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100644 index 0000000000000000000000000000000000000000..cdee41345d258606468b7de3764ba326b28da226 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,101 @@ +# Codeowners are designated by their GitHub username. They are +# the people who are responsible for reviewing and approving PRs +# that modify the files that match the pattern. +# +# Codeowners are not the same as contributors. They are not +# automatically added to the PR, but they will be requested to +# review the PR when it is created. +# +# As a general rule, the codeowners are the people who are +# most familiar with the code that the PR is modifying. If you +# are not sure who to add, ask in the issue or in the PR itself. +# +# The format of the file is as follows: +# + + +# App experience files +# These are the files that are used to launch the app with the correct settings and configurations +/apps/ @kellyguo11 @hhansen-bdai @Mayankm96 + +# Core Framework +/source/isaaclab/isaaclab/actuators @Mayankm96 @jtigue-bdai +/source/isaaclab/isaaclab/app @hhansen-bdai @kellyguo11 +/source/isaaclab/isaaclab/assets @kellyguo11 @Mayankm96 @jtigue-bdai +/source/isaaclab/isaaclab/assets/deformable_object @masoudmoghani @ooctipus +/source/isaaclab/isaaclab/controllers @Mayankm96 +/source/isaaclab/isaaclab/envs/manager_based_* @Mayankm96 @jtigue-bdai @ooctipus +/source/isaaclab/isaaclab/envs/direct_* @kellyguo11 +/source/isaaclab/isaaclab/envs/mdp @ooctipus +/source/isaaclab/isaaclab/envs/mimic_* @peterd-NV +/source/isaaclab/isaaclab/envs/ui @ooctipus @ossamaAhmed +/source/isaaclab/isaaclab/envs/utils @Toni-SM +/source/isaaclab/isaaclab/managers @jtigue-bdai @Mayankm96 @ooctipus +/source/isaaclab/isaaclab/sensors/sensor_base* @pascal-roth +/source/isaaclab/isaaclab/sensors/camera @kellyguo11 @pascal-roth +/source/isaaclab/isaaclab/sensors/contact_sensor @jtigue-bdai @ooctipus +/source/isaaclab/isaaclab/sensors/imu @jtigue-bdai @pascal-roth +/source/isaaclab/isaaclab/sensors/ray_caster @pascal-roth +/source/isaaclab/isaaclab/sensors/frame_transformer @jtigue-bdai +/source/isaaclab/isaaclab/sim/converters @Mayankm96 @jtigue-bdai @kellyguo11 +/source/isaaclab/isaaclab/sim/schemas @Mayankm96 @jtigue-bdai @kellyguo11 +/source/isaaclab/isaaclab/sim/spawners @Mayankm96 @jtigue-bdai @ooctipus +/source/isaaclab/isaaclab/sim/simulation_* @matthewtrepte @ossamaAhmed @kellyguo11 +/source/isaaclab/isaaclab/terrains @Mayankm96 +/source/isaaclab/isaaclab/ui @pascal-roth @jtigue-bdai +/source/isaaclab/isaaclab/utils/buffers @ooctipus @jtigue-bdai +/source/isaaclab/isaaclab/utils/datasets @Peter-NV +/source/isaaclab/isaaclab/utils/interpolation @jtigue-bdai +/source/isaaclab/isaaclab/utils/io @ooctipus +/source/isaaclab/isaaclab/utils/modifiers @jtigue-bdai +/source/isaaclab/isaaclab/utils/noise @jtigue-bdai @kellyguo11 +/source/isaaclab/isaaclab/utils/warp @pascal-roth +/source/isaaclab/isaaclab/utils/assets.py @kellyguo11 @Mayankm96 +/source/isaaclab/isaaclab/utils/math.py @jtigue-bdai @Mayankm96 +/source/isaaclab/isaaclab/utils/configclass.py @Mayankm96 +/source/isaaclab/isaaclab/utils/sensors.py @kellyguo11 @pascal-roth + +# RL Environment +/source/isaaclab_tasks/isaaclab_tasks/direct @kellyguo11 +/source/isaaclab_tasks/isaaclab_tasks/manager_based @Mayankm96 +/source/isaaclab_tasks/isaaclab_tasks/utils @Mayankm96 + +# Assets +/source/isaaclab_assets/isaaclab_assets/ @pascal-roth + +# Mimic +/source/isaaclab_mimic/isaaclab_mimic @peterd-NV +/source/isaaclab_mimic/isaaclab_mimic @njawale42 +/source/isaaclab_mimic/isaaclab_mimic @michaellin6 +/source/isaaclab_mimic/isaaclab_mimic @jaybdub +/source/isaaclab_mimic/isaaclab_mimic @huihuaNvidia2023 +/source/isaaclab_mimic/isaaclab_mimic @xyao-nv + +# RL +/source/isaaclab_rl/isaaclab_rl/rsl_rl @Mayankm96 @ClemensSchwarke +/source/isaaclab_rl/isaaclab_rl/rl_games @Toni-SM +/source/isaaclab_rl/isaaclab_rl/sb3 @Toni-SM +/source/isaaclab_rl/isaaclab_rl/skrl @Toni-SM + +# Standalone Scripts +/scripts/benchmarks/ @ooctipus @kellyguo11 +/scripts/demos/ @ooctipus +/scripts/environments/ @ooctipus +/scripts/imitation_learning/ @Peter-NV +/scripts/reinforcement_learning/ @ooctipus @Toni-NV +/scripts/tools/ @jtigue-bdai @Mayankm96 +/scripts/tutorials/ @jtigue-bdai @pascal-roth + +# Github Actions +# This list is for people wanting to be notified every time there's a change +# related to Github Actions +/.github/ @kellyguo11 @hhansen-bdai + +# Visual Studio Code +/.vscode/ @hhansen-bdai @Mayankm96 + +# Infrastructure (Docker, Docs, Tools) +/docker/ @hhansen-bdai @pascal-roth +/docs/ @jtigue-bdai @kellyguo11 @Mayankm96 +/tools/ @hhansen-bdai +/isaaclab.* @hhansen-bdai @Mayankm96 @kellyguo11 diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md new file mode 100644 index 0000000000000000000000000000000000000000..54d6f21a8086972743fe78298aebb6fed1d20f70 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -0,0 +1,54 @@ +--- +name: Bug Report +about: Submit a bug report +title: "[Bug Report] Bug title" + +--- + +If you are submitting a bug report, please fill in the following details and use the tag [bug]. + +### Describe the bug + +A clear and concise description of what the bug is. + +### Steps to reproduce + +Please try to provide a minimal example to reproduce the bug. Error messages and stack traces are also helpful. + + + +### System Info + +Describe the characteristic of your environment: + + +- Commit: [e.g. 8f3b9ca] +- Isaac Sim Version: [e.g. 5.0, this can be obtained by `cat ${ISAACSIM_PATH}/VERSION`] +- OS: [e.g. Ubuntu 22.04] +- GPU: [e.g. RTX 5090] +- CUDA: [e.g. 12.8] +- GPU Driver: [e.g. 553.05, this can be seen by using `nvidia-smi` command.] + +### Additional context + +Add any other context about the problem here. + +### Checklist + +- [ ] I have checked that there is no similar issue in the repo (**required**) +- [ ] I have checked that the issue is not in running Isaac Sim itself and is related to the repo + +### Acceptance Criteria + +Add the criteria for which this task is considered **done**. If not known at issue creation time, you can add this once the issue is assigned. + +- [ ] Criteria 1 +- [ ] Criteria 2 diff --git a/.github/ISSUE_TEMPLATE/proposal.md b/.github/ISSUE_TEMPLATE/proposal.md new file mode 100644 index 0000000000000000000000000000000000000000..02f89f30aa40b56b523a4c8d0e5ca14c926beca5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/proposal.md @@ -0,0 +1,45 @@ +--- +name: Proposal +about: Propose changes that are not bug fixes +title: "[Proposal] Proposal title" +--- + + +### Proposal + +A clear and concise description of the proposal. In a few sentences, describe the feature and its core capabilities. + +### Motivation + +Please outline the motivation for the proposal. Summarize the core use cases and user problems and needs you are trying to solve. + +Is your feature request related to a problem? e.g.,"I'm always frustrated when [...]". + +If this is related to another GitHub issue, please link here too. + +### Alternatives + +A clear and concise description of any alternative solutions or features you've considered, if any. + +### Build Info + +Describe the versions where you are observing the missing feature in: + + +- Isaac Lab Version: [e.g. 2.3.0] +- Isaac Sim Version: [e.g. 5.1, this can be obtained by `cat ${ISAACSIM_PATH}/VERSION`] + +### Additional context + +Add any other context or screenshots about the feature request here. + +### Checklist + +- [ ] I have checked that there is no similar issue in the repo (**required**) + +### Acceptance Criteria + +Add the criteria for which this task is considered **done**. If not known at issue creation time, you can add this once the issue is assigned. + +- [ ] Criteria 1 +- [ ] Criteria 2 diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000000000000000000000000000000000000..489b46ee060ce8e12673c745d80b78f3841c449c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,21 @@ +--- +name: Question +about: Ask a question +title: "[Question] Question title" +--- + +### Question + +Basic questions, related to robot learning, that are not bugs or feature requests will be closed without reply, because GitHub issues are not an appropriate venue for these. + +Advanced/nontrivial questions, especially in areas where documentation is lacking, are very much welcome. + +For questions that are related to running and understanding Isaac Sim, please post them at the official [Isaac Sim forums](https://forums.developer.nvidia.com/c/omniverse/simulation/69). + +### Build Info + +Describe the versions that you are currently using: + + +- Isaac Lab Version: [e.g. 2.3.0] +- Isaac Sim Version: [e.g. 5.1, this can be obtained by `cat ${ISAACSIM_PATH}/VERSION`] diff --git a/.github/LICENSE_HEADER.txt b/.github/LICENSE_HEADER.txt new file mode 100644 index 0000000000000000000000000000000000000000..f078a3a4e8a725254e9354e313dd11959ebf4a48 --- /dev/null +++ b/.github/LICENSE_HEADER.txt @@ -0,0 +1,4 @@ +Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +All rights reserved. + +SPDX-License-Identifier: BSD-3-Clause diff --git a/.github/LICENSE_HEADER_MIMIC.txt b/.github/LICENSE_HEADER_MIMIC.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5779ee27290639abbb9e6ecbf51e77c13e81902 --- /dev/null +++ b/.github/LICENSE_HEADER_MIMIC.txt @@ -0,0 +1,4 @@ +Copyright (c) 2024-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +All rights reserved. + +SPDX-License-Identifier: Apache-2.0 diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000000000000000000000000000000000..ee9fa4ebdc5e2a8a3c1acd3019743c514ba3a2da --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,59 @@ +# Description + + + +Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. +List any dependencies that are required for this change. + +Fixes # (issue) + + + +## Type of change + + + +- Bug fix (non-breaking change which fixes an issue) +- New feature (non-breaking change which adds functionality) +- Breaking change (existing functionality will not work without user modification) +- Documentation update + +## Screenshots + +Please attach before and after screenshots of the change if applicable. + + + +## Checklist + +- [ ] I have read and understood the [contribution guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html) +- [ ] I have run the [`pre-commit` checks](https://pre-commit.com/) with `./isaaclab.sh --format` +- [ ] I have made corresponding changes to the documentation +- [ ] My changes generate no new warnings +- [ ] I have added tests that prove my fix is effective or that my feature works +- [ ] I have updated the changelog and the corresponding version in the extension's `config/extension.toml` file +- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already exists there + + diff --git a/.github/actions/combine-results/action.yml b/.github/actions/combine-results/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..8ed66e3b460326ecd22537c3f25a86353d143742 --- /dev/null +++ b/.github/actions/combine-results/action.yml @@ -0,0 +1,103 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: 'Combine XML Test Results' +description: 'Combines multiple XML test result files into a single file' + +inputs: + tests-dir: + description: 'Directory containing test result files' + default: 'tests' + required: false + output-file: + description: 'Output combined XML file path' + required: true + reports-dir: + description: 'Directory to store the combined results' + default: 'reports' + required: false + +runs: + using: composite + steps: + - name: Combine XML Test Results + shell: sh + run: | + # Function to combine multiple XML test results + combine_xml_results() { + local tests_dir="$1" + local output_file="$2" + local reports_dir="$3" + + echo "Combining test results from: $tests_dir" + echo "Output file: $output_file" + echo "Reports directory: $reports_dir" + + # Check if reports directory exists + if [ ! -d "$reports_dir" ]; then + echo "⚠️ Reports directory does not exist: $reports_dir" + mkdir -p "$reports_dir" + fi + + # Check if tests directory exists + if [ ! -d "$tests_dir" ]; then + echo "⚠️ Tests directory does not exist: $tests_dir" + echo "Creating fallback XML..." + echo 'Tests directory was not found' > "$output_file" + return + fi + + # Find all XML files in the tests directory + echo "Searching for XML files in: $tests_dir" + xml_files=$(find "$tests_dir" -name "*.xml" -type f 2>/dev/null | sort) + + if [ -z "$xml_files" ]; then + echo "⚠️ No XML files found in: $tests_dir" + echo "Creating fallback XML..." + echo 'No XML test result files were found' > "$output_file" + return + fi + + # Count XML files found + file_count=$(echo "$xml_files" | wc -l) + echo "✅ Found $file_count XML file(s):" + echo "$xml_files" | while read -r file; do + echo " - $file ($(wc -c < "$file") bytes)" + done + + # Create combined XML + echo "🔄 Combining $file_count XML files..." + echo '' > "$output_file" + echo '' >> "$output_file" + + # Process each XML file + combined_count=0 + echo "$xml_files" | while read -r file; do + if [ -f "$file" ]; then + echo " Processing: $file" + # Remove XML declaration and outer testsuites wrapper from each file + # Remove first line (XML declaration) and strip outer / tags + sed '1d; s/^//; s/<\/testsuites>$//' "$file" >> "$output_file" 2>/dev/null || { + echo " ⚠️ Warning: Could not process $file, skipping..." + } + combined_count=$((combined_count + 1)) + fi + done + + echo '' >> "$output_file" + echo "✅ Successfully combined $combined_count files into: $output_file" + + # Verify output file was created + if [ -f "$output_file" ]; then + echo "✅ Final output file created: $output_file" + echo "📊 Output file size: $(wc -c < "$output_file") bytes" + else + echo "❌ Failed to create output file: $output_file" + exit 1 + fi + } + + # Call the function with provided parameters + combine_xml_results "${{ inputs.tests-dir }}" "${{ inputs.output-file }}" "${{ inputs.reports-dir }}" diff --git a/.github/actions/docker-build/action.yml b/.github/actions/docker-build/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..2db402d42042cdfae1530c1935b9223c1ccda262 --- /dev/null +++ b/.github/actions/docker-build/action.yml @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: 'Build Docker Image' +description: 'Builds a Docker image with IsaacSim and IsaacLab dependencies' + +inputs: + image-tag: + description: 'Docker image tag to use' + required: true + isaacsim-base-image: + description: 'IsaacSim base image' + required: true + isaacsim-version: + description: 'IsaacSim version' + required: true + dockerfile-path: + description: 'Path to Dockerfile' + default: 'docker/Dockerfile.curobo' + required: false + context-path: + description: 'Build context path' + default: '.' + required: false + +runs: + using: composite + steps: + - name: NGC Login + shell: sh + run: | + # Only attempt NGC login if API key is available + if [ -n "${{ env.NGC_API_KEY }}" ]; then + echo "Logging into NGC registry..." + docker login -u \$oauthtoken -p ${{ env.NGC_API_KEY }} nvcr.io + echo "✅ Successfully logged into NGC registry" + else + echo "⚠️ NGC_API_KEY not available - skipping NGC login" + echo "This is normal for PRs from forks or when secrets are not configured" + fi + + - name: Build Docker Image + shell: sh + run: | + # Function to build Docker image + build_docker_image() { + local image_tag="$1" + local isaacsim_base_image="$2" + local isaacsim_version="$3" + local dockerfile_path="$4" + local context_path="$5" + + echo "Building Docker image: $image_tag" + echo "Using Dockerfile: $dockerfile_path" + echo "Build context: $context_path" + + # Build Docker image + docker buildx build --progress=plain --platform linux/amd64 \ + -t isaac-lab-dev \ + -t $image_tag \ + --build-arg ISAACSIM_BASE_IMAGE_ARG="$isaacsim_base_image" \ + --build-arg ISAACSIM_VERSION_ARG="$isaacsim_version" \ + --build-arg ISAACSIM_ROOT_PATH_ARG=/isaac-sim \ + --build-arg ISAACLAB_PATH_ARG=/workspace/isaaclab \ + --build-arg DOCKER_USER_HOME_ARG=/root \ + --cache-from type=gha \ + --cache-to type=gha,mode=max \ + -f $dockerfile_path \ + --load $context_path + + echo "✅ Docker image built successfully: $image_tag" + docker images | grep isaac-lab-dev + } + + # Call the function with provided parameters + build_docker_image "${{ inputs.image-tag }}" "${{ inputs.isaacsim-base-image }}" "${{ inputs.isaacsim-version }}" "${{ inputs.dockerfile-path }}" "${{ inputs.context-path }}" diff --git a/.github/actions/run-tests/action.yml b/.github/actions/run-tests/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..467122860141617062de2b54c991d00ab1828cbe --- /dev/null +++ b/.github/actions/run-tests/action.yml @@ -0,0 +1,157 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: 'Run Tests in Docker Container' +description: 'Runs pytest tests in a Docker container with GPU support and result collection' + +inputs: + test-path: + description: 'Path to test directory or pytest arguments' + required: true + result-file: + description: 'Name of the result XML file' + required: true + container-name: + description: 'Name for the Docker container' + required: true + image-tag: + description: 'Docker image tag to use' + required: true + reports-dir: + description: 'Directory to store test results' + default: 'reports' + required: false + pytest-options: + description: 'Additional pytest options (e.g., -k filter)' + default: '' + required: false + filter-pattern: + description: 'Pattern to filter test files (e.g., isaaclab_tasks)' + default: '' + required: false + +runs: + using: composite + steps: + - name: Run Tests in Docker Container + shell: bash + run: | + # Function to run tests in Docker container + run_tests() { + local test_path="$1" + local result_file="$2" + local container_name="$3" + local image_tag="$4" + local reports_dir="$5" + local pytest_options="$6" + local filter_pattern="$7" + + echo "Running tests in: $test_path" + if [ -n "$pytest_options" ]; then + echo "With pytest options: $pytest_options" + fi + if [ -n "$filter_pattern" ]; then + echo "With filter pattern: $filter_pattern" + fi + + # Create reports directory + mkdir -p "$reports_dir" + + # Clean up any existing container + docker rm -f $container_name 2>/dev/null || true + + # Build Docker environment variables + docker_env_vars="\ + -e OMNI_KIT_ACCEPT_EULA=yes \ + -e ACCEPT_EULA=Y \ + -e OMNI_KIT_DISABLE_CUP=1 \ + -e ISAAC_SIM_HEADLESS=1 \ + -e ISAAC_SIM_LOW_MEMORY=1 \ + -e PYTHONUNBUFFERED=1 \ + -e PYTHONIOENCODING=utf-8 \ + -e TEST_RESULT_FILE=$result_file" + + if [ -n "$filter_pattern" ]; then + if [[ "$filter_pattern" == not* ]]; then + # Handle "not pattern" case + exclude_pattern="${filter_pattern#not }" + docker_env_vars="$docker_env_vars -e TEST_EXCLUDE_PATTERN=$exclude_pattern" + echo "Setting exclude pattern: $exclude_pattern" + else + # Handle positive pattern case + docker_env_vars="$docker_env_vars -e TEST_FILTER_PATTERN=$filter_pattern" + echo "Setting include pattern: $filter_pattern" + fi + else + echo "No filter pattern provided" + fi + + echo "Docker environment variables: '$docker_env_vars'" + + # Run tests in container with error handling + echo "🚀 Starting Docker container for tests..." + if docker run --name $container_name \ + --entrypoint bash --gpus all --network=host \ + --security-opt=no-new-privileges:true \ + --memory=$(echo "$(free -m | awk '/^Mem:/{print $2}') * 0.9 / 1" | bc)m \ + --cpus=$(echo "$(nproc) * 0.9" | bc) \ + --oom-kill-disable=false \ + --ulimit nofile=65536:65536 \ + --ulimit nproc=4096:4096 \ + $docker_env_vars \ + $image_tag \ + -c " + set -e + cd /workspace/isaaclab + mkdir -p tests + echo 'Starting pytest with path: $test_path' + /isaac-sim/python.sh -m pytest --ignore=tools/conftest.py $test_path $pytest_options -v --junitxml=tests/$result_file || echo 'Pytest completed with exit code: $?' + "; then + echo "✅ Docker container completed successfully" + else + echo "⚠️ Docker container failed, but continuing to copy results..." + fi + + # Copy test results with error handling + echo "📋 Attempting to copy test results..." + if docker cp $container_name:/workspace/isaaclab/tests/$result_file "$reports_dir/$result_file" 2>/dev/null; then + echo "✅ Test results copied successfully" + else + echo "❌ Failed to copy specific result file, trying to copy all test results..." + if docker cp $container_name:/workspace/isaaclab/tests/ "$reports_dir/" 2>/dev/null; then + echo "✅ All test results copied successfully" + # Look for any XML files and use the first one found + if [ -f "$reports_dir/full_report.xml" ]; then + mv "$reports_dir/full_report.xml" "$reports_dir/$result_file" + echo "✅ Found and renamed full_report.xml to $result_file" + elif [ -f "$reports_dir/test-reports-"*".xml" ]; then + # Combine individual test reports if no full report exists + echo "📊 Combining individual test reports..." + echo '' > "$reports_dir/$result_file" + for xml_file in "$reports_dir"/test-reports-*.xml; do + if [ -f "$xml_file" ]; then + echo " Processing: $xml_file" + sed '1d; /^> "$reports_dir/$result_file" 2>/dev/null || true + fi + done + echo '' >> "$reports_dir/$result_file" + echo "✅ Combined individual test reports into $result_file" + else + echo "❌ No test result files found, creating fallback" + echo "Container may have failed to generate any results" > "$reports_dir/$result_file" + fi + else + echo "❌ Failed to copy any test results, creating fallback" + echo "Container may have failed to generate results" > "$reports_dir/$result_file" + fi + fi + + # Clean up container + echo "🧹 Cleaning up Docker container..." + docker rm $container_name 2>/dev/null || echo "⚠️ Container cleanup failed, but continuing..." + } + + # Call the function with provided parameters + run_tests "${{ inputs.test-path }}" "${{ inputs.result-file }}" "${{ inputs.container-name }}" "${{ inputs.image-tag }}" "${{ inputs.reports-dir }}" "${{ inputs.pytest-options }}" "${{ inputs.filter-pattern }}" diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 0000000000000000000000000000000000000000..2e6837fdb71a7ad7536919f5ebbcbd24e9171ff4 --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,76 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Documentation-related changes +documentation: + - all: + - changed-files: + - any-glob-to-any-file: + - 'docs/**' + - '**/README.md' + - all-globs-to-all-files: + - '!docs/licenses/**' + +# Infrastructure changes +infrastructure: + - changed-files: + - any-glob-to-any-file: + - .github/** + - docker/** + - .dockerignore + - tools/** + - .vscode/** + - environment.yml + - setup.py + - pyproject.toml + - .pre-commit-config.yaml + - isaaclab.sh + - isaaclab.bat + - docs/licenses/** + +# Assets (USD, glTF, etc.) related changes. +asset: + - changed-files: + - any-glob-to-any-file: + - source/isaaclab_assets/** + +# Isaac Sim team related changes. +isaac-sim: + - changed-files: + - any-glob-to-any-file: + - apps/** + +# Isaac Mimic team related changes. +isaac-mimic: + - changed-files: + - any-glob-to-any-file: + - source/isaaclab/isaaclab/devices/** + - source/isaaclab_mimic/** + - source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack** + - source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_and_place** + - scripts/imitation_learning/** + +# Isaac Lab team related changes. +isaac-lab: + - all: + - changed-files: + - any-glob-to-any-file: + - source/** + - scripts/** + - all-globs-to-all-files: + - '!source/isaaclab_assets/**' + - '!source/isaaclab_mimic/**' + - '!source/isaaclab/isaaclab/devices' + - '!scripts/imitation_learning/**' + +# Add 'enhancement' label to any PR where the head branch name +# starts with `feature` or has a `feature` section in the name +enhancement: + - head-branch: ['^feature', 'feature'] + +# Add 'bug' label to any PR where the head branch name +# starts with `fix`/`bug` or has a `fix`/`bug` section in the name +bug: + - head-branch: ['^fix', 'fix', '^bug', 'bug'] diff --git a/.github/stale.yml b/.github/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..6205170b38bd6ba9f59fd43e72c826650d197d80 --- /dev/null +++ b/.github/stale.yml @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Configuration for probot-stale - https://github.com/probot/stale + +# Number of days of inactivity before an Issue or Pull Request becomes stale +daysUntilStale: 60 + +# Number of days of inactivity before an Issue or Pull Request with the stale label is closed. +# Set to false to disable. If disabled, issues still need to be closed manually, but will remain marked as stale. +daysUntilClose: 14 + +# Only issues or pull requests with all of these labels are check if stale. Defaults to `[]` (disabled) +onlyLabels: + - more-information-needed + +# Issues or Pull Requests with these labels will never be considered stale. Set to `[]` to disable +exemptLabels: + - pinned + - security + - "[Status] Maybe Later" + +# Set to true to ignore issues in a project (defaults to false) +exemptProjects: true + +# Set to true to ignore issues in a milestone (defaults to false) +exemptMilestones: true + +# Set to true to ignore issues with an assignee (defaults to false) +exemptAssignees: true + +# Label to use when marking as stale +staleLabel: stale + +# Comment to post when marking as stale. Set to `false` to disable +markComment: > + This issue has been automatically marked as stale because it has not had + recent activity. It will be closed if no further activity occurs. Thank you + for your contributions. + +# Comment to post when removing the stale label. +# unmarkComment: > +# Your comment here. + +# Comment to post when closing a stale Issue or Pull Request. +# closeComment: > +# Your comment here. + +# Limit the number of actions per hour, from 1-30. Default is 30 +limitPerRun: 30 + +# Limit to only `issues` or `pulls` +only: issues + +# Optionally, specify configuration settings that are specific to just 'issues' or 'pulls': +# pulls: +# daysUntilStale: 30 +# markComment: > +# This pull request has been automatically marked as stale because it has not had +# recent activity. It will be closed if no further activity occurs. Thank you +# for your contributions. + +# issues: +# exemptLabels: +# - confirmed diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 0000000000000000000000000000000000000000..cbaa8f7b8e9971835dfba7e09ee77eb5fea1afcc --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,222 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Build and Test + +on: + pull_request: + branches: + - devel + - main + - 'release/**' + +# Concurrency control to prevent parallel runs on the same PR +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + pull-requests: write + checks: write + issues: read + +env: + NGC_API_KEY: ${{ secrets.NGC_API_KEY }} + ISAACSIM_BASE_IMAGE: ${{ vars.ISAACSIM_BASE_IMAGE || 'nvcr.io/nvidia/isaac-sim' }} + ISAACSIM_BASE_VERSION: ${{ vars.ISAACSIM_BASE_VERSION || '5.1.0' }} + DOCKER_IMAGE_TAG: isaac-lab-dev:${{ github.event_name == 'pull_request' && format('pr-{0}', github.event.pull_request.number) || github.ref_name }}-${{ github.sha }} + +jobs: + test-isaaclab-tasks: + runs-on: [self-hosted, gpu] + timeout-minutes: 180 + continue-on-error: true + + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + lfs: true + + - name: Build Docker Image + uses: ./.github/actions/docker-build + with: + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + isaacsim-base-image: ${{ env.ISAACSIM_BASE_IMAGE }} + isaacsim-version: ${{ env.ISAACSIM_BASE_VERSION }} + + - name: Run IsaacLab Tasks Tests + uses: ./.github/actions/run-tests + with: + test-path: "tools" + result-file: "isaaclab-tasks-report.xml" + container-name: "isaac-lab-tasks-test-$$" + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + pytest-options: "" + filter-pattern: "isaaclab_tasks" + + - name: Copy Test Results from IsaacLab Tasks Container + run: | + CONTAINER_NAME="isaac-lab-tasks-test-$$" + if docker ps -a | grep -q $CONTAINER_NAME; then + echo "Copying test results from IsaacLab Tasks container..." + docker cp $CONTAINER_NAME:/workspace/isaaclab/tests/isaaclab-tasks-report.xml reports/ 2>/dev/null || echo "No test results to copy from IsaacLab Tasks container" + fi + + - name: Upload IsaacLab Tasks Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: isaaclab-tasks-test-results + path: reports/isaaclab-tasks-report.xml + retention-days: 1 + compression-level: 9 + + - name: Check Test Results for Fork PRs + if: github.event.pull_request.head.repo.full_name != github.repository + run: | + if [ -f "reports/isaaclab-tasks-report.xml" ]; then + # Check if the test results contain any failures + if grep -q 'failures="[1-9]' reports/isaaclab-tasks-report.xml || grep -q 'errors="[1-9]' reports/isaaclab-tasks-report.xml; then + echo "Tests failed for PR from fork. The test report is in the logs. Failing the job." + exit 1 + fi + else + echo "No test results file found. This might indicate test execution failed." + exit 1 + fi + + test-general: + runs-on: [self-hosted, gpu] + timeout-minutes: 180 + + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + lfs: true + + - name: Build Docker Image + uses: ./.github/actions/docker-build + with: + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + isaacsim-base-image: ${{ env.ISAACSIM_BASE_IMAGE }} + isaacsim-version: ${{ env.ISAACSIM_BASE_VERSION }} + + - name: Run General Tests + id: run-general-tests + uses: ./.github/actions/run-tests + with: + test-path: "tools" + result-file: "general-tests-report.xml" + container-name: "isaac-lab-general-test-$$" + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + pytest-options: "" + filter-pattern: "not isaaclab_tasks" + + - name: Copy Test Results from General Tests Container + run: | + CONTAINER_NAME="isaac-lab-general-test-$$" + if docker ps -a | grep -q $CONTAINER_NAME; then + echo "Copying test results from General Tests container..." + docker cp $CONTAINER_NAME:/workspace/isaaclab/tests/general-tests-report.xml reports/ 2>/dev/null || echo "No test results to copy from General Tests container" + fi + + - name: Upload General Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: general-test-results + path: reports/general-tests-report.xml + retention-days: 1 + compression-level: 9 + + - name: Check Test Results for Fork PRs + if: github.event.pull_request.head.repo.full_name != github.repository + run: | + if [ -f "reports/general-tests-report.xml" ]; then + # Check if the test results contain any failures + if grep -q 'failures="[1-9]' reports/general-tests-report.xml || grep -q 'errors="[1-9]' reports/general-tests-report.xml; then + echo "Tests failed for PR from fork. The test report is in the logs. Failing the job." + exit 1 + fi + else + echo "No test results file found. This might indicate test execution failed." + exit 1 + fi + + combine-results: + needs: [test-isaaclab-tasks, test-general] + runs-on: [self-hosted, gpu] + if: always() + + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + lfs: false + + - name: Create Reports Directory + run: | + mkdir -p reports + + - name: Download Test Results + uses: actions/download-artifact@v4 + with: + name: isaaclab-tasks-test-results + path: reports/ + continue-on-error: true + + - name: Download General Test Results + uses: actions/download-artifact@v4 + with: + name: general-test-results + path: reports/ + + - name: Combine All Test Results + uses: ./.github/actions/combine-results + with: + tests-dir: "reports" + output-file: "reports/combined-results.xml" + + - name: Upload Combined Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: pr-${{ github.event.pull_request.number }}-combined-test-results + path: reports/combined-results.xml + retention-days: 7 + compression-level: 9 + + - name: Comment on Test Results + id: test-reporter + if: github.event.pull_request.head.repo.full_name == github.repository + uses: EnricoMi/publish-unit-test-result-action@v2 + with: + files: "reports/combined-results.xml" + check_name: "Tests Summary" + comment_mode: changes + comment_title: "Test Results Summary" + report_individual_runs: false + deduplicate_classes_by_file_name: true + compare_to_earlier_commit: true + fail_on: errors + action_fail_on_inconclusive: true + + - name: Report Test Results + if: github.event.pull_request.head.repo.full_name == github.repository + uses: dorny/test-reporter@v1 + with: + name: IsaacLab Build and Test Results + path: reports/combined-results.xml + reporter: java-junit + fail-on-error: true + only-summary: false + max-annotations: '50' + report-title: "IsaacLab Test Results - ${{ github.workflow }}" diff --git a/.github/workflows/check-links.yml b/.github/workflows/check-links.yml new file mode 100644 index 0000000000000000000000000000000000000000..a5f91e934032826006d45c67a56a7adc73cdff64 --- /dev/null +++ b/.github/workflows/check-links.yml @@ -0,0 +1,120 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Check Documentation Links + +on: + # Run on pull requests that modify documentation + pull_request: + paths: + - 'docs/**' + - '**.md' + - '.github/workflows/check-links.yml' + # Run on pushes to main branches + push: + branches: + - main + - devel + - 'release/**' + paths: + - 'docs/**' + - '**.md' + - '.github/workflows/check-links.yml' + # Allow manual trigger + workflow_dispatch: + # Run weekly to catch external links that break over time + schedule: + - cron: '0 0 * * 0' # Every Sunday at midnight UTC + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + check-links: + name: Check for Broken Links + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Restore lychee cache + uses: actions/cache@v4 + with: + path: .lycheecache + key: cache-lychee-${{ github.sha }} + restore-keys: cache-lychee- + + - name: Run Link Checker + uses: lycheeverse/lychee-action@v2 + with: + # Check all markdown files and documentation + args: >- + --verbose + --no-progress + --cache + --max-cache-age 1d + --exclude-path './docs/_build' + --exclude-path './apps/warp-*' + --exclude-path './logs' + --exclude-path './outputs' + --exclude-loopback + --exclude '^file://' + --exclude '^mailto:' + --exclude 'localhost' + --exclude '127\.0\.0\.1' + --exclude 'example\.com' + --exclude 'your-organization' + --exclude 'YOUR_' + --exclude 'yourdomain' + --exclude 'user@' + --exclude 'helm\.ngc\.nvidia\.com' + --exclude 'slurm\.schedmd\.com' + --max-retries 3 + --retry-wait-time 5 + --timeout 30 + --accept 200,201,202,203,204,206,301,302,303,307,308,429 + --scheme https + --scheme http + '*.md' + '**/*.md' + 'docs/**/*.rst' + 'docs/**/*.html' + # Output results to a file + output: ./lychee-output.md + # Fail action on broken links + fail: true + # Optional: Use GitHub token for authenticated requests (higher rate limit) + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Print results to logs + if: always() + run: | + echo "========================================" + echo "Link Checker Results:" + echo "========================================" + if [ -f ./lychee-output.md ]; then + cat ./lychee-output.md + echo "" + echo "========================================" + + # Also add to GitHub step summary for easy viewing + echo "## Link Checker Results" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + cat ./lychee-output.md >> $GITHUB_STEP_SUMMARY + else + echo "No output file generated" + echo "========================================" + fi + + - name: Fail job if broken links found + if: failure() + run: | + echo "❌ Broken links were found in the documentation!" + echo "Please review the link checker report above and fix all broken links." + exit 1 diff --git a/.github/workflows/daily-compatibility.yml b/.github/workflows/daily-compatibility.yml new file mode 100644 index 0000000000000000000000000000000000000000..bbf59e45160d97f824c0772ffdaa25d6a6d32486 --- /dev/null +++ b/.github/workflows/daily-compatibility.yml @@ -0,0 +1,257 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Backwards Compatibility Tests + +on: + schedule: + # Run daily at 8 PM PST (4 AM UTC) + - cron: '0 4 * * *' + + workflow_dispatch: + inputs: + isaacsim_version: + description: 'IsaacSim version image tag to test' + required: true + default: '4.5.0' + type: string + +# Concurrency control to prevent parallel runs +concurrency: + group: compatibility-${{ github.ref }}-${{ github.event_name }} + cancel-in-progress: true + +permissions: + contents: read + pull-requests: write + +env: + NGC_API_KEY: ${{ secrets.NGC_API_KEY }} + ISAACSIM_BASE_IMAGE: ${{ vars.ISAACSIM_BASE_IMAGE || 'nvcr.io/nvidia/isaac-sim' }} + +jobs: + setup-versions: + runs-on: ubuntu-latest + outputs: + versions: ${{ steps.set-versions.outputs.versions }} + steps: + - name: Set Isaac Sim Versions + id: set-versions + run: | + # Define all versions to test in one place + DEFAULT_VERSIONS='["4.5.0", "5.0.0"]' + + if [ -n "${{ github.event.inputs.isaacsim_version }}" ]; then + # If a specific version is provided via workflow_dispatch, use only that + echo "versions=[\"${{ github.event.inputs.isaacsim_version }}\"]" >> $GITHUB_OUTPUT + else + # Otherwise, use all default versions + echo "versions=$DEFAULT_VERSIONS" >> $GITHUB_OUTPUT + fi + + test-isaaclab-tasks-compat: + needs: setup-versions + runs-on: [self-hosted, gpu] + timeout-minutes: 180 + continue-on-error: true + strategy: + matrix: + isaacsim_version: ${{ fromJson(needs.setup-versions.outputs.versions) }} + fail-fast: false + env: + CUDA_VISIBLE_DEVICES: all + NVIDIA_VISIBLE_DEVICES: all + NVIDIA_DRIVER_CAPABILITIES: all + CUDA_HOME: /usr/local/cuda + LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64 + DOCKER_IMAGE_TAG: isaac-lab-compat:${{ github.ref_name }}-${{ github.sha }}-${{ matrix.isaacsim_version }} + + steps: + - name: Checkout Code + uses: actions/checkout@v3 + with: + fetch-depth: 0 + lfs: true + + - name: Build Docker Image + uses: ./.github/actions/docker-build + with: + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + isaacsim-base-image: ${{ env.ISAACSIM_BASE_IMAGE }} + isaacsim-version: ${{ matrix.isaacsim_version }} + + - name: Run IsaacLab Tasks Tests + uses: ./.github/actions/run-tests + with: + test-path: "tools" + result-file: "isaaclab-tasks-compat-report.xml" + container-name: "isaac-lab-tasks-compat-test-$$" + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + pytest-options: "" + filter-pattern: "isaaclab_tasks" + + - name: Copy All Test Results from IsaacLab Tasks Container + run: | + CONTAINER_NAME="isaac-lab-tasks-compat-test-$$" + if docker ps -a | grep -q $CONTAINER_NAME; then + echo "Copying all test results from IsaacLab Tasks container..." + docker cp $CONTAINER_NAME:/workspace/isaaclab/tests/isaaclab-tasks-compat-report.xml reports/ 2>/dev/null || echo "No test results to copy from IsaacLab Tasks container" + fi + + - name: Upload IsaacLab Tasks Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: isaaclab-tasks-compat-results-${{ matrix.isaacsim_version }} + path: reports/isaaclab-tasks-compat-report.xml + retention-days: 7 + compression-level: 9 + + test-general-compat: + needs: setup-versions + runs-on: [self-hosted, gpu] + timeout-minutes: 180 + strategy: + matrix: + isaacsim_version: ${{ fromJson(needs.setup-versions.outputs.versions) }} + fail-fast: false + env: + CUDA_VISIBLE_DEVICES: all + NVIDIA_VISIBLE_DEVICES: all + NVIDIA_DRIVER_CAPABILITIES: all + CUDA_HOME: /usr/local/cuda + LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64 + DOCKER_IMAGE_TAG: isaac-lab-compat:${{ github.ref_name }}-${{ github.sha }}-${{ matrix.isaacsim_version }} + + steps: + - name: Checkout Code + uses: actions/checkout@v3 + with: + fetch-depth: 0 + lfs: true + + - name: Build Docker Image + uses: ./.github/actions/docker-build + with: + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + isaacsim-base-image: ${{ env.ISAACSIM_BASE_IMAGE }} + isaacsim-version: ${{ matrix.isaacsim_version }} + + - name: Run General Tests + uses: ./.github/actions/run-tests + with: + test-path: "tools" + result-file: "general-tests-compat-report.xml" + container-name: "isaac-lab-general-compat-test-$$" + image-tag: ${{ env.DOCKER_IMAGE_TAG }} + pytest-options: "" + filter-pattern: "not isaaclab_tasks" + + - name: Copy All Test Results from General Tests Container + run: | + CONTAINER_NAME="isaac-lab-general-compat-test-$$" + if docker ps -a | grep -q $CONTAINER_NAME; then + echo "Copying all test results from General Tests container..." + docker cp $CONTAINER_NAME:/workspace/isaaclab/tests/general-tests-compat-report.xml reports/ 2>/dev/null || echo "No test results to copy from General Tests container" + fi + + - name: Upload General Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: general-tests-compat-results-${{ matrix.isaacsim_version }} + path: reports/general-tests-compat-report.xml + retention-days: 7 + compression-level: 9 + + combine-compat-results: + needs: [test-isaaclab-tasks-compat, test-general-compat] + runs-on: [self-hosted, gpu] + if: always() + + steps: + - name: Checkout Code + uses: actions/checkout@v3 + with: + fetch-depth: 0 + lfs: false + + - name: Create Reports Directory + run: | + mkdir -p reports + + - name: Download All Test Results + uses: actions/download-artifact@v4 + with: + pattern: '*-compat-results-*' + path: reports/ + merge-multiple: true + continue-on-error: true + + - name: Combine All Test Results + uses: ./.github/actions/combine-results + with: + tests-dir: "reports" + output-file: "reports/combined-compat-results.xml" + + - name: Upload Combined Test Results + uses: actions/upload-artifact@v4 + if: always() + with: + name: daily-compat-${{ github.run_id }}-combined-test-results + path: reports/combined-compat-results.xml + retention-days: 30 + compression-level: 9 + + - name: Report Test Results + uses: dorny/test-reporter@v1 + if: always() + with: + name: IsaacLab Compatibility Test Results (${{ github.event_name }}) + path: reports/combined-compat-results.xml + reporter: java-junit + max-annotations: '50' + report-title: "IsaacLab Compatibility Test Results - ${{ github.event_name }} - ${{ github.ref_name }}" + + notify-compatibility-status: + needs: [setup-versions, combine-compat-results] + runs-on: [self-hosted, gpu] + if: always() + + steps: + - name: Checkout Code + uses: actions/checkout@v3 + with: + fetch-depth: 0 + lfs: false + + - name: Create Compatibility Report + run: | + TRIGGER_INFO="**Trigger:** ${{ github.event_name }}" + ISAACSIM_VERSIONS="${{ join(fromJson(needs.setup-versions.outputs.versions), ', ') }}" + echo "## Daily Backwards Compatibility Test Results" > compatibility-report.md + echo "" >> compatibility-report.md + echo "$TRIGGER_INFO" >> compatibility-report.md + echo "**IsaacSim Versions Tested:** $ISAACSIM_VERSIONS" >> compatibility-report.md + echo "**Branch:** ${{ github.ref_name }}" >> compatibility-report.md + echo "**Commit:** ${{ github.sha }}" >> compatibility-report.md + echo "**Run ID:** ${{ github.run_id }}" >> compatibility-report.md + echo "" >> compatibility-report.md + echo "### Test Status:" >> compatibility-report.md + echo "- Results: ${{ needs.combine-compat-results.result }}" >> compatibility-report.md + echo "" >> compatibility-report.md + echo "### Artifacts:" >> compatibility-report.md + echo "- [Combined Test Results](https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }})" >> compatibility-report.md + echo "" >> compatibility-report.md + echo "---" >> compatibility-report.md + echo "*This report was generated automatically by the daily compatibility workflow.*" >> compatibility-report.md + + - name: Upload Compatibility Report + uses: actions/upload-artifact@v4 + if: always() + with: + name: compatibility-report-${{ github.run_id }} + path: compatibility-report.md + retention-days: 30 diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9af2d6e94f0b904358c400ec7f18535b8f34af86 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Build & deploy docs + +on: + push: + branches: + - main + - devel + - 'release/**' + pull_request: + types: [opened, synchronize, reopened] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + check-secrets: + name: Check secrets + runs-on: ubuntu-latest + outputs: + trigger-deploy: ${{ steps.trigger-deploy.outputs.defined }} + steps: + - id: trigger-deploy + env: + REPO_NAME: ${{ secrets.REPO_NAME }} + if: "${{ github.repository == env.REPO_NAME && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/devel' || startsWith(github.ref, 'refs/heads/release/')) }}" + run: echo "defined=true" >> "$GITHUB_OUTPUT" + + build-docs: + name: Build Docs + runs-on: ubuntu-latest + needs: [check-secrets] + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Setup python + uses: actions/setup-python@v2 + with: + python-version: "3.11" + architecture: x64 + + - name: Install dev requirements + working-directory: ./docs + run: pip install -r requirements.txt + + - name: Check branch docs building + working-directory: ./docs + if: needs.check-secrets.outputs.trigger-deploy != 'true' + run: make current-docs + + - name: Generate multi-version docs + working-directory: ./docs + run: | + git fetch --prune --unshallow --tags + make multi-docs + + - name: Upload docs artifact + uses: actions/upload-artifact@v4 + with: + name: docs-html + path: ./docs/_build + + deploy-docs: + name: Deploy Docs + runs-on: ubuntu-latest + needs: [check-secrets, build-docs] + if: needs.check-secrets.outputs.trigger-deploy == 'true' + + steps: + - name: Download docs artifact + uses: actions/download-artifact@v4 + with: + name: docs-html + path: ./docs/_build + + - name: Deploy to gh-pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./docs/_build diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 0000000000000000000000000000000000000000..593aec9a2cb0ed34d3a174e3327f846cab97a1d2 --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,17 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + labeler: + permissions: + contents: read + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/labeler@v6 diff --git a/.github/workflows/license-check.yaml b/.github/workflows/license-check.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3753ffcb6b51de4bc32353fe4fb595432798069 --- /dev/null +++ b/.github/workflows/license-check.yaml @@ -0,0 +1,136 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Check Python Dependency Licenses + +on: + pull_request: + types: [opened, synchronize, reopened] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + license-check: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + # - name: Install jq + # run: sudo apt-get update && sudo apt-get install -y jq + + - name: Clean up disk space + run: | + rm -rf /opt/hostedtoolcache + rm -rf /usr/share/dotnet + rm -rf /opt/ghc + docker container prune -f + docker image prune -af + docker volume prune -f || true + + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' # Adjust as needed + + - name: Install dependencies using ./isaaclab.sh -i + run: | + # first install isaac sim + pip install --upgrade pip + pip install 'isaacsim[all,extscache]==${{ vars.ISAACSIM_BASE_VERSION || '5.0.0' }}' --extra-index-url https://pypi.nvidia.com + chmod +x ./isaaclab.sh # Make sure the script is executable + # install all lab dependencies + ./isaaclab.sh -i + + - name: Install pip-licenses + run: | + pip install pip-licenses + pip install -r tools/template/requirements.txt + pip install -r docs/requirements.txt + + # Optional: Print the license report for visibility + - name: Print License Report + run: pip-licenses --from=mixed --format=markdown + + # Print pipdeptree + - name: Print pipdeptree + run: | + pip install pipdeptree + pipdeptree + + - name: Check licenses against whitelist and exceptions + run: | + # Define the whitelist of allowed licenses + ALLOWED_LICENSES="MIT Apache BSD ISC zlib" + + # Load the exceptions list from the exceptions.json file + EXCEPTIONS_FILE=".github/workflows/license-exceptions.json" + + # Initialize counter for failed packages + FAILED_PACKAGES=0 + + # Get the list of installed packages and their licenses + pip-licenses --from=mixed --format=json > licenses.json + + # Check the output of pip-licenses to ensure it is valid JSON + if ! jq empty licenses.json; then + echo "ERROR: Failed to parse pip-licenses output. Exiting..." + exit 1 + fi + + # Split ALLOWED_LICENSES into individual words + IFS=' ' read -r -a allowed_licenses <<< "$ALLOWED_LICENSES" + + # Loop through the installed packages and their licenses + for pkg in $(jq -r '.[].Name' licenses.json); do + LICENSE=$(jq -r --arg pkg "$pkg" '.[] | select(.Name == $pkg) | .License' licenses.json) + + # Check if any of the allowed licenses are a substring of the package's license + match_found=false + for allowed_license in "${allowed_licenses[@]}"; do + if [[ "$LICENSE" == *"$allowed_license"* ]]; then + match_found=true + break + fi + done + + if [ "$match_found" = false ]; then + # Check if the package is in the exceptions list + EXCEPTION=$(jq -r --arg pkg "$pkg" --arg license "$LICENSE" \ + '.[] | select(.package == $pkg)' "$EXCEPTIONS_FILE") + + # If the package is in the exceptions list + if [ -n "$EXCEPTION" ]; then + # If the license is provided in the exceptions list, check the license + EXCEPTION_LICENSE=$(echo "$EXCEPTION" | jq -r '.license') + + # echo "Comparing licenses for $pkg:" + # echo " EXCEPTION_LICENSE='${EXCEPTION_LICENSE}' (len=${#EXCEPTION_LICENSE})" + # echo " LICENSE='${LICENSE}' (len=${#LICENSE})" + + # If the exceptions list has a license and doesn't match the current license + if [ "$EXCEPTION_LICENSE" != "null" ] && [ "$EXCEPTION_LICENSE" != "$LICENSE" ]; then + echo "ERROR: $pkg has license: $LICENSE" + FAILED_PACKAGES=$((FAILED_PACKAGES + 1)) # Increment the counter + fi + else + # If the package is not in the exceptions list + echo "ERROR: $pkg has license: $LICENSE" + FAILED_PACKAGES=$((FAILED_PACKAGES + 1)) # Increment the counter + fi + fi + done + + # After all packages are processed, check if there were any errors + if [ "$FAILED_PACKAGES" -gt 0 ]; then + echo "ERROR: $FAILED_PACKAGES packages were flagged." + exit 1 # Fail the build + else + echo "All packages were checked." + fi diff --git a/.github/workflows/license-exceptions.json b/.github/workflows/license-exceptions.json new file mode 100644 index 0000000000000000000000000000000000000000..27b3b9c65522f1c6f9046e1c984db115321ae73a --- /dev/null +++ b/.github/workflows/license-exceptions.json @@ -0,0 +1,448 @@ +[ + { + "package": "isaaclab", + "license": null + }, + { + "package": "isaaclab_assets", + "license": null + }, + { + "package": "isaaclab_contrib", + "license": null + }, + { + "package": "isaaclab_mimic", + "license": null + }, + { + "package": "isaaclab_rl", + "license": null + }, + { + "package": "isaaclab_tasks", + "license": null + }, + { + "package": "isaacsim", + "license": null + }, + { + "package": "isaacsim-app", + "license": null + }, + { + "package": "isaacsim-asset", + "license": null + }, + { + "package": "isaacsim-benchmark", + "license": null + }, + { + "package": "isaacsim-code-editor", + "license": null + }, + { + "package": "isaacsim-core", + "license": null + }, + { + "package": "isaacsim-cortex", + "license": null + }, + { + "package": "isaacsim-example", + "license": null + }, + { + "package": "isaacsim-extscache-kit", + "license": null + }, + { + "package": "isaacsim-extscache-kit-sdk", + "license": null + }, + { + "package": "isaacsim-extscache-physics", + "license": null + }, + { + "package": "isaacsim-gui", + "license": null + }, + { + "package": "isaacsim-kernel", + "license": null + }, + { + "package": "isaacsim-replicator", + "license": null + }, + { + "package": "isaacsim-rl", + "license": null + }, + { + "package": "isaacsim-robot", + "license": null + }, + { + "package": "isaacsim-robot-motion", + "license": null + }, + { + "package": "isaacsim-robot-setup", + "license": null + }, + { + "package": "isaacsim-ros1", + "license": null + }, + { + "package": "isaacsim-ros2", + "license": null + }, + { + "package": "isaacsim-sensor", + "license": null + }, + { + "package": "isaacsim-storage", + "license": null + }, + { + "package": "isaacsim-template", + "license": null + }, + { + "package": "isaacsim-test", + "license": null + }, + { + "package": "isaacsim-utils", + "license": null + }, + { + "package": "nvidia-cublas-cu12", + "license": null + }, + { + "package": "nvidia-cuda-cupti-cu12", + "license": null + }, + { + "package": "nvidia-cuda-nvrtc-cu12", + "license": null + }, + { + "package": "nvidia-cuda-runtime-cu12", + "license": null + }, + { + "package": "nvidia-cudnn-cu12", + "license": null + }, + { + "package": "nvidia-cufft-cu12", + "license": null + }, + { + "package": "nvidia-cufile-cu12", + "license": null + }, + { + "package": "nvidia-curand-cu12", + "license": null + }, + { + "package": "nvidia-cusolver-cu12", + "license": null + }, + { + "package": "nvidia-cusparse-cu12", + "license": null + }, + { + "package": "nvidia-cusparselt-cu12", + "license": null + }, + { + "package": "nvidia-nccl-cu12", + "license": null + }, + { + "package": "nvidia-nvjitlink-cu12", + "license": null + }, + { + "package": "nvidia-nvtx-cu12", + "license": null + }, + { + "package": "omniverse-kit", + "license": null + }, + { + "package": "warp-lang", + "license": null + }, + { + "package": "cmeel", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "cmeel-assimp", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "cmeel-boost", + "license": "BSL-1.0", + "comment": "BSL" + }, + { + "package": "cmeel-console-bridge", + "license": "Zlib", + "comment": "ZLIBL" + }, + { + "package": "cmeel-octomap", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "cmeel-qhull", + "license": "UNKNOWN", + "comment": "custom / OSRB" + }, + { + "package": "cmeel-tinyxml", + "license": "Zlib", + "comment": "ZLIBL" + }, + { + "package": "cmeel-urdfdom", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "cmeel-zlib", + "license": "Zlib", + "comment": "ZLIBL" + }, + { + "package": "matplotlib", + "license": "Python Software Foundation License" + }, + { + "package": "certifi", + "license": "Mozilla Public License 2.0 (MPL 2.0)" + }, + { + "package": "rl_games", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "robomimic", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "hpp-fcl", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "pin", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "eigenpy", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "qpsolvers", + "license": "GNU Lesser General Public License v3 (LGPLv3)", + "comment": "OSRB" + }, + { + "package": "quadprog", + "license": "GNU General Public License v2 or later (GPLv2+)", + "comment": "OSRB" + }, + { + "package": "Markdown", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "anytree", + "license": "UNKNOWN", + "comment": "Apache" + }, + { + "package": "click", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "egl_probe", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "filelock", + "license": "Unlicense", + "comment": "no condition" + }, + { + "package": "proglog", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "termcolor", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "typing_extensions", + "license": "Python Software Foundation License", + "comment": "PSFL / OSRB" + }, + { + "package": "urllib3", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "h5py", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "pillow", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "pygame", + "license": "GNU Library or Lesser General Public License (LGPL)", + "comment": "OSRB" + }, + { + "package": "scikit-learn", + "license": "UNKNOWN", + "comment": "BSD" + }, + { + "package": "tensorboardX", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "attrs", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "jsonschema", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "jsonschema-specifications", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "referencing", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "regex", + "license": "UNKNOWN", + "comment": "Apache 2.0" + }, + { + "package": "anyio", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package" : "hf-xet", + "license" : "UNKNOWN", + "comment": "Apache 2.0" + }, + { + "package": "rpds-py", + "license" : "UNKNOWN", + "comment": "MIT" + }, + { + "package": "typing-inspection", + "license" : "UNKNOWN", + "comment": "MIT" + }, + { + "package": "ml_dtypes", + "license" : "UNKNOWN", + "comment": "Apache 2.0" + }, + { + "package": "zipp", + "license" : "UNKNOWN", + "comment": "MIT" + }, + { + "package": "fsspec", + "license" : "UNKNOWN", + "comment": "BSD" + }, + { + "package": "numpy-quaternion", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "aiohappyeyeballs", + "license": "Other/Proprietary License; Python Software Foundation License", + "comment": "PSFL / OSRB" + }, + { + "package": "cffi", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "trio", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "pipdeptree", + "license": "UNKNOWN", + "comment": "MIT" + }, + { + "package": "msgpack", + "license": "UNKNOWN", + "comment": "Apache 2.0" + }, + { + "package": "onnx-ir", + "license": "UNKNOWN", + "comment": "Apache 2.0" + }, + { + "package": "matplotlib-inline", + "license": "UNKNOWN", + "comment": "BSD-3" + } +] diff --git a/.github/workflows/postmerge-ci.yml b/.github/workflows/postmerge-ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..4a1f34d38a16152e34548f235917ac48887d38db --- /dev/null +++ b/.github/workflows/postmerge-ci.yml @@ -0,0 +1,170 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Post-Merge CI + +on: + push: + branches: + - main + - devel + - release/** + +# Concurrency control to prevent parallel runs +concurrency: + group: postmerge-ci-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +env: + NGC_API_KEY: ${{ secrets.NGC_API_KEY }} + ISAACSIM_BASE_IMAGE: ${{ vars.ISAACSIM_BASE_IMAGE || 'nvcr.io/nvidia/isaac-sim' }} + ISAACSIM_BASE_VERSIONS_STRING: ${{ vars.ISAACSIM_BASE_VERSIONS_STRING || '5.1.0' }} + ISAACLAB_IMAGE_NAME: ${{ vars.ISAACLAB_IMAGE_NAME || 'isaac-lab-base' }} + +jobs: + build-and-push-images: + runs-on: [self-hosted, gpu] + timeout-minutes: 180 + environment: + name: postmerge-production + url: https://github.com/${{ github.repository }} + env: + DOCKER_HOST: unix:///var/run/docker.sock + DOCKER_TLS_CERTDIR: "" + + steps: + - name: Checkout Code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + lfs: true + + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + with: + platforms: linux/arm64 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + with: + platforms: linux/amd64,linux/arm64 + driver-opts: | + image=moby/buildkit:buildx-stable-1 + + - name: Login to NGC + run: | + # Only attempt NGC login if API key is available + if [ -n "${{ env.NGC_API_KEY }}" ]; then + echo "Logging into NGC registry..." + docker login -u \$oauthtoken -p ${{ env.NGC_API_KEY }} nvcr.io + echo "✅ Successfully logged into NGC registry" + else + echo "⚠️ NGC_API_KEY not available - skipping NGC login" + echo "This is normal when secrets are not configured" + fi + + - name: Build and Push Docker Images + run: | + # Determine branch name + BRANCH_NAME="${{ github.ref_name }}" + + # Replace '/' with '-' and remove any invalid characters for Docker tag + SAFE_BRANCH_NAME=$(echo $BRANCH_NAME | sed 's/[^a-zA-Z0-9._-]/-/g') + + # Use "latest" if branch name is empty or only contains invalid characters + if [ -z "$SAFE_BRANCH_NAME" ]; then + SAFE_BRANCH_NAME="latest" + fi + + # Get the git repository short name + REPO_SHORT_NAME="${{ github.event.repository.name }}" + if [ -z "$REPO_SHORT_NAME" ]; then + REPO_SHORT_NAME="verification" + fi + + echo "Building images for branch: $BRANCH_NAME" + echo "Safe branch name: $SAFE_BRANCH_NAME" + echo "Repository name: $REPO_SHORT_NAME" + echo "IsaacSim versions: ${{ env.ISAACSIM_BASE_VERSIONS_STRING }}" + + # Parse the env variable string into an array + IMAGE_BASE_VERSIONS_STRING="${{ env.ISAACSIM_BASE_VERSIONS_STRING }}" + # Use set to split the string into positional parameters, then convert to array + set -- $IMAGE_BASE_VERSIONS_STRING + IMAGE_BASE_VERSIONS=("$@") + + for IMAGE_BASE_VERSION in "${IMAGE_BASE_VERSIONS[@]}"; do + IMAGE_BASE_VERSION=$(echo "$IMAGE_BASE_VERSION" | tr -d '[:space:]') + + # Skip empty versions + if [ -z "$IMAGE_BASE_VERSION" ]; then + continue + fi + + # Combine repo short name and branch name for the tag + COMBINED_TAG="${REPO_SHORT_NAME}-${SAFE_BRANCH_NAME}-${IMAGE_BASE_VERSION}" + BUILD_TAG="${COMBINED_TAG}-b${{ github.run_number }}" + + # Determine if multiarch is supported by inspecting the base image manifest + echo "Checking if base image supports multiarch..." + BASE_IMAGE_FULL="${{ env.ISAACSIM_BASE_IMAGE }}:${IMAGE_BASE_VERSION}" + + # Get architectures from the base image manifest + ARCHITECTURES=$(docker manifest inspect "$BASE_IMAGE_FULL" 2>/dev/null | grep -o '"architecture": "[^"]*"' | cut -d'"' -f4 | sort -u) + + if [ -z "$ARCHITECTURES" ]; then + echo "Could not inspect base image manifest: $BASE_IMAGE_FULL" + echo "Defaulting to AMD64 only for safety" + BUILD_PLATFORMS="linux/amd64" + else + echo "Base image architectures found:" + echo "$ARCHITECTURES" | sed 's/^/ - /' + + # Check if both amd64 and arm64 are present + HAS_AMD64=$(echo "$ARCHITECTURES" | grep -c "amd64" || true) + HAS_ARM64=$(echo "$ARCHITECTURES" | grep -c "arm64" || true) + + if [ "$HAS_AMD64" -gt 0 ] && [ "$HAS_ARM64" -gt 0 ]; then + echo "Base image supports multiarch (amd64 + arm64)" + BUILD_PLATFORMS="linux/amd64,linux/arm64" + elif [ "$HAS_AMD64" -gt 0 ]; then + echo "Base image only supports amd64" + BUILD_PLATFORMS="linux/amd64" + elif [ "$HAS_ARM64" -gt 0 ]; then + echo "Base image only supports arm64" + BUILD_PLATFORMS="linux/arm64" + else + echo "Unknown architecture support, defaulting to amd64" + BUILD_PLATFORMS="linux/amd64" + fi + fi + + echo "Building image: ${{ env.ISAACLAB_IMAGE_NAME }}:$COMBINED_TAG" + echo "IsaacSim version: $IMAGE_BASE_VERSION" + echo "Base image: $BASE_IMAGE_FULL" + echo "Target platforms: $BUILD_PLATFORMS" + + # Build Docker image once with both tags for multiple architectures + docker buildx build \ + --platform $BUILD_PLATFORMS \ + --progress=plain \ + -t ${{ env.ISAACLAB_IMAGE_NAME }}:$COMBINED_TAG \ + -t ${{ env.ISAACLAB_IMAGE_NAME }}:$BUILD_TAG \ + --build-arg ISAACSIM_BASE_IMAGE_ARG=${{ env.ISAACSIM_BASE_IMAGE }} \ + --build-arg ISAACSIM_VERSION_ARG=$IMAGE_BASE_VERSION \ + --build-arg ISAACSIM_ROOT_PATH_ARG=/isaac-sim \ + --build-arg ISAACLAB_PATH_ARG=/workspace/isaaclab \ + --build-arg DOCKER_USER_HOME_ARG=/root \ + --cache-from type=gha \ + --cache-to type=gha,mode=max \ + -f docker/Dockerfile.base \ + --push . + + echo "✅ Successfully built and pushed: ${{ env.ISAACLAB_IMAGE_NAME }}:$COMBINED_TAG (platforms: $BUILD_PLATFORMS)" + echo "✅ Successfully built and pushed: ${{ env.ISAACLAB_IMAGE_NAME }}:$BUILD_TAG (platforms: $BUILD_PLATFORMS)" + done diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f71f1f373899d7d5fd29c9aaf0f0c36af4e2d21d --- /dev/null +++ b/.github/workflows/pre-commit.yaml @@ -0,0 +1,20 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +name: Run linters using pre-commit + +on: + pull_request: + types: [opened, synchronize, reopened] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v3 + with: + python-version: "3.12" + - uses: pre-commit/action@v3.0.0 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b4063f1a73f9fc349e6e738fb413f8291a87a9cd --- /dev/null +++ b/.gitignore @@ -0,0 +1,78 @@ +# C++ +**/cmake-build*/ +**/build*/ +**/*.so +**/*.log* + +# Omniverse +**/*.dmp +**/.thumbs + +# No USD files allowed in the repo +# **/*.usd +**/*.usda +**/*.usdc +**/*.usdz + +# Python +.DS_Store +**/*.egg-info/ +**/__pycache__/ +**/.pytest_cache/ +**/*.pyc +**/*.pb + +# Docker/Singularity +**/*.sif +docker/cluster/exports/ +docker/.container.cfg + +# IDE +**/.idea/ +**/.vscode/ +# Don't ignore the top-level .vscode directory as it is +# used to configure VS Code settings +!.vscode + +# Outputs +**/output/* +**/outputs/* +**/videos/* +**/wandb/* +**/.neptune/* +docker/artifacts/ +*.tmp + +# Doc Outputs +**/docs/_build/* +**/generated/* + +# Isaac-Sim packman +_isaac_sim* +_repo +_build +.lastformat + +# RL-Games +**/runs/* +**/logs/* +**/recordings/* + +# Pre-Trained Checkpoints +/.pretrained_checkpoints/ + +# Teleop Recorded Dataset +/large_datasets/ + +# Tests +tests/ + +# Docker history +.isaac-lab-docker-history + +# TacSL sensor +**/tactile_record/* +**/gelsight_r15_data/* + +# IsaacSim Assets +assets/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..426a84b59b79b1df3de2c4695927c393705b63b8 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,71 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.10 + hooks: + # Run the linter + - id: ruff + args: ["--fix"] + # Run the formatter + - id: ruff-format + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + - id: check-symlinks + - id: destroyed-symlinks + - id: check-added-large-files + args: ["--maxkb=2000"] # restrict files more than 2 MB. Should use git-lfs instead. + - id: check-yaml + - id: check-merge-conflict + - id: check-case-conflict + - id: check-executables-have-shebangs + - id: check-toml + - id: end-of-file-fixer + - id: check-shebang-scripts-are-executable + - id: detect-private-key + - id: debug-statements + - repo: https://github.com/codespell-project/codespell + rev: v2.4.1 + hooks: + - id: codespell + additional_dependencies: + - tomli + exclude: "CONTRIBUTORS.md|docs/source/setup/walkthrough/concepts_env_design.rst" + # FIXME: Figure out why this is getting stuck under VPN. + # - repo: https://github.com/RobertCraigie/pyright-python + # rev: v1.1.315 + # hooks: + # - id: pyright + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: insert-license + files: \.(py|ya?ml)$ + args: + # - --remove-header # Remove existing license headers. Useful when updating license. + - --license-filepath + - .github/LICENSE_HEADER.txt + - --use-current-year + exclude: "source/isaaclab_mimic/|scripts/imitation_learning/isaaclab_mimic/" + # Apache 2.0 license for mimic files + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: insert-license + files: ^(source/isaaclab_mimic|scripts/imitation_learning/isaaclab_mimic)/.*\.py$ + args: + # - --remove-header # Remove existing license headers. Useful when updating license. + - --license-filepath + - .github/LICENSE_HEADER_MIMIC.txt + - --use-current-year + - repo: https://github.com/pre-commit/pygrep-hooks + rev: v1.10.0 + hooks: + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal diff --git a/.vscode/.gitignore b/.vscode/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..10b0af342ce38ac614e378cac00d334c5106edd0 --- /dev/null +++ b/.vscode/.gitignore @@ -0,0 +1,10 @@ +# Note: These files are kept for development purposes only. +!tools/launch.template.json +!tools/settings.template.json +!tools/setup_vscode.py +!extensions.json +!tasks.json + +# Ignore all other files +.python.env +*.json diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 0000000000000000000000000000000000000000..b4f0b50306ef981fea8f7d5f70edd483527b91fe --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,12 @@ +{ + // See http://go.microsoft.com/fwlink/?LinkId=827846 + // for the documentation about the extensions.json format + "recommendations": [ + "ms-vscode.cpptools", + "ms-python.python", + "ms-python.vscode-pylance", + "ban.spellright", + "ms-iot.vscode-ros", + "ExecutableBookProject.myst-highlight", + ] +} diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..256f4556d11ca8bf381e93ede98c070d9c87d4b6 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,61 @@ +// This file is a template and is automatically generated by the setup_vscode.py script. +// Do not edit this file directly. +// +// Generated from: /home/user/isaaclab/.vscode/tools/launch.template.json +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Python: Attach (windows-x86_64/linux-x86_64)", + "type": "python", + "request": "attach", + "port": 3000, + "host": "localhost" + }, + { + "name": "Python: Train Environment", + "type": "python", + "request": "launch", + "args" : ["--task", "Isaac-Reach-Franka-v0", "--headless"], + "program": "${workspaceFolder}/scripts/reinforcement_learning/rsl_rl/train.py", + "console": "integratedTerminal" + }, + { + "name": "Python: Play Environment", + "type": "python", + "request": "launch", + "args" : ["--task", "Isaac-Reach-Franka-v0", "--num_envs", "32"], + "program": "${workspaceFolder}/scripts/reinforcement_learning/rsl_rl/play.py", + "console": "integratedTerminal" + }, + { + "name": "Python: SinglePytest", + "type": "python", + "request": "launch", + "module": "pytest", + "args": [ + "${file}" + ], + "console": "integratedTerminal" + }, + { + "name": "Python: ALL Pytest", + "type": "python", + "request": "launch", + "module": "pytest", + "args": ["source/isaaclab/test"], + "console": "integratedTerminal", + "justMyCode": false + } + ] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..c93d97e858439d0daf7c5c1c3f3d8a08cfa38f7f --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,96 @@ +// This file is a template and is automatically generated by the setup_vscode.py script. +// Do not edit this file directly. +// +// Generated from: /home/user/isaaclab/.vscode/tools/settings.template.json +{ + "files.exclude": { + "**/.mypy_cache": true, + "**/__pycache__": true, + "**/*.egg-info": true + }, + "files.associations": { + "*.tpp": "cpp", + "*.kit": "toml", + "*.rst": "restructuredtext" + }, + "editor.rulers": [120], + + // files to be ignored by the linter + "files.watcherExclude": { + "**/.git/objects/**": true, + "**/.git/subtree-cache/**": true, + "**/node_modules/**": true, + "**/_isaac_sim/**": true, + "**/_compiler/**": true + }, + // Configuration for spelling checker + "spellright.language": [ + "en-US-10-1." + ], + "spellright.documentTypes": [ + "markdown", + "latex", + "plaintext", + "cpp", + "asciidoc", + "python", + "restructuredtext" + ], + "cSpell.words": [ + "literalinclude", + "linenos", + "instanceable", + "isaacSim", + "jacobians", + "pointcloud", + "ridgeback", + "rllib", + "robomimic", + "teleoperation", + "xform", + "numpy", + "flatcache", + "physx", + "dpad", + "gamepad", + "linspace", + "upsampled", + "downsampled", + "arange", + "discretization", + "trimesh", + "uninstanceable", + "coeff", + "prestartup" + ], + // This enables python language server. Seems to work slightly better than jedi: + "python.languageServer": "Pylance", + // Use ruff as a formatter and linter + "ruff.configuration": "${workspaceFolder}/pyproject.toml", + // Use docstring generator + "autoDocstring.docstringFormat": "google", + "autoDocstring.guessTypes": true, + // Python environment path + // note: the default interpreter is overridden when user selects a workspace interpreter + // in the status bar. For example, the virtual environment python interpreter + "python.defaultInterpreterPath": "/home/user/miniconda3/envs/env_isaaclab_51/bin/python", + // ROS distribution + "ros.distro": "noetic", + // Language specific settings + "[python]": { + "editor.tabSize": 4 + }, + "[restructuredtext]": { + "editor.tabSize": 2 + }, + // Python extra paths + // Note: this is filled up when "./isaaclab.sh -i" is run + "python.analysis.extraPaths": [ + "${workspaceFolder}/source/isaaclab_tasks", + "${workspaceFolder}/source/isaaclab", + "${workspaceFolder}/source/isaaclab_assets", + "${workspaceFolder}/source/isaaclab_contrib", + "${workspaceFolder}/source/isaaclab_mimic", + "${workspaceFolder}/source/isaaclab_rl" + ] +} diff --git a/.vscode/tasks.json b/.vscode/tasks.json new file mode 100644 index 0000000000000000000000000000000000000000..f7896a038b45e0c939b41b9b0bd5b99d22d838bd --- /dev/null +++ b/.vscode/tasks.json @@ -0,0 +1,29 @@ +{ + // See https://go.microsoft.com/fwlink/?LinkId=733558 + // for the documentation about the tasks.json format + "version": "2.0.0", + "tasks": [ + { + // setup python env + "label": "setup_python_env", + "type": "shell", + "linux": { + "command": "${workspaceFolder}/isaaclab.sh -p ${workspaceFolder}/.vscode/tools/setup_vscode.py" + }, + "windows": { + "command": "${workspaceFolder}/isaaclab.bat -p ${workspaceFolder}/.vscode/tools/setup_vscode.py" + } + }, + { + // run formatter + "label": "run_formatter", + "type": "shell", + "linux": { + "command": "${workspaceFolder}/isaaclab.sh --format" + }, + "windows": { + "command": "${workspaceFolder}/isaaclab.bat --format" + } + } + ] +} diff --git a/.vscode/tools/launch.template.json b/.vscode/tools/launch.template.json new file mode 100644 index 0000000000000000000000000000000000000000..a44f114c822bbb29b3fda1f2649c48b8cdf1687d --- /dev/null +++ b/.vscode/tools/launch.template.json @@ -0,0 +1,57 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Python: Attach (windows-x86_64/linux-x86_64)", + "type": "python", + "request": "attach", + "port": 3000, + "host": "localhost" + }, + { + "name": "Python: Train Environment", + "type": "python", + "request": "launch", + "args" : ["--task", "Isaac-Reach-Franka-v0", "--headless"], + "program": "${workspaceFolder}/scripts/reinforcement_learning/rsl_rl/train.py", + "console": "integratedTerminal" + }, + { + "name": "Python: Play Environment", + "type": "python", + "request": "launch", + "args" : ["--task", "Isaac-Reach-Franka-v0", "--num_envs", "32"], + "program": "${workspaceFolder}/scripts/reinforcement_learning/rsl_rl/play.py", + "console": "integratedTerminal" + }, + { + "name": "Python: SinglePytest", + "type": "python", + "request": "launch", + "module": "pytest", + "args": [ + "${file}" + ], + "console": "integratedTerminal" + }, + { + "name": "Python: ALL Pytest", + "type": "python", + "request": "launch", + "module": "pytest", + "args": ["source/isaaclab/test"], + "console": "integratedTerminal", + "justMyCode": false + } + ] +} diff --git a/.vscode/tools/settings.template.json b/.vscode/tools/settings.template.json new file mode 100644 index 0000000000000000000000000000000000000000..4b07a6a8f9adfa8d5ed30b1e5603ee4aebd74a61 --- /dev/null +++ b/.vscode/tools/settings.template.json @@ -0,0 +1,85 @@ +{ + "files.exclude": { + "**/.mypy_cache": true, + "**/__pycache__": true, + "**/*.egg-info": true + }, + "files.associations": { + "*.tpp": "cpp", + "*.kit": "toml", + "*.rst": "restructuredtext" + }, + "editor.rulers": [120], + + // files to be ignored by the linter + "files.watcherExclude": { + "**/.git/objects/**": true, + "**/.git/subtree-cache/**": true, + "**/node_modules/**": true, + "**/_isaac_sim/**": true, + "**/_compiler/**": true + }, + // Configuration for spelling checker + "spellright.language": [ + "en-US-10-1." + ], + "spellright.documentTypes": [ + "markdown", + "latex", + "plaintext", + "cpp", + "asciidoc", + "python", + "restructuredtext" + ], + "cSpell.words": [ + "literalinclude", + "linenos", + "instanceable", + "isaacSim", + "jacobians", + "pointcloud", + "ridgeback", + "rllib", + "robomimic", + "teleoperation", + "xform", + "numpy", + "flatcache", + "physx", + "dpad", + "gamepad", + "linspace", + "upsampled", + "downsampled", + "arange", + "discretization", + "trimesh", + "uninstanceable", + "coeff", + "prestartup" + ], + // This enables python language server. Seems to work slightly better than jedi: + "python.languageServer": "Pylance", + // Use ruff as a formatter and linter + "ruff.configuration": "${workspaceFolder}/pyproject.toml", + // Use docstring generator + "autoDocstring.docstringFormat": "google", + "autoDocstring.guessTypes": true, + // Python environment path + // note: the default interpreter is overridden when user selects a workspace interpreter + // in the status bar. For example, the virtual environment python interpreter + "python.defaultInterpreterPath": "${workspaceFolder}/_isaac_sim/python.sh", + // ROS distribution + "ros.distro": "noetic", + // Language specific settings + "[python]": { + "editor.tabSize": 4 + }, + "[restructuredtext]": { + "editor.tabSize": 2 + }, + // Python extra paths + // Note: this is filled up when "./isaaclab.sh -i" is run + "python.analysis.extraPaths": [] +} diff --git a/.vscode/tools/setup_vscode.py b/.vscode/tools/setup_vscode.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2c6375ab75e4932bf495c00f86c16f593760bd --- /dev/null +++ b/.vscode/tools/setup_vscode.py @@ -0,0 +1,205 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script sets up the vs-code settings for the Isaac Lab project. + +This script merges the python.analysis.extraPaths from the "{ISAACSIM_DIR}/.vscode/settings.json" file into +the ".vscode/settings.json" file. + +This is necessary because Isaac Sim 2022.2.1 onwards does not add the necessary python packages to the python path +when the "setup_python_env.sh" is run as part of the vs-code launch configuration. +""" + +import re +import sys +import os +import pathlib + + +ISAACLAB_DIR = pathlib.Path(__file__).parents[2] +"""Path to the Isaac Lab directory.""" + +try: + import isaacsim # noqa: F401 + + isaacsim_dir = os.environ.get("ISAAC_PATH", "") +except ModuleNotFoundError or ImportError: + isaacsim_dir = os.path.join(ISAACLAB_DIR, "_isaac_sim") +except EOFError: + print("Unable to trigger EULA acceptance. This is likely due to the script being run in a non-interactive shell.") + print("Please run the script in an interactive shell to accept the EULA.") + print("Skipping the setup of the VSCode settings...") + sys.exit(0) + +# check if the isaac-sim directory exists +if not os.path.exists(isaacsim_dir): + raise FileNotFoundError( + f"Could not find the isaac-sim directory: {isaacsim_dir}. There are two possible reasons for this:" + f"\n\t1. The Isaac Sim directory does not exist as a symlink at: {os.path.join(ISAACLAB_DIR, '_isaac_sim')}" + "\n\t2. The script could not import the 'isaacsim' package. This could be due to the 'isaacsim' package not " + "being installed in the Python environment.\n" + "\nPlease make sure that the Isaac Sim directory exists or that the 'isaacsim' package is installed." + ) + +ISAACSIM_DIR = isaacsim_dir +"""Path to the isaac-sim directory.""" + + +def overwrite_python_analysis_extra_paths(isaaclab_settings: str) -> str: + """Overwrite the python.analysis.extraPaths in the Isaac Lab settings file. + + The extraPaths are replaced with the path names from the isaac-sim settings file that exists in the + "{ISAACSIM_DIR}/.vscode/settings.json" file. + + If the isaac-sim settings file does not exist, the extraPaths are not overwritten. + + Args: + isaaclab_settings: The settings string to use as template. + + Returns: + The settings string with overwritten python analysis extra paths. + """ + # isaac-sim settings + isaacsim_vscode_filename = os.path.join(ISAACSIM_DIR, ".vscode", "settings.json") + + # we use the isaac-sim settings file to get the python.analysis.extraPaths for kit extensions + # if this file does not exist, we will not add any extra paths + if os.path.exists(isaacsim_vscode_filename): + # read the path names from the isaac-sim settings file + with open(isaacsim_vscode_filename) as f: + vscode_settings = f.read() + # extract the path names + # search for the python.analysis.extraPaths section and extract the contents + settings = re.search( + r"\"python.analysis.extraPaths\": \[.*?\]", vscode_settings, flags=re.MULTILINE | re.DOTALL + ) + settings = settings.group(0) + settings = settings.split('"python.analysis.extraPaths": [')[-1] + settings = settings.split("]")[0] + + # read the path names from the isaac-sim settings file + path_names = settings.split(",") + path_names = [path_name.strip().strip('"') for path_name in path_names] + path_names = [path_name for path_name in path_names if len(path_name) > 0] + + # change the path names to be relative to the Isaac Lab directory + rel_path = os.path.relpath(ISAACSIM_DIR, ISAACLAB_DIR) + path_names = ['"${workspaceFolder}/' + rel_path + "/" + path_name + '"' for path_name in path_names] + else: + path_names = [] + print( + f"[WARN] Could not find Isaac Sim VSCode settings: {isaacsim_vscode_filename}." + "\n\tThis will result in missing 'python.analysis.extraPaths' in the VSCode" + "\n\tsettings, which limits the functionality of the Python language server." + "\n\tHowever, it does not affect the functionality of the Isaac Lab project." + "\n\tWe are working on a fix for this issue with the Isaac Sim team." + ) + + # add the path names that are in the Isaac Lab extensions directory + isaaclab_extensions = os.listdir(os.path.join(ISAACLAB_DIR, "source")) + path_names.extend(['"${workspaceFolder}/source/' + ext + '"' for ext in isaaclab_extensions]) + + # combine them into a single string + path_names = ",\n\t\t".expandtabs(4).join(path_names) + # deal with the path separator being different on Windows and Unix + path_names = path_names.replace("\\", "/") + + # replace the path names in the Isaac Lab settings file with the path names parsed + isaaclab_settings = re.sub( + r"\"python.analysis.extraPaths\": \[.*?\]", + '"python.analysis.extraPaths": [\n\t\t'.expandtabs(4) + path_names + "\n\t]".expandtabs(4), + isaaclab_settings, + flags=re.DOTALL, + ) + # return the Isaac Lab settings string + return isaaclab_settings + + +def overwrite_default_python_interpreter(isaaclab_settings: str) -> str: + """Overwrite the default python interpreter in the Isaac Lab settings file. + + The default python interpreter is replaced with the path to the python interpreter used by the + isaac-sim project. This is necessary because the default python interpreter is the one shipped with + isaac-sim. + + Args: + isaaclab_settings: The settings string to use as template. + + Returns: + The settings string with overwritten default python interpreter. + """ + # read executable name + python_exe = sys.executable.replace("\\", "/") + + # We make an exception for replacing the default interpreter if the + # path (/kit/python/bin/python3) indicates that we are using a local/container + # installation of IsaacSim. We will preserve the calling script as the default, python.sh. + # We want to use python.sh because it modifies LD_LIBRARY_PATH and PYTHONPATH + # (among other envars) that we need for all of our dependencies to be accessible. + if "kit/python/bin/python3" in python_exe: + return isaaclab_settings + # replace the default python interpreter in the Isaac Lab settings file with the path to the + # python interpreter in the Isaac Lab directory + isaaclab_settings = re.sub( + r"\"python.defaultInterpreterPath\": \".*?\"", + f'"python.defaultInterpreterPath": "{python_exe}"', + isaaclab_settings, + flags=re.DOTALL, + ) + # return the Isaac Lab settings file + return isaaclab_settings + + +def main(): + # Isaac Lab template settings + isaaclab_vscode_template_filename = os.path.join(ISAACLAB_DIR, ".vscode", "tools", "settings.template.json") + # make sure the Isaac Lab template settings file exists + if not os.path.exists(isaaclab_vscode_template_filename): + raise FileNotFoundError( + f"Could not find the Isaac Lab template settings file: {isaaclab_vscode_template_filename}" + ) + # read the Isaac Lab template settings file + with open(isaaclab_vscode_template_filename) as f: + isaaclab_template_settings = f.read() + + # overwrite the python.analysis.extraPaths in the Isaac Lab settings file with the path names + isaaclab_settings = overwrite_python_analysis_extra_paths(isaaclab_template_settings) + # overwrite the default python interpreter in the Isaac Lab settings file with the path to the + # python interpreter used to call this script + isaaclab_settings = overwrite_default_python_interpreter(isaaclab_settings) + + # add template notice to the top of the file + header_message = ( + "// This file is a template and is automatically generated by the setup_vscode.py script.\n" + "// Do not edit this file directly.\n" + "// \n" + f"// Generated from: {isaaclab_vscode_template_filename}\n" + ) + isaaclab_settings = header_message + isaaclab_settings + + # write the Isaac Lab settings file + isaaclab_vscode_filename = os.path.join(ISAACLAB_DIR, ".vscode", "settings.json") + with open(isaaclab_vscode_filename, "w") as f: + f.write(isaaclab_settings) + + # copy the launch.json file if it doesn't exist + isaaclab_vscode_launch_filename = os.path.join(ISAACLAB_DIR, ".vscode", "launch.json") + isaaclab_vscode_template_launch_filename = os.path.join(ISAACLAB_DIR, ".vscode", "tools", "launch.template.json") + if not os.path.exists(isaaclab_vscode_launch_filename): + # read template launch settings + with open(isaaclab_vscode_template_launch_filename) as f: + isaaclab_template_launch_settings = f.read() + # add header + header_message = header_message.replace( + isaaclab_vscode_template_filename, isaaclab_vscode_template_launch_filename + ) + isaaclab_launch_settings = header_message + isaaclab_template_launch_settings + # write the Isaac Lab launch settings file + with open(isaaclab_vscode_launch_filename, "w") as f: + f.write(isaaclab_launch_settings) + + +if __name__ == "__main__": + main() diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..d382de9d0e3015e5215f90656e74060b902c8283 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,227 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite the technical report of Isaac Lab." +title: Isaac Lab +version: 2.3.0 +repository-code: https://github.com/isaac-sim/IsaacLab +type: software +authors: + - name: Isaac Lab Project Developers +identifiers: + - type: url + value: https://github.com/isaac-sim/IsaacLab + - type: doi + value: 10.48550/arXiv.2511.04831 +url: https://isaac-sim.github.io/IsaacLab +license: BSD-3-Clause +preferred-citation: + type: article + title: Isaac Lab - A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning + authors: + - family-names: Mittal + given-names: Mayank + - family-names: Roth + given-names: Pascal + - family-names: Tigue + given-names: James + - family-names: Richard + given-names: Antoine + - family-names: Zhang + given-names: Octi + - family-names: Du + given-names: Peter + - family-names: Serrano-Muñoz + given-names: Antonio + - family-names: Yao + given-names: Xinjie + - family-names: Zurbrügg + given-names: René + - family-names: Rudin + given-names: Nikita + - family-names: Wawrzyniak + given-names: Lukasz + - family-names: Rakhsha + given-names: Milad + - family-names: Denzler + given-names: Alain + - family-names: Heiden + given-names: Eric + - family-names: Borovicka + given-names: Ales + - family-names: Ahmed + given-names: Ossama + - family-names: Akinola + given-names: Iretiayo + - family-names: Anwar + given-names: Abrar + - family-names: Carlson + given-names: Mark T. + - family-names: Feng + given-names: Ji Yuan + - family-names: Garg + given-names: Animesh + - family-names: Gasoto + given-names: Renato + - family-names: Gulich + given-names: Lionel + - family-names: Guo + given-names: Yijie + - family-names: Gussert + given-names: M. + - family-names: Hansen + given-names: Alex + - family-names: Kulkarni + given-names: Mihir + - family-names: Li + given-names: Chenran + - family-names: Liu + given-names: Wei + - family-names: Makoviychuk + given-names: Viktor + - family-names: Malczyk + given-names: Grzegorz + - family-names: Mazhar + given-names: Hammad + - family-names: Moghani + given-names: Masoud + - family-names: Murali + given-names: Adithyavairavan + - family-names: Noseworthy + given-names: Michael + - family-names: Poddubny + given-names: Alexander + - family-names: Ratliff + given-names: Nathan + - family-names: Rehberg + given-names: Welf + - family-names: Schwarke + given-names: Clemens + - family-names: Singh + given-names: Ritvik + - family-names: Smith + given-names: James Latham + - family-names: Tang + given-names: Bingjie + - family-names: Thaker + given-names: Ruchik + - family-names: Trepte + given-names: Matthew + - family-names: Van Wyk + given-names: Karl + - family-names: Yu + given-names: Fangzhou + - family-names: Millane + given-names: Alex + - family-names: Ramasamy + given-names: Vikram + - family-names: Steiner + given-names: Remo + - family-names: Subramanian + given-names: Sangeeta + - family-names: Volk + given-names: Clemens + - family-names: Chen + given-names: CY + - family-names: Jawale + given-names: Neel + - family-names: Kuruttukulam + given-names: Ashwin Varghese + - family-names: Lin + given-names: Michael A. + - family-names: Mandlekar + given-names: Ajay + - family-names: Patzwaldt + given-names: Karsten + - family-names: Welsh + given-names: John + - family-names: Lafleche + given-names: Jean-Francois + - family-names: Moënne-Loccoz + given-names: Nicolas + - family-names: Park + given-names: Soowan + - family-names: Stepinski + given-names: Rob + - family-names: Van Gelder + given-names: Dirk + - family-names: Amevor + given-names: Chris + - family-names: Carius + given-names: Jan + - family-names: Chang + given-names: Jumyung + - family-names: He Chen + given-names: Anka + - family-names: Ciechomski + given-names: Pablo de Heras + - family-names: Daviet + given-names: Gilles + - family-names: Mohajerani + given-names: Mohammad + - family-names: von Muralt + given-names: Julia + - family-names: Reutskyy + given-names: Viktor + - family-names: Sauter + given-names: Michael + - family-names: Schirm + given-names: Simon + - family-names: Shi + given-names: Eric L. + - family-names: Terdiman + given-names: Pierre + - family-names: Vilella + given-names: Kenny + - family-names: Widmer + given-names: Tobias + - family-names: Yeoman + given-names: Gordon + - family-names: Chen + given-names: Tiffany + - family-names: Grizan + given-names: Sergey + - family-names: Li + given-names: Cathy + - family-names: Li + given-names: Lotus + - family-names: Smith + given-names: Connor + - family-names: Wiltz + given-names: Rafael + - family-names: Alexis + given-names: Kostas + - family-names: Chang + given-names: Yan + - family-names: Fan + given-names: Linxi "Jim" + - family-names: Farshidian + given-names: Farbod + - family-names: Handa + given-names: Ankur + - family-names: Huang + given-names: Spencer + - family-names: Hutter + given-names: Marco + - family-names: Narang + given-names: Yashraj + - family-names: Pouya + given-names: Soha + - family-names: Sheng + given-names: Shiwei + - family-names: Zhu + given-names: Yuke + - family-names: Macklin + given-names: Miles + - family-names: Moravanszky + given-names: Adam + - family-names: Reist + given-names: Philipp + - family-names: Guo + given-names: Yunrong + - family-names: Hoeller + given-names: David + - family-names: State + given-names: Gavriel + journal: arXiv preprint arXiv:2511.04831 + year: 2025 + url: https://arxiv.org/abs/2511.04831 + doi: 10.48550/arXiv.2511.04831 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..d627fa2e27b65a25d7b10874b4bb621155dce57a --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,45 @@ +# Contribution Guidelines + +Isaac Lab is a community maintained project. We wholeheartedly welcome contributions to the project to make +the framework more mature and useful for everyone. These may happen in forms of bug reports, feature requests, +design proposals and more. + +For general information on how to contribute see +. + +--- + +Developer Certificate of Origin +Version 1.1 + +Copyright (C) 2004, 2006 The Linux Foundation and its contributors. + +Everyone is permitted to copy and distribute verbatim copies of this +license document, but changing it is not allowed. + + +Developer's Certificate of Origin 1.1 + +By making a contribution to this project, I certify that: + +(a) The contribution was created in whole or in part by me and I + have the right to submit it under the open source license + indicated in the file; or + +(b) The contribution is based upon previous work that, to the best + of my knowledge, is covered under an appropriate open source + license and I have the right under that license to submit that + work with modifications, whether created in whole or in part + by me, under the same open source license (unless I am + permitted to submit under a different license), as indicated + in the file; or + +(c) The contribution was provided directly to me by some other + person who certified (a), (b) or (c) and I have not modified + it. + +(d) I understand and agree that this project and the contribution + are public and that a record of the contribution (including all + personal information I submit with it, including my sign-off) is + maintained indefinitely and may be redistributed consistent with + this project or the open source license(s) involved. diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md new file mode 100644 index 0000000000000000000000000000000000000000..9fbfe7f1bf3024d9a11d962e11dff58c5bbc7561 --- /dev/null +++ b/CONTRIBUTORS.md @@ -0,0 +1,188 @@ +# Isaac Lab Developers and Contributors + +This is the official list of Isaac Lab Project developers and contributors. + +To see the full list of contributors, please check the revision history in the source control. + +Guidelines for modifications: + +* Please keep the **lists sorted alphabetically**. +* Names should be added to this file as: *individual names* or *organizations*. +* E-mail addresses are tracked elsewhere to avoid spam. + +## Developers + +* Boston Dynamics AI Institute, Inc. +* ETH Zurich +* NVIDIA Corporation & Affiliates +* University of Toronto + +--- + +* Antonio Serrano-Muñoz +* Ben Johnston +* Brian McCann +* Clemens Schwarke +* David Hoeller +* Farbod Farshidian +* Hunter Hansen +* James Smith +* James Tigue +* Kelly (Yunrong) Guo +* Matthew Trepte +* Mayank Mittal +* Nikita Rudin +* Octi (Zhengyu) Zhang +* Pascal Roth +* Sheikh Dawood +* Ossama Ahmed +* Greg Attra + +## Contributors + +* Alessandro Assirelli +* Alex Omar +* Alice Zhou +* Amr Mousa +* Andrej Orsula +* Anton Bjørndahl Mortensen +* Antonin Raffin +* Arjun Bhardwaj +* Ashwin Varghese Kuruttukulam +* Bikram Pandit +* Bingjie Tang +* Brayden Zhang +* Brian Bingham +* Brian McCann +* Cameron Upright +* Calvin Yu +* Cathy Y. Li +* Cheng-Rong Lai +* Chenyu Yang +* Connor Smith +* CY (Chien-Ying) Chen +* David Yang +* Dhananjay Shendre +* Dongxuan Fan +* Dorsa Rohani +* Emily Sturman +* Emmanuel Ferdman +* Fabian Jenelten +* Felipe Mohr +* Felix Yu +* Gary Lvov +* Giulio Romualdi +* Grzegorz Malczyk +* Haoran Zhou +* Harsh Patel +* HoJin Jeon +* Hongwei Xiong +* Hongyu Li +* Hougant Chen +* Huihua Zhao +* Iretiayo Akinola +* Jack Zeng +* Jan Kerner +* Jean Tampon +* Jeonghwan Kim +* Jia Lin Yuan +* Jiakai Zhang +* Jinghuan Shang +* Jingzhou Liu +* Jinqi Wei +* Jinyeob Kim +* Johnson Sun +* Juana Du +* Kaixi Bao +* Kris Wilson +* Krishna Lakhi +* Kourosh Darvish +* Kousheek Chakraborty +* Lionel Gulich +* Lotus Li +* Louis Le Lay +* Lorenz Wellhausen +* Lukas Fröhlich +* Manuel Schweiger +* Masoud Moghani +* Mateo Guaman Castro +* Maurice Rahme +* Michael Gussert +* Michael Noseworthy +* Michael Lin +* Miguel Alonso Jr +* Mihir Kulkarni +* Mingxue Gu +* Mingyu Lee +* Muhong Guo +* Narendra Dahile +* Neel Anand Jawale +* Nicola Loi +* Norbert Cygiert +* Nuoyan Chen (Alvin) +* Nuralem Abizov +* Ori Gadot +* Oyindamola Omotuyi +* Özhan Özen +* Patrick Yin +* Peter Du +* Philipp Reist +* Pulkit Goyal +* Qian Wan +* Qingyang Jiang +* Qinxi Yu +* Rafael Wiltz +* Renaud Poncelet +* René Zurbrügg +* Ritvik Singh +* Rosario Scalise +* Ryan Gresia +* Ryley McCarroll +* Sahara Yuta +* Sergey Grizan +* Shafeef Omar +* Shane Reetz +* Shaoshu Su +* Shaurya Dewan +* Sixiang Chen +* Shundo Kishi +* Stefan Van de Mosselaer +* Stephan Pleines +* Tiffany Chen +* Trushant Adeshara +* Tyler Lum +* Victor Khaustov +* Virgilio Gómez Lambo +* Vladimir Fokow +* Wei Yang +* Welf Rehberg +* Xavier Nal +* Xiaodi Yuan +* Xinjie Yao +* Xinpeng Liu +* Yang Jin +* Yanzi Zhu +* Yijie Guo +* Yohan Choi +* Yujian Zhang +* Yun Liu +* Zehao Wang +* Zijian Li +* Ziqi Fan +* Zoe McCarthy +* David Leon +* Song Yi +* Weihua Zhang +* Tsz Ki GAO +* Anke Zhao + +## Acknowledgements + +* Ajay Mandlekar +* Animesh Garg +* Buck Babich +* Gavriel State +* Hammad Mazhar +* Marco Hutter +* Yan Chang +* Yashraj Narang diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..dee9ba551f428dd44471e7ee461528374233ad3c --- /dev/null +++ b/LICENSE @@ -0,0 +1,30 @@ +Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). + +All rights reserved. + +SPDX-License-Identifier: BSD-3-Clause + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +1. 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However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ca5dc349d67c74938e5524121600f7466276b819 --- /dev/null +++ b/README.md @@ -0,0 +1,141 @@ +![Isaac Lab](docs/source/_static/isaaclab.jpg) + +--- + +# Isaac Lab + +[![IsaacSim](https://img.shields.io/badge/IsaacSim-5.1.0-silver.svg)](https://docs.isaacsim.omniverse.nvidia.com/latest/index.html) +[![Python](https://img.shields.io/badge/python-3.11-blue.svg)](https://docs.python.org/3/whatsnew/3.11.html) +[![Linux platform](https://img.shields.io/badge/platform-linux--64-orange.svg)](https://releases.ubuntu.com/22.04/) +[![Windows platform](https://img.shields.io/badge/platform-windows--64-orange.svg)](https://www.microsoft.com/en-us/) +[![pre-commit](https://img.shields.io/github/actions/workflow/status/isaac-sim/IsaacLab/pre-commit.yaml?logo=pre-commit&logoColor=white&label=pre-commit&color=brightgreen)](https://github.com/isaac-sim/IsaacLab/actions/workflows/pre-commit.yaml) +[![docs status](https://img.shields.io/github/actions/workflow/status/isaac-sim/IsaacLab/docs.yaml?label=docs&color=brightgreen)](https://github.com/isaac-sim/IsaacLab/actions/workflows/docs.yaml) +[![License](https://img.shields.io/badge/license-BSD--3-yellow.svg)](https://opensource.org/licenses/BSD-3-Clause) +[![License](https://img.shields.io/badge/license-Apache--2.0-yellow.svg)](https://opensource.org/license/apache-2-0) + + +**Isaac Lab** is a GPU-accelerated, open-source framework designed to unify and simplify robotics research workflows, +such as reinforcement learning, imitation learning, and motion planning. Built on [NVIDIA Isaac Sim](https://docs.isaacsim.omniverse.nvidia.com/latest/index.html), +it combines fast and accurate physics and sensor simulation, making it an ideal choice for sim-to-real +transfer in robotics. + +Isaac Lab provides developers with a range of essential features for accurate sensor simulation, such as RTX-based +cameras, LIDAR, or contact sensors. The framework's GPU acceleration enables users to run complex simulations and +computations faster, which is key for iterative processes like reinforcement learning and data-intensive tasks. +Moreover, Isaac Lab can run locally or be distributed across the cloud, offering flexibility for large-scale deployments. + +A detailed description of Isaac Lab can be found in our [arXiv paper](https://arxiv.org/abs/2511.04831). + +## Key Features + +Isaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning: + +- **Robots**: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with more than 16 commonly available models. +- **Environments**: Ready-to-train implementations of more than 30 environments, which can be trained with popular reinforcement learning frameworks such as RSL RL, SKRL, RL Games, or Stable Baselines. We also support multi-agent reinforcement learning. +- **Physics**: Rigid bodies, articulated systems, deformable objects +- **Sensors**: RGB/depth/segmentation cameras, camera annotations, IMU, contact sensors, ray casters. + + +## Getting Started + +### Documentation + +Our [documentation page](https://isaac-sim.github.io/IsaacLab) provides everything you need to get started, including +detailed tutorials and step-by-step guides. Follow these links to learn more about: + +- [Installation steps](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html#local-installation) +- [Reinforcement learning](https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_existing_scripts.html) +- [Tutorials](https://isaac-sim.github.io/IsaacLab/main/source/tutorials/index.html) +- [Available environments](https://isaac-sim.github.io/IsaacLab/main/source/overview/environments.html) + + +## Isaac Sim Version Dependency + +Isaac Lab is built on top of Isaac Sim and requires specific versions of Isaac Sim that are compatible with each +release of Isaac Lab. Below, we outline the recent Isaac Lab releases and GitHub branches and their corresponding +dependency versions for Isaac Sim. + +| Isaac Lab Version | Isaac Sim Version | +| ----------------------------- | ------------------------- | +| `main` branch | Isaac Sim 4.5 / 5.0 / 5.1 | +| `v2.3.X` | Isaac Sim 4.5 / 5.0 / 5.1 | +| `v2.2.X` | Isaac Sim 4.5 / 5.0 | +| `v2.1.X` | Isaac Sim 4.5 | +| `v2.0.X` | Isaac Sim 4.5 | + + +## Contributing to Isaac Lab + +We wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone. +These may happen as bug reports, feature requests, or code contributions. For details, please check our +[contribution guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html). + +## Show & Tell: Share Your Inspiration + +We encourage you to utilize our [Show & Tell](https://github.com/isaac-sim/IsaacLab/discussions/categories/show-and-tell) +area in the `Discussions` section of this repository. This space is designed for you to: + +* Share the tutorials you've created +* Showcase your learning content +* Present exciting projects you've developed + +By sharing your work, you'll inspire others and contribute to the collective knowledge +of our community. Your contributions can spark new ideas and collaborations, fostering +innovation in robotics and simulation. + +## Troubleshooting + +Please see the [troubleshooting](https://isaac-sim.github.io/IsaacLab/main/source/refs/troubleshooting.html) section for +common fixes or [submit an issue](https://github.com/isaac-sim/IsaacLab/issues). + +For issues related to Isaac Sim, we recommend checking its [documentation](https://docs.isaacsim.omniverse.nvidia.com/latest/index.html) +or opening a question on its [forums](https://forums.developer.nvidia.com/c/agx-autonomous-machines/isaac/67). + +## Support + +* Please use GitHub [Discussions](https://github.com/isaac-sim/IsaacLab/discussions) for discussing ideas, + asking questions, and requests for new features. +* Github [Issues](https://github.com/isaac-sim/IsaacLab/issues) should only be used to track executable pieces of + work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features, + or general updates. + +## Connect with the NVIDIA Omniverse Community + +Do you have a project or resource you'd like to share more widely? We'd love to hear from you! +Reach out to the NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to explore opportunities +to spotlight your work. + +You can also join the conversation on the [Omniverse Discord](https://discord.com/invite/nvidiaomniverse) to +connect with other developers, share your projects, and help grow a vibrant, collaborative ecosystem +where creativity and technology intersect. Your contributions can make a meaningful impact on the Isaac Lab +community and beyond! + +## License + +The Isaac Lab framework is released under [BSD-3 License](LICENSE). The `isaaclab_mimic` extension and its +corresponding standalone scripts are released under [Apache 2.0](LICENSE-mimic). The license files of its +dependencies and assets are present in the [`docs/licenses`](docs/licenses) directory. + +Note that Isaac Lab requires Isaac Sim, which includes components under proprietary licensing terms. Please see the [Isaac Sim license](docs/licenses/dependencies/isaacsim-license.txt) for information on Isaac Sim licensing. + +Note that the `isaaclab_mimic` extension requires cuRobo, which has proprietary licensing terms that can be found in [`docs/licenses/dependencies/cuRobo-license.txt`](docs/licenses/dependencies/cuRobo-license.txt). + + +## Citation + +If you use Isaac Lab in your research, please cite the technical report: + +``` +@article{mittal2025isaaclab, + title={Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning}, + author={Mayank Mittal and Pascal Roth and James Tigue and Antoine Richard and Octi Zhang and Peter Du and Antonio Serrano-Muñoz and Xinjie Yao and René Zurbrügg and Nikita Rudin and Lukasz Wawrzyniak and Milad Rakhsha and Alain Denzler and Eric Heiden and Ales Borovicka and Ossama Ahmed and Iretiayo Akinola and Abrar Anwar and Mark T. Carlson and Ji Yuan Feng and Animesh Garg and Renato Gasoto and Lionel Gulich and Yijie Guo and M. Gussert and Alex Hansen and Mihir Kulkarni and Chenran Li and Wei Liu and Viktor Makoviychuk and Grzegorz Malczyk and Hammad Mazhar and Masoud Moghani and Adithyavairavan Murali and Michael Noseworthy and Alexander Poddubny and Nathan Ratliff and Welf Rehberg and Clemens Schwarke and Ritvik Singh and James Latham Smith and Bingjie Tang and Ruchik Thaker and Matthew Trepte and Karl Van Wyk and Fangzhou Yu and Alex Millane and Vikram Ramasamy and Remo Steiner and Sangeeta Subramanian and Clemens Volk and CY Chen and Neel Jawale and Ashwin Varghese Kuruttukulam and Michael A. Lin and Ajay Mandlekar and Karsten Patzwaldt and John Welsh and Huihua Zhao and Fatima Anes and Jean-Francois Lafleche and Nicolas Moënne-Loccoz and Soowan Park and Rob Stepinski and Dirk Van Gelder and Chris Amevor and Jan Carius and Jumyung Chang and Anka He Chen and Pablo de Heras Ciechomski and Gilles Daviet and Mohammad Mohajerani and Julia von Muralt and Viktor Reutskyy and Michael Sauter and Simon Schirm and Eric L. Shi and Pierre Terdiman and Kenny Vilella and Tobias Widmer and Gordon Yeoman and Tiffany Chen and Sergey Grizan and Cathy Li and Lotus Li and Connor Smith and Rafael Wiltz and Kostas Alexis and Yan Chang and David Chu and Linxi "Jim" Fan and Farbod Farshidian and Ankur Handa and Spencer Huang and Marco Hutter and Yashraj Narang and Soha Pouya and Shiwei Sheng and Yuke Zhu and Miles Macklin and Adam Moravanszky and Philipp Reist and Yunrong Guo and David Hoeller and Gavriel State}, + journal={arXiv preprint arXiv:2511.04831}, + year={2025}, + url={https://arxiv.org/abs/2511.04831} +} +``` + +## Acknowledgement + +Isaac Lab development initiated from the [Orbit](https://isaac-orbit.github.io/) framework. +We gratefully acknowledge the authors of Orbit for their foundational contributions. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000000000000000000000000000000000000..ea14ff8175beff926f8fcf0c22a42c60e3da0463 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,38 @@ +# Security + +NVIDIA is dedicated to the security and trust of our software products and services, including all source code +repositories managed through our organization. + +If you need to report a security issue, please use the appropriate contact points outlined below. **Please do +not report security vulnerabilities through GitHub.** + +## Reporting Potential Security Vulnerability in an NVIDIA Product + +To report a potential security vulnerability in any NVIDIA product: + +- Web: [Security Vulnerability Submission Form](https://www.nvidia.com/object/submit-security-vulnerability.html) + +- E-Mail: psirt@nvidia.com + + - We encourage you to use the following PGP key for secure email communication: [NVIDIA public PGP Key for communication](https://www.nvidia.com/en-us/security/pgp-key) + + - Please include the following information: + + - Product/Driver name and version/branch that contains the vulnerability + + - Type of vulnerability (code execution, denial of service, buffer overflow, etc.) + + - Instructions to reproduce the vulnerability + + - Proof-of-concept or exploit code + + - Potential impact of the vulnerability, including how an attacker could exploit the vulnerability + +While NVIDIA currently does not have a bug bounty program, we do offer acknowledgement when an +externally reported security issue is addressed under our coordinated vulnerability disclosure policy. Please +visit our [Product Security Incident Response Team (PSIRT)](https://www.nvidia.com/en-us/security/psirt-policies/) +policies page for more information. + +## NVIDIA Product Security + +For all security-related concerns, please visit NVIDIA's Product Security portal at: https://www.nvidia.com/en-us/security diff --git a/VERSION b/VERSION new file mode 100644 index 0000000000000000000000000000000000000000..276cbf9e2858c779297bb9f73b34170302949ec4 --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +2.3.0 diff --git a/apps/isaaclab.python.headless.kit b/apps/isaaclab.python.headless.kit new file mode 100644 index 0000000000000000000000000000000000000000..5e93d229c043b19b0017f1d2e5caf7103df5b872 --- /dev/null +++ b/apps/isaaclab.python.headless.kit @@ -0,0 +1,220 @@ +## +# Adapted from: _isaac_sim/apps/omni.isaac.sim.python.gym.headless.kit +## + +[package] +title = "Isaac Lab Python Headless" +description = "An app for running Isaac Lab headlessly" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "headless"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "5.1.0" + +################################## +# Omniverse related dependencies # +################################## +[dependencies] +"omni.physx" = {} +"omni.physx.tensors" = {} +"omni.physx.fabric" = {} +"omni.warp.core" = {} +"usdrt.scenegraph" = {} +"omni.kit.telemetry" = {} +"omni.kit.loop" = {} +# this is needed to create physics material through CreatePreviewSurfaceMaterialPrim +"omni.kit.usd.mdl" = {} +# this is used for converting assets that have the wrong units +"omni.usd.metrics.assembler.ui" = {} + +[settings] +app.content.emptyStageOnStart = false + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# default viewport is fill +app.runLoops.rendering_0.fillResolution = false +exts."omni.kit.window.viewport".blockingGetViewportDrawable = false + +# Fix PlayButtonGroup error +exts."omni.kit.widget.toolbar".PlayButton.enabled = false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.app.settings] +persistent = true +dev_build = false +fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 still true? + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings] +# MGPU is always on, you can turn it from the settings, and force this off to save even more resource if you +# only want to use a single GPU on your MGPU system +# False for Isaac Sim +renderer.multiGpu.enabled = true +renderer.multiGpu.autoEnable = true +'rtx-transient'.resourcemanager.enableTextureStreaming = true +app.asyncRendering = false +app.asyncRenderingLowLatency = false +app.hydraEngine.waitIdle = false +# app.hydra.aperture.conform = 4 # in 105.1 pixels are square by default +omni.replicator.asyncRendering = false + +### FSD +# this .kit file is used for headless, no-rendering cases. There won't be a scene delegate +# created, but setting useFSD to false here is done to not do full fabric population, but +# instead to minimal population +app.useFabricSceneDelegate = false + +# Enable Iray and pxr by setting this to "rtx,iray,pxr" +renderer.enabled = "rtx" + +# Avoid warning on shutdown from audio context +app.audio.enabled = false + +# Enable Vulkan - avoids torch+cu12 error on windows +app.vulkan = true + +# Set profiler backend to NVTX by default +app.profilerBackend = "nvtx" + +# Disables rate limit in runloop +app.runLoops.main.rateLimitEnabled=false + +# hide NonToggleable Exts +exts."omni.kit.window.extensions".hideNonToggleableExts = true +exts."omni.kit.window.extensions".showFeatureOnly = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# disable the metrics assembler change listener, we don't want to do any runtime changes +metricsAssembler.changeListenerEnabled = false + +# Extensions +############################### +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/107/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.extensions] +skipPublishVerification = false +registryEnabled = true + +[settings.crashreporter.data] +experience = "Isaac Sim" + +[settings.persistent] +app.file.recentFiles = [] +app.stage.upAxis = "Z" +app.stage.movePrimInPlace = false +app.stage.instanceableOnCreatingReference = false +app.stage.materialStrength = "weakerThanDescendants" + +app.transform.gizmoUseSRT = true +app.viewport.grid.scale = 1.0 +app.viewport.pickingMode = "kind:model.ALL" +app.viewport.camMoveVelocity = 0.05 # 5 m/s +app.viewport.gizmo.scale = 0.01 # scaled to meters +app.viewport.previewOnPeek = false +app.viewport.snapToSurface = false +app.viewport.displayOptions = 31951 # Disable Frame Rate and Resolution by default +app.window.uiStyle = "NvidiaDark" +app.primCreation.DefaultXformOpType = "Scale, Orient, Translate" +app.primCreation.DefaultXformOpOrder="xformOp:translate, xformOp:orient, xformOp:scale" +app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0] +simulation.minFrameRate = 15 +simulation.defaultMetersPerUnit = 1.0 +omnigraph.updateToUsd = false +omnigraph.useSchemaPrims = true +omnigraph.disablePrimNodes = true +omni.replicator.captureOnPlay = true +omnihydra.useSceneGraphInstancing = true +renderer.startupMessageDisplayed = true # hides the IOMMU popup window + +# Make Detail panel visible by default +app.omniverse.content_browser.options_menu.show_details = true +app.omniverse.filepicker.options_menu.show_details = true + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false +### When Direct GPU mode is enabled (suppressReadback=true) use direct interop between PhysX GPU and Fabric +fabricUseGPUInterop = true + +# Performance improvement +resourcemonitor.timeBetweenQueries = 100 + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +[settings.ngx] +enabled=true # Enable this for DLSS + +######################## +# Isaac Sim Extensions # +######################## +[dependencies] +"isaacsim.simulation_app" = {} +"isaacsim.core.api" = {} +"isaacsim.core.cloner" = {} +"isaacsim.core.utils" = {} +"isaacsim.core.version" = {} + +######################## +# Isaac Lab Extensions # +######################## + +# Load Isaac Lab extensions last +"isaaclab" = {order = 1000} +"isaaclab_assets" = {order = 1000} +"isaaclab_tasks" = {order = 1000} +"isaaclab_mimic" = {order = 1000} +"isaaclab_rl" = {order = 1000} + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaaclab.python.headless.rendering.kit b/apps/isaaclab.python.headless.rendering.kit new file mode 100644 index 0000000000000000000000000000000000000000..b37f33999bf48d3ea07b0d3ad52eee7ca7346619 --- /dev/null +++ b/apps/isaaclab.python.headless.rendering.kit @@ -0,0 +1,161 @@ +## +# Adapted from: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/blob/main/apps/omni.isaac.sim.python.gym.camera.kit +# +# This app file designed specifically towards vision-based RL tasks. It provides necessary settings to enable +# multiple cameras to be rendered each frame. Additional settings are also applied to increase performance when +# rendering cameras across multiple environments. +## + +[package] +title = "Isaac Lab Python Headless Camera" +description = "An app for running Isaac Lab headlessly with rendering enabled" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "camera", "minimal"] + +[dependencies] +# Isaac Lab minimal app +"isaaclab.python.headless" = {} +"omni.replicator.core" = {} + +# Rendering +"omni.kit.material.library" = {} +"omni.kit.viewport.rtx" = {} + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "5.1.0" + +### FSD +app.useFabricSceneDelegate = true +# Temporary, should be enabled by default in Kit soon +rtx.hydra.readTransformsFromFabricInRenderDelegate = true + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Flags for better rendering performance +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.translucency.enabled = false +rtx.reflections.enabled = false +rtx.indirectDiffuse.enabled = false +rtx-transient.dlssg.enabled = false +rtx.directLighting.sampledLighting.enabled = true +rtx.directLighting.sampledLighting.samplesPerPixel = 1 +rtx.sceneDb.ambientLightIntensity = 1.0 +# rtx.shadows.enabled = false + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 + +# Disable present thread to improve performance +exts."omni.renderer.core".present.enabled=false + +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.raytracing.cached.enabled = false +rtx.ambientOcclusion.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids unnecessary GPU context initialization +renderer.multiGpu.maxGpuCount=1 + +# Force synchronous rendering to improve training results +omni.replicator.asyncRendering = false + +# Avoids frame offset issue +app.updateOrder.checkForHydraRenderComplete = 1000 +app.renderer.waitIdle=true +app.hydraEngine.waitIdle=true + +# Forces serial processing for omni graph to avoid NCCL timeout hangs in distributed training +app.execution.debug.forceSerial = true + +app.audio.enabled = false + +# Enable Vulkan - avoids torch+cu12 error on windows +app.vulkan = true + +# Set profiler backend to NVTX by default +app.profilerBackend = "nvtx" + +# Disables rate limit in runloop +app.runLoops.main.rateLimitEnabled=false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +# disable the metrics assembler change listener, we don't want to do any runtime changes +metricsAssembler.changeListenerEnabled = false + +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/107/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +skipWhileMinimized = false +sleepMsOnFocus = 0 +sleepMsOutOfFocus = 0 + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false +### When Direct GPU mode is enabled (suppressReadback=true) use direct interop between PhysX GPU and Fabric +fabricUseGPUInterop = true + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaaclab.python.kit b/apps/isaaclab.python.kit new file mode 100644 index 0000000000000000000000000000000000000000..de4252f2995bdda9c75bd370f83c81a663cce592 --- /dev/null +++ b/apps/isaaclab.python.kit @@ -0,0 +1,307 @@ +## +# Adapted from: _isaac_sim/apps/isaacsim.exp.base.kit +## + +[package] +title = "Isaac Lab Python" +description = "An app for running Isaac Lab" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd"] + +[dependencies] +# Isaac Sim extensions +"isaacsim.app.about" = {} +"isaacsim.asset.browser" = {} +"isaacsim.core.api" = {} +"isaacsim.core.cloner" = {} +"isaacsim.core.nodes" = {} +"isaacsim.core.simulation_manager" = {} +"isaacsim.core.throttling" = {} +"isaacsim.core.utils" = {} +"isaacsim.core.version" = {} +"isaacsim.gui.menu" = {} +"isaacsim.gui.property" = {} +"isaacsim.replicator.behavior" = {} +"isaacsim.robot.manipulators" = {} +"isaacsim.robot.policy.examples" = {} +"isaacsim.robot.schema" = {} +"isaacsim.robot.wheeled_robots" = {} +"isaacsim.sensors.camera" = {} +"isaacsim.sensors.physics" = {} +"isaacsim.sensors.physx" = {} +"isaacsim.sensors.rtx" = {} +"isaacsim.simulation_app" = {} +"isaacsim.storage.native" = {} +"isaacsim.util.debug_draw" = {} + +# Isaac Sim Extra +"isaacsim.asset.importer.mjcf" = {} +"isaacsim.asset.importer.urdf" = {version = "2.4.31", exact = true} +"omni.physx.bundle" = {} +"omni.physx.tensors" = {} +"omni.replicator.core" = {} +"omni.replicator.replicator_yaml" = {} +"omni.syntheticdata" = {} +"semantics.schema.editor" = {} +"semantics.schema.property" = {} + +# Kit based editor extensions +"omni.anim.curve.core" = {} +"omni.graph.action" = {} +"omni.graph.core" = {} +"omni.graph.nodes" = {} +"omni.graph.scriptnode" = {} +"omni.graph.ui_nodes" = {} +"omni.hydra.engine.stats" = {} +"omni.hydra.rtx" = {} +"omni.kit.mainwindow" = {} +"omni.kit.manipulator.camera" = {} +"omni.kit.manipulator.prim" = {} +"omni.kit.manipulator.selection" = {} +"omni.kit.material.library" = {} +"omni.kit.menu.common" = { order = 1000 } +"omni.kit.menu.create" = {} +"omni.kit.menu.stage" = {} +"omni.kit.menu.utils" = {} +"omni.kit.primitive.mesh" = {} +"omni.kit.property.bundle" = {} +"omni.kit.raycast.query" = {} +"omni.kit.stagerecorder.bundle" = {} +"omni.kit.stage_template.core" = {} +"omni.kit.telemetry" = {} +"omni.kit.tool.asset_importer" = {} +"omni.kit.tool.collect" = {} +"omni.kit.viewport.legacy_gizmos" = {} +"omni.kit.viewport.menubar.camera" = {} +"omni.kit.viewport.menubar.display" = {} +"omni.kit.viewport.menubar.lighting" = {} +"omni.kit.viewport.menubar.render" = {} +"omni.kit.viewport.menubar.settings" = {} +"omni.kit.viewport.scene_camera_model" = {} +"omni.kit.viewport.window" = {} +"omni.kit.window.console" = {} +"omni.kit.window.content_browser" = {} +"omni.kit.window.property" = {} +"omni.kit.window.script_editor" = {} +"omni.kit.window.stage" = {} +"omni.kit.window.status_bar" = {} +"omni.kit.window.toolbar" = {} +"omni.physics.stageupdate" = {} +"omni.rtx.settings.core" = {} +"omni.uiaudio" = {} +"omni.usd.metrics.assembler.ui" = {} +"omni.usd.schema.metrics.assembler" = {} +"omni.warp.core" = {} + +######################## +# Isaac Lab Extensions # +######################## + +# Load Isaac Lab extensions last +"isaaclab" = {order = 1000} +"isaaclab_assets" = {order = 1000} +"isaaclab_tasks" = {order = 1000} +"isaaclab_mimic" = {order = 1000} +"isaaclab_rl" = {order = 1000} + +[settings] +exts."omni.kit.material.library".ui_show_list = [ + "OmniPBR", + "OmniGlass", + "OmniSurface", + "USD Preview Surface", +] +exts."omni.kit.renderer.core".present.enabled = false # Fixes MGPU stability issue +exts."omni.kit.viewport.window".windowMenu.entryCount = 2 # Allow user to create two viewports by default +exts."omni.kit.viewport.window".windowMenu.label = "" # Put Viewport menuitem under Window menu +exts."omni.rtx.window.settings".window_menu = "Window" # Where to put the render settings menuitem +exts."omni.usd".locking.onClose = false # reduce time it takes to close/create stage +renderer.asyncInit = true # Don't block while renderer inits +renderer.gpuEnumeration.glInterop.enabled = false # Improves startup speed. +rendergraph.mgpu.backend = "copyQueue" # In MGPU configurations, This setting can be removed if IOMMU is disabled for better performance, copyQueue improves stability and performance when IOMMU is enabled +rtx-transient.dlssg.enabled = false # DLSSG frame generation is not compatible with synthetic data generation +rtx.hydra.mdlMaterialWarmup = true # start loading the MDL shaders needed before any delegate is actually created. +omni.replicator.asyncRendering = false # Async rendering must be disabled for SDG +exts."omni.kit.test".includeTests = ["*isaac*"] # Add isaac tests to test runner +foundation.verifyOsVersion.enabled = false + +# disable the metrics assembler change listener, we don't want to do any runtime changes +metricsAssembler.changeListenerEnabled = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Disable for base application +[settings."filter:platform"."windows-x86_64"] +isaac.startup.ros_bridge_extension = "" +[settings."filter:platform"."linux-x86_64"] +isaac.startup.ros_bridge_extension = "" + +# menu styling +[settings.exts."omni.kit.menu.utils"] +logDeprecated = false +margin_size = [18, 3] +tick_spacing = [10, 6] +margin_size_posttick = [0, 3] +separator_size = [14, 10] +root_spacing = 3 +post_label_spaces = 6 +color_tick_enabled = 0xFFFAC434 +color_separator = 0xFF7E7E7E +color_label_enabled = 0xFFEEEEEE +menu_title_color = 0xFF202020 +menu_title_line_color = 0xFF5E5E5E +menu_title_text_color = 0xFF8F8F8F +menu_title_text_height = 24 +menu_title_close_color = 0xFFC6C6C6 +indent_all_ticks = false +show_menu_titles = true + +[settings.app] +name = "Isaac-Sim" +version = "5.1.0" +versionFile = "${exe-path}/VERSION" +content.emptyStageOnStart = true +fastShutdown = true +file.ignoreUnsavedOnExit = true +font.file = "${fonts}/OpenSans-SemiBold.ttf" +font.size = 16 +gatherRenderResults = true # True to prevent artifacts in multiple viewport configurations, can be set to false for better performance in some cases +hangDetector.enabled = true +hangDetector.timeout = 120 +player.useFixedTimeStepping = true +settings.fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 still true? +settings.persistent = true # settings are persistent for this app + +vulkan = true # Explicitly enable Vulkan (on by default on Linux, off by default on Windows) +### async rendering settings +asyncRendering = false +asyncRenderingLowLatency = false + +[settings.app.window] +iconPath = "${isaacsim.simulation_app}/data/omni.isaac.sim.png" +title = "Isaac Sim" + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +resolution.height = 720 +resolution.width = 1280 +skipWhileMinimized = false # python app does not throttle +sleepMsOnFocus = 0 # python app does not throttle +sleepMsOutOfFocus = 0 # python app does not throttle + +[settings.app.viewport] +defaultCamPos.x = 5 +defaultCamPos.y = 5 +defaultCamPos.z = 5 +defaults.fillViewport = false # default to not fill viewport +grid.enabled = true +outline.enabled = true +boundingBoxes.enabled = false +show.camera=false +show.lights=false + +[settings.telemetry] +enableAnonymousAppName = true # Anonymous Kit application usage telemetry +enableAnonymousData = true # Anonymous Kit application usage telemetry + +[settings.persistent] +app.primCreation.DefaultXformOpOrder = "xformOp:translate, xformOp:orient, xformOp:scale" +app.primCreation.DefaultXformOpType = "Scale, Orient, Translate" +app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0] # Meters default +app.primCreation.DefaultXformOpPrecision = "Double" +app.primCreation.DefaultRotationOrder = "ZYX" +app.primCreation.PrimCreationWithDefaultXformOps = true +app.stage.timeCodeRange = [0, 1000000] +app.stage.upAxis = "Z" # Isaac Sim default Z up +app.viewport.camMoveVelocity = 0.05 # Meters default +app.viewport.gizmo.scale = 0.01 # Meters default +app.viewport.grid.scale = 1.0 # Meters default +app.viewport.camShowSpeedOnStart = false # Hide camera speed on startup +app.omniverse.gamepadCameraControl = false # Disable gamepad control for camera by default +exts."omni.anim.navigation.core".navMesh.config.autoRebakeOnChanges = false +exts."omni.anim.navigation.core".navMesh.viewNavMesh = false +physics.visualizationDisplayJoints = false # improves performance +physics.visualizationSimulationOutput = false # improves performance +physics.resetOnStop = true # Physics state is reset on stop +renderer.startupMessageDisplayed = true # hides the IOMMU popup window +resourcemonitor.timeBetweenQueries = 100 # improves performance +simulation.defaultMetersPerUnit = 1.0 # Meters default +omni.replicator.captureOnPlay = true + +[settings] +### async rendering settings +omni.replicator.asyncRendering = false +app.asyncRendering = false +app.asyncRenderingLowLatency = false + +### FSD +app.useFabricSceneDelegate = true +rtx.hydra.readTransformsFromFabricInRenderDelegate = true + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.app.livestream] +outDirectory = "${data}" + +# Extensions +############################### +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/107/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.extensions] +skipPublishVerification = false +registryEnabled = true + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +[settings.physics] +autoPopupSimulationOutputWindow = false +updateToUsd = false +updateVelocitiesToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false +### When Direct GPU mode is enabled (suppressReadback=true) use direct interop between PhysX GPU and Fabric +fabricUseGPUInterop = true + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaaclab.python.rendering.kit b/apps/isaaclab.python.rendering.kit new file mode 100644 index 0000000000000000000000000000000000000000..73c181a0d685bd82c8f96500547b16451a73c069 --- /dev/null +++ b/apps/isaaclab.python.rendering.kit @@ -0,0 +1,150 @@ +## +# Adapted from: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/blob/main/apps/omni.isaac.sim.python.gym.camera.kit +# +# This app file designed specifically towards vision-based RL tasks. It provides necessary settings to enable +# multiple cameras to be rendered each frame. Additional settings are also applied to increase performance when +# rendering cameras across multiple environments. +## + +[package] +title = "Isaac Lab Python Camera" +description = "An app for running Isaac Lab with rendering enabled" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "camera", "minimal"] + +[dependencies] +# Isaac Lab minimal app +"isaaclab.python" = {} + +# PhysX +"omni.kit.property.physx" = {} + +# Rendering +"omni.kit.material.library" = {} + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "5.1.0" + +### FSD +app.useFabricSceneDelegate = true +# Temporary, should be enabled by default in Kit soon +rtx.hydra.readTransformsFromFabricInRenderDelegate = true + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Flags for better rendering performance +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.translucency.enabled = false +rtx.reflections.enabled = false +rtx.indirectDiffuse.enabled = false +rtx-transient.dlssg.enabled = false +rtx.directLighting.sampledLighting.enabled = true +rtx.directLighting.sampledLighting.samplesPerPixel = 1 +rtx.sceneDb.ambientLightIntensity = 1.0 +# rtx.shadows.enabled = false + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 + +# Disable present thread to improve performance +exts."omni.renderer.core".present.enabled=false + +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.raytracing.cached.enabled = false +rtx.ambientOcclusion.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids unnecessary GPU context initialization +renderer.multiGpu.maxGpuCount=1 + +# Force synchronous rendering to improve training results +omni.replicator.asyncRendering = false + +# Avoids frame offset issue +app.updateOrder.checkForHydraRenderComplete = 1000 +app.renderer.waitIdle=true +app.hydraEngine.waitIdle=true + +app.audio.enabled = false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +# disable the metrics assembler change listener, we don't want to do any runtime changes +metricsAssembler.changeListenerEnabled = false + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false +### When Direct GPU mode is enabled (suppressReadback=true) use direct interop between PhysX GPU and Fabric +fabricUseGPUInterop = true + +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/107/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +skipWhileMinimized = false +sleepMsOnFocus = 0 +sleepMsOutOfFocus = 0 + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaaclab.python.xr.openxr.headless.kit b/apps/isaaclab.python.xr.openxr.headless.kit new file mode 100644 index 0000000000000000000000000000000000000000..4fa2bfc098506f794d5927006bde41d7a703f237 --- /dev/null +++ b/apps/isaaclab.python.xr.openxr.headless.kit @@ -0,0 +1,64 @@ +## +# Adapted from: apps/isaaclab.python.xr.openxr.kit +## + +[package] +title = "Isaac Lab Python OpenXR Headless" +description = "An app for running Isaac Lab with OpenXR in headless mode" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd", "headless"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "5.1.0" + +### FSD +app.useFabricSceneDelegate = true +# Temporary, should be enabled by default in Kit soon +rtx.hydra.readTransformsFromFabricInRenderDelegate = true + +# xr optimizations +xr.skipInputDeviceUSDWrites = true +'rtx-transient'.resourcemanager.enableTextureStreaming = false + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[dependencies] +"isaaclab.python.xr.openxr" = {} + +[settings] +xr.profile.ar.enabled = true + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaaclab.python.xr.openxr.kit b/apps/isaaclab.python.xr.openxr.kit new file mode 100644 index 0000000000000000000000000000000000000000..4150eae64494aafa077084975b9d5a452dcffa9a --- /dev/null +++ b/apps/isaaclab.python.xr.openxr.kit @@ -0,0 +1,93 @@ +## +# Adapted from: _isaac_sim/apps/isaacsim.exp.xr.openxr.kit +## + +[package] +title = "Isaac Lab Python OpenXR" +description = "An app for running Isaac Lab with OpenXR" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "5.1.0" + +### async rendering settings +# omni.replicator.asyncRendering needs to be false for external camera rendering +omni.replicator.asyncRendering = false +app.asyncRendering = true +app.asyncRenderingLowLatency = true + +# For XR, set this back to default "#define OMNI_MAX_DEVICE_GROUP_DEVICE_COUNT 16" +renderer.multiGpu.maxGpuCount = 16 +renderer.gpuEnumeration.glInterop.enabled = true # Allow Kit XR OpenXR to render headless + +### FSD +app.useFabricSceneDelegate = true +# Temporary, should be enabled by default in Kit soon +rtx.hydra.readTransformsFromFabricInRenderDelegate = true + +# xr optimizations +xr.skipInputDeviceUSDWrites = true +'rtx-transient'.resourcemanager.enableTextureStreaming = false + +[dependencies] +"isaaclab.python" = {} + +# Kit extensions +"omni.kit.xr.system.openxr" = {} +"omni.kit.xr.profile.ar" = {} + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[settings] +app.xr.enabled = true +# Set profiler backend to NVTX by default +app.profilerBackend = "nvtx" + +# xr settings +xr.ui.enabled = false +xr.depth.aov = "GBufferDepth" +defaults.xr.profile.ar.anchorMode = "custom anchor" +rtx.rendermode = "RaytracedLighting" +persistent.xr.profile.ar.renderQuality = "performance" +persistent.xr.profile.ar.render.nearPlane = 0.15 +xr.openxr.components."omni.kit.xr.openxr.ext.hand_tracking".enabled = true +xr.openxr.components."isaacsim.xr.openxr.hand_tracking".enabled = true + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.cloud = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" +persistent.isaac.asset_root.nvidia = "https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/5.1" diff --git a/apps/isaacsim_4_5/extension.toml b/apps/isaacsim_4_5/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..f0668b9184d126682bb868596d119d0112e46e80 --- /dev/null +++ b/apps/isaacsim_4_5/extension.toml @@ -0,0 +1 @@ +# This is not an extension diff --git a/apps/isaacsim_4_5/isaaclab.python.headless.kit b/apps/isaacsim_4_5/isaaclab.python.headless.kit new file mode 100644 index 0000000000000000000000000000000000000000..944e284c45219450b71f683daad6f32747a552f0 --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.headless.kit @@ -0,0 +1,202 @@ +## +# Adapted from: _isaac_sim/apps/omni.isaac.sim.python.gym.headless.kit +## + +[package] +title = "Isaac Lab Python Headless" +description = "An app for running Isaac Lab headlessly" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "headless"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "4.5.0" + +################################## +# Omniverse related dependencies # +################################## +[dependencies] +"omni.physx" = {} +"omni.physx.tensors" = {} +"omni.physx.fabric" = {} +"omni.warp.core" = {} +"usdrt.scenegraph" = {} +"omni.kit.telemetry" = {} +"omni.kit.loop" = {} + +[settings] +app.content.emptyStageOnStart = false + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# default viewport is fill +app.runLoops.rendering_0.fillResolution = false +exts."omni.kit.window.viewport".blockingGetViewportDrawable = false + +# Fix PlayButtonGroup error +exts."omni.kit.widget.toolbar".PlayButton.enabled = false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.app.settings] +persistent = true +dev_build = false +fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 still true? + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings] +# MGPU is always on, you can turn it from the settings, and force this off to save even more resource if you +# only want to use a single GPU on your MGPU system +# False for Isaac Sim +renderer.multiGpu.enabled = true +renderer.multiGpu.autoEnable = true +'rtx-transient'.resourcemanager.enableTextureStreaming = true +app.asyncRendering = false +app.asyncRenderingLowLatency = false +app.hydraEngine.waitIdle = false +# app.hydra.aperture.conform = 4 # in 105.1 pixels are square by default +omni.replicator.asyncRendering = false + +# Enable Iray and pxr by setting this to "rtx,iray,pxr" +renderer.enabled = "rtx" + +# Avoid warning on shutdown from audio context +app.audio.enabled = false + +# Enable Vulkan - avoids torch+cu12 error on windows +app.vulkan = true + +# Set profiler backend to NVTX by default +app.profilerBackend = "nvtx" + +# hide NonToggleable Exts +exts."omni.kit.window.extensions".hideNonToggleableExts = true +exts."omni.kit.window.extensions".showFeatureOnly = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Extensions +############################### +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/106/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.extensions] +skipPublishVerification = false +registryEnabled = true + +[settings.crashreporter.data] +experience = "Isaac Sim" + +[settings.persistent] +app.file.recentFiles = [] +app.stage.upAxis = "Z" +app.stage.movePrimInPlace = false +app.stage.instanceableOnCreatingReference = false +app.stage.materialStrength = "weakerThanDescendants" + +app.transform.gizmoUseSRT = true +app.viewport.grid.scale = 1.0 +app.viewport.pickingMode = "kind:model.ALL" +app.viewport.camMoveVelocity = 0.05 # 5 m/s +app.viewport.gizmo.scale = 0.01 # scaled to meters +app.viewport.previewOnPeek = false +app.viewport.snapToSurface = false +app.viewport.displayOptions = 31951 # Disable Frame Rate and Resolution by default +app.window.uiStyle = "NvidiaDark" +app.primCreation.DefaultXformOpType = "Scale, Orient, Translate" +app.primCreation.DefaultXformOpOrder="xformOp:translate, xformOp:orient, xformOp:scale" +app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0] +simulation.minFrameRate = 15 +simulation.defaultMetersPerUnit = 1.0 +omnigraph.updateToUsd = false +omnigraph.useSchemaPrims = true +omnigraph.disablePrimNodes = true +omni.replicator.captureOnPlay = true +omnihydra.useSceneGraphInstancing = true +renderer.startupMessageDisplayed = true # hides the IOMMU popup window + +# Make Detail panel visible by default +app.omniverse.content_browser.options_menu.show_details = true +app.omniverse.filepicker.options_menu.show_details = true + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false + +# Performance improvement +resourcemonitor.timeBetweenQueries = 100 + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] + +[settings.ngx] +enabled=true # Enable this for DLSS + +######################## +# Isaac Sim Extensions # +######################## +[dependencies] +"isaacsim.simulation_app" = {} +"isaacsim.core.api" = {} +"isaacsim.core.cloner" = {} +"isaacsim.core.utils" = {} +"isaacsim.core.version" = {} + +######################## +# Isaac Lab Extensions # +######################## + +# Load Isaac Lab extensions last +"isaaclab" = {order = 1000} +"isaaclab_assets" = {order = 1000} +"isaaclab_tasks" = {order = 1000} +"isaaclab_mimic" = {order = 1000} +"isaaclab_rl" = {order = 1000} + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.cloud = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.nvidia = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" diff --git a/apps/isaacsim_4_5/isaaclab.python.headless.rendering.kit b/apps/isaacsim_4_5/isaaclab.python.headless.rendering.kit new file mode 100644 index 0000000000000000000000000000000000000000..cb1b4e8a25defba24049f54ce75f45bcc5083195 --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.headless.rendering.kit @@ -0,0 +1,145 @@ +## +# Adapted from: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/blob/main/apps/omni.isaac.sim.python.gym.camera.kit +# +# This app file designed specifically towards vision-based RL tasks. It provides necessary settings to enable +# multiple cameras to be rendered each frame. Additional settings are also applied to increase performance when +# rendering cameras across multiple environments. +## + +[package] +title = "Isaac Lab Python Headless Camera" +description = "An app for running Isaac Lab headlessly with rendering enabled" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "camera", "minimal"] + +[dependencies] +# Isaac Lab minimal app +"isaaclab.python.headless" = {} +"omni.replicator.core" = {} + +# Rendering +"omni.kit.material.library" = {} +"omni.kit.viewport.rtx" = {} + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "4.5.0" + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Flags for better rendering performance +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.translucency.enabled = false +rtx.reflections.enabled = false +rtx.indirectDiffuse.enabled = false +rtx-transient.dlssg.enabled = false +rtx.directLighting.sampledLighting.enabled = true +rtx.directLighting.sampledLighting.samplesPerPixel = 1 +rtx.sceneDb.ambientLightIntensity = 1.0 +# rtx.shadows.enabled = false + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 + +# Disable present thread to improve performance +exts."omni.renderer.core".present.enabled=false + +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.raytracing.cached.enabled = false +rtx.ambientOcclusion.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids unnecessary GPU context initialization +renderer.multiGpu.maxGpuCount=1 + +# Force synchronous rendering to improve training results +omni.replicator.asyncRendering = false + +# Avoids frame offset issue +app.updateOrder.checkForHydraRenderComplete = 1000 +app.renderer.waitIdle=true +app.hydraEngine.waitIdle=true + +app.audio.enabled = false + +# Enable Vulkan - avoids torch+cu12 error on windows +app.vulkan = true + +# Set profiler backend to NVTX by default +app.profilerBackend = "nvtx" + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/106/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +skipWhileMinimized = false +sleepMsOnFocus = 0 +sleepMsOutOfFocus = 0 + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.cloud = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.nvidia = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" diff --git a/apps/isaacsim_4_5/isaaclab.python.kit b/apps/isaacsim_4_5/isaaclab.python.kit new file mode 100644 index 0000000000000000000000000000000000000000..89db9ffb0d6ed6f566c69da118fd10446ce1c183 --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.kit @@ -0,0 +1,301 @@ +## +# Adapted from: _isaac_sim/apps/isaacsim.exp.base.kit +## + +[package] +title = "Isaac Lab Python" +description = "An app for running Isaac Lab" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd"] + +[dependencies] +# Isaac Sim extensions +"isaacsim.app.about" = {} +"isaacsim.asset.browser" = {} +"isaacsim.core.api" = {} +"isaacsim.core.cloner" = {} +"isaacsim.core.nodes" = {} +"isaacsim.core.simulation_manager" = {} +"isaacsim.core.throttling" = {} +"isaacsim.core.utils" = {} +"isaacsim.core.version" = {} +"isaacsim.gui.menu" = {} +"isaacsim.gui.property" = {} +"isaacsim.replicator.behavior" = {} +"isaacsim.robot.manipulators" = {} +"isaacsim.robot.policy.examples" = {} +"isaacsim.robot.schema" = {} +"isaacsim.robot.surface_gripper" = {} +"isaacsim.robot.wheeled_robots" = {} +"isaacsim.sensors.camera" = {} +"isaacsim.sensors.physics" = {} +"isaacsim.sensors.physx" = {} +"isaacsim.sensors.rtx" = {} +"isaacsim.simulation_app" = {} +"isaacsim.storage.native" = {} +"isaacsim.util.debug_draw" = {} + +# Isaac Sim Extra +"isaacsim.asset.importer.mjcf" = {} +"isaacsim.asset.importer.urdf" = {} +"omni.physx.bundle" = {} +"omni.physx.tensors" = {} +"omni.replicator.core" = {} +"omni.replicator.replicator_yaml" = {} +"omni.syntheticdata" = {} +"semantics.schema.editor" = {} +"semantics.schema.property" = {} + +# Kit based editor extensions +"omni.anim.curve.core" = {} +"omni.graph.action" = {} +"omni.graph.core" = {} +"omni.graph.nodes" = {} +"omni.graph.scriptnode" = {} +"omni.graph.ui_nodes" = {} +"omni.hydra.engine.stats" = {} +"omni.hydra.rtx" = {} +"omni.kit.loop" = {} +"omni.kit.mainwindow" = {} +"omni.kit.manipulator.camera" = {} +"omni.kit.manipulator.prim" = {} +"omni.kit.manipulator.selection" = {} +"omni.kit.material.library" = {} +"omni.kit.menu.common" = { order = 1000 } +"omni.kit.menu.create" = {} +"omni.kit.menu.edit" = {} +"omni.kit.menu.file" = {} +"omni.kit.menu.stage" = {} +"omni.kit.menu.utils" = {} +"omni.kit.primitive.mesh" = {} +"omni.kit.property.bundle" = {} +"omni.kit.raycast.query" = {} +"omni.kit.stage_template.core" = {} +"omni.kit.stagerecorder.bundle" = {} +"omni.kit.telemetry" = {} +"omni.kit.tool.asset_importer" = {} +"omni.kit.tool.collect" = {} +"omni.kit.viewport.legacy_gizmos" = {} +"omni.kit.viewport.menubar.camera" = {} +"omni.kit.viewport.menubar.display" = {} +"omni.kit.viewport.menubar.lighting" = {} +"omni.kit.viewport.menubar.render" = {} +"omni.kit.viewport.menubar.settings" = {} +"omni.kit.viewport.scene_camera_model" = {} +"omni.kit.viewport.window" = {} +"omni.kit.window.console" = {} +"omni.kit.window.content_browser" = {} +"omni.kit.window.property" = {} +"omni.kit.window.stage" = {} +"omni.kit.window.status_bar" = {} +"omni.kit.window.toolbar" = {} +"omni.physx.stageupdate" = {} +"omni.rtx.settings.core" = {} +"omni.uiaudio" = {} +"omni.usd.metrics.assembler.ui" = {} +"omni.usd.schema.metrics.assembler" = {} +"omni.warp.core" = {} + +######################## +# Isaac Lab Extensions # +######################## + +# Load Isaac Lab extensions last +"isaaclab" = {order = 1000} +"isaaclab_assets" = {order = 1000} +"isaaclab_tasks" = {order = 1000} +"isaaclab_mimic" = {order = 1000} +"isaaclab_rl" = {order = 1000} + +[settings] +exts."omni.kit.material.library".ui_show_list = [ + "OmniPBR", + "OmniGlass", + "OmniSurface", + "USD Preview Surface", +] +exts."omni.kit.renderer.core".present.enabled = false # Fixes MGPU stability issue +exts."omni.kit.viewport.window".windowMenu.entryCount = 2 # Allow user to create two viewports by default +exts."omni.kit.viewport.window".windowMenu.label = "" # Put Viewport menuitem under Window menu +exts."omni.rtx.window.settings".window_menu = "Window" # Where to put the render settings menuitem +exts."omni.usd".locking.onClose = false # reduce time it takes to close/create stage +renderer.asyncInit = true # Don't block while renderer inits +renderer.gpuEnumeration.glInterop.enabled = false # Improves startup speed. +rendergraph.mgpu.backend = "copyQueue" # In MGPU configurations, This setting can be removed if IOMMU is disabled for better performance, copyQueue improves stability and performance when IOMMU is enabled +rtx-transient.dlssg.enabled = false # DLSSG frame generation is not compatible with synthetic data generation +rtx.hydra.mdlMaterialWarmup = true # start loading the MDL shaders needed before any delegate is actually created. +omni.replicator.asyncRendering = false # Async rendering must be disabled for SDG +exts."omni.kit.test".includeTests = ["*isaac*"] # Add isaac tests to test runner +foundation.verifyOsVersion.enabled = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Disable for base application +[settings."filter:platform"."windows-x86_64"] +isaac.startup.ros_bridge_extension = "" +[settings."filter:platform"."linux-x86_64"] +isaac.startup.ros_bridge_extension = "" + +# menu styling +[settings.exts."omni.kit.menu.utils"] +logDeprecated = false +margin_size = [18, 3] +tick_spacing = [10, 6] +margin_size_posttick = [0, 3] +separator_size = [14, 10] +root_spacing = 3 +post_label_spaces = 6 +color_tick_enabled = 0xFFFAC434 +color_separator = 0xFF7E7E7E +color_label_enabled = 0xFFEEEEEE +menu_title_color = 0xFF202020 +menu_title_line_color = 0xFF5E5E5E +menu_title_text_color = 0xFF8F8F8F +menu_title_text_height = 24 +menu_title_close_color = 0xFFC6C6C6 +indent_all_ticks = false +show_menu_titles = true + +[settings.app] +name = "Isaac-Sim" +version = "4.5.0" +versionFile = "${exe-path}/VERSION" +content.emptyStageOnStart = true +fastShutdown = true +file.ignoreUnsavedOnExit = true +font.file = "${fonts}/OpenSans-SemiBold.ttf" +font.size = 16 +gatherRenderResults = true # True to prevent artifacts in multiple viewport configurations, can be set to false for better performance in some cases +hangDetector.enabled = true +hangDetector.timeout = 120 +player.useFixedTimeStepping = true +settings.fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 still true? +settings.persistent = true # settings are persistent for this app + +vulkan = true # Explicitly enable Vulkan (on by default on Linux, off by default on Windows) +### async rendering settings +asyncRendering = false +asyncRenderingLowLatency = false + +[settings.app.window] +iconPath = "${isaacsim.simulation_app}/data/omni.isaac.sim.png" +title = "Isaac Sim" + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +resolution.height = 720 +resolution.width = 1280 +skipWhileMinimized = false # python app does not throttle +sleepMsOnFocus = 0 # python app does not throttle +sleepMsOutOfFocus = 0 # python app does not throttle + +[settings.app.viewport] +defaultCamPos.x = 5 +defaultCamPos.y = 5 +defaultCamPos.z = 5 +defaults.fillViewport = false # default to not fill viewport +grid.enabled = true +outline.enabled = true +boundingBoxes.enabled = false +show.camera=false +show.lights=false + +[settings.telemetry] +enableAnonymousAppName = true # Anonymous Kit application usage telemetry +enableAnonymousData = true # Anonymous Kit application usage telemetry + +[settings.persistent] +app.primCreation.DefaultXformOpOrder = "xformOp:translate, xformOp:orient, xformOp:scale" +app.primCreation.DefaultXformOpType = "Scale, Orient, Translate" +app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0] # Meters default +app.primCreation.DefaultXformOpPrecision = "Double" +app.primCreation.DefaultRotationOrder = "ZYX" +app.primCreation.PrimCreationWithDefaultXformOps = true +app.stage.timeCodeRange = [0, 1000000] +app.stage.upAxis = "Z" # Isaac Sim default Z up +app.viewport.camMoveVelocity = 0.05 # Meters default +app.viewport.gizmo.scale = 0.01 # Meters default +app.viewport.grid.scale = 1.0 # Meters default +app.viewport.camShowSpeedOnStart = false # Hide camera speed on startup +app.omniverse.gamepadCameraControl = false # Disable gamepad control for camera by default +exts."omni.anim.navigation.core".navMesh.config.autoRebakeOnChanges = false +exts."omni.anim.navigation.core".navMesh.viewNavMesh = false +physics.visualizationDisplayJoints = false # improves performance +physics.visualizationSimulationOutput = false # improves performance +physics.resetOnStop = true # Physics state is reset on stop +renderer.startupMessageDisplayed = true # hides the IOMMU popup window +resourcemonitor.timeBetweenQueries = 100 # improves performance +simulation.defaultMetersPerUnit = 1.0 # Meters default +omni.replicator.captureOnPlay = true + +[settings] +### async rendering settings +omni.replicator.asyncRendering = false +app.asyncRendering = false +app.asyncRenderingLowLatency = false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.app.livestream] +outDirectory = "${data}" + +# Extensions +############################### +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/106/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.extensions] +skipPublishVerification = false +registryEnabled = true + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] + +[settings.physics] +autoPopupSimulationOutputWindow = false +updateToUsd = false +updateVelocitiesToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.cloud = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.nvidia = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" diff --git a/apps/isaacsim_4_5/isaaclab.python.rendering.kit b/apps/isaacsim_4_5/isaaclab.python.rendering.kit new file mode 100644 index 0000000000000000000000000000000000000000..df2ee90bf1668fc23b28d7937948ab77c7d1f1e9 --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.rendering.kit @@ -0,0 +1,140 @@ +## +# Adapted from: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/blob/main/apps/omni.isaac.sim.python.gym.camera.kit +# +# This app file designed specifically towards vision-based RL tasks. It provides necessary settings to enable +# multiple cameras to be rendered each frame. Additional settings are also applied to increase performance when +# rendering cameras across multiple environments. +## + +[package] +title = "Isaac Lab Python Camera" +description = "An app for running Isaac Lab with rendering enabled" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "isaaclab", "python", "camera", "minimal"] + +[dependencies] +# Isaac Lab minimal app +"isaaclab.python" = {} + +# PhysX +"omni.kit.property.physx" = {} + +# Rendering +"omni.kit.material.library" = {} + +[settings.isaaclab] +# This is used to check that this experience file is loaded when using cameras +cameras_enabled = true + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "4.5.0" + +# Disable print outs on extension startup information +# this only disables the app print_and_log function +app.enableStdoutOutput = false + +# set the default ros bridge to disable on startup +isaac.startup.ros_bridge_extension = "" + +# Flags for better rendering performance +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.translucency.enabled = false +rtx.reflections.enabled = false +rtx.indirectDiffuse.enabled = false +rtx-transient.dlssg.enabled = false +rtx.directLighting.sampledLighting.enabled = true +rtx.directLighting.sampledLighting.samplesPerPixel = 1 +rtx.sceneDb.ambientLightIntensity = 1.0 +# rtx.shadows.enabled = false + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 + +# Disable present thread to improve performance +exts."omni.renderer.core".present.enabled=false + +# Disabling these settings reduces renderer VRAM usage and improves rendering performance, but at some quality cost +rtx.raytracing.cached.enabled = false +rtx.ambientOcclusion.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids unnecessary GPU context initialization +renderer.multiGpu.maxGpuCount=1 + +# Force synchronous rendering to improve training results +omni.replicator.asyncRendering = false + +# Avoids frame offset issue +app.updateOrder.checkForHydraRenderComplete = 1000 +app.renderer.waitIdle=true +app.hydraEngine.waitIdle=true + +app.audio.enabled = false + +# disable replicator orchestrator for better runtime perf +exts."omni.replicator.core".Orchestrator.enabled = false + +[settings.physics] +updateToUsd = false +updateParticlesToUsd = false +updateVelocitiesToUsd = false +updateForceSensorsToUsd = false +outputVelocitiesLocalSpace = false +useFastCache = false +visualizationDisplayJoints = false +fabricUpdateTransformations = false +fabricUpdateVelocities = false +fabricUpdateForceSensors = false +fabricUpdateJointStates = false + +[settings.exts."omni.kit.registry.nucleus"] +registries = [ + { name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/106/shared" }, + { name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" }, + { name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" }, +] + +[settings.app.python] +# These disable the kit app from also printing out python output, which gets confusing +interceptSysStdOutput = false +logSysStdOutput = false + +[settings.app.renderer] +skipWhileMinimized = false +sleepMsOnFocus = 0 +sleepMsOutOfFocus = 0 + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.cloud = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.nvidia = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" diff --git a/apps/isaacsim_4_5/isaaclab.python.xr.openxr.headless.kit b/apps/isaacsim_4_5/isaaclab.python.xr.openxr.headless.kit new file mode 100644 index 0000000000000000000000000000000000000000..5839ae8acc311a50148232bda4d4bc3b3413866f --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.xr.openxr.headless.kit @@ -0,0 +1,41 @@ +## +# Adapted from: apps/isaaclab.python.xr.openxr.kit +## + +[package] +title = "Isaac Lab Python OpenXR Headless" +description = "An app for running Isaac Lab with OpenXR in headless mode" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd", "headless"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "4.5.0" + +[dependencies] +"isaaclab.python.xr.openxr" = {} + +[settings] +xr.profile.ar.enabled = true + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] diff --git a/apps/isaacsim_4_5/isaaclab.python.xr.openxr.kit b/apps/isaacsim_4_5/isaaclab.python.xr.openxr.kit new file mode 100644 index 0000000000000000000000000000000000000000..24f4663c2e05908c22190ffd59fbe02662c37229 --- /dev/null +++ b/apps/isaacsim_4_5/isaaclab.python.xr.openxr.kit @@ -0,0 +1,71 @@ +## +# Adapted from: _isaac_sim/apps/isaacsim.exp.xr.openxr.kit +## + +[package] +title = "Isaac Lab Python OpenXR" +description = "An app for running Isaac Lab with OpenXR" +version = "2.3.0" + +# That makes it browsable in UI with "experience" filter +keywords = ["experience", "app", "usd"] + +[settings] +# Note: This path was adapted to be respective to the kit-exe file location +app.versionFile = "${exe-path}/VERSION" +app.folder = "${exe-path}/" +app.name = "Isaac-Sim" +app.version = "4.5.0" + +### async rendering settings +omni.replicator.asyncRendering = true +app.asyncRendering = true +app.asyncRenderingLowLatency = true + +# For XR, set this back to default "#define OMNI_MAX_DEVICE_GROUP_DEVICE_COUNT 16" +renderer.multiGpu.maxGpuCount = 16 +renderer.gpuEnumeration.glInterop.enabled = true # Allow Kit XR OpenXR to render headless + +[dependencies] +"isaaclab.python" = {} +"isaacsim.xr.openxr" = {} + +# Kit extensions +"omni.kit.xr.system.openxr" = {} +"omni.kit.xr.profile.ar" = {} + +[settings] +app.xr.enabled = true + +# xr settings +xr.ui.enabled = false +xr.depth.aov = "GBufferDepth" +defaults.xr.profile.ar.renderQuality = "off" +defaults.xr.profile.ar.anchorMode = "custom anchor" +rtx.rendermode = "RaytracedLighting" +persistent.xr.profile.ar.render.nearPlane = 0.15 + +# Register extension folder from this repo in kit +[settings.app.exts] +folders = [ + "${exe-path}/exts", # kit extensions + "${exe-path}/extscore", # kit core extensions + "${exe-path}/../exts", # isaac extensions + "${exe-path}/../extsDeprecated", # deprecated isaac extensions + "${exe-path}/../extscache", # isaac cache extensions + "${exe-path}/../extsPhysics", # isaac physics extensions + "${exe-path}/../isaacsim/exts", # isaac extensions for pip + "${exe-path}/../isaacsim/extsDeprecated", # deprecated isaac extensions + "${exe-path}/../isaacsim/extscache", # isaac cache extensions for pip + "${exe-path}/../isaacsim/extsPhysics", # isaac physics extensions for pip + "${app}", # needed to find other app files + "${app}/../../source", # needed to find extensions in Isaac Lab +] + +# Asset path +# set the S3 directory manually to the latest published S3 +# note: this is done to ensure prior versions of Isaac Sim still use the latest assets +[settings] +persistent.isaac.asset_root.default = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.cloud = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" +persistent.isaac.asset_root.nvidia = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5" diff --git a/apps/isaacsim_4_5/rendering_modes/balanced.kit b/apps/isaacsim_4_5/rendering_modes/balanced.kit new file mode 100644 index 0000000000000000000000000000000000000000..ee92625fd7e7ade27d2ca0c4f3253fb384560ba6 --- /dev/null +++ b/apps/isaacsim_4_5/rendering_modes/balanced.kit @@ -0,0 +1,36 @@ +rtx.translucency.enabled = false + +rtx.reflections.enabled = false +rtx.reflections.denoiser.enabled = true + +# this will be ignored when RR (dldenoiser) is enabled +# rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = true + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = false +rtx.indirectDiffuse.denoiser.enabled = true + +# rtx.domeLight.upperLowerStrategy = 3 + +rtx.ambientOcclusion.enabled = false +rtx.ambientOcclusion.denoiserMode = 1 + +rtx.raytracing.subpixel.mode = 0 +rtx.raytracing.cached.enabled = true + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = true + +# Set the DLSS model +rtx.post.dlss.execMode = 1 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/isaacsim_4_5/rendering_modes/performance.kit b/apps/isaacsim_4_5/rendering_modes/performance.kit new file mode 100644 index 0000000000000000000000000000000000000000..3cfe6e8c0e2c2bb57e43fa9caa5ffd8692007cd9 --- /dev/null +++ b/apps/isaacsim_4_5/rendering_modes/performance.kit @@ -0,0 +1,35 @@ +rtx.translucency.enabled = false + +rtx.reflections.enabled = false +rtx.reflections.denoiser.enabled = false + +rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = false + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = false +rtx.indirectDiffuse.denoiser.enabled = false + +rtx.domeLight.upperLowerStrategy = 3 + +rtx.ambientOcclusion.enabled = false +rtx.ambientOcclusion.denoiserMode = 1 + +rtx.raytracing.subpixel.mode = 0 +rtx.raytracing.cached.enabled = false + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/isaacsim_4_5/rendering_modes/quality.kit b/apps/isaacsim_4_5/rendering_modes/quality.kit new file mode 100644 index 0000000000000000000000000000000000000000..8e966ddfd3b7c428d7773c6a8e2d0221943fde79 --- /dev/null +++ b/apps/isaacsim_4_5/rendering_modes/quality.kit @@ -0,0 +1,36 @@ +rtx.translucency.enabled = true + +rtx.reflections.enabled = true +rtx.reflections.denoiser.enabled = true + +# this will be ignored when RR (dldenoiser) is enabled +# rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = true + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = true +rtx.indirectDiffuse.denoiser.enabled = true + +# rtx.domeLight.upperLowerStrategy = 4 + +rtx.ambientOcclusion.enabled = true +rtx.ambientOcclusion.denoiserMode = 0 + +rtx.raytracing.subpixel.mode = 1 +rtx.raytracing.cached.enabled = true + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = true + +# Set the DLSS model +rtx.post.dlss.execMode = 2 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/isaacsim_4_5/rendering_modes/xr.kit b/apps/isaacsim_4_5/rendering_modes/xr.kit new file mode 100644 index 0000000000000000000000000000000000000000..8cfc2c988d78d8e2b8483744ee74878e851ca329 --- /dev/null +++ b/apps/isaacsim_4_5/rendering_modes/xr.kit @@ -0,0 +1,35 @@ +rtx.translucency.enabled = true + +rtx.reflections.enabled = true +rtx.reflections.denoiser.enabled = true + +rtx.directLighting.sampledLighting.denoisingTechnique = 5 +rtx.directLighting.sampledLighting.enabled = true + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = true +rtx.indirectDiffuse.denoiser.enabled = true + +rtx.domeLight.upperLowerStrategy = 4 + +rtx.ambientOcclusion.enabled = true +rtx.ambientOcclusion.denoiserMode = 0 + +rtx.raytracing.subpixel.mode = 1 +rtx.raytracing.cached.enabled = true + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = true + +# Set the DLSS model +rtx.post.dlss.execMode = 2 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/rendering_modes/balanced.kit b/apps/rendering_modes/balanced.kit new file mode 100644 index 0000000000000000000000000000000000000000..ee92625fd7e7ade27d2ca0c4f3253fb384560ba6 --- /dev/null +++ b/apps/rendering_modes/balanced.kit @@ -0,0 +1,36 @@ +rtx.translucency.enabled = false + +rtx.reflections.enabled = false +rtx.reflections.denoiser.enabled = true + +# this will be ignored when RR (dldenoiser) is enabled +# rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = true + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = false +rtx.indirectDiffuse.denoiser.enabled = true + +# rtx.domeLight.upperLowerStrategy = 3 + +rtx.ambientOcclusion.enabled = false +rtx.ambientOcclusion.denoiserMode = 1 + +rtx.raytracing.subpixel.mode = 0 +rtx.raytracing.cached.enabled = true + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = true + +# Set the DLSS model +rtx.post.dlss.execMode = 1 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/rendering_modes/extension.toml b/apps/rendering_modes/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..f0668b9184d126682bb868596d119d0112e46e80 --- /dev/null +++ b/apps/rendering_modes/extension.toml @@ -0,0 +1 @@ +# This is not an extension diff --git a/apps/rendering_modes/performance.kit b/apps/rendering_modes/performance.kit new file mode 100644 index 0000000000000000000000000000000000000000..3cfe6e8c0e2c2bb57e43fa9caa5ffd8692007cd9 --- /dev/null +++ b/apps/rendering_modes/performance.kit @@ -0,0 +1,35 @@ +rtx.translucency.enabled = false + +rtx.reflections.enabled = false +rtx.reflections.denoiser.enabled = false + +rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = false + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = false +rtx.indirectDiffuse.denoiser.enabled = false + +rtx.domeLight.upperLowerStrategy = 3 + +rtx.ambientOcclusion.enabled = false +rtx.ambientOcclusion.denoiserMode = 1 + +rtx.raytracing.subpixel.mode = 0 +rtx.raytracing.cached.enabled = false + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = false + +# Set the DLSS model +rtx.post.dlss.execMode = 0 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/apps/rendering_modes/quality.kit b/apps/rendering_modes/quality.kit new file mode 100644 index 0000000000000000000000000000000000000000..8e966ddfd3b7c428d7773c6a8e2d0221943fde79 --- /dev/null +++ b/apps/rendering_modes/quality.kit @@ -0,0 +1,36 @@ +rtx.translucency.enabled = true + +rtx.reflections.enabled = true +rtx.reflections.denoiser.enabled = true + +# this will be ignored when RR (dldenoiser) is enabled +# rtx.directLighting.sampledLighting.denoisingTechnique = 0 +rtx.directLighting.sampledLighting.enabled = true + +rtx.sceneDb.ambientLightIntensity = 1.0 + +rtx.shadows.enabled = true + +rtx.indirectDiffuse.enabled = true +rtx.indirectDiffuse.denoiser.enabled = true + +# rtx.domeLight.upperLowerStrategy = 4 + +rtx.ambientOcclusion.enabled = true +rtx.ambientOcclusion.denoiserMode = 0 + +rtx.raytracing.subpixel.mode = 1 +rtx.raytracing.cached.enabled = true + +# DLSS frame gen does not yet support tiled camera well +rtx-transient.dlssg.enabled = false +rtx-transient.dldenoiser.enabled = true + +# Set the DLSS model +rtx.post.dlss.execMode = 2 # can be 0 (Performance), 1 (Balanced), 2 (Quality), or 3 (Auto) + +# Avoids replicator warning +rtx.pathtracing.maxSamplesPerLaunch = 1000000 + +# Avoids silent trimming of tiles +rtx.viewTile.limit = 1000000 diff --git a/datasets/apple_pick_place.hdf5 b/datasets/apple_pick_place.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..be0074833bf66b50298ed033df397da40c7472f4 Binary files /dev/null and b/datasets/apple_pick_place.hdf5 differ diff --git a/datasets/apple_pick_place_annotated.hdf5 b/datasets/apple_pick_place_annotated.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..188bbb225b27b559de8c9458d8ca311fe65dbe79 --- /dev/null +++ b/datasets/apple_pick_place_annotated.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cdd75e7702c0cdfd89ddb700f7a499c524c07359de86327ed45f40d401cad146 +size 75382054 diff --git a/datasets/apple_pick_place_generated_small.hdf5 b/datasets/apple_pick_place_generated_small.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..2d120eb24abd1a9b45468a2ed2d3c84680f19353 --- /dev/null +++ b/datasets/apple_pick_place_generated_small.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:606a51ec98856f200d952da51f61c437c5acc3571f64a7a1bc2192d1c7560197 +size 235878882 diff --git a/datasets/dataset_pick_place_g1.hdf5 b/datasets/dataset_pick_place_g1.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..0923d8dba507b3d6431a2f8bf9fb42fc393f40ac --- /dev/null +++ b/datasets/dataset_pick_place_g1.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9022310c8f5a0e01ec890fc3f9f6f511c0fbc15683c8c97c1ead62608eef91b +size 1505422 diff --git a/datasets/dataset_pick_place_g1_23.hdf5 b/datasets/dataset_pick_place_g1_23.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..445aba9842d5b6149d44e83c18c938dd1a6a9827 Binary files /dev/null and b/datasets/dataset_pick_place_g1_23.hdf5 differ diff --git a/datasets/steering_wheel.hdf5 b/datasets/steering_wheel.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..ef0c956cb19d9a5598712395087272305c9f49c5 --- /dev/null +++ b/datasets/steering_wheel.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24bb0969f52d98b753053481492cec50f029a546b7f1069cf48924f81fc3c75f +size 2417593 diff --git a/datasets/steering_wheel_annotated.hdf5 b/datasets/steering_wheel_annotated.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..578d6491e5de5605d6264d07d98b70cb66082a5e --- /dev/null +++ b/datasets/steering_wheel_annotated.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d89784d5b41dad09f83b126f2fb29b15b05f6344273388113abd941e087e4b0 +size 35629259 diff --git a/datasets/steering_wheel_generated.hdf5 b/datasets/steering_wheel_generated.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..e028df8fd94cd0a5abc613f8e84ad737bdc8e874 --- /dev/null +++ b/datasets/steering_wheel_generated.hdf5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a65bdcde7ad10f962f163d0f1c2c43f64ed3c500dc5df38348d3488a9739fc70 +size 381532081 diff --git a/datasets/steering_wheel_student.hdf5 b/datasets/steering_wheel_student.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..038d095a228ca4cba848f225dbac7e25e3d9dbd2 Binary files /dev/null and b/datasets/steering_wheel_student.hdf5 differ diff --git a/docker/.env.base b/docker/.env.base new file mode 100644 index 0000000000000000000000000000000000000000..be1dd4f6221349238623ee4eae1c1d0ab6717afd --- /dev/null +++ b/docker/.env.base @@ -0,0 +1,19 @@ +### +# General settings +### + +# Accept the NVIDIA Omniverse EULA by default +ACCEPT_EULA=Y +# NVIDIA Isaac Sim base image +ISAACSIM_BASE_IMAGE=nvcr.io/nvidia/isaac-sim +# NVIDIA Isaac Sim version to use (e.g. 5.1.0) +ISAACSIM_VERSION=5.1.0 +# Derived from the default path in the NVIDIA provided Isaac Sim container +DOCKER_ISAACSIM_ROOT_PATH=/isaac-sim +# The Isaac Lab path in the container +DOCKER_ISAACLAB_PATH=/workspace/isaaclab +# Docker user directory - by default this is the root user's home directory +DOCKER_USER_HOME=/root +# Docker image and container name suffix (default "", set by the container_interface.py script) +# Example: "-custom" +DOCKER_NAME_SUFFIX="" diff --git a/docker/.env.cloudxr-runtime b/docker/.env.cloudxr-runtime new file mode 100644 index 0000000000000000000000000000000000000000..65b6d1373ac3808182b18178706bb518a8548bd1 --- /dev/null +++ b/docker/.env.cloudxr-runtime @@ -0,0 +1,8 @@ +### +# General settings +### + +# NVIDIA CloudXR Runtime base image +CLOUDXR_RUNTIME_BASE_IMAGE_ARG=nvcr.io/nvidia/cloudxr-runtime +# NVIDIA CloudXR Runtime version to use +CLOUDXR_RUNTIME_VERSION_ARG=5.0.1 diff --git a/docker/.env.ros2 b/docker/.env.ros2 new file mode 100644 index 0000000000000000000000000000000000000000..609704f456747b35a1c0f893caedfb7b56a7c00e --- /dev/null +++ b/docker/.env.ros2 @@ -0,0 +1,14 @@ +### +# ROS2 specific settings +### +# Set the version of the ROS2 apt package to install (ros-base, desktop, desktop-full) +ROS2_APT_PACKAGE=ros-base +# Set ROS2 middleware implementation to use (e.g. rmw_fastrtps_cpp, rmw_cyclonedds_cpp) +RMW_IMPLEMENTATION=rmw_fastrtps_cpp +# Path to fastdds.xml file to use (only needed when using fastdds) +FASTRTPS_DEFAULT_PROFILES_FILE=${DOCKER_USER_HOME}/.ros/fastdds.xml +# Path to cyclonedds.xml file to use (only needed when using cyclonedds) +CYCLONEDDS_URI=${DOCKER_USER_HOME}/.ros/cyclonedds.xml +# Docker image and container name suffix (default "", set by the container_interface.py script) +# Example: "-custom" +DOCKER_NAME_SUFFIX="" diff --git a/docker/.ros/cyclonedds.xml b/docker/.ros/cyclonedds.xml new file mode 100644 index 0000000000000000000000000000000000000000..782c4ae317626dfbf31e2684b04e202878e0447a --- /dev/null +++ b/docker/.ros/cyclonedds.xml @@ -0,0 +1,15 @@ + + + + + + + + true + + + auto + 120 + + + diff --git a/docker/.ros/fastdds.xml b/docker/.ros/fastdds.xml new file mode 100644 index 0000000000000000000000000000000000000000..93ca9c403dad0b2fca3c7d8ac10e5528ed24bd3c --- /dev/null +++ b/docker/.ros/fastdds.xml @@ -0,0 +1,27 @@ + + +Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +NVIDIA CORPORATION and its licensors retain all intellectual property +and proprietary rights in and to this software, related documentation +and any modifications thereto. Any use, reproduction, disclosure or +distribution of this software and related documentation without an express +license agreement from NVIDIA CORPORATION is strictly prohibited. + + + + + + UdpTransport + UDPv4 + + + + + + + UdpTransport + + false + + + diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base new file mode 100644 index 0000000000000000000000000000000000000000..9aff6b165c930a43b79ecd84a7a8669d1ccdf6a3 --- /dev/null +++ b/docker/Dockerfile.base @@ -0,0 +1,111 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Nvidia Dockerfiles: https://github.com/NVIDIA-Omniverse/IsaacSim-dockerfiles +# Please check above link for license information. + +# Base image +ARG ISAACSIM_BASE_IMAGE_ARG +ARG ISAACSIM_VERSION_ARG +FROM ${ISAACSIM_BASE_IMAGE_ARG}:${ISAACSIM_VERSION_ARG} AS base +ENV ISAACSIM_VERSION=${ISAACSIM_VERSION_ARG} + +# Set default RUN shell to bash +SHELL ["/bin/bash", "-c"] + +# Adds labels to the Dockerfile +LABEL version="2.1.1" +LABEL description="Dockerfile for building and running the Isaac Lab framework inside Isaac Sim container image." + +# Arguments +# Path to Isaac Sim root folder +ARG ISAACSIM_ROOT_PATH_ARG +ENV ISAACSIM_ROOT_PATH=${ISAACSIM_ROOT_PATH_ARG} +# Path to the Isaac Lab directory +ARG ISAACLAB_PATH_ARG +ENV ISAACLAB_PATH=${ISAACLAB_PATH_ARG} +# Home dir of docker user, typically '/root' +ARG DOCKER_USER_HOME_ARG +ENV DOCKER_USER_HOME=${DOCKER_USER_HOME_ARG} + +# Set environment variables +ENV LANG=C.UTF-8 +ENV DEBIAN_FRONTEND=noninteractive + +USER root + +# Install dependencies and remove cache +RUN --mount=type=cache,target=/var/cache/apt \ + apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + libglib2.0-0 \ + ncurses-term \ + wget && \ + apt -y autoremove && apt clean autoclean && \ + rm -rf /var/lib/apt/lists/* + +# Copy the Isaac Lab directory (files to exclude are defined in .dockerignore) +COPY ../ ${ISAACLAB_PATH} + +# Ensure isaaclab.sh has execute permissions +RUN chmod +x ${ISAACLAB_PATH}/isaaclab.sh + +# Set up a symbolic link between the installed Isaac Sim root folder and _isaac_sim in the Isaac Lab directory +RUN ln -sf ${ISAACSIM_ROOT_PATH} ${ISAACLAB_PATH}/_isaac_sim + +# Install toml dependency +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install toml + +# Install apt dependencies for extensions that declare them in their extension.toml +RUN --mount=type=cache,target=/var/cache/apt \ + ${ISAACLAB_PATH}/isaaclab.sh -p ${ISAACLAB_PATH}/tools/install_deps.py apt ${ISAACLAB_PATH}/source && \ + apt -y autoremove && apt clean autoclean && \ + rm -rf /var/lib/apt/lists/* + +# for singularity usage, have to create the directories that will binded +RUN mkdir -p ${ISAACSIM_ROOT_PATH}/kit/cache && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/ov && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/pip && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/nvidia/GLCache && \ + mkdir -p ${DOCKER_USER_HOME}/.nv/ComputeCache && \ + mkdir -p ${DOCKER_USER_HOME}/.nvidia-omniverse/logs && \ + mkdir -p ${DOCKER_USER_HOME}/.local/share/ov/data && \ + mkdir -p ${DOCKER_USER_HOME}/Documents + +# for singularity usage, create NVIDIA binary placeholders +RUN touch /bin/nvidia-smi && \ + touch /bin/nvidia-debugdump && \ + touch /bin/nvidia-persistenced && \ + touch /bin/nvidia-cuda-mps-control && \ + touch /bin/nvidia-cuda-mps-server && \ + touch /etc/localtime && \ + mkdir -p /var/run/nvidia-persistenced && \ + touch /var/run/nvidia-persistenced/socket + +# installing Isaac Lab dependencies +# use pip caching to avoid reinstalling large packages +RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ + ${ISAACLAB_PATH}/isaaclab.sh --install + +# HACK: Remove install of quadprog dependency +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip uninstall -y quadprog + +# aliasing isaaclab.sh and python for convenience +RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ + echo "alias isaaclab=${ISAACLAB_PATH}/isaaclab.sh" >> ${HOME}/.bashrc && \ + echo "alias python=${ISAACLAB_PATH}/_isaac_sim/python.sh" >> ${HOME}/.bashrc && \ + echo "alias python3=${ISAACLAB_PATH}/_isaac_sim/python.sh" >> ${HOME}/.bashrc && \ + echo "alias pip='${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip'" >> ${HOME}/.bashrc && \ + echo "alias pip3='${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip'" >> ${HOME}/.bashrc && \ + echo "alias tensorboard='${ISAACLAB_PATH}/_isaac_sim/python.sh ${ISAACLAB_PATH}/_isaac_sim/tensorboard'" >> ${HOME}/.bashrc && \ + echo "export TZ=$(date +%Z)" >> ${HOME}/.bashrc && \ + echo "shopt -s histappend" >> /root/.bashrc && \ + echo "PROMPT_COMMAND='history -a'" >> /root/.bashrc + +# make working directory as the Isaac Lab directory +# this is the default directory when the container is run +WORKDIR ${ISAACLAB_PATH} diff --git a/docker/Dockerfile.curobo b/docker/Dockerfile.curobo new file mode 100644 index 0000000000000000000000000000000000000000..8e7ea4baffba30612617c09d7aeec0dff0abfdf7 --- /dev/null +++ b/docker/Dockerfile.curobo @@ -0,0 +1,144 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Nvidia Dockerfiles: https://github.com/NVIDIA-Omniverse/IsaacSim-dockerfiles +# Please check above link for license information. + +# Base image +ARG ISAACSIM_BASE_IMAGE_ARG +ARG ISAACSIM_VERSION_ARG +FROM ${ISAACSIM_BASE_IMAGE_ARG}:${ISAACSIM_VERSION_ARG} AS base +ENV ISAACSIM_VERSION=${ISAACSIM_VERSION_ARG} + +# Set default RUN shell to bash +SHELL ["/bin/bash", "-c"] + +# Adds labels to the Dockerfile +LABEL version="2.1.1" +LABEL description="Dockerfile for building and running the Isaac Lab framework inside Isaac Sim container image." + +# Arguments +# Path to Isaac Sim root folder +ARG ISAACSIM_ROOT_PATH_ARG +ENV ISAACSIM_ROOT_PATH=${ISAACSIM_ROOT_PATH_ARG} +# Path to the Isaac Lab directory +ARG ISAACLAB_PATH_ARG +ENV ISAACLAB_PATH=${ISAACLAB_PATH_ARG} +# Home dir of docker user, typically '/root' +ARG DOCKER_USER_HOME_ARG +ENV DOCKER_USER_HOME=${DOCKER_USER_HOME_ARG} + +# Set environment variables +ENV LANG=C.UTF-8 +ENV DEBIAN_FRONTEND=noninteractive + +USER root + +# Install dependencies and remove cache +RUN --mount=type=cache,target=/var/cache/apt \ + apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + libglib2.0-0 \ + ncurses-term \ + wget && \ + apt -y autoremove && apt clean autoclean && \ + rm -rf /var/lib/apt/lists/* + +# Detect Ubuntu version and install CUDA 12.8 via NVIDIA network repo (cuda-keyring) +RUN set -euo pipefail && \ + . /etc/os-release && \ + case "$ID" in \ + ubuntu) \ + case "$VERSION_ID" in \ + "20.04") cuda_repo="ubuntu2004";; \ + "22.04") cuda_repo="ubuntu2204";; \ + "24.04") cuda_repo="ubuntu2404";; \ + *) echo "Unsupported Ubuntu $VERSION_ID"; exit 1;; \ + esac ;; \ + *) echo "Unsupported base OS: $ID"; exit 1 ;; \ + esac && \ + apt-get update && apt-get install -y --no-install-recommends wget gnupg ca-certificates && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-keyring_1.1-1_all.deb && \ + dpkg -i cuda-keyring_1.1-1_all.deb && \ + rm -f cuda-keyring_1.1-1_all.deb && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-${cuda_repo}.pin && \ + mv cuda-${cuda_repo}.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \ + apt-get update && \ + apt-get install -y --no-install-recommends cuda-toolkit-12-8 && \ + apt-get -y autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* + + +ENV CUDA_HOME=/usr/local/cuda-12.8 +ENV PATH=${CUDA_HOME}/bin:${PATH} +ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} +ENV TORCH_CUDA_ARCH_LIST=8.0+PTX + +# Copy the Isaac Lab directory (files to exclude are defined in .dockerignore) +COPY ../ ${ISAACLAB_PATH} + +# Ensure isaaclab.sh has execute permissions +RUN chmod +x ${ISAACLAB_PATH}/isaaclab.sh + +# Set up a symbolic link between the installed Isaac Sim root folder and _isaac_sim in the Isaac Lab directory +RUN ln -sf ${ISAACSIM_ROOT_PATH} ${ISAACLAB_PATH}/_isaac_sim + +# Install toml dependency +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install toml + +# Install apt dependencies for extensions that declare them in their extension.toml +RUN --mount=type=cache,target=/var/cache/apt \ + ${ISAACLAB_PATH}/isaaclab.sh -p ${ISAACLAB_PATH}/tools/install_deps.py apt ${ISAACLAB_PATH}/source && \ + apt -y autoremove && apt clean autoclean && \ + rm -rf /var/lib/apt/lists/* + +# for singularity usage, have to create the directories that will binded +RUN mkdir -p ${ISAACSIM_ROOT_PATH}/kit/cache && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/ov && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/pip && \ + mkdir -p ${DOCKER_USER_HOME}/.cache/nvidia/GLCache && \ + mkdir -p ${DOCKER_USER_HOME}/.nv/ComputeCache && \ + mkdir -p ${DOCKER_USER_HOME}/.nvidia-omniverse/logs && \ + mkdir -p ${DOCKER_USER_HOME}/.local/share/ov/data && \ + mkdir -p ${DOCKER_USER_HOME}/Documents + +# for singularity usage, create NVIDIA binary placeholders +RUN touch /bin/nvidia-smi && \ + touch /bin/nvidia-debugdump && \ + touch /bin/nvidia-persistenced && \ + touch /bin/nvidia-cuda-mps-control && \ + touch /bin/nvidia-cuda-mps-server && \ + touch /etc/localtime && \ + mkdir -p /var/run/nvidia-persistenced && \ + touch /var/run/nvidia-persistenced/socket + +# installing Isaac Lab dependencies +# use pip caching to avoid reinstalling large packages +RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ + ${ISAACLAB_PATH}/isaaclab.sh --install + +# Install cuRobo from source (pinned commit); needs CUDA env and Torch +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install --no-build-isolation \ + "nvidia-curobo @ git+https://github.com/NVlabs/curobo.git@ebb71702f3f70e767f40fd8e050674af0288abe8" + +# HACK: Remove install of quadprog dependency +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip uninstall -y quadprog + +# aliasing isaaclab.sh and python for convenience +RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ + echo "alias isaaclab=${ISAACLAB_PATH}/isaaclab.sh" >> ${HOME}/.bashrc && \ + echo "alias python=${ISAACLAB_PATH}/_isaac_sim/python.sh" >> ${HOME}/.bashrc && \ + echo "alias python3=${ISAACLAB_PATH}/_isaac_sim/python.sh" >> ${HOME}/.bashrc && \ + echo "alias pip='${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip'" >> ${HOME}/.bashrc && \ + echo "alias pip3='${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip'" >> ${HOME}/.bashrc && \ + echo "alias tensorboard='${ISAACLAB_PATH}/_isaac_sim/python.sh ${ISAACLAB_PATH}/_isaac_sim/tensorboard'" >> ${HOME}/.bashrc && \ + echo "export TZ=$(date +%Z)" >> ${HOME}/.bashrc && \ + echo "shopt -s histappend" >> /root/.bashrc && \ + echo "PROMPT_COMMAND='history -a'" >> /root/.bashrc + +# make working directory as the Isaac Lab directory +# this is the default directory when the container is run +WORKDIR ${ISAACLAB_PATH} diff --git a/docker/Dockerfile.ros2 b/docker/Dockerfile.ros2 new file mode 100644 index 0000000000000000000000000000000000000000..2e00fc7ec396939a25ba55ef2886883f643d927d --- /dev/null +++ b/docker/Dockerfile.ros2 @@ -0,0 +1,39 @@ +# Everything past this stage is to install +# ROS2 Humble + +# What is the docker name suffix for the base image to load? (defaults to empty string) +ARG DOCKER_NAME_SUFFIX="" + +FROM isaac-lab-base${DOCKER_NAME_SUFFIX} AS ros2 + +# Which ROS2 apt package to install +ARG ROS2_APT_PACKAGE + +# ROS2 Humble Apt installations +RUN --mount=type=cache,target=/var/cache/apt \ + apt-get update && apt-get install -y --no-install-recommends \ + curl \ + # Install ROS2 Humble \ + software-properties-common && \ + add-apt-repository universe && \ + curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg && \ + echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo jammy) main" | tee /etc/apt/sources.list.d/ros2.list > /dev/null && \ + apt-get update && apt-get install -y --no-install-recommends \ + ros-humble-${ROS2_APT_PACKAGE} \ + ros-humble-vision-msgs \ + # Install both FastRTPS and CycloneDDS + ros-humble-rmw-cyclonedds-cpp \ + ros-humble-rmw-fastrtps-cpp \ + # This includes various dev tools including colcon + ros-dev-tools && \ + # Install rosdeps for extensions that declare a ros_ws in + # their extension.toml + ${ISAACLAB_PATH}/isaaclab.sh -p ${ISAACLAB_PATH}/tools/install_deps.py rosdep ${ISAACLAB_PATH}/source && \ + apt -y autoremove && apt clean autoclean && \ + rm -rf /var/lib/apt/lists/* && \ + # Add sourcing of setup.bash to .bashrc + echo "source /opt/ros/humble/setup.bash" >> ${HOME}/.bashrc + +# Copy the RMW specifications for ROS2 +# https://docs.isaacsim.omniverse.nvidia.com/latest/installation/install_ros.html +COPY docker/.ros/ ${DOCKER_USER_HOME}/.ros/ diff --git a/docker/cluster/.env.cluster b/docker/cluster/.env.cluster new file mode 100644 index 0000000000000000000000000000000000000000..7a2759d9fa6bd764b4c1cbf07640c62e0cb01de6 --- /dev/null +++ b/docker/cluster/.env.cluster @@ -0,0 +1,22 @@ +### +# Cluster specific settings +### + +# Job scheduler used by cluster. +# Currently supports PBS and SLURM +CLUSTER_JOB_SCHEDULER=SLURM +# Docker cache dir for Isaac Sim (has to end on docker-isaac-sim) +# e.g. /cluster/scratch/$USER/docker-isaac-sim +CLUSTER_ISAAC_SIM_CACHE_DIR=/some/path/on/cluster/docker-isaac-sim +# Isaac Lab directory on the cluster (has to end on isaaclab) +# e.g. /cluster/home/$USER/isaaclab +CLUSTER_ISAACLAB_DIR=/some/path/on/cluster/isaaclab +# Cluster login +CLUSTER_LOGIN=username@cluster_ip +# Cluster scratch directory to store the SIF file +# e.g. /cluster/scratch/$USER +CLUSTER_SIF_PATH=/some/path/on/cluster/ +# Remove the temporary isaaclab code copy after the job is done +REMOVE_CODE_COPY_AFTER_JOB=false +# Python executable within Isaac Lab directory to run with the submitted job +CLUSTER_PYTHON_EXECUTABLE=scripts/reinforcement_learning/rsl_rl/train.py diff --git a/docker/cluster/cluster_interface.sh b/docker/cluster/cluster_interface.sh new file mode 100644 index 0000000000000000000000000000000000000000..6684d835c99d58d56b2a66d6ff3f5d2601c222e9 --- /dev/null +++ b/docker/cluster/cluster_interface.sh @@ -0,0 +1,211 @@ +#!/usr/bin/env bash + +#== +# Configurations +#== + +# Exits if error occurs +set -e + +# Set tab-spaces +tabs 4 + +# get script directory +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" + +#== +# Functions +#== +# Function to display warnings in red +display_warning() { + echo -e "\033[31mWARNING: $1\033[0m" +} + +# Helper function to compare version numbers +version_gte() { + # Returns 0 if the first version is greater than or equal to the second, otherwise 1 + [ "$(printf '%s\n' "$1" "$2" | sort -V | head -n 1)" == "$2" ] +} + +# Function to check docker versions +check_docker_version() { + # check if docker is installed + if ! command -v docker &> /dev/null; then + echo "[Error] Docker is not installed! Please check the 'Docker Guide' for instruction." >&2; + exit 1 + fi + # Retrieve Docker version + docker_version=$(docker --version | awk '{ print $3 }') + apptainer_version=$(apptainer --version | awk '{ print $3 }') + + # Check if Docker version is exactly 24.0.7 or Apptainer version is exactly 1.2.5 + if [ "$docker_version" = "24.0.7" ] && [ "$apptainer_version" = "1.2.5" ]; then + echo "[INFO]: Docker version ${docker_version} and Apptainer version ${apptainer_version} are tested and compatible." + + # Check if Docker version is >= 27.0.0 and Apptainer version is >= 1.3.4 + elif version_gte "$docker_version" "27.0.0" && version_gte "$apptainer_version" "1.3.4"; then + echo "[INFO]: Docker version ${docker_version} and Apptainer version ${apptainer_version} are tested and compatible." + + # Else, display a warning for non-tested versions + else + display_warning "Docker version ${docker_version} and Apptainer version ${apptainer_version} are non-tested versions. There could be issues, please try to update them. More info: https://isaac-sim.github.io/IsaacLab/source/deployment/cluster.html" + fi +} + +# Checks if a docker image exists, otherwise prints warning and exists +check_image_exists() { + image_name="$1" + if ! docker image inspect $image_name &> /dev/null; then + echo "[Error] The '$image_name' image does not exist!" >&2; + echo "[Error] You might be able to build it with /IsaacLab/docker/container.py." >&2; + exit 1 + fi +} + +# Check if the singularity image exists on the remote host, otherwise print warning and exit +check_singularity_image_exists() { + image_name="$1" + if ! ssh "$CLUSTER_LOGIN" "[ -f $CLUSTER_SIF_PATH/$image_name.tar ]"; then + echo "[Error] The '$image_name' image does not exist on the remote host $CLUSTER_LOGIN!" >&2; + exit 1 + fi +} + +submit_job() { + + echo "[INFO] Arguments passed to job script ${@}" + + case $CLUSTER_JOB_SCHEDULER in + "SLURM") + job_script_file=submit_job_slurm.sh + ;; + "PBS") + job_script_file=submit_job_pbs.sh + ;; + *) + echo "[ERROR] Unsupported job scheduler specified: '$CLUSTER_JOB_SCHEDULER'. Supported options are: ['SLURM', 'PBS']" + exit 1 + ;; + esac + + ssh $CLUSTER_LOGIN "cd $CLUSTER_ISAACLAB_DIR && bash $CLUSTER_ISAACLAB_DIR/docker/cluster/$job_script_file \"$CLUSTER_ISAACLAB_DIR\" \"isaac-lab-$profile\" ${@}" +} + +#== +# Main +#== + +#!/bin/bash + +help() { + echo -e "\nusage: $(basename "$0") [-h] [] [...] -- Utility for interfacing between IsaacLab and compute clusters." + echo -e "\noptions:" + echo -e " -h Display this help message." + echo -e "\ncommands:" + echo -e " push [] Push the docker image to the cluster." + echo -e " job [] [] Submit a job to the cluster." + echo -e "\nwhere:" + echo -e " is the optional container profile specification. Defaults to 'base'." + echo -e " are optional arguments specific to the job command." + echo -e "\n" >&2 +} + +# Parse options +while getopts ":h" opt; do + case ${opt} in + h ) + help + exit 0 + ;; + \? ) + echo "Invalid option: -$OPTARG" >&2 + help + exit 1 + ;; + esac +done +shift $((OPTIND -1)) + +# Check for command +if [ $# -lt 1 ]; then + echo "Error: Command is required." >&2 + help + exit 1 +fi + +command=$1 +shift +profile="base" + +case $command in + push) + if [ $# -gt 1 ]; then + echo "Error: Too many arguments for push command." >&2 + help + exit 1 + fi + [ $# -eq 1 ] && profile=$1 + echo "Executing push command" + [ -n "$profile" ] && echo "Using profile: $profile" + if ! command -v apptainer &> /dev/null; then + echo "[INFO] Exiting because apptainer was not installed" + echo "[INFO] You may follow the installation procedure from here: https://apptainer.org/docs/admin/main/installation.html#install-ubuntu-packages" + exit + fi + # Check if Docker image exists + check_image_exists isaac-lab-$profile:latest + # Check docker and apptainer version + check_docker_version + # source env file to get cluster login and path information + source $SCRIPT_DIR/.env.cluster + # make sure exports directory exists + mkdir -p /$SCRIPT_DIR/exports + # clear old exports for selected profile + rm -rf /$SCRIPT_DIR/exports/isaac-lab-$profile* + # create singularity image + # NOTE: we create the singularity image as non-root user to allow for more flexibility. If this causes + # issues, remove the --fakeroot flag and open an issue on the IsaacLab repository. + cd /$SCRIPT_DIR/exports + APPTAINER_NOHTTPS=1 apptainer build --sandbox --fakeroot isaac-lab-$profile.sif docker-daemon://isaac-lab-$profile:latest + # tar image (faster to send single file as opposed to directory with many files) + tar -cvf /$SCRIPT_DIR/exports/isaac-lab-$profile.tar isaac-lab-$profile.sif + # make sure target directory exists + ssh $CLUSTER_LOGIN "mkdir -p $CLUSTER_SIF_PATH" + # send image to cluster + scp $SCRIPT_DIR/exports/isaac-lab-$profile.tar $CLUSTER_LOGIN:$CLUSTER_SIF_PATH/isaac-lab-$profile.tar + ;; + job) + if [ $# -ge 1 ]; then + passed_profile=$1 + if [ -f "$SCRIPT_DIR/../.env.$passed_profile" ]; then + profile=$passed_profile + shift + fi + fi + job_args="$@" + echo "[INFO] Executing job command" + [ -n "$profile" ] && echo -e "\tUsing profile: $profile" + [ -n "$job_args" ] && echo -e "\tJob arguments: $job_args" + source $SCRIPT_DIR/.env.cluster + # Get current date and time + current_datetime=$(date +"%Y%m%d_%H%M%S") + # Append current date and time to CLUSTER_ISAACLAB_DIR + CLUSTER_ISAACLAB_DIR="${CLUSTER_ISAACLAB_DIR}_${current_datetime}" + # Check if singularity image exists on the remote host + check_singularity_image_exists isaac-lab-$profile + # make sure target directory exists + ssh $CLUSTER_LOGIN "mkdir -p $CLUSTER_ISAACLAB_DIR" + # Sync Isaac Lab code + echo "[INFO] Syncing Isaac Lab code..." + rsync -rh --exclude="*.git*" --filter=':- .dockerignore' /$SCRIPT_DIR/../.. $CLUSTER_LOGIN:$CLUSTER_ISAACLAB_DIR + # execute job script + echo "[INFO] Executing job script..." + # check whether the second argument is a profile or a job argument + submit_job $job_args + ;; + *) + echo "Error: Invalid command: $command" >&2 + help + exit 1 + ;; +esac diff --git a/docker/cluster/run_singularity.sh b/docker/cluster/run_singularity.sh new file mode 100644 index 0000000000000000000000000000000000000000..51d8bcc4cf415d93762488d051f6e1b2f0e5ee1d --- /dev/null +++ b/docker/cluster/run_singularity.sh @@ -0,0 +1,82 @@ +#!/usr/bin/env bash + +echo "(run_singularity.py): Called on compute node from current isaaclab directory $1 with container profile $2 and arguments ${@:3}" + +#== +# Helper functions +#== + +setup_directories() { + # Check and create directories + for dir in \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/cache/kit" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/cache/ov" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/cache/pip" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/cache/glcache" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/cache/computecache" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/logs" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/data" \ + "${CLUSTER_ISAAC_SIM_CACHE_DIR}/documents"; do + if [ ! -d "$dir" ]; then + mkdir -p "$dir" + echo "Created directory: $dir" + fi + done +} + + +#== +# Main +#== + + +# get script directory +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" + +# load variables to set the Isaac Lab path on the cluster +source $SCRIPT_DIR/.env.cluster +source $SCRIPT_DIR/../.env.base + +# make sure that all directories exists in cache directory +setup_directories +# copy all cache files +cp -r $CLUSTER_ISAAC_SIM_CACHE_DIR $TMPDIR + +# make sure logs directory exists (in the permanent isaaclab directory) +mkdir -p "$CLUSTER_ISAACLAB_DIR/logs" +touch "$CLUSTER_ISAACLAB_DIR/logs/.keep" + +# copy the temporary isaaclab directory with the latest changes to the compute node +cp -r $1 $TMPDIR +# Get the directory name +dir_name=$(basename "$1") + +# copy container to the compute node +tar -xf $CLUSTER_SIF_PATH/$2.tar -C $TMPDIR + +# execute command in singularity container +# NOTE: ISAACLAB_PATH is normally set in `isaaclab.sh` but we directly call the isaac-sim python because we sync the entire +# Isaac Lab directory to the compute node and remote the symbolic link to isaac-sim +singularity exec \ + -B $TMPDIR/docker-isaac-sim/cache/kit:${DOCKER_ISAACSIM_ROOT_PATH}/kit/cache:rw \ + -B $TMPDIR/docker-isaac-sim/cache/ov:${DOCKER_USER_HOME}/.cache/ov:rw \ + -B $TMPDIR/docker-isaac-sim/cache/pip:${DOCKER_USER_HOME}/.cache/pip:rw \ + -B $TMPDIR/docker-isaac-sim/cache/glcache:${DOCKER_USER_HOME}/.cache/nvidia/GLCache:rw \ + -B $TMPDIR/docker-isaac-sim/cache/computecache:${DOCKER_USER_HOME}/.nv/ComputeCache:rw \ + -B $TMPDIR/docker-isaac-sim/logs:${DOCKER_USER_HOME}/.nvidia-omniverse/logs:rw \ + -B $TMPDIR/docker-isaac-sim/data:${DOCKER_USER_HOME}/.local/share/ov/data:rw \ + -B $TMPDIR/docker-isaac-sim/documents:${DOCKER_USER_HOME}/Documents:rw \ + -B $TMPDIR/$dir_name:/workspace/isaaclab:rw \ + -B $CLUSTER_ISAACLAB_DIR/logs:/workspace/isaaclab/logs:rw \ + --nv --writable --containall $TMPDIR/$2.sif \ + bash -c "export ISAACLAB_PATH=/workspace/isaaclab && cd /workspace/isaaclab && /isaac-sim/python.sh ${CLUSTER_PYTHON_EXECUTABLE} ${@:3}" + +# copy resulting cache files back to host +rsync -azPv $TMPDIR/docker-isaac-sim $CLUSTER_ISAAC_SIM_CACHE_DIR/.. + +# if defined, remove the temporary isaaclab directory pushed when the job was submitted +if $REMOVE_CODE_COPY_AFTER_JOB; then + rm -rf $1 +fi + +echo "(run_singularity.py): Return" diff --git a/docker/cluster/submit_job_pbs.sh b/docker/cluster/submit_job_pbs.sh new file mode 100644 index 0000000000000000000000000000000000000000..c3a44f438e43a81052285863daa47b6ab68bd755 --- /dev/null +++ b/docker/cluster/submit_job_pbs.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +# in the case you need to load specific modules on the cluster, add them here +# e.g., `module load eth_proxy` + +# create job script with compute demands +### MODIFY HERE FOR YOUR JOB ### +cat < job.sh +#!/bin/bash + +#PBS -l select=1:ncpus=8:mpiprocs=1:ngpus=1 +#PBS -l walltime=01:00:00 +#PBS -j oe +#PBS -q gpu +#PBS -N isaaclab +#PBS -m bea -M "user@mail" + +# Pass the container profile first to run_singularity.sh, then all arguments intended for the executed script +bash "$1/docker/cluster/run_singularity.sh" "$1" "$2" "${@:3}" +EOT + +qsub job.sh +rm job.sh diff --git a/docker/cluster/submit_job_slurm.sh b/docker/cluster/submit_job_slurm.sh new file mode 100644 index 0000000000000000000000000000000000000000..22c216d95910279d30f2fe72afb29a2e9587d2bb --- /dev/null +++ b/docker/cluster/submit_job_slurm.sh @@ -0,0 +1,25 @@ +#!/usr/bin/env bash + +# in the case you need to load specific modules on the cluster, add them here +# e.g., `module load eth_proxy` + +# create job script with compute demands +### MODIFY HERE FOR YOUR JOB ### +cat < job.sh +#!/bin/bash + +#SBATCH -n 1 +#SBATCH --cpus-per-task=8 +#SBATCH --gpus=rtx_3090:1 +#SBATCH --time=23:00:00 +#SBATCH --mem-per-cpu=4048 +#SBATCH --mail-type=END +#SBATCH --mail-user=name@mail +#SBATCH --job-name="training-$(date +"%Y-%m-%dT%H:%M")" + +# Pass the container profile first to run_singularity.sh, then all arguments intended for the executed script +bash "$1/docker/cluster/run_singularity.sh" "$1" "$2" "${@:3}" +EOT + +sbatch < job.sh +rm job.sh diff --git a/docker/container.py b/docker/container.py new file mode 100644 index 0000000000000000000000000000000000000000..ab92d816ffac36bdfe095d7db290fdc8ef31c38a --- /dev/null +++ b/docker/container.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import shutil +from pathlib import Path + +from utils import ContainerInterface, x11_utils + + +def parse_cli_args() -> argparse.Namespace: + """Parse command line arguments. + + This function creates a parser object and adds subparsers for each command. The function then parses the + command line arguments and returns the parsed arguments. + + Returns: + The parsed command line arguments. + """ + parser = argparse.ArgumentParser(description="Utility for using Docker with Isaac Lab.") + + # We have to create separate parent parsers for common options to our subparsers + parent_parser = argparse.ArgumentParser(add_help=False) + parent_parser.add_argument( + "profile", nargs="?", default="base", help="Optional container profile specification. Example: 'base' or 'ros'." + ) + parent_parser.add_argument( + "--files", + nargs="*", + default=None, + help=( + "Allows additional '.yaml' files to be passed to the docker compose command. These files will be merged" + " with 'docker-compose.yaml' in their provided order." + ), + ) + parent_parser.add_argument( + "--env-files", + nargs="*", + default=None, + help=( + "Allows additional '.env' files to be passed to the docker compose command. These files will be merged with" + " '.env.base' in their provided order." + ), + ) + parent_parser.add_argument( + "--suffix", + nargs="?", + default=None, + help=( + "Optional docker image and container name suffix. Defaults to None, in which case, the docker name" + " suffix is set to the empty string. A hyphen is inserted in between the profile and the suffix if" + ' the suffix is a nonempty string. For example, if "base" is passed to profile, and "custom" is' + " passed to suffix, then the produced docker image and container will be named ``isaac-lab-base-custom``." + ), + ) + parent_parser.add_argument( + "--info", + action="store_true", + help="Print the container interface information. This is useful for debugging purposes.", + ) + + # Actual command definition begins here + subparsers = parser.add_subparsers(dest="command", required=True) + subparsers.add_parser( + "build", + help="Build the docker image without creating the container.", + parents=[parent_parser], + ) + subparsers.add_parser( + "start", + help="Build the docker image and create the container in detached mode.", + parents=[parent_parser], + ) + subparsers.add_parser( + "enter", help="Begin a new bash process within an existing Isaac Lab container.", parents=[parent_parser] + ) + config = subparsers.add_parser( + "config", + help=( + "Generate a docker-compose.yaml from the passed yamls, .envs, and either print to the terminal or create a" + " yaml at output_yaml" + ), + parents=[parent_parser], + ) + config.add_argument( + "--output-yaml", nargs="?", default=None, help="Yaml file to write config output to. Defaults to None." + ) + subparsers.add_parser( + "copy", help="Copy build and logs artifacts from the container to the host machine.", parents=[parent_parser] + ) + subparsers.add_parser("stop", help="Stop the docker container and remove it.", parents=[parent_parser]) + + # parse the arguments to determine the command + args = parser.parse_args() + + return args + + +def main(args: argparse.Namespace): + """Main function for the Docker utility.""" + # check if docker is installed + if not shutil.which("docker"): + raise RuntimeError( + "Docker is not installed! Please check the 'Docker Guide' for instruction: " + "https://isaac-sim.github.io/IsaacLab/source/deployment/docker.html" + ) + + # creating container interface + ci = ContainerInterface( + context_dir=Path(__file__).resolve().parent, + profile=args.profile, + yamls=args.files, + envs=args.env_files, + suffix=args.suffix, + ) + if args.info: + print("[INFO] Printing container interface information...\n") + ci.print_info() + return + + print(f"[INFO] Using container profile: {ci.profile}") + if args.command == "build": + # check if x11 forwarding is enabled + x11_outputs = x11_utils.x11_check(ci.statefile) + # if x11 forwarding is enabled, add the x11 yaml and environment variables + if x11_outputs is not None: + (x11_yaml, x11_envar) = x11_outputs + ci.add_yamls += x11_yaml + ci.environ.update(x11_envar) + # build the image + ci.build() + elif args.command == "start": + # check if x11 forwarding is enabled + x11_outputs = x11_utils.x11_check(ci.statefile) + # if x11 forwarding is enabled, add the x11 yaml and environment variables + if x11_outputs is not None: + (x11_yaml, x11_envar) = x11_outputs + ci.add_yamls += x11_yaml + ci.environ.update(x11_envar) + # start the container + ci.start() + elif args.command == "enter": + # refresh the x11 forwarding + x11_utils.x11_refresh(ci.statefile) + # enter the container + ci.enter() + elif args.command == "config": + ci.config(args.output_yaml) + elif args.command == "copy": + ci.copy() + elif args.command == "stop": + # stop the container + ci.stop() + # cleanup the x11 forwarding + x11_utils.x11_cleanup(ci.statefile) + else: + raise RuntimeError(f"Invalid command provided: {args.command}. Please check the help message.") + + +if __name__ == "__main__": + args_cli = parse_cli_args() + main(args_cli) diff --git a/docker/container.sh b/docker/container.sh new file mode 100644 index 0000000000000000000000000000000000000000..f6fc2af49d61989cb8deda70453130cb631aaa0e --- /dev/null +++ b/docker/container.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# print warning of deprecated script in yellow +echo -e "\e[33m------------------------------------------------------------" +echo -e "WARNING: This script is deprecated and will be removed in the future. Please use 'docker/container.py' instead." +echo -e "------------------------------------------------------------\e[0m\n" + +# obtain current directory +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# call the python script +python3 "${SCRIPT_DIR}/container.py" "${@:1}" diff --git a/docker/docker-compose.cloudxr-runtime.patch.yaml b/docker/docker-compose.cloudxr-runtime.patch.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5615aed29ac7469043afbd791f068141195afea1 --- /dev/null +++ b/docker/docker-compose.cloudxr-runtime.patch.yaml @@ -0,0 +1,52 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +services: + cloudxr-runtime: + image: ${CLOUDXR_RUNTIME_BASE_IMAGE_ARG}:${CLOUDXR_RUNTIME_VERSION_ARG} + ports: + - "48010:48010/tcp" # signaling + - "47998:47998/udp" # media + - "47999:47999/udp" # media + - "48000:48000/udp" # media + - "48005:48005/udp" # media + - "48008:48008/udp" # media + - "48012:48012/udp" # media + healthcheck: + test: ["CMD", "test", "-S", "/openxr/run/ipc_cloudxr"] + interval: 1s + timeout: 1s + retries: 10 + start_period: 5s + environment: + - ACCEPT_EULA=${ACCEPT_EULA} + volumes: + - openxr-volume:/openxr:rw + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + + isaac-lab-base: + environment: + - XDG_RUNTIME_DIR=/openxr/run + - XR_RUNTIME_JSON=/openxr/share/openxr/1/openxr_cloudxr.json + volumes: + - openxr-volume:/openxr:rw + depends_on: + - cloudxr-runtime + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + +volumes: + openxr-volume: diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml new file mode 100644 index 0000000000000000000000000000000000000000..09fde19be7c2e08ba5c161c76a06a4b6a6df3284 --- /dev/null +++ b/docker/docker-compose.yaml @@ -0,0 +1,153 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Here we set the parts that would +# be reused between services to an +# extension field +# https://docs.docker.com/compose/compose-file/compose-file-v3/#extension-fields +x-default-isaac-lab-volumes: &default-isaac-lab-volumes + # These volumes follow from this page + # https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/install_faq.html#save-isaac-sim-configs-on-local-disk + - type: volume + source: isaac-cache-kit + target: ${DOCKER_ISAACSIM_ROOT_PATH}/kit/cache + - type: volume + source: isaac-cache-ov + target: ${DOCKER_USER_HOME}/.cache/ov + - type: volume + source: isaac-cache-pip + target: ${DOCKER_USER_HOME}/.cache/pip + - type: volume + source: isaac-cache-gl + target: ${DOCKER_USER_HOME}/.cache/nvidia/GLCache + - type: volume + source: isaac-cache-compute + target: ${DOCKER_USER_HOME}/.nv/ComputeCache + - type: volume + source: isaac-logs + target: ${DOCKER_USER_HOME}/.nvidia-omniverse/logs + - type: volume + source: isaac-carb-logs + target: ${DOCKER_ISAACSIM_ROOT_PATH}/kit/logs/Kit/Isaac-Sim + - type: volume + source: isaac-data + target: ${DOCKER_USER_HOME}/.local/share/ov/data + - type: volume + source: isaac-docs + target: ${DOCKER_USER_HOME}/Documents + # This overlay allows changes on the local files to + # be reflected within the container immediately + - type: bind + source: ../source + target: ${DOCKER_ISAACLAB_PATH}/source + - type: bind + source: ../scripts + target: ${DOCKER_ISAACLAB_PATH}/scripts + - type: bind + source: ../docs + target: ${DOCKER_ISAACLAB_PATH}/docs + - type: bind + source: ../tools + target: ${DOCKER_ISAACLAB_PATH}/tools + # The effect of these volumes is twofold: + # 1. Prevent root-owned files from flooding the _build and logs dir + # on the host machine + # 2. Preserve the artifacts in persistent volumes for later copying + # to the host machine + - type: volume + source: isaac-lab-docs + target: ${DOCKER_ISAACLAB_PATH}/docs/_build + - type: volume + source: isaac-lab-logs + target: ${DOCKER_ISAACLAB_PATH}/logs + - type: volume + source: isaac-lab-data + target: ${DOCKER_ISAACLAB_PATH}/data_storage + # This volume is used to store the history of the bash shell + - type: bind + source: .isaac-lab-docker-history + target: ${DOCKER_USER_HOME}/.bash_history + +x-default-isaac-lab-environment: &default-isaac-lab-environment + - ISAACSIM_PATH=${DOCKER_ISAACLAB_PATH}/_isaac_sim + - OMNI_KIT_ALLOW_ROOT=1 + +x-default-isaac-lab-deploy: &default-isaac-lab-deploy + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + +services: + # This service is the base Isaac Lab image + isaac-lab-base: + profiles: [ "base" ] + env_file: .env.base + build: + context: ../ + dockerfile: docker/Dockerfile.base + args: + - ISAACSIM_BASE_IMAGE_ARG=${ISAACSIM_BASE_IMAGE} + - ISAACSIM_VERSION_ARG=${ISAACSIM_VERSION} + - ISAACSIM_ROOT_PATH_ARG=${DOCKER_ISAACSIM_ROOT_PATH} + - ISAACLAB_PATH_ARG=${DOCKER_ISAACLAB_PATH} + - DOCKER_USER_HOME_ARG=${DOCKER_USER_HOME} + image: isaac-lab-base${DOCKER_NAME_SUFFIX-} + container_name: isaac-lab-base${DOCKER_NAME_SUFFIX-} + environment: *default-isaac-lab-environment + volumes: *default-isaac-lab-volumes + network_mode: host + deploy: *default-isaac-lab-deploy + # This is the entrypoint for the container + entrypoint: bash + stdin_open: true + tty: true + + # This service adds a ROS2 Humble + # installation on top of the base image + isaac-lab-ros2: + profiles: [ "ros2" ] + env_file: + - .env.base + - .env.ros2 + build: + context: ../ + dockerfile: docker/Dockerfile.ros2 + args: + # ROS2_APT_PACKAGE will default to NONE. This is to + # avoid a warning message when building only the base profile + # with the .env.base file + - ROS2_APT_PACKAGE=${ROS2_APT_PACKAGE:-NONE} + # Make sure that the correct Docker Name Suffix is being passed to the dockerfile, to know which base image to + # start from. + - DOCKER_NAME_SUFFIX=${DOCKER_NAME_SUFFIX-} + image: isaac-lab-ros2${DOCKER_NAME_SUFFIX-} + container_name: isaac-lab-ros2${DOCKER_NAME_SUFFIX-} + environment: *default-isaac-lab-environment + volumes: *default-isaac-lab-volumes + network_mode: host + deploy: *default-isaac-lab-deploy + # This is the entrypoint for the container + entrypoint: bash + stdin_open: true + tty: true + +volumes: + # isaac-sim + isaac-cache-kit: + isaac-cache-ov: + isaac-cache-pip: + isaac-cache-gl: + isaac-cache-compute: + isaac-logs: + isaac-carb-logs: + isaac-data: + isaac-docs: + # isaac-lab + isaac-lab-docs: + isaac-lab-logs: + isaac-lab-data: diff --git a/docker/test/test_docker.py b/docker/test/test_docker.py new file mode 100644 index 0000000000000000000000000000000000000000..85fd66348f855185368375d91285874782f2a681 --- /dev/null +++ b/docker/test/test_docker.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +import subprocess +from pathlib import Path + +import pytest + + +def start_stop_docker(profile, suffix): + """Test starting and stopping docker profile with suffix.""" + environ = os.environ + context_dir = Path(__file__).resolve().parent.parent + + # generate parameters for the arguments + if suffix != "": + container_name = f"isaac-lab-{profile}-{suffix}" + suffix_args = ["--suffix", suffix] + else: + container_name = f"isaac-lab-{profile}" + suffix_args = [] + + run_kwargs = { + "check": False, + "capture_output": True, + "text": True, + "cwd": context_dir, + "env": environ, + } + + # start the container + docker_start = subprocess.run(["python", "container.py", "start", profile] + suffix_args, **run_kwargs) + assert docker_start.returncode == 0 + + # verify that the container is running + docker_running_true = subprocess.run(["docker", "ps"], **run_kwargs) + assert docker_running_true.returncode == 0 + assert container_name in docker_running_true.stdout + + # stop the container + docker_stop = subprocess.run(["python", "container.py", "stop", profile] + suffix_args, **run_kwargs) + assert docker_stop.returncode == 0 + + # verify that the container has stopped + docker_running_false = subprocess.run(["docker", "ps"], **run_kwargs) + assert docker_running_false.returncode == 0 + assert container_name not in docker_running_false.stdout + + +@pytest.mark.parametrize( + "profile,suffix", + [ + ("base", ""), + ("base", "test"), + ("ros2", ""), + ("ros2", "test"), + ], +) +def test_docker_profiles(profile, suffix): + """Test starting and stopping docker profiles with and without suffixes.""" + start_stop_docker(profile, suffix) diff --git a/docker/utils/__init__.py b/docker/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4d29d62425fad8ecc8d94578f95dcb24d717a7eb --- /dev/null +++ b/docker/utils/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .container_interface import ContainerInterface + +__all__ = ["ContainerInterface"] diff --git a/docker/utils/container_interface.py b/docker/utils/container_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..f8b3eb07ee2289d0bb1dc2f3c51119c0fb82e406 --- /dev/null +++ b/docker/utils/container_interface.py @@ -0,0 +1,347 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import os +import shutil +import subprocess +from pathlib import Path +from typing import Any + +from .state_file import StateFile + + +class ContainerInterface: + """A helper class for managing Isaac Lab containers.""" + + def __init__( + self, + context_dir: Path, + profile: str = "base", + yamls: list[str] | None = None, + envs: list[str] | None = None, + statefile: StateFile | None = None, + suffix: str | None = None, + ): + """Initialize the container interface with the given parameters. + + Args: + context_dir: + The context directory for Docker operations. + profile: + The profile name for the container. Defaults to "base". + yamls: + A list of yaml files to extend ``docker-compose.yaml`` settings. These are extended in the order + they are provided. Defaults to None, in which case no additional yaml files are added. + envs: + A list of environment variable files to extend the ``.env.base`` file. These are extended in the order + they are provided. Defaults to None, in which case no additional environment variable files are added. + statefile: + An instance of the :class:`Statefile` class to manage state variables. Defaults to None, in + which case a new configuration object is created by reading the configuration file at the path + ``context_dir/.container.cfg``. + suffix: + Optional docker image and container name suffix. Defaults to None, in which case, the docker name + suffix is set to the empty string. A hyphen is inserted in between the profile and the suffix if + the suffix is a nonempty string. For example, if "base" is passed to profile, and "custom" is + passed to suffix, then the produced docker image and container will be named ``isaac-lab-base-custom``. + """ + # set the context directory + self.context_dir = context_dir + + # create a state-file if not provided + # the state file is a manager of run-time state variables that are saved to a file + if statefile is None: + self.statefile = StateFile(path=self.context_dir / ".container.cfg") + else: + self.statefile = statefile + + # set the profile and container name + self.profile = profile + if self.profile == "isaaclab": + # Silently correct from isaaclab to base, because isaaclab is a commonly passed arg + # but not a real profile + self.profile = "base" + + # set the docker image and container name suffix + if suffix is None or suffix == "": + # if no name suffix is given, default to the empty string as the name suffix + self.suffix = "" + else: + # insert a hyphen before the suffix if a suffix is given + self.suffix = f"-{suffix}" + + # set names for easier reference + self.base_service_name = "isaac-lab-base" + self.service_name = f"isaac-lab-{self.profile}" + self.container_name = f"{self.service_name}{self.suffix}" + self.image_name = f"{self.service_name}{self.suffix}:latest" + + # keep the environment variables from the current environment, + # except make sure that the docker name suffix is set from the script + self.environ = os.environ.copy() + self.environ["DOCKER_NAME_SUFFIX"] = self.suffix + + # resolve the image extension through the passed yamls and envs + self._resolve_image_extension(yamls, envs) + # load the environment variables from the .env files + self._parse_dot_vars() + + def print_info(self): + """Print the container interface information.""" + print("=" * 60) + print(f"{'DOCKER CONTAINER INFO':^60}") # Centered title + print("=" * 60) + + print(f"{'Profile:':25} {self.profile}") + print(f"{'Suffix:':25} {self.suffix}") + print(f"{'Service Name:':25} {self.service_name}") + print(f"{'Image Name:':25} {self.image_name}") + print(f"{'Container Name:':25} {self.container_name}") + + print("-" * 60) + print(f"{'Docker Compose Arguments':^60}") + print("-" * 60) + print(f"{'YAMLs:':25} {' '.join(self.add_yamls)}") + print(f"{'Profiles:':25} {' '.join(self.add_profiles)}") + print(f"{'Env Files:':25} {' '.join(self.add_env_files)}") + print("=" * 60) + + """ + Operations. + """ + + def is_container_running(self) -> bool: + """Check if the container is running. + + Returns: + True if the container is running, otherwise False. + """ + status = subprocess.run( + ["docker", "container", "inspect", "-f", "{{.State.Status}}", self.container_name], + capture_output=True, + text=True, + check=False, + ).stdout.strip() + return status == "running" + + def does_image_exist(self) -> bool: + """Check if the Docker image exists. + + Returns: + True if the image exists, otherwise False. + """ + result = subprocess.run(["docker", "image", "inspect", self.image_name], capture_output=True, text=True) + return result.returncode == 0 + + def build(self): + """Build the Docker image.""" + print("[INFO] Building the docker image for the profile 'base'...\n") + # build the image for the base profile + cmd = ( + ["docker", "compose"] + + ["--file", "docker-compose.yaml"] + + ["--profile", "base"] + + ["--env-file", ".env.base"] + + ["build", self.base_service_name] + ) + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + print("[INFO] Finished building the docker image for the profile 'base'.\n") + + # build the image for the profile + if self.profile != "base": + print(f"[INFO] Building the docker image for the profile '{self.profile}'...\n") + cmd = ( + ["docker", "compose"] + + self.add_yamls + + self.add_profiles + + self.add_env_files + + ["build", self.service_name] + ) + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + print(f"[INFO] Finished building the docker image for the profile '{self.profile}'.\n") + + def start(self): + """Build and start the Docker container using the Docker compose command.""" + print( + f"[INFO] Building the docker image and starting the container '{self.container_name}' in the" + " background...\n" + ) + # Check if the container history file exists + container_history_file = self.context_dir / ".isaac-lab-docker-history" + if not container_history_file.exists(): + # Create the file with sticky bit on the group + container_history_file.touch(mode=0o2644, exist_ok=True) + + # build the image for the base profile if not running base (up will build base already if profile is base) + if self.profile != "base": + cmd = ( + ["docker", "compose"] + + ["--file", "docker-compose.yaml"] + + ["--profile", "base"] + + ["--env-file", ".env.base"] + + ["build", self.base_service_name] + ) + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + + # start the container and build the image if not available + cmd = ( + ["docker", "compose"] + + self.add_yamls + + self.add_profiles + + self.add_env_files + + ["up", "--detach", "--build", "--remove-orphans"] + ) + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + + def enter(self): + """Enter the running container by executing a bash shell. + + Raises: + RuntimeError: If the container is not running. + """ + if self.is_container_running(): + print(f"[INFO] Entering the existing '{self.container_name}' container in a bash session...\n") + cmd = ( + ["docker", "exec", "--interactive", "--tty"] + + (["-e", f"DISPLAY={os.environ['DISPLAY']}"] if "DISPLAY" in os.environ else []) + + [self.container_name, "bash"] + ) + subprocess.run(cmd) + else: + raise RuntimeError(f"The container '{self.container_name}' is not running.") + + def stop(self): + """Stop the running container using the Docker compose command.""" + if self.is_container_running(): + print(f"[INFO] Stopping the launched docker container '{self.container_name}'...\n") + # stop running services + cmd = ( + ["docker", "compose"] + self.add_yamls + self.add_profiles + self.add_env_files + ["down", "--volumes"] + ) + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + else: + print( + f"[INFO] Can't stop container '{self.container_name}' as it is not running." + " To check if the container is running, run 'docker ps' or 'docker container ls'.\n" + ) + + def copy(self, output_dir: Path | None = None): + """Copy artifacts from the running container to the host machine. + + Args: + output_dir: The directory to copy the artifacts to. Defaults to None, in which case + the context directory is used. + + Raises: + RuntimeError: If the container is not running. + """ + if self.is_container_running(): + print(f"[INFO] Copying artifacts from the '{self.container_name}' container...\n") + if output_dir is None: + output_dir = self.context_dir + + # create a directory to store the artifacts + output_dir = output_dir.joinpath("artifacts") + if not output_dir.is_dir(): + output_dir.mkdir() + + # define dictionary of mapping from docker container path to host machine path + docker_isaac_lab_path = Path(self.dot_vars["DOCKER_ISAACLAB_PATH"]) + artifacts = { + docker_isaac_lab_path.joinpath("logs"): output_dir.joinpath("logs"), + docker_isaac_lab_path.joinpath("docs/_build"): output_dir.joinpath("docs"), + docker_isaac_lab_path.joinpath("data_storage"): output_dir.joinpath("data_storage"), + } + # print the artifacts to be copied + for container_path, host_path in artifacts.items(): + print(f"\t -{container_path} -> {host_path}") + # remove the existing artifacts + for path in artifacts.values(): + shutil.rmtree(path, ignore_errors=True) + + # copy the artifacts + for container_path, host_path in artifacts.items(): + cmd = ["docker", "cp", f"{self.container_name}:{container_path}/", host_path] + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + print("\n[INFO] Finished copying the artifacts from the container.") + else: + raise RuntimeError(f"The container '{self.container_name}' is not running.") + + def config(self, output_yaml: Path | None = None): + """Process the Docker compose configuration based on the passed yamls and environment files. + + If the :attr:`output_yaml` is not None, the configuration is written to the file. Otherwise, it is printed to + the terminal. + + Args: + output_yaml: The path to the yaml file where the configuration is written to. Defaults + to None, in which case the configuration is printed to the terminal. + """ + print("[INFO] Configuring the passed options into a yaml...\n") + + # resolve the output argument + if output_yaml is not None: + output = ["--output", output_yaml] + else: + output = [] + + # run the docker compose config command to generate the configuration + cmd = ["docker", "compose"] + self.add_yamls + self.add_profiles + self.add_env_files + ["config"] + output + subprocess.run(cmd, check=False, cwd=self.context_dir, env=self.environ) + + """ + Helper functions. + """ + + def _resolve_image_extension(self, yamls: list[str] | None = None, envs: list[str] | None = None): + """Resolve the image extension by setting up YAML files, profiles, and environment files for the + Docker compose command. + + Args: + yamls: A list of yaml files to extend ``docker-compose.yaml`` settings. These are extended in the order + they are provided. + envs: A list of environment variable files to extend the ``.env.base`` file. These are extended in the order + they are provided. + """ + self.add_yamls = ["--file", "docker-compose.yaml"] + self.add_profiles = ["--profile", f"{self.profile}"] + self.add_env_files = ["--env-file", ".env.base"] + + # extend env file based on profile + if self.profile != "base": + self.add_env_files += ["--env-file", f".env.{self.profile}"] + + # extend the env file based on the passed envs + if envs is not None: + for env in envs: + self.add_env_files += ["--env-file", env] + + # extend the docker-compose.yaml based on the passed yamls + if yamls is not None: + for yaml in yamls: + self.add_yamls += ["--file", yaml] + + def _parse_dot_vars(self): + """Parse the environment variables from the .env files. + + Based on the passed ".env" files, this function reads the environment variables and stores them in a dictionary. + The environment variables are read in order and overwritten if there are name conflicts, mimicking the behavior + of Docker compose. + """ + self.dot_vars: dict[str, Any] = {} + + # check if the number of arguments is even for the env files + if len(self.add_env_files) % 2 != 0: + raise RuntimeError( + "The parameters for env files are configured incorrectly. There should be an even number of arguments." + f" Received: {self.add_env_files}." + ) + + # read the environment variables from the .env files + for i in range(1, len(self.add_env_files), 2): + with open(self.context_dir / self.add_env_files[i]) as f: + self.dot_vars.update(dict(line.strip().split("=", 1) for line in f if "=" in line)) diff --git a/docker/utils/state_file.py b/docker/utils/state_file.py new file mode 100644 index 0000000000000000000000000000000000000000..505f272f41013a1b2e68018e58fb38d3b53db246 --- /dev/null +++ b/docker/utils/state_file.py @@ -0,0 +1,151 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import configparser +from configparser import ConfigParser +from pathlib import Path +from typing import Any + + +class StateFile: + """A class to manage state variables parsed from a configuration file. + + This class provides a simple interface to set, get, and delete variables from a configuration + object. It also provides the ability to save the configuration object to a file. + + It thinly wraps around the ConfigParser class from the configparser module. + """ + + def __init__(self, path: Path, namespace: str | None = None): + """Initialize the class instance and load the configuration file. + + Args: + path: The path to the configuration file. + namespace: The default namespace to use when setting and getting variables. + Namespace corresponds to a section in the configuration file. Defaults to None, + meaning all member functions will have to specify the section explicitly, + or :attr:`StateFile.namespace` must be set manually. + """ + self.path = path + self.namespace = namespace + + # load the configuration file + self.load() + + def __del__(self): + """ + Save the loaded configuration to the initial file path upon deconstruction. This helps + ensure that the configuration file is always up to date. + """ + # save the configuration file + self.save() + + """ + Operations. + """ + + def set_variable(self, key: str, value: Any, section: str | None = None): + """Set a variable into the configuration object. + + Note: + Since we use the ConfigParser class, the section names are case-sensitive but the keys are not. + + Args: + key: The key of the variable to be set. + value: The value of the variable to be set. + section: The section of the configuration object to set the variable in. + Defaults to None, in which case the default section is used. + + Raises: + configparser.Error: If no section is specified and the default section is None. + """ + # resolve the section + if section is None: + if self.namespace is None: + raise configparser.Error("No section specified. Please specify a section or set StateFile.namespace.") + section = self.namespace + + # create section if it does not exist + if section not in self.loaded_cfg.sections(): + self.loaded_cfg.add_section(section) + # set the variable + self.loaded_cfg.set(section, key, value) + + def get_variable(self, key: str, section: str | None = None) -> Any: + """Get a variable from the configuration object. + + Note: + Since we use the ConfigParser class, the section names are case-sensitive but the keys are not. + + Args: + key: The key of the variable to be loaded. + section: The section of the configuration object to read the variable from. + Defaults to None, in which case the default section is used. + + Returns: + The value of the variable. It is None if the key does not exist. + + Raises: + configparser.Error: If no section is specified and the default section is None. + """ + # resolve the section + if section is None: + if self.namespace is None: + raise configparser.Error("No section specified. Please specify a section or set StateFile.namespace.") + section = self.namespace + + return self.loaded_cfg.get(section, key, fallback=None) + + def delete_variable(self, key: str, section: str | None = None): + """Delete a variable from the configuration object. + + Note: + Since we use the ConfigParser class, the section names are case-sensitive but the keys are not. + + Args: + key: The key of the variable to be deleted. + section: The section of the configuration object to remove the variable from. + Defaults to None, in which case the default section is used. + + Raises: + configparser.Error: If no section is specified and the default section is None. + configparser.NoSectionError: If the section does not exist in the configuration object. + configparser.NoOptionError: If the key does not exist in the section. + """ + # resolve the section + if section is None: + if self.namespace is None: + raise configparser.Error("No section specified. Please specify a section or set StateFile.namespace.") + section = self.namespace + + # check if the section exists + if section not in self.loaded_cfg.sections(): + raise configparser.NoSectionError(f"Section '{section}' does not exist in the file: {self.path}") + + # check if the key exists + if self.loaded_cfg.has_option(section, key): + self.loaded_cfg.remove_option(section, key) + else: + raise configparser.NoOptionError(option=key, section=section) + + """ + Operations - File I/O. + """ + + def load(self): + """Load the configuration file into memory. + + This function reads the contents of the configuration file into memory. + If the file does not exist, it creates an empty file. + """ + self.loaded_cfg = ConfigParser() + self.loaded_cfg.read(self.path) + + def save(self): + """Save the configuration file to disk.""" + with open(self.path, "w+") as f: + self.loaded_cfg.write(f) diff --git a/docker/utils/x11_utils.py b/docker/utils/x11_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4d7d0e3639f3b14807ce188db183cbc916021df0 --- /dev/null +++ b/docker/utils/x11_utils.py @@ -0,0 +1,227 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utility functions for managing X11 forwarding in the docker container.""" + +from __future__ import annotations + +import os +import shutil +import subprocess +import sys +from pathlib import Path + +from .state_file import StateFile + + +# This method of x11 enabling forwarding was inspired by osrf/rocker +# https://github.com/osrf/rocker +def configure_x11(statefile: StateFile) -> dict[str, str]: + """Configure X11 forwarding by creating and managing a temporary .xauth file. + + If xauth is not installed, the function prints an error message and exits. The message + instructs the user to install xauth with 'apt install xauth'. + + If the .xauth file does not exist, the function creates it and configures it with the necessary + xauth cookie. + + Args: + statefile: An instance of the configuration file class. + + Returns: + A dictionary with two key-value pairs: + + - "__ISAACLAB_TMP_XAUTH": The path to the temporary .xauth file. + - "__ISAACLAB_TMP_DIR": The path to the directory where the temporary .xauth file is stored. + + """ + # check if xauth is installed + if not shutil.which("xauth"): + print("[INFO] xauth is not installed.") + print("[INFO] Please install it with 'apt install xauth'") + exit(1) + + # set the namespace to X11 for the statefile + statefile.namespace = "X11" + # load the value of the temporary xauth file + tmp_xauth_value = statefile.get_variable("__ISAACLAB_TMP_XAUTH") + + if tmp_xauth_value is None or not Path(tmp_xauth_value).exists(): + # create a temporary directory to store the .xauth file + tmp_dir = subprocess.run(["mktemp", "-d"], capture_output=True, text=True, check=True).stdout.strip() + # create the .xauth file + tmp_xauth_value = create_x11_tmpfile(tmpdir=Path(tmp_dir)) + # set the statefile variable + statefile.set_variable("__ISAACLAB_TMP_XAUTH", str(tmp_xauth_value)) + else: + tmp_dir = Path(tmp_xauth_value).parent + + return {"__ISAACLAB_TMP_XAUTH": str(tmp_xauth_value), "__ISAACLAB_TMP_DIR": str(tmp_dir)} + + +def x11_check(statefile: StateFile) -> tuple[list[str], dict[str, str]] | None: + """Check and configure X11 forwarding based on user input and existing state. + + This function checks if X11 forwarding is enabled in the configuration file. If it is not configured, + the function prompts the user to enable or disable X11 forwarding. If X11 forwarding is enabled, the function + configures X11 forwarding by creating a temporary .xauth file. + + Args: + statefile: An instance of the configuration file class. + + Returns: + If X11 forwarding is enabled, the function returns a tuple containing the following: + + - A list containing the x11.yaml file configuration option for docker-compose. + - A dictionary containing the environment variables for the container. + + If X11 forwarding is disabled, the function returns None. + """ + # set the namespace to X11 for the statefile + statefile.namespace = "X11" + # check if X11 forwarding is enabled + is_x11_forwarding_enabled = statefile.get_variable("X11_FORWARDING_ENABLED") + + if is_x11_forwarding_enabled is None: + print("[INFO] X11 forwarding from the Isaac Lab container is disabled by default.") + print( + "[INFO] It will fail if there is no display, or this script is being run via ssh without proper" + " configuration." + ) + x11_answer = input("Would you like to enable it? (y/N) ") + + # parse the user's input + if x11_answer.lower() == "y": + is_x11_forwarding_enabled = "1" + print("[INFO] X11 forwarding is enabled from the container.") + else: + is_x11_forwarding_enabled = "0" + print("[INFO] X11 forwarding is disabled from the container.") + + # remember the user's choice and set the statefile variable + statefile.set_variable("X11_FORWARDING_ENABLED", is_x11_forwarding_enabled) + else: + # print the current configuration + print(f"[INFO] X11 Forwarding is configured as '{is_x11_forwarding_enabled}' in '.container.cfg'.") + + # print help message to enable/disable X11 forwarding + if is_x11_forwarding_enabled == "1": + print("\tTo disable X11 forwarding, set 'X11_FORWARDING_ENABLED=0' in '.container.cfg'.") + else: + print("\tTo enable X11 forwarding, set 'X11_FORWARDING_ENABLED=1' in '.container.cfg'.") + + if is_x11_forwarding_enabled == "1": + x11_envars = configure_x11(statefile) + # If X11 forwarding is enabled, return the proper args to + # compose the x11.yaml file. Else, return an empty string. + return ["--file", "x11.yaml"], x11_envars + + return None + + +def x11_cleanup(statefile: StateFile): + """Clean up the temporary .xauth file used for X11 forwarding. + + If the .xauth file exists, this function deletes it and remove the corresponding state variable. + + Args: + statefile: An instance of the configuration file class. + """ + # set the namespace to X11 for the statefile + statefile.namespace = "X11" + + # load the value of the temporary xauth file + tmp_xauth_value = statefile.get_variable("__ISAACLAB_TMP_XAUTH") + + # if the file exists, delete it and remove the state variable + if tmp_xauth_value is not None and Path(tmp_xauth_value).exists(): + print(f"[INFO] Removing temporary Isaac Lab '.xauth' file: {tmp_xauth_value}.") + Path(tmp_xauth_value).unlink() + statefile.delete_variable("__ISAACLAB_TMP_XAUTH") + + +def create_x11_tmpfile(tmpfile: Path | None = None, tmpdir: Path | None = None) -> Path: + """Creates an .xauth file with an MIT-MAGIC-COOKIE derived from the current ``DISPLAY`` environment variable. + + Args: + tmpfile: A Path to a file which will be filled with the correct .xauth info. + tmpdir: A Path to the directory where a random tmp file will be made. + This is used as an ``--tmpdir arg`` to ``mktemp`` bash command. + + Returns: + The Path to the .xauth file. + """ + if tmpfile is None: + if tmpdir is None: + add_tmpdir = "" + else: + add_tmpdir = f"--tmpdir={tmpdir}" + # Create .tmp file with .xauth suffix + tmp_xauth = Path( + subprocess.run( + ["mktemp", "--suffix=.xauth", f"{add_tmpdir}"], capture_output=True, text=True, check=True + ).stdout.strip() + ) + else: + tmpfile.touch() + tmp_xauth = tmpfile + + # Derive current MIT-MAGIC-COOKIE and make it universally addressable + xauth_cookie = subprocess.run( + ["xauth", "nlist", os.environ["DISPLAY"]], capture_output=True, text=True, check=True + ).stdout.replace("ffff", "") + + # Merge the new cookie into the create .tmp file + subprocess.run(["xauth", "-f", tmp_xauth, "nmerge", "-"], input=xauth_cookie, text=True, check=True) + + return tmp_xauth + + +def x11_refresh(statefile: StateFile): + """Refresh the temporary .xauth file used for X11 forwarding. + + If x11 is enabled, this function generates a new .xauth file with the current MIT-MAGIC-COOKIE-1. + The new file uses the same filename so that the bind-mount and ``XAUTHORITY`` var from build-time + still work. + + As the envar ``DISPLAY` informs the contents of the MIT-MAGIC-COOKIE-1, that value within the container + will also need to be updated to the current value on the host. Currently, this done automatically in + :meth:`ContainerInterface.enter` method. + + The function exits if X11 forwarding is enabled but the temporary .xauth file does not exist. In this case, + the user must rebuild the container. + + Args: + statefile: An instance of the configuration file class. + """ + # set the namespace to X11 for the statefile + statefile.namespace = "X11" + + # check if X11 forwarding is enabled + is_x11_forwarding_enabled = statefile.get_variable("X11_FORWARDING_ENABLED") + # load the value of the temporary xauth file + tmp_xauth_value = statefile.get_variable("__ISAACLAB_TMP_XAUTH") + + # print the current configuration + if is_x11_forwarding_enabled is not None: + status = "enabled" if is_x11_forwarding_enabled == "1" else "disabled" + print(f"[INFO] X11 Forwarding is {status} from the settings in '.container.cfg'") + + # if the file exists, delete it and create a new one + if tmp_xauth_value is not None and Path(tmp_xauth_value).exists(): + # remove the file and create a new one + Path(tmp_xauth_value).unlink() + create_x11_tmpfile(tmpfile=Path(tmp_xauth_value)) + # update the statefile with the new path + statefile.set_variable("__ISAACLAB_TMP_XAUTH", str(tmp_xauth_value)) + elif tmp_xauth_value is None: + if is_x11_forwarding_enabled is not None and is_x11_forwarding_enabled == "1": + print( + "[ERROR] X11 forwarding is enabled but the temporary .xauth file does not exist." + " Please rebuild the container by running: './docker/container.py start'" + ) + sys.exit(1) + else: + print("[INFO] X11 forwarding is disabled. No action taken.") diff --git a/docker/x11.yaml b/docker/x11.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd9b22f16b702a0b1db06813749b22ae5c9b6c35 --- /dev/null +++ b/docker/x11.yaml @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +services: + isaac-lab-base: + environment: + - DISPLAY + - TERM + - QT_X11_NO_MITSHM=1 + - XAUTHORITY=${__ISAACLAB_TMP_XAUTH} + volumes: + - type: bind + source: ${__ISAACLAB_TMP_DIR} + target: ${__ISAACLAB_TMP_DIR} + - type: bind + source: /tmp/.X11-unix + target: /tmp/.X11-unix + - type: bind + source: /etc/localtime + target: /etc/localtime + read_only: true + + isaac-lab-ros2: + environment: + - DISPLAY + - TERM + - QT_X11_NO_MITSHM=1 + - XAUTHORITY=${__ISAACLAB_TMP_XAUTH} + volumes: + - type: bind + source: ${__ISAACLAB_TMP_DIR} + target: ${__ISAACLAB_TMP_DIR} + - type: bind + source: /tmp/.X11-unix + target: /tmp/.X11-unix + - type: bind + source: /etc/localtime + target: /etc/localtime + read_only: true diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..0bff236671c8c27bcfc30fa9809d4345d9da58ee --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +.PHONY: multi-docs +multi-docs: + @sphinx-multiversion "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) + @cp _redirect/index.html $(BUILDDIR)/index.html + +.PHONY: current-docs +current-docs: + @rm -rf "$(BUILDDIR)/current" + @$(SPHINXBUILD) -W --keep-going "$(SOURCEDIR)" "$(BUILDDIR)/current" $(SPHINXOPTS) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..69a77a48d9048488bad3980cec241cf1a1957bf6 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,75 @@ +# Building Documentation + +We use [Sphinx](https://www.sphinx-doc.org/en/master/) with the [Book Theme](https://sphinx-book-theme.readthedocs.io/en/stable/) for maintaining and generating our documentation. + +> **Note:** To avoid dependency conflicts, we strongly recommend using a Python virtual environment to isolate the required dependencies from your system's global Python environment. + +## Current-Version Documentation + +This section describes how to build the documentation for the current version of the project. + +
+Linux + +```bash +# 1. Navigate to the docs directory and install dependencies +cd docs +pip install -r requirements.txt + +# 2. Build the current documentation +make current-docs + +# 3. Open the current docs +xdg-open _build/current/index.html +``` +
+ +
Windows + +```batch +:: 1. Navigate to the docs directory and install dependencies +cd docs +pip install -r requirements.txt + +:: 2. Build the current documentation +make current-docs + +:: 3. Open the current docs +start _build\current\index.html +``` +
+ + +## Multi-Version Documentation + +This section describes how to build the multi-version documentation, which includes previous tags and the main branch. + +
Linux + +```bash +# 1. Navigate to the docs directory and install dependencies +cd docs +pip install -r requirements.txt + +# 2. Build the multi-version documentation +make multi-docs + +# 3. Open the multi-version docs +xdg-open _build/index.html +``` +
+ +
Windows + +```batch +:: 1. Navigate to the docs directory and install dependencies +cd docs +pip install -r requirements.txt + +:: 2. Build the multi-version documentation +make multi-docs + +:: 3. Open the multi-version docs +start _build\index.html +``` +
diff --git a/docs/_redirect/index.html b/docs/_redirect/index.html new file mode 100644 index 0000000000000000000000000000000000000000..5208597ed15e9f9778a0f4888d463b92c7475596 --- /dev/null +++ b/docs/_redirect/index.html @@ -0,0 +1,8 @@ + + + + Redirecting to the latest Isaac Lab documentation + + + + diff --git a/docs/_templates/versioning.html b/docs/_templates/versioning.html new file mode 100644 index 0000000000000000000000000000000000000000..eb67be60e1c469b9ed31bb8bc04ccf1b85bca8a2 --- /dev/null +++ b/docs/_templates/versioning.html @@ -0,0 +1,21 @@ +{% if versions %} + +{% endif %} diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..248e14c3f89c997bcd3f79038df9f0330dda98fb --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,310 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath("../source/isaaclab")) +sys.path.insert(0, os.path.abspath("../source/isaaclab/isaaclab")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_assets")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_assets/isaaclab_assets")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_tasks")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_tasks/isaaclab_tasks")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_rl")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_rl/isaaclab_rl")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_mimic")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_mimic/isaaclab_mimic")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_contrib")) +sys.path.insert(0, os.path.abspath("../source/isaaclab_contrib/isaaclab_contrib")) + +# -- Project information ----------------------------------------------------- + +project = "Isaac Lab" +copyright = "2022-2025, The Isaac Lab Project Developers." +author = "The Isaac Lab Project Developers." + +# Read version from the package +with open(os.path.join(os.path.dirname(__file__), "..", "VERSION")) as f: + full_version = f.read().strip() + version = ".".join(full_version.split(".")[:3]) + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "autodocsumm", + "myst_parser", + "sphinx.ext.napoleon", + "sphinxemoji.sphinxemoji", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.githubpages", + "sphinx.ext.intersphinx", + "sphinx.ext.mathjax", + "sphinx.ext.todo", + "sphinx.ext.viewcode", + "sphinxcontrib.bibtex", + "sphinxcontrib.icon", + "sphinx_copybutton", + "sphinx_design", + "sphinx_tabs.tabs", # backwards compatibility for building docs on v1.0.0 + "sphinx_multiversion", +] + +# mathjax hacks +mathjax3_config = { + "tex": { + "inlineMath": [["\\(", "\\)"]], + "displayMath": [["\\[", "\\]"]], + }, +} + +# panels hacks +panels_add_bootstrap_css = False +panels_add_fontawesome_css = True + +# supported file extensions for source files +source_suffix = { + ".rst": "restructuredtext", + ".md": "markdown", +} + +# make sure we don't have any unknown references +# TODO: Enable this by default once we have fixed all the warnings +# nitpicky = True + +nitpick_ignore = [ + ("py:obj", "slice(None)"), +] + +nitpick_ignore_regex = [ + (r"py:.*", r"pxr.*"), # we don't have intersphinx mapping for pxr + (r"py:.*", r"trimesh.*"), # we don't have intersphinx mapping for trimesh +] + +# emoji style +sphinxemoji_style = "twemoji" # options: "twemoji" or "unicode" +# put type hints inside the signature instead of the description (easier to maintain) +autodoc_typehints = "signature" +# autodoc_typehints_format = "fully-qualified" +# document class *and* __init__ methods +autoclass_content = "class" # +# separate class docstring from __init__ docstring +autodoc_class_signature = "separated" +# sort members by source order +autodoc_member_order = "bysource" +# inherit docstrings from base classes +autodoc_inherit_docstrings = True +# BibTeX configuration +bibtex_bibfiles = ["source/_static/refs.bib"] +# generate autosummary even if no references +autosummary_generate = True +autosummary_generate_overwrite = False +# default autodoc settings +autodoc_default_options = { + "autosummary": True, +} + +# generate links to the documentation of objects in external projects +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "numpy": ("https://numpy.org/doc/stable/", None), + "trimesh": ("https://trimesh.org/", None), + "torch": ("https://docs.pytorch.org/docs/stable/", None), + "isaacsim": ("https://docs.isaacsim.omniverse.nvidia.com/5.1.0/py/", None), + "gymnasium": ("https://gymnasium.farama.org/", None), + "warp": ("https://nvidia.github.io/warp/", None), + "omniverse": ("https://docs.omniverse.nvidia.com/dev-guide/latest", None), +} + +# Add any paths that contain templates here, relative to this directory. +templates_path = [] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "_redirect", "_templates", "Thumbs.db", ".DS_Store", "README.md", "licenses/*"] + +# Mock out modules that are not available on RTD +autodoc_mock_imports = [ + "torch", + "torchvision", + "numpy", + "matplotlib", + "scipy", + "carb", + "warp", + "pxr", + "isaacsim", + "omni", + "omni.kit", + "omni.log", + "omni.usd", + "omni.client", + "omni.physx", + "omni.physics", + "usdrt", + "pxr.PhysxSchema", + "pxr.PhysicsSchemaTools", + "omni.replicator", + "isaacsim", + "isaacsim.core.api", + "isaacsim.core.cloner", + "isaacsim.core.version", + "isaacsim.core.utils", + "isaacsim.robot_motion.motion_generation", + "isaacsim.gui.components", + "isaacsim.asset.importer.urdf", + "isaacsim.asset.importer.mjcf", + "omni.syntheticdata", + "omni.timeline", + "omni.ui", + "gym", + "skrl", + "stable_baselines3", + "rsl_rl", + "rl_games", + "ray", + "h5py", + "hid", + "prettytable", + "tqdm", + "tensordict", + "trimesh", + "toml", + "pink", + "pinocchio", + "nvidia.srl", + "flatdict", + "IPython", + "cv2", + "imageio", + "ipywidgets", + "mpl_toolkits", +] + +# List of zero or more Sphinx-specific warning categories to be squelched (i.e., +# suppressed, ignored). +suppress_warnings = [ + # Generally speaking, we do want Sphinx to inform + # us about cross-referencing failures. Remove this entirely after Sphinx + # resolves this open issue: + # https://github.com/sphinx-doc/sphinx/issues/4961 + # Squelch mostly ignorable warnings resembling: + # WARNING: more than one target found for cross-reference 'TypeHint': + # beartype.door._doorcls.TypeHint, beartype.door.TypeHint + # + # Sphinx currently emits *MANY* of these warnings against our + # documentation. All of these warnings appear to be ignorable. Although we + # could explicitly squelch *SOME* of these warnings by canonicalizing + # relative to absolute references in docstrings, Sphinx emits still others + # of these warnings when parsing PEP-compliant type hints via static + # analysis. Since those hints are actual hints that *CANNOT* by definition + # by canonicalized, our only recourse is to squelch warnings altogether. + "ref.python", +] + +# -- Internationalization ---------------------------------------------------- + +# specifying the natural language populates some key tags +language = "en" + +# -- Options for HTML output ------------------------------------------------- + +import sphinx_book_theme + +html_title = "Isaac Lab Documentation" +html_theme_path = [sphinx_book_theme.get_html_theme_path()] +html_theme = "sphinx_book_theme" +html_favicon = "source/_static/favicon.ico" +html_show_copyright = True +html_show_sphinx = False +html_last_updated_fmt = "" # to reveal the build date in the pages meta + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["source/_static/css"] +html_css_files = ["custom.css"] + +html_theme_options = { + "path_to_docs": "docs/", + "collapse_navigation": True, + "repository_url": "https://github.com/isaac-sim/IsaacLab", + "use_repository_button": True, + "use_issues_button": True, + "use_edit_page_button": True, + "show_toc_level": 1, + "use_sidenotes": True, + "logo": { + "text": "Isaac Lab Documentation", + "image_light": "source/_static/NVIDIA-logo-white.png", + "image_dark": "source/_static/NVIDIA-logo-black.png", + }, + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/isaac-sim/IsaacLab", + "icon": "fa-brands fa-square-github", + "type": "fontawesome", + }, + { + "name": "Isaac Sim", + "url": "https://developer.nvidia.com/isaac-sim", + "icon": "https://img.shields.io/badge/IsaacSim-5.1.0-silver.svg", + "type": "url", + }, + { + "name": "Stars", + "url": "https://img.shields.io/github/stars/isaac-sim/IsaacLab?color=fedcba", + "icon": "https://img.shields.io/github/stars/isaac-sim/IsaacLab?color=fedcba", + "type": "url", + }, + ], + "icon_links_label": "Quick Links", +} + +templates_path = [ + "_templates", +] + +# Whitelist pattern for remotes +smv_remote_whitelist = r"^.*$" +# Whitelist pattern for branches (set to None to ignore all branches) +smv_branch_whitelist = os.getenv("SMV_BRANCH_WHITELIST", r"^(main|devel|release/.*)$") +# Whitelist pattern for tags (set to None to ignore all tags) +smv_tag_whitelist = os.getenv("SMV_TAG_WHITELIST", r"^v[1-9]\d*\.\d+\.\d+$") +html_sidebars = { + "**": ["navbar-logo.html", "versioning.html", "icon-links.html", "search-field.html", "sbt-sidebar-nav.html"] +} + + +# -- Advanced configuration ------------------------------------------------- + + +def skip_member(app, what, name, obj, skip, options): + # List the names of the functions you want to skip here + exclusions = ["from_dict", "to_dict", "replace", "copy", "validate", "__post_init__"] + if name in exclusions: + return True + return None + + +def setup(app): + app.connect("autodoc-skip-member", skip_member) diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..97b5f851e082770bcba4772394e5bdf4bf2f57b7 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,190 @@ +Welcome to Isaac Lab! +===================== + +.. figure:: source/_static/isaaclab.jpg + :width: 100% + :alt: H1 Humanoid example using Isaac Lab + +**Isaac Lab** is a unified and modular framework for robot learning that aims to simplify common workflows +in robotics research (such as reinforcement learning, learning from demonstrations, and motion planning). It is built on +`NVIDIA Isaac Sim`_ to leverage the latest simulation capabilities for photo-realistic scenes, and fast +and efficient simulation. + +The core objectives of the framework are: + +- **Modularity**: Easily customize and add new environments, robots, and sensors. +- **Agility**: Adapt to the changing needs of the community. +- **Openness**: Remain open-sourced to allow the community to contribute and extend the framework. +- **Batteries-included**: Include a number of environments, sensors, and tasks that are ready to use. + +Key features available in Isaac Lab include fast and accurate physics simulation provided by PhysX, +tiled rendering APIs for vectorized rendering, domain randomization for improving robustness and adaptability, +and support for running in the cloud. + +Additionally, Isaac Lab provides a variety of environments, and we are actively working on adding more environments +to the list. These include classic control tasks, fixed-arm and dexterous manipulation tasks, legged locomotion tasks, +and navigation tasks. A complete list is available in the `environments `_ section. + +Isaac lab is developed with specific robot assets that are now **Batteries-included** as part of the platform and are ready to learn! These robots include... + +- **Classic** Cartpole, Humanoid, Ant +- **Fixed-Arm and Hands**: UR10, Franka, Allegro, Shadow Hand +- **Quadrupeds**: Anybotics Anymal-B, Anymal-C, Anymal-D, Unitree A1, Unitree Go1, Unitree Go2, Boston Dynamics Spot +- **Humanoids**: Unitree H1, Unitree G1 +- **Quadcopter**: Crazyflie + +The platform is also designed so that you can add your own robots! Please refer to the +:ref:`how-to` section for details. + +For more information about the framework, please refer to the `technical report `_ +:cite:`mittal2025isaaclab`. For clarifications on NVIDIA Isaac ecosystem, please check out the +:ref:`isaac-lab-ecosystem` section. + +.. figure:: source/_static/tasks.jpg + :width: 100% + :alt: Example tasks created using Isaac Lab + + +License +======= + +The Isaac Lab framework is open-sourced under the BSD-3-Clause license, +with certain parts under Apache-2.0 license. Please refer to :ref:`license` for more details. + +Citation +======== + +If you use Isaac Lab in your research, please cite our technical report: + +.. code:: bibtex + + @article{mittal2025isaaclab, + title={Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning}, + author={Mayank Mittal and Pascal Roth and James Tigue and Antoine Richard and Octi Zhang and Peter Du and Antonio Serrano-Muñoz and Xinjie Yao and René Zurbrügg and Nikita Rudin and Lukasz Wawrzyniak and Milad Rakhsha and Alain Denzler and Eric Heiden and Ales Borovicka and Ossama Ahmed and Iretiayo Akinola and Abrar Anwar and Mark T. Carlson and Ji Yuan Feng and Animesh Garg and Renato Gasoto and Lionel Gulich and Yijie Guo and M. Gussert and Alex Hansen and Mihir Kulkarni and Chenran Li and Wei Liu and Viktor Makoviychuk and Grzegorz Malczyk and Hammad Mazhar and Masoud Moghani and Adithyavairavan Murali and Michael Noseworthy and Alexander Poddubny and Nathan Ratliff and Welf Rehberg and Clemens Schwarke and Ritvik Singh and James Latham Smith and Bingjie Tang and Ruchik Thaker and Matthew Trepte and Karl Van Wyk and Fangzhou Yu and Alex Millane and Vikram Ramasamy and Remo Steiner and Sangeeta Subramanian and Clemens Volk and CY Chen and Neel Jawale and Ashwin Varghese Kuruttukulam and Michael A. Lin and Ajay Mandlekar and Karsten Patzwaldt and John Welsh and Huihua Zhao and Fatima Anes and Jean-Francois Lafleche and Nicolas Moënne-Loccoz and Soowan Park and Rob Stepinski and Dirk Van Gelder and Chris Amevor and Jan Carius and Jumyung Chang and Anka He Chen and Pablo de Heras Ciechomski and Gilles Daviet and Mohammad Mohajerani and Julia von Muralt and Viktor Reutskyy and Michael Sauter and Simon Schirm and Eric L. Shi and Pierre Terdiman and Kenny Vilella and Tobias Widmer and Gordon Yeoman and Tiffany Chen and Sergey Grizan and Cathy Li and Lotus Li and Connor Smith and Rafael Wiltz and Kostas Alexis and Yan Chang and David Chu and Linxi "Jim" Fan and Farbod Farshidian and Ankur Handa and Spencer Huang and Marco Hutter and Yashraj Narang and Soha Pouya and Shiwei Sheng and Yuke Zhu and Miles Macklin and Adam Moravanszky and Philipp Reist and Yunrong Guo and David Hoeller and Gavriel State}, + journal={arXiv preprint arXiv:2511.04831}, + year={2025}, + url={https://arxiv.org/abs/2511.04831} + } + + +Acknowledgement +=============== + +Isaac Lab development initiated from the `Orbit `_ framework. +We gratefully acknowledge the authors of Orbit for their foundational contributions. + + +Table of Contents +================= + +.. toctree:: + :maxdepth: 1 + :caption: Isaac Lab + + source/setup/ecosystem + source/setup/installation/index + source/deployment/index + source/setup/installation/cloud_installation + source/refs/reference_architecture/index + + +.. toctree:: + :maxdepth: 2 + :caption: Getting Started + :titlesonly: + + source/setup/quickstart + source/overview/own-project/index + source/setup/walkthrough/index + source/tutorials/index + source/how-to/index + source/overview/developer-guide/index + + +.. toctree:: + :maxdepth: 3 + :caption: Overview + :titlesonly: + + + source/overview/core-concepts/index + source/overview/environments + source/overview/reinforcement-learning/index + source/overview/imitation-learning/index + source/overview/showroom + source/overview/simple_agents + + +.. toctree:: + :maxdepth: 2 + :caption: Features + + source/features/hydra + source/features/multi_gpu + source/features/population_based_training + Tiled Rendering + source/features/ray + source/features/reproducibility + + +.. toctree:: + :maxdepth: 3 + :caption: Experimental Features + + source/experimental-features/bleeding-edge + source/experimental-features/newton-physics-integration/index + +.. toctree:: + :maxdepth: 1 + :caption: Resources + :titlesonly: + + source/setup/installation/cloud_installation + source/policy_deployment/index + +.. toctree:: + :maxdepth: 1 + :caption: Migration Guides + :titlesonly: + + source/migration/migrating_from_isaacgymenvs + source/migration/migrating_from_omniisaacgymenvs + source/migration/migrating_from_orbit + +.. toctree:: + :maxdepth: 1 + :caption: Source API + + source/api/index + +.. toctree:: + :maxdepth: 1 + :caption: References + + + source/refs/additional_resources + source/refs/contributing + source/refs/troubleshooting + source/refs/migration + source/refs/issues + source/refs/release_notes + source/refs/changelog + source/refs/license + source/refs/bibliography + +.. toctree:: + :hidden: + :caption: Project Links + + GitHub + NVIDIA Isaac Sim + NVIDIA PhysX + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + +.. _NVIDIA Isaac Sim: https://docs.isaacsim.omniverse.nvidia.com/latest/index.html diff --git a/docs/licenses/dependencies/Farama-Notifications-license.txt b/docs/licenses/dependencies/Farama-Notifications-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..44a6bbb5b5c238ea5810499dd00d89028faa2737 --- /dev/null +++ b/docs/licenses/dependencies/Farama-Notifications-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Farama Foundation + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "{}" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/assimp-license.txt b/docs/licenses/dependencies/assimp-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..c66fa94f441ef17845141f417580226b32be8e94 --- /dev/null +++ b/docs/licenses/dependencies/assimp-license.txt @@ -0,0 +1,78 @@ +Open Asset Import Library (assimp) + +Copyright (c) 2006-2021, assimp team +All rights reserved. + +Redistribution and use of this software in source and binary forms, +with or without modification, are permitted provided that the +following conditions are met: + +* Redistributions of source code must retain the above + copyright notice, this list of conditions and the + following disclaimer. + +* Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the + following disclaimer in the documentation and/or other + materials provided with the distribution. + +* Neither the name of the assimp team, nor the names of its + contributors may be used to endorse or promote products + derived from this software without specific prior + written permission of the assimp team. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + + +****************************************************************************** + +AN EXCEPTION applies to all files in the ./test/models-nonbsd folder. +These are 3d models for testing purposes, from various free sources +on the internet. They are - unless otherwise stated - copyright of +their respective creators, which may impose additional requirements +on the use of their work. For any of these models, see +.source.txt for more legal information. Contact us if you +are a copyright holder and believe that we credited you improperly or +if you don't want your files to appear in the repository. + + +****************************************************************************** + +Poly2Tri Copyright (c) 2009-2010, Poly2Tri Contributors +http://code.google.com/p/poly2tri/ + +All rights reserved. +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +* Neither the name of Poly2Tri nor the names of its contributors may be + used to endorse or promote products derived from this software without specific + prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/asttokens-license.txt b/docs/licenses/dependencies/asttokens-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..8dada3edaf50dbc082c9a125058f25def75e625a --- /dev/null +++ b/docs/licenses/dependencies/asttokens-license.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "{}" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/attrs-license b/docs/licenses/dependencies/attrs-license new file mode 100644 index 0000000000000000000000000000000000000000..2bd6453d255e19b973f19b128596a8b6dd65b2c3 --- /dev/null +++ b/docs/licenses/dependencies/attrs-license @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2015 Hynek Schlawack and the attrs contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/autodocsumm-license.txt b/docs/licenses/dependencies/autodocsumm-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/docs/licenses/dependencies/autodocsumm-license.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/babel-license.txt b/docs/licenses/dependencies/babel-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..f31575ec773bb199aeb7c0d0f1612cfe1c7038f1 --- /dev/null +++ b/docs/licenses/dependencies/babel-license.txt @@ -0,0 +1,22 @@ +MIT License + +Copyright (c) 2014-present Sebastian McKenzie and other contributors + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/docs/licenses/dependencies/beautifulsoup4-license.txt b/docs/licenses/dependencies/beautifulsoup4-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..d668d13f041ec95e1fdd810a18299f20076a37fb --- /dev/null +++ b/docs/licenses/dependencies/beautifulsoup4-license.txt @@ -0,0 +1,26 @@ +Beautiful Soup is made available under the MIT license: + + Copyright (c) 2004-2012 Leonard Richardson + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal in the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS + BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN + ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE, DAMMIT. + +Beautiful Soup incorporates code from the html5lib library, which is +also made available under the MIT license. diff --git a/docs/licenses/dependencies/black-license.txt b/docs/licenses/dependencies/black-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a9b891f713e1d0a522f042a28f28ba0c5f390d4 --- /dev/null +++ b/docs/licenses/dependencies/black-license.txt @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2018 Łukasz Langa + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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These were automatically extracted from Mozilla's root certificates +file (certdata.txt). This file can be found in the mozilla source tree: +https://hg.mozilla.org/mozilla-central/file/tip/security/nss/lib/ckfw/builtins/certdata.txt +It contains the certificates in PEM format and therefore +can be directly used with curl / libcurl / php_curl, or with +an Apache+mod_ssl webserver for SSL client authentication. +Just configure this file as the SSLCACertificateFile.# + +***** BEGIN LICENSE BLOCK ***** +This Source Code Form is subject to the terms of the Mozilla Public License, +v. 2.0. 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The Free Software Foundation may publish revised and/or new versions +of the General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + +Each version is given a distinguishing version number. If the Program +specifies a version number of this License which applies to it and "any +later version", you have the option of following the terms and conditions +either of that version or of any later version published by the Free +Software Foundation. If the Program does not specify a version number of +this License, you may choose any version ever published by the Free Software +Foundation. + + 10. If you wish to incorporate parts of the Program into other free +programs whose distribution conditions are different, write to the author +to ask for permission. For software which is copyrighted by the Free +Software Foundation, write to the Free Software Foundation; we sometimes +make exceptions for this. Our decision will be guided by the two goals +of preserving the free status of all derivatives of our free software and +of promoting the sharing and reuse of software generally. + + NO WARRANTY + + 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY +FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN +OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED +OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS +TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE +PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, +REPAIR OR CORRECTION. + + 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, +INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING +OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED +TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY +YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER +PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +convey the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 2 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + +Also add information on how to contact you by electronic and paper mail. + +If the program is interactive, make it output a short notice like this +when it starts in an interactive mode: + + Gnomovision version 69, Copyright (C) year name of author + Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, the commands you use may +be called something other than `show w' and `show c'; they could even be +mouse-clicks or menu items--whatever suits your program. + +You should also get your employer (if you work as a programmer) or your +school, if any, to sign a "copyright disclaimer" for the program, if +necessary. Here is a sample; alter the names: + + Yoyodyne, Inc., hereby disclaims all copyright interest in the program + `Gnomovision' (which makes passes at compilers) written by James Hacker. + + , 1 April 1989 + Ty Coon, President of Vice + +This General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may +consider it more useful to permit linking proprietary applications with the +library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/docs/licenses/dependencies/colorama-license.txt b/docs/licenses/dependencies/colorama-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..3105888ec149d10cad51c11d332779e94b548661 --- /dev/null +++ b/docs/licenses/dependencies/colorama-license.txt @@ -0,0 +1,27 @@ +Copyright (c) 2010 Jonathan Hartley +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holders, nor those of its contributors + may be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/comm-license.txt b/docs/licenses/dependencies/comm-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..eee1b58d9ed05bdf064d2550c708a6015285de67 --- /dev/null +++ b/docs/licenses/dependencies/comm-license.txt @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2022, Jupyter +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/console-bridge-license.txt b/docs/licenses/dependencies/console-bridge-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..574ef07902908574cba9a471d7e5eb0b684691e4 --- /dev/null +++ b/docs/licenses/dependencies/console-bridge-license.txt @@ -0,0 +1,25 @@ +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/contourpy-license.txt b/docs/licenses/dependencies/contourpy-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..93e41fb6965a930641ba181a3ae4e1008246d9d5 --- /dev/null +++ b/docs/licenses/dependencies/contourpy-license.txt @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2021-2025, ContourPy Developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/cuRobo-license.txt b/docs/licenses/dependencies/cuRobo-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b76a56cbf86ed8510ad730a3f7b6104987bc217 --- /dev/null +++ b/docs/licenses/dependencies/cuRobo-license.txt @@ -0,0 +1,93 @@ +NVIDIA ISAAC LAB ADDITIONAL SOFTWARE AND MATERIALS LICENSE + +IMPORTANT NOTICE – PLEASE READ AND AGREE BEFORE USING THE SOFTWARE + +This software license agreement ("Agreement") is a legal agreement between you, whether an individual or entity, ("you") and NVIDIA Corporation ("NVIDIA") and governs the use of the NVIDIA cuRobo and related software and materials that NVIDIA delivers to you under this Agreement ("Software"). NVIDIA and you are each a "party" and collectively the "parties." + +By using the Software, you are affirming that you have read and agree to this Agreement. + +If you don't accept all the terms and conditions below, do not use the Software. + +1. License Grant. The Software made available by NVIDIA to you is licensed, not sold. Subject to the terms of this Agreement, NVIDIA grants you a limited, non-exclusive, revocable, non-transferable, and non-sublicensable (except as expressly granted in this Agreement), license to install and use copies of the Software together with NVIDIA Isaac Lab in systems with NVIDIA GPUs ("Purpose"). + +2. License Restrictions. Your license to use the Software is restricted as stated in this Section 2 ("License Restrictions"). You will cooperate with NVIDIA and, upon NVIDIA's written request, you will confirm in writing and provide reasonably requested information to verify your compliance with the terms of this Agreement. You may not: + +2.1 Use the Software for any purpose other than the Purpose, and for clarity use of NVIDIA cuRobo apart from use with Isaac Lab is outside of the Purpose; + +2.2 Sell, rent, sublicense, transfer, distribute or otherwise make available to others (except authorized users as stated in Section 3 ("Authorized Users")) any portion of the Software, except as expressly granted in Section 1 ("License Grant"); + +2.3 Reverse engineer, decompile, or disassemble the Software components provided in binary form, nor attempt in any other manner to obtain source code of such Software; + +2.4 Modify or create derivative works of the Software; + +2.5 Change or remove copyright or other proprietary notices in the Software; + +2.6 Bypass, disable, or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism in the Software; + +2.7 Use the Software in any manner that would cause them to become subject to an open source software license, subject to the terms in Section 7 ("Components Under Other Licenses"); or + +2.8 Use the Software in violation of any applicable law or regulation in relevant jurisdictions. + +3. Authorized Users. You may allow employees and contractors of your entity or of your subsidiary(ies), and for educational institutions also enrolled students, to internally access and use the Software as authorized by this Agreement from your secure network to perform the work authorized by this Agreement on your behalf. You are responsible for the compliance with the terms of this Agreement by your authorized users. Any act or omission that if committed by you would constitute a breach of this Agreement will be deemed to constitute a breach of this Agreement if committed by your authorized users. + +4. Pre-Release. Software versions identified as alpha, beta, preview, early access or otherwise as pre-release ("Pre-Release") may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, availability and reliability standards relative to NVIDIA commercial offerings. You use Pre-Release Software at your own risk. NVIDIA did not design or test the Software for use in production or business-critical systems. NVIDIA may choose not to make available a commercial version of Pre-Release Software. NVIDIA may also choose to abandon development and terminate the availability of Pre-Release Software at any time without liability. + +5. Updates. NVIDIA may at any time and at its option, change, discontinue, or deprecate any part, or all, of the Software, or change or remove features or functionality, or make available patches, workarounds or other updates to the Software. Unless the updates are provided with their separate governing terms, they are deemed part of the Software licensed to you under this Agreement, and your continued use of the Software is deemed acceptance of such changes. + +6. Components Under Other Licenses. The Software may include or be distributed with components provided with separate legal notices or terms that accompany the components, such as open source software licenses and other license terms ("Other Licenses"). The components are subject to the applicable Other Licenses, including any proprietary notices, disclaimers, requirements and extended use rights; except that this Agreement will prevail regarding the use of third-party open source software, unless a third-party open source software license requires its license terms to prevail. Open source software license means any software, data or documentation subject to any license identified as an open source license by the Open Source Initiative (http://opensource.org), Free Software Foundation (http://www.fsf.org) or other similar open source organization or listed by the Software Package Data Exchange (SPDX) Workgroup under the Linux Foundation (http://www.spdx.org). + +7. Ownership. The Software, including all intellectual property rights, is and will remain the sole and exclusive property of NVIDIA or its licensors. Except as expressly granted in this Agreement, (a) NVIDIA reserves all rights, interests and remedies in connection with the Software, and (b) no other license or right is granted to you by implication, estoppel or otherwise. + +8. Feedback. You may, but you are not obligated to, provide suggestions, requests, fixes, modifications, enhancements, or other feedback regarding the Software (collectively, "Feedback"). Feedback, even if designated as confidential by you, will not create any confidentiality obligation for NVIDIA or its affiliates. If you provide Feedback, you grant NVIDIA, its affiliates and its designees a non-exclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit the Feedback at NVIDIA's discretion. + +9. Term and Termination. + +9.1 Term and Termination for Convenience. This license ends by July 31, 2026 or earlier at your choice if you finished using the Software for the Purpose. Either party may terminate this Agreement at any time with thirty (30) days' advance written notice to the other party. + +9.2 Termination for Cause. If you commence or participate in any legal proceeding against NVIDIA with respect to the Software, this Agreement will terminate immediately without notice. Either party may terminate this Agreement for cause if: + +(a) The other party fails to cure a material breach of this Agreement within ten (10) days of the non-breaching party's written notice of the breach; or + +(b) the other party breaches its confidentiality obligations or license rights under this Agreement, which termination will be effective immediately upon written notice. + +9.3 Effect of Termination. Upon any expiration or termination of this Agreement, you will promptly stop using and return, delete or destroy NVIDIA confidential information and all Software received under this Agreement. Upon written request, you will certify in writing that you have complied with your obligations under this Section 9.3 ("Effect of Termination"). + +9.4 Survival. Section 5 ("Updates"), Section 6 ("Components Under Other Licenses"), Section 7 ("Ownership"), Section 8 ("Feedback"), Section 9.3 ("Effect of Termination"), Section 9.4 ("Survival"), Section 10 ("Disclaimer of Warranties"), Section 11 ("Limitation of Liability"), Section 12 ("Use in Mission Critical Applications"), Section 13 ("Governing Law and Jurisdiction") and Section 14 ("General") will survive any expiration or termination of this Agreement. + +10. Disclaimer of Warranties. THE SOFTWARE IS PROVIDED BY NVIDIA AS-IS AND WITH ALL FAULTS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA DISCLAIMS ALL WARRANTIES AND REPRESENTATIONS OF ANY KIND, WHETHER EXPRESS, IMPLIED OR STATUTORY, RELATING TO OR ARISING UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF TITLE, NONINFRINGEMENT, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, USAGE OF TRADE AND COURSE OF DEALING. NVIDIA DOES NOT WARRANT OR ASSUME RESPONSIBILITY FOR THE ACCURACY OR COMPLETENESS OF ANY THIRD-PARTY INFORMATION, TEXT, GRAPHICS, LINKS CONTAINED IN THE SOFTWARE. WITHOUT LIMITING THE FOREGOING, NVIDIA DOES NOT WARRANT THAT THE SOFTWARE WILL MEET YOUR REQUIREMENTS, ANY DEFECTS OR ERRORS WILL BE CORRECTED, ANY CERTAIN CONTENT WILL BE AVAILABLE; OR THAT THE SOFTWARE IS FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS. NO INFORMATION OR ADVICE GIVEN BY NVIDIA WILL IN ANY WAY INCREASE THE SCOPE OF ANY WARRANTY EXPRESSLY PROVIDED IN THIS AGREEMENT. + +11. Limitations of Liability. + +11.1 EXCLUSIONS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY (I) INDIRECT, PUNITIVE, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES, OR (II) DAMAGES FOR (A) THE COST OF PROCURING SUBSTITUTE GOODS, OR (B) LOSS OF PROFITS, REVENUES, USE, DATA OR GOODWILL ARISING OUT OF OR RELATED TO THIS AGREEMENT, WHETHER BASED ON BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY, OR OTHERWISE, AND EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES AND EVEN IF A PARTY'S REMEDIES FAIL THEIR ESSENTIAL PURPOSE. + +11.2 DAMAGES CAP. ADDITIONALLY, TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA'S TOTAL CUMULATIVE AGGREGATE LIABILITY FOR ANY AND ALL LIABILITIES, OBLIGATIONS OR CLAIMS ARISING OUT OF OR RELATED TO THIS AGREEMENT WILL NOT EXCEED FIVE U.S. DOLLARS (US$5). + +12. Use in Mission Critical Applications. You acknowledge that the Software provided under this Agreement is not designed or tested by NVIDIA for use in any system or application where the use or failure of such system or application developed with NVIDIA's Software could result in injury, death or catastrophic damage (each, a "Mission Critical Application"). Examples of Mission Critical Applications include use in avionics, navigation, autonomous vehicle applications, AI solutions for automotive products, military, medical, life support or other mission-critical or life-critical applications. NVIDIA will not be liable to you or any third party, in whole or in part, for any claims or damages arising from these uses. You are solely responsible for ensuring that systems and applications developed with the Software include sufficient safety and redundancy features and comply with all applicable legal and regulatory standards and requirements. + +13. Governing Law and Jurisdiction. This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods. The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts; except that either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction. + +14. General. + +14.1 Indemnity. By using the Software you agree to defend, indemnify and hold harmless NVIDIA and its affiliates and their respective officers, directors, employees and agents from and against any claims, disputes, demands, liabilities, damages, losses, costs and expenses arising out of or in any way connected with (i) products or services that have been developed or deployed with or use the Software, or claims that they violate laws, or infringe, violate, or misappropriate any third party right; or (ii) your use of the Software in breach of the terms of this Agreement. + +14.2 Independent Contractors. The parties are independent contractors, and this Agreement does not create a joint venture, partnership, agency, or other form of business association between the parties. Neither party will have the power to bind the other party or incur any obligation on its behalf without the other party's prior written consent. Nothing in this Agreement prevents either party from participating in similar arrangements with third parties. + +14.3 No Assignment. NVIDIA may assign, delegate or transfer its rights or obligations under this Agreement by any means or operation of law. You may not, without NVIDIA's prior written consent, assign, delegate or transfer any of your rights or obligations under this Agreement by any means or operation of law, and any attempt to do so is null and void. + +14.4 No Waiver. No failure or delay by a party to enforce any term or obligation of this Agreement will operate as a waiver by that party, or prevent the enforcement of such term or obligation later. + +14.5 Trade Compliance. You agree to comply with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations. You confirm (a) your understanding that export or reexport of certain NVIDIA products or technologies may require a license or other approval from appropriate authorities and (b) that you will not export or reexport any products or technology, directly or indirectly, without first obtaining any required license or other approval from appropriate authorities, (i) to any countries that are subject to any U.S. or local export restrictions (currently including, but not necessarily limited to, Belarus, Cuba, Iran, North Korea, Russia, Syria, the Region of Crimea, Donetsk People's Republic Region and Luhansk People's Republic Region); (ii) to any end-user who you know or have reason to know will utilize them in the design, development or production of nuclear, chemical or biological weapons, missiles, rocket systems, unmanned air vehicles capable of a maximum range of at least 300 kilometers, regardless of payload, or intended for military end-use, or any weapons of mass destruction; (iii) to any end-user who has been prohibited from participating in the U.S. or local export transactions by any governing authority; or (iv) to any known military or military-intelligence end-user or for any known military or military-intelligence end-use in accordance with U.S. trade compliance laws and regulations. + +14.6 Government Rights. The Software, documentation and technology ("Protected Items") are "Commercial products" as this term is defined at 48 C.F.R. 2.101, consisting of "commercial computer software" and "commercial computer software documentation" as such terms are used in, respectively, 48 C.F.R. 12.212 and 48 C.F.R. 227.7202 & 252.227-7014(a)(1). Before any Protected Items are supplied to the U.S. Government, you will (i) inform the U.S. Government in writing that the Protected Items are and must be treated as commercial computer software and commercial computer software documentation developed at private expense; (ii) inform the U.S. Government that the Protected Items are provided subject to the terms of the Agreement; and (iii) mark the Protected Items as commercial computer software and commercial computer software documentation developed at private expense. In no event will you permit the U.S. Government to acquire rights in Protected Items beyond those specified in 48 C.F.R. 52.227-19(b)(1)-(2) or 252.227-7013(c) except as expressly approved by NVIDIA in writing. + +14.7 Notices. Please direct your legal notices or other correspondence to legalnotices@nvidia.com with a copy mailed to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department. If NVIDIA needs to contact you, you consent to receive the notices by email and agree that such notices will satisfy any legal communication requirements. + +14.8 Severability. If a court of competent jurisdiction rules that a provision of this Agreement is unenforceable, that provision will be deemed modified to the extent necessary to make it enforceable and the remainder of this Agreement will continue in full force and effect. + +14.9 Construction. The headings in the Agreement are included solely for convenience and are not intended to affect the meaning or interpretation of the Agreement. As required by the context of the Agreement, the singular of a term includes the plural and vice versa. + +14.10 Amendment. Any amendment to this Agreement must be in writing and signed by authorized representatives of both parties. + +14.11 Entire Agreement. Regarding the subject matter of this Agreement, the parties agree that (a) this Agreement constitutes the entire and exclusive agreement between the parties and supersedes all prior and contemporaneous communications and (b) any additional or different terms or conditions, whether contained in purchase orders, order acknowledgments, invoices or otherwise, will not be binding and are null and void. + +(v. August 15, 2025) diff --git a/docs/licenses/dependencies/cycler-license.txt b/docs/licenses/dependencies/cycler-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..539c7c1f9c7c115b3aa759bf7c56ac66c64cae95 --- /dev/null +++ b/docs/licenses/dependencies/cycler-license.txt @@ -0,0 +1,27 @@ +Copyright (c) 2015, matplotlib project +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the matplotlib project nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/decorator-license.txt b/docs/licenses/dependencies/decorator-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e01d05b189d7ebe54b01717c4e365fe45bb765b --- /dev/null +++ b/docs/licenses/dependencies/decorator-license.txt @@ -0,0 +1,27 @@ +Copyright (c) 2005-2025, Michele Simionato +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +* Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/docker-pycreds-license.txt b/docs/licenses/dependencies/docker-pycreds-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/docs/licenses/dependencies/docker-pycreds-license.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. 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If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/egl_probe-license.txt b/docs/licenses/dependencies/egl_probe-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..934eaa87bb98d79ced50c8f27849625dc97b934d --- /dev/null +++ b/docs/licenses/dependencies/egl_probe-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Stanford Vision and Learning Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/einops-license.txt b/docs/licenses/dependencies/einops-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..3a654e906619009358eb2cfe80609bd12b43fa7f --- /dev/null +++ b/docs/licenses/dependencies/einops-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Alex Rogozhnikov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/executing-license.txt b/docs/licenses/dependencies/executing-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..473e36e246edd5800325e9fa1eaa7697c95be1ef --- /dev/null +++ b/docs/licenses/dependencies/executing-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 Alex Hall + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/filelock-license.txt b/docs/licenses/dependencies/filelock-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf1ab25da0349f84a3fdd40032f0ce99db813b8b --- /dev/null +++ b/docs/licenses/dependencies/filelock-license.txt @@ -0,0 +1,24 @@ +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to diff --git a/docs/licenses/dependencies/flake8-license.txt b/docs/licenses/dependencies/flake8-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..e5e3d6f94008a5b394c0ec3fc3d873dc13cf07f0 --- /dev/null +++ b/docs/licenses/dependencies/flake8-license.txt @@ -0,0 +1,22 @@ +== Flake8 License (MIT) == + +Copyright (C) 2011-2013 Tarek Ziade +Copyright (C) 2012-2016 Ian Cordasco + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/flaky-license.txt b/docs/licenses/dependencies/flaky-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..167ec4d66df8f9c0f49d9ba06eb284c8d5ab472d --- /dev/null +++ b/docs/licenses/dependencies/flaky-license.txt @@ -0,0 +1,166 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. Definitions. + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + +2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + +3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + +5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + +6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + +7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + +9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS diff --git a/docs/licenses/dependencies/flatdict-license.txt b/docs/licenses/dependencies/flatdict-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..b0e19d4258482e9b56cfa2653e588524fb09e20a --- /dev/null +++ b/docs/licenses/dependencies/flatdict-license.txt @@ -0,0 +1,25 @@ +Copyright (c) 2013-2020 Gavin M. Roy +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + * Neither the name of the copyright holder nor the names of its contributors + may be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/fonttools-license.txt b/docs/licenses/dependencies/fonttools-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc633905d333c4b42c1a0c8b34e9f734adeb6e1e --- /dev/null +++ b/docs/licenses/dependencies/fonttools-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Just van Rossum + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/fsspec-license.txt b/docs/licenses/dependencies/fsspec-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..67590a5e5be5a5a2dde3fe53a7512e404a896c22 --- /dev/null +++ b/docs/licenses/dependencies/fsspec-license.txt @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2018, Martin Durant +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/gitdb-license.txt b/docs/licenses/dependencies/gitdb-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..c4986edc27e05a3f9bdbdaae6f0172e89252b167 --- /dev/null +++ b/docs/licenses/dependencies/gitdb-license.txt @@ -0,0 +1,42 @@ +Copyright (C) 2010, 2011 Sebastian Thiel and contributors +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +* Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. + +* Neither the name of the GitDB project nor the names of +its contributors may be used to endorse or promote products derived +from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED +TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +Additional Licenses +------------------- +The files at +gitdb/test/fixtures/packs/pack-11fdfa9e156ab73caae3b6da867192221f2089c2.idx +and +gitdb/test/fixtures/packs/pack-11fdfa9e156ab73caae3b6da867192221f2089c2.pack +are licensed under GNU GPL as part of the git source repository, +see http://en.wikipedia.org/wiki/Git_%28software%29 for more information. + +They are not required for the actual operation, which is why they are not found +in the distribution package. diff --git a/docs/licenses/dependencies/grpcio-license.txt b/docs/licenses/dependencies/grpcio-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..b44484922db16b39ce794ba43843bda5a45adb37 --- /dev/null +++ b/docs/licenses/dependencies/grpcio-license.txt @@ -0,0 +1,609 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +----------------------------------------------------------- + +BSD 3-Clause License + +Copyright 2016, Google Inc. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its +contributors may be used to endorse or promote products derived from this +software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +THE POSSIBILITY OF SUCH DAMAGE. + +----------------------------------------------------------- + +Mozilla Public License Version 2.0 +================================== + +1. Definitions +-------------- + +1.1. "Contributor" + means each individual or legal entity that creates, contributes to + the creation of, or owns Covered Software. + +1.2. "Contributor Version" + means the combination of the Contributions of others (if any) used + by a Contributor and that particular Contributor's Contribution. + +1.3. "Contribution" + means Covered Software of a particular Contributor. + +1.4. "Covered Software" + means Source Code Form to which the initial Contributor has attached + the notice in Exhibit A, the Executable Form of such Source Code + Form, and Modifications of such Source Code Form, in each case + including portions thereof. + +1.5. "Incompatible With Secondary Licenses" + means + + (a) that the initial Contributor has attached the notice described + in Exhibit B to the Covered Software; or + + (b) that the Covered Software was made available under the terms of + version 1.1 or earlier of the License, but not also under the + terms of a Secondary License. + +1.6. "Executable Form" + means any form of the work other than Source Code Form. + +1.7. "Larger Work" + means a work that combines Covered Software with other material, in + a separate file or files, that is not Covered Software. + +1.8. "License" + means this document. + +1.9. "Licensable" + means having the right to grant, to the maximum extent possible, + whether at the time of the initial grant or subsequently, any and + all of the rights conveyed by this License. + +1.10. "Modifications" + means any of the following: + + (a) any file in Source Code Form that results from an addition to, + deletion from, or modification of the contents of Covered + Software; or + + (b) any new file in Source Code Form that contains any Covered + Software. + +1.11. "Patent Claims" of a Contributor + means any patent claim(s), including without limitation, method, + process, and apparatus claims, in any patent Licensable by such + Contributor that would be infringed, but for the grant of the + License, by the making, using, selling, offering for sale, having + made, import, or transfer of either its Contributions or its + Contributor Version. + +1.12. "Secondary License" + means either the GNU General Public License, Version 2.0, the GNU + Lesser General Public License, Version 2.1, the GNU Affero General + Public License, Version 3.0, or any later versions of those + licenses. + +1.13. "Source Code Form" + means the form of the work preferred for making modifications. + +1.14. "You" (or "Your") + means an individual or a legal entity exercising rights under this + License. For legal entities, "You" includes any entity that + controls, is controlled by, or is under common control with You. For + purposes of this definition, "control" means (a) the power, direct + or indirect, to cause the direction or management of such entity, + whether by contract or otherwise, or (b) ownership of more than + fifty percent (50%) of the outstanding shares or beneficial + ownership of such entity. + +2. License Grants and Conditions +-------------------------------- + +2.1. Grants + +Each Contributor hereby grants You a world-wide, royalty-free, +non-exclusive license: + +(a) under intellectual property rights (other than patent or trademark) + Licensable by such Contributor to use, reproduce, make available, + modify, display, perform, distribute, and otherwise exploit its + Contributions, either on an unmodified basis, with Modifications, or + as part of a Larger Work; and + +(b) under Patent Claims of such Contributor to make, use, sell, offer + for sale, have made, import, and otherwise transfer either its + Contributions or its Contributor Version. + +2.2. Effective Date + +The licenses granted in Section 2.1 with respect to any Contribution +become effective for each Contribution on the date the Contributor first +distributes such Contribution. + +2.3. Limitations on Grant Scope + +The licenses granted in this Section 2 are the only rights granted under +this License. No additional rights or licenses will be implied from the +distribution or licensing of Covered Software under this License. +Notwithstanding Section 2.1(b) above, no patent license is granted by a +Contributor: + +(a) for any code that a Contributor has removed from Covered Software; + or + +(b) for infringements caused by: (i) Your and any other third party's + modifications of Covered Software, or (ii) the combination of its + Contributions with other software (except as part of its Contributor + Version); or + +(c) under Patent Claims infringed by Covered Software in the absence of + its Contributions. + +This License does not grant any rights in the trademarks, service marks, +or logos of any Contributor (except as may be necessary to comply with +the notice requirements in Section 3.4). + +2.4. Subsequent Licenses + +No Contributor makes additional grants as a result of Your choice to +distribute the Covered Software under a subsequent version of this +License (see Section 10.2) or under the terms of a Secondary License (if +permitted under the terms of Section 3.3). + +2.5. Representation + +Each Contributor represents that the Contributor believes its +Contributions are its original creation(s) or it has sufficient rights +to grant the rights to its Contributions conveyed by this License. + +2.6. Fair Use + +This License is not intended to limit any rights You have under +applicable copyright doctrines of fair use, fair dealing, or other +equivalents. + +2.7. Conditions + +Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted +in Section 2.1. + +3. Responsibilities +------------------- + +3.1. Distribution of Source Form + +All distribution of Covered Software in Source Code Form, including any +Modifications that You create or to which You contribute, must be under +the terms of this License. You must inform recipients that the Source +Code Form of the Covered Software is governed by the terms of this +License, and how they can obtain a copy of this License. You may not +attempt to alter or restrict the recipients' rights in the Source Code +Form. + +3.2. Distribution of Executable Form + +If You distribute Covered Software in Executable Form then: + +(a) such Covered Software must also be made available in Source Code + Form, as described in Section 3.1, and You must inform recipients of + the Executable Form how they can obtain a copy of such Source Code + Form by reasonable means in a timely manner, at a charge no more + than the cost of distribution to the recipient; and + +(b) You may distribute such Executable Form under the terms of this + License, or sublicense it under different terms, provided that the + license for the Executable Form does not attempt to limit or alter + the recipients' rights in the Source Code Form under this License. + +3.3. Distribution of a Larger Work + +You may create and distribute a Larger Work under terms of Your choice, +provided that You also comply with the requirements of this License for +the Covered Software. If the Larger Work is a combination of Covered +Software with a work governed by one or more Secondary Licenses, and the +Covered Software is not Incompatible With Secondary Licenses, this +License permits You to additionally distribute such Covered Software +under the terms of such Secondary License(s), so that the recipient of +the Larger Work may, at their option, further distribute the Covered +Software under the terms of either this License or such Secondary +License(s). + +3.4. Notices + +You may not remove or alter the substance of any license notices +(including copyright notices, patent notices, disclaimers of warranty, +or limitations of liability) contained within the Source Code Form of +the Covered Software, except that You may alter any license notices to +the extent required to remedy known factual inaccuracies. + +3.5. Application of Additional Terms + +You may choose to offer, and to charge a fee for, warranty, support, +indemnity or liability obligations to one or more recipients of Covered +Software. However, You may do so only on Your own behalf, and not on +behalf of any Contributor. You must make it absolutely clear that any +such warranty, support, indemnity, or liability obligation is offered by +You alone, and You hereby agree to indemnify every Contributor for any +liability incurred by such Contributor as a result of warranty, support, +indemnity or liability terms You offer. You may include additional +disclaimers of warranty and limitations of liability specific to any +jurisdiction. + +4. Inability to Comply Due to Statute or Regulation +--------------------------------------------------- + +If it is impossible for You to comply with any of the terms of this +License with respect to some or all of the Covered Software due to +statute, judicial order, or regulation then You must: (a) comply with +the terms of this License to the maximum extent possible; and (b) +describe the limitations and the code they affect. Such description must +be placed in a text file included with all distributions of the Covered +Software under this License. Except to the extent prohibited by statute +or regulation, such description must be sufficiently detailed for a +recipient of ordinary skill to be able to understand it. + +5. Termination +-------------- + +5.1. The rights granted under this License will terminate automatically +if You fail to comply with any of its terms. However, if You become +compliant, then the rights granted under this License from a particular +Contributor are reinstated (a) provisionally, unless and until such +Contributor explicitly and finally terminates Your grants, and (b) on an +ongoing basis, if such Contributor fails to notify You of the +non-compliance by some reasonable means prior to 60 days after You have +come back into compliance. Moreover, Your grants from a particular +Contributor are reinstated on an ongoing basis if such Contributor +notifies You of the non-compliance by some reasonable means, this is the +first time You have received notice of non-compliance with this License +from such Contributor, and You become compliant prior to 30 days after +Your receipt of the notice. + +5.2. If You initiate litigation against any entity by asserting a patent +infringement claim (excluding declaratory judgment actions, +counter-claims, and cross-claims) alleging that a Contributor Version +directly or indirectly infringes any patent, then the rights granted to +You by any and all Contributors for the Covered Software under Section +2.1 of this License shall terminate. + +5.3. In the event of termination under Sections 5.1 or 5.2 above, all +end user license agreements (excluding distributors and resellers) which +have been validly granted by You or Your distributors under this License +prior to termination shall survive termination. + +************************************************************************ +* * +* 6. Disclaimer of Warranty * +* ------------------------- * +* * +* Covered Software is provided under this License on an "as is" * +* basis, without warranty of any kind, either expressed, implied, or * +* statutory, including, without limitation, warranties that the * +* Covered Software is free of defects, merchantable, fit for a * +* particular purpose or non-infringing. The entire risk as to the * +* quality and performance of the Covered Software is with You. * +* Should any Covered Software prove defective in any respect, You * +* (not any Contributor) assume the cost of any necessary servicing, * +* repair, or correction. This disclaimer of warranty constitutes an * +* essential part of this License. No use of any Covered Software is * +* authorized under this License except under this disclaimer. * +* * +************************************************************************ + +************************************************************************ +* * +* 7. Limitation of Liability * +* -------------------------- * +* * +* Under no circumstances and under no legal theory, whether tort * +* (including negligence), contract, or otherwise, shall any * +* Contributor, or anyone who distributes Covered Software as * +* permitted above, be liable to You for any direct, indirect, * +* special, incidental, or consequential damages of any character * +* including, without limitation, damages for lost profits, loss of * +* goodwill, work stoppage, computer failure or malfunction, or any * +* and all other commercial damages or losses, even if such party * +* shall have been informed of the possibility of such damages. This * +* limitation of liability shall not apply to liability for death or * +* personal injury resulting from such party's negligence to the * +* extent applicable law prohibits such limitation. Some * +* jurisdictions do not allow the exclusion or limitation of * +* incidental or consequential damages, so this exclusion and * +* limitation may not apply to You. * +* * +************************************************************************ + +8. Litigation +------------- + +Any litigation relating to this License may be brought only in the +courts of a jurisdiction where the defendant maintains its principal +place of business and such litigation shall be governed by laws of that +jurisdiction, without reference to its conflict-of-law provisions. +Nothing in this Section shall prevent a party's ability to bring +cross-claims or counter-claims. + +9. Miscellaneous +---------------- + +This License represents the complete agreement concerning the subject +matter hereof. If any provision of this License is held to be +unenforceable, such provision shall be reformed only to the extent +necessary to make it enforceable. Any law or regulation which provides +that the language of a contract shall be construed against the drafter +shall not be used to construe this License against a Contributor. + +10. Versions of the License +--------------------------- + +10.1. New Versions + +Mozilla Foundation is the license steward. Except as provided in Section +10.3, no one other than the license steward has the right to modify or +publish new versions of this License. Each version will be given a +distinguishing version number. + +10.2. Effect of New Versions + +You may distribute the Covered Software under the terms of the version +of the License under which You originally received the Covered Software, +or under the terms of any subsequent version published by the license +steward. + +10.3. Modified Versions + +If you create software not governed by this License, and you want to +create a new license for such software, you may create and use a +modified version of this License if you rename the license and remove +any references to the name of the license steward (except to note that +such modified license differs from this License). + +10.4. Distributing Source Code Form that is Incompatible With Secondary +Licenses + +If You choose to distribute Source Code Form that is Incompatible With +Secondary Licenses under the terms of this version of the License, the +notice described in Exhibit B of this License must be attached. + +Exhibit A - Source Code Form License Notice +------------------------------------------- + + This Source Code Form is subject to the terms of the Mozilla Public + License, v. 2.0. If a copy of the MPL was not distributed with this + file, You can obtain one at http://mozilla.org/MPL/2.0/. + +If it is not possible or desirable to put the notice in a particular +file, then You may include the notice in a location (such as a LICENSE +file in a relevant directory) where a recipient would be likely to look +for such a notice. + +You may add additional accurate notices of copyright ownership. + +Exhibit B - "Incompatible With Secondary Licenses" Notice +--------------------------------------------------------- + + This Source Code Form is "Incompatible With Secondary Licenses", as + defined by the Mozilla Public License, v. 2.0. diff --git a/docs/licenses/dependencies/gym-license.txt b/docs/licenses/dependencies/gym-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..979a5ce5ae1b2b3f8105dccd739c10ec086d8a42 --- /dev/null +++ b/docs/licenses/dependencies/gym-license.txt @@ -0,0 +1,34 @@ +The MIT License + +Copyright (c) 2016 OpenAI (https://openai.com) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + +# Mujoco models +This work is derived from [MuJuCo models](http://www.mujoco.org/forum/index.php?resources/) used under the following license: +``` +This file is part of MuJoCo. +Copyright 2009-2015 Roboti LLC. +Mujoco :: Advanced physics simulation engine +Source : www.roboti.us +Version : 1.31 +Released : 23Apr16 +Author :: Vikash Kumar +Contacts : kumar@roboti.us +``` diff --git a/docs/licenses/dependencies/gym-notices-license.txt b/docs/licenses/dependencies/gym-notices-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..96f1555dfe572f6dd2af1b7db9e100cd85bf9687 --- /dev/null +++ b/docs/licenses/dependencies/gym-notices-license.txt @@ -0,0 +1,19 @@ +Copyright (c) 2018 The Python Packaging Authority + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/gymnasium-license.txt b/docs/licenses/dependencies/gymnasium-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac10bf443756cdc02724943ac09021092ce9e737 --- /dev/null +++ b/docs/licenses/dependencies/gymnasium-license.txt @@ -0,0 +1,22 @@ +The MIT License + +Copyright (c) 2016 OpenAI +Copyright (c) 2022 Farama Foundation + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/docs/licenses/dependencies/h5py-license.txt b/docs/licenses/dependencies/h5py-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..28ca56277f0c598235b91158fc8f99755997fb0e --- /dev/null +++ b/docs/licenses/dependencies/h5py-license.txt @@ -0,0 +1,30 @@ +Copyright (c) 2008 Andrew Collette and contributors +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/hidapi-license.txt b/docs/licenses/dependencies/hidapi-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..538cdf95cf63e6f8fab8e9c4c7222a35357b4ce1 --- /dev/null +++ b/docs/licenses/dependencies/hidapi-license.txt @@ -0,0 +1,26 @@ +Copyright (c) 2010, Alan Ott, Signal 11 Software +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of Signal 11 Software nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. 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Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. 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Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. 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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/jsonschema-license.txt b/docs/licenses/dependencies/jsonschema-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..af9cfbdb134f42e5205ecbad597421d778826481 --- /dev/null +++ b/docs/licenses/dependencies/jsonschema-license.txt @@ -0,0 +1,19 @@ +Copyright (c) 2013 Julian Berman + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. 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For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. 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If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. 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The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/open3d-license.txt b/docs/licenses/dependencies/open3d-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..79d162876d440fdaabaf292892fd3b6acb0db5af --- /dev/null +++ b/docs/licenses/dependencies/open3d-license.txt @@ -0,0 +1,22 @@ +The MIT License (MIT) + +Open3D: www.open3d.org +Copyright (c) 2018-2021 www.open3d.org + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/docs/licenses/dependencies/opencv-python-license.txt b/docs/licenses/dependencies/opencv-python-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..09ad12ac89f5ef952bf48b030b7c9a15f4748a46 --- /dev/null +++ b/docs/licenses/dependencies/opencv-python-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Olli-Pekka Heinisuo + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/packaging-license.txt b/docs/licenses/dependencies/packaging-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..d9a10c0d8e868ebf8da0b3dc95bb0be634c34bfe --- /dev/null +++ b/docs/licenses/dependencies/packaging-license.txt @@ -0,0 +1,176 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS diff --git a/docs/licenses/dependencies/pandas-license.txt b/docs/licenses/dependencies/pandas-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..c343da2ebe870934eb5c9840d9b92f7d5cd7b134 --- /dev/null +++ b/docs/licenses/dependencies/pandas-license.txt @@ -0,0 +1,31 @@ +BSD 3-Clause License + +Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team +All rights reserved. + +Copyright (c) 2011-2025, Open source contributors. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/pathtools-license.txt b/docs/licenses/dependencies/pathtools-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e6adc9dadd859189c3de9d2f42cfb72e0a64abb --- /dev/null +++ b/docs/licenses/dependencies/pathtools-license.txt @@ -0,0 +1,21 @@ +Copyright (C) 2010 by Yesudeep Mangalapilly + +MIT License +----------- +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/docs/licenses/dependencies/pexpect-license.txt b/docs/licenses/dependencies/pexpect-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ee60f42b44e250ccd8d6064b71e7f622f498836 --- /dev/null +++ b/docs/licenses/dependencies/pexpect-license.txt @@ -0,0 +1,19 @@ +ISC LICENSE + + This license is approved by the OSI and FSF as GPL-compatible. + http://opensource.org/licenses/isc-license.txt + + Copyright (c) 2013-2014, Pexpect development team + Copyright (c) 2012, Noah Spurrier + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted, provided that the above + copyright notice and this permission notice appear in all copies. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES + WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF + MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR + ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES + WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN + ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF + OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. diff --git a/docs/licenses/dependencies/pillow-license.txt b/docs/licenses/dependencies/pillow-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..40aabc3239f150d0657212032fb21211601272b7 --- /dev/null +++ b/docs/licenses/dependencies/pillow-license.txt @@ -0,0 +1,30 @@ +The Python Imaging Library (PIL) is + + Copyright © 1997-2011 by Secret Labs AB + Copyright © 1995-2011 by Fredrik Lundh + +Pillow is the friendly PIL fork. It is + + Copyright © 2010-2022 by Alex Clark and contributors + +Like PIL, Pillow is licensed under the open source HPND License: + +By obtaining, using, and/or copying this software and/or its associated +documentation, you agree that you have read, understood, and will comply +with the following terms and conditions: + +Permission to use, copy, modify, and distribute this software and its +associated documentation for any purpose and without fee is hereby granted, +provided that the above copyright notice appears in all copies, and that +both that copyright notice and this permission notice appear in supporting +documentation, and that the name of Secret Labs AB or the author not be +used in advertising or publicity pertaining to distribution of the software +without specific, written prior permission. + +SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS +SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. +IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR BE LIABLE FOR ANY SPECIAL, +INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM +LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. diff --git a/docs/licenses/dependencies/pin-license.txt b/docs/licenses/dependencies/pin-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..dfacb67314841410e216c4702d05e484e163d323 --- /dev/null +++ b/docs/licenses/dependencies/pin-license.txt @@ -0,0 +1,26 @@ +BSD 2-Clause License + +Copyright (c) 2014-2023, CNRS +Copyright (c) 2018-2025, INRIA +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/pin-pink-license.txt b/docs/licenses/dependencies/pin-pink-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/docs/licenses/dependencies/pin-pink-license.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. 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Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. diff --git a/docs/licenses/dependencies/rich-license.txt b/docs/licenses/dependencies/rich-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..4415505566f261c802b671426be529a31f914137 --- /dev/null +++ b/docs/licenses/dependencies/rich-license.txt @@ -0,0 +1,19 @@ +Copyright (c) 2020 Will McGugan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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All rights reserved. + +All contributions by Facebook: +Copyright (c) 2016 Facebook Inc. + +All contributions by Google: +Copyright (c) 2015 Google Inc. +All rights reserved. + +All contributions by Yangqing Jia: +Copyright (c) 2015 Yangqing Jia +All rights reserved. + +All contributions by Kakao Brain: +Copyright 2019-2020 Kakao Brain + +All contributions from Caffe: +Copyright(c) 2013, 2014, 2015, the respective contributors +All rights reserved. + +All other contributions: +Copyright(c) 2015, 2016 the respective contributors +All rights reserved. + +Caffe2 uses a copyright model similar to Caffe: each contributor holds +copyright over their contributions to Caffe2. The project versioning records +all such contribution and copyright details. If a contributor wants to further +mark their specific copyright on a particular contribution, they should +indicate their copyright solely in the commit message of the change when it is +committed. + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America + and IDIAP Research Institute nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +end of terms and conditions + +The externally maintained libraries from which parts of the Software is derived +are: + +- autoflake, licensed as follows: + """ + Copyright (C) 2012-2018 Steven Myint + + Permission is hereby granted, free of charge, to any person obtaining a copy of + this software and associated documentation files (the "Software"), to deal in + the Software without restriction, including without limitation the rights to + use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies + of the Software, and to permit persons to whom the Software is furnished to do + so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- autotyping, licensed as follows: + """ + MIT License + + Copyright (c) 2023 Jelle Zijlstra + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- Flake8, licensed as follows: + """ + == Flake8 License (MIT) == + + Copyright (C) 2011-2013 Tarek Ziade + Copyright (C) 2012-2016 Ian Cordasco + + Permission is hereby granted, free of charge, to any person obtaining a copy of + this software and associated documentation files (the "Software"), to deal in + the Software without restriction, including without limitation the rights to + use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies + of the Software, and to permit persons to whom the Software is furnished to do + so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- flake8-eradicate, licensed as follows: + """ + MIT License + + Copyright (c) 2018 Nikita Sobolev + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- flake8-pyi, licensed as follows: + """ + The MIT License (MIT) + + Copyright (c) 2016 Łukasz Langa + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- flake8-simplify, licensed as follows: + """ + MIT License + + Copyright (c) 2020 Martin Thoma + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- isort, licensed as follows: + """ + The MIT License (MIT) + + Copyright (c) 2013 Timothy Edmund Crosley + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + """ + +- pygrep-hooks, licensed as follows: + """ + Copyright (c) 2018 Anthony Sottile + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + """ + +- pycodestyle, licensed as follows: + """ + Copyright © 2006-2009 Johann C. Rocholl + Copyright © 2009-2014 Florent Xicluna + Copyright © 2014-2020 Ian Lee + + Licensed under the terms of the Expat License + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation files + (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS + BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN + ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- pydocstyle, licensed as follows: + """ + Copyright (c) 2012 GreenSteam, + + Copyright (c) 2014-2020 Amir Rachum, + + Copyright (c) 2020 Sambhav Kothari, + + Permission is hereby granted, free of charge, to any person obtaining a copy of + this software and associated documentation files (the "Software"), to deal in + the Software without restriction, including without limitation the rights to + use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies + of the Software, and to permit persons to whom the Software is furnished to do + so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- Pyflakes, licensed as follows: + """ + Copyright 2005-2011 Divmod, Inc. + Copyright 2013-2014 Florent Xicluna + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal in the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE + LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION + OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION + WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + """ + +- Pyright, licensed as follows: + """ + MIT License + + Pyright - A static type checker for the Python language + Copyright (c) Microsoft Corporation. All rights reserved. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE + """ + +- pyupgrade, licensed as follows: + """ + Copyright (c) 2017 Anthony Sottile + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + """ + +- rome/tools, licensed under the MIT license: + """ + MIT License + + Copyright (c) Rome Tools, Inc. and its affiliates. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- RustPython, licensed as follows: + """ + MIT License + + Copyright (c) 2020 RustPython Team + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + +- rust-analyzer/text-size, licensed under the MIT license: + """ + Permission is hereby granted, free of charge, to any + person obtaining a copy of this software and associated + documentation files (the "Software"), to deal in the + Software without restriction, including without + limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of + the Software, and to permit persons to whom the Software + is furnished to do so, subject to the following + conditions: + + The above copyright notice and this permission notice + shall be included in all copies or substantial portions + of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF + ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED + TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A + PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT + SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION + OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS IN THE SOFTWARE. + """ diff --git a/docs/licenses/dependencies/ruff-pre-commit-license.txt b/docs/licenses/dependencies/ruff-pre-commit-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..c16d8bb1db2621505a8bdd37d260d6f312b049ad --- /dev/null +++ b/docs/licenses/dependencies/ruff-pre-commit-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Astral Software Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/safetensors-license.txt b/docs/licenses/dependencies/safetensors-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/docs/licenses/dependencies/safetensors-license.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2017, The TensorFlow Authors. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/tensorboardx-license.txt b/docs/licenses/dependencies/tensorboardx-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..bed02cde0be5fcac374c51db9902eef7fcaa90b5 --- /dev/null +++ b/docs/licenses/dependencies/tensorboardx-license.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Tzu-Wei Huang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/licenses/dependencies/toml-license.txt b/docs/licenses/dependencies/toml-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..576d83e85523a28ebea0abb9789d5cdcb11e1154 --- /dev/null +++ b/docs/licenses/dependencies/toml-license.txt @@ -0,0 +1,27 @@ +The MIT License + +Copyright 2013-2019 William Pearson +Copyright 2015-2016 Julien Enselme +Copyright 2016 Google Inc. +Copyright 2017 Samuel Vasko +Copyright 2017 Nate Prewitt +Copyright 2017 Jack Evans +Copyright 2019 Filippo Broggini + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/licenses/dependencies/transformers-license.txt b/docs/licenses/dependencies/transformers-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..68b7d66c97d66c58de883ed0c451af2b3183e6f3 --- /dev/null +++ b/docs/licenses/dependencies/transformers-license.txt @@ -0,0 +1,203 @@ +Copyright 2018- The Hugging Face team. All rights reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/licenses/dependencies/triton-license.txt b/docs/licenses/dependencies/triton-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d0238e86b1b43e123c9eb136eb0b2c0e1658d63 --- /dev/null +++ b/docs/licenses/dependencies/triton-license.txt @@ -0,0 +1,23 @@ +/* +* Copyright 2018-2020 Philippe Tillet +* Copyright 2020-2022 OpenAI +* +* Permission is hereby granted, free of charge, to any person obtaining +* a copy of this software and associated documentation files +* (the "Software"), to deal in the Software without restriction, +* including without limitation the rights to use, copy, modify, merge, +* publish, distribute, sublicense, and/or sell copies of the Software, +* and to permit persons to whom the Software is furnished to do so, +* subject to the following conditions: +* +* The above copyright notice and this permission notice shall be +* included in all copies or substantial portions of the Software. +* +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ diff --git a/docs/licenses/dependencies/typing-extensions-license.txt b/docs/licenses/dependencies/typing-extensions-license.txt new file mode 100644 index 0000000000000000000000000000000000000000..f26bcf4d2de6eb136e31006ca3ab447d5e488adf --- /dev/null +++ b/docs/licenses/dependencies/typing-extensions-license.txt @@ -0,0 +1,279 @@ +A. HISTORY OF THE SOFTWARE +========================== + +Python was created in the early 1990s by Guido van Rossum at Stichting +Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands +as a successor of a language called ABC. Guido remains Python's +principal author, although it includes many contributions from others. + +In 1995, Guido continued his work on Python at the Corporation for +National Research Initiatives (CNRI, see https://www.cnri.reston.va.us) +in Reston, Virginia where he released several versions of the +software. + +In May 2000, Guido and the Python core development team moved to +BeOpen.com to form the BeOpen PythonLabs team. In October of the same +year, the PythonLabs team moved to Digital Creations, which became +Zope Corporation. In 2001, the Python Software Foundation (PSF, see +https://www.python.org/psf/) was formed, a non-profit organization +created specifically to own Python-related Intellectual Property. +Zope Corporation was a sponsoring member of the PSF. + +All Python releases are Open Source (see https://opensource.org for +the Open Source Definition). Historically, most, but not all, Python +releases have also been GPL-compatible; the table below summarizes +the various releases. + + Release Derived Year Owner GPL- + from compatible? (1) + + 0.9.0 thru 1.2 1991-1995 CWI yes + 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes + 1.6 1.5.2 2000 CNRI no + 2.0 1.6 2000 BeOpen.com no + 1.6.1 1.6 2001 CNRI yes (2) + 2.1 2.0+1.6.1 2001 PSF no + 2.0.1 2.0+1.6.1 2001 PSF yes + 2.1.1 2.1+2.0.1 2001 PSF yes + 2.1.2 2.1.1 2002 PSF yes + 2.1.3 2.1.2 2002 PSF yes + 2.2 and above 2.1.1 2001-now PSF yes + +Footnotes: + +(1) GPL-compatible doesn't mean that we're distributing Python under + the GPL. All Python licenses, unlike the GPL, let you distribute + a modified version without making your changes open source. The + GPL-compatible licenses make it possible to combine Python with + other software that is released under the GPL; the others don't. + +(2) According to Richard Stallman, 1.6.1 is not GPL-compatible, + because its license has a choice of law clause. According to + CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1 + is "not incompatible" with the GPL. + +Thanks to the many outside volunteers who have worked under Guido's +direction to make these releases possible. + + +B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON +=============================================================== + +Python software and documentation are licensed under the +Python Software Foundation License Version 2. + +Starting with Python 3.8.6, examples, recipes, and other code in +the documentation are dual licensed under the PSF License Version 2 +and the Zero-Clause BSD license. + +Some software incorporated into Python is under different licenses. +The licenses are listed with code falling under that license. + + +PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 +-------------------------------------------- + +1. This LICENSE AGREEMENT is between the Python Software Foundation +("PSF"), and the Individual or Organization ("Licensee") accessing and +otherwise using this software ("Python") in source or binary form and +its associated documentation. + +2. Subject to the terms and conditions of this License Agreement, PSF hereby +grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, +analyze, test, perform and/or display publicly, prepare derivative works, +distribute, and otherwise use Python alone or in any derivative version, +provided, however, that PSF's License Agreement and PSF's notice of copyright, +i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, +2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 Python Software Foundation; +All Rights Reserved" are retained in Python alone or in any derivative version +prepared by Licensee. + +3. In the event Licensee prepares a derivative work that is based on +or incorporates Python or any part thereof, and wants to make +the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to Python. + +4. PSF is making Python available to Licensee on an "AS IS" +basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT +INFRINGE ANY THIRD PARTY RIGHTS. + +5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON +FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS +A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, +OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. Nothing in this License Agreement shall be deemed to create any +relationship of agency, partnership, or joint venture between PSF and +Licensee. This License Agreement does not grant permission to use PSF +trademarks or trade name in a trademark sense to endorse or promote +products or services of Licensee, or any third party. + +8. By copying, installing or otherwise using Python, Licensee +agrees to be bound by the terms and conditions of this License +Agreement. + + +BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 +------------------------------------------- + +BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 + +1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an +office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the +Individual or Organization ("Licensee") accessing and otherwise using +this software in source or binary form and its associated +documentation ("the Software"). + +2. 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BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE +SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS +AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY +DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + +5. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +6. This License Agreement shall be governed by and interpreted in all +respects by the law of the State of California, excluding conflict of +law provisions. Nothing in this License Agreement shall be deemed to +create any relationship of agency, partnership, or joint venture +between BeOpen and Licensee. This License Agreement does not grant +permission to use BeOpen trademarks or trade names in a trademark +sense to endorse or promote products or services of Licensee, or any +third party. As an exception, the "BeOpen Python" logos available at +http://www.pythonlabs.com/logos.html may be used according to the +permissions granted on that web page. + +7. By copying, installing or otherwise using the software, Licensee +agrees to be bound by the terms and conditions of this License +Agreement. + + +CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 +--------------------------------------- + +1. This LICENSE AGREEMENT is between the Corporation for National +Research Initiatives, having an office at 1895 Preston White Drive, +Reston, VA 20191 ("CNRI"), and the Individual or Organization +("Licensee") accessing and otherwise using Python 1.6.1 software in +source or binary form and its associated documentation. + +2. Subject to the terms and conditions of this License Agreement, CNRI +hereby grants Licensee a nonexclusive, royalty-free, world-wide +license to reproduce, analyze, test, perform and/or display publicly, +prepare derivative works, distribute, and otherwise use Python 1.6.1 +alone or in any derivative version, provided, however, that CNRI's +License Agreement and CNRI's notice of copyright, i.e., "Copyright (c) +1995-2001 Corporation for National Research Initiatives; All Rights +Reserved" are retained in Python 1.6.1 alone or in any derivative +version prepared by Licensee. Alternately, in lieu of CNRI's License +Agreement, Licensee may substitute the following text (omitting the +quotes): "Python 1.6.1 is made available subject to the terms and +conditions in CNRI's License Agreement. This Agreement together with +Python 1.6.1 may be located on the internet using the following +unique, persistent identifier (known as a handle): 1895.22/1013. This +Agreement may also be obtained from a proxy server on the internet +using the following URL: http://hdl.handle.net/1895.22/1013". + +3. In the event Licensee prepares a derivative work that is based on +or incorporates Python 1.6.1 or any part thereof, and wants to make +the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to Python 1.6.1. + +4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" +basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT +INFRINGE ANY THIRD PARTY RIGHTS. + +5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON +1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS +A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, +OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. This License Agreement shall be governed by the federal +intellectual property law of the United States, including without +limitation the federal copyright law, and, to the extent such +U.S. federal law does not apply, by the law of the Commonwealth of +Virginia, excluding Virginia's conflict of law provisions. +Notwithstanding the foregoing, with regard to derivative works based +on Python 1.6.1 that incorporate non-separable material that was +previously distributed under the GNU General Public License (GPL), the +law of the Commonwealth of Virginia shall govern this License +Agreement only as to issues arising under or with respect to +Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this +License Agreement shall be deemed to create any relationship of +agency, partnership, or joint venture between CNRI and Licensee. This +License Agreement does not grant permission to use CNRI trademarks or +trade name in a trademark sense to endorse or promote products or +services of Licensee, or any third party. + +8. By clicking on the "ACCEPT" button where indicated, or by copying, +installing or otherwise using Python 1.6.1, Licensee agrees to be +bound by the terms and conditions of this License Agreement. + + ACCEPT + + +CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 +-------------------------------------------------- + +Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, +The Netherlands. All rights reserved. + +Permission to use, copy, modify, and distribute this software and its +documentation for any purpose and without fee is hereby granted, +provided that the above copyright notice appear in all copies and that +both that copyright notice and this permission notice appear in +supporting documentation, and that the name of Stichting Mathematisch +Centrum or CWI not be used in advertising or publicity pertaining to +distribution of the software without specific, written prior +permission. + +STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO +THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND +FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE +FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT +OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION +---------------------------------------------------------------------- + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. 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In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + + Jean-loup Gailly Mark Adler + jloup@gzip.org madler@alumni.caltech.edu + +*/ diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000000000000000000000000000000000000..941689ef03c8e844b3c33b68bb573c0872a2f8d8 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,65 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file to build Sphinx documentation + +set SOURCEDIR=. +set BUILDDIR=_build + +REM Check if a specific target was passed +if "%1" == "multi-docs" ( + REM Check if SPHINXBUILD is set, if not default to sphinx-multiversion + if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-multiversion + ) + where %SPHINXBUILD% >NUL 2>NUL + if errorlevel 1 ( + echo. + echo.The 'sphinx-multiversion' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-multiversion' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 + ) + %SPHINXBUILD% %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + + REM Copy the redirect index.html to the build directory + copy _redirect\index.html %BUILDDIR%\index.html + goto end +) + +if "%1" == "current-docs" ( + REM Check if SPHINXBUILD is set, if not default to sphinx-build + if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build + ) + where %SPHINXBUILD% >NUL 2>NUL + if errorlevel 1 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 + ) + if exist "%BUILDDIR%\current" rmdir /s /q "%BUILDDIR%\current" + %SPHINXBUILD% -W "%SOURCEDIR%" "%BUILDDIR%\current" %SPHINXOPTS% + goto end +) + +REM If no valid target is passed, show usage instructions +echo. +echo.Usage: +echo. make.bat multi-docs - To build the multi-version documentation. +echo. make.bat current-docs - To build the current documentation. +echo. + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..13b2bfe9d69e7528b50aa930a9a4be007d16df78 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,18 @@ +# for building the docs +sphinx-book-theme==1.0.1 +myst-parser +sphinxcontrib-bibtex==2.5.0 +autodocsumm +sphinx-copybutton +sphinx-icon +sphinx_design +sphinxemoji +sphinx-tabs # backwards compatibility for building docs on v1.0.0 +sphinx-multiversion==0.2.4 + +# basic python +numpy +matplotlib +warp-lang +# learning +gymnasium diff --git a/docs/source/_static/NVIDIA-logo-black.png b/docs/source/_static/NVIDIA-logo-black.png new file mode 100644 index 0000000000000000000000000000000000000000..5a982e2e94bb2ecad6f3a96bf67ef5a3c6ac99d0 --- /dev/null +++ b/docs/source/_static/NVIDIA-logo-black.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a30acc905d80587f70b7368591377156e5135dffc9a84850cd0e25fccc86a27d +size 39261 diff --git a/docs/source/_static/NVIDIA-logo-white.png b/docs/source/_static/NVIDIA-logo-white.png new file mode 100644 index 0000000000000000000000000000000000000000..457ffe18452de043c4e8065e03a7f75126156f7b --- /dev/null +++ b/docs/source/_static/NVIDIA-logo-white.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c305f6ac606b24ce4e1db63b9d47d8e99e436ed4ecc94ccb5824185734f3f464 +size 130540 diff --git a/docs/source/_static/actuator-group/actuator-dark.svg b/docs/source/_static/actuator-group/actuator-dark.svg new file mode 100644 index 0000000000000000000000000000000000000000..b9e0682395d4ae1f1769a8039f79a14dce813076 --- /dev/null +++ b/docs/source/_static/actuator-group/actuator-dark.svg @@ -0,0 +1,522 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + DC Motor + Actuator Net(MLP/LSTM) + + Gripper + + + Arm + Base + Mimic Group + + + + open/close (1) + joint position(6) + joint position(12) + joint torque(12) + joint torque(6) + joint velocity(6) + + Simulation + + + + Actions + + + + + + + + + + + diff --git a/docs/source/_static/actuator-group/actuator-light.svg b/docs/source/_static/actuator-group/actuator-light.svg new file mode 100644 index 0000000000000000000000000000000000000000..214b5a7fee2d45248b0fa38c2e7ff438a7ebde23 --- /dev/null +++ b/docs/source/_static/actuator-group/actuator-light.svg @@ -0,0 +1,10214 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + DC Motor + Actuator Net(MLP/LSTM) + + Gripper + + + Arm + Base + Mimic Group + + + + open/close (1) + joint position(6) + joint position(12) + joint torque(12) + joint torque(6) + joint velocity(6) + + Simulation + + + + Actions + + + + + + + + + + + diff --git a/docs/source/_static/actuator-group/dc_motor_clipping.jpg b/docs/source/_static/actuator-group/dc_motor_clipping.jpg new file mode 100644 index 0000000000000000000000000000000000000000..bc44bcff8d28d4012a2cd38e95afe9b941821e80 --- /dev/null +++ b/docs/source/_static/actuator-group/dc_motor_clipping.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85b08723a42530b70f2d54e109b1f1914827aedb5bb6f963f64794ac2c83219c +size 63276 diff --git a/docs/source/_static/benchmarks/cartpole.jpg b/docs/source/_static/benchmarks/cartpole.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a407fa75277a9cfa962db02ba3a4b084a5d4afb8 --- /dev/null +++ b/docs/source/_static/benchmarks/cartpole.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ebd6fb905977ca8469f83d5e781f71539548fdf4a391e65853a6f8ae1b26b46 +size 139764 diff --git a/docs/source/_static/benchmarks/cartpole_camera.jpg b/docs/source/_static/benchmarks/cartpole_camera.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1776ea8403a671eb157b30273811708aff836ec1 --- /dev/null +++ b/docs/source/_static/benchmarks/cartpole_camera.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93298b069d5e386d56a138e8677bd6f2525e6ef35956c80196485fa20e9cb93b +size 59002 diff --git a/docs/source/_static/benchmarks/g1_rough.jpg b/docs/source/_static/benchmarks/g1_rough.jpg new file mode 100644 index 0000000000000000000000000000000000000000..71c716a3886ec649a0bdbf601bb443e973ab4839 --- /dev/null +++ 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b/docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f44840c0f908bd7b2bc2579e952771b5c69f5904 --- /dev/null +++ b/docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml @@ -0,0 +1,349 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +actions: +- action_type: JointAction + clip: null + dtype: torch.float32 + extras: + description: Joint action term that applies the processed actions to the articulation's + joints as position commands. + full_path: isaaclab.envs.mdp.actions.joint_actions.JointPositionAction + joint_names: + - LF_HAA + - LH_HAA + - RF_HAA + - RH_HAA + - LF_HFE + - LH_HFE + - RF_HFE + - RH_HFE + - LF_KFE + - LH_KFE + - RF_KFE + - RH_KFE + mdp_type: Action + name: joint_position_action + offset: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.4000000059604645 + - -0.4000000059604645 + - 0.4000000059604645 + - -0.4000000059604645 + - -0.800000011920929 + - 0.800000011920929 + - -0.800000011920929 + - 0.800000011920929 + scale: 0.5 + shape: + - 12 +articulations: + robot: + default_joint_armature: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + default_joint_damping: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + default_joint_friction: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + default_joint_pos: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.4000000059604645 + - -0.4000000059604645 + - 0.4000000059604645 + - -0.4000000059604645 + - -0.800000011920929 + - 0.800000011920929 + - -0.800000011920929 + - 0.800000011920929 + default_joint_pos_limits: + - - -0.7853984236717224 + - 0.6108654141426086 + - - -0.7853984236717224 + - 0.6108654141426086 + - - -0.6108654141426086 + - 0.7853984236717224 + - - -0.6108654141426086 + - 0.7853984236717224 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + - - -9.42477798461914 + - 9.42477798461914 + default_joint_stiffness: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + default_joint_vel: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + joint_names: + - LF_HAA + - LH_HAA + - RF_HAA + - RH_HAA + - LF_HFE + - LH_HFE + - RF_HFE + - RH_HFE + - LF_KFE + - LH_KFE + - RF_KFE + - RH_KFE +observations: + policy: + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Root linear velocity in the asset's root frame. + modifiers: null + units: m/s + full_path: isaaclab.envs.mdp.observations.base_lin_vel + mdp_type: Observation + name: base_lin_vel + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Root angular velocity in the asset's root frame. + modifiers: null + units: rad/s + full_path: isaaclab.envs.mdp.observations.base_ang_vel + mdp_type: Observation + name: base_ang_vel + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Gravity projection on the asset's root frame. + modifiers: null + units: m/s^2 + full_path: isaaclab.envs.mdp.observations.projected_gravity + mdp_type: Observation + name: projected_gravity + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + description: The generated command from command term in the command manager + with the given name. + modifiers: null + full_path: isaaclab.envs.mdp.observations.generated_commands + mdp_type: Observation + name: generated_commands + observation_type: Command + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + description: 'The joint positions of the asset w.r.t. the default joint positions. + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have + their positions returned.' + modifiers: null + units: rad + full_path: isaaclab.envs.mdp.observations.joint_pos_rel + joint_names: + - LF_HAA + - LH_HAA + - RF_HAA + - RH_HAA + - LF_HFE + - LH_HFE + - RF_HFE + - RH_HFE + - LF_KFE + - LH_KFE + - RF_KFE + - RH_KFE + joint_pos_offsets: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.4000000059604645 + - -0.4000000059604645 + - 0.4000000059604645 + - -0.4000000059604645 + - -0.800000011920929 + - 0.800000011920929 + - -0.800000011920929 + - 0.800000011920929 + mdp_type: Observation + name: joint_pos_rel + observation_type: JointState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 12 + - dtype: torch.float32 + extras: + description: 'The joint velocities of the asset w.r.t. the default joint velocities. + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have + their velocities returned.' + modifiers: null + units: rad/s + full_path: isaaclab.envs.mdp.observations.joint_vel_rel + joint_names: + - LF_HAA + - LH_HAA + - RF_HAA + - RH_HAA + - LF_HFE + - LH_HFE + - RF_HFE + - RH_HFE + - LF_KFE + - LH_KFE + - RF_KFE + - RH_KFE + joint_vel_offsets: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + mdp_type: Observation + name: joint_vel_rel + observation_type: JointState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 12 + - dtype: torch.float32 + extras: + description: The last input action to the environment. The name of the action + term for which the action is required. If None, the entire action tensor is + returned. + modifiers: null + full_path: isaaclab.envs.mdp.observations.last_action + mdp_type: Observation + name: last_action + observation_type: Action + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 12 +scene: + decimation: 4 + dt: 0.02 + physics_dt: 0.005 diff --git a/docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_g1_v0_IO_descriptors.yaml b/docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_g1_v0_IO_descriptors.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d932800eaf6eae1ff0387c8b56fe733921b48931 --- /dev/null +++ b/docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_g1_v0_IO_descriptors.yaml @@ -0,0 +1,724 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +actions: +- action_type: JointAction + clip: null + dtype: torch.float32 + extras: + description: Joint action term that applies the processed actions to the articulation's + joints as position commands. + full_path: isaaclab.envs.mdp.actions.joint_actions.JointPositionAction + joint_names: + - left_hip_pitch_joint + - right_hip_pitch_joint + - torso_joint + - left_hip_roll_joint + - right_hip_roll_joint + - left_shoulder_pitch_joint + - right_shoulder_pitch_joint + - left_hip_yaw_joint + - right_hip_yaw_joint + - left_shoulder_roll_joint + - right_shoulder_roll_joint + - left_knee_joint + - right_knee_joint + - left_shoulder_yaw_joint + - right_shoulder_yaw_joint + - left_ankle_pitch_joint + - right_ankle_pitch_joint + - left_elbow_pitch_joint + - right_elbow_pitch_joint + - left_ankle_roll_joint + - right_ankle_roll_joint + - left_elbow_roll_joint + - right_elbow_roll_joint + - left_five_joint + - left_three_joint + - left_zero_joint + - right_five_joint + - right_three_joint + - right_zero_joint + - left_six_joint + - left_four_joint + - left_one_joint + - right_six_joint + - right_four_joint + - right_one_joint + - left_two_joint + - right_two_joint + mdp_type: Action + name: joint_position_action + offset: + - -0.20000000298023224 + - -0.20000000298023224 + - 0.0 + - 0.0 + - 0.0 + - 0.3499999940395355 + - 0.3499999940395355 + - 0.0 + - 0.0 + - 0.1599999964237213 + - -0.1599999964237213 + - 0.41999998688697815 + - 0.41999998688697815 + - 0.0 + - 0.0 + - -0.23000000417232513 + - -0.23000000417232513 + - 0.8700000047683716 + - 0.8700000047683716 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 1.0 + - 0.0 + - 0.0 + - -1.0 + - 0.5199999809265137 + - -0.5199999809265137 + scale: 0.5 + shape: + - 37 +articulations: + robot: + default_joint_armature: + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.009999999776482582 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + default_joint_damping: + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - 10.0 + - 10.0 + - 2.0 + - 2.0 + - 10.0 + - 10.0 + - 2.0 + - 2.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + - 10.0 + default_joint_friction: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + default_joint_pos: + - -0.20000000298023224 + - -0.20000000298023224 + - 0.0 + - 0.0 + - 0.0 + - 0.3499999940395355 + - 0.3499999940395355 + - 0.0 + - 0.0 + - 0.1599999964237213 + - -0.1599999964237213 + - 0.41999998688697815 + - 0.41999998688697815 + - 0.0 + - 0.0 + - -0.23000000417232513 + - -0.23000000417232513 + - 0.8700000047683716 + - 0.8700000047683716 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 1.0 + - 0.0 + - 0.0 + - -1.0 + - 0.5199999809265137 + - -0.5199999809265137 + default_joint_pos_limits: + - - -2.3499996662139893 + - 3.049999952316284 + - - -2.3499996662139893 + - 3.049999952316284 + - - -2.618000030517578 + - 2.618000030517578 + - - -0.25999996066093445 + - 2.5299997329711914 + - - -2.5299997329711914 + - 0.25999996066093445 + - - -2.967099666595459 + - 2.7924997806549072 + - - -2.967099666595459 + - 2.7924997806549072 + - - -2.749999761581421 + - 2.749999761581421 + - - -2.749999761581421 + - 2.749999761581421 + - - -1.5881999731063843 + - 2.251499652862549 + - - -2.251499652862549 + - 1.5881999731063843 + - - -0.3348899781703949 + - 2.5448997020721436 + - - -0.3348899781703949 + - 2.5448997020721436 + - - -2.618000030517578 + - 2.618000030517578 + - - -2.618000030517578 + - 2.618000030517578 + - - -0.6799999475479126 + - 0.7299999594688416 + - - -0.6799999475479126 + - 0.7299999594688416 + - - -0.22679997980594635 + - 3.420799732208252 + - - -0.22679997980594635 + - 3.420799732208252 + - - -0.26179996132850647 + - 0.26179996132850647 + - - -0.26179996132850647 + - 0.26179996132850647 + - - -2.094299793243408 + - 2.094299793243408 + - - -2.094299793243408 + - 2.094299793243408 + - - -1.8399999141693115 + - 0.30000001192092896 + - - -1.8399999141693115 + - 0.30000001192092896 + - - -0.5235979557037354 + - 0.5235979557037354 + - - -0.30000001192092896 + - 1.8399999141693115 + - - -0.30000001192092896 + - 1.8399999141693115 + - - -0.5235979557037354 + - 0.5235979557037354 + - - -1.8399999141693115 + - 0.0 + - - -1.8399999141693115 + - 0.0 + - - -0.9999999403953552 + - 1.2000000476837158 + - - 0.0 + - 1.8399999141693115 + - - 0.0 + - 1.8399999141693115 + - - -1.2000000476837158 + - 0.9999999403953552 + - - 0.0 + - 1.8399999141693115 + - - -1.8399999141693115 + - 0.0 + default_joint_stiffness: + - 200.0 + - 200.0 + - 200.0 + - 150.0 + - 150.0 + - 40.0 + - 40.0 + - 150.0 + - 150.0 + - 40.0 + - 40.0 + - 200.0 + - 200.0 + - 40.0 + - 40.0 + - 20.0 + - 20.0 + - 40.0 + - 40.0 + - 20.0 + - 20.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + - 40.0 + default_joint_vel: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + joint_names: + - left_hip_pitch_joint + - right_hip_pitch_joint + - torso_joint + - left_hip_roll_joint + - right_hip_roll_joint + - left_shoulder_pitch_joint + - right_shoulder_pitch_joint + - left_hip_yaw_joint + - right_hip_yaw_joint + - left_shoulder_roll_joint + - right_shoulder_roll_joint + - left_knee_joint + - right_knee_joint + - left_shoulder_yaw_joint + - right_shoulder_yaw_joint + - left_ankle_pitch_joint + - right_ankle_pitch_joint + - left_elbow_pitch_joint + - right_elbow_pitch_joint + - left_ankle_roll_joint + - right_ankle_roll_joint + - left_elbow_roll_joint + - right_elbow_roll_joint + - left_five_joint + - left_three_joint + - left_zero_joint + - right_five_joint + - right_three_joint + - right_zero_joint + - left_six_joint + - left_four_joint + - left_one_joint + - right_six_joint + - right_four_joint + - right_one_joint + - left_two_joint + - right_two_joint +observations: + policy: + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Root linear velocity in the asset's root frame. + modifiers: null + units: m/s + full_path: isaaclab.envs.mdp.observations.base_lin_vel + mdp_type: Observation + name: base_lin_vel + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Root angular velocity in the asset's root frame. + modifiers: null + units: rad/s + full_path: isaaclab.envs.mdp.observations.base_ang_vel + mdp_type: Observation + name: base_ang_vel + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + axes: + - X + - Y + - Z + description: Gravity projection on the asset's root frame. + modifiers: null + units: m/s^2 + full_path: isaaclab.envs.mdp.observations.projected_gravity + mdp_type: Observation + name: projected_gravity + observation_type: RootState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + description: The generated command from command term in the command manager + with the given name. + modifiers: null + full_path: isaaclab.envs.mdp.observations.generated_commands + mdp_type: Observation + name: generated_commands + observation_type: Command + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 3 + - dtype: torch.float32 + extras: + description: 'The joint positions of the asset w.r.t. the default joint positions. + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have + their positions returned.' + modifiers: null + units: rad + full_path: isaaclab.envs.mdp.observations.joint_pos_rel + joint_names: + - left_hip_pitch_joint + - right_hip_pitch_joint + - torso_joint + - left_hip_roll_joint + - right_hip_roll_joint + - left_shoulder_pitch_joint + - right_shoulder_pitch_joint + - left_hip_yaw_joint + - right_hip_yaw_joint + - left_shoulder_roll_joint + - right_shoulder_roll_joint + - left_knee_joint + - right_knee_joint + - left_shoulder_yaw_joint + - right_shoulder_yaw_joint + - left_ankle_pitch_joint + - right_ankle_pitch_joint + - left_elbow_pitch_joint + - right_elbow_pitch_joint + - left_ankle_roll_joint + - right_ankle_roll_joint + - left_elbow_roll_joint + - right_elbow_roll_joint + - left_five_joint + - left_three_joint + - left_zero_joint + - right_five_joint + - right_three_joint + - right_zero_joint + - left_six_joint + - left_four_joint + - left_one_joint + - right_six_joint + - right_four_joint + - right_one_joint + - left_two_joint + - right_two_joint + joint_pos_offsets: + - -0.20000000298023224 + - -0.20000000298023224 + - 0.0 + - 0.0 + - 0.0 + - 0.3499999940395355 + - 0.3499999940395355 + - 0.0 + - 0.0 + - 0.1599999964237213 + - -0.1599999964237213 + - 0.41999998688697815 + - 0.41999998688697815 + - 0.0 + - 0.0 + - -0.23000000417232513 + - -0.23000000417232513 + - 0.8700000047683716 + - 0.8700000047683716 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 1.0 + - 0.0 + - 0.0 + - -1.0 + - 0.5199999809265137 + - -0.5199999809265137 + mdp_type: Observation + name: joint_pos_rel + observation_type: JointState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 37 + - dtype: torch.float32 + extras: + description: 'The joint velocities of the asset w.r.t. the default joint velocities. + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have + their velocities returned.' + modifiers: null + units: rad/s + full_path: isaaclab.envs.mdp.observations.joint_vel_rel + joint_names: + - left_hip_pitch_joint + - right_hip_pitch_joint + - torso_joint + - left_hip_roll_joint + - right_hip_roll_joint + - left_shoulder_pitch_joint + - right_shoulder_pitch_joint + - left_hip_yaw_joint + - right_hip_yaw_joint + - left_shoulder_roll_joint + - right_shoulder_roll_joint + - left_knee_joint + - right_knee_joint + - left_shoulder_yaw_joint + - right_shoulder_yaw_joint + - left_ankle_pitch_joint + - right_ankle_pitch_joint + - left_elbow_pitch_joint + - right_elbow_pitch_joint + - left_ankle_roll_joint + - right_ankle_roll_joint + - left_elbow_roll_joint + - right_elbow_roll_joint + - left_five_joint + - left_three_joint + - left_zero_joint + - right_five_joint + - right_three_joint + - right_zero_joint + - left_six_joint + - left_four_joint + - left_one_joint + - right_six_joint + - right_four_joint + - right_one_joint + - left_two_joint + - right_two_joint + joint_vel_offsets: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + mdp_type: Observation + name: joint_vel_rel + observation_type: JointState + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 37 + - dtype: torch.float32 + extras: + description: The last input action to the environment. The name of the action + term for which the action is required. If None, the entire action tensor is + returned. + modifiers: null + full_path: isaaclab.envs.mdp.observations.last_action + mdp_type: Observation + name: last_action + observation_type: Action + overloads: + clip: null + flatten_history_dim: true + history_length: 0 + scale: null + shape: + - 37 +scene: + decimation: 4 + dt: 0.02 + physics_dt: 0.005 diff --git a/docs/source/_static/policy_deployment/02_gear_assembly/gear_assembly_sim_real.webm b/docs/source/_static/policy_deployment/02_gear_assembly/gear_assembly_sim_real.webm new file mode 100644 index 0000000000000000000000000000000000000000..f9ae96c07e1b3090f36969550ae34b50c5d6e845 --- /dev/null +++ b/docs/source/_static/policy_deployment/02_gear_assembly/gear_assembly_sim_real.webm @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc563544ad5a1fa4033b761134c0d271ad08feb5bdbd6bb90ec415821517f044 +size 252667 diff --git a/docs/source/_static/policy_deployment/02_gear_assembly/sim_real_gear_assembly_train.jpg b/docs/source/_static/policy_deployment/02_gear_assembly/sim_real_gear_assembly_train.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d2760d571dcb6a0a78f6f1e39da541f362ea681c --- /dev/null +++ b/docs/source/_static/policy_deployment/02_gear_assembly/sim_real_gear_assembly_train.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26356af696ec0ebce8c5ff95b789e21594cd59f0afc341768f1e42504a8464b5 +size 124831 diff --git a/docs/source/_static/reference-architecture/deployment-dark.svg b/docs/source/_static/reference-architecture/deployment-dark.svg new file mode 100644 index 0000000000000000000000000000000000000000..ba8c739724a5627df9268174906e183658134d1f --- /dev/null +++ b/docs/source/_static/reference-architecture/deployment-dark.svg @@ -0,0 +1,3 @@ + + +
Robot Hardware
NVIDIA Isaac Perceptor
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Robot Assets (.usd, .urdf)
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 diff --git a/docs/source/_static/refs.bib b/docs/source/_static/refs.bib new file mode 100644 index 0000000000000000000000000000000000000000..cdb8577dff514cf9e0e54dc3b860ad9d4d5c71a6 --- /dev/null +++ b/docs/source/_static/refs.bib @@ -0,0 +1,185 @@ +@inproceedings{rudin2022learning, + title={Learning to walk in minutes using massively parallel deep reinforcement learning}, + author={Rudin, Nikita and Hoeller, David and Reist, Philipp and Hutter, Marco}, + booktitle={Conference on Robot Learning}, + pages={91--100}, + year={2022}, + organization={PMLR} +} + +@article{hwangbo2019learning, + title={Learning agile and dynamic motor skills for legged robots}, + author={Hwangbo, Jemin and Lee, Joonho and Dosovitskiy, Alexey and Bellicoso, Dario and Tsounis, Vassilios and Koltun, Vladlen and Hutter, Marco}, + journal={Science Robotics}, + volume={4}, + number={26}, + pages={eaau5872}, + year={2019}, + publisher={American Association for the Advancement of Science} +} + +@article{khatib1987osc, + author={Khatib, O.}, + journal={IEEE Journal on Robotics and Automation}, + title={A unified approach for motion and force control of robot manipulators: The operational space formulation}, + year={1987}, + volume={3}, + number={1}, + pages={43-53}, + doi={10.1109/JRA.1987.1087068} +} + +@book{siciliano2009force, + title={Force control}, + author={Siciliano, Bruno and Sciavicco, Lorenzo and Villani, Luigi and Oriolo, Giuseppe}, + year={2009}, + publisher={Springer} +} + +@article{cheng2021rmpflow, + author={Cheng, Ching-An and Mukadam, Mustafa and Issac, Jan and Birchfield, Stan and Fox, Dieter and Boots, Byron and Ratliff, Nathan}, + journal={IEEE Transactions on Automation Science and Engineering}, + title={RMPflow: A Geometric Framework for Generation of Multitask Motion Policies}, + year={2021}, + volume={18}, + number={3}, + pages={968-987}, + doi={10.1109/TASE.2021.3053422} +} + +@article{buss2004ik, + author = {Buss, Samuel}, + year = {2004}, + pages = {}, + title = {Introduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methods}, + volume = {17}, + journal={IEEE Transactions in Robotics and Automation}, +} + +@article{sucan2012ompl, + Author = {Ioan A. {\c{S}}ucan and Mark Moll and Lydia E. Kavraki}, + Doi = {10.1109/MRA.2012.2205651}, + Journal = {{IEEE} Robotics \& Automation Magazine}, + Month = {December}, + Number = {4}, + Pages = {72--82}, + Title = {The {O}pen {M}otion {P}lanning {L}ibrary}, + Note = {\url{https://ompl.kavrakilab.org}}, + Volume = {19}, + Year = {2012} +} + +@article{mittal2021articulated, + title={Articulated object interaction in unknown scenes with whole-body mobile manipulation}, + author={Mittal, Mayank and Hoeller, David and Farshidian, Farbod and Hutter, Marco and Garg, Animesh}, + journal={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + year={2022} +} + +@INPROCEEDINGS{rudin2022advanced, + author={Rudin, Nikita and Hoeller, David and Bjelonic, Marko and Hutter, Marco}, + booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + title={Advanced Skills by Learning Locomotion and Local Navigation End-to-End}, + year={2022}, + volume={}, + number={}, + pages={2497-2503}, + doi={10.1109/IROS47612.2022.9981198} +} + +@ARTICLE{frankhauser2018probabilistic, + author={Fankhauser, Péter and Bloesch, Michael and Hutter, Marco}, + journal={IEEE Robotics and Automation Letters}, + title={Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization}, + year={2018}, + volume={3}, + number={4}, + pages={3019-3026}, + doi={10.1109/LRA.2018.2849506} +} + +@article{makoviychuk2021isaac, + title={Isaac gym: High performance gpu-based physics simulation for robot learning}, + author={Makoviychuk, Viktor and Wawrzyniak, Lukasz and Guo, Yunrong and Lu, Michelle and Storey, Kier and Macklin, Miles and Hoeller, David and Rudin, Nikita and Allshire, Arthur and Handa, Ankur and State, Gavriel}, + journal={arXiv preprint arXiv:2108.10470}, + year={2021} +} + + +@article{handa2022dextreme, + title={DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality}, + author={Handa, Ankur and Allshire, Arthur and Makoviychuk, Viktor and Petrenko, Aleksei and Singh, Ritvik and Liu, Jingzhou and Makoviichuk, Denys and Van Wyk, Karl and Zhurkevich, Alexander and Sundaralingam, Balakumar and Narang, Yashraj and Lafleche, Jean-Francois and Fox, Dieter and State, Gavriel}, + journal={arXiv preprint arXiv:2210.13702}, + year={2022} +} + +@article{narang2022factory, + title={Factory: Fast contact for robotic assembly}, + author={Narang, Yashraj and Storey, Kier and Akinola, Iretiayo and Macklin, Miles and Reist, Philipp and Wawrzyniak, Lukasz and Guo, Yunrong and Moravanszky, Adam and State, Gavriel and Lu, Michelle and Handa, Ankur and Fox, Dieter}, + journal={arXiv preprint arXiv:2205.03532}, + year={2022} +} + +@inproceedings{allshire2022transferring, + title={Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger}, + author={Allshire, Arthur and Mittal, Mayank and Lodaya, Varun and Makoviychuk, Viktor and Makoviichuk, Denys and Widmaier, Felix and W{\"u}thrich, Manuel and Bauer, Stefan and Handa, Ankur and Garg, Animesh}, + booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + pages={11802--11809}, + year={2022}, + organization={IEEE} +} + +@article{mittal2025isaaclab, + title={Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning}, + author={Mayank Mittal and Pascal Roth and James Tigue and Antoine Richard and Octi Zhang and Peter Du and Antonio Serrano-Muñoz and Xinjie Yao and René Zurbrügg and Nikita Rudin and Lukasz Wawrzyniak and Milad Rakhsha and Alain Denzler and Eric Heiden and Ales Borovicka and Ossama Ahmed and Iretiayo Akinola and Abrar Anwar and Mark T. Carlson and Ji Yuan Feng and Animesh Garg and Renato Gasoto and Lionel Gulich and Yijie Guo and M. Gussert and Alex Hansen and Mihir Kulkarni and Chenran Li and Wei Liu and Viktor Makoviychuk and Grzegorz Malczyk and Hammad Mazhar and Masoud Moghani and Adithyavairavan Murali and Michael Noseworthy and Alexander Poddubny and Nathan Ratliff and Welf Rehberg and Clemens Schwarke and Ritvik Singh and James Latham Smith and Bingjie Tang and Ruchik Thaker and Matthew Trepte and Karl Van Wyk and Fangzhou Yu and Alex Millane and Vikram Ramasamy and Remo Steiner and Sangeeta Subramanian and Clemens Volk and CY Chen and Neel Jawale and Ashwin Varghese Kuruttukulam and Michael A. Lin and Ajay Mandlekar and Karsten Patzwaldt and John Welsh and Huihua Zhao and Fatima Anes and Jean-Francois Lafleche and Nicolas Moënne-Loccoz and Soowan Park and Rob Stepinski and Dirk Van Gelder and Chris Amevor and Jan Carius and Jumyung Chang and Anka He Chen and Pablo de Heras Ciechomski and Gilles Daviet and Mohammad Mohajerani and Julia von Muralt and Viktor Reutskyy and Michael Sauter and Simon Schirm and Eric L. Shi and Pierre Terdiman and Kenny Vilella and Tobias Widmer and Gordon Yeoman and Tiffany Chen and Sergey Grizan and Cathy Li and Lotus Li and Connor Smith and Rafael Wiltz and Kostas Alexis and Yan Chang and David Chu and Linxi "Jim" Fan and Farbod Farshidian and Ankur Handa and Spencer Huang and Marco Hutter and Yashraj Narang and Soha Pouya and Shiwei Sheng and Yuke Zhu and Miles Macklin and Adam Moravanszky and Philipp Reist and Yunrong Guo and David Hoeller and Gavriel State}, + journal={arXiv preprint arXiv:2511.04831}, + year={2025}, + url={https://arxiv.org/abs/2511.04831} +} + +@article{mittal2023orbit, + author={Mittal, Mayank and Yu, Calvin and Yu, Qinxi and Liu, Jingzhou and Rudin, Nikita and Hoeller, David and Yuan, Jia Lin and Singh, Ritvik and Guo, Yunrong and Mazhar, Hammad and Mandlekar, Ajay and Babich, Buck and State, Gavriel and Hutter, Marco and Garg, Animesh}, + journal={IEEE Robotics and Automation Letters}, + title={Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments}, + year={2023}, + volume={8}, + number={6}, + pages={3740-3747}, + doi={10.1109/LRA.2023.3270034} +} + +@article{shang2024theia, + title={Theia: Distilling diverse vision foundation models for robot learning}, + author={Shang, Jinghuan and Schmeckpeper, Karl and May, Brandon B and Minniti, Maria Vittoria and Kelestemur, Tarik and Watkins, David and Herlant, Laura}, + journal={arXiv preprint arXiv:2407.20179}, + year={2024} +} + +@inproceedings{he2016deep, + title={Deep residual learning for image recognition}, + author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={770--778}, + year={2016} +} + +@InProceedings{schwarke2023curiosity, + title = {Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks}, + author = {Schwarke, Clemens and Klemm, Victor and Boon, Matthijs van der and Bjelonic, Marko and Hutter, Marco}, + booktitle = {Proceedings of The 7th Conference on Robot Learning}, + pages = {2594--2610}, + year = {2023}, + volume = {229}, + series = {Proceedings of Machine Learning Research}, + publisher = {PMLR}, + url = {https://proceedings.mlr.press/v229/schwarke23a.html}, +} + +@InProceedings{mittal2024symmetry, + author={Mittal, Mayank and Rudin, Nikita and Klemm, Victor and Allshire, Arthur and Hutter, Marco}, + booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, + title={Symmetry Considerations for Learning Task Symmetric Robot Policies}, + year={2024}, + pages={7433-7439}, + doi={10.1109/ICRA57147.2024.10611493} +} diff --git a/docs/source/_static/setup/asset_caching.jpg b/docs/source/_static/setup/asset_caching.jpg new file mode 100644 index 0000000000000000000000000000000000000000..55992e7ea27e2180eb4eb4d43303e1fd6bcd577c --- /dev/null +++ b/docs/source/_static/setup/asset_caching.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:70b5d31518826c6e57c4182b20abbb88f0b247d596a616de4e5f7e5790f79b2c +size 983040 diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..6e0457d93e45e8f9d6849bafcc1bd274fb62dbae --- /dev/null +++ b/docs/source/api/index.rst @@ -0,0 +1,96 @@ +API Reference +============= + +This page gives an overview of all the modules and classes in the Isaac Lab extensions. + +isaaclab extension +------------------ + +The following modules are available in the ``isaaclab`` extension: + +.. currentmodule:: isaaclab + +.. autosummary:: + :toctree: lab + + app + actuators + assets + controllers + devices + envs + managers + markers + scene + sensors + sim + terrains + utils + +.. toctree:: + :hidden: + + lab/isaaclab.envs.mdp + lab/isaaclab.envs.ui + lab/isaaclab.sensors.patterns + lab/isaaclab.sim.converters + lab/isaaclab.sim.schemas + lab/isaaclab.sim.spawners + lab/isaaclab.sim.views + lab/isaaclab.sim.utils + + +isaaclab_rl extension +--------------------- + +The following wrappers are available in the ``isaaclab_rl`` extension: + +.. currentmodule:: isaaclab_rl + +.. toctree:: + :maxdepth: 2 + + lab_rl/isaaclab_rl + + +isaaclab_mimic extension +------------------------ + +The following modules are available in the ``isaaclab_mimic`` extension: + +.. currentmodule:: isaaclab_mimic + +.. autosummary:: + :toctree: lab_mimic + + datagen + envs + +isaaclab_contrib extension +----------------------------- + +The following modules are available in the ``isaaclab_contrib`` extension: + +.. currentmodule:: isaaclab_contrib + +.. autosummary:: + :toctree: lab_contrib + + actuators + assets + mdp + +isaaclab_tasks extension +------------------------ + +This package ``isaaclab_tasks`` contains the tasks that are available in the Isaac Lab. +For more information, please refer to the :ref:`environments`. + +It includes the following modules: + +.. currentmodule:: isaaclab_tasks + +.. autosummary:: + :toctree: lab_tasks + + utils diff --git a/docs/source/api/lab/isaaclab.actuators.rst b/docs/source/api/lab/isaaclab.actuators.rst new file mode 100644 index 0000000000000000000000000000000000000000..5ab005de5b3b3a08ea2377eae71157915f2d8b8f --- /dev/null +++ b/docs/source/api/lab/isaaclab.actuators.rst @@ -0,0 +1,135 @@ +isaaclab.actuators +================== + +.. automodule:: isaaclab.actuators + + .. rubric:: Classes + + .. autosummary:: + + ActuatorBase + ActuatorBaseCfg + ImplicitActuator + ImplicitActuatorCfg + IdealPDActuator + IdealPDActuatorCfg + DCMotor + DCMotorCfg + DelayedPDActuator + DelayedPDActuatorCfg + RemotizedPDActuator + RemotizedPDActuatorCfg + ActuatorNetMLP + ActuatorNetMLPCfg + ActuatorNetLSTM + ActuatorNetLSTMCfg + +Actuator Base +------------- + +.. autoclass:: ActuatorBase + :members: + :inherited-members: + +.. autoclass:: ActuatorBaseCfg + :members: + :inherited-members: + :exclude-members: __init__, class_type + +Implicit Actuator +----------------- + +.. autoclass:: ImplicitActuator + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ImplicitActuatorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Ideal PD Actuator +----------------- + +.. autoclass:: IdealPDActuator + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: IdealPDActuatorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +DC Motor Actuator +----------------- + +.. autoclass:: DCMotor + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DCMotorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Delayed PD Actuator +------------------- + +.. autoclass:: DelayedPDActuator + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DelayedPDActuatorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Remotized PD Actuator +--------------------- + +.. autoclass:: RemotizedPDActuator + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RemotizedPDActuatorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +MLP Network Actuator +--------------------- + +.. autoclass:: ActuatorNetMLP + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ActuatorNetMLPCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +LSTM Network Actuator +--------------------- + +.. autoclass:: ActuatorNetLSTM + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ActuatorNetLSTMCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type diff --git a/docs/source/api/lab/isaaclab.app.rst b/docs/source/api/lab/isaaclab.app.rst new file mode 100644 index 0000000000000000000000000000000000000000..b170fa8fa8ffa19078202f4607173afd1be24914 --- /dev/null +++ b/docs/source/api/lab/isaaclab.app.rst @@ -0,0 +1,115 @@ +isaaclab.app +============ + +.. automodule:: isaaclab.app + + .. rubric:: Classes + + .. autosummary:: + + AppLauncher + + +Environment variables +--------------------- + +The following details the behavior of the class based on the environment variables: + +* **Headless mode**: If the environment variable ``HEADLESS=1``, then SimulationApp will be started in headless mode. + If ``LIVESTREAM={1,2}``, then it will supersede the ``HEADLESS`` envvar and force headlessness. + + * ``HEADLESS=1`` causes the app to run in headless mode. + +* **Livestreaming**: If the environment variable ``LIVESTREAM={1,2}`` , then `livestream`_ is enabled. Any + of the livestream modes being true forces the app to run in headless mode. + + * ``LIVESTREAM=1`` enables streaming via the `WebRTC Livestream`_ extension over **public networks**. This allows users to + connect through the WebRTC Client using the WebRTC protocol. + * ``LIVESTREAM=2`` enables streaming via the `WebRTC Livestream`_ extension over **private and local networks**. This allows users to + connect through the WebRTC Client using the WebRTC protocol. + + .. note:: + + Each Isaac Sim instance can only connect to one streaming client. + Connecting to an Isaac Sim instance that is currently serving a streaming client + results in an error for the second user. + +* **Public IP Address**: When using the environment variable ``LIVESTREAM={1,2}``, set the ``PUBLIC_IP`` envvar to define the public IP address endpoint for livestreaming remotely. + +* **Enable cameras**: If the environment variable ``ENABLE_CAMERAS`` is set to 1, then the + cameras are enabled. This is useful for running the simulator without a GUI but still rendering the + viewport and camera images. + + * ``ENABLE_CAMERAS=1``: Enables the offscreen-render pipeline which allows users to render + the scene without launching a GUI. + + .. note:: + + The off-screen rendering pipeline only works when used in conjunction with the + :class:`isaaclab.sim.SimulationContext` class. This is because the off-screen rendering + pipeline enables flags that are internally used by the SimulationContext class. + + +To set the environment variables, one can use the following command in the terminal: + +.. code:: bash + + export LIVESTREAM=2 + export ENABLE_CAMERAS=1 + # run the python script + ./isaaclab.sh -p scripts/demos/quadrupeds.py + +Alternatively, one can set the environment variables to the python script directly: + +.. code:: bash + + LIVESTREAM=2 ENABLE_CAMERAS=1 ./isaaclab.sh -p scripts/demos/quadrupeds.py + + +Overriding the environment variables +------------------------------------ + +The environment variables can be overridden in the python script itself using the :class:`AppLauncher`. +These can be passed as a dictionary, a :class:`argparse.Namespace` object or as keyword arguments. +When the passed arguments are not the default values, then they override the environment variables. + +The following snippet shows how use the :class:`AppLauncher` in different ways: + +.. code:: python + + import argparser + + from isaaclab.app import AppLauncher + + # add argparse arguments + parser = argparse.ArgumentParser() + # add your own arguments + # .... + # add app launcher arguments for cli + AppLauncher.add_app_launcher_args(parser) + # parse arguments + args = parser.parse_args() + + # launch omniverse isaac-sim app + # -- Option 1: Pass the settings as a Namespace object + app_launcher = AppLauncher(args).app + # -- Option 2: Pass the settings as keywords arguments + app_launcher = AppLauncher(headless=args.headless, livestream=args.livestream) + # -- Option 3: Pass the settings as a dictionary + app_launcher = AppLauncher(vars(args)) + # -- Option 4: Pass no settings + app_launcher = AppLauncher() + + # obtain the launched app + simulation_app = app_launcher.app + + +Simulation App Launcher +----------------------- + +.. autoclass:: AppLauncher + :members: + + +.. _livestream: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/manual_livestream_clients.html +.. _`WebRTC Livestream`: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/manual_livestream_clients.html#isaac-sim-short-webrtc-streaming-client diff --git a/docs/source/api/lab/isaaclab.assets.rst b/docs/source/api/lab/isaaclab.assets.rst new file mode 100644 index 0000000000000000000000000000000000000000..c91066966e8043469bb1a54c7e1d02a46cee08f5 --- /dev/null +++ b/docs/source/api/lab/isaaclab.assets.rst @@ -0,0 +1,115 @@ +isaaclab.assets +=============== + +.. automodule:: isaaclab.assets + + .. rubric:: Classes + + .. autosummary:: + + AssetBase + AssetBaseCfg + RigidObject + RigidObjectData + RigidObjectCfg + RigidObjectCollection + RigidObjectCollectionData + RigidObjectCollectionCfg + Articulation + ArticulationData + ArticulationCfg + DeformableObject + DeformableObjectData + DeformableObjectCfg + +.. currentmodule:: isaaclab.assets + +Asset Base +---------- + +.. autoclass:: AssetBase + :members: + +.. autoclass:: AssetBaseCfg + :members: + :exclude-members: __init__, class_type, InitialStateCfg + +Rigid Object +------------ + +.. autoclass:: RigidObject + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RigidObjectData + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: RigidObjectCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Rigid Object Collection +----------------------- + +.. autoclass:: RigidObjectCollection + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RigidObjectCollectionData + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: RigidObjectCollectionCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Articulation +------------ + +.. autoclass:: Articulation + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ArticulationData + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: ArticulationCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Deformable Object +----------------- + +.. autoclass:: DeformableObject + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DeformableObjectData + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: DeformableObjectCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type diff --git a/docs/source/api/lab/isaaclab.controllers.rst b/docs/source/api/lab/isaaclab.controllers.rst new file mode 100644 index 0000000000000000000000000000000000000000..1ef31448ab86ef44cbe57414a3606b77d96b5117 --- /dev/null +++ b/docs/source/api/lab/isaaclab.controllers.rst @@ -0,0 +1,66 @@ +isaaclab.controllers +==================== + +.. automodule:: isaaclab.controllers + + .. rubric:: Classes + + .. autosummary:: + + DifferentialIKController + DifferentialIKControllerCfg + OperationalSpaceController + OperationalSpaceControllerCfg + pink_ik.PinkIKController + pink_ik.PinkIKControllerCfg + pink_ik.NullSpacePostureTask + +Differential Inverse Kinematics +------------------------------- + +.. autoclass:: DifferentialIKController + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DifferentialIKControllerCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Operational Space controllers +----------------------------- + +.. autoclass:: OperationalSpaceController + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: OperationalSpaceControllerCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + + +Pink IK Controller +------------------ + +.. automodule:: isaaclab.controllers.pink_ik + +.. autoclass:: PinkIKController + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: PinkIKControllerCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Available Pink IK Tasks +^^^^^^^^^^^^^^^^^^^^^^^ + +.. autoclass:: NullSpacePostureTask diff --git a/docs/source/api/lab/isaaclab.devices.rst b/docs/source/api/lab/isaaclab.devices.rst new file mode 100644 index 0000000000000000000000000000000000000000..5f04c4733cf6db63fd248c7645d253ce6796ff65 --- /dev/null +++ b/docs/source/api/lab/isaaclab.devices.rst @@ -0,0 +1,141 @@ +isaaclab.devices +================ + +.. automodule:: isaaclab.devices + + .. rubric:: Classes + + .. autosummary:: + + DeviceBase + RetargeterBase + Se2Gamepad + Se3Gamepad + Se2Keyboard + Se3Keyboard + Se2SpaceMouse + Se3SpaceMouse + HaplyDevice + OpenXRDevice + ManusVive + isaaclab.devices.openxr.retargeters.GripperRetargeter + isaaclab.devices.openxr.retargeters.Se3AbsRetargeter + isaaclab.devices.openxr.retargeters.Se3RelRetargeter + isaaclab.devices.openxr.retargeters.GR1T2Retargeter + + .. rubric:: Modules + + .. autosummary:: + + isaaclab.devices.openxr.retargeters + +Device Base +----------- + +.. autoclass:: DeviceBase + :members: + +Retargeter Base +--------------- + +.. autoclass:: RetargeterBase + :members: + +Game Pad +-------- + +.. autoclass:: Se2Gamepad + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: Se3Gamepad + :members: + :inherited-members: + :show-inheritance: + :noindex: + +Keyboard +-------- + +.. autoclass:: Se2Keyboard + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: Se3Keyboard + :members: + :inherited-members: + :show-inheritance: + :noindex: + +Space Mouse +----------- + +.. autoclass:: Se2SpaceMouse + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: Se3SpaceMouse + :members: + :inherited-members: + :show-inheritance: + :noindex: + +Haply +----- + +.. autoclass:: HaplyDevice + :members: + :inherited-members: + :show-inheritance: + :noindex: + +OpenXR +------ + +.. autoclass:: OpenXRDevice + :members: + :inherited-members: + :show-inheritance: + :noindex: + +Manus + Vive +------------ + +.. autoclass:: ManusVive + :members: + :inherited-members: + :show-inheritance: + :noindex: + +Retargeters +----------- + +.. autoclass:: isaaclab.devices.openxr.retargeters.GripperRetargeter + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: isaaclab.devices.openxr.retargeters.Se3AbsRetargeter + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: isaaclab.devices.openxr.retargeters.Se3RelRetargeter + :members: + :inherited-members: + :show-inheritance: + :noindex: + +.. autoclass:: isaaclab.devices.openxr.retargeters.GR1T2Retargeter + :members: + :inherited-members: + :show-inheritance: + :noindex: diff --git a/docs/source/api/lab/isaaclab.envs.mdp.rst b/docs/source/api/lab/isaaclab.envs.mdp.rst new file mode 100644 index 0000000000000000000000000000000000000000..bffa5279b125f358caa8df8340cab509fbe0c5a5 --- /dev/null +++ b/docs/source/api/lab/isaaclab.envs.mdp.rst @@ -0,0 +1,54 @@ +isaaclab.envs.mdp +================= + +.. automodule:: isaaclab.envs.mdp + +Observations +------------ + +.. automodule:: isaaclab.envs.mdp.observations + :members: + +Actions +------- + +.. automodule:: isaaclab.envs.mdp.actions + +.. automodule:: isaaclab.envs.mdp.actions.actions_cfg + :members: + :show-inheritance: + :exclude-members: __init__, class_type + +Events +------ + +.. automodule:: isaaclab.envs.mdp.events + :members: + +Commands +-------- + +.. automodule:: isaaclab.envs.mdp.commands + +.. automodule:: isaaclab.envs.mdp.commands.commands_cfg + :members: + :show-inheritance: + :exclude-members: __init__, class_type + +Rewards +------- + +.. automodule:: isaaclab.envs.mdp.rewards + :members: + +Terminations +------------ + +.. automodule:: isaaclab.envs.mdp.terminations + :members: + +Curriculum +---------- + +.. automodule:: isaaclab.envs.mdp.curriculums + :members: diff --git a/docs/source/api/lab/isaaclab.envs.rst b/docs/source/api/lab/isaaclab.envs.rst new file mode 100644 index 0000000000000000000000000000000000000000..51b6e1866888d1cce3db2ca1af0f039fe7a96105 --- /dev/null +++ b/docs/source/api/lab/isaaclab.envs.rst @@ -0,0 +1,114 @@ +isaaclab.envs +============= + +.. automodule:: isaaclab.envs + + .. rubric:: Submodules + + .. autosummary:: + + mdp + ui + + .. rubric:: Classes + + .. autosummary:: + + ManagerBasedEnv + ManagerBasedEnvCfg + ManagerBasedRLEnv + ManagerBasedRLEnvCfg + DirectRLEnv + DirectRLEnvCfg + DirectMARLEnv + DirectMARLEnvCfg + ManagerBasedRLMimicEnv + MimicEnvCfg + SubTaskConfig + SubTaskConstraintConfig + ViewerCfg + +Manager Based Environment +------------------------- + +.. autoclass:: ManagerBasedEnv + :members: + +.. autoclass:: ManagerBasedEnvCfg + :members: + :exclude-members: __init__, class_type + +Manager Based RL Environment +---------------------------- + +.. autoclass:: ManagerBasedRLEnv + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ManagerBasedRLEnvCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Direct RL Environment +--------------------- + +.. autoclass:: DirectRLEnv + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DirectRLEnvCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Direct Multi-Agent RL Environment +--------------------------------- + +.. autoclass:: DirectMARLEnv + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: DirectMARLEnvCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Mimic Environment +----------------- + +.. autoclass:: ManagerBasedRLMimicEnv + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +.. autoclass:: SubTaskConfig + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +.. autoclass:: SubTaskConstraintConfig + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Common +------ + +.. autoclass:: ViewerCfg + :members: + :exclude-members: __init__ diff --git a/docs/source/api/lab/isaaclab.envs.ui.rst b/docs/source/api/lab/isaaclab.envs.ui.rst new file mode 100644 index 0000000000000000000000000000000000000000..0e48034c7feefb024f95bbd2dc23148e12e9d2a6 --- /dev/null +++ b/docs/source/api/lab/isaaclab.envs.ui.rst @@ -0,0 +1,31 @@ +isaaclab.envs.ui +================ + +.. automodule:: isaaclab.envs.ui + + .. rubric:: Classes + + .. autosummary:: + + BaseEnvWindow + ManagerBasedRLEnvWindow + ViewportCameraController + +Base Environment UI +------------------- + +.. autoclass:: BaseEnvWindow + :members: + +Config Based RL Environment UI +------------------------------ + +.. autoclass:: ManagerBasedRLEnvWindow + :members: + :show-inheritance: + +Viewport Camera Controller +-------------------------- + +.. autoclass:: ViewportCameraController + :members: diff --git a/docs/source/api/lab/isaaclab.managers.rst b/docs/source/api/lab/isaaclab.managers.rst new file mode 100644 index 0000000000000000000000000000000000000000..184e976e32a0878fee50acadc85b0fab0b268e8c --- /dev/null +++ b/docs/source/api/lab/isaaclab.managers.rst @@ -0,0 +1,160 @@ +isaaclab.managers +================= + +.. automodule:: isaaclab.managers + + .. rubric:: Classes + + .. autosummary:: + + SceneEntityCfg + ManagerBase + ManagerTermBase + ManagerTermBaseCfg + ObservationManager + ObservationGroupCfg + ObservationTermCfg + ActionManager + ActionTerm + ActionTermCfg + EventManager + EventTermCfg + CommandManager + CommandTerm + CommandTermCfg + RewardManager + RewardTermCfg + TerminationManager + TerminationTermCfg + CurriculumManager + CurriculumTermCfg + RecorderManager + RecorderTermCfg + +Scene Entity +------------ + +.. autoclass:: SceneEntityCfg + :members: + :exclude-members: __init__ + +Manager Base +------------ + +.. autoclass:: ManagerBase + :members: + +.. autoclass:: ManagerTermBase + :members: + +.. autoclass:: ManagerTermBaseCfg + :members: + :exclude-members: __init__ + +Observation Manager +------------------- + +.. autoclass:: ObservationManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ObservationGroupCfg + :members: + :exclude-members: __init__ + +.. autoclass:: ObservationTermCfg + :members: + :exclude-members: __init__ + +Action Manager +-------------- + +.. autoclass:: ActionManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ActionTerm + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ActionTermCfg + :members: + :exclude-members: __init__ + +Event Manager +------------- + +.. autoclass:: EventManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: EventTermCfg + :members: + :exclude-members: __init__ + + +Command Manager +--------------- + +.. autoclass:: CommandManager + :members: + +.. autoclass:: CommandTerm + :members: + :exclude-members: __init__, class_type + +.. autoclass:: CommandTermCfg + :members: + :exclude-members: __init__, class_type + + +Reward Manager +-------------- + +.. autoclass:: RewardManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RewardTermCfg + :exclude-members: __init__ + +Termination Manager +------------------- + +.. autoclass:: TerminationManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: TerminationTermCfg + :members: + :exclude-members: __init__ + +Curriculum Manager +------------------ + +.. autoclass:: CurriculumManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: CurriculumTermCfg + :members: + :exclude-members: __init__ + +Recorder Manager +---------------- + +.. autoclass:: RecorderManager + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RecorderTermCfg + :members: + :exclude-members: __init__ diff --git a/docs/source/api/lab/isaaclab.markers.rst b/docs/source/api/lab/isaaclab.markers.rst new file mode 100644 index 0000000000000000000000000000000000000000..9382de11840aa9ca84e8e2be3aff467a7deb7e3f --- /dev/null +++ b/docs/source/api/lab/isaaclab.markers.rst @@ -0,0 +1,23 @@ +isaaclab.markers +================ + +.. automodule:: isaaclab.markers + + .. rubric:: Classes + + .. autosummary:: + + VisualizationMarkers + VisualizationMarkersCfg + +Visualization Markers +--------------------- + +.. autoclass:: VisualizationMarkers + :members: + :undoc-members: + :show-inheritance: + +.. autoclass:: VisualizationMarkersCfg + :members: + :exclude-members: __init__ diff --git a/docs/source/api/lab/isaaclab.scene.rst b/docs/source/api/lab/isaaclab.scene.rst new file mode 100644 index 0000000000000000000000000000000000000000..86866dfdadb32c0af057c033c02b7a05b4b7f8de --- /dev/null +++ b/docs/source/api/lab/isaaclab.scene.rst @@ -0,0 +1,23 @@ +isaaclab.scene +============== + +.. automodule:: isaaclab.scene + + .. rubric:: Classes + + .. autosummary:: + + InteractiveScene + InteractiveSceneCfg + +interactive Scene +----------------- + +.. autoclass:: InteractiveScene + :members: + :undoc-members: + :show-inheritance: + +.. autoclass:: InteractiveSceneCfg + :members: + :exclude-members: __init__ diff --git a/docs/source/api/lab/isaaclab.sensors.patterns.rst b/docs/source/api/lab/isaaclab.sensors.patterns.rst new file mode 100644 index 0000000000000000000000000000000000000000..ac5a44808beeb7c753b3aca17b594fbd8046c40f --- /dev/null +++ b/docs/source/api/lab/isaaclab.sensors.patterns.rst @@ -0,0 +1,61 @@ +isaaclab.sensors.patterns +========================= + +.. automodule:: isaaclab.sensors.patterns + + .. rubric:: Classes + + .. autosummary:: + + PatternBaseCfg + GridPatternCfg + PinholeCameraPatternCfg + BpearlPatternCfg + +Pattern Base +------------ + +.. autoclass:: PatternBaseCfg + :members: + :inherited-members: + :exclude-members: __init__ + +Grid Pattern +------------ + +.. autofunction:: isaaclab.sensors.patterns.grid_pattern + +.. autoclass:: GridPatternCfg + :members: + :inherited-members: + :exclude-members: __init__, func + +Pinhole Camera Pattern +---------------------- + +.. autofunction:: isaaclab.sensors.patterns.pinhole_camera_pattern + +.. autoclass:: PinholeCameraPatternCfg + :members: + :inherited-members: + :exclude-members: __init__, func + +RS-Bpearl Pattern +----------------- + +.. autofunction:: isaaclab.sensors.patterns.bpearl_pattern + +.. autoclass:: BpearlPatternCfg + :members: + :inherited-members: + :exclude-members: __init__, func + +LiDAR Pattern +------------- + +.. autofunction:: isaaclab.sensors.patterns.lidar_pattern + +.. autoclass:: LidarPatternCfg + :members: + :inherited-members: + :exclude-members: __init__, func diff --git a/docs/source/api/lab/isaaclab.sensors.rst b/docs/source/api/lab/isaaclab.sensors.rst new file mode 100644 index 0000000000000000000000000000000000000000..90588d48216b2c7ef2a0db6aad24efce8fce5f47 --- /dev/null +++ b/docs/source/api/lab/isaaclab.sensors.rst @@ -0,0 +1,228 @@ +isaaclab.sensors +================ + +.. automodule:: isaaclab.sensors + + .. rubric:: Submodules + + .. autosummary:: + + patterns + + .. rubric:: Classes + + .. autosummary:: + + SensorBase + SensorBaseCfg + Camera + CameraData + CameraCfg + TiledCamera + TiledCameraCfg + ContactSensor + ContactSensorData + ContactSensorCfg + FrameTransformer + FrameTransformerData + FrameTransformerCfg + RayCaster + RayCasterData + RayCasterCfg + RayCasterCamera + RayCasterCameraCfg + MultiMeshRayCaster + MultiMeshRayCasterData + MultiMeshRayCasterCfg + MultiMeshRayCasterCamera + MultiMeshRayCasterCameraCfg + Imu + ImuCfg + VisuoTactileSensor + VisuoTactileSensorCfg + VisuoTactileSensorData + +Sensor Base +----------- + +.. autoclass:: SensorBase + :members: + +.. autoclass:: SensorBaseCfg + :members: + :exclude-members: __init__, class_type + +USD Camera +---------- + +.. autoclass:: Camera + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: CameraData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: CameraCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type, OffsetCfg + +Tile-Rendered USD Camera +------------------------ + +.. autoclass:: TiledCamera + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: TiledCameraCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Contact Sensor +-------------- + +.. autoclass:: ContactSensor + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ContactSensorData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: ContactSensorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Frame Transformer +----------------- + +.. autoclass:: FrameTransformer + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: FrameTransformerData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: FrameTransformerCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +.. autoclass:: OffsetCfg + :members: + :inherited-members: + :exclude-members: __init__ + +Ray-Cast Sensor +--------------- + +.. autoclass:: RayCaster + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RayCasterData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: RayCasterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Ray-Cast Camera +--------------- + +.. autoclass:: RayCasterCamera + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: RayCasterCameraCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type, OffsetCfg + +Multi-Mesh Ray-Cast Sensor +-------------------------- + +.. autoclass:: MultiMeshRayCaster + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MultiMeshRayCasterData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: MultiMeshRayCasterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type, OffsetCfg + +Multi-Mesh Ray-Cast Camera +-------------------------- + +.. autoclass:: MultiMeshRayCasterCamera + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MultiMeshRayCasterCameraCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type, OffsetCfg, RaycastTargetCfg + +Inertia Measurement Unit +------------------------ + +.. autoclass:: Imu + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ImuCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Visuo-Tactile Sensor +-------------------- + +.. autoclass:: VisuoTactileSensor + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: VisuoTactileSensorData + :members: + :inherited-members: + :exclude-members: __init__ + +.. autoclass:: VisuoTactileSensorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type diff --git a/docs/source/api/lab/isaaclab.sim.converters.rst b/docs/source/api/lab/isaaclab.sim.converters.rst new file mode 100644 index 0000000000000000000000000000000000000000..6fd5155c4e534ceb917db858abe0488a1e0c38a9 --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.converters.rst @@ -0,0 +1,70 @@ +isaaclab.sim.converters +======================= + +.. automodule:: isaaclab.sim.converters + + .. rubric:: Classes + + .. autosummary:: + + AssetConverterBase + AssetConverterBaseCfg + MeshConverter + MeshConverterCfg + UrdfConverter + UrdfConverterCfg + MjcfConverter + MjcfConverterCfg + +Asset Converter Base +-------------------- + +.. autoclass:: AssetConverterBase + :members: + +.. autoclass:: AssetConverterBaseCfg + :members: + :exclude-members: __init__ + +Mesh Converter +-------------- + +.. autoclass:: MeshConverter + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MeshConverterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + + +URDF Converter +-------------- + +.. autoclass:: UrdfConverter + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: UrdfConverterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ + +MJCF Converter +-------------- + +.. autoclass:: MjcfConverter + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MjcfConverterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__ diff --git a/docs/source/api/lab/isaaclab.sim.rst b/docs/source/api/lab/isaaclab.sim.rst new file mode 100644 index 0000000000000000000000000000000000000000..b6b5c372bc1787959f72f5a7076f5f40c7180873 --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.rst @@ -0,0 +1,58 @@ +isaaclab.sim +============ + +.. automodule:: isaaclab.sim + + .. rubric:: Submodules + + .. autosummary:: + + converters + schemas + spawners + utils + + .. rubric:: Classes + + .. autosummary:: + + SimulationContext + SimulationCfg + PhysxCfg + RenderCfg + + .. rubric:: Functions + + .. autosummary:: + + simulation_context.build_simulation_context + +Simulation Context +------------------ + +.. autoclass:: SimulationContext + :members: + :show-inheritance: + +Simulation Configuration +------------------------ + +.. autoclass:: SimulationCfg + :members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: PhysxCfg + :members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: RenderCfg + :members: + :show-inheritance: + :exclude-members: __init__ + +Simulation Context Builder +-------------------------- + +.. automethod:: simulation_context.build_simulation_context diff --git a/docs/source/api/lab/isaaclab.sim.schemas.rst b/docs/source/api/lab/isaaclab.sim.schemas.rst new file mode 100644 index 0000000000000000000000000000000000000000..77ac6512dbcecbd021a77da4139113ee1d38535f --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.schemas.rst @@ -0,0 +1,103 @@ +isaaclab.sim.schemas +==================== + +.. automodule:: isaaclab.sim.schemas + + .. rubric:: Classes + + .. autosummary:: + + ArticulationRootPropertiesCfg + RigidBodyPropertiesCfg + CollisionPropertiesCfg + MassPropertiesCfg + JointDrivePropertiesCfg + FixedTendonPropertiesCfg + DeformableBodyPropertiesCfg + + .. rubric:: Functions + + .. autosummary:: + + define_articulation_root_properties + modify_articulation_root_properties + define_rigid_body_properties + modify_rigid_body_properties + activate_contact_sensors + define_collision_properties + modify_collision_properties + define_mass_properties + modify_mass_properties + modify_joint_drive_properties + modify_fixed_tendon_properties + define_deformable_body_properties + modify_deformable_body_properties + +Articulation Root +----------------- + +.. autoclass:: ArticulationRootPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: define_articulation_root_properties +.. autofunction:: modify_articulation_root_properties + +Rigid Body +---------- + +.. autoclass:: RigidBodyPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: define_rigid_body_properties +.. autofunction:: modify_rigid_body_properties +.. autofunction:: activate_contact_sensors + +Collision +--------- + +.. autoclass:: CollisionPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: define_collision_properties +.. autofunction:: modify_collision_properties + +Mass +---- + +.. autoclass:: MassPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: define_mass_properties +.. autofunction:: modify_mass_properties + +Joint Drive +----------- + +.. autoclass:: JointDrivePropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: modify_joint_drive_properties + +Fixed Tendon +------------ + +.. autoclass:: FixedTendonPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: modify_fixed_tendon_properties + +Deformable Body +--------------- + +.. autoclass:: DeformableBodyPropertiesCfg + :members: + :exclude-members: __init__ + +.. autofunction:: define_deformable_body_properties +.. autofunction:: modify_deformable_body_properties diff --git a/docs/source/api/lab/isaaclab.sim.spawners.rst b/docs/source/api/lab/isaaclab.sim.spawners.rst new file mode 100644 index 0000000000000000000000000000000000000000..701efda84e187c67deed8e199029976968d69412 --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.spawners.rst @@ -0,0 +1,329 @@ +isaaclab.sim.spawners +===================== + +.. automodule:: isaaclab.sim.spawners + + .. rubric:: Submodules + + .. autosummary:: + + shapes + meshes + lights + sensors + from_files + materials + wrappers + + .. rubric:: Classes + + .. autosummary:: + + SpawnerCfg + RigidObjectSpawnerCfg + DeformableObjectSpawnerCfg + +Spawners +-------- + +.. autoclass:: SpawnerCfg + :members: + :exclude-members: __init__ + +.. autoclass:: RigidObjectSpawnerCfg + :members: + :show-inheritance: + :exclude-members: __init__ + +.. autoclass:: DeformableObjectSpawnerCfg + :members: + :show-inheritance: + :exclude-members: __init__ + +Shapes +------ + +.. automodule:: isaaclab.sim.spawners.shapes + + .. rubric:: Classes + + .. autosummary:: + + ShapeCfg + CapsuleCfg + ConeCfg + CuboidCfg + CylinderCfg + SphereCfg + +.. autoclass:: ShapeCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_capsule + +.. autoclass:: CapsuleCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_cone + +.. autoclass:: ConeCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_cuboid + +.. autoclass:: CuboidCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_cylinder + +.. autoclass:: CylinderCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_sphere + +.. autoclass:: SphereCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +Meshes +------ + +.. automodule:: isaaclab.sim.spawners.meshes + + .. rubric:: Classes + + .. autosummary:: + + MeshCfg + MeshCapsuleCfg + MeshConeCfg + MeshCuboidCfg + MeshCylinderCfg + MeshSphereCfg + +.. autoclass:: MeshCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_mesh_capsule + +.. autoclass:: MeshCapsuleCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_mesh_cone + +.. autoclass:: MeshConeCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_mesh_cuboid + +.. autoclass:: MeshCuboidCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_mesh_cylinder + +.. autoclass:: MeshCylinderCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +.. autofunction:: spawn_mesh_sphere + +.. autoclass:: MeshSphereCfg + :members: + :show-inheritance: + :exclude-members: __init__, func + +Lights +------ + +.. automodule:: isaaclab.sim.spawners.lights + + .. rubric:: Classes + + .. autosummary:: + + LightCfg + CylinderLightCfg + DiskLightCfg + DistantLightCfg + DomeLightCfg + SphereLightCfg + +.. autofunction:: spawn_light + +.. autoclass:: LightCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: CylinderLightCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: DiskLightCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: DistantLightCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: DomeLightCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: SphereLightCfg + :members: + :exclude-members: __init__, func + +Sensors +------- + +.. automodule:: isaaclab.sim.spawners.sensors + + .. rubric:: Classes + + .. autosummary:: + + PinholeCameraCfg + FisheyeCameraCfg + +.. autofunction:: spawn_camera + +.. autoclass:: PinholeCameraCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: FisheyeCameraCfg + :members: + :exclude-members: __init__, func + +From Files +---------- + +.. automodule:: isaaclab.sim.spawners.from_files + + .. rubric:: Classes + + .. autosummary:: + + UrdfFileCfg + UsdFileCfg + GroundPlaneCfg + +.. autofunction:: spawn_from_urdf + +.. autoclass:: UrdfFileCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_from_usd + +.. autoclass:: UsdFileCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_ground_plane + +.. autoclass:: GroundPlaneCfg + :members: + :exclude-members: __init__, func + +Materials +--------- + +.. automodule:: isaaclab.sim.spawners.materials + + .. rubric:: Classes + + .. autosummary:: + + VisualMaterialCfg + PreviewSurfaceCfg + MdlFileCfg + GlassMdlCfg + PhysicsMaterialCfg + RigidBodyMaterialCfg + DeformableBodyMaterialCfg + +Visual Materials +~~~~~~~~~~~~~~~~ + +.. autoclass:: VisualMaterialCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_preview_surface + +.. autoclass:: PreviewSurfaceCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_from_mdl_file + +.. autoclass:: MdlFileCfg + :members: + :exclude-members: __init__, func + +.. autoclass:: GlassMdlCfg + :members: + :exclude-members: __init__, func + +Physical Materials +~~~~~~~~~~~~~~~~~~ + +.. autoclass:: PhysicsMaterialCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_rigid_body_material + +.. autoclass:: RigidBodyMaterialCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_deformable_body_material + +.. autoclass:: DeformableBodyMaterialCfg + :members: + :exclude-members: __init__, func + +Wrappers +-------- + +.. automodule:: isaaclab.sim.spawners.wrappers + + .. rubric:: Classes + + .. autosummary:: + + MultiAssetSpawnerCfg + MultiUsdFileCfg + +.. autofunction:: spawn_multi_asset + +.. autoclass:: MultiAssetSpawnerCfg + :members: + :exclude-members: __init__, func + +.. autofunction:: spawn_multi_usd_file + +.. autoclass:: MultiUsdFileCfg + :members: + :exclude-members: __init__, func diff --git a/docs/source/api/lab/isaaclab.sim.utils.rst b/docs/source/api/lab/isaaclab.sim.utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..f27e574efb9a77ffe59730776205c327b92fb111 --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.utils.rst @@ -0,0 +1,57 @@ +isaaclab.sim.utils +================== + +.. automodule:: isaaclab.sim.utils + + .. rubric:: Submodules + + .. autosummary:: + + stage + queries + prims + transforms + semantics + legacy + +Stage +----- + +.. automodule:: isaaclab.sim.utils.stage + :members: + :show-inheritance: + +Queries +------- + +.. automodule:: isaaclab.sim.utils.queries + :members: + :show-inheritance: + +Prims +----- + +.. automodule:: isaaclab.sim.utils.prims + :members: + :show-inheritance: + +Transforms +---------- + +.. automodule:: isaaclab.sim.utils.transforms + :members: + :show-inheritance: + +Semantics +--------- + +.. automodule:: isaaclab.sim.utils.semantics + :members: + :show-inheritance: + +Legacy +------ + +.. automodule:: isaaclab.sim.utils.legacy + :members: + :show-inheritance: diff --git a/docs/source/api/lab/isaaclab.sim.views.rst b/docs/source/api/lab/isaaclab.sim.views.rst new file mode 100644 index 0000000000000000000000000000000000000000..3a5f9bdecfe9cbfc12a1be73f5c6ef23a556b68d --- /dev/null +++ b/docs/source/api/lab/isaaclab.sim.views.rst @@ -0,0 +1,17 @@ +isaaclab.sim.views +================== + +.. automodule:: isaaclab.sim.views + + .. rubric:: Classes + + .. autosummary:: + + XformPrimView + +XForm Prim View +--------------- + +.. autoclass:: XformPrimView + :members: + :show-inheritance: diff --git a/docs/source/api/lab/isaaclab.terrains.rst b/docs/source/api/lab/isaaclab.terrains.rst new file mode 100644 index 0000000000000000000000000000000000000000..8e0e80cb4543e9fb814e83d66efb8323ecf7c517 --- /dev/null +++ b/docs/source/api/lab/isaaclab.terrains.rst @@ -0,0 +1,261 @@ +isaaclab.terrains +================= + +.. automodule:: isaaclab.terrains + + .. rubric:: Classes + + .. autosummary:: + + TerrainImporter + TerrainImporterCfg + TerrainGenerator + TerrainGeneratorCfg + SubTerrainBaseCfg + + +Terrain importer +---------------- + +.. autoclass:: TerrainImporter + :members: + :show-inheritance: + +.. autoclass:: TerrainImporterCfg + :members: + :exclude-members: __init__, class_type + +Terrain generator +----------------- + +.. autoclass:: TerrainGenerator + :members: + +.. autoclass:: TerrainGeneratorCfg + :members: + :exclude-members: __init__ + +.. autoclass:: SubTerrainBaseCfg + :members: + :exclude-members: __init__ + +Height fields +------------- + +.. automodule:: isaaclab.terrains.height_field + +All sub-terrains must inherit from the :class:`HfTerrainBaseCfg` class which contains the common +parameters for all terrains generated from height fields. + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfTerrainBaseCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Random Uniform Terrain +^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.random_uniform_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfRandomUniformTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Pyramid Sloped Terrain +^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.pyramid_sloped_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfPyramidSlopedTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfInvertedPyramidSlopedTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Pyramid Stairs Terrain +^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.pyramid_stairs_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfPyramidStairsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfInvertedPyramidStairsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Discrete Obstacles Terrain +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.discrete_obstacles_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfDiscreteObstaclesTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Wave Terrain +^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.wave_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfWaveTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Stepping Stones Terrain +^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.height_field.hf_terrains.stepping_stones_terrain + +.. autoclass:: isaaclab.terrains.height_field.hf_terrains_cfg.HfSteppingStonesTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Trimesh terrains +---------------- + +.. automodule:: isaaclab.terrains.trimesh + + +Flat terrain +^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.flat_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshPlaneTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Pyramid terrain +^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.pyramid_stairs_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshPyramidStairsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Inverted pyramid terrain +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.inverted_pyramid_stairs_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshInvertedPyramidStairsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Random grid terrain +^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.random_grid_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRandomGridTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Rails terrain +^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.rails_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRailsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Pit terrain +^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.pit_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshPitTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Box terrain +^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.box_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshBoxTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Gap terrain +^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.gap_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshGapTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Floating ring terrain +^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.floating_ring_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshFloatingRingTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Star terrain +^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.star_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshStarTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Repeated Objects Terrain +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: isaaclab.terrains.trimesh.mesh_terrains.repeated_objects_terrain + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRepeatedObjectsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRepeatedPyramidsTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRepeatedBoxesTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +.. autoclass:: isaaclab.terrains.trimesh.mesh_terrains_cfg.MeshRepeatedCylindersTerrainCfg + :members: + :show-inheritance: + :exclude-members: __init__, function + +Utilities +--------- + +.. automodule:: isaaclab.terrains.utils + :members: + :undoc-members: diff --git a/docs/source/api/lab/isaaclab.utils.rst b/docs/source/api/lab/isaaclab.utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..5b352152e0b542e22d7a5bd8e2d87bbd5e884bce --- /dev/null +++ b/docs/source/api/lab/isaaclab.utils.rst @@ -0,0 +1,190 @@ +isaaclab.utils +============== + +.. automodule:: isaaclab.utils + + .. Rubric:: Submodules + + .. autosummary:: + + io + array + assets + buffers + datasets + dict + interpolation + logger + math + mesh + modifiers + noise + seed + sensors + string + timer + types + version + warp + + .. Rubric:: Functions + + .. autosummary:: + + configclass + +Configuration class +~~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.configclass + :members: + :show-inheritance: + +IO operations +~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.io + :members: + :imported-members: + :show-inheritance: + +Array operations +~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.array + :members: + :show-inheritance: + +Asset operations +~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.assets + :members: + :show-inheritance: + +Buffer operations +~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.buffers + :members: + :imported-members: + :inherited-members: + :show-inheritance: + +Datasets operations +~~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.datasets + :members: + :show-inheritance: + :exclude-members: __init__, func + +Dictionary operations +~~~~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.dict + :members: + :show-inheritance: + +Interpolation operations +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.interpolation + :members: + :imported-members: + :inherited-members: + :show-inheritance: + +Logger operations +~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.logger + :members: + :show-inheritance: + +Math operations +~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.math + :members: + :inherited-members: + :show-inheritance: + +Mesh operations +~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.mesh + :members: + :imported-members: + :show-inheritance: + +Modifier operations +~~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.modifiers + :members: + :imported-members: + :special-members: __call__ + :inherited-members: + :show-inheritance: + :exclude-members: __init__, func + +Noise operations +~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.noise + :members: + :imported-members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, func + +Seed operations +~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.seed + :members: + :show-inheritance: + +Sensor operations +~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.sensors + :members: + :show-inheritance: + +String operations +~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.string + :members: + :show-inheritance: + +Timer operations +~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.timer + :members: + :show-inheritance: + +Type operations +~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.types + :members: + :show-inheritance: + +Version operations +~~~~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.version + :members: + :show-inheritance: + +Warp operations +~~~~~~~~~~~~~~~ + +.. automodule:: isaaclab.utils.warp + :members: + :imported-members: + :show-inheritance: diff --git a/docs/source/api/lab_contrib/isaaclab_contrib.actuators.rst b/docs/source/api/lab_contrib/isaaclab_contrib.actuators.rst new file mode 100644 index 0000000000000000000000000000000000000000..1171c31e5eaff8b510ef5d899674df0a77680743 --- /dev/null +++ b/docs/source/api/lab_contrib/isaaclab_contrib.actuators.rst @@ -0,0 +1,25 @@ +isaaclab_contrib.actuators +============================= + +.. automodule:: isaaclab_contrib.actuators + + .. rubric:: Classes + + .. autosummary:: + + Thruster + ThrusterCfg + +Thruster Actuator +----------------- + +.. autoclass:: Thruster + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: ThrusterCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type diff --git a/docs/source/api/lab_contrib/isaaclab_contrib.assets.rst b/docs/source/api/lab_contrib/isaaclab_contrib.assets.rst new file mode 100644 index 0000000000000000000000000000000000000000..24375f918c8fa389b00ceb6bff48df2048991775 --- /dev/null +++ b/docs/source/api/lab_contrib/isaaclab_contrib.assets.rst @@ -0,0 +1,31 @@ +isaaclab_contrib.assets +========================== + +.. automodule:: isaaclab_contrib.assets + + .. rubric:: Classes + + .. autosummary:: + + Multirotor + MultirotorCfg + MultirotorData + +Multirotor Asset +---------------- + +.. autoclass:: Multirotor + :members: + :inherited-members: + :show-inheritance: + +.. autoclass:: MultirotorCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +.. autoclass:: MultirotorData + :members: + :inherited-members: + :show-inheritance: diff --git a/docs/source/api/lab_contrib/isaaclab_contrib.mdp.rst b/docs/source/api/lab_contrib/isaaclab_contrib.mdp.rst new file mode 100644 index 0000000000000000000000000000000000000000..32421ef9b1f88942ae5caaa0098d4ab88aff65df --- /dev/null +++ b/docs/source/api/lab_contrib/isaaclab_contrib.mdp.rst @@ -0,0 +1,33 @@ +isaaclab_contrib.mdp +======================= + +.. automodule:: isaaclab_contrib.mdp + +Actions +------- + +.. automodule:: isaaclab_contrib.mdp.actions + + .. rubric:: Classes + + .. autosummary:: + + ThrustActionCfg + ThrustAction + +Thrust Action Configuration +--------------------------- + +.. autoclass:: isaaclab_contrib.mdp.actions.thrust_actions_cfg.ThrustActionCfg + :members: + :inherited-members: + :show-inheritance: + :exclude-members: __init__, class_type + +Thrust Action Implementation +---------------------------- + +.. autoclass:: isaaclab_contrib.mdp.actions.thrust_actions.ThrustAction + :members: + :inherited-members: + :show-inheritance: diff --git a/docs/source/api/lab_mimic/isaaclab_mimic.datagen.rst b/docs/source/api/lab_mimic/isaaclab_mimic.datagen.rst new file mode 100644 index 0000000000000000000000000000000000000000..73b4c4feba146218abc1e98a6194fc85bbf68892 --- /dev/null +++ b/docs/source/api/lab_mimic/isaaclab_mimic.datagen.rst @@ -0,0 +1,82 @@ +isaaclab_mimic.datagen +====================== + +.. automodule:: isaaclab_mimic.datagen + + .. rubric:: Classes + + .. autosummary:: + + DataGenerator + DatagenInfo + DataGenInfoPool + SelectionStrategy + RandomStrategy + NearestNeighborObjectStrategy + NearestNeighborRobotDistanceStrategy + Waypoint + WaypointSequence + WaypointTrajectory + +Data Generator +-------------- + +.. autoclass:: DataGenerator + :members: + :inherited-members: + +Datagen Info +------------ + +.. autoclass:: DatagenInfo + :members: + :inherited-members: + +Datagen Info Pool +----------------- + +.. autoclass:: DataGenInfoPool + :members: + :inherited-members: + +Random Strategy +--------------- + +.. autoclass:: RandomStrategy + :members: + :inherited-members: + +Nearest Neighbor Object Strategy +-------------------------------- + +.. autoclass:: NearestNeighborObjectStrategy + :members: + :inherited-members: + +Nearest Neighbor Robot Distance Strategy +---------------------------------------- + +.. autoclass:: NearestNeighborRobotDistanceStrategy + :members: + :inherited-members: + +Waypoint +-------- + +.. autoclass:: Waypoint + :members: + :inherited-members: + +Waypoint Sequence +----------------- + +.. autoclass:: WaypointSequence + :members: + :inherited-members: + +Waypoint Trajectory +------------------- + +.. autoclass:: WaypointTrajectory + :members: + :inherited-members: diff --git a/docs/source/api/lab_mimic/isaaclab_mimic.envs.rst b/docs/source/api/lab_mimic/isaaclab_mimic.envs.rst new file mode 100644 index 0000000000000000000000000000000000000000..ea8dc82d16155497c54f8d8af8a38a16c37a6f96 --- /dev/null +++ b/docs/source/api/lab_mimic/isaaclab_mimic.envs.rst @@ -0,0 +1,144 @@ +isaaclab_mimic.envs +=================== + +.. automodule:: isaaclab_mimic.envs + + .. rubric:: Classes + + .. autosummary:: + + isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env.FrankaCubeStackIKRelMimicEnv + isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env_cfg.FrankaCubeStackIKRelMimicEnvCfg + isaaclab_mimic.envs.franka_stack_ik_abs_mimic_env.FrankaCubeStackIKAbsMimicEnv + isaaclab_mimic.envs.franka_stack_ik_abs_mimic_env_cfg.FrankaCubeStackIKAbsMimicEnvCfg + isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env.RmpFlowGalbotCubeStackRelMimicEnv + isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env_cfg.RmpFlowGalbotLeftArmGripperCubeStackRelMimicEnvCfg + isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env_cfg.RmpFlowGalbotRightArmSuctionCubeStackRelMimicEnvCfg + isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env.RmpFlowGalbotCubeStackAbsMimicEnv + isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env_cfg.RmpFlowGalbotLeftArmGripperCubeStackAbsMimicEnvCfg + isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env_cfg.RmpFlowGalbotRightArmSuctionCubeStackAbsMimicEnvCfg + isaaclab_mimic.envs.pick_place_mimic_env.PickPlaceRelMimicEnv + isaaclab_mimic.envs.pick_place_mimic_env.PickPlaceAbsMimicEnv + isaaclab_mimic.envs.agibot_place_upright_mug_mimic_env_cfg.RmpFlowAgibotPlaceUprightMugMimicEnvCfg + isaaclab_mimic.envs.agibot_place_toy2box_mimic_env_cfg.RmpFlowAgibotPlaceToy2BoxMimicEnvCfg + +Franka Environments +------------------- + +Franka Cube Stack IK Rel Mimic Env +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env.FrankaCubeStackIKRelMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Franka Cube Stack IK Rel Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env_cfg.FrankaCubeStackIKRelMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Franka Cube Stack IK Abs Mimic Env +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.franka_stack_ik_abs_mimic_env.FrankaCubeStackIKAbsMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Franka Cube Stack IK Abs Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.franka_stack_ik_abs_mimic_env_cfg.FrankaCubeStackIKAbsMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Galbot Environments +------------------- + +Galbot Cube Stack Rel Mimic Env (RmpFlow) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env.RmpFlowGalbotCubeStackRelMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Galbot Left Arm Gripper Cube Stack Rel Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env_cfg.RmpFlowGalbotLeftArmGripperCubeStackRelMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Galbot Right Arm Suction Cube Stack Rel Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_rel_mimic_env_cfg.RmpFlowGalbotRightArmSuctionCubeStackRelMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Galbot Cube Stack Abs Mimic Env (RmpFlow) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env.RmpFlowGalbotCubeStackAbsMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Galbot Left Arm Gripper Cube Stack Abs Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env_cfg.RmpFlowGalbotLeftArmGripperCubeStackAbsMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Galbot Right Arm Suction Cube Stack Abs Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.galbot_stack_rmp_abs_mimic_env_cfg.RmpFlowGalbotRightArmSuctionCubeStackAbsMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Agibot Environments +------------------- + +Pick Place Rel Mimic Env +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.pick_place_mimic_env.PickPlaceRelMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Pick Place Abs Mimic Env +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.pick_place_mimic_env.PickPlaceAbsMimicEnv + :members: + :inherited-members: + :show-inheritance: + +Agibot Place Upright Mug Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.agibot_place_upright_mug_mimic_env_cfg.RmpFlowAgibotPlaceUprightMugMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: + +Agibot Place Toy2Box Mimic Env Cfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: isaaclab_mimic.envs.agibot_place_toy2box_mimic_env_cfg.RmpFlowAgibotPlaceToy2BoxMimicEnvCfg + :members: + :inherited-members: + :show-inheritance: diff --git a/docs/source/api/lab_rl/isaaclab_rl.rst b/docs/source/api/lab_rl/isaaclab_rl.rst new file mode 100644 index 0000000000000000000000000000000000000000..32b4b4c62468d44fa3abf1e8a6ce0d7ed77dbd3e --- /dev/null +++ b/docs/source/api/lab_rl/isaaclab_rl.rst @@ -0,0 +1,35 @@ +.. _api-isaaclab-rl: + +isaaclab_rl +=========== + +.. automodule:: isaaclab_rl + +RL-Games Wrapper +---------------- + +.. automodule:: isaaclab_rl.rl_games + :members: + :show-inheritance: + +RSL-RL Wrapper +-------------- + +.. automodule:: isaaclab_rl.rsl_rl + :members: + :imported-members: + :show-inheritance: + +SKRL Wrapper +------------ + +.. automodule:: isaaclab_rl.skrl + :members: + :show-inheritance: + +Stable-Baselines3 Wrapper +------------------------- + +.. automodule:: isaaclab_rl.sb3 + :members: + :show-inheritance: diff --git a/docs/source/api/lab_tasks/isaaclab_tasks.utils.rst b/docs/source/api/lab_tasks/isaaclab_tasks.utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..3ffd8f075bc56e785780b464a3a0694bcf7d02b9 --- /dev/null +++ b/docs/source/api/lab_tasks/isaaclab_tasks.utils.rst @@ -0,0 +1,6 @@ +isaaclab_tasks.utils +==================== + +.. automodule:: isaaclab_tasks.utils + :members: + :imported-members: diff --git a/docs/source/deployment/cloudxr_teleoperation_cluster.rst b/docs/source/deployment/cloudxr_teleoperation_cluster.rst new file mode 100644 index 0000000000000000000000000000000000000000..9548e29eb70d9dd67e5865bb54cbd24c67f19c81 --- /dev/null +++ b/docs/source/deployment/cloudxr_teleoperation_cluster.rst @@ -0,0 +1,207 @@ +.. _cloudxr-teleoperation-cluster: + +Deploying CloudXR Teleoperation on Kubernetes +============================================= + +.. currentmodule:: isaaclab + +This section explains how to deploy CloudXR Teleoperation for Isaac Lab on a Kubernetes (K8s) cluster. + +.. _k8s-system-requirements: + +System Requirements +------------------- + +* **Minimum requirement**: Kubernetes cluster with a node that has at least 1 NVIDIA RTX PRO 6000 / L40 GPU or equivalent +* **Recommended requirement**: Kubernetes cluster with a node that has at least 2 RTX PRO 6000 / L40 GPUs or equivalent + +.. note:: + If you are using DGX Spark, check `DGX Spark Limitations `_ for compatibility. + +Software Dependencies +--------------------- + +* ``kubectl`` on your host computer + + * If you use MicroK8s, you already have ``microk8s kubectl`` + * Otherwise follow the `official kubectl installation guide `_ + +* ``helm`` on your host computer + + * If you use MicroK8s, you already have ``microk8s helm`` + * Otherwise follow the `official Helm installation guide `_ + +* Access to NGC public registry from your Kubernetes cluster, in particular these container images: + + * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-lab`` + * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cloudxr-runtime`` + +* NVIDIA GPU Operator or equivalent installed in your Kubernetes cluster to expose NVIDIA GPUs +* NVIDIA Container Toolkit installed on the nodes of your Kubernetes cluster + +Preparation +----------- + +On your host computer, you should have already configured ``kubectl`` to access your Kubernetes cluster. To validate, run the following command and verify it returns your nodes correctly: + +.. code:: bash + + kubectl get node + +If you are installing this to your own Kubernetes cluster instead of using the setup described in the :ref:`k8s-appendix`, your role in the K8s cluster should have at least the following RBAC permissions: + +.. code:: yaml + + rules: + - apiGroups: [""] + resources: ["configmaps"] + verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] + - apiGroups: ["apps"] + resources: ["deployments", "replicasets"] + verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] + - apiGroups: [""] + resources: ["pods"] + verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] + - apiGroups: [""] + resources: ["services"] + verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] + +.. _k8s-installation: + +Installation +------------ + +.. note:: + + The following steps are verified on a MicroK8s cluster with GPU Operator installed (see configurations in the :ref:`k8s-appendix`). You can configure your own K8s cluster accordingly if you encounter issues. + +#. Download the Helm chart from NGC (get your NGC API key based on the `public guide `_): + + .. code:: bash + + helm fetch https://helm.ngc.nvidia.com/nvidia/charts/isaac-lab-teleop-2.3.0.tgz \ + --username='$oauthtoken' \ + --password= + +#. Install and run the CloudXR Teleoperation for Isaac Lab pod in the default namespace, consuming all host GPUs: + + .. code:: bash + + helm upgrade --install hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz \ + --set fullnameOverride=hello-isaac-teleop \ + --set hostNetwork="true" + + .. note:: + + You can remove the need for host network by creating an external LoadBalancer VIP (e.g., with MetalLB), and setting the environment variable ``NV_CXR_ENDPOINT_IP`` when deploying the Helm chart: + + .. code:: yaml + + # local_values.yml file example: + fullnameOverride: hello-isaac-teleop + streamer: + extraEnvs: + - name: NV_CXR_ENDPOINT_IP + value: "" + - name: ACCEPT_EULA + value: "Y" + + .. code:: bash + + # command + helm upgrade --install --values local_values.yml \ + hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz + +#. Verify the deployment is completed: + + .. code:: bash + + kubectl wait --for=condition=available --timeout=300s \ + deployment/hello-isaac-teleop + + After the pod is running, it might take approximately 5-8 minutes to complete loading assets and start streaming. + +Uninstallation +-------------- + +You can uninstall by simply running: + +.. code:: bash + + helm uninstall hello-isaac-teleop + +.. _k8s-appendix: + +Appendix: Setting Up a Local K8s Cluster with MicroK8s +------------------------------------------------------ + +Your local workstation should have the NVIDIA Container Toolkit and its dependencies installed. Otherwise, the following setup will not work. + +Cleaning Up Existing Installations (Optional) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + # Clean up the system to ensure we start fresh + sudo snap remove microk8s + sudo snap remove helm + sudo apt-get remove docker-ce docker-ce-cli containerd.io + # If you have snap docker installed, remove it as well + sudo snap remove docker + +Installing MicroK8s +~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + sudo snap install microk8s --classic + +Installing NVIDIA GPU Operator +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + microk8s helm repo add nvidia https://helm.ngc.nvidia.com/nvidia + microk8s helm repo update + microk8s helm install gpu-operator \ + -n gpu-operator \ + --create-namespace nvidia/gpu-operator \ + --set toolkit.env[0].name=CONTAINERD_CONFIG \ + --set toolkit.env[0].value=/var/snap/microk8s/current/args/containerd-template.toml \ + --set toolkit.env[1].name=CONTAINERD_SOCKET \ + --set toolkit.env[1].value=/var/snap/microk8s/common/run/containerd.sock \ + --set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \ + --set toolkit.env[2].value=nvidia \ + --set toolkit.env[3].name=CONTAINERD_SET_AS_DEFAULT \ + --set-string toolkit.env[3].value=true + +.. note:: + + If you have configured the GPU operator to use volume mounts for ``DEVICE_LIST_STRATEGY`` on the device plugin and disabled ``ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED`` on the toolkit, this configuration is currently unsupported, as there is no method to ensure the assigned GPU resource is consistently shared between containers of the same pod. + +Verifying Installation +~~~~~~~~~~~~~~~~~~~~~~ + +Run the following command to verify that all pods are running correctly: + +.. code:: bash + + microk8s kubectl get pods -n gpu-operator + +You should see output similar to: + +.. code:: text + + NAMESPACE NAME READY STATUS RESTARTS AGE + gpu-operator gpu-operator-node-feature-discovery-gc-76dc6664b8-npkdg 1/1 Running 0 77m + gpu-operator gpu-operator-node-feature-discovery-master-7d6b448f6d-76fqj 1/1 Running 0 77m + gpu-operator gpu-operator-node-feature-discovery-worker-8wr4n 1/1 Running 0 77m + gpu-operator gpu-operator-86656466d6-wjqf4 1/1 Running 0 77m + gpu-operator nvidia-container-toolkit-daemonset-qffh6 1/1 Running 0 77m + gpu-operator nvidia-dcgm-exporter-vcxsf 1/1 Running 0 77m + gpu-operator nvidia-cuda-validator-x9qn4 0/1 Completed 0 76m + gpu-operator nvidia-device-plugin-daemonset-t4j4k 1/1 Running 0 77m + gpu-operator gpu-feature-discovery-8dms9 1/1 Running 0 77m + gpu-operator nvidia-operator-validator-gjs9m 1/1 Running 0 77m + +Once all pods are running, you can proceed to the :ref:`k8s-installation` section. diff --git a/docs/source/deployment/cluster.rst b/docs/source/deployment/cluster.rst new file mode 100644 index 0000000000000000000000000000000000000000..ab9e03874e7a9453f8476bb71fd0886df14b1554 --- /dev/null +++ b/docs/source/deployment/cluster.rst @@ -0,0 +1,211 @@ +.. _deployment-cluster: + + +Cluster Guide +============= + +Clusters are a great way to speed up training and evaluation of learning algorithms. +While the Isaac Lab Docker image can be used to run jobs on a cluster, many clusters only +support singularity images. This is because `singularity`_ is designed for +ease-of-use on shared multi-user systems and high performance computing (HPC) environments. +It does not require root privileges to run containers and can be used to run user-defined +containers. + +Singularity is compatible with all Docker images. In this section, we describe how to +convert the Isaac Lab Docker image into a singularity image and use it to submit jobs to a cluster. + +.. attention:: + + Cluster setup varies across different institutions. The following instructions have been + tested on the `ETH Zurich Euler`_ cluster (which uses the SLURM workload manager), and the + IIT Genoa Franklin cluster (which uses PBS workload manager). + + The instructions may need to be adapted for other clusters. If you have successfully + adapted the instructions for another cluster, please consider contributing to the + documentation. + + +Setup Instructions +------------------ + +In order to export the Docker Image to a singularity image, `apptainer`_ is required. +A detailed overview of the installation procedure for ``apptainer`` can be found in its +`documentation`_. For convenience, we summarize the steps here for a local installation: + +.. code:: bash + + sudo apt update + sudo apt install -y software-properties-common + sudo add-apt-repository -y ppa:apptainer/ppa + sudo apt update + sudo apt install -y apptainer + +For simplicity, we recommend that an SSH connection is set up between the local +development machine and the cluster. Such a connection will simplify the file transfer and prevent +the user cluster password from being requested multiple times. + +.. attention:: + The workflow has been tested with: + + - ``apptainer version 1.2.5-1.el7`` and ``docker version 24.0.7`` + - ``apptainer version 1.3.4`` and ``docker version 27.3.1`` + + In the case of issues, please try to switch to those versions. + + +Configuring the cluster parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First, you need to configure the cluster-specific parameters in ``docker/cluster/.env.cluster`` file. +The following describes the parameters that need to be configured: + +.. list-table:: + :header-rows: 1 + :widths: 20 80 + + * - Parameter + - Description + * - CLUSTER_JOB_SCHEDULER + - The job scheduler/workload manager used by your cluster. Currently, we support 'SLURM' and + 'PBS' workload managers. + * - CLUSTER_ISAAC_SIM_CACHE_DIR + - The directory on the cluster where the Isaac Sim cache is stored. This directory + has to end on ``docker-isaac-sim``. It will be copied to the compute node + and mounted into the singularity container. This should increase the speed of starting + the simulation. + * - CLUSTER_ISAACLAB_DIR + - The directory on the cluster where the Isaac Lab logs are stored. This directory has to + end on ``isaaclab``. It will be copied to the compute node and mounted into + the singularity container. When a job is submitted, the latest local changes will + be copied to the cluster to a new directory in the format ``${CLUSTER_ISAACLAB_DIR}_${datetime}`` + with the date and time of the job submission. This allows to run multiple jobs with different code versions at + the same time. + * - CLUSTER_LOGIN + - The login to the cluster. Typically, this is the user and cluster names, + e.g., ``your_user@euler.ethz.ch``. + * - CLUSTER_SIF_PATH + - The path on the cluster where the singularity image will be stored. The image will be + copied to the compute node but not uploaded again to the cluster when a job is submitted. + * - REMOVE_CODE_COPY_AFTER_JOB + - Whether the copied code should be removed after the job is finished or not. The logs from the job will not be deleted + as these are saved under the permanent ``CLUSTER_ISAACLAB_DIR``. This feature is useful + to save disk space on the cluster. If set to ``true``, the code copy will be removed. + * - CLUSTER_PYTHON_EXECUTABLE + - The path within Isaac Lab to the Python executable that should be executed in the submitted job. + +When a ``job`` is submitted, it will also use variables defined in ``docker/.env.base``, though these +should be correct by default. + +Exporting to singularity image +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Next, we need to export the Docker image to a singularity image and upload +it to the cluster. This step is only required once when the first job is submitted +or when the Docker image is updated. For instance, due to an upgrade of the Isaac Sim +version, or additional requirements for your project. + +To export to a singularity image, execute the following command: + +.. code:: bash + + ./docker/cluster/cluster_interface.sh push [profile] + +This command will create a singularity image under ``docker/exports`` directory and +upload it to the defined location on the cluster. It requires that you have previously +built the image with the ``container.py`` interface. Be aware that creating the singularity +image can take a while. +``[profile]`` is an optional argument that specifies the container profile to be used. If no profile is +specified, the default profile ``base`` will be used. + +.. note:: + By default, the singularity image is created without root access by providing the ``--fakeroot`` flag to + the ``apptainer build`` command. In case the image creation fails, you can try to create it with root + access by removing the flag in ``docker/cluster/cluster_interface.sh``. + + +Defining the job parameters +--------------------------- + +The job parameters need to be defined based on the job scheduler used by your cluster. +You only need to update the appropriate script for the scheduler available to you. + +- For SLURM, update the parameters in ``docker/cluster/submit_job_slurm.sh``. +- For PBS, update the parameters in ``docker/cluster/submit_job_pbs.sh``. + +For SLURM +~~~~~~~~~ + +The job parameters are defined inside the ``docker/cluster/submit_job_slurm.sh``. +A typical SLURM operation requires specifying the number of CPUs and GPUs, the memory, and +the time limit. For more information, please check the `SLURM documentation`_. + +The default configuration is as follows: + +.. literalinclude:: ../../../docker/cluster/submit_job_slurm.sh + :language: bash + :lines: 12-19 + :linenos: + :lineno-start: 12 + +An essential requirement for the cluster is that the compute node has access to the internet at all times. +This is required to load assets from the Nucleus server. For some cluster architectures, extra modules +must be loaded to allow internet access. + +For instance, on ETH Zurich Euler cluster, the ``eth_proxy`` module needs to be loaded. This can be done +by adding the following line to the ``submit_job_slurm.sh`` script: + +.. literalinclude:: ../../../docker/cluster/submit_job_slurm.sh + :language: bash + :lines: 3-5 + :linenos: + :lineno-start: 3 + +For PBS +~~~~~~~ + +The job parameters are defined inside the ``docker/cluster/submit_job_pbs.sh``. +A typical PBS operation requires specifying the number of CPUs and GPUs, and the time limit. For more +information, please check the `PBS Official Site`_. + +The default configuration is as follows: + +.. literalinclude:: ../../../docker/cluster/submit_job_pbs.sh + :language: bash + :lines: 11-17 + :linenos: + :lineno-start: 11 + + +Submitting a job +---------------- + +To submit a job on the cluster, the following command can be used: + +.. code:: bash + + ./docker/cluster/cluster_interface.sh job [profile] "argument1" "argument2" ... + +This command will copy the latest changes in your code to the cluster and submit a job. Please ensure that +your Python executable's output is stored under ``isaaclab/logs`` as this directory is synced between the compute +node and ``CLUSTER_ISAACLAB_DIR``. + +``[profile]`` is an optional argument that specifies which singularity image corresponding to the container profile +will be used. If no profile is specified, the default profile ``base`` will be used. The profile has be defined +directlty after the ``job`` command. All other arguments are passed to the Python executable. If no profile is +defined, all arguments are passed to the Python executable. + +The training arguments are passed to the Python executable. As an example, the standard +ANYmal rough terrain locomotion training can be executed with the following command: + +.. code:: bash + + ./docker/cluster/cluster_interface.sh job --task Isaac-Velocity-Rough-Anymal-C-v0 --headless --video --enable_cameras + +The above will, in addition, also render videos of the training progress and store them under ``isaaclab/logs`` directory. + +.. _Singularity: https://docs.sylabs.io/guides/2.6/user-guide/index.html +.. _ETH Zurich Euler: https://www.gdc-docs.ethz.ch/EulerManual/site/overview/ +.. _PBS Official Site: https://openpbs.org/ +.. _apptainer: https://apptainer.org/ +.. _documentation: https://www.apptainer.org/docs/admin/main/installation.html#install-ubuntu-packages +.. _SLURM documentation: https://www.slurm.schedmd.com/sbatch.html diff --git a/docs/source/deployment/docker.rst b/docs/source/deployment/docker.rst new file mode 100644 index 0000000000000000000000000000000000000000..2c4aeb7cbeaedda5695ae554adb469a383099586 --- /dev/null +++ b/docs/source/deployment/docker.rst @@ -0,0 +1,369 @@ +.. _deployment-docker: + + +Docker Guide +============ + +.. caution:: + + Due to the dependency on Isaac Sim docker image, by running this container you are implicitly + agreeing to the `NVIDIA Software License Agreement`_. If you do not agree to the EULA, do not run this container. + +Setup Instructions +------------------ + +.. note:: + + The following steps are taken from the Isaac Sim documentation on `container installation`_. + They have been added here for the sake of completeness. + + +Docker and Docker Compose +~~~~~~~~~~~~~~~~~~~~~~~~~ + +We have tested the container using Docker Engine version 26.0.0 and Docker Compose version 2.25.0 +We recommend using these versions or newer. + +* To install Docker, please follow the instructions for your operating system on the `Docker website`_. +* To install Docker Compose, please follow the instructions for your operating system on the `docker compose`_ page. +* Follow the post-installation steps for Docker on the `post-installation steps`_ page. These steps allow you to run + Docker without using ``sudo``. +* To build and run GPU-accelerated containers, you also need install the `NVIDIA Container Toolkit`_. + Please follow the instructions on the `Container Toolkit website`_ for installation steps. + +.. note:: + + Due to limitations with `snap `_, please make sure + the Isaac Lab directory is placed under the ``/home`` directory tree when using docker. + + +Directory Organization +---------------------- + +The root of the Isaac Lab repository contains the ``docker`` directory that has various files and scripts +needed to run Isaac Lab inside a Docker container. A subset of these are summarized below: + +* **Dockerfile.base**: Defines the base Isaac Lab image by overlaying its dependencies onto the Isaac Sim Docker image. + Dockerfiles which end with something else, (i.e. ``Dockerfile.ros2``) build an `image extension <#isaac-lab-image-extensions>`_. +* **docker-compose.yaml**: Creates mounts to allow direct editing of Isaac Lab code from the host machine that runs + the container. It also creates several named volumes such as ``isaac-cache-kit`` to + store frequently reused resources compiled by Isaac Sim, such as shaders, and to retain logs, data, and documents. +* **.env.base**: Stores environment variables required for the ``base`` build process and the container itself. ``.env`` + files which end with something else (i.e. ``.env.ros2``) define these for `image extension <#isaac-lab-image-extensions>`_. +* **docker-compose.cloudxr-runtime.patch.yaml**: A patch file that is applied to enable CloudXR Runtime support for + streaming to compatible XR devices. It defines services and volumes for CloudXR Runtime and the base. +* **.env.cloudxr-runtime**: Environment variables for the CloudXR Runtime support. +* **container.py**: A utility script that interfaces with tools in ``utils`` to configure and build the image, + and run and interact with the container. + +Running the Container +--------------------- + +.. note:: + + The docker container copies all the files from the repository into the container at the + location ``/workspace/isaaclab`` at build time. This means that any changes made to the files in the container would not + normally be reflected in the repository after the image has been built, i.e. after ``./container.py start`` is run. + + For a faster development cycle, we mount the following directories in the Isaac Lab repository into the container + so that you can edit their files from the host machine: + + * **IsaacLab/source**: This is the directory that contains the Isaac Lab source code. + * **IsaacLab/docs**: This is the directory that contains the source code for Isaac Lab documentation. This is overlaid except + for the ``_build`` subdirectory where build artifacts are stored. + + +The script ``container.py`` parallels basic ``docker compose`` commands. Each can accept an `image extension argument <#isaac-lab-image-extensions>`_, +or else they will default to the ``base`` image extension. These commands are: + +* **build**: This builds the image for the given profile. It does not bring up the container. +* **start**: This builds the image and brings up the container in detached mode (i.e. in the background). +* **enter**: This begins a new bash process in an existing Isaac Lab container, and which can be exited + without bringing down the container. +* **config**: This outputs the compose.yaml which would be result from the inputs given to ``container.py start``. This command is useful + for debugging a compose configuration. +* **copy**: This copies the ``logs``, ``data_storage`` and ``docs/_build`` artifacts, from the ``isaac-lab-logs``, ``isaac-lab-data`` and ``isaac-lab-docs`` + volumes respectively, to the ``docker/artifacts`` directory. These artifacts persist between docker container instances and are shared between image extensions. +* **stop**: This brings down the container and removes it. + +The following shows how to launch the container in a detached state and enter it: + +.. code:: bash + + # Launch the container in detached mode + # We don't pass an image extension arg, so it defaults to 'base' + ./docker/container.py start + + # If we want to add .env or .yaml files to customize our compose config, + # we can simply specify them in the same manner as the compose cli + # ./docker/container.py start --file my-compose.yaml --env-file .env.my-vars + + # Enter the container + # We pass 'base' explicitly, but if we hadn't it would default to 'base' + ./docker/container.py enter base + +To copy files from the base container to the host machine, you can use the following command: + +.. code:: bash + + # Copy the file /workspace/isaaclab/logs to the current directory + docker cp isaac-lab-base:/workspace/isaaclab/logs . + +The script ``container.py`` provides a wrapper around this command to copy the ``logs`` , ``data_storage`` and ``docs/_build`` +directories to the ``docker/artifacts`` directory. This is useful for copying the logs, data and documentation: + +.. code:: bash + + # stop the container + ./docker/container.py stop + + +CloudXR Runtime Support +~~~~~~~~~~~~~~~~~~~~~~~ + +To enable CloudXR Runtime for streaming to compatible XR devices, you need to apply the patch file +``docker-compose.cloudxr-runtime.patch.yaml`` to run CloudXR Runtime container. The patch file defines services and +volumes for CloudXR Runtime and base. The environment variables required for CloudXR Runtime are specified in the +``.env.cloudxr-runtime`` file. To start or stop the CloudXR runtime container with base, use the following command: + +.. code:: bash + + # Start CloudXR Runtime container with base. + ./docker/container.py start --files docker-compose.cloudxr-runtime.patch.yaml --env-file .env.cloudxr-runtime + + # Stop CloudXR Runtime container and base. + ./docker/container.py stop --files docker-compose.cloudxr-runtime.patch.yaml --env-file .env.cloudxr-runtime + + +X11 forwarding +~~~~~~~~~~~~~~ + +The container supports X11 forwarding, which allows the user to run GUI applications from the container +and display them on the host machine. + +The first time a container is started with ``./docker/container.py start``, the script prompts +the user whether to activate X11 forwarding. This will create a file at ``docker/.container.cfg`` +to store the user's choice for future runs. + +If you want to change the choice, you can set the parameter ``X11_FORWARDING_ENABLED`` to '0' or '1' +in the ``docker/.container.cfg`` file to disable or enable X11 forwarding, respectively. After that, you need to +re-build the container by running ``./docker/container.py start``. The rebuilding process ensures that the changes +are applied to the container. Otherwise, the changes will not take effect. + +After the container is started, you can enter the container and run GUI applications from it with X11 forwarding enabled. +The display will be forwarded to the host machine. + + +Python Interpreter +~~~~~~~~~~~~~~~~~~ + +The container uses the Python interpreter provided by Isaac Sim. This interpreter is located at +``/isaac-sim/python.sh``. We set aliases inside the container to make it easier to run the Python +interpreter. You can use the following commands to run the Python interpreter: + +.. code:: bash + + # Run the Python interpreter -> points to /isaac-sim/python.sh + python + + +Understanding the mounted volumes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``docker-compose.yaml`` file creates several named volumes that are mounted to the container. +These are summarized below: + +.. list-table:: + :header-rows: 1 + :widths: 23 45 32 + + * - Volume Name + - Description + - Container Path + * - isaac-cache-kit + - Stores cached Kit resources + - /isaac-sim/kit/cache + * - isaac-cache-ov + - Stores cached OV resources + - /root/.cache/ov + * - isaac-cache-pip + - Stores cached pip resources + - /root/.cache/pip + * - isaac-cache-gl + - Stores cached GLCache resources + - /root/.cache/nvidia/GLCache + * - isaac-cache-compute + - Stores cached compute resources + - /root/.nv/ComputeCache + * - isaac-logs + - Stores logs generated by Omniverse + - /root/.nvidia-omniverse/logs + * - isaac-carb-logs + - Stores logs generated by carb + - /isaac-sim/kit/logs/Kit/Isaac-Sim + * - isaac-data + - Stores data generated by Omniverse + - /root/.local/share/ov/data + * - isaac-docs + - Stores documents generated by Omniverse + - /root/Documents + * - isaac-lab-docs + - Stores documentation of Isaac Lab when built inside the container + - /workspace/isaaclab/docs/_build + * - isaac-lab-logs + - Stores logs generated by Isaac Lab workflows when run inside the container + - /workspace/isaaclab/logs + * - isaac-lab-data + - Stores whatever data users may want to preserve between container runs + - /workspace/isaaclab/data_storage + +To view the contents of these volumes, you can use the following command: + +.. code:: bash + + # list all volumes + docker volume ls + # inspect a specific volume, e.g. isaac-cache-kit + docker volume inspect isaac-cache-kit + + + +Isaac Lab Image Extensions +-------------------------- + +The produced image depends on the arguments passed to ``container.py start`` and ``container.py stop``. These +commands accept an image extension parameter as an additional argument. If no argument is passed, then this +parameter defaults to ``base``. Currently, the only valid values are (``base``, ``ros2``). +Only one image extension can be passed at a time. The produced image and container will be named +``isaac-lab-${profile}``, where ``${profile}`` is the image extension name. + +``suffix`` is an optional string argument to ``container.py`` that specifies a docker image and +container name suffix, which can be useful for development purposes. By default ``${suffix}`` is the empty string. +If ``${suffix}`` is a nonempty string, then the produced docker image and container will be named +``isaac-lab-${profile}-${suffix}``, where a hyphen is inserted between ``${profile}`` and ``${suffix}`` in +the name. ``suffix`` should not be used with cluster deployments. + +.. code:: bash + + # start base by default, named isaac-lab-base + ./docker/container.py start + # stop base explicitly, named isaac-lab-base + ./docker/container.py stop base + # start ros2 container named isaac-lab-ros2 + ./docker/container.py start ros2 + # stop ros2 container named isaac-lab-ros2 + ./docker/container.py stop ros2 + + # start base container named isaac-lab-base-custom + ./docker/container.py start base --suffix custom + # stop base container named isaac-lab-base-custom + ./docker/container.py stop base --suffix custom + # start ros2 container named isaac-lab-ros2-custom + ./docker/container.py start ros2 --suffix custom + # stop ros2 container named isaac-lab-ros2-custom + ./docker/container.py stop ros2 --suffix custom + +The passed image extension argument will build the image defined in ``Dockerfile.${image_extension}``, +with the corresponding `profile`_ in the ``docker-compose.yaml`` and the envars from ``.env.${image_extension}`` +in addition to the ``.env.base``, if any. + +ROS2 Image Extension +~~~~~~~~~~~~~~~~~~~~ + +In ``Dockerfile.ros2``, the container installs ROS2 Humble via an `apt package`_, and it is sourced in the ``.bashrc``. +The exact version is specified by the variable ``ROS_APT_PACKAGE`` in the ``.env.ros2`` file, +defaulting to ``ros-base``. Other relevant ROS2 variables are also specified in the ``.env.ros2`` file, +including variables defining the `various middleware`_ options. + +The container defaults to ``FastRTPS``, but ``CylconeDDS`` is also supported. Each of these middlewares can be +`tuned`_ using their corresponding ``.xml`` files under ``docker/.ros``. + + +.. dropdown:: Parameters for ROS2 Image Extension + :icon: code + + .. literalinclude:: ../../../docker/.env.ros2 + :language: bash + + +Running Pre-Built Isaac Lab Container +------------------------------------- + +In Isaac Lab 2.0 release, we introduced a minimal pre-built container that contains a very minimal set +of Isaac Sim and Omniverse dependencies, along with Isaac Lab 2.0 pre-built into the container. +This container allows users to pull the container directly from NGC without requiring a local build of +the docker image. The Isaac Lab source code will be available in this container under ``/workspace/IsaacLab``. + +This container is designed for running **headless** only and does not allow for X11 forwarding or running +with the GUI. Please only use this container for headless training. For other use cases, we recommend +following the above steps to build your own Isaac Lab docker image. + +.. note:: + + Currently, we only provide docker images with every major release of Isaac Lab. + For example, we provide the docker image for release 2.0.0 and 2.1.0, but not 2.0.2. + In the future, we will provide docker images for every minor release of Isaac Lab. + +To pull the minimal Isaac Lab container, run: + +.. code:: bash + + docker pull nvcr.io/nvidia/isaac-lab:2.3.0 + +To run the Isaac Lab container with an interactive bash session, run: + +.. code:: bash + + docker run --name isaac-lab --entrypoint bash -it --gpus all -e "ACCEPT_EULA=Y" --rm --network=host \ + -e "PRIVACY_CONSENT=Y" \ + -v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \ + -v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \ + -v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \ + -v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \ + -v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \ + -v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \ + -v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \ + -v ~/docker/isaac-sim/documents:/root/Documents:rw \ + nvcr.io/nvidia/isaac-lab:2.3.0 + +To enable rendering through X11 forwarding, run: + +.. code:: bash + + xhost + + docker run --name isaac-lab --entrypoint bash -it --gpus all -e "ACCEPT_EULA=Y" --rm --network=host \ + -e "PRIVACY_CONSENT=Y" \ + -e DISPLAY \ + -v $HOME/.Xauthority:/root/.Xauthority \ + -v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \ + -v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \ + -v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \ + -v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \ + -v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \ + -v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \ + -v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \ + -v ~/docker/isaac-sim/documents:/root/Documents:rw \ + nvcr.io/nvidia/isaac-lab:2.3.0 + +To run an example within the container, run: + +.. code:: bash + + ./isaaclab.sh -p scripts/tutorials/00_sim/log_time.py --headless + + +.. _`NVIDIA Software License Agreement`: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement +.. _`container installation`: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/install_container.html +.. _`Docker website`: https://docs.docker.com/desktop/install/linux-install/ +.. _`docker compose`: https://docs.docker.com/compose/install/linux/#install-using-the-repository +.. _`NVIDIA Container Toolkit`: https://github.com/NVIDIA/nvidia-container-toolkit +.. _`Container Toolkit website`: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html +.. _`post-installation steps`: https://docs.docker.com/engine/install/linux-postinstall/ +.. _`Isaac Sim container`: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-sim +.. _`NGC API key`: https://docs.nvidia.com/ngc/gpu-cloud/ngc-user-guide/index.html#generating-api-key +.. _`several streaming clients`: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/manual_livestream_clients.html +.. _`known issue`: https://forums.developer.nvidia.com/t/unable-to-use-webrtc-when-i-run-runheadless-webrtc-sh-in-remote-headless-container/222916 +.. _`profile`: https://docs.docker.com/compose/compose-file/15-profiles/ +.. _`apt package`: https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debians.html#install-ros-2-packages +.. _`various middleware`: https://docs.ros.org/en/humble/How-To-Guides/Working-with-multiple-RMW-implementations.html +.. _`tuned`: https://docs.ros.org/en/foxy/How-To-Guides/DDS-tuning.html diff --git a/docs/source/deployment/index.rst b/docs/source/deployment/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..235a23c9d754a74dc9d7d773109ae2e7b6c6bc9c --- /dev/null +++ b/docs/source/deployment/index.rst @@ -0,0 +1,77 @@ +.. _container-deployment: + +Container Deployment +==================== + +Docker is a tool that allows for the creation of containers, which are isolated environments that can +be used to run applications. They are useful for ensuring that an application can run on any machine +that has Docker installed, regardless of the host machine's operating system or installed libraries. + +We include a Dockerfile and docker-compose.yaml file that can be used to build a Docker image that +contains Isaac Lab and all of its dependencies. This image can then be used to run Isaac Lab in a container. +The Dockerfile is based on the Isaac Sim image provided by NVIDIA, which includes the Omniverse +application launcher and the Isaac Sim application. The Dockerfile installs Isaac Lab and its dependencies +on top of this image. + +Cloning the Repository +---------------------- + +Before building the container, clone the Isaac Lab repository (if not already done): + +.. tab-set:: + + .. tab-item:: SSH + + .. code:: bash + + git clone git@github.com:isaac-sim/IsaacLab.git + + .. tab-item:: HTTPS + + .. code:: bash + + git clone https://github.com/isaac-sim/IsaacLab.git + +Next Steps +---------- + +After cloning, you can choose the deployment workflow that fits your needs: + +- :doc:`docker` + + - Learn how to build, configure, and run Isaac Lab in Docker containers. + - Explains the repository's ``docker/`` setup, the ``container.py`` helper script, mounted volumes, + image extensions (like ROS 2), and optional CloudXR streaming support. + - Covers running pre-built Isaac Lab containers from NVIDIA NGC for headless training. + +- :doc:`run_docker_example` + + - Learn how to run a development workflow inside the Isaac Lab Docker container. + - Demonstrates building the container, entering it, executing a sample Python script (`log_time.py`), + and retrieving logs using mounted volumes. + - Highlights bind-mounted directories for live code editing and explains how to stop or remove the container + while keeping the image and artifacts. + +- :doc:`cluster` + + - Learn how to run Isaac Lab on high-performance computing (HPC) clusters. + - Explains how to export the Docker image to a Singularity (Apptainer) image, configure cluster-specific parameters, + and submit jobs using common workload managers (SLURM or PBS). + - Includes tested workflows for ETH Zurich's Euler cluster and IIT Genoa's Franklin cluster, + with notes on adapting to other environments. + +- :doc:`cloudxr_teleoperation_cluster` + + - Deploy CloudXR Teleoperation for Isaac Lab on a Kubernetes cluster. + - Covers system requirements, software dependencies, and preparation steps including RBAC permissions. + - Demonstrates how to install and verify the Helm chart, run the pod, and uninstall it. + + +.. toctree:: + :maxdepth: 1 + :hidden: + + docker + run_docker_example + cluster + cloudxr_teleoperation_cluster diff --git a/docs/source/deployment/run_docker_example.rst b/docs/source/deployment/run_docker_example.rst new file mode 100644 index 0000000000000000000000000000000000000000..8da716585f7356817f0d99725510daba8fe4feac --- /dev/null +++ b/docs/source/deployment/run_docker_example.rst @@ -0,0 +1,141 @@ +Running an example with Docker +============================== + +From the root of the Isaac Lab repository, the ``docker`` directory contains all the Docker relevant files. These include the three files +(**Dockerfile**, **docker-compose.yaml**, **.env**) which are used by Docker, and an additional script that we use to interface with them, +**container.py**. + +In this tutorial, we will learn how to use the Isaac Lab Docker container for development. For a detailed description of the Docker setup, +including installation and obtaining access to an Isaac Sim image, please reference the :ref:`deployment-docker`. For a description +of Docker in general, please refer to `their official documentation `_. + + +Building the Container +~~~~~~~~~~~~~~~~~~~~~~ + +To build the Isaac Lab container from the root of the Isaac Lab repository, we will run the following: + + +.. code-block:: console + + python docker/container.py start + + +The terminal will first pull the base IsaacSim image, build the Isaac Lab image's additional layers on top of it, and run the Isaac Lab container. +This should take several minutes for the first build but will be shorter in subsequent runs as Docker's caching prevents repeated work. +If we run the command ``docker container ls`` on the terminal, the output will list the containers that are running on the system. If +everything has been set up correctly, a container with the ``NAME`` **isaac-lab-base** should appear, similar to below: + + +.. code-block:: console + + CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES + 483d1d5e2def isaac-lab-base "bash" 30 seconds ago Up 30 seconds isaac-lab-base + + +Once the container is up and running, we can enter it from our terminal. + +.. code-block:: console + + python docker/container.py enter + + +On entering the Isaac Lab container, we are in the terminal as the superuser, ``root``. This environment contains a copy of the +Isaac Lab repository, but also has access to the directories and libraries of Isaac Sim. We can run experiments from this environment +using a few convenient aliases that have been put into the ``root`` **.bashrc**. For instance, we have made the **isaaclab.sh** script +usable from anywhere by typing its alias ``isaaclab``. + +Additionally in the container, we have `bind mounted`_ the ``IsaacLab/source`` directory from the +host machine. This means that if we modify files under this directory from an editor on the host machine, the changes are +reflected immediately within the container without requiring us to rebuild the Docker image. + +We will now run a sample script from within the container to demonstrate how to extract artifacts +from the Isaac Lab Docker container. + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``log_time.py`` script in the ``IsaacLab/scripts/tutorials/00_sim`` directory. + +.. dropdown:: Code for log_time.py + :icon: code + + .. literalinclude:: ../../../scripts/tutorials/00_sim/log_time.py + :language: python + :emphasize-lines: 46-55, 72-79 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +The Isaac Lab Docker container has several `volumes`_ to facilitate persistent storage between the host computer and the +container. One such volume is the ``/workspace/isaaclab/logs`` directory. +The ``log_time.py`` script designates this directory as the location to which a ``log.txt`` should be written: + +.. literalinclude:: ../../../scripts/tutorials/00_sim/log_time.py + :language: python + :start-at: # Specify that the logs must be in logs/docker_tutorial + :end-at: print(f"[INFO] Logging experiment to directory: {log_dir_path}") + + +As the comments note, :func:`os.path.abspath()` will prepend ``/workspace/isaaclab`` because in +the Docker container all python execution is done through ``/workspace/isaaclab/isaaclab.sh``. +The output will be a file, ``log.txt``, with the ``sim_time`` written on a newline at every simulation step: + +.. literalinclude:: ../../../scripts/tutorials/00_sim/log_time.py + :language: python + :start-at: # Prepare to count sim_time + :end-at: sim_time += sim_dt + + +Executing the Script +~~~~~~~~~~~~~~~~~~~~ + +We will execute the script to produce a log, adding a ``--headless`` flag to our execution to prevent a GUI: + +.. code-block:: bash + + isaaclab -p scripts/tutorials/00_sim/log_time.py --headless + + +Now ``log.txt`` will have been produced at ``/workspace/isaaclab/logs/docker_tutorial``. If we exit the container +by typing ``exit``, we will return to ``IsaacLab/docker`` in our host terminal environment. We can then enter +the following command to retrieve our logs from the Docker container and put them on our host machine: + +.. code-block:: console + + ./container.py copy + + +We will see a terminal readout reporting the artifacts we have retrieved from the container. If we navigate to +``/isaaclab/docker/artifacts/logs/docker_tutorial``, we will see a copy of the ``log.txt`` file which was produced +by the script above. + +Each of the directories under ``artifacts`` corresponds to Docker `volumes`_ mapped to directories +within the container and the ``container.py copy`` command copies them from those `volumes`_ to these directories. + +We could return to the Isaac Lab Docker terminal environment by running ``container.py enter`` again, +but we have retrieved our logs and wish to go inspect them. We can stop the Isaac Lab Docker container with the following command: + +.. code-block:: console + + ./container.py stop + + +This will bring down the Docker Isaac Lab container. The image will persist and remain available for further use, as will +the contents of any `volumes`_. If we wish to free up the disk space taken by the image, (~20.1GB), and do not mind repeating +the build process when we next run ``./container.py start``, we may enter the following command to delete the **isaac-lab-base** image: + +.. code-block:: console + + docker image rm isaac-lab-base + +A subsequent run of ``docker image ls`` will show that the image tagged **isaac-lab-base** is now gone. We can repeat the process for the +underlying NVIDIA container if we wish to free up more space. If a more powerful method of freeing resources from Docker is desired, +please consult the documentation for the `docker prune`_ commands. + + +.. _volumes: https://docs.docker.com/storage/volumes/ +.. _bind mounted: https://docs.docker.com/storage/bind-mounts/ +.. _docker prune: https://docs.docker.com/config/pruning/ diff --git a/docs/source/experimental-features/bleeding-edge.rst b/docs/source/experimental-features/bleeding-edge.rst new file mode 100644 index 0000000000000000000000000000000000000000..5927ba1ae8df5b78378fbe7ae5cf3f9d313754f8 --- /dev/null +++ b/docs/source/experimental-features/bleeding-edge.rst @@ -0,0 +1,11 @@ +Welcome to the bleeding edge! +============================= + +Isaac Lab is open source because our intention is to grow a community of open collaboration for robotic simulation. +We believe that robust tools are crucial for the future of robotics. + +Sometimes new features may require extensive changes to the internal structure of Isaac Lab. +Directly integrating such features before they are complete and without feedback from the full community could cause serious issues for users caught unaware. + +To address this, some major features will be released as Experimental Feature Branches. +This way, the community can experiment with and contribute to the feature before it's fully integrated, reducing the likelihood of being derailed by unexpected and new errors. diff --git a/docs/source/experimental-features/newton-physics-integration/index.rst b/docs/source/experimental-features/newton-physics-integration/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..48d19caca874973bcb725a49946673b12791bc54 --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/index.rst @@ -0,0 +1,47 @@ +Newton Physics Integration +=========================== + +`Newton `_ is a GPU-accelerated, extensible, and differentiable physics simulation engine designed for robotics, research, +and advanced simulation workflows. Built on top of `NVIDIA Warp `_ and integrating MuJoCo Warp, Newton provides high-performance +simulation, modern Python APIs, and a flexible architecture for both users and developers. + +Newton is an Open Source community-driven project with contributions from NVIDIA, Google Deep Mind, and Disney Research, +managed through the Linux Foundation. + +This `experimental feature branch `_ of Isaac Lab provides an initial integration with the Newton Physics Engine, and is +under active development. Many features are not yet supported, and only a limited set of classic RL and flat terrain locomotion +reinforcement learning examples are included at the moment. + +Both this Isaac Lab integration branch and Newton itself are under heavy development. We intend to support additional +features for other reinforcement learning and imitation learning workflows in the future, but the above tasks should be +a good lens through which to understand how Newton integration works in Isaac Lab. + +We have validated Newton simulation against PhysX by transferring learned policies from Newton to PhysX and vice versa +Furthermore, we have also successfully deployed a Newton-trained locomotion policy to a G1 robot. Please see :ref:`here ` for more information. + +Newton can support `multiple solvers `_ for handling different types of physics simulation, but for the moment, the Isaac +Lab integration focuses primarily on the MuJoCo-Warp solver. + +Future updates of this branch and Newton should include both ongoing improvements in performance as well as integration +with additional solvers. + +Note that this branch does not include support for the PhysX physics engine - only Newton is supported. We are considering +several possible paths to continue to support PhysX within Lab, and feedback from users about their needs around that would be appreciated. + +During the early development phase of both Newton and this Isaac Lab integration, you are likely to encounter breaking +changes as well as limited documentation. We do not expect to be able to provide official support or debugging assistance +until the framework has reached an official release. We appreciate your understanding and patience as we work to deliver a robust and polished framework! + + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + installation + isaaclab_newton-beta-2 + training-environments + visualization + limitations-and-known-bugs + solver-transitioning + sim-to-sim + sim-to-real diff --git a/docs/source/experimental-features/newton-physics-integration/installation.rst b/docs/source/experimental-features/newton-physics-integration/installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..be0f82632e66c196ce2013fb9bc039908468019b --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/installation.rst @@ -0,0 +1,78 @@ +Installation +============ + +Installing the Newton physics integration branch requires three things: + +1) The ``feature/newton`` branch of Isaac Lab +2) Ubuntu 22.04 or 24.04 (Windows will be supported soon) +3) [Optional] Isaac sim 5.1 (Isaac Sim is not required if the Omniverse visualizer is not used) + +To begin, verify the version of Isaac Sim by checking the title of the window created when launching the simulation app. Alternatively, you can +find more explicit version information under the ``Help -> About`` menu within the app. +If your version is less than 5.1, you must first `update or reinstall Isaac Sim `_ before +you can proceed further. + +Next, navigate to the root directory of your local copy of the Isaac Lab repository and open a terminal. + +Make sure we are on the ``feature/newton`` branch by running the following command: + +.. code-block:: bash + + git checkout feature/newton + +Below, we provide instructions for installing Isaac Sim through pip. + + +Pip Installation +---------------- + +We recommend using conda for managing your python environments. Conda can be downloaded and installed from `here `_. + +If you previously already have a virtual environment for Isaac Lab, please ensure to start from a fresh environment to avoid any dependency conflicts. +If you have installed earlier versions of mujoco, mujoco-warp, or newton packages through pip, we recommend first +cleaning your pip cache with ``pip cache purge`` to remove any cache of earlier versions that may be conflicting with the latest. + +Create a new conda environment: + +.. code-block:: bash + + conda create -n env_isaaclab python=3.11 + +Activate the environment: + +.. code-block:: bash + + conda activate env_isaaclab + +Install the correct version of torch and torchvision: + +.. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + +[Optional] Install Isaac Sim 5.1: + +.. code-block:: bash + + pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com + +Install Isaac Lab extensions and dependencies: + +.. code-block:: bash + + ./isaaclab.sh -i + + +Testing the Installation +------------------------ + +To verify that the installation was successful, run the following command from the root directory of your Isaac Lab repository: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/zero_agent.py --task Isaac-Cartpole-Direct-v0 --num_envs 128 + + +Note that since Newton requires a more recent version of Warp than Isaac Sim 5.1, there may be some incompatibility issues +that could result in errors such as ``ModuleNotFoundError: No module named 'warp.sim'``. These are ok to ignore and should not +impact usability. diff --git a/docs/source/experimental-features/newton-physics-integration/isaaclab_newton-beta-2.rst b/docs/source/experimental-features/newton-physics-integration/isaaclab_newton-beta-2.rst new file mode 100644 index 0000000000000000000000000000000000000000..2e7b4dd9ec425412b17a394ae03ec1b5a14829ed --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/isaaclab_newton-beta-2.rst @@ -0,0 +1,52 @@ +Isaac Lab - Newton Beta 2 +========================= + +Isaac Lab - Newton Beta 2 (feature/newton branch) provides Newton physics engine integration for Isaac Lab. We refactored our code so that we can not only support PhysX and Newton, but +any other physics engine, enabling users to bring their own physics engine to Isaac Lab if they desire. To enable this, we introduce base implementations of +our ``simulation interfaces``, :class:`~isaaclab.assets.articulation.Articulation` or :class:`~isaaclab.sensors.ContactSensor` for instance. These provide a +set of abstract methods that all physics engines must implement. In turn this allows all of the default Isaac Lab environments to work with any physics engine. +This also allows us to ensure that Isaac Lab - Newton Beta 2 is backwards compatible with Isaac Lab 2.X. For engine specific calls, users could get the underlying view of +the physics engine and call the engine specific APIs directly. + +However, as we are refactoring the code, we are also looking at ways to limit the overhead of Isaac Lab's. In an effort to minimize the overhead, we are moving +all our low level code away from torch, and instead will rely heavily on warp. This will allow us to write low level code that is more efficient, and also +to take advantage of the cuda-graphing. However, this means that the ``data classes`` such as :class:`~isaaclab.assets.articulation.ArticulationData` or +:class:`~isaaclab.sensors.ContactSensorData` will only return warp arrays. Users will hence have to call ``wp.to_torch`` to convert them to torch tensors if they desire. +Our setters/writers will support both warp arrays and torch tensors, and will use the most optimal strategy to update the warp arrays under the hood. This minimizes the +amount of changes required for users to migrate to Isaac Lab - Newton Beta 2. + +Another new feature of the writers and setters is the ability to provide them with masks and complete data (as opposed to indices and partial data in Isaac Lab 2.X). +Note that this feature will be available along with the ability to provide indices and partial data, and that the default behavior will still be to provide indices and partial data. +However, if using warp, users will have to provide masks and complete data. In general we encourage users to move to adopt this new feature as, if done well, it will +reduce on the fly memory allocations, and should result in better performance. + +On the optimization front, we decided to change quaternion conventions. Originally, Isaac Lab and Isaac Sim both adopted the ``wxyz`` convention. However, we were doing several +conversions to and from ``xyzw`` in our setters/writers as PhysX uses the ``xyzw`` convention. Since both Newton and Warp, also use the ``xyzw`` convention, we decided to change +our default convention to ``xyzw``. This means that all our APIs will now return quaternions in the ``xyzw`` convention. This is likely a breaking change for all the custom +mdps that are not using our :mod:`~isaaclab.utils.math` module. While this change is substantial, it should make things more consistent for when users are using the simulation +views directly, and will remove needless conversions. + +Finally, alongside the new isaaclab_newton extension, we are also introducing new isaaclab_experimental and isaaclab_task_experimental extensions. These extensions will allow +us to quickly bring new features to Isaac Lab main while giving them the time they need to mature before being fully integrated into the core Isaac Lab extensions. In this release, +we are introducing cuda-graphing support for direct rl tasks. This drastically reduces Isaac Lab's overhead making training faster. Try them out and let us know what you think! + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-Direct-Warp-v0 --num_envs 4096 --headless + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Ant-Direct-Warp-v0 --num_envs 4096 --headless + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Humanoid-Direct-Warp-v0 --num_envs 4096 --headless + + +What's Next? +============ + +Isaac Lab 3.0 is the upcoming release of Isaac Lab, which will be compatible with Isaac Sim 6.0, and at the same time will support the new Newton physics engine. +This will allow users to train policies on the Newton physics engine, or PhysX. To accommodate this major code refactoring are required. In this section, we +will go over some of the changes, how that will affect Isaac Lab 2.X users, and how to migrate to Isaac Lab 3.0. The current branch of ``feature/newton`` gives +a glance of what is to come. While the changes to the internal code structure are significant, the changes to the user API are minimal. diff --git a/docs/source/experimental-features/newton-physics-integration/limitations-and-known-bugs.rst b/docs/source/experimental-features/newton-physics-integration/limitations-and-known-bugs.rst new file mode 100644 index 0000000000000000000000000000000000000000..e5eab3996d8af50579e97358888b2a5a36cd9739 --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/limitations-and-known-bugs.rst @@ -0,0 +1,55 @@ +Limitations +=========== + +During the early development phase of both Newton and this Isaac Lab integration, +you are likely to encounter breaking changes as well as limited documentation. + +We do not expect to be able to provide support or debugging assistance until the framework has reached an official release. + +Here is a non-exhaustive list of capabilities currently supported in the Newton experimental feature branch grouped by extension: + +* isaaclab: + * Articulation API (supports both articulations and single-body articulations as rigid bodies) + * Contact Sensor + * Direct & Manager single agent workflows + * Omniverse Kit visualizer + * Newton visualizer +* isaaclab_assets: + * Quadrupeds + * Anymal-B, Anymal-C, Anymal-D + * Unitree A1, Go1, Go2 + * Spot + * Humanoids + * Unitree H1 & G1 + * Cassie + * Arms and Hands + * Franka + * UR10 + * Allegro Hand + * Toy examples + * Cartpole + * Ant + * Humanoid +* isaaclab_tasks: + * Direct: + * Cartpole (State, RGB, Depth) + * Ant + * Humanoid + * Allegro Hand Repose Cube + * Manager based: + * Cartpole (State) + * Ant + * Humanoid + * Locomotion (velocity flat terrain) + * Anymal-B + * Anymal-C + * Anymal-D + * Cassie + * A1 + * Go1 + * Go2 + * Unitree G1 + * Unitree H1 + * Manipulation reach + * Franka + * UR10 diff --git a/docs/source/experimental-features/newton-physics-integration/sim-to-real.rst b/docs/source/experimental-features/newton-physics-integration/sim-to-real.rst new file mode 100644 index 0000000000000000000000000000000000000000..6b7a952a76c4ccfd45de37464d505d75272c6420 --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/sim-to-real.rst @@ -0,0 +1,92 @@ +.. _sim2real: + +Sim-to-Real Policy Transfer +=========================== +Deploying policies from simulation to real robots involves important nuances that must be addressed. +This section provides a high-level guide for training policies that can be deployed on a real Unitree G1 robot. +The key challenge is that not all observations available in simulation can be directly measured by real robot sensors. +This means RL-trained policies cannot be directly deployed unless they use only sensor-available observations. For example, while real robot IMU sensors provide angular acceleration (which can be integrated to get angular velocity), they cannot directly measure linear velocity. Therefore, if a policy relies on base linear velocity during training, this information must be removed before real robot deployment. + + +Requirements +~~~~~~~~~~~~ + +We assume that policies from this workflow are first verified through sim-to-sim transfer before real robot deployment. +Please see :ref:`here ` for more information. + + +Overview +-------- + +This section demonstrates a sim-to-real workflow using teacher–student distillation for the Unitree G1 +velocity-tracking task with the Newton backend. + +The teacher–student distillation workflow consists of three stages: + +1. Train a teacher policy with privileged observations that are not available in real-world sensors. +2. Distill a student policy that excludes privileged terms (e.g., root linear velocity) by behavior cloning from the teacher policy. +3. Fine-tune the student policy with RL using only real-sensor observations. + +The teacher and student observation groups are implemented in the velocity task configuration. See the following source for details: + +- Teacher observations: ``PolicyCfg(ObsGroup)`` in `velocity_env_cfg.py `__ +- Student observations: ``StudentPolicyCfg(ObsGroup)`` in `velocity_env_cfg.py `__ + + +1. Train the teacher policy +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Train the teacher policy for the G1 velocity task using the Newton backend. The task ID is ``Isaac-Velocity-Flat-G1-v1`` + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Flat-G1-v1 --num_envs=4096 + +The teacher policy includes privileged observations (e.g., root linear velocity) defined in ``PolicyCfg(ObsGroup)``. + + +2. Distill the student policy (remove privileged terms) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +During distillation, the student policy learns to mimic the teacher through behavior cloning by minimizing the mean squared error +between their actions: :math:`loss = MSE(\pi(O_{teacher}), \pi(O_{student}))`. + +The student policy only uses observations available from real sensors (see ``StudentPolicyCfg(ObsGroup)`` +in `velocity_env_cfg.py `__). +Specifically: **Root angular velocity** and **Projected gravity** come from the IMU sensor, **Joint positions and velocities** come from joint encoders, and **Actions** are the joint torques applied by the controller. + +Run the student distillation task ``Velocity-G1-Distillation-v1`` using ``--load_run`` and ``--checkpoint`` to specify the teacher policy you want to distill from. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Velocity-G1-Distillation-v1 --num_envs=4096 --load_run 2025-08-13_23-53-28 --checkpoint model_1499.pt + +.. note:: + + Use the correct ``--load_run`` and ``--checkpoint`` to ensure you distill from the intended teacher policy. + + +3. Fine-tune the student policy with RL +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Fine-tune the distilled student policy using RL with the ``Velocity-G1-Student-Finetune-v1`` task. +Use ``--load_run`` and ``--checkpoint`` to initialize from the distilled policy. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Velocity-G1-Student-Finetune-v1 --num_envs=4096 --load_run 2025-08-20_16-06-52_distillation --checkpoint model_1499.pt + +This starts from the distilled student policy and improves it further with RL training. + +.. note:: + + Make sure ``--load_run`` and ``--checkpoint`` point to the correct initial policy (usually the latest checkpoint from the distillation step). + +You can replay the student policy via: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task=Velocity-G1-Student-Finetune-v1 --num_envs=32 --visualizer newton + + +This exports the policy as ``.pt`` and ``.onnx`` files in the run's export directory, ready for real robot deployment. diff --git a/docs/source/experimental-features/newton-physics-integration/sim-to-sim.rst b/docs/source/experimental-features/newton-physics-integration/sim-to-sim.rst new file mode 100644 index 0000000000000000000000000000000000000000..8eebbdffac3f8e1ebeaabecf304834eb2f47950a --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/sim-to-sim.rst @@ -0,0 +1,171 @@ +.. _sim2sim: + +Sim-to-Sim Policy Transfer +========================== +This section provides examples of sim-to-sim policy transfer between PhysX and Newton backends. Sim-to-sim transfer is an essential step before real robot deployment because it verifies that policies work across different simulators. Policies that pass sim-to-sim verification are much more likely to succeed on real robots. + + +Overview +-------- + +This guide shows how to transfer policies between PhysX and Newton backends in both directions. The main challenge is that different physics engines may parse the same robot model with different joint and link ordering. + +Policies trained in one backend expect joints and links in a specific order determined by how that backend parses the robot model. When transferring to another backend, the joint ordering may be different, requiring remapping of observations and actions. + +In the future, we plan to solve this using **robot schema** that standardizes joint and link ordering across different backends. + +Currently, we solve this by remapping observations and actions using joint mappings defined in YAML files. These files specify joint names in both source and target backend orders. During policy execution, we use this mapping to reorder observations and actions so they work correctly with the target backend. + +The method has been tested with Unitree G1, Unitree Go2, Unitree H1, and ANYmal-D robots for both transfer directions. + + +What you need +~~~~~~~~~~~~~ + +- A policy checkpoint trained with either PhysX or Newton (RSL-RL). +- A joint mapping YAML for your robot under ``scripts/sim2sim_transfer/config/``. +- The provided player script: ``scripts/sim2sim_transfer/rsl_rl_transfer.py``. + +To add a new robot, create a YAML file with two lists where each joint name appears exactly once in both: + +.. code-block:: yaml + + # Example structure + source_joint_names: # Source backend joint order + - joint_1 + - joint_2 + # ... + target_joint_names: # Target backend joint order + - joint_1 + - joint_2 + # ... + +The script automatically computes the necessary mappings for locomotion tasks. + + +PhysX-to-Newton Transfer +~~~~~~~~~~~~~~~~~~~~~~~~ + +To run a PhysX-trained policy with the Newton backend, use this command template: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task= \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file \ + --visualizer newton + +Here are examples for different robots: + +1. Unitree G1 + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-G1-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/physx_to_newton_g1.yaml \ + --visualizer newton + +2. Unitree H1 + + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-H1-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/physx_to_newton_h1.yaml \ + --visualizer newton + + +3. Unitree Go2 + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-Go2-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/physx_to_newton_go2.yaml \ + --visualizer newton + + +4. ANYmal-D + + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-Anymal-D-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/physx_to_newton_anymal_d.yaml \ + --visualizer newton + +Note that to run this, you need to checkout the Newton-based branch of IsaacLab such as ``feature/newton``. + +Newton-to-PhysX Transfer +~~~~~~~~~~~~~~~~~~~~~~~~ + +To transfer Newton-trained policies to PhysX-based IsaacLab, use the reverse mapping files: + +Here are examples for different robots: + +1. Unitree G1 + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-G1-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/newton_to_physx_g1.yaml + + +2. Unitree H1 + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-H1-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/newton_to_physx_h1.yaml + + +3. Unitree Go2 + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-Go2-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/newton_to_physx_go2.yaml + + +4. ANYmal-D + +.. code-block:: bash + + ./isaaclab.sh -p scripts/sim2sim_transfer/rsl_rl_transfer.py \ + --task=Isaac-Velocity-Flat-Anymal-D-v0 \ + --num_envs=32 \ + --checkpoint \ + --policy_transfer_file scripts/sim2sim_transfer/config/newton_to_physx_anymal_d.yaml + +The key difference is using the ``newton_to_physx_*.yaml`` mapping files instead of ``physx_to_newton_*.yaml`` files. Also note that you need to checkout a PhysX-based IsaacLab branch such as ``main``. + +Notes and Limitations +~~~~~~~~~~~~~~~~~~~~~ + +- Both transfer directions have been tested with Unitree G1, Unitree Go2, Unitree H1, and ANYmal-D robots. +- PhysX-to-Newton transfer uses ``physx_to_newton_*.yaml`` mapping files. +- Newton-to-PhysX transfer requires the corresponding ``newton_to_physx_*.yaml`` mapping files and the PhysX branch of IsaacLab. +- The observation remapping assumes a locomotion layout with base observations followed by joint observations. For different observation layouts, you'll need to modify the ``get_joint_mappings`` function in ``scripts/sim2sim_transfer/rsl_rl_transfer.py``. +- When adding new robots or backends, make sure both source and target have identical joint names, and that the YAML lists reflect how each backend orders these joints. diff --git a/docs/source/experimental-features/newton-physics-integration/solver-transitioning.rst b/docs/source/experimental-features/newton-physics-integration/solver-transitioning.rst new file mode 100644 index 0000000000000000000000000000000000000000..db85df0f991f45bda96f1a12a126ded9afe90f7c --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/solver-transitioning.rst @@ -0,0 +1,73 @@ +Solver Transitioning +==================== + +Transitioning to the Newton physics engine introduces new physics solvers that handle simulation using different numerical approaches. +While Newton supports several different solvers, our initial focus for Isaac Lab is on using the MuJoCo-Warp solver from Google DeepMind. + +The way the physics scene itself is defined does not change - we continue to use USD as the primary way to set basic parameters of objects and robots in the scene, +and for current environments, the exact same USD files used for the PhysX-based Isaac Lab are used. +In the future, that may change, as new USD schemas are under development that capture additional physics parameters. + +What does require change is the way that some solver-specific settings are configured. +Tuning these parameters can have a significant impact on both simulation performance and behaviour. + +For now, we will show an example of setting these parameters to help provide a feel for these changes. +Note that the :class:`~isaaclab.sim.NewtonCfg` replaces the :class:`~isaaclab.sim.PhysxCfg` and is used to set everything related to the physical simulation parameters except for the ``dt``: + +.. code-block:: python + + from isaaclab.sim._impl.newton_manager_cfg import NewtonCfg + from isaaclab.sim._impl.solvers_cfg import MJWarpSolverCfg + + solver_cfg = MJWarpSolverCfg( + nefc_per_env=35, + ls_iterations=10, + cone="pyramidal", + ls_parallel=True, + impratio=1, + ) + newton_cfg = NewtonCfg( + solver_cfg=solver_cfg, + num_substeps=1, + debug_mode=False, + ) + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation, newton_cfg=newton_cfg) + + +Here is a very brief explanation of some of the key parameters above: + +* ``nefc_per_env``: This is the size of the buffer constraints we want MuJoCo warp to + pre-allocate for a given environment. A large value will slow down the simulation, + while a too small value may lead to some contacts being missed. + +* ``ls_iterations``: The number of line searches performed by the MuJoCo Warp solver. + Line searches are used to find an optimal step size, and for each solver step, + at most ``ls_iterations`` line searches will be performed. Keeping this number low + is important for performance. This number is also an upper bound when + ``ls_parallel`` is not set. + +* ``cone``: This parameter provides a choice between pyramidal and elliptic + approximations for the friction cone used in contact handling. Please see the + MuJoCo documentation for additional information on contact: + https://mujoco.readthedocs.io/en/stable/computation/index.html#contact + +* ``ls_parallel``: This switches line searches from iterative to parallel execution. + Enabling ``ls_parallel`` provides a performance boost, but at the cost of some + simulation stability. To ensure good simulation behaviour when enabled, a higher + ``ls_iterations`` setting is required. Usually an increase of approximately 50% is + best over the ``ls_iterations`` setting when ``ls_parallel`` is disabled. + +* ``impratio``: This is the frictional-to-normal constraint impedance ratio that + enables finer-grained control of the significance of the tangential forces + compared to the normal forces. Larger values signify more emphasis on harder + frictional constraints to avoid slip. More on how to tune this parameter (and + cone) can be found in the MuJoCo documentation here: + https://mujoco.readthedocs.io/en/stable/XMLreference.html#option-impratio + +* ``num_substeps``: The number of substeps to perform when running the simulation. + Setting this to a number larger than one allows to decimate the simulation + without requiring Isaac Lab to process data between two substeps. This can be + of value when using implicit actuators, for example. + + +A more detailed transition guide covering the full set of available parameters and describing tuning approaches will follow in an upcoming release. diff --git a/docs/source/experimental-features/newton-physics-integration/training-environments.rst b/docs/source/experimental-features/newton-physics-integration/training-environments.rst new file mode 100644 index 0000000000000000000000000000000000000000..5e25564a136059d6a6be467f946db757c37cbecf --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/training-environments.rst @@ -0,0 +1,89 @@ +Training Environments +====================== + +To run training, we follow the standard Isaac Lab workflow. If you are new to Isaac Lab, we recommend that you review the `Quickstart Guide here `_. + +The currently supported tasks are as follows: + +* Isaac-Cartpole-Direct-v0 +* Isaac-Cartpole-v0 +* Isaac-Cartpole-RGB-Camera-Direct-v0 +* Isaac-Cartpole-Depth-Camera-Direct-v0 +* Isaac-Ant-Direct-v0 +* Isaac-Ant-v0 +* Isaac-Humanoid-Direct-v0 +* Isaac-Humanoid-v0 +* Isaac-Velocity-Flat-Anymal-B-v0 +* Isaac-Velocity-Flat-Anymal-C-v0 +* Isaac-Velocity-Flat-Anymal-D-v0 +* Isaac-Velocity-Flat-Cassie-v0 +* Isaac-Velocity-Flat-G1-v0 +* Isaac-Velocity-Flat-G1-v1 (Sim-to-Real tested) +* Isaac-Velocity-Flat-H1-v0 +* Isaac-Velocity-Flat-Unitree-A1-v0 +* Isaac-Velocity-Flat-Unitree-Go1-v0 +* Isaac-Velocity-Flat-Unitree-Go2-v0 +* Isaac-Reach-Franka-v0 +* Isaac-Reach-UR10-v0 +* Isaac-Repose-Cube-Allegro-Direct-v0 + +New experimental warp-based enviromnets: + +* Isaac-Cartpole-Direct-Warp-v0 +* Isaac-Ant-Direct-Warp-v0 +* Isaac-Humanoid-Direct-Warp-v0 + +To launch an environment and check that it loads as expected, we can start by trying it out with zero actions sent to its actuators. +This can be done as follows, where ``TASK_NAME`` is the name of the task you’d like to run, and ``NUM_ENVS`` is the number of instances of the task that you’d like to create. + +.. code-block:: shell + + ./isaaclab.sh -p scripts/environments/zero_agent.py --task TASK_NAME --num_envs NUM_ENVS + +For cartpole with 128 instances it would look like this: + +.. code-block:: shell + + ./isaaclab.sh -p scripts/environments/zero_agent.py --task Isaac-Cartpole-Direct-v0 --num_envs 128 + +To run the same environment with random actions we can use a different script: + +.. code-block:: shell + + ./isaaclab.sh -p scripts/environments/random_agent.py --task Isaac-Cartpole-Direct-v0 --num_envs 128 + +To train the environment we provide hooks to different rl frameworks. See the `Reinforcement Learning Scripts documentation `_ for more information. + +Here are some examples on how to run training on several different RL frameworks. Note that we are explicitly setting the number of environments to +4096 to benefit more from GPU parallelization. + +By default, environments will train in headless mode. If visualization is required, use ``--visualizer`` and specify the desired visualizer. +Available options are ``newton``, ``rerun``, and ``omniverse`` (requires Isaac Sim installation). Note, multiple visualizers can be selected and launched. + +.. code-block:: shell + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-Direct-v0 --num_envs 4096 + +.. code-block:: shell + + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Cartpole-Direct-v0 --num_envs 4096 + +.. code-block:: shell + + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-Cartpole-Direct-v0 --num_envs 4096 + +Once a policy is trained we can visualize it by using the play scripts. But first, we need to find the checkpoint of the trained policy. Typically, these are stored under: +``logs/NAME_OF_RL_FRAMEWORK/TASK_NAME/DATE``. + +For instance with our rsl_rl example it could look like this: +``logs/rsl_rl/cartpole_direct/2025-08-21_15-45-30/model_299.pt`` + +To then run this policy we can use the following command, note that we reduced the number of environments and added the ``--visualizer newton`` option so that we can see our policy in action! + +.. code-block:: shell + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Cartpole-Direct-v0 --num_envs 128 --visualizer newton --checkpoint logs/rsl_rl/cartpole_direct/2025-08-21_15-45-30/model_299.pt + +The same approach applies to all other frameworks. + +Note that not all environments are supported in all frameworks. For example, several of the locomotion environments are only supported in the rsl_rl framework. diff --git a/docs/source/experimental-features/newton-physics-integration/visualization.rst b/docs/source/experimental-features/newton-physics-integration/visualization.rst new file mode 100644 index 0000000000000000000000000000000000000000..f54435733934b4f39bf6747d7c5f506f9f5d1bba --- /dev/null +++ b/docs/source/experimental-features/newton-physics-integration/visualization.rst @@ -0,0 +1,310 @@ +Visualization +============= + +.. currentmodule:: isaaclab + +Isaac Lab offers several lightweight visualizers for real-time simulation inspection and debugging. Unlike renderers that process sensor data, visualizers are meant for fast, interactive feedback. + +You can use any visualizer regardless of your chosen physics engine or rendering backend. + + +Overview +-------- + +Isaac Lab supports three visualizer backends, each optimized for different use cases: + +.. list-table:: Visualizer Comparison + :widths: 15 35 50 + :header-rows: 1 + + * - Visualizer + - Best For + - Key Features + * - **Omniverse** + - High-fidelity, Isaac Sim integration + - USD, visual markers, live plots + * - **Newton** + - Fast iteration + - Low overhead, visual markers + * - **Rerun** + - Remote viewing, replay + - Webviewer, time scrubbing, recording export + + +*The following visualizers are shown training the Isaac-Velocity-Flat-Anymal-D-v0 environment.* + +.. figure:: ../../_static/visualizers/ov_viz.jpg + :width: 100% + :alt: Omniverse Visualizer + + Omniverse Visualizer + +.. figure:: ../../_static/visualizers/newton_viz.jpg + :width: 100% + :alt: Newton Visualizer + + Newton Visualizer + +.. figure:: ../../_static/visualizers/rerun_viz.jpg + :width: 100% + :alt: Rerun Visualizer + + Rerun Visualizer + + +Quick Start +----------- + +Launch visualizers from the command line with ``--visualizer``: + +.. code-block:: bash + + # Launch all visualizers + python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --visualizer omniverse newton rerun + + # Launch just newton visualizer + python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --visualizer newton + + +If ``--headless`` is given, no visualizers will be launched. + +.. note:: + + The ``--headless`` argument may be deprecated in future versions to avoid confusion with the ``--visualizer`` + argument. For now, ``--headless`` takes precedence and disables all visualizers. + + +Configuration +~~~~~~~~~~~~~ + +Launching visualizers with the command line will use default visualizer configurations. Default configs can be found and edited in ``source/isaaclab/isaaclab/visualizers``. + +You can also configure custom visualizers in the code by defining new ``VisualizerCfg`` instances for the ``SimulationCfg``, for example: + +.. code-block:: python + + from isaaclab.sim import SimulationCfg + from isaaclab.visualizers import NewtonVisualizerCfg, OVVisualizerCfg, RerunVisualizerCfg + + sim_cfg = SimulationCfg( + visualizer_cfgs=[ + OVVisualizerCfg( + viewport_name="Visualizer Viewport", + create_viewport=True, + dock_position="SAME", + window_width=1280, + window_height=720, + camera_position=(0.0, 0.0, 20.0), # high top down view + camera_target=(0.0, 0.0, 0.0), + ), + NewtonVisualizerCfg( + camera_position=(5.0, 5.0, 5.0), # closer quarter view + camera_target=(0.0, 0.0, 0.0), + show_joints=True, + ), + RerunVisualizerCfg( + keep_historical_data=True, + keep_scalar_history=True, + record_to_rrd="my_training.rrd", + ), + ] + ) + + +Visualizer Backends +------------------- + +Omniverse Visualizer +~~~~~~~~~~~~~~~~~~~~ + +**Main Features:** + +- Native USD stage integration +- Visualization markers for debugging (arrows, frames, points, etc.) +- Live plots for monitoring training metrics +- Full Isaac Sim rendering capabilities and tooling + +**Core Configuration:** + +.. code-block:: python + + from isaaclab.visualizers import OVVisualizerCfg + + visualizer_cfg = OVVisualizerCfg( + # Viewport settings + viewport_name="Visualizer Viewport", # Viewport window name + create_viewport=True, # Create new viewport vs. use existing + dock_position="SAME", # Docking: 'LEFT', 'RIGHT', 'BOTTOM', 'SAME' + window_width=1280, # Viewport width in pixels + window_height=720, # Viewport height in pixels + + # Camera settings + camera_position=(8.0, 8.0, 3.0), # Initial camera position (x, y, z) + camera_target=(0.0, 0.0, 0.0), # Camera look-at target + + # Feature toggles + enable_markers=True, # Enable visualization markers + enable_live_plots=True, # Enable live plots (auto-expands frames) + ) + + +Newton Visualizer +~~~~~~~~~~~~~~~~~~~~~~~~~ + +**Main Features:** + +- Lightweight OpenGL rendering with low overhead +- Visualization markers (joints, contacts, springs, COM) +- Training and rendering pause controls +- Adjustable update frequency for performance tuning +- Some customizable rendering options (shadows, sky, wireframe) + + +**Interactive Controls:** + +.. list-table:: + :widths: 30 70 + :header-rows: 1 + + * - Key/Input + - Action + * - **W, A, S, D** or **Arrow Keys** + - Forward / Left / Back / Right + * - **Q, E** + - Down / Up + * - **Left Click + Drag** + - Look around + * - **Mouse Scroll** + - Zoom in/out + * - **Space** + - Pause/resume rendering (physics continues) + * - **H** + - Toggle UI sidebar + * - **ESC** + - Exit viewer + +**Core Configuration:** + +.. code-block:: python + + from isaaclab.visualizers import NewtonVisualizerCfg + + visualizer_cfg = NewtonVisualizerCfg( + # Window settings + window_width=1920, # Window width in pixels + window_height=1080, # Window height in pixels + + # Camera settings + camera_position=(8.0, 8.0, 3.0), # Initial camera position (x, y, z) + camera_target=(0.0, 0.0, 0.0), # Camera look-at target + + # Performance tuning + update_frequency=1, # Update every N frames (1=every frame) + + # Physics debug visualization + show_joints=False, # Show joint visualizations + show_contacts=False, # Show contact points and normals + show_springs=False, # Show spring constraints + show_com=False, # Show center of mass markers + + # Rendering options + enable_shadows=True, # Enable shadow rendering + enable_sky=True, # Enable sky rendering + enable_wireframe=False, # Enable wireframe mode + + # Color customization + background_color=(0.53, 0.81, 0.92), # Sky/background color (RGB [0,1]) + ground_color=(0.18, 0.20, 0.25), # Ground plane color (RGB [0,1]) + light_color=(1.0, 1.0, 1.0), # Directional light color (RGB [0,1]) + ) + + +Rerun Visualizer +~~~~~~~~~~~~~~~~ + +**Main Features:** + +- Web viewer interface accessible from local or remote browser +- Metadata logging and filtering +- Recording to .rrd files for offline replay (.rrd files can be opened with ctrl+O from the web viewer) +- Timeline scrubbing and playback controls of recordings + +**Core Configuration:** + +.. code-block:: python + + from isaaclab.visualizers import RerunVisualizerCfg + + visualizer_cfg = RerunVisualizerCfg( + # Server settings + app_id="isaaclab-simulation", # Application identifier for viewer + web_port=9090, # Port for local web viewer (launched in browser) + + # Camera settings + camera_position=(8.0, 8.0, 3.0), # Initial camera position (x, y, z) + camera_target=(0.0, 0.0, 0.0), # Camera look-at target + + # History settings + keep_historical_data=False, # Keep transforms for time scrubbing + keep_scalar_history=False, # Keep scalar/plot history + + # Recording + record_to_rrd="recording.rrd", # Path to save .rrd file (None = no recording) + ) + + +Performance Note +---------------- + +To reduce overhead when visualizing large-scale environments, consider: + +- Using Newton instead of Omniverse or Rerun +- Reducing window sizes +- Higher update frequencies +- Pausing visualizers while they are not being used + + +Limitations +----------- + +**Rerun Visualizer Performance** + +The Rerun web-based visualizer may experience performance issues or crashes when visualizing large-scale +environments. For large-scale simulations, the Newton visualizer is recommended. Alternatively, to reduce load, +the num of environments can be overwritten and decreased using ``--num_envs``: + +.. code-block:: bash + + python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --visualizer rerun --num_envs 512 + + +.. note:: + + A future feature will support visualizing only a subset of environments, which will improve visualization performance + and reduce resource usage while maintaining full-scale training in the background. + + +**Rerun Visualizer FPS Control** + +The FPS control in the Rerun visualizer UI may not affect the visualization frame rate in all configurations. + + +**Newton Visualizer Contact and Center of Mass Markers** + +Contact and center of mass markers are not yet supported in the Newton visualizer. This will be addressed in a future release. + + +**Newton Visualizer CUDA/OpenGL Interoperability Warnings** + +On some system configurations, the Newton visualizer may display warnings about CUDA/OpenGL interoperability: + +.. code-block:: text + + Warning: Could not get MSAA config, falling back to non-AA. + Warp CUDA error 999: unknown error (in function wp_cuda_graphics_register_gl_buffer) + Warp UserWarning: Could not register GL buffer since CUDA/OpenGL interoperability + is not available. Falling back to copy operations between the Warp array and the + OpenGL buffer. + +The visualizer will still function correctly but may experience reduced performance due to falling back to +CPU copy operations instead of direct GPU memory sharing. diff --git a/docs/source/features/hydra.rst b/docs/source/features/hydra.rst new file mode 100644 index 0000000000000000000000000000000000000000..47e84fb328c658af1c29a1436bc1b6729e58c302 --- /dev/null +++ b/docs/source/features/hydra.rst @@ -0,0 +1,129 @@ +Hydra Configuration System +========================== + +.. currentmodule:: isaaclab + +Isaac Lab supports the `Hydra `_ configuration system to modify the task's +configuration using command line arguments, which can be useful to automate experiments and perform hyperparameter tuning. + +Any parameter of the environment can be modified by adding one or multiple elements of the form ``env.a.b.param1=value`` +to the command line input, where ``a.b.param1`` reflects the parameter's hierarchy, for example ``env.actions.joint_effort.scale=10.0``. +Similarly, the agent's parameters can be modified by using the ``agent`` prefix, for example ``agent.seed=2024``. + +The way these command line arguments are set follow the exact structure of the configuration files. Since the different +RL frameworks use different conventions, there might be differences in the way the parameters are set. For example, +with *rl_games* the seed will be set with ``agent.params.seed``, while with *rsl_rl*, *skrl* and *sb3* it will be set with +``agent.seed``. + +As a result, training with hydra arguments can be run with the following syntax: + +.. tab-set:: + :sync-group: rl-train + + .. tab-item:: rsl_rl + :sync: rsl_rl + + .. code-block:: shell + + python scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024 + + .. tab-item:: rl_games + :sync: rl_games + + .. code-block:: shell + + python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.params.seed=2024 + + .. tab-item:: skrl + :sync: skrl + + .. code-block:: shell + + python scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024 + + .. tab-item:: sb3 + :sync: sb3 + + .. code-block:: shell + + python scripts/reinforcement_learning/sb3/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024 + +The above command will run the training script with the task ``Isaac-Cartpole-v0`` in headless mode, and set the +``env.actions.joint_effort.scale`` parameter to 10.0 and the ``agent.seed`` parameter to 2024. + +.. note:: + + To keep backwards compatibility, and to provide a more user-friendly experience, we have kept the old cli arguments + of the form ``--param``, for example ``--num_envs``, ``--seed``, ``--max_iterations``. These arguments have precedence + over the hydra arguments, and will overwrite the values set by the hydra arguments. + + +Modifying advanced parameters +----------------------------- + +Callables +^^^^^^^^^ + +It is possible to modify functions and classes in the configuration files by using the syntax ``module:attribute_name``. +For example, in the Cartpole environment: + +.. literalinclude:: ../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :start-at: class ObservationsCfg + :end-at: policy: PolicyCfg = PolicyCfg() + :emphasize-lines: 9 + +we could modify ``joint_pos_rel`` to compute absolute positions instead of relative positions with +``env.observations.policy.joint_pos_rel.func=isaaclab.envs.mdp:joint_pos``. + +Setting parameters to None +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To set parameters to None, use the ``null`` keyword, which is a special keyword in Hydra that is automatically converted to None. +In the above example, we could also disable the ``joint_pos_rel`` observation by setting it to None with +``env.observations.policy.joint_pos_rel=null``. + +Dictionaries +^^^^^^^^^^^^ +Elements in dictionaries are handled as a parameters in the hierarchy. For example, in the Cartpole environment: + +.. literalinclude:: ../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :lines: 90-114 + :emphasize-lines: 11 + +the ``position_range`` parameter can be modified with ``env.events.reset_cart_position.params.position_range="[-2.0, 2.0]"``. +This example shows two noteworthy points: + +- The parameter we set has a space, so it must be enclosed in quotes. +- The parameter is a list while it is a tuple in the config. This is due to the fact that Hydra does not support tuples. + + +Modifying inter-dependent parameters +------------------------------------ + +Particular care should be taken when modifying the parameters using command line arguments. Some of the configurations +perform intermediate computations based on other parameters. These computations will not be updated when the parameters +are modified. + +For example, for the configuration of the Cartpole camera depth environment: + +.. literalinclude:: ../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_camera_env.py + :language: python + :start-at: class CartpoleDepthCameraEnvCfg + :end-at: tiled_camera.width + :emphasize-lines: 10, 15 + +If the user were to modify the width of the camera, i.e. ``env.tiled_camera.width=128``, then the parameter +``env.observation_space=[80,128,1]`` must be updated and given as input as well. + +Similarly, the ``__post_init__`` method is not updated with the command line inputs. In the ``LocomotionVelocityRoughEnvCfg``, for example, +the post init update is as follows: + +.. literalinclude:: ../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/velocity_env_cfg.py + :language: python + :start-at: class LocomotionVelocityRoughEnvCfg + :emphasize-lines: 23, 29, 31 + +Here, when modifying ``env.decimation`` or ``env.sim.dt``, the user needs to give the updated ``env.sim.render_interval``, +``env.scene.height_scanner.update_period``, and ``env.scene.contact_forces.update_period`` as input as well. diff --git a/docs/source/features/multi_gpu.rst b/docs/source/features/multi_gpu.rst new file mode 100644 index 0000000000000000000000000000000000000000..2537e5eff25baed44e578a0e633176cf3ecc6421 --- /dev/null +++ b/docs/source/features/multi_gpu.rst @@ -0,0 +1,222 @@ +Multi-GPU and Multi-Node Training +================================= + +.. currentmodule:: isaaclab + +Isaac Lab supports multi-GPU and multi-node reinforcement learning. Currently, this feature is only +available for RL-Games, RSL-RL and skrl libraries workflows. We are working on extending this feature to +other workflows. + +.. attention:: + + Multi-GPU and multi-node training is only supported on Linux. Windows support is not available at this time. + This is due to limitations of the NCCL library on Windows. + + +Multi-GPU Training +------------------ + +Isaac Lab supports the following multi-GPU training frameworks: + +* `Torchrun `_ through `PyTorch distributed `_ +* `JAX distributed `_ + +Pytorch Torchrun Implementation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We are using `Pytorch Torchrun `_ to manage multi-GPU +training. Torchrun manages the distributed training by: + +* **Process Management**: Launching one process per GPU, where each process is assigned to a specific GPU. +* **Script Execution**: Running the same training script (e.g., RL Games trainer) on each process. +* **Environment Instances**: Each process creates its own instance of the Isaac Lab environment. +* **Gradient Synchronization**: Aggregating gradients across all processes and broadcasting the synchronized + gradients back to each process after each training step. + +.. tip:: + Check out this `3 minute youtube video from PyTorch `_ + to understand how Torchrun works. + +The key components in this setup are: + +* **Torchrun**: Handles process spawning, communication, and gradient synchronization. +* **RL Library**: The reinforcement learning library that runs the actual training algorithm. +* **Isaac Lab**: Provides the simulation environment that each process instantiates independently. + +Under the hood, Torchrun uses the `DistributedDataParallel `_ +module to manage the distributed training. When training with multiple GPUs using Torchrun, the following happens: + +* Each GPU runs an independent process +* Each process executes the full training script +* Each process maintains its own: + + * Isaac Lab environment instance (with *n* parallel environments) + * Policy network copy + * Experience buffer for rollout collection + +* All processes synchronize only for gradient updates + +For a deeper dive into how Torchrun works, checkout +`PyTorch Docs: DistributedDataParallel - Internal Design `_. + +Jax Implementation +^^^^^^^^^^^^^^^^^^ + +.. tip:: + JAX is only supported with the skrl library. + +With JAX, we are using `skrl.utils.distributed.jax `_ +Since the ML framework doesn't automatically start multiple processes from a single program invocation, +the skrl library provides a module to start them. + +.. image:: ../_static/multi-gpu-rl/a3c-light.svg + :class: only-light + :align: center + :alt: Multi-GPU training paradigm + :width: 80% + +.. image:: ../_static/multi-gpu-rl/a3c-dark.svg + :class: only-dark + :align: center + :width: 80% + :alt: Multi-GPU training paradigm + +| + +Running Multi-GPU Training +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To train with multiple GPUs, use the following command, where ``--nproc_per_node`` represents the number of available GPUs: + +.. tab-set:: + :sync-group: rl-train + + .. tab-item:: rl_games + :sync: rl_games + + .. code-block:: shell + + python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: rsl_rl + :sync: rsl_rl + + .. code-block:: shell + + python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: skrl + :sync: skrl + + .. tab-set:: + + .. tab-item:: PyTorch + :sync: torch + + .. code-block:: shell + + python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: JAX + :sync: jax + + .. code-block:: shell + + python -m skrl.utils.distributed.jax --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax + +Multi-Node Training +------------------- + +To scale up training beyond multiple GPUs on a single machine, it is also possible to train across multiple nodes. +To train across multiple nodes/machines, it is required to launch an individual process on each node. + +For the master node, use the following command, where ``--nproc_per_node`` represents the number of available GPUs, and +``--nnodes`` represents the number of nodes: + +.. tab-set:: + :sync-group: rl-train + + .. tab-item:: rl_games + :sync: rl_games + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr= --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: rsl_rl + :sync: rsl_rl + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr= --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: skrl + :sync: skrl + + .. tab-set:: + + .. tab-item:: PyTorch + :sync: torch + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr= --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: JAX + :sync: jax + + .. code-block:: shell + + python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=0 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax + +Note that the port (``5555``) can be replaced with any other available port. + +For non-master nodes, use the following command, replacing ``--node_rank`` with the index of each machine: + +.. tab-set:: + :sync-group: rl-train + + .. tab-item:: rl_games + :sync: rl_games + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr= --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: rsl_rl + :sync: rsl_rl + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr= --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: skrl + :sync: skrl + + .. tab-set:: + + .. tab-item:: PyTorch + :sync: torch + + .. code-block:: shell + + python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr= --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed + + .. tab-item:: JAX + :sync: jax + + .. code-block:: shell + + python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=1 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax + +For more details on multi-node training with PyTorch, please visit the +`PyTorch documentation `_. +For more details on multi-node training with JAX, please visit the +`skrl documentation `_ and the +`JAX documentation `_. + +.. note:: + + As mentioned in the PyTorch documentation, "multi-node training is bottlenecked by inter-node communication + latencies". When this latency is high, it is possible multi-node training will perform worse than running on + a single node instance. diff --git a/docs/source/features/population_based_training.rst b/docs/source/features/population_based_training.rst new file mode 100644 index 0000000000000000000000000000000000000000..d88b8195bc7c4dc15ffbb35eededc8118933ce80 --- /dev/null +++ b/docs/source/features/population_based_training.rst @@ -0,0 +1,140 @@ +Population Based Training +========================= + +What PBT Does +------------- + +* Trains *N* policies in parallel (a "population") on the **same task**. +* Every ``interval_steps``: + + #. Save each policy's checkpoint and objective. + #. Score the population and identify **leaders** and **underperformers**. + #. For underperformers, replace weights from a random leader and **mutate** selected hyperparameters. + #. Restart that process with the new weights/params automatically. + +Leader / Underperformer Selection +--------------------------------- + +Let ``o_i`` be each initialized policy's objective, with mean ``μ`` and std ``σ``. + +Upper and lower performance cuts are:: + + upper_cut = max(μ + threshold_std * σ, μ + threshold_abs) + lower_cut = min(μ - threshold_std * σ, μ - threshold_abs) + +* **Leaders**: ``o_i > upper_cut`` +* **Underperformers**: ``o_i < lower_cut`` + +The "Natural-Selection" rules: + +1. Only underperformers are acted on (mutated or replaced). +2. If leaders exist, replace an underperformer with a random leader; otherwise, self-mutate. + +Mutation (Hyperparameters) +-------------------------- + +* Each param has a mutation function (e.g., ``mutate_float``, ``mutate_discount``, etc.). +* A param is mutated with probability ``mutation_rate``. +* When mutated, its value is perturbed within ``change_range = (min, max)``. +* Only whitelisted keys (from the PBT config) are considered. + +Example Config +-------------- + +.. code-block:: yaml + + pbt: + enabled: True + policy_idx: 0 + num_policies: 8 + directory: . + workspace: "pbt_workspace" + objective: episode.Curriculum/difficulty_level + interval_steps: 50000000 + threshold_std: 0.1 + threshold_abs: 0.025 + mutation_rate: 0.25 + change_range: [1.1, 2.0] + mutation: + agent.params.config.learning_rate: "mutate_float" + agent.params.config.grad_norm: "mutate_float" + agent.params.config.entropy_coef: "mutate_float" + agent.params.config.critic_coef: "mutate_float" + agent.params.config.bounds_loss_coef: "mutate_float" + agent.params.config.kl_threshold: "mutate_float" + agent.params.config.gamma: "mutate_discount" + agent.params.config.tau: "mutate_discount" + + +``objective: episode.Curriculum/difficulty_level`` is the dotted expression that uses +``infos["episode"]["Curriculum/difficulty_level"]`` as the scalar to **rank policies** (higher is better). +With ``num_policies: 8``, launch eight processes sharing the same ``workspace`` and unique ``policy_idx`` (0-7). + + +Launching PBT +------------- + +You must start **one process per policy** and point them to the **same workspace**. Set a unique +``policy_idx`` for each process and the common ``num_policies``. + +Minimal flags you need: + +* ``agent.pbt.enabled=True`` +* ``agent.pbt.directory=`` +* ``agent.pbt.policy_idx=<0..num_policies-1>`` + +.. note:: + All processes must use the same ``agent.pbt.workspace`` so they can see each other's checkpoints. + +.. caution:: + PBT is currently supported **only** with the **rl_games** library. Other RL libraries are not supported yet. + +Tips +---- + +* Keep checkpoints reasonable: reduce ``interval_steps`` only if you really need tighter PBT cadence. +* Use larger ``threshold_std`` and ``threshold_abs`` for greater population diversity. +* It is recommended to run 6+ workers to see benefit of pbt. + + +Training Example +---------------- + +We provide a reference PPO config here for task: +`Isaac-Dexsuite-Kuka-Allegro-Lift-v0 `_. +For the best logging experience, we recommend using wandb for the logging in the script. + +Launch *N* workers, where *n* indicates each worker index: + +.. code-block:: bash + + # Run this once per worker (n = 0..N-1), all pointing to the same directory/workspace + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py \ + --seed= \ + --task=Isaac-Dexsuite-Kuka-Allegro-Lift-v0 \ + --num_envs=8192 \ + --headless \ + --track \ + --wandb-name=idx \ + --wandb-entity=<**entity**> \ + --wandb-project-name=<**project**> + agent.pbt.enabled=True \ + agent.pbt.num_policies= \ + agent.pbt.policy_idx= \ + agent.pbt.workspace=<**pbt_workspace_name**> \ + agent.pbt.directory=<**/path/to/shared_folder**> \ + + +References +---------- + +This PBT implementation reimplements and is inspired by *Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training* (Petrenko et al., 2023). + +.. code-block:: bibtex + + @article{petrenko2023dexpbt, + title={Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training}, + author={Petrenko, Aleksei and Allshire, Arthur and State, Gavriel and Handa, Ankur and Makoviychuk, Viktor}, + journal={arXiv preprint arXiv:2305.12127}, + year={2023} + } diff --git a/docs/source/features/ray.rst b/docs/source/features/ray.rst new file mode 100644 index 0000000000000000000000000000000000000000..0edf935e8389e3d3da7e73c350e06a40cd992f01 --- /dev/null +++ b/docs/source/features/ray.rst @@ -0,0 +1,432 @@ +=========================== +Ray Job Dispatch and Tuning +=========================== + +.. currentmodule:: isaaclab + +Isaac Lab supports `Ray `_ for streamlining dispatching multiple training jobs (in parallel and in series), +and hyperparameter tuning, both on local and remote configurations. + +This `independent community contributed walkthrough video `_ +demonstrates some of the core functionality of the Ray integration covered in this overview. Although there may be some +differences in the codebase (such as file names being shortened) since the creation of the video, +the general workflow is the same. + +.. attention:: + + This functionality is experimental, and has been tested only on Linux. + +.. warning:: + + **Security Notice**: Due to security risks associated with Ray, + this workflow is not intended for use outside of a strictly controlled + network environment. Ray clusters should only be deployed in trusted, + isolated networks with appropriate access controls and security measures in place. + + + +Overview +-------- + +The Ray integration is useful for the following. + +- Dispatching several training jobs in parallel or sequentially with minimal interaction. +- Tuning hyperparameters; in parallel or sequentially with support for multiple GPUs and/or multiple GPU Nodes. +- Using the same training setup everywhere (on cloud and local) with minimal overhead. +- Resource Isolation for training jobs (resource-wrapped jobs). + +The core functionality of the Ray workflow consists of two main scripts that enable the orchestration +of resource-wrapped and tuning aggregate jobs. In resource-wrapped aggregate jobs, each sub-job and its +resource requirements are defined manually, enabling resource isolation. +For tuning aggregate jobs, individual jobs are generated automatically based on a hyperparameter +sweep configuration. + +Both resource-wrapped and tuning aggregate jobs dispatch individual jobs to a designated Ray +cluster, which leverages the cluster's resources (e.g., a single workstation node or multiple nodes) +to execute these jobs with workers in parallel and/or sequentially. + +By default, jobs use all \ +available resources on each available GPU-enabled node for each sub-job worker. This can be changed through +specifying the ``--num_workers`` argument for resource-wrapped jobs, or ``--num_workers_per_node`` +for tuning jobs, which is especially critical for parallel aggregate +job processing on local/virtual multi-GPU machines. Tuning jobs assume homogeneous node resource composition for nodes with GPUs. + +The three following files contain the core functionality of the Ray integration. + +.. dropdown:: scripts/reinforcement_learning/ray/wrap_resources.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/wrap_resources.py + :language: python + :emphasize-lines: 10-63 + +.. dropdown:: scripts/reinforcement_learning/ray/tuner.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/tuner.py + :language: python + :emphasize-lines: 18-59 + +.. dropdown:: scripts/reinforcement_learning/ray/task_runner.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/task_runner.py + :language: python + :emphasize-lines: 13-105 + +The following script can be used to submit aggregate +jobs to one or more Ray cluster(s), which can be used for +running jobs on a remote cluster or simultaneous jobs with heterogeneous +resource requirements. + +.. dropdown:: scripts/reinforcement_learning/ray/submit_job.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/submit_job.py + :language: python + :emphasize-lines: 13-61 + +The following script can be used to extract KubeRay cluster information for aggregate job submission. + +.. dropdown:: scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py + :language: python + :emphasize-lines: 14-26 + +The following script can be used to easily create clusters on Google GKE. + +.. dropdown:: scripts/reinforcement_learning/ray/launch.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/launch.py + :language: python + :emphasize-lines: 15-36 + +Docker-based Local Quickstart +----------------------------- + +First, follow the `Docker Guide `_ +to set up the NVIDIA Container Toolkit and Docker Compose. + +Then, run the following steps to start a tuning run. + +.. code-block:: bash + + # Build the base image, but we don't need to run it + python3 docker/container.py start && python3 docker/container.py stop + # Build the tuning image with extra deps + docker build -t isaacray -f scripts/reinforcement_learning/ray/cluster_configs/Dockerfile . + # Start the tuning image - symlink so that changes in the source folder show up in the container + docker run -v $(pwd)/source:/workspace/isaaclab/source -it --gpus all --net=host --entrypoint /bin/bash isaacray + # Start the Ray server within the tuning image + echo "import ray; ray.init(); import time; [time.sleep(10) for _ in iter(int, 1)]" | ./isaaclab.sh -p + + + +In a different terminal, run the following. + + +.. code-block:: bash + + # In a new terminal (don't close the above) , enter the image with a new shell. + docker container ps + docker exec -it /bin/bash + # Start a tuning run, with one parallel worker per GPU + ./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py \ + --cfg_file scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py \ + --cfg_class CartpoleTheiaJobCfg \ + --run_mode local \ + --workflow scripts/reinforcement_learning/rl_games/train.py \ + --num_workers_per_node + + +To view the training logs, in a different terminal, run the following and visit ``localhost:6006`` in a browser afterwards. + +.. code-block:: bash + + # In a new terminal (don't close the above) , enter the image with a new shell. + docker container ps + docker exec -it /bin/bash + # Start a tuning run, with one parallel worker per GPU + tensorboard --logdir=. + + +Submitting resource-wrapped individual jobs instead of automatic tuning runs is described in the following file. + +.. dropdown:: scripts/reinforcement_learning/ray/wrap_resources.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/wrap_resources.py + :language: python + :emphasize-lines: 10-63 + +The ``task_runner.py`` dispatches Python tasks to a Ray cluster via a single declarative YAML file. This approach allows users to specify additional pip packages and Python modules for each run. Fine-grained resource allocation is supported, with explicit control over the number of CPUs, GPUs, and memory assigned to each task. The runner also offers advanced scheduling capabilities: tasks can be restricted to specific nodes by hostname or node ID, and supports two launch modes: tasks can be executed independently as resources become available, or grouped into a simultaneous batch—ideal for multi-node training jobs—which ensures that all tasks launch together only when sufficient resources are available across the cluster. + +.. dropdown:: scripts/reinforcement_learning/ray/task_runner.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/task_runner.py + :language: python + :emphasize-lines: 13-105 + +To use this script, run a command similar to the following (replace ``tasks.yaml`` with your actual configuration file): + +.. code-block:: bash + + python3 scripts/reinforcement_learning/ray/submit_job.py --aggregate_jobs task_runner.py --task_cfg tasks.yaml + +For detailed instructions on how to write your ``tasks.yaml`` file, please refer to the comments in ``task_runner.py``. + +**Tip:** Place the ``tasks.yaml`` file in the ``scripts/reinforcement_learning/ray`` directory so that it is included when the ``working_dir`` is uploaded. You can then reference it using a relative path in the command. + +Transferring files from the running container can be done as follows. + +.. code-block:: bash + + docker container ps + docker cp : + + +For tuning jobs, specify the tuning job / hyperparameter sweep as child class of :class:`JobCfg` . +The included :class:`JobCfg` only supports the ``rl_games`` workflow due to differences in +environment entrypoints and hydra arguments, although other workflows will work if provided a compatible +:class:`JobCfg`. + +.. dropdown:: scripts/reinforcement_learning/ray/tuner.py (JobCfg definition) + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/tuner.py + :language: python + :start-at: class JobCfg + :end-at: self.cfg = cfg + +For example, see the following Cartpole Example configurations. + +.. dropdown:: scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py + :language: python + + +Remote Clusters +--------------- + +Select one of the following methods to create a Ray cluster to accept and execute dispatched jobs. + +KubeRay Setup +~~~~~~~~~~~~~ + +If using KubeRay clusters on Google GKE with the batteries-included cluster launch file, +the following dependencies are also needed. + +.. code-block:: bash + + python3 -p -m pip install kubernetes Jinja2 + +For use on Kubernetes clusters with KubeRay, +such as Google Kubernetes Engine or Amazon Elastic Kubernetes Service, ``kubectl`` is required, and can +be installed via the `Kubernetes website `_ . + +Google Cloud is currently the only platform tested, although +any cloud provider should work if one configures the following. + +.. attention:: + The ``ray`` command should be modified to use Isaac python, which could be achieved in a fashion similar to + ``sed -i "1i $(echo "#!/workspace/isaaclab/_isaac_sim/python.sh")" \ + /isaac-sim/kit/python/bin/ray && ln -s /isaac-sim/kit/python/bin/ray /usr/local/bin/ray``. + +- An container registry (NGC, GCS artifact registry, AWS ECR, etc) with + an Isaac Lab image configured to support Ray. See ``cluster_configs/Dockerfile`` to see how to modify the ``isaac-lab-base`` + container for Ray compatibility. Ray should use the isaac sim python shebang, and ``nvidia-smi`` + should work within the container. Be careful with the setup here as + paths need to be configured correctly for everything to work. It's likely that + the example dockerfile will work out of the box and can be pushed to the registry, as + long as the base image has already been built as in the container guide. +- A Kubernetes setup with available NVIDIA RTX (likely ``l4`` or ``l40`` or ``tesla-t4`` or ``a10``) GPU-passthrough node-pool resources, + that has access to your container registry/storage bucket and has the Ray operator enabled with correct IAM + permissions. This can be easily achieved with services such as Google GKE or AWS EKS, + provided that your account or organization has been granted a GPU-budget. It is recommended + to use manual kubernetes services as opposed to "autopilot" services for cost-effective + experimentation as this way clusters can be completely shut down when not in use, although + this may require installing the `Nvidia GPU Operator `_ . +- An `MLFlow server `_ that your cluster has access to + (already included for Google Cloud, which can be referenced for the format and MLFlow integration). +- A ``kuberay.yaml.ninja`` file that describes how to allocate resources (already included for + Google Cloud, which can be referenced for the format and MLFlow integration). + +Ray Clusters (Without Kubernetes) Setup +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. attention:: + Modify the Ray command to use Isaac Python like in KubeRay clusters, and follow the same + steps for creating an image/cluster permissions. + +See the `Ray Clusters Overview `_ or +`Anyscale `_ for more information. + +Also, create an `MLFlow server `_ that your local +host and cluster have access to. + +Shared Steps Between KubeRay and Pure Ray Part I +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1.) Install Ray on your local machine. + +.. code-block:: bash + + python3 -p -m pip install ray[default]==2.31.0 + +2.) Build the Isaac Ray image, and upload it to your container registry of choice. + +.. code-block:: bash + + # Login with NGC (nvcr.io) registry first, see docker steps in repo. + python3 docker/container.py start + # Build the special Isaac Lab Ray Image + docker build -t -f scripts/reinforcement_learning/ray/cluster_configs/Dockerfile . + # Push the image to your registry of choice. + docker push + +KubeRay Clusters Only +~~~~~~~~~~~~~~~~~~~~~ +`k9s `_ is a great tool for monitoring your clusters that can +easily be installed with ``snap install k9s --devmode``. + +1.) Verify cluster access, and that the correct operators are installed. + +.. code-block:: bash + + # Verify cluster access + kubectl cluster-info + # If using a manually managed cluster (not Autopilot or the like) + # verify that there are node pools + kubectl get nodes + # Check that the ray operator is installed on the cluster + # should list rayclusters.ray.io , rayjobs.ray.io , and rayservices.ray.io + kubectl get crds | grep ray + # Check that the NVIDIA Driver Operator is installed on the cluster + # should list clusterpolicies.nvidia.com + kubectl get crds | grep nvidia + +2.) Create the KubeRay cluster and an MLFlow server for receiving logs +that your cluster has access to. +This can be done automatically for Google GKE, +where instructions are included in the following creation file. + +.. dropdown:: scripts/reinforcement_learning/ray/launch.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/launch.py + :language: python + :emphasize-lines: 15-36 + +For other cloud services, the ``kuberay.yaml.ninja`` will be similar to that of +Google's. + + +.. dropdown:: scripts/reinforcement_learning/ray/cluster_configs/google_cloud/kuberay.yaml.ninja + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/cluster_configs/google_cloud/kuberay.yaml.jinja + :language: python + + + +3.) Fetch the KubeRay cluster IP addresses, and the MLFLow Server IP. +This can be done automatically for KubeRay clusters, +where instructions are included in the following fetching file. +The KubeRay clusters are saved to a file, but the MLFLow Server IP is +printed. + +.. dropdown:: scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py + :language: python + :emphasize-lines: 14-26 + +Ray Clusters Only (Without Kubernetes) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +1.) Verify cluster access. + +2.) Create a ``~/.cluster_config`` file, where ``name: address: http://:`` is on +a new line for each unique cluster. For one cluster, there should only be one line in this file. + +3.) Start an MLFLow Server to receive the logs that the ray cluster has access to, +and determine the server URI. + +Dispatching Steps Shared Between KubeRay and Pure Ray Part II +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +1.) Test that your cluster is operational with the following. + +.. code-block:: bash + + # Test that NVIDIA GPUs are visible and that Ray is operation with the following command: + python3 scripts/reinforcement_learning/ray/submit_job.py --aggregate_jobs wrap_resources.py --test + +2.) Submitting tuning and/or resource-wrapped jobs is described in the :file:`submit_job.py` file. + +.. dropdown:: scripts/reinforcement_learning/ray/submit_job.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/submit_job.py + :language: python + :emphasize-lines: 13-61 + +3.) For tuning jobs, specify the tuning job / hyperparameter sweep as a :class:`JobCfg` . +The included :class:`JobCfg` only supports the ``rl_games`` workflow due to differences in +environment entrypoints and hydra arguments, although other workflows will work if provided a compatible +:class:`JobCfg`. + +.. dropdown:: scripts/reinforcement_learning/ray/tuner.py (JobCfg definition) + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/tuner.py + :language: python + :start-at: class JobCfg + :end-at: self.cfg = cfg + +For example, see the following Cartpole Example configurations. + +.. dropdown:: scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py + :icon: code + + .. literalinclude:: ../../../scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py + :language: python + + +To view the tuning results, view the MLFlow dashboard of the server that you created. +For KubeRay, this can be done through port forwarding the MLFlow dashboard with the following. + +``kubectl port-forward service/isaacray-mlflow 5000:5000`` + +Then visit the following address in a browser. + +``localhost:5000`` + +If the MLFlow port is forwarded like above, it can be converted into tensorboard logs with +this following command. + +``./isaaclab.sh -p scripts/reinforcement_learning/ray/mlflow_to_local_tensorboard.py \ +--uri http://localhost:5000 --experiment-name IsaacRay--tune --download-dir test`` + + +Kubernetes Cluster Cleanup +'''''''''''''''''''''''''' + +For the sake of conserving resources, and potentially freeing precious GPU resources for other people to use +on shared compute platforms, please destroy the Ray cluster after use. They can be easily +recreated! For KubeRay clusters, this can be done as follows. + +.. code-block:: bash + + kubectl get raycluster | egrep 'isaacray' | awk '{print $1}' | xargs kubectl delete raycluster && + kubectl get deployments | egrep 'mlflow' | awk '{print $1}' | xargs kubectl delete deployment && + kubectl get services | egrep 'mlflow' | awk '{print $1}' | xargs kubectl delete service && + kubectl get services | egrep 'isaacray' | awk '{print $1}' | xargs kubectl delete service diff --git a/docs/source/features/reproducibility.rst b/docs/source/features/reproducibility.rst new file mode 100644 index 0000000000000000000000000000000000000000..631e138376c9440189728ef3d6bae3dd60717dce --- /dev/null +++ b/docs/source/features/reproducibility.rst @@ -0,0 +1,42 @@ +Reproducibility and Determinism +------------------------------- + +Given the same hardware and Isaac Sim (and consequently PhysX) version, the simulation produces +identical results for scenes with rigid bodies and articulations. However, the simulation results can +vary across different hardware configurations due to floating point precision and rounding errors. +At present, PhysX does not guarantee determinism for any scene with non-rigid bodies, such as cloth +or soft bodies. For more information, please refer to the `PhysX Determinism documentation`_. + +Based on above, Isaac Lab provides a deterministic simulation that ensures consistent simulation +results across different runs. This is achieved by using the same random seed for the +simulation environment and the physics engine. At construction of the environment, the random seed +is set to a fixed value using the :meth:`~isaaclab.utils.seed.configure_seed` method. This method sets the +random seed for both the CPU and GPU globally across different libraries, including PyTorch and +NumPy. + +In the included workflow scripts, the seed specified in the learning agent's configuration file or the +command line argument is used to set the random seed for the environment. This ensures that the +simulation results are reproducible across different runs. The seed is set into the environment +parameters :attr:`isaaclab.envs.ManagerBasedEnvCfg.seed` or :attr:`isaaclab.envs.DirectRLEnvCfg.seed` +depending on the manager-based or direct environment implementation respectively. + +For results on our determinacy testing for RL training, please check the GitHub Pull Request `#940`_. + +.. tip:: + + Due to GPU work scheduling, there's a possibility that runtime changes to simulation parameters + may alter the order in which operations take place. This occurs because environment updates can + happen while the GPU is occupied with other tasks. Due to the inherent nature of floating-point + numeric storage, any modification to the execution ordering can result in minor changes in the + least significant bits of output data. These changes may lead to divergent execution over the + course of simulating thousands of environments and simulation frames. + + An illustrative example of this issue is observed with the runtime domain randomization of object's + physics materials. This process can introduce both determinacy and simulation issues when executed + on the GPU due to the way these parameters are passed from the CPU to the GPU in the lower-level APIs. + Consequently, it is strongly advised to perform this operation only at setup time, before the + environment stepping commences. + + +.. _PhysX Determinism documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/API.html#determinism +.. _#940: https://github.com/isaac-sim/IsaacLab/pull/940 diff --git a/docs/source/how-to/add_own_library.rst b/docs/source/how-to/add_own_library.rst new file mode 100644 index 0000000000000000000000000000000000000000..8a0347d65979e1f342d1fb2cc534e28129ba1a78 --- /dev/null +++ b/docs/source/how-to/add_own_library.rst @@ -0,0 +1,102 @@ +.. _how-to-add-library: + +Adding your own learning library +================================ + +Isaac Lab comes pre-integrated with a number of libraries (such as RSL-RL, RL-Games, SKRL, Stable Baselines, etc.). +However, you may want to integrate your own library with Isaac Lab or use a different version of the libraries than +the one installed by Isaac Lab. This is possible as long as the library is available as Python package that supports +the Python version used by the underlying simulator. For instance, if you are using Isaac Sim 4.0.0 onwards, you need +to ensure that the library is available for Python 3.11. + +Using a different version of a library +-------------------------------------- + +If you want to use a different version of a library than the one installed by Isaac Lab, you can install the library +by building it from source or using a different version of the library available on PyPI. + +For instance, if you want to use your own modified version of the `rsl-rl`_ library, you can follow these steps: + +1. Follow the instructions for installing Isaac Lab. This will install the default version of the ``rsl-rl`` library. +2. Clone the ``rsl-rl`` library from the GitHub repository: + + .. code-block:: bash + + git clone git@github.com:leggedrobotics/rsl_rl.git + + +3. Install the library in your Python environment: + + .. code-block:: bash + + # Assuming you are in the root directory of the Isaac Lab repository + cd IsaacLab + + # Note: If you are using a virtual environment, make sure to activate it before running the following command + ./isaaclab.sh -p -m pip install -e /path/to/rsl_rl + +In this case, the ``rsl-rl`` library will be installed in the Python environment used by Isaac Lab. You can now use the +``rsl-rl`` library in your experiments. To check the library version and other details, you can use the following +command: + +.. code-block:: bash + + ./isaaclab.sh -p -m pip show rsl-rl-lib + +This should now show the location of the ``rsl-rl`` library as the directory where you cloned the library. +For instance, if you cloned the library to ``/home/user/git/rsl_rl``, the output of the above command should be: + +.. code-block:: bash + + Name: rsl_rl + Version: 3.0.1 + Summary: Fast and simple RL algorithms implemented in pytorch + Home-page: https://github.com/leggedrobotics/rsl_rl + Author: ETH Zurich, NVIDIA CORPORATION + Author-email: + License: BSD-3 + Location: /home/user/git/rsl_rl + Requires: torch, torchvision, numpy, GitPython, onnx + Required-by: + + +Integrating a new library +------------------------- + +Adding a new library to Isaac Lab is similar to using a different version of a library. You can install the library +in your Python environment and use it in your experiments. However, if you want to integrate the library with +Isaac Lab, you will first need to make a wrapper for the library, as explained in +:ref:`how-to-env-wrappers`. + +The following steps can be followed to integrate a new library with Isaac Lab: + +1. Add your library as an extra-dependency in the ``setup.py`` for the extension ``isaaclab_rl``. + This will ensure that the library is installed when you install Isaac Lab or it will complain if the library is not + installed or available. +2. Install your library in the Python environment used by Isaac Lab. You can do this by following the steps mentioned + in the previous section. +3. Create a wrapper for the library. You can check the module :mod:`isaaclab_rl` + for examples of wrappers for different libraries. You can create a new wrapper for your library and add it to the + module. You can also create a new module for the wrapper if you prefer. +4. Create workflow scripts for your library to train and evaluate agents. You can check the existing workflow scripts + in the ``scripts/reinforcement_learning`` directory for examples. You can create new workflow + scripts for your library and add them to the directory. + +Optionally, you can also add some tests and documentation for the wrapper. This will help ensure that the wrapper +works as expected and can guide users on how to use the wrapper. + +* Add some tests to ensure that the wrapper works as expected and remains compatible with the library. + These tests can be added to the ``source/isaaclab_rl/test`` directory. +* Add some documentation for the wrapper. You can add the API documentation to the + :ref:`API documentation` for the ``isaaclab_rl`` module. + + +Configuring an RL Agent +----------------------- + +Once you have integrated a new library with Isaac Lab, you can configure the example environment to use the new library. +You can check the :ref:`tutorial-configure-rl-training` for an example of how to configure the training process to use a +different library. + + +.. _rsl-rl: https://github.com/leggedrobotics/rsl_rl diff --git a/docs/source/how-to/cloudxr_teleoperation.rst b/docs/source/how-to/cloudxr_teleoperation.rst new file mode 100644 index 0000000000000000000000000000000000000000..7cf243d4b01706a1eb090e0a05d5d76600d2bc87 --- /dev/null +++ b/docs/source/how-to/cloudxr_teleoperation.rst @@ -0,0 +1,1132 @@ +.. _cloudxr-teleoperation: + +Setting up CloudXR Teleoperation +================================ + +.. currentmodule:: isaaclab + +`NVIDIA CloudXR`_ enables seamless, high-fidelity immersive streaming to extended reality (XR) +devices over any network. + +Isaac Lab developers can use CloudXR with Isaac Lab to build teleoperation workflows that require +immersive XR rendering for increased spatial acuity and/or hand tracking for teleoperation of +dextrous robots. + +In these workflows, Isaac Lab renders and submits stereo views of the robot simulation to CloudXR, +which then encodes and streams the rendered views to a compatible XR device in realtime using a +low-latency, GPU-accelerated pipeline. Control inputs such as hand tracking data are sent from the +XR device back to Isaac Lab through CloudXR, where they can be used to control the robot. + +This guide explains how to use CloudXR and `Apple Vision Pro`_ for immersive streaming and +teleoperation in Isaac Lab. + +.. note:: + + See :ref:`manus-vive-handtracking` for more information on supported hand-tracking peripherals. + +.. note:: + + **Meta Quest 3 and Pico 4 Ultra Support (Early Access)** + + Meta Quest 3 and Pico 4 Ultra are now supported via the `CloudXR Early Access program`_. + Join the program by mentioning isaac use cases. Once approved, you'll receive email to set up NGC, + then download `CloudXR.js with Isaac Teleop samples`_ and follow its guide. + Pico 4 Ultra must use HTTPS mode (see NGC documentation for details). General availability + will be provided in a future version of Isaac Lab. + +.. _`CloudXR Early Access program`: https://developer.nvidia.com/cloudxr-sdk-early-access-program/join +.. _`CloudXR.js with Isaac Teleop samples`: https://catalog.ngc.nvidia.com/orgs/nvidia/resources/cloudxr-js-early-access?version=6.0.0-beta + +Overview +-------- + +Using CloudXR with Isaac Lab involves the following components: + +* **Isaac Lab** is used to simulate the robot environment and apply control data received from the + teleoperator. + +* The **NVIDIA CloudXR Runtime** runs on the Isaac Lab workstation in a Docker container, and streams + the virtual simulation from Isaac Lab to compatible XR devices. + +* The **Isaac XR Teleop Sample Client** is a sample app for Apple Vision Pro which enables + immersive streaming and teleoperation of an Isaac Lab simulation using CloudXR. + +This guide will walk you through how to: + +* :ref:`run-isaac-lab-with-the-cloudxr-runtime` + +* :ref:`use-apple-vision-pro`, including how to :ref:`build-apple-vision-pro`, + :ref:`teleoperate-apple-vision-pro`, and :ref:`manus-vive-handtracking`. + +* :ref:`develop-xr-isaac-lab`, including how to :ref:`run-isaac-lab-with-xr`, + :ref:`configure-scene-placement`, and :ref:`optimize-xr-performance`. + +* :ref:`control-robot-with-xr`, including the :ref:`openxr-device-architecture`, + :ref:`control-robot-with-xr-retargeters`, and how to implement :ref:`control-robot-with-xr-callbacks`. + +As well as :ref:`xr-known-issues`. + + +System Requirements +------------------- + +Prior to using CloudXR with Isaac Lab, please review the following system requirements: + + * Isaac Lab workstation + + * Ubuntu 22.04 or Ubuntu 24.04 + * Hardware requirements to sustain 45 FPS with a 120Hz physics simulation: + * CPU: 16-Cores AMD Ryzen Threadripper Pro 5955WX or higher + * Memory: 64GB RAM + * GPU: 1x RTX PRO 6000 GPUs (or equivalent e.g. 1x RTX 5090) or higher + * For details on driver requirements, please see the `Technical Requirements `_ guide + * `Docker`_ 26.0.0+, `Docker Compose`_ 2.25.0+, and the `NVIDIA Container Toolkit`_. Refer to + the Isaac Lab :ref:`deployment-docker` for how to install. + + * Apple Vision Pro + + * visionOS 26 + * Apple M3 Pro chip with an 11-core CPU with at least 5 performance cores and 6 efficiency cores + * 16GB unified memory + * 256 GB SSD + + * Apple Silicon based Mac (for building the Isaac XR Teleop Sample Client App for Apple Vision Pro + with Xcode) + + * macOS Sequoia 15.6 or later + * Xcode 26.0 + + * Wifi 6 capable router + + * A strong wireless connection is essential for a high-quality streaming experience. Refer to the + requirements of `Omniverse Spatial Streaming`_ for more details. + * We recommend using a dedicated router, as concurrent usage will degrade quality + * The Apple Vision Pro and Isaac Lab workstation must be IP-reachable from one another (note: + many institutional wireless networks will prevent devices from reaching each other, resulting + in the Apple Vision Pro being unable to find the Isaac Lab workstation on the network) + +.. note:: + If you are using DGX Spark, check `DGX Spark Limitations `_ for compatibility. + + +.. _`Omniverse Spatial Streaming`: https://docs.omniverse.nvidia.com/avp/latest/setup-network.html + + +.. _run-isaac-lab-with-the-cloudxr-runtime: + +Run Isaac Lab with the CloudXR Runtime +-------------------------------------- + +The CloudXR Runtime runs in a Docker container on your Isaac Lab workstation, and is responsible for +streaming the Isaac Lab simulation to a compatible XR device. + +Ensure that `Docker`_, `Docker Compose`_, and the `NVIDIA Container Toolkit`_ are installed on your +Isaac Lab workstation as described in the Isaac Lab :ref:`deployment-docker`. + +Also ensure that your firewall allows connections to the ports used by CloudXR by running: + +.. code:: bash + + sudo ufw allow 47998:48000,48005,48008,48012/udp + sudo ufw allow 48010/tcp + +There are two options to run the CloudXR Runtime Docker container: + +.. dropdown:: Option 1 (Recommended): Use Docker Compose to run the Isaac Lab and CloudXR Runtime + containers together + :open: + + On your Isaac Lab workstation: + + #. From the root of the Isaac Lab repository, start the Isaac Lab and CloudXR Runtime containers + using the Isaac Lab ``container.py`` script + + .. code:: bash + + ./docker/container.py start \ + --files docker-compose.cloudxr-runtime.patch.yaml \ + --env-file .env.cloudxr-runtime + + If prompted, elect to activate X11 forwarding, which is necessary to see the Isaac Sim UI. + + .. note:: + + The ``container.py`` script is a thin wrapper around Docker Compose. The additional + ``--files`` and ``--env-file`` arguments augment the base Docker Compose configuration to + additionally run the CloudXR Runtime + + For more details on ``container.py`` and running Isaac Lab with Docker Compose, see the + :ref:`deployment-docker`. + + #. Enter the Isaac Lab base container with: + + .. code:: bash + + ./docker/container.py enter base + + From within the Isaac Lab base container, you can run Isaac Lab scripts that use XR. + + #. Run an example teleop task with: + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --teleop_device handtracking \ + --enable_pinocchio + + #. You'll want to leave the container running for the next steps. But once you are finished, you can + stop the containers with: + + .. code:: bash + + ./docker/container.py stop \ + --files docker-compose.cloudxr-runtime.patch.yaml \ + --env-file .env.cloudxr-runtime + + .. tip:: + + If you encounter issues on restart, you can run the following command to clean up orphaned + containers: + + .. code:: bash + + docker system prune -f + +.. dropdown:: Option 2: Run Isaac Lab as a local process and CloudXR Runtime container with Docker + + Isaac Lab can be run as a local process that connects to the CloudXR Runtime Docker container. + However, this method requires manually specifying a shared directory for communication between + the Isaac Lab instance and the CloudXR Runtime. + + On your Isaac Lab workstation: + + #. From the root of the Isaac Lab repository, create a local folder for temporary cache files: + + .. code:: bash + + mkdir -p $(pwd)/openxr + + #. Start the CloudXR Runtime, mounting the directory created above to the ``/openxr`` directory in + the container: + + .. code:: bash + + docker run -it --rm --name cloudxr-runtime \ + --user $(id -u):$(id -g) \ + --gpus=all \ + -e "ACCEPT_EULA=Y" \ + --mount type=bind,src=$(pwd)/openxr,dst=/openxr \ + -p 48010:48010 \ + -p 47998:47998/udp \ + -p 47999:47999/udp \ + -p 48000:48000/udp \ + -p 48005:48005/udp \ + -p 48008:48008/udp \ + -p 48012:48012/udp \ + nvcr.io/nvidia/cloudxr-runtime:5.0.1 + + .. note:: + If you choose a particular GPU instead of ``all``, you need to make sure Isaac Lab also runs + on that GPU. + + .. tip:: + + If you encounter issues on running cloudxr-runtime container, you can run the following + command to clean up the orphaned container: + + .. code:: bash + + docker stop cloudxr-runtime + docker rm cloudxr-runtime + + #. In a new terminal where you intend to run Isaac Lab, export the following environment + variables, which reference the directory created above: + + .. code:: bash + + export XDG_RUNTIME_DIR=$(pwd)/openxr/run + export XR_RUNTIME_JSON=$(pwd)/openxr/share/openxr/1/openxr_cloudxr.json + + You can now run Isaac Lab scripts that use XR. + + #. Run an example teleop task with: + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --teleop_device handtracking \ + --enable_pinocchio + +With Isaac Lab and the CloudXR Runtime running: + +#. In the Isaac Sim UI: locate the Panel named **AR** and choose the following options: + + * Selected Output Plugin: **OpenXR** + + * OpenXR Runtime: **System OpenXR Runtime** + + .. figure:: ../_static/setup/cloudxr_ar_panel.jpg + :align: center + :figwidth: 50% + :alt: Isaac Sim UI: AR Panel + + .. note:: + Isaac Sim lets you choose from several OpenXR runtime options: + + * **System OpenXR Runtime**: Use a runtime installed outside of Isaac Lab, such as the CloudXR Runtime set up via Docker in this tutorial. + + * **CloudXR Runtime (5.0)**: Use the built-in CloudXR Runtime. + + * **Custom**: Allow you to specify and run any custom OpenXR Runtime of your choice. + +#. Click **Start AR**. + +The Viewport should show two eyes being rendered, and you should see the status "AR profile is +active". + +.. figure:: ../_static/setup/cloudxr_viewport.jpg + :align: center + :figwidth: 100% + :alt: Isaac Lab viewport rendering two eyes + +Isaac Lab is now ready to receive connections from a CloudXR client. The next sections will walk +you through building and connecting a CloudXR client. + +.. admonition:: Learn More about Teleoperation and Imitation Learning in Isaac Lab + + To learn more about the Isaac Lab teleoperation scripts, and how to build new teleoperation and + imitation learning workflows in Isaac Lab, see :ref:`teleoperation-imitation-learning`. + + +.. _use-apple-vision-pro: + +Use Apple Vision Pro for Teleoperation +-------------------------------------- + +This section will walk you through building and installing the Isaac XR Teleop Sample Client for +Apple Vision Pro, connecting to Isaac Lab, and teleoperating a simulated robot. + + +.. _build-apple-vision-pro: + +Build and Install the Isaac XR Teleop Sample Client App for Apple Vision Pro +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +On your Mac: + +#. Clone the `Isaac XR Teleop Sample Client`_ GitHub repository: + + .. code-block:: bash + + git clone git@github.com:isaac-sim/isaac-xr-teleop-sample-client-apple.git + +#. Check out the App version that matches your Isaac Lab version: + + +-------------------+---------------------+ + | Isaac Lab Version | Client App Version | + +-------------------+---------------------+ + | 2.3 | v2.3.0 | + +-------------------+---------------------+ + | 2.2 | v2.2.0 | + +-------------------+---------------------+ + | 2.1 | v1.0.0 | + +-------------------+---------------------+ + + .. code-block:: bash + + git checkout + +#. Follow the README in the repository to build and install the app on your Apple Vision Pro. + + +.. _teleoperate-apple-vision-pro: + +Teleoperate an Isaac Lab Robot with Apple Vision Pro +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +With the Isaac XR Teleop Sample Client installed on your Apple Vision Pro, you are ready to connect +to Isaac Lab. + +.. tip:: + + **Before wearing the headset**, you can first verify connectivity from your Mac: + + .. code:: bash + + # Test signaling port (replace with your workstation IP) + nc -vz 48010 + + Expected output: ``Connection to port 48010 [tcp/*] succeeded!`` + + If the connection fails, check that the runtime container is running (``docker ps``) and no stale + runtime container is blocking ports. + +On your Isaac Lab workstation: + +#. Ensure that Isaac Lab and CloudXR are both running as described in + :ref:`run-isaac-lab-with-the-cloudxr-runtime`, including starting Isaac Lab with a script that + supports teleoperation. For example: + + .. code-block:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --teleop_device handtracking \ + --enable_pinocchio + + .. note:: + Recall that the script above should either be run within the Isaac Lab Docker container + (Option 1, recommended), or with environment variables configured to a directory shared by a + running CloudXR Runtime Docker container (Option 2). + +#. Locate the Panel named **AR**. + +#. Click **Start AR** and ensure that the Viewport shows two eyes being rendered. + +Back on your Apple Vision Pro: + +#. Open the Isaac XR Teleop Sample Client. You should see a UI window: + + .. figure:: ../_static/setup/cloudxr_avp_connect_ui.jpg + :align: center + :figwidth: 50% + :alt: Isaac Sim UI: AR Panel + +#. Enter the IP address of your Isaac Lab workstation. + + .. note:: + The Apple Vision Pro and Isaac Lab machine must be IP-reachable from one another. + + We recommend using a dedicated Wifi 6 router for this process, as many institutional wireless + networks will prevent devices from reaching each other, resulting in the Apple Vision Pro + being unable to find the Isaac Lab workstation on the network. + +#. Click **Connect**. + + The first time you attempt to connect, you may need to allow the application access to + permissions such as hand tracking and local network usage, and then connect again. + +#. After a brief period, you should see the Isaac Lab simulation rendered in the Apple Vision Pro, + as well as a set of controls for teleoperation. + + .. figure:: ../_static/setup/cloudxr_avp_teleop_ui.jpg + :align: center + :figwidth: 50% + :alt: Isaac Sim UI: AR Panel + +#. Click **Play** to begin teleoperating the simulated robot. The robot motion should now be + directed by your hand movements. + + You may repeatedly **Play**, **Stop**, and **Reset** the teleoperation session using the UI + controls. + + .. tip:: + For teleoperation tasks that require bimanual manipulation, visionOS accessibility features + can be used to control teleoperation without the use of hand gestures. For example, in order + to enable voice control of the UI: + + #. In **Settings** > **Accessibility** > **Voice Control**, Turn on **Voice Control** + + #. In **Settings** > **Accessibility** > **Voice Control** > **Commands** > **Basic + Navigation** > Turn on **** + + #. Now you can say "Play", "Stop", and "Reset" to control teleoperation while the app is + connected. + +#. Teleoperate the simulated robot by moving your hands. + + .. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/cloudxr_bimanual_teleop.gif + :align: center + :alt: Isaac Lab teleoperation of a bimanual dexterous robot with CloudXR + + .. note:: + + The red dots represent the tracked position of the hand joints. Latency or offset between the + motion of the dots and the robot may be caused by the limits of the robot joints and/or robot + controller. + + .. note:: + When the inverse kinematics solver fails to find a valid solution, an error message will appear + in the XR device display. To recover from this state, click the **Reset** button to return + the robot to its original pose and continue teleoperation. + + .. figure:: ../_static/setup/cloudxr_avp_ik_error.jpg + :align: center + :figwidth: 80% + :alt: IK Error Message Display in XR Device + + + +#. When you are finished with the example, click **Disconnect** to disconnect from Isaac Lab. + +.. admonition:: Learn More about Teleoperation and Imitation Learning in Isaac Lab + + See :ref:`teleoperation-imitation-learning` to learn how to record teleoperated demonstrations + and build teleoperation and imitation learning workflows with Isaac Lab. + + +.. _manus-vive-handtracking: + +Manus + Vive Hand Tracking +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Manus gloves and HTC Vive trackers can provide hand tracking when optical hand tracking from a headset is occluded. +This setup expects Manus gloves with a Manus SDK license and Vive trackers attached to the gloves. +Requires Isaac Sim 5.1 or later. + +Run the teleoperation example with Manus + Vive tracking: + +.. dropdown:: Installation instructions + :open: + + Vive tracker integration is provided through the libsurvive library. + + To install, clone the repository, build the python package, and install the required udev rules. + In your Isaac Lab virtual environment, run the following commands: + + .. code-block:: bash + + git clone https://github.com/collabora/libsurvive.git + cd libsurvive + pip install scikit-build + python setup.py install + + sudo cp ./useful_files/81-vive.rules /etc/udev/rules.d/ + sudo udevadm control --reload-rules && sudo udevadm trigger + + + The Manus integration is provided through the Isaac Sim teleoperation input plugin framework. + Install the plugin by following the build and installation steps in `isaac-teleop-device-plugins `_. + +In the same terminal from which you will launch Isaac Lab, set: + +.. code-block:: bash + + export ISAACSIM_HANDTRACKER_LIB=/build-manus-default/lib/libIsaacSimManusHandTracking.so + +Once the plugin is installed, run the teleoperation example: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --teleop_device manusvive \ + --xr \ + --enable_pinocchio + +The recommended workflow, is to start Isaac Lab, click **Start AR**, and then put on the Manus gloves, vive trackers, and +headset. Once you are ready to begin the session, use voice commands to launch the Isaac XR teleop sample client and +connect to Isaac Lab. + +Isaac Lab automatically calibrates the Vive trackers using wrist pose data from the Apple Vision Pro during the initial +frames of the session. If calibration fails, for example, if the red dots do not accurately follow the teleoperator's +hands, restart Isaac Lab and begin with your hands in a palm-up position to improve calibration reliability. + +For optimal performance, position the lighthouse above the hands, tilted slightly downward. +Ensure the lighthouse remains stable; a stand is recommended to prevent wobbling. + +Ensure that while the task is being teleoperated, the hands remain stable and visible to the lighthouse at all times. +See: `Installing the Base Stations `_ +and `Tips for Setting Up the Base Stations `_ + +.. note:: + + On first launch of the Manus Vive device, the Vive lighthouses may take a few seconds to calibrate. Keep the Vive trackers + stable and visible to the lighthouse during this time. If the light houses are moved or if tracking fails or is unstable, + calibration can be forced by deleting the calibration file at: ``$XDG_RUNTIME_DIR/libsurvive/config.json``. If XDG_RUNTIME_DIR + is not set, the default directory is ``~/.config/libsurvive``. + + For more information consult the libsurvive documentation: `libsurvive `_. + +For optimal performance, position the lighthouse above the hands, tilted slightly downward. +One lighthouse is sufficient if both hands are visible. +Ensure the lighthouse remains stable; a stand is recommended to prevent wobbling. + +.. note:: + + To avoid resource contention and crashes, ensure Manus and Vive devices are connected to different USB controllers/buses. + Use ``lsusb -t`` to identify different buses and connect devices accordingly. + + Vive trackers are automatically calculated to map to the left and right wrist joints obtained from a stable + OpenXR hand tracking wrist pose. + This auto-mapping calculation supports up to 2 Vive trackers; + if more than 2 Vive trackers are detected, it uses the first two trackers detected for calibration, which may not be correct. + +.. _develop-xr-isaac-lab: + +Develop for XR in Isaac Lab +--------------------------- + +This section will walk you through how to develop XR environments in Isaac Lab for building +teleoperation workflows. + + +.. _run-isaac-lab-with-xr: + +Run Isaac Lab with XR Extensions Enabled +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In order to enable extensions necessary for XR, and to see the AR Panel in the UI, Isaac Lab must be +loaded with an XR experience file. This can be done automatically by passing the ``--xr`` flag to +any Isaac Lab script that uses :class:`app.AppLauncher`. + +For example: you can enable and use XR in any of the :ref:`tutorials` by invoking them with the +additional ``--xr`` flag. + + +.. _configure-scene-placement: + +Configure XR Scene Placement +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Placement of the robot simulation within the XR device's local coordinate frame can be achieved +using an XR anchor, and is configurable using the ``xr`` field (type :class:`openxr.XrCfg`) in the +environment configuration. + +Specifically: the pose specified by the ``anchor_pos`` and ``anchor_rot`` fields of the +:class:`openxr.XrCfg` will appear at the origin of the XR device's local coordinate frame, which +should be on the floor. + +.. note:: + + On Apple Vision Pro, the local coordinate frame can be reset to a point on the floor beneath the + user by holding the digital crown. + +For example: if a robot should appear at the position of the user, the ``anchor_pos`` and +``anchor_rot`` properties should be set to a pose on the floor directly beneath the robot. + +.. note:: + + The XR anchor configuration is applied in :class:`openxr.OpenXRDevice` by creating a prim at the + position of the anchor, and modifying the ``xr/profile/ar/anchorMode`` and + ``/xrstage/profile/ar/customAnchor`` settings. + + If you are running a script that does not use :class:`openxr.OpenXRDevice`, you will need to do + this explicitly. + + +.. _optimize-xr-performance: + +Optimize XR Performance +~~~~~~~~~~~~~~~~~~~~~~~ + +.. dropdown:: Configure the physics and render time step + :open: + + In order to provide a high-fidelity immersive experience, it is recommended to ensure that the + simulation render time step roughly matches the XR device display time step. + + It is also important to ensure that this time step can be simulated and rendered in real time. + + The Apple Vision Pro display runs at 90Hz, but many Isaac Lab simulations will not achieve 90Hz + performance when rendering stereo views for XR; so for best experience on Apple Vision Pro, we + suggest running with a simulation dt of 90Hz and a render interval of 2, meaning that the + simulation is rendered once for every two simulation steps, or at 45Hz, if performance allows. + + You can still set the simulation dt lower or higher depending on your requirements, but this may + result in the simulation appearing faster or slower when rendered in XR. + + Overriding the time step configuration for an environment can be done by modifying the + :class:`sim.SimulationCfg` in the environment's ``__post_init__`` function. For instance: + + .. code-block:: python + + @configclass + class XrTeleopEnvCfg(ManagerBasedRLEnvCfg): + + def __post_init__(self): + self.sim.dt = 1.0 / 90 + self.sim.render_interval = 2 + + Also note that by default the CloudXR Runtime attempts to dynamically adjust its pacing based on + how long Isaac Lab takes to render. If render times are highly variable, this can lead to the + simulation appearing to speed up or slow down when rendered in XR. If this is an issue, the + CloudXR Runtime can be configured to use a fixed time step by setting the environment variable + ``NV_PACER_FIXED_TIME_STEP_MS`` to an integer quantity when starting the CloudXR Runtime Docker + containere. + + +.. dropdown:: Try running physics on CPU + :open: + + It is currently recommended to try running Isaac Lab teleoperation scripts with the ``--device + cpu`` flag. This will cause Physics calculations to be done on the CPU, which may be reduce + latency when only a single environment is present in the simulation. + + +.. _control-robot-with-xr: + +Control the Robot with XR Device Inputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Isaac Lab provides a flexible architecture for using XR tracking data to control +simulated robots. This section explains the components of this architecture and how they work together. + +.. _openxr-device-architecture: + +OpenXR Device +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The :class:`isaaclab.devices.OpenXRDevice` is the core component that enables XR-based teleoperation in Isaac Lab. +This device interfaces with CloudXR to receive tracking data from the XR headset and transform it into robot control +commands. + +At its heart, XR teleoperation requires mapping (or "retargeting") user inputs, such as hand movements and poses, +into robot control signals. Isaac Lab makes this straightforward through its OpenXRDevice and Retargeter architecture. +The OpenXRDevice captures hand tracking data via Isaac Sim's OpenXR API, then passes this data through one or more +Retargeters that convert it into robot actions. + +The OpenXRDevice also integrates with the XR device's user interface when using CloudXR, allowing users to trigger +simulation events directly from their XR environment. + +.. _control-robot-with-xr-retargeters: + +Retargeting Architecture +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Retargeters are specialized components that convert raw tracking data into meaningful control signals +for robots. They implement the :class:`isaaclab.devices.RetargeterBase` interface and are passed to +the OpenXRDevice during initialization. + +Isaac Lab provides three main retargeters for hand tracking: + +.. dropdown:: Se3RelRetargeter (:class:`isaaclab.devices.openxr.retargeters.Se3RelRetargeter`) + + * Generates incremental robot commands from relative hand movements + * Best for precise manipulation tasks + +.. dropdown:: Se3AbsRetargeter (:class:`isaaclab.devices.openxr.retargeters.Se3AbsRetargeter`) + + * Maps hand position directly to robot end-effector position + * Enables 1:1 spatial control + +.. dropdown:: GripperRetargeter (:class:`isaaclab.devices.openxr.retargeters.GripperRetargeter`) + + * Controls gripper state based on thumb-index finger distance + * Used alongside position retargeters for full robot control + +.. dropdown:: GR1T2Retargeter (:class:`isaaclab.devices.openxr.retargeters.GR1T2Retargeter`) + + * Retargets OpenXR hand tracking data to GR1T2 hand end-effector commands + * Handles both left and right hands, converting hand poses to joint angles for the GR1T2 robot's hands + * Supports visualization of tracked hand joints + +.. dropdown:: UnitreeG1Retargeter (:class:`isaaclab.devices.openxr.retargeters.UnitreeG1Retargeter`) + + * Retargets OpenXR hand tracking data to Unitree G1 using Inspire 5-finger hand end-effector commands + * Handles both left and right hands, converting hand poses to joint angles for the G1 robot's hands + * Supports visualization of tracked hand joints + +Retargeters can be combined to control different robot functions simultaneously. + +Using Retargeters with Hand Tracking +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Here's an example of setting up hand tracking: + +.. code-block:: python + + from isaaclab.devices import OpenXRDevice, OpenXRDeviceCfg + from isaaclab.devices.openxr.retargeters import Se3AbsRetargeter, GripperRetargeter + + # Create retargeters + position_retargeter = Se3AbsRetargeter( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_position=False # Use pinch position (thumb-index midpoint) instead of wrist + ) + gripper_retargeter = GripperRetargeter(bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT) + + # Create OpenXR device with hand tracking and both retargeters + device = OpenXRDevice( + OpenXRDeviceCfg(xr_cfg=env_cfg.xr), + retargeters=[position_retargeter, gripper_retargeter], + ) + + # Main control loop + while True: + # Get the latest commands from the XR device + commands = device.advance() + if commands is None: + continue + + # Apply the commands to the environment + obs, reward, terminated, truncated, info = env.step(commands) + + if terminated or truncated: + break + +Here's a diagram for the dataflow and algorithm used in humanoid teleoperation. Using Apple Vision Pro, we collect 26 keypoints for each hand. +The wrist keypoint is used to control the hand end-effector, while the remaining hand keypoints are used for hand retargeting. + +.. figure:: ../_static/teleop/teleop_diagram.jpg + :align: center + :figwidth: 80% + :alt: teleop_diagram + +For dex-retargeting, we are currently using the Dexpilot optimizer, which relies on the five fingertips and the palm for retargeting. It is essential +that the links used for retargeting are defined exactly at the fingertips—not in the middle of the fingers—to ensure accurate optimization.Please refer +to the image below for hand asset selection, find a suitable hand asset, or add fingertip links in IsaacLab as needed. + +.. figure:: ../_static/teleop/hand_asset.jpg + :align: center + :figwidth: 60% + :alt: hand_asset + +.. _control-robot-with-xr-callbacks: + +Adding Callbacks for XR UI Events +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The OpenXRDevice can handle events triggered by user interactions with XR UI elements like buttons and menus. +When a user interacts with these elements, the device triggers registered callback functions: + +.. code-block:: python + + # Register callbacks for teleop control events + device.add_callback("RESET", reset_callback) + device.add_callback("START", start_callback) + device.add_callback("STOP", stop_callback) + +When the user interacts with the XR UI, these callbacks will be triggered to control the simulation +or recording process. You can also add custom messages from the client side using custom keys that will +trigger these callbacks, allowing for programmatic control of the simulation alongside direct user interaction. +The custom keys can be any string value that matches the callback registration. + + +Teleop Environment Configuration +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +XR-based teleoperation can be integrated with Isaac Lab's environment configuration system using the +``teleop_devices`` field in your environment configuration: + +.. code-block:: python + + from dataclasses import field + from isaaclab.envs import ManagerBasedEnvCfg + from isaaclab.devices import DevicesCfg, OpenXRDeviceCfg + from isaaclab.devices.openxr import XrCfg + from isaaclab.devices.openxr.retargeters import Se3AbsRetargeterCfg, GripperRetargeterCfg + + @configclass + class MyEnvironmentCfg(ManagerBasedEnvCfg): + """Configuration for a teleoperation-enabled environment.""" + + # Add XR configuration with custom anchor position + xr: XrCfg = XrCfg( + anchor_pos=[0.0, 0.0, 0.0], + anchor_rot=[1.0, 0.0, 0.0, 0.0] + ) + + # Define teleoperation devices + teleop_devices: DevicesCfg = field(default_factory=lambda: DevicesCfg( + # Configuration for hand tracking with absolute position control + handtracking=OpenXRDeviceCfg( + xr_cfg=None, # Will use environment's xr config + retargeters=[ + Se3AbsRetargeterCfg( + bound_hand=0, # HAND_LEFT enum value + zero_out_xy_rotation=True, + use_wrist_position=False, + ), + GripperRetargeterCfg(bound_hand=0), + ] + ), + # Add other device configurations as needed + )) + + +Teleop Device Factory +^^^^^^^^^^^^^^^^^^^^^ + +To create a teleoperation device from your environment configuration, use the ``create_teleop_device`` factory function: + +.. code-block:: python + + from isaaclab.devices import create_teleop_device + from isaaclab.envs import ManagerBasedEnv + + # Create environment from configuration + env_cfg = MyEnvironmentCfg() + env = ManagerBasedEnv(env_cfg) + + # Define callbacks for teleop events + callbacks = { + "RESET": lambda: print("Reset simulation"), + "START": lambda: print("Start teleoperation"), + "STOP": lambda: print("Stop teleoperation"), + } + + # Create teleop device from configuration with callbacks + device_name = "handtracking" # Must match a key in teleop_devices + device = create_teleop_device( + device_name, + env_cfg.teleop_devices, + callbacks=callbacks + ) + + # Use device in control loop + while True: + # Get the latest commands from the device + commands = device.advance() + if commands is None: + continue + + # Apply commands to environment + obs, reward, terminated, truncated, info = env.step(commands) + + +Extending the Retargeting System +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The retargeting system is designed to be extensible. You can create custom retargeters by following these steps: + +1. Create a configuration dataclass for your retargeter: + +.. code-block:: python + + from dataclasses import dataclass + from isaaclab.devices.retargeter_base import RetargeterCfg + + @dataclass + class MyCustomRetargeterCfg(RetargeterCfg): + """Configuration for my custom retargeter.""" + scaling_factor: float = 1.0 + filter_strength: float = 0.5 + # Add any other configuration parameters your retargeter needs + +2. Implement your retargeter class by extending the RetargeterBase: + +.. code-block:: python + + from isaaclab.devices.retargeter_base import RetargeterBase + from isaaclab.devices import OpenXRDevice + import torch + from typing import Any + + class MyCustomRetargeter(RetargeterBase): + """A custom retargeter that processes OpenXR tracking data.""" + + def __init__(self, cfg: MyCustomRetargeterCfg): + """Initialize retargeter with configuration. + + Args: + cfg: Configuration object for retargeter settings. + """ + super().__init__() + self.scaling_factor = cfg.scaling_factor + self.filter_strength = cfg.filter_strength + # Initialize any other required attributes + + def retarget(self, data: dict) -> Any: + """Transform raw tracking data into robot control commands. + + Args: + data: Dictionary containing tracking data from OpenXRDevice. + Keys are TrackingTarget enum values, values are joint pose dictionaries. + + Returns: + Any: The transformed control commands for the robot. + """ + # Access hand tracking data using TrackingTarget enum + right_hand_data = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + # Extract specific joint positions and orientations + wrist_pose = right_hand_data.get("wrist") + thumb_tip_pose = right_hand_data.get("thumb_tip") + index_tip_pose = right_hand_data.get("index_tip") + + # Access head tracking data + head_pose = data[DeviceBase.TrackingTarget.HEAD] + + # Process the tracking data and apply your custom logic + # ... + + # Return control commands in appropriate format + return torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]) # Example output + +3. Register your retargeter by setting ``retargeter_type`` on the config class: + +.. code-block:: python + + # Import your retargeter at the top of your module + from my_package.retargeters import MyCustomRetargeter, MyCustomRetargeterCfg + + # Link the config to the implementation for factory construction + MyCustomRetargeterCfg.retargeter_type = MyCustomRetargeter + +4. Now you can use your custom retargeter in teleop device configurations: + +.. code-block:: python + + from isaaclab.devices import OpenXRDeviceCfg, DevicesCfg + from isaaclab.devices.openxr import XrCfg + from my_package.retargeters import MyCustomRetargeterCfg + + # Create XR configuration for proper scene placement + xr_config = XrCfg(anchor_pos=[0.0, 0.0, 0.0], anchor_rot=[1.0, 0.0, 0.0, 0.0]) + + # Define teleop devices with custom retargeter + teleop_devices = DevicesCfg( + handtracking=OpenXRDeviceCfg( + xr_cfg=xr_config, + retargeters=[ + MyCustomRetargeterCfg( + scaling_factor=1.5, + filter_strength=0.7, + ), + ] + ), + ) + +As the OpenXR capabilities expand beyond hand tracking to include head tracking and other features, +additional retargeters can be developed to map this data to various robot control paradigms. + + +Creating Custom Teleop Devices +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can create and register your own custom teleoperation devices by following these steps: + +1. Create a configuration dataclass for your device: + +.. code-block:: python + + from dataclasses import dataclass + from isaaclab.devices import DeviceCfg + + @dataclass + class MyCustomDeviceCfg(DeviceCfg): + """Configuration for my custom device.""" + sensitivity: float = 1.0 + invert_controls: bool = False + # Add any other configuration parameters your device needs + +2. Implement your device class by inheriting from DeviceBase: + +.. code-block:: python + + from isaaclab.devices import DeviceBase + import torch + + class MyCustomDevice(DeviceBase): + """A custom teleoperation device.""" + + def __init__(self, cfg: MyCustomDeviceCfg): + """Initialize the device with configuration. + + Args: + cfg: Configuration object for device settings. + """ + super().__init__() + self.sensitivity = cfg.sensitivity + self.invert_controls = cfg.invert_controls + # Initialize any other required attributes + self._device_input = torch.zeros(7) # Example: 6D pose + gripper + + def reset(self): + """Reset the device state.""" + self._device_input.zero_() + # Reset any other state variables + + def add_callback(self, key: str, func): + """Add callback function for a button/event. + + Args: + key: Button or event name. + func: Callback function to be called when event occurs. + """ + # Implement callback registration + pass + + def advance(self) -> torch.Tensor: + """Get the latest commands from the device. + + Returns: + torch.Tensor: Control commands (e.g., delta pose + gripper). + """ + # Update internal state based on device input + # Return command tensor + return self._device_input + +3. Register your device with the teleoperation device factory by adding it to the ``DEVICE_MAP``: + +.. code-block:: python + + # Import your device at the top of your module + from my_package.devices import MyCustomDevice, MyCustomDeviceCfg + + # Add your device to the factory + from isaaclab.devices.teleop_device_factory import DEVICE_MAP + + # Register your device type with its constructor + DEVICE_MAP[MyCustomDeviceCfg] = MyCustomDevice + +4. Now you can use your custom device in environment configurations: + +.. code-block:: python + + from dataclasses import field + from isaaclab.envs import ManagerBasedEnvCfg + from isaaclab.devices import DevicesCfg + from my_package.devices import MyCustomDeviceCfg + + @configclass + class MyEnvironmentCfg(ManagerBasedEnvCfg): + """Environment configuration with custom teleop device.""" + + teleop_devices: DevicesCfg = field(default_factory=lambda: DevicesCfg( + my_custom_device=MyCustomDeviceCfg( + sensitivity=1.5, + invert_controls=True, + ), + )) + + +.. _xr-known-issues: + +Known Issues +------------ + +* ``XR_ERROR_VALIDATION_FAILURE: xrWaitFrame(frameState->type == 0)`` when stopping AR Mode + + This error message can be safely ignored. It is caused by a race condition in the exit handler for + AR Mode. + +* ``XR_ERROR_INSTANCE_LOST in xrPollEvent: Call to "xrt_session_poll_events" failed`` + + This error may occur if the CloudXR runtime exits before Isaac Lab. Restart the CloudXR + runtime to resume teleoperation. + +* ``[omni.usd] TF_PYTHON_EXCEPTION`` when starting/stopping AR Mode + + This error message can be safely ignored. It is caused by a race condition in the enter/exit + handler for AR Mode. + +* ``Invalid version string in _ParseVersionString`` + + This error message can be caused by shader assets authored with older versions of USD, and can + typically be ignored. + +* The XR device connects successfully, but no video is displayed, even though the Isaac Lab viewport responds to tracking. + + This error occurs when the GPU index differs between the host and the container, causing CUDA + to load on the wrong GPU. To fix this, set ``NV_GPU_INDEX`` in the runtime container to ``0``, ``1``, + or ``2`` to ensure the GPU selected by CUDA matches the host. + + +Kubernetes Deployment +--------------------- + +For information on deploying XR Teleop for Isaac Lab on a Kubernetes cluster, see :ref:`cloudxr-teleoperation-cluster`. + +.. + References +.. _`Apple Vision Pro`: https://www.apple.com/apple-vision-pro/ +.. _`Docker Compose`: https://docs.docker.com/compose/install/linux/#install-using-the-repository +.. _`Docker`: https://docs.docker.com/desktop/install/linux-install/ +.. _`NVIDIA CloudXR`: https://developer.nvidia.com/cloudxr-sdk +.. _`NVIDIA Container Toolkit`: https://github.com/NVIDIA/nvidia-container-toolkit +.. _`Isaac XR Teleop Sample Client`: https://github.com/isaac-sim/isaac-xr-teleop-sample-client-apple diff --git a/docs/source/how-to/configure_rendering.rst b/docs/source/how-to/configure_rendering.rst new file mode 100644 index 0000000000000000000000000000000000000000..adfa8b5556ccac48940a2728ca2f3ece8ed1539a --- /dev/null +++ b/docs/source/how-to/configure_rendering.rst @@ -0,0 +1,151 @@ +Configuring Rendering Settings +============================== + +Isaac Lab offers 3 preset rendering modes: performance, balanced, and quality. +You can select a mode via a command line argument or from within a script, and customize settings as needed. +Adjust and fine-tune rendering to achieve the ideal balance for your workflow. + +Selecting a Rendering Mode +-------------------------- + +Rendering modes can be selected in 2 ways. + +1. using the ``rendering_mode`` input class argument in :class:`~sim.RenderCfg` + + .. code-block:: python + + # for an example of how this can be used, checkout the tutorial script + # scripts/tutorials/00_sim/set_rendering_mode.py + render_cfg = sim_utils.RenderCfg(rendering_mode="performance") + +2. using the ``--rendering_mode`` CLI argument, which takes precedence over the ``rendering_mode`` argument in :class:`~sim.RenderCfg`. + + .. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/00_sim/set_rendering_mode.py --rendering_mode {performance/balanced/quality} + + +Note, the ``rendering_mode`` defaults to ``balanced``. +However, in the case where the launcher argument ``--enable_cameras`` is not set, then +the default ``rendering_mode`` is not applied and, instead, the default kit rendering settings are used. + + +Example renders from the ``set_rendering_mode.py`` script. +To help assess rendering, the example scene includes some reflections, translucency, direct and ambient lighting, and several material types. + +- Quality Mode + + .. image:: ../_static/how-to/howto_rendering_example_quality.jpg + :width: 100% + :alt: Quality Rendering Mode Example + +- Balanced Mode + + .. image:: ../_static/how-to/howto_rendering_example_balanced.jpg + :width: 100% + :alt: Balanced Rendering Mode Example + +- Performance Mode + + .. image:: ../_static/how-to/howto_rendering_example_performance.jpg + :width: 100% + :alt: Performance Rendering Mode Example + +Overwriting Specific Rendering Settings +--------------------------------------- + +Preset rendering settings can be overwritten via the :class:`~sim.RenderCfg` class. + +There are 2 ways to provide settings that overwrite presets. + +1. :class:`~sim.RenderCfg` supports overwriting specific settings via user-friendly setting names that map to underlying RTX settings. + For example: + + .. code-block:: python + + render_cfg = sim_utils.RenderCfg( + rendering_mode="performance", + # user friendly setting overwrites + enable_translucency=True, # defaults to False in performance mode + enable_reflections=True, # defaults to False in performance mode + dlss_mode="3", # defaults to 1 in performance mode + ) + + List of user-friendly settings. + + .. table:: + :widths: 25 75 + + +----------------------------+--------------------------------------------------------------------------+ + | enable_translucency | Bool. Enables translucency for specular transmissive surfaces such as | + | | glass at the cost of some performance. | + +----------------------------+--------------------------------------------------------------------------+ + | enable_reflections | Bool. Enables reflections at the cost of some performance. | + +----------------------------+--------------------------------------------------------------------------+ + | enable_global_illumination | Bool. Enables Diffused Global Illumination at the cost of some | + | | performance. | + +----------------------------+--------------------------------------------------------------------------+ + | antialiasing_mode | Literal["Off", "FXAA", "DLSS", "TAA", "DLAA"]. | + | | | + | | DLSS: Boosts performance by using AI to output higher resolution frames | + | | from a lower resolution input. DLSS samples multiple lower resolution | + | | images and uses motion data and feedback from prior frames to reconstruct| + | | native quality images. | + | | DLAA: Provides higher image quality with an AI-based anti-aliasing | + | | technique. DLAA uses the same Super Resolution technology developed for | + | | DLSS, reconstructing a native resolution image to maximize image quality.| + +----------------------------+--------------------------------------------------------------------------+ + | enable_dlssg | Bool. Enables the use of DLSS-G. DLSS Frame Generation boosts performance| + | | by using AI to generate more frames. This feature requires an Ada | + | | Lovelace architecture GPU and can hurt performance due to additional | + | | thread-related activities. | + +----------------------------+--------------------------------------------------------------------------+ + | enable_dl_denoiser | Bool. Enables the use of a DL denoiser, which improves the quality of | + | | renders at the cost of performance. | + +----------------------------+--------------------------------------------------------------------------+ + | dlss_mode | Literal[0, 1, 2, 3]. For DLSS anti-aliasing, selects the performance/ | + | | quality tradeoff mode. Valid values are 0 (Performance), 1 (Balanced), | + | | 2 (Quality), or 3 (Auto). | + +----------------------------+--------------------------------------------------------------------------+ + | enable_direct_lighting | Bool. Enable direct light contributions from lights. | + +----------------------------+--------------------------------------------------------------------------+ + | samples_per_pixel | Int. Defines the Direct Lighting samples per pixel. Higher values | + | | increase the direct lighting quality at the cost of performance. | + +----------------------------+--------------------------------------------------------------------------+ + | enable_shadows | Bool. Enables shadows at the cost of performance. When disabled, lights | + | | will not cast shadows. | + +----------------------------+--------------------------------------------------------------------------+ + | enable_ambient_occlusion | Bool. Enables ambient occlusion at the cost of some performance. | + +----------------------------+--------------------------------------------------------------------------+ + + +2. For more control, :class:`~sim.RenderCfg` allows you to overwrite any RTX setting by using the ``carb_settings`` argument. + + Examples of RTX settings can be found from within the repo, in the render mode preset files located in ``apps/rendering_modes``. + + In addition, the RTX documentation can be found here - https://docs.omniverse.nvidia.com/materials-and-rendering/latest/rtx-renderer.html. + + An example usage of ``carb_settings``. + + .. code-block:: python + + render_cfg = sim_utils.RenderCfg( + rendering_mode="quality", + # carb setting overwrites + carb_settings={ + "rtx.translucency.enabled": False, + "rtx.reflections.enabled": False, + "rtx.domeLight.upperLowerStrategy": 3, + } + ) + + +Current Limitations +------------------- + +For performance reasons, we default to using DLSS for denoising, which generally provides better performance. +This may result in renders of lower quality, which may be especially evident at lower resolutions. +Due to this, we recommend using per-tile or per-camera resolution of at least 100 x 100. +For renders at lower resolutions, we advice setting the ``antialiasing_mode`` attribute in :class:`~sim.RenderCfg` to +``DLAA``, and also potentially enabling ``enable_dl_denoiser``. Both of these settings should help improve render +quality, but also comes at a cost of performance. Additional rendering parameters can also be specified in :class:`~sim.RenderCfg`. diff --git a/docs/source/how-to/curriculums.rst b/docs/source/how-to/curriculums.rst new file mode 100644 index 0000000000000000000000000000000000000000..8c2a94e82cbe21b8f2768e6c38acc0e8a9513c87 --- /dev/null +++ b/docs/source/how-to/curriculums.rst @@ -0,0 +1,122 @@ +Curriculum Utilities +==================== + +.. currentmodule:: isaaclab.managers + +This guide walks through the common curriculum helper functions and terms that can be used to create flexible curricula +for RL environments in Isaac Lab. These utilities can be passed to a :class:`~isaaclab.managers.CurriculumTermCfg` +object to enable dynamic modification of reward weights and environment parameters during training. + +.. note:: + + We cover three utilities in this guide: + - The simple function modifies reward :func:`modify_reward_weight` + - The term modify any environment parameters :class:`modify_env_param` + - The term modify term_cfg :class:`modify_term_cfg` + +.. dropdown:: Full source for curriculum utilities + :icon: code + + .. literalinclude:: ../../../source/isaaclab/isaaclab/envs/mdp/curriculums.py + :language: python + + +Modifying Reward Weights +------------------------ + +The function :func:`modify_reward_weight` updates the weight of a reward term after a specified number of simulation +steps. This can be passed directly as the ``func`` in a ``CurriculumTermCfg``. + +.. literalinclude:: ../../../source/isaaclab/isaaclab/envs/mdp/curriculums.py + :language: python + :pyobject: modify_reward_weight + +**Usage example**: + +.. code-block:: python + + from isaaclab.managers import CurriculumTermCfg + import isaaclab.managers.mdp as mdp + + # After 100k steps, set the "sparse_reward" term weight to 0.5 + sparse_reward_schedule = CurriculumTermCfg( + func=mdp.modify_reward_weight, + params={ + "term_name": "sparse_reward", + "weight": 0.5, + "num_steps": 100_000, + } + ) + + +Dynamically Modifying Environment Parameters +-------------------------------------------- + +The class :class:`modify_env_param` is a :class:`~isaaclab.managers.ManagerTermBase` subclass that lets you target any +dotted attribute path in the environment and apply a user-supplied function to compute a new value at runtime. It +handles nested attributes, dictionary keys, list or tuple indexing, and respects a ``NO_CHANGE`` sentinel if no update +is desired. + +.. literalinclude:: ../../../source/isaaclab/isaaclab/envs/mdp/curriculums.py + :language: python + :pyobject: modify_env_param + +**Usage example**: + +.. code-block:: python + + import torch + from isaaclab.managers import CurriculumTermCfg + import isaaclab.managers.mdp as mdp + + def resample_friction(env, env_ids, old_value, low, high, num_steps): + # After num_steps, sample a new friction coefficient uniformly + if env.common_step_counter > num_steps: + return torch.empty((len(env_ids),), device="cpu").uniform_(low, high) + return mdp.modify_env_param.NO_CHANGE + + friction_curriculum = CurriculumTermCfg( + func=mdp.modify_env_param, + params={ + "address": "event_manager.cfg.object_physics_material.func.material_buckets", + "modify_fn": resample_friction, + "modify_params": { + "low": 0.3, + "high": 1.0, + "num_steps": 120_000, + } + } + ) + + +Modify Term Configuration +------------------------- + +The subclass :class:`modify_term_cfg` provides a more concise style address syntax, using consistent with hydra config +syntax. It otherwise behaves identically to :class:`modify_env_param`. + +.. literalinclude:: ../../../source/isaaclab/isaaclab/envs/mdp/curriculums.py + :language: python + :pyobject: modify_term_cfg + +**Usage example**: + +.. code-block:: python + + def override_command_range(env, env_ids, old_value, value, num_steps): + # Override after num_steps + if env.common_step_counter > num_steps: + return value + return mdp.modify_term_cfg.NO_CHANGE + + range_override = CurriculumTermCfg( + func=mdp.modify_term_cfg, + params={ + "address": "commands.object_pose.ranges.pos_x", + "modify_fn": override_command_range, + "modify_params": { + "value": (-0.75, -0.25), + "num_steps": 12_000, + } + } + ) diff --git a/docs/source/how-to/draw_markers.rst b/docs/source/how-to/draw_markers.rst new file mode 100644 index 0000000000000000000000000000000000000000..58b1e0ed3790e07d4421cb63dd9220f314e2d01c --- /dev/null +++ b/docs/source/how-to/draw_markers.rst @@ -0,0 +1,75 @@ +Creating Visualization Markers +============================== + +.. currentmodule:: isaaclab + +Visualization markers are useful to debug the state of the environment. They can be used to visualize +the frames, commands, and other information in the simulation. + +While Isaac Sim provides its own :mod:`isaacsim.util.debug_draw` extension, it is limited to rendering only +points, lines and splines. For cases, where you need to render more complex shapes, you can use the +:class:`markers.VisualizationMarkers` class. + +This guide is accompanied by a sample script ``markers.py`` in the ``IsaacLab/scripts/demos`` directory. + +.. dropdown:: Code for markers.py + :icon: code + + .. literalinclude:: ../../../scripts/demos/markers.py + :language: python + :emphasize-lines: 45-90, 106-107, 136-142 + :linenos: + + + +Configuring the markers +----------------------- + +The :class:`~markers.VisualizationMarkersCfg` class provides a simple interface to configure +different types of markers. It takes in the following parameters: + +- :attr:`~markers.VisualizationMarkersCfg.prim_path`: The corresponding prim path for the marker class. +- :attr:`~markers.VisualizationMarkersCfg.markers`: A dictionary specifying the different marker prototypes + handled by the class. The key is the name of the marker prototype and the value is its spawn configuration. + +.. note:: + + In case the marker prototype specifies a configuration with physics properties, these are removed. + This is because the markers are not meant to be simulated. + +Here we show all the different types of markers that can be configured. These range from simple shapes like +cones and spheres to more complex geometries like a frame or arrows. The marker prototypes can also be +configured from USD files. + +.. literalinclude:: ../../../scripts/demos/markers.py + :language: python + :lines: 45-90 + :dedent: + + +Drawing the markers +------------------- + +To draw the markers, we call the :class:`~markers.VisualizationMarkers.visualize` method. This method takes in +as arguments the pose of the markers and the corresponding marker prototypes to draw. + +.. literalinclude:: ../../../scripts/demos/markers.py + :language: python + :lines: 136-142 + :dedent: + + +Executing the Script +-------------------- + +To run the accompanying script, execute the following command: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/demos/markers.py + +The simulation should start, and you can observe the different types of markers arranged in a grid pattern. +The markers will rotating around their respective axes. Additionally every few rotations, they will +roll forward on the grid. + +To stop the simulation, close the window, or use ``Ctrl+C`` in the terminal. diff --git a/docs/source/how-to/estimate_how_many_cameras_can_run.rst b/docs/source/how-to/estimate_how_many_cameras_can_run.rst new file mode 100644 index 0000000000000000000000000000000000000000..ec9fa72cfb641726859746409935c5087bb4e9b0 --- /dev/null +++ b/docs/source/how-to/estimate_how_many_cameras_can_run.rst @@ -0,0 +1,121 @@ +.. _how-to-estimate-how-cameras-can-run: + + +Find How Many/What Cameras You Should Train With +================================================ + +.. currentmodule:: isaaclab + +Currently in Isaac Lab, there are several camera types; USD Cameras (standard), Tiled Cameras, +and Ray Caster cameras. These camera types differ in functionality and performance. The ``benchmark_cameras.py`` +script can be used to understand the difference in cameras types, as well to characterize their relative performance +at different parameters such as camera quantity, image dimensions, and data types. + +This utility is provided so that one easily can find the camera type/parameters that are the most performant +while meeting the requirements of the user's scenario. This utility also helps estimate +the maximum number of cameras one can realistically run, assuming that one wants to maximize the number +of environments while minimizing step time. + +This utility can inject cameras into an existing task from the gym registry, +which can be useful for benchmarking cameras in a specific scenario. Also, +if you install ``pynvml``, you can let this utility automatically find the maximum +numbers of cameras that can run in your task environment up to a +certain specified system resource utilization threshold (without training; taking zero actions +at each timestep). + +This guide accompanies the ``benchmark_cameras.py`` script in the ``scripts/benchmarks`` +directory. + +.. dropdown:: Code for benchmark_cameras.py + :icon: code + + .. literalinclude:: ../../../scripts/benchmarks/benchmark_cameras.py + :language: python + :linenos: + + +Possible Parameters +------------------- + +First, run + +.. code-block:: bash + + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py -h + +to see all possible parameters you can vary with this utility. + + +See the command line parameters related to ``autotune`` for more information about +automatically determining maximum camera count. + + +Compare Performance in Task Environments and Automatically Determine Task Max Camera Count +------------------------------------------------------------------------------------------ + +Currently, tiled cameras are the most performant camera that can handle multiple dynamic objects. + +For example, to see how your system could handle 100 tiled cameras in +the cartpole environment, with 2 cameras per environment (so 50 environments total) +only in RGB mode, run + +.. code-block:: bash + + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py \ + --task Isaac-Cartpole-v0 --num_tiled_cameras 100 \ + --task_num_cameras_per_env 2 \ + --tiled_camera_data_types rgb + +If you have pynvml installed, (``./isaaclab.sh -p -m pip install pynvml``), you can also +find the maximum number of cameras that you could run in the specified environment up to +a certain performance threshold (specified by max CPU utilization percent, max RAM utilization percent, +max GPU compute percent, and max GPU memory percent). For example, to find the maximum number of cameras +you can run with cartpole, you could run: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py \ + --task Isaac-Cartpole-v0 --num_tiled_cameras 100 \ + --task_num_cameras_per_env 2 \ + --tiled_camera_data_types rgb --autotune \ + --autotune_max_percentage_util 100 80 50 50 + +Autotune may lead to the program crashing, which means that it tried to run too many cameras at once. +However, the max percentage utilization parameter is meant to prevent this from happening. + +The output of the benchmark doesn't include the overhead of training the network, so consider +decreasing the maximum utilization percentages to account for this overhead. The final output camera +count is for all cameras, so to get the total number of environments, divide the output camera count +by the number of cameras per environment. + + +Compare Camera Type and Performance (Without a Specified Task) +-------------------------------------------------------------- + +This tool can also asses performance without a task environment. +For example, to view 100 random objects with 2 standard cameras, one could run + +.. code-block:: bash + + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py \ + --height 100 --width 100 --num_standard_cameras 2 \ + --standard_camera_data_types instance_segmentation_fast normals --num_objects 100 \ + --experiment_length 100 + +If your system cannot handle this due to performance reasons, then the process will be killed. +It's recommended to monitor CPU/RAM utilization and GPU utilization while running this script, to get +an idea of how many resources rendering the desired camera requires. In Ubuntu, you can use tools like ``htop`` and ``nvtop`` +to live monitor resources while running this script, and in Windows, you can use the Task Manager. + +If your system has a hard time handling the desired cameras, you can try the following + + - Switch to headless mode (supply ``--headless``) + - Ensure you are using the GPU pipeline not CPU! + - If you aren't using Tiled Cameras, switch to Tiled Cameras + - Decrease camera resolution + - Decrease how many data_types there are for each camera. + - Decrease the number of cameras + - Decrease the number of objects in the scene + +If your system is able to handle the amount of cameras, then the time statistics will be printed to the terminal. +After the simulations stops it can be closed with CTRL+C. diff --git a/docs/source/how-to/haply_teleoperation.rst b/docs/source/how-to/haply_teleoperation.rst new file mode 100644 index 0000000000000000000000000000000000000000..1f8d1d6e2522412c97068d880a9edf280050c269 --- /dev/null +++ b/docs/source/how-to/haply_teleoperation.rst @@ -0,0 +1,240 @@ +.. _haply-teleoperation: + +Setting up Haply Teleoperation +=============================== + +.. currentmodule:: isaaclab + +`Haply Devices`_ provides haptic devices that enable intuitive robot teleoperation with +directional force feedback. The Haply Inverse3 paired with the VerseGrip creates an +end-effector control system with force feedback capabilities. + +Isaac Lab supports Haply devices for teleoperation workflows that require precise spatial +control with haptic feedback. This enables operators to feel contact forces during manipulation +tasks, improving control quality and task performance. + +This guide explains how to set up and use Haply devices with Isaac Lab for robot teleoperation. + +.. _Haply Devices: https://haply.co/ + + +Overview +-------- + +Using Haply with Isaac Lab involves the following components: + +* **Isaac Lab** simulates the robot environment and streams contact forces back to the operator + +* **Haply Inverse3** provides 3-DOF position tracking and force feedback in the operator's workspace + +* **Haply VerseGrip** adds orientation sensing and button inputs for gripper control + +* **Haply SDK** manages WebSocket communication between Isaac Lab and the Haply hardware + +This guide will walk you through: + +* :ref:`haply-system-requirements` +* :ref:`haply-installation` +* :ref:`haply-device-setup` +* :ref:`haply-running-demo` +* :ref:`haply-troubleshooting` + + +.. _haply-system-requirements: + +System Requirements +------------------- + +Hardware Requirements +~~~~~~~~~~~~~~~~~~~~~ + +* **Isaac Lab Workstation** + + * Ubuntu 22.04 or Ubuntu 24.04 + * Hardware requirements for 200Hz physics simulation: + + * CPU: 8-Core Intel Core i7 or AMD Ryzen 7 (or higher) + * Memory: 32GB RAM (64GB recommended) + * GPU: RTX 3090 or higher + + * Network: Same local network as Haply devices for WebSocket communication + +* **Haply Devices** + + * Haply Inverse3 - Haptic device for position tracking and force feedback + * Haply VerseGrip - Wireless controller for orientation and button inputs + * Both devices must be powered on and connected to the Haply SDK + +Software Requirements +~~~~~~~~~~~~~~~~~~~~~ + +* Isaac Lab (follow the :ref:`installation guide `) +* Haply SDK (provided by Haply Robotics) +* Python 3.10+ +* ``websockets`` Python package (automatically installed with Isaac Lab) + + +.. _haply-installation: + +Installation +------------ + +1. Install Isaac Lab +~~~~~~~~~~~~~~~~~~~~ + +Follow the Isaac Lab :ref:`installation guide ` to set up your environment. + +The ``websockets`` dependency is automatically included in Isaac Lab's requirements. + +2. Install Haply SDK +~~~~~~~~~~~~~~~~~~~~ + +Download the Haply SDK from the `Haply Devices`_ website. +Install the SDK software and configure the devices. + +3. Verify Installation +~~~~~~~~~~~~~~~~~~~~~~ + +Test that your Haply devices are detected by the Haply Device Manager. +You should see both Inverse3 and VerseGrip as connected. + + +.. _haply-device-setup: + +Device Setup +------------ + +1. Physical Setup +~~~~~~~~~~~~~~~~~ + +* Place the Haply Inverse3 on a stable surface +* Ensure the VerseGrip is charged and paired +* Position yourself comfortably to reach the Inverse3 workspace +* Keep the workspace clear of obstacles + +2. Start Haply SDK +~~~~~~~~~~~~~~~~~~ + +Launch the Haply SDK according to Haply's documentation. The SDK typically: + +* Runs a WebSocket server on ``localhost:10001`` +* Streams device data at 200Hz +* Displays connection status for both devices + +3. Test Communication +~~~~~~~~~~~~~~~~~~~~~ + +You can test the WebSocket connection using the following Python script: + +.. code:: python + + import asyncio + import websockets + import json + + async def test_haply(): + uri = "ws://localhost:10001" + async with websockets.connect(uri) as ws: + response = await ws.recv() + data = json.loads(response) + print("Inverse3:", data.get("inverse3", [])) + print("VerseGrip:", data.get("wireless_verse_grip", [])) + + asyncio.run(test_haply()) + +You should see device data streaming from both Inverse3 and VerseGrip. + + +.. _haply-running-demo: + +Running the Demo +---------------- + +The Haply teleoperation demo showcases robot manipulation with force feedback using +a Franka Panda arm. + +Basic Usage +~~~~~~~~~~~ + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Ensure Haply SDK is running + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py --websocket_uri ws://localhost:10001 --pos_sensitivity 1.65 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + REM Ensure Haply SDK is running + isaaclab.bat -p scripts\demos\haply_teleoperation.py --websocket_uri ws://localhost:10001 --pos_sensitivity 1.65 + +The demo will: + +1. Connect to the Haply devices via WebSocket +2. Spawn a Franka Panda robot and a cube in simulation +3. Map Haply position to robot end-effector position +4. Stream contact forces back to the Inverse3 for haptic feedback + +Controls +~~~~~~~~ + +* **Move Inverse3**: Controls the robot end-effector position +* **VerseGrip Button A**: Open gripper +* **VerseGrip Button B**: Close gripper +* **VerseGrip Button C**: Rotate end-effector by 60° + +Advanced Options +~~~~~~~~~~~~~~~~ + +Customize the demo with command-line arguments: + +.. code:: bash + + # Use custom WebSocket URI + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py \ + --websocket_uri ws://192.168.1.100:10001 + + # Adjust position sensitivity (default: 1.0) + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py \ + --websocket_uri ws://localhost:10001 \ + --pos_sensitivity 2.0 + +Demo Features +~~~~~~~~~~~~~ + +* **Workspace Mapping**: Haply workspace is mapped to robot reachable space with safety limits +* **Inverse Kinematics**: Inverse Kinematics (IK) computes joint positions for desired end-effector pose +* **Force Feedback**: Contact forces from end-effector sensors are sent to Inverse3 for haptic feedback + + +.. _haply-troubleshooting: + +Troubleshooting +--------------- + +No Haptic Feedback +~~~~~~~~~~~~~~~~~~ + +**Problem**: No haptic feedback felt on Inverse3 + +Solutions: + +* Verify Inverse3 is the active device in Haply SDK +* Check contact forces are non-zero in simulation (try grasping the cube) +* Ensure ``limit_force`` is not set too low (default: 2.0N) + + +Next Steps +---------- + +* **Customize the demo**: Modify the workspace mapping or add custom button behaviors +* **Implement your own controller**: Use :class:`~isaaclab.devices.HaplyDevice` in your own scripts + +For more information on device APIs, see :class:`~isaaclab.devices.HaplyDevice` in the API documentation. diff --git a/docs/source/how-to/import_new_asset.rst b/docs/source/how-to/import_new_asset.rst new file mode 100644 index 0000000000000000000000000000000000000000..41eacc48673de97ba0bb666cf3a9cc65dd33d8a6 --- /dev/null +++ b/docs/source/how-to/import_new_asset.rst @@ -0,0 +1,314 @@ +Importing a New Asset +===================== + +.. currentmodule:: isaaclab + +NVIDIA Omniverse relies on the Universal Scene Description (USD) file format to +import and export assets. USD is an open source file format developed by Pixar +Animation Studios. It is a scene description format optimized for large-scale, +complex data sets. While this format is widely used in the film and animation +industry, it is less common in the robotics community. + +To this end, NVIDIA has developed various importers that allow you to import +assets from other file formats into USD. These importers are available as +extensions to Omniverse Kit: + +* **URDF Importer** - Import assets from URDF files. +* **MJCF Importer** - Import assets from MJCF files. +* **Mesh Importer** - Import assets from various file formats, including + OBJ, FBX, STL, and glTF. + +The recommended workflow from NVIDIA is to use the above importers to convert +the asset into its USD representation. Once the asset is in USD format, you can +use the Omniverse Kit to edit the asset and export it to other file formats. Isaac Sim includes +these importers by default. They can also be enabled manually in Omniverse Kit. + + +An important note to use assets for large-scale simulation is to ensure that they +are in `instanceable`_ format. This allows the asset to be efficiently loaded +into memory and used multiple times in a scene. Otherwise, the asset will be +loaded into memory multiple times, which can cause performance issues. +For more details on instanceable assets, please check the Isaac Sim `documentation`_. + + +Using URDF Importer +------------------- + +For using the URDF importer in the GUI, please check the documentation at `URDF importer`_. For using the URDF importer from Python scripts, we include a utility tool called ``convert_urdf.py``. This script creates an instance of :class:`~sim.converters.UrdfConverterCfg` which +is then passed to the :class:`~sim.converters.UrdfConverter` class. + +The URDF importer has various configuration parameters that can be set to control the behavior of the importer. +The default values for the importer's configuration parameters are specified are in the :class:`~sim.converters.UrdfConverterCfg` class, and they are listed below. We made a few commonly modified settings to be available as command-line arguments when calling the ``convert_urdf.py``, and they are marked with ``*`` in the list. For a comprehensive list of the configuration parameters, please check the the documentation at `URDF importer`_. + +* :attr:`~sim.converters.UrdfConverterCfg.fix_base` * - Whether to fix the base of the robot. + This depends on whether you have a floating-base or fixed-base robot. The command-line flag is + ``--fix-base`` where when set, the importer will fix the base of the robot, otherwise it will default to floating-base. +* :attr:`~sim.converters.UrdfConverterCfg.root_link_name` - The link on which the PhysX articulation root is placed. +* :attr:`~sim.converters.UrdfConverterCfg.merge_fixed_joints` * - Whether to merge the fixed joints. + Usually, this should be set to ``True`` to reduce the asset complexity. The command-line flag is + ``--merge-joints`` where when set, the importer will merge the fixed joints, otherwise it will default to not merging the fixed joints. +* :attr:`~sim.converters.UrdfConverterCfg.joint_drive` - The configuration for the joint drives on the robot. + + * :attr:`~sim.converters.UrdfConverterCfg.JointDriveCfg.drive_type` - The drive type for the joints. + This can be either ``"acceleration"`` or ``"force"``. We recommend using ``"force"`` for most cases. + * :attr:`~sim.converters.UrdfConverterCfg.JointDriveCfg.target_type` - The target type for the joints. + This can be either ``"none"``, ``"position"``, or ``"velocity"``. We recommend using ``"position"`` for most cases. + Setting this to ``"none"`` will disable the drive and set the joint gains to 0.0. + * :attr:`~sim.converters.UrdfConverterCfg.JointDriveCfg.gains` - The drive stiffness and damping gains for the joint. + We support two ways to set the gains: + + * :attr:`~sim.converters.UrdfConverterCfg.JointDriveCfg.PDGainsCfg` - To directly set the stiffness and damping. + * :attr:`~sim.converters.UrdfConverterCfg.JointDriveCfg.NaturalFrequencyGainsCfg` - To set the gains using the + desired natural frequency response of the system. + +For more detailed information on the configuration parameters, please check the documentation for :class:`~sim.converters.UrdfConverterCfg`. + +Example Usage +~~~~~~~~~~~~~ + +In this example, we use the pre-processed URDF file of the ANYmal-D robot. To check the +pre-process URDF, please check the file the `anymal.urdf`_. The main difference between the +pre-processed URDF and the original URDF are: + +* We removed the ```` tag from the URDF. This tag is not supported by the URDF importer. +* We removed the ```` tag from the URDF. This tag is not supported by the URDF importer. +* We removed various collision bodies from the URDF to reduce the complexity of the asset. +* We changed all the joint's damping and friction parameters to ``0.0``. This ensures that we can perform + effort-control on the joints without PhysX adding additional damping. +* We added the ```` tag to fixed joints. This ensures that the importer does + not merge these fixed joints. + +The following shows the steps to clone the repository and run the converter: + + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # clone a repository with URDF files + git clone git@github.com:isaac-orbit/anymal_d_simple_description.git + + # go to top of the Isaac Lab repository + cd IsaacLab + # run the converter + ./isaaclab.sh -p scripts/tools/convert_urdf.py \ + ../anymal_d_simple_description/urdf/anymal.urdf \ + source/isaaclab_assets/data/Robots/ANYbotics/anymal_d.usd \ + --merge-joints \ + --joint-stiffness 0.0 \ + --joint-damping 0.0 \ + --joint-target-type none + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + :: clone a repository with URDF files + git clone git@github.com:isaac-orbit/anymal_d_simple_description.git + + :: go to top of the Isaac Lab repository + cd IsaacLab + :: run the converter + isaaclab.bat -p scripts\tools\convert_urdf.py ^ + ..\anymal_d_simple_description\urdf\anymal.urdf ^ + source\isaaclab_assets\data\Robots\ANYbotics\anymal_d.usd ^ + --merge-joints ^ + --joint-stiffness 0.0 ^ + --joint-damping 0.0 ^ + --joint-target-type none + +Executing the above script will create a USD file inside the +``source/isaaclab_assets/data/Robots/ANYbotics/`` directory: + +* ``anymal_d.usd`` - This is the main asset file. + + +To run the script headless, you can add the ``--headless`` flag. This will not open the GUI and +exit the script after the conversion is complete. + +You can press play on the opened window to see the asset in the scene. The asset should fall under gravity. If it blows up, then it might be that you have self-collisions present in the URDF. + + +.. figure:: ../_static/tutorials/tutorial_convert_urdf.jpg + :align: center + :figwidth: 100% + :alt: result of convert_urdf.py + + + +Using MJCF Importer +------------------- + +Similar to the URDF Importer, the MJCF Importer also has a GUI interface. Please check the documentation at +`MJCF importer`_ for more details. For using the MJCF importer from Python scripts, we include a utility tool +called ``convert_mjcf.py``. This script creates an instance of :class:`~sim.converters.MjcfConverterCfg` +which is then passed to the :class:`~sim.converters.MjcfConverter` class. + +The default values for the importer's configuration parameters are specified in the +:class:`~sim.converters.MjcfConverterCfg` class. The configuration parameters are listed below. +We made a few commonly modified settings to be available as command-line arguments when calling the +``convert_mjcf.py``, and they are marked with ``*`` in the list. For a comprehensive list of the configuration +parameters, please check the the documentation at `MJCF importer`_. + + +* :attr:`~sim.converters.MjcfConverterCfg.fix_base*` - Whether to fix the base of the robot. + This depends on whether you have a floating-base or fixed-base robot. The command-line flag is + ``--fix-base`` where when set, the importer will fix the base of the robot, otherwise it will default to floating-base. +* :attr:`~sim.converters.MjcfConverterCfg.make_instanceable*` - Whether to create instanceable assets. + Usually, this should be set to ``True``. The command-line flag is ``--make-instanceable`` where + when set, the importer will create instanceable assets, otherwise it will default to non-instanceable. +* :attr:`~sim.converters.MjcfConverterCfg.import_sites*` - Whether to parse the tag in the MJCF. + Usually, this should be set to ``True``. The command-line flag is ``--import-sites`` where when set, the importer will parse the tag, otherwise it will default to not parsing the tag. + + +Example Usage +~~~~~~~~~~~~~ + +In this example, we use the MuJoCo model of the Unitree's H1 humanoid robot in the `mujoco_menagerie`_. + +The following shows the steps to clone the repository and run the converter: + + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # clone a repository with URDF files + git clone git@github.com:google-deepmind/mujoco_menagerie.git + + # go to top of the Isaac Lab repository + cd IsaacLab + # run the converter + ./isaaclab.sh -p scripts/tools/convert_mjcf.py \ + ../mujoco_menagerie/unitree_h1/h1.xml \ + source/isaaclab_assets/data/Robots/Unitree/h1.usd \ + --import-sites \ + --make-instanceable + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + :: clone a repository with URDF files + git clone git@github.com:google-deepmind/mujoco_menagerie.git + + :: go to top of the Isaac Lab repository + cd IsaacLab + :: run the converter + isaaclab.bat -p scripts\tools\convert_mjcf.py ^ + ..\mujoco_menagerie\unitree_h1\h1.xml ^ + source\isaaclab_assets\data\Robots\Unitree\h1.usd ^ + --import-sites ^ + --make-instanceable + +Executing the above script will create USD files inside the +``source/isaaclab_assets/data/Robots/Unitree/`` directory: + +* ``h1.usd`` - This is the main asset file. It contains all the non-mesh data. +* ``Props/instanceable_assets.usd`` - This is the mesh data file. + +.. figure:: ../_static/tutorials/tutorial_convert_mjcf.jpg + :align: center + :figwidth: 100% + :alt: result of convert_mjcf.py + + +Using Mesh Importer +------------------- + +Omniverse Kit includes the mesh converter tool that uses the ASSIMP library to import assets +from various mesh formats (e.g. OBJ, FBX, STL, glTF, etc.). The asset converter tool is available +as an extension to Omniverse Kit. Please check the `asset converter`_ documentation for more details. +However, unlike Isaac Sim's URDF and MJCF importers, the asset converter tool does not support +creating instanceable assets. This means that the asset will be loaded into memory multiple times +if it is used multiple times in a scene. + +Thus, we include a utility tool called ``convert_mesh.py`` that uses the asset converter tool to +import the asset and then converts it into an instanceable asset. Internally, this script creates +an instance of :class:`~sim.converters.MeshConverterCfg` which is then passed to the +:class:`~sim.converters.MeshConverter` class. Since the mesh file does not contain any physics +information, the configuration class accepts different physics properties (such as mass, collision +shape, etc.) as input. Please check the documentation for :class:`~sim.converters.MeshConverterCfg` +for more details. + +Example Usage +~~~~~~~~~~~~~ + +We use an OBJ file of a cube to demonstrate the usage of the mesh converter. The following shows +the steps to clone the repository and run the converter: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # clone a repository with URDF files + git clone git@github.com:NVIDIA-Omniverse/IsaacGymEnvs.git + + # go to top of the Isaac Lab repository + cd IsaacLab + # run the converter + ./isaaclab.sh -p scripts/tools/convert_mesh.py \ + ../IsaacGymEnvs/assets/trifinger/objects/meshes/cube_multicolor.obj \ + source/isaaclab_assets/data/Props/CubeMultiColor/cube_multicolor.usd \ + --make-instanceable \ + --collision-approximation convexDecomposition \ + --mass 1.0 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + :: clone a repository with URDF files + git clone git@github.com:NVIDIA-Omniverse/IsaacGymEnvs.git + + :: go to top of the Isaac Lab repository + cd IsaacLab + :: run the converter + isaaclab.bat -p scripts\tools\convert_mesh.py ^ + ..\IsaacGymEnvs\assets\trifinger\objects\meshes\cube_multicolor.obj ^ + source\isaaclab_assets\data\Props\CubeMultiColor\cube_multicolor.usd ^ + --make-instanceable ^ + --collision-approximation convexDecomposition ^ + --mass 1.0 + +You may need to press 'F' to zoom in on the asset after import. + +Similar to the URDF and MJCF converter, executing the above script will create two USD files inside the +``source/isaaclab_assets/data/Props/CubeMultiColor/`` directory. Additionally, +if you press play on the opened window, you should see the asset fall down under the influence +of gravity. + +* If you do not set the ``--mass`` flag, then no rigid body properties will be added to the asset. + It will be imported as a static asset. +* If you also do not set the ``--collision-approximation`` flag, then the asset will not have any collider + properties as well and will be imported as a visual asset. + + +.. figure:: ../_static/tutorials/tutorial_convert_mesh.jpg + :align: center + :figwidth: 100% + :alt: result of convert_mesh.py + + +.. _instanceable: https://openusd.org/dev/api/_usd__page__scenegraph_instancing.html +.. _documentation: https://docs.isaacsim.omniverse.nvidia.com/latest/isaac_lab_tutorials/tutorial_instanceable_assets.html +.. _MJCF importer: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_mjcf.html +.. _URDF importer: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_urdf.html +.. _anymal.urdf: https://github.com/isaac-orbit/anymal_d_simple_description/blob/master/urdf/anymal.urdf +.. _asset converter: https://docs.omniverse.nvidia.com/extensions/latest/ext_asset-converter.html +.. _mujoco_menagerie: https://github.com/google-deepmind/mujoco_menagerie/tree/main/unitree_h1 diff --git a/docs/source/how-to/index.rst b/docs/source/how-to/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..02c0ff99ae1432e4a5defcfa9d99040d19f7ef44 --- /dev/null +++ b/docs/source/how-to/index.rst @@ -0,0 +1,185 @@ +.. _how-to: + +How-to Guides +============= + +This section includes guides that help you use Isaac Lab. These are intended for users who +have already worked through the tutorials and are looking for more information on how to +use Isaac Lab. If you are new to Isaac Lab, we recommend you start with the tutorials. + +.. note:: + + This section is a work in progress. If you have a question that is not answered here, + please open an issue on our `GitHub page `_. + +Importing a New Asset +--------------------- + +Importing an asset into Isaac Lab is a common task. It contains two steps: importing the asset into +a USD format and then setting up the configuration object for the asset. The following guide explains +how to import a new asset into Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + import_new_asset + write_articulation_cfg + +Creating a Fixed Asset +---------------------- + +Often you may want to create a fixed asset in your scene. For instance, making a floating base robot +a fixed base robot. This guide goes over the various considerations and steps to create a fixed asset. + +.. toctree:: + :maxdepth: 1 + + make_fixed_prim + +Spawning Multiple Assets +------------------------ + +This guide explains how to import and configure different assets in each environment. This is +useful when you want to create diverse environments with different objects. + +.. toctree:: + :maxdepth: 1 + + multi_asset_spawning + +Saving Camera Output +-------------------- + +This guide explains how to save the camera output in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + save_camera_output + +Estimate How Many Cameras Can Run On Your Machine +------------------------------------------------- + +This guide demonstrates how to estimate the number of cameras one can run on their machine under the desired parameters. + +.. toctree:: + :maxdepth: 1 + + estimate_how_many_cameras_can_run + +Configure Rendering +------------------- + +This guide demonstrates how to select rendering mode presets and overwrite preset rendering settings. + +.. toctree:: + :maxdepth: 1 + + configure_rendering + +Drawing Markers +--------------- + +This guide explains how to use the :class:`~isaaclab.markers.VisualizationMarkers` class to draw markers in +Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + draw_markers + + +Interfacing with Environments +----------------------------- + +These guides explain how to interface with reinforcement learning environments in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + wrap_rl_env + add_own_library + + +Recording an Animation and Video +-------------------------------- + +This guide explains how to record an animation and video in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + record_animation + record_video + + +Dynamically Modifying Environment Parameters With CurriculumTerm +---------------------------------------------------------------- + +This guide explains how to dynamically modify environment parameters during training in Isaac Lab. +It covers the use of curriculum utilities to change environment parameters at runtime. + +.. toctree:: + :maxdepth: 1 + + curriculums + + +Mastering Omniverse +------------------- + +Omniverse is a powerful platform that provides a wide range of features. This guide links to +additional resources that help you use Omniverse features in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + master_omniverse + + +Setting up CloudXR Teleoperation +-------------------------------- + +This guide explains how to use CloudXR and Apple Vision Pro for immersive streaming and +teleoperation in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + cloudxr_teleoperation + + +Setting up Haply Teleoperation +------------------------------ + +This guide explains how to use Haply Inverse3 and VerseGrip devices for robot teleoperation +with directional force feedback in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + haply_teleoperation + + +Understanding Simulation Performance +------------------------------------ + +This guide provides tips on optimizing simulation performance for different simulation use cases. +Additional resources are also linked to provide relevant performance guides for Isaac Sim and +Omniverse Physics. + +.. toctree:: + :maxdepth: 1 + + simulation_performance + + +Optimize Stage Creation +----------------------- + +This guide explains 2 features that can speed up stage initialization, **fabric cloning** and **stage in memory**. + +.. toctree:: + :maxdepth: 1 + + optimize_stage_creation diff --git a/docs/source/how-to/make_fixed_prim.rst b/docs/source/how-to/make_fixed_prim.rst new file mode 100644 index 0000000000000000000000000000000000000000..6d9d50232dc53e78568fda451361793027409e03 --- /dev/null +++ b/docs/source/how-to/make_fixed_prim.rst @@ -0,0 +1,179 @@ +Making a physics prim fixed in the simulation +============================================= + +.. currentmodule:: isaaclab + +When a USD prim has physics schemas applied on it, it is affected by physics simulation. +This means that the prim can move, rotate, and collide with other prims in the simulation world. +However, there are cases where it is desirable to make certain prims static in the simulation world, +i.e. the prim should still participate in collisions but its position and orientation should not change. + +The following sections describe how to spawn a prim with physics schemas and make it static in the simulation world. + +Static colliders +---------------- + +Static colliders are prims that are not affected by physics but can collide with other prims in the simulation world. +These don't have any rigid body properties applied on them. However, this also means that they can't be accessed +using the physics tensor API (i.e., through the :class:`assets.RigidObject` class). + +For instance, to spawn a cone static in the simulation world, the following code can be used: + +.. code-block:: python + + import isaaclab.sim as sim_utils + + cone_spawn_cfg = sim_utils.ConeCfg( + radius=0.15, + height=0.5, + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ) + cone_spawn_cfg.func( + "/World/Cone", cone_spawn_cfg, translation=(0.0, 0.0, 2.0), orientation=(0.5, 0.0, 0.5, 0.0) + ) + + +Rigid object +------------ + +Rigid objects (i.e. object only has a single body) can be made static by setting the parameter +:attr:`sim.schemas.RigidBodyPropertiesCfg.kinematic_enabled` as True. This will make the object +kinematic and it will not be affected by physics. + +For instance, to spawn a cone static in the simulation world but with rigid body schema on it, +the following code can be used: + +.. code-block:: python + + import isaaclab.sim as sim_utils + + cone_spawn_cfg = sim_utils.ConeCfg( + radius=0.15, + height=0.5, + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ) + cone_spawn_cfg.func( + "/World/Cone", cone_spawn_cfg, translation=(0.0, 0.0, 2.0), orientation=(0.5, 0.0, 0.5, 0.0) + ) + + +Articulation +------------ + +Fixing the root of an articulation requires having a fixed joint to the root rigid body link of the articulation. +This can be achieved by setting the parameter :attr:`sim.schemas.ArticulationRootPropertiesCfg.fix_root_link` +as True. Based on the value of this parameter, the following cases are possible: + +* If set to :obj:`None`, the root link is not modified. +* If the articulation already has a fixed root link, this flag will enable or disable the fixed joint. +* If the articulation does not have a fixed root link, this flag will create a fixed joint between the world + frame and the root link. The joint is created with the name "FixedJoint" under the root link. + +For instance, to spawn an ANYmal robot and make it static in the simulation world, the following code can be used: + +.. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + + anymal_spawn_cfg = sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + fix_root_link=True, + ), + ) + anymal_spawn_cfg.func( + "/World/ANYmal", anymal_spawn_cfg, translation=(0.0, 0.0, 0.8), orientation=(1.0, 0.0, 0.0, 0.0) + ) + + +This will create a fixed joint between the world frame and the root link of the ANYmal robot +at the prim path ``"/World/ANYmal/base/FixedJoint"`` since the root link is at the path ``"/World/ANYmal/base"``. + + +Further notes +------------- + +Given the flexibility of USD asset designing the following possible scenarios are usually encountered: + +1. **Articulation root schema on the rigid body prim without a fixed joint**: + + This is the most common and recommended scenario for floating-base articulations. The root prim + has both the rigid body and the articulation root properties. In this case, the articulation root + is parsed as a floating-base with the root prim of the articulation ``Link0Xform``. + + .. code-block:: text + + ArticulationXform + └── Link0Xform (RigidBody and ArticulationRoot schema) + +2. **Articulation root schema on the parent prim with a fixed joint**: + + This is the expected arrangement for fixed-base articulations. The root prim has only the rigid body + properties and the articulation root properties are applied to its parent prim. In this case, the + articulation root is parsed as a fixed-base with the root prim of the articulation ``Link0Xform``. + + .. code-block:: text + + ArticulationXform (ArticulationRoot schema) + └── Link0Xform (RigidBody schema) + └── FixedJoint (connecting the world frame and Link0Xform) + +3. **Articulation root schema on the parent prim without a fixed joint**: + + This is a scenario where the root prim has only the rigid body properties and the articulation root properties + are applied to its parent prim. However, the fixed joint is not created between the world frame and the root link. + In this case, the articulation is parsed as a floating-base system. However, the PhysX parser uses its own + heuristic (such as alphabetical order) to determine the root prim of the articulation. It may select the root prim + at ``Link0Xform`` or choose another prim as the root prim. + + .. code-block:: text + + ArticulationXform (ArticulationRoot schema) + └── Link0Xform (RigidBody schema) + +4. **Articulation root schema on the rigid body prim with a fixed joint**: + + While this is a valid scenario, it is not recommended as it may lead to unexpected behavior. In this case, + the articulation is still parsed as a floating-base system. However, the fixed joint, created between the + world frame and the root link, is considered as a part of the maximal coordinate tree. This is different from + PhysX considering the articulation as a fixed-base system. Hence, the simulation may not behave as expected. + + .. code-block:: text + + ArticulationXform + └── Link0Xform (RigidBody and ArticulationRoot schema) + └── FixedJoint (connecting the world frame and Link0Xform) + +For floating base articulations, the root prim usually has both the rigid body and the articulation +root properties. However, directly connecting this prim to the world frame will cause the simulation +to consider the fixed joint as a part of the maximal coordinate tree. This is different from PhysX +considering the articulation as a fixed-base system. + +Internally, when the parameter :attr:`sim.schemas.ArticulationRootPropertiesCfg.fix_root_link` is set to True +and the articulation is detected as a floating-base system, the fixed joint is created between the world frame +the root rigid body link of the articulation. However, to make the PhysX parser consider the articulation as a +fixed-base system, the articulation root properties are removed from the root rigid body prim and applied to +its parent prim instead. + +.. note:: + + In future release of Isaac Sim, an explicit flag will be added to the articulation root schema from PhysX + to toggle between fixed-base and floating-base systems. This will resolve the need of the above workaround. diff --git a/docs/source/how-to/master_omniverse.rst b/docs/source/how-to/master_omniverse.rst new file mode 100644 index 0000000000000000000000000000000000000000..ee3e0d55c4e45ced9ad4af73a7e9f029aa82c238 --- /dev/null +++ b/docs/source/how-to/master_omniverse.rst @@ -0,0 +1,123 @@ +Mastering Omniverse for Robotics +================================ + +NVIDIA Omniverse offers a large suite of tools for 3D content workflows. +There are three main components (relevant to robotics) in Omniverse: + +- **USD Composer**: This is based on a novel file format (Universal Scene + Description) from the animation (originally Pixar) community that is + used in Omniverse +- **PhysX SDK**: This is the main physics engine behind Omniverse that + leverages GPU-based parallelization for massive scenes +- **RTX-enabled Renderer**: This uses ray-tracing kernels in NVIDIA RTX + GPUs for real-time physically-based rendering + +Of these, the first two require a deeper understanding to start working +with Omniverse and its constituent applications (Isaac Sim and Isaac Lab). + +The main things to learn: + +- How to use the Composer GUI efficiently? +- What are USD prims and schemas? +- How do you compose a USD scene? +- What is the difference between references and payloads in USD? +- What is meant by scene-graph instancing? +- How to apply PhysX schemas on prims? What all schemas are possible? +- How to write basic operations in USD for creating prims and modifying + their attributes? + + +Part 1: Using USD Composer +-------------------------- + +While several `video +tutorials `__ and +`documentation `__ exist +out there on NVIDIA Omniverse, going through all of them would take an +extensive amount of time and effort. Thus, we have curated these +resources to guide you through using Omniverse, specifically for +robotics. + +Introduction to Omniverse and USD + +- `What is NVIDIA Omniverse? `__ +- `What is the USD File Type? \| Getting Started in NVIDIA Omniverse `__ +- `What Makes USD Unique in NVIDIA Omniverse `__ + +Using Omniverse USD Composer + +- `Introduction to Omniverse USD Composer `__ +- `Navigation Basics in Omniverse USD Composer `__ +- `Lighting Basics in NVIDIA Omniverse USD Composer `__ +- `Rendering Overview in NVIDIA Omniverse USD Composer `__ + +Materials and MDL + +- `Five Things to Know About Materials in NVIDIA Omniverse `__ +- `How to apply materials? `__ + +Omniverse Physics and PhysX SDK + +- `Basics - Setting Up Physics and Toolbar Overview `__ +- `Basics - Demos Overview `__ +- `Rigid Bodies - Mass Editing `__ +- `Materials - Friction Restitution and Defaults `__ +- `Overview of Simulation Ready Assets Physics in Omniverse `__ + +Importing assets + +- `Omniverse Create - Importing FBX Files \| NVIDIA Omniverse Tutorials `__ +- `Omniverse Asset Importer `__ +- `Isaac Sim URDF impoter `__ + + +Part 2: Scripting in Omniverse +------------------------------ + +The above links mainly introduced how to use the USD Composer and its +functionalities through UI operations. However, often developers +need to write scripts to perform operations. This is especially true +when you want to automate certain tasks or create custom applications +that use Omniverse as a backend. This section will introduce you to +scripting in Omniverse. + +USD is the main file format Omniverse operates with. So naturally, the +APIs (from OpenUSD) for modifying USD are at the core of Omniverse. +Most of the APIs are in C++ and Python bindings are provided for them. +Thus, to script in Omniverse, you need to understand the USD APIs. + +.. note:: + + While Isaac Sim and Isaac Lab try to "relieve" users from understanding + the core USD concepts and APIs, understanding these basics still + help a lot once you start diving inside the codebase and modifying + it for your own application. + +Before diving into USD scripting, it is good to get acquainted with the +terminologies used in USD. We recommend the following `introduction to +USD basics `__ by +Houdini, which is a 3D animation software. +Make sure to go through the following sections: + +- `Quick example `__ +- `Attributes and primvars `__ +- `Composition `__ +- `Schemas `__ +- `Instances `__ + and `Scene-graph Instancing `__ + +As a test of understanding, make sure you can answer the following: + +- What are prims? What is meant by a prim path in a stage? +- How are attributes related to prims? +- How are schemas related to prims? +- What is the difference between attributes and schemas? +- What is asset instancing? + +Part 3: More Resources +---------------------- + +- `Omniverse Glossary of Terms `__ +- `Omniverse Code Samples `__ +- `PhysX Limitations `__ +- `PhysX Documentation `__. diff --git a/docs/source/how-to/multi_asset_spawning.rst b/docs/source/how-to/multi_asset_spawning.rst new file mode 100644 index 0000000000000000000000000000000000000000..0dbcf48ba784b2d1d9615eba77f54fc39f8aad86 --- /dev/null +++ b/docs/source/how-to/multi_asset_spawning.rst @@ -0,0 +1,129 @@ + +Spawning Multiple Assets +======================== + +.. currentmodule:: isaaclab + +Typical spawning configurations (introduced in the :ref:`tutorial-spawn-prims` tutorial) copy the same +asset (or USD primitive) across the different resolved prim paths from the expressions. +For instance, if the user specifies to spawn the asset at "/World/Table\_.*/Object", the same +asset is created at the paths "/World/Table_0/Object", "/World/Table_1/Object" and so on. + +However, we also support multi-asset spawning with two mechanisms: + +1. Rigid object collections. This allows the user to spawn multiple rigid objects in each environment and access/modify + them with a unified API, improving performance. + +2. Spawning different assets under the same prim path. This allows the user to create diverse simulations, where each + environment has a different asset. + +This guide describes how to use these two mechanisms. + +The sample script ``multi_asset.py`` is used as a reference, located in the +``IsaacLab/scripts/demos`` directory. + +.. dropdown:: Code for multi_asset.py + :icon: code + + .. literalinclude:: ../../../scripts/demos/multi_asset.py + :language: python + :emphasize-lines: 109-131, 135-179, 184-203 + :linenos: + +This script creates multiple environments, where each environment has: + +* a rigid object collection containing a cone, a cube, and a sphere +* a rigid object that is either a cone, a cube, or a sphere, chosen at random +* an articulation that is either the ANYmal-C or ANYmal-D robot, chosen at random + +.. image:: ../_static/demos/multi_asset.jpg + :width: 100% + :alt: result of multi_asset.py + + +Rigid Object Collections +------------------------ + +Multiple rigid objects can be spawned in each environment and accessed/modified with a unified ``(env_ids, obj_ids)`` API. +While the user could also create multiple rigid objects by spawning them individually, the API is more user-friendly and +more efficient since it uses a single physics view under the hood to handle all the objects. + +.. literalinclude:: ../../../scripts/demos/multi_asset.py + :language: python + :lines: 135-179 + :dedent: + +The configuration :class:`~assets.RigidObjectCollectionCfg` is used to create the collection. It's attribute :attr:`~assets.RigidObjectCollectionCfg.rigid_objects` +is a dictionary containing :class:`~assets.RigidObjectCfg` objects. The keys serve as unique identifiers for each +rigid object in the collection. + + +Spawning different assets under the same prim path +-------------------------------------------------- + +It is possible to spawn different assets and USDs under the same prim path in each environment using the spawners +:class:`~sim.spawners.wrappers.MultiAssetSpawnerCfg` and :class:`~sim.spawners.wrappers.MultiUsdFileCfg`: + +* We set the spawn configuration in :class:`~assets.RigidObjectCfg` to be + :class:`~sim.spawners.wrappers.MultiAssetSpawnerCfg`: + + .. literalinclude:: ../../../scripts/demos/multi_asset.py + :language: python + :lines: 107-133 + :dedent: + + This function allows you to define a list of different assets that can be spawned as rigid objects. + When :attr:`~sim.spawners.wrappers.MultiAssetSpawnerCfg.random_choice` is set to True, one asset from the list + is randomly selected and spawned at the specified prim path. + +* Similarly, we set the spawn configuration in :class:`~assets.ArticulationCfg` to be + :class:`~sim.spawners.wrappers.MultiUsdFileCfg`: + + .. literalinclude:: ../../../scripts/demos/multi_asset.py + :language: python + :lines: 182-215 + :dedent: + + Similar to before, this configuration allows the selection of different USD files representing articulated assets. + + +Things to Note +~~~~~~~~~~~~~~ + +Similar asset structuring +~~~~~~~~~~~~~~~~~~~~~~~~~ + +While spawning and handling multiple assets using the same physics interface (the rigid object or articulation classes), +it is essential to have the assets at all the prim locations follow a similar structure. In case of an articulation, +this means that they all must have the same number of links and joints, the same number of collision bodies and +the same names for them. If that is not the case, the physics parsing of the prims can get affected and fail. + +The main purpose of this functionality is to enable the user to create randomized versions of the same asset, +for example robots with different link lengths, or rigid objects with different collider shapes. + +Disabling physics replication in interactive scene +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, the flag :attr:`scene.InteractiveScene.replicate_physics` is set to True. This flag informs the physics +engine that the simulation environments are copies of one another so it just needs to parse the first environment +to understand the entire simulation scene. This helps speed up the simulation scene parsing. + +However, in the case of spawning different assets in different environments, this assumption does not hold +anymore. Hence the flag :attr:`scene.InteractiveScene.replicate_physics` must be disabled. + +.. literalinclude:: ../../../scripts/demos/multi_asset.py + :language: python + :lines: 280-283 + :dedent: + +The Code Execution +------------------ + +To execute the script with multiple environments and randomized assets, use the following command: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/demos/multi_asset.py --num_envs 2048 + +This command runs the simulation with 2048 environments, each with randomly selected assets. +To stop the simulation, you can close the window, or press ``Ctrl+C`` in the terminal. diff --git a/docs/source/how-to/optimize_stage_creation.rst b/docs/source/how-to/optimize_stage_creation.rst new file mode 100644 index 0000000000000000000000000000000000000000..b262878d6671d761e0db81fa9fe0bf5e7c5fce48 --- /dev/null +++ b/docs/source/how-to/optimize_stage_creation.rst @@ -0,0 +1,146 @@ +Optimize Stage Creation +======================= + +Isaac Lab supports two experimental features to speed-up stage creation: **fabric cloning** and **stage in memory**. +These features are particularly effective for large-scale RL setups with thousands of environments. + +What These Features Do +----------------------- + +**Fabric Cloning** + +- Clones environments using Fabric library (see `USD Fabric USDRT Documentation `_) +- Partially supported and enabled by default on some environments (see `Limitations`_ section for a list) + +**Stage in Memory** + +- Constructs the stage in memory, rather than with a USD file, avoiding overhead from disk I/O +- After stage creation, if rendering is required, the stage is attached to the USD context, returning to the default stage configuration +- Not enabled by default + +Usage Examples +-------------- + +Fabric cloning can be toggled by setting the :attr:`isaaclab.scene.InteractiveSceneCfg.clone_in_fabric` flag. + +**Using Fabric Cloning with a RL environment** + +.. code-block:: python + + # create environment configuration + env_cfg = CartpoleEnvCfg() + env_cfg.scene.clone_in_fabric = True + # setup RL environment + env = ManagerBasedRLEnv(cfg=env_cfg) + + +Stage in memory can be toggled by setting the :attr:`isaaclab.sim.SimulationCfg.create_stage_in_memory` flag. + +**Using Stage in Memory with a RL environment** + +.. code-block:: python + + # create config and set flag + cfg = CartpoleEnvCfg() + cfg.scene.num_envs = 1024 + cfg.sim.create_stage_in_memory = True + # create env with stage in memory + env = ManagerBasedRLEnv(cfg=cfg) + +Note, if stage in memory is enabled without using an existing RL environment class, a few more steps are need. +The stage creation steps should be wrapped in a :py:keyword:`with` statement to set the stage context. +If the stage needs to be attached, the :meth:`~isaaclab.sim.utils.attach_stage_to_usd_context` function should +be called after the stage is created. + +**Using Stage in Memory with a manual scene setup** + +.. code-block:: python + + # init simulation context with stage in memory + sim = SimulationContext(cfg=SimulationCfg(create_stage_in_memory=True)) + + # grab stage in memory and set stage context + stage_in_memory = sim.get_initial_stage() + with stage_utils.use_stage(stage_in_memory): + # create cartpole scene + scene_cfg = CartpoleSceneCfg(num_envs=1024) + scene = InteractiveScene(scene_cfg) + # attach stage to memory after stage is created + sim_utils.attach_stage_to_usd_context() + + sim.play() + + +Limitations +----------- + +**Fabric Cloning** + +- Fabric-cloned environments must be accessed using USDRT functions, rather than USD functions. +- Fabric cloning is partially supported and enabled by default on some environments, listed here. + +.. code-block:: none + + 1. Isaac-Ant-Direct-v0 + 2. Isaac-Ant-v0 + 3. Isaac-Cartpole-Direct-v0 + 4. Isaac-Cartpole-Showcase-Box-Box-Direct-v0 + 5. Isaac-Cartpole-Showcase-Box-Discrete-Direct-v0 + 6. Isaac-Cartpole-Showcase-Box-MultiDiscrete-Direct-v0 + 7. Isaac-Cartpole-Showcase-Dict-Box-Direct-v0 + 8. Isaac-Cartpole-Showcase-Dict-Discrete-Direct-v0 + 9. Isaac-Cartpole-Showcase-Dict-MultiDiscrete-Direct-v0 + 10. Isaac-Cartpole-Showcase-Discrete-Box-Direct-v0 + 11. Isaac-Cartpole-Showcase-Discrete-Discrete-Direct-v0 + 12. Isaac-Cartpole-Showcase-Discrete-MultiDiscrete-Direct-v0 + 13. Isaac-Cartpole-Showcase-MultiDiscrete-Box-Direct-v0 + 14. Isaac-Cartpole-Showcase-MultiDiscrete-Discrete-Direct-v0 + 15. Isaac-Cartpole-Showcase-MultiDiscrete-MultiDiscrete-Direct-v0 + 16. Isaac-Cartpole-Showcase-Tuple-Box-Direct-v0 + 17. Isaac-Cartpole-Showcase-Tuple-Discrete-Direct-v0 + 18. Isaac-Cartpole-Showcase-Tuple-MultiDiscrete-Direct-v0 + 19. Isaac-Cartpole-v0 + 20. Isaac-Factory-GearMesh-Direct-v0 + 21. Isaac-Factory-NutThread-Direct-v0 + 22. Isaac-Factory-PegInsert-Direct-v0 + 23. Isaac-Franka-Cabinet-Direct-v0 + 24. Isaac-Humanoid-Direct-v0 + 25. Isaac-Humanoid-v0 + 26. Isaac-Quadcopter-Direct-v0 + 27. Isaac-Repose-Cube-Allegro-Direct-v0 + 28. Isaac-Repose-Cube-Allegro-NoVelObs-v0 + 29. Isaac-Repose-Cube-Allegro-v0 + 30. Isaac-Repose-Cube-Shadow-Direct-v0 + 31. Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0 + 32. Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0 + +**Stage in Memory** + +- Cannot be currently enabled at the same time as **Fabric Cloning**. + +- Attaching stage in memory to the USD context can be slow, offsetting some or all of the performance benefits. + + - Note, attaching is only necessary when rendering is enabled. For example, in headless mode, attachment is not required. + +- Certain low-level Kit APIs do not yet support stage in memory. + + - In most cases, when these APIs are hit, existing scripts will automatically early attach the stage and print a warning message. + - In one particular case, for some environments, the API call to color the ground plane is skipped, when stage in memory is enabled. + + +Benchmark Results +----------------- + +Performance comparison cloning 4000 ShadowHand robots with rendering enabled + ++--------+-----------------+-------------------+------------------------+---------------------------+------------------------+------------------------+ +| Test # | Stage in Memory | Clone in Fabric | Attach Stage Time (s) | Fabric Attach Time (s) | Clone Paths Time (s) | First Step Time (s) | ++========+=================+===================+========================+===========================+========================+========================+ +| 1 | Yes | Yes | 3.88 | 0.15 | 4.84 | 1.39 | ++--------+-----------------+-------------------+------------------------+---------------------------+------------------------+------------------------+ +| 2 | No | No | — | 60.17 | 4.46 | 3.52 | ++--------+-----------------+-------------------+------------------------+---------------------------+------------------------+------------------------+ +| 3 | No | Yes | — | 0.47 | 4.72 | 2.56 | ++--------+-----------------+-------------------+------------------------+---------------------------+------------------------+------------------------+ +| 4 | Yes | No | 42.64 | 21.75 | 1.87 | 2.16 | ++--------+-----------------+-------------------+------------------------+---------------------------+------------------------+------------------------+ diff --git a/docs/source/how-to/record_animation.rst b/docs/source/how-to/record_animation.rst new file mode 100644 index 0000000000000000000000000000000000000000..2d675684fbf4273bbd5f5897740c8128bba33457 --- /dev/null +++ b/docs/source/how-to/record_animation.rst @@ -0,0 +1,127 @@ +Recording Animations of Simulations +=================================== + +.. currentmodule:: isaaclab + +Isaac Lab supports two approaches for recording animations of physics simulations: the **Stage Recorder** and the **OVD Recorder**. +Both generate USD outputs that can be played back in Omniverse, but they differ in how they work and when you’d use them. + +The `Stage Recorder`_ extension listens to all motion and USD property changes in the stage during simulation +and records them as **time-sampled data**. The result is a USD file that captures only the animated changes—**not** the +full scene—and matches the hierarchy of the original stage at the time of recording. +This makes it easy to add as a sublayer for playback or rendering. + +This method is built into Isaac Lab’s UI through the :class:`~isaaclab.envs.ui.BaseEnvWindow`. +However, to record the animation of a simulation, you need to disable `Fabric`_ to allow reading and writing +all the changes (such as motion and USD properties) to the USD stage. + +The **OVD Recorder** is designed for more scalable or automated workflows. It uses OmniPVD to capture simulated physics from a played stage +and then **bakes** that directly into an animated USD file. It works with Fabric enabled and runs with CLI arguments. +The animated USD can be quickly replayed and reviewed by scrubbing through the timeline window, without simulating expensive physics operations. + +.. note:: + + Omniverse only supports **either** physics simulation **or** animation playback on a USD prim—never both at once. + Disable physics on the prims you want to animate. + + +Stage Recorder +-------------- + +In Isaac Lab, the Stage Recorder is integrated into the :class:`~isaaclab.envs.ui.BaseEnvWindow` class. +It’s the easiest way to capture physics simulations visually and works directly through the UI. + +To record, Fabric must be disabled—this allows the recorder to track changes to USD and write them out. + +Stage Recorder Settings +~~~~~~~~~~~~~~~~~~~~~~~ + +Isaac Lab sets up the Stage Recorder with sensible defaults in ``base_env_window.py``. If needed, +you can override or inspect these by using the Stage Recorder extension directly in Omniverse Create. + +.. dropdown:: Settings used in base_env_window.py + :icon: code + + .. literalinclude:: ../../../source/isaaclab/isaaclab/envs/ui/base_env_window.py + :language: python + :linenos: + :pyobject: BaseEnvWindow._toggle_recording_animation_fn + +Example Usage +~~~~~~~~~~~~~ + +In standalone Isaac Lab environments, pass the ``--disable_fabric`` flag: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/lift_cube_sm.py --num_envs 8 --device cpu --disable_fabric + +After launching, the Isaac Lab UI window will display a "Record Animation" button. +Click to begin recording. Click again to stop. + +The following files are saved to the ``recordings/`` folder: + +- ``Stage.usd`` — the original stage with physics disabled +- ``TimeSample_tk001.usd`` — the animation (time-sampled) layer + +To play back: + +.. code-block:: bash + + ./isaaclab.sh -s # Opens Isaac Sim + +Inside the Layers panel, insert both ``Stage.usd`` and ``TimeSample_tk001.usd`` as sublayers. +The animation will now play back when you hit the play button. + +See the `tutorial on layering in Omniverse`_ for more on working with layers. + + +OVD Recorder +------------ + +The OVD Recorder uses OmniPVD to record simulation data and bake it directly into a new USD stage. +This method is more scalable and better suited for large-scale training scenarios (e.g. multi-env RL). + +It’s not UI-controlled—the whole process is enabled through CLI flags and runs automatically. + + +Workflow Summary +~~~~~~~~~~~~~~~~ + +1. User runs Isaac Lab with animation recording enabled via CLI +2. Isaac Lab starts simulation +3. OVD data is recorded as the simulation runs +4. At the specified stop time, the simulation is baked into an outputted USD file, and IsaacLab is closed +5. The final result is a fully baked, self-contained USD animation + +Example Usage +~~~~~~~~~~~~~ + +To record an animation: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/run_cartpole_rl_env.py \ + --anim_recording_enabled \ + --anim_recording_start_time 1 \ + --anim_recording_stop_time 3 + +.. note:: + + The provided ``--anim_recording_stop_time`` should be greater than the simulation time. + +.. warning:: + + Currently, the final recording step can output many warning logs from [omni.usd]. This is a known issue, and these warning messages can be ignored. + +After the stop time is reached, a file will be saved to: + +.. code-block:: none + + anim_recordings//baked_animation_recording.usda + + +.. _Stage Recorder: https://docs.omniverse.nvidia.com/extensions/latest/ext_animation_stage-recorder.html +.. _Fabric: https://docs.omniverse.nvidia.com/kit/docs/usdrt/latest/docs/usd_fabric_usdrt.html +.. _Omniverse Launcher: https://docs.omniverse.nvidia.com/launcher/latest/index.html +.. _tutorial on layering in Omniverse: https://www.youtube.com/watch?v=LTwmNkSDh-c&ab_channel=NVIDIAOmniverse diff --git a/docs/source/how-to/record_video.rst b/docs/source/how-to/record_video.rst new file mode 100644 index 0000000000000000000000000000000000000000..aba743631295e3d8c7ce9d50b18727eb91d71673 --- /dev/null +++ b/docs/source/how-to/record_video.rst @@ -0,0 +1,25 @@ +Recording video clips during training +===================================== + +Isaac Lab supports recording video clips during training using the +`gymnasium.wrappers.RecordVideo `_ class. + +This feature can be enabled by installing ``ffmpeg`` and using the following command line arguments with the training +script: + +* ``--video``: enables video recording during training +* ``--video_length``: length of each recorded video (in steps) +* ``--video_interval``: interval between each video recording (in steps) + +Make sure to also add the ``--enable_cameras`` argument when running headless. +Note that enabling recording is equivalent to enabling rendering during training, which will slow down both startup and runtime performance. + +Example usage: + +.. code-block:: shell + + python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --video --video_length 100 --video_interval 500 + + +The recorded videos will be saved in the same directory as the training checkpoints, under +``IsaacLab/logs////videos/train``. diff --git a/docs/source/how-to/save_camera_output.rst b/docs/source/how-to/save_camera_output.rst new file mode 100644 index 0000000000000000000000000000000000000000..f92f9bcc93d5a4e354ef169eb28bdac19ce15210 --- /dev/null +++ b/docs/source/how-to/save_camera_output.rst @@ -0,0 +1,102 @@ +.. _how-to-save-images-and-3d-reprojection: + + +Saving rendered images and 3D re-projection +=========================================== + +.. currentmodule:: isaaclab + +This guide accompanied with the ``run_usd_camera.py`` script in the ``IsaacLab/scripts/tutorials/04_sensors`` +directory. + +.. dropdown:: Code for run_usd_camera.py + :icon: code + + .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :emphasize-lines: 171-179, 229-247, 251-264 + :linenos: + + +Saving using Replicator Basic Writer +------------------------------------ + +To save camera outputs, we use the basic write class from Omniverse Replicator. This class allows us to save the +images in a numpy format. For more information on the basic writer, please check the +`documentation `_. + +.. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :start-at: rep_writer = rep.BasicWriter( + :end-before: # Camera positions, targets, orientations + +While stepping the simulator, the images can be saved to the defined folder. Since the BasicWriter only supports +saving data using NumPy format, we first need to convert the PyTorch sensors to NumPy arrays before packing +them in a dictionary. + +.. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :start-at: # Save images from camera at camera_index + :end-at: single_cam_info = camera.data.info[camera_index] + +After this step, we can save the images using the BasicWriter. + +.. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :start-at: # Pack data back into replicator format to save them using its writer + :end-at: rep_writer.write(rep_output) + + +Projection into 3D Space +------------------------ + +We include utilities to project the depth image into 3D Space. The re-projection operations are done using +PyTorch operations which allows faster computation. + +.. code-block:: python + + from isaaclab.utils.math import transform_points, unproject_depth + + # Pointcloud in world frame + points_3d_cam = unproject_depth( + camera.data.output["distance_to_image_plane"], camera.data.intrinsic_matrices + ) + + points_3d_world = transform_points(points_3d_cam, camera.data.pos_w, camera.data.quat_w_ros) + +Alternately, we can use the :meth:`isaaclab.sensors.camera.utils.create_pointcloud_from_depth` function +to create a point cloud from the depth image and transform it to the world frame. + +.. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :start-at: # Derive pointcloud from camera at camera_index + :end-before: # In the first few steps, things are still being instanced and Camera.data + +The resulting point cloud can be visualized using the :mod:`isaacsim.util.debug_draw` extension from Isaac Sim. +This makes it easy to visualize the point cloud in the 3D space. + +.. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py + :language: python + :start-at: # In the first few steps, things are still being instanced and Camera.data + :end-at: pc_markers.visualize(translations=pointcloud) + + +Executing the script +-------------------- + +To run the accompanying script, execute the following command: + +.. code-block:: bash + + # Usage with saving and drawing + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --save --draw --enable_cameras + + # Usage with saving only in headless mode + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --save --headless --enable_cameras + + +The simulation should start, and you can observe different objects falling down. An output folder will be created +in the ``IsaacLab/scripts/tutorials/04_sensors`` directory, where the images will be saved. Additionally, +you should see the point cloud in the 3D space drawn on the viewport. + +To stop the simulation, close the window, or use ``Ctrl+C`` in the terminal. diff --git a/docs/source/how-to/simulation_performance.rst b/docs/source/how-to/simulation_performance.rst new file mode 100644 index 0000000000000000000000000000000000000000..3dd113a1285d9b07c6541cc61398bde0afd3d070 --- /dev/null +++ b/docs/source/how-to/simulation_performance.rst @@ -0,0 +1,72 @@ +Simulation Performance and Tuning +==================================== + +The performance of the simulation can be affected by various factors, including the number of objects in the scene, +the complexity of the physics simulation, and the hardware being used. Here are some tips to improve performance: + +1. **Use Headless Mode**: Running the simulation in headless mode can significantly improve performance, especially + when rendering is not required. You can enable headless mode by using the ``--headless`` flag when running the + simulator. +2. **Avoid Unnecessary Collisions**: If possible, reduce the number of object overlaps to reduce overhead in the simulation. + Excessive contacts and collisions in the simulation can be expensive in the collision phase in the simulation. +3. **Use Simplified Physics**: Consider using simplified physics collision geometries or lowering simulation fidelity + for better performance. This can be done by modifying the assets and adjusting the physics parameters in the simulation configuration. +4. **Use CPU/GPU Simulation**: If your scene consists of just a few articulations or rigid bodies, consider using CPU simulation + for better performance. For larger scenes, using GPU simulation can significantly improve performance. + +Collision Geometries +-------------------- + +Collision geometries are used to define the shape of objects in the simulation for collision detection. Using +simplified collision geometries can improve performance and reduce the complexity of the simulation. + +For example, if you have a complex mesh, you can create a simplified collision geometry that approximates the shape +of the mesh. This can be done in Isaac Sim through the UI by modifying the collision mesh and approximation methods. + +Additionally, we can often remove collision geometries on areas of the robot that are not important for training. +In the Anymal-C robot, we keep the collision geometries for the kneeds and feet, but remove the collision geometries +on other parts of the legs to optimize for performance. + +Simpler collision geometries such as primitive shapes like spheres will also yield better performance than complex meshes. +For example, an SDF mesh collider will be more expensive than a simple sphere. + +Note that cylinder and cone collision geometries have special support for smooth collisions with triangle meshes for +better wheeled simulation behavior. This comes at a cost of performance and may not always be desired. To disable this feature, +we can set the stage settings ``--/physics/collisionApproximateCylinders=true`` and ``--/physics/collisionApproximateCones=true``. + +Another item to watch out for in GPU RL workloads is warnings about GPU compatibility of ``Convex Hull`` approximated mesh collision geometry. +If the input mesh has a high aspect ratio (e.g. a long thin shape), the convex hull approximation may be incompatible with GPU simulation, +triggering a CPU fallback that can significantly impact performance. + +A CPU-fallback warning looks as follows: ``[Warning] [omni.physx.cooking.plugin] ConvexMeshCookingTask: failed to cook GPU-compatible mesh, +collision detection will fall back to CPU. Collisions with particles and deformables will not work with this mesh.``. +Suitable workarounds include switching to a bounding cube approximation, or using a static triangle mesh collider +if the geometry is not part of a dynamic rigid body. + +CPU Governor Settings on Linux +------------------------------ + +CPU governors dictate the operating clock frequency range and scaling of the CPU. This can be a limiting factor for Isaac Sim performance. For maximum performance, the CPU governor should be set to ``performance``. To modify the CPU governor, run the following commands: + +.. code-block:: bash + + sudo apt-get install linux-tools-common + cpupower frequency-info # Check available governors + sudo cpupower frequency-set -g performance # Set governor with root permissions + +.. note:: + + Not all governors are available on all systems. Governors enabling higher clock speed are typically more performance-centric and will yield better performance for Isaac Sim. + +Additional Performance Guides +----------------------------- + +There are many ways to "tune" the performance of the simulation, but the way you choose largely depends on what you are trying to simulate. In general, the first place +you will want to look for performance gains is with the `physics engine `_. Next to rendering +and running deep learning models, the physics engine is the most computationally costly. Tuning the physics sim to limit the scope to only the task of interest is a great place to +start hunting for performance gains. + +We have recently released a new `gripper tuning guide `_ , specific to contact and grasp tuning. Please check it first if you intend to use robot grippers. For additional details, you should also checkout these guides! + +* `Isaac Sim Performance Optimization Handbook `_ +* `Omni Physics Simulation Performance Guide `_ diff --git a/docs/source/how-to/wrap_rl_env.rst b/docs/source/how-to/wrap_rl_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..54433dfe0334080ccea7a6a808230a714b121704 --- /dev/null +++ b/docs/source/how-to/wrap_rl_env.rst @@ -0,0 +1,167 @@ +.. _how-to-env-wrappers: + + +Wrapping environments +===================== + +.. currentmodule:: isaaclab + +Environment wrappers are a way to modify the behavior of an environment without modifying the environment itself. +This can be used to apply functions to modify observations or rewards, record videos, enforce time limits, etc. +A detailed description of the API is available in the :class:`gymnasium.Wrapper` class. + +At present, all RL environments inheriting from the :class:`~envs.ManagerBasedRLEnv` or :class:`~envs.DirectRLEnv` classes +are compatible with :class:`gymnasium.Wrapper`, since the base class implements the :class:`gymnasium.Env` interface. +In order to wrap an environment, you need to first initialize the base environment. After that, you can +wrap it with as many wrappers as you want by calling ``env = wrapper(env, *args, **kwargs)`` repeatedly. + +For example, here is how you would wrap an environment to enforce that reset is called before step or render: + +.. code-block:: python + + """Launch Isaac Sim Simulator first.""" + + + from isaaclab.app import AppLauncher + + # launch omniverse app in headless mode + app_launcher = AppLauncher(headless=True) + simulation_app = app_launcher.app + + """Rest everything follows.""" + + import gymnasium as gym + + import isaaclab_tasks # noqa: F401 + from isaaclab_tasks.utils import load_cfg_from_registry + + # create base environment + cfg = load_cfg_from_registry("Isaac-Reach-Franka-v0", "env_cfg_entry_point") + env = gym.make("Isaac-Reach-Franka-v0", cfg=cfg) + # wrap environment to enforce that reset is called before step + env = gym.wrappers.OrderEnforcing(env) + + +Wrapper for recording videos +---------------------------- + +The :class:`gymnasium.wrappers.RecordVideo` wrapper can be used to record videos of the environment. +The wrapper takes a ``video_dir`` argument, which specifies where to save the videos. The videos are saved in +`mp4 `__ format at specified intervals for specified +number of environment steps or episodes. + +To use the wrapper, you need to first install ``ffmpeg``. On Ubuntu, you can install it by running: + +.. code-block:: bash + + sudo apt-get install ffmpeg + +.. attention:: + + By default, when running an environment in headless mode, the Omniverse viewport is disabled. This is done to + improve performance by avoiding unnecessary rendering. + + We notice the following performance in different rendering modes with the ``Isaac-Reach-Franka-v0`` environment + using an RTX 3090 GPU: + + * No GUI execution without off-screen rendering enabled: ~65,000 FPS + * No GUI execution with off-screen enabled: ~57,000 FPS + * GUI execution with full rendering: ~13,000 FPS + + +The viewport camera used for rendering is the default camera in the scene called ``"/OmniverseKit_Persp"``. +The camera's pose and image resolution can be configured through the +:class:`~envs.ViewerCfg` class. + + +.. dropdown:: Default parameters of the ViewerCfg class: + :icon: code + + .. literalinclude:: ../../../source/isaaclab/isaaclab/envs/common.py + :language: python + :pyobject: ViewerCfg + + +After adjusting the parameters, you can record videos by wrapping the environment with the +:class:`gymnasium.wrappers.RecordVideo` wrapper and enabling the off-screen rendering +flag. Additionally, you need to specify the render mode of the environment as ``"rgb_array"``. + +As an example, the following code records a video of the ``Isaac-Reach-Franka-v0`` environment +for 200 steps, and saves it in the ``videos`` folder at a step interval of 1500 steps. + +.. code:: python + + """Launch Isaac Sim Simulator first.""" + + + from isaaclab.app import AppLauncher + + # launch omniverse app in headless mode with off-screen rendering + app_launcher = AppLauncher(headless=True, enable_cameras=True) + simulation_app = app_launcher.app + + """Rest everything follows.""" + + import gymnasium as gym + + # adjust camera resolution and pose + env_cfg.viewer.resolution = (640, 480) + env_cfg.viewer.eye = (1.0, 1.0, 1.0) + env_cfg.viewer.lookat = (0.0, 0.0, 0.0) + # create isaac-env instance + # set render mode to rgb_array to obtain images on render calls + env = gym.make(task_name, cfg=env_cfg, render_mode="rgb_array") + # wrap for video recording + video_kwargs = { + "video_folder": "videos/train", + "step_trigger": lambda step: step % 1500 == 0, + "video_length": 200, + } + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + +Wrapper for learning frameworks +------------------------------- + +Every learning framework has its own API for interacting with environments. For example, the +`Stable-Baselines3`_ library uses the `gym.Env `_ +interface to interact with environments. However, libraries like `RL-Games`_, `RSL-RL`_ or `SKRL`_ +use their own API for interfacing with a learning environments. Since there is no one-size-fits-all +solution, we do not base the :class:`~envs.ManagerBasedRLEnv` and :class:`~envs.DirectRLEnv` classes on any particular learning framework's +environment definition. Instead, we implement wrappers to make it compatible with the learning +framework's environment definition. + +As an example of how to use the RL task environment with Stable-Baselines3: + +.. code:: python + + from isaaclab_rl.sb3 import Sb3VecEnvWrapper + + # create isaac-env instance + env = gym.make(task_name, cfg=env_cfg) + # wrap around environment for stable baselines + env = Sb3VecEnvWrapper(env) + + +.. caution:: + + Wrapping the environment with the respective learning framework's wrapper should happen in the end, + i.e. after all other wrappers have been applied. This is because the learning framework's wrapper + modifies the interpretation of environment's APIs which may no longer be compatible with :class:`gymnasium.Env`. + + +Adding new wrappers +------------------- + +All new wrappers should be added to the :mod:`isaaclab_rl` module. +They should check that the underlying environment is an instance of :class:`isaaclab.envs.ManagerBasedRLEnv` +or :class:`~envs.DirectRLEnv` +before applying the wrapper. This can be done by using the :func:`unwrapped` property. + +We include a set of wrappers in this module that can be used as a reference to implement your own wrappers. +If you implement a new wrapper, please consider contributing it to the framework by opening a pull request. + +.. _Stable-Baselines3: https://stable-baselines3.readthedocs.io/en/master/ +.. _SKRL: https://skrl.readthedocs.io +.. _RL-Games: https://github.com/Denys88/rl_games +.. _RSL-RL: https://github.com/leggedrobotics/rsl_rl diff --git a/docs/source/how-to/write_articulation_cfg.rst b/docs/source/how-to/write_articulation_cfg.rst new file mode 100644 index 0000000000000000000000000000000000000000..d681f281473b00c5f68d0360e9a01f28829a25fb --- /dev/null +++ b/docs/source/how-to/write_articulation_cfg.rst @@ -0,0 +1,268 @@ +.. _how-to-write-articulation-config: + + +Writing an Asset Configuration +============================== + +.. currentmodule:: isaaclab + +This guide walks through the process of creating an :class:`~assets.ArticulationCfg`. +The :class:`~assets.ArticulationCfg` is a configuration object that defines the +properties of an :class:`~assets.Articulation` in Isaac Lab. + +.. note:: + + While we only cover the creation of an :class:`~assets.ArticulationCfg` in this guide, + the process is similar for creating any other asset configuration object. + +We will use the Cartpole example to demonstrate how to create an :class:`~assets.ArticulationCfg`. +The Cartpole is a simple robot that consists of a cart with a pole attached to it. The cart +is free to move along a rail, and the pole is free to rotate about the cart. The file for this configuration example is +``source/isaaclab_assets/isaaclab_assets/robots/cartpole.py``. + +.. dropdown:: Code for Cartpole configuration + :icon: code + + .. literalinclude:: ../../../source/isaaclab_assets/isaaclab_assets/robots/cartpole.py + :language: python + :linenos: + + +Defining the spawn configuration +-------------------------------- + +As explained in :ref:`tutorial-spawn-prims` tutorials, the spawn configuration defines +the properties of the assets to be spawned. This spawning may happen procedurally, or +through an existing asset file (e.g. USD or URDF). In this example, we will spawn the +Cartpole from a USD file. + +When spawning an asset from a USD file, we define its :class:`~sim.spawners.from_files.UsdFileCfg`. +This configuration object takes in the following parameters: + +* :class:`~sim.spawners.from_files.UsdFileCfg.usd_path`: The USD file path to spawn from +* :class:`~sim.spawners.from_files.UsdFileCfg.rigid_props`: The properties of the articulation's root +* :class:`~sim.spawners.from_files.UsdFileCfg.articulation_props`: The properties of all the articulation's links + +The last two parameters are optional. If not specified, they are kept at their default values in the USD file. + +.. literalinclude:: ../../../source/isaaclab_assets/isaaclab_assets/robots/cartpole.py + :language: python + :lines: 19-35 + :dedent: + +To import articulation from a URDF file instead of a USD file, you can replace the +:class:`~sim.spawners.from_files.UsdFileCfg` with a :class:`~sim.spawners.from_files.UrdfFileCfg`. +For more details, please check the API documentation. + + +Defining the initial state +-------------------------- + +Every asset requires defining their initial or *default* state in the simulation through its configuration. +This configuration is stored into the asset's default state buffers that can be accessed when the asset's +state needs to be reset. + +.. note:: + The initial state of an asset is defined w.r.t. its local environment frame. This then needs to + be transformed into the global simulation frame when resetting the asset's state. For more + details, please check the :ref:`tutorial-interact-articulation` tutorial. + + +For an articulation, the :class:`~assets.ArticulationCfg.InitialStateCfg` object defines the +initial state of the root of the articulation and the initial state of all its joints. In this +example, we will spawn the Cartpole at the origin of the XY plane at a Z height of 2.0 meters. +Meanwhile, the joint positions and velocities are set to 0.0. + +.. literalinclude:: ../../../source/isaaclab_assets/isaaclab_assets/robots/cartpole.py + :language: python + :lines: 36-38 + :dedent: + +Defining the actuator configuration +----------------------------------- + +Actuators are a crucial component of an articulation. Through this configuration, it is possible +to define the type of actuator model to use. We can use the internal actuator model provided by +the physics engine (i.e. the implicit actuator model), or use a custom actuator model which is +governed by a user-defined system of equations (i.e. the explicit actuator model). +For more details on actuators, see :ref:`overview-actuators`. + +The cartpole's articulation has two actuators, one corresponding to its each joint: +``cart_to_pole`` and ``slider_to_cart``. We use two different actuator models for these actuators as +an example. However, since they are both using the same actuator model, it is possible +to combine them into a single actuator model. + +.. dropdown:: Actuator model configuration with separate actuator models + :icon: code + + .. literalinclude:: ../../../source/isaaclab_assets/isaaclab_assets/robots/cartpole.py + :language: python + :lines: 39-49 + :dedent: + + +.. dropdown:: Actuator model configuration with a single actuator model + :icon: code + + .. code-block:: python + + actuators={ + "all_joints": ImplicitActuatorCfg( + joint_names_expr=[".*"], + effort_limit=400.0, + velocity_limit=100.0, + stiffness={"slider_to_cart": 0.0, "cart_to_pole": 0.0}, + damping={"slider_to_cart": 10.0, "cart_to_pole": 0.0}, + ), + }, + + +ActuatorCfg velocity/effort limits considerations +------------------------------------------------- + +In IsaacLab v1.4.0, the plain ``velocity_limit`` and ``effort_limit`` attributes were **not** consistently +pushed into the physics solver: + +- **Implicit actuators** + - velocity_limit was ignored (never set in simulation) + - effort_limit was set into simulation + +- **Explicit actuators** + - both velocity_limit and effort_limit were used only by the drive model, not by the solver + + +In v2.0.1 we accidentally changed this: all velocity_limit & effort_limit, implicit or +explicit, were being applied to the solver. That caused many training under the old default uncaped solver +limits to break. + +To restore the original behavior while still giving users full control over solver limits, we introduced two new flags: + +* **velocity_limit_sim** + Sets the physics-solver's maximum joint-velocity cap in simulation. + +* **effort_limit_sim** + Sets the physics-solver's maximum joint-effort cap in simulation. + + +These explicitly set the solver's joint-velocity and joint-effort caps at simulation level. + +On the other hand, velocity_limit and effort_limit model the motor's hardware-level constraints in torque +computation for all explicit actuators rather than limiting simulation-level constraint. +For implicit actuators, since they do not model motor hardware limitations, ``velocity_limit`` were removed in v2.1.1 +and marked as deprecated. This preserves same behavior as they did in v1.4.0. Eventually, ``velocity_limit`` and +``effort_limit`` will be deprecated for implicit actuators, preserving only ``velocity_limit_sim`` and +``effort_limit_sim`` + + +.. table:: Limit Options Comparison + + .. list-table:: + :header-rows: 1 + :widths: 20 40 40 + + * - **Attribute** + - **Implicit Actuator** + - **Explicit Actuator** + * - ``velocity_limit`` + - Deprecated (alias for ``velocity_limit_sim``) + - Used by the model (e.g. DC motor), not set into simulation + * - ``effort_limit`` + - Deprecated (alias for ``effort_limit_sim``) + - Used by the model, not set into simulation + * - ``velocity_limit_sim`` + - Set into simulation + - Set into simulation + * - ``effort_limit_sim`` + - Set into simulation + - Set into simulation + + + +Users who want to tune the underlying physics-solver limits should set the ``_sim`` flags. + + +USD vs. ActuatorCfg discrepancy resolution +------------------------------------------ + +USD having default value and the fact that ActuatorCfg can be specified with None, or a overriding value can sometime be +confusing what exactly gets written into simulation. The resolution follows these simple rules,per joint and per +property: + +.. table:: Resolution Rules for USD vs. ActuatorCfg + + +------------------------+------------------------+--------------------+ + | **Condition** | **ActuatorCfg Value** | **Applied** | + +========================+========================+====================+ + | No override provided | Not Specified | USD Value | + +------------------------+------------------------+--------------------+ + | Override provided | User's ActuatorCfg | Same as ActuatorCfg| + +------------------------+------------------------+--------------------+ + + +Digging into USD can sometime be unconvinent, to help clarify what exact value is written, we designed a flag +:attr:`~isaaclab.assets.ArticulationCfg.actuator_value_resolution_debug_print`, +to help user figure out what exact value gets used in simulation. + +Whenever an actuator parameter is overridden in the user's ActuatorCfg (or left unspecified), +we compare it to the value read from the USD definition and record any differences. For each joint and each property, +if unmatching value is found, we log the resolution: + + 1. **USD Value** + The default limit or gain parsed from the USD asset. + + 2. **ActuatorCfg Value** + The user-provided override (or “Not Specified” if none was given). + + 3. **Applied** + The final value actually used for simulation: if the user didn't override it, this matches the USD value; + otherwise it reflects the user's setting. + +This resolution info is emitted as a warning table only when discrepancies exist. +Here's an example of what you'll see:: + + +----------------+--------------------+---------------------+----+-------------+--------------------+----------+ + | Group | Property | Name | ID | USD Value | ActuatorCfg Value | Applied | + +----------------+--------------------+---------------------+----+-------------+--------------------+----------+ + | panda_shoulder | velocity_limit_sim | panda_joint1 | 0 | 2.17e+00 | Not Specified | 2.17e+00 | + | | | panda_joint2 | 1 | 2.17e+00 | Not Specified | 2.17e+00 | + | | | panda_joint3 | 2 | 2.17e+00 | Not Specified | 2.17e+00 | + | | | panda_joint4 | 3 | 2.17e+00 | Not Specified | 2.17e+00 | + | | stiffness | panda_joint1 | 0 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | | panda_joint2 | 1 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | | panda_joint3 | 2 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | | panda_joint4 | 3 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | damping | panda_joint1 | 0 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | | panda_joint2 | 1 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | | panda_joint3 | 2 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | | panda_joint4 | 3 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | armature | panda_joint1 | 0 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint2 | 1 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint3 | 2 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint4 | 3 | 0.00e+00 | Not Specified | 0.00e+00 | + | panda_forearm | velocity_limit_sim | panda_joint5 | 4 | 2.61e+00 | Not Specified | 2.61e+00 | + | | | panda_joint6 | 5 | 2.61e+00 | Not Specified | 2.61e+00 | + | | | panda_joint7 | 6 | 2.61e+00 | Not Specified | 2.61e+00 | + | | stiffness | panda_joint5 | 4 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | | panda_joint6 | 5 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | | panda_joint7 | 6 | 2.29e+04 | 8.00e+01 | 8.00e+01 | + | | damping | panda_joint5 | 4 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | | panda_joint6 | 5 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | | panda_joint7 | 6 | 4.58e+03 | 4.00e+00 | 4.00e+00 | + | | armature | panda_joint5 | 4 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint6 | 5 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint7 | 6 | 0.00e+00 | Not Specified | 0.00e+00 | + | | friction | panda_joint5 | 4 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint6 | 5 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_joint7 | 6 | 0.00e+00 | Not Specified | 0.00e+00 | + | panda_hand | velocity_limit_sim | panda_finger_joint1 | 7 | 2.00e-01 | Not Specified | 2.00e-01 | + | | | panda_finger_joint2 | 8 | 2.00e-01 | Not Specified | 2.00e-01 | + | | stiffness | panda_finger_joint1 | 7 | 1.00e+06 | 2.00e+03 | 2.00e+03 | + | | | panda_finger_joint2 | 8 | 1.00e+06 | 2.00e+03 | 2.00e+03 | + | | armature | panda_finger_joint1 | 7 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_finger_joint2 | 8 | 0.00e+00 | Not Specified | 0.00e+00 | + | | friction | panda_finger_joint1 | 7 | 0.00e+00 | Not Specified | 0.00e+00 | + | | | panda_finger_joint2 | 8 | 0.00e+00 | Not Specified | 0.00e+00 | + +----------------+--------------------+---------------------+----+-------------+--------------------+----------+ + +To keep the cleaniness of logging, :attr:`~isaaclab.assets.ArticulationCfg.actuator_value_resolution_debug_print` +default to False, remember to turn it on when wishes. diff --git a/docs/source/migration/migrating_from_isaacgymenvs.rst b/docs/source/migration/migrating_from_isaacgymenvs.rst new file mode 100644 index 0000000000000000000000000000000000000000..0346ef76d791f96a7a384718d657a4392fa37822 --- /dev/null +++ b/docs/source/migration/migrating_from_isaacgymenvs.rst @@ -0,0 +1,929 @@ +.. _migrating-from-isaacgymenvs: + +From IsaacGymEnvs +================= + +.. currentmodule:: isaaclab + + +`IsaacGymEnvs`_ was a reinforcement learning framework designed for the `Isaac Gym Preview Release`_. +As both IsaacGymEnvs and the Isaac Gym Preview Release are now deprecated, the following guide walks through +the key differences between IsaacGymEnvs and Isaac Lab, as well as differences in APIs between Isaac Gym Preview +Release and Isaac Sim. + +.. note:: + + The following changes are with respect to Isaac Lab 1.0 release. Please refer to the `release notes`_ for any changes + in the future releases. + + +Task Config Setup +~~~~~~~~~~~~~~~~~ + +In IsaacGymEnvs, task config files were defined in ``.yaml`` format. With Isaac Lab, configs are now specified using +a specialized Python class :class:`~isaaclab.utils.configclass`. The :class:`~isaaclab.utils.configclass` +module provides a wrapper on top of Python's ``dataclasses`` module. Each environment should specify its own config +class annotated by ``@configclass`` that inherits from :class:`~envs.DirectRLEnvCfg`, which can include simulation +parameters, environment scene parameters, robot parameters, and task-specific parameters. + +Below is an example skeleton of a task config class: + +.. code-block:: python + + from isaaclab.envs import DirectRLEnvCfg + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.sim import SimulationCfg + + @configclass + class MyEnvCfg(DirectRLEnvCfg): + # simulation + sim: SimulationCfg = SimulationCfg() + # robot + robot_cfg: ArticulationCfg = ArticulationCfg() + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg() + # env + decimation = 2 + episode_length_s = 5.0 + action_space = 1 + observation_space = 4 + state_space = 0 + # task-specific parameters + ... + +Simulation Config +----------------- + +Simulation related parameters are defined as part of the :class:`~isaaclab.sim.SimulationCfg` class, +which is a :class:`~isaaclab.utils.configclass` module that holds simulation parameters such as ``dt``, +``device``, and ``gravity``. Each task config must have a variable named ``sim`` defined that holds the type +:class:`~isaaclab.sim.SimulationCfg`. + +In Isaac Lab, the use of ``substeps`` has been replaced +by a combination of the simulation ``dt`` and the ``decimation`` parameters. For example, in IsaacGymEnvs, having +``dt=1/60`` and ``substeps=2`` is equivalent to taking 2 simulation steps with ``dt=1/120``, but running the task step +at ``1/60`` seconds. The ``decimation`` parameter is a task parameter that controls the number of simulation steps to +take for each task (or RL) step, replacing the ``controlFrequencyInv`` parameter in IsaacGymEnvs. +Thus, the same setup in Isaac Lab will become ``dt=1/120`` and ``decimation=2``. + +In Isaac Sim, physx simulation parameters such as ``num_position_iterations``, ``num_velocity_iterations``, +``contact_offset``, ``rest_offset``, ``bounce_threshold_velocity``, ``max_depenetration_velocity`` can all +be specified on a per-actor basis. These parameters have been moved from the physx simulation config +to each individual articulation and rigid body config. + +When running simulation on the GPU, buffers in PhysX require pre-allocation for computing and storing +information such as contacts, collisions and aggregate pairs. These buffers may need to be adjusted +depending on the complexity of the environment, the number of expected contacts and collisions, +and the number of actors in the environment. The :class:`~isaaclab.sim.PhysxCfg` class provides access for +setting the GPU buffer dimensions. + ++--------------------------------------------------------------+-------------------------------------------------------------------+ +| | | +|.. code-block:: yaml |.. code-block:: python | +| | | +| # IsaacGymEnvs | # IsaacLab | +| sim: | sim: SimulationCfg = SimulationCfg( | +| | device = "cuda:0" # can be "cpu", "cuda", "cuda:" | +| dt: 0.0166 # 1/60 s | dt=1 / 120, | +| substeps: 2 | # decimation will be set in the task config | +| up_axis: "z" | # up axis will always be Z in isaac sim | +| use_gpu_pipeline: ${eq:${...pipeline},"gpu"} | # use_gpu_pipeline is deduced from the device | +| gravity: [0.0, 0.0, -9.81] | gravity=(0.0, 0.0, -9.81), | +| physx: | physx: PhysxCfg = PhysxCfg( | +| num_threads: ${....num_threads} | # num_threads is no longer needed | +| solver_type: ${....solver_type} | solver_type=1, | +| use_gpu: ${contains:"cuda",${....sim_device}} | # use_gpu is deduced from the device | +| num_position_iterations: 4 | max_position_iteration_count=4, | +| num_velocity_iterations: 0 | max_velocity_iteration_count=0, | +| contact_offset: 0.02 | # moved to actor config | +| rest_offset: 0.001 | # moved to actor config | +| bounce_threshold_velocity: 0.2 | bounce_threshold_velocity=0.2, | +| max_depenetration_velocity: 100.0 | # moved to actor config | +| default_buffer_size_multiplier: 2.0 | # default_buffer_size_multiplier is no longer needed | +| max_gpu_contact_pairs: 1048576 # 1024*1024 | gpu_max_rigid_contact_count=2**23 | +| num_subscenes: ${....num_subscenes} | # num_subscenes is no longer needed | +| contact_collection: 0 | # contact_collection is no longer needed | +| | )) | ++--------------------------------------------------------------+-------------------------------------------------------------------+ + +Scene Config +------------ + +The :class:`~isaaclab.scene.InteractiveSceneCfg` class can be used to specify parameters related to the scene, +such as the number of environments and the spacing between environments. Each task config must have a variable named +``scene`` defined that holds the type :class:`~isaaclab.scene.InteractiveSceneCfg`. + ++--------------------------------------------------------------+-------------------------------------------------------------------+ +| | | +|.. code-block:: yaml |.. code-block:: python | +| | | +| # IsaacGymEnvs | # IsaacLab | +| env: | scene: InteractiveSceneCfg = InteractiveSceneCfg( | +| numEnvs: ${resolve_default:512,${...num_envs}} | num_envs=512, | +| envSpacing: 4.0 | env_spacing=4.0) | ++--------------------------------------------------------------+-------------------------------------------------------------------+ + +Task Config +----------- + +Each environment should specify its own config class that holds task specific parameters, such as the dimensions of the +observation and action buffers. Reward term scaling parameters can also be specified in the config class. + +The following parameters must be set for each environment config: + +.. code-block:: python + + decimation = 2 + episode_length_s = 5.0 + action_space = 1 + observation_space = 4 + state_space = 0 + +Note that the maximum episode length parameter (now ``episode_length_s``) is in seconds instead of steps as it was +in IsaacGymEnvs. To convert between step count to seconds, use the equation: +``episode_length_s = dt * decimation * num_steps`` + + +RL Config Setup +~~~~~~~~~~~~~~~ + +RL config files for the rl_games library can continue to be defined in ``.yaml`` files in Isaac Lab. +Most of the content of the config file can be copied directly from IsaacGymEnvs. +Note that in Isaac Lab, we do not use hydra to resolve relative paths in config files. +Please replace any relative paths such as ``${....device}`` with the actual values of the parameters. + +Additionally, the observation and action clip ranges have been moved to the RL config file. +For any ``clipObservations`` and ``clipActions`` parameters that were defined in the IsaacGymEnvs task config file, +they should be moved to the RL config file in Isaac Lab. + ++--------------------------+----------------------------+ +| | | +| IsaacGymEnvs Task Config | Isaac Lab RL Config | ++--------------------------+----------------------------+ +|.. code-block:: yaml |.. code-block:: yaml | +| | | +| # IsaacGymEnvs | # IsaacLab | +| env: | params: | +| clipObservations: 5.0 | env: | +| clipActions: 1.0 | clip_observations: 5.0 | +| | clip_actions: 1.0 | ++--------------------------+----------------------------+ + +Environment Creation +~~~~~~~~~~~~~~~~~~~~ + +In IsaacGymEnvs, environment creation generally included four components: creating the sim object with ``create_sim()``, +creating the ground plane, importing the assets from MJCF or URDF files, and finally creating the environments +by looping through each environment and adding actors into the environments. + +Isaac Lab no longer requires calling the ``create_sim()`` method to retrieve the sim object. Instead, the simulation +context is retrieved automatically by the framework. It is also no longer required to use the ``sim`` as an +argument for the simulation APIs. + +In replacement of ``create_sim()``, tasks can implement the ``_setup_scene()`` method in Isaac Lab. +This method can be used for adding actors into the scene, adding ground plane, cloning the actors, and +adding any other optional objects into the scene, such as lights. + ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def create_sim(self): | def _setup_scene(self): | +| # set the up axis to be z-up | self.cartpole = Articulation(self.cfg.robot_cfg) | +| self.up_axis = self.cfg["sim"]["up_axis"] | # add ground plane | +| | spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg() | +| self.sim = super().create_sim(self.device_id, self.graphics_device_id, | # clone, filter, and replicate | +| self.physics_engine, self.sim_params) | self.scene.clone_environments(copy_from_source=False) | +| self._create_ground_plane() | self.scene.filter_collisions(global_prim_paths=[]) | +| self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], | # add articulation to scene | +| int(np.sqrt(self.num_envs))) | self.scene.articulations["cartpole"] = self.cartpole | +| | # add lights | +| | light_cfg = sim_utils.DomeLightCfg(intensity=2000.0) | +| | light_cfg.func("/World/Light", light_cfg) | ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ + + +Ground Plane +------------ + +In Isaac Lab, most of the environment creation process has been simplified into configs with the :class:`~isaaclab.utils.configclass` module. + +The ground plane can be defined using the :class:`~terrains.TerrainImporterCfg` class. + +.. code-block:: python + + from isaaclab.terrains import TerrainImporterCfg + + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + +The terrain can then be added to the scene in ``_setup_scene(self)`` by referencing the ``TerrainImporterCfg`` object: + +.. code-block::python + + def _setup_scene(self): + ... + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self._terrain = self.cfg.terrain.class_type(self.cfg.terrain) + + +Actors +------ + +Isaac Lab and Isaac Sim both use the `USD (Universal Scene Description) `_ +library for describing the scene. Assets defined in MJCF and URDF formats can be imported to USD using importer +tools described in the `Importing a New Asset <../how-to/import_new_asset.html>`_ tutorial. + +Each Articulation and Rigid Body actor can also have its own config class. The +:class:`~isaaclab.assets.ArticulationCfg` class can be used to define parameters for articulation actors, +including file path, simulation parameters, actuator properties, and initial states. + +.. code-block::python + + from isaaclab.actuators import ImplicitActuatorCfg + from isaaclab.assets import ArticulationCfg + + CARTPOLE_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Classic/Cartpole/cartpole.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=100.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 2.0), joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0} + ), + actuators={ + "cart_actuator": ImplicitActuatorCfg( + joint_names_expr=["slider_to_cart"], + effort_limit=400.0, + velocity_limit=100.0, + stiffness=0.0, + damping=10.0, + ), + "pole_actuator": ImplicitActuatorCfg( + joint_names_expr=["cart_to_pole"], effort_limit=400.0, velocity_limit=100.0, stiffness=0.0, damping=0.0 + ), + }, + ) + +Within the :class:`~assets.ArticulationCfg`, the ``spawn`` attribute can be used to add the robot to the scene by +specifying the path to the robot file. In addition, :class:`~isaaclab.sim.schemas.RigidBodyPropertiesCfg` can +be used to specify simulation properties for the rigid bodies in the articulation. +Similarly, the :class:`~isaaclab.sim.schemas.ArticulationRootPropertiesCfg` class can be used to specify +simulation properties for the articulation. Joint properties are now specified as part of the ``actuators`` +dictionary using :class:`~actuators.ImplicitActuatorCfg`. Joints with the same properties can be grouped into +regex expressions or provided as a list of names or expressions. + +Actors are added to the scene by simply calling ``self.cartpole = Articulation(self.cfg.robot_cfg)``, +where ``self.cfg.robot_cfg`` is an :class:`~assets.ArticulationCfg` object. Once initialized, they should also +be added to the :class:`~scene.InteractiveScene` by calling ``self.scene.articulations["cartpole"] = self.cartpole`` +so that the :class:`~scene.InteractiveScene` can traverse through actors in the scene for writing values to the +simulation and resetting. + +Simulation Parameters for Actors +"""""""""""""""""""""""""""""""" + +Some simulation parameters related to Rigid Bodies and Articulations may have different +default values between Isaac Gym Preview Release and Isaac Sim. +It may be helpful to double check the USD assets to ensure that the default values are +applicable for the asset. + +For instance, the following parameters in the ``RigidBodyAPI`` could be different +between Isaac Gym Preview Release and Isaac Sim: + +.. list-table:: + :widths: 50 50 50 + :header-rows: 1 + + * - RigidBodyAPI Parameter + - Default Value in Isaac Sim + - Default Value in Isaac Gym Preview Release + * - Linear Damping + - 0.00 + - 0.00 + * - Angular Damping + - 0.05 + - 0.0 + * - Max Linear Velocity + - inf + - 1000 + * - Max Angular Velocity + - 5729.58008 (degree/s) + - 64.0 (rad/s) + * - Max Contact Impulse + - inf + - 1e32 + +Articulation parameters for the ``JointAPI`` and ``DriveAPI`` could be altered as well. Note +that the Isaac Sim UI assumes the unit of angle to be degrees. It is particularly +worth noting that the ``Damping`` and ``Stiffness`` parameters in the ``DriveAPI`` have the unit +of ``1/deg`` in the Isaac Sim UI but ``1/rad`` in Isaac Gym Preview Release. + +.. list-table:: + :widths: 50 50 50 + :header-rows: 1 + + * - Joint Parameter + - Default Value in Isaac Sim + - Default Value in Isaac Gym Preview Releases + * - Maximum Joint Velocity + - 1000000.0 (deg) + - 100.0 (rad) + + +Cloner +------ + +Isaac Sim introduced a concept of ``Cloner``, which is a class designed for replication during the scene creation process. +In IsaacGymEnvs, scenes had to be created by looping through the number of environments. +Within each iteration, actors were added to each environment and their handles had to be cached. +Isaac Lab eliminates the need for looping through the environments by using the ``Cloner`` APIs. +The scene creation process is as follow: + +#. Construct a single environment (what the scene would look like if number of environments = 1) +#. Call ``clone_environments()`` to replicate the single environment +#. Call ``filter_collisions()`` to filter out collision between environments (if required) + + +.. code-block:: python + + # construct a single environment with the Cartpole robot + self.cartpole = Articulation(self.cfg.robot_cfg) + # clone the environment + self.scene.clone_environments(copy_from_source=False) + # filter collisions + self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path]) + + +Accessing States from Simulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +APIs for accessing physics states in Isaac Lab require the creation of an :class:`~assets.Articulation` or :class:`~assets.RigidObject` +object. Multiple objects can be initialized for different articulations or rigid bodies in the scene by defining +corresponding :class:`~assets.ArticulationCfg` or :class:`~assets.RigidObjectCfg` config as outlined in the section above. +This approach eliminates the need of retrieving body handles to slice states for specific bodies in the scene. + + +.. code-block:: python + + self._robot = Articulation(self.cfg.robot) + self._cabinet = Articulation(self.cfg.cabinet) + self._object = RigidObject(self.cfg.object_cfg) + + +We have also removed ``acquire`` and ``refresh`` APIs in Isaac Lab. Physics states can be directly applied or retrieved +using APIs defined for the articulations and rigid objects. + +APIs provided in Isaac Lab no longer require explicit wrapping and un-wrapping of underlying buffers. +APIs can now work with tensors directly for reading and writing data. + ++------------------------------------------------------------------+-----------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------+-----------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) | self.joint_pos = self._robot.data.joint_pos | +| self.dof_state = gymtorch.wrap_tensor(dof_state_tensor) | self.joint_vel = self._robot.data.joint_vel | +| self.gym.refresh_dof_state_tensor(self.sim) | | ++------------------------------------------------------------------+-----------------------------------------------------------------+ + +Note some naming differences between APIs in Isaac Gym Preview Release and Isaac Lab. Most ``dof`` related APIs have been +named to ``joint`` in Isaac Lab. +APIs in Isaac Lab also no longer follow the explicit ``_tensors`` or ``_tensor_indexed`` suffixes in naming. +Indexed versions of APIs now happen implicitly through the optional ``indices`` parameter. + +Most APIs in Isaac Lab also provide +the option to specify an ``indices`` parameter, which can be used when reading or writing data for a subset +of environments. Note that when setting states with the ``indices`` parameter, the shape of the states buffer +should match with the dimension of the ``indices`` list. + ++---------------------------------------------------------------------------+---------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++---------------------------------------------------------------------------+---------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| env_ids_int32 = env_ids.to(dtype=torch.int32) | self._robot.write_joint_state_to_sim(joint_pos, joint_vel, | +| self.gym.set_dof_state_tensor_indexed(self.sim, | joint_ids, env_ids) | +| gymtorch.unwrap_tensor(self.dof_state), | | +| gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) | | ++---------------------------------------------------------------------------+---------------------------------------------------------------+ + +Quaternion Convention +--------------------- + +Isaac Lab and Isaac Sim both adopt ``wxyz`` as the quaternion convention. However, the quaternion +convention used in Isaac Gym Preview Release was ``xyzw``. +Remember to switch all quaternions to use the ``xyzw`` convention when working indexing rotation data. +Similarly, please ensure all quaternions are in ``wxyz`` before passing them to Isaac Lab APIs. + + +Articulation Joint Order +------------------------ + +Physics simulation in Isaac Sim and Isaac Lab assumes a breadth-first +ordering for the joints in a given kinematic tree. +However, Isaac Gym Preview Release assumed a depth-first ordering for joints in the kinematic tree. +This means that indexing joints based on their ordering may be different in IsaacGymEnvs and Isaac Lab. + +In Isaac Lab, the list of joint names can be retrieved with ``Articulation.data.joint_names``, which will +also correspond to the ordering of the joints in the Articulation. + + +Creating a New Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Each environment in Isaac Lab should be in its own directory following this structure: + +.. code-block:: none + + my_environment/ + - agents/ + - __init__.py + - rl_games_ppo_cfg.py + - __init__.py + my_env.py + +* ``my_environment`` is the root directory of the task. +* ``my_environment/agents`` is the directory containing all RL config files for the task. Isaac Lab supports multiple RL libraries that can each have its own individual config file. +* ``my_environment/__init__.py`` is the main file that registers the environment with the Gymnasium interface. This allows the training and inferencing scripts to find the task by its name. The content of this file should be as follow: + +.. code-block:: python + + import gymnasium as gym + + from . import agents + from .cartpole_env import CartpoleEnv, CartpoleEnvCfg + + ## + # Register Gym environments. + ## + + gym.register( + id="Isaac-Cartpole-Direct-v0", + entry_point="isaaclab_tasks.direct_workflow.cartpole:CartpoleEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": CartpoleEnvCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml" + }, + ) + +* ``my_environment/my_env.py`` is the main python script that implements the task logic and task config class for the environment. + + +Task Logic +~~~~~~~~~~ + +In Isaac Lab, the ``post_physics_step`` function has been moved to the framework in the base class. +Tasks are not required to implement this method, but can choose to override it if a different workflow is desired. + +By default, Isaac Lab follows the following flow in logic: + ++----------------------------------+----------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++----------------------------------+----------------------------------+ +|.. code-block:: none |.. code-block:: none | +| | | +| pre_physics_step | pre_physics_step | +| |-- apply_action | |-- _pre_physics_step(action)| +| | |-- _apply_action() | +| | | +| post_physics_step | post_physics_step | +| |-- reset_idx() | |-- _get_dones() | +| |-- compute_observation() | |-- _get_rewards() | +| |-- compute_reward() | |-- _reset_idx() | +| | |-- _get_observations() | ++----------------------------------+----------------------------------+ + +In Isaac Lab, we also separate the ``pre_physics_step`` API for processing actions from the policy with +the ``apply_action`` API, which sets the actions into the simulation. This provides more flexibility in controlling +when actions should be written to simulation when ``decimation`` is used. +``pre_physics_step`` will be called once per step before stepping simulation. +``apply_actions`` will be called ``decimation`` number of times for each RL step, once before each simulation step call. + +With this approach, resets are performed based on actions from the current step instead of the previous step. +Observations will also be computed with the correct states after resets. + +We have also performed some renamings of APIs: + +* ``create_sim(self)`` --> ``_setup_scene(self)`` +* ``pre_physics_step(self, actions)`` --> ``_pre_physics_step(self, actions)`` and ``_apply_action(self)`` +* ``reset_idx(self, env_ids)`` --> ``_reset_idx(self, env_ids)`` +* ``compute_observations(self)`` --> ``_get_observations(self)`` - ``_get_observations()`` should now return a dictionary ``{"policy": obs}`` +* ``compute_reward(self)`` --> ``_get_rewards(self)`` - ``_get_rewards()`` should now return the reward buffer +* ``post_physics_step(self)`` --> moved to the base class +* In addition, Isaac Lab requires the implementation of ``_is_done(self)``, which should return two buffers: the ``reset`` buffer and the ``time_out`` buffer. + + +Putting It All Together +~~~~~~~~~~~~~~~~~~~~~~~ + +The Cartpole environment is shown here in completion to fully show the comparison between the IsaacGymEnvs implementation and the Isaac Lab implementation. + +Task Config +----------- + ++--------------------------------------------------------+---------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++--------------------------------------------------------+---------------------------------------------------------------------+ +|.. code-block:: yaml |.. code-block:: python | +| | | +| # used to create the object | @configclass | +| name: Cartpole | class CartpoleEnvCfg(DirectRLEnvCfg): | +| | | +| physics_engine: ${..physics_engine} | # simulation | +| | sim: SimulationCfg = SimulationCfg(dt=1 / 120) | +| # if given, will override the device setting in gym. | # robot | +| env: | robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace( | +| numEnvs: ${resolve_default:512,${...num_envs}} | prim_path="/World/envs/env_.*/Robot") | +| envSpacing: 4.0 | cart_dof_name = "slider_to_cart" | +| resetDist: 3.0 | pole_dof_name = "cart_to_pole" | +| maxEffort: 400.0 | # scene | +| | scene: InteractiveSceneCfg = InteractiveSceneCfg( | +| clipObservations: 5.0 | num_envs=4096, env_spacing=4.0, replicate_physics=True) | +| clipActions: 1.0 | # env | +| | decimation = 2 | +| asset: | episode_length_s = 5.0 | +| assetRoot: "../../assets" | action_scale = 100.0 # [N] | +| assetFileName: "urdf/cartpole.urdf" | action_space = 1 | +| | observation_space = 4 | +| enableCameraSensors: False | state_space = 0 | +| | # reset | +| sim: | max_cart_pos = 3.0 | +| dt: 0.0166 # 1/60 s | initial_pole_angle_range = [-0.25, 0.25] | +| substeps: 2 | # reward scales | +| up_axis: "z" | rew_scale_alive = 1.0 | +| use_gpu_pipeline: ${eq:${...pipeline},"gpu"} | rew_scale_terminated = -2.0 | +| gravity: [0.0, 0.0, -9.81] | rew_scale_pole_pos = -1.0 | +| physx: | rew_scale_cart_vel = -0.01 | +| num_threads: ${....num_threads} | rew_scale_pole_vel = -0.005 | +| solver_type: ${....solver_type} | | +| use_gpu: ${contains:"cuda",${....sim_device}} | | +| num_position_iterations: 4 | | +| num_velocity_iterations: 0 | | +| contact_offset: 0.02 | | +| rest_offset: 0.001 | | +| bounce_threshold_velocity: 0.2 | | +| max_depenetration_velocity: 100.0 | | +| default_buffer_size_multiplier: 2.0 | | +| max_gpu_contact_pairs: 1048576 # 1024*1024 | | +| num_subscenes: ${....num_subscenes} | | +| contact_collection: 0 | | ++--------------------------------------------------------+---------------------------------------------------------------------+ + + + +Task Setup +---------- + +Isaac Lab no longer requires pre-initialization of buffers through the ``acquire_*`` APIs that were used in IsaacGymEnvs. +It is also no longer necessary to ``wrap`` and ``unwrap`` tensors. + ++-------------------------------------------------------------------------+-------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++-------------------------------------------------------------------------+-------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| class Cartpole(VecTask): | class CartpoleEnv(DirectRLEnv): | +| | cfg: CartpoleEnvCfg | +| def __init__(self, cfg, rl_device, sim_device, graphics_device_id, | def __init__(self, cfg: CartpoleEnvCfg, | +| headless, virtual_screen_capture, force_render): | render_mode: str | None = None, **kwargs): | +| self.cfg = cfg | | +| | super().__init__(cfg, render_mode, **kwargs) | +| self.reset_dist = self.cfg["env"]["resetDist"] | | +| | self._cart_dof_idx, _ = self.cartpole.find_joints( | +| self.max_push_effort = self.cfg["env"]["maxEffort"] | self.cfg.cart_dof_name) | +| self.max_episode_length = 500 | self._pole_dof_idx, _ = self.cartpole.find_joints( | +| | self.cfg.pole_dof_name) | +| self.cfg["env"]["numObservations"] = 4 | self.action_scale = self.cfg.action_scale | +| self.cfg["env"]["numActions"] = 1 | | +| | self.joint_pos = self.cartpole.data.joint_pos | +| super().__init__(config=self.cfg, | self.joint_vel = self.cartpole.data.joint_vel | +| rl_device=rl_device, sim_device=sim_device, | | +| graphics_device_id=graphics_device_id, headless=headless, | | +| virtual_screen_capture=virtual_screen_capture, | | +| force_render=force_render) | | +| | | +| dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) | | +| self.dof_state = gymtorch.wrap_tensor(dof_state_tensor) | | +| self.dof_pos = self.dof_state.view( | | +| self.num_envs, self.num_dof, 2)[..., 0] | | +| self.dof_vel = self.dof_state.view( | | +| self.num_envs, self.num_dof, 2)[..., 1] | | ++-------------------------------------------------------------------------+-------------------------------------------------------------+ + + + +Scene Setup +----------- + +Scene setup is now done through the ``Cloner`` API and by specifying actor attributes in config objects. +This eliminates the need to loop through the number of environments to set up the environments and avoids +the need to set simulation parameters for actors in the task implementation. + ++------------------------------------------------------------------------+---------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------------+---------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def create_sim(self): | def _setup_scene(self): | +| # set the up axis to be z-up given that assets are y-up by default | self.cartpole = Articulation(self.cfg.robot_cfg) | +| self.up_axis = self.cfg["sim"]["up_axis"] | # add ground plane | +| | spawn_ground_plane(prim_path="/World/ground", | +| self.sim = super().create_sim(self.device_id, | cfg=GroundPlaneCfg()) | +| self.graphics_device_id, self.physics_engine, | # clone, filter, and replicate | +| self.sim_params) | self.scene.clone_environments( | +| self._create_ground_plane() | copy_from_source=False) | +| self._create_envs(self.num_envs, | self.scene.filter_collisions( | +| self.cfg["env"]['envSpacing'], | global_prim_paths=[]) | +| int(np.sqrt(self.num_envs))) | # add articulation to scene | +| | self.scene.articulations["cartpole"] = self.cartpole | +| def _create_ground_plane(self): | # add lights | +| plane_params = gymapi.PlaneParams() | light_cfg = sim_utils.DomeLightCfg( | +| # set the normal force to be z dimension | intensity=2000.0, color=(0.75, 0.75, 0.75)) | +| plane_params.normal = (gymapi.Vec3(0.0, 0.0, 1.0) | light_cfg.func("/World/Light", light_cfg) | +| if self.up_axis == 'z' | | +| else gymapi.Vec3(0.0, 1.0, 0.0)) | CARTPOLE_CFG = ArticulationCfg( | +| self.gym.add_ground(self.sim, plane_params) | spawn=sim_utils.UsdFileCfg( | +| | usd_path=f"{ISAACLAB_NUCLEUS_DIR}/.../cartpole.usd", | +| def _create_envs(self, num_envs, spacing, num_per_row): | rigid_props=sim_utils.RigidBodyPropertiesCfg( | +| # define plane on which environments are initialized | rigid_body_enabled=True, | +| lower = (gymapi.Vec3(0.5 * -spacing, -spacing, 0.0) | max_linear_velocity=1000.0, | +| if self.up_axis == 'z' | max_angular_velocity=1000.0, | +| else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)) | max_depenetration_velocity=100.0, | +| upper = gymapi.Vec3(0.5 * spacing, spacing, spacing) | enable_gyroscopic_forces=True, | +| | ), | +| asset_root = os.path.join(os.path.dirname( | articulation_props=sim_utils.ArticulationRootPropertiesCfg( | +| os.path.abspath(__file__)), "../../assets") | enabled_self_collisions=False, | +| asset_file = "urdf/cartpole.urdf" | solver_position_iteration_count=4, | +| | solver_velocity_iteration_count=0, | +| if "asset" in self.cfg["env"]: | sleep_threshold=0.005, | +| asset_root = os.path.join(os.path.dirname( | stabilization_threshold=0.001, | +| os.path.abspath(__file__)), | ), | +| self.cfg["env"]["asset"].get("assetRoot", asset_root)) | ), | +| asset_file = self.cfg["env"]["asset"].get( | init_state=ArticulationCfg.InitialStateCfg( | +| "assetFileName", asset_file) | pos=(0.0, 0.0, 2.0), | +| | joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0} | +| asset_path = os.path.join(asset_root, asset_file) | ), | +| asset_root = os.path.dirname(asset_path) | actuators={ | +| asset_file = os.path.basename(asset_path) | "cart_actuator": ImplicitActuatorCfg( | +| | joint_names_expr=["slider_to_cart"], | +| asset_options = gymapi.AssetOptions() | effort_limit_sim=400.0, | +| asset_options.fix_base_link = True | velocity_limit_sim=100.0, | +| cartpole_asset = self.gym.load_asset(self.sim, | stiffness=0.0, | +| asset_root, asset_file, asset_options) | damping=10.0, | +| self.num_dof = self.gym.get_asset_dof_count( | ), | +| cartpole_asset) | "pole_actuator": ImplicitActuatorCfg( | +| | joint_names_expr=["cart_to_pole"], | +| pose = gymapi.Transform() | effort_limit_sim=400.0, velocity_limit_sim=100.0, | +| if self.up_axis == 'z': | stiffness=0.0, damping=0.0 | +| pose.p.z = 2.0 | ), | +| pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) | }, | +| else: | ) | +| pose.p.y = 2.0 | | +| pose.r = gymapi.Quat( | | +| -np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2) | | +| | | +| self.cartpole_handles = [] | | +| self.envs = [] | | +| for i in range(self.num_envs): | | +| # create env instance | | +| env_ptr = self.gym.create_env( | | +| self.sim, lower, upper, num_per_row | | +| ) | | +| cartpole_handle = self.gym.create_actor( | | +| env_ptr, cartpole_asset, pose, | | +| "cartpole", i, 1, 0) | | +| | | +| dof_props = self.gym.get_actor_dof_properties( | | +| env_ptr, cartpole_handle) | | +| dof_props['driveMode'][0] = gymapi.DOF_MODE_EFFORT | | +| dof_props['driveMode'][1] = gymapi.DOF_MODE_NONE | | +| dof_props['stiffness'][:] = 0.0 | | +| dof_props['damping'][:] = 0.0 | | +| self.gym.set_actor_dof_properties(env_ptr, c | | +| artpole_handle, dof_props) | | +| | | +| self.envs.append(env_ptr) | | +| self.cartpole_handles.append(cartpole_handle) | | ++------------------------------------------------------------------------+---------------------------------------------------------------------+ + + +Pre and Post Physics Step +------------------------- + +In IsaacGymEnvs, due to limitations of the GPU APIs, observations had stale data when environments had to perform resets. +This restriction has been eliminated in Isaac Lab, and thus, tasks follow the correct workflow of applying actions, stepping simulation, +collecting states, computing dones, calculating rewards, performing resets, and finally computing observations. +This workflow is done automatically by the framework such that a ``post_physics_step`` API is not required in the task. +However, individual tasks can override the ``step()`` API to control the workflow. + ++------------------------------------------------------------------+-------------------------------------------------------------+ +| IsaacGymEnvs | IsaacLab | ++------------------------------------------------------------------+-------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def pre_physics_step(self, actions): | def _pre_physics_step(self, actions: torch.Tensor) -> None: | +| actions_tensor = torch.zeros( | self.actions = self.action_scale * actions | +| self.num_envs * self.num_dof, | | +| device=self.device, dtype=torch.float) | def _apply_action(self) -> None: | +| actions_tensor[::self.num_dof] = actions.to( | self.cartpole.set_joint_effort_target( | +| self.device).squeeze() * self.max_push_effort | self.actions, joint_ids=self._cart_dof_idx) | +| forces = gymtorch.unwrap_tensor(actions_tensor) | | +| self.gym.set_dof_actuation_force_tensor( | | +| self.sim, forces) | | +| | | +| def post_physics_step(self): | | +| self.progress_buf += 1 | | +| | | +| env_ids = self.reset_buf.nonzero( | | +| as_tuple=False).squeeze(-1) | | +| if len(env_ids) > 0: | | +| self.reset_idx(env_ids) | | +| | | +| self.compute_observations() | | +| self.compute_reward() | | ++------------------------------------------------------------------+-------------------------------------------------------------+ + + +Dones and Resets +---------------- + +In Isaac Lab, ``dones`` are computed in the ``_get_dones()`` method and should return two variables: ``resets`` and ``time_out``. +Tracking of the ``progress_buf`` has been moved to the base class and is now automatically incremented and reset by the framework. +The ``progress_buf`` variable has also been renamed to ``episode_length_buf``. + ++-----------------------------------------------------------------------+---------------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++-----------------------------------------------------------------------+---------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def reset_idx(self, env_ids): | def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: | +| positions = 0.2 * (torch.rand((len(env_ids), self.num_dof), | self.joint_pos = self.cartpole.data.joint_pos | +| device=self.device) - 0.5) | self.joint_vel = self.cartpole.data.joint_vel | +| velocities = 0.5 * (torch.rand((len(env_ids), self.num_dof), | | +| device=self.device) - 0.5) | time_out = self.episode_length_buf >= self.max_episode_length - 1 | +| | out_of_bounds = torch.any(torch.abs( | +| self.dof_pos[env_ids, :] = positions[:] | self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, | +| self.dof_vel[env_ids, :] = velocities[:] | dim=1) | +| | out_of_bounds = out_of_bounds | torch.any( | +| env_ids_int32 = env_ids.to(dtype=torch.int32) | torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, | +| self.gym.set_dof_state_tensor_indexed(self.sim, | dim=1) | +| gymtorch.unwrap_tensor(self.dof_state), | return out_of_bounds, time_out | +| gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32)) | | +| self.reset_buf[env_ids] = 0 | def _reset_idx(self, env_ids: Sequence[int] | None): | +| self.progress_buf[env_ids] = 0 | if env_ids is None: | +| | env_ids = self.cartpole._ALL_INDICES | +| | super()._reset_idx(env_ids) | +| | | +| | joint_pos = self.cartpole.data.default_joint_pos[env_ids] | +| | joint_pos[:, self._pole_dof_idx] += sample_uniform( | +| | self.cfg.initial_pole_angle_range[0] * math.pi, | +| | self.cfg.initial_pole_angle_range[1] * math.pi, | +| | joint_pos[:, self._pole_dof_idx].shape, | +| | joint_pos.device, | +| | ) | +| | joint_vel = self.cartpole.data.default_joint_vel[env_ids] | +| | | +| | default_root_state = self.cartpole.data.default_root_state[env_ids] | +| | default_root_state[:, :3] += self.scene.env_origins[env_ids] | +| | | +| | self.joint_pos[env_ids] = joint_pos | +| | | +| | self.cartpole.write_root_pose_to_sim( | +| | default_root_state[:, :7], env_ids) | +| | self.cartpole.write_root_velocity_to_sim( | +| | default_root_state[:, 7:], env_ids) | +| | self.cartpole.write_joint_state_to_sim( | +| | joint_pos, joint_vel, None, env_ids) | ++-----------------------------------------------------------------------+---------------------------------------------------------------------------+ + + +Observations +------------ + +In Isaac Lab, the ``_get_observations()`` API should now return a dictionary containing the ``policy`` key with the observation +buffer as the value. +For asymmetric policies, the dictionary should also include a ``critic`` key that holds the state buffer. + ++--------------------------------------------------------------------------+---------------------------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++--------------------------------------------------------------------------+---------------------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def compute_observations(self, env_ids=None): | def _get_observations(self) -> dict: | +| if env_ids is None: | obs = torch.cat( | +| env_ids = np.arange(self.num_envs) | ( | +| | self.joint_pos[:, self._pole_dof_idx[0]], | +| self.gym.refresh_dof_state_tensor(self.sim) | self.joint_vel[:, self._pole_dof_idx[0]], | +| | self.joint_pos[:, self._cart_dof_idx[0]], | +| self.obs_buf[env_ids, 0] = self.dof_pos[env_ids, 0] | self.joint_vel[:, self._cart_dof_idx[0]], | +| self.obs_buf[env_ids, 1] = self.dof_vel[env_ids, 0] | ), | +| self.obs_buf[env_ids, 2] = self.dof_pos[env_ids, 1] | dim=-1, | +| self.obs_buf[env_ids, 3] = self.dof_vel[env_ids, 1] | ) | +| | observations = {"policy": obs} | +| return self.obs_buf | return observations | ++--------------------------------------------------------------------------+---------------------------------------------------------------------------------------+ + + +Rewards +------- + +In Isaac Lab, the reward method ``_get_rewards`` should return the reward buffer as a return value. +Similar to IsaacGymEnvs, computations in the reward function can also be performed using pytorch jit +by adding the ``@torch.jit.script`` annotation. + ++--------------------------------------------------------------------------+----------------------------------------------------------------------------------------+ +| IsaacGymEnvs | Isaac Lab | ++--------------------------------------------------------------------------+----------------------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def compute_reward(self): | def _get_rewards(self) -> torch.Tensor: | +| # retrieve environment observations from buffer | total_reward = compute_rewards( | +| pole_angle = self.obs_buf[:, 2] | self.cfg.rew_scale_alive, | +| pole_vel = self.obs_buf[:, 3] | self.cfg.rew_scale_terminated, | +| cart_vel = self.obs_buf[:, 1] | self.cfg.rew_scale_pole_pos, | +| cart_pos = self.obs_buf[:, 0] | self.cfg.rew_scale_cart_vel, | +| | self.cfg.rew_scale_pole_vel, | +| self.rew_buf[:], self.reset_buf[:] = compute_cartpole_reward( | self.joint_pos[:, self._pole_dof_idx[0]], | +| pole_angle, pole_vel, cart_vel, cart_pos, | self.joint_vel[:, self._pole_dof_idx[0]], | +| self.reset_dist, self.reset_buf, | self.joint_pos[:, self._cart_dof_idx[0]], | +| self.progress_buf, self.max_episode_length | self.joint_vel[:, self._cart_dof_idx[0]], | +| ) | self.reset_terminated, | +| | ) | +| @torch.jit.script | return total_reward | +| def compute_cartpole_reward(pole_angle, pole_vel, | | +| cart_vel, cart_pos, | @torch.jit.script | +| reset_dist, reset_buf, | def compute_rewards( | +| progress_buf, max_episode_length): | rew_scale_alive: float, | +| | rew_scale_terminated: float, | +| reward = (1.0 - pole_angle * pole_angle - | rew_scale_pole_pos: float, | +| 0.01 * torch.abs(cart_vel) - | rew_scale_cart_vel: float, | +| 0.005 * torch.abs(pole_vel)) | rew_scale_pole_vel: float, | +| | pole_pos: torch.Tensor, | +| # adjust reward for reset agents | pole_vel: torch.Tensor, | +| reward = torch.where(torch.abs(cart_pos) > reset_dist, | cart_pos: torch.Tensor, | +| torch.ones_like(reward) * -2.0, reward) | cart_vel: torch.Tensor, | +| reward = torch.where(torch.abs(pole_angle) > np.pi / 2, | reset_terminated: torch.Tensor, | +| torch.ones_like(reward) * -2.0, reward) | ): | +| | rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) | +| reset = torch.where(torch.abs(cart_pos) > reset_dist, | rew_termination = rew_scale_terminated * reset_terminated.float() | +| torch.ones_like(reset_buf), reset_buf) | rew_pole_pos = rew_scale_pole_pos * torch.sum( | +| reset = torch.where(torch.abs(pole_angle) > np.pi / 2, | torch.square(pole_pos), dim=-1) | +| torch.ones_like(reset_buf), reset_buf) | rew_cart_vel = rew_scale_cart_vel * torch.sum( | +| reset = torch.where(progress_buf >= max_episode_length - 1, | torch.abs(cart_vel), dim=-1) | +| torch.ones_like(reset_buf), reset) | rew_pole_vel = rew_scale_pole_vel * torch.sum( | +| | torch.abs(pole_vel), dim=-1) | +| | total_reward = (rew_alive + rew_termination | +| | + rew_pole_pos + rew_cart_vel + rew_pole_vel) | +| | return total_reward | ++--------------------------------------------------------------------------+----------------------------------------------------------------------------------------+ + + + +Launching Training +~~~~~~~~~~~~~~~~~~ + +To launch a training in Isaac Lab, use the command: + +.. code-block:: bash + + python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 --headless + +Launching Inferencing +~~~~~~~~~~~~~~~~~~~~~ + +To launch inferencing in Isaac Lab, use the command: + +.. code-block:: bash + + python scripts/reinforcement_learning/rl_games/play.py --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint= + + +.. _IsaacGymEnvs: https://github.com/isaac-sim/IsaacGymEnvs +.. _Isaac Gym Preview Release: https://developer.nvidia.com/isaac-gym +.. _release notes: https://github.com/isaac-sim/IsaacLab/releases diff --git a/docs/source/migration/migrating_from_omniisaacgymenvs.rst b/docs/source/migration/migrating_from_omniisaacgymenvs.rst new file mode 100644 index 0000000000000000000000000000000000000000..b3a46f0a518f0be0e07efd8a3167b52f76810ca7 --- /dev/null +++ b/docs/source/migration/migrating_from_omniisaacgymenvs.rst @@ -0,0 +1,999 @@ +.. _migrating-from-omniisaacgymenvs: + +From OmniIsaacGymEnvs +===================== + +.. currentmodule:: isaaclab + + +`OmniIsaacGymEnvs`_ was a reinforcement learning framework using the Isaac Sim platform. +Features from OmniIsaacGymEnvs have been integrated into the Isaac Lab framework. +We have updated OmniIsaacGymEnvs to Isaac Sim version 4.0.0 to support the migration process +to Isaac Lab. Moving forward, OmniIsaacGymEnvs will be deprecated and future development +will continue in Isaac Lab. + +.. note:: + + The following changes are with respect to Isaac Lab 1.0 release. Please refer to the `release notes`_ for any changes + in the future releases. + +Task Config Setup +~~~~~~~~~~~~~~~~~ + +In OmniIsaacGymEnvs, task config files were defined in ``.yaml`` format. With Isaac Lab, configs are now specified +using a specialized Python class :class:`~isaaclab.utils.configclass`. The +:class:`~isaaclab.utils.configclass` module provides a wrapper on top of Python's ``dataclasses`` module. +Each environment should specify its own config class annotated by ``@configclass`` that inherits from the +:class:`~envs.DirectRLEnvCfg` class, which can include simulation parameters, environment scene parameters, +robot parameters, and task-specific parameters. + +Below is an example skeleton of a task config class: + +.. code-block:: python + + from isaaclab.envs import DirectRLEnvCfg + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.sim import SimulationCfg + + @configclass + class MyEnvCfg(DirectRLEnvCfg): + # simulation + sim: SimulationCfg = SimulationCfg() + # robot + robot_cfg: ArticulationCfg = ArticulationCfg() + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg() + # env + decimation = 2 + episode_length_s = 5.0 + action_space = 1 + observation_space = 4 + state_space = 0 + # task-specific parameters + ... + +Simulation Config +----------------- + +Simulation related parameters are defined as part of the :class:`~isaaclab.sim.SimulationCfg` class, +which is a :class:`~isaaclab.utils.configclass` module that holds simulation parameters such as ``dt``, +``device``, and ``gravity``. Each task config must have a variable named ``sim`` defined that holds the type +:class:`~isaaclab.sim.SimulationCfg`. + +Simulation parameters for articulations and rigid bodies such as ``num_position_iterations``, ``num_velocity_iterations``, +``contact_offset``, ``rest_offset``, ``bounce_threshold_velocity``, ``max_depenetration_velocity`` can all +be specified on a per-actor basis in the config class for each individual articulation and rigid body. + +When running simulation on the GPU, buffers in PhysX require pre-allocation for computing and storing +information such as contacts, collisions and aggregate pairs. These buffers may need to be adjusted +depending on the complexity of the environment, the number of expected contacts and collisions, +and the number of actors in the environment. The :class:`~isaaclab.sim.PhysxCfg` class provides access +for setting the GPU buffer dimensions. + ++--------------------------------------------------+---------------------------------------------------------------+ +|| || | +|| || | +|| # OmniIsaacGymEnvs || # IsaacLab | +|| sim: || sim: SimulationCfg = SimulationCfg( | +|| || device = "cuda:0" # can be "cpu", "cuda", "cuda:" | +|| dt: 0.0083 # 1/120 s || dt=1 / 120, | +|| use_gpu_pipeline: ${eq:${...pipeline},"gpu"} || # use_gpu_pipeline is deduced from the device | +|| use_fabric: True || use_fabric=True, | +|| enable_scene_query_support: False || enable_scene_query_support=False, | +|| disable_contact_processing: False || | +|| gravity: [0.0, 0.0, -9.81] || gravity=(0.0, 0.0, -9.81), | +|| || | +|| default_physics_material: || physics_material=RigidBodyMaterialCfg( | +|| static_friction: 1.0 || static_friction=1.0, | +|| dynamic_friction: 1.0 || dynamic_friction=1.0, | +|| restitution: 0.0 || restitution=0.0 | +|| || ) | +|| physx: || physx: PhysxCfg = PhysxCfg( | +|| worker_thread_count: ${....num_threads} || # worker_thread_count is no longer needed | +|| solver_type: ${....solver_type} || solver_type=1, | +|| use_gpu: ${contains:"cuda",${....sim_device}} || # use_gpu is deduced from the device | +|| solver_position_iteration_count: 4 || max_position_iteration_count=4, | +|| solver_velocity_iteration_count: 0 || max_velocity_iteration_count=0, | +|| contact_offset: 0.02 || # moved to actor config | +|| rest_offset: 0.001 || # moved to actor config | +|| bounce_threshold_velocity: 0.2 || bounce_threshold_velocity=0.2, | +|| friction_offset_threshold: 0.04 || friction_offset_threshold=0.04, | +|| friction_correlation_distance: 0.025 || friction_correlation_distance=0.025, | +|| enable_sleeping: True || # enable_sleeping is no longer needed | +|| enable_stabilization: True || enable_stabilization=True, | +|| max_depenetration_velocity: 100.0 || # moved to RigidBodyPropertiesCfg | +|| || | +|| gpu_max_rigid_contact_count: 524288 || gpu_max_rigid_contact_count=2**23, | +|| gpu_max_rigid_patch_count: 81920 || gpu_max_rigid_patch_count=5 * 2**15, | +|| gpu_found_lost_pairs_capacity: 1024 || gpu_found_lost_pairs_capacity=2**21, | +|| gpu_found_lost_aggregate_pairs_capacity: 262144 || gpu_found_lost_aggregate_pairs_capacity=2**25, | +|| gpu_total_aggregate_pairs_capacity: 1024 || gpu_total_aggregate_pairs_capacity=2**21, | +|| gpu_heap_capacity: 67108864 || gpu_heap_capacity=2**26, | +|| gpu_temp_buffer_capacity: 16777216 || gpu_temp_buffer_capacity=2**24, | +|| gpu_max_num_partitions: 8 || gpu_max_num_partitions=8, | +|| gpu_max_soft_body_contacts: 1048576 || gpu_max_soft_body_contacts=2**20, | +|| gpu_max_particle_contacts: 1048576 || gpu_max_particle_contacts=2**20, | +|| || ) | +|| || ) | ++--------------------------------------------------+---------------------------------------------------------------+ + +Parameters such as ``add_ground_plane`` and ``add_distant_light`` are now part of the task logic when creating the scene. +``enable_cameras`` is now a command line argument ``--enable_cameras`` that can be passed directly to the training script. + + +Scene Config +------------ + +The :class:`~isaaclab.scene.InteractiveSceneCfg` class can be used to specify parameters related to the scene, +such as the number of environments and the spacing between environments. Each task config must have a variable named +``scene`` defined that holds the type :class:`~isaaclab.scene.InteractiveSceneCfg`. + ++--------------------------------------------------------------+-------------------------------------------------------------------+ +| | | +|.. code-block:: yaml |.. code-block:: python | +| | | +| # OmniIsaacGymEnvs | # IsaacLab | +| env: | scene: InteractiveSceneCfg = InteractiveSceneCfg( | +| numEnvs: ${resolve_default:512,${...num_envs}} | num_envs=512, | +| envSpacing: 4.0 | env_spacing=4.0) | ++--------------------------------------------------------------+-------------------------------------------------------------------+ + +Task Config +----------- + +Each environment should specify its own config class that holds task specific parameters, such as the dimensions of the +observation and action buffers. Reward term scaling parameters can also be specified in the config class. + +In Isaac Lab, the ``controlFrequencyInv`` parameter has been renamed to ``decimation``, +which must be specified as a parameter in the config class. + +In addition, the maximum episode length parameter (now ``episode_length_s``) is in seconds instead of steps as it was +in OmniIsaacGymEnvs. To convert between step count to seconds, use the equation: +``episode_length_s = dt * decimation * num_steps``. + +The following parameters must be set for each environment config: + +.. code-block:: python + + decimation = 2 + episode_length_s = 5.0 + action_space = 1 + observation_space = 4 + state_space = 0 + + +RL Config Setup +~~~~~~~~~~~~~~~ + +RL config files for the rl_games library can continue to be defined in ``.yaml`` files in Isaac Lab. +Most of the content of the config file can be copied directly from OmniIsaacGymEnvs. +Note that in Isaac Lab, we do not use hydra to resolve relative paths in config files. +Please replace any relative paths such as ``${....device}`` with the actual values of the parameters. + +Additionally, the observation and action clip ranges have been moved to the RL config file. +For any ``clipObservations`` and ``clipActions`` parameters that were defined in the IsaacGymEnvs task config file, +they should be moved to the RL config file in Isaac Lab. + ++--------------------------+----------------------------+ +| | | +| IsaacGymEnvs Task Config | Isaac Lab RL Config | ++--------------------------+----------------------------+ +|.. code-block:: yaml |.. code-block:: yaml | +| | | +| # OmniIsaacGymEnvs | # IsaacLab | +| env: | params: | +| clipObservations: 5.0 | env: | +| clipActions: 1.0 | clip_observations: 5.0 | +| | clip_actions: 1.0 | ++--------------------------+----------------------------+ + +Environment Creation +~~~~~~~~~~~~~~~~~~~~ + +In OmniIsaacGymEnvs, environment creation generally happened in the ``set_up_scene()`` API, +which involved creating the initial environment, cloning the environment, filtering collisions, +adding the ground plane and lights, and creating the ``View`` classes for the actors. + +Similar functionality is performed in Isaac Lab in the ``_setup_scene()`` API. +The main difference is that the base class ``_setup_scene()`` no longer performs operations for +cloning the environment and adding ground plane and lights. Instead, these operations +should now be implemented in individual tasks' ``_setup_scene`` implementations to provide more +flexibility around the scene setup process. + +Also note that by defining an ``Articulation`` or ``RigidObject`` object, the actors will be +added to the scene by parsing the ``spawn`` parameter in the actor config and a ``View`` class +will automatically be created for the actor. This avoids the need to separately define an +``ArticulationView`` or ``RigidPrimView`` object for the actors. + + ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def set_up_scene(self, scene) -> None: | def _setup_scene(self): | +| self.get_cartpole() | self.cartpole = Articulation(self.cfg.robot_cfg) | +| super().set_up_scene(scene) | # add ground plane | +| | spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg() | +| self._cartpoles = ArticulationView( | # clone, filter, and replicate | +| prim_paths_expr="/World/envs/.*/Cartpole", | self.scene.clone_environments(copy_from_source=False) | +| name="cartpole_view", reset_xform_properties=False | self.scene.filter_collisions(global_prim_paths=[]) | +| ) | # add articulation to scene | +| scene.add(self._cartpoles) | self.scene.articulations["cartpole"] = self.cartpole | +| | # add lights | +| | light_cfg = sim_utils.DomeLightCfg(intensity=2000.0) | +| | light_cfg.func("/World/Light", light_cfg) | ++------------------------------------------------------------------------------+------------------------------------------------------------------------+ + + +Ground Plane +------------ + +In addition to the above example, more sophisticated ground planes can be defined using the :class:`~terrains.TerrainImporterCfg` class. + +.. code-block:: python + + from isaaclab.terrains import TerrainImporterCfg + + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + +The terrain can then be added to the scene in ``_setup_scene(self)`` by referencing the ``TerrainImporterCfg`` object: + +.. code-block::python + + def _setup_scene(self): + ... + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self._terrain = self.cfg.terrain.class_type(self.cfg.terrain) + + +Actors +------ + +In Isaac Lab, each Articulation and Rigid Body actor can have its own config class. The +:class:`~isaaclab.assets.ArticulationCfg` class can be used to define parameters for articulation actors, +including file path, simulation parameters, actuator properties, and initial states. + +.. code-block::python + + from isaaclab.actuators import ImplicitActuatorCfg + from isaaclab.assets import ArticulationCfg + + CARTPOLE_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Classic/Cartpole/cartpole.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=100.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 2.0), joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0} + ), + actuators={ + "cart_actuator": ImplicitActuatorCfg( + joint_names_expr=["slider_to_cart"], + effort_limit_sim=400.0, + velocity_limit_sim=100.0, + stiffness=0.0, + damping=10.0, + ), + "pole_actuator": ImplicitActuatorCfg( + joint_names_expr=["cart_to_pole"], effort_limit=400.0, velocity_limit=100.0, stiffness=0.0, damping=0.0 + ), + }, + ) + +Within the :class:`~assets.ArticulationCfg`, the ``spawn`` attribute can be used to add the robot to the scene +by specifying the path to the robot file. In addition, the :class:`~isaaclab.sim.schemas.RigidBodyPropertiesCfg` +class can be used to specify simulation properties for the rigid bodies in the articulation. Similarly, the +:class:`~isaaclab.sim.schemas.ArticulationRootPropertiesCfg` class can be used to specify simulation properties +for the articulation. The joint properties are now specified as part of the ``actuators`` dictionary using +:class:`~actuators.ImplicitActuatorCfg`. Joints with the same properties can be grouped into regex expressions or +provided as a list of names or expressions. + +Actors are added to the scene by simply calling ``self.cartpole = Articulation(self.cfg.robot_cfg)``, where +``self.cfg.robot_cfg`` is an :class:`~assets.ArticulationCfg` object. Once initialized, they should also be added +to the :class:`~scene.InteractiveScene` by calling ``self.scene.articulations["cartpole"] = self.cartpole`` so that +the :class:`~scene.InteractiveScene` can traverse through actors in the scene for writing values to the simulation +and resetting. + + +Accessing States from Simulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +APIs for accessing physics states in Isaac Lab require the creation of an :class:`~assets.Articulation` or +:class:`~assets.RigidObject` object. Multiple objects can be initialized for different articulations or rigid bodies +in the scene by defining corresponding :class:`~assets.ArticulationCfg` or :class:`~assets.RigidObjectCfg` config, +as outlined in the section above. This replaces the previously used :class:`~omni.isaac.core.articulations.ArticulationView` +and :class:`omni.isaac.core.prims.RigidPrimView` classes used in OmniIsaacGymEnvs. + +However, functionality between the classes are similar: + ++------------------------------------------------------------------+-----------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------+-----------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| dof_pos = self._cartpoles.get_joint_positions(clone=False) | self.joint_pos = self._robot.data.joint_pos | +| dof_vel = self._cartpoles.get_joint_velocities(clone=False) | self.joint_vel = self._robot.data.joint_vel | ++------------------------------------------------------------------+-----------------------------------------------------------------+ + +In Isaac Lab, :class:`~assets.Articulation` and :class:`~assets.RigidObject` classes both have a ``data`` class. +The data classes (:class:`~assets.ArticulationData` and :class:`~assets.RigidObjectData`) contain +buffers that hold the states for the articulation and rigid objects and provide +a more performant way of retrieving states from the actors. + +Apart from some renamings of APIs, setting states for actors can also be performed similarly between OmniIsaacGymEnvs and Isaac Lab. + ++---------------------------------------------------------------------------+---------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++---------------------------------------------------------------------------+---------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| indices = env_ids.to(dtype=torch.int32) | self._robot.write_joint_state_to_sim(joint_pos, joint_vel, | +| self._cartpoles.set_joint_positions(dof_pos, indices=indices) | joint_ids, env_ids) | +| self._cartpoles.set_joint_velocities(dof_vel, indices=indices) | | ++---------------------------------------------------------------------------+---------------------------------------------------------------+ + +In Isaac Lab, ``root_pose`` and ``root_velocity`` have been combined into single buffers and no longer split between +``root_position``, ``root_orientation``, ``root_linear_velocity`` and ``root_angular_velocity``. + +.. code-block::python + + self.cartpole.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.cartpole.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + + +Creating a New Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Each environment in Isaac Lab should be in its own directory following this structure: + +.. code-block:: none + + my_environment/ + - agents/ + - __init__.py + - rl_games_ppo_cfg.py + - __init__.py + my_env.py + +* ``my_environment`` is the root directory of the task. +* ``my_environment/agents`` is the directory containing all RL config files for the task. Isaac Lab supports multiple + RL libraries that can each have its own individual config file. +* ``my_environment/__init__.py`` is the main file that registers the environment with the Gymnasium interface. + This allows the training and inferencing scripts to find the task by its name. + The content of this file should be as follow: + + .. code-block:: python + + import gymnasium as gym + + from . import agents + from .cartpole_env import CartpoleEnv, CartpoleEnvCfg + + ## + # Register Gym environments. + ## + + gym.register( + id="Isaac-Cartpole-Direct-v0", + entry_point="isaaclab_tasks.direct_workflow.cartpole:CartpoleEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": CartpoleEnvCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml" + }, + ) + +* ``my_environment/my_env.py`` is the main python script that implements the task logic and task config class for + the environment. + + +Task Logic +~~~~~~~~~~ + +The ``post_reset`` API in OmniIsaacGymEnvs is no longer required in Isaac Lab. Everything that was previously +done in ``post_reset`` can be done in the ``__init__`` method after executing the base class's +``__init__``. At this point, simulation has already started. + +In OmniIsaacGymEnvs, due to limitations of the GPU APIs, resets could not be performed based on states of the current +step. Instead, resets have to be performed at the beginning of the next time step. +This restriction has been eliminated in Isaac Lab, and thus, tasks follow the correct workflow of applying actions, +stepping simulation, collecting states, computing dones, calculating rewards, performing resets, and finally computing +observations. This workflow is done automatically by the framework such that a ``post_physics_step`` API is not +required in the task. However, individual tasks can override the ``step()`` API to control the workflow. + +In Isaac Lab, we also separate the ``pre_physics_step`` API for processing actions from the policy with +the ``apply_action`` API, which sets the actions into the simulation. This provides more flexibility in controlling +when actions should be written to simulation when ``decimation`` is used. +The ``pre_physics_step`` method will be called once per step before stepping simulation. +The ``apply_actions`` method will be called ``decimation`` number of times for each RL step, +once before each simulation step call. + +The ordering of the calls are as follow: + ++----------------------------------+----------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++----------------------------------+----------------------------------+ +|.. code-block:: none |.. code-block:: none | +| | | +| pre_physics_step | pre_physics_step | +| |-- reset_idx() | |-- _pre_physics_step(action)| +| |-- apply_action | |-- _apply_action() | +| | | +| post_physics_step | post_physics_step | +| |-- get_observations() | |-- _get_dones() | +| |-- calculate_metrics() | |-- _get_rewards() | +| |-- is_done() | |-- _reset_idx() | +| | |-- _get_observations() | ++----------------------------------+----------------------------------+ + +With this approach, resets are performed based on actions from the current step instead of the previous step. +Observations will also be computed with the correct states after resets. + +We have also performed some renamings of APIs: + +* ``set_up_scene(self, scene)`` --> ``_setup_scene(self)`` +* ``post_reset(self)`` --> ``__init__(...)`` +* ``pre_physics_step(self, actions)`` --> ``_pre_physics_step(self, actions)`` and ``_apply_action(self)`` +* ``reset_idx(self, env_ids)`` --> ``_reset_idx(self, env_ids)`` +* ``get_observations(self)`` --> ``_get_observations(self)`` - ``_get_observations()`` should now return a dictionary ``{"policy": obs}`` +* ``calculate_metrics(self)`` --> ``_get_rewards(self)`` - ``_get_rewards()`` should now return the reward buffer +* ``is_done(self)`` --> ``_get_dones(self)`` - ``_get_dones()`` should now return 2 buffers: ``reset`` and ``time_out`` buffers + + + +Putting It All Together +~~~~~~~~~~~~~~~~~~~~~~~ + +The Cartpole environment is shown here in completion to fully show the comparison between the OmniIsaacGymEnvs +implementation and the Isaac Lab implementation. + +Task Config +----------- + +Task config in Isaac Lab can be split into the main task configuration class and individual config objects for the actors. + ++-----------------------------------------------------------------+-----------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++-----------------------------------------------------------------+-----------------------------------------------------------------+ +|.. code-block:: yaml |.. code-block:: python | +| | | +| # used to create the object | @configclass | +| | class CartpoleEnvCfg(DirectRLEnvCfg): | +| name: Cartpole | | +| | # simulation | +| physics_engine: ${..physics_engine} | sim: SimulationCfg = SimulationCfg(dt=1 / 120) | +| | # robot | +| # if given, will override the device setting in gym. | robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace( | +| env: | prim_path="/World/envs/env_.*/Robot") | +| | cart_dof_name = "slider_to_cart" | +| numEnvs: ${resolve_default:512,${...num_envs}} | pole_dof_name = "cart_to_pole" | +| envSpacing: 4.0 | # scene | +| resetDist: 3.0 | scene: InteractiveSceneCfg = InteractiveSceneCfg( | +| maxEffort: 400.0 | num_envs=4096, env_spacing=4.0, replicate_physics=True) | +| | # env | +| clipObservations: 5.0 | decimation = 2 | +| clipActions: 1.0 | episode_length_s = 5.0 | +| controlFrequencyInv: 2 # 60 Hz | action_scale = 100.0 # [N] | +| | action_space = 1 | +| sim: | observation_space = 4 | +| | state_space = 0 | +| dt: 0.0083 # 1/120 s | # reset | +| use_gpu_pipeline: ${eq:${...pipeline},"gpu"} | max_cart_pos = 3.0 | +| gravity: [0.0, 0.0, -9.81] | initial_pole_angle_range = [-0.25, 0.25] | +| add_ground_plane: True | # reward scales | +| add_distant_light: False | rew_scale_alive = 1.0 | +| use_fabric: True | rew_scale_terminated = -2.0 | +| enable_scene_query_support: False | rew_scale_pole_pos = -1.0 | +| disable_contact_processing: False | rew_scale_cart_vel = -0.01 | +| | rew_scale_pole_vel = -0.005 | +| enable_cameras: False | | +| | | +| default_physics_material: | CARTPOLE_CFG = ArticulationCfg( | +| static_friction: 1.0 | spawn=sim_utils.UsdFileCfg( | +| dynamic_friction: 1.0 | usd_path=f"{ISAACLAB_NUCLEUS_DIR}/.../cartpole.usd", | +| restitution: 0.0 | rigid_props=sim_utils.RigidBodyPropertiesCfg( | +| | rigid_body_enabled=True, | +| physx: | max_linear_velocity=1000.0, | +| worker_thread_count: ${....num_threads} | max_angular_velocity=1000.0, | +| solver_type: ${....solver_type} | max_depenetration_velocity=100.0, | +| use_gpu: ${eq:${....sim_device},"gpu"} # set to False to... | enable_gyroscopic_forces=True, | +| solver_position_iteration_count: 4 | ), | +| solver_velocity_iteration_count: 0 | articulation_props=sim_utils.ArticulationRootPropertiesCfg( | +| contact_offset: 0.02 | enabled_self_collisions=False, | +| rest_offset: 0.001 | solver_position_iteration_count=4, | +| bounce_threshold_velocity: 0.2 | solver_velocity_iteration_count=0, | +| friction_offset_threshold: 0.04 | sleep_threshold=0.005, | +| friction_correlation_distance: 0.025 | stabilization_threshold=0.001, | +| enable_sleeping: True | ), | +| enable_stabilization: True | ), | +| max_depenetration_velocity: 100.0 | init_state=ArticulationCfg.InitialStateCfg( | +| | pos=(0.0, 0.0, 2.0), | +| # GPU buffers | joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0} | +| gpu_max_rigid_contact_count: 524288 | ), | +| gpu_max_rigid_patch_count: 81920 | actuators={ | +| gpu_found_lost_pairs_capacity: 1024 | "cart_actuator": ImplicitActuatorCfg( | +| gpu_found_lost_aggregate_pairs_capacity: 262144 | joint_names_expr=["slider_to_cart"], | +| gpu_total_aggregate_pairs_capacity: 1024 | effort_limit=400.0, | +| gpu_max_soft_body_contacts: 1048576 | velocity_limit=100.0, | +| gpu_max_particle_contacts: 1048576 | stiffness=0.0, | +| gpu_heap_capacity: 67108864 | damping=10.0, | +| gpu_temp_buffer_capacity: 16777216 | ), | +| gpu_max_num_partitions: 8 | "pole_actuator": ImplicitActuatorCfg( | +| | joint_names_expr=["cart_to_pole"], effort_limit=400.0, | +| Cartpole: | velocity_limit=100.0, stiffness=0.0, damping=0.0 | +| override_usd_defaults: False | ), | +| enable_self_collisions: False | }, | +| enable_gyroscopic_forces: True | ) | +| solver_position_iteration_count: 4 | | +| solver_velocity_iteration_count: 0 | | +| sleep_threshold: 0.005 | | +| stabilization_threshold: 0.001 | | +| density: -1 | | +| max_depenetration_velocity: 100.0 | | +| contact_offset: 0.02 | | +| rest_offset: 0.001 | | ++-----------------------------------------------------------------+-----------------------------------------------------------------+ + + + +Task Setup +---------- + +The ``post_reset`` API in OmniIsaacGymEnvs is no longer required in Isaac Lab. +Everything that was previously done in ``post_reset`` can be done in the ``__init__`` method after +executing the base class's ``__init__``. At this point, simulation has already started. + ++-------------------------------------------------------------------------+-------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++-------------------------------------------------------------------------+-------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| class CartpoleTask(RLTask): | class CartpoleEnv(DirectRLEnv): | +| | cfg: CartpoleEnvCfg | +| def __init__(self, name, sim_config, env, offset=None) -> None: | def __init__(self, cfg: CartpoleEnvCfg, | +| | render_mode: str | None = None, **kwargs): | +| self.update_config(sim_config) | super().__init__(cfg, render_mode, **kwargs) | +| self._max_episode_length = 500 | | +| | | +| self._num_observations = 4 | self._cart_dof_idx, _ = self.cartpole.find_joints( | +| self._num_actions = 1 | self.cfg.cart_dof_name) | +| | self._pole_dof_idx, _ = self.cartpole.find_joints( | +| RLTask.__init__(self, name, env) | self.cfg.pole_dof_name) | +| | self.action_scale=self.cfg.action_scale | +| def update_config(self, sim_config): | | +| self._sim_config = sim_config | self.joint_pos = self.cartpole.data.joint_pos | +| self._cfg = sim_config.config | self.joint_vel = self.cartpole.data.joint_vel | +| self._task_cfg = sim_config. | | +| task_config | | +| | | +| self._num_envs = self._task_cfg["env"]["numEnvs"] | | +| self._env_spacing = self._task_cfg["env"]["envSpacing"] | | +| self._cartpole_positions = torch.tensor([0.0, 0.0, 2.0]) | | +| | | +| self._reset_dist = self._task_cfg["env"]["resetDist"] | | +| self._max_push_effort = self._task_cfg["env"]["maxEffort"] | | +| | | +| | | +| def post_reset(self): | | +| self._cart_dof_idx = self._cartpoles.get_dof_index( | | +| "cartJoint") | | +| self._pole_dof_idx = self._cartpoles.get_dof_index( | | +| "poleJoint") | | +| # randomize all envs | | +| indices = torch.arange( | | +| self._cartpoles.count, dtype=torch.int64, | | +| device=self._device) | | +| self.reset_idx(indices) | | ++-------------------------------------------------------------------------+-------------------------------------------------------------+ + + + +Scene Setup +----------- + +The ``set_up_scene`` method in OmniIsaacGymEnvs has been replaced by the ``_setup_scene`` API in the task class in +Isaac Lab. Additionally, scene cloning and collision filtering have been provided as APIs for the task class to +call when necessary. Similarly, adding ground plane and lights should also be taken care of in the task class. +Adding actors to the scene has been replaced by ``self.scene.articulations["cartpole"] = self.cartpole``. + ++-----------------------------------------------------------+----------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++-----------------------------------------------------------+----------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def set_up_scene(self, scene) -> None: | def _setup_scene(self): | +| | self.cartpole = Articulation(self.cfg.robot_cfg) | +| self.get_cartpole() | # add ground plane | +| super().set_up_scene(scene) | spawn_ground_plane(prim_path="/World/ground", | +| self._cartpoles = ArticulationView( | cfg=GroundPlaneCfg()) | +| prim_paths_expr="/World/envs/.*/Cartpole", | # clone, filter, and replicate | +| name="cartpole_view", | self.scene.clone_environments( | +| reset_xform_properties=False | copy_from_source=False) | +| ) | self.scene.filter_collisions( | +| scene.add(self._cartpoles) | global_prim_paths=[]) | +| return | # add articulation to scene | +| | self.scene.articulations["cartpole"] = self.cartpole | +| def get_cartpole(self): | | +| cartpole = Cartpole( | # add lights | +| prim_path=self.default_zero_env_path+"/Cartpole", | light_cfg = sim_utils.DomeLightCfg( | +| name="Cartpole", | intensity=2000.0, color=(0.75, 0.75, 0.75)) | +| translation=self._cartpole_positions | light_cfg.func("/World/Light", light_cfg) | +| ) | | +| # applies articulation settings from the | | +| # task configuration yaml file | | +| self._sim_config.apply_articulation_settings( | | +| "Cartpole", get_prim_at_path(cartpole.prim_path), | | +| self._sim_config.parse_actor_config("Cartpole") | | +| ) | | ++-----------------------------------------------------------+----------------------------------------------------------+ + + +Pre-Physics Step +---------------- + +Note that resets are no longer performed in the ``pre_physics_step`` API. In addition, the separation of the +``_pre_physics_step`` and ``_apply_action`` methods allow for more flexibility in processing the action buffer +and setting actions into simulation. + ++------------------------------------------------------------------+-------------------------------------------------------------+ +| OmniIsaacGymEnvs | IsaacLab | ++------------------------------------------------------------------+-------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def pre_physics_step(self, actions) -> None: | def _pre_physics_step(self, | +| if not self.world.is_playing(): | actions: torch.Tensor) -> None: | +| return | self.actions = self.action_scale * actions | +| | | +| reset_env_ids = self.reset_buf.nonzero( | def _apply_action(self) -> None: | +| as_tuple=False).squeeze(-1) | self.cartpole.set_joint_effort_target( | +| if len(reset_env_ids) > 0: | self.actions, joint_ids=self._cart_dof_idx) | +| self.reset_idx(reset_env_ids) | | +| | | +| actions = actions.to(self._device) | | +| | | +| forces = torch.zeros((self._cartpoles.count, | | +| self._cartpoles.num_dof), | | +| dtype=torch.float32, device=self._device) | | +| forces[:, self._cart_dof_idx] = | | +| self._max_push_effort * actions[:, 0] | | +| | | +| indices = torch.arange(self._cartpoles.count, | | +| dtype=torch.int32, device=self._device) | | +| self._cartpoles.set_joint_efforts( | | +| forces, indices=indices) | | ++------------------------------------------------------------------+-------------------------------------------------------------+ + + +Dones and Resets +---------------- + +In Isaac Lab, the ``dones`` are computed in the ``_get_dones()`` method and should return two variables: ``resets`` and +``time_out``. The ``_reset_idx()`` method is also called after stepping simulation instead of before, as it was done in +OmniIsaacGymEnvs. The ``progress_buf`` tensor has been renamed to ``episode_length_buf`` in Isaac Lab and the +bookkeeping is now done automatically by the framework. Task implementations no longer need to increment or +reset the ``episode_length_buf`` buffer. + ++------------------------------------------------------------------+--------------------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------+--------------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def is_done(self) -> None: | def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: | +| resets = torch.where( | self.joint_pos = self.cartpole.data.joint_pos | +| torch.abs(self.cart_pos) > self._reset_dist, 1, 0) | self.joint_vel = self.cartpole.data.joint_vel | +| resets = torch.where( | | +| torch.abs(self.pole_pos) > math.pi / 2, 1, resets) | time_out = self.episode_length_buf >= self.max_episode_length - 1 | +| resets = torch.where( | out_of_bounds = torch.any(torch.abs( | +| self.progress_buf >= self._max_episode_length, 1, resets) | self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, | +| self.reset_buf[:] = resets | dim=1) | +| | out_of_bounds = out_of_bounds | torch.any( | +| | torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, | +| | dim=1) | +| | return out_of_bounds, time_out | +| | | +| def reset_idx(self, env_ids): | def _reset_idx(self, env_ids: Sequence[int] | None): | +| num_resets = len(env_ids) | if env_ids is None: | +| | env_ids = self.cartpole._ALL_INDICES | +| # randomize DOF positions | super()._reset_idx(env_ids) | +| dof_pos = torch.zeros((num_resets, self._cartpoles.num_dof), | | +| device=self._device) | joint_pos = self.cartpole.data.default_joint_pos[env_ids] | +| dof_pos[:, self._cart_dof_idx] = 1.0 * ( | joint_pos[:, self._pole_dof_idx] += sample_uniform( | +| 1.0 - 2.0 * torch.rand(num_resets, device=self._device)) | self.cfg.initial_pole_angle_range[0] * math.pi, | +| dof_pos[:, self._pole_dof_idx] = 0.125 * math.pi * ( | self.cfg.initial_pole_angle_range[1] * math.pi, | +| 1.0 - 2.0 * torch.rand(num_resets, device=self._device)) | joint_pos[:, self._pole_dof_idx].shape, | +| | joint_pos.device, | +| # randomize DOF velocities | ) | +| dof_vel = torch.zeros((num_resets, self._cartpoles.num_dof), | joint_vel = self.cartpole.data.default_joint_vel[env_ids] | +| device=self._device) | | +| dof_vel[:, self._cart_dof_idx] = 0.5 * ( | default_root_state = self.cartpole.data.default_root_state[env_ids] | +| 1.0 - 2.0 * torch.rand(num_resets, device=self._device)) | default_root_state[:, :3] += self.scene.env_origins[env_ids] | +| dof_vel[:, self._pole_dof_idx] = 0.25 * math.pi * ( | | +| 1.0 - 2.0 * torch.rand(num_resets, device=self._device)) | self.joint_pos[env_ids] = joint_pos | +| | self.joint_vel[env_ids] = joint_vel | +| # apply resets | | +| indices = env_ids.to(dtype=torch.int32) | self.cartpole.write_root_pose_to_sim( | +| self._cartpoles.set_joint_positions(dof_pos, indices=indices) | default_root_state[:, :7], env_ids) | +| self._cartpoles.set_joint_velocities(dof_vel, indices=indices) | self.cartpole.write_root_velocity_to_sim( | +| | default_root_state[:, 7:], env_ids) | +| # bookkeeping | self.cartpole.write_joint_state_to_sim( | +| self.reset_buf[env_ids] = 0 | joint_pos, joint_vel, None, env_ids) | +| self.progress_buf[env_ids] = 0 | | +| | | +| | | ++------------------------------------------------------------------+--------------------------------------------------------------------------+ + + +Rewards +------- + +In Isaac Lab, rewards are implemented in the ``_get_rewards`` API and should return the reward buffer instead of assigning +it directly to ``self.rew_buf``. Computation in the reward function can also be performed using pytorch jit +through defining functions with the ``@torch.jit.script`` annotation. + ++-------------------------------------------------------+-----------------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++-------------------------------------------------------+-----------------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: python | +| | | +| def calculate_metrics(self) -> None: | def _get_rewards(self) -> torch.Tensor: | +| reward = (1.0 - self.pole_pos * self.pole_pos | total_reward = compute_rewards( | +| - 0.01 * torch.abs(self.cart_vel) - 0.005 | self.cfg.rew_scale_alive, | +| * torch.abs(self.pole_vel)) | self.cfg.rew_scale_terminated, | +| reward = torch.where( | self.cfg.rew_scale_pole_pos, | +| torch.abs(self.cart_pos) > self._reset_dist, | self.cfg.rew_scale_cart_vel, | +| torch.ones_like(reward) * -2.0, reward) | self.cfg.rew_scale_pole_vel, | +| reward = torch.where( | self.joint_pos[:, self._pole_dof_idx[0]], | +| torch.abs(self.pole_pos) > np.pi / 2, | self.joint_vel[:, self._pole_dof_idx[0]], | +| torch.ones_like(reward) * -2.0, reward) | self.joint_pos[:, self._cart_dof_idx[0]], | +| | self.joint_vel[:, self._cart_dof_idx[0]], | +| self.rew_buf[:] = reward | self.reset_terminated, | +| | ) | +| | return total_reward | +| | | +| | @torch.jit.script | +| | def compute_rewards( | +| | rew_scale_alive: float, | +| | rew_scale_terminated: float, | +| | rew_scale_pole_pos: float, | +| | rew_scale_cart_vel: float, | +| | rew_scale_pole_vel: float, | +| | pole_pos: torch.Tensor, | +| | pole_vel: torch.Tensor, | +| | cart_pos: torch.Tensor, | +| | cart_vel: torch.Tensor, | +| | reset_terminated: torch.Tensor, | +| | ): | +| | rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) | +| | rew_termination = rew_scale_terminated * reset_terminated.float() | +| | rew_pole_pos = rew_scale_pole_pos * torch.sum( | +| | torch.square(pole_pos), dim=-1) | +| | rew_cart_vel = rew_scale_cart_vel * torch.sum( | +| | torch.abs(cart_vel), dim=-1) | +| | rew_pole_vel = rew_scale_pole_vel * torch.sum( | +| | torch.abs(pole_vel), dim=-1) | +| | total_reward = (rew_alive + rew_termination | +| | + rew_pole_pos + rew_cart_vel + rew_pole_vel) | +| | return total_reward | ++-------------------------------------------------------+-----------------------------------------------------------------------+ + + +Observations +------------ + +In Isaac Lab, the ``_get_observations()`` API must return a dictionary with the key ``policy`` that has the observation buffer as the value. +When working with asymmetric actor-critic states, the states for the critic should have the key ``critic`` and be returned +with the observation buffer in the same dictionary. + ++------------------------------------------------------------------+-------------------------------------------------------------+ +| OmniIsaacGymEnvs | Isaac Lab | ++------------------------------------------------------------------+-------------------------------------------------------------+ +|.. code-block:: python |.. code-block:: | +| | | +| def get_observations(self) -> dict: | def _get_observations(self) -> dict: | +| dof_pos = self._cartpoles.get_joint_positions(clone=False) | obs = torch.cat( | +| dof_vel = self._cartpoles.get_joint_velocities(clone=False) | ( | +| | self.joint_pos[:, self._pole_dof_idx[0]], | +| self.cart_pos = dof_pos[:, self._cart_dof_idx] | self.joint_vel[:, self._pole_dof_idx[0]], | +| self.cart_vel = dof_vel[:, self._cart_dof_idx] | self.joint_pos[:, self._cart_dof_idx[0]], | +| self.pole_pos = dof_pos[:, self._pole_dof_idx] | self.joint_vel[:, self._cart_dof_idx[0]], | +| self.pole_vel = dof_vel[:, self._pole_dof_idx] | ), | +| self.obs_buf[:, 0] = self.cart_pos | dim=-1, | +| self.obs_buf[:, 1] = self.cart_vel | ) | +| self.obs_buf[:, 2] = self.pole_pos | observations = {"policy": obs} | +| self.obs_buf[:, 3] = self.pole_vel | return observations | +| | | +| observations = {self._cartpoles.name: | | +| {"obs_buf": self.obs_buf}} | | +| return observations | | ++------------------------------------------------------------------+-------------------------------------------------------------+ + + +Domain Randomization +~~~~~~~~~~~~~~~~~~~~ + +In OmniIsaacGymEnvs, domain randomization was specified through the task ``.yaml`` config file. +In Isaac Lab, the domain randomization configuration uses the :class:`~isaaclab.utils.configclass` module +to specify a configuration class consisting of :class:`~managers.EventTermCfg` variables. + +Below is an example of a configuration class for domain randomization: + +.. code-block:: python + + @configclass + class EventCfg: + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + reset_gravity = EventTerm( + func=mdp.randomize_physics_scene_gravity, + mode="interval", + is_global_time=True, + interval_range_s=(36.0, 36.0), # time_s = num_steps * (decimation * dt) + params={ + "gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.4]), + "operation": "add", + "distribution": "gaussian", + }, + ) + +Each ``EventTerm`` object is of the :class:`~managers.EventTermCfg` class and takes in a ``func`` parameter +for specifying the function to call during randomization, a ``mode`` parameter, which can be ``startup``, +``reset`` or ``interval``. THe ``params`` dictionary should provide the necessary arguments to the +function that is specified in the ``func`` parameter. +Functions specified as ``func`` for the ``EventTerm`` can be found in the :class:`~envs.mdp.events` module. + +Note that as part of the ``"asset_cfg": SceneEntityCfg("robot", body_names=".*")`` parameter, the name of +the actor ``"robot"`` is provided, along with the body or joint names specified as a regex expression, +which will be the actors and bodies/joints that will have randomization applied. + +One difference with OmniIsaacGymEnvs is that ``interval`` randomization is now specified as seconds instead of +steps. When ``mode="interval"``, the ``interval_range_s`` parameter must also be provided, which specifies +the range of seconds for which randomization should be applied. This range will then be randomized to +determine a specific time in seconds when the next randomization will occur for the term. +To convert between steps to seconds, use the equation ``time_s = num_steps * (decimation * dt)``. + +Similar to OmniIsaacGymEnvs, randomization APIs are available for randomizing articulation properties, +such as joint stiffness and damping, joint limits, rigid body materials, fixed tendon properties, +as well as rigid body properties, such as mass and rigid body materials. Randomization of the +physics scene gravity is also supported. Note that randomization of scale is current not supported +in Isaac Lab. To randomize scale, please set up the scene in a way where each environment holds the actor +at a different scale. + +Once the ``configclass`` for the randomization terms have been set up, the class must be added +to the base config class for the task and be assigned to the variable ``events``. + +.. code-block:: python + + @configclass + class MyTaskConfig: + events: EventCfg = EventCfg() + + +Action and Observation Noise +---------------------------- + +Actions and observation noise can also be added using the :class:`~utils.configclass` module. +Action and observation noise configs must be added to the main task config using the +``action_noise_model`` and ``observation_noise_model`` variables: + +.. code-block:: python + + @configclass + class MyTaskConfig: + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + action_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.05, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.015, operation="abs"), + ) + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + observation_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.002, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.0001, operation="abs"), + ) + + +:class:`~.utils.noise.NoiseModelWithAdditiveBiasCfg` can be used to sample both uncorrelated noise +per step as well as correlated noise that is re-sampled at reset time. +The ``noise_cfg`` term specifies the Gaussian distribution that will be sampled at each +step for all environments. This noise will be added to the corresponding actions and +observations buffers at every step. +The ``bias_noise_cfg`` term specifies the Gaussian distribution for the correlated noise +that will be sampled at reset time for the environments being reset. The same noise +will be applied each step for the remaining of the episode for the environments and +resampled at the next reset. + +This replaces the following setup in OmniIsaacGymEnvs: + +.. code-block:: yaml + + domain_randomization: + randomize: True + randomization_params: + observations: + on_reset: + operation: "additive" + distribution: "gaussian" + distribution_parameters: [0, .0001] + on_interval: + frequency_interval: 1 + operation: "additive" + distribution: "gaussian" + distribution_parameters: [0, .002] + actions: + on_reset: + operation: "additive" + distribution: "gaussian" + distribution_parameters: [0, 0.015] + on_interval: + frequency_interval: 1 + operation: "additive" + distribution: "gaussian" + distribution_parameters: [0., 0.05] + + +Launching Training +~~~~~~~~~~~~~~~~~~ + +To launch a training in Isaac Lab, use the command: + +.. code-block:: bash + + python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 --headless + +Launching Inferencing +~~~~~~~~~~~~~~~~~~~~~ + +To launch inferencing in Isaac Lab, use the command: + +.. code-block:: bash + + python scripts/reinforcement_learning/rl_games/play.py --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint= + + +.. _`OmniIsaacGymEnvs`: https://github.com/isaac-sim/OmniIsaacGymEnvs +.. _release notes: https://github.com/isaac-sim/IsaacLab/releases diff --git a/docs/source/migration/migrating_from_orbit.rst b/docs/source/migration/migrating_from_orbit.rst new file mode 100644 index 0000000000000000000000000000000000000000..ee88909c7eca995071fec4dc43e2ae51071ba36c --- /dev/null +++ b/docs/source/migration/migrating_from_orbit.rst @@ -0,0 +1,150 @@ +.. _migrating-from-orbit: + +From Orbit +========== + +.. currentmodule:: isaaclab + +Since `Orbit`_ was used as basis for Isaac Lab, migrating from Orbit to Isaac Lab is straightforward. +The following sections describe the changes that need to be made to your code to migrate from Orbit to Isaac Lab. + +.. note:: + + The following changes are with respect to Isaac Lab 1.0 release. Please refer to the `release notes`_ for any changes + in the future releases. + + +Renaming of the launch script +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The script ``orbit.sh`` has been renamed to ``isaaclab.sh``. + + +Updates to extensions +~~~~~~~~~~~~~~~~~~~~~ + +The extensions ``omni.isaac.orbit``, ``omni.isaac.orbit_tasks``, and ``omni.isaac.orbit_assets`` have been renamed +to ``isaaclab``, ``isaaclab_tasks``, and ``isaaclab_assets``, respectively. Thus, +the new folder structure looks like this: + +- ``source/isaaclab/isaaclab`` +- ``source/isaaclab_tasks/isaaclab_tasks`` +- ``source/isaaclab_assets/isaaclab_assets`` + +The high level imports have to be updated as well: + ++-------------------------------------+-----------------------------------+ +| Orbit | Isaac Lab | ++=====================================+===================================+ +| ``from omni.isaac.orbit...`` | ``from isaaclab...`` | ++-------------------------------------+-----------------------------------+ +| ``from omni.isaac.orbit_tasks...`` | ``from isaaclab_tasks...`` | ++-------------------------------------+-----------------------------------+ +| ``from omni.isaac.orbit_assets...`` | ``from isaaclab_assets...`` | ++-------------------------------------+-----------------------------------+ + + +Updates to class names +~~~~~~~~~~~~~~~~~~~~~~ + +In Isaac Lab, we introduced the concept of task design workflows (see :ref:`feature-workflows`). The Orbit code is using +the manager-based workflow and the environment specific class names have been updated to reflect this change: + ++------------------------+---------------------------------------------------------+ +| Orbit | Isaac Lab | ++========================+=========================================================+ +| ``BaseEnv`` | :class:`isaaclab.envs.ManagerBasedEnv` | ++------------------------+---------------------------------------------------------+ +| ``BaseEnvCfg`` | :class:`isaaclab.envs.ManagerBasedEnvCfg` | ++------------------------+---------------------------------------------------------+ +| ``RLTaskEnv`` | :class:`isaaclab.envs.ManagerBasedRLEnv` | ++------------------------+---------------------------------------------------------+ +| ``RLTaskEnvCfg`` | :class:`isaaclab.envs.ManagerBasedRLEnvCfg` | ++------------------------+---------------------------------------------------------+ +| ``RLTaskEnvWindow`` | :class:`isaaclab.envs.ui.ManagerBasedRLEnvWindow` | ++------------------------+---------------------------------------------------------+ + + +Updates to the tasks folder structure +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To support the manager-based and direct workflows, we have added two folders in the tasks extension: + +- ``source/isaaclab_tasks/isaaclab_tasks/manager_based`` +- ``source/isaaclab_tasks/isaaclab_tasks/direct`` + +The tasks from Orbit can now be found under the ``manager_based`` folder. +This change must also be reflected in the imports for your tasks. For example, + +.. code-block:: python + + from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg ... + +should now be: + +.. code-block:: python + + from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg ... + + +Other Breaking changes +~~~~~~~~~~~~~~~~~~~~~~ + +Setting the device +------------------ + +The argument ``--cpu`` has been removed in favor of ``--device device_name``. Valid options for ``device_name`` are: + +- ``cpu``: Use CPU. +- ``cuda``: Use GPU with device ID ``0``. +- ``cuda:N``: Use GPU, where N is the device ID. For example, ``cuda:0``. + +The default value is ``cuda:0``. + + +Offscreen rendering +------------------- + +The input argument ``--offscreen_render`` given to :class:`isaaclab.app.AppLauncher` and the environment variable +``OFFSCREEN_RENDER`` have been renamed to ``--enable_cameras`` and ``ENABLE_CAMERAS`` respectively. + + +Event term distribution configuration +------------------------------------- + +Some of the event functions in `events.py `_ +accepted a ``distribution`` parameter and a ``range`` to sample from. In an effort to support arbitrary distributions, +we have renamed the input argument ``AAA_range`` to ``AAA_distribution_params`` for these functions. +Therefore, event term configurations whose functions have a ``distribution`` argument should be updated. For example, + +.. code-block:: python + :emphasize-lines: 6 + + add_base_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "mass_range": (-5.0, 5.0), + "operation": "add", + }, + ) + +should now be: + +.. code-block:: python + :emphasize-lines: 6 + + add_base_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "mass_distribution_params": (-5.0, 5.0), + "operation": "add", + }, + ) + + +.. _Orbit: https://isaac-orbit.github.io/ +.. _release notes: https://github.com/isaac-sim/IsaacLab/releases diff --git a/docs/source/overview/core-concepts/actuators.rst b/docs/source/overview/core-concepts/actuators.rst new file mode 100644 index 0000000000000000000000000000000000000000..de34e42028689c85dfd6fa5b22f162458cefeffc --- /dev/null +++ b/docs/source/overview/core-concepts/actuators.rst @@ -0,0 +1,100 @@ +.. _overview-actuators: + + +Actuators +========= + +An articulated system comprises of actuated joints, also called the degrees of freedom (DOF). +In a physical system, the actuation typically happens either through active components, such as +electric or hydraulic motors, or passive components, such as springs. These components can introduce +certain non-linear characteristics which includes delays or maximum producible velocity or torque. + +In simulation, the joints are either position, velocity, or torque-controlled. For position and velocity +control, the physics engine internally implements a spring-damp (PD) controller which computes the torques +applied on the actuated joints. In torque-control, the commands are set directly as the joint efforts. +While this mimics an ideal behavior of the joint mechanism, it does not truly model how the drives work +in the physical world. Thus, we provide a mechanism to inject external models to compute the +joint commands that would represent the physical robot's behavior. + +Actuator models +--------------- + +We name two different types of actuator models: + +1. **implicit**: corresponds to the ideal simulation mechanism (provided by physics engine). +2. **explicit**: corresponds to external drive models (implemented by user). + +The explicit actuator model performs two steps: 1) it computes the desired joint torques for tracking +the input commands, and 2) it clips the desired torques based on the motor capabilities. The clipped +torques are the desired actuation efforts that are set into the simulation. + +As an example of an ideal explicit actuator model, we provide the :class:`isaaclab.actuators.IdealPDActuator` +class, which implements a PD controller with feed-forward effort, and simple clipping based on the configured +maximum effort: + +.. math:: + + \tau_{j, computed} & = k_p * (q_{des} - q) + k_d * (\dot{q}_{des} - \dot{q}) + \tau_{ff} \\ + \tau_{j, max} & = \gamma \times \tau_{motor, max} \\ + \tau_{j, applied} & = clip(\tau_{computed}, -\tau_{j, max}, \tau_{j, max}) + + +where, :math:`k_p` and :math:`k_d` are joint stiffness and damping gains, :math:`q` and :math:`\dot{q}` +are the current joint positions and velocities, :math:`q_{des}`, :math:`\dot{q}_{des}` and :math:`\tau_{ff}` +are the desired joint positions, velocities and torques commands. The parameters :math:`\gamma` and +:math:`\tau_{motor, max}` are the gear box ratio and the maximum motor effort possible. + +Actuator groups +--------------- + +The actuator models by themselves are computational blocks that take as inputs the desired joint commands +and output the joint commands to apply into the simulator. They do not contain any knowledge about the +joints they are acting on themselves. These are handled by the :class:`isaaclab.assets.Articulation` +class, which wraps around the physics engine's articulation class. + +Actuator are collected as a set of actuated joints on an articulation that are using the same actuator model. +For instance, the quadruped, ANYmal-C, uses series elastic actuator, ANYdrive 3.0, for all its joints. This +grouping configures the actuator model for those joints, translates the input commands to the joint level +commands, and returns the articulation action to set into the simulator. Having an arm with a different +actuator model, such as a DC motor, would require configuring a different actuator group. + +The following figure shows the actuator groups for a legged mobile manipulator: + +.. image:: ../../_static/actuator-group/actuator-light.svg + :class: only-light + :align: center + :alt: Actuator models for a legged mobile manipulator + :width: 80% + +.. image:: ../../_static/actuator-group/actuator-dark.svg + :class: only-dark + :align: center + :width: 80% + :alt: Actuator models for a legged mobile manipulator + +.. seealso:: + + We provide implementations for various explicit actuator models. These are detailed in + `isaaclab.actuators <../../api/lab/isaaclab.actuators.html>`_ sub-package. + +Considerations when using actuators +----------------------------------- + +As explained in the previous sections, there are two main types of actuator models: implicit and explicit. +The implicit actuator model is provided by the physics engine. This means that when the user sets either +a desired position or velocity, the physics engine will internally compute the efforts that need to be +applied to the joints to achieve the desired behavior. In PhysX, the PD controller adds numerical damping +to the desired effort, which results in more stable behavior. + +The explicit actuator model is provided by the user. This means that when the user sets either a desired +position or velocity, the user's model will compute the efforts that need to be applied to the joints to +achieve the desired behavior. While this provides more flexibility, it can also lead to some numerical +instabilities. One way to mitigate this is to use the ``armature`` parameter of the actuator model, either in +the USD file or in the articulation config. This parameter is used to dampen the joint response and helps +improve the numerical stability of the simulation. More details on how to improve articulation stability +can be found in the `OmniPhysics documentation `_. + +What does this mean for the user? It means that policies trained with implicit actuators may not transfer +to the exact same robot model when using explicit actuators. If you are running into issues like this, or +in cases where policies do not converge on explicit actuators while they do on implicit ones, increasing +or setting the ``armature`` parameter to a higher value may help. diff --git a/docs/source/overview/core-concepts/index.rst b/docs/source/overview/core-concepts/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..930ab665c1880ddce961c1260fa550c234d4ab4e --- /dev/null +++ b/docs/source/overview/core-concepts/index.rst @@ -0,0 +1,13 @@ +Core Concepts +============= + +This section we introduce core concepts in Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + + task_workflows + actuators + sensors/index.rst + motion_generators diff --git a/docs/source/overview/core-concepts/motion_generators.rst b/docs/source/overview/core-concepts/motion_generators.rst new file mode 100644 index 0000000000000000000000000000000000000000..e4e09f2f17db841ed468267113b4c804d4c87601 --- /dev/null +++ b/docs/source/overview/core-concepts/motion_generators.rst @@ -0,0 +1,229 @@ +Motion Generators +================= + +Robotic tasks are typically defined in task-space in terms of desired +end-effector trajectory, while control actions are executed in the +joint-space. This naturally leads to *joint-space* and *task-space* +(operational-space) control methods. However, successful execution of +interaction tasks using motion control often requires an accurate model +of both the robot manipulator as well as its environment. While a +sufficiently precise manipulator's model might be known, detailed +description of environment is hard to obtain :cite:p:`siciliano2009force`. +Planning errors caused by this mismatch can be overcome by introducing a +*compliant* behavior during interaction. + +While compliance is achievable passively through robot's structure (such +as elastic actuators, soft robot arms), we are more interested in +controller designs that focus on active interaction control. These are +broadly categorized into: + +1. **impedance control:** indirect control method where motion deviations + caused during interaction relates to contact force as a mass-spring-damper + system with adjustable parameters (stiffness and damping). A specialized case + of this is *stiffness* control where only the static relationship between + position error and contact force is considered. + +2. **hybrid force/motion control:** active control method which controls motion + and force along unconstrained and constrained task directions respectively. + Among the various schemes for hybrid motion control, the provided implementation + is based on inverse dynamics control in the operational space :cite:p:`khatib1987osc`. + +.. note:: + + To provide an even broader set of motion generators, we welcome contributions from the + community. If you are interested, please open an issue to start a discussion! + + +Joint-space controllers +----------------------- + +Torque control +~~~~~~~~~~~~~~ + +Action dimensions: ``"n"`` (number of joints) + +In torque control mode, the input actions are directly set as feed-forward +joint torque commands, i.e. at every time-step, + +.. math:: + + \tau = \tau_{des} + +Thus, this control mode is achievable by setting the command type for the actuator group, via +the :class:`ActuatorControlCfg` class, to ``"t_abs"``. + + +Velocity control +~~~~~~~~~~~~~~~~ + +Action dimensions: ``"n"`` (number of joints) + +In velocity control mode, a proportional control law is required to reduce the error between the +current and desired joint velocities. Based on input actions, the joint torques commands are computed as: + +.. math:: + + \tau = k_d (\dot{q}_{des} - \dot{q}) + +where :math:`k_d` are the gains parsed from configuration. + +This control mode is achievable by setting the command type for the actuator group, via +the :class:`ActuatorControlCfg` class, to ``"v_abs"`` or ``"v_rel"``. + +.. attention:: + + While performing velocity control, in many cases, gravity compensation is required to ensure better + tracking of the command. In this case, we suggest disabling gravity for the links in the articulation + in simulation. + +Position control with fixed impedance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Action dimensions: ``"n"`` (number of joints) + +In position control mode, a proportional-damping (PD) control law is employed to track the desired joint +positions and ensuring the articulation remains still at the desired location (i.e., desired joint velocities +are zero). Based on the input actions, the joint torque commands are computed as: + +.. math:: + + \tau = k_p (q_{des} - q) - k_d \dot{q} + +where :math:`k_p` and :math:`k_d` are the gains parsed from configuration. + +In its simplest above form, the control mode is achievable by setting the command type for the actuator group, +via the :class:`ActuatorControlCfg` class, to ``"p_abs"`` or ``"p_rel"``. + +However, a more complete formulation which considers the dynamics of the articulation would be: + +.. math:: + + \tau = M \left( k_p (q_{des} - q) - k_d \dot{q} \right) + g + +where :math:`M` is the joint-space inertia matrix of size :math:`n \times n`, and :math:`g` is the joint-space +gravity vector. This implementation is available through the :class:`JointImpedanceController` class by setting the +impedance mode to ``"fixed"``. The gains :math:`k_p` are parsed from the input configuration and :math:`k_d` +are computed while considering the system as a decoupled point-mass oscillator, i.e., + +.. math:: + + k_d = 2 \sqrt{k_p} \times D + +where :math:`D` is the damping ratio of the system. Critical damping is achieved for :math:`D = 1`, overcritical +damping for :math:`D > 1` and undercritical damping for :math:`D < 1`. + +Additionally, it is possible to disable the inertial or gravity compensation in the controller by setting the +flags :attr:`inertial_compensation` and :attr:`gravity_compensation` in the configuration to :obj:`False`, +respectively. + +Position control with variable stiffness +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Action dimensions: ``"2n"`` (number of joints) + +In stiffness control, the same formulation as above is employed, however, the gains :math:`k_p` are part of +the input commands. This implementation is available through the :class:`JointImpedanceController` class by +setting the impedance mode to ``"variable_kp"``. + +Position control with variable impedance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Action dimensions: ``"3n"`` (number of joints) + +In impedance control, the same formulation as above is employed, however, both :math:`k_p` and :math:`k_d` +are part of the input commands. This implementation is available through the :class:`JointImpedanceController` +class by setting the impedance mode to ``"variable"``. + +Task-space controllers +---------------------- + +Differential inverse kinematics (IK) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Action dimensions: ``"3"`` (relative/absolute position), ``"6"`` (relative pose), or ``"7"`` (absolute pose) + +Inverse kinematics converts the task-space tracking error to joint-space error. In its most typical implementation, +the pose error in the task-sace, :math:`\Delta \chi_e = (\Delta p_e, \Delta \phi_e)`, is computed as the cartesian +distance between the desired and current task-space positions, and the shortest distance in :math:`\mathbb{SO}(3)` +between the desired and current task-space orientations. + +Using the geometric Jacobian :math:`J_{eO} \in \mathbb{R}^{6 \times n}`, that relates task-space velocity to joint-space velocities, +we design the control law to obtain the desired joint positions as: + +.. math:: + + q_{des} = q + \eta J_{eO}^{-} \Delta \chi_e + +where :math:`\eta` is a scaling parameter and :math:`J_{eO}^{-}` is the pseudo-inverse of the Jacobian. + +It is possible to compute the pseudo-inverse of the Jacobian using different formulations: + +* Moore-Penrose pseduo-inverse: :math:`A^{-} = A^T(AA^T)^{-1}`. +* Levenberg-Marquardt pseduo-inverse (damped least-squares): :math:`A^{-} = A^T (AA^T + \lambda \mathbb{I})^{-1}`. +* Tanspose pseudo-inverse: :math:`A^{-} = A^T`. +* Adaptive singular-vale decomposition (SVD) pseduo-inverse from :cite:t:`buss2004ik`. + +These implementations are available through the :class:`DifferentialInverseKinematics` class. + +Impedance controller +~~~~~~~~~~~~~~~~~~~~ + + +It uses task-space pose error and Jacobian to compute join torques through mass-spring-damper system +with a) fixed stiffness, b) variable stiffness (stiffness control), +and c) variable stiffness and damping (impedance control). + +Operational-space controller +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Similar to task-space impedance +control but uses the Equation of Motion (EoM) for computing the +task-space force + +Closed-loop proportional force controller +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +It uses a proportional term +to track the desired wrench command with respect to current wrench at +the end-effector. + +Hybrid force-motion controller +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +It combines closed-loop force control +and operational-space motion control to compute the desired wrench at +the end-effector. It uses selection matrices that define the +unconstrainted and constrained task directions. + + +Reactive planners +----------------- + +Typical task-space controllers do not account for motion constraints +such as joint limits, self-collision and environment collision. Instead +they rely on high-level planners (such as RRT) to handle these +non-Euclidean constraints and give joint/task-space way-points to the +controller. However, these methods are often conservative and have +undesirable deceleration when close to an object. More recently, +different approaches combine the constraints directly into an +optimization problem, thereby providing a holistic solution for motion +generation and control. + +We currently support the following planners: + +- **RMPFlow (lula):** An acceleration-based policy that composes various Reimannian Motion Policies (RMPs) to + solve a hierarchy of tasks :cite:p:`cheng2021rmpflow`. It is capable of performing dynamic collision + avoidance while navigating the end-effector to a target. + +- **MPC (OCS2):** A receding horizon control policy based on sequential linear-quadratic (SLQ) programming. + It formulates various constraints into a single optimization problem via soft-penalties and uses automatic + differentiation to compute derivatives of the system dynamics, constraints and costs. Currently, we support + the MPC formulation for end-effector trajectory tracking in fixed-arm and mobile manipulators. The formulation + considers a kinematic system model with joint limits and self-collision avoidance :cite:p:`mittal2021articulated`. + + +.. warning:: + + We wrap around the python bindings for these reactive planners to perform a batched computing of + robot actions. However, their current implementations are CPU-based which may cause certain + slowdown for learning. diff --git a/docs/source/overview/core-concepts/sensors/camera.rst b/docs/source/overview/core-concepts/sensors/camera.rst new file mode 100644 index 0000000000000000000000000000000000000000..6a34c6fab30c7642837d6aa58fbc62424bf61e6b --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/camera.rst @@ -0,0 +1,159 @@ +.. _overview_sensors_camera: + +.. currentmodule:: isaaclab + +Camera +====== + +Camera sensors are uniquely defined by the use of the ``render_product``, a structure for managing data generated by the rendering pipeline (images). Isaac Lab provides the ability to fully control how these renderings are created through camera parameters like focal length, pose, type, etc... and what kind of data you want to render through the use of Annotators, allowing you to record not only RGB, but also Instance segmentation, object pose, object ID, etc... + +Rendered images are unique among the supported data types in Isaac Lab due to the inherently large bandwidth requirements for moving those data. A single 800 x 600 image with 32-bit color (a single float per pixel) clocks in at just under 2 MB. If we render at 60 fps and record every frame, that camera needs to move 120 MB/s. Multiply this by the number of cameras in an environment and environments in a simulation, and you can quickly see how scaling a naive vectorization of camera data could lead to bandwidth challenges. NVIDIA's Isaac Lab leverages our expertise in GPU hardware to provide an API that specifically addresses these scaling challenges in the rendering pipeline. + +Tiled Rendering +~~~~~~~~~~~~~~~ + +.. note:: + + This feature is only available from Isaac Sim version 4.2.0 onwards. + + Tiled rendering in combination with image processing networks require heavy memory resources, especially + at larger resolutions. We recommend running 512 cameras in the scene on RTX 4090 GPUs or similar. + +The Tiled Rendering APIs provide a vectorized interface for collecting data from camera sensors. This is useful for reinforcement learning environments where parallelization can be exploited to accelerate data collection and thus the training loop. Tiled rendering works by using a single ``render_product`` for **all** clones of a single camera in the scene. The desired dimensions of a single image and the number of environments are used to compute a much larger ``render_product``, consisting of the tiled individual renders from the separate clones of the camera. When all cameras have populated their buffers the render product is "completed" and can be moved around as a single, large image, dramatically reducing the overhead for moving the data from the host to the device, for example. Only a single call is used to synchronize the device data, instead of one call per camera, and this is a big part of what makes the Tiled Rendering API more efficient for working with vision data. + +Isaac Lab provides tiled rendering APIs for RGB, depth, along with other annotators through the :class:`~sensors.TiledCamera` class. Configurations for the tiled rendering APIs can be defined through the :class:`~sensors.TiledCameraCfg` class, specifying parameters such as the regex expression for all camera paths, the transform for the cameras, the desired data type, the type of cameras to add to the scene, and the camera resolution. + +.. code-block:: python + + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="/World/envs/env_.*/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-7.0, 0.0, 3.0), rot=(0.9945, 0.0, 0.1045, 0.0), convention="world"), + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=80, + height=80, + ) + +To access the tiled rendering interface, a :class:`~sensors.TiledCamera` object can be created and used to retrieve data from the cameras. + +.. code-block:: python + + tiled_camera = TiledCamera(cfg.tiled_camera) + data_type = "rgb" + data = tiled_camera.data.output[data_type] + +The returned data will be transformed into the shape (num_cameras, height, width, num_channels), which can be used directly as observation for reinforcement learning. + +When working with rendering, make sure to add the ``--enable_cameras`` argument when launching the environment. For example: + +.. code-block:: shell + + python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-RGB-Camera-Direct-v0 --headless --enable_cameras + + +Annotators +~~~~~~~~~~ + +Both :class:`~sensors.TiledCamera` and :class:`~sensors.Camera` classes provide APIs for retrieving various types annotator data from replicator: + +* ``"rgb"``: A 3-channel rendered color image. +* ``"rgba"``: A 4-channel rendered color image with alpha channel. +* ``"distance_to_camera"``: An image containing the distance to camera optical center. +* ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis. +* ``"depth"``: The same as ``"distance_to_image_plane"``. +* ``"normals"``: An image containing the local surface normal vectors at each pixel. +* ``"motion_vectors"``: An image containing the motion vector data at each pixel. +* ``"semantic_segmentation"``: The semantic segmentation data. +* ``"instance_segmentation_fast"``: The instance segmentation data. +* ``"instance_id_segmentation_fast"``: The instance id segmentation data. + +RGB and RGBA +~~~~~~~~~~~~ + +.. figure:: ../../../_static/overview/sensors/camera_rgb.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``rgb`` data type returns a 3-channel RGB colored image of type ``torch.uint8``, with dimension (B, H, W, 3). + +``rgba`` data type returns a 4-channel RGBA colored image of type ``torch.uint8``, with dimension (B, H, W, 4). + +To convert the ``torch.uint8`` data to ``torch.float32``, divide the buffer by 255.0 to obtain a ``torch.float32`` buffer containing data from 0 to 1. + +Depth and Distances +~~~~~~~~~~~~~~~~~~~ + +.. figure:: ../../../_static/overview/sensors/camera_depth.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``distance_to_camera`` returns a single-channel depth image with distance to the camera optical center. The dimension for this annotator is (B, H, W, 1) and has type ``torch.float32``. + +``distance_to_image_plane`` returns a single-channel depth image with distances of 3D points from the camera plane along the camera's Z-axis. The dimension for this annotator is (B, H, W, 1) and has type ``torch.float32``. + +``depth`` is provided as an alias for ``distance_to_image_plane`` and will return the same data as the ``distance_to_image_plane`` annotator, with dimension (B, H, W, 1) and type ``torch.float32``. + +Normals +~~~~~~~ + +.. figure:: ../../../_static/overview/sensors/camera_normals.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``normals`` returns an image containing the local surface normal vectors at each pixel. The buffer has dimension (B, H, W, 3), containing the (x, y, z) information for each vector, and has data type ``torch.float32``. + +Motion Vectors +~~~~~~~~~~~~~~ + +``motion_vectors`` returns the per-pixel motion vectors in image space, with a 2D array of motion vectors representing the relative motion of a pixel in the camera’s viewport between frames. The buffer has dimension (B, H, W, 2), representing x - the motion distance in the horizontal axis (image width) with movement to the left of the image being positive and movement to the right being negative and y - motion distance in the vertical axis (image height) with movement towards the top of the image being positive and movement to the bottom being negative. The data type is ``torch.float32``. + +Semantic Segmentation +~~~~~~~~~~~~~~~~~~~~~ + +.. figure:: ../../../_static/overview/sensors/camera_semantic.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``semantic_segmentation`` outputs semantic segmentation of each entity in the camera’s viewport that has semantic labels. In addition to the image buffer, an ``info`` dictionary can be retrieved with ``tiled_camera.data.info['semantic_segmentation']`` containing ID to labels information. + +- If ``colorize_semantic_segmentation=True`` in the camera config, a 4-channel RGBA image will be returned with dimension (B, H, W, 4) and type ``torch.uint8``. The info ``idToLabels`` dictionary will be the mapping from color to semantic labels. + +- If ``colorize_semantic_segmentation=False``, a buffer of dimension (B, H, W, 1) of type ``torch.int32`` will be returned, containing the semantic ID of each pixel. The info ``idToLabels`` dictionary will be the mapping from semantic ID to semantic labels. + +Instance ID Segmentation +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. figure:: ../../../_static/overview/sensors/camera_instanceID.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``instance_id_segmentation_fast`` outputs instance ID segmentation of each entity in the camera’s viewport. The instance ID is unique for each prim in the scene with different paths. In addition to the image buffer, an ``info`` dictionary can be retrieved with ``tiled_camera.data.info['instance_id_segmentation_fast']`` containing ID to labels information. + +The main difference between ``instance_id_segmentation_fast`` and ``instance_segmentation_fast`` are that instance segmentation annotator goes down the hierarchy to the lowest level prim which has semantic labels, where instance ID segmentation always goes down to the leaf prim. + +- If ``colorize_instance_id_segmentation=True`` in the camera config, a 4-channel RGBA image will be returned with dimension (B, H, W, 4) and type ``torch.uint8``. The info ``idToLabels`` dictionary will be the mapping from color to USD prim path of that entity. + +- If ``colorize_instance_id_segmentation=False``, a buffer of dimension (B, H, W, 1) of type ``torch.int32`` will be returned, containing the instance ID of each pixel. The info ``idToLabels`` dictionary will be the mapping from instance ID to USD prim path of that entity. + +Instance Segmentation +""""""""""""""""""""" + +.. figure:: ../../../_static/overview/sensors/camera_instance.jpg + :align: center + :figwidth: 100% + :alt: A scene captured in RGB + +``instance_segmentation_fast`` outputs instance segmentation of each entity in the camera’s viewport. In addition to the image buffer, an ``info`` dictionary can be retrieved with ``tiled_camera.data.info['instance_segmentation_fast']`` containing ID to labels and ID to semantic information. + +- If ``colorize_instance_segmentation=True`` in the camera config, a 4-channel RGBA image will be returned with dimension (B, H, W, 4) and type ``torch.uint8``. + +- If ``colorize_instance_segmentation=False``, a buffer of dimension (B, H, W, 1) of type ``torch.int32`` will be returned, containing the instance ID of each pixel. + +The info ``idToLabels`` dictionary will be the mapping from color to USD prim path of that semantic entity. The info ``idToSemantics`` dictionary will be the mapping from color to semantic labels of that semantic entity. diff --git a/docs/source/overview/core-concepts/sensors/contact_sensor.rst b/docs/source/overview/core-concepts/sensors/contact_sensor.rst new file mode 100644 index 0000000000000000000000000000000000000000..4ddd2d10c077cfafd76fbd28e38b61747493f343 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/contact_sensor.rst @@ -0,0 +1,104 @@ +.. _overview_sensors_contact: + +.. currentmodule:: isaaclab + +Contact Sensor +============== + +.. figure:: ../../../_static/overview/sensors/contact_diagram.jpg + :align: center + :figwidth: 100% + :alt: A contact sensor with filtering + +The contact sensor is designed to return the net contact force acting on a given ridgid body. The sensor is written to behave as a physical object, and so the "scope" of the contact sensor is limited to the body (or bodies) that defines it. There are multiple ways to define this scope, depending on your need to filter the forces coming from the contact. + +By default, the reported force is the total contact force, but your application may only care about contact forces due to specific objects. Retrieving contact forces from specific objects requires filtering, and this can only be done in a "many-to-one" way. A multi-legged robot that needs filterable contact information for its feet would require one sensor per foot to be defined in the environment, but a robotic hand with contact sensors on the tips of each finger can be defined with a single sensor. + +Consider a simple environment with an Anymal Quadruped and a block + +.. literalinclude:: ../../../../../scripts/demos/sensors/contact_sensor.py + :language: python + :lines: 40-90 + +We define the sensors on the feet of the robot in two different ways. The front feet are independent sensors (one sensor body per foot) and the "Cube" is placed under the left foot. The hind feet are defined as a single sensor with multiple bodies. + +We can then run the scene and print the data from the sensors + +.. code-block:: python + + def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + . + . + . + # Simulate physics + while simulation_app.is_running(): + . + . + . + # print information from the sensors + print("-------------------------------") + print(scene["contact_forces_LF"]) + print("Received force matrix of: ", scene["contact_forces_LF"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_LF"].data.net_forces_w) + print("-------------------------------") + print(scene["contact_forces_RF"]) + print("Received force matrix of: ", scene["contact_forces_RF"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_RF"].data.net_forces_w) + print("-------------------------------") + print(scene["contact_forces_H"]) + print("Received force matrix of: ", scene["contact_forces_H"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_H"].data.net_forces_w) + +Here, we print both the net contact force and the filtered force matrix for each contact sensor defined in the scene. The front left and front right feet report the following + +.. code-block:: bash + + ------------------------------- + Contact sensor @ '/World/envs/env_.*/Robot/LF_FOOT': + view type : + update period (s) : 0.0 + number of bodies : 1 + body names : ['LF_FOOT'] + + Received force matrix of: tensor([[[[-1.3923e-05, 1.5727e-04, 1.1032e+02]]]], device='cuda:0') + Received contact force of: tensor([[[-1.3923e-05, 1.5727e-04, 1.1032e+02]]], device='cuda:0') + ------------------------------- + Contact sensor @ '/World/envs/env_.*/Robot/RF_FOOT': + view type : + update period (s) : 0.0 + number of bodies : 1 + body names : ['RF_FOOT'] + + Received force matrix of: tensor([[[[0., 0., 0.]]]], device='cuda:0') + Received contact force of: tensor([[[1.3529e-05, 0.0000e+00, 1.0069e+02]]], device='cuda:0') + + +.. figure:: ../../../_static/overview/sensors/contact_visualization.jpg + :align: center + :figwidth: 100% + :alt: The contact sensor visualization + + +Notice that even with filtering, both sensors report the net contact force acting on the foot. However, the "force matrix" on the right foot is zero because that foot isn't in contact with the filtered body, ``/World/envs/env_.*/Cube``. Now, checkout the data coming from the hind feet! + +.. code-block:: bash + + ------------------------------- + Contact sensor @ '/World/envs/env_.*/Robot/.*H_FOOT': + view type : + update period (s) : 0.0 + number of bodies : 2 + body names : ['LH_FOOT', 'RH_FOOT'] + + Received force matrix of: None + Received contact force of: tensor([[[9.7227e-06, 0.0000e+00, 7.2364e+01], + [2.4322e-05, 0.0000e+00, 1.8102e+02]]], device='cuda:0') + +In this case, the contact sensor has two bodies: the left and right hind feet. When the force matrix is queried, the result is ``None`` because this is a many body sensor, and presently Isaac Lab only supports "many to one" contact force filtering. Unlike the single body contact sensor, the reported force tensor has multiple entries, with each "row" corresponding to the contact force on a single body of the sensor (matching the ordering at construction). + +.. dropdown:: Code for contact_sensor.py + :icon: code + + .. literalinclude:: ../../../../../scripts/demos/sensors/contact_sensor.py + :language: python + :linenos: diff --git a/docs/source/overview/core-concepts/sensors/frame_transformer.rst b/docs/source/overview/core-concepts/sensors/frame_transformer.rst new file mode 100644 index 0000000000000000000000000000000000000000..7ebe0020cdb1583e1f8baad7e0941b4184f62192 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/frame_transformer.rst @@ -0,0 +1,111 @@ +.. _overview_sensors_frame_transformer: + +.. currentmodule:: isaaclab + +Frame Transformer +================= + +.. figure:: ../../../_static/overview/sensors/frame_transformer.jpg + :align: center + :figwidth: 100% + :alt: A diagram outlining the basic geometry of frame transformations + +.. + Do YOU want to know where things are relative to other things at a glance? Then the frame transformer is the sensor for you!* + +One of the most common operations that needs to be performed within a physics simulation is the frame transformation: rewriting a vector or quaternion in the basis of an arbitrary euclidean coordinate system. There are many ways to accomplish this within Isaac and USD, but these methods can be cumbersome to implement within Isaac Lab's GPU based simulation and cloned environments. To mitigate this problem, we have designed the Frame Transformer Sensor, that tracks and calculate the relative frame transformations for rigid bodies of interest to the scene. + +The sensory is minimally defined by a source frame and a list of target frames. These definitions take the form of a prim path (for the source) and list of regex capable prim paths the rigid bodies to be tracked (for the targets). + +.. literalinclude:: ../../../../../scripts/demos/sensors/frame_transformer_sensor.py + :language: python + :lines: 38-86 + +We can now run the scene and query the sensor for data + +.. code-block:: python + + def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + . + . + . + # Simulate physics + while simulation_app.is_running(): + . + . + . + + # print information from the sensors + print("-------------------------------") + print(scene["specific_transforms"]) + print("relative transforms:", scene["specific_transforms"].data.target_pos_source) + print("relative orientations:", scene["specific_transforms"].data.target_quat_source) + print("-------------------------------") + print(scene["cube_transform"]) + print("relative transform:", scene["cube_transform"].data.target_pos_source) + print("-------------------------------") + print(scene["robot_transforms"]) + print("relative transforms:", scene["robot_transforms"].data.target_pos_source) + +Let's take a look at the result for tracking specific objects. First, we can take a look at the data coming from the +sensors on the feet + +.. code-block:: bash + + ------------------------------- + FrameTransformer @ '/World/envs/env_.*/Robot/base': + tracked body frames: ['base', 'LF_FOOT', 'RF_FOOT'] + number of envs: 1 + source body frame: base + target frames (count: ['LF_FOOT', 'RF_FOOT']): 2 + + relative transforms: tensor([[[ 0.4658, 0.3085, -0.4840], + [ 0.4487, -0.2959, -0.4828]]], device='cuda:0') + relative orientations: tensor([[[ 0.9623, 0.0072, -0.2717, -0.0020], + [ 0.9639, 0.0052, -0.2663, -0.0014]]], device='cuda:0') + +.. figure:: ../../../_static/overview/sensors/frame_transformer_visualizer.jpg + :align: center + :figwidth: 100% + :alt: The frame transformer visualizer + +By activating the visualizer, we can see that the frames of the feet are rotated "upward" slightly. +We can also see the explicit relative positions and rotations by querying the sensor for data, which +returns these values as a list with the same order as the tracked frames. This becomes even more +apparent if we examine the transforms specified by regex. + +.. code-block:: bash + + ------------------------------- + FrameTransformer @ '/World/envs/env_.*/Robot/base': + tracked body frames: ['base', 'LF_FOOT', 'LF_HIP', 'LF_SHANK', 'LF_THIGH', 'LH_FOOT', 'LH_HIP', 'LH_SHANK', 'LH_THIGH', 'RF_FOOT', 'RF_HIP', 'RF_SHANK', 'RF_THIGH', 'RH_FOOT', 'RH_HIP', 'RH_SHANK', 'RH_THIGH', 'base'] + number of envs: 1 + source body frame: base + target frames (count: ['LF_FOOT', 'LF_HIP', 'LF_SHANK', 'LF_THIGH', 'LH_FOOT', 'LH_HIP', 'LH_SHANK', 'LH_THIGH', 'RF_FOOT', 'RF_HIP', 'RF_SHANK', 'RF_THIGH', 'RH_FOOT', 'RH_HIP', 'RH_SHANK', 'RH_THIGH', 'base']): 17 + + relative transforms: tensor([[[ 4.6581e-01, 3.0846e-01, -4.8398e-01], + [ 2.9990e-01, 1.0400e-01, -1.7062e-09], + [ 2.1409e-01, 2.9177e-01, -2.4214e-01], + [ 3.5980e-01, 1.8780e-01, 1.2608e-03], + [-4.8813e-01, 3.0973e-01, -4.5927e-01], + [-2.9990e-01, 1.0400e-01, 2.7044e-09], + [-2.1495e-01, 2.9264e-01, -2.4198e-01], + [-3.5980e-01, 1.8780e-01, 1.5582e-03], + [ 4.4871e-01, -2.9593e-01, -4.8277e-01], + [ 2.9990e-01, -1.0400e-01, -2.7057e-09], + [ 1.9971e-01, -2.8554e-01, -2.3778e-01], + [ 3.5980e-01, -1.8781e-01, -9.1049e-04], + [-5.0090e-01, -2.9095e-01, -4.5746e-01], + [-2.9990e-01, -1.0400e-01, 6.3592e-09], + [-2.1860e-01, -2.8251e-01, -2.5163e-01], + [-3.5980e-01, -1.8779e-01, -1.8792e-03], + [ 0.0000e+00, 0.0000e+00, 0.0000e+00]]], device='cuda:0') + +Here, the sensor is tracking all rigid body children of ``Robot/base``, but this expression is **inclusive**, meaning that the source body itself is also a target. This can be seen both by examining the source and target list, where ``base`` appears twice, and also in the returned data, where the sensor returns the relative transform to itself, (0, 0, 0). + +.. dropdown:: Code for frame_transformer_sensor.py + :icon: code + + .. literalinclude:: ../../../../../scripts/demos/sensors/frame_transformer_sensor.py + :language: python + :linenos: diff --git a/docs/source/overview/core-concepts/sensors/imu.rst b/docs/source/overview/core-concepts/sensors/imu.rst new file mode 100644 index 0000000000000000000000000000000000000000..fe429f77a2b304f9593066f52283af5c13401926 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/imu.rst @@ -0,0 +1,89 @@ +.. _overview_sensors_imu: + +.. currentmodule:: isaaclab + +Inertial Measurement Unit (IMU) +=================================== + +.. figure:: ../../../_static/overview/sensors/imu_diagram.jpg + :align: center + :figwidth: 100% + :alt: A diagram outlining the basic force relationships for the IMU sensor + +Inertial Measurement Units (IMUs) are a type of sensor for measuring the acceleration of an object. These sensors are traditionally designed report linear accelerations and angular velocities, and function on similar principles to that of a digital scale: They report accelerations derived from **net force acting on the sensor**. + +A naive implementation of an IMU would report a negative acceleration due to gravity while the sensor is at rest in some local gravitational field. This is not generally needed for most practical applications, and so most real IMU sensors often include a **gravity bias** and assume that the device is operating on the surface of the Earth. The IMU we provide in Isaac Lab includes a similar bias term, which defaults to +g. This means that if you add an IMU to your simulation, and do not change this bias term, you will detect an acceleration of :math:`+ 9.81 m/s^{2}` anti-parallel to gravity acceleration. + +Consider a simple environment with an Anymal Quadruped equipped with an IMU on each of its two front feet. + +.. literalinclude:: ../../../../../scripts/demos/sensors/imu_sensor.py + :language: python + :lines: 39-63 + +Here we have explicitly removed the bias from one of the sensors, and we can see how this affects the reported values by visualizing the sensor when we run the sample script + +.. figure:: ../../../_static/overview/sensors/imu_visualizer.jpg + :align: center + :figwidth: 100% + :alt: IMU visualized + +Notice that the right front foot explicitly has a bias of (0,0,0). In the visualization, you should see that the arrow indicating the acceleration from the right IMU rapidly changes over time, while the arrow visualizing the left IMU points constantly along the vertical axis. + +Retrieving values form the sensor is done in the usual way + +.. code-block:: python + + def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + . + . + . + # Simulate physics + while simulation_app.is_running(): + . + . + . + # print information from the sensors + print("-------------------------------") + print(scene["imu_LF"]) + print("Received linear velocity: ", scene["imu_LF"].data.lin_vel_b) + print("Received angular velocity: ", scene["imu_LF"].data.ang_vel_b) + print("Received linear acceleration: ", scene["imu_LF"].data.lin_acc_b) + print("Received angular acceleration: ", scene["imu_LF"].data.ang_acc_b) + print("-------------------------------") + print(scene["imu_RF"]) + print("Received linear velocity: ", scene["imu_RF"].data.lin_vel_b) + print("Received angular velocity: ", scene["imu_RF"].data.ang_vel_b) + print("Received linear acceleration: ", scene["imu_RF"].data.lin_acc_b) + print("Received angular acceleration: ", scene["imu_RF"].data.ang_acc_b) + +The oscillations in the values reported by the sensor are a direct result of of how the sensor calculates the acceleration, which is through a finite difference approximation between adjacent ground truth velocity values as reported by the sim. We can see this in the reported result (pay attention to the **linear acceleration**) because the acceleration from the right foot is small, but explicitly zero. + +.. code-block:: bash + + Imu sensor @ '/World/envs/env_.*/Robot/LF_FOOT': + view type : + update period (s) : 0.0 + number of sensors : 1 + + Received linear velocity: tensor([[ 0.0203, -0.0054, 0.0380]], device='cuda:0') + Received angular velocity: tensor([[-0.0104, -0.1189, 0.0080]], device='cuda:0') + Received linear acceleration: tensor([[ 4.8344, -0.0205, 8.5305]], device='cuda:0') + Received angular acceleration: tensor([[-0.0389, -0.0262, -0.0045]], device='cuda:0') + ------------------------------- + Imu sensor @ '/World/envs/env_.*/Robot/RF_FOOT': + view type : + update period (s) : 0.0 + number of sensors : 1 + + Received linear velocity: tensor([[0.0244, 0.0077, 0.0431]], device='cuda:0') + Received angular velocity: tensor([[ 0.0122, -0.1360, -0.0042]], device='cuda:0') + Received linear acceleration: tensor([[-0.0018, 0.0010, -0.0032]], device='cuda:0') + Received angular acceleration: tensor([[-0.0373, -0.0050, -0.0053]], device='cuda:0') + ------------------------------- + +.. dropdown:: Code for imu_sensor.py + :icon: code + + .. literalinclude:: ../../../../../scripts/demos/sensors/imu_sensor.py + :language: python + :linenos: diff --git a/docs/source/overview/core-concepts/sensors/index.rst b/docs/source/overview/core-concepts/sensors/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..d2c63f212b76496da960c41bf0be87d88a4495e8 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/index.rst @@ -0,0 +1,22 @@ +.. _overview_sensors: + +Sensors +========= + +In this section, we will overview the various sensor APIs provided by Isaac Lab. + +Every sensor in Isaac Lab inherits from the ``SensorBase`` abstract class that provides the core functionality inherent to all sensors, which is to provide access to "measurements" of the scene. These measurements can take many forms such as ray-casting results, camera rendered images, or even simply ground truth data queried directly from the simulation (such as poses). Whatever the data may be, we can think of the sensor as having a buffer that is periodically updated with measurements by querying the scene. This ``update_period`` is defined in "simulated" seconds, meaning that even if the flow of time in the simulation is dilated relative to the real world, the sensor will update at the appropriate rate. The ``SensorBase`` is also designed with vectorizability in mind, holding the buffers for all copies of the sensor across cloned environments. + +Updating the buffers is done by overriding the ``_update_buffers_impl`` abstract method of the ``SensorBase`` class. On every time-step of the simulation, ``dt``, all sensors are queried for an update. During this query, the total time since the last update is incremented by ``dt`` for every buffer managed by that particular sensor. If the total time is greater than or equal to the ``update_period`` for a buffer, then that buffer is flagged to be updated on the next query. + +The following pages describe the available sensors in more detail: + +.. toctree:: + :maxdepth: 1 + + camera + contact_sensor + frame_transformer + imu + ray_caster + visuo_tactile_sensor diff --git a/docs/source/overview/core-concepts/sensors/ray_caster.rst b/docs/source/overview/core-concepts/sensors/ray_caster.rst new file mode 100644 index 0000000000000000000000000000000000000000..8b2f12020b3fc3ef4e6bad49d520bc4a315326f7 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/ray_caster.rst @@ -0,0 +1,77 @@ +.. _overview_sensors_ray_caster: + +.. currentmodule:: isaaclab + +Ray Caster +============= + +.. figure:: ../../../_static/overview/sensors/raycaster_patterns.jpg + :align: center + :figwidth: 100% + :alt: A diagram outlining the basic geometry of frame transformations + +The Ray Caster sensor (and the ray caster camera) are similar to RTX based rendering in that they both involve casting rays. The difference here is that the rays cast by the Ray Caster sensor return strictly collision information along the cast, and the direction of each individual ray can be specified. They do not bounce, nor are they affected by things like materials or opacity. For each ray specified by the sensor, a line is traced along the path of the ray and the location of first collision with the specified mesh is returned. This is the method used by some of our quadruped examples to measure the local height field. + +To keep the sensor performant when there are many cloned environments, the line tracing is done directly in `Warp `_. This is the reason why specific meshes need to be identified to cast against: that mesh data is loaded onto the device by warp when the sensor is initialized. As a consequence, the current iteration of this sensor only works for literally static meshes (meshes that *are not changed from the defaults specified in their USD file*). This constraint will be removed in future releases. + +Using a ray caster sensor requires a **pattern** and a parent xform to be attached to. The pattern defines how the rays are cast, while the prim properties defines the orientation and position of the sensor (additional offsets can be specified for more exact placement). Isaac Lab supports a number of ray casting pattern configurations, including a generic LIDAR and grid pattern. + +.. literalinclude:: ../../../../../scripts/demos/sensors/raycaster_sensor.py + :language: python + :lines: 40-71 + +Notice that the units on the pattern config is in degrees! Also, we enable visualization here to explicitly show the pattern in the rendering, but this is not required and should be disabled for performance tuning. + +.. figure:: ../../../_static/overview/sensors/raycaster_visualizer.jpg + :align: center + :figwidth: 100% + :alt: Lidar Pattern visualized + +Querying the sensor for data can be done at simulation run time like any other sensor. + +.. code-block:: python + + def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + . + . + . + # Simulate physics + while simulation_app.is_running(): + . + . + . + # print information from the sensors + print("-------------------------------") + print(scene["ray_caster"]) + print("Ray cast hit results: ", scene["ray_caster"].data.ray_hits_w) + + +.. code-block:: bash + + ------------------------------- + Ray-caster @ '/World/envs/env_.*/Robot/base/lidar_cage': + view type : + update period (s) : 0.016666666666666666 + number of meshes : 1 + number of sensors : 1 + number of rays/sensor: 18000 + total number of rays : 18000 + Ray cast hit results: tensor([[[-0.3698, 0.0357, 0.0000], + [-0.3698, 0.0357, 0.0000], + [-0.3698, 0.0357, 0.0000], + ..., + [ inf, inf, inf], + [ inf, inf, inf], + [ inf, inf, inf]]], device='cuda:0') + ------------------------------- + +Here we can see the data returned by the sensor itself. Notice first that there are 3 closed brackets at the beginning and the end: this is because the data returned is batched by the number of sensors. The ray cast pattern itself has also been flattened, and so the dimensions of the array are ``[N, B, 3]`` where ``N`` is the number of sensors, ``B`` is the number of cast rays in the pattern, and 3 is the dimension of the casting space. Finally, notice that the first several values in this casting pattern are the same: this is because the lidar pattern is spherical and we have specified our FOV to be hemispherical, which includes the poles. In this configuration, the "flattening pattern" becomes apparent: the first 180 entries will be the same because it's the bottom pole of this hemisphere, and there will be 180 of them because our horizontal FOV is 180 degrees with a resolution of 1 degree. + +You can use this script to experiment with pattern configurations and build an intuition about how the data is stored by altering the ``triggered`` variable on line 81. + +.. dropdown:: Code for raycaster_sensor.py + :icon: code + + .. literalinclude:: ../../../../../scripts/demos/sensors/raycaster_sensor.py + :language: python + :linenos: diff --git a/docs/source/overview/core-concepts/sensors/visuo_tactile_sensor.rst b/docs/source/overview/core-concepts/sensors/visuo_tactile_sensor.rst new file mode 100644 index 0000000000000000000000000000000000000000..ad00d1136b3c6d38696f7e92eaaadfdc896b4748 --- /dev/null +++ b/docs/source/overview/core-concepts/sensors/visuo_tactile_sensor.rst @@ -0,0 +1,204 @@ +.. _overview_sensors_tactile: + +.. currentmodule:: isaaclab + +Visuo-Tactile Sensor +==================== + + +The visuo-tactile sensor in Isaac Lab provides realistic tactile feedback through integration with TacSL (Tactile Sensor Learning) [Akinola2025]_. It is designed to simulate high-fidelity tactile interactions, generating both visual and force-based data that mirror real-world tactile sensors like GelSight devices. The sensor can provide tactile RGB images, force field distributions, and other intermediate tactile measurements essential for robotic manipulation tasks requiring fine tactile feedback. + + +.. figure:: ../../../_static/overview/sensors/tacsl_diagram.jpg + :align: center + :figwidth: 100% + :alt: Tactile sensor with RGB visualization and force fields + + +Configuration +~~~~~~~~~~~~~ + +Tactile sensors require specific configuration parameters to define their behavior and data collection properties. The sensor can be configured with various parameters including sensor resolution, force sensitivity, and output data types. + +.. code-block:: python + + from isaaclab.sensors.tacsl_sensor import VisuoTactileSensorCfg + from isaaclab.sensors import TiledCameraCfg + from isaaclab_assets.sensors import GELSIGHT_R15_CFG + import isaaclab.sim as sim_utils + + # Tactile sensor configuration + tactile_sensor = VisuoTactileSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/elastomer/tactile_sensor", + ## Sensor configuration + render_cfg=GELSIGHT_R15_CFG, + enable_camera_tactile=True, + enable_force_field=True, + ## Elastomer configuration + tactile_array_size=(20, 25), + tactile_margin=0.003, + ## Contact object configuration + contact_object_prim_path_expr="{ENV_REGEX_NS}/contact_object", + ## Force field physics parameters + normal_contact_stiffness=1.0, + friction_coefficient=2.0, + tangential_stiffness=0.1, + ## Camera configuration + camera_cfg=TiledCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/elastomer_tip/cam", + update_period=1 / 60, # 60 Hz + height=320, + width=240, + data_types=["distance_to_image_plane"], + spawn=None, # camera already spawned in USD file + ), + ) + +The configuration supports customization of: + +* **Render Configuration**: Specify the GelSight sensor rendering parameters using predefined configs + (e.g., ``GELSIGHT_R15_CFG``, ``GELSIGHT_MINI_CFG`` from ``isaaclab_assets.sensors``) +* **Tactile Modalities**: + * ``enable_camera_tactile`` - Enable tactile RGB imaging through camera sensors + * ``enable_force_field`` - Enable force field computation and visualization +* **Force Field Grid**: Set tactile grid dimensions (``tactile_array_size``) and margins, which directly affects the spatial resolution of the computed force field +* **Contact Object Configuration**: Define properties of interacting objects using prim path expressions to locate objects with SDF collision meshes +* **Physics Parameters**: Control the sensor's force field computation: + * ``normal_contact_stiffness``, ``friction_coefficient``, ``tangential_stiffness`` - Normal stiffness, friction coefficient, and tangential stiffness +* **Camera Settings**: Configure resolution, update rates, and data types, currently only ``distance_to_image_plane`` (alias for ``depth``) is supported. + ``spawn`` is set to ``None`` by default, which means that the camera is already spawned in the USD file. + If you want to spawn the camera yourself and set focal length, etc., you can set the spawn configuration to a valid spawn configuration. + +Configuration Requirements +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. important:: + The following requirements must be satisfied for proper sensor operation: + + **Camera Tactile Imaging** + If ``enable_camera_tactile=True``, a valid ``camera_cfg`` (TiledCameraCfg) must be provided with appropriate camera parameters. + + **Force Field Computation** + If ``enable_force_field=True``, the following parameters are required: + + * ``contact_object_prim_path_expr`` - Prim path expression to locate contact objects with SDF collision meshes + + **SDF Computation** + When force field computation is enabled, penalty-based normal and shear forces are computed using Signed Distance Field (SDF) queries. To achieve GPU acceleration: + + * Interacting objects should have SDF collision meshes + * An SDFView must be defined during initialization, therefore interacting objects should be specified before simulation. + + **Elastomer Configuration** + The sensor's ``prim_path`` must be configured as a child of the elastomer prim in the USD hierarchy. + The query points for the force field computation is computed from the surface of the elastomer mesh, which is searched for under the prim path of the elastomer. + + **Physics Materials** + The sensor uses physics materials to configure the compliant contact properties of the elastomer. + By default, physics material properties are pre-configured in the USD asset. However, you can override + these properties by specifying the following parameters in ``UsdFileWithCompliantContactCfg`` when + spawning the robot: + + * ``compliant_contact_stiffness`` - Contact stiffness for the elastomer surface + * ``compliant_contact_damping`` - Contact damping for the elastomer surface + * ``physics_material_prim_path`` - Prim path where physics material is applied (typically ``"elastomer"``) + + If any parameter is set to ``None``, the corresponding property from the USD asset will be retained. + + +Usage Example +~~~~~~~~~~~~~ + +To use the tactile sensor in a simulation environment, run the demo: + +.. code-block:: bash + + cd scripts/demos/sensors + python tacsl_sensor.py --use_tactile_rgb --use_tactile_ff --tactile_compliance_stiffness 100.0 --tactile_compliant_damping 1.0 --contact_object_type nut --num_envs 16 --save_viz --enable_cameras + +Available command-line options include: + +* ``--use_tactile_rgb``: Enable camera-based tactile sensing +* ``--use_tactile_ff``: Enable force field tactile sensing +* ``--contact_object_type``: Specify the type of contact object (nut, cube, etc.) +* ``--num_envs``: Number of parallel environments +* ``--save_viz``: Save visualization outputs for analysis +* ``--tactile_compliance_stiffness``: Override compliant contact stiffness (default: use USD asset values) +* ``--tactile_compliant_damping``: Override compliant contact damping (default: use USD asset values) +* ``--normal_contact_stiffness``: Normal contact stiffness for force field computation +* ``--tangential_stiffness``: Tangential stiffness for shear forces +* ``--friction_coefficient``: Friction coefficient for shear forces +* ``--debug_sdf_closest_pts``: Visualize closest SDF points for debugging +* ``--debug_tactile_sensor_pts``: Visualize tactile sensor points for debugging +* ``--trimesh_vis_tactile_points``: Enable trimesh-based visualization of tactile points + +For a complete list of available options: + +.. code-block:: bash + + python tacsl_sensor.py -h + +.. note:: + The demo examples are based on the Gelsight R1.5, which is a prototype sensor that is now discontinued. The same procedure can be adapted for other visuotactile sensors. + +.. figure:: ../../../_static/overview/sensors/tacsl_demo.jpg + :align: center + :figwidth: 100% + :alt: TacSL tactile sensor demo showing RGB tactile images and force field visualizations + +The tactile sensor supports multiple data modalities that provide comprehensive information about contact interactions: + + +Output Tactile Data +~~~~~~~~~~~~~~~~~~~ +**RGB Tactile Images** + Real-time generation of tactile RGB images as objects make contact with the sensor surface. These images show deformation patterns and contact geometry similar to gel-based tactile sensors [Si2022]_ + + +**Force Fields** + Detailed contact force field and pressure distributions across the sensor surface, including normal and shear components. + +.. list-table:: + :widths: 50 50 + :class: borderless + + * - .. figure:: ../../../_static/overview/sensors/tacsl_taxim_example.jpg + :align: center + :figwidth: 80% + :alt: Tactile output with RGB visualization + + - .. figure:: ../../../_static/overview/sensors/tacsl_force_field_example.jpg + :align: center + :figwidth: 80% + :alt: Tactile output with force field visualization + +Integration with Learning Frameworks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The tactile sensor is designed to integrate seamlessly with reinforcement learning and imitation learning frameworks. The structured tensor outputs can be directly used as observations in learning algorithms: + +.. code-block:: python + + def get_tactile_observations(self): + """Extract tactile observations for learning.""" + tactile_data = self.scene["tactile_sensor"].data + + # tactile RGB image + tactile_rgb = tactile_data.tactile_rgb_image + + # tactile depth image + tactile_depth = tactile_data.tactile_depth_image + + # force field + tactile_normal_force = tactile_data.tactile_normal_force + tactile_shear_force = tactile_data.tactile_shear_force + + return [tactile_rgb, tactile_depth, tactile_normal_force, tactile_shear_force] + + + +References +~~~~~~~~~~ + +.. [Akinola2025] Akinola, I., Xu, J., Carius, J., Fox, D., & Narang, Y. (2025). TacSL: A library for visuotactile sensor simulation and learning. *IEEE Transactions on Robotics*. +.. [Si2022] Si, Z., & Yuan, W. (2022). Taxim: An example-based simulation model for GelSight tactile sensors. *IEEE Robotics and Automation Letters*, 7(2), 2361-2368. diff --git a/docs/source/overview/core-concepts/task_workflows.rst b/docs/source/overview/core-concepts/task_workflows.rst new file mode 100644 index 0000000000000000000000000000000000000000..18bd0f93b66a1b8fb434f03d49f4083f490a716e --- /dev/null +++ b/docs/source/overview/core-concepts/task_workflows.rst @@ -0,0 +1,118 @@ +.. _feature-workflows: + + +Task Design Workflows +===================== + +.. currentmodule:: isaaclab + +A **Task** is defined by an environment with specific interfaces for observations to and actions from a specific agent (robot). The environment is what provides an agent with the current observations and executes that agent's actions by updating the simulation forward in time. There are many common components of simulating a robot in an environment, regardless of what you might want that robot to do or how it might be trained to do it. + +This is especially true of Reinforcement Learning (RL), where managing the actions, observations, rewards, etc... across a vectorized GPU simulation can be daunting to even think about! To meet this need, Isaac Lab provides the ability to build your RL environments within our **Manager-based** system, allowing you to trust various minutia of the appropriate manager classes. However, we also recognize the need to exert granular control over an environment, especially during development. For this need, we also provide a **Direct** interface into the simulation, giving you full control! + +* **Manager-based**: The environment is decomposed into individual components (or managers) that handle different + aspects of the environment (such as computing observations, applying actions, and applying randomization). The + user defines configuration classes for each component and the environment is responsible for coordinating the + managers and calling their functions. + +* **Direct**: The user defines a single class that implements the entire environment directly without the need for + separate managers. This class is responsible for computing observations, applying actions, and computing rewards. + +Both workflows have their own advantages and disadvantages. The manager-based workflow is more modular and allows +different components of the environment to be swapped out easily. This is useful when prototyping the environment +and experimenting with different configurations. On the other hand, the direct workflow is more efficient and allows +for more fine-grained control over the environment logic. This is useful when optimizing the environment for performance +or when implementing complex logic that is difficult to decompose into separate components. + + +Manager-Based Environments +-------------------------- + +.. image:: ../../_static/task-workflows/manager-based-light.svg + :class: only-light + :align: center + :alt: Manager-based Task Workflow + +.. image:: ../../_static/task-workflows/manager-based-dark.svg + :class: only-dark + :align: center + :alt: Manager-based Task Workflow + +Manager-based environments promote modular implementations of tasks by decomposing it into individually managed components. Each component of the task, such as calculating rewards, observations, etc... can be specified as configurations for a corresponding manager. These managers define configurable functions that are responsible for executing the specific computations as needed. Coordinating a collection of different managers is handled by an Environment class that inherits from :class:`envs.ManagerBasedEnv`. Configurations likewise must all inherit from :class:`envs.ManagerBasedEnvCfg`. + +When developing new training environments, it is often beneficial to break the environment into independent components. This can be highly effective for collaboration, as it lets individual developers focus on different aspects of the environment, while allowing those disparate efforts to be joined back together into a single runnable task. For example, you may have multiple robots with differing sensoriums, requiring different observation managers to process those sensory data into a form that's useful for downstream components. You might have multiple members on the team with different ideas about what the reward should be to achieve your goals, and by having each one develop their own reward manager, you can swap and test as you see fit. The modular nature of the manager workflow is essential for more complex projects! + +For reinforcement learning, much of this has been done for you already! In most cases, it will be enough to write your environment to inherit from +:class:`envs.ManagerBasedRLEnv` and and your configuration from :class:`envs.ManagerBasedRLEnvCfg`. + +.. dropdown:: Example for defining the reward function for the Cartpole task using the manager-style + :icon: plus + + The following class is a part of the Cartpole environment configuration class. The :class:`RewardsCfg` class + defines individual terms that compose the reward function. Each reward term is defined by its function + implementation, weight and additional parameters to be passed to the function. Users can define multiple + reward terms and their weights to be used in the reward function. + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :pyobject: RewardsCfg + +.. seealso:: + + We provide a more detailed tutorial for setting up an environment using the manager-based workflow at + :ref:`tutorial-create-manager-rl-env`. + + +Direct Environments +------------------- + +.. image:: ../../_static/task-workflows/direct-based-light.svg + :class: only-light + :align: center + :alt: Direct-based Task Workflow + +.. image:: ../../_static/task-workflows/direct-based-dark.svg + :class: only-dark + :align: center + :alt: Direct-based Task Workflow + +The direct-style environment aligns more closely with traditional implementations of environments from other libraries. +A single class implements the reward function, observation function, resets, and all the other components +of the environment. This approach does not require the manager classes. Instead, users are provided the complete freedom +to implement their task through the APIs of either :class:`envs.DirectRLEnv` or :class:`envs.DirectMARLEnv`. All direct task environments must inherit from one of these two classes. +Direct environments still require configurations to be defined, specifically by inheriting from either :class:`envs.DirectRLEnvCfg` or :class:`envs.DirectMARLEnvCfg`. +This workflow may be the most familiar for users migrating from the `IsaacGymEnvs`_ and `OmniIsaacGymEnvs`_ frameworks. + +.. dropdown:: Example for defining the reward function for the Cartpole task using the direct-style + :icon: plus + + The following function is a part of the Cartpole environment class and is responsible for computing the rewards. + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._get_rewards + :dedent: 4 + + It calls the :meth:`compute_rewards` function which is Torch JIT compiled for performance benefits. + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: compute_rewards + +This approach provides more transparency in the implementations of the environments, as logic is defined within the task +class instead of abstracted with the use of managers. This may be beneficial when implementing complex logic that is +difficult to decompose into separate components. Additionally, the direct-style implementation may bring more performance +benefits for the environment, as it allows implementing large chunks of logic with optimized frameworks such as +`PyTorch JIT`_ or `Warp`_. This may be valuable when scaling up training tremendously which requires optimizing individual +operations in the environment. + +.. seealso:: + + We provide a more detailed tutorial for setting up a RL environment using the direct workflow at + :ref:`tutorial-create-direct-rl-env`. + + +.. _IsaacGymEnvs: https://github.com/isaac-sim/IsaacGymEnvs +.. _OmniIsaacGymEnvs: https://github.com/isaac-sim/OmniIsaacGymEnvs +.. _Pytorch JIT: https://pytorch.org/docs/stable/jit.html +.. _Warp: https://github.com/NVIDIA/warp diff --git a/docs/source/overview/developer-guide/development.rst b/docs/source/overview/developer-guide/development.rst new file mode 100644 index 0000000000000000000000000000000000000000..48a5019609fab428e44891a573bab2e916ba88ff --- /dev/null +++ b/docs/source/overview/developer-guide/development.rst @@ -0,0 +1,170 @@ +Extension Development +======================= + +Everything in Omniverse is either an extension or a collection of extensions (an application). They are +modularized packages that form the atoms of the Omniverse ecosystem. Each extension +provides a set of functionalities that can be used by other extensions or +standalone applications. A folder is recognized as an extension if it contains +an ``extension.toml`` file in the ``config`` directory. More information on extensions can be found in the +`Omniverse documentation `__. + +Each extension in Isaac Lab is written as a python package and follows the following structure: + +.. code:: bash + + + ├── config + │   └── extension.toml + ├── docs + │   ├── CHANGELOG.md + │   └── README.md + ├── + │ ├── __init__.py + │ ├── .... + │ └── scripts + ├── setup.py + └── tests + +The ``config/extension.toml`` file contains the metadata of the extension. This +includes the name, version, description, dependencies, etc. This information is used +by the Omniverse API to load the extension. The ``docs`` directory contains the documentation +for the extension with more detailed information about the extension and a CHANGELOG +file that contains the changes made to the extension in each version. + +The ```` directory contains the main python package for the extension. +It may also contain the ``scripts`` directory for keeping python-based applications +that are loaded into Omniverse when the extension is enabled using the +`Extension Manager `__. + +More specifically, when an extension is enabled, the python module specified in the +``config/extension.toml`` file is loaded and scripts that contain children of the +:class:`omni.ext.IExt` class are executed. + +.. code:: python + + import omni.ext + + class MyExt(omni.ext.IExt): + """My extension application.""" + + def on_startup(self, ext_id): + """Called when the extension is loaded.""" + pass + + def on_shutdown(self): + """Called when the extension is unloaded. + + It releases all references to the extension and cleans up any resources. + """ + pass + +While loading extensions into Omniverse happens automatically, using the python package +in standalone applications requires additional steps. To simplify the build process and +avoid the need to understand the `premake `__ +build system used by Omniverse, we directly use the `setuptools `__ +python package to build the python module provided by the extensions. This is done by the +``setup.py`` file in the extension directory. + +.. note:: + + The ``setup.py`` file is not required for extensions that are only loaded into Omniverse + using the `Extension Manager `__. + +Lastly, the ``tests`` directory contains the unit tests for the extension. These are written +using the `unittest `__ framework. It is +important to note that Omniverse also provides a similar +`testing framework `__. +However, it requires going through the build process and does not support testing of the python module in +standalone applications. + +Custom Extension Dependency Management +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Certain extensions may have dependencies which require the installation of additional packages before the extension +can be used. While Python dependencies are handled by the `setuptools `__ +package and specified in the ``setup.py`` file, non-Python dependencies such as `ROS `__ +packages or `apt `__ packages are not handled by setuptools. +Handling these kinds of dependencies requires an additional procedure. + +There are two types of dependencies that can be specified in the ``extension.toml`` file +under the ``isaac_lab_settings`` section: + +1. **apt_deps**: A list of apt packages that need to be installed. These are installed using the + `apt `__ package manager. +2. **ros_ws**: The path to the ROS workspace that contains the ROS packages. These are installed using + the `rosdep `__ dependency manager. + +As an example, the following ``extension.toml`` file specifies the dependencies for the extension: + +.. code-block:: toml + + [isaac_lab_settings] + # apt dependencies + apt_deps = ["libboost-all-dev"] + + # ROS workspace + # note: if this path is relative, it is relative to the extension directory's root + ros_ws = "/home/user/catkin_ws" + +These dependencies are installed using the ``install_deps.py`` script provided in the ``tools`` directory. +To install all dependencies for all extensions, run the following command: + +.. code-block:: bash + + # execute from the root of the repository + # the script expects the type of dependencies to install and the path to the extensions directory + # available types are: 'apt', 'rosdep' and 'all' + python tools/install_deps.py all ${ISAACLAB_PATH}/source + +.. note:: + Currently, this script is automatically executed during the build process of the ``Dockerfile.base`` + and ``Dockerfile.ros2``. This ensures that all the 'apt' and 'rosdep' dependencies are installed + before building the extensions respectively. + + +Standalone applications +~~~~~~~~~~~~~~~~~~~~~~~ + +In a typical Omniverse workflow, the simulator is launched first and then the extensions are +enabled. The loading of python modules and other python applications happens automagically, under the hood, and while this is the recommended +workflow, it is not always possible. + +For example, consider robot reinforcement learning. It is essential to have complete control over the simulation step +and when things update instead of asynchronously waiting for the result. In +such cases, we require direct control of the simulation, and so it is necessary to write a standalone application. These applications are functionally similar in that they launch the simulator using the :class:`~isaaclab.app.AppLauncher` and +then control the simulation directly through the :class:`~isaaclab.sim.SimulationContext`. In these cases, python modules from extensions **must** be imported after the app is launched. Doing so before the app is launched will cause missing module errors. + +The following snippet shows how to write a standalone application: + +.. code:: python + + """Launch Isaac Sim Simulator first.""" + + from isaaclab.app import AppLauncher + + # launch omniverse app + app_launcher = AppLauncher(headless=False) + simulation_app = app_launcher.app + + + """Rest everything follows.""" + + from isaaclab.sim import SimulationContext + + if __name__ == "__main__": + # get simulation context + simulation_context = SimulationContext() + # reset and play simulation + simulation_context.reset() + # step simulation + simulation_context.step() + # stop simulation + simulation_context.stop() + + # close the simulation + simulation_app.close() + + +It is necessary to launch the simulator before running any other code because extensions are hot-loaded +when the simulator starts. Many Omniverse modules become available only after the simulator is launched. +For further details, we recommend exploring the Isaac Lab :ref:`tutorials`. diff --git a/docs/source/overview/developer-guide/index.rst b/docs/source/overview/developer-guide/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..59f603fbfad2361551328a2821a307a693fb34d7 --- /dev/null +++ b/docs/source/overview/developer-guide/index.rst @@ -0,0 +1,15 @@ +Developer's Guide +================= + +For development, we suggest using `Microsoft Visual Studio Code +(VSCode) `__. This is also suggested by +NVIDIA Omniverse and there exists tutorials on how to `debug Omniverse +extensions `__ +using VSCode. + +.. toctree:: + :maxdepth: 1 + + VS Code + repo_structure + development diff --git a/docs/source/overview/developer-guide/repo_structure.rst b/docs/source/overview/developer-guide/repo_structure.rst new file mode 100644 index 0000000000000000000000000000000000000000..a201886c0f8dac4fa39392bbecf385c84fa46260 --- /dev/null +++ b/docs/source/overview/developer-guide/repo_structure.rst @@ -0,0 +1,68 @@ +Repository organization +----------------------- + +.. code-block:: bash + + IsaacLab + ├── .vscode + ├── CONTRIBUTING.md + ├── CONTRIBUTORS.md + ├── LICENSE + ├── isaaclab.bat + ├── isaaclab.sh + ├── pyproject.toml + ├── README.md + ├── docs + ├── docker + ├── source + │   ├── isaaclab + │   ├── isaaclab_assets + │   ├── isaaclab_mimic + │   ├── isaaclab_rl + │   └── isaaclab_tasks + ├── scripts + │   ├── benchmarks + │   ├── demos + │   ├── environments + │   ├── imitation_learning + │   ├── reinforcement_learning + │   ├── tools + │   ├── tutorials + ├── tools + └── VERSION + +Isaac Lab is built on the same back end as Isaac Sim. As such, it exists as a collection of **extensions** that can be assembled into **applications**. +The ``source`` directory contains the majority of the code in the repository and the specific extensions that compose Isaac lab, while ``scripts`` containing python scripts for launching customized standalone apps (Like our workflows). +These are the two primary ways of interacting with the simulation and Isaac lab supports both! +Checkout this `Isaac Sim introduction to workflows `__ for more details. + +Extensions +~~~~~~~~~~ + +The extensions that compose Isaac Lab are kept in the ``source`` directory. To simplify the build process, Isaac Lab directly use `setuptools `__. It is strongly recommend that you adhere to this process if you create your own extensions using Isaac Lab. + +The extensions are organized as follows: + +* **isaaclab**: Contains the core interface extension for Isaac Lab. This provides the main modules for actuators, + objects, robots and sensors. +* **isaaclab_assets**: Contains the extension with pre-configured assets for Isaac Lab. +* **isaaclab_tasks**: Contains the extension with pre-configured environments for Isaac Lab. +* **isaaclab_mimic**: Contains APIs and pre-configured environments for data generation for imitation learning. +* **isaaclab_rl**: Contains wrappers for using the above environments with different reinforcement learning agents. + + +Standalone +~~~~~~~~~~ + +The ``scripts`` directory contains various standalone applications written in python. +They are structured as follows: + +* **benchmarks**: Contains scripts for benchmarking different framework components. +* **demos**: Contains various demo applications that showcase the core framework :mod:`isaaclab`. +* **environments**: Contains applications for running environments defined in :mod:`isaaclab_tasks` with + different agents. These include a random policy, zero-action policy, teleoperation or scripted state machines. +* **tools**: Contains applications for using the tools provided by the framework. These include converting assets, + generating datasets, etc. +* **tutorials**: Contains step-by-step tutorials for using the APIs provided by the framework. +* **workflows**: Contains applications for using environments with various learning-based frameworks. These include different + reinforcement learning or imitation learning libraries. diff --git a/docs/source/overview/developer-guide/vs_code.rst b/docs/source/overview/developer-guide/vs_code.rst new file mode 100644 index 0000000000000000000000000000000000000000..a19889d1bdb8572e11a6da1a1548a68b018de004 --- /dev/null +++ b/docs/source/overview/developer-guide/vs_code.rst @@ -0,0 +1,92 @@ +.. _setup-vs-code: + +Setting up Visual Studio Code +----------------------------- + +**This is optional. You do not need to use VScode to use Isaac Lab** + +`Visual Studio Code `_ has proven an invaluable tool for the development of Isaac Lab. The Isaac Lab repository includes the VSCode files for setting up your development environment. These are included in the ``.vscode`` directory and include the following files: + +.. code-block:: bash + + .vscode + ├── tools + │   ├── launch.template.json + │   ├── settings.template.json + │   └── setup_vscode.py + ├── extensions.json + ├── launch.json # <- this is generated by setup_vscode.py + ├── settings.json # <- this is generated by setup_vscode.py + └── tasks.json + + +.. attention:: + + The following instructions on setting up Visual Studio Code only work with + :ref:`Isaac Sim Binaries Installation ` and not with + :ref:`Pip Installation `. + + +To setup the IDE, please follow these instructions: + +1. Open the ``IsaacLab`` directory on Visual Studio Code IDE +2. Run VSCode `Tasks `__, by + pressing ``Ctrl+Shift+P``, selecting ``Tasks: Run Task`` and running the + ``setup_python_env`` in the drop down menu. + + .. image:: ../../_static/vscode_tasks.png + :width: 600px + :align: center + :alt: VSCode Tasks + + +.. note:: + If this is your first time running tasks in VS Code, you may be prompted to select how to handle warnings. Simply follow + the prompts until the task window closes. + +If everything executes correctly, it should create the following files: + +* ``.vscode/launch.json``: Contains the launch configurations for debugging python code. +* ``.vscode/settings.json``: Contains the settings for the python interpreter and the python environment. + +For more information on VSCode support for Omniverse, please refer to the +following links: + +* `Isaac Sim VSCode support `__ + + +Configuring the python interpreter +---------------------------------- + +In the provided configuration, we set the default python interpreter to use the +python executable provided by Omniverse. This is specified in the +``.vscode/settings.json`` file: + +.. code-block:: json + + { + "python.defaultInterpreterPath": "${workspaceFolder}/_isaac_sim/python.sh", + } + +If you want to use a different python interpreter (for instance, from your conda or uv environment), +you need to change the python interpreter used by selecting and activating the python interpreter +of your choice in the bottom left corner of VSCode, or opening the command palette (``Ctrl+Shift+P``) +and selecting ``Python: Select Interpreter``. + +For more information on how to set python interpreter for VSCode, please +refer to the `VSCode documentation `_. + + +Setting up formatting and linting +--------------------------------- + +We use `ruff `_ as a formatter and linter. +These are configured in the ``.vscode/settings.json`` file: + +.. code-block:: json + + { + "ruff.configuration": "${workspaceFolder}/pyproject.toml", + } + +The ruff linter will show warnings and errors in your code to help you follow Python best practices and the project's coding standards. diff --git a/docs/source/overview/environments.rst b/docs/source/overview/environments.rst new file mode 100644 index 0000000000000000000000000000000000000000..c1cfb7dcb2c4c618c743ca388d21cc6fc240024b --- /dev/null +++ b/docs/source/overview/environments.rst @@ -0,0 +1,1198 @@ +.. _environments: + +Available Environments +====================== + +The following lists comprises of all the RL and IL tasks implementations that are available in Isaac Lab. +While we try to keep this list up-to-date, you can always get the latest list of environments by +running the following command: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. note:: + Use ``--keyword `` (optional) to filter environments by keyword. + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/list_envs.py --keyword + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. note:: + Use ``--keyword `` (optional) to filter environments by keyword. + + .. code:: batch + + isaaclab.bat -p scripts\environments\list_envs.py --keyword + + +We are actively working on adding more environments to the list. If you have any environments that +you would like to add to Isaac Lab, please feel free to open a pull request! + +Single-agent +------------ + +Classic +~~~~~~~ + +Classic environments that are based on IsaacGymEnvs implementation of MuJoCo-style environments. + +.. table:: + :widths: 33 37 30 + + +------------------+-----------------------------+-------------------------------------------------------------------------+ + | World | Environment ID | Description | + +==================+=============================+=========================================================================+ + | |humanoid| | |humanoid-link| | Move towards a direction with the MuJoCo humanoid robot | + | | | | + | | |humanoid-direct-link| | | + +------------------+-----------------------------+-------------------------------------------------------------------------+ + | |ant| | |ant-link| | Move towards a direction with the MuJoCo ant robot | + | | | | + | | |ant-direct-link| | | + +------------------+-----------------------------+-------------------------------------------------------------------------+ + | |cartpole| | |cartpole-link| | Move the cart to keep the pole upwards in the classic cartpole control | + | | | | + | | |cartpole-direct-link| | | + +------------------+-----------------------------+-------------------------------------------------------------------------+ + | |cartpole| | |cartpole-rgb-link| | Move the cart to keep the pole upwards in the classic cartpole control | + | | | and perceptive inputs. Requires running with ``--enable_cameras``. | + | | |cartpole-depth-link| | | + | | | | + | | |cartpole-rgb-direct-link| | | + | | | | + | | |cartpole-depth-direct-link|| | + +------------------+-----------------------------+-------------------------------------------------------------------------+ + | |cartpole| | |cartpole-resnet-link| | Move the cart to keep the pole upwards in the classic cartpole control | + | | | based off of features extracted from perceptive inputs with pre-trained | + | | |cartpole-theia-link| | frozen vision encoders. Requires running with ``--enable_cameras``. | + +------------------+-----------------------------+-------------------------------------------------------------------------+ + +.. |humanoid| image:: ../_static/tasks/classic/humanoid.jpg +.. |ant| image:: ../_static/tasks/classic/ant.jpg +.. |cartpole| image:: ../_static/tasks/classic/cartpole.jpg + +.. |humanoid-link| replace:: `Isaac-Humanoid-v0 `__ +.. |ant-link| replace:: `Isaac-Ant-v0 `__ +.. |cartpole-link| replace:: `Isaac-Cartpole-v0 `__ +.. |cartpole-rgb-link| replace:: `Isaac-Cartpole-RGB-v0 `__ +.. |cartpole-depth-link| replace:: `Isaac-Cartpole-Depth-v0 `__ +.. |cartpole-resnet-link| replace:: `Isaac-Cartpole-RGB-ResNet18-v0 `__ +.. |cartpole-theia-link| replace:: `Isaac-Cartpole-RGB-TheiaTiny-v0 `__ + + +.. |humanoid-direct-link| replace:: `Isaac-Humanoid-Direct-v0 `__ +.. |ant-direct-link| replace:: `Isaac-Ant-Direct-v0 `__ +.. |cartpole-direct-link| replace:: `Isaac-Cartpole-Direct-v0 `__ +.. |cartpole-rgb-direct-link| replace:: `Isaac-Cartpole-RGB-Camera-Direct-v0 `__ +.. |cartpole-depth-direct-link| replace:: `Isaac-Cartpole-Depth-Camera-Direct-v0 `__ + +Manipulation +~~~~~~~~~~~~ + +Environments based on fixed-arm manipulation tasks. + +For many of these tasks, we include configurations with different arm action spaces. For example, +for the lift-cube environment: + +* |lift-cube-link|: Franka arm with joint position control +* |lift-cube-ik-abs-link|: Franka arm with absolute IK control +* |lift-cube-ik-rel-link|: Franka arm with relative IK control + +.. table:: + :widths: 33 37 30 + + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +=========================+==============================+=============================================================================+ + | |reach-franka| | |reach-franka-link| | Move the end-effector to a sampled target pose with the Franka robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |reach-ur10| | |reach-ur10-link| | Move the end-effector to a sampled target pose with the UR10 robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |deploy-reach-ur10e| | |deploy-reach-ur10e-link| | Move the end-effector to a sampled target pose with the UR10e robot | + | | | This policy has been deployed to a real robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |lift-cube| | |lift-cube-link| | Pick a cube and bring it to a sampled target position with the Franka robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |stack-cube| | |stack-cube-link| | Stack three cubes (bottom to top: blue, red, green) with the Franka robot. | + | | | Blueprint env used for the NVIDIA Isaac GR00T blueprint for synthetic | + | | |stack-cube-bp-link| | manipulation motion generation | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |surface-gripper| | |long-suction-link| | Stack three cubes (bottom to top: blue, red, green) | + | | | with the UR10 arm and long surface gripper | + | | |short-suction-link| | or short surface gripper. | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |cabi-franka| | |cabi-franka-link| | Grasp the handle of a cabinet's drawer and open it with the Franka robot | + | | | | + | | |franka-direct-link| | | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |cube-allegro| | |cube-allegro-link| | In-hand reorientation of a cube using Allegro hand | + | | | | + | | |allegro-direct-link| | | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |cube-shadow| | |cube-shadow-link| | In-hand reorientation of a cube using Shadow hand | + | | | | + | | |cube-shadow-ff-link| | | + | | | | + | | |cube-shadow-lstm-link| | | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |cube-shadow| | |cube-shadow-vis-link| | In-hand reorientation of a cube using Shadow hand using perceptive inputs. | + | | | Requires running with ``--enable_cameras``. | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |gr1_pick_place| | |gr1_pick_place-link| | Pick up and place an object in a basket with a GR-1 humanoid robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |gr1_pp_waist| | |gr1_pp_waist-link| | Pick up and place an object in a basket with a GR-1 humanoid robot | + | | | with waist degrees-of-freedom enables that provides a wider reach space. | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |g1_pick_place| | |g1_pick_place-link| | Pick up and place an object in a basket with a Unitree G1 humanoid robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |g1_pick_place_fixed| | |g1_pick_place_fixed-link| | Pick up and place an object in a basket with a Unitree G1 humanoid robot | + | | | with three-fingered hands. Robot is set up with the base fixed in place. | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |g1_pick_place_lm| | |g1_pick_place_lm-link| | Pick up and place an object in a basket with a Unitree G1 humanoid robot | + | | | with three-fingered hands and in-place locomanipulation capabilities | + | | | enabled (i.e. Robot lower body balances in-place while upper body is | + | | | controlled via Inverse Kinematics). | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |kuka-allegro-lift| | |kuka-allegro-lift-link| | Pick up a primitive shape on the table and lift it to target position | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |kuka-allegro-reorient| | |kuka-allegro-reorient-link| | Pick up a primitive shape on the table and orient it to target pose | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |galbot_stack| | |galbot_stack-link| | Stack three cubes (bottom to top: blue, red, green) with the left arm of | + | | | a Galbot humanoid robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |agibot_place_mug| | |agibot_place_mug-link| | Pick up and place a mug upright with a Agibot A2D humanoid robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |agibot_place_toy| | |agibot_place_toy-link| | Pick up and place an object in a box with a Agibot A2D humanoid robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |reach_openarm_bi| | |reach_openarm_bi-link| | Move the end-effector to sampled target poses with the OpenArm robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |reach_openarm_uni| | |reach_openarm_uni-link| | Move the end-effector to a sampled target pose with the OpenArm robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |lift_openarm_uni| | |lift_openarm_uni-link| | Pick a cube and bring it to a sampled target position with the OpenArm robot| + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + | |cabi_openarm_uni| | |cabi_openarm_uni-link| | Grasp the handle of a cabinet's drawer and open it with the OpenArm robot | + +-------------------------+------------------------------+-----------------------------------------------------------------------------+ + +.. |reach-franka| image:: ../_static/tasks/manipulation/franka_reach.jpg +.. |reach-ur10| image:: ../_static/tasks/manipulation/ur10_reach.jpg +.. |deploy-reach-ur10e| image:: ../_static/tasks/manipulation/ur10e_reach.jpg +.. |lift-cube| image:: ../_static/tasks/manipulation/franka_lift.jpg +.. |cabi-franka| image:: ../_static/tasks/manipulation/franka_open_drawer.jpg +.. |cube-allegro| image:: ../_static/tasks/manipulation/allegro_cube.jpg +.. |cube-shadow| image:: ../_static/tasks/manipulation/shadow_cube.jpg +.. |stack-cube| image:: ../_static/tasks/manipulation/franka_stack.jpg +.. |gr1_pick_place| image:: ../_static/tasks/manipulation/gr-1_pick_place.jpg +.. |g1_pick_place| image:: ../_static/tasks/manipulation/g1_pick_place.jpg +.. |g1_pick_place_fixed| image:: ../_static/tasks/manipulation/g1_pick_place_fixed_base.jpg +.. |g1_pick_place_lm| image:: ../_static/tasks/manipulation/g1_pick_place_locomanipulation.jpg +.. |surface-gripper| image:: ../_static/tasks/manipulation/ur10_stack_surface_gripper.jpg +.. |gr1_pp_waist| image:: ../_static/tasks/manipulation/gr-1_pick_place_waist.jpg +.. |galbot_stack| image:: ../_static/tasks/manipulation/galbot_stack_cube.jpg +.. |agibot_place_mug| image:: ../_static/tasks/manipulation/agibot_place_mug.jpg +.. |agibot_place_toy| image:: ../_static/tasks/manipulation/agibot_place_toy.jpg +.. |kuka-allegro-lift| image:: ../_static/tasks/manipulation/kuka_allegro_lift.jpg +.. |kuka-allegro-reorient| image:: ../_static/tasks/manipulation/kuka_allegro_reorient.jpg +.. |reach_openarm_bi| image:: ../_static/tasks/manipulation/openarm_bi_reach.jpg +.. |reach_openarm_uni| image:: ../_static/tasks/manipulation/openarm_uni_reach.jpg +.. |lift_openarm_uni| image:: ../_static/tasks/manipulation/openarm_uni_lift.jpg +.. |cabi_openarm_uni| image:: ../_static/tasks/manipulation/openarm_uni_open_drawer.jpg + +.. |reach-franka-link| replace:: `Isaac-Reach-Franka-v0 `__ +.. |reach-ur10-link| replace:: `Isaac-Reach-UR10-v0 `__ +.. |deploy-reach-ur10e-link| replace:: `Isaac-Deploy-Reach-UR10e-v0 `__ +.. |lift-cube-link| replace:: `Isaac-Lift-Cube-Franka-v0 `__ +.. |lift-cube-ik-abs-link| replace:: `Isaac-Lift-Cube-Franka-IK-Abs-v0 `__ +.. |lift-cube-ik-rel-link| replace:: `Isaac-Lift-Cube-Franka-IK-Rel-v0 `__ +.. |cabi-franka-link| replace:: `Isaac-Open-Drawer-Franka-v0 `__ +.. |franka-direct-link| replace:: `Isaac-Franka-Cabinet-Direct-v0 `__ +.. |cube-allegro-link| replace:: `Isaac-Repose-Cube-Allegro-v0 `__ +.. |allegro-direct-link| replace:: `Isaac-Repose-Cube-Allegro-Direct-v0 `__ +.. |stack-cube-link| replace:: `Isaac-Stack-Cube-Franka-v0 `__ +.. |stack-cube-bp-link| replace:: `Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0 `__ +.. |gr1_pick_place-link| replace:: `Isaac-PickPlace-GR1T2-Abs-v0 `__ +.. |g1_pick_place-link| replace:: `Isaac-PickPlace-G1-InspireFTP-Abs-v0 `__ +.. |g1_pick_place_fixed-link| replace:: `Isaac-PickPlace-FixedBaseUpperBodyIK-G1-Abs-v0 `__ +.. |g1_pick_place_lm-link| replace:: `Isaac-PickPlace-Locomanipulation-G1-Abs-v0 `__ +.. |long-suction-link| replace:: `Isaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0 `__ +.. |short-suction-link| replace:: `Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0 `__ +.. |gr1_pp_waist-link| replace:: `Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0 `__ +.. |galbot_stack-link| replace:: `Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0 `__ +.. |kuka-allegro-lift-link| replace:: `Isaac-Dexsuite-Kuka-Allegro-Lift-v0 `__ +.. |kuka-allegro-reorient-link| replace:: `Isaac-Dexsuite-Kuka-Allegro-Reorient-v0 `__ +.. |cube-shadow-link| replace:: `Isaac-Repose-Cube-Shadow-Direct-v0 `__ +.. |cube-shadow-ff-link| replace:: `Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0 `__ +.. |cube-shadow-lstm-link| replace:: `Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0 `__ +.. |cube-shadow-vis-link| replace:: `Isaac-Repose-Cube-Shadow-Vision-Direct-v0 `__ +.. |agibot_place_mug-link| replace:: `Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0 `__ +.. |agibot_place_toy-link| replace:: `Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0 `__ +.. |reach_openarm_bi-link| replace:: `Isaac-Reach-OpenArm-Bi-v0 `__ +.. |reach_openarm_uni-link| replace:: `Isaac-Reach-OpenArm-v0 `__ +.. |lift_openarm_uni-link| replace:: `Isaac-Lift-Cube-OpenArm-v0 `__ +.. |cabi_openarm_uni-link| replace:: `Isaac-Open-Drawer-OpenArm-v0 `__ + + +Contact-rich Manipulation +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Environments based on contact-rich manipulation tasks such as peg insertion, gear meshing and nut-bolt fastening. + +These tasks share the same task configurations and control options. You can switch between them by specifying the task name. +For example: + +* |factory-peg-link|: Peg insertion with the Franka arm +* |factory-gear-link|: Gear meshing with the Franka arm +* |factory-nut-link|: Nut-Bolt fastening with the Franka arm + +.. table:: + :widths: 33 37 30 + + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +====================+=========================+=============================================================================+ + | |factory-peg| | |factory-peg-link| | Insert peg into the socket with the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |factory-gear| | |factory-gear-link| | Insert and mesh gear into the base with other gears, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |factory-nut| | |factory-nut-link| | Thread the nut onto the first 2 threads of the bolt, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + +.. |factory-peg| image:: ../_static/tasks/factory/peg_insert.jpg +.. |factory-gear| image:: ../_static/tasks/factory/gear_mesh.jpg +.. |factory-nut| image:: ../_static/tasks/factory/nut_thread.jpg + +.. |factory-peg-link| replace:: `Isaac-Factory-PegInsert-Direct-v0 `__ +.. |factory-gear-link| replace:: `Isaac-Factory-GearMesh-Direct-v0 `__ +.. |factory-nut-link| replace:: `Isaac-Factory-NutThread-Direct-v0 `__ + +AutoMate +~~~~~~~~ + +Environments based on 100 diverse assembly tasks, each involving the insertion of a plug into a socket. These tasks share a common configuration and differ by th geometry and properties of the parts. + +You can switch between tasks by specifying the corresponding asset ID. Available asset IDs include: + +'00004', '00007', '00014', '00015', '00016', '00021', '00028', '00030', '00032', '00042', '00062', '00074', '00077', '00078', '00081', '00083', '00103', '00110', '00117', '00133', '00138', '00141', '00143', '00163', '00175', '00186', '00187', '00190', '00192', '00210', '00211', '00213', '00255', '00256', '00271', '00293', '00296', '00301', '00308', '00318', '00319', '00320', '00329', '00340', '00345', '00346', '00360', '00388', '00410', '00417', '00422', '00426', '00437', '00444', '00446', '00470', '00471', '00480', '00486', '00499', '00506', '00514', '00537', '00553', '00559', '00581', '00597', '00614', '00615', '00638', '00648', '00649', '00652', '00659', '00681', '00686', '00700', '00703', '00726', '00731', '00741', '00755', '00768', '00783', '00831', '00855', '00860', '00863', '01026', '01029', '01036', '01041', '01053', '01079', '01092', '01102', '01125', '01129', '01132', '01136'. + +We provide environments for both disassembly and assembly. + +.. attention:: + + CUDA is recommended for running the AutoMate environments with 570 drivers. If running with Nvidia driver 570 on Linux with architecture x86_64, we follow the below steps to install CUDA 12.8. This allows for computing rewards in AutoMate environments with CUDA. If you have a different operation system or architecture, please refer to the `CUDA installation page `_ for additional instruction. + + .. code-block:: bash + + wget https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run + sudo sh cuda_12.8.0_570.86.10_linux.run --toolkit + + When using conda, cuda toolkit can be installed with: + + .. code-block:: bash + + conda install cudatoolkit + + With 580 drivers and CUDA 13, we are currently unable to enable CUDA for computing the rewards. The code automatically fallbacks to CPU, resulting in slightly slower performance. + +* |disassembly-link|: The plug starts inserted in the socket. A low-level controller lifts the plug out and moves it to a random position. This process is purely scripted and does not involve any learned policy. Therefore, it does not require policy training or evaluation. The resulting trajectories serve as demonstrations for the reverse process, i.e., learning to assemble. To run disassembly for a specific task: ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py --assembly_id=ASSEMBLY_ID --disassembly_dir=DISASSEMBLY_DIR``. All generated trajectories are saved to a local directory ``DISASSEMBLY_DIR``. +* |assembly-link|: The goal is to insert the plug into the socket. You can use this environment to train a policy via reinforcement learning or evaluate a pre-trained checkpoint. + + * To train an assembly policy, we run the command ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --train``. We can customize the training process using the optional flags: ``--headless`` to run without opening the GUI windows, ``--max_iterations=MAX_ITERATIONS`` to set the number of training iterations, ``--num_envs=NUM_ENVS`` to set the number of parallel environments during training, ``--seed=SEED`` to assign the random seed. The policy checkpoints will be saved automatically during training in the directory ``logs/rl_games/Assembly/test``. + * To evaluate an assembly policy, we run the command ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --checkpoint=CHECKPOINT --log_eval``. The evaluation results are stored in ``evaluation_{ASSEMBLY_ID}.h5``. + +.. table:: + :widths: 33 37 30 + + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +====================+=========================+=============================================================================+ + | |disassembly| | |disassembly-link| | Lift a plug out of the socket with the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |assembly| | |assembly-link| | Insert a plug into its corresponding socket with the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + +.. |assembly| image:: ../_static/tasks/automate/00004.jpg +.. |disassembly| image:: ../_static/tasks/automate/01053_disassembly.jpg + +.. |assembly-link| replace:: `Isaac-AutoMate-Assembly-Direct-v0 `__ +.. |disassembly-link| replace:: `Isaac-AutoMate-Disassembly-Direct-v0 `__ + +FORGE +~~~~~~~~ + +FORGE environments extend Factory environments with: + +* Force sensing: Add observations for force experienced by the end-effector. +* Excessive force penalty: Add an option to penalize the agent for excessive contact forces. +* Dynamics randomization: Randomize controller gains, asset properties (friction, mass), and dead-zone. +* Success prediction: Add an extra action that predicts task success. + +These tasks share the same task configurations and control options. You can switch between them by specifying the task name. + +* |forge-peg-link|: Peg insertion with the Franka arm +* |forge-gear-link|: Gear meshing with the Franka arm +* |forge-nut-link|: Nut-Bolt fastening with the Franka arm + +.. table:: + :widths: 33 37 30 + + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +====================+=========================+=============================================================================+ + | |forge-peg| | |forge-peg-link| | Insert peg into the socket with the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |forge-gear| | |forge-gear-link| | Insert and mesh gear into the base with other gears, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |forge-nut| | |forge-nut-link| | Thread the nut onto the first 2 threads of the bolt, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + +.. |forge-peg| image:: ../_static/tasks/factory/peg_insert.jpg +.. |forge-gear| image:: ../_static/tasks/factory/gear_mesh.jpg +.. |forge-nut| image:: ../_static/tasks/factory/nut_thread.jpg + +.. |forge-peg-link| replace:: `Isaac-Forge-PegInsert-Direct-v0 `__ +.. |forge-gear-link| replace:: `Isaac-Forge-GearMesh-Direct-v0 `__ +.. |forge-nut-link| replace:: `Isaac-Forge-NutThread-Direct-v0 `__ + + +Locomotion +~~~~~~~~~~ + +Environments based on legged locomotion tasks. + +.. table:: + :widths: 33 37 30 + + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | World | Environment ID | Description | + +==============================+==============================================+==============================================================================+ + | |velocity-flat-anymal-b| | |velocity-flat-anymal-b-link| | Track a velocity command on flat terrain with the Anymal B robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-anymal-b| | |velocity-rough-anymal-b-link| | Track a velocity command on rough terrain with the Anymal B robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-anymal-c| | |velocity-flat-anymal-c-link| | Track a velocity command on flat terrain with the Anymal C robot | + | | | | + | | |velocity-flat-anymal-c-direct-link| | | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-anymal-c| | |velocity-rough-anymal-c-link| | Track a velocity command on rough terrain with the Anymal C robot | + | | | | + | | |velocity-rough-anymal-c-direct-link| | | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-anymal-d| | |velocity-flat-anymal-d-link| | Track a velocity command on flat terrain with the Anymal D robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-anymal-d| | |velocity-rough-anymal-d-link| | Track a velocity command on rough terrain with the Anymal D robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-unitree-a1| | |velocity-flat-unitree-a1-link| | Track a velocity command on flat terrain with the Unitree A1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-unitree-a1| | |velocity-rough-unitree-a1-link| | Track a velocity command on rough terrain with the Unitree A1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-unitree-go1| | |velocity-flat-unitree-go1-link| | Track a velocity command on flat terrain with the Unitree Go1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-unitree-go1| | |velocity-rough-unitree-go1-link| | Track a velocity command on rough terrain with the Unitree Go1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-unitree-go2| | |velocity-flat-unitree-go2-link| | Track a velocity command on flat terrain with the Unitree Go2 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-unitree-go2| | |velocity-rough-unitree-go2-link| | Track a velocity command on rough terrain with the Unitree Go2 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-spot| | |velocity-flat-spot-link| | Track a velocity command on flat terrain with the Boston Dynamics Spot robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-h1| | |velocity-flat-h1-link| | Track a velocity command on flat terrain with the Unitree H1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-h1| | |velocity-rough-h1-link| | Track a velocity command on rough terrain with the Unitree H1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-g1| | |velocity-flat-g1-link| | Track a velocity command on flat terrain with the Unitree G1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-g1| | |velocity-rough-g1-link| | Track a velocity command on rough terrain with the Unitree G1 robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-flat-digit| | |velocity-flat-digit-link| | Track a velocity command on flat terrain with the Agility Digit robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |velocity-rough-digit| | |velocity-rough-digit-link| | Track a velocity command on rough terrain with the Agility Digit robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + | |tracking-loco-manip-digit| | |tracking-loco-manip-digit-link| | Track a root velocity and hand pose command with the Agility Digit robot | + +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+ + +.. |velocity-flat-anymal-b-link| replace:: `Isaac-Velocity-Flat-Anymal-B-v0 `__ +.. |velocity-rough-anymal-b-link| replace:: `Isaac-Velocity-Rough-Anymal-B-v0 `__ + +.. |velocity-flat-anymal-c-link| replace:: `Isaac-Velocity-Flat-Anymal-C-v0 `__ +.. |velocity-rough-anymal-c-link| replace:: `Isaac-Velocity-Rough-Anymal-C-v0 `__ + +.. |velocity-flat-anymal-c-direct-link| replace:: `Isaac-Velocity-Flat-Anymal-C-Direct-v0 `__ +.. |velocity-rough-anymal-c-direct-link| replace:: `Isaac-Velocity-Rough-Anymal-C-Direct-v0 `__ + +.. |velocity-flat-anymal-d-link| replace:: `Isaac-Velocity-Flat-Anymal-D-v0 `__ +.. |velocity-rough-anymal-d-link| replace:: `Isaac-Velocity-Rough-Anymal-D-v0 `__ + +.. |velocity-flat-unitree-a1-link| replace:: `Isaac-Velocity-Flat-Unitree-A1-v0 `__ +.. |velocity-rough-unitree-a1-link| replace:: `Isaac-Velocity-Rough-Unitree-A1-v0 `__ + +.. |velocity-flat-unitree-go1-link| replace:: `Isaac-Velocity-Flat-Unitree-Go1-v0 `__ +.. |velocity-rough-unitree-go1-link| replace:: `Isaac-Velocity-Rough-Unitree-Go1-v0 `__ + +.. |velocity-flat-unitree-go2-link| replace:: `Isaac-Velocity-Flat-Unitree-Go2-v0 `__ +.. |velocity-rough-unitree-go2-link| replace:: `Isaac-Velocity-Rough-Unitree-Go2-v0 `__ + +.. |velocity-flat-spot-link| replace:: `Isaac-Velocity-Flat-Spot-v0 `__ + +.. |velocity-flat-h1-link| replace:: `Isaac-Velocity-Flat-H1-v0 `__ +.. |velocity-rough-h1-link| replace:: `Isaac-Velocity-Rough-H1-v0 `__ + +.. |velocity-flat-g1-link| replace:: `Isaac-Velocity-Flat-G1-v0 `__ +.. |velocity-rough-g1-link| replace:: `Isaac-Velocity-Rough-G1-v0 `__ + +.. |velocity-flat-digit-link| replace:: `Isaac-Velocity-Flat-Digit-v0 `__ +.. |velocity-rough-digit-link| replace:: `Isaac-Velocity-Rough-Digit-v0 `__ +.. |tracking-loco-manip-digit-link| replace:: `Isaac-Tracking-LocoManip-Digit-v0 `__ + +.. |velocity-flat-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_flat.jpg +.. |velocity-rough-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_rough.jpg +.. |velocity-flat-anymal-c| image:: ../_static/tasks/locomotion/anymal_c_flat.jpg +.. |velocity-rough-anymal-c| image:: ../_static/tasks/locomotion/anymal_c_rough.jpg +.. |velocity-flat-anymal-d| image:: ../_static/tasks/locomotion/anymal_d_flat.jpg +.. |velocity-rough-anymal-d| image:: ../_static/tasks/locomotion/anymal_d_rough.jpg +.. |velocity-flat-unitree-a1| image:: ../_static/tasks/locomotion/a1_flat.jpg +.. |velocity-rough-unitree-a1| image:: ../_static/tasks/locomotion/a1_rough.jpg +.. |velocity-flat-unitree-go1| image:: ../_static/tasks/locomotion/go1_flat.jpg +.. |velocity-rough-unitree-go1| image:: ../_static/tasks/locomotion/go1_rough.jpg +.. |velocity-flat-unitree-go2| image:: ../_static/tasks/locomotion/go2_flat.jpg +.. |velocity-rough-unitree-go2| image:: ../_static/tasks/locomotion/go2_rough.jpg +.. |velocity-flat-spot| image:: ../_static/tasks/locomotion/spot_flat.jpg +.. |velocity-flat-h1| image:: ../_static/tasks/locomotion/h1_flat.jpg +.. |velocity-rough-h1| image:: ../_static/tasks/locomotion/h1_rough.jpg +.. |velocity-flat-g1| image:: ../_static/tasks/locomotion/g1_flat.jpg +.. |velocity-rough-g1| image:: ../_static/tasks/locomotion/g1_rough.jpg +.. |velocity-flat-digit| image:: ../_static/tasks/locomotion/agility_digit_flat.jpg +.. |velocity-rough-digit| image:: ../_static/tasks/locomotion/agility_digit_rough.jpg +.. |tracking-loco-manip-digit| image:: ../_static/tasks/locomotion/agility_digit_loco_manip.jpg + +Navigation +~~~~~~~~~~ + +.. table:: + :widths: 33 37 30 + + +----------------+---------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +================+=====================+=============================================================================+ + | |anymal_c_nav| | |anymal_c_nav-link| | Navigate towards a target x-y position and heading with the ANYmal C robot. | + +----------------+---------------------+-----------------------------------------------------------------------------+ + +.. |anymal_c_nav-link| replace:: `Isaac-Navigation-Flat-Anymal-C-v0 `__ + +.. |anymal_c_nav| image:: ../_static/tasks/navigation/anymal_c_nav.jpg + + +Multirotor +~~~~~~~~~~ + +.. note:: + The multirotor entry provides an environment configuration for flying the ARL robot. + See the `drone_arl` folder and the ARL robot config + (`ARL_ROBOT_1_CFG`) in the codebase for details. + +.. |arl_robot_track_position_state_based-link| replace:: `Isaac-TrackPositionNoObstacles-ARL-Robot-1-v0 `__ + +.. |arl_robot_track_position_state_based| image:: ../_static/tasks/drone_arl/arl_robot_1_track_position_state_based.jpg + +.. table:: + :widths: 33 37 30 + + +----------------------------------------+---------------------------------------------+----------------------------------------------------------------------------------------+ + | World | Environment ID | Description | + +========================================+=============================================+========================================================================================+ + | |arl_robot_track_position_state_based| | |arl_robot_track_position_state_based-link| | Setpoint position control for the ARL robot using the track_position_state_based task. | + +----------------------------------------+---------------------------------------------+----------------------------------------------------------------------------------------+ + + +Others +~~~~~~ + +.. note:: + + Adversarial Motion Priors (AMP) training is only available with the `skrl` library, as it is the only one of the currently + integrated libraries that supports it out-of-the-box (for the other libraries, it is necessary to implement the algorithm and architectures). + See the `skrl's AMP Documentation `_ for more information. + The AMP algorithm can be activated by adding the command line input ``--algorithm AMP`` to the train/play script. + + For evaluation, the play script's command line input ``--real-time`` allows the interaction loop between the environment and the agent to run in real time, if possible. + +.. table:: + :widths: 33 37 30 + + +----------------+---------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +================+===========================+=============================================================================+ + | |quadcopter| | |quadcopter-link| | Fly and hover the Crazyflie copter at a goal point by applying thrust. | + +----------------+---------------------------+-----------------------------------------------------------------------------+ + | |humanoid_amp| | |humanoid_amp_dance-link| | Move a humanoid robot by imitating different pre-recorded human animations | + | | | (Adversarial Motion Priors). | + | | |humanoid_amp_run-link| | | + | | | | + | | |humanoid_amp_walk-link| | | + +----------------+---------------------------+-----------------------------------------------------------------------------+ + +.. |quadcopter-link| replace:: `Isaac-Quadcopter-Direct-v0 `__ +.. |humanoid_amp_dance-link| replace:: `Isaac-Humanoid-AMP-Dance-Direct-v0 `__ +.. |humanoid_amp_run-link| replace:: `Isaac-Humanoid-AMP-Run-Direct-v0 `__ +.. |humanoid_amp_walk-link| replace:: `Isaac-Humanoid-AMP-Walk-Direct-v0 `__ + +.. |quadcopter| image:: ../_static/tasks/others/quadcopter.jpg +.. |humanoid_amp| image:: ../_static/tasks/others/humanoid_amp.jpg + +Spaces showcase +~~~~~~~~~~~~~~~ + +The |cartpole_showcase| folder contains showcase tasks (based on the *Cartpole* and *Cartpole-Camera* Direct tasks) +for the definition/use of the various Gymnasium observation and action spaces supported in Isaac Lab. + +.. |cartpole_showcase| replace:: `cartpole_showcase `__ + +.. note:: + + Currently, only Isaac Lab's Direct workflow supports the definition of observation and action spaces other than ``Box``. + See Direct workflow's :py:obj:`~isaaclab.envs.DirectRLEnvCfg.observation_space` / :py:obj:`~isaaclab.envs.DirectRLEnvCfg.action_space` + documentation for more details. + +The following tables summarize the different pairs of showcased spaces for the *Cartpole* and *Cartpole-Camera* tasks. +Replace ```` and ```` with the observation and action spaces to be explored in the task names for training and evaluation. + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

Showcase spaces for the Cartpole task

+

Isaac-Cartpole-Showcase-<OBSERVATION>-<ACTION>-Direct-v0

+
action space
 Box Discrete MultiDiscrete

observation

space

 Boxxxx
 Discretexxx
 MultiDiscretexxx
 Dictxxx
 Tuplexxx
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

Showcase spaces for the Cartpole-Camera task

+

Isaac-Cartpole-Camera-Showcase-<OBSERVATION>-<ACTION>-Direct-v0

+
action space
 Box Discrete MultiDiscrete

observation

space

 Boxxxx
 Discrete---
 MultiDiscrete---
 Dictxxx
 Tuplexxx
+ +Multi-agent +------------ + +.. note:: + + True mutli-agent training is only available with the `skrl` library, see the `Multi-Agents Documentation `_ for more information. + It supports the `IPPO` and `MAPPO` algorithms, which can be activated by adding the command line input ``--algorithm IPPO`` or ``--algorithm MAPPO`` to the train/play script. + If these environments are run with other libraries or without the `IPPO` or `MAPPO` flags, they will be converted to single-agent environments under the hood. + + +Classic +~~~~~~~ + +.. table:: + :widths: 33 37 30 + + +------------------------+------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ + | World | Environment ID | Description | + +========================+====================================+=======================================================================================================================+ + | |cart-double-pendulum| | |cart-double-pendulum-direct-link| | Move the cart and the pendulum to keep the last one upwards in the classic inverted double pendulum on a cart control | + +------------------------+------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ + +.. |cart-double-pendulum| image:: ../_static/tasks/classic/cart_double_pendulum.jpg + +.. |cart-double-pendulum-direct-link| replace:: `Isaac-Cart-Double-Pendulum-Direct-v0 `__ + +Manipulation +~~~~~~~~~~~~ + +Environments based on fixed-arm manipulation tasks. + +.. table:: + :widths: 33 37 30 + + +----------------------+--------------------------------+--------------------------------------------------------+ + | World | Environment ID | Description | + +======================+================================+========================================================+ + | |shadow-hand-over| | |shadow-hand-over-direct-link| | Passing an object from one hand over to the other hand | + +----------------------+--------------------------------+--------------------------------------------------------+ + +.. |shadow-hand-over| image:: ../_static/tasks/manipulation/shadow_hand_over.jpg + +.. |shadow-hand-over-direct-link| replace:: `Isaac-Shadow-Hand-Over-Direct-v0 `__ + +| + +Comprehensive List of Environments +================================== + +For environments that have a different task name listed under ``Inference Task Name``, please use the Inference Task Name +provided when running ``play.py`` or any inferencing workflows. These tasks provide more suitable configurations for +inferencing, including reading from an already trained checkpoint and disabling runtime perturbations used for training. + +.. list-table:: + :widths: 33 25 19 25 + + * - **Task Name** + - **Inference Task Name** + - **Workflow** + - **RL Library** + * - Isaac-Ant-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Ant-v0 + - + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Cart-Double-Pendulum-Direct-v0 + - + - Direct + - **rl_games** (PPO), **skrl** (IPPO, PPO, MAPPO) + * - Isaac-Cartpole-Camera-Showcase-Box-Box-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Box-Discrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Box-MultiDiscrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Dict-Box-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Dict-Discrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Dict-MultiDiscrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Tuple-Box-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Tuple-Discrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Camera-Showcase-Tuple-MultiDiscrete-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Depth-Camera-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Cartpole-Depth-v0 (Requires running with ``--enable_cameras``) + - + - Manager Based + - **rl_games** (PPO) + * - Isaac-Cartpole-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Cartpole-RGB-Camera-Direct-v0 (Requires running with ``--enable_cameras``) + - + - Direct + - **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Cartpole-RGB-ResNet18-v0 (Requires running with ``--enable_cameras``) + - + - Manager Based + - **rl_games** (PPO) + * - Isaac-Cartpole-RGB-TheiaTiny-v0 (Requires running with ``--enable_cameras``) + - + - Manager Based + - **rl_games** (PPO) + * - Isaac-Cartpole-RGB-v0 (Requires running with ``--enable_cameras``) + - + - Manager Based + - **rl_games** (PPO) + * - Isaac-Cartpole-Showcase-Box-Box-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Box-Discrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Box-MultiDiscrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Dict-Box-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Dict-Discrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Dict-MultiDiscrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Discrete-Box-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Discrete-Discrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Discrete-MultiDiscrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-MultiDiscrete-Box-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-MultiDiscrete-Discrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-MultiDiscrete-MultiDiscrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Tuple-Box-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Tuple-Discrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-Showcase-Tuple-MultiDiscrete-Direct-v0 + - + - Direct + - **skrl** (PPO) + * - Isaac-Cartpole-v0 + - + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Factory-GearMesh-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Factory-NutThread-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Factory-PegInsert-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-AutoMate-Assembly-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-AutoMate-Disassembly-Direct-v0 + - + - Direct + - + * - Isaac-Forge-GearMesh-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Forge-NutThread-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Forge-PegInsert-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Franka-Cabinet-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Humanoid-AMP-Dance-Direct-v0 + - + - Direct + - **skrl** (AMP) + * - Isaac-Humanoid-AMP-Run-Direct-v0 + - + - Direct + - **skrl** (AMP) + * - Isaac-Humanoid-AMP-Walk-Direct-v0 + - + - Direct + - **skrl** (AMP) + * - Isaac-Humanoid-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Humanoid-v0 + - + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Lift-Cube-Franka-IK-Abs-v0 + - + - Manager Based + - + * - Isaac-Lift-Cube-Franka-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Lift-Cube-Franka-v0 + - Isaac-Lift-Cube-Franka-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO), **rl_games** (PPO), **sb3** (PPO) + * - Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0 + - + - Manager Based + - + * - Isaac-Tracking-LocoManip-Digit-v0 + - Isaac-Tracking-LocoManip-Digit-Play-v0 + - Manager Based + - **rsl_rl** (PPO) + * - Isaac-Navigation-Flat-Anymal-C-v0 + - Isaac-Navigation-Flat-Anymal-C-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Open-Drawer-Franka-IK-Abs-v0 + - + - Manager Based + - + * - Isaac-Open-Drawer-Franka-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Open-Drawer-Franka-v0 + - Isaac-Open-Drawer-Franka-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Quadcopter-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Reach-Franka-IK-Abs-v0 + - + - Manager Based + - + * - Isaac-Reach-Franka-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Reach-Franka-OSC-v0 + - Isaac-Reach-Franka-OSC-Play-v0 + - Manager Based + - **rsl_rl** (PPO) + * - Isaac-Reach-Franka-v0 + - Isaac-Reach-Franka-Play-v0 + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Reach-UR10-v0 + - Isaac-Reach-UR10-Play-v0 + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Deploy-Reach-UR10e-v0 + - Isaac-Deploy-Reach-UR10e-Play-v0 + - Manager Based + - **rsl_rl** (PPO) + * - Isaac-Repose-Cube-Allegro-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Repose-Cube-Allegro-NoVelObs-v0 + - Isaac-Repose-Cube-Allegro-NoVelObs-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Repose-Cube-Allegro-v0 + - Isaac-Repose-Cube-Allegro-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Repose-Cube-Shadow-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0 + - + - Direct + - **rl_games** (FF), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0 + - + - Direct + - **rl_games** (LSTM) + * - Isaac-Repose-Cube-Shadow-Vision-Direct-v0 (Requires running with ``--enable_cameras``) + - Isaac-Repose-Cube-Shadow-Vision-Direct-Play-v0 (Requires running with ``--enable_cameras``) + - Direct + - **rsl_rl** (PPO), **rl_games** (VISION) + * - Isaac-Shadow-Hand-Over-Direct-v0 + - + - Direct + - **rl_games** (PPO), **skrl** (IPPO, PPO, MAPPO) + * - Isaac-Stack-Cube-Franka-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Dexsuite-Kuka-Allegro-Lift-v0 + - Isaac-Dexsuite-Kuka-Allegro-Lift-Play-v0 + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO) + * - Isaac-Dexsuite-Kuka-Allegro-Reorient-v0 + - Isaac-Dexsuite-Kuka-Allegro-Reorient-Play-v0 + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO) + * - Isaac-Stack-Cube-Franka-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Instance-Randomize-Franka-v0 + - + - Manager Based + - + * - Isaac-PickPlace-G1-InspireFTP-Abs-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0 + - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Play-v0 + - Manager Based + - + * - Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0 + - Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Play-v0 + - Manager Based + - + * - Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0 + - + - Manager Based + - + * - Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0 + - + - Manager Based + - + + * - Isaac-Velocity-Flat-Anymal-B-v0 + - Isaac-Velocity-Flat-Anymal-B-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Anymal-C-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Anymal-C-v0 + - Isaac-Velocity-Flat-Anymal-C-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Anymal-D-v0 + - Isaac-Velocity-Flat-Anymal-D-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Cassie-v0 + - Isaac-Velocity-Flat-Cassie-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Digit-v0 + - Isaac-Velocity-Flat-Digit-Play-v0 + - Manager Based + - **rsl_rl** (PPO) + * - Isaac-Velocity-Flat-G1-v0 + - Isaac-Velocity-Flat-G1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-H1-v0 + - Isaac-Velocity-Flat-H1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Spot-v0 + - Isaac-Velocity-Flat-Spot-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Unitree-A1-v0 + - Isaac-Velocity-Flat-Unitree-A1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Velocity-Flat-Unitree-Go1-v0 + - Isaac-Velocity-Flat-Unitree-Go1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Flat-Unitree-Go2-v0 + - Isaac-Velocity-Flat-Unitree-Go2-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Anymal-B-v0 + - Isaac-Velocity-Rough-Anymal-B-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Anymal-C-Direct-v0 + - + - Direct + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Anymal-C-v0 + - Isaac-Velocity-Rough-Anymal-C-Play-v0 + - Manager Based + - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Anymal-D-v0 + - Isaac-Velocity-Rough-Anymal-D-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Cassie-v0 + - Isaac-Velocity-Rough-Cassie-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Digit-v0 + - Isaac-Velocity-Rough-Digit-Play-v0 + - Manager Based + - **rsl_rl** (PPO) + * - Isaac-Velocity-Rough-G1-v0 + - Isaac-Velocity-Rough-G1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-H1-v0 + - Isaac-Velocity-Rough-H1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Unitree-A1-v0 + - Isaac-Velocity-Rough-Unitree-A1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Velocity-Rough-Unitree-Go1-v0 + - Isaac-Velocity-Rough-Unitree-Go1-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Velocity-Rough-Unitree-Go2-v0 + - Isaac-Velocity-Rough-Unitree-Go2-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO) + * - Isaac-Reach-OpenArm-Bi-v0 + - Isaac-Reach-OpenArm-Bi-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO) + * - Isaac-Reach-OpenArm-v0 + - Isaac-Reach-OpenArm-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **skrl** (PPO), **rl_games** (PPO) + * - Isaac-Lift-Cube-OpenArm-v0 + - Isaac-Lift-Cube-OpenArm-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO) + * - Isaac-Open-Drawer-OpenArm-v0 + - Isaac-Open-Drawer-OpenArm-Play-v0 + - Manager Based + - **rsl_rl** (PPO), **rl_games** (PPO) diff --git a/docs/source/overview/imitation-learning/augmented_imitation.rst b/docs/source/overview/imitation-learning/augmented_imitation.rst new file mode 100644 index 0000000000000000000000000000000000000000..b3593f22e62248c40f8c4875c1ddcc26e38815b4 --- /dev/null +++ b/docs/source/overview/imitation-learning/augmented_imitation.rst @@ -0,0 +1,436 @@ +.. _augmented-imitation-learning: + +Augmented Imitation Learning +============================ + +This section describes how to use Isaac Lab's imitation learning capabilities with the visual augmentation capabilities of `Cosmos `_ models to generate demonstrations at scale to train visuomotor policies robust against visual variations. + +Generating Demonstrations +~~~~~~~~~~~~~~~~~~~~~~~~~ + +We use the Isaac Lab Mimic feature that allows the generation of additional demonstrations automatically from a handful of annotated demonstrations. + +.. note:: + This section assumes you already have an annotated dataset of collected demonstrations. If you don't, you can follow the instructions in :ref:`teleoperation-imitation-learning` to collect and annotate your own demonstrations. + +In the following example, we will show you how to use Isaac Lab Mimic to generate additional demonstrations that can be used to train a visuomotor policy directly or can be augmented with visual variations using Cosmos (using the ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0`` environment). + +.. note:: + The ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0`` environment is similar to the standard visuomotor environment (``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Mimic-v0``), but with the addition of segmentation masks, depth maps, and normal maps in the generated dataset. These additional modalities are required to get the best results from the visual augmentation done using Cosmos. + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --enable_cameras --headless --num_envs 10 --generation_num_trials 1000 \ + --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/mimic_dataset_1k.hdf5 \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0 \ + --rendering_mode performance + +The number of demonstrations can be increased or decreased, 1000 demonstrations have been shown to provide good training results for this task. + +Additionally, the number of environments in the ``--num_envs`` parameter can be adjusted to speed up data generation. +The suggested number of 10 can be executed on a moderate laptop CPU. +On a more powerful desktop machine, use a larger number of environments for a significant speedup of this step. + +Cosmos Augmentation +~~~~~~~~~~~~~~~~~~~ + +HDF5 to MP4 Conversion +^^^^^^^^^^^^^^^^^^^^^^ + +The ``hdf5_to_mp4.py`` script converts camera frames stored in HDF5 demonstration files to MP4 videos. It supports multiple camera modalities including RGB, segmentation, depth and normal maps. This conversion is necessary for visual augmentation using Cosmos as it only works with video files rather than HDF5 data. + +.. rubric:: Required Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--input_file`` + - Path to the input HDF5 file. + * - ``--output_dir`` + - Directory to save the output MP4 files. + +.. rubric:: Optional Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--input_keys`` + - List of input keys to process from the HDF5 file. (default: ["table_cam", "wrist_cam", "table_cam_segmentation", "table_cam_normals", "table_cam_shaded_segmentation", "table_cam_depth"]) + * - ``--video_height`` + - Height of the output video in pixels. (default: 704) + * - ``--video_width`` + - Width of the output video in pixels. (default: 1280) + * - ``--framerate`` + - Frames per second for the output video. (default: 30) + +.. note:: + The default input keys cover all camera modalities as per the naming convention followed in the ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0`` environment. We include an additional modality "table_cam_shaded_segmentation" which is not a part of the generated modalities from simulation in the HDF5 data file. Instead, it is automatically generated by this script using a combination of the segmentation and normal maps to get a pseudo-textured segmentation video for better controlling the Cosmos augmentation. + +.. note:: + We recommend using the default values given above for the output video height, width and framerate for the best results with Cosmos augmentation. + +Example usage for the cube stacking task: + +.. code:: bash + + python scripts/tools/hdf5_to_mp4.py \ + --input_file datasets/mimic_dataset_1k.hdf5 \ + --output_dir datasets/mimic_dataset_1k_mp4 + +.. _running-cosmos: + +Running Cosmos for Visual Augmentation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +After converting the demonstrations to MP4 format, you can use a `Cosmos`_ model to visually augment the videos. Follow the Cosmos documentation for details on the augmentation process. Visual augmentation can include changes to lighting, textures, backgrounds, and other visual elements while preserving the essential task-relevant features. + +We use the RGB, depth and shaded segmentation videos from the previous step as input to the Cosmos model as seen below: + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/cosmos_inputs.gif + :width: 100% + :align: center + :alt: RGB, depth and segmentation control inputs to Cosmos + +We provide an example augmentation output from `Cosmos Transfer1 `_ below: + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/cosmos_output.gif + :width: 100% + :align: center + :alt: Cosmos Transfer1 augmentation output + +We recommend using the `Cosmos Transfer1 `_ model for visual augmentation as we found it to produce the best results in the form of a highly diverse dataset with a wide range of visual variations. You can refer to the `installation instructions `_, the `checkpoint download instructions `_ and `this example `_ for reference on how to use Transfer1 for this usecase. We further recommend the following settings to be used with the Transfer1 model for this task: + +.. note:: + This workflow has been tested with commit ``e4055e39ee9c53165e85275bdab84ed20909714a`` of the Cosmos Transfer1 repository, and it is the recommended version to use. After cloning the Cosmos Transfer1 repository, checkout to this specific commit by running ``git checkout e4055e39ee9c53165e85275bdab84ed20909714a``. + +.. rubric:: Hyperparameters + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``negative_prompt`` + - "The video captures a game playing, with bad crappy graphics and cartoonish frames. It represents a recording of old outdated games. The images are very pixelated and of poor CG quality. There are many subtitles in the footage. Overall, the video is unrealistic and appears cg. Plane background." + * - ``sigma_max`` + - 50 + * - ``control_weight`` + - "0.3,0.3,0.6,0.7" + * - ``hint_key`` + - "blur,canny,depth,segmentation" + +Another crucial aspect to get good augmentations is the set of prompts used to control the Cosmos generation. We provide a script, ``cosmos_prompt_gen.py``, to construct prompts from a set of carefully chosen templates that handle various aspects of the augmentation process. + +.. rubric:: Required Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--templates_path`` + - Path to the file containing templates for the prompts. + +.. rubric:: Optional Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--num_prompts`` + - Number of prompts to generate (default: 1). + * - ``--output_path`` + - Path to the output file to write generated prompts. (default: prompts.txt) + +.. code:: bash + + python scripts/tools/cosmos/cosmos_prompt_gen.py \ + --templates_path scripts/tools/cosmos/transfer1_templates.json \ + --num_prompts 10 --output_path prompts.txt + +In case you want to create your own prompts, we suggest you refer to the following guidelines: + +1. Keep the prompts as detailed as possible. It is best to have some instruction on how the generation should handle each visible object/region of interest. For instance, the prompts that we provide cover explicit details for the table, lighting, background, robot arm, cubes, and the general setting. + +2. Try to keep the augmentation instructions as realistic and coherent as possible. The more unrealistic or unconventional the prompt is, the worse the model does at retaining key features of the input control video(s). + +3. Keep the augmentation instructions in-sync for each aspect. What we mean by this is that the augmentation for all the objects/regions of interest should be coherent and conventional with respect to each other. For example, it is better to have a prompt such as "The table is of old dark wood with faded polish and food stains and the background consists of a suburban home" instead of something like "The table is of old dark wood with faded polish and food stains and the background consists of a spaceship hurtling through space". + +4. It is vital to include details on key aspects of the input control video(s) that should be retained or left unchanged. In our prompts, we very clearly mention that the cube colors should be left unchanged such that the bottom cube is blue, the middle is red and the top is green. Note that we not only mention what should be left unchanged but also give details on what form that aspect currently has. + +Example command to use the Cosmos Transfer1 model for this usecase: + +.. code:: bash + + export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:=0}" + export CHECKPOINT_DIR="${CHECKPOINT_DIR:=./checkpoints}" + export NUM_GPU="${NUM_GPU:=1}" + PYTHONPATH=$(pwd) torchrun --nproc_per_node=$NUM_GPU --nnodes=1 --node_rank=0 cosmos_transfer1/diffusion/inference/transfer.py \ + --checkpoint_dir $CHECKPOINT_DIR \ + --video_save_folder outputs/cosmos_dataset_1k_mp4 \ + --controlnet_specs ./controlnet_specs/demo_0.json \ + --offload_text_encoder_model \ + --offload_guardrail_models \ + --num_gpus $NUM_GPU + +Example ``./controlnet_specs/demo_0.json`` json file to use with the above command: + +.. code:: json + + { + "prompt": "A robotic arm is picking up and stacking cubes inside a foggy industrial scrapyard at dawn, surrounded by piles of old robotic parts and twisted metal. The background includes large magnetic cranes, rusted conveyor belts, and flickering yellow floodlights struggling to penetrate the fog. The robot arm is bright teal with a glossy surface and silver stripes on the outer edges; the joints rotate smoothly and the pistons reflect a pale cyan hue. The robot arm is mounted on a table that is light oak wood with a natural grain pattern and a glossy varnish that reflects overhead lights softly; small burn marks dot one corner. The arm is connected to the base mounted on the table. The bottom cube is deep blue, the second cube is bright red, and the top cube is vivid green, maintaining their correct order after stacking. Sunlight pouring in from a large, open window bathes the table and robotic arm in a warm golden light. The shadows are soft, and the scene feels natural and inviting with a slight contrast between light and shadow.", + "negative_prompt": "The video captures a game playing, with bad crappy graphics and cartoonish frames. It represents a recording of old outdated games. The images are very pixelated and of poor CG quality. There are many subtitles in the footage. Overall, the video is unrealistic and appears cg. Plane background.", + "input_video_path" : "mimic_dataset_1k_mp4/demo_0_table_cam.mp4", + "sigma_max": 50, + "vis": { + "input_control": "mimic_dataset_1k_mp4/demo_0_table_cam.mp4", + "control_weight": 0.3 + }, + "edge": { + "control_weight": 0.3 + }, + "depth": { + "input_control": "mimic_dataset_1k_mp4/demo_0_table_cam_depth.mp4", + "control_weight": 0.6 + }, + "seg": { + "input_control": "mimic_dataset_1k_mp4/demo_0_table_cam_shaded_segmentation.mp4", + "control_weight": 0.7 + } + } + +MP4 to HDF5 Conversion +^^^^^^^^^^^^^^^^^^^^^^ + +The ``mp4_to_hdf5.py`` script converts the visually augmented MP4 videos back to HDF5 format for training. This step is crucial as it ensures the augmented visual data is in the correct format for training visuomotor policies in Isaac Lab and pairs the videos with the corresponding demonstration data from the original dataset. + +.. rubric:: Required Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--input_file`` + - Path to the input HDF5 file containing the original demonstrations. + * - ``--videos_dir`` + - Directory containing the visually augmented MP4 videos. + * - ``--output_file`` + - Path to save the new HDF5 file with augmented videos. + +.. note:: + The input HDF5 file is used to preserve the non-visual data (such as robot states and actions) while replacing the visual data with the augmented versions. + +.. important:: + The visually augmented MP4 files must follow the naming convention ``demo_{demo_id}_*.mp4``, where: + + - ``demo_id`` matches the demonstration ID from the original MP4 file + + - ``*`` signifies that the file name can be as per user preference starting from this point + + This naming convention is required for the script to correctly pair the augmented videos with their corresponding demonstrations. + +Example usage for the cube stacking task: + +.. code:: bash + + python scripts/tools/mp4_to_hdf5.py \ + --input_file datasets/mimic_dataset_1k.hdf5 \ + --videos_dir datasets/cosmos_dataset_1k_mp4 \ + --output_file datasets/cosmos_dataset_1k.hdf5 + +Pre-generated Dataset +^^^^^^^^^^^^^^^^^^^^^ + +We provide a pre-generated dataset in HDF5 format containing visually augmented demonstrations for the cube stacking task. This dataset can be used if you do not wish to run Cosmos locally to generate your own augmented data. The dataset is available on `Hugging Face `_ and contains both (as separate dataset files), original and augmented demonstrations, that can be used for training visuomotor policies. + +Merging Datasets +^^^^^^^^^^^^^^^^ + +The ``merge_hdf5_datasets.py`` script combines multiple HDF5 datasets into a single file. This is useful when you want to combine the original demonstrations with the augmented ones to create a larger, more diverse training dataset. + +.. rubric:: Required Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--input_files`` + - A list of paths to HDF5 files to merge. + +.. rubric:: Optional Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--output_file`` + - File path to merged output. (default: merged_dataset.hdf5) + +.. tip:: + Merging datasets can help improve policy robustness by exposing the model to both original and augmented visual conditions during training. + +Example usage for the cube stacking task: + +.. code:: bash + + python scripts/tools/merge_hdf5_datasets.py \ + --input_files datasets/mimic_dataset_1k.hdf5 datasets/cosmos_dataset_1k.hdf5 \ + --output_file datasets/mimic_cosmos_dataset.hdf5 + +Model Training and Evaluation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Robomimic Setup +^^^^^^^^^^^^^^^ + +As an example, we will train a BC agent implemented in `Robomimic `__ to train a policy. Any other framework or training method could be used. + +To install the robomimic framework, use the following commands: + +.. code:: bash + + # install the dependencies + sudo apt install cmake build-essential + # install python module (for robomimic) + ./isaaclab.sh -i robomimic + +Training an agent +^^^^^^^^^^^^^^^^^ + +Using the generated data, we can now train a visuomotor BC agent for ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0``: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0 --algo bc \ + --dataset ./datasets/mimic_cosmos_dataset.hdf5 \ + --name bc_rnn_image_franka_stack_mimic_cosmos + +.. note:: + By default the trained models and logs will be saved to ``IssacLab/logs/robomimic``. + +Evaluation +^^^^^^^^^^ + +The ``robust_eval.py`` script evaluates trained visuomotor policies in simulation. This evaluation helps assess how well the policy generalizes to different visual variations and whether the visually augmented data has improved the policy's robustness. + +Below is an explanation of the different settings used for evaluation: + +.. rubric:: Evaluation Settings + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``Vanilla`` + - Exact same setting as that used during Mimic data generation. + * - ``Light Intensity`` + - Light intensity/brightness is varied, all other aspects remain the same. + * - ``Light Color`` + - Light color is varied, all other aspects remain the same. + * - ``Light Texture (Background)`` + - Light texture/background is varied, all other aspects remain the same. + * - ``Table Texture`` + - Table's visual texture is varied, all other aspects remain the same. + * - ``Robot Arm Texture`` + - Robot arm's visual texture is varied, all other aspects remain the same. + +.. rubric:: Required Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--task`` + - Name of the environment. + * - ``--input_dir`` + - Directory containing the model checkpoints to evaluate. + +.. rubric:: Optional Arguments + +.. list-table:: + :widths: 30 70 + :header-rows: 0 + + * - ``--start_epoch`` + - Epoch of the checkpoint to start the evaluation from. (default: 100) + * - ``--horizon`` + - Step horizon of each rollout. (default: 400) + * - ``--num_rollouts`` + - Number of rollouts per model per setting. (default: 15) + * - ``--num_seeds`` + - Number of random seeds to evaluate. (default: 3) + * - ``--seeds`` + - List of specific seeds to use instead of random ones. + * - ``--log_dir`` + - Directory to write results to. (default: /tmp/policy_evaluation_results) + * - ``--log_file`` + - Name of the output file. (default: results) + * - ``--norm_factor_min`` + - Minimum value of the action space normalization factor. + * - ``--norm_factor_max`` + - Maximum value of the action space normalization factor. + * - ``--disable_fabric`` + - Whether to disable fabric and use USD I/O operations. + * - ``--enable_pinocchio`` + - Whether to enable Pinocchio for IK controllers. + +.. note:: + The evaluation results will help you understand if the visual augmentation has improved the policy's performance and robustness. Compare these results with evaluations on the original dataset to measure the impact of augmentation. + +Example usage for the cube stacking task: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/robust_eval.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0 \ + --input_dir logs/robomimic/Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0/bc_rnn_image_franka_stack_mimic_cosmos/*/models \ + --log_dir robust_results/bc_rnn_image_franka_stack_mimic_cosmos \ + --log_file result \ + --enable_cameras \ + --seeds 0 \ + --num_rollouts 15 \ + --rendering_mode performance + +.. note:: + This script can take over a day or even longer to run (depending on the hardware being used). This behavior is expected. + +We use the above script to compare models trained with 1000 Mimic-generated demonstrations, 2000 Mimic-generated demonstrations and 2000 Cosmos-Mimic-generated demonstrations (1000 original mimic + 1000 Cosmos augmented) respectively. We use the same seeds (0, 1000 and 5000) for all three models and provide the metrics (averaged across best checkpoints for each seed) below: + +.. rubric:: Model Comparison + +.. list-table:: + :widths: 25 25 25 25 + :header-rows: 0 + + * - **Evaluation Setting** + - **Mimic 1k Baseline** + - **Mimic 2k Baseline** + - **Cosmos-Mimic 2k** + * - ``Vanilla`` + - 62% + - 96.6% + - 86.6% + * - ``Light Intensity`` + - 11.1% + - 20% + - 62.2% + * - ``Light Color`` + - 24.6% + - 30% + - 77.7% + * - ``Light Texture (Background)`` + - 16.6% + - 20% + - 68.8% + * - ``Table Texture`` + - 0% + - 0% + - 20% + * - ``Robot Arm Texture`` + - 0% + - 0% + - 4.4% + +The above trained models' checkpoints can be accessed `here `_ in case you wish to use the models directly. diff --git a/docs/source/overview/imitation-learning/index.rst b/docs/source/overview/imitation-learning/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..f8f77d031fb0de0828a8d28813cd5d63706dd694 --- /dev/null +++ b/docs/source/overview/imitation-learning/index.rst @@ -0,0 +1,12 @@ +Imitation Learning +================== + +In this section, we show existing scripts for running imitation learning +with Isaac Lab. + +.. toctree:: + :maxdepth: 1 + + teleop_imitation + augmented_imitation + skillgen diff --git a/docs/source/overview/imitation-learning/skillgen.rst b/docs/source/overview/imitation-learning/skillgen.rst new file mode 100644 index 0000000000000000000000000000000000000000..b577f82e13ae8f716ad2ff140db9bd7ed5e19457 --- /dev/null +++ b/docs/source/overview/imitation-learning/skillgen.rst @@ -0,0 +1,548 @@ +.. _skillgen: + +SkillGen for Automated Demonstration Generation +=============================================== + +SkillGen is an advanced demonstration generation system that enhances Isaac Lab Mimic by integrating motion planning. It generates high-quality, adaptive, collision-free robot demonstrations by combining human-provided subtask segments with automated motion planning. + +What is SkillGen? +~~~~~~~~~~~~~~~~~ + +SkillGen addresses key limitations in traditional demonstration generation: + +* **Motion Quality**: Uses cuRobo's GPU-accelerated motion planner to generate smooth, collision-free trajectories +* **Validity**: Generates kinematically feasible plans between skill segments +* **Diversity**: Generates varied demonstrations through configurable sampling and planning parameters +* **Adaptability**: Generates demonstrations that can be adapted to new object placements and scene configurations during data generation + +The system works by taking manually annotated human demonstrations, extracting localized subtask skills (see `Subtasks in SkillGen`_), and using cuRobo to plan feasible motions between these skill segments while respecting robot kinematics and collision constraints. + +Prerequisites +~~~~~~~~~~~~~ + +Before using SkillGen, you must understand: + +1. **Teleoperation**: How to control robots and record demonstrations using keyboard, SpaceMouse, or hand tracking +2. **Isaac Lab Mimic**: The complete workflow including data collection, annotation, generation, and policy training + +.. important:: + + Review the :ref:`teleoperation-imitation-learning` documentation thoroughly before proceeding with SkillGen. + +.. _skillgen-installation: + +Installation +~~~~~~~~~~~~ + +SkillGen requires Isaac Lab, Isaac Sim, and cuRobo. Follow these steps in your Isaac Lab conda environment. + +Step 1: Install and verify Isaac Sim and Isaac Lab +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Follow the official Isaac Sim and Isaac Lab installation guide `here `__. + +Step 2: Install cuRobo +^^^^^^^^^^^^^^^^^^^^^^ + +cuRobo provides the motion planning capabilities for SkillGen. This installation is tested to work with Isaac Lab's PyTorch and CUDA requirements: + +.. code:: bash + + # One line installation of cuRobo (formatted for readability) + conda install -c nvidia cuda-toolkit=12.8 -y && \ + export CUDA_HOME="$CONDA_PREFIX" && \ + export PATH="$CUDA_HOME/bin:$PATH" && \ + export LD_LIBRARY_PATH="$CUDA_HOME/lib:$LD_LIBRARY_PATH" && \ + export TORCH_CUDA_ARCH_LIST="8.0+PTX" && \ + pip install -e "git+https://github.com/NVlabs/curobo.git@ebb71702f3f70e767f40fd8e050674af0288abe8#egg=nvidia-curobo" --no-build-isolation + +.. note:: + * The commit hash ``ebb71702f3f70e767f40fd8e050674af0288abe8`` is tested with Isaac Lab - using other versions may cause compatibility issues. This commit has the support for quad face mesh triangulation, required for cuRobo to parse usds as collision objects. + + * cuRobo is installed from source and is editable installed. This means that the cuRobo source code will be cloned in the current directory under ``src/nvidia-curobo``. Users can choose their working directory to install cuRobo. + + * ``TORCH_CUDA_ARCH_LIST`` in the above command should match your GPU's CUDA compute capability (e.g., ``8.0`` for A100, ``8.6`` for many RTX 30‑series, ``8.9`` for RTX 4090); the ``+PTX`` suffix embeds PTX for forward compatibility so newer GPUs can JIT‑compile when native SASS isn’t included. + +.. warning:: + + **cuRobo installation may fail if Isaac Sim environment scripts are sourced** + + Sourcing Omniverse Kit/Isaac Sim environment scripts (for example, ``setup_conda_env.sh``) exports ``PYTHONHOME`` and ``PYTHONPATH`` to the Kit runtime and its pre-bundled Python packages. During cuRobo installation this can cause ``conda`` to import Omniverse's bundled libraries (e.g., ``requests``/``urllib3``) before initialization, resulting in a crash (often seen as a ``TypeError`` referencing ``omni.kit.pip_archive``). + + Do one of the following: + + - Install cuRobo from a clean shell that has not sourced any Omniverse/Isaac Sim scripts. + - Temporarily reset or ignore inherited Python environment variables (notably ``PYTHONPATH`` and ``PYTHONHOME``) before invoking Conda, so Kit's Python does not shadow your Conda environment. + - Use Conda mechanisms that do not rely on shell activation and avoid inheriting the current shell's Python variables. + + After installation completes, you may source Isaac Lab/Isaac Sim scripts again for normal use. + + + +Step 3: Install Rerun +^^^^^^^^^^^^^^^^^^^^^ + +For trajectory visualization during development: + +.. code:: bash + + pip install rerun-sdk==0.23 + +.. note:: + + **Rerun Visualization Setup:** + + * Rerun is optional but highly recommended for debugging and validating planned trajectories during development + * Enable trajectory visualization by setting ``visualize_plan = True`` in the cuRobo planner configuration + * When enabled, cuRobo planner interface will stream planned end-effector trajectories, waypoints, and collision data to Rerun for interactive inspection + * Visualization helps identify planning issues, collision problems, and trajectory smoothness before full dataset generation + * Can also be ran with ``--headless`` to disable isaacsim visualization but still visualize and debug end effector trajectories + +Step 4: Verify Installation +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Test that cuRobo works with Isaac Lab: + +.. code:: bash + + # This should run without import errors + python -c "import curobo; print('cuRobo installed successfully')" + +.. tip:: + + If you run into ``libstdc++.so.6: version 'GLIBCXX_3.4.30' not found`` error, you can try these commands to fix it: + + .. code:: bash + + conda config --env --set channel_priority strict + conda config --env --add channels conda-forge + conda install -y -c conda-forge "libstdcxx-ng>=12" "libgcc-ng>=12" + +Download the SkillGen Dataset +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We provide a pre-annotated dataset to help you get started quickly with SkillGen. + +Dataset Contents +^^^^^^^^^^^^^^^^ + +The dataset contains: + +* Human demonstrations of Franka arm cube stacking +* Manually annotated subtask boundaries for each demonstration +* Compatible with both basic cube stacking and adaptive bin cube stacking tasks + +Download and Setup +^^^^^^^^^^^^^^^^^^ + +1. Download the pre-annotated dataset by clicking `here `__. + +2. Prepare the datasets directory and move the downloaded file: + +.. code:: bash + + # Make sure you are in the root directory of your Isaac Lab workspace + cd /path/to/your/IsaacLab + + # Create the datasets directory if it does not exist + mkdir -p datasets + + # Move the downloaded dataset into the datasets directory + mv /path/to/annotated_dataset_skillgen.hdf5 datasets/annotated_dataset_skillgen.hdf5 + +.. tip:: + + A major advantage of SkillGen is that the same annotated dataset can be reused across multiple related tasks (e.g., basic stacking and adaptive bin stacking). This avoids collecting and annotating new data per variant. + +.. admonition:: {Optional for the tasks in this tutorial} Collect a fresh dataset (source + annotated) + + If you want to collect a fresh source dataset and then create an annotated dataset for SkillGen, follow these commands. The user is expected to have knowledge of the Isaac Lab Mimic workflow. + + **Important pointers before you begin** + + * Using the provided annotated dataset is the fastest path to get started with SkillGen tasks in this tutorial. + * If you create your own dataset, SkillGen requires manual annotation of both subtask start and termination boundaries (no auto-annotation). + * Start boundary signals are mandatory for SkillGen; use ``--annotate_subtask_start_signals`` during annotation or data generation will fail. + * Keep your subtask definitions (``object_ref``, ``subtask_term_signal``) consistent with the SkillGen environment config. + + **Record demonstrations** (any teleop device is supported; replace ``spacemouse`` if needed): + + .. code:: bash + + ./isaaclab.sh -p scripts/tools/record_demos.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --teleop_device spacemouse \ + --dataset_file ./datasets/dataset_skillgen.hdf5 \ + --num_demos 10 + + **Annotate demonstrations for SkillGen** (writes both term and start boundaries): + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --input_file ./datasets/dataset_skillgen.hdf5 \ + --output_file ./datasets/annotated_dataset_skillgen.hdf5 \ + --annotate_subtask_start_signals + +Understanding Dataset Annotation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +SkillGen requires datasets with annotated subtask start and termination boundaries. Auto-annotation is not supported. + +Subtasks in SkillGen +^^^^^^^^^^^^^^^^^^^^ + +**Technical definition:** A subtask is a contiguous demo segment that achieves a manipulation objective, defined via ``SubTaskConfig``: + +* ``object_ref``: the object (or ``None``) used as the spatial reference for this subtask +* ``subtask_term_signal``: the binary termination signal name (transitions 0 to 1 when the subtask completes) +* ``subtask_start_signal``: the binary start signal name (transitions 0 to 1 when the subtask begins; required for SkillGen) + +The subtask localization process performs: + +* detection of signal transition points (0 to 1) to identify subtask boundaries ``[t_start, t_end]``; +* extraction of the subtask segment between boundaries; +* computation of end-effector trajectories and key poses in an object- or task-relative frame (using ``object_ref`` if provided); + +This converts absolute, scene-specific motions into object-relative skill segments that can be adapted to new object placements and scene configurations during data generation. + +Manual Annotation Workflow +^^^^^^^^^^^^^^^^^^^^^^^^^^ +Contrary to the Isaac Lab Mimic workflow, SkillGen requires manual annotation of subtask start and termination boundaries. For example, for grasping a cube, the start signal is right before the gripper closes and the termination signal is right after the object is grasped. You can adjust the start and termination signals to fit your subtask definition. + +.. tip:: + + **Manual Annotation Controls:** + + * Press ``N`` to start/continue playback + * Press ``B`` to pause + * Press ``S`` to mark subtask boundary + * Press ``Q`` to skip current demonstration + + When annotating the start and end signals for a skill segment (e.g., grasp, stack, etc.), pause the playback using ``B`` a few steps before the skill, annotate the start signal using ``S``, and then resume playback using ``N``. After the skill is completed, pause again a few steps later to annotate the end signal using ``S``. + +Data Generation with SkillGen +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +SkillGen transforms annotated demonstrations into diverse, high-quality datasets using motion planning. + +How SkillGen Works +^^^^^^^^^^^^^^^^^^ + +The SkillGen pipeline uses your annotated dataset and the environment's Mimic API to synthesize new demonstrations: + +1. **Subtask boundary use**: Reads per-subtask start and termination indices from the annotated dataset +2. **Goal sampling**: Samples target poses per subtask according to task constraints and datagen config +3. **Trajectory planning**: Plans collision-free motions between subtask segments using cuRobo (when ``--use_skillgen``) +4. **Trajectory stitching**: Stitches skill segments and planned trajectories into complete demonstrations. +5. **Success evaluation**: Validates task success terms; only successful trials are written to the output dataset + +Usage Parameters +^^^^^^^^^^^^^^^^ + +Key parameters for SkillGen data generation: + +* ``--use_skillgen``: Enables SkillGen planner (required) +* ``--generation_num_trials``: Number of demonstrations to generate +* ``--num_envs``: Parallel environments (tune based on GPU memory) +* ``--device``: Computation device (cpu/cuda). Use cpu for stable physics +* ``--headless``: Disable visualization for faster generation + +.. _task-basic-cube-stacking: + +Task 1: Basic Cube Stacking +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Generate demonstrations for the standard Isaac Lab Mimic cube stacking task. In this task, the Franka robot must: + +1. Pick up the red cube and place it on the blue cube +2. Pick up the green cube and place it on the red cube +3. Final stack order: blue (bottom), red (middle), green (top). + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/cube_stack_data_gen_skillgen.gif + :width: 75% + :align: center + :alt: Cube stacking task generated with SkillGen + :figclass: align-center + + Cube stacking dataset example. + +Small-Scale Generation +^^^^^^^^^^^^^^^^^^^^^^ + +Start with a small dataset to verify everything works: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu \ + --num_envs 1 \ + --generation_num_trials 10 \ + --input_file ./datasets/annotated_dataset_skillgen.hdf5 \ + --output_file ./datasets/generated_dataset_small_skillgen_cube_stack.hdf5 \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --use_skillgen + +Full-Scale Generation +^^^^^^^^^^^^^^^^^^^^^ + +Once satisfied with small-scale results, generate a full training dataset: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu \ + --headless \ + --num_envs 1 \ + --generation_num_trials 1000 \ + --input_file ./datasets/annotated_dataset_skillgen.hdf5 \ + --output_file ./datasets/generated_dataset_skillgen_cube_stack.hdf5 \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --use_skillgen + +.. note:: + + * Use ``--headless`` to disable visualization for faster generation. Rerun visualization can be enabled by setting ``visualize_plan = True`` in the cuRobo planner configuration with ``--headless`` enabled as well for debugging. + * Adjust ``--num_envs`` based on your GPU memory (start with 1, increase gradually). The performance gain is not very significant when num_envs is greater than 1. A value of 5 seems to be a sweet spot for most GPUs to balance performance and memory usage between cuRobo instances and simulation environments. + * Generation time: ~90 to 120 minutes for one environment with ``--headless`` enabled for 1000 demonstrations on a RTX 6000 Ada GPU. Time depends on the GPU, the number of environments, and the success rate of the demonstrations (which depends on quality of the annotated dataset). + * cuRobo planner interface and configurations are described in :ref:`cuRobo-interface-features`. + +.. _task-bin-cube-stacking: + +Task 2: Adaptive Cube Stacking in a Bin +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +SkillGen can also be used to generate datasets for adaptive tasks. In this example, we generate a dataset for adaptive cube stacking in a narrow bin. The bin is placed at a fixed position and orientation in the workspace and a blue cube is placed at the center of the bin. The robot must generate successful demonstrations for stacking the red and green cubes on the blue cube without colliding with the bin. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/bin_cube_stack_data_gen_skillgen.gif + :width: 75% + :align: center + :alt: Adaptive bin cube stacking task generated with SkillGen + :figclass: align-center + + Adaptive bin stacking data generation example. + +Small-Scale Generation +^^^^^^^^^^^^^^^^^^^^^^ + +Test the adaptive stacking setup: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu \ + --num_envs 1 \ + --generation_num_trials 10 \ + --input_file ./datasets/annotated_dataset_skillgen.hdf5 \ + --output_file ./datasets/generated_dataset_small_skillgen_bin_cube_stack.hdf5 \ + --task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \ + --use_skillgen + +Full-Scale Generation +^^^^^^^^^^^^^^^^^^^^^ + +Generate the complete adaptive stacking dataset: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu \ + --headless \ + --num_envs 1 \ + --generation_num_trials 1000 \ + --input_file ./datasets/annotated_dataset_skillgen.hdf5 \ + --output_file ./datasets/generated_dataset_skillgen_bin_cube_stack.hdf5 \ + --task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \ + --use_skillgen + +.. warning:: + + Adaptive tasks typically have lower success rates and higher data generation time due to increased complexity. The time taken to generate the dataset is also longer due to lower success rates than vanilla cube stacking and difficult planning problems. + +.. note:: + + If the pre-annotated dataset is used and the data generation command is run with ``--headless`` enabled, the generation time is typically around ~220 minutes for 1000 demonstrations for a single environment on a RTX 6000 Ada GPU. + +.. note:: + + **VRAM usage and GPU recommendations** + + Figures measured over 10 generated demonstrations on an RTX 6000 Ada. + * Vanilla Cube Stacking: 1 env ~9.3–9.6 GB steady; 5 envs ~21.8–22.2 GB steady (briefly higher during initialization). + * Adaptive Bin Cube Stacking: 1 env ~9.3–9.6 GB steady; 5 envs ~22.0–22.3 GB steady (briefly higher during initialization). + * Minimum recommended GPU: ≥24 GB VRAM for ``--num_envs`` 1–2; ≥48 GB VRAM for ``--num_envs`` up to ~5. + * To reduce VRAM: prefer ``--headless`` and keep ``--num_envs`` modest. Numbers can vary with scene assets and number of demonstrations. + +Learning Policies from SkillGen Data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Similar to the Isaac Lab Mimic workflow, you can train imitation learning policies using the generated SkillGen datasets with Robomimic. + +Basic Cube Stacking Policy +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Train a state-based policy for the basic cube stacking task: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --algo bc \ + --dataset ./datasets/generated_dataset_skillgen_cube_stack.hdf5 + +Adaptive Bin Cube Stacking Policy +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Train a policy for the more complex adaptive bin stacking: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \ + --algo bc \ + --dataset ./datasets/generated_dataset_skillgen_bin_cube_stack.hdf5 + +.. note:: + + The training script will save the model checkpoints in the model directory under ``IssacLab/logs/robomimic``. + +Evaluating Trained Policies +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Test your trained policies: + +.. code:: bash + + # Basic cube stacking evaluation + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ + --num_rollouts 50 \ + --checkpoint /path/to/model_checkpoint.pth + +.. code:: bash + + # Adaptive bin cube stacking evaluation + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu \ + --task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \ + --num_rollouts 50 \ + --checkpoint /path/to/model_checkpoint.pth + +.. note:: + + **Expected Success Rates and Recommendations for Cube Stacking and Bin Cube Stacking Tasks** + + * SkillGen data generation and downstream policy success are sensitive to the task and the quality of dataset annotation, and can show high variance. + * For cube stacking and bin cube stacking, data generation success is typically 40% to 70% when the dataset is properly annotated per the instructions. + * Behavior Cloning (BC) policy success from 1000 generated demonstrations trained for 2000 epochs (default) is typically 40% to 85% for these tasks, depending on data quality. + * Training the policy with 1000 demonstrations and for 2000 epochs takes about 30 to 35 minutes on a RTX 6000 Ada GPU. Training time increases with the number of demonstrations and epochs. + * For dataset generation time, see :ref:`task-basic-cube-stacking` and :ref:`task-bin-cube-stacking`. + * Recommendation: Train for the default 2000 epochs with about 1000 generated demonstrations, and evaluate multiple checkpoints saved after the 1000th epoch to select the best-performing policy. + +.. _cuRobo-interface-features: + +cuRobo Interface Features +~~~~~~~~~~~~~~~~~~~~~~~~~ + +This section summarizes the cuRobo planner interface and features. The SkillGen pipeline uses the cuRobo planner to generate collision-free motions between subtask segments. However, the user can use cuRobo as a standalone motion planner for your own tasks. The user can also implement their own motion planner by subclassing the base motion planner and implementing the same API. + +Base Motion Planner (Extensible) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Location: ``isaaclab_mimic/motion_planners/base_motion_planner.py`` +* Purpose: Uniform interface for all motion planners used by SkillGen +* Extensibility: New planners can be added by subclassing and implementing the same API; SkillGen consumes the API without code changes + +cuRobo Planner (GPU, collision-aware) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Location: ``isaaclab_mimic/motion_planners/curobo`` +* Multi-phase planning: + + * Retreat → Contact → Approach phases per subtask + * Configurable collision filtering in contact phases + * For SkillGen, retreat and approach phases are collision-free. The transit phase is collision-checked. + +* World synchronization: + + * Updates robot state, attached objects, and collision spheres from the Isaac Lab scene each trial + * Dynamic attach/detach of objects during grasp/place + +* Collision representation: + + * Contact-aware sphere sets with per-phase enables/filters + +* Outputs: + + * Time-parameterized, collision-checked trajectories for stitching + +* Tests: + + * ``source/isaaclab_mimic/test/test_curobo_planner_cube_stack.py`` + * ``source/isaaclab_mimic/test/test_curobo_planner_franka.py`` + * ``source/isaaclab_mimic/test/test_generate_dataset_skillgen.py`` + +.. list-table:: + :widths: 50 50 + :header-rows: 0 + + * - .. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/cube_stack_end_to_end_curobo.gif + :height: 260px + :align: center + :alt: cuRobo planner test on cube stack using Franka Panda robot + + Cube stack planner test. + - .. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/obstacle_avoidance_curobo.gif + :height: 260px + :align: center + :alt: cuRobo planner test on obstacle avoidance using Franka Panda robot + + Franka planner test. + +These tests can also serve as a reference for how to use cuRobo as a standalone motion planner. + +.. note:: + + For detailed cuRobo config creation and parameters, please see the file ``isaaclab_mimic/motion_planners/curobo/curobo_planner_config.py``. + +Generation Pipeline Integration +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When ``--use_skillgen`` is enabled in ``generate_dataset.py``, the following pipeline is executed: + +1. **Randomize subtask boundaries**: Randomize per-demo start and termination indices for each subtask using task-configured offset ranges. + +2. **Build per-subtask trajectories**: + For each end-effector and subtask: + + - Select a source demonstration segment (strategy-driven; respects coordination/sequential constraints) + - Transform the segment to the current scene (object-relative or coordination delta; optional first-pose interpolation) + - Wrap the transformed segment into a waypoint trajectory + +3. **Transition between subtasks**: + - Plan a collision-aware transition with cuRobo to the subtask's first waypoint (world sync, optional attach/detach), execute the planned waypoints, then resume the subtask trajectory + +4. **Execute with constraints**: + - Execute waypoints step-by-step across end-effectors while enforcing subtask constraints (sequential, coordination with synchronous steps); optionally update planner visualization if enabled + +5. **Record and export**: + - Accumulate states/observations/actions, set the episode success flag, and export the episode (the outer pipeline filters/consumes successes) + +Visualization and Debugging +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Users can visualize the planned trajectories and debug for collisions using Rerun-based plan visualizer. This can be enabled by setting ``visualize_plan = True`` in the cuRobo planner configuration. Note that rerun needs to be installed to visualize the planned trajectories. Refer to Step 3 in :ref:`skillgen-installation` for installation instructions. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/rerun_cube_stack.gif + :width: 80% + :align: center + :alt: Rerun visualization of planned trajectories and collisions + :figclass: align-center + + Rerun integration: planned trajectories with collision spheres. + +.. note:: + + Check cuRobo usage license in ``docs/licenses/dependencies/cuRobo-license.txt`` diff --git a/docs/source/overview/imitation-learning/teleop_imitation.rst b/docs/source/overview/imitation-learning/teleop_imitation.rst new file mode 100644 index 0000000000000000000000000000000000000000..14017e65b5dae46e462511082ea981711556238f --- /dev/null +++ b/docs/source/overview/imitation-learning/teleop_imitation.rst @@ -0,0 +1,1070 @@ +.. _teleoperation-imitation-learning: + +Teleoperation and Imitation Learning with Isaac Lab Mimic +========================================================= + + +Teleoperation +~~~~~~~~~~~~~ + +We provide interfaces for providing commands in SE(2) and SE(3) space +for robot control. In case of SE(2) teleoperation, the returned command +is the linear x-y velocity and yaw rate, while in SE(3), the returned +command is a 6-D vector representing the change in pose. + +.. note:: + + Presently, Isaac Lab Mimic is only supported in Linux. + +To play inverse kinematics (IK) control with a keyboard device: + +.. code:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_envs 1 --teleop_device keyboard + +For smoother operation and off-axis operation, we recommend using a SpaceMouse as the input device. Providing smoother demonstrations will make it easier for the policy to clone the behavior. To use a SpaceMouse, simply change the teleop device accordingly: + +.. code:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_envs 1 --teleop_device spacemouse + +.. note:: + + If the SpaceMouse is not detected, you may need to grant additional user permissions by running ``sudo chmod 666 /dev/hidraw<#>`` where ``<#>`` corresponds to the device index + of the connected SpaceMouse. + + To determine the device index, list all ``hidraw`` devices by running ``ls -l /dev/hidraw*``. + Identify the device corresponding to the SpaceMouse by running ``cat /sys/class/hidraw/hidraw<#>/device/uevent`` on each of the devices listed + from the prior step. + + We recommend using local deployment of Isaac Lab to use the SpaceMouse. If using container deployment (:ref:`deployment-docker`), you must manually mount the SpaceMouse to the ``isaac-lab-base`` container by + adding a ``devices`` attribute with the path to the device in your ``docker-compose.yaml`` file: + + .. code:: yaml + + devices: + - /dev/hidraw<#>:/dev/hidraw<#> + + where ``<#>`` is the device index of the connected SpaceMouse. + + If you are using the IsaacLab + CloudXR container deployment (:ref:`cloudxr-teleoperation`), you can add the ``devices`` attribute under the ``services -> isaac-lab-base`` section of the + ``docker/docker-compose.cloudxr-runtime.patch.yaml`` file. + + Isaac Lab is only compatible with the SpaceMouse Wireless and SpaceMouse Compact models from 3Dconnexion. + + +For tasks that benefit from the use of an extended reality (XR) device with hand tracking, Isaac Lab supports using NVIDIA CloudXR to immersively stream the scene to compatible XR devices for teleoperation. Note that when using hand tracking we recommend using the absolute variant of the task (``Isaac-Stack-Cube-Franka-IK-Abs-v0``), which requires the ``handtracking`` device: + +.. code:: bash + + ./isaaclab.sh -p scripts/environments/teleoperation/teleop_se3_agent.py --task Isaac-Stack-Cube-Franka-IK-Abs-v0 --teleop_device handtracking --device cpu + +.. note:: + + See :ref:`cloudxr-teleoperation` to learn how to use CloudXR and experience teleoperation with Isaac Lab. + + +The script prints the teleoperation events configured. For keyboard, +these are as follows: + +.. code:: text + + Keyboard Controller for SE(3): Se3Keyboard + Reset all commands: R + Toggle gripper (open/close): K + Move arm along x-axis: W/S + Move arm along y-axis: A/D + Move arm along z-axis: Q/E + Rotate arm along x-axis: Z/X + Rotate arm along y-axis: T/G + Rotate arm along z-axis: C/V + +For SpaceMouse, these are as follows: + +.. code:: text + + SpaceMouse Controller for SE(3): Se3SpaceMouse + Reset all commands: Right click + Toggle gripper (open/close): Click the left button on the SpaceMouse + Move arm along x/y-axis: Tilt the SpaceMouse + Move arm along z-axis: Push or pull the SpaceMouse + Rotate arm: Twist the SpaceMouse + +The next section describes how teleoperation devices can be used for data collection for imitation learning. + + +Imitation Learning with Isaac Lab Mimic +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Using the teleoperation devices, it is also possible to collect data for +learning from demonstrations (LfD). For this, we provide scripts to collect data into the open HDF5 format. + +Collecting demonstrations +^^^^^^^^^^^^^^^^^^^^^^^^^ + +To collect demonstrations with teleoperation for the environment ``Isaac-Stack-Cube-Franka-IK-Rel-v0``, use the following commands: + +.. code:: bash + + # step a: create folder for datasets + mkdir -p datasets + # step b: collect data with a selected teleoperation device. Replace with your preferred input device. + # Available options: spacemouse, keyboard, handtracking + ./isaaclab.sh -p scripts/tools/record_demos.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --device cpu --teleop_device --dataset_file ./datasets/dataset.hdf5 --num_demos 10 + # step a: replay the collected dataset + ./isaaclab.sh -p scripts/tools/replay_demos.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --device cpu --dataset_file ./datasets/dataset.hdf5 + + +.. note:: + + The order of the stacked cubes should be blue (bottom), red (middle), green (top). + +.. tip:: + + When using an XR device, we suggest collecting demonstrations with the ``Isaac-Stack-Cube-Frank-IK-Abs-v0`` version of the task and ``--teleop_device handtracking``, which controls the end effector using the absolute position of the hand. + +About 10 successful demonstrations are required in order for the following steps to succeed. + +Here are some tips to perform demonstrations that lead to successful policy training: + +* Keep demonstrations short. Shorter demonstrations mean fewer decisions for the policy, making training easier. +* Take a direct path. Do not follow along arbitrary axis, but move straight toward the goal. +* Do not pause. Perform smooth, continuous motions instead. It is not obvious for a policy why and when to pause, hence continuous motions are easier to learn. + +If, while performing a demonstration, a mistake is made, or the current demonstration should not be recorded for some other reason, press the ``R`` key to discard the current demonstration, and reset to a new starting position. + +.. note:: + Non-determinism may be observed during replay as physics in IsaacLab are not determimnistically reproducible when using ``env.reset``. + +Pre-recorded demonstrations +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We provide a pre-recorded ``dataset.hdf5`` containing 10 human demonstrations for ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` +here: `[Franka Dataset] `__. +This dataset may be downloaded and used in the remaining tutorial steps if you do not wish to collect your own demonstrations. + +.. note:: + Use of the pre-recorded dataset is optional. + +.. _generating-additional-demonstrations: + +Generating additional demonstrations with Isaac Lab Mimic +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Additional demonstrations can be generated using Isaac Lab Mimic. + +Isaac Lab Mimic is a feature in Isaac Lab that allows generation of additional demonstrations automatically, allowing a policy to learn successfully even from just a handful of manual demonstrations. + +In the following example, we will show how to use Isaac Lab Mimic to generate additional demonstrations that can be used to train either a state-based policy +(using the ``Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0`` environment) or visuomotor policy (using the ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Mimic-v0`` environment). + +.. note:: + The following commands are run using CPU mode as a small number of envs are used which are I/O bound rather than compute bound. + +.. important:: + + All commands in the following sections must keep a consistent policy type. For example, if choosing to use a state-based policy, then all commands used should be from the "State-based policy" tab. + +In order to use Isaac Lab Mimic with the recorded dataset, first annotate the subtasks in the recording: + +.. tab-set:: + :sync-group: policy_type + + .. tab-item:: State-based policy + :sync: state + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu --task Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0 --auto \ + --input_file ./datasets/dataset.hdf5 --output_file ./datasets/annotated_dataset.hdf5 + + .. tab-item:: Visuomotor policy + :sync: visuomotor + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu --enable_cameras --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Mimic-v0 --auto \ + --input_file ./datasets/dataset.hdf5 --output_file ./datasets/annotated_dataset.hdf5 + + +Then, use Isaac Lab Mimic to generate some additional demonstrations: + +.. tab-set:: + :sync-group: policy_type + + .. tab-item:: State-based policy + :sync: state + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --num_envs 10 --generation_num_trials 10 \ + --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/generated_dataset_small.hdf5 + + .. tab-item:: Visuomotor policy + :sync: visuomotor + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --enable_cameras --num_envs 10 --generation_num_trials 10 \ + --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/generated_dataset_small.hdf5 + +.. note:: + + The output_file of the ``annotate_demos.py`` script is the input_file to the ``generate_dataset.py`` script + +Inspect the output of generated data (filename: ``generated_dataset_small.hdf5``), and if satisfactory, generate the full dataset: + +.. tab-set:: + :sync-group: policy_type + + .. tab-item:: State-based policy + :sync: state + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --headless --num_envs 10 --generation_num_trials 1000 \ + --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/generated_dataset.hdf5 + + .. tab-item:: Visuomotor policy + :sync: visuomotor + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --enable_cameras --headless --num_envs 10 --generation_num_trials 1000 \ + --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/generated_dataset.hdf5 + + +The number of demonstrations can be increased or decreased, 1000 demonstrations have been shown to provide good training results for this task. + +Additionally, the number of environments in the ``--num_envs`` parameter can be adjusted to speed up data generation. +The suggested number of 10 can be executed on a moderate laptop GPU. +On a more powerful desktop machine, use a larger number of environments for a significant speedup of this step. + +Robomimic setup +^^^^^^^^^^^^^^^ + +As an example, we will train a BC agent implemented in `Robomimic `__ to train a policy. Any other framework or training method could be used. + +To install the robomimic framework, use the following commands: + +.. code:: bash + + # install the dependencies + sudo apt install cmake build-essential + # install python module (for robomimic) + ./isaaclab.sh -i robomimic + +Training an agent +^^^^^^^^^^^^^^^^^ + +Using the Mimic generated data we can now train a state-based BC agent for ``Isaac-Stack-Cube-Franka-IK-Rel-v0``, or a visuomotor BC agent for ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0``: + +.. tab-set:: + :sync-group: policy_type + + .. tab-item:: State-based policy + :sync: state + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --algo bc \ + --dataset ./datasets/generated_dataset.hdf5 + + .. tab-item:: Visuomotor policy + :sync: visuomotor + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0 --algo bc \ + --dataset ./datasets/generated_dataset.hdf5 + +.. note:: + By default the trained models and logs will be saved to ``IssacLab/logs/robomimic``. + +Visualizing results +^^^^^^^^^^^^^^^^^^^ + +.. tip:: + + **Important: Testing Multiple Checkpoint Epochs** + + When evaluating policy performance, it is common for different training epochs to yield significantly different results. + If you don't see the expected performance, **always test policies from various epochs** (not just the final checkpoint) + to find the best-performing model. Model performance can vary substantially across training, and the final epoch + is not always optimal. + +By inferencing using the generated model, we can visualize the results of the policy: + +.. tab-set:: + :sync-group: policy_type + + .. tab-item:: State-based policy + :sync: state + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 \ + --checkpoint /PATH/TO/desired_model_checkpoint.pth + + .. tab-item:: Visuomotor policy + :sync: visuomotor + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu --enable_cameras --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0 --num_rollouts 50 \ + --checkpoint /PATH/TO/desired_model_checkpoint.pth + +.. tip:: + + **If you don't see expected performance results:** Test policies from multiple checkpoint epochs, not just the final one. + Policy performance can vary significantly across training epochs, and intermediate checkpoints often outperform the final model. + +.. note:: + + **Expected Success Rates and Timings for Franka Cube Stack Task** + + * Data generation success rate: ~50% (for both state + visuomotor) + * Data generation time: ~30 mins for state, ~4 hours for visuomotor (varies based on num envs the user runs) + * BC RNN training time: 1000 epochs + ~30 mins (for state), 600 epochs + ~6 hours (for visuomotor) + * BC RNN policy success rate: ~40-60% (for both state + visuomotor) + * **Recommendation:** Evaluate checkpoints from various epochs throughout training to identify the best-performing model + + +Demo 1: Data Generation and Policy Training for a Humanoid Robot +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_steering_wheel_pick_place.gif + :width: 100% + :align: center + :alt: GR-1 humanoid robot performing a pick and place task + :figclass: align-center + + +Isaac Lab Mimic supports data generation for robots with multiple end effectors. In the following demonstration, we will show how to generate data +to train a Fourier GR-1 humanoid robot to perform a pick and place task. + +Optional: Collect and annotate demonstrations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Collect human demonstrations +"""""""""""""""""""""""""""" +.. note:: + + Data collection for the GR-1 humanoid robot environment requires use of an Apple Vision Pro headset. If you do not have access to + an Apple Vision Pro, you may skip this step and continue on to the next step: `Generate the dataset`_. + A pre-recorded annotated dataset is provided in the next step. + +.. tip:: + The GR1 scene utilizes the wrist poses from the Apple Vision Pro (AVP) as setpoints for a differential IK controller (Pink-IK). + The differential IK controller requires the user's wrist pose to be close to the robot's initial or current pose for optimal performance. + Rapid movements of the user's wrist may cause it to deviate significantly from the goal state, which could prevent the IK controller from finding the optimal solution. + This may result in a mismatch between the user's wrist and the robot's wrist. + You can increase the gain of all the `Pink-IK controller's FrameTasks `__ to track the AVP wrist poses with lower latency. + However, this may lead to more jerky motion. + Separately, the finger joints of the robot are retargeted to the user's finger joints using the `dex-retargeting `_ library. + +Set up the CloudXR Runtime and Apple Vision Pro for teleoperation by following the steps in :ref:`cloudxr-teleoperation`. +CPU simulation is used in the following steps for better XR performance when running a single environment. + +Collect a set of human demonstrations. +A success demo requires the object to be placed in the bin and for the robot's right arm to be retracted to the starting position. + +The Isaac Lab Mimic Env GR-1 humanoid robot is set up such that the left hand has a single subtask, while the right hand has two subtasks. +The first subtask involves the right hand remaining idle while the left hand picks up and moves the object to the position where the right hand will grasp it. +This setup allows Isaac Lab Mimic to interpolate the right hand's trajectory accurately by using the object's pose, especially when poses are randomized during data generation. +Therefore, avoid moving the right hand while the left hand picks up the object and brings it to a stable position. + + +.. |good_demo| image:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_steering_wheel_pick_place_good_demo.gif + :width: 49% + :alt: GR-1 humanoid robot performing a good pick and place demonstration + +.. |bad_demo| image:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_steering_wheel_pick_place_bad_demo.gif + :width: 49% + :alt: GR-1 humanoid robot performing a bad pick and place demonstration + +|good_demo| |bad_demo| + +.. centered:: Left: A good human demonstration with smooth and steady motion. Right: A bad demonstration with jerky and exaggerated motion. + + +Collect five demonstrations by running the following command: + +.. code:: bash + + ./isaaclab.sh -p scripts/tools/record_demos.py \ + --device cpu \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --teleop_device handtracking \ + --dataset_file ./datasets/dataset_gr1.hdf5 \ + --num_demos 5 --enable_pinocchio + +.. note:: + We also provide a GR-1 pick and place task with waist degrees-of-freedom enabled ``Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0`` (see :ref:`environments` for details on the available environments, including the GR1 Waist Enabled variant). The same command above applies but with the task name changed to ``Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0``. + +.. tip:: + If a demo fails during data collection, the environment can be reset using the teleoperation controls panel in the XR teleop client + on the Apple Vision Pro or via voice control by saying "reset". See :ref:`teleoperate-apple-vision-pro` for more details. + + The robot uses simplified collision meshes for physics calculations that differ from the detailed visual meshes displayed in the simulation. Due to this difference, you may occasionally observe visual artifacts where parts of the robot appear to penetrate other objects or itself, even though proper collision handling is occurring in the physics simulation. + +You can replay the collected demonstrations by running the following command: + +.. code:: bash + + ./isaaclab.sh -p scripts/tools/replay_demos.py \ + --device cpu \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --dataset_file ./datasets/dataset_gr1.hdf5 --enable_pinocchio + +.. note:: + Non-determinism may be observed during replay as physics in IsaacLab are not determimnistically reproducible when using ``env.reset``. + + +Annotate the demonstrations +""""""""""""""""""""""""""" + +Unlike the prior Franka stacking task, the GR-1 pick and place task uses manual annotation to define subtasks. + +The pick and place task has one subtask for the left arm (pick) and two subtasks for the right arm (idle, place). +Annotations denote the end of a subtask. For the pick and place task, this means there are no annotations for the left arm and one annotation for the right arm (the end of the final subtask is always implicit). + +Each demo requires a single annotation between the first and second subtask of the right arm. This annotation ("S" button press) should be done when the right robot arm finishes the "idle" subtask and begins to +move towards the target object. An example of a correct annotation is shown below: + +.. figure:: ../../_static/tasks/manipulation/gr-1_pick_place_annotation.jpg + :width: 100% + :align: center + +Annotate the demonstrations by running the following command: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu \ + --task Isaac-PickPlace-GR1T2-Abs-Mimic-v0 \ + --input_file ./datasets/dataset_gr1.hdf5 \ + --output_file ./datasets/dataset_annotated_gr1.hdf5 --enable_pinocchio + +.. note:: + + The script prints the keyboard commands for manual annotation and the current subtask being annotated: + + .. code:: text + + Annotating episode #0 (demo_0) + Playing the episode for subtask annotations for eef "right". + Subtask signals to annotate: + - Termination: ['idle_right'] + + Press "N" to begin. + Press "B" to pause. + Press "S" to annotate subtask signals. + Press "Q" to skip the episode. + +.. tip:: + + If the object does not get placed in the bin during annotation, you can press "N" to replay the episode and annotate again. Or you can press "Q" to skip the episode and annotate the next one. + +Generate the dataset +^^^^^^^^^^^^^^^^^^^^ + +If you skipped the prior collection and annotation step, download the pre-recorded annotated dataset ``dataset_annotated_gr1.hdf5`` from +here: `[Annotated GR1 Dataset] `_. +Place the file under ``IsaacLab/datasets`` and run the following command to generate a new dataset with 1000 demonstrations. + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --headless --num_envs 20 --generation_num_trials 1000 --enable_pinocchio \ + --input_file ./datasets/dataset_annotated_gr1.hdf5 --output_file ./datasets/generated_dataset_gr1.hdf5 + +Train a policy +^^^^^^^^^^^^^^ + +Use `Robomimic `__ to train a policy for the generated dataset. + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-PickPlace-GR1T2-Abs-v0 --algo bc \ + --normalize_training_actions \ + --dataset ./datasets/generated_dataset_gr1.hdf5 + +The training script will normalize the actions in the dataset to the range [-1, 1]. +The normalization parameters are saved in the model directory under ``PATH_TO_MODEL_DIRECTORY/logs/normalization_params.txt``. +Record the normalization parameters for later use in the visualization step. + +.. note:: + By default the trained models and logs will be saved to ``IssacLab/logs/robomimic``. + +Visualize the results +^^^^^^^^^^^^^^^^^^^^^ + +Visualize the results of the trained policy by running the following command, using the normalization parameters recorded in the prior training step: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu \ + --enable_pinocchio \ + --task Isaac-PickPlace-GR1T2-Abs-v0 \ + --num_rollouts 50 \ + --horizon 400 \ + --norm_factor_min \ + --norm_factor_max \ + --checkpoint /PATH/TO/desired_model_checkpoint.pth + +.. note:: + Change the ``NORM_FACTOR`` in the above command with the values generated in the training step. + +.. tip:: + + **If you don't see expected performance results:** It is critical to test policies from various checkpoint epochs. + Performance can vary significantly between epochs, and the best-performing checkpoint is often not the final one. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_steering_wheel_pick_place_policy.gif + :width: 100% + :align: center + :alt: GR-1 humanoid robot performing a pick and place task + :figclass: align-center + + The trained policy performing the pick and place task in Isaac Lab. + +.. note:: + + **Expected Success Rates and Timings for Pick and Place GR1T2 Task** + + * Success rate for data generation depends on the quality of human demonstrations (how well the user performs them) and dataset annotation quality. Both data generation and downstream policy success are sensitive to these factors and can show high variance. See :ref:`Common Pitfalls when Generating Data ` for tips to improve your dataset. + * Data generation success for this task is typically 65-80% over 1000 demonstrations, taking 18-40 minutes depending on GPU hardware and success rate (19 minutes on a RTX ADA 6000 @ 80% success rate). + * Behavior Cloning (BC) policy success is typically 75-86% (evaluated on 50 rollouts) when trained on 1000 generated demonstrations for 2000 epochs (default), depending on demonstration quality. Training takes approximately 29 minutes on a RTX ADA 6000. + * **Recommendation:** Train for 2000 epochs with 1000 generated demonstrations, and **evaluate multiple checkpoints saved between the 1000th and 2000th epochs** to select the best-performing policy. Testing various epochs is essential for finding optimal performance. + + +Demo 2: Data Generation and Policy Training for Humanoid Robot Locomanipulation with Unitree G1 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this demo, we showcase the integration of locomotion and manipulation capabilities within a single humanoid robot system. +This locomanipulation environment enables data collection for complex tasks that combine navigation and object manipulation. +The demonstration follows a multi-step process: first, it generates pick and place tasks similar to Demo 1, then introduces +a navigation component that uses specialized scripts to generate scenes where the humanoid robot must move from point A to point B. +The robot picks up an object at the initial location (point A) and places it at the target destination (point B). + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/locomanipulation-g-1_steering_wheel_pick_place.gif + :width: 100% + :align: center + :alt: G1 humanoid robot with locomanipulation performing a pick and place task + :figclass: align-center + +.. note:: + **Locomotion policy training** + + The locomotion policy used in this integration example was trained using the `AGILE `__ framework. + AGILE is an officially supported humanoid control training pipeline that leverages the manager based environment in Isaac Lab. It will also be + seamlessly integrated with other evaluation and deployment tools across Isaac products. This allows teams to rely on a single, maintained stack + covering all necessary infrastructure and tooling for policy training, with easy export to real-world deployment. The AGILE repository contains + updated pre-trained policies with separate upper and lower body policies for flexibtility. They have been verified in the real world and can be + directly deployed. Users can also train their own locomotion or whole-body control policies using the AGILE framework. + +Generate the manipulation dataset +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The same data generation and policy training steps from Demo 1.0 can be applied to the G1 humanoid robot with locomanipulation capabilities. +This demonstration shows how to train a G1 robot to perform pick and place tasks with full-body locomotion and manipulation. + +The process follows the same workflow as Demo 1.0, but uses the ``Isaac-PickPlace-Locomanipulation-G1-Abs-v0`` task environment. + +Follow the same data collection, annotation, and generation process as demonstrated in Demo 1.0, but adapted for the G1 locomanipulation task. + +.. hint:: + + If desired, data collection and annotation can be done using the same commands as the prior examples for validation of the dataset. + + The G1 robot with locomanipulation capabilities combines full-body locomotion with manipulation to perform pick and place tasks. + + **Note that the following commands are only for your reference and dataset validation purposes - they are not required for this demo.** + + To collect demonstrations: + + .. code:: bash + + ./isaaclab.sh -p scripts/tools/record_demos.py \ + --device cpu \ + --task Isaac-PickPlace-Locomanipulation-G1-Abs-v0 \ + --teleop_device handtracking \ + --dataset_file ./datasets/dataset_g1_locomanip.hdf5 \ + --num_demos 5 --enable_pinocchio + + .. note:: + + Depending on how the Apple Vision Pro app was initialized, the hands of the operator might be very far up or far down compared to the hands of the G1 robot. If this is the case, you can click **Stop AR** in the AR tab in Isaac Lab, and move the AR Anchor prim. Adjust it down to bring the hands of the operator lower, and up to bring them higher. Click **Start AR** to resume teleoperation session. Make sure to match the hands of the robot before clicking **Play** in the Apple Vision Pro, otherwise there will be an undesired large force generated initially. + + You can replay the collected demonstrations by running: + + .. code:: bash + + ./isaaclab.sh -p scripts/tools/replay_demos.py \ + --device cpu \ + --task Isaac-PickPlace-Locomanipulation-G1-Abs-v0 \ + --dataset_file ./datasets/dataset_g1_locomanip.hdf5 --enable_pinocchio + + To annotate the demonstrations: + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu \ + --task Isaac-Locomanipulation-G1-Abs-Mimic-v0 \ + --input_file ./datasets/dataset_g1_locomanip.hdf5 \ + --output_file ./datasets/dataset_annotated_g1_locomanip.hdf5 --enable_pinocchio + + +If you skipped the prior collection and annotation step, download the pre-recorded annotated dataset ``dataset_annotated_g1_locomanip.hdf5`` from +here: `[Annotated G1 Dataset] `_. +Place the file under ``IsaacLab/datasets`` and run the following command to generate a new dataset with 1000 demonstrations. + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu --headless --num_envs 20 --generation_num_trials 1000 --enable_pinocchio \ + --input_file ./datasets/dataset_annotated_g1_locomanip.hdf5 --output_file ./datasets/generated_dataset_g1_locomanip.hdf5 + + +Train a manipulation-only policy +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +At this point you can train a policy that only performs manipulation tasks using the generated dataset: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-PickPlace-Locomanipulation-G1-Abs-v0 --algo bc \ + --normalize_training_actions \ + --dataset ./datasets/generated_dataset_g1_locomanip.hdf5 + +Visualize the results +^^^^^^^^^^^^^^^^^^^^^ + +Visualize the trained policy performance: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu \ + --enable_pinocchio \ + --task Isaac-PickPlace-Locomanipulation-G1-Abs-v0 \ + --num_rollouts 50 \ + --horizon 400 \ + --norm_factor_min \ + --norm_factor_max \ + --checkpoint /PATH/TO/desired_model_checkpoint.pth + +.. note:: + Change the ``NORM_FACTOR`` in the above command with the values generated in the training step. + +.. tip:: + + **If you don't see expected performance results:** Always test policies from various checkpoint epochs. + Different epochs can produce significantly different results, so evaluate multiple checkpoints to find the optimal model. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/locomanipulation-g-1_steering_wheel_pick_place.gif + :width: 100% + :align: center + :alt: G1 humanoid robot performing a pick and place task + :figclass: align-center + + The trained policy performing the pick and place task in Isaac Lab. + +.. note:: + + **Expected Success Rates and Timings for Locomanipulation Pick and Place Task** + + * Success rate for data generation depends on the quality of human demonstrations (how well the user performs them) and dataset annotation quality. Both data generation and downstream policy success are sensitive to these factors and can show high variance. See :ref:`Common Pitfalls when Generating Data ` for tips to improve your dataset. + * Data generation success for this task is typically 65-82% over 1000 demonstrations, taking 18-40 minutes depending on GPU hardware and success rate (18 minutes on a RTX ADA 6000 @ 82% success rate). + * Behavior Cloning (BC) policy success is typically 75-85% (evaluated on 50 rollouts) when trained on 1000 generated demonstrations for 2000 epochs (default), depending on demonstration quality. Training takes approximately 40 minutes on a RTX ADA 6000. + * **Recommendation:** Train for 2000 epochs with 1000 generated demonstrations, and **evaluate multiple checkpoints saved between the 1000th and 2000th epochs** to select the best-performing policy. Testing various epochs is essential for finding optimal performance. + +Generate the dataset with manipulation and point-to-point navigation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To create a comprehensive locomanipulation dataset that combines both manipulation and navigation capabilities, you can generate a navigation dataset using the manipulation dataset from the previous step as input. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/disjoint_navigation.gif + :width: 100% + :align: center + :alt: G1 humanoid robot combining navigation with locomanipulation + :figclass: align-center + + G1 humanoid robot performing locomanipulation with navigation capabilities. + +The locomanipulation dataset generation process takes the previously generated manipulation dataset and creates scenarios where the robot must navigate from one location to another while performing manipulation tasks. This creates a more complex dataset that includes both locomotion and manipulation behaviors. + +To generate the locomanipulation dataset, use the following command: + +.. code:: bash + + ./isaaclab.sh -p \ + scripts/imitation_learning/locomanipulation_sdg/generate_data.py \ + --device cpu \ + --kit_args="--enable isaacsim.replicator.mobility_gen" \ + --task="Isaac-G1-SteeringWheel-Locomanipulation" \ + --dataset ./datasets/generated_dataset_g1_locomanip.hdf5 \ + --num_runs 1 \ + --lift_step 60 \ + --navigate_step 130 \ + --enable_pinocchio \ + --output_file ./datasets/generated_dataset_g1_locomanipulation_sdg.hdf5 \ + --enable_cameras + +.. note:: + + The input dataset (``--dataset``) should be the manipulation dataset generated in the previous step. You can specify any output filename using the ``--output_file_name`` parameter. + +The key parameters for locomanipulation dataset generation are: + +* ``--lift_step 70``: Number of steps for the lifting phase of the manipulation task. This should mark the point immediately after the robot has grasped the object. +* ``--navigate_step 120``: Number of steps for the navigation phase between locations. This should make the point where the robot has lifted the object and is ready to walk. +* ``--output_file``: Name of the output dataset file + +This process creates a dataset where the robot performs the manipulation task at different locations, requiring it to navigate between points while maintaining the learned manipulation behaviors. The resulting dataset can be used to train policies that combine both locomotion and manipulation capabilities. + +.. note:: + + You can visualize the robot trajectory results with the following script command: + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/locomanipulation_sdg/plot_navigation_trajectory.py --input_file datasets/generated_dataset_g1_locomanipulation_sdg.hdf5 --output_dir /PATH/TO/DESIRED_OUTPUT_DIR + +The data generated from this locomanipulation pipeline can also be used to finetune an imitation learning policy using GR00T N1.5. To do this, +you may convert the generated dataset to LeRobot format as expected by GR00T N1.5, and then run the finetuning script provided +in the GR00T N1.5 repository. An example closed-loop policy rollout is shown in the video below: + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/locomanipulation_sdg_disjoint_nav_groot_policy_4x.gif + :width: 100% + :align: center + :alt: Simulation rollout of GR00T N1.5 policy finetuned for locomanipulation + :figclass: align-center + + Simulation rollout of GR00T N1.5 policy finetuned for locomanipulation. + +The policy shown above uses the camera image, hand poses, hand joint positions, object pose, and base goal pose as inputs. +The output of the model is the target base velocity, hand poses, and hand joint positions for the next several timesteps. + + +Demo 3: Visuomotor Policy for a Humanoid Robot +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_nut_pouring_policy.gif + :width: 100% + :align: center + :alt: GR-1 humanoid robot performing a pouring task + :figclass: align-center + +Download the Dataset +^^^^^^^^^^^^^^^^^^^^ + +Download the pre-generated dataset from `here `__ and place it under ``IsaacLab/datasets/generated_dataset_gr1_nut_pouring.hdf5`` +(**Note: The dataset size is approximately 12GB**). The dataset contains 1000 demonstrations of a humanoid robot performing a pouring/placing task that was +generated using Isaac Lab Mimic for the ``Isaac-NutPour-GR1T2-Pink-IK-Abs-Mimic-v0`` task. + +.. hint:: + + If desired, data collection, annotation, and generation can be done using the same commands as the prior examples. + + The robot first picks up the red beaker and pours the contents into the yellow bowl. + Then, it drops the red beaker into the blue bin. Lastly, it places the yellow bowl onto the white scale. + See the video in the :ref:`visualize-results-demo-2` section below for a visual demonstration of the task. + + **The success criteria for this task requires the red beaker to be placed in the blue bin, the green nut to be in the yellow bowl, + and the yellow bowl to be placed on top of the white scale.** + + .. attention:: + **The following commands are only for your reference and are not required for this demo.** + + To collect demonstrations: + + .. code:: bash + + ./isaaclab.sh -p scripts/tools/record_demos.py \ + --device cpu \ + --task Isaac-NutPour-GR1T2-Pink-IK-Abs-v0 \ + --teleop_device handtracking \ + --dataset_file ./datasets/dataset_gr1_nut_pouring.hdf5 \ + --num_demos 5 --enable_pinocchio + + Since this is a visuomotor environment, the ``--enable_cameras`` flag must be added to the annotation and data generation commands. + + To annotate the demonstrations: + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py \ + --device cpu \ + --enable_cameras \ + --rendering_mode balanced \ + --task Isaac-NutPour-GR1T2-Pink-IK-Abs-Mimic-v0 \ + --input_file ./datasets/dataset_gr1_nut_pouring.hdf5 \ + --output_file ./datasets/dataset_annotated_gr1_nut_pouring.hdf5 --enable_pinocchio + + .. warning:: + There are multiple right eef annotations for this task. Annotations for subtasks for the same eef cannot have the same action index. + Make sure to annotate the right eef subtasks with different action indices. + + + To generate the dataset: + + .. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \ + --device cpu \ + --headless \ + --enable_pinocchio \ + --enable_cameras \ + --rendering_mode balanced \ + --task Isaac-NutPour-GR1T2-Pink-IK-Abs-Mimic-v0 \ + --generation_num_trials 1000 \ + --num_envs 5 \ + --input_file ./datasets/dataset_annotated_gr1_nut_pouring.hdf5 \ + --output_file ./datasets/generated_dataset_gr1_nut_pouring.hdf5 + + +Train a policy +^^^^^^^^^^^^^^ + +Use `Robomimic `__ to train a visuomotor BC agent for the task. + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ + --task Isaac-NutPour-GR1T2-Pink-IK-Abs-v0 --algo bc \ + --normalize_training_actions \ + --dataset ./datasets/generated_dataset_gr1_nut_pouring.hdf5 + +The training script will normalize the actions in the dataset to the range [-1, 1]. +The normalization parameters are saved in the model directory under ``PATH_TO_MODEL_DIRECTORY/logs/normalization_params.txt``. +Record the normalization parameters for later use in the visualization step. + +.. note:: + By default the trained models and logs will be saved to ``IsaacLab/logs/robomimic``. + +You can also post-train a `GR00T `__ foundation model to deploy a Vision-Language-Action policy for the task. + +Please refer to the `IsaacLabEvalTasks `__ repository for more details. + +.. _visualize-results-demo-2: + +Visualize the results +^^^^^^^^^^^^^^^^^^^^^ + +Visualize the results of the trained policy by running the following command, using the normalization parameters recorded in the prior training step: + +.. code:: bash + + ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ + --device cpu \ + --enable_pinocchio \ + --enable_cameras \ + --rendering_mode balanced \ + --task Isaac-NutPour-GR1T2-Pink-IK-Abs-v0 \ + --num_rollouts 50 \ + --horizon 350 \ + --norm_factor_min \ + --norm_factor_max \ + --checkpoint /PATH/TO/desired_model_checkpoint.pth + +.. note:: + Change the ``NORM_FACTOR`` in the above command with the values generated in the training step. + +.. tip:: + + **If you don't see expected performance results:** Test policies from various checkpoint epochs, not just the final one. + Policy performance can vary substantially across training, and intermediate checkpoints often yield better results. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/gr-1_nut_pouring_policy.gif + :width: 100% + :align: center + :alt: GR-1 humanoid robot performing a pouring task + :figclass: align-center + + The trained visuomotor policy performing the pouring task in Isaac Lab. + +.. note:: + + **Expected Success Rates and Timings for Visuomotor Nut Pour GR1T2 Task** + + * Success rate for data generation depends on the quality of human demonstrations (how well the user performs them) and dataset annotation quality. Both data generation and downstream policy success are sensitive to these factors and can show high variance. See :ref:`Common Pitfalls when Generating Data ` for tips to improve your dataset. + * Data generation for 1000 demonstrations takes approximately 10 hours on a RTX ADA 6000. + * Behavior Cloning (BC) policy success is typically 50-60% (evaluated on 50 rollouts) when trained on 1000 generated demonstrations for 600 epochs (default). Training takes approximately 15 hours on a RTX ADA 6000. + * **Recommendation:** Train for 600 epochs with 1000 generated demonstrations, and **evaluate multiple checkpoints saved between the 300th and 600th epochs** to select the best-performing policy. Testing various epochs is critical for achieving optimal performance. + +.. _common-pitfalls-generating-data: + +Common Pitfalls when Generating Data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**Demonstrations are too long:** + +* Longer time horizon is harder to learn for a policy +* Start close to the first object and minimize motions + +**Demonstrations are not smooth:** + +* Irregular motion is hard for policy to decipher +* Better teleop devices result in better data (i.e. SpaceMouse is better than Keyboard) + +**Pauses in demonstrations:** + +* Pauses are difficult to learn +* Keep the human motions smooth and fluid + +**Excessive number of subtasks:** + +* Minimize the number of defined subtasks for completing a given task +* Less subtacks results in less stitching of trajectories, yielding higher data generation success rate + +**Lack of action noise:** + +* Action noise makes policies more robust + +**Recording cropped too tight:** + +* If recording stops on the frame the success term triggers, it may not re-trigger during replay +* Allow for some buffer at the end of recording + +**Non-deterministic replay:** + +* Physics in IsaacLab are not deterministically reproducible when using ``env.reset`` so demonstrations may fail on replay +* Collect more human demos than needed, use the ones that succeed during annotation +* All data in Isaac Lab Mimic generated HDF5 file represent a successful demo and can be used for training (even if non-determinism causes failure when replayed) + + +Creating Your Own Isaac Lab Mimic Compatible Environments +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +How it works +^^^^^^^^^^^^ + +Isaac Lab Mimic works by splitting the input demonstrations into subtasks. Subtasks are user-defined segments in the demonstrations that are common to all demonstrations. Examples for subtasks are "grasp an object", "move end effector to some pre-defined position", "release object" etc.. Note that most subtasks are defined with respect to some object that the robot interacts with. + +Subtasks need to be defined, and then annotated for each input demonstration. Annotation can either happen algorithmically by defining heuristics for subtask detection, as was done in the example above, or it can be done manually. + +With subtasks defined and annotated, Isaac Lab Mimic utilizes a small number of helper methods to then transform the subtask segments, and generate new demonstrations by stitching them together to match the new task at hand. + +For each thusly generated candidate demonstration, Isaac Lab Mimic uses a boolean success criteria to determine whether the demonstration succeeded in performing the task, and if so, add it to the output dataset. Success rate of candidate demonstrations can be as high as 70% in simple cases, and as low as <1%, depending on the difficulty of the task, and the complexity of the robot itself. + +Configuration and subtask definition +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Subtasks, among other configuration settings for Isaac Lab Mimic, are defined in a Mimic compatible environment configuration class that is created by extending the existing environment config with additional Mimic required parameters. + +All Mimic required config parameters are specified in the :class:`~isaaclab.envs.MimicEnvCfg` class. + +The config class :class:`~isaaclab_mimic.envs.FrankaCubeStackIKRelMimicEnvCfg` serves as an example of creating a Mimic compatible environment config class for the Franka stacking task that was used in the examples above. + +The ``DataGenConfig`` member contains various parameters that influence how data is generated. It is initially sufficient to just set the ``name`` parameter, and revise the rest later. + +Subtasks are a list of :class:`~isaaclab.envs.SubTaskConfig` objects, of which the most important members are: + +* ``object_ref`` is the object that is being interacted with. This will be used to adjust motions relative to this object during data generation. Can be ``None`` if the current subtask does not involve any object. +* ``subtask_term_signal`` is the ID of the signal indicating whether the subtask is active or not. + +For multi end-effector environments, subtask ordering between end-effectors can be enforced by specifying subtask constraints. These constraints are defined in the :class:`~isaaclab.envs.SubTaskConstraintConfig` class. + +Subtask annotation +^^^^^^^^^^^^^^^^^^ + +Once the subtasks are defined, they need to be annotated in the source data. There are two methods to annotate source demonstrations for subtask boundaries: Manual annotation or using heuristics. + +It is often easiest to perform manual annotations, since the number of input demonstrations is usually very small. To perform manual annotations, use the ``annotate_demos.py`` script without the ``--auto`` flag. Then press ``B`` to pause, ``N`` to continue, and ``S`` to annotate a subtask boundary. + +For more accurate boundaries, or to speed up repeated processing of a given task for experiments, heuristics can be implemented to perform the same task. Heuristics are observations in the environment. An example how to add subtask terms can be found in ``source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_env_cfg.py``, where they are added as an observation group called ``SubtaskCfg``. This example is using prebuilt heuristics, but custom heuristics are easily implemented. + + +Helpers for demonstration generation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Helpers needed for Isaac Lab Mimic are defined in the environment. All tasks that are to be used with Isaac Lab Mimic are derived from the :class:`~isaaclab.envs.ManagerBasedRLMimicEnv` base class, and must implement the following functions: + +* ``get_robot_eef_pose``: Returns the current robot end effector pose in the same frame as used by the robot end effector controller. + +* ``target_eef_pose_to_action``: Takes a target pose and a gripper action for the end effector controller and returns an action which achieves the target pose. + +* ``action_to_target_eef_pose``: Takes an action and returns a target pose for the end effector controller. + +* ``actions_to_gripper_actions``: Takes a sequence of actions and returns the gripper actuation part of the actions. + +* ``get_object_poses``: Returns the pose of each object in the scene that is used for data generation. + +* ``get_subtask_term_signals``: Returns a dictionary of binary flags for each subtask in a task. The flag of true is set when the subtask has been completed and false otherwise. + +The class :class:`~isaaclab_mimic.envs.FrankaCubeStackIKRelMimicEnv` shows an example of creating a Mimic compatible environment from an existing Isaac Lab environment. + +Registering the environment +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Once both Mimic compatible environment and environment config classes have been created, a new Mimic compatible environment can be registered using ``gym.register``. For the Franka stacking task in the examples above, the Mimic environment is registered as ``Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0``. + +The registered environment is now ready to be used with Isaac Lab Mimic. + + +Tips for Successful Data Generation with Isaac Lab Mimic +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Splitting subtasks +^^^^^^^^^^^^^^^^^^ + +A general rule of thumb is to split the task into as few subtasks as possible, while still being able to complete the task. Isaac Lab Mimic data generation uses linear interpolation to bridge and stitch together subtask segments. +More subtasks result in more stitching of trajectories which can result in less smooth motions and more failed demonstrations. For this reason, it is often best to annoatate subtask boundaries where the robot's motion is unlikely to collide with other objects. + +For example, in the scenario below, there is a subtask partition after the robot's left arm grasps the object. On the left, the subtask annotation is marked immediately after the grasp, while on the right, the annotation is marked after the robot has grasped and lifted the object. +In the left case, the interpolation causes the robot's left arm to collide with the table and it's motion lags while on the right the motion is continuous and smooth. + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/lagging_subtask.gif + :width: 99% + :align: center + :alt: Subtask splitting example + :figclass: align-center + +.. centered:: Motion lag/collision caused by poor subtask splitting (left) + + +Selecting number of interpolation steps +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The number of interpolation steps between subtask segments can be specified in the :class:`~isaaclab.envs.SubTaskConfig` class. Once transformed, the subtask segments don't start/end at the same spot, thus to create a continuous motion, Isaac Lab Mimic +will apply linear interpolation between the last point of the previous subtask and the first point of the next subtask. + +The number of interpolation steps can be tuned to control the smoothness of the generated demonstrations during this stitching process. +The appropriate number of interpolation steps depends on the speed of the robot and the complexity of the task. A complex task with a large object reset distribution will have larger gaps between subtask segments and require more interpolation steps to create a smooth motion. +Alternatively, a task with small gaps between subtask segments should use a small number of interpolation steps to avoid unnecessary motion lag caused by too many steps. + +An example of how the number of interpolation steps can affect the generated demonstrations is shown below. +In the example, an interpolation is applied to the right arm of the robot to bridge the gap between the left arm's grasp and the right arm's placement. With 0 steps, the right arm exhibits a jerky jump in motion while with 20 steps, the motion is laggy. With 5 steps, the motion is +smooth and natural. + +.. |0_interp_steps| image:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/0_interpolation_steps.gif + :width: 32% + :alt: GR-1 robot with 0 interpolation steps + +.. |5_interp_steps| image:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/5_interpolation_steps.gif + :width: 32% + :alt: GR-1 robot with 5 interpolation steps + +.. |20_interp_steps| image:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/20_interpolation_steps.gif + :width: 32% + :alt: GR-1 robot with 20 interpolation steps + +|0_interp_steps| |5_interp_steps| |20_interp_steps| + +.. centered:: Left: 0 steps. Middle: 5 steps. Right: 20 steps. diff --git a/docs/source/overview/own-project/index.rst b/docs/source/overview/own-project/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..36ef4443f5b124f53c85679692ebb1a7aa476e0c --- /dev/null +++ b/docs/source/overview/own-project/index.rst @@ -0,0 +1,14 @@ +.. _own-project: + +Build your Own Project or Task +============================== + +To get started, first create a new project or task with the template generator :ref:`template-generator`. +For more detailed information on how your project is structured, see :ref:`project-structure`. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + template + project_structure diff --git a/docs/source/overview/own-project/project_structure.rst b/docs/source/overview/own-project/project_structure.rst new file mode 100644 index 0000000000000000000000000000000000000000..a0e17f0344d42665c287b02cc2f4dfef208d631e --- /dev/null +++ b/docs/source/overview/own-project/project_structure.rst @@ -0,0 +1,44 @@ +.. _project-structure: + + +Project Structure +================= + +There are four nested structures you need to be aware of when working in the direct workflow with an Isaac Lab template +project: the **Project**, the **Extension**, the **Modules**, and the **Task**. + +.. figure:: ../../_static/setup/walkthrough_project_setup.svg + :align: center + :figwidth: 100% + :alt: The structure of the isaac lab template project. + +The **Project** is the root directory of the generated template. It contains the source and scripts directories, as well as +a ``README.md`` file. When we created the template, we named the project *IsaacLabTutorial* and this defined the root directory +of a git repository. If you examine the project root with hidden files visible you will see a number of files defining +the behavior of the project with respect to git. The ``scripts`` directory contains the ``train.py`` and ``play.py`` scripts for the +various RL libraries you chose when generating the template, while the source directory contains the python packages for the project. + +The **Extension** is the name of the python package we installed via pip. By default, the template generates a project +with a single extension of the same name. A project can have multiple extensions, and so they are kept in a common ``source`` +directory. Traditional python packages are defined by the presence of a ``pyproject.toml`` file that describes the package +metadata, but packages using Isaac Lab must also be Isaac Sim extensions and so require a ``config`` directory and an accompanying +``extension.toml`` file that describes the metadata needed by the Isaac Sim extension manager. Finally, because the template +is intended to be installed via pip, it needs a ``setup.py`` file to complete the setup procedure using the ``extension.toml`` +config. A project can have multiple extensions, as evidenced by the Isaac Lab repository itself! + +The **Modules** are what actually gets loaded by Isaac Lab to run training (the meat of the code). By default, the template +generates an extension with a single module that is named the same as the project. The structure of the various sub-modules +in the extension is what determines the ``entry_point`` for an environment in Isaac Lab. This is why our template project needed +to be installed before we could call ``train.py``: the path to the necessary components to run the task needed to be exposed +to python for Isaac Lab to find them. + +Finally, the **Task** is the heart of the direct workflow. By default, the template generates a single task with the same name +as the project. The environment and configuration files are stored here, as well as placeholder, RL library dependent ``agents``. +Critically, note the contents of the ``__init__.py``! Specifically, the ``gym.register`` function needs to be called at least once +before an environment and task can be used with the Isaac Lab ``train.py`` and ``play.py`` scripts. +This function should be included in one of the module ``__init__.py`` files so it is called at installation. The path to +this init file is what defines the entry point for the task! + +For the template, ``gym.register`` is called within ``isaac_lab_tutorial/source/isaac_lab_tutorial/isaac_lab_tutorial/tasks/direct/isaac_lab_tutorial/__init__.py``. +The repeated name is a consequence of needing default names for the template, but now we can see the structure of the project. +**Project**/source/**Extension**/**Module**/tasks/direct/**Task**/__init__.py diff --git a/docs/source/overview/own-project/template.rst b/docs/source/overview/own-project/template.rst new file mode 100644 index 0000000000000000000000000000000000000000..cb52effde62ca07a898460c00f61b470b3f306cf --- /dev/null +++ b/docs/source/overview/own-project/template.rst @@ -0,0 +1,230 @@ +.. _template-generator: + + +Create new project or task +========================== + +Traditionally, building new projects that utilize Isaac Lab's features required creating your own +extensions within the Isaac Lab repository. However, this approach can obscure project visibility and +complicate updates from one version of Isaac Lab to another. To circumvent these challenges, +we now provide a command-line tool (**template generator**) for creating Isaac Lab-based projects and tasks. + +The template generator enables you to create an: + +* **External project** (recommended): An isolated project that is not part of the Isaac Lab repository. This approach + works outside of the core Isaac Lab repository, ensuring that your development efforts remain self-contained. Also, + it allows your code to be run as an extension in Omniverse. + + .. hint:: + + For the external project, the template generator will initialize a new Git repository in the specified directory. + You can push the generated content to your own remote repository (e.g. GitHub) and share it with others. + +* **Internal task**: A task that is part of the Isaac Lab repository. This approach should only be used to create + new tasks within the Isaac Lab repository in order to contribute to it. + + .. warning:: + + Pip installations of Isaac Lab do not support *Internal* templates. + If ``isaaclab`` is loaded from ``site-packages`` or ``dist-packages``, the *Internal* option is disabled + and the *External* template will be used instead. + +Running the template generator +------------------------------ + +Install Isaac Lab by following the `installation guide <../../setup/installation/index.html>`_. +We recommend using conda or uv installation as it simplifies calling Python scripts from the terminal. + +Then, run the following command to generate a new external project or internal task: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./isaaclab.sh --new # or "./isaaclab.sh -n" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + isaaclab.bat --new :: or "isaaclab.bat -n" + +The generator will guide you in setting up the project/task for your needs by asking you the following questions: + +* Type of project/task (external or internal), and project/task path or names according to the selected type. +* Isaac Lab workflows (see :ref:`feature-workflows`). +* Reinforcement learning libraries (see :ref:`rl-frameworks`), and algorithms (if the selected libraries support multiple algorithms). + +External project usage (once generated) +--------------------------------------- + +Once the external project is generated, a ``README.md`` file will be created in the specified directory. +This file will contain instructions on how to install the project and run the tasks. + +Here are some general commands to get started with it: + +.. note:: + + If Isaac Lab is not installed in a conda environment or in a (virtual) Python environment, use ``FULL_PATH_TO_ISAACLAB/isaaclab.sh -p`` + (or ``FULL_PATH_TO_ISAACLAB\isaaclab.bat -p`` on Windows) instead of ``python`` to run the commands below. + +* Install the project (in editable mode). + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python -m pip install -e source/ + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python -m pip install -e source\ + +* List the tasks available in the project. + + .. warning:: + + If the task names change, it may be necessary to update the search pattern ``"Template-"`` + (in the ``scripts/list_envs.py`` file) so that they can be listed. + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts/list_envs.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\list_envs.py + +* Run a task. + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts//train.py --task= + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\\train.py --task= + +For more details, please follow the instructions in the generated project's ``README.md`` file. + +Internal task usage (once generated) +--------------------------------------- + +Once the internal task is generated, it will be available along with the rest of the Isaac Lab tasks. + +Here are some general commands to get started with it: + +.. note:: + + If Isaac Lab is not installed in a conda environment or in a (virtual) Python environment, use ``./isaaclab.sh -p`` + (or ``isaaclab.bat -p`` on Windows) instead of ``python`` to run the commands below. + +* List the tasks available in Isaac Lab. + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts/environments/list_envs.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\environments\list_envs.py + +* Run a task. + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts/reinforcement_learning//train.py --task= + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\reinforcement_learning\\train.py --task= + +* Run a task with dummy agents. + + These include dummy agents that output zero or random agents. They are useful to ensure that the environments are configured correctly. + + * Zero-action agent + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts/zero_agent.py --task= + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\zero_agent.py --task= + + * Random-action agent + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + python scripts/random_agent.py --task= + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python scripts\random_agent.py --task= diff --git a/docs/source/overview/reinforcement-learning/index.rst b/docs/source/overview/reinforcement-learning/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..730b0751f82a531e737f3b3e2acce62cd21bc2d1 --- /dev/null +++ b/docs/source/overview/reinforcement-learning/index.rst @@ -0,0 +1,15 @@ +Reinforcement Learning +====================== + +Isaac Lab supports multiple reinforcement learning frameworks. +In this section, we show existing scripts for running reinforcement learning +with supported RL libraries and provide a comparison of the supported +learning frameworks. + +.. toctree:: + :maxdepth: 1 + + rl_existing_scripts + rl_frameworks + performance_benchmarks + training_guide diff --git a/docs/source/overview/reinforcement-learning/performance_benchmarks.rst b/docs/source/overview/reinforcement-learning/performance_benchmarks.rst new file mode 100644 index 0000000000000000000000000000000000000000..2a6845a18f5cb37160ac74d02905f2a4a3a3faf8 --- /dev/null +++ b/docs/source/overview/reinforcement-learning/performance_benchmarks.rst @@ -0,0 +1,151 @@ +Performance Benchmarks +====================== + +Isaac Lab leverages end-to-end GPU training for reinforcement learning workflows, +allowing for fast parallel training across thousands of environments. +In this section, we provide runtime performance benchmark results for reinforcement learning +training of various example environments on different GPU setups. +Multi-GPU and multi-node training performance results are also outlined. + + +Benchmark Results +----------------- + +All benchmarking results were performed with the RL Games library with ``--headless`` flag on Ubuntu 22.04. +``Isaac-Velocity-Rough-G1-v0`` environment benchmarks were performed with the RSL RL library. + + +Memory Consumption +^^^^^^^^^^^^^^^^^^ + ++------------------------------------+----------------+-------------------+----------+-----------+ +| Environment Name | | # of Environments | RAM (GB) | VRAM (GB) | ++====================================+================+===================+==========+===========+ +| Isaac-Cartpole-Direct-v0 | |cartpole| | 4096 | 3.7 | 3.3 | ++------------------------------------+----------------+-------------------+----------+-----------+ +| Isaac-Cartpole-RGB-Camera-Direct-v0| |cartpole-cam| | 1024 | 7.5 | 16.7 | ++------------------------------------+----------------+-------------------+----------+-----------+ +| Isaac-Velocity-Rough-G1-v0 | |g1| | 4096 | 6.5 | 6.1 | ++------------------------------------+----------------+-------------------+----------+-----------+ +| Isaac-Repose-Cube-Shadow-Direct-v0 | |shadow| | 8192 | 6.7 | 6.4 | ++------------------------------------+----------------+-------------------+----------+-----------+ + +.. |cartpole| image:: ../../_static/benchmarks/cartpole.jpg + :width: 80 + :height: 45 +.. |cartpole-cam| image:: ../../_static/benchmarks/cartpole_camera.jpg + :width: 80 + :height: 45 +.. |g1| image:: ../../_static/benchmarks/g1_rough.jpg + :width: 80 + :height: 45 +.. |shadow| image:: ../../_static/benchmarks/shadow.jpg + :width: 80 + :height: 45 + + +Single GPU - RTX 4090 +^^^^^^^^^^^^^^^^^^^^^ + +CPU: AMD Ryzen 9 7950X 16-Core Processor + ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Environment Name | # of Environments | Environment | Environment Step | Environment Step, | +| | | Step FPS | and | Inference, | +| | | | Inference FPS | and Train FPS | ++=====================================+===================+==============+===================+====================+ +| Isaac-Cartpole-Direct-v0 | 4096 | 1100000 | 910000 | 510000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Cartpole-RGB-Camera-Direct-v0 | 1024 | 50000 | 45000 | 32000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Velocity-Rough-G1-v0 | 4096 | 94000 | 88000 | 82000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Repose-Cube-Shadow-Direct-v0 | 8192 | 200000 | 190000 | 170000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ + + +Single GPU - L40 +^^^^^^^^^^^^^^^^ + +CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz + ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Environment Name | # of Environments | Environment | Environment Step | Environment Step, | +| | | Step FPS | and | Inference, | +| | | | Inference FPS | and Train FPS | ++=====================================+===================+==============+===================+====================+ +| Isaac-Cartpole-Direct-v0 | 4096 | 620000 | 490000 | 260000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Cartpole-RGB-Camera-Direct-v0 | 1024 | 30000 | 28000 | 21000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Velocity-Rough-G1-v0 | 4096 | 72000 | 64000 | 62000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Repose-Cube-Shadow-Direct-v0 | 8192 | 170000 | 140000 | 120000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ + + +Single-Node, 4 x L40 GPUs +^^^^^^^^^^^^^^^^^^^^^^^^^ + +CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz + ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Environment Name | # of Environments | Environment | Environment Step | Environment Step, | +| | | Step FPS | and | Inference, | +| | | | Inference FPS | and Train FPS | ++=====================================+===================+==============+===================+====================+ +| Isaac-Cartpole-Direct-v0 | 4096 | 2700000 | 2100000 | 950000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Cartpole-RGB-Camera-Direct-v0 | 1024 | 130000 | 120000 | 90000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Velocity-Rough-G1-v0 | 4096 | 290000 | 270000 | 250000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Repose-Cube-Shadow-Direct-v0 | 8192 | 440000 | 420000 | 390000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ + + +4 Nodes, 4 x L40 GPUs per node +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz + ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Environment Name | # of Environments | Environment | Environment Step | Environment Step, | +| | | Step FPS | and | Inference, | +| | | | Inference FPS | and Train FPS | ++=====================================+===================+==============+===================+====================+ +| Isaac-Cartpole-Direct-v0 | 4096 | 10200000 | 8200000 | 3500000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Cartpole-RGB-Camera-Direct-v0 | 1024 | 530000 | 490000 | 260000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Velocity-Rough-G1-v0 | 4096 | 1200000 | 1100000 | 960000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ +| Isaac-Repose-Cube-Shadow-Direct-v0 | 8192 | 2400000 | 2300000 | 1800000 | ++-------------------------------------+-------------------+--------------+-------------------+--------------------+ + + +Benchmark Scripts +----------------- + +For ease of reproducibility, we provide benchmarking scripts available at ``scripts/benchmarks``. +This folder contains individual benchmark scripts that resemble the ``train.py`` script for RL-Games +and RSL RL. In addition, we also provide a benchmarking script that runs only the environment implementation +without any reinforcement learning library. + +Example scripts can be run similarly to training scripts: + +.. code-block:: bash + + # benchmark with RSL RL + python scripts/benchmarks/benchmark_rsl_rl.py --task=Isaac-Cartpole-v0 --headless + + # benchmark with RL Games + python scripts/benchmarks/benchmark_rlgames.py --task=Isaac-Cartpole-v0 --headless + + # benchmark without RL libraries + python scripts/benchmarks/benchmark_non_rl.py --task=Isaac-Cartpole-v0 --headless + +Each script will generate a set of KPI files at the end of the run, which includes data on the +startup times, runtime statistics, such as the time taken for each simulation or rendering step, +as well as overall environment FPS for stepping the environment, performing inference during +rollout, as well as training. diff --git a/docs/source/overview/reinforcement-learning/rl_existing_scripts.rst b/docs/source/overview/reinforcement-learning/rl_existing_scripts.rst new file mode 100644 index 0000000000000000000000000000000000000000..9ffd47b401e2cbd5a271e228a366bbd272e88bba --- /dev/null +++ b/docs/source/overview/reinforcement-learning/rl_existing_scripts.rst @@ -0,0 +1,298 @@ +Reinforcement Learning Scripts +============================== + +We provide wrappers to different reinforcement libraries. These wrappers convert the data +from the environments into the respective libraries function argument and return types. + + +RL-Games +-------- + +.. attention:: + + When using RL-Games with the Ray workflow for distributed training or hyperparameter tuning, + please be aware that due to security risks associated with Ray, this workflow is not intended + for use outside of a strictly controlled network environment. + +- Training an agent with + `RL-Games `__ on ``Isaac-Ant-v0``: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for rl-games) + ./isaaclab.sh -i rl_games + # run script for training + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-Ant-v0 --headless + # run script for playing with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth + # run script for playing a pre-trained checkpoint with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --num_envs 32 --use_pretrained_checkpoint + # run script for recording video of a trained agent (requires installing `ffmpeg`) + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --headless --video --video_length 200 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for rl-games) + isaaclab.bat -i rl_games + :: run script for training + isaaclab.bat -p scripts\reinforcement_learning\rl_games\train.py --task Isaac-Ant-v0 --headless + :: run script for playing with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth + :: run script for playing a pre-trained checkpoint with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --num_envs 32 --use_pretrained_checkpoint + :: run script for recording video of a trained agent (requires installing `ffmpeg`) + isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --headless --video --video_length 200 + +RSL-RL +------ + +- Training an agent with + `RSL-RL `__ on ``Isaac-Reach-Franka-v0``: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for rsl-rl) + ./isaaclab.sh -i rsl_rl + # run script for training + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Reach-Franka-v0 --headless + # run script for playing with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint /PATH/TO/model.pt + # run script for playing a pre-trained checkpoint with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint + # run script for recording video of a trained agent (requires installing `ffmpeg`) + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for rsl-rl) + isaaclab.bat -i rsl_rl + :: run script for training + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\train.py --task Isaac-Reach-Franka-v0 --headless + :: run script for playing with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint /PATH/TO/model.pt + :: run script for playing a pre-trained checkpoint with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint + :: run script for recording video of a trained agent (requires installing `ffmpeg`) + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200 + +- Training and distilling an agent with + `RSL-RL `__ on ``Isaac-Velocity-Flat-Anymal-D-v0``: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for rsl-rl) + ./isaaclab.sh -i rsl_rl + # run script for rl training of the teacher agent + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless + # run script for distilling the teacher agent into a student agent + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless --agent rsl_rl_distillation_cfg_entry_point --load_run teacher_run_folder_name + # run script for playing the student with 64 environments + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Velocity-Flat-Anymal-D-v0 --num_envs 64 --agent rsl_rl_distillation_cfg_entry_point + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for rsl-rl) + isaaclab.bat -i rsl_rl + :: run script for rl training of the teacher agent + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless + :: run script for distilling the teacher agent into a student agent + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless --agent rsl_rl_distillation_cfg_entry_point --load_run teacher_run_folder_name + :: run script for playing the student with 64 environments + isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Velocity-Flat-Anymal-D-v0 --num_envs 64 --agent rsl_rl_distillation_cfg_entry_point + +SKRL +---- + +- Training an agent with + `SKRL `__ on ``Isaac-Reach-Franka-v0``: + + .. tab-set:: + + .. tab-item:: PyTorch + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for skrl) + ./isaaclab.sh -i skrl + # run script for training + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Reach-Franka-v0 --headless + # run script for playing with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt + # run script for playing a pre-trained checkpoint with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint + # run script for recording video of a trained agent (requires installing `ffmpeg`) + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for skrl) + isaaclab.bat -i skrl + :: run script for training + isaaclab.bat -p scripts\reinforcement_learning\skrl\train.py --task Isaac-Reach-Franka-v0 --headless + :: run script for playing with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt + :: run script for playing a pre-trained checkpoint with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint + :: run script for recording video of a trained agent (requires installing `ffmpeg`) + isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200 + + .. tab-item:: JAX + + .. warning:: + + It is recommended to `install JAX `_ manually before proceeding to install skrl and its dependencies, as JAX installs its CPU version by default. + Visit the **skrl** `installation `_ page for more details. + Note that JAX GPU support is only available on Linux. + + JAX 0.6.0 or higher (built on CuDNN v9.8) is incompatible with Isaac Lab's PyTorch 2.7 (built on CuDNN v9.7), and therefore not supported. + To install a compatible version of JAX for CUDA 12 use ``pip install "jax[cuda12]<0.6.0" "flax<0.10.7"``, for example. + + .. code:: bash + + # install python module (for skrl) + ./isaaclab.sh -i skrl + # install jax<0.6.0 for torch 2.7 + ./isaaclab.sh -p -m pip install "jax[cuda12]<0.6.0" "flax<0.10.7" + # install skrl dependencies for JAX + ./isaaclab.sh -p -m pip install skrl["jax"] + # run script for training + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax + # run script for playing with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --ml_framework jax --checkpoint /PATH/TO/model.pt + # run script for recording video of a trained agent (requires installing `ffmpeg`) + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax --video --video_length 200 + + - Training the multi-agent environment ``Isaac-Shadow-Hand-Over-Direct-v0`` with skrl: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for skrl) + ./isaaclab.sh -i skrl + # run script for training with the MAPPO algorithm (IPPO is also supported) + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Shadow-Hand-Over-Direct-v0 --headless --algorithm MAPPO + # run script for playing with 32 environments with the MAPPO algorithm (IPPO is also supported) + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Shadow-Hand-Over-Direct-v0 --num_envs 32 --algorithm MAPPO --checkpoint /PATH/TO/model.pt + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for skrl) + isaaclab.bat -i skrl + :: run script for training with the MAPPO algorithm (IPPO is also supported) + isaaclab.bat -p scripts\reinforcement_learning\skrl\train.py --task Isaac-Shadow-Hand-Over-Direct-v0 --headless --algorithm MAPPO + :: run script for playing with 32 environments with the MAPPO algorithm (IPPO is also supported) + isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Shadow-Hand-Over-Direct-v0 --num_envs 32 --algorithm MAPPO --checkpoint /PATH/TO/model.pt + +Stable-Baselines3 +----------------- + +- Training an agent with + `Stable-Baselines3 `__ + on ``Isaac-Velocity-Flat-Unitree-A1-v0``: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # install python module (for stable-baselines3) + ./isaaclab.sh -i sb3 + # run script for training + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless + # run script for playing with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip + # run script for playing a pre-trained checkpoint with 32 environments + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint + # run script for recording video of a trained agent (requires installing `ffmpeg`) + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: install python module (for stable-baselines3) + isaaclab.bat -i sb3 + :: run script for training + isaaclab.bat -p scripts\reinforcement_learning\sb3\train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless + :: run script for playing with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip + :: run script for playing a pre-trained checkpoint with 32 environments + isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint + :: run script for recording video of a trained agent (requires installing `ffmpeg`) + isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200 + +All the scripts above log the training progress to `Tensorboard`_ in the ``logs`` directory in the root of +the repository. The logs directory follows the pattern ``logs///``, where ```` +is the name of the learning framework, ```` is the task name, and ```` is the timestamp at +which the training script was executed. + +To view the logs, run: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # execute from the root directory of the repository + ./isaaclab.sh -p -m tensorboard.main --logdir=logs + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: execute from the root directory of the repository + isaaclab.bat -p -m tensorboard.main --logdir=logs + +.. _Tensorboard: https://www.tensorflow.org/tensorboard diff --git a/docs/source/overview/reinforcement-learning/rl_frameworks.rst b/docs/source/overview/reinforcement-learning/rl_frameworks.rst new file mode 100644 index 0000000000000000000000000000000000000000..5f9d25e06e0522cf1ec574779fd6f1a832b2e2ad --- /dev/null +++ b/docs/source/overview/reinforcement-learning/rl_frameworks.rst @@ -0,0 +1,96 @@ +.. _rl-frameworks: + +Reinforcement Learning Library Comparison +========================================= + +In this section, we provide an overview of the supported reinforcement learning libraries in Isaac Lab, +along with performance benchmarks across the libraries. + +The supported libraries are: + +- `SKRL `__ +- `RSL-RL `__ +- `RL-Games `__ +- `Stable-Baselines3 `__ + +Feature Comparison +------------------ + +.. list-table:: + :widths: 20 20 20 20 20 + :header-rows: 1 + + * - Feature + - RL-Games + - RSL RL + - SKRL + - Stable Baselines3 + * - Algorithms Included + - PPO, SAC, A2C + - PPO, Distillation + - `Extensive List `__ + - `Extensive List `__ + * - Vectorized Training + - Yes + - Yes + - Yes + - No + * - Distributed Training + - Yes + - Yes + - Yes + - No + * - ML Frameworks Supported + - PyTorch + - PyTorch + - PyTorch, JAX + - PyTorch + * - Multi-Agent Support + - PPO + - PPO + - PPO + Multi-Agent algorithms + - External projects support + * - Documentation + - Low + - Low + - Comprehensive + - Extensive + * - Community Support + - Small Community + - Small Community + - Small Community + - Large Community + * - Available Examples in Isaac Lab + - Large + - Large + - Large + - Small + + +Training Performance +-------------------- + +We performed training with each RL library on the same ``Isaac-Humanoid-v0`` environment +with ``--headless`` on a single NVIDIA GeForce RTX 4090 and logged the total training time +for 65.5M steps (4096 environments x 32 rollout steps x 500 iterations). + ++--------------------+-----------------+ +| RL Library | Time in seconds | ++====================+=================+ +| RL-Games | 201 | ++--------------------+-----------------+ +| SKRL | 201 | ++--------------------+-----------------+ +| RSL RL | 198 | ++--------------------+-----------------+ +| Stable-Baselines3 | 287 | ++--------------------+-----------------+ + +Training commands (check for the *'Training time: XXX seconds'* line in the terminal output): + +.. code:: bash + + python scripts/reinforcement_learning/rl_games/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless + python scripts/reinforcement_learning/skrl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless + python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless + python scripts/reinforcement_learning/sb3/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless diff --git a/docs/source/overview/reinforcement-learning/training_guide.rst b/docs/source/overview/reinforcement-learning/training_guide.rst new file mode 100644 index 0000000000000000000000000000000000000000..9a220a52003b6c69d79f3cc097258991f1a91777 --- /dev/null +++ b/docs/source/overview/reinforcement-learning/training_guide.rst @@ -0,0 +1,163 @@ +Debugging and Training Guide +============================ + +In this tutorial, we'll guide developers working with Isaac Lab to understand the +impact of various parameters on training time, GPU utilization, and memory usage. +This is especially helpful for addressing Out of Memory (OOM) errors that commonly +occur during reinforcement learning (RL) training. We will touch on common errors seen +during RL training in Isaac Lab and provide some guidance on troubleshooting steps. + + +Training with Parallel Environments +----------------------------------- + +The key RL paradigm of Isaac Lab is to train with many environments in parallel. +Here, we define an environment as an instance of a robot or multiple robots interacting with other robots or objects in simulation. +By creating multiple environments in parallel, we generate multiple copies of the environment such that the robots in each environment can explore the world independently of other environments. +The number of environments thus becomes an important hyperparameter for training. +In general, the more environments we have running in parallel, +the more data we can collect during rollout, which in turn, provides more data +for RL training and allows for faster training since the RL agent can learn from parallel experiences. + +However, the number of environments can also be bounded by other factors. +Memory can often be a hard constraint on the number of environments we can run in parallel. +When more environments are added to the world, the simulation also requires more memory to represent and simulate each object in the scene. +The number of environments we can simulate in parallel thus often depend on the amount of memory resources available on the machine. +In addition, different forms of simulation can also consume various amounts of memory. +For example, objects with high fidelity visual and collision meshes will consume more memory than simple primitive shapes. +Deformable simulation will also likely require more memory to simulate than rigid bodies. + +Training with rendering often consumes much higher memory than running with only physics simulation. This is especially true when rendering at relatively large resolutions. Additionally, when training RL policies with image observations, we often also require more memory to hold the rollout trajectories of image buffers and larger networks for the policies. Both of these components will also impact the amount of memory available for the simulation. + +To reduce memory consumption, one method is to simplify collision meshes of the assets where possible to keep only bare minimum collision shapes required for correct simulation of contacts. +Additionally, we recommend only running with the viewport when debugging with a small number of environments. +When training with larger number of environments in parallel, it is recommended to run in headless mode to avoid any rendering overhead. +If the RL pipeline requires rendering in the loop, make sure to reduce the number of environments, taking into consideration for the dimensions of the image buffers and the size of the policy networks. When hitting out of memory errors, the simplest solution may be to reduce the number of environments. + + +Hyperparameter Tuning +--------------------- + +Although in many cases, simulating more environments in parallel can yield faster training and better results, there are also cases where diminishing returns are observed when the number of environments reaches certain thresholds. +This threshold will vary depending on the complexity of the environment, task, policy setup, and RL algorithm. +When more environments are simulated in parallel, each simulation step requires more time to simulate, which will impact the overall training time. +When the number of environments is small, this increase in per-step simulation time is often insignificant compared to the increase in training performance from more experiences collected. +However, when the number of environments reaches a point, the benefits from having even more experiences for the RL algorithm may start to saturate, and the amount of increased simulation time can outweigh the benefits in training performance. + +In contrast to diminishing returns on number of environments that are too large, training with low number of environments can also be challenging. +This is often due to the RL policies not getting enough experiences to learn from. +To address this issue, it may be helpful to increase the batch size or the horizon length to accommodate for the smaller amount of data collected from lower number of parallel environments. +When the number of environments is constrained by available resources, running with parallel GPUs or training across multiple nodes can also help alleviate issues due to limited rollouts. + + +Debugging NaNs during Training +------------------------------ + +One common error seen during RL training is the appearance of NaNs in the observation buffers, which often get propagated into the policy networks and cause crashes in the downstream training pipeline. +In most cases, the appearance of NaNs occur when the simulation becomes unstable. +This could be due to drastic actions being applied to the robots that exceed the limits of the simulation, or resets of the assets into invalid states. +Some helpful tips to reduce the occurrence of NaNs include proper tuning of the physics parameters for the assets to ensure that joint, velocity, and force limits are within reasonable ranges and the gains are correctly tuned for the robot. +It is also a good idea to check that actions applied to the robots are reasonable and will not impose large forces or impulses on the objects. +Reducing the timestep of the physics simulation can also help improve accuracy and stability of the simulation, in addition to increasing the solver iterations. + + +Understanding Training Outputs +------------------------------ + +Each RL library produces its own output data during training. +Some libraries are more verbose and generate logs that contain more detailed information on the training process, while others are more compact. +In this section, we will explain the common outputs from the RL libraries. + + +RL-Games +^^^^^^^^ + +For each iteration, RL-Games prints statistics of the data collection, inference, and training performance. + +.. code:: bash + + fps step: 112918 fps step and policy inference: 104337 fps total: 78179 epoch: 1/150 frames: 0 + +``fps step`` refers to the environment step FPS, which includes the applying actions, computing observations, rewards, dones, and resets, as well as stepping simulation. + +``step and policy inference`` measure everything in ``fps step`` along with the time it takes for the policy inference to compute the actions. + +``fps total`` measure the above and the time it takes for the training iteration. + +At specified intervals, it will also log the current best reward and the path of the intermmediate checkpoints saved to file. + +.. code:: bash + + => saving checkpoint 'IsaacLab/logs/rl_games/cartpole_direct/2024-12-28_20-23-06/nn/last_cartpole_direct_ep_150_rew_294.18793.pth' + saving next best rewards: [294.18793] + + +RSL RL +^^^^^^ + +For each iteration, RSL RL provides the following output: + +.. code:: bash + + Learning iteration 0/150 + + Computation: 50355 steps/s (collection: 1.106s, learning 0.195s) + Value function loss: 22.0539 + Surrogate loss: -0.0086 + Mean action noise std: 1.00 + Mean reward: -5.49 + Mean episode length: 15.79 + -------------------------------------------------------------------------------- + Total timesteps: 65536 + Iteration time: 1.30s + Total time: 1.30s + ETA: 195.2s + + +This output encapsulates the total FPS for data collection, inference, and learning, along with the per-step breakdown for collection and learning time per step. +In addition, statistics for the training losses are provided, along with the current average reward and episode length. + +In the bottom section, it logs the total number of steps completed so far, the total ieration time for the current ieration, the total overall training time, and the estimated training time to complete the full number of iterations. + + +SKRL +^^^^ + +SKRL provides a very simplistic output showing the training progress as a percentage of the total number of timesteps (divided by the number of environments). It also includes the total elapsed time so far and the estimated time to complete training. + +.. code:: bash + + 0%| | 2/4800 [00:00<10:02, 7.96it/s] + + +Stable-Baselines3 +^^^^^^^^^^^^^^^^^ + +Stable-Baselines3 provides a detailed output, outlining the rollout statistics, timing, and policy data. + +.. code:: bash + + ------------------------------------------ + | rollout/ | | + | ep_len_mean | 30.8 | + | ep_rew_mean | 2.87 | + | time/ | | + | fps | 8824 | + | iterations | 2 | + | time_elapsed | 14 | + | total_timesteps | 131072 | + | train/ | | + | approx_kl | 0.0079056695 | + | clip_fraction | 0.0842 | + | clip_range | 0.2 | + | entropy_loss | -1.42 | + | explained_variance | 0.0344 | + | learning_rate | 0.0003 | + | loss | 10.4 | + | n_updates | 20 | + | policy_gradient_loss | -0.0119 | + | std | 1 | + | value_loss | 17 | + ------------------------------------------ + +Under the ``rollout/`` section, average episode length and reward are logged for the iteration. Under ``time/``, data for the total FPS, number of iterations, total time elapsed, and the total number of timesteps are provided. Finally, under ``train/``, statistics of the training parameters are logged, such as KL, losses, learning rates, and more. diff --git a/docs/source/overview/showroom.rst b/docs/source/overview/showroom.rst new file mode 100644 index 0000000000000000000000000000000000000000..bb2248375749d484a70f42a709d9a10f8a181a33 --- /dev/null +++ b/docs/source/overview/showroom.rst @@ -0,0 +1,384 @@ +Showroom Demos +============== + +The main core interface extension in Isaac Lab ``isaaclab`` provides +the main modules for actuators, objects, robots and sensors. We provide +a list of demo scripts and tutorials. These showcase how to use the provided +interfaces within a code in a minimal way. + +A few quick showroom scripts to run and checkout: + + +- Spawn different arms and apply random joint position commands: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/arms.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\arms.py + + .. image:: ../_static/demos/arms.jpg + :width: 100% + :alt: Arms in Isaac Lab + + +- Spawn different biped robots: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/bipeds.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\bipeds.py + + .. image:: ../_static/demos/bipeds.jpg + :width: 100% + :alt: Biped robots in Isaac Lab + + +- Spawn different deformable (soft) bodies and let them fall from a height: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/deformables.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\deformables.py + + .. image:: ../_static/demos/deformables.jpg + :width: 100% + :alt: Deformable primitive-shaped objects in Isaac Lab + + +- Interactive inference of trained H1 rough terrain locomotion policy: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/h1_locomotion.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\h1_locomotion.py + + .. image:: ../_static/demos/h1_locomotion.jpg + :width: 100% + :alt: H1 locomotion in Isaac Lab + + This is an interactive demo that can be run using the mouse and keyboard. + To enter third-person perspective, click on a humanoid character in the scene. + Once entered into third-person view, the humanoid can be controlled by keyboard using: + + * ``UP``: go forward + * ``LEFT``: turn left + * ``RIGHT``: turn right + * ``DOWN``: stop + * ``C``: switch between third-person and perspective views + * ``ESC``: exit current third-person view + + If a misclick happens outside of the humanoid bodies when selecting a humanoid, + a message is printed to console indicating the error, such as + ``The selected prim was not a H1 robot`` or + ``Multiple prims are selected. Please only select one!``. + + +- Spawn different hands and command them to open and close: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/hands.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\hands.py + + .. image:: ../_static/demos/hands.jpg + :width: 100% + :alt: Dexterous hands in Isaac Lab + + +- Define multiple markers that are useful for visualizations: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/markers.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\markers.py + + .. image:: ../_static/demos/markers.jpg + :width: 100% + :alt: Markers in Isaac Lab + + +- Use the interactive scene and spawn varying assets in individual environments: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/multi_asset.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\multi_asset.py + + .. image:: ../_static/demos/multi_asset.jpg + :width: 100% + :alt: Multiple assets managed through the same simulation handles + + +- Use the RigidObjectCollection spawn and view manipulation to demonstrate bin-packing example: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/bin_packing.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\bin_packing.py + + .. image:: ../_static/demos/bin_packing.jpg + :width: 100% + :alt: Spawning random number of random asset per env_id using combination of MultiAssetSpawner and RigidObjectCollection + + + +- Use the interactive scene and spawn a simple parallel robot for pick and place: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/pick_and_place.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\pick_and_place.py + + .. image:: ../_static/demos/pick_and_place.jpg + :width: 100% + :alt: User controlled pick and place with a parallel robot + + This is an interactive demo that can be run using the mouse and keyboard. + Your goal is pick up the purple cube and to drop it on the red sphere! + Use the following controls to interact with the simulation: + + * Hold the ``A`` key to have the gripper track the cube position. + * Hold the ``D`` key to have the gripper track the target position + * Press the ``W`` or ``S`` keys to move the gantry UP or DOWN respectively + * Press ``Q`` or ``E`` to OPEN or CLOSE the gripper respectively + + + +- Teleoperate a Franka Panda robot using Haply haptic device with force feedback: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py --websocket_uri ws://localhost:10001 --pos_sensitivity 1.65 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\haply_teleoperation.py --websocket_uri ws://localhost:10001 --pos_sensitivity 1.65 + + .. image:: ../_static/demos/haply_teleop_franka.jpg + :width: 100% + :alt: Haply teleoperation with force feedback + + This demo requires Haply Inverse3 and VerseGrip devices. + The goal of this demo is to pick up the cube or touch it with the end-effector. + The Haply devices provide: + + * 3 dimensional position tracking for end-effector control + * Directional force feedback for contact sensing + * Button inputs for gripper and end-effector rotation control + + See :ref:`haply-teleoperation` for detailed setup instructions. + + + +- Create and spawn procedurally generated terrains with different configurations: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/procedural_terrain.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\procedural_terrain.py + + .. image:: ../_static/demos/procedural_terrain.jpg + :width: 100% + :alt: Procedural Terrains in Isaac Lab + + + +- Spawn a quadcopter in the default environment: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/quadcopter.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\quadcopter.py + + .. image:: ../_static/demos/quadcopter.jpg + :width: 100% + :alt: Quadcopter in Isaac Lab + + +- Spawn different quadrupeds and make robots stand using position commands: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/quadrupeds.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\quadrupeds.py + + .. image:: ../_static/demos/quadrupeds.jpg + :width: 100% + :alt: Quadrupeds in Isaac Lab + + +- Spawn a multi-mesh ray caster that uses Warp kernels for raycasting + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type objects + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\demos\sensors\multi_mesh_raycaster.py --num_envs 16 --asset_type objects + + .. image:: ../_static/demos/multi-mesh-raycast.jpg + :width: 100% + :alt: Multi-mesh raycaster in Isaac Lab diff --git a/docs/source/overview/simple_agents.rst b/docs/source/overview/simple_agents.rst new file mode 100644 index 0000000000000000000000000000000000000000..bb17ae3fbe85582aee7cc7a2250bdfab84ac287d --- /dev/null +++ b/docs/source/overview/simple_agents.rst @@ -0,0 +1,120 @@ +Simple Agents +============= + +Workflows +--------- + +With Isaac Lab, we also provide a suite of benchmark environments included +in the ``isaaclab_tasks`` extension. We use the OpenAI Gym registry +to register these environments. For each environment, we provide a default +configuration file that defines the scene, observations, rewards and action spaces. + +The list of environments available registered with OpenAI Gym can be found by running: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/list_envs.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\environments\list_envs.py + +Dummy agents +~~~~~~~~~~~~ + +These include dummy agents that output zero or random agents. They are +useful to ensure that the environments are configured correctly. + +- Zero-action agent on the Cart-pole example + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/zero_agent.py --task Isaac-Cartpole-v0 --num_envs 32 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\environments\zero_agent.py --task Isaac-Cartpole-v0 --num_envs 32 + +- Random-action agent on the Cart-pole example: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/random_agent.py --task Isaac-Cartpole-v0 --num_envs 32 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\environments\random_agent.py --task Isaac-Cartpole-v0 --num_envs 32 + + +State machine +~~~~~~~~~~~~~ + +We include examples on hand-crafted state machines for the environments. These +help in understanding the environment and how to use the provided interfaces. +The state machines are written in `warp `__ which +allows efficient execution for large number of environments using CUDA kernels. + +- Picking up a cube and placing it at a desired pose with a robotic arm: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/lift_cube_sm.py --num_envs 32 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\environments\state_machine\lift_cube_sm.py --num_envs 32 + +- Picking up a deformable teddy bear and placing it at a desired pose with a robotic arm: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/lift_teddy_bear.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts\environments\state_machine\lift_teddy_bear.py diff --git a/docs/source/policy_deployment/00_hover/hover_policy.rst b/docs/source/policy_deployment/00_hover/hover_policy.rst new file mode 100644 index 0000000000000000000000000000000000000000..b7efd80a15e9e5d3d64366932a8cb6d267d50d4a --- /dev/null +++ b/docs/source/policy_deployment/00_hover/hover_policy.rst @@ -0,0 +1,194 @@ +Training & Deploying HOVER Policy +================================= + +This tutorial shows you an example of how to train and deploy HOVER which is a whole-body control (WBC) policy for humanoid robots in the Isaac Lab simulation environment. +It uses the `HOVER`_ repository, which provides an Isaac Lab extension for training neural whole-body control policy for humanoids, as described in the `HOVER Paper`_ and `OMNIH2O Paper`_ papers. +For video demonstrations and more details about the project, please visit the `HOVER Project Website`_ and the `OMNIH2O Project Website`_. + +.. figure:: ../../_static/policy_deployment/00_hover/hover_training_robots.png + :align: center + :figwidth: 100% + :alt: visualization of training the policy + +Installation +------------ + +.. note:: + + This tutorial is for linux only. + + HOVER supports Isaac Lab 2.0 and Isaac Sim 4.5. Please ensure you have the correct version of Isaac Lab and Isaac Sim installed to run the HOVER workflow. + + +1. Install Isaac Lab following the instructions in the `Isaac Lab Installation Guide`_. + +2. Define the following environment variable to specify the path to your Isaac Lab installation: + +.. code-block:: bash + + # Set the ISAACLAB_PATH environment variable to point to your Isaac Lab installation directory + export ISAACLAB_PATH= + +3. Clone the `HOVER`_ repository and its submodules in your workspace. + +.. code-block:: bash + + git clone --recurse-submodules https://github.com/NVlabs/HOVER.git + +4. Install the dependencies. + +.. code-block:: bash + + cd HOVER + ./install_deps.sh + + +Training the Policy +------------------- + +Dataset +~~~~~~~ +Refer to the `HOVER Dataset`_ repository for the steps to obtain and process data for training the policy. + + +Training the teacher policy +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Execute the following command from the ``HOVER`` directory to train the teacher policy. + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/train_teacher_policy.py \ + --num_envs 1024 \ + --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ + --headless + +The teacher policy is trained for 10000000 iterations, or until the user interrupts the training. +The resulting checkpoint is stored in ``neural_wbc/data/data/policy/h1:teacher/`` and the filename is ``model_.pt``. + +Training the student policy +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Execute the following command from the ``HOVER`` directory to train the student policy using teacher policy checkpoint. + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/train_student_policy.py \ + --num_envs 1024 \ + --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ + --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ + --teacher_policy.checkpoint model_.pt \ + --headless + +This assumes that you have already trained the teacher policy as there is no provided teacher policy in the repo. + +Please refer to these sections on the HOVER repository for more details about training configurations: + - `General Remarks for Training`_ + - `Generalist vs Specialist Policy`_ + +Testing the trained policy +-------------------------- + +Play teacher policy +~~~~~~~~~~~~~~~~~~~ +Execute the following command from the ``HOVER`` directory to play the trained teacher policy checkpoint. + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/play.py \ + --num_envs 10 \ + --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ + --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ + --teacher_policy.checkpoint model_.pt + +Play student policy +~~~~~~~~~~~~~~~~~~~ +Execute the following command from the ``HOVER`` directory to play the trained student policy checkpoint. + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/play.py \ + --num_envs 10 \ + --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ + --student_player \ + --student_path neural_wbc/data/data/policy/h1:student \ + --student_checkpoint model_.pt + + +Evaluate the trained policy +--------------------------- +Evaluate the trained policy checkpoint in the Isaac Lab environment. +The evaluation iterates through all the reference motions included in the dataset specified by the ``--reference_motion_path`` option and exits when all motions are evaluated. Randomization is turned off during evaluation. + +Refer to the `HOVER Evaluation`_ repository for more details about the evaluation pipeline and the metrics used. + +The evaluation script, ``scripts/rsl_rl/eval.py``, uses the same arguments as the play script, ``scripts/rsl_rl/play.py``. You can use it for both teacher and student policies. + +.. code-block:: bash + + ${ISAACLAB_PATH}/isaaclab.sh -p scripts/rsl_rl/eval.py \ + --num_envs 10 \ + --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ + --teacher_policy.checkpoint model_.pt + + +Validation of the policy +------------------------ +The trained policy in Isaac Lab can be validated in another simulation environment or on the real robot. + +.. figure:: ../../_static/policy_deployment/00_hover/hover_stable_wave.png + :align: center + :width: 100% + + Stable Wave - Mujoco (left) & Real Robot (right) + +Sim-to-Sim Validation +~~~~~~~~~~~~~~~~~~~~~ +Use the provided `Mujoco Environment`_ for conducting sim-to-sim validation of the trained policy. To run the evaluation of Sim2Sim, + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p neural_wbc/inference_env/scripts/eval.py \ + --num_envs 1 \ + --headless \ + --student_path neural_wbc/data/data/policy/h1:student/ \ + --student_checkpoint model_.pt + +Please be aware that the mujoco_wrapper only supports one environment at a time. For reference, it will take up to 5h to evaluate 8k reference motions. The inference_env is designed for maximum versatility. + + +Sim-to-Real Deployment +~~~~~~~~~~~~~~~~~~~~~~ +For sim-to-real deployment, we provide a `Hardware Environment`_ for `Unitree H1 Robot`_. +Detailed steps of setting up a Sim-to-Real deployment workflow is explained at `README of Sim2Real deployment`_. + +To deploy the trained policy on the H1 robot, + +.. code-block:: bash + + ${ISAACLAB_PATH:?}/isaaclab.sh -p neural_wbc/inference_env/scripts/s2r_player.py \ + --student_path neural_wbc/data/data/policy/h1:student/ \ + --student_checkpoint model_.pt \ + --reference_motion_path neural_wbc/data/data/motions/.pkl \ + --robot unitree_h1 \ + --max_iterations 5000 \ + --num_envs 1 \ + --headless + +.. note:: + + The sim-to-real deployment wrapper currently only supports the Unitree H1 robot. It can be extended to other robots by implementing the corresponding hardware wrapper interface. + + +.. _Isaac Lab Installation Guide: https://isaac-sim.github.io/IsaacLab/v2.0.0/source/setup/installation/index.html +.. _HOVER: https://github.com/NVlabs/HOVER +.. _HOVER Dataset: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#data-processing +.. _HOVER Evaluation: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#evaluation +.. _General Remarks for Training: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#general-remarks-for-training +.. _Generalist vs Specialist Policy: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#generalist-vs-specialist-policy +.. _HOVER Paper: https://arxiv.org/abs/2410.21229 +.. _HOVER Project Website: https://omni.human2humanoid.com/ +.. _OMNIH2O Paper: https://arxiv.org/abs/2410.21229 +.. _OMNIH2O Project Website: https://hover-versatile-humanoid.github.io/ +.. _README of Sim2Real deployment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md +.. _Hardware Environment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md +.. _Mujoco Environment: https://github.com/NVlabs/HOVER/tree/main/neural_wbc/mujoco_wrapper +.. _Unitree H1 Robot: https://unitree.com/h1 diff --git a/docs/source/policy_deployment/01_io_descriptors/io_descriptors_101.rst b/docs/source/policy_deployment/01_io_descriptors/io_descriptors_101.rst new file mode 100644 index 0000000000000000000000000000000000000000..d31de818399af09c9d5989ba838411976630f2ae --- /dev/null +++ b/docs/source/policy_deployment/01_io_descriptors/io_descriptors_101.rst @@ -0,0 +1,281 @@ +IO Descriptors 101 +================== + +.. currentmodule:: isaaclab + +In this tutorial, we will learn about IO descriptors, what they are, how to export them, and how to add them to +your environments. We will use the Anymal-D robot as an example to demonstrate how to export IO descriptors from +an environment, and use our own terms to demonstrate how to attach IO descriptors to custom action and observation terms. + + +What are IO Descriptors? +------------------------ + +Before we dive into IO descriptors, let's first understand what they are and how they can be useful. + +IO descriptors are a way to describe the inputs and outputs of a policy trained using the ManagerBasedRLEnv in Isaac +Lab. In other words, they describe the action and observation terms of a policy. This description is used to generate +a YAML file that can be loaded in an external tool to run the policies without having to manually input the +configuration of the action and observation terms. + +In addition to this the IO Descriptors provide the following information: +- The parameters of all the joints in the articulation. +- Some simulation parameters including the simulation time step, and the policy time step. +- For some action and observation terms, it provides the joint names or body names in the same order as they appear in the action/observation terms. +- For both the observation and action terms, it provides the terms in the exact same order as they appear in the managers. Making it easy to reconstruct them from the YAML file. + +Here is an example of what the action part of the YAML generated from the IO descriptors looks like for the Anymal-D robot: + +.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml + :language: yaml + :lines: 1-39 + +Here is an example of what a portion of the observation part of the YAML generated from the IO descriptors looks like for the Anymal-D robot: + +.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml + :language: yaml + :lines: 158-199 + +.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml + :language: yaml + :lines: 236-279 + +Something to note here is that both the action and observation terms are returned as list of dictionaries, and not a dictionary of dictionaries. +This is done to ensure the order of the terms is preserved. Hence, to retrieve the action or observation term, the users need to look for the +``name`` key in the dictionaries. + +For example, in the following snippet, we are looking at the ``projected_gravity`` observation term. The ``name`` key is used to identify the term. +The ``full_path`` key is used to provide an explicit path to the function in Isaac Lab's source code that is used to compute this term. Some flags +like ``mdp_type`` and ``observation_type`` are also provided, these don't have any functional impact. They are here to inform the user that this is the +category this term belongs to. + +.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml + :language: yaml + :lines: 200-219 + :emphasize-lines: 9, 11 + + +Exporting IO Descriptors from an Environment +-------------------------------------------- + +In this section, we will cover how to export IO descriptors from an environment. +Keep in mind that this feature is only available to the manager based RL environments. + +If a policy has already been trained using a given configuration, then the IO descriptors can be exported using: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/export_io_descriptors.py --task --output_dir + +For example, if we want to export the IO descriptors for the Anymal-D robot, we can run: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/export_io_descriptors.py --task Isaac-Velocity-Flat-Anymal-D-v0 --output_dir ./io_descriptors + +When training a policy, it is also possible to request the IO descriptors to be exported at the beginning of the training. +This can be done by setting the ``export_io_descriptors`` flag in the command line. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors + ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors + + +Attaching IO Descriptors to Custom Observation Terms +---------------------------------------------------- + +In this section, we will cover how to attach IO descriptors to custom observation terms. + +Let's take a look at how we can attach an IO descriptor to a simple observation term: + +.. code-block:: python + + @generic_io_descriptor( + units="m/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] + ) + def base_lin_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Root linear velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_lin_vel_b + +Here, we are defining a custom observation term called ``base_lin_vel`` that computes the root linear velocity of the robot. +We are also attaching an IO descriptor to this term. The IO descriptor is defined using the ``@generic_io_descriptor`` decorator. + +The ``@generic_io_descriptor`` decorator is a special decorator that is used to attach an IO descriptor to a custom observation term. +It takes arbitrary arguments that are used to describe the observation term, in this case we provide extra information that could be +useful for the end user: + +- ``units``: The units of the observation term. +- ``axes``: The axes of the observation term. +- ``observation_type``: The type of the observation term. + +You'll also notice that there is an ``on_inspect`` argument that is provided. This is a list of functions that are used to inspect the observation term. +In this case, we are using the ``record_shape`` and ``record_dtype`` functions to record the shape and dtype of the output of the observation term. + +These functions are defined like so: + +.. code-block:: python + + def record_shape(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the shape of the output tensor. + + Args: + output: The output tensor. + descriptor: The descriptor to record the shape to. + **kwargs: Additional keyword arguments. + """ + descriptor.shape = (output.shape[-1],) + + + def record_dtype(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the dtype of the output tensor. + + Args: + output: The output tensor. + descriptor: The descriptor to record the dtype to. + **kwargs: Additional keyword arguments. + """ + descriptor.dtype = str(output.dtype) + +They always take the output tensor of the observation term as the first argument, and the descriptor as the second argument. +In the ``kwargs`` all the inputs of the observation term are provided. In addition to the ``on_inspect`` functions, the decorator +will also call call some functions in the background to collect the ``name``, the ``description``, and the ``full_path`` of the +observation term. Note that adding this decorator does not change the signature of the observation term, so it can be used safely +with the observation manager! + +Let us now take a look at a more complex example: getting the relative joint positions of the robot. + +.. code-block:: python + + @generic_io_descriptor( + observation_type="JointState", + on_inspect=[record_joint_names, record_dtype, record_shape, record_joint_pos_offsets], + units="rad", + ) + def joint_pos_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint positions of the asset w.r.t. the default joint positions. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids] + +Similarly to the previous example, we are adding an IO descriptor to a custom observation term with a set of functions that probe the observation term. + +To get the name of the joints we can write the following function: + +.. code-block:: python + + def record_joint_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the joint names of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the joint names to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + joint_ids = kwargs["asset_cfg"].joint_ids + if joint_ids == slice(None, None, None): + joint_ids = list(range(len(asset.joint_names))) + descriptor.joint_names = [asset.joint_names[i] for i in joint_ids] + +Note that we can access all the inputs of the observation term in the ``kwargs`` dictionary. Hence we can access the ``asset_cfg``, which contains the +configuration of the articulation that the observation term is computed on. + +To get the offsets, we can write the following function: + +.. code-block:: python + + def record_joint_pos_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs): + """Record the joint position offsets of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the joint position offsets to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + ids = kwargs["asset_cfg"].joint_ids + # Get the offsets of the joints for the first robot in the scene. + # This assumes that all robots have the same joint offsets. + descriptor.joint_pos_offsets = asset.data.default_joint_pos[:, ids][0] + +With this in mind, you should now be able to attach an IO descriptor to your own custom observation terms! However, before +we close this tutorial, let's take a look at how we can attach an IO descriptor to a custom action term. + + +Attaching IO Descriptors to Custom Action Terms +----------------------------------------------- + +In this section, we will cover how to attach IO descriptors to custom action terms. Action terms are classes that +inherit from the :class:`managers.ActionTerm` class. To add an IO descriptor to an action term, we need to expand +upon its :meth:`~managers.ActionTerm.IO_descriptor` property. + +By default, the :meth:`~managers.ActionTerm.IO_descriptor` property returns the base descriptor and fills the following fields: +- ``name``: The name of the action term. +- ``full_path``: The full path of the action term. +- ``description``: The description of the action term. +- ``export``: Whether to export the action term. + +.. code-block:: python + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor for the action term.""" + self._IO_descriptor.name = re.sub(r"([a-z])([A-Z])", r"\1_\2", self.__class__.__name__).lower() + self._IO_descriptor.full_path = f"{self.__class__.__module__}.{self.__class__.__name__}" + self._IO_descriptor.description = " ".join(self.__class__.__doc__.split()) + self._IO_descriptor.export = self.export_IO_descriptor + return self._IO_descriptor + +To add more information to the descriptor, we need to override the :meth:`~managers.ActionTerm.IO_descriptor` property. +Let's take a look at an example on how to add the joint names, scale, offset, and clip to the descriptor. + +.. code-block:: python + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the joint action. + It adds the following information to the base descriptor: + - joint_names: The names of the joints. + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "JointAction" + self._IO_descriptor.joint_names = self._joint_names + self._IO_descriptor.scale = self._scale + # This seems to be always [4xNum_joints] IDK why. Need to check. + if isinstance(self._offset, torch.Tensor): + self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.offset = self._offset + # FIXME: This is not correct. Add list support. + if self.cfg.clip is not None: + if isinstance(self._clip, torch.Tensor): + self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.clip = self._clip + else: + self._IO_descriptor.clip = None + return self._IO_descriptor + +This is it! You should now be able to attach an IO descriptor to your own custom action terms which concludes this tutorial. diff --git a/docs/source/policy_deployment/02_gear_assembly/gear_assembly_policy.rst b/docs/source/policy_deployment/02_gear_assembly/gear_assembly_policy.rst new file mode 100644 index 0000000000000000000000000000000000000000..885b7fb6733a5dff9a471a12a331d2d2faa0dc9d --- /dev/null +++ b/docs/source/policy_deployment/02_gear_assembly/gear_assembly_policy.rst @@ -0,0 +1,605 @@ +.. _walkthrough_sim_to_real: + +Training a Gear Insertion Policy and ROS Deployment +==================================================== + +This tutorial walks you through how to train a gear insertion assembly reinforcement learning (RL) policy that transfers from simulation to a real robot. The workflow consists of two main stages: + +1. **Simulation Training in Isaac Lab**: Train the policy in a high-fidelity physics simulation with domain randomization +2. **Real Robot Deployment with Isaac ROS**: Deploy the trained policy on real hardware using Isaac ROS and a custom ROS inference node + +This walkthrough covers the key principles and best practices for sim-to-real transfer using Isaac Lab, illustrated with a real-world example: + +- the Gear Assembly task for the UR10e robot with the Robotiq 2F-140 gripper or 2F-85 gripper + +**Task Details:** + +The gear assembly policy operates as follows: + +1. **Initial State**: The policy assumes the gear is already grasped by the gripper at the start of the episode +2. **Input Observations**: The policy receives the pose of the gear shaft (position and orientation) in which the gear should be inserted, obtained from a separate perception pipeline +3. **Policy Output**: The policy outputs delta joint positions (incremental changes to joint angles) to control the robot arm and perform the insertion +4. **Generalization**: The trained policy generalizes across 3 different gear sizes without requiring retraining for each size + + +.. figure:: ../../_static/policy_deployment/02_gear_assembly/gear_assembly_sim_real.webm + :align: center + :figwidth: 100% + :alt: Comparison of gear assembly in simulation versus real hardware + + Sim-to-real transfer: Gear assembly policy trained in Isaac Lab (left) successfully deployed on real UR10e robot (right). + +This environment has been successfully deployed on real UR10e robots without an IsaacLab dependency. + +**Scope of This Tutorial:** + +This tutorial focuses exclusively on the **training part** of the sim-to-real transfer workflow in Isaac Lab. For the complete deployment workflow on the real robot, including the exact steps to set up the vision pipeline, robot interface and the ROS inference node to run your trained policy on real hardware, please refer to the `Isaac ROS Documentation `_. + +Overview +-------- + +Successful sim-to-real transfer requires addressing three fundamental aspects: + +1. **Input Consistency**: Ensuring the observations your policy receives in simulation match those available on the real robot +2. **System Response Consistency**: Ensuring the robot and environment respond to actions in simulation the same way they do in reality +3. **Output Consistency**: Ensuring any post-processing applied to policy outputs in Isaac Lab is also applied during real-world inference + +When all three aspects are properly addressed, policies trained purely in simulation can achieve robust performance on real hardware without any real-world training data. + +**Debugging Tip**: When your policy fails on the real robot, the best approach to debug is to set up the real robot with the same initial observations as in simulation, then compare how the controller/system respond. This isolates whether the problem is from observation mismatch (Input Consistency) or physics/controller mismatch (System Response Consistency). + +Part 1: Input Consistency +-------------------------- + +The observations your policy receives must be consistent between simulation and reality. This means: + +1. The observation space should only include information available from real sensors +2. Sensor noise and delays should be modeled appropriately + +Using Real-Robot-Available Observations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Your simulation environment should only use observations that are available on the real robot and not use "privileged" information that wouldn't be available in deployment. + + +Observation Specification: Isaac-Deploy-GearAssembly-UR10e-2F140-v0 +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The Gear Assembly environment uses both proprioceptive and exteroceptive (vision) observations: + +.. list-table:: Gear Assembly Environment Observations + :widths: 25 25 25 25 + :header-rows: 1 + + * - Observation + - Dimension + - Real-World Source + - Noise in Training + * - ``joint_pos`` + - 6 (UR10e arm joints) + - UR10e controller + - None (proprioceptive) + * - ``joint_vel`` + - 6 (UR10e arm joints) + - UR10e controller + - None (proprioceptive) + * - ``gear_shaft_pos`` + - 3 (x, y, z position) + - FoundationPose + RealSense depth + - ±0.005 m (5mm, estimated error from FoundationPose + RealSense depth pipeline) + * - ``gear_shaft_quat`` + - 4 (quaternion orientation) + - FoundationPose + RealSense depth + - ±0.01 per component (~5° angular error, estimated error from FoundationPose + RealSense depth pipeline) + +**Implementation:** + +.. code-block:: python + + from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # Robot joint states - NO noise for proprioceptive observations + joint_pos = ObsTerm( + func=mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["shoulder_pan_joint", ...])}, + ) + + joint_vel = ObsTerm( + func=mdp.joint_vel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["shoulder_pan_joint", ...])}, + ) + + # Gear shaft pose from FoundationPose perception + # ADD noise for exteroceptive (vision-based) observations + # Calibrated to match FoundationPose + RealSense D435 error + # Typical error: 3-8mm position, 3-7° orientation + gear_shaft_pos = ObsTerm( + func=mdp.gear_shaft_pos_w, + params={"asset_cfg": SceneEntityCfg("factory_gear_base")}, + noise=Unoise(n_min=-0.005, n_max=0.005), # ±5mm + ) + + # Quaternion noise: small uniform noise on each component + # Results in ~5° orientation error + gear_shaft_quat = ObsTerm( + func=mdp.gear_shaft_quat_w, + params={"asset_cfg": SceneEntityCfg("factory_gear_base")}, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + + def __post_init__(self): + self.enable_corruption = True # Enable for perception observations only + self.concatenate_terms = True + +**Why No Noise for Proprioceptive Observations?** + +Empirically, we found that policies trained without noise on proprioceptive observations (joint positions and velocities) transfer well to real hardware. The UR10e controller provides sufficiently accurate joint state feedback that modeling sensor noise doesn't improve sim-to-real transfer for these tasks. + + +Part 2: System Response Consistency +------------------------------------ + +Once your observations are consistent, you need to ensure the simulated robot and environment respond to actions the same way the real system does. In this use case, this involves three main aspects: + +1. Physics simulation parameters (friction, contact properties) +2. Actuator modeling (PD controller gains, effort limits) +3. Domain randomization + +Physics Parameter Tuning +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Accurate physics simulation is critical for contact-rich tasks. Key parameters include: + +- Friction coefficients (static and dynamic) +- Contact solver parameters +- Material properties +- Rigid body properties + +**Example: Gear Assembly Physics Configuration** + +The Gear Assembly task requires accurate contact modeling for insertion. Here's how friction is configured: + +.. code-block:: python + + # From joint_pos_env_cfg.py in Isaac-Deploy-GearAssembly-UR10e-2F140-v0 + + @configclass + class EventCfg: + """Configuration for events including physics randomization.""" + + # Randomize friction for gear objects + small_gear_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_gear_small", body_names=".*"), + "static_friction_range": (0.75, 0.75), # Calibrated to real gear material + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), # No bounce + "num_buckets": 16, + }, + ) + + # Similar configuration for gripper fingers + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*finger"), + "static_friction_range": (0.75, 0.75), # Calibrated to real gripper + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + +These friction values (0.75) were determined through iterative visual comparison: + +1. Record videos of the gear being grasped and manipulated on real hardware +2. Start training in simulation and observe the live simulation viewer +3. Look for physics issues (penetration, unrealistic slipping, poor contact) +4. Adjust friction coefficients and solver parameters and retry +5. Compare the gear's behavior in the gripper visually between sim and real +6. Repeat adjustments until behavior matches (no need to wait for full policy training) +7. Once physics looks good, train in headless mode with video recording: + + .. code-block:: bash + + python scripts/reinforcement_learning/rsl_rl/train.py \ + --task Isaac-Deploy-GearAssembly-UR10e-2F140-v0 \ + --headless \ + --video --video_length 800 --video_interval 5000 + +8. Review the recorded videos and compare with real hardware videos to verify physics behavior + +**Contact Solver Configuration** + +Contact-rich manipulation requires careful solver tuning. These parameters were calibrated through the same iterative visual comparison process as the friction coefficients: + +.. code-block:: python + + # Robot rigid body properties + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, # Robot is mounted, no gravity + max_depenetration_velocity=5.0, # Control interpenetration resolution + linear_damping=0.0, # No artificial damping + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, # Important for accurate dynamics + solver_position_iteration_count=4, # Balance accuracy vs performance + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, # Allow large contact forces + ), + +**Important**: The ``solver_position_iteration_count`` is a critical parameter for contact-rich tasks. Increasing this value improves collision simulation stability and reduces penetration issues, but it also increases simulation and training time. For the gear assembly task, we use ``solver_position_iteration_count=4`` as a balance between physics accuracy and computational performance. If you observe penetration or unstable contacts, try increasing to 8 or 16, but expect slower training. + +.. code-block:: python + + # Articulation properties + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=1, + ), + + # Contact properties + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, # 5mm contact detection distance + rest_offset=0.0, # Objects touch at 0 distance + ), + +Actuator Modeling +~~~~~~~~~~~~~~~~~ + +Accurate actuator modeling ensures the simulated robot moves like the real one. This includes: + +- PD controller gains (stiffness and damping) +- Effort and velocity limits +- Joint friction + +**Controller Choice: Impedance Control** + +For the UR10e deployment, we use an impedance controller interface. Using a simpler controller like impedance control reduces the chances of variation between simulation and reality compared to more complex controllers (e.g., operational space control, hybrid force-position control). Simpler controllers: + +- Have fewer parameters that can mismatch between sim and real +- Are easier to model accurately in simulation +- Have more predictable behavior that's easier to replicate +- Reduce the controller complexity as a source of sim-real gap + +**Example: UR10e Actuator Configuration** + +.. code-block:: python + + # Default UR10e actuator configuration + actuators = { + "arm": ImplicitActuatorCfg( + joint_names_expr=["shoulder_pan_joint", "shoulder_lift_joint", + "elbow_joint", "wrist_1_joint", "wrist_2_joint", "wrist_3_joint"], + effort_limit=87.0, # From UR10e specifications + velocity_limit=2.0, # From UR10e specifications + stiffness=800.0, # Calibrated to match real behavior + damping=40.0, # Calibrated to match real behavior + ), + } + +**Domain Randomization of Actuator Parameters** + +To account for variations in real robot behavior, randomize actuator gains during training: + +.. code-block:: python + + # From EventCfg in the Gear Assembly environment + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["shoulder_.*", "elbow_.*", "wrist_.*"]), + "stiffness_distribution_params": (0.75, 1.5), # 75% to 150% of nominal + "damping_distribution_params": (0.3, 3.0), # 30% to 300% of nominal + "operation": "scale", + "distribution": "log_uniform", + }, + ) + + +**Joint Friction Randomization** + +Real robots have friction in their joints that varies with position, velocity, and temperature. For the UR10e with impedance controller interface, we observed significant stiction (static friction) causing the controller to not reach target joint positions. + +**Characterizing Real Robot Behavior:** + +To quantify this behavior, we plotted the step response of the impedance controller on the real robot and observed contact offsets of approximately 0.25 degrees from the commanded setpoint. This steady-state error is caused by joint friction opposing the controller's commanded motion. Based on these measurements, we added joint friction modeling in simulation to replicate this behavior: + +.. code-block:: python + + joint_friction = EventTerm( + func=mdp.randomize_joint_parameters, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["shoulder_.*", "elbow_.*", "wrist_.*"]), + "friction_distribution_params": (0.3, 0.7), # Add 0.3 to 0.7 Nm friction + "operation": "add", + "distribution": "uniform", + }, + ) + +**Why Joint Friction Matters**: Without modeling joint friction in simulation, the policy learns to expect that commanded joint positions are always reached. On the real robot, stiction prevents small movements and causes steady-state errors. By adding friction during training, the policy learns to account for these effects and commands appropriately larger motions to overcome friction. + +**Compensating for Stiction with Action Scaling:** + +To help the policy overcome stiction on the real robot, we also increased the output action scaling. The Isaac ROS documentation notes that a higher action scale (0.0325 vs 0.025) is needed to overcome the higher static friction (stiction) compared to the 2F-85 gripper. This increased scaling ensures the policy commands are large enough to overcome the friction forces observed in the step response analysis. + +Action Space Design +~~~~~~~~~~~~~~~~~~~ + +Your action space should match what the real robot controller can execute. For this task we found that **incremental joint position control** is the most reliable approach. + +**Example: Gear Assembly Action Configuration** + +.. code-block:: python + + # For contact-rich manipulation, smaller action scale for more precise control + self.joint_action_scale = 0.025 # ±2.5 degrees per step + + self.actions.arm_action = mdp.RelativeJointPositionActionCfg( + asset_name="robot", + joint_names=["shoulder_pan_joint", "shoulder_lift_joint", "elbow_joint", + "wrist_1_joint", "wrist_2_joint", "wrist_3_joint"], + scale=self.joint_action_scale, + use_zero_offset=True, + ) + +The action scale is a critical hyperparameter that should be tuned based on: + +- Task precision requirements (smaller for contact-rich tasks) +- Control frequency (higher frequency allows larger steps) + +Domain Randomization Strategy +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Domain randomization should cover the range of conditions in which you want the real robot to perform. Increasing randomization ranges makes it harder for the policy to learn, but allows for larger variations in inputs and system parameters. The key is to balance training difficulty with robustness: randomize enough to cover real-world variations, but not so much that the policy cannot learn effectively. + +**Pose Randomization** + +For manipulation tasks, randomize object poses to ensure the policy works across the workspace: + +.. code-block:: python + + # From Gear Assembly environment + randomize_gears_and_base_pose = EventTerm( + func=gear_assembly_events.randomize_gears_and_base_pose, + mode="reset", + params={ + "pose_range": { + "x": [-0.1, 0.1], # ±10cm + "y": [-0.25, 0.25], # ±25cm + "z": [-0.1, 0.1], # ±10cm + "roll": [-math.pi/90, math.pi/90], # ±2 degrees + "pitch": [-math.pi/90, math.pi/90], # ±2 degrees + "yaw": [-math.pi/6, math.pi/6], # ±30 degrees + }, + "gear_pos_range": { + "x": [-0.02, 0.02], # ±2cm relative to base + "y": [-0.02, 0.02], + "z": [0.0575, 0.0775], # 5.75-7.75cm above base + }, + "rot_randomization_range": { + "roll": [-math.pi/36, math.pi/36], # ±5 degrees + "pitch": [-math.pi/36, math.pi/36], + "yaw": [-math.pi/36, math.pi/36], + }, + }, + ) + +**Initial State Randomization** + +Randomizing the robot's initial configuration helps the policy handle different starting conditions: + +.. code-block:: python + + set_robot_to_grasp_pose = EventTerm( + func=gear_assembly_events.set_robot_to_grasp_pose, + mode="reset", + params={ + "robot_asset_cfg": SceneEntityCfg("robot"), + "rot_offset": [0.0, math.sqrt(2)/2, math.sqrt(2)/2, 0.0], # Base gripper orientation + "pos_randomization_range": { + "x": [-0.0, 0.0], + "y": [-0.005, 0.005], # ±5mm variation + "z": [-0.003, 0.003], # ±3mm variation + }, + "gripper_type": "2f_140", + }, + ) + +Part 3: Training the Policy in Isaac Lab +----------------------------------------- + +Now that we've covered the key principles for sim-to-real transfer, let's train the gear assembly policy in Isaac Lab. + +Step 1: Visualize the Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First, launch the training with a small number of environments and visualization enabled to verify that the environment is set up correctly: + +.. code-block:: bash + + # Launch training with visualization + python scripts/reinforcement_learning/rsl_rl/train.py \ + --task Isaac-Deploy-GearAssembly-UR10e-2F140-v0 \ + --num_envs 4 + +.. note:: + + For the Robotiq 2F-85 gripper, use ``--task Isaac-Deploy-GearAssembly-UR10e-2F85-v0`` instead. + +This will open the Isaac Sim viewer where you can observe the training process in real-time. + +.. figure:: ../../_static/policy_deployment/02_gear_assembly/sim_real_gear_assembly_train.jpg + :align: center + :figwidth: 100% + :alt: Gear assembly training visualization in Isaac Lab + + Training visualization showing multiple parallel environments with robots grasping gears. + +**What to Expect:** + +In the early stages of training, you should see the robots moving around with the gears grasped by the grippers, but they won't be successfully inserting the gears yet. This is expected behavior as the policy is still learning. The robots will move the grasped gear in various directions. Once you've verified the environment looks correct, stop the training (Ctrl+C) and proceed to full-scale training. + +Step 2: Full-Scale Training with Video Recording +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now launch the full training run with more parallel environments in headless mode for faster training. We'll also enable video recording to monitor progress: + +.. code-block:: bash + + # Full training with video recording + python scripts/reinforcement_learning/rsl_rl/train.py \ + --task Isaac-Deploy-GearAssembly-UR10e-2F140-v0 \ + --headless \ + --num_envs 256 \ + --video --video_length 800 --video_interval 5000 + +This command will: + +- Run 256 parallel environments for efficient training +- Run in headless mode (no visualization) for maximum performance +- Record videos every 5000 steps to monitor training progress +- Save videos with 800 frames each + +Training typically takes ~12-24 hours for a robust insertion policy. The videos will be saved in the ``logs`` directory and can be reviewed to assess policy performance during training. + +.. note:: + + **GPU Memory Considerations**: The default configuration uses 256 parallel environments, which should work on most modern GPUs (e.g., RTX 3090, RTX 4090, A100). For better sim-to-real transfer performance, you can increase ``solver_position_iteration_count`` from 4 to 196 in ``gear_assembly_env_cfg.py`` and ``joint_pos_env_cfg.py`` for more realistic contact simulation, but this requires a larger GPU (e.g., RTX PRO 6000 with 40GB+ VRAM). Higher solver iteration counts reduce penetration and improve contact stability but significantly increase GPU memory usage. + + +**Monitoring Training Progress with TensorBoard:** + +You can monitor training metrics in real-time using TensorBoard. Open a new terminal and run: + +.. code-block:: bash + + ./isaaclab.sh -p -m tensorboard.main --logdir + +Replace ```` with the path to your training logs (e.g., ``logs/rsl_rl/gear_assembly_ur10e/2025-11-19_19-31-01``). TensorBoard will display plots showing rewards, episode lengths, and other metrics. Verify that the rewards are increasing over iterations to ensure the policy is learning successfully. + + +Step 3: Deploy on Real Robot +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once training is complete, follow the `Isaac ROS inference documentation `_ to deploy your policy. + +The Isaac ROS deployment pipeline directly uses the trained model checkpoint (``.pt`` file) along with the ``agent.yaml`` and ``env.yaml`` configuration files generated during training. No additional export step is required. + +The deployment pipeline uses Isaac ROS and a custom ROS inference node to run the policy on real hardware. The pipeline includes: + +1. **Perception**: Camera-based pose estimation (FoundationPose, Segment Anything) +2. **Motion Planning**: cuMotion for collision-free trajectories +3. **Policy Inference**: Your trained policy running at control frequency in a custom ROS inference node +4. **Robot Control**: Low-level controller executing commands + + +Troubleshooting +--------------- + +This section covers common errors you may encounter during training and their solutions. + +PhysX Collision Stack Overflow +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**Error Message:** + +.. code-block:: text + + PhysX error: PxGpuDynamicsMemoryConfig::collisionStackSize buffer overflow detected, + please increase its size to at least 269452544 in the scene desc! + Contacts have been dropped. + +**Cause:** This error occurs when the GPU collision detection buffer is too small for the number of contacts being simulated. This is common in contact-rich environments like gear assembly. + +**Solution:** Increase the ``gpu_collision_stack_size`` parameter in ``gear_assembly_env_cfg.py``: + +.. code-block:: python + + # In GearAssemblyEnvCfg class + sim: SimulationCfg = SimulationCfg( + physx=PhysxCfg( + gpu_collision_stack_size=2**31, # Increase this value if you see overflow errors + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + ), + ) + +The error message will suggest a minimum size. Set ``gpu_collision_stack_size`` to at least the recommended value (e.g., if the error says "at least 269452544", set it to ``2**28`` or ``2**29``). Note that increasing this value increases GPU memory usage. + +CUDA Out of Memory +~~~~~~~~~~~~~~~~~~ + +**Error Message:** + +.. code-block:: text + + torch.OutOfMemoryError: CUDA out of memory. + +**Cause:** The GPU does not have enough memory to run the requested number of parallel environments with the current simulation parameters. + +**Solutions (in order of preference):** + +1. **Reduce the number of parallel environments:** + + .. code-block:: bash + + python scripts/reinforcement_learning/rsl_rl/train.py \ + --task Isaac-Deploy-GearAssembly-UR10e-2F140-v0 \ + --headless \ + --num_envs 128 # Reduce from 256 to 128, 64, etc. + + **Trade-off:** Using fewer environments will reduce sample diversity per training iteration and may slow down training convergence. You may need to train for more iterations to achieve the same performance. However, the final policy quality should be similar. + +2. **If using increased solver iteration counts** (values higher than the default 4): + + In both ``gear_assembly_env_cfg.py`` and ``joint_pos_env_cfg.py``, reduce ``solver_position_iteration_count`` back to the default value of 4, or use intermediate values like 8 or 16: + + .. code-block:: python + + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, # Use default value + # ... other parameters + ), + + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + solver_position_iteration_count=4, # Use default value + # ... other parameters + ), + + **Trade-off:** Lower solver iteration counts may result in less realistic contact dynamics and more penetration issues. The default value of 4 provides a good balance for most use cases. + +3. **Disable video recording during training:** + + Remove the ``--video`` flags to save GPU memory: + + .. code-block:: bash + + python scripts/reinforcement_learning/rsl_rl/train.py \ + --task Isaac-Deploy-GearAssembly-UR10e-2F140-v0 \ + --headless \ + --num_envs 256 + + You can always evaluate the trained policy later with visualization. + + +Further Resources +----------------- + +- `IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality `_ +- `FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty `_ +- Sim-to-Real Policy Transfer for Whole Body Controllers: :ref:`sim2real` - Shows how to train and deploy a whole body controller for legged robots using Isaac Lab with the Newton backend +- `Isaac ROS Manipulation Documentation `_ +- `Isaac ROS Gear Assembly Tutorial `_ +- RL Training Tutorial: :ref:`tutorial-run-rl-training` diff --git a/docs/source/policy_deployment/index.rst b/docs/source/policy_deployment/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..3ee100f221749e1df40c120de896b00bf28800f6 --- /dev/null +++ b/docs/source/policy_deployment/index.rst @@ -0,0 +1,13 @@ +Sim2Real Deployment of Policies Trained in Isaac Lab +==================================================== + +Welcome to the Policy Deployment Guide! This section provides examples of training policies in Isaac Lab and deploying them to both simulation and real robots. + +Below, you’ll find detailed examples of various policies for training and deploying them, along with essential configuration details. + +.. toctree:: + :maxdepth: 1 + + 00_hover/hover_policy + 01_io_descriptors/io_descriptors_101 + 02_gear_assembly/gear_assembly_policy diff --git a/docs/source/refs/additional_resources.rst b/docs/source/refs/additional_resources.rst new file mode 100644 index 0000000000000000000000000000000000000000..16913b36d2eea2ced9413ccca2a38a6e327080e9 --- /dev/null +++ b/docs/source/refs/additional_resources.rst @@ -0,0 +1,36 @@ +Additional Resources +==================== + +Here we provide external links to tools and various resources that you may also find useful. + + +Sim-to-Real Resources +--------------------- + +One of the core goals of the broader Isaac project is to bring real robots to life through the power of NVIDIA technology. There are many ways to do this, and thus, many tools that you could use. These resources are dedicated to helping you navigate these possibilities by providing examples and discussions about closing the Sim-to-Real gap and deploying policies to actual real robots. + +* `Closing the Sim-to-Real Gap: Training Spot Quadruped Locomotion with NVIDIA Isaac Lab `_ is a detailed guide for training a quadruped locomotion policy for the Spot Quadruped from Boston Dynamics, and deploying it to the real robot. + + +LLM Generated Reward Functions +------------------------------ + +Our research endeavor, ``Eureka!``, has resulted in a pipeline for generating and tuning Reinforcement Learning (RL) reward functions using an LLM. These resources are dedicated to helping you utilize this pipeline to create RL based solutions to tasks that were once thought impossible! + +* `Isaac Lab Eureka `_ is a github repository where you can setup your own LLM reward generation pipeline for your direct RL environments built in Isaac Lab! + +* `Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning `_ is a blog post that covers the broad idea of this reward generation process. + + +Simulation Features +------------------- + +At the heart of Isaac Lab is Isaac Sim, which is itself a feature rich tool that is useful for robotics in general, and not only for RL. The stronger your understanding of the simulation, the readily you will be able to exploit its capabilities for your own projects and applications. These resources are dedicated to informing you about the other features of the simulation that may be useful to you given your specific interest in Isaac Lab! + +* `Simulation Performance Guide `_ is a best practice guide for obtaining the best simulation performance from OmniPhysics. + +* `Deploying Policies in Isaac Sim `_ is an Isaac Sim tutorial on how to use trained policies within the simulation. + +* `Supercharge Robotics Workflows with AI and Simulation Using NVIDIA Isaac Sim 4.0 and NVIDIA Isaac Lab `_ is a blog post covering the newest features of Isaac Sim 4.0, including ``pip install``, a more advanced physics engine, updated sensor simulations, and more! + +* `Fast-Track Robot Learning in Simulation Using NVIDIA Isaac Lab `_ is a blog post covering the gamut of features for accelerated robot learning through Isaac Lab. diff --git a/docs/source/refs/bibliography.rst b/docs/source/refs/bibliography.rst new file mode 100644 index 0000000000000000000000000000000000000000..0d21440d01c35639698c298f9415e74db4134ecc --- /dev/null +++ b/docs/source/refs/bibliography.rst @@ -0,0 +1,4 @@ +Bibliography +============ + +.. bibliography:: diff --git a/docs/source/refs/changelog.rst b/docs/source/refs/changelog.rst new file mode 100644 index 0000000000000000000000000000000000000000..1fb156e6d444a93d0d3b3b8271c1af7c414b5b32 --- /dev/null +++ b/docs/source/refs/changelog.rst @@ -0,0 +1,38 @@ +Extensions Changelog +==================== + +All notable changes to this project are documented in this file. The format is based on +`Keep a Changelog `__ and this project adheres to +`Semantic Versioning `__. For a broader information +about the changes in the framework, please refer to the +`release notes `__. + +Each extension has its own changelog. The changelog for each extension is located in the +``docs`` directory of the extension. The changelog for each extension is also included in +this changelog to make it easier to find the changelog for a specific extension. + +isaaclab +-------------- + +Extension containing the core framework of Isaac Lab. + +.. include:: ../../../source/isaaclab/docs/CHANGELOG.rst + :start-line: 3 + + +isaaclab_assets +--------------------- + +Extension for configurations of various assets and sensors for Isaac Lab. + +.. include:: ../../../source/isaaclab_assets/docs/CHANGELOG.rst + :start-line: 3 + + +isaaclab_tasks +-------------------- + +Extension containing the environments built using Isaac Lab. + +.. include:: ../../../source/isaaclab_tasks/docs/CHANGELOG.rst + :start-line: 3 diff --git a/docs/source/refs/contributing.rst b/docs/source/refs/contributing.rst new file mode 100644 index 0000000000000000000000000000000000000000..411742fd19e81bb1b2a810ba8c556e68fa1bcaf9 --- /dev/null +++ b/docs/source/refs/contributing.rst @@ -0,0 +1,539 @@ +Contribution Guidelines +======================= + +We wholeheartedly welcome contributions to the project to make the framework more mature +and useful for everyone. These may happen in forms of: + +* Bug reports: Please report any bugs you find in the `issue tracker `__. +* Feature requests: Please suggest new features you would like to see in the `discussions `__. +* Code contributions: Please submit a `pull request `__. + + * Bug fixes + * New features + * Documentation improvements + * Tutorials and tutorial improvements + +We prefer GitHub `discussions `_ for discussing ideas, +asking questions, conversations and requests for new features. + +Please use the +`issue tracker `_ only to track executable pieces of work +with a definite scope and a clear deliverable. These can be fixing bugs, new features, or general updates. + + +Contributing Code +----------------- + +.. attention:: + + Please refer to the `Google Style Guide `__ + for the coding style before contributing to the codebase. In the coding style section, + we outline the specific deviations from the style guide that we follow in the codebase. + +We use `GitHub `__ for code hosting. Please +follow the following steps to contribute code: + +1. Create an issue in the `issue tracker `__ to discuss + the changes or additions you would like to make. This helps us to avoid duplicate work and to make + sure that the changes are aligned with the roadmap of the project. +2. Fork the repository. +3. Create a new branch for your changes. +4. Make your changes and commit them. +5. Push your changes to your fork. +6. Submit a pull request to the `main branch `__. +7. Ensure all the checks on the pull request template are performed. + +After sending a pull request, the maintainers will review your code and provide feedback. + +Please ensure that your code is well-formatted, documented and passes all the tests. + +.. tip:: + + It is important to keep the pull request as small as possible. This makes it easier for the + maintainers to review your code. If you are making multiple changes, please send multiple pull requests. + Large pull requests are difficult to review and may take a long time to merge. + + +More details on the code style and testing can be found in the `Coding Style`_ and `Unit Testing`_ sections. + + +Contributing Documentation +-------------------------- + +Contributing to the documentation is as easy as contributing to the codebase. All the source files +for the documentation are located in the ``IsaacLab/docs`` directory. The documentation is written in +`reStructuredText `__ format. + +We use `Sphinx `__ with the +`Book Theme `__ +for maintaining the documentation. + +Sending a pull request for the documentation is the same as sending a pull request for the codebase. +Please follow the steps mentioned in the `Contributing Code`_ section. + +.. caution:: + + To build the documentation, we recommend creating a `virtual environment `__ + to install the dependencies. This can also be a `conda environment `__. + + +To build the documentation, run the following command in the terminal which installs the required python packages and +builds the documentation using the ``docs/Makefile``: + +.. code:: bash + + ./isaaclab.sh --docs # or "./isaaclab.sh -d" + +The documentation is generated in the ``docs/_build`` directory. To view the documentation, open +the ``index.html`` file in the ``html`` directory. This can be done by running the following command +in the terminal: + +.. code:: bash + + xdg-open docs/_build/current/index.html + +.. hint:: + + The ``xdg-open`` command is used to open the ``index.html`` file in the default browser. If you are + using a different operating system, you can use the appropriate command to open the file in the browser. + + +To do a clean build, run the following command in the terminal: + +.. code:: bash + + rm -rf docs/_build && ./isaaclab.sh --docs + + +Contributing assets +------------------- + +Currently, we host the assets for the extensions on `NVIDIA Nucleus Server `__. +Nucleus is a cloud-based storage service that allows users to store and share large files. It is +integrated with the `NVIDIA Omniverse Platform `__. + +Since all assets are hosted on Nucleus, we do not need to include them in the repository. However, +we need to include the links to the assets in the documentation. + +Please checkout the `Isaac Sim Assets `__ +for more information on what is presently available. + +.. attention:: + + We are currently working on a better way to contribute assets. We will update this section once we + have a solution. In the meantime, please follow the steps mentioned below. + +To host your own assets, the current solution is: + +1. Create a separate repository for the assets and add it over there +2. Make sure the assets are licensed for use and distribution +3. Include images of the assets in the README file of the repository +4. Send a pull request with a link to the repository + +We will then verify the assets, its licensing, and include the assets into the Nucleus server for hosting. +In case you have any questions, please feel free to reach out to us through e-mail or by opening an issue +in the repository. + + +Maintaining a changelog and extension.toml +------------------------------------------ + +Each extension maintains a changelog in the ``CHANGELOG.rst`` file in the ``docs`` directory, +as well as a ``extension.toml`` file in the ``config`` directory. + +The ``extension.toml`` file contains the metadata for the extension. It is used to describe the +name, version, description, and other metadata of the extension. + +The ``CHANGELOG.rst`` is a file that contains the curated, chronologically ordered list of notable changes +for each version of the extension. + +.. note:: + + The version number on the ``extension.toml`` file should be updated according to + `Semantic Versioning `__ and should match the version number in the + ``CHANGELOG.rst`` file. + +The changelog file is written in `reStructuredText `__ format. +The goal of this changelog is to help users and contributors see precisely what notable changes have +been made between each release (or version) of the extension. This is a *MUST* for every extension. + +For updating the changelog, please follow the following guidelines: + +* Each version should have a section with the version number and the release date. +* The version number is updated according to `Semantic Versioning `__. The + release date is the date on which the version is released. +* Each version is divided into subsections based on the type of changes made. + + * ``Added``: For new features. + * ``Changed``: For changes in existing functionality. + * ``Deprecated``: For soon-to-be removed features. + * ``Removed``: For now removed features. + * ``Fixed``: For any bug fixes. + +* Each change is described in its corresponding sub-section with a bullet point. +* The bullet points are written in the **past tense**. + + * This means that the change is described as if it has already happened. + * The bullet points should be concise and to the point. They should not be verbose. + * The bullet point should also include the reason for the change, if applicable. + + +.. tip:: + + When in doubt, please check the style in the existing changelog files and follow the same style. + +For example, the following is a sample changelog: + +.. code:: rst + + Changelog + --------- + + 0.1.0 (2021-02-01) + ~~~~~~~~~~~~~~~~~~ + + Added + ^^^^^ + + * Added a new feature that helps in a 10x speedup. + + Changed + ^^^^^^^ + + * Changed an existing feature. Earlier, we were using :meth:`torch.bmm` to perform the matrix multiplication. + However, this was slow for large matrices. We have now switched to using :meth:`torch.einsum` which is + significantly faster. + + Deprecated + ^^^^^^^^^^ + + * Deprecated an existing feature in favor of a new feature. + + Removed + ^^^^^^^ + + * Removed an existing feature. This was done to simplify the codebase and reduce the complexity. + + Fixed + ^^^^^ + + * Fixed crashing of the :meth:`my_function` when the input was too large. + We now use :meth:`torch.einsum` that is able to handle larger inputs. + + +Coding Style +------------ + +We follow the `Google Style +Guides `__ for the +codebase. For Python code, the PEP guidelines are followed. Most +important ones are `PEP-8 `__ +for code comments and layout, +`PEP-484 `__ and +`PEP-585 `__ for +type-hinting. + +For documentation, we adopt the `Google Style Guide `__ +for docstrings. We use `Sphinx `__ for generating the documentation. +Please make sure that your code is well-documented and follows the guidelines. + +Code Structure +^^^^^^^^^^^^^^ + +We follow a specific structure for the codebase. This helps in maintaining the codebase and makes it easier to +understand. + +In a Python file, we follow the following structure: + +.. code:: python + + # Imports: These are sorted by the pre-commit hooks. + # Constants + # Functions (public) + # Classes (public) + # _Functions (private) + # _Classes (private) + +Imports are sorted by the pre-commit hooks. Unless there is a good reason to do otherwise, please do not +import the modules inside functions or classes. To deal with circular imports, we use the +:obj:`typing.TYPE_CHECKING` variable. Please refer to the `Circular Imports`_ section for more details. + +Python does not have a concept of private and public classes and functions. However, we follow the +convention of prefixing the private functions and classes with an underscore. +The public functions and classes are the ones that are intended to be used by the users. The private +functions and classes are the ones that are intended to be used internally in that file. +Irrespective of the public or private nature of the functions and classes, we follow the Style Guide +for the code and make sure that the code and documentation are consistent. + +Similarly, within Python classes, we follow the following structure: + +.. code:: python + + # Constants + # Class variables (public or private): Must have the type hint ClassVar[type] + # Dunder methods: __init__, __del__ + # Representation: __repr__, __str__ + # Properties: @property + # Instance methods (public) + # Class methods (public) + # Static methods (public) + # _Instance methods (private) + # _Class methods (private) + # _Static methods (private) + +The rule of thumb is that the functions within the classes are ordered in the way a user would +expect to use them. For instance, if the class contains the method :meth:`initialize`, :meth:`reset`, +:meth:`update`, and :meth:`close`, then they should be listed in the order of their usage. +The same applies for private functions in the class. Their order is based on the order of call inside the +class. + +.. dropdown:: Code skeleton + :icon: code + + .. literalinclude:: snippets/code_skeleton.py + :language: python + +Circular Imports +^^^^^^^^^^^^^^^^ + +Circular imports happen when two modules import each other, which is a common issue in Python. +You can prevent circular imports by adhering to the best practices outlined in this +`StackOverflow post `__. + +In general, it is essential to avoid circular imports as they can lead to unpredictable behavior. + +However, in our codebase, we encounter circular imports at a sub-package level. This situation arises +due to our specific code structure. We organize classes or functions and their corresponding configuration +objects into separate files. This separation enhances code readability and maintainability. Nevertheless, +it can result in circular imports because, in many configuration objects, we specify classes or functions +as default values using the attributes ``class_type`` and ``func`` respectively. + +To address circular imports, we leverage the `typing.TYPE_CHECKING +`_ variable. This special variable is +evaluated only during type-checking, allowing us to import classes or functions in the configuration objects +without triggering circular imports. + +It is important to note that this is the sole instance within our codebase where circular imports are used +and are acceptable. In all other scenarios, we adhere to best practices and recommend that you do the same. + +Type-hinting +^^^^^^^^^^^^ + +To make the code more readable, we use `type hints `__ for +all the functions and classes. This helps in understanding the code and makes it easier to maintain. Following +this practice also helps in catching bugs early with static type checkers like `mypy `__. + +**Type-hinting only in the function signature** + +To avoid duplication of efforts, we do not specify type hints for the arguments and return values in the docstrings. + +For instance, the following are bad examples for various reasons: + +.. code:: python + + def my_function(a, b): + """Adds two numbers. + + This function is a bad example. Reason: No type hints anywhere. + + Args: + a: The first argument. + b: The second argument. + + Returns: + The sum of the two arguments. + """ + return a + b + +.. code:: python + + def my_function(a, b): + """Adds two numbers. + + This function is a bad example. Reason: Type hints in the docstring and not in the + function signature. + + Args: + a (int): The first argument. + b (int): The second argument. + + Returns: + int: The sum of the two arguments. + """ + return a + b + +.. code:: python + + def my_function(a: int, b: int) -> int: + """Adds two numbers. + + This function is a bad example. Reason: Type hints in the docstring and in the function + signature. Redundancy. + + Args: + a (int): The first argument. + b (int): The second argument. + + Returns: + int: The sum of the two arguments. + """ + return a + b + +The following is how we expect you to write the docstrings and type hints: + +.. code:: python + + def my_function(a: int, b: int) -> int: + """Adds two numbers. + + This function is a good example. Reason: Type hints in the function signature and not in the + docstring. + + Args: + a: The first argument. + b: The second argument. + + Returns: + The sum of the two arguments. + """ + return a + b + +**No type-hinting for None** + +We do not specify the return type of :obj:`None` in the docstrings. This is because +it is not necessary and can be inferred from the function signature. + +For instance, the following is a bad example: + +.. code:: python + + def my_function(x: int | None) -> None: + pass + +Instead, we recommend the following: + +.. code:: python + + def my_function(x: int | None): + pass + +Documenting the code +^^^^^^^^^^^^^^^^^^^^ + +The code documentation is as important as the code itself. It helps in understanding the code and makes +it easier to maintain. However, more often than not, the documentation is an afterthought or gets rushed +to keep up with the development pace. + +**What is considered as a bad documentation?** + +* If someone else wants to use the code, they cannot understand the code just by reading the documentation. + + What this means is that the documentation is not complete or is not written in a way that is easy to understand. + The next time someone wants to use the code, they will have to spend time understanding the code (in the best + case scenario), or scrap the code and start from scratch (in the worst case scenario). + +* Certain design subtleties are not documented and are only apparent from the code. + + Often certain design decisions are made to address specific use cases. These use cases are not + obvious to someone who wants to use the code. They may change the code in a way that is not intuitive + and unintentionally break the code. + +* The documentation is not updated when the code is updated. + + This means that the documentation is not kept up to date with the code. It is important to update the + documentation when the code is updated. This helps in keeping the documentation up to date and in sync + with the code. + +**What is considered good documentation?** + +We recommend thinking of the code documentation as a living document that helps the reader understand +the *what, why and how* of the code. Often we see documentation that only explains the +what but not the how or why. This is not helpful in the long run. + +We suggest always thinking of the documentation from a new user's perspective. They should be able to directly +check the documentation and have a good understanding of the code. + +For information on how to write good documentation, please check the notes on +`Dart's effective documentation `__ +and `technical writing `__. +We summarize the key points below: + +* Inform (educate the reader) and persuade (convince the reader). + * Have a clear aim in mind, and make sure everything you write is towards that aim alone. + * Use examples and analogies before introducing abstract concepts. +* Use the right tone for the audience. +* Compose simple sentences in active voice. +* Avoid unnecessary jargon and repetition. Use plain English. +* Avoid ambiguous phrases such as 'kind of', 'sort of', 'a bit', etc. +* State important information at the beginning of the sentence. +* Say exactly what you mean. Don't avoid writing the uncomfortable truth. + + +Unit Testing +------------ + +We use `pytest `__ for unit testing. +Good tests not only cover the basic functionality of the code but also the edge cases. +They should be able to catch regressions and ensure that the code is working as expected. +Please make sure that you add tests for your changes. + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # Run all tests + ./isaaclab.sh --test # or "./isaaclab.sh -t" + + # Run all tests in a particular file + ./isaaclab.sh -p -m pytest source/isaaclab/test/deps/test_torch.py + + # Run a particular test + ./isaaclab.sh -p -m pytest source/isaaclab/test/deps/test_torch.py::test_array_slicing + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: bash + + # Run all tests + isaaclab.bat --test # or "isaaclab.bat -t" + + # Run all tests in a particular file + isaaclab.bat -p -m pytest source/isaaclab/test/deps/test_torch.py + + # Run a particular test + isaaclab.bat -p -m pytest source/isaaclab/test/deps/test_torch.py::test_array_slicing + + +Tools +----- + +We use the following tools for maintaining code quality: + +* `pre-commit `__: Runs a list of formatters and linters over the codebase. +* `ruff `__: An extremely fast Python linter and formatter. + +Please check `here `__ for instructions +to set these up. To run over the entire repository, please execute the +following command in the terminal: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./isaaclab.sh --format # or "./isaaclab.sh -f" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: bash + + isaaclab.bat --format # or "isaaclab.bat -f" diff --git a/docs/source/refs/issues.rst b/docs/source/refs/issues.rst new file mode 100644 index 0000000000000000000000000000000000000000..c4bb56182e510c9f2ec31c2c20cd6351eb60e22f --- /dev/null +++ b/docs/source/refs/issues.rst @@ -0,0 +1,114 @@ +Known Issues +============ + +.. attention:: + + Please also refer to the `Omniverse Isaac Sim documentation`_ for known issues and workarounds. + +Stale values after resetting the environment +-------------------------------------------- + +When resetting the environment, some of the data fields of assets and sensors are not updated. +These include the poses of links in a kinematic chain, the camera images, the contact sensor readings, +and the lidar point clouds. This is a known issue which has to do with the way the PhysX and +rendering engines work in Omniverse. + +Many physics engines do a simulation step as a two-level call: ``forward()`` and ``simulate()``, +where the kinematic and dynamic states are updated, respectively. Unfortunately, PhysX has only a +single ``step()`` call where the two operations are combined. Due to computations through GPU +kernels, it is not so straightforward for them to split these operations. Thus, at the moment, +it is not possible to set the root and/or joint states and do a forward call to update the +kinematic states of links. This affects both initialization as well as episodic resets. + +Similarly for RTX rendering related sensors (such as cameras), the sensor data is not updated +immediately after setting the state of the sensor. The rendering engine update is bundled with +the simulator's ``step()`` call which only gets called when the simulation is stepped forward. +This means that the sensor data is not updated immediately after a reset and it will hold +outdated values. + +While the above is erroneous, there is currently no direct workaround for it. From our experience in +using IsaacGym, the reset values affect the agent learning critically depending on how frequently +the environment terminates. Eventually if the agent is learning successfully, this number drops +and does not affect the performance that critically. + +We have made a feature request to the respective Omniverse teams to have complete control +over stepping different parts of the simulation app. However, at this point, there is no set +timeline for this feature request. + +.. note:: + With Isaac Lab 1.2, we have introduced a PhysX kinematic update call inside the + :attr:`~isaaclab.assets.ArticulationData.body_state_w` attribute. This workaround + ensures that the states of the links are updated when the root state or joint state + of an articulation is set. + + +Blank initial frames from the camera +------------------------------------ + +When using the :class:`~isaaclab.sensors.Camera` sensor in standalone scripts, the first few frames +may be blank. This is a known issue with the simulator where it needs a few steps to load the material +textures properly and fill up the render targets. + +A hack to work around this is to add the following after initializing the camera sensor and setting +its pose: + +.. code-block:: python + + from isaaclab.sim import SimulationContext + + sim = SimulationContext.instance() + + # note: the number of steps might vary depending on how complicated the scene is. + for _ in range(12): + sim.render() + + +Using instanceable assets for markers +------------------------------------- + +When using `instanceable assets`_ for markers, the markers do not work properly, since Omniverse does not support +instanceable assets when using the :class:`UsdGeom.PointInstancer` schema. This is a known issue and will hopefully +be fixed in a future release. + +If you use an instanceable assets for markers, the marker class removes all the physics properties of the asset. +This is then replicated across other references of the same asset since physics properties of instanceable assets +are stored in the instanceable asset's USD file and not in its stage reference's USD file. + +.. _instanceable assets: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_gym_instanceable_assets.html +.. _Omniverse Isaac Sim documentation: https://docs.isaacsim.omniverse.nvidia.com/latest/overview/known_issues.html# + + +Exiting the process +------------------- + +When exiting a process with ``Ctrl+C``, occasionally the below error may appear: + +.. code-block:: bash + + [Error] [omni.physx.plugin] Subscription cannot be changed during the event call. + +This is due to the termination occurring in the middle of a physics event call and +should not affect the functionality of Isaac Lab. It is safe to ignore the error +message and continue with terminating the process. On Windows systems, please use +``Ctrl+Break`` or ``Ctrl+fn+B`` to terminate the process. + + +URDF Importer: Unresolved references for fixed joints +----------------------------------------------------- + +Starting with Isaac Sim 5.1, links connected through ``fixed_joint`` elements are no longer merged when +their URDF link entries specify mass and inertia even if ``merge-joint`` set to True. +This is expected behaviour—those links are treated as full bodies rather than zero-mass reference frames. +However, the USD importer currently raises ``ReportError`` warnings showing unresolved references for such links +when they lack visuals or colliders. This is a known bug in the importer; it creates references to visuals +that do not exist. The warnings can be safely ignored until the importer is updated. + + +GLIBCXX errors in Conda +----------------------- + +In Isaac Sim 5.0, we have observed some workflows exiting with an ``OSError`` indicating +``version 'GLIBCXX_3.4.30' not found`` when running from a conda environment. +The issue apperas to be stemming from importing torch or torch-related packages, such as tensorboard, +prior to launching ``AppLauncher``. As a workaround, ensure that all torch imports happen after +the ``AppLauncher`` instance has been created, which should resolve the error. diff --git a/docs/source/refs/license.rst b/docs/source/refs/license.rst new file mode 100644 index 0000000000000000000000000000000000000000..4e1a296587332f7cd9c571a7981ddd8214c21937 --- /dev/null +++ b/docs/source/refs/license.rst @@ -0,0 +1,46 @@ +.. _license: + +License +======== + +NVIDIA Isaac Sim is licensed under Apache 2.0. For more information +about its license terms, please check `here `_. +The license files for all its dependencies and included assets are available in its +`documentation `_. + + +The Isaac Lab framework is open-sourced under the +`BSD-3-Clause license `_, with some dependencies licensed under other terms. + + +.. code-block:: text + + Copyright (c) 2022-2025, The Isaac Lab Project Developers. + All rights reserved. + + SPDX-License-Identifier: BSD-3-Clause + + Redistribution and use in source and binary forms, with or without modification, + are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + 3. Neither the name of the copyright holder nor the names of its contributors + may be used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/source/refs/migration.rst b/docs/source/refs/migration.rst new file mode 100644 index 0000000000000000000000000000000000000000..c83193de92a8525e2a3f1ae9038e6cb91d8eb0ee --- /dev/null +++ b/docs/source/refs/migration.rst @@ -0,0 +1,137 @@ +.. _migration_guide: + +Migration Guide (Isaac Sim) +=========================== + +Moving from Isaac Sim 4.2 to 4.5 and later brings in a number of changes to the +APIs and Isaac Sim extensions and classes. This document outlines the changes +and how to migrate your code to the new APIs. + + +Renaming of Isaac Sim Extensions +-------------------------------- + +Previously, Isaac Sim extensions have been following the convention of ``omni.isaac.*``, +such as ``omni.isaac.core``. In Isaac Sim 4.5, Isaac Sim extensions have been renamed +to use the prefix ``isaacsim``, replacing ``omni.isaac``. In addition, many extensions +have been renamed and split into multiple extensions to prepare for a more modular +framework that can be customized by users through the use of app templates. + +Notably, the following commonly used Isaac Sim extensions in Isaac Lab are renamed as follow: + +* ``omni.isaac.cloner`` --> ``isaacsim.core.cloner`` +* ``omni.isaac.core.prims`` --> ``isaacsim.core.prims`` +* ``omni.isaac.core.simulation_context`` --> ``isaacsim.core.api.simulation_context`` +* ``omni.isaac.core.utils`` --> ``isaacsim.core.utils`` +* ``omni.isaac.core.world`` --> ``isaacsim.core.api.world`` +* ``omni.isaac.kit.SimulationApp`` --> ``isaacsim.SimulationApp`` +* ``omni.isaac.ui`` --> ``isaacsim.gui.components`` + + +Renaming of the URDF and MJCF Importers +--------------------------------------- + +Starting from Isaac Sim 4.5, the URDF and MJCF importers have been renamed to be more consistent +with the other extensions in Isaac Sim. The importers are available on isaac-sim GitHub +as open source projects. + +Due to the extension name change, the Python module names have also been changed: + +* URDF Importer: :mod:`isaacsim.asset.importer.urdf` (previously :mod:`omni.importer.urdf`) +* MJCF Importer: :mod:`isaacsim.asset.importer.mjcf` (previously :mod:`omni.importer.mjcf`) + +From the Isaac Sim UI, both URDF and MJCF importers can now be accessed directly from the File > Import +menu when selecting a corresponding .urdf or .xml file in the file browser. + + +Changes in URDF Importer +------------------------ + +Isaac Sim 4.5 brings some updates to the URDF Importer, with a fresh UI to allow for better configurations +when importing robots from URDF. As a result, the Isaac Lab URDF Converter has also been updated to +reflect these changes. The :class:`UrdfConverterCfg` includes some new settings, such as :class:`PDGainsCfg` +and :class:`NaturalFrequencyGainsCfg` classes for configuring the gains of the drives. + +One breaking change to note is that the :attr:`UrdfConverterCfg.JointDriveCfg.gains` attribute must +be of class type :class:`PDGainsCfg` or :class:`NaturalFrequencyGainsCfg`. + +The stiffness of the :class:`PDGainsCfg` must be specified, as such: + +.. code::python + + joint_drive=sim_utils.UrdfConverterCfg.JointDriveCfg( + gains=sim_utils.UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=None, damping=None) + ) + +The :attr:`natural_frequency` must be specified for :class:`NaturalFrequencyGainsCfg`. + + +Renaming of omni.isaac.core Classes +----------------------------------- + +Isaac Sim 4.5 introduced some naming changes to the core prim classes that are commonly +used in Isaac Lab. These affect the single and ``View`` variations of the prim classes, including +Articulation, RigidPrim, XFormPrim, and others. Single-object classes are now prefixed with +``Single``, such as ``SingleArticulation``, while tensorized View classes now have the ``View`` +suffix removed. + +The exact renaming of the classes are as follow: + +* ``Articulation`` --> ``SingleArticulation`` +* ``ArticulationView`` --> ``Articulation`` +* ``ClothPrim`` --> ``SingleClothPrim`` +* ``ClothPrimView`` --> ``ClothPrim`` +* ``DeformablePrim`` --> ``SingleDeformablePrim`` +* ``DeformablePrimView`` --> ``DeformablePrim`` +* ``GeometryPrim`` --> ``SingleGeometryPrim`` +* ``GeometryPrimView`` --> ``GeometryPrim`` +* ``ParticleSystem`` --> ``SingleParticleSystem`` +* ``ParticleSystemView`` --> ``ParticleSystem`` +* ``RigidPrim`` --> ``SingleRigidPrim`` +* ``RigidPrimView`` --> ``RigidPrim`` +* ``XFormPrim`` --> ``SingleXFormPrim`` +* ``XFormPrimView`` --> ``XFormPrim`` + + +Renaming of Isaac Lab Extensions and Folders +-------------------------------------------- + +Corresponding to Isaac Sim 4.5 changes, we have also made some updates to the Isaac Lab directories and extensions. +All extensions that were previously under ``source/extensions`` are now under the ``source/`` directory directly. +The ``source/apps`` and ``source/standalone`` folders have been moved to the root directory and are now called +``apps/`` and ``scripts/``. + +Isaac Lab extensions have been renamed to: + +* ``omni.isaac.lab`` --> ``isaaclab`` +* ``omni.isaac.lab_assets`` --> ``isaaclab_assets`` +* ``omni.isaac.lab_tasks`` --> ``isaaclab_tasks`` + +In addition, we have split up the previous ``source/standalone/workflows`` directory into ``scripts/imitation_learning`` +and ``scripts/reinforcement_learning`` directories. The RSL RL, Stable-Baselines, RL_Games, SKRL, and Ray directories +are under ``scripts/reinforcement_learning``, while Robomimic and the new Isaac Lab Mimic directories are under +``scripts/imitation_learning``. + +To assist with the renaming of Isaac Lab extensions in your project, we have provided a `simple script`_ that will traverse +through the ``source`` and ``docs`` directories in your local Isaac Lab project and replace any instance of the renamed +directories and imports. **Please use the script at your own risk as it will overwrite source files directly.** + + +Restructuring of Isaac Lab Extensions +------------------------------------- + +With the introduction of ``isaaclab_mimic``, designed for supporting data generation workflows for imitation learning, +we have also split out the previous ``wrappers`` folder under ``isaaclab_tasks`` to its own module, named ``isaaclab_rl``. +This new extension will contain reinforcement learning specific wrappers for the various RL libraries supported by Isaac Lab. + +The new ``isaaclab_mimic`` extension will also replace the previous imitation learning scripts under the ``robomimic`` folder. +We have removed the old scripts for data collection and dataset preparation in favor of the new mimic workflow. For users +who prefer to use the previous scripts, they will be available in previous release branches. + +Additionally, we have also restructured the ``isaaclab_assets`` extension to be split into ``robots`` and ``sensors`` +subdirectories. This allows for clearer separation between the pre-defined configurations provided in the extension. +For any existing imports such as ``from omni.isaac.lab_assets.anymal import ANYMAL_C_CFG``, please replace it with +``from isaaclab.robots.anymal import ANYMAL_C_CFG``. + + +.. _simple script: https://gist.github.com/kellyguo11/3e8f73f739b1c013b1069ad372277a85 diff --git a/docs/source/refs/reference_architecture/index.rst b/docs/source/refs/reference_architecture/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..c875c964e26c21260dcc11a116bcd948bf469b5e --- /dev/null +++ b/docs/source/refs/reference_architecture/index.rst @@ -0,0 +1,378 @@ +.. _ref_arch: + +Reference Architecture +====================== + +This document presents an overview of the end-to-end robot learning process with +Isaac Lab and Isaac Sim. This is demonstrated using a reference architecture that highlights +the major building blocks for training and deployment workflows. It provides a comprehensive, +user-friendly guide on the entire process of developing applications from training to deploying +the trained model in the real world, including links to demos, working examples, and documentation. + +Who is this document for? +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This document is designed to assist robotics developers and researchers working with NVIDIA Isaac Lab +in the robot learning field, including those at research labs, Original Equipment Manufacturers (OEM), +Solutions Providers, Solutions Integrators (SI), and independent software vendors (ISV). It offers +guidance on utilizing Isaac Lab’s robot training framework and workflows as a foundational starting +point for environment configuration, task design, and policy training and testing. + + + +.. image:: ../../_static/reference-architecture/isaac-lab-ra-light.svg + :class: only-light + :align: center + :alt: Isaac Lab Reference Architecture + +.. image:: ../../_static/reference-architecture/isaac-lab-ra-dark.svg + :class: only-dark + :align: center + :alt: Isaac Lab Reference Architecture + + +| + +The reference architecture for Isaac Lab comprises the following components: + +1. :ref:`Asset Input` +2. :ref:`Configuration - Assets & Scene` +3. :ref:`Robot Learning Task Design` +4. :ref:`Register with Gymnasium` +5. :ref:`Environment Wrapping` +6. :ref:`Run Training` +7. :ref:`Run Testing` + + + + +Components +~~~~~~~~~~~ +In this section, we will briefly discuss the individual blocks for creating a +sample reference application in Isaac Lab. + + +.. _ra-asset-input: + +Component 1 - Asset Input +--------------------------- +Isaac Lab accepts URDF, MJCF XML or USD files for the assets. The first step to training using Isaac Lab is to +have the USD file of your asset and the USD or URDF file of your robot. This can be achieved in +the following ways: + + +1. Design your assets or robot in Isaac Sim and export the USD file. + +2. Design your assets or robot in any software of your choice and export it to USD using Isaac Sim converters. Isaac Sim supports the different converters/importers to USD such as the `CAD Converter`_, `URDF Importer`_, `MJCF Importer`_, `Onshape Importer`_, etc. More details are found in the `Importing Assets section`_ in the `Isaac Sim Reference Architecture`_. + +3. If you already have the URDF or MJCF file of your robot, you do not need to convert to USD as Isaac Lab takes URDF and MJCF XML. + + +.. _ra-configuration: + +Component 2 - Configuration (Assets and Scene) +------------------------------------------------------ + +Asset Configuration +^^^^^^^^^^^^^^^^^^^^^^^^ + +Given that you have the asset file for your robot and other assets such as environment objects based on the task, the next step is to import them into Isaac Lab. Isaac Lab uses asset configuration classes to spawn various objects (or prims) into the scene using Python. The first step is to write a configuration class to define the properties for the assets needed to complete the task. For example, a simple go-to-goal task for a mobile robot will include the robot asset, an object like cubes to signify the goal pose visually, lights, ground plane, etc. Isaac Lab understands these assets using the configuration classes. Isaac Lab provides various sim-ready assets such as physically accurate +3D objects that encompass accurate physical properties and behavior. It also provides connected data streams to represent the real world in simulated digital worlds such as `robots `__ +like ANYbotics Anymal, Unitree H1 Humanoid, etc. as well as `sensors `__. We provide these assets configuration classes. Users can also define their own assets using the configuration classes. + +Follow the tutorial on `how to write an Articulation and ArticulationCfg class `__. + +Scene Configuration +^^^^^^^^^^^^^^^^^^^^^^^^ + +Given the individual asset configurations, the next step is to put all the assets together into a +scene. The scene configuration is a simple config class that initializes all the assets in the +scene that are needed for the task and for visualization. This is an example for the +`Cartpole example scene configuration `__, +which includes the cartpole, ground plane, and dome light. + + +.. _ra-robot-learning-task-design: + +Component 3 - Robot Learning Task Design +------------------------------------------------------ +Now, we have the scene for the task, but we need to define the robot learning task. We will focus on +`reinforcement learning (RL) `__ algorithm here. We define the RL task +that the agent is going to do. RL tasks are defined as a Markov Decision Process (MDP), +which is a stochastic decision-making process where optional decisions are made for the agents +considering their current state and environment they interact with. The environment provides the +agents’ current state or observations, and executes the actions provided by the agent. +The environment responds to the agents by providing the next states, reward of taking the +action, done flag and information about the current episode. Therefore, different components +of the MDP formulation (the environment) – states, actions, rewards, reset, done, etc. — must +be defined by the user for the agent to perform the given task. + +In Isaac Lab, we provide two different workflows for designing environments. + +Manager-based +^^^^^^^^^^^^^^^^^ +.. image:: ../../_static/task-workflows/manager-based-light.svg + :class: only-light + :align: center + :alt: Manager-based Task Workflow + +.. image:: ../../_static/task-workflows/manager-based-dark.svg + :class: only-dark + :align: center + :alt: Manager-based Task Workflow + +This workflow is modular, and the environment is decomposed into individual components (or managers) +that handle the different aspects of the environment, such as computing observations, +applying actions, and applying randomization. As a user, you define different configuration classes +for each component. + +- An RL task should have the following configuration classes: + + - Observations Config: Defines the agents’ observations for the task. + - Actions Config: Defines the agent’s action type, i.e. how the output of the agent are mapped to + the robot's control inputs. + - Rewards Config: Defines the reward function for the task + - Terminations Config: Defines the conditions for termination of an episode or when the task + is completed. + +- You can add other optional configuration classes such as Event Config which defines the set of randomizations and noisification for the agent and environment, Curriculum Config for tasks that require `curriculum learning`_ and Commands Config for tasks where the input is from a controller/setpoint controls e.g. a gamepad controller. + +.. tip:: + + To learn more on how you can design your own manager-based environment, see :ref:`tutorial-create-manager-rl-env`. + + + +Direct +^^^^^^^^ +.. image:: ../../_static/task-workflows/direct-based-light.svg + :class: only-light + :align: center + :alt: Direct-based Task Workflow + +.. image:: ../../_static/task-workflows/direct-based-dark.svg + :class: only-dark + :align: center + :alt: Direct-based Task Workflow + +In this workflow, you implement a single class that is responsible for computing observations, applying actions, and computing rewards. This workflow allows for direct control of the environment logic. + +.. tip:: + To learn more on how you can design your own direct environment, see :ref:`tutorial-create-direct-rl-env`. + +Users can choose from Isaac Lab’s large suite of pre-configured environments or users can define +their own environments. For more technical information about the two workflows, please see the +`documentation `__. + + +In addition to designing the RL task, you will need to design your agent’s model, the neural +network policy and value function. To train the RL agent to solve the task, you need to define +the hyperparameters such as number of epochs, learning rate, etc. for training and the +policy/value model architecture. This is defined in the training configuration file specific +to the RL library you want to use. Examples are created under the agent's folder in each task directory. +See an example of `RSL-RL `__ for Anymal-B. + + +.. _ra-register-gym: + +Component 4 - Register with Gymnasium +------------------------------------------------------ + +The next step is to register the environments with the gymnasium registry to allow you to create the environment using the unique environment name. +Registration is a way to make the environment accessible and reusable across different +RL algorithms and experiments. This is common in the RL community. Follow the tutorial on +`Registering an Environment `__ to learn more about how to register in your own environment. + +.. _ra-env-wrap: + +Component 5 - Environment Wrapping +------------------------------------------------------ +In running your RL task, you might want to change the behavior of your environment without +changing the environment itself. For example, you might want to create functions to modify +observations or rewards, record videos, or enforce time limits. Isaac Lab utilizes the API +available in the `gymnasium.Wrapper `__ class to create interfaces to the simulated environments. + +Some wrappers include: + +* `Video Wrappers `__ +* `RL Libraries Wrappers `__ + +.. currentmodule:: isaaclab_rl + +Most RL libraries expect their own variation of an environment interface. This means the +data types needed by each library differs. Isaac Lab provides its own wrappers to convert +the environment into the expected interface by the RL library a user wants to use. These are +specified in :class:`isaaclab_rl` + +See the `full list `__ of other wrappers APIs. For more information on how these wrappers work, +please refer to the `Wrapping environments `__ documentation. + +Adding your own wrappers +^^^^^^^^^^^^^^^^^^^^^^^^ + +You can define your own wrappers by adding them to the Isaac Lab utils wrapper module. More information is available `on the GitHub page for wrapping environments `__. + +.. _ra-run-training: + +Component 6 - Run Training +--------------------------- + +Finally, the last step is to run the training of the RL agent. Isaac Lab provides scripts which utilizes four popular RL libraries for training the models (GPU-based training): + +* `StableBaselines3 `__ +* `RSL-RL `__ +* `RL-Games `__ +* `SKRL `__ + + +.. note:: + + Isaac Lab does not provide the implementation of these RL libraries. They are already implemented by different authors. We provide the environments and framework wrappers for the RL libraries. + + + +If you want to integrate a different version of the provided algorithms or your learning library, you can follow +`these instructions `__. + + + +Single GPU Training +^^^^^^^^^^^^^^^^^^^^^^^^ +.. image:: ../../_static/reference-architecture/single-gpu-training-light.svg + :class: only-light + :align: center + :alt: Single GPU Training Data Flow + +.. image:: ../../_static/reference-architecture/single-gpu-training-dark.svg + :class: only-dark + :align: center + :alt: Single GPU Training Data Flow + +Isaac Lab supports training massively parallel environments to speed up RL training and provides rich data for the model to train. +For single GPU training, the following steps show how training works in Isaac Sim and Isaac Lab: + +1. **In Isaac Sim** + +* Isaac Sim provides the asset states such as robot and sensor states, including the observations defined in the task observation config class. + +2. **In Isaac Lab** + +* Randomizations are added to the states defined in the event configuration class to obtain the observation for the task. Randomizations are however optional. If not defined, the states are the observations. +* The observations are computed as PyTorch tensors, and it can optionally include the action provided by the trained model based on the task. + +3. **In the RL library** + +* The observation is passed to the policy. +* The policy is trained to output the right actions for the robot using RL library algorithms such as PPO, TRPO, etc. +* The actions can serve either as a setpoint for a controller that generates the action to the robot or used directly as the action to the robot based on the task. +* Action types such as joint position for a quadruped is an input to a joint controller, velocity of 1 or 0 is used to control the cart directly in the cartpole task, etc. +* In addition, based on how the task is defined, the previous action can be part of the next set of observations that is sent. + +4. **In Isaac Sim** + +* The actions from the policy are sent back to Isaac Sim to control the agent that is learning i.e. the robot. This is the physics simulation (sim) step. This generates the next states in Isaac Sim and the rewards are calculated in Isaac Lab. + +5. **Rendering** + +* The scene can be rendered to produce the cameras' images. + + +The next state is then passed in the flow till the training reaches the specified training steps or epochs. The final product is the trained model/agent. + + + +Multi-GPU and Multi-Node Training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. image:: ../../_static/reference-architecture/multi-gpu-training-light.svg + :class: only-light + :align: center + :alt: Multi GPU Training Data Flow + +.. image:: ../../_static/reference-architecture/multi-gpu-training-dark.svg + :class: only-dark + :align: center + :alt: Multi GPU Training Data Flow + + +Isaac Lab supports scaling up training by taking advantage of multi-GPU and multi-node training on Linux. Follow the tutorial on `Multi-GPU training `__ and `Multi-Node training `__ to get started. + + +Cloud-Based Training +^^^^^^^^^^^^^^^^^^^^^^^^ +Isaac Lab can be deployed alongside Isaac Sim onto the public clouds with `Isaac Automator `__. AWS, GCP, Azure, and Alibaba Cloud are currently supported. Follow the tutorial on `how to run Isaac Lab in the cloud `__. + +.. note:: + + Both multi-GPU and multi-node jobs can be easily scaled across heterogeneous environments with `OSMO `__, a cloud-native, orchestration platform for scheduling complex multi-stage and multi-container heterogeneous computing workflows. Isaac Lab also provides the tools to run your RL task in Docker. See more details on `container deployment `__. + +.. _ra-run-testing: + +Component 7: Run Testing +----------------------------- +Isaac Lab provides scripts for `testing/playing the trained policy `__ on the environment and functions for converting the trained model from .pt to +.jit and .onnx for deployment. + + +Deployment on Physical Robots +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/reference-architecture/deployment-light.svg + :class: only-light + :align: center + :alt: Isaac Lab Trained Policy Deployment + +.. image:: ../../_static/reference-architecture/deployment-dark.svg + :class: only-dark + :align: center + :alt: Isaac Lab Trained Policy Deployment + + +To deploy your trained model on a real robot, you would need what is shown in the flow diagram. Note, this is a sample reference architecture, hence it can be tweaked for a different application. +First, you need a robot with the required sensors and processing computer such as `NVIDIA Jetson `__ to deploy on. Next, you need a state estimator for your robot. The state estimator should be able to deliver the list of observations used for training. + +Once the observations are extracted, they are passed into the model which delivers the action using the model inferencing runtime. The commanded action from the model serves as setpoints for the action controller. The action controller outputs scaled actions which are then used to control the robot to get to the next state, and this continues till the task is done. + +NVIDIA Isaac platform provides some tools for state estimation, including visual slam and inferencing engines such as `TensorRT `__. Other inferencing runtime includes `OnnxRuntime `__, direct inferencing on the PyTorch model, etc. + + + + +Summary +~~~~~~~~~~~ + +This document presents a reference architecture for Isaac Lab that has undergone SQA testing. We have provided a user-friendly guide to end-to-end robot learning with Isaac Lab and Isaac Sim from training to real-world deployment, including demos, examples, and documentation links. + + +How to Get Started +~~~~~~~~~~~~~~~~~~~~~~ +Check out our resources on using Isaac Lab with your robots. + +Review Our Documentation & Samples Resources + +* :ref:`Isaac Lab Tutorials ` +* `Fast-Track Robot Learning in Simulation Using NVIDIA Isaac Lab`_ +* `Supercharge Robotics Workflows with AI and Simulation Using NVIDIA Isaac Sim 4.0 and NVIDIA Isaac Lab`_ +* `Closing the Sim-to-Real Gap: Training Spot Quadruped Locomotion with NVIDIA Isaac Lab `__ +* `Additional Resources`_ + +Learn More About Featured NVIDIA Solutions + +* `Scale AI-Enabled Robotics Development Workloads with NVIDIA OSMO`_ +* `Parkour and More: How Simulation-Based RL Helps to Push the Boundaries in Legged Locomotion (GTC session) `__ +* `Isaac Perceptor`_ +* `Isaac Manipulator`_ + +.. _curriculum learning: https://arxiv.org/abs/2109.11978 +.. _CAD Converter: https://docs.omniverse.nvidia.com/extensions/latest/ext_cad-converter.html +.. _URDF Importer: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_urdf.html +.. _MJCF Importer: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_mjcf.html +.. _Onshape Importer: https://docs.omniverse.nvidia.com/extensions/latest/ext_onshape.html +.. _Isaac Sim Reference Architecture: https://docs.isaacsim.omniverse.nvidia.com/latest/introduction/reference_architecture.html +.. _Importing Assets section: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/importers_exporters.html + +.. _Scale AI-Enabled Robotics Development Workloads with NVIDIA OSMO: https://developer.nvidia.com/blog/scale-ai-enabled-robotics-development-workloads-with-nvidia-osmo/ +.. _Isaac Perceptor: https://developer.nvidia.com/isaac/perceptor +.. _Isaac Manipulator: https://developer.nvidia.com/isaac/manipulator +.. _Additional Resources: https://isaac-sim.github.io/IsaacLab/main/source/refs/additional_resources.html +.. _Fast-Track Robot Learning in Simulation Using NVIDIA Isaac Lab: https://developer.nvidia.com/blog/fast-track-robot-learning-in-simulation-using-nvidia-isaac-lab/ +.. _Supercharge Robotics Workflows with AI and Simulation Using NVIDIA Isaac Sim 4.0 and NVIDIA Isaac Lab: https://developer.nvidia.com/blog/supercharge-robotics-workflows-with-ai-and-simulation-using-nvidia-isaac-sim-4-0-and-nvidia-isaac-lab/ diff --git a/docs/source/refs/release_notes.rst b/docs/source/refs/release_notes.rst new file mode 100644 index 0000000000000000000000000000000000000000..57d5e1891cc9660e327ff2822ee2e69269497d88 --- /dev/null +++ b/docs/source/refs/release_notes.rst @@ -0,0 +1,1635 @@ +Release Notes +############# + +The release notes are now available in the `Isaac Lab GitHub repository `_. +We summarize the release notes here for convenience. + +v2.3.0 +====== + +What's Changed +-------------- + +The Isaac Lab 2.3.0 release, built on Isaac Sim 5.1, delivers enhancements across dexterous manipulation, +teleoperation, and learning workflows. It introduces new dexterous environments with advanced training capabilities, +expands surface gripper and teleoperation support for a wider range of robots and devices, +and integrates SkillGen with the Mimic imitation learning pipeline to enable GPU-accelerated motion planning +and skill-based data generation with cuRobo integration. + +Key highlights of this release include: + +* **Dexterous RL (DexSuite)**: Introduction of two new dexterous manipulation environments using the Kuka arm and + Allegro hand setup, with addition of support for Automatic Domain Randomization (ADR) and PBT (Population-Based Training). +* **Surface gripper updates**: Surface gripper has been extended to support Manager-based workflows, + including the addition of ``SurfaceGripperAction`` and ``SurfaceGripperActionCfg``, along with several new environments + demonstrating teleoperation examples with surface grippers and the RMPFlow controller. + New robots and variations are introduced, including Franka and UR10 with robotiq grippers and suction cups, + and Galbot and Agibot robots. +* **Mimic - SkillGen**: SkillGen support has been added for the Mimic Imitation Learning pipeline, + introducing cuRobo integration, integrating GPU motion planning with skill-segmented data generation. + Note that cuRobo has proprietary licensing terms, please review the + `cuRobo license `_ + carefully before use. +* **Mimic - Locomanipulation**: Added a new G1 humanoid environment combining RL-based locomotion with IK-based + manipulation. A full robot navigation stack is integrated to augment demonstrations with randomization of + tabletop pick/place locations, destination and ground obstacles. By segmenting tasks into pick-navigate-place + phases, this method enables generation of large-scale loco-manipulation datasets from manipulation-only + demonstrations. +* **Teleoperation**: Upper body inverse kinematics controller is improved by adding a null space posture task that + helps enable waist movement on humanoid tasks while regularizing redundant degrees-of-freedom to a preferred + upright posture. Additionally, support for Vive and Manus Glove are introduced, providing more options for + teleoperation devices. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v2.2.1...v2.3.0 + +Isaac Sim 5.1 Updates +---------------------- + +* Introduced support for `DGX Spark `_, + including multi-architecture Docker images with support for ARM platforms. +* PhysX now offers a new joint parameter tuning `tutorial `_ + for robotic grippers, along with a new feature for solving articulation collision contacts last to improve on + gripper penetration issues, especially for cases with sub-optimally tuned joints. +* Surface grippers has been optimized for better performance. Although support continues to be CPU-only, + performance has improved by several orders of magnitude compared to previous releases. +* Windows 10 support ended on October 14, 2025. Microsoft will no longer provide free security, feature, or technical + updates for Windows 10. As a result, we will be dropping support for Windows 10 in future releases of Isaac Sim and Lab + to ensure the security and functionality of our software. + +New Features +------------ + +Core +~~~~ + +* Supports rl games wrapper with dictionary observation by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3340 +* Adds surface gripper support in manager-based workflow by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3174 +* Adds two new robots with grippers by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3229 +* Adds new Collision Mesh Schema properties by @hapatel-bdai in https://github.com/isaac-sim/IsaacLab/pull/2249 +* Adds PBT algorithm to rl games by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3399 + +Mimic and Teleoperation +~~~~~~~~~~~~~~~~~~~~~~~ + +* Adds SkillGen framework to Isaac Lab with cuRobo support by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3303 +* Adds locomanipulation data generation via. disjoint navigation by @jaybdub in https://github.com/isaac-sim/IsaacLab/pull/3259 +* Adds support for manus and vive by @cathyliyuanchen in https://github.com/isaac-sim/IsaacLab/pull/3357 +* Adds notification widgets at IK error status and Teleop task completion by @lotusl-code in https://github.com/isaac-sim/IsaacLab/pull/3356 + +Environments +~~~~~~~~~~~~ + +* Adds dexterous lift and reorientation manipulation environments by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3378 +* Adds task Reach-UR10e, an end-effector tracking environment by @ashwinvkNV in https://github.com/isaac-sim/IsaacLab/pull/3147 +* Adds a configuration example for Student-Teacher Distillation by @ClemensSchwarke in https://github.com/isaac-sim/IsaacLab/pull/3100 +* Adds Locomanipulation Environment with G1 for Mimic workflow by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3150 +* Adds teleop support for Unitree G1 with Inspire 5-finger hand, take PickPlace task as an example by @yami007007 in https://github.com/isaac-sim/IsaacLab/pull/3242 +* Adds galbot stack cube tasks, with left_arm_gripper and right_arm_suction, using RMPFlow controller by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3210 +* Adds AVP teleop support for Galbot stack tasks by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3669 +* Adds camera to G1 Steering Wheel environment by @jaybdub in https://github.com/isaac-sim/IsaacLab/pull/3549 + +Infrastructure +~~~~~~~~~~~~~~ + +* Adds YAML Resource Specification To Ray Integration by @binw666 in https://github.com/isaac-sim/IsaacLab/pull/2847 +* Installs cuda13 on arm builds for Spark by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3396 +* Adds arm64 platform for Pink IK setup by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3686 +* Updates torch installation version to 2.9 for Linux-aarch, and updates opset version from 11 to 18. by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3706 + + +Improvements +------------ + +Core and Infrastructure +~~~~~~~~~~~~~~~~~~~~~~~ + +* Adds changes for rsl_rl 3.0.1 by @ClemensSchwarke in https://github.com/isaac-sim/IsaacLab/pull/2962 +* Simplifies cross platform installation setup.py by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3294 +* Updated image build logic and details by @nv-apoddubny in https://github.com/isaac-sim/IsaacLab/pull/3649 +* Applies the pre-merge CI failure control to the tasks by @nv-apoddubny in https://github.com/isaac-sim/IsaacLab/pull/3457 +* Updates Isaac Sim 5.1 staging server to production by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3691 +* Removes scikit-learn dependency by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3799 +* Removes extra calls to write simulation after reset_idx by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3446 +* Exposes render parameter ``/rtx/domeLight/upperLowerStrategy`` for dome light by @shauryadNv in https://github.com/isaac-sim/IsaacLab/pull/3694 +* Adds onnxscript dependency to isaaclab_rl module by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3722 +* Configures mesh collision schemas in ``convert_mesh.py`` by @zehao-wang in https://github.com/isaac-sim/IsaacLab/pull/3558 + +Mimic and Teleoperation +~~~~~~~~~~~~~~~~~~~~~~~ + +* Improves recorder performance and add additional recording capability by @peterd-NV in https://github.com/isaac-sim/IsaacLab/pull/3302 +* Optimizes Kit XR Teleop CPU time by @hougantc-nvda in https://github.com/isaac-sim/IsaacLab/pull/3487 +* Improves dataset file names and low success rate for trained model on g1 locomanipulation dataset by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3503 +* Updates the teleop_se3 and record_demos scripts with more helpful description for teleop_device parameter by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3642 + + +Documentation +------------- + +Core +~~~~ + +* Updates documentation to explain known issue of missing references when uses URDF importer by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3729 +* Fixes symbol in training_jetbot_reward_exploration.rst by @dougfulop in https://github.com/isaac-sim/IsaacLab/pull/2722 +* Clarifies asset classes' default_inertia tensor coordinate frame by @preist-nvidia in https://github.com/isaac-sim/IsaacLab/pull/3405 +* Adds limitation note in docs for Multi Node Training on DGX Spark by @matthewtrepte in https://github.com/isaac-sim/IsaacLab/pull/3806 +* Updates locomanip task name and link in docs by @fan-ziqi in https://github.com/isaac-sim/IsaacLab/pull/3342 + +Mimic and Teleoperation +~~~~~~~~~~~~~~~~~~~~~~~ + +* Fixes G1 dataset link in teleop_imitation tutorial by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3463 +* Updates dataset instruction in ``teleop_imitation.rst`` (#3462) by @peterd-NV in https://github.com/isaac-sim/IsaacLab/pull/3489 +* Fixes teleop doc in Isaac Lab by @tifchen-nvda in https://github.com/isaac-sim/IsaacLab/pull/3539 +* Updates cloudxr teleop doc in Isaac Lab by @tifchen-nvda in https://github.com/isaac-sim/IsaacLab/pull/3540 +* Adds instructions on how to position the lighthouse for manus+vive by @cathyliyuanchen in https://github.com/isaac-sim/IsaacLab/pull/3548 +* Corrects versions for the cloudxr teleop doc by @tifchen-nvda in https://github.com/isaac-sim/IsaacLab/pull/3580 +* Adds link to IsaacLabEvalTasks repo from mimic section in doc (#3621) by @xyao-nv in https://github.com/isaac-sim/IsaacLab/pull/3627 +* Fixes ordering of docs for imitation learning by @shauryadNv in https://github.com/isaac-sim/IsaacLab/pull/3634 +* Updates documentation for manus teleop by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3605 +* Updates SkillGen documentation for data gen command and success rates by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3703 +* Fixes typo in mimic teleop documentation for locomanipulation by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3704 +* Updates dataset paths in teleop documentation and adds note in documentation to adjusting AR Anchors by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3707 +* Adds pysurvive installation instructions by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3747 +* Adds to mimic documentation expected generation and training timings and success rates by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3742 +* Adds data gen and policy learning times in SkillGen documentation by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3774 +* Updates doc to describe ways to clean up orphaned container and check connectivity for teleop by @yanziz-nvidia in https://github.com/isaac-sim/IsaacLab/pull/3787 +* Updates cloudxr teleop doc to explain openxr plugin by @tifchen-nvda in https://github.com/isaac-sim/IsaacLab/pull/3786 +* Updates Mimic docs to clarify CPU mode usage and DGX Spark support by @peterd-NV in https://github.com/isaac-sim/IsaacLab/pull/3794 +* Updates cuRobo installation instructions and added VRAM baseline perf to SkillGen docs by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3797 +* Adds dgx spark limitations link to teleop docs by @lotusl-code in https://github.com/isaac-sim/IsaacLab/pull/3805 +* Adds Cosmos Transfer1 limitation for DGX spark by @shauryadNv in https://github.com/isaac-sim/IsaacLab/pull/3817 +* Updates DGX spark limitations for SkillGen in the documentation by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3748 +* Adds the Isaac-PickPlace-G1-InspireFTP-Abs-v0 Task into Envs Docs by @yami007007 in https://github.com/isaac-sim/IsaacLab/pull/3479 + +Infrastructure +~~~~~~~~~~~~~~ + +* Change GLIBC version requirement to 2.35 for pip by @GiulioRomualdi in https://github.com/isaac-sim/IsaacLab/pull/3360 +* Updates Isaac Sim license by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3393 +* Updates jax installation instructions by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3561 +* Adds section for the DGX spark limitations by @mpgussert in https://github.com/isaac-sim/IsaacLab/pull/3652 +* Fixes broken links in the documentation by @mpgussert in https://github.com/isaac-sim/IsaacLab/pull/3721 +* Adds windows pip installation instruction in local pip installation documentation by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3723 +* Adds note about potential security risks with Ray by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3711 +* Fixes errors while building the docs by @Mayankm96 in https://github.com/isaac-sim/IsaacLab/pull/3370 + + +Bug Fixes +--------- + +Core +~~~~ + +* Fixes missing visible attribute in spawn_ground_plane by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3304 +* Moves parameter ``platform_height`` to the correct mesh terrain configuration by @Mayankm96 in https://github.com/isaac-sim/IsaacLab/pull/3316 +* Fixes invalid callbacks for debug vis when simulation is restarted by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3338 +* Deletes unused asset.py in isaaclab by @fan-ziqi in https://github.com/isaac-sim/IsaacLab/pull/3389 +* Moves location of serve file check to the correct module by @Mayankm96 in https://github.com/isaac-sim/IsaacLab/pull/3368 +* Fixes SurfaceGripper API to accommodate for Isaac Sim 5.1 changes by @AntoineRichard in https://github.com/isaac-sim/IsaacLab/pull/3528 +* Fixes keyboard unsubscribe carb call by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3662 +* Fixes GCC error for raycaster demo when running in conda by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3712 +* Corrects materials and objects imports in ``check_terrain_importer.py`` by @PeterL-NV in https://github.com/isaac-sim/IsaacLab/pull/3411 +* Fixes tensor construction warning in ``events.py`` by @louislelay in https://github.com/isaac-sim/IsaacLab/pull/3251 +* Fixes skrl train/play script configurations when using the ``--agent`` argument and rename agent configuration variable by @Toni-SM in https://github.com/isaac-sim/IsaacLab/pull/3643 +* Fixes TiledCamera data types and rlgames training on CPU by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3808 + +Mimic and Teleoperation +~~~~~~~~~~~~~~~~~~~~~~~ + +* Updates the Path to Isaaclab Dir in SkillGen Documentation by @njawale42 in https://github.com/isaac-sim/IsaacLab/pull/3483 +* Fixes the reach task regression with teleop devices returning the gripper by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3327 +* Fixes teleop G1 with Inspire hand issues by @yami007007 in https://github.com/isaac-sim/IsaacLab/pull/3440 +* Updates default viewer pose to see the whole scene for Agibot environment by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3525 +* Fixes XR UI when used with teleop devices other than "handtracking" by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3566 +* Fixes manus joint indices mapping for teleoperation by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3592 +* Updates gr1t2 dex pilot hand scaling by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3607 +* Fixes unreal surface_gripper behavior by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3679 +* Fixes G1 finger PD gains configs for locomanipulation by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3749 +* Fixes the bug of right_arm suction cup passing through cubes by @rebeccazhang0707 in https://github.com/isaac-sim/IsaacLab/pull/3764 +* Updates the xr anchor for g1 tasks to me more natural for standing teleop by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3775 +* Suppresses dex_retargeting::yourdfpy warnings for G1 by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3798 +* Refines height of xr view for G1 envs by @rwiltz in https://github.com/isaac-sim/IsaacLab/pull/3813 + +Infrastructure +~~~~~~~~~~~~~~ + +* Fixes the missing Ray initialization by @ozhanozen in https://github.com/isaac-sim/IsaacLab/pull/3350 +* Fixes torch nightly version install in arm system by @ooctipus in https://github.com/isaac-sim/IsaacLab/pull/3464 +* Fixes unintentional removal of '=' from command by @ndahile-nvidia in https://github.com/isaac-sim/IsaacLab/pull/3600 +* Updates installation script for aarch64 to fix LD_PRELOAD issues by @matthewtrepte in https://github.com/isaac-sim/IsaacLab/pull/3708 +* Fixes hanging issue in test_manager_based_rl_env_obs_spaces.py by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3717 +* Fixes for missing desktop icon when running scripts on DGX Spark by @matthewtrepte in https://github.com/isaac-sim/IsaacLab/pull/3804 + + +Breaking Changes +---------------- + +* Removes unused 'relevant_link_name' parameter in nutpour and exhaust pipe envs by @michaellin6 in https://github.com/isaac-sim/IsaacLab/pull/3651 +* Moves IO descriptor log dir to logs by @kellyguo11 in https://github.com/isaac-sim/IsaacLab/pull/3434 + +Known Issues +~~~~~~~~~~~~ + +* The ROS2 docker image is not currently expected to work due to the update to Python 3.11. We are actively working on + a fix to resolve this. +* We have received reports of performance regressions in the previous Isaac Sim release for both physics and rendering + workflows. We are still working on addressing some of these, but have also found some workarounds. + For viewport regressions, Omniverse settings can be set by adding + ``--kit_args="--/app/usdrt/hierarchy/partialGpuUpdate=1 --/rtx/post/dlss/execMode=0 --/app/runLoops/main/rateLimitEnabled=false --/app/runLoops/main/manualModeEnabled=true --enable omni.kit.loop-isaac"``. Additionally, Isaac Sim 5.0 + introduced new actuator models for PhysX, including drive model and friction model improvements. + These improvements also introduced a small performance regression. We have observed up to ~20% slowdown in some + state-based environments. + +v2.2.1 +====== + +Overview +-------- + +This is a minor patch release with some improvements and bug fixes. + +Full Changelog: https://github.com/isaac-sim/IsaacLab/compare/v2.2.0...v2.2.1 + +New Features +------------ + +- Adds contact point location reporting to ContactSensor by @jtigue-bdai +- Adds environments actions/observations descriptors for export by @AntoineRichard +- Adds RSL-RL symmetry example for cartpole and ANYmal locomotion by @Mayankm96 + +Improvements +------------ + +Core API +~~~~~~~~ + +- Enhances Pink IK controller with null-space posture control and improvements by @michaellin6 +- Adds periodic logging when checking USD path on Nucleus server by @matthewtrepte +- Disallows string value written in sb3_ppo_cfg.yaml from being evaluated in process_sb3_cfg by @ooctipus + +Infrastructure +~~~~~~~~~~~~~~ + +* **Application Settings** + - Disables rate limit for headless and headless rendering app by @matthewtrepte, @kellyguo11 + - Disables ``rtx.indirrectDiffuse.enabled`` in render preset balanced and performance modes by @matthewtrepte + - Sets profiler backend to NVTX by default by @soowanpNV, @rwiltz +* **Dependencies** + - Adds hf-xet license by @hhansen-bdai + - Fixes new typing-inspection dependency license by @kellyguo11 +* **Testing & Benchmarking** + - Adds basic validation tests for scale-based randomization ranges by @louislelay + - Adds ``SensorBase`` tests by @jtigue-bdai +* **Repository Utilities** + - Adds improved readout from install_deps.py by @hhansen-bdai + - Fixes isaaclab.sh to detect isaacsim_version accurately 4.5 or >= 5.0 by @ooctipus + - Disables verbose printing in conftest.py by @ooctipus + - Updates pytest flags for isaacsim integration testing by @ben-johnston-nv + - Updates CodeOwners to be more fine-grained by @pascal-roth + - Fixes minor issues in CI by @nv-apoddubny + +Bug Fixes +--------- + +Core API +~~~~~~~~ + +* **Asset Interfaces** + - Fixes setting friction coefficients into PhysX in the articulation classes by @ossamaAhmed + - Sets joint_friction_coeff only for selected physx_env_ids by @ashwinvkNV +* **Manager Interfaces** + - Fixes observation space Dict for non-concatenated groups only keeping the last term by @CSCSX +* **MDP Terms** + - Fixes termination term effort limit check logic by @moribots + - Broadcasts environment ids inside ``mdp.randomize_rigid_body_com`` by @Foruck + - Fixes IndexError in reset_joints_by_scale and reset_joints_by_offset by @Creampelt + - Fixes ``terrain_out_of_bounds`` to return tensor instead of bool by @fan-ziqi + +Infrastructure +~~~~~~~~~~~~~~ + +- Fixes distributed training hanging issue by @kellyguo11 +- Disables generation of internal template when detecting isaaclab install via pip by @ooctipus +- Fixes typo in isaaclab.bat by @ooctipus +- Updates app pathing for user-provided rendering preset mode by @matthewtrepte + +Documentation +------------- + +- Adds documentation for Newton integration by @mpgussert +- Adapts FAQ section in docs with Isaac Sim open-sourcing by @Mayankm96 +- Changes checkpoint path in rsl-rl to an absolute path in documentation by @fan-ziqi +- Fixes MuJoCo link in docs by @fan-ziqi +- Adds client version direction to XR document by @lotusl-code +- Fixes broken link in doc by @kellyguo11 +- Fixes typo in list_envs.py script path by @fbeltrao +- Fixes Franka blueprint env ID in docs by @louislelay + +Breaking Changes +---------------- + +- Improves termination manager logging to report aggregated percentage of environments done due to each term by @ooctipus + + +v2.2.0 +====== + +Overview +-------- + +**Isaac Lab 2.2** brings major upgrades across simulation capabilities, tooling, and developer experience. It expands support for advanced physics features, new environments, and improved testing and documentation workflows. This release includes full compatibility with **Isaac Sim 5.0** as well as backwards compatibility with **Isaac Sim 4.5**. + +Key highlights of this release include: + +- **Enhanced Physics Support**: Updated `joint friction modeling using the latest PhysX APIs `_, added support for `spatial tendons `_, and improved surface gripper interactions. +- **New Environments for Imitation Learning**: Introduction of two new GR1 mimic environments, with domain randomization and visual robustness evaluation, and improved pick-and-place tasks. +- **New Contact-Rich Manipulation Tasks**: Integration of `FORGE `_ and `AutoMate `_ tasks for learning fine-grained contact interactions in simulation. +- **Teleoperation Improvements**: Teleoperation tools have been enhanced with configurable parameters and CloudXR runtime updates, including head tracking and hand tracking. +- **Performance & Usability Improvements**: Includes support for Stage in Memory and Cloning in Fabric for faster scene creation, new OVD recorder for large-scene GPU-based animation recording, and FSD (Fabric Scene Delegate) for improved rendering speed. +- **Improved Documentation**: The documentation has been extended and updated to cover new features, resolve common issues, and streamline setup, including updates to teleop system requirements, VS Code integration, and Python environment management. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v2.1.1...v2.2.0 + + +Isaac Sim 5.0 Updates +--------------------- + +* Fixes rendering issues on Blackwell GPUs that previously resulted in overly noisy renders +* Updates Python version from 3.10 to 3.11 +* Updates PyTorch version to torch 2.7.0+cu128, which will include Blackwell support +* Drops official support for Ubuntu 20.04, we now officially support Ubuntu 22.04 and 24.04 Linux platforms +* Isaac Sim 5.0 no longer sets ``/app/player/useFixedTimeStepping=False`` by default. We now do this in Isaac Lab. +* :attr:`~isaaclab.sim.spawners.PhysicsMaterialCfg.improve_patch_friction` is now removed. The simulation will always behave as if this attribute is set to true. +* Native Livestreaming support has been removed. ``LIVESTREAM=1`` can now be used for WebRTC streaming over public networks and + ``LIVESTREAM=2`` for private and local networks with WebRTC streaming. +* Some assets in Isaac Sim have been reworked and restructured. Notably, the following asset paths were updated: + + * ``Robots/Ant/ant_instanceable.usd`` --> ``Robots/IsaacSim/Ant/ant_instanceable.usd`` + * ``Robots/Humanoid/humanoid_instanceable.usd`` --> ``Robots/IsaacSim/Humanoid/humanoid_instanceable.usd`` + * ``Robots/ANYbotics/anymal_instanceable.usd`` --> ``Robots/ANYbotics/anymal_c/anymal_c.usd`` + * ``Robots/ANYbotics/anymal_c.usd`` --> ``Robots/ANYbotics/anymal_c/anymal_c.usd`` + * ``Robots/Franka/franka.usd`` --> ``Robots/FrankaRobotics/FrankaPanda/franka.usd`` + * ``Robots/AllegroHand/allegro_hand_instanceable.usd`` --> ``Robots/WonikRobotics/AllegroHand/allegro_hand_instanceable.usd`` + * ``Robots/Crazyflie/cf2x.usd`` --> ``Robots/Bitcraze/Crazyflie/cf2x.usd`` + * ``Robots/RethinkRobotics/sawyer_instanceable.usd`` --> ``Robots/RethinkRobotics/Sawyer/sawyer_instanceable.usd`` + * ``Robots/ShadowHand/shadow_hand_instanceable.usd`` --> ``Robots/ShadowRobot/ShadowHand/shadow_hand_instanceable.usd`` + + +New Features +------------ + +* Adds FORGE tasks for contact-rich manipulation with force sensing to IsaacLab by @noseworm in #2968 +* Adds two new GR1 environments for IsaacLab Mimic by @peterd-NV +* Adds stack environment, scripts for Cosmos, and visual robustness evaluation by @shauryadNv +* Updates Joint Friction Parameters to Isaac Sim 5.0 PhysX APIs by @ossamaAhmed +* Adds support for spatial tendons by @ossamaAhmed +* Adds support and example for SurfaceGrippers by @AntoineRichard +* Adds support for stage in memory by @matthewtrepte +* Adds OVD animation recording feature by @matthewtrepte + +Improvements +------------ + +* Enables FSD for faster rendering by @nv-mm +* Sets rtx.indirectDiffuse.enabled to True for performance & balanced rendering presets by @matthewtrepte +* Changes runner for post-merge pipeline on self-hosted runners by @nv-apoddubny +* Fixes and improvements for CI pipeline by @nv-apoddubny +* Adds flaky annotation for tests by @kellyguo11 +* Updates Mimic test cases to pytest format by @peterd-NV +* Updates cosmos test files to use pytest by @shauryadNv +* Updates onnx and protobuf version due to vulnerabilities by @kellyguo11 +* Updates minimum skrl version to 1.4.3 by @Toni-SM +* Updates to Isaac Sim 5.0 by @kellyguo11 +* Updates docker CloudXR runtime version by @lotusl-code +* Removes xr rendering mode by @rwiltz +* Migrates OpenXRDevice from isaacsim.xr.openxr to omni.xr.kitxr by @rwiltz +* Implements teleop config parameters and device factory by @rwiltz +* Updates pick place env to use steering wheel asset by @peterd-NV +* Adds a CLI argument to set epochs for Robomimic training script by @peterd-NV + +Bug Fixes +--------- + +* Fixes operational space unit test to avoid pi rotation error by @ooctipus +* Fixes GLIBC errors with importing torch before AppLauncher by @kellyguo11 +* Fixes rendering preset by @matthewtrepte in cc0dab6cd50778507efc3c9c2d74a28919ab2092 +* Fixes callbacks with stage in memory and organize environment tests by @matthewtrepte +* Fixes XR and external camera bug with async rendering by @rwiltz +* Disables selection for rl_games when marl is selected for template generator by @ooctipus +* Adds check for .gitignore when generating template by @kellyguo11 +* Fixes camera obs errors in stack instance randomize envs by @peterd-NV +* Fixes parsing for play envs by @matthewtrepte +* Fixes issues with consecutive python exe calls in isaaclab.bat by @kellyguo11 +* Fixes spacemouse add callback function by @peterd-NV +* Fixes humanoid training with new velocity_limit_sim by @AntoineRichard + +Documentation +------------- + +* Adds note to mimic cosmos pipeline doc for eval by @shauryadNv +* Updates teleop docs for 2.2 release by @rwiltz +* Fixes outdated dofbot path in tutorial scripts by @mpgussert +* Updates docs for VS Code IntelliSense setup and JAX installation by @Toni-SM +* Updates Jax doc to overwrite version < 0.6.0 for torch by @kellyguo11 +* Adds docs for fabric cloning & stage in memory by @matthewtrepte +* Updates driver requirements to point to our official technical docs by @mpgussert +* Adds warning for ovd recording warning logs spam by @matthewtrepte +* Adds documentation to specify HOVER version and known GLIBCXX error by @kellyguo11 +* Updates teleop system requirements doc by @lotusl-code +* Add network requirements to cloudxr teleop doc by @lotusl-code + + +v2.1.1 +====== + +Overview +-------- + +This release has been in development over the past few months and includes a significant number of updates, +enhancements, and new features across the entire codebase. Given the volume of changes, we've grouped them +into relevant categories to improve readability. This version is compatible with +`NVIDIA Isaac Sim 4.5 `__. + +We appreciate the community's patience and contributions in ensuring quality and stability throughout. +We're aiming for more frequent patch releases moving forward to improve the developer experience. + +**Note:** This minor release does not include a Docker image or pip package. + +**Full Changelog:** https://github.com/isaac-sim/IsaacLab/compare/v2.1.0...v2.1.1 + +New Features +------------ + +* **Asset Interfaces** + * Adds ``position`` argument to set external forces and torques at different locations on the rigid body by @AntoineRichard + * Adds ``body_incoming_joint_wrench_b`` to ArticulationData field by @jtigue-bdai + * Allows selecting articulation root prim explicitly by @lgulich +* **Sensor Interfaces** + * Draws connection lines for FrameTransformer visualization by @Mayankm96 + * Uses visualization marker for connecting lines inside FrameTransformer by @bikcrum +* **MDP Terms** + * Adds ``body_pose_w`` and ``body_projected_gravity_b`` observations by @jtigue-bdai + * Adds joint effort observation by @jtigue-bdai + * Adds CoM randomization term to manager-based events by @shendredm + * Adds time-based mdp (observation) functions by @TheIndoorDad + * Adds curriculum mdp term to modify any environment parameters by @ooctipus +* **New Example Tasks** + * Adds assembly tasks from the Automate project by @yijieg + * Adds digit locomotion examples by @lgulich + +Improvements +------------ + +Core API +~~~~~~~~ + +* **Actuator Interfaces** + * Fixes implicit actuator limits configs for assets by @ooctipus + * Updates actuator configs for Franka arm by @reeceomahoney +* **Asset Interfaces** + * Optimizes getters of data inside asset classes by @Mayankm96 + * Adds method to set the visibility of the Asset's prims by @Mayankm96 +* **Sensor Interfaces** + * Updates to ray caster ray alignment and customizable drift sampling by @jsmith-bdai + * Extends ``ContactSensorData`` by ``force_matrix_w_history`` attribute by @bikcrum + * Adds IMU ``projected_gravity_b`` and optimizations by @jtigue-bdai +* **Manager Interfaces** + * Adds serialization to observation and action managers by @jsmith-bdai + * Adds concatenation dimension to ``ObservationManager`` by @pascal-roth + * Supports composite observation space with min/max by @ooctipus + * Changes counter update in ``CommandManager`` by @pascal-roth + * Integrates ``NoiseModel`` to manager-based workflows by @ozhanozen + * Updates ``NoiseModelWithAdditiveBias`` to apply per-feature bias by @ozhanozen + * Fixes :meth:`isaaclab.scene.reset_to` to accept ``None`` by @ooctipus + * Resets step reward buffer properly by @bikcrum +* **Terrain Generation** + * Custom ``TerrainGenerator`` support by @pascal-roth + * Adds terrain border options by @pascal-roth + * Platform height independent of object height by @jtigue-bdai + * Adds noise to ``MeshRepeatedObjectsTerrain`` by @jtigue-bdai +* **Simulation** + * Raises exceptions from SimContext init callbacks + * Applies ``semantic_tags`` to ground by @KumoLiu + * Sets ``enable_stabilization`` to false by default by @AntoineRichard + * Fixes deprecation for ``pxr.Semantics`` by @kellyguo11 +* **Math Utilities** + * Improves ``euler_xyz_from_quat`` by @ShaoshuSu + * Optimizes ``yaw_quat`` by @hapatel-bdai + * Changes ``quat_apply`` and ``quat_apply_inverse`` by @jtigue-bdai + * Changes ``quat_box_minus`` by @jtigue-bdai + * Adds ``quat_box_plus`` and ``rigid_body_twist_transform`` by @jtigue-bdai + * Adds math tests for transforms by @jtigue-bdai +* **General Utilities** + * Simplifies buffer validation for ``CircularBuffer`` by @Mayankm96 + * Modifies ``update_class_from_dict()`` by @ozhanozen + * Allows slicing from list values in dicts by @LinghengMeng @kellyguo11 + +Tasks API +~~~~~~~~~ + +* Adds support for ``module:task`` and gymnasium >=1.0 by @kellyguo11 +* Adds RL library error hints by @Toni-SM +* Enables hydra for ``play.py`` scripts by @ooctipus +* Fixes ray metric reporting and hangs by @ozhanozen +* Adds gradient clipping for distillation (RSL-RL) by @alessandroassirelli98 +* GRU-based RNNs ONNX export in RSL RL by @WT-MM +* Adds wandb support in rl_games by @ooctipus +* Optimizes SB3 wrapper by @araffin +* Enables SB3 checkpoint loading by @ooctipus +* Pre-processes SB3 env image obs-space for CNN pipeline by @ooctipus + +Infrastructure +~~~~~~~~~~~~~~ + +* **Dependencies** + * Updates torch to 2.7.0 with CUDA 12.8 by @kellyguo11 + * Updates gymnasium to 1.2.0 by @kellyguo11 + * Fixes numpy version to <2 by @ooctipus + * Adds license file for OSS by @kellyguo11 + * Sets robomimic to v0.4.0 by @masoudmoghani + * Upgrades pillow for Kit 107.3.1 by @ooctipus + * Removes protobuf upper pin by @kwlzn +* **Docker** + * Uses ``--gpus`` instead of Nvidia runtime by @yanziz-nvidia + * Adds docker name suffix parameter by @zoemcc + * Adds bash history support in docker by @AntoineRichard +* **Testing & Benchmarking** + * Switches unittest to pytest by @kellyguo11 @pascal-roth + * Adds training benchmark unit tests by @matthewtrepte + * Fixes env and IK test failures by @kellyguo11 +* **Repository Utilities** + * Adds URDF to USD batch conversion script by @hapatel-bdai + * Adds repository citation link by @kellyguo11 + * Adds pip install warning for internal templates by @ooctipus + +Bug Fixes +--------- + +Core API +~~~~~~~~ + +* **Actuator Interfaces** + * Fixes DCMotor clipping for negative power by @jtigue-bdai +* **Asset Interfaces** + * Fixes inconsistent data reads for body/link/com by @ooctipus +* **Sensor Interfaces** + * Fixes pose update in ``Camera`` and ``TiledCamera`` by @pascal-roth + * Fixes CPU fallback in camera.py by @renaudponcelet + * Fixes camera intrinsics logic by @jtigue-bdai +* **Manager Interfaces** + * Fixes ``ObservationManager`` buffer overwrite by @patrickhaoy + * Fixes term check in event manager by @miguelalonsojr + * Fixes ``Modifiers`` and history buffer bug by @ZiwenZhuang + * Fixes re-init check in ``ManagerBase`` by @Mayankm96 + * Fixes CPU collision filtering by @kellyguo11 + * Fixes imports in InteractiveScene/LiveVisualizer by @Mayankm96 + * Fixes image plot import in Live Visualizer by @pascal-roth +* **MDP Terms** + * Fixes CoM randomization shape mismatch by @shendredm + * Fixes visual prim handling in texture randomization by @KumoLiu + * Resets joint targets in ``reset_scene_to_default`` by @wghou + * Fixes joint limit terminations by @GiulioRomualdi + * Fixes joint reset scope in ``SceneEntityCfg`` by @ooctipus +* **Math Utilities** + * Fixes ``quat_inv()`` implementation by @ozhanozen + +Tasks API +~~~~~~~~~ + +* Fixes LSTM to ONNX export by @jtigue-bdai + +Example Tasks +~~~~~~~~~~~~~ + +* Removes contact termination redundancy by @louislelay +* Fixes memory leak in SDF by @leondavi +* Changes ``randomization`` to ``events`` in Digit envs by @fan-ziqi + +Documentation +------------- + +* Adds Isaac Sim version section to README by @kellyguo11 +* Adds physics performance guide by @kellyguo11 +* Adds jetbot tutorial to walkthrough docs by @mpgussert +* Changes quickstart install to conda by @mpgussert +* Fixes typo in library docs by @norbertcygiert +* Updates docs for conda, fabric, inference by @kellyguo11 +* Adds license/contributing updates with DCO by @kellyguo11 +* Updates pytest docs and help by @louislelay +* Adds actuator reference docs by @AntoineRichard +* Updates multi-GPU PyTorch setup docs by @Alex-Omar-Nvidia +* Removes deprecated env var in docs by @Kyu3224 + + +v2.1.0 +====== + +Overview +-------- + +This release introduces the official support for teleoperation using the Apple Vision Pro for collecting high-quality +and dexterous hand data, including the addition of bi-manual teleoperation and imitation learning workflows through Isaac Lab Mimic. + +We have also introduced new randomization methods for USD attributes, including the randomization of +scale, color, and textures. In this release, we updated RSL RL to v2.3.1, which introduces many additional features +including distributed training, student-teacher distillation, and recurrent student-teacher distillation. + +Additionally, we revamped the `Extension Template `_ +to include an automatic template generator tool from within the Isaac Lab repo. The extension template is +a powerful method for users to develop new projects in user-hosted repos, allowing for isolation from the core +Isaac Lab repo and changes. The previous IsaacLabExtensionTemplate repo showed a limited example pertaining only +to the Manager-based workflow and RSL RL. In the new template generator, users can choose from any supported +workflow and RL library, along with the desired RL algorithm. We will be deprecating the standalone +`IsaacLabExtensionTemplate `_ in the near future. + +NVIDIA has also released `HOVER `_ as an independent repo, hosting a neural whole body +controller for humanoids built on top of Isaac Lab. HOVER includes sim-to-real workflows for deployment on the Unitree +H1 robot, which we have also added a tutorial guide for the deployment process in the Isaac Lab documentation. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v2.0.2...v2.1.0 + +New Features +------------ + +* Adds new external project / internal task template generator by @Toni-SM +* Adds dummy agents to the external task template generator by @louislelay +* Adds USD-level randomization mode to event manager by @Mayankm96 +* Adds texture and scale randomization event terms by @hapatel-bdai +* Adds replicator event for randomizing colors by @Mayankm96 +* Adds interactive demo script for H1 locomotion by @kellyguo11 +* Adds blueprint environment for Franka stacking mimic by @chengronglai +* Adds action clipping to rsl-rl wrapper by @Mayankm96 +* Adds Gymnasium spaces showcase tasks by @Toni-SM +* Add configs and adapt exporter for RSL-RL distillation by @ClemensSchwarke +* Adds support for head pose for Open XR device by @rwiltz +* Adds handtracking joints and retargetting pipeline by @rwiltz +* Adds documentation for openxr device and retargeters by @rwiltz +* Adds tutorial for training & validating HOVER policy using Isaac Lab by @pulkitg01 +* Adds rendering mode presets by @matthewtrepte +* Adds GR1 scene with Pink IK + Groot Mimic data generation and training by @ashwinvkNV +* Adds absolute pose franka cube stacking environment for mimic by @rwiltz +* Enables CloudXR OpenXR runtime container by @jaczhangnv +* Adds a quick start guide for quick installation and introduction by @mpgussert + +Improvements +------------ + +* Clarifies the default parameters in ArticulationData by @Mayankm96 +* Removes storage of meshes inside the TerrainImporter class by @Mayankm96 +* Adds more details about state in InteractiveScene by @Mayankm96 +* Mounts scripts to docker container by @Mayankm96 +* Initializes manager term classes only when sim starts by @Mayankm96 +* Updates to latest RSL-RL v2.3.0 release by @Mayankm96 +* Skips dependency installation for directories with no extension.toml by @jsmith-bdai +* Clarifies layer instructions in animation docs by @tylerlum +* Lowers the default number of environments for camera envs by @kellyguo11 +* Updates Rendering Mode guide in documentation by @matthewtrepte +* Adds task instruction UI support for mimic by @chengronglai +* Adds ExplicitAction class to track argument usage in AppLauncher by @nv-mhaselton +* Allows physics reset during simulation by @oahmednv +* Updates mimic to support multi-eef (DexMimicGen) data generation by @nvcyc + +Bug Fixes +--------- + +* Fixes default effort limit behavior for implicit actuators by @jtigue-bdai +* Fixes docstrings inconsistencies the code by @Bardreamaster +* Fixes missing stage recorder extension for animation recorder by @kellyguo11 +* Fixes ground height in factory environment by @louislelay +* Removes double definition of render settings by @pascal-roth +* Fixes device settings in env tutorials by @Mayankm96 +* Changes default ground color back to dark grey by @Mayankm96 +* Initializes extras dict before loading managers by @kousheekc +* Fixes typos in development.rst by @vi3itor +* Fixes SE gamepad omniverse subscription API by @PinkPanther-ny +* Fixes modify_action_space in RslRlVecEnvWrapper by @felipemohr +* Fixes distributed setup in benchmarking scripts by @kellyguo11 +* Fixes typo ``RF_FOOT`` to ``RH_FOOT`` in tutorials by @likecanyon +* Checks if success term exists before recording in RecorderManager by @peterd-NV +* Unsubscribes from debug vis handle when timeline is stopped by @jsmith-bdai +* Fixes wait time in ``play.py`` by using ``env.step_dt`` by @tylerlum +* Fixes 50 series installation instruction to include torchvision by @kellyguo11 +* Fixes importing MotionViewer from external scripts by @T-K-233 +* Resets cuda device after each app.update call by @kellyguo11 +* Fixes resume flag in rsl-rl cli args by @Mayankm96 + + +v2.0.2 +====== + +Overview +-------- + +This patch release focuses on improving actuator configuration and fixing key bugs while reverting unintended +behavioral changes from v2.0.1. **We strongly recommend switching** to this new version if you're migrating +from a pre-2.0 release of Isaac Lab. + +**Key Changes:** + +* **Actuator Limit Handling**: Introduced :attr:`~isaaclab.actuators.ActuatorBaseCfg.velocity_limit_sim` + and :attr:`~isaaclab.actuators.ActuatorBaseCfg.effort_limit_sim` to clearly distinguish + simulation solver limits from actuator model constraints. Reverted implicit actuator velocity limits + to pre-v2.0 behavior +* **Simulation configuration update**: Removed :attr:`~isaaclab.sim.SimulationCfg.disable_contact_processing` + flag to simplify behavior +* **Rendering configuration update**: Reverted to pre-2.0 configuration to improve the quality of the + render product +* **Tiled camera fixes**: Fixed motion vector processing and added a hotfix for retrieving semantic + images from the :class:`~isaaclab.sensors.TiledCamera` +* **WebRTC Support**: Added IP specification for live-streaming + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v2.0.1...v2.0.2 + +New Features +------------ + +* Adds :attr:`~isaaclab.actuators.ActuatorBaseCfg.velocity_limit_sim` and + :attr:`~isaaclab.actuators.ActuatorBaseCfg.effort_limit_sim` to actuator. +* Adds WebRTC livestreaming support with IP specification. + +Improvements +------------ + +* Adds guidelines and examples for code contribution +* Separates joint state setters inside Articulation class +* Implements deterministic evaluation for skrl's multi-agent algorithms +* Adds new extensions to ``pyproject.toml`` +* Updates docs on Isaac Sim binary installation path and VSCode integration +* Removes remaining deprecation warning in RigidObject deprecation +* Adds security and show&tell notes to documentation +* Updates docs for segmentation and 50 series GPUs +* Adds workaround for semantic segmentation issue with tiled camera + +Bug Fixes +--------- + +* Fixes offset from object obs for Franka stacking env when using parallel envs +* Adds scene update to ManagerBasedEnv, DirectRLEnv, and MARL envs initialization +* Loads actuator networks in eval() mode to prevent gradients +* Fixes instructions on importing ANYmal URDF in docs +* Fixes setting of root velocities in the event term :func:`~isaaclab.mdp.reset_root_state_from_terrain` +* Fixes ``activate_contact_sensors`` when using :class:`~isaaclab.sim.MultiUsdFileCfg` +* Fixes misalignment in motion vectors from :class:`~isaaclab.sim.TiledCamera` +* Sets default tensor device to CPU for Camera rot buffer + +Breaking Changes +---------------- + +* Reverts the setting of joint velocity limits for implicit actuators +* Removes ``disable_contact_processing`` flag from SimulationContext +* Reverts to old render settings in kit experience files + +Migration Guide +--------------- + +.. attention:: + + We strongly recommend reviewing the details to fully understand the change in behavior, + as it may impact the deployment of learned policies. Please open an issue on GitHub if + you face any problems. + + +Introduction of simulation's effort and velocity limits parameters in ActuatorBaseCfg +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We have introduced the configuration variables :attr:`~isaaclab.actuators.ActuatorBaseCfg.velocity_limit_sim` +and :attr:`~isaaclab.actuators.ActuatorBaseCfg.effort_limit_sim` to the +:class:`isaaclab.actuators.ActuatorBaseCfg` to allow users to set the **simulation** joint velocity +and effort limits through the actuator configuration class. + +Previously, we were overusing the attributes :attr:`~isaaclab.actuators.ActuatorBaseCfg.velocity_limit` +and :attr:`~isaaclab.actuators.ActuatorBaseCfg.effort_limit` inside the actuator configuration. A series +of changes in-between led to a regression from v1.4.0 to v2.0.1 release of IsaacLab. To make this +clearer to understand, we note the change in their behavior in a tabular form: + ++---------------+-------------------------+--------------------------------------------------------------------+----------------------------------------------------------------+ +| Actuator Type | Attribute | v1.4.0 Behavior | v2.0.1 Behavior | ++---------------+-------------------------+--------------------------------------------------------------------+----------------------------------------------------------------+ +| Implicit | :attr:`velocity_limit` | Ignored, not set into simulation | Set into simulation | +| Implicit | :attr:`effort_limit` | Set into simulation | Set into simulation | +| Explicit | :attr:`velocity_limit` | Used by actuator models (e.g., DC Motor), not set into simulation | Used by actuator models (e.g., DC Motor), set into simulation | +| Explicit | :attr:`effort_limit` | Used by actuator models, not set into simulation | Used by actuator models, set into simulation | ++---------------+-------------------------+--------------------------------------------------------------------+----------------------------------------------------------------+ + +Setting the limits from the configuration into the simulation directly affects the behavior +of the underlying physics engine solver. This impact is particularly noticeable when velocity +limits are too restrictive, especially in joints with high stiffness, where it becomes easier +to reach these limits. As a result, the change in behavior caused previously trained policies +to not function correctly in IsaacLab v2.0.1. + +Consequently, we have reverted back to the prior behavior and added :attr:`velocity_limit_sim` and +:attr:`effort_limit_sim` attributes to make it clear that setting those parameters means +changing solver's configuration. The new behavior is as follows: + ++----------------------------+--------------------------------------------------------+-------------------------------------------------------------+ +| Attribute | Implicit Actuator | Explicit Actuator | ++============================+========================================================+=============================================================+ +| :attr:`velocity_limit` | Ignored, not set into simulation | Used by the model (e.g., DC Motor), not set into simulation | +| :attr:`effort_limit` | Set into simulation (same as :attr:`effort_limit_sim`) | Used by the models, not set into simulation | +| :attr:`velocity_limit_sim` | Set into simulation | Set into simulation | +| :attr:`effort_limit_sim` | Set into simulation (same as :attr:`effort_limit`) | Set into simulation | ++----------------------------+--------------------------------------------------------+-------------------------------------------------------------+ + +Users are advised to use the ``xxx_sim`` flag if they want to directly modify the solver limits. + +Removal of ``disable_contact_processing`` flag in ``SimulationCfg`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We have now removed the ``disable_contact_processing`` flag from the :class:`isaaclab.sim.SimulationCfg` +to not have the user worry about these intricacies of the simulator. The flag is always True by +default unless a contact sensor is created (which will internally set this flag to False). + +Previously, the flag ``disable_contact_processing`` led to confusion about its +behavior. As the name suggests, the flag controls the contact reporting from the +underlying physics engine, PhysX. Disabling this flag (note the double negation) +means that PhysX collects the contact information from its solver and allows +reporting them to the user. Enabling this flag means this operation is not performed and +the overhead of it is avoided. + +Many of our examples (for instance, the locomotion environments) were setting this +flag to True which meant the contacts should **not** get reported. However, this issue +was not noticed earlier since GPU simulation bypasses this flag, and only CPU simulation +gets affected. Running the same examples on CPU device led to different behaviors +because of this reason. + +Existing users, who currently set this flag themselves, should receive a deprecated +warning mentioning the removal of this flag and the switch to the new default behavior. + +Switch to older rendering settings to improve render quality +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +With the IsaacLab 2.0.0 release, we switched to new render settings aimed at improving +tiled-rendering performance, but at the cost of reduced rendering quality. This change +particularly affected dome lighting in the scene, which is the default in many of our examples. + +As reported by several users, this change negatively impacted render quality, even in +cases where it wasn't necessary (such as when recording videos of the simulation). In +response to this feedback, we have reverted to the previous render settings by default +to restore the quality users expected. + +For users looking to trade render quality for speed, we will provide guidelines in the future. + + +v2.0.1 +====== + +Overview +-------- + +This release contains a small set of fixes and improvements. + +The main change was to maintain combability with the updated library name for RSL RL, which breaks the previous +installation methods for Isaac Lab. This release provides the necessary fixes and updates in Isaac Lab to accommodate +for the name change and maintain compatibility with installation for RSL RL. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v2.0.0...v2.0.1 + +Improvements +------------ + +* Switches to RSL-RL install from PyPI by @Mayankm96 +* Updates the script path in the document by @fan-ziqi +* Disables extension auto-reload when saving files by @kellyguo11 +* Updates documentation for v2.0.1 installation by @kellyguo11 + +Bug Fixes +--------- + +* Fixes timestamp of com and link buffers when writing articulation pose to sim by @Jackkert +* Fixes incorrect local documentation preview path in xdg-open command by @louislelay +* Fixes no matching distribution found for rsl-rl (unavailable) by @samibouziri +* Fixes reset of sensor drift inside the RayCaster sensor by @zoctipus + +v2.0.0 +====== + +Overview +-------- + +Isaac Lab 2.0 brings some exciting new features, including a new addition to the Imitation Learning workflow with +the **Isaac Lab Mimic** extension. + +Isaac Lab Mimic provides the ability to automatically generate additional trajectories based on just a few human +collected demonstrations, allowing for larger training datasets with less human effort. This work is based on the +`MimicGen `_ work for Scalable Robot Learning using Human Demonstrations. + +Additionally, we introduced a new set of AMP tasks based on +`Adversarial Motion Priors `_, training humanoid robots to walk, run, +and dance. + +Along with Isaac Lab 2.0, Isaac Sim 4.5 brings several new and breaking changes, including a full refactor of the +Isaac Sim extensions, an improved URDF importer, an update to the PyTorch dependency to version 2.5.1, and many +fixes for tiled rendering that now supports multiple tiled cameras at different resolutions. + +To follow the refactoring in Isaac Sim, we made similar refactoring and restructuring changes to Isaac Lab. +These breaking changes will no longer be compatible with previous Isaac Sim versions. + +.. attention:: + + Please make sure to update to Isaac Sim 4.5 when using the Isaac Lab 2.0 release. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.4.1...v2.0.0 + +Highlights from the Isaac Sim 4.5 release +----------------------------------------- + +* Support for multiple ``TiledCamera`` instances and varying resolutions +* Improved rendering performance by up to 1.2x +* Faster startup time through optimizations in the Cloner class that improves startup time by 30% +* Enhanced OmniPVD for debugging physics simulation, enabling capturing reinforcement learning simulation +* Physics simulation performance optimizations improving throughput of up to 70% +* Physics support for dedicated cylinder and cone geometry designed for robot wheels that is fully GPU accelerated +* A new physics GPU filtering mechanism allowing co-location of reinforcement learning environments at the + origin with minimal performance loss for scenes with limited collider counts +* Improvements in simulation stability for mimic joints at high joint gains + +New Features +------------ + +* Adds humanoid AMP tasks for direct workflow by @Toni-SM +* Adds Isaac Lab Mimic based on MimicGen data generation for Imitation Learning by @peterd-NV @nvcyc @ashwinvkNV @karsten-nvidia +* Adds consolidated demo script for showcasing recording and mimic dataset generation in real-time in one simulation script by @nvcyc +* Adds Franka stacking environment for GR00T mimic by @peterd-NV @nvcyc +* Adds option to filter collisions and real-time playback by @kellyguo11 + +Improvements +------------ + +* Adds a tutorial for policy inference in a prebuilt USD scene by @oahmednv +* Adds unit tests for multi-tiled cameras by @matthewtrepte +* Updates render setting defaults for better quality by @kellyguo11 +* Adds a flag to wait for texture loading completion when reset by @oahmednv +* Adds pre-trained checkpoints and tools for generating and uploading checkpoints by @nv-cupright +* Adds new denoiser optimization flags for rendering by @kellyguo11 +* Updates torch to 2.5.1 by @kellyguo11 + +Bug Fixes +--------- + +* Fixes external force buffers to set to zero when no forces/torques are applied by @matthewtrepte +* Fixes RSL-RL package name in ``setup.py`` according to PyPI installation by @samibouziri + +Breaking Changes +---------------- + +* Updates the URDF and MJCF importers for Isaac Sim 4.5 by @Dhoeller19 +* Renames Isaac Lab extensions and folders by @kellyguo11 +* Restructures extension folders and removes old imitation learning scripts by @kellyguo11 +* Renames default conda and venv Python environment from ``isaaclab`` to ``env_isaaclab`` by @Toni-SM + +.. attention:: + + We have identified a breaking feature for semantic segmentation and instance segmentation when using + ``Camera`` and ``TiledCamera`` with instanceable assets. Since the Isaac Sim 4.5 / Isaac Lab 2.0 release, semantic and instance + segmentation outputs only render the first tile correctly and produces blank outputs for the remaining tiles. + We will be introducing a workaround for this fix to remove scene instancing if semantic segmentation or instance + segmentation is required for ``Camera`` and ``TiledCamera`` until we receive a proper fix from Omniverse as part of the next Isaac Sim release. + +Migration Guide +--------------- + +Renaming of Isaac Sim Extensions +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Previously, Isaac Sim extensions have been following the convention of ``omni.isaac.*``, +such as ``omni.isaac.core``. In Isaac Sim 4.5, Isaac Sim extensions have been renamed +to use the prefix ``isaacsim``, replacing ``omni.isaac``. In addition, many extensions +have been renamed and split into multiple extensions to prepare for a more modular +framework that can be customized by users through the use of app templates. + +Notably, the following commonly used Isaac Sim extensions in Isaac Lab are renamed as follow: + +* ``omni.isaac.cloner`` --> :mod:`isaacsim.core.cloner` +* ``omni.isaac.core.prims`` --> :mod:`isaacsim.core.prims` +* ``omni.isaac.core.simulation_context`` --> :mod:`isaacsim.core.api.simulation_context` +* ``omni.isaac.core.utils`` --> :mod:`isaacsim.core.utils` +* ``omni.isaac.core.world`` --> :mod:`isaacsim.core.api.world` +* ``omni.isaac.kit.SimulationApp`` --> :mod:`isaacsim.SimulationApp` +* ``omni.isaac.ui`` --> :mod:`isaacsim.gui.components` + +Renaming of the URDF and MJCF Importers +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Starting from Isaac Sim 4.5, the URDF and MJCF importers have been renamed to be more consistent +with the other extensions in Isaac Sim. The importers are available on isaac-sim GitHub +as open source projects. + +Due to the extension name change, the Python module names have also been changed: + +* URDF Importer: :mod:`isaacsim.asset.importer.urdf` (previously :mod:`omni.importer.urdf`) +* MJCF Importer: :mod:`isaacsim.asset.importer.mjcf` (previously :mod:`omni.importer.mjcf`) + +From the Isaac Sim UI, both URDF and MJCF importers can now be accessed directly from the File > Import +menu when selecting a corresponding .urdf or .xml file in the file browser. + +Changes in URDF Importer +~~~~~~~~~~~~~~~~~~~~~~~~ + +Isaac Sim 4.5 brings some updates to the URDF Importer, with a fresh UI to allow for better configurations +when importing robots from URDF. As a result, the Isaac Lab URDF Converter has also been updated to +reflect these changes. The :class:`isaaclab.sim.converters.UrdfConverterCfg` includes some new settings, +such as :class:`~isaaclab.sim.converters.JointDriveCfg.PDGainsCfg` +and :class:`~isaaclab.sim.converters.JointDriveCfg.NaturalFrequencyGainsCfg` classes for configuring +the gains of the drives. + +One breaking change to note is that the :attr:`~isaaclab.sim.converters.UrdfConverterCfg.JointDriveCfg.gains` +attribute must be of class type :class:`~isaaclab.sim.converters.JointDriveCfg.PDGainsCfg` or +:class:`~isaaclab.sim.converters.JointDriveCfg.NaturalFrequencyGainsCfg`. + +The stiffness of the :class:`~isaaclab.sim.converters.JointDriveCfg.PDGainsCfg` must be specified, as such: + +.. code-block:: python + + joint_drive=sim_utils.UrdfConverterCfg.JointDriveCfg( + gains=sim_utils.UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=None, damping=None) + ) + + +The :attr:`~isaaclab.sim.converters.JointDriveCfg.NaturalFrequencyGainsCfg.natural_frequency` attribute must +be specified for :class:`~isaaclab.sim.converters.JointDriveCfg.NaturalFrequencyGainsCfg`. + + +Renaming of Isaac Lab Extensions and Folders +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Corresponding to Isaac Sim 4.5 changes, we have also made some updates to the Isaac Lab directories and extensions. +All extensions that were previously under ``source/extensions`` are now under the ``source/`` directory directly. +The ``source/apps`` and ``source/standalone`` folders have been moved to the root directory and are now called +``apps/`` and ``scripts/``. + +Isaac Lab extensions have been renamed to: + +* ``omni.isaac.lab`` --> :mod:`isaaclab` +* ``omni.isaac.lab_assets`` --> :mod:`isaaclab_assets` +* ``omni.isaac.lab_tasks`` --> :mod:`isaaclab_tasks` + +In addition, we have split up the previous ``source/standalone/workflows`` directory into ``scripts/imitation_learning`` +and ``scripts/reinforcement_learning`` directories. The RSL RL, Stable-Baselines, RL_Games, SKRL, and Ray directories +are under ``scripts/reinforcement_learning``, while Robomimic and the new Isaac Lab Mimic directories are under +``scripts/imitation_learning``. + +To assist with the renaming of Isaac Lab extensions in your project, we have provided a +`simple script `_ that will traverse +through the ``source`` and ``docs`` directories in your local Isaac Lab project and replace any instance of the renamed +directories and imports. **Please use the script at your own risk as it will overwrite source files directly.** + + +Restructuring of Isaac Lab Extensions +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +With the introduction of :mod:`isaaclab_mimic`, designed for supporting data generation workflows for imitation learning, +we have also split out the previous ``wrappers`` folder under ``isaaclab_tasks`` to its own module, named :mod:`isaaclab_rl`. +This new extension will contain reinforcement learning specific wrappers for the various RL libraries supported by Isaac Lab. + +The new :mod:`isaaclab_mimic` extension will also replace the previous imitation learning scripts under the ``robomimic`` folder. +We have removed the old scripts for data collection and dataset preparation in favor of the new mimic workflow. For users +who prefer to use the previous scripts, they will be available in previous release branches. + +Additionally, we have also restructured the :mod:`isaaclab_assets` extension to be split into ``robots`` and ``sensors`` +subdirectories. This allows for clearer separation between the pre-defined configurations provided in the extension. + +As an example, the following import: + +.. code-block:: python + + from omni.isaac.lab_assets.anymal import ANYMAL_C_CFG + +should be replaced with: + +.. code-block:: python + + from isaaclab_assets.robots.anymal import ANYMAL_C_CFG + + +v1.4.1 +====== + +Overview +-------- + +This release contains a set of improvements and bug fixes. + +Most importantly, we reverted one of the `changes from the previous release `_ +to ensure the training throughput performance remains the same. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.4.0...v1.4.1 + +This is the **final release compatible with Isaac Sim 4.2**. The next release will target Isaac Sim 4.5, +which introduces breaking changes that will make Isaac Lab incompatible with earlier versions of Isaac Sim. + +New Features +------------ + +* Adds documentation and demo script for IMU sensor by @mpgussert + +Improvements +------------ + +* Removes deprecation for root_state_w properties and setters by @jtigue-bdai +* Fixes MARL workflows for recording videos during training/inferencing by @Rishi-V +* Adds body tracking option to ViewerCfg by @KyleM73 +* Fixes the ``joint_parameter_lookup`` type in ``RemotizedPDActuatorCfg`` to support list format by @fan-ziqi +* Updates pip installation documentation to clarify options by @steple +* Fixes docstrings in Articulation Data that report wrong return dimension by @zoctipus +* Fixes documentation error for PD Actuator by @kellyguo11 +* Clarifies ray documentation and fixes minor issues by @garylvov +* Updates code snippets in documentation to reference scripts by @mpgussert +* Adds dict conversion test for ActuatorBase configs by @mschweig + +Bug Fixes +--------- + +* Fixes JointAction not preserving order when using all joints by @T-K-233 +* Fixes event term for pushing root by setting velocity by @Mayankm96 +* Fixes error in Articulation where ``default_joint_stiffness`` and ``default_joint_damping`` are not correctly set for implicit actuator by @zoctipus +* Fixes action reset of ``pre_trained_policy_action`` in navigation environment by @nicolaloi +* Fixes rigid object's root com velocities timestamp check by @ori-gadot +* Adds interval resampling on event manager's reset call by @Mayankm96 +* Corrects calculation of target height adjustment based on sensor data by @fan-ziqi +* Fixes infinite loop in ``repeated_objects_terrain`` method by @nicolaloi +* Fixes issue where the indices were not created correctly for articulation setters by @AntoineRichard + + +v1.4.0 +====== + +Overview +-------- + +Due to a great amount of amazing updates, we are putting out one more Isaac Lab release based off of Isaac Sim 4.2. +This release contains many great new additions and bug fixes, including several new environments, distributed training +and hyperparameter support with Ray, new live plot feature for Manager-based environments, and more. + +We will now spend more focus on the next Isaac Lab release geared towards the new Isaac Sim 4.5 release coming +soon. The upcoming release will contain breaking changes in both Isaac Lab and Isaac Sim and breaks backwards +compatibility, but will come with many great fixes and improvements. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.3.0...v1.4.0 + +New Features +------------ + +* Adds Factory contact-rich manipulation tasks to IsaacLab by @noseworm +* Adds a Franka stacking ManagerBasedRLEnv by @peterd-NV +* Adds recorder manager in manager-based environments by @nvcyc +* Adds Ray Workflow: Multiple Run Support, Distributed Hyperparameter Tuning, and Consistent Setup Across Local/Cloud by @glvov-bdai +* Adds ``OperationSpaceController`` to docs and tests and implement corresponding action/action_cfg classes by @ozhanozen +* Adds null-space control option within ``OperationSpaceController`` by @ozhanozen +* Adds observation term history support to Observation Manager by @jtigue-bdai +* Adds live plots to managers by @pascal-roth + +Improvements +------------ + +* Adds documentation and example scripts for sensors by @mpgussert +* Removes duplicated ``TerminationsCfg`` code in G1 and H1 RoughEnvCfg by @fan-ziqi +* Adds option to change the clipping behavior for all Cameras and unifies the default by @pascal-roth +* Adds check that no articulation root API is applied on rigid bodies by @lgulich +* Adds RayCaster rough terrain base height to reward by @Andy-xiong6 +* Adds position threshold check for state transitions by @DorsaRoh +* Adds clip range for JointAction by @fan-ziqi + +Bug Fixes +--------- + +* Fixes noise_model initialized in direct_marl_env by @NoneJou072 +* Fixes entry_point and kwargs in isaaclab_tasks README by @fan-ziqi +* Fixes syntax for checking if pre-commit is installed in isaaclab.sh by @louislelay +* Corrects fisheye camera projection types in spawner configuration by @command-z-z +* Fixes actuator velocity limits propagation down the articulation root_physx_view by @jtigue-bdai +* Computes Jacobian in the root frame inside the ``DifferentialInverseKinematicsAction`` class by @zoctipus +* Adds transform for mesh_prim of ray caster sensor by @clearsky-mio +* Fixes configclass dict conversion for torch tensors by @lgulich +* Fixes error in apply_actions method in ``NonHolonomicAction`` action term. by @KyleM73 +* Fixes outdated sensor data after reset by @kellyguo11 +* Fixes order of logging metrics and sampling commands in command manager by @Mayankm96 + +Breaking Changes +---------------- + +* Refactors pose and velocities to link frame and COM frame APIs by @jtigue-bdai + + +v1.3.0 +====== + +Overview +-------- + +This release will be a final release based on Isaac Sim 4.2 before the transition to Isaac Sim 4.5, which will +likely contain breaking changes and no longer backwards compatible with Isaac Sim 4.2 and earlier. In this release, +we introduce many features, improvements, and bug fixes, including IMU sensors, support for various types of +gymnasium spaces, manager-based perception environments, and more. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.2.0...v1.3.0 + +New Features +------------ + +* Adds ``IMU`` sensor by @pascal-roth +* Add Camera Benchmark Tool and Allow Correct Unprojection of distance_to_camera depth image by @glvov-bdai +* Creates Manager Based Cartpole Vision Example Environments by @glvov-bdai +* Adds image extracted features observation term and cartpole examples for it by @glvov-bdai +* Supports other gymnasium spaces in Direct workflow by @Toni-SM +* Adds configuration classes for spawning different assets at prim paths by @Mayankm96 +* Adds a rigid body collection class by @Dhoeller19 +* Adds option to scale/translate/rotate meshes in the ``mesh_converter`` by @pascal-roth +* Adds event term to randomize gains of explicit actuators by @MoreTore +* Adds Isaac Lab Reference Architecture documentation by @OOmotuyi + +Improvements +------------ + +* Expands functionality of FrameTransformer to allow multi-body transforms by @jsmith-bdai +* Inverts SE-2 keyboard device actions (Z, X) for yaw command by @riccardorancan +* Disables backward pass compilation of warp kernels by @Mayankm96 +* Replaces TensorDict with native dictionary by @Toni-SM +* Improves omni.isaac.lab_tasks loading time by @Toni-SM +* Caches PhysX view's joint paths when processing fixed articulation tendons by @Toni-SM +* Replaces hardcoded module paths with ``__name__`` dunder by @Mayankm96 +* Expands observation term scaling to support list of floats by @pascal-roth +* Removes extension startup messages from the Simulation App by @Mayankm96 +* Adds a render config to the simulation and tiledCamera limitations to the docs by @kellyguo11 +* Adds Kit command line argument support by @kellyguo11 +* Modifies workflow scripts to generate random seed when seed=-1 by @kellyguo11 +* Adds benchmark script to measure robot loading by @Mayankm96 +* Switches from ``carb`` to ``omni.log`` for logging by @Mayankm96 +* Excludes cache files from vscode explorer by @Divelix +* Adds versioning to the docs by @sheikh-nv +* Adds better error message for invalid actuator parameters by @lgulich +* Updates tested docker and apptainer versions for cluster deployment by @pascal-roth +* Removes ``ml_archive`` as a dependency of ``omni.isaac.lab`` extension by @fan-ziqi +* Adds a validity check for configclasses by @Dhoeller19 +* Ensures mesh name is compatible with USD convention in mesh converter by @fan-ziqi +* Adds sanity check for the term type inside the command manager by @command-z-z +* Allows configclass ``to_dict`` operation to handle a list of configclasses by @jtigue-bdai + +Bug Fixes +--------- + +* Disables replicate physics for deformable teddy lift environment by @Mayankm96 +* Fixes Jacobian joint indices for floating base articulations by @lorenwel +* Fixes setting the seed from CLI for RSL-RL by @kaixi287 +* Fixes camera MDP term name and reprojection docstrings by @Mayankm96 +* Fixes deprecation notice for using ``pxr.Semantics`` by @Mayankm96 +* Fixes scaling of default ground plane by @kellyguo11 +* Fixes Isaac Sim executable on pip installation by @Toni-SM +* Passes device from CLI args to simulation config in standalone scripts by @Mayankm96 +* Fixes the event for randomizing rigid body material by @pascal-roth +* Fixes the ray_caster_camera tutorial script when saving the data by @mpgussert +* Fixes running the docker container when the DISPLAY env variable is not defined by @GiulioRomualdi +* Fixes default joint pos when setting joint limits by @kellyguo11 +* Fixes device propagation for noise and adds noise tests by @jtigue-bdai +* Removes additional sbatch and fixes default profile in cluster deployment by @pascal-roth +* Fixes the checkpoint loading error in RSL-RL training script by @bearpaw +* Fixes pytorch broadcasting issue in ``EMAJointPositionToLimitsAction`` by @bearpaw +* Fixes body IDs selection when computing ``feet_slide`` reward for locomotion-velocity task by @dtc103 +* Fixes broken URLs in markdown files by @DorsaRoh +* Fixes ``net_arch`` in ``sb3_ppo_cfg.yaml`` for Isaac-Lift-Cube-Franka-v0 task by @LinghengMeng + + +v1.2.0 +====== + +Overview +-------- + +We leverage the new release of Isaac Sim, 4.2.0, and bring RTX-based tiled rendering, support for multi-agent +environments, and introduce many bug fixes and improvements. + +Additionally, we have published an example for generating rewards using an LLM based on +`Eureka `_, available here: https://github.com/isaac-sim/IsaacLabEureka + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.1.0...v1.2.0 + +New Features +------------ + +* Adds RTX-based tiled rendering. This improves the overall rendering speed and quality. +* Adds the direct workflow perceptive Shadowhand Cube Repose environment ``Isaac-Repose-Cube-Shadow-Vision-Direct-v0`` by @kellyguo11. +* Adds support for multi-agent environments with the Direct workflow, with support for MAPPO and IPPO in SKRL by @Toni-SM +* Adds the direct workflow multi-agent environments ``Isaac-Cart-Double-Pendulum-Direct-v0`` and ``Isaac-Shadow-Hand-Over-Direct-v0`` by @Toni-SM +* Adds throughput benchmarking scripts for the different learning workflows by @kellyguo11 +* Adds results for the benchmarks in the documentation + `here `__ + for different types of hardware by @kellyguo11 +* Adds the direct workflow Allegro hand environment by @kellyguo11 +* Adds video recording to the play scripts in RL workflows by @j3soon +* Adds comparison tables for the supported RL libraries + `here `__ by @kellyguo11 +* Add APIs for deformable asset by @masoudmoghani +* Adds support for MJCF converter by @qqqwan +* Adds a function to define camera configs through intrinsic matrix by @pascal-roth +* Adds configurable modifiers to observation manager by @jtigue-bdai +* Adds the Hydra configuration system for RL training by @Dhoeller19 + +Improvements +------------ + +* Uses PhysX accelerations for rigid body acceleration data by @Mayankm96 +* Adds documentation on the frames for asset data by @Mayankm96 +* Renames Unitree configs in locomotion tasks to match properly by @Mayankm96 +* Adds option to set the height of the border in the ``TerrainGenerator`` by @pascal-roth +* Adds a cli arg to ``run_all_tests.py`` for testing a selected extension by @jsmith-bdai +* Decouples rigid object and articulation asset classes by @Mayankm96 +* Adds performance optimizations for domain randomization by @kellyguo11 +* Allows having hybrid dimensional terms inside an observation group by @Mayankm96 +* Adds a flag to preserve joint order inside ``JointActionCfg`` action term by @xav-nal +* Adds the ability to resume training from a checkpoint with rl_games by @sizsJEon +* Adds windows configuration to VS code tasks by @johnBuffer +* Adapts A and D button bindings in the keyboard device by @zoctipus +* Uses ``torch.einsum`` for quat_rotate and quat_rotate_inverse operations by @dxyy1 +* Expands on articulation test for multiple instances and devices by @jsmith-bdai +* Adds setting of environment seed at initialization by @Mayankm96 +* Disables default viewport when headless but cameras are enabled by @kellyguo11 +* Simplifies the return type for ``parse_env_cfg`` method by @Mayankm96 +* Simplifies the if-elses inside the event manager apply method by @Mayankm96 + +Bug Fixes +--------- + +* Fixes rendering frame delays. Rendered images now faithfully represent the latest state of the physics scene. + We added the flag ``rerender_on_reset`` in the environment configs to toggle an additional render step when a + reset happens. When activated, the images/observation always represent the latest state of the environment, but + this also reduces performance. +* Fixes ``wrap_to_pi`` function in math utilities by @Mayankm96 +* Fixes setting of pose when spawning a mesh by @masoudmoghani +* Fixes caching of the terrain using the terrain generator by @Mayankm96 +* Fixes running train scripts when rsl_rl is not installed by @Dhoeller19 +* Adds flag to recompute inertia when randomizing the mass of a rigid body by @Mayankm96 +* Fixes support for ``classmethod`` when defining a configclass by @Mayankm96 +* Fixes ``Sb3VecEnvWrapper`` to clear buffer on reset by @EricJin2002 +* Fixes venv and conda pip installation on windows by @kellyguo11 +* Sets native livestream extensions to Isaac Sim 4.1-4.0 defaults by @jtigue-bdai +* Defaults the gym video recorder fps to match episode decimation by @ozhanozen +* Fixes the event manager's apply method by @kellyguo11 +* Updates camera docs with world units and introduces new test for intrinsics by @pascal-roth +* Adds the ability to resume training from a checkpoint with rl_games by @sizsJEon + +Breaking Changes +---------------- + +* Simplifies device setting in SimulationCfg and AppLauncher by @Dhoeller19 +* Fixes conflict in teleop-device command line argument in scripts by @Dhoeller19 +* Converts container.sh into Python utilities by @hhansen-bdai +* Drops support for ``TiledCamera`` for Isaac Sim 4.1 + +Migration Guide +--------------- + +Setting the simulation device into the simulation context +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Previously, changing the simulation device to CPU required users to set other simulation parameters +(such as disabling GPU physics and GPU pipelines). This made setting up the device appear complex. +We now simplify the checks for device directly inside the simulation context, so users only need to +specify the device through the configuration object. + +Before: + +.. code:: python + + sim_utils.SimulationCfg(device="cpu", use_gpu_pipeline=False, dt=0.01, physx=sim_utils.PhysxCfg(use_gpu=False)) + +Now: + +.. code:: python + + sim_utils.SimulationCfg(device="cpu", dt=0.01, physx=sim_utils.PhysxCfg()) + +Setting the simulation device from CLI +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Previously, users could specify the device through the command line argument ``--device_id``. However, +this made it ambiguous when users wanted to set the device to CPU. Thus, instead of the device ID, +users need to specify the device explicitly through the argument ``--device``. +The valid options for the device name are: + +* "cpu": runs simulation on CPU +* "cuda": runs simulation on GPU with device ID at default index +* "cuda:N": runs simulation on GPU with device ID at ``N``. For instance, "cuda:0" will use device at index "0". + +Due to the above change, the command line interaction with some of the scripts has changed. + +Before: + +.. code:: bash + + ./isaaclab.sh -p source/standalone/workflows/sb3/train.py --task Isaac-Cartpole-v0 --headless --cpu + +Now: + +.. code:: bash + + ./isaaclab.sh -p source/standalone/workflows/sb3/train.py --task Isaac-Cartpole-v0 --headless --device cpu + +Renaming of teleoperation device CLI in standalone scripts +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Since ``--device`` is now an argument provided by the AppLauncher, it conflicted with the command-line +argument used for specifying the teleoperation-device in some of the standalone scripts. Thus, to fix +this conflict, the teleoperation-device now needs to be specified through ``--teleop_device`` argument. + +Before: + +.. code:: bash + + ./isaaclab.sh -p source/standalone/environments/teleoperation/teleop_se3_agent.py --task Isaac-Lift-Cube-Franka-IK-Rel-v0 --num_envs 1 --device keyboard + +Now: + +.. code:: bash + + ./isaaclab.sh -p source/standalone/environments/teleoperation/teleop_se3_agent.py --task Isaac-Lift-Cube-Franka-IK-Rel-v0 --num_envs 1 --teleop_device keyboard + + +Using Python-version of container utility script +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The prior `container.sh `_ became quite +complex as it had many different use cases in one script. For instance, building a docker image for "base" or "ros2", +as well as cluster deployment. As more users wanted to have the flexibility to overlay their own docker settings, +maintaining this bash script became cumbersome. Hence, we migrated its features into a Python script in this release. +Additionally, we split the cluster-related utilities into their own script inside the ``docker/cluster`` directory. + +We still maintain backward compatibility for ``container.sh``. Internally, it calls the Python script ``container.py``. +We request users to use the Python script directly. + +Before: + +.. code:: bash + + ./docker/container.sh start + + +Now: + +.. code:: bash + + ./docker/container.py start + + +Using separate directories for logging videos in RL workflows +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Previously, users could record videos during the RL training by specifying the ``--video`` flag to the +``train.py`` script. The videos would be saved inside the ``videos`` directory in the corresponding log +directory of the run. + +Since many users requested to also be able to record videos while inferencing the policy, recording +videos have also been added to the ``play.py`` script. Since changing the prefix of the video file +names is not possible, the videos from the train and play scripts are saved inside the ``videos/train`` +and ``videos/play`` directories, respectively. + +Drops support for the tiled camera with Isaac Sim 4.1 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Various fixes have been made to the tiled camera rendering pipeline in Isaac Sim 4.2. This made +supporting the tiled camera with Isaac Sim 4.1 difficult. Hence, for the best experience, we advice +switching to Isaac Sim 4.2 with this release of Isaac Lab. + + +v1.1.0 +====== + +Overview +-------- + +With the release of Isaac Sim 4.0 and 4.1, support for Isaac Sim 2023.1.1 has been discontinued. +We strongly encourage all users to upgrade to Isaac Sim 4.1 to take advantage of the latest features +and improvements. For detailed information on this upgrade, please refer to the release notes available +`here `__. + +Besides the above, the Isaac Lab release brings new features and improvements, as detailed below. We thank +all our contributors for their continued support. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v1.0.0...v1.1.0 + +New Features +------------ + +* Adds distributed multi-GPU learning support for skrl by @Toni-SM +* Updates skrl integration to support training/evaluation using JAX by @Toni-SM +* Adds lidar pattern for raycaster sensor by @pascal-roth +* Adds support for PBS job scheduler-based clusters by @shafeef901 +* Adds APIs for spawning deformable meshes by @Mayankm96 + +Improvements +------------ + +* Changes documentation color to the green theme by @Mayankm96 +* Fixes sphinx tabs to make them work in dark theme by @Mayankm96 +* Fixes VSCode settings to work with pip installation of Isaac Sim by @Mayankm96 +* Fixes ``isaaclab`` scripts to deal with Isaac Sim pip installation by @Mayankm96 +* Optimizes interactive scene for homogeneous cloning by @kellyguo11 +* Improves docker X11 forwarding documentation by @j3soon + +Bug Fixes +--------- + +* Reads gravity direction from simulation inside ``RigidObjectData`` by @Mayankm96 +* Fixes reference count in asset instances due to circular references by @Mayankm96 +* Fixes issue with asset deinitialization due to torch > 2.1 by @Mayankm96 +* Fixes the rendering logic regression in environments by @Dhoeller19 +* Fixes the check for action-space inside Stable-Baselines3 wrapper by @Mayankm96 +* Fixes warning message in Articulation config processing by @locoxsoco +* Fixes action term in the reach environment by @masoudmoghani +* Fixes training UR10 reach with RL_GAMES and SKRL by @sudhirpratapyadav +* Adds event manager call to simple manage-based env by @Mayankm96 + +Breaking Changes +---------------- + +* Drops official support for Isaac Sim 2023.1.1 +* Removes the use of body view inside the asset classes by @Mayankm96 +* Renames ``SimulationCfg.substeps`` to ``SimulationCfg.render_interval`` by @Dhoeller19 + +Migration Guide +--------------- + +Renaming of ``SimulationCfg.substeps`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Previously, the users set both ``omni.isaac.lab.sim.SimulationCfg.dt`` and +``omni.isaac.lab.sim.SimulationCfg.substeps``, which marked the physics insulation time-step and sub-steps, +respectively. It was unclear whether sub-steps meant the number of integration steps inside the physics time-step +``dt`` or the number of physics steps inside a rendering step. + +Since in the code base, the attribute was used as the latter, it has been renamed to ``render_interval`` for clarity. + +Removal of Deprecated Attributes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +As notified in previous releases, we removed the classes and attributes marked as deprecated. These are as follows: + +* The ``mdp.add_body_mass`` method in the events. Please use the ``mdp.randomize_rigid_body_mass`` instead. +* The classes ``managers.RandomizationManager`` and ``managers.RandomizationTermCfg``. Please use the + ``managers.EventManager`` and ``managers.EventTermCfg`` classes instead. +* The following properties in ``omni.isaac.lab.sensors.FrameTransformerData``: + * ``target_rot_source`` --> ``target_quat_w`` + * ``target_rot_w`` --> ``target_quat_source`` + * ``source_rot_w`` --> ``source_quat_w`` + +* The attribute ``body_physx_view`` from the ``omni.isaac.lab.assets.Articulation`` and + ``omni.isaac.lab.assets.RigidObject`` classes. These caused confusion when used with the articulation view + since the body names did not follow the same ordering. + +v1.0.0 +====== + +Overview +-------- + +Welcome to the first official release of Isaac Lab! + +Building upon the foundation of the `Orbit `_ framework, we have integrated +the RL environment designing workflow from `OmniIsaacGymEnvs `_. +This allows users to choose a suitable :ref:`task-design approach ` +for their applications. + +While we maintain backward compatibility with Isaac Sim 2023.1.1, we highly recommend using Isaac Lab with +Isaac Sim 4.0.0 version for the latest features and improvements. + +**Full Changelog**: https://github.com/isaac-sim/IsaacLab/compare/v0.3.1...v1.0.0 + +New Features +------------ + +* Integrated CI/CD pipeline, which is triggered on pull requests and publishes the results publicly +* Extended support for Windows OS platforms +* Added tiled render based Camera + sensor implementation. This provides optimized RGB-D rendering throughputs of up to 10k frames per second. +* Added support for multi-GPU and multi-node training for the RL-Games library +* Integrated APIs for environment designing (direct workflow) without relying on managers +* Added implementation of delayed PD actuator model +* Added various new learning environments: + * Cartpole balancing using images + * Shadow hand cube reorientation + * Boston Dynamics Spot locomotion + * Unitree H1 and G1 locomotion + * ANYmal-C navigation + * Quadcopter target reaching + +Improvements +------------ + +* Reduced start-up time for scripts (inherited from Isaac Sim 4.0 improvements) +* Added lazy buffer implementation for rigid object and articulation data. Instead of updating all the quantities + at every step call, the lazy buffers are updated only when the user queries them +* Added SKRL support to more environments + +Breaking Changes +---------------- + +For users coming from Orbit, this release brings certain breaking changes. Please check the migration guide for more information. + +Migration Guide +--------------- + +Please find detailed migration guides as follows: + +* `From Orbit to IsaacLab `_ +* `From OmniIsaacGymEnvs to IsaacLab `_ + +.. _simple script: https://gist.github.com/kellyguo11/3e8f73f739b1c013b1069ad372277a85 diff --git a/docs/source/refs/snippets/code_skeleton.py b/docs/source/refs/snippets/code_skeleton.py new file mode 100644 index 0000000000000000000000000000000000000000..759ca38ae0f96d2f2e8d72e653fd8ca530c9cb20 --- /dev/null +++ b/docs/source/refs/snippets/code_skeleton.py @@ -0,0 +1,152 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from typing import ClassVar + +DEFAULT_TIMEOUT: int = 30 +"""Default timeout for the task.""" + +_MAX_RETRIES: int = 3 # private constant (note the underscore) +"""Maximum number of retries for the task.""" + + +def run_task(task_name: str): + """Run a task by name. + + Args: + task_name: The name of the task to run. + """ + print(f"Running task: {task_name}") + + +class TaskRunner: + """Runs and manages tasks.""" + + DEFAULT_NAME: ClassVar[str] = "runner" + """Default name for the runner.""" + + _registry: ClassVar[dict] = {} + """Registry of runners.""" + + def __init__(self, name: str): + """Initialize the runner. + + Args: + name: The name of the runner. + """ + self.name = name + self._tasks = [] # private instance variable + + def __del__(self): + """Clean up the runner.""" + print(f"Cleaning up {self.name}") + + def __repr__(self) -> str: + return f"TaskRunner(name={self.name!r})" + + def __str__(self) -> str: + return f"TaskRunner: {self.name}" + + """ + Properties. + """ + + @property + def task_count(self) -> int: + return len(self._tasks) + + """ + Operations. + """ + + def initialize(self): + """Initialize the runner.""" + print("Initializing runner...") + + def update(self, task: str): + """Update the runner with a new task. + + Args: + task: The task to add. + """ + self._tasks.append(task) + print(f"Added task: {task}") + + def close(self): + """Close the runner.""" + print("Closing runner...") + + """ + Operations: Registration. + """ + + @classmethod + def register(cls, name: str, runner: "TaskRunner"): + """Register a runner. + + Args: + name: The name of the runner. + runner: The runner to register. + """ + if name in cls._registry: + _log_error(f"Runner {name} already registered. Skipping registration.") + return + cls._registry[name] = runner + + @staticmethod + def validate_task(task: str) -> bool: + """Validate a task. + + Args: + task: The task to validate. + + Returns: + True if the task is valid, False otherwise. + """ + return bool(task and task.strip()) + + """ + Internal operations. + """ + + def _reset(self): + """Reset the runner.""" + self._tasks.clear() + + @classmethod + def _get_registry(cls) -> dict: + """Get the registry.""" + return cls._registry + + @staticmethod + def _internal_helper(): + """Internal helper.""" + print("Internal helper called.") + + +""" +Helper operations. +""" + + +def _log_error(message: str): + """Internal helper to log errors. + + Args: + message: The message to log. + """ + print(f"[ERROR] {message}") + + +class _TaskHelper: + """Private utility class for internal task logic.""" + + def compute(self) -> int: + """Compute the result. + + Returns: + The result of the computation. + """ + return 42 diff --git a/docs/source/refs/snippets/tutorial_modify_direct_rl_env.py b/docs/source/refs/snippets/tutorial_modify_direct_rl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..e839abf20099bb878f288af7797f9431f7eda62d --- /dev/null +++ b/docs/source/refs/snippets/tutorial_modify_direct_rl_env.py @@ -0,0 +1,61 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ruff: noqa +# fmt: off + +# [start-init-import] +from .h1_env import H1Env, H1EnvCfg +# [end-init-import] + +# [start-init-register] +gym.register( + id="Isaac-H1-Direct-v0", + entry_point="isaaclab_tasks.direct.humanoid:H1Env", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": H1EnvCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:HumanoidPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) +# [end-init-register] + +# [start-h1_env-import] +from isaaclab_assets import H1_CFG +# [end-h1_env-import] + +# [start-h1_env-spaces] +action_space = 19 +observation_space = 69 +# [end-h1_env-spaces] + +# [start-h1_env-robot] +robot: ArticulationCfg = H1_CFG.replace(prim_path="/World/envs/env_.*/Robot") +joint_gears: list = [ + 50.0, # left_hip_yaw + 50.0, # right_hip_yaw + 50.0, # torso + 50.0, # left_hip_roll + 50.0, # right_hip_roll + 50.0, # left_shoulder_pitch + 50.0, # right_shoulder_pitch + 50.0, # left_hip_pitch + 50.0, # right_hip_pitch + 50.0, # left_shoulder_roll + 50.0, # right_shoulder_roll + 50.0, # left_knee + 50.0, # right_knee + 50.0, # left_shoulder_yaw + 50.0, # right_shoulder_yaw + 50.0, # left_ankle + 50.0, # right_ankle + 50.0, # left_elbow + 50.0, # right_elbow +] +# [end-h1_env-robot] + +# fmt: on diff --git a/docs/source/refs/troubleshooting.rst b/docs/source/refs/troubleshooting.rst new file mode 100644 index 0000000000000000000000000000000000000000..8f3a82f3f150df1b14d2d68d524e69e16eb8f467 --- /dev/null +++ b/docs/source/refs/troubleshooting.rst @@ -0,0 +1,260 @@ +Tricks and Troubleshooting +========================== + +.. note:: + + The following lists some of the common tricks and troubleshooting methods that we use in our common workflows. + Please also check the `troubleshooting page on Omniverse + `__ for more + assistance. + + +Debugging physics simulation stability issues +--------------------------------------------- + +When importing new robots into Isaac Lab or setting up a new environment, simulation instability +can often appear if the assets have not been tuned with reasonable simulation parameters. +In reinforcement learning scenarios, this will often result in NaNs propagating into the learning pipeline +due to invalid states in the simulation. + +If this happens, we recommend consulting the +`Articulation and Robot Simulation Stability Guide `_ +which recommends various simulation parameters and best practices to achieve better stability in robot simulations. + +Additionally, `Omniverse PhysX Visual Debugger `_ +allows for recording of data of PhysX simulations, which can often help simulation issues and aid the debugging process. + +To enable OmniPVD capture in Isaac Lab, add the relevant kit arguments to the command line prompt when launching an Isaac Lab process + +.. code:: bash + + ./isaaclab.sh -p scripts/demos/bipeds.py --kit_args "--/persistent/physics/omniPvdOvdRecordingDirectory=/tmp/ --/physics/omniPvdOutputEnabled=true" --headless + + +Checking the internal logs from the simulator +--------------------------------------------- + +When running the simulator from a standalone script, it logs warnings and errors to the terminal. At the same time, +it also logs internal messages to a file. These are useful for debugging and understanding the internal state of the +simulator. Depending on your system, the log file can be found in the locations listed +`here `_. + +To obtain the exact location of the log file, you need to check the first few lines of the terminal output when +you run the standalone script. The log file location is printed at the start of the terminal output. For example: + +.. code:: bash + + [INFO] Using python from: /home/${USER}/git/IsaacLab/_isaac_sim/python.sh + ... + Passing the following args to the base kit application: [] + Loading user config located at: '.../data/Kit/Isaac-Sim/2023.1/user.config.json' + [Info] [carb] Logging to file: '.../logs/Kit/Isaac-Sim/2023.1/kit_20240328_183346.log' + + +In the above example, the log file is located at ``.../logs/Kit/Isaac-Sim/2023.1/kit_20240328_183346.log``, +``...`` is the path to the user's log directory. The log file is named ``kit_20240328_183346.log`` + +You can open this file to check the internal logs from the simulator. Also when reporting issues, please include +this log file to help us debug the issue. + +Changing logging channel levels for the simulator +------------------------------------------------- + +By default, the simulator logs messages at the ``WARN`` level and above on the terminal. You can change the logging +channel levels to get more detailed logs. The logging channel levels can be set through Omniverse's logging system. + +To obtain more detailed logs, you can run your application with the following flags: + +* ``--info``: This flag logs messages at the ``INFO`` level and above. +* ``--verbose``: This flag logs messages at the ``VERBOSE`` level and above. + +For instance, to run a standalone script with verbose logging, you can use the following command: + +.. code-block:: bash + + # Run the standalone script with info logging + ./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py --headless --info + +For more fine-grained control, you can modify the logging channels through the ``logger`` module. +For more information, please refer to its `documentation `__. + + +Observing long load times at the start of the simulation +-------------------------------------------------------- + +The first time you run the simulator, it will take a long time to load up. This is because the +simulator is compiling shaders and loading assets. Subsequent runs should be faster to start up, +but may still take some time. + +Please note that once the Isaac Sim app loads, the environment creation time may scale linearly with +the number of environments. Please expect a longer load time if running with thousands of +environments or if each environment contains a larger number of assets. We are continually working +on improving the time needed for this. + +When an instance of Isaac Sim is already running, launching another Isaac Sim instance in a different +process may appear to hang at startup for the first time. Please be patient and give it some time as +the second process will take longer to start up due to slower shader compilation. + + +Receiving a “PhysX error” when running simulation on GPU +-------------------------------------------------------- + +When using the GPU pipeline, the buffers used for the physics simulation are allocated on the GPU only +once at the start of the simulation. This means that they do not grow dynamically as the number of +collisions or objects in the scene changes. If the number of collisions or objects in the scene +exceeds the size of the buffers, the simulation will fail with an error such as the following: + +.. code:: bash + + PhysX error: the application need to increase the PxgDynamicsMemoryConfig::foundLostPairsCapacity + parameter to 3072, otherwise the simulation will miss interactions + +In this case, you need to increase the size of the buffers passed to the +:class:`~isaaclab.sim.SimulationContext` class. The size of the buffers can be increased by setting +the :attr:`~isaaclab.sim.PhysxCfg.gpu_found_lost_pairs_capacity` parameter in the +:class:`~isaaclab.sim.PhysxCfg` class. For example, to increase the size of the buffers to +4096, you can use the following code: + +.. code:: python + + import isaaclab.sim as sim_utils + + sim_cfg = sim_utils.SimulationConfig() + sim_cfg.physx.gpu_found_lost_pairs_capacity = 4096 + sim = SimulationContext(sim_params=sim_cfg) + +Please see the documentation for :class:`~isaaclab.sim.SimulationCfg` for more details +on the parameters that can be used to configure the simulation. + + +Preventing memory leaks in the simulator +---------------------------------------- + +Memory leaks in the Isaac Sim simulator can occur when C++ callbacks are registered with Python objects. +This happens when callback functions within classes maintain references to the Python objects they are +associated with. As a result, Python's garbage collection is unable to reclaim memory associated with +these objects, preventing the corresponding C++ objects from being destroyed. Over time, this can lead +to memory leaks and increased resource usage. + +To prevent memory leaks in the Isaac Sim simulator, it is essential to use weak references when registering +callbacks with the simulator. This ensures that Python objects can be garbage collected when they are no +longer needed, thereby avoiding memory leaks. The `weakref `_ +module from the Python standard library can be employed for this purpose. + + +For example, consider a class with a callback function ``on_event_callback`` that needs to be registered +with the simulator. If you use a strong reference to the ``MyClass`` object when passing the callback, +the reference count of the ``MyClass`` object will be incremented. This prevents the ``MyClass`` object +from being garbage collected when it is no longer needed, i.e., the ``__del__`` destructor will not be +called. + +.. code:: python + + import omni.kit + + class MyClass: + def __init__(self): + app_interface = omni.kit.app.get_app_interface() + self._handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + self.on_event_callback + ) + + def __del__(self): + self._handle.unsubscribe() + self._handle = None + + def on_event_callback(self, event): + # do something with the message + + +To fix this issue, it's crucial to employ weak references when registering the callback. While this approach +adds some verbosity to the code, it ensures that the ``MyClass`` object can be garbage collected when no longer +in use. Here's the modified code: + +.. code:: python + + import omni.kit + import weakref + + class MyClass: + def __init__(self): + app_interface = omni.kit.app.get_app_interface() + self._handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj.on_event_callback(event) + ) + + def __del__(self): + self._handle.unsubscribe() + self._handle = None + + def on_event_callback(self, event): + # do something with the message + + +In this revised code, the weak reference ``weakref.proxy(self)`` is used when registering the callback, +allowing the ``MyClass`` object to be properly garbage collected. + +By following this pattern, you can prevent memory leaks and maintain a more efficient and stable simulation. + + +Understanding the error logs from crashes +----------------------------------------- + +Many times the simulator crashes due to a bug in the implementation. +This swamps the terminal with exceptions, some of which are coming from +the python interpreter calling ``__del__()`` destructor of the +simulation application. These typically look like the following: + +.. code:: bash + + ... + + [INFO]: Completed setting up the environment... + + Traceback (most recent call last): + File "scripts/imitation_learning/robomimic/collect_demonstrations.py", line 166, in + main() + File "scripts/imitation_learning/robomimic/collect_demonstrations.py", line 126, in main + actions = pre_process_actions(delta_pose, gripper_command) + File "scripts/imitation_learning/robomimic/collect_demonstrations.py", line 57, in pre_process_actions + return torch.concat([delta_pose, gripper_vel], dim=1) + TypeError: expected Tensor as element 1 in argument 0, but got int + Exception ignored in: ._Registry.__del__ at 0x7f94ac097f80> + Traceback (most recent call last): + File "../IsaacLab/_isaac_sim/kit/extscore/omni.kit.viewport.registry/omni/kit/viewport/registry/registry.py", line 103, in __del__ + File "../IsaacLab/_isaac_sim/kit/extscore/omni.kit.viewport.registry/omni/kit/viewport/registry/registry.py", line 98, in destroy + TypeError: 'NoneType' object is not callable + Exception ignored in: ._Registry.__del__ at 0x7f94ac097f80> + Traceback (most recent call last): + File "../IsaacLab/_isaac_sim/kit/extscore/omni.kit.viewport.registry/omni/kit/viewport/registry/registry.py", line 103, in __del__ + File "../IsaacLab/_isaac_sim/kit/extscore/omni.kit.viewport.registry/omni/kit/viewport/registry/registry.py", line 98, in destroy + TypeError: 'NoneType' object is not callable + Exception ignored in: + Traceback (most recent call last): + File "../IsaacLab/_isaac_sim/kit/kernel/py/omni/kit/app/_impl/__init__.py", line 114, in __del__ + AttributeError: 'NoneType' object has no attribute 'get_settings' + Exception ignored in: + Traceback (most recent call last): + File "../IsaacLab/_isaac_sim/extscache/omni.kit.viewport.menubar.lighting-104.0.7/omni/kit/viewport/menubar/lighting/actions.py", line 345, in __del__ + File "../IsaacLab/_isaac_sim/extscache/omni.kit.viewport.menubar.lighting-104.0.7/omni/kit/viewport/menubar/lighting/actions.py", line 350, in destroy + TypeError: 'NoneType' object is not callable + 2022-12-02 15:41:54 [18,514ms] [Warning] [carb.audio.context] 1 contexts were leaked + ../IsaacLab/_isaac_sim/python.sh: line 41: 414372 Segmentation fault (core dumped) $python_exe "$@" $args + There was an error running python + +This is a known error with running standalone scripts with the Isaac Sim +simulator. Please scroll above the exceptions thrown with +``registry`` to see the actual error log. + +In the above case, the actual error is: + +.. code:: bash + + Traceback (most recent call last): + File "scripts/imitation_learning/robomimic/tools/collect_demonstrations.py", line 166, in + main() + File "scripts/imitation_learning/robomimic/tools/collect_demonstrations.py", line 126, in main + actions = pre_process_actions(delta_pose, gripper_command) + File "scripts/imitation_learning/robomimic/tools/collect_demonstrations.py", line 57, in pre_process_actions + return torch.concat([delta_pose, gripper_vel], dim=1) + TypeError: expected Tensor as element 1 in argument 0, but got int diff --git a/docs/source/setup/ecosystem.rst b/docs/source/setup/ecosystem.rst new file mode 100644 index 0000000000000000000000000000000000000000..5978443ac21cb24e2fcd91a76a8c3bde8d713878 --- /dev/null +++ b/docs/source/setup/ecosystem.rst @@ -0,0 +1,156 @@ +.. _isaac-lab-ecosystem: + +Isaac Lab Ecosystem +=================== + +Isaac Lab is built on top of Isaac Sim to provide a unified and flexible framework +for robot learning that exploits latest simulation technologies. It is designed to be modular and extensible, +and aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and +motion planning). While it includes some pre-built environments, sensors, and tasks, its main goal is to +provide an open-sourced, unified, and easy-to-use interface for developing and testing custom environments +and robot learning algorithms. + +Working with Isaac Lab requires the installation of Isaac Sim, which is packaged with core robotics tools +that Isaac Lab depends on, including URDF and MJCF importers, simulation managers, and ROS features. Isaac +Sim also builds on top of the NVIDIA Omniverse platform, leveraging advanced physics simulation from PhysX, +photorealistic rendering technologies, and Universal Scene Description (USD) for scene creation. + +Isaac Lab not only inherits the capabilities of Isaac Sim, but also adds a number +of new features that pertain to robot learning research. For example, including actuator dynamics in the +simulation, procedural terrain generation, and support to collect data from human demonstrations. + +.. image:: ../_static/setup/ecosystem-light.jpg + :class: only-light + :align: center + :alt: The Isaac Lab, Isaac Sim, and NVIDIA Omniverse ecosystem + +.. image:: ../_static/setup/ecosystem-dark.jpg + :class: only-dark + :align: center + :alt: The Isaac Lab, Isaac Sim, and NVIDIA Omniverse ecosystem + + +Where does Isaac Lab fit in the Isaac ecosystem? +------------------------------------------------ + +Over the years, NVIDIA has developed a number of tools for robotics and AI. These tools leverage +the power of GPUs to accelerate the simulation both in terms of speed and realism. They show great +promise in the field of simulation technology and are being used by many researchers and companies +worldwide. + +`Isaac Gym`_ :cite:`makoviychuk2021isaac` provides a high performance GPU-based physics simulation +for robot learning. It is built on top of `PhysX`_ which supports GPU-accelerated simulation of rigid bodies +and a Python API to directly access physics simulation data. Through an end-to-end GPU pipeline, it is possible +to achieve high frame rates compared to CPU-based physics engines. The tool has been used successfully in a +number of research projects, including legged locomotion :cite:`rudin2022learning` :cite:`rudin2022advanced`, +in-hand manipulation :cite:`handa2022dextreme` :cite:`allshire2022transferring`, and industrial assembly +:cite:`narang2022factory`. + +Despite the success of Isaac Gym, it is not designed to be a general purpose simulator for +robotics. For example, it does not include interaction between deformable and rigid objects, high-fidelity +rendering, and support for ROS. The tool has been primarily designed as a preview release to showcase the +capabilities of the underlying physics engine. With the release of `Isaac Sim`_, NVIDIA is building +a general purpose simulator for robotics and has integrated the functionalities of Isaac Gym into +Isaac Sim. + +`Isaac Sim`_ is a robot simulation toolkit built on top of Omniverse, which is a general purpose platform +that aims to unite complex 3D workflows. Isaac Sim leverages the latest advances in graphics and +physics simulation to provide a high-fidelity simulation environment for robotics. It supports +ROS/ROS2, various sensor simulation, tools for domain randomization and synthetic data creation. +Tiled rendering support in Isaac Sim allows for vectorized rendering across environments, along with +support for running in the cloud using `Isaac Automator`_. +Overall, it is a powerful tool for roboticists and is a huge step forward in the field of robotics +simulation. + +With the release of above two tools, NVIDIA also released an open-sourced set of environments called +`IsaacGymEnvs`_ and `OmniIsaacGymEnvs`_, that have been built on top of Isaac Gym and Isaac Sim respectively. +These environments have been designed to display the capabilities of the underlying simulators and provide +a starting point to understand what is possible with the simulators for robot learning. These environments +can be used for benchmarking but are not designed for developing and testing custom environments and algorithms. +This is where Isaac Lab comes in. + +Isaac Lab is built on top of Isaac Sim to provide a unified and flexible framework +for robot learning that exploits latest simulation technologies. It is designed to be modular and extensible, +and aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and +motion planning). While it includes some pre-built environments, sensors, and tasks, its main goal is to +provide an open-sourced, unified, and easy-to-use interface for developing and testing custom environments +and robot learning algorithms. It not only inherits the capabilities of Isaac Sim, but also adds a number +of new features that pertain to robot learning research. For example, including actuator dynamics in the +simulation, procedural terrain generation, and support to collect data from human demonstrations. + +Isaac Lab replaces the previous `IsaacGymEnvs`_, `OmniIsaacGymEnvs`_ and `Orbit`_ frameworks and will +be the single robot learning framework for Isaac Sim. Previously released frameworks are deprecated +and we encourage users to follow our migration guides to transition over to Isaac Lab. + + +Is Isaac Lab a simulator? +------------------------- + +Often, when people think of simulators, they think of various commonly available engines, such as +`MuJoCo`_, `Bullet`_, and `Flex`_. These engines are powerful and have been used in a number of +research projects. However, they are not designed to be a general purpose simulator for robotics. +Rather they are primarily physics engines that are used to simulate the dynamics of rigid and +deformable bodies. They are shipped with some basic rendering capabilities to visualize the +simulation and provide parsing capabilities of different scene description formats. + +Various recent works combine these physics engines with different rendering engines to provide +a more complete simulation environment. They include APIs that allow reading and writing to the +physics and rendering engines. In some cases, they support ROS and hardware-in-the-loop simulation +for more robotic-specific applications. An example of these include `AirSim`_, `DoorGym`_, `ManiSkill`_, +`ThreeDWorld`_ and lastly, `Isaac Sim`_. + +At its core, Isaac Lab is **not** a robotics simulator, but a framework for building robot learning +applications on top of Isaac Sim. An equivalent example of such a framework is `RoboSuite`_, which +is built on top of `MuJoCo`_ and is specific to fixed-base robots. Other examples include +`MuJoCo Playground`_ and `Isaac Gym`_ which use `MJX`_ and `PhysX`_ respectively. They +include a number of pre-built tasks with separated out stand-alone implementations for individual +tasks. While this is a good starting point (and often convenient), a lot of code +repetition occurs across different task implementations, which can reduce code-reuse for larger +projects and teams. + +The main goal of Isaac Lab is to provide a unified framework for robot learning that includes +a variety of tooling and features that are required for robot learning, while being easy to +use and extend. It includes design patterns that simplify many of the common requirements for +robotics research. These include simulating sensors at different frequencies, connecting to different +teleoperation interfaces for data collection, switching action spaces for policy learning, +using Hydra for configuration management, supporting different learning libraries and more. +Isaac Lab supports designing tasks using *manager-based (modularized)* and *direct (single-script +similar to Isaac Gym)* patterns, leaving it up to the user to choose the best approach for their +use-case. For each of these patterns, Isaac Lab includes a number of pre-built tasks that can be +used for benchmarking and research. + + +Why should I use Isaac Lab? +--------------------------- + +Isaac Lab provides an open-sourced platform for the community to drive progress with consolidated efforts +toward designing benchmarks and robot learning systems as a joint initiative. This allows us to reuse +existing components and algorithms, and to build on top of each other's work. Doing so not only saves +time and effort, but also allows us to focus on the more important aspects of research. Our hope with +Isaac Lab is that it becomes the de-facto platform for robot learning research and an environment *zoo* +that leverages Isaac Sim. As the framework matures, we foresee it benefitting hugely from the latest +simulation developments (as part of internal developments at NVIDIA and collaborating partners) +and research in robotics. + +We are already working with labs in universities and research institutions to integrate their work into Isaac Lab +and hope that others in the community will join us too in this effort. If you are interested in contributing +to Isaac Lab, please reach out to us. + + +.. _PhysX: https://developer.nvidia.com/physx-sdk +.. _Isaac Sim: https://developer.nvidia.com/isaac-sim +.. _Isaac Gym: https://developer.nvidia.com/isaac-gym +.. _IsaacGymEnvs: https://github.com/isaac-sim/IsaacGymEnvs +.. _OmniIsaacGymEnvs: https://github.com/isaac-sim/OmniIsaacGymEnvs +.. _Orbit: https://isaac-orbit.github.io/ +.. _Isaac Automator: https://github.com/isaac-sim/IsaacAutomator +.. _AirSim: https://microsoft.github.io/AirSim/ +.. _DoorGym: https://github.com/PSVL/DoorGym/ +.. _ManiSkill: https://github.com/haosulab/ManiSkill +.. _ThreeDWorld: https://www.threedworld.org/ +.. _RoboSuite: https://robosuite.ai/ +.. _MuJoCo: https://mujoco.org/ +.. _MuJoCo Playground: https://playground.mujoco.org/ +.. _MJX: https://mujoco.readthedocs.io/en/stable/mjx.html +.. _Bullet: https://github.com/bulletphysics/bullet3 +.. _Flex: https://developer.nvidia.com/flex diff --git a/docs/source/setup/installation/asset_caching.rst b/docs/source/setup/installation/asset_caching.rst new file mode 100644 index 0000000000000000000000000000000000000000..5cee207fae36bb5346b3514852b184a53ab12ce5 --- /dev/null +++ b/docs/source/setup/installation/asset_caching.rst @@ -0,0 +1,58 @@ +Asset Caching +============= + +Assets used in Isaac Lab are hosted on AWS S3 buckets on the cloud. +Asset loading time can depend on your network connection and geographical location. +In some cases, it is possible that asset loading times can be long when assets are pulled from the AWS servers. + +If you run into cases where assets take a few minutes to load for each run, +we recommend enabling asset caching following the below steps. + +First, launch the Isaac Sim application: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -s + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -s + +On the top right of the Isaac Lab or Isaac Sim app, look for the icon labeled ``CACHE:``. +You may see a message such as ``HUB NOT DETECTED`` or ``NEW VERSION DETECTED``. + +Click the message to enable `Hub `_. +Hub automatically manages local caching for Isaac Lab assets, so subsequent runs will use cached files instead of +downloading from AWS each time. + +.. figure:: /source/_static/setup/asset_caching.jpg + :align: center + :figwidth: 100% + :alt: Simulator with cache messaging. + +Hub provides better control and management of cached assets, making workflows faster and more reliable, especially +in environments with limited or intermittent internet access. + +.. note:: + The first time you run Isaac Lab, assets will still need to be pulled from the cloud, which could lead + to longer loading times. Once cached, loading times will be significantly reduced on subsequent runs. + +Nucleus +------- + + +Before Isaac Sim 4.5, assets were accessed via the Omniverse Nucleus server, including setups with local Nucleus instances. + +.. warning:: + Starting with Isaac Sim 4.5, the Omniverse Nucleus server and Omniverse Launcher are deprecated. + Existing Nucleus setups will continue to work, so if you have a local Nucleus server already configured, + you may continue to use it. diff --git a/docs/source/setup/installation/binaries_installation.rst b/docs/source/setup/installation/binaries_installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..82754d6871e3b92745e504fbcf690dc5eb0fd975 --- /dev/null +++ b/docs/source/setup/installation/binaries_installation.rst @@ -0,0 +1,80 @@ +.. _isaaclab-binaries-installation: + +Installation using Isaac Sim Pre-built Binaries +=============================================== + +The following steps first installs Isaac Sim from its pre-built binaries, then Isaac Lab from source code. + +Installing Isaac Sim +-------------------- + +Downloading pre-built binaries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Isaac Sim binaries can be downloaded directly as a zip file from +`here `__. +If you wish to use the older Isaac Sim 4.5 release, please check the older download page +`here `__. + +Once the zip file is downloaded, you can unzip it to the desired directory. +As an example set of instructions for unzipping the Isaac Sim binaries, +please refer to the `Isaac Sim documentation `__. + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + On Linux systems, we assume the Isaac Sim directory is named ``${HOME}/isaacsim``. + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + On Windows systems, we assume the Isaac Sim directory is named ``C:\isaacsim``. + +Verifying the Isaac Sim installation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To avoid the overhead of finding and locating the Isaac Sim installation +directory every time, we recommend exporting the following environment +variables to your terminal for the remaining of the installation instructions: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Isaac Sim root directory + export ISAACSIM_PATH="${HOME}/isaacsim" + # Isaac Sim python executable + export ISAACSIM_PYTHON_EXE="${ISAACSIM_PATH}/python.sh" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: Isaac Sim root directory + set ISAACSIM_PATH="C:\isaacsim" + :: Isaac Sim python executable + set ISAACSIM_PYTHON_EXE="%ISAACSIM_PATH:"=%\python.bat" + + +.. include:: include/bin_verify_isaacsim.rst + +Installing Isaac Lab +-------------------- + +.. include:: include/src_clone_isaaclab.rst + +.. include:: include/src_symlink_isaacsim.rst + +.. include:: include/src_python_virtual_env.rst + +.. include:: include/src_build_isaaclab.rst + +.. include:: include/src_verify_isaaclab.rst diff --git a/docs/source/setup/installation/cloud_installation.rst b/docs/source/setup/installation/cloud_installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..25572e74396ea15da497ba93198069e8c8f6a12e --- /dev/null +++ b/docs/source/setup/installation/cloud_installation.rst @@ -0,0 +1,238 @@ +Cloud Deployment +================ + +Isaac Lab can be run in various cloud infrastructures with the use of +`Isaac Automator `__. + +Isaac Automator allows for quick deployment of Isaac Sim and Isaac Lab onto +the public clouds (AWS, GCP, Azure, and Alibaba Cloud are currently supported). +The result is a fully configured remote desktop cloud workstation, which can +be used for development and testing of Isaac Lab within minutes and on a budget. +Isaac Automator supports variety of GPU instances and stop-start functionality +to save on cloud costs and a variety of tools to aid the workflow +(such as uploading and downloading data, autorun, deployment management, etc). + + +System Requirements +------------------- + +Isaac Automator requires having ``docker`` pre-installed on the system. + +* To install Docker, please follow the instructions for your operating system on the + `Docker website`_. A minimum version of 26.0.0 for Docker Engine and 2.25.0 for Docker + compose are required to work with Isaac Automator. +* Follow the post-installation steps for Docker on the `post-installation steps`_ page. + These steps allow you to run Docker without using ``sudo``. + + +Installing Isaac Automator +-------------------------- + +For the most update-to-date and complete installation instructions, please refer to +`Isaac Automator `__. + +To use Isaac Automator, first clone the repo: + +.. tab-set:: + + .. tab-item:: HTTPS + + .. code-block:: bash + + git clone https://github.com/isaac-sim/IsaacAutomator.git + + .. tab-item:: SSH + + .. code-block:: bash + + git clone git@github.com:isaac-sim/IsaacAutomator.git + + +Isaac Automator requires obtaining a NGC API key. + +* Get access to the `Isaac Sim container`_ by joining the NVIDIA Developer Program credentials. +* Generate your `NGC API key`_ to access locked container images from NVIDIA GPU Cloud (NGC). + + * This step requires you to create an NGC account if you do not already have one. + * Once you have your generated API key, you need to log in to NGC + from the terminal. + + .. code:: bash + + docker login nvcr.io + + * For the username, enter ``$oauthtoken`` exactly as shown. It is a special username that is used to + authenticate with NGC. + + .. code:: text + + Username: $oauthtoken + Password: + + +Building the container +---------------------- + +To run Isaac Automator, first build the Isaac Automator container: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./build + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + docker build --platform linux/x86_64 -t isa . + + +This will build the Isaac Automator container and tag it as ``isa``. + + +Running the Automator Commands +------------------------------ + +First, enter the Automator container: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./run + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + docker run --platform linux/x86_64 -it --rm -v .:/app isa bash + +Next, run the deployment script for your preferred cloud: + +.. note:: + + The ``--isaaclab`` flag is used to specify the version of Isaac Lab to deploy. + The ``v2.3.0`` tag is the latest release of Isaac Lab. + +.. tab-set:: + :sync-group: cloud + + .. tab-item:: AWS + :sync: aws + + .. code-block:: bash + + ./deploy-aws --isaaclab v2.3.0 + + .. tab-item:: Azure + :sync: azure + + .. code-block:: bash + + ./deploy-azure --isaaclab v2.3.0 + + .. tab-item:: GCP + :sync: gcp + + .. code-block:: bash + + ./deploy-gcp --isaaclab v2.3.0 + + .. tab-item:: Alibaba Cloud + :sync: alicloud + + .. code-block:: bash + + ./deploy-alicloud --isaaclab v2.3.0 + +Follow the prompts for entering information regarding the environment setup and credentials. +Once successful, instructions for connecting to the cloud instance will be available +in the terminal. The deployed Isaac Sim instances can be accessed via: + +- SSH +- noVCN (browser-based VNC client) +- NoMachine (remote desktop client) + +Look for the connection instructions at the end of the deployment command output. +Additionally, this info is saved in ``state//info.txt`` file. + +For details on the credentials and setup required for each cloud, please visit the +`Isaac Automator `__ +page for more instructions. + + +Running Isaac Lab on the Cloud +------------------------------ + +Once connected to the cloud instance, the desktop will have an icon showing ``isaaclab.sh``. +Launch the ``isaaclab.sh`` executable, which will open a new Terminal. Within the terminal, +Isaac Lab commands can be executed in the same way as running locally. + +For example: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + isaaclab.bat -p scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 + + +Destroying a Deployment +----------------------- + +To save costs, deployments can be destroyed when not being used. +This can be done from within the Automator container. + +Enter the Automator container with the command described in the previous section: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + ./run + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + docker run --platform linux/x86_64 -it --rm -v .:/app isa bash + + +To destroy a deployment, run the following command from within the container: + +.. code:: bash + + ./destroy + + +.. _`Docker website`: https://docs.docker.com/desktop/install/linux-install/ +.. _`post-installation steps`: https://docs.docker.com/engine/install/linux-postinstall/ +.. _`Isaac Sim container`: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-sim +.. _`NGC API key`: https://docs.nvidia.com/ngc/gpu-cloud/ngc-user-guide/index.html#generating-api-key diff --git a/docs/source/setup/installation/include/bin_verify_isaacsim.rst b/docs/source/setup/installation/include/bin_verify_isaacsim.rst new file mode 100644 index 0000000000000000000000000000000000000000..19da95e16236864475aa9be90b961c9e791d3ce8 --- /dev/null +++ b/docs/source/setup/installation/include/bin_verify_isaacsim.rst @@ -0,0 +1,74 @@ +Check that the simulator runs as expected: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # note: you can pass the argument "--help" to see all arguments possible. + ${ISAACSIM_PATH}/isaac-sim.sh + + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: note: you can pass the argument "--help" to see all arguments possible. + %ISAACSIM_PATH%\isaac-sim.bat + + +Check that the simulator runs from a standalone python script: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # checks that python path is set correctly + ${ISAACSIM_PYTHON_EXE} -c "print('Isaac Sim configuration is now complete.')" + # checks that Isaac Sim can be launched from python + ${ISAACSIM_PYTHON_EXE} ${ISAACSIM_PATH}/standalone_examples/api/isaacsim.core.api/add_cubes.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: checks that python path is set correctly + %ISAACSIM_PYTHON_EXE% -c "print('Isaac Sim configuration is now complete.')" + :: checks that Isaac Sim can be launched from python + %ISAACSIM_PYTHON_EXE% %ISAACSIM_PATH%\standalone_examples\api\isaacsim.core.api\add_cubes.py + +.. caution:: + + If you have been using a previous version of Isaac Sim, you need to run the following command for the *first* + time after installation to remove all the old user data and cached variables: + + .. tab-set:: + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + + .. code:: bash + + ${ISAACSIM_PATH}/isaac-sim.sh --reset-user + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + + .. code:: batch + + %ISAACSIM_PATH%\isaac-sim.bat --reset-user + + +If the simulator does not run or crashes while following the above +instructions, it means that something is incorrectly configured. To +debug and troubleshoot, please check Isaac Sim +`documentation `__ +and the +`Isaac Sim Forums `_. diff --git a/docs/source/setup/installation/include/pip_python_virtual_env.rst b/docs/source/setup/installation/include/pip_python_virtual_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..ae6a290fae862d02754daa644aa84d507dbd79f4 --- /dev/null +++ b/docs/source/setup/installation/include/pip_python_virtual_env.rst @@ -0,0 +1,130 @@ +Preparing a Python Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Creating a dedicated Python environment is **strongly recommended**. It helps: + +- **Avoid conflicts with system Python** or other projects installed on your machine. +- **Keep dependencies isolated**, so that package upgrades or experiments in other projects + do not break Isaac Sim. +- **Easily manage multiple environments** for setups with different versions of dependencies. +- **Simplify reproducibility** — the environment contains only the packages needed for the current project, + making it easier to share setups with colleagues or run on different machines. + +You can choose different package managers to create a virtual environment. + +- **UV**: A modern, fast, and secure package manager for Python. +- **Conda**: A cross-platform, language-agnostic package manager for Python. +- **venv**: The standard library for creating virtual environments in Python. + +.. caution:: + + The Python version of the virtual environment must match the Python version of Isaac Sim. + + - For Isaac Sim 5.X, the required Python version is 3.11. + - For Isaac Sim 4.X, the required Python version is 3.10. + + Using a different Python version will result in errors when running Isaac Lab. + +The following instructions are for Isaac Sim 5.X, which requires Python 3.11. +If you wish to install Isaac Sim 4.5, please use modify the instructions accordingly to use Python 3.10. + +- Create a virtual environment using one of the package managers: + + .. tab-set:: + + .. tab-item:: UV Environment + + To install ``uv``, please follow the instructions `here `__. + + .. note:: + + A virtual environment created by ``uv venv`` does **not** include ``pip``. + Since Isaac Lab installation requires ``pip``, please install it manually + after activating the environment. + + You can create the Isaac Lab environment using the following commands: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # create a virtual environment named env_isaaclab with python3.11 and pip + uv venv --python 3.11 --seed env_isaaclab + # activate the virtual environment + source env_isaaclab/bin/activate + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + :: create a virtual environment named env_isaaclab with python3.11 and pip + uv venv --python 3.11 --seed env_isaaclab + :: activate the virtual environment + env_isaaclab\Scripts\activate + + .. tab-item:: Conda Environment + + To install conda, please follow the instructions `here `__. + You can create the Isaac Lab environment using the following commands. + + We recommend using `Miniconda `_, + since it is light-weight and resource-efficient environment management system. + + .. code-block:: bash + + conda create -n env_isaaclab python=3.11 + conda activate env_isaaclab + + .. tab-item:: venv Environment + + To create a virtual environment using the standard library, you can use the + following commands: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # create a virtual environment named env_isaaclab with python3.11 + python3.11 -m venv env_isaaclab + # activate the virtual environment + source env_isaaclab/bin/activate + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + :: create a virtual environment named env_isaaclab with python3.11 + python3.11 -m venv env_isaaclab + :: activate the virtual environment + env_isaaclab\Scripts\activate + + +- Ensure the latest pip version is installed. To update pip, run the following command + from inside the virtual environment: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + pip install --upgrade pip + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python -m pip install --upgrade pip diff --git a/docs/source/setup/installation/include/pip_verify_isaacsim.rst b/docs/source/setup/installation/include/pip_verify_isaacsim.rst new file mode 100644 index 0000000000000000000000000000000000000000..111b47d271bb41e142eaf673c93349cf364f6507 --- /dev/null +++ b/docs/source/setup/installation/include/pip_verify_isaacsim.rst @@ -0,0 +1,46 @@ + +Verifying the Isaac Sim installation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- Make sure that your virtual environment is activated (if applicable) + +- Check that the simulator runs as expected: + + .. code:: bash + + # note: you can pass the argument "--help" to see all arguments possible. + isaacsim + +- It's also possible to run with a specific experience file, run: + + .. code:: bash + + # experience files can be absolute path, or relative path searched in isaacsim/apps or omni/apps + isaacsim isaacsim.exp.full.kit + + +.. note:: + + When running Isaac Sim for the first time, all dependent extensions will be pulled from the registry. + This process can take upwards of 10 minutes and is required on the first run of each experience file. + Once the extensions are pulled, consecutive runs using the same experience file will use the cached extensions. + +.. attention:: + + The first run will prompt users to accept the Nvidia Omniverse License Agreement. + To accept the EULA, reply ``Yes`` when prompted with the below message: + + .. code:: bash + + By installing or using Isaac Sim, I agree to the terms of NVIDIA OMNIVERSE LICENSE AGREEMENT (EULA) + in https://docs.isaacsim.omniverse.nvidia.com/latest/common/NVIDIA_Omniverse_License_Agreement.html + + Do you accept the EULA? (Yes/No): Yes + + +If the simulator does not run or crashes while following the above +instructions, it means that something is incorrectly configured. To +debug and troubleshoot, please check Isaac Sim +`documentation `__ +and the +`Isaac Sim Forums `_. diff --git a/docs/source/setup/installation/include/src_build_isaaclab.rst b/docs/source/setup/installation/include/src_build_isaaclab.rst new file mode 100644 index 0000000000000000000000000000000000000000..ba822ae7b2c55ba91d296d18880a774eaaedef71 --- /dev/null +++ b/docs/source/setup/installation/include/src_build_isaaclab.rst @@ -0,0 +1,56 @@ +Installation +~~~~~~~~~~~~ + +- Install dependencies using ``apt`` (on Linux only): + + .. code:: bash + + # these dependency are needed by robomimic which is not available on Windows + sudo apt install cmake build-essential + +- Run the install command that iterates over all the extensions in ``source`` directory and installs them + using pip (with ``--editable`` flag): + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh --install # or "./isaaclab.sh -i" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat --install :: or "isaaclab.bat -i" + + + By default, the above will install **all** the learning frameworks. These include + ``rl_games``, ``rsl_rl``, ``sb3``, ``skrl``, ``robomimic``. + + If you want to install only a specific framework, you can pass the name of the framework + as an argument. For example, to install only the ``rl_games`` framework, you can run: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh --install rl_games # or "./isaaclab.sh -i rl_games" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat --install rl_games :: or "isaaclab.bat -i rl_games" + + The valid options are ``all``, ``rl_games``, ``rsl_rl``, ``sb3``, ``skrl``, ``robomimic``, + and ``none``. If ``none`` is passed, then no learning frameworks will be installed. diff --git a/docs/source/setup/installation/include/src_clone_isaaclab.rst b/docs/source/setup/installation/include/src_clone_isaaclab.rst new file mode 100644 index 0000000000000000000000000000000000000000..844cac2f3fd11fc80bd0146e6dbc1ec2a4bf0ee5 --- /dev/null +++ b/docs/source/setup/installation/include/src_clone_isaaclab.rst @@ -0,0 +1,78 @@ +Cloning Isaac Lab +~~~~~~~~~~~~~~~~~ + +.. note:: + + We recommend making a `fork `_ of the Isaac Lab repository to contribute + to the project but this is not mandatory to use the framework. If you + make a fork, please replace ``isaac-sim`` with your username + in the following instructions. + +Clone the Isaac Lab repository into your project's workspace: + +.. tab-set:: + + .. tab-item:: SSH + + .. code:: bash + + git clone git@github.com:isaac-sim/IsaacLab.git + + .. tab-item:: HTTPS + + .. code:: bash + + git clone https://github.com/isaac-sim/IsaacLab.git + + +We provide a helper executable `isaaclab.sh `_ +and `isaaclab.bat `_ for Linux and Windows +respectively that provides utilities to manage extensions. + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: text + + ./isaaclab.sh --help + + usage: isaaclab.sh [-h] [-i] [-f] [-p] [-s] [-t] [-o] [-v] [-d] [-n] [-c] -- Utility to manage Isaac Lab. + + optional arguments: + -h, --help Display the help content. + -i, --install [LIB] Install the extensions inside Isaac Lab and learning frameworks (rl_games, rsl_rl, sb3, skrl) as extra dependencies. Default is 'all'. + -f, --format Run pre-commit to format the code and check lints. + -p, --python Run the python executable provided by Isaac Sim or virtual environment (if active). + -s, --sim Run the simulator executable (isaac-sim.sh) provided by Isaac Sim. + -t, --test Run all python pytest tests. + -o, --docker Run the docker container helper script (docker/container.sh). + -v, --vscode Generate the VSCode settings file from template. + -d, --docs Build the documentation from source using sphinx. + -n, --new Create a new external project or internal task from template. + -c, --conda [NAME] Create the conda environment for Isaac Lab. Default name is 'env_isaaclab'. + -u, --uv [NAME] Create the uv environment for Isaac Lab. Default name is 'env_isaaclab'. + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: text + + isaaclab.bat --help + + usage: isaaclab.bat [-h] [-i] [-f] [-p] [-s] [-v] [-d] [-n] [-c] -- Utility to manage Isaac Lab. + + optional arguments: + -h, --help Display the help content. + -i, --install [LIB] Install the extensions inside Isaac Lab and learning frameworks (rl_games, rsl_rl, sb3, skrl) as extra dependencies. Default is 'all'. + -f, --format Run pre-commit to format the code and check lints. + -p, --python Run the python executable provided by Isaac Sim or virtual environment (if active). + -s, --sim Run the simulator executable (isaac-sim.bat) provided by Isaac Sim. + -t, --test Run all python pytest tests. + -v, --vscode Generate the VSCode settings file from template. + -d, --docs Build the documentation from source using sphinx. + -n, --new Create a new external project or internal task from template. + -c, --conda [NAME] Create the conda environment for Isaac Lab. Default name is 'env_isaaclab'. + -u, --uv [NAME] Create the uv environment for Isaac Lab. Default name is 'env_isaaclab'. diff --git a/docs/source/setup/installation/include/src_python_virtual_env.rst b/docs/source/setup/installation/include/src_python_virtual_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..d94d908d831941033321351f155409a4c8b14036 --- /dev/null +++ b/docs/source/setup/installation/include/src_python_virtual_env.rst @@ -0,0 +1,112 @@ +Setting up a Python Environment (optional) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. attention:: + This step is optional. If you are using the bundled Python with Isaac Sim, you can skip this step. + +Creating a dedicated Python environment for Isaac Lab is **strongly recommended**, even though +it is optional. Using a virtual environment helps: + +- **Avoid conflicts with system Python** or other projects installed on your machine. +- **Keep dependencies isolated**, so that package upgrades or experiments in other projects + do not break Isaac Sim. +- **Easily manage multiple environments** for setups with different versions of dependencies. +- **Simplify reproducibility** — the environment contains only the packages needed for the current project, + making it easier to share setups with colleagues or run on different machines. + + +You can choose different package managers to create a virtual environment. + +- **UV**: A modern, fast, and secure package manager for Python. +- **Conda**: A cross-platform, language-agnostic package manager for Python. + +Once created, you can use the default Python in the virtual environment (*python* or *python3*) +instead of *./isaaclab.sh -p* or *isaaclab.bat -p*. + +.. caution:: + + The Python version of the virtual environment must match the Python version of Isaac Sim. + + - For Isaac Sim 5.X, the required Python version is 3.11. + - For Isaac Sim 4.X, the required Python version is 3.10. + + Using a different Python version will result in errors when running Isaac Lab. + + +.. tab-set:: + + .. tab-item:: UV Environment + + To install ``uv``, please follow the instructions `here `__. + You can create the Isaac Lab environment using the following commands: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Option 1: Default environment name 'env_isaaclab' + ./isaaclab.sh --uv # or "./isaaclab.sh -u" + # Option 2: Custom name + ./isaaclab.sh --uv my_env # or "./isaaclab.sh -u my_env" + + .. code:: bash + + # Activate environment + source ./env_isaaclab/bin/activate # or "source ./my_env/bin/activate" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. warning:: + Windows support for UV is currently unavailable. Please check + `issue #3483 `_ to track progress. + + .. tab-item:: Conda Environment + + To install conda, please follow the instructions `here `__. + You can create the Isaac Lab environment using the following commands. + + We recommend using `Miniconda `_, + since it is light-weight and resource-efficient environment management system. + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Option 1: Default environment name 'env_isaaclab' + ./isaaclab.sh --conda # or "./isaaclab.sh -c" + # Option 2: Custom name + ./isaaclab.sh --conda my_env # or "./isaaclab.sh -c my_env" + + .. code:: bash + + # Activate environment + conda activate env_isaaclab # or "conda activate my_env" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: Option 1: Default environment name 'env_isaaclab' + isaaclab.bat --conda :: or "isaaclab.bat -c" + :: Option 2: Custom name + isaaclab.bat --conda my_env :: or "isaaclab.bat -c my_env" + + .. code:: batch + + :: Activate environment + conda activate env_isaaclab # or "conda activate my_env" + +Once you are in the virtual environment, you do not need to use ``./isaaclab.sh -p`` or +``isaaclab.bat -p`` to run python scripts. You can use the default python executable in your +environment by running ``python`` or ``python3``. However, for the rest of the documentation, +we will assume that you are using ``./isaaclab.sh -p`` or ``isaaclab.bat -p`` to run python scripts. diff --git a/docs/source/setup/installation/include/src_symlink_isaacsim.rst b/docs/source/setup/installation/include/src_symlink_isaacsim.rst new file mode 100644 index 0000000000000000000000000000000000000000..be8ae17cdbd2825150b5dec7c47321193c5aec85 --- /dev/null +++ b/docs/source/setup/installation/include/src_symlink_isaacsim.rst @@ -0,0 +1,43 @@ +Creating the Isaac Sim Symbolic Link +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Set up a symbolic link between the installed Isaac Sim root folder +and ``_isaac_sim`` in the Isaac Lab directory. This makes it convenient +to index the python modules and look for extensions shipped with Isaac Sim. + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # enter the cloned repository + cd IsaacLab + # create a symbolic link + ln -s ${ISAACSIM_PATH} _isaac_sim + + # For example: + # Option 1: If pre-built binaries were installed: + # ln -s ${HOME}/isaacsim _isaac_sim + # + # Option 2: If Isaac Sim was built from source: + # ln -s ${HOME}/IsaacSim/_build/linux-x86_64/release _isaac_sim + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: enter the cloned repository + cd IsaacLab + :: create a symbolic link - requires launching Command Prompt with Administrator access + mklink /D _isaac_sim %ISAACSIM_PATH% + + :: For example: + :: Option 1: If pre-built binaries were installed: + :: mklink /D _isaac_sim C:\isaacsim + :: + :: Option 2: If Isaac Sim was built from source: + :: mklink /D _isaac_sim C:\IsaacSim\_build\windows-x86_64\release diff --git a/docs/source/setup/installation/include/src_verify_isaaclab.rst b/docs/source/setup/installation/include/src_verify_isaaclab.rst new file mode 100644 index 0000000000000000000000000000000000000000..a747a1ccdc35fcdc5d0fc58bf2611e52e6f8052c --- /dev/null +++ b/docs/source/setup/installation/include/src_verify_isaaclab.rst @@ -0,0 +1,102 @@ +Verifying the Isaac Lab installation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To verify that the installation was successful, run the following command from the +top of the repository: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Option 1: Using the isaaclab.sh executable + # note: this works for both the bundled python and the virtual environment + ./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py + + # Option 2: Using python in your virtual environment + python scripts/tutorials/00_sim/create_empty.py + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: Option 1: Using the isaaclab.bat executable + :: note: this works for both the bundled python and the virtual environment + isaaclab.bat -p scripts\tutorials\00_sim\create_empty.py + + :: Option 2: Using python in your virtual environment + python scripts\tutorials\00_sim\create_empty.py + + +The above command should launch the simulator and display a window with a black +viewport. You can exit the script by pressing ``Ctrl+C`` on your terminal. +On Windows machines, please terminate the process from Command Prompt using +``Ctrl+Break`` or ``Ctrl+fn+B``. + +.. figure:: /source/_static/setup/verify_install.jpg + :align: center + :figwidth: 100% + :alt: Simulator with a black window. + + +If you see this, then the installation was successful! |:tada:| + +.. note:: + + If you see an error ``ModuleNotFoundError: No module named 'isaacsim'``, please ensure that the virtual + environment is activated and ``source _isaac_sim/setup_conda_env.sh`` has been executed (for uv as well). + + +Train a robot! +~~~~~~~~~~~~~~ + +You can now use Isaac Lab to train a robot through Reinforcement Learning! The quickest way to use Isaac Lab is through the predefined workflows using one of our **Batteries-included** robot tasks. Execute the following command to quickly train an ant to walk! +We recommend adding ``--headless`` for faster training. + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Ant-v0 --headless + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Ant-v0 --headless + +... Or a robot dog! + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 --headless + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + isaaclab.bat -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 --headless + +Isaac Lab provides the tools you'll need to create your own **Tasks** and **Workflows** for whatever your project needs may be. +Take a look at our :ref:`how-to` guides like :ref:`Adding your own learning Library ` or :ref:`Wrapping Environments ` for details. + +.. figure:: /source/_static/setup/isaac_ants_example.jpg + :align: center + :figwidth: 100% + :alt: Idle hands... diff --git a/docs/source/setup/installation/index.rst b/docs/source/setup/installation/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..b8b794ec237e23d9f6fc065572c4ef2c141565a9 --- /dev/null +++ b/docs/source/setup/installation/index.rst @@ -0,0 +1,192 @@ +.. _isaaclab-installation-root: + +Local Installation +================== + +.. image:: https://img.shields.io/badge/IsaacSim-5.1.0-silver.svg + :target: https://developer.nvidia.com/isaac-sim + :alt: IsaacSim 5.1.0 + +.. image:: https://img.shields.io/badge/python-3.11-blue.svg + :target: https://www.python.org/downloads/release/python-31013/ + :alt: Python 3.11 + +.. image:: https://img.shields.io/badge/platform-linux--64-orange.svg + :target: https://releases.ubuntu.com/22.04/ + :alt: Ubuntu 22.04 + +.. image:: https://img.shields.io/badge/platform-windows--64-orange.svg + :target: https://www.microsoft.com/en-ca/windows/windows-11 + :alt: Windows 11 + + +Isaac Lab installation is available for Windows and Linux. Since it is built on top of Isaac Sim, +it is required to install Isaac Sim before installing Isaac Lab. This guide explains the +recommended installation methods for both Isaac Sim and Isaac Lab. + +.. caution:: + + We have dropped support for Isaac Sim versions 4.2.0 and below. We recommend using the latest + Isaac Sim 5.1.0 release to benefit from the latest features and improvements. + + For more information, please refer to the + `Isaac Sim release notes `__. + + +System Requirements +------------------- + +General Requirements +~~~~~~~~~~~~~~~~~~~~ + +For detailed requirements, please see the +`Isaac Sim system requirements `_. +The basic requirements are: + +- **OS:** Ubuntu 22.04 (Linux x64) or Windows 11 (x64) +- **RAM:** 32 GB or more +- **GPU VRAM:** 16 GB or more (additional VRAM may be required for rendering workflows) + +**Isaac Sim is built against a specific Python version**, making +it essential to use the same Python version when installing Isaac Lab. +The required Python version is as follows: + +- For Isaac Sim 5.X, the required Python version is 3.11. +- For Isaac Sim 4.X, the required Python version is 3.10. + + +Driver Requirements +~~~~~~~~~~~~~~~~~~~ + +Drivers other than those recommended on `Omniverse Technical Requirements `_ +may work but have not been validated against all Omniverse tests. + +- Use the **latest NVIDIA production branch driver**. +- On Linux, version ``580.65.06`` or later is recommended, especially when upgrading to + **Ubuntu 22.04.5 with kernel 6.8.0-48-generic** or newer. +- On Spark, version ``580.95.05`` is recommended. +- On Windows, version ``580.88`` is recommended. +- If you are using a new GPU or encounter driver issues, install the latest production branch + driver from the `Unix Driver Archive `_ + using the ``.run`` installer. + +DGX Spark: details and limitations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The DGX spark is a standalone machine learning device with aarch64 architecture. As a consequence, some +features of Isaac Lab are not currently supported on the DGX spark. The most noteworthy is that the architecture *requires* CUDA ≥ 13, and thus the cu13 build of PyTorch or newer. +Other notable limitations with respect to Isaac Lab include... + +#. `SkillGen `_ is not supported out of the box. This + is because cuRobo builds native CUDA/C++ extensions that requires specific tooling and library versions which are not validated for use with DGX spark. + +#. Extended reality teleoperation tools such as `OpenXR `_ is not supported. This is due + to encoding performance limitations that have not yet been fully investigated. + +#. SKRL training with `JAX `_ has not been explicitly validated or tested in Isaac Lab on the DGX Spark. + JAX provides pre-built CUDA wheels only for Linux on x86_64, so on aarch64 systems (e.g., DGX Spark) it runs on CPU only by default. + GPU support requires building JAX from source, which has not been validated in Isaac Lab. + +#. Livestream and Hub Workstation Cache are not supported on the DGX spark. + +#. :ref:`Running Cosmos Transfer1 ` is not currently supported on the DGX Spark. + +Troubleshooting +~~~~~~~~~~~~~~~ + +Please refer to the `Linux Troubleshooting `_ +to resolve installation issues in Linux. + +You can use `Isaac Sim Compatibility Checker `_ +to automatically check if the above requirements are met for running Isaac Sim on your system. + +Quick Start (Recommended) +------------------------- + +For most users, the simplest and fastest way to install Isaac Lab is by following the +:doc:`pip_installation` guide. + +This method will install Isaac Sim via pip and Isaac Lab through its source code. +If you are new to Isaac Lab, start here. + + +Choosing an Installation Method +------------------------------- + +Different workflows require different installation methods. +Use this table to decide: + ++-------------------+------------------------------+------------------------------+---------------------------+------------+ +| Method | Isaac Sim | Isaac Lab | Best For | Difficulty | ++===================+==============================+==============================+===========================+============+ +| **Recommended** | |:package:| pip install | |:floppy_disk:| source (git) | Beginners, standard use | Easy | ++-------------------+------------------------------+------------------------------+---------------------------+------------+ +| Binary + Source | |:inbox_tray:| binary | |:floppy_disk:| source (git) | Users preferring binary | Easy | +| | download | | install of Isaac Sim | | ++-------------------+------------------------------+------------------------------+---------------------------+------------+ +| Full Source Build | |:floppy_disk:| source (git) | |:floppy_disk:| source (git) | Developers modifying both | Advanced | ++-------------------+------------------------------+------------------------------+---------------------------+------------+ +| Pip Only | |:package:| pip install | |:package:| pip install | External extensions only | Special | +| | | | (no training/examples) | case | ++-------------------+------------------------------+------------------------------+---------------------------+------------+ +| Docker | |:whale:| Docker | |:floppy_disk:| source (git) | Docker users | Advanced | ++-------------------+------------------------------+------------------------------+---------------------------+------------+ + +Next Steps +---------- + +Once you've reviewed the installation methods, continue with the guide that matches your workflow: + +- |:smiley:| :doc:`pip_installation` + + - Install Isaac Sim via pip and Isaac Lab from source. + - Best for beginners and most users. + +- :doc:`binaries_installation` + + - Install Isaac Sim from its binary package (website download). + - Install Isaac Lab from its source code. + - Choose this if you prefer not to use pip for Isaac Sim (for instance, on Ubuntu 20.04). + +- :doc:`source_installation` + + - Build Isaac Sim from source. + - Install Isaac Lab from its source code. + - Recommended only if you plan to modify Isaac Sim itself. + +- :doc:`isaaclab_pip_installation` + + - Install Isaac Sim and Isaac Lab as pip packages. + - Best for advanced users building **external extensions** with custom runner scripts. + - Note: This does **not** include training or example scripts. + +- :ref:`container-deployment` + + - Install Isaac Sim and Isaac Lab in a Docker container. + - Best for users who want to use Isaac Lab in a containerized environment. + + +Asset Caching +------------- + +Isaac Lab assets are hosted on **AWS S3 cloud storage**. Loading times can vary +depending on your **network connection** and **geographical location**, and in some cases, +assets may take several minutes to load for each run. To improve performance or support +**offline workflows**, we recommend enabling **asset caching**. + +- Cached assets are stored locally, reducing repeated downloads. +- This is especially useful if you have a slow or intermittent internet connection, + or if your deployment environment is offline. + +Please follow the steps :doc:`asset_caching` to enable asset caching and speed up your workflow. + + +.. toctree:: + :maxdepth: 1 + :hidden: + + pip_installation + binaries_installation + source_installation + isaaclab_pip_installation + asset_caching diff --git a/docs/source/setup/installation/isaaclab_pip_installation.rst b/docs/source/setup/installation/isaaclab_pip_installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..7638e0b5a6b8d57a1706be75ec57c207a104a321 --- /dev/null +++ b/docs/source/setup/installation/isaaclab_pip_installation.rst @@ -0,0 +1,116 @@ +Installation using Isaac Lab Pip Packages +========================================= + +From Isaac Lab 2.0, pip packages are provided to install both Isaac Sim and Isaac Lab extensions from pip. +Note that this installation process is only recommended for advanced users working on additional extension projects +that are built on top of Isaac Lab. Isaac Lab pip packages **does not** include any standalone python scripts for +training, inferencing, or running standalone workflows such as demos and examples. Therefore, users are required +to define their own runner scripts when installing Isaac Lab from pip. + +To learn about how to set up your own project on top of Isaac Lab, please see :ref:`template-generator`. + +.. note:: + + Currently, we only provide pip packages for every major release of Isaac Lab. + For example, we provide the pip package for release 2.1.0 and 2.2.0, but not 2.1.1. + In the future, we will provide pip packages for every minor release of Isaac Lab. + +.. include:: include/pip_python_virtual_env.rst + +Installing dependencies +~~~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + + In case you used UV to create your virtual environment, please replace ``pip`` with ``uv pip`` + in the following commands. + +- Install a CUDA-enabled PyTorch 2.7.0 build for CUDA 12.8 that matches your system architecture: + + .. tab-set:: + :sync-group: pip-platform + + .. tab-item:: :icon:`fa-brands fa-linux` Linux (x86_64) + :sync: linux-x86_64 + + .. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows (x86_64) + :sync: windows-x86_64 + + .. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + + .. tab-item:: :icon:`fa-brands fa-linux` Linux (aarch64) + :sync: linux-aarch64 + + .. code-block:: bash + + pip install -U torch==2.9.0 torchvision==0.24.0 --index-url https://download.pytorch.org/whl/cu130 + + .. note:: + + After installing Isaac Lab on aarch64, you may encounter warnings such as: + + .. code-block:: none + + ERROR: ld.so: object '...torch.libs/libgomp-XXXX.so.1.0.0' cannot be preloaded: ignored. + + This occurs when both the system and PyTorch ``libgomp`` (GNU OpenMP) libraries are preloaded. + Isaac Sim expects the **system** OpenMP runtime, while PyTorch sometimes bundles its own. + + To fix this, unset any existing ``LD_PRELOAD`` and set it to use the system library only: + + .. code-block:: bash + + unset LD_PRELOAD + export LD_PRELOAD="$LD_PRELOAD:/lib/aarch64-linux-gnu/libgomp.so.1" + + This ensures the correct ``libgomp`` library is preloaded for both Isaac Sim and Isaac Lab, + removing the preload warnings during runtime. + +- Install the Isaac Lab packages along with Isaac Sim: + + .. code-block:: none + + pip install isaaclab[isaacsim,all]==2.3.0 --extra-index-url https://pypi.nvidia.com + +- If you want to use ``rl_games`` for training and inferencing, install + its Python 3.11 enabled fork: + + .. code-block:: none + + pip install git+https://github.com/isaac-sim/rl_games.git@python3.11 + +.. include:: include/pip_verify_isaacsim.rst + +Running Isaac Lab Scripts +~~~~~~~~~~~~~~~~~~~~~~~~~ + +By following the above scripts, your Python environment should now have access to all of the Isaac Lab extensions. +To run a user-defined script for Isaac Lab, simply run + +.. code:: bash + + python my_awesome_script.py + +Generating VS Code Settings +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Due to the structure resulting from the installation, VS Code IntelliSense (code completion, parameter info +and member lists, etc.) will not work by default. To set it up (define the search paths for import resolution, +the path to the default Python interpreter, and other settings), for a given workspace folder, +run the following command: + +.. code-block:: bash + + python -m isaaclab --generate-vscode-settings + + +.. warning:: + + The command will generate a ``.vscode/settings.json`` file in the workspace folder. + If the file already exists, it will be overwritten (a confirmation prompt will be shown first). diff --git a/docs/source/setup/installation/pip_installation.rst b/docs/source/setup/installation/pip_installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..5a6a5a7956d31746e5e46c7ff325d3170dada451 --- /dev/null +++ b/docs/source/setup/installation/pip_installation.rst @@ -0,0 +1,107 @@ +.. _isaaclab-pip-installation: + +Installation using Isaac Sim Pip Package +======================================== + +The following steps first installs Isaac Sim from pip, then Isaac Lab from source code. + +.. attention:: + + Installing Isaac Sim with pip requires GLIBC 2.35+ version compatibility. + To check the GLIBC version on your system, use command ``ldd --version``. + + This may pose compatibility issues with some Linux distributions. For instance, Ubuntu 20.04 LTS + has GLIBC 2.31 by default. If you encounter compatibility issues, we recommend following the + :ref:`Isaac Sim Binaries Installation ` approach. + +.. note:: + + If you plan to :ref:`Set up Visual Studio Code ` later, we recommend following the + :ref:`Isaac Sim Binaries Installation ` approach. + +Installing Isaac Sim +-------------------- + +From Isaac Sim 4.0 onwards, it is possible to install Isaac Sim using pip. +This approach makes it easier to install Isaac Sim without requiring to download the Isaac Sim binaries. +If you encounter any issues, please report them to the +`Isaac Sim Forums `_. + +.. attention:: + + On Windows, it may be necessary to `enable long path support `_ + to avoid installation errors due to OS limitations. + +.. include:: include/pip_python_virtual_env.rst + +Installing dependencies +~~~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + + In case you used UV to create your virtual environment, please replace ``pip`` with ``uv pip`` + in the following commands. + +- Install Isaac Sim pip packages: + + .. code-block:: none + + pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com + +- Install a CUDA-enabled PyTorch build that matches your system architecture: + + .. tab-set:: + :sync-group: pip-platform + + .. tab-item:: :icon:`fa-brands fa-linux` Linux (x86_64) + :sync: linux-x86_64 + + .. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + + .. tab-item:: :icon:`fa-brands fa-windows` Windows (x86_64) + :sync: windows-x86_64 + + .. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + + .. tab-item:: :icon:`fa-brands fa-linux` Linux (aarch64) + :sync: linux-aarch64 + + .. code-block:: bash + + pip install -U torch==2.9.0 torchvision==0.24.0 --index-url https://download.pytorch.org/whl/cu130 + + .. note:: + + After installing Isaac Lab on aarch64, you may encounter warnings such as: + + .. code-block:: none + + ERROR: ld.so: object '...torch.libs/libgomp-XXXX.so.1.0.0' cannot be preloaded: ignored. + + This occurs when both the system and PyTorch ``libgomp`` (GNU OpenMP) libraries are preloaded. + Isaac Sim expects the **system** OpenMP runtime, while PyTorch sometimes bundles its own. + + To fix this, unset any existing ``LD_PRELOAD`` and set it to use the system library only: + + .. code-block:: bash + + unset LD_PRELOAD + export LD_PRELOAD="$LD_PRELOAD:/lib/aarch64-linux-gnu/libgomp.so.1" + + This ensures the correct ``libgomp`` library is preloaded for both Isaac Sim and Isaac Lab, + removing the preload warnings during runtime. + +.. include:: include/pip_verify_isaacsim.rst + +Installing Isaac Lab +-------------------- + +.. include:: include/src_clone_isaaclab.rst + +.. include:: include/src_build_isaaclab.rst + +.. include:: include/src_verify_isaaclab.rst diff --git a/docs/source/setup/installation/source_installation.rst b/docs/source/setup/installation/source_installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..c697c1dd2054bce02da223e1cdd34eb9dc48907b --- /dev/null +++ b/docs/source/setup/installation/source_installation.rst @@ -0,0 +1,109 @@ +.. _isaaclab-source-installation: + +Installation using Isaac Sim Source Code +======================================== + +The following steps first installs Isaac Sim from source, then Isaac Lab from source code. + +.. note:: + + This is a more advanced installation method and is not recommended for most users. Only follow this method + if you wish to modify the source code of Isaac Sim as well. + +Installing Isaac Sim +-------------------- + +Building from source +~~~~~~~~~~~~~~~~~~~~ + +From Isaac Sim 5.0 release, it is possible to build Isaac Sim from its source code. +This approach is meant for users who wish to modify the source code of Isaac Sim as well, +or want to test Isaac Lab with the nightly version of Isaac Sim. + +The following instructions are adapted from the `Isaac Sim documentation `_ +for the convenience of users. + +.. attention:: + + Building Isaac Sim from source requires Ubuntu 22.04 LTS or higher. + +.. attention:: + + For details on driver requirements, please see the `Technical Requirements `_ guide! + + On Windows, it may be necessary to `enable long path support `_ to avoid installation errors due to OS limitations. + + +- Clone the Isaac Sim repository into your workspace: + + .. code:: bash + + git clone https://github.com/isaac-sim/IsaacSim.git + +- Build Isaac Sim from source: + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + cd IsaacSim + ./build.sh + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: bash + + cd IsaacSim + build.bat + + +Verifying the Isaac Sim installation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To avoid the overhead of finding and locating the Isaac Sim installation +directory every time, we recommend exporting the following environment +variables to your terminal for the remaining of the installation instructions: + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + # Isaac Sim root directory + export ISAACSIM_PATH="${pwd}/_build/linux-x86_64/release" + # Isaac Sim python executable + export ISAACSIM_PYTHON_EXE="${ISAACSIM_PATH}/python.sh" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: batch + + :: Isaac Sim root directory + set ISAACSIM_PATH="%cd%\_build\windows-x86_64\release" + :: Isaac Sim python executable + set ISAACSIM_PYTHON_EXE="%ISAACSIM_PATH:"=%\python.bat" + +.. include:: include/bin_verify_isaacsim.rst + + +Installing Isaac Lab +-------------------- + +.. include:: include/src_clone_isaaclab.rst + +.. include:: include/src_symlink_isaacsim.rst + +.. include:: include/src_python_virtual_env.rst + +.. include:: include/src_build_isaaclab.rst + +.. include:: include/src_verify_isaaclab.rst diff --git a/docs/source/setup/quickstart.rst b/docs/source/setup/quickstart.rst new file mode 100644 index 0000000000000000000000000000000000000000..d65d738a85a7d323395cb6f39b229a62c8c8a181 --- /dev/null +++ b/docs/source/setup/quickstart.rst @@ -0,0 +1,397 @@ +.. _isaac-lab-quickstart: + +Quickstart Guide +======================= + + +This guide is written for those who just can't wait to get their hands dirty and will touch on the most common concepts you will encounter as you build your own +projects with Isaac Lab! This includes installation, running RL, finding environments, creating new projects, and more! + +The power of Isaac Lab comes from from a few key features that we will very briefly touch on in this guide. + +1) **Vectorization**: Reinforcement Learning requires attempting a task many times. Isaac Lab speeds this process along by vectorizing the + environment, a process by which training can be run in parallel across many copies of the same environment, thus reducing the amount of time + spent on collecting data before the weights of the model can be updated. Most of the codebase is devoted to defining those parts of the environment + that need to be touched by this vectorization system + +2) **Modular Design**: Isaac Lab is designed to be modular, meaning that you can design your projects to have various components that can be + swapped out for different needs. For example, suppose you want to train a policy that supports a specific subset of robots. You could design + the environment and task to be robot agnostic by writing a controller interface layer in the form of one of our Manager classes (the ``ActionManager`` + in this specific case). Most of the rest of the codebase is devoted to defining those parts of your project that need to be touched by this manager system. + +To get started, we will first install Isaac Lab and launch a training script. + +Quick Installation Guide +------------------------- + +There are many ways to :ref:`install ` Isaac Lab, but for the purposes of this quickstart guide, we will follow the +pip install route using virtual environments. + +To begin, we first define our virtual environment. + +.. tab-set:: + + .. tab-item:: conda + + .. code-block:: bash + + # create a virtual environment named env_isaaclab with python3.11 and pip + conda create -n env_isaaclab python=3.11 + # activate the virtual environment + conda activate env_isaaclab + + .. tab-item:: uv + + .. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + # create a virtual environment named env_isaaclab with python3.11 and pip + uv venv --python 3.11 --seed env_isaaclab + # activate the virtual environment + source env_isaaclab/bin/activate + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + # create a virtual environment named env_isaaclab with python3.11 + uv venv --python 3.11 env_isaaclab + # activate the virtual environment + env_isaaclab\Scripts\activate + + +Next, install a CUDA-enabled PyTorch 2.7.0 build. + + .. code-block:: bash + + pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128 + + +Before we can install Isaac Sim, we need to make sure pip is updated. To update pip, run + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code-block:: bash + + pip install --upgrade pip + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code-block:: batch + + python -m pip install --upgrade pip + +and now we can install the Isaac Sim packages. + +.. code-block:: none + + pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com + +Finally, we can install Isaac Lab. To start, clone the repository using the following + +.. tab-set:: + + .. tab-item:: SSH + + .. code:: bash + + git clone git@github.com:isaac-sim/IsaacLab.git + + .. tab-item:: HTTPS + + .. code:: bash + + git clone https://github.com/isaac-sim/IsaacLab.git + +Installation is now as easy as navigating to the repo and then calling the root script with the ``--install`` flag! + +.. tab-set:: + :sync-group: os + + .. tab-item:: :icon:`fa-brands fa-linux` Linux + :sync: linux + + .. code:: bash + + ./isaaclab.sh --install # or "./isaaclab.sh -i" + + .. tab-item:: :icon:`fa-brands fa-windows` Windows + :sync: windows + + .. code:: bash + + isaaclab.bat --install :: or "isaaclab.bat -i" + + +Quick Start Using Isaac Launchable +---------------------------------- + +For users first learning Isaac Lab, without sufficient local compute resources, the `Isaac Launchable `_ project is a quick way to get started without manual installation. + +Through this project, users can interact with Isaac Sim and Isaac Lab purely from a web browser, with one tab running Visual Studio Code for development and command execution, and another tab providing the streamed user interface for Isaac Sim. + +This method uses `NVIDIA Brev `_, a platform that offers easily configurable pay-by-the-hour cloud compute. Brev Launchables are preconfigured, optimized compute and software environments. + +To try now, click the button below. To learn more about how to use this project, or how to create your own Launchable, please see the project repo `here `_. + +.. image:: https://brev-assets.s3.us-west-1.amazonaws.com/nv-lb-dark.svg + :target: https://brev.nvidia.com/launchable/deploy/now?launchableID=env-35JP2ywERLgqtD0b0MIeK1HnF46 + :alt: Click here to deploy + + +Launch Training +------------------- + +The various backends of Isaac Lab are accessed through their corresponding ``train.py`` and ``play.py`` scripts located in the ``isaaclab/scripts/reinforcement_learning`` directory. +Invoking these scripts will require a **Task Name** and a corresponding **Entry Point** to the gymnasium API. For example + +.. code-block:: bash + + python scripts/reinforcement_learning/skrl/train.py --task=Isaac-Ant-v0 + +This will train the mujoco ant to "run". You can see the various launch option available to you with the ``--help`` flag. Note specifically the ``--num_envs`` option and the ``--headless`` flag, +both of which can be useful when trying to develop and debug a new environment. Options specified at this level automatically overwrite any configuration equivalent that may be defined in the code +(so long as those definitions are part of a ``@configclass``, see below). + +List Available Environments +----------------------------- + +Above, ``Isaac-Ant-v0`` is the task name and ``skrl`` is the RL framework being used. The ``Isaac-Ant-v0`` environment +has been registered with the `Gymnasium API `_, and you can see how the entry point is defined +by calling the ``list_envs.py`` script, which can be found in ``isaaclab/scripts/environments/list_envs.py``. You should see entries like the following + +.. code-block:: bash + + $> python scripts/environments/list_envs.py + + +--------------------------------------------------------------------------------------------------------------------------------------------+ + | Available Environments in Isaac Lab + +--------+----------------------+--------------------------------------------+---------------------------------------------------------------+ + | S. No. | Task Name | Entry Point | Config + . + . + . + +--------+----------------------+--------------------------------------------+---------------------------------------------------------------+ + | 2 | Isaac-Ant-Direct-v0 | isaaclab_tasks.direct.ant.ant_env:AntEnv | isaaclab_tasks.direct.ant.ant_env:AntEnvCfg + +--------+----------------------+--------------------------------------------+---------------------------------------------------------------+ + . + . + . + +--------+----------------------+--------------------------------------------+---------------------------------------------------------------+ + | 48 | Isaac-Ant-v0 | isaaclab.envs:ManagerBasedRLEnv | isaaclab_tasks.manager_based.classic.ant.ant_env_cfg:AntEnvCfg + +--------+----------------------+--------------------------------------------+---------------------------------------------------------------+ + +Notice that there are two different ``Ant`` tasks, one for a ``Direct`` environment and one for a ``ManagerBased`` environment. +These are the :ref:`two primary workflows` that you can use with Isaac Lab out of the box. The Direct workflow will give you the +shortest path to a working custom environment for reinforcement learning, but the Manager based workflow will give your project the modularity required +for more generalized development. For the purposes of this quickstart guide, we will only focus on the Direct workflow. + + +Generate Your Own Project +-------------------------- + +Getting a new project started with Isaac Lab can seem daunting at first, but this is why we provide the :ref:`template +generator`, to rapidly boilerplate a new project via the command line. + +.. code-block:: bash + + ./isaaclab.sh --new + +This will create a new project for you based on the settings you choose + +* **External vs Internal**: Determines if the project is meant to be built as a part of the isaac lab repository, or if + it is meant to be loaded as an external extension. +* **Direct vs Manager**: A direct task primarily contains all the implementation details within the environment definition, + while a manager based project is meant to use our modular definitions for the different "parts" of an environment. +* **Framework**: You can select more than one option here. This determines which RL frameworks you intend to natively use with your project + (which specific algorithm implementations you want to use for training). + +Once created, navigate to the installed project and run + +.. code-block:: bash + + python -m pip install -e source/ + +to complete the installation process and register the environment. Within the directories created by the template +generator, you will find at least one ``__init__.py`` file with something that looks like the following + +.. code-block:: python + + import gymnasium as gym + + gym.register( + id="Template-isaaclabtutorial_env-v0", + entry_point=f"{__name__}.isaaclabtutorial_env:IsaaclabtutorialEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.isaaclabtutorial_env_cfg:IsaaclabtutorialEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}.skrl_ppo_cfg:PPORunnerCfg", + }, + ) + +This is the function that actually registers an environment for future use. Notice that the ``entry_point`` is literally +just the python module path to the environment definition. This is why we need to install the project as a package: the module path **is** the +entry point for the gymnasium API. + +Configurations +--------------- + +Regardless of what you are going to be doing with Isaac Lab, you will need to deal with **Configurations**. Configurations +can all be identified by the inclusion of the ``@configclass`` decorator above their class definition and the lack of an ``__init__`` function. For example, consider +this configuration class for the :ref:`cartpole environment `. + +.. code-block:: python + + @configclass + class CartpoleEnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + action_scale = 100.0 # [N] + action_space = 1 + observation_space = 4 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot + robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # reset + max_cart_pos = 3.0 # the cart is reset if it exceeds that position [m] + initial_pole_angle_range = [-0.25, 0.25] # the range in which the pole angle is sampled from on reset [rad] + + # reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_pole_pos = -1.0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_vel = -0.005 + +Notice that the entire class definition is just a list of value fields and other configurations. Configuration classes are +necessary for anything that needs to care about being vectorized by the lab during training. If you want to be able to copy an +environment thousands of times, and manage the data from each asynchronously, you need to somehow "label" what parts of the scene matter +to this copying process (vectorization). This is what the configuration classes accomplish! + +In this case, the class defines the configuration for the entire training environment! Notice also the ``num_envs`` variable in the ``InteractiveSceneCfg``. This actually gets overwritten +by the CLI argument from within the ``train.py`` script. Configurations provide a direct path to any variable in the configuration hierarchy, making it easy +to modify anything "configured" by the environment at launch time. + +Robots +------- + +Robots are entirely defined as instances of configurations within Isaac Lab. If you examine ``source/isaaclab_assets/isaaclab_assets/robots``, you will see a number of files, each of which +contains configurations for the robot in question. The purpose of these individual files is to better define scope for all the different robots, but there is nothing preventing +you from :ref:`adding your own ` to your project or even to the ``isaaclab`` repository! For example, consider the following configuration for +the Dofbot + +.. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.actuators import ImplicitActuatorCfg + from isaaclab.assets.articulation import ArticulationCfg + from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + DOFBOT_CONFIG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Dofbot/dofbot.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "joint1": 0.0, + "joint2": 0.0, + "joint3": 0.0, + "joint4": 0.0, + }, + pos=(0.25, -0.25, 0.0), + ), + actuators={ + "front_joints": ImplicitActuatorCfg( + joint_names_expr=["joint[1-2]"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + "joint3_act": ImplicitActuatorCfg( + joint_names_expr=["joint3"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + "joint4_act": ImplicitActuatorCfg( + joint_names_expr=["joint4"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + }, + ) + +This completely defines the dofbot! You could copy this into a ``.py`` file and import it as a module and you would be able to use the dofbot in +your own lab sims. One common feature you will see in any config defining things with state is the presence of an ``InitialStateCfg``. Remember, the configurations +are what informs vectorization, and it's the ``InitialStateCfg`` that describes the state of the joints of our robot when it gets created in each environment. The +``ImplicitActuatorCfg`` defines the joints of the robot using the default actuation model determined by the joint time. Not all joints need to be actuated, but you +will get warnings if you don't. If you aren't planning on using those undefined joints, you can generally ignore these. + +Apps and Sims +-------------- + +Using the simulation means launching the Isaac Sim app to provide simulation context. If you are not running a task defined by the standard workflows, then you +are responsible for creating the app, managing the context, and stepping the simulation forward through time. This is the "third workflow": a **Standalone** app, which +is what we call the scripts for the frameworks, demos, benchmarks, etc... + +The Standalone workflow gives you total control over *everything* in the app and simulation +context. Developing standalone apps is discussed at length in the `Isaac Sim documentation `_ but there +are a few points worth touching on that can be incredibly useful. + +.. code-block:: python + + import argparse + + from isaaclab.app import AppLauncher + # add argparse arguments + parser = argparse.ArgumentParser( + description="This script demonstrates adding a custom robot to an Isaac Lab environment." + ) + parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") + # append AppLauncher cli args + AppLauncher.add_app_launcher_args(parser) + # parse the arguments + args_cli = parser.parse_args() + + # launch omniverse app + app_launcher = AppLauncher(args_cli) + simulation_app = app_launcher.app + +The ``AppLauncher`` is the entrypoint to any and all Isaac Sim applications, like Isaac Lab! *Many Isaac Lab and Isaac Sim modules +cannot be imported until the app is launched!*. This is done on the second to last line of the code above, when the ``AppLauncher`` is constructed. +The ``app_launcher.app`` is our interface to the Kit App Framework; the broader interstitial code that binds the simulation to things the extension +management system, or the GUI, etc... In the standalone workflow, this interface, often called the ``simulation_app`` is predominantly used +to check if the simulation is running, and cleanup after the simulation finishes. diff --git a/docs/source/setup/walkthrough/api_env_design.rst b/docs/source/setup/walkthrough/api_env_design.rst new file mode 100644 index 0000000000000000000000000000000000000000..07471ec2ea5addbd3165cedb41df13924eff38ff --- /dev/null +++ b/docs/source/setup/walkthrough/api_env_design.rst @@ -0,0 +1,158 @@ +.. _walkthrough_api_env_design: + +Classes and Configs +==================================== + +To begin, navigate to the task: ``source/isaac_lab_tutorial/isaac_lab_tutorial/tasks/direct/isaac_lab_tutorial``, and take a look +and the contents of ``isaac_lab_tutorial_env_cfg.py``. You should see something that looks like the following + +.. code-block:: python + + from isaaclab_assets.robots.cartpole import CARTPOLE_CFG + + from isaaclab.assets import ArticulationCfg + from isaaclab.envs import DirectRLEnvCfg + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.sim import SimulationCfg + from isaaclab.utils import configclass + + + @configclass + class IsaacLabTutorialEnvCfg(DirectRLEnvCfg): + + # Some useful fields + . + . + . + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=2) + + # robot(s) + robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # Some more useful fields + . + . + . + +This is the default configuration for a simple cartpole environment that comes with the template and defines the ``self`` scope +for anything you do within the corresponding environment. + +.. currentmodule:: isaaclab.envs + +The first thing to note is the presence of the ``@configclass`` decorator. This defines a class as a configuration class, which holds +a special place in Isaac Lab. Configuration classes are part of how Isaac Lab determines what to "care" about when it comes to cloning +the environment to scale up training. Isaac Lab provides different base configuration classes depending on your goals, and in this +case we are using the :class:`DirectRLEnvCfg` class because we are interested in performing reinforcement learning in the direct workflow. + +.. currentmodule:: isaaclab.sim + +The second thing to note is the content of the configuration class. As the author, you can specify any fields you desire but, generally speaking, there are three things you +will always define here: The **sim**, the **scene**, and the **robot**. Notice that these fields are also configuration classes! Configuration classes +are compositional in this way as a solution for cloning arbitrarily complex environments. + +The **sim** is an instance of :class:`SimulationCfg`, and this is the config that controls the nature of the simulated reality we are building. This field is a member +of the base class, ``DirecRLEnvCfg``, but has a default sim configuration, so it's *technically* optional. The ``SimulationCfg`` dictates +how finely to step through time (dt), the direction of gravity, and even how physics should be simulated. In this case we only specify the time step and the render interval, with the +former indicating that each step through time should simulate :math:`1/120` th of a second, and the latter being how many steps we should take before we render a frame (a value of 2 means +render every other frame). + +.. currentmodule:: isaaclab.scene + +The **scene** is an instance of :class:`InteractiveSceneCfg`. The scene describes what goes "on the stage" and manages those simulation entities to be cloned across environments. +The scene is also a member of the base class ``DirectRLEnvCfg``, but unlike the sim it has no default and must be defined in every ``DirectRLEnvCfg``. The ``InteractiveSceneCfg`` +describes how many copies of the scene we want to create for training purposes, as well as how far apart they should be spaced on the stage. + +.. currentmodule:: isaaclab.assets + +Finally we have the **robot** definition, which is an instance of :class:`ArticulationCfg`. An environment could have multiple articulations, and so the presence of +an ``ArticulationCfg`` is not strictly required in order to define a ``DirectRLEnv``. Instead, the usual workflow is to define a regex path to the robot, and replace +the ``prim_path`` attribute in the base configuration. In this case, ``CARTPOLE_CFG`` is a configuration defined in ``isaaclab_assets.robots.cartpole`` and by replacing +the prim path with ``/World/envs/env_.*/Robot`` we are implicitly saying that every copy of the scene will have a robot named ``Robot``. + + +The Environment +----------------- + +Next, let's take a look at the contents of the other python file in our task directory: ``isaac_lab_tutorial_env.py`` + +.. code-block:: python + + # imports + . + . + . + from .isaac_lab_tutorial_env_cfg import IsaacLabTutorialEnvCfg + + class IsaacLabTutorialEnv(DirectRLEnv): + cfg: IsaacLabTutorialEnvCfg + + def __init__(self, cfg: IsaacLabTutorialEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + . . . + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + . . . + + def _apply_action(self) -> None: + . . . + + def _get_observations(self) -> dict: + . . . + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards(...) + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + . . . + + def _reset_idx(self, env_ids: Sequence[int] | None): + . . . + + @torch.jit.script + def compute_rewards(...): + . . . + return total_reward + + +.. currentmodule:: isaaclab.envs + +Some of the code has been omitted for clarity, in order to aid in discussion. This is where the actual "meat" of the +direct workflow exists and where most of our modifications will take place as we tweak the template to suit our needs. +Currently, all of the member functions of ``IsaacLabTutorialEnv`` are directly inherited from the :class:`DirectRLEnv`. This +known interface is how Isaac Lab and its supported RL frameworks interact with the environment. + +When the environment is initialized, it receives its own config as an argument, which is then immediately passed to super in order +to initialize the ``DirectRLEnv``. This super call also calls ``_setup_scene``, which actually constructs the scene and clones +it appropriately. Notably is how the robot is created and registered to the scene in ``_setup_scene``. First, the robot articulation +is created by using the ``robot_config`` we defined in ``IsaacLabTutorialEnvCfg``: it doesn't exist before this point! When the +articulation is created, the robot exists on the stage at ``/World/envs/env_0/Robot``. The call to ``scene.clone_environments`` then +copies ``env_0`` appropriately. At this point the robot exists as many copies on the stage, so all that's left is to notify the ``scene`` +object of the existence of this articulation to be tracked. The articulations of the scene are kept as a dictionary, so ``scene.articulations["robot"] = self.robot`` +creates a new ``robot`` element of the ``articulations`` dictionary and sets the value to be ``self.robot``. + +Notice also that the remaining functions do not take additional arguments except ``_reset_idx``. This is because the environment only manages the application of +actions to the agent being simulated, and then updating the sim. This is what the ``_pre_physics_step`` and ``_apply_action`` steps are for: we set the drive commands +to the robot so that when the simulation steps forward, the actions are applied and the joints are driven to new targets. This process is broken into steps like this +in order to ensure systematic control over how the environment is executed, and is especially important in the manager workflow. A similar relationship exists between the +``_get_dones`` function and ``_reset_idx``. The former, ``_get_dones`` determines if each of the environments is in a terminal state, and populates tensors of boolean +values to indicate which environments terminated due to entering a terminal state vs time out (the two returned tensors of the function). The latter, ``_reset_idx`` takes a +list environment index values (integers) and then actually resets those environments. It is important that things like updating drive targets or resetting environments +do not happen **during** the physics or rendering steps, and breaking up the interface in this way helps prevent that. diff --git a/docs/source/setup/walkthrough/concepts_env_design.rst b/docs/source/setup/walkthrough/concepts_env_design.rst new file mode 100644 index 0000000000000000000000000000000000000000..d446820a1472d96d22f726c687f45efee462b355 --- /dev/null +++ b/docs/source/setup/walkthrough/concepts_env_design.rst @@ -0,0 +1,70 @@ +.. _walkthrough_concepts_env_design: + +Environment Design Background +============================== + +Now that we have our project installed, we can start designing the environment. In the traditional description +of a reinforcement learning (RL) problem, the environment is responsible for using the actions produced by the agent to +update the state of the "world", and finally compute and return the observations and the reward signal. However, there are +some additional concepts that are unique to Isaac Sim and Lab regarding the mechanics of the simulation itself. +The traditional description of a reinforcement learning problem presumes a "world", but we get no such luxury; we must define +the world ourselves, and success depends on understanding on how to construct that world and how it will fit into the simulation. + +App, Sim, World, Stage, and Scene +---------------------------------- + +.. figure:: ../../_static/setup/walkthrough_sim_stage_scene.svg + :align: center + :figwidth: 100% + :alt: How the sim is organized. + +The **World** is defined by the origin of a cartesian coordinate system and the units that define it. How big or how small? How +near or how far? The answers to questions like these can only be defined *relative* to some contextual reference frame, and that +reference frame is what defines the world. + +"Above" the world in structure is the **Sim**\ ulation and the **App**\ lication. The **Application** is "the thing responsible for +everything else": It governs all resource management as well as launching and destroying the simulation when we are done with it. +When we :ref:`launched training with the template`, the window that appears with the viewport of cartpoles +training is the Application window. The application is not defined by the GUI however, and even when running in headless mode all +simulations have an application that governs them. + +The **Simulation** controls the "rules" of the world. It defines the laws of physics, such as how time and gravity should work, and how frequently to perform +rendering. If the application holds the sim, then the sim holds the world. The simulation governs a single step through time by dividing it into many different +sub-steps, each devoted to a specific aspect of updating the world into a state. Many of the APIs in Isaac Lab are written to specifically hook into +these various steps and you will often see functions named like ``_pre_XYZ_step`` and ``_post_XYZ_step`` where ``XYZ_step`` is the name of one of these sub-steps of +the simulation, such as the ``physics_step`` or the ``render_step``. + +"Below" the world in structure is the **Stage** and the **Scene**. If the world provides spatial context to the sim, then +the **Stage** provides the *compositional context* for the world. Suppose we want to simulate a table set for a meal in a room: +the room is the "world" in this case, and we choose the origin of the world to be one of the corners of the room. The position of the +table in the room is defined as a vector from the origin of the world to some point on the table that we choose to be the origin of a *new* coordinate +system, fixed to the table. It's not useful to us, *the agent*\ , to talk about the location of the food and the utensils on the table with respect to the +corner of the room: instead it is preferable to use the coordinates defined with respect to the table. However, the simulation needs to know +these global coordinates in order to properly simulate the next time step, so we must define how these two coordinate systems are *composed* together. + +This is what the stage accomplishes: everything in the simulation is a `USD primitive `_ and the +stage represents the relationships between these primitives as a tree, with the context being defined by the relative path in the tree. Every prim on the stage +has a name and therefore a path in this tree, such as ``/room/table/food`` or ``room/table/utensils``. Relationships are defined by the "parents" and "children" +of a given node in this tree: the ``table`` is a child of the ``room`` but a parent of ``food``. Compositional properties of the parent are applied to all of its +children, but child prims have the ability to override parent properties if necessary, as is often the case for materials. + +.. figure:: ../../_static/setup/walkthrough_stage_context.svg + :align: center + :figwidth: 100% + :alt: How the stage organizes context + +Armed with this vocabulary, we can finally talk about the **Scene**, one of the most critical elements to understand about Isaac Lab. Deep learning, in +all its forms, is rooted in the analysis of data. This is true even in robot learning, where data is acquired through the sensors of the robot being trained. +The time required to setup the robot, collect data, and reset the robot to collect more, is a fundamental bottleneck in teaching robots to do *anything*, with any method. +Isaac Sim gives us access to robots without the need for literal physical robots, but Isaac Lab gives us access to *vectorization*: the ability to simulate many copies +of a training procedure efficiently, thus multiplying the rate of data generation and accelerating training proportionally. The scene governs those primitives on the stage +that matter to this vectorization process, known as **simulation entities**. + +Suppose the reason why you want to simulate a table set for a meal is because you would like to train a robot to place the table settings for you! The robot, the table, +and all the things on it can be registered to the scene of an environment. We can then specify how many copies we want and the scene will automatically +construct and run those copies on the stage. These copies are placed at new coordinates on the stage, defining a new reference frame from which observations +and rewards can be computed. Every copy of the scene exists on the stage and is being simulated by the same world. This is much more efficient +than running unique simulations for each copy, but it does open up the possibility of unwanted interactions between copies of the scene, so it's important +to keep this in mind while debugging. + +Now that we have a grasp on the mechanics, we can take a look at the code generated for our template project! diff --git a/docs/source/setup/walkthrough/index.rst b/docs/source/setup/walkthrough/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..2ba226625583b310ade151eabfadb516c9af44f8 --- /dev/null +++ b/docs/source/setup/walkthrough/index.rst @@ -0,0 +1,24 @@ +.. _walkthrough: + +Walkthrough +======================== + +So you finished installing Isaac Sim and Isaac Lab, and you verified that everything is working as expected... + +Now what? + +The following walkthrough will guide you through setting up an Isaac Lab extension project, adding a new robot to lab, designing an environment, and training a policy for that robot. +For this walkthrough, we will be starting with the Jetbot, a simple two wheeled differential base robot with a camera mounted on top, but the intent is for these guides to be general enough that you can use them to add your own robots and environments to Isaac Lab! + +The end result of this walkthrough can be found in our tutorial project repository `here `_. Each branch of this repository +represents a different stage of modifying the default template project to achieve our goals. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + concepts_env_design + api_env_design + technical_env_design + training_jetbot_gt + training_jetbot_reward_exploration diff --git a/docs/source/setup/walkthrough/technical_env_design.rst b/docs/source/setup/walkthrough/technical_env_design.rst new file mode 100644 index 0000000000000000000000000000000000000000..982a579f6831fae5d0c96940ac053dc4398ccc2a --- /dev/null +++ b/docs/source/setup/walkthrough/technical_env_design.rst @@ -0,0 +1,204 @@ +.. _walkthrough_technical_env_design: + +Environment Design +==================== + +Armed with our understanding of the project and its structure, we are ready to start modifying the code to suit our Jetbot training needs. +Our template is set up for the **direct** workflow, which means the environment class will manage all of these details +centrally. We will need to write the code that will... + +#. Define the robot +#. Define the training simulation and manage cloning +#. Apply the actions from the agent to the robot +#. Calculate and return the rewards and observations +#. Manage resetting and terminal states + +As a first step, our goal will be to get the environment training pipeline to load and run. We will use a dummy reward signal +for the purposes of this part of the walkthrough. You can find the code for these modifications `here `_! + +Define the Robot +------------------ + +As our project grows, we may have many robots that we want to train. With malice aforethought we will add a new ``module`` to our +tutorial ``extension`` named ``robots`` where we will keep the definitions for robots as individual python scripts. Navigate +to ``isaac_lab_tutorial/source/isaac_lab_tutorial/isaac_lab_tutorial`` and create a new folder called ``robots``. Within this folder +create two files: ``__init__.py`` and ``jetbot.py``. The ``__init__.py`` file marks this directory as a python module and we will +be able to import the contents of ``jetbot.py`` in the usual way. + +The contents of ``jetbot.py`` is fairly minimal + +.. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.assets import ArticulationCfg + from isaaclab.actuators import ImplicitActuatorCfg + from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + JETBOT_CONFIG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/NVIDIA/Jetbot/jetbot.usd"), + actuators={"wheel_acts": ImplicitActuatorCfg(joint_names_expr=[".*"], damping=None, stiffness=None)}, + ) + +The only purpose of this file is to define a unique scope in which to save our configurations. The details of robot configurations +can be explored in :ref:`this tutorial ` but most noteworthy for this walkthrough is the ``usd_path`` for the ``spawn`` +argument of this ``ArticulationCfg``. The Jetbot asset is available to the public via a hosted nucleus server, and that path is defined by +``ISAAC_NUCLEUS_DIR``, however any path to a USD file is valid, including local ones! + +Environment Configuration +--------------------------- + +Navigate to the environment configuration, ``isaac_lab_tutorial/source/isaac_lab_tutorial/isaac_lab_tutorial/tasks/direct/isaac_lab_tutorial/isaac_lab_tutorial_env_cfg.py``, and +replace its contents with the following + +.. code-block:: python + + from isaac_lab_tutorial.robots.jetbot import JETBOT_CONFIG + + from isaaclab.assets import ArticulationCfg + from isaaclab.envs import DirectRLEnvCfg + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.sim import SimulationCfg + from isaaclab.utils import configclass + + @configclass + class IsaacLabTutorialEnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + # - spaces definition + action_space = 2 + observation_space = 3 + state_space = 0 + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + # robot(s) + robot_cfg: ArticulationCfg = JETBOT_CONFIG.replace(prim_path="/World/envs/env_.*/Robot") + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=100, env_spacing=4.0, replicate_physics=True) + dof_names = ["left_wheel_joint", "right_wheel_joint"] + +Here we have, effectively, the same environment configuration as before, but with the Jetbot instead of the cartpole. The +parameters ``decimation``, ``episode_length_s``, ``action_space``, ``observation_space``, and ``state_space`` are members of +the base class, ``DirectRLEnvCfg``, and must be defined for every ``DirectRLEnv``. The space parameters are interpreted as vectors of +the given integer dimension, but they can also be defined as `gymnasium spaces `_! + +Notice the difference in the action and observation spaces. As the designers of the environment, we get to choose these. For the Jetbot, we want to +directly control the joints of the robot, of which only two are actuated (hence the action space of two). The observation space is *chosen* to be +3 because we are just going to feed the agent the linear velocity of the Jetbot, for now. We will change these later as we develop the environment. Our policy isn't going +to need an internal state maintained, so our state space is zero. + +Attack of the clones +--------------------- + +With the config defined, it's time to fill in the details of the environment, starting with the initialization and setup. +Navigate to the environment definition, ``isaac_lab_tutorial/source/isaac_lab_tutorial/isaac_lab_tutorial/tasks/direct/isaac_lab_tutorial/isaac_lab_tutorial_env.py``, and +replace the contents of the ``__init__`` and ``_setup_scene`` methods with the following. + +.. code-block:: python + + class IsaacLabTutorialEnv(DirectRLEnv): + cfg: IsaacLabTutorialEnvCfg + + def __init__(self, cfg: IsaacLabTutorialEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self.dof_idx, _ = self.robot.find_joints(self.cfg.dof_names) + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + +Notice that the ``_setup_scene`` method doesn't change and the ``_init__`` method is simply grabbing the joint indices from the robot (remember, setup is called in super). + +The next thing our environment needs is the definitions for how to handle actions, observations, and rewards. First, replace the contents of ``_pre_physics_step`` and +``_apply_action`` with the following. + +.. code-block:: python + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + + def _apply_action(self) -> None: + self.robot.set_joint_velocity_target(self.actions, joint_ids=self.dof_idx) + +Here the act of applying actions to the robot in the environment is broken into two steps: ``_pre_physics_step`` and ``_apply_action``. The physics +simulation is decimated with respect to querying the policy for actions, meaning that multiple physics steps may occur per action taken by the policy. +The ``_pre_physics_step`` method is called just before this simulation step takes place and lets us detach the process of getting data from the +policy being trained and applying updates to the physics simulation. The ``_apply_action`` method is where those actions are actually applied to the robots +on the stage, after which the simulation is actually stepped forward in time. + +Next is the observations and rewards, which is just going to depend on the linear velocity of the Jetbot in the body frame of the robot. Replace the contents of ``_get_observations`` +and ``_get_rewards`` with the following. + +.. code-block:: python + + def _get_observations(self) -> dict: + self.velocity = self.robot.data.root_com_lin_vel_b + observations = {"policy": self.velocity} + return observations + + def _get_rewards(self) -> torch.Tensor: + total_reward = torch.linalg.norm(self.velocity, dim=-1, keepdim=True) + return total_reward + +The robot exists as an Articulation object within the Isaac Lab API. That object carries a data class, the ``ArticulationData``, which contains all the data for **specific** robots on the stage. +When we talk about a scene entity like the robot, we can either be talking about the robot broadly, as an entity that exists in every scene, or we can be describing a specific, singular clone +of the robot on the stage. The ``ArticulationData`` contains the data for those individual clones. This includes things like various kinematic vectors (like ``root_com_lin_vel_b``) and reference +vectors (like ``robot.data.FORWARD_VEC_B``). + +Notice how in the ``_apply_action`` method, we are calling a method of ``self.robot`` which is a method of ``Articulation``. The actions being applied are in the form of a 2D tensor +of shape ``[num_envs, num_actions]``. We are applying actions to **all** robots on the stage at once! Here, when we need to get the observations, we need the body frame velocity for all robots on the +stage, and so access ``self.robot.data`` to get that information. The ``root_com_lin_vel_b`` is a property of the ``ArticulationData`` that handles the conversion of the center-of-mass linear velocity from the world frame +to the body frame for us. Finally, Isaac Lab expects the observations to be returned as a dictionary, with ``policy`` defining those observations for the policy model and ``critic`` defining those observations for +the critic model (in the case of asymmetric actor critic training). Since we are not doing asymmetric actor critic, we only need to define ``policy``. + +The rewards are more straightforward. For each clone of the scene, we need to compute a reward value and return it as a tensor of shape ``[num_envs, 1]``. As a place holder, we will make the reward the +magnitude of the linear velocity of the Jetbot in the body frame. With this reward and observation space, the agent should learn to drive the Jetbot forward or backward, with the direction determined at random +shortly after training starts. + +Finally, we can write the parts of the environment to handle termination and resetting. Replace the contents of ``_get_dones`` and ``_reset_idx`` with the following. + +.. code-block:: python + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + time_out = self.episode_length_buf >= self.max_episode_length - 1 + + return False, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.robot._ALL_INDICES + super()._reset_idx(env_ids) + + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.robot.write_root_state_to_sim(default_root_state, env_ids) + +Like the actions, termination and resetting are handled in two parts. First is the ``_get_dones`` method, the goal of which is simply to mark which environments need to be reset and why. +Traditionally in reinforcement learning, an "episode" ends in one of two ways: either the agent reaches a terminal state, or the episode reaches a maximum duration. +Isaac Lab is kind to us, because it manages all of this episode duration tracking behind the scenes. The configuration parameter ``episode_length_s`` defines this maximum episode length in +seconds and the parameters ``episode_length_buff`` and ``max_episode_length`` contain the number of steps taken by individual scenes (allowing for asynchronous running of the environment) and the +maximum length of the episode as converted from ``episode_length_s``. The boolean operation computing ``time_out`` just compares the current buffer size to the max and returns true if it's greater, thus +indicating which scenes are at the episode length limit. Since our current environment is a dummy, we don't define terminal states and so just return ``False`` for the first tensor (this gets projected automatically +to the correct shape through the power of pytorch). + +Finally, the ``_reset_idx`` method accepts a tensor of booleans indicating which scenes need to be reset, and resets them. Notice that this is the only other method of ``DirectRLEnv`` that directly calls +``super``, which is done so here to manage the internal buffers related to episode length. For those environments indicated by ``env_ids`` we retrieve the root default state, and reset the robot to that state while +also offsetting the position of each robot according to the origin of the corresponding scene. This is a consequence of the cloning procedure, which starts with a single robot and a single default state defined in the world +frame. Don't forget this step for your own custom environments! + +With these changes complete, you should see the Jetbot slowly learn to drive forward when you launch the task with the template ``train.py`` script. + +.. figure:: ../../_static/setup/walkthrough_1_1_result.jpg + :align: center + :figwidth: 100% + :alt: The Jetbot invasion begins! diff --git a/docs/source/setup/walkthrough/training_jetbot_gt.rst b/docs/source/setup/walkthrough/training_jetbot_gt.rst new file mode 100644 index 0000000000000000000000000000000000000000..05e89ef4564463d7361a7c7767ac10a9d5d6d0ed --- /dev/null +++ b/docs/source/setup/walkthrough/training_jetbot_gt.rst @@ -0,0 +1,221 @@ +.. _walkthrough_training_jetbot_gt: + +Training the Jetbot: Ground Truth +====================================== + +With the environment defined, we can now start modifying our observations and rewards in order to train a policy +to act as a controller for the Jetbot. As a user, we would like to be able to specify the desired direction for the Jetbot to drive, +and have the wheels turn such that the robot drives in that specified direction as fast as possible. How do we achieve this with +Reinforcement Learning (RL)? If you want to cut to the end and checkout the result of this stage of the walk through, checkout +`this branch of the tutorial repository `_! + +Expanding the Environment +-------------------------- + +The very first thing we need to do is create the logic for setting commands for each Jetbot on the stage. Each command will be a unit vector, and +we need one for every clone of the robot on the stage, which means a tensor of shape ``[num_envs, 3]``. Even though the Jetbot only navigates in the +2D plane, by working with 3D vectors we get to make use of all the math utilities provided by Isaac Lab. + +It would also be a good idea to setup visualizations, so we can more easily tell what the policy is doing during training and inference. +In this case, we will define two arrow ``VisualizationMarkers``: one to represent the "forward" direction of the robot, and one to +represent the command direction. When the policy is fully trained, these arrows should be aligned! Having these visualizations in place +early helps us avoid "silent bugs": issues in the code that do not cause it to crash. + +To begin, we need to define the marker config and then instantiate the markers with that config. Add the following to the global scope of ``isaac_lab_tutorial_env.py`` + +.. code-block:: python + + from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + import isaaclab.utils.math as math_utils + + def define_markers() -> VisualizationMarkers: + """Define markers with various different shapes.""" + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/myMarkers", + markers={ + "forward": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(0.25, 0.25, 0.5), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)), + ), + "command": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(0.25, 0.25, 0.5), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + return VisualizationMarkers(cfg=marker_cfg) + +The ``VisualizationMarkersCfg`` defines USD prims to serve as the "marker". Any prim will do, but generally you want to keep markers as simple as possible because the cloning of markers occurs at runtime on every time step. +This is because the purpose of these markers is for *debug visualization only* and not to be a part of the simulation: the user has full control over how many markers to draw when and where. +NVIDIA provides several simple meshes on our public nucleus server, located at ``ISAAC_NUCLEUS_DIR``, and for obvious reasons we choose to use ``arrow_x.usd``. + +For a more detailed example of using ``VisualizationMarkers`` checkout the ``markers.py`` demo! + +.. dropdown:: Code for the markers.py demo + :icon: code + + .. literalinclude:: ../../../../scripts/demos/markers.py + :language: python + :linenos: + +Next, we need to expand the initialization and setup steps to construct the data we need for tracking the commands as well as the marker positions and rotations. Replace the contents of +``_setup_scene`` with the following + +.. code-block:: python + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + self.visualization_markers = define_markers() + + # setting aside useful variables for later + self.up_dir = torch.tensor([0.0, 0.0, 1.0]).cuda() + self.yaws = torch.zeros((self.cfg.scene.num_envs, 1)).cuda() + self.commands = torch.randn((self.cfg.scene.num_envs, 3)).cuda() + self.commands[:,-1] = 0.0 + self.commands = self.commands/torch.linalg.norm(self.commands, dim=1, keepdim=True) + + # offsets to account for atan range and keep things on [-pi, pi] + ratio = self.commands[:,1]/(self.commands[:,0]+1E-8) + gzero = torch.where(self.commands > 0, True, False) + lzero = torch.where(self.commands < 0, True, False) + plus = lzero[:,0]*gzero[:,1] + minus = lzero[:,0]*lzero[:,1] + offsets = torch.pi*plus - torch.pi*minus + self.yaws = torch.atan(ratio).reshape(-1,1) + offsets.reshape(-1,1) + + self.marker_locations = torch.zeros((self.cfg.scene.num_envs, 3)).cuda() + self.marker_offset = torch.zeros((self.cfg.scene.num_envs, 3)).cuda() + self.marker_offset[:,-1] = 0.5 + self.forward_marker_orientations = torch.zeros((self.cfg.scene.num_envs, 4)).cuda() + self.command_marker_orientations = torch.zeros((self.cfg.scene.num_envs, 4)).cuda() + +Most of this is setting up the book keeping for the commands and markers, but the command initialization and the yaw calculations are worth diving into. The commands +are sampled from a multivariate normal distribution via ``torch.randn`` with the z component fixed to zero and then normalized to unit length. In order to point our +command markers along these vectors, we need to rotate the base arrow mesh appropriately. This means we need to define a `quaternion `_ that will rotate the arrow +prim about the z axis by some angle defined by the command. By convention, rotations about the z axis are called a "yaw" rotation (akin to roll and pitch). + +Luckily for us, Isaac Lab provides a utility to generate a quaternion from an axis of rotation and an angle: :func:`isaaclab.utils.math.quat_from_axis_angle`, so the only +tricky part now is determining that angle. + +.. figure:: ../../_static/setup/walkthrough_training_vectors.svg + :align: center + :figwidth: 100% + :alt: Useful vector definitions for training + +The yaw is defined about the z axis, with a yaw of 0 aligning with the x axis and positive angles opening counterclockwise. The x and y components of the command vector +define the tangent of this angle, and so we need the *arctangent* of that ratio to get the yaw. + +Now, consider two commands: Command A is in quadrant 2 at (-x, y), while command B is in quadrant 4 at (x, -y). The ratio of the +y component to the x component is identical for both A and B. If we do not account for this, then some of our command arrows will be +pointing in the opposite direction of the command! Essentially, our commands are defined on ``[-pi, pi]`` but ``arctangent`` is +only defined on ``[-pi/2, pi/2]``. + +To remedy this, we add or subtract ``pi`` from the yaw depending on the quadrant of the command. + +.. code-block:: python + + ratio = self.commands[:,1]/(self.commands[:,0]+1E-8) #in case the x component is zero + gzero = torch.where(self.commands > 0, True, False) + lzero = torch.where(self.commands < 0, True, False) + plus = lzero[:,0]*gzero[:,1] + minus = lzero[:,0]*lzero[:,1] + offsets = torch.pi*plus - torch.pi*minus + self.yaws = torch.atan(ratio).reshape(-1,1) + offsets.reshape(-1,1) + +Boolean expressions involving tensors can have ambiguous definitions and pytorch will throw errors regarding this. Pytorch provides +various methods to make the definitions explicit. The method ``torch.where`` produces a tensor with the same shape as the input +with each element of the output is determined by the evaluation of that expression on only that element. A reliable way to handle +boolean operations with tensors is to simply produce boolean indexing tensors and then represent the operation algebraically, with ``AND`` +as multiplication and ``OR`` as addition, which is what we do above. This is equivalent to the pseudocode: + +.. code-block:: python + + yaws = torch.atan(ratio) + yaws[commands[:,0] < 0 and commands[:,1] > 0] += torch.pi + yaws[commands[:,0] < 0 and commands[:,1] < 0] -= torch.pi + +Next we have the method for actually visualizing the markers. Remember, these markers aren't scene entities! We need to "draw" them whenever we +want to see them. + +.. code-block:: python + + def _visualize_markers(self): + # get marker locations and orientations + self.marker_locations = self.robot.data.root_pos_w + self.forward_marker_orientations = self.robot.data.root_quat_w + self.command_marker_orientations = math_utils.quat_from_angle_axis(self.yaws, self.up_dir).squeeze() + + # offset markers so they are above the jetbot + loc = self.marker_locations + self.marker_offset + loc = torch.vstack((loc, loc)) + rots = torch.vstack((self.forward_marker_orientations, self.command_marker_orientations)) + + # render the markers + all_envs = torch.arange(self.cfg.scene.num_envs) + indices = torch.hstack((torch.zeros_like(all_envs), torch.ones_like(all_envs))) + self.visualization_markers.visualize(loc, rots, marker_indices=indices) + +The ``visualize`` method of ``VisualizationMarkers`` is like this "draw" function. It accepts tensors for the spatial +transformations of the markers, and a ``marker_indices`` tensor to specify which marker prototype to use for each marker. So +long as the first dimension of all of these tensors match, this function will draw those markers with the specified transformations. +This is why we stack the locations, rotations, and indices. + +Now we just need to call ``_visualize_markers`` on the pre physics step to make the arrows visible. Replace ``_pre_physics_step`` with the following + +.. code-block:: python + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + self._visualize_markers() + +The last major modification before we dig into the RL training is to update the ``_reset_idx`` method to account for the commands and markers. Whenever we reset an environment, +we need to generate a new command and reset the markers. The logic for this is already covered above. Replace the contents of ``_reset_idx`` with the following: + +.. code-block:: python + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.robot._ALL_INDICES + super()._reset_idx(env_ids) + + # pick new commands for reset envs + self.commands[env_ids] = torch.randn((len(env_ids), 3)).cuda() + self.commands[env_ids,-1] = 0.0 + self.commands[env_ids] = self.commands[env_ids]/torch.linalg.norm(self.commands[env_ids], dim=1, keepdim=True) + + # recalculate the orientations for the command markers with the new commands + ratio = self.commands[env_ids][:,1]/(self.commands[env_ids][:,0]+1E-8) + gzero = torch.where(self.commands[env_ids] > 0, True, False) + lzero = torch.where(self.commands[env_ids]< 0, True, False) + plus = lzero[:,0]*gzero[:,1] + minus = lzero[:,0]*lzero[:,1] + offsets = torch.pi*plus - torch.pi*minus + self.yaws[env_ids] = torch.atan(ratio).reshape(-1,1) + offsets.reshape(-1,1) + + # set the root state for the reset envs + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.robot.write_root_state_to_sim(default_root_state, env_ids) + self._visualize_markers() + + +And that's it! We now generate commands and can visualize it the heading of the Jetbot. We are ready to start tinkering with the observations and rewards. + +.. figure:: ../../_static/setup/walkthrough_1_2_arrows.jpg + :align: center + :figwidth: 100% + :alt: Visualization of the command markers diff --git a/docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst b/docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst new file mode 100644 index 0000000000000000000000000000000000000000..efdce4689c99dfe12266e418dba6d1f77291fbe8 --- /dev/null +++ b/docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst @@ -0,0 +1,146 @@ +.. _walkthrough_training_jetbot_reward_exploration: + +Exploring the RL problem +========================= + +The command to the Jetbot is a unit vector in specifying the desired drive direction and we must make the agent aware of this somehow +so it can adjust its actions accordingly. There are many possible ways to do this, with the "zeroth order" approach to simply change the observation space to include +this command. To start, **edit the ``IsaacLabTutorialEnvCfg`` to set the observation space to 9**: the world velocity vector contains the linear and angular velocities +of the robot, which is 6 dimensions and if we append the command to this vector, that's 9 dimensions for the observation space in total. + +Next, we just need to do that appending when we get the observations. We also need to calculate our forward vectors for later use. The forward vector for the Jetbot is +the x axis, so we apply the ``root_link_quat_w`` to ``[1,0,0]`` to get the forward vector in the world frame. Replace the ``_get_observations`` method with the following: + +.. code-block:: python + + def _get_observations(self) -> dict: + self.velocity = self.robot.data.root_com_vel_w + self.forwards = math_utils.quat_apply(self.robot.data.root_link_quat_w, self.robot.data.FORWARD_VEC_B) + obs = torch.hstack((self.velocity, self.commands)) + observations = {"policy": obs} + return observations + +So now what should the reward be? + +When the robot is behaving as desired, it will be driving at full speed in the direction of the command. If we reward both +"driving forward" and "alignment to the command", then maximizing that combined signal should result in driving to the command... right? + +Let's give it a try! Replace the ``_get_rewards`` method with the following: + +.. code-block:: python + + def _get_rewards(self) -> torch.Tensor: + forward_reward = self.robot.data.root_com_lin_vel_b[:,0].reshape(-1,1) + alignment_reward = torch.sum(self.forwards * self.commands, dim=-1, keepdim=True) + total_reward = forward_reward + alignment_reward + return total_reward + +The ``forward_reward`` is the x component of the linear center of mass velocity of the robot in the body frame. We know that +the x direction is the forward direction for the asset, so this should be equivalent to inner product between the forward vector and +the linear velocity in the world frame. The alignment term is the inner product between the forward vector and the command vector: when they are +pointing in the same direction this term will be 1, but in the opposite direction it will be -1. We add them together to get the combined reward and +we can finally run training! Let's see what happens! + +.. code-block:: bash + + python scripts/skrl/train.py --task=Template-Isaac-Lab-Tutorial-Direct-v0 + + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/walkthrough_naive_webp.webp + :align: center + :figwidth: 100% + :alt: Naive results + +Surely we can do better! + +Reward and Observation Tuning +------------------------------- + +When tuning an environment for training, as a rule of thumb, you want to keep the observation space as small as possible. This is to +reduce the number parameters in the model (the literal interpretation of Occam's razor) and thus improve training time. In this case we +need to somehow encode our alignment to the command and our forward speed. One way to do this is to exploit the dot and cross products +from linear algebra! Replace the contents of ``_get_observations`` with the following: + +.. code-block:: python + + def _get_observations(self) -> dict: + self.velocity = self.robot.data.root_com_vel_w + self.forwards = math_utils.quat_apply(self.robot.data.root_link_quat_w, self.robot.data.FORWARD_VEC_B) + + dot = torch.sum(self.forwards * self.commands, dim=-1, keepdim=True) + cross = torch.cross(self.forwards, self.commands, dim=-1)[:,-1].reshape(-1,1) + forward_speed = self.robot.data.root_com_lin_vel_b[:,0].reshape(-1,1) + obs = torch.hstack((dot, cross, forward_speed)) + + observations = {"policy": obs} + return observations + +We also need to **edit the ``IsaacLabTutorialEnvCfg`` to set the observation space back to 3** which includes the dot product, the z component of the cross product, and the forward speed. + +The dot or inner product tells us how aligned two vectors are as a single scalar quantity. If they are very aligned and pointed in the same direction, then the inner +product will be large and positive, but if they are aligned and in opposite directions, it will be large and negative. If two vectors are +perpendicular, the inner product is zero. This means that the inner product between the forward vector and the command vector can tell us +how much we are facing towards or away from the command, but not which direction we need to turn to improve alignment. + +The cross product also tells us how aligned two vectors are, but it expresses this relationship as a vector. The cross product between any +two vectors defines an axis that is perpendicular to the plane containing the two argument vectors, where the direction of the result vector along this axis is +determined by the chirality (dimension ordering, or handedness) of the coordinate system. In our case, we can exploit the fact that we are operating in 2D to only +examine the z component of the result of :math:`\vec{forward} \times \vec{command}`. This component will be zero if the vectors are colinear, positive if the +command vector is to the left of forward, and negative if it's to the right. + +Finally, the x component of the center of mass linear velocity tells us our forward speed, with positive being forward and negative being backwards. We stack these together +"horizontally" (along dim 1) to generate the observations for each Jetbot. This alone improves performance! + + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/walkthrough_improved_webp.webp + :align: center + :figwidth: 100% + :alt: Improved results + +It seems to qualitatively train better, and the Jetbots are somewhat inching forward... Surely we can do better still! + +Another rule of thumb for training is to reduce and simplify the reward function as much as possible. Terms in the reward behave similarly to +the logical "OR" operation. In our case, we are rewarding driving forward and being aligned to the command by adding them together, so our agent +can be reward for driving forward OR being aligned to the command. To force the agent to learn to drive in the direction of the command, we should only +reward the agent driving forward AND being aligned. Logical AND suggests multiplication and therefore the following reward function: + +.. code-block:: python + + def _get_rewards(self) -> torch.Tensor: + forward_reward = self.robot.data.root_com_lin_vel_b[:,0].reshape(-1,1) + alignment_reward = torch.sum(self.forwards * self.commands, dim=-1, keepdim=True) + total_reward = forward_reward*alignment_reward + return total_reward + +Now we will only get rewarded for driving forward if our alignment reward is non zero. Let's see what kind of result this produces! + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/walkthrough_tuned_webp.webp + :align: center + :figwidth: 100% + :alt: Tuned results + +It definitely trains faster, but the Jetbots have learned to drive in reverse if the command is pointed behind them. This may be desirable in our +case, but it shows just how dependent the policy behavior is on the reward function. In this case, there are **degenerate solutions** to our +reward function: The reward is maximized for driving forward and aligned to the command, but if the Jetbot drives in reverse, then the forward +term is negative, and if its driving in reverse towards the command, then the alignment term is **also negative**, meaning hat the reward is positive! +When you design your own environments, you will run into degenerate solutions like this and a significant amount of reward engineering is devoted to +suppressing or supporting these behaviors by modifying the reward function. + +Let's say, in our case, we don't want this behavior. In our case, the alignment term has a domain of ``[-1, 1]``, but we would much prefer it to be mapped +only to positive values. We don't want to *eliminate* the sign on the alignment term, rather, we would like large negative values to be near zero, so if we +are misaligned, we don't get rewarded. The exponential function accomplishes this! + +.. code-block:: python + + def _get_rewards(self) -> torch.Tensor: + forward_reward = self.robot.data.root_com_lin_vel_b[:,0].reshape(-1,1) + alignment_reward = torch.sum(self.forwards * self.commands, dim=-1, keepdim=True) + total_reward = forward_reward*torch.exp(alignment_reward) + return total_reward + +Now when we train, the Jetbots will turn to always drive towards the command in the forward direction! + +.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/walkthrough_directed_webp.webp + :align: center + :figwidth: 100% + :alt: Directed results diff --git a/docs/source/tutorials/00_sim/create_empty.rst b/docs/source/tutorials/00_sim/create_empty.rst new file mode 100644 index 0000000000000000000000000000000000000000..89f28201c41025d19b4c0cc749d1c4bc9fab1924 --- /dev/null +++ b/docs/source/tutorials/00_sim/create_empty.rst @@ -0,0 +1,167 @@ +Creating an empty scene +======================= + +.. currentmodule:: isaaclab + +This tutorial shows how to launch and control Isaac Sim simulator from a standalone Python script. It sets up an +empty scene in Isaac Lab and introduces the two main classes used in the framework, :class:`app.AppLauncher` and +:class:`sim.SimulationContext`. + +Please review `Isaac Sim Workflows`_ prior to beginning this tutorial to get +an initial understanding of working with the simulator. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``create_empty.py`` script in the ``scripts/tutorials/00_sim`` directory. + +.. dropdown:: Code for create_empty.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :emphasize-lines: 18-30,34,40-44,46-47,51-54,60-61 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Launching the simulator +----------------------- + +The first step when working with standalone Python scripts is to launch the simulation application. +This is necessary to do at the start since various dependency modules of Isaac Sim are only available +after the simulation app is running. + +This can be done by importing the :class:`app.AppLauncher` class. This utility class wraps around +:class:`isaacsim.SimulationApp` class to launch the simulator. It provides mechanisms to +configure the simulator using command-line arguments and environment variables. + +For this tutorial, we mainly look at adding the command-line options to a user-defined +:class:`argparse.ArgumentParser`. This is done by passing the parser instance to the +:meth:`app.AppLauncher.add_app_launcher_args` method, which appends different parameters +to it. These include launching the app headless, configuring different Livestream options, +and enabling off-screen rendering. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :start-at: import argparse + :end-at: simulation_app = app_launcher.app + +Importing python modules +------------------------ + +Once the simulation app is running, it is possible to import different Python modules from +Isaac Sim and other libraries. Here we import the following module: + +* :mod:`isaaclab.sim`: A sub-package in Isaac Lab for all the core simulator-related operations. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :start-at: from isaaclab.sim import SimulationCfg, SimulationContext + :end-at: from isaaclab.sim import SimulationCfg, SimulationContext + + +Configuring the simulation context +---------------------------------- + +When launching the simulator from a standalone script, the user has complete control over playing, +pausing and stepping the simulator. All these operations are handled through the **simulation +context**. It takes care of various timeline events and also configures the `physics scene`_ for +simulation. + +In Isaac Lab, the :class:`sim.SimulationContext` class inherits from Isaac Sim's +:class:`isaacsim.core.api.simulation_context.SimulationContext` to allow configuring the simulation +through Python's ``dataclass`` object and handle certain intricacies of the simulation stepping. + +For this tutorial, we set the physics and rendering time step to 0.01 seconds. This is done +by passing these quantities to the :class:`sim.SimulationCfg`, which is then used to create an +instance of the simulation context. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :start-at: # Initialize the simulation context + :end-at: sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + +Following the creation of the simulation context, we have only configured the physics acting on the +simulated scene. This includes the device to use for simulation, the gravity vector, and other advanced +solver parameters. There are now two main steps remaining to run the simulation: + +1. Designing the simulation scene: Adding sensors, robots and other simulated objects +2. Running the simulation loop: Stepping the simulator, and setting and getting data from the simulator + +In this tutorial, we look at Step 2 first for an empty scene to focus on the simulation control first. +In the following tutorials, we will look into Step 1 and working with simulation handles for interacting +with the simulator. + +Running the simulation +---------------------- + +The first thing, after setting up the simulation scene, is to call the :meth:`sim.SimulationContext.reset` +method. This method plays the timeline and initializes the physics handles in the simulator. It must always +be called the first time before stepping the simulator. Otherwise, the simulation handles are not initialized +properly. + +.. note:: + + :meth:`sim.SimulationContext.reset` is different from :meth:`sim.SimulationContext.play` method as the latter + only plays the timeline and does not initializes the physics handles. + +After playing the simulation timeline, we set up a simple simulation loop where the simulator is stepped repeatedly +while the simulation app is running. The method :meth:`sim.SimulationContext.step` takes in as argument :attr:`render`, +which dictates whether the step includes updating the rendering-related events or not. By default, this flag is +set to True. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :start-at: # Play the simulator + :end-at: sim.step() + +Exiting the simulation +---------------------- + +Lastly, the simulation application is stopped and its window is closed by calling +:meth:`isaacsim.SimulationApp.close` method. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/create_empty.py + :language: python + :start-at: # close sim app + :end-at: simulation_app.close() + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py + + +The simulation should be playing, and the stage should be rendering. To stop the simulation, +you can either close the window, or press ``Ctrl+C`` in the terminal. + +.. figure:: ../../_static/tutorials/tutorial_create_empty.jpg + :align: center + :figwidth: 100% + :alt: result of create_empty.py + +Passing ``--help`` to the above script will show the different command-line arguments added +earlier by the :class:`app.AppLauncher` class. To run the script headless, you can execute the +following: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py --headless + + +Now that we have a basic understanding of how to run a simulation, let's move on to the +following tutorial where we will learn how to add assets to the stage. + +.. _`Isaac Sim Workflows`: https://docs.isaacsim.omniverse.nvidia.com/latest/introduction/workflows.html +.. _carb: https://docs.omniverse.nvidia.com/kit/docs/carbonite/latest/index.html +.. _`physics scene`: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_physics.html#physics-scene diff --git a/docs/source/tutorials/00_sim/launch_app.rst b/docs/source/tutorials/00_sim/launch_app.rst new file mode 100644 index 0000000000000000000000000000000000000000..05fa32c4648da1858e275897dac4ac53bb14d5ae --- /dev/null +++ b/docs/source/tutorials/00_sim/launch_app.rst @@ -0,0 +1,176 @@ +Deep-dive into AppLauncher +========================== + +.. currentmodule:: isaaclab + +In this tutorial, we will dive into the :class:`app.AppLauncher` class to configure the simulator using +CLI arguments and environment variables (envars). Particularly, we will demonstrate how to use +:class:`~app.AppLauncher` to enable livestreaming and configure the :class:`isaacsim.simulation_app.SimulationApp` +instance it wraps, while also allowing user-provided options. + +The :class:`~app.AppLauncher` is a wrapper for :class:`~isaacsim.simulation_app.SimulationApp` to simplify +its configuration. The :class:`~isaacsim.simulation_app.SimulationApp` has many extensions that must be +loaded to enable different capabilities, and some of these extensions are order- and inter-dependent. +Additionally, there are startup options such as ``headless`` which must be set at instantiation time, +and which have an implied relationship with some extensions, e.g. the livestreaming extensions. +The :class:`~app.AppLauncher` presents an interface that can handle these extensions and startup +options in a portable manner across a variety of use cases. To achieve this, we offer CLI and envar +flags which can be merged with user-defined CLI args, while passing forward arguments intended +for :class:`~isaacsim.simulation_app.SimulationApp`. + + +The Code +-------- + +The tutorial corresponds to the ``launch_app.py`` script in the +``scripts/tutorials/00_sim`` directory. + +.. dropdown:: Code for launch_app.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/00_sim/launch_app.py + :language: python + :emphasize-lines: 18-40 + :linenos: + +The Code Explained +------------------ + +Adding arguments to the argparser +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:class:`~app.AppLauncher` is designed to be compatible with custom CLI args that users need for +their own scripts, while still providing a portable CLI interface. + +In this tutorial, a standard :class:`argparse.ArgumentParser` is instantiated and given the +script-specific ``--size`` argument, as well as the arguments ``--height`` and ``--width``. +The latter are ingested by :class:`~isaacsim.simulation_app.SimulationApp`. + +The argument ``--size`` is not used by :class:`~app.AppLauncher`, but will merge seamlessly +with the :class:`~app.AppLauncher` interface. In-script arguments can be merged with the +:class:`~app.AppLauncher` interface via the :meth:`~app.AppLauncher.add_app_launcher_args` method, +which will return a modified :class:`~argparse.ArgumentParser` with the :class:`~app.AppLauncher` +arguments appended. This can then be processed into an :class:`argparse.Namespace` using the +standard :meth:`argparse.ArgumentParser.parse_args` method and passed directly to +:class:`~app.AppLauncher` for instantiation. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/launch_app.py + :language: python + :start-at: import argparse + :end-at: simulation_app = app_launcher.app + +The above only illustrates only one of several ways of passing arguments to :class:`~app.AppLauncher`. +Please consult its documentation page to see further options. + +Understanding the output of --help +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +While executing the script, we can pass the ``--help`` argument and see the combined outputs of the +custom arguments and those from :class:`~app.AppLauncher`. + +.. code-block:: console + + ./isaaclab.sh -p scripts/tutorials/00_sim/launch_app.py --help + + [INFO] Using python from: /isaac-sim/python.sh + [INFO][AppLauncher]: The argument 'width' will be used to configure the SimulationApp. + [INFO][AppLauncher]: The argument 'height' will be used to configure the SimulationApp. + usage: launch_app.py [-h] [--size SIZE] [--width WIDTH] [--height HEIGHT] [--headless] [--livestream {0,1,2}] + [--enable_cameras] [--verbose] [--experience EXPERIENCE] + + Tutorial on running IsaacSim via the AppLauncher. + + options: + -h, --help show this help message and exit + --size SIZE Side-length of cuboid + --width WIDTH Width of the viewport and generated images. Defaults to 1280 + --height HEIGHT Height of the viewport and generated images. Defaults to 720 + + app_launcher arguments: + --headless Force display off at all times. + --livestream {0,1,2} + Force enable livestreaming. Mapping corresponds to that for the "LIVESTREAM" environment variable. + --enable_cameras Enable cameras when running without a GUI. + --verbose Enable verbose terminal logging from the SimulationApp. + --experience EXPERIENCE + The experience file to load when launching the SimulationApp. + + * If an empty string is provided, the experience file is determined based on the headless flag. + * If a relative path is provided, it is resolved relative to the `apps` folder in Isaac Sim and + Isaac Lab (in that order). + +This readout details the ``--size``, ``--height``, and ``--width`` arguments defined in the script directly, +as well as the :class:`~app.AppLauncher` arguments. + +The ``[INFO]`` messages preceding the help output also reads out which of these arguments are going +to be interpreted as arguments to the :class:`~isaacsim.simulation_app.SimulationApp` instance which the +:class:`~app.AppLauncher` class wraps. In this case, it is ``--height`` and ``--width``. These +are classified as such because they match the name and type of an argument which can be processed +by :class:`~isaacsim.simulation_app.SimulationApp`. Please refer to the `specification`_ for such arguments +for more examples. + +Using environment variables +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +As noted in the help message, the :class:`~app.AppLauncher` arguments (``--livestream``, ``--headless``) +have corresponding environment variables (envar) as well. These are detailed in :mod:`isaaclab.app` +documentation. Providing any of these arguments through CLI is equivalent to running the script in a shell +environment where the corresponding envar is set. + +The support for :class:`~app.AppLauncher` envars are simply a convenience to provide session-persistent +configurations, and can be set in the user's ``${HOME}/.bashrc`` for persistent settings between sessions. +In the case where these arguments are provided from the CLI, they will override their corresponding envar, +as we will demonstrate later in this tutorial. + +These arguments can be used with any script that starts the simulation using :class:`~app.AppLauncher`, +with one exception, ``--enable_cameras``. This setting sets the rendering pipeline to use the +offscreen renderer. However, this setting is only compatible with the :class:`isaaclab.sim.SimulationContext`. +It will not work with Isaac Sim's :class:`isaacsim.core.api.simulation_context.SimulationContext` class. +For more information on this flag, please see the :class:`~app.AppLauncher` API documentation. + + +The Code Execution +------------------ + +We will now run the example script: + +.. code-block:: console + + LIVESTREAM=2 ./isaaclab.sh -p scripts/tutorials/00_sim/launch_app.py --size 0.5 + +This will spawn a 0.5m\ :sup:`3` volume cuboid in the simulation. No GUI will appear, equivalent +to if we had passed the ``--headless`` flag because headlessness is implied by our ``LIVESTREAM`` +envar. If a visualization is desired, we could get one via Isaac's `WebRTC Livestreaming`_. Streaming +is currently the only supported method of visualization from within the container. The +process can be killed by pressing ``Ctrl+C`` in the launching terminal. + +.. figure:: ../../_static/tutorials/tutorial_launch_app.jpg + :align: center + :figwidth: 100% + :alt: result of launch_app.py + +Now, let's look at how :class:`~app.AppLauncher` handles conflicting commands: + +.. code-block:: console + + LIVESTREAM=0 ./isaaclab.sh -p scripts/tutorials/00_sim/launch_app.py --size 0.5 --livestream 2 + +This will cause the same behavior as in the previous run, because although we have set ``LIVESTREAM=0`` +in our envars, CLI args such as ``--livestream`` take precedence in determining behavior. The process can +be killed by pressing ``Ctrl+C`` in the launching terminal. + +Finally, we will examine passing arguments to :class:`~isaacsim.simulation_app.SimulationApp` through +:class:`~app.AppLauncher`: + +.. code-block:: console + + LIVESTREAM=2 ./isaaclab.sh -p scripts/tutorials/00_sim/launch_app.py --size 0.5 --width 1920 --height 1080 + +This will cause the same behavior as before, but now the viewport will be rendered at 1920x1080p resolution. +This can be useful when we want to gather high-resolution video, or we can specify a lower resolution if we +want our simulation to be more performant. The process can be killed by pressing ``Ctrl+C`` in the launching +terminal. + + +.. _specification: https://docs.isaacsim.omniverse.nvidia.com/latest/py/source/extensions/isaacsim.simulation_app/docs/index.html#isaacsim.simulation_app.SimulationApp.DEFAULT_LAUNCHER_CONFIG +.. _WebRTC Livestreaming: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/manual_livestream_clients.html#isaac-sim-short-webrtc-streaming-client diff --git a/docs/source/tutorials/00_sim/spawn_prims.rst b/docs/source/tutorials/00_sim/spawn_prims.rst new file mode 100644 index 0000000000000000000000000000000000000000..66941ddb1c7fe326bd1a327665b6f0323b88d652 --- /dev/null +++ b/docs/source/tutorials/00_sim/spawn_prims.rst @@ -0,0 +1,192 @@ +.. _tutorial-spawn-prims: + + +Spawning prims into the scene +============================= + +.. currentmodule:: isaaclab + +This tutorial explores how to spawn various objects (or prims) into the scene in Isaac Lab from Python. +It builds on the previous tutorial on running the simulator from a standalone script and +demonstrates how to spawn a ground plane, lights, primitive shapes, and meshes from USD files. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``spawn_prims.py`` script in the ``scripts/tutorials/00_sim`` directory. +Let's take a look at the Python script: + +.. dropdown:: Code for spawn_prims.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :emphasize-lines: 40-88, 100-101 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Scene designing in Omniverse is built around a software system and file format called USD (Universal Scene Description). +It allows describing 3D scenes in a hierarchical manner, similar to a file system. Since USD is a comprehensive framework, +we recommend reading the `USD documentation`_ to learn more about it. + +For completeness, we introduce the must know concepts of USD in this tutorial. + +* **Primitives (Prims)**: These are the basic building blocks of a USD scene. They can be thought of as nodes in a scene + graph. Each node can be a mesh, a light, a camera, or a transform. It can also be a group of other prims under it. +* **Attributes**: These are the properties of a prim. They can be thought of as key-value pairs. For example, a prim can + have an attribute called ``color`` with a value of ``red``. +* **Relationships**: These are the connections between prims. They can be thought of as pointers to other prims. For + example, a mesh prim can have a relationship to a material prim for shading. + +A collection of these prims, with their attributes and relationships, is called a **USD stage**. It can be thought of +as a container for all prims in a scene. When we say we are designing a scene, we are actually designing a USD stage. + +While working with direct USD APIs provides a lot of flexibility, it can be cumbersome to learn and use. To make it +easier to design scenes, Isaac Lab builds on top of the USD APIs to provide a configuration-driven interface to spawn prims +into a scene. These are included in the :mod:`sim.spawners` module. + +When spawning prims into the scene, each prim requires a configuration class instance that defines the prim's attributes +and relationships (through material and shading information). The configuration class is then passed to its respective +function where the prim name and transformation are specified. The function then spawns the prim into the scene. + +At a high-level, this is how it works: + +.. code-block:: python + + # Create a configuration class instance + cfg = MyPrimCfg() + prim_path = "/path/to/prim" + + # Spawn the prim into the scene using the corresponding spawner function + spawn_my_prim(prim_path, cfg, translation=[0, 0, 0], orientation=[1, 0, 0, 0], scale=[1, 1, 1]) + # OR + # Use the spawner function directly from the configuration class + cfg.func(prim_path, cfg, translation=[0, 0, 0], orientation=[1, 0, 0, 0], scale=[1, 1, 1]) + + +In this tutorial, we demonstrate the spawning of various different prims into the scene. For more +information on the available spawners, please refer to the :mod:`sim.spawners` module in Isaac Lab. + +.. attention:: + + All the scene designing must happen before the simulation starts. Once the simulation starts, we recommend keeping + the scene frozen and only altering the properties of the prim. This is particularly important for GPU simulation + as adding new prims during simulation may alter the physics simulation buffers on GPU and lead to unexpected + behaviors. + + +Spawning a ground plane +----------------------- + +The :class:`~sim.spawners.from_files.GroundPlaneCfg` configures a grid-like ground plane with +modifiable properties such as its appearance and size. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # Ground-plane + :end-at: cfg_ground.func("/World/defaultGroundPlane", cfg_ground) + + +Spawning lights +--------------- + +It is possible to spawn `different light prims`_ into the stage. These include distant lights, sphere lights, disk +lights, and cylinder lights. In this tutorial, we spawn a distant light which is a light that is infinitely far away +from the scene and shines in a single direction. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # spawn distant light + :end-at: cfg_light_distant.func("/World/lightDistant", cfg_light_distant, translation=(1, 0, 10)) + + +Spawning primitive shapes +------------------------- + +Before spawning primitive shapes, we introduce the concept of a transform prim or Xform. A transform prim is a prim that +contains only transformation properties. It is used to group other prims under it and to transform them as a group. +Here we make an Xform prim to group all the primitive shapes under it. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # create a new xform prim for all objects to be spawned under + :end-at: sim_utils.create_prim("/World/Objects", "Xform") + +Next, we spawn a cone using the :class:`~sim.spawners.shapes.ConeCfg` class. It is possible to specify +the radius, height, physics properties, and material properties of the cone. By default, the physics and material +properties are disabled. + +The first two cones we spawn ``Cone1`` and ``Cone2`` are visual elements and do not have physics enabled. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # spawn a red cone + :end-at: cfg_cone.func("/World/Objects/Cone2", cfg_cone, translation=(-1.0, -1.0, 1.0)) + +For the third cone ``ConeRigid``, we add rigid body physics to it by setting the attributes for that in the configuration +class. Through these attributes, we can specify the mass, friction, and restitution of the cone. If unspecified, they +default to the default values set by USD Physics. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # spawn a green cone with colliders and rigid body + :end-before: # spawn a blue cuboid with deformable body + +Lastly, we spawn a cuboid ``CuboidDeformable`` which contains deformable body physics properties. Unlike the +rigid body simulation, a deformable body can have relative motion between its vertices. This is useful for simulating +soft bodies like cloth, rubber, or jello. It is important to note that deformable bodies are only supported in +GPU simulation and require a mesh object to be spawned with the deformable body physics properties. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # spawn a blue cuboid with deformable body + :end-before: # spawn a usd file of a table into the scene + +Spawning from another file +-------------------------- + +Lastly, it is possible to spawn prims from other file formats such as other USD, URDF, or OBJ files. In this tutorial, +we spawn a USD file of a table into the scene. The table is a mesh prim and has a material prim associated with it. +All of this information is stored in its USD file. + +.. literalinclude:: ../../../../scripts/tutorials/00_sim/spawn_prims.py + :language: python + :start-at: # spawn a usd file of a table into the scene + :end-at: cfg.func("/World/Objects/Table", cfg, translation=(0.0, 0.0, 1.05)) + +The table above is added as a reference to the scene. In layman terms, this means that the table is not actually added +to the scene, but a ``pointer`` to the table asset is added. This allows us to modify the table asset and have the changes +reflected in the scene in a non-destructive manner. For example, we can change the material of the table without +actually modifying the underlying file for the table asset directly. Only the changes are stored in the USD stage. + + +Executing the Script +~~~~~~~~~~~~~~~~~~~~ + +Similar to the tutorial before, to run the script, execute the following command: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/00_sim/spawn_prims.py + +Once the simulation starts, you should see a window with a ground plane, a light, some cones, and a table. +The green cone, which has rigid body physics enabled, should fall and collide with the table and the ground +plane. The other cones are visual elements and should not move. To stop the simulation, you can close the window, +or press ``Ctrl+C`` in the terminal. + +.. figure:: ../../_static/tutorials/tutorial_spawn_prims.jpg + :align: center + :figwidth: 100% + :alt: result of spawn_prims.py + +This tutorial provided a foundation for spawning various prims into the scene in Isaac Lab. Although simple, it +demonstrates the basic concepts of scene designing in Isaac Lab and how to use the spawners. In the coming tutorials, +we will now look at how to interact with the scene and the simulation. + + +.. _`USD documentation`: https://graphics.pixar.com/usd/docs/index.html +.. _`different light prims`: https://youtu.be/c7qyI8pZvF4?feature=shared diff --git a/docs/source/tutorials/01_assets/add_new_robot.rst b/docs/source/tutorials/01_assets/add_new_robot.rst new file mode 100644 index 0000000000000000000000000000000000000000..a4d258f82c15a7dbc89ec9a99ad3ce0ea95586bc --- /dev/null +++ b/docs/source/tutorials/01_assets/add_new_robot.rst @@ -0,0 +1,113 @@ +.. _tutorial-add-new-robot: + +Adding a New Robot to Isaac Lab +=============================== + +.. currentmodule:: isaaclab + +Simulating and training a new robot is a multi-step process that starts with importing the robot into Isaac Sim. +This is covered in depth in the Isaac Sim documentation `here `_. +Once the robot is imported and tuned for simulation, we must define those interfaces necessary to clone the robot across multiple environments, drive its joints, +and properly reset it, regardless of the chosen workflow or training framework. + +In this tutorial, we will examine how to add a new robot to Isaac Lab. The key step is creating an ``AssetBaseCfg`` that defines +the interface between the USD articulation of the robot and the learning algorithms available through Isaac Lab. + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``add_new_robot`` script in the ``scripts/tutorials/01_assets`` directory. + +.. dropdown:: Code for add_new_robot.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/01_assets/add_new_robot.py + :language: python + :linenos: + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Fundamentally, a robot is an articulation with joint drives. To move a robot around in the simulation, we must apply +targets to its drives and step the sim forward in time. However, to control a robot strictly through joint drives is tedious, especially if +you want to control anything complex, and doubly so if you want to clone the robot across multiple environments. + +To facilitate this, Isaac Lab provides a collection of ``configuration`` classes that define which parts of the USD need +to be cloned, which parts are actuators to be controlled by an agent, how it should be reset, etc... There are many ways +you can configure a single robot asset for Isaac Lab depending on how much fine tuning the asset requires. To demonstrate, +the tutorial script imports two robots: The first robot, the ``Jetbot``, is configured minimally while the second robot, the ``Dofbot``, is configured with additional parameters. + +The Jetbot is a simple, two wheeled differential base with a camera on top. The asset is used for a number of demonstrations and +tutorials in Isaac Sim, so we know it's good to go! To bring it into Isaac lab, we must first define one of these configurations. +Because a robot is an articulation with joint drives, we define an ``ArticulationCfg`` that describes the robot. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/add_new_robot.py + :language: python + :lines: 27-38 + +This is the minimal configuration for a robot in Isaac Lab. There are only two required parameters: ``spawn`` and ``actuators``. + +The ``spawn`` parameter is looking for a ``SpawnerCfg``, and is used to specify the USD asset that defines the robot in the sim. +The Isaac Lab simulation utilities, ``isaaclab.sim``, provides us with a ``USDFileCfg`` class that consumes a path to our USD +asset, and generates the ``SpawnerCfg`` we need. In this case, the ``jetbot.usd`` is located +with the `Isaac Assets `_ under ``Robots/Jetbot/jetbot.usd``. + +The ``actuators`` parameter is a dictionary of Actuator Configs and defines what parts of the robot we intend to control with an agent. +There are many different ways to update the state of a joint in time towards some target. Isaac Lab provides a collection of actuator +classes that can be used to match common actuator models or even implement your own! In this case, we are using the ``ImplicitActuatorCfg`` class to specify +the actuators for the robot, because they are simple wheels and the defaults are fine. + +Specifying joint name keys for this dictionary can be done to varying levels of specificity. +The jetbot only has a few joints, and we are just going to use the defaults specified in the USD asset, so we can use a simple regex, ``.*`` to specify all joints. +Other regex can also be used to group joints and associated configurations. + +.. note:: + + Both stiffness and damping must be specified in the implicit actuator, but a value of ``None`` will use the defaults defined in the USD asset. + +While this is the minimal configuration, there are a number of other parameters we could specify + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/add_new_robot.py + :language: python + :lines: 39-82 + +This configuration can be used to add a Dofbot to the scene, and it contains some of those parameters. +The Dofbot is a hobbiest robot arm with several joints, and so we have more options available for configuration. +The two most notable differences though is the addition of configurations for physics properties, and the initial state of the robot, ``init_state``. + +The ``USDFileCfg`` has special parameters for rigid bodies and robots, among others. The ``rigid_props`` parameter expects +a ``RigidBodyPropertiesCfg`` that allows you to specify body link properties of the robot being spawned relating to its behavior +as a "physical object" in the simulation. The ``articulation_props`` meanwhile governs the properties relating to the solver +being used to step the joints through time, and so it expects an ``ArticulationRootPropertiesCfg`` to be configured. +There are many other physics properties and parameters that can be specified through configurations provided by :class:`isaaclab.sim.schemas`. + +The ``ArticulationCfg`` can optionally include the ``init_state`` parameter, that defines the initial state of the articulation. +The initial state of an articulation is a special, user defined state that is used when the robot is spawned or reset by Isaac Lab. +The initial joint state, ``joint_pos``, is specified by a dictionary of floats with the USD joint names as keys (**not** the actuator names). +Something else worth noting here is the coordinate system of the initial position, ``pos``, which is that of the environment. +In this case, by specifying a position of ``(0.25, -0.25, 0.0)`` we are offsetting the spawn position of the robot **from the origin of the environment**, and not the world. + +Armed with the configurations for these robots, we can now add them to the scene and interact with them in the usual way +for the direct workflow: by defining an ``InteractiveSceneCfg`` containing the articulation configs for the robots ... + + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/add_new_robot.py + :language: python + :lines: 85 - 99 + + +...and then stepping the simulation while updating the scene entities appropriately. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/add_new_robot.py + :language: python + :lines: 101 - 158 + + +.. note:: + + You may see a warning that not all actuators are configured! This is expected because we don't handle the gripper for this tutorial. + +.. figure:: ../../_static/tutorials/tutorial_add_new_robot_result.jpg + :align: center + :figwidth: 100% + :alt: The new robots say hi! diff --git a/docs/source/tutorials/01_assets/run_articulation.rst b/docs/source/tutorials/01_assets/run_articulation.rst new file mode 100644 index 0000000000000000000000000000000000000000..6adf96d98d260ca1dcd543cdba0f6ae0d499a4da --- /dev/null +++ b/docs/source/tutorials/01_assets/run_articulation.rst @@ -0,0 +1,146 @@ +.. _tutorial-interact-articulation: + +Interacting with an articulation +================================ + +.. currentmodule:: isaaclab + + +This tutorial shows how to interact with an articulated robot in the simulation. It is a continuation of the +:ref:`tutorial-interact-rigid-object` tutorial, where we learned how to interact with a rigid object. +On top of setting the root state, we will see how to set the joint state and apply commands to the articulated +robot. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_articulation.py`` script in the ``scripts/tutorials/01_assets`` +directory. + +.. dropdown:: Code for run_articulation.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/01_assets/run_articulation.py + :language: python + :emphasize-lines: 58-69, 91-104, 108-111, 116-117 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Designing the scene +------------------- + +Similar to the previous tutorial, we populate the scene with a ground plane and a distant light. Instead of +spawning rigid objects, we now spawn a cart-pole articulation from its USD file. The cart-pole is a simple robot +consisting of a cart and a pole attached to it. The cart is free to move along the x-axis, and the pole is free to +rotate about the cart. The USD file for the cart-pole contains the robot's geometry, joints, and other physical +properties. + +For the cart-pole, we use its pre-defined configuration object, which is an instance of the +:class:`assets.ArticulationCfg` class. This class contains information about the articulation's spawning strategy, +default initial state, actuator models for different joints, and other meta-information. A deeper-dive into how to +create this configuration object is provided in the :ref:`how-to-write-articulation-config` tutorial. + +As seen in the previous tutorial, we can spawn the articulation into the scene in a similar fashion by creating +an instance of the :class:`assets.Articulation` class by passing the configuration object to its constructor. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_articulation.py + :language: python + :start-at: # Create separate groups called "Origin1", "Origin2" + :end-at: cartpole = Articulation(cfg=cartpole_cfg) + + +Running the simulation loop +--------------------------- + +Continuing from the previous tutorial, we reset the simulation at regular intervals, set commands to the articulation, +step the simulation, and update the articulation's internal buffers. + +Resetting the simulation +"""""""""""""""""""""""" + +Similar to a rigid object, an articulation also has a root state. This state corresponds to the root body in the +articulation tree. On top of the root state, an articulation also has joint states. These states correspond to the +joint positions and velocities. + +To reset the articulation, we first set the root state by calling the :meth:`Articulation.write_root_pose_to_sim` and :meth:`Articulation.write_root_velocity_to_sim` +methods. Similarly, we set the joint states by calling the :meth:`Articulation.write_joint_state_to_sim` method. +Finally, we call the :meth:`Articulation.reset` method to reset any internal buffers and caches. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_articulation.py + :language: python + :start-at: # reset the scene entities + :end-at: robot.reset() + +Stepping the simulation +""""""""""""""""""""""" + +Applying commands to the articulation involves two steps: + +1. *Setting the joint targets*: This sets the desired joint position, velocity, or effort targets for the articulation. +2. *Writing the data to the simulation*: Based on the articulation's configuration, this step handles any + :ref:`actuation conversions ` and writes the converted values to the PhysX buffer. + +In this tutorial, we control the articulation using joint effort commands. For this to work, we need to set the +articulation's stiffness and damping parameters to zero. This is done a-priori inside the cart-pole's pre-defined +configuration object. + +At every step, we randomly sample joint efforts and set them to the articulation by calling the +:meth:`Articulation.set_joint_effort_target` method. After setting the targets, we call the +:meth:`Articulation.write_data_to_sim` method to write the data to the PhysX buffer. Finally, we step +the simulation. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_articulation.py + :language: python + :start-at: # Apply random action + :end-at: robot.write_data_to_sim() + + +Updating the state +"""""""""""""""""" + +Every articulation class contains a :class:`assets.ArticulationData` object. This stores the state of the +articulation. To update the state inside the buffer, we call the :meth:`assets.Articulation.update` method. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_articulation.py + :language: python + :start-at: # Update buffers + :end-at: robot.update(sim_dt) + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + +To run the code and see the results, let's run the script from the terminal: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/01_assets/run_articulation.py + + +This command should open a stage with a ground plane, lights, and two cart-poles that are moving around randomly. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal. + +.. figure:: ../../_static/tutorials/tutorial_run_articulation.jpg + :align: center + :figwidth: 100% + :alt: result of run_articulation.py + +In this tutorial, we learned how to create and interact with a simple articulation. We saw how to set the state +of an articulation (its root and joint state) and how to apply commands to it. We also saw how to update its +buffers to read the latest state from the simulation. + +In addition to this tutorial, we also provide a few other scripts that spawn different robots. These are included +in the ``scripts/demos`` directory. You can run these scripts as: + +.. code-block:: bash + + # Spawn many different single-arm manipulators + ./isaaclab.sh -p scripts/demos/arms.py + + # Spawn many different quadrupeds + ./isaaclab.sh -p scripts/demos/quadrupeds.py diff --git a/docs/source/tutorials/01_assets/run_deformable_object.rst b/docs/source/tutorials/01_assets/run_deformable_object.rst new file mode 100644 index 0000000000000000000000000000000000000000..46489378ace112bf2ce90df63e043881f86c48b9 --- /dev/null +++ b/docs/source/tutorials/01_assets/run_deformable_object.rst @@ -0,0 +1,181 @@ +.. _tutorial-interact-deformable-object: + + +Interacting with a deformable object +==================================== + +.. currentmodule:: isaaclab + +While deformable objects sometimes refer to a broader class of objects, such as cloths, fluids and soft bodies, +in PhysX, deformable objects syntactically correspond to soft bodies. Unlike rigid objects, soft bodies can deform +under external forces and collisions. + +Soft bodies are simulated using Finite Element Method (FEM) in PhysX. The soft body comprises of two tetrahedral +meshes -- a simulation mesh and a collision mesh. The simulation mesh is used to simulate the deformations of +the soft body, while the collision mesh is used to detect collisions with other objects in the scene. +For more details, please check the `PhysX documentation`_. + +This tutorial shows how to interact with a deformable object in the simulation. We will spawn a +set of soft cubes and see how to set their nodal positions and velocities, along with apply kinematic +commands to the mesh nodes to move the soft body. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_deformable_object.py`` script in the ``scripts/tutorials/01_assets`` directory. + +.. dropdown:: Code for run_deformable_object.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :emphasize-lines: 61-73, 75-77, 102-110, 112-115, 117-118, 123-130, 132-133, 139-140 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Designing the scene +------------------- + +Similar to the :ref:`tutorial-interact-rigid-object` tutorial, we populate the scene with a ground plane +and a light source. In addition, we add a deformable object to the scene using the :class:`assets.DeformableObject` +class. This class is responsible for spawning the prims at the input path and initializes their corresponding +deformable body physics handles. + +In this tutorial, we create a cubical soft object using the spawn configuration similar to the deformable cube +in the :ref:`Spawn Objects ` tutorial. The only difference is that now we wrap +the spawning configuration into the :class:`assets.DeformableObjectCfg` class. This class contains information about +the asset's spawning strategy and default initial state. When this class is passed to +the :class:`assets.DeformableObject` class, it spawns the object and initializes the corresponding physics handles +when the simulation is played. + +.. note:: + The deformable object is only supported in GPU simulation and requires a mesh object to be spawned with the + deformable body physics properties on it. + + +As seen in the rigid body tutorial, we can spawn the deformable object into the scene in a similar fashion by creating +an instance of the :class:`assets.DeformableObject` class by passing the configuration object to its constructor. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # Create separate groups called "Origin1", "Origin2", "Origin3" + :end-at: cube_object = DeformableObject(cfg=cfg) + +Running the simulation loop +--------------------------- + +Continuing from the rigid body tutorial, we reset the simulation at regular intervals, apply kinematic commands +to the deformable body, step the simulation, and update the deformable object's internal buffers. + +Resetting the simulation state +"""""""""""""""""""""""""""""" + +Unlike rigid bodies and articulations, deformable objects have a different state representation. The state of a +deformable object is defined by the nodal positions and velocities of the mesh. The nodal positions and velocities +are defined in the **simulation world frame** and are stored in the :attr:`assets.DeformableObject.data` attribute. + +We use the :attr:`assets.DeformableObject.data.default_nodal_state_w` attribute to get the default nodal state of the +spawned object prims. This default state can be configured from the :attr:`assets.DeformableObjectCfg.init_state` +attribute, which we left as identity in this tutorial. + +.. attention:: + The initial state in the configuration :attr:`assets.DeformableObjectCfg` specifies the pose + of the deformable object at the time of spawning. Based on this initial state, the default nodal state is + obtained when the simulation is played for the first time. + +We apply transformations to the nodal positions to randomize the initial state of the deformable object. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # reset the nodal state of the object + :end-at: nodal_state[..., :3] = cube_object.transform_nodal_pos(nodal_state[..., :3], pos_w, quat_w) + +To reset the deformable object, we first set the nodal state by calling the :meth:`assets.DeformableObject.write_nodal_state_to_sim` +method. This method writes the nodal state of the deformable object prim into the simulation buffer. +Additionally, we free all the kinematic targets set for the nodes in the previous simulation step by calling +the :meth:`assets.DeformableObject.write_nodal_kinematic_target_to_sim` method. We explain the +kinematic targets in the next section. + +Finally, we call the :meth:`assets.DeformableObject.reset` method to reset any internal buffers and caches. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # write nodal state to simulation + :end-at: cube_object.reset() + +Stepping the simulation +""""""""""""""""""""""" + +Deformable bodies support user-driven kinematic control where a user can specify position targets for some of +the mesh nodes while the rest of the nodes are simulated using the FEM solver. This `partial kinematic`_ control +is useful for applications where the user wants to interact with the deformable object in a controlled manner. + +In this tutorial, we apply kinematic commands to two out of the four cubes in the scene. We set the position +targets for the node at index 0 (bottom-left corner) to move the cube along the z-axis. + +At every step, we increment the kinematic position target for the node by a small value. Additionally, +we set the flag to indicate that the target is a kinematic target for that node in the simulation buffer. +These are set into the simulation buffer by calling the :meth:`assets.DeformableObject.write_nodal_kinematic_target_to_sim` +method. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # update the kinematic target for cubes at index 0 and 3 + :end-at: cube_object.write_nodal_kinematic_target_to_sim(nodal_kinematic_target) + +Similar to the rigid object and articulation, we perform the :meth:`assets.DeformableObject.write_data_to_sim` method +before stepping the simulation. For deformable objects, this method does not apply any external forces to the object. +However, we keep this method for completeness and future extensions. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # write internal data to simulation + :end-at: cube_object.write_data_to_sim() + +Updating the state +"""""""""""""""""" + +After stepping the simulation, we update the internal buffers of the deformable object prims to reflect their new state +inside the :class:`assets.DeformableObject.data` attribute. This is done using the :meth:`assets.DeformableObject.update` method. + +At a fixed interval, we print the root position of the deformable object to the terminal. As mentioned +earlier, there is no concept of a root state for deformable objects. However, we compute the root position as +the average position of all the nodes in the mesh. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_deformable_object.py + :language: python + :start-at: # update buffers + :end-at: print(f"Root position (in world): {cube_object.data.root_pos_w[:, :3]}") + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/01_assets/run_deformable_object.py + + +This should open a stage with a ground plane, lights, and several green cubes. Two of the four cubes must be dropping +from a height and settling on to the ground. Meanwhile the other two cubes must be moving along the z-axis. You +should see a marker showing the kinematic target position for the nodes at the bottom-left corner of the cubes. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal + +.. figure:: ../../_static/tutorials/tutorial_run_deformable_object.jpg + :align: center + :figwidth: 100% + :alt: result of run_deformable_object.py + +This tutorial showed how to spawn deformable objects and wrap them in a :class:`DeformableObject` class to initialize their +physics handles which allows setting and obtaining their state. We also saw how to apply kinematic commands to the +deformable object to move the mesh nodes in a controlled manner. In the next tutorial, we will see how to create +a scene using the :class:`InteractiveScene` class. + +.. _PhysX documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/SoftBodies.html +.. _partial kinematic: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/SoftBodies.html#kinematic-soft-bodies diff --git a/docs/source/tutorials/01_assets/run_rigid_object.rst b/docs/source/tutorials/01_assets/run_rigid_object.rst new file mode 100644 index 0000000000000000000000000000000000000000..80232c76e7d5961f5fb0ce84a49d4d62b143ed1d --- /dev/null +++ b/docs/source/tutorials/01_assets/run_rigid_object.rst @@ -0,0 +1,153 @@ +.. _tutorial-interact-rigid-object: + + +Interacting with a rigid object +=============================== + +.. currentmodule:: isaaclab + +In the previous tutorials, we learned the essential workings of the standalone script and how to +spawn different objects (or *prims*) into the simulation. This tutorial shows how to create and interact +with a rigid object. For this, we will use the :class:`assets.RigidObject` class provided in Isaac Lab. + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_rigid_object.py`` script in the ``scripts/tutorials/01_assets`` directory. + +.. dropdown:: Code for run_rigid_object.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :emphasize-lines: 55-74, 76-78, 98-108, 111-112, 118-119, 132-134, 139-140 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +In this script, we split the ``main`` function into two separate functions, which highlight the two main +steps of setting up any simulation in the simulator: + +1. **Design scene**: As the name suggests, this part is responsible for adding all the prims to the scene. +2. **Run simulation**: This part is responsible for stepping the simulator, interacting with the prims + in the scene, e.g., changing their poses, and applying any commands to them. + +A distinction between these two steps is necessary because the second step only happens after the first step +is complete and the simulator is reset. Once the simulator is reset (which automatically plays the simulation), +no new (physics-enabled) prims should be added to the scene as it may lead to unexpected behaviors. However, +the prims can be interacted with through their respective handles. + + +Designing the scene +------------------- + +Similar to the previous tutorial, we populate the scene with a ground plane and a light source. In addition, +we add a rigid object to the scene using the :class:`assets.RigidObject` class. This class is responsible for +spawning the prims at the input path and initializes their corresponding rigid body physics handles. + +In this tutorial, we create a conical rigid object using the spawn configuration similar to the rigid cone +in the :ref:`Spawn Objects ` tutorial. The only difference is that now we wrap +the spawning configuration into the :class:`assets.RigidObjectCfg` class. This class contains information about +the asset's spawning strategy, default initial state, and other meta-information. When this class is passed to +the :class:`assets.RigidObject` class, it spawns the object and initializes the corresponding physics handles +when the simulation is played. + +As an example on spawning the rigid object prim multiple times, we create its parent Xform prims, +``/World/Origin{i}``, that correspond to different spawn locations. When the regex expression +``/World/Origin.*/Cone`` is passed to the :class:`assets.RigidObject` class, it spawns the rigid object prim at +each of the ``/World/Origin{i}`` locations. For instance, if ``/World/Origin1`` and ``/World/Origin2`` are +present in the scene, the rigid object prims are spawned at the locations ``/World/Origin1/Cone`` and +``/World/Origin2/Cone`` respectively. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :start-at: # Create separate groups called "Origin1", "Origin2", "Origin3" + :end-at: cone_object = RigidObject(cfg=cone_cfg) + +Since we want to interact with the rigid object, we pass this entity back to the main function. This entity +is then used to interact with the rigid object in the simulation loop. In later tutorials, we will see a more +convenient way to handle multiple scene entities using the :class:`scene.InteractiveScene` class. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :start-at: # return the scene information + :end-at: return scene_entities, origins + + +Running the simulation loop +--------------------------- + +We modify the simulation loop to interact with the rigid object to include three steps -- resetting the +simulation state at fixed intervals, stepping the simulation, and updating the internal buffers of the +rigid object. For the convenience of this tutorial, we extract the rigid object's entity from the scene +dictionary and store it in a variable. + +Resetting the simulation state +"""""""""""""""""""""""""""""" + +To reset the simulation state of the spawned rigid object prims, we need to set their pose and velocity. +Together they define the root state of the spawned rigid objects. It is important to note that this state +is defined in the **simulation world frame**, and not of their parent Xform prim. This is because the physics +engine only understands the world frame and not the parent Xform prim's frame. Thus, we need to transform +desired state of the rigid object prim into the world frame before setting it. + +We use the :attr:`assets.RigidObject.data.default_root_state` attribute to get the default root state of the +spawned rigid object prims. This default state can be configured from the :attr:`assets.RigidObjectCfg.init_state` +attribute, which we left as identity in this tutorial. We then randomize the translation of the root state and +set the desired state of the rigid object prim using the :meth:`assets.RigidObject.write_root_pose_to_sim` and :meth:`assets.RigidObject.write_root_velocity_to_sim` methods. +As the name suggests, this method writes the root state of the rigid object prim into the simulation buffer. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :start-at: # reset root state + :end-at: cone_object.reset() + +Stepping the simulation +""""""""""""""""""""""" + +Before stepping the simulation, we perform the :meth:`assets.RigidObject.write_data_to_sim` method. This method +writes other data, such as external forces, into the simulation buffer. In this tutorial, we do not apply any +external forces to the rigid object, so this method is not necessary. However, it is included for completeness. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :start-at: # apply sim data + :end-at: cone_object.write_data_to_sim() + +Updating the state +"""""""""""""""""" + +After stepping the simulation, we update the internal buffers of the rigid object prims to reflect their new state +inside the :class:`assets.RigidObject.data` attribute. This is done using the :meth:`assets.RigidObject.update` method. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_rigid_object.py + :language: python + :start-at: # update buffers + :end-at: cone_object.update(sim_dt) + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/01_assets/run_rigid_object.py + + +This should open a stage with a ground plane, lights, and several green cones. The cones must be dropping from +a random height and settling on to the ground. To stop the simulation, you can either close the window, or press +the ``STOP`` button in the UI, or press ``Ctrl+C`` in the terminal + +.. figure:: ../../_static/tutorials/tutorial_run_rigid_object.jpg + :align: center + :figwidth: 100% + :alt: result of run_rigid_object.py + + +This tutorial showed how to spawn rigid objects and wrap them in a :class:`RigidObject` class to initialize their +physics handles which allows setting and obtaining their state. In the next tutorial, we will see how to interact +with an articulated object which is a collection of rigid objects connected by joints. diff --git a/docs/source/tutorials/01_assets/run_surface_gripper.rst b/docs/source/tutorials/01_assets/run_surface_gripper.rst new file mode 100644 index 0000000000000000000000000000000000000000..402d8e0847018b6acbd1620d94a463f5e8936cbd --- /dev/null +++ b/docs/source/tutorials/01_assets/run_surface_gripper.rst @@ -0,0 +1,170 @@ +.. _tutorial-interact-surface-gripper: + +Interacting with a surface gripper +================================== + +.. currentmodule:: isaaclab + + +This tutorial shows how to interact with an articulated robot with a surface gripper attached to its end-effector in +the simulation. It is a continuation of the :ref:`tutorial-interact-articulation` tutorial, where we learned how to +interact with an articulated robot. Note that as of IsaacSim 5.0 the surface gripper are only supported on the cpu +backend. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_surface_gripper.py`` script in the ``scripts/tutorials/01_assets`` +directory. + +.. dropdown:: Code for run_surface_gripper.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/01_assets/run_surface_gripper.py + :language: python + :emphasize-lines: 61-85, 124-125, 128-142, 147-150 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Designing the scene +------------------- + +Similarly to the previous tutorial, we populate the scene with a ground plane and a distant light. Then, we spawn +an articulation from its USD file. This time a pick-and-place robot is spawned. The pick-and-place robot is a simple +robot with 3 driven axes, its gantry allows it to move along the x and y axes, as well as up and down along the z-axis. +Furthermore, the robot end-effector is outfitted with a surface gripper. +The USD file for the pick-and-place robot contains the robot's geometry, joints, and other physical properties +as well as the surface gripper. Before implementing a similar gripper on your own robot, we recommend to +check out the USD file for the gripper found on Isaaclab's Nucleus. + +For the pick-and-place robot, we use its pre-defined configuration object, you can find out more about it in the +:ref:`how-to-write-articulation-config` tutorial. For the surface gripper, we also need to create a configuration +object. This is done by instantiating a :class:`assets.SurfaceGripperCfg` object and passing it the relevant +parameters. + +The available parameters are: + +- ``max_grip_distance``: The maximum distance at which the gripper can grasp an object. +- ``shear_force_limit``: The maximum force the gripper can exert in the direction perpendicular to the gripper's axis. +- ``coaxial_force_limit``: The maximum force the gripper can exert in the direction of the gripper's axis. +- ``retry_interval``: The time the gripper will stay in a grasping state. + +As seen in the previous tutorial, we can spawn the articulation into the scene in a similar fashion by creating +an instance of the :class:`assets.Articulation` class by passing the configuration object to its constructor. The same +principle applies to the surface gripper. By passing the configuration object to the :class:`assets.SurfaceGripper` +constructor, the surface gripper is created and can be added to the scene. In practice, the object will only be +initialized when the play button is pressed. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_surface_gripper.py + :language: python + :start-at: # Create separate groups called "Origin1", "Origin2" + :end-at: surface_gripper = SurfaceGripper(cfg=surface_gripper_cfg) + + +Running the simulation loop +--------------------------- + +Continuing from the previous tutorial, we reset the simulation at regular intervals, set commands to the articulation, +step the simulation, and update the articulation's internal buffers. + +Resetting the simulation +"""""""""""""""""""""""" + +To reset the surface gripper, we only need to call the :meth:`SurfaceGripper.reset` method which will reset the +internal buffers and caches. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_surface_gripper.py + :language: python + :start-at: # Opens the gripper and makes sure the gripper is in the open state + :end-at: surface_gripper.reset() + +Stepping the simulation +""""""""""""""""""""""" + +Applying commands to the surface gripper involves two steps: + +1. *Setting the desired commands*: This sets the desired gripper commands (Open, Close, or Idle). +2. *Writing the data to the simulation*: Based on the surface gripper's configuration, this step handles writes the + converted values to the PhysX buffer. + +In this tutorial, we use a random command to set the gripper's command. The gripper behavior is as follows: + +- -1 < command < -0.3 --> Gripper is Opening +- -0.3 < command < 0.3 --> Gripper is Idle +- 0.3 < command < 1 --> Gripper is Closing + +At every step, we randomly sample commands and set them to the gripper by calling the +:meth:`SurfaceGripper.set_grippers_command` method. After setting the commands, we call the +:meth:`SurfaceGripper.write_data_to_sim` method to write the data to the PhysX buffer. Finally, we step +the simulation. + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_surface_gripper.py + :language: python + :start-at: # Sample a random command between -1 and 1. + :end-at: surface_gripper.write_data_to_sim() + + +Updating the state +"""""""""""""""""" + +To know the current state of the surface gripper, we can query the :meth:`assets.SurfaceGripper.state` property. +This property returns a tensor of size ``[num_envs]`` where each element is either ``-1``, ``0``, or ``1`` +corresponding to the gripper state. This property is updated every time the :meth:`assets.SurfaceGripper.update` method +is called. + +- ``-1`` --> Gripper is Open +- ``0`` --> Gripper is Closing +- ``1`` --> Gripper is Closed + +.. literalinclude:: ../../../../scripts/tutorials/01_assets/run_surface_gripper.py + :language: python + :start-at: # Read the gripper state from the simulation + :end-at: surface_gripper_state = surface_gripper.state + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + +To run the code and see the results, let's run the script from the terminal: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/01_assets/run_surface_gripper.py --device cpu + + +This command should open a stage with a ground plane, lights, and two pick-and-place robots. +In the terminal, you should see the gripper state and the command being printed. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal. + +.. figure:: ../../_static/tutorials/tutorial_run_surface_gripper.jpg + :align: center + :figwidth: 100% + :alt: result of run_surface_gripper.py + +In this tutorial, we learned how to create and interact with a surface gripper. We saw how to set commands and +query the gripper state. We also saw how to update its buffers to read the latest state from the simulation. + +In addition to this tutorial, we also provide a few other scripts that spawn different robots. These are included +in the ``scripts/demos`` directory. You can run these scripts as: + +.. code-block:: bash + + # Spawn many pick-and-place robots and perform a pick-and-place task + ./isaaclab.sh -p scripts/demos/pick_and_place.py + +Note that in practice, the users would be expected to register their :class:`assets.SurfaceGripper` instances inside +a :class:`isaaclab.InteractiveScene` object, which will automatically handle the calls to the +:meth:`assets.SurfaceGripper.write_data_to_sim` and :meth:`assets.SurfaceGripper.update` methods. + +.. code-block:: python + + # Create a scene + scene = InteractiveScene() + + # Register the surface gripper + scene.surface_grippers["gripper"] = surface_gripper diff --git a/docs/source/tutorials/02_scene/create_scene.rst b/docs/source/tutorials/02_scene/create_scene.rst new file mode 100644 index 0000000000000000000000000000000000000000..a2d34cf57e78bed7f6d0e16c9466cfebf777c3d1 --- /dev/null +++ b/docs/source/tutorials/02_scene/create_scene.rst @@ -0,0 +1,169 @@ +.. _tutorial-interactive-scene: + +Using the Interactive Scene +=========================== + +.. currentmodule:: isaaclab + +So far in the tutorials, we manually spawned assets into the simulation and created +object instances to interact with them. However, as the complexity of the scene +increases, it becomes tedious to perform these tasks manually. In this tutorial, +we will introduce the :class:`scene.InteractiveScene` class, which provides a convenient +interface for spawning prims and managing them in the simulation. + +At a high-level, the interactive scene is a collection of scene entities. Each entity +can be either a non-interactive prim (e.g. ground plane, light source), an interactive +prim (e.g. articulation, rigid object), or a sensor (e.g. camera, lidar). The interactive +scene provides a convenient interface for spawning these entities and managing them +in the simulation. + +Compared the manual approach, it provides the following benefits: + +* Alleviates the user needing to spawn each asset separately as this is handled implicitly. +* Enables user-friendly cloning of scene prims for multiple environments. +* Collects all the scene entities into a single object, which makes them easier to manage. + +In this tutorial, we take the cartpole example from the :ref:`tutorial-interact-articulation` +tutorial and replace the ``design_scene`` function with an :class:`scene.InteractiveScene` object. +While it may seem like overkill to use the interactive scene for this simple example, it will +become more useful in the future as more assets and sensors are added to the scene. + + +The Code +~~~~~~~~ + +This tutorial corresponds to the ``create_scene.py`` script within +``scripts/tutorials/02_scene``. + +.. dropdown:: Code for create_scene.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/02_scene/create_scene.py + :language: python + :emphasize-lines: 50-63, 68-70, 91-92, 99-100, 105-106, 116-118 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +While the code is similar to the previous tutorial, there are a few key differences +that we will go over in detail. + +Scene configuration +------------------- + +The scene is composed of a collection of entities, each with their own configuration. +These are specified in a configuration class that inherits from :class:`scene.InteractiveSceneCfg`. +The configuration class is then passed to the :class:`scene.InteractiveScene` constructor +to create the scene. + +For the cartpole example, we specify the same scene as in the previous tutorial, but list +them now in the configuration class :class:`CartpoleSceneCfg` instead of manually spawning them. + +.. literalinclude:: ../../../../scripts/tutorials/02_scene/create_scene.py + :language: python + :pyobject: CartpoleSceneCfg + +The variable names in the configuration class are used as keys to access the corresponding +entity from the :class:`scene.InteractiveScene` object. For example, the cartpole can +be accessed via ``scene["cartpole"]``. However, we will get to that later. First, let's +look at how individual scene entities are configured. + +Similar to how a rigid object and articulation were configured in the previous tutorials, +the configurations are specified using a configuration class. However, there is a key +difference between the configurations for the ground plane and light source and the +configuration for the cartpole. The ground plane and light source are non-interactive +prims, while the cartpole is an interactive prim. This distinction is reflected in the +configuration classes used to specify them. The configurations for the ground plane and +light source are specified using an instance of the :class:`assets.AssetBaseCfg` class +while the cartpole is configured using an instance of the :class:`assets.ArticulationCfg`. +Anything that is not an interactive prim (i.e., neither an asset nor a sensor) is not +*handled* by the scene during simulation steps. + +Another key difference to note is in the specification of the prim paths for the +different prims: + +* Ground plane: ``/World/defaultGroundPlane`` +* Light source: ``/World/Light`` +* Cartpole: ``{ENV_REGEX_NS}/Robot`` + +As we learned earlier, Omniverse creates a graph of prims in the USD stage. The prim +paths are used to specify the location of the prim in the graph. The ground plane and +light source are specified using absolute paths, while the cartpole is specified using +a relative path. The relative path is specified using the ``ENV_REGEX_NS`` variable, +which is a special variable that is replaced with the environment name during scene creation. +Any entity that has the ``ENV_REGEX_NS`` variable in its prim path will be cloned for each +environment. This path is replaced by the scene object with ``/World/envs/env_{i}`` where +``i`` is the environment index. + +Scene instantiation +------------------- + +Unlike before where we called the ``design_scene`` function to create the scene, we now +create an instance of the :class:`scene.InteractiveScene` class and pass in the configuration +object to its constructor. While creating the configuration instance of ``CartpoleSceneCfg`` +we specify how many environment copies we want to create using the ``num_envs`` argument. +This will be used to clone the scene for each environment. + +.. literalinclude:: ../../../../scripts/tutorials/02_scene/create_scene.py + :language: python + :start-at: # Design scene + :end-at: scene = InteractiveScene(scene_cfg) + +Accessing scene elements +------------------------ + +Similar to how entities were accessed from a dictionary in the previous tutorials, the +scene elements can be accessed from the :class:`InteractiveScene` object using the +``[]`` operator. The operator takes in a string key and returns the corresponding +entity. The key is specified through the configuration class for each entity. For example, +the cartpole is specified using the key ``"cartpole"`` in the configuration class. + +.. literalinclude:: ../../../../scripts/tutorials/02_scene/create_scene.py + :language: python + :start-at: # Extract scene entities + :end-at: robot = scene["cartpole"] + +Running the simulation loop +--------------------------- + +The rest of the script looks similar to previous scripts that interfaced with :class:`assets.Articulation`, +with a few small differences in the methods called: + +* :meth:`assets.Articulation.reset` ⟶ :meth:`scene.InteractiveScene.reset` +* :meth:`assets.Articulation.write_data_to_sim` ⟶ :meth:`scene.InteractiveScene.write_data_to_sim` +* :meth:`assets.Articulation.update` ⟶ :meth:`scene.InteractiveScene.update` + +Under the hood, the methods of :class:`scene.InteractiveScene` call the corresponding +methods of the entities in the scene. + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + + +Let's run the script to simulate 32 cartpoles in the scene. We can do this by passing +the ``--num_envs`` argument to the script. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/02_scene/create_scene.py --num_envs 32 + +This should open a stage with 32 cartpoles swinging around randomly. You can use the +mouse to rotate the camera and the arrow keys to move around the scene. + + +.. figure:: ../../_static/tutorials/tutorial_creating_a_scene.jpg + :align: center + :figwidth: 100% + :alt: result of create_scene.py + +In this tutorial, we saw how to use :class:`scene.InteractiveScene` to create a +scene with multiple assets. We also saw how to use the ``num_envs`` argument +to clone the scene for multiple environments. + +There are many more example usages of the :class:`scene.InteractiveSceneCfg` in the tasks found +under the ``isaaclab_tasks`` extension. Please check out the source code to see +how they are used for more complex scenes. diff --git a/docs/source/tutorials/03_envs/configuring_rl_training.rst b/docs/source/tutorials/03_envs/configuring_rl_training.rst new file mode 100644 index 0000000000000000000000000000000000000000..2eb2b0b5e763ca4b56b5d971d23469b2c056f8c6 --- /dev/null +++ b/docs/source/tutorials/03_envs/configuring_rl_training.rst @@ -0,0 +1,140 @@ +.. _tutorial-configure-rl-training: + +Configuring an RL Agent +======================= + +.. currentmodule:: isaaclab + +In the previous tutorial, we saw how to train an RL agent to solve the cartpole balancing task +using the `Stable-Baselines3`_ library. In this tutorial, we will see how to configure the +training process to use different RL libraries and different training algorithms. + +In the directory ``scripts/reinforcement_learning``, you will find the scripts for +different RL libraries. These are organized into subdirectories named after the library name. +Each subdirectory contains the training and playing scripts for the library. + +To configure a learning library with a specific task, you need to create a configuration file +for the learning agent. This configuration file is used to create an instance of the learning agent +and is used to configure the training process. Similar to the environment registration shown in +the :ref:`tutorial-register-rl-env-gym` tutorial, you can register the learning agent with the +``gymnasium.register`` method. + +The Code +-------- + +As an example, we will look at the configuration included for the task ``Isaac-Cartpole-v0`` +in the ``isaaclab_tasks`` package. This is the same task that we used in the +:ref:`tutorial-run-rl-training` tutorial. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/__init__.py + :language: python + :lines: 18-29 + +The Code Explained +------------------ + +Under the attribute ``kwargs``, we can see the configuration for the different learning libraries. +The key is the name of the library and the value is the path to the configuration instance. +This configuration instance can be a string, a class, or an instance of the class. +For example, the value of the key ``"rl_games_cfg_entry_point"`` is a string that points to the +configuration YAML file for the RL-Games library. Meanwhile, the value of the key +``"rsl_rl_cfg_entry_point"`` points to the configuration class for the RSL-RL library. + +The pattern used for specifying an agent configuration class follows closely to that used for +specifying the environment configuration entry point. This means that while the following +are equivalent: + + +.. dropdown:: Specifying the configuration entry point as a string + :icon: code + + .. code-block:: python + + from . import agents + + gym.register( + id="Isaac-Cartpole-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg", + }, + ) + +.. dropdown:: Specifying the configuration entry point as a class + :icon: code + + .. code-block:: python + + from . import agents + + gym.register( + id="Isaac-Cartpole-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg", + "rsl_rl_cfg_entry_point": agents.rsl_rl_ppo_cfg.CartpolePPORunnerCfg, + }, + ) + +The first code block is the preferred way to specify the configuration entry point. +The second code block is equivalent to the first one, but it leads to import of the configuration +class which slows down the import time. This is why we recommend using strings for the configuration +entry point. + +All the scripts in the ``scripts/reinforcement_learning`` directory are configured by default to read the +``_cfg_entry_point`` from the ``kwargs`` dictionary to retrieve the configuration instance. + +For instance, the following code block shows how the ``train.py`` script reads the configuration +instance for the Stable-Baselines3 library: + +.. dropdown:: Code for train.py with SB3 + :icon: code + + .. literalinclude:: ../../../../scripts/reinforcement_learning/sb3/train.py + :language: python + :emphasize-lines: 26-28, 102-103 + :linenos: + +The argument ``--agent`` is used to specify the learning library to use. This is used to +retrieve the configuration instance from the ``kwargs`` dictionary. You can manually specify +alternate configuration instances by passing the ``--agent`` argument. + +The Code Execution +------------------ + +Since for the cartpole balancing task, RSL-RL library offers two configuration instances, +we can use the ``--agent`` argument to specify the configuration instance to use. + +* Training with the standard PPO configuration: + + .. code-block:: bash + + # standard PPO training + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \ + --run_name ppo + +* Training with the PPO configuration with symmetry augmentation: + + .. code-block:: bash + + # PPO training with symmetry augmentation + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \ + --agent rsl_rl_with_symmetry_cfg_entry_point \ + --run_name ppo_with_symmetry_data_augmentation + + # you can use hydra to disable symmetry augmentation but enable mirror loss computation + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \ + --agent rsl_rl_with_symmetry_cfg_entry_point \ + --run_name ppo_without_symmetry_data_augmentation \ + agent.algorithm.symmetry_cfg.use_data_augmentation=false + +The ``--run_name`` argument is used to specify the name of the run. This is used to +create a directory for the run in the ``logs/rsl_rl/cartpole`` directory. + +.. _Stable-Baselines3: https://stable-baselines3.readthedocs.io/en/master/ +.. _RL-Games: https://github.com/Denys88/rl_games +.. _RSL-RL: https://github.com/leggedrobotics/rsl_rl +.. _SKRL: https://skrl.readthedocs.io diff --git a/docs/source/tutorials/03_envs/create_direct_rl_env.rst b/docs/source/tutorials/03_envs/create_direct_rl_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..05ef7a336620e1f74984628c386c3cd7eb320441 --- /dev/null +++ b/docs/source/tutorials/03_envs/create_direct_rl_env.rst @@ -0,0 +1,339 @@ +.. _tutorial-create-direct-rl-env: + + +Creating a Direct Workflow RL Environment +========================================= + +.. currentmodule:: isaaclab + +In addition to the :class:`envs.ManagerBasedRLEnv` class, which encourages the use of configuration classes +for more modular environments, the :class:`~isaaclab.envs.DirectRLEnv` class allows for more direct control +in the scripting of environment. + +Instead of using Manager classes for defining rewards and observations, the direct workflow tasks +implement the full reward and observation functions directly in the task script. +This allows for more control in the implementation of the methods, such as using pytorch jit +features, and provides a less abstracted framework that makes it easier to find the various +pieces of code. + +In this tutorial, we will configure the cartpole environment using the direct workflow implementation to create a task +for balancing the pole upright. We will learn how to specify the task using by implementing functions +for scene creation, actions, resets, rewards and observations. + + +The Code +~~~~~~~~ + +For this tutorial, we use the cartpole environment defined in ``isaaclab_tasks.direct.cartpole`` module. + +.. dropdown:: Code for cartpole_env.py + :icon: code + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Similar to the manager-based environments, a configuration class is defined for the task to hold settings +for the simulation parameters, the scene, the actors, and the task. With the direct workflow implementation, +the :class:`envs.DirectRLEnvCfg` class is used as the base class for configurations. +Since the direct workflow implementation does not use Action and Observation managers, the task +config should define the number of actions and observations for the environment. + +.. code-block:: python + + @configclass + class CartpoleEnvCfg(DirectRLEnvCfg): + ... + action_space = 1 + observation_space = 4 + state_space = 0 + +The config class can also be used to define task-specific attributes, such as scaling for reward terms +and thresholds for reset conditions. + +.. code-block:: python + + @configclass + class CartpoleEnvCfg(DirectRLEnvCfg): + ... + # reset + max_cart_pos = 3.0 + initial_pole_angle_range = [-0.25, 0.25] + + # reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_pole_pos = -1.0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_vel = -0.005 + +When creating a new environment, the code should define a new class that inherits from :class:`~isaaclab.envs.DirectRLEnv`. + +.. code-block:: python + + class CartpoleEnv(DirectRLEnv): + cfg: CartpoleEnvCfg + + def __init__(self, cfg: CartpoleEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + +The class can also hold class variables that are accessible by all functions in the class, +including functions for applying actions, computing resets, rewards, and observations. + +Scene Creation +-------------- + +In contrast to manager-based environments where the scene creation is taken care of by the framework, +the direct workflow implementation provides flexibility for users to implement their own scene creation +function. This includes adding actors into the stage, cloning the environments, filtering collisions +between the environments, adding the actors into the scene, and adding any additional props to the +scene, such as ground plane and lights. These operations should be implemented in the +``_setup_scene(self)`` method. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._setup_scene + +Defining Rewards +---------------- + +Reward function should be defined in the ``_get_rewards(self)`` API, which returns the reward +buffer as a return value. Within this function, the task is free to implement the logic of +the reward function. In this example, we implement a Pytorch JIT function that computes +the various components of the reward function. + +.. code-block:: python + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_vel, + self.joint_pos[:, self._pole_dof_idx[0]], + self.joint_vel[:, self._pole_dof_idx[0]], + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + self.reset_terminated, + ) + return total_reward + + @torch.jit.script + def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_pole_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_vel: float, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + reset_terminated: torch.Tensor, + ): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos), dim=-1) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel), dim=-1) + total_reward = rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel + return total_reward + + +Defining Observations +--------------------- + +The observation buffer should be computed in the ``_get_observations(self)`` function, +which constructs the observation buffer for the environment. At the end of this API, +a dictionary should be returned that contains ``policy`` as the key, and the full +observation buffer as the value. For asymmetric policies, the dictionary should also +include the key ``critic`` and the states buffer as the value. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._get_observations + +Computing Dones and Performing Resets +------------------------------------- + +Populating the ``dones`` buffer should be done in the ``_get_dones(self)`` method. +This method is free to implement logic that computes which environments would need to be reset +and which environments have reached the episode length limit. Both results should be +returned by the ``_get_dones(self)`` function, in the form of a tuple of boolean tensors. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._get_dones + +Once the indices for environments requiring reset have been computed, the ``_reset_idx(self, env_ids)`` +function performs the reset operations on those environments. Within this function, new states +for the environments requiring reset should be set directly into simulation. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._reset_idx + +Applying Actions +---------------- + +There are two APIs that are designed for working with actions. The ``_pre_physics_step(self, actions)`` takes in actions +from the policy as an argument and is called once per RL step, prior to taking any physics steps. This function can +be used to process the actions buffer from the policy and cache the data in a class variable for the environment. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._pre_physics_step + +The ``_apply_action(self)`` API is called ``decimation`` number of times for each RL step, prior to taking +each physics step. This provides more flexibility for environments where actions should be applied +for each physics step. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py + :language: python + :pyobject: CartpoleEnv._apply_action + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +To run training for the direct workflow Cartpole environment, we can use the following command: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 + +.. figure:: ../../_static/tutorials/tutorial_create_direct_workflow.jpg + :align: center + :figwidth: 100% + :alt: result of train.py + +All direct workflow tasks have the suffix ``-Direct`` added to the task name to differentiate the implementation style. + + +Domain Randomization +~~~~~~~~~~~~~~~~~~~~ + +In the direct workflow, domain randomization configuration uses the :class:`~isaaclab.utils.configclass` module +to specify a configuration class consisting of :class:`~managers.EventTermCfg` variables. + +Below is an example of a configuration class for domain randomization: + +.. code-block:: python + + @configclass + class EventCfg: + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + reset_gravity = EventTerm( + func=mdp.randomize_physics_scene_gravity, + mode="interval", + is_global_time=True, + interval_range_s=(36.0, 36.0), # time_s = num_steps * (decimation * dt) + params={ + "gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.4]), + "operation": "add", + "distribution": "gaussian", + }, + ) + +Each ``EventTerm`` object is of the :class:`~managers.EventTermCfg` class and takes in a ``func`` parameter +for specifying the function to call during randomization, a ``mode`` parameter, which can be ``startup``, +``reset`` or ``interval``. THe ``params`` dictionary should provide the necessary arguments to the +function that is specified in the ``func`` parameter. +Functions specified as ``func`` for the ``EventTerm`` can be found in the :class:`~envs.mdp.events` module. + +Note that as part of the ``"asset_cfg": SceneEntityCfg("robot", body_names=".*")`` parameter, the name of +the actor ``"robot"`` is provided, along with the body or joint names specified as a regex expression, +which will be the actors and bodies/joints that will have randomization applied. + +Once the ``configclass`` for the randomization terms have been set up, the class must be added +to the base config class for the task and be assigned to the variable ``events``. + +.. code-block:: python + + @configclass + class MyTaskConfig: + events: EventCfg = EventCfg() + + +Action and Observation Noise +---------------------------- + +Actions and observation noise can also be added using the :class:`~utils.configclass` module. +Action and observation noise configs must be added to the main task config using the +``action_noise_model`` and ``observation_noise_model`` variables: + +.. code-block:: python + + @configclass + class MyTaskConfig: + + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + action_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.05, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.015, operation="abs"), + ) + + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + observation_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.002, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.0001, operation="abs"), + ) + + +:class:`~.utils.noise.NoiseModelWithAdditiveBiasCfg` can be used to sample both uncorrelated noise +per step as well as correlated noise that is re-sampled at reset time. + +The ``noise_cfg`` term specifies the Gaussian distribution that will be sampled at each +step for all environments. This noise will be added to the corresponding actions and +observations buffers at every step. + +The ``bias_noise_cfg`` term specifies the Gaussian distribution for the correlated noise +that will be sampled at reset time for the environments being reset. The same noise +will be applied each step for the remaining of the episode for the environments and +resampled at the next reset. + +If only per-step noise is desired, :class:`~utils.noise.GaussianNoiseCfg` can be used +to specify an additive Gaussian distribution that adds the sampled noise to the input buffer. + +.. code-block:: python + + @configclass + class MyTaskConfig: + action_noise_model: GaussianNoiseCfg = GaussianNoiseCfg(mean=0.0, std=0.05, operation="add") + + + + +In this tutorial, we learnt how to create a direct workflow task environment for reinforcement learning. We do this +by extending the base environment to include the scene setup, actions, dones, reset, reward and observaion functions. + +While it is possible to manually create an instance of :class:`~isaaclab.envs.DirectRLEnv` class for a desired task, +this is not scalable as it requires specialized scripts for each task. Thus, we exploit the +:meth:`gymnasium.make` function to create the environment with the gym interface. We will learn how to do this +in the next tutorial. diff --git a/docs/source/tutorials/03_envs/create_manager_base_env.rst b/docs/source/tutorials/03_envs/create_manager_base_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..b8a0fc23534c4f4a645ccdfd259f664bfc65640f --- /dev/null +++ b/docs/source/tutorials/03_envs/create_manager_base_env.rst @@ -0,0 +1,220 @@ +.. _tutorial-create-manager-base-env: + + +Creating a Manager-Based Base Environment +========================================= + +.. currentmodule:: isaaclab + +Environments bring together different aspects of the simulation such as +the scene, observations and actions spaces, reset events etc. to create a +coherent interface for various applications. In Isaac Lab, manager-based environments are +implemented as :class:`envs.ManagerBasedEnv` and :class:`envs.ManagerBasedRLEnv` classes. +The two classes are very similar, but :class:`envs.ManagerBasedRLEnv` is useful for +reinforcement learning tasks and contains rewards, terminations, curriculum +and command generation. The :class:`envs.ManagerBasedEnv` class is useful for +traditional robot control and doesn't contain rewards and terminations. + +In this tutorial, we will look at the base class :class:`envs.ManagerBasedEnv` and its +corresponding configuration class :class:`envs.ManagerBasedEnvCfg` for the manager-based workflow. +We will use the +cartpole environment from earlier to illustrate the different components +in creating a new :class:`envs.ManagerBasedEnv` environment. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``create_cartpole_base_env`` script in the ``scripts/tutorials/03_envs`` +directory. + +.. dropdown:: Code for create_cartpole_base_env.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :emphasize-lines: 47-51, 54-71, 74-108, 111-130, 135-139, 144, 148, 153-154, 160-161 + :linenos: + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +The base class :class:`envs.ManagerBasedEnv` wraps around many intricacies of the simulation interaction +and provides a simple interface for the user to run the simulation and interact with it. It +is composed of the following components: + +* :class:`scene.InteractiveScene` - The scene that is used for the simulation. +* :class:`managers.ActionManager` - The manager that handles actions. +* :class:`managers.ObservationManager` - The manager that handles observations. +* :class:`managers.EventManager` - The manager that schedules operations (such as domain randomization) + at specified simulation events. For instance, at startup, on resets, or periodic intervals. + +By configuring these components, the user can create different variations of the same environment +with minimal effort. In this tutorial, we will go through the different components of the +:class:`envs.ManagerBasedEnv` class and how to configure them to create a new environment. + +Designing the scene +------------------- + +The first step in creating a new environment is to configure its scene. For the cartpole +environment, we will be using the scene from the previous tutorial. Thus, we omit the +scene configuration here. For more details on how to configure a scene, see +:ref:`tutorial-interactive-scene`. + +Defining actions +---------------- + +In the previous tutorial, we directly input the action to the cartpole using +the :meth:`assets.Articulation.set_joint_effort_target` method. In this tutorial, we will +use the :class:`managers.ActionManager` to handle the actions. + +The action manager can comprise of multiple :class:`managers.ActionTerm`. Each action term +is responsible for applying *control* over a specific aspect of the environment. For instance, +for robotic arm, we can have two action terms -- one for controlling the joints of the arm, +and the other for controlling the gripper. This composition allows the user to define +different control schemes for different aspects of the environment. + +In the cartpole environment, we want to control the force applied to the cart to balance the pole. +Thus, we will create an action term that controls the force applied to the cart. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :pyobject: ActionsCfg + +Defining observations +--------------------- + +While the scene defines the state of the environment, the observations define the states +that are observable by the agent. These observations are used by the agent to make decisions +on what actions to take. In Isaac Lab, the observations are computed by the +:class:`managers.ObservationManager` class. + +Similar to the action manager, the observation manager can comprise of multiple observation terms. +These are further grouped into observation groups which are used to define different observation +spaces for the environment. For instance, for hierarchical control, we may want to define +two observation groups -- one for the low level controller and the other for the high level +controller. It is assumed that all the observation terms in a group have the same dimensions. + +For this tutorial, we will only define one observation group named ``"policy"``. While not completely +prescriptive, this group is a necessary requirement for various wrappers in Isaac Lab. +We define a group by inheriting from the :class:`managers.ObservationGroupCfg` class. This class +collects different observation terms and help define common properties for the group, such +as enabling noise corruption or concatenating the observations into a single tensor. + +The individual terms are defined by inheriting from the :class:`managers.ObservationTermCfg` class. +This class takes in the :attr:`managers.ObservationTermCfg.func` that specifies the function or +callable class that computes the observation for that term. It includes other parameters for +defining the noise model, clipping, scaling, etc. However, we leave these parameters to their +default values for this tutorial. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :pyobject: ObservationsCfg + +Defining events +--------------- + +At this point, we have defined the scene, actions and observations for the cartpole environment. +The general idea for all these components is to define the configuration classes and then +pass them to the corresponding managers. The event manager is no different. + +The :class:`managers.EventManager` class is responsible for events corresponding to changes +in the simulation state. This includes resetting (or randomizing) the scene, randomizing physical +properties (such as mass, friction, etc.), and varying visual properties (such as colors, textures, etc.). +Each of these are specified through the :class:`managers.EventTermCfg` class, which +takes in the :attr:`managers.EventTermCfg.func` that specifies the function or callable +class that performs the event. + +Additionally, it expects the **mode** of the event. The mode specifies when the event term should be applied. +It is possible to specify your own mode. For this, you'll need to adapt the :class:`~envs.ManagerBasedEnv` class. +However, out of the box, Isaac Lab provides three commonly used modes: + +* ``"startup"`` - Event that takes place only once at environment startup. +* ``"reset"`` - Event that occurs on environment termination and reset. +* ``"interval"`` - Event that are executed at a given interval, i.e., periodically after a certain number of steps. + +For this example, we define events that randomize the pole's mass on startup. This is done only once since this +operation is expensive and we don't want to do it on every reset. We also create an event to randomize the initial +joint state of the cartpole and the pole at every reset. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :pyobject: EventCfg + +Tying it all together +--------------------- + +Having defined the scene and manager configurations, we can now define the environment configuration +through the :class:`envs.ManagerBasedEnvCfg` class. This class takes in the scene, action, observation and +event configurations. + +In addition to these, it also takes in the :attr:`envs.ManagerBasedEnvCfg.sim` which defines the simulation +parameters such as the timestep, gravity, etc. This is initialized to the default values, but can +be modified as needed. We recommend doing so by defining the :meth:`__post_init__` method in the +:class:`envs.ManagerBasedEnvCfg` class, which is called after the configuration is initialized. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :pyobject: CartpoleEnvCfg + +Running the simulation +---------------------- + +Lastly, we revisit the simulation execution loop. This is now much simpler since we have +abstracted away most of the details into the environment configuration. We only need to +call the :meth:`envs.ManagerBasedEnv.reset` method to reset the environment and :meth:`envs.ManagerBasedEnv.step` +method to step the environment. Both these functions return the observation and an info dictionary +which may contain additional information provided by the environment. These can be used by an +agent for decision-making. + +The :class:`envs.ManagerBasedEnv` class does not have any notion of terminations since that concept is +specific for episodic tasks. Thus, the user is responsible for defining the termination condition +for the environment. In this tutorial, we reset the simulation at regular intervals. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/create_cartpole_base_env.py + :language: python + :pyobject: main + +An important thing to note above is that the entire simulation loop is wrapped inside the +:meth:`torch.inference_mode` context manager. This is because the environment uses PyTorch +operations under-the-hood and we want to ensure that the simulation is not slowed down by +the overhead of PyTorch's autograd engine and gradients are not computed for the simulation +operations. + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +To run the base environment made in this tutorial, you can use the following command: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32 + +This should open a stage with a ground plane, light source, and cartpoles. The simulation should be +playing with random actions on the cartpole. Additionally, it opens a UI window on the bottom +right corner of the screen named ``"Isaac Lab"``. This window contains different UI elements that +can be used for debugging and visualization. + + +.. figure:: ../../_static/tutorials/tutorial_create_manager_rl_env.jpg + :align: center + :figwidth: 100% + :alt: result of create_cartpole_base_env.py + +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal where you +started the simulation. + +In this tutorial, we learned about the different managers that help define a base environment. We +include more examples of defining the base environment in the ``scripts/tutorials/03_envs`` +directory. For completeness, they can be run using the following commands: + +.. code-block:: bash + + # Floating cube environment with custom action term for PD control + ./isaaclab.sh -p scripts/tutorials/03_envs/create_cube_base_env.py --num_envs 32 + + # Quadrupedal locomotion environment with a policy that interacts with the environment + ./isaaclab.sh -p scripts/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32 + +In the following tutorial, we will look at the :class:`envs.ManagerBasedRLEnv` class and how to use it +to create a Markovian Decision Process (MDP). diff --git a/docs/source/tutorials/03_envs/create_manager_rl_env.rst b/docs/source/tutorials/03_envs/create_manager_rl_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..940bc60be243e8d38f5d18e1a64e20059cd030ee --- /dev/null +++ b/docs/source/tutorials/03_envs/create_manager_rl_env.rst @@ -0,0 +1,190 @@ +.. _tutorial-create-manager-rl-env: + + +Creating a Manager-Based RL Environment +======================================= + +.. currentmodule:: isaaclab + +Having learnt how to create a base environment in :ref:`tutorial-create-manager-base-env`, we will now look at how to create a manager-based +task environment for reinforcement learning. + +The base environment is designed as an sense-act environment where the agent can send commands to the environment +and receive observations from the environment. This minimal interface is sufficient for many applications such as +traditional motion planning and controls. However, many applications require a task-specification which often +serves as the learning objective for the agent. For instance, in a navigation task, the agent may be required to +reach a goal location. To this end, we use the :class:`envs.ManagerBasedRLEnv` class which extends the base environment +to include a task specification. + +Similar to other components in Isaac Lab, instead of directly modifying the base class :class:`envs.ManagerBasedRLEnv`, we +encourage users to simply implement a configuration :class:`envs.ManagerBasedRLEnvCfg` for their task environment. +This practice allows us to separate the task specification from the environment implementation, making it easier +to reuse components of the same environment for different tasks. + +In this tutorial, we will configure the cartpole environment using the :class:`envs.ManagerBasedRLEnvCfg` to create a manager-based task +for balancing the pole upright. We will learn how to specify the task using reward terms, termination criteria, +curriculum and commands. + + +The Code +~~~~~~~~ + +For this tutorial, we use the cartpole environment defined in ``isaaclab_tasks.manager_based.classic.cartpole`` module. + +.. dropdown:: Code for cartpole_env_cfg.py + :icon: code + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :emphasize-lines: 117-141, 144-154, 172-174 + :linenos: + +The script for running the environment ``run_cartpole_rl_env.py`` is present in the +``isaaclab/scripts/tutorials/03_envs`` directory. The script is similar to the +``cartpole_base_env.py`` script in the previous tutorial, except that it uses the +:class:`envs.ManagerBasedRLEnv` instead of the :class:`envs.ManagerBasedEnv`. + +.. dropdown:: Code for run_cartpole_rl_env.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/03_envs/run_cartpole_rl_env.py + :language: python + :emphasize-lines: 38-42, 56-57 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +We already went through parts of the above in the :ref:`tutorial-create-manager-base-env` tutorial to learn +about how to specify the scene, observations, actions and events. Thus, in this tutorial, we +will focus only on the RL components of the environment. + +In Isaac Lab, we provide various implementations of different terms in the :mod:`envs.mdp` module. We will use +some of these terms in this tutorial, but users are free to define their own terms as well. These +are usually placed in their task-specific sub-package +(for instance, in :mod:`isaaclab_tasks.manager_based.classic.cartpole.mdp`). + + +Defining rewards +---------------- + +The :class:`managers.RewardManager` is used to compute the reward terms for the agent. Similar to the other +managers, its terms are configured using the :class:`managers.RewardTermCfg` class. The +:class:`managers.RewardTermCfg` class specifies the function or callable class that computes the reward +as well as the weighting associated with it. It also takes in dictionary of arguments, ``"params"`` +that are passed to the reward function when it is called. + +For the cartpole task, we will use the following reward terms: + +* **Alive Reward**: Encourage the agent to stay alive for as long as possible. +* **Terminating Reward**: Similarly penalize the agent for terminating. +* **Pole Angle Reward**: Encourage the agent to keep the pole at the desired upright position. +* **Cart Velocity Reward**: Encourage the agent to keep the cart velocity as small as possible. +* **Pole Velocity Reward**: Encourage the agent to keep the pole velocity as small as possible. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :pyobject: RewardsCfg + +Defining termination criteria +----------------------------- + +Most learning tasks happen over a finite number of steps that we call an episode. For instance, in the cartpole +task, we want the agent to balance the pole for as long as possible. However, if the agent reaches an unstable +or unsafe state, we want to terminate the episode. On the other hand, if the agent is able to balance the pole +for a long time, we want to terminate the episode and start a new one so that the agent can learn to balance the +pole from a different starting configuration. + +The :class:`managers.TerminationsCfg` configures what constitutes for an episode to terminate. In this example, +we want the task to terminate when either of the following conditions is met: + +* **Episode Length** The episode length is greater than the defined max_episode_length +* **Cart out of bounds** The cart goes outside of the bounds [-3, 3] + +The flag :attr:`managers.TerminationsCfg.time_out` specifies whether the term is a time-out (truncation) term +or terminated term. These are used to indicate the two types of terminations as described in `Gymnasium's documentation +`_. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :pyobject: TerminationsCfg + +Defining commands +----------------- + +For various goal-conditioned tasks, it is useful to specify the goals or commands for the agent. These are +handled through the :class:`managers.CommandManager`. The command manager handles resampling and updating the +commands at each step. It can also be used to provide the commands as an observation to the agent. + +For this simple task, we do not use any commands. Hence, we leave this attribute as its default value, which is None. +You can see an example of how to define a command manager in the other locomotion or manipulation tasks. + +Defining curriculum +------------------- + +Often times when training a learning agent, it helps to start with a simple task and gradually increase the +tasks's difficulty as the agent training progresses. This is the idea behind curriculum learning. In Isaac Lab, +we provide a :class:`managers.CurriculumManager` class that can be used to define a curriculum for your environment. + +In this tutorial we don't implement a curriculum for simplicity, but you can see an example of a +curriculum definition in the other locomotion or manipulation tasks. + +Tying it all together +--------------------- + +With all the above components defined, we can now create the :class:`ManagerBasedRLEnvCfg` configuration for the +cartpole environment. This is similar to the :class:`ManagerBasedEnvCfg` defined in :ref:`tutorial-create-manager-base-env`, +only with the added RL components explained in the above sections. + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py + :language: python + :pyobject: CartpoleEnvCfg + +Running the simulation loop +--------------------------- + +Coming back to the ``run_cartpole_rl_env.py`` script, the simulation loop is similar to the previous tutorial. +The only difference is that we create an instance of :class:`envs.ManagerBasedRLEnv` instead of the +:class:`envs.ManagerBasedEnv`. Consequently, now the :meth:`envs.ManagerBasedRLEnv.step` method returns additional signals +such as the reward and termination status. The information dictionary also maintains logging of quantities +such as the reward contribution from individual terms, the termination status of each term, the episode length etc. + +.. literalinclude:: ../../../../scripts/tutorials/03_envs/run_cartpole_rl_env.py + :language: python + :pyobject: main + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + +Similar to the previous tutorial, we can run the environment by executing the ``run_cartpole_rl_env.py`` script. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/run_cartpole_rl_env.py --num_envs 32 + + +This should open a similar simulation as in the previous tutorial. However, this time, the environment +returns more signals that specify the reward and termination status. Additionally, the individual +environments reset themselves when they terminate based on the termination criteria specified in the +configuration. + +.. figure:: ../../_static/tutorials/tutorial_create_manager_rl_env.jpg + :align: center + :figwidth: 100% + :alt: result of run_cartpole_rl_env.py + +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal +where you started the simulation. + +In this tutorial, we learnt how to create a task environment for reinforcement learning. We do this +by extending the base environment to include the rewards, terminations, commands and curriculum terms. +We also learnt how to use the :class:`envs.ManagerBasedRLEnv` class to run the environment and receive various +signals from it. + +While it is possible to manually create an instance of :class:`envs.ManagerBasedRLEnv` class for a desired task, +this is not scalable as it requires specialized scripts for each task. Thus, we exploit the +:meth:`gymnasium.make` function to create the environment with the gym interface. We will learn how to do this +in the next tutorial. diff --git a/docs/source/tutorials/03_envs/modify_direct_rl_env.rst b/docs/source/tutorials/03_envs/modify_direct_rl_env.rst new file mode 100644 index 0000000000000000000000000000000000000000..e362a7b0e97df8b1a50edb4f9af6cd3df3479783 --- /dev/null +++ b/docs/source/tutorials/03_envs/modify_direct_rl_env.rst @@ -0,0 +1,133 @@ +.. _tutorial-modify-direct-rl-env: + + +Modifying an existing Direct RL Environment +=========================================== + +.. currentmodule:: isaaclab + +Having learnt how to create a task in :ref:`tutorial-create-direct-rl-env`, register it in :ref:`tutorial-register-rl-env-gym`, +and train it in :ref:`tutorial-run-rl-training`, we will now look at how to make minor modifications to an existing task. + +Sometimes it is necessary to create, due to complexity or variations from existing examples, tasks from scratch. However, in certain situations, +it is possible to start from the existing code and introduce minor changes, one by one, to transform them according to our needs. + +In this tutorial, we will make minor modifications to the direct workflow Humanoid task to change the simple +humanoid model to the Unitree H1 humanoid robot without affecting the original code. + + +The Base Code +~~~~~~~~~~~~~ + +For this tutorial, we start from the direct workflow Humanoid environment defined in ``isaaclab_tasks.direct.humanoid`` module. + +.. dropdown:: Code for humanoid_env.py + :icon: code + + .. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/humanoid_env.py + :language: python + :linenos: + + +The Changes Explained +~~~~~~~~~~~~~~~~~~~~~ + +Duplicating the file and registering a new task +----------------------------------------------- + +To avoid modifying the code of the existing task, we will make a copy of the file containing the Python +code and perform the modification on this copy. Then, in the Isaac Lab project +``source/isaaclab_tasks/isaaclab_tasks/direct/humanoid`` +folder we make a copy of the ``humanoid_env.py`` file and rename it to ``h1_env.py``. + +Open the ``h1_env.py`` file in a code editor and replace all the humanoid task name (``HumanoidEnv``) and its configuration +(``HumanoidEnvCfg``) instances to ``H1Env`` and ``H1EnvCfg`` respectively. +This is necessary to avoid name conflicts during import when registering the environment. + +Once the name change has been made, we proceed to add a new entry to register the task under the name ``Isaac-H1-Direct-v0``. +To do this, we modify the ``__init__.py`` file in the same working folder and add the following entry. +Refer to the :ref:`tutorial-register-rl-env-gym` tutorial for more details about environment registrations. + +.. hint:: + + If the changes in the task are minimal, it is very likely that the same RL library agent configurations can be used to train it successfully. + Otherwise, it is advisable to create new configuration files (adjusting their name during registration under the ``kwargs`` parameter) + to avoid altering the original configurations. + + +.. literalinclude:: ../../refs/snippets/tutorial_modify_direct_rl_env.py + :language: python + :start-after: [start-init-import] + :end-before: [end-init-import] + +.. literalinclude:: ../../refs/snippets/tutorial_modify_direct_rl_env.py + :language: python + :start-after: [start-init-register] + :end-before: [end-init-register] + +Changing the robot +------------------ + +The ``H1EnvCfg`` class (in the new created ``h1_env.py`` file) encapsulates the configuration values of the environment, +including the assets to be instantiated. Particularly in this example, the ``robot`` property holds the target articulation configuration. + +Since the Unitree H1 robot is included in the Isaac Lab assets extension (``isaaclab_assets``) we can just import it +and do the replacement directly (under the ``H1EnvCfg.robot`` property), as shown below. Note that we also need to modify the +``joint_gears`` property as it holds robot-specific configuration values. + +.. |franka-direct-link| replace:: `Isaac-Franka-Cabinet-Direct-v0 `__ + +.. hint:: + + If the target robot is not included in the Isaac Lab assets extension, it is possible to load and configure it, from a USD file, + by using the :class:`~isaaclab.assets.ArticulationCfg` class. + + * See the |franka-direct-link| source code for an example of loading and configuring a robot from a USD file. + * Refer to the `Importing a New Asset <../../how-to/import_new_asset.html>`_ tutorial for details on how to import an asset from URDF or MJCF file, and other formats. + +.. literalinclude:: ../../refs/snippets/tutorial_modify_direct_rl_env.py + :language: python + :start-after: [start-h1_env-import] + :end-before: [end-h1_env-import] + +.. literalinclude:: ../../refs/snippets/tutorial_modify_direct_rl_env.py + :language: python + :start-after: [start-h1_env-robot] + :end-before: [end-h1_env-robot] + +The robot changed, and with it the number of joints to control or the number of rigid bodies that compose the articulation, for example. +Therefore, it is also necessary to adjust other values in the environment configuration that depend on the characteristics of the robot, +such as the number of elements in the observation and action space. + +.. literalinclude:: ../../refs/snippets/tutorial_modify_direct_rl_env.py + :language: python + :start-after: [start-h1_env-spaces] + :end-before: [end-h1_env-spaces] + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +After the minor modification has been done, and similar to the previous tutorial, we can train on the task using one of the available RL workflows for such task. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-H1-Direct-v0 --headless + +When the training is finished, we can visualize the result with the following command. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal +where you started the simulation. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-H1-Direct-v0 --num_envs 64 + +.. figure:: ../../_static/tutorials/tutorial_modify_direct_rl_env.jpg + :align: center + :figwidth: 100% + :alt: result of training Isaac-H1-Direct-v0 task + +In this tutorial, we learnt how to make minor modifications to an existing environment without affecting the original code. + +It is important to note, however, that while the changes to be made may be small, they may not always work on the first try, +as there may be deeper dependencies on the original assets in the environment being modified. +In these cases, it is advisable to analyze the code of the available examples in detail in order to make an appropriate adjustment. diff --git a/docs/source/tutorials/03_envs/policy_inference_in_usd.rst b/docs/source/tutorials/03_envs/policy_inference_in_usd.rst new file mode 100644 index 0000000000000000000000000000000000000000..fa004352610b8f3a4366f5a3f14b4ac752e4bfe6 --- /dev/null +++ b/docs/source/tutorials/03_envs/policy_inference_in_usd.rst @@ -0,0 +1,74 @@ +.. _tutorial-policy-inference-in-usd: + + +Policy Inference in USD Environment +=================================== + +.. currentmodule:: isaaclab + +Having learnt how to modify a task in :ref:`tutorial-modify-direct-rl-env`, we will now look at how to run a trained policy in a prebuilt USD scene. + +In this tutorial, we will use the RSL RL library and the trained policy from the Humanoid Rough Terrain ``Isaac-Velocity-Rough-H1-v0`` task in a simple warehouse USD. + + +The Tutorial Code +~~~~~~~~~~~~~~~~~ + +For this tutorial, we use the trained policy's checkpoint exported as jit (which is an offline version of the policy). + +The ``H1RoughEnvCfg_PLAY`` cfg encapsulates the configuration values of the inference environment, including the assets to +be instantiated. + +In order to use a prebuilt USD environment instead of the terrain generator specified, we make the +following changes to the config before passing it to the ``ManagerBasedRLEnv``. + +.. dropdown:: Code for policy_inference_in_usd.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/03_envs/policy_inference_in_usd.py + :language: python + :linenos: + :emphasize-lines: 60-69 + + +Note that we have set the device to ``CPU`` and disabled the use of Fabric for inferencing. +This is because when simulating a small number of environment, CPU simulation can often perform faster than GPU simulation. + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +First, we need to train the ``Isaac-Velocity-Rough-H1-v0`` task by running the following: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Rough-H1-v0 --headless + +When the training is finished, we can visualize the result with the following command. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal +where you started the simulation. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Velocity-Rough-H1-v0 --num_envs 64 --checkpoint logs/rsl_rl/h1_rough/EXPERIMENT_NAME/POLICY_FILE.pt + + +After running the play script, the policy will be exported to jit and onnx files under the experiment logs directory. +Note that not all learning libraries support exporting the policy to a jit or onnx file. +For libraries that don't currently support this functionality, please refer to the corresponding ``play.py`` script for the library +to learn about how to initialize the policy. + +We can then load the warehouse asset and run inference on the H1 robot using the exported jit policy +(``policy.pt`` file in the ``exported/`` directory). + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/policy_inference_in_usd.py --checkpoint logs/rsl_rl/h1_rough/EXPERIMENT_NAME/exported/policy.pt + + +.. figure:: ../../_static/tutorials/tutorial_policy_inference_in_usd.jpg + :align: center + :figwidth: 100% + :alt: result of training Isaac-H1-Direct-v0 task + +In this tutorial, we learnt how to make minor modifications to an existing environment config to run policy inference in a prebuilt usd environment. diff --git a/docs/source/tutorials/03_envs/register_rl_env_gym.rst b/docs/source/tutorials/03_envs/register_rl_env_gym.rst new file mode 100644 index 0000000000000000000000000000000000000000..53e653a42755c596e10e41a8dd7b0e58fb49f244 --- /dev/null +++ b/docs/source/tutorials/03_envs/register_rl_env_gym.rst @@ -0,0 +1,172 @@ +.. _tutorial-register-rl-env-gym: + +Registering an Environment +========================== + +.. currentmodule:: isaaclab + +In the previous tutorial, we learned how to create a custom cartpole environment. We manually +created an instance of the environment by importing the environment class and its configuration +class. + +.. dropdown:: Environment creation in the previous tutorial + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/03_envs/run_cartpole_rl_env.py + :language: python + :start-at: # create environment configuration + :end-at: env = ManagerBasedRLEnv(cfg=env_cfg) + +While straightforward, this approach is not scalable as we have a large suite of environments. +In this tutorial, we will show how to use the :meth:`gymnasium.register` method to register +environments with the ``gymnasium`` registry. This allows us to create the environment through +the :meth:`gymnasium.make` function. + + +.. dropdown:: Environment creation in this tutorial + :icon: code + + .. literalinclude:: ../../../../scripts/environments/random_agent.py + :language: python + :lines: 36-47 + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``random_agent.py`` script in the ``scripts/environments`` directory. + +.. dropdown:: Code for random_agent.py + :icon: code + + .. literalinclude:: ../../../../scripts/environments/random_agent.py + :language: python + :emphasize-lines: 36-37, 42-47 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +The :class:`envs.ManagerBasedRLEnv` class inherits from the :class:`gymnasium.Env` class to follow +a standard interface. However, unlike the traditional Gym environments, the :class:`envs.ManagerBasedRLEnv` +implements a *vectorized* environment. This means that multiple environment instances +are running simultaneously in the same process, and all the data is returned in a batched +fashion. + +Similarly, the :class:`envs.DirectRLEnv` class also inherits from the :class:`gymnasium.Env` class +for the direct workflow. For :class:`envs.DirectMARLEnv`, although it does not inherit +from Gymnasium, it can be registered and created in the same way. + +Using the gym registry +---------------------- + +To register an environment, we use the :meth:`gymnasium.register` method. This method takes +in the environment name, the entry point to the environment class, and the entry point to the +environment configuration class. + +.. note:: + The :mod:`gymnasium` registry is a global registry. Hence, it is important to ensure that the + environment names are unique. Otherwise, the registry will throw an error when registering + the environment. + +Manager-Based Environments +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +For manager-based environments, the following shows the registration +call for the cartpole environment in the ``isaaclab_tasks.manager_based.classic.cartpole`` sub-package: + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/__init__.py + :language: python + :lines: 10- + :emphasize-lines: 4, 11, 12, 15 + +The ``id`` argument is the name of the environment. As a convention, we name all the environments +with the prefix ``Isaac-`` to make it easier to search for them in the registry. The name of the +environment is typically followed by the name of the task, and then the name of the robot. +For instance, for legged locomotion with ANYmal C on flat terrain, the environment is called +``Isaac-Velocity-Flat-Anymal-C-v0``. The version number ``v`` is typically used to specify different +variations of the same environment. Otherwise, the names of the environments can become too long +and difficult to read. + +The ``entry_point`` argument is the entry point to the environment class. The entry point is a string +of the form ``:``. In the case of the cartpole environment, the entry point is +``isaaclab.envs:ManagerBasedRLEnv``. The entry point is used to import the environment class +when creating the environment instance. + +The ``env_cfg_entry_point`` argument specifies the default configuration for the environment. The default +configuration is loaded using the :meth:`isaaclab_tasks.utils.parse_env_cfg` function. +It is then passed to the :meth:`gymnasium.make` function to create the environment instance. +The configuration entry point can be both a YAML file or a python configuration class. + +Direct Environments +^^^^^^^^^^^^^^^^^^^ + +For direct-based environments, the environment registration follows a similar pattern. Instead of +registering the environment's entry point as the :class:`~isaaclab.envs.ManagerBasedRLEnv` class, +we register the environment's entry point as the implementation class of the environment. +Additionally, we add the suffix ``-Direct`` to the environment name to differentiate it from the +manager-based environments. + +As an example, the following shows the registration call for the cartpole environment in the +``isaaclab_tasks.direct.cartpole`` sub-package: + +.. literalinclude:: ../../../../source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/__init__.py + :language: python + :lines: 10-31 + :emphasize-lines: 5, 12, 13, 16 + + +Creating the environment +------------------------ + +To inform the ``gym`` registry with all the environments provided by the ``isaaclab_tasks`` +extension, we must import the module at the start of the script. This will execute the ``__init__.py`` +file which iterates over all the sub-packages and registers their respective environments. + +.. literalinclude:: ../../../../scripts/environments/random_agent.py + :language: python + :start-at: import isaaclab_tasks # noqa: F401 + :end-at: import isaaclab_tasks # noqa: F401 + +In this tutorial, the task name is read from the command line. The task name is used to parse +the default configuration as well as to create the environment instance. In addition, other +parsed command line arguments such as the number of environments, the simulation device, +and whether to render, are used to override the default configuration. + +.. literalinclude:: ../../../../scripts/environments/random_agent.py + :language: python + :start-at: # create environment configuration + :end-at: env = gym.make(args_cli.task, cfg=env_cfg) + +Once creating the environment, the rest of the execution follows the standard resetting and stepping. + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/random_agent.py --task Isaac-Cartpole-v0 --num_envs 32 + + +This should open a stage with everything similar to the :ref:`tutorial-create-manager-rl-env` tutorial. +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal. + + +.. figure:: ../../_static/tutorials/tutorial_register_environment.jpg + :align: center + :figwidth: 100% + :alt: result of random_agent.py + + +In addition, you can also change the simulation device from GPU to CPU by setting the value of the ``--device`` flag explicitly: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/random_agent.py --task Isaac-Cartpole-v0 --num_envs 32 --device cpu + +With the ``--device cpu`` flag, the simulation will run on the CPU. This is useful for debugging the simulation. +However, the simulation will run much slower than on the GPU. diff --git a/docs/source/tutorials/03_envs/run_rl_training.rst b/docs/source/tutorials/03_envs/run_rl_training.rst new file mode 100644 index 0000000000000000000000000000000000000000..c11dc8f27c07cbbedd691f1bf48e2254ea18db36 --- /dev/null +++ b/docs/source/tutorials/03_envs/run_rl_training.rst @@ -0,0 +1,156 @@ +.. _tutorial-run-rl-training: + +Training with an RL Agent +========================= + +.. currentmodule:: isaaclab + +In the previous tutorials, we covered how to define an RL task environment, register +it into the ``gym`` registry, and interact with it using a random agent. We now move +on to the next step: training an RL agent to solve the task. + +Although the :class:`envs.ManagerBasedRLEnv` conforms to the :class:`gymnasium.Env` interface, +it is not exactly a ``gym`` environment. The input and outputs of the environment are +not numpy arrays, but rather based on torch tensors with the first dimension being the +number of environment instances. + +Additionally, most RL libraries expect their own variation of an environment interface. +For example, `Stable-Baselines3`_ expects the environment to conform to its +`VecEnv API`_ which expects a list of numpy arrays instead of a single tensor. Similarly, +`RSL-RL`_, `RL-Games`_ and `SKRL`_ expect a different interface. Since there is no one-size-fits-all +solution, we do not base the :class:`envs.ManagerBasedRLEnv` on any particular learning library. +Instead, we implement wrappers to convert the environment into the expected interface. +These are specified in the :mod:`isaaclab_rl` module. + +In this tutorial, we will use `Stable-Baselines3`_ to train an RL agent to solve the +cartpole balancing task. + +.. caution:: + + Wrapping the environment with the respective learning framework's wrapper should happen in the end, + i.e. after all other wrappers have been applied. This is because the learning framework's wrapper + modifies the interpretation of environment's APIs which may no longer be compatible with :class:`gymnasium.Env`. + +The Code +-------- + +For this tutorial, we use the training script from `Stable-Baselines3`_ workflow in the +``scripts/reinforcement_learning/sb3`` directory. + +.. dropdown:: Code for train.py + :icon: code + + .. literalinclude:: ../../../../scripts/reinforcement_learning/sb3/train.py + :language: python + :emphasize-lines: 57, 66, 68-70, 81, 90-98, 100, 105-113, 115-116, 121-126, 133-136 + :linenos: + +The Code Explained +------------------ + +.. currentmodule:: isaaclab_rl.utils + +Most of the code above is boilerplate code to create logging directories, saving the parsed configurations, +and setting up different Stable-Baselines3 components. For this tutorial, the important part is creating +the environment and wrapping it with the Stable-Baselines3 wrapper. + +There are three wrappers used in the code above: + +1. :class:`gymnasium.wrappers.RecordVideo`: This wrapper records a video of the environment + and saves it to the specified directory. This is useful for visualizing the agent's behavior + during training. +2. :class:`wrappers.sb3.Sb3VecEnvWrapper`: This wrapper converts the environment + into a Stable-Baselines3 compatible environment. +3. `stable_baselines3.common.vec_env.VecNormalize`_: This wrapper normalizes the + environment's observations and rewards. + +Each of these wrappers wrap around the previous wrapper by following ``env = wrapper(env, *args, **kwargs)`` +repeatedly. The final environment is then used to train the agent. For more information on how these +wrappers work, please refer to the :ref:`how-to-env-wrappers` documentation. + +The Code Execution +------------------ + +We train a PPO agent from Stable-Baselines3 to solve the cartpole balancing task. + +Training the agent +~~~~~~~~~~~~~~~~~~ + +There are three main ways to train the agent. Each of them has their own advantages and disadvantages. +It is up to you to decide which one you prefer based on your use case. + +Headless execution +"""""""""""""""""" + +If the ``--headless`` flag is set, the simulation is not rendered during training. This is useful +when training on a remote server or when you do not want to see the simulation. Typically, it speeds +up the training process since only physics simulation step is performed. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64 --headless + + +Headless execution with off-screen render +""""""""""""""""""""""""""""""""""""""""" + +Since the above command does not render the simulation, it is not possible to visualize the agent's +behavior during training. To visualize the agent's behavior, we pass the ``--enable_cameras`` which +enables off-screen rendering. Additionally, we pass the flag ``--video`` which records a video of the +agent's behavior during training. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64 --headless --video + +The videos are saved to the ``logs/sb3/Isaac-Cartpole-v0//videos/train`` directory. You can open these videos +using any video player. + +Interactive execution +""""""""""""""""""""" + +.. currentmodule:: isaaclab + +While the above two methods are useful for training the agent, they don't allow you to interact with the +simulation to see what is happening. In this case, you can ignore the ``--headless`` flag and run the +training script as follows: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64 + +This will open the Isaac Sim window and you can see the agent training in the environment. However, this +will slow down the training process since the simulation is rendered on the screen. As a workaround, you +can switch between different render modes in the ``"Isaac Lab"`` window that is docked on the bottom-right +corner of the screen. To learn more about these render modes, please check the +:class:`sim.SimulationContext.RenderMode` class. + +Viewing the logs +~~~~~~~~~~~~~~~~ + +On a separate terminal, you can monitor the training progress by executing the following command: + +.. code:: bash + + # execute from the root directory of the repository + ./isaaclab.sh -p -m tensorboard.main --logdir logs/sb3/Isaac-Cartpole-v0 + +Playing the trained agent +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once the training is complete, you can visualize the trained agent by executing the following command: + +.. code:: bash + + # execute from the root directory of the repository + ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Cartpole-v0 --num_envs 32 --use_last_checkpoint + +The above command will load the latest checkpoint from the ``logs/sb3/Isaac-Cartpole-v0`` +directory. You can also specify a specific checkpoint by passing the ``--checkpoint`` flag. + +.. _Stable-Baselines3: https://stable-baselines3.readthedocs.io/en/master/ +.. _VecEnv API: https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html#vecenv-api-vs-gym-api +.. _`stable_baselines3.common.vec_env.VecNormalize`: https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html#vecnormalize +.. _RL-Games: https://github.com/Denys88/rl_games +.. _RSL-RL: https://github.com/leggedrobotics/rsl_rl +.. _SKRL: https://skrl.readthedocs.io diff --git a/docs/source/tutorials/04_sensors/add_sensors_on_robot.rst b/docs/source/tutorials/04_sensors/add_sensors_on_robot.rst new file mode 100644 index 0000000000000000000000000000000000000000..e5815e800a55c7ef9925de27642946884cac3910 --- /dev/null +++ b/docs/source/tutorials/04_sensors/add_sensors_on_robot.rst @@ -0,0 +1,210 @@ +.. _tutorial-add-sensors-on-robot: + +Adding sensors on a robot +========================= + +.. currentmodule:: isaaclab + + +While the asset classes allow us to create and simulate the physical embodiment of the robot, +sensors help in obtaining information about the environment. They typically update at a lower +frequency than the simulation and are useful for obtaining different proprioceptive and +exteroceptive information. For example, a camera sensor can be used to obtain the visual +information of the environment, and a contact sensor can be used to obtain the contact +information of the robot with the environment. + +In this tutorial, we will see how to add different sensors to a robot. We will use the +ANYmal-C robot for this tutorial. The ANYmal-C robot is a quadrupedal robot with 12 degrees +of freedom. It has 4 legs, each with 3 degrees of freedom. The robot has the following +sensors: + +- A camera sensor on the head of the robot which provides RGB-D images +- A height scanner sensor that provides terrain height information +- Contact sensors on the feet of the robot that provide contact information + +We continue this tutorial from the previous tutorial on :ref:`tutorial-interactive-scene`, +where we learned about the :class:`scene.InteractiveScene` class. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``add_sensors_on_robot.py`` script in the +``scripts/tutorials/04_sensors`` directory. + +.. dropdown:: Code for add_sensors_on_robot.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :emphasize-lines: 72-95, 143-153, 167-168 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +Similar to the previous tutorials, where we added assets to the scene, the sensors are also added +to the scene using the scene configuration. All sensors inherit from the :class:`sensors.SensorBase` class +and are configured through their respective config classes. Each sensor instance can define its own +update period, which is the frequency at which the sensor is updated. The update period is specified +in seconds through the :attr:`sensors.SensorBaseCfg.update_period` attribute. + +Depending on the specified path and the sensor type, the sensors are attached to the prims in the scene. +They may have an associated prim that is created in the scene or they may be attached to an existing prim. +For instance, the camera sensor has a corresponding prim that is created in the scene, whereas for the +contact sensor, the activating the contact reporting is a property on a rigid body prim. + +In the following, we introduce the different sensors we use in this tutorial and how they are configured. +For more description about them, please check the :mod:`sensors` module. + +Camera sensor +------------- + +A camera is defined using the :class:`sensors.CameraCfg`. It is based on the USD Camera sensor and +the different data types are captured using Omniverse Replicator API. Since it has a corresponding prim +in the scene, the prims are created in the scene at the specified prim path. + +The configuration of the camera sensor includes the following parameters: + +* :attr:`~sensors.CameraCfg.spawn`: The type of USD camera to create. This can be either + :class:`~sim.spawners.sensors.PinholeCameraCfg` or :class:`~sim.spawners.sensors.FisheyeCameraCfg`. +* :attr:`~sensors.CameraCfg.offset`: The offset of the camera sensor from the parent prim. +* :attr:`~sensors.CameraCfg.data_types`: The data types to capture. This can be ``rgb``, + ``distance_to_image_plane``, ``normals``, or other types supported by the USD Camera sensor. + +To attach an RGB-D camera sensor to the head of the robot, we specify an offset relative to the base +frame of the robot. The offset is specified as a translation and rotation relative to the base frame, +and the :attr:`~sensors.CameraCfg.OffsetCfg.convention` in which the offset is specified. + +In the following, we show the configuration of the camera sensor used in this tutorial. We set the +update period to 0.1s, which means that the camera sensor is updated at 10Hz. The prim path expression is +set to ``{ENV_REGEX_NS}/Robot/base/front_cam`` where the ``{ENV_REGEX_NS}`` is the environment namespace, +``"Robot"`` is the name of the robot, ``"base"`` is the name of the prim to which the camera is attached, +and ``"front_cam"`` is the name of the prim associated with the camera sensor. + +.. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :start-at: camera = CameraCfg( + :end-before: height_scanner = RayCasterCfg( + +Height scanner +-------------- + +The height-scanner is implemented as a virtual sensor using the NVIDIA Warp ray-casting kernels. +Through the :class:`sensors.RayCasterCfg`, we can specify the pattern of rays to cast and the +meshes against which to cast the rays. Since they are virtual sensors, there is no corresponding +prim created in the scene for them. Instead they are attached to a prim in the scene, which is +used to specify the location of the sensor. + +For this tutorial, the ray-cast based height scanner is attached to the base frame of the robot. +The pattern of rays is specified using the :attr:`~sensors.RayCasterCfg.pattern` attribute. For +a uniform grid pattern, we specify the pattern using :class:`~sensors.patterns.GridPatternCfg`. +Since we only care about the height information, we do not need to consider the roll and pitch +of the robot. Hence, we set the :attr:`~sensors.RayCasterCfg.ray_alignment` to "yaw". + +For the height-scanner, you can visualize the points where the rays hit the mesh. This is done +by setting the :attr:`~sensors.SensorBaseCfg.debug_vis` attribute to true. + +The entire configuration of the height-scanner is as follows: + +.. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :start-at: height_scanner = RayCasterCfg( + :end-before: contact_forces = ContactSensorCfg( + +Contact sensor +-------------- + +Contact sensors wrap around the PhysX contact reporting API to obtain the contact information of the robot +with the environment. Since it relies of PhysX, the contact sensor expects the contact reporting API +to be enabled on the rigid bodies of the robot. This can be done by setting the +:attr:`~sim.spawners.RigidObjectSpawnerCfg.activate_contact_sensors` to true in the asset configuration. + +Through the :class:`sensors.ContactSensorCfg`, it is possible to specify the prims for which we want to +obtain the contact information. Additional flags can be set to obtain more information about +the contact, such as the contact air time, contact forces between filtered prims, etc. + +In this tutorial, we attach the contact sensor to the feet of the robot. The feet of the robot are +named ``"LF_FOOT"``, ``"RF_FOOT"``, ``"LH_FOOT"``, and ``"RH_FOOT"``. We pass a Regex expression +``".*_FOOT"`` to simplify the prim path specification. This Regex expression matches all prims that +end with ``"_FOOT"``. + +We set the update period to 0 to update the sensor at the same frequency as the simulation. Additionally, +for contact sensors, we can specify the history length of the contact information to store. For this +tutorial, we set the history length to 6, which means that the contact information for the last 6 +simulation steps is stored. + +The entire configuration of the contact sensor is as follows: + +.. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :start-at: contact_forces = ContactSensorCfg( + :lines: 1-3 + +Running the simulation loop +--------------------------- + +Similar to when using assets, the buffers and physics handles for the sensors are initialized only +when the simulation is played, i.e., it is important to call ``sim.reset()`` after creating the scene. + +.. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :start-at: # Play the simulator + :end-at: sim.reset() + +Besides that, the simulation loop is similar to the previous tutorials. The sensors are updated as part +of the scene update and they internally handle the updating of their buffers based on their update +periods. + +The data from the sensors can be accessed through their ``data`` attribute. As an example, we show how +to access the data for the different sensors created in this tutorial: + +.. literalinclude:: ../../../../scripts/tutorials/04_sensors/add_sensors_on_robot.py + :language: python + :start-at: # print information from the sensors + :end-at: print("Received max contact force of: ", torch.max(scene["contact_forces"].data.net_forces_w).item()) + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/04_sensors/add_sensors_on_robot.py --num_envs 2 --enable_cameras + + +This command should open a stage with a ground plane, lights, and two quadrupedal robots. +Around the robots, you should see red spheres that indicate the points where the rays hit the mesh. +Additionally, you can switch the viewport to the camera view to see the RGB image captured by the +camera sensor. Please check `here `_ for more information +on how to switch the viewport to the camera view. + +.. figure:: ../../_static/tutorials/tutorial_add_sensors. jpg + :align: center + :figwidth: 100% + :alt: result of add_sensors_on_robot.py + +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal. + +While in this tutorial, we went over creating and using different sensors, there are many more sensors +available in the :mod:`sensors` module. We include minimal examples of using these sensors in the +``scripts/tutorials/04_sensors`` directory. For completeness, these scripts can be run using the +following commands: + +.. code-block:: bash + + # Frame Transformer + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_frame_transformer.py + + # Ray Caster + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_ray_caster.py + + # Ray Caster Camera + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_ray_caster_camera.py + + # USD Camera + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --enable_cameras diff --git a/docs/source/tutorials/05_controllers/run_diff_ik.rst b/docs/source/tutorials/05_controllers/run_diff_ik.rst new file mode 100644 index 0000000000000000000000000000000000000000..dda5568c0f41d122b2431f73e132c3cdfbd085a1 --- /dev/null +++ b/docs/source/tutorials/05_controllers/run_diff_ik.rst @@ -0,0 +1,159 @@ +Using a task-space controller +============================= + +.. currentmodule:: isaaclab + +In the previous tutorials, we have joint-space controllers to control the robot. However, in many +cases, it is more intuitive to control the robot using a task-space controller. For example, if we +want to teleoperate the robot, it is easier to specify the desired end-effector pose rather than +the desired joint positions. + +In this tutorial, we will learn how to use a task-space controller to control the robot. +We will use the :class:`controllers.DifferentialIKController` class to track a desired +end-effector pose command. + + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_diff_ik.py`` script in the +``scripts/tutorials/05_controllers`` directory. + + +.. dropdown:: Code for run_diff_ik.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :emphasize-lines: 98-100, 121-136, 155-157, 161-171 + :linenos: + + +The Code Explained +~~~~~~~~~~~~~~~~~~ + +While using any task-space controller, it is important to ensure that the provided +quantities are in the correct frames. When parallelizing environment instances, they are +all existing in the same unique simulation world frame. However, typically, we want each +environment itself to have its own local frame. This is accessible through the +:attr:`scene.InteractiveScene.env_origins` attribute. + +In our APIs, we use the following notation for frames: + +- The simulation world frame (denoted as ``w``), which is the frame of the entire simulation. +- The local environment frame (denoted as ``e``), which is the frame of the local environment. +- The robot's base frame (denoted as ``b``), which is the frame of the robot's base link. + +Since the asset instances are not "aware" of the local environment frame, they return +their states in the simulation world frame. Thus, we need to convert the obtained +quantities to the local environment frame. This is done by subtracting the local environment +origin from the obtained quantities. + + +Creating an IK controller +------------------------- + +The :class:`~controllers.DifferentialIKController` class computes the desired joint +positions for a robot to reach a desired end-effector pose. The included implementation +performs the computation in a batched format and uses PyTorch operations. It supports +different types of inverse kinematics solvers, including the damped least-squares method +and the pseudo-inverse method. These solvers can be specified using the +:attr:`~controllers.DifferentialIKControllerCfg.ik_method` argument. +Additionally, the controller can handle commands as both relative and absolute poses. + +In this tutorial, we will use the damped least-squares method to compute the desired +joint positions. Additionally, since we want to track desired end-effector poses, we +will use the absolute pose command mode. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :start-at: # Create controller + :end-at: diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) + +Obtaining the robot's joint and body indices +-------------------------------------------- + +The IK controller implementation is a computation-only class. Thus, it expects the +user to provide the necessary information about the robot. This includes the robot's +joint positions, current end-effector pose, and the Jacobian matrix. + +While the attribute :attr:`assets.ArticulationData.joint_pos` provides the joint positions, +we only want the joint positions of the robot's arm, and not the gripper. Similarly, while +the attribute :attr:`assets.ArticulationData.body_state_w` provides the state of all the +robot's bodies, we only want the state of the robot's end-effector. Thus, we need to +index into these arrays to obtain the desired quantities. + +For this, the articulation class provides the methods :meth:`~assets.Articulation.find_joints` +and :meth:`~assets.Articulation.find_bodies`. These methods take in the names of the joints +and bodies and return their corresponding indices. + +While you may directly use these methods to obtain the indices, we recommend using the +:attr:`~managers.SceneEntityCfg` class to resolve the indices. This class is used in various +places in the APIs to extract certain information from a scene entity. Internally, it +calls the above methods to obtain the indices. However, it also performs some additional +checks to ensure that the provided names are valid. Thus, it is a safer option to use +this class. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :start-at: # Specify robot-specific parameters + :end-before: # Define simulation stepping + + +Computing robot command +----------------------- + +The IK controller separates the operation of setting the desired command and +computing the desired joint positions. This is done to allow for the user to +run the IK controller at a different frequency than the robot's control frequency. + +The :meth:`~controllers.DifferentialIKController.set_command` method takes in +the desired end-effector pose as a single batched array. The pose is specified in +the robot's base frame. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :start-at: # reset controller + :end-at: diff_ik_controller.set_command(ik_commands) + +We can then compute the desired joint positions using the +:meth:`~controllers.DifferentialIKController.compute` method. +The method takes in the current end-effector pose (in base frame), Jacobian, and +current joint positions. We read the Jacobian matrix from the robot's data, which uses +its value computed from the physics engine. + + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :start-at: # obtain quantities from simulation + :end-at: joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + +The computed joint position targets can then be applied on the robot, as done in the +previous tutorials. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_diff_ik.py + :language: python + :start-at: # apply actions + :end-at: scene.write_data_to_sim() + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + + +Now that we have gone through the code, let's run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/05_controllers/run_diff_ik.py --robot franka_panda --num_envs 128 + +The script will start a simulation with 128 robots. The robots will be controlled using the IK controller. +The current and desired end-effector poses should be displayed using frame markers. When the robot reaches +the desired pose, the command should cycle through to the next pose specified in the script. + +.. figure:: ../../_static/tutorials/tutorial_task_space_controller.jpg + :align: center + :figwidth: 100% + :alt: result of run_diff_ik.py + +To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal. diff --git a/docs/source/tutorials/05_controllers/run_osc.rst b/docs/source/tutorials/05_controllers/run_osc.rst new file mode 100644 index 0000000000000000000000000000000000000000..b8dbab6ae347b47c126143001634b2d6af6a2896 --- /dev/null +++ b/docs/source/tutorials/05_controllers/run_osc.rst @@ -0,0 +1,191 @@ +Using an operational space controller +===================================== + +.. currentmodule:: isaaclab + +Sometimes, controlling the end-effector pose of the robot using a differential IK controller is not sufficient. +For example, we might want to enforce a very specific pose tracking error dynamics in the task space, actuate the robot +with joint effort/torque commands, or apply a contact force at a specific direction while controlling the motion of +the other directions (e.g., washing the surface of the table with a cloth). In such tasks, we can use an +operational space controller (OSC). + +.. rubric:: References for the operational space control: + +1. O Khatib. A unified approach for motion and force control of robot manipulators: + The operational space formulation. IEEE Journal of Robotics and Automation, 3(1):43–53, 1987. URL http://dx.doi.org/10.1109/JRA.1987.1087068. + +2. Robot Dynamics Lecture Notes by Marco Hutter (ETH Zurich). URL https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2017/RD_HS2017script.pdf + +In this tutorial, we will learn how to use an OSC to control the robot. +We will use the :class:`controllers.OperationalSpaceController` class to apply a constant force perpendicular to a +tilted wall surface while tracking a desired end-effector pose in all the other directions. + +The Code +~~~~~~~~ + +The tutorial corresponds to the ``run_osc.py`` script in the +``scripts/tutorials/05_controllers`` directory. + + +.. dropdown:: Code for run_osc.py + :icon: code + + .. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :linenos: + + +Creating an Operational Space Controller +---------------------------------------- + +The :class:`~controllers.OperationalSpaceController` class computes the joint +efforts/torques for a robot to do simultaneous motion and force control in task space. + +The reference frame of this task space could be an arbitrary coordinate frame in Euclidean space. By default, +it is the robot's base frame. However, in certain cases, it could be easier to define target coordinates w.r.t. a +different frame. In such cases, the pose of this task reference frame, w.r.t. to the robot's base frame, should be +provided in the ``set_command`` method's ``current_task_frame_pose_b`` argument. For example, in this tutorial, it +makes sense to define the target commands w.r.t. a frame that is parallel to the wall surface, as the force control +direction would be then only nonzero in the z-axis of this frame. The target pose, which is set to have the same +orientation as the wall surface, is such a candidate and is used as the task frame in this tutorial. Therefore, all +the arguments to the :class:`~controllers.OperationalSpaceControllerCfg` should be set with this task reference frame +in mind. + +For the motion control, the task space targets could be given as absolute (i.e., defined w.r.t. the robot base, +``target_types: "pose_abs"``) or relative the the end-effector's current pose (i.e., ``target_types: "pose_rel"``). +For the force control, the task space targets could be given as absolute (i.e., defined w.r.t. the robot base, +``target_types: "force_abs"``). If it is desired to apply pose and force control simultaneously, the ``target_types`` +should be a list such as ``["pose_abs", "wrench_abs"]`` or ``["pose_rel", "wrench_abs"]``. + +The axes that the motion and force control will be applied can be specified using the ``motion_control_axes_task`` and +``force_control_axes_task`` arguments, respectively. These lists should consist of 0/1 for all six axes (position and +rotation) and be complementary to each other (e.g., for the x-axis, if the ``motion_control_axes_task`` is ``0``, the +``force_control_axes_task`` should be ``1``). + +For the motion control axes, desired stiffness, and damping ratio values can be specified using the +``motion_control_stiffness`` and ``motion_damping_ratio_task`` arguments, which can be a scalar (same value for all +axes) or a list of six scalars, one value corresponding to each axis. If desired, the stiffness and damping ratio +values could be a command parameter (e.g., to learn the values using RL or change them on the go). For this, +``impedance_mode`` should be either ``"variable_kp"`` to include the stiffness values within the command or +``"variable"`` to include both the stiffness and damping ratio values. In these cases, ``motion_stiffness_limits_task`` +and ``motion_damping_limits_task`` should be set as well, which puts bounds on the stiffness and damping ratio values. + +For contact force control, it is possible to apply an open-loop force control by not setting the +``contact_wrench_stiffness_task``, or apply a closed-loop force control (with the feed-forward term) by setting +the desired stiffness values using the ``contact_wrench_stiffness_task`` argument, which can be a scalar or a list +of six scalars. Please note that, currently, only the linear part of the contact wrench (hence the first three +elements of the ``contact_wrench_stiffness_task``) is considered in the closed-loop control, as the rotational part +cannot be measured with the contact sensors. + +For the motion control, ``inertial_dynamics_decoupling`` should be set to ``True`` to use the robot's inertia matrix +to decouple the desired accelerations in the task space. This is important for the motion control to be accurate, +especially for rapid movements. This inertial decoupling accounts for the coupling between all the six motion axes. +If desired, the inertial coupling between the translational and rotational axes could be ignored by setting the +``partial_inertial_dynamics_decoupling`` to ``True``. + +If it is desired to include the gravity compensation in the operational space command, the ``gravity_compensation`` +should be set to ``True``. + +A final consideration regarding the operational space control is what to do with the null-space of redundant robots. +The null-space is the subspace of the joint space that does not affect the task space coordinates. If nothing is done +to control the null-space, the robot joints will float without moving the end-effector. This might be undesired (e.g., +the robot joints might get close to their limits), and one might want to control the robot behaviour within its +null-space. One way to do is to set ``nullspace_control`` to ``"position"`` (by default it is ``"none"``) which +integrates a null-space PD controller to attract the robot joints to desired targets without affecting the task +space. The behaviour of this null-space controller can be defined using the ``nullspace_stiffness`` and +``nullspace_damping_ratio`` arguments. Please note that theoretical decoupling of the null-space and task space +accelerations is only possible when ``inertial_dynamics_decoupling`` is set to ``True`` and +``partial_inertial_dynamics_decoupling`` is set to ``False``. + +The included OSC implementation performs the computation in a batched format and uses PyTorch operations. + +In this tutorial, we will use ``"pose_abs"`` for controlling the motion in all axes except the z-axis and +``"wrench_abs"`` for controlling the force in the z-axis. Moreover, we will include the full inertia decoupling in +the motion control and not include the gravity compensation, as the gravity is disabled from the robot configuration. +We set the impedance mode to ``"variable_kp"`` to dynamically change the stiffness values +(``motion_damping_ratio_task`` is set to ``1``: the kd values adapt according to kp values to maintain a critically +damped response). Finally, ``nullspace_control`` is set to use ``"position"`` where the joint set points are provided +to be the center of the joint position limits. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # Create the OSC + :end-at: osc = OperationalSpaceController(osc_cfg, num_envs=scene.num_envs, device=sim.device) + +Updating the states of the robot +-------------------------------------------- + +The OSC implementation is a computation-only class. Thus, it expects the user to provide the necessary information +about the robot. This includes the robot's Jacobian matrix, mass/inertia matrix, end-effector pose, velocity, contact +force (all in the root frame), and finally, the joint positions and velocities. Moreover, the user should provide +gravity compensation vector and null-space joint position targets if required. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # Update robot states + :end-before: # Update the target commands + + +Computing robot command +----------------------- + +The OSC separates the operation of setting the desired command and computing the desired joint positions. +To set the desired command, the user should provide command vector, which includes the target commands +(i.e., in the order they appear in the ``target_types`` argument of the OSC configuration), +and the desired stiffness and damping ratio values if the impedance_mode is set to ``"variable_kp"`` or ``"variable"``. +They should be all in the same coordinate frame as the task frame (e.g., indicated with ``_task`` subscript) and +concatanated together. + +In this tutorial, the desired wrench is already defined w.r.t. the task frame, and the desired pose is transformed +to the task frame as the following: + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # Convert the target commands to the task frame + :end-at: return command, task_frame_pose_b + +The OSC command is set with the command vector in the task frame, the end-effector pose in the base frame, and the +task (reference) frame pose in the base frame as the following. This information is needed, as the internal +computations are done in the base frame. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # set the osc command + :end-at: osc.set_command(command=command, current_ee_pose_b=ee_pose_b, current_task_frame_pose_b=task_frame_pose_b) + +The joint effort/torque values are computed using the provided robot states and the desired command as the following: + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # compute the joint commands + :end-at: ) + + +The computed joint effort/torque targets can then be applied on the robot. + +.. literalinclude:: ../../../../scripts/tutorials/05_controllers/run_osc.py + :language: python + :start-at: # apply actions + :end-at: robot.write_data_to_sim() + + +The Code Execution +~~~~~~~~~~~~~~~~~~ + +You can now run the script and see the result: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/05_controllers/run_osc.py --num_envs 128 + +The script will start a simulation with 128 robots. The robots will be controlled using the OSC. +The current and desired end-effector poses should be displayed using frame markers in addition to the red tilted wall. +You should see that the robot reaches the desired pose while applying a constant force perpendicular to the wall +surface. + +.. figure:: ../../_static/tutorials/tutorial_operational_space_controller.jpg + :align: center + :figwidth: 100% + :alt: result of run_osc.py + +To stop the simulation, you can either close the window or press ``Ctrl+C`` in the terminal. diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..f1096e6c05b0fa59eb0e306b42195a6d6bae2fa8 --- /dev/null +++ b/docs/source/tutorials/index.rst @@ -0,0 +1,110 @@ +.. _tutorials: + +Tutorials +========= + +Welcome to the Isaac Lab tutorials! These tutorials provide a step-by-step guide to help you understand +and use various features of the framework. All the tutorials are written as Python scripts. You can +find the source code for each tutorial in the ``scripts/tutorials`` directory of the Isaac Lab +repository. + +.. note:: + + We would love to extend the tutorials to cover more topics and use cases, so please let us know if + you have any suggestions. + +We recommend that you go through the tutorials in the order they are listed here. + + +Setting up a Simple Simulation +------------------------------- + +These tutorials show you how to launch the simulation with different settings and spawn objects in the +simulated scene. They cover the following APIs: :class:`~isaaclab.app.AppLauncher`, +:class:`~isaaclab.sim.SimulationContext`, and :class:`~isaaclab.sim.spawners`. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 00_sim/create_empty + 00_sim/spawn_prims + 00_sim/launch_app + +Interacting with Assets +----------------------- + +Having spawned objects in the scene, these tutorials show you how to create physics handles for these +objects and interact with them. These revolve around the :class:`~isaaclab.assets.AssetBase` +class and its derivatives such as :class:`~isaaclab.assets.RigidObject`, +:class:`~isaaclab.assets.Articulation` and :class:`~isaaclab.assets.DeformableObject`. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 01_assets/add_new_robot + 01_assets/run_rigid_object + 01_assets/run_articulation + 01_assets/run_deformable_object + 01_assets/run_surface_gripper + +Creating a Scene +---------------- + +With the basic concepts of the framework covered, the tutorials move to a more intuitive scene +interface that uses the :class:`~isaaclab.scene.InteractiveScene` class. This class +provides a higher level abstraction for creating scenes easily. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 02_scene/create_scene + +Designing an Environment +------------------------ + +The following tutorials introduce the concept of manager-based environments: :class:`~isaaclab.envs.ManagerBasedEnv` +and its derivative :class:`~isaaclab.envs.ManagerBasedRLEnv`, as well as the direct workflow base class +:class:`~isaaclab.envs.DirectRLEnv`. These environments bring-in together +different aspects of the framework to create a simulation environment for agent interaction. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 03_envs/create_manager_base_env + 03_envs/create_manager_rl_env + 03_envs/create_direct_rl_env + 03_envs/register_rl_env_gym + 03_envs/run_rl_training + 03_envs/configuring_rl_training + 03_envs/modify_direct_rl_env + 03_envs/policy_inference_in_usd + +Integrating Sensors +------------------- + +The following tutorial shows you how to integrate sensors into the simulation environment. The +tutorials introduce the :class:`~isaaclab.sensors.SensorBase` class and its derivatives +such as :class:`~isaaclab.sensors.Camera` and :class:`~isaaclab.sensors.RayCaster`. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 04_sensors/add_sensors_on_robot + +Using motion generators +----------------------- + +While the robots in the simulation environment can be controlled at the joint-level, the following +tutorials show you how to use motion generators to control the robots at the task-level. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + 05_controllers/run_diff_ik + 05_controllers/run_osc diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..053fef4e99db5fb473e56435af0f17ed8845291c --- /dev/null +++ b/environment.yml @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +channels: + - conda-forge + - defaults +dependencies: + - python=3.11 + - importlib_metadata diff --git a/fetch_assets.sh b/fetch_assets.sh new file mode 100644 index 0000000000000000000000000000000000000000..b7077aff838e416885f2aaad85c4d3ea566e8a7d --- /dev/null +++ b/fetch_assets.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +set -e # Exit script if any command fails +set -o pipefail + +# 1. Clone repository +echo "Cloning repository..." +git lfs install +git clone https://huggingface.co/datasets/unitreerobotics/unitree_sim_isaaclab_usds + +# 2. Enter repository directory +cd unitree_sim_isaaclab_usds + +# 3. Check if assets.zip exists and is greater than 1GB +if [ ! -f "assets.zip" ]; then + echo "Error: assets.zip does not exist" + exit 1 +fi + +filesize=$(stat -c%s "assets.zip") +if [ "$filesize" -le $((1024 * 1024 * 1024)) ]; then + echo "Error: assets.zip is less than 1GB" + exit 1 +fi + +echo "assets.zip check passed, size is $((filesize / 1024 / 1024)) MB" + +# 4. Unzip assets.zip +echo "Unzipping assets.zip..." +unzip -q assets.zip + +# 5. Move assets folder to parent directory +if [ -d "assets" ]; then + echo "Moving assets to parent directory..." + mv assets ../ +else + echo "Error: assets unzip failed or folder does not exist" + exit 1 +fi + +# 6. Return to parent directory and delete original folder +cd .. +echo "Deleting unitree_sim_isaaclab_usds folder..." +rm -rf unitree_sim_isaaclab_usds + +echo "✅ All done!" diff --git a/greptile.json b/greptile.json new file mode 100644 index 0000000000000000000000000000000000000000..1f195339e5b77d1a2d75b8e1a2086c103a15b477 --- /dev/null +++ b/greptile.json @@ -0,0 +1,3 @@ +{ + "triggerOnUpdates": false +} diff --git a/isaaclab.bat b/isaaclab.bat new file mode 100644 index 0000000000000000000000000000000000000000..4f166251d82a95580708c6a6d197068968ef47e4 --- /dev/null +++ b/isaaclab.bat @@ -0,0 +1,673 @@ +@echo off +setlocal EnableExtensions EnableDelayedExpansion + +rem Copyright (c) 2022-2025, The Isaac Lab Project Developers. +rem All rights reserved. +rem +rem SPDX-License-Identifier: BSD-3-Clause + +rem Configurations +set "ISAACLAB_PATH=%~dp0" +goto main + +rem Helper functions + +rem extract Isaac Sim directory +:extract_isaacsim_path +rem Use the sym-link path to Isaac Sim directory +set isaac_path=%ISAACLAB_PATH%\_isaac_sim +rem Check if directory exists +if not exist "%isaac_path%" ( + rem Find the Python executable + call :extract_python_exe + rem retrieve the isaacsim path from the installed package + set "isaac_path=" + for /f "delims=" %%i in ('!python_exe! -c "import isaacsim; import os; print(os.environ['ISAAC_PATH'])"') do ( + if not defined isaac_path ( + set "isaac_path=%%i" + ) + ) +) +rem Check if the directory exists +if not exist "%isaac_path%" ( + echo [ERROR] Unable to find the Isaac Sim directory: %isaac_path% + echo %tab%This could be due to the following reasons: + echo %tab%1. Conda environment with Isaac Sim pip package is not activated. + echo %tab%2. Isaac Sim directory is not available at the default path: %ISAACLAB_PATH%\_isaac_sim + exit /b 1 +) +goto :eof + +rem --- Ensure CUDA PyTorch helper ------------------------------------------ +:ensure_cuda_torch +rem expects: !python_exe! set by :extract_python_exe +setlocal EnableExtensions EnableDelayedExpansion +set "TORCH_VER=2.7.0" +set "TV_VER=0.22.0" +set "CUDA_TAG=cu128" +set "PYTORCH_INDEX=https://download.pytorch.org/whl/%CUDA_TAG%" + +rem Do we already have torch? +call "!python_exe!" -m pip show torch >nul 2>&1 +if errorlevel 1 ( + echo [INFO] Installing PyTorch !TORCH_VER! with CUDA !CUDA_TAG!... + call "!python_exe!" -m pip install "torch==!TORCH_VER!" "torchvision==!TV_VER!" --index-url "!PYTORCH_INDEX!" +) else ( + for /f "tokens=2" %%V in ('"!python_exe!" -m pip show torch ^| findstr /B /C:"Version:"') do set "TORCH_CUR=%%V" + echo [INFO] Found PyTorch version !TORCH_CUR!. + if /I not "!TORCH_CUR!"=="!TORCH_VER!+!CUDA_TAG!" ( + echo [INFO] Replacing PyTorch !TORCH_CUR! -> !TORCH_VER!+!CUDA_TAG!... + call "!python_exe!" -m pip uninstall -y torch torchvision torchaudio >nul 2>&1 + call "!python_exe!" -m pip install "torch==!TORCH_VER!" "torchvision==!TV_VER!" --index-url "!PYTORCH_INDEX!" + ) else ( + echo [INFO] PyTorch !TORCH_VER!+!CUDA_TAG! already installed. + ) +) +endlocal & exit /b 0 + +rem ----------------------------------------------------------------------- +rem Returns success (exit code 0) if Isaac Sim's version starts with "4.5" +rem ----------------------------------------------------------------------- +:is_isaacsim_version_4_5 + rem make sure we have %python_exe% + call :extract_python_exe + + rem 1) try to locate the VERSION file via the kit install + for /f "delims=" %%V in ('!python_exe! -c "import isaacsim,os;print(os.path.abspath(os.path.join(os.path.dirname(isaacsim.__file__), os.pardir, os.pardir, 'VERSION')))"') do set "VERSION_PATH=%%V" + if exist "!VERSION_PATH!" ( + for /f "usebackq delims=" %%L in ("!VERSION_PATH!") do set "ISAACSIM_VER=%%L" + ) else ( + rem 2) fallback to importlib.metadata if no VERSION file + for /f "delims=" %%L in ('!python_exe! -c "from importlib.metadata import version;print(version(''isaacsim''))"') do set "ISAACSIM_VER=%%L" + ) + + rem Clean up the version string (remove any trailing whitespace or newlines) + set "ISAACSIM_VER=!ISAACSIM_VER: =!" + + rem Use string comparison instead of findstr for more reliable matching + if "!ISAACSIM_VER:~0,3!"=="4.5" ( + exit /b 0 + ) else ( + exit /b 1 + ) + goto :eof + +rem extract the python from isaacsim +:extract_python_exe +rem check if using conda +if not "%CONDA_PREFIX%"=="" ( + rem use conda python + set python_exe=%CONDA_PREFIX%\python.exe +) else ( + rem use kit python + set python_exe=%ISAACLAB_PATH%\_isaac_sim\python.bat +) +rem check for if isaac sim was installed to system python +if not exist "%python_exe%" ( + set "python_exe=" + python -m pip show isaacsim-rl > nul 2>&1 + if %ERRORLEVEL% equ 0 ( + for /f "delims=" %%i in ('where python') do ( + if not defined python_exe ( + set "python_exe=%%i" + ) + ) + ) +) +if not exist "%python_exe%" ( + echo [ERROR] Unable to find any Python executable at path: %python_exe% + echo %tab%This could be due to the following reasons: + echo %tab%1. Conda environment is not activated. + echo %tab%2. Python executable is not available at the default path: %ISAACLAB_PATH%\_isaac_sim\python.bat + exit /b 1 +) +goto :eof + + +rem extract the simulator exe from isaacsim +:extract_isaacsim_exe +call :extract_python_exe +call !python_exe! -m pip show isaacsim-rl > nul 2>&1 +if errorlevel 1 ( + rem obtain isaacsim path + call :extract_isaacsim_path + rem python executable to use + set isaacsim_exe=!isaac_path!\isaac-sim.bat +) else ( + rem if isaac sim installed from pip + set isaacsim_exe=isaacsim isaacsim.exp.full +) +rem check if there is a python path available +if not exist "%isaacsim_exe%" ( + echo [ERROR] No isaac-sim executable found at path: %isaacsim_exe% + exit /b 1 +) +goto :eof + + +rem check if input directory is a python extension and install the module +:install_isaaclab_extension +echo %ext_folder% +rem retrieve the python executable +call :extract_python_exe +rem if the directory contains setup.py then install the python module +if exist "%ext_folder%\setup.py" ( + echo module: %ext_folder% + call !python_exe! -m pip install --editable %ext_folder% +) +goto :eof + + +rem setup anaconda environment for Isaac Lab +:setup_conda_env +rem get environment name from input +set env_name=%conda_env_name% +rem check if conda is installed +where conda >nul 2>nul +if errorlevel 1 ( + echo [ERROR] Conda could not be found. Please install conda and try again. + exit /b 1 +) + +rem check if _isaac_sim symlink exists and isaacsim-rl is not installed via pip +if not exist "%ISAACLAB_PATH%\_isaac_sim" ( + python -m pip list | findstr /C:"isaacsim-rl" >nul + if errorlevel 1 ( + echo [WARNING] _isaac_sim symlink not found at %ISAACLAB_PATH%\_isaac_sim + echo This warning can be ignored if you plan to install Isaac Sim via pip. + echo If you are using a binary installation of Isaac Sim, please ensure the symlink is created before setting up the conda environment. + ) +) + +rem check if the environment exists +call conda env list | findstr /c:"%env_name%" >nul +if %errorlevel% equ 0 ( + echo [INFO] Conda environment named '%env_name%' already exists. +) else ( + echo [INFO] Creating conda environment named '%env_name%'... + echo [INFO] Installing dependencies from %ISAACLAB_PATH%\environment.yml + rem ———————————————————————————————— + rem patch Python version if needed, but back up first + rem ———————————————————————————————— + copy "%ISAACLAB_PATH%environment.yml" "%ISAACLAB_PATH%environment.yml.bak" >nul + call :is_isaacsim_version_4_5 + if !ERRORLEVEL! EQU 0 ( + echo [INFO] Detected Isaac Sim 4.5 --^> forcing python=3.10 + rem Use findstr to replace the python version line + ( + for /f "delims=" %%L in ('type "%ISAACLAB_PATH%environment.yml"') do ( + set "line=%%L" + set "line=!line: =!" + if "!line:~0,15!"=="-python=3.11" ( + echo - python=3.10 + ) else ( + echo %%L + ) + ) + ) > "%ISAACLAB_PATH%environment.yml.tmp" + rem Replace the original file with the modified version + move /y "%ISAACLAB_PATH%environment.yml.tmp" "%ISAACLAB_PATH%environment.yml" >nul + ) else ( + echo [INFO] Isaac Sim ^>=5.0, installing python=3.11 + ) + call conda env create -y --file %ISAACLAB_PATH%\environment.yml -n %env_name% +) +rem cache current paths for later +set "cache_pythonpath=%PYTHONPATH%" +set "cache_ld_library_path=%LD_LIBRARY_PATH%" +rem clear any existing files +echo %CONDA_PREFIX% +del "%CONDA_PREFIX%\etc\conda\activate.d\setenv.bat" 2>nul +del "%CONDA_PREFIX%\etc\conda\deactivate.d\unsetenv.bat" 2>nul +rem activate the environment +call conda activate %env_name% +rem setup directories to load isaac-sim variables +mkdir "%CONDA_PREFIX%\etc\conda\activate.d" 2>nul +mkdir "%CONDA_PREFIX%\etc\conda\deactivate.d" 2>nul + +rem obtain isaacsim path +call :extract_isaacsim_path +if exist "%isaac_path%" ( + rem add variables to environment during activation + ( + echo @echo off + echo rem for isaac-sim + echo set "RESOURCE_NAME=IsaacSim" + echo set CARB_APP_PATH=!isaac_path!\kit + echo set EXP_PATH=!isaac_path!\apps + echo set ISAAC_PATH=!isaac_path! + echo set PYTHONPATH=%PYTHONPATH%;!isaac_path!\site + echo. + echo rem for isaac-lab + echo doskey isaaclab=isaaclab.bat $* + ) > "%CONDA_PREFIX%\etc\conda\activate.d\env_vars.bat" + ( + echo $env:CARB_APP_PATH="!isaac_path!\kit" + echo $env:EXP_PATH="!isaac_path!\apps" + echo $env:ISAAC_PATH="!isaac_path!" + echo $env:PYTHONPATH="%PYTHONPATH%;!isaac_path!\site" + echo $env:RESOURCE_NAME="IsaacSim" + ) > "%CONDA_PREFIX%\etc\conda\activate.d\env_vars.ps1" +) else ( + rem assume isaac sim will be installed from pip + rem add variables to environment during activation + ( + echo @echo off + echo rem for isaac-sim + echo set "RESOURCE_NAME=IsaacSim" + echo. + echo rem for isaac-lab + echo doskey isaaclab=isaaclab.bat $* + ) > "%CONDA_PREFIX%\etc\conda\activate.d\env_vars.bat" + ( + echo $env:RESOURCE_NAME="IsaacSim" + ) > "%CONDA_PREFIX%\etc\conda\activate.d\env_vars.ps1" +) + +rem reactivate the environment to load the variables +call conda activate %env_name% + +rem remove variables from environment during deactivation +( + echo @echo off + echo rem for isaac-sim + echo set "CARB_APP_PATH=" + echo set "EXP_PATH=" + echo set "ISAAC_PATH=" + echo set "RESOURCE_NAME=" + echo. + echo rem for isaac-lab + echo doskey isaaclab = + echo. + echo rem restore paths + echo set "PYTHONPATH=%cache_pythonpath%" + echo set "LD_LIBRARY_PATH=%cache_ld_library_path%" +) > "%CONDA_PREFIX%\etc\conda\deactivate.d\unsetenv_vars.bat" +( + echo $env:RESOURCE_NAME="" + echo $env:PYTHONPATH="%cache_pythonpath%" + echo $env:LD_LIBRARY_PATH="%cache_pythonpath%" +) > "%CONDA_PREFIX%\etc\conda\deactivate.d\unsetenv_vars.ps1" + +rem deactivate the environment +call conda deactivate +rem add information to the user about alias +echo [INFO] Added 'isaaclab' alias to conda environment for 'isaaclab.bat' script. +echo [INFO] Created conda environment named '%env_name%'. +echo. +echo 1. To activate the environment, run: conda activate %env_name% +echo 2. To install Isaac Lab extensions, run: isaaclab -i +echo 3. To perform formatting, run: isaaclab -f +echo 4. To deactivate the environment, run: conda deactivate +echo. +goto :eof + + +rem Update the vscode settings from template and Isaac Sim settings +:update_vscode_settings +echo [INFO] Setting up vscode settings... +rem Retrieve the python executable +call :extract_python_exe +rem Path to setup_vscode.py +set "setup_vscode_script=%ISAACLAB_PATH%\.vscode\tools\setup_vscode.py" +rem Check if the file exists before attempting to run it +if exist "%setup_vscode_script%" ( + call !python_exe! "%setup_vscode_script%" +) else ( + echo [WARNING] setup_vscode.py not found. Aborting vscode settings setup. +) +goto :eof + + +rem Print the usage description +:print_help +echo. +echo usage: %~nx0 [-h] [-i] [-f] [-p] [-s] [-v] [-d] [-n] [-c] -- Utility to manage extensions in Isaac Lab. +echo. +echo optional arguments: +echo -h, --help Display the help content. +echo -i, --install [LIB] Install the extensions inside Isaac Lab and learning frameworks as extra dependencies. Default is 'all'. +echo -f, --format Run pre-commit to format the code and check lints. +echo -p, --python Run the python executable (python.bat) provided by Isaac Sim. +echo -s, --sim Run the simulator executable (isaac-sim.bat) provided by Isaac Sim. +echo -t, --test Run all python pytest tests. +echo -v, --vscode Generate the VSCode settings file from template. +echo -d, --docs Build the documentation from source using sphinx. +echo -n, --new Create a new external project or internal task from template. +echo -c, --conda [NAME] Create the conda environment for Isaac Lab. Default name is 'env_isaaclab'. +echo. +goto :eof + + +rem Main +:main + +rem check argument provided +if "%~1"=="" ( + echo [Error] No arguments provided. + call :print_help + exit /b 1 +) + +rem pass the arguments +:loop +if "%~1"=="" goto :end +set "arg=%~1" + +rem read the key +if "%arg%"=="-i" ( + rem install the python packages in isaaclab/source directory + echo [INFO] Installing extensions inside the Isaac Lab repository... + call :extract_python_exe + rem check if pytorch is installed and its version + rem install pytorch with cuda 12.8 for blackwell support + call :ensure_cuda_torch + + for /d %%d in ("%ISAACLAB_PATH%\source\*") do ( + set ext_folder="%%d" + call :install_isaaclab_extension + ) + rem install the python packages for supported reinforcement learning frameworks + echo [INFO] Installing extra requirements such as learning frameworks... + if "%~2"=="" ( + echo [INFO] Installing all rl-frameworks. + set framework_name=all + ) else if "%~2"=="none" ( + echo [INFO] No rl-framework will be installed. + set framework_name=none + shift + ) else ( + echo [INFO] Installing rl-framework: %2. + set framework_name=%2 + shift + ) + rem install the rl-frameworks specified + call !python_exe! -m pip install -e %ISAACLAB_PATH%\source\isaaclab_rl[!framework_name!] + rem in rare case if some packages or flaky setup override default torch installation, ensure right torch is + rem installed again + call :ensure_cuda_torch + rem update the vscode settings + rem once we have a docker container, we need to disable vscode settings + call :update_vscode_settings + shift + shift +) else if "%arg%"=="--install" ( + rem install the python packages in source directory + echo [INFO] Installing extensions inside the Isaac Lab repository... + call :extract_python_exe + rem check if pytorch is installed and its version + rem install pytorch with cuda 12.8 for blackwell support + call :ensure_cuda_torch + + for /d %%d in ("%ISAACLAB_PATH%\source\*") do ( + set ext_folder="%%d" + call :install_isaaclab_extension + ) + rem install the python packages for supported reinforcement learning frameworks + echo [INFO] Installing extra requirements such as learning frameworks... + if "%~2"=="" ( + echo [INFO] Installing all rl-frameworks. + set framework_name=all + ) else if "%~2"=="none" ( + echo [INFO] No rl-framework will be installed. + set framework_name=none + shift + ) else ( + echo [INFO] Installing rl-framework: %2. + set framework_name=%2 + shift + ) + rem install the rl-frameworks specified + call !python_exe! -m pip install -e %ISAACLAB_PATH%\source\isaaclab_rl[!framework_name!] + rem in rare case if some packages or flaky setup override default torch installation, ensure right torch is + rem installed again + call :ensure_cuda_torch + rem update the vscode settings + rem once we have a docker container, we need to disable vscode settings + call :update_vscode_settings + shift +) else if "%arg%"=="-c" ( + rem use default name if not provided + if not "%~2"=="" ( + echo [INFO] Using conda environment name: %2 + set conda_env_name=%2 + shift + ) else ( + echo [INFO] Using default conda environment name: env_isaaclab + set conda_env_name=env_isaaclab + ) + call :setup_conda_env %conda_env_name% + shift +) else if "%arg%"=="--conda" ( + rem use default name if not provided + if not "%~2"=="" ( + echo [INFO] Using conda environment name: %2 + set conda_env_name=%2 + shift + ) else ( + echo [INFO] Using default conda environment name: env_isaaclab + set conda_env_name=env_isaaclab + ) + call :setup_conda_env %conda_env_name% + shift +) else if "%arg%"=="-f" ( + rem reset the python path to avoid conflicts with pre-commit + rem this is needed because the pre-commit hooks are installed in a separate virtual environment + rem and it uses the system python to run the hooks + if not "%CONDA_DEFAULT_ENV%"=="" ( + set cache_pythonpath=%PYTHONPATH% + set PYTHONPATH= + ) + + rem run the formatter over the repository + rem check if pre-commit is installed + pip show pre-commit > nul 2>&1 + if errorlevel 1 ( + echo [INFO] Installing pre-commit... + pip install pre-commit + ) + + rem always execute inside the Isaac Lab directory + echo [INFO] Formatting the repository... + pushd %ISAACLAB_PATH% + call python -m pre_commit run --all-files + popd >nul + + rem set the python path back to the original value + if not "%CONDA_DEFAULT_ENV%"=="" ( + set PYTHONPATH=%cache_pythonpath% + ) + goto :end +) else if "%arg%"=="--format" ( + rem reset the python path to avoid conflicts with pre-commit + rem this is needed because the pre-commit hooks are installed in a separate virtual environment + rem and it uses the system python to run the hooks + if not "%CONDA_DEFAULT_ENV%"=="" ( + set cache_pythonpath=%PYTHONPATH% + set PYTHONPATH= + ) + + rem run the formatter over the repository + rem check if pre-commit is installed + pip show pre-commit > nul 2>&1 + if errorlevel 1 ( + echo [INFO] Installing pre-commit... + pip install pre-commit + ) + + rem always execute inside the Isaac Lab directory + echo [INFO] Formatting the repository... + pushd %ISAACLAB_PATH% + call python -m pre_commit run --all-files + popd >nul + + rem set the python path back to the original value + if not "%CONDA_DEFAULT_ENV%"=="" ( + set PYTHONPATH=%cache_pythonpath% + ) + goto :end +) else if "%arg%"=="-p" ( + rem run the python provided by Isaac Sim + call :extract_python_exe + echo [INFO] Using python from: !python_exe! + REM Loop through all arguments - mimic shift + for /f "tokens=1,* delims= " %%a in ("%*") do ( + set "allArgs=%%b" + ) + call !python_exe! !allArgs! + goto :end +) else if "%arg%"=="--python" ( + rem run the python provided by Isaac Sim + call :extract_python_exe + echo [INFO] Using python from: !python_exe! + REM Loop through all arguments - mimic shift + for /f "tokens=1,* delims= " %%a in ("%*") do ( + set "allArgs=%%b" + ) + call !python_exe! !allArgs! + goto :end +) else if "%arg%"=="-s" ( + rem run the simulator exe provided by isaacsim + call :extract_isaacsim_exe + echo [INFO] Running isaac-sim from: !isaacsim_exe! + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + !isaacsim_exe! --ext-folder %ISAACLAB_PATH%\source !allArgs! + goto :end +) else if "%arg%"=="--sim" ( + rem run the simulator exe provided by Isaac Sim + call :extract_isaacsim_exe + echo [INFO] Running isaac-sim from: !isaacsim_exe! + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + !isaacsim_exe! --ext-folder %ISAACLAB_PATH%\source !allArgs! + goto :end +) else if "%arg%"=="-n" ( + rem run the template generator script + call :extract_python_exe + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + echo [INFO] Installing template dependencies... + call !python_exe! -m pip install -q -r tools\template\requirements.txt + echo. + echo [INFO] Running template generator... + echo. + call !python_exe! tools\template\cli.py !allArgs! + goto :end +) else if "%arg%"=="--new" ( + rem run the template generator script + call :extract_python_exe + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + echo [INFO] Installing template dependencies... + call !python_exe! -m pip install -q -r tools\template\requirements.txt + echo. + echo [INFO] Running template generator... + echo. + call !python_exe! tools\template\cli.py !allArgs! + goto :end +) else if "%arg%"=="-t" ( + rem run the python provided by Isaac Sim + call :extract_python_exe + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + call !python_exe! -m pytest tools !allArgs! + goto :end +) else if "%arg%"=="--test" ( + rem run the python provided by Isaac Sim + call :extract_python_exe + set "allArgs=" + for %%a in (%*) do ( + REM Append each argument to the variable, skip the first one + if defined skip ( + set "allArgs=!allArgs! %%a" + ) else ( + set "skip=1" + ) + ) + call !python_exe! -m pytest tools !allArgs! + goto :end +) else if "%arg%"=="-v" ( + rem update the vscode settings + call :update_vscode_settings + shift + goto :end +) else if "%arg%"=="--vscode" ( + rem update the vscode settings + call :update_vscode_settings + shift + goto :end +) else if "%arg%"=="-d" ( + rem build the documentation + echo [INFO] Building documentation... + call :extract_python_exe + pushd %ISAACLAB_PATH%\docs + call !python_exe! -m pip install -r requirements.txt >nul + call !python_exe! -m sphinx -b html -d _build\doctrees . _build\html + echo [INFO] To open documentation on default browser, run: + echo xdg-open "%ISAACLAB_PATH%\docs\_build\html\index.html" + popd >nul + shift + goto :end +) else if "%arg%"=="--docs" ( + rem build the documentation + echo [INFO] Building documentation... + call :extract_python_exe + pushd %ISAACLAB_PATH%\docs + call !python_exe! -m pip install -r requirements.txt >nul + call !python_exe! -m sphinx -b html -d _build\doctrees . _build\current + echo [INFO] To open documentation on default browser, run: + echo xdg-open "%ISAACLAB_PATH%\docs\_build\current\index.html" + popd >nul + shift + goto :end +) else if "%arg%"=="-h" ( + call :print_help + goto :end +) else if "%arg%"=="--help" ( + call :print_help + goto :end +) else ( + echo Invalid argument provided: %arg% + call :print_help + exit /b 1 +) +goto loop + +:end +exit /b 0 diff --git a/isaaclab.sh b/isaaclab.sh new file mode 100644 index 0000000000000000000000000000000000000000..00008d6ec8275d131be671768a55892667c5a0e6 --- /dev/null +++ b/isaaclab.sh @@ -0,0 +1,774 @@ +#!/usr/bin/env bash + +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +#== +# Configurations +#== + +# Exits if error occurs +set -e + +# Set tab-spaces +tabs 4 + +# get source directory +export ISAACLAB_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +#== +# Helper functions +#== + +# install system dependencies +install_system_deps() { + # check if cmake is already installed + if command -v cmake &> /dev/null; then + echo "[INFO] cmake is already installed." + else + # check if running as root + if [ "$EUID" -ne 0 ]; then + echo "[INFO] Installing system dependencies..." + sudo apt-get update && sudo apt-get install -y --no-install-recommends \ + cmake \ + build-essential + else + echo "[INFO] Installing system dependencies..." + apt-get update && apt-get install -y --no-install-recommends \ + cmake \ + build-essential + fi + fi +} + +# Returns success (exit code 0 / "true") if the detected Isaac Sim version starts with 4.5, +# otherwise returns non-zero ("false"). Works with both symlinked binary installs and pip installs. +is_isaacsim_version_4_5() { + local version="" + local python_exe + python_exe=$(extract_python_exe) + + # 0) Fast path: read VERSION file from the symlinked _isaac_sim directory (binary install) + # If the repository has _isaac_sim → symlink, the VERSION file is the simplest source of truth. + if [[ -f "${ISAACLAB_PATH}/_isaac_sim/VERSION" ]]; then + # Read first line of the VERSION file; don't fail the whole script on errors. + version=$(head -n1 "${ISAACLAB_PATH}/_isaac_sim/VERSION" || true) + fi + + # 1) Package-path probe: import isaacsim and walk up to ../../VERSION (pip or nonstandard layouts) + # If we still don't know the version, ask Python where the isaacsim package lives + if [[ -z "$version" ]]; then + local sim_file="" + # Print isaacsim.__file__; suppress errors so set -e won't abort. + sim_file=$("${python_exe}" -c 'import isaacsim, os; print(isaacsim.__file__)' 2>/dev/null || true) + if [[ -n "$sim_file" ]]; then + local version_path + version_path="$(dirname "$sim_file")/../../VERSION" + # If that VERSION file exists, read it. + [[ -f "$version_path" ]] && version=$(head -n1 "$version_path" || true) + fi + fi + + # 2) Fallback: use package metadata via importlib.metadata.version("isaacsim") + if [[ -z "$version" ]]; then + version=$("${python_exe}" <<'PY' 2>/dev/null || true +from importlib.metadata import version, PackageNotFoundError +try: + print(version("isaacsim")) +except PackageNotFoundError: + pass +PY +) + fi + + # Final decision: return success if version begins with "4.5", 0 if match, 1 otherwise. + [[ "$version" == 4.5* ]] +} + +# check if running in docker +is_docker() { + [ -f /.dockerenv ] || [ -f /run/.containerenv ] || \ + grep -qaE '(docker|containerd|kubepods|podman)' /proc/1/cgroup || \ + [[ $(cat /proc/1/comm) == "containerd-shim" ]] +} + +# check if running on ARM architecture +is_arm() { + [[ "$(uname -m)" == "aarch64" ]] || [[ "$(uname -m)" == "arm64" ]] +} + +ensure_cuda_torch() { + local python_exe=$(extract_python_exe) + local pip_install_command=$(extract_pip_command) + local pip_uninstall_command=$(extract_pip_uninstall_command) + # base index for torch + local base_index="https://download.pytorch.org/whl" + + # choose pins per arch + local torch_ver tv_ver cuda_ver + if is_arm; then + torch_ver="2.9.0" + tv_ver="0.24.0" + cuda_ver="130" + else + torch_ver="2.7.0" + tv_ver="0.22.0" + cuda_ver="128" + fi + + local index="${base_index}/cu${cuda_ver}" + local want_torch="${torch_ver}+cu${cuda_ver}" + + # check current torch version (may be empty) + local cur="" + cur="$(${python_exe} - <<'PY' 2>/dev/null || true +try: + import torch +except Exception: + pass +else: + print(torch.__version__, end="") +PY +)" + + # skip install if version is already satisfied + if [[ "$cur" == "$want_torch" ]]; then + return 0 + fi + + # clean install torch + echo "[INFO] Installing torch==${torch_ver} and torchvision==${tv_ver} (cu${cuda_ver}) from ${index}..." + ${pip_uninstall_command} torch torchvision torchaudio >/dev/null 2>&1 || true + ${pip_install_command} -U --index-url "${index}" "torch==${torch_ver}" "torchvision==${tv_ver}" +} + +# extract isaac sim path +extract_isaacsim_path() { + # Use the sym-link path to Isaac Sim directory + local isaac_path=${ISAACLAB_PATH}/_isaac_sim + # If above path is not available, try to find the path using python + if [ ! -d "${isaac_path}" ]; then + # Use the python executable to get the path + local python_exe=$(extract_python_exe) + # Retrieve the path importing isaac sim and getting the environment path + if [ $(${python_exe} -m pip list | grep -c 'isaacsim-rl') -gt 0 ]; then + local isaac_path=$(${python_exe} -c "import isaacsim; import os; print(os.environ['ISAAC_PATH'])") + fi + fi + # check if there is a path available + if [ ! -d "${isaac_path}" ]; then + # throw an error if no path is found + echo -e "[ERROR] Unable to find the Isaac Sim directory: '${isaac_path}'" >&2 + echo -e "\tThis could be due to the following reasons:" >&2 + echo -e "\t1. Conda environment is not activated." >&2 + echo -e "\t2. Isaac Sim pip package 'isaacsim-rl' is not installed." >&2 + echo -e "\t3. Isaac Sim directory is not available at the default path: ${ISAACLAB_PATH}/_isaac_sim" >&2 + # exit the script + exit 1 + fi + # return the result + echo ${isaac_path} +} + +# extract the python from isaacsim +extract_python_exe() { + # check if using conda + if ! [[ -z "${CONDA_PREFIX}" ]]; then + # use conda python + local python_exe=${CONDA_PREFIX}/bin/python + elif ! [[ -z "${VIRTUAL_ENV}" ]]; then + # use uv virtual environment python + local python_exe=${VIRTUAL_ENV}/bin/python + else + # use kit python + local python_exe=${ISAACLAB_PATH}/_isaac_sim/python.sh + + if [ ! -f "${python_exe}" ]; then + # note: we need to check system python for cases such as docker + # inside docker, if user installed into system python, we need to use that + # otherwise, use the python from the kit + if [ $(python -m pip list | grep -c 'isaacsim-rl') -gt 0 ]; then + local python_exe=$(which python) + fi + fi + fi + # check if there is a python path available + if [ ! -f "${python_exe}" ]; then + echo -e "[ERROR] Unable to find any Python executable at path: '${python_exe}'" >&2 + echo -e "\tThis could be due to the following reasons:" >&2 + echo -e "\t1. Conda or uv environment is not activated." >&2 + echo -e "\t2. Isaac Sim pip package 'isaacsim-rl' is not installed." >&2 + echo -e "\t3. Python executable is not available at the default path: ${ISAACLAB_PATH}/_isaac_sim/python.sh" >&2 + exit 1 + fi + # return the result + echo ${python_exe} +} + +# extract the simulator exe from isaacsim +extract_isaacsim_exe() { + # obtain the isaac sim path + local isaac_path=$(extract_isaacsim_path) + # isaac sim executable to use + local isaacsim_exe=${isaac_path}/isaac-sim.sh + # check if there is a python path available + if [ ! -f "${isaacsim_exe}" ]; then + # check for installation using Isaac Sim pip + # note: pip installed Isaac Sim can only come from a direct + # python environment, so we can directly use 'python' here + if [ $(python -m pip list | grep -c 'isaacsim-rl') -gt 0 ]; then + # Isaac Sim - Python packages entry point + local isaacsim_exe="isaacsim isaacsim.exp.full" + else + echo "[ERROR] No Isaac Sim executable found at path: ${isaac_path}" >&2 + exit 1 + fi + fi + # return the result + echo ${isaacsim_exe} +} + +# find pip command based on virtualization +extract_pip_command() { + # detect if we're in a uv environment + if [ -n "${VIRTUAL_ENV}" ] && [ -f "${VIRTUAL_ENV}/pyvenv.cfg" ] && grep -q "uv" "${VIRTUAL_ENV}/pyvenv.cfg"; then + pip_command="uv pip install" + else + # retrieve the python executable + python_exe=$(extract_python_exe) + pip_command="${python_exe} -m pip install" + fi + + echo ${pip_command} +} + +extract_pip_uninstall_command() { + # detect if we're in a uv environment + if [ -n "${VIRTUAL_ENV}" ] && [ -f "${VIRTUAL_ENV}/pyvenv.cfg" ] && grep -q "uv" "${VIRTUAL_ENV}/pyvenv.cfg"; then + pip_uninstall_command="uv pip uninstall" + else + # retrieve the python executable + python_exe=$(extract_python_exe) + pip_uninstall_command="${python_exe} -m pip uninstall -y" + fi + + echo ${pip_uninstall_command} +} + +# check if input directory is a python extension and install the module +install_isaaclab_extension() { + # retrieve the python executable + python_exe=$(extract_python_exe) + pip_command=$(extract_pip_command) + + # if the directory contains setup.py then install the python module + if [ -f "$1/setup.py" ]; then + echo -e "\t module: $1" + $pip_command --editable "$1" + fi +} + +# Resolve Torch-bundled libgomp and prepend to LD_PRELOAD, once per shell session +write_torch_gomp_hooks() { + mkdir -p "${CONDA_PREFIX}/etc/conda/activate.d" "${CONDA_PREFIX}/etc/conda/deactivate.d" + + # activation: resolve Torch's libgomp via this env's Python and prepend to LD_PRELOAD + cat > "${CONDA_PREFIX}/etc/conda/activate.d/torch_gomp.sh" <<'EOS' +# Resolve Torch-bundled libgomp and prepend to LD_PRELOAD (quiet + idempotent) +: "${_IL_PREV_LD_PRELOAD:=${LD_PRELOAD-}}" + +__gomp="$("$CONDA_PREFIX/bin/python" - <<'PY' 2>/dev/null || true +import pathlib +try: + import torch + p = pathlib.Path(torch.__file__).parent / 'lib' / 'libgomp.so.1' + print(p if p.exists() else "", end="") +except Exception: + pass +PY +)" + +if [ -n "$__gomp" ] && [ -r "$__gomp" ]; then + case ":${LD_PRELOAD:-}:" in + *":$__gomp:"*) : ;; # already present + *) export LD_PRELOAD="$__gomp${LD_PRELOAD:+:$LD_PRELOAD}";; + esac +fi +unset __gomp +EOS + + # deactivation: restore original LD_PRELOAD + cat > "${CONDA_PREFIX}/etc/conda/deactivate.d/torch_gomp_unset.sh" <<'EOS' +# restore LD_PRELOAD to pre-activation value +if [ -v _IL_PREV_LD_PRELOAD ]; then + export LD_PRELOAD="$_IL_PREV_LD_PRELOAD" + unset _IL_PREV_LD_PRELOAD +fi +EOS +} + +# Temporarily unset LD_PRELOAD (ARM only) for a block of commands +begin_arm_install_sandbox() { + if is_arm && [[ -n "${LD_PRELOAD:-}" ]]; then + export _IL_SAVED_LD_PRELOAD="$LD_PRELOAD" + unset LD_PRELOAD + echo "[INFO] ARM install sandbox: temporarily unsetting LD_PRELOAD for installation." + fi + # ensure we restore even if a command fails (set -e) + trap 'end_arm_install_sandbox' EXIT +} + +end_arm_install_sandbox() { + if [[ -n "${_IL_SAVED_LD_PRELOAD:-}" ]]; then + export LD_PRELOAD="$_IL_SAVED_LD_PRELOAD" + unset _IL_SAVED_LD_PRELOAD + fi + # remove trap so later exits don’t re-run restore + trap - EXIT +} + +# setup anaconda environment for Isaac Lab +setup_conda_env() { + # get environment name from input + local env_name=$1 + # check conda is installed + if ! command -v conda &> /dev/null + then + echo "[ERROR] Conda could not be found. Please install conda and try again." + exit 1 + fi + + # check if _isaac_sim symlink exists and isaacsim-rl is not installed via pip + if [ ! -L "${ISAACLAB_PATH}/_isaac_sim" ] && ! python -m pip list | grep -q 'isaacsim-rl'; then + echo -e "[WARNING] _isaac_sim symlink not found at ${ISAACLAB_PATH}/_isaac_sim" + echo -e "\tThis warning can be ignored if you plan to install Isaac Sim via pip." + echo -e "\tIf you are using a binary installation of Isaac Sim, please ensure the symlink is created before setting up the conda environment." + fi + + # check if the environment exists + if { conda env list | grep -w ${env_name}; } >/dev/null 2>&1; then + echo -e "[INFO] Conda environment named '${env_name}' already exists." + else + echo -e "[INFO] Creating conda environment named '${env_name}'..." + echo -e "[INFO] Installing dependencies from ${ISAACLAB_PATH}/environment.yml" + + # patch Python version if needed, but back up first + cp "${ISAACLAB_PATH}/environment.yml"{,.bak} + if is_isaacsim_version_4_5; then + echo "[INFO] Detected Isaac Sim 4.5 → forcing python=3.10" + sed -i 's/^ - python=3\.11/ - python=3.10/' "${ISAACLAB_PATH}/environment.yml" + else + echo "[INFO] Isaac Sim >= 5.0 detected, installing python=3.11" + fi + + conda env create -y --file ${ISAACLAB_PATH}/environment.yml -n ${env_name} + # (optional) restore original environment.yml: + if [[ -f "${ISAACLAB_PATH}/environment.yml.bak" ]]; then + mv "${ISAACLAB_PATH}/environment.yml.bak" "${ISAACLAB_PATH}/environment.yml" + fi + fi + + # cache current paths for later + cache_pythonpath=$PYTHONPATH + cache_ld_library_path=$LD_LIBRARY_PATH + # clear any existing files + rm -f ${CONDA_PREFIX}/etc/conda/activate.d/setenv.sh + rm -f ${CONDA_PREFIX}/etc/conda/deactivate.d/unsetenv.sh + # activate the environment + source $(conda info --base)/etc/profile.d/conda.sh + conda activate ${env_name} + # setup directories to load Isaac Sim variables + mkdir -p ${CONDA_PREFIX}/etc/conda/activate.d + mkdir -p ${CONDA_PREFIX}/etc/conda/deactivate.d + + # add variables to environment during activation + printf '%s\n' '#!/usr/bin/env bash' '' \ + '# for Isaac Lab' \ + 'export ISAACLAB_PATH='${ISAACLAB_PATH}'' \ + 'alias isaaclab='${ISAACLAB_PATH}'/isaaclab.sh' \ + '' \ + '# show icon if not running headless' \ + 'export RESOURCE_NAME="IsaacSim"' \ + '' > ${CONDA_PREFIX}/etc/conda/activate.d/setenv.sh + + write_torch_gomp_hooks + # check if we have _isaac_sim directory -> if so that means binaries were installed. + # we need to setup conda variables to load the binaries + local isaacsim_setup_conda_env_script=${ISAACLAB_PATH}/_isaac_sim/setup_conda_env.sh + + if [ -f "${isaacsim_setup_conda_env_script}" ]; then + # add variables to environment during activation + printf '%s\n' \ + '# for Isaac Sim' \ + 'source '${isaacsim_setup_conda_env_script}'' \ + '' >> ${CONDA_PREFIX}/etc/conda/activate.d/setenv.sh + fi + + # reactivate the environment to load the variables + # needed because deactivate complains about Isaac Lab alias since it otherwise doesn't exist + conda activate ${env_name} + + # remove variables from environment during deactivation + printf '%s\n' '#!/usr/bin/env bash' '' \ + '# for Isaac Lab' \ + 'unalias isaaclab &>/dev/null' \ + 'unset ISAACLAB_PATH' \ + '' \ + '# restore paths' \ + 'export PYTHONPATH='${cache_pythonpath}'' \ + 'export LD_LIBRARY_PATH='${cache_ld_library_path}'' \ + '' \ + '# for Isaac Sim' \ + 'unset RESOURCE_NAME' \ + '' > ${CONDA_PREFIX}/etc/conda/deactivate.d/unsetenv.sh + + # check if we have _isaac_sim directory -> if so that means binaries were installed. + if [ -f "${isaacsim_setup_conda_env_script}" ]; then + # add variables to environment during activation + printf '%s\n' \ + '# for Isaac Sim' \ + 'unset CARB_APP_PATH' \ + 'unset EXP_PATH' \ + 'unset ISAAC_PATH' \ + '' >> ${CONDA_PREFIX}/etc/conda/deactivate.d/unsetenv.sh + fi + + # deactivate the environment + conda deactivate + # add information to the user about alias + echo -e "[INFO] Added 'isaaclab' alias to conda environment for 'isaaclab.sh' script." + echo -e "[INFO] Created conda environment named '${env_name}'.\n" + echo -e "\t\t1. To activate the environment, run: conda activate ${env_name}" + echo -e "\t\t2. To install Isaac Lab extensions, run: isaaclab -i" + echo -e "\t\t3. To perform formatting, run: isaaclab -f" + echo -e "\t\t4. To deactivate the environment, run: conda deactivate" + echo -e "\n" +} + +# setup uv environment for Isaac Lab +setup_uv_env() { + # get environment name from input + local env_name="$1" + local python_path="$2" + + # check uv is installed + if ! command -v uv &>/dev/null; then + echo "[ERROR] uv could not be found. Please install uv and try again." + echo "[ERROR] uv can be installed here:" + echo "[ERROR] https://docs.astral.sh/uv/getting-started/installation/" + exit 1 + fi + + # check if _isaac_sim symlink exists and isaacsim-rl is not installed via pip + if [ ! -L "${ISAACLAB_PATH}/_isaac_sim" ] && ! python -m pip list | grep -q 'isaacsim-rl'; then + echo -e "[WARNING] _isaac_sim symlink not found at ${ISAACLAB_PATH}/_isaac_sim" + echo -e "\tThis warning can be ignored if you plan to install Isaac Sim via pip." + echo -e "\tIf you are using a binary installation of Isaac Sim, please ensure the symlink is created before setting up the conda environment." + fi + + # check if the environment exists + local env_path="${ISAACLAB_PATH}/${env_name}" + if [ ! -d "${env_path}" ]; then + echo -e "[INFO] Creating uv environment named '${env_name}'..." + uv venv --clear --python "${python_path}" "${env_path}" + else + echo "[INFO] uv environment '${env_name}' already exists." + fi + + # define root path for activation hooks + local isaaclab_root="${ISAACLAB_PATH}" + + # cache current paths for later + cache_pythonpath=$PYTHONPATH + cache_ld_library_path=$LD_LIBRARY_PATH + + # ensure activate file exists + touch "${env_path}/bin/activate" + + # add variables to environment during activation + cat >> "${env_path}/bin/activate" <&2 +} + + +#== +# Main +#== + +# check argument provided +if [ -z "$*" ]; then + echo "[Error] No arguments provided." >&2; + print_help + exit 0 +fi + +# pass the arguments +while [[ $# -gt 0 ]]; do + # read the key + case "$1" in + -i|--install) + # install system dependencies first + install_system_deps + # install the python packages in IsaacLab/source directory + echo "[INFO] Installing extensions inside the Isaac Lab repository..." + python_exe=$(extract_python_exe) + pip_command=$(extract_pip_command) + pip_uninstall_command=$(extract_pip_uninstall_command) + + # if on ARM arch, temporarily clear LD_PRELOAD + # LD_PRELOAD is restored below, after installation + begin_arm_install_sandbox + + # install pytorch (version based on arch) + ensure_cuda_torch + # recursively look into directories and install them + # this does not check dependencies between extensions + export -f extract_python_exe + export -f extract_pip_command + export -f extract_pip_uninstall_command + export -f install_isaaclab_extension + # source directory + find -L "${ISAACLAB_PATH}/source" -mindepth 1 -maxdepth 1 -type d -exec bash -c 'install_isaaclab_extension "{}"' \; + # install the python packages for supported reinforcement learning frameworks + echo "[INFO] Installing extra requirements such as learning frameworks..." + # check if specified which rl-framework to install + if [ -z "$2" ]; then + echo "[INFO] Installing all rl-frameworks..." + framework_name="all" + elif [ "$2" = "none" ]; then + echo "[INFO] No rl-framework will be installed." + framework_name="none" + shift # past argument + else + echo "[INFO] Installing rl-framework: $2" + framework_name=$2 + shift # past argument + fi + # install the learning frameworks specified + ${pip_command} -e "${ISAACLAB_PATH}/source/isaaclab_rl[${framework_name}]" + ${pip_command} -e "${ISAACLAB_PATH}/source/isaaclab_mimic[${framework_name}]" + + # in some rare cases, torch might not be installed properly by setup.py, add one more check here + # can prevent that from happening + ensure_cuda_torch + + # restore LD_PRELOAD if we cleared it + end_arm_install_sandbox + + # check if we are inside a docker container or are building a docker image + # in that case don't setup VSCode since it asks for EULA agreement which triggers user interaction + if is_docker; then + echo "[INFO] Running inside a docker container. Skipping VSCode settings setup." + echo "[INFO] To setup VSCode settings, run 'isaaclab -v'." + else + # update the vscode settings + update_vscode_settings + fi + + # unset local variables + unset extract_python_exe + unset extract_pip_command + unset extract_pip_uninstall_command + unset install_isaaclab_extension + shift # past argument + ;; + -c|--conda) + # use default name if not provided + if [ -z "$2" ]; then + echo "[INFO] Using default conda environment name: env_isaaclab" + conda_env_name="env_isaaclab" + else + echo "[INFO] Using conda environment name: $2" + conda_env_name=$2 + shift # past argument + fi + # setup the conda environment for Isaac Lab + setup_conda_env ${conda_env_name} + shift # past argument + ;; + -u|--uv) + # use default name if not provided + if [ -z "$2" ]; then + echo "[INFO] Using default uv environment name: env_isaaclab" + uv_env_name="env_isaaclab" + else + echo "[INFO] Using uv environment name: $2" + uv_env_name=$2 + shift # past argument + fi + # setup the uv environment for Isaac Lab + setup_uv_env ${uv_env_name} + shift # past argument + ;; + -f|--format) + # reset the python path to avoid conflicts with pre-commit + # this is needed because the pre-commit hooks are installed in a separate virtual environment + # and it uses the system python to run the hooks + if [ -n "${CONDA_DEFAULT_ENV}" ] || [ -n "${VIRTUAL_ENV}" ]; then + cache_pythonpath=${PYTHONPATH} + export PYTHONPATH="" + fi + # run the formatter over the repository + # check if pre-commit is installed + if ! command -v pre-commit &>/dev/null; then + echo "[INFO] Installing pre-commit..." + pip_command=$(extract_pip_command) + ${pip_command} pre-commit + sudo apt-get install -y pre-commit + fi + # always execute inside the Isaac Lab directory + echo "[INFO] Formatting the repository..." + cd ${ISAACLAB_PATH} + pre-commit run --all-files + cd - > /dev/null + # set the python path back to the original value + if [ -n "${CONDA_DEFAULT_ENV}" ] || [ -n "${VIRTUAL_ENV}" ]; then + export PYTHONPATH=${cache_pythonpath} + fi + + shift # past argument + # exit neatly + break + ;; + -p|--python) + # ensures Kit loads Isaac Sim’s icon instead of a generic icon on aarch64 + if is_arm; then + export RESOURCE_NAME="${RESOURCE_NAME:-IsaacSim}" + fi + # run the python provided by isaacsim + python_exe=$(extract_python_exe) + echo "[INFO] Using python from: ${python_exe}" + shift # past argument + ${python_exe} "$@" + # exit neatly + break + ;; + -s|--sim) + # run the simulator exe provided by isaacsim + isaacsim_exe=$(extract_isaacsim_exe) + echo "[INFO] Running isaac-sim from: ${isaacsim_exe}" + shift # past argument + ${isaacsim_exe} --ext-folder ${ISAACLAB_PATH}/source $@ + # exit neatly + break + ;; + -n|--new) + # run the template generator script + python_exe=$(extract_python_exe) + pip_command=$(extract_pip_command) + shift # past argument + echo "[INFO] Installing template dependencies..." + ${pip_command} -q -r ${ISAACLAB_PATH}/tools/template/requirements.txt + echo -e "\n[INFO] Running template generator...\n" + ${python_exe} ${ISAACLAB_PATH}/tools/template/cli.py $@ + # exit neatly + break + ;; + -t|--test) + # run the python provided by isaacsim + python_exe=$(extract_python_exe) + shift # past argument + ${python_exe} -m pytest ${ISAACLAB_PATH}/tools $@ + # exit neatly + break + ;; + -o|--docker) + # run the docker container helper script + docker_script=${ISAACLAB_PATH}/docker/container.sh + echo "[INFO] Running docker utility script from: ${docker_script}" + shift # past argument + bash ${docker_script} $@ + # exit neatly + break + ;; + -v|--vscode) + # update the vscode settings + update_vscode_settings + shift # past argument + # exit neatly + break + ;; + -d|--docs) + # build the documentation + echo "[INFO] Building documentation..." + # retrieve the python executable + python_exe=$(extract_python_exe) + pip_command=$(extract_pip_command) + # install pip packages + cd ${ISAACLAB_PATH}/docs + ${pip_command} -r requirements.txt > /dev/null + # build the documentation + ${python_exe} -m sphinx -b html -d _build/doctrees . _build/current + # open the documentation + echo -e "[INFO] To open documentation on default browser, run:" + echo -e "\n\t\txdg-open $(pwd)/_build/current/index.html\n" + # exit neatly + cd - > /dev/null + shift # past argument + # exit neatly + break + ;; + -h|--help) + print_help + exit 0 + ;; + *) # unknown option + echo "[Error] Invalid argument provided: $1" + print_help + exit 1 + ;; + esac +done diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..19424c641de24574270b3e893a812e9b43e78938 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,140 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +[tool.ruff] +line-length = 120 +target-version = "py310" + +# Exclude directories +extend-exclude = [ + "logs", + "_isaac_sim", + ".vscode", + "_*", + ".git", +] + +[tool.ruff.lint] +# Enable flake8 rules and other useful ones +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "UP", # pyupgrade + "C90", # mccabe complexity + # "D", # pydocstyle + "SIM", # flake8-simplify + "RET", # flake8-return +] + +# Ignore specific rules (matching your flake8 config) +ignore = [ + "E402", # Module level import not at top of file + "D401", # First line should be in imperative mood + "RET504", # Unnecessary variable assignment before return statement + "RET505", # Unnecessary elif after return statement + "SIM102", # Use a single if-statement instead of nested if-statements + "SIM103", # Return the negated condition directly + "SIM108", # Use ternary operator instead of if-else statement + "SIM117", # Merge with statements for context managers + "SIM118", # Use {key} in {dict} instead of {key} in {dict}.keys() + "UP006", # Use 'dict' instead of 'Dict' type annotation + "UP018", # Unnecessary `float` call (rewrite as a literal) +] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] # Allow unused imports in __init__.py files + +[tool.ruff.lint.mccabe] +max-complexity = 30 + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.isort] + +# Custom import sections with separate sections for each Isaac Lab extension +section-order = [ + "future", + "standard-library", + "third-party", + # Group omniverse extensions separately since they are run-time dependencies + # which are pulled in by Isaac Lab extensions + "omniverse-extensions", + # Group Isaac Lab extensions together since they are all part of the Isaac Lab project + "isaaclab", + "isaaclab-rl", + "isaaclab-mimic", + "isaaclab-tasks", + "isaaclab-assets", + # First-party is reserved for project templates + "first-party", + "local-folder", +] + +[tool.ruff.lint.isort.sections] +# Define what belongs in each custom section + +"omniverse-extensions" = [ + "isaacsim", + "omni", + "pxr", + "carb", + "usdrt", + "Semantics", + "curobo", +] + +"isaaclab" = ["isaaclab"] +"isaaclab-assets" = ["isaaclab_assets"] +"isaaclab-rl" = ["isaaclab_rl"] +"isaaclab-mimic" = ["isaaclab_mimic"] +"isaaclab-tasks" = ["isaaclab_tasks"] + +[tool.ruff.format] + +docstring-code-format = true + +[tool.pyright] + +include = ["source", "scripts"] +exclude = [ + "**/__pycache__", + "**/_isaac_sim", + "**/docs", + "**/logs", + ".git", + ".vscode", +] + +typeCheckingMode = "basic" +pythonVersion = "3.11" +pythonPlatform = "Linux" +enableTypeIgnoreComments = true + +# This is required as the CI pre-commit does not download the module (i.e. numpy, torch, prettytable) +# Therefore, we have to ignore missing imports +reportMissingImports = "none" +# This is required to ignore for type checks of modules with stubs missing. +reportMissingModuleSource = "none" # -> most common: prettytable in mdp managers + +reportGeneralTypeIssues = "none" # -> raises 218 errors (usage of literal MISSING in dataclasses) +reportOptionalMemberAccess = "warning" # -> raises 8 errors +reportPrivateUsage = "warning" + + +[tool.codespell] +skip = '*.usd,*.usda,*.usdz,*.svg,*.png,_isaac_sim*,*.bib,*.css,*/_build' +quiet-level = 0 +# the world list should always have words in lower case +ignore-words-list = "haa,slq,collapsable,buss,reacher,thirdparty" + + +[tool.pytest.ini_options] + +markers = [ + "isaacsim_ci: mark test to run in isaacsim ci", +] diff --git a/scripts/benchmarks/benchmark_cameras.py b/scripts/benchmarks/benchmark_cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d6a0c00267d8684efc07294f8ac543cc69462a --- /dev/null +++ b/scripts/benchmarks/benchmark_cameras.py @@ -0,0 +1,864 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script might help you determine how many cameras your system can realistically run +at different desired settings. + +You can supply different task environments to inject cameras into, or just test a sample scene. +Additionally, you can automatically find the maximum amount of cameras you can run a task with +through the auto-tune functionality. + +.. code-block:: bash + + # Usage with GUI + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py -h + + # Usage with headless + ./isaaclab.sh -p scripts/benchmarks/benchmark_cameras.py -h --headless + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +from collections.abc import Callable + +from isaaclab.app import AppLauncher + +# parse the arguments +args_cli = argparse.Namespace() + +parser = argparse.ArgumentParser(description="This script can help you benchmark how many cameras you could run.") + +""" +The following arguments only need to be supplied for when one wishes +to try injecting cameras into their environment, and automatically determining +the maximum camera count. +""" +parser.add_argument( + "--task", + type=str, + default=None, + required=False, + help="Supply this argument to spawn cameras within an known manager-based task environment.", +) + +parser.add_argument( + "--autotune", + default=False, + action="store_true", + help=( + "Autotuning is only supported for provided task environments." + " Supply this argument to increase the number of environments until a desired threshold is reached." + "Install pynvml in your environment; ./isaaclab.sh -m pip install pynvml" + ), +) + +parser.add_argument( + "--task_num_cameras_per_env", + type=int, + default=1, + help="The number of cameras per environment to use when using a known task.", +) + +parser.add_argument( + "--use_fabric", action="store_true", default=False, help="Enable fabric and use USD I/O operations." +) + +parser.add_argument( + "--autotune_max_percentage_util", + nargs="+", + type=float, + default=[100.0, 80.0, 80.0, 80.0], + required=False, + help=( + "The system utilization percentage thresholds to reach before an autotune is finished. " + "If any one of these limits are hit, the autotune stops." + "Thresholds are, in order, maximum CPU percentage utilization," + "maximum RAM percentage utilization, maximum GPU compute percent utilization, " + "amd maximum GPU memory utilization." + ), +) + +parser.add_argument( + "--autotune_max_camera_count", type=int, default=4096, help="The maximum amount of cameras allowed in an autotune." +) + +parser.add_argument( + "--autotune_camera_count_interval", + type=int, + default=25, + help=( + "The number of cameras to try to add to the environment if the current camera count" + " falls within permitted system resource utilization limits." + ), +) + +""" +The following arguments are shared for when injecting cameras into a task environment, +as well as when creating cameras independent of a task environment. +""" + +parser.add_argument( + "--num_tiled_cameras", + type=int, + default=0, + required=False, + help="Number of tiled cameras to create. For autotuning, this is how many cameras to start with.", +) + +parser.add_argument( + "--num_standard_cameras", + type=int, + default=0, + required=False, + help="Number of standard cameras to create. For autotuning, this is how many cameras to start with.", +) + +parser.add_argument( + "--num_ray_caster_cameras", + type=int, + default=0, + required=False, + help="Number of ray caster cameras to create. For autotuning, this is how many cameras to start with.", +) + +parser.add_argument( + "--tiled_camera_data_types", + nargs="+", + type=str, + default=["rgb", "depth"], + help="The data types rendered by the tiled camera", +) + +parser.add_argument( + "--standard_camera_data_types", + nargs="+", + type=str, + default=["rgb", "distance_to_image_plane", "distance_to_camera"], + help="The data types rendered by the standard camera", +) + +parser.add_argument( + "--ray_caster_camera_data_types", + nargs="+", + type=str, + default=["distance_to_image_plane"], + help="The data types rendered by the ray caster camera.", +) + +parser.add_argument( + "--ray_caster_visible_mesh_prim_paths", + nargs="+", + type=str, + default=["/World/ground"], + help="WARNING: Ray Caster can currently only cast against a single, static, object", +) + +parser.add_argument( + "--convert_depth_to_camera_to_image_plane", + action="store_true", + default=True, + help=( + "Enable undistorting from perspective view (distance to camera data_type)" + "to orthogonal view (distance to plane data_type) for depth." + "This is currently needed to create undisorted depth images/point cloud." + ), +) + +parser.add_argument( + "--keep_raw_depth", + dest="convert_depth_to_camera_to_image_plane", + action="store_false", + help=( + "Disable undistorting from perspective view (distance to camera)" + "to orthogonal view (distance to plane data_type) for depth." + ), +) + +parser.add_argument( + "--height", + type=int, + default=120, + required=False, + help="Height in pixels of cameras", +) + +parser.add_argument( + "--width", + type=int, + default=140, + required=False, + help="Width in pixels of cameras", +) + +parser.add_argument( + "--warm_start_length", + type=int, + default=3, + required=False, + help=( + "Number of steps to run the sim before starting benchmark." + "Needed to avoid blank images at the start of the simulation." + ), +) + +parser.add_argument( + "--experiment_length", + type=int, + default=15, + required=False, + help="Number of steps to average over", +) + +# This argument is only used when a task is not provided. +parser.add_argument( + "--num_objects", + type=int, + default=10, + required=False, + help="Number of objects to spawn into the scene when not using a known task.", +) + + +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() +args_cli.enable_cameras = True + +if args_cli.autotune: + import pynvml + +if len(args_cli.ray_caster_visible_mesh_prim_paths) > 1: + print("[WARNING]: Ray Casting is only currently supported for a single, static object") +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random +import time + +import gymnasium as gym +import numpy as np +import psutil +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.scene.interactive_scene import InteractiveScene +from isaaclab.sensors import ( + Camera, + CameraCfg, + RayCasterCamera, + RayCasterCameraCfg, + TiledCamera, + TiledCameraCfg, + patterns, +) +from isaaclab.utils.math import orthogonalize_perspective_depth, unproject_depth + +from isaaclab_tasks.utils import load_cfg_from_registry + +""" +Camera Creation +""" + + +def create_camera_base( + camera_cfg: type[CameraCfg | TiledCameraCfg], + num_cams: int, + data_types: list[str], + height: int, + width: int, + prim_path: str | None = None, + instantiate: bool = True, +) -> Camera | TiledCamera | CameraCfg | TiledCameraCfg | None: + """Generalized function to create a camera or tiled camera sensor.""" + # Determine prim prefix based on the camera class + name = camera_cfg.class_type.__name__ + + if instantiate: + # Create the necessary prims + for idx in range(num_cams): + sim_utils.create_prim(f"/World/{name}_{idx:02d}", "Xform") + if prim_path is None: + prim_path = f"/World/{name}_.*/{name}" + # If valid camera settings are provided, create the camera + if num_cams > 0 and len(data_types) > 0 and height > 0 and width > 0: + cfg = camera_cfg( + prim_path=prim_path, + update_period=0, + height=height, + width=width, + data_types=data_types, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1e4) + ), + ) + if instantiate: + return camera_cfg.class_type(cfg=cfg) + else: + return cfg + else: + return None + + +def create_tiled_cameras( + num_cams: int = 2, data_types: list[str] | None = None, height: int = 100, width: int = 120 +) -> TiledCamera | None: + if data_types is None: + data_types = ["rgb", "depth"] + """Defines the tiled camera sensor to add to the scene.""" + return create_camera_base( + camera_cfg=TiledCameraCfg, + num_cams=num_cams, + data_types=data_types, + height=height, + width=width, + ) + + +def create_cameras( + num_cams: int = 2, data_types: list[str] | None = None, height: int = 100, width: int = 120 +) -> Camera | None: + """Defines the Standard cameras.""" + if data_types is None: + data_types = ["rgb", "depth"] + return create_camera_base( + camera_cfg=CameraCfg, num_cams=num_cams, data_types=data_types, height=height, width=width + ) + + +def create_ray_caster_cameras( + num_cams: int = 2, + data_types: list[str] = ["distance_to_image_plane"], + mesh_prim_paths: list[str] = ["/World/ground"], + height: int = 100, + width: int = 120, + prim_path: str = "/World/RayCasterCamera_.*/RayCaster", + instantiate: bool = True, +) -> RayCasterCamera | RayCasterCameraCfg | None: + """Create the raycaster cameras; different configuration than Standard/Tiled camera""" + for idx in range(num_cams): + sim_utils.create_prim(f"/World/RayCasterCamera_{idx:02d}/RayCaster", "Xform") + + if num_cams > 0 and len(data_types) > 0 and height > 0 and width > 0: + cam_cfg = RayCasterCameraCfg( + prim_path=prim_path, + mesh_prim_paths=mesh_prim_paths, + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + data_types=data_types, + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=480, + width=640, + ), + ) + if instantiate: + return RayCasterCamera(cfg=cam_cfg) + else: + return cam_cfg + + else: + return None + + +def create_tiled_camera_cfg(prim_path: str) -> TiledCameraCfg: + """Grab a simple tiled camera config for injecting into task environments.""" + return create_camera_base( + TiledCameraCfg, + num_cams=args_cli.num_tiled_cameras, + data_types=args_cli.tiled_camera_data_types, + width=args_cli.width, + height=args_cli.height, + prim_path="{ENV_REGEX_NS}/" + prim_path, + instantiate=False, + ) + + +def create_standard_camera_cfg(prim_path: str) -> CameraCfg: + """Grab a simple standard camera config for injecting into task environments.""" + return create_camera_base( + CameraCfg, + num_cams=args_cli.num_standard_cameras, + data_types=args_cli.standard_camera_data_types, + width=args_cli.width, + height=args_cli.height, + prim_path="{ENV_REGEX_NS}/" + prim_path, + instantiate=False, + ) + + +def create_ray_caster_camera_cfg(prim_path: str) -> RayCasterCameraCfg: + """Grab a simple ray caster config for injecting into task environments.""" + return create_ray_caster_cameras( + num_cams=args_cli.num_ray_caster_cameras, + data_types=args_cli.ray_caster_camera_data_types, + width=args_cli.width, + height=args_cli.height, + prim_path="{ENV_REGEX_NS}/" + prim_path, + ) + + +""" +Scene Creation +""" + + +def design_scene( + num_tiled_cams: int = 2, + num_standard_cams: int = 0, + num_ray_caster_cams: int = 0, + tiled_camera_data_types: list[str] | None = None, + standard_camera_data_types: list[str] | None = None, + ray_caster_camera_data_types: list[str] | None = None, + height: int = 100, + width: int = 200, + num_objects: int = 20, + mesh_prim_paths: list[str] = ["/World/ground"], +) -> dict: + """Design the scene.""" + if tiled_camera_data_types is None: + tiled_camera_data_types = ["rgb"] + if standard_camera_data_types is None: + standard_camera_data_types = ["rgb"] + if ray_caster_camera_data_types is None: + ray_caster_camera_data_types = ["distance_to_image_plane"] + + # Populate scene + # -- Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/ground", cfg) + # -- Lights + cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create a dictionary for the scene entities + scene_entities = {} + + # Xform to hold objects + sim_utils.create_prim("/World/Objects", "Xform") + # Random objects + for i in range(num_objects): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # sample random color + color = (random.random(), random.random(), random.random()) + # choose random prim type + prim_type = random.choice(["Cube", "Cone", "Cylinder"]) + common_properties = { + "rigid_props": sim_utils.RigidBodyPropertiesCfg(), + "mass_props": sim_utils.MassPropertiesCfg(mass=5.0), + "collision_props": sim_utils.CollisionPropertiesCfg(), + "visual_material": sim_utils.PreviewSurfaceCfg(diffuse_color=color, metallic=0.5), + "semantic_tags": [("class", prim_type)], + } + if prim_type == "Cube": + shape_cfg = sim_utils.CuboidCfg(size=(0.25, 0.25, 0.25), **common_properties) + elif prim_type == "Cone": + shape_cfg = sim_utils.ConeCfg(radius=0.1, height=0.25, **common_properties) + elif prim_type == "Cylinder": + shape_cfg = sim_utils.CylinderCfg(radius=0.25, height=0.25, **common_properties) + # Rigid Object + obj_cfg = RigidObjectCfg( + prim_path=f"/World/Objects/Obj_{i:02d}", + spawn=shape_cfg, + init_state=RigidObjectCfg.InitialStateCfg(pos=position), + ) + scene_entities[f"rigid_object{i}"] = RigidObject(cfg=obj_cfg) + + # Sensors + standard_camera = create_cameras( + num_cams=num_standard_cams, data_types=standard_camera_data_types, height=height, width=width + ) + tiled_camera = create_tiled_cameras( + num_cams=num_tiled_cams, data_types=tiled_camera_data_types, height=height, width=width + ) + ray_caster_camera = create_ray_caster_cameras( + num_cams=num_ray_caster_cams, + data_types=ray_caster_camera_data_types, + mesh_prim_paths=mesh_prim_paths, + height=height, + width=width, + ) + # return the scene information + if tiled_camera is not None: + scene_entities["tiled_camera"] = tiled_camera + if standard_camera is not None: + scene_entities["standard_camera"] = standard_camera + if ray_caster_camera is not None: + scene_entities["ray_caster_camera"] = ray_caster_camera + return scene_entities + + +def inject_cameras_into_task( + task: str, + num_cams: int, + camera_name_prefix: str, + camera_creation_callable: Callable, + num_cameras_per_env: int = 1, +) -> gym.Env: + """Loads the task, sticks cameras into the config, and creates the environment.""" + cfg = load_cfg_from_registry(task, "env_cfg_entry_point") + cfg.sim.device = args_cli.device + cfg.sim.use_fabric = args_cli.use_fabric + scene_cfg = cfg.scene + + num_envs = int(num_cams / num_cameras_per_env) + scene_cfg.num_envs = num_envs + + for idx in range(num_cameras_per_env): + suffix = "" if idx == 0 else str(idx) + name = camera_name_prefix + suffix + setattr(scene_cfg, name, camera_creation_callable(name)) + cfg.scene = scene_cfg + env = gym.make(task, cfg=cfg) + return env + + +""" +System diagnosis +""" + + +def get_utilization_percentages(reset: bool = False, max_values: list[float] = [0.0, 0.0, 0.0, 0.0]) -> list[float]: + """Get the maximum CPU, RAM, GPU utilization (processing), and + GPU memory usage percentages since the last time reset was true.""" + if reset: + max_values[:] = [0, 0, 0, 0] # Reset the max values + + # CPU utilization + cpu_usage = psutil.cpu_percent(interval=0.1) + max_values[0] = max(max_values[0], cpu_usage) + + # RAM utilization + memory_info = psutil.virtual_memory() + ram_usage = memory_info.percent + max_values[1] = max(max_values[1], ram_usage) + + # GPU utilization using pynvml + if torch.cuda.is_available(): + if args_cli.autotune: + pynvml.nvmlInit() # Initialize NVML + for i in range(torch.cuda.device_count()): + handle = pynvml.nvmlDeviceGetHandleByIndex(i) + + # GPU Utilization + gpu_utilization = pynvml.nvmlDeviceGetUtilizationRates(handle) + gpu_processing_utilization_percent = gpu_utilization.gpu # GPU core utilization + max_values[2] = max(max_values[2], gpu_processing_utilization_percent) + + # GPU Memory Usage + memory_info = pynvml.nvmlDeviceGetMemoryInfo(handle) + gpu_memory_total = memory_info.total + gpu_memory_used = memory_info.used + gpu_memory_utilization_percent = (gpu_memory_used / gpu_memory_total) * 100 + max_values[3] = max(max_values[3], gpu_memory_utilization_percent) + + pynvml.nvmlShutdown() # Shutdown NVML after usage + else: + gpu_processing_utilization_percent = None + gpu_memory_utilization_percent = None + return max_values + + +""" +Experiment +""" + + +def run_simulator( + sim: sim_utils.SimulationContext | None, + scene_entities: dict | InteractiveScene, + warm_start_length: int = 10, + experiment_length: int = 100, + tiled_camera_data_types: list[str] | None = None, + standard_camera_data_types: list[str] | None = None, + ray_caster_camera_data_types: list[str] | None = None, + depth_predicate: Callable = lambda x: "to" in x or x == "depth", + perspective_depth_predicate: Callable = lambda x: x == "distance_to_camera", + convert_depth_to_camera_to_image_plane: bool = True, + max_cameras_per_env: int = 1, + env: gym.Env | None = None, +) -> dict: + """Run the simulator with all cameras, and return timing analytics. Visualize if desired.""" + + if tiled_camera_data_types is None: + tiled_camera_data_types = ["rgb"] + if standard_camera_data_types is None: + standard_camera_data_types = ["rgb"] + if ray_caster_camera_data_types is None: + ray_caster_camera_data_types = ["distance_to_image_plane"] + + # Initialize camera lists + tiled_cameras = [] + standard_cameras = [] + ray_caster_cameras = [] + + # Dynamically extract cameras from the scene entities up to max_cameras_per_env + for i in range(max_cameras_per_env): + # Extract tiled cameras + tiled_camera_key = f"tiled_camera{i}" if i > 0 else "tiled_camera" + standard_camera_key = f"standard_camera{i}" if i > 0 else "standard_camera" + ray_caster_camera_key = f"ray_caster_camera{i}" if i > 0 else "ray_caster_camera" + + try: # if instead you checked ... if key is in scene_entities... # errors out always even if key present + tiled_cameras.append(scene_entities[tiled_camera_key]) + standard_cameras.append(scene_entities[standard_camera_key]) + ray_caster_cameras.append(scene_entities[ray_caster_camera_key]) + except KeyError: + break + + # Initialize camera counts + camera_lists = [tiled_cameras, standard_cameras, ray_caster_cameras] + camera_data_types = [tiled_camera_data_types, standard_camera_data_types, ray_caster_camera_data_types] + labels = ["tiled", "standard", "ray_caster"] + + if sim is not None: + # Set camera world poses + for camera_list in camera_lists: + for camera in camera_list: + num_cameras = camera.data.intrinsic_matrices.size(0) + positions = torch.tensor([[2.5, 2.5, 2.5]], device=sim.device).repeat(num_cameras, 1) + targets = torch.tensor([[0.0, 0.0, 0.0]], device=sim.device).repeat(num_cameras, 1) + camera.set_world_poses_from_view(positions, targets) + + # Initialize timing variables + timestep = 0 + total_time = 0.0 + valid_timesteps = 0 + sim_step_time = 0.0 + + while simulation_app.is_running() and timestep < experiment_length: + print(f"On timestep {timestep} of {experiment_length}, with warm start of {warm_start_length}") + get_utilization_percentages() + + # Measure the total simulation step time + step_start_time = time.time() + + if sim is not None: + sim.step() + + if env is not None: + with torch.inference_mode(): + # compute zero actions + actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device) + # apply actions + env.step(actions) + + # Update cameras and process vision data within the simulation step + clouds = {} + images = {} + depth_images = {} + + # Loop through all camera lists and their data_types + for camera_list, data_types, label in zip(camera_lists, camera_data_types, labels): + for cam_idx, camera in enumerate(camera_list): + if env is None: # No env, need to step cams manually + # Only update the camera if it hasn't been updated as part of scene_entities.update ... + camera.update(dt=sim.get_physics_dt()) + + for data_type in data_types: + data_label = f"{label}_{cam_idx}_{data_type}" + + if depth_predicate(data_type): # is a depth image, want to create cloud + depth = camera.data.output[data_type] + depth_images[data_label + "_raw"] = depth + if perspective_depth_predicate(data_type) and convert_depth_to_camera_to_image_plane: + depth = orthogonalize_perspective_depth( + camera.data.output[data_type], camera.data.intrinsic_matrices + ) + depth_images[data_label + "_undistorted"] = depth + + pointcloud = unproject_depth(depth=depth, intrinsics=camera.data.intrinsic_matrices) + clouds[data_label] = pointcloud + else: # rgb image, just save it + image = camera.data.output[data_type] + images[data_label] = image + + # End timing for the step + step_end_time = time.time() + sim_step_time += step_end_time - step_start_time + + if timestep > warm_start_length: + get_utilization_percentages(reset=True) + total_time += step_end_time - step_start_time + valid_timesteps += 1 + + timestep += 1 + + # Calculate average timings + if valid_timesteps > 0: + avg_timestep_duration = total_time / valid_timesteps + avg_sim_step_duration = sim_step_time / experiment_length + else: + avg_timestep_duration = 0.0 + avg_sim_step_duration = 0.0 + + # Package timing analytics in a dictionary + timing_analytics = { + "average_timestep_duration": avg_timestep_duration, + "average_sim_step_duration": avg_sim_step_duration, + "total_simulation_time": sim_step_time, + "total_experiment_duration": sim_step_time, + } + + system_utilization_analytics = get_utilization_percentages() + + print("--- Benchmark Results ---") + print(f"Average timestep duration: {avg_timestep_duration:.6f} seconds") + print(f"Average simulation step duration: {avg_sim_step_duration:.6f} seconds") + print(f"Total simulation time: {sim_step_time:.6f} seconds") + print("\nSystem Utilization Statistics:") + print( + f"| CPU:{system_utilization_analytics[0]}% | " + f"RAM:{system_utilization_analytics[1]}% | " + f"GPU Compute:{system_utilization_analytics[2]}% | " + f" GPU Memory: {system_utilization_analytics[3]:.2f}% |" + ) + + return {"timing_analytics": timing_analytics, "system_utilization_analytics": system_utilization_analytics} + + +def main(): + """Main function.""" + # Load simulation context + if args_cli.num_tiled_cameras + args_cli.num_standard_cameras + args_cli.num_ray_caster_cameras <= 0: + raise ValueError("You must select at least one camera.") + if ( + (args_cli.num_tiled_cameras > 0 and args_cli.num_standard_cameras > 0) + or (args_cli.num_ray_caster_cameras > 0 and args_cli.num_standard_cameras > 0) + or (args_cli.num_ray_caster_cameras > 0 and args_cli.num_tiled_cameras > 0) + ): + print("[WARNING]: You have elected to use more than one camera type.") + print("[WARNING]: For a benchmark to be meaningful, use ONLY ONE camera type at a time.") + print( + "[WARNING]: For example, if num_tiled_cameras=100, for a meaningful benchmark," + "num_standard_cameras should be 0, and num_ray_caster_cameras should be 0" + ) + raise ValueError("Benchmark one camera at a time.") + + print("[INFO]: Designing the scene") + if args_cli.task is None: + print("[INFO]: No task environment provided, creating random scene.") + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + scene_entities = design_scene( + num_tiled_cams=args_cli.num_tiled_cameras, + num_standard_cams=args_cli.num_standard_cameras, + num_ray_caster_cams=args_cli.num_ray_caster_cameras, + tiled_camera_data_types=args_cli.tiled_camera_data_types, + standard_camera_data_types=args_cli.standard_camera_data_types, + ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types, + height=args_cli.height, + width=args_cli.width, + num_objects=args_cli.num_objects, + mesh_prim_paths=args_cli.ray_caster_visible_mesh_prim_paths, + ) + # Play simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run simulator + run_simulator( + sim=sim, + scene_entities=scene_entities, + warm_start_length=args_cli.warm_start_length, + experiment_length=args_cli.experiment_length, + tiled_camera_data_types=args_cli.tiled_camera_data_types, + standard_camera_data_types=args_cli.standard_camera_data_types, + ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types, + convert_depth_to_camera_to_image_plane=args_cli.convert_depth_to_camera_to_image_plane, + ) + else: + print("[INFO]: Using known task environment, injecting cameras.") + autotune_iter = 0 + max_sys_util_thresh = [0.0, 0.0, 0.0] + max_num_cams = max(args_cli.num_tiled_cameras, args_cli.num_standard_cameras, args_cli.num_ray_caster_cameras) + cur_num_cams = max_num_cams + cur_sys_util = max_sys_util_thresh + interval = args_cli.autotune_camera_count_interval + + if args_cli.autotune: + max_sys_util_thresh = args_cli.autotune_max_percentage_util + max_num_cams = args_cli.autotune_max_camera_count + print("[INFO]: Auto tuning until any of the following threshold are met") + print(f"|CPU: {max_sys_util_thresh[0]}% | RAM {max_sys_util_thresh[1]}% | GPU: {max_sys_util_thresh[2]}% |") + print(f"[INFO]: Maximum number of cameras allowed: {max_num_cams}") + # Determine which camera is being tested... + tiled_camera_cfg = create_tiled_camera_cfg("tiled_camera") + standard_camera_cfg = create_standard_camera_cfg("standard_camera") + ray_caster_camera_cfg = create_ray_caster_camera_cfg("ray_caster_camera") + camera_name_prefix = "" + camera_creation_callable = None + num_cams = 0 + if tiled_camera_cfg is not None: + camera_name_prefix = "tiled_camera" + camera_creation_callable = create_tiled_camera_cfg + num_cams = args_cli.num_tiled_cameras + elif standard_camera_cfg is not None: + camera_name_prefix = "standard_camera" + camera_creation_callable = create_standard_camera_cfg + num_cams = args_cli.num_standard_cameras + elif ray_caster_camera_cfg is not None: + camera_name_prefix = "ray_caster_camera" + camera_creation_callable = create_ray_caster_camera_cfg + num_cams = args_cli.num_ray_caster_cameras + + while ( + all(cur <= max_thresh for cur, max_thresh in zip(cur_sys_util, max_sys_util_thresh)) + and cur_num_cams <= max_num_cams + ): + cur_num_cams = num_cams + interval * autotune_iter + autotune_iter += 1 + + env = inject_cameras_into_task( + task=args_cli.task, + num_cams=cur_num_cams, + camera_name_prefix=camera_name_prefix, + camera_creation_callable=camera_creation_callable, + num_cameras_per_env=args_cli.task_num_cameras_per_env, + ) + env.reset() + print(f"Testing with {cur_num_cams} {camera_name_prefix}") + analysis = run_simulator( + sim=None, + scene_entities=env.unwrapped.scene, + warm_start_length=args_cli.warm_start_length, + experiment_length=args_cli.experiment_length, + tiled_camera_data_types=args_cli.tiled_camera_data_types, + standard_camera_data_types=args_cli.standard_camera_data_types, + ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types, + convert_depth_to_camera_to_image_plane=args_cli.convert_depth_to_camera_to_image_plane, + max_cameras_per_env=args_cli.task_num_cameras_per_env, + env=env, + ) + + cur_sys_util = analysis["system_utilization_analytics"] + print("Triggering reset...") + env.close() + sim_utils.create_new_stage() + print("[INFO]: DONE! Feel free to CTRL + C Me ") + print(f"[INFO]: If you've made it this far, you can likely simulate {cur_num_cams} {camera_name_prefix}") + print("Keep in mind, this is without any training running on the GPU.") + print("Set lower utilization thresholds to account for training.") + + if not args_cli.autotune: + print("[WARNING]: GPU Util Statistics only correct while autotuning, ignore above.") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/benchmarks/benchmark_load_robot.py b/scripts/benchmarks/benchmark_load_robot.py new file mode 100644 index 0000000000000000000000000000000000000000..45d066162c8f625227897db3ff1a490aa09dd457 --- /dev/null +++ b/scripts/benchmarks/benchmark_load_robot.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to benchmark loading multiple copies of a robot. + +.. code-block python + + ./isaaclab.sh -p scripts/benchmarks/benchmark_load_robot.py --num_envs 2048 --robot g1 --headless + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import time + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Benchmark loading different robots.") +parser.add_argument("--num_envs", type=int, default=32, help="Number of robots to simulate.") +parser.add_argument( + "--robot", + type=str, + choices=["anymal_d", "h1", "g1"], + default="h1", + help="Choose which robot to load: anymal_d, h1, or g1.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, _ = parser.parse_known_args() + +# Start the timer for app start +app_start_time_begin = time.perf_counter_ns() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +# End the timer for app start +app_start_time_end = time.perf_counter_ns() + +print(f"[INFO]: App start time: {(app_start_time_end - app_start_time_begin) / 1e6:.2f} ms") + +"""Rest everything follows.""" + +# Start the timer for imports +imports_time_begin = time.perf_counter_ns() + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets import ANYMAL_D_CFG, G1_MINIMAL_CFG, H1_MINIMAL_CFG # isort:skip + + +# Stop the timer for imports +imports_time_end = time.perf_counter_ns() + +print(f"[INFO]: Imports time: {(imports_time_end - imports_time_begin) / 1e6:.2f} ms") + + +@configclass +class RobotSceneCfg(InteractiveSceneCfg): + """Configuration for a simple scene with a robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # articulation + if args_cli.robot == "h1": + robot: ArticulationCfg = H1_MINIMAL_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + elif args_cli.robot == "g1": + robot: ArticulationCfg = G1_MINIMAL_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + elif args_cli.robot == "anymal_d": + robot: ArticulationCfg = ANYMAL_D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + else: + raise ValueError(f"Unsupported robot type: {args_cli.robot}.") + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. + robot = scene["robot"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + + # Start the timer for creating the scene + step_time_begin = time.perf_counter_ns() + num_steps = 2000 + + # Simulation loop + for count in range(num_steps): + # Reset + if count % 500 == 0: + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + joint_pos += torch.rand_like(joint_pos) * 0.1 + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + # Apply random action + # -- generate random joint efforts + efforts = torch.randn_like(robot.data.joint_pos) * 5.0 + # -- apply action to the robot + robot.set_joint_effort_target(efforts) + # -- write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + # Update buffers + scene.update(sim_dt) + + # Stop the timer for reset + step_time_end = time.perf_counter_ns() + print(f"[INFO]: Per step time: {(step_time_end - step_time_begin) / num_steps / 1e6:.2f} ms") + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device="cuda:0") + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 0.0, 4.0], [0.0, 0.0, 2.0]) + + # Start the timer for creating the scene + setup_time_begin = time.perf_counter_ns() + # Design scene + scene_cfg = RobotSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Stop the timer for creating the scene + setup_time_end = time.perf_counter_ns() + print(f"[INFO]: Scene creation time: {(setup_time_end - setup_time_begin) / 1e6:.2f} ms") + + # Start the timer for reset + reset_time_begin = time.perf_counter_ns() + # Play the simulator + sim.reset() + # Stop the timer for reset + reset_time_end = time.perf_counter_ns() + print(f"[INFO]: Sim start time: {(reset_time_end - reset_time_begin) / 1e6:.2f} ms") + + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/benchmarks/benchmark_non_rl.py b/scripts/benchmarks/benchmark_non_rl.py new file mode 100644 index 0000000000000000000000000000000000000000..20d4221bc30e45f1a911f89fe9d06e81b9efb319 --- /dev/null +++ b/scripts/benchmarks/benchmark_non_rl.py @@ -0,0 +1,216 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to benchmark non-RL environment.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import os +import sys +import time + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument("--num_frames", type=int, default=100, help="Number of environment frames to run benchmark for.") +parser.add_argument( + "--benchmark_backend", + type=str, + default="OmniPerfKPIFile", + choices=["LocalLogMetrics", "JSONFileMetrics", "OsmoKPIFile", "OmniPerfKPIFile"], + help="Benchmarking backend options, defaults OmniPerfKPIFile", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +app_start_time_begin = time.perf_counter_ns() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +app_start_time_end = time.perf_counter_ns() + +"""Rest everything follows.""" + +# enable benchmarking extension +from isaacsim.core.utils.extensions import enable_extension + +enable_extension("isaacsim.benchmark.services") +from isaacsim.benchmark.services import BaseIsaacBenchmark + +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) + +from isaaclab.utils.timer import Timer + +from scripts.benchmarks.utils import ( + log_app_start_time, + log_python_imports_time, + log_runtime_step_times, + log_scene_creation_time, + log_simulation_start_time, + log_task_start_time, + log_total_start_time, +) + +imports_time_begin = time.perf_counter_ns() + +import os +from datetime import datetime + +import gymnasium as gym +import numpy as np +import torch + +from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg +from isaaclab.utils.dict import print_dict + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config + +imports_time_end = time.perf_counter_ns() + + +# Create the benchmark +benchmark = BaseIsaacBenchmark( + benchmark_name="benchmark_non_rl", + workflow_metadata={ + "metadata": [ + {"name": "task", "data": args_cli.task}, + {"name": "seed", "data": args_cli.seed}, + {"name": "num_envs", "data": args_cli.num_envs}, + {"name": "num_frames", "data": args_cli.num_frames}, + ] + }, + backend_type=args_cli.benchmark_backend, +) + + +@hydra_task_config(args_cli.task, None) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Benchmark without RL in the loop.""" + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + env_cfg.seed = args_cli.seed + + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # process distributed + world_size = 1 + world_rank = 0 + if args_cli.distributed: + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + world_size = int(os.getenv("WORLD_SIZE", 1)) + world_rank = app_launcher.global_rank + + task_startup_time_begin = time.perf_counter_ns() + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + # wrap for video recording + if args_cli.video: + log_root_path = os.path.abs(f"benchmark/{args_cli.task}") + log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + video_kwargs = { + "video_folder": os.path.join(log_root_path, log_dir, "videos"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + task_startup_time_end = time.perf_counter_ns() + + env.reset() + + benchmark.set_phase("sim_runtime") + + # counter for number of frames to run for + num_frames = 0 + # log frame times + step_times = [] + while simulation_app.is_running(): + while num_frames < args_cli.num_frames: + # get upper and lower bounds of action space, sample actions randomly on this interval + action_high = 1 + action_low = -1 + actions = (action_high - action_low) * torch.rand( + env.unwrapped.num_envs, env.unwrapped.single_action_space.shape[0], device=env.unwrapped.device + ) - action_high + + # env stepping + env_step_time_begin = time.perf_counter_ns() + _ = env.step(actions) + end_step_time_end = time.perf_counter_ns() + step_times.append(end_step_time_end - env_step_time_begin) + + num_frames += 1 + + # terminate + break + + if world_rank == 0: + benchmark.store_measurements() + + # compute stats + step_times = np.array(step_times) / 1e6 # ns to ms + fps = 1.0 / (step_times / 1000) + effective_fps = fps * env.unwrapped.num_envs * world_size + + # prepare step timing dict + environment_step_times = { + "Environment step times": step_times.tolist(), + "Environment step FPS": fps.tolist(), + "Environment step effective FPS": effective_fps.tolist(), + } + + log_app_start_time(benchmark, (app_start_time_end - app_start_time_begin) / 1e6) + log_python_imports_time(benchmark, (imports_time_end - imports_time_begin) / 1e6) + log_task_start_time(benchmark, (task_startup_time_end - task_startup_time_begin) / 1e6) + log_scene_creation_time(benchmark, Timer.get_timer_info("scene_creation") * 1000) + log_simulation_start_time(benchmark, Timer.get_timer_info("simulation_start") * 1000) + log_total_start_time(benchmark, (task_startup_time_end - app_start_time_begin) / 1e6) + log_runtime_step_times(benchmark, environment_step_times, compute_stats=True) + + benchmark.stop() + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/benchmarks/benchmark_rlgames.py b/scripts/benchmarks/benchmark_rlgames.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f20ecd02a4b0432747ac9a3370e1bee6a01f79 --- /dev/null +++ b/scripts/benchmarks/benchmark_rlgames.py @@ -0,0 +1,271 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to benchmark RL agent with RL-Games.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys +import time + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument("--max_iterations", type=int, default=10, help="RL Policy training iterations.") +parser.add_argument( + "--benchmark_backend", + type=str, + default="OmniPerfKPIFile", + choices=["LocalLogMetrics", "JSONFileMetrics", "OsmoKPIFile", "OmniPerfKPIFile"], + help="Benchmarking backend options, defaults OmniPerfKPIFile", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +app_start_time_begin = time.perf_counter_ns() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +app_start_time_end = time.perf_counter_ns() + +"""Rest everything follows.""" + +# enable benchmarking extension +from isaacsim.core.utils.extensions import enable_extension + +enable_extension("isaacsim.benchmark.services") +from isaacsim.benchmark.services import BaseIsaacBenchmark + +imports_time_begin = time.perf_counter_ns() + +import math +import os +import random +from datetime import datetime + +import gymnasium as gym +import torch +from rl_games.common import env_configurations, vecenv +from rl_games.common.algo_observer import IsaacAlgoObserver +from rl_games.torch_runner import Runner + +from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config + +imports_time_end = time.perf_counter_ns() + +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) + +from isaaclab.utils.timer import Timer + +from scripts.benchmarks.utils import ( + log_app_start_time, + log_python_imports_time, + log_rl_policy_episode_lengths, + log_rl_policy_rewards, + log_runtime_step_times, + log_scene_creation_time, + log_simulation_start_time, + log_task_start_time, + log_total_start_time, + parse_tf_logs, +) + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = False + + +# Create the benchmark +benchmark = BaseIsaacBenchmark( + benchmark_name="benchmark_rlgames_train", + workflow_metadata={ + "metadata": [ + {"name": "task", "data": args_cli.task}, + {"name": "seed", "data": args_cli.seed}, + {"name": "num_envs", "data": args_cli.num_envs}, + {"name": "max_iterations", "data": args_cli.max_iterations}, + ] + }, + backend_type=args_cli.benchmark_backend, +) + + +@hydra_task_config(args_cli.task, "rl_games_cfg_entry_point") +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Train with RL-Games agent.""" + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # update agent device to match simulation device + if args_cli.device is not None: + agent_cfg["params"]["config"]["device"] = args_cli.device + agent_cfg["params"]["config"]["device_name"] = args_cli.device + + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + agent_cfg["params"]["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["params"]["seed"] + + # process distributed + world_rank = 0 + if args_cli.distributed: + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + agent_cfg["params"]["config"]["device"] = f"cuda:{app_launcher.local_rank}" + world_rank = app_launcher.global_rank + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rl_games", agent_cfg["params"]["config"]["name"]) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # specify directory for logging runs + log_dir = agent_cfg["params"]["config"].get("full_experiment_name", datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) + # set directory into agent config + # logging directory path: / + agent_cfg["params"]["config"]["train_dir"] = log_root_path + agent_cfg["params"]["config"]["full_experiment_name"] = log_dir + + # multi-gpu training config + if args_cli.distributed: + agent_cfg["params"]["seed"] += app_launcher.global_rank + agent_cfg["params"]["config"]["device"] = f"cuda:{app_launcher.local_rank}" + agent_cfg["params"]["config"]["device_name"] = f"cuda:{app_launcher.local_rank}" + agent_cfg["params"]["config"]["multi_gpu"] = True + # update env config device + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + + # max iterations + if args_cli.max_iterations: + agent_cfg["params"]["config"]["max_epochs"] = args_cli.max_iterations + + # dump the configuration into log-directory + dump_yaml(os.path.join(log_root_path, log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_root_path, log_dir, "params", "agent.yaml"), agent_cfg) + + # read configurations about the agent-training + rl_device = agent_cfg["params"]["config"]["device"] + clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf) + clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf) + + task_startup_time_begin = time.perf_counter_ns() + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_root_path, log_dir, "videos"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + # wrap around environment for rl-games + env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions) + + task_startup_time_end = time.perf_counter_ns() + + # register the environment to rl-games registry + # note: in agents configuration: environment name must be "rlgpu" + vecenv.register( + "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs) + ) + env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env}) + + # set number of actors into agent config + agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs + # create runner from rl-games + runner = Runner(IsaacAlgoObserver()) + runner.load(agent_cfg) + + # set seed of the env + env.seed(agent_cfg["params"]["seed"]) + # reset the agent and env + runner.reset() + + benchmark.set_phase("sim_runtime") + + # train the agent + runner.run({"train": True, "play": False, "sigma": None}) + + if world_rank == 0: + benchmark.store_measurements() + + # parse tensorboard file stats + tensorboard_log_dir = os.path.join(log_root_path, log_dir, "summaries") + log_data = parse_tf_logs(tensorboard_log_dir) + + # prepare RL timing dict + rl_training_times = { + "Environment only step time": log_data["performance/step_time"], + "Environment + Inference step time": log_data["performance/step_inference_time"], + "Environment + Inference + Policy update time": log_data["performance/rl_update_time"], + "Environment only FPS": log_data["performance/step_fps"], + "Environment + Inference FPS": log_data["performance/step_inference_fps"], + "Environment + Inference + Policy update FPS": log_data["performance/step_inference_rl_update_fps"], + } + + # log additional metrics to benchmark services + log_app_start_time(benchmark, (app_start_time_end - app_start_time_begin) / 1e6) + log_python_imports_time(benchmark, (imports_time_end - imports_time_begin) / 1e6) + log_task_start_time(benchmark, (task_startup_time_end - task_startup_time_begin) / 1e6) + log_scene_creation_time(benchmark, Timer.get_timer_info("scene_creation") * 1000) + log_simulation_start_time(benchmark, Timer.get_timer_info("simulation_start") * 1000) + log_total_start_time(benchmark, (task_startup_time_end - app_start_time_begin) / 1e6) + log_runtime_step_times(benchmark, rl_training_times, compute_stats=True) + log_rl_policy_rewards(benchmark, log_data["rewards/iter"]) + log_rl_policy_episode_lengths(benchmark, log_data["episode_lengths/iter"]) + + benchmark.stop() + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/benchmarks/benchmark_rsl_rl.py b/scripts/benchmarks/benchmark_rsl_rl.py new file mode 100644 index 0000000000000000000000000000000000000000..8e3b4e132a5914335bc984ebb67f5c559f03c8b7 --- /dev/null +++ b/scripts/benchmarks/benchmark_rsl_rl.py @@ -0,0 +1,265 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2022-2025, The IsaacLab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to benchmark RL agent with RSL-RL.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import os +import sys +import time + +from isaaclab.app import AppLauncher + +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) +import scripts.reinforcement_learning.rsl_rl.cli_args as cli_args # isort: skip + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=4096, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--seed", type=int, default=42, help="Seed used for the environment") +parser.add_argument("--max_iterations", type=int, default=10, help="RL Policy training iterations.") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument( + "--benchmark_backend", + type=str, + default="OmniPerfKPIFile", + choices=["LocalLogMetrics", "JSONFileMetrics", "OsmoKPIFile", "OmniPerfKPIFile"], + help="Benchmarking backend options, defaults OmniPerfKPIFile", +) + +# append RSL-RL cli arguments +cli_args.add_rsl_rl_args(parser) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# to ensure kit args don't break the benchmark arg parsing +args_cli, hydra_args = parser.parse_known_args() + +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +app_start_time_begin = time.perf_counter_ns() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +app_start_time_end = time.perf_counter_ns() + +imports_time_begin = time.perf_counter_ns() + +from datetime import datetime + +import gymnasium as gym +import numpy as np +import torch +from rsl_rl.runners import OnPolicyRunner + +from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +imports_time_end = time.perf_counter_ns() + +from isaacsim.core.utils.extensions import enable_extension + +enable_extension("isaacsim.benchmark.services") +from isaacsim.benchmark.services import BaseIsaacBenchmark + +from isaaclab.utils.timer import Timer + +from scripts.benchmarks.utils import ( + log_app_start_time, + log_python_imports_time, + log_rl_policy_episode_lengths, + log_rl_policy_rewards, + log_runtime_step_times, + log_scene_creation_time, + log_simulation_start_time, + log_task_start_time, + log_total_start_time, + parse_tf_logs, +) + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = False + +# Create the benchmark +benchmark = BaseIsaacBenchmark( + benchmark_name="benchmark_rsl_rl_train", + workflow_metadata={ + "metadata": [ + {"name": "task", "data": args_cli.task}, + {"name": "seed", "data": args_cli.seed}, + {"name": "num_envs", "data": args_cli.num_envs}, + {"name": "max_iterations", "data": args_cli.max_iterations}, + ] + }, + backend_type=args_cli.benchmark_backend, +) + + +@hydra_task_config(args_cli.task, "rsl_rl_cfg_entry_point") +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg): + """Train with RSL-RL agent.""" + # parse configuration + benchmark.set_phase("loading", start_recording_frametime=False, start_recording_runtime=True) + # override configurations with non-hydra CLI arguments + agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli) + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + agent_cfg.max_iterations = ( + args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations + ) + + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg.seed + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # multi-gpu training configuration + world_rank = 0 + world_size = 1 + if args_cli.distributed: + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + agent_cfg.device = f"cuda:{app_launcher.local_rank}" + + # set seed to have diversity in different threads + seed = agent_cfg.seed + app_launcher.local_rank + env_cfg.seed = seed + agent_cfg.seed = seed + world_rank = app_launcher.global_rank + world_size = int(os.getenv("WORLD_SIZE", 1)) + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # specify directory for logging runs: {time-stamp}_{run_name} + log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + if agent_cfg.run_name: + log_dir += f"_{agent_cfg.run_name}" + log_dir = os.path.join(log_root_path, log_dir) + + # max iterations for training + if args_cli.max_iterations: + agent_cfg.max_iterations = args_cli.max_iterations + + task_startup_time_begin = time.perf_counter_ns() + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + # wrap around environment for rsl-rl + env = RslRlVecEnvWrapper(env) + + task_startup_time_end = time.perf_counter_ns() + + # create runner from rsl-rl + runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device) + # write git state to logs + runner.add_git_repo_to_log(__file__) + # save resume path before creating a new log_dir + if agent_cfg.resume: + # get path to previous checkpoint + resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint) + print(f"[INFO]: Loading model checkpoint from: {resume_path}") + # load previously trained model + runner.load(resume_path) + + # set seed of the environment + env.seed(agent_cfg.seed) + + # dump the configuration into log-directory + dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg) + + benchmark.set_phase("sim_runtime") + + # run training + runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True) + + if world_rank == 0: + benchmark.store_measurements() + + # parse tensorboard file stats + log_data = parse_tf_logs(log_dir) + + # prepare RL timing dict + collection_fps = ( + 1 + / (np.array(log_data["Perf/collection time"])) + * env.unwrapped.num_envs + * agent_cfg.num_steps_per_env + * world_size + ) + rl_training_times = { + "Collection Time": (np.array(log_data["Perf/collection time"]) / 1000).tolist(), + "Learning Time": (np.array(log_data["Perf/learning_time"]) / 1000).tolist(), + "Collection FPS": collection_fps.tolist(), + "Total FPS": log_data["Perf/total_fps"] * world_size, + } + + # log additional metrics to benchmark services + log_app_start_time(benchmark, (app_start_time_end - app_start_time_begin) / 1e6) + log_python_imports_time(benchmark, (imports_time_end - imports_time_begin) / 1e6) + log_task_start_time(benchmark, (task_startup_time_end - task_startup_time_begin) / 1e6) + log_scene_creation_time(benchmark, Timer.get_timer_info("scene_creation") * 1000) + log_simulation_start_time(benchmark, Timer.get_timer_info("simulation_start") * 1000) + log_total_start_time(benchmark, (task_startup_time_end - app_start_time_begin) / 1e6) + log_runtime_step_times(benchmark, rl_training_times, compute_stats=True) + log_rl_policy_rewards(benchmark, log_data["Train/mean_reward"]) + log_rl_policy_episode_lengths(benchmark, log_data["Train/mean_episode_length"]) + + benchmark.stop() + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/benchmarks/benchmark_view_comparison.py b/scripts/benchmarks/benchmark_view_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..61e7893fbbfc36f571fb16c3b9f94fa9ec3938d5 --- /dev/null +++ b/scripts/benchmarks/benchmark_view_comparison.py @@ -0,0 +1,492 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Benchmark script comparing XformPrimView vs PhysX RigidBodyView for transform operations. + +This script tests the performance of batched transform operations using: + +- Isaac Lab's XformPrimView (USD-based) +- PhysX RigidBodyView (PhysX tensors-based, as used in RigidObject) + +Note: + XformPrimView operates on USD attributes directly (useful for non-physics prims), + while RigidBodyView requires rigid body physics components and operates on PhysX tensors. + This benchmark helps understand the performance trade-offs between the two approaches. + +Usage: + # Basic benchmark + ./isaaclab.sh -p scripts/benchmarks/benchmark_view_comparison.py --num_envs 1024 --device cuda:0 --headless + + # With profiling enabled (for snakeviz visualization) + ./isaaclab.sh -p scripts/benchmarks/benchmark_view_comparison.py --num_envs 1024 --profile --headless + + # Then visualize with snakeviz: + snakeviz profile_results/xform_view_benchmark.prof + snakeviz profile_results/physx_view_benchmark.prof +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# parse the arguments +args_cli = argparse.Namespace() + +parser = argparse.ArgumentParser(description="Benchmark XformPrimView vs PhysX RigidBodyView performance.") + +parser.add_argument("--num_envs", type=int, default=100, help="Number of environments to simulate.") +parser.add_argument("--num_iterations", type=int, default=50, help="Number of iterations for each test.") +parser.add_argument( + "--profile", + action="store_true", + help="Enable profiling with cProfile. Results saved as .prof files for snakeviz visualization.", +) +parser.add_argument( + "--profile-dir", + type=str, + default="./profile_results", + help="Directory to save profile results. Default: ./profile_results", +) + +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import cProfile +import time + +import torch + +from isaacsim.core.simulation_manager import SimulationManager + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.sim.views import XformPrimView + + +@torch.no_grad() +def benchmark_view(view_type: str, num_iterations: int) -> tuple[dict[str, float], dict[str, torch.Tensor]]: + """Benchmark the specified view class. + + Args: + view_type: Type of view to benchmark ("xform" or "physx"). + num_iterations: Number of iterations to run. + + Returns: + A tuple of (timing_results, computed_results) where: + - timing_results: Dictionary containing timing results for various operations + - computed_results: Dictionary containing the computed values for validation + """ + timing_results = {} + computed_results = {} + + # Setup scene + print(" Setting up scene") + # Clear stage + sim_utils.create_new_stage() + # Create simulation context + start_time = time.perf_counter() + sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.01, device=args_cli.device)) + stage = sim_utils.get_current_stage() + + print(f" Time taken to create simulation context: {time.perf_counter() - start_time:.4f} seconds") + + # create a rigid object + object_cfg = sim_utils.ConeCfg( + radius=0.15, + height=0.5, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ) + # Create prims + for i in range(args_cli.num_envs): + sim_utils.create_prim(f"/World/Env_{i}", "Xform", stage=stage, translation=(i * 2.0, 0.0, 0.0)) + object_cfg.func(f"/World/Env_{i}/Object", object_cfg, translation=(0.0, 0.0, 1.0)) + + # Play simulation + sim.reset() + + # Pattern to match all prims + pattern = "/World/Env_.*/Object" if view_type == "xform" else "/World/Env_*/Object" + print(f" Pattern: {pattern}") + + # Create view based on type + start_time = time.perf_counter() + if view_type == "xform": + view = XformPrimView(pattern, device=args_cli.device, validate_xform_ops=False) + num_prims = view.count + view_name = "XformPrimView" + else: # physx + physics_sim_view = SimulationManager.get_physics_sim_view() + view = physics_sim_view.create_rigid_body_view(pattern) + num_prims = view.count + view_name = "PhysX RigidBodyView" + timing_results["init"] = time.perf_counter() - start_time + # prepare indices for benchmarking + all_indices = torch.arange(num_prims, device=args_cli.device) + + print(f" {view_name} managing {num_prims} prims") + + # Benchmark get_world_poses + start_time = time.perf_counter() + for _ in range(num_iterations): + if view_type == "xform": + positions, orientations = view.get_world_poses() + else: # physx + transforms = view.get_transforms() + positions = transforms[:, :3] + orientations = transforms[:, 3:7] + # Convert quaternion from xyzw to wxyz + orientations = math_utils.convert_quat(orientations, to="wxyz") + timing_results["get_world_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Store initial world poses + computed_results["initial_world_positions"] = positions.clone() + computed_results["initial_world_orientations"] = orientations.clone() + + # Benchmark set_world_poses + new_positions = positions.clone() + new_positions[:, 2] += 0.5 + start_time = time.perf_counter() + for _ in range(num_iterations): + if view_type == "xform": + view.set_world_poses(new_positions, orientations) + else: # physx + # Convert quaternion from wxyz to xyzw for PhysX + orientations_xyzw = math_utils.convert_quat(orientations, to="xyzw") + new_transforms = torch.cat([new_positions, orientations_xyzw], dim=-1) + view.set_transforms(new_transforms, indices=all_indices) + timing_results["set_world_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Get world poses after setting to verify + if view_type == "xform": + positions_after_set, orientations_after_set = view.get_world_poses() + else: # physx + transforms_after = view.get_transforms() + positions_after_set = transforms_after[:, :3] + orientations_after_set = math_utils.convert_quat(transforms_after[:, 3:7], to="wxyz") + computed_results["world_positions_after_set"] = positions_after_set.clone() + computed_results["world_orientations_after_set"] = orientations_after_set.clone() + + # close simulation + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + return timing_results, computed_results + + +def compare_results( + results_dict: dict[str, dict[str, torch.Tensor]], tolerance: float = 1e-4 +) -> dict[str, dict[str, dict[str, float]]]: + """Compare computed results across implementations. + + Args: + results_dict: Dictionary mapping implementation names to their computed values. + tolerance: Tolerance for numerical comparison. + + Returns: + Nested dictionary: {comparison_pair: {metric: {stats}}} + """ + comparison_stats = {} + impl_names = list(results_dict.keys()) + + # Compare each pair of implementations + for i, impl1 in enumerate(impl_names): + for impl2 in impl_names[i + 1 :]: + pair_key = f"{impl1}_vs_{impl2}" + comparison_stats[pair_key] = {} + + computed1 = results_dict[impl1] + computed2 = results_dict[impl2] + + for key in computed1.keys(): + if key not in computed2: + continue + + val1 = computed1[key] + val2 = computed2[key] + + # Skip zero tensors (not applicable tests) + if torch.all(val1 == 0) or torch.all(val2 == 0): + continue + + # Compute differences + diff = torch.abs(val1 - val2) + max_diff = torch.max(diff).item() + mean_diff = torch.mean(diff).item() + + # Check if within tolerance + all_close = torch.allclose(val1, val2, atol=tolerance, rtol=0) + + comparison_stats[pair_key][key] = { + "max_diff": max_diff, + "mean_diff": mean_diff, + "all_close": all_close, + } + + return comparison_stats + + +def print_comparison_results(comparison_stats: dict[str, dict[str, dict[str, float]]], tolerance: float): + """Print comparison results. + + Args: + comparison_stats: Nested dictionary containing comparison statistics. + tolerance: Tolerance used for comparison. + """ + for pair_key, pair_stats in comparison_stats.items(): + if not pair_stats: # Skip if no comparable results + continue + + # Format the pair key for display + impl1, impl2 = pair_key.split("_vs_") + display_impl1 = impl1.replace("_", " ").title() + display_impl2 = impl2.replace("_", " ").title() + comparison_title = f"{display_impl1} vs {display_impl2}" + + # Check if all results match + all_match = all(stats["all_close"] for stats in pair_stats.values()) + + if all_match: + # Compact output when everything matches + print("\n" + "=" * 100) + print(f"RESULT COMPARISON: {comparison_title}") + print("=" * 100) + print(f"✓ All computed values match within tolerance ({tolerance})") + print("=" * 100) + else: + # Detailed output when there are mismatches + print("\n" + "=" * 100) + print(f"RESULT COMPARISON: {comparison_title}") + print("=" * 100) + print(f"{'Computed Value':<40} {'Max Diff':<15} {'Mean Diff':<15} {'Match':<10}") + print("-" * 100) + + for key, stats in pair_stats.items(): + # Format the key for display + display_key = key.replace("_", " ").title() + match_str = "✓ Yes" if stats["all_close"] else "✗ No" + + print(f"{display_key:<40} {stats['max_diff']:<15.6e} {stats['mean_diff']:<15.6e} {match_str:<10}") + + print("=" * 100) + print(f"\n✗ Some results differ beyond tolerance ({tolerance})") + print(f" This may indicate implementation differences between {display_impl1} and {display_impl2}") + + print() + + +def print_results(results_dict: dict[str, dict[str, float]], num_prims: int, num_iterations: int): + """Print benchmark results in a formatted table. + + Args: + results_dict: Dictionary mapping implementation names to their timing results. + num_prims: Number of prims tested. + num_iterations: Number of iterations run. + """ + print("\n" + "=" * 100) + print(f"BENCHMARK RESULTS: {num_prims} prims, {num_iterations} iterations") + print("=" * 100) + + impl_names = list(results_dict.keys()) + # Format names for display + display_names = [name.replace("_", " ").title() for name in impl_names] + + # Calculate column width + col_width = 20 + + # Print header + header = f"{'Operation':<30}" + for display_name in display_names: + header += f" {display_name + ' (ms)':<{col_width}}" + print(header) + print("-" * 100) + + # Print each operation + operations = [ + ("Initialization", "init"), + ("Get World Poses", "get_world_poses"), + ("Set World Poses", "set_world_poses"), + ] + + for op_name, op_key in operations: + row = f"{op_name:<30}" + for impl_name in impl_names: + impl_time = results_dict[impl_name].get(op_key, 0) * 1000 # Convert to ms + row += f" {impl_time:>{col_width - 1}.4f}" + print(row) + + print("=" * 100) + + # Calculate and print total time (excluding N/A operations) + total_row = f"{'Total Time':<30}" + for impl_name in impl_names: + if impl_name == "physx_view": + # Exclude local pose operations for PhysX + total_time = ( + results_dict[impl_name].get("init", 0) * 1000 + + results_dict[impl_name].get("get_world_poses", 0) * 1000 + + results_dict[impl_name].get("set_world_poses", 0) * 1000 + ) + else: + total_time = sum(results_dict[impl_name].values()) * 1000 + total_row += f" {total_time:>{col_width - 1}.4f}" + print(f"\n{total_row}") + + # Calculate speedups relative to XformPrimView + if "xform_view" in impl_names: + print("\n" + "=" * 100) + print("SPEEDUP vs XformPrimView") + print("=" * 100) + print(f"{'Operation':<30}", end="") + for display_name in display_names: + if "xform" not in display_name.lower(): + print(f" {display_name + ' Speedup':<{col_width}}", end="") + print() + print("-" * 100) + + xform_results = results_dict["xform_view"] + for op_name, op_key in operations: + print(f"{op_name:<30}", end="") + xform_time = xform_results.get(op_key, 0) + for impl_name, display_name in zip(impl_names, display_names): + if impl_name != "xform_view": + impl_time = results_dict[impl_name].get(op_key, 0) + if xform_time > 0 and impl_time > 0: + speedup = impl_time / xform_time + print(f" {speedup:>{col_width - 1}.2f}x", end="") + else: + print(f" {'N/A':>{col_width}}", end="") + print() + + # Overall speedup (only world pose operations) + print("=" * 100) + print(f"{'Overall Speedup (World Ops)':<30}", end="") + total_xform = ( + xform_results.get("init", 0) + + xform_results.get("get_world_poses", 0) + + xform_results.get("set_world_poses", 0) + ) + for impl_name, display_name in zip(impl_names, display_names): + if impl_name != "xform_view": + total_impl = ( + results_dict[impl_name].get("init", 0) + + results_dict[impl_name].get("get_world_poses", 0) + + results_dict[impl_name].get("set_world_poses", 0) + ) + if total_xform > 0 and total_impl > 0: + overall_speedup = total_impl / total_xform + print(f" {overall_speedup:>{col_width - 1}.2f}x", end="") + else: + print(f" {'N/A':>{col_width}}", end="") + print() + + print("\n" + "=" * 100) + print("\nNotes:") + print(" - Times are averaged over all iterations") + print(" - Speedup = (PhysX View time) / (XformPrimView time)") + print(" - Speedup > 1.0 means XformPrimView is faster") + print(" - Speedup < 1.0 means PhysX View is faster") + print(" - PhysX View requires rigid body physics components") + print(" - XformPrimView works with any Xform prim (physics or non-physics)") + print(" - PhysX View does not support local pose operations directly") + print() + + +def main(): + """Main benchmark function.""" + print("=" * 100) + print("View Comparison Benchmark - XformPrimView vs PhysX RigidBodyView") + print("=" * 100) + print("Configuration:") + print(f" Number of environments: {args_cli.num_envs}") + print(f" Iterations per test: {args_cli.num_iterations}") + print(f" Device: {args_cli.device}") + print(f" Profiling: {'Enabled' if args_cli.profile else 'Disabled'}") + if args_cli.profile: + print(f" Profile directory: {args_cli.profile_dir}") + print() + + # Create profile directory if profiling is enabled + if args_cli.profile: + import os + + os.makedirs(args_cli.profile_dir, exist_ok=True) + + # Dictionary to store all results + all_timing_results = {} + all_computed_results = {} + profile_files = {} + + # Implementations to benchmark + implementations = [ + ("xform_view", "XformPrimView", "xform"), + ("physx_view", "PhysX RigidBodyView", "physx"), + ] + + # Benchmark each implementation + for impl_key, impl_name, view_type in implementations: + print(f"Benchmarking {impl_name}...") + + if args_cli.profile: + profiler = cProfile.Profile() + profiler.enable() + + timing, computed = benchmark_view(view_type=view_type, num_iterations=args_cli.num_iterations) + + if args_cli.profile: + profiler.disable() + profile_file = f"{args_cli.profile_dir}/{impl_key}_benchmark.prof" + profiler.dump_stats(profile_file) + profile_files[impl_key] = profile_file + print(f" Profile saved to: {profile_file}") + + all_timing_results[impl_key] = timing + all_computed_results[impl_key] = computed + + print(" Done!") + print() + + # Print timing results + print_results(all_timing_results, args_cli.num_envs, args_cli.num_iterations) + + # Compare computed results + print("\nComparing computed results across implementations...") + comparison_stats = compare_results(all_computed_results, tolerance=1e-4) + print_comparison_results(comparison_stats, tolerance=1e-4) + + # Print profiling instructions if enabled + if args_cli.profile: + print("\n" + "=" * 100) + print("PROFILING RESULTS") + print("=" * 100) + print("Profile files have been saved. To visualize with snakeviz, run:") + for impl_key, profile_file in profile_files.items(): + impl_display = impl_key.replace("_", " ").title() + print(f" # {impl_display}") + print(f" snakeviz {profile_file}") + print("\nAlternatively, use pstats to analyze in terminal:") + print(" python -m pstats ") + print("=" * 100) + print() + + # Clean up + sim_utils.SimulationContext.clear_instance() + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmarks/benchmark_xform_prim_view.py b/scripts/benchmarks/benchmark_xform_prim_view.py new file mode 100644 index 0000000000000000000000000000000000000000..f6665f6eba8b94f0e2349601701f7e7a2c292028 --- /dev/null +++ b/scripts/benchmarks/benchmark_xform_prim_view.py @@ -0,0 +1,510 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Benchmark script comparing XformPrimView implementations across different APIs. + +This script tests the performance of batched transform operations using: +- Isaac Lab's XformPrimView implementation +- Isaac Sim's XformPrimView implementation (legacy) +- Isaac Sim Experimental's XformPrim implementation (latest) + +Usage: + # Basic benchmark (all APIs) + ./isaaclab.sh -p scripts/benchmarks/benchmark_xform_prim_view.py --num_envs 1024 --device cuda:0 --headless + + # With profiling enabled (for snakeviz visualization) + ./isaaclab.sh -p scripts/benchmarks/benchmark_xform_prim_view.py --num_envs 1024 --profile --headless + + # Then visualize with snakeviz: + snakeviz profile_results/isaaclab_XformPrimView.prof + snakeviz profile_results/isaacsim_XformPrimView.prof + snakeviz profile_results/isaacsim_experimental_XformPrim.prof +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# parse the arguments +args_cli = argparse.Namespace() + +parser = argparse.ArgumentParser(description="This script can help you benchmark the performance of XformPrimView.") + +parser.add_argument("--num_envs", type=int, default=100, help="Number of environments to simulate.") +parser.add_argument("--num_iterations", type=int, default=50, help="Number of iterations for each test.") +parser.add_argument( + "--profile", + action="store_true", + help="Enable profiling with cProfile. Results saved as .prof files for snakeviz visualization.", +) +parser.add_argument( + "--profile-dir", + type=str, + default="./profile_results", + help="Directory to save profile results. Default: ./profile_results", +) + +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import cProfile +import time +from typing import Literal + +import torch + +from isaacsim.core.prims import XFormPrim as IsaacSimXformPrimView +from isaacsim.core.utils.extensions import enable_extension + +# compare against latest Isaac Sim implementation +enable_extension("isaacsim.core.experimental.prims") +from isaacsim.core.experimental.prims import XformPrim as IsaacSimExperimentalXformPrimView + +import isaaclab.sim as sim_utils +from isaaclab.sim.views import XformPrimView as IsaacLabXformPrimView + + +@torch.no_grad() +def benchmark_xform_prim_view( + api: Literal["isaaclab", "isaacsim", "isaacsim-exp"], num_iterations: int +) -> tuple[dict[str, float], dict[str, torch.Tensor]]: + """Benchmark the Xform view class from Isaac Lab, Isaac Sim, or Isaac Sim Experimental. + + Args: + api: Which API to benchmark ("isaaclab", "isaacsim", or "isaacsim-exp"). + num_iterations: Number of iterations to run. + + Returns: + A tuple of (timing_results, computed_results) where: + - timing_results: Dictionary containing timing results for various operations + - computed_results: Dictionary containing the computed values for validation + """ + timing_results = {} + computed_results = {} + + # Setup scene + print(" Setting up scene") + # Clear stage + sim_utils.create_new_stage() + # Create simulation context + start_time = time.perf_counter() + sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.01, device=args_cli.device)) + stage = sim_utils.get_current_stage() + + print(f" Time taken to create simulation context: {time.perf_counter() - start_time} seconds") + + # Create prims + prim_paths = [] + for i in range(args_cli.num_envs): + sim_utils.create_prim(f"/World/Env_{i}", "Xform", stage=stage, translation=(i * 2.0, 0.0, 1.0)) + sim_utils.create_prim(f"/World/Env_{i}/Object", "Xform", stage=stage, translation=(0.0, 0.0, 0.0)) + prim_paths.append(f"/World/Env_{i}/Object") + # Play simulation + sim.reset() + + # Pattern to match all prims + pattern = "/World/Env_.*/Object" + print(f" Pattern: {pattern}") + + # Create view + start_time = time.perf_counter() + if api == "isaaclab": + xform_view = IsaacLabXformPrimView(pattern, device=args_cli.device, validate_xform_ops=False) + elif api == "isaacsim": + xform_view = IsaacSimXformPrimView(pattern, reset_xform_properties=False) + elif api == "isaacsim-exp": + xform_view = IsaacSimExperimentalXformPrimView(pattern) + else: + raise ValueError(f"Invalid API: {api}") + timing_results["init"] = time.perf_counter() - start_time + + if api in ("isaaclab", "isaacsim"): + num_prims = xform_view.count + elif api == "isaacsim-exp": + num_prims = len(xform_view.prims) + print(f" XformView managing {num_prims} prims") + + # Benchmark get_world_poses + start_time = time.perf_counter() + for _ in range(num_iterations): + positions, orientations = xform_view.get_world_poses() + # Ensure tensors are torch tensors + if not isinstance(positions, torch.Tensor): + positions = torch.tensor(positions, dtype=torch.float32) + if not isinstance(orientations, torch.Tensor): + orientations = torch.tensor(orientations, dtype=torch.float32) + + timing_results["get_world_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Store initial world poses + computed_results["initial_world_positions"] = positions.clone() + computed_results["initial_world_orientations"] = orientations.clone() + + # Benchmark set_world_poses + new_positions = positions.clone() + new_positions[:, 2] += 0.1 + start_time = time.perf_counter() + for _ in range(num_iterations): + if api in ("isaaclab", "isaacsim"): + xform_view.set_world_poses(new_positions, orientations) + elif api == "isaacsim-exp": + xform_view.set_world_poses(new_positions.cpu().numpy(), orientations.cpu().numpy()) + timing_results["set_world_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Get world poses after setting to verify + positions_after_set, orientations_after_set = xform_view.get_world_poses() + if not isinstance(positions_after_set, torch.Tensor): + positions_after_set = torch.tensor(positions_after_set, dtype=torch.float32) + if not isinstance(orientations_after_set, torch.Tensor): + orientations_after_set = torch.tensor(orientations_after_set, dtype=torch.float32) + computed_results["world_positions_after_set"] = positions_after_set.clone() + computed_results["world_orientations_after_set"] = orientations_after_set.clone() + + # Benchmark get_local_poses + start_time = time.perf_counter() + for _ in range(num_iterations): + translations, orientations_local = xform_view.get_local_poses() + # Ensure tensors are torch tensors + if not isinstance(translations, torch.Tensor): + translations = torch.tensor(translations, dtype=torch.float32, device=args_cli.device) + if not isinstance(orientations_local, torch.Tensor): + orientations_local = torch.tensor(orientations_local, dtype=torch.float32, device=args_cli.device) + + timing_results["get_local_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Store initial local poses + computed_results["initial_local_translations"] = translations.clone() + computed_results["initial_local_orientations"] = orientations_local.clone() + + # Benchmark set_local_poses + new_translations = translations.clone() + new_translations[:, 2] += 0.1 + start_time = time.perf_counter() + for _ in range(num_iterations): + if api in ("isaaclab", "isaacsim"): + xform_view.set_local_poses(new_translations, orientations_local) + elif api == "isaacsim-exp": + xform_view.set_local_poses(new_translations.cpu().numpy(), orientations_local.cpu().numpy()) + timing_results["set_local_poses"] = (time.perf_counter() - start_time) / num_iterations + + # Get local poses after setting to verify + translations_after_set, orientations_local_after_set = xform_view.get_local_poses() + if not isinstance(translations_after_set, torch.Tensor): + translations_after_set = torch.tensor(translations_after_set, dtype=torch.float32) + if not isinstance(orientations_local_after_set, torch.Tensor): + orientations_local_after_set = torch.tensor(orientations_local_after_set, dtype=torch.float32) + computed_results["local_translations_after_set"] = translations_after_set.clone() + computed_results["local_orientations_after_set"] = orientations_local_after_set.clone() + + # Benchmark combined get operation + start_time = time.perf_counter() + for _ in range(num_iterations): + positions, orientations = xform_view.get_world_poses() + translations, local_orientations = xform_view.get_local_poses() + timing_results["get_both"] = (time.perf_counter() - start_time) / num_iterations + + # close simulation + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + return timing_results, computed_results + + +def compare_results( + results_dict: dict[str, dict[str, torch.Tensor]], tolerance: float = 1e-4 +) -> dict[str, dict[str, dict[str, float]]]: + """Compare computed results across multiple implementations. + + Args: + results_dict: Dictionary mapping API names to their computed values. + tolerance: Tolerance for numerical comparison. + + Returns: + Nested dictionary: {comparison_pair: {metric: {stats}}}, e.g., + {"isaaclab_vs_isaacsim": {"initial_world_positions": {"max_diff": 0.001, ...}}} + """ + comparison_stats = {} + api_names = list(results_dict.keys()) + + # Compare each pair of APIs + for i, api1 in enumerate(api_names): + for api2 in api_names[i + 1 :]: + pair_key = f"{api1}_vs_{api2}" + comparison_stats[pair_key] = {} + + computed1 = results_dict[api1] + computed2 = results_dict[api2] + + for key in computed1.keys(): + if key not in computed2: + print(f" Warning: Key '{key}' not found in {api2} results") + continue + + val1 = computed1[key] + val2 = computed2[key] + + # Compute differences + diff = torch.abs(val1 - val2) + max_diff = torch.max(diff).item() + mean_diff = torch.mean(diff).item() + + # Check if within tolerance + all_close = torch.allclose(val1, val2, atol=tolerance, rtol=0) + + comparison_stats[pair_key][key] = { + "max_diff": max_diff, + "mean_diff": mean_diff, + "all_close": all_close, + } + + return comparison_stats + + +def print_comparison_results(comparison_stats: dict[str, dict[str, dict[str, float]]], tolerance: float): + """Print comparison results across implementations. + + Args: + comparison_stats: Nested dictionary containing comparison statistics for each API pair. + tolerance: Tolerance used for comparison. + """ + for pair_key, pair_stats in comparison_stats.items(): + # Format the pair key for display (e.g., "isaaclab_vs_isaacsim" -> "Isaac Lab vs Isaac Sim") + api1, api2 = pair_key.split("_vs_") + display_api1 = api1.replace("-", " ").title() + display_api2 = api2.replace("-", " ").title() + comparison_title = f"{display_api1} vs {display_api2}" + + # Check if all results match + all_match = all(stats["all_close"] for stats in pair_stats.values()) + + if all_match: + # Compact output when everything matches + print("\n" + "=" * 100) + print(f"RESULT COMPARISON: {comparison_title}") + print("=" * 100) + print(f"✓ All computed values match within tolerance ({tolerance})") + print("=" * 100) + else: + # Detailed output when there are mismatches + print("\n" + "=" * 100) + print(f"RESULT COMPARISON: {comparison_title}") + print("=" * 100) + print(f"{'Computed Value':<40} {'Max Diff':<15} {'Mean Diff':<15} {'Match':<10}") + print("-" * 100) + + for key, stats in pair_stats.items(): + # Format the key for display + display_key = key.replace("_", " ").title() + match_str = "✓ Yes" if stats["all_close"] else "✗ No" + + print(f"{display_key:<40} {stats['max_diff']:<15.6e} {stats['mean_diff']:<15.6e} {match_str:<10}") + + print("=" * 100) + print(f"\n✗ Some results differ beyond tolerance ({tolerance})") + print(f" This may indicate implementation differences between {display_api1} and {display_api2}") + + print() + + +def print_results(results_dict: dict[str, dict[str, float]], num_prims: int, num_iterations: int): + """Print benchmark results in a formatted table. + + Args: + results_dict: Dictionary mapping API names to their timing results. + num_prims: Number of prims tested. + num_iterations: Number of iterations run. + """ + print("\n" + "=" * 100) + print(f"BENCHMARK RESULTS: {num_prims} prims, {num_iterations} iterations") + print("=" * 100) + + api_names = list(results_dict.keys()) + # Format API names for display + display_names = [name.replace("-", " ").replace("_", " ").title() for name in api_names] + + # Calculate column width based on number of APIs + col_width = 20 + + # Print header + header = f"{'Operation':<25}" + for display_name in display_names: + header += f" {display_name + ' (ms)':<{col_width}}" + print(header) + print("-" * 100) + + # Print each operation + operations = [ + ("Initialization", "init"), + ("Get World Poses", "get_world_poses"), + ("Set World Poses", "set_world_poses"), + ("Get Local Poses", "get_local_poses"), + ("Set Local Poses", "set_local_poses"), + ("Get Both (World+Local)", "get_both"), + ] + + for op_name, op_key in operations: + row = f"{op_name:<25}" + for api_name in api_names: + api_time = results_dict[api_name].get(op_key, 0) * 1000 # Convert to ms + row += f" {api_time:>{col_width - 1}.4f}" + print(row) + + print("=" * 100) + + # Calculate and print total time + total_row = f"{'Total Time':<25}" + for api_name in api_names: + total_time = sum(results_dict[api_name].values()) * 1000 + total_row += f" {total_time:>{col_width - 1}.4f}" + print(f"\n{total_row}") + + # Calculate speedups relative to Isaac Lab + if "isaaclab" in api_names: + print("\n" + "=" * 100) + print("SPEEDUP vs Isaac Lab") + print("=" * 100) + print(f"{'Operation':<25}", end="") + for display_name in display_names: + if "isaaclab" not in display_name.lower(): + print(f" {display_name + ' Speedup':<{col_width}}", end="") + print() + print("-" * 100) + + isaaclab_results = results_dict["isaaclab"] + for op_name, op_key in operations: + print(f"{op_name:<25}", end="") + isaaclab_time = isaaclab_results.get(op_key, 0) + for api_name, display_name in zip(api_names, display_names): + if api_name != "isaaclab": + api_time = results_dict[api_name].get(op_key, 0) + if isaaclab_time > 0 and api_time > 0: + speedup = api_time / isaaclab_time + print(f" {speedup:>{col_width - 1}.2f}x", end="") + else: + print(f" {'N/A':>{col_width}}", end="") + print() + + # Overall speedup + print("=" * 100) + print(f"{'Overall Speedup':<25}", end="") + total_isaaclab = sum(isaaclab_results.values()) + for api_name, display_name in zip(api_names, display_names): + if api_name != "isaaclab": + total_api = sum(results_dict[api_name].values()) + if total_isaaclab > 0 and total_api > 0: + overall_speedup = total_api / total_isaaclab + print(f" {overall_speedup:>{col_width - 1}.2f}x", end="") + else: + print(f" {'N/A':>{col_width}}", end="") + print() + + print("\n" + "=" * 100) + print("\nNotes:") + print(" - Times are averaged over all iterations") + print(" - Speedup = (Other API time) / (Isaac Lab time)") + print(" - Speedup > 1.0 means Isaac Lab is faster") + print(" - Speedup < 1.0 means the other API is faster") + print() + + +def main(): + """Main benchmark function.""" + print("=" * 100) + print("XformPrimView Benchmark - Comparing Multiple APIs") + print("=" * 100) + print("Configuration:") + print(f" Number of environments: {args_cli.num_envs}") + print(f" Iterations per test: {args_cli.num_iterations}") + print(f" Device: {args_cli.device}") + print(f" Profiling: {'Enabled' if args_cli.profile else 'Disabled'}") + if args_cli.profile: + print(f" Profile directory: {args_cli.profile_dir}") + print() + + # Create profile directory if profiling is enabled + if args_cli.profile: + import os + + os.makedirs(args_cli.profile_dir, exist_ok=True) + + # Dictionary to store all results + all_timing_results = {} + all_computed_results = {} + profile_files = {} + + # APIs to benchmark + apis_to_test = [ + ("isaaclab", "Isaac Lab XformPrimView"), + ("isaacsim", "Isaac Sim XformPrimView (Legacy)"), + ("isaacsim-exp", "Isaac Sim Experimental XformPrim"), + ] + + # Benchmark each API + for api_key, api_name in apis_to_test: + print(f"Benchmarking {api_name}...") + + if args_cli.profile: + profiler = cProfile.Profile() + profiler.enable() + + # Cast api_key to Literal type for type checker + timing, computed = benchmark_xform_prim_view( + api=api_key, # type: ignore[arg-type] + num_iterations=args_cli.num_iterations, + ) + + if args_cli.profile: + profiler.disable() + profile_file = f"{args_cli.profile_dir}/{api_key.replace('-', '_')}_benchmark.prof" + profiler.dump_stats(profile_file) + profile_files[api_key] = profile_file + print(f" Profile saved to: {profile_file}") + + all_timing_results[api_key] = timing + all_computed_results[api_key] = computed + + print(" Done!") + print() + + # Print timing results + print_results(all_timing_results, args_cli.num_envs, args_cli.num_iterations) + + # Compare computed results + print("\nComparing computed results across APIs...") + comparison_stats = compare_results(all_computed_results, tolerance=1e-6) + print_comparison_results(comparison_stats, tolerance=1e-4) + + # Print profiling instructions if enabled + if args_cli.profile: + print("\n" + "=" * 100) + print("PROFILING RESULTS") + print("=" * 100) + print("Profile files have been saved. To visualize with snakeviz, run:") + for api_key, profile_file in profile_files.items(): + api_display = api_key.replace("-", " ").title() + print(f" # {api_display}") + print(f" snakeviz {profile_file}") + print("\nAlternatively, use pstats to analyze in terminal:") + print(" python -m pstats ") + print("=" * 100) + print() + + # Clean up + sim_utils.SimulationContext.clear_instance() + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmarks/utils.py b/scripts/benchmarks/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8401320f4e5081128c3f16d471d9d969d272f19d --- /dev/null +++ b/scripts/benchmarks/utils.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import glob +import os + +from tensorboard.backend.event_processing import event_accumulator + +from isaacsim.benchmark.services import BaseIsaacBenchmark +from isaacsim.benchmark.services.metrics.measurements import DictMeasurement, ListMeasurement, SingleMeasurement + + +def parse_tf_logs(log_dir: str): + """Search for the latest tfevents file in log_dir folder and returns + the tensorboard logs in a dictionary. + + Args: + log_dir: directory used to search for tfevents files + """ + + # search log directory for latest log file + list_of_files = glob.glob(f"{log_dir}/events*") # * means all if need specific format then *.csv + latest_file = max(list_of_files, key=os.path.getctime) + + log_data = {} + ea = event_accumulator.EventAccumulator(latest_file) + ea.Reload() + tags = ea.Tags()["scalars"] + for tag in tags: + log_data[tag] = [] + for event in ea.Scalars(tag): + log_data[tag].append(event.value) + + return log_data + + +############################# +# logging benchmark metrics # +############################# + + +def log_min_max_mean_stats(benchmark: BaseIsaacBenchmark, values: dict): + for k, v in values.items(): + measurement = SingleMeasurement(name=f"Min {k}", value=min(v), unit="ms") + benchmark.store_custom_measurement("runtime", measurement) + measurement = SingleMeasurement(name=f"Max {k}", value=max(v), unit="ms") + benchmark.store_custom_measurement("runtime", measurement) + measurement = SingleMeasurement(name=f"Mean {k}", value=sum(v) / len(v), unit="ms") + benchmark.store_custom_measurement("runtime", measurement) + + +def log_app_start_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="App Launch Time", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_python_imports_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="Python Imports Time", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_task_start_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="Task Creation and Start Time", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_scene_creation_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="Scene Creation Time", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_simulation_start_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="Simulation Start Time", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_total_start_time(benchmark: BaseIsaacBenchmark, value: float): + measurement = SingleMeasurement(name="Total Start Time (Launch to Train)", value=value, unit="ms") + benchmark.store_custom_measurement("startup", measurement) + + +def log_runtime_step_times(benchmark: BaseIsaacBenchmark, value: dict, compute_stats=True): + measurement = DictMeasurement(name="Step Frametimes", value=value) + benchmark.store_custom_measurement("runtime", measurement) + if compute_stats: + log_min_max_mean_stats(benchmark, value) + + +def log_rl_policy_rewards(benchmark: BaseIsaacBenchmark, value: list): + measurement = ListMeasurement(name="Rewards", value=value) + benchmark.store_custom_measurement("train", measurement) + # log max reward + measurement = SingleMeasurement(name="Max Rewards", value=max(value), unit="float") + benchmark.store_custom_measurement("train", measurement) + + +def log_rl_policy_episode_lengths(benchmark: BaseIsaacBenchmark, value: list): + measurement = ListMeasurement(name="Episode Lengths", value=value) + benchmark.store_custom_measurement("train", measurement) + # log max episode length + measurement = SingleMeasurement(name="Max Episode Lengths", value=max(value), unit="float") + benchmark.store_custom_measurement("train", measurement) diff --git a/scripts/demos/arms.py b/scripts/demos/arms.py new file mode 100644 index 0000000000000000000000000000000000000000..92bd4499d6d5a399be983764fe0fd950974456e6 --- /dev/null +++ b/scripts/demos/arms.py @@ -0,0 +1,231 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates different single-arm manipulators. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/arms.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates different single-arm manipulators.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Pre-defined configs +## +# isort: off +from isaaclab_assets import ( + FRANKA_PANDA_CFG, + UR10_CFG, + KINOVA_JACO2_N7S300_CFG, + KINOVA_JACO2_N6S300_CFG, + KINOVA_GEN3_N7_CFG, + SAWYER_CFG, +) + +# isort: on + + +def define_origins(num_origins: int, spacing: float) -> list[list[float]]: + """Defines the origins of the the scene.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_origins, 3) + # create a grid of origins + num_rows = np.floor(np.sqrt(num_origins)) + num_cols = np.ceil(num_origins / num_rows) + xx, yy = torch.meshgrid(torch.arange(num_rows), torch.arange(num_cols), indexing="xy") + env_origins[:, 0] = spacing * xx.flatten()[:num_origins] - spacing * (num_rows - 1) / 2 + env_origins[:, 1] = spacing * yy.flatten()[:num_origins] - spacing * (num_cols - 1) / 2 + env_origins[:, 2] = 0.0 + # return the origins + return env_origins.tolist() + + +def design_scene() -> tuple[dict, list[list[float]]]: + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a mount and a robot on top of it + origins = define_origins(num_origins=6, spacing=2.0) + + # Origin 1 with Franka Panda + sim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0]) + # -- Table + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func("/World/Origin1/Table", cfg, translation=(0.55, 0.0, 1.05)) + # -- Robot + franka_arm_cfg = FRANKA_PANDA_CFG.replace(prim_path="/World/Origin1/Robot") + franka_arm_cfg.init_state.pos = (0.0, 0.0, 1.05) + franka_panda = Articulation(cfg=franka_arm_cfg) + + # Origin 2 with UR10 + sim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1]) + # -- Table + cfg = sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) + ) + cfg.func("/World/Origin2/Table", cfg, translation=(0.0, 0.0, 1.03)) + # -- Robot + ur10_cfg = UR10_CFG.replace(prim_path="/World/Origin2/Robot") + ur10_cfg.init_state.pos = (0.0, 0.0, 1.03) + ur10 = Articulation(cfg=ur10_cfg) + + # Origin 3 with Kinova JACO2 (7-Dof) arm + sim_utils.create_prim("/World/Origin3", "Xform", translation=origins[2]) + # -- Table + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/ThorlabsTable/table_instanceable.usd") + cfg.func("/World/Origin3/Table", cfg, translation=(0.0, 0.0, 0.8)) + # -- Robot + kinova_arm_cfg = KINOVA_JACO2_N7S300_CFG.replace(prim_path="/World/Origin3/Robot") + kinova_arm_cfg.init_state.pos = (0.0, 0.0, 0.8) + kinova_j2n7s300 = Articulation(cfg=kinova_arm_cfg) + + # Origin 4 with Kinova JACO2 (6-Dof) arm + sim_utils.create_prim("/World/Origin4", "Xform", translation=origins[3]) + # -- Table + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/ThorlabsTable/table_instanceable.usd") + cfg.func("/World/Origin4/Table", cfg, translation=(0.0, 0.0, 0.8)) + # -- Robot + kinova_arm_cfg = KINOVA_JACO2_N6S300_CFG.replace(prim_path="/World/Origin4/Robot") + kinova_arm_cfg.init_state.pos = (0.0, 0.0, 0.8) + kinova_j2n6s300 = Articulation(cfg=kinova_arm_cfg) + + # Origin 5 with Sawyer + sim_utils.create_prim("/World/Origin5", "Xform", translation=origins[4]) + # -- Table + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func("/World/Origin5/Table", cfg, translation=(0.55, 0.0, 1.05)) + # -- Robot + kinova_arm_cfg = KINOVA_GEN3_N7_CFG.replace(prim_path="/World/Origin5/Robot") + kinova_arm_cfg.init_state.pos = (0.0, 0.0, 1.05) + kinova_gen3n7 = Articulation(cfg=kinova_arm_cfg) + + # Origin 6 with Kinova Gen3 (7-Dof) arm + sim_utils.create_prim("/World/Origin6", "Xform", translation=origins[5]) + # -- Table + cfg = sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) + ) + cfg.func("/World/Origin6/Table", cfg, translation=(0.0, 0.0, 1.03)) + # -- Robot + sawyer_arm_cfg = SAWYER_CFG.replace(prim_path="/World/Origin6/Robot") + sawyer_arm_cfg.init_state.pos = (0.0, 0.0, 1.03) + sawyer = Articulation(cfg=sawyer_arm_cfg) + + # return the scene information + scene_entities = { + "franka_panda": franka_panda, + "ur10": ur10, + "kinova_j2n7s300": kinova_j2n7s300, + "kinova_j2n6s300": kinova_j2n6s300, + "kinova_gen3n7": kinova_gen3n7, + "sawyer": sawyer, + } + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, Articulation], origins: torch.Tensor): + """Runs the simulation loop.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 200 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset the scene entities + for index, robot in enumerate(entities.values()): + # root state + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins[index] + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + robot.reset() + print("[INFO]: Resetting robots state...") + # apply random actions to the robots + for robot in entities.values(): + # generate random joint positions + joint_pos_target = robot.data.default_joint_pos + torch.randn_like(robot.data.joint_pos) * 0.1 + joint_pos_target = joint_pos_target.clamp_( + robot.data.soft_joint_pos_limits[..., 0], robot.data.soft_joint_pos_limits[..., 1] + ) + # apply action to the robot + robot.set_joint_position_target(joint_pos_target) + # write data to sim + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + for robot in entities.values(): + robot.update(sim_dt) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([3.5, 0.0, 3.2], [0.0, 0.0, 0.5]) + # design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/bin_packing.py b/scripts/demos/bin_packing.py new file mode 100644 index 0000000000000000000000000000000000000000..a43cbf199b25b11e158334627e33583340b83075 --- /dev/null +++ b/scripts/demos/bin_packing.py @@ -0,0 +1,354 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Demonstration of randomized bin-packing with Isaac Lab. + +This script tiles multiple environments, spawns a configurable set of grocery +objects, and continuously randomizes their poses, velocities, mass properties, +and active/cached state to mimic a bin filling workflow. It showcases how to +use ``RigidObjectCollection`` utilities for bulk pose resets, cache management, +and out-of-bounds recovery inside an interactive simulation loop. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/bin_packing.py --num_envs 32 + +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Demo usage of RigidObjectCollection through bin packing example") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import math + +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.assets import AssetBaseCfg, RigidObjectCfg, RigidObjectCollection, RigidObjectCollectionCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import Timer, configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Scene Configuration +## + +# Layout and spawn counts. +MAX_NUM_OBJECTS = 24 # Hard cap on objects managed per environment (active + cached). +MAX_OBJECTS_PER_BIN = 24 # Maximum active objects we plan to fit inside the bin. +MIN_OBJECTS_PER_BIN = 1 # Lower bound for randomized active object count. +NUM_OBJECTS_PER_LAYER = 4 # Number of groceries spawned on each layer of the active stack. + +# Cached staging area and grid spacing. +CACHE_HEIGHT = 2.5 # Height (m) at which inactive groceries wait out of view. +ACTIVE_LAYER_SPACING = 0.1 # Vertical spacing (m) between layers inside the bin. +CACHE_SPACING = 0.25 # XY spacing (m) between cached groceries. + +# Bin dimensions and bounds. +BIN_DIMENSIONS = (0.2, 0.3, 0.15) # Physical size (m) of the storage bin. +BIN_XY_BOUND = ((-0.2, -0.3), (0.2, 0.3)) # Valid XY region (min/max) for active groceries. + +# Randomization ranges (radians for rotations, m/s and rad/s for velocities). +POSE_RANGE = {"roll": (-3.14, 3.14), "pitch": (-3.14, 3.14), "yaw": (-3.14, 3.14)} +VELOCITY_RANGE = {"roll": (-0.2, 1.0), "pitch": (-0.2, 1.0), "yaw": (-0.2, 1.0)} + +# Object layout configuration + +GROCERIES = { + "OBJECT_A": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/YCB/Axis_Aligned_Physics/004_sugar_box.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(solver_position_iteration_count=4), + ), + "OBJECT_B": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/YCB/Axis_Aligned_Physics/003_cracker_box.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(solver_position_iteration_count=4), + ), + "OBJECT_C": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/YCB/Axis_Aligned_Physics/005_tomato_soup_can.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(solver_position_iteration_count=4), + ), + "OBJECT_D": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/YCB/Axis_Aligned_Physics/006_mustard_bottle.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(solver_position_iteration_count=4), + ), +} + + +@configclass +class MultiObjectSceneCfg(InteractiveSceneCfg): + """Configuration for a multi-object scene.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # rigid object + object: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/Object", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/KLT_Bin/small_KLT.usd", + scale=(2.0, 2.0, 2.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0, kinematic_enabled=True + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 0.15)), + ) + + groceries: RigidObjectCollectionCfg = RigidObjectCollectionCfg( + # Instantiate four grocery variants per layer and replicate across all layers in each environment. + rigid_objects={ + f"Object_{label}_Layer{layer}": RigidObjectCfg( + prim_path=f"/World/envs/env_.*/Object_{label}_Layer{layer}", + init_state=RigidObjectCfg.InitialStateCfg(pos=(x, y, 0.2 + (layer) * 0.2)), + spawn=GROCERIES.get(f"OBJECT_{label}"), + ) + for layer in range(MAX_NUM_OBJECTS // NUM_OBJECTS_PER_LAYER) + for label, (x, y) in zip(["A", "B", "C", "D"], [(-0.035, -0.1), (-0.035, 0.1), (0.035, 0.1), (0.035, -0.1)]) + } + ) + + +def reset_object_collections( + scene: InteractiveScene, asset_name: str, view_states: torch.Tensor, view_ids: torch.Tensor, noise: bool = False +) -> None: + """Apply states to a subset of a collection, with optional noise. + + Updates ``view_states`` in-place for ``view_ids`` and writes transforms/velocities + to the PhysX view for the collection ``asset_name``. When ``noise`` is True, adds + uniform perturbations to pose (XYZ + Euler) and velocities using ``POSE_RANGE`` and + ``VELOCITY_RANGE``. + + Args: + scene: Interactive scene containing the collection. + asset_name: Key in the scene (e.g., ``"groceries"``) for the RigidObjectCollection. + view_states: Flat tensor (N, 13) with [x, y, z, qx, qy, qz, qw, lin(3), ang(3)] in world frame. + view_ids: 1D tensor of indices into ``view_states`` to update. + noise: If True, apply pose and velocity noise before writing. + + Returns: + None: This function updates ``view_states`` and the underlying PhysX view in-place. + """ + rigid_object_collection: RigidObjectCollection = scene[asset_name] + sel_view_states = view_states[view_ids] + positions = sel_view_states[:, :3] + orientations = sel_view_states[:, 3:7] + # poses + if noise: + range_list = [POSE_RANGE.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=scene.device) + samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(view_ids), 6), device=scene.device) + positions += samples[..., 0:3] + + # Compose new orientations by applying the sampled euler noise in quaternion space. + orientations_delta = math_utils.quat_from_euler_xyz(samples[..., 3], samples[..., 4], samples[..., 5]) + orientations = math_utils.convert_quat(orientations, to="wxyz") + orientations = math_utils.quat_mul(orientations, orientations_delta) + orientations = math_utils.convert_quat(orientations, to="xyzw") + + # velocities + new_velocities = sel_view_states[:, 7:13] + if noise: + range_list = [VELOCITY_RANGE.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=scene.device) + samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(view_ids), 6), device=scene.device) + new_velocities += samples + else: + new_velocities[:] = 0.0 + + view_states[view_ids, :7] = torch.concat((positions, orientations), dim=-1) + view_states[view_ids, 7:] = new_velocities + + rigid_object_collection.root_physx_view.set_transforms(view_states[:, :7], indices=view_ids) + rigid_object_collection.root_physx_view.set_velocities(view_states[:, 7:], indices=view_ids) + + +def build_grocery_defaults( + num_envs: int, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor]: + """Create default active/cached spawn poses for all environments. + + - Active poses: stacked 3D grid over the bin with ``ACTIVE_LAYER_SPACING`` per layer. + - Cached poses: 2D grid at ``CACHE_HEIGHT`` to park inactive objects out of view. + + Args: + num_envs: Number of environments to tile the poses for. + device: Torch device for allocation (e.g., ``"cuda:0"`` or ``"cpu"``). + + Returns: + tuple[torch.Tensor, torch.Tensor]: Active and cached spawn poses, each shaped + ``(num_envs, M, 7)`` with ``[x, y, z, qx, qy, qz, qw]`` where ``M`` equals + ``MAX_NUM_OBJECTS``. + """ + + # The bin has a size of 0.2 x 0.3 x 0.15 m + bin_x_dim, bin_y_dim, bin_z_dim = BIN_DIMENSIONS + # First, we calculate the number of layers and objects per layer + num_layers = math.ceil(MAX_OBJECTS_PER_BIN / NUM_OBJECTS_PER_LAYER) + num_x_objects = math.ceil(math.sqrt(NUM_OBJECTS_PER_LAYER)) + num_y_objects = math.ceil(NUM_OBJECTS_PER_LAYER / num_x_objects) + total_objects = num_x_objects * num_y_objects * num_layers + # Then, we create a 3D grid that allows for IxJxN objects to be placed on top of the bin. + x = torch.linspace(-bin_x_dim * (2 / 6), bin_x_dim * (2 / 6), num_x_objects, device=device) + y = torch.linspace(-bin_y_dim * (2 / 6), bin_y_dim * (2 / 6), num_y_objects, device=device) + z = torch.linspace(0, ACTIVE_LAYER_SPACING * (num_layers - 1), num_layers, device=device) + bin_z_dim * 2 + grid_z, grid_y, grid_x = torch.meshgrid(z, y, x, indexing="ij") # Note Z first, this stacks the layers. + # Using this grid plus a reference quaternion, create the poses for the groceries to be spawned above the bin. + ref_quat = torch.tensor([[0.0, 0.0, 0.0, 1.0]], device=device).repeat(total_objects, 1) + positions = torch.stack((grid_x.flatten(), grid_y.flatten(), grid_z.flatten()), dim=-1) + poses = torch.cat((positions, ref_quat), dim=-1) + # Duplicate across environments, cap at max_num_objects + active_spawn_poses = poses.unsqueeze(0).repeat(num_envs, 1, 1)[:, :MAX_NUM_OBJECTS, :] + + # We'll also create a buffer for the cached groceries. They'll be spawned below the bin so they can't be seen. + num_x_objects = math.ceil(math.sqrt(MAX_NUM_OBJECTS)) + num_y_objects = math.ceil(MAX_NUM_OBJECTS / num_x_objects) + # We create a XY grid only and fix the Z height for the cache. + x = CACHE_SPACING * torch.arange(num_x_objects, device=device) + y = CACHE_SPACING * torch.arange(num_y_objects, device=device) + grid_y, grid_x = torch.meshgrid(y, x, indexing="ij") + grid_z = CACHE_HEIGHT * torch.ones_like(grid_x) + # We can then create the poses for the cached groceries. + ref_quat = torch.tensor([[1.0, 0.0, 0.0, 0.0]], device=device).repeat(num_x_objects * num_y_objects, 1) + positions = torch.stack((grid_x.flatten(), grid_y.flatten(), grid_z.flatten()), dim=-1) + poses = torch.cat((positions, ref_quat), dim=-1) + # Duplicate across environments, cap at max_num_objects + cached_spawn_poses = poses.unsqueeze(0).repeat(num_envs, 1, 1)[:, :MAX_NUM_OBJECTS, :] + + return active_spawn_poses, cached_spawn_poses + + +## +# Simulation Loop +## + + +def run_simulator(sim: SimulationContext, scene: InteractiveScene) -> None: + """Runs the simulation loop that coordinates spawn randomization and stepping. + + Returns: + None: The simulator side-effects are applied through ``scene`` and ``sim``. + """ + # Extract scene entities + # note: we only do this here for readability. + groceries: RigidObjectCollection = scene["groceries"] + num_objects = groceries.num_objects + num_envs = scene.num_envs + device = scene.device + view_indices = torch.arange(num_envs * num_objects, device=device) + default_state_w = groceries.data.default_object_state.clone() + default_state_w[..., :3] = default_state_w[..., :3] + scene.env_origins.unsqueeze(1) + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + + # Pre-compute canonical spawn poses for each object both inside the bin and in the cache. + active_spawn_poses, cached_spawn_poses = build_grocery_defaults(num_envs, device) + # Offset poses into each environment's world frame. + active_spawn_poses[..., :3] += scene.env_origins.view(-1, 1, 3) + cached_spawn_poses[..., :3] += scene.env_origins.view(-1, 1, 3) + active_spawn_poses = groceries.reshape_data_to_view(active_spawn_poses) + cached_spawn_poses = groceries.reshape_data_to_view(cached_spawn_poses) + spawn_w = groceries.reshape_data_to_view(default_state_w).clone() + + groceries_mask_helper = torch.arange(num_objects * num_envs, device=device) % num_objects + # Precompute a helper mask to toggle objects between active and cached sets. + # Precompute XY bounds [[x_min,y_min],[x_max,y_max]] + bounds_xy = torch.as_tensor(BIN_XY_BOUND, device=device, dtype=spawn_w.dtype) + # Simulation loop + while simulation_app.is_running(): + # Reset + if count % 250 == 0: + # reset counter + count = 0 + # Randomly choose how many groceries stay active in each environment. + num_active_groceries = torch.randint(MIN_OBJECTS_PER_BIN, num_objects, (num_envs, 1), device=device) + groceries_mask = (groceries_mask_helper.view(num_envs, -1) < num_active_groceries).view(-1, 1) + spawn_w[:, :7] = cached_spawn_poses * (~groceries_mask) + active_spawn_poses * groceries_mask + # Retrieve positions + with Timer("[INFO] Time to reset scene: "): + reset_object_collections(scene, "groceries", spawn_w, view_indices[~groceries_mask.view(-1)]) + reset_object_collections(scene, "groceries", spawn_w, view_indices[groceries_mask.view(-1)], noise=True) + # Vary the mass and gravity settings so cached objects stay parked. + random_masses = torch.rand(groceries.num_instances * num_objects, device=device) * 0.2 + 0.2 + groceries.root_physx_view.set_masses(random_masses.cpu(), view_indices.cpu()) + groceries.root_physx_view.set_disable_gravities((~groceries_mask).cpu(), indices=view_indices.cpu()) + scene.reset() + + # Write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + + # Bring out-of-bounds objects back to the bin in one pass. + xy = groceries.reshape_data_to_view(groceries.data.object_pos_w - scene.env_origins.unsqueeze(1))[:, :2] + out_bound = torch.nonzero(~((xy >= bounds_xy[0]) & (xy <= bounds_xy[1])).all(dim=1), as_tuple=False).flatten() + if out_bound.numel(): + # Teleport stray objects back into the active stack to keep the bin tidy. + reset_object_collections(scene, "groceries", spawn_w, out_bound) + # Increment counter + count += 1 + # Update buffers + scene.update(sim_dt) + + +def main() -> None: + """Main function. + + Returns: + None: The function drives the simulation for its side-effects. + """ + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view((2.5, 0.0, 4.0), (0.0, 0.0, 2.0)) + + # Design scene + scene_cfg = MultiObjectSceneCfg(num_envs=args_cli.num_envs, env_spacing=1.0, replicate_physics=False) + with Timer("[INFO] Time to create scene: "): + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main execution + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/bipeds.py b/scripts/demos/bipeds.py new file mode 100644 index 0000000000000000000000000000000000000000..91421c105ff67e21f6773ba29ddb71dc276f4c29 --- /dev/null +++ b/scripts/demos/bipeds.py @@ -0,0 +1,139 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to simulate bipedal robots. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/bipeds.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to simulate bipedal robots.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.sim import SimulationContext + +## +# Pre-defined configs +## +from isaaclab_assets.robots.cassie import CASSIE_CFG # isort:skip +from isaaclab_assets import H1_CFG # isort:skip +from isaaclab_assets import G1_CFG # isort:skip + + +def design_scene(sim: sim_utils.SimulationContext) -> tuple[list, torch.Tensor]: + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Define origins + origins = torch.tensor( + [ + [0.0, -1.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ).to(device=sim.device) + + # Robots + cassie = Articulation(CASSIE_CFG.replace(prim_path="/World/Cassie")) + h1 = Articulation(H1_CFG.replace(prim_path="/World/H1")) + g1 = Articulation(G1_CFG.replace(prim_path="/World/G1")) + robots = [cassie, h1, g1] + + return robots, origins + + +def run_simulator(sim: sim_utils.SimulationContext, robots: list[Articulation], origins: torch.Tensor): + """Runs the simulation loop.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 200 == 0: + # reset counters + sim_time = 0.0 + count = 0 + for index, robot in enumerate(robots): + # reset dof state + joint_pos, joint_vel = robot.data.default_joint_pos, robot.data.default_joint_vel + robot.write_joint_state_to_sim(joint_pos, joint_vel) + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins[index] + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + robot.reset() + # reset command + print(">>>>>>>> Reset!") + # apply action to the robot + for robot in robots: + robot.set_joint_position_target(robot.data.default_joint_pos.clone()) + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + for robot in robots: + robot.update(sim_dt) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.0, 0.0, 2.25], target=[0.0, 0.0, 1.0]) + + # design scene + robots, origins = design_scene(sim) + + # Play the simulator + sim.reset() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # Run the simulator + run_simulator(sim, robots, origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/deformables.py b/scripts/demos/deformables.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9a962c26d53e887229aea47379aaedece4211c --- /dev/null +++ b/scripts/demos/deformables.py @@ -0,0 +1,202 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn deformable prims into the scene. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/deformables.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="This script demonstrates how to spawn deformable prims into the scene.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +import numpy as np +import torch +import tqdm + +import isaaclab.sim as sim_utils +from isaaclab.assets import DeformableObject, DeformableObjectCfg + + +def define_origins(num_origins: int, spacing: float) -> list[list[float]]: + """Defines the origins of the the scene.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_origins, 3) + # create a grid of origins + num_cols = np.floor(np.sqrt(num_origins)) + num_rows = np.ceil(num_origins / num_cols) + xx, yy = torch.meshgrid(torch.arange(num_rows), torch.arange(num_cols), indexing="xy") + env_origins[:, 0] = spacing * xx.flatten()[:num_origins] - spacing * (num_rows - 1) / 2 + env_origins[:, 1] = spacing * yy.flatten()[:num_origins] - spacing * (num_cols - 1) / 2 + env_origins[:, 2] = torch.rand(num_origins) + 1.0 + # return the origins + return env_origins.tolist() + + +def design_scene() -> tuple[dict, list[list[float]]]: + """Designs the scene.""" + # Ground-plane + cfg_ground = sim_utils.GroundPlaneCfg() + cfg_ground.func("/World/defaultGroundPlane", cfg_ground) + + # spawn distant light + cfg_light = sim_utils.DomeLightCfg( + intensity=3000.0, + color=(0.75, 0.75, 0.75), + ) + cfg_light.func("/World/light", cfg_light) + + # spawn a red cone + cfg_sphere = sim_utils.MeshSphereCfg( + radius=0.25, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + cfg_cuboid = sim_utils.MeshCuboidCfg( + size=(0.2, 0.2, 0.2), + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + cfg_cylinder = sim_utils.MeshCylinderCfg( + radius=0.15, + height=0.5, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + cfg_capsule = sim_utils.MeshCapsuleCfg( + radius=0.15, + height=0.5, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + cfg_cone = sim_utils.MeshConeCfg( + radius=0.15, + height=0.5, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + # create a dictionary of all the objects to be spawned + objects_cfg = { + "sphere": cfg_sphere, + "cuboid": cfg_cuboid, + "cylinder": cfg_cylinder, + "capsule": cfg_capsule, + "cone": cfg_cone, + } + + # Create separate groups of deformable objects + origins = define_origins(num_origins=64, spacing=0.6) + print("[INFO]: Spawning objects...") + # Iterate over all the origins and randomly spawn objects + for idx, origin in tqdm.tqdm(enumerate(origins), total=len(origins)): + # randomly select an object to spawn + obj_name = random.choice(list(objects_cfg.keys())) + obj_cfg = objects_cfg[obj_name] + # randomize the young modulus (somewhere between a Silicone 30 and Silicone 70) + obj_cfg.physics_material.youngs_modulus = random.uniform(0.7e6, 3.3e6) + # randomize the poisson's ratio + obj_cfg.physics_material.poissons_ratio = random.uniform(0.25, 0.5) + # randomize the color + obj_cfg.visual_material.diffuse_color = (random.random(), random.random(), random.random()) + # spawn the object + obj_cfg.func(f"/World/Origin/Object{idx:02d}", obj_cfg, translation=origin) + + # create a view for all the deformables + # note: since we manually spawned random deformable meshes above, we don't need to + # specify the spawn configuration for the deformable object + cfg = DeformableObjectCfg( + prim_path="/World/Origin/Object.*", + spawn=None, + init_state=DeformableObjectCfg.InitialStateCfg(), + ) + deformable_object = DeformableObject(cfg=cfg) + + # return the scene information + scene_entities = {"deformable_object": deformable_object} + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, DeformableObject], origins: torch.Tensor): + """Runs the simulation loop.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 400 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset deformable object state + for _, deform_body in enumerate(entities.values()): + # root state + nodal_state = deform_body.data.default_nodal_state_w.clone() + deform_body.write_nodal_state_to_sim(nodal_state) + # reset the internal state + deform_body.reset() + print("[INFO]: Resetting deformable object state...") + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + for deform_body in entities.values(): + deform_body.update(sim_dt) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([4.0, 4.0, 3.0], [0.5, 0.5, 0.0]) + + # Design scene by adding assets to it + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/h1_locomotion.py b/scripts/demos/h1_locomotion.py new file mode 100644 index 0000000000000000000000000000000000000000..6de384af9bf0e5bf62c1bf8b34c3271c56ef8f89 --- /dev/null +++ b/scripts/demos/h1_locomotion.py @@ -0,0 +1,227 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates an interactive demo with the H1 rough terrain environment. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/h1_locomotion.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) +import scripts.reinforcement_learning.rsl_rl.cli_args as cli_args # isort: skip + + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script demonstrates an interactive demo with the H1 rough terrain environment." +) +# append RSL-RL cli arguments +cli_args.add_rsl_rl_args(parser) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch +from rsl_rl.runners import OnPolicyRunner + +import carb +import omni +from omni.kit.viewport.utility import get_viewport_from_window_name +from omni.kit.viewport.utility.camera_state import ViewportCameraState +from pxr import Gf, Sdf + +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.utils.math import quat_apply + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper +from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint + +from isaaclab_tasks.manager_based.locomotion.velocity.config.h1.rough_env_cfg import H1RoughEnvCfg_PLAY + +TASK = "Isaac-Velocity-Rough-H1-v0" +RL_LIBRARY = "rsl_rl" + + +class H1RoughDemo: + """This class provides an interactive demo for the H1 rough terrain environment. + It loads a pre-trained checkpoint for the Isaac-Velocity-Rough-H1-v0 task, trained with RSL RL + and defines a set of keyboard commands for directing motion of selected robots. + + A robot can be selected from the scene through a mouse click. Once selected, the following + keyboard controls can be used to control the robot: + + * UP: go forward + * LEFT: turn left + * RIGHT: turn right + * DOWN: stop + * C: switch between third-person and perspective views + * ESC: exit current third-person view""" + + def __init__(self): + """Initializes environment config designed for the interactive model and sets up the environment, + loads pre-trained checkpoints, and registers keyboard events.""" + agent_cfg: RslRlOnPolicyRunnerCfg = cli_args.parse_rsl_rl_cfg(TASK, args_cli) + # load the trained jit policy + checkpoint = get_published_pretrained_checkpoint(RL_LIBRARY, TASK) + # create envionrment + env_cfg = H1RoughEnvCfg_PLAY() + env_cfg.scene.num_envs = 25 + env_cfg.episode_length_s = 1000000 + env_cfg.curriculum = None + env_cfg.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0) + env_cfg.commands.base_velocity.ranges.heading = (-1.0, 1.0) + # wrap around environment for rsl-rl + self.env = RslRlVecEnvWrapper(ManagerBasedRLEnv(cfg=env_cfg)) + self.device = self.env.unwrapped.device + # load previously trained model + ppo_runner = OnPolicyRunner(self.env, agent_cfg.to_dict(), log_dir=None, device=self.device) + ppo_runner.load(checkpoint) + # obtain the trained policy for inference + self.policy = ppo_runner.get_inference_policy(device=self.device) + + self.create_camera() + self.commands = torch.zeros(env_cfg.scene.num_envs, 4, device=self.device) + self.commands[:, 0:3] = self.env.unwrapped.command_manager.get_command("base_velocity") + self.set_up_keyboard() + self._prim_selection = omni.usd.get_context().get_selection() + self._selected_id = None + self._previous_selected_id = None + self._camera_local_transform = torch.tensor([-2.5, 0.0, 0.8], device=self.device) + + def create_camera(self): + """Creates a camera to be used for third-person view.""" + stage = get_current_stage() + self.viewport = get_viewport_from_window_name("Viewport") + # Create camera + self.camera_path = "/World/Camera" + self.perspective_path = "/OmniverseKit_Persp" + camera_prim = stage.DefinePrim(self.camera_path, "Camera") + camera_prim.GetAttribute("focalLength").Set(8.5) + coi_prop = camera_prim.GetProperty("omni:kit:centerOfInterest") + if not coi_prop or not coi_prop.IsValid(): + camera_prim.CreateAttribute( + "omni:kit:centerOfInterest", Sdf.ValueTypeNames.Vector3d, True, Sdf.VariabilityUniform + ).Set(Gf.Vec3d(0, 0, -10)) + self.viewport.set_active_camera(self.perspective_path) + + def set_up_keyboard(self): + """Sets up interface for keyboard input and registers the desired keys for control.""" + self._input = carb.input.acquire_input_interface() + self._keyboard = omni.appwindow.get_default_app_window().get_keyboard() + self._sub_keyboard = self._input.subscribe_to_keyboard_events(self._keyboard, self._on_keyboard_event) + T = 1 + R = 0.5 + self._key_to_control = { + "UP": torch.tensor([T, 0.0, 0.0, 0.0], device=self.device), + "DOWN": torch.tensor([0.0, 0.0, 0.0, 0.0], device=self.device), + "LEFT": torch.tensor([T, 0.0, 0.0, -R], device=self.device), + "RIGHT": torch.tensor([T, 0.0, 0.0, R], device=self.device), + "ZEROS": torch.tensor([0.0, 0.0, 0.0, 0.0], device=self.device), + } + + def _on_keyboard_event(self, event): + """Checks for a keyboard event and assign the corresponding command control depending on key pressed.""" + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + # Arrow keys map to pre-defined command vectors to control navigation of robot + if event.input.name in self._key_to_control: + if self._selected_id: + self.commands[self._selected_id] = self._key_to_control[event.input.name] + # Escape key exits out of the current selected robot view + elif event.input.name == "ESCAPE": + self._prim_selection.clear_selected_prim_paths() + # C key swaps between third-person and perspective views + elif event.input.name == "C": + if self._selected_id is not None: + if self.viewport.get_active_camera() == self.camera_path: + self.viewport.set_active_camera(self.perspective_path) + else: + self.viewport.set_active_camera(self.camera_path) + # On key release, the robot stops moving + elif event.type == carb.input.KeyboardEventType.KEY_RELEASE: + if self._selected_id: + self.commands[self._selected_id] = self._key_to_control["ZEROS"] + + def update_selected_object(self): + """Determines which robot is currently selected and whether it is a valid H1 robot. + For valid robots, we enter the third-person view for that robot. + When a new robot is selected, we reset the command of the previously selected + to continue random commands.""" + + self._previous_selected_id = self._selected_id + selected_prim_paths = self._prim_selection.get_selected_prim_paths() + if len(selected_prim_paths) == 0: + self._selected_id = None + self.viewport.set_active_camera(self.perspective_path) + elif len(selected_prim_paths) > 1: + print("Multiple prims are selected. Please only select one!") + else: + prim_splitted_path = selected_prim_paths[0].split("/") + # a valid robot was selected, update the camera to go into third-person view + if len(prim_splitted_path) >= 4 and prim_splitted_path[3][0:4] == "env_": + self._selected_id = int(prim_splitted_path[3][4:]) + if self._previous_selected_id != self._selected_id: + self.viewport.set_active_camera(self.camera_path) + self._update_camera() + else: + print("The selected prim was not a H1 robot") + + # Reset commands for previously selected robot if a new one is selected + if self._previous_selected_id is not None and self._previous_selected_id != self._selected_id: + self.env.unwrapped.command_manager.reset([self._previous_selected_id]) + self.commands[:, 0:3] = self.env.unwrapped.command_manager.get_command("base_velocity") + + def _update_camera(self): + """Updates the per-frame transform of the third-person view camera to follow + the selected robot's torso transform.""" + + base_pos = self.env.unwrapped.scene["robot"].data.root_pos_w[self._selected_id, :] # - env.scene.env_origins + base_quat = self.env.unwrapped.scene["robot"].data.root_quat_w[self._selected_id, :] + + camera_pos = quat_apply(base_quat, self._camera_local_transform) + base_pos + + camera_state = ViewportCameraState(self.camera_path, self.viewport) + eye = Gf.Vec3d(camera_pos[0].item(), camera_pos[1].item(), camera_pos[2].item()) + target = Gf.Vec3d(base_pos[0].item(), base_pos[1].item(), base_pos[2].item() + 0.6) + camera_state.set_position_world(eye, True) + camera_state.set_target_world(target, True) + + +def main(): + """Main function.""" + demo_h1 = H1RoughDemo() + obs, _ = demo_h1.env.reset() + while simulation_app.is_running(): + # check for selected robots + demo_h1.update_selected_object() + with torch.inference_mode(): + action = demo_h1.policy(obs) + obs, _, _, _ = demo_h1.env.step(action) + # overwrite command based on keyboard input + obs[:, 9:13] = demo_h1.commands + + +if __name__ == "__main__": + main() + simulation_app.close() diff --git a/scripts/demos/hands.py b/scripts/demos/hands.py new file mode 100644 index 0000000000000000000000000000000000000000..a0fa04e0fbfdca3dd67be5380554bdf8149b58ca --- /dev/null +++ b/scripts/demos/hands.py @@ -0,0 +1,165 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates different dexterous hands. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/hands.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates different dexterous hands.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation + +## +# Pre-defined configs +## +from isaaclab_assets.robots.allegro import ALLEGRO_HAND_CFG # isort:skip +from isaaclab_assets.robots.shadow_hand import SHADOW_HAND_CFG # isort:skip + + +def define_origins(num_origins: int, spacing: float) -> list[list[float]]: + """Defines the origins of the the scene.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_origins, 3) + # create a grid of origins + num_cols = np.floor(np.sqrt(num_origins)) + num_rows = np.ceil(num_origins / num_cols) + xx, yy = torch.meshgrid(torch.arange(num_rows), torch.arange(num_cols), indexing="xy") + env_origins[:, 0] = spacing * xx.flatten()[:num_origins] - spacing * (num_rows - 1) / 2 + env_origins[:, 1] = spacing * yy.flatten()[:num_origins] - spacing * (num_cols - 1) / 2 + env_origins[:, 2] = 0.0 + # return the origins + return env_origins.tolist() + + +def design_scene() -> tuple[dict, list[list[float]]]: + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a mount and a robot on top of it + origins = define_origins(num_origins=2, spacing=0.5) + + # Origin 1 with Allegro Hand + sim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0]) + # -- Robot + allegro = Articulation(ALLEGRO_HAND_CFG.replace(prim_path="/World/Origin1/Robot")) + + # Origin 2 with Shadow Hand + sim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1]) + # -- Robot + shadow_hand = Articulation(SHADOW_HAND_CFG.replace(prim_path="/World/Origin2/Robot")) + + # return the scene information + scene_entities = { + "allegro": allegro, + "shadow_hand": shadow_hand, + } + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, Articulation], origins: torch.Tensor): + """Runs the simulation loop.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Start with hand open + grasp_mode = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 1000 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset robots + for index, robot in enumerate(entities.values()): + # root state + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins[index] + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # joint state + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # reset the internal state + robot.reset() + print("[INFO]: Resetting robots state...") + # toggle grasp mode + if count % 100 == 0: + grasp_mode = 1 - grasp_mode + # apply default actions to the hands robots + for robot in entities.values(): + # generate joint positions + joint_pos_target = robot.data.soft_joint_pos_limits[..., grasp_mode] + # apply action to the robot + robot.set_joint_position_target(joint_pos_target) + # write data to sim + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + for robot in entities.values(): + robot.update(sim_dt) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[0.0, -0.5, 1.5], target=[0.0, -0.2, 0.5]) + # design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main execution + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/haply_teleoperation.py b/scripts/demos/haply_teleoperation.py new file mode 100644 index 0000000000000000000000000000000000000000..b6d02900baf43f10b48bd2437ee571b8b72f62bb --- /dev/null +++ b/scripts/demos/haply_teleoperation.py @@ -0,0 +1,361 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Demonstration of Haply device teleoperation with a robotic arm. + +This script demonstrates how to use a Haply device (Inverse3 + VerseGrip) to +teleoperate a robotic arm in Isaac Lab. The Haply provides: +- Position tracking from the Inverse3 device +- Orientation and button inputs from the VerseGrip device +- Force feedback + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py + + # With custom WebSocket URI + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py --websocket_uri ws://localhost:10001 + + # With sensitivity adjustment + ./isaaclab.sh -p scripts/demos/haply_teleoperation.py --pos_sensitivity 2.0 + +Prerequisites: + 1. Install websockets package: pip install websockets + 2. Have Haply SDK running and accessible via WebSocket + 3. Connect Inverse3 and VerseGrip devices +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Demonstration of Haply device teleoperation with Isaac Lab.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +parser.add_argument( + "--websocket_uri", + type=str, + default="ws://localhost:10001", + help="WebSocket URI for Haply SDK connection.", +) +parser.add_argument( + "--pos_sensitivity", + type=float, + default=1.0, + help="Position sensitivity scaling factor.", +) + +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, AssetBaseCfg, RigidObject, RigidObjectCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.devices import HaplyDevice, HaplyDeviceCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ContactSensor, ContactSensorCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + +# Workspace mapping constants +HAPLY_Z_OFFSET = 0.35 +WORKSPACE_LIMITS = { + "x": (0.1, 0.9), + "y": (-0.50, 0.50), + "z": (1.05, 1.85), +} + + +def apply_haply_to_robot_mapping( + haply_pos: np.ndarray | torch.Tensor, + haply_initial_pos: np.ndarray | list, + robot_initial_pos: np.ndarray | torch.Tensor, +) -> np.ndarray: + """Apply coordinate mapping from Haply workspace to Franka Panda end-effector. + + Uses absolute position control: robot position = robot_initial_pos + haply_pos (transformed) + + Args: + haply_pos: Current Haply absolute position [x, y, z] in meters + haply_initial_pos: Haply's zero reference position [x, y, z] + robot_initial_pos: Base offset for robot end-effector + + Returns: + robot_pos: Target position for robot EE in world frame [x, y, z] + + """ + # Convert to numpy + if isinstance(haply_pos, torch.Tensor): + haply_pos = haply_pos.cpu().numpy() + if isinstance(robot_initial_pos, torch.Tensor): + robot_initial_pos = robot_initial_pos.cpu().numpy() + + haply_delta = haply_pos - haply_initial_pos + + # Coordinate system mapping: Haply (X, Y, Z) -> Robot (-Y, X, Z-offset) + robot_offset = np.array([-haply_delta[1], haply_delta[0], haply_delta[2] - HAPLY_Z_OFFSET]) + robot_pos = robot_initial_pos + robot_offset + + # Apply workspace limits for safety + robot_pos[0] = np.clip(robot_pos[0], WORKSPACE_LIMITS["x"][0], WORKSPACE_LIMITS["x"][1]) + robot_pos[1] = np.clip(robot_pos[1], WORKSPACE_LIMITS["y"][0], WORKSPACE_LIMITS["y"][1]) + robot_pos[2] = np.clip(robot_pos[2], WORKSPACE_LIMITS["z"][0], WORKSPACE_LIMITS["z"][1]) + + return robot_pos + + +@configclass +class FrankaHaplySceneCfg(InteractiveSceneCfg): + """Configuration for Franka scene with Haply teleoperation and contact sensors.""" + + ground = AssetBaseCfg( + prim_path="/World/defaultGroundPlane", + spawn=sim_utils.GroundPlaneCfg(), + ) + + dome_light = AssetBaseCfg( + prim_path="/World/Light", + spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)), + ) + + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", + scale=(1.0, 1.0, 1.0), + ), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.50, 0.0, 1.05), rot=(0.707, 0, 0, 0.707)), + ) + + robot: Articulation = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + robot.init_state.pos = (-0.02, 0.0, 1.05) + robot.spawn.activate_contact_sensors = True + + cube = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube", + spawn=sim_utils.CuboidCfg( + size=(0.06, 0.06, 0.06), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=0.5, dynamic_friction=0.5), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.2, 0.8, 0.2), metallic=0.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.60, 0.00, 1.15)), + ) + + left_finger_contact_sensor = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + update_period=0.0, + history_length=3, + debug_vis=True, + track_pose=True, + ) + + right_finger_contact_sensor = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + update_period=0.0, + history_length=3, + debug_vis=True, + track_pose=True, + ) + + +def run_simulator( + sim: sim_utils.SimulationContext, + scene: InteractiveScene, + haply_device: HaplyDevice, +): + """Runs the simulation loop with Haply teleoperation.""" + sim_dt = sim.get_physics_dt() + count = 1 + + robot: Articulation = scene["robot"] + cube: RigidObject = scene["cube"] + left_finger_sensor: ContactSensor = scene["left_finger_contact_sensor"] + right_finger_sensor: ContactSensor = scene["right_finger_contact_sensor"] + + ee_body_name = "panda_hand" + ee_body_idx = robot.body_names.index(ee_body_name) + + joint_pos = robot.data.default_joint_pos.clone() + joint_pos[0, :7] = torch.tensor([0.0, -0.569, 0.0, -2.81, 0.0, 3.037, 0.741], device=robot.device) + joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + + for _ in range(10): + scene.write_data_to_sim() + sim.step() + scene.update(sim_dt) + + # Initialize the position of franka + robot_initial_pos = robot.data.body_pos_w[0, ee_body_idx].cpu().numpy() + haply_initial_pos = np.array([0.0, 0.0, 0.0], dtype=np.float32) + + ik_controller_cfg = DifferentialIKControllerCfg( + command_type="position", + use_relative_mode=False, + ik_method="dls", + ik_params={"lambda_val": 0.05}, + ) + + # IK joints control arms, buttons control ee rotation and gripper open/close + arm_joint_names = [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + ] + arm_joint_indices = [robot.joint_names.index(name) for name in arm_joint_names] + + # Initialize IK controller + ik_controller = DifferentialIKController(cfg=ik_controller_cfg, num_envs=scene.num_envs, device=sim.device) + initial_ee_quat = robot.data.body_quat_w[:, ee_body_idx] + ik_controller.set_command(command=torch.zeros(scene.num_envs, 3, device=sim.device), ee_quat=initial_ee_quat) + + prev_button_a = False + prev_button_b = False + prev_button_c = False + gripper_target = 0.04 + + # Initialize the rotation of franka end-effector + ee_rotation_angle = robot.data.joint_pos[0, 6].item() + rotation_step = np.pi / 3 + + print("\n[INFO] Teleoperation ready!") + print(" Move handler: Control pose of the end-effector") + print(" Button A: Open | Button B: Close | Button C: Rotate EE (60°)\n") + + while simulation_app.is_running(): + if count % 10000 == 0: + count = 1 + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + + joint_pos = robot.data.default_joint_pos.clone() + joint_pos[0, :7] = torch.tensor([0.0, -0.569, 0.0, -2.81, 0.0, 3.037, 0.741], device=robot.device) + joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + + cube_state = cube.data.default_root_state.clone() + cube_state[:, :3] += scene.env_origins + cube.write_root_pose_to_sim(cube_state[:, :7]) + cube.write_root_velocity_to_sim(cube_state[:, 7:]) + + scene.reset() + haply_device.reset() + ik_controller.reset() + print("[INFO]: Resetting robot state...") + + # Get the data from Haply device + haply_data = haply_device.advance() + + haply_pos = haply_data[:3] + button_a = haply_data[7].item() > 0.5 + button_b = haply_data[8].item() > 0.5 + button_c = haply_data[9].item() > 0.5 + + if button_a and not prev_button_a: + gripper_target = 0.04 # Open gripper + if button_b and not prev_button_b: + gripper_target = 0.0 # Close gripper + if button_c and not prev_button_c: + joint_7_limit = 3.0 + ee_rotation_angle += rotation_step + + if ee_rotation_angle > joint_7_limit: + ee_rotation_angle = -joint_7_limit + (ee_rotation_angle - joint_7_limit) + elif ee_rotation_angle < -joint_7_limit: + ee_rotation_angle = joint_7_limit + (ee_rotation_angle + joint_7_limit) + + prev_button_a = button_a + prev_button_b = button_b + prev_button_c = button_c + + # Compute IK + target_pos = apply_haply_to_robot_mapping( + haply_pos, + haply_initial_pos, + robot_initial_pos, + ) + + target_pos_tensor = torch.tensor(target_pos, dtype=torch.float32, device=sim.device).unsqueeze(0) + + current_joint_pos = robot.data.joint_pos[:, arm_joint_indices] + ee_pos_w = robot.data.body_pos_w[:, ee_body_idx] + ee_quat_w = robot.data.body_quat_w[:, ee_body_idx] + + # get jacobian to IK controller + jacobian = robot.root_physx_view.get_jacobians()[:, ee_body_idx, :, arm_joint_indices] + ik_controller.set_command(command=target_pos_tensor, ee_quat=ee_quat_w) + joint_pos_des = ik_controller.compute(ee_pos_w, ee_quat_w, jacobian, current_joint_pos) + + joint_pos_target = robot.data.joint_pos[0].clone() + + # Update joints: 6 from IK + 1 from button control (correct by design) + joint_pos_target[arm_joint_indices] = joint_pos_des[0] # panda_joint1-6 from IK + joint_pos_target[6] = ee_rotation_angle # panda_joint7 - end-effector rotation (button C) + joint_pos_target[[-2, -1]] = gripper_target # gripper + + robot.set_joint_position_target(joint_pos_target.unsqueeze(0)) + + for _ in range(5): + scene.write_data_to_sim() + sim.step() + + scene.update(sim_dt) + count += 1 + + # get contact forces and apply force feedback + left_finger_forces = left_finger_sensor.data.net_forces_w[0, 0] + right_finger_forces = right_finger_sensor.data.net_forces_w[0, 0] + total_contact_force = (left_finger_forces + right_finger_forces) * 0.5 + haply_device.push_force(forces=total_contact_force.unsqueeze(0), position=torch.tensor([0])) + + +def main(): + """Main function to set up and run the Haply teleoperation demo.""" + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device, dt=1 / 200) + sim = sim_utils.SimulationContext(sim_cfg) + + # set the simulation view + sim.set_camera_view([1.6, 1.0, 1.70], [0.4, 0.0, 1.0]) + + scene_cfg = FrankaHaplySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + + # Create Haply device + haply_cfg = HaplyDeviceCfg( + websocket_uri=args_cli.websocket_uri, + pos_sensitivity=args_cli.pos_sensitivity, + sim_device=args_cli.device, + limit_force=2.0, + ) + haply_device = HaplyDevice(cfg=haply_cfg) + print(f"[INFO] Haply connected: {args_cli.websocket_uri}") + + sim.reset() + + run_simulator(sim, scene, haply_device) + + +if __name__ == "__main__": + main() + simulation_app.close() diff --git a/scripts/demos/markers.py b/scripts/demos/markers.py new file mode 100644 index 0000000000000000000000000000000000000000..6152dcf5226fa5555db9a581a7272a756f2eb544 --- /dev/null +++ b/scripts/demos/markers.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates different types of markers. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/markers.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates different types of markers.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.math import quat_from_angle_axis + + +def define_markers() -> VisualizationMarkers: + """Define markers with various different shapes.""" + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/myMarkers", + markers={ + "frame": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/frame_prim.usd", + scale=(0.5, 0.5, 0.5), + ), + "arrow_x": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(1.0, 0.5, 0.5), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)), + ), + "cube": sim_utils.CuboidCfg( + size=(1.0, 1.0, 1.0), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + "sphere": sim_utils.SphereCfg( + radius=0.5, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + "cylinder": sim_utils.CylinderCfg( + radius=0.5, + height=1.0, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + "cone": sim_utils.ConeCfg( + radius=0.5, + height=1.0, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 1.0, 0.0)), + ), + "mesh": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(10.0, 10.0, 10.0), + ), + "mesh_recolored": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(10.0, 10.0, 10.0), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.25, 0.0)), + ), + "robot_mesh": sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + scale=(2.0, 2.0, 2.0), + visual_material=sim_utils.GlassMdlCfg(glass_color=(0.0, 0.1, 0.0)), + ), + }, + ) + return VisualizationMarkers(marker_cfg) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([0.0, 18.0, 12.0], [0.0, 3.0, 0.0]) + + # Spawn things into stage + # Lights + cfg = sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # create markers + my_visualizer = define_markers() + + # define a grid of positions where the markers should be placed + num_markers_per_type = 5 + grid_spacing = 2.0 + # Calculate the half-width and half-height + half_width = (num_markers_per_type - 1) / 2.0 + half_height = (my_visualizer.num_prototypes - 1) / 2.0 + # Create the x and y ranges centered around the origin + x_range = torch.arange(-half_width * grid_spacing, (half_width + 1) * grid_spacing, grid_spacing) + y_range = torch.arange(-half_height * grid_spacing, (half_height + 1) * grid_spacing, grid_spacing) + # Create the grid + x_grid, y_grid = torch.meshgrid(x_range, y_range, indexing="ij") + x_grid = x_grid.reshape(-1) + y_grid = y_grid.reshape(-1) + z_grid = torch.zeros_like(x_grid) + # marker locations + marker_locations = torch.stack([x_grid, y_grid, z_grid], dim=1) + marker_indices = torch.arange(my_visualizer.num_prototypes).repeat(num_markers_per_type) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Yaw angle + yaw = torch.zeros_like(marker_locations[:, 0]) + # Simulate physics + while simulation_app.is_running(): + # rotate the markers around the z-axis for visualization + marker_orientations = quat_from_angle_axis(yaw, torch.tensor([0.0, 0.0, 1.0])) + # visualize + my_visualizer.visualize(marker_locations, marker_orientations, marker_indices=marker_indices) + # roll corresponding indices to show how marker prototype can be changed + if yaw[0].item() % (0.5 * torch.pi) < 0.01: + marker_indices = torch.roll(marker_indices, 1) + # perform step + sim.step() + # increment yaw + yaw += 0.01 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/multi_asset.py b/scripts/demos/multi_asset.py new file mode 100644 index 0000000000000000000000000000000000000000..d104eb161d381f421e9808a4244394d2a6e41f3e --- /dev/null +++ b/scripts/demos/multi_asset.py @@ -0,0 +1,306 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn multiple objects in multiple environments. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/multi_asset.py --num_envs 2048 + +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Demo on spawning different objects in multiple environments.") +parser.add_argument("--num_envs", type=int, default=512, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +from pxr import Gf, Sdf + +import isaaclab.sim as sim_utils +from isaaclab.assets import ( + Articulation, + ArticulationCfg, + AssetBaseCfg, + RigidObject, + RigidObjectCfg, + RigidObjectCollection, + RigidObjectCollectionCfg, +) +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.utils import Timer, configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Pre-defined Configuration +## + +from isaaclab_assets.robots.anymal import ANYDRIVE_3_LSTM_ACTUATOR_CFG # isort: skip + + +## +# Randomization events. +## + + +def randomize_shape_color(prim_path_expr: str): + """Randomize the color of the geometry.""" + # get stage handle + stage = get_current_stage() + # resolve prim paths for spawning and cloning + prim_paths = sim_utils.find_matching_prim_paths(prim_path_expr) + # manually clone prims if the source prim path is a regex expression + with Sdf.ChangeBlock(): + for prim_path in prim_paths: + # spawn single instance + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_path) + + # DO YOUR OWN OTHER KIND OF RANDOMIZATION HERE! + # Note: Just need to acquire the right attribute about the property you want to set + # Here is an example on setting color randomly + color_spec = prim_spec.GetAttributeAtPath(prim_path + "/geometry/material/Shader.inputs:diffuseColor") + color_spec.default = Gf.Vec3f(random.random(), random.random(), random.random()) + + +## +# Scene Configuration +## + + +@configclass +class MultiObjectSceneCfg(InteractiveSceneCfg): + """Configuration for a multi-object scene.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # rigid object + object: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/Object", + spawn=sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.ConeCfg( + radius=0.3, + height=0.6, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + sim_utils.CuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0), metallic=0.2), + ), + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 2.0)), + ) + + # object collection + object_collection: RigidObjectCollectionCfg = RigidObjectCollectionCfg( + rigid_objects={ + "object_A": RigidObjectCfg( + prim_path="/World/envs/env_.*/Object_A", + spawn=sim_utils.SphereCfg( + radius=0.1, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -0.5, 2.0)), + ), + "object_B": RigidObjectCfg( + prim_path="/World/envs/env_.*/Object_B", + spawn=sim_utils.CuboidCfg( + size=(0.1, 0.1, 0.1), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.5, 2.0)), + ), + "object_C": RigidObjectCfg( + prim_path="/World/envs/env_.*/Object_C", + spawn=sim_utils.ConeCfg( + radius=0.1, + height=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.5, 0.0, 2.0)), + ), + } + ) + + # articulation + robot: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.MultiUsdFileCfg( + usd_path=[ + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d.usd", + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=True, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.6), + joint_pos={ + ".*HAA": 0.0, # all HAA + ".*F_HFE": 0.4, # both front HFE + ".*H_HFE": -0.4, # both hind HFE + ".*F_KFE": -0.8, # both front KFE + ".*H_KFE": 0.8, # both hind KFE + }, + ), + actuators={"legs": ANYDRIVE_3_LSTM_ACTUATOR_CFG}, + ) + + +## +# Simulation Loop +## + + +def run_simulator(sim: SimulationContext, scene: InteractiveScene): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. + rigid_object: RigidObject = scene["object"] + rigid_object_collection: RigidObjectCollection = scene["object_collection"] + robot: Articulation = scene["robot"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # Reset + if count % 250 == 0: + # reset counter + count = 0 + # reset the scene entities + # object + root_state = rigid_object.data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + rigid_object.write_root_pose_to_sim(root_state[:, :7]) + rigid_object.write_root_velocity_to_sim(root_state[:, 7:]) + # object collection + object_state = rigid_object_collection.data.default_object_state.clone() + object_state[..., :3] += scene.env_origins.unsqueeze(1) + rigid_object_collection.write_object_link_pose_to_sim(object_state[..., :7]) + rigid_object_collection.write_object_com_velocity_to_sim(object_state[..., 7:]) + # robot + # -- root state + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # -- joint state + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting scene state...") + + # Apply action to robot + robot.set_joint_position_target(robot.data.default_joint_pos) + # Write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + # Increment counter + count += 1 + # Update buffers + scene.update(sim_dt) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 0.0, 4.0], [0.0, 0.0, 2.0]) + + # Design scene + scene_cfg = MultiObjectSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0, replicate_physics=False) + with Timer("[INFO] Time to create scene: "): + scene = InteractiveScene(scene_cfg) + + with Timer("[INFO] Time to randomize scene: "): + # DO YOUR OWN OTHER KIND OF RANDOMIZATION HERE! + # Note: Just need to acquire the right attribute about the property you want to set + # Here is an example on setting color randomly + randomize_shape_color(scene_cfg.object.prim_path) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main execution + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/pick_and_place.py b/scripts/demos/pick_and_place.py new file mode 100644 index 0000000000000000000000000000000000000000..c98998de12429ba032e29e2e8b740dc1262d684e --- /dev/null +++ b/scripts/demos/pick_and_place.py @@ -0,0 +1,427 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Keyboard control for Isaac Lab Pick and Place.") +parser.add_argument("--num_envs", type=int, default=32, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +from collections.abc import Sequence + +import torch + +import carb +import omni + +import isaaclab.sim as sim_utils +from isaaclab.assets import ( + Articulation, + ArticulationCfg, + RigidObject, + RigidObjectCfg, + SurfaceGripper, + SurfaceGripperCfg, +) +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg +from isaaclab.markers import SPHERE_MARKER_CFG, VisualizationMarkers +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils import configclass +from isaaclab.utils.math import sample_uniform + +from isaaclab_assets.robots.pick_and_place import PICK_AND_PLACE_CFG + + +@configclass +class PickAndPlaceEnvCfg(DirectRLEnvCfg): + """Example configuration for a PickAndPlace robot using suction-cups. + + This example follows what would be typically done in a DirectRL pipeline. + """ + + # env + decimation = 4 + episode_length_s = 240.0 + action_space = 4 + observation_space = 6 + state_space = 0 + + # Simulation cfg. Surface grippers are currently only supported on CPU. + # Surface grippers also require scene query support to function. + sim: SimulationCfg = SimulationCfg( + dt=1 / 60, + device="cpu", + render_interval=decimation, + use_fabric=True, + enable_scene_query_support=True, + ) + debug_vis = True + + # robot + robot_cfg: ArticulationCfg = PICK_AND_PLACE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + x_dof_name = "x_axis" + y_dof_name = "y_axis" + z_dof_name = "z_axis" + + # We add a cube to pick-up + cube_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/Robot/Cube", + spawn=sim_utils.CuboidCfg( + size=(0.4, 0.4, 0.4), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.8)), + ), + init_state=RigidObjectCfg.InitialStateCfg(), + ) + + # Surface Gripper, the prim_expr need to point to a unique surface gripper per environment. + gripper = SurfaceGripperCfg( + prim_path="/World/envs/env_.*/Robot/picker_head/SurfaceGripper", + max_grip_distance=0.1, + shear_force_limit=500.0, + coaxial_force_limit=500.0, + retry_interval=0.2, + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=12.0, replicate_physics=True) + + # reset logic + # Initial position of the robot + initial_x_pos_range = [-2.0, 2.0] + initial_y_pos_range = [-2.0, 2.0] + initial_z_pos_range = [0.0, 0.5] + + # Initial position of the cube + initial_object_x_pos_range = [-2.0, 2.0] + initial_object_y_pos_range = [-2.0, -0.5] + initial_object_z_pos = 0.2 + + # Target position of the cube + target_x_pos_range = [-2.0, 2.0] + target_y_pos_range = [2.0, 0.5] + target_z_pos = 0.2 + + +class PickAndPlaceEnv(DirectRLEnv): + """Example environment for a PickAndPlace robot using suction-cups. + + This example follows what would be typically done in a DirectRL pipeline. + Here we substitute the policy by keyboard inputs. + """ + + cfg: PickAndPlaceEnvCfg + + def __init__(self, cfg: PickAndPlaceEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + # Indices used to control the different axes of the gantry + self._x_dof_idx, _ = self.pick_and_place.find_joints(self.cfg.x_dof_name) + self._y_dof_idx, _ = self.pick_and_place.find_joints(self.cfg.y_dof_name) + self._z_dof_idx, _ = self.pick_and_place.find_joints(self.cfg.z_dof_name) + + # joints info + self.joint_pos = self.pick_and_place.data.joint_pos + self.joint_vel = self.pick_and_place.data.joint_vel + + # Buffers + self.go_to_cube = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) + self.go_to_target = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) + self.target_pos = torch.zeros((self.num_envs, 3), device=self.device, dtype=torch.float32) + self.instant_controls = torch.zeros((self.num_envs, 3), device=self.device, dtype=torch.float32) + self.permanent_controls = torch.zeros((self.num_envs, 1), device=self.device, dtype=torch.float32) + + # Visual marker for the target + self.set_debug_vis(self.cfg.debug_vis) + + # Sets up the keyboard callback and settings + self.set_up_keyboard() + + def set_up_keyboard(self): + """Sets up interface for keyboard input and registers the desired keys for control.""" + # Acquire keyboard interface + self._input = carb.input.acquire_input_interface() + self._keyboard = omni.appwindow.get_default_app_window().get_keyboard() + self._sub_keyboard = self._input.subscribe_to_keyboard_events(self._keyboard, self._on_keyboard_event) + # Open / Close / Idle commands for gripper + self._instant_key_controls = { + "Q": torch.tensor([0, 0, -1]), + "E": torch.tensor([0, 0, 1]), + "ZEROS": torch.tensor([0, 0, 0]), + } + # Move up or down + self._permanent_key_controls = { + "W": torch.tensor([-200.0], device=self.device), + "S": torch.tensor([100.0], device=self.device), + } + # Aiming manually is painful we can automate this. + self._auto_aim_cube = "A" + self._auto_aim_target = "D" + + # Task description: + print("Keyboard set up!") + print("The simulation is ready for you to try it out!") + print("Your goal is pick up the purple cube and to drop it on the red sphere!") + print(f"Number of environments: {self.num_envs}") + print("Use the following controls to interact with ALL environments simultaneously:") + print("Press the 'A' key to have all grippers track the cube position.") + print("Press the 'D' key to have all grippers track the target position") + print("Press the 'W' or 'S' keys to move all gantries UP or DOWN respectively") + print("Press 'Q' or 'E' to OPEN or CLOSE all grippers respectively") + + def _on_keyboard_event(self, event): + """Checks for a keyboard event and assign the corresponding command control depending on key pressed.""" + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + # Logic on key press - apply to ALL environments + if event.input.name == self._auto_aim_target: + self.go_to_target[:] = True + self.go_to_cube[:] = False + if event.input.name == self._auto_aim_cube: + self.go_to_cube[:] = True + self.go_to_target[:] = False + if event.input.name in self._instant_key_controls: + self.go_to_cube[:] = False + self.go_to_target[:] = False + self.instant_controls[:] = self._instant_key_controls[event.input.name] + if event.input.name in self._permanent_key_controls: + self.go_to_cube[:] = False + self.go_to_target[:] = False + self.permanent_controls[:] = self._permanent_key_controls[event.input.name] + # On key release, all robots stop moving + elif event.type == carb.input.KeyboardEventType.KEY_RELEASE: + self.go_to_cube[:] = False + self.go_to_target[:] = False + self.instant_controls[:] = self._instant_key_controls["ZEROS"] + + def _setup_scene(self): + self.pick_and_place = Articulation(self.cfg.robot_cfg) + self.cube = RigidObject(self.cfg.cube_cfg) + self.gripper = SurfaceGripper(self.cfg.gripper) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene + self.scene.articulations["pick_and_place"] = self.pick_and_place + self.scene.rigid_objects["cube"] = self.cube + self.scene.surface_grippers["gripper"] = self.gripper + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + # Store the actions + self.actions = actions.clone() + + def _apply_action(self) -> None: + # We use the keyboard outputs as an action. + # Process each environment independently + if self.go_to_cube.any(): + # Effort based proportional controller to track the cube position + head_pos_x = self.pick_and_place.data.joint_pos[self.go_to_cube, self._x_dof_idx[0]] + head_pos_y = self.pick_and_place.data.joint_pos[self.go_to_cube, self._y_dof_idx[0]] + cube_pos_x = self.cube.data.root_pos_w[self.go_to_cube, 0] - self.scene.env_origins[self.go_to_cube, 0] + cube_pos_y = self.cube.data.root_pos_w[self.go_to_cube, 1] - self.scene.env_origins[self.go_to_cube, 1] + d_cube_robot_x = cube_pos_x - head_pos_x + d_cube_robot_y = cube_pos_y - head_pos_y + self.instant_controls[self.go_to_cube] = torch.stack( + [d_cube_robot_x * 5.0, d_cube_robot_y * 5.0, torch.zeros_like(d_cube_robot_x)], dim=1 + ) + if self.go_to_target.any(): + # Effort based proportional controller to track the target position + head_pos_x = self.pick_and_place.data.joint_pos[self.go_to_target, self._x_dof_idx[0]] + head_pos_y = self.pick_and_place.data.joint_pos[self.go_to_target, self._y_dof_idx[0]] + target_pos_x = self.target_pos[self.go_to_target, 0] + target_pos_y = self.target_pos[self.go_to_target, 1] + d_target_robot_x = target_pos_x - head_pos_x + d_target_robot_y = target_pos_y - head_pos_y + self.instant_controls[self.go_to_target] = torch.stack( + [d_target_robot_x * 5.0, d_target_robot_y * 5.0, torch.zeros_like(d_target_robot_x)], dim=1 + ) + + # Set the joint effort targets for the picker + self.pick_and_place.set_joint_effort_target( + self.instant_controls[:, 0].unsqueeze(dim=1), joint_ids=self._x_dof_idx + ) + self.pick_and_place.set_joint_effort_target( + self.instant_controls[:, 1].unsqueeze(dim=1), joint_ids=self._y_dof_idx + ) + self.pick_and_place.set_joint_effort_target( + self.permanent_controls[:, 0].unsqueeze(dim=1), joint_ids=self._z_dof_idx + ) + # Set the gripper command + self.gripper.set_grippers_command(self.instant_controls[:, 2]) + + def _get_observations(self) -> dict: + # Get the observations + gripper_state = self.gripper.state.clone() + obs = torch.cat( + ( + self.joint_pos[:, self._x_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._x_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._y_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._y_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._z_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._z_dof_idx[0]].unsqueeze(dim=1), + self.target_pos[:, 0].unsqueeze(dim=1), + self.target_pos[:, 1].unsqueeze(dim=1), + gripper_state.unsqueeze(dim=1), + ), + dim=-1, + ) + + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + return torch.zeros_like(self.reset_terminated, dtype=torch.float32) + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + # Dones + self.joint_pos = self.pick_and_place.data.joint_pos + self.joint_vel = self.pick_and_place.data.joint_vel + # Check for time out + time_out = self.episode_length_buf >= self.max_episode_length - 1 + # Check if the cube reached the target + cube_to_target_x_dist = self.cube.data.root_pos_w[:, 0] - self.target_pos[:, 0] - self.scene.env_origins[:, 0] + cube_to_target_y_dist = self.cube.data.root_pos_w[:, 1] - self.target_pos[:, 1] - self.scene.env_origins[:, 1] + cube_to_target_z_dist = self.cube.data.root_pos_w[:, 2] - self.target_pos[:, 2] - self.scene.env_origins[:, 2] + cube_to_target_distance = torch.norm( + torch.stack((cube_to_target_x_dist, cube_to_target_y_dist, cube_to_target_z_dist), dim=1), dim=1 + ) + self.target_reached = cube_to_target_distance < 0.3 + # Check if the cube is out of bounds (that is outside of the picking area) + cube_to_origin_xy_diff = self.cube.data.root_pos_w[:, :2] - self.scene.env_origins[:, :2] + cube_to_origin_x_dist = torch.abs(cube_to_origin_xy_diff[:, 0]) + cube_to_origin_y_dist = torch.abs(cube_to_origin_xy_diff[:, 1]) + self.cube_out_of_bounds = (cube_to_origin_x_dist > 2.5) | (cube_to_origin_y_dist > 2.5) + + time_out = time_out | self.target_reached + return self.cube_out_of_bounds, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.pick_and_place._ALL_INDICES + # Reset the environment, this must be done first! As it releases the objects held by the grippers. + # (And that's an operation that should be done before the gripper or the gripped objects are moved) + super()._reset_idx(env_ids) + num_resets = len(env_ids) + + # Set a target position for the cube + self.target_pos[env_ids, 0] = sample_uniform( + self.cfg.target_x_pos_range[0], + self.cfg.target_x_pos_range[1], + num_resets, + self.device, + ) + self.target_pos[env_ids, 1] = sample_uniform( + self.cfg.target_y_pos_range[0], + self.cfg.target_y_pos_range[1], + num_resets, + self.device, + ) + self.target_pos[env_ids, 2] = self.cfg.target_z_pos + + # Set the initial position of the cube + cube_pos = self.cube.data.default_root_state[env_ids, :7] + cube_pos[:, 0] = sample_uniform( + self.cfg.initial_object_x_pos_range[0], + self.cfg.initial_object_x_pos_range[1], + cube_pos[:, 0].shape, + self.device, + ) + cube_pos[:, 1] = sample_uniform( + self.cfg.initial_object_y_pos_range[0], + self.cfg.initial_object_y_pos_range[1], + cube_pos[:, 1].shape, + self.device, + ) + cube_pos[:, 2] = self.cfg.initial_object_z_pos + cube_pos[:, :3] += self.scene.env_origins[env_ids] + self.cube.write_root_pose_to_sim(cube_pos, env_ids) + + # Set the initial position of the robot + joint_pos = self.pick_and_place.data.default_joint_pos[env_ids] + joint_pos[:, self._x_dof_idx] += sample_uniform( + self.cfg.initial_x_pos_range[0], + self.cfg.initial_x_pos_range[1], + joint_pos[:, self._x_dof_idx].shape, + self.device, + ) + joint_pos[:, self._y_dof_idx] += sample_uniform( + self.cfg.initial_y_pos_range[0], + self.cfg.initial_y_pos_range[1], + joint_pos[:, self._y_dof_idx].shape, + self.device, + ) + joint_pos[:, self._z_dof_idx] += sample_uniform( + self.cfg.initial_z_pos_range[0], + self.cfg.initial_z_pos_range[1], + joint_pos[:, self._z_dof_idx].shape, + self.device, + ) + joint_vel = self.pick_and_place.data.default_joint_vel[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self.pick_and_place.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + def _set_debug_vis_impl(self, debug_vis: bool): + # create markers if necessary for the first tome + if debug_vis: + if not hasattr(self, "goal_pos_visualizer"): + marker_cfg = SPHERE_MARKER_CFG.copy() + marker_cfg.markers["sphere"].radius = 0.25 + # -- goal pose + marker_cfg.prim_path = "/Visuals/Command/goal_position" + self.goal_pos_visualizer = VisualizationMarkers(marker_cfg) + # set their visibility to true + self.goal_pos_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_pos_visualizer"): + self.goal_pos_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # update the markers + self.goal_pos_visualizer.visualize(self.target_pos + self.scene.env_origins) + + +def main(): + """Main function.""" + # create environment configuration + env_cfg = PickAndPlaceEnvCfg() + env_cfg.scene.num_envs = args_cli.num_envs + # create environment + pick_and_place = PickAndPlaceEnv(env_cfg) + obs, _ = pick_and_place.reset() + while simulation_app.is_running(): + # check for selected robots + with torch.inference_mode(): + actions = torch.zeros((pick_and_place.num_envs, 4), device=pick_and_place.device, dtype=torch.float32) + pick_and_place.step(actions) + + +if __name__ == "__main__": + main() + simulation_app.close() diff --git a/scripts/demos/procedural_terrain.py b/scripts/demos/procedural_terrain.py new file mode 100644 index 0000000000000000000000000000000000000000..f0a2fb4e2ef7f9b689c31eb918ac0a371f0edaca --- /dev/null +++ b/scripts/demos/procedural_terrain.py @@ -0,0 +1,175 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates procedural terrains with flat patches. + +Example usage: + +.. code-block:: bash + + # Generate terrain with height color scheme + ./isaaclab.sh -p scripts/demos/procedural_terrain.py --color_scheme height + + # Generate terrain with random color scheme + ./isaaclab.sh -p scripts/demos/procedural_terrain.py --color_scheme random + + # Generate terrain with no color scheme + ./isaaclab.sh -p scripts/demos/procedural_terrain.py --color_scheme none + + # Generate terrain with curriculum + ./isaaclab.sh -p scripts/demos/procedural_terrain.py --use_curriculum + + # Generate terrain with curriculum along with flat patches + ./isaaclab.sh -p scripts/demos/procedural_terrain.py --use_curriculum --show_flat_patches + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates procedural terrain generation.") +parser.add_argument( + "--color_scheme", + type=str, + default="none", + choices=["height", "random", "none"], + help="Color scheme to use for the terrain generation.", +) +parser.add_argument( + "--use_curriculum", + action="store_true", + default=False, + help="Whether to use the curriculum for the terrain generation.", +) +parser.add_argument( + "--show_flat_patches", + action="store_true", + default=False, + help="Whether to show the flat patches computed during the terrain generation.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBase +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg +from isaaclab.terrains import FlatPatchSamplingCfg, TerrainImporter, TerrainImporterCfg + +## +# Pre-defined configs +## +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort:skip + + +def design_scene() -> tuple[dict, torch.Tensor]: + """Designs the scene.""" + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Parse terrain generation + terrain_gen_cfg = ROUGH_TERRAINS_CFG.replace(curriculum=args_cli.use_curriculum, color_scheme=args_cli.color_scheme) + + # Add flat patch configuration + # Note: To have separate colors for each sub-terrain type, we set the flat patch sampling configuration name + # to the sub-terrain name. However, this is not how it should be used in practice. The key name should be + # the intention of the flat patch. For instance, "source" or "target" for spawn and command related flat patches. + if args_cli.show_flat_patches: + for sub_terrain_name, sub_terrain_cfg in terrain_gen_cfg.sub_terrains.items(): + sub_terrain_cfg.flat_patch_sampling = { + sub_terrain_name: FlatPatchSamplingCfg(num_patches=10, patch_radius=0.5, max_height_diff=0.05) + } + + # Handler for terrains importing + terrain_importer_cfg = TerrainImporterCfg( + num_envs=2048, + env_spacing=3.0, + prim_path="/World/ground", + max_init_terrain_level=None, + terrain_type="generator", + terrain_generator=terrain_gen_cfg, + debug_vis=True, + ) + # Remove visual material for height and random color schemes to use the default material + if args_cli.color_scheme in ["height", "random"]: + terrain_importer_cfg.visual_material = None + # Create terrain importer + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # Show the flat patches computed + if args_cli.show_flat_patches: + # Configure the flat patches + vis_cfg = VisualizationMarkersCfg(prim_path="/Visuals/TerrainFlatPatches", markers={}) + for name in terrain_importer.flat_patches: + vis_cfg.markers[name] = sim_utils.CylinderCfg( + radius=0.5, # note: manually set to the patch radius for visualization + height=0.1, + visual_material=sim_utils.GlassMdlCfg(glass_color=(random.random(), random.random(), random.random())), + ) + flat_patches_visualizer = VisualizationMarkers(vis_cfg) + + # Visualize the flat patches + all_patch_locations = [] + all_patch_indices = [] + for i, patch_locations in enumerate(terrain_importer.flat_patches.values()): + num_patch_locations = patch_locations.view(-1, 3).shape[0] + # store the patch locations and indices + all_patch_locations.append(patch_locations.view(-1, 3)) + all_patch_indices += [i] * num_patch_locations + # combine the patch locations and indices + flat_patches_visualizer.visualize(torch.cat(all_patch_locations), marker_indices=all_patch_indices) + + # return the scene information + scene_entities = {"terrain": terrain_importer} + return scene_entities, terrain_importer.env_origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, AssetBase], origins: torch.Tensor): + """Runs the simulation loop.""" + # Simulate physics + while simulation_app.is_running(): + # perform step + sim.step() + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[5.0, 5.0, 5.0], target=[0.0, 0.0, 0.0]) + # design scene + scene_entities, scene_origins = design_scene() + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/quadcopter.py b/scripts/demos/quadcopter.py new file mode 100644 index 0000000000000000000000000000000000000000..bf42a04f8501290da98ce8caa91ff3627f3994d4 --- /dev/null +++ b/scripts/demos/quadcopter.py @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to simulate a quadcopter. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/quadcopter.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to simulate a quadcopter.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.sim import SimulationContext + +## +# Pre-defined configs +## +from isaaclab_assets import CRAZYFLIE_CFG # isort:skip + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[0.5, 0.5, 1.0], target=[0.0, 0.0, 0.5]) + + # Spawn things into stage + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Robots + robot_cfg = CRAZYFLIE_CFG.replace(prim_path="/World/Crazyflie") + robot_cfg.spawn.func("/World/Crazyflie", robot_cfg.spawn, translation=robot_cfg.init_state.pos) + + # create handles for the robots + robot = Articulation(robot_cfg) + + # Play the simulator + sim.reset() + + # Fetch relevant parameters to make the quadcopter hover in place + prop_body_ids = robot.find_bodies("m.*_prop")[0] + robot_mass = robot.root_physx_view.get_masses().sum() + gravity = torch.tensor(sim.cfg.gravity, device=sim.device).norm() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 2000 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset dof state + joint_pos, joint_vel = robot.data.default_joint_pos, robot.data.default_joint_vel + robot.write_joint_state_to_sim(joint_pos, joint_vel) + robot.write_root_pose_to_sim(robot.data.default_root_state[:, :7]) + robot.write_root_velocity_to_sim(robot.data.default_root_state[:, 7:]) + robot.reset() + # reset command + print(">>>>>>>> Reset!") + # apply action to the robot (make the robot float in place) + forces = torch.zeros(robot.num_instances, 4, 3, device=sim.device) + torques = torch.zeros_like(forces) + forces[..., 2] = robot_mass * gravity / 4.0 + robot.permanent_wrench_composer.set_forces_and_torques( + forces=forces, + torques=torques, + body_ids=prop_body_ids, + ) + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + robot.update(sim_dt) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/quadrupeds.py b/scripts/demos/quadrupeds.py new file mode 100644 index 0000000000000000000000000000000000000000..b9935de30dabb41730c227e05abba21e92e82118 --- /dev/null +++ b/scripts/demos/quadrupeds.py @@ -0,0 +1,191 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates different legged robots. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/quadrupeds.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates different legged robots.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_B_CFG, ANYMAL_C_CFG, ANYMAL_D_CFG # isort:skip +from isaaclab_assets.robots.spot import SPOT_CFG # isort:skip +from isaaclab_assets.robots.unitree import UNITREE_A1_CFG, UNITREE_GO1_CFG, UNITREE_GO2_CFG # isort:skip + + +def define_origins(num_origins: int, spacing: float) -> list[list[float]]: + """Defines the origins of the scene.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_origins, 3) + # create a grid of origins + num_cols = np.floor(np.sqrt(num_origins)) + num_rows = np.ceil(num_origins / num_cols) + xx, yy = torch.meshgrid(torch.arange(num_rows), torch.arange(num_cols), indexing="xy") + env_origins[:, 0] = spacing * xx.flatten()[:num_origins] - spacing * (num_rows - 1) / 2 + env_origins[:, 1] = spacing * yy.flatten()[:num_origins] - spacing * (num_cols - 1) / 2 + env_origins[:, 2] = 0.0 + # return the origins + return env_origins.tolist() + + +def design_scene() -> tuple[dict, list[list[float]]]: + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a mount and a robot on top of it + origins = define_origins(num_origins=7, spacing=1.25) + + # Origin 1 with Anymal B + sim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0]) + # -- Robot + anymal_b = Articulation(ANYMAL_B_CFG.replace(prim_path="/World/Origin1/Robot")) + + # Origin 2 with Anymal C + sim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1]) + # -- Robot + anymal_c = Articulation(ANYMAL_C_CFG.replace(prim_path="/World/Origin2/Robot")) + + # Origin 3 with Anymal D + sim_utils.create_prim("/World/Origin3", "Xform", translation=origins[2]) + # -- Robot + anymal_d = Articulation(ANYMAL_D_CFG.replace(prim_path="/World/Origin3/Robot")) + + # Origin 4 with Unitree A1 + sim_utils.create_prim("/World/Origin4", "Xform", translation=origins[3]) + # -- Robot + unitree_a1 = Articulation(UNITREE_A1_CFG.replace(prim_path="/World/Origin4/Robot")) + + # Origin 5 with Unitree Go1 + sim_utils.create_prim("/World/Origin5", "Xform", translation=origins[4]) + # -- Robot + unitree_go1 = Articulation(UNITREE_GO1_CFG.replace(prim_path="/World/Origin5/Robot")) + + # Origin 6 with Unitree Go2 + sim_utils.create_prim("/World/Origin6", "Xform", translation=origins[5]) + # -- Robot + unitree_go2 = Articulation(UNITREE_GO2_CFG.replace(prim_path="/World/Origin6/Robot")) + + # Origin 7 with Boston Dynamics Spot + sim_utils.create_prim("/World/Origin7", "Xform", translation=origins[6]) + # -- Robot + spot = Articulation(SPOT_CFG.replace(prim_path="/World/Origin7/Robot")) + + # return the scene information + scene_entities = { + "anymal_b": anymal_b, + "anymal_c": anymal_c, + "anymal_d": anymal_d, + "unitree_a1": unitree_a1, + "unitree_go1": unitree_go1, + "unitree_go2": unitree_go2, + "spot": spot, + } + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, Articulation], origins: torch.Tensor): + """Runs the simulation loop.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 200 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset robots + for index, robot in enumerate(entities.values()): + # root state + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins[index] + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # joint state + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # reset the internal state + robot.reset() + print("[INFO]: Resetting robots state...") + # apply default actions to the quadrupedal robots + for robot in entities.values(): + # generate random joint positions + joint_pos_target = robot.data.default_joint_pos + torch.randn_like(robot.data.joint_pos) * 0.1 + # apply action to the robot + robot.set_joint_position_target(joint_pos_target) + # write data to sim + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + for robot in entities.values(): + robot.update(sim_dt) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.01)) + # Set main camera + sim.set_camera_view(eye=[2.5, 2.5, 2.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/cameras.py b/scripts/demos/sensors/cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..83214f7e4cf2d9b4019b235f434521018ae7fdea --- /dev/null +++ b/scripts/demos/sensors/cameras.py @@ -0,0 +1,302 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates the different camera sensors that can be attached to a robot. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/demos/sensors/cameras.py --enable_cameras + + # Usage in headless mode + ./isaaclab.sh -p scripts/demos/sensors/cameras.py --headless --enable_cameras + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the different camera sensor implementations.") +parser.add_argument("--num_envs", type=int, default=4, help="Number of environments to spawn.") +parser.add_argument("--disable_fabric", action="store_true", help="Disable Fabric API and use USD instead.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os + +import matplotlib.pyplot as plt +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import CameraCfg, RayCasterCameraCfg, TiledCameraCfg +from isaaclab.sensors.ray_caster import patterns +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort:skip +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class SensorsSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = TerrainImporterCfg( + prim_path="/World/ground", + max_init_terrain_level=None, + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG.replace(color_scheme="random"), + visual_material=None, + debug_vis=False, + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors + camera = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/base/front_cam", + update_period=0.1, + height=480, + width=640, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + tiled_camera = TiledCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/base/front_cam", + update_period=0.1, + height=480, + width=640, + data_types=["rgb", "distance_to_image_plane"], + spawn=None, # the camera is already spawned in the scene + offset=TiledCameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + raycast_camera = RayCasterCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + mesh_prim_paths=["/World/ground"], + update_period=0.1, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + data_types=["distance_to_image_plane", "normals"], + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=480, + width=640, + ), + ) + + +def save_images_grid( + images: list[torch.Tensor], + cmap: str | None = None, + nrow: int = 1, + subtitles: list[str] | None = None, + title: str | None = None, + filename: str | None = None, +): + """Save images in a grid with optional subtitles and title. + + Args: + images: A list of images to be plotted. Shape of each image should be (H, W, C). + cmap: Colormap to be used for plotting. Defaults to None, in which case the default colormap is used. + nrows: Number of rows in the grid. Defaults to 1. + subtitles: A list of subtitles for each image. Defaults to None, in which case no subtitles are shown. + title: Title of the grid. Defaults to None, in which case no title is shown. + filename: Path to save the figure. Defaults to None, in which case the figure is not saved. + """ + # show images in a grid + n_images = len(images) + ncol = int(np.ceil(n_images / nrow)) + + fig, axes = plt.subplots(nrow, ncol, figsize=(ncol * 2, nrow * 2)) + if isinstance(axes, np.ndarray): + axes = axes.flatten() + else: + axes = np.array([axes]) + + # plot images + for idx, (img, ax) in enumerate(zip(images, axes)): + img = img.detach().cpu().numpy() + ax.imshow(img, cmap=cmap) + ax.axis("off") + if subtitles: + ax.set_title(subtitles[idx]) + # remove extra axes if any + for ax in axes[n_images:]: + fig.delaxes(ax) + # set title + if title: + plt.suptitle(title) + + # adjust layout to fit the title + plt.tight_layout() + # save the figure + if filename: + os.makedirs(os.path.dirname(filename), exist_ok=True) + plt.savefig(filename) + # close the figure + plt.close() + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Create output directory to save images + output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") + os.makedirs(output_dir, exist_ok=True) + + # Simulate physics + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["camera"]) + print("Received shape of rgb image: ", scene["camera"].data.output["rgb"].shape) + print("Received shape of depth image: ", scene["camera"].data.output["distance_to_image_plane"].shape) + print("-------------------------------") + print(scene["tiled_camera"]) + print("Received shape of rgb image: ", scene["tiled_camera"].data.output["rgb"].shape) + print("Received shape of depth image: ", scene["tiled_camera"].data.output["distance_to_image_plane"].shape) + print("-------------------------------") + print(scene["raycast_camera"]) + print("Received shape of depth: ", scene["raycast_camera"].data.output["distance_to_image_plane"].shape) + print("Received shape of normals: ", scene["raycast_camera"].data.output["normals"].shape) + + # save every 10th image (for visualization purposes only) + # note: saving images will slow down the simulation + if count % 10 == 0: + # compare generated RGB images across different cameras + rgb_images = [scene["camera"].data.output["rgb"][0, ..., :3], scene["tiled_camera"].data.output["rgb"][0]] + save_images_grid( + rgb_images, + subtitles=["Camera", "TiledCamera"], + title="RGB Image: Cam0", + filename=os.path.join(output_dir, "rgb", f"{count:04d}.jpg"), + ) + + # compare generated Depth images across different cameras + depth_images = [ + scene["camera"].data.output["distance_to_image_plane"][0], + scene["tiled_camera"].data.output["distance_to_image_plane"][0, ..., 0], + scene["raycast_camera"].data.output["distance_to_image_plane"][0], + ] + save_images_grid( + depth_images, + cmap="turbo", + subtitles=["Camera", "TiledCamera", "RaycasterCamera"], + title="Depth Image: Cam0", + filename=os.path.join(output_dir, "distance_to_camera", f"{count:04d}.jpg"), + ) + + # save all tiled RGB images + tiled_images = scene["tiled_camera"].data.output["rgb"] + save_images_grid( + tiled_images, + subtitles=[f"Cam{i}" for i in range(tiled_images.shape[0])], + title="Tiled RGB Image", + filename=os.path.join(output_dir, "tiled_rgb", f"{count:04d}.jpg"), + ) + + # save all camera RGB images + cam_images = scene["camera"].data.output["rgb"][..., :3] + save_images_grid( + cam_images, + subtitles=[f"Cam{i}" for i in range(cam_images.shape[0])], + title="Camera RGB Image", + filename=os.path.join(output_dir, "cam_rgb", f"{count:04d}.jpg"), + ) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device, use_fabric=not args_cli.disable_fabric) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = SensorsSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/contact_sensor.py b/scripts/demos/sensors/contact_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..0ee672ec16a6270eb5d2809937a641153202739d --- /dev/null +++ b/scripts/demos/sensors/contact_sensor.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the contact sensor.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg, RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class ContactSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Rigid Object + cube = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube", + spawn=sim_utils.CuboidCfg( + size=(0.5, 0.5, 0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=100.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.5, 0.5, 0.05)), + ) + + contact_forces_LF = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT", + update_period=0.0, + history_length=6, + debug_vis=True, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube"], + ) + + contact_forces_RF = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT", + update_period=0.0, + history_length=6, + debug_vis=True, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube"], + ) + + contact_forces_H = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/.*H_FOOT", + update_period=0.0, + history_length=6, + debug_vis=True, + ) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["contact_forces_LF"]) + print("Received force matrix of: ", scene["contact_forces_LF"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_LF"].data.net_forces_w) + print("-------------------------------") + print(scene["contact_forces_RF"]) + print("Received force matrix of: ", scene["contact_forces_RF"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_RF"].data.net_forces_w) + print("-------------------------------") + print(scene["contact_forces_H"]) + print("Received force matrix of: ", scene["contact_forces_H"].data.force_matrix_w) + print("Received contact force of: ", scene["contact_forces_H"].data.net_forces_w) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = ContactSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/frame_transformer_sensor.py b/scripts/demos/sensors/frame_transformer_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..8827b23cea71b8e662751358293e08c4ddad5ade --- /dev/null +++ b/scripts/demos/sensors/frame_transformer_sensor.py @@ -0,0 +1,170 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the frame transformer sensor.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg, RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class FrameTransformerSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Rigid Object + cube = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube", + spawn=sim_utils.CuboidCfg( + size=(1, 1, 1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=100.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(5, 0, 0.5)), + ) + + specific_transforms = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT"), + FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT"), + ], + debug_vis=True, + ) + + cube_transform = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Cube")], + debug_vis=False, + ) + + robot_transforms = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/.*")], + debug_vis=False, + ) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["specific_transforms"]) + print("relative transforms:", scene["specific_transforms"].data.target_pos_source) + print("relative orientations:", scene["specific_transforms"].data.target_quat_source) + print("-------------------------------") + print(scene["cube_transform"]) + print("relative transform:", scene["cube_transform"].data.target_pos_source) + print("-------------------------------") + print(scene["robot_transforms"]) + print("relative transforms:", scene["robot_transforms"].data.target_pos_source) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = FrameTransformerSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/imu_sensor.py b/scripts/demos/sensors/imu_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..af649fd94a97fc297fdd2727f633c001b97c19ca --- /dev/null +++ b/scripts/demos/sensors/imu_sensor.py @@ -0,0 +1,143 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the IMU sensor.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ImuCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class ImuSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + imu_RF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT", debug_vis=True) + + imu_LF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT", gravity_bias=(0, 0, 0), debug_vis=True) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_link_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_com_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["imu_LF"]) + print("Received linear velocity: ", scene["imu_LF"].data.lin_vel_b) + print("Received angular velocity: ", scene["imu_LF"].data.ang_vel_b) + print("Received linear acceleration: ", scene["imu_LF"].data.lin_acc_b) + print("Received angular acceleration: ", scene["imu_LF"].data.ang_acc_b) + print("-------------------------------") + print(scene["imu_RF"]) + print("Received linear velocity: ", scene["imu_RF"].data.lin_vel_b) + print("Received angular velocity: ", scene["imu_RF"].data.ang_vel_b) + print("Received linear acceleration: ", scene["imu_RF"].data.lin_acc_b) + print("Received angular acceleration: ", scene["imu_RF"].data.ang_acc_b) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = ImuSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/multi_mesh_raycaster.py b/scripts/demos/sensors/multi_mesh_raycaster.py new file mode 100644 index 0000000000000000000000000000000000000000..07b36573501bda6a694f251b4ee9bb0cadaad255 --- /dev/null +++ b/scripts/demos/sensors/multi_mesh_raycaster.py @@ -0,0 +1,303 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Example on using the Multi-Mesh Raycaster sensor. + +.. code-block:: bash + + # with allegro hand + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type allegro_hand + + # with anymal-D bodies + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type anymal_d + + # with random multiple objects + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type objects + +""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the multi-mesh raycaster sensor.") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") +parser.add_argument( + "--asset_type", + type=str, + default="allegro_hand", + help="Asset type to use.", + choices=["allegro_hand", "anymal_d", "objects"], +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +import torch + +import omni.usd +from pxr import Gf, Sdf + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, AssetBaseCfg, RigidObjectCfg +from isaaclab.markers.config import VisualizationMarkersCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors.ray_caster import MultiMeshRayCasterCfg, patterns +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Pre-defined configs +## +from isaaclab_assets.robots.allegro import ALLEGRO_HAND_CFG +from isaaclab_assets.robots.anymal import ANYMAL_D_CFG + +RAY_CASTER_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "hit": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, +) + + +if args_cli.asset_type == "allegro_hand": + asset_cfg = ALLEGRO_HAND_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + ray_caster_cfg = MultiMeshRayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot", + update_period=1 / 60, + offset=MultiMeshRayCasterCfg.OffsetCfg(pos=(0, -0.1, 0.3)), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/thumb_link_.*/visuals_xform"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/index_link.*/visuals_xform"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/middle_link_.*/visuals_xform"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/ring_link_.*/visuals_xform"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/palm_link/visuals_xform"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/allegro_mount/visuals_xform"), + ], + ray_alignment="world", + pattern_cfg=patterns.GridPatternCfg(resolution=0.005, size=(0.4, 0.4), direction=(0, 0, -1)), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) + +elif args_cli.asset_type == "anymal_d": + asset_cfg = ANYMAL_D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + ray_caster_cfg = MultiMeshRayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + update_period=1 / 60, + offset=MultiMeshRayCasterCfg.OffsetCfg(pos=(0, -0.1, 0.3)), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LF_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RF_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LH_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RH_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/base/visuals"), + ], + ray_alignment="world", + pattern_cfg=patterns.GridPatternCfg(resolution=0.02, size=(2.5, 2.5), direction=(0, 0, -1)), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) + +elif args_cli.asset_type == "objects": + asset_cfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + spawn=sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.CuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0), metallic=0.2), + ), + sim_utils.CylinderCfg( + radius=0.2, + height=0.5, + axis="Y", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + sim_utils.CapsuleCfg( + radius=0.15, + height=0.5, + axis="Z", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 1.0, 0.0), metallic=0.2), + ), + sim_utils.ConeCfg( + radius=0.2, + height=0.5, + axis="Z", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 1.0), metallic=0.2), + ), + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 2.0)), + ) + ray_caster_cfg = MultiMeshRayCasterCfg( + prim_path="{ENV_REGEX_NS}/Object", + update_period=1 / 60, + offset=MultiMeshRayCasterCfg.OffsetCfg(pos=(0, 0.0, 0.6)), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Object"), + ], + ray_alignment="world", + pattern_cfg=patterns.GridPatternCfg(resolution=0.01, size=(0.6, 0.6), direction=(0, 0, -1)), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) +else: + raise ValueError(f"Unknown asset type: {args_cli.asset_type}") + + +@configclass +class RaycasterSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the asset.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/Ground", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + scale=(1, 1, 1), + ), + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # asset + asset = asset_cfg + # ray caster + ray_caster = ray_caster_cfg + + +def randomize_shape_color(prim_path_expr: str): + """Randomize the color of the geometry.""" + + # acquire stage + stage = omni.usd.get_context().get_stage() + # resolve prim paths for spawning and cloning + prim_paths = sim_utils.find_matching_prim_paths(prim_path_expr) + # manually clone prims if the source prim path is a regex expression + + with Sdf.ChangeBlock(): + for prim_path in prim_paths: + print("Applying prim scale to:", prim_path) + # spawn single instance + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_path) + + # DO YOUR OWN OTHER KIND OF RANDOMIZATION HERE! + # Note: Just need to acquire the right attribute about the property you want to set + # Here is an example on setting color randomly + color_spec = prim_spec.GetAttributeAtPath(prim_path + "/geometry/material/Shader.inputs:diffuseColor") + color_spec.default = Gf.Vec3f(random.random(), random.random(), random.random()) + + # randomize scale + scale_spec = prim_spec.GetAttributeAtPath(prim_path + ".xformOp:scale") + scale_spec.default = Gf.Vec3f(random.uniform(0.5, 1.5), random.uniform(0.5, 1.5), random.uniform(0.5, 1.5)) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + root_state = scene["asset"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["asset"].write_root_pose_to_sim(root_state[:, :7]) + scene["asset"].write_root_velocity_to_sim(root_state[:, 7:]) + + if isinstance(scene["asset"], Articulation): + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["asset"].data.default_joint_pos.clone(), + scene["asset"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["asset"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting Asset state...") + + if isinstance(scene["asset"], Articulation): + # -- generate actions/commands + targets = scene["asset"].data.default_joint_pos + 5 * ( + torch.rand_like(scene["asset"].data.default_joint_pos) - 0.5 + ) + # -- apply action to the asset + scene["asset"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = RaycasterSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0, replicate_physics=False) + scene = InteractiveScene(scene_cfg) + + if args_cli.asset_type == "objects": + randomize_shape_color(scene_cfg.asset.prim_path.format(ENV_REGEX_NS="/World/envs/env_.*")) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/multi_mesh_raycaster_camera.py b/scripts/demos/sensors/multi_mesh_raycaster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef3a188f3d165be1fc3be4ce72076ffea947490 --- /dev/null +++ b/scripts/demos/sensors/multi_mesh_raycaster_camera.py @@ -0,0 +1,329 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Example on using the Multi-Mesh Raycaster Camera sensor. + +.. code-block:: bash + + # with allegro hand + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type allegro_hand + + # with anymal-D bodies + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type anymal_d + + # with random multiple objects + python scripts/demos/sensors/multi_mesh_raycaster.py --num_envs 16 --asset_type objects + +""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the multi-mesh raycaster sensor.") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") +parser.add_argument( + "--asset_type", + type=str, + default="allegro_hand", + help="Asset type to use.", + choices=["allegro_hand", "anymal_d", "objects"], +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +import torch + +import omni.usd +from pxr import Gf, Sdf + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, AssetBaseCfg, RigidObjectCfg +from isaaclab.markers.config import VisualizationMarkersCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors.ray_caster import MultiMeshRayCasterCameraCfg, patterns +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Pre-defined configs +## +from isaaclab_assets.robots.allegro import ALLEGRO_HAND_CFG +from isaaclab_assets.robots.anymal import ANYMAL_D_CFG + +RAY_CASTER_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "hit": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, +) + +if args_cli.asset_type == "allegro_hand": + asset_cfg = ALLEGRO_HAND_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + ray_caster_cfg = MultiMeshRayCasterCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot", + update_period=1 / 60, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg( + pos=(-0.70, -0.7, -0.25), rot=(0.268976, 0.268976, 0.653951, 0.653951) + ), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/thumb_link_.*/visuals_xform"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/index_link.*/visuals_xform"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/middle_link_.*/visuals_xform"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/ring_link_.*/visuals_xform"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/palm_link/visuals_xform"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/allegro_mount/visuals_xform"), + ], + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=120, + width=240, + ), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) + +elif args_cli.asset_type == "anymal_d": + asset_cfg = ANYMAL_D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + ray_caster_cfg = MultiMeshRayCasterCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + update_period=1 / 60, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0, -0.1, 1.5), rot=(0.0, 1.0, 0.0, 0.0)), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LF_.*/visuals"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RF_.*/visuals"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LH_.*/visuals"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RH_.*/visuals"), + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/base/visuals"), + ], + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=120, + width=240, + ), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) + +elif args_cli.asset_type == "objects": + asset_cfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + spawn=sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.CuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0), metallic=0.2), + ), + sim_utils.CylinderCfg( + radius=0.2, + height=0.5, + axis="Y", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + sim_utils.CapsuleCfg( + radius=0.15, + height=0.5, + axis="Z", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 1.0, 0.0), metallic=0.2), + ), + sim_utils.ConeCfg( + radius=0.2, + height=0.5, + axis="Z", + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 1.0), metallic=0.2), + ), + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 2.0)), + ) + ray_caster_cfg = MultiMeshRayCasterCameraCfg( + prim_path="{ENV_REGEX_NS}/Object", + update_period=1 / 60, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0, 0.0, 1.5), rot=(0.0, 1.0, 0.0, 0.0)), + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCameraCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Object"), + ], + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=120, + width=240, + ), + debug_vis=not args_cli.headless, + visualizer_cfg=RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster"), + ) +else: + raise ValueError(f"Unknown asset type: {args_cli.asset_type}") + + +@configclass +class RaycasterSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the asset.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/Ground", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + scale=(1, 1, 1), + ), + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # asset + asset = asset_cfg + # ray caster + ray_caster = ray_caster_cfg + + +def randomize_shape_color(prim_path_expr: str): + """Randomize the color of the geometry.""" + + # acquire stage + stage = omni.usd.get_context().get_stage() + # resolve prim paths for spawning and cloning + prim_paths = sim_utils.find_matching_prim_paths(prim_path_expr) + # manually clone prims if the source prim path is a regex expression + + with Sdf.ChangeBlock(): + for prim_path in prim_paths: + print("Applying prim scale to:", prim_path) + # spawn single instance + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_path) + + # DO YOUR OWN OTHER KIND OF RANDOMIZATION HERE! + # Note: Just need to acquire the right attribute about the property you want to set + # Here is an example on setting color randomly + color_spec = prim_spec.GetAttributeAtPath(prim_path + "/geometry/material/Shader.inputs:diffuseColor") + color_spec.default = Gf.Vec3f(random.random(), random.random(), random.random()) + + # randomize scale + scale_spec = prim_spec.GetAttributeAtPath(prim_path + ".xformOp:scale") + scale_spec.default = Gf.Vec3f(random.uniform(0.5, 1.5), random.uniform(0.5, 1.5), random.uniform(0.5, 1.5)) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + triggered = True + countdown = 42 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + root_state = scene["asset"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["asset"].write_root_pose_to_sim(root_state[:, :7]) + scene["asset"].write_root_velocity_to_sim(root_state[:, 7:]) + + if isinstance(scene["asset"], Articulation): + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["asset"].data.default_joint_pos.clone(), + scene["asset"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["asset"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting Asset state...") + + if isinstance(scene["asset"], Articulation): + # -- generate actions/commands + targets = scene["asset"].data.default_joint_pos + 5 * ( + torch.rand_like(scene["asset"].data.default_joint_pos) - 0.5 + ) + # -- apply action to the asset + scene["asset"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + if not triggered: + if countdown > 0: + countdown -= 1 + continue + + data = scene["ray_caster"].data.ray_hits_w.cpu().numpy() # noqa: F841 + triggered = True + else: + continue + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = RaycasterSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0, replicate_physics=False) + scene = InteractiveScene(scene_cfg) + + if args_cli.asset_type == "objects": + randomize_shape_color(scene_cfg.asset.prim_path.format(ENV_REGEX_NS="/World/envs/env_.*")) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/raycaster_sensor.py b/scripts/demos/sensors/raycaster_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..02c55222e836adced5cd1dcf6927fcf41e350f13 --- /dev/null +++ b/scripts/demos/sensors/raycaster_sensor.py @@ -0,0 +1,161 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Example on using the raycaster sensor.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors.ray_caster import RayCasterCfg, patterns +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class RaycasterSensorSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/Ground", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + scale=(1, 1, 1), + ), + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + ray_caster = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base/lidar_cage", + update_period=1 / 60, + offset=RayCasterCfg.OffsetCfg(pos=(0, 0, 0.5)), + mesh_prim_paths=["/World/Ground"], + ray_alignment="yaw", + pattern_cfg=patterns.LidarPatternCfg( + channels=100, vertical_fov_range=[-90, 90], horizontal_fov_range=[-90, 90], horizontal_res=1.0 + ), + debug_vis=not args_cli.headless, + ) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + triggered = True + countdown = 42 + + # Simulate physics + while simulation_app.is_running(): + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["ray_caster"]) + print("Ray cast hit results: ", scene["ray_caster"].data.ray_hits_w) + + if not triggered: + if countdown > 0: + countdown -= 1 + continue + data = scene["ray_caster"].data.ray_hits_w.cpu().numpy() + np.save("cast_data.npy", data) + triggered = True + else: + continue + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = RaycasterSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/demos/sensors/tacsl_sensor.py b/scripts/demos/sensors/tacsl_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..e1e205df6333e5bcc2109eb3d49263f66a1f6187 --- /dev/null +++ b/scripts/demos/sensors/tacsl_sensor.py @@ -0,0 +1,415 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Example script demonstrating the TacSL tactile sensor implementation in IsaacLab. + +This script shows how to use the TactileSensor for both camera-based and force field +tactile sensing with the gelsight finger setup. + +.. code-block:: bash + + # Usage + python scripts/demos/sensors/tacsl_sensor.py \ + --use_tactile_rgb \ + --use_tactile_ff \ + --tactile_compliance_stiffness 100.0 \ + --num_envs 16 \ + --contact_object_type nut \ + --save_viz \ + --enable_cameras + +""" + +import argparse +import math +import os + +import cv2 +import numpy as np +import torch + +from isaaclab.app import AppLauncher + +# Add argparse arguments +parser = argparse.ArgumentParser(description="TacSL tactile sensor example.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +parser.add_argument("--normal_contact_stiffness", type=float, default=1.0, help="Tactile normal stiffness.") +parser.add_argument("--tangential_stiffness", type=float, default=0.1, help="Tactile tangential stiffness.") +parser.add_argument("--friction_coefficient", type=float, default=2.0, help="Tactile friction coefficient.") +parser.add_argument( + "--tactile_compliance_stiffness", + type=float, + default=None, + help="Optional: Override compliant contact stiffness (default: use USD asset values)", +) +parser.add_argument( + "--tactile_compliant_damping", + type=float, + default=None, + help="Optional: Override compliant contact damping (default: use USD asset values)", +) +parser.add_argument("--save_viz", action="store_true", help="Visualize tactile data.") +parser.add_argument("--save_viz_dir", type=str, default="tactile_record", help="Directory to save tactile data.") +parser.add_argument("--use_tactile_rgb", action="store_true", help="Use tactile RGB sensor data collection.") +parser.add_argument("--use_tactile_ff", action="store_true", help="Use tactile force field sensor data collection.") +parser.add_argument("--debug_sdf_closest_pts", action="store_true", help="Visualize closest SDF points.") +parser.add_argument("--debug_tactile_sensor_pts", action="store_true", help="Visualize tactile sensor points.") +parser.add_argument("--trimesh_vis_tactile_points", action="store_true", help="Visualize tactile points using trimesh.") +parser.add_argument( + "--contact_object_type", + type=str, + default="nut", + choices=["none", "cube", "nut"], + help="Type of contact object to use.", +) + +# Append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# Parse the arguments +args_cli = parser.parse_args() + +# Launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg + +# Import our TactileSensor +from isaaclab.sensors import TiledCameraCfg, VisuoTactileSensorCfg +from isaaclab.sensors.tacsl_sensor.visuotactile_render import compute_tactile_shear_image +from isaaclab.sensors.tacsl_sensor.visuotactile_sensor_data import VisuoTactileSensorData +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.timer import Timer + +from isaaclab_assets.sensors import GELSIGHT_R15_CFG + + +@configclass +class TactileSensorsSceneCfg(InteractiveSceneCfg): + """Design the scene with tactile sensors on the robot.""" + + # Ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # Lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # Robot with tactile sensor + robot = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileWithCompliantContactCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/TacSL/gelsight_r15_finger/gelsight_r15_finger.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + ), + compliant_contact_stiffness=args_cli.tactile_compliance_stiffness, + compliant_contact_damping=args_cli.tactile_compliant_damping, + physics_material_prim_path="elastomer", + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=12, + solver_velocity_iteration_count=1, + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.001, rest_offset=-0.0005), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(math.sqrt(2) / 2, -math.sqrt(2) / 2, 0.0, 0.0), # 90° rotation + joint_pos={}, + joint_vel={}, + ), + actuators={}, + ) + + # Camera configuration for tactile sensing + + # TacSL Tactile Sensor + tactile_sensor = VisuoTactileSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/elastomer/tactile_sensor", + history_length=0, + debug_vis=args_cli.debug_tactile_sensor_pts or args_cli.debug_sdf_closest_pts, + # Sensor configuration + render_cfg=GELSIGHT_R15_CFG, + enable_camera_tactile=args_cli.use_tactile_rgb, + enable_force_field=args_cli.use_tactile_ff, + # Elastomer configuration + tactile_array_size=(20, 25), + tactile_margin=0.003, + # Contact object configuration + contact_object_prim_path_expr="{ENV_REGEX_NS}/contact_object", + # Force field physics parameters + normal_contact_stiffness=args_cli.normal_contact_stiffness, + friction_coefficient=args_cli.friction_coefficient, + tangential_stiffness=args_cli.tangential_stiffness, + # Camera configuration + # Note: the camera is already spawned in the scene, properties are set in the + # 'gelsight_r15_finger.usd' USD file + camera_cfg=TiledCameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/elastomer_tip/cam", + height=GELSIGHT_R15_CFG.image_height, + width=GELSIGHT_R15_CFG.image_width, + data_types=["distance_to_image_plane"], + spawn=None, + ), + # Debug Visualization + trimesh_vis_tactile_points=args_cli.trimesh_vis_tactile_points, + visualize_sdf_closest_pts=args_cli.debug_sdf_closest_pts, + ) + + +@configclass +class CubeTactileSceneCfg(TactileSensorsSceneCfg): + """Scene with cube contact object.""" + + # Cube contact object + contact_object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/contact_object", + spawn=sim_utils.CuboidCfg( + size=(0.01, 0.01, 0.01), + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=True), + mass_props=sim_utils.MassPropertiesCfg(mass=0.00327211), + collision_props=sim_utils.CollisionPropertiesCfg(), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.1, 0.1)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0 + 0.06776, 0.51), rot=(1.0, 0.0, 0.0, 0.0)), + ) + + +@configclass +class NutTactileSceneCfg(TactileSensorsSceneCfg): + """Scene with nut contact object.""" + + # Nut contact object + contact_object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/contact_object", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Factory/factory_nut_m16.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + solver_position_iteration_count=12, + solver_velocity_iteration_count=1, + max_angular_velocity=180.0, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=0.1), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0), + articulation_props=sim_utils.ArticulationRootPropertiesCfg(articulation_enabled=False), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.0 + 0.06776, 0.498), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ) + + +def mkdir_helper(dir_path: str) -> tuple[str, str]: + """Create directories for saving tactile sensor visualizations. + + Args: + dir_path: The base directory path where visualizations will be saved. + + Returns: + A tuple containing paths to the force field directory and RGB image directory. + """ + tactile_img_folder = dir_path + os.makedirs(tactile_img_folder, exist_ok=True) + tactile_force_field_dir = os.path.join(tactile_img_folder, "tactile_force_field") + os.makedirs(tactile_force_field_dir, exist_ok=True) + tactile_rgb_image_dir = os.path.join(tactile_img_folder, "tactile_rgb_image") + os.makedirs(tactile_rgb_image_dir, exist_ok=True) + return tactile_force_field_dir, tactile_rgb_image_dir + + +def save_viz_helper( + dir_path_list: tuple[str, str], + count: int, + tactile_data: VisuoTactileSensorData, + num_envs: int, + nrows: int, + ncols: int, +): + """Save visualization of tactile sensor data. + + Args: + dir_path_list: A tuple containing paths to the force field directory and RGB image directory. + count: The current simulation step count, used for naming saved files. + tactile_data: The data object containing tactile sensor readings (forces, images). + num_envs: Number of environments in the simulation. + nrows: Number of rows in the tactile array. + ncols: Number of columns in the tactile array. + """ + # Only save the first 2 environments + + tactile_force_field_dir, tactile_rgb_image_dir = dir_path_list + + if tactile_data.tactile_shear_force is not None and tactile_data.tactile_normal_force is not None: + # visualize tactile forces + tactile_normal_force = tactile_data.tactile_normal_force.view((num_envs, nrows, ncols)) + tactile_shear_force = tactile_data.tactile_shear_force.view((num_envs, nrows, ncols, 2)) + + tactile_image = compute_tactile_shear_image( + tactile_normal_force[0, :, :].detach().cpu().numpy(), tactile_shear_force[0, :, :].detach().cpu().numpy() + ) + + if tactile_normal_force.shape[0] > 1: + tactile_image_1 = compute_tactile_shear_image( + tactile_normal_force[1, :, :].detach().cpu().numpy(), + tactile_shear_force[1, :, :].detach().cpu().numpy(), + ) + combined_image = np.vstack([tactile_image, tactile_image_1]) + cv2.imwrite(os.path.join(tactile_force_field_dir, f"{count}.png"), (combined_image * 255).astype(np.uint8)) + else: + cv2.imwrite(os.path.join(tactile_force_field_dir, f"{count}.png"), (tactile_image * 255).astype(np.uint8)) + + if tactile_data.tactile_rgb_image is not None: + tactile_rgb_data = tactile_data.tactile_rgb_image.cpu().numpy() + tactile_rgb_data = np.transpose(tactile_rgb_data, axes=(0, 2, 1, 3)) + tactile_rgb_data_first_2 = tactile_rgb_data[:2] if len(tactile_rgb_data) >= 2 else tactile_rgb_data + tactile_rgb_tiled = np.concatenate(tactile_rgb_data_first_2, axis=0) + # Convert to uint8 if not already + if tactile_rgb_tiled.dtype != np.uint8: + tactile_rgb_tiled = ( + (tactile_rgb_tiled * 255).astype(np.uint8) + if tactile_rgb_tiled.max() <= 1.0 + else tactile_rgb_tiled.astype(np.uint8) + ) + cv2.imwrite(os.path.join(tactile_rgb_image_dir, f"{count}.png"), tactile_rgb_tiled) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Assign different masses to contact objects in different environments + num_envs = scene.num_envs + + if args_cli.save_viz: + # Create output directories for tactile data + dir_path_list = mkdir_helper(args_cli.save_viz_dir) + + # Create constant downward force + force_tensor = torch.zeros(scene.num_envs, 1, 3, device=sim.device) + torque_tensor = torch.zeros(scene.num_envs, 1, 3, device=sim.device) + force_tensor[:, 0, 2] = -1.0 + + nrows = scene["tactile_sensor"].cfg.tactile_array_size[0] + ncols = scene["tactile_sensor"].cfg.tactile_array_size[1] + + physics_timer = Timer() + physics_total_time = 0.0 + physics_total_count = 0 + + entity_list = ["robot"] + if "contact_object" in scene.keys(): + entity_list.append("contact_object") + + while simulation_app.is_running(): + if count == 122: + # Reset robot and contact object positions + count = 0 + for entity in entity_list: + root_state = scene[entity].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene[entity].write_root_state_to_sim(root_state) + + scene.reset() + print("[INFO]: Resetting robot and contact object state...") + + if "contact_object" in scene.keys(): + # rotation + if count > 20: + env_indices = torch.arange(scene.num_envs, device=sim.device) + odd_mask = env_indices % 2 == 1 + even_mask = env_indices % 2 == 0 + torque_tensor[odd_mask, 0, 2] = 10 # rotation for odd environments + torque_tensor[even_mask, 0, 2] = -10 # rotation for even environments + scene["contact_object"].set_external_force_and_torque(force_tensor, torque_tensor) + + # Step simulation + scene.write_data_to_sim() + physics_timer.start() + sim.step() + physics_timer.stop() + physics_total_time += physics_timer.total_run_time + physics_total_count += 1 + sim_time += sim_dt + count += 1 + scene.update(sim_dt) + + # Access tactile sensor data + tactile_data = scene["tactile_sensor"].data + + if args_cli.save_viz: + save_viz_helper(dir_path_list, count, tactile_data, num_envs, nrows, ncols) + + # Get timing summary from sensor and add physics timing + timing_summary = scene["tactile_sensor"].get_timing_summary() + + # Add physics timing to the summary + physics_avg = physics_total_time / (physics_total_count * scene.num_envs) if physics_total_count > 0 else 0.0 + timing_summary["physics_total"] = physics_total_time + timing_summary["physics_average"] = physics_avg + timing_summary["physics_fps"] = 1 / physics_avg if physics_avg > 0 else 0.0 + + print(timing_summary) + + +def main(): + """Main function.""" + # Initialize simulation + # Note: We set the gpu_collision_stack_size to prevent buffer overflow in contact-rich environments. + sim_cfg = sim_utils.SimulationCfg( + dt=0.005, + device=args_cli.device, + physx=sim_utils.PhysxCfg(gpu_collision_stack_size=2**30), + ) + sim = sim_utils.SimulationContext(sim_cfg) + + # Set main camera + sim.set_camera_view(eye=[1.5, 1.5, 1.5], target=[0.0, 0.0, 0.0]) + + # Create scene based on contact object type + if args_cli.contact_object_type == "cube": + scene_cfg = CubeTactileSceneCfg(num_envs=args_cli.num_envs, env_spacing=0.2) + # disabled force field for cube contact object because a SDF collision mesh cannot + # be created for the Shape Prims + scene_cfg.tactile_sensor.enable_force_field = False + elif args_cli.contact_object_type == "nut": + scene_cfg = NutTactileSceneCfg(num_envs=args_cli.num_envs, env_spacing=0.2) + elif args_cli.contact_object_type == "none": + scene_cfg = TactileSensorsSceneCfg(num_envs=args_cli.num_envs, env_spacing=0.2) + scene_cfg.tactile_sensor.contact_object_prim_path_expr = None + # this flag is to visualize the tactile sensor points + scene_cfg.tactile_sensor.debug_vis = True + + scene = InteractiveScene(scene_cfg) + + # Initialize simulation + sim.reset() + print("[INFO]: Setup complete...") + + # Get initial render + scene["tactile_sensor"].get_initial_render() + # Run simulation + run_simulator(sim, scene) + + +if __name__ == "__main__": + # Run the main function + main() + # Close sim app + simulation_app.close() diff --git a/scripts/environments/export_IODescriptors.py b/scripts/environments/export_IODescriptors.py new file mode 100644 index 0000000000000000000000000000000000000000..3f515a166f98e753dca153855a400876cbdd90bb --- /dev/null +++ b/scripts/environments/export_IODescriptors.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to an environment with random action agent.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import os + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--output_dir", type=str, default=None, help="Path to the output directory.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +args_cli.headless = True + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import gymnasium as gym +import torch + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import parse_env_cfg + +# PLACEHOLDER: Extension template (do not remove this comment) + + +def main(): + """Random actions agent with Isaac Lab environment.""" + # create environment configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=True) + # create environment + env = gym.make(args_cli.task, cfg=env_cfg) + + # print info (this is vectorized environment) + print(f"[INFO]: Gym observation space: {env.observation_space}") + print(f"[INFO]: Gym action space: {env.action_space}") + # reset environment + env.reset() + + outs = env.unwrapped.get_IO_descriptors + out_observations = outs["observations"] + out_actions = outs["actions"] + out_articulations = outs["articulations"] + out_scene = outs["scene"] + # Make a yaml file with the output + import yaml + + name = args_cli.task.lower().replace("-", "_") + name = name.replace(" ", "_") + + if not os.path.exists(args_cli.output_dir): + os.makedirs(args_cli.output_dir) + + with open(os.path.join(args_cli.output_dir, f"{name}_IO_descriptors.yaml"), "w") as f: + print(f"[INFO]: Exporting IO descriptors to {os.path.join(args_cli.output_dir, f'{name}_IO_descriptors.yaml')}") + yaml.safe_dump(outs, f) + + for k in out_actions: + print(f"--- Action term: {k['name']} ---") + k.pop("name") + for k1, v1 in k.items(): + print(f"{k1}: {v1}") + + for obs_group_name, obs_group in out_observations.items(): + print(f"--- Obs group: {obs_group_name} ---") + for k in obs_group: + print(f"--- Obs term: {k['name']} ---") + k.pop("name") + for k1, v1 in k.items(): + print(f"{k1}: {v1}") + + for articulation_name, articulation_data in out_articulations.items(): + print(f"--- Articulation: {articulation_name} ---") + for k1, v1 in articulation_data.items(): + print(f"{k1}: {v1}") + + for k1, v1 in out_scene.items(): + print(f"{k1}: {v1}") + + env.step(torch.zeros(env.action_space.shape, device=env.unwrapped.device)) + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/list_envs.py b/scripts/environments/list_envs.py new file mode 100644 index 0000000000000000000000000000000000000000..0beb83e9213105af9ea388f55be75ee5c22a7ce7 --- /dev/null +++ b/scripts/environments/list_envs.py @@ -0,0 +1,72 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to print all the available environments in Isaac Lab. + +The script iterates over all registered environments and stores the details in a table. +It prints the name of the environment, the entry point and the config file. + +All the environments are registered in the `isaaclab_tasks` extension. They start +with `Isaac` in their name. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="List Isaac Lab environments.") +parser.add_argument("--keyword", type=str, default=None, help="Keyword to filter environments.") +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import gymnasium as gym +from prettytable import PrettyTable + +import isaaclab_tasks # noqa: F401 + + +def main(): + """Print all environments registered in `isaaclab_tasks` extension.""" + # print all the available environments + table = PrettyTable(["S. No.", "Task Name", "Entry Point", "Config"]) + table.title = "Available Environments in Isaac Lab" + # set alignment of table columns + table.align["Task Name"] = "l" + table.align["Entry Point"] = "l" + table.align["Config"] = "l" + + # count of environments + index = 0 + # acquire all Isaac environments names + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id and (args_cli.keyword is None or args_cli.keyword in task_spec.id): + # add details to table + table.add_row([index + 1, task_spec.id, task_spec.entry_point, task_spec.kwargs["env_cfg_entry_point"]]) + # increment count + index += 1 + + print(table) + + +if __name__ == "__main__": + try: + # run the main function + main() + except Exception as e: + raise e + finally: + # close the app + simulation_app.close() diff --git a/scripts/environments/random_agent.py b/scripts/environments/random_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..6a40060d64b17d2e058e58ff3b6cb1653afc76dc --- /dev/null +++ b/scripts/environments/random_agent.py @@ -0,0 +1,72 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to an environment with random action agent.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import gymnasium as gym +import torch + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import parse_env_cfg + +# PLACEHOLDER: Extension template (do not remove this comment) + + +def main(): + """Random actions agent with Isaac Lab environment.""" + # create environment configuration + env_cfg = parse_env_cfg( + args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric + ) + # create environment + env = gym.make(args_cli.task, cfg=env_cfg) + + # print info (this is vectorized environment) + print(f"[INFO]: Gym observation space: {env.observation_space}") + print(f"[INFO]: Gym action space: {env.action_space}") + # reset environment + env.reset() + # simulate environment + while simulation_app.is_running(): + # run everything in inference mode + with torch.inference_mode(): + # sample actions from -1 to 1 + actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions + env.step(actions) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/state_machine/lift_cube_sm.py b/scripts/environments/state_machine/lift_cube_sm.py new file mode 100644 index 0000000000000000000000000000000000000000..6136e2e3a351afe0e2d922e4cf9cdf8d5ea1814e --- /dev/null +++ b/scripts/environments/state_machine/lift_cube_sm.py @@ -0,0 +1,319 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to run an environment with a pick and lift state machine. + +The state machine is implemented in the kernel function `infer_state_machine`. +It uses the `warp` library to run the state machine in parallel on the GPU. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/lift_cube_sm.py --num_envs 32 + +""" + +"""Launch Omniverse Toolkit first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Pick and lift state machine for lift environments.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=args_cli.headless) +simulation_app = app_launcher.app + +"""Rest everything else.""" + +from collections.abc import Sequence + +import gymnasium as gym +import torch +import warp as wp + +from isaaclab.assets.rigid_object.rigid_object_data import RigidObjectData + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.manager_based.manipulation.lift.lift_env_cfg import LiftEnvCfg +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + + +class PickSmState: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + + +class PickSmWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.2) + APPROACH_ABOVE_OBJECT = wp.constant(0.5) + APPROACH_OBJECT = wp.constant(0.6) + GRASP_OBJECT = wp.constant(0.3) + LIFT_OBJECT = wp.constant(1.0) + + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), + object_pose: wp.array(dtype=wp.transform), + des_object_pose: wp.array(dtype=wp.transform), + des_ee_pose: wp.array(dtype=wp.transform), + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == PickSmState.REST: + des_ee_pose[tid] = ee_pose[tid] + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.REST: + # move to next state and reset wait time + sm_state[tid] = PickSmState.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.APPROACH_ABOVE_OBJECT: + des_ee_pose[tid] = wp.transform_multiply(offset[tid], object_pose[tid]) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.APPROACH_OBJECT: + des_ee_pose[tid] = object_pose[tid] + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + if sm_wait_time[tid] >= PickSmWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.GRASP_OBJECT: + des_ee_pose[tid] = object_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.LIFT_OBJECT: + des_ee_pose[tid] = des_object_pose[tid] + gripper_state[tid] = GripperState.CLOSE + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + + +class PickAndLiftSm: + """A simple state machine in a robot's task space to pick and lift an object. + + The state machine is implemented as a warp kernel. It takes in the current state of + the robot's end-effector and the object, and outputs the desired state of the robot's + end-effector and the gripper. The state machine is implemented as a finite state + machine with the following states: + + 1. REST: The robot is at rest. + 2. APPROACH_ABOVE_OBJECT: The robot moves above the object. + 3. APPROACH_OBJECT: The robot moves to the object. + 4. GRASP_OBJECT: The robot grasps the object. + 5. LIFT_OBJECT: The robot lifts the object to the desired pose. This is the final state. + """ + + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.01): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + + # approach above object offset + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.1 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def compute(self, ee_pose: torch.Tensor, object_pose: torch.Tensor, des_object_pose: torch.Tensor) -> torch.Tensor: + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + object_pose = object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + des_object_pose = des_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + object_pose_wp = wp.from_torch(object_pose.contiguous(), wp.transform) + des_object_pose_wp = wp.from_torch(des_object_pose.contiguous(), wp.transform) + + # run state machine + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + object_pose_wp, + des_object_pose_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + ], + device=self.device, + ) + + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) + + +def main(): + # parse configuration + env_cfg: LiftEnvCfg = parse_env_cfg( + "Isaac-Lift-Cube-Franka-IK-Abs-v0", + device=args_cli.device, + num_envs=args_cli.num_envs, + use_fabric=not args_cli.disable_fabric, + ) + # create environment + env = gym.make("Isaac-Lift-Cube-Franka-IK-Abs-v0", cfg=env_cfg) + # reset environment at start + env.reset() + + # create action buffers (position + quaternion) + actions = torch.zeros(env.unwrapped.action_space.shape, device=env.unwrapped.device) + actions[:, 3] = 1.0 + # desired object orientation (we only do position control of object) + desired_orientation = torch.zeros((env.unwrapped.num_envs, 4), device=env.unwrapped.device) + desired_orientation[:, 1] = 1.0 + # create state machine + pick_sm = PickAndLiftSm( + env_cfg.sim.dt * env_cfg.decimation, env.unwrapped.num_envs, env.unwrapped.device, position_threshold=0.01 + ) + + while simulation_app.is_running(): + # run everything in inference mode + with torch.inference_mode(): + # step environment + dones = env.step(actions)[-2] + + # observations + # -- end-effector frame + ee_frame_sensor = env.unwrapped.scene["ee_frame"] + tcp_rest_position = ee_frame_sensor.data.target_pos_w[..., 0, :].clone() - env.unwrapped.scene.env_origins + tcp_rest_orientation = ee_frame_sensor.data.target_quat_w[..., 0, :].clone() + # -- object frame + object_data: RigidObjectData = env.unwrapped.scene["object"].data + object_position = object_data.root_pos_w - env.unwrapped.scene.env_origins + # -- target object frame + desired_position = env.unwrapped.command_manager.get_command("object_pose")[..., :3] + + # advance state machine + actions = pick_sm.compute( + torch.cat([tcp_rest_position, tcp_rest_orientation], dim=-1), + torch.cat([object_position, desired_orientation], dim=-1), + torch.cat([desired_position, desired_orientation], dim=-1), + ) + + # reset state machine + if dones.any(): + pick_sm.reset_idx(dones.nonzero(as_tuple=False).squeeze(-1)) + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/state_machine/lift_teddy_bear.py b/scripts/environments/state_machine/lift_teddy_bear.py new file mode 100644 index 0000000000000000000000000000000000000000..2eb4ae7100992ab5ceedf5961f93394d87795169 --- /dev/null +++ b/scripts/environments/state_machine/lift_teddy_bear.py @@ -0,0 +1,341 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to demonstrate lifting a deformable object with a robotic arm. + +The state machine is implemented in the kernel function `infer_state_machine`. +It uses the `warp` library to run the state machine in parallel on the GPU. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/lift_teddy_bear.py + +""" + +"""Launch Omniverse Toolkit first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Pick and lift a teddy bear with a robotic arm.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=args_cli.headless) +simulation_app = app_launcher.app + +# disable metrics assembler due to scene graph instancing +from isaacsim.core.utils.extensions import disable_extension + +disable_extension("omni.usd.metrics.assembler.ui") + +"""Rest everything else.""" + +from collections.abc import Sequence + +import gymnasium as gym +import torch +import warp as wp + +from isaaclab.assets.rigid_object.rigid_object_data import RigidObjectData + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.manager_based.manipulation.lift.lift_env_cfg import LiftEnvCfg +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + + +class PickSmState: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + OPEN_GRIPPER = wp.constant(5) + + +class PickSmWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.2) + APPROACH_ABOVE_OBJECT = wp.constant(0.5) + APPROACH_OBJECT = wp.constant(0.6) + GRASP_OBJECT = wp.constant(0.6) + LIFT_OBJECT = wp.constant(1.0) + OPEN_GRIPPER = wp.constant(0.0) + + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), + object_pose: wp.array(dtype=wp.transform), + des_object_pose: wp.array(dtype=wp.transform), + des_ee_pose: wp.array(dtype=wp.transform), + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == PickSmState.REST: + des_ee_pose[tid] = ee_pose[tid] + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.REST: + # move to next state and reset wait time + sm_state[tid] = PickSmState.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.APPROACH_ABOVE_OBJECT: + des_ee_pose[tid] = wp.transform_multiply(offset[tid], object_pose[tid]) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.APPROACH_OBJECT: + des_ee_pose[tid] = object_pose[tid] + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.GRASP_OBJECT: + des_ee_pose[tid] = object_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + elif state == PickSmState.LIFT_OBJECT: + des_ee_pose[tid] = des_object_pose[tid] + gripper_state[tid] = GripperState.CLOSE + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + sm_state[tid] = PickSmState.OPEN_GRIPPER + sm_wait_time[tid] = 0.0 + elif state == PickSmState.OPEN_GRIPPER: + # des_ee_pose[tid] = object_pose[tid] + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= PickSmWaitTime.OPEN_GRIPPER: + # move to next state and reset wait time + sm_state[tid] = PickSmState.OPEN_GRIPPER + sm_wait_time[tid] = 0.0 + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + + +class PickAndLiftSm: + """A simple state machine in a robot's task space to pick and lift an object. + + The state machine is implemented as a warp kernel. It takes in the current state of + the robot's end-effector and the object, and outputs the desired state of the robot's + end-effector and the gripper. The state machine is implemented as a finite state + machine with the following states: + + 1. REST: The robot is at rest. + 2. APPROACH_ABOVE_OBJECT: The robot moves above the object. + 3. APPROACH_OBJECT: The robot moves to the object. + 4. GRASP_OBJECT: The robot grasps the object. + 5. LIFT_OBJECT: The robot lifts the object to the desired pose. This is the final state. + """ + + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.01): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + + # approach above object offset + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.2 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def compute(self, ee_pose: torch.Tensor, object_pose: torch.Tensor, des_object_pose: torch.Tensor): + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + object_pose = object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + des_object_pose = des_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + object_pose_wp = wp.from_torch(object_pose.contiguous(), wp.transform) + des_object_pose_wp = wp.from_torch(des_object_pose.contiguous(), wp.transform) + + # run state machine + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + object_pose_wp, + des_object_pose_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + ], + device=self.device, + ) + + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) + + +def main(): + # parse configuration + env_cfg: LiftEnvCfg = parse_env_cfg( + "Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0", + device=args_cli.device, + num_envs=args_cli.num_envs, + ) + + env_cfg.viewer.eye = (2.1, 1.0, 1.3) + + # create environment + env = gym.make("Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0", cfg=env_cfg) + # reset environment at start + env.reset() + + # create action buffers (position + quaternion) + actions = torch.zeros(env.unwrapped.action_space.shape, device=env.unwrapped.device) + actions[:, 3] = 1.0 + # desired rotation after grasping + desired_orientation = torch.zeros((env.unwrapped.num_envs, 4), device=env.unwrapped.device) + desired_orientation[:, 1] = 1.0 + + object_grasp_orientation = torch.zeros((env.unwrapped.num_envs, 4), device=env.unwrapped.device) + # z-axis pointing down and 45 degrees rotation + object_grasp_orientation[:, 1] = 0.9238795 + object_grasp_orientation[:, 2] = -0.3826834 + object_local_grasp_position = torch.tensor([0.02, -0.08, 0.0], device=env.unwrapped.device) + + # create state machine + pick_sm = PickAndLiftSm(env_cfg.sim.dt * env_cfg.decimation, env.unwrapped.num_envs, env.unwrapped.device) + + while simulation_app.is_running(): + # run everything in inference mode + with torch.inference_mode(): + # step environment + dones = env.step(actions)[-2] + + # observations + # -- end-effector frame + ee_frame_sensor = env.unwrapped.scene["ee_frame"] + tcp_rest_position = ee_frame_sensor.data.target_pos_w[..., 0, :].clone() - env.unwrapped.scene.env_origins + tcp_rest_orientation = ee_frame_sensor.data.target_quat_w[..., 0, :].clone() + # -- object frame + object_data: RigidObjectData = env.unwrapped.scene["object"].data + object_position = object_data.root_pos_w - env.unwrapped.scene.env_origins + object_position += object_local_grasp_position + + # -- target object frame + desired_position = env.unwrapped.command_manager.get_command("object_pose")[..., :3] + + # advance state machine + actions = pick_sm.compute( + torch.cat([tcp_rest_position, tcp_rest_orientation], dim=-1), + torch.cat([object_position, object_grasp_orientation], dim=-1), + torch.cat([desired_position, desired_orientation], dim=-1), + ) + + # reset state machine + if dones.any(): + pick_sm.reset_idx(dones.nonzero(as_tuple=False).squeeze(-1)) + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/state_machine/open_cabinet_sm.py b/scripts/environments/state_machine/open_cabinet_sm.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb88d31a1a2a2e9bf638d83da481971fff631e8 --- /dev/null +++ b/scripts/environments/state_machine/open_cabinet_sm.py @@ -0,0 +1,333 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to run an environment with a cabinet opening state machine. + +The state machine is implemented in the kernel function `infer_state_machine`. +It uses the `warp` library to run the state machine in parallel on the GPU. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/environments/state_machine/open_cabinet_sm.py --num_envs 32 + +""" + +"""Launch Omniverse Toolkit first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Pick and lift state machine for cabinet environments.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=args_cli.headless) +simulation_app = app_launcher.app + +"""Rest everything else.""" + +from collections.abc import Sequence + +import gymnasium as gym +import torch +import warp as wp + +from isaaclab.sensors import FrameTransformer + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.manager_based.manipulation.cabinet.cabinet_env_cfg import CabinetEnvCfg +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + + +class OpenDrawerSmState: + """States for the cabinet drawer opening state machine.""" + + REST = wp.constant(0) + APPROACH_INFRONT_HANDLE = wp.constant(1) + APPROACH_HANDLE = wp.constant(2) + GRASP_HANDLE = wp.constant(3) + OPEN_DRAWER = wp.constant(4) + RELEASE_HANDLE = wp.constant(5) + + +class OpenDrawerSmWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.5) + APPROACH_INFRONT_HANDLE = wp.constant(1.25) + APPROACH_HANDLE = wp.constant(1.0) + GRASP_HANDLE = wp.constant(1.0) + OPEN_DRAWER = wp.constant(3.0) + RELEASE_HANDLE = wp.constant(0.2) + + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), + handle_pose: wp.array(dtype=wp.transform), + des_ee_pose: wp.array(dtype=wp.transform), + gripper_state: wp.array(dtype=float), + handle_approach_offset: wp.array(dtype=wp.transform), + handle_grasp_offset: wp.array(dtype=wp.transform), + drawer_opening_rate: wp.array(dtype=wp.transform), + position_threshold: float, +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == OpenDrawerSmState.REST: + des_ee_pose[tid] = ee_pose[tid] + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.REST: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.APPROACH_INFRONT_HANDLE + sm_wait_time[tid] = 0.0 + elif state == OpenDrawerSmState.APPROACH_INFRONT_HANDLE: + des_ee_pose[tid] = wp.transform_multiply(handle_approach_offset[tid], handle_pose[tid]) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.APPROACH_INFRONT_HANDLE: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.APPROACH_HANDLE + sm_wait_time[tid] = 0.0 + elif state == OpenDrawerSmState.APPROACH_HANDLE: + des_ee_pose[tid] = handle_pose[tid] + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.APPROACH_HANDLE: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.GRASP_HANDLE + sm_wait_time[tid] = 0.0 + elif state == OpenDrawerSmState.GRASP_HANDLE: + des_ee_pose[tid] = wp.transform_multiply(handle_grasp_offset[tid], handle_pose[tid]) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.GRASP_HANDLE: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.OPEN_DRAWER + sm_wait_time[tid] = 0.0 + elif state == OpenDrawerSmState.OPEN_DRAWER: + des_ee_pose[tid] = wp.transform_multiply(drawer_opening_rate[tid], handle_pose[tid]) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.OPEN_DRAWER: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.RELEASE_HANDLE + sm_wait_time[tid] = 0.0 + elif state == OpenDrawerSmState.RELEASE_HANDLE: + des_ee_pose[tid] = ee_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= OpenDrawerSmWaitTime.RELEASE_HANDLE: + # move to next state and reset wait time + sm_state[tid] = OpenDrawerSmState.RELEASE_HANDLE + sm_wait_time[tid] = 0.0 + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + + +class OpenDrawerSm: + """A simple state machine in a robot's task space to open a drawer in the cabinet. + + The state machine is implemented as a warp kernel. It takes in the current state of + the robot's end-effector and the object, and outputs the desired state of the robot's + end-effector and the gripper. The state machine is implemented as a finite state + machine with the following states: + + 1. REST: The robot is at rest. + 2. APPROACH_HANDLE: The robot moves towards the handle of the drawer. + 3. GRASP_HANDLE: The robot grasps the handle of the drawer. + 4. OPEN_DRAWER: The robot opens the drawer. + 5. RELEASE_HANDLE: The robot releases the handle of the drawer. This is the final state. + """ + + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.01): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + + # approach in front of the handle + self.handle_approach_offset = torch.zeros((self.num_envs, 7), device=self.device) + self.handle_approach_offset[:, 0] = -0.1 + self.handle_approach_offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # handle grasp offset + self.handle_grasp_offset = torch.zeros((self.num_envs, 7), device=self.device) + self.handle_grasp_offset[:, 0] = 0.025 + self.handle_grasp_offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # drawer opening rate + self.drawer_opening_rate = torch.zeros((self.num_envs, 7), device=self.device) + self.drawer_opening_rate[:, 0] = -0.015 + self.drawer_opening_rate[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.handle_approach_offset_wp = wp.from_torch(self.handle_approach_offset, wp.transform) + self.handle_grasp_offset_wp = wp.from_torch(self.handle_grasp_offset, wp.transform) + self.drawer_opening_rate_wp = wp.from_torch(self.drawer_opening_rate, wp.transform) + + def reset_idx(self, env_ids: Sequence[int] | None = None): + """Reset the state machine.""" + if env_ids is None: + env_ids = slice(None) + # reset state machine + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def compute(self, ee_pose: torch.Tensor, handle_pose: torch.Tensor): + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + handle_pose = handle_pose[:, [0, 1, 2, 4, 5, 6, 3]] + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + handle_pose_wp = wp.from_torch(handle_pose.contiguous(), wp.transform) + + # run state machine + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + handle_pose_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.handle_approach_offset_wp, + self.handle_grasp_offset_wp, + self.drawer_opening_rate_wp, + self.position_threshold, + ], + device=self.device, + ) + + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) + + +def main(): + # parse configuration + env_cfg: CabinetEnvCfg = parse_env_cfg( + "Isaac-Open-Drawer-Franka-IK-Abs-v0", + device=args_cli.device, + num_envs=args_cli.num_envs, + use_fabric=not args_cli.disable_fabric, + ) + # create environment + env = gym.make("Isaac-Open-Drawer-Franka-IK-Abs-v0", cfg=env_cfg) + # reset environment at start + env.reset() + + # create action buffers (position + quaternion) + actions = torch.zeros(env.unwrapped.action_space.shape, device=env.unwrapped.device) + actions[:, 3] = 1.0 + # desired object orientation (we only do position control of object) + desired_orientation = torch.zeros((env.unwrapped.num_envs, 4), device=env.unwrapped.device) + desired_orientation[:, 1] = 1.0 + # create state machine + open_sm = OpenDrawerSm(env_cfg.sim.dt * env_cfg.decimation, env.unwrapped.num_envs, env.unwrapped.device) + + while simulation_app.is_running(): + # run everything in inference mode + with torch.inference_mode(): + # step environment + dones = env.step(actions)[-2] + + # observations + # -- end-effector frame + ee_frame_tf: FrameTransformer = env.unwrapped.scene["ee_frame"] + tcp_rest_position = ee_frame_tf.data.target_pos_w[..., 0, :].clone() - env.unwrapped.scene.env_origins + tcp_rest_orientation = ee_frame_tf.data.target_quat_w[..., 0, :].clone() + # -- handle frame + cabinet_frame_tf: FrameTransformer = env.unwrapped.scene["cabinet_frame"] + cabinet_position = cabinet_frame_tf.data.target_pos_w[..., 0, :].clone() - env.unwrapped.scene.env_origins + cabinet_orientation = cabinet_frame_tf.data.target_quat_w[..., 0, :].clone() + + # advance state machine + actions = open_sm.compute( + torch.cat([tcp_rest_position, tcp_rest_orientation], dim=-1), + torch.cat([cabinet_position, cabinet_orientation], dim=-1), + ) + + # reset state machine + if dones.any(): + open_sm.reset_idx(dones.nonzero(as_tuple=False).squeeze(-1)) + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main execution + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/teleoperation/teleop_se3_agent.py b/scripts/environments/teleoperation/teleop_se3_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..8492ad77f3ce110aed56c3768e5378bb083bb662 --- /dev/null +++ b/scripts/environments/teleoperation/teleop_se3_agent.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to run teleoperation with Isaac Lab manipulation environments. + +Supports multiple input devices (e.g., keyboard, spacemouse, gamepad) and devices +configured within the environment (including OpenXR-based hand tracking or motion +controllers).""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +from collections.abc import Callable + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Teleoperation for Isaac Lab environments.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.") +parser.add_argument( + "--teleop_device", + type=str, + default="keyboard", + help=( + "Teleop device. Set here (legacy) or via the environment config. If using the environment config, pass the" + " device key/name defined under 'teleop_devices' (it can be a custom name, not necessarily 'handtracking')." + " Built-ins: keyboard, spacemouse, gamepad. Not all tasks support all built-ins." + ), +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--sensitivity", type=float, default=1.0, help="Sensitivity factor.") +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +app_launcher_args = vars(args_cli) + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and + # not the one installed by Isaac Sim pinocchio is required by the Pink IK controllers and the + # GR1T2 retargeter + import pinocchio # noqa: F401 +if "handtracking" in args_cli.teleop_device.lower(): + app_launcher_args["xr"] = True + +# launch omniverse app +app_launcher = AppLauncher(app_launcher_args) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +import logging + +import gymnasium as gym +import torch + +from isaaclab.devices import Se3Gamepad, Se3GamepadCfg, Se3Keyboard, Se3KeyboardCfg, Se3SpaceMouse, Se3SpaceMouseCfg +from isaaclab.devices.openxr import remove_camera_configs +from isaaclab.devices.teleop_device_factory import create_teleop_device +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.manager_based.manipulation.lift import mdp +from isaaclab_tasks.utils import parse_env_cfg + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +# import logger +logger = logging.getLogger(__name__) + + +def main() -> None: + """ + Run teleoperation with an Isaac Lab manipulation environment. + + Creates the environment, sets up teleoperation interfaces and callbacks, + and runs the main simulation loop until the application is closed. + + Returns: + None + """ + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs) + env_cfg.env_name = args_cli.task + if not isinstance(env_cfg, ManagerBasedRLEnvCfg): + raise ValueError( + "Teleoperation is only supported for ManagerBasedRLEnv environments. " + f"Received environment config type: {type(env_cfg).__name__}" + ) + # modify configuration + env_cfg.terminations.time_out = None + if "Lift" in args_cli.task: + # set the resampling time range to large number to avoid resampling + env_cfg.commands.object_pose.resampling_time_range = (1.0e9, 1.0e9) + # add termination condition for reaching the goal otherwise the environment won't reset + env_cfg.terminations.object_reached_goal = DoneTerm(func=mdp.object_reached_goal) + + if args_cli.xr: + env_cfg = remove_camera_configs(env_cfg) + env_cfg.sim.render.antialiasing_mode = "DLSS" + + try: + # create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + # check environment name (for reach , we don't allow the gripper) + if "Reach" in args_cli.task: + logger.warning( + f"The environment '{args_cli.task}' does not support gripper control. The device command will be" + " ignored." + ) + except Exception as e: + logger.error(f"Failed to create environment: {e}") + simulation_app.close() + return + + # Flags for controlling teleoperation flow + should_reset_recording_instance = False + teleoperation_active = True + + # Callback handlers + def reset_recording_instance() -> None: + """ + Reset the environment to its initial state. + + Sets a flag to reset the environment on the next simulation step. + + Returns: + None + """ + nonlocal should_reset_recording_instance + should_reset_recording_instance = True + print("Reset triggered - Environment will reset on next step") + + def start_teleoperation() -> None: + """ + Activate teleoperation control of the robot. + + Enables the application of teleoperation commands to the environment. + + Returns: + None + """ + nonlocal teleoperation_active + teleoperation_active = True + print("Teleoperation activated") + + def stop_teleoperation() -> None: + """ + Deactivate teleoperation control of the robot. + + Disables the application of teleoperation commands to the environment. + + Returns: + None + """ + nonlocal teleoperation_active + teleoperation_active = False + print("Teleoperation deactivated") + + # Create device config if not already in env_cfg + teleoperation_callbacks: dict[str, Callable[[], None]] = { + "R": reset_recording_instance, + "START": start_teleoperation, + "STOP": stop_teleoperation, + "RESET": reset_recording_instance, + } + + # For hand tracking devices, add additional callbacks + if args_cli.xr: + # Default to inactive for hand tracking + teleoperation_active = False + else: + # Always active for other devices + teleoperation_active = True + + # Create teleop device from config if present, otherwise create manually + teleop_interface = None + try: + if hasattr(env_cfg, "teleop_devices") and args_cli.teleop_device in env_cfg.teleop_devices.devices: + teleop_interface = create_teleop_device( + args_cli.teleop_device, env_cfg.teleop_devices.devices, teleoperation_callbacks + ) + else: + logger.warning( + f"No teleop device '{args_cli.teleop_device}' found in environment config. Creating default." + ) + # Create fallback teleop device + sensitivity = args_cli.sensitivity + if args_cli.teleop_device.lower() == "keyboard": + teleop_interface = Se3Keyboard( + Se3KeyboardCfg(pos_sensitivity=0.05 * sensitivity, rot_sensitivity=0.05 * sensitivity) + ) + elif args_cli.teleop_device.lower() == "spacemouse": + teleop_interface = Se3SpaceMouse( + Se3SpaceMouseCfg(pos_sensitivity=0.05 * sensitivity, rot_sensitivity=0.05 * sensitivity) + ) + elif args_cli.teleop_device.lower() == "gamepad": + teleop_interface = Se3Gamepad( + Se3GamepadCfg(pos_sensitivity=0.1 * sensitivity, rot_sensitivity=0.1 * sensitivity) + ) + else: + logger.error(f"Unsupported teleop device: {args_cli.teleop_device}") + logger.error("Configure the teleop device in the environment config.") + env.close() + simulation_app.close() + return + + # Add callbacks to fallback device + for key, callback in teleoperation_callbacks.items(): + try: + teleop_interface.add_callback(key, callback) + except (ValueError, TypeError) as e: + logger.warning(f"Failed to add callback for key {key}: {e}") + except Exception as e: + logger.error(f"Failed to create teleop device: {e}") + env.close() + simulation_app.close() + return + + if teleop_interface is None: + logger.error("Failed to create teleop interface") + env.close() + simulation_app.close() + return + + print(f"Using teleop device: {teleop_interface}") + + # reset environment + env.reset() + teleop_interface.reset() + + print("Teleoperation started. Press 'R' to reset the environment.") + + # simulate environment + while simulation_app.is_running(): + try: + # run everything in inference mode + with torch.inference_mode(): + # get device command + action = teleop_interface.advance() + + # Only apply teleop commands when active + if teleoperation_active: + # process actions + actions = action.repeat(env.num_envs, 1) + # apply actions + env.step(actions) + else: + env.sim.render() + + if should_reset_recording_instance: + env.reset() + teleop_interface.reset() + should_reset_recording_instance = False + print("Environment reset complete") + except Exception as e: + logger.error(f"Error during simulation step: {e}") + break + + # close the simulator + env.close() + print("Environment closed") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/zero_agent.py b/scripts/environments/zero_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..edd9317a6287d290f5e47d4c45af20069e8a2902 --- /dev/null +++ b/scripts/environments/zero_agent.py @@ -0,0 +1,72 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to run an environment with zero action agent.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Zero agent for Isaac Lab environments.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import gymnasium as gym +import torch + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import parse_env_cfg + +# PLACEHOLDER: Extension template (do not remove this comment) + + +def main(): + """Zero actions agent with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg( + args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric + ) + # create environment + env = gym.make(args_cli.task, cfg=env_cfg) + + # print info (this is vectorized environment) + print(f"[INFO]: Gym observation space: {env.observation_space}") + print(f"[INFO]: Gym action space: {env.action_space}") + # reset environment + env.reset() + # simulate environment + while simulation_app.is_running(): + # run everything in inference mode + with torch.inference_mode(): + # compute zero actions + actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device) + # apply actions + env.step(actions) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py b/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py new file mode 100644 index 0000000000000000000000000000000000000000..a60f7913549f1fa6b5bf3553f43198a00fca8a44 --- /dev/null +++ b/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py @@ -0,0 +1,539 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Script to add mimic annotations to demos to be used as source demos for mimic dataset generation. +""" + +import argparse +import math + +from isaaclab.app import AppLauncher + +# Launching Isaac Sim Simulator first. + + +# add argparse arguments +parser = argparse.ArgumentParser(description="Annotate demonstrations for Isaac Lab environments.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--input_file", type=str, default="./datasets/dataset.hdf5", help="File name of the dataset to be annotated." +) +parser.add_argument( + "--output_file", + type=str, + default="./datasets/dataset_annotated.hdf5", + help="File name of the annotated output dataset file.", +) +parser.add_argument("--auto", action="store_true", default=False, help="Automatically annotate subtasks.") +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +parser.add_argument( + "--annotate_subtask_start_signals", + action="store_true", + default=False, + help="Enable annotating start points of subtasks.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed + # by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import contextlib +import os + +import gymnasium as gym +import torch + +import isaaclab_mimic.envs # noqa: F401 + +if args_cli.enable_pinocchio: + import isaaclab_mimic.envs.pinocchio_envs # noqa: F401 + +# Only enables inputs if this script is NOT headless mode +if not args_cli.headless and not os.environ.get("HEADLESS", 0): + from isaaclab.devices import Se3Keyboard, Se3KeyboardCfg + +from isaaclab.envs import ManagerBasedRLMimicEnv +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.managers import RecorderTerm, RecorderTermCfg, TerminationTermCfg +from isaaclab.utils import configclass +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +is_paused = False +current_action_index = 0 +marked_subtask_action_indices = [] +skip_episode = False + + +def play_cb(): + global is_paused + is_paused = False + + +def pause_cb(): + global is_paused + is_paused = True + + +def skip_episode_cb(): + global skip_episode + skip_episode = True + + +def mark_subtask_cb(): + global current_action_index, marked_subtask_action_indices + marked_subtask_action_indices.append(current_action_index) + print(f"Marked a subtask signal at action index: {current_action_index}") + + +class PreStepDatagenInfoRecorder(RecorderTerm): + """Recorder term that records the datagen info data in each step.""" + + def record_pre_step(self): + eef_pose_dict = {} + for eef_name in self._env.cfg.subtask_configs.keys(): + eef_pose_dict[eef_name] = self._env.get_robot_eef_pose(eef_name=eef_name) + + datagen_info = { + "object_pose": self._env.get_object_poses(), + "eef_pose": eef_pose_dict, + "target_eef_pose": self._env.action_to_target_eef_pose(self._env.action_manager.action), + } + return "obs/datagen_info", datagen_info + + +@configclass +class PreStepDatagenInfoRecorderCfg(RecorderTermCfg): + """Configuration for the datagen info recorder term.""" + + class_type: type[RecorderTerm] = PreStepDatagenInfoRecorder + + +class PreStepSubtaskStartsObservationsRecorder(RecorderTerm): + """Recorder term that records the subtask start observations in each step.""" + + def record_pre_step(self): + return "obs/datagen_info/subtask_start_signals", self._env.get_subtask_start_signals() + + +@configclass +class PreStepSubtaskStartsObservationsRecorderCfg(RecorderTermCfg): + """Configuration for the subtask start observations recorder term.""" + + class_type: type[RecorderTerm] = PreStepSubtaskStartsObservationsRecorder + + +class PreStepSubtaskTermsObservationsRecorder(RecorderTerm): + """Recorder term that records the subtask completion observations in each step.""" + + def record_pre_step(self): + return "obs/datagen_info/subtask_term_signals", self._env.get_subtask_term_signals() + + +@configclass +class PreStepSubtaskTermsObservationsRecorderCfg(RecorderTermCfg): + """Configuration for the step subtask terms observation recorder term.""" + + class_type: type[RecorderTerm] = PreStepSubtaskTermsObservationsRecorder + + +@configclass +class MimicRecorderManagerCfg(ActionStateRecorderManagerCfg): + """Mimic specific recorder terms.""" + + record_pre_step_datagen_info = PreStepDatagenInfoRecorderCfg() + record_pre_step_subtask_start_signals = PreStepSubtaskStartsObservationsRecorderCfg() + record_pre_step_subtask_term_signals = PreStepSubtaskTermsObservationsRecorderCfg() + + +def main(): + """Add Isaac Lab Mimic annotations to the given demo dataset file.""" + global is_paused, current_action_index, marked_subtask_action_indices + + # Load input dataset to be annotated + if not os.path.exists(args_cli.input_file): + raise FileNotFoundError(f"The input dataset file {args_cli.input_file} does not exist.") + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(args_cli.input_file) + env_name = dataset_file_handler.get_env_name() + episode_count = dataset_file_handler.get_num_episodes() + + if episode_count == 0: + print("No episodes found in the dataset.") + return 0 + + # get output directory path and file name (without extension) from cli arguments + output_dir = os.path.dirname(args_cli.output_file) + output_file_name = os.path.splitext(os.path.basename(args_cli.output_file))[0] + # create output directory if it does not exist + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + if args_cli.task is not None: + env_name = args_cli.task.split(":")[-1] + if env_name is None: + raise ValueError("Task/env name was not specified nor found in the dataset.") + + env_cfg = parse_env_cfg(env_name, device=args_cli.device, num_envs=1) + + env_cfg.env_name = env_name + + # extract success checking function to invoke manually + success_term = None + if hasattr(env_cfg.terminations, "success"): + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + else: + raise NotImplementedError("No success termination term was found in the environment.") + + # Disable all termination terms + env_cfg.terminations = None + + # Set up recorder terms for mimic annotations + env_cfg.recorders = MimicRecorderManagerCfg() + if not args_cli.auto: + # disable subtask term signals recorder term if in manual mode + env_cfg.recorders.record_pre_step_subtask_term_signals = None + + if not args_cli.auto or (args_cli.auto and not args_cli.annotate_subtask_start_signals): + # disable subtask start signals recorder term if in manual mode or no need for subtask start annotations + env_cfg.recorders.record_pre_step_subtask_start_signals = None + + env_cfg.recorders.dataset_export_dir_path = output_dir + env_cfg.recorders.dataset_filename = output_file_name + + # create environment from loaded config + env: ManagerBasedRLMimicEnv = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + if not isinstance(env, ManagerBasedRLMimicEnv): + raise ValueError("The environment should be derived from ManagerBasedRLMimicEnv") + + if args_cli.auto: + # check if the mimic API env.get_subtask_term_signals() is implemented + if env.get_subtask_term_signals.__func__ is ManagerBasedRLMimicEnv.get_subtask_term_signals: + raise NotImplementedError( + "The environment does not implement the get_subtask_term_signals method required " + "to run automatic annotations." + ) + if ( + args_cli.annotate_subtask_start_signals + and env.get_subtask_start_signals.__func__ is ManagerBasedRLMimicEnv.get_subtask_start_signals + ): + raise NotImplementedError( + "The environment does not implement the get_subtask_start_signals method required " + "to run automatic annotations." + ) + else: + # get subtask termination signal names for each eef from the environment configs + subtask_term_signal_names = {} + subtask_start_signal_names = {} + for eef_name, eef_subtask_configs in env.cfg.subtask_configs.items(): + subtask_start_signal_names[eef_name] = ( + [subtask_config.subtask_term_signal for subtask_config in eef_subtask_configs] + if args_cli.annotate_subtask_start_signals + else [] + ) + subtask_term_signal_names[eef_name] = [ + subtask_config.subtask_term_signal for subtask_config in eef_subtask_configs + ] + # Validation: if annotating start signals, every subtask (including the last) must have a name + if args_cli.annotate_subtask_start_signals: + if any(name in (None, "") for name in subtask_start_signal_names[eef_name]): + raise ValueError( + f"Missing 'subtask_term_signal' for one or more subtasks in eef '{eef_name}'. When" + " '--annotate_subtask_start_signals' is enabled, each subtask (including the last) must" + " specify 'subtask_term_signal'. The last subtask's term signal name is used as the final" + " start signal name." + ) + # no need to annotate the last subtask term signal, so remove it from the list + subtask_term_signal_names[eef_name].pop() + + # reset environment + env.reset() + + # Only enables inputs if this script is NOT headless mode + if not args_cli.headless and not os.environ.get("HEADLESS", 0): + keyboard_interface = Se3Keyboard(Se3KeyboardCfg(pos_sensitivity=0.1, rot_sensitivity=0.1)) + keyboard_interface.add_callback("N", play_cb) + keyboard_interface.add_callback("B", pause_cb) + keyboard_interface.add_callback("Q", skip_episode_cb) + if not args_cli.auto: + keyboard_interface.add_callback("S", mark_subtask_cb) + keyboard_interface.reset() + + # simulate environment -- run everything in inference mode + exported_episode_count = 0 + processed_episode_count = 0 + successful_task_count = 0 # Counter for successful task completions + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + while simulation_app.is_running() and not simulation_app.is_exiting(): + # Iterate over the episodes in the loaded dataset file + for episode_index, episode_name in enumerate(dataset_file_handler.get_episode_names()): + processed_episode_count += 1 + print(f"\nAnnotating episode #{episode_index} ({episode_name})") + episode = dataset_file_handler.load_episode(episode_name, env.device) + + is_episode_annotated_successfully = False + if args_cli.auto: + is_episode_annotated_successfully = annotate_episode_in_auto_mode(env, episode, success_term) + else: + is_episode_annotated_successfully = annotate_episode_in_manual_mode( + env, episode, success_term, subtask_term_signal_names, subtask_start_signal_names + ) + + if is_episode_annotated_successfully and not skip_episode: + # set success to the recorded episode data and export to file + env.recorder_manager.set_success_to_episodes( + None, torch.tensor([[True]], dtype=torch.bool, device=env.device) + ) + env.recorder_manager.export_episodes() + exported_episode_count += 1 + successful_task_count += 1 # Increment successful task counter + print("\tExported the annotated episode.") + else: + print("\tSkipped exporting the episode due to incomplete subtask annotations.") + break + + print( + f"\nExported {exported_episode_count} (out of {processed_episode_count}) annotated" + f" episode{'s' if exported_episode_count > 1 else ''}." + ) + print( + f"Successful task completions: {successful_task_count}" + ) # This line is used by the dataset generation test case to check if the expected number of demos were annotated + print("Exiting the app.") + + # Close environment after annotation is complete + env.close() + + return successful_task_count + + +def replay_episode( + env: ManagerBasedRLMimicEnv, + episode: EpisodeData, + success_term: TerminationTermCfg | None = None, +) -> bool: + """Replays an episode in the environment. + + This function replays the given recorded episode in the environment. It can optionally check if the task + was successfully completed using a success termination condition input. + + Args: + env: The environment to replay the episode in. + episode: The recorded episode data to replay. + success_term: Optional termination term to check for task success. + + Returns: + True if the episode was successfully replayed and the success condition was met (if provided), + False otherwise. + """ + global current_action_index, skip_episode, is_paused + # read initial state and actions from the loaded episode + initial_state = episode.data["initial_state"] + actions = episode.data["actions"] + env.sim.reset() + env.recorder_manager.reset() + env.reset_to(initial_state, None, is_relative=True) + first_action = True + for action_index, action in enumerate(actions): + current_action_index = action_index + if first_action: + first_action = False + else: + while is_paused or skip_episode: + env.sim.render() + if skip_episode: + return False + continue + action_tensor = torch.Tensor(action).reshape([1, action.shape[0]]) + env.step(torch.Tensor(action_tensor)) + if success_term is not None: + if not bool(success_term.func(env, **success_term.params)[0]): + return False + return True + + +def annotate_episode_in_auto_mode( + env: ManagerBasedRLMimicEnv, + episode: EpisodeData, + success_term: TerminationTermCfg | None = None, +) -> bool: + """Annotates an episode in automatic mode. + + This function replays the given episode in the environment and checks if the task was successfully completed. + If the task was not completed, it will print a message and return False. Otherwise, it will check if all the + subtask term signals are annotated and return True if they are, False otherwise. + + Args: + env: The environment to replay the episode in. + episode: The recorded episode data to replay. + success_term: Optional termination term to check for task success. + + Returns: + True if the episode was successfully annotated, False otherwise. + """ + global skip_episode + skip_episode = False + is_episode_annotated_successfully = replay_episode(env, episode, success_term) + if skip_episode: + print("\tSkipping the episode.") + return False + if not is_episode_annotated_successfully: + print("\tThe final task was not completed.") + else: + # check if all the subtask term signals are annotated + annotated_episode = env.recorder_manager.get_episode(0) + subtask_term_signal_dict = annotated_episode.data["obs"]["datagen_info"]["subtask_term_signals"] + for signal_name, signal_flags in subtask_term_signal_dict.items(): + signal_flags = torch.tensor(signal_flags, device=env.device) + if not torch.any(signal_flags): + is_episode_annotated_successfully = False + print(f'\tDid not detect completion for the subtask "{signal_name}".') + if args_cli.annotate_subtask_start_signals: + subtask_start_signal_dict = annotated_episode.data["obs"]["datagen_info"]["subtask_start_signals"] + for signal_name, signal_flags in subtask_start_signal_dict.items(): + if not torch.any(signal_flags): + is_episode_annotated_successfully = False + print(f'\tDid not detect start for the subtask "{signal_name}".') + return is_episode_annotated_successfully + + +def annotate_episode_in_manual_mode( + env: ManagerBasedRLMimicEnv, + episode: EpisodeData, + success_term: TerminationTermCfg | None = None, + subtask_term_signal_names: dict[str, list[str]] = {}, + subtask_start_signal_names: dict[str, list[str]] = {}, +) -> bool: + """Annotates an episode in manual mode. + + This function replays the given episode in the environment and allows for manual marking of subtask term signals. + It iterates over each eef and prompts the user to mark the subtask term signals for that eef. + + Args: + env: The environment to replay the episode in. + episode: The recorded episode data to replay. + success_term: Optional termination term to check for task success. + subtask_term_signal_names: Dictionary mapping eef names to lists of subtask term signal names. + subtask_start_signal_names: Dictionary mapping eef names to lists of subtask start signal names. + Returns: + True if the episode was successfully annotated, False otherwise. + """ + global is_paused, marked_subtask_action_indices, skip_episode + # iterate over the eefs for marking subtask term signals + subtask_term_signal_action_indices = {} + subtask_start_signal_action_indices = {} + for eef_name, eef_subtask_term_signal_names in subtask_term_signal_names.items(): + eef_subtask_start_signal_names = subtask_start_signal_names[eef_name] + # skip if no subtask annotation is needed for this eef + if len(eef_subtask_term_signal_names) == 0 and len(eef_subtask_start_signal_names) == 0: + continue + + while True: + is_paused = True + skip_episode = False + print(f'\tPlaying the episode for subtask annotations for eef "{eef_name}".') + print("\tSubtask signals to annotate:") + if len(eef_subtask_start_signal_names) > 0: + print(f"\t\t- Start:\t{eef_subtask_start_signal_names}") + print(f"\t\t- Termination:\t{eef_subtask_term_signal_names}") + + print('\n\tPress "N" to begin.') + print('\tPress "B" to pause.') + print('\tPress "S" to annotate subtask signals.') + print('\tPress "Q" to skip the episode.\n') + marked_subtask_action_indices = [] + task_success_result = replay_episode(env, episode, success_term) + if skip_episode: + print("\tSkipping the episode.") + return False + + print(f"\tSubtasks marked at action indices: {marked_subtask_action_indices}") + expected_subtask_signal_count = len(eef_subtask_term_signal_names) + len(eef_subtask_start_signal_names) + if task_success_result and expected_subtask_signal_count == len(marked_subtask_action_indices): + print(f'\tAll {expected_subtask_signal_count} subtask signals for eef "{eef_name}" were annotated.') + for marked_signal_index in range(expected_subtask_signal_count): + if args_cli.annotate_subtask_start_signals and marked_signal_index % 2 == 0: + subtask_start_signal_action_indices[ + eef_subtask_start_signal_names[int(marked_signal_index / 2)] + ] = marked_subtask_action_indices[marked_signal_index] + if not args_cli.annotate_subtask_start_signals: + # Direct mapping when only collecting termination signals + subtask_term_signal_action_indices[eef_subtask_term_signal_names[marked_signal_index]] = ( + marked_subtask_action_indices[marked_signal_index] + ) + elif args_cli.annotate_subtask_start_signals and marked_signal_index % 2 == 1: + # Every other signal is a termination when collecting both types + subtask_term_signal_action_indices[ + eef_subtask_term_signal_names[math.floor(marked_signal_index / 2)] + ] = marked_subtask_action_indices[marked_signal_index] + break + + if not task_success_result: + print("\tThe final task was not completed.") + return False + + if expected_subtask_signal_count != len(marked_subtask_action_indices): + print( + f"\tOnly {len(marked_subtask_action_indices)} out of" + f' {expected_subtask_signal_count} subtask signals for eef "{eef_name}" were' + " annotated." + ) + + print(f'\tThe episode will be replayed again for re-marking subtask signals for the eef "{eef_name}".\n') + + annotated_episode = env.recorder_manager.get_episode(0) + for ( + subtask_term_signal_name, + subtask_term_signal_action_index, + ) in subtask_term_signal_action_indices.items(): + # subtask termination signal is false until subtask is complete, and true afterwards + subtask_signals = torch.ones(len(episode.data["actions"]), dtype=torch.bool) + subtask_signals[:subtask_term_signal_action_index] = False + annotated_episode.add(f"obs/datagen_info/subtask_term_signals/{subtask_term_signal_name}", subtask_signals) + + if args_cli.annotate_subtask_start_signals: + for ( + subtask_start_signal_name, + subtask_start_signal_action_index, + ) in subtask_start_signal_action_indices.items(): + subtask_signals = torch.ones(len(episode.data["actions"]), dtype=torch.bool) + subtask_signals[:subtask_start_signal_action_index] = False + annotated_episode.add( + f"obs/datagen_info/subtask_start_signals/{subtask_start_signal_name}", subtask_signals + ) + + return True + + +if __name__ == "__main__": + # run the main function + successful_task_count = main() + # close sim app + simulation_app.close() + # exit with the number of successful task completions as return code + exit(successful_task_count) diff --git a/scripts/imitation_learning/isaaclab_mimic/consolidated_demo.py b/scripts/imitation_learning/isaaclab_mimic/consolidated_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..d180dffd7ccf885632b965aed47688c7b4f24515 --- /dev/null +++ b/scripts/imitation_learning/isaaclab_mimic/consolidated_demo.py @@ -0,0 +1,474 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Script to record teleoperated demos and run mimic dataset generation in real-time. +""" + +# Launching Isaac Sim Simulator first. + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="Record demonstrations and run mimic dataset generation for Isaac Lab environments." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--num_demos", type=int, default=0, help="Number of demonstrations to record. Set to 0 for infinite." +) +parser.add_argument( + "--num_success_steps", + type=int, + default=10, + help="Number of continuous steps with task success for concluding a demo as successful. Default is 10.", +) +parser.add_argument( + "--num_envs", + type=int, + default=5, + help=( + "Number of environments to instantiate to test recording and generating datasets. The environment specified by" + " `teleop_env_index` will be used for teleoperation and recording while the remaining environments will be used" + " for real-time data generation. Default is 5." + ), +) +parser.add_argument( + "--teleop_env_index", + type=int, + default=0, + help="Index of the environment to be used for teleoperation. Set -1 for disabling the teleop robot. Default is 0.", +) +parser.add_argument("--teleop_device", type=str, default="keyboard", help="Device for interacting with environment.") +parser.add_argument( + "--step_hz", type=int, default=0, help="Environment stepping rate in Hz. Set to 0 for maximum speed." +) +parser.add_argument("--input_file", type=str, default=None, help="File path to the source demo dataset file.") +parser.add_argument( + "--output_file", + type=str, + default="./datasets/output_dataset.hdf5", + help="File path to export recorded episodes.", +) +parser.add_argument( + "--generated_output_file", + type=str, + default=None, + help="File path to export generated episodes by mimic.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import asyncio +import contextlib +import os +import random +import time + +import gymnasium as gym +import numpy as np +import torch + +from isaaclab.devices import Se3Keyboard, Se3KeyboardCfg, Se3SpaceMouse, Se3SpaceMouseCfg +from isaaclab.envs import ManagerBasedRLMimicEnv +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.managers import DatasetExportMode, RecorderTerm, RecorderTermCfg +from isaaclab.utils import configclass +from isaaclab.utils.datasets import HDF5DatasetFileHandler + +import isaaclab_mimic.envs # noqa: F401 +from isaaclab_mimic.datagen.data_generator import DataGenerator +from isaaclab_mimic.datagen.datagen_info_pool import DataGenInfoPool + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# global variable to keep track of the data generation statistics +num_recorded = 0 +num_success = 0 +num_failures = 0 +num_attempts = 0 + + +class PreStepDatagenInfoRecorder(RecorderTerm): + """Recorder term that records the datagen info data in each step.""" + + def record_pre_step(self): + eef_pose_dict = {} + for eef_name in self._env.cfg.subtask_configs.keys(): + eef_pose_dict[eef_name] = self._env.get_robot_eef_pose(eef_name) + + datagen_info = { + "object_pose": self._env.get_object_poses(), + "eef_pose": eef_pose_dict, + "target_eef_pose": self._env.action_to_target_eef_pose(self._env.action_manager.action), + } + return "obs/datagen_info", datagen_info + + +@configclass +class PreStepDatagenInfoRecorderCfg(RecorderTermCfg): + """Configuration for the datagen info recorder term.""" + + class_type: type[RecorderTerm] = PreStepDatagenInfoRecorder + + +class PreStepSubtaskTermsObservationsRecorder(RecorderTerm): + """Recorder term that records the subtask completion observations in each step.""" + + def record_pre_step(self): + return "obs/datagen_info/subtask_term_signals", self._env.get_subtask_term_signals() + + +@configclass +class PreStepSubtaskTermsObservationsRecorderCfg(RecorderTermCfg): + """Configuration for the step subtask terms observation recorder term.""" + + class_type: type[RecorderTerm] = PreStepSubtaskTermsObservationsRecorder + + +@configclass +class MimicRecorderManagerCfg(ActionStateRecorderManagerCfg): + """Mimic specific recorder terms.""" + + record_pre_step_datagen_info = PreStepDatagenInfoRecorderCfg() + record_pre_step_subtask_term_signals = PreStepSubtaskTermsObservationsRecorderCfg() + + +class RateLimiter: + """Convenience class for enforcing rates in loops.""" + + def __init__(self, hz): + """ + Args: + hz (int): frequency to enforce + """ + self.hz = hz + self.last_time = time.time() + self.sleep_duration = 1.0 / hz + self.render_period = min(0.033, self.sleep_duration) + + def sleep(self, env): + """Attempt to sleep at the specified rate in hz.""" + next_wakeup_time = self.last_time + self.sleep_duration + while time.time() < next_wakeup_time: + time.sleep(self.render_period) + env.unwrapped.sim.render() + + self.last_time = self.last_time + self.sleep_duration + + # detect time jumping forwards (e.g. loop is too slow) + if self.last_time < time.time(): + while self.last_time < time.time(): + self.last_time += self.sleep_duration + + +def pre_process_actions(delta_pose: torch.Tensor, gripper_command: bool) -> torch.Tensor: + """Pre-process actions for the environment.""" + # compute actions based on environment + if "Reach" in args_cli.task: + # note: reach is the only one that uses a different action space + # compute actions + return delta_pose + else: + # resolve gripper command + gripper_vel = torch.zeros((delta_pose.shape[0], 1), dtype=torch.float, device=delta_pose.device) + gripper_vel[:] = -1 if gripper_command else 1 + # compute actions + return torch.concat([delta_pose, gripper_vel], dim=1) + + +async def run_teleop_robot( + env, env_id, env_action_queue, shared_datagen_info_pool, success_term, exported_dataset_path, teleop_interface=None +): + """Run teleop robot.""" + global num_recorded + should_reset_teleop_instance = False + # create controller if needed + if teleop_interface is None: + if args_cli.teleop_device.lower() == "keyboard": + teleop_interface = Se3Keyboard(Se3KeyboardCfg(pos_sensitivity=0.2, rot_sensitivity=0.5)) + elif args_cli.teleop_device.lower() == "spacemouse": + teleop_interface = Se3SpaceMouse(Se3SpaceMouseCfg(pos_sensitivity=0.2, rot_sensitivity=0.5)) + else: + raise ValueError( + f"Invalid device interface '{args_cli.teleop_device}'. Supported: 'keyboard', 'spacemouse'." + ) + + # add teleoperation key for reset current recording instance + def reset_teleop_instance(): + nonlocal should_reset_teleop_instance + should_reset_teleop_instance = True + + teleop_interface.add_callback("R", reset_teleop_instance) + + teleop_interface.reset() + print(teleop_interface) + + recorded_episode_dataset_file_handler = HDF5DatasetFileHandler() + recorded_episode_dataset_file_handler.create(exported_dataset_path, env_name=env.unwrapped.cfg.env_name) + + env_id_tensor = torch.tensor([env_id], dtype=torch.int64, device=env.device) + success_step_count = 0 + num_recorded = 0 + while True: + if should_reset_teleop_instance: + env.unwrapped.recorder_manager.reset(env_id_tensor) + env.unwrapped.reset(env_ids=env_id_tensor) + should_reset_teleop_instance = False + success_step_count = 0 + + # get keyboard command + delta_pose, gripper_command = teleop_interface.advance() + # convert to torch + delta_pose = torch.tensor(delta_pose, dtype=torch.float, device=env.device).repeat(1, 1) + # compute actions based on environment + teleop_action = pre_process_actions(delta_pose, gripper_command) + + await env_action_queue.put((env_id, teleop_action)) + await env_action_queue.join() + + if success_term is not None: + if bool(success_term.func(env, **success_term.params)[env_id]): + success_step_count += 1 + if success_step_count >= args_cli.num_success_steps: + env.recorder_manager.set_success_to_episodes( + env_id_tensor, torch.tensor([[True]], dtype=torch.bool, device=env.device) + ) + teleop_episode = env.unwrapped.recorder_manager.get_episode(env_id) + await shared_datagen_info_pool.add_episode(teleop_episode) + + recorded_episode_dataset_file_handler.write_episode(teleop_episode) + recorded_episode_dataset_file_handler.flush() + env.recorder_manager.reset(env_id_tensor) + num_recorded += 1 + should_reset_teleop_instance = True + else: + success_step_count = 0 + + +async def run_data_generator( + env, env_id, env_action_queue, shared_datagen_info_pool, success_term, pause_subtask=False, export_demo=True +): + """Run data generator.""" + global num_success, num_failures, num_attempts + data_generator = DataGenerator(env=env.unwrapped, src_demo_datagen_info_pool=shared_datagen_info_pool) + idle_action = torch.zeros(env.unwrapped.action_space.shape)[0] + while True: + while data_generator.src_demo_datagen_info_pool.num_datagen_infos < 1: + await env_action_queue.put((env_id, idle_action)) + await env_action_queue.join() + + results = await data_generator.generate( + env_id=env_id, + success_term=success_term, + env_action_queue=env_action_queue, + select_src_per_subtask=env.unwrapped.cfg.datagen_config.generation_select_src_per_subtask, + transform_first_robot_pose=env.unwrapped.cfg.datagen_config.generation_transform_first_robot_pose, + interpolate_from_last_target_pose=env.unwrapped.cfg.datagen_config.generation_interpolate_from_last_target_pose, + pause_subtask=pause_subtask, + export_demo=export_demo, + ) + if bool(results["success"]): + num_success += 1 + else: + num_failures += 1 + num_attempts += 1 + + +def env_loop(env, env_action_queue, shared_datagen_info_pool, asyncio_event_loop): + """Main loop for the environment.""" + global num_recorded, num_success, num_failures, num_attempts + prev_num_attempts = 0 + prev_num_recorded = 0 + + rate_limiter = None + if args_cli.step_hz > 0: + rate_limiter = RateLimiter(args_cli.step_hz) + + # simulate environment -- run everything in inference mode + is_first_print = True + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + while True: + actions = torch.zeros(env.unwrapped.action_space.shape) + + # get actions from all the data generators + for i in range(env.unwrapped.num_envs): + # an async-blocking call to get an action from a data generator + env_id, action = asyncio_event_loop.run_until_complete(env_action_queue.get()) + actions[env_id] = action + + # perform action on environment + env.step(actions) + + # mark done so the data generators can continue with the step results + for i in range(env.unwrapped.num_envs): + env_action_queue.task_done() + + if prev_num_attempts != num_attempts or prev_num_recorded != num_recorded: + prev_num_attempts = num_attempts + prev_num_recorded = num_recorded + generated_sucess_rate = 100 * num_success / num_attempts if num_attempts > 0 else 0.0 + if is_first_print: + is_first_print = False + else: + print("\r", "\033[F" * 5, end="") + print("") + print("*" * 50, "\033[K") + print(f"{num_recorded} teleoperated demos recorded\033[K") + print( + f"{num_success}/{num_attempts} ({generated_sucess_rate:.1f}%) successful demos generated by" + " mimic\033[K" + ) + print("*" * 50, "\033[K") + + if args_cli.num_demos > 0 and num_recorded >= args_cli.num_demos: + print(f"All {args_cli.num_demos} demonstrations recorded. Exiting the app.") + break + + # check that simulation is stopped or not + if env.unwrapped.sim.is_stopped(): + break + + if rate_limiter: + rate_limiter.sleep(env.unwrapped) + env.close() + + +def main(): + num_envs = args_cli.num_envs + + # create output directory for recorded episodes if it does not exist + recorded_output_dir = os.path.dirname(args_cli.output_file) + if not os.path.exists(recorded_output_dir): + os.makedirs(recorded_output_dir) + + # check if the given input dataset file exists + if args_cli.input_file and not os.path.exists(args_cli.input_file): + raise FileNotFoundError(f"The dataset file {args_cli.input_file} does not exist.") + + # get the environment name + if args_cli.task is not None: + env_name = args_cli.task.split(":")[-1] + elif args_cli.input_file: + # if the environment name is not specified, try to get it from the dataset file + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(args_cli.input_file) + env_name = dataset_file_handler.get_env_name() + else: + raise ValueError("Task/env name was not specified nor found in the dataset.") + + # parse configuration + env_cfg = parse_env_cfg(env_name, device=args_cli.device, num_envs=num_envs) + env_cfg.env_name = env_name + + # extract success checking function to invoke manually + success_term = None + if hasattr(env_cfg.terminations, "success"): + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + else: + raise NotImplementedError("No success termination term was found in the environment.") + + # data generator is in charge of resetting the environment + env_cfg.terminations = None + + env_cfg.observations.policy.concatenate_terms = False + + env_cfg.recorders = MimicRecorderManagerCfg() + + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_NONE + if args_cli.generated_output_file: + # create output directory for generated episodes if it does not exist + generated_output_dir = os.path.dirname(args_cli.generated_output_file) + if not os.path.exists(generated_output_dir): + os.makedirs(generated_output_dir) + generated_output_file_name = os.path.splitext(os.path.basename(args_cli.generated_output_file))[0] + env_cfg.recorders.dataset_export_dir_path = generated_output_dir + env_cfg.recorders.dataset_filename = generated_output_file_name + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_SUCCEEDED_ONLY + + # create environment + env = gym.make(args_cli.task, cfg=env_cfg) + + if not isinstance(env.unwrapped, ManagerBasedRLMimicEnv): + raise ValueError("The environment should be derived from ManagerBasedRLMimicEnv") + + # check if the mimic API env.unwrapped.get_subtask_term_signals() is implemented + if env.unwrapped.get_subtask_term_signals.__func__ is ManagerBasedRLMimicEnv.get_subtask_term_signals: + raise NotImplementedError( + "The environment does not implement the get_subtask_term_signals method required to run this script." + ) + + # set seed for generation + random.seed(env.unwrapped.cfg.datagen_config.seed) + np.random.seed(env.unwrapped.cfg.datagen_config.seed) + torch.manual_seed(env.unwrapped.cfg.datagen_config.seed) + + # reset before starting + env.reset() + + # Set up asyncio stuff + asyncio_event_loop = asyncio.get_event_loop() + env_action_queue = asyncio.Queue() + + shared_datagen_info_pool_lock = asyncio.Lock() + shared_datagen_info_pool = DataGenInfoPool( + env.unwrapped, env.unwrapped.cfg, env.unwrapped.device, asyncio_lock=shared_datagen_info_pool_lock + ) + if args_cli.input_file: + shared_datagen_info_pool.load_from_dataset_file(args_cli.input_file) + print(f"Loaded {shared_datagen_info_pool.num_datagen_infos} to datagen info pool") + + # make data generator object + data_generator_asyncio_tasks = [] + for i in range(num_envs): + if args_cli.teleop_env_index is not None and i == args_cli.teleop_env_index: + data_generator_asyncio_tasks.append( + asyncio_event_loop.create_task( + run_teleop_robot( + env, i, env_action_queue, shared_datagen_info_pool, success_term, args_cli.output_file + ) + ) + ) + continue + data_generator_asyncio_tasks.append( + asyncio_event_loop.create_task( + run_data_generator( + env, + i, + env_action_queue, + shared_datagen_info_pool, + success_term, + export_demo=bool(args_cli.generated_output_file), + ) + ) + ) + + try: + asyncio.ensure_future(asyncio.gather(*data_generator_asyncio_tasks)) + except asyncio.CancelledError: + print("Tasks were cancelled.") + + env_loop(env, env_action_queue, shared_datagen_info_pool, asyncio_event_loop) + + +if __name__ == "__main__": + try: + main() + except KeyboardInterrupt: + print("\nProgram interrupted by user. Exiting...") + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py b/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..527792ea9038c9e4cadb8b2487920401f480b29a --- /dev/null +++ b/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py @@ -0,0 +1,205 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Main data generation script. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Generate demonstrations for Isaac Lab environments.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--generation_num_trials", type=int, help="Number of demos to be generated.", default=None) +parser.add_argument( + "--num_envs", type=int, default=1, help="Number of environments to instantiate for generating datasets." +) +parser.add_argument("--input_file", type=str, default=None, required=True, help="File path to the source dataset file.") +parser.add_argument( + "--output_file", + type=str, + default="./datasets/output_dataset.hdf5", + help="File path to export recorded and generated episodes.", +) +parser.add_argument( + "--pause_subtask", + action="store_true", + help="pause after every subtask during generation for debugging - only useful with render flag", +) +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +parser.add_argument( + "--use_skillgen", + action="store_true", + default=False, + help="use skillgen to generate motion trajectories", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version + # installed by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import asyncio +import inspect +import logging +import random + +import gymnasium as gym +import numpy as np +import torch + +from isaaclab.envs import ManagerBasedRLMimicEnv + +import isaaclab_mimic.envs # noqa: F401 + +if args_cli.enable_pinocchio: + import isaaclab_mimic.envs.pinocchio_envs # noqa: F401 + +from isaaclab_mimic.datagen.generation import env_loop, setup_async_generation, setup_env_config +from isaaclab_mimic.datagen.utils import get_env_name_from_dataset, setup_output_paths + +import isaaclab_tasks # noqa: F401 + +# import logger +logger = logging.getLogger(__name__) + + +def main(): + num_envs = args_cli.num_envs + + # Setup output paths and get env name + output_dir, output_file_name = setup_output_paths(args_cli.output_file) + task_name = args_cli.task + if task_name: + task_name = args_cli.task.split(":")[-1] + env_name = task_name or get_env_name_from_dataset(args_cli.input_file) + + # Configure environment + env_cfg, success_term = setup_env_config( + env_name=env_name, + output_dir=output_dir, + output_file_name=output_file_name, + num_envs=num_envs, + device=args_cli.device, + generation_num_trials=args_cli.generation_num_trials, + ) + + # Create environment + env = gym.make(env_name, cfg=env_cfg).unwrapped + + if not isinstance(env, ManagerBasedRLMimicEnv): + raise ValueError("The environment should be derived from ManagerBasedRLMimicEnv") + + # Check if the mimic API from this environment contains decprecated signatures + if "action_noise_dict" not in inspect.signature(env.target_eef_pose_to_action).parameters: + logger.warning( + f'The "noise" parameter in the "{env_name}" environment\'s mimic API "target_eef_pose_to_action", ' + "is deprecated. Please update the API to take action_noise_dict instead." + ) + + # Set seed for generation + random.seed(env.cfg.datagen_config.seed) + np.random.seed(env.cfg.datagen_config.seed) + torch.manual_seed(env.cfg.datagen_config.seed) + + # Reset before starting + env.reset() + + motion_planners = None + if args_cli.use_skillgen: + from isaaclab_mimic.motion_planners.curobo.curobo_planner import CuroboPlanner + from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg + + # Create one motion planner per environment + motion_planners = {} + for env_id in range(num_envs): + print(f"Initializing motion planner for environment {env_id}") + # Create a config instance from the task name + planner_config = CuroboPlannerCfg.from_task_name(env_name) + + # Ensure visualization is only enabled for the first environment + # If not, sphere and plan visualization will be too slow in isaac lab + # It is efficient to visualize the spheres and plan for the first environment in rerun + if env_id != 0: + planner_config.visualize_spheres = False + planner_config.visualize_plan = False + + motion_planners[env_id] = CuroboPlanner( + env=env, + robot=env.scene["robot"], + config=planner_config, # Pass the config object + env_id=env_id, # Pass environment ID + ) + + env.cfg.datagen_config.use_skillgen = True + + # Setup and run async data generation + async_components = setup_async_generation( + env=env, + num_envs=args_cli.num_envs, + input_file=args_cli.input_file, + success_term=success_term, + pause_subtask=args_cli.pause_subtask, + motion_planners=motion_planners, # Pass the motion planners dictionary + ) + + try: + data_gen_tasks = asyncio.ensure_future(asyncio.gather(*async_components["tasks"])) + env_loop( + env, + async_components["reset_queue"], + async_components["action_queue"], + async_components["info_pool"], + async_components["event_loop"], + ) + except asyncio.CancelledError: + print("Tasks were cancelled.") + finally: + # Cancel all async tasks when env_loop finishes + data_gen_tasks.cancel() + try: + # Wait for tasks to be cancelled + async_components["event_loop"].run_until_complete(data_gen_tasks) + except asyncio.CancelledError: + print("Remaining async tasks cancelled and cleaned up.") + except Exception as e: + print(f"Error cancelling remaining async tasks: {e}") + # Cleanup of motion planners and their visualizers + if motion_planners is not None: + for env_id, planner in motion_planners.items(): + if getattr(planner, "plan_visualizer", None) is not None: + print(f"Closing plan visualizer for environment {env_id}") + planner.plan_visualizer.close() + planner.plan_visualizer = None + motion_planners.clear() + + +if __name__ == "__main__": + try: + main() + except KeyboardInterrupt: + print("\nProgram interrupted by user. Exiting...") + # Close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/locomanipulation_sdg/generate_data.py b/scripts/imitation_learning/locomanipulation_sdg/generate_data.py new file mode 100644 index 0000000000000000000000000000000000000000..4999f2d3fefcbceb9c5be1106a9a8d3ad08a0acd --- /dev/null +++ b/scripts/imitation_learning/locomanipulation_sdg/generate_data.py @@ -0,0 +1,774 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to replay demonstrations with Isaac Lab environments.""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse +import os + +from isaaclab.app import AppLauncher + +# Launch Isaac Lab +parser = argparse.ArgumentParser(description="Locomanipulation SDG") +parser.add_argument("--task", type=str, help="The Isaac Lab locomanipulation SDG task to load for data generation.") +parser.add_argument("--dataset", type=str, help="The static manipulation dataset recorded via teleoperation.") +parser.add_argument("--output_file", type=str, help="The file name for the generated output dataset.") +parser.add_argument( + "--lift_step", + type=int, + help=( + "The step index in the input recording where the robot is ready to lift the object. Aka, where the grasp is" + " finished." + ), +) +parser.add_argument( + "--navigate_step", + type=int, + help=( + "The step index in the input recording where the robot is ready to navigate. Aka, where it has finished" + " lifting the object" + ), +) +parser.add_argument("--demo", type=str, default=None, help="The demo in the input dataset to use.") +parser.add_argument("--num_runs", type=int, default=1, help="The number of trajectories to generate.") +parser.add_argument( + "--draw_visualization", type=bool, default=False, help="Draw the occupancy map and path planning visualization." +) +parser.add_argument( + "--angular_gain", + type=float, + default=2.0, + help=( + "The angular gain to use for determining an angular control velocity when driving the robot during navigation." + ), +) +parser.add_argument( + "--linear_gain", + type=float, + default=1.0, + help="The linear gain to use for determining the linear control velocity when driving the robot during navigation.", +) +parser.add_argument( + "--linear_max", type=float, default=1.0, help="The maximum linear control velocity allowable during navigation." +) +parser.add_argument( + "--distance_threshold", + type=float, + default=0.2, + help="The distance threshold in meters to perform state transitions between navigation and manipulation tasks.", +) +parser.add_argument( + "--following_offset", + type=float, + default=0.6, + help=( + "The target point offset distance used for local path following during navigation. A larger value will result" + " in smoother trajectories, but may cut path corners." + ), +) +parser.add_argument( + "--angle_threshold", + type=float, + default=0.2, + help=( + "The angle threshold in radians to determine when the robot can move forward or transition between navigation" + " and manipulation tasks." + ), +) +parser.add_argument( + "--approach_distance", + type=float, + default=0.5, + help="An offset distance added to the destination to allow a buffer zone for reliably approaching the goal.", +) +parser.add_argument( + "--randomize_placement", + type=bool, + default=True, + help="Whether or not to randomize the placement of fixtures in the scene upon environment initialization.", +) +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version + # installed by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +import enum +import random + +import gymnasium as gym +import torch + +import omni.kit + +from isaaclab.utils import configclass +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + +import isaaclab_mimic.locomanipulation_sdg.envs # noqa: F401 +from isaaclab_mimic.locomanipulation_sdg.data_classes import LocomanipulationSDGOutputData +from isaaclab_mimic.locomanipulation_sdg.envs.locomanipulation_sdg_env import LocomanipulationSDGEnv +from isaaclab_mimic.locomanipulation_sdg.occupancy_map_utils import ( + OccupancyMap, + merge_occupancy_maps, + occupancy_map_add_to_stage, +) +from isaaclab_mimic.locomanipulation_sdg.path_utils import ParameterizedPath, plan_path +from isaaclab_mimic.locomanipulation_sdg.scene_utils import RelativePose, place_randomly +from isaaclab_mimic.locomanipulation_sdg.transform_utils import transform_inv, transform_mul, transform_relative_pose + +from isaaclab_tasks.utils import parse_env_cfg + + +class LocomanipulationSDGDataGenerationState(enum.IntEnum): + """States for the locomanipulation SDG data generation state machine.""" + + GRASP_OBJECT = 0 + """Robot grasps object at start position""" + + LIFT_OBJECT = 1 + """Robot lifts object while stationary""" + + NAVIGATE = 2 + """Robot navigates to approach position with object""" + + APPROACH = 3 + """Robot approaches final goal position""" + + DROP_OFF_OBJECT = 4 + """Robot places object at end position""" + + DONE = 5 + """Task completed""" + + +@configclass +class LocomanipulationSDGControlConfig: + """Configuration for navigation control parameters.""" + + angular_gain: float = 2.0 + """Proportional gain for angular velocity control""" + + linear_gain: float = 1.0 + """Proportional gain for linear velocity control""" + + linear_max: float = 1.0 + """Maximum allowed linear velocity (m/s)""" + + distance_threshold: float = 0.1 + """Distance threshold for state transitions (m)""" + + following_offset: float = 0.6 + """Look-ahead distance for path following (m)""" + + angle_threshold: float = 0.2 + """Angular threshold for orientation control (rad)""" + + approach_distance: float = 1.0 + """Buffer distance from final goal (m)""" + + +def compute_navigation_velocity( + current_pose: torch.Tensor, target_xy: torch.Tensor, config: LocomanipulationSDGControlConfig +) -> tuple[torch.Tensor, torch.Tensor]: + """Compute linear and angular velocities for navigation control. + + Args: + current_pose: Current robot pose [x, y, yaw] + target_xy: Target position [x, y] + config: Navigation control configuration + + Returns: + Tuple of (linear_velocity, angular_velocity) + """ + current_xy = current_pose[:2] + current_yaw = current_pose[2] + + # Compute position and orientation errors + delta_xy = target_xy - current_xy + delta_distance = torch.sqrt(torch.sum(delta_xy**2)) + + target_yaw = torch.arctan2(delta_xy[1], delta_xy[0]) + delta_yaw = target_yaw - current_yaw + # Normalize angle to [-π, π] + delta_yaw = (delta_yaw + torch.pi) % (2 * torch.pi) - torch.pi + + # Compute control commands + angular_velocity = config.angular_gain * delta_yaw + linear_velocity = torch.clip(config.linear_gain * delta_distance, 0.0, config.linear_max) / ( + 1 + torch.abs(angular_velocity) + ) + + return linear_velocity, angular_velocity + + +def load_and_transform_recording_data( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + reference_pose: torch.Tensor, + target_pose: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + """Load recording data and transform hand targets to current reference frame. + + Args: + env: The locomanipulation SDG environment + input_episode_data: Input episode data from static manipulation + recording_step: Current step in the recording + reference_pose: Original reference pose for the hand targets + target_pose: Current target pose to transform to + + Returns: + Tuple of transformed (left_hand_pose, right_hand_pose) + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + if recording_item is None: + return None, None + + left_hand_pose = transform_relative_pose(recording_item.left_hand_pose_target, reference_pose, target_pose)[0] + right_hand_pose = transform_relative_pose(recording_item.right_hand_pose_target, reference_pose, target_pose)[0] + + return left_hand_pose, right_hand_pose + + +def setup_navigation_scene( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + approach_distance: float, + randomize_placement: bool = True, +) -> tuple[OccupancyMap, ParameterizedPath, RelativePose, RelativePose]: + """Set up the navigation scene with occupancy map and path planning. + + Args: + env: The locomanipulation SDG environment + input_episode_data: Input episode data + approach_distance: Buffer distance from final goal + randomize_placement: Whether to randomize fixture placement + + Returns: + Tuple of (occupancy_map, path_helper, base_goal, base_goal_approach) + """ + # Create base occupancy map + occupancy_map = merge_occupancy_maps( + [ + OccupancyMap.make_empty(start=(-7, -7), end=(7, 7), resolution=0.05), + env.get_start_fixture().get_occupancy_map(), + ] + ) + + # Randomize fixture placement if enabled + if randomize_placement: + fixtures = [env.get_end_fixture()] + env.get_obstacle_fixtures() + for fixture in fixtures: + place_randomly(fixture, occupancy_map.buffered_meters(1.0)) + occupancy_map = merge_occupancy_maps([occupancy_map, fixture.get_occupancy_map()]) + + # Compute goal poses from initial state + initial_state = env.load_input_data(input_episode_data, 0) + base_goal = RelativePose( + relative_pose=transform_mul(transform_inv(initial_state.fixture_pose), initial_state.base_pose), + parent=env.get_end_fixture(), + ) + base_goal_approach = RelativePose( + relative_pose=torch.tensor([-approach_distance, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), parent=base_goal + ) + + # Plan navigation path + base_path = plan_path( + start=env.get_base(), end=base_goal_approach, occupancy_map=occupancy_map.buffered_meters(0.15) + ) + base_path_helper = ParameterizedPath(base_path) + + return occupancy_map, base_path_helper, base_goal, base_goal_approach + + +def handle_grasp_state( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + lift_step: int, + output_data: LocomanipulationSDGOutputData, +) -> tuple[int, LocomanipulationSDGDataGenerationState]: + """Handle the GRASP_OBJECT state logic. + + Args: + env: The environment + input_episode_data: Input episode data + recording_step: Current recording step + lift_step: Step to transition to lift phase + output_data: Output data to populate + + Returns: + Tuple of (next_recording_step, next_state) + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + + # Set control targets - robot stays stationary during grasping + output_data.data_generation_state = int(LocomanipulationSDGDataGenerationState.GRASP_OBJECT) + output_data.recording_step = recording_step + output_data.base_velocity_target = torch.tensor([0.0, 0.0, 0.0]) + + # Transform hand poses relative to object + output_data.left_hand_pose_target = transform_relative_pose( + recording_item.left_hand_pose_target, recording_item.object_pose, env.get_object().get_pose() + )[0] + output_data.right_hand_pose_target = transform_relative_pose( + recording_item.right_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.left_hand_joint_positions_target = recording_item.left_hand_joint_positions_target + output_data.right_hand_joint_positions_target = recording_item.right_hand_joint_positions_target + + # Update state + + next_recording_step = recording_step + 1 + next_state = ( + LocomanipulationSDGDataGenerationState.LIFT_OBJECT + if next_recording_step > lift_step + else LocomanipulationSDGDataGenerationState.GRASP_OBJECT + ) + + return next_recording_step, next_state + + +def handle_lift_state( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + navigate_step: int, + output_data: LocomanipulationSDGOutputData, +) -> tuple[int, LocomanipulationSDGDataGenerationState]: + """Handle the LIFT_OBJECT state logic. + + Args: + env: The environment + input_episode_data: Input episode data + recording_step: Current recording step + navigate_step: Step to transition to navigation phase + output_data: Output data to populate + + Returns: + Tuple of (next_recording_step, next_state) + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + + # Set control targets - robot stays stationary during lifting + output_data.data_generation_state = int(LocomanipulationSDGDataGenerationState.LIFT_OBJECT) + output_data.recording_step = recording_step + output_data.base_velocity_target = torch.tensor([0.0, 0.0, 0.0]) + + # Transform hand poses relative to base + output_data.left_hand_pose_target = transform_relative_pose( + recording_item.left_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.right_hand_pose_target = transform_relative_pose( + recording_item.right_hand_pose_target, recording_item.object_pose, env.get_object().get_pose() + )[0] + output_data.left_hand_joint_positions_target = recording_item.left_hand_joint_positions_target + output_data.right_hand_joint_positions_target = recording_item.right_hand_joint_positions_target + + # Update state + next_recording_step = recording_step + 1 + next_state = ( + LocomanipulationSDGDataGenerationState.NAVIGATE + if next_recording_step > navigate_step + else LocomanipulationSDGDataGenerationState.LIFT_OBJECT + ) + + return next_recording_step, next_state + + +def handle_navigate_state( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + base_path_helper: ParameterizedPath, + base_goal_approach: RelativePose, + config: LocomanipulationSDGControlConfig, + output_data: LocomanipulationSDGOutputData, +) -> LocomanipulationSDGDataGenerationState: + """Handle the NAVIGATE state logic. + + Args: + env: The environment + input_episode_data: Input episode data + recording_step: Current recording step + base_path_helper: Parameterized path for navigation + base_goal_approach: Approach pose goal + config: Navigation control configuration + output_data: Output data to populate + + Returns: + Next state + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + current_pose = env.get_base().get_pose_2d()[0] + + # Find target point along path using pure pursuit algorithm + _, nearest_path_length, _, _ = base_path_helper.find_nearest(current_pose[:2]) + target_xy = base_path_helper.get_point_by_distance(distance=nearest_path_length + config.following_offset) + + # Compute navigation velocities + linear_velocity, angular_velocity = compute_navigation_velocity(current_pose, target_xy, config) + + # Set control targets + output_data.data_generation_state = int(LocomanipulationSDGDataGenerationState.NAVIGATE) + output_data.recording_step = recording_step + output_data.base_velocity_target = torch.tensor([linear_velocity, 0.0, angular_velocity]) + + # Transform hand poses relative to base + output_data.left_hand_pose_target = transform_relative_pose( + recording_item.left_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.right_hand_pose_target = transform_relative_pose( + recording_item.right_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.left_hand_joint_positions_target = recording_item.left_hand_joint_positions_target + output_data.right_hand_joint_positions_target = recording_item.right_hand_joint_positions_target + + # Check if close enough to approach goal to transition + goal_xy = base_goal_approach.get_pose_2d()[0, :2] + distance_to_goal = torch.sqrt(torch.sum((current_pose[:2] - goal_xy) ** 2)) + + return ( + LocomanipulationSDGDataGenerationState.APPROACH + if distance_to_goal < config.distance_threshold + else LocomanipulationSDGDataGenerationState.NAVIGATE + ) + + +def handle_approach_state( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + base_goal: RelativePose, + config: LocomanipulationSDGControlConfig, + output_data: LocomanipulationSDGOutputData, +) -> LocomanipulationSDGDataGenerationState: + """Handle the APPROACH state logic. + + Args: + env: The environment + input_episode_data: Input episode data + recording_step: Current recording step + base_goal: Final goal pose + config: Navigation control configuration + output_data: Output data to populate + + Returns: + Next state + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + current_pose = env.get_base().get_pose_2d()[0] + + # Navigate directly to final goal position + goal_xy = base_goal.get_pose_2d()[0, :2] + linear_velocity, angular_velocity = compute_navigation_velocity(current_pose, goal_xy, config) + + # Set control targets + output_data.data_generation_state = int(LocomanipulationSDGDataGenerationState.APPROACH) + output_data.recording_step = recording_step + output_data.base_velocity_target = torch.tensor([linear_velocity, 0.0, angular_velocity]) + + # Transform hand poses relative to base + output_data.left_hand_pose_target = transform_relative_pose( + recording_item.left_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.right_hand_pose_target = transform_relative_pose( + recording_item.right_hand_pose_target, recording_item.base_pose, env.get_base().get_pose() + )[0] + output_data.left_hand_joint_positions_target = recording_item.left_hand_joint_positions_target + output_data.right_hand_joint_positions_target = recording_item.right_hand_joint_positions_target + + # Check if close enough to final goal to start drop-off + distance_to_goal = torch.sqrt(torch.sum((current_pose[:2] - goal_xy) ** 2)) + + return ( + LocomanipulationSDGDataGenerationState.DROP_OFF_OBJECT + if distance_to_goal < config.distance_threshold + else LocomanipulationSDGDataGenerationState.APPROACH + ) + + +def handle_drop_off_state( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + recording_step: int, + base_goal: RelativePose, + config: LocomanipulationSDGControlConfig, + output_data: LocomanipulationSDGOutputData, +) -> tuple[int, LocomanipulationSDGDataGenerationState | None]: + """Handle the DROP_OFF_OBJECT state logic. + + Args: + env: The environment + input_episode_data: Input episode data + recording_step: Current recording step + base_goal: Final goal pose + config: Navigation control configuration + output_data: Output data to populate + + Returns: + Tuple of (next_recording_step, next_state) + """ + recording_item = env.load_input_data(input_episode_data, recording_step) + if recording_item is None: + return recording_step, None + + # Compute orientation control to face target orientation + current_pose = env.get_base().get_pose_2d()[0] + target_pose = base_goal.get_pose_2d()[0] + current_yaw = current_pose[2] + target_yaw = target_pose[2] + delta_yaw = target_yaw - current_yaw + delta_yaw = (delta_yaw + torch.pi) % (2 * torch.pi) - torch.pi + + angular_velocity = config.angular_gain * delta_yaw + linear_velocity = 0.0 # Stay in place while orienting + + # Set control targets + output_data.data_generation_state = int(LocomanipulationSDGDataGenerationState.DROP_OFF_OBJECT) + output_data.recording_step = recording_step + output_data.base_velocity_target = torch.tensor([linear_velocity, 0.0, angular_velocity]) + + # Transform hand poses relative to end fixture + output_data.left_hand_pose_target = transform_relative_pose( + recording_item.left_hand_pose_target, + recording_item.fixture_pose, + env.get_end_fixture().get_pose(), + )[0] + output_data.right_hand_pose_target = transform_relative_pose( + recording_item.right_hand_pose_target, + recording_item.fixture_pose, + env.get_end_fixture().get_pose(), + )[0] + output_data.left_hand_joint_positions_target = recording_item.left_hand_joint_positions_target + output_data.right_hand_joint_positions_target = recording_item.right_hand_joint_positions_target + + # Continue playback if orientation is within threshold + next_recording_step = recording_step + 1 if abs(delta_yaw) < config.angle_threshold else recording_step + + return next_recording_step, LocomanipulationSDGDataGenerationState.DROP_OFF_OBJECT + + +def populate_output_data( + env: LocomanipulationSDGEnv, + output_data: LocomanipulationSDGOutputData, + base_goal: RelativePose, + base_goal_approach: RelativePose, + base_path: torch.Tensor, +) -> None: + """Populate remaining output data fields. + + Args: + env: The environment + output_data: Output data to populate + base_goal: Final goal pose + base_goal_approach: Approach goal pose + base_path: Planned navigation path + """ + output_data.base_pose = env.get_base().get_pose() + output_data.object_pose = env.get_object().get_pose() + output_data.start_fixture_pose = env.get_start_fixture().get_pose() + output_data.end_fixture_pose = env.get_end_fixture().get_pose() + output_data.base_goal_pose = base_goal.get_pose() + output_data.base_goal_approach_pose = base_goal_approach.get_pose() + output_data.base_path = base_path + + # Collect obstacle poses + obstacle_poses = [] + for obstacle in env.get_obstacle_fixtures(): + obstacle_poses.append(obstacle.get_pose()) + if obstacle_poses: + output_data.obstacle_fixture_poses = torch.cat(obstacle_poses, dim=0)[None, :] + else: + output_data.obstacle_fixture_poses = torch.empty((1, 0, 7)) # Empty tensor with correct shape + + +def replay( + env: LocomanipulationSDGEnv, + input_episode_data: EpisodeData, + lift_step: int, + navigate_step: int, + draw_visualization: bool = False, + angular_gain: float = 2.0, + linear_gain: float = 1.0, + linear_max: float = 1.0, + distance_threshold: float = 0.1, + following_offset: float = 0.6, + angle_threshold: float = 0.2, + approach_distance: float = 1.0, + randomize_placement: bool = True, +) -> None: + """Replay a locomanipulation SDG episode with state machine control. + + This function implements a state machine for locomanipulation SDG, where the robot: + 1. Grasps an object at the start position + 2. Lifts the object while stationary + 3. Navigates with the object to an approach position + 4. Approaches the final goal position + 5. Places the object at the end position + + Args: + env: The locomanipulation SDG environment + input_episode_data: Static manipulation episode data to replay + lift_step: Recording step where lifting phase begins + navigate_step: Recording step where navigation phase begins + draw_visualization: Whether to visualize occupancy map and path + angular_gain: Proportional gain for angular velocity control + linear_gain: Proportional gain for linear velocity control + linear_max: Maximum linear velocity (m/s) + distance_threshold: Distance threshold for state transitions (m) + following_offset: Look-ahead distance for path following (m) + angle_threshold: Angular threshold for orientation control (rad) + approach_distance: Buffer distance from final goal (m) + randomize_placement: Whether to randomize obstacle placement + """ + + # Initialize environment to starting state + env.reset_to(state=input_episode_data.get_initial_state(), env_ids=torch.tensor([0]), is_relative=True) + + # Create navigation control configuration + config = LocomanipulationSDGControlConfig( + angular_gain=angular_gain, + linear_gain=linear_gain, + linear_max=linear_max, + distance_threshold=distance_threshold, + following_offset=following_offset, + angle_threshold=angle_threshold, + approach_distance=approach_distance, + ) + + # Set up navigation scene and path planning + occupancy_map, base_path_helper, base_goal, base_goal_approach = setup_navigation_scene( + env, input_episode_data, approach_distance, randomize_placement + ) + + # Visualize occupancy map and path if requested + if draw_visualization: + occupancy_map_add_to_stage( + occupancy_map, + stage=omni.usd.get_context().get_stage(), + path="/OccupancyMap", + z_offset=0.01, + draw_path=base_path_helper.points, + ) + + # Initialize state machine + output_data = LocomanipulationSDGOutputData() + current_state = LocomanipulationSDGDataGenerationState.GRASP_OBJECT + recording_step = 0 + + # Main simulation loop with state machine + while simulation_app.is_running() and not simulation_app.is_exiting(): + print(f"Current state: {current_state.name}, Recording step: {recording_step}") + + # Execute state-specific logic using helper functions + if current_state == LocomanipulationSDGDataGenerationState.GRASP_OBJECT: + recording_step, current_state = handle_grasp_state( + env, input_episode_data, recording_step, lift_step, output_data + ) + + elif current_state == LocomanipulationSDGDataGenerationState.LIFT_OBJECT: + recording_step, current_state = handle_lift_state( + env, input_episode_data, recording_step, navigate_step, output_data + ) + + elif current_state == LocomanipulationSDGDataGenerationState.NAVIGATE: + current_state = handle_navigate_state( + env, input_episode_data, recording_step, base_path_helper, base_goal_approach, config, output_data + ) + + elif current_state == LocomanipulationSDGDataGenerationState.APPROACH: + current_state = handle_approach_state( + env, input_episode_data, recording_step, base_goal, config, output_data + ) + + elif current_state == LocomanipulationSDGDataGenerationState.DROP_OFF_OBJECT: + recording_step, next_state = handle_drop_off_state( + env, input_episode_data, recording_step, base_goal, config, output_data + ) + if next_state is None: # End of episode data + break + current_state = next_state + + # Populate additional output data fields + populate_output_data(env, output_data, base_goal, base_goal_approach, base_path_helper.points) + + # Attach output data to environment for recording + env._locomanipulation_sdg_output_data = output_data + + # Build and execute action + action = env.build_action_vector( + base_velocity_target=output_data.base_velocity_target, + left_hand_joint_positions_target=output_data.left_hand_joint_positions_target, + right_hand_joint_positions_target=output_data.right_hand_joint_positions_target, + left_hand_pose_target=output_data.left_hand_pose_target, + right_hand_pose_target=output_data.right_hand_pose_target, + ) + + env.step(action) + + +if __name__ == "__main__": + with torch.no_grad(): + # Create environment + if args_cli.task is not None: + env_name = args_cli.task.split(":")[-1] + if env_name is None: + raise ValueError("Task/env name was not specified nor found in the dataset.") + + env_cfg = parse_env_cfg(env_name, device=args_cli.device, num_envs=1) + env_cfg.sim.device = "cpu" + env_cfg.recorders.dataset_export_dir_path = os.path.dirname(args_cli.output_file) + env_cfg.recorders.dataset_filename = os.path.basename(args_cli.output_file) + + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Load input data + input_dataset_file_handler = HDF5DatasetFileHandler() + input_dataset_file_handler.open(args_cli.dataset) + + for i in range(args_cli.num_runs): + if args_cli.demo is None: + demo = random.choice(list(input_dataset_file_handler.get_episode_names())) + else: + demo = args_cli.demo + + input_episode_data = input_dataset_file_handler.load_episode(demo, args_cli.device) + + replay( + env=env, + input_episode_data=input_episode_data, + lift_step=args_cli.lift_step, + navigate_step=args_cli.navigate_step, + draw_visualization=args_cli.draw_visualization, + angular_gain=args_cli.angular_gain, + linear_gain=args_cli.linear_gain, + linear_max=args_cli.linear_max, + distance_threshold=args_cli.distance_threshold, + following_offset=args_cli.following_offset, + angle_threshold=args_cli.angle_threshold, + approach_distance=args_cli.approach_distance, + randomize_placement=args_cli.randomize_placement, + ) + + env.reset() # FIXME: hack to handle missing final recording + env.close() + + simulation_app.close() diff --git a/scripts/imitation_learning/locomanipulation_sdg/plot_navigation_trajectory.py b/scripts/imitation_learning/locomanipulation_sdg/plot_navigation_trajectory.py new file mode 100644 index 0000000000000000000000000000000000000000..e65059d7d65aa545e7159f3a6d76f6e33803010c --- /dev/null +++ b/scripts/imitation_learning/locomanipulation_sdg/plot_navigation_trajectory.py @@ -0,0 +1,110 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to visualize navigation datasets. + +Loads a navigation dataset and generates plots showing paths, poses and obstacles. + +Args: + dataset: Path to the HDF5 dataset file containing recorded demonstrations. + output_dir: Directory path where visualization plots will be saved. + figure_size: Size of the generated figures (width, height). + demo_filter: If provided, only visualize specific demo(s). Can be a single demo name or comma-separated list. +""" + +import argparse +import os + +import h5py +import matplotlib.pyplot as plt + + +def main(): + """Main function to process dataset and generate visualizations.""" + # add argparse arguments + parser = argparse.ArgumentParser( + description="Visualize navigation dataset from locomanipulation sdg demonstrations." + ) + parser.add_argument( + "--input_file", type=str, help="Path to the HDF5 dataset file containing recorded demonstrations." + ) + parser.add_argument("--output_dir", type=str, help="Directory path where visualization plots will be saved.") + parser.add_argument( + "--figure_size", + type=int, + nargs=2, + default=[20, 20], + help="Size of the generated figures (width, height). Default: [20, 20]", + ) + parser.add_argument( + "--demo_filter", + type=str, + default=None, + help="If provided, only visualize specific demo(s). Can be a single demo name or comma-separated list.", + ) + + # parse the arguments + args = parser.parse_args() + + # Validate inputs + if not os.path.exists(args.input_file): + raise FileNotFoundError(f"Dataset file not found: {args.input_file}") + + # Create output directory if it doesn't exist + os.makedirs(args.output_dir, exist_ok=True) + + # Load dataset + dataset = h5py.File(args.input_file, "r") + + demos = list(dataset["data"].keys()) + + # Filter demos if specified + if args.demo_filter: + filter_demos = [d.strip() for d in args.demo_filter.split(",")] + demos = [d for d in demos if d in filter_demos] + if not demos: + print(f"Warning: No demos found matching filter '{args.demo_filter}'") + return + + print(f"Visualizing {len(demos)} demonstrations...") + + for i, demo in enumerate(demos): + print(f"Processing demo {i + 1}/{len(demos)}: {demo}") + + replay_data = dataset["data"][demo]["locomanipulation_sdg_output_data"] + path = replay_data["base_path"] + base_pose = replay_data["base_pose"] + object_pose = replay_data["object_pose"] + start_pose = replay_data["start_fixture_pose"] + end_pose = replay_data["end_fixture_pose"] + obstacle_poses = replay_data["obstacle_fixture_poses"] + + plt.figure(figsize=args.figure_size) + plt.plot(path[0, :, 0], path[0, :, 1], "r-", label="Target Path", linewidth=2) + plt.plot(base_pose[:, 0], base_pose[:, 1], "g--", label="Base Pose", linewidth=2) + plt.plot(object_pose[:, 0], object_pose[:, 1], "b--", label="Object Pose", linewidth=2) + plt.plot(obstacle_poses[0, :, 0], obstacle_poses[0, :, 1], "ro", label="Obstacles", markersize=8) + + # Add start and end markers + plt.plot(start_pose[0, 0], start_pose[0, 1], "gs", label="Start", markersize=12) + plt.plot(end_pose[0, 0], end_pose[0, 1], "rs", label="End", markersize=12) + + plt.legend(loc="upper right", ncol=1, fontsize=12) + plt.axis("equal") + plt.grid(True, alpha=0.3) + plt.title(f"Navigation Visualization - {demo}", fontsize=16) + plt.xlabel("X Position (m)", fontsize=14) + plt.ylabel("Y Position (m)", fontsize=14) + + output_path = os.path.join(args.output_dir, f"{demo}.png") + plt.savefig(output_path, dpi=150, bbox_inches="tight") + plt.close() # Close the figure to free memory + + dataset.close() + print(f"Visualization complete! Plots saved to: {args.output_dir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/imitation_learning/robomimic/play.py b/scripts/imitation_learning/robomimic/play.py new file mode 100644 index 0000000000000000000000000000000000000000..f663bc3acb2bf89844a9c32e498655d984b5d655 --- /dev/null +++ b/scripts/imitation_learning/robomimic/play.py @@ -0,0 +1,194 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=800, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version + # installed by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import copy +import random + +import gymnasium as gym +import numpy as np +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +import torch + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg + + +def rollout(policy, env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + policy.start_episode() + obs_dict, _ = env.reset() + traj = dict(actions=[], obs=[], next_obs=[]) + + for i in range(horizon): + # Prepare observations + obs = copy.deepcopy(obs_dict["policy"]) + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + traj["obs"].append(obs) + + # Compute actions + actions = policy(obs) + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + obs = obs_dict["policy"] + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + # Set termination conditions + env_cfg.terminations.time_out = None + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed + torch.manual_seed(args_cli.seed) + np.random.seed(args_cli.seed) + random.seed(args_cli.seed) + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # Run policy + results = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=args_cli.checkpoint, device=device) + terminated, traj = rollout(policy, env, success_term, args_cli.horizon, device) + results.append(terminated) + print(f"[INFO] Trial {trial}: {terminated}\n") + + print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") + print(f"Success rate: {results.count(True) / len(results)}") + print(f"Trial Results: {results}\n") + + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/robust_eval.py b/scripts/imitation_learning/robomimic/robust_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..0e1e9014ba922fe047ad32c67c7ca1fbfffcb814 --- /dev/null +++ b/scripts/imitation_learning/robomimic/robust_eval.py @@ -0,0 +1,335 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to evaluate a trained policy from robomimic across multiple evaluation settings. + +This script loads a trained robomimic policy and evaluates it in an Isaac Lab environment +across multiple evaluation settings (lighting, textures, etc.) and seeds. It saves the results +to a specified output directory. + +Args: + task: Name of the environment. + input_dir: Directory containing the model checkpoints to evaluate. + horizon: Step horizon of each rollout. + num_rollouts: Number of rollouts per model per setting. + num_seeds: Number of random seeds to evaluate. + seeds: Optional list of specific seeds to use instead of random ones. + log_dir: Directory to write results to. + log_file: Name of the output file. + output_vis_file: File path to export recorded episodes. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. + disable_fabric: Whether to disable fabric and use USD I/O operations. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--input_dir", type=str, default=None, help="Directory containing models to evaluate.") +parser.add_argument( + "--start_epoch", type=int, default=100, help="Epoch of the checkpoint to start the evaluation from." +) +parser.add_argument("--horizon", type=int, default=400, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=15, help="Number of rollouts for each setting.") +parser.add_argument("--num_seeds", type=int, default=3, help="Number of random seeds to evaluate.") +parser.add_argument("--seeds", nargs="+", type=int, default=None, help="List of specific seeds to use.") +parser.add_argument( + "--log_dir", type=str, default="/tmp/policy_evaluation_results", help="Directory to write results to." +) +parser.add_argument("--log_file", type=str, default="results", help="Name of output file.") +parser.add_argument( + "--output_vis_file", type=str, default="visuals.hdf5", help="File path to export recorded episodes." +) +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed + # by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import copy +import os +import pathlib +import random + +import gymnasium as gym +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +import torch + +from isaaclab_tasks.utils import parse_env_cfg + + +def rollout(policy, env: gym.Env, success_term, horizon: int, device: torch.device) -> tuple[bool, dict]: + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimic policy to evaluate. + env: The environment to evaluate in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + args_cli: Command line arguments containing normalization factors. + + Returns: + terminated: Whether the rollout terminated successfully. + traj: The trajectory of the rollout. + """ + policy.start_episode() + obs_dict, _ = env.reset() + traj = dict(actions=[], obs=[], next_obs=[]) + + for _ in range(horizon): + # Prepare policy observations + obs = copy.deepcopy(obs_dict["policy"]) + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + traj["obs"].append(obs) + + # Compute actions + actions = policy(obs) + + # Unnormalize actions if normalization factors are provided + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + obs = obs_dict["policy"] + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj + + +def evaluate_model( + model_path: str, + env: gym.Env, + device: torch.device, + success_term, + num_rollouts: int, + horizon: int, + seed: int, + output_file: str, +) -> float: + """Evaluate a single model checkpoint across multiple rollouts. + + Args: + model_path: Path to the model checkpoint. + env: The environment to evaluate in. + device: The device to run the policy on. + num_rollouts: Number of rollouts to perform. + horizon: Step horizon of each rollout. + seed: Random seed to use. + output_file: File to write results to. + + Returns: + float: Success rate of the model + """ + # Set seed + torch.manual_seed(seed) + env.seed(seed) + random.seed(seed) + + # Load policy + policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=model_path, device=device, verbose=False) + + # Run policy + results = [] + for trial in range(num_rollouts): + print(f"[Model: {os.path.basename(model_path)}] Starting trial {trial}") + terminated, _ = rollout(policy, env, success_term, horizon, device) + results.append(terminated) + with open(output_file, "a") as file: + file.write(f"[Model: {os.path.basename(model_path)}] Trial {trial}: {terminated}\n") + print(f"[Model: {os.path.basename(model_path)}] Trial {trial}: {terminated}") + + # Calculate and log results + success_rate = results.count(True) / len(results) + with open(output_file, "a") as file: + file.write( + f"[Model: {os.path.basename(model_path)}] Successful trials: {results.count(True)}, out of" + f" {len(results)} trials\n" + ) + file.write(f"[Model: {os.path.basename(model_path)}] Success rate: {success_rate}\n") + file.write(f"[Model: {os.path.basename(model_path)}] Results: {results}\n") + file.write("-" * 80 + "\n\n") + + print( + f"\n[Model: {os.path.basename(model_path)}] Successful trials: {results.count(True)}, out of" + f" {len(results)} trials" + ) + print(f"[Model: {os.path.basename(model_path)}] Success rate: {success_rate}\n") + print(f"[Model: {os.path.basename(model_path)}] Results: {results}\n") + + return success_rate + + +def main() -> None: + """Run evaluation of trained policies from robomimic with Isaac Lab environment.""" + # Parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + # Set termination conditions + env_cfg.terminations.time_out = None + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Set evaluation settings + env_cfg.eval_mode = True + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=False) + + # Get model checkpoints + model_checkpoints = [f.name for f in os.scandir(args_cli.input_dir) if f.is_file()] + + # Set up seeds + seeds = random.sample(range(0, 10000), args_cli.num_seeds) if args_cli.seeds is None else args_cli.seeds + + # Define evaluation settings + settings = ["vanilla", "light_intensity", "light_color", "light_texture", "table_texture", "robot_texture", "all"] + + # Create log directory if it doesn't exist + os.makedirs(args_cli.log_dir, exist_ok=True) + + # Evaluate each seed + for seed in seeds: + output_path = os.path.join(args_cli.log_dir, f"{args_cli.log_file}_seed_{seed}") + path = pathlib.Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + # Initialize results summary + results_summary = dict() + results_summary["overall"] = {} + for setting in settings: + results_summary[setting] = {} + + with open(output_path, "w") as file: + # Evaluate each setting + for setting in settings: + env.cfg.eval_type = setting + + file.write(f"Evaluation setting: {setting}\n") + file.write("=" * 80 + "\n\n") + + print(f"Evaluation setting: {setting}") + print("=" * 80) + + # Evaluate each model + for model in model_checkpoints: + # Skip early checkpoints + model_epoch = int(model.split(".")[0].split("_")[-1]) + if model_epoch < args_cli.start_epoch: + continue + + model_path = os.path.join(args_cli.input_dir, model) + success_rate = evaluate_model( + model_path=model_path, + env=env, + device=device, + success_term=success_term, + num_rollouts=args_cli.num_rollouts, + horizon=args_cli.horizon, + seed=seed, + output_file=output_path, + ) + + # Store results + results_summary[setting][model] = success_rate + if model not in results_summary["overall"].keys(): + results_summary["overall"][model] = 0.0 + results_summary["overall"][model] += success_rate + + env.reset() + + file.write("=" * 80 + "\n\n") + env.reset() + + # Calculate overall success rates + for model in results_summary["overall"].keys(): + results_summary["overall"][model] /= len(settings) + + # Write final summary + file.write("\nResults Summary (success rate):\n") + for setting in results_summary.keys(): + file.write(f"\nSetting: {setting}\n") + for model in results_summary[setting].keys(): + file.write(f"{model}: {results_summary[setting][model]}\n") + max_key = max(results_summary[setting], key=results_summary[setting].get) + file.write( + f"\nBest model for setting {setting} is {max_key} with success rate" + f" {results_summary[setting][max_key]}\n" + ) + + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/train.py b/scripts/imitation_learning/robomimic/train.py new file mode 100644 index 0000000000000000000000000000000000000000..11dd9814de6d76157af82a3ab566861ce3d84230 --- /dev/null +++ b/scripts/imitation_learning/robomimic/train.py @@ -0,0 +1,457 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# MIT License +# +# Copyright (c) 2021 Stanford Vision and Learning Lab +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +""" +The main entry point for training policies from pre-collected data. + +This script loads dataset(s), creates a model based on the algorithm specified, +and trains the model. It supports training on various environments with multiple +algorithms from robomimic. + +Args: + algo: Name of the algorithm to run. + task: Name of the environment. + name: If provided, override the experiment name defined in the config. + dataset: If provided, override the dataset path defined in the config. + log_dir: Directory to save logs. + normalize_training_actions: Whether to normalize actions in the training data. + +This file has been modified from the original robomimic version to integrate with IsaacLab. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import argparse +import importlib +import json +import os +import shutil +import sys +import time +import traceback +from collections import OrderedDict + +import gymnasium as gym +import h5py +import numpy as np +import psutil +import robomimic.utils.env_utils as EnvUtils +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.train_utils as TrainUtils +import torch +from robomimic.algo import algo_factory +from robomimic.config import Config, config_factory +from robomimic.utils.log_utils import DataLogger, PrintLogger +from torch.utils.data import DataLoader + +import isaaclab_tasks # noqa: F401 +import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 +import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + + +def normalize_hdf5_actions(config: Config, log_dir: str) -> str: + """Normalizes actions in hdf5 dataset to [-1, 1] range. + + Args: + config: The configuration object containing dataset path. + log_dir: Directory to save normalization parameters. + + Returns: + Path to the normalized dataset. + """ + base, ext = os.path.splitext(config.train.data) + normalized_path = base + "_normalized" + ext + + # Copy the original dataset + print(f"Creating normalized dataset at {normalized_path}") + shutil.copyfile(config.train.data, normalized_path) + + # Open the new dataset and normalize the actions + with h5py.File(normalized_path, "r+") as f: + dataset_paths = [f"/data/demo_{str(i)}/actions" for i in range(len(f["data"].keys()))] + + # Compute the min and max of the dataset + dataset = np.array(f[dataset_paths[0]]).flatten() + for i, path in enumerate(dataset_paths): + if i != 0: + data = np.array(f[path]).flatten() + dataset = np.append(dataset, data) + + max = np.max(dataset) + min = np.min(dataset) + + # Normalize the actions + for i, path in enumerate(dataset_paths): + data = np.array(f[path]) + normalized_data = 2 * ((data - min) / (max - min)) - 1 # Scale to [-1, 1] range + del f[path] + f[path] = normalized_data + + # Save the min and max values to log directory + with open(os.path.join(log_dir, "normalization_params.txt"), "w") as f: + f.write(f"min: {min}\n") + f.write(f"max: {max}\n") + + return normalized_path + + +def train(config: Config, device: str, log_dir: str, ckpt_dir: str, video_dir: str): + """Train a model using the algorithm specified in config. + + Args: + config: Configuration object. + device: PyTorch device to use for training. + log_dir: Directory to save logs. + ckpt_dir: Directory to save checkpoints. + video_dir: Directory to save videos. + """ + # first set seeds + np.random.seed(config.train.seed) + torch.manual_seed(config.train.seed) + + print("\n============= New Training Run with Config =============") + print(config) + print("") + + print(f">>> Saving logs into directory: {log_dir}") + print(f">>> Saving checkpoints into directory: {ckpt_dir}") + print(f">>> Saving videos into directory: {video_dir}") + + if config.experiment.logging.terminal_output_to_txt: + # log stdout and stderr to a text file + logger = PrintLogger(os.path.join(log_dir, "log.txt")) + sys.stdout = logger + sys.stderr = logger + + # read config to set up metadata for observation modalities (e.g. detecting rgb observations) + ObsUtils.initialize_obs_utils_with_config(config) + + # make sure the dataset exists + dataset_path = os.path.expanduser(config.train.data) + if not os.path.exists(dataset_path): + raise FileNotFoundError(f"Dataset at provided path {dataset_path} not found!") + + # load basic metadata from training file + print("\n============= Loaded Environment Metadata =============") + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) + shape_meta = FileUtils.get_shape_metadata_from_dataset( + dataset_path=config.train.data, all_obs_keys=config.all_obs_keys, verbose=True + ) + + if config.experiment.env is not None: + env_meta["env_name"] = config.experiment.env + print("=" * 30 + "\n" + "Replacing Env to {}\n".format(env_meta["env_name"]) + "=" * 30) + + # create environment + envs = OrderedDict() + if config.experiment.rollout.enabled: + # create environments for validation runs + env_names = [env_meta["env_name"]] + + if config.experiment.additional_envs is not None: + for name in config.experiment.additional_envs: + env_names.append(name) + + for env_name in env_names: + env = EnvUtils.create_env_from_metadata( + env_meta=env_meta, + env_name=env_name, + render=False, + render_offscreen=config.experiment.render_video, + use_image_obs=shape_meta["use_images"], + ) + envs[env.name] = env + print(envs[env.name]) + + print("") + + # setup for a new training run + data_logger = DataLogger(log_dir, config=config, log_tb=config.experiment.logging.log_tb) + model = algo_factory( + algo_name=config.algo_name, + config=config, + obs_key_shapes=shape_meta["all_shapes"], + ac_dim=shape_meta["ac_dim"], + device=device, + ) + + # save the config as a json file + with open(os.path.join(log_dir, "..", "config.json"), "w") as outfile: + json.dump(config, outfile, indent=4) + + print("\n============= Model Summary =============") + print(model) # print model summary + print("") + + # load training data + trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) + train_sampler = trainset.get_dataset_sampler() + print("\n============= Training Dataset =============") + print(trainset) + print("") + + # maybe retrieve statistics for normalizing observations + obs_normalization_stats = None + if config.train.hdf5_normalize_obs: + obs_normalization_stats = trainset.get_obs_normalization_stats() + + # initialize data loaders + train_loader = DataLoader( + dataset=trainset, + sampler=train_sampler, + batch_size=config.train.batch_size, + shuffle=(train_sampler is None), + num_workers=config.train.num_data_workers, + drop_last=True, + ) + + if config.experiment.validate: + # cap num workers for validation dataset at 1 + num_workers = min(config.train.num_data_workers, 1) + valid_sampler = validset.get_dataset_sampler() + valid_loader = DataLoader( + dataset=validset, + sampler=valid_sampler, + batch_size=config.train.batch_size, + shuffle=(valid_sampler is None), + num_workers=num_workers, + drop_last=True, + ) + else: + valid_loader = None + + # main training loop + best_valid_loss = None + last_ckpt_time = time.time() + + # number of learning steps per epoch (defaults to a full dataset pass) + train_num_steps = config.experiment.epoch_every_n_steps + valid_num_steps = config.experiment.validation_epoch_every_n_steps + + for epoch in range(1, config.train.num_epochs + 1): # epoch numbers start at 1 + step_log = TrainUtils.run_epoch(model=model, data_loader=train_loader, epoch=epoch, num_steps=train_num_steps) + model.on_epoch_end(epoch) + + # setup checkpoint path + epoch_ckpt_name = f"model_epoch_{epoch}" + + # check for recurring checkpoint saving conditions + should_save_ckpt = False + if config.experiment.save.enabled: + time_check = (config.experiment.save.every_n_seconds is not None) and ( + time.time() - last_ckpt_time > config.experiment.save.every_n_seconds + ) + epoch_check = ( + (config.experiment.save.every_n_epochs is not None) + and (epoch > 0) + and (epoch % config.experiment.save.every_n_epochs == 0) + ) + epoch_list_check = epoch in config.experiment.save.epochs + last_epoch_check = epoch == config.train.num_epochs + should_save_ckpt = time_check or epoch_check or epoch_list_check or last_epoch_check + ckpt_reason = None + if should_save_ckpt: + last_ckpt_time = time.time() + ckpt_reason = "time" + + print(f"Train Epoch {epoch}") + print(json.dumps(step_log, sort_keys=True, indent=4)) + for k, v in step_log.items(): + if k.startswith("Time_"): + data_logger.record(f"Timing_Stats/Train_{k[5:]}", v, epoch) + else: + data_logger.record(f"Train/{k}", v, epoch) + + # Evaluate the model on validation set + if config.experiment.validate: + with torch.no_grad(): + step_log = TrainUtils.run_epoch( + model=model, data_loader=valid_loader, epoch=epoch, validate=True, num_steps=valid_num_steps + ) + for k, v in step_log.items(): + if k.startswith("Time_"): + data_logger.record(f"Timing_Stats/Valid_{k[5:]}", v, epoch) + else: + data_logger.record(f"Valid/{k}", v, epoch) + + print(f"Validation Epoch {epoch}") + print(json.dumps(step_log, sort_keys=True, indent=4)) + + # save checkpoint if achieve new best validation loss + valid_check = "Loss" in step_log + if valid_check and (best_valid_loss is None or (step_log["Loss"] <= best_valid_loss)): + best_valid_loss = step_log["Loss"] + if config.experiment.save.enabled and config.experiment.save.on_best_validation: + epoch_ckpt_name += f"_best_validation_{best_valid_loss}" + should_save_ckpt = True + ckpt_reason = "valid" if ckpt_reason is None else ckpt_reason + + # Save model checkpoints based on conditions (success rate, validation loss, etc) + if should_save_ckpt: + TrainUtils.save_model( + model=model, + config=config, + env_meta=env_meta, + shape_meta=shape_meta, + ckpt_path=os.path.join(ckpt_dir, epoch_ckpt_name + ".pth"), + obs_normalization_stats=obs_normalization_stats, + ) + + # Finally, log memory usage in MB + process = psutil.Process(os.getpid()) + mem_usage = int(process.memory_info().rss / 1000000) + data_logger.record("System/RAM Usage (MB)", mem_usage, epoch) + print(f"\nEpoch {epoch} Memory Usage: {mem_usage} MB\n") + + # terminate logging + data_logger.close() + + +def main(args: argparse.Namespace): + """Train a model on a task using a specified algorithm. + + Args: + args: Command line arguments. + """ + # load config + if args.task is not None: + # obtain the configuration entry point + cfg_entry_point_key = f"robomimic_{args.algo}_cfg_entry_point" + task_name = args.task.split(":")[-1] + + print(f"Loading configuration for task: {task_name}") + print(gym.envs.registry.keys()) + print(" ") + cfg_entry_point_file = gym.spec(task_name).kwargs.pop(cfg_entry_point_key) + # check if entry point exists + if cfg_entry_point_file is None: + raise ValueError( + f"Could not find configuration for the environment: '{task_name}'." + f" Please check that the gym registry has the entry point: '{cfg_entry_point_key}'." + ) + + # resolve module path if needed + if ":" in cfg_entry_point_file: + mod_name, file_name = cfg_entry_point_file.split(":") + mod = importlib.import_module(mod_name) + if mod.__file__ is None: + raise ValueError(f"Could not find module file for: '{mod_name}'") + mod_path = os.path.dirname(mod.__file__) + config_file = os.path.join(mod_path, file_name) + else: + config_file = cfg_entry_point_file + + with open(config_file) as f: + ext_cfg = json.load(f) + config = config_factory(ext_cfg["algo_name"]) + # update config with external json - this will throw errors if + # the external config has keys not present in the base algo config + with config.values_unlocked(): + config.update(ext_cfg) + else: + raise ValueError("Please provide a task name through CLI arguments.") + + if args.dataset is not None: + config.train.data = args.dataset + + if args.name is not None: + config.experiment.name = args.name + + if args.epochs is not None: + config.train.num_epochs = args.epochs + + # change location of experiment directory + config.train.output_dir = os.path.abspath(os.path.join("./logs", args.log_dir, args.task)) + + log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) + + if args.normalize_training_actions: + config.train.data = normalize_hdf5_actions(config, log_dir) + + # get torch device + device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) + + config.lock() + + # catch error during training and print it + res_str = "finished run successfully!" + try: + train(config, device, log_dir, ckpt_dir, video_dir) + except Exception as e: + res_str = f"run failed with error:\n{e}\n\n{traceback.format_exc()}" + print(res_str) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # Experiment Name (for tensorboard, saving models, etc.) + parser.add_argument( + "--name", + type=str, + default=None, + help="(optional) if provided, override the experiment name defined in the config", + ) + + # Dataset path, to override the one in the config + parser.add_argument( + "--dataset", + type=str, + default=None, + help="(optional) if provided, override the dataset path defined in the config", + ) + + parser.add_argument("--task", type=str, default=None, help="Name of the task.") + parser.add_argument("--algo", type=str, default=None, help="Name of the algorithm.") + parser.add_argument("--log_dir", type=str, default="robomimic", help="Path to log directory") + parser.add_argument("--normalize_training_actions", action="store_true", default=False, help="Normalize actions") + parser.add_argument( + "--epochs", + type=int, + default=None, + help=( + "Optional: Number of training epochs. If specified, overrides the number of epochs from the JSON training" + " config." + ), + ) + + args = parser.parse_args() + + # run training + main(args) + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/ray/cluster_configs/Dockerfile b/scripts/reinforcement_learning/ray/cluster_configs/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..e75b756057fe4fca45b17cb68c04a6ba96ba437b --- /dev/null +++ b/scripts/reinforcement_learning/ray/cluster_configs/Dockerfile @@ -0,0 +1,23 @@ +FROM isaac-lab-base:latest + +# WGet is needed so that GCS or other cloud providers can mark the container as ready. +# Otherwise the Ray liveliness checks fail. +RUN apt-get update && apt-get install wget + +# Set NVIDIA paths +ENV PATH="/usr/local/nvidia/bin:$PATH" +ENV LD_LIBRARY_PATH="/usr/local/nvidia/lib64" + +# Link NVIDIA binaries +RUN ln -sf /usr/local/nvidia/bin/nvidia* /usr/bin + +# Install Ray and configure it +RUN /workspace/isaaclab/_isaac_sim/python.sh -m pip install "ray[default, tune]"==2.31.0 && \ +sed -i "1i $(echo "#!/workspace/isaaclab/_isaac_sim/python.sh")" \ +/isaac-sim/kit/python/bin/ray && ln -s /isaac-sim/kit/python/bin/ray /usr/local/bin/ray + +# Install tuning dependencies +RUN /workspace/isaaclab/_isaac_sim/python.sh -m pip install optuna bayesian-optimization + +# Install MLflow for logging +RUN /workspace/isaaclab/_isaac_sim/python.sh -m pip install mlflow diff --git a/scripts/reinforcement_learning/ray/cluster_configs/google_cloud/kuberay.yaml.jinja b/scripts/reinforcement_learning/ray/cluster_configs/google_cloud/kuberay.yaml.jinja new file mode 100644 index 0000000000000000000000000000000000000000..40ccccf7c683f5f5750486ca7c135afb552fa88b --- /dev/null +++ b/scripts/reinforcement_learning/ray/cluster_configs/google_cloud/kuberay.yaml.jinja @@ -0,0 +1,203 @@ +# Jinja is used for templating here as full helm setup is excessive for application +apiVersion: ray.io/v1alpha1 +kind: RayCluster +metadata: + name: {{ name }} + namespace: {{ namespace }} +spec: + rayVersion: "2.8.0" + enableInTreeAutoscaling: true + autoscalerOptions: + upscalingMode: Default + idleTimeoutSeconds: 120 + imagePullPolicy: Always + securityContext: {} + envFrom: [] + + headGroupSpec: + rayStartParams: + block: "true" + dashboard-host: 0.0.0.0 + dashboard-port: "8265" + port: "6379" + include-dashboard: "true" + ray-debugger-external: "true" + object-manager-port: "8076" + num-gpus: "0" + num-cpus: "0" # prevent scheduling jobs to the head node - workers only + headService: + apiVersion: v1 + kind: Service + metadata: + name: {{ name }}-head + spec: + type: LoadBalancer + template: + metadata: + labels: + app.kubernetes.io/instance: tuner + app.kubernetes.io/name: kuberay + cloud.google.com/gke-ray-node-type: head + spec: + serviceAccountName: {{ service_account_name }} + affinity: {} + securityContext: + fsGroup: 100 + containers: + - env: + image: {{ image }} + imagePullPolicy: Always + name: head + resources: + limits: + cpu: "{{ num_head_cpu }}" + memory: {{ head_ram_gb }}G + nvidia.com/gpu: "0" + requests: + cpu: "{{ num_head_cpu }}" + memory: {{ head_ram_gb }}G + nvidia.com/gpu: "0" + securityContext: {} + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + command: ["/bin/bash", "-c", "ray start --head --port=6379 --object-manager-port=8076 --dashboard-host=0.0.0.0 --dashboard-port=8265 --include-dashboard=true && tail -f /dev/null"] + - image: fluent/fluent-bit:1.9.6 + name: fluentbit + resources: + limits: + cpu: 100m + memory: 128Mi + requests: + cpu: 100m + memory: 128Mi + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + imagePullSecrets: [] + nodeSelector: + iam.gke.io/gke-metadata-server-enabled: "true" + volumes: + - configMap: + name: fluentbit-config + name: fluentbit-config + - name: ray-logs + emptyDir: {} + + workerGroupSpecs: + {% for it in range(gpu_per_worker|length) %} + - groupName: "{{ worker_accelerator[it] }}x{{ gpu_per_worker[it] }}-cpu-{{ cpu_per_worker[it] }}-ram-gb-{{ ram_gb_per_worker[it] }}" + replicas: {{ num_workers[it] }} + maxReplicas: {{ num_workers[it] }} + minReplicas: {{ num_workers[it] }} + rayStartParams: + block: "true" + ray-debugger-external: "true" + replicas: "{{num_workers[it]}}" + template: + metadata: + annotations: {} + labels: + app.kubernetes.io/instance: tuner + app.kubernetes.io/name: kuberay + cloud.google.com/gke-ray-node-type: worker + spec: + serviceAccountName: {{ service_account_name }} + affinity: {} + securityContext: + fsGroup: 100 + containers: + - env: + - name: NVIDIA_VISIBLE_DEVICES + value: "all" + - name: NVIDIA_DRIVER_CAPABILITIES + value: "compute,utility" + + image: {{ image }} + imagePullPolicy: Always + name: ray-worker + resources: + limits: + cpu: "{{ cpu_per_worker[it] }}" + memory: {{ ram_gb_per_worker[it] }}G + nvidia.com/gpu: "{{ gpu_per_worker[it] }}" + requests: + cpu: "{{ cpu_per_worker[it] }}" + memory: {{ ram_gb_per_worker[it] }}G + nvidia.com/gpu: "{{ gpu_per_worker[it] }}" + securityContext: {} + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + command: ["/bin/bash", "-c", "ray start --address={{name}}-head.{{ namespace }}.svc.cluster.local:6379 && tail -f /dev/null"] + - image: fluent/fluent-bit:1.9.6 + name: fluentbit + resources: + limits: + cpu: 100m + memory: 128Mi + requests: + cpu: 100m + memory: 128Mi + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + + imagePullSecrets: [] + nodeSelector: + cloud.google.com/gke-accelerator: {{ worker_accelerator[it] }} + iam.gke.io/gke-metadata-server-enabled: "true" + tolerations: + - key: "nvidia.com/gpu" + operator: "Exists" + effect: "NoSchedule" + volumes: + - configMap: + name: fluentbit-config + name: fluentbit-config + - name: ray-logs + emptyDir: {} + {% endfor %} + +--- +# ML Flow Server - for fetching logs +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{name}}-mlflow + namespace: {{ namespace }} +spec: + replicas: 1 + selector: + matchLabels: + app: mlflow + template: + metadata: + labels: + app: mlflow + spec: + containers: + - name: mlflow + image: ghcr.io/mlflow/mlflow:v2.9.2 + ports: + - containerPort: 5000 + command: ["mlflow"] + args: + - server + - --host=0.0.0.0 + - --port=5000 + - --backend-store-uri=sqlite:///mlflow.db +--- +# ML Flow Service (for port forwarding, kubectl port-forward service/{name}-mlflow 5000:5000) +apiVersion: v1 +kind: Service +metadata: + name: {{name}}-mlflow + namespace: {{ namespace }} +spec: + selector: + app: mlflow + ports: + - port: 5000 + targetPort: 5000 + type: ClusterIP diff --git a/scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py b/scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py new file mode 100644 index 0000000000000000000000000000000000000000..b7b3c5cf89e258aea16788d1daa9c74f19ab3f5b --- /dev/null +++ b/scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py @@ -0,0 +1,275 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import os +import re +import subprocess +import threading +import time +from concurrent.futures import ThreadPoolExecutor, as_completed + +""" +This script requires that kubectl is installed and KubeRay was used to create the cluster. + +Creates a config file containing ``name: address: http://:`` on +a new line for each cluster, and also fetches the MLFlow URI. + +Usage: + +.. code-block:: bash + + python3 scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py + # For options, supply -h arg +""" + + +def get_namespace() -> str: + """Get the current Kubernetes namespace from the context, fallback to default if not set""" + try: + namespace = ( + subprocess.check_output(["kubectl", "config", "view", "--minify", "--output", "jsonpath={..namespace}"]) + .decode() + .strip() + ) + if not namespace: + namespace = "default" + except subprocess.CalledProcessError: + namespace = "default" + return namespace + + +def get_pods(namespace: str = "default") -> list[tuple]: + """Get a list of all of the pods in the namespace""" + cmd = ["kubectl", "get", "pods", "-n", namespace, "--no-headers"] + output = subprocess.check_output(cmd).decode() + pods = [] + for line in output.strip().split("\n"): + fields = line.split() + pod_name = fields[0] + status = fields[2] + pods.append((pod_name, status)) + return pods + + +def get_clusters(pods: list, cluster_name_prefix: str) -> set: + """ + Get unique cluster name(s). Works for one or more clusters, based off of the number of head nodes. + Excludes MLflow deployments. + """ + clusters = set() + for pod_name, _ in pods: + # Skip MLflow pods + if "-mlflow" in pod_name: + continue + + match = re.match(r"(" + re.escape(cluster_name_prefix) + r"[-\w]+)", pod_name) + if match: + # Get base name without head/worker suffix (skip workers) + if "head" in pod_name: + base_name = match.group(1).split("-head")[0] + clusters.add(base_name) + return sorted(clusters) + + +def get_mlflow_info(namespace: str = None, cluster_prefix: str = "isaacray") -> str: + """ + Get MLflow service information if it exists in the namespace with the given prefix. + Only works for a single cluster instance. + Args: + namespace: Kubernetes namespace + cluster_prefix: Base cluster name (without -head/-worker suffixes) + Returns: + MLflow service URL + """ + # Strip any -head or -worker suffixes to get base name + if namespace is None: + namespace = get_namespace() + pods = get_pods(namespace=namespace) + clusters = get_clusters(pods=pods, cluster_name_prefix=cluster_prefix) + if len(clusters) > 1: + raise ValueError("More than one cluster matches prefix, could not automatically determine mlflow info.") + mlflow_name = f"{cluster_prefix}-mlflow" + + cmd = ["kubectl", "get", "svc", mlflow_name, "-n", namespace, "--no-headers"] + try: + output = subprocess.check_output(cmd).decode() + fields = output.strip().split() + + # Get cluster IP + cluster_ip = fields[2] + port = "5000" # Default MLflow port + # This needs to be http to be resolved. HTTPS can't be resolved + # This should be fine as it is on a subnet on the cluster regardless + return f"http://{cluster_ip}:{port}" + except subprocess.CalledProcessError as e: + raise ValueError(f"Could not grok MLflow: {e}") # Fixed f-string + + +def check_clusters_running(pods: list, clusters: set) -> bool: + """ + Check that all of the pods in all provided clusters are running. + + Args: + pods (list): A list of tuples where each tuple contains the pod name and its status. + clusters (set): A set of cluster names to check. + + Returns: + bool: True if all pods in any of the clusters are running, False otherwise. + """ + clusters_running = False + for cluster in clusters: + cluster_pods = [p for p in pods if p[0].startswith(cluster)] + total_pods = len(cluster_pods) + running_pods = len([p for p in cluster_pods if p[1] == "Running"]) + if running_pods == total_pods and running_pods > 0: + clusters_running = True + break + return clusters_running + + +def get_ray_address(head_pod: str, namespace: str = "default", ray_head_name: str = "head") -> str: + """ + Given a cluster head pod, check its logs, which should include the ray address which can accept job requests. + + Args: + head_pod (str): The name of the head pod. + namespace (str, optional): The Kubernetes namespace. Defaults to "default". + ray_head_name (str, optional): The name of the ray head container. Defaults to "head". + + Returns: + str: The ray address if found, None otherwise. + + Raises: + ValueError: If the logs cannot be retrieved or the ray address is not found. + """ + cmd = ["kubectl", "logs", head_pod, "-c", ray_head_name, "-n", namespace] + try: + output = subprocess.check_output(cmd).decode() + except subprocess.CalledProcessError as e: + raise ValueError( + f"Could not enter head container with cmd {cmd}: {e}Perhaps try a different namespace or ray head name." + ) + match = re.search(r"RAY_ADDRESS='([^']+)'", output) + if match: + return match.group(1) + else: + return None + + +def process_cluster(cluster_info: dict, ray_head_name: str = "head") -> str: + """ + For each cluster, check that it is running, and get the Ray head address that will accept jobs. + + Args: + cluster_info: A dictionary containing cluster information with keys 'cluster', 'pods', and 'namespace'. + ray_head_name: The name of the ray head container. Defaults to "head". + + Returns: + A string containing the cluster name and its Ray head address, or an error message if + the head pod or Ray address is not found. + """ + cluster, pods, namespace = cluster_info + head_pod = None + for pod_name, status in pods: + if pod_name.startswith(cluster + "-head"): + head_pod = pod_name + break + if not head_pod: + return f"Error: Could not find head pod for cluster {cluster}\n" + + # Get RAY_ADDRESS and status + ray_address = get_ray_address(head_pod, namespace=namespace, ray_head_name=ray_head_name) + if not ray_address: + return f"Error: Could not find RAY_ADDRESS for cluster {cluster}\n" + + # Return only cluster and ray address + return f"name: {cluster} address: {ray_address}\n" + + +def main(): + # Parse command-line arguments + parser = argparse.ArgumentParser(description="Process Ray clusters and save their specifications.") + parser.add_argument("--prefix", default="isaacray", help="The prefix for the cluster names.") + parser.add_argument("--output", default="~/.cluster_config", help="The file to save cluster specifications.") + parser.add_argument("--ray_head_name", default="head", help="The metadata name for the ray head container") + parser.add_argument( + "--namespace", help="Kubernetes namespace to use. If not provided, will detect from current context." + ) + args = parser.parse_args() + + # Get namespace from args or detect it + current_namespace = args.namespace if args.namespace else get_namespace() + print(f"Using namespace: {current_namespace}") + + cluster_name_prefix = args.prefix + cluster_spec_file = os.path.expanduser(args.output) + + # Get all pods + pods = get_pods(namespace=current_namespace) + + # Get clusters + clusters = get_clusters(pods, cluster_name_prefix) + if not clusters: + print(f"No clusters found with prefix {cluster_name_prefix}") + return + + # Wait for clusters to be running + while True: + pods = get_pods(namespace=current_namespace) + if check_clusters_running(pods, clusters): + break + print("Waiting for all clusters to spin up...") + time.sleep(5) + + print("Checking for MLflow:") + # Check MLflow status for each cluster + for cluster in clusters: + try: + mlflow_address = get_mlflow_info(current_namespace, cluster) + print(f"MLflow address for {cluster}: {mlflow_address}") + except ValueError as e: + print(f"ML Flow not located: {e}") + print() + + # Prepare cluster info for parallel processing + cluster_infos = [] + for cluster in clusters: + cluster_pods = [p for p in pods if p[0].startswith(cluster)] + cluster_infos.append((cluster, cluster_pods, current_namespace)) + + # Use ThreadPoolExecutor to process clusters in parallel + results = [] + results_lock = threading.Lock() + + with ThreadPoolExecutor() as executor: + future_to_cluster = { + executor.submit(process_cluster, info, args.ray_head_name): info[0] for info in cluster_infos + } + for future in as_completed(future_to_cluster): + cluster_name = future_to_cluster[future] + try: + result = future.result() + with results_lock: + results.append(result) + except Exception as exc: + print(f"{cluster_name} generated an exception: {exc}") + + # Sort results alphabetically by cluster name + results.sort() + + # Write sorted results to the output file (Ray info only) + with open(cluster_spec_file, "w") as f: + for result in results: + f.write(result) + + print(f"Cluster spec information saved to {cluster_spec_file}") + # Display the contents of the config file + with open(cluster_spec_file) as f: + print(f.read()) + + +if __name__ == "__main__": + main() diff --git a/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py b/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f43ae7ecaaaa4c534057f644e8746ddbe7791027 --- /dev/null +++ b/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import pathlib +import sys +from typing import Any + +# Allow for import of items from the ray workflow. +CUR_DIR = pathlib.Path(__file__).parent +UTIL_DIR = CUR_DIR.parent +sys.path.extend([str(UTIL_DIR), str(CUR_DIR)]) +import util +import vision_cfg +from ray import tune +from ray.tune.progress_reporter import CLIReporter +from ray.tune.stopper import Stopper + + +class CartpoleRGBNoTuneJobCfg(vision_cfg.CameraJobCfg): + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-v0"]) + super().__init__(cfg, vary_env_count=False, vary_cnn=False, vary_mlp=False) + + +class CartpoleRGBCNNOnlyJobCfg(vision_cfg.CameraJobCfg): + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-v0"]) + super().__init__(cfg, vary_env_count=False, vary_cnn=True, vary_mlp=False) + + +class CartpoleRGBJobCfg(vision_cfg.CameraJobCfg): + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-v0"]) + super().__init__(cfg, vary_env_count=True, vary_cnn=True, vary_mlp=True) + + +class CartpoleResNetJobCfg(vision_cfg.ResNetCameraJob): + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-ResNet18-v0"]) + super().__init__(cfg) + + +class CartpoleTheiaJobCfg(vision_cfg.TheiaCameraJob): + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-TheiaTiny-v0"]) + super().__init__(cfg) + + +class CustomCartpoleProgressReporter(CLIReporter): + def __init__(self): + super().__init__( + metric_columns={ + "training_iteration": "iter", + "time_total_s": "total time (s)", + "Episode/Episode_Reward/alive": "alive", + "Episode/Episode_Reward/cart_vel": "cart velocity", + "rewards/time": "rewards/time", + }, + max_report_frequency=5, + sort_by_metric=True, + ) + + +class CartpoleEarlyStopper(Stopper): + def __init__(self): + self._bad_trials = set() + + def __call__(self, trial_id: str, result: dict[str, Any]) -> bool: + iter = result.get("training_iteration", 0) + out_of_bounds = result.get("Episode/Episode_Termination/cart_out_of_bounds") + + # Mark the trial for stopping if conditions are met + if iter >= 20 and out_of_bounds is not None and out_of_bounds > 0.85: + self._bad_trials.add(trial_id) + + return trial_id in self._bad_trials + + def stop_all(self) -> bool: + return False # only stop individual trials diff --git a/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cfg.py b/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..cb59a993368df7c0fffc758a51d4f698fe0dc7f3 --- /dev/null +++ b/scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cfg.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import pathlib +import sys + +# Allow for import of items from the ray workflow. +UTIL_DIR = pathlib.Path(__file__).parent.parent.parent +sys.path.append(str(UTIL_DIR)) +import tuner +import util +from ray import tune + + +class CameraJobCfg(tuner.JobCfg): + """In order to be compatible with :meth: invoke_tuning_run, and + :class:IsaacLabTuneTrainable , configurations should + be in a similar format to this class. This class can vary env count/horizon length, + CNN structure, and MLP structure. Broad possible ranges are set, the specific values + that work can be found via tuning. Tuning results can inform better ranges for a second tuning run. + These ranges were selected for demonstration purposes. Best ranges are run/task specific.""" + + @staticmethod + def _get_batch_size_divisors(batch_size: int, min_size: int = 128) -> list[int]: + """Get valid batch divisors to combine with num_envs and horizon length""" + divisors = [i for i in range(min_size, batch_size + 1) if batch_size % i == 0] + return divisors if divisors else [min_size] + + def __init__(self, cfg={}, vary_env_count: bool = False, vary_cnn: bool = False, vary_mlp: bool = False): + cfg = util.populate_isaac_ray_cfg_args(cfg) + + # Basic configuration + cfg["runner_args"]["headless_singleton"] = "--headless" + cfg["runner_args"]["enable_cameras_singleton"] = "--enable_cameras" + cfg["hydra_args"]["agent.params.config.max_epochs"] = 200 + + if vary_env_count: # Vary the env count, and horizon length, and select a compatible mini-batch size + # Check from 512 to 8196 envs in powers of 2 + # check horizon lengths of 8 to 256 + # More envs should be better, but different batch sizes can improve gradient estimation + env_counts = [2**x for x in range(9, 13)] + horizon_lengths = [2**x for x in range(3, 8)] + + selected_env_count = tune.choice(env_counts) + selected_horizon = tune.choice(horizon_lengths) + + cfg["runner_args"]["--num_envs"] = selected_env_count + cfg["hydra_args"]["agent.params.config.horizon_length"] = selected_horizon + + def get_valid_batch_size(config): + num_envs = config["runner_args"]["--num_envs"] + horizon_length = config["hydra_args"]["agent.params.config.horizon_length"] + total_batch = horizon_length * num_envs + divisors = self._get_batch_size_divisors(total_batch) + return divisors[0] + + cfg["hydra_args"]["agent.params.config.minibatch_size"] = tune.sample_from(get_valid_batch_size) + + if vary_cnn: # Vary the depth, and size of the layers in the CNN part of the agent + # Also varies kernel size, and stride. + num_layers = tune.randint(2, 3) + cfg["hydra_args"]["agent.params.network.cnn.type"] = "conv2d" + cfg["hydra_args"]["agent.params.network.cnn.activation"] = tune.choice(["relu", "elu"]) + cfg["hydra_args"]["agent.params.network.cnn.initializer"] = "{name:default}" + cfg["hydra_args"]["agent.params.network.cnn.regularizer"] = "{name:None}" + + def get_cnn_layers(_): + layers = [] + size = 64 # Initial input size + + for _ in range(num_layers.sample()): + # Get valid kernel sizes for current size + valid_kernels = [k for k in [3, 4, 6, 8, 10, 12] if k <= size] + if not valid_kernels: + break + + kernel = int(tune.choice([str(k) for k in valid_kernels]).sample()) + stride = int(tune.choice(["1", "2", "3", "4"]).sample()) + padding = int(tune.choice(["0", "1"]).sample()) + + # Calculate next size + next_size = ((size + 2 * padding - kernel) // stride) + 1 + if next_size <= 0: + break + + layers.append( + { + "filters": tune.randint(16, 32).sample(), + "kernel_size": str(kernel), + "strides": str(stride), + "padding": str(padding), + } + ) + size = next_size + + return layers + + cfg["hydra_args"]["agent.params.network.cnn.convs"] = tune.sample_from(get_cnn_layers) + + if vary_mlp: # Vary the MLP structure; neurons (units) per layer, number of layers, + max_num_layers = 6 + max_neurons_per_layer = 128 + if "env.observations.policy.image.params.model_name" in cfg["hydra_args"]: + # By decreasing MLP size when using pretrained helps prevent out of memory on L4 + max_num_layers = 3 + max_neurons_per_layer = 32 + if "agent.params.network.cnn.convs" in cfg["hydra_args"]: + # decrease MLP size to prevent running out of memory on L4 + max_num_layers = 2 + max_neurons_per_layer = 32 + + num_layers = tune.randint(1, max_num_layers) + + def get_mlp_layers(_): + return [tune.randint(4, max_neurons_per_layer).sample() for _ in range(num_layers.sample())] + + cfg["hydra_args"]["agent.params.network.mlp.units"] = tune.sample_from(get_mlp_layers) + cfg["hydra_args"]["agent.params.network.mlp.initializer.name"] = tune.choice(["default"]).sample() + cfg["hydra_args"]["agent.params.network.mlp.activation"] = tune.choice( + ["relu", "tanh", "sigmoid", "elu"] + ).sample() + + super().__init__(cfg) + + +class ResNetCameraJob(CameraJobCfg): + """Try different ResNet sizes.""" + + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["hydra_args"]["env.observations.policy.image.params.model_name"] = tune.choice( + ["resnet18", "resnet34", "resnet50", "resnet101"] + ) + super().__init__(cfg, vary_env_count=True, vary_cnn=False, vary_mlp=True) + + +class TheiaCameraJob(CameraJobCfg): + """Try different Theia sizes.""" + + def __init__(self, cfg: dict = {}): + cfg = util.populate_isaac_ray_cfg_args(cfg) + cfg["hydra_args"]["env.observations.policy.image.params.model_name"] = tune.choice( + [ + "theia-tiny-patch16-224-cddsv", + "theia-tiny-patch16-224-cdiv", + "theia-small-patch16-224-cdiv", + "theia-base-patch16-224-cdiv", + "theia-small-patch16-224-cddsv", + "theia-base-patch16-224-cddsv", + ] + ) + super().__init__(cfg, vary_env_count=True, vary_cnn=False, vary_mlp=True) diff --git a/scripts/reinforcement_learning/ray/launch.py b/scripts/reinforcement_learning/ray/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..3a3be716702e2cbb09cc78f12bb09750eff3b69a --- /dev/null +++ b/scripts/reinforcement_learning/ray/launch.py @@ -0,0 +1,183 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script helps create one or more KubeRay clusters. + +Usage: + +.. code-block:: bash + # If the head node is stuck on container creating, make sure to create a secret + python3 scripts/reinforcement_learning/ray/launch.py -h + + # Examples + + # The following creates 8 GPUx1 nvidia l4 workers + python3 scripts/reinforcement_learning/ray/launch.py --cluster_host google_cloud \ + --namespace --image \ + --num_workers 8 --num_clusters 1 --worker_accelerator nvidia-l4 --gpu_per_worker 1 + + # The following creates 1 GPUx1 nvidia l4 worker, 2 GPUx2 nvidia-tesla-t4 workers, + # and 2 GPUx4 nvidia-tesla-t4 GPU workers + python3 scripts/reinforcement_learning/ray/launch.py --cluster_host google_cloud \ + --namespace --image \ + --num_workers 1 2 --num_clusters 1 \ + --worker_accelerator nvidia-l4 nvidia-tesla-t4 --gpu_per_worker 1 2 4 +""" + +import argparse +import pathlib +import subprocess + +import yaml +from jinja2 import Environment, FileSystemLoader +from kubernetes import config + +# Local imports +import util # isort: skip + +RAY_DIR = pathlib.Path(__file__).parent + + +def apply_manifest(args: argparse.Namespace) -> None: + """Provided a Jinja templated ray.io/v1alpha1 file, + populate the arguments and create the cluster. Additionally, create + kubernetes containers for resources separated by '---' from the rest + of the file. + + Args: + args: Possible arguments concerning cluster parameters. + """ + # Load Kubernetes configuration + config.load_kube_config() + + # Set up Jinja2 environment for loading templates + templates_dir = RAY_DIR / "cluster_configs" / args.cluster_host + file_loader = FileSystemLoader(str(templates_dir)) + jinja_env = Environment(loader=file_loader, keep_trailing_newline=True, autoescape=True) + + # Define template filename + template_file = "kuberay.yaml.jinja" + + # Convert args namespace to a dictionary + template_params = vars(args) + + # Load and render the template + template = jinja_env.get_template(template_file) + file_contents = template.render(template_params) + + # Parse all YAML documents in the rendered template + all_yamls = [] + for doc in yaml.safe_load_all(file_contents): + all_yamls.append(doc) + + # Convert back to YAML string, preserving multiple documents + cleaned_yaml_string = "" + for i, doc in enumerate(all_yamls): + if i > 0: + cleaned_yaml_string += "\n---\n" + cleaned_yaml_string += yaml.dump(doc) + + # Apply the Kubernetes manifest using kubectl + try: + print(cleaned_yaml_string) + subprocess.run(["kubectl", "apply", "-f", "-"], input=cleaned_yaml_string, text=True, check=True) + except subprocess.CalledProcessError as e: + exit(f"An error occurred while running `kubectl`: {e}") + + +def parse_args() -> argparse.Namespace: + """ + Parse command-line arguments for Kubernetes deployment script. + + Returns: + argparse.Namespace: Parsed command-line arguments. + """ + arg_parser = argparse.ArgumentParser( + description="Script to apply manifests to create Kubernetes objects for Ray clusters.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + arg_parser.add_argument( + "--cluster_host", + type=str, + default="google_cloud", + choices=["google_cloud"], + help=( + "In the cluster_configs directory, the name of the folder where a tune.yaml.jinja" + "file exists defining the KubeRay config. Currently only google_cloud is supported." + ), + ) + + arg_parser.add_argument( + "--name", + type=str, + required=False, + default="isaacray", + help="Name of the Kubernetes deployment.", + ) + + arg_parser.add_argument( + "--namespace", + type=str, + required=False, + default="default", + help="Kubernetes namespace to deploy the Ray cluster.", + ) + + arg_parser.add_argument( + "--service_acount_name", type=str, required=False, default="default", help="The service account name to use." + ) + + arg_parser.add_argument( + "--image", + type=str, + required=True, + help="Docker image for the Ray cluster pods.", + ) + + arg_parser.add_argument( + "--worker_accelerator", + nargs="+", + type=str, + default=["nvidia-l4"], + help="GPU accelerator name. Supply more than one for heterogeneous resources.", + ) + + arg_parser = util.add_resource_arguments(arg_parser, cluster_create_defaults=True) + + arg_parser.add_argument( + "--num_clusters", + type=int, + default=1, + help="How many Ray Clusters to create.", + ) + arg_parser.add_argument( + "--num_head_cpu", + type=float, # to be able to schedule partial CPU heads + default=8, + help="The number of CPUs to give the Ray head.", + ) + + arg_parser.add_argument("--head_ram_gb", type=int, default=8, help="How many gigs of ram to give the Ray head") + args = arg_parser.parse_args() + return util.fill_in_missing_resources(args, cluster_creation_flag=True) + + +def main(): + args = parse_args() + + if "head" in args.name: + raise ValueError("For compatibility with other scripts, do not include head in the name") + if args.num_clusters == 1: + apply_manifest(args) + else: + default_name = args.name + for i in range(args.num_clusters): + args.name = default_name + "-" + str(i) + apply_manifest(args) + + +if __name__ == "__main__": + main() diff --git a/scripts/reinforcement_learning/ray/mlflow_to_local_tensorboard.py b/scripts/reinforcement_learning/ray/mlflow_to_local_tensorboard.py new file mode 100644 index 0000000000000000000000000000000000000000..2c45f1cd0a8d0c5251921c09f3b07cebe58e6e07 --- /dev/null +++ b/scripts/reinforcement_learning/ray/mlflow_to_local_tensorboard.py @@ -0,0 +1,150 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import logging +import multiprocessing as mp +import os +import sys +from concurrent.futures import ProcessPoolExecutor, as_completed + +import mlflow +from mlflow.tracking import MlflowClient +from torch.utils.tensorboard import SummaryWriter + + +def setup_logging(level=logging.INFO): + logging.basicConfig(level=level, format="%(asctime)s - %(levelname)s - %(message)s") + + +def get_existing_runs(download_dir: str) -> set[str]: + """Get set of run IDs that have already been downloaded.""" + existing_runs = set() + tensorboard_dir = os.path.join(download_dir, "tensorboard") + if os.path.exists(tensorboard_dir): + for entry in os.listdir(tensorboard_dir): + if entry.startswith("run_"): + existing_runs.add(entry[4:]) + return existing_runs + + +def process_run(args): + """Convert MLflow run to TensorBoard format.""" + run_id, download_dir, tracking_uri = args + + try: + # Set up MLflow client + mlflow.set_tracking_uri(tracking_uri) + client = MlflowClient() + run = client.get_run(run_id) + + # Create TensorBoard writer + tensorboard_log_dir = os.path.join(download_dir, "tensorboard", f"run_{run_id}") + writer = SummaryWriter(log_dir=tensorboard_log_dir) + + # Log parameters + for key, value in run.data.params.items(): + writer.add_text(f"params/{key}", str(value)) + + # Log metrics with history + for key in run.data.metrics.keys(): + history = client.get_metric_history(run_id, key) + for m in history: + writer.add_scalar(f"metrics/{key}", m.value, m.step) + + # Log tags + for key, value in run.data.tags.items(): + writer.add_text(f"tags/{key}", str(value)) + + writer.close() + return run_id, True + except Exception: + return run_id, False + + +def download_experiment_tensorboard_logs(uri: str, experiment_name: str, download_dir: str) -> None: + """Download MLflow experiment logs and convert to TensorBoard format.""" + # import logger + logger = logging.getLogger(__name__) + + try: + # Set up MLflow + mlflow.set_tracking_uri(uri) + logger.info(f"Connected to MLflow tracking server at {uri}") + + # Get experiment + experiment = mlflow.get_experiment_by_name(experiment_name) + if experiment is None: + raise ValueError(f"Experiment '{experiment_name}' not found at URI '{uri}'.") + + # Get all runs + runs = mlflow.search_runs([experiment.experiment_id]) + logger.info(f"Found {len(runs)} total runs in experiment '{experiment_name}'") + + # Check existing runs + existing_runs = get_existing_runs(download_dir) + logger.info(f"Found {len(existing_runs)} existing runs in {download_dir}") + + # Create directory structure + os.makedirs(os.path.join(download_dir, "tensorboard"), exist_ok=True) + + # Process new runs + new_run_ids = [run.run_id for _, run in runs.iterrows() if run.run_id not in existing_runs] + + if not new_run_ids: + logger.info("No new runs to process") + return + + logger.info(f"Processing {len(new_run_ids)} new runs...") + + # Process runs in parallel + num_processes = min(mp.cpu_count(), len(new_run_ids)) + processed = 0 + + with ProcessPoolExecutor(max_workers=num_processes) as executor: + future_to_run = { + executor.submit(process_run, (run_id, download_dir, uri)): run_id for run_id in new_run_ids + } + + for future in as_completed(future_to_run): + run_id = future_to_run[future] + try: + run_id, success = future.result() + processed += 1 + if success: + logger.info(f"[{processed}/{len(new_run_ids)}] Successfully processed run {run_id}") + else: + logger.error(f"[{processed}/{len(new_run_ids)}] Failed to process run {run_id}") + except Exception as e: + logger.error(f"Error processing run {run_id}: {e}") + + logger.info(f"\nAll data saved to {download_dir}/tensorboard") + + except Exception as e: + logger.error(f"Error during download: {e}") + raise + + +def main(): + parser = argparse.ArgumentParser(description="Download MLflow experiment logs for TensorBoard visualization.") + parser.add_argument("--uri", required=True, help="The MLflow tracking URI (e.g., http://localhost:5000)") + parser.add_argument("--experiment-name", required=True, help="Name of the experiment to download") + parser.add_argument("--download-dir", required=True, help="Directory to save TensorBoard logs") + parser.add_argument("--debug", action="store_true", help="Enable debug logging") + + args = parser.parse_args() + setup_logging(level=logging.DEBUG if args.debug else logging.INFO) + + try: + download_experiment_tensorboard_logs(args.uri, args.experiment_name, args.download_dir) + print("\nSuccess! To view the logs, run:") + print(f"tensorboard --logdir {os.path.join(args.download_dir, 'tensorboard')}") + except Exception as e: + logging.error(f"Failed to download experiment logs: {e}") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/scripts/reinforcement_learning/ray/submit_job.py b/scripts/reinforcement_learning/ray/submit_job.py new file mode 100644 index 0000000000000000000000000000000000000000..21fb6a3d9b39299994b90ba27e5311c220be5e5d --- /dev/null +++ b/scripts/reinforcement_learning/ray/submit_job.py @@ -0,0 +1,158 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script submits aggregate job(s) to cluster(s) described in a +config file containing ``name: address: http://:`` on +a new line for each cluster. For KubeRay clusters, this file +can be automatically created with :file:`grok_cluster_with_kubectl.py` + +Aggregate job(s) are matched with cluster(s) via the following relation: +cluster_line_index_submitted_to = job_index % total_cluster_count + +Aggregate jobs are separated by the * delimiter. The ``--aggregate_jobs`` argument must be +the last argument supplied to the script. + +An aggregate job could be a :file:`../tuner.py` tuning job, which automatically +creates several individual jobs when started on a cluster. Alternatively, an aggregate job +could be a :file:'../wrap_resources.py` resource-wrapped job, +which may contain several individual sub-jobs separated by +the + delimiter. An aggregate job could also be a :file:`../task_runner.py` multi-task submission job, +where each sub-job and its resource requirements are defined in a YAML configuration file. +In this mode, :file:`../task_runner.py` will read the YAML file (via --task_cfg), and +submit all defined sub-tasks to the Ray cluster, supporting per-job resource specification and +real-time streaming of sub-job outputs. + +If there are more aggregate jobs than cluster(s), aggregate jobs will be submitted +as clusters become available via the defined relation above. If there are less aggregate job(s) +than clusters, some clusters will not receive aggregate job(s). The maximum number of +aggregate jobs that can be run simultaneously is equal to the number of workers created by +default by a ThreadPoolExecutor on the machine submitting jobs due to fetching the log output after +jobs finish, which is unlikely to constrain overall-job submission. + +Usage: + +.. code-block:: bash + + # Example; submitting a tuning job + python3 scripts/reinforcement_learning/ray/submit_job.py \ + --aggregate_jobs /workspace/isaaclab/scripts/reinforcement_learning/ray/tuner.py \ + --cfg_file hyperparameter_tuning/vision_cartpole_cfg.py \ + --cfg_class CartpoleTheiaJobCfg --mlflow_uri + + # Example: Submitting resource wrapped job + python3 scripts/reinforcement_learning/ray/submit_job.py --aggregate_jobs wrap_resources.py --test + + # Example: submitting tasks with specific resources, and supporting pip packages and py_modules + # You may use relative paths for task_cfg and py_modules, placing them in the + # "scripts/reinforcement_learning/ray" directory, which will be uploaded to the cluster. + python3 scripts/reinforcement_learning/ray/submit_job.py --aggregate_jobs task_runner.py --task_cfg tasks.yaml + + # For all command line arguments + python3 scripts/reinforcement_learning/ray/submit_job.py -h +""" + +import argparse +import os +import time +from concurrent.futures import ThreadPoolExecutor + +from ray import job_submission + +script_directory = os.path.dirname(os.path.abspath(__file__)) +CONFIG = {"working_dir": script_directory, "executable": "/workspace/isaaclab/isaaclab.sh -p"} + + +def read_cluster_spec(fn: str | None = None) -> list[dict]: + if fn is None: + cluster_spec_path = os.path.expanduser("~/.cluster_config") + else: + cluster_spec_path = os.path.expanduser(fn) + + if not os.path.exists(cluster_spec_path): + raise FileNotFoundError(f"Cluster spec file not found at {cluster_spec_path}") + + clusters = [] + with open(cluster_spec_path) as f: + for line in f: + parts = line.strip().split(" ") + http_address = parts[3] + cluster_info = {"name": parts[1], "address": http_address} + print(f"[INFO] Setting {cluster_info['name']}") # with {cluster_info['num_gpu']} GPUs.") + clusters.append(cluster_info) + + return clusters + + +def submit_job(cluster: dict, job_command: str) -> None: + """ + Submits a job to a single cluster, prints the final result and Ray dashboard URL at the end. + """ + address = cluster["address"] + cluster_name = cluster["name"] + print(f"[INFO]: Submitting job to cluster '{cluster_name}' at {address}") # with {num_gpus} GPUs.") + client = job_submission.JobSubmissionClient(address) + runtime_env = {"working_dir": CONFIG["working_dir"], "executable": CONFIG["executable"]} + print(f"[INFO]: Checking contents of the directory: {CONFIG['working_dir']}") + try: + dir_contents = os.listdir(CONFIG["working_dir"]) + print(f"[INFO]: Directory contents: {dir_contents}") + except Exception as e: + print(f"[INFO]: Failed to list directory contents: {str(e)}") + entrypoint = f"{CONFIG['executable']} {job_command}" + print(f"[INFO]: Attempting entrypoint {entrypoint=} in cluster {cluster}") + job_id = client.submit_job(entrypoint=entrypoint, runtime_env=runtime_env) + status = client.get_job_status(job_id) + while status in [job_submission.JobStatus.PENDING, job_submission.JobStatus.RUNNING]: + time.sleep(5) + status = client.get_job_status(job_id) + + final_logs = client.get_job_logs(job_id) + print("----------------------------------------------------") + print(f"[INFO]: Cluster {cluster_name} Logs: \n") + print(final_logs) + print("----------------------------------------------------") + + +def submit_jobs_to_clusters(jobs: list[str], clusters: list[dict]) -> None: + """ + Submit all jobs to their respective clusters, cycling through clusters if there are more jobs than clusters. + """ + if not clusters: + raise ValueError("No clusters available for job submission.") + + if len(jobs) < len(clusters): + print("[INFO]: Less jobs than clusters, some clusters will not receive jobs") + elif len(jobs) == len(clusters): + print("[INFO]: Exactly one job per cluster") + else: + print("[INFO]: More jobs than clusters, jobs submitted as clusters become available.") + with ThreadPoolExecutor() as executor: + for idx, job_command in enumerate(jobs): + # Cycle through clusters using modulus to wrap around if there are more jobs than clusters + cluster = clusters[idx % len(clusters)] + executor.submit(submit_job, cluster, job_command) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Submit multiple GPU jobs to multiple Ray clusters.") + parser.add_argument("--config_file", default="~/.cluster_config", help="The cluster config path.") + parser.add_argument( + "--aggregate_jobs", + type=str, + nargs=argparse.REMAINDER, + help="This should be last argument. The aggregate jobs to submit separated by the * delimiter.", + ) + args = parser.parse_args() + if args.aggregate_jobs is not None: + jobs = " ".join(args.aggregate_jobs) + formatted_jobs = jobs.split("*") + if len(formatted_jobs) > 1: + print("Warning; Split jobs by cluster with the * delimiter") + else: + formatted_jobs = [] + print(f"[INFO]: Isaac Ray Wrapper received jobs {formatted_jobs=}") + clusters = read_cluster_spec(args.config_file) + submit_jobs_to_clusters(formatted_jobs, clusters) diff --git a/scripts/reinforcement_learning/ray/task_runner.py b/scripts/reinforcement_learning/ray/task_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..cbf12293035a65941362859531f7c976aa85773f --- /dev/null +++ b/scripts/reinforcement_learning/ray/task_runner.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script dispatches one or more user-defined Python tasks to workers in a Ray cluster. +Each task, along with its resource requirements and execution parameters, is specified in a YAML configuration file. +Users may define the number of CPUs, GPUs, and the amount of memory to allocate per task via the config file. + +Key features: +------------- +- Fine-grained, per-task resource management via config fields (`num_gpus`, `num_cpus`, `memory`). +- Parallel execution of multiple tasks using available resources across the Ray cluster. +- Option to specify node affinity for tasks, e.g., by hostname, node ID, or any node. +- Optional batch (simultaneous) or independent scheduling of tasks. + +Task scheduling and distribution are handled via Ray’s built-in resource manager. + +YAML configuration fields: +-------------------------- +- `pip`: List of extra pip packages to install before running any tasks. +- `py_modules`: List of additional Python module paths (directories or files) to include in the runtime environment. +- `concurrent`: (bool) It determines task dispatch semantics: + - If `concurrent: true`, **all tasks are scheduled as a batch**. The script waits until + sufficient resources are available for every task in the batch, then launches all tasks + together. If resources are insufficient, all tasks remain blocked until the cluster can + support the full batch. + - If `concurrent: false`, tasks are launched as soon as resources are available for each + individual task, and Ray independently schedules them. This may result in non-simultaneous + task start times. +- `tasks`: List of task specifications, each with: + - `name`: String identifier for the task. + - `py_args`: Arguments to the Python interpreter (e.g., script/module, flags, user arguments). + - `num_gpus`: Number of GPUs to allocate (float or string arithmetic, e.g., "2*2"). + - `num_cpus`: Number of CPUs to allocate (float or string). + - `memory`: Amount of RAM in bytes (int or string). + - `node` (optional): Node placement constraints. + - `specific` (str): Type of node placement, support `hostname`, `node_id`, or `any`. + - `any`: Place the task on any available node. + - `hostname`: Place the task on a specific hostname. `hostname` must be specified + in the node field. + - `node_id`: Place the task on a specific node ID. `node_id` must be specified in + the node field. + - `hostname` (str): Specific hostname to place the task on. + - `node_id` (str): Specific node ID to place the task on. + + +Typical usage: +-------------- + +.. code-block:: bash + + # Print help and argument details: + python task_runner.py -h + + # Submit tasks defined in a YAML file to the Ray cluster (auto-detects Ray head address): + python task_runner.py --task_cfg /path/to/tasks.yaml + +YAML configuration example-1: +----------------------------- + +.. code-block:: yaml + + pip: ["xxx"] + py_modules: ["my_package/my_package"] + concurrent: false + tasks: + - name: "Isaac-Cartpole-v0" + py_args: "-m torch.distributed.run --nnodes=1 --nproc_per_node=2 --rdzv_endpoint=localhost:29501 /workspace/isaaclab/scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --max_iterations 200 --headless --distributed" + num_gpus: 2 + num_cpus: 10 + memory: 10737418240 + - name: "script need some dependencies" + py_args: "script.py --option arg" + num_gpus: 0 + num_cpus: 1 + memory: 10*1024*1024*1024 + +YAML configuration example-2: +----------------------------- + +.. code-block:: yaml + + pip: ["xxx"] + py_modules: ["my_package/my_package"] + concurrent: true + tasks: + - name: "Isaac-Cartpole-v0-multi-node-train-1" + py_args: "-m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 /workspace/isaaclab/scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --max_iterations 1000" + num_gpus: 1 + num_cpus: 10 + memory: 10*1024*1024*1024 + node: + specific: "hostname" + hostname: "xxx" + - name: "Isaac-Cartpole-v0-multi-node-train-2" + py_args: "-m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=x.x.x.x:5555 /workspace/isaaclab/scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --max_iterations 1000" + num_gpus: 1 + num_cpus: 10 + memory: 10*1024*1024*1024 + node: + specific: "hostname" + hostname: "xxx" + +To stop all tasks early, press Ctrl+C; the script will cancel all running Ray tasks. +""" # noqa: E501 + +import argparse +from datetime import datetime + +import yaml + +# Local imports +import util # isort: skip + + +def parse_args() -> argparse.Namespace: + """ + Parse command-line arguments for the Ray task runner. + + Returns: + A namespace containing parsed CLI arguments: + - task_cfg (str): Path to the YAML task file. + - ray_address (str): Ray cluster address. + - test (bool): Whether to run a GPU resource isolation sanity check. + """ + parser = argparse.ArgumentParser(description="Run tasks from a YAML config file.") + parser.add_argument("--task_cfg", type=str, required=True, help="Path to the YAML task file.") + parser.add_argument("--ray_address", type=str, default="auto", help="the Ray address.") + parser.add_argument( + "--test", + action="store_true", + help=( + "Run nvidia-smi test instead of the arbitrary job," + "can use as a sanity check prior to any jobs to check " + "that GPU resources are correctly isolated." + ), + ) + return parser.parse_args() + + +def parse_task_resource(task: dict) -> util.JobResource: + """ + Parse task resource requirements from the YAML configuration. + + Args: + task (dict): Dictionary representing a single task's configuration. + Keys may include `num_gpus`, `num_cpus`, and `memory`, each either + as a number or evaluatable string expression. + + Returns: + util.JobResource: Resource object with the parsed values. + """ + resource = util.JobResource() + if "num_gpus" in task: + resource.num_gpus = eval(task["num_gpus"]) if isinstance(task["num_gpus"], str) else task["num_gpus"] + if "num_cpus" in task: + resource.num_cpus = eval(task["num_cpus"]) if isinstance(task["num_cpus"], str) else task["num_cpus"] + if "memory" in task: + resource.memory = eval(task["memory"]) if isinstance(task["memory"], str) else task["memory"] + return resource + + +def run_tasks( + tasks: list[dict], args: argparse.Namespace, runtime_env: dict | None = None, concurrent: bool = False +) -> None: + """ + Submit tasks to the Ray cluster for execution. + + Args: + tasks (list[dict]): A list of task configuration dictionaries. + args (argparse.Namespace): Parsed command-line arguments. + runtime_env (dict | None): Ray runtime environment configuration containing: + - pip (list[str] | None): Additional pip packages to install. + - py_modules (list[str] | None): Python modules to include in the environment. + concurrent (bool): Whether to launch tasks simultaneously as a batch, + or independently as resources become available. + + Returns: + None + """ + job_objs = [] + util.ray_init(ray_address=args.ray_address, runtime_env=runtime_env, log_to_driver=False) + for task in tasks: + resource = parse_task_resource(task) + print(f"[INFO] Creating job {task['name']} with resource={resource}") + job = util.Job( + name=task["name"], + py_args=task["py_args"], + resources=resource, + node=util.JobNode( + specific=task.get("node", {}).get("specific"), + hostname=task.get("node", {}).get("hostname"), + node_id=task.get("node", {}).get("node_id"), + ), + ) + job_objs.append(job) + start = datetime.now() + print(f"[INFO] Creating {len(job_objs)} jobs at {start.strftime('%H:%M:%S.%f')} with runtime env={runtime_env}") + # submit jobs + util.submit_wrapped_jobs( + jobs=job_objs, + test_mode=args.test, + concurrent=concurrent, + ) + end = datetime.now() + print( + f"[INFO] All jobs completed at {end.strftime('%H:%M:%S.%f')}, took {(end - start).total_seconds():.2f} seconds." + ) + + +def main() -> None: + """ + Main entry point for the Ray task runner script. + + Reads the YAML task configuration file, parses CLI arguments, + and dispatches tasks to the Ray cluster. + + Returns: + None + """ + args = parse_args() + with open(args.task_cfg) as f: + config = yaml.safe_load(f) + tasks = config["tasks"] + runtime_env = { + "pip": None if not config.get("pip") else config["pip"], + "py_modules": None if not config.get("py_modules") else config["py_modules"], + } + concurrent = config.get("concurrent", False) + run_tasks( + tasks=tasks, + args=args, + runtime_env=runtime_env, + concurrent=concurrent, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/reinforcement_learning/ray/tuner.py b/scripts/reinforcement_learning/ray/tuner.py new file mode 100644 index 0000000000000000000000000000000000000000..99dc7e8d08f54af8063d1d410ca6718b93998836 --- /dev/null +++ b/scripts/reinforcement_learning/ray/tuner.py @@ -0,0 +1,556 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import argparse +import importlib.util +import os +import random +import subprocess +import sys +from time import sleep, time + +import ray +import util +from ray import air, tune +from ray.tune import Callback +from ray.tune.progress_reporter import ProgressReporter +from ray.tune.search.optuna import OptunaSearch +from ray.tune.search.repeater import Repeater +from ray.tune.stopper import CombinedStopper + +""" +This script breaks down an aggregate tuning job, as defined by a hyperparameter sweep configuration, +into individual jobs (shell commands) to run on the GPU-enabled nodes of the cluster. +By default, one worker is created for each GPU-enabled node in the cluster for each individual job. +To use more than one worker per node (likely the case for multi-GPU machines), supply the +num_workers_per_node argument. + +Each hyperparameter sweep configuration should include the workflow, +runner arguments, and hydra arguments to vary. + +This assumes that all workers in a cluster are homogeneous. For heterogeneous workloads, +create several heterogeneous clusters (with homogeneous nodes in each cluster), +then submit several overall-cluster jobs with :file:`../submit_job.py`. +KubeRay clusters on Google GKE can be created with :file:`../launch.py` + +To report tune metrics on clusters, a running MLFlow server with a known URI that the cluster has +access to is required. For KubeRay clusters configured with :file:`../launch.py`, this is included +automatically, and can be easily found with with :file:`grok_cluster_with_kubectl.py` + +Usage: + +.. code-block:: bash + + ./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py -h + + # Examples + # Local + ./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py --run_mode local \ + --cfg_file scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py \ + --cfg_class CartpoleTheiaJobCfg + # Local with a custom progress reporter + ./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py \ + --cfg_file scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py \ + --cfg_class CartpoleTheiaJobCfg \ + --progress_reporter CustomCartpoleProgressReporter + # Remote (run grok cluster or create config file mentioned in :file:`submit_job.py`) + ./isaaclab.sh -p scripts/reinforcement_learning/ray/submit_job.py \ + --aggregate_jobs tuner.py \ + --cfg_file hyperparameter_tuning/vision_cartpole_cfg.py \ + --cfg_class CartpoleTheiaJobCfg --mlflow_uri + +""" + +DOCKER_PREFIX = "/workspace/isaaclab/" +BASE_DIR = os.path.expanduser("~") +PYTHON_EXEC = "./isaaclab.sh -p" +WORKFLOW = "scripts/reinforcement_learning/rl_games/train.py" +NUM_WORKERS_PER_NODE = 1 # needed for local parallelism +PROCESS_RESPONSE_TIMEOUT = 200.0 # seconds to wait before killing the process when it stops responding +MAX_LINES_TO_SEARCH_EXPERIMENT_LOGS = 1000 # maximum number of lines to read from the training process logs +MAX_LOG_EXTRACTION_ERRORS = 10 # maximum allowed LogExtractionErrors before we abort the whole training + + +class IsaacLabTuneTrainable(tune.Trainable): + """The Isaac Lab Ray Tune Trainable. + This class uses the standalone workflows to start jobs, along with the hydra integration. + This class achieves Ray-based logging through reading the tensorboard logs from + the standalone workflows. This depends on a config generated in the format of + :class:`JobCfg` + """ + + def setup(self, config: dict) -> None: + """Get the invocation command, return quick for easy scheduling.""" + self.data = None + self.time_since_last_proc_response = 0.0 + self.invoke_cmd = util.get_invocation_command_from_cfg(cfg=config, python_cmd=PYTHON_EXEC, workflow=WORKFLOW) + print(f"[INFO]: Recovered invocation with {self.invoke_cmd}") + self.experiment = None + + def reset_config(self, new_config: dict): + """Allow environments to be reused by fetching a new invocation command""" + self.setup(new_config) + return True + + def step(self) -> dict: + if self.experiment is None: # start experiment + # When including this as first step instead of setup, experiments get scheduled faster + # Don't want to block the scheduler while the experiment spins up + print(f"[INFO]: Invoking experiment as first step with {self.invoke_cmd}...") + try: + experiment = util.execute_job( + self.invoke_cmd, + identifier_string="", + extract_experiment=True, # Keep this as True to return a valid dictionary + persistent_dir=BASE_DIR, + max_lines_to_search_logs=MAX_LINES_TO_SEARCH_EXPERIMENT_LOGS, + max_time_to_search_logs=PROCESS_RESPONSE_TIMEOUT, + ) + except util.LogExtractionError: + self.data = { + "LOG_EXTRACTION_ERROR_STOPPER_FLAG": True, + "done": True, + } + return self.data + self.experiment = experiment + print(f"[INFO]: Tuner recovered experiment info {experiment}") + self.proc = experiment["proc"] + self.experiment_name = experiment["experiment_name"] + self.isaac_logdir = experiment["logdir"] + self.tensorboard_logdir = self.isaac_logdir + "/" + self.experiment_name + self.done = False + + if self.proc is None: + raise ValueError("Could not start trial.") + proc_status = self.proc.poll() + if proc_status is not None: # process finished, signal finish + self.data["done"] = True + print(f"[INFO]: Process finished with {proc_status}, returning...") + else: # wait until the logs are ready or fresh + data = util.load_tensorboard_logs(self.tensorboard_logdir) + + while data is None: + data = util.load_tensorboard_logs(self.tensorboard_logdir) + proc_status = self.proc.poll() + if proc_status is not None: + break + sleep(2) # Lazy report metrics to avoid performance overhead + + if self.data is not None: + data_ = {k: v for k, v in data.items() if k != "done"} + self_data_ = {k: v for k, v in self.data.items() if k != "done"} + unresponsiveness_start_time = time() + while util._dicts_equal(data_, self_data_): + self.time_since_last_proc_response = time() - unresponsiveness_start_time + data = util.load_tensorboard_logs(self.tensorboard_logdir) + data_ = {k: v for k, v in data.items() if k != "done"} + proc_status = self.proc.poll() + if proc_status is not None: + break + if self.time_since_last_proc_response > PROCESS_RESPONSE_TIMEOUT: + self.time_since_last_proc_response = 0.0 + print("[WARNING]: Training workflow process is not responding, terminating...") + self.proc.terminate() + try: + self.proc.wait(timeout=20) + except subprocess.TimeoutExpired: + print("[ERROR]: The process did not terminate within timeout duration.") + self.proc.kill() + self.proc.wait() + self.data = data + self.data["done"] = True + return self.data + sleep(2) # Lazy report metrics to avoid performance overhead + + self.data = data + self.data["done"] = False + return self.data + + def default_resource_request(self): + """How many resources each trainable uses. Assumes homogeneous resources across gpu nodes, + and that each trainable is meant for one node, where it uses all available resources.""" + resources = util.get_gpu_node_resources(one_node_only=True) + if NUM_WORKERS_PER_NODE != 1: + print("[WARNING]: Splitting node into more than one worker") + return tune.PlacementGroupFactory( + [{"CPU": resources["CPU"] / NUM_WORKERS_PER_NODE, "GPU": resources["GPU"] / NUM_WORKERS_PER_NODE}], + strategy="STRICT_PACK", + ) + + +class LogExtractionErrorStopper(tune.Stopper): + """Stopper that stops all trials if multiple LogExtractionErrors occur. + + Args: + max_errors: The maximum number of LogExtractionErrors allowed before terminating the experiment. + """ + + def __init__(self, max_errors: int): + self.max_errors = max_errors + self.error_count = 0 + + def __call__(self, trial_id, result): + """Increments the error count if trial has encountered a LogExtractionError. + + It does not stop the trial based on the metrics, always returning False. + """ + if result.get("LOG_EXTRACTION_ERROR_STOPPER_FLAG", False): + self.error_count += 1 + print( + f"[ERROR]: Encountered LogExtractionError {self.error_count} times. " + f"Maximum allowed is {self.max_errors}." + ) + return False + + def stop_all(self): + """Returns true if number of LogExtractionErrors exceeds the maximum allowed, terminating the experiment.""" + if self.error_count > self.max_errors: + print("[FATAL]: Encountered LogExtractionError more than allowed, aborting entire tuning run... ") + return True + else: + return False + + +class ProcessCleanupCallback(Callback): + """Callback to clean up processes when trials are stopped.""" + + def on_trial_error(self, iteration, trials, trial, error, **info): + """Called when a trial encounters an error.""" + self._cleanup_trial(trial) + + def on_trial_complete(self, iteration, trials, trial, **info): + """Called when a trial completes.""" + self._cleanup_trial(trial) + + def _cleanup_trial(self, trial): + """Clean up processes for a trial using SIGKILL.""" + try: + subprocess.run(["pkill", "-9", "-f", f"rid {trial.config['runner_args']['-rid']}"], check=False) + sleep(5) + except Exception as e: + print(f"[ERROR]: Failed to cleanup trial {trial.trial_id}: {e}") + + +def invoke_tuning_run( + cfg: dict, + args: argparse.Namespace, + progress_reporter: ProgressReporter | None = None, + stopper: tune.Stopper | None = None, +) -> None: + """Invoke an Isaac-Ray tuning run. + + Log either to a local directory or to MLFlow. + Args: + cfg: Configuration dictionary extracted from job setup + args: Command-line arguments related to tuning. + progress_reporter: Custom progress reporter. Defaults to CLIReporter or JupyterNotebookReporter if not provided. + stopper: Custom stopper, optional. + """ + # Allow for early exit + os.environ["TUNE_DISABLE_STRICT_METRIC_CHECKING"] = "1" + + print("[WARNING]: Not saving checkpoints, just running experiment...") + print("[INFO]: Model parameters and metrics will be preserved.") + print("[WARNING]: For homogeneous cluster resources only...") + + # Initialize Ray + util.ray_init( + ray_address=args.ray_address, + log_to_driver=True, + ) + + # Get available resources + resources = util.get_gpu_node_resources() + print(f"[INFO]: Available resources {resources}") + + print(f"[INFO]: Using config {cfg}") + + # Configure the search algorithm and the repeater + searcher = OptunaSearch( + metric=args.metric, + mode=args.mode, + ) + repeat_search = Repeater(searcher, repeat=args.repeat_run_count) + + # Configure the stoppers + stoppers: CombinedStopper = CombinedStopper( + *[ + LogExtractionErrorStopper(max_errors=MAX_LOG_EXTRACTION_ERRORS), + *([stopper] if stopper is not None else []), + ] + ) + + if progress_reporter is not None: + os.environ["RAY_AIR_NEW_OUTPUT"] = "0" + if ( + getattr(progress_reporter, "_metric", None) is not None + or getattr(progress_reporter, "_mode", None) is not None + ): + raise ValueError( + "Do not set or directly in the custom progress reporter class, " + "provide them as arguments to tuner.py instead." + ) + + if args.run_mode == "local": # Standard config, to file + run_config = air.RunConfig( + storage_path="/tmp/ray", + name=f"IsaacRay-{args.cfg_class}-tune", + callbacks=[ProcessCleanupCallback()], + verbose=1, + checkpoint_config=air.CheckpointConfig( + checkpoint_frequency=0, # Disable periodic checkpointing + checkpoint_at_end=False, # Disable final checkpoint + ), + stop=stoppers, + progress_reporter=progress_reporter, + ) + + elif args.run_mode == "remote": # MLFlow, to MLFlow server + mlflow_callback = MLflowLoggerCallback( + tracking_uri=args.mlflow_uri, + experiment_name=f"IsaacRay-{args.cfg_class}-tune", + save_artifact=False, + tags={"run_mode": "remote", "cfg_class": args.cfg_class}, + ) + + run_config = ray.train.RunConfig( + name="mlflow", + storage_path="/tmp/ray", + callbacks=[ProcessCleanupCallback(), mlflow_callback], + checkpoint_config=ray.train.CheckpointConfig(checkpoint_frequency=0, checkpoint_at_end=False), + stop=stoppers, + progress_reporter=progress_reporter, + ) + else: + raise ValueError("Unrecognized run mode.") + # RID isn't optimized as it is sampled from, but useful for cleanup later + cfg["runner_args"]["-rid"] = tune.sample_from(lambda _: str(random.randint(int(1e9), int(1e10) - 1))) + # Configure the tuning job + tuner = tune.Tuner( + IsaacLabTuneTrainable, + param_space=cfg, + tune_config=tune.TuneConfig( + metric=args.metric, + mode=args.mode, + search_alg=repeat_search, + num_samples=args.num_samples, + reuse_actors=True, + ), + run_config=run_config, + ) + + # Execute the tuning + tuner.fit() + + # Save results to mounted volume + if args.run_mode == "local": + print("[DONE!]: Check results with tensorboard dashboard") + else: + print("[DONE!]: Check results with MLFlow dashboard") + + +class JobCfg: + """To be compatible with :meth: invoke_tuning_run and :class:IsaacLabTuneTrainable, + at a minimum, the tune job should inherit from this class.""" + + def __init__(self, cfg: dict): + """ + Runner args include command line arguments passed to the task. + For example: + cfg["runner_args"]["headless_singleton"] = "--headless" + cfg["runner_args"]["enable_cameras_singleton"] = "--enable_cameras" + """ + assert "runner_args" in cfg, "No runner arguments specified." + """ + Task is the desired task to train on. For example: + cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-TheiaTiny-v0"]) + """ + assert "--task" in cfg["runner_args"], "No task specified." + """ + Hydra args define the hyperparameters varied within the sweep. For example: + cfg["hydra_args"]["agent.params.network.cnn.activation"] = tune.choice(["relu", "elu"]) + """ + assert "hydra_args" in cfg, "No hyperparameters specified." + self.cfg = cfg + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Tune Isaac Lab hyperparameters.") + parser.add_argument("--ray_address", type=str, default="auto", help="the Ray address.") + parser.add_argument( + "--cfg_file", + type=str, + default="hyperparameter_tuning/vision_cartpole_cfg.py", + required=False, + help="The relative filepath where a hyperparameter sweep is defined", + ) + parser.add_argument( + "--cfg_class", + type=str, + default="CartpoleRGBNoTuneJobCfg", + required=False, + help="Name of the hyperparameter sweep class to use", + ) + parser.add_argument( + "--run_mode", + choices=["local", "remote"], + default="remote", + help=( + "Set to local to use ./isaaclab.sh -p python, set to " + "remote to use /workspace/isaaclab/isaaclab.sh -p python" + ), + ) + parser.add_argument( + "--workflow", + default=None, # populated with RL Games + help="The absolute path of the workflow to use for the experiment. By default, RL Games is used.", + ) + parser.add_argument( + "--mlflow_uri", + type=str, + default=None, + required=False, + help="The MLFlow Uri.", + ) + parser.add_argument( + "--num_workers_per_node", + type=int, + default=1, + help="Number of workers to run on each GPU node. Only supply for parallelism on multi-gpu nodes", + ) + + parser.add_argument("--metric", type=str, default="rewards/time", help="What metric to tune for.") + + parser.add_argument( + "--mode", + choices=["max", "min"], + default="max", + help="What to optimize the metric to while tuning", + ) + parser.add_argument( + "--num_samples", + type=int, + default=100, + help="How many hyperparameter runs to try total.", + ) + parser.add_argument( + "--repeat_run_count", + type=int, + default=3, + help="How many times to repeat each hyperparameter config.", + ) + parser.add_argument( + "--process_response_timeout", + type=float, + default=PROCESS_RESPONSE_TIMEOUT, + help="Training workflow process response timeout.", + ) + parser.add_argument( + "--max_lines_to_search_experiment_logs", + type=float, + default=MAX_LINES_TO_SEARCH_EXPERIMENT_LOGS, + help="Max number of lines to search for experiment logs before terminating the training workflow process.", + ) + parser.add_argument( + "--max_log_extraction_errors", + type=float, + default=MAX_LOG_EXTRACTION_ERRORS, + help="Max number number of LogExtractionError failures before we abort the whole tuning run.", + ) + parser.add_argument( + "--progress_reporter", + type=str, + default=None, + help=( + "Optional: name of a custom reporter class defined in the cfg_file. " + "Must subclass ray.tune.ProgressReporter " + "(e.g., CustomCartpoleProgressReporter)." + ), + ) + parser.add_argument( + "--stopper", + type=str, + default=None, + help="A stop criteria in the cfg_file, must be a tune.Stopper instance.", + ) + + args = parser.parse_args() + PROCESS_RESPONSE_TIMEOUT = args.process_response_timeout + MAX_LINES_TO_SEARCH_EXPERIMENT_LOGS = int(args.max_lines_to_search_experiment_logs) + print( + "[INFO]: The max number of lines to search for experiment logs before (early) terminating the training " + f"workflow process is set to {MAX_LINES_TO_SEARCH_EXPERIMENT_LOGS}.\n" + "[INFO]: The process response timeout, used while updating tensorboard scalars and searching for " + f"experiment logs, is set to {PROCESS_RESPONSE_TIMEOUT} seconds." + ) + MAX_LOG_EXTRACTION_ERRORS = int(args.max_log_extraction_errors) + print( + "[INFO]: Max number of LogExtractionError failures before we abort the whole tuning run is " + f"set to {MAX_LOG_EXTRACTION_ERRORS}.\n" + ) + NUM_WORKERS_PER_NODE = args.num_workers_per_node + print(f"[INFO]: Using {NUM_WORKERS_PER_NODE} workers per node.") + if args.run_mode == "remote": + BASE_DIR = DOCKER_PREFIX # ensure logs are dumped to persistent location + PYTHON_EXEC = DOCKER_PREFIX + PYTHON_EXEC[2:] + if args.workflow is None: + WORKFLOW = DOCKER_PREFIX + WORKFLOW + else: + WORKFLOW = args.workflow + print(f"[INFO]: Using remote mode {PYTHON_EXEC=} {WORKFLOW=}") + + if args.mlflow_uri is not None: + import mlflow + + mlflow.set_tracking_uri(args.mlflow_uri) + from ray.air.integrations.mlflow import MLflowLoggerCallback + else: + raise ValueError("Please provide a result MLFLow URI server.") + else: # local + PYTHON_EXEC = os.getcwd() + "/" + PYTHON_EXEC[2:] + if args.workflow is None: + WORKFLOW = os.getcwd() + "/" + WORKFLOW + else: + WORKFLOW = args.workflow + BASE_DIR = os.getcwd() + print(f"[INFO]: Using local mode {PYTHON_EXEC=} {WORKFLOW=}") + file_path = args.cfg_file + class_name = args.cfg_class + print(f"[INFO]: Attempting to use sweep config from {file_path=} {class_name=}") + module_name = os.path.splitext(os.path.basename(file_path))[0] + + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + print(f"[INFO]: Successfully imported {module_name} from {file_path}") + if hasattr(module, class_name): + ClassToInstantiate = getattr(module, class_name) + print(f"[INFO]: Found correct class {ClassToInstantiate}") + instance = ClassToInstantiate() + print(f"[INFO]: Successfully instantiated class '{class_name}' from {file_path}") + cfg = instance.cfg + print(f"[INFO]: Grabbed the following hyperparameter sweep config: \n {cfg}") + # Load optional stopper config + stopper = None + if args.stopper and hasattr(module, args.stopper): + stopper = getattr(module, args.stopper) + if isinstance(stopper, type) and issubclass(stopper, tune.Stopper): + stopper = stopper() + else: + raise TypeError(f"[ERROR]: Unsupported stop criteria type: {type(stopper)}") + print(f"[INFO]: Loaded custom stop criteria from '{args.stopper}'") + # Load optional progress reporter config + progress_reporter = None + if args.progress_reporter and hasattr(module, args.progress_reporter): + progress_reporter = getattr(module, args.progress_reporter) + if isinstance(progress_reporter, type) and issubclass(progress_reporter, tune.ProgressReporter): + progress_reporter = progress_reporter() + else: + raise TypeError(f"[ERROR]: {args.progress_reporter} is not a valid ProgressReporter.") + print(f"[INFO]: Loaded custom progress reporter from '{args.progress_reporter}'") + invoke_tuning_run(cfg, args, progress_reporter=progress_reporter, stopper=stopper) + + else: + raise AttributeError(f"[ERROR]:Class '{class_name}' not found in {file_path}") diff --git a/scripts/reinforcement_learning/ray/util.py b/scripts/reinforcement_learning/ray/util.py new file mode 100644 index 0000000000000000000000000000000000000000..a73ebdf493dc8edd5ef8cd116365e0ab89a68a99 --- /dev/null +++ b/scripts/reinforcement_learning/ray/util.py @@ -0,0 +1,720 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import argparse +import os +import re +import select +import subprocess +import sys +import threading +from collections.abc import Sequence +from dataclasses import dataclass +from datetime import datetime +from math import isclose +from time import time +from typing import Any + +import ray +from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy +from tensorboard.backend.event_processing.directory_watcher import DirectoryDeletedError +from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + + +def load_tensorboard_logs(directory: str) -> dict: + """From a tensorboard directory, get the latest scalar values. If the logs can't be + found, check the summaries sublevel. + + Args: + directory: The directory of the tensorboard logging. + + Returns: + The latest available scalar values. + """ + + # replace any non-alnum/underscore/dot with "_", then collapse runs of "_" + def replace_invalid_chars(t): + t2 = re.sub(r"[^0-9A-Za-z_./]", "_", t) + t2 = re.sub(r"_+", "_", t2) + return t2.strip("_") + + # Initialize the event accumulator with a size guidance for only the latest entry + def get_latest_scalars(path: str) -> dict: + event_acc = EventAccumulator(path, size_guidance={"scalars": 1}) + try: + event_acc.Reload() + if event_acc.Tags()["scalars"]: + return { + replace_invalid_chars(tag): event_acc.Scalars(tag)[-1].value + for tag in event_acc.Tags()["scalars"] + if event_acc.Scalars(tag) + } + except (KeyError, OSError, RuntimeError, DirectoryDeletedError): + return {} + + scalars = get_latest_scalars(directory) + return scalars or get_latest_scalars(os.path.join(directory, "summaries")) + + +def get_invocation_command_from_cfg( + cfg: dict, + python_cmd: str = "/workspace/isaaclab/isaaclab.sh -p", + workflow: str = "scripts/reinforcement_learning/rl_games/train.py", +) -> str: + """Generate command with proper Hydra arguments""" + runner_args = [] + hydra_args = [] + + def process_args(args, target_list, is_hydra=False): + for key, value in args.items(): + if not is_hydra: + if key.endswith("_singleton"): + target_list.append(value) + elif key.startswith("--") or key.startswith("-"): + target_list.append(f"{key} {value}") # Space instead of = for runner args + else: + target_list.append(f"{value}") + else: + if isinstance(value, list): + # Check the type of the first item to determine formatting + if value and isinstance(value[0], dict): + # Handle list of dictionaries (e.g., CNN convs) + formatted_items = [f"{{{','.join(f'{k}:{v}' for k, v in item.items())}}}" for item in value] + else: + # Handle list of primitives (e.g., MLP units) + formatted_items = [str(x) for x in value] + target_list.append(f"'{key}=[{','.join(formatted_items)}]'") + elif isinstance(value, str) and ("{" in value or "}" in value): + target_list.append(f"'{key}={value}'") + else: + target_list.append(f"{key}={value}") + + print(f"[INFO]: Starting workflow {workflow}") + process_args(cfg["runner_args"], runner_args) + print(f"[INFO]: Retrieved workflow runner args: {runner_args}") + process_args(cfg["hydra_args"], hydra_args, is_hydra=True) + print(f"[INFO]: Retrieved hydra args: {hydra_args}") + + invoke_cmd = f"{python_cmd} {workflow} " + invoke_cmd += " ".join(runner_args) + " " + " ".join(hydra_args) + return invoke_cmd + + +@ray.remote +def remote_execute_job( + job_cmd: str, identifier_string: str, test_mode: bool = False, extract_experiment: bool = False +) -> str | dict: + """This method has an identical signature to :meth:`execute_job`, with the ray remote decorator""" + return execute_job( + job_cmd=job_cmd, identifier_string=identifier_string, test_mode=test_mode, extract_experiment=extract_experiment + ) + + +class LogExtractionError(Exception): + """Raised when we cannot extract experiment_name/logdir from the trainer output.""" + + pass + + +def execute_job( + job_cmd: str, + identifier_string: str = "job 0", + test_mode: bool = False, + extract_experiment: bool = False, + persistent_dir: str | None = None, + log_all_output: bool = False, + max_lines_to_search_logs: int = 1000, + max_time_to_search_logs: float = 200.0, +) -> str | dict: + """Issue a job (shell command). + + Args: + job_cmd: The shell command to run. + identifier_string: What prefix to add to make logs easier to differentiate + across clusters or jobs. Defaults to "job 0". + test_mode: When true, only run 'nvidia-smi'. Defaults to False. + extract_experiment: When true, search for experiment details from a training run. Defaults to False. + persistent_dir: When supplied, change to run the directory in a persistent + directory. Can be used to avoid losing logs in the /tmp directory. Defaults to None. + log_all_output: When true, print all output to the console. Defaults to False. + max_lines_to_search_logs: Maximum number of lines to search for experiment info. Defaults to 1000. + max_time_to_search_logs: Maximum time to wait for experiment info before giving up. Defaults to 200.0 seconds. + Raises: + ValueError: If the job is unable to start, or throws an error. Most likely to happen + due to running out of memory. + + Returns: + Relevant information from the job + """ + start_time = datetime.now().strftime("%H:%M:%S.%f") + result_details = [f"{identifier_string}: ---------------------------------\n"] + result_details.append(f"{identifier_string}:[INFO]: Invocation {job_cmd} \n") + node_id = ray.get_runtime_context().get_node_id() + result_details.append(f"{identifier_string}:[INFO]: Ray Node ID: {node_id} \n") + + if test_mode: + import torch + + try: + result = subprocess.run( + ["nvidia-smi", "--query-gpu=name,memory.free,serial", "--format=csv,noheader,nounits"], + capture_output=True, + check=True, + text=True, + ) + output = result.stdout.strip().split("\n") + for gpu_info in output: + name, memory_free, serial = gpu_info.split(", ") + result_details.append( + f"{identifier_string}[INFO]: Name: {name}|Memory Available: {memory_free} MB|Serial Number" + f" {serial} \n" + ) + + # Get GPU count from PyTorch + num_gpus_detected = torch.cuda.device_count() + result_details.append(f"{identifier_string}[INFO]: Detected GPUs from PyTorch: {num_gpus_detected} \n") + + # Check CUDA_VISIBLE_DEVICES and count the number of visible GPUs + cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES") + if cuda_visible_devices: + visible_devices_count = len(cuda_visible_devices.split(",")) + result_details.append( + f"{identifier_string}[INFO]: GPUs visible via CUDA_VISIBLE_DEVICES: {visible_devices_count} \n" + ) + else: + visible_devices_count = len(output) # All GPUs visible if CUDA_VISIBLE_DEVICES is not set + result_details.append( + f"{identifier_string}[INFO]: CUDA_VISIBLE_DEVICES not set; all GPUs visible" + f" ({visible_devices_count}) \n" + ) + + # If PyTorch GPU count disagrees with nvidia-smi, reset CUDA_VISIBLE_DEVICES and rerun detection + if num_gpus_detected != len(output): + result_details.append( + f"{identifier_string}[WARNING]: PyTorch and nvidia-smi disagree on GPU count! Re-running with all" + " GPUs visible. \n" + ) + result_details.append(f"{identifier_string}[INFO]: This shows that GPU resources were isolated.\n") + os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in range(len(output))]) + num_gpus_detected_after_reset = torch.cuda.device_count() + result_details.append( + f"{identifier_string}[INFO]: After setting CUDA_VISIBLE_DEVICES, PyTorch detects" + f" {num_gpus_detected_after_reset} GPUs \n" + ) + + except subprocess.CalledProcessError as e: + print(f"Error calling nvidia-smi: {e.stderr}") + result_details.append({"error": "Failed to retrieve GPU information"}) + else: + if persistent_dir: + og_dir = os.getcwd() + os.chdir(persistent_dir) + process = subprocess.Popen( + job_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1 + ) + process_file_descriptor = process.stdout.fileno() + + if persistent_dir: + os.chdir(og_dir) + experiment_name = None + logdir = None + experiment_info_pattern = re.compile("Exact experiment name requested from command line: (.+)") + logdir_pattern = re.compile(r"\[INFO\] Logging experiment in directory: (.+)$") + err_pattern = re.compile("There was an error (.+)$") + + def stream_reader(stream, identifier_string, result_details): + for line in iter(stream.readline, ""): + line = line.strip() + result_details.append(f"{identifier_string}: {line}\n") + if log_all_output: + print(f"{identifier_string}: {line}") + + # Read stdout until we find exp. info, up to max_lines_to_search_logs lines, max_time_to_search_logs, or EOF. + # Do some careful handling prevent overflowing the pipe reading buffer with error 141 + lines_read = 0 + search_duration = 0.0 + search_start_time = time() + while True: + new_line_ready, _, _ = select.select([process_file_descriptor], [], [], 1.0) # Wait up to 1s for stdout + if new_line_ready: + line = process.stdout.readline() + if not line: # EOF + break + + lines_read += 1 + line = line.strip() + result_details.append(f"{identifier_string}: {line} \n") + + if log_all_output: + print(f"{identifier_string}: {line}") + + if extract_experiment: + exp_match = experiment_info_pattern.search(line) + log_match = logdir_pattern.search(line) + err_match = err_pattern.search(line) + + if err_match: + raise ValueError(f"Encountered an error during trial run. {' '.join(result_details)}") + + if exp_match: + experiment_name = exp_match.group(1) + if log_match: + logdir = log_match.group(1) + + if experiment_name and logdir: + # Start stderr reader after finding experiment info + stderr_thread = threading.Thread( + target=stream_reader, args=(process.stderr, identifier_string, result_details) + ) + stderr_thread.daemon = True + stderr_thread.start() + + # Start stdout reader to continue reading to flush buffer + stdout_thread = threading.Thread( + target=stream_reader, args=(process.stdout, identifier_string, result_details) + ) + stdout_thread.daemon = True + stdout_thread.start() + + return { + "experiment_name": experiment_name, + "logdir": logdir, + "proc": process, + "result": " ".join(result_details), + } + + if extract_experiment: # if we are looking for experiment info, check for timeouts and line limits + search_duration = time() - search_start_time + if search_duration > max_time_to_search_logs: + print(f"[ERROR]: Could not find experiment logs within {max_time_to_search_logs} seconds.") + break + if lines_read >= max_lines_to_search_logs: + print(f"[ERROR]: Could not find experiment logs within first {max_lines_to_search_logs} lines.") + break + + # If we reach here, we didn't find experiment info in the output + if extract_experiment and not (experiment_name and logdir): + error_msg = ( + "Could not extract experiment_name/logdir from trainer output " + f"(experiment_name={experiment_name!r}, logdir={logdir!r}).\n" + "\tMake sure your training script prints the following correctly:\n" + "\t\tExact experiment name requested from command line: \n" + "\t\t[INFO] Logging experiment in directory: \n\n" + ) + print(f"[ERROR]: {error_msg}") + raise LogExtractionError("Could not extract experiment_name/logdir from training workflow output.") + process.wait() + now = datetime.now().strftime("%H:%M:%S.%f") + completion_info = f"\n[INFO]: {identifier_string}: Job Started at {start_time}, completed at {now}\n" + print(completion_info) + result_details.append(completion_info) + return " ".join(result_details) + + +def ray_init(ray_address: str = "auto", runtime_env: dict[str, Any] | None = None, log_to_driver: bool = False): + """Initialize Ray with the given address and runtime environment.""" + if not ray.is_initialized(): + print( + f"[INFO] Initializing Ray with address {ray_address}, log_to_driver={log_to_driver}," + f" runtime_env={runtime_env}" + ) + ray.init(address=ray_address, runtime_env=runtime_env, log_to_driver=log_to_driver) + else: + print("[WARNING]: Attempting to initialize Ray but it is already initialized!") + + +def get_gpu_node_resources( + total_resources: bool = False, + one_node_only: bool = False, + include_gb_ram: bool = False, + include_id: bool = False, +) -> list[dict] | dict: + """Get information about available GPU node resources. + + Args: + total_resources: When true, return total available resources. Defaults to False. + one_node_only: When true, return resources for a single node. Defaults to False. + include_gb_ram: Set to true to convert MB to GB in result + include_id: Set to true to include node ID + ray_address: The ray address to connect to. + + Returns: + Resource information for all nodes, sorted by descending GPU count, then descending CPU + count, then descending RAM capacity, and finally by node ID in ascending order if available, + or simply the resource for a single node if requested. + """ + if not ray.is_initialized(): + raise RuntimeError("Ray must be initialized before calling get_gpu_node_resources().") + nodes = ray.nodes() + node_resources = [] + total_cpus = 0 + total_gpus = 0 + total_memory = 0 # in bytes + + for node in nodes: + if node["Alive"] and "GPU" in node["Resources"]: + node_id = node["NodeID"] + resources = node["Resources"] + cpus = resources.get("CPU", 0) + gpus = resources.get("GPU", 0) + memory = resources.get("memory", 0) + node_resources.append({"CPU": cpus, "GPU": gpus, "memory": memory}) + + if include_id: + node_resources[-1]["id"] = node_id + if include_gb_ram: + node_resources[-1]["ram_gb"] = memory / 1024**3 + + total_cpus += cpus + total_gpus += gpus + total_memory += memory + node_resources = sorted(node_resources, key=lambda x: (-x["GPU"], -x["CPU"], -x["memory"], x.get("id", ""))) + + if total_resources: + # Return summed total resources + return {"CPU": total_cpus, "GPU": total_gpus, "memory": total_memory} + + if one_node_only and node_resources: + return node_resources[0] + + return node_resources + + +def add_resource_arguments( + arg_parser: argparse.ArgumentParser, + defaults: list | None = None, + cluster_create_defaults: bool = False, +) -> argparse.ArgumentParser: + """Add resource arguments to a cluster; this is shared across both + wrapping resources and launching clusters. + + Args: + arg_parser: the argparser to add the arguments to. This argparser is mutated. + defaults: The default values for GPUs, CPUs, RAM, and Num Workers + cluster_create_defaults: Set to true to populate reasonable defaults for creating clusters. + Returns: + The argparser with the standard resource arguments. + """ + if defaults is None: + if cluster_create_defaults: + defaults = [[1], [8], [16], [1]] + else: + defaults = [None, None, None, [1]] + arg_parser.add_argument( + "--gpu_per_worker", + nargs="+", + type=int, + default=defaults[0], + help="Number of GPUs per worker node. Supply more than one for heterogeneous resources", + ) + arg_parser.add_argument( + "--cpu_per_worker", + nargs="+", + type=int, + default=defaults[1], + help="Number of CPUs per worker node. Supply more than one for heterogeneous resources", + ) + arg_parser.add_argument( + "--ram_gb_per_worker", + nargs="+", + type=int, + default=defaults[2], + help="RAM in GB per worker node. Supply more than one for heterogeneous resources.", + ) + arg_parser.add_argument( + "--num_workers", + nargs="+", + type=int, + default=defaults[3], + help="Number of desired workers. Supply more than one for heterogeneous resources.", + ) + return arg_parser + + +def fill_in_missing_resources( + args: argparse.Namespace, resources: dict | None = None, cluster_creation_flag: bool = False, policy: callable = max +): + """Normalize the lengths of resource lists based on the longest list provided.""" + print("[INFO]: Filling in missing command line arguments with best guess...") + if resources is None: + resources = { + "gpu_per_worker": args.gpu_per_worker, + "cpu_per_worker": args.cpu_per_worker, + "ram_gb_per_worker": args.ram_gb_per_worker, + "num_workers": args.num_workers, + } + if cluster_creation_flag: + cluster_creation_resources = {"worker_accelerator": args.worker_accelerator} + resources.update(cluster_creation_resources) + + # Calculate the maximum length of any list + max_length = max(len(v) for v in resources.values()) + print("[INFO]: Resource list lengths:") + for key, value in resources.items(): + print(f"[INFO] {key}: {len(value)} values {value}") + + # Extend each list to match the maximum length using the maximum value in each list + for key, value in resources.items(): + potential_value = getattr(args, key) + if potential_value is not None: + max_value = policy(policy(value), policy(potential_value)) + else: + max_value = policy(value) + extension_length = max_length - len(value) + if extension_length > 0: # Only extend if the current list is shorter than max_length + print(f"\n[WARNING]: Resource '{key}' needs extension:") + print(f"[INFO] Current length: {len(value)}") + print(f"[INFO] Target length: {max_length}") + print(f"[INFO] Filling in {extension_length} missing values with {max_value}") + print(f"[INFO] To avoid auto-filling, provide {extension_length} more {key} value(s)") + value.extend([max_value] * extension_length) + setattr(args, key, value) + resources[key] = value + print(f"[INFO] Final {key} values: {getattr(args, key)}") + print("[INFO]: Done filling in command line arguments...\n\n") + return args + + +def populate_isaac_ray_cfg_args(cfg: dict = {}) -> dict: + """Small utility method to create empty fields if needed for a configuration.""" + if "runner_args" not in cfg: + cfg["runner_args"] = {} + if "hydra_args" not in cfg: + cfg["hydra_args"] = {} + return cfg + + +def _dicts_equal(d1: dict, d2: dict, tol=1e-9) -> bool: + """Check if two dicts are equal; helps ensure only new logs are returned.""" + if d1.keys() != d2.keys(): + return False + for key in d1: + if isinstance(d1[key], float) and isinstance(d2[key], float): + if not isclose(d1[key], d2[key], abs_tol=tol): + return False + elif d1[key] != d2[key]: + return False + return True + + +@dataclass +class JobResource: + """A dataclass to represent a resource request for a job.""" + + num_gpus: float | None = None + num_cpus: float | None = None + memory: int | None = None # in bytes + + def to_opt(self) -> dict[str, Any]: + """Convert the resource request to a dictionary.""" + opt = {} + if self.num_gpus is not None: + opt["num_gpus"] = self.num_gpus + if self.num_cpus is not None: + opt["num_cpus"] = self.num_cpus + if self.memory is not None: + opt["memory"] = self.memory + return opt + + def to_pg_resources(self) -> dict[str, Any]: + """Convert the resource request to a dictionary suitable for placement groups.""" + res = {} + if self.num_gpus is not None: + res["GPU"] = self.num_gpus + if self.num_cpus is not None: + res["CPU"] = self.num_cpus + if self.memory is not None: + res["memory"] = self.memory + return res + + +@dataclass +class JobNode: + """A dataclass to represent a node for job affinity.""" + + specific: str | None = None + hostname: str | None = None + node_id: str | None = None + + def to_opt(self, nodes: list[dict[str, Any]]) -> dict[str, Any]: + """ + Convert node affinity settings into a dictionary of Ray actor scheduling options. + + Args: + nodes (list[dict[str, Any]]): List of node metadata from `ray.nodes()` which looks like this: + [{ + 'NodeID': 'xxx', + 'Alive': True, + 'NodeManagerAddress': 'x.x.x.x', + 'NodeManagerHostname': 'ray-head-mjzzf', + 'NodeManagerPort': 44039, + 'ObjectManagerPort': 35689, + 'ObjectStoreSocketName': '/tmp/ray/session_xxx/sockets/plasma_store', + 'RayletSocketName': '/tmp/ray/session_xxx/sockets/raylet', + 'MetricsExportPort': 8080, + 'NodeName': 'x.x.x.x', + 'RuntimeEnvAgentPort': 63725, + 'DeathReason': 0, + 'DeathReasonMessage': '', + 'alive': True, + 'Resources': { + 'node:__internal_head__': 1.0, + 'object_store_memory': 422449279795.0, + 'memory': 1099511627776.0, + 'GPU': 8.0, + 'node:x.x.x.x': 1.0, + 'CPU': 192.0, + 'accelerator_type:H20': 1.0 + }, + 'Labels': { + 'ray.io/node_id': 'xxx' + } + },...] + + Returns: + dict[str, Any]: A dictionary with possible scheduling options: + - Empty if no specific placement requirement. + - "scheduling_strategy" key set to `NodeAffinitySchedulingStrategy` + if hostname or node_id placement is specified. + + Raises: + ValueError: If hostname/node_id is specified but not found in the cluster + or the node is not alive. + """ + opt = {} + if self.specific is None or self.specific == "any": + return opt + elif self.specific == "hostname": + if self.hostname is None: + raise ValueError("Hostname must be specified when specific is 'hostname'") + for node in nodes: + if node["NodeManagerHostname"] == self.hostname: + if node["alive"] is False: + raise ValueError(f"Node {node['NodeID']} is not alive") + opt["scheduling_strategy"] = NodeAffinitySchedulingStrategy(node_id=node["NodeID"], soft=False) + return opt + raise ValueError(f"Hostname {self.hostname} not found in nodes: {nodes}") + elif self.specific == "node_id": + if self.node_id is None: + raise ValueError("Node ID must be specified when specific is 'node_id'") + for node in nodes: + if node["NodeID"] == self.node_id: + if node["alive"] is False: + raise ValueError(f"Node {node['NodeID']} is not alive") + opt["scheduling_strategy"] = NodeAffinitySchedulingStrategy(node_id=node["NodeID"], soft=False) + return opt + raise ValueError(f"Node ID {self.node_id} not found in nodes: {nodes}") + else: + raise ValueError(f"Invalid specific value: {self.specific}. Must be 'any', 'hostname', or 'node_id'.") + + +@dataclass +class Job: + """A dataclass to represent a job to be submitted to Ray.""" + + # job command + cmd: str | None = None + py_args: str | None = None + # identifier string for the job, e.g., "job 0" + name: str = "" + # job resources, e.g., {"CPU": 4, "GPU": 1} + resources: JobResource | None = None + # specify the node to run the job on, if needed to run on a specific node + node: JobNode | None = None + + def to_opt(self, nodes: list[dict[str, Any]]) -> dict[str, Any]: + """ + Convert the job definition into a dictionary of Ray scheduling options. + + Args: + nodes (list[dict[str, Any]]): Node information from `ray.nodes()`. + + Returns: + dict[str, Any]: Combined scheduling options from: + - `JobResource.to_opt()` for resource requirements + - `JobNode.to_opt()` for node placement constraints + """ + opt = {} + if self.resources is not None: + opt.update(self.resources.to_opt()) + if self.node is not None: + opt.update(self.node.to_opt(nodes)) + return opt + + +@ray.remote +class JobActor: + """Actor to run job in Ray cluster.""" + + def __init__(self, job: Job, test_mode: bool, log_all_output: bool, extract_experiment: bool = False): + self.job = job + self.test_mode = test_mode + self.log_all_output = log_all_output + self.extract_experiment = extract_experiment + self.done = True + + def ready(self) -> bool: + """Check if the job is ready to run.""" + return self.done + + def run(self): + """Run the job.""" + cmd = self.job.cmd if self.job.cmd else " ".join([sys.executable, *self.job.py_args.split()]) + return execute_job( + job_cmd=cmd, + identifier_string=self.job.name, + test_mode=self.test_mode, + extract_experiment=self.extract_experiment, + log_all_output=self.log_all_output, + ) + + +def submit_wrapped_jobs( + jobs: Sequence[Job], + log_realtime: bool = True, + test_mode: bool = False, + concurrent: bool = False, +) -> None: + """ + Submit a list of jobs to the Ray cluster and manage their execution. + + Args: + jobs (Sequence[Job]): A sequence of Job objects to execute on Ray. + log_realtime (bool): Whether to log stdout/stderr in real-time. Defaults to True. + test_mode (bool): If True, run in GPU sanity-check mode instead of actual jobs. Defaults to False. + concurrent (bool): Whether to launch tasks simultaneously as a batch, + or independently as resources become available. Defaults to False. + + Returns: + None + """ + if jobs is None or len(jobs) == 0: + print("[WARNING]: No jobs to submit") + return + if not ray.is_initialized(): + raise Exception("Ray is not initialized. Please initialize Ray before submitting jobs.") + nodes = ray.nodes() + actors = [] + for i, job in enumerate(jobs): + opts = job.to_opt(nodes) + name = job.name or f"job_{i + 1}" + print(f"[INFO] Create {name} with opts={opts}") + job_actor = JobActor.options(**opts).remote(job, test_mode, log_realtime) + actors.append(job_actor) + try: + if concurrent: + ray.get([actor.ready.remote() for actor in actors]) + print("[INFO] All actors are ready to run.") + future = [actor.run.remote() for actor in actors] + while future: + ready, not_ready = ray.wait(future, timeout=5) + for result in ray.get(ready): + print(f"\n{result}\n") + future = not_ready + print("[INFO] all jobs completed.") + except KeyboardInterrupt: + print("[INFO] KeyboardInterrupt received, cancelling …") + for actor in actors: + ray.cancel(actor, force=True) + sys.exit(0) diff --git a/scripts/reinforcement_learning/ray/wrap_resources.py b/scripts/reinforcement_learning/ray/wrap_resources.py new file mode 100644 index 0000000000000000000000000000000000000000..158bd0d824602ea423b07288ae52758381dacf52 --- /dev/null +++ b/scripts/reinforcement_learning/ray/wrap_resources.py @@ -0,0 +1,162 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script dispatches sub-job(s) (individual jobs, use :file:`tuner.py` for tuning jobs) +to worker(s) on GPU-enabled node(s) of a specific cluster as part of an resource-wrapped aggregate +job. If no desired compute resources for each sub-job are specified, +this script creates one worker per available node for each node with GPU(s) in the cluster. +If the desired resources for each sub-job is specified, +the maximum number of workers possible with the desired resources are created for each node +with GPU(s) in the cluster. It is also possible to split available node resources for each node +into the desired number of workers with the ``--num_workers`` flag, to be able to easily +parallelize sub-jobs on multi-GPU nodes. Due to Isaac Lab requiring a GPU, +this ignores all CPU only nodes such as loggers. + +Sub-jobs are matched with node(s) in a cluster via the following relation: +sorted_nodes = Node sorted by descending GPUs, then descending CPUs, then descending RAM, then node ID +node_submitted_to = sorted_nodes[job_index % total_node_count] + +To check the ordering of sorted nodes, supply the ``--test`` argument and run the script. + +Sub-jobs are separated by the + delimiter. The ``--sub_jobs`` argument must be the last +argument supplied to the script. + +If there is more than one available worker, and more than one sub-job, +sub-jobs will be executed in parallel. If there are more sub-jobs than workers, sub-jobs will +be dispatched to workers as they become available. There is no limit on the number +of sub-jobs that can be near-simultaneously submitted. + +This script is meant to be executed on a Ray cluster head node as an aggregate cluster job. +To submit aggregate cluster jobs such as this script to one or more remote clusters, +see :file:`../submit_isaac_ray_job.py`. + +KubeRay clusters on Google GKE can be created with :file:`../launch.py` + +Usage: + +.. code-block:: bash + # **Ensure that sub-jobs are separated by the ``+`` delimiter.** + # Generic Templates----------------------------------- + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py -h + # No resource isolation; no parallelization: + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py + --sub_jobs ++ + # Automatic Resource Isolation; Example A: needed for parallelization + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py \ + --num_workers \ + --sub_jobs + + # Manual Resource Isolation; Example B: needed for parallelization + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py --num_cpu_per_worker \ + --gpu_per_worker --ram_gb_per_worker --sub_jobs + + # Manual Resource Isolation; Example C: Needed for parallelization, for heterogeneous workloads + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py --num_cpu_per_worker \ + --gpu_per_worker --ram_gb_per_worker --sub_jobs + + # to see all arguments + ./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py -h +""" + +import argparse + +import util + + +def wrap_resources_to_jobs(jobs: list[str], args: argparse.Namespace) -> None: + """ + Provided a list of jobs, dispatch jobs to one worker per available node, + unless otherwise specified by resource constraints. + + Args: + jobs: bash commands to execute on a Ray cluster + args: The arguments for resource allocation + + """ + job_objs = [] + util.ray_init( + ray_address=args.ray_address, + runtime_env={ + "py_modules": None if not args.py_modules else args.py_modules, + }, + log_to_driver=False, + ) + gpu_node_resources = util.get_gpu_node_resources(include_id=True, include_gb_ram=True) + + if any([args.gpu_per_worker, args.cpu_per_worker, args.ram_gb_per_worker]) and args.num_workers: + raise ValueError("Either specify only num_workers or only granular resources(GPU,CPU,RAM_GB).") + + num_nodes = len(gpu_node_resources) + # Populate arguments + formatted_node_resources = { + "gpu_per_worker": [gpu_node_resources[i]["GPU"] for i in range(num_nodes)], + "cpu_per_worker": [gpu_node_resources[i]["CPU"] for i in range(num_nodes)], + "ram_gb_per_worker": [gpu_node_resources[i]["ram_gb"] for i in range(num_nodes)], + "num_workers": args.num_workers, # By default, 1 worker por node + } + args = util.fill_in_missing_resources(args, resources=formatted_node_resources, policy=min) + print(f"[INFO]: Number of GPU nodes found: {num_nodes}") + if args.test: + jobs = ["nvidia-smi"] * num_nodes + for i, job in enumerate(jobs): + gpu_node = gpu_node_resources[i % num_nodes] + print(f"[INFO]: Creating job {i + 1} of {len(jobs)} with job '{job}' to node {gpu_node}") + print( + f"[INFO]: Resource parameters: GPU: {args.gpu_per_worker[i]}" + f" CPU: {args.cpu_per_worker[i]} RAM {args.ram_gb_per_worker[i]}" + ) + print(f"[INFO] For the node parameters, creating {args.num_workers[i]} workers") + num_gpus = args.gpu_per_worker[i] / args.num_workers[i] + num_cpus = args.cpu_per_worker[i] / args.num_workers[i] + memory = (args.ram_gb_per_worker[i] * 1024**3) / args.num_workers[i] + job_objs.append( + util.Job( + cmd=job, + name=f"Job-{i + 1}", + resources=util.JobResource(num_gpus=num_gpus, num_cpus=num_cpus, memory=memory), + node=util.JobNode( + specific="node_id", + node_id=gpu_node["id"], + ), + ) + ) + # submit jobs + util.submit_wrapped_jobs(jobs=job_objs, test_mode=args.test, concurrent=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Submit multiple jobs with optional GPU testing.") + parser = util.add_resource_arguments(arg_parser=parser) + parser.add_argument("--ray_address", type=str, default="auto", help="the Ray address.") + parser.add_argument( + "--test", + action="store_true", + help=( + "Run nvidia-smi test instead of the arbitrary job," + "can use as a sanity check prior to any jobs to check " + "that GPU resources are correctly isolated." + ), + ) + parser.add_argument( + "--py_modules", + type=str, + nargs="*", + default=[], + help=( + "List of python modules or paths to add before running the job. Example: --py_modules my_package/my_package" + ), + ) + parser.add_argument( + "--sub_jobs", + type=str, + nargs=argparse.REMAINDER, + help="This should be last wrapper argument. Jobs separated by the + delimiter to run on a cluster.", + ) + args = parser.parse_args() + if args.sub_jobs is not None: + jobs = " ".join(args.sub_jobs) + formatted_jobs = jobs.split("+") + else: + formatted_jobs = [] + print(f"[INFO]: Isaac Ray Wrapper received jobs {formatted_jobs=}") + wrap_resources_to_jobs(jobs=formatted_jobs, args=args) diff --git a/scripts/reinforcement_learning/rl_games/play.py b/scripts/reinforcement_learning/rl_games/play.py new file mode 100644 index 0000000000000000000000000000000000000000..ee2dbcdbb149be90ff2ed152341b54848c8301a7 --- /dev/null +++ b/scripts/reinforcement_learning/rl_games/play.py @@ -0,0 +1,239 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play a checkpoint if an RL agent from RL-Games.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from RL-Games.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="rl_games_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.") +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--use_pretrained_checkpoint", + action="store_true", + help="Use the pre-trained checkpoint from Nucleus.", +) +parser.add_argument( + "--use_last_checkpoint", + action="store_true", + help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.", +) +parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +import math +import os +import random +import time + +import gymnasium as gym +import torch +from rl_games.common import env_configurations, vecenv +from rl_games.common.player import BasePlayer +from rl_games.torch_runner import Runner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.dict import print_dict + +from isaaclab_rl.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper +from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +# PLACEHOLDER: Extension template (do not remove this comment) + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Play with RL-Games agent.""" + # grab task name for checkpoint path + task_name = args_cli.task.split(":")[-1] + train_task_name = task_name.replace("-Play", "") + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + agent_cfg["params"]["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["params"]["seed"] + # set the environment seed (after multi-gpu config for updated rank from agent seed) + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg["params"]["seed"] + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rl_games", agent_cfg["params"]["config"]["name"]) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Loading experiment from directory: {log_root_path}") + # find checkpoint + if args_cli.use_pretrained_checkpoint: + resume_path = get_published_pretrained_checkpoint("rl_games", train_task_name) + if not resume_path: + print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.") + return + elif args_cli.checkpoint is None: + # specify directory for logging runs + run_dir = agent_cfg["params"]["config"].get("full_experiment_name", ".*") + # specify name of checkpoint + if args_cli.use_last_checkpoint: + checkpoint_file = ".*" + else: + # this loads the best checkpoint + checkpoint_file = f"{agent_cfg['params']['config']['name']}.pth" + # get path to previous checkpoint + resume_path = get_checkpoint_path(log_root_path, run_dir, checkpoint_file, other_dirs=["nn"]) + else: + resume_path = retrieve_file_path(args_cli.checkpoint) + log_dir = os.path.dirname(os.path.dirname(resume_path)) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # wrap around environment for rl-games + rl_device = agent_cfg["params"]["config"]["device"] + clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf) + clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf) + obs_groups = agent_cfg["params"]["env"].get("obs_groups") + concate_obs_groups = agent_cfg["params"]["env"].get("concate_obs_groups", True) + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_root_path, log_dir, "videos", "play"), + "step_trigger": lambda step: step == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + # wrap around environment for rl-games + env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions, obs_groups, concate_obs_groups) + + # register the environment to rl-games registry + # note: in agents configuration: environment name must be "rlgpu" + vecenv.register( + "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs) + ) + env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env}) + + # load previously trained model + agent_cfg["params"]["load_checkpoint"] = True + agent_cfg["params"]["load_path"] = resume_path + print(f"[INFO]: Loading model checkpoint from: {agent_cfg['params']['load_path']}") + + # set number of actors into agent config + agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs + # create runner from rl-games + runner = Runner() + runner.load(agent_cfg) + # obtain the agent from the runner + agent: BasePlayer = runner.create_player() + agent.restore(resume_path) + agent.reset() + + dt = env.unwrapped.step_dt + + # reset environment + obs = env.reset() + if isinstance(obs, dict): + obs = obs["obs"] + timestep = 0 + # required: enables the flag for batched observations + _ = agent.get_batch_size(obs, 1) + # initialize RNN states if used + if agent.is_rnn: + agent.init_rnn() + # simulate environment + # note: We simplified the logic in rl-games player.py (:func:`BasePlayer.run()`) function in an + # attempt to have complete control over environment stepping. However, this removes other + # operations such as masking that is used for multi-agent learning by RL-Games. + while simulation_app.is_running(): + start_time = time.time() + # run everything in inference mode + with torch.inference_mode(): + # convert obs to agent format + obs = agent.obs_to_torch(obs) + # agent stepping + actions = agent.get_action(obs, is_deterministic=agent.is_deterministic) + # env stepping + obs, _, dones, _ = env.step(actions) + + # perform operations for terminated episodes + if len(dones) > 0: + # reset rnn state for terminated episodes + if agent.is_rnn and agent.states is not None: + for s in agent.states: + s[:, dones, :] = 0.0 + if args_cli.video: + timestep += 1 + # exit the play loop after recording one video + if timestep == args_cli.video_length: + break + + # time delay for real-time evaluation + sleep_time = dt - (time.time() - start_time) + if args_cli.real_time and sleep_time > 0: + time.sleep(sleep_time) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/rl_games/train.py b/scripts/reinforcement_learning/rl_games/train.py new file mode 100644 index 0000000000000000000000000000000000000000..5b85ba5b429d4a907b8008bbebf7fc0b698979d0 --- /dev/null +++ b/scripts/reinforcement_learning/rl_games/train.py @@ -0,0 +1,261 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to train RL agent with RL-Games.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys +from distutils.util import strtobool + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="rl_games_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.") +parser.add_argument("--sigma", type=str, default=None, help="The policy's initial standard deviation.") +parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.") +parser.add_argument("--wandb-project-name", type=str, default=None, help="the wandb's project name") +parser.add_argument("--wandb-entity", type=str, default=None, help="the entity (team) of wandb's project") +parser.add_argument("--wandb-name", type=str, default=None, help="the name of wandb's run") +parser.add_argument( + "--track", + type=lambda x: bool(strtobool(x)), + default=False, + nargs="?", + const=True, + help="if toggled, this experiment will be tracked with Weights and Biases", +) +parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.") +parser.add_argument( + "--ray-proc-id", "-rid", type=int, default=None, help="Automatically configured by Ray integration, otherwise None." +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import logging +import math +import os +import random +import time +from datetime import datetime + +import gymnasium as gym +from rl_games.common import env_configurations, vecenv +from rl_games.common.algo_observer import IsaacAlgoObserver +from rl_games.torch_runner import Runner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.rl_games import MultiObserver, PbtAlgoObserver, RlGamesGpuEnv, RlGamesVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config + +# import logger +logger = logging.getLogger(__name__) + +# PLACEHOLDER: Extension template (do not remove this comment) + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Train with RL-Games agent.""" + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + agent_cfg["params"]["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["params"]["seed"] + agent_cfg["params"]["config"]["max_epochs"] = ( + args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg["params"]["config"]["max_epochs"] + ) + if args_cli.checkpoint is not None: + resume_path = retrieve_file_path(args_cli.checkpoint) + agent_cfg["params"]["load_checkpoint"] = True + agent_cfg["params"]["load_path"] = resume_path + print(f"[INFO]: Loading model checkpoint from: {agent_cfg['params']['load_path']}") + train_sigma = float(args_cli.sigma) if args_cli.sigma is not None else None + + # multi-gpu training config + if args_cli.distributed: + agent_cfg["params"]["seed"] += app_launcher.global_rank + agent_cfg["params"]["config"]["device"] = f"cuda:{app_launcher.local_rank}" + agent_cfg["params"]["config"]["device_name"] = f"cuda:{app_launcher.local_rank}" + agent_cfg["params"]["config"]["multi_gpu"] = True + # update env config device + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + + # set the environment seed (after multi-gpu config for updated rank from agent seed) + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg["params"]["seed"] + + # specify directory for logging experiments + config_name = agent_cfg["params"]["config"]["name"] + log_root_path = os.path.join("logs", "rl_games", config_name) + if "pbt" in agent_cfg and agent_cfg["pbt"]["directory"] != ".": + log_root_path = os.path.join(agent_cfg["pbt"]["directory"], log_root_path) + else: + log_root_path = os.path.abspath(log_root_path) + + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # specify directory for logging runs + log_dir = agent_cfg["params"]["config"].get("full_experiment_name", datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) + # set directory into agent config + # logging directory path: / + agent_cfg["params"]["config"]["train_dir"] = log_root_path + agent_cfg["params"]["config"]["full_experiment_name"] = log_dir + wandb_project = config_name if args_cli.wandb_project_name is None else args_cli.wandb_project_name + experiment_name = log_dir if args_cli.wandb_name is None else args_cli.wandb_name + + # dump the configuration into log-directory + dump_yaml(os.path.join(log_root_path, log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_root_path, log_dir, "params", "agent.yaml"), agent_cfg) + print(f"Exact experiment name requested from command line: {os.path.join(log_root_path, log_dir)}") + + # read configurations about the agent-training + rl_device = agent_cfg["params"]["config"]["device"] + clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf) + clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf) + obs_groups = agent_cfg["params"]["env"].get("obs_groups") + concate_obs_groups = agent_cfg["params"]["env"].get("concate_obs_groups", True) + + # set the IO descriptors export flag if requested + if isinstance(env_cfg, ManagerBasedRLEnvCfg): + env_cfg.export_io_descriptors = args_cli.export_io_descriptors + else: + logger.warning( + "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported." + ) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = os.path.join(log_root_path, log_dir) + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_root_path, log_dir, "videos", "train"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + start_time = time.time() + + # wrap around environment for rl-games + env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions, obs_groups, concate_obs_groups) + + # register the environment to rl-games registry + # note: in agents configuration: environment name must be "rlgpu" + vecenv.register( + "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs) + ) + env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env}) + + # set number of actors into agent config + agent_cfg["params"]["config"]["num_actors"] = env.unwrapped.num_envs + # create runner from rl-games + + if "pbt" in agent_cfg and agent_cfg["pbt"]["enabled"]: + observers = MultiObserver([IsaacAlgoObserver(), PbtAlgoObserver(agent_cfg, args_cli)]) + runner = Runner(observers) + else: + runner = Runner(IsaacAlgoObserver()) + + runner.load(agent_cfg) + + # reset the agent and env + runner.reset() + # train the agent + + global_rank = int(os.getenv("RANK", "0")) + if args_cli.track and global_rank == 0: + if args_cli.wandb_entity is None: + raise ValueError("Weights and Biases entity must be specified for tracking.") + import wandb + + wandb.init( + project=wandb_project, + entity=args_cli.wandb_entity, + name=experiment_name, + sync_tensorboard=True, + monitor_gym=True, + save_code=True, + ) + if not wandb.run.resumed: + wandb.config.update({"env_cfg": env_cfg.to_dict()}) + wandb.config.update({"agent_cfg": agent_cfg}) + + if args_cli.checkpoint is not None: + runner.run({"train": True, "play": False, "sigma": train_sigma, "checkpoint": resume_path}) + else: + runner.run({"train": True, "play": False, "sigma": train_sigma}) + + print(f"Training time: {round(time.time() - start_time, 2)} seconds") + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/rsl_rl/cli_args.py b/scripts/reinforcement_learning/rsl_rl/cli_args.py new file mode 100644 index 0000000000000000000000000000000000000000..51cf868b5cd51953d4514458f8e7e9c70cfbf72a --- /dev/null +++ b/scripts/reinforcement_learning/rsl_rl/cli_args.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import argparse +import random +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg + + +def add_rsl_rl_args(parser: argparse.ArgumentParser): + """Add RSL-RL arguments to the parser. + + Args: + parser: The parser to add the arguments to. + """ + # create a new argument group + arg_group = parser.add_argument_group("rsl_rl", description="Arguments for RSL-RL agent.") + # -- experiment arguments + arg_group.add_argument( + "--experiment_name", type=str, default=None, help="Name of the experiment folder where logs will be stored." + ) + arg_group.add_argument("--run_name", type=str, default=None, help="Run name suffix to the log directory.") + # -- load arguments + arg_group.add_argument("--resume", action="store_true", default=False, help="Whether to resume from a checkpoint.") + arg_group.add_argument("--load_run", type=str, default=None, help="Name of the run folder to resume from.") + arg_group.add_argument("--checkpoint", type=str, default=None, help="Checkpoint file to resume from.") + # -- logger arguments + arg_group.add_argument( + "--logger", type=str, default=None, choices={"wandb", "tensorboard", "neptune"}, help="Logger module to use." + ) + arg_group.add_argument( + "--log_project_name", type=str, default=None, help="Name of the logging project when using wandb or neptune." + ) + + +def parse_rsl_rl_cfg(task_name: str, args_cli: argparse.Namespace) -> RslRlBaseRunnerCfg: + """Parse configuration for RSL-RL agent based on inputs. + + Args: + task_name: The name of the environment. + args_cli: The command line arguments. + + Returns: + The parsed configuration for RSL-RL agent based on inputs. + """ + from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry + + # load the default configuration + rslrl_cfg: RslRlBaseRunnerCfg = load_cfg_from_registry(task_name, "rsl_rl_cfg_entry_point") + rslrl_cfg = update_rsl_rl_cfg(rslrl_cfg, args_cli) + return rslrl_cfg + + +def update_rsl_rl_cfg(agent_cfg: RslRlBaseRunnerCfg, args_cli: argparse.Namespace): + """Update configuration for RSL-RL agent based on inputs. + + Args: + agent_cfg: The configuration for RSL-RL agent. + args_cli: The command line arguments. + + Returns: + The updated configuration for RSL-RL agent based on inputs. + """ + # override the default configuration with CLI arguments + if hasattr(args_cli, "seed") and args_cli.seed is not None: + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + agent_cfg.seed = args_cli.seed + if args_cli.resume is not None: + agent_cfg.resume = args_cli.resume + if args_cli.load_run is not None: + agent_cfg.load_run = args_cli.load_run + if args_cli.checkpoint is not None: + agent_cfg.load_checkpoint = args_cli.checkpoint + if args_cli.run_name is not None: + agent_cfg.run_name = args_cli.run_name + if args_cli.logger is not None: + agent_cfg.logger = args_cli.logger + # set the project name for wandb and neptune + if agent_cfg.logger in {"wandb", "neptune"} and args_cli.log_project_name: + agent_cfg.wandb_project = args_cli.log_project_name + agent_cfg.neptune_project = args_cli.log_project_name + + return agent_cfg diff --git a/scripts/reinforcement_learning/rsl_rl/play.py b/scripts/reinforcement_learning/rsl_rl/play.py new file mode 100644 index 0000000000000000000000000000000000000000..beb920721738a46eb9189dac78f823d29161e50b --- /dev/null +++ b/scripts/reinforcement_learning/rsl_rl/play.py @@ -0,0 +1,210 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play a checkpoint if an RL agent from RSL-RL.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# local imports +import cli_args # isort: skip + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--use_pretrained_checkpoint", + action="store_true", + help="Use the pre-trained checkpoint from Nucleus.", +) +parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.") +# append RSL-RL cli arguments +cli_args.add_rsl_rl_args(parser) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import time + +import gymnasium as gym +import torch +from rsl_rl.runners import DistillationRunner, OnPolicyRunner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.dict import print_dict + +from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper, export_policy_as_jit, export_policy_as_onnx +from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +# PLACEHOLDER: Extension template (do not remove this comment) + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlBaseRunnerCfg): + """Play with RSL-RL agent.""" + # grab task name for checkpoint path + task_name = args_cli.task.split(":")[-1] + train_task_name = task_name.replace("-Play", "") + + # override configurations with non-hydra CLI arguments + agent_cfg: RslRlBaseRunnerCfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli) + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg.seed + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Loading experiment from directory: {log_root_path}") + if args_cli.use_pretrained_checkpoint: + resume_path = get_published_pretrained_checkpoint("rsl_rl", train_task_name) + if not resume_path: + print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.") + return + elif args_cli.checkpoint: + resume_path = retrieve_file_path(args_cli.checkpoint) + else: + resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint) + + log_dir = os.path.dirname(resume_path) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "play"), + "step_trigger": lambda step: step == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + # wrap around environment for rsl-rl + env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) + + print(f"[INFO]: Loading model checkpoint from: {resume_path}") + # load previously trained model + if agent_cfg.class_name == "OnPolicyRunner": + runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device) + elif agent_cfg.class_name == "DistillationRunner": + runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device) + else: + raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}") + runner.load(resume_path) + + # obtain the trained policy for inference + policy = runner.get_inference_policy(device=env.unwrapped.device) + + # extract the neural network module + # we do this in a try-except to maintain backwards compatibility. + try: + # version 2.3 onwards + policy_nn = runner.alg.policy + except AttributeError: + # version 2.2 and below + policy_nn = runner.alg.actor_critic + + # extract the normalizer + if hasattr(policy_nn, "actor_obs_normalizer"): + normalizer = policy_nn.actor_obs_normalizer + elif hasattr(policy_nn, "student_obs_normalizer"): + normalizer = policy_nn.student_obs_normalizer + else: + normalizer = None + + # export policy to onnx/jit + export_model_dir = os.path.join(os.path.dirname(resume_path), "exported") + export_policy_as_jit(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.pt") + export_policy_as_onnx(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.onnx") + + dt = env.unwrapped.step_dt + + # reset environment + obs = env.get_observations() + timestep = 0 + # simulate environment + while simulation_app.is_running(): + start_time = time.time() + # run everything in inference mode + with torch.inference_mode(): + # agent stepping + actions = policy(obs) + # env stepping + obs, _, dones, _ = env.step(actions) + # reset recurrent states for episodes that have terminated + policy_nn.reset(dones) + if args_cli.video: + timestep += 1 + # Exit the play loop after recording one video + if timestep == args_cli.video_length: + break + + # time delay for real-time evaluation + sleep_time = dt - (time.time() - start_time) + if args_cli.real_time and sleep_time > 0: + time.sleep(sleep_time) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/rsl_rl/train.py b/scripts/reinforcement_learning/rsl_rl/train.py new file mode 100644 index 0000000000000000000000000000000000000000..0cce12d7eba00683cd2415d63f604219ed223ab9 --- /dev/null +++ b/scripts/reinforcement_learning/rsl_rl/train.py @@ -0,0 +1,229 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to train RL agent with RSL-RL.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# local imports +import cli_args # isort: skip + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.") +parser.add_argument( + "--ray-proc-id", "-rid", type=int, default=None, help="Automatically configured by Ray integration, otherwise None." +) +# append RSL-RL cli arguments +cli_args.add_rsl_rl_args(parser) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +args_cli, hydra_args = parser.parse_known_args() + +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Check for minimum supported RSL-RL version.""" + +import importlib.metadata as metadata +import platform + +from packaging import version + +# check minimum supported rsl-rl version +RSL_RL_VERSION = "3.0.1" +installed_version = metadata.version("rsl-rl-lib") +if version.parse(installed_version) < version.parse(RSL_RL_VERSION): + if platform.system() == "Windows": + cmd = [r".\isaaclab.bat", "-p", "-m", "pip", "install", f"rsl-rl-lib=={RSL_RL_VERSION}"] + else: + cmd = ["./isaaclab.sh", "-p", "-m", "pip", "install", f"rsl-rl-lib=={RSL_RL_VERSION}"] + print( + f"Please install the correct version of RSL-RL.\nExisting version is: '{installed_version}'" + f" and required version is: '{RSL_RL_VERSION}'.\nTo install the correct version, run:" + f"\n\n\t{' '.join(cmd)}\n" + ) + exit(1) + +"""Rest everything follows.""" + +import logging +import os +import time +from datetime import datetime + +import gymnasium as gym +import torch +from rsl_rl.runners import DistillationRunner, OnPolicyRunner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +# import logger +logger = logging.getLogger(__name__) + +# PLACEHOLDER: Extension template (do not remove this comment) + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = False + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlBaseRunnerCfg): + """Train with RSL-RL agent.""" + # override configurations with non-hydra CLI arguments + agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli) + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + agent_cfg.max_iterations = ( + args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations + ) + + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg.seed + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # multi-gpu training configuration + if args_cli.distributed: + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + agent_cfg.device = f"cuda:{app_launcher.local_rank}" + + # set seed to have diversity in different threads + seed = agent_cfg.seed + app_launcher.local_rank + env_cfg.seed = seed + agent_cfg.seed = seed + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # specify directory for logging runs: {time-stamp}_{run_name} + log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not + # change it (see PR #2346, comment-2819298849) + print(f"Exact experiment name requested from command line: {log_dir}") + if agent_cfg.run_name: + log_dir += f"_{agent_cfg.run_name}" + log_dir = os.path.join(log_root_path, log_dir) + + # set the IO descriptors export flag if requested + if isinstance(env_cfg, ManagerBasedRLEnvCfg): + env_cfg.export_io_descriptors = args_cli.export_io_descriptors + else: + logger.warning( + "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported." + ) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # save resume path before creating a new log_dir + if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation": + resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "train"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + start_time = time.time() + + # wrap around environment for rsl-rl + env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) + + # create runner from rsl-rl + if agent_cfg.class_name == "OnPolicyRunner": + runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device) + elif agent_cfg.class_name == "DistillationRunner": + runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device) + else: + raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}") + # write git state to logs + runner.add_git_repo_to_log(__file__) + # load the checkpoint + if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation": + print(f"[INFO]: Loading model checkpoint from: {resume_path}") + # load previously trained model + runner.load(resume_path) + + # dump the configuration into log-directory + dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg) + + # run training + runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True) + + print(f"Training time: {round(time.time() - start_time, 2)} seconds") + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/sb3/play.py b/scripts/reinforcement_learning/sb3/play.py new file mode 100644 index 0000000000000000000000000000000000000000..4afe943f62fd6a1bfcc74f93525c1d659477d143 --- /dev/null +++ b/scripts/reinforcement_learning/sb3/play.py @@ -0,0 +1,213 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play a checkpoint if an RL agent from Stable-Baselines3.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys +from pathlib import Path + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from Stable-Baselines3.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="sb3_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.") +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--use_pretrained_checkpoint", + action="store_true", + help="Use the pre-trained checkpoint from Nucleus.", +) +parser.add_argument( + "--use_last_checkpoint", + action="store_true", + help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.", +) +parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.") +parser.add_argument( + "--keep_all_info", + action="store_true", + default=False, + help="Use a slower SB3 wrapper but keep all the extra training info.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() + +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import random +import time + +import gymnasium as gym +import torch +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import VecNormalize + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.dict import print_dict + +from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg +from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config +from isaaclab_tasks.utils.parse_cfg import get_checkpoint_path + +# PLACEHOLDER: Extension template (do not remove this comment) + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Play with stable-baselines agent.""" + # grab task name for checkpoint path + task_name = args_cli.task.split(":")[-1] + train_task_name = task_name.replace("-Play", "") + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"] + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg["seed"] + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # directory for logging into + log_root_path = os.path.join("logs", "sb3", train_task_name) + log_root_path = os.path.abspath(log_root_path) + # checkpoint and log_dir stuff + if args_cli.use_pretrained_checkpoint: + checkpoint_path = get_published_pretrained_checkpoint("sb3", train_task_name) + if not checkpoint_path: + print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.") + return + elif args_cli.checkpoint is None: + # FIXME: last checkpoint doesn't seem to really use the last one' + if args_cli.use_last_checkpoint: + checkpoint = "model_.*.zip" + else: + checkpoint = "model.zip" + checkpoint_path = get_checkpoint_path(log_root_path, ".*", checkpoint, sort_alpha=False) + else: + checkpoint_path = args_cli.checkpoint + log_dir = os.path.dirname(checkpoint_path) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # post-process agent configuration + agent_cfg = process_sb3_cfg(agent_cfg, env.unwrapped.num_envs) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "play"), + "step_trigger": lambda step: step == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + # wrap around environment for stable baselines + env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info) + + vec_norm_path = checkpoint_path.replace("/model", "/model_vecnormalize").replace(".zip", ".pkl") + vec_norm_path = Path(vec_norm_path) + + # normalize environment (if needed) + if vec_norm_path.exists(): + print(f"Loading saved normalization: {vec_norm_path}") + env = VecNormalize.load(vec_norm_path, env) + # do not update them at test time + env.training = False + # reward normalization is not needed at test time + env.norm_reward = False + elif "normalize_input" in agent_cfg: + env = VecNormalize( + env, + training=True, + norm_obs="normalize_input" in agent_cfg and agent_cfg.pop("normalize_input"), + clip_obs="clip_obs" in agent_cfg and agent_cfg.pop("clip_obs"), + ) + + # create agent from stable baselines + print(f"Loading checkpoint from: {checkpoint_path}") + agent = PPO.load(checkpoint_path, env, print_system_info=True) + + dt = env.unwrapped.step_dt + + # reset environment + obs = env.reset() + timestep = 0 + # simulate environment + while simulation_app.is_running(): + start_time = time.time() + # run everything in inference mode + with torch.inference_mode(): + # agent stepping + actions, _ = agent.predict(obs, deterministic=True) + # env stepping + obs, _, _, _ = env.step(actions) + if args_cli.video: + timestep += 1 + # Exit the play loop after recording one video + if timestep == args_cli.video_length: + break + + # time delay for real-time evaluation + sleep_time = dt - (time.time() - start_time) + if args_cli.real_time and sleep_time > 0: + time.sleep(sleep_time) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/sb3/train.py b/scripts/reinforcement_learning/sb3/train.py new file mode 100644 index 0000000000000000000000000000000000000000..32549dcd4ea3a5b2c5dcea3d706322cc78ea8da7 --- /dev/null +++ b/scripts/reinforcement_learning/sb3/train.py @@ -0,0 +1,240 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Script to train RL agent with Stable Baselines3.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import contextlib +import signal +import sys +from pathlib import Path + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with Stable-Baselines3.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="sb3_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument("--log_interval", type=int, default=100_000, help="Log data every n timesteps.") +parser.add_argument("--checkpoint", type=str, default=None, help="Continue the training from checkpoint.") +parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.") +parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.") +parser.add_argument( + "--keep_all_info", + action="store_true", + default=False, + help="Use a slower SB3 wrapper but keep all the extra training info.", +) +parser.add_argument( + "--ray-proc-id", "-rid", type=int, default=None, help="Automatically configured by Ray integration, otherwise None." +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + + +def cleanup_pbar(*args): + """ + A small helper to stop training and + cleanup progress bar properly on ctrl+c + """ + import gc + + tqdm_objects = [obj for obj in gc.get_objects() if "tqdm" in type(obj).__name__] + for tqdm_object in tqdm_objects: + if "tqdm_rich" in type(tqdm_object).__name__: + tqdm_object.close() + raise KeyboardInterrupt + + +# disable KeyboardInterrupt override +signal.signal(signal.SIGINT, cleanup_pbar) + +"""Rest everything follows.""" + +import logging +import os +import random +import time +from datetime import datetime + +import gymnasium as gym +import numpy as np +from stable_baselines3 import PPO +from stable_baselines3.common.callbacks import CheckpointCallback, LogEveryNTimesteps +from stable_baselines3.common.vec_env import VecNormalize + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config + +# import logger +logger = logging.getLogger(__name__) +# PLACEHOLDER: Extension template (do not remove this comment) + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Train with stable-baselines agent.""" + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"] + # max iterations for training + if args_cli.max_iterations is not None: + agent_cfg["n_timesteps"] = args_cli.max_iterations * agent_cfg["n_steps"] * env_cfg.scene.num_envs + + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg["seed"] + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # directory for logging into + run_info = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + log_root_path = os.path.abspath(os.path.join("logs", "sb3", args_cli.task)) + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # The Ray Tune workflow extracts experiment name using the logging line below, hence, + # do not change it (see PR #2346, comment-2819298849) + print(f"Exact experiment name requested from command line: {run_info}") + log_dir = os.path.join(log_root_path, run_info) + # dump the configuration into log-directory + dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg) + + # save command used to run the script + command = " ".join(sys.orig_argv) + (Path(log_dir) / "command.txt").write_text(command) + + # post-process agent configuration + agent_cfg = process_sb3_cfg(agent_cfg, env_cfg.scene.num_envs) + # read configurations about the agent-training + policy_arch = agent_cfg.pop("policy") + n_timesteps = agent_cfg.pop("n_timesteps") + + # set the IO descriptors export flag if requested + if isinstance(env_cfg, ManagerBasedRLEnvCfg): + env_cfg.export_io_descriptors = args_cli.export_io_descriptors + else: + logger.warning( + "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported." + ) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "train"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + start_time = time.time() + + # wrap around environment for stable baselines + env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info) + + norm_keys = {"normalize_input", "normalize_value", "clip_obs"} + norm_args = {} + for key in norm_keys: + if key in agent_cfg: + norm_args[key] = agent_cfg.pop(key) + + if norm_args and norm_args.get("normalize_input"): + print(f"Normalizing input, {norm_args=}") + env = VecNormalize( + env, + training=True, + norm_obs=norm_args["normalize_input"], + norm_reward=norm_args.get("normalize_value", False), + clip_obs=norm_args.get("clip_obs", 100.0), + gamma=agent_cfg["gamma"], + clip_reward=np.inf, + ) + + # create agent from stable baselines + agent = PPO(policy_arch, env, verbose=1, tensorboard_log=log_dir, **agent_cfg) + if args_cli.checkpoint is not None: + agent = agent.load(args_cli.checkpoint, env, print_system_info=True) + + # callbacks for agent + checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_dir, name_prefix="model", verbose=2) + callbacks = [checkpoint_callback, LogEveryNTimesteps(n_steps=args_cli.log_interval)] + + # train the agent + with contextlib.suppress(KeyboardInterrupt): + agent.learn( + total_timesteps=n_timesteps, + callback=callbacks, + progress_bar=True, + log_interval=None, + ) + # save the final model + agent.save(os.path.join(log_dir, "model")) + print("Saving to:") + print(os.path.join(log_dir, "model.zip")) + + if isinstance(env, VecNormalize): + print("Saving normalization") + env.save(os.path.join(log_dir, "model_vecnormalize.pkl")) + + print(f"Training time: {round(time.time() - start_time, 2)} seconds") + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/skrl/play.py b/scripts/reinforcement_learning/skrl/play.py new file mode 100644 index 0000000000000000000000000000000000000000..089ec756197914199d3d852d1bc0a1c229cd30d0 --- /dev/null +++ b/scripts/reinforcement_learning/skrl/play.py @@ -0,0 +1,250 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to play a checkpoint of an RL agent from skrl. + +Visit the skrl documentation (https://skrl.readthedocs.io) to see the examples structured in +a more user-friendly way. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from skrl.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", + type=str, + default=None, + help=( + "Name of the RL agent configuration entry point. Defaults to None, in which case the argument " + "--algorithm is used to determine the default agent configuration entry point." + ), +) +parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.") +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--use_pretrained_checkpoint", + action="store_true", + help="Use the pre-trained checkpoint from Nucleus.", +) +parser.add_argument( + "--ml_framework", + type=str, + default="torch", + choices=["torch", "jax", "jax-numpy"], + help="The ML framework used for training the skrl agent.", +) +parser.add_argument( + "--algorithm", + type=str, + default="PPO", + choices=["AMP", "PPO", "IPPO", "MAPPO"], + help="The RL algorithm used for training the skrl agent.", +) +parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import random +import time + +import gymnasium as gym +import skrl +import torch +from packaging import version + +# check for minimum supported skrl version +SKRL_VERSION = "1.4.3" +if version.parse(skrl.__version__) < version.parse(SKRL_VERSION): + skrl.logger.error( + f"Unsupported skrl version: {skrl.__version__}. " + f"Install supported version using 'pip install skrl>={SKRL_VERSION}'" + ) + exit() + +if args_cli.ml_framework.startswith("torch"): + from skrl.utils.runner.torch import Runner +elif args_cli.ml_framework.startswith("jax"): + from skrl.utils.runner.jax import Runner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.dict import print_dict + +from isaaclab_rl.skrl import SkrlVecEnvWrapper +from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +# PLACEHOLDER: Extension template (do not remove this comment) + +# config shortcuts +if args_cli.agent is None: + algorithm = args_cli.algorithm.lower() + agent_cfg_entry_point = "skrl_cfg_entry_point" if algorithm in ["ppo"] else f"skrl_{algorithm}_cfg_entry_point" +else: + agent_cfg_entry_point = args_cli.agent + algorithm = agent_cfg_entry_point.split("_cfg")[0].split("skrl_")[-1].lower() + + +@hydra_task_config(args_cli.task, agent_cfg_entry_point) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, experiment_cfg: dict): + """Play with skrl agent.""" + # grab task name for checkpoint path + task_name = args_cli.task.split(":")[-1] + train_task_name = task_name.replace("-Play", "") + + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # configure the ML framework into the global skrl variable + if args_cli.ml_framework.startswith("jax"): + skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy" + + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + # set the agent and environment seed from command line + # note: certain randomization occur in the environment initialization so we set the seed here + experiment_cfg["seed"] = args_cli.seed if args_cli.seed is not None else experiment_cfg["seed"] + env_cfg.seed = experiment_cfg["seed"] + + # specify directory for logging experiments (load checkpoint) + log_root_path = os.path.join("logs", "skrl", experiment_cfg["agent"]["experiment"]["directory"]) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Loading experiment from directory: {log_root_path}") + # get checkpoint path + if args_cli.use_pretrained_checkpoint: + resume_path = get_published_pretrained_checkpoint("skrl", train_task_name) + if not resume_path: + print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.") + return + elif args_cli.checkpoint: + resume_path = os.path.abspath(args_cli.checkpoint) + else: + resume_path = get_checkpoint_path( + log_root_path, run_dir=f".*_{algorithm}_{args_cli.ml_framework}", other_dirs=["checkpoints"] + ) + log_dir = os.path.dirname(os.path.dirname(resume_path)) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv) and algorithm in ["ppo"]: + env = multi_agent_to_single_agent(env) + + # get environment (step) dt for real-time evaluation + try: + dt = env.step_dt + except AttributeError: + dt = env.unwrapped.step_dt + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "play"), + "step_trigger": lambda step: step == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + # wrap around environment for skrl + env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework) # same as: `wrap_env(env, wrapper="auto")` + + # configure and instantiate the skrl runner + # https://skrl.readthedocs.io/en/latest/api/utils/runner.html + experiment_cfg["trainer"]["close_environment_at_exit"] = False + experiment_cfg["agent"]["experiment"]["write_interval"] = 0 # don't log to TensorBoard + experiment_cfg["agent"]["experiment"]["checkpoint_interval"] = 0 # don't generate checkpoints + runner = Runner(env, experiment_cfg) + + print(f"[INFO] Loading model checkpoint from: {resume_path}") + runner.agent.load(resume_path) + # set agent to evaluation mode + runner.agent.set_running_mode("eval") + + # reset environment + obs, _ = env.reset() + timestep = 0 + # simulate environment + while simulation_app.is_running(): + start_time = time.time() + + # run everything in inference mode + with torch.inference_mode(): + # agent stepping + outputs = runner.agent.act(obs, timestep=0, timesteps=0) + # - multi-agent (deterministic) actions + if hasattr(env, "possible_agents"): + actions = {a: outputs[-1][a].get("mean_actions", outputs[0][a]) for a in env.possible_agents} + # - single-agent (deterministic) actions + else: + actions = outputs[-1].get("mean_actions", outputs[0]) + # env stepping + obs, _, _, _, _ = env.step(actions) + if args_cli.video: + timestep += 1 + # exit the play loop after recording one video + if timestep == args_cli.video_length: + break + + # time delay for real-time evaluation + sleep_time = dt - (time.time() - start_time) + if args_cli.real_time and sleep_time > 0: + time.sleep(sleep_time) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/reinforcement_learning/skrl/train.py b/scripts/reinforcement_learning/skrl/train.py new file mode 100644 index 0000000000000000000000000000000000000000..cf2edce47435021929c1a2875d4f1b47cc26d499 --- /dev/null +++ b/scripts/reinforcement_learning/skrl/train.py @@ -0,0 +1,246 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to train RL agent with skrl. + +Visit the skrl documentation (https://skrl.readthedocs.io) to see the examples structured in +a more user-friendly way. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Train an RL agent with skrl.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).") +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", + type=str, + default=None, + help=( + "Name of the RL agent configuration entry point. Defaults to None, in which case the argument " + "--algorithm is used to determine the default agent configuration entry point." + ), +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument( + "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes." +) +parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint to resume training.") +parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.") +parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.") +parser.add_argument( + "--ml_framework", + type=str, + default="torch", + choices=["torch", "jax", "jax-numpy"], + help="The ML framework used for training the skrl agent.", +) +parser.add_argument( + "--algorithm", + type=str, + default="PPO", + choices=["AMP", "PPO", "IPPO", "MAPPO"], + help="The RL algorithm used for training the skrl agent.", +) +parser.add_argument( + "--ray-proc-id", "-rid", type=int, default=None, help="Automatically configured by Ray integration, otherwise None." +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import logging +import os +import random +import time +from datetime import datetime + +import gymnasium as gym +import skrl +from packaging import version + +# check for minimum supported skrl version +SKRL_VERSION = "1.4.3" +if version.parse(skrl.__version__) < version.parse(SKRL_VERSION): + skrl.logger.error( + f"Unsupported skrl version: {skrl.__version__}. " + f"Install supported version using 'pip install skrl>={SKRL_VERSION}'" + ) + exit() + +if args_cli.ml_framework.startswith("torch"): + from skrl.utils.runner.torch import Runner +elif args_cli.ml_framework.startswith("jax"): + from skrl.utils.runner.jax import Runner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.dict import print_dict +from isaaclab.utils.io import dump_yaml + +from isaaclab_rl.skrl import SkrlVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import hydra_task_config + +# import logger +logger = logging.getLogger(__name__) + +# PLACEHOLDER: Extension template (do not remove this comment) + +# config shortcuts +if args_cli.agent is None: + algorithm = args_cli.algorithm.lower() + agent_cfg_entry_point = "skrl_cfg_entry_point" if algorithm in ["ppo"] else f"skrl_{algorithm}_cfg_entry_point" +else: + agent_cfg_entry_point = args_cli.agent + algorithm = agent_cfg_entry_point.split("_cfg")[0].split("skrl_")[-1].lower() + + +@hydra_task_config(args_cli.task, agent_cfg_entry_point) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict): + """Train with skrl agent.""" + # override configurations with non-hydra CLI arguments + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # check for invalid combination of CPU device with distributed training + if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device: + raise ValueError( + "Distributed training is not supported when using CPU device. " + "Please use GPU device (e.g., --device cuda) for distributed training." + ) + + # multi-gpu training config + if args_cli.distributed: + env_cfg.sim.device = f"cuda:{app_launcher.local_rank}" + # max iterations for training + if args_cli.max_iterations: + agent_cfg["trainer"]["timesteps"] = args_cli.max_iterations * agent_cfg["agent"]["rollouts"] + agent_cfg["trainer"]["close_environment_at_exit"] = False + # configure the ML framework into the global skrl variable + if args_cli.ml_framework.startswith("jax"): + skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy" + + # randomly sample a seed if seed = -1 + if args_cli.seed == -1: + args_cli.seed = random.randint(0, 10000) + + # set the agent and environment seed from command line + # note: certain randomization occur in the environment initialization so we set the seed here + agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"] + env_cfg.seed = agent_cfg["seed"] + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "skrl", agent_cfg["agent"]["experiment"]["directory"]) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Logging experiment in directory: {log_root_path}") + # specify directory for logging runs: {time-stamp}_{run_name} + log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + f"_{algorithm}_{args_cli.ml_framework}" + # The Ray Tune workflow extracts experiment name using the logging line below, hence, + # do not change it (see PR #2346, comment-2819298849) + print(f"Exact experiment name requested from command line: {log_dir}") + if agent_cfg["agent"]["experiment"]["experiment_name"]: + log_dir += f"_{agent_cfg['agent']['experiment']['experiment_name']}" + # set directory into agent config + agent_cfg["agent"]["experiment"]["directory"] = log_root_path + agent_cfg["agent"]["experiment"]["experiment_name"] = log_dir + # update log_dir + log_dir = os.path.join(log_root_path, log_dir) + + # dump the configuration into log-directory + dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg) + dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg) + + # get checkpoint path (to resume training) + resume_path = retrieve_file_path(args_cli.checkpoint) if args_cli.checkpoint else None + + # set the IO descriptors export flag if requested + if isinstance(env_cfg, ManagerBasedRLEnvCfg): + env_cfg.export_io_descriptors = args_cli.export_io_descriptors + else: + logger.warning( + "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported." + ) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv) and algorithm in ["ppo"]: + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "train"), + "step_trigger": lambda step: step % args_cli.video_interval == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + start_time = time.time() + + # wrap around environment for skrl + env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework) # same as: `wrap_env(env, wrapper="auto")` + + # configure and instantiate the skrl runner + # https://skrl.readthedocs.io/en/latest/api/utils/runner.html + runner = Runner(env, agent_cfg) + + # load checkpoint (if specified) + if resume_path: + print(f"[INFO] Loading model checkpoint from: {resume_path}") + runner.agent.load(resume_path) + + # run training + runner.run() + + print(f"Training time: {round(time.time() - start_time, 2)} seconds") + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/sim2sim_transfer/config/newton_to_physx_anymal_d.yaml b/scripts/sim2sim_transfer/config/newton_to_physx_anymal_d.yaml new file mode 100644 index 0000000000000000000000000000000000000000..00d2925345b161e9470d353340a325a0ae22fac4 --- /dev/null +++ b/scripts/sim2sim_transfer/config/newton_to_physx_anymal_d.yaml @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Joint names in the source physics engine where policy is trained (Newton) +source_joint_names: + - "LF_HAA" + - "LF_HFE" + - "LF_KFE" + - "LH_HAA" + - "LH_HFE" + - "LH_KFE" + - "RF_HAA" + - "RF_HFE" + - "RF_KFE" + - "RH_HAA" + - "RH_HFE" + - "RH_KFE" + +# Joint names in the target physics engine where policy is deployed (PhysX) +target_joint_names: + - "LF_HAA" + - "LH_HAA" + - "RF_HAA" + - "RH_HAA" + - "LF_HFE" + - "LH_HFE" + - "RF_HFE" + - "RH_HFE" + - "LF_KFE" + - "LH_KFE" + - "RF_KFE" + - "RH_KFE" diff --git a/scripts/sim2sim_transfer/config/newton_to_physx_g1.yaml b/scripts/sim2sim_transfer/config/newton_to_physx_g1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..839980c4d10e74818fc4618dc9b563f42aa5ab2f --- /dev/null +++ b/scripts/sim2sim_transfer/config/newton_to_physx_g1.yaml @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Joint names in the source physics engine where policy is trained (Newton) +source_joint_names: + - "left_hip_pitch_joint" + - "left_hip_roll_joint" + - "left_hip_yaw_joint" + - "left_knee_joint" + - "left_ankle_pitch_joint" + - "left_ankle_roll_joint" + - "right_hip_pitch_joint" + - "right_hip_roll_joint" + - "right_hip_yaw_joint" + - "right_knee_joint" + - "right_ankle_pitch_joint" + - "right_ankle_roll_joint" + - "torso_joint" + - "left_shoulder_pitch_joint" + - "left_shoulder_roll_joint" + - "left_shoulder_yaw_joint" + - "left_elbow_pitch_joint" + - "left_elbow_roll_joint" + - "left_five_joint" + - "left_six_joint" + - "left_three_joint" + - "left_four_joint" + - "left_zero_joint" + - "left_one_joint" + - "left_two_joint" + - "right_shoulder_pitch_joint" + - "right_shoulder_roll_joint" + - "right_shoulder_yaw_joint" + - "right_elbow_pitch_joint" + - "right_elbow_roll_joint" + - "right_five_joint" + - "right_six_joint" + - "right_three_joint" + - "right_four_joint" + - "right_zero_joint" + - "right_one_joint" + - "right_two_joint" + +# Joint names in the target physics engine where policy is deployed (PhysX) +target_joint_names: + - "left_hip_pitch_joint" + - "right_hip_pitch_joint" + - "torso_joint" + - "left_hip_roll_joint" + - "right_hip_roll_joint" + - "left_shoulder_pitch_joint" + - "right_shoulder_pitch_joint" + - "left_hip_yaw_joint" + - "right_hip_yaw_joint" + - "left_shoulder_roll_joint" + - "right_shoulder_roll_joint" + - "left_knee_joint" + - "right_knee_joint" + - "left_shoulder_yaw_joint" + - "right_shoulder_yaw_joint" + - "left_ankle_pitch_joint" + - "right_ankle_pitch_joint" + - "left_elbow_pitch_joint" + - "right_elbow_pitch_joint" + - "left_ankle_roll_joint" + - "right_ankle_roll_joint" + - "left_elbow_roll_joint" + - "right_elbow_roll_joint" + - "left_five_joint" + - "left_three_joint" + - "left_zero_joint" + - "right_five_joint" + - "right_three_joint" + - "right_zero_joint" + - "left_six_joint" + - "left_four_joint" + - "left_one_joint" + - "right_six_joint" + - "right_four_joint" + - "right_one_joint" + - "left_two_joint" + - "right_two_joint" diff --git a/scripts/sim2sim_transfer/config/newton_to_physx_go2.yaml b/scripts/sim2sim_transfer/config/newton_to_physx_go2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9f976ee1bb683a943aafc9460193d8b5cb417cf --- /dev/null +++ b/scripts/sim2sim_transfer/config/newton_to_physx_go2.yaml @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Joint names in the source physics engine where policy is trained (Newton) +source_joint_names: + - "FL_hip_joint" + - "FL_thigh_joint" + - "FL_calf_joint" + - "FR_hip_joint" + - "FR_thigh_joint" + - "FR_calf_joint" + - "RL_hip_joint" + - "RL_thigh_joint" + - "RL_calf_joint" + - "RR_hip_joint" + - "RR_thigh_joint" + - "RR_calf_joint" +# Joint names in the target physics engine where policy is deployed (PhysX) +target_joint_names: + - "FL_hip_joint" + - "FR_hip_joint" + - "RL_hip_joint" + - "RR_hip_joint" + - "FL_thigh_joint" + - "FR_thigh_joint" + - "RL_thigh_joint" + - "RR_thigh_joint" + - "FL_calf_joint" + - "FR_calf_joint" + - "RL_calf_joint" + - "RR_calf_joint" diff --git a/scripts/sim2sim_transfer/config/newton_to_physx_h1.yaml b/scripts/sim2sim_transfer/config/newton_to_physx_h1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb0e0996f054dae891c3881edc582776a55f42c1 --- /dev/null +++ b/scripts/sim2sim_transfer/config/newton_to_physx_h1.yaml @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Joint names in the source physics engine where policy is trained (Newton) +source_joint_names: + - "left_hip_yaw" + - "left_hip_roll" + - "left_hip_pitch" + - "left_knee" + - "left_ankle" + - "right_hip_yaw" + - "right_hip_roll" + - "right_hip_pitch" + - "right_knee" + - "right_ankle" + - "torso" + - "left_shoulder_pitch" + - "left_shoulder_roll" + - "left_shoulder_yaw" + - "left_elbow" + - "right_shoulder_pitch" + - "right_shoulder_roll" + - "right_shoulder_yaw" + - "right_elbow" + +# Joint names in the target physics engine where policy is deployed (PhysX) +target_joint_names: + - "left_hip_yaw" + - "right_hip_yaw" + - "torso" + - "left_hip_roll" + - "right_hip_roll" + - "left_shoulder_pitch" + - "right_shoulder_pitch" + - "left_hip_pitch" + - "right_hip_pitch" + - "left_shoulder_roll" + - "right_shoulder_roll" + - "left_knee" + - "right_knee" + - "left_shoulder_yaw" + - "right_shoulder_yaw" + - "left_ankle" + - "right_ankle" + - "left_elbow" + - "right_elbow" diff --git a/scripts/sim2sim_transfer/rsl_rl_transfer.py b/scripts/sim2sim_transfer/rsl_rl_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec1b389879fa84b18664ab269630536529316ca --- /dev/null +++ b/scripts/sim2sim_transfer/rsl_rl_transfer.py @@ -0,0 +1,286 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play a checkpoint of an RL agent from RSL-RL with policy transfer capabilities.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import os +import sys + +from isaaclab.app import AppLauncher + +# local imports +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) +from scripts.reinforcement_learning.rsl_rl import cli_args # isort: skip + +# add argparse arguments +parser = argparse.ArgumentParser(description="Play an RL agent with RSL-RL with policy transfer.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument( + "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point." +) +parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment") +parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.") +# Joint ordering arguments +parser.add_argument( + "--policy_transfer_file", + type=str, + default=None, + help="Path to YAML file containing joint mapping configuration for policy transfer between physics engines.", +) +# append RSL-RL cli arguments +cli_args.add_rsl_rl_args(parser) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli, hydra_args = parser.parse_known_args() +# always enable cameras to record video +if args_cli.video: + args_cli.enable_cameras = True + +# clear out sys.argv for Hydra +sys.argv = [sys.argv[0]] + hydra_args + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import time + +import gymnasium as gym +import torch +import yaml +from rsl_rl.runners import DistillationRunner, OnPolicyRunner + +from isaaclab.envs import ( + DirectMARLEnv, + DirectMARLEnvCfg, + DirectRLEnvCfg, + ManagerBasedRLEnvCfg, + multi_agent_to_single_agent, +) +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.dict import print_dict + +from isaaclab_rl.rsl_rl import RslRlBaseRunnerCfg, RslRlVecEnvWrapper, export_policy_as_jit, export_policy_as_onnx + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import get_checkpoint_path +from isaaclab_tasks.utils.hydra import hydra_task_config + +# PLACEHOLDER: Extension template (do not remove this comment) + + +def get_joint_mappings(args_cli, action_space_dim): + """Get joint mappings based on command line arguments. + + Args: + args_cli: Command line arguments + action_space_dim: Dimension of the action space (number of joints) + + Returns: + tuple: (source_to_target_list, target_to_source_list, source_to_target_obs_list) + """ + num_joints = action_space_dim + if args_cli.policy_transfer_file: + # Load from YAML file + try: + with open(args_cli.policy_transfer_file) as file: + config = yaml.safe_load(file) + except Exception as e: + raise RuntimeError(f"Failed to load joint mapping from {args_cli.policy_transfer_file}: {e}") + + source_joint_names = config["source_joint_names"] + target_joint_names = config["target_joint_names"] + # Find joint mapping + source_to_target = [] + target_to_source = [] + + # Create source to target mapping + for joint_name in source_joint_names: + if joint_name in target_joint_names: + source_to_target.append(target_joint_names.index(joint_name)) + else: + raise ValueError(f"Joint '{joint_name}' not found in target joint names") + + # Create target to source mapping + for joint_name in target_joint_names: + if joint_name in source_joint_names: + target_to_source.append(source_joint_names.index(joint_name)) + else: + raise ValueError(f"Joint '{joint_name}' not found in source joint names") + print(f"[INFO] Loaded joint mapping for policy transfer from YAML: {args_cli.policy_transfer_file}") + assert len(source_to_target) == len(target_to_source) == num_joints, ( + "Number of source and target joints must match" + ) + else: + # Use identity mapping (one-to-one) + identity_map = list(range(num_joints)) + source_to_target, target_to_source = identity_map, identity_map + + # Create observation mapping (first 12 values stay the same for locomotion examples, then map joint-related values) + obs_map = ( + [0, 1, 2] + + [3, 4, 5] + + [6, 7, 8] + + [9, 10, 11] + + [i + 12 + num_joints * 0 for i in source_to_target] + + [i + 12 + num_joints * 1 for i in source_to_target] + + [i + 12 + num_joints * 2 for i in source_to_target] + ) + + return source_to_target, target_to_source, obs_map + + +@hydra_task_config(args_cli.task, args_cli.agent) +def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlBaseRunnerCfg): + """Play with RSL-RL agent with policy transfer capabilities.""" + + # override configurations with non-hydra CLI arguments + agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli) + env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs + + # set the environment seed + # note: certain randomizations occur in the environment initialization so we set the seed here + env_cfg.seed = agent_cfg.seed + env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device + + # specify directory for logging experiments + log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name) + log_root_path = os.path.abspath(log_root_path) + print(f"[INFO] Loading experiment from directory: {log_root_path}") + if args_cli.checkpoint: + resume_path = retrieve_file_path(args_cli.checkpoint) + else: + resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint) + + log_dir = os.path.dirname(resume_path) + + # set the log directory for the environment (works for all environment types) + env_cfg.log_dir = log_dir + + # create isaac environment + env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + + # wrap for video recording + if args_cli.video: + video_kwargs = { + "video_folder": os.path.join(log_dir, "videos", "play"), + "step_trigger": lambda step: step == 0, + "video_length": args_cli.video_length, + "disable_logger": True, + } + print("[INFO] Recording videos during training.") + print_dict(video_kwargs, nesting=4) + env = gym.wrappers.RecordVideo(env, **video_kwargs) + + # wrap around environment for rsl-rl + env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) + + print(f"[INFO]: Loading model checkpoint from: {resume_path}") + # load previously trained model + if agent_cfg.class_name == "OnPolicyRunner": + runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device) + elif agent_cfg.class_name == "DistillationRunner": + runner = DistillationRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device) + else: + raise ValueError(f"Unsupported runner class: {agent_cfg.class_name}") + runner.load(resume_path) + + # obtain the trained policy for inference + policy = runner.get_inference_policy(device=env.unwrapped.device) + + # extract the neural network module + # we do this in a try-except to maintain backwards compatibility. + try: + # version 2.3 onwards + policy_nn = runner.alg.policy + except AttributeError: + # version 2.2 and below + policy_nn = runner.alg.actor_critic + + # extract the normalizer + if hasattr(policy_nn, "actor_obs_normalizer"): + normalizer = policy_nn.actor_obs_normalizer + elif hasattr(policy_nn, "student_obs_normalizer"): + normalizer = policy_nn.student_obs_normalizer + else: + normalizer = None + + # export policy to onnx/jit + export_model_dir = os.path.join(os.path.dirname(resume_path), "exported") + export_policy_as_jit(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.pt") + export_policy_as_onnx(policy_nn, normalizer=normalizer, path=export_model_dir, filename="policy.onnx") + + dt = env.unwrapped.step_dt + + # reset environment + obs = env.get_observations() + timestep = 0 + + # Get joint mappings for policy transfer + _, target_to_source, obs_map = get_joint_mappings(args_cli, env.action_space.shape[1]) + + # Create torch tensors for mappings + device = args_cli.device if args_cli.device else "cuda:0" + target_to_source_tensor = torch.tensor(target_to_source, device=device) if target_to_source else None + obs_map_tensor = torch.tensor(obs_map, device=device) if obs_map else None + + def remap_obs(obs): + """Remap the observation to the target observation space.""" + if obs_map_tensor is not None: + obs = obs[:, obs_map_tensor] + return obs + + def remap_actions(actions): + """Remap the actions to the target action space.""" + if target_to_source_tensor is not None: + actions = actions[:, target_to_source_tensor] + return actions + + # simulate environment + while simulation_app.is_running(): + start_time = time.time() + # run everything in inference mode + with torch.inference_mode(): + # agent stepping + actions = policy(remap_obs(obs)) + # env stepping + obs, _, _, _ = env.step(remap_actions(actions)) + if args_cli.video: + timestep += 1 + # Exit the play loop after recording one video + if timestep == args_cli.video_length: + break + + # time delay for real-time evaluation + sleep_time = dt - (time.time() - start_time) + if args_cli.real_time and sleep_time > 0: + time.sleep(sleep_time) + + # close the simulator + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/blender_obj.py b/scripts/tools/blender_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..c03a525fae499ca88f49d993b95bb87c3295c5ed --- /dev/null +++ b/scripts/tools/blender_obj.py @@ -0,0 +1,100 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Convert a mesh file to `.obj` using blender. + +This file processes a given dae mesh file and saves the resulting mesh file in obj format. + +It needs to be called using the python packaged with blender, i.e.: + + blender --background --python blender_obj.py -- -in_file FILE -out_file FILE + +For more information: https://docs.blender.org/api/current/index.html + +The script was tested on Blender 3.2 on Ubuntu 20.04LTS. +""" + +import os +import sys + +import bpy + + +def parse_cli_args(): + """Parse the input command line arguments.""" + import argparse + + # get the args passed to blender after "--", all of which are ignored by + # blender so scripts may receive their own arguments + argv = sys.argv + + if "--" not in argv: + argv = [] # as if no args are passed + else: + argv = argv[argv.index("--") + 1 :] # get all args after "--" + + # When --help or no args are given, print this help + usage_text = ( + f"Run blender in background mode with this script:\n\tblender --background --python {__file__} -- [options]" + ) + parser = argparse.ArgumentParser(description=usage_text) + # Add arguments + parser.add_argument("-i", "--in_file", metavar="FILE", type=str, required=True, help="Path to input OBJ file.") + parser.add_argument("-o", "--out_file", metavar="FILE", type=str, required=True, help="Path to output OBJ file.") + args = parser.parse_args(argv) + # Check if any arguments provided + if not argv or not args.in_file or not args.out_file: + parser.print_help() + return None + # return arguments + return args + + +def convert_to_obj(in_file: str, out_file: str, save_usd: bool = False): + """Convert a mesh file to `.obj` using blender. + + Args: + in_file: Input mesh file to process. + out_file: Path to store output obj file. + """ + # check valid input file + if not os.path.exists(in_file): + raise FileNotFoundError(in_file) + # add ending of file format + if not out_file.endswith(".obj"): + out_file += ".obj" + # create directory if it doesn't exist for destination file + if not os.path.exists(os.path.dirname(out_file)): + os.makedirs(os.path.dirname(out_file), exist_ok=True) + # reset scene to empty + bpy.ops.wm.read_factory_settings(use_empty=True) + # load object into scene + if in_file.endswith(".dae"): + bpy.ops.wm.collada_import(filepath=in_file) + elif in_file.endswith(".stl") or in_file.endswith(".STL"): + bpy.ops.import_mesh.stl(filepath=in_file) + else: + raise ValueError(f"Input file not in dae/stl format: {in_file}") + # convert to obj format and store with z up + # TODO: Read the convention from dae file instead of manually fixing it. + # Reference: https://docs.blender.org/api/2.79/bpy.ops.export_scene.html + bpy.ops.export_scene.obj( + filepath=out_file, check_existing=False, axis_forward="Y", axis_up="Z", global_scale=1, path_mode="RELATIVE" + ) + # save it as usd as well + if save_usd: + out_file = out_file.replace("obj", "usd") + bpy.ops.wm.usd_export(filepath=out_file, check_existing=False) + + +if __name__ == "__main__": + # read arguments + cli_args = parse_cli_args() + # check CLI args + if cli_args is None: + sys.exit() + # process via blender + convert_to_obj(cli_args.in_file, cli_args.out_file) diff --git a/scripts/tools/check_hdf5.py b/scripts/tools/check_hdf5.py new file mode 100644 index 0000000000000000000000000000000000000000..d59ccafefb67a182600823219e71266553571e2f --- /dev/null +++ b/scripts/tools/check_hdf5.py @@ -0,0 +1,57 @@ +"""Quick HDF5 structure sanity-check. + +This script is intentionally lightweight and is meant for quick inspection from the terminal. +""" + +from __future__ import annotations + +import argparse + +import h5py + + +def _first_data_demo_group(h5_file: h5py.File) -> h5py.Group: + if "data" not in h5_file: + raise KeyError("HDF5 file missing top-level 'data' group") + data_grp = h5_file["data"] + demo_keys = sorted(data_grp.keys()) + if len(demo_keys) == 0: + raise ValueError("No demos found under 'data'") + return data_grp[demo_keys[0]] + + +def main() -> None: + parser = argparse.ArgumentParser(description="Inspect basic structure of an IsaacLab HDF5 dataset") + parser.add_argument( + "path", + nargs="?", + default="datasets/generated_dataset_pick_place_camera_g1.hdf5", + help="Path to the HDF5 dataset", + ) + args = parser.parse_args() + + with h5py.File(args.path, "r") as f: + demo0 = _first_data_demo_group(f) + + print("dataset:", args.path) + print("episode keys:", list(demo0.keys())) + print("obs keys:", list(demo0["obs"].keys()) if "obs" in demo0 else None) + print("states keys:", list(demo0["states"].keys()) if "states" in demo0 else None) + + datagen_info = demo0.get("obs", {}).get("datagen_info") if "obs" in demo0 else None + if datagen_info is None: + print("datagen_info: ") + return + + print("datagen_info keys:", list(datagen_info.keys())) + + subtask_term_signals = datagen_info.get("subtask_term_signals") + if subtask_term_signals is None: + print("subtask_term_signals: ") + return + + print("subtask_term_signals keys:", list(subtask_term_signals.keys())) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/tools/check_instanceable.py b/scripts/tools/check_instanceable.py new file mode 100644 index 0000000000000000000000000000000000000000..fedb771f6113f5830336da751ab7e2e5bbb00595 --- /dev/null +++ b/scripts/tools/check_instanceable.py @@ -0,0 +1,136 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script uses the cloner API to check if asset has been instanced properly. + +Usage with different inputs (replace `` and `` with the path to the +original asset and the instanced asset respectively): + +```bash +./isaaclab.sh -p source/tools/check_instanceable.py -n 4096 --headless --physics +./isaaclab.sh -p source/tools/check_instanceable.py -n 4096 --headless --physics +./isaaclab.sh -p source/tools/check_instanceable.py -n 4096 --headless +./isaaclab.sh -p source/tools/check_instanceable.py -n 4096 --headless +``` + +Output from the above commands: + +```bash +>>> Cloning time (cloner.clone): 0.648198 seconds +>>> Setup time (sim.reset): : 5.843589 seconds +[#clones: 4096, physics: True] Asset: : 6.491870 seconds + +>>> Cloning time (cloner.clone): 0.693133 seconds +>>> Setup time (sim.reset): 50.860526 seconds +[#clones: 4096, physics: True] Asset: : 51.553743 seconds + +>>> Cloning time (cloner.clone) : 0.687201 seconds +>>> Setup time (sim.reset) : 6.302215 seconds +[#clones: 4096, physics: False] Asset: : 6.989500 seconds + +>>> Cloning time (cloner.clone) : 0.678150 seconds +>>> Setup time (sim.reset) : 52.854054 seconds +[#clones: 4096, physics: False] Asset: : 53.532287 seconds +``` + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import contextlib +import os + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser("Utility to empirically check if asset in instanced properly.") +parser.add_argument("input", type=str, help="The path to the USD file.") +parser.add_argument("-n", "--num_clones", type=int, default=128, help="Number of clones to spawn.") +parser.add_argument("-s", "--spacing", type=float, default=1.5, help="Spacing between instances in a grid.") +parser.add_argument("-p", "--physics", action="store_true", default=False, help="Clone assets using physics cloner.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner + +import isaaclab.sim as sim_utils +from isaaclab.utils import Timer +from isaaclab.utils.assets import check_file_path + + +def main(): + """Spawns the USD asset robot and clones it using Isaac Gym Cloner API.""" + # check valid file path + if not check_file_path(args_cli.input): + raise ValueError(f"Invalid file path: {args_cli.input}") + # Load kit helper + sim = SimulationContext( + stage_units_in_meters=1.0, physics_dt=0.01, rendering_dt=0.01, backend="torch", device="cuda:0" + ) + + # get stage handle + stage = sim_utils.get_current_stage() + + # enable fabric which avoids passing data over to USD structure + # this speeds up the read-write operation of GPU buffers + if sim.get_physics_context().use_gpu_pipeline: + sim.get_physics_context().enable_fabric(True) + # increase GPU buffer dimensions + sim.get_physics_context().set_gpu_found_lost_aggregate_pairs_capacity(2**25) + sim.get_physics_context().set_gpu_total_aggregate_pairs_capacity(2**21) + # enable hydra scene-graph instancing + # this is needed to visualize the scene when fabric is enabled + sim._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Create interface to clone the scene + cloner = GridCloner(spacing=args_cli.spacing, stage=stage) + cloner.define_base_env("/World/envs") + stage.DefinePrim("/World/envs/env_0", "Xform") + # Spawn things into stage + sim_utils.create_prim("/World/Light", "DistantLight") + + # Everything under the namespace "/World/envs/env_0" will be cloned + sim_utils.create_prim("/World/envs/env_0/Asset", "Xform", usd_path=os.path.abspath(args_cli.input)) + # Clone the scene + num_clones = args_cli.num_clones + + # Create a timer to measure the cloning time + with Timer(f"[#clones: {num_clones}, physics: {args_cli.physics}] Asset: {args_cli.input}"): + # Clone the scene + with Timer(">>> Cloning time (cloner.clone)"): + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_clones) + _ = cloner.clone( + source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=args_cli.physics + ) + # Play the simulator + with Timer(">>> Setup time (sim.reset)"): + sim.reset() + + # Simulate scene (if not headless) + if not args_cli.headless: + with contextlib.suppress(KeyboardInterrupt): + while sim.is_playing(): + # perform step + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/convert_instanceable.py b/scripts/tools/convert_instanceable.py new file mode 100644 index 0000000000000000000000000000000000000000..7713bdc728f35eb3d19f0d60c16f668e5d7b17e4 --- /dev/null +++ b/scripts/tools/convert_instanceable.py @@ -0,0 +1,160 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Utility to bulk convert URDFs or mesh files into instanceable USD format. + +Unified Robot Description Format (URDF) is an XML file format used in ROS to describe all elements of +a robot. For more information, see: http://wiki.ros.org/urdf + +This script uses the URDF importer extension from Isaac Sim (``omni.isaac.urdf_importer``) to convert a +URDF asset into USD format. It is designed as a convenience script for command-line use. For more +information on the URDF importer, see the documentation for the extension: +https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/ext_omni_isaac_urdf.html + + +positional arguments: + input The path to the input directory containing URDFs and Meshes. + output The path to directory to store the instanceable files. + +optional arguments: + -h, --help Show this help message and exit + --conversion-type Select file type to convert, urdf or mesh. (default: urdf) + --merge-joints Consolidate links that are connected by fixed joints. (default: False) + --fix-base Fix the base to where it is imported. (default: False) + --make-instanceable Make the asset instanceable for efficient cloning. (default: False) + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Utility to convert a URDF or mesh into an Instanceable asset.") +parser.add_argument("input", type=str, help="The path to the input directory.") +parser.add_argument("output", type=str, help="The path to directory to store converted instanceable files.") +parser.add_argument( + "--conversion-type", type=str, default="both", help="Select file type to convert, urdf, mesh, or both." +) +parser.add_argument( + "--merge-joints", + action="store_true", + default=False, + help="Consolidate links that are connected by fixed joints.", +) +parser.add_argument("--fix-base", action="store_true", default=False, help="Fix the base to where it is imported.") +parser.add_argument( + "--make-instanceable", + action="store_true", + default=True, + help="Make the asset instanceable for efficient cloning.", +) +parser.add_argument( + "--collision-approximation", + type=str, + default="convexDecomposition", + choices=["convexDecomposition", "convexHull", "none"], + help=( + 'The method used for approximating collision mesh. Set to "none" ' + "to not add a collision mesh to the converted mesh." + ), +) +parser.add_argument( + "--mass", + type=float, + default=None, + help="The mass (in kg) to assign to the converted asset. If not provided, then no mass is added.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os + +from isaaclab.sim.converters import MeshConverter, MeshConverterCfg, UrdfConverter, UrdfConverterCfg +from isaaclab.sim.schemas import schemas_cfg + + +def main(): + # Define conversion time given + conversion_type = args_cli.conversion_type.lower() + # Warning if conversion type input is not valid + if conversion_type != "urdf" and conversion_type != "mesh" and conversion_type != "both": + raise Warning("Conversion type is not valid, please select either 'urdf', 'mesh', or 'both'.") + + if not os.path.exists(args_cli.input): + print(f"Error: The directory {args_cli.input} does not exist.") + + # For each file and subsequent sub-directory + for root, dirs, files in os.walk(args_cli.input): + # For each file + for filename in files: + # Check for URDF extensions + if (conversion_type == "urdf" or conversion_type == "both") and filename.lower().endswith(".urdf"): + # URDF converter call + urdf_converter_cfg = UrdfConverterCfg( + asset_path=f"{root}/{filename}", + usd_dir=f"{args_cli.output}/{filename[:-5]}", + usd_file_name=f"{filename[:-5]}.usd", + fix_base=args_cli.fix_base, + merge_fixed_joints=args_cli.merge_joints, + force_usd_conversion=True, + make_instanceable=args_cli.make_instanceable, + ) + # Create Urdf converter and import the file + urdf_converter = UrdfConverter(urdf_converter_cfg) + print(f"Generated USD file: {urdf_converter.usd_path}") + + elif (conversion_type == "mesh" or conversion_type == "both") and ( + filename.lower().endswith(".fbx") + or filename.lower().endswith(".obj") + or filename.lower().endswith(".dae") + or filename.lower().endswith(".stl") + ): + # Mass properties + if args_cli.mass is not None: + mass_props = schemas_cfg.MassPropertiesCfg(mass=args_cli.mass) + rigid_props = schemas_cfg.RigidBodyPropertiesCfg() + else: + mass_props = None + rigid_props = None + + # Collision properties + collision_props = schemas_cfg.CollisionPropertiesCfg( + collision_enabled=args_cli.collision_approximation != "none" + ) + # Mesh converter call + mesh_converter_cfg = MeshConverterCfg( + mass_props=mass_props, + rigid_props=rigid_props, + collision_props=collision_props, + asset_path=f"{root}/{filename}", + force_usd_conversion=True, + usd_dir=f"{args_cli.output}/{filename[:-4]}", + usd_file_name=f"{filename[:-4]}.usd", + make_instanceable=args_cli.make_instanceable, + collision_approximation=args_cli.collision_approximation, + ) + # Create mesh converter and import the file + mesh_converter = MeshConverter(mesh_converter_cfg) + print(f"Generated USD file: {mesh_converter.usd_path}") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/convert_mesh.py b/scripts/tools/convert_mesh.py new file mode 100644 index 0000000000000000000000000000000000000000..6e9fd46befd301d97e11274fa51f8004c43ff4bc --- /dev/null +++ b/scripts/tools/convert_mesh.py @@ -0,0 +1,205 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Utility to convert a OBJ/STL/FBX into USD format. + +The OBJ file format is a simple data-format that represents 3D geometry alone — namely, the position +of each vertex, the UV position of each texture coordinate vertex, vertex normals, and the faces that +make each polygon defined as a list of vertices, and texture vertices. + +An STL file describes a raw, unstructured triangulated surface by the unit normal and vertices (ordered +by the right-hand rule) of the triangles using a three-dimensional Cartesian coordinate system. + +FBX files are a type of 3D model file created using the Autodesk FBX software. They can be designed and +modified in various modeling applications, such as Maya, 3ds Max, and Blender. Moreover, FBX files typically +contain mesh, material, texture, and skeletal animation data. +Link: https://www.autodesk.com/products/fbx/overview + + +This script uses the asset converter extension from Isaac Sim (``omni.kit.asset_converter``) to convert a +OBJ/STL/FBX asset into USD format. It is designed as a convenience script for command-line use. + + +positional arguments: + input The path to the input mesh (.OBJ/.STL/.FBX) file. + output The path to store the USD file. + +optional arguments: + -h, --help Show this help message and exit + --make-instanceable, Make the asset instanceable for efficient cloning. (default: False) + --collision-approximation The method used for approximating collision mesh. Defaults to convexDecomposition. + Set to \"none\" to not add a collision mesh to the converted mesh. + (default: convexDecomposition) + --mass The mass (in kg) to assign to the converted asset. (default: None) + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# Define collision approximation choices (must be defined before parser) +_valid_collision_approx = [ + "convexDecomposition", + "convexHull", + "triangleMesh", + "meshSimplification", + "sdf", + "boundingCube", + "boundingSphere", + "none", +] + +# add argparse arguments +parser = argparse.ArgumentParser(description="Utility to convert a mesh file into USD format.") +parser.add_argument("input", type=str, help="The path to the input mesh file.") +parser.add_argument("output", type=str, help="The path to store the USD file.") +parser.add_argument( + "--make-instanceable", + action="store_true", + default=False, + help="Make the asset instanceable for efficient cloning.", +) +parser.add_argument( + "--collision-approximation", + type=str, + default="convexDecomposition", + choices=_valid_collision_approx, + help="The method used for approximating the collision mesh. Set to 'none' to disable collision mesh generation.", +) +parser.add_argument( + "--mass", + type=float, + default=None, + help="The mass (in kg) to assign to the converted asset. If not provided, then no mass is added.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import contextlib +import os + +import carb +import omni.kit.app + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import MeshConverter, MeshConverterCfg +from isaaclab.sim.schemas import schemas_cfg +from isaaclab.utils.assets import check_file_path +from isaaclab.utils.dict import print_dict + +collision_approximation_map = { + "convexDecomposition": schemas_cfg.ConvexDecompositionPropertiesCfg, + "convexHull": schemas_cfg.ConvexHullPropertiesCfg, + "triangleMesh": schemas_cfg.TriangleMeshPropertiesCfg, + "meshSimplification": schemas_cfg.TriangleMeshSimplificationPropertiesCfg, + "sdf": schemas_cfg.SDFMeshPropertiesCfg, + "boundingCube": schemas_cfg.BoundingCubePropertiesCfg, + "boundingSphere": schemas_cfg.BoundingSpherePropertiesCfg, + "none": None, +} + + +def main(): + # check valid file path + mesh_path = args_cli.input + if not os.path.isabs(mesh_path): + mesh_path = os.path.abspath(mesh_path) + if not check_file_path(mesh_path): + raise ValueError(f"Invalid mesh file path: {mesh_path}") + + # create destination path + dest_path = args_cli.output + if not os.path.isabs(dest_path): + dest_path = os.path.abspath(dest_path) + + # Mass properties + if args_cli.mass is not None: + mass_props = schemas_cfg.MassPropertiesCfg(mass=args_cli.mass) + rigid_props = schemas_cfg.RigidBodyPropertiesCfg() + else: + mass_props = None + rigid_props = None + + # Collision properties + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=args_cli.collision_approximation != "none") + + # Create Mesh converter config + cfg_class = collision_approximation_map.get(args_cli.collision_approximation) + if cfg_class is None and args_cli.collision_approximation != "none": + valid_keys = ", ".join(sorted(collision_approximation_map.keys())) + raise ValueError( + f"Invalid collision approximation type '{args_cli.collision_approximation}'. " + f"Valid options are: {valid_keys}." + ) + collision_cfg = cfg_class() if cfg_class is not None else None + + mesh_converter_cfg = MeshConverterCfg( + mass_props=mass_props, + rigid_props=rigid_props, + collision_props=collision_props, + asset_path=mesh_path, + force_usd_conversion=True, + usd_dir=os.path.dirname(dest_path), + usd_file_name=os.path.basename(dest_path), + make_instanceable=args_cli.make_instanceable, + mesh_collision_props=collision_cfg, + ) + + # Print info + print("-" * 80) + print("-" * 80) + print(f"Input Mesh file: {mesh_path}") + print("Mesh importer config:") + print_dict(mesh_converter_cfg.to_dict(), nesting=0) + print("-" * 80) + print("-" * 80) + + # Create Mesh converter and import the file + mesh_converter = MeshConverter(mesh_converter_cfg) + # print output + print("Mesh importer output:") + print(f"Generated USD file: {mesh_converter.usd_path}") + print("-" * 80) + print("-" * 80) + + # Determine if there is a GUI to update: + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # read flag for whether a local GUI is enabled + local_gui = carb_settings_iface.get("/app/window/enabled") + # read flag for whether livestreaming GUI is enabled + livestream_gui = carb_settings_iface.get("/app/livestream/enabled") + + # Simulate scene (if not headless) + if local_gui or livestream_gui: + # Open the stage with USD + sim_utils.open_stage(mesh_converter.usd_path) + # Reinitialize the simulation + app = omni.kit.app.get_app_interface() + # Run simulation + with contextlib.suppress(KeyboardInterrupt): + while app.is_running(): + # perform step + app.update() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/convert_mjcf.py b/scripts/tools/convert_mjcf.py new file mode 100644 index 0000000000000000000000000000000000000000..40e46b82d59da9febff3dc04e9b2caf778a1b721 --- /dev/null +++ b/scripts/tools/convert_mjcf.py @@ -0,0 +1,139 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Utility to convert a MJCF into USD format. + +MuJoCo XML Format (MJCF) is an XML file format used in MuJoCo to describe all elements of a robot. +For more information, see: http://www.mujoco.org/book/XMLreference.html + +This script uses the MJCF importer extension from Isaac Sim (``isaacsim.asset.importer.mjcf``) to convert +a MJCF asset into USD format. It is designed as a convenience script for command-line use. For more information +on the MJCF importer, see the documentation for the extension: +https://docs.isaacsim.omniverse.nvidia.com/latest/robot_setup/ext_isaacsim_asset_importer_mjcf.html + + +positional arguments: + input The path to the input URDF file. + output The path to store the USD file. + +optional arguments: + -h, --help Show this help message and exit + --fix-base Fix the base to where it is imported. (default: False) + --import-sites Import sites by parse tag. (default: True) + --make-instanceable Make the asset instanceable for efficient cloning. (default: False) + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Utility to convert a MJCF into USD format.") +parser.add_argument("input", type=str, help="The path to the input MJCF file.") +parser.add_argument("output", type=str, help="The path to store the USD file.") +parser.add_argument("--fix-base", action="store_true", default=False, help="Fix the base to where it is imported.") +parser.add_argument( + "--import-sites", action="store_true", default=False, help="Import sites by parsing the tag." +) +parser.add_argument( + "--make-instanceable", + action="store_true", + default=False, + help="Make the asset instanceable for efficient cloning.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import contextlib +import os + +import carb +import omni.kit.app + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import MjcfConverter, MjcfConverterCfg +from isaaclab.utils.assets import check_file_path +from isaaclab.utils.dict import print_dict + + +def main(): + # check valid file path + mjcf_path = args_cli.input + if not os.path.isabs(mjcf_path): + mjcf_path = os.path.abspath(mjcf_path) + if not check_file_path(mjcf_path): + raise ValueError(f"Invalid file path: {mjcf_path}") + # create destination path + dest_path = args_cli.output + if not os.path.isabs(dest_path): + dest_path = os.path.abspath(dest_path) + + # create the converter configuration + mjcf_converter_cfg = MjcfConverterCfg( + asset_path=mjcf_path, + usd_dir=os.path.dirname(dest_path), + usd_file_name=os.path.basename(dest_path), + fix_base=args_cli.fix_base, + import_sites=args_cli.import_sites, + force_usd_conversion=True, + make_instanceable=args_cli.make_instanceable, + ) + + # Print info + print("-" * 80) + print("-" * 80) + print(f"Input MJCF file: {mjcf_path}") + print("MJCF importer config:") + print_dict(mjcf_converter_cfg.to_dict(), nesting=0) + print("-" * 80) + print("-" * 80) + + # Create mjcf converter and import the file + mjcf_converter = MjcfConverter(mjcf_converter_cfg) + # print output + print("MJCF importer output:") + print(f"Generated USD file: {mjcf_converter.usd_path}") + print("-" * 80) + print("-" * 80) + + # Determine if there is a GUI to update: + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # read flag for whether a local GUI is enabled + local_gui = carb_settings_iface.get("/app/window/enabled") + # read flag for whether livestreaming GUI is enabled + livestream_gui = carb_settings_iface.get("/app/livestream/enabled") + + # Simulate scene (if not headless) + if local_gui or livestream_gui: + # Open the stage with USD + sim_utils.open_stage(mjcf_converter.usd_path) + # Reinitialize the simulation + app = omni.kit.app.get_app_interface() + # Run simulation + with contextlib.suppress(KeyboardInterrupt): + while app.is_running(): + # perform step + app.update() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/convert_urdf.py b/scripts/tools/convert_urdf.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7a74708c598bcd9a1e4cccba5247721e48812f --- /dev/null +++ b/scripts/tools/convert_urdf.py @@ -0,0 +1,163 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Utility to convert a URDF into USD format. + +Unified Robot Description Format (URDF) is an XML file format used in ROS to describe all elements of +a robot. For more information, see: http://wiki.ros.org/urdf + +This script uses the URDF importer extension from Isaac Sim (``isaacsim.asset.importer.urdf``) to convert a +URDF asset into USD format. It is designed as a convenience script for command-line use. For more +information on the URDF importer, see the documentation for the extension: +https://docs.isaacsim.omniverse.nvidia.com/latest/robot_setup/ext_isaacsim_asset_importer_urdf.html + + +positional arguments: + input The path to the input URDF file. + output The path to store the USD file. + +optional arguments: + -h, --help Show this help message and exit + --merge-joints Consolidate links that are connected by fixed joints. (default: False) + --fix-base Fix the base to where it is imported. (default: False) + --joint-stiffness The stiffness of the joint drive. (default: 100.0) + --joint-damping The damping of the joint drive. (default: 1.0) + --joint-target-type The type of control to use for the joint drive. (default: "position") + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Utility to convert a URDF into USD format.") +parser.add_argument("input", type=str, help="The path to the input URDF file.") +parser.add_argument("output", type=str, help="The path to store the USD file.") +parser.add_argument( + "--merge-joints", + action="store_true", + default=False, + help="Consolidate links that are connected by fixed joints.", +) +parser.add_argument("--fix-base", action="store_true", default=False, help="Fix the base to where it is imported.") +parser.add_argument( + "--joint-stiffness", + type=float, + default=100.0, + help="The stiffness of the joint drive.", +) +parser.add_argument( + "--joint-damping", + type=float, + default=1.0, + help="The damping of the joint drive.", +) +parser.add_argument( + "--joint-target-type", + type=str, + default="position", + choices=["position", "velocity", "none"], + help="The type of control to use for the joint drive.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import contextlib +import os + +import carb +import omni.kit.app + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import UrdfConverter, UrdfConverterCfg +from isaaclab.utils.assets import check_file_path +from isaaclab.utils.dict import print_dict + + +def main(): + # check valid file path + urdf_path = args_cli.input + if not os.path.isabs(urdf_path): + urdf_path = os.path.abspath(urdf_path) + if not check_file_path(urdf_path): + raise ValueError(f"Invalid file path: {urdf_path}") + # create destination path + dest_path = args_cli.output + if not os.path.isabs(dest_path): + dest_path = os.path.abspath(dest_path) + + # Create Urdf converter config + urdf_converter_cfg = UrdfConverterCfg( + asset_path=urdf_path, + usd_dir=os.path.dirname(dest_path), + usd_file_name=os.path.basename(dest_path), + fix_base=args_cli.fix_base, + merge_fixed_joints=args_cli.merge_joints, + force_usd_conversion=True, + joint_drive=UrdfConverterCfg.JointDriveCfg( + gains=UrdfConverterCfg.JointDriveCfg.PDGainsCfg( + stiffness=args_cli.joint_stiffness, + damping=args_cli.joint_damping, + ), + target_type=args_cli.joint_target_type, + ), + ) + + # Print info + print("-" * 80) + print("-" * 80) + print(f"Input URDF file: {urdf_path}") + print("URDF importer config:") + print_dict(urdf_converter_cfg.to_dict(), nesting=0) + print("-" * 80) + print("-" * 80) + + # Create Urdf converter and import the file + urdf_converter = UrdfConverter(urdf_converter_cfg) + # print output + print("URDF importer output:") + print(f"Generated USD file: {urdf_converter.usd_path}") + print("-" * 80) + print("-" * 80) + + # Determine if there is a GUI to update: + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # read flag for whether a local GUI is enabled + local_gui = carb_settings_iface.get("/app/window/enabled") + # read flag for whether livestreaming GUI is enabled + livestream_gui = carb_settings_iface.get("/app/livestream/enabled") + + # Simulate scene (if not headless) + if local_gui or livestream_gui: + # Open the stage with USD + sim_utils.open_stage(urdf_converter.usd_path) + # Reinitialize the simulation + app = omni.kit.app.get_app_interface() + # Run simulation + with contextlib.suppress(KeyboardInterrupt): + while app.is_running(): + # perform step + app.update() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/cosmos/cosmos_prompt_gen.py b/scripts/tools/cosmos/cosmos_prompt_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..32db884adc566ba963cac823312194b6b92e2646 --- /dev/null +++ b/scripts/tools/cosmos/cosmos_prompt_gen.py @@ -0,0 +1,85 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to construct prompts to control the Cosmos model's generation. + +Required arguments: + --templates_path Path to the file containing templates for the prompts. + +Optional arguments: + --num_prompts Number of prompts to generate (default: 1). + --output_path Path to the output file to write generated prompts (default: prompts.txt). +""" + +import argparse +import json +import random + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description="Generate prompts for controlling Cosmos model's generation.") + parser.add_argument( + "--templates_path", type=str, required=True, help="Path to the JSON file containing prompt templates" + ) + parser.add_argument("--num_prompts", type=int, default=1, help="Number of prompts to generate (default: 1)") + parser.add_argument( + "--output_path", type=str, default="prompts.txt", help="Path to the output file to write generated prompts" + ) + args = parser.parse_args() + + return args + + +def generate_prompt(templates_path: str): + """Generate a random prompt for controlling the Cosmos model's visual augmentation. + + The prompt describes the scene and desired visual variations, which the model + uses to guide the augmentation process while preserving the core robotic actions. + + Args: + templates_path (str): Path to the JSON file containing prompt templates. + + Returns: + str: Generated prompt string that specifies visual aspects to modify in the video. + """ + try: + with open(templates_path) as f: + templates = json.load(f) + except FileNotFoundError: + raise FileNotFoundError(f"Prompt templates file not found: {templates_path}") + except json.JSONDecodeError: + raise ValueError(f"Invalid JSON in prompt templates file: {templates_path}") + + prompt_parts = [] + + for section_name, section_options in templates.items(): + if not isinstance(section_options, list): + continue + if len(section_options) == 0: + continue + selected_option = random.choice(section_options) + prompt_parts.append(selected_option) + + return " ".join(prompt_parts) + + +def main(): + # Parse command line arguments + args = parse_args() + + prompts = [generate_prompt(args.templates_path) for _ in range(args.num_prompts)] + + try: + with open(args.output_path, "w") as f: + for prompt in prompts: + f.write(prompt + "\n") + except Exception as e: + print(f"Failed to write to {args.output_path}: {e}") + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/cosmos/transfer1_templates.json b/scripts/tools/cosmos/transfer1_templates.json new file mode 100644 index 0000000000000000000000000000000000000000..d2d4b063a2682c9c10eac2e2f06648da269f91af --- /dev/null +++ b/scripts/tools/cosmos/transfer1_templates.json @@ -0,0 +1,96 @@ +{ + "env": [ + "A robotic arm is picking up and stacking cubes inside a foggy industrial scrapyard at dawn, surrounded by piles of old robotic parts and twisted metal. The background includes large magnetic cranes, rusted conveyor belts, and flickering yellow floodlights struggling to penetrate the fog.", + "A robotic arm is picking up and stacking cubes inside a luxury penthouse showroom during sunset. The background includes minimalist designer furniture, a panoramic view of a glowing city skyline, and hovering autonomous drones offering refreshments.", + "A robotic arm is picking up and stacking cubes within an ancient temple-themed robotics exhibit in a museum. The background includes stone columns with hieroglyphic-style etchings, interactive display panels, and a few museum visitors observing silently from behind glass barriers.", + "A robotic arm is picking up and stacking cubes inside a futuristic daycare facility for children. The background includes robotic toys, soft padded walls, holographic storybooks floating in mid-air, and tiny humanoid robots assisting toddlers.", + "A robotic arm is picking up and stacking cubes inside a deep underwater laboratory where pressure-resistant glass panels reveal a shimmering ocean outside. The background includes jellyfish drifting outside the windows, robotic submarines gliding by, and walls lined with wet-surface equipment panels.", + "A robotic arm is picking up and stacking cubes inside a post-apocalyptic lab, partially collapsed and exposed to the open sky. The background includes ruined machinery, exposed rebar, and a distant city skyline covered in ash and fog.", + "A robotic arm is picking up and stacking cubes in a biotech greenhouse surrounded by lush plant life. The background includes rows of bio-engineered plants, misting systems, and hovering inspection drones checking crop health.", + "A robotic arm is picking up and stacking cubes inside a dark, volcanic research outpost. The background includes robotic arms encased in heat-resistant suits, seismic monitors, and distant lava fountains occasionally illuminating the space.", + "A robotic arm is picking up and stacking cubes inside an icy arctic base, with frost-covered walls and equipment glinting under bright artificial white lights. The background includes heavy-duty heaters, control consoles wrapped in thermal insulation, and a large window looking out onto a frozen tundra with polar winds swirling snow outside.", + "A robotic arm is picking up and stacking cubes inside a zero-gravity chamber on a rotating space habitat. The background includes floating lab instruments, panoramic windows showing stars and Earth in rotation, and astronauts monitoring data.", + "A robotic arm is picking up and stacking cubes inside a mystical tech-art installation blending robotics with generative art. The background includes sculptural robotics, shifting light patterns on the walls, and visitors interacting with the exhibit using gestures.", + "A robotic arm is picking up and stacking cubes in a Martian colony dome, under a terraformed red sky filtering through thick glass. The background includes pressure-locked entry hatches, Martian rovers parked outside, and domed hydroponic farms stretching into the distance.", + "A robotic arm is picking up and stacking cubes inside a high-security military robotics testing bunker, with matte green steel walls and strict order. The background includes surveillance cameras, camouflage netting over equipment racks, and military personnel observing from a secure glass-walled control room.", + "A robotic arm is picking up and stacking cubes inside a retro-futuristic robotics lab from the 1980s with checkered floors and analog computer panels. The background includes CRT monitors with green code, rotary dials, printed schematics on the walls, and operators in lab coats typing on clunky terminals.", + "A robotic arm is picking up and stacking cubes inside a sunken ancient ruin repurposed for modern robotics experiments. The background includes carved pillars, vines creeping through gaps in stone, and scattered crates of modern equipment sitting on ancient floors.", + "A robotic arm is picking up and stacking cubes on a luxury interstellar yacht cruising through deep space. The background includes elegant furnishings, ambient synth music systems, and holographic butlers attending to other passengers.", + "A robotic arm is picking up and stacking cubes in a rebellious underground cybernetic hacker hideout. The background includes graffiti-covered walls, tangled wires, makeshift workbenches, and anonymous figures hunched over terminals with scrolling code.", + "A robotic arm is picking up and stacking cubes inside a dense jungle outpost where technology is being tested in extreme organic environments. The background includes humid control panels, vines creeping onto the robotics table, and occasional wildlife observed from a distance by researchers in camo gear.", + "A robotic arm is picking up and stacking cubes in a minimalist Zen tech temple. The background includes bonsai trees on floating platforms, robotic monks sweeping floors silently, and smooth stone pathways winding through digital meditation alcoves." + ], + + "robot": [ + "The robot arm is matte dark green with yellow diagonal hazard stripes along the upper arm; the joints are rugged and chipped, and the hydraulics are exposed with faded red tubing.", + "The robot arm is worn orange with black caution tape markings near the wrist; the elbow joint is dented and the pistons have visible scarring from long use.", + "The robot arm is steel gray with smooth curved panels and subtle blue stripes running down the length; the joints are sealed tight and the hydraulics have a glossy black casing.", + "The robot arm is bright yellow with alternating black bands around each segment; the joints show minor wear, and the hydraulics gleam with fresh lubrication.", + "The robot arm is navy blue with white serial numbers stenciled along the arm; the joints are well-maintained and the hydraulic shafts are matte silver with no visible dirt.", + "The robot arm is deep red with a matte finish and faint white grid lines across the panels; the joints are squared off and the hydraulic units look compact and embedded.", + "The robot arm is dirty white with dark gray speckled patches from wear; the joints are squeaky with exposed rivets, and the hydraulics are rusted at the base.", + "The robot arm is olive green with chipped paint and a black triangle warning icon near the shoulder; the joints are bulky and the hydraulics leak slightly around the seals.", + "The robot arm is bright teal with a glossy surface and silver stripes on the outer edges; the joints rotate smoothly and the pistons reflect a pale cyan hue.", + "The robot arm is orange-red with carbon fiber textures and white racing-style stripes down the forearm; the joints have minimal play and the hydraulics are tightly sealed in synthetic tubing.", + "The robot arm is flat black with uneven camouflage blotches in dark gray; the joints are reinforced and the hydraulic tubes are dusty and loose-fitting.", + "The robot arm is dull maroon with vertical black grooves etched into the panels; the joints show corrosion on the bolts and the pistons are thick and slow-moving.", + "The robot arm is powder blue with repeating geometric patterns printed in light gray; the joints are square and the hydraulic systems are internal and silent.", + "The robot arm is brushed silver with high-gloss finish and blue LED strips along the seams; the joints are shiny and tight, and the hydraulics hiss softly with every movement.", + "The robot arm is lime green with paint faded from sun exposure and white warning labels near each joint; the hydraulics are scraped and the fittings show heat marks.", + "The robot arm is dusty gray with chevron-style black stripes pointing toward the claw; the joints have uneven wear, and the pistons are dented and slightly bent.", + "The robot arm is cobalt blue with glossy texture and stylized angular black patterns across each segment; the joints are clean and the hydraulics show new flexible tubing.", + "The robot arm is industrial brown with visible welded seams and red caution tape wrapped loosely around the middle section; the joints are clunky and the hydraulics are slow and loud.", + "The robot arm is flat tan with dark green splotches and faint stencil text across the forearm; the joints have dried mud stains and the pistons are partially covered in grime.", + "The robot arm is light orange with chrome hexagon detailing and black number codes on the side; the joints are smooth and the hydraulic actuators shine under the lab lights." + ], + + "table": [ + "The robot arm is mounted on a table that is dull gray metal with scratches and scuff marks across the surface; faint rust rings are visible where older machinery used to be mounted.", + "The robot arm is mounted on a table that is smooth black plastic with a matte finish and faint fingerprint smudges near the edges; corners are slightly worn from regular use.", + "The robot arm is mounted on a table that is light oak wood with a natural grain pattern and a glossy varnish that reflects overhead lights softly; small burn marks dot one corner.", + "The robot arm is mounted on a table that is rough concrete with uneven texture and visible air bubbles; some grease stains and faded yellow paint markings suggest heavy usage.", + "The robot arm is mounted on a table that is brushed aluminum with a clean silver tone and very fine linear grooves; surface reflects light evenly, giving a soft glow.", + "The robot arm is mounted on a table that is pale green composite with chipped corners and scratches revealing darker material beneath; tape residue is stuck along the edges.", + "The robot arm is mounted on a table that is dark brown with a slightly cracked synthetic coating; patches of discoloration suggest exposure to heat or chemicals over time.", + "The robot arm is mounted on a table that is polished steel with mirror-like reflections; every small movement of the robot is mirrored faintly across the surface.", + "The robot arm is mounted on a table that is white with a slightly textured ceramic top, speckled with tiny black dots; the surface is clean but the edges are chipped.", + "The robot arm is mounted on a table that is glossy black glass with a deep shine and minimal dust; any lights above are clearly reflected, and fingerprints are visible under certain angles.", + "The robot arm is mounted on a table that is matte red plastic with wide surface scuffs and paint transfer from other objects; faint gridlines are etched into one side.", + "The robot arm is mounted on a table that is dark navy laminate with a low-sheen surface and subtle wood grain texture; the edge banding is slightly peeling off.", + "The robot arm is mounted on a table that is yellow-painted steel with diagonal black warning stripes running along one side; the paint is scratched and faded in high-contact areas.", + "The robot arm is mounted on a table that is translucent pale blue polymer with internal striations and slight glow under overhead lights; small bubbles are frozen inside the material.", + "The robot arm is mounted on a table that is cold concrete with embedded metal panels bolted into place; the surface has oil stains, welding marks, and tiny debris scattered around.", + "The robot arm is mounted on a table that is shiny chrome with heavy smudging and streaks; the table reflects distorted shapes of everything around it, including the arm itself.", + "The robot arm is mounted on a table that is matte forest green with shallow dents and drag marks from prior mechanical operations; a small sticker label is half-torn in one corner.", + "The robot arm is mounted on a table that is textured black rubber with slight give under pressure; scratches from the robot's base and clamp marks are clearly visible.", + "The robot arm is mounted on a table that is medium gray ceramic tile with visible grout lines and chips along the edges; some tiles have tiny cracks or stains.", + "The robot arm is mounted on a table that is old dark wood with faded polish and visible circular stains from spilled liquids; a few deep grooves are carved into the surface near the center." + ], + + "cubes": [ + "The arm is connected to the base mounted on the table. The bottom cube is deep blue, the second cube is bright red, and the top cube is vivid green, maintaining their correct order after stacking." + ], + + "light": [ + "The lighting is soft and diffused from large windows, allowing daylight to fill the room, creating gentle shadows that elongate throughout the space, with a natural warmth due to the sunlight streaming in.", + "Bright fluorescent tubes overhead cast a harsh, even light across the scene, creating sharp, well-defined shadows under the arm and cubes, with a sterile, clinical feel due to the cold white light.", + "Warm tungsten lights in the ceiling cast a golden glow over the table, creating long, soft shadows and a cozy, welcoming atmosphere. The light contrasts with cool blue tones from the robot arm.", + "The lighting comes from several intense spotlights mounted above, each casting focused beams of light that create stark, dramatic shadows around the cubes and the robotic arm, producing a high-contrast look.", + "A single adjustable desk lamp with a soft white bulb casts a directional pool of light over the cubes, causing deep, hard shadows and a quiet, intimate feel in the dimly lit room.", + "The space is illuminated with bright daylight filtering in through a skylight above, casting diffused, soft shadows and giving the scene a clean and natural look, with a cool tint from the daylight.", + "Soft, ambient lighting from hidden LEDs embedded in the ceiling creates a halo effect around the robotic arm, while subtle, elongated shadows stretch across the table surface, giving a sleek modern vibe.", + "Neon strip lights line the walls, casting a cool blue and purple glow across the scene. The robot and table are bathed in this colored light, producing sharp-edged shadows with a futuristic feel.", + "Bright artificial lights overhead illuminate the scene in a harsh white, with scattered, uneven shadows across the table and robot arm. There's a slight yellow hue to the light, giving it an industrial ambiance.", + "Soft morning sunlight spills through a large open window, casting long shadows across the floor and the robot arm. The warm, golden light creates a peaceful, natural atmosphere with a slight coolness in the shadows.", + "Dim ambient lighting with occasional flashes of bright blue light from overhead digital screens creates a high-tech, slightly eerie atmosphere. The shadows are soft, stretching in an almost surreal manner.", + "Lighting from tall lamps outside the room filters in through large glass doors, casting angled shadows across the table and robot arm. The ambient light creates a relaxing, slightly diffused atmosphere.", + "Artificial overhead lighting casts a harsh, stark white light with little warmth, producing sharply defined, almost clinical shadows on the robot arm and cubes. The space feels cold and industrial.", + "Soft moonlight from a large window at night creates a cool, ethereal glow on the table and arm. The shadows are long and faint, and the lighting provides a calm and serene atmosphere.", + "Bright overhead LED panels illuminate the scene with clean, white light, casting neutral shadows that give the environment a modern, sleek feel with minimal distortion or softness in the shadows.", + "A floodlight positioned outside casts bright, almost blinding natural light through an open door, creating high-contrast, sharp-edged shadows across the table and robot arm, adding dramatic tension to the scene.", + "Dim lighting from vintage tungsten bulbs hanging from the ceiling gives the room a warm, nostalgic glow, casting elongated, soft shadows that provide a cozy atmosphere around the robotic arm.", + "Bright fluorescent lights directly above produce a harsh, clinical light that creates sharp, defined shadows on the table and robotic arm, enhancing the industrial feel of the scene.", + "Neon pink and purple lights flicker softly from the walls, illuminating the robot arm with an intense glow that produces sharp, angular shadows across the cubes. The atmosphere feels futuristic and edgy.", + "Sunlight pouring in from a large, open window bathes the table and robotic arm in a warm golden light. The shadows are soft, and the scene feels natural and inviting with a slight contrast between light and shadow." + ] +} diff --git a/scripts/tools/hdf5_to_mp4.py b/scripts/tools/hdf5_to_mp4.py new file mode 100644 index 0000000000000000000000000000000000000000..0cd8a40c78f232845dd5408220dca640c75c93b9 --- /dev/null +++ b/scripts/tools/hdf5_to_mp4.py @@ -0,0 +1,208 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to convert HDF5 demonstration files to MP4 videos. + +This script converts camera frames stored in HDF5 demonstration files to MP4 videos. +It supports multiple camera modalities including RGB, segmentation, and normal maps. +The output videos are saved in the specified directory with appropriate naming. + +required arguments: + --input_file Path to the input HDF5 file. + --output_dir Directory to save the output MP4 files. + +optional arguments: + --input_keys List of input keys to process from the HDF5 file. + (default: ["table_cam", "wrist_cam", "table_cam_segmentation", + "table_cam_normals", "table_cam_shaded_segmentation"]) + --video_height Height of the output video in pixels. (default: 704) + --video_width Width of the output video in pixels. (default: 1280) + --framerate Frames per second for the output video. (default: 30) +""" + +import argparse +import os + +import cv2 +import h5py +import numpy as np + +# Constants +DEFAULT_VIDEO_HEIGHT = 704 +DEFAULT_VIDEO_WIDTH = 1280 +DEFAULT_INPUT_KEYS = [ + "table_cam", + "wrist_cam", + "table_cam_segmentation", + "table_cam_normals", + "table_cam_shaded_segmentation", + "table_cam_depth", +] +DEFAULT_FRAMERATE = 30 +LIGHT_SOURCE = np.array([0.0, 0.0, 1.0]) +MIN_DEPTH = 0.0 +MAX_DEPTH = 1.5 + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description="Convert HDF5 demonstration files to MP4 videos.") + parser.add_argument( + "--input_file", + type=str, + required=True, + help="Path to the input HDF5 file containing demonstration data.", + ) + parser.add_argument( + "--output_dir", + type=str, + required=True, + help="Directory path where the output MP4 files will be saved.", + ) + + parser.add_argument( + "--input_keys", + type=str, + nargs="+", + default=DEFAULT_INPUT_KEYS, + help="List of input keys to process.", + ) + parser.add_argument( + "--video_height", + type=int, + default=DEFAULT_VIDEO_HEIGHT, + help="Height of the output video in pixels.", + ) + parser.add_argument( + "--video_width", + type=int, + default=DEFAULT_VIDEO_WIDTH, + help="Width of the output video in pixels.", + ) + parser.add_argument( + "--framerate", + type=int, + default=DEFAULT_FRAMERATE, + help="Frames per second for the output video.", + ) + + args = parser.parse_args() + + return args + + +def write_demo_to_mp4( + hdf5_file, + demo_id, + frames_path, + input_key, + output_dir, + video_height, + video_width, + framerate=DEFAULT_FRAMERATE, +): + """Convert frames from an HDF5 file to an MP4 video. + + Args: + hdf5_file (str): Path to the HDF5 file containing the frames. + demo_id (int): ID of the demonstration to convert. + frames_path (str): Path to the frames data in the HDF5 file. + input_key (str): Name of the input key to convert. + output_dir (str): Directory to save the output MP4 file. + video_height (int): Height of the output video in pixels. + video_width (int): Width of the output video in pixels. + framerate (int, optional): Frames per second for the output video. Defaults to 30. + """ + with h5py.File(hdf5_file, "r") as f: + # Get frames based on input key type + if "shaded_segmentation" in input_key: + temp_key = input_key.replace("shaded_segmentation", "segmentation") + frames = f[f"data/demo_{demo_id}/obs/{temp_key}"] + else: + frames = f[frames_path + "/" + input_key] + + # Setup video writer + output_path = os.path.join(output_dir, f"demo_{demo_id}_{input_key}.mp4") + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + if "depth" in input_key: + video = cv2.VideoWriter(output_path, fourcc, framerate, (video_width, video_height), isColor=False) + else: + video = cv2.VideoWriter(output_path, fourcc, framerate, (video_width, video_height)) + + # Process and write frames + for ix, frame in enumerate(frames): + # Convert normal maps to uint8 if needed + if "normals" in input_key: + frame = (frame * 255.0).astype(np.uint8) + + # Process shaded segmentation frames + elif "shaded_segmentation" in input_key: + seg = frame[..., :-1] + normals_key = input_key.replace("shaded_segmentation", "normals") + normals = f[f"data/demo_{demo_id}/obs/{normals_key}"][ix] + shade = 0.5 + (normals * LIGHT_SOURCE[None, None, :]).sum(axis=-1) * 0.5 + shaded_seg = (shade[..., None] * seg).astype(np.uint8) + frame = np.concatenate((shaded_seg, frame[..., -1:]), axis=-1) + + # Convert RGB to BGR + if "depth" not in input_key: + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + else: + frame = (frame[..., 0] - MIN_DEPTH) / (MAX_DEPTH - MIN_DEPTH) + frame = np.where(frame < 0.01, 1.0, frame) + frame = 1.0 - frame + frame = (frame * 255.0).astype(np.uint8) + + # Resize to video resolution + frame = cv2.resize(frame, (video_width, video_height), interpolation=cv2.INTER_CUBIC) + video.write(frame) + + video.release() + + +def get_num_demos(hdf5_file): + """Get the number of demonstrations in the HDF5 file. + + Args: + hdf5_file (str): Path to the HDF5 file. + + Returns: + int: Number of demonstrations found in the file. + """ + with h5py.File(hdf5_file, "r") as f: + return len(f["data"].keys()) + + +def main(): + """Main function to convert all demonstrations to MP4 videos.""" + # Parse command line arguments + args = parse_args() + + # Create output directory if it doesn't exist + os.makedirs(args.output_dir, exist_ok=True) + + # Get number of demonstrations from the file + num_demos = get_num_demos(args.input_file) + print(f"Found {num_demos} demonstrations in {args.input_file}") + + # Convert each demonstration + for i in range(num_demos): + frames_path = f"data/demo_{str(i)}/obs" + for input_key in args.input_keys: + write_demo_to_mp4( + args.input_file, + i, + frames_path, + input_key, + args.output_dir, + args.video_height, + args.video_width, + args.framerate, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/merge_hdf5_datasets.py b/scripts/tools/merge_hdf5_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..a9fe1c63561d28218d2a7914cafd124e8b4a4098 --- /dev/null +++ b/scripts/tools/merge_hdf5_datasets.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import os + +import h5py + +parser = argparse.ArgumentParser(description="Merge a set of HDF5 datasets.") +parser.add_argument( + "--input_files", + type=str, + nargs="+", + default=[], + help="A list of paths to HDF5 files to merge.", +) +parser.add_argument("--output_file", type=str, default="merged_dataset.hdf5", help="File path to merged output.") + +args_cli = parser.parse_args() + + +def merge_datasets(): + for filepath in args_cli.input_files: + if not os.path.exists(filepath): + raise FileNotFoundError(f"The dataset file {filepath} does not exist.") + + with h5py.File(args_cli.output_file, "w") as output: + episode_idx = 0 + copy_attributes = True + + for filepath in args_cli.input_files: + with h5py.File(filepath, "r") as input: + for episode, data in input["data"].items(): + input.copy(f"data/{episode}", output, f"data/demo_{episode_idx}") + episode_idx += 1 + + if copy_attributes: + output["data"].attrs["env_args"] = input["data"].attrs["env_args"] + copy_attributes = False + + print(f"Merged dataset saved to {args_cli.output_file}") + + +if __name__ == "__main__": + merge_datasets() diff --git a/scripts/tools/mp4_to_hdf5.py b/scripts/tools/mp4_to_hdf5.py new file mode 100644 index 0000000000000000000000000000000000000000..61f7b5b0b40b0f2112f03da8190383f44e9469ac --- /dev/null +++ b/scripts/tools/mp4_to_hdf5.py @@ -0,0 +1,169 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to create a new dataset by combining existing HDF5 demonstrations with visually augmented MP4 videos. + +This script takes an existing HDF5 dataset containing demonstrations and a directory of MP4 videos +that are visually augmented versions of the original demonstration videos (e.g., with different lighting, +color schemes, or visual effects). It creates a new HDF5 dataset that preserves all the original +demonstration data (actions, robot state, etc.) but replaces the video frames with the augmented versions. + +required arguments: + --input_file Path to the input HDF5 file containing original demonstrations. + --output_file Path to save the new HDF5 file with augmented videos. + --videos_dir Directory containing the visually augmented MP4 videos. +""" + +import argparse +import glob +import os + +import cv2 +import h5py +import numpy as np + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description="Create a new dataset with visually augmented videos.") + parser.add_argument( + "--input_file", + type=str, + required=True, + help="Path to the input HDF5 file containing original demonstrations.", + ) + parser.add_argument( + "--videos_dir", + type=str, + required=True, + help="Directory containing the visually augmented MP4 videos.", + ) + parser.add_argument( + "--output_file", + type=str, + required=True, + help="Path to save the new HDF5 file with augmented videos.", + ) + + args = parser.parse_args() + + return args + + +def get_frames_from_mp4(video_path, target_height=None, target_width=None): + """Extract frames from an MP4 video file. + + Args: + video_path (str): Path to the MP4 video file. + target_height (int, optional): Target height for resizing frames. If None, no resizing is done. + target_width (int, optional): Target width for resizing frames. If None, no resizing is done. + + Returns: + np.ndarray: Array of frames from the video in RGB format. + """ + # Open the video file + video = cv2.VideoCapture(video_path) + + # Get video properties + frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + + # Read all frames into a numpy array + frames = [] + for _ in range(frame_count): + ret, frame = video.read() + if not ret: + break + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if target_height is not None and target_width is not None: + frame = cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_LINEAR) + frames.append(frame) + + # Convert to numpy array + frames = np.array(frames).astype(np.uint8) + + # Release the video object + video.release() + + return frames + + +def process_video_and_demo(f_in, f_out, video_path, orig_demo_id, new_demo_id): + """Process a single video and create a new demo with augmented video frames. + + Args: + f_in (h5py.File): Input HDF5 file. + f_out (h5py.File): Output HDF5 file. + video_path (str): Path to the augmented video file. + orig_demo_id (int): ID of the original demo to copy. + new_demo_id (int): ID for the new demo. + """ + # Get original demo data + actions = f_in[f"data/demo_{str(orig_demo_id)}/actions"] + eef_pos = f_in[f"data/demo_{str(orig_demo_id)}/obs/eef_pos"] + eef_quat = f_in[f"data/demo_{str(orig_demo_id)}/obs/eef_quat"] + gripper_pos = f_in[f"data/demo_{str(orig_demo_id)}/obs/gripper_pos"] + wrist_cam = f_in[f"data/demo_{str(orig_demo_id)}/obs/wrist_cam"] + + # Get original video resolution + orig_video = f_in[f"data/demo_{str(orig_demo_id)}/obs/table_cam"] + target_height, target_width = orig_video.shape[1:3] + + # Extract frames from video with original resolution + frames = get_frames_from_mp4(video_path, target_height, target_width) + + # Create new datasets + f_out.create_dataset(f"data/demo_{str(new_demo_id)}/actions", data=actions, compression="gzip") + f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/eef_pos", data=eef_pos, compression="gzip") + f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/eef_quat", data=eef_quat, compression="gzip") + f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/gripper_pos", data=gripper_pos, compression="gzip") + f_out.create_dataset( + f"data/demo_{str(new_demo_id)}/obs/table_cam", data=frames.astype(np.uint8), compression="gzip" + ) + f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/wrist_cam", data=wrist_cam, compression="gzip") + + # Copy attributes + f_out[f"data/demo_{str(new_demo_id)}"].attrs["num_samples"] = f_in[f"data/demo_{str(orig_demo_id)}"].attrs[ + "num_samples" + ] + + +def main(): + """Main function to create a new dataset with augmented videos.""" + # Parse command line arguments + args = parse_args() + + # Get list of MP4 videos + search_path = os.path.join(args.videos_dir, "*.mp4") + video_paths = glob.glob(search_path) + video_paths.sort() + print(f"Found {len(video_paths)} MP4 videos in {args.videos_dir}") + + # Create output directory if it doesn't exist + os.makedirs(os.path.dirname(args.output_file), exist_ok=True) + + with h5py.File(args.input_file, "r") as f_in, h5py.File(args.output_file, "w") as f_out: + # Copy all data from input to output + f_in.copy("data", f_out) + + # Get the largest demo ID to start new demos from + demo_ids = [int(key.split("_")[1]) for key in f_in["data"].keys()] + next_demo_id = max(demo_ids) + 1 # noqa: SIM113 + print(f"Starting new demos from ID: {next_demo_id}") + + # Process each video and create new demo + for video_path in video_paths: + # Extract original demo ID from video filename + video_filename = os.path.basename(video_path) + orig_demo_id = int(video_filename.split("_")[1]) + + process_video_and_demo(f_in, f_out, video_path, orig_demo_id, next_demo_id) + next_demo_id += 1 + + print(f"Augmented data saved to {args.output_file}") + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/process_meshes_to_obj.py b/scripts/tools/process_meshes_to_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..2c5be04c0e5ce30aa1254704489043f2d445e67c --- /dev/null +++ b/scripts/tools/process_meshes_to_obj.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Convert all mesh files to `.obj` in given folders.""" + +import argparse +import os +import shutil +import subprocess + +# Constants +# Path to blender +BLENDER_EXE_PATH = shutil.which("blender") + + +def parse_cli_args(): + """Parse the input command line arguments.""" + # add argparse arguments + parser = argparse.ArgumentParser("Utility to convert all mesh files to `.obj` in given folders.") + parser.add_argument("input_dir", type=str, help="The input directory from which to load meshes.") + parser.add_argument( + "-o", + "--output_dir", + type=str, + default=None, + help="The output directory to save converted meshes into. Default is same as input directory.", + ) + args_cli = parser.parse_args() + # resolve output directory + if args_cli.output_dir is None: + args_cli.output_dir = args_cli.input_dir + # return arguments + return args_cli + + +def run_blender_convert2obj(in_file: str, out_file: str): + """Calls the python script using `subprocess` to perform processing of mesh file. + + Args: + in_file: Input mesh file. + out_file: Output obj file. + """ + # resolve for python file + tools_dirname = os.path.dirname(os.path.abspath(__file__)) + script_file = os.path.join(tools_dirname, "blender_obj.py") + # complete command + command_exe = f"{BLENDER_EXE_PATH} --background --python {script_file} -- -i {in_file} -o {out_file}" + # break command into list + command_exe_list = command_exe.split(" ") + # run command + subprocess.run(command_exe_list) + + +def convert_meshes(source_folders: list[str], destination_folders: list[str]): + """Processes all mesh files of supported format into OBJ file using blender. + + Args: + source_folders: List of directories to search for meshes. + destination_folders: List of directories to dump converted files. + """ + # create folder for corresponding destination + for folder in destination_folders: + os.makedirs(folder, exist_ok=True) + # iterate over each folder + for in_folder, out_folder in zip(source_folders, destination_folders): + # extract all dae files in the directory + mesh_filenames = [f for f in os.listdir(in_folder) if f.endswith("dae")] + mesh_filenames += [f for f in os.listdir(in_folder) if f.endswith("stl")] + mesh_filenames += [f for f in os.listdir(in_folder) if f.endswith("STL")] + # print status + print(f"Found {len(mesh_filenames)} files to process in directory: {in_folder}") + # iterate over each OBJ file + for mesh_file in mesh_filenames: + # extract meshname + mesh_name = os.path.splitext(mesh_file)[0] + # complete path of input and output files + in_file_path = os.path.join(in_folder, mesh_file) + out_file_path = os.path.join(out_folder, mesh_name + ".obj") + # perform blender processing + print("Processing: ", in_file_path) + run_blender_convert2obj(in_file_path, out_file_path) + + +if __name__ == "__main__": + # Parse command line arguments + args = parse_cli_args() + # Run conversion + convert_meshes([args.input_dir], [args.output_dir]) diff --git a/scripts/tools/record_demos.py b/scripts/tools/record_demos.py new file mode 100644 index 0000000000000000000000000000000000000000..afde4260b5dd3675a9e3345c034ec91a50ec147c --- /dev/null +++ b/scripts/tools/record_demos.py @@ -0,0 +1,596 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +""" +Script to record demonstrations with Isaac Lab environments using human teleoperation. + +This script allows users to record demonstrations operated by human teleoperation for a specified task. +The recorded demonstrations are stored as episodes in a hdf5 file. Users can specify the task, teleoperation +device, dataset directory, and environment stepping rate through command-line arguments. + +required arguments: + --task Name of the task. + +optional arguments: + -h, --help Show this help message and exit + --teleop_device Device for interacting with environment. (default: keyboard) + --dataset_file File path to export recorded demos. (default: "./datasets/dataset.hdf5") + --step_hz Environment stepping rate in Hz. (default: 30) + --num_demos Number of demonstrations to record. (default: 0) + --num_success_steps Number of continuous steps with task success for concluding a demo as successful. + (default: 10) +""" + +"""Launch Isaac Sim Simulator first.""" + +# Standard library imports +import argparse +import contextlib + +# Isaac Lab AppLauncher +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Record demonstrations for Isaac Lab environments.") +parser.add_argument("--task", type=str, required=True, help="Name of the task.") +parser.add_argument( + "--teleop_device", + type=str, + default="keyboard", + help=( + "Teleop device. Set here (legacy) or via the environment config. If using the environment config, pass the" + " device key/name defined under 'teleop_devices' (it can be a custom name, not necessarily 'handtracking')." + " Built-ins: keyboard, spacemouse, gamepad. Not all tasks support all built-ins." + ), +) +parser.add_argument( + "--dataset_file", type=str, default="./datasets/dataset.hdf5", help="File path to export recorded demos." +) +parser.add_argument("--step_hz", type=int, default=30, help="Environment stepping rate in Hz.") +parser.add_argument( + "--num_demos", type=int, default=0, help="Number of demonstrations to record. Set to 0 for infinite." +) +parser.add_argument( + "--num_success_steps", + type=int, + default=10, + help="Number of continuous steps with task success for concluding a demo as successful. Default is 10.", +) +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# Validate required arguments +if args_cli.task is None: + parser.error("--task is required") + +app_launcher_args = vars(args_cli) + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version + # installed by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +if "handtracking" in args_cli.teleop_device.lower(): + app_launcher_args["xr"] = True + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +# Third-party imports +import logging +import os +import time +from typing import cast + +import gymnasium as gym +import torch + +import omni.ui as ui + +from isaaclab.devices import Se3Keyboard, Se3KeyboardCfg, Se3SpaceMouse, Se3SpaceMouseCfg +from isaaclab.devices.openxr import remove_camera_configs +from isaaclab.devices.teleop_device_factory import create_teleop_device + +import isaaclab_mimic.envs # noqa: F401 +from isaaclab_mimic.ui.instruction_display import InstructionDisplay, show_subtask_instructions + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from collections.abc import Callable + +from isaaclab.envs import DirectRLEnvCfg, ManagerBasedRLEnvCfg +from isaaclab.envs import ManagerBasedEnv +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.envs.ui import EmptyWindow +from isaaclab.managers import DatasetExportMode + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class RateLimiter: + """Convenience class for enforcing rates in loops.""" + + def __init__(self, hz: int): + """Initialize a RateLimiter with specified frequency. + + Args: + hz: Frequency to enforce in Hertz. + """ + self.hz = hz + self.last_time = time.time() + self.sleep_duration = 1.0 / hz + self.render_period = min(0.033, self.sleep_duration) + + def sleep(self, env: gym.Env): + """Attempt to sleep at the specified rate in hz. + + Args: + env: Environment to render during sleep periods. + """ + next_wakeup_time = self.last_time + self.sleep_duration + while time.time() < next_wakeup_time: + time.sleep(self.render_period) + env.sim.render() + + self.last_time = self.last_time + self.sleep_duration + + # detect time jumping forwards (e.g. loop is too slow) + if self.last_time < time.time(): + while self.last_time < time.time(): + self.last_time += self.sleep_duration + + +def setup_output_directories() -> tuple[str, str]: + """Set up output directories for saving demonstrations. + + Creates the output directory if it doesn't exist and extracts the file name + from the dataset file path. + + Returns: + tuple[str, str]: A tuple containing: + - output_dir: The directory path where the dataset will be saved + - output_file_name: The filename (without extension) for the dataset + """ + # get directory path and file name (without extension) from cli arguments + output_dir = os.path.dirname(args_cli.dataset_file) + output_file_name = os.path.splitext(os.path.basename(args_cli.dataset_file))[0] + + # create directory if it does not exist + if not os.path.exists(output_dir): + os.makedirs(output_dir) + print(f"Created output directory: {output_dir}") + + return output_dir, output_file_name + + +def create_environment_config( + output_dir: str, output_file_name: str +) -> tuple[ManagerBasedRLEnvCfg | DirectRLEnvCfg, object | None]: + """Create and configure the environment configuration. + + Parses the environment configuration and makes necessary adjustments for demo recording. + Extracts the success termination function and configures the recorder manager. + + Args: + output_dir: Directory where recorded demonstrations will be saved + output_file_name: Name of the file to store the demonstrations + + Returns: + tuple[isaaclab_tasks.utils.parse_cfg.EnvCfg, Optional[object]]: A tuple containing: + - env_cfg: The configured environment configuration + - success_term: The success termination object or None if not available + + Raises: + Exception: If parsing the environment configuration fails + """ + # parse configuration + try: + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1) + env_cfg.env_name = args_cli.task.split(":")[-1] + except Exception as e: + logger.error(f"Failed to parse environment configuration: {e}") + exit(1) + + # extract success checking function to invoke in the main loop + success_term = None + if hasattr(env_cfg.terminations, "success"): + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + else: + logger.warning( + "No success termination term was found in the environment." + " Will not be able to mark recorded demos as successful." + ) + + if args_cli.xr: + # If cameras are not enabled and XR is enabled, remove camera configs + if not args_cli.enable_cameras: + env_cfg = remove_camera_configs(env_cfg) + env_cfg.sim.render.antialiasing_mode = "DLSS" + + # modify configuration such that the environment runs indefinitely until + # the goal is reached or other termination conditions are met + env_cfg.terminations.time_out = None + env_cfg.observations.policy.concatenate_terms = False + + env_cfg.recorders: ActionStateRecorderManagerCfg = ActionStateRecorderManagerCfg() + env_cfg.recorders.dataset_export_dir_path = output_dir + env_cfg.recorders.dataset_filename = output_file_name + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_SUCCEEDED_ONLY + + return env_cfg, success_term + + +def create_environment(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg) -> gym.Env: + """Create the environment from the configuration. + + Args: + env_cfg: The environment configuration object that defines the environment properties. + This should be an instance of EnvCfg created by parse_env_cfg(). + + Returns: + gym.Env: A Gymnasium environment instance for the specified task. + + Raises: + Exception: If environment creation fails for any reason. + """ + try: + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + return env + except Exception as e: + logger.error(f"Failed to create environment: {e}") + exit(1) + + +def setup_teleop_device(callbacks: dict[str, Callable]) -> object: + """Set up the teleoperation device based on configuration. + + Attempts to create a teleoperation device based on the environment configuration. + Falls back to default devices if the specified device is not found in the configuration. + + Args: + callbacks: Dictionary mapping callback keys to functions that will be + attached to the teleop device + + Returns: + object: The configured teleoperation device interface + + Raises: + Exception: If teleop device creation fails + """ + teleop_interface = None + try: + if hasattr(env_cfg, "teleop_devices") and args_cli.teleop_device in env_cfg.teleop_devices.devices: + teleop_interface = create_teleop_device(args_cli.teleop_device, env_cfg.teleop_devices.devices, callbacks) + else: + logger.warning( + f"No teleop device '{args_cli.teleop_device}' found in environment config. Creating default." + ) + # Create fallback teleop device + if args_cli.teleop_device.lower() == "keyboard": + teleop_interface = Se3Keyboard(Se3KeyboardCfg(pos_sensitivity=0.2, rot_sensitivity=0.5)) + elif args_cli.teleop_device.lower() == "spacemouse": + teleop_interface = Se3SpaceMouse(Se3SpaceMouseCfg(pos_sensitivity=0.2, rot_sensitivity=0.5)) + else: + logger.error(f"Unsupported teleop device: {args_cli.teleop_device}") + logger.error("Supported devices: keyboard, spacemouse, handtracking") + exit(1) + + # Add callbacks to fallback device + for key, callback in callbacks.items(): + teleop_interface.add_callback(key, callback) + except Exception as e: + logger.error(f"Failed to create teleop device: {e}") + exit(1) + + if teleop_interface is None: + logger.error("Failed to create teleop interface") + exit(1) + + return teleop_interface + + +def setup_ui(label_text: str, env: gym.Env) -> InstructionDisplay: + """Set up the user interface elements. + + Creates instruction display and UI window with labels for showing information + to the user during demonstration recording. + + Args: + label_text: Text to display showing current recording status + env: The environment instance for which UI is being created + + Returns: + InstructionDisplay: The configured instruction display object + """ + instruction_display = InstructionDisplay(args_cli.xr) + if not args_cli.xr: + window = EmptyWindow(env, "Instruction") + with window.ui_window_elements["main_vstack"]: + demo_label = ui.Label(label_text) + subtask_label = ui.Label("") + instruction_display.set_labels(subtask_label, demo_label) + + return instruction_display + + +def process_success_condition(env: gym.Env, success_term: object | None, success_step_count: int) -> tuple[int, bool]: + """Process the success condition for the current step. + + Checks if the environment has met the success condition for the required + number of consecutive steps. Marks the episode as successful if criteria are met. + + Args: + env: The environment instance to check + success_term: The success termination object or None if not available + success_step_count: Current count of consecutive successful steps + + Returns: + tuple[int, bool]: A tuple containing: + - updated success_step_count: The updated count of consecutive successful steps + - success_reset_needed: Boolean indicating if reset is needed due to success + """ + if success_term is None: + return success_step_count, False + + if bool(success_term.func(env, **success_term.params)[0]): + success_step_count += 1 + if success_step_count >= args_cli.num_success_steps: + env.recorder_manager.record_pre_reset([0], force_export_or_skip=False) + env.recorder_manager.set_success_to_episodes( + [0], torch.tensor([[True]], dtype=torch.bool, device=env.device) + ) + env.recorder_manager.export_episodes([0]) + print("Success condition met! Recording completed.") + return success_step_count, True + else: + success_step_count = 0 + + return success_step_count, False + + +def handle_reset( + env: gym.Env, success_step_count: int, instruction_display: InstructionDisplay, label_text: str +) -> int: + """Handle resetting the environment. + + Resets the environment, recorder manager, and related state variables. + Updates the instruction display with current status. + + Args: + env: The environment instance to reset + success_step_count: Current count of consecutive successful steps + instruction_display: The display object to update + label_text: Text to display showing current recording status + + Returns: + int: Reset success step count (0) + """ + print("Resetting environment...") + env.sim.reset() + env.recorder_manager.reset() + env.reset() + success_step_count = 0 + instruction_display.show_demo(label_text) + return success_step_count + + +def run_simulation_loop( + env: gym.Env, + teleop_interface: object | None, + success_term: object | None, + rate_limiter: RateLimiter | None, +) -> int: + """Run the main simulation loop for collecting demonstrations. + + Sets up callback functions for teleop device, initializes the UI, + and runs the main loop that processes user inputs and environment steps. + Records demonstrations when success conditions are met. + + Args: + env: The environment instance + teleop_interface: Optional teleop interface (will be created if None) + success_term: The success termination object or None if not available + rate_limiter: Optional rate limiter to control simulation speed + + Returns: + int: Number of successful demonstrations recorded + """ + current_recorded_demo_count = 0 + success_step_count = 0 + should_reset_recording_instance = False + running_recording_instance = not args_cli.xr + + # Callback closures for the teleop device + def reset_recording_instance(): + nonlocal should_reset_recording_instance + should_reset_recording_instance = True + print("Recording instance reset requested") + + def start_recording_instance(): + nonlocal running_recording_instance + running_recording_instance = True + print("Recording started") + + def stop_recording_instance(): + nonlocal running_recording_instance + running_recording_instance = False + print("Recording paused") + + # Set up teleoperation callbacks + teleoperation_callbacks = { + "R": reset_recording_instance, + "START": start_recording_instance, + "STOP": stop_recording_instance, + "RESET": reset_recording_instance, + } + + teleop_interface = setup_teleop_device(teleoperation_callbacks) + teleop_interface.add_callback("R", reset_recording_instance) + + # Reset before starting + env.sim.reset() + env.reset() + teleop_interface.reset() + + label_text = f"Recorded {current_recorded_demo_count} successful demonstrations." + instruction_display = setup_ui(label_text, env) + + subtasks = {} + + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + while simulation_app.is_running(): + # Get keyboard command + action = teleop_interface.advance() + # Expand to batch dimension + actions = action.repeat(env.num_envs, 1) + + # Perform action on environment + if running_recording_instance: + # Compute actions based on environment + obv = env.step(actions) + if subtasks is not None: + if subtasks == {}: + subtasks = obv[0].get("subtask_terms") + elif subtasks: + show_subtask_instructions(instruction_display, subtasks, obv, env.cfg) + else: + env.sim.render() + + # Check for success condition + success_step_count, success_reset_needed = process_success_condition(env, success_term, success_step_count) + if success_reset_needed: + should_reset_recording_instance = True + + # Update demo count if it has changed + if env.recorder_manager.exported_successful_episode_count > current_recorded_demo_count: + current_recorded_demo_count = env.recorder_manager.exported_successful_episode_count + label_text = f"Recorded {current_recorded_demo_count} successful demonstrations." + print(label_text) + + # Check if we've reached the desired number of demos + if args_cli.num_demos > 0 and env.recorder_manager.exported_successful_episode_count >= args_cli.num_demos: + label_text = f"All {current_recorded_demo_count} demonstrations recorded.\nExiting the app." + instruction_display.show_demo(label_text) + print(label_text) + target_time = time.time() + 0.8 + while time.time() < target_time: + if rate_limiter: + rate_limiter.sleep(env) + else: + env.sim.render() + break + + # Handle reset if requested + if should_reset_recording_instance: + success_step_count = handle_reset(env, success_step_count, instruction_display, label_text) + should_reset_recording_instance = False + + # Check if simulation is stopped + if env.sim.is_stopped(): + break + + # Rate limiting + if rate_limiter: + rate_limiter.sleep(env) + + return current_recorded_demo_count + + +def main() -> None: + """Collect demonstrations from the environment using teleop interfaces. + + Main function that orchestrates the entire process: + 1. Sets up rate limiting based on configuration + 2. Creates output directories for saving demonstrations + 3. Configures the environment + 4. Runs the simulation loop to collect demonstrations + 5. Cleans up resources when done + + Raises: + Exception: Propagates exceptions from any of the called functions + """ + # if handtracking is selected, rate limiting is achieved via OpenXR + if args_cli.xr: + rate_limiter = None + from isaaclab.ui.xr_widgets import TeleopVisualizationManager, XRVisualization + + # Assign the teleop visualization manager to the visualization system + XRVisualization.assign_manager(TeleopVisualizationManager) + else: + rate_limiter = RateLimiter(args_cli.step_hz) + + # Set up output directories + output_dir, output_file_name = setup_output_directories() + + # Create and configure environment + global env_cfg # Make env_cfg available to setup_teleop_device + env_cfg, success_term = create_environment_config(output_dir, output_file_name) + + # Create environment + env = create_environment(env_cfg) + + # Print robot joint names (runtime scene access; useful for debugging) + try: + env_mb = cast(ManagerBasedEnv, env) + robot = env_mb.scene["robot"] + print(f"===== JOINT NAMES =====\n{robot.data.joint_names}") + except Exception as e: + logger.warning(f"Failed to print robot joint names: {e}") + + # Print action term information (dimensions and joint mapping when available) + try: + env_mb = cast(ManagerBasedEnv, env) + print("===== ACTION TERMS =====") + print(env_mb.action_manager) + + # For some action terms (e.g. Pink IK), the IO descriptor includes the resolved joint-name mapping. + for desc in env_mb.action_manager.get_IO_descriptors: + name = desc.get("name", "") + action_type = desc.get("action_type", "") + shape = desc.get("shape", None) + print(f"- {name}: type={action_type}, shape={shape}") + + pink_joints = desc.get("pink_controller_joint_names", None) + hand_joints = desc.get("hand_joint_names", None) + if pink_joints is not None: + print(f" pink_controller_joint_names ({len(pink_joints)}): {pink_joints}") + if hand_joints is not None: + print(f" hand_joint_names ({len(hand_joints)}): {hand_joints}") + except Exception as e: + logger.warning(f"Failed to print action-space/joint mapping: {e}") + + # Run simulation loop + current_recorded_demo_count = run_simulation_loop(env, None, success_term, rate_limiter) + + # Clean up + env.close() + print(f"Recording session completed with {current_recorded_demo_count} successful demonstrations") + print(f"Demonstrations saved to: {args_cli.dataset_file}") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/replay_demos.py b/scripts/tools/replay_demos.py new file mode 100644 index 0000000000000000000000000000000000000000..7d5e477267bf35110e883b79ab0fda84afb67611 --- /dev/null +++ b/scripts/tools/replay_demos.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Script to replay demonstrations with Isaac Lab environments.""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Replay demonstrations in Isaac Lab environments.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to replay episodes.") +parser.add_argument("--task", type=str, default=None, help="Force to use the specified task.") +parser.add_argument( + "--select_episodes", + type=int, + nargs="+", + default=[], + help="A list of episode indices to be replayed. Keep empty to replay all in the dataset file.", +) +parser.add_argument("--dataset_file", type=str, default="datasets/dataset.hdf5", help="Dataset file to be replayed.") +parser.add_argument( + "--validate_states", + action="store_true", + default=False, + help=( + "Validate if the states, if available, match between loaded from datasets and replayed. Only valid if" + " --num_envs is 1." + ), +) +parser.add_argument( + "--validate_success_rate", + action="store_true", + default=False, + help="Validate the replay success rate using the task environment termination criteria", +) +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# args_cli.headless = True + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version + # installed by IsaacLab and not the one installed by Isaac Sim. + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import contextlib +import os + +import gymnasium as gym +import torch + +from isaaclab.devices import Se3Keyboard, Se3KeyboardCfg +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +is_paused = False + + +def play_cb(): + global is_paused + is_paused = False + + +def pause_cb(): + global is_paused + is_paused = True + + +def compare_states(state_from_dataset, runtime_state, runtime_env_index) -> (bool, str): + """Compare states from dataset and runtime. + + Args: + state_from_dataset: State from dataset. + runtime_state: State from runtime. + runtime_env_index: Index of the environment in the runtime states to be compared. + + Returns: + bool: True if states match, False otherwise. + str: Log message if states don't match. + """ + states_matched = True + output_log = "" + for asset_type in ["articulation", "rigid_object"]: + for asset_name in runtime_state[asset_type].keys(): + for state_name in runtime_state[asset_type][asset_name].keys(): + runtime_asset_state = runtime_state[asset_type][asset_name][state_name][runtime_env_index] + dataset_asset_state = state_from_dataset[asset_type][asset_name][state_name] + if len(dataset_asset_state) != len(runtime_asset_state): + raise ValueError(f"State shape of {state_name} for asset {asset_name} don't match") + for i in range(len(dataset_asset_state)): + if abs(dataset_asset_state[i] - runtime_asset_state[i]) > 0.01: + states_matched = False + output_log += f'\tState ["{asset_type}"]["{asset_name}"]["{state_name}"][{i}] don\'t match\r\n' + output_log += f"\t Dataset:\t{dataset_asset_state[i]}\r\n" + output_log += f"\t Runtime: \t{runtime_asset_state[i]}\r\n" + return states_matched, output_log + + +def main(): + """Replay episodes loaded from a file.""" + global is_paused + + # Load dataset + if not os.path.exists(args_cli.dataset_file): + raise FileNotFoundError(f"The dataset file {args_cli.dataset_file} does not exist.") + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(args_cli.dataset_file) + env_name = dataset_file_handler.get_env_name() + episode_count = dataset_file_handler.get_num_episodes() + + if episode_count == 0: + print("No episodes found in the dataset.") + exit() + + episode_indices_to_replay = args_cli.select_episodes + if len(episode_indices_to_replay) == 0: + episode_indices_to_replay = list(range(episode_count)) + + if args_cli.task is not None: + env_name = args_cli.task.split(":")[-1] + if env_name is None: + raise ValueError("Task/env name was not specified nor found in the dataset.") + + num_envs = args_cli.num_envs + + env_cfg = parse_env_cfg(env_name, device=args_cli.device, num_envs=num_envs) + + # extract success checking function to invoke in the main loop + success_term = None + if args_cli.validate_success_rate: + if hasattr(env_cfg.terminations, "success"): + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + else: + print( + "No success termination term was found in the environment." + " Will not be able to mark recorded demos as successful." + ) + + # Disable all recorders and terminations + env_cfg.recorders = {} + env_cfg.terminations = {} + + # create environment from loaded config + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + teleop_interface = Se3Keyboard(Se3KeyboardCfg(pos_sensitivity=0.1, rot_sensitivity=0.1)) + teleop_interface.add_callback("N", play_cb) + teleop_interface.add_callback("B", pause_cb) + print('Press "B" to pause and "N" to resume the replayed actions.') + + # Determine if state validation should be conducted + state_validation_enabled = False + if args_cli.validate_states and num_envs == 1: + state_validation_enabled = True + elif args_cli.validate_states and num_envs > 1: + print("Warning: State validation is only supported with a single environment. Skipping state validation.") + + # Get idle action (idle actions are applied to envs without next action) + if hasattr(env_cfg, "idle_action"): + idle_action = env_cfg.idle_action.repeat(num_envs, 1) + else: + idle_action = torch.zeros(env.action_space.shape) + + # reset before starting + env.reset() + teleop_interface.reset() + + # simulate environment -- run everything in inference mode + episode_names = list(dataset_file_handler.get_episode_names()) + replayed_episode_count = 0 + recorded_episode_count = 0 + + # Track current episode indices for each environment + current_episode_indices = [None] * num_envs + + # Track failed demo IDs + failed_demo_ids = [] + + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + while simulation_app.is_running() and not simulation_app.is_exiting(): + env_episode_data_map = {index: EpisodeData() for index in range(num_envs)} + first_loop = True + has_next_action = True + episode_ended = [False] * num_envs + while has_next_action: + # initialize actions with idle action so those without next action will not move + actions = idle_action + has_next_action = False + for env_id in range(num_envs): + env_next_action = env_episode_data_map[env_id].get_next_action() + if env_next_action is None: + # check if the episode is successful after the whole episode_data is + if ( + (success_term is not None) + and (current_episode_indices[env_id]) is not None + and (not episode_ended[env_id]) + ): + if bool(success_term.func(env, **success_term.params)[env_id]): + recorded_episode_count += 1 + plural_trailing_s = "s" if recorded_episode_count > 1 else "" + + print( + f"Successfully replayed {recorded_episode_count} episode{plural_trailing_s} out" + f" of {replayed_episode_count} demos." + ) + else: + # if not successful, add to failed demo IDs list + if ( + current_episode_indices[env_id] is not None + and current_episode_indices[env_id] not in failed_demo_ids + ): + failed_demo_ids.append(current_episode_indices[env_id]) + + episode_ended[env_id] = True + + next_episode_index = None + while episode_indices_to_replay: + next_episode_index = episode_indices_to_replay.pop(0) + + if next_episode_index < episode_count: + episode_ended[env_id] = False + break + next_episode_index = None + + if next_episode_index is not None: + replayed_episode_count += 1 + current_episode_indices[env_id] = next_episode_index + print(f"{replayed_episode_count:4}: Loading #{next_episode_index} episode to env_{env_id}") + episode_data = dataset_file_handler.load_episode( + episode_names[next_episode_index], env.device + ) + env_episode_data_map[env_id] = episode_data + # Set initial state for the new episode + initial_state = episode_data.get_initial_state() + env.reset_to(initial_state, torch.tensor([env_id], device=env.device), is_relative=True) + # Get the first action for the new episode + env_next_action = env_episode_data_map[env_id].get_next_action() + has_next_action = True + else: + continue + else: + has_next_action = True + actions[env_id] = env_next_action + if first_loop: + first_loop = False + else: + while is_paused: + env.sim.render() + continue + env.step(actions) + + if state_validation_enabled: + state_from_dataset = env_episode_data_map[0].get_next_state() + if state_from_dataset is not None: + print( + f"Validating states at action-index: {env_episode_data_map[0].next_state_index - 1:4}", + end="", + ) + current_runtime_state = env.scene.get_state(is_relative=True) + states_matched, comparison_log = compare_states(state_from_dataset, current_runtime_state, 0) + if states_matched: + print("\t- matched.") + else: + print("\t- mismatched.") + print(comparison_log) + break + # Close environment after replay in complete + plural_trailing_s = "s" if replayed_episode_count > 1 else "" + print(f"Finished replaying {replayed_episode_count} episode{plural_trailing_s}.") + + # Print success statistics only if validation was enabled + if success_term is not None: + print(f"Successfully replayed: {recorded_episode_count}/{replayed_episode_count}") + + # Print failed demo IDs if any + if failed_demo_ids: + print(f"\nFailed demo IDs ({len(failed_demo_ids)} total):") + print(f" {sorted(failed_demo_ids)}") + + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tools/test/test_cosmos_prompt_gen.py b/scripts/tools/test/test_cosmos_prompt_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..17f1764d914b4f85195cced469ddf1d3b004d196 --- /dev/null +++ b/scripts/tools/test/test_cosmos_prompt_gen.py @@ -0,0 +1,169 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for Cosmos prompt generation script.""" + +import json +import os +import tempfile + +import pytest + +from scripts.tools.cosmos.cosmos_prompt_gen import generate_prompt, main + + +@pytest.fixture(scope="class") +def temp_templates_file(): + """Create temporary templates file.""" + temp_file = tempfile.NamedTemporaryFile(suffix=".json", delete=False) # noqa: SIM115 + + # Create test templates + test_templates = { + "lighting": ["with bright lighting", "with dim lighting", "with natural lighting"], + "color": ["in warm colors", "in cool colors", "in vibrant colors"], + "style": ["in a realistic style", "in an artistic style", "in a minimalist style"], + "empty_section": [], # Test empty section + "invalid_section": "not a list", # Test invalid section + } + + # Write templates to file + with open(temp_file.name, "w") as f: + json.dump(test_templates, f) + + yield temp_file.name + # Cleanup + os.remove(temp_file.name) + + +@pytest.fixture +def temp_output_file(): + """Create temporary output file.""" + temp_file = tempfile.NamedTemporaryFile(suffix=".txt", delete=False) # noqa: SIM115 + yield temp_file.name + # Cleanup + os.remove(temp_file.name) + + +class TestCosmosPromptGen: + """Test cases for Cosmos prompt generation functionality.""" + + def test_generate_prompt_valid_templates(self, temp_templates_file): + """Test generating a prompt with valid templates.""" + prompt = generate_prompt(temp_templates_file) + + # Check that prompt is a string + assert isinstance(prompt, str) + + # Check that prompt contains at least one word + assert len(prompt.split()) > 0 + + # Check that prompt contains valid sections + valid_sections = ["lighting", "color", "style"] + found_sections = [section for section in valid_sections if section in prompt.lower()] + assert len(found_sections) > 0 + + def test_generate_prompt_invalid_file(self): + """Test generating a prompt with invalid file path.""" + with pytest.raises(FileNotFoundError): + generate_prompt("nonexistent_file.json") + + def test_generate_prompt_invalid_json(self): + """Test generating a prompt with invalid JSON file.""" + # Create a temporary file with invalid JSON + with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as temp_file: + temp_file.write(b"invalid json content") + temp_file.flush() + + try: + with pytest.raises(ValueError): + generate_prompt(temp_file.name) + finally: + os.remove(temp_file.name) + + def test_main_function_single_prompt(self, temp_templates_file, temp_output_file): + """Test main function with single prompt generation.""" + # Mock command line arguments + import sys + + original_argv = sys.argv + sys.argv = [ + "cosmos_prompt_gen.py", + "--templates_path", + temp_templates_file, + "--num_prompts", + "1", + "--output_path", + temp_output_file, + ] + + try: + main() + + # Check if output file was created + assert os.path.exists(temp_output_file) + + # Check content of output file + with open(temp_output_file) as f: + content = f.read().strip() + assert len(content) > 0 + assert len(content.split("\n")) == 1 + finally: + # Restore original argv + sys.argv = original_argv + + def test_main_function_multiple_prompts(self, temp_templates_file, temp_output_file): + """Test main function with multiple prompt generation.""" + # Mock command line arguments + import sys + + original_argv = sys.argv + sys.argv = [ + "cosmos_prompt_gen.py", + "--templates_path", + temp_templates_file, + "--num_prompts", + "3", + "--output_path", + temp_output_file, + ] + + try: + main() + + # Check if output file was created + assert os.path.exists(temp_output_file) + + # Check content of output file + with open(temp_output_file) as f: + content = f.read().strip() + assert len(content) > 0 + assert len(content.split("\n")) == 3 + + # Check that each line is a valid prompt + for line in content.split("\n"): + assert len(line) > 0 + finally: + # Restore original argv + sys.argv = original_argv + + def test_main_function_default_output(self, temp_templates_file): + """Test main function with default output path.""" + # Mock command line arguments + import sys + + original_argv = sys.argv + sys.argv = ["cosmos_prompt_gen.py", "--templates_path", temp_templates_file, "--num_prompts", "1"] + + try: + main() + + # Check if default output file was created + assert os.path.exists("prompts.txt") + + # Clean up default output file + os.remove("prompts.txt") + finally: + # Restore original argv + sys.argv = original_argv diff --git a/scripts/tools/test/test_hdf5_to_mp4.py b/scripts/tools/test/test_hdf5_to_mp4.py new file mode 100644 index 0000000000000000000000000000000000000000..33ccd0d1723eab2e2200a537abd49f974af93dee --- /dev/null +++ b/scripts/tools/test/test_hdf5_to_mp4.py @@ -0,0 +1,173 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for HDF5 to MP4 conversion script.""" + +import os +import tempfile + +import h5py +import numpy as np +import pytest + +from scripts.tools.hdf5_to_mp4 import get_num_demos, main, write_demo_to_mp4 + + +@pytest.fixture(scope="class") +def temp_hdf5_file(): + """Create temporary HDF5 file with test data.""" + temp_file = tempfile.NamedTemporaryFile(suffix=".h5", delete=False) # noqa: SIM115 + with h5py.File(temp_file.name, "w") as h5f: + # Create test data structure + for demo_id in range(2): # Create 2 demos + demo_group = h5f.create_group(f"data/demo_{demo_id}/obs") + + # Create RGB frames (2 frames per demo) + rgb_data = np.random.randint(0, 255, (2, 704, 1280, 3), dtype=np.uint8) + demo_group.create_dataset("table_cam", data=rgb_data) + + # Create segmentation frames + seg_data = np.random.randint(0, 255, (2, 704, 1280, 4), dtype=np.uint8) + demo_group.create_dataset("table_cam_segmentation", data=seg_data) + + # Create normal maps + normals_data = np.random.rand(2, 704, 1280, 3).astype(np.float32) + demo_group.create_dataset("table_cam_normals", data=normals_data) + + # Create depth maps + depth_data = np.random.rand(2, 704, 1280, 1).astype(np.float32) + demo_group.create_dataset("table_cam_depth", data=depth_data) + + yield temp_file.name + # Cleanup + os.remove(temp_file.name) + + +@pytest.fixture +def temp_output_dir(): + """Create temporary output directory.""" + temp_dir = tempfile.mkdtemp() # noqa: SIM115 + yield temp_dir + # Cleanup + for file in os.listdir(temp_dir): + os.remove(os.path.join(temp_dir, file)) + os.rmdir(temp_dir) + + +class TestHDF5ToMP4: + """Test cases for HDF5 to MP4 conversion functionality.""" + + def test_get_num_demos(self, temp_hdf5_file): + """Test the get_num_demos function.""" + num_demos = get_num_demos(temp_hdf5_file) + assert num_demos == 2 + + def test_write_demo_to_mp4_rgb(self, temp_hdf5_file, temp_output_dir): + """Test writing RGB frames to MP4.""" + write_demo_to_mp4(temp_hdf5_file, 0, "data/demo_0/obs", "table_cam", temp_output_dir, 704, 1280) + + output_file = os.path.join(temp_output_dir, "demo_0_table_cam.mp4") + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + + def test_write_demo_to_mp4_segmentation(self, temp_hdf5_file, temp_output_dir): + """Test writing segmentation frames to MP4.""" + write_demo_to_mp4(temp_hdf5_file, 0, "data/demo_0/obs", "table_cam_segmentation", temp_output_dir, 704, 1280) + + output_file = os.path.join(temp_output_dir, "demo_0_table_cam_segmentation.mp4") + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + + def test_write_demo_to_mp4_normals(self, temp_hdf5_file, temp_output_dir): + """Test writing normal maps to MP4.""" + write_demo_to_mp4(temp_hdf5_file, 0, "data/demo_0/obs", "table_cam_normals", temp_output_dir, 704, 1280) + + output_file = os.path.join(temp_output_dir, "demo_0_table_cam_normals.mp4") + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + + def test_write_demo_to_mp4_shaded_segmentation(self, temp_hdf5_file, temp_output_dir): + """Test writing shaded_segmentation frames to MP4.""" + write_demo_to_mp4( + temp_hdf5_file, + 0, + "data/demo_0/obs", + "table_cam_shaded_segmentation", + temp_output_dir, + 704, + 1280, + ) + + output_file = os.path.join(temp_output_dir, "demo_0_table_cam_shaded_segmentation.mp4") + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + + def test_write_demo_to_mp4_depth(self, temp_hdf5_file, temp_output_dir): + """Test writing depth maps to MP4.""" + write_demo_to_mp4(temp_hdf5_file, 0, "data/demo_0/obs", "table_cam_depth", temp_output_dir, 704, 1280) + + output_file = os.path.join(temp_output_dir, "demo_0_table_cam_depth.mp4") + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + + def test_write_demo_to_mp4_invalid_demo(self, temp_hdf5_file, temp_output_dir): + """Test writing with invalid demo ID.""" + with pytest.raises(KeyError): + write_demo_to_mp4( + temp_hdf5_file, + 999, # Invalid demo ID + "data/demo_999/obs", + "table_cam", + temp_output_dir, + 704, + 1280, + ) + + def test_write_demo_to_mp4_invalid_key(self, temp_hdf5_file, temp_output_dir): + """Test writing with invalid input key.""" + with pytest.raises(KeyError): + write_demo_to_mp4(temp_hdf5_file, 0, "data/demo_0/obs", "invalid_key", temp_output_dir, 704, 1280) + + def test_main_function(self, temp_hdf5_file, temp_output_dir): + """Test the main function.""" + # Mock command line arguments + import sys + + original_argv = sys.argv + sys.argv = [ + "hdf5_to_mp4.py", + "--input_file", + temp_hdf5_file, + "--output_dir", + temp_output_dir, + "--input_keys", + "table_cam", + "table_cam_segmentation", + "--video_height", + "704", + "--video_width", + "1280", + "--framerate", + "30", + ] + + try: + main() + + # Check if output files were created + expected_files = [ + "demo_0_table_cam.mp4", + "demo_0_table_cam_segmentation.mp4", + "demo_1_table_cam.mp4", + "demo_1_table_cam_segmentation.mp4", + ] + + for file in expected_files: + output_file = os.path.join(temp_output_dir, file) + assert os.path.exists(output_file) + assert os.path.getsize(output_file) > 0 + finally: + # Restore original argv + sys.argv = original_argv diff --git a/scripts/tools/test/test_mp4_to_hdf5.py b/scripts/tools/test/test_mp4_to_hdf5.py new file mode 100644 index 0000000000000000000000000000000000000000..631ac41da228573228e2a73f572c29805b0c661b --- /dev/null +++ b/scripts/tools/test/test_mp4_to_hdf5.py @@ -0,0 +1,181 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for MP4 to HDF5 conversion script.""" + +import os +import tempfile + +import cv2 +import h5py +import numpy as np +import pytest + +from scripts.tools.mp4_to_hdf5 import get_frames_from_mp4, main, process_video_and_demo + + +@pytest.fixture(scope="class") +def temp_hdf5_file(): + """Create temporary HDF5 file with test data.""" + temp_file = tempfile.NamedTemporaryFile(suffix=".h5", delete=False) # noqa: SIM115 + with h5py.File(temp_file.name, "w") as h5f: + # Create test data structure for 2 demos + for demo_id in range(2): + demo_group = h5f.create_group(f"data/demo_{demo_id}") + obs_group = demo_group.create_group("obs") + + # Create actions data + actions_data = np.random.rand(10, 7).astype(np.float32) + demo_group.create_dataset("actions", data=actions_data) + + # Create robot state data + eef_pos_data = np.random.rand(10, 3).astype(np.float32) + eef_quat_data = np.random.rand(10, 4).astype(np.float32) + gripper_pos_data = np.random.rand(10, 1).astype(np.float32) + obs_group.create_dataset("eef_pos", data=eef_pos_data) + obs_group.create_dataset("eef_quat", data=eef_quat_data) + obs_group.create_dataset("gripper_pos", data=gripper_pos_data) + + # Create camera data + table_cam_data = np.random.randint(0, 255, (10, 704, 1280, 3), dtype=np.uint8) + wrist_cam_data = np.random.randint(0, 255, (10, 704, 1280, 3), dtype=np.uint8) + obs_group.create_dataset("table_cam", data=table_cam_data) + obs_group.create_dataset("wrist_cam", data=wrist_cam_data) + + # Set attributes + demo_group.attrs["num_samples"] = 10 + + yield temp_file.name + # Cleanup + os.remove(temp_file.name) + + +@pytest.fixture(scope="class") +def temp_videos_dir(): + """Create temporary MP4 files.""" + temp_dir = tempfile.mkdtemp() # noqa: SIM115 + video_paths = [] + + for demo_id in range(2): + video_path = os.path.join(temp_dir, f"demo_{demo_id}_table_cam.mp4") + video_paths.append(video_path) + + # Create a test video + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + video = cv2.VideoWriter(video_path, fourcc, 30, (1280, 704)) + + # Write some random frames + for _ in range(10): + frame = np.random.randint(0, 255, (704, 1280, 3), dtype=np.uint8) + video.write(frame) + video.release() + + yield temp_dir, video_paths + + # Cleanup + for video_path in video_paths: + os.remove(video_path) + os.rmdir(temp_dir) + + +@pytest.fixture +def temp_output_file(): + """Create temporary output file.""" + temp_file = tempfile.NamedTemporaryFile(suffix=".h5", delete=False) # noqa: SIM115 + yield temp_file.name + # Cleanup + os.remove(temp_file.name) + + +class TestMP4ToHDF5: + """Test cases for MP4 to HDF5 conversion functionality.""" + + def test_get_frames_from_mp4(self, temp_videos_dir): + """Test extracting frames from MP4 video.""" + _, video_paths = temp_videos_dir + frames = get_frames_from_mp4(video_paths[0]) + + # Check frame properties + assert frames.shape[0] == 10 # Number of frames + assert frames.shape[1:] == (704, 1280, 3) # Frame dimensions + assert frames.dtype == np.uint8 # Data type + + def test_get_frames_from_mp4_resize(self, temp_videos_dir): + """Test extracting frames with resizing.""" + _, video_paths = temp_videos_dir + target_height, target_width = 352, 640 + frames = get_frames_from_mp4(video_paths[0], target_height, target_width) + + # Check resized frame properties + assert frames.shape[0] == 10 # Number of frames + assert frames.shape[1:] == (target_height, target_width, 3) # Resized dimensions + assert frames.dtype == np.uint8 # Data type + + def test_process_video_and_demo(self, temp_hdf5_file, temp_videos_dir, temp_output_file): + """Test processing a single video and creating a new demo.""" + _, video_paths = temp_videos_dir + with h5py.File(temp_hdf5_file, "r") as f_in, h5py.File(temp_output_file, "w") as f_out: + process_video_and_demo(f_in, f_out, video_paths[0], 0, 2) + + # Check if new demo was created with correct data + assert "data/demo_2" in f_out + assert "data/demo_2/actions" in f_out + assert "data/demo_2/obs/eef_pos" in f_out + assert "data/demo_2/obs/eef_quat" in f_out + assert "data/demo_2/obs/gripper_pos" in f_out + assert "data/demo_2/obs/table_cam" in f_out + assert "data/demo_2/obs/wrist_cam" in f_out + + # Check data shapes + assert f_out["data/demo_2/actions"].shape == (10, 7) + assert f_out["data/demo_2/obs/eef_pos"].shape == (10, 3) + assert f_out["data/demo_2/obs/eef_quat"].shape == (10, 4) + assert f_out["data/demo_2/obs/gripper_pos"].shape == (10, 1) + assert f_out["data/demo_2/obs/table_cam"].shape == (10, 704, 1280, 3) + assert f_out["data/demo_2/obs/wrist_cam"].shape == (10, 704, 1280, 3) + + # Check attributes + assert f_out["data/demo_2"].attrs["num_samples"] == 10 + + def test_main_function(self, temp_hdf5_file, temp_videos_dir, temp_output_file): + """Test the main function.""" + # Mock command line arguments + import sys + + original_argv = sys.argv + sys.argv = [ + "mp4_to_hdf5.py", + "--input_file", + temp_hdf5_file, + "--videos_dir", + temp_videos_dir[0], + "--output_file", + temp_output_file, + ] + + try: + main() + + # Check if output file was created with correct data + with h5py.File(temp_output_file, "r") as f: + # Check if original demos were copied + assert "data/demo_0" in f + assert "data/demo_1" in f + + # Check if new demos were created + assert "data/demo_2" in f + assert "data/demo_3" in f + + # Check data in new demos + for demo_id in [2, 3]: + assert f"data/demo_{demo_id}/actions" in f + assert f"data/demo_{demo_id}/obs/eef_pos" in f + assert f"data/demo_{demo_id}/obs/eef_quat" in f + assert f"data/demo_{demo_id}/obs/gripper_pos" in f + assert f"data/demo_{demo_id}/obs/table_cam" in f + assert f"data/demo_{demo_id}/obs/wrist_cam" in f + finally: + # Restore original argv + sys.argv = original_argv diff --git a/scripts/tools/train_and_publish_checkpoints.py b/scripts/tools/train_and_publish_checkpoints.py new file mode 100644 index 0000000000000000000000000000000000000000..97ebb6f4c5f7d9a73024be94d72e73c2f2ef9534 --- /dev/null +++ b/scripts/tools/train_and_publish_checkpoints.py @@ -0,0 +1,414 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to manage pretrained checkpoints for Isaac Lab environments. + +This script is used to train and publish pretrained checkpoints for Isaac Lab environments. +It supports multiple workflows: rl_games, rsl_rl, sb3, and skrl. + +* To train an agent using the rl_games workflow on the Isaac-Cartpole-v0 environment: + + .. code-block:: shell + + python scripts/tools/train_and_publish_checkpoints.py --train rl_games:Isaac-Cartpole-v0 + +* To train and publish the checkpoints for all workflows on only the direct Cartpole environments: + + .. code-block:: shell + + python scripts/tools/train_and_publish_checkpoints.py \ + -tp "*:Isaac-Cartpole-*Direct-v0" \ + --/persistent/isaaclab/asset_root/pretrained_checkpoints="/some/path" + +* To review all repose cube jobs, excluding the 'Play' tasks and 'skrl' workflows: + + .. code-block:: shell + + python scripts/tools/train_and_publish_checkpoints.py \ + -r "*:*Repose-Cube*" \ + --exclude "*:*Play*" \ + --exclude skrl:* + +* To publish all results (that have been reviewed and approved). + + .. code-block:: shell + + python scripts/tools/train_and_publish_checkpoints.py \ + --publish --all \ + --/persistent/isaaclab/asset_root/pretrained_checkpoints="/some/path" + +""" + +import argparse + +from isaaclab.app import AppLauncher + +# Initialize the parser +parser = argparse.ArgumentParser( + description=""" +Script for training and publishing pre-trained checkpoints in Isaac Lab. + +Examples: + # Train an agent using the rl_games workflow for the Isaac-Cartpole-v0 environment. + train_and_publish_checkpoints.py --train rl_games:Isaac-Cartpole-v0 + + # Train and publish checkpoints for all workflows, targeting only direct Cartpole environments. + train_and_publish_checkpoints.py -tp "*:Isaac-Cartpole-*Direct-v0" \\ + --/persistent/isaaclab/asset_root/pretrained_checkpoints="/some/path" + + # Review all Repose Cube jobs, excluding Play tasks and skrl jobs. + train_and_publish_checkpoints.py -r "*:*Repose-Cube*" --exclude "*:*Play*" --exclude skrl:* + + # Publish all results that have been reviewed and approved. + train_and_publish_checkpoints.py --publish --all \\ + --/persistent/isaaclab/asset_root/pretrained_checkpoints="/some/path" +""", + formatter_class=argparse.RawTextHelpFormatter, +) + +# Add positional arguments that can accept zero or more values +parser.add_argument( + "jobs", + nargs="*", + help=""" +A job consists of a workflow and a task name, separated by a colon (wildcards are optional). Examples: + + rl_games:Isaac-Humanoid-*v0 # Wildcard for any Humanoid version + rsl_rl:Isaac-Ant-*-v0 # Wildcard for any Ant environment + *:Isaac-Velocity-Flat-Spot-v0 # Wildcard for any workflow, specific task + +Wildcards can be used in either the workflow or task name to match multiple entries. +""", +) +parser.add_argument("-t", "--train", action="store_true", help="Train checkpoints for later publishing.") +parser.add_argument("-p", "--publish_checkpoint", action="store_true", help="Publish pre-trained checkpoints.") +parser.add_argument("-r", "--review", action="store_true", help="Review checkpoints.") +parser.add_argument("-l", "--list", action="store_true", help="List all available environments and workflows.") +parser.add_argument("-f", "--force", action="store_true", help="Force training when results already exist.") +parser.add_argument("-a", "--all", action="store_true", help="Run all valid workflow task pairs.") +parser.add_argument( + "-E", + "--exclude", + action="append", + type=str, + default=[], + help="Excludes jobs matching the argument, with wildcard support.", +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") +parser.add_argument("--force_review", action="store_true", help="Forces review when one already exists.") +parser.add_argument("--force_publish", action="store_true", help="Publish checkpoints without review.") +parser.add_argument("--headless", action="store_true", help="Run training without the UI.") + +args, _ = parser.parse_known_args() + +# Need something to do +if len(args.jobs) == 0 and not args.all: + parser.error("Jobs must be provided, or --all.") + +# Must train, publish, review or list +if not (args.train or args.publish_checkpoint or args.review or args.list): + parser.error("A train, publish, review or list flag must be given.") + +# List excludes train and publish +if args.list and (args.train or args.publish_checkpoint): + parser.error("Can't train or publish when listing.") + +# launch omniverse app +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +import csv + +# Now everything else +import fnmatch +import json +import os +import subprocess +import sys + +import gymnasium as gym +import numpy as np + +import omni.client +from omni.client._omniclient import CopyBehavior + +from isaaclab_rl.utils.pretrained_checkpoint import ( + WORKFLOW_EXPERIMENT_NAME_VARIABLE, + WORKFLOW_PLAYER, + WORKFLOW_TRAINER, + WORKFLOWS, + get_log_root_path, + get_pretrained_checkpoint_path, + get_pretrained_checkpoint_publish_path, + get_pretrained_checkpoint_review, + get_pretrained_checkpoint_review_path, + has_pretrained_checkpoint_job_finished, + has_pretrained_checkpoint_job_run, + has_pretrained_checkpoints_asset_root_dir, +) + +# Need somewhere to publish +if args.publish_checkpoint and not has_pretrained_checkpoints_asset_root_dir(): + raise Exception("A /persistent/isaaclab/asset_root/pretrained_checkpoints setting is required to publish.") + + +def train_job(workflow, task_name, headless=False, force=False, num_envs=None): + """ + This trains a task using the workflow's train.py script, overriding the experiment name to ensure unique + log directories. By default it will return if an experiment has already been run. + + Args: + workflow: The workflow. + task_name: The task name. + headless: Should the training run without the UI. + force: Run training even if previous experiments have been run. + num_envs: How many simultaneous environments to simulate, overriding the config. + """ + + log_root_path = get_log_root_path(workflow, task_name) + + # We already ran this + if not force and os.path.exists(log_root_path) and len(os.listdir(log_root_path)) > 0: + print(f"Skipping training of {workflow}:{task_name}, already has been run") + return + + print(f"Training {workflow}:{task_name}") + + # Construct our command + cmd = [ + sys.executable, + WORKFLOW_TRAINER[workflow], + "--task", + task_name, + "--enable_cameras", + ] + + # Changes the directory name for logging + if WORKFLOW_EXPERIMENT_NAME_VARIABLE[workflow]: + cmd.append(f"{WORKFLOW_EXPERIMENT_NAME_VARIABLE[workflow]}={task_name}") + + if headless: + cmd.append("--headless") + if num_envs: + cmd.extend(["--num_envs", str(num_envs)]) + + print("Running : " + " ".join(cmd)) + + subprocess.run(cmd) + + +def review_pretrained_checkpoint(workflow, task_name, force_review=False, num_envs=None): + """ + This initiates a review of the pretrained checkpoint. The play.py script for the workflow is run, and the user + inspects the results. When done they close the simulator and will be prompted for their review. + + Args: + workflow: The workflow. + task_name: The task name. + force_review: Performs the review even if a review already exists. + num_envs: How many simultaneous environments to simulate, overriding the config. + """ + + # This workflow task pair hasn't been trained + if not has_pretrained_checkpoint_job_run(workflow, task_name): + print(f"Skipping review of {workflow}:{task_name}, hasn't been trained yet") + return + + # Couldn't find the checkpoint + if not has_pretrained_checkpoint_job_finished(workflow, task_name): + print(f"Training not complete for {workflow}:{task_name}") + return + + review = get_pretrained_checkpoint_review(workflow, task_name) + + if not force_review and review and review["reviewed"]: + print(f"Review already complete for {workflow}:{task_name}") + return + + print(f"Reviewing {workflow}:{task_name}") + + # Construct our command + cmd = [ + sys.executable, + WORKFLOW_PLAYER[workflow], + "--task", + task_name, + "--checkpoint", + get_pretrained_checkpoint_path(workflow, task_name), + "--enable_cameras", + ] + + if num_envs: + cmd.extend(["--num_envs", str(num_envs)]) + + print("Running : " + " ".join(cmd)) + + subprocess.run(cmd) + + # Give user a chance to leave the old review + if force_review and review and review["reviewed"]: + result = review["result"] + notes = review.get("notes") + print(f"A review already exists for {workflow}:{task_name}, it was marked as '{result}'.") + print(f" Notes: {notes}") + answer = input("Would you like to replace it? Please answer yes or no (y/n) [n]: ").strip().lower() + if answer != "y": + return + + # Get the verdict from the user + print(f"Do you accept this checkpoint for {workflow}:{task_name}?") + + answer = input("Please answer yes, no or undetermined (y/n/u) [u]: ").strip().lower() + if answer not in {"y", "n", "u"}: + answer = "u" + answer_map = { + "y": "accepted", + "n": "rejected", + "u": "undetermined", + } + + # Create the review dict + review = { + "reviewed": True, + "result": answer_map[answer], + } + + # Maybe add some notes + notes = input("Please add notes or hit enter: ").strip().lower() + if notes: + review["notes"] = notes + + # Save the review JSON file + path = get_pretrained_checkpoint_review_path(workflow, task_name) + if not path: + raise Exception("This shouldn't be possible, something went very wrong.") + + with open(path, "w") as f: + json.dump(review, f, indent=4) + + +def publish_pretrained_checkpoint(workflow, task_name, force_publish=False): + """ + This publishes the pretrained checkpoint to Nucleus using the asset path in the + /persistent/isaaclab/asset_root/pretrained_checkpoints Carb variable. + + Args: + workflow: The workflow. + task_name: The task name. + force_publish: Publish without review. + """ + + # This workflow task pair hasn't been trained + if not has_pretrained_checkpoint_job_run(workflow, task_name): + print(f"Skipping publishing of {workflow}:{task_name}, hasn't been trained yet") + return + + # Couldn't find the checkpoint + if not has_pretrained_checkpoint_job_finished(workflow, task_name): + print(f"Training not complete for {workflow}:{task_name}") + return + + # Get local pretrained checkpoint path + local_path = get_pretrained_checkpoint_path(workflow, task_name) + if not local_path: + raise Exception("This shouldn't be possible, something went very wrong.") + + # Not forcing, need to check review results + if not force_publish: + # Grab the review if it exists + review = get_pretrained_checkpoint_review(workflow, task_name) + + if not review or not review["reviewed"]: + print(f"Skipping publishing of {workflow}:{task_name}, hasn't been reviewed yet") + return + + result = review["result"] + if result != "accepted": + print(f'Skipping publishing of {workflow}:{task_name}, review result was "{result}"') + return + + print(f"Publishing {workflow}:{task_name}") + + # Copy the file + publish_path = get_pretrained_checkpoint_publish_path(workflow, task_name) + omni.client.copy_file(local_path, publish_path, CopyBehavior.OVERWRITE) + + +def get_job_summary_row(workflow, task_name): + """Returns a single row summary of the job""" + + has_run = has_pretrained_checkpoint_job_run(workflow, task_name) + has_finished = has_pretrained_checkpoint_job_finished(workflow, task_name) + review = get_pretrained_checkpoint_review(workflow, task_name) + + if review: + result = review.get("result", "undetermined") + notes = review.get("notes", "") + else: + result = "" + notes = "" + + return [workflow, task_name, has_run, has_finished, result, notes] + + +def main(): + # Figure out what workflows and tasks we'll be using + if args.all: + jobs = ["*:*"] + else: + jobs = args.jobs + + if args.list: + print() + print("# Workflow, Task, Ran, Finished, Review, Notes") + + summary_rows = [] + + # Could be implemented more efficiently, but the performance gain would be inconsequential + for workflow in WORKFLOWS: + for task_spec in sorted(gym.registry.values(), key=lambda t: t.id): + job_id = f"{workflow}:{task_spec.id}" + + # We've excluded this job + if any(fnmatch.fnmatch(job_id, e) for e in args.exclude): + continue + + # None of our jobs match this pair + if not np.any(np.array([fnmatch.fnmatch(job_id, job) for job in jobs])): + continue + + # No config for this workflow + if workflow + "_cfg_entry_point" not in task_spec.kwargs: + continue + + if args.list: + summary_rows.append(get_job_summary_row(workflow, task_spec.id)) + continue + + # Training reviewing and publishing + if args.train: + train_job(workflow, task_spec.id, args.headless, args.force, args.num_envs) + + if args.review: + review_pretrained_checkpoint(workflow, task_spec.id, args.force_review, args.num_envs) + + if args.publish_checkpoint: + publish_pretrained_checkpoint(workflow, task_spec.id, args.force_publish) + + if args.list: + writer = csv.writer(sys.stdout, quotechar='"', quoting=csv.QUOTE_MINIMAL) + writer.writerows(summary_rows) + + +if __name__ == "__main__": + try: + # Run the main function + main() + except Exception as e: + raise e + finally: + # Close the app + simulation_app.close() diff --git a/scripts/tutorials/00_sim/create_empty.py b/scripts/tutorials/00_sim/create_empty.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa283a68f1f026a4959c28cee6e6ba6cad33767 --- /dev/null +++ b/scripts/tutorials/00_sim/create_empty.py @@ -0,0 +1,61 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to create a simple stage in Isaac Sim. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="Tutorial on creating an empty stage.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +from isaaclab.sim import SimulationCfg, SimulationContext + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = SimulationCfg(dt=0.01) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Simulate physics + while simulation_app.is_running(): + # perform step + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/00_sim/launch_app.py b/scripts/tutorials/00_sim/launch_app.py new file mode 100644 index 0000000000000000000000000000000000000000..1622d3ba956e8bc4e16ff4b19ee4bd6fc217fc8c --- /dev/null +++ b/scripts/tutorials/00_sim/launch_app.py @@ -0,0 +1,96 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to run IsaacSim via the AppLauncher + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/00_sim/launch_app.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="Tutorial on running IsaacSim via the AppLauncher.") +parser.add_argument("--size", type=float, default=1.0, help="Side-length of cuboid") +# SimulationApp arguments https://docs.omniverse.nvidia.com/py/isaacsim/source/isaacsim.simulation_app/docs/index.html?highlight=simulationapp#isaacsim.simulation_app.SimulationApp +parser.add_argument( + "--width", type=int, default=1280, help="Width of the viewport and generated images. Defaults to 1280" +) +parser.add_argument( + "--height", type=int, default=720, help="Height of the viewport and generated images. Defaults to 720" +) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils + + +def design_scene(): + """Designs the scene by spawning ground plane, light, objects and meshes from usd files.""" + # Ground-plane + cfg_ground = sim_utils.GroundPlaneCfg() + cfg_ground.func("/World/defaultGroundPlane", cfg_ground) + + # spawn distant light + cfg_light_distant = sim_utils.DistantLightCfg( + intensity=3000.0, + color=(0.75, 0.75, 0.75), + ) + cfg_light_distant.func("/World/lightDistant", cfg_light_distant, translation=(1, 0, 10)) + + # spawn a cuboid + cfg_cuboid = sim_utils.CuboidCfg( + size=[args_cli.size] * 3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 1.0, 1.0)), + ) + # Spawn cuboid, altering translation on the z-axis to scale to its size + cfg_cuboid.func("/World/Object", cfg_cuboid, translation=(0.0, 0.0, args_cli.size / 2)) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.0, 0.0, 2.5], [-0.5, 0.0, 0.5]) + + # Design scene by adding assets to it + design_scene() + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Simulate physics + while simulation_app.is_running(): + # perform step + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/00_sim/log_time.py b/scripts/tutorials/00_sim/log_time.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4445c3d47005ece02946d03e0a7b709d2ab191 --- /dev/null +++ b/scripts/tutorials/00_sim/log_time.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to generate log outputs while the simulation plays. +It accompanies the tutorial on docker usage. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/00_sim/log_time.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse +import os + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="Tutorial on creating logs from within the docker container.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +from isaaclab.sim import SimulationCfg, SimulationContext + + +def main(): + """Main function.""" + # Specify that the logs must be in logs/docker_tutorial + log_dir_path = os.path.join("logs") + if not os.path.isdir(log_dir_path): + os.mkdir(log_dir_path) + # In the container, the absolute path will be + # /workspace/isaaclab/logs/docker_tutorial, because + # all python execution is done through /workspace/isaaclab/isaaclab.sh + # and the calling process' path will be /workspace/isaaclab + log_dir_path = os.path.abspath(os.path.join(log_dir_path, "docker_tutorial")) + if not os.path.isdir(log_dir_path): + os.mkdir(log_dir_path) + print(f"[INFO] Logging experiment to directory: {log_dir_path}") + + # Initialize the simulation context + sim_cfg = SimulationCfg(dt=0.01) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Prepare to count sim_time + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + + # Open logging file + with open(os.path.join(log_dir_path, "log.txt"), "w") as log_file: + # Simulate physics + while simulation_app.is_running(): + log_file.write(f"{sim_time}" + "\n") + # perform step + sim.step() + sim_time += sim_dt + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/00_sim/set_rendering_mode.py b/scripts/tutorials/00_sim/set_rendering_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..38a1d5b2ba8887ecaa9cf6e16371be5fb90d1509 --- /dev/null +++ b/scripts/tutorials/00_sim/set_rendering_mode.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn prims into the scene. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/00_sim/set_rendering_mode.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser( + description="Tutorial on viewing a warehouse scene with a given rendering mode preset." +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + +def main(): + """Main function.""" + + # rendering modes include performance, balanced, and quality + # note, the rendering_mode specified in the CLI argument (--rendering_mode) takes precedence over + # this Render Config setting + rendering_mode = "performance" + + # carb setting dictionary can include any rtx carb setting which will overwrite the native preset setting + carb_settings = {"rtx.reflections.enabled": True} + + # Initialize render config + render_cfg = sim_utils.RenderCfg( + rendering_mode=rendering_mode, + carb_settings=carb_settings, + ) + + # Initialize the simulation context with render coofig + sim_cfg = sim_utils.SimulationCfg(render=render_cfg) + sim = sim_utils.SimulationContext(sim_cfg) + + # Pose camera in the hospital lobby area + sim.set_camera_view([-11, -0.5, 2], [0, 0, 0.5]) + + # Load hospital scene + hospital_usd_path = f"{ISAAC_NUCLEUS_DIR}/Environments/Hospital/hospital.usd" + cfg = sim_utils.UsdFileCfg(usd_path=hospital_usd_path) + cfg.func("/Scene", cfg) + + # Play the simulator + sim.reset() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # Run simulation and view scene + while simulation_app.is_running(): + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/00_sim/spawn_prims.py b/scripts/tutorials/00_sim/spawn_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..7c120bd308dd65b7dbb3f88760715baf4bf73c47 --- /dev/null +++ b/scripts/tutorials/00_sim/spawn_prims.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn prims into the scene. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/00_sim/spawn_prims.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="Tutorial on spawning prims into the scene.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + +def design_scene(): + """Designs the scene by spawning ground plane, light, objects and meshes from usd files.""" + # Ground-plane + cfg_ground = sim_utils.GroundPlaneCfg() + cfg_ground.func("/World/defaultGroundPlane", cfg_ground) + + # spawn distant light + cfg_light_distant = sim_utils.DistantLightCfg( + intensity=3000.0, + color=(0.75, 0.75, 0.75), + ) + cfg_light_distant.func("/World/lightDistant", cfg_light_distant, translation=(1, 0, 10)) + + # create a new xform prim for all objects to be spawned under + sim_utils.create_prim("/World/Objects", "Xform") + # spawn a red cone + cfg_cone = sim_utils.ConeCfg( + radius=0.15, + height=0.5, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ) + cfg_cone.func("/World/Objects/Cone1", cfg_cone, translation=(-1.0, 1.0, 1.0)) + cfg_cone.func("/World/Objects/Cone2", cfg_cone, translation=(-1.0, -1.0, 1.0)) + + # spawn a green cone with colliders and rigid body + cfg_cone_rigid = sim_utils.ConeCfg( + radius=0.15, + height=0.5, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ) + cfg_cone_rigid.func( + "/World/Objects/ConeRigid", cfg_cone_rigid, translation=(-0.2, 0.0, 2.0), orientation=(0.5, 0.0, 0.5, 0.0) + ) + + # spawn a blue cuboid with deformable body + cfg_cuboid_deformable = sim_utils.MeshCuboidCfg( + size=(0.2, 0.5, 0.2), + deformable_props=sim_utils.DeformableBodyPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + physics_material=sim_utils.DeformableBodyMaterialCfg(), + ) + cfg_cuboid_deformable.func("/World/Objects/CuboidDeformable", cfg_cuboid_deformable, translation=(0.15, 0.0, 2.0)) + + # spawn a usd file of a table into the scene + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func("/World/Objects/Table", cfg, translation=(0.0, 0.0, 1.05)) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.0, 0.0, 2.5], [-0.5, 0.0, 0.5]) + # Design scene + design_scene() + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Simulate physics + while simulation_app.is_running(): + # perform step + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/01_assets/add_new_robot.py b/scripts/tutorials/01_assets/add_new_robot.py new file mode 100644 index 0000000000000000000000000000000000000000..bc322d10947908b0d7d3f60d8c64cea3fcfd5ae9 --- /dev/null +++ b/scripts/tutorials/01_assets/add_new_robot.py @@ -0,0 +1,179 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script demonstrates adding a custom robot to an Isaac Lab environment." +) +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import AssetBaseCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +JETBOT_CONFIG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/NVIDIA/Jetbot/jetbot.usd"), + actuators={"wheel_acts": ImplicitActuatorCfg(joint_names_expr=[".*"], damping=None, stiffness=None)}, +) + +DOFBOT_CONFIG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Yahboom/Dofbot/dofbot.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "joint1": 0.0, + "joint2": 0.0, + "joint3": 0.0, + "joint4": 0.0, + }, + pos=(0.25, -0.25, 0.0), + ), + actuators={ + "front_joints": ImplicitActuatorCfg( + joint_names_expr=["joint[1-2]"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + "joint3_act": ImplicitActuatorCfg( + joint_names_expr=["joint3"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + "joint4_act": ImplicitActuatorCfg( + joint_names_expr=["joint4"], + effort_limit_sim=100.0, + velocity_limit_sim=100.0, + stiffness=10000.0, + damping=100.0, + ), + }, +) + + +class NewRobotsSceneCfg(InteractiveSceneCfg): + """Designs the scene.""" + + # Ground-plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + Jetbot = JETBOT_CONFIG.replace(prim_path="{ENV_REGEX_NS}/Jetbot") + Dofbot = DOFBOT_CONFIG.replace(prim_path="{ENV_REGEX_NS}/Dofbot") + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + while simulation_app.is_running(): + # reset + if count % 500 == 0: + # reset counters + count = 0 + # reset the scene entities to their initial positions offset by the environment origins + root_jetbot_state = scene["Jetbot"].data.default_root_state.clone() + root_jetbot_state[:, :3] += scene.env_origins + root_dofbot_state = scene["Dofbot"].data.default_root_state.clone() + root_dofbot_state[:, :3] += scene.env_origins + + # copy the default root state to the sim for the jetbot's orientation and velocity + scene["Jetbot"].write_root_pose_to_sim(root_jetbot_state[:, :7]) + scene["Jetbot"].write_root_velocity_to_sim(root_jetbot_state[:, 7:]) + scene["Dofbot"].write_root_pose_to_sim(root_dofbot_state[:, :7]) + scene["Dofbot"].write_root_velocity_to_sim(root_dofbot_state[:, 7:]) + + # copy the default joint states to the sim + joint_pos, joint_vel = ( + scene["Jetbot"].data.default_joint_pos.clone(), + scene["Jetbot"].data.default_joint_vel.clone(), + ) + scene["Jetbot"].write_joint_state_to_sim(joint_pos, joint_vel) + joint_pos, joint_vel = ( + scene["Dofbot"].data.default_joint_pos.clone(), + scene["Dofbot"].data.default_joint_vel.clone(), + ) + scene["Dofbot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting Jetbot and Dofbot state...") + + # drive around + if count % 100 < 75: + # Drive straight by setting equal wheel velocities + action = torch.Tensor([[10.0, 10.0]]) + else: + # Turn by applying different velocities + action = torch.Tensor([[5.0, -5.0]]) + + scene["Jetbot"].set_joint_velocity_target(action) + + # wave + wave_action = scene["Dofbot"].data.default_joint_pos + wave_action[:, 0:4] = 0.25 * np.sin(2 * np.pi * 0.5 * sim_time) + scene["Dofbot"].set_joint_position_target(wave_action) + + scene.write_data_to_sim() + sim.step() + sim_time += sim_dt + count += 1 + scene.update(sim_dt) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + sim.set_camera_view([3.5, 0.0, 3.2], [0.0, 0.0, 0.5]) + # Design scene + scene_cfg = NewRobotsSceneCfg(args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + main() + simulation_app.close() diff --git a/scripts/tutorials/01_assets/run_articulation.py b/scripts/tutorials/01_assets/run_articulation.py new file mode 100644 index 0000000000000000000000000000000000000000..bc4254cbae1f289da26064df4609e8a119943213 --- /dev/null +++ b/scripts/tutorials/01_assets/run_articulation.py @@ -0,0 +1,141 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn a cart-pole and interact with it. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/01_assets/run_articulation.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on spawning and interacting with an articulation.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.sim import SimulationContext + +## +# Pre-defined configs +## +from isaaclab_assets import CARTPOLE_CFG # isort:skip + + +def design_scene() -> tuple[dict, list[list[float]]]: + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2" + # Each group will have a robot in it + origins = [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]] + # Origin 1 + sim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0]) + # Origin 2 + sim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1]) + + # Articulation + cartpole_cfg = CARTPOLE_CFG.copy() + cartpole_cfg.prim_path = "/World/Origin.*/Robot" + cartpole = Articulation(cfg=cartpole_cfg) + + # return the scene information + scene_entities = {"cartpole": cartpole} + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, Articulation], origins: torch.Tensor): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. In general, it is better to access the entities directly from + # the dictionary. This dictionary is replaced by the InteractiveScene class in the next tutorial. + robot = entities["cartpole"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + joint_pos += torch.rand_like(joint_pos) * 0.1 + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + robot.reset() + print("[INFO]: Resetting robot state...") + # Apply random action + # -- generate random joint efforts + efforts = torch.randn_like(robot.data.joint_pos) * 5.0 + # -- apply action to the robot + robot.set_joint_effort_target(efforts) + # -- write data to sim + robot.write_data_to_sim() + # Perform step + sim.step() + # Increment counter + count += 1 + # Update buffers + robot.update(sim_dt) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 0.0, 4.0], [0.0, 0.0, 2.0]) + # Design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/01_assets/run_deformable_object.py b/scripts/tutorials/01_assets/run_deformable_object.py new file mode 100644 index 0000000000000000000000000000000000000000..3623bb3d8a19c62870529cbe38fb4e992501162c --- /dev/null +++ b/scripts/tutorials/01_assets/run_deformable_object.py @@ -0,0 +1,166 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to work with the deformable object and interact with it. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/01_assets/run_deformable_object.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on interacting with a deformable object.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.assets import DeformableObject, DeformableObjectCfg +from isaaclab.sim import SimulationContext + + +def design_scene(): + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.8, 0.8, 0.8)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a robot in it + origins = [[0.25, 0.25, 0.0], [-0.25, 0.25, 0.0], [0.25, -0.25, 0.0], [-0.25, -0.25, 0.0]] + for i, origin in enumerate(origins): + sim_utils.create_prim(f"/World/Origin{i}", "Xform", translation=origin) + + # Deformable Object + cfg = DeformableObjectCfg( + prim_path="/World/Origin.*/Cube", + spawn=sim_utils.MeshCuboidCfg( + size=(0.2, 0.2, 0.2), + deformable_props=sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0, contact_offset=0.001), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.1, 0.0)), + physics_material=sim_utils.DeformableBodyMaterialCfg(poissons_ratio=0.4, youngs_modulus=1e5), + ), + init_state=DeformableObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 1.0)), + debug_vis=True, + ) + cube_object = DeformableObject(cfg=cfg) + + # return the scene information + scene_entities = {"cube_object": cube_object} + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, DeformableObject], origins: torch.Tensor): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. In general, it is better to access the entities directly from + # the dictionary. This dictionary is replaced by the InteractiveScene class in the next tutorial. + cube_object = entities["cube_object"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Nodal kinematic targets of the deformable bodies + nodal_kinematic_target = cube_object.data.nodal_kinematic_target.clone() + + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 250 == 0: + # reset counters + sim_time = 0.0 + count = 0 + + # reset the nodal state of the object + nodal_state = cube_object.data.default_nodal_state_w.clone() + # apply random pose to the object + pos_w = torch.rand(cube_object.num_instances, 3, device=sim.device) * 0.1 + origins + quat_w = math_utils.random_orientation(cube_object.num_instances, device=sim.device) + nodal_state[..., :3] = cube_object.transform_nodal_pos(nodal_state[..., :3], pos_w, quat_w) + + # write nodal state to simulation + cube_object.write_nodal_state_to_sim(nodal_state) + + # Write the nodal state to the kinematic target and free all vertices + nodal_kinematic_target[..., :3] = nodal_state[..., :3] + nodal_kinematic_target[..., 3] = 1.0 + cube_object.write_nodal_kinematic_target_to_sim(nodal_kinematic_target) + + # reset buffers + cube_object.reset() + + print("----------------------------------------") + print("[INFO]: Resetting object state...") + + # update the kinematic target for cubes at index 0 and 3 + # we slightly move the cube in the z-direction by picking the vertex at index 0 + nodal_kinematic_target[[0, 3], 0, 2] += 0.001 + # set vertex at index 0 to be kinematically constrained + # 0: constrained, 1: free + nodal_kinematic_target[[0, 3], 0, 3] = 0.0 + # write kinematic target to simulation + cube_object.write_nodal_kinematic_target_to_sim(nodal_kinematic_target) + + # write internal data to simulation + cube_object.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + cube_object.update(sim_dt) + # print the root position + if count % 50 == 0: + print(f"Root position (in world): {cube_object.data.root_pos_w[:, :3]}") + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.0, 0.0, 1.0], target=[0.0, 0.0, 0.5]) + # Design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/01_assets/run_rigid_object.py b/scripts/tutorials/01_assets/run_rigid_object.py new file mode 100644 index 0000000000000000000000000000000000000000..dc1a88c57eb1f60646c8c68bf3b06c18b7bb85d6 --- /dev/null +++ b/scripts/tutorials/01_assets/run_rigid_object.py @@ -0,0 +1,146 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to create a rigid object and interact with it. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/01_assets/run_rigid_object.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on spawning and interacting with a rigid object.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.sim import SimulationContext + + +def design_scene(): + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.8, 0.8, 0.8)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a robot in it + origins = [[0.25, 0.25, 0.0], [-0.25, 0.25, 0.0], [0.25, -0.25, 0.0], [-0.25, -0.25, 0.0]] + for i, origin in enumerate(origins): + sim_utils.create_prim(f"/World/Origin{i}", "Xform", translation=origin) + + # Rigid Object + cone_cfg = RigidObjectCfg( + prim_path="/World/Origin.*/Cone", + spawn=sim_utils.ConeCfg( + radius=0.1, + height=0.2, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(), + ) + cone_object = RigidObject(cfg=cone_cfg) + + # return the scene information + scene_entities = {"cone": cone_object} + return scene_entities, origins + + +def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, RigidObject], origins: torch.Tensor): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. In general, it is better to access the entities directly from + # the dictionary. This dictionary is replaced by the InteractiveScene class in the next tutorial. + cone_object = entities["cone"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # reset + if count % 250 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset root state + root_state = cone_object.data.default_root_state.clone() + # sample a random position on a cylinder around the origins + root_state[:, :3] += origins + root_state[:, :3] += math_utils.sample_cylinder( + radius=0.1, h_range=(0.25, 0.5), size=cone_object.num_instances, device=cone_object.device + ) + # write root state to simulation + cone_object.write_root_pose_to_sim(root_state[:, :7]) + cone_object.write_root_velocity_to_sim(root_state[:, 7:]) + # reset buffers + cone_object.reset() + print("----------------------------------------") + print("[INFO]: Resetting object state...") + # apply sim data + cone_object.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + cone_object.update(sim_dt) + # print the root position + if count % 50 == 0: + print(f"Root position (in world): {cone_object.data.root_pos_w}") + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[1.5, 0.0, 1.0], target=[0.0, 0.0, 0.0]) + # Design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/01_assets/run_surface_gripper.py b/scripts/tutorials/01_assets/run_surface_gripper.py new file mode 100644 index 0000000000000000000000000000000000000000..066dd9a077d2d656bdfab485ff965e24c08260a6 --- /dev/null +++ b/scripts/tutorials/01_assets/run_surface_gripper.py @@ -0,0 +1,181 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to spawn a pick-and-place robot equipped with a surface gripper and interact with it. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/01_assets/run_surface_gripper.py --device=cpu + +When running this script make sure the --device flag is set to cpu. This is because the surface gripper is +currently only supported on the CPU. +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on spawning and interacting with a Surface Gripper.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, SurfaceGripper, SurfaceGripperCfg +from isaaclab.sim import SimulationContext + +## +# Pre-defined configs +## +from isaaclab_assets import PICK_AND_PLACE_CFG # isort:skip + + +def design_scene(): + """Designs the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create separate groups called "Origin1", "Origin2" + # Each group will have a robot in it + origins = [[2.75, 0.0, 0.0], [-2.75, 0.0, 0.0]] + # Origin 1 + sim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0]) + # Origin 2 + sim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1]) + + # Articulation: First we define the robot config + pick_and_place_robot_cfg = PICK_AND_PLACE_CFG.copy() + pick_and_place_robot_cfg.prim_path = "/World/Origin.*/Robot" + pick_and_place_robot = Articulation(cfg=pick_and_place_robot_cfg) + + # Surface Gripper: Next we define the surface gripper config + surface_gripper_cfg = SurfaceGripperCfg() + # We need to tell the View which prim to use for the surface gripper + surface_gripper_cfg.prim_path = "/World/Origin.*/Robot/picker_head/SurfaceGripper" + # We can then set different parameters for the surface gripper, note that if these parameters are not set, + # the View will try to read them from the prim. + surface_gripper_cfg.max_grip_distance = 0.1 # [m] (Maximum distance at which the gripper can grasp an object) + surface_gripper_cfg.shear_force_limit = 500.0 # [N] (Force limit in the direction perpendicular direction) + surface_gripper_cfg.coaxial_force_limit = 500.0 # [N] (Force limit in the direction of the gripper's axis) + surface_gripper_cfg.retry_interval = 0.1 # seconds (Time the gripper will stay in a grasping state) + # We can now spawn the surface gripper + surface_gripper = SurfaceGripper(cfg=surface_gripper_cfg) + + # return the scene information + scene_entities = {"pick_and_place_robot": pick_and_place_robot, "surface_gripper": surface_gripper} + return scene_entities, origins + + +def run_simulator( + sim: sim_utils.SimulationContext, entities: dict[str, Articulation | SurfaceGripper], origins: torch.Tensor +): + """Runs the simulation loop.""" + # Extract scene entities + robot: Articulation = entities["pick_and_place_robot"] + surface_gripper: SurfaceGripper = entities["surface_gripper"] + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + joint_pos += torch.rand_like(joint_pos) * 0.1 + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + robot.reset() + print("[INFO]: Resetting robot state...") + # Opens the gripper and makes sure the gripper is in the open state + surface_gripper.reset() + print("[INFO]: Resetting gripper state...") + + # Sample a random command between -1 and 1. + gripper_commands = torch.rand(surface_gripper.num_instances) * 2.0 - 1.0 + # The gripper behavior is as follows: + # -1 < command < -0.3 --> Gripper is Opening + # -0.3 < command < 0.3 --> Gripper is Idle + # 0.3 < command < 1 --> Gripper is Closing + print(f"[INFO]: Gripper commands: {gripper_commands}") + mapped_commands = [ + "Opening" if command < -0.3 else "Closing" if command > 0.3 else "Idle" for command in gripper_commands + ] + print(f"[INFO]: Mapped commands: {mapped_commands}") + # Set the gripper command + surface_gripper.set_grippers_command(gripper_commands) + # Write data to sim + surface_gripper.write_data_to_sim() + # Perform step + sim.step() + # Increment counter + count += 1 + # Read the gripper state from the simulation + surface_gripper.update(sim_dt) + # Read the gripper state from the buffer + surface_gripper_state = surface_gripper.state + # The gripper state is a list of integers that can be mapped to the following: + # -1 --> Open + # 0 --> Closing + # 1 --> Closed + # Print the gripper state + print(f"[INFO]: Gripper state: {surface_gripper_state}") + mapped_commands = [ + "Open" if state == -1 else "Closing" if state == 0 else "Closed" for state in surface_gripper_state.tolist() + ] + print(f"[INFO]: Mapped commands: {mapped_commands}") + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.75, 7.5, 10.0], [2.75, 0.0, 0.0]) + # Design scene + scene_entities, scene_origins = design_scene() + scene_origins = torch.tensor(scene_origins, device=sim.device) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities, scene_origins) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/02_scene/create_scene.py b/scripts/tutorials/02_scene/create_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..82b5b21c0097d612816590482cdf447a8fd62a63 --- /dev/null +++ b/scripts/tutorials/02_scene/create_scene.py @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to use the interactive scene interface to setup a scene with multiple prims. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/02_scene/create_scene.py --num_envs 32 + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on using the interactive scene interface.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets import CARTPOLE_CFG # isort:skip + + +@configclass +class CartpoleSceneCfg(InteractiveSceneCfg): + """Configuration for a cart-pole scene.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # articulation + cartpole: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. + robot = scene["cartpole"] + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = robot.data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone() + joint_pos += torch.rand_like(joint_pos) * 0.1 + robot.write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply random action + # -- generate random joint efforts + efforts = torch.randn_like(robot.data.joint_pos) * 5.0 + # -- apply action to the robot + robot.set_joint_effort_target(efforts) + # -- write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + # Increment counter + count += 1 + # Update buffers + scene.update(sim_dt) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 0.0, 4.0], [0.0, 0.0, 2.0]) + # Design scene + scene_cfg = CartpoleSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/03_envs/create_cartpole_base_env.py b/scripts/tutorials/03_envs/create_cartpole_base_env.py new file mode 100644 index 0000000000000000000000000000000000000000..35c3650e6811965b47bac9b1016f666c4bddd4f2 --- /dev/null +++ b/scripts/tutorials/03_envs/create_cartpole_base_env.py @@ -0,0 +1,175 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to create a simple environment with a cartpole. It combines the concepts of +scene, action, observation and event managers to create an environment. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32 + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on creating a cartpole base environment.") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import math + +import torch + +import isaaclab.envs.mdp as mdp +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleSceneCfg + + +@configclass +class ActionsCfg: + """Action specifications for the environment.""" + + joint_efforts = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=5.0) + + +@configclass +class ObservationsCfg: + """Observation specifications for the environment.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) + + def __post_init__(self) -> None: + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # on startup + add_pole_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]), + "mass_distribution_params": (0.1, 0.5), + "operation": "add", + }, + ) + + # on reset + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.1, 0.1), + }, + ) + + reset_pole_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), + "position_range": (-0.125 * math.pi, 0.125 * math.pi), + "velocity_range": (-0.01 * math.pi, 0.01 * math.pi), + }, + ) + + +@configclass +class CartpoleEnvCfg(ManagerBasedEnvCfg): + """Configuration for the cartpole environment.""" + + # Scene settings + scene = CartpoleSceneCfg(num_envs=1024, env_spacing=2.5) + # Basic settings + observations = ObservationsCfg() + actions = ActionsCfg() + events = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # viewer settings + self.viewer.eye = [4.5, 0.0, 6.0] + self.viewer.lookat = [0.0, 0.0, 2.0] + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + + +def main(): + """Main function.""" + # parse the arguments + env_cfg = CartpoleEnvCfg() + env_cfg.scene.num_envs = args_cli.num_envs + env_cfg.sim.device = args_cli.device + # setup base environment + env = ManagerBasedEnv(cfg=env_cfg) + + # simulate physics + count = 0 + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 300 == 0: + count = 0 + env.reset() + print("-" * 80) + print("[INFO]: Resetting environment...") + # sample random actions + joint_efforts = torch.randn_like(env.action_manager.action) + # step the environment + obs, _ = env.step(joint_efforts) + # print current orientation of pole + print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) + # update counter + count += 1 + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/03_envs/create_cube_base_env.py b/scripts/tutorials/03_envs/create_cube_base_env.py new file mode 100644 index 0000000000000000000000000000000000000000..641512607e3128ca8fd7e4198ff93a3a89eb4037 --- /dev/null +++ b/scripts/tutorials/03_envs/create_cube_base_env.py @@ -0,0 +1,352 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script creates a simple environment with a floating cube. The cube is controlled by a PD +controller to track an arbitrary target position. + +While going through this tutorial, we recommend you to pay attention to how a custom action term +is defined. The action term is responsible for processing the raw actions and applying them to the +scene entities. + +We also define an event term called 'randomize_scale' that randomizes the scale of +the cube. This event term has the mode 'prestartup', which means that it is applied on the USD stage +before the simulation starts. Additionally, the flag 'replicate_physics' is set to False, +which means that the cube is not replicated across multiple environments but rather each +environment gets its own cube instance. + +The rest of the environment is similar to the previous tutorials. + +.. code-block:: bash + + # Run the script + ./isaaclab.sh -p scripts/tutorials/03_envs/create_cube_base_env.py --num_envs 32 + +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on creating a floating cube environment.") +parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import ActionTerm, ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Custom action term +## + + +class CubeActionTerm(ActionTerm): + """Simple action term that implements a PD controller to track a target position. + + The action term is applied to the cube asset. It involves two steps: + + 1. **Process the raw actions**: Typically, this includes any transformations of the raw actions + that are required to map them to the desired space. This is called once per environment step. + 2. **Apply the processed actions**: This step applies the processed actions to the asset. + It is called once per simulation step. + + In this case, the action term simply applies the raw actions to the cube asset. The raw actions + are the desired target positions of the cube in the environment frame. The pre-processing step + simply copies the raw actions to the processed actions as no additional processing is required. + The processed actions are then applied to the cube asset by implementing a PD controller to + track the target position. + """ + + _asset: RigidObject + """The articulation asset on which the action term is applied.""" + + def __init__(self, cfg: CubeActionTermCfg, env: ManagerBasedEnv): + # call super constructor + super().__init__(cfg, env) + # create buffers + self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._vel_command = torch.zeros(self.num_envs, 6, device=self.device) + # gains of controller + self.p_gain = cfg.p_gain + self.d_gain = cfg.d_gain + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._raw_actions.shape[1] + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + """ + Operations + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # no-processing of actions + self._processed_actions[:] = self._raw_actions[:] + + def apply_actions(self): + # implement a PD controller to track the target position + pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins) + vel_error = -self._asset.data.root_lin_vel_w + # set velocity targets + self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error + self._asset.write_root_velocity_to_sim(self._vel_command) + + +@configclass +class CubeActionTermCfg(ActionTermCfg): + """Configuration for the cube action term.""" + + class_type: type = CubeActionTerm + """The class corresponding to the action term.""" + + p_gain: float = 5.0 + """Proportional gain of the PD controller.""" + d_gain: float = 0.5 + """Derivative gain of the PD controller.""" + + +## +# Custom observation term +## + + +def base_position(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Root linear velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w - env.scene.env_origins + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration. + + The scene comprises of a ground plane, light source and floating cubes (gravity disabled). + """ + + # add terrain + terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False) + + # add cube + cube: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube", + spawn=sim_utils.CuboidCfg( + size=(0.2, 0.2, 0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.0, 0.0)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2000.0), + ) + + +## +# Environment settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = CubeActionTermCfg(asset_name="cube") + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # cube velocity + position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube")}) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # This event term resets the base position of the cube. + # The mode is set to 'reset', which means that the base position is reset whenever + # the environment instance is reset (because of terminations defined in 'TerminationCfg'). + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.5, 0.5), + "y": (-0.5, 0.5), + "z": (-0.5, 0.5), + }, + "asset_cfg": SceneEntityCfg("cube"), + }, + ) + + # This event term randomizes the scale of the cube. + # The mode is set to 'prestartup', which means that the scale is randomize on the USD stage before the + # simulation starts. + # Note: USD-level randomizations require the flag 'replicate_physics' to be set to False. + randomize_scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (0.5, 1.5), "y": (0.5, 1.5), "z": (0.5, 1.5)}, + "asset_cfg": SceneEntityCfg("cube"), + }, + ) + + # This event term randomizes the visual color of the cube. + # Similar to the scale randomization, this is also a USD-level randomization and requires the flag + # 'replicate_physics' to be set to False. + randomize_color = EventTerm( + func=mdp.randomize_visual_color, + mode="prestartup", + params={ + "colors": {"r": (0.0, 1.0), "g": (0.0, 1.0), "b": (0.0, 1.0)}, + "asset_cfg": SceneEntityCfg("cube"), + "mesh_name": "geometry/mesh", + "event_name": "rep_cube_randomize_color", + }, + ) + + +## +# Environment configuration +## + + +@configclass +class CubeEnvCfg(ManagerBasedEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + # The flag 'replicate_physics' is set to False, which means that the cube is not replicated + # across multiple environments but rather each environment gets its own cube instance. + # This allows modifying the cube's properties independently for each environment. + scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5, replicate_physics=False) + + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + # simulation settings + self.sim.dt = 0.01 + self.sim.physics_material = self.scene.terrain.physics_material + self.sim.render_interval = 2 # render interval should be a multiple of decimation + self.sim.device = args_cli.device + # viewer settings + self.viewer.eye = (5.0, 5.0, 5.0) + self.viewer.lookat = (0.0, 0.0, 2.0) + + +def main(): + """Main function.""" + + # setup base environment + env_cfg = CubeEnvCfg() + env = ManagerBasedEnv(cfg=env_cfg) + + # setup target position commands + target_position = torch.rand(env.num_envs, 3, device=env.device) * 2 + target_position[:, 2] += 2.0 + # offset all targets so that they move to the world origin + target_position -= env.scene.env_origins + + # simulate physics + count = 0 + obs, _ = env.reset() + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 300 == 0: + count = 0 + obs, _ = env.reset() + print("-" * 80) + print("[INFO]: Resetting environment...") + # step env + obs, _ = env.step(target_position) + # print mean squared position error between target and current position + error = torch.norm(obs["policy"] - target_position).mean().item() + print(f"[Step: {count:04d}]: Mean position error: {error:.4f}") + # update counter + count += 1 + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/03_envs/create_quadruped_base_env.py b/scripts/tutorials/03_envs/create_quadruped_base_env.py new file mode 100644 index 0000000000000000000000000000000000000000..78f5b75ec5f8e3a506d1f7e16d8778f9583b5379 --- /dev/null +++ b/scripts/tutorials/03_envs/create_quadruped_base_env.py @@ -0,0 +1,245 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates the environment for a quadruped robot with height-scan sensor. + +In this example, we use a locomotion policy to control the robot. The robot is commanded to +move forward at a constant velocity. The height-scan sensor is used to detect the height of +the terrain. + +.. code-block:: bash + + # Run the script + ./isaaclab.sh -p scripts/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32 + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on creating a quadruped base environment.") +parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import RayCasterCfg, patterns +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, check_file_path, read_file +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +## +# Pre-defined configs +## +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +## +# Custom observation terms +## + + +def constant_commands(env: ManagerBasedEnv) -> torch.Tensor: + """The generated command from the command generator.""" + return torch.tensor([[1, 0, 0]], device=env.device).repeat(env.num_envs, 1) + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # add terrain + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + max_init_terrain_level=5, + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + debug_vis=False, + ) + + # add robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/ground"], + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2)) + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObsTerm(func=constant_commands) + joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5)) + actions = ObsTerm(func=mdp.last_action) + height_scan = ObsTerm( + func=mdp.height_scan, + params={"sensor_cfg": SceneEntityCfg("height_scanner")}, + noise=Unoise(n_min=-0.1, n_max=0.1), + clip=(-1.0, 1.0), + ) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_scene = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + +## +# Environment configuration +## + + +@configclass +class QuadrupedEnvCfg(ManagerBasedEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 # env decimation -> 50 Hz control + # simulation settings + self.sim.dt = 0.005 # simulation timestep -> 200 Hz physics + self.sim.physics_material = self.scene.terrain.physics_material + self.sim.device = args_cli.device + # update sensor update periods + # we tick all the sensors based on the smallest update period (physics update period) + if self.scene.height_scanner is not None: + self.scene.height_scanner.update_period = self.decimation * self.sim.dt # 50 Hz + + +def main(): + """Main function.""" + # setup base environment + env_cfg = QuadrupedEnvCfg() + env = ManagerBasedEnv(cfg=env_cfg) + + # load level policy + policy_path = ISAACLAB_NUCLEUS_DIR + "/Policies/ANYmal-C/HeightScan/policy.pt" + # check if policy file exists + if not check_file_path(policy_path): + raise FileNotFoundError(f"Policy file '{policy_path}' does not exist.") + file_bytes = read_file(policy_path) + # jit load the policy + policy = torch.jit.load(file_bytes).to(env.device).eval() + + # simulate physics + count = 0 + obs, _ = env.reset() + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 1000 == 0: + obs, _ = env.reset() + count = 0 + print("-" * 80) + print("[INFO]: Resetting environment...") + # infer action + action = policy(obs["policy"]) + # step env + obs, _ = env.step(action) + # update counter + count += 1 + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/03_envs/policy_inference_in_usd.py b/scripts/tutorials/03_envs/policy_inference_in_usd.py new file mode 100644 index 0000000000000000000000000000000000000000..f4add0f617cf6a46a3bc705140a5563475dbe25b --- /dev/null +++ b/scripts/tutorials/03_envs/policy_inference_in_usd.py @@ -0,0 +1,88 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates policy inference in a prebuilt USD environment. + +In this example, we use a locomotion policy to control the H1 robot. The robot was trained +using Isaac-Velocity-Rough-H1-v0. The robot is commanded to move forward at a constant velocity. + +.. code-block:: bash + + # Run the script + ./isaaclab.sh -p scripts/tutorials/03_envs/policy_inference_in_usd.py --checkpoint /path/to/jit/checkpoint.pt + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on inferencing a policy on an H1 robot in a warehouse.") +parser.add_argument("--checkpoint", type=str, help="Path to model checkpoint exported as jit.", required=True) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" +import io +import os + +import torch + +import omni + +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.locomotion.velocity.config.h1.rough_env_cfg import H1RoughEnvCfg_PLAY + + +def main(): + """Main function.""" + # load the trained jit policy + policy_path = os.path.abspath(args_cli.checkpoint) + file_content = omni.client.read_file(policy_path)[2] + file = io.BytesIO(memoryview(file_content).tobytes()) + policy = torch.jit.load(file, map_location=args_cli.device) + + # setup environment + env_cfg = H1RoughEnvCfg_PLAY() + env_cfg.scene.num_envs = 1 + env_cfg.curriculum = None + env_cfg.scene.terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="usd", + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Simple_Warehouse/warehouse.usd", + ) + env_cfg.sim.device = args_cli.device + if args_cli.device == "cpu": + env_cfg.sim.use_fabric = False + + # create environment + env = ManagerBasedRLEnv(cfg=env_cfg) + + # run inference with the policy + obs, _ = env.reset() + with torch.inference_mode(): + while simulation_app.is_running(): + action = policy(obs["policy"]) + obs, _, _, _, _ = env.step(action) + + +if __name__ == "__main__": + main() + simulation_app.close() diff --git a/scripts/tutorials/03_envs/run_cartpole_rl_env.py b/scripts/tutorials/03_envs/run_cartpole_rl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..eb66a744b958edadcefe91fb63caabd1d7bad946 --- /dev/null +++ b/scripts/tutorials/03_envs/run_cartpole_rl_env.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to run the RL environment for the cartpole balancing task. + +.. code-block:: bash + + ./isaaclab.sh -p scripts/tutorials/03_envs/run_cartpole_rl_env.py --num_envs 32 + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on running the cartpole RL environment.") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +from isaaclab.envs import ManagerBasedRLEnv + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleEnvCfg + + +def main(): + """Main function.""" + # create environment configuration + env_cfg = CartpoleEnvCfg() + env_cfg.scene.num_envs = args_cli.num_envs + env_cfg.sim.device = args_cli.device + # setup RL environment + env = ManagerBasedRLEnv(cfg=env_cfg) + + # simulate physics + count = 0 + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 300 == 0: + count = 0 + env.reset() + print("-" * 80) + print("[INFO]: Resetting environment...") + # sample random actions + joint_efforts = torch.randn_like(env.action_manager.action) + # step the environment + obs, rew, terminated, truncated, info = env.step(joint_efforts) + # print current orientation of pole + print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) + # update counter + count += 1 + + # close the environment + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/04_sensors/add_sensors_on_robot.py b/scripts/tutorials/04_sensors/add_sensors_on_robot.py new file mode 100644 index 0000000000000000000000000000000000000000..5cc6de6778b67bad537812d4619a1c4d616f8b4e --- /dev/null +++ b/scripts/tutorials/04_sensors/add_sensors_on_robot.py @@ -0,0 +1,179 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to add and simulate on-board sensors for a robot. + +We add the following sensors on the quadruped robot, ANYmal-C (ANYbotics): + +* USD-Camera: This is a camera sensor that is attached to the robot's base. +* Height Scanner: This is a height scanner sensor that is attached to the robot's base. +* Contact Sensor: This is a contact sensor that is attached to the robot's feet. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/04_sensors/add_sensors_on_robot.py --enable_cameras + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on adding sensors on a robot.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import CameraCfg, ContactSensorCfg, RayCasterCfg, patterns +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class SensorsSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors + camera = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/base/front_cam", + update_period=0.1, + height=480, + width=640, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + update_period=0.02, + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/defaultGroundPlane"], + ) + contact_forces = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/.*_FOOT", update_period=0.0, history_length=6, debug_vis=True + ) + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + # we offset the root state by the origin since the states are written in simulation world frame + # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + joint_pos += torch.rand_like(joint_pos) * 0.1 + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + # print information from the sensors + print("-------------------------------") + print(scene["camera"]) + print("Received shape of rgb image: ", scene["camera"].data.output["rgb"].shape) + print("Received shape of depth image: ", scene["camera"].data.output["distance_to_image_plane"].shape) + print("-------------------------------") + print(scene["height_scanner"]) + print("Received max height value: ", torch.max(scene["height_scanner"].data.ray_hits_w[..., -1]).item()) + print("-------------------------------") + print(scene["contact_forces"]) + print("Received max contact force of: ", torch.max(scene["contact_forces"].data.net_forces_w).item()) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # Design scene + scene_cfg = SensorsSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/04_sensors/run_frame_transformer.py b/scripts/tutorials/04_sensors/run_frame_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d12665ada942f45e71474a234d600f2f0f8786 --- /dev/null +++ b/scripts/tutorials/04_sensors/run_frame_transformer.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates the FrameTransformer sensor by visualizing the frames that it creates. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_frame_transformer.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script checks the FrameTransformer sensor by visualizing the frames that it creates." +) +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=args_cli.headless) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import math + +import torch + +import isaacsim.util.debug_draw._debug_draw as omni_debug_draw + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.sensors import FrameTransformer, FrameTransformerCfg, OffsetCfg +from isaaclab.sim import SimulationContext + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort:skip + + +def define_sensor() -> FrameTransformer: + """Defines the FrameTransformer sensor to add to the scene.""" + # define offset + rot_offset = math_utils.quat_from_euler_xyz(torch.zeros(1), torch.zeros(1), torch.tensor(-math.pi / 2)) + pos_offset = math_utils.quat_apply(rot_offset, torch.tensor([0.08795, 0.01305, -0.33797])) + + # Example using .* to get full body + LF_FOOT + frame_transformer_cfg = FrameTransformerCfg( + prim_path="/World/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg(prim_path="/World/Robot/.*"), + FrameTransformerCfg.FrameCfg( + prim_path="/World/Robot/LF_SHANK", + name="LF_FOOT_USER", + offset=OffsetCfg(pos=tuple(pos_offset.tolist()), rot=tuple(rot_offset[0].tolist())), + ), + ], + debug_vis=False, + ) + frame_transformer = FrameTransformer(frame_transformer_cfg) + + return frame_transformer + + +def design_scene() -> dict: + """Design the scene.""" + # Populate scene + # -- Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # -- Lights + cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + # -- Robot + robot = Articulation(ANYMAL_C_CFG.replace(prim_path="/World/Robot")) + # -- Sensors + frame_transformer = define_sensor() + + # return the scene information + scene_entities = {"robot": robot, "frame_transformer": frame_transformer} + return scene_entities + + +def run_simulator(sim: sim_utils.SimulationContext, scene_entities: dict): + """Run the simulator.""" + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # extract entities for simplified notation + robot: Articulation = scene_entities["robot"] + frame_transformer: FrameTransformer = scene_entities["frame_transformer"] + + # We only want one visualization at a time. This visualizer will be used + # to step through each frame so the user can verify that the correct frame + # is being visualized as the frame names are printing to console + if not args_cli.headless: + cfg = FRAME_MARKER_CFG.replace(prim_path="/Visuals/FrameVisualizerFromScript") + cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + transform_visualizer = VisualizationMarkers(cfg) + # debug drawing for lines connecting the frame + draw_interface = omni_debug_draw.acquire_debug_draw_interface() + else: + transform_visualizer = None + draw_interface = None + + frame_index = 0 + # Simulate physics + while simulation_app.is_running(): + # perform this loop at policy control freq (50 Hz) + robot.set_joint_position_target(robot.data.default_joint_pos.clone()) + robot.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # read data from sim + robot.update(sim_dt) + frame_transformer.update(dt=sim_dt) + + # Change the frame that we are visualizing to ensure that frame names + # are correctly associated with the frames + if not args_cli.headless: + if count % 50 == 0: + # get frame names + frame_names = frame_transformer.data.target_frame_names + # increment frame index + frame_index += 1 + frame_index = frame_index % len(frame_names) + print(f"Displaying Frame ID {frame_index}: {frame_names[frame_index]}") + + # visualize frame + source_pos = frame_transformer.data.source_pos_w + source_quat = frame_transformer.data.source_quat_w + target_pos = frame_transformer.data.target_pos_w[:, frame_index] + target_quat = frame_transformer.data.target_quat_w[:, frame_index] + # draw the frames + transform_visualizer.visualize( + torch.cat([source_pos, target_pos], dim=0), torch.cat([source_quat, target_quat], dim=0) + ) + # draw the line connecting the frames + draw_interface.clear_lines() + # plain color for lines + lines_colors = [[1.0, 1.0, 0.0, 1.0]] * source_pos.shape[0] + line_thicknesses = [5.0] * source_pos.shape[0] + draw_interface.draw_lines(source_pos.tolist(), target_pos.tolist(), lines_colors, line_thicknesses) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device) + sim = SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[2.5, 2.5, 2.5], target=[0.0, 0.0, 0.0]) + # Design scene + scene_entities = design_scene() + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene_entities) + + +if __name__ == "__main__": + # Run the main function + main() + # Close the simulator + simulation_app.close() diff --git a/scripts/tutorials/04_sensors/run_ray_caster.py b/scripts/tutorials/04_sensors/run_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..51780accbdd283a11f8d20f3ae18ae1bb9394288 --- /dev/null +++ b/scripts/tutorials/04_sensors/run_ray_caster.py @@ -0,0 +1,149 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the ray-caster sensor. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_ray_caster.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Ray Caster Test Script") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.sensors.ray_caster import RayCaster, RayCasterCfg, patterns +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.timer import Timer + + +def define_sensor() -> RayCaster: + """Defines the ray-caster sensor to add to the scene.""" + # Create a ray-caster sensor + ray_caster_cfg = RayCasterCfg( + prim_path="/World/Origin.*/ball", + mesh_prim_paths=["/World/ground"], + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=(2.0, 2.0)), + ray_alignment="yaw", + debug_vis=not args_cli.headless, + ) + ray_caster = RayCaster(cfg=ray_caster_cfg) + + return ray_caster + + +def design_scene() -> dict: + """Design the scene.""" + # Populate scene + # -- Rough terrain + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd") + cfg.func("/World/ground", cfg) + # -- Light + cfg = sim_utils.DistantLightCfg(intensity=2000) + cfg.func("/World/light", cfg) + + # Create separate groups called "Origin1", "Origin2", "Origin3" + # Each group will have a robot in it + origins = [[0.25, 0.25, 0.0], [-0.25, 0.25, 0.0], [0.25, -0.25, 0.0], [-0.25, -0.25, 0.0]] + for i, origin in enumerate(origins): + sim_utils.create_prim(f"/World/Origin{i}", "Xform", translation=origin) + # -- Balls + cfg = RigidObjectCfg( + prim_path="/World/Origin.*/ball", + spawn=sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + ) + balls = RigidObject(cfg) + # -- Sensors + ray_caster = define_sensor() + + # return the scene information + scene_entities = {"balls": balls, "ray_caster": ray_caster} + return scene_entities + + +def run_simulator(sim: sim_utils.SimulationContext, scene_entities: dict): + """Run the simulator.""" + # Extract scene_entities for simplified notation + ray_caster: RayCaster = scene_entities["ray_caster"] + balls: RigidObject = scene_entities["balls"] + + # define an initial position of the sensor + ball_default_state = balls.data.default_root_state.clone() + ball_default_state[:, :3] = torch.rand_like(ball_default_state[:, :3]) * 10 + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # Reset the scene + if step_count % 250 == 0: + # reset the balls + balls.write_root_pose_to_sim(ball_default_state[:, :7]) + balls.write_root_velocity_to_sim(ball_default_state[:, 7:]) + # reset the sensor + ray_caster.reset() + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update the ray-caster + with Timer( + f"Ray-caster update with {4} x {ray_caster.num_rays} rays with max height of" + f" {torch.max(ray_caster.data.pos_w).item():.2f}" + ): + ray_caster.update(dt=sim.get_physics_dt(), force_recompute=True) + # Update counter + step_count += 1 + + +def main(): + """Main function.""" + # Load simulation context + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([0.0, 15.0, 15.0], [0.0, 0.0, -2.5]) + # Design scene + scene_entities = design_scene() + # Play simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run simulator + run_simulator(sim=sim, scene_entities=scene_entities) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/04_sensors/run_ray_caster_camera.py b/scripts/tutorials/04_sensors/run_ray_caster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..375a0cf8f08b1d32463b25a175eb19614186bf2b --- /dev/null +++ b/scripts/tutorials/04_sensors/run_ray_caster_camera.py @@ -0,0 +1,184 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use the ray-cast camera sensor from the Isaac Lab framework. + +The camera sensor is based on using Warp kernels which do ray-casting against static meshes. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_ray_caster_camera.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to use the ray-cast camera sensor.") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to generate.") +parser.add_argument("--save", action="store_true", default=False, help="Save the obtained data to disk.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os + +import torch + +import omni.replicator.core as rep + +import isaaclab.sim as sim_utils +from isaaclab.sensors.ray_caster import RayCasterCamera, RayCasterCameraCfg, patterns +from isaaclab.utils import convert_dict_to_backend +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.math import project_points, unproject_depth + + +def define_sensor() -> RayCasterCamera: + """Defines the ray-cast camera sensor to add to the scene.""" + # Camera base frames + # In contras to the USD camera, we associate the sensor to the prims at these locations. + # This means that parent prim of the sensor is the prim at this location. + sim_utils.create_prim("/World/Origin_00/CameraSensor", "Xform") + sim_utils.create_prim("/World/Origin_01/CameraSensor", "Xform") + + # Setup camera sensor + camera_cfg = RayCasterCameraCfg( + prim_path="/World/Origin_.*/CameraSensor", + mesh_prim_paths=["/World/ground"], + update_period=0.1, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + data_types=["distance_to_image_plane", "normals", "distance_to_camera"], + debug_vis=True, + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=480, + width=640, + ), + ) + # Create camera + camera = RayCasterCamera(cfg=camera_cfg) + + return camera + + +def design_scene(): + # Populate scene + # -- Rough terrain + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd") + cfg.func("/World/ground", cfg) + # -- Lights + cfg = sim_utils.DistantLightCfg(intensity=600.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + # -- Sensors + camera = define_sensor() + + # return the scene information + scene_entities = {"camera": camera} + return scene_entities + + +def run_simulator(sim: sim_utils.SimulationContext, scene_entities: dict): + """Run the simulator.""" + # extract entities for simplified notation + camera: RayCasterCamera = scene_entities["camera"] + + # Create replicator writer + output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output", "ray_caster_camera") + rep_writer = rep.BasicWriter(output_dir=output_dir, frame_padding=3) + + # Set pose: There are two ways to set the pose of the camera. + # -- Option-1: Set pose using view + eyes = torch.tensor([[2.5, 2.5, 2.5], [-2.5, -2.5, 2.5]], device=sim.device) + targets = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=sim.device) + camera.set_world_poses_from_view(eyes, targets) + # -- Option-2: Set pose using ROS + # position = torch.tensor([[2.5, 2.5, 2.5]], device=sim.device) + # orientation = torch.tensor([[-0.17591989, 0.33985114, 0.82047325, -0.42470819]], device=sim.device) + # camera.set_world_poses(position, orientation, indices=[0], convention="ros") + + # Simulate physics + while simulation_app.is_running(): + # Step simulation + sim.step() + # Update camera data + camera.update(dt=sim.get_physics_dt()) + + # Print camera info + print(camera) + print("Received shape of depth image: ", camera.data.output["distance_to_image_plane"].shape) + print("-------------------------------") + + # Extract camera data + if args_cli.save: + # Extract camera data + camera_index = 0 + # note: BasicWriter only supports saving data in numpy format, so we need to convert the data to numpy. + single_cam_data = convert_dict_to_backend( + {k: v[camera_index] for k, v in camera.data.output.items()}, backend="numpy" + ) + # Extract the other information + single_cam_info = camera.data.info[camera_index] + + # Pack data back into replicator format to save them using its writer + rep_output = {"annotators": {}} + for key, data, info in zip(single_cam_data.keys(), single_cam_data.values(), single_cam_info.values()): + if info is not None: + rep_output["annotators"][key] = {"render_product": {"data": data, **info}} + else: + rep_output["annotators"][key] = {"render_product": {"data": data}} + # Save images + rep_output["trigger_outputs"] = {"on_time": camera.frame[camera_index]} + rep_writer.write(rep_output) + + # Pointcloud in world frame + points_3d_cam = unproject_depth( + camera.data.output["distance_to_image_plane"], camera.data.intrinsic_matrices + ) + + # Check methods are valid + im_height, im_width = camera.image_shape + # -- project points to (u, v, d) + reproj_points = project_points(points_3d_cam, camera.data.intrinsic_matrices) + reproj_depths = reproj_points[..., -1].view(-1, im_width, im_height).transpose_(1, 2) + sim_depths = camera.data.output["distance_to_image_plane"].squeeze(-1) + torch.testing.assert_close(reproj_depths, sim_depths) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg() + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 3.5], [0.0, 0.0, 0.0]) + # Design scene + scene_entities = design_scene() + # Play simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run simulator + run_simulator(sim=sim, scene_entities=scene_entities) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/04_sensors/run_usd_camera.py b/scripts/tutorials/04_sensors/run_usd_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..c2462aaaec897b25023a971b602cf91d21189982 --- /dev/null +++ b/scripts/tutorials/04_sensors/run_usd_camera.py @@ -0,0 +1,289 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use the camera sensor from the Isaac Lab framework. + +The camera sensor is created and interfaced through the Omniverse Replicator API. However, instead of using +the simulator or OpenGL convention for the camera, we use the robotics or ROS convention. + +.. code-block:: bash + + # Usage with GUI + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --enable_cameras + + # Usage with headless + ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --headless --enable_cameras + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to use the camera sensor.") +parser.add_argument( + "--draw", + action="store_true", + default=False, + help="Draw the pointcloud from camera at index specified by ``--camera_id``.", +) +parser.add_argument( + "--save", + action="store_true", + default=False, + help="Save the data from camera at index specified by ``--camera_id``.", +) +parser.add_argument( + "--camera_id", + type=int, + choices={0, 1}, + default=0, + help=( + "The camera ID to use for displaying points or saving the camera data. Default is 0." + " The viewport will always initialize with the perspective of camera 0." + ), +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import random + +import numpy as np +import torch + +import omni.replicator.core as rep + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import RAY_CASTER_MARKER_CFG +from isaaclab.sensors.camera import Camera, CameraCfg +from isaaclab.sensors.camera.utils import create_pointcloud_from_depth +from isaaclab.utils import convert_dict_to_backend + + +def define_sensor() -> Camera: + """Defines the camera sensor to add to the scene.""" + # Setup camera sensor + # In contrast to the ray-cast camera, we spawn the prim at these locations. + # This means the camera sensor will be attached to these prims. + sim_utils.create_prim("/World/Origin_00", "Xform") + sim_utils.create_prim("/World/Origin_01", "Xform") + camera_cfg = CameraCfg( + prim_path="/World/Origin_.*/CameraSensor", + update_period=0, + height=480, + width=640, + data_types=[ + "rgb", + "distance_to_image_plane", + "normals", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ], + colorize_semantic_segmentation=True, + colorize_instance_id_segmentation=True, + colorize_instance_segmentation=True, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + ) + # Create camera + camera = Camera(cfg=camera_cfg) + + return camera + + +def design_scene() -> dict: + """Design the scene.""" + # Populate scene + # -- Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # -- Lights + cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + cfg.func("/World/Light", cfg) + + # Create a dictionary for the scene entities + scene_entities = {} + + # Xform to hold objects + sim_utils.create_prim("/World/Objects", "Xform") + # Random objects + for i in range(8): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # sample random color + color = (random.random(), random.random(), random.random()) + # choose random prim type + prim_type = random.choice(["Cube", "Cone", "Cylinder"]) + common_properties = { + "rigid_props": sim_utils.RigidBodyPropertiesCfg(), + "mass_props": sim_utils.MassPropertiesCfg(mass=5.0), + "collision_props": sim_utils.CollisionPropertiesCfg(), + "visual_material": sim_utils.PreviewSurfaceCfg(diffuse_color=color, metallic=0.5), + "semantic_tags": [("class", prim_type)], + } + if prim_type == "Cube": + shape_cfg = sim_utils.CuboidCfg(size=(0.25, 0.25, 0.25), **common_properties) + elif prim_type == "Cone": + shape_cfg = sim_utils.ConeCfg(radius=0.1, height=0.25, **common_properties) + elif prim_type == "Cylinder": + shape_cfg = sim_utils.CylinderCfg(radius=0.25, height=0.25, **common_properties) + # Rigid Object + obj_cfg = RigidObjectCfg( + prim_path=f"/World/Objects/Obj_{i:02d}", + spawn=shape_cfg, + init_state=RigidObjectCfg.InitialStateCfg(pos=position), + ) + scene_entities[f"rigid_object{i}"] = RigidObject(cfg=obj_cfg) + + # Sensors + camera = define_sensor() + + # return the scene information + scene_entities["camera"] = camera + return scene_entities + + +def run_simulator(sim: sim_utils.SimulationContext, scene_entities: dict): + """Run the simulator.""" + # extract entities for simplified notation + camera: Camera = scene_entities["camera"] + + # Create replicator writer + output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output", "camera") + rep_writer = rep.BasicWriter( + output_dir=output_dir, + frame_padding=0, + colorize_instance_id_segmentation=camera.cfg.colorize_instance_id_segmentation, + colorize_instance_segmentation=camera.cfg.colorize_instance_segmentation, + colorize_semantic_segmentation=camera.cfg.colorize_semantic_segmentation, + ) + + # Camera positions, targets, orientations + camera_positions = torch.tensor([[2.5, 2.5, 2.5], [-2.5, -2.5, 2.5]], device=sim.device) + camera_targets = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=sim.device) + # These orientations are in ROS-convention, and will position the cameras to view the origin + camera_orientations = torch.tensor( # noqa: F841 + [[-0.1759, 0.3399, 0.8205, -0.4247], [-0.4247, 0.8205, -0.3399, 0.1759]], device=sim.device + ) + + # Set pose: There are two ways to set the pose of the camera. + # -- Option-1: Set pose using view + camera.set_world_poses_from_view(camera_positions, camera_targets) + # -- Option-2: Set pose using ROS + # camera.set_world_poses(camera_positions, camera_orientations, convention="ros") + + # Index of the camera to use for visualization and saving + camera_index = args_cli.camera_id + + # Create the markers for the --draw option outside of is_running() loop + if sim.has_gui() and args_cli.draw: + cfg = RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/CameraPointCloud") + cfg.markers["hit"].radius = 0.002 + pc_markers = VisualizationMarkers(cfg) + + # Simulate physics + while simulation_app.is_running(): + # Step simulation + sim.step() + # Update camera data + camera.update(dt=sim.get_physics_dt()) + + # Print camera info + print(camera) + if "rgb" in camera.data.output.keys(): + print("Received shape of rgb image : ", camera.data.output["rgb"].shape) + if "distance_to_image_plane" in camera.data.output.keys(): + print("Received shape of depth image : ", camera.data.output["distance_to_image_plane"].shape) + if "normals" in camera.data.output.keys(): + print("Received shape of normals : ", camera.data.output["normals"].shape) + if "semantic_segmentation" in camera.data.output.keys(): + print("Received shape of semantic segm. : ", camera.data.output["semantic_segmentation"].shape) + if "instance_segmentation_fast" in camera.data.output.keys(): + print("Received shape of instance segm. : ", camera.data.output["instance_segmentation_fast"].shape) + if "instance_id_segmentation_fast" in camera.data.output.keys(): + print("Received shape of instance id segm.: ", camera.data.output["instance_id_segmentation_fast"].shape) + print("-------------------------------") + + # Extract camera data + if args_cli.save: + # Save images from camera at camera_index + # note: BasicWriter only supports saving data in numpy format, so we need to convert the data to numpy. + single_cam_data = convert_dict_to_backend( + {k: v[camera_index] for k, v in camera.data.output.items()}, backend="numpy" + ) + + # Extract the other information + single_cam_info = camera.data.info[camera_index] + + # Pack data back into replicator format to save them using its writer + rep_output = {"annotators": {}} + for key, data, info in zip(single_cam_data.keys(), single_cam_data.values(), single_cam_info.values()): + if info is not None: + rep_output["annotators"][key] = {"render_product": {"data": data, **info}} + else: + rep_output["annotators"][key] = {"render_product": {"data": data}} + # Save images + # Note: We need to provide On-time data for Replicator to save the images. + rep_output["trigger_outputs"] = {"on_time": camera.frame[camera_index]} + rep_writer.write(rep_output) + + # Draw pointcloud if there is a GUI and --draw has been passed + if sim.has_gui() and args_cli.draw and "distance_to_image_plane" in camera.data.output.keys(): + # Derive pointcloud from camera at camera_index + pointcloud = create_pointcloud_from_depth( + intrinsic_matrix=camera.data.intrinsic_matrices[camera_index], + depth=camera.data.output["distance_to_image_plane"][camera_index], + position=camera.data.pos_w[camera_index], + orientation=camera.data.quat_w_ros[camera_index], + device=sim.device, + ) + + # In the first few steps, things are still being instanced and Camera.data + # can be empty. If we attempt to visualize an empty pointcloud it will crash + # the sim, so we check that the pointcloud is not empty. + if pointcloud.size()[0] > 0: + pc_markers.visualize(translations=pointcloud) + + +def main(): + """Main function.""" + # Load simulation context + sim_cfg = sim_utils.SimulationCfg(device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + # Design scene + scene_entities = design_scene() + # Play simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run simulator + run_simulator(sim, scene_entities) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/05_controllers/run_diff_ik.py b/scripts/tutorials/05_controllers/run_diff_ik.py new file mode 100644 index 0000000000000000000000000000000000000000..22d17963235f2896b1aac9b215ac5c3aeb7b1b75 --- /dev/null +++ b/scripts/tutorials/05_controllers/run_diff_ik.py @@ -0,0 +1,212 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the differential inverse kinematics controller with the simulator. + +The differential IK controller can be configured in different modes. It uses the Jacobians computed by +PhysX. This helps perform parallelized computation of the inverse kinematics. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/05_controllers/run_diff_ik.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on using the differential IK controller.") +parser.add_argument("--robot", type=str, default="franka_panda", help="Name of the robot.") +parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.managers import SceneEntityCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.math import subtract_frame_transforms + +## +# Pre-defined configs +## +from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG # isort:skip + + +@configclass +class TableTopSceneCfg(InteractiveSceneCfg): + """Configuration for a cart-pole scene.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/defaultGroundPlane", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # mount + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) + ), + ) + + # articulation + if args_cli.robot == "franka_panda": + robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + elif args_cli.robot == "ur10": + robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + else: + raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. + robot = scene["robot"] + + # Create controller + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) + + # Markers + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current")) + goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + + # Define goals for the arm + ee_goals = [ + [0.5, 0.5, 0.7, 0.707, 0, 0.707, 0], + [0.5, -0.4, 0.6, 0.707, 0.707, 0.0, 0.0], + [0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0], + ] + ee_goals = torch.tensor(ee_goals, device=sim.device) + # Track the given command + current_goal_idx = 0 + # Create buffers to store actions + ik_commands = torch.zeros(scene.num_envs, diff_ik_controller.action_dim, device=robot.device) + ik_commands[:] = ee_goals[current_goal_idx] + + # Specify robot-specific parameters + if args_cli.robot == "franka_panda": + robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + elif args_cli.robot == "ur10": + robot_entity_cfg = SceneEntityCfg("robot", joint_names=[".*"], body_names=["ee_link"]) + else: + raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") + # Resolving the scene entities + robot_entity_cfg.resolve(scene) + # Obtain the frame index of the end-effector + # For a fixed base robot, the frame index is one less than the body index. This is because + # the root body is not included in the returned Jacobians. + if robot.is_fixed_base: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 + else: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # reset + if count % 150 == 0: + # reset time + count = 0 + # reset joint state + joint_pos = robot.data.default_joint_pos.clone() + joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + robot.reset() + # reset actions + ik_commands[:] = ee_goals[current_goal_idx] + joint_pos_des = joint_pos[:, robot_entity_cfg.joint_ids].clone() + # reset controller + diff_ik_controller.reset() + diff_ik_controller.set_command(ik_commands) + # change goal + current_goal_idx = (current_goal_idx + 1) % len(ee_goals) + else: + # obtain quantities from simulation + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] + ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] + root_pose_w = robot.data.root_pose_w + joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] + # compute frame in root frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + # compute the joint commands + joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + + # apply actions + robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + count += 1 + # update buffers + scene.update(sim_dt) + + # obtain quantities from simulation + ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7] + # update marker positions + ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]) + goal_marker.visualize(ik_commands[:, 0:3] + scene.env_origins, ik_commands[:, 3:7]) + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + # Design scene + scene_cfg = TableTopSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/tutorials/05_controllers/run_osc.py b/scripts/tutorials/05_controllers/run_osc.py new file mode 100644 index 0000000000000000000000000000000000000000..98b2532a0a2d3878150f3d62460f587743308f5b --- /dev/null +++ b/scripts/tutorials/05_controllers/run_osc.py @@ -0,0 +1,484 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the operational space controller (OSC) with the simulator. + +The OSC controller can be configured in different modes. It uses the dynamical quantities such as Jacobians and +mass matricescomputed by PhysX. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/05_controllers/run_osc.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on using the operational space controller.") +parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, AssetBaseCfg +from isaaclab.controllers import OperationalSpaceController, OperationalSpaceControllerCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg +from isaaclab.utils import configclass +from isaaclab.utils.math import ( + combine_frame_transforms, + matrix_from_quat, + quat_apply_inverse, + quat_inv, + subtract_frame_transforms, +) + +## +# Pre-defined configs +## +from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG # isort:skip + + +@configclass +class SceneCfg(InteractiveSceneCfg): + """Configuration for a simple scene with a tilted wall.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/defaultGroundPlane", + spawn=sim_utils.GroundPlaneCfg(), + ) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # Tilted wall + tilted_wall = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/TiltedWall", + spawn=sim_utils.CuboidCfg( + size=(2.0, 1.5, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ), + init_state=AssetBaseCfg.InitialStateCfg( + pos=(0.6 + 0.085, 0.0, 0.3), rot=(0.9238795325, 0.0, -0.3826834324, 0.0) + ), + ) + + contact_forces = ContactSensorCfg( + prim_path="/World/envs/env_.*/TiltedWall", + update_period=0.0, + history_length=2, + debug_vis=False, + ) + + robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + robot.actuators["panda_shoulder"].stiffness = 0.0 + robot.actuators["panda_shoulder"].damping = 0.0 + robot.actuators["panda_forearm"].stiffness = 0.0 + robot.actuators["panda_forearm"].damping = 0.0 + robot.spawn.rigid_props.disable_gravity = True + + +def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): + """Runs the simulation loop. + + Args: + sim: (SimulationContext) Simulation context. + scene: (InteractiveScene) Interactive scene. + """ + + # Extract scene entities for readability. + robot = scene["robot"] + contact_forces = scene["contact_forces"] + + # Obtain indices for the end-effector and arm joints + ee_frame_name = "panda_leftfinger" + arm_joint_names = ["panda_joint.*"] + ee_frame_idx = robot.find_bodies(ee_frame_name)[0][0] + arm_joint_ids = robot.find_joints(arm_joint_names)[0] + + # Create the OSC + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs", "wrench_abs"], + impedance_mode="variable_kp", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_damping_ratio_task=1.0, + contact_wrench_stiffness_task=[0.0, 0.0, 0.1, 0.0, 0.0, 0.0], + motion_control_axes_task=[1, 1, 0, 1, 1, 1], + contact_wrench_control_axes_task=[0, 0, 1, 0, 0, 0], + nullspace_control="position", + ) + osc = OperationalSpaceController(osc_cfg, num_envs=scene.num_envs, device=sim.device) + + # Markers + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current")) + goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + + # Define targets for the arm + ee_goal_pose_set_tilted_b = torch.tensor( + [ + [0.6, 0.15, 0.3, 0.0, 0.92387953, 0.0, 0.38268343], + [0.6, -0.3, 0.3, 0.0, 0.92387953, 0.0, 0.38268343], + [0.8, 0.0, 0.5, 0.0, 0.92387953, 0.0, 0.38268343], + ], + device=sim.device, + ) + ee_goal_wrench_set_tilted_task = torch.tensor( + [ + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + ], + device=sim.device, + ) + kp_set_task = torch.tensor( + [ + [360.0, 360.0, 360.0, 360.0, 360.0, 360.0], + [420.0, 420.0, 420.0, 420.0, 420.0, 420.0], + [320.0, 320.0, 320.0, 320.0, 320.0, 320.0], + ], + device=sim.device, + ) + ee_target_set = torch.cat([ee_goal_pose_set_tilted_b, ee_goal_wrench_set_tilted_task, kp_set_task], dim=-1) + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + + # Update existing buffers + # Note: We need to update buffers before the first step for the controller. + robot.update(dt=sim_dt) + + # Get the center of the robot soft joint limits + joint_centers = torch.mean(robot.data.soft_joint_pos_limits[:, arm_joint_ids, :], dim=-1) + + # get the updated states + ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) = update_states(sim, scene, robot, ee_frame_idx, arm_joint_ids, contact_forces) + + # Track the given target command + current_goal_idx = 0 # Current goal index for the arm + command = torch.zeros( + scene.num_envs, osc.action_dim, device=sim.device + ) # Generic target command, which can be pose, position, force, etc. + ee_target_pose_b = torch.zeros(scene.num_envs, 7, device=sim.device) # Target pose in the body frame + ee_target_pose_w = torch.zeros(scene.num_envs, 7, device=sim.device) # Target pose in the world frame (for marker) + + # Set joint efforts to zero + zero_joint_efforts = torch.zeros(scene.num_envs, robot.num_joints, device=sim.device) + joint_efforts = torch.zeros(scene.num_envs, len(arm_joint_ids), device=sim.device) + + count = 0 + # Simulation loop + while simulation_app.is_running(): + # reset every 500 steps + if count % 500 == 0: + # reset joint state to default + default_joint_pos = robot.data.default_joint_pos.clone() + default_joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(default_joint_pos, default_joint_vel) + robot.set_joint_effort_target(zero_joint_efforts) # Set zero torques in the initial step + robot.write_data_to_sim() + robot.reset() + # reset contact sensor + contact_forces.reset() + # reset target pose + robot.update(sim_dt) + _, _, _, ee_pose_b, _, _, _, _, _, _ = update_states( + sim, scene, robot, ee_frame_idx, arm_joint_ids, contact_forces + ) # at reset, the jacobians are not updated to the latest state + command, ee_target_pose_b, ee_target_pose_w, current_goal_idx = update_target( + sim, scene, osc, root_pose_w, ee_target_set, current_goal_idx + ) + # set the osc command + osc.reset() + command, task_frame_pose_b = convert_to_task_frame(osc, command=command, ee_target_pose_b=ee_target_pose_b) + osc.set_command(command=command, current_ee_pose_b=ee_pose_b, current_task_frame_pose_b=task_frame_pose_b) + else: + # get the updated states + ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) = update_states(sim, scene, robot, ee_frame_idx, arm_joint_ids, contact_forces) + # compute the joint commands + joint_efforts = osc.compute( + jacobian_b=jacobian_b, + current_ee_pose_b=ee_pose_b, + current_ee_vel_b=ee_vel_b, + current_ee_force_b=ee_force_b, + mass_matrix=mass_matrix, + gravity=gravity, + current_joint_pos=joint_pos, + current_joint_vel=joint_vel, + nullspace_joint_pos_target=joint_centers, + ) + # apply actions + robot.set_joint_effort_target(joint_efforts, joint_ids=arm_joint_ids) + robot.write_data_to_sim() + + # update marker positions + ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]) + goal_marker.visualize(ee_target_pose_w[:, 0:3], ee_target_pose_w[:, 3:7]) + + # perform step + sim.step(render=True) + # update robot buffers + robot.update(sim_dt) + # update buffers + scene.update(sim_dt) + # update sim-time + count += 1 + + +# Update robot states +def update_states( + sim: sim_utils.SimulationContext, + scene: InteractiveScene, + robot: Articulation, + ee_frame_idx: int, + arm_joint_ids: list[int], + contact_forces, +): + """Update the robot states. + + Args: + sim: (SimulationContext) Simulation context. + scene: (InteractiveScene) Interactive scene. + robot: (Articulation) Robot articulation. + ee_frame_idx: (int) End-effector frame index. + arm_joint_ids: (list[int]) Arm joint indices. + contact_forces: (ContactSensor) Contact sensor. + + Returns: + jacobian_b (torch.tensor): Jacobian in the body frame. + mass_matrix (torch.tensor): Mass matrix. + gravity (torch.tensor): Gravity vector. + ee_pose_b (torch.tensor): End-effector pose in the body frame. + ee_vel_b (torch.tensor): End-effector velocity in the body frame. + root_pose_w (torch.tensor): Root pose in the world frame. + ee_pose_w (torch.tensor): End-effector pose in the world frame. + ee_force_b (torch.tensor): End-effector force in the body frame. + joint_pos (torch.tensor): The joint positions. + joint_vel (torch.tensor): The joint velocities. + + Raises: + ValueError: Undefined target_type. + """ + # obtain dynamics related quantities from simulation + ee_jacobi_idx = ee_frame_idx - 1 + jacobian_w = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, arm_joint_ids] + mass_matrix = robot.root_physx_view.get_generalized_mass_matrices()[:, arm_joint_ids, :][:, :, arm_joint_ids] + gravity = robot.root_physx_view.get_gravity_compensation_forces()[:, arm_joint_ids] + # Convert the Jacobian from world to root frame + jacobian_b = jacobian_w.clone() + root_rot_matrix = matrix_from_quat(quat_inv(robot.data.root_quat_w)) + jacobian_b[:, :3, :] = torch.bmm(root_rot_matrix, jacobian_b[:, :3, :]) + jacobian_b[:, 3:, :] = torch.bmm(root_rot_matrix, jacobian_b[:, 3:, :]) + + # Compute current pose of the end-effector + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + ee_pos_w = robot.data.body_pos_w[:, ee_frame_idx] + ee_quat_w = robot.data.body_quat_w[:, ee_frame_idx] + ee_pos_b, ee_quat_b = subtract_frame_transforms(root_pos_w, root_quat_w, ee_pos_w, ee_quat_w) + root_pose_w = torch.cat([root_pos_w, root_quat_w], dim=-1) + ee_pose_w = torch.cat([ee_pos_w, ee_quat_w], dim=-1) + ee_pose_b = torch.cat([ee_pos_b, ee_quat_b], dim=-1) + + # Compute the current velocity of the end-effector + ee_vel_w = robot.data.body_vel_w[:, ee_frame_idx, :] # Extract end-effector velocity in the world frame + root_vel_w = robot.data.root_vel_w # Extract root velocity in the world frame + relative_vel_w = ee_vel_w - root_vel_w # Compute the relative velocity in the world frame + ee_lin_vel_b = quat_apply_inverse(robot.data.root_quat_w, relative_vel_w[:, 0:3]) # From world to root frame + ee_ang_vel_b = quat_apply_inverse(robot.data.root_quat_w, relative_vel_w[:, 3:6]) + ee_vel_b = torch.cat([ee_lin_vel_b, ee_ang_vel_b], dim=-1) + + # Calculate the contact force + ee_force_w = torch.zeros(scene.num_envs, 3, device=sim.device) + sim_dt = sim.get_physics_dt() + contact_forces.update(sim_dt) # update contact sensor + # Calculate the contact force by averaging over last four time steps (i.e., to smoothen) and + # taking the max of three surfaces as only one should be the contact of interest + ee_force_w, _ = torch.max(torch.mean(contact_forces.data.net_forces_w_history, dim=1), dim=1) + + # This is a simplification, only for the sake of testing. + ee_force_b = ee_force_w + + # Get joint positions and velocities + joint_pos = robot.data.joint_pos[:, arm_joint_ids] + joint_vel = robot.data.joint_vel[:, arm_joint_ids] + + return ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) + + +# Update the target commands +def update_target( + sim: sim_utils.SimulationContext, + scene: InteractiveScene, + osc: OperationalSpaceController, + root_pose_w: torch.tensor, + ee_target_set: torch.tensor, + current_goal_idx: int, +): + """Update the targets for the operational space controller. + + Args: + sim: (SimulationContext) Simulation context. + scene: (InteractiveScene) Interactive scene. + osc: (OperationalSpaceController) Operational space controller. + root_pose_w: (torch.tensor) Root pose in the world frame. + ee_target_set: (torch.tensor) End-effector target set. + current_goal_idx: (int) Current goal index. + + Returns: + command (torch.tensor): Updated target command. + ee_target_pose_b (torch.tensor): Updated target pose in the body frame. + ee_target_pose_w (torch.tensor): Updated target pose in the world frame. + next_goal_idx (int): Next goal index. + + Raises: + ValueError: Undefined target_type. + """ + + # update the ee desired command + command = torch.zeros(scene.num_envs, osc.action_dim, device=sim.device) + command[:] = ee_target_set[current_goal_idx] + + # update the ee desired pose + ee_target_pose_b = torch.zeros(scene.num_envs, 7, device=sim.device) + for target_type in osc.cfg.target_types: + if target_type == "pose_abs": + ee_target_pose_b[:] = command[:, :7] + elif target_type == "wrench_abs": + pass # ee_target_pose_b could stay at the root frame for force control, what matters is ee_target_b + else: + raise ValueError("Undefined target_type within update_target().") + + # update the target desired pose in world frame (for marker) + ee_target_pos_w, ee_target_quat_w = combine_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_target_pose_b[:, 0:3], ee_target_pose_b[:, 3:7] + ) + ee_target_pose_w = torch.cat([ee_target_pos_w, ee_target_quat_w], dim=-1) + + next_goal_idx = (current_goal_idx + 1) % len(ee_target_set) + + return command, ee_target_pose_b, ee_target_pose_w, next_goal_idx + + +# Convert the target commands to the task frame +def convert_to_task_frame(osc: OperationalSpaceController, command: torch.tensor, ee_target_pose_b: torch.tensor): + """Converts the target commands to the task frame. + + Args: + osc: OperationalSpaceController object. + command: Command to be converted. + ee_target_pose_b: Target pose in the body frame. + + Returns: + command (torch.tensor): Target command in the task frame. + task_frame_pose_b (torch.tensor): Target pose in the task frame. + + Raises: + ValueError: Undefined target_type. + """ + command = command.clone() + task_frame_pose_b = ee_target_pose_b.clone() + + cmd_idx = 0 + for target_type in osc.cfg.target_types: + if target_type == "pose_abs": + command[:, :3], command[:, 3:7] = subtract_frame_transforms( + task_frame_pose_b[:, :3], task_frame_pose_b[:, 3:], command[:, :3], command[:, 3:7] + ) + cmd_idx += 7 + elif target_type == "wrench_abs": + # These are already defined in target frame for ee_goal_wrench_set_tilted_task (since it is + # easier), so not transforming + cmd_idx += 6 + else: + raise ValueError("Undefined target_type within _convert_to_task_frame().") + + return command, task_frame_pose_b + + +def main(): + """Main function.""" + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + # Design scene + scene_cfg = SceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/config/extension.toml b/source/isaaclab/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..7b3b218b3b02e7c24fa7b9f5b436d95e5b6b642c --- /dev/null +++ b/source/isaaclab/config/extension.toml @@ -0,0 +1,41 @@ +[package] + +# Note: Semantic Versioning is used: https://semver.org/ +version = "0.53.1" + +# Description +title = "Isaac Lab framework for Robot Learning" +description="Extension providing main framework interfaces and abstractions for robot learning." +readme = "docs/README.md" +repository = "https://github.com/isaac-sim/IsaacLab" +category = "robotics" +keywords = ["kit", "robotics", "learning", "ai"] + +[python.pipapi] +requirements = [ + "numpy", + "prettytable==3.3.0", + "toml", + "hidapi", + "gymnasium==0.29.0", + "trimesh", + "websockets" +] + +modules = [ + "numpy", + "prettytable", + "toml", + "hid", + "gymnasium", + "trimesh", + "websockets" +] + +use_online_index=true + +[core] +reloadable = false + +[[python.module]] +name = "isaaclab" diff --git a/source/isaaclab/docs/CHANGELOG.rst b/source/isaaclab/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..287059f72b94b09fb1a96617fad207a7a8635a31 --- /dev/null +++ b/source/isaaclab/docs/CHANGELOG.rst @@ -0,0 +1,5808 @@ +Changelog +--------- + +0.53.2 (2026-01-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.assets.utils.wrench_composer.WrenchComposer` to compose forces and torques at the body's center of mass frame. +* Added :meth:`~isaaclab.assets.Articulation.instantaneous_wrench_composer` to add or set instantaneous external wrenches to the articulation. +* Added :meth:`~isaaclab.assets.Articulation.permanent_wrench_composer` to add or set permanent external wrenches to the articulation. +* Added :meth:`~isaaclab.assets.RigidObject.instantaneous_wrench_composer` to add or set instantaneous external wrenches to the rigid object. +* Added :meth:`~isaaclab.assets.RigidObject.permanent_wrench_composer` to add or set permanent external wrenches to the rigid object. +* Added :meth:`~isaaclab.assets.RigidObjectCollection.instantaneous_wrench_composer` to add or set instantaneous external wrenches to the rigid object collection. +* Added :meth:`~isaaclab.assets.RigidObjectCollection.permanent_wrench_composer` to add or set permanent external wrenches to the rigid object collection. +* Added unit tests for the wrench composer. +* Added kernels for the wrench composer in the :mod:`isaaclab.utils.warp.kernels` module. + +Changed +^^^^^^^ + +* Deprecated :meth:`~isaaclab.assets.Articulation.set_external_force_and_torque` in favor of :meth:`~isaaclab.assets.Articulation.permanent_wrench_composer.set_forces_and_torques`. +* Deprecated :meth:`~isaaclab.assets.RigidObject.set_external_force_and_torque` in favor of :meth:`~isaaclab.assets.RigidObject.permanent_wrench_composer.set_forces_and_torques`. +* Deprecated :meth:`~isaaclab.assets.RigidObjectCollection.set_external_force_and_torque` in favor of :meth:`~isaaclab.assets.RigidObjectCollection.permanent_wrench_composer.set_forces_and_torques`. +* Modified the tests of the articulation, rigid object, and rigid object collection to use the new permanent and instantaneous external wrench functions and test them. + +0.53.1 (2026-01-08) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added function :func:`~isaaclab.sim.utils.prims.change_prim_property` to change attributes on a USD prim. + This replaces the previously used USD command ``ChangeProperty`` that depends on Omniverse Kit API. + +Changed +^^^^^^^ + +* Replaced occurrences of ``ChangeProperty`` USD command to :func:`~isaaclab.sim.utils.prims.change_prim_property`. + + +0.53.0 (2026-01-07) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.sim.views.XformPrimView` class to provide a + view of the USD Xform operations. Compared to Isaac Sim implementation, + this class optimizes several operations using USD SDF API. + +Changed +^^^^^^^ + +* Switched the sensor classes to use the :class:`~isaaclab.sim.views.XformPrimView` + class for the internal view wherever applicable. + +Removed +^^^^^^^ + +* Removed the usage of :class:`isaacsim.core.utils.prims.XformPrim` + class from the sensor classes. + + +0.52.2 (2026-01-06) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Improved logic for the URDF importer extension version pinning: the older extension version + is now pinned only on Isaac Sim 5.1 and later, while older Isaac Sim versions no longer + attempt to pin to a version that does not exist. + + +0.52.1 (2026-01-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed FrameTransformer body name collision when tracking bodies with the same name but different hierarchical paths + (e.g., Robot/left_hand vs Robot_1/left_hand). The sensor now uses the full prim path (with env_* patterns normalized) + as the unique body identifier instead of just the leaf body name. This ensures bodies at different hierarchy levels + are tracked separately. The change is backwards compatible: user-facing frame names still default to leaf names when + not explicitly provided, while internal body tracking uses full paths to avoid collisions. Works for both + environment-scoped paths (e.g., /World/envs/env_0/Robot) and non-environment paths (e.g., /World/Robot). + + +0.52.0 (2026-01-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :mod:`~isaaclab.sim.utils.transforms` module to handle USD Xform operations. +* Added passing of ``stage`` to the :func:`~isaaclab.sim.utils.prims.create_prim` function + inside spawning functions to allow for the creation of prims in a specific stage. + +Changed +^^^^^^^ + +* Changed :func:`~isaaclab.sim.utils.prims.create_prim` function to use the :mod:`~isaaclab.sim.utils.transforms` + module for USD Xform operations. It removes the usage of Isaac Sim's XformPrim class. + + +0.51.2 (2025-12-30) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :attr:`~isaaclab.managers.ObservationManager.get_active_iterable_terms` + to handle observation data when not concatenated along the last dimension. + + +0.51.1 (2025-12-29) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :func:`~isaaclab.utils.version.get_isaac_sim_version` to get the version of Isaac Sim. + This function caches the version of Isaac Sim and returns it immediately if it has already been computed. + This helps avoid parsing the VERSION file from disk multiple times. + +Changed +^^^^^^^ + +* Changed the function :meth:`~isaaclab.utils.version.compare_versions` to use :mod:`packaging.version.Version` module. +* Changed occurrences of :func:`isaacsim.core.version.get_version` to :func:`~isaaclab.utils.version.get_isaac_sim_version`. + +Removed +^^^^^^^ + +* Removed storing of Isaac Sim version inside the environment base classes defined inside + :mod:`isaaclab.envs` module. + + +0.51.0 (2025-12-29) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added tests for the :mod:`isaaclab.sim.utils.prims` module. +* Added tests for the :mod:`isaaclab.sim.utils.stage` module. +* Created :mod:`isaaclab.sim.utils.legacy` sub-module to keep deprecated functions. + +Removed +^^^^^^^ + +* Removed many unused USD prim and stage related operations from the :mod:`isaaclab.sim.utils` module. +* Moved :mod:`isaaclab.sim.utils.nucleus` sub-module to the ``tests/deps/isaacsim`` directory as it + is only being used for Isaac Sim check scripts. + +Changed +^^^^^^^ + +* Changed the organization of the :mod:`isaaclab.sim.utils` module to make it clearer which functions + are related to the stage and which are related to the prims. +* Modified imports of :mod:`~isaaclab.sim.utils.stage` and :mod:`~isaaclab.sim.utils.prims` modules + to only use the :mod:`isaaclab.sim.utils` module. +* Moved ``logger.py`` to the :mod:`isaaclab.utils` module. + + +0.50.7 (2025-12-29) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved ``pretrained_checkpoint.py`` to the :mod:`isaaclab_rl.utils` module. + + +0.50.6 (2025-12-18) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed issue where :meth:~isaaclab.envs.mdp.observations.body_pose_w` was modifying the original body pose data + when using slice or int for body_ids in the observation config. A clone of the data is now created to avoid modifying + the original data. + + +0.50.5 (2025-12-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.sensors.MultiMeshRayCaster` sensor to support tracking of dynamic meshes for ray-casting. + We keep the previous implementation of :class:`~isaaclab.sensors.RayCaster` for backwards compatibility. +* Added :mod:`isaaclab.utils.mesh` sub-module to perform various Trimesh and USD Mesh related operations. + + +0.50.4 (2025-12-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`~isaaclab.sim.PhysxCfg.enable_external_forces_every_iteration` to enable external forces every position + iteration. This can help improve the accuracy of velocity updates. Consider enabling this flag if the velocities + generated by the simulation are noisy. +* Added warning when :attr:`~isaaclab.sim.PhysxCfg.enable_external_forces_every_iteration` is set to False and + the solver type is TGS. + + +0.50.3 (2025-12-11) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed missing mesh collision approximation attribute when running :class:`~isaaclab.sim.converters.MeshConverter`. + The collision approximation attribute is now properly set on the USD prim when converting meshes with mesh collision + properties. + + +0.50.2 (2025-11-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Prevent randomizing mass to zero in :meth:`~isaaclab.envs.mdp.events.randomize_mass_by_scale` to avoid physics errors. + + +0.50.1 (2025-11-25) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed advanced indexing issue in resetting prev action + in :class:`~isaaclab.envs.mdp.actions.JointPositionToLimitsAction` . + + +0.50.0 (2025-12-8) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Implemented ability to attach an imu sensor to xform primitives in a usd file. This PR is based on work by '@GiulioRomualdi' + here: #3094 Addressing issue #3088. + + +0.49.3 (2025-12-03) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`G1TriHandUpperBodyMotionControllerGripperRetargeter` and :class:`G1TriHandUpperBodyMotionControllerGripperRetargeterCfg` for retargeting the gripper state from motion controllers. +* Added unit tests for the retargeters. + + +0.49.2 (2025-11-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`~isaaclab.sensors.contact_sensor.ContactSensorCfg.track_friction_forces` to toggle tracking of friction forces between sensor bodies and filtered bodies. +* Added :attr:`~isaaclab.sensors.contact_sensor.ContactSensorData.friction_forces_w` data field for tracking friction forces. + + +0.49.1 (2025-11-26) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed import from ``isaacsim.core.utils.prims`` to ``isaaclab.sim.utils.prims`` across repo to reduce IsaacLab dependencies. + +0.49.0 (2025-11-10) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Updated the URDF Importer version to 2.4.31 to avoid issues with merging joints on the latest URDF importer in Isaac Sim 5.1 + + +0.48.9 (2025-11-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Add navigation state API to IsaacLabManagerBasedRLMimicEnv +* Add optional custom recorder config to MimicEnvCfg + + +0.48.8 (2025-10-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`preserve_order` flag to :class:`~isaaclab.envs.mdp.actions.actions_cfg.JointPositionToLimitsActionCfg` + + +0.48.7 (2025-11-25) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed import from ``isaaclab.sim.utils`` to ``isaaclab.sim.utils.stage`` in ``isaaclab.devices.openxr.xr_anchor_utils.py`` + to properly propagate the Isaac Sim stage context. + + + + +0.48.6 (2025-11-18) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added OpenXR motion controller support for the G1 robot locomanipulation environment + ``Isaac-PickPlace-Locomanipulation-G1-Abs-v0``. This enables teleoperation using XR motion controllers + in addition to hand tracking. +* Added :class:`OpenXRDeviceMotionController` for motion controller-based teleoperation with headset anchoring control. +* Added motion controller-specific retargeters: + * :class:`G1TriHandControllerUpperBodyRetargeterCfg` for upper body and hand control using motion controllers. + * :class:`G1LowerBodyStandingControllerRetargeterCfg` for lower body control using motion controllers. + + +0.48.5 (2025-11-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed import from ``isaacsim.core.utils.stage`` to ``isaaclab.sim.utils.stage`` to reduce IsaacLab dependencies. + + +0.48.4 (2025-11-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Refactored modules related to the actuator configs in order to remediate a circular import necessary to support future + actuator drive model improvements. + + +0.48.3 (2025-11-13) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved retargeter and device declaration out of factory and into the devices/retargeters themselves. + + +0.48.2 (2025-11-13) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed from using :meth:`isaacsim.core.utils.torch.set_seed` to :meth:`~isaaclab.utils.seed.configure_seed` + + +0.48.1 (2025-11-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.devices.haply.HaplyDevice` class for SE(3) teleoperation with dual Haply Inverse3 and Versegrip devices, + supporting robot manipulation with haptic feedback. +* Added demo script ``scripts/demos/haply_teleoperation.py`` and documentation guide in + ``docs/source/how-to/haply_teleoperation.rst`` for Haply-based robot teleoperation. + + +0.48.0 (2025-11-03) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Detected contacts are reported with the threshold of 0.0 (instead of 1.0). This increases the sensitivity of contact + detection. + +Fixed +^^^^^ + +* Removed passing the boolean flag to :meth:`isaaclab.sim.schemas.activate_contact_sensors` when activating contact + sensors. This was incorrectly modifying the threshold attribute to 1.0 when contact sensors were activated. + + +0.47.11 (2025-11-03) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the bug where effort limits were being overridden in :class:`~isaaclab.actuators.ActuatorBase` when the ``effort_limit`` parameter is set to None. +* Corrected the unit tests for three effort limit scenarios with proper assertions + + +0.47.10 (2025-11-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``num_rerenders_on_reset`` parameter to ManagerBasedEnvCfg and DirectRLEnvCfg to configure the number + of render steps to perform after reset. This enables more control over DLSS rendering behavior after reset. + +Changed +^^^^^^^ + +* Added deprecation warning for ``rerender_on_reset`` parameter in ManagerBasedEnv and DirectRLEnv. + + +0.47.9 (2025-11-05) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Fixed termination term bookkeeping in :class:`~isaaclab.managers.TerminationManager`: + per-step termination and last-episode termination bookkeeping are now separated. + last-episode dones are now updated once per step from all term outputs, avoiding per-term overwrites + and ensuring Episode_Termination metrics reflect the actual triggering terms. + + +0.47.8 (2025-11-06) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added parameter :attr:`~isaaclab.terrains.TerrainImporterCfg.use_terrain_origins` to allow generated sub terrains with grid origins. + + +0.47.7 (2025-10-31) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed Pink IK controller qpsolver from osqp to daqp. +* Changed Null Space matrix computation in Pink IK's Null Space Posture Task to a faster matrix pseudo inverse computation. + + +0.47.6 (2025-11-01) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed an issue in recurrent policy evaluation in RSL-RL framework where the recurrent state was not reset after an episode termination. + + +0.47.5 (2025-10-30) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added docstrings notes to clarify the friction coefficient modeling in Isaac Sim 4.5 and 5.0. + + +0.47.4 (2025-10-30) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Enhanced :meth:`~isaaclab.managers.RecorderManager.export_episodes` method to support customizable sequence of demo IDs: + + - Added argument ``demo_ids`` to :meth:`~isaaclab.managers.RecorderManager.export_episodes` to accept a sequence of integers + for custom episode identifiers. + +* Enhanced :meth:`~isaaclab.utils.datasets.HDF5DatasetFileHandler.write_episode` method to support customizable episode identifiers: + + - Added argument ``demo_id`` to :meth:`~isaaclab.utils.datasets.HDF5DatasetFileHandler.write_episode` to accept a custom integer + for episode identifier. + + +0.47.3 (2025-10-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the data type conversion in :class:`~isaaclab.sensors.tiled_camera.TiledCamera` to + support the correct data type when converting from numpy arrays to warp arrays on the CPU. + + +0.47.2 (2025-10-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :meth:`~isaaclab.sim.utils.resolve_prim_pose` to resolve the pose of a prim with respect to another prim. +* Added :meth:`~isaaclab.sim.utils.resolve_prim_scale` to resolve the scale of a prim in the world frame. + + +0.47.1 (2025-10-17) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Suppressed yourdfpy warnings when trying to load meshes from hand urdfs in dex_retargeting. These mesh files are not + used by dex_retargeting, but the parser is incorrectly configured by dex_retargeting to load them anyway which results + in warning spam. + + +0.47.0 (2025-10-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed pickle utilities for saving and loading configurations as pickle contains security vulnerabilities in its APIs. + Configurations can continue to be saved and loaded through yaml. + + +0.46.11 (2025-10-15) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for modifying the :attr:`/rtx/domeLight/upperLowerStrategy` Sim rendering setting. + + +0.46.10 (2025-10-13) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ARM64 architecture for pink ik and dex-retargetting setup installations. + + +0.46.9 (2025-10-09) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`~isaaclab.devices.keyboard.se3_keyboard.Se3Keyboard.__del__` to use the correct method name + for unsubscribing from keyboard events "unsubscribe_to_keyboard_events" instead of "unsubscribe_from_keyboard_events". + + +0.46.8 (2025-10-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed scaling factor for retargeting of GR1T2 hand. + + +0.46.7 (2025-09-30) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed finger joint indices with manus extension. + + +0.46.6 (2025-09-30) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added argument :attr:`traverse_instance_prims` to :meth:`~isaaclab.sim.utils.get_all_matching_child_prims` and + :meth:`~isaaclab.sim.utils.get_first_matching_child_prim` to control whether to traverse instance prims + during the traversal. Earlier, instanced prims were skipped since :meth:`Usd.Prim.GetChildren` did not return + instanced prims, which is now fixed. + +Changed +^^^^^^^ + +* Made parsing of instanced prims in :meth:`~isaaclab.sim.utils.get_all_matching_child_prims` and + :meth:`~isaaclab.sim.utils.get_first_matching_child_prim` as the default behavior. +* Added parsing of instanced prims in :meth:`~isaaclab.sim.utils.make_uninstanceable` to make all prims uninstanceable. + + +0.46.5 (2025-10-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Exposed parameter :attr:`~isaaclab.sim.spawners.PhysxCfg.solve_articulation_contact_last` + to configure USD attribute ``physxscene:solveArticulationContactLast``. This parameter may + help improve solver stability with grippers, which previously required reducing simulation time-steps. + :class:`~isaaclab.sim.spawners.PhysxCfg` + + +0.46.4 (2025-10-06) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Fixed :attr:`~isaaclab.sim.simulation_context.SimulationContext.device` to return the device from the configuration. + Previously, it was returning the device from the simulation manager, which was causing a performance overhead. + + +0.46.3 (2025-09-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Modified setter to support for viscous and dynamic joint friction coefficients in articulation based on IsaacSim 5.0. +* Added randomization of viscous and dynamic joint friction coefficients in event term. + + +0.46.2 (2025-09-13) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Fixed missing actuator indices in :meth:`~isaaclab.envs.mdp.events.randomize_actuator_gains` + + +0.46.1 (2025-09-10) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved IO descriptors output directory to a subfolder under the task log directory. + + +0.46.0 (2025-09-06) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added argument :attr:`traverse_instance_prims` to :meth:`~isaaclab.sim.utils.get_all_matching_child_prims` and + :meth:`~isaaclab.sim.utils.get_first_matching_child_prim` to control whether to traverse instance prims + during the traversal. Earlier, instanced prims were skipped since :meth:`Usd.Prim.GetChildren` did not return + instanced prims, which is now fixed. + +Changed +^^^^^^^ + +* Made parsing of instanced prims in :meth:`~isaaclab.sim.utils.get_all_matching_child_prims` and + :meth:`~isaaclab.sim.utils.get_first_matching_child_prim` as the default behavior. +* Added parsing of instanced prims in :meth:`~isaaclab.sim.utils.make_uninstanceable` to make all prims uninstanceable. + + +0.45.16 (2025-09-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added teleoperation environments for Unitree G1. This includes an environment with lower body fixed and upper body + controlled by IK, and an environment with the lower body controlled by a policy and the upper body controlled by IK. + + +0.45.15 (2025-09-05) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added action terms for using RMPFlow in Manager-Based environments. + + +0.45.14 (2025-09-08) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.ui.xr_widgets.TeleopVisualizationManager` and :class:`~isaaclab.ui.xr_widgets.XRVisualization` + classes to provide real-time visualization of teleoperation and inverse kinematics status in XR environments. + + +0.45.13 (2025-09-08) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.devices.openxr.manus_vive.ManusVive` to support teleoperation with Manus gloves and Vive trackers. + + +0.45.12 (2025-09-05) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.envs.mdp.actions.SurfaceGripperBinaryAction` for supporting surface grippers in Manager-Based workflows. + +Changed +^^^^^^^ + +* Added AssetBase inheritance for :class:`~isaaclab.assets.surface_gripper.SurfaceGripper`. + + +0.45.11 (2025-09-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixes a high memory usage and perf slowdown issue in episode data by removing the use of torch.cat when appending to the episode data + at each timestep. The use of torch.cat was causing the episode data to be copied at each timestep, which causes high memory usage and + significant performance slowdown when recording longer episode data. +* Patches the configclass to allow validate dict with key is not a string. + +Added +^^^^^ + +* Added optional episode metadata (ep_meta) to be stored in the HDF5 data attributes. +* Added option to record data pre-physics step. +* Added joint_target data to episode data. Joint target data can be optionally recorded by the user and replayed to improve + determinism of replay. + + +0.45.10 (2025-09-02) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed regression in reach task configuration where the gripper command was being returned. +* Added :attr:`~isaaclab.devices.Se3GamepadCfg.gripper_term` to :class:`~isaaclab.devices.Se3GamepadCfg` + to control whether the gamepad device should return a gripper command. +* Added :attr:`~isaaclab.devices.Se3SpaceMouseCfg.gripper_term` to :class:`~isaaclab.devices.Se3SpaceMouseCfg` + to control whether the spacemouse device should return a gripper command. +* Added :attr:`~isaaclab.devices.Se3KeyboardCfg.gripper_term` to :class:`~isaaclab.devices.Se3KeyboardCfg` + to control whether the keyboard device should return a gripper command. + + +0.45.9 (2025-08-27) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed removing import of pink_ik controller from isaaclab.controllers which is causing pinocchio import error. + + +0.45.8 (2025-07-25) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Created :attr:`~isaaclab.controllers.pink_ik.PinkIKControllerCfg.target_eef_link_names` to :class:`~isaaclab.controllers.pink_ik.PinkIKControllerCfg` + to specify the target end-effector link names for the pink inverse kinematics controller. + +Changed +^^^^^^^ + +* Updated pink inverse kinematics controller configuration for the following tasks (Isaac-PickPlace-GR1T2, Isaac-NutPour-GR1T2, Isaac-ExhaustPipe-GR1T2) + to increase end-effector tracking accuracy and speed. Also added a null-space regularizer that enables turning on of waist degrees-of-freedom. +* Improved the test_pink_ik script to more comprehensive test on controller accuracy. Also, migrated to use pytest. With the current IK controller + improvements, our unit tests pass position and orientation accuracy test within **(1 mm, 1 degree)**. Previously, the position accuracy tolerances + were set to **(30 mm, 10 degrees)**. +* Included a new config parameter :attr:`fail_on_ik_error` to :class:`~isaaclab.controllers.pink_ik.PinkIKControllerCfg` + to control whether the IK controller raise an exception if robot joint limits are exceeded. In the case of an exception, the controller will hold the + last joint position. This adds to stability of the controller and avoids operator experiencing what is perceived as sudden large delays in robot control. + + +0.45.7 (2025-08-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added periodic logging when checking if a USD path exists on a Nucleus server + to improve user experience when the checks takes a while. + + +0.45.6 (2025-08-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`~isaaclab.envs.mdp.events.randomize_rigid_body_com` to broadcasts the environment ids. + + +0.45.5 (2025-08-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`~isaaclab.assets.Articulation.write_joint_friction_coefficient_to_sim` to set the friction coefficients in the simulation. +* Fixed :meth:`~isaaclab.assets.Articulation.write_joint_dynamic_friction_coefficient_to_sim` to set the friction coefficients in the simulation.* Added :meth:`~isaaclab.envs.ManagerBasedEnvCfg.export_io_descriptors` to toggle the export of the IO descriptors. +* Fixed :meth:`~isaaclab.assets.Articulation.write_joint_viscous_friction_coefficient_to_sim` to set the friction coefficients in the simulation. + + + +0.45.4 (2025-08-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added unit tests for :class:`~isaaclab.sensor.sensor_base` + + +0.45.3 (2025-08-20) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`isaaclab.envs.mdp.terminations.joint_effort_out_of_limit` so that it correctly reports whether a joint + effort limit has been violated. Previously, the implementation marked a violation when the applied and computed + torques were equal; in fact, equality should indicate no violation, and vice versa. + + +0.45.2 (2025-08-18) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :meth:`~isaaclab.managers.ObservationManager.get_IO_descriptors` to export the IO descriptors for the observation manager. +* Added :meth:`~isaaclab.envs.ManagerBasedEnvCfg.io_descriptors_output_dir` to configure the directory to export the IO descriptors to. +* Added :meth:`~isaaclab.envs.ManagerBasedEnvCfg.export_io_descriptors` to toggle the export of the IO descriptors. +* Added the option to export the Observation and Action of the managed environments into a YAML file. This can be used to more easily + deploy policies trained in Isaac Lab. + + +0.45.1 (2025-08-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added validations for scale-based randomization ranges across mass, actuator, joint, and tendon parameters. + +Changed +^^^^^^^ + +* Refactored randomization functions into classes with initialization-time checks to avoid runtime overhead. + + +0.45.0 (2025-08-07) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`~isaaclab.sensors.contact_sensor.ContactSensorCfg.track_contact_points` to toggle tracking of contact + point locations between sensor bodies and filtered bodies. +* Added :attr:`~isaaclab.sensors.contact_sensor.ContactSensorCfg.max_contact_data_per_prim` to configure the maximum + amount of contacts per sensor body. +* Added :attr:`~isaaclab.sensors.contact_sensor.ContactSensorData.contact_pos_w` data field for tracking contact point + locations. + + +0.44.12 (2025-08-12) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed IndexError in :meth:`isaaclab.envs.mdp.events.reset_joints_by_scale`, + :meth:`isaaclab.envs.mdp.events.reset_joints_by_offsets` by adding dimension to env_ids when indexing. + + +0.44.11 (2025-08-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed rendering preset mode when an experience CLI arg is provided. + + +0.44.10 (2025-08-06) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the old termination manager in :class:`~isaaclab.managers.TerminationManager` term_done logging that + logs the instantaneous term done count at reset. This let to inaccurate aggregation of termination count, + obscuring the what really happening during the training. Instead we log the episodic term done. + + +0.44.9 (2025-07-30) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``from __future__ import annotations`` to manager_based_rl_mimic_env.py to fix Sphinx + doc warnings for IsaacLab Mimic docs. + + +0.44.8 (2025-07-30) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Improved handling of deprecated flag :attr:`~isaaclab.sensors.RayCasterCfg.attach_yaw_only`. + Previously, the flag was only handled if it was set to True. This led to a bug where the yaw was not accounted for + when the flag was set to False. +* Fixed the handling of interval-based events inside :class:`~isaaclab.managers.EventManager` to properly handle + their resets. Previously, only class-based events were properly handled. + + +0.44.7 (2025-07-30) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new argument ``is_global`` to :meth:`~isaaclab.assets.Articulation.set_external_force_and_torque`, + :meth:`~isaaclab.assets.RigidObject.set_external_force_and_torque`, and + :meth:`~isaaclab.assets.RigidObjectCollection.set_external_force_and_torque` allowing to set external wrenches + in the global frame directly from the method call rather than having to set the frame in the configuration. + +Removed +^^^^^^^^ + +* Removed :attr:`xxx_external_wrench_frame` flag from asset configuration classes in favor of direct argument + passed to the :meth:`set_external_force_and_torque` function. + + +0.44.6 (2025-07-28) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Tweak default behavior for rendering preset modes. + + +0.44.5 (2025-07-28) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`isaaclab.scene.reset_to` to properly accept None as valid argument. +* Added tests to verify that argument types. + + +0.44.4 (2025-07-22) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added safe callbacks for stage in memory attaching. +* Remove on prim deletion callback workaround + + +0.44.3 (2025-07-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed rendering preset mode regression. + + +0.44.2 (2025-07-22) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated teleop scripts to print to console vs omni log. + + +0.44.1 (2025-07-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated test_pink_ik.py test case to pytest format. + + +0.44.0 (2025-07-21) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the way clipping is handled for :class:`~isaaclab.actuator.DCMotor` for torque-speed points in when in + negative power regions. + +Added +^^^^^ + +* Added unit tests for :class:`~isaaclab.actuator.ImplicitActuator`, :class:`~isaaclab.actuator.IdealPDActuator`, + and :class:`~isaaclab.actuator.DCMotor` independent of :class:`~isaaclab.assets.Articulation` + + +0.43.0 (2025-07-21) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updates torch version to 2.7.0 and torchvision to 0.22.0. + Some dependencies now require torch>=2.6, and given the vulnerabilities in Torch 2.5.1, + we are updating the torch version to 2.7.0 to also include Blackwell support. Since Isaac Sim 4.5 has not updated the + torch version, we are now overwriting the torch installation step in isaaclab.sh when running ``./isaaclab.sh -i``. + + +0.42.26 (2025-06-29) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added MangerBasedRLEnv support for composite gym observation spaces. +* A test for the composite gym observation spaces in ManagerBasedRLEnv is added to ensure that the observation spaces + are correctly configured base on the clip. + + +0.42.25 (2025-07-11) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`~isaaclab.sensors.ContactSensorData.force_matrix_w_history` that tracks the history of the filtered + contact forces in the world frame. + + +0.42.24 (2025-06-25) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added new curriculum mdp :func:`~isaaclab.envs.mdp.curriculums.modify_env_param` and + :func:`~isaaclab.envs.mdp.curriculums.modify_env_param` that enables flexible changes to any configurations in the + env instance + + +0.42.23 (2025-07-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`isaaclab.envs.mdp.events.reset_joints_by_scale`, :meth:`isaaclab.envs.mdp.events.reset_joints_by_offsets` + restricting the resetting joint indices be that user defined joint indices. + + +0.42.22 (2025-07-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed missing attribute in :class:`~isaaclab.sensors.ray_caster.RayCasterCamera` class and its reset method when no + env_ids are passed. + + +0.42.21 (2025-07-09) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added input param ``update_history`` to :meth:`~isaaclab.managers.ObservationManager.compute` + to control whether the history buffer should be updated. +* Added unit test for :class:`~isaaclab.envs.ManagerBasedEnv`. + +Fixed +^^^^^ + +* Fixed :class:`~isaaclab.envs.ManagerBasedEnv` and :class:`~isaaclab.envs.ManagerBasedRLEnv` to not update the history + buffer on recording. + + +0.42.20 (2025-07-10) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added unit tests for multiple math functions: + :func:`~isaaclab.utils.math.scale_transform`. + :func:`~isaaclab.utils.math.unscale_transform`. + :func:`~isaaclab.utils.math.saturate`. + :func:`~isaaclab.utils.math.normalize`. + :func:`~isaaclab.utils.math.copysign`. + :func:`~isaaclab.utils.math.convert_quat`. + :func:`~isaaclab.utils.math.quat_conjugate`. + :func:`~isaaclab.utils.math.quat_from_euler_xyz`. + :func:`~isaaclab.utils.math.quat_from_matrix`. + :func:`~isaaclab.utils.math.euler_xyz_from_quat`. + :func:`~isaaclab.utils.math.matrix_from_euler`. + :func:`~isaaclab.utils.math.quat_from_angle_axis`. + :func:`~isaaclab.utils.math.axis_angle_from_quat`. + :func:`~isaaclab.utils.math.skew_symmetric_matrix`. + :func:`~isaaclab.utils.math.combine_transform`. + :func:`~isaaclab.utils.math.subtract_transform`. + :func:`~isaaclab.utils.math.compute_pose_error`. + +Changed +^^^^^^^ + +* Changed the implementation of :func:`~isaaclab.utils.math.copysign` to better reflect the documented functionality. + + +0.42.19 (2025-07-09) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added clone_in_fabric config flag to :class:`~isaaclab.scene.interactive_scene_cfg.InteractiveSceneCfg` +* Enable clone_in_fabric for envs which work with limited benchmark_non_rl.py script + + +0.42.18 (2025-07-07) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed texture and color randomization to use new replicator functional APIs. + + +0.42.17 (2025-07-08) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed hanging quat_rotate calls to point to quat_apply in :class:`~isaaclab.assets.articulation.ArticulationData` and + :class:`~isaaclab.assets.articulation.RigidObjectCollectionData` + + +0.42.16 (2025-07-08) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ability to set platform height independent of object height for trimesh terrains. + + +0.42.15 (2025-07-01) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`abs_height_noise` and :attr:`rel_height_noise` to give minimum and maximum absolute and relative noise to + :class:`isaaclab.terrrains.trimesh.MeshRepeatedObjectsTerrainCfg` +* Added deprecation warnings to the existing :attr:`max_height_noise` but still functions. + + +0.42.14 (2025-07-03) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed unittest tests that are floating inside pytests for articulation and rendering + + +0.42.13 (2025-07-07) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated gymnasium to v1.2.0. This update includes fixes for a memory leak that appears when recording + videos with the ``--video`` flag. + + +0.42.12 (2025-06-27) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added unit test for :func:`~isaaclab.utils.math.quat_inv`. + +Fixed +^^^^^ + +* Fixed the implementation mistake in :func:`~isaaclab.utils.math.quat_inv`. + + +0.42.11 (2025-06-25) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :func:`~isaaclab.utils.dict.update_class_from_dict` preventing setting flat Iterables with different lengths. + + +0.42.10 (2025-06-25) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``sample_bias_per_component`` flag to :class:`~isaaclab.utils.noise.noise_model.NoiseModelWithAdditiveBias` + to enable independent per-component bias sampling, which is now the default behavior. If set to False, the previous + behavior of sharing the same bias value across all components is retained. + + +0.42.9 (2025-06-18) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed data inconsistency between read_body, read_link, read_com when write_body, write_com, write_joint performed, in + :class:`~isaaclab.assets.Articulation`, :class:`~isaaclab.assets.RigidObject`, and + :class:`~isaaclab.assets.RigidObjectCollection` +* added pytest that check against these data consistencies + + +0.42.8 (2025-06-24) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* :class:`~isaaclab.utils.noise.NoiseModel` support for manager-based workflows. + +Changed +^^^^^^^ + +* Renamed :func:`~isaaclab.utils.noise.NoiseModel.apply` method to :func:`~isaaclab.utils.noise.NoiseModel.__call__`. + + +0.42.7 (2025-06-12) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed potential issues in :func:`~isaaclab.envs.mdp.events.randomize_visual_texture_material` related to handling + visual prims during texture randomization. + + +0.42.6 (2025-06-11) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Remove deprecated usage of quat_rotate from articulation data class and replace with quat_apply. + + +0.42.5 (2025-05-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed collision filtering logic for CPU simulation. The automatic collision filtering feature + currently has limitations for CPU simulation. Collision filtering needs to be manually enabled when using + CPU simulation. + + +0.42.4 (2025-06-03) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removes the hardcoding to :class:`~isaaclab.terrains.terrain_generator.TerrainGenerator` in + :class:`~isaaclab.terrains.terrain_generator.TerrainImporter` and instead the ``class_type`` is used which is + passed in the ``TerrainGeneratorCfg``. + + +0.42.3 (2025-03-20) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Made separate data buffers for poses and velocities for the :class:`~isaaclab.assets.Articulation`, + :class:`~isaaclab.assets.RigidObject`, and :class:`~isaaclab.assets.RigidObjectCollection` classes. + Previously, the two data buffers were stored together in a single buffer requiring an additional + concatenation operation when accessing the data. +* Cleaned up ordering of members inside the data classes for the assets to make them easier + to comprehend. This reduced the code duplication within the class and made the class + more readable. + + +0.42.2 (2025-05-31) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Updated gymnasium to >= 1.0 +* Added support for specifying module:task_name as task name to avoid module import for ``gym.make`` + + +0.42.1 (2025-06-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added time observation functions to ~isaaclab.envs.mdp.observations module, + :func:`~isaaclab.envs.mdp.observations.current_time_s` and :func:`~isaaclab.envs.mdp.observations.remaining_time_s`. + +Changed +^^^^^^^ + +* Moved initialization of ``episode_length_buf`` outside of :meth:`load_managers()` of + :class:`~isaaclab.envs.ManagerBasedRLEnv` to make it available for mdp functions. + + +0.42.0 (2025-06-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for stage in memory and cloning in fabric. This will help improve performance for scene setup and lower + overall startup time. + + +0.41.0 (2025-05-19) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added simulation schemas for spatial tendons. These can be configured for assets imported + from file formats. +* Added support for spatial tendons. + + +0.40.14 (2025-05-29) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added deprecation warning for :meth:`~isaaclab.utils.math.quat_rotate` and + :meth:`~isaaclab.utils.math.quat_rotate_inverse` + +Changed +^^^^^^^ + +* Changed all calls to :meth:`~isaaclab.utils.math.quat_rotate` and :meth:`~isaaclab.utils.math.quat_rotate_inverse` to + :meth:`~isaaclab.utils.math.quat_apply` and :meth:`~isaaclab.utils.math.quat_apply_inverse` for speed. + + +0.40.13 (2025-05-19) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Raising exceptions in step, render and reset if they occurred inside the initialization callbacks + of assets and sensors.used from the experience files and the double definition is removed. + + +0.40.12 (2025-01-30) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added method :meth:`omni.isaac.lab.assets.AssetBase.set_visibility` to set the visibility of the asset + in the simulation. + + +0.40.11 (2025-05-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for concatenation of observations along different dimensions in + :class:`~isaaclab.managers.observation_manager.ObservationManager`. + +Changed +^^^^^^^ + +* Updated the :class:`~isaaclab.managers.command_manager.CommandManager` to update the command counter after the + resampling call. + + +0.40.10 (2025-05-16) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed penetration issue for negative border height in :class:`~isaaclab.terrains.terrain_generator.TerrainGeneratorCfg`. + + +0.40.9 (2025-05-20) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the implementation of :meth:`~isaaclab.utils.math.quat_box_minus` + +Added +^^^^^ + +* Added :meth:`~isaaclab.utils.math.quat_box_plus` +* Added :meth:`~isaaclab.utils.math.rigid_body_twist_transform` + + +0.40.8 (2025-05-15) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`omni.isaac.lab.sensors.camera.camera.Camera.set_intrinsic_matrices` preventing setting of unused USD + camera parameters. +* Fixed :meth:`omni.isaac.lab.sensors.camera.camera.Camera._update_intrinsic_matrices` preventing unused USD camera + parameters from being used to calculate :attr:`omni.isaac.lab.sensors.camera.CameraData.intrinsic_matrices` +* Fixed :meth:`omni.isaac.lab.spawners.sensors.sensors_cfg.PinholeCameraCfg.from_intrinsic_matrix` preventing setting of + unused USD camera parameters. + + +0.40.7 (2025-05-14) +~~~~~~~~~~~~~~~~~~~ + +* Added a new attribute :attr:`articulation_root_prim_path` to the :class:`~isaaclab.assets.ArticulationCfg` class + to allow explicitly specifying the prim path of the articulation root. + + +0.40.6 (2025-05-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Enabled external cameras in XR. + + +0.40.5 (2025-05-23) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added feature for animation recording through baking physics operations into OVD files. + + +0.40.4 (2025-05-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed livestreaming options to use ``LIVESTREAM=1`` for WebRTC over public networks and ``LIVESTREAM=2`` for WebRTC over private networks. + + +0.40.3 (2025-05-20) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Made modifications to :func:`isaaclab.envs.mdp.image` to handle image normalization for normal maps. + + +0.40.2 (2025-05-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Refactored remove_camera_configs to be a function that can be used in the record_demos and teleop scripts. + + +0.40.1 (2025-05-14) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed spacemouse device add callback function to work with record_demos/teleop_se3_agent scripts. + + +0.40.0 (2025-05-03) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added check in RecorderManager to ensure that the success indicator is only set if the termination manager is present. +* Added semantic tags in :func:`isaaclab.sim.spawners.from_files.spawn_ground_plane`. + This allows for :attr:`semantic_segmentation_mapping` to be used when using the ground plane spawner. + + +0.39.0 (2025-04-01) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :meth:`~isaaclab.env.mdp.observations.joint_effort` + + +0.38.0 (2025-04-01) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :meth:`~isaaclab.envs.mdp.observations.body_pose_w` +* Added :meth:`~isaaclab.envs.mdp.observations.body_projected_gravity_b` + + +0.37.5 (2025-05-12) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new teleop configuration class :class:`~isaaclab.devices.DevicesCfg` to support multiple teleoperation + devices declared in the environment configuration file. +* Implemented a factory function to create teleoperation devices based on the device configuration. + + +0.37.4 (2025-05-12) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Remove isaacsim.xr.openxr from openxr experience file. +* Use Performance AR profile for XR rendering. + + +0.37.3 (2025-05-08) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Updated PINK task space action to record processed actions. +* Added new recorder term for recording post step processed actions. + + +0.37.2 (2025-05-06) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Migrated OpenXR device to use the new OpenXR handtracking API from omni.kit.xr.core. + + +0.37.1 (2025-05-05) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed xr rendering mode. + + +0.37.0 (2025-04-24) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated pytorch to latest 2.7.0 with cuda 12.8 for Blackwell support. + Torch is now installed as part of the isaaclab.sh/bat scripts to ensure the correct version is installed. +* Removed :attr:`~isaaclab.sim.spawners.PhysicsMaterialCfg.improve_patch_friction` as it has been deprecated and removed from the simulation. + The simulation will always behave as if this attribute is set to true. + + +0.36.23 (2025-04-24) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed ``return_latest_camera_pose`` option in :class:`~isaaclab.sensors.TiledCameraCfg` from not being used to the + argument ``update_latest_camera_pose`` in :class:`~isaaclab.sensors.CameraCfg` with application in both + :class:`~isaaclab.sensors.Camera` and :class:`~isaaclab.sensors.TiledCamera`. + + +0.36.22 (2025-04-23) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^^^ + +* Adds correct type check for ManagerTermBase class in event_manager.py. + + +0.36.21 (2025-04-15) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed direct call of qpsovlers library from pink_ik controller and changed solver from quadprog to osqp. + + +0.36.20 (2025-04-09) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added call to set cuda device after each ``app.update()`` call in :class:`~isaaclab.sim.SimulationContext`. + This is now required for multi-GPU workflows because some underlying logic in ``app.update()`` is modifying + the cuda device, which results in NCCL errors on distributed setups. + + +0.36.19 (2025-04-01) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added check in RecorderManager to ensure that the success indicator is only set if the termination manager is present. + + +0.36.18 (2025-03-26) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a dynamic text instruction widget that provides real-time feedback + on the number of successful recordings during demonstration sessions. + + +0.36.17 (2025-03-26) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added override in AppLauncher to apply patch for ``pxr.Gf.Matrix4d`` to work with Pinocchio 2.7.0. + + +0.36.16 (2025-03-25) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified rendering mode default behavior when the launcher arg :attr:`enable_cameras` is not set. + + +0.36.15 (2025-03-25) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added near plane distance configuration for XR device. + + +0.36.14 (2025-03-24) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed default render settings in :class:`~isaaclab.sim.SimulationCfg` to None, which means that + the default settings will be used from the experience files and the double definition is removed. + + +0.36.13 (2025-03-24) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added headpose support to OpenXRDevice. + + +0.36.12 (2025-03-19) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added parameter to show warning if Pink IK solver fails to find a solution. + + +0.36.11 (2025-03-19) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed default behavior of :class:`~isaaclab.actuators.ImplicitActuator` if no :attr:`effort_limits_sim` or + :attr:`effort_limit` is set. + + +0.36.10 (2025-03-17) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* App launcher to update the cli arguments if conditional defaults are used. + + +0.36.9 (2025-03-18) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^^ + +* Xr rendering mode, which is default when xr is used. + + +0.36.8 (2025-03-17) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Removed ``scalar_first`` from scipy function usage to support older versions of scipy. + + +0.36.7 (2025-03-14) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Changed the import structure to only import ``pinocchio`` when ``pink-ik`` or ``dex-retargeting`` is being used. + This also solves for the problem that ``pink-ik`` and ``dex-retargeting`` are not supported in windows. +* Removed ``isaacsim.robot_motion.lula`` and ``isaacsim.robot_motion.motion_generation`` from the default loaded Isaac Sim extensions. +* Moved pink ik action config to a separate file. + + +0.36.6 (2025-03-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Worked around an issue where the render mode is set to ``"RayTracedLighting"`` instead of ``"RaytracedLighting"`` by + some dependencies. + + +0.36.5 (2025-03-11) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^^ + +* Added 3 rendering mode presets: performance, balanced, and quality. +* Preset settings are stored in ``apps/rendering_modes``. +* Presets can be set with cli arg ``--rendering_mode`` or with :class:`RenderCfg`. +* Preset rendering settings can be overwritten with :class:`RenderCfg`. +* :class:`RenderCfg` supports all native RTX carb settings. + +Changed +^^^^^^^ +* :class:`RenderCfg` default settings are unset. + + +0.36.4 (2025-03-11) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated the OpenXR kit file ``isaaclab.python.xr.openxr.kit`` to inherit from ``isaaclab.python.kit`` instead of + ``isaaclab.python.rendering.kit`` which is not appropriate. + + +0.36.3 (2025-03-10) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added the PinkIKController controller class that interfaces Isaac Lab with the Pink differential inverse kinematics solver + to allow control of multiple links in a robot using a single solver. + + +0.36.2 (2025-03-07) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Allowed users to exit on 1 Ctrl+C instead of consecutive 2 key strokes. +* Allowed physics reset during simulation through :meth:`reset` in :class:`~isaaclab.sim.SimulationContext`. + + +0.36.1 (2025-03-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`semantic_segmentation_mapping` for camera configs to allow specifying colors for semantics. + + +0.36.0 (2025-03-07) +~~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Removed the storage of tri-meshes and warp meshes inside the :class:`~isaaclab.terrains.TerrainImporter` class. + Initially these meshes were added for ray-casting purposes. However, since the ray-caster reads the terrains + directly from the USD files, these meshes are no longer needed. +* Deprecated the :attr:`warp_meshes` and :attr:`meshes` attributes from the + :class:`~isaaclab.terrains.TerrainImporter` class. These attributes now return an empty dictionary + with a deprecation warning. + +Changed +^^^^^^^ + +* Changed the prim path of the "plane" terrain inside the :class:`~isaaclab.terrains.TerrainImporter` class. + Earlier, the terrain was imported directly as the importer's prim path. Now, the terrain is imported as + ``{importer_prim_path}/{name}``, where ``name`` is the name of the terrain. + + +0.35.0 (2025-03-07) +~~~~~~~~~~~~~~~~~~~ + +* Improved documentation of various attributes in the :class:`~isaaclab.assets.ArticulationData` class to make + it clearer which values represent the simulation and internal class values. In the new convention, + the ``default_xxx`` attributes are whatever the user configured from their configuration of the articulation + class, while the ``xxx`` attributes are the values from the simulation. +* Updated the soft joint position limits inside the :meth:`~isaaclab.assets.Articulation.write_joint_pos_limits_to_sim` + method to use the new limits passed to the function. +* Added setting of :attr:`~isaaclab.assets.ArticulationData.default_joint_armature` and + :attr:`~isaaclab.assets.ArticulationData.default_joint_friction` attributes in the + :class:`~isaaclab.assets.Articulation` class based on user configuration. + +Changed +^^^^^^^ + +* Removed unnecessary buffer creation operations inside the :class:`~isaaclab.assets.Articulation` class. + Earlier, the class initialized a variety of buffer data with zeros and in the next function assigned + them the value from PhysX. This made the code bulkier and more complex for no reason. +* Renamed parameters for a consistent nomenclature. These changes are backwards compatible with previous releases + with a deprecation warning for the old names. + + * ``joint_velocity_limits`` → ``joint_vel_limits`` (to match attribute ``joint_vel`` and ``joint_vel_limits``) + * ``joint_limits`` → ``joint_pos_limits`` (to match attribute ``joint_pos`` and ``soft_joint_pos_limits``) + * ``default_joint_limits`` → ``default_joint_pos_limits`` + * ``write_joint_limits_to_sim`` → ``write_joint_position_limit_to_sim`` + * ``joint_friction`` → ``joint_friction_coeff`` + * ``default_joint_friction`` → ``default_joint_friction_coeff`` + * ``write_joint_friction_to_sim`` → ``write_joint_friction_coefficient_to_sim`` + * ``fixed_tendon_limit`` → ``fixed_tendon_pos_limits`` + * ``default_fixed_tendon_limit`` → ``default_fixed_tendon_pos_limits`` + * ``set_fixed_tendon_limit`` → ``set_fixed_tendon_position_limit`` + + +0.34.13 (2025-03-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new event mode called "prestartup", which gets called right after the scene design is complete + and before the simulation is played. +* Added a callback to resolve the scene entity configurations separately once the simulation plays, + since the scene entities cannot be resolved before the simulation starts playing + (as we currently rely on PhysX to provide us with the joint/body ordering) + + +0.34.12 (2025-03-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Updated the mimic API :meth:`target_eef_pose_to_action` in :class:`isaaclab.envs.ManagerBasedRLMimicEnv` to take a dictionary of + eef noise values instead of a single noise value. +* Added support for optional subtask constraints based on DexMimicGen to the mimic configuration class :class:`isaaclab.envs.MimicEnvCfg`. +* Enabled data compression in HDF5 dataset file handler :class:`isaaclab.utils.datasets.hdf5_dataset_file_handler.HDF5DatasetFileHandler`. + + +0.34.11 (2025-03-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed issue in :class:`~isaaclab.sensors.TiledCamera` and :class:`~isaaclab.sensors.Camera` where segmentation outputs only display the first tile + when scene instancing is enabled. A workaround is added for now to disable instancing when segmentation + outputs are requested. + + +0.34.10 (2025-03-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the issue of misalignment in the motion vectors from the :class:`TiledCamera` + with other modalities such as RGBA and depth. + + +0.34.9 (2025-03-04) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added methods inside the :class:`omni.isaac.lab.assets.Articulation` class to set the joint + position and velocity for the articulation. Previously, the joint position and velocity could + only be set using the :meth:`omni.isaac.lab.assets.Articulation.write_joint_state_to_sim` method, + which didn't allow setting the joint position and velocity separately. + + +0.34.8 (2025-03-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the propagation of the :attr:`activate_contact_sensors` attribute to the + :class:`~isaaclab.sim.spawners.wrappers.wrappers_cfg.MultiAssetSpawnerCfg` class. Previously, this value + was always set to False, which led to incorrect contact sensor settings for the spawned assets. + + +0.34.7 (2025-03-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Enabled the physics flag for disabling contact processing in the :class:`~isaaclab.sim.SimulationContact` + class. This means that by default, no contact reporting is done by the physics engine, which should provide + a performance boost in simulations with no contact processing requirements. +* Disabled the physics flag for disabling contact processing in the :class:`~isaaclab.sensors.ContactSensor` + class when the sensor is created to allow contact reporting for the sensor. + +Removed +^^^^^^^ + +* Removed the attribute ``disable_contact_processing`` from :class:`~isaaclab.sim.SimulationContact`. + + +0.34.6 (2025-03-01) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new attribute :attr:`is_implicit_model` to the :class:`isaaclab.actuators.ActuatorBase` class to + indicate if the actuator model is implicit or explicit. This helps checking that the correct model type + is being used when initializing the actuator models. + +Fixed +^^^^^ + +* Added copy of configurations to :class:`~isaaclab.assets.AssetBase` and :class:`~isaaclab.sensors.SensorBase` + to prevent modifications of the configurations from leaking outside of the classes. +* Fixed the case where setting velocity/effort limits for the simulation in the + :class:`~isaaclab.actuators.ActuatorBaseCfg` class was not being used to update the actuator-specific + velocity/effort limits. + +Changed +^^^^^^^ + +* Moved warnings and checks for implicit actuator models to the :class:`~isaaclab.actuators.ImplicitActuator` class. +* Reverted to IsaacLab v1.3 behavior where :attr:`isaaclab.actuators.ImplicitActuatorCfg.velocity_limit` + attribute was not used for setting the velocity limits in the simulation. This makes it possible to deploy + policies from previous release without any changes. If users want to set the velocity limits for the simulation, + they should use the :attr:`isaaclab.actuators.ImplicitActuatorCfg.velocity_limit_sim` attribute instead. + + +0.34.5 (2025-02-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added IP address support for WebRTC livestream to allow specifying IP address to stream across networks. + This feature requires an updated livestream extension, which is current only available in the pre-built Isaac Lab 2.0.1 docker image. + Support for other Isaac Sim builds will become available in Isaac Sim 5.0. + + +0.34.4 (2025-02-27) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Refactored retargeting code from Se3Handtracking class into separate modules for better modularity +* Added scaffolding for developing additional retargeters (e.g. dex) + + +0.34.3 (2025-02-26) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Enablec specifying the placement of the simulation when viewed in an XR device. This is achieved by + adding an ``XrCfg`` environment configuration with ``anchor_pos`` and ``anchor_rot`` parameters. + + +0.34.2 (2025-02-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed setting of root velocities inside the event term :meth:`reset_root_state_from_terrain`. Earlier, the indexing + based on the environment IDs was missing. + + +0.34.1 (2025-02-17) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Ensured that the loaded torch JIT models inside actuator networks are correctly set to eval mode + to prevent any unexpected behavior during inference. + + +0.34.0 (2025-02-14) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added attributes :attr:`velocity_limits_sim` and :attr:`effort_limits_sim` to the + :class:`isaaclab.actuators.ActuatorBaseCfg` class to separate solver limits from actuator limits. + + +0.33.17 (2025-02-13) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed Imu sensor based observations at first step by updating scene during initialization for + :class:`~isaaclab.envs.ManagerBasedEnv`, :class:`~isaaclab.envs.DirectRLEnv`, and :class:`~isaaclab.envs.DirectMARLEnv` + + +0.33.16 (2025-02-09) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Removes old deprecation warning from :attr:`isaaclab.assets.RigidObectData.body_state_w` + + +0.33.15 (2025-02-09) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed not updating the ``drift`` when calling :func:`~isaaclab.sensors.RayCaster.reset` + + +0.33.14 (2025-02-01) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed not updating the timestamp of ``body_link_state_w`` and ``body_com_state_w`` when ``write_root_pose_to_sim`` and ``write_joint_state_to_sim`` in the ``Articulation`` class are called. + + +0.33.13 (2025-01-30) +~~~~~~~~~~~~~~~~~~~~ + +* Fixed resampling of interval time left for the next event in the :class:`~isaaclab.managers.EventManager` + class. Earlier, the time left for interval-based events was not being resampled on episodic resets. This led + to the event being triggered at the wrong time after the reset. + + +0.33.12 (2025-01-28) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed missing import in ``line_plot.py`` + + +0.33.11 (2025-01-25) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`isaaclab.scene.InteractiveSceneCfg.filter_collisions` to allow specifying whether collision masking across environments is desired. + +Changed +^^^^^^^ + +* Automatic collision filtering now happens as part of the replicate_physics call. When replicate_physics is not enabled, we call the previous + ``filter_collisions`` API to mask collisions between environments. + + +0.33.10 (2025-01-22) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* In :meth:`isaaclab.assets.Articulation.write_joint_limits_to_sim`, we previously added a check for if default joint positions exceed the + new limits being set. When this is True, we log a warning message to indicate that the default joint positions will be clipped to be within + the range of the new limits. However, the warning message can become overly verbose in a randomization setting where this API is called on + every environment reset. We now default to only writing the message to info level logging if called within randomization, and expose a + parameter that can be used to choose the logging level desired. + + +0.33.9 (2025-01-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed typo in /physics/autoPopupSimulationOutputWindow setting in :class:`~isaaclab.sim.SimulationContext` + + +0.33.8 (2025-01-17) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Removed deprecation of :attr:`isaaclab.assets.ArticulationData.root_state_w` and + :attr:`isaaclab.assets.ArticulationData.body_state_w` derived properties. +* Removed deprecation of :meth:`isaaclab.assets.Articulation.write_root_state_to_sim`. +* Replaced calls to :attr:`isaaclab.assets.ArticulationData.root_com_state_w` and + :attr:`isaaclab.assets.ArticulationData.root_link_state_w` with corresponding calls to + :attr:`isaaclab.assets.ArticulationData.root_state_w`. +* Replaced calls to :attr:`isaaclab.assets.ArticulationData.body_com_state_w` and + :attr:`isaaclab.assets.ArticulationData.body_link_state_w` properties with corresponding calls to + :attr:`isaaclab.assets.ArticulationData.body_state_w` properties. +* Removed deprecation of :attr:`isaaclab.assets.RigidObjectData.root_state_w` derived properties. +* Removed deprecation of :meth:`isaaclab.assets.RigidObject.write_root_state_to_sim`. +* Replaced calls to :attr:`isaaclab.assets.RigidObjectData.root_com_state_w` and + :attr:`isaaclab.assets.RigidObjectData.root_link_state_w` properties with corresponding calls to + :attr:`isaaclab.assets.RigidObjectData.root_state_w` properties. +* Removed deprecation of :attr:`isaaclab.assets.RigidObjectCollectionData.root_state_w` derived properties. +* Removed deprecation of :meth:`isaaclab.assets.RigidObjectCollection.write_root_state_to_sim`. +* Replaced calls to :attr:`isaaclab.assets.RigidObjectCollectionData.root_com_state_w` and + :attr:`isaaclab.assets.RigidObjectData.root_link_state_w` properties with corresponding calls to + :attr:`isaaclab.assets.RigidObjectData.root_state_w` properties. +* Fixed indexing issue in ``write_root_link_velocity_to_sim`` in :class:`isaaclab.assets.RigidObject` +* Fixed index broadcasting in ``write_object_link_velocity_to_sim`` and ``write_object_com_pose_to_sim`` in + the :class:`isaaclab.assets.RigidObjectCollection` class. + + +0.33.7 (2025-01-14) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the respawn of only wrong object samples in :func:`repeated_objects_terrain` of :mod:`isaaclab.terrains.trimesh` module. + Previously, the function was respawning all objects in the scene instead of only the wrong object samples, which in worst case + could lead to infinite respawn loop. + + +0.33.6 (2025-01-16) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added initial unit tests for multiple tiled cameras, including tests for initialization, groundtruth annotators, different poses, and different resolutions. + + +0.33.5 (2025-01-13) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the definition of ``/persistent/isaac/asset_root/*`` settings from :class:`AppLauncher` to the app files. + This is needed to prevent errors where ``isaaclab_assets`` was loaded prior to the carbonite setting being set. + + +0.33.4 (2025-01-10) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added an optional parameter in the :meth:`record_pre_reset` method in + :class:`~isaaclab.managers.RecorderManager` to override the export config upon invoking. + + +0.33.3 (2025-01-08) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed docstring in articulation data :class:`isaaclab.assets.ArticulationData`. + In body properties sections, the second dimension should be num_bodies but was documented as 1. + + +0.33.2 (2025-01-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added body tracking as an origin type to :class:`isaaclab.envs.ViewerCfg` and :class:`isaaclab.envs.ui.ViewportCameraController`. + + +0.33.1 (2024-12-26) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added kinematics initialization call for populating kinematic prim transforms to fabric for rendering. +* Added ``enable_env_ids`` flag for cloning and replication to replace collision filtering. + + +0.33.0 (2024-12-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed populating default_joint_stiffness and default_joint_damping values for ImplicitActuator instances in :class:`isaaclab.assets.Articulation` + + +0.32.2 (2024-12-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added null-space (position) control option to :class:`isaaclab.controllers.OperationalSpaceController`. +* Added test cases that uses null-space control for :class:`isaaclab.controllers.OperationalSpaceController`. +* Added information regarding null-space control to the tutorial script and documentation of + :class:`isaaclab.controllers.OperationalSpaceController`. +* Added arguments to set specific null-space joint position targets within + :class:`isaaclab.envs.mdp.actions.OperationalSpaceControllerAction` class. + + +0.32.1 (2024-12-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added a default and generic implementation of the :meth:`get_object_poses` function + in the :class:`ManagerBasedRLMimicEnv` class. +* Added a ``EXPORT_NONE`` mode in the :class:`DatasetExportMode` class and updated + :class:`~isaaclab.managers.RecorderManager` to enable recording without exporting + the data to a file. + + +0.32.0 (2024-12-16) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Previously, physx returns the rigid bodies and articulations velocities in the com of bodies rather than the + link frame, while poses are in link frames. We now explicitly provide :attr:`body_link_state` and + :attr:`body_com_state` APIs replacing the previous :attr:`body_state` API. Previous APIs are now marked as + deprecated. Please update any code using the previous pose and velocity APIs to use the new + ``*_link_*`` or ``*_com_*`` APIs in :attr:`isaaclab.assets.RigidBody`, + :attr:`isaaclab.assets.RigidBodyCollection`, and :attr:`isaaclab.assets.Articulation`. + + +0.31.0 (2024-12-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`ManagerBasedRLMimicEnv` and config classes for mimic data generation workflow for imitation learning. + + +0.30.3 (2024-12-16) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed ordering of logging and resamping in the command manager, where we were logging the metrics + after resampling the commands. This leads to incorrect logging of metrics when inside the resample call, + the metrics tensors get reset. + + +0.30.2 (2024-12-16) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed errors within the calculations of :class:`isaaclab.controllers.OperationalSpaceController`. + +Added +^^^^^ + +* Added :class:`isaaclab.controllers.OperationalSpaceController` to API documentation. +* Added test cases for :class:`isaaclab.controllers.OperationalSpaceController`. +* Added a tutorial for :class:`isaaclab.controllers.OperationalSpaceController`. +* Added the implementation of :class:`isaaclab.envs.mdp.actions.OperationalSpaceControllerAction` class. + + +0.30.1 (2024-12-15) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added call to update articulation kinematics after reset to ensure states are updated for non-rendering sensors. + Previously, some changes in reset such as modifying joint states would not be reflected in the rigid body + states immediately after reset. + + +0.30.0 (2024-12-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added UI interface to the Managers in the ManagerBasedEnv and MangerBasedRLEnv classes. +* Added UI widgets for :class:`LiveLinePlot` and :class:`ImagePlot`. +* Added ``ManagerLiveVisualizer/Cfg``: Given a ManagerBase (i.e. action_manager, observation_manager, etc) and a + config file this class creates the the interface between managers and the UI. +* Added :class:`EnvLiveVisualizer`: A 'manager' of ManagerLiveVisualizer. This is added to the ManagerBasedEnv + but is only called during the initialization of the managers in load_managers +* Added ``get_active_iterable_terms`` implementation methods to ActionManager, ObservationManager, CommandsManager, + CurriculumManager, RewardManager, and TerminationManager. This method exports the active term data and labels + for each manager and is called by ManagerLiveVisualizer. +* Additions to :class:`BaseEnvWindow` and :class:`RLEnvWindow` to register ManagerLiveVisualizer UI interfaces + for the chosen managers. + + +0.29.0 (2024-12-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added observation history computation to :class:`isaaclab.manager.observation_manager.ObservationManager`. +* Added ``history_length`` and ``flatten_history_dim`` configuration parameters to :class:`isaaclab.manager.manager_term_cfg.ObservationTermCfg` +* Added ``history_length`` and ``flatten_history_dim`` configuration parameters to :class:`isaaclab.manager.manager_term_cfg.ObservationGroupCfg` +* Added full buffer property to :class:`isaaclab.utils.buffers.circular_buffer.CircularBuffer` + + +0.28.4 (2024-12-15) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added action clip to all :class:`isaaclab.envs.mdp.actions`. + + +0.28.3 (2024-12-14) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added check for error below threshold in state machines to ensure the state has been reached. + + +0.28.2 (2024-12-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the shape of ``quat_w`` in the ``apply_actions`` method of :attr:`~isaaclab.env.mdp.NonHolonomicAction` + (previously (N,B,4), now (N,4) since the number of root bodies B is required to be 1). Previously ``apply_actions`` + errored because ``euler_xyz_from_quat`` requires inputs of shape (N,4). + + +0.28.1 (2024-12-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the internal buffers for ``set_external_force_and_torque`` where the buffer values would be stale if zero + values are sent to the APIs. + + +0.28.0 (2024-12-12) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Adapted the :class:`~isaaclab.sim.converters.UrdfConverter` to use the latest URDF converter API from Isaac Sim 4.5. + The physics articulation root can now be set separately, and the joint drive gains can be set on a per joint basis. + + +0.27.33 (2024-12-11) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Introduced an optional ``sensor_cfg`` parameter to the :meth:`~isaaclab.envs.mdp.rewards.base_height_l2` function, + enabling the use of :class:`~isaaclab.sensors.RayCaster` for height adjustments. For flat terrains, the function + retains its previous behavior. +* Improved documentation to clarify the usage of the :meth:`~isaaclab.envs.mdp.rewards.base_height_l2` function in + both flat and rough terrain settings. + + +0.27.32 (2024-12-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Modified :class:`isaaclab.envs.mdp.actions.DifferentialInverseKinematicsAction` class to use the geometric + Jacobian computed w.r.t. to the root frame of the robot. This helps ensure that root pose does not affect the tracking. + + +0.27.31 (2024-12-09) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Introduced configuration options in :class:`Se3HandTracking` to: + + - Zero out rotation around the x/y axes + - Apply smoothing and thresholding to position and rotation deltas for reduced jitter + - Use wrist-based rotation reference as an alternative to fingertip-based rotation + +* Switched the default position reference in :class:`Se3HandTracking` to the wrist joint pose, providing more stable + relative-based positioning. + + +0.27.30 (2024-12-09) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the initial state recorder term in :class:`isaaclab.envs.mdp.recorders.InitialStateRecorder` to + return only the states of the specified environment IDs. + + +0.27.29 (2024-12-06) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the enforcement of :attr:`~isaaclab.actuators.ActuatorBaseCfg.velocity_limits` at the + :attr:`~isaaclab.assets.Articulation.root_physx_view` level. + + +0.27.28 (2024-12-06) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* If a USD that contains an articulation root is loaded using a + :attr:`isaaclab.assets.RigidBody` we now fail unless the articulation root is explicitly + disabled. Using an articulation root for rigid bodies is not needed and decreases overall performance. + + +0.27.27 (2024-12-06) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Corrected the projection types of fisheye camera in :class:`isaaclab.sim.spawners.sensors.sensors_cfg.FisheyeCameraCfg`. + Earlier, the projection names used snakecase instead of camelcase. + + +0.27.26 (2024-12-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added option to define the clipping behavior for depth images generated by + :class:`~isaaclab.sensors.RayCasterCamera`, :class:`~isaaclab.sensors.Camera`, and :class:`~isaaclab.sensors.TiledCamera` + +Changed +^^^^^^^ + +* Unified the clipping behavior for the depth images of all camera implementations. Per default, all values exceeding + the range are clipped to zero for both ``distance_to_image_plane`` and ``distance_to_camera`` depth images. Prev. + :class:`~isaaclab.sensors.RayCasterCamera` clipped the values to the maximum value of the depth image, + :class:`~isaaclab.sensors.Camera` did not clip them and had a different behavior for both types. + + +0.27.25 (2024-12-05) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the condition in ``isaaclab.sh`` that checks whether ``pre-commit`` is installed before attempting installation. + + +0.27.24 (2024-12-05) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Removed workaround in :class:`isaaclab.sensors.TiledCamera` and :class:`isaaclab.sensors.Camera` + that was previously required to prevent frame offsets in renders. The denoiser setting is no longer + automatically modified based on the resolution of the cameras. + + +0.27.23 (2024-12-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added the attributes :attr:`~isaaclab.envs.DirectRLEnvCfg.wait_for_textures` and + :attr:`~isaaclab.envs.ManagerBasedEnvCfg.wait_for_textures` to enable assets loading check + during :class:`~isaaclab.DirectRLEnv` and :class:`~isaaclab.ManagerBasedEnv` reset method when + rtx sensors are added to the scene. + + +0.27.22 (2024-12-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the order of the incoming parameters in :class:`isaaclab.envs.DirectMARLEnv` to correctly use + ``NoiseModel`` in marl-envs. + + +0.27.21 (2024-12-04) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.managers.RecorderManager` and its utility classes to record data from the simulation. +* Added :class:`~isaaclab.utils.datasets.EpisodeData` to store data for an episode. +* Added :class:`~isaaclab.utils.datasets.DatasetFileHandlerBase` as a base class for handling dataset files. +* Added :class:`~isaaclab.utils.datasets.HDF5DatasetFileHandler` as a dataset file handler implementation to + export and load episodes from HDF5 files. +* Added ``record_demos.py`` script to record human-teleoperated demos for a specified task and export to an HDF5 file. +* Added ``replay_demos.py`` script to replay demos loaded from an HDF5 file. + + +0.27.20 (2024-12-02) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed :class:`isaaclab.envs.DirectMARLEnv` to inherit from ``Gymnasium.Env`` due to requirement from Gymnasium + v1.0.0 requiring all environments to be a subclass of ``Gymnasium.Env`` when using the ``make`` interface. + + +0.27.19 (2024-12-02) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``isaaclab.utils.pretrained_checkpoints`` containing constants and utility functions used to manipulate + paths and load checkpoints from Nucleus. + + +0.27.18 (2024-11-28) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed Isaac Sim imports to follow Isaac Sim 4.5 naming conventions. + + +0.27.17 (2024-11-20) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``create_new_stage`` setting in :class:`~isaaclab.app.AppLauncher` to avoid creating a default new + stage on startup in Isaac Sim. This helps reduce the startup time when launching Isaac Lab. + + +0.27.16 (2024-11-15) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the class :class:`~isaaclab.devices.Se3HandTracking` which enables XR teleop for manipulators. + + +0.27.15 (2024-11-09) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed indexing in :meth:`isaaclab.assets.Articulation.write_joint_limits_to_sim` to correctly process + non-None ``env_ids`` and ``joint_ids``. + + +0.27.14 (2024-10-23) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the class :class:`~isaaclab.assets.RigidObjectCollection` which allows to spawn + multiple objects in each environment and access/modify the quantities with a unified (env_ids, object_ids) API. + + +0.27.13 (2024-10-30) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the attributes :attr:`~isaaclab.sim.converters.MeshConverterCfg.translation`, :attr:`~isaaclab.sim.converters.MeshConverterCfg.rotation`, + :attr:`~isaaclab.sim.converters.MeshConverterCfg.scale` to translate, rotate, and scale meshes + when importing them with :class:`~isaaclab.sim.converters.MeshConverter`. + + +0.27.12 (2024-11-04) +~~~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Removed TensorDict usage in favor of Python dictionary in sensors + + +0.27.11 (2024-10-31) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support to define tuple of floats to scale observation terms by expanding the + :attr:`isaaclab.managers.manager_term_cfg.ObservationManagerCfg.scale` attribute. + + +0.27.10 (2024-11-01) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Cached the PhysX view's joint paths before looping over them when processing fixed joint tendons + inside the :class:`Articulation` class. This helps improve the processing time for the tendons. + + +0.27.9 (2024-11-01) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`isaaclab.utils.types.ArticulationActions` class to store the joint actions + for an articulation. Earlier, the class from Isaac Sim was being used. However, it used a different + type for the joint actions which was not compatible with the Isaac Lab framework. + + +0.27.8 (2024-11-01) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added sanity check if the term is a valid type inside the command manager. +* Corrected the iteration over ``group_cfg_items`` inside the observation manager. + + +0.27.7 (2024-10-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added frozen encoder feature extraction observation space with ResNet and Theia + + +0.27.6 (2024-10-25) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed usage of ``meshes`` property in :class:`isaaclab.sensors.RayCasterCamera` to use ``self.meshes`` + instead of the undefined ``RayCaster.meshes``. +* Fixed issue in :class:`isaaclab.envs.ui.BaseEnvWindow` where undefined configs were being accessed when + creating debug visualization elements in UI. + + +0.27.5 (2024-10-25) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added utilities for serializing/deserializing Gymnasium spaces. + + +0.27.4 (2024-10-18) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Updated installation path instructions for Windows in the Isaac Lab documentation to remove redundancy in the + use of %USERPROFILE% for path definitions. + + +0.27.3 (2024-10-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the issue with using list or tuples of ``configclass`` within a ``configclass``. Earlier, the list of + configclass objects were not converted to dictionary properly when ``to_dict`` function was called. + + +0.27.2 (2024-10-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``--kit_args`` to :class:`~isaaclab.app.AppLauncher` to allow passing command line arguments directly to + Omniverse Kit SDK. + + +0.27.1 (2024-10-20) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`~isaaclab.sim.RenderCfg` and the attribute :attr:`~isaaclab.sim.SimulationCfg.render` for + specifying render related settings. + + +0.27.0 (2024-10-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a method to :class:`~isaaclab.utils.configclass` to check for attributes with values of + type ``MISSING``. This is useful when the user wants to check if a certain attribute has been set or not. +* Added the configuration validation check inside the constructor of all the core classes + (such as sensor base, asset base, scene and environment base classes). +* Added support for environments without commands by leaving the attribute + :attr:`isaaclab.envs.ManagerBasedRLEnvCfg.commands` as None. Before, this had to be done using + the class :class:`isaaclab.command_generators.NullCommandGenerator`. +* Moved the ``meshes`` attribute in the :class:`isaaclab.sensors.RayCaster` class from class variable to instance variable. + This prevents the meshes to overwrite each other. + + +0.26.0 (2024-10-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added Imu sensor implementation that directly accesses the physx view :class:`isaaclab.sensors.Imu`. The + sensor comes with a configuration class :class:`isaaclab.sensors.ImuCfg` and data class + :class:`isaaclab.sensors.ImuData`. +* Moved and renamed :meth:`isaaclab.sensors.camera.utils.convert_orientation_convention` to + :meth:`isaaclab.utils.math.convert_camera_frame_orientation_convention` +* Moved :meth:`isaaclab.sensors.camera.utils.create_rotation_matrix_from_view` to + :meth:`isaaclab.utils.math.create_rotation_matrix_from_view` + + +0.25.2 (2024-10-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for different Gymnasium spaces (``Box``, ``Discrete``, ``MultiDiscrete``, ``Tuple`` and ``Dict``) + to define observation, action and state spaces in the direct workflow. +* Added :meth:`sample_space` to environment utils to sample supported spaces where data containers are torch tensors. + +Changed +^^^^^^^ + +* Mark the :attr:`num_observations`, :attr:`num_actions` and :attr:`num_states` in :class:`DirectRLEnvCfg` as deprecated + in favor of :attr:`observation_space`, :attr:`action_space` and :attr:`state_space` respectively. +* Mark the :attr:`num_observations`, :attr:`num_actions` and :attr:`num_states` in :class:`DirectMARLEnvCfg` as deprecated + in favor of :attr:`observation_spaces`, :attr:`action_spaces` and :attr:`state_space` respectively. + + +0.25.1 (2024-10-10) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed potential issue where default joint positions can fall outside of the limits being set with Articulation's + ``write_joint_limits_to_sim`` API. + + +0.25.0 (2024-10-06) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configuration classes for spawning assets from a list of individual asset configurations randomly + at the specified prim paths. + + +0.24.20 (2024-10-07) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :meth:`isaaclab.envs.mdp.events.randomize_rigid_body_material` function to + correctly sample friction and restitution from the given ranges. + + +0.24.19 (2024-10-05) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added new functionalities to the FrameTransformer to make it more general. It is now possible to track: + + * Target frames that aren't children of the source frame prim_path + * Target frames that are based upon the source frame prim_path + + +0.24.18 (2024-10-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixes parsing and application of ``size`` parameter for :class:`~isaaclab.sim.spawn.GroundPlaneCfg` to correctly + scale the grid-based ground plane. + + +0.24.17 (2024-10-04) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the deprecation notice for using ``pxr.Semantics``. The corresponding modules use ``Semantics`` module + directly. + + +0.24.16 (2024-10-03) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed the observation function :meth:`grab_images` to :meth:`image` to follow convention of noun-based naming. +* Renamed the function :meth:`convert_perspective_depth_to_orthogonal_depth` to a shorter name + :meth:`isaaclab.utils.math.orthogonalize_perspective_depth`. + + +0.24.15 (2024-09-20) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :meth:`grab_images` to be able to use images for an observation term in manager-based environments. + + +0.24.14 (2024-09-20) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the method :meth:`convert_perspective_depth_to_orthogonal_depth` to convert perspective depth + images to orthogonal depth images. This is useful for the :meth:`~isaaclab.utils.math.unproject_depth`, + since it expects orthogonal depth images as inputs. + + +0.24.13 (2024-09-08) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the configuration of visualization markers for the command terms to their respective configuration classes. + This allows users to modify the markers for the command terms without having to modify the command term classes. + + +0.24.12 (2024-09-18) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed outdated fetching of articulation data by using the method ``update_articulations_kinematic`` in + :class:`isaaclab.assets.ArticulationData`. Before if an articulation was moved during a reset, the pose of the + links were outdated if fetched before the next physics step. Adding this method ensures that the pose of the links + is always up-to-date. Similarly ``update_articulations_kinematic`` was added before any render step to ensure that the + articulation displays correctly after a reset. + + +0.24.11 (2024-09-11) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added skrl's JAX environment variables to :class:`~isaaclab.app.AppLauncher` + to support distributed multi-GPU and multi-node training using JAX + + +0.24.10 (2024-09-10) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added config class, support, and tests for MJCF conversion via standalone python scripts. + + +0.24.9 (2024-09-09) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a seed parameter to the :attr:`isaaclab.envs.ManagerBasedEnvCfg` and :attr:`isaaclab.envs.DirectRLEnvCfg` + classes to set the seed for the environment. This seed is used to initialize the random number generator for the environment. +* Adapted the workflow scripts to set the seed for the environment using the seed specified in the learning agent's configuration + file or the command line argument. This ensures that the simulation results are reproducible across different runs. + + +0.24.8 (2024-09-08) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified:meth:`quat_rotate` and :meth:`quat_rotate_inverse` operations to use :meth:`torch.einsum` + for faster processing of high dimensional input tensors. + + +0.24.7 (2024-09-06) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for property attributes in the :meth:``isaaclab.utils.configclass`` method. + Earlier, the configclass decorator failed to parse the property attributes correctly and made them + instance variables instead. + + +0.24.6 (2024-09-05) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Adapted the ``A`` and ``D`` button bindings inside :meth:`isaaclab.device.Se3Keyboard` to make them now + more-intuitive to control the y-axis motion based on the right-hand rule. + + +0.24.5 (2024-08-29) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added alternative data type "distance_to_camera" in :class:`isaaclab.sensors.TiledCamera` class to be + consistent with all other cameras (equal to type "depth"). + + +0.24.4 (2024-09-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added missing SI units to the documentation of :class:`isaaclab.sensors.Camera` and + :class:`isaaclab.sensors.RayCasterCamera`. +* Added test to check :attr:`isaaclab.sensors.RayCasterCamera.set_intrinsic_matrices` + + +0.24.3 (2024-08-29) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the support for class-bounded methods when creating a configclass + out of them. Earlier, these methods were being made as instance methods + which required initialization of the class to call the class-methods. + + +0.24.2 (2024-08-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a class method to initialize camera configurations with an intrinsic matrix in the + :class:`isaaclab.sim.spawner.sensors.PinholeCameraCfg` + :class:`isaaclab.sensors.ray_caster.patterns_cfg.PinholeCameraPatternCfg` classes. + +Fixed +^^^^^ + +* Fixed the ray direction in :func:`isaaclab.sensors.ray_caster.patterns.patterns.pinhole_camera_pattern` to + point to the center of the pixel instead of the top-left corner. +* Fixed the clipping of the "distance_to_image_plane" depth image obtained using the + :class:`isaaclab.sensors.ray_caster.RayCasterCamera` class. Earlier, the depth image was being clipped + before the depth image was generated. Now, the clipping is applied after the depth image is generated. This makes + the behavior equal to the USD Camera. + + +0.24.1 (2024-08-21) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Disabled default viewport in certain headless scenarios for better performance. + + +0.24.0 (2024-08-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added additional annotators for :class:`isaaclab.sensors.camera.TiledCamera` class. + +Changed +^^^^^^^ + +* Updated :class:`isaaclab.sensors.TiledCamera` to latest RTX tiled rendering API. +* Single channel outputs for :class:`isaaclab.sensors.TiledCamera`, :class:`isaaclab.sensors.Camera` and :class:`isaaclab.sensors.RayCasterCamera` now has shape (H, W, 1). +* Data type for RGB output for :class:`isaaclab.sensors.TiledCamera` changed from ``torch.float`` to ``torch.uint8``. +* Dimension of RGB output for :class:`isaaclab.sensors.Camera` changed from (H, W, 4) to (H, W, 3). Use type ``rgba`` to retrieve the previous dimension. + + +0.23.1 (2024-08-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated torch to version 2.4.0. + + +0.23.0 (2024-08-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added direct workflow base class :class:`isaaclab.envs.DirectMARLEnv` for multi-agent environments. + + +0.22.1 (2024-08-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added APIs to interact with the physics simulation of deformable objects. This includes setting the + material properties, setting kinematic targets, and getting the state of the deformable object. + For more information, please refer to the :mod:`isaaclab.assets.DeformableObject` class. + + +0.22.0 (2024-08-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :mod:`~isaaclab.utils.modifiers` module to provide framework for configurable and custom + observation data modifiers. +* Adapted the :class:`~isaaclab.managers.ObservationManager` class to support custom modifiers. + These are applied to the observation data before applying any noise or scaling operations. + + +0.21.2 (2024-08-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Moved event mode-based checks in the :meth:`isaaclab.managers.EventManager.apply` method outside + the loop that iterates over the event terms. This prevents unnecessary checks and improves readability. +* Fixed the logic for global and per environment interval times when using the "interval" mode inside the + event manager. Earlier, the internal lists for these times were of unequal lengths which led to wrong indexing + inside the loop that iterates over the event terms. + + +0.21.1 (2024-08-06) +~~~~~~~~~~~~~~~~~~~ + +* Added a flag to preserve joint ordering inside the :class:`isaaclab.envs.mdp.JointAction` action term. + + +0.21.0 (2024-08-05) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the command line argument ``--device`` in :class:`~isaaclab.app.AppLauncher`. Valid options are: + + * ``cpu``: Use CPU. + * ``cuda``: Use GPU with device ID ``0``. + * ``cuda:N``: Use GPU, where N is the device ID. For example, ``cuda:0``. The default value is ``cuda:0``. + +Changed +^^^^^^^ + +* Simplified setting the device throughout the code by relying on :attr:`isaaclab.sim.SimulationCfg.device` + to activate gpu/cpu pipelines. + +Removed +^^^^^^^ + +* Removed the parameter :attr:`isaaclab.sim.SimulationCfg.use_gpu_pipeline`. This is now directly inferred from + :attr:`isaaclab.sim.SimulationCfg.device`. +* Removed the command line input argument ``--device_id`` in :class:`~isaaclab.app.AppLauncher`. The device id can + now be set using the ``--device`` argument, for example with ``--device cuda:0``. + + +0.20.8 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the handling of observation terms with different shapes in the + :class:`~isaaclab.managers.ObservationManager` class. Earlier, the constructor would throw an error if the + shapes of the observation terms were different. Now, this operation only happens when the terms in an observation + group are being concatenated. Otherwise, the terms are stored as a dictionary of tensors. +* Improved the error message when the observation terms are not of the same shape in the + :class:`~isaaclab.managers.ObservationManager` class and the terms are being concatenated. + + +0.20.7 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Performance improvements for material randomization in events. + +Added +^^^^^ + +* Added minimum randomization frequency for reset mode randomizations. + + +0.20.6 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed the hierarchy from :class:`~isaaclab.assets.RigidObject` class to + :class:`~isaaclab.assets.Articulation` class. Previously, the articulation class overrode almost + all the functions of the rigid object class making the hierarchy redundant. Now, the articulation class + is a standalone class that does not inherit from the rigid object class. This does add some code + duplication but the simplicity and clarity of the code is improved. + + +0.20.5 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :attr:`isaaclab.terrain.TerrainGeneratorCfg.border_height` to set the height of the border + around the terrain. + + +0.20.4 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the caching of terrains when using the :class:`isaaclab.terrains.TerrainGenerator` class. + Earlier, the random sampling of the difficulty levels led to different hash values for the same terrain + configuration. This caused the terrains to be re-generated even when the same configuration was used. + Now, the numpy random generator is seeded with the same seed to ensure that the difficulty levels are + sampled in the same order between different runs. + + +0.20.3 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the setting of translation and orientation when spawning a mesh prim. Earlier, the translation + and orientation was being applied both on the parent Xform and the mesh prim. This was causing the + mesh prim to be offset by the translation and orientation of the parent Xform, which is not the intended + behavior. + + +0.20.2 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified the computation of body acceleration for rigid body data to use PhysX APIs instead of + numerical finite-differencing. This removes the need for computation of body acceleration at + every update call of the data buffer. + + +0.20.1 (2024-07-30) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :meth:`isaaclab.utils.math.wrap_to_pi` method to handle the wrapping of angles correctly. + Earlier, the method was not wrapping the angles to the range [-pi, pi] correctly when the angles were outside + the range [-2*pi, 2*pi]. + + +0.20.0 (2024-07-26) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Support for the Isaac Sim 4.1.0 release. + +Removed +^^^^^^^ + +* The ``mdp.add_body_mass`` method in the events. Please use the + :meth:`isaaclab.envs.mdp.randomize_rigid_body_mass` method instead. +* The classes ``managers.RandomizationManager`` and ``managers.RandomizationTermCfg`` are replaced with + :class:`isaaclab.managers.EventManager` and :class:`isaaclab.managers.EventTermCfg` classes. +* The following properties in :class:`isaaclab.sensors.FrameTransformerData`: + + * ``target_rot_source`` --> :attr:`~isaaclab.sensors.FrameTransformerData.target_quat_w` + * ``target_rot_w`` --> :attr:`~isaaclab.sensors.FrameTransformerData.target_quat_source` + * ``source_rot_w`` --> :attr:`~isaaclab.sensors.FrameTransformerData.source_quat_w` + +* The kit experience file ``isaaclab.backwards.compatible.kit``. This is followed by dropping the support for + Isaac Sim 2023.1.1 completely. + + +0.19.4 (2024-07-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added the call to "startup" events when using the :class:`~isaaclab.envs.ManagerBasedEnv` class. + Earlier, the "startup" events were not being called when the environment was initialized. This issue + did not occur when using the :class:`~isaaclab.envs.ManagerBasedRLEnv` class since the "startup" + events were called in the constructor. + + +0.19.3 (2024-07-13) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added schemas for setting and modifying deformable body properties on a USD prim. +* Added API to spawn a deformable body material in the simulation. +* Added APIs to spawn rigid and deformable meshes of primitive shapes (cone, cylinder, sphere, box, capsule) + in the simulation. This is possible through the :mod:`isaaclab.sim.spawners.meshes` module. + + +0.19.2 (2024-07-05) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified cloning scheme based on the attribute :attr:`~isaaclab.scene.InteractiveSceneCfg.replicate_physics` + to determine whether environment is homogeneous or heterogeneous. + + +0.19.1 (2024-07-05) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a lidar pattern function :func:`~isaaclab.sensors.ray_caster.patterns.patterns.lidar_pattern` with + corresponding config :class:`~isaaclab.sensors.ray_caster.patterns_cfg.LidarPatternCfg`. + + +0.19.0 (2024-07-04) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed parsing of articulations with nested rigid links while using the :class:`isaaclab.assets.Articulation` + class. Earlier, the class initialization failed when the articulation had nested rigid links since the rigid + links were not being parsed correctly by the PhysX view. + +Removed +^^^^^^^ + +* Removed the attribute :attr:`body_physx_view` from the :class:`isaaclab.assets.Articulation` and + :class:`isaaclab.assets.RigidObject` classes. These were causing confusions when used with articulation + view since the body names were not following the same ordering. +* Dropped support for Isaac Sim 2023.1.1. The minimum supported version is now Isaac Sim 4.0.0. + + +0.18.6 (2024-07-01) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the environment stepping logic. Earlier, the environments' rendering logic was updating the kit app which + would in turn step the physics :attr:`isaaclab.sim.SimulationCfg.render_interval` times. Now, a render + call only does rendering and does not step the physics. + + +0.18.5 (2024-06-26) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the gravity vector direction used inside the :class:`isaaclab.assets.RigidObjectData` class. + Earlier, the gravity direction was hard-coded as (0, 0, -1) which may be different from the actual + gravity direction in the simulation. Now, the gravity direction is obtained from the simulation context + and used to compute the projection of the gravity vector on the object. + + +0.18.4 (2024-06-26) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed double reference count of the physics sim view inside the asset classes. This was causing issues + when destroying the asset class instance since the physics sim view was not being properly released. + +Added +^^^^^ + +* Added the attribute :attr:`~isaaclab.assets.AssetBase.is_initialized` to check if the asset and sensor + has been initialized properly. This can be used to ensure that the asset or sensor is ready to use in the simulation. + + +0.18.3 (2024-06-25) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the docstrings at multiple places related to the different buffer implementations inside the + :mod:`isaaclab.utils.buffers` module. The docstrings were not clear and did not provide enough + information about the classes and their methods. + +Added +^^^^^ + +* Added the field for fixed tendom names in the :class:`isaaclab.assets.ArticulationData` class. + Earlier, this information was not exposed which was inconsistent with other name related information + such as joint or body names. + +Changed +^^^^^^^ + +* Renamed the fields ``min_num_time_lags`` and ``max_num_time_lags`` to ``min_delay`` and + ``max_delay`` in the :class:`isaaclab.actuators.DelayedPDActuatorCfg` class. This is to make + the naming simpler to understand. + + +0.18.2 (2024-06-25) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the configuration for tile-rendered camera into its own file named ``tiled_camera_cfg.py``. + This makes it easier to follow where the configuration is located and how it is related to the class. + + +0.18.1 (2024-06-25) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Ensured that a parity between class and its configuration class is explicitly visible in the + :mod:`isaaclab.envs` module. This makes it easier to follow where definitions are located and how + they are related. This should not be a breaking change as the classes are still accessible through the same module. + + +0.18.0 (2024-06-13) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the rendering logic to render at the specified interval. Earlier, the substep parameter had no effect and rendering + would happen once every env.step() when active. + +Changed +^^^^^^^ + +* Renamed :attr:`isaaclab.sim.SimulationCfg.substeps` to :attr:`isaaclab.sim.SimulationCfg.render_interval`. + The render logic is now integrated in the decimation loop of the environment. + + +0.17.13 (2024-06-13) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the orientation reset logic in :func:`isaaclab.envs.mdp.events.reset_root_state_uniform` to make it relative to + the default orientation. Earlier, the position was sampled relative to the default and the orientation not. + + +0.17.12 (2024-06-13) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the class :class:`isaaclab.utils.buffers.TimestampedBuffer` to store timestamped data. + +Changed +^^^^^^^ + +* Added time-stamped buffers in the classes :class:`isaaclab.assets.RigidObjectData` and :class:`isaaclab.assets.ArticulationData` + to update some values lazily and avoid unnecessary computations between physics updates. Before, all the data was always + updated at every step, even if it was not used by the task. + + +0.17.11 (2024-05-30) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :class:`isaaclab.sensor.ContactSensor` not loading correctly in extension mode. + Earlier, the :attr:`isaaclab.sensor.ContactSensor.body_physx_view` was not initialized when + :meth:`isaaclab.sensor.ContactSensor._debug_vis_callback` is called which references it. + + +0.17.10 (2024-05-30) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed compound classes being directly assigned in ``default_factory`` generator method + :meth:`isaaclab.utils.configclass._return_f`, which resulted in shared references such that modifications to + compound objects were reflected across all instances generated from the same ``default_factory`` method. + + +0.17.9 (2024-05-30) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``variants`` attribute to the :class:`isaaclab.sim.from_files.UsdFileCfg` class to select USD + variants when loading assets from USD files. + + +0.17.8 (2024-05-28) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Implemented the reset methods in the action terms to avoid returning outdated data. + + +0.17.7 (2024-05-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added debug visualization utilities in the :class:`isaaclab.managers.ActionManager` class. + + +0.17.6 (2024-05-27) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``wp.init()`` call in Warp utils. + + +0.17.5 (2024-05-22) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Websocket livestreaming is no longer supported. Valid livestream options are {0, 1, 2}. +* WebRTC livestream is now set with livestream=2. + + +0.17.4 (2024-05-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified the noise functions to also support add, scale, and abs operations on the data. Added aliases + to ensure backward compatibility with the previous functions. + + * Added :attr:`isaaclab.utils.noise.NoiseCfg.operation` for the different operations. + * Renamed ``constant_bias_noise`` to :func:`isaaclab.utils.noise.constant_noise`. + * Renamed ``additive_uniform_noise`` to :func:`isaaclab.utils.noise.uniform_noise`. + * Renamed ``additive_gaussian_noise`` to :func:`isaaclab.utils.noise.gaussian_noise`. + + +0.17.3 (2024-05-15) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Set ``hide_ui`` flag in the app launcher for livestream. +* Fix native client livestream extensions. + + +0.17.2 (2024-05-09) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed ``_range`` to ``distribution_params`` in ``events.py`` for methods that defined a distribution. +* Apply additive/scaling randomization noise on default data instead of current data. +* Changed material bucketing logic to prevent exceeding 64k materials. + +Fixed +^^^^^ + +* Fixed broadcasting issues with indexing when environment and joint IDs are provided. +* Fixed incorrect tensor dimensions when setting a subset of environments. + +Added +^^^^^ + +* Added support for randomization of fixed tendon parameters. +* Added support for randomization of dof limits. +* Added support for randomization of gravity. +* Added support for Gaussian sampling. +* Added default buffers to Articulation/Rigid object data classes for randomization. + + +0.17.1 (2024-05-10) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added attribute :attr:`isaaclab.sim.converters.UrdfConverterCfg.override_joint_dynamics` to properly parse + joint dynamics in :class:`isaaclab.sim.converters.UrdfConverter`. + + +0.17.0 (2024-05-07) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed ``BaseEnv`` to :class:`isaaclab.envs.ManagerBasedEnv`. +* Renamed ``base_env.py`` to ``manager_based_env.py``. +* Renamed ``BaseEnvCfg`` to :class:`isaaclab.envs.ManagerBasedEnvCfg`. +* Renamed ``RLTaskEnv`` to :class:`isaaclab.envs.ManagerBasedRLEnv`. +* Renamed ``rl_task_env.py`` to ``manager_based_rl_env.py``. +* Renamed ``RLTaskEnvCfg`` to :class:`isaaclab.envs.ManagerBasedRLEnvCfg`. +* Renamed ``rl_task_env_cfg.py`` to ``rl_env_cfg.py``. +* Renamed ``OIGEEnv`` to :class:`isaaclab.envs.DirectRLEnv`. +* Renamed ``oige_env.py`` to ``direct_rl_env.py``. +* Renamed ``RLTaskEnvWindow`` to :class:`isaaclab.envs.ui.ManagerBasedRLEnvWindow`. +* Renamed ``rl_task_env_window.py`` to ``manager_based_rl_env_window.py``. +* Renamed all references of ``BaseEnv``, ``BaseEnvCfg``, ``RLTaskEnv``, ``RLTaskEnvCfg``, ``OIGEEnv``, and ``RLTaskEnvWindow``. + +Added +^^^^^ + +* Added direct workflow base class :class:`isaaclab.envs.DirectRLEnv`. + + +0.16.4 (2024-05-06) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added :class:`isaaclab.sensors.TiledCamera` to support tiled rendering with RGB and depth. + + +0.16.3 (2024-04-26) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed parsing of filter prim path expressions in the :class:`isaaclab.sensors.ContactSensor` class. + Earlier, the filter prim paths given to the physics view was not being parsed since they were specified as + regex expressions instead of glob expressions. + + +0.16.2 (2024-04-25) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Simplified the installation procedure, isaaclab -e is no longer needed +* Updated torch dependency to 2.2.2 + + +0.16.1 (2024-04-20) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added attribute :attr:`isaaclab.sim.ArticulationRootPropertiesCfg.fix_root_link` to fix the root link + of an articulation to the world frame. + + +0.16.0 (2024-04-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the function :meth:`isaaclab.utils.math.quat_unique` to standardize quaternion representations, + i.e. always have a non-negative real part. +* Added events terms for randomizing mass by scale, simulation joint properties (stiffness, damping, armature, + and friction) + +Fixed +^^^^^ + +* Added clamping of joint positions and velocities in event terms for resetting joints. The simulation does not + throw an error if the set values are out of their range. Hence, users are expected to clamp them before setting. +* Fixed :class:`isaaclab.envs.mdp.EMAJointPositionToLimitsActionCfg` to smoothen the actions + at environment frequency instead of simulation frequency. + +* Renamed the following functions in :meth:`isaaclab.envs.mdp` to avoid confusions: + + * Observation: :meth:`joint_pos_norm` -> :meth:`joint_pos_limit_normalized` + * Action: :class:`ExponentialMovingAverageJointPositionAction` -> :class:`EMAJointPositionToLimitsAction` + * Termination: :meth:`base_height` -> :meth:`root_height_below_minimum` + * Termination: :meth:`joint_pos_limit` -> :meth:`joint_pos_out_of_limit` + * Termination: :meth:`joint_pos_manual_limit` -> :meth:`joint_pos_out_of_manual_limit` + * Termination: :meth:`joint_vel_limit` -> :meth:`joint_vel_out_of_limit` + * Termination: :meth:`joint_vel_manual_limit` -> :meth:`joint_vel_out_of_manual_limit` + * Termination: :meth:`joint_torque_limit` -> :meth:`joint_effort_out_of_limit` + +Deprecated +^^^^^^^^^^ + +* Deprecated the function :meth:`isaaclab.envs.mdp.add_body_mass` in favor of + :meth:`isaaclab.envs.mdp.randomize_rigid_body_mass`. This supports randomizing the mass based on different + operations (add, scale, or set) and sampling distributions. + + +0.15.13 (2024-04-16) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Improved startup performance by enabling rendering-based extensions only when necessary and caching of nucleus directory. +* Renamed the flag ``OFFSCREEN_RENDER`` or ``--offscreen_render`` to ``ENABLE_CAMERAS`` or ``--enable_cameras`` respectively. + + +0.15.12 (2024-04-16) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Replaced calls to the ``check_file_path`` function in the :mod:`isaaclab.sim.spawners.from_files` + with the USD stage resolve identifier function. This helps speed up the loading of assets from file paths + by avoiding Nucleus server calls. + + +0.15.11 (2024-04-15) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :meth:`isaaclab.sim.SimulationContext.has_rtx_sensors` method to check if any + RTX-related sensors such as cameras have been created in the simulation. This is useful to determine + if simulation requires RTX rendering during step or not. + +Fixed +^^^^^ + +* Fixed the rendering of RTX-related sensors such as cameras inside the :class:`isaaclab.envs.RLTaskEnv` class. + Earlier the rendering did not happen inside the step function, which caused the sensor data to be empty. + + +0.15.10 (2024-04-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed sharing of the same memory address between returned tensors from observation terms + in the :class:`isaaclab.managers.ObservationManager` class. Earlier, the returned + tensors could map to the same memory address, causing issues when the tensors were modified + during scaling, clipping or other operations. + + +0.15.9 (2024-04-04) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed assignment of individual termination terms inside the :class:`isaaclab.managers.TerminationManager` + class. Earlier, the terms were being assigned their values through an OR operation which resulted in incorrect + values. This regression was introduced in version 0.15.1. + + +0.15.8 (2024-04-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added option to define ordering of points for the mesh-grid generation in the + :func:`isaaclab.sensors.ray_caster.patterns.grid_pattern`. This parameter defaults to 'xy' + for backward compatibility. + + +0.15.7 (2024-03-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Adds option to return indices/data in the specified query keys order in + :class:`isaaclab.managers.SceneEntityCfg` class, and the respective + :func:`isaaclab.utils.string.resolve_matching_names_values` and + :func:`isaaclab.utils.string.resolve_matching_names` functions. + + +0.15.6 (2024-03-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Extended the :class:`isaaclab.app.AppLauncher` class to support the loading of experience files + from the command line. This allows users to load a specific experience file when running the application + (such as for multi-camera rendering or headless mode). + +Changed +^^^^^^^ + +* Changed default loading of experience files in the :class:`isaaclab.app.AppLauncher` class from the ones + provided by Isaac Sim to the ones provided in Isaac Lab's ``apps`` directory. + + +0.15.5 (2024-03-23) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the env origins in :meth:`_compute_env_origins_grid` of :class:`isaaclab.terrain.TerrainImporter` + to match that obtained from the Isaac Sim :class:`isaacsim.core.cloner.GridCloner` class. + +Added +^^^^^ + +* Added unit test to ensure consistency between environment origins generated by IsaacSim's Grid Cloner and those + produced by the TerrainImporter. + + +0.15.4 (2024-03-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :class:`isaaclab.envs.mdp.actions.NonHolonomicActionCfg` class to use + the correct variable when applying actions. + + +0.15.3 (2024-03-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added unit test to check that :class:`isaaclab.scene.InteractiveScene` entity data is not shared between separate instances. + +Fixed +^^^^^ + +* Moved class variables in :class:`isaaclab.scene.InteractiveScene` to correctly be assigned as + instance variables. +* Removed custom ``__del__`` magic method from :class:`isaaclab.scene.InteractiveScene`. + + +0.15.2 (2024-03-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added resolving of relative paths for the main asset USD file when using the + :class:`isaaclab.sim.converters.UrdfConverter` class. This is to ensure that the material paths are + resolved correctly when the main asset file is moved to a different location. + + +0.15.1 (2024-03-19) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the imitation learning workflow example script, updating Isaac Lab and Robomimic API calls. +* Removed the resetting of :attr:`_term_dones` in the :meth:`isaaclab.managers.TerminationManager.reset`. + Previously, the environment cleared out all the terms. However, it impaired reading the specific term's values externally. + + +0.15.0 (2024-03-17) +~~~~~~~~~~~~~~~~~~~ + +Deprecated +^^^^^^^^^^ + +* Renamed :class:`isaaclab.managers.RandomizationManager` to :class:`isaaclab.managers.EventManager` + class for clarification as the manager takes care of events such as reset in addition to pure randomizations. +* Renamed :class:`isaaclab.managers.RandomizationTermCfg` to :class:`isaaclab.managers.EventTermCfg` + for consistency with the class name change. + + +0.14.1 (2024-03-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added simulation schemas for joint drive and fixed tendons. These can be configured for assets imported + from file formats. +* Added logging of tendon properties to the articulation class (if they are present in the USD prim). + + +0.14.0 (2024-03-15) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the ordering of body names used in the :class:`isaaclab.assets.Articulation` class. Earlier, + the body names were not following the same ordering as the bodies in the articulation. This led + to issues when using the body names to access data related to the links from the articulation view + (such as Jacobians, mass matrices, etc.). + +Removed +^^^^^^^ + +* Removed the attribute :attr:`body_physx_view` from the :class:`isaaclab.assets.RigidObject` + and :class:`isaaclab.assets.Articulation` classes. These were causing confusions when used + with articulation view since the body names were not following the same ordering. + + +0.13.1 (2024-03-14) +~~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Removed the :mod:`isaaclab.compat` module. This module was used to provide compatibility + with older versions of Isaac Sim. It is no longer needed since we have most of the functionality + absorbed into the main classes. + + +0.13.0 (2024-03-12) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for the following data types inside the :class:`isaaclab.sensors.Camera` class: + ``instance_segmentation_fast`` and ``instance_id_segmentation_fast``. These are GPU-supported annotations + and are faster than the regular annotations. + +Fixed +^^^^^ + +* Fixed handling of semantic filtering inside the :class:`isaaclab.sensors.Camera` class. Earlier, + the annotator was given ``semanticTypes`` as an argument. However, with Isaac Sim 2023.1, the annotator + does not accept this argument. Instead the mapping needs to be set to the synthetic data interface directly. +* Fixed the return shape of colored images for segmentation data types inside the + :class:`isaaclab.sensors.Camera` class. Earlier, the images were always returned as ``int32``. Now, + they are casted to ``uint8`` 4-channel array before returning if colorization is enabled for the annotation type. + +Removed +^^^^^^^ + +* Dropped support for ``instance_segmentation`` and ``instance_id_segmentation`` annotations in the + :class:`isaaclab.sensors.Camera` class. Their "fast" counterparts should be used instead. +* Renamed the argument :attr:`isaaclab.sensors.CameraCfg.semantic_types` to + :attr:`isaaclab.sensors.CameraCfg.semantic_filter`. This is more aligned with Replicator's terminology + for semantic filter predicates. +* Replaced the argument :attr:`isaaclab.sensors.CameraCfg.colorize` with separate colorized + arguments for each annotation type (:attr:`~isaaclab.sensors.CameraCfg.colorize_instance_segmentation`, + :attr:`~isaaclab.sensors.CameraCfg.colorize_instance_id_segmentation`, and + :attr:`~isaaclab.sensors.CameraCfg.colorize_semantic_segmentation`). + + +0.12.4 (2024-03-11) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + + +* Adapted randomization terms to deal with ``slice`` for the body indices. Earlier, the terms were not + able to handle the slice object and were throwing an error. +* Added ``slice`` type-hinting to all body and joint related methods in the rigid body and articulation + classes. This is to make it clear that the methods can handle both list of indices and slices. + + +0.12.3 (2024-03-11) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added signal handler to the :class:`isaaclab.app.AppLauncher` class to catch the ``SIGINT`` signal + and close the application gracefully. This is to prevent the application from crashing when the user + presses ``Ctrl+C`` to close the application. + + +0.12.2 (2024-03-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added observation terms for states of a rigid object in world frame. +* Added randomization terms to set root state with randomized orientation and joint state within user-specified limits. +* Added reward term for penalizing specific termination terms. + +Fixed +^^^^^ + +* Improved sampling of states inside randomization terms. Earlier, the code did multiple torch calls + for sampling different components of the vector. Now, it uses a single call to sample the entire vector. + + +0.12.1 (2024-03-09) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added an option to the last actions observation term to get a specific term by name from the action manager. + If None, the behavior remains the same as before (the entire action is returned). + + +0.12.0 (2024-03-08) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added functionality to sample flat patches on a generated terrain. This can be configured using + :attr:`isaaclab.terrains.SubTerrainBaseCfg.flat_patch_sampling` attribute. +* Added a randomization function for setting terrain-aware root state. Through this, an asset can be + reset to a randomly sampled flat patches. + +Fixed +^^^^^ + +* Separated normal and terrain-base position commands. The terrain based commands rely on the + terrain to sample flat patches for setting the target position. +* Fixed command resample termination function. + +Changed +^^^^^^^ + +* Added the attribute :attr:`isaaclab.envs.mdp.commands.UniformVelocityCommandCfg.heading_control_stiffness` + to control the stiffness of the heading control term in the velocity command term. Earlier, this was + hard-coded to 0.5 inside the term. + +Removed +^^^^^^^ + +* Removed the function :meth:`sample_new_targets` in the terrain importer. Instead the attribute + :attr:`isaaclab.terrains.TerrainImporter.flat_patches` should be used to sample new targets. + + +0.11.3 (2024-03-04) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Corrects the functions :func:`isaaclab.utils.math.axis_angle_from_quat` and :func:`isaaclab.utils.math.quat_error_magnitude` + to accept tensors of the form (..., 4) instead of (N, 4). This brings us in line with our documentation and also upgrades one of our functions + to handle higher dimensions. + + +0.11.2 (2024-03-04) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added checks for default joint position and joint velocity in the articulation class. This is to prevent + users from configuring values for these quantities that might be outside the valid range from the simulation. + + +0.11.1 (2024-02-29) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Replaced the default values for ``joint_ids`` and ``body_ids`` from ``None`` to ``slice(None)`` + in the :class:`isaaclab.managers.SceneEntityCfg`. +* Adapted rewards and observations terms so that the users can query a subset of joints and bodies. + + +0.11.0 (2024-02-27) +~~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Dropped support for Isaac Sim<=2022.2. As part of this, removed the components of :class:`isaaclab.app.AppLauncher` + which handled ROS extension loading. We no longer need them in Isaac Sim>=2023.1 to control the load order to avoid crashes. +* Upgraded Dockerfile to use ISAACSIM_VERSION=2023.1.1 by default. + + +0.10.28 (2024-02-29) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Implemented relative and moving average joint position action terms. These allow the user to specify + the target joint positions as relative to the current joint positions or as a moving average of the + joint positions over a window of time. + + +0.10.27 (2024-02-28) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added UI feature to start and stop animation recording in the stage when running an environment. + To enable this feature, please pass the argument ``--disable_fabric`` to the environment script to allow + USD read/write operations. Be aware that this will slow down the simulation. + + +0.10.26 (2024-02-26) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a viewport camera controller class to the :class:`isaaclab.envs.BaseEnv`. This is useful + for applications where the user wants to render the viewport from different perspectives even when the + simulation is running in headless mode. + + +0.10.25 (2024-02-26) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Ensures that all path arguments in :mod:`isaaclab.sim.utils` are cast to ``str``. Previously, + we had handled path types as strings without casting. + + +0.10.24 (2024-02-26) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added tracking of contact time in the :class:`isaaclab.sensors.ContactSensor` class. Previously, + only the air time was being tracked. +* Added contact force threshold, :attr:`isaaclab.sensors.ContactSensorCfg.force_threshold`, to detect + when the contact sensor is in contact. Previously, this was set to hard-coded 1.0 in the sensor class. + + +0.10.23 (2024-02-21) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixes the order of size arguments in :meth:`isaaclab.terrains.height_field.random_uniform_terrain`. Previously, the function + would crash if the size along x and y were not the same. + + +0.10.22 (2024-02-14) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed "divide by zero" bug in :class:`~isaaclab.sim.SimulationContext` when setting gravity vector. + Now, it is correctly disabled when the gravity vector is set to zero. + + +0.10.21 (2024-02-12) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the printing of articulation joint information when the articulation has only one joint. + Earlier, the function was performing a squeeze operation on the tensor, which caused an error when + trying to index the tensor of shape (1,). + + +0.10.20 (2024-02-12) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Adds :attr:`isaaclab.sim.PhysxCfg.enable_enhanced_determinism` to enable improved + determinism from PhysX. Please note this comes at the expense of performance. + + +0.10.19 (2024-02-08) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed environment closing so that articulations, objects, and sensors are cleared properly. + + +0.10.18 (2024-02-05) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Pinned :mod:`torch` version to 2.0.1 in the setup.py to keep parity version of :mod:`torch` supplied by + Isaac 2023.1.1, and prevent version incompatibility between :mod:`torch` ==2.2 and + :mod:`typing-extensions` ==3.7.4.3 + + +0.10.17 (2024-02-02) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^^ + +* Fixed carb setting ``/app/livestream/enabled`` to be set as False unless live-streaming is specified + by :class:`isaaclab.app.AppLauncher` settings. This fixes the logic of :meth:`SimulationContext.render`, + which depended on the config in previous versions of Isaac defaulting to false for this setting. + + +0.10.16 (2024-01-29) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^ + +* Added an offset parameter to the height scan observation term. This allows the user to specify the + height offset of the scan from the tracked body. Previously it was hard-coded to be 0.5. + + +0.10.15 (2024-01-29) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed joint torque computation for implicit actuators. Earlier, the torque was always zero for implicit + actuators. Now, it is computed approximately by applying the PD law. + + +0.10.14 (2024-01-22) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the tensor shape of :attr:`isaaclab.sensors.ContactSensorData.force_matrix_w`. Earlier, the reshaping + led to a mismatch with the data obtained from PhysX. + + +0.10.13 (2024-01-15) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed running of environments with a single instance even if the :attr:`replicate_physics`` flag is set to True. + + +0.10.12 (2024-01-10) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed indexing of source and target frames in the :class:`isaaclab.sensors.FrameTransformer` class. + Earlier, it always assumed that the source frame body is at index 0. Now, it uses the body index of the + source frame to compute the transformation. + +Deprecated +^^^^^^^^^^ + +* Renamed quantities in the :class:`isaaclab.sensors.FrameTransformerData` class to be more + consistent with the terminology used in the asset classes. The following quantities are deprecated: + + * ``target_rot_w`` -> ``target_quat_w`` + * ``source_rot_w`` -> ``source_quat_w`` + * ``target_rot_source`` -> ``target_quat_source`` + + +0.10.11 (2024-01-08) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed attribute error raised when calling the :class:`isaaclab.envs.mdp.TerrainBasedPositionCommand` + command term. +* Added a dummy function in :class:`isaaclab.terrain.TerrainImporter` that returns environment + origins as terrain-aware sampled targets. This function should be implemented by child classes based on + the terrain type. + + +0.10.10 (2023-12-21) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed reliance on non-existent ``Viewport`` in :class:`isaaclab.sim.SimulationContext` when loading livestreaming + by ensuring that the extension ``omni.kit.viewport.window`` is enabled in :class:`isaaclab.app.AppLauncher` when + livestreaming is enabled + + +0.10.9 (2023-12-21) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed invalidation of physics views inside the asset and sensor classes. Earlier, they were left initialized + even when the simulation was stopped. This caused issues when closing the application. + + +0.10.8 (2023-12-20) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :class:`isaaclab.envs.mdp.actions.DifferentialInverseKinematicsAction` class + to account for the offset pose of the end-effector. + + +0.10.7 (2023-12-19) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added a check to ray-cast and camera sensor classes to ensure that the sensor prim path does not + have a regex expression at its leaf. For instance, ``/World/Robot/camera_.*`` is not supported + for these sensor types. This behavior needs to be fixed in the future. + + +0.10.6 (2023-12-19) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for using articulations as visualization markers. This disables all physics APIs from + the articulation and allows the user to use it as a visualization marker. It is useful for creating + visualization markers for the end-effectors or base of the robot. + +Fixed +^^^^^ + +* Fixed hiding of debug markers from secondary images when using the + :class:`isaaclab.markers.VisualizationMarkers` class. Earlier, the properties were applied on + the XForm prim instead of the Mesh prim. + + +0.10.5 (2023-12-18) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed test ``check_base_env_anymal_locomotion.py``, which + previously called :func:`torch.jit.load` with the path to a policy (which would work + for a local file), rather than calling + :func:`isaaclab.utils.assets.read_file` on the path to get the file itself. + + +0.10.4 (2023-12-14) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed potentially breaking import of omni.kit.widget.toolbar by ensuring that + if live-stream is enabled, then the :mod:`omni.kit.widget.toolbar` + extension is loaded. + +0.10.3 (2023-12-12) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the attribute :attr:`isaaclab.actuators.ActuatorNetMLPCfg.input_order` + to specify the order of the input tensors to the MLP network. + +Fixed +^^^^^ + +* Fixed computation of metrics for the velocity command term. Earlier, the norm was being computed + over the entire batch instead of the last dimension. +* Fixed the clipping inside the :class:`isaaclab.actuators.DCMotor` class. Earlier, it was + not able to handle the case when configured saturation limit was set to None. + + +0.10.2 (2023-12-12) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added a check in the simulation stop callback in the :class:`isaaclab.sim.SimulationContext` class + to not render when an exception is raised. The while loop in the callback was preventing the application + from closing when an exception was raised. + + +0.10.1 (2023-12-06) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added command manager class with terms defined by :class:`isaaclab.managers.CommandTerm`. This + allow for multiple types of command generators to be used in the same environment. + + +0.10.0 (2023-12-04) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Modified the sensor and asset base classes to use the underlying PhysX views instead of Isaac Sim views. + Using Isaac Sim classes led to a very high load time (of the order of minutes) when using a scene with + many assets. This is because Isaac Sim supports USD paths which are slow and not required. + +Added +^^^^^ + +* Added faster implementation of USD stage traversal methods inside the :class:`isaaclab.sim.utils` module. +* Added properties :attr:`isaaclab.assets.AssetBase.num_instances` and + :attr:`isaaclab.sensor.SensorBase.num_instances` to obtain the number of instances of the asset + or sensor in the simulation respectively. + +Removed +^^^^^^^ + +* Removed dependencies on Isaac Sim view classes. It is no longer possible to use :attr:`root_view` and + :attr:`body_view`. Instead use :attr:`root_physx_view` and :attr:`body_physx_view` to access the underlying + PhysX views. + + +0.9.55 (2023-12-03) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the Nucleus directory path in the :attr:`isaaclab.utils.assets.NVIDIA_NUCLEUS_DIR`. + Earlier, it was referring to the ``NVIDIA/Assets`` directory instead of ``NVIDIA``. + + +0.9.54 (2023-11-29) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed pose computation in the :class:`isaaclab.sensors.Camera` class to obtain them from XFormPrimView + instead of using ``UsdGeomCamera.ComputeLocalToWorldTransform`` method. The latter is not updated correctly + during GPU simulation. +* Fixed initialization of the annotator info in the class :class:`isaaclab.sensors.Camera`. Previously + all dicts had the same memory address which caused all annotators to have the same info. +* Fixed the conversion of ``uint32`` warp arrays inside the :meth:`isaaclab.utils.array.convert_to_torch` + method. PyTorch does not support this type, so it is converted to ``int32`` before converting to PyTorch tensor. +* Added render call inside :meth:`isaaclab.sim.SimulationContext.reset` to initialize Replicator + buffers when the simulation is reset. + + +0.9.53 (2023-11-29) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the behavior of passing :obj:`None` to the :class:`isaaclab.actuators.ActuatorBaseCfg` + class. Earlier, they were resolved to fixed default values. Now, they imply that the values are loaded + from the USD joint drive configuration. + +Added +^^^^^ + +* Added setting of joint armature and friction quantities to the articulation class. + + +0.9.52 (2023-11-29) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the warning print in :meth:`isaaclab.sim.utils.apply_nested` method + to be more descriptive. Earlier, it was printing a warning for every instanced prim. + Now, it only prints a warning if it could not apply the attribute to any of the prims. + +Added +^^^^^ + +* Added the method :meth:`isaaclab.utils.assets.retrieve_file_path` to + obtain the absolute path of a file on the Nucleus server or locally. + +Fixed +^^^^^ + +* Fixed hiding of STOP button in the :class:`AppLauncher` class when running the + simulation in headless mode. +* Fixed a bug with :meth:`isaaclab.sim.utils.clone` failing when the input prim path + had no parent (example: "/Table"). + + +0.9.51 (2023-11-29) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the :meth:`isaaclab.sensor.SensorBase.update` method to always recompute the buffers if + the sensor is in visualization mode. + +Added +^^^^^ + +* Added available entities to the error message when accessing a non-existent entity in the + :class:`InteractiveScene` class. +* Added a warning message when the user tries to reference an invalid prim in the :class:`FrameTransformer` sensor. + + +0.9.50 (2023-11-28) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Hid the ``STOP`` button in the UI when running standalone Python scripts. This is to prevent + users from accidentally clicking the button and stopping the simulation. They should only be able to + play and pause the simulation from the UI. + +Removed +^^^^^^^ + +* Removed :attr:`isaaclab.sim.SimulationCfg.shutdown_app_on_stop`. The simulation is always rendering + if it is stopped from the UI. The user needs to close the window or press ``Ctrl+C`` to close the simulation. + + +0.9.49 (2023-11-27) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added an interface class, :class:`isaaclab.managers.ManagerTermBase`, to serve as the parent class + for term implementations that are functional classes. +* Adapted all managers to support terms that are classes and not just functions clearer. This allows the user to + create more complex terms that require additional state information. + + +0.9.48 (2023-11-24) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed initialization of drift in the :class:`isaaclab.sensors.RayCasterCamera` class. + + +0.9.47 (2023-11-24) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Automated identification of the root prim in the :class:`isaaclab.assets.RigidObject` and + :class:`isaaclab.assets.Articulation` classes. Earlier, the root prim was hard-coded to + the spawn prim path. Now, the class searches for the root prim under the spawn prim path. + + +0.9.46 (2023-11-24) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed a critical issue in the asset classes with writing states into physics handles. + Earlier, the states were written over all the indices instead of the indices of the + asset that were being updated. This caused the physics handles to refresh the states + of all the assets in the scene, which is not desirable. + + +0.9.45 (2023-11-24) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`isaaclab.command_generators.UniformPoseCommandGenerator` to generate + poses in the asset's root frame by uniformly sampling from a given range. + + +0.9.44 (2023-11-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added methods :meth:`reset` and :meth:`step` to the :class:`isaaclab.envs.BaseEnv`. This unifies + the environment interface for simple standalone applications with the class. + + +0.9.43 (2023-11-16) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Replaced subscription of physics play and stop events in the :class:`isaaclab.assets.AssetBase` and + :class:`isaaclab.sensors.SensorBase` classes with subscription to time-line play and stop events. + This is to prevent issues in cases where physics first needs to perform mesh cooking and handles are not + available immediately. For instance, with deformable meshes. + + +0.9.42 (2023-11-16) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed setting of damping values from the configuration for :class:`ActuatorBase` class. Earlier, + the stiffness values were being set into damping when a dictionary configuration was passed to the + actuator model. +* Added dealing with :class:`int` and :class:`float` values in the configurations of :class:`ActuatorBase`. + Earlier, a type-error was thrown when integer values were passed to the actuator model. + + +0.9.41 (2023-11-16) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the naming and shaping issues in the binary joint action term. + + +0.9.40 (2023-11-09) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Simplified the manual initialization of Isaac Sim :class:`ArticulationView` class. Earlier, we basically + copied the code from the Isaac Sim source code. Now, we just call their initialize method. + +Changed +^^^^^^^ + +* Changed the name of attribute :attr:`default_root_state_w` to :attr:`default_root_state`. The latter is + more correct since the data is actually in the local environment frame and not the simulation world frame. + + +0.9.39 (2023-11-08) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Changed the reference of private ``_body_view`` variable inside the :class:`RigidObject` class + to the public ``body_view`` property. For a rigid object, the private variable is not defined. + + +0.9.38 (2023-11-07) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Upgraded the :class:`isaaclab.envs.RLTaskEnv` class to support Gym 0.29.0 environment definition. + +Added +^^^^^ + +* Added computation of ``time_outs`` and ``terminated`` signals inside the termination manager. These follow the + definition mentioned in `Gym 0.29.0 `_. +* Added proper handling of observation and action spaces in the :class:`isaaclab.envs.RLTaskEnv` class. + These now follow closely to how Gym VecEnv handles the spaces. + + +0.9.37 (2023-11-06) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed broken visualization in :mod:`isaaclab.sensors.FrameTramsformer` class by overwriting the + correct ``_debug_vis_callback`` function. +* Moved the visualization marker configurations of sensors to their respective sensor configuration classes. + This allows users to set these configurations from the configuration object itself. + + +0.9.36 (2023-11-03) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added explicit deleting of different managers in the :class:`isaaclab.envs.BaseEnv` and + :class:`isaaclab.envs.RLTaskEnv` classes. This is required since deleting the managers + is order-sensitive (many managers need to be deleted before the scene is deleted). + + +0.9.35 (2023-11-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the error: ``'str' object has no attribute '__module__'`` introduced by adding the future import inside the + :mod:`isaaclab.utils.warp.kernels` module. Warp language does not support the ``__future__`` imports. + + +0.9.34 (2023-11-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added missing import of ``from __future__ import annotations`` in the :mod:`isaaclab.utils.warp` + module. This is needed to have a consistent behavior across Python versions. + + +0.9.33 (2023-11-02) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :class:`isaaclab.command_generators.NullCommandGenerator` class. Earlier, + it was having a runtime error due to infinity in the resampling time range. Now, the class just + overrides the parent methods to perform no operations. + + +0.9.32 (2023-11-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed the :class:`isaaclab.envs.RLEnv` class to :class:`isaaclab.envs.RLTaskEnv` to + avoid confusions in terminologies between environments and tasks. + + +0.9.31 (2023-11-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`isaaclab.sensors.RayCasterCamera` class, as a ray-casting based camera for + "distance_to_camera", "distance_to_image_plane" and "normals" annotations. It has the same interface and + functionalities as the USD Camera while it is on average 30% faster. + + +0.9.30 (2023-11-01) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added skipping of None values in the :class:`InteractiveScene` class when creating the scene from configuration + objects. Earlier, it was throwing an error when the user passed a None value for a scene element. +* Added ``kwargs`` to the :class:`RLEnv` class to allow passing additional arguments from gym registry function. + This is now needed since the registry function passes args beyond the ones specified in the constructor. + + +0.9.29 (2023-11-01) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the material path resolution inside the :class:`isaaclab.sim.converters.UrdfConverter` class. + With Isaac Sim 2023.1, the material paths from the importer are always saved as absolute paths. This caused + issues when the generated USD file was moved to a different location. The fix now resolves the material paths + relative to the USD file location. + + +0.9.28 (2023-11-01) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the way the :func:`isaaclab.sim.spawners.from_files.spawn_ground_plane` function sets the + height of the ground. Earlier, it was reading the height from the configuration object. Now, it expects the + desired transformation as inputs to the function. This makes it consistent with the other spawner functions. + + +0.9.27 (2023-10-31) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed the default value of the argument ``camel_case`` in setters of USD attributes. This is to avoid + confusion with the naming of the attributes in the USD file. + +Fixed +^^^^^ + +* Fixed the selection of material prim in the :class:`isaaclab.sim.spawners.materials.spawn_preview_surface` + method. Earlier, the created prim was being selected in the viewport which interfered with the selection of + prims by the user. +* Updated :class:`isaaclab.sim.converters.MeshConverter` to use a different stage than the default stage + for the conversion. This is to avoid the issue of the stage being closed when the conversion is done. + + +0.9.26 (2023-10-31) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the sensor implementation for :class:`isaaclab.sensors.FrameTransformer` class. Currently, + it handles obtaining the transformation between two frames in the same articulation. + + +0.9.25 (2023-10-27) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :mod:`isaaclab.envs.ui` module to put all the UI-related classes in one place. This currently + implements the :class:`isaaclab.envs.ui.BaseEnvWindow` and :class:`isaaclab.envs.ui.RLEnvWindow` + classes. Users can inherit from these classes to create their own UI windows. +* Added the attribute :attr:`isaaclab.envs.BaseEnvCfg.ui_window_class_type` to specify the UI window class + to be used for the environment. This allows the user to specify their own UI window class to be used for the + environment. + + +0.9.24 (2023-10-27) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the behavior of setting up debug visualization for assets, sensors and command generators. + Earlier it was raising an error if debug visualization was not enabled in the configuration object. + Now it checks whether debug visualization is implemented and only sets up the callback if it is + implemented. + + +0.9.23 (2023-10-27) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed a typo in the :class:`AssetBase` and :class:`SensorBase` that effected the class destructor. + Earlier, a tuple was being created in the constructor instead of the actual object. + + +0.9.22 (2023-10-26) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a :class:`isaaclab.command_generators.NullCommandGenerator` class for no command environments. + This is easier to work with than having checks for :obj:`None` in the command generator. + +Fixed +^^^^^ + +* Moved the randomization manager to the :class:`isaaclab.envs.BaseEnv` class with the default + settings to reset the scene to the defaults specified in the configurations of assets. +* Moved command generator to the :class:`isaaclab.envs.RlEnv` class to have all task-specification + related classes in the same place. + + +0.9.21 (2023-10-26) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Decreased the priority of callbacks in asset and sensor base classes. This may help in preventing + crashes when warm starting the simulation. +* Fixed no rendering mode when running the environment from the GUI. Earlier the function + :meth:`SimulationContext.set_render_mode` was erroring out. + + +0.9.20 (2023-10-25) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Changed naming in :class:`isaaclab.sim.SimulationContext.RenderMode` to use ``NO_GUI_OR_RENDERING`` + and ``NO_RENDERING`` instead of ``HEADLESS`` for clarity. +* Changed :class:`isaaclab.sim.SimulationContext` to be capable of handling livestreaming and + offscreen rendering. +* Changed :class:`isaaclab.app.AppLauncher` envvar ``VIEWPORT_RECORD`` to the more descriptive + ``OFFSCREEN_RENDER``. + + +0.9.19 (2023-10-25) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added Gym observation and action spaces for the :class:`isaaclab.envs.RLEnv` class. + + +0.9.18 (2023-10-23) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Created :class:`isaaclab.sim.converters.asset_converter.AssetConverter` to serve as a base + class for all asset converters. +* Added :class:`isaaclab.sim.converters.mesh_converter.MeshConverter` to handle loading and conversion + of mesh files (OBJ, STL and FBX) into USD format. +* Added script ``convert_mesh.py`` to ``source/tools`` to allow users to convert a mesh to USD via command line arguments. + +Changed +^^^^^^^ + +* Renamed the submodule :mod:`isaaclab.sim.loaders` to :mod:`isaaclab.sim.converters` to be more + general with the functionality of the module. +* Updated ``check_instanceable.py`` script to convert relative paths to absolute paths. + + +0.9.17 (2023-10-22) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added setters and getters for term configurations in the :class:`RandomizationManager`, :class:`RewardManager` + and :class:`TerminationManager` classes. This allows the user to modify the term configurations after the + manager has been created. +* Added the method :meth:`compute_group` to the :class:`isaaclab.managers.ObservationManager` class to + compute the observations for only a given group. +* Added the curriculum term for modifying reward weights after certain environment steps. + + +0.9.16 (2023-10-22) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for keyword arguments for terms in the :class:`isaaclab.managers.ManagerBase`. + +Fixed +^^^^^ + +* Fixed resetting of buffers in the :class:`TerminationManager` class. Earlier, the values were being set + to ``0.0`` instead of ``False``. + + +0.9.15 (2023-10-22) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added base yaw heading and body acceleration into :class:`isaaclab.assets.RigidObjectData` class. + These quantities are computed inside the :class:`RigidObject` class. + +Fixed +^^^^^ + +* Fixed the :meth:`isaaclab.assets.RigidObject.set_external_force_and_torque` method to correctly + deal with the body indices. +* Fixed a bug in the :meth:`isaaclab.utils.math.wrap_to_pi` method to prevent self-assignment of + the input tensor. + + +0.9.14 (2023-10-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added 2-D drift (i.e. along x and y) to the :class:`isaaclab.sensors.RayCaster` class. +* Added flags to the :class:`isaaclab.sensors.ContactSensorCfg` to optionally obtain the + sensor origin and air time information. Since these are not required by default, they are + disabled by default. + +Fixed +^^^^^ + +* Fixed the handling of contact sensor history buffer in the :class:`isaaclab.sensors.ContactSensor` class. + Earlier, the buffer was not being updated correctly. + + +0.9.13 (2023-10-20) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the issue with double :obj:`Ellipsis` when indexing tensors with multiple dimensions. + The fix now uses :obj:`slice(None)` instead of :obj:`Ellipsis` to index the tensors. + + +0.9.12 (2023-10-18) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed bugs in actuator model implementation for actuator nets. Earlier the DC motor clipping was not working. +* Fixed bug in applying actuator model in the :class:`isaaclab.asset.Articulation` class. The new + implementation caches the outputs from explicit actuator model into the ``joint_pos_*_sim`` buffer to + avoid feedback loops in the tensor operation. + + +0.9.11 (2023-10-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the support for semantic tags into the :class:`isaaclab.sim.spawner.SpawnerCfg` class. This allows + the user to specify the semantic tags for a prim when spawning it into the scene. It follows the same format as + Omniverse Replicator. + + +0.9.10 (2023-10-16) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``--livestream`` and ``--ros`` CLI args to :class:`isaaclab.app.AppLauncher` class. +* Added a static function :meth:`isaaclab.app.AppLauncher.add_app_launcher_args`, which + appends the arguments needed for :class:`isaaclab.app.AppLauncher` to the argument parser. + +Changed +^^^^^^^ + +* Within :class:`isaaclab.app.AppLauncher`, removed ``REMOTE_DEPLOYMENT`` env-var processing + in the favor of ``HEADLESS`` and ``LIVESTREAM`` env-vars. These have clearer uses and better parity + with the CLI args. + + +0.9.9 (2023-10-12) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the property :attr:`isaaclab.assets.Articulation.is_fixed_base` to the articulation class to + check if the base of the articulation is fixed or floating. +* Added the task-space action term corresponding to the differential inverse-kinematics controller. + +Fixed +^^^^^ + +* Simplified the :class:`isaaclab.controllers.DifferentialIKController` to assume that user provides the + correct end-effector poses and Jacobians. Earlier it was doing internal frame transformations which made the + code more complicated and error-prone. + + +0.9.8 (2023-09-30) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the boundedness of class objects that register callbacks into the simulator. + These include devices, :class:`AssetBase`, :class:`SensorBase` and :class:`CommandGenerator`. + The fix ensures that object gets deleted when the user deletes the object. + + +0.9.7 (2023-09-26) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Modified the :class:`isaaclab.markers.VisualizationMarkers` to use the + :class:`isaaclab.sim.spawner.SpawnerCfg` class instead of their + own configuration objects. This makes it consistent with the other ways to spawn assets in the scene. + +Added +^^^^^ + +* Added the method :meth:`copy` to configclass to allow copying of configuration objects. + + +0.9.6 (2023-09-26) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Changed class-level configuration classes to refer to class types using ``class_type`` attribute instead + of ``cls`` or ``cls_name``. + + +0.9.5 (2023-09-25) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added future import of ``annotations`` to have a consistent behavior across Python versions. +* Removed the type-hinting from docstrings to simplify maintenance of the documentation. All type-hints are + now in the code itself. + + +0.9.4 (2023-08-29) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`isaaclab.scene.InteractiveScene`, as the central scene unit that contains all entities + that are part of the simulation. These include the terrain, sensors, articulations, rigid objects etc. + The scene groups the common operations of these entities and allows to access them via their unique names. +* Added :mod:`isaaclab.envs` module that contains environment definitions that encapsulate the different + general (scene, action manager, observation manager) and RL-specific (reward and termination manager) managers. +* Added :class:`isaaclab.managers.SceneEntityCfg` to handle which scene elements are required by the + manager's terms. This allows the manager to parse useful information from the scene elements, such as the + joint and body indices, and pass them to the term. +* Added :class:`isaaclab.sim.SimulationContext.RenderMode` to handle different rendering modes based on + what the user wants to update (viewport, cameras, or UI elements). + +Fixed +^^^^^ + +* Fixed the :class:`isaaclab.command_generators.CommandGeneratorBase` to register a debug visualization + callback similar to how sensors and robots handle visualization. + + +0.9.3 (2023-08-23) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Enabled the `faulthander `_ to catch segfaults and print + the stack trace. This is enabled by default in the :class:`isaaclab.app.AppLauncher` class. + +Fixed +^^^^^ + +* Re-added the :mod:`isaaclab.utils.kit` to the ``compat`` directory and fixed all the references to it. +* Fixed the deletion of Replicator nodes for the :class:`isaaclab.sensors.Camera` class. Earlier, the + Replicator nodes were not being deleted when the camera was deleted. However, this does not prevent the random + crashes that happen when the camera is deleted. +* Fixed the :meth:`isaaclab.utils.math.convert_quat` to support both numpy and torch tensors. + +Changed +^^^^^^^ + +* Renamed all the scripts inside the ``test`` directory to follow the convention: + + * ``test_.py``: Tests for the module ```` using unittest. + * ``check_``: Check for the module ```` using python main function. + + +0.9.2 (2023-08-22) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the ability to color meshes in the :class:`isaaclab.terrain.TerrainGenerator` class. Currently, + it only supports coloring the mesh randomly (``"random"``), based on the terrain height (``"height"``), and + no coloring (``"none"``). + +Fixed +^^^^^ + +* Modified the :class:`isaaclab.terrain.TerrainImporter` class to configure visual and physics materials + based on the configuration object. + + +0.9.1 (2023-08-18) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Introduced three different rotation conventions in the :class:`isaaclab.sensors.Camera` class. These + conventions are: + + * ``opengl``: the camera is looking down the -Z axis with the +Y axis pointing up + * ``ros``: the camera is looking down the +Z axis with the +Y axis pointing down + * ``world``: the camera is looking along the +X axis with the -Z axis pointing down + + These can be used to declare the camera offset in :class:`isaaclab.sensors.CameraCfg.OffsetCfg` class + and in :meth:`isaaclab.sensors.Camera.set_world_pose` method. Additionally, all conventions are + saved to :class:`isaaclab.sensors.CameraData` class for easy access. + +Changed +^^^^^^^ + +* Adapted all the sensor classes to follow a structure similar to the :class:`isaaclab.assets.AssetBase`. + Hence, the spawning and initialization of sensors manually by the users is avoided. +* Removed the :meth:`debug_vis` function since that this functionality is handled by a render callback automatically + (based on the passed configuration for the :class:`isaaclab.sensors.SensorBaseCfg.debug_vis` flag). + + +0.9.0 (2023-08-18) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Introduces a new set of asset interfaces. These interfaces simplify the spawning of assets into the scene + and initializing the physics handle by putting that inside post-startup physics callbacks. With this, users + no longer need to worry about the :meth:`spawn` and :meth:`initialize` calls. +* Added utility methods to :mod:`isaaclab.utils.string` module that resolve regex expressions based + on passed list of target keys. + +Changed +^^^^^^^ + +* Renamed all references of joints in an articulation from "dof" to "joint". This makes it consistent with the + terminology used in robotics. + +Deprecated +^^^^^^^^^^ + +* Removed the previous modules for objects and robots. Instead the :class:`Articulation` and :class:`RigidObject` + should be used. + + +0.8.12 (2023-08-18) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added other properties provided by ``PhysicsScene`` to the :class:`isaaclab.sim.SimulationContext` + class to allow setting CCD, solver iterations, etc. +* Added commonly used functions to the :class:`SimulationContext` class itself to avoid having additional + imports from Isaac Sim when doing simple tasks such as setting camera view or retrieving the simulation settings. + +Fixed +^^^^^ + +* Switched the notations of default buffer values in :class:`isaaclab.sim.PhysxCfg` from multiplication + to scientific notation to avoid confusion with the values. + + +0.8.11 (2023-08-18) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Adds utility functions and configuration objects in the :mod:`isaaclab.sim.spawners` + to create the following prims in the scene: + + * :mod:`isaaclab.sim.spawners.from_file`: Create a prim from a USD/URDF file. + * :mod:`isaaclab.sim.spawners.shapes`: Create USDGeom prims for shapes (box, sphere, cylinder, capsule, etc.). + * :mod:`isaaclab.sim.spawners.materials`: Create a visual or physics material prim. + * :mod:`isaaclab.sim.spawners.lights`: Create a USDLux prim for different types of lights. + * :mod:`isaaclab.sim.spawners.sensors`: Create a USD prim for supported sensors. + +Changed +^^^^^^^ + +* Modified the :class:`SimulationContext` class to take the default physics material using the material spawn + configuration object. + + +0.8.10 (2023-08-17) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added methods for defining different physics-based schemas in the :mod:`isaaclab.sim.schemas` module. + These methods allow creating the schema if it doesn't exist at the specified prim path and modify + its properties based on the configuration object. + + +0.8.9 (2023-08-09) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the :class:`isaaclab.asset_loader.UrdfLoader` class to the :mod:`isaaclab.sim.loaders` + module to make it more accessible to the user. + + +0.8.8 (2023-08-09) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configuration classes and functions for setting different physics-based schemas in the + :mod:`isaaclab.sim.schemas` module. These allow modifying properties of the physics solver + on the asset using configuration objects. + + +0.8.7 (2023-08-03) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added support for `__post_init__ `_ in + the :class:`isaaclab.utils.configclass` decorator. + + +0.8.6 (2023-08-03) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for callable classes in the :class:`isaaclab.managers.ManagerBase`. + + +0.8.5 (2023-08-03) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :class:`isaaclab.markers.Visualizationmarkers` class so that the markers are not visible in camera rendering mode. + +Changed +^^^^^^^ + +* Simplified the creation of the point instancer in the :class:`isaaclab.markers.Visualizationmarkers` class. It now creates a new + prim at the next available prim path if a prim already exists at the given path. + + +0.8.4 (2023-08-02) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`isaaclab.sim.SimulationContext` class to the :mod:`isaaclab.sim` module. + This class inherits from the :class:`isaacsim.core.api.simulation_context.SimulationContext` class and adds + the ability to create a simulation context from a configuration object. + + +0.8.3 (2023-08-02) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the :class:`ActuatorBase` class to the :mod:`isaaclab.actuators.actuator_base` module. +* Renamed the :mod:`isaaclab.actuators.actuator` module to :mod:`isaaclab.actuators.actuator_pd` + to make it more explicit that it contains the PD actuator models. + + +0.8.2 (2023-08-02) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Cleaned up the :class:`isaaclab.terrain.TerrainImporter` class to take all the parameters from the configuration + object. This makes it consistent with the other classes in the package. +* Moved the configuration classes for terrain generator and terrain importer into separate files to resolve circular + dependency issues. + + +0.8.1 (2023-08-02) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added a hack into :class:`isaaclab.app.AppLauncher` class to remove Isaac Lab packages from the path before launching + the simulation application. This prevents the warning messages that appears when the user launches the ``SimulationApp``. + +Added +^^^^^ + +* Enabled necessary viewport extensions in the :class:`isaaclab.app.AppLauncher` class itself if ``VIEWPORT_ENABLED`` + flag is true. + + +0.8.0 (2023-07-26) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`ActionManager` class to the :mod:`isaaclab.managers` module to handle actions in the + environment through action terms. +* Added contact force history to the :class:`isaaclab.sensors.ContactSensor` class. The history is stored + in the ``net_forces_w_history`` attribute of the sensor data. + +Changed +^^^^^^^ + +* Implemented lazy update of buffers in the :class:`isaaclab.sensors.SensorBase` class. This allows the user + to update the sensor data only when required, i.e. when the data is requested by the user. This helps avoid double + computation of sensor data when a reset is called in the environment. + +Deprecated +^^^^^^^^^^ + +* Removed the support for different backends in the sensor class. We only use Pytorch as the backend now. +* Removed the concept of actuator groups. They are now handled by the :class:`isaaclab.managers.ActionManager` + class. The actuator models are now directly handled by the robot class itself. + + +0.7.4 (2023-07-26) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the behavior of the :class:`isaaclab.terrains.TerrainImporter` class. It now expects the terrain + type to be specified in the configuration object. This allows the user to specify everything in the configuration + object and not have to do an explicit call to import a terrain. + +Fixed +^^^^^ + +* Fixed setting of quaternion orientations inside the :class:`isaaclab.markers.Visualizationmarkers` class. + Earlier, the orientation was being set into the point instancer in the wrong order (``wxyz`` instead of ``xyzw``). + + +0.7.3 (2023-07-25) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the issue with multiple inheritance in the :class:`isaaclab.utils.configclass` decorator. + Earlier, if the inheritance tree was more than one level deep and the lowest level configuration class was + not updating its values from the middle level classes. + + +0.7.2 (2023-07-24) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the method :meth:`replace` to the :class:`isaaclab.utils.configclass` decorator to allow + creating a new configuration object with values replaced from keyword arguments. This function internally + calls the `dataclasses.replace `_. + +Fixed +^^^^^ + +* Fixed the handling of class types as member values in the :meth:`isaaclab.utils.configclass`. Earlier it was + throwing an error since class types were skipped in the if-else block. + + +0.7.1 (2023-07-22) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`TerminationManager`, :class:`CurriculumManager`, and :class:`RandomizationManager` classes + to the :mod:`isaaclab.managers` module to handle termination, curriculum, and randomization respectively. + + +0.7.0 (2023-07-22) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Created a new :mod:`isaaclab.managers` module for all the managers related to the environment / scene. + This includes the :class:`isaaclab.managers.ObservationManager` and :class:`isaaclab.managers.RewardManager` + classes that were previously in the :mod:`isaaclab.utils.mdp` module. +* Added the :class:`isaaclab.managers.ManagerBase` class to handle the creation of managers. +* Added configuration classes for :class:`ObservationTermCfg` and :class:`RewardTermCfg` to allow easy creation of + observation and reward terms. + +Changed +^^^^^^^ + +* Changed the behavior of :class:`ObservationManager` and :class:`RewardManager` classes to accept the key ``func`` + in each configuration term to be a callable. This removes the need to inherit from the base class + and allows more reusability of the functions across different environments. +* Moved the old managers to the :mod:`isaaclab.compat.utils.mdp` module. +* Modified the necessary scripts to use the :mod:`isaaclab.compat.utils.mdp` module. + + +0.6.2 (2023-07-21) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :mod:`isaaclab.command_generators` to generate different commands based on the desired task. + It allows the user to generate commands for different tasks in the same environment without having to write + custom code for each task. + + +0.6.1 (2023-07-16) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :meth:`isaaclab.utils.math.quat_apply_yaw` to compute the yaw quaternion correctly. + +Added +^^^^^ + +* Added functions to convert string and callable objects in :mod:`isaaclab.utils.string`. + + +0.6.0 (2023-07-16) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the argument :attr:`sort_keys` to the :meth:`isaaclab.utils.io.yaml.dump_yaml` method to allow + enabling/disabling of sorting of keys in the output yaml file. + +Fixed +^^^^^ + +* Fixed the ordering of terms in :mod:`isaaclab.utils.configclass` to be consistent in the order in which + they are defined. Previously, the ordering was done alphabetically which made it inconsistent with the order in which + the parameters were defined. + +Changed +^^^^^^^ + +* Changed the default value of the argument :attr:`sort_keys` in the :meth:`isaaclab.utils.io.yaml.dump_yaml` + method to ``False``. +* Moved the old config classes in :mod:`isaaclab.utils.configclass` to + :mod:`isaaclab.compat.utils.configclass` so that users can still run their old code where alphabetical + ordering was used. + + +0.5.0 (2023-07-04) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a generalized :class:`isaaclab.sensors.SensorBase` class that leverages the ideas of views to + handle multiple sensors in a single class. +* Added the classes :class:`isaaclab.sensors.RayCaster`, :class:`isaaclab.sensors.ContactSensor`, + and :class:`isaaclab.sensors.Camera` that output a batched tensor of sensor data. + +Changed +^^^^^^^ + +* Renamed the parameter ``sensor_tick`` to ``update_freq`` to make it more intuitive. +* Moved the old sensors in :mod:`isaaclab.sensors` to :mod:`isaaclab.compat.sensors`. +* Modified the standalone scripts to use the :mod:`isaaclab.compat.sensors` module. + + +0.4.4 (2023-07-05) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :meth:`isaaclab.terrains.trimesh.utils.make_plane` method to handle the case when the + plane origin does not need to be centered. +* Added the :attr:`isaaclab.terrains.TerrainGeneratorCfg.seed` to make generation of terrains reproducible. + The default value is ``None`` which means that the seed is not set. + +Changed +^^^^^^^ + +* Changed the saving of ``origins`` in :class:`isaaclab.terrains.TerrainGenerator` class to be in CSV format + instead of NPY format. + + +0.4.3 (2023-06-28) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`isaaclab.markers.PointInstancerMarker` class that wraps around + `UsdGeom.PointInstancer `_ + to directly work with torch and numpy arrays. + +Changed +^^^^^^^ + +* Moved the old markers in :mod:`isaaclab.markers` to :mod:`isaaclab.compat.markers`. +* Modified the standalone scripts to use the :mod:`isaaclab.compat.markers` module. + + +0.4.2 (2023-06-28) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the sub-module :mod:`isaaclab.terrains` to allow procedural generation of terrains and supporting + importing of terrains from different sources (meshes, usd files or default ground plane). + + +0.4.1 (2023-06-27) +~~~~~~~~~~~~~~~~~~ + +* Added the :class:`isaaclab.app.AppLauncher` class to allow controlled instantiation of + the SimulationApp and extension loading for remote deployment and ROS bridges. + +Changed +^^^^^^^ + +* Modified all standalone scripts to use the :class:`isaaclab.app.AppLauncher` class. + + +0.4.0 (2023-05-27) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a helper class :class:`isaaclab.asset_loader.UrdfLoader` that converts a URDF file to instanceable USD + file based on the input configuration object. + + +0.3.2 (2023-04-27) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added safe-printing of functions while using the :meth:`isaaclab.utils.dict.print_dict` function. + + +0.3.1 (2023-04-23) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a modified version of ``lula_franka_gen.urdf`` which includes an end-effector frame. +* Added a standalone script ``play_rmpflow.py`` to show RMPFlow controller. + +Fixed +^^^^^ + +* Fixed the splitting of commands in the :meth:`ActuatorGroup.compute` method. Earlier it was reshaping the + commands to the shape ``(num_actuators, num_commands)`` which was causing the commands to be split incorrectly. +* Fixed the processing of actuator command in the :meth:`RobotBase._process_actuators_cfg` to deal with multiple + command types when using "implicit" actuator group. + +0.3.0 (2023-04-20) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added the destructor to the keyboard devices to unsubscribe from carb. + +Added +^^^^^ + +* Added the :class:`Se2Gamepad` and :class:`Se3Gamepad` for gamepad teleoperation support. + + +0.2.8 (2023-04-10) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed bugs in :meth:`axis_angle_from_quat` in the ``isaaclab.utils.math`` to handle quaternion with negative w component. +* Fixed bugs in :meth:`subtract_frame_transforms` in the ``isaaclab.utils.math`` by adding the missing final rotation. + + +0.2.7 (2023-04-07) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed repetition in applying mimic multiplier for "p_abs" in the :class:`GripperActuatorGroup` class. +* Fixed bugs in :meth:`reset_buffers` in the :class:`RobotBase` and :class:`LeggedRobot` classes. + +0.2.6 (2023-03-16) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the :class:`CollisionPropertiesCfg` to rigid/articulated object and robot base classes. +* Added the :class:`PhysicsMaterialCfg` to the :class:`SingleArm` class for tool sites. + +Changed +^^^^^^^ + +* Changed the default control mode of the :obj:`PANDA_HAND_MIMIC_GROUP_CFG` to be from ``"v_abs"`` to ``"p_abs"``. + Using velocity control for the mimic group can cause the hand to move in a jerky manner. + + +0.2.5 (2023-03-08) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the indices used for the Jacobian and dynamics quantities in the :class:`MobileManipulator` class. + + +0.2.4 (2023-03-04) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :meth:`apply_nested_physics_material` to the ``isaaclab.utils.kit``. +* Added the :meth:`sample_cylinder` to sample points from a cylinder's surface. +* Added documentation about the issue in using instanceable asset as markers. + +Fixed +^^^^^ + +* Simplified the physics material application in the rigid object and legged robot classes. + +Removed +^^^^^^^ + +* Removed the ``geom_prim_rel_path`` argument in the :class:`RigidObjectCfg.MetaInfoCfg` class. + + +0.2.3 (2023-02-24) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the end-effector body index used for getting the Jacobian in the :class:`SingleArm` and :class:`MobileManipulator` classes. + + +0.2.2 (2023-01-27) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :meth:`set_world_pose_ros` and :meth:`set_world_pose_from_view` in the :class:`Camera` class. + +Deprecated +^^^^^^^^^^ + +* Removed the :meth:`set_world_pose_from_ypr` method from the :class:`Camera` class. + + +0.2.1 (2023-01-26) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the :class:`Camera` class to support different fisheye projection types. + + +0.2.0 (2023-01-25) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for warp backend in camera utilities. +* Extended the ``play_camera.py`` with ``--gpu`` flag to use GPU replicator backend. + +0.1.1 (2023-01-24) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed setting of physics material on the ground plane when using :meth:`isaaclab.utils.kit.create_ground_plane` function. + + +0.1.0 (2023-01-17) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Initial release of the extension with experimental API. +* Available robot configurations: + + * **Quadrupeds:** Unitree A1, ANYmal B, ANYmal C + * **Single-arm manipulators:** Franka Emika arm, UR5 + * **Mobile manipulators:** Clearpath Ridgeback with Franka Emika arm or UR5 diff --git a/source/isaaclab/docs/README.md b/source/isaaclab/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f5ded99b04773e465321109359134f5357efde34 --- /dev/null +++ b/source/isaaclab/docs/README.md @@ -0,0 +1,11 @@ +# Isaac Lab: Framework + +Isaac Lab includes its own set of interfaces and wrappers around Isaac Sim classes. One of the main goals behind this +decision is to have a unified description for different systems. While isaac Sim tries to be general for a wider +variety of simulation requires, our goal has been to specialize these for learning requirements. These include +features such as augmenting simulators with non-ideal actuator models, managing different observation and reward +settings, integrate different sensors, as well as provide interfaces to features that are currently not available in +Isaac Sim but are available from the physics side (such as deformable bodies). + +We recommend the users to try out the demo scripts present in `scripts/demos` that display how different parts +of the framework can be integrated together. diff --git a/source/isaaclab/isaaclab/__init__.py b/source/isaaclab/isaaclab/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..73bd3e99d3abe8440330c4421f25a4849f47d078 --- /dev/null +++ b/source/isaaclab/isaaclab/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Package containing the core framework.""" + +import os +import toml + +# Conveniences to other module directories via relative paths +ISAACLAB_EXT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../")) +"""Path to the extension source directory.""" + +ISAACLAB_METADATA = toml.load(os.path.join(ISAACLAB_EXT_DIR, "config", "extension.toml")) +"""Extension metadata dictionary parsed from the extension.toml file.""" + +# Configure the module-level variables +__version__ = ISAACLAB_METADATA["package"]["version"] diff --git a/source/isaaclab/isaaclab/actuators/__init__.py b/source/isaaclab/isaaclab/actuators/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..db7d36b00a5e4110951258d2c547532e50c7323c --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package for different actuator models. + +Actuator models are used to model the behavior of the actuators in an articulation. These +are usually meant to be used in simulation to model different actuator dynamics and delays. + +There are two main categories of actuator models that are supported: + +- **Implicit**: Motor model with ideal PD from the physics engine. This is similar to having a continuous time + PD controller. The motor model is implicit in the sense that the motor model is not explicitly defined by the user. +- **Explicit**: Motor models based on physical drive models. + + - **Physics-based**: Derives the motor models based on first-principles. + - **Neural Network-based**: Learned motor models from actuator data. + +Every actuator model inherits from the :class:`isaaclab.actuators.ActuatorBase` class, +which defines the common interface for all actuator models. The actuator models are handled +and called by the :class:`isaaclab.assets.Articulation` class. +""" + +from .actuator_base import ActuatorBase +from .actuator_base_cfg import ActuatorBaseCfg +from .actuator_net import ActuatorNetLSTM, ActuatorNetMLP +from .actuator_net_cfg import ActuatorNetLSTMCfg, ActuatorNetMLPCfg +from .actuator_pd import DCMotor, DelayedPDActuator, IdealPDActuator, ImplicitActuator, RemotizedPDActuator +from .actuator_pd_cfg import ( + DCMotorCfg, + DelayedPDActuatorCfg, + IdealPDActuatorCfg, + ImplicitActuatorCfg, + RemotizedPDActuatorCfg, +) diff --git a/source/isaaclab/isaaclab/actuators/actuator_base.py b/source/isaaclab/isaaclab/actuators/actuator_base.py new file mode 100644 index 0000000000000000000000000000000000000000..4489983366d30b4570ae77cb4f8f1a94b8230426 --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_base.py @@ -0,0 +1,381 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING, ClassVar + +import torch + +import isaaclab.utils.string as string_utils +from isaaclab.utils.types import ArticulationActions + +if TYPE_CHECKING: + from .actuator_base_cfg import ActuatorBaseCfg + + +class ActuatorBase(ABC): + """Base class for actuator models over a collection of actuated joints in an articulation. + + Actuator models augment the simulated articulation joints with an external drive dynamics model. + The model is used to convert the user-provided joint commands (positions, velocities and efforts) + into the desired joint positions, velocities and efforts that are applied to the simulated articulation. + + The base class provides the interface for the actuator models. It is responsible for parsing the + actuator parameters from the configuration and storing them as buffers. It also provides the + interface for resetting the actuator state and computing the desired joint commands for the simulation. + + For each actuator model, a corresponding configuration class is provided. The configuration class + is used to parse the actuator parameters from the configuration. It also specifies the joint names + for which the actuator model is applied. These names can be specified as regular expressions, which + are matched against the joint names in the articulation. + + To see how the class is used, check the :class:`isaaclab.assets.Articulation` class. + """ + + is_implicit_model: ClassVar[bool] = False + """Flag indicating if the actuator is an implicit or explicit actuator model. + + If a class inherits from :class:`ImplicitActuator`, then this flag should be set to :obj:`True`. + """ + + computed_effort: torch.Tensor + """The computed effort for the actuator group. Shape is (num_envs, num_joints).""" + + applied_effort: torch.Tensor + """The applied effort for the actuator group. Shape is (num_envs, num_joints). + + This is the effort obtained after clipping the :attr:`computed_effort` based on the + actuator characteristics. + """ + + effort_limit: torch.Tensor + """The effort limit for the actuator group. Shape is (num_envs, num_joints). + + This limit is used differently depending on the actuator type: + + - **Explicit actuators**: Used for internal torque clipping within the actuator model + (e.g., motor torque limits in DC motor models). + - **Implicit actuators**: Same as :attr:`effort_limit_sim` (aliased for consistency). + """ + + effort_limit_sim: torch.Tensor + """The effort limit for the actuator group in the simulation. Shape is (num_envs, num_joints). + + For implicit actuators, the :attr:`effort_limit` and :attr:`effort_limit_sim` are the same. + + - **Explicit actuators**: Typically set to a large value (1.0e9) to avoid double-clipping, + since the actuator model already clips efforts using :attr:`effort_limit`. + - **Implicit actuators**: Same as :attr:`effort_limit` (both values are synchronized). + """ + + velocity_limit: torch.Tensor + """The velocity limit for the actuator group. Shape is (num_envs, num_joints). + + For implicit actuators, the :attr:`velocity_limit` and :attr:`velocity_limit_sim` are the same. + """ + + velocity_limit_sim: torch.Tensor + """The velocity limit for the actuator group in the simulation. Shape is (num_envs, num_joints). + + For implicit actuators, the :attr:`velocity_limit` and :attr:`velocity_limit_sim` are the same. + """ + + stiffness: torch.Tensor + """The stiffness (P gain) of the PD controller. Shape is (num_envs, num_joints).""" + + damping: torch.Tensor + """The damping (D gain) of the PD controller. Shape is (num_envs, num_joints).""" + + armature: torch.Tensor + """The armature of the actuator joints. Shape is (num_envs, num_joints).""" + + friction: torch.Tensor + """The joint static friction of the actuator joints. Shape is (num_envs, num_joints).""" + + dynamic_friction: torch.Tensor + """The joint dynamic friction of the actuator joints. Shape is (num_envs, num_joints).""" + + viscous_friction: torch.Tensor + """The joint viscous friction of the actuator joints. Shape is (num_envs, num_joints).""" + + _DEFAULT_MAX_EFFORT_SIM: ClassVar[float] = 1.0e9 + """The default maximum effort for the actuator joints in the simulation. Defaults to 1.0e9. + + If the :attr:`ActuatorBaseCfg.effort_limit_sim` is not specified and the actuator is an explicit + actuator, then this value is used. + """ + + def __init__( + self, + cfg: ActuatorBaseCfg, + joint_names: list[str], + joint_ids: slice | torch.Tensor, + num_envs: int, + device: str, + stiffness: torch.Tensor | float = 0.0, + damping: torch.Tensor | float = 0.0, + armature: torch.Tensor | float = 0.0, + friction: torch.Tensor | float = 0.0, + dynamic_friction: torch.Tensor | float = 0.0, + viscous_friction: torch.Tensor | float = 0.0, + effort_limit: torch.Tensor | float = torch.inf, + velocity_limit: torch.Tensor | float = torch.inf, + ): + """Initialize the actuator. + + The actuator parameters are parsed from the configuration and stored as buffers. If the parameters + are not specified in the configuration, then their values provided in the constructor are used. + + .. note:: + The values in the constructor are typically obtained through the USD values passed from the PhysX API calls + corresponding to the joints in the actuator model; these values serve as default values if the parameters + are not specified in the cfg. + + + + Args: + cfg: The configuration of the actuator model. + joint_names: The joint names in the articulation. + joint_ids: The joint indices in the articulation. If :obj:`slice(None)`, then all + the joints in the articulation are part of the group. + num_envs: Number of articulations in the view. + device: Device used for processing. + stiffness: The default joint stiffness (P gain). Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + damping: The default joint damping (D gain). Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + armature: The default joint armature. Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + friction: The default joint static friction. Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + dynamic_friction: The default joint dynamic friction. Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + viscous_friction: The default joint viscous friction. Defaults to 0.0. + If a tensor, then the shape is (num_envs, num_joints). + effort_limit: The default effort limit. Defaults to infinity. + If a tensor, then the shape is (num_envs, num_joints). + velocity_limit: The default velocity limit. Defaults to infinity. + If a tensor, then the shape is (num_envs, num_joints). + """ + # save parameters + self.cfg = cfg + self._num_envs = num_envs + self._device = device + self._joint_names = joint_names + self._joint_indices = joint_ids + self.joint_property_resolution_table: dict[str, list] = {} + # For explicit models, we do not want to enforce the effort limit through the solver + # (unless it is explicitly set) + if not self.is_implicit_model and self.cfg.effort_limit_sim is None: + self.cfg.effort_limit_sim = self._DEFAULT_MAX_EFFORT_SIM + + # resolve usd, actuator configuration values + # case 1: if usd_value == actuator_cfg_value: all good, + # case 2: if usd_value != actuator_cfg_value: we use actuator_cfg_value + # case 3: if actuator_cfg_value is None: we use usd_value + + to_check = [ + ("velocity_limit_sim", velocity_limit), + ("effort_limit_sim", effort_limit), + ("stiffness", stiffness), + ("damping", damping), + ("armature", armature), + ("friction", friction), + ("dynamic_friction", dynamic_friction), + ("viscous_friction", viscous_friction), + ] + for param_name, usd_val in to_check: + cfg_val = getattr(self.cfg, param_name) + setattr(self, param_name, self._parse_joint_parameter(cfg_val, usd_val)) + new_val = getattr(self, param_name) + + allclose = ( + torch.all(new_val == usd_val) if isinstance(usd_val, (float, int)) else torch.allclose(new_val, usd_val) + ) + if cfg_val is None or not allclose: + self._record_actuator_resolution( + cfg_val=getattr(self.cfg, param_name), + new_val=new_val[0], # new val always has the shape of (num_envs, num_joints) + usd_val=usd_val, + joint_names=joint_names, + joint_ids=joint_ids, + actuator_param=param_name, + ) + + self.velocity_limit = self._parse_joint_parameter(self.cfg.velocity_limit, self.velocity_limit_sim) + # Parse effort_limit with special default handling: + # - If cfg.effort_limit is None, use the original USD value (effort_limit parameter from constructor) + # - Otherwise, use effort_limit_sim as the default + # Please refer to the documentation of the effort_limit and effort_limit_sim parameters for more details. + effort_default = effort_limit if self.cfg.effort_limit is None else self.effort_limit_sim + self.effort_limit = self._parse_joint_parameter(self.cfg.effort_limit, effort_default) + + # create commands buffers for allocation + self.computed_effort = torch.zeros(self._num_envs, self.num_joints, device=self._device) + self.applied_effort = torch.zeros_like(self.computed_effort) + + def __str__(self) -> str: + """Returns: A string representation of the actuator group.""" + # resolve joint indices for printing + joint_indices = self.joint_indices + if joint_indices == slice(None): + joint_indices = list(range(self.num_joints)) + # resolve model type (implicit or explicit) + model_type = "implicit" if self.is_implicit_model else "explicit" + + return ( + f" object:\n" + f"\tModel type : {model_type}\n" + f"\tNumber of joints : {self.num_joints}\n" + f"\tJoint names expression: {self.cfg.joint_names_expr}\n" + f"\tJoint names : {self.joint_names}\n" + f"\tJoint indices : {joint_indices}\n" + ) + + """ + Properties. + """ + + @property + def num_joints(self) -> int: + """Number of actuators in the group.""" + return len(self._joint_names) + + @property + def joint_names(self) -> list[str]: + """Articulation's joint names that are part of the group.""" + return self._joint_names + + @property + def joint_indices(self) -> slice | torch.Tensor: + """Articulation's joint indices that are part of the group. + + Note: + If :obj:`slice(None)` is returned, then the group contains all the joints in the articulation. + We do this to avoid unnecessary indexing of the joints for performance reasons. + """ + return self._joint_indices + + """ + Operations. + """ + + @abstractmethod + def reset(self, env_ids: Sequence[int]): + """Reset the internals within the group. + + Args: + env_ids: List of environment IDs to reset. + """ + raise NotImplementedError + + @abstractmethod + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + """Process the actuator group actions and compute the articulation actions. + + It computes the articulation actions based on the actuator model type + + Args: + control_action: The joint action instance comprising of the desired joint positions, joint velocities + and (feed-forward) joint efforts. + joint_pos: The current joint positions of the joints in the group. Shape is (num_envs, num_joints). + joint_vel: The current joint velocities of the joints in the group. Shape is (num_envs, num_joints). + + Returns: + The computed desired joint positions, joint velocities and joint efforts. + """ + raise NotImplementedError + + """ + Helper functions. + """ + + def _record_actuator_resolution(self, cfg_val, new_val, usd_val, joint_names, joint_ids, actuator_param: str): + if actuator_param not in self.joint_property_resolution_table: + self.joint_property_resolution_table[actuator_param] = [] + table = self.joint_property_resolution_table[actuator_param] + + ids = joint_ids if isinstance(joint_ids, torch.Tensor) else list(range(len(joint_names))) + for idx, name in enumerate(joint_names): + cfg_val_log = "Not Specified" if cfg_val is None else float(new_val[idx]) + default_usd_val = usd_val if isinstance(usd_val, (float, int)) else float(usd_val[0][idx]) + applied_val_log = default_usd_val if cfg_val is None else float(new_val[idx]) + table.append([name, int(ids[idx]), default_usd_val, cfg_val_log, applied_val_log]) + + def _parse_joint_parameter( + self, cfg_value: float | dict[str, float] | None, default_value: float | torch.Tensor | None + ) -> torch.Tensor: + """Parse the joint parameter from the configuration. + + Args: + cfg_value: The parameter value from the configuration. If None, then use the default value. + default_value: The default value to use if the parameter is None. If it is also None, + then an error is raised. + + Returns: + The parsed parameter value. + + Raises: + TypeError: If the parameter value is not of the expected type. + TypeError: If the default value is not of the expected type. + ValueError: If the parameter value is None and no default value is provided. + ValueError: If the default value tensor is the wrong shape. + """ + # create parameter buffer + param = torch.zeros(self._num_envs, self.num_joints, device=self._device) + # parse the parameter + if cfg_value is not None: + if isinstance(cfg_value, (float, int)): + # if float, then use the same value for all joints + param[:] = float(cfg_value) + elif isinstance(cfg_value, dict): + # if dict, then parse the regular expression + indices, _, values = string_utils.resolve_matching_names_values(cfg_value, self.joint_names) + # note: need to specify type to be safe (e.g. values are ints, but we want floats) + param[:, indices] = torch.tensor(values, dtype=torch.float, device=self._device) + else: + raise TypeError( + f"Invalid type for parameter value: {type(cfg_value)} for " + + f"actuator on joints {self.joint_names}. Expected float or dict." + ) + elif default_value is not None: + if isinstance(default_value, (float, int)): + # if float, then use the same value for all joints + param[:] = float(default_value) + elif isinstance(default_value, torch.Tensor): + # if tensor, then use the same tensor for all joints + if default_value.shape == (self._num_envs, self.num_joints): + param = default_value.float() + else: + raise ValueError( + "Invalid default value tensor shape.\n" + f"Got: {default_value.shape}\n" + f"Expected: {(self._num_envs, self.num_joints)}" + ) + else: + raise TypeError( + f"Invalid type for default value: {type(default_value)} for " + + f"actuator on joints {self.joint_names}. Expected float or Tensor." + ) + else: + raise ValueError("The parameter value is None and no default value is provided.") + + return param + + def _clip_effort(self, effort: torch.Tensor) -> torch.Tensor: + """Clip the desired torques based on the motor limits. + + Args: + desired_torques: The desired torques to clip. + + Returns: + The clipped torques. + """ + return torch.clip(effort, min=-self.effort_limit, max=self.effort_limit) diff --git a/source/isaaclab/isaaclab/actuators/actuator_base_cfg.py b/source/isaaclab/isaaclab/actuators/actuator_base_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e7a26b52aef451134c5251c18210a0f2a1fd8a21 --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_base_cfg.py @@ -0,0 +1,165 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + + +@configclass +class ActuatorBaseCfg: + """Configuration for default actuators in an articulation.""" + + class_type: type = MISSING + """The associated actuator class. + + The class should inherit from :class:`isaaclab.actuators.ActuatorBase`. + """ + + joint_names_expr: list[str] = MISSING + """Articulation's joint names that are part of the group. + + Note: + This can be a list of joint names or a list of regex expressions (e.g. ".*"). + """ + + effort_limit: dict[str, float] | float | None = None + """Force/Torque limit of the joints in the group. Defaults to None. + + This limit is used to clip the computed torque sent to the simulation. If None, the + limit is set to the value specified in the USD joint prim. + + .. attention:: + + The :attr:`effort_limit_sim` attribute should be used to set the effort limit for + the simulation physics solver. + + The :attr:`effort_limit` attribute is used for clipping the effort output of the + actuator model **only** in the case of explicit actuators, such as the + :class:`~isaaclab.actuators.IdealPDActuator`. + + .. note:: + + For implicit actuators, the attributes :attr:`effort_limit` and :attr:`effort_limit_sim` + are equivalent. However, we suggest using the :attr:`effort_limit_sim` attribute because + it is more intuitive. + + """ + + velocity_limit: dict[str, float] | float | None = None + """Velocity limit of the joints in the group. Defaults to None. + + This limit is used by the actuator model. If None, the limit is set to the value specified + in the USD joint prim. + + .. attention:: + + The :attr:`velocity_limit_sim` attribute should be used to set the velocity limit for + the simulation physics solver. + + The :attr:`velocity_limit` attribute is used for clipping the effort output of the + actuator model **only** in the case of explicit actuators, such as the + :class:`~isaaclab.actuators.IdealPDActuator`. + + .. note:: + + For implicit actuators, the attribute :attr:`velocity_limit` is not used. This is to stay + backwards compatible with previous versions of the Isaac Lab, where this parameter was + unused since PhysX did not support setting the velocity limit for the joints using the + PhysX Tensor API. + """ + + effort_limit_sim: dict[str, float] | float | None = None + """Effort limit of the joints in the group applied to the simulation physics solver. Defaults to None. + + The effort limit is used to constrain the computed joint efforts in the physics engine. If the + computed effort exceeds this limit, the physics engine will clip the effort to this value. + + Since explicit actuators (e.g. DC motor), compute and clip the effort in the actuator model, this + limit is by default set to a large value to prevent the physics engine from any additional clipping. + However, at times, it may be necessary to set this limit to a smaller value as a safety measure. + + If None, the limit is resolved based on the type of actuator model: + + * For implicit actuators, the limit is set to the value specified in the USD joint prim. + * For explicit actuators, the limit is set to 1.0e9. + + """ + + velocity_limit_sim: dict[str, float] | float | None = None + """Velocity limit of the joints in the group applied to the simulation physics solver. Defaults to None. + + The velocity limit is used to constrain the joint velocities in the physics engine. The joint will only + be able to reach this velocity if the joint's effort limit is sufficiently large. If the joint is moving + faster than this velocity, the physics engine will actually try to brake the joint to reach this velocity. + + If None, the limit is set to the value specified in the USD joint prim for both implicit and explicit actuators. + + .. tip:: + If the velocity limit is too tight, the physics engine may have trouble converging to a solution. + In such cases, we recommend either keeping this value sufficiently large or tuning the stiffness and + damping parameters of the joint to ensure the limits are not violated. + + """ + + stiffness: dict[str, float] | float | None = MISSING + """Stiffness gains (also known as p-gain) of the joints in the group. + + The behavior of the stiffness is different for implicit and explicit actuators. For implicit actuators, + the stiffness gets set into the physics engine directly. For explicit actuators, the stiffness is used + by the actuator model to compute the joint efforts. + + If None, the stiffness is set to the value from the USD joint prim. + """ + + damping: dict[str, float] | float | None = MISSING + """Damping gains (also known as d-gain) of the joints in the group. + + The behavior of the damping is different for implicit and explicit actuators. For implicit actuators, + the damping gets set into the physics engine directly. For explicit actuators, the damping gain is used + by the actuator model to compute the joint efforts. + + If None, the damping is set to the value from the USD joint prim. + """ + + armature: dict[str, float] | float | None = None + """Armature of the joints in the group. Defaults to None. + + The armature is directly added to the corresponding joint-space inertia. It helps improve the + simulation stability by reducing the joint velocities. + + It is a physics engine solver parameter that gets set into the simulation. + + If None, the armature is set to the value from the USD joint prim. + """ + + friction: dict[str, float] | float | None = None + r"""The static friction coefficient of the joints in the group. Defaults to None. + + The joint static friction is a unitless quantity. It relates the magnitude of the spatial force transmitted + from the parent body to the child body to the maximal static friction force that may be applied by the solver + to resist the joint motion. + + Mathematically, this means that: :math:`F_{resist} \leq \mu F_{spatial}`, where :math:`F_{resist}` + is the resisting force applied by the solver and :math:`F_{spatial}` is the spatial force + transmitted from the parent body to the child body. The simulated static friction effect is therefore + similar to static and Coulomb static friction. + + If None, the joint static friction is set to the value from the USD joint prim. + + Note: In Isaac Sim 4.5, this parameter is modeled as a coefficient. In Isaac Sim 5.0 and later, + it is modeled as an effort (torque or force). + """ + + dynamic_friction: dict[str, float] | float | None = None + """The dynamic friction coefficient of the joints in the group. Defaults to None. + + Note: In Isaac Sim 4.5, this parameter is modeled as a coefficient. In Isaac Sim 5.0 and later, + it is modeled as an effort (torque or force). + """ + + viscous_friction: dict[str, float] | float | None = None + """The viscous friction coefficient of the joints in the group. Defaults to None. + """ diff --git a/source/isaaclab/isaaclab/actuators/actuator_cfg.py b/source/isaaclab/isaaclab/actuators/actuator_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5cacb8ffa4084c00fca41c6b106511d70a0c007c --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_cfg.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import sys +import warnings + +from . import actuator_base_cfg, actuator_net_cfg, actuator_pd_cfg + + +def __getattr__(name): + new_module = None + if name in dir(actuator_pd_cfg): + new_module = actuator_pd_cfg + elif name in dir(actuator_net_cfg): + new_module = actuator_net_cfg + elif name in dir(actuator_base_cfg): + new_module = actuator_base_cfg + + if new_module is not None: + warnings.warn( + f"The module actuator_cfg.py is deprecated. Please import {name} directly from the isaaclab.actuators" + f" package, or from its new module {new_module.__name__}.", + DeprecationWarning, + stacklevel=2, + ) + return getattr(new_module, name) + if name in dir(sys.modules[__name__]): + return vars(sys.modules[__name__])[name] + if name == "__path__": + return __file__ + raise ImportError( + f"Failed to import attribute {name} from actuator_cfg.py. Warning: actuator_cfg.py has been " + + "deprecated. Please import actuator config classes directly from the isaaclab.actuators package.", + ) diff --git a/source/isaaclab/isaaclab/actuators/actuator_net.py b/source/isaaclab/isaaclab/actuators/actuator_net.py new file mode 100644 index 0000000000000000000000000000000000000000..2274d1b78db39003ef3d555b9bc230c67b4fc02a --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_net.py @@ -0,0 +1,188 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Neural network models for actuators. + +Currently, the following models are supported: + +* Multi-Layer Perceptron (MLP) +* Long Short-Term Memory (LSTM) + +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.utils.assets import read_file +from isaaclab.utils.types import ArticulationActions + +from .actuator_pd import DCMotor + +if TYPE_CHECKING: + from .actuator_net_cfg import ActuatorNetLSTMCfg, ActuatorNetMLPCfg + + +class ActuatorNetLSTM(DCMotor): + """Actuator model based on recurrent neural network (LSTM). + + Unlike the MLP implementation :cite:t:`hwangbo2019learning`, this class implements + the learned model as a temporal neural network (LSTM) based on the work from + :cite:t:`rudin2022learning`. This removes the need of storing a history as the + hidden states of the recurrent network captures the history. + + Note: + Only the desired joint positions are used as inputs to the network. + """ + + cfg: ActuatorNetLSTMCfg + """The configuration of the actuator model.""" + + def __init__(self, cfg: ActuatorNetLSTMCfg, *args, **kwargs): + super().__init__(cfg, *args, **kwargs) + + # load the model from JIT file + file_bytes = read_file(self.cfg.network_file) + self.network = torch.jit.load(file_bytes, map_location=self._device).eval() + + # extract number of lstm layers and hidden dim from the shape of weights + num_layers = len(self.network.lstm.state_dict()) // 4 + hidden_dim = self.network.lstm.state_dict()["weight_hh_l0"].shape[1] + # create buffers for storing LSTM inputs + self.sea_input = torch.zeros(self._num_envs * self.num_joints, 1, 2, device=self._device) + self.sea_hidden_state = torch.zeros( + num_layers, self._num_envs * self.num_joints, hidden_dim, device=self._device + ) + self.sea_cell_state = torch.zeros(num_layers, self._num_envs * self.num_joints, hidden_dim, device=self._device) + # reshape via views (doesn't change the actual memory layout) + layer_shape_per_env = (num_layers, self._num_envs, self.num_joints, hidden_dim) + self.sea_hidden_state_per_env = self.sea_hidden_state.view(layer_shape_per_env) + self.sea_cell_state_per_env = self.sea_cell_state.view(layer_shape_per_env) + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int]): + # reset the hidden and cell states for the specified environments + with torch.no_grad(): + self.sea_hidden_state_per_env[:, env_ids] = 0.0 + self.sea_cell_state_per_env[:, env_ids] = 0.0 + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # compute network inputs + self.sea_input[:, 0, 0] = (control_action.joint_positions - joint_pos).flatten() + self.sea_input[:, 0, 1] = joint_vel.flatten() + + # run network inference + with torch.inference_mode(): + torques, (self.sea_hidden_state[:], self.sea_cell_state[:]) = self.network( + self.sea_input, (self.sea_hidden_state, self.sea_cell_state) + ) + self.computed_effort = torques.reshape(self._num_envs, self.num_joints) + + # clip the computed effort based on the motor limits + self.applied_effort = self._clip_effort(self.computed_effort) + + # return torques + control_action.joint_efforts = self.applied_effort + control_action.joint_positions = None + control_action.joint_velocities = None + return control_action + + +class ActuatorNetMLP(DCMotor): + """Actuator model based on multi-layer perceptron and joint history. + + Many times the analytical model is not sufficient to capture the actuator dynamics, the + delay in the actuator response, or the non-linearities in the actuator. In these cases, + a neural network model can be used to approximate the actuator dynamics. This model is + trained using data collected from the physical actuator and maps the joint state and the + desired joint command to the produced torque by the actuator. + + This class implements the learned model as a neural network based on the work from + :cite:t:`hwangbo2019learning`. The class stores the history of the joint positions errors + and velocities which are used to provide input to the neural network. The model is loaded + as a TorchScript. + + Note: + Only the desired joint positions are used as inputs to the network. + + """ + + cfg: ActuatorNetMLPCfg + """The configuration of the actuator model.""" + + def __init__(self, cfg: ActuatorNetMLPCfg, *args, **kwargs): + super().__init__(cfg, *args, **kwargs) + + # load the model from JIT file + file_bytes = read_file(self.cfg.network_file) + self.network = torch.jit.load(file_bytes, map_location=self._device).eval() + + # create buffers for MLP history + history_length = max(self.cfg.input_idx) + 1 + self._joint_pos_error_history = torch.zeros( + self._num_envs, history_length, self.num_joints, device=self._device + ) + self._joint_vel_history = torch.zeros(self._num_envs, history_length, self.num_joints, device=self._device) + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int]): + # reset the history for the specified environments + self._joint_pos_error_history[env_ids] = 0.0 + self._joint_vel_history[env_ids] = 0.0 + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # move history queue by 1 and update top of history + # -- positions + self._joint_pos_error_history = self._joint_pos_error_history.roll(1, 1) + self._joint_pos_error_history[:, 0] = control_action.joint_positions - joint_pos + # -- velocity + self._joint_vel_history = self._joint_vel_history.roll(1, 1) + self._joint_vel_history[:, 0] = joint_vel + # save current joint vel for dc-motor clipping + self._joint_vel[:] = joint_vel + + # compute network inputs + # -- positions + pos_input = torch.cat([self._joint_pos_error_history[:, i].unsqueeze(2) for i in self.cfg.input_idx], dim=2) + pos_input = pos_input.view(self._num_envs * self.num_joints, -1) + # -- velocity + vel_input = torch.cat([self._joint_vel_history[:, i].unsqueeze(2) for i in self.cfg.input_idx], dim=2) + vel_input = vel_input.view(self._num_envs * self.num_joints, -1) + # -- scale and concatenate inputs + if self.cfg.input_order == "pos_vel": + network_input = torch.cat([pos_input * self.cfg.pos_scale, vel_input * self.cfg.vel_scale], dim=1) + elif self.cfg.input_order == "vel_pos": + network_input = torch.cat([vel_input * self.cfg.vel_scale, pos_input * self.cfg.pos_scale], dim=1) + else: + raise ValueError( + f"Invalid input order for MLP actuator net: {self.cfg.input_order}. Must be 'pos_vel' or 'vel_pos'." + ) + + # run network inference + with torch.inference_mode(): + torques = self.network(network_input).view(self._num_envs, self.num_joints) + self.computed_effort = torques.view(self._num_envs, self.num_joints) * self.cfg.torque_scale + + # clip the computed effort based on the motor limits + self.applied_effort = self._clip_effort(self.computed_effort) + + # return torques + control_action.joint_efforts = self.applied_effort + control_action.joint_positions = None + control_action.joint_velocities = None + return control_action diff --git a/source/isaaclab/isaaclab/actuators/actuator_net_cfg.py b/source/isaaclab/isaaclab/actuators/actuator_net_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..eecfe8050ab34a6fe890e4b65a846ce37ea95f11 --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_net_cfg.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Iterable +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from . import actuator_net +from .actuator_pd_cfg import DCMotorCfg + + +@configclass +class ActuatorNetLSTMCfg(DCMotorCfg): + """Configuration for LSTM-based actuator model.""" + + class_type: type = actuator_net.ActuatorNetLSTM + # we don't use stiffness and damping for actuator net + stiffness = None + damping = None + + network_file: str = MISSING + """Path to the file containing network weights.""" + + +@configclass +class ActuatorNetMLPCfg(DCMotorCfg): + """Configuration for MLP-based actuator model.""" + + class_type: type = actuator_net.ActuatorNetMLP + # we don't use stiffness and damping for actuator net + + stiffness = None + damping = None + + network_file: str = MISSING + """Path to the file containing network weights.""" + + pos_scale: float = MISSING + """Scaling of the joint position errors input to the network.""" + vel_scale: float = MISSING + """Scaling of the joint velocities input to the network.""" + torque_scale: float = MISSING + """Scaling of the joint efforts output from the network.""" + + input_order: Literal["pos_vel", "vel_pos"] = MISSING + """Order of the inputs to the network. + + The order can be one of the following: + + * ``"pos_vel"``: joint position errors followed by joint velocities + * ``"vel_pos"``: joint velocities followed by joint position errors + """ + + input_idx: Iterable[int] = MISSING + """ + Indices of the actuator history buffer passed as inputs to the network. + + The index *0* corresponds to current time-step, while *n* corresponds to n-th + time-step in the past. The allocated history length is `max(input_idx) + 1`. + """ diff --git a/source/isaaclab/isaaclab/actuators/actuator_pd.py b/source/isaaclab/isaaclab/actuators/actuator_pd.py new file mode 100644 index 0000000000000000000000000000000000000000..ff014fa7a58eaaf9fa058161f1d93c669075050c --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_pd.py @@ -0,0 +1,451 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.utils import DelayBuffer, LinearInterpolation +from isaaclab.utils.types import ArticulationActions + +from .actuator_base import ActuatorBase + +if TYPE_CHECKING: + from .actuator_pd_cfg import ( + DCMotorCfg, + DelayedPDActuatorCfg, + IdealPDActuatorCfg, + ImplicitActuatorCfg, + RemotizedPDActuatorCfg, + ) + +# import logger +logger = logging.getLogger(__name__) + +""" +Implicit Actuator Models. +""" + + +class ImplicitActuator(ActuatorBase): + """Implicit actuator model that is handled by the simulation. + + This performs a similar function as the :class:`IdealPDActuator` class. However, the PD control is handled + implicitly by the simulation which performs continuous-time integration of the PD control law. This is + generally more accurate than the explicit PD control law used in :class:`IdealPDActuator` when the simulation + time-step is large. + + The articulation class sets the stiffness and damping parameters from the implicit actuator configuration + into the simulation. Thus, the class does not perform its own computations on the joint action that + needs to be applied to the simulation. However, it computes the approximate torques for the actuated joint + since PhysX does not expose this quantity explicitly. + + .. caution:: + + The class is only provided for consistency with the other actuator models. It does not implement any + functionality and should not be used. All values should be set to the simulation directly. + """ + + cfg: ImplicitActuatorCfg + """The configuration for the actuator model.""" + + def __init__(self, cfg: ImplicitActuatorCfg, *args, **kwargs): + # effort limits + if cfg.effort_limit_sim is None and cfg.effort_limit is not None: + # throw a warning that we have a replacement for the deprecated parameter + logger.warning( + "The object has a value for 'effort_limit'." + " This parameter will be removed in the future." + " To set the effort limit, please use 'effort_limit_sim' instead." + ) + cfg.effort_limit_sim = cfg.effort_limit + elif cfg.effort_limit_sim is not None and cfg.effort_limit is None: + # TODO: Eventually we want to get rid of 'effort_limit' for implicit actuators. + # We should do this once all parameters have an "_sim" suffix. + cfg.effort_limit = cfg.effort_limit_sim + elif cfg.effort_limit_sim is not None and cfg.effort_limit is not None: + if cfg.effort_limit_sim != cfg.effort_limit: + raise ValueError( + "The object has set both 'effort_limit_sim' and 'effort_limit'" + f" and they have different values {cfg.effort_limit_sim} != {cfg.effort_limit}." + " Please only set 'effort_limit_sim' for implicit actuators." + ) + + # velocity limits + if cfg.velocity_limit_sim is None and cfg.velocity_limit is not None: + # throw a warning that previously this was not set + # it leads to different simulation behavior so we want to remain backwards compatible + logger.warning( + "The object has a value for 'velocity_limit'." + " Previously, although this value was specified, it was not getting used by implicit" + " actuators. Since this parameter affects the simulation behavior, we continue to not" + " use it. This parameter will be removed in the future." + " To set the velocity limit, please use 'velocity_limit_sim' instead." + ) + cfg.velocity_limit = None + elif cfg.velocity_limit_sim is not None and cfg.velocity_limit is None: + # TODO: Eventually we want to get rid of 'velocity_limit' for implicit actuators. + # We should do this once all parameters have an "_sim" suffix. + cfg.velocity_limit = cfg.velocity_limit_sim + elif cfg.velocity_limit_sim is not None and cfg.velocity_limit is not None: + if cfg.velocity_limit_sim != cfg.velocity_limit: + raise ValueError( + "The object has set both 'velocity_limit_sim' and 'velocity_limit'" + f" and they have different values {cfg.velocity_limit_sim} != {cfg.velocity_limit}." + " Please only set 'velocity_limit_sim' for implicit actuators." + ) + + # set implicit actuator model flag + ImplicitActuator.is_implicit_model = True + # call the base class + super().__init__(cfg, *args, **kwargs) + + """ + Operations. + """ + + def reset(self, *args, **kwargs): + # This is a no-op. There is no state to reset for implicit actuators. + pass + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + """Process the actuator group actions and compute the articulation actions. + + In case of implicit actuator, the control action is directly returned as the computed action. + This function is a no-op and does not perform any computation on the input control action. + However, it computes the approximate torques for the actuated joint since PhysX does not compute + this quantity explicitly. + + Args: + control_action: The joint action instance comprising of the desired joint positions, joint velocities + and (feed-forward) joint efforts. + joint_pos: The current joint positions of the joints in the group. Shape is (num_envs, num_joints). + joint_vel: The current joint velocities of the joints in the group. Shape is (num_envs, num_joints). + + Returns: + The computed desired joint positions, joint velocities and joint efforts. + """ + # store approximate torques for reward computation + error_pos = control_action.joint_positions - joint_pos + error_vel = control_action.joint_velocities - joint_vel + self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts + # clip the torques based on the motor limits + self.applied_effort = self._clip_effort(self.computed_effort) + return control_action + + +""" +Explicit Actuator Models. +""" + + +class IdealPDActuator(ActuatorBase): + r"""Ideal torque-controlled actuator model with a simple saturation model. + + It employs the following model for computing torques for the actuated joint :math:`j`: + + .. math:: + + \tau_{j, computed} = k_p * (q_{des} - q) + k_d * (\dot{q}_{des} - \dot{q}) + \tau_{ff} + + where, :math:`k_p` and :math:`k_d` are joint stiffness and damping gains, :math:`q` and :math:`\dot{q}` + are the current joint positions and velocities, :math:`q_{des}`, :math:`\dot{q}_{des}` and :math:`\tau_{ff}` + are the desired joint positions, velocities and torques commands. + + The clipping model is based on the maximum torque applied by the motor. It is implemented as: + + .. math:: + + \tau_{j, max} & = \gamma \times \tau_{motor, max} \\ + \tau_{j, applied} & = clip(\tau_{computed}, -\tau_{j, max}, \tau_{j, max}) + + where the clipping function is defined as :math:`clip(x, x_{min}, x_{max}) = min(max(x, x_{min}), x_{max})`. + The parameters :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends, + and :math:`\tau_{motor, max}` is the maximum motor effort possible. These parameters are read from + the configuration instance passed to the class. + """ + + cfg: IdealPDActuatorCfg + """The configuration for the actuator model.""" + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int]): + pass + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # compute errors + error_pos = control_action.joint_positions - joint_pos + error_vel = control_action.joint_velocities - joint_vel + # calculate the desired joint torques + self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts + # clip the torques based on the motor limits + self.applied_effort = self._clip_effort(self.computed_effort) + # set the computed actions back into the control action + control_action.joint_efforts = self.applied_effort + control_action.joint_positions = None + control_action.joint_velocities = None + return control_action + + +class DCMotor(IdealPDActuator): + r"""Direct control (DC) motor actuator model with velocity-based saturation model. + + It uses the same model as the :class:`IdealPDActuator` for computing the torques from input commands. + However, it implements a saturation model defined by a linear four quadrant DC motor torque-speed curve. + + A DC motor is a type of electric motor that is powered by direct current electricity. In most cases, + the motor is connected to a constant source of voltage supply, and the current is controlled by a rheostat. + Depending on various design factors such as windings and materials, the motor can draw a limited maximum power + from the electronic source, which limits the produced motor torque and speed. + + A DC motor characteristics are defined by the following parameters: + + * No-load speed (:math:`\dot{q}_{motor, max}`) : The maximum-rated speed of the motor at + zero torque (:attr:`velocity_limit`). + * Stall torque (:math:`\tau_{motor, stall}`): The maximum-rated torque produced at + zero speed (:attr:`saturation_effort`). + * Continuous torque (:math:`\tau_{motor, con}`): The maximum torque that can be outputted for a short period. + This is often enforced on the current drives for a DC motor to limit overheating, prevent mechanical damage, + or enforced by electrical limitations (:attr:`effort_limit`). + * Corner velocity (:math:`V_{c}`): The velocity where the torque-speed curve intersects with continuous torque. + + Based on these parameters, the instantaneous minimum and maximum torques for velocities between corner velocities + (where torque-speed curve intersects with continuous torque) are defined as follows: + + .. math:: + + \tau_{j, max}(\dot{q}) & = clip \left (\tau_{j, stall} \times \left(1 - + \frac{\dot{q}}{\dot{q}_{j, max}}\right), -∞, \tau_{j, con} \right) \\ + \tau_{j, min}(\dot{q}) & = clip \left (\tau_{j, stall} \times \left( -1 - + \frac{\dot{q}}{\dot{q}_{j, max}}\right), - \tau_{j, con}, ∞ \right) + + where :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends, + :math:`\dot{q}_{j, max} = \gamma^{-1} \times \dot{q}_{motor, max}`, :math:`\tau_{j, con} = + \gamma \times \tau_{motor, con}` and :math:`\tau_{j, stall} = \gamma \times \tau_{motor, stall}` + are the maximum joint velocity, continuous joint torque and stall torque, respectively. These parameters + are read from the configuration instance passed to the class. + + Using these values, the computed torques are clipped to the minimum and maximum values based on the + instantaneous joint velocity: + + .. math:: + + \tau_{j, applied} = clip(\tau_{computed}, \tau_{j, min}(\dot{q}), \tau_{j, max}(\dot{q})) + + If the velocity of the joint is outside corner velocities (this would be due to external forces) the + applied output torque will be driven to the Continuous Torque (`effort_limit`). + + The figure below demonstrates the clipping action for example (velocity, torque) pairs. + + .. figure:: ../../_static/actuator-group/dc_motor_clipping.jpg + :align: center + :figwidth: 100% + :alt: The effort clipping as a function of joint velocity for a linear DC Motor. + + """ + + cfg: DCMotorCfg + """The configuration for the actuator model.""" + + def __init__(self, cfg: DCMotorCfg, *args, **kwargs): + super().__init__(cfg, *args, **kwargs) + # parse configuration + if self.cfg.saturation_effort is None: + raise ValueError("The saturation_effort must be provided for the DC motor actuator model.") + self._saturation_effort = self.cfg.saturation_effort + # find the velocity on the torque-speed curve that intersects effort_limit in the second and fourth quadrant + self._vel_at_effort_lim = self.velocity_limit * (1 + self.effort_limit / self._saturation_effort) + # prepare joint vel buffer for max effort computation + self._joint_vel = torch.zeros_like(self.computed_effort) + # create buffer for zeros effort + self._zeros_effort = torch.zeros_like(self.computed_effort) + # check that quantities are provided + if self.cfg.velocity_limit is None: + raise ValueError("The velocity limit must be provided for the DC motor actuator model.") + + """ + Operations. + """ + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # save current joint vel + self._joint_vel[:] = joint_vel + # calculate the desired joint torques + return super().compute(control_action, joint_pos, joint_vel) + + """ + Helper functions. + """ + + def _clip_effort(self, effort: torch.Tensor) -> torch.Tensor: + # save current joint vel + self._joint_vel[:] = torch.clip(self._joint_vel, min=-self._vel_at_effort_lim, max=self._vel_at_effort_lim) + # compute torque limits + torque_speed_top = self._saturation_effort * (1.0 - self._joint_vel / self.velocity_limit) + torque_speed_bottom = self._saturation_effort * (-1.0 - self._joint_vel / self.velocity_limit) + # -- max limit + max_effort = torch.clip(torque_speed_top, max=self.effort_limit) + # -- min limit + min_effort = torch.clip(torque_speed_bottom, min=-self.effort_limit) + # clip the torques based on the motor limits + clamped = torch.clip(effort, min=min_effort, max=max_effort) + return clamped + + +class DelayedPDActuator(IdealPDActuator): + """Ideal PD actuator with delayed command application. + + This class extends the :class:`IdealPDActuator` class by adding a delay to the actuator commands. The delay + is implemented using a circular buffer that stores the actuator commands for a certain number of physics steps. + The most recent actuation value is pushed to the buffer at every physics step, but the final actuation value + applied to the simulation is lagged by a certain number of physics steps. + + The amount of time lag is configurable and can be set to a random value between the minimum and maximum time + lag bounds at every reset. The minimum and maximum time lag values are set in the configuration instance passed + to the class. + """ + + cfg: DelayedPDActuatorCfg + """The configuration for the actuator model.""" + + def __init__(self, cfg: DelayedPDActuatorCfg, *args, **kwargs): + super().__init__(cfg, *args, **kwargs) + # instantiate the delay buffers + self.positions_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device) + self.velocities_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device) + self.efforts_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device) + # all of the envs + self._ALL_INDICES = torch.arange(self._num_envs, dtype=torch.long, device=self._device) + + def reset(self, env_ids: Sequence[int]): + super().reset(env_ids) + # number of environments (since env_ids can be a slice) + if env_ids is None or env_ids == slice(None): + num_envs = self._num_envs + else: + num_envs = len(env_ids) + # set a new random delay for environments in env_ids + time_lags = torch.randint( + low=self.cfg.min_delay, + high=self.cfg.max_delay + 1, + size=(num_envs,), + dtype=torch.int, + device=self._device, + ) + # set delays + self.positions_delay_buffer.set_time_lag(time_lags, env_ids) + self.velocities_delay_buffer.set_time_lag(time_lags, env_ids) + self.efforts_delay_buffer.set_time_lag(time_lags, env_ids) + # reset buffers + self.positions_delay_buffer.reset(env_ids) + self.velocities_delay_buffer.reset(env_ids) + self.efforts_delay_buffer.reset(env_ids) + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # apply delay based on the delay the model for all the setpoints + control_action.joint_positions = self.positions_delay_buffer.compute(control_action.joint_positions) + control_action.joint_velocities = self.velocities_delay_buffer.compute(control_action.joint_velocities) + control_action.joint_efforts = self.efforts_delay_buffer.compute(control_action.joint_efforts) + # compte actuator model + return super().compute(control_action, joint_pos, joint_vel) + + +class RemotizedPDActuator(DelayedPDActuator): + """Ideal PD actuator with angle-dependent torque limits. + + This class extends the :class:`DelayedPDActuator` class by adding angle-dependent torque limits to the actuator. + The torque limits are applied by querying a lookup table describing the relationship between the joint angle + and the maximum output torque. The lookup table is provided in the configuration instance passed to the class. + + The torque limits are interpolated based on the current joint positions and applied to the actuator commands. + """ + + def __init__( + self, + cfg: RemotizedPDActuatorCfg, + joint_names: list[str], + joint_ids: Sequence[int], + num_envs: int, + device: str, + stiffness: torch.Tensor | float = 0.0, + damping: torch.Tensor | float = 0.0, + armature: torch.Tensor | float = 0.0, + friction: torch.Tensor | float = 0.0, + dynamic_friction: torch.Tensor | float = 0.0, + viscous_friction: torch.Tensor | float = 0.0, + effort_limit: torch.Tensor | float = torch.inf, + velocity_limit: torch.Tensor | float = torch.inf, + ): + # remove effort and velocity box constraints from the base class + cfg.effort_limit = torch.inf + cfg.velocity_limit = torch.inf + # call the base method and set default effort_limit and velocity_limit to inf + super().__init__( + cfg, + joint_names, + joint_ids, + num_envs, + device, + stiffness, + damping, + armature, + friction, + dynamic_friction, + viscous_friction, + effort_limit, + velocity_limit, + ) + self._joint_parameter_lookup = torch.tensor(cfg.joint_parameter_lookup, device=device) + # define remotized joint torque limit + self._torque_limit = LinearInterpolation(self.angle_samples, self.max_torque_samples, device=device) + + """ + Properties. + """ + + @property + def angle_samples(self) -> torch.Tensor: + return self._joint_parameter_lookup[:, 0] + + @property + def transmission_ratio_samples(self) -> torch.Tensor: + return self._joint_parameter_lookup[:, 1] + + @property + def max_torque_samples(self) -> torch.Tensor: + return self._joint_parameter_lookup[:, 2] + + """ + Operations. + """ + + def compute( + self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor + ) -> ArticulationActions: + # call the base method + control_action = super().compute(control_action, joint_pos, joint_vel) + # compute the absolute torque limits for the current joint positions + abs_torque_limits = self._torque_limit.compute(joint_pos) + # apply the limits + control_action.joint_efforts = torch.clamp( + control_action.joint_efforts, min=-abs_torque_limits, max=abs_torque_limits + ) + self.applied_effort = control_action.joint_efforts + return control_action diff --git a/source/isaaclab/isaaclab/actuators/actuator_pd_cfg.py b/source/isaaclab/isaaclab/actuators/actuator_pd_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f5a6282ad85bd2f83224da77559999e67c933b --- /dev/null +++ b/source/isaaclab/isaaclab/actuators/actuator_pd_cfg.py @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from . import actuator_pd +from .actuator_base_cfg import ActuatorBaseCfg + +""" +Implicit Actuator Models. +""" + + +@configclass +class ImplicitActuatorCfg(ActuatorBaseCfg): + """Configuration for an implicit actuator. + + Note: + The PD control is handled implicitly by the simulation. + """ + + class_type: type = actuator_pd.ImplicitActuator + + +""" +Explicit Actuator Models. +""" + + +@configclass +class IdealPDActuatorCfg(ActuatorBaseCfg): + """Configuration for an ideal PD actuator.""" + + class_type: type = actuator_pd.IdealPDActuator + + +@configclass +class DCMotorCfg(IdealPDActuatorCfg): + """Configuration for direct control (DC) motor actuator model.""" + + class_type: type = actuator_pd.DCMotor + + saturation_effort: float = MISSING + """Peak motor force/torque of the electric DC motor (in N-m).""" + + +@configclass +class DelayedPDActuatorCfg(IdealPDActuatorCfg): + """Configuration for a delayed PD actuator.""" + + class_type: type = actuator_pd.DelayedPDActuator + + min_delay: int = 0 + """Minimum number of physics time-steps with which the actuator command may be delayed. Defaults to 0.""" + + max_delay: int = 0 + """Maximum number of physics time-steps with which the actuator command may be delayed. Defaults to 0.""" + + +@configclass +class RemotizedPDActuatorCfg(DelayedPDActuatorCfg): + """Configuration for a remotized PD actuator. + + Note: + The torque output limits for this actuator is derived from a linear interpolation of a lookup table + in :attr:`joint_parameter_lookup`. This table describes the relationship between joint angles and + the output torques. + """ + + class_type: type = actuator_pd.RemotizedPDActuator + + joint_parameter_lookup: list[list[float]] = MISSING + """Joint parameter lookup table. Shape is (num_lookup_points, 3). + + This tensor describes the relationship between the joint angle (rad), the transmission ratio (in/out), + and the output torque (N*m). The table is used to interpolate the output torque based on the joint angle. + """ diff --git a/source/isaaclab/isaaclab/app/__init__.py b/source/isaaclab/isaaclab/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b81b6e3c9e5e8e7ee2ced8eccf4431208196b6a8 --- /dev/null +++ b/source/isaaclab/isaaclab/app/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package containing app-specific functionalities. + +These include: + +* Ability to launch the simulation app with different configurations +* Run tests with the simulation app + +""" + +from .app_launcher import AppLauncher # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/app/app_launcher.py b/source/isaaclab/isaaclab/app/app_launcher.py new file mode 100644 index 0000000000000000000000000000000000000000..e986d4b664a008dfead172953fd6d82fcd9ac095 --- /dev/null +++ b/source/isaaclab/isaaclab/app/app_launcher.py @@ -0,0 +1,1083 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package with the utility class to configure the :class:`isaacsim.simulation_app.SimulationApp`. + +The :class:`AppLauncher` parses environment variables and input CLI arguments to launch the simulator in +various different modes. This includes with or without GUI and switching between different Omniverse remote +clients. Some of these require the extensions to be loaded in a specific order, otherwise a segmentation +fault occurs. The launched :class:`isaacsim.simulation_app.SimulationApp` instance is accessible via the +:attr:`AppLauncher.app` property. +""" + +import argparse +import contextlib +import logging +import os +import re +import signal +import sys +from typing import Any, Literal + +with contextlib.suppress(ModuleNotFoundError): + import isaacsim # noqa: F401 + +from isaacsim import SimulationApp + +# import logger +logger = logging.getLogger(__name__) + + +class ExplicitAction(argparse.Action): + """Custom action to track if an argument was explicitly passed by the user.""" + + def __call__(self, parser, namespace, values, option_string=None): + # Set the parameter value + setattr(namespace, self.dest, values) + # Set a flag indicating the parameter was explicitly passed + setattr(namespace, f"{self.dest}_explicit", True) + + +class AppLauncher: + """A utility class to launch Isaac Sim application based on command-line arguments and environment variables. + + The class resolves the simulation app settings that appear through environments variables, + command-line arguments (CLI) or as input keyword arguments. Based on these settings, it launches the + simulation app and configures the extensions to load (as a part of post-launch setup). + + The input arguments provided to the class are given higher priority than the values set + from the corresponding environment variables. This provides flexibility to deal with different + users' preferences. + + .. note:: + Explicitly defined arguments are only given priority when their value is set to something outside + their default configuration. For example, the ``livestream`` argument is -1 by default. It only + overrides the ``LIVESTREAM`` environment variable when ``livestream`` argument is set to a + value >-1. In other words, if ``livestream=-1``, then the value from the environment variable + ``LIVESTREAM`` is used. + + """ + + def __init__(self, launcher_args: argparse.Namespace | dict | None = None, **kwargs): + """Create a `SimulationApp`_ instance based on the input settings. + + Args: + launcher_args: Input arguments to parse using the AppLauncher and set into the SimulationApp. + Defaults to None, which is equivalent to passing an empty dictionary. A detailed description of + the possible arguments is available in the `SimulationApp`_ documentation. + **kwargs : Additional keyword arguments that will be merged into :attr:`launcher_args`. + They serve as a convenience for those who want to pass some arguments using the argparse + interface and others directly into the AppLauncher. Duplicated arguments with + the :attr:`launcher_args` will raise a ValueError. + + Raises: + ValueError: If there are common/duplicated arguments between ``launcher_args`` and ``kwargs``. + ValueError: If combination of ``launcher_args`` and ``kwargs`` are missing the necessary arguments + that are needed by the AppLauncher to resolve the desired app configuration. + ValueError: If incompatible or undefined values are assigned to relevant environment values, + such as ``LIVESTREAM``. + + .. _argparse.Namespace: https://docs.python.org/3/library/argparse.html?highlight=namespace#argparse.Namespace + .. _SimulationApp: https://docs.isaacsim.omniverse.nvidia.com/latest/py/source/extensions/isaacsim.simulation_app/docs/index.html#isaacsim.simulation_app.SimulationApp + """ + # We allow users to pass either a dict or an argparse.Namespace into + # __init__, anticipating that these will be all of the argparse arguments + # used by the calling script. Those which we appended via add_app_launcher_args + # will be used to control extension loading logic. Additional arguments are allowed, + # and will be passed directly to the SimulationApp initialization. + # + # We could potentially require users to enter each argument they want passed here + # as a kwarg, but this would require them to pass livestream, headless, and + # any other options we choose to add here explicitly, and with the correct keywords. + # + # @hunter: I feel that this is cumbersome and could introduce error, and would prefer to do + # some sanity checking in the add_app_launcher_args function + if launcher_args is None: + launcher_args = {} + elif isinstance(launcher_args, argparse.Namespace): + launcher_args = launcher_args.__dict__ + + # Check that arguments are unique + if len(kwargs) > 0: + if not set(kwargs.keys()).isdisjoint(launcher_args.keys()): + overlapping_args = set(kwargs.keys()).intersection(launcher_args.keys()) + raise ValueError( + f"Input `launcher_args` and `kwargs` both provided common attributes: {overlapping_args}." + " Please ensure that each argument is supplied to only one of them, as the AppLauncher cannot" + " discern priority between them." + ) + launcher_args.update(kwargs) + + # Define config members that are read from env-vars or keyword args + self._headless: bool # 0: GUI, 1: Headless + self._livestream: Literal[0, 1, 2] # 0: Disabled, 1: WebRTC public, 2: WebRTC private + self._offscreen_render: bool # 0: Disabled, 1: Enabled + self._sim_experience_file: str # Experience file to load + + # Exposed to train scripts + self.device_id: int # device ID for GPU simulation (defaults to 0) + self.local_rank: int # local rank of GPUs in the current node + self.global_rank: int # global rank for multi-node training + + # Integrate env-vars and input keyword args into simulation app config + self._config_resolution(launcher_args) + + # Internal: Override SimulationApp._start_app method to apply patches after app has started. + self.__patch_simulation_start_app(launcher_args) + + # Create SimulationApp, passing the resolved self._config to it for initialization + self._create_app() + # Load IsaacSim extensions + self._load_extensions() + # Hide the stop button in the toolbar + self._hide_stop_button() + # Set settings from the given rendering mode + self._set_rendering_mode_settings(launcher_args) + # Set animation recording settings + self._set_animation_recording_settings(launcher_args) + + # Hide play button callback if the timeline is stopped + import omni.timeline + + self._hide_play_button_callback = ( + omni.timeline.get_timeline_interface() + .get_timeline_event_stream() + .create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.STOP), lambda e: self._hide_play_button(True) + ) + ) + self._unhide_play_button_callback = ( + omni.timeline.get_timeline_interface() + .get_timeline_event_stream() + .create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.PLAY), lambda e: self._hide_play_button(False) + ) + ) + # Set up signal handlers for graceful shutdown + # -- during explicit `kill` commands + signal.signal(signal.SIGTERM, self._abort_signal_handle_callback) + # -- during segfaults + signal.signal(signal.SIGABRT, self._abort_signal_handle_callback) + signal.signal(signal.SIGSEGV, self._abort_signal_handle_callback) + + """ + Properties. + """ + + @property + def app(self) -> SimulationApp: + """The launched SimulationApp.""" + if self._app is not None: + return self._app + else: + raise RuntimeError("The `AppLauncher.app` member cannot be retrieved until the class is initialized.") + + """ + Operations. + """ + + @staticmethod + def add_app_launcher_args(parser: argparse.ArgumentParser) -> None: + """Utility function to configure AppLauncher arguments with an existing argument parser object. + + This function takes an ``argparse.ArgumentParser`` object and does some sanity checking on the existing + arguments for ingestion by the SimulationApp. It then appends custom command-line arguments relevant + to the SimulationApp to the input :class:`argparse.ArgumentParser` instance. This allows overriding the + environment variables using command-line arguments. + + Currently, it adds the following parameters to the argparser object: + + * ``headless`` (bool): If True, the app will be launched in headless (no-gui) mode. The values map the same + as that for the ``HEADLESS`` environment variable. If False, then headless mode is determined by the + ``HEADLESS`` environment variable. + * ``livestream`` (int): If one of {1, 2}, then livestreaming and headless mode is enabled. The values + map the same as that for the ``LIVESTREAM`` environment variable. If :obj:`-1`, then livestreaming is + determined by the ``LIVESTREAM`` environment variable. + Valid options are: + + - ``0``: Disabled + - ``1``: `WebRTC`_ over public network + - ``2``: `WebRTC`_ over local/private network + + * ``enable_cameras`` (bool): If True, the app will enable camera sensors and render them, even when in + headless mode. This flag must be set to True if the environments contains any camera sensors. + The values map the same as that for the ``ENABLE_CAMERAS`` environment variable. + If False, then enable_cameras mode is determined by the ``ENABLE_CAMERAS`` environment variable. + * ``device`` (str): The device to run the simulation on. + Valid options are: + + - ``cpu``: Use CPU. + - ``cuda``: Use GPU with device ID ``0``. + - ``cuda:N``: Use GPU, where N is the device ID. For example, "cuda:0". + + * ``experience`` (str): The experience file to load when launching the SimulationApp. If a relative path + is provided, it is resolved relative to the ``apps`` folder in Isaac Sim and Isaac Lab (in that order). + + If provided as an empty string, the experience file is determined based on the command-line flags: + + * If headless and enable_cameras are True, the experience file is set to + ``isaaclab.python.headless.rendering.kit``. + * If headless is False and enable_cameras is True, the experience file is set to + ``isaaclab.python.rendering.kit``. + * If headless and enable_cameras are False, the experience file is set to + ``isaaclab.python.kit``. + * If headless is True and enable_cameras is False, the experience file is set to + ``isaaclab.python.headless.kit``. + + * ``kit_args`` (str): Optional command line arguments to be passed to Omniverse Kit directly. + Arguments should be combined into a single string separated by space. + Example usage: --kit_args "--ext-folder=/path/to/ext1 --ext-folder=/path/to/ext2" + + + .. _`WebRTC`: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/manual_livestream_clients.html#isaac-sim-short-webrtc-streaming-client + + Args: + parser: An argument parser instance to be extended with the AppLauncher specific options. + """ + # If the passed parser has an existing _HelpAction when passed, + # we here remove the options which would invoke it, + # to be added back after the additional AppLauncher args + # have been added. This is equivalent to + # initially constructing the ArgParser with add_help=False, + # but this means we don't have to require that behavior + # in users and can handle it on our end. + # We do this because calling parse_known_args() will handle + # any -h/--help options being passed and then exit immediately, + # before the additional arguments can be added to the help readout. + parser_help = None + if len(parser._actions) > 0 and isinstance(parser._actions[0], argparse._HelpAction): # type: ignore + parser_help = parser._actions[0] + parser._option_string_actions.pop("-h") + parser._option_string_actions.pop("--help") + + # Parse known args for potential name collisions/type mismatches + # between the config fields SimulationApp expects and the ArgParse + # arguments that the user passed. + known, _ = parser.parse_known_args() + config = vars(known) + if len(config) == 0: + print( + "[WARN][AppLauncher]: There are no arguments attached to the ArgumentParser object." + " If you have your own arguments, please load your own arguments before calling the" + " `AppLauncher.add_app_launcher_args` method. This allows the method to check the validity" + " of the arguments and perform checks for argument names." + ) + else: + AppLauncher._check_argparser_config_params(config) + + # Add custom arguments to the parser + arg_group = parser.add_argument_group( + "app_launcher arguments", + description="Arguments for the AppLauncher. For more details, please check the documentation.", + ) + arg_group.add_argument( + "--headless", + action="store_true", + default=AppLauncher._APPLAUNCHER_CFG_INFO["headless"][1], + help="Force display off at all times.", + ) + arg_group.add_argument( + "--livestream", + type=int, + default=AppLauncher._APPLAUNCHER_CFG_INFO["livestream"][1], + choices={0, 1, 2}, + help="Force enable livestreaming. Mapping corresponds to that for the `LIVESTREAM` environment variable.", + ) + arg_group.add_argument( + "--enable_cameras", + action="store_true", + default=AppLauncher._APPLAUNCHER_CFG_INFO["enable_cameras"][1], + help="Enable camera sensors and relevant extension dependencies.", + ) + arg_group.add_argument( + "--xr", + action="store_true", + default=AppLauncher._APPLAUNCHER_CFG_INFO["xr"][1], + help="Enable XR mode for VR/AR applications.", + ) + arg_group.add_argument( + "--device", + type=str, + action=ExplicitAction, + default=AppLauncher._APPLAUNCHER_CFG_INFO["device"][1], + help='The device to run the simulation on. Can be "cpu", "cuda", "cuda:N", where N is the device ID', + ) + # Add the deprecated cpu flag to raise an error if it is used + arg_group.add_argument("--cpu", action="store_true", help=argparse.SUPPRESS) + arg_group.add_argument( + "--verbose", # Note: This is read by SimulationApp through sys.argv + action="store_true", + help="Enable verbose-level log output from the SimulationApp.", + ) + arg_group.add_argument( + "--info", # Note: This is read by SimulationApp through sys.argv + action="store_true", + help="Enable info-level log output from the SimulationApp.", + ) + arg_group.add_argument( + "--experience", + type=str, + default="", + help=( + "The experience file to load when launching the SimulationApp. If an empty string is provided," + " the experience file is determined based on the headless flag. If a relative path is provided," + " it is resolved relative to the `apps` folder in Isaac Sim and Isaac Lab (in that order)." + ), + ) + arg_group.add_argument( + "--rendering_mode", + type=str, + action=ExplicitAction, + choices={"performance", "balanced", "quality"}, + help=( + "Sets the rendering mode. Preset settings files can be found in apps/rendering_modes." + ' Can be "performance", "balanced", or "quality".' + " Individual settings can be overwritten by using the RenderCfg class." + ), + ) + arg_group.add_argument( + "--kit_args", + type=str, + default="", + help=( + "Command line arguments for Omniverse Kit as a string separated by a space delimiter." + ' Example usage: --kit_args "--ext-folder=/path/to/ext1 --ext-folder=/path/to/ext2"' + ), + ) + arg_group.add_argument( + "--anim_recording_enabled", + action="store_true", + help="Enable recording time-sampled USD animations from IsaacLab PhysX simulations.", + ) + arg_group.add_argument( + "--anim_recording_start_time", + type=float, + default=0, + help=( + "Set time that animation recording begins playing. If not set, the recording will start from the" + " beginning." + ), + ) + arg_group.add_argument( + "--anim_recording_stop_time", + type=float, + default=10, + help=( + "Set time that animation recording stops playing. If the process is shutdown before the stop time is" + " exceeded, then the animation is not recorded." + ), + ) + # special flag for backwards compatibility + + # Corresponding to the beginning of the function, + # if we have removed -h/--help handling, we add it back. + if parser_help is not None: + parser._option_string_actions["-h"] = parser_help + parser._option_string_actions["--help"] = parser_help + + """ + Internal functions. + """ + + _APPLAUNCHER_CFG_INFO: dict[str, tuple[list[type], Any]] = { + "headless": ([bool], False), + "livestream": ([int], -1), + "enable_cameras": ([bool], False), + "xr": ([bool], False), + "device": ([str], "cuda:0"), + "experience": ([str], ""), + "rendering_mode": ([str], "balanced"), + } + """A dictionary of arguments added manually by the :meth:`AppLauncher.add_app_launcher_args` method. + + The values are a tuple of the expected type and default value. This is used to check against name collisions + for arguments passed to the :class:`AppLauncher` class as well as for type checking. + + They have corresponding environment variables as detailed in the documentation. + """ + + # TODO: Find some internally managed NVIDIA list of these types. + # SimulationApp.DEFAULT_LAUNCHER_CONFIG almost works, except that + # it is ambiguous where the default types are None + _SIM_APP_CFG_TYPES: dict[str, list[type]] = { + "headless": [bool], + "hide_ui": [bool, type(None)], + "active_gpu": [int, type(None)], + "physics_gpu": [int], + "multi_gpu": [bool], + "sync_loads": [bool], + "width": [int], + "height": [int], + "window_width": [int], + "window_height": [int], + "display_options": [int], + "subdiv_refinement_level": [int], + "renderer": [str], + "anti_aliasing": [int], + "samples_per_pixel_per_frame": [int], + "denoiser": [bool], + "max_bounces": [int], + "max_specular_transmission_bounces": [int], + "max_volume_bounces": [int], + "open_usd": [str, type(None)], + "livesync_usd": [str, type(None)], + "fast_shutdown": [bool], + "experience": [str], + } + """A dictionary containing the type of arguments passed to SimulationApp. + + This is used to check against name collisions for arguments passed to the :class:`AppLauncher` class + as well as for type checking. It corresponds closely to the :attr:`SimulationApp.DEFAULT_LAUNCHER_CONFIG`, + but specifically denotes where None types are allowed. + """ + + @staticmethod + def _check_argparser_config_params(config: dict) -> None: + """Checks that input argparser object has parameters with valid settings with no name conflicts. + + First, we inspect the dictionary to ensure that the passed ArgParser object is not attempting to add arguments + which should be assigned by calling :meth:`AppLauncher.add_app_launcher_args`. + + Then, we check that if the key corresponds to a config setting expected by SimulationApp, then the type of + that key's value corresponds to the type expected by the SimulationApp. If it passes the check, the function + prints out that the setting with be passed to the SimulationApp. Otherwise, we raise a ValueError exception. + + Args: + config: A configuration parameters which will be passed to the SimulationApp constructor. + + Raises: + ValueError: If a key is an already existing field in the configuration parameters but + should be added by calling the :meth:`AppLauncher.add_app_launcher_args. + ValueError: If keys corresponding to those used to initialize SimulationApp + (as found in :attr:`_SIM_APP_CFG_TYPES`) are of the wrong value type. + """ + # check that no config key conflicts with AppLauncher config names + applauncher_keys = set(AppLauncher._APPLAUNCHER_CFG_INFO.keys()) + for key, value in config.items(): + if key in applauncher_keys: + raise ValueError( + f"The passed ArgParser object already has the field '{key}'. This field will be added by" + " `AppLauncher.add_app_launcher_args()`, and should not be added directly. Please remove the" + " argument or rename it to a non-conflicting name." + ) + # check that type of the passed keys are valid + simulationapp_keys = set(AppLauncher._SIM_APP_CFG_TYPES.keys()) + for key, value in config.items(): + if key in simulationapp_keys: + given_type = type(value) + expected_types = AppLauncher._SIM_APP_CFG_TYPES[key] + if type(value) not in set(expected_types): + raise ValueError( + f"Invalid value type for the argument '{key}': {given_type}. Expected one of {expected_types}," + " if intended to be ingested by the SimulationApp object. Please change the type if this" + " intended for the SimulationApp or change the name of the argument to avoid name conflicts." + ) + # Print out values which will be used + print(f"[INFO][AppLauncher]: The argument '{key}' will be used to configure the SimulationApp.") + + def _config_resolution(self, launcher_args: dict): + """Resolve the input arguments and environment variables. + + Args: + launcher_args: A dictionary of all input arguments passed to the class object. + """ + # Handle core settings + livestream_arg, livestream_env = self._resolve_livestream_settings(launcher_args) + self._resolve_headless_settings(launcher_args, livestream_arg, livestream_env) + self._resolve_camera_settings(launcher_args) + self._resolve_xr_settings(launcher_args) + self._resolve_viewport_settings(launcher_args) + + # Handle device and distributed settings + self._resolve_device_settings(launcher_args) + + # Handle experience file settings + self._resolve_experience_file(launcher_args) + + # Handle animation recording settings + self._resolve_anim_recording_settings(launcher_args) + + # Handle additional arguments + self._resolve_kit_args(launcher_args) + + # Prepare final simulation app config + # Remove all values from input keyword args which are not meant for SimulationApp + # Assign all the passed settings to a dictionary for the simulation app + self._sim_app_config = { + key: launcher_args[key] for key in set(AppLauncher._SIM_APP_CFG_TYPES.keys()) & set(launcher_args.keys()) + } + + def _resolve_livestream_settings(self, launcher_args: dict) -> tuple[int, int]: + """Resolve livestream related settings.""" + livestream_env = int(os.environ.get("LIVESTREAM", 0)) + livestream_arg = launcher_args.pop("livestream", AppLauncher._APPLAUNCHER_CFG_INFO["livestream"][1]) + livestream_valid_vals = {0, 1, 2} + # Value checking on LIVESTREAM + if livestream_env not in livestream_valid_vals: + raise ValueError( + f"Invalid value for environment variable `LIVESTREAM`: {livestream_env} ." + f" Expected: {livestream_valid_vals}." + ) + # We allow livestream kwarg to supersede LIVESTREAM envvar + if livestream_arg >= 0: + if livestream_arg in livestream_valid_vals: + self._livestream = livestream_arg + # print info that we overrode the env-var + print( + f"[INFO][AppLauncher]: Input keyword argument `livestream={livestream_arg}` has overridden" + f" the environment variable `LIVESTREAM={livestream_env}`." + ) + else: + raise ValueError( + f"Invalid value for input keyword argument `livestream`: {livestream_arg} ." + f" Expected: {livestream_valid_vals}." + ) + else: + self._livestream = livestream_env + + # Set public IP address of a remote instance + public_ip_env = os.environ.get("PUBLIC_IP", "127.0.0.1") + + # Process livestream here before launching kit because some of the extensions only work + # when launched with the kit file + self._livestream_args = [] + if self._livestream >= 1: + # Note: Only one livestream extension can be enabled at a time + if self._livestream == 1: + # WebRTC public network + self._livestream_args += [ + f"--/app/livestream/publicEndpointAddress={public_ip_env}", + "--/app/livestream/port=49100", + "--enable", + "omni.services.livestream.nvcf", + ] + elif self._livestream == 2: + # WebRTC private network + self._livestream_args += [ + "--enable", + "omni.services.livestream.nvcf", + ] + else: + raise ValueError(f"Invalid value for livestream: {self._livestream}. Expected: 1, 2 .") + sys.argv += self._livestream_args + + return livestream_arg, livestream_env + + def _resolve_headless_settings(self, launcher_args: dict, livestream_arg: int, livestream_env: int): + """Resolve headless related settings.""" + # Resolve headless execution of simulation app + # HEADLESS is initially passed as an int instead of + # the bool of headless_arg to avoid messy string processing, + headless_env = int(os.environ.get("HEADLESS", 0)) + headless_arg = launcher_args.pop("headless", AppLauncher._APPLAUNCHER_CFG_INFO["headless"][1]) + headless_valid_vals = {0, 1} + # Value checking on HEADLESS + if headless_env not in headless_valid_vals: + raise ValueError( + f"Invalid value for environment variable `HEADLESS`: {headless_env} . Expected: {headless_valid_vals}." + ) + # We allow headless kwarg to supersede HEADLESS envvar if headless_arg does not have the default value + # Note: Headless is always true when livestreaming + if headless_arg is True: + self._headless = headless_arg + elif self._livestream in {1, 2}: + # we are always headless on the host machine + self._headless = True + # inform who has toggled the headless flag + if self._livestream == livestream_arg: + print( + f"[INFO][AppLauncher]: Input keyword argument `livestream={self._livestream}` has implicitly" + f" overridden the environment variable `HEADLESS={headless_env}` to True." + ) + elif self._livestream == livestream_env: + print( + f"[INFO][AppLauncher]: Environment variable `LIVESTREAM={self._livestream}` has implicitly" + f" overridden the environment variable `HEADLESS={headless_env}` to True." + ) + else: + # Headless needs to be a bool to be ingested by SimulationApp + self._headless = bool(headless_env) + # Headless needs to be passed to the SimulationApp so we keep it here + launcher_args["headless"] = self._headless + + def _resolve_camera_settings(self, launcher_args: dict): + """Resolve camera related settings.""" + enable_cameras_env = int(os.environ.get("ENABLE_CAMERAS", 0)) + enable_cameras_arg = launcher_args.get("enable_cameras", AppLauncher._APPLAUNCHER_CFG_INFO["enable_cameras"][1]) + enable_cameras_valid_vals = {0, 1} + if enable_cameras_env not in enable_cameras_valid_vals: + raise ValueError( + f"Invalid value for environment variable `ENABLE_CAMERAS`: {enable_cameras_env} ." + f"Expected: {enable_cameras_valid_vals} ." + ) + # We allow enable_cameras kwarg to supersede ENABLE_CAMERAS envvar + if enable_cameras_arg is True: + self._enable_cameras = enable_cameras_arg + else: + self._enable_cameras = bool(enable_cameras_env) + self._offscreen_render = False + if self._enable_cameras and self._headless: + self._offscreen_render = True + + def _resolve_xr_settings(self, launcher_args: dict): + """Resolve XR related settings.""" + xr_env = int(os.environ.get("XR", 0)) + xr_arg = launcher_args.get("xr", AppLauncher._APPLAUNCHER_CFG_INFO["xr"][1]) + xr_valid_vals = {0, 1} + if xr_env not in xr_valid_vals: + raise ValueError(f"Invalid value for environment variable `XR`: {xr_env} .Expected: {xr_valid_vals} .") + # We allow xr kwarg to supersede XR envvar + if xr_arg is True: + self._xr = xr_arg + else: + self._xr = bool(xr_env) + + def _resolve_viewport_settings(self, launcher_args: dict): + """Resolve viewport related settings.""" + # Check if we can disable the viewport to improve performance + # This should only happen if we are running headless and do not require livestreaming or video recording + # This is different from offscreen_render because this only affects the default viewport and + # not other render-products in the scene + self._render_viewport = True + if self._headless and not self._livestream and not launcher_args.get("video", False): + self._render_viewport = False + + # hide_ui flag + launcher_args["hide_ui"] = False + if self._headless and not self._livestream: + launcher_args["hide_ui"] = True + + # avoid creating new stage at startup by default for performance reasons + launcher_args["create_new_stage"] = False + + def _resolve_device_settings(self, launcher_args: dict): + """Resolve simulation GPU device related settings.""" + self.device_id = 0 + device = launcher_args.get("device", AppLauncher._APPLAUNCHER_CFG_INFO["device"][1]) + + device_explicitly_passed = launcher_args.pop("device_explicit", False) + if self._xr and not device_explicitly_passed: + # If no device is specified, default to the CPU device if we are running in XR + device = "cpu" + + # Overwrite for downstream consumers + launcher_args["device"] = "cpu" + + if "cuda" not in device and "cpu" not in device: + raise ValueError( + f"Invalid value for input keyword argument `device`: {device}." + " Expected: a string with the format 'cuda', 'cuda:', or 'cpu'." + ) + + if "cuda:" in device: + self.device_id = int(device.split(":")[-1]) + + # Raise an error for the deprecated cpu flag + if launcher_args.get("cpu", False): + raise ValueError("The `--cpu` flag is deprecated. Please use `--device cpu` instead.") + + if "distributed" in launcher_args and launcher_args["distributed"]: + # local rank (GPU id) in a current multi-gpu mode + self.local_rank = int(os.getenv("LOCAL_RANK", "0")) + int(os.getenv("JAX_LOCAL_RANK", "0")) + # global rank (GPU id) in multi-gpu multi-node mode + self.global_rank = int(os.getenv("RANK", "0")) + int(os.getenv("JAX_RANK", "0")) + + self.device_id = self.local_rank + device = "cuda:" + str(self.device_id) + launcher_args["multi_gpu"] = False + # limit CPU threads to minimize thread context switching + # this ensures processes do not take up all available threads and fight for resources + num_cpu_cores = os.cpu_count() + num_threads_per_process = num_cpu_cores // int(os.getenv("WORLD_SIZE", 1)) + # set environment variables to limit CPU threads + os.environ["PXR_WORK_THREAD_LIMIT"] = str(num_threads_per_process) + os.environ["OPENBLAS_NUM_THREADS"] = str(num_threads_per_process) + # pass command line variable to kit + sys.argv.append(f"--/plugins/carb.tasking.plugin/threadCount={num_threads_per_process}") + + # set rendering device. We do not need to set physics_gpu because it will automatically pick the same one + # as the active_gpu device. Setting physics_gpu explicitly may result in a different device to be used. + launcher_args["physics_gpu"] = self.device_id + launcher_args["active_gpu"] = self.device_id + + print(f"[INFO][AppLauncher]: Using device: {device}") + + def _resolve_experience_file(self, launcher_args: dict): + """Resolve experience file related settings.""" + # Check if input keywords contain an 'experience' file setting + # Note: since experience is taken as a separate argument by Simulation App, we store it separately + self._sim_experience_file = launcher_args.pop("experience", "") + + # If nothing is provided resolve the experience file based on the headless flag + kit_app_exp_path = os.environ["EXP_PATH"] + isaaclab_app_exp_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), *[".."] * 4, "apps") + # For Isaac Sim 4.5 compatibility, we use the 4.5 app files in a different folder + # if launcher_args.get("use_isaacsim_45", False): + if self.is_isaac_sim_version_4_5(): + isaaclab_app_exp_path = os.path.join(isaaclab_app_exp_path, "isaacsim_4_5") + + if self._sim_experience_file == "": + # check if the headless flag is set + # xr rendering overrides camera rendering settings + if self._enable_cameras and not self._xr: + if self._headless and not self._livestream: + self._sim_experience_file = os.path.join( + isaaclab_app_exp_path, "isaaclab.python.headless.rendering.kit" + ) + else: + self._sim_experience_file = os.path.join(isaaclab_app_exp_path, "isaaclab.python.rendering.kit") + elif self._xr: + if self._headless: + self._sim_experience_file = os.path.join( + isaaclab_app_exp_path, "isaaclab.python.xr.openxr.headless.kit" + ) + else: + self._sim_experience_file = os.path.join(isaaclab_app_exp_path, "isaaclab.python.xr.openxr.kit") + elif self._headless and not self._livestream: + self._sim_experience_file = os.path.join(isaaclab_app_exp_path, "isaaclab.python.headless.kit") + else: + self._sim_experience_file = os.path.join(isaaclab_app_exp_path, "isaaclab.python.kit") + elif not os.path.isabs(self._sim_experience_file): + option_1_app_exp_path = os.path.join(kit_app_exp_path, self._sim_experience_file) + option_2_app_exp_path = os.path.join(isaaclab_app_exp_path, self._sim_experience_file) + if os.path.exists(option_1_app_exp_path): + self._sim_experience_file = option_1_app_exp_path + elif os.path.exists(option_2_app_exp_path): + self._sim_experience_file = option_2_app_exp_path + else: + raise FileNotFoundError( + f"Invalid value for input keyword argument `experience`: {self._sim_experience_file}." + "\n No such file exists in either the Kit or Isaac Lab experience paths. Checked paths:" + f"\n\t [1]: {option_1_app_exp_path}" + f"\n\t [2]: {option_2_app_exp_path}" + ) + elif not os.path.exists(self._sim_experience_file): + raise FileNotFoundError( + f"Invalid value for input keyword argument `experience`: {self._sim_experience_file}." + " The file does not exist." + ) + + # Resolve the absolute path of the experience file + self._sim_experience_file = os.path.abspath(self._sim_experience_file) + print(f"[INFO][AppLauncher]: Loading experience file: {self._sim_experience_file}") + + def _resolve_anim_recording_settings(self, launcher_args: dict): + """Resolve animation recording settings.""" + + # Enable omni.physx.pvd extension if recording is enabled + recording_enabled = launcher_args.get("anim_recording_enabled", False) + if recording_enabled: + if self._headless: + raise ValueError("Animation recording is not supported in headless mode.") + if self.is_isaac_sim_version_4_5(): + raise RuntimeError( + "Animation recording is not supported in Isaac Sim 4.5. Please update to Isaac Sim 5.0." + ) + sys.argv += ["--enable", "omni.physx.pvd"] + + def _resolve_kit_args(self, launcher_args: dict): + """Resolve additional arguments passed to Kit.""" + # Resolve additional arguments passed to Kit + self._kit_args = [] + if "kit_args" in launcher_args: + self._kit_args = [arg for arg in launcher_args["kit_args"].split()] + sys.argv += self._kit_args + + def _create_app(self): + """Launch and create the SimulationApp based on the parsed simulation config.""" + # Initialize SimulationApp + # hack sys module to make sure that the SimulationApp is initialized correctly + # this is to avoid the warnings from the simulation app about not ok modules + r = re.compile(".*lab.*") + found_modules = list(filter(r.match, list(sys.modules.keys()))) + # remove Isaac Lab modules from sys.modules + hacked_modules = dict() + for key in found_modules: + hacked_modules[key] = sys.modules[key] + del sys.modules[key] + + # disable sys stdout and stderr to avoid printing the warning messages + # this is mainly done to purge the print statements from the simulation app + if "--verbose" not in sys.argv and "--info" not in sys.argv: + sys.stdout = open(os.devnull, "w") # noqa: SIM115 + + # pytest may have left some things in sys.argv, this will check for some of those + # do a mark and sweep to remove any -m pytest and -m isaacsim_ci and -c **/pyproject.toml + indexes_to_remove = [] + for idx, arg in enumerate(sys.argv[:-1]): + if arg == "-m": + value_for_dash_m = sys.argv[idx + 1] + if "pytest" in value_for_dash_m or "isaacsim_ci" in value_for_dash_m: + indexes_to_remove.append(idx) + indexes_to_remove.append(idx + 1) + if arg.startswith("--config-file=") and "pyproject.toml" in arg: + indexes_to_remove.append(idx) + if arg == "--capture=no": + indexes_to_remove.append(idx) + for idx in sorted(indexes_to_remove, reverse=True): + sys.argv = sys.argv[:idx] + sys.argv[idx + 1 :] + + # launch simulation app + self._app = SimulationApp(self._sim_app_config, experience=self._sim_experience_file) + # enable sys stdout and stderr + sys.stdout = sys.__stdout__ + + # add Isaac Lab modules back to sys.modules + for key, value in hacked_modules.items(): + sys.modules[key] = value + # remove the threadCount argument from sys.argv if it was added for distributed training + pattern = r"--/plugins/carb\.tasking\.plugin/threadCount=\d+" + sys.argv = [arg for arg in sys.argv if not re.match(pattern, arg)] + + # remove additional OV args from sys.argv + if len(self._kit_args) > 0: + sys.argv = [arg for arg in sys.argv if arg not in self._kit_args] + if len(self._livestream_args) > 0: + sys.argv = [arg for arg in sys.argv if arg not in self._livestream_args] + + def _rendering_enabled(self) -> bool: + """Check if rendering is required by the app.""" + # Indicates whether rendering is required by the app. + # Extensions required for rendering bring startup and simulation costs, so we do not + # enable them if not required. + return not self._headless or self._livestream >= 1 or self._enable_cameras or self._xr + + def _load_extensions(self): + """Load correct extensions based on AppLauncher's resolved config member variables.""" + # These have to be loaded after SimulationApp is initialized + import carb + + # Retrieve carb settings for modification + carb_settings_iface = carb.settings.get_settings() + + # set carb setting to indicate Isaac Lab's offscreen_render pipeline should be enabled + # this flag is used by the SimulationContext class to enable the offscreen_render pipeline + # when the render() method is called. + carb_settings_iface.set_bool("/isaaclab/render/offscreen", self._offscreen_render) + + # set carb setting to indicate Isaac Lab's render_viewport pipeline should be enabled + # this flag is used by the SimulationContext class to enable the render_viewport pipeline + # when the render() method is called. + carb_settings_iface.set_bool("/isaaclab/render/active_viewport", self._render_viewport) + + # set carb setting to indicate no RTX sensors are used + # this flag is set to True when an RTX-rendering related sensor is created + # for example: the `Camera` sensor class + carb_settings_iface.set_bool("/isaaclab/render/rtx_sensors", False) + + # set fabric update flag to disable updating transforms when rendering is disabled + carb_settings_iface.set_bool("/physics/fabricUpdateTransformations", self._rendering_enabled()) + + # in theory, this should ensure that dt is consistent across time stepping, but this is not the case + # for now, we use the custom loop runner from Isaac Sim to achieve this + carb_settings_iface.set_bool("/app/player/useFixedTimeStepping", False) + + def _hide_stop_button(self): + """Hide the stop button in the toolbar. + + For standalone executions, having a stop button is confusing since it invalidates the whole simulation. + Thus, we hide the button so that users don't accidentally click it. + """ + # when we are truly headless, then we can't import the widget toolbar + # thus, we only hide the stop button when we are not headless (i.e. GUI is enabled) + if self._livestream >= 1 or not self._headless: + import omni.kit.widget.toolbar + + # grey out the stop button because we don't want to stop the simulation manually in standalone mode + toolbar = omni.kit.widget.toolbar.get_instance() + play_button_group = toolbar._builtin_tools._play_button_group # type: ignore + if play_button_group is not None: + play_button_group._stop_button.visible = False # type: ignore + play_button_group._stop_button.enabled = False # type: ignore + play_button_group._stop_button = None # type: ignore + + def _set_rendering_mode_settings(self, launcher_args: dict) -> None: + """Store RTX rendering mode in carb settings.""" + import carb + + rendering_mode = launcher_args.get("rendering_mode") + + if rendering_mode is None: + # use default kit rendering settings if cameras are disabled and a rendering mode is not selected + if not self._enable_cameras: + return + rendering_mode = "" + + # store rendering mode in carb settings + carb_settings = carb.settings.get_settings() + carb_settings.set_string("/isaaclab/rendering/rendering_mode", rendering_mode) + + def _set_animation_recording_settings(self, launcher_args: dict) -> None: + """Store animation recording settings in carb settings.""" + import carb + + # check if recording is enabled + recording_enabled = launcher_args.get("anim_recording_enabled", False) + if not recording_enabled: + return + + # arg checks + if launcher_args.get("anim_recording_start_time") >= launcher_args.get("anim_recording_stop_time"): + raise ValueError( + f"'anim_recording_start_time' {launcher_args.get('anim_recording_start_time')} must be less than" + f" 'anim_recording_stop_time' {launcher_args.get('anim_recording_stop_time')}" + ) + + # grab config + start_time = launcher_args.get("anim_recording_start_time") + stop_time = launcher_args.get("anim_recording_stop_time") + + # store config in carb settings + carb_settings = carb.settings.get_settings() + carb_settings.set_bool("/isaaclab/anim_recording/enabled", recording_enabled) + carb_settings.set_float("/isaaclab/anim_recording/start_time", start_time) + carb_settings.set_float("/isaaclab/anim_recording/stop_time", stop_time) + + def _interrupt_signal_handle_callback(self, signal, frame): + """Handle the interrupt signal from the keyboard.""" + # close the app + self._app.close() + # raise the error for keyboard interrupt + raise KeyboardInterrupt + + def is_isaac_sim_version_4_5(self) -> bool: + if not hasattr(self, "_is_sim_ver_4_5"): + # 1) Try to read the VERSION file (for manual / binary installs) + version_path = os.path.abspath(os.path.join(os.path.dirname(isaacsim.__file__), "../../VERSION")) + if os.path.isfile(version_path): + with open(version_path) as f: + ver = f.readline().strip() + if ver.startswith("4.5"): + self._is_sim_ver_4_5 = True + return True + + # 2) Fall back to metadata (for pip installs) + from importlib.metadata import version as pkg_version + + try: + ver = pkg_version("isaacsim") + if ver.startswith("4.5"): + self._is_sim_ver_4_5 = True + else: + self._is_sim_ver_4_5 = False + except Exception: + self._is_sim_ver_4_5 = False + return self._is_sim_ver_4_5 + + def _hide_play_button(self, flag): + """Hide/Unhide the play button in the toolbar. + + This is used if the timeline is stopped by a GUI action like "save as" to not allow the user to + resume the timeline afterwards. + """ + # when we are truly headless, then we can't import the widget toolbar + # thus, we only hide the play button when we are not headless (i.e. GUI is enabled) + if self._livestream >= 1 or not self._headless: + import omni.kit.widget.toolbar + + toolbar = omni.kit.widget.toolbar.get_instance() + play_button_group = toolbar._builtin_tools._play_button_group # type: ignore + if play_button_group is not None: + play_button_group._play_button.visible = not flag # type: ignore + play_button_group._play_button.enabled = not flag # type: ignore + + def _abort_signal_handle_callback(self, signal, frame): + """Handle the abort/segmentation/kill signals.""" + # close the app + self._app.close() + + def __patch_simulation_start_app(self, launcher_args: dict): + if not launcher_args.get("enable_pinocchio", False): + return + + if launcher_args.get("disable_pinocchio_patch", False): + return + + original_start_app = SimulationApp._start_app + + def _start_app_patch(sim_app_instance, *args, **kwargs): + original_start_app(sim_app_instance, *args, **kwargs) + self.__patch_pxr_gf_matrix4d(launcher_args) + + SimulationApp._start_app = _start_app_patch + + def __patch_pxr_gf_matrix4d(self, launcher_args: dict): + import traceback + + from pxr import Gf + + logger.warning( + "Due to an issue with Pinocchio and pxr.Gf.Matrix4d, patching the Matrix4d constructor to convert arguments" + " into a list of floats." + ) + + # Store the original Matrix4d constructor + original_matrix4d = Gf.Matrix4d.__init__ + + # Define a wrapper function to handle different input types + def patch_matrix4d(self, *args, **kwargs): + try: + # Case 1: No arguments (identity matrix) + if len(args) == 0: + original_matrix4d(self, *args, **kwargs) + return + + # Case 2: Single argument + elif len(args) == 1: + arg = args[0] + + # Case 2a: Already a Matrix4d + if isinstance(arg, Gf.Matrix4d): + original_matrix4d(self, arg) + return + + # Case 2b: Tuple of tuples (4x4 matrix) OR List of lists (4x4 matrix) + elif (isinstance(arg, tuple) and len(arg) == 4 and all(isinstance(row, tuple) for row in arg)) or ( + isinstance(arg, list) and len(arg) == 4 and all(isinstance(row, list) for row in arg) + ): + float_list = [float(item) for row in arg for item in row] + original_matrix4d(self, *float_list) + return + + # Case 2c: Flat list of 16 elements + elif isinstance(arg, (list, tuple)) and len(arg) == 16: + float_list = [float(item) for item in arg] + original_matrix4d(self, *float_list) + return + + # Case 2d: Another matrix-like object with elements accessible via indexing + elif hasattr(arg, "__getitem__") and hasattr(arg, "__len__"): + with contextlib.suppress(IndexError, TypeError): + if len(arg) == 16: + float_list = [float(arg[i]) for i in range(16)] + original_matrix4d(self, *float_list) + return + # Try to extract as 4x4 matrix + elif len(arg) == 4 and all(len(row) == 4 for row in arg): + float_list = [float(arg[i][j]) for i in range(4) for j in range(4)] + original_matrix4d(self, *float_list) + return + + # Case 3: 16 separate arguments (individual matrix elements) + elif len(args) == 16: + float_list = [float(arg) for arg in args] + original_matrix4d(self, *float_list) + return + + # Default: Use original constructor + original_matrix4d(self, *args, **kwargs) + + except Exception as e: + logger.error(f"Matrix4d wrapper error: {e}") + traceback.print_stack() + # Fall back to original constructor as last resort + try: + original_matrix4d(self, *args, **kwargs) + except Exception as inner_e: + logger.error(f"Original Matrix4d constructor also failed: {inner_e}") + # Initialize as identity matrix if all else fails + original_matrix4d(self) + + Gf.Matrix4d.__init__ = patch_matrix4d diff --git a/source/isaaclab/isaaclab/controllers/__init__.py b/source/isaaclab/isaaclab/controllers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3a055c508e8d343f950a84d2dea533ac814888f9 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package for different controllers and motion-generators. + +Controllers or motion generators are responsible for closed-loop tracking of a given command. The +controller can be a simple PID controller or a more complex controller such as impedance control +or inverse kinematics control. The controller is responsible for generating the desired joint-level +commands to be sent to the robot. +""" + +from .differential_ik import DifferentialIKController +from .differential_ik_cfg import DifferentialIKControllerCfg +from .operational_space import OperationalSpaceController +from .operational_space_cfg import OperationalSpaceControllerCfg diff --git a/source/isaaclab/isaaclab/controllers/config/__init__.py b/source/isaaclab/isaaclab/controllers/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/config/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab/isaaclab/controllers/config/data/lula_franka_gen.urdf b/source/isaaclab/isaaclab/controllers/config/data/lula_franka_gen.urdf new file mode 100644 index 0000000000000000000000000000000000000000..2d00a71e12d5ef85e0d20d64aea2be49539a6b1a --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/config/data/lula_franka_gen.urdf @@ -0,0 +1,415 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/isaaclab/isaaclab/controllers/config/rmp_flow.py b/source/isaaclab/isaaclab/controllers/config/rmp_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..55c6d8e1fbaaae5c9baf4e3652e507e49bebd5b5 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/config/rmp_flow.py @@ -0,0 +1,100 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os + +from isaacsim.core.utils.extensions import get_extension_path_from_name + +from isaaclab.controllers.rmp_flow import RmpFlowControllerCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +# Directory on Nucleus Server for RMP-Flow assets (URDFs, collision models, etc.) +ISAACLAB_NUCLEUS_RMPFLOW_DIR = os.path.join(ISAACLAB_NUCLEUS_DIR, "Controllers", "RmpFlowAssets") + +# Note: RMP-Flow config files for supported robots are stored in the motion_generation extension +# We need to move it here for doc building purposes. +try: + _RMP_CONFIG_DIR = os.path.join( + get_extension_path_from_name("isaacsim.robot_motion.motion_generation"), + "motion_policy_configs", + ) +except Exception: + _RMP_CONFIG_DIR = "" + +# Path to current directory +_CUR_DIR = os.path.dirname(os.path.realpath(__file__)) + +FRANKA_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join(_RMP_CONFIG_DIR, "franka", "rmpflow", "franka_rmpflow_common.yaml"), + urdf_file=os.path.join(_CUR_DIR, "data", "lula_franka_gen.urdf"), + collision_file=os.path.join(_RMP_CONFIG_DIR, "franka", "rmpflow", "robot_descriptor.yaml"), + frame_name="panda_end_effector", + evaluations_per_frame=5, +) +"""Configuration of RMPFlow for Franka arm (default from `isaacsim.robot_motion.motion_generation`).""" + + +UR10_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join(_RMP_CONFIG_DIR, "ur10", "rmpflow", "ur10_rmpflow_config.yaml"), + urdf_file=os.path.join(_RMP_CONFIG_DIR, "ur10", "ur10_robot.urdf"), + collision_file=os.path.join(_RMP_CONFIG_DIR, "ur10", "rmpflow", "ur10_robot_description.yaml"), + frame_name="ee_link", + evaluations_per_frame=5, +) +"""Configuration of RMPFlow for UR10 arm (default from `isaacsim.robot_motion.motion_generation`).""" + +GALBOT_LEFT_ARM_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join( + ISAACLAB_NUCLEUS_RMPFLOW_DIR, + "galbot_one_charlie", + "rmpflow", + "galbot_one_charlie_left_arm_rmpflow_config.yaml", + ), + urdf_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "galbot_one_charlie", "galbot_one_charlie.urdf"), + collision_file=os.path.join( + ISAACLAB_NUCLEUS_RMPFLOW_DIR, "galbot_one_charlie", "rmpflow", "galbot_one_charlie_left_arm_gripper.yaml" + ), + frame_name="left_gripper_tcp_link", + evaluations_per_frame=5, + ignore_robot_state_updates=True, +) + +GALBOT_RIGHT_ARM_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join( + ISAACLAB_NUCLEUS_RMPFLOW_DIR, + "galbot_one_charlie", + "rmpflow", + "galbot_one_charlie_right_arm_rmpflow_config.yaml", + ), + urdf_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "galbot_one_charlie", "galbot_one_charlie.urdf"), + collision_file=os.path.join( + ISAACLAB_NUCLEUS_RMPFLOW_DIR, "galbot_one_charlie", "rmpflow", "galbot_one_charlie_right_arm_suction.yaml" + ), + frame_name="right_suction_cup_tcp_link", + evaluations_per_frame=5, + ignore_robot_state_updates=True, +) + +"""Configuration of RMPFlow for Galbot humanoid.""" + +AGIBOT_LEFT_ARM_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "rmpflow", "agibot_left_arm_rmpflow_config.yaml"), + urdf_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "agibot.urdf"), + collision_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "rmpflow", "agibot_left_arm_gripper.yaml"), + frame_name="gripper_center", + evaluations_per_frame=5, + ignore_robot_state_updates=True, +) + +AGIBOT_RIGHT_ARM_RMPFLOW_CFG = RmpFlowControllerCfg( + config_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "rmpflow", "agibot_right_arm_rmpflow_config.yaml"), + urdf_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "agibot.urdf"), + collision_file=os.path.join(ISAACLAB_NUCLEUS_RMPFLOW_DIR, "agibot", "rmpflow", "agibot_right_arm_gripper.yaml"), + frame_name="right_gripper_center", + evaluations_per_frame=5, + ignore_robot_state_updates=True, +) + +"""Configuration of RMPFlow for Agibot humanoid.""" diff --git a/source/isaaclab/isaaclab/controllers/differential_ik.py b/source/isaaclab/isaaclab/controllers/differential_ik.py new file mode 100644 index 0000000000000000000000000000000000000000..8841dbe4fb5e050e8c5cec6e01f2bcf5972f54d8 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/differential_ik.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.utils.math import apply_delta_pose, compute_pose_error + +if TYPE_CHECKING: + from .differential_ik_cfg import DifferentialIKControllerCfg + + +class DifferentialIKController: + r"""Differential inverse kinematics (IK) controller. + + This controller is based on the concept of differential inverse kinematics [1, 2] which is a method for computing + the change in joint positions that yields the desired change in pose. + + .. math:: + + \Delta \mathbf{q} &= \mathbf{J}^{\dagger} \Delta \mathbf{x} \\ + \mathbf{q}_{\text{desired}} &= \mathbf{q}_{\text{current}} + \Delta \mathbf{q} + + where :math:`\mathbf{J}^{\dagger}` is the pseudo-inverse of the Jacobian matrix :math:`\mathbf{J}`, + :math:`\Delta \mathbf{x}` is the desired change in pose, and :math:`\mathbf{q}_{\text{current}}` + is the current joint positions. + + To deal with singularity in Jacobian, the following methods are supported for computing inverse of the Jacobian: + + - "pinv": Moore-Penrose pseudo-inverse + - "svd": Adaptive singular-value decomposition (SVD) + - "trans": Transpose of matrix + - "dls": Damped version of Moore-Penrose pseudo-inverse (also called Levenberg-Marquardt) + + + .. caution:: + The controller does not assume anything about the frames of the current and desired end-effector pose, + or the joint-space velocities. It is up to the user to ensure that these quantities are given + in the correct format. + + Reference: + + 1. `Robot Dynamics Lecture Notes `_ + by Marco Hutter (ETH Zurich) + 2. `Introduction to Inverse Kinematics `_ + by Samuel R. Buss (University of California, San Diego) + + """ + + def __init__(self, cfg: DifferentialIKControllerCfg, num_envs: int, device: str): + """Initialize the controller. + + Args: + cfg: The configuration for the controller. + num_envs: The number of environments. + device: The device to use for computations. + """ + # store inputs + self.cfg = cfg + self.num_envs = num_envs + self._device = device + # create buffers + self.ee_pos_des = torch.zeros(self.num_envs, 3, device=self._device) + self.ee_quat_des = torch.zeros(self.num_envs, 4, device=self._device) + # -- input command + self._command = torch.zeros(self.num_envs, self.action_dim, device=self._device) + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + """Dimension of the controller's input command.""" + if self.cfg.command_type == "position": + return 3 # (x, y, z) + elif self.cfg.command_type == "pose" and self.cfg.use_relative_mode: + return 6 # (dx, dy, dz, droll, dpitch, dyaw) + else: + return 7 # (x, y, z, qw, qx, qy, qz) + + """ + Operations. + """ + + def reset(self, env_ids: torch.Tensor = None): + """Reset the internals. + + Args: + env_ids: The environment indices to reset. If None, then all environments are reset. + """ + pass + + def set_command( + self, command: torch.Tensor, ee_pos: torch.Tensor | None = None, ee_quat: torch.Tensor | None = None + ): + """Set target end-effector pose command. + + Based on the configured command type and relative mode, the method computes the desired end-effector pose. + It is up to the user to ensure that the command is given in the correct frame. The method only + applies the relative mode if the command type is ``position_rel`` or ``pose_rel``. + + Args: + command: The input command in shape (N, 3) or (N, 6) or (N, 7). + ee_pos: The current end-effector position in shape (N, 3). + This is only needed if the command type is ``position_rel`` or ``pose_rel``. + ee_quat: The current end-effector orientation (w, x, y, z) in shape (N, 4). + This is only needed if the command type is ``position_*`` or ``pose_rel``. + + Raises: + ValueError: If the command type is ``position_*`` and :attr:`ee_quat` is None. + ValueError: If the command type is ``position_rel`` and :attr:`ee_pos` is None. + ValueError: If the command type is ``pose_rel`` and either :attr:`ee_pos` or :attr:`ee_quat` is None. + """ + # store command + self._command[:] = command + # compute the desired end-effector pose + if self.cfg.command_type == "position": + # we need end-effector orientation even though we are in position mode + # this is only needed for display purposes + if ee_quat is None: + raise ValueError("End-effector orientation can not be None for `position_*` command type!") + # compute targets + if self.cfg.use_relative_mode: + if ee_pos is None: + raise ValueError("End-effector position can not be None for `position_rel` command type!") + self.ee_pos_des[:] = ee_pos + self._command + self.ee_quat_des[:] = ee_quat + else: + self.ee_pos_des[:] = self._command + self.ee_quat_des[:] = ee_quat + else: + # compute targets + if self.cfg.use_relative_mode: + if ee_pos is None or ee_quat is None: + raise ValueError( + "Neither end-effector position nor orientation can be None for `pose_rel` command type!" + ) + self.ee_pos_des, self.ee_quat_des = apply_delta_pose(ee_pos, ee_quat, self._command) + else: + self.ee_pos_des = self._command[:, 0:3] + self.ee_quat_des = self._command[:, 3:7] + + def compute( + self, ee_pos: torch.Tensor, ee_quat: torch.Tensor, jacobian: torch.Tensor, joint_pos: torch.Tensor + ) -> torch.Tensor: + """Computes the target joint positions that will yield the desired end effector pose. + + Args: + ee_pos: The current end-effector position in shape (N, 3). + ee_quat: The current end-effector orientation in shape (N, 4). + jacobian: The geometric jacobian matrix in shape (N, 6, num_joints). + joint_pos: The current joint positions in shape (N, num_joints). + + Returns: + The target joint positions commands in shape (N, num_joints). + """ + # compute the delta in joint-space + if "position" in self.cfg.command_type: + position_error = self.ee_pos_des - ee_pos + jacobian_pos = jacobian[:, 0:3] + delta_joint_pos = self._compute_delta_joint_pos(delta_pose=position_error, jacobian=jacobian_pos) + else: + position_error, axis_angle_error = compute_pose_error( + ee_pos, ee_quat, self.ee_pos_des, self.ee_quat_des, rot_error_type="axis_angle" + ) + pose_error = torch.cat((position_error, axis_angle_error), dim=1) + delta_joint_pos = self._compute_delta_joint_pos(delta_pose=pose_error, jacobian=jacobian) + # return the desired joint positions + return joint_pos + delta_joint_pos + + """ + Helper functions. + """ + + def _compute_delta_joint_pos(self, delta_pose: torch.Tensor, jacobian: torch.Tensor) -> torch.Tensor: + """Computes the change in joint position that yields the desired change in pose. + + The method uses the Jacobian mapping from joint-space velocities to end-effector velocities + to compute the delta-change in the joint-space that moves the robot closer to a desired + end-effector position. + + Args: + delta_pose: The desired delta pose in shape (N, 3) or (N, 6). + jacobian: The geometric jacobian matrix in shape (N, 3, num_joints) or (N, 6, num_joints). + + Returns: + The desired delta in joint space. Shape is (N, num-jointsß). + """ + if self.cfg.ik_params is None: + raise RuntimeError(f"Inverse-kinematics parameters for method '{self.cfg.ik_method}' is not defined!") + # compute the delta in joint-space + if self.cfg.ik_method == "pinv": # Jacobian pseudo-inverse + # parameters + k_val = self.cfg.ik_params["k_val"] + # computation + jacobian_pinv = torch.linalg.pinv(jacobian) + delta_joint_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_joint_pos = delta_joint_pos.squeeze(-1) + elif self.cfg.ik_method == "svd": # adaptive SVD + # parameters + k_val = self.cfg.ik_params["k_val"] + min_singular_value = self.cfg.ik_params["min_singular_value"] + # computation + # U: 6xd, S: dxd, V: d x num-joint + U, S, Vh = torch.linalg.svd(jacobian) + S_inv = 1.0 / S + S_inv = torch.where(min_singular_value < S, S_inv, torch.zeros_like(S_inv)) + jacobian_pinv = ( + torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] + @ torch.diag_embed(S_inv) + @ torch.transpose(U, dim0=1, dim1=2) + ) + delta_joint_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_joint_pos = delta_joint_pos.squeeze(-1) + elif self.cfg.ik_method == "trans": # Jacobian transpose + # parameters + k_val = self.cfg.ik_params["k_val"] + # computation + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + delta_joint_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1) + delta_joint_pos = delta_joint_pos.squeeze(-1) + elif self.cfg.ik_method == "dls": # damped least squares + # parameters + lambda_val = self.cfg.ik_params["lambda_val"] + # computation + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + lambda_matrix = (lambda_val**2) * torch.eye(n=jacobian.shape[1], device=self._device) + delta_joint_pos = ( + jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1) + ) + delta_joint_pos = delta_joint_pos.squeeze(-1) + else: + raise ValueError(f"Unsupported inverse-kinematics method: {self.cfg.ik_method}") + + return delta_joint_pos diff --git a/source/isaaclab/isaaclab/controllers/differential_ik_cfg.py b/source/isaaclab/isaaclab/controllers/differential_ik_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..315a762752ca18797047a4574f3f3501daa0e952 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/differential_ik_cfg.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .differential_ik import DifferentialIKController + + +@configclass +class DifferentialIKControllerCfg: + """Configuration for differential inverse kinematics controller.""" + + class_type: type = DifferentialIKController + """The associated controller class.""" + + command_type: Literal["position", "pose"] = MISSING + """Type of task-space command to control the articulation's body. + + If "position", then the controller only controls the position of the articulation's body. + Otherwise, the controller controls the pose of the articulation's body. + """ + + use_relative_mode: bool = False + """Whether to use relative mode for the controller. Defaults to False. + + If True, then the controller treats the input command as a delta change in the position/pose. + Otherwise, the controller treats the input command as the absolute position/pose. + """ + + ik_method: Literal["pinv", "svd", "trans", "dls"] = MISSING + """Method for computing inverse of Jacobian.""" + + ik_params: dict[str, float] | None = None + """Parameters for the inverse-kinematics method. Defaults to None, in which case the default + parameters for the method are used. + + - Moore-Penrose pseudo-inverse ("pinv"): + - "k_val": Scaling of computed delta-joint positions (default: 1.0). + - Adaptive Singular Value Decomposition ("svd"): + - "k_val": Scaling of computed delta-joint positions (default: 1.0). + - "min_singular_value": Single values less than this are suppressed to zero (default: 1e-5). + - Jacobian transpose ("trans"): + - "k_val": Scaling of computed delta-joint positions (default: 1.0). + - Damped Moore-Penrose pseudo-inverse ("dls"): + - "lambda_val": Damping coefficient (default: 0.01). + """ + + def __post_init__(self): + # check valid input + if self.command_type not in ["position", "pose"]: + raise ValueError(f"Unsupported inverse-kinematics command: {self.command_type}.") + if self.ik_method not in ["pinv", "svd", "trans", "dls"]: + raise ValueError(f"Unsupported inverse-kinematics method: {self.ik_method}.") + # default parameters for different inverse kinematics approaches. + default_ik_params = { + "pinv": {"k_val": 1.0}, + "svd": {"k_val": 1.0, "min_singular_value": 1e-5}, + "trans": {"k_val": 1.0}, + "dls": {"lambda_val": 0.01}, + } + # update parameters for IK-method if not provided + ik_params = default_ik_params[self.ik_method].copy() + if self.ik_params is not None: + ik_params.update(self.ik_params) + self.ik_params = ik_params diff --git a/source/isaaclab/isaaclab/controllers/joint_impedance.py b/source/isaaclab/isaaclab/controllers/joint_impedance.py new file mode 100644 index 0000000000000000000000000000000000000000..bd35089b81af3613dcfd69a785f5244cf5981a8f --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/joint_impedance.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Sequence +from dataclasses import MISSING + +import torch + +from isaaclab.utils import configclass + + +@configclass +class JointImpedanceControllerCfg: + """Configuration for joint impedance regulation controller.""" + + command_type: str = "p_abs" + """Type of command: p_abs (absolute) or p_rel (relative).""" + + dof_pos_offset: Sequence[float] | None = None + """Offset to DOF position command given to controller. (default: None). + + If None then position offsets are set to zero. + """ + + impedance_mode: str = MISSING + """Type of gains: "fixed", "variable", "variable_kp".""" + + inertial_compensation: bool = False + """Whether to perform inertial compensation (inverse dynamics).""" + + gravity_compensation: bool = False + """Whether to perform gravity compensation.""" + + stiffness: float | Sequence[float] = MISSING + """The positional gain for determining desired torques based on joint position error.""" + + damping_ratio: float | Sequence[float] | None = None + """The damping ratio is used in-conjunction with positional gain to compute desired torques + based on joint velocity error. + + The following math operation is performed for computing velocity gains: + :math:`d_gains = 2 * sqrt(p_gains) * damping_ratio`. + """ + + stiffness_limits: tuple[float, float] = (0, 300) + """Minimum and maximum values for positional gains. + + Note: Used only when :obj:`impedance_mode` is "variable" or "variable_kp". + """ + + damping_ratio_limits: tuple[float, float] = (0, 100) + """Minimum and maximum values for damping ratios used to compute velocity gains. + + Note: Used only when :obj:`impedance_mode` is "variable". + """ + + +class JointImpedanceController: + """Joint impedance regulation control. + + Reference: + [1] https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2017/RD_HS2017script.pdf + """ + + def __init__(self, cfg: JointImpedanceControllerCfg, num_robots: int, dof_pos_limits: torch.Tensor, device: str): + """Initialize joint impedance controller. + + Args: + cfg: The configuration for the controller. + num_robots: The number of robots to control. + dof_pos_limits: The joint position limits for each robot. This is a tensor of shape + (num_robots, num_dof, 2) where the last dimension contains the lower and upper limits. + device: The device to use for computations. + + Raises: + ValueError: When the shape of :obj:`dof_pos_limits` is not (num_robots, num_dof, 2). + """ + # check valid inputs + if len(dof_pos_limits.shape) != 3: + raise ValueError(f"Joint position limits has shape '{dof_pos_limits.shape}'. Expected length of shape = 3.") + # store inputs + self.cfg = cfg + self.num_robots = num_robots + self.num_dof = dof_pos_limits.shape[1] # (num_robots, num_dof, 2) + self._device = device + + # create buffers + # -- commands + self._dof_pos_target = torch.zeros(self.num_robots, self.num_dof, device=self._device) + # -- offsets + self._dof_pos_offset = torch.zeros(self.num_robots, self.num_dof, device=self._device) + # -- limits + self._dof_pos_limits = dof_pos_limits + # -- positional gains + self._p_gains = torch.zeros(self.num_robots, self.num_dof, device=self._device) + self._p_gains[:] = torch.tensor(self.cfg.stiffness, device=self._device) + # -- velocity gains + self._d_gains = torch.zeros(self.num_robots, self.num_dof, device=self._device) + self._d_gains[:] = 2 * torch.sqrt(self._p_gains) * torch.tensor(self.cfg.damping_ratio, device=self._device) + # -- position offsets + if self.cfg.dof_pos_offset is not None: + self._dof_pos_offset[:] = torch.tensor(self.cfg.dof_pos_offset, device=self._device) + # -- position gain limits + self._p_gains_limits = torch.zeros_like(self._dof_pos_limits) + self._p_gains_limits[..., 0] = self.cfg.stiffness_limits[0] + self._p_gains_limits[..., 1] = self.cfg.stiffness_limits[1] + # -- damping ratio limits + self._damping_ratio_limits = torch.zeros_like(self._dof_pos_limits) + self._damping_ratio_limits[..., 0] = self.cfg.damping_ratio_limits[0] + self._damping_ratio_limits[..., 1] = self.cfg.damping_ratio_limits[1] + + """ + Properties. + """ + + @property + def num_actions(self) -> int: + """Dimension of the action space of controller.""" + # impedance mode + if self.cfg.impedance_mode == "fixed": + # joint positions + return self.num_dof + elif self.cfg.impedance_mode == "variable_kp": + # joint positions + stiffness + return self.num_dof * 2 + elif self.cfg.impedance_mode == "variable": + # joint positions + stiffness + damping + return self.num_dof * 3 + else: + raise ValueError(f"Invalid impedance mode: {self.cfg.impedance_mode}.") + + """ + Operations. + """ + + def initialize(self): + """Initialize the internals.""" + pass + + def reset_idx(self, robot_ids: torch.Tensor = None): + """Reset the internals.""" + pass + + def set_command(self, command: torch.Tensor): + """Set target end-effector pose command. + + Args: + command: The command to set. This is a tensor of shape (num_robots, num_actions) where + :obj:`num_actions` is the dimension of the action space of the controller. + """ + # check input size + if command.shape != (self.num_robots, self.num_actions): + raise ValueError( + f"Invalid command shape '{command.shape}'. Expected: '{(self.num_robots, self.num_actions)}'." + ) + # impedance mode + if self.cfg.impedance_mode == "fixed": + # joint positions + self._dof_pos_target[:] = command + elif self.cfg.impedance_mode == "variable_kp": + # split input command + dof_pos_command, stiffness = torch.tensor_split(command, 2, dim=-1) + # format command + stiffness = stiffness.clip_(min=self._p_gains_limits[0], max=self._p_gains_limits[1]) + # joint positions + stiffness + self._dof_pos_target[:] = dof_pos_command + self._p_gains[:] = stiffness + self._d_gains[:] = 2 * torch.sqrt(self._p_gains) # critically damped + elif self.cfg.impedance_mode == "variable": + # split input command + dof_pos_command, stiffness, damping_ratio = torch.tensor_split(command, 3, dim=-1) + # format command + stiffness = stiffness.clip_(min=self._p_gains_limits[0], max=self._p_gains_limits[1]) + damping_ratio = damping_ratio.clip_(min=self._damping_ratio_limits[0], max=self._damping_ratio_limits[1]) + # joint positions + stiffness + damping + self._dof_pos_target[:] = dof_pos_command + self._p_gains[:] = stiffness + self._d_gains[:] = 2 * torch.sqrt(self._p_gains) * damping_ratio + else: + raise ValueError(f"Invalid impedance mode: {self.cfg.impedance_mode}.") + + def compute( + self, + dof_pos: torch.Tensor, + dof_vel: torch.Tensor, + mass_matrix: torch.Tensor | None = None, + gravity: torch.Tensor | None = None, + ) -> torch.Tensor: + """Performs inference with the controller. + + Args: + dof_pos: The current joint positions. + dof_vel: The current joint velocities. + mass_matrix: The joint-space inertial matrix. Defaults to None. + gravity: The joint-space gravity vector. Defaults to None. + + Raises: + ValueError: When the command type is invalid. + + Returns: + The target joint torques commands. + """ + # resolve the command type + if self.cfg.command_type == "p_abs": + desired_dof_pos = self._dof_pos_target + self._dof_pos_offset + elif self.cfg.command_type == "p_rel": + desired_dof_pos = self._dof_pos_target + dof_pos + else: + raise ValueError(f"Invalid dof position command mode: {self.cfg.command_type}.") + # compute errors + desired_dof_pos = desired_dof_pos.clip_(min=self._dof_pos_limits[..., 0], max=self._dof_pos_limits[..., 1]) + dof_pos_error = desired_dof_pos - dof_pos + dof_vel_error = -dof_vel + # compute acceleration + des_dof_acc = self._p_gains * dof_pos_error + self._d_gains * dof_vel_error + # compute torques + # -- inertial compensation + if self.cfg.inertial_compensation: + # inverse dynamics control + desired_torques = mass_matrix @ des_dof_acc + else: + # decoupled spring-mass control + desired_torques = des_dof_acc + # -- gravity compensation (bias correction) + if self.cfg.gravity_compensation: + desired_torques += gravity + + return desired_torques diff --git a/source/isaaclab/isaaclab/controllers/operational_space.py b/source/isaaclab/isaaclab/controllers/operational_space.py new file mode 100644 index 0000000000000000000000000000000000000000..2505768e0581905cd34636c1f3c88c5e131b0c9b --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/operational_space.py @@ -0,0 +1,548 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.utils.math import ( + apply_delta_pose, + combine_frame_transforms, + compute_pose_error, + matrix_from_quat, + subtract_frame_transforms, +) + +if TYPE_CHECKING: + from .operational_space_cfg import OperationalSpaceControllerCfg + + +class OperationalSpaceController: + """Operational-space controller. + + Reference: + + 1. `A unified approach for motion and force control of robot manipulators: The operational space formulation `_ + by Oussama Khatib (Stanford University) + 2. `Robot Dynamics Lecture Notes `_ + by Marco Hutter (ETH Zurich) + """ + + def __init__(self, cfg: OperationalSpaceControllerCfg, num_envs: int, device: str): + """Initialize operational-space controller. + + Args: + cfg: The configuration for operational-space controller. + num_envs: The number of environments. + device: The device to use for computations. + + Raises: + ValueError: When invalid control command is provided. + """ + # store inputs + self.cfg = cfg + self.num_envs = num_envs + self._device = device + + # resolve tasks-pace target dimensions + self.target_list = list() + for command_type in self.cfg.target_types: + if command_type == "pose_rel": + self.target_list.append(6) + elif command_type == "pose_abs": + self.target_list.append(7) + elif command_type == "wrench_abs": + self.target_list.append(6) + else: + raise ValueError(f"Invalid control command: {command_type}.") + self.target_dim = sum(self.target_list) + + # create buffers + # -- selection matrices, which might be defined in the task reference frame different from the root frame + self._selection_matrix_motion_task = torch.diag_embed( + torch.tensor(self.cfg.motion_control_axes_task, dtype=torch.float, device=self._device) + .unsqueeze(0) + .repeat(self.num_envs, 1) + ) + self._selection_matrix_force_task = torch.diag_embed( + torch.tensor(self.cfg.contact_wrench_control_axes_task, dtype=torch.float, device=self._device) + .unsqueeze(0) + .repeat(self.num_envs, 1) + ) + # -- selection matrices in root frame + self._selection_matrix_motion_b = torch.zeros_like(self._selection_matrix_motion_task) + self._selection_matrix_force_b = torch.zeros_like(self._selection_matrix_force_task) + # -- commands + self._task_space_target_task = torch.zeros(self.num_envs, self.target_dim, device=self._device) + # -- Placeholders for motion/force control + self.desired_ee_pose_task = None + self.desired_ee_pose_b = None + self.desired_ee_wrench_task = None + self.desired_ee_wrench_b = None + # -- buffer for operational space mass matrix + self._os_mass_matrix_b = torch.zeros(self.num_envs, 6, 6, device=self._device) + # -- Placeholder for the inverse of joint space mass matrix + self._mass_matrix_inv = None + # -- motion control gains + self._motion_p_gains_task = torch.diag_embed( + torch.ones(self.num_envs, 6, device=self._device) + * torch.tensor(self.cfg.motion_stiffness_task, dtype=torch.float, device=self._device) + ) + # -- -- zero out the axes that are not motion controlled, as keeping them non-zero will cause other axes + # -- -- to act due to coupling + self._motion_p_gains_task[:] = self._selection_matrix_motion_task @ self._motion_p_gains_task[:] + self._motion_d_gains_task = torch.diag_embed( + 2 + * torch.diagonal(self._motion_p_gains_task, dim1=-2, dim2=-1).sqrt() + * torch.as_tensor(self.cfg.motion_damping_ratio_task, dtype=torch.float, device=self._device).reshape(1, -1) + ) + # -- -- motion control gains in root frame + self._motion_p_gains_b = torch.zeros_like(self._motion_p_gains_task) + self._motion_d_gains_b = torch.zeros_like(self._motion_d_gains_task) + # -- force control gains + if self.cfg.contact_wrench_stiffness_task is not None: + self._contact_wrench_p_gains_task = torch.diag_embed( + torch.ones(self.num_envs, 6, device=self._device) + * torch.tensor(self.cfg.contact_wrench_stiffness_task, dtype=torch.float, device=self._device) + ) + self._contact_wrench_p_gains_task[:] = ( + self._selection_matrix_force_task @ self._contact_wrench_p_gains_task[:] + ) + # -- -- force control gains in root frame + self._contact_wrench_p_gains_b = torch.zeros_like(self._contact_wrench_p_gains_task) + else: + self._contact_wrench_p_gains_task = None + self._contact_wrench_p_gains_b = None + # -- position gain limits + self._motion_p_gains_limits = torch.zeros(self.num_envs, 6, 2, device=self._device) + self._motion_p_gains_limits[..., 0], self._motion_p_gains_limits[..., 1] = ( + self.cfg.motion_stiffness_limits_task[0], + self.cfg.motion_stiffness_limits_task[1], + ) + # -- damping ratio limits + self._motion_damping_ratio_limits = torch.zeros_like(self._motion_p_gains_limits) + self._motion_damping_ratio_limits[..., 0], self._motion_damping_ratio_limits[..., 1] = ( + self.cfg.motion_damping_ratio_limits_task[0], + self.cfg.motion_damping_ratio_limits_task[1], + ) + # -- end-effector contact wrench + self._ee_contact_wrench_b = torch.zeros(self.num_envs, 6, device=self._device) + + # -- buffers for null-space control gains + self._nullspace_p_gain = torch.tensor(self.cfg.nullspace_stiffness, dtype=torch.float, device=self._device) + self._nullspace_d_gain = ( + 2 + * torch.sqrt(self._nullspace_p_gain) + * torch.tensor(self.cfg.nullspace_damping_ratio, dtype=torch.float, device=self._device) + ) + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + """Dimension of the action space of controller.""" + # impedance mode + if self.cfg.impedance_mode == "fixed": + # task-space targets + return self.target_dim + elif self.cfg.impedance_mode == "variable_kp": + # task-space targets + stiffness + return self.target_dim + 6 + elif self.cfg.impedance_mode == "variable": + # task-space targets + stiffness + damping + return self.target_dim + 6 + 6 + else: + raise ValueError(f"Invalid impedance mode: {self.cfg.impedance_mode}.") + + """ + Operations. + """ + + def reset(self): + """Reset the internals.""" + self.desired_ee_pose_b = None + self.desired_ee_pose_task = None + self.desired_ee_wrench_b = None + self.desired_ee_wrench_task = None + + def set_command( + self, + command: torch.Tensor, + current_ee_pose_b: torch.Tensor | None = None, + current_task_frame_pose_b: torch.Tensor | None = None, + ): + """Set the task-space targets and impedance parameters. + + Args: + command (torch.Tensor): A concatenated tensor of shape (``num_envs``, ``action_dim``) containing task-space + targets (i.e., pose/wrench) and impedance parameters. + current_ee_pose_b (torch.Tensor, optional): Current end-effector pose, in root frame, of shape + (``num_envs``, 7), containing position and quaternion ``(w, x, y, z)``. Required for relative + commands. Defaults to None. + current_task_frame_pose_b: Current pose of the task frame, in root frame, in which the targets and the + (motion/wrench) control axes are defined. It is a tensor of shape (``num_envs``, 7), + containing position and the quaternion ``(w, x, y, z)``. Defaults to None. + + Format: + Task-space targets, ordered according to 'command_types': + + Absolute pose: shape (``num_envs``, 7), containing position and quaternion ``(w, x, y, z)``. + Relative pose: shape (``num_envs``, 6), containing delta position and rotation in axis-angle form. + Absolute wrench: shape (``num_envs``, 6), containing force and torque. + + Impedance parameters: stiffness for ``variable_kp``, or stiffness, followed by damping ratio for + ``variable``: + + Stiffness: shape (``num_envs``, 6) + Damping ratio: shape (``num_envs``, 6) + + Raises: + ValueError: When the command dimensions are invalid. + ValueError: When an invalid impedance mode is provided. + ValueError: When the current end-effector pose is not provided for the ``pose_rel`` command. + ValueError: When an invalid control command is provided. + """ + # Check the input dimensions + if command.shape != (self.num_envs, self.action_dim): + raise ValueError( + f"Invalid command shape '{command.shape}'. Expected: '{(self.num_envs, self.action_dim)}'." + ) + + # Resolve the impedance parameters + if self.cfg.impedance_mode == "fixed": + # task space targets (i.e., pose/wrench) + self._task_space_target_task[:] = command + elif self.cfg.impedance_mode == "variable_kp": + # split input command + task_space_command, stiffness = torch.split(command, [self.target_dim, 6], dim=-1) + # format command + stiffness = stiffness.clip_( + min=self._motion_p_gains_limits[..., 0], max=self._motion_p_gains_limits[..., 1] + ) + # task space targets + stiffness + self._task_space_target_task[:] = task_space_command.squeeze(dim=-1) + self._motion_p_gains_task[:] = torch.diag_embed(stiffness) + self._motion_p_gains_task[:] = self._selection_matrix_motion_task @ self._motion_p_gains_task[:] + self._motion_d_gains_task = torch.diag_embed( + 2 + * torch.diagonal(self._motion_p_gains_task, dim1=-2, dim2=-1).sqrt() + * torch.as_tensor(self.cfg.motion_damping_ratio_task, dtype=torch.float, device=self._device).reshape( + 1, -1 + ) + ) + elif self.cfg.impedance_mode == "variable": + # split input command + task_space_command, stiffness, damping_ratio = torch.split(command, [self.target_dim, 6, 6], dim=-1) + # format command + stiffness = stiffness.clip_( + min=self._motion_p_gains_limits[..., 0], max=self._motion_p_gains_limits[..., 1] + ) + damping_ratio = damping_ratio.clip_( + min=self._motion_damping_ratio_limits[..., 0], max=self._motion_damping_ratio_limits[..., 1] + ) + # task space targets + stiffness + damping + self._task_space_target_task[:] = task_space_command + self._motion_p_gains_task[:] = torch.diag_embed(stiffness) + self._motion_p_gains_task[:] = self._selection_matrix_motion_task @ self._motion_p_gains_task[:] + self._motion_d_gains_task[:] = torch.diag_embed( + 2 * torch.diagonal(self._motion_p_gains_task, dim1=-2, dim2=-1).sqrt() * damping_ratio + ) + else: + raise ValueError(f"Invalid impedance mode: {self.cfg.impedance_mode}.") + + if current_task_frame_pose_b is None: + current_task_frame_pose_b = torch.tensor( + [[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]] * self.num_envs, device=self._device + ) + + # Resolve the target commands + target_groups = torch.split(self._task_space_target_task, self.target_list, dim=1) + for command_type, target in zip(self.cfg.target_types, target_groups): + if command_type == "pose_rel": + # check input is provided + if current_ee_pose_b is None: + raise ValueError("Current pose is required for 'pose_rel' command.") + # Transform the current pose from base/root frame to task frame + current_ee_pos_task, current_ee_rot_task = subtract_frame_transforms( + current_task_frame_pose_b[:, :3], + current_task_frame_pose_b[:, 3:], + current_ee_pose_b[:, :3], + current_ee_pose_b[:, 3:], + ) + # compute targets in task frame + desired_ee_pos_task, desired_ee_rot_task = apply_delta_pose( + current_ee_pos_task, current_ee_rot_task, target + ) + self.desired_ee_pose_task = torch.cat([desired_ee_pos_task, desired_ee_rot_task], dim=-1) + elif command_type == "pose_abs": + # compute targets + self.desired_ee_pose_task = target.clone() + elif command_type == "wrench_abs": + # compute targets + self.desired_ee_wrench_task = target.clone() + else: + raise ValueError(f"Invalid control command: {command_type}.") + + # Rotation of task frame wrt root frame, converts a coordinate from task frame to root frame. + R_task_b = matrix_from_quat(current_task_frame_pose_b[:, 3:]) + # Rotation of root frame wrt task frame, converts a coordinate from root frame to task frame. + R_b_task = R_task_b.mT + + # Transform motion control stiffness gains from task frame to root frame + self._motion_p_gains_b[:, 0:3, 0:3] = R_task_b @ self._motion_p_gains_task[:, 0:3, 0:3] @ R_b_task + self._motion_p_gains_b[:, 3:6, 3:6] = R_task_b @ self._motion_p_gains_task[:, 3:6, 3:6] @ R_b_task + + # Transform motion control damping gains from task frame to root frame + self._motion_d_gains_b[:, 0:3, 0:3] = R_task_b @ self._motion_d_gains_task[:, 0:3, 0:3] @ R_b_task + self._motion_d_gains_b[:, 3:6, 3:6] = R_task_b @ self._motion_d_gains_task[:, 3:6, 3:6] @ R_b_task + + # Transform contact wrench gains from task frame to root frame (if applicable) + if self._contact_wrench_p_gains_task is not None and self._contact_wrench_p_gains_b is not None: + self._contact_wrench_p_gains_b[:, 0:3, 0:3] = ( + R_task_b @ self._contact_wrench_p_gains_task[:, 0:3, 0:3] @ R_b_task + ) + self._contact_wrench_p_gains_b[:, 3:6, 3:6] = ( + R_task_b @ self._contact_wrench_p_gains_task[:, 3:6, 3:6] @ R_b_task + ) + + # Transform selection matrices from target frame to base frame + self._selection_matrix_motion_b[:, 0:3, 0:3] = ( + R_task_b @ self._selection_matrix_motion_task[:, 0:3, 0:3] @ R_b_task + ) + self._selection_matrix_motion_b[:, 3:6, 3:6] = ( + R_task_b @ self._selection_matrix_motion_task[:, 3:6, 3:6] @ R_b_task + ) + self._selection_matrix_force_b[:, 0:3, 0:3] = ( + R_task_b @ self._selection_matrix_force_task[:, 0:3, 0:3] @ R_b_task + ) + self._selection_matrix_force_b[:, 3:6, 3:6] = ( + R_task_b @ self._selection_matrix_force_task[:, 3:6, 3:6] @ R_b_task + ) + + # Transform desired pose from task frame to root frame + if self.desired_ee_pose_task is not None: + self.desired_ee_pose_b = torch.zeros_like(self.desired_ee_pose_task) + self.desired_ee_pose_b[:, :3], self.desired_ee_pose_b[:, 3:] = combine_frame_transforms( + current_task_frame_pose_b[:, :3], + current_task_frame_pose_b[:, 3:], + self.desired_ee_pose_task[:, :3], + self.desired_ee_pose_task[:, 3:], + ) + + # Transform desired wrenches to root frame + if self.desired_ee_wrench_task is not None: + self.desired_ee_wrench_b = torch.zeros_like(self.desired_ee_wrench_task) + self.desired_ee_wrench_b[:, :3] = (R_task_b @ self.desired_ee_wrench_task[:, :3].unsqueeze(-1)).squeeze(-1) + self.desired_ee_wrench_b[:, 3:] = (R_task_b @ self.desired_ee_wrench_task[:, 3:].unsqueeze(-1)).squeeze( + -1 + ) + torch.cross(current_task_frame_pose_b[:, :3], self.desired_ee_wrench_b[:, :3], dim=-1) + + def compute( + self, + jacobian_b: torch.Tensor, + current_ee_pose_b: torch.Tensor | None = None, + current_ee_vel_b: torch.Tensor | None = None, + current_ee_force_b: torch.Tensor | None = None, + mass_matrix: torch.Tensor | None = None, + gravity: torch.Tensor | None = None, + current_joint_pos: torch.Tensor | None = None, + current_joint_vel: torch.Tensor | None = None, + nullspace_joint_pos_target: torch.Tensor | None = None, + ) -> torch.Tensor: + """Performs inference with the controller. + + Args: + jacobian_b: The Jacobian matrix of the end-effector in root frame. It is a tensor of shape + (``num_envs``, 6, ``num_DoF``). + current_ee_pose_b: The current end-effector pose in root frame. It is a tensor of shape + (``num_envs``, 7), which contains the position and quaternion ``(w, x, y, z)``. Defaults to ``None``. + current_ee_vel_b: The current end-effector velocity in root frame. It is a tensor of shape + (``num_envs``, 6), which contains the linear and angular velocities. Defaults to None. + current_ee_force_b: The current external force on the end-effector in root frame. It is a tensor of + shape (``num_envs``, 3), which contains the linear force. Defaults to ``None``. + mass_matrix: The joint-space mass/inertia matrix. It is a tensor of shape (``num_envs``, ``num_DoF``, + ``num_DoF``). Defaults to ``None``. + gravity: The joint-space gravity vector. It is a tensor of shape (``num_envs``, ``num_DoF``). Defaults + to ``None``. + current_joint_pos: The current joint positions. It is a tensor of shape (``num_envs``, ``num_DoF``). + Defaults to ``None``. + current_joint_vel: The current joint velocities. It is a tensor of shape (``num_envs``, ``num_DoF``). + Defaults to ``None``. + nullspace_joint_pos_target: The target joint positions the null space controller is trying to enforce, when + possible. It is a tensor of shape (``num_envs``, ``num_DoF``). + + Raises: + ValueError: When motion-control is enabled but the current end-effector pose or velocity is not provided. + ValueError: When inertial dynamics decoupling is enabled but the mass matrix is not provided. + ValueError: When the current end-effector pose is not provided for the ``pose_rel`` command. + ValueError: When closed-loop force control is enabled but the current end-effector force is not provided. + ValueError: When gravity compensation is enabled but the gravity vector is not provided. + ValueError: When null-space control is enabled but the system is not redundant. + ValueError: When dynamically consistent pseudo-inverse is enabled but the mass matrix inverse is not + provided. + ValueError: When null-space control is enabled but the current joint positions and velocities are not + provided. + ValueError: When target joint positions are provided for null-space control but their dimensions do not + match the current joint positions. + ValueError: When an invalid null-space control method is provided. + + Returns: + Tensor: The joint efforts computed by the controller. It is a tensor of shape (``num_envs``, ``num_DoF``). + """ + + # deduce number of DoF + num_DoF = jacobian_b.shape[2] + # create joint effort vector + joint_efforts = torch.zeros(self.num_envs, num_DoF, device=self._device) + + # compute joint efforts for motion-control + if self.desired_ee_pose_b is not None: + # check input is provided + if current_ee_pose_b is None or current_ee_vel_b is None: + raise ValueError("Current end-effector pose and velocity are required for motion control.") + # -- end-effector tracking error + pose_error_b = torch.cat( + compute_pose_error( + current_ee_pose_b[:, :3], + current_ee_pose_b[:, 3:], + self.desired_ee_pose_b[:, :3], + self.desired_ee_pose_b[:, 3:], + rot_error_type="axis_angle", + ), + dim=-1, + ) + velocity_error_b = -current_ee_vel_b # zero target velocity. The target is assumed to be stationary. + # -- desired end-effector acceleration (spring-damper system) + des_ee_acc_b = self._motion_p_gains_b @ pose_error_b.unsqueeze( + -1 + ) + self._motion_d_gains_b @ velocity_error_b.unsqueeze(-1) + # -- Inertial dynamics decoupling + if self.cfg.inertial_dynamics_decoupling: + # check input is provided + if mass_matrix is None: + raise ValueError("Mass matrix is required for inertial decoupling.") + # Compute operational space mass matrix + self._mass_matrix_inv = torch.inverse(mass_matrix) + if self.cfg.partial_inertial_dynamics_decoupling: + # Fill in the translational and rotational parts of the inertia separately, ignoring their coupling + self._os_mass_matrix_b[:, 0:3, 0:3] = torch.inverse( + jacobian_b[:, 0:3] @ self._mass_matrix_inv @ jacobian_b[:, 0:3].mT + ) + self._os_mass_matrix_b[:, 3:6, 3:6] = torch.inverse( + jacobian_b[:, 3:6] @ self._mass_matrix_inv @ jacobian_b[:, 3:6].mT + ) + else: + # Calculate the operational space mass matrix fully accounting for the couplings + self._os_mass_matrix_b[:] = torch.inverse(jacobian_b @ self._mass_matrix_inv @ jacobian_b.mT) + # (Generalized) operational space command forces + # F = (J M^(-1) J^T)^(-1) * \ddot(x_des) = M_task * \ddot(x_des) + os_command_forces_b = self._os_mass_matrix_b @ des_ee_acc_b + else: + # Task-space impedance control: command forces = \ddot(x_des). + # Please note that the definition of task-space impedance control varies in literature. + # This implementation ignores the inertial term. For inertial decoupling, + # use inertial_dynamics_decoupling=True. + os_command_forces_b = des_ee_acc_b + # -- joint-space commands + joint_efforts += (jacobian_b.mT @ self._selection_matrix_motion_b @ os_command_forces_b).squeeze(-1) + + # compute joint efforts for contact wrench/force control + if self.desired_ee_wrench_b is not None: + # -- task-space contact wrench + if self.cfg.contact_wrench_stiffness_task is not None: + # check input is provided + if current_ee_force_b is None: + raise ValueError("Current end-effector force is required for closed-loop force control.") + # We can only measure the force component at the contact, so only apply the feedback for only the force + # component, keep the control of moment components open loop + self._ee_contact_wrench_b[:, 0:3] = current_ee_force_b + self._ee_contact_wrench_b[:, 3:6] = self.desired_ee_wrench_b[:, 3:6] + # closed-loop control with feedforward term + os_contact_wrench_command_b = self.desired_ee_wrench_b.unsqueeze( + -1 + ) + self._contact_wrench_p_gains_b @ (self.desired_ee_wrench_b - self._ee_contact_wrench_b).unsqueeze( + -1 + ) + else: + # open-loop control + os_contact_wrench_command_b = self.desired_ee_wrench_b.unsqueeze(-1) + # -- joint-space commands + joint_efforts += (jacobian_b.mT @ self._selection_matrix_force_b @ os_contact_wrench_command_b).squeeze(-1) + + # add gravity compensation (bias correction) + if self.cfg.gravity_compensation: + # check input is provided + if gravity is None: + raise ValueError("Gravity vector is required for gravity compensation.") + # add gravity compensation + joint_efforts += gravity + + # Add null-space control + # -- Free null-space control + if self.cfg.nullspace_control == "none": + # No additional control is applied in the null space. + pass + else: + # Check if the system is redundant + if num_DoF <= 6: + raise ValueError("Null-space control is only applicable for redundant manipulators.") + + # Calculate the pseudo-inverse of the Jacobian + if self.cfg.inertial_dynamics_decoupling and not self.cfg.partial_inertial_dynamics_decoupling: + # Dynamically consistent pseudo-inverse allows decoupling of null space and task space + if self._mass_matrix_inv is None or mass_matrix is None: + raise ValueError("Mass matrix inverse is required for dynamically consistent pseudo-inverse") + jacobian_pinv_transpose = self._os_mass_matrix_b @ jacobian_b @ self._mass_matrix_inv + else: + # Moore-Penrose pseudo-inverse if full inertia matrix is not available (e.g., no/partial decoupling) + jacobian_pinv_transpose = torch.pinverse(jacobian_b).mT + + # Calculate the null-space projector + nullspace_jacobian_transpose = ( + torch.eye(n=num_DoF, device=self._device) - jacobian_b.mT @ jacobian_pinv_transpose + ) + + # Null space position control + if self.cfg.nullspace_control == "position": + # Check if the current joint positions and velocities are provided + if current_joint_pos is None or current_joint_vel is None: + raise ValueError("Current joint positions and velocities are required for null-space control.") + + # Calculate the joint errors for nullspace position control + if nullspace_joint_pos_target is None: + nullspace_joint_pos_target = torch.zeros_like(current_joint_pos) + # Check if the dimensions of the target nullspace joint positions match the current joint positions + elif nullspace_joint_pos_target.shape != current_joint_pos.shape: + raise ValueError( + f"The target nullspace joint positions shape '{nullspace_joint_pos_target.shape}' does not" + f"match the current joint positions shape '{current_joint_pos.shape}'." + ) + + joint_pos_error_nullspace = nullspace_joint_pos_target - current_joint_pos + joint_vel_error_nullspace = -current_joint_vel + + # Calculate the desired joint accelerations + joint_acc_nullspace = ( + self._nullspace_p_gain * joint_pos_error_nullspace + + self._nullspace_d_gain * joint_vel_error_nullspace + ).unsqueeze(-1) + + # Calculate the projected torques in null-space + if mass_matrix is not None: + tau_null = (nullspace_jacobian_transpose @ mass_matrix @ joint_acc_nullspace).squeeze(-1) + else: + tau_null = nullspace_jacobian_transpose @ joint_acc_nullspace + + # Add the null-space joint efforts to the total joint efforts + joint_efforts += tau_null + + else: + raise ValueError(f"Invalid null-space control method: {self.cfg.nullspace_control}.") + + return joint_efforts diff --git a/source/isaaclab/isaaclab/controllers/operational_space_cfg.py b/source/isaaclab/isaaclab/controllers/operational_space_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d2fc3575bd7371eb7bfe312ce25625021cf26a85 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/operational_space_cfg.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Sequence +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from .operational_space import OperationalSpaceController + + +@configclass +class OperationalSpaceControllerCfg: + """Configuration for operational-space controller.""" + + class_type: type = OperationalSpaceController + """The associated controller class.""" + + target_types: Sequence[str] = MISSING + """Type of task-space targets. + + It has two sub-strings joined by underscore: + - type of task-space target: ``"pose"``, ``"wrench"`` + - reference for the task-space targets: ``"abs"`` (absolute), ``"rel"`` (relative, only for pose) + """ + + motion_control_axes_task: Sequence[int] = (1, 1, 1, 1, 1, 1) + """Motion direction to control in task reference frame. Mark as ``0/1`` for each axis.""" + + contact_wrench_control_axes_task: Sequence[int] = (0, 0, 0, 0, 0, 0) + """Contact wrench direction to control in task reference frame. Mark as 0/1 for each axis.""" + + inertial_dynamics_decoupling: bool = False + """Whether to perform inertial dynamics decoupling for motion control (inverse dynamics).""" + + partial_inertial_dynamics_decoupling: bool = False + """Whether to ignore the inertial coupling between the translational & rotational motions.""" + + gravity_compensation: bool = False + """Whether to perform gravity compensation.""" + + impedance_mode: str = "fixed" + """Type of gains for motion control: ``"fixed"``, ``"variable"``, ``"variable_kp"``.""" + + motion_stiffness_task: float | Sequence[float] = (100.0, 100.0, 100.0, 100.0, 100.0, 100.0) + """The positional gain for determining operational space command forces based on task-space pose error.""" + + motion_damping_ratio_task: float | Sequence[float] = (1.0, 1.0, 1.0, 1.0, 1.0, 1.0) + """The damping ratio is used in-conjunction with positional gain to compute operational space command forces + based on task-space velocity error. + + The following math operation is performed for computing velocity gains: + :math:`d_gains = 2 * sqrt(p_gains) * damping_ratio`. + """ + + motion_stiffness_limits_task: tuple[float, float] = (0, 1000) + """Minimum and maximum values for positional gains. + + Note: Used only when :obj:`impedance_mode` is ``"variable"`` or ``"variable_kp"``. + """ + + motion_damping_ratio_limits_task: tuple[float, float] = (0, 100) + """Minimum and maximum values for damping ratios used to compute velocity gains. + + Note: Used only when :obj:`impedance_mode` is ``"variable"``. + """ + + contact_wrench_stiffness_task: float | Sequence[float] | None = None + """The proportional gain for determining operational space command forces for closed-loop contact force control. + + If ``None``, then open-loop control of desired contact wrench is performed. + + Note: since only the linear forces could be measured at the moment, + only the first three elements are used for the feedback loop. + """ + + nullspace_control: str = "none" + """The null space control method for redundant manipulators: ``"none"``, ``"position"``. + + Note: ``"position"`` is used to drive the redundant manipulator to zero configuration by default. If + ``target_joint_pos`` is provided in the ``compute()`` method, it will be driven to this configuration. + """ + + nullspace_stiffness: float = 10.0 + """The stiffness for null space control.""" + + nullspace_damping_ratio: float = 1.0 + """The damping ratio for null space control.""" diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/__init__.py b/source/isaaclab/isaaclab/controllers/pink_ik/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..17ed7a67b0775279e78f6c7b693ee08503feb988 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Pink IK controller package for IsaacLab. + +This package provides integration between Pink inverse kinematics solver and IsaacLab. +""" + +from .null_space_posture_task import NullSpacePostureTask +from .pink_ik import PinkIKController +from .pink_ik_cfg import PinkIKControllerCfg diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/local_frame_task.py b/source/isaaclab/isaaclab/controllers/pink_ik/local_frame_task.py new file mode 100644 index 0000000000000000000000000000000000000000..ff8c6b9b03d837c669836e04a52cd2d1aca488bb --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/local_frame_task.py @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Sequence + +import numpy as np +import pinocchio as pin +from pink.tasks.frame_task import FrameTask + +from .pink_kinematics_configuration import PinkKinematicsConfiguration + + +class LocalFrameTask(FrameTask): + """ + A task that computes error in a local (custom) frame. + Inherits from FrameTask but overrides compute_error. + """ + + def __init__( + self, + frame: str, + base_link_frame_name: str, + position_cost: float | Sequence[float], + orientation_cost: float | Sequence[float], + lm_damping: float = 0.0, + gain: float = 1.0, + ): + """ + Initialize the LocalFrameTask with configuration. + + This task computes pose errors in a local (custom) frame rather than the world frame, + allowing for more flexible control strategies where the reference frame can be + specified independently. + + Args: + frame: Name of the frame to control (end-effector or target frame). + base_link_frame_name: Name of the base link frame used as reference frame + for computing transforms and errors. + position_cost: Cost weight(s) for position error. Can be a single float + for uniform weighting or a sequence of 3 floats for per-axis weighting. + orientation_cost: Cost weight(s) for orientation error. Can be a single float + for uniform weighting or a sequence of 3 floats for per-axis weighting. + lm_damping: Levenberg-Marquardt damping factor for numerical stability. + Defaults to 0.0 (no damping). + gain: Task gain factor that scales the overall task contribution. + Defaults to 1.0. + """ + super().__init__(frame, position_cost, orientation_cost, lm_damping, gain) + self.base_link_frame_name = base_link_frame_name + self.transform_target_to_base = None + + def set_target(self, transform_target_to_base: pin.SE3) -> None: + """Set task target pose in the world frame. + + Args: + transform_target_to_world: Transform from the task target frame to + the world frame. + """ + self.transform_target_to_base = transform_target_to_base.copy() + + def set_target_from_configuration(self, configuration: PinkKinematicsConfiguration) -> None: + """Set task target pose from a robot configuration. + + Args: + configuration: Robot configuration. + """ + if not isinstance(configuration, PinkKinematicsConfiguration): + raise ValueError("configuration must be a PinkKinematicsConfiguration") + self.set_target(configuration.get_transform(self.frame, self.base_link_frame_name)) + + def compute_error(self, configuration: PinkKinematicsConfiguration) -> np.ndarray: + """ + Compute the error between current and target pose in a local frame. + """ + if not isinstance(configuration, PinkKinematicsConfiguration): + raise ValueError("configuration must be a PinkKinematicsConfiguration") + if self.transform_target_to_base is None: + raise ValueError(f"no target set for frame '{self.frame}'") + + transform_frame_to_base = configuration.get_transform(self.frame, self.base_link_frame_name) + transform_target_to_frame = transform_frame_to_base.actInv(self.transform_target_to_base) + + error_in_frame: np.ndarray = pin.log(transform_target_to_frame).vector + return error_in_frame + + def compute_jacobian(self, configuration: PinkKinematicsConfiguration) -> np.ndarray: + r"""Compute the frame task Jacobian. + + The task Jacobian :math:`J(q) \in \mathbb{R}^{6 \times n_v}` is the + derivative of the task error :math:`e(q) \in \mathbb{R}^6` with respect + to the configuration :math:`q`. The formula for the frame task is: + + .. math:: + + J(q) = -\text{Jlog}_6(T_{tb}) {}_b J_{0b}(q) + + The derivation of the formula for this Jacobian is detailed in + [Caron2023]_. See also + :func:`pink.tasks.task.Task.compute_jacobian` for more context on task + Jacobians. + + Args: + configuration: Robot configuration :math:`q`. + + Returns: + Jacobian matrix :math:`J`, expressed locally in the frame. + """ + if self.transform_target_to_base is None: + raise Exception(f"no target set for frame '{self.frame}'") + transform_frame_to_base = configuration.get_transform(self.frame, self.base_link_frame_name) + transform_frame_to_target = self.transform_target_to_base.actInv(transform_frame_to_base) + jacobian_in_frame = configuration.get_frame_jacobian(self.frame) + J = -pin.Jlog6(transform_frame_to_target) @ jacobian_in_frame + return J diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/null_space_posture_task.py b/source/isaaclab/isaaclab/controllers/pink_ik/null_space_posture_task.py new file mode 100644 index 0000000000000000000000000000000000000000..8ab6ddcc2dc02ad020196382b12b45a2637a854b --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/null_space_posture_task.py @@ -0,0 +1,275 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import pinocchio as pin +import scipy.linalg.blas as blas +import scipy.linalg.lapack as lapack +from pink.configuration import Configuration +from pink.tasks import Task + + +class NullSpacePostureTask(Task): + r"""Pink-based task that adds a posture objective that is in the null space projection of other tasks. + + This task implements posture control in the null space of higher priority tasks + (typically end-effector pose tasks) within the Pink inverse kinematics framework. + + **Mathematical Formulation:** + + For details on Pink Inverse Kinematics optimization formulation visit: https://github.com/stephane-caron/pink + + **Null Space Posture Task Implementation:** + + This task consists of two components: + + 1. **Error Function**: The posture error is computed as: + + .. math:: + + \mathbf{e}(\mathbf{q}) = \mathbf{M} \cdot (\mathbf{q}^* - \mathbf{q}) + + where: + - :math:`\mathbf{q}^*` is the target joint configuration + - :math:`\mathbf{q}` is the current joint configuration + - :math:`\mathbf{M}` is a joint selection mask matrix + + 2. **Jacobian Matrix**: The task Jacobian is the null space projector: + + .. math:: + + \mathbf{J}_{\text{posture}}(\mathbf{q}) = \mathbf{N}(\mathbf{q}) = + \mathbf{I} -\mathbf{J}_{\text{primary}}^+ \mathbf{J}_{\text{primary}} + + where: + - :math:`\mathbf{J}_{\text{primary}}` is the combined Jacobian of all higher priority tasks + - :math:`\mathbf{J}_{\text{primary}}^+` is the pseudoinverse of the primary task Jacobian + - :math:`\mathbf{N}(\mathbf{q})` is the null space projector matrix + + For example, if there are two frame tasks (e.g., controlling the pose of two end-effectors), the combined Jacobian + :math:`\mathbf{J}_{\text{primary}}` is constructed by stacking the individual Jacobians for each frame vertically: + + .. math:: + + \mathbf{J}_{\text{primary}} = + \begin{bmatrix} + \mathbf{J}_1(\mathbf{q}) \\ + \mathbf{J}_2(\mathbf{q}) + \end{bmatrix} + + where :math:`\mathbf{J}_1(\mathbf{q})` and :math:`\mathbf{J}_2(\mathbf{q})` are the Jacobians for the + first and second frame tasks, respectively. + + The null space projector ensures that joint velocities in the null space produce zero velocity + for the primary tasks: :math:`\mathbf{J}_{\text{primary}} \cdot \dot{\mathbf{q}}_{\text{null}} = \mathbf{0}`. + + **Task Integration:** + + When integrated into the Pink framework, this task contributes to the optimization as: + + .. math:: + + \left\| + \mathbf{N}(\mathbf{q}) \mathbf{v} + \mathbf{M} \cdot (\mathbf{q}^* - \mathbf{q}) + \right\|_{W_{\text{posture}}}^2 + + This formulation allows the robot to maintain a desired posture while respecting the constraints + imposed by higher priority tasks (e.g., end-effector positioning). + + """ + + # Regularization factor for pseudoinverse computation to ensure numerical stability + PSEUDOINVERSE_DAMPING_FACTOR: float = 1e-9 + + def __init__( + self, + cost: float, + lm_damping: float = 0.0, + gain: float = 1.0, + controlled_frames: list[str] | None = None, + controlled_joints: list[str] | None = None, + ) -> None: + r"""Initialize the null space posture task. + + This task maintains a desired joint posture in the null space of higher-priority + frame tasks. Joint selection allows excluding specific joints (e.g., wrist joints + in humanoid manipulation) to prevent large rotational ranges from overwhelming + errors in critical joints like shoulders and waist. + + Args: + cost: Task weighting factor in the optimization objective. + Units: :math:`[\text{cost}] / [\text{rad}]`. + lm_damping: Levenberg-Marquardt regularization scale (unitless). Defaults to 0.0. + gain: Task gain :math:`\alpha \in [0, 1]` for low-pass filtering. + Defaults to 1.0 (no filtering). + controlled_frames: Frame names whose Jacobians define the primary tasks for + null space projection. If None or empty, no projection is applied. + controlled_joints: Joint names to control in the posture task. If None or + empty, all actuated joints are controlled. + """ + super().__init__(cost=cost, gain=gain, lm_damping=lm_damping) + self.target_q: np.ndarray | None = None + self.controlled_frames: list[str] = controlled_frames or [] + self.controlled_joints: list[str] = controlled_joints or [] + self._joint_mask: np.ndarray | None = None + self._frame_names: list[str] | None = None + + def __repr__(self) -> str: + """Human-readable representation of the task.""" + return ( + f"NullSpacePostureTask(cost={self.cost}, gain={self.gain}, lm_damping={self.lm_damping}," + f" controlled_frames={self.controlled_frames}, controlled_joints={self.controlled_joints})" + ) + + def _build_joint_mapping(self, configuration: Configuration) -> None: + """Build joint mask and cache frequently used values. + + Creates a binary mask that selects which joints should be controlled + in the posture task. + + Args: + configuration: Robot configuration containing the model and joint information. + """ + # Create joint mask for full configuration size + self._joint_mask = np.zeros(configuration.model.nq) + + # Create dictionary for joint names to indices (exclude root joint) + joint_names = configuration.model.names.tolist()[1:] + + # Build joint mask efficiently + for i, joint_name in enumerate(joint_names): + if joint_name in self.controlled_joints: + self._joint_mask[i] = 1.0 + + # Cache frame names for performance + self._frame_names = list(self.controlled_frames) + + def set_target(self, target_q: np.ndarray) -> None: + """Set target posture configuration. + + Args: + target_q: Target vector in the configuration space. If the model + has a floating base, then this vector should include + floating-base coordinates (although they have no effect on the + posture task since only actuated joints are controlled). + """ + self.target_q = target_q.copy() + + def set_target_from_configuration(self, configuration: Configuration) -> None: + """Set target posture from a robot configuration. + + Args: + configuration: Robot configuration whose joint angles will be used + as the target posture. + """ + self.set_target(configuration.q) + + def compute_error(self, configuration: Configuration) -> np.ndarray: + r"""Compute posture task error. + + The error computation follows: + + .. math:: + + \mathbf{e}(\mathbf{q}) = \mathbf{M} \cdot (\mathbf{q}^* - \mathbf{q}) + + where :math:`\mathbf{M}` is the joint selection mask and :math:`\mathbf{q}^* - \mathbf{q}` + is computed using Pinocchio's difference function to handle joint angle wrapping. + + Args: + configuration: Robot configuration :math:`\mathbf{q}`. + + Returns: + Posture task error :math:`\mathbf{e}(\mathbf{q})` with the same dimension + as the configuration vector, but with zeros for non-controlled joints. + + Raises: + ValueError: If no posture target has been set. + """ + if self.target_q is None: + raise ValueError("No posture target has been set. Call set_target() first.") + + # Initialize joint mapping if needed + if self._joint_mask is None: + self._build_joint_mapping(configuration) + + # Compute configuration difference using Pinocchio's difference function + # This handles joint angle wrapping correctly + err = pin.difference( + configuration.model, + self.target_q, + configuration.q, + ) + + # Apply pre-computed joint mask to select only controlled joints + return self._joint_mask * err + + def compute_jacobian(self, configuration: Configuration) -> np.ndarray: + r"""Compute the null space projector Jacobian. + + The null space projector is defined as: + + .. math:: + + \mathbf{N}(\mathbf{q}) = \mathbf{I} - \mathbf{J}_{\text{primary}}^+ \mathbf{J}_{\text{primary}} + + where: + - :math:`\mathbf{J}_{\text{primary}}` is the combined Jacobian of all controlled frames + - :math:`\mathbf{J}_{\text{primary}}^+` is the pseudoinverse of the primary task Jacobian + - :math:`\mathbf{I}` is the identity matrix + + The null space projector ensures that joint velocities in the null space produce + zero velocity for the primary tasks: + :math:`\mathbf{J}_{\text{primary}} \cdot \dot{\mathbf{q}}_{\text{null}} = \mathbf{0}`. + + If no controlled frames are specified, returns the identity matrix. + + Args: + configuration: Robot configuration :math:`\mathbf{q}`. + + Returns: + Null space projector matrix :math:`\mathbf{N}(\mathbf{q})` with dimensions + :math:`n_q \times n_q` where :math:`n_q` is the number of configuration variables. + """ + # Initialize joint mapping if needed + if self._frame_names is None: + self._build_joint_mapping(configuration) + + # If no frame tasks are defined, return identity matrix (no null space projection) + if not self._frame_names: + return np.eye(configuration.model.nq) + + # Get Jacobians for all frame tasks and combine them + J_frame_tasks = [configuration.get_frame_jacobian(frame_name) for frame_name in self._frame_names] + J_combined = np.concatenate(J_frame_tasks, axis=0) + + # Compute null space projector: N = I - J^+ * J + # Use fast pseudoinverse computation with direct LAPACK/BLAS calls + m, n = J_combined.shape + + # Wide matrix (typical for robotics): use left pseudoinverse + # J^+ = J^T @ inv(J @ J^T + λ²I) + # This is faster because we invert an m×m matrix instead of n×n + + # Compute J @ J^T using BLAS (faster than numpy) + JJT = blas.dgemm(1.0, J_combined, J_combined.T) + np.fill_diagonal(JJT, JJT.diagonal() + self.PSEUDOINVERSE_DAMPING_FACTOR**2) + + # Use LAPACK's Cholesky factorization (dpotrf = Positive definite TRiangular Factorization) + L, info = lapack.dpotrf(JJT, lower=1, clean=False, overwrite_a=True) + + if info != 0: + # Fallback if not positive definite: use numpy's pseudoinverse + J_pinv = np.linalg.pinv(J_combined) + return np.eye(n) - J_pinv @ J_combined + + # Solve (J @ J^T + λ²I) @ X = J using LAPACK's triangular solver (dpotrs) + # This directly solves the system without computing the full inverse + X, _ = lapack.dpotrs(L, J_combined, lower=1) + + # Compute null space projector: N = I - J^T @ X + N_combined = np.eye(n) - J_combined.T @ X + + return N_combined diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik.py b/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik.py new file mode 100644 index 0000000000000000000000000000000000000000..788a5da670538667227428b4da52d09cc694e2dd --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik.py @@ -0,0 +1,255 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Pink IK controller implementation for IsaacLab. + +This module provides integration between Pink inverse kinematics solver and IsaacLab. +Pink is a differentiable inverse kinematics solver framework that provides task-space control capabilities. + +Reference: + Pink IK Solver: https://github.com/stephane-caron/pink +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import torch +from pink import solve_ik + +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.string import resolve_matching_names_values + +from .null_space_posture_task import NullSpacePostureTask +from .pink_kinematics_configuration import PinkKinematicsConfiguration + +if TYPE_CHECKING: + from .pink_ik_cfg import PinkIKControllerCfg + + +class PinkIKController: + """Integration of Pink IK controller with Isaac Lab. + + The Pink IK controller solves differential inverse kinematics through weighted tasks. Each task is defined + by a residual function e(q) that is driven to zero (e.g., e(q) = p_target - p_ee(q) for end-effector positioning). + The controller computes joint velocities v satisfying J_e(q)v = -αe(q), where J_e(q) is the task Jacobian. + Multiple tasks are resolved through weighted optimization, formulating a quadratic program that minimizes + weighted task errors while respecting joint velocity limits. + + It supports user defined tasks, and we have provided a NullSpacePostureTask for maintaining desired + joint configurations. + + Reference: + Pink IK Solver: https://github.com/stephane-caron/pink + """ + + def __init__( + self, cfg: PinkIKControllerCfg, robot_cfg: ArticulationCfg, device: str, controlled_joint_indices: list[int] + ): + """Initialize the Pink IK Controller. + + Args: + cfg: The configuration for the Pink IK controller containing task definitions, solver parameters, + and joint configurations. + robot_cfg: The robot articulation configuration containing initial joint positions and robot + specifications. + device: The device to use for computations (e.g., 'cuda:0', 'cpu'). + controlled_joint_indices: A list of joint indices in the USD asset controlled by the Pink IK controller. + + Raises: + ValueError: When joint_names or all_joint_names are not provided in the configuration. + """ + if cfg.joint_names is None: + raise ValueError("joint_names must be provided in the configuration") + if cfg.all_joint_names is None: + raise ValueError("all_joint_names must be provided in the configuration") + + self.cfg = cfg + self.device = device + self.controlled_joint_indices = controlled_joint_indices + + # Validate consistency between controlled_joint_indices and configuration + self._validate_consistency(cfg, controlled_joint_indices) + + # Initialize the Kinematics model used by pink IK to control robot + self.pink_configuration = PinkKinematicsConfiguration( + urdf_path=cfg.urdf_path, + mesh_path=cfg.mesh_path, + controlled_joint_names=cfg.joint_names, + ) + + # Find the initial joint positions by matching Pink's joint names to robot_cfg.init_state.joint_pos, + # where the joint_pos keys may be regex patterns and the values are the initial positions. + # We want to assign to each Pink joint name the value from the first matching regex key in joint_pos. + pink_joint_names = self.pink_configuration.model.names.tolist()[1:] + joint_pos_dict = robot_cfg.init_state.joint_pos + + # Use resolve_matching_names_values to match Pink joint names to joint_pos values + indices, _, values = resolve_matching_names_values( + joint_pos_dict, pink_joint_names, preserve_order=False, strict=False + ) + self.init_joint_positions = np.zeros(len(pink_joint_names)) + self.init_joint_positions[indices] = np.array(values) + + # Set the default targets for each task from the configuration + for task in cfg.variable_input_tasks: + # If task is a NullSpacePostureTask, set the target to the initial joint positions + if isinstance(task, NullSpacePostureTask): + task.set_target(self.init_joint_positions) + continue + task.set_target_from_configuration(self.pink_configuration) + for task in cfg.fixed_input_tasks: + task.set_target_from_configuration(self.pink_configuration) + + # Create joint ordering mappings + self._setup_joint_ordering_mappings() + + def _validate_consistency(self, cfg: PinkIKControllerCfg, controlled_joint_indices: list[int]) -> None: + """Validate consistency between controlled_joint_indices and controller configuration. + + Args: + cfg: The Pink IK controller configuration. + controlled_joint_indices: List of joint indices in Isaac Lab joint space. + + Raises: + ValueError: If any consistency checks fail. + """ + # Check: Length consistency + if cfg.joint_names is None: + raise ValueError("cfg.joint_names cannot be None") + if len(controlled_joint_indices) != len(cfg.joint_names): + raise ValueError( + f"Length mismatch: controlled_joint_indices has {len(controlled_joint_indices)} elements " + f"but cfg.joint_names has {len(cfg.joint_names)} elements" + ) + + # Check: Joint name consistency - verify that the indices point to the expected joint names + actual_joint_names = [cfg.all_joint_names[idx] for idx in controlled_joint_indices] + if actual_joint_names != cfg.joint_names: + mismatches = [] + for i, (actual, expected) in enumerate(zip(actual_joint_names, cfg.joint_names)): + if actual != expected: + mismatches.append( + f"Index {i}: index {controlled_joint_indices[i]} points to '{actual}' but expected '{expected}'" + ) + if mismatches: + raise ValueError( + "Joint name mismatch between controlled_joint_indices and cfg.joint_names:\n" + + "\n".join(mismatches) + ) + + def _setup_joint_ordering_mappings(self): + """Setup joint ordering mappings between Isaac Lab and Pink conventions.""" + pink_joint_names = self.pink_configuration.all_joint_names_pinocchio_order + isaac_lab_joint_names = self.cfg.all_joint_names + + if pink_joint_names is None: + raise ValueError("pink_joint_names should not be None") + if isaac_lab_joint_names is None: + raise ValueError("isaac_lab_joint_names should not be None") + + # Create reordering arrays for all joints + self.isaac_lab_to_pink_ordering = np.array( + [isaac_lab_joint_names.index(pink_joint) for pink_joint in pink_joint_names] + ) + self.pink_to_isaac_lab_ordering = np.array( + [pink_joint_names.index(isaac_lab_joint) for isaac_lab_joint in isaac_lab_joint_names] + ) + # Create reordering arrays for controlled joints only + pink_controlled_joint_names = self.pink_configuration.controlled_joint_names_pinocchio_order + isaac_lab_controlled_joint_names = self.cfg.joint_names + + if pink_controlled_joint_names is None: + raise ValueError("pink_controlled_joint_names should not be None") + if isaac_lab_controlled_joint_names is None: + raise ValueError("isaac_lab_controlled_joint_names should not be None") + + self.isaac_lab_to_pink_controlled_ordering = np.array( + [isaac_lab_controlled_joint_names.index(pink_joint) for pink_joint in pink_controlled_joint_names] + ) + self.pink_to_isaac_lab_controlled_ordering = np.array( + [pink_controlled_joint_names.index(isaac_lab_joint) for isaac_lab_joint in isaac_lab_controlled_joint_names] + ) + + def update_null_space_joint_targets(self, curr_joint_pos: np.ndarray): + """Update the null space joint targets. + + This method updates the target joint positions for null space posture tasks based on the current + joint configuration. This is useful for maintaining desired joint configurations when the primary + task allows redundancy. + + Args: + curr_joint_pos: The current joint positions of shape (num_joints,). + """ + for task in self.cfg.variable_input_tasks: + if isinstance(task, NullSpacePostureTask): + task.set_target(curr_joint_pos) + + def compute( + self, + curr_joint_pos: np.ndarray, + dt: float, + ) -> torch.Tensor: + """Compute the target joint positions based on current state and tasks. + + Performs inverse kinematics using the Pink solver to compute target joint positions that satisfy + the defined tasks. The solver uses quadratic programming to find optimal joint velocities that + minimize task errors while respecting constraints. + + Args: + curr_joint_pos: The current joint positions of shape (num_joints,). + dt: The time step for computing joint position changes in seconds. + + Returns: + The target joint positions as a tensor of shape (num_joints,) on the specified device. + If the IK solver fails, returns the current joint positions unchanged to maintain stability. + """ + # Get the current controlled joint positions + curr_controlled_joint_pos = [curr_joint_pos[i] for i in self.controlled_joint_indices] + + # Initialize joint positions for Pink, change from isaac_lab to pink/pinocchio joint ordering. + joint_positions_pink = curr_joint_pos[self.isaac_lab_to_pink_ordering] + + # Update Pink's robot configuration with the current joint positions + self.pink_configuration.update(joint_positions_pink) + + # Solve IK using Pink's solver + try: + velocity = solve_ik( + self.pink_configuration, + self.cfg.variable_input_tasks + self.cfg.fixed_input_tasks, + dt, + solver="daqp", + safety_break=self.cfg.fail_on_joint_limit_violation, + ) + joint_angle_changes = velocity * dt + except (AssertionError, Exception) as e: + # Print warning and return the current joint positions as the target + if self.cfg.show_ik_warnings: + print( + "Warning: IK quadratic solver could not find a solution! Did not update the target joint" + f" positions.\nError: {e}" + ) + + if self.cfg.xr_enabled: + from isaaclab.ui.xr_widgets import XRVisualization + + XRVisualization.push_event("ik_error", {"error": e}) + return torch.tensor(curr_controlled_joint_pos, device=self.device, dtype=torch.float32) + + # Reorder the joint angle changes back to Isaac Lab conventions + joint_vel_isaac_lab = torch.tensor( + joint_angle_changes[self.pink_to_isaac_lab_controlled_ordering], + device=self.device, + dtype=torch.float32, + ) + + # Add the velocity changes to the current joint positions to get the target joint positions + target_joint_pos = torch.add( + joint_vel_isaac_lab, torch.tensor(curr_controlled_joint_pos, device=self.device, dtype=torch.float32) + ) + + return target_joint_pos diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik_cfg.py b/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a66c4aec6658b472f2345216624a9f3d3a9c40da --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/pink_ik_cfg.py @@ -0,0 +1,89 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for Pink IK controller.""" + +from dataclasses import MISSING + +from pink.tasks import FrameTask + +from isaaclab.utils import configclass + + +@configclass +class PinkIKControllerCfg: + """Configuration settings for the Pink IK Controller. + + The Pink IK controller can be found at: https://github.com/stephane-caron/pink + """ + + urdf_path: str | None = None + """Path to the robot's URDF file. This file is used by Pinocchio's ``robot_wrapper.BuildFromURDF`` + to load the robot model. + """ + + mesh_path: str | None = None + """Path to the mesh files associated with the robot. These files are also loaded by Pinocchio's + ``robot_wrapper.BuildFromURDF``. + """ + + num_hand_joints: int = 0 + """The number of hand joints in the robot. + + The action space for the controller contains the ``pose_dim(7) * num_controlled_frames + num_hand_joints``. + The last ``num_hand_joints`` values of the action are the hand joint angles. + """ + + variable_input_tasks: list[FrameTask] = MISSING + """A list of tasks for the Pink IK controller. + + These tasks are controllable by the environment action. + + These tasks can be used to control the pose of a frame or the angles of joints. + For more details, visit: https://github.com/stephane-caron/pink + """ + + fixed_input_tasks: list[FrameTask] = MISSING + """ + A list of tasks for the Pink IK controller. These tasks are fixed and not controllable by the env action. + + These tasks can be used to fix the pose of a frame or the angles of joints to a desired configuration. + For more details, visit: https://github.com/stephane-caron/pink + """ + + joint_names: list[str] | None = None + """A list of joint names in the USD asset controlled by the Pink IK controller. + + This is required because the joint naming conventions differ between USD and URDF files. This value is + currently designed to be automatically populated by the action term in a manager based environment. + """ + + all_joint_names: list[str] | None = None + """A list of joint names in the USD asset. + + This is required because the joint naming conventions differ between USD and URDF files. This value is + currently designed to be automatically populated by the action term in a manager based environment. + """ + + articulation_name: str = "robot" + """The name of the articulation USD asset in the scene.""" + + base_link_name: str = "base_link" + """The name of the base link in the USD asset.""" + + show_ik_warnings: bool = True + """Show warning if IK solver fails to find a solution.""" + + fail_on_joint_limit_violation: bool = True + """Whether to fail on joint limit violation. + + If True, the Pink IK solver will fail and raise an error if any joint limit is violated during optimization. + The PinkIKController will handle the error by setting the last joint positions. + + If False, the solver will ignore joint limit violations and return the closest solution found. + """ + + xr_enabled: bool = False + """If True, the Pink IK controller will send information to the XRVisualization.""" diff --git a/source/isaaclab/isaaclab/controllers/pink_ik/pink_kinematics_configuration.py b/source/isaaclab/isaaclab/controllers/pink_ik/pink_kinematics_configuration.py new file mode 100644 index 0000000000000000000000000000000000000000..cd935390f0aa4308f9d64dc732e8d30e86ac980f --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/pink_ik/pink_kinematics_configuration.py @@ -0,0 +1,188 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import numpy as np +import pinocchio as pin +from pink.configuration import Configuration +from pink.exceptions import FrameNotFound +from pinocchio.robot_wrapper import RobotWrapper + + +class PinkKinematicsConfiguration(Configuration): + """ + A configuration class that maintains both a "controlled" (reduced) model and a "full" model. + + This class extends the standard Pink Configuration to allow for selective joint control: + + - The "controlled" model/data/q represent the subset of joints being actively controlled + (e.g., a kinematic chain or arm). + - The "full" model/data/q represent the complete robot, including all joints. + + This is useful for scenarios where only a subset of joints are being optimized or controlled, but + full-model kinematics (e.g., for collision checking, full-body Jacobians, or visualization) are still required. + + The class ensures that both models are kept up to date, and provides methods to update both the controlled and full + configurations as needed. + """ + + def __init__( + self, + controlled_joint_names: list[str], + urdf_path: str, + mesh_path: str | None = None, + copy_data: bool = True, + forward_kinematics: bool = True, + ): + """ + Initialize PinkKinematicsConfiguration. + + + This constructor initializes the PinkKinematicsConfiguration, which maintains both a "controlled" + (reduced) model and a "full" model. The controlled model/data/q represent the subset of joints + being actively controlled, while the full model/data/q represent the complete robot. This is useful + for scenarios where only a subset of joints are being optimized or controlled, but full-model + kinematics are still required. + + Args: + urdf_path: Path to the robot URDF file. + mesh_path: Path to the mesh files for the robot. + controlled_joint_names: List of joint names to be actively controlled. + copy_data: If True, work on an internal copy of the input data. Defaults to True. + forward_kinematics: If True, compute forward kinematics from the configuration vector. Defaults to True. + """ + self._controlled_joint_names = controlled_joint_names + + # Build robot model with all joints + if mesh_path: + self.robot_wrapper = RobotWrapper.BuildFromURDF(urdf_path, mesh_path) + else: + self.robot_wrapper = RobotWrapper.BuildFromURDF(urdf_path) + self.full_model = self.robot_wrapper.model + self.full_data = self.robot_wrapper.data + self.full_q = self.robot_wrapper.q0 + + # import pdb; pdb.set_trace() + self._all_joint_names = self.full_model.names.tolist()[1:] + # controlled_joint_indices: indices in all_joint_names for joints that are in controlled_joint_names, + # preserving all_joint_names order + self._controlled_joint_indices = [ + idx for idx, joint_name in enumerate(self._all_joint_names) if joint_name in self._controlled_joint_names + ] + + # Build the reduced model with only the controlled joints + joints_to_lock = [] + for joint_name in self._all_joint_names: + if joint_name not in self._controlled_joint_names: + joints_to_lock.append(self.full_model.getJointId(joint_name)) + + if len(joints_to_lock) == 0: + # No joints to lock, controlled model is the same as full model + self.controlled_model = self.full_model + self.controlled_data = self.full_data + self.controlled_q = self.full_q + else: + self.controlled_model = pin.buildReducedModel(self.full_model, joints_to_lock, self.full_q) + self.controlled_data = self.controlled_model.createData() + self.controlled_q = self.full_q[self._controlled_joint_indices] + + # Pink will should only have the controlled model + super().__init__(self.controlled_model, self.controlled_data, self.controlled_q, copy_data, forward_kinematics) + + def update(self, q: np.ndarray | None = None) -> None: + """Update configuration to a new vector. + + Calling this function runs forward kinematics and computes + collision-pair distances, if applicable. + + Args: + q: New configuration vector. + """ + if q is not None and len(q) != len(self._all_joint_names): + raise ValueError("q must have the same length as the number of joints in the model") + if q is not None: + super().update(q[self._controlled_joint_indices]) + + q_readonly = q.copy() + q_readonly.setflags(write=False) + self.full_q = q_readonly + pin.computeJointJacobians(self.full_model, self.full_data, q) + pin.updateFramePlacements(self.full_model, self.full_data) + else: + super().update() + pin.computeJointJacobians(self.full_model, self.full_data, self.full_q) + pin.updateFramePlacements(self.full_model, self.full_data) + + def get_frame_jacobian(self, frame: str) -> np.ndarray: + r"""Compute the Jacobian matrix of a frame velocity. + + Denoting our frame by :math:`B` and the world frame by :math:`W`, the + Jacobian matrix :math:`{}_B J_{WB}` is related to the body velocity + :math:`{}_B v_{WB}` by: + + .. math:: + + {}_B v_{WB} = {}_B J_{WB} \dot{q} + + Args: + frame: Name of the frame, typically a link name from the URDF. + + Returns: + Jacobian :math:`{}_B J_{WB}` of the frame. + + When the robot model includes a floating base + (pin.JointModelFreeFlyer), the configuration vector :math:`q` consists + of: + + - ``q[0:3]``: position in [m] of the floating base in the inertial + frame, formatted as :math:`[p_x, p_y, p_z]`. + - ``q[3:7]``: unit quaternion for the orientation of the floating base + in the inertial frame, formatted as :math:`[q_x, q_y, q_z, q_w]`. + - ``q[7:]``: joint angles in [rad]. + """ + if not self.full_model.existFrame(frame): + raise FrameNotFound(frame, self.full_model.frames) + frame_id = self.full_model.getFrameId(frame) + J: np.ndarray = pin.getFrameJacobian(self.full_model, self.full_data, frame_id, pin.ReferenceFrame.LOCAL) + return J[:, self._controlled_joint_indices] + + def get_transform_frame_to_world(self, frame: str) -> pin.SE3: + """Get the pose of a frame in the current configuration. + + We override this method from the super class to solve the issue that in the default + Pink implementation, the frame placements do not take into account the non-controlled joints + being not at initial pose (which is a bad assumption when they are controlled by other + controllers like a lower body controller). + + Args: + frame: Name of a frame, typically a link name from the URDF. + + Returns: + Current transform from the given frame to the world frame. + + Raises: + FrameNotFound: if the frame name is not found in the robot model. + """ + frame_id = self.full_model.getFrameId(frame) + try: + return self.full_data.oMf[frame_id].copy() + except IndexError as index_error: + raise FrameNotFound(frame, self.full_model.frames) from index_error + + def check_limits(self, tol: float = 1e-6, safety_break: bool = True) -> None: + """Check if limits are violated only if safety_break is enabled""" + if safety_break: + super().check_limits(tol, safety_break) + + @property + def controlled_joint_names_pinocchio_order(self) -> list[str]: + """Get the names of the controlled joints in the order of the pinocchio model.""" + return [self._all_joint_names[i] for i in self._controlled_joint_indices] + + @property + def all_joint_names_pinocchio_order(self) -> list[str]: + """Get the names of all joints in the order of the pinocchio model.""" + return self._all_joint_names diff --git a/source/isaaclab/isaaclab/controllers/rmp_flow.py b/source/isaaclab/isaaclab/controllers/rmp_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..70e2ee1306c076bb55f4fcedfc582de147b53ce8 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/rmp_flow.py @@ -0,0 +1,164 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import torch + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.prims import SingleArticulation + +# enable motion generation extensions +from isaacsim.core.utils.extensions import enable_extension + +enable_extension("isaacsim.robot_motion.lula") +enable_extension("isaacsim.robot_motion.motion_generation") + +from isaacsim.robot_motion.motion_generation import ArticulationMotionPolicy +from isaacsim.robot_motion.motion_generation.lula.motion_policies import RmpFlow, RmpFlowSmoothed + +import isaaclab.sim.utils as sim_utils +from isaaclab.utils import configclass +from isaaclab.utils.assets import retrieve_file_path + + +@configclass +class RmpFlowControllerCfg: + """Configuration for RMP-Flow controller (provided through LULA library).""" + + name: str = "rmp_flow" + """Name of the controller. Supported: "rmp_flow", "rmp_flow_smoothed". Defaults to "rmp_flow".""" + config_file: str = MISSING + """Path to the configuration file for the controller.""" + urdf_file: str = MISSING + """Path to the URDF model of the robot.""" + collision_file: str = MISSING + """Path to collision model description of the robot.""" + frame_name: str = MISSING + """Name of the robot frame for task space (must be present in the URDF).""" + evaluations_per_frame: float = MISSING + """Number of substeps during Euler integration inside LULA world model.""" + ignore_robot_state_updates: bool = False + """If true, then state of the world model inside controller is rolled out. Defaults to False.""" + + +class RmpFlowController: + """Wraps around RMPFlow from IsaacSim for batched environments.""" + + def __init__(self, cfg: RmpFlowControllerCfg, device: str): + """Initialize the controller. + + Args: + cfg: The configuration for the controller. + device: The device to use for computation. + """ + # store input + self.cfg = cfg + self._device = device + # display info + print(f"[INFO]: Loading RMPFlow controller URDF from: {self.cfg.urdf_file}") + + """ + Properties. + """ + + @property + def num_actions(self) -> int: + """Dimension of the action space of controller.""" + return 7 + + """ + Operations. + """ + + def initialize(self, prim_paths_expr: str): + """Initialize the controller. + + Args: + prim_paths_expr: The expression to find the articulation prim paths. + """ + # obtain the simulation time + physics_dt = SimulationContext.instance().get_physics_dt() + # find all prims + self._prim_paths = sim_utils.find_matching_prim_paths(prim_paths_expr) + self.num_robots = len(self._prim_paths) + # resolve controller + if self.cfg.name == "rmp_flow": + controller_cls = RmpFlow + elif self.cfg.name == "rmp_flow_smoothed": + controller_cls = RmpFlowSmoothed + else: + raise ValueError(f"Unsupported controller in Lula library: {self.cfg.name}") + # create all franka robots references and their controllers + self.articulation_policies = list() + for prim_path in self._prim_paths: + # add robot reference + robot = SingleArticulation(prim_path) + robot.initialize() + # download files if they are not local + + local_urdf_file = retrieve_file_path(self.cfg.urdf_file, force_download=True) + local_collision_file = retrieve_file_path(self.cfg.collision_file, force_download=True) + local_config_file = retrieve_file_path(self.cfg.config_file, force_download=True) + + # add controller + rmpflow = controller_cls( + robot_description_path=local_collision_file, + urdf_path=local_urdf_file, + rmpflow_config_path=local_config_file, + end_effector_frame_name=self.cfg.frame_name, + maximum_substep_size=physics_dt / self.cfg.evaluations_per_frame, + ignore_robot_state_updates=self.cfg.ignore_robot_state_updates, + ) + # wrap rmpflow to connect to the Franka robot articulation + articulation_policy = ArticulationMotionPolicy(robot, rmpflow, physics_dt) + self.articulation_policies.append(articulation_policy) + # get number of active joints + self.active_dof_names = self.articulation_policies[0].get_motion_policy().get_active_joints() + self.num_dof = len(self.active_dof_names) + # create buffers + # -- for storing command + self._command = torch.zeros(self.num_robots, self.num_actions, device=self._device) + # -- for policy output + self.dof_pos_target = torch.zeros((self.num_robots, self.num_dof), device=self._device) + self.dof_vel_target = torch.zeros((self.num_robots, self.num_dof), device=self._device) + + def reset_idx(self, robot_ids: torch.Tensor = None): + """Reset the internals.""" + # if no robot ids are provided, then reset all robots + if robot_ids is None: + robot_ids = torch.arange(self.num_robots, device=self._device) + # reset policies for specified robots + for index in robot_ids: + self.articulation_policies[index].motion_policy.reset() + + def set_command(self, command: torch.Tensor): + """Set target end-effector pose command.""" + # store command + self._command[:] = command + + def compute(self) -> tuple[torch.Tensor, torch.Tensor]: + """Performs inference with the controller. + + Returns: + The target joint positions and velocity commands. + """ + # convert command to numpy + command = self._command.cpu().numpy() + # compute control actions + for i, policy in enumerate(self.articulation_policies): + # enable type-hinting + policy: ArticulationMotionPolicy + # set rmpflow target to be the current position of the target cube. + policy.get_motion_policy().set_end_effector_target( + target_position=command[i, 0:3], target_orientation=command[i, 3:7] + ) + # apply action on the robot + action = policy.get_next_articulation_action() + # copy actions into buffer + self.dof_pos_target[i, :] = torch.from_numpy(action.joint_positions[:]).to(self.dof_pos_target) + self.dof_vel_target[i, :] = torch.from_numpy(action.joint_velocities[:]).to(self.dof_vel_target) + + return self.dof_pos_target, self.dof_vel_target diff --git a/source/isaaclab/isaaclab/controllers/utils.py b/source/isaaclab/isaaclab/controllers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7e72912fdfda4a21a9ab18c66f8cdf03abe4c224 --- /dev/null +++ b/source/isaaclab/isaaclab/controllers/utils.py @@ -0,0 +1,138 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Helper functions for Isaac Lab controllers. + +This module provides utility functions to help with controller implementations. +""" + +import logging +import os +import re + +from isaacsim.core.utils.extensions import enable_extension + +enable_extension("isaacsim.asset.exporter.urdf") + +from nvidia.srl.from_usd.to_urdf import UsdToUrdf + +# import logger +logger = logging.getLogger(__name__) + + +def convert_usd_to_urdf(usd_path: str, output_path: str, force_conversion: bool = True) -> tuple[str, str]: + """Convert a USD file to URDF format. + + Args: + usd_path: Path to the USD file to convert. + output_path: Directory to save the converted URDF and mesh files. + force_conversion: Whether to force the conversion even if the URDF and mesh files already exist. + Returns: + A tuple containing the paths to the URDF file and the mesh directory. + """ + usd_to_urdf_kwargs = { + "node_names_to_remove": None, + "edge_names_to_remove": None, + "root": None, + "parent_link_is_body_1": None, + "log_level": logging.ERROR, + } + + urdf_output_dir = os.path.join(output_path, "urdf") + urdf_file_name = os.path.basename(usd_path).split(".")[0] + ".urdf" + urdf_output_path = urdf_output_dir + "/" + urdf_file_name + urdf_meshes_output_dir = os.path.join(output_path, "meshes") + + if not os.path.exists(urdf_output_path) or not os.path.exists(urdf_meshes_output_dir) or force_conversion: + usd_to_urdf = UsdToUrdf.init_from_file(usd_path, **usd_to_urdf_kwargs) + os.makedirs(urdf_output_dir, exist_ok=True) + os.makedirs(urdf_meshes_output_dir, exist_ok=True) + + output_path = usd_to_urdf.save_to_file( + urdf_output_path=urdf_output_path, + visualize_collision_meshes=False, + mesh_dir=urdf_meshes_output_dir, + mesh_path_prefix="", + ) + + # The current version of the usd to urdf converter creates "inf" effort, + # This has to be replaced with a max value for the urdf to be valid + # Open the file for reading and writing + with open(urdf_output_path) as file: + # Read the content of the file + content = file.read() + + # Replace all occurrences of 'inf' with '0' + content = content.replace("inf", "0.") + + # Open the file again to write the modified content + with open(urdf_output_path, "w") as file: + # Write the modified content back to the file + file.write(content) + return urdf_output_path, urdf_meshes_output_dir + + +def change_revolute_to_fixed(urdf_path: str, fixed_joints: list[str], verbose: bool = False): + """Change revolute joints to fixed joints in a URDF file. + + This function modifies a URDF file by changing specified revolute joints to fixed joints. + This is useful when you want to disable certain joints in a robot model. + + Args: + urdf_path: Path to the URDF file to modify. + fixed_joints: List of joint names to convert from revolute to fixed. + verbose: Whether to print information about the changes being made. + """ + with open(urdf_path) as file: + content = file.read() + + for joint in fixed_joints: + old_str = f'' + new_str = f'' + if verbose: + logger.warning(f"Replacing {joint} with fixed joint") + logger.warning(old_str) + logger.warning(new_str) + if old_str not in content: + logger.warning(f"Error: Could not find revolute joint named '{joint}' in URDF file") + content = content.replace(old_str, new_str) + + with open(urdf_path, "w") as file: + file.write(content) + + +def change_revolute_to_fixed_regex(urdf_path: str, fixed_joints: list[str], verbose: bool = False): + """Change revolute joints to fixed joints in a URDF file. + + This function modifies a URDF file by changing specified revolute joints to fixed joints. + This is useful when you want to disable certain joints in a robot model. + + Args: + urdf_path: Path to the URDF file to modify. + fixed_joints: List of regular expressions matching joint names to convert from revolute to fixed. + verbose: Whether to print information about the changes being made. + """ + + with open(urdf_path) as file: + content = file.read() + + # Find all revolute joints in the URDF + revolute_joints = re.findall(r'', content) + + for joint in revolute_joints: + # Check if this joint matches any of the fixed joint patterns + should_fix = any(re.match(pattern, joint) for pattern in fixed_joints) + + if should_fix: + old_str = f'' + new_str = f'' + if verbose: + logger.warning(f"Replacing {joint} with fixed joint") + logger.warning(old_str) + logger.warning(new_str) + content = content.replace(old_str, new_str) + + with open(urdf_path, "w") as file: + file.write(content) diff --git a/source/isaaclab/isaaclab/devices/__init__.py b/source/isaaclab/isaaclab/devices/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b2605d39ca16dc53d9670e73aa96e08ac898eefa --- /dev/null +++ b/source/isaaclab/isaaclab/devices/__init__.py @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package providing interfaces to different teleoperation devices. + +Currently, the following categories of devices are supported: + +* **Keyboard**: Standard keyboard with WASD and arrow keys. +* **Spacemouse**: 3D mouse with 6 degrees of freedom. +* **Gamepad**: Gamepad with 2D two joysticks and buttons. Example: Xbox controller. +* **OpenXR**: Uses hand tracking of index/thumb tip avg to drive the target pose. Gripping is done with pinching. +* **Haply**: Haptic device (Inverse3 + VerseGrip) with position, orientation tracking and force feedback. + +All device interfaces inherit from the :class:`DeviceBase` class, which provides a +common interface for all devices. The device interface reads the input data when +the :meth:`DeviceBase.advance` method is called. It also provides the function :meth:`DeviceBase.add_callback` +to add user-defined callback functions to be called when a particular input is pressed from +the peripheral device. +""" + +from .device_base import DeviceBase, DeviceCfg, DevicesCfg +from .gamepad import Se2Gamepad, Se2GamepadCfg, Se3Gamepad, Se3GamepadCfg +from .haply import HaplyDevice, HaplyDeviceCfg +from .keyboard import Se2Keyboard, Se2KeyboardCfg, Se3Keyboard, Se3KeyboardCfg +from .openxr import ManusVive, ManusViveCfg, OpenXRDevice, OpenXRDeviceCfg +from .retargeter_base import RetargeterBase, RetargeterCfg +from .spacemouse import Se2SpaceMouse, Se2SpaceMouseCfg, Se3SpaceMouse, Se3SpaceMouseCfg +from .teleop_device_factory import create_teleop_device diff --git a/source/isaaclab/isaaclab/devices/device_base.py b/source/isaaclab/isaaclab/devices/device_base.py new file mode 100644 index 0000000000000000000000000000000000000000..a434bcc73cfda36a112de2ad3e82591c17c1f96d --- /dev/null +++ b/source/isaaclab/isaaclab/devices/device_base.py @@ -0,0 +1,160 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Base class for teleoperation interface.""" + +from abc import ABC, abstractmethod +from collections.abc import Callable +from dataclasses import dataclass, field +from enum import Enum +from typing import Any + +import torch + +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +@dataclass +class DeviceCfg: + """Configuration for teleoperation devices.""" + + # Whether teleoperation should start active by default + teleoperation_active_default: bool = True + # Torch device string to place output tensors on + sim_device: str = "cpu" + # Retargeters that transform device data into robot commands + retargeters: list[RetargeterCfg] = field(default_factory=list) + # Concrete device class to construct for this config. Set by each device module. + class_type: type["DeviceBase"] | None = None + + +@dataclass +class DevicesCfg: + """Configuration for all supported teleoperation devices.""" + + devices: dict[str, DeviceCfg] = field(default_factory=dict) + + +class DeviceBase(ABC): + """An interface class for teleoperation devices. + + Derived classes have two implementation options: + + 1. Override _get_raw_data() and use the base advance() implementation: + This approach is suitable for devices that want to leverage the built-in + retargeting logic but only need to customize the raw data acquisition. + + 2. Override advance() completely: + This approach gives full control over the command generation process, + and _get_raw_data() can be ignored entirely. + """ + + def __init__(self, retargeters: list[RetargeterBase] | None = None): + """Initialize the teleoperation interface. + + Args: + retargeters: List of components that transform device data into robot commands. + If None or empty list, the device will output its native data format. + """ + # Initialize empty list if None is provided + self._retargeters = retargeters or [] + # Aggregate required features across all retargeters + self._required_features = set() + for retargeter in self._retargeters: + self._required_features.update(retargeter.get_requirements()) + + def __str__(self) -> str: + """Returns: A string identifier for the device.""" + return f"{self.__class__.__name__}" + + """ + Operations + """ + + @abstractmethod + def reset(self): + """Reset the internals.""" + raise NotImplementedError + + @abstractmethod + def add_callback(self, key: Any, func: Callable): + """Add additional functions to bind keyboard. + + Args: + key: The button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + raise NotImplementedError + + def _get_raw_data(self) -> Any: + """Internal method to get the raw data from the device. + + This method is intended for internal use by the advance() implementation. + Derived classes can override this method to customize raw data acquisition + while still using the base class's advance() implementation. + + Returns: + Raw device data in a device-specific format + + Note: + This is an internal implementation detail. Clients should call advance() + instead of this method. + """ + raise NotImplementedError("Derived class must implement _get_raw_data() or override advance()") + + def advance(self) -> torch.Tensor: + """Process current device state and return control commands. + + This method retrieves raw data from the device and optionally applies + retargeting to convert it to robot commands. + + Derived classes can either: + 1. Override _get_raw_data() and use this base implementation, or + 2. Override this method completely for custom command processing + + Returns: + When no retargeters are configured, returns raw device data in its native format. + When retargeters are configured, returns a torch.Tensor containing the concatenated + outputs from all retargeters. + """ + raw_data = self._get_raw_data() + + # If no retargeters, return raw data directly (not as a tuple) + if not self._retargeters: + return raw_data + + # With multiple retargeters, return a tuple of outputs + # Concatenate retargeted outputs into a single tensor + return torch.cat([retargeter.retarget(raw_data) for retargeter in self._retargeters], dim=-1) + + # ----------------------------- + # Shared data layout helpers (for retargeters across devices) + # ----------------------------- + class TrackingTarget(Enum): + """Standard tracking targets shared across devices.""" + + HAND_LEFT = 0 + HAND_RIGHT = 1 + HEAD = 2 + CONTROLLER_LEFT = 3 + CONTROLLER_RIGHT = 4 + + class MotionControllerDataRowIndex(Enum): + """Rows in the motion-controller 2x7 array.""" + + POSE = 0 + INPUTS = 1 + + class MotionControllerInputIndex(Enum): + """Indices in the motion-controller input row.""" + + THUMBSTICK_X = 0 + THUMBSTICK_Y = 1 + TRIGGER = 2 + SQUEEZE = 3 + BUTTON_0 = 4 + BUTTON_1 = 5 + PADDING = 6 diff --git a/source/isaaclab/isaaclab/devices/gamepad/__init__.py b/source/isaaclab/isaaclab/devices/gamepad/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8f8ec66aa4ecb5a55d935f396f0e0df8a14d2e27 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/gamepad/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Gamepad device for SE(2) and SE(3) control.""" + +from .se2_gamepad import Se2Gamepad, Se2GamepadCfg +from .se3_gamepad import Se3Gamepad, Se3GamepadCfg diff --git a/source/isaaclab/isaaclab/devices/gamepad/se2_gamepad.py b/source/isaaclab/isaaclab/devices/gamepad/se2_gamepad.py new file mode 100644 index 0000000000000000000000000000000000000000..5954c3c6918e742fe7e363b3e1b8314da92f5a7b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/gamepad/se2_gamepad.py @@ -0,0 +1,215 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Gamepad controller for SE(2) control.""" + +from __future__ import annotations + +import weakref +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch + +import carb +import carb.input +import omni + +from ..device_base import DeviceBase, DeviceCfg + + +class Se2Gamepad(DeviceBase): + r"""A gamepad controller for sending SE(2) commands as velocity commands. + + This class is designed to provide a gamepad controller for mobile base (such as quadrupeds). + It uses the Omniverse gamepad interface to listen to gamepad events and map them to robot's + task-space commands. + + The command comprises of the base linear and angular velocity: :math:`(v_x, v_y, \omega_z)`. + + Key bindings: + ====================== ========================= ======================== + Command Key (+ve axis) Key (-ve axis) + ====================== ========================= ======================== + Move along x-axis left stick up left stick down + Move along y-axis left stick right left stick left + Rotate along z-axis right stick right right stick left + ====================== ========================= ======================== + + .. seealso:: + + The official documentation for the gamepad interface: `Carb Gamepad Interface `__. + + """ + + def __init__( + self, + cfg: Se2GamepadCfg, + ): + """Initialize the gamepad layer. + + Args: + v_x_sensitivity: Magnitude of linear velocity along x-direction scaling. Defaults to 1.0. + v_y_sensitivity: Magnitude of linear velocity along y-direction scaling. Defaults to 1.0. + omega_z_sensitivity: Magnitude of angular velocity along z-direction scaling. Defaults to 1.0. + dead_zone: Magnitude of dead zone for gamepad. An event value from the gamepad less than + this value will be ignored. Defaults to 0.01. + """ + # turn off simulator gamepad control + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/persistent/app/omniverse/gamepadCameraControl", False) + # store inputs + self.v_x_sensitivity = cfg.v_x_sensitivity + self.v_y_sensitivity = cfg.v_y_sensitivity + self.omega_z_sensitivity = cfg.omega_z_sensitivity + self.dead_zone = cfg.dead_zone + self._sim_device = cfg.sim_device + # acquire omniverse interfaces + self._appwindow = omni.appwindow.get_default_app_window() + self._input = carb.input.acquire_input_interface() + self._gamepad = self._appwindow.get_gamepad(0) + # note: Use weakref on callbacks to ensure that this object can be deleted when its destructor is called + self._gamepad_sub = self._input.subscribe_to_gamepad_events( + self._gamepad, + lambda event, *args, obj=weakref.proxy(self): obj._on_gamepad_event(event, *args), + ) + # bindings for gamepad to command + self._create_key_bindings() + # command buffers + # When using the gamepad, two values are provided for each axis. + # For example: when the left stick is moved down, there are two evens: `left_stick_down = 0.8` + # and `left_stick_up = 0.0`. If only the value of left_stick_up is used, the value will be 0.0, + # which is not the desired behavior. Therefore, we save both the values into the buffer and use + # the maximum value. + # (positive, negative), (x, y, yaw) + self._base_command_raw = np.zeros([2, 3]) + # dictionary for additional callbacks + self._additional_callbacks = dict() + + def __del__(self): + """Unsubscribe from gamepad events.""" + self._input.unsubscribe_to_gamepad_events(self._gamepad, self._gamepad_sub) + self._gamepad_sub = None + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Gamepad Controller for SE(2): {self.__class__.__name__}\n" + msg += f"\tDevice name: {self._input.get_gamepad_name(self._gamepad)}\n" + msg += "\t----------------------------------------------\n" + msg += "\tMove in X-Y plane: left stick\n" + msg += "\tRotate in Z-axis: right stick\n" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._base_command_raw.fill(0.0) + + def add_callback(self, key: carb.input.GamepadInput, func: Callable): + """Add additional functions to bind gamepad. + + A list of available gamepad keys are present in the + `carb documentation `__. + + Args: + key: The gamepad button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from gamepad event state. + + Returns: + A tensor containing the linear (x,y) and angular velocity (z). + """ + numpy_result = self._resolve_command_buffer(self._base_command_raw) + return torch.tensor(numpy_result, dtype=torch.float32, device=self._sim_device) + + """ + Internal helpers. + """ + + def _on_gamepad_event(self, event: carb.input.GamepadEvent, *args, **kwargs): + """Subscriber callback to when kit is updated. + + Reference: + https://docs.omniverse.nvidia.com/dev-guide/latest/programmer_ref/input-devices/gamepad.html + """ + + # check if the event is a button press + cur_val = event.value + if abs(cur_val) < self.dead_zone: + cur_val = 0 + # -- left and right stick + if event.input in self._INPUT_STICK_VALUE_MAPPING: + direction, axis, value = self._INPUT_STICK_VALUE_MAPPING[event.input] + # change the value only if the stick is moved (soft press) + self._base_command_raw[direction, axis] = value * cur_val + + # additional callbacks + if event.input in self._additional_callbacks: + self._additional_callbacks[event.input]() + + # since no error, we are fine :) + return True + + def _create_key_bindings(self): + """Creates default key binding.""" + self._INPUT_STICK_VALUE_MAPPING = { + # forward command + carb.input.GamepadInput.LEFT_STICK_UP: (0, 0, self.v_x_sensitivity), + # backward command + carb.input.GamepadInput.LEFT_STICK_DOWN: (1, 0, self.v_x_sensitivity), + # right command + carb.input.GamepadInput.LEFT_STICK_RIGHT: (0, 1, self.v_y_sensitivity), + # left command + carb.input.GamepadInput.LEFT_STICK_LEFT: (1, 1, self.v_y_sensitivity), + # yaw command (positive) + carb.input.GamepadInput.RIGHT_STICK_RIGHT: (0, 2, self.omega_z_sensitivity), + # yaw command (negative) + carb.input.GamepadInput.RIGHT_STICK_LEFT: (1, 2, self.omega_z_sensitivity), + } + + def _resolve_command_buffer(self, raw_command: np.ndarray) -> np.ndarray: + """Resolves the command buffer. + + Args: + raw_command: The raw command from the gamepad. Shape is (2, 3) + This is a 2D array since gamepad dpad/stick returns two values corresponding to + the positive and negative direction. The first index is the direction (0: positive, 1: negative) + and the second index is value (absolute) of the command. + + Returns: + Resolved command. Shape is (3,) + """ + # compare the positive and negative value decide the sign of the value + # if the positive value is larger, the sign is positive (i.e. False, 0) + # if the negative value is larger, the sign is positive (i.e. True, 1) + command_sign = raw_command[1, :] > raw_command[0, :] + # extract the command value + command = raw_command.max(axis=0) + # apply the sign + # if the sign is positive, the value is already positive. + # if the sign is negative, the value is negative after applying the sign. + command[command_sign] *= -1 + + return command + + +@dataclass +class Se2GamepadCfg(DeviceCfg): + """Configuration for SE2 gamepad devices.""" + + v_x_sensitivity: float = 1.0 + v_y_sensitivity: float = 1.0 + omega_z_sensitivity: float = 1.0 + dead_zone: float = 0.01 + class_type: type[DeviceBase] = Se2Gamepad diff --git a/source/isaaclab/isaaclab/devices/gamepad/se3_gamepad.py b/source/isaaclab/isaaclab/devices/gamepad/se3_gamepad.py new file mode 100644 index 0000000000000000000000000000000000000000..2520de6247ebf0a2a0cd07c94c89d672e8ef58b6 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/gamepad/se3_gamepad.py @@ -0,0 +1,269 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Gamepad controller for SE(3) control.""" + +from __future__ import annotations + +import weakref +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation + +import carb +import omni + +from ..device_base import DeviceBase, DeviceCfg + + +class Se3Gamepad(DeviceBase): + """A gamepad controller for sending SE(3) commands as delta poses and binary command (open/close). + + This class is designed to provide a gamepad controller for a robotic arm with a gripper. + It uses the gamepad interface to listen to gamepad events and map them to the robot's + task-space commands. + + The command comprises of two parts: + + * delta pose: a 6D vector of (x, y, z, roll, pitch, yaw) in meters and radians. + * gripper: a binary command to open or close the gripper. + + Stick and Button bindings: + ============================ ========================= ========================= + Description Stick/Button (+ve axis) Stick/Button (-ve axis) + ============================ ========================= ========================= + Toggle gripper(open/close) X Button X Button + Move along x-axis Left Stick Up Left Stick Down + Move along y-axis Left Stick Left Left Stick Right + Move along z-axis Right Stick Up Right Stick Down + Rotate along x-axis D-Pad Left D-Pad Right + Rotate along y-axis D-Pad Down D-Pad Up + Rotate along z-axis Right Stick Left Right Stick Right + ============================ ========================= ========================= + + .. seealso:: + + The official documentation for the gamepad interface: `Carb Gamepad Interface `__. + + """ + + def __init__( + self, + cfg: Se3GamepadCfg, + ): + """Initialize the gamepad layer. + + Args: + cfg: Configuration object for gamepad settings. + """ + # turn off simulator gamepad control + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/persistent/app/omniverse/gamepadCameraControl", False) + # store inputs + self.pos_sensitivity = cfg.pos_sensitivity + self.rot_sensitivity = cfg.rot_sensitivity + self.dead_zone = cfg.dead_zone + self.gripper_term = cfg.gripper_term + self._sim_device = cfg.sim_device + # acquire omniverse interfaces + self._appwindow = omni.appwindow.get_default_app_window() + self._input = carb.input.acquire_input_interface() + self._gamepad = self._appwindow.get_gamepad(0) + # note: Use weakref on callbacks to ensure that this object can be deleted when its destructor is called + self._gamepad_sub = self._input.subscribe_to_gamepad_events( + self._gamepad, + lambda event, *args, obj=weakref.proxy(self): obj._on_gamepad_event(event, *args), + ) + # bindings for gamepad to command + self._create_key_bindings() + # command buffers + self._close_gripper = False + # When using the gamepad, two values are provided for each axis. + # For example: when the left stick is moved down, there are two evens: `left_stick_down = 0.8` + # and `left_stick_up = 0.0`. If only the value of left_stick_up is used, the value will be 0.0, + # which is not the desired behavior. Therefore, we save both the values into the buffer and use + # the maximum value. + # (positive, negative), (x, y, z, roll, pitch, yaw) + self._delta_pose_raw = np.zeros([2, 6]) + # dictionary for additional callbacks + self._additional_callbacks = dict() + + def __del__(self): + """Unsubscribe from gamepad events.""" + self._input.unsubscribe_to_gamepad_events(self._gamepad, self._gamepad_sub) + self._gamepad_sub = None + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Gamepad Controller for SE(3): {self.__class__.__name__}\n" + msg += f"\tDevice name: {self._input.get_gamepad_name(self._gamepad)}\n" + msg += "\t----------------------------------------------\n" + msg += "\tToggle gripper (open/close): X\n" + msg += "\tMove arm along x-axis: Left Stick Up/Down\n" + msg += "\tMove arm along y-axis: Left Stick Left/Right\n" + msg += "\tMove arm along z-axis: Right Stick Up/Down\n" + msg += "\tRotate arm along x-axis: D-Pad Right/Left\n" + msg += "\tRotate arm along y-axis: D-Pad Down/Up\n" + msg += "\tRotate arm along z-axis: Right Stick Left/Right\n" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._close_gripper = False + self._delta_pose_raw.fill(0.0) + + def add_callback(self, key: carb.input.GamepadInput, func: Callable): + """Add additional functions to bind gamepad. + + A list of available gamepad keys are present in the + `carb documentation `__. + + Args: + key: The gamepad button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from gamepad event state. + + Returns: + torch.Tensor: A 7-element tensor containing: + - delta pose: First 6 elements as [x, y, z, rx, ry, rz] in meters and radians. + - gripper command: Last element as a binary value (+1.0 for open, -1.0 for close). + """ + # -- resolve position command + delta_pos = self._resolve_command_buffer(self._delta_pose_raw[:, :3]) + # -- resolve rotation command + delta_rot = self._resolve_command_buffer(self._delta_pose_raw[:, 3:]) + # -- convert to rotation vector + rot_vec = Rotation.from_euler("XYZ", delta_rot).as_rotvec() + # return the command and gripper state + command = np.concatenate([delta_pos, rot_vec]) + if self.gripper_term: + gripper_value = -1.0 if self._close_gripper else 1.0 + command = np.append(command, gripper_value) + + return torch.tensor(command, dtype=torch.float32, device=self._sim_device) + + """ + Internal helpers. + """ + + def _on_gamepad_event(self, event, *args, **kwargs): + """Subscriber callback to when kit is updated. + + Reference: + https://docs.omniverse.nvidia.com/dev-guide/latest/programmer_ref/input-devices/gamepad.html + """ + # check if the event is a button press + cur_val = event.value + if abs(cur_val) < self.dead_zone: + cur_val = 0 + # -- button + if event.input == carb.input.GamepadInput.X: + # toggle gripper based on the button pressed + if cur_val > 0.5: + self._close_gripper = not self._close_gripper + # -- left and right stick + if event.input in self._INPUT_STICK_VALUE_MAPPING: + direction, axis, value = self._INPUT_STICK_VALUE_MAPPING[event.input] + # change the value only if the stick is moved (soft press) + self._delta_pose_raw[direction, axis] = value * cur_val + # -- dpad (4 arrow buttons on the console) + if event.input in self._INPUT_DPAD_VALUE_MAPPING: + direction, axis, value = self._INPUT_DPAD_VALUE_MAPPING[event.input] + # change the value only if button is pressed on the DPAD + if cur_val > 0.5: + self._delta_pose_raw[direction, axis] = value + self._delta_pose_raw[1 - direction, axis] = 0 + else: + self._delta_pose_raw[:, axis] = 0 + # additional callbacks + if event.input in self._additional_callbacks: + self._additional_callbacks[event.input]() + + # since no error, we are fine :) + return True + + def _create_key_bindings(self): + """Creates default key binding.""" + # map gamepad input to the element in self._delta_pose_raw + # the first index is the direction (0: positive, 1: negative) + # the second index is the axis (0: x, 1: y, 2: z, 3: roll, 4: pitch, 5: yaw) + # the third index is the sensitivity of the command + self._INPUT_STICK_VALUE_MAPPING = { + # forward command + carb.input.GamepadInput.LEFT_STICK_UP: (0, 0, self.pos_sensitivity), + # backward command + carb.input.GamepadInput.LEFT_STICK_DOWN: (1, 0, self.pos_sensitivity), + # right command + carb.input.GamepadInput.LEFT_STICK_RIGHT: (0, 1, self.pos_sensitivity), + # left command + carb.input.GamepadInput.LEFT_STICK_LEFT: (1, 1, self.pos_sensitivity), + # upward command + carb.input.GamepadInput.RIGHT_STICK_UP: (0, 2, self.pos_sensitivity), + # downward command + carb.input.GamepadInput.RIGHT_STICK_DOWN: (1, 2, self.pos_sensitivity), + # yaw command (positive) + carb.input.GamepadInput.RIGHT_STICK_RIGHT: (0, 5, self.rot_sensitivity), + # yaw command (negative) + carb.input.GamepadInput.RIGHT_STICK_LEFT: (1, 5, self.rot_sensitivity), + } + + self._INPUT_DPAD_VALUE_MAPPING = { + # pitch command (positive) + carb.input.GamepadInput.DPAD_UP: (1, 4, self.rot_sensitivity * 0.8), + # pitch command (negative) + carb.input.GamepadInput.DPAD_DOWN: (0, 4, self.rot_sensitivity * 0.8), + # roll command (positive) + carb.input.GamepadInput.DPAD_RIGHT: (1, 3, self.rot_sensitivity * 0.8), + # roll command (negative) + carb.input.GamepadInput.DPAD_LEFT: (0, 3, self.rot_sensitivity * 0.8), + } + + def _resolve_command_buffer(self, raw_command: np.ndarray) -> np.ndarray: + """Resolves the command buffer. + + Args: + raw_command: The raw command from the gamepad. Shape is (2, 3) + This is a 2D array since gamepad dpad/stick returns two values corresponding to + the positive and negative direction. The first index is the direction (0: positive, 1: negative) + and the second index is value (absolute) of the command. + + Returns: + Resolved command. Shape is (3,) + """ + # compare the positive and negative value decide the sign of the value + # if the positive value is larger, the sign is positive (i.e. False, 0) + # if the negative value is larger, the sign is positive (i.e. True, 1) + delta_command_sign = raw_command[1, :] > raw_command[0, :] + # extract the command value + delta_command = raw_command.max(axis=0) + # apply the sign + # if the sign is positive, the value is already positive. + # if the sign is negative, the value is negative after applying the sign. + delta_command[delta_command_sign] *= -1 + + return delta_command + + +@dataclass +class Se3GamepadCfg(DeviceCfg): + """Configuration for SE3 gamepad devices.""" + + gripper_term: bool = True + dead_zone: float = 0.01 # For gamepad devices + pos_sensitivity: float = 1.0 + rot_sensitivity: float = 1.6 + class_type: type[DeviceBase] = Se3Gamepad diff --git a/source/isaaclab/isaaclab/devices/haply/__init__.py b/source/isaaclab/isaaclab/devices/haply/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b86030f80e7ad32cf9d91a4e564f7110d4175620 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/haply/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Haply device interface for teleoperation.""" + +from .se3_haply import HaplyDevice, HaplyDeviceCfg + +__all__ = ["HaplyDevice", "HaplyDeviceCfg"] diff --git a/source/isaaclab/isaaclab/devices/haply/se3_haply.py b/source/isaaclab/isaaclab/devices/haply/se3_haply.py new file mode 100644 index 0000000000000000000000000000000000000000..9d9c06c92dc7e27b1a3ea498f119b9cda2c66585 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/haply/se3_haply.py @@ -0,0 +1,395 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Haply device controller for SE3 control with force feedback.""" + +from __future__ import annotations + +import asyncio +import json +import threading +import time +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch + +try: + import websockets + + WEBSOCKETS_AVAILABLE = True +except ImportError: + WEBSOCKETS_AVAILABLE = False + +from ..device_base import DeviceBase, DeviceCfg +from ..retargeter_base import RetargeterBase + + +class HaplyDevice(DeviceBase): + """A Haply device controller for sending SE(3) commands with force feedback. + + This class provides an interface to Haply robotic devices (Inverse3 + VerseGrip) + for teleoperation. It communicates via WebSocket and supports: + + - Position tracking from Inverse3 device + - Orientation and button inputs from VerseGrip device + - Directional force feedback to Inverse3 + - Real-time data streaming at configurable rates + + The device provides raw data: + + * Position: 3D position (x, y, z) in meters from Inverse3 + * Orientation: Quaternion (x, y, z, w) from VerseGrip + * Buttons: Three buttons (a, b, c) from VerseGrip with state (pressed/not pressed) + + Note: All button logic (e.g., gripper control, reset, mode switching) should be + implemented in the application layer using the raw button states from advance(). + + Note: + Requires the Haply SDK to be running and accessible via WebSocket. + Install dependencies: pip install websockets + + """ + + def __init__(self, cfg: HaplyDeviceCfg, retargeters: list[RetargeterBase] | None = None): + """Initialize the Haply device interface. + + Args: + cfg: Configuration object for Haply device settings. + retargeters: Optional list of retargeting components that transform device data + into robot commands. If None or empty, the device outputs its native data format. + + Raises: + ImportError: If websockets module is not installed. + RuntimeError: If connection to Haply device fails. + """ + super().__init__(retargeters) + + if not WEBSOCKETS_AVAILABLE: + raise ImportError("websockets module is required for Haply device. Install with: pip install websockets") + + # Store configuration + self.websocket_uri = cfg.websocket_uri + self.pos_sensitivity = cfg.pos_sensitivity + self.data_rate = cfg.data_rate + self._sim_device = cfg.sim_device + self.limit_force = cfg.limit_force + + # Device status (True only when both Inverse3 and VerseGrip are connected) + self.connected = False + self._connected_lock = threading.Lock() + + # Device IDs (will be set after first message) + self.inverse3_device_id = None + self.verse_grip_device_id = None + + # Current data cache + self.cached_data = { + "position": np.zeros(3, dtype=np.float32), + "quaternion": np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32), + "buttons": {"a": False, "b": False, "c": False}, + "inverse3_connected": False, + "versegrip_connected": False, + } + + self.data_lock = threading.Lock() + + # Force feedback + self.feedback_force = {"x": 0.0, "y": 0.0, "z": 0.0} + self.force_lock = threading.Lock() + + self._additional_callbacks = dict() + + # Button state tracking + self._prev_buttons = {"a": False, "b": False, "c": False} + + # Connection monitoring + self.consecutive_timeouts = 0 + self.max_consecutive_timeouts = 10 # ~10 seconds at 1s timeout + self.timeout_warning_issued = False + + # Start WebSocket connection + self.running = True + self._websocket_thread = None + self._start_websocket_thread() + + # Wait for both devices to connect + timeout = 5.0 + start_time = time.time() + while (time.time() - start_time) < timeout: + with self._connected_lock: + if self.connected: + break + time.sleep(0.1) + + with self._connected_lock: + if not self.connected: + raise RuntimeError(f"Failed to connect both Inverse3 and VerseGrip devices within {timeout}s. ") + + def __del__(self): + """Cleanup on deletion: shutdown WebSocket connection and background thread.""" + if not hasattr(self, "running") or not self.running: + return + + self.running = False + + # Reset force feedback before closing + if hasattr(self, "force_lock") and hasattr(self, "feedback_force"): + with self.force_lock: + self.feedback_force = {"x": 0.0, "y": 0.0, "z": 0.0} + + # Explicitly wait for WebSocket thread to finish + if hasattr(self, "_websocket_thread") and self._websocket_thread is not None: + if self._websocket_thread.is_alive(): + self._websocket_thread.join(timeout=2.0) + if self._websocket_thread.is_alive(): + self._websocket_thread.daemon = True + + def __str__(self) -> str: + """Returns: A string containing the information of the device.""" + msg = f"Haply Device Controller: {self.__class__.__name__}\n" + msg += f"\tWebSocket URI: {self.websocket_uri}\n" + msg += f"\tInverse3 ID: {self.inverse3_device_id}\n" + msg += f"\tVerseGrip ID: {self.verse_grip_device_id}\n" + msg += "\t----------------------------------------------\n" + msg += "\tOutput: [x, y, z, qx, qy, qz, qw, btn_a, btn_b, btn_c]\n" + msg += "\tInverse3: Provides position (x, y, z) and force feedback\n" + msg += "\tVerseGrip: Provides orientation (quaternion) and buttons (a, b, c)" + return msg + + def reset(self): + """Reset the device internal state.""" + with self.force_lock: + self.feedback_force = {"x": 0.0, "y": 0.0, "z": 0.0} + + # Reset button state tracking + self._prev_buttons = {"a": False, "b": False, "c": False} + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind to button events. + + Args: + key: The button to check against. Valid values are "a", "b", "c". + func: The function to call when button is pressed. The callback function should not + take any arguments. + """ + if key not in ["a", "b", "c"]: + raise ValueError(f"Invalid button key: {key}. Valid keys are 'a', 'b', 'c'.") + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from Haply device state. + + Returns: + torch.Tensor: A tensor containing the raw device data: + - 10 elements: [x, y, z, qx, qy, qz, qw, button_a, button_b, button_c] + where (x, y, z) is position, (qx, qy, qz, qw) is quaternion orientation, + and buttons are 1.0 (pressed) or 0.0 (not pressed) + """ + with self.data_lock: + if not (self.cached_data["inverse3_connected"] and self.cached_data["versegrip_connected"]): + raise RuntimeError("Haply devices not connected. Both Inverse3 and VerseGrip must be connected.") + + # Safe copy within lock + position = self.cached_data["position"].copy() * self.pos_sensitivity + quaternion = self.cached_data["quaternion"].copy() + button_a = self.cached_data["buttons"].get("a", False) + button_b = self.cached_data["buttons"].get("b", False) + button_c = self.cached_data["buttons"].get("c", False) + + # Button callbacks execute OUTSIDE lock to prevent deadlock + for button_key, current_state in [("a", button_a), ("b", button_b), ("c", button_c)]: + prev_state = self._prev_buttons.get(button_key, False) + + if current_state and not prev_state: + if button_key in self._additional_callbacks: + self._additional_callbacks[button_key]() + + self._prev_buttons[button_key] = current_state + + button_states = np.array( + [ + 1.0 if button_a else 0.0, + 1.0 if button_b else 0.0, + 1.0 if button_c else 0.0, + ], + dtype=np.float32, + ) + + # Construct command tensor: [position(3), quaternion(4), buttons(3)] + command = np.concatenate([position, quaternion, button_states]) + + return torch.tensor(command, dtype=torch.float32, device=self._sim_device) + + def push_force(self, forces: torch.Tensor, position: torch.Tensor) -> None: + """Push force vector to Haply Inverse3 device. + + Overrides DeviceBase.push_force() to provide force feedback for Haply Inverse3. + Forces are clipped to [-limit_force, limit_force] range for safety. + + Args: + forces: Tensor of shape (N, 3) with forces [fx, fy, fz]. + position: Tensor of shape (N) with indices specifying which forces to use. + """ + # Check if forces is empty + if forces.shape[0] == 0: + raise ValueError("No forces provided") + + # Select forces using position indices + selected_forces = forces[position] if position.ndim > 0 else forces[position].unsqueeze(0) + force = selected_forces.sum(dim=0) + force = force.cpu().numpy() if force.is_cuda else force.numpy() + + fx = np.clip(force[0], -self.limit_force, self.limit_force) + fy = np.clip(force[1], -self.limit_force, self.limit_force) + fz = np.clip(force[2], -self.limit_force, self.limit_force) + + with self.force_lock: + self.feedback_force = {"x": float(fx), "y": float(fy), "z": float(fz)} + + def _start_websocket_thread(self): + """Start WebSocket connection thread.""" + + def websocket_thread(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(self._websocket_loop()) + + self._websocket_thread = threading.Thread(target=websocket_thread, daemon=False) + self._websocket_thread.start() + + async def _websocket_loop(self): + """WebSocket data reading and writing loop.""" + while self.running: + try: + async with websockets.connect(self.websocket_uri, ping_interval=None, ping_timeout=None) as ws: + first_message = True + + while self.running: + try: + response = await asyncio.wait_for(ws.recv(), timeout=1.0) + data = json.loads(response) + + self.consecutive_timeouts = 0 + if self.timeout_warning_issued: + self.timeout_warning_issued = False + + # Safe array access - no IndexError risk with ternary operator + inverse3_list = data.get("inverse3", []) + verse_grip_list = data.get("wireless_verse_grip", []) + inverse3_data = inverse3_list[0] if inverse3_list else {} + verse_grip_data = verse_grip_list[0] if verse_grip_list else {} + + if first_message: + first_message = False + if inverse3_data: + self.inverse3_device_id = inverse3_data.get("device_id") + if verse_grip_data: + self.verse_grip_device_id = verse_grip_data.get("device_id") + + with self.data_lock: + inverse3_connected = False + versegrip_connected = False + + if inverse3_data and "state" in inverse3_data: + cursor_pos = inverse3_data["state"].get("cursor_position", {}) + if cursor_pos: + self.cached_data["position"] = np.array( + [cursor_pos.get(k, 0.0) for k in ("x", "y", "z")], dtype=np.float32 + ) + inverse3_connected = True + + if verse_grip_data and "state" in verse_grip_data: + state = verse_grip_data["state"] + self.cached_data["buttons"] = { + k: state.get("buttons", {}).get(k, False) for k in ("a", "b", "c") + } + orientation = state.get("orientation", {}) + if orientation: + self.cached_data["quaternion"] = np.array( + [ + orientation.get(k, 1.0 if k == "w" else 0.0) + for k in ("x", "y", "z", "w") + ], + dtype=np.float32, + ) + versegrip_connected = True + + self.cached_data["inverse3_connected"] = inverse3_connected + self.cached_data["versegrip_connected"] = versegrip_connected + # Both devices required (AND logic): Inverse3 for position/force, + both_connected = inverse3_connected and versegrip_connected + + with self._connected_lock: + self.connected = both_connected + + # Send force feedback + if self.inverse3_device_id: + with self.force_lock: + current_force = self.feedback_force.copy() + + request_msg = { + "inverse3": [ + { + "device_id": self.inverse3_device_id, + "commands": {"set_cursor_force": {"values": current_force}}, + } + ] + } + await ws.send(json.dumps(request_msg)) + + await asyncio.sleep(1.0 / self.data_rate) + + except asyncio.TimeoutError: + self.consecutive_timeouts += 1 + + # Check if timeout + if ( + self.consecutive_timeouts >= self.max_consecutive_timeouts + and not self.timeout_warning_issued + ): + self.timeout_warning_issued = True + with self.data_lock: + self.cached_data["inverse3_connected"] = False + self.cached_data["versegrip_connected"] = False + with self._connected_lock: + self.connected = False + continue + except Exception as e: + print(f"[ERROR] Error in WebSocket receive loop: {e}") + break + + except Exception: + with self.data_lock: + self.cached_data["inverse3_connected"] = False + self.cached_data["versegrip_connected"] = False + with self._connected_lock: + self.connected = False + self.consecutive_timeouts = 0 + self.timeout_warning_issued = False + + if self.running: + await asyncio.sleep(2.0) + else: + break + + +@dataclass +class HaplyDeviceCfg(DeviceCfg): + """Configuration for Haply device. + + Attributes: + websocket_uri: WebSocket URI for Haply SDK connection + pos_sensitivity: Position sensitivity scaling factor + data_rate: Data exchange rate in Hz + limit_force: Maximum force magnitude in Newtons (safety limit) + """ + + websocket_uri: str = "ws://localhost:10001" + pos_sensitivity: float = 1.0 + data_rate: float = 200.0 + limit_force: float = 2.0 + class_type: type[DeviceBase] = HaplyDevice diff --git a/source/isaaclab/isaaclab/devices/keyboard/__init__.py b/source/isaaclab/isaaclab/devices/keyboard/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eff757a6d1321912a112a52e86aeaf58642a407f --- /dev/null +++ b/source/isaaclab/isaaclab/devices/keyboard/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Keyboard device for SE(2) and SE(3) control.""" + +from .se2_keyboard import Se2Keyboard, Se2KeyboardCfg +from .se3_keyboard import Se3Keyboard, Se3KeyboardCfg diff --git a/source/isaaclab/isaaclab/devices/keyboard/se2_keyboard.py b/source/isaaclab/isaaclab/devices/keyboard/se2_keyboard.py new file mode 100644 index 0000000000000000000000000000000000000000..beb19d1835d781143e8b6e9e4e8c234309556cc4 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/keyboard/se2_keyboard.py @@ -0,0 +1,184 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Keyboard controller for SE(2) control.""" + +from __future__ import annotations + +import weakref +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch + +import carb +import omni + +from ..device_base import DeviceBase, DeviceCfg + + +class Se2Keyboard(DeviceBase): + r"""A keyboard controller for sending SE(2) commands as velocity commands. + + This class is designed to provide a keyboard controller for mobile base (such as quadrupeds). + It uses the Omniverse keyboard interface to listen to keyboard events and map them to robot's + task-space commands. + + The command comprises of the base linear and angular velocity: :math:`(v_x, v_y, \omega_z)`. + + Key bindings: + ====================== ========================= ======================== + Command Key (+ve axis) Key (-ve axis) + ====================== ========================= ======================== + Move along x-axis Numpad 8 / Arrow Up Numpad 2 / Arrow Down + Move along y-axis Numpad 4 / Arrow Right Numpad 6 / Arrow Left + Rotate along z-axis Numpad 7 / Z Numpad 9 / X + ====================== ========================= ======================== + + .. seealso:: + + The official documentation for the keyboard interface: `Carb Keyboard Interface `__. + + """ + + def __init__(self, cfg: Se2KeyboardCfg): + """Initialize the keyboard layer. + + Args: + v_x_sensitivity: Magnitude of linear velocity along x-direction scaling. Defaults to 0.8. + v_y_sensitivity: Magnitude of linear velocity along y-direction scaling. Defaults to 0.4. + omega_z_sensitivity: Magnitude of angular velocity along z-direction scaling. Defaults to 1.0. + """ + # store inputs + self.v_x_sensitivity = cfg.v_x_sensitivity + self.v_y_sensitivity = cfg.v_y_sensitivity + self.omega_z_sensitivity = cfg.omega_z_sensitivity + self._sim_device = cfg.sim_device + + # acquire omniverse interfaces + self._appwindow = omni.appwindow.get_default_app_window() + self._input = carb.input.acquire_input_interface() + self._keyboard = self._appwindow.get_keyboard() + # note: Use weakref on callbacks to ensure that this object can be deleted when its destructor is called + self._keyboard_sub = self._input.subscribe_to_keyboard_events( + self._keyboard, + lambda event, *args, obj=weakref.proxy(self): obj._on_keyboard_event(event, *args), + ) + # bindings for keyboard to command + self._create_key_bindings() + # command buffers + self._base_command = np.zeros(3) + # dictionary for additional callbacks + self._additional_callbacks = dict() + + def __del__(self): + """Release the keyboard interface.""" + self._input.unsubscribe_to_keyboard_events(self._keyboard, self._keyboard_sub) + self._keyboard_sub = None + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Keyboard Controller for SE(2): {self.__class__.__name__}\n" + msg += f"\tKeyboard name: {self._input.get_keyboard_name(self._keyboard)}\n" + msg += "\t----------------------------------------------\n" + msg += "\tReset all commands: L\n" + msg += "\tMove forward (along x-axis): Numpad 8 / Arrow Up\n" + msg += "\tMove backward (along x-axis): Numpad 2 / Arrow Down\n" + msg += "\tMove right (along y-axis): Numpad 4 / Arrow Right\n" + msg += "\tMove left (along y-axis): Numpad 6 / Arrow Left\n" + msg += "\tYaw positively (along z-axis): Numpad 7 / Z\n" + msg += "\tYaw negatively (along z-axis): Numpad 9 / X" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._base_command.fill(0.0) + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind keyboard. + + A list of available keys are present in the + `carb documentation `__. + + Args: + key: The keyboard button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from keyboard event state. + + Returns: + Tensor containing the linear (x,y) and angular velocity (z). + """ + return torch.tensor(self._base_command, dtype=torch.float32, device=self._sim_device) + + """ + Internal helpers. + """ + + def _on_keyboard_event(self, event, *args, **kwargs): + """Subscriber callback to when kit is updated. + + Reference: + https://docs.omniverse.nvidia.com/dev-guide/latest/programmer_ref/input-devices/keyboard.html + """ + # apply the command when pressed + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + if event.input.name == "L": + self.reset() + elif event.input.name in self._INPUT_KEY_MAPPING: + self._base_command += self._INPUT_KEY_MAPPING[event.input.name] + # remove the command when un-pressed + if event.type == carb.input.KeyboardEventType.KEY_RELEASE: + if event.input.name in self._INPUT_KEY_MAPPING: + self._base_command -= self._INPUT_KEY_MAPPING[event.input.name] + # additional callbacks + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + if event.input.name in self._additional_callbacks: + self._additional_callbacks[event.input.name]() + + # since no error, we are fine :) + return True + + def _create_key_bindings(self): + """Creates default key binding.""" + self._INPUT_KEY_MAPPING = { + # forward command + "NUMPAD_8": np.asarray([1.0, 0.0, 0.0]) * self.v_x_sensitivity, + "UP": np.asarray([1.0, 0.0, 0.0]) * self.v_x_sensitivity, + # back command + "NUMPAD_2": np.asarray([-1.0, 0.0, 0.0]) * self.v_x_sensitivity, + "DOWN": np.asarray([-1.0, 0.0, 0.0]) * self.v_x_sensitivity, + # right command + "NUMPAD_4": np.asarray([0.0, 1.0, 0.0]) * self.v_y_sensitivity, + "LEFT": np.asarray([0.0, 1.0, 0.0]) * self.v_y_sensitivity, + # left command + "NUMPAD_6": np.asarray([0.0, -1.0, 0.0]) * self.v_y_sensitivity, + "RIGHT": np.asarray([0.0, -1.0, 0.0]) * self.v_y_sensitivity, + # yaw command (positive) + "NUMPAD_7": np.asarray([0.0, 0.0, 1.0]) * self.omega_z_sensitivity, + "Z": np.asarray([0.0, 0.0, 1.0]) * self.omega_z_sensitivity, + # yaw command (negative) + "NUMPAD_9": np.asarray([0.0, 0.0, -1.0]) * self.omega_z_sensitivity, + "X": np.asarray([0.0, 0.0, -1.0]) * self.omega_z_sensitivity, + } + + +@dataclass +class Se2KeyboardCfg(DeviceCfg): + """Configuration for SE2 keyboard devices.""" + + v_x_sensitivity: float = 0.8 + v_y_sensitivity: float = 0.4 + omega_z_sensitivity: float = 1.0 + class_type: type[DeviceBase] = Se2Keyboard diff --git a/source/isaaclab/isaaclab/devices/keyboard/se3_keyboard.py b/source/isaaclab/isaaclab/devices/keyboard/se3_keyboard.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b17d1702abf1f6d5bd45e545c00c5b8d15be3 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/keyboard/se3_keyboard.py @@ -0,0 +1,212 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Keyboard controller for SE(3) control.""" + +from __future__ import annotations + +import weakref +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation + +import carb +import omni + +from ..device_base import DeviceBase, DeviceCfg + + +class Se3Keyboard(DeviceBase): + """A keyboard controller for sending SE(3) commands as delta poses and binary command (open/close). + + This class is designed to provide a keyboard controller for a robotic arm with a gripper. + It uses the Omniverse keyboard interface to listen to keyboard events and map them to robot's + task-space commands. + + The command comprises of two parts: + + * delta pose: a 6D vector of (x, y, z, roll, pitch, yaw) in meters and radians. + * gripper: a binary command to open or close the gripper. + + Key bindings: + ============================== ================= ================= + Description Key (+ve axis) Key (-ve axis) + ============================== ================= ================= + Toggle gripper (open/close) K + Move along x-axis W S + Move along y-axis A D + Move along z-axis Q E + Rotate along x-axis Z X + Rotate along y-axis T G + Rotate along z-axis C V + ============================== ================= ================= + + .. seealso:: + + The official documentation for the keyboard interface: `Carb Keyboard Interface `__. + + """ + + def __init__(self, cfg: Se3KeyboardCfg): + """Initialize the keyboard layer. + + Args: + cfg: Configuration object for keyboard settings. + """ + # store inputs + self.pos_sensitivity = cfg.pos_sensitivity + self.rot_sensitivity = cfg.rot_sensitivity + self.gripper_term = cfg.gripper_term + self._sim_device = cfg.sim_device + # acquire omniverse interfaces + self._appwindow = omni.appwindow.get_default_app_window() + self._input = carb.input.acquire_input_interface() + self._keyboard = self._appwindow.get_keyboard() + # note: Use weakref on callbacks to ensure that this object can be deleted when its destructor is called. + self._keyboard_sub = self._input.subscribe_to_keyboard_events( + self._keyboard, + lambda event, *args, obj=weakref.proxy(self): obj._on_keyboard_event(event, *args), + ) + # bindings for keyboard to command + self._create_key_bindings() + # command buffers + self._close_gripper = False + self._delta_pos = np.zeros(3) # (x, y, z) + self._delta_rot = np.zeros(3) # (roll, pitch, yaw) + # dictionary for additional callbacks + self._additional_callbacks = dict() + + def __del__(self): + """Release the keyboard interface.""" + self._input.unsubscribe_to_keyboard_events(self._keyboard, self._keyboard_sub) + self._keyboard_sub = None + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Keyboard Controller for SE(3): {self.__class__.__name__}\n" + msg += f"\tKeyboard name: {self._input.get_keyboard_name(self._keyboard)}\n" + msg += "\t----------------------------------------------\n" + msg += "\tToggle gripper (open/close): K\n" + msg += "\tMove arm along x-axis: W/S\n" + msg += "\tMove arm along y-axis: A/D\n" + msg += "\tMove arm along z-axis: Q/E\n" + msg += "\tRotate arm along x-axis: Z/X\n" + msg += "\tRotate arm along y-axis: T/G\n" + msg += "\tRotate arm along z-axis: C/V" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._close_gripper = False + self._delta_pos = np.zeros(3) # (x, y, z) + self._delta_rot = np.zeros(3) # (roll, pitch, yaw) + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind keyboard. + + A list of available keys are present in the + `carb documentation `__. + + Args: + key: The keyboard button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from keyboard event state. + + Returns: + torch.Tensor: A 7-element tensor containing: + - delta pose: First 6 elements as [x, y, z, rx, ry, rz] in meters and radians. + - gripper command: Last element as a binary value (+1.0 for open, -1.0 for close). + """ + # convert to rotation vector + rot_vec = Rotation.from_euler("XYZ", self._delta_rot).as_rotvec() + # return the command and gripper state + command = np.concatenate([self._delta_pos, rot_vec]) + if self.gripper_term: + gripper_value = -1.0 if self._close_gripper else 1.0 + command = np.append(command, gripper_value) + + return torch.tensor(command, dtype=torch.float32, device=self._sim_device) + + """ + Internal helpers. + """ + + def _on_keyboard_event(self, event, *args, **kwargs): + """Subscriber callback to when kit is updated. + + Reference: + https://docs.omniverse.nvidia.com/dev-guide/latest/programmer_ref/input-devices/keyboard.html + """ + # apply the command when pressed + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + if event.input.name == "L": + self.reset() + if event.input.name == "K": + self._close_gripper = not self._close_gripper + elif event.input.name in ["W", "S", "A", "D", "Q", "E"]: + self._delta_pos += self._INPUT_KEY_MAPPING[event.input.name] + elif event.input.name in ["Z", "X", "T", "G", "C", "V"]: + self._delta_rot += self._INPUT_KEY_MAPPING[event.input.name] + # remove the command when un-pressed + if event.type == carb.input.KeyboardEventType.KEY_RELEASE: + if event.input.name in ["W", "S", "A", "D", "Q", "E"]: + self._delta_pos -= self._INPUT_KEY_MAPPING[event.input.name] + elif event.input.name in ["Z", "X", "T", "G", "C", "V"]: + self._delta_rot -= self._INPUT_KEY_MAPPING[event.input.name] + # additional callbacks + if event.type == carb.input.KeyboardEventType.KEY_PRESS: + if event.input.name in self._additional_callbacks: + self._additional_callbacks[event.input.name]() + + # since no error, we are fine :) + return True + + def _create_key_bindings(self): + """Creates default key binding.""" + self._INPUT_KEY_MAPPING = { + # toggle: gripper command + "K": True, + # x-axis (forward) + "W": np.asarray([1.0, 0.0, 0.0]) * self.pos_sensitivity, + "S": np.asarray([-1.0, 0.0, 0.0]) * self.pos_sensitivity, + # y-axis (left-right) + "A": np.asarray([0.0, 1.0, 0.0]) * self.pos_sensitivity, + "D": np.asarray([0.0, -1.0, 0.0]) * self.pos_sensitivity, + # z-axis (up-down) + "Q": np.asarray([0.0, 0.0, 1.0]) * self.pos_sensitivity, + "E": np.asarray([0.0, 0.0, -1.0]) * self.pos_sensitivity, + # roll (around x-axis) + "Z": np.asarray([1.0, 0.0, 0.0]) * self.rot_sensitivity, + "X": np.asarray([-1.0, 0.0, 0.0]) * self.rot_sensitivity, + # pitch (around y-axis) + "T": np.asarray([0.0, 1.0, 0.0]) * self.rot_sensitivity, + "G": np.asarray([0.0, -1.0, 0.0]) * self.rot_sensitivity, + # yaw (around z-axis) + "C": np.asarray([0.0, 0.0, 1.0]) * self.rot_sensitivity, + "V": np.asarray([0.0, 0.0, -1.0]) * self.rot_sensitivity, + } + + +@dataclass +class Se3KeyboardCfg(DeviceCfg): + """Configuration for SE3 keyboard devices.""" + + gripper_term: bool = True + pos_sensitivity: float = 0.4 + rot_sensitivity: float = 0.8 + retargeters: None = None + class_type: type[DeviceBase] = Se3Keyboard diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/__init__.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..31779f5eafa9ed82764efd394decbe5f253ac7d5 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Keyboard device for SE(2) and SE(3) control.""" + +from .opencv_handtracking_device import HandTrackingDevice, HandTrackingDeviceCfg \ No newline at end of file diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/common.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/common.py new file mode 100644 index 0000000000000000000000000000000000000000..088641c2886ae1772ab14e854431282c715f7911 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/common.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Standard set of hand joint names based on OpenXR specification. +# Input devices for dexterous hands can use this as a reference, +# but may provide any subset or superset of these joints. +HAND_JOINT_NAMES = [ + # Palm + "palm", + # Wrist + "wrist", + # Thumb + "thumb_metacarpal", + "thumb_proximal", + "thumb_distal", + "thumb_tip", + # Index + "index_metacarpal", + "index_proximal", + "index_intermediate", + "index_distal", + "index_tip", + # Middle + "middle_metacarpal", + "middle_proximal", + "middle_intermediate", + "middle_distal", + "middle_tip", + # Ring + "ring_metacarpal", + "ring_proximal", + "ring_intermediate", + "ring_distal", + "ring_tip", + # Little + "little_metacarpal", + "little_proximal", + "little_intermediate", + "little_distal", + "little_tip", +] diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/opencv_handtracking_device.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/opencv_handtracking_device.py new file mode 100644 index 0000000000000000000000000000000000000000..310806853ecf328b70ac5cd3c4518a024853a6d4 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/opencv_handtracking_device.py @@ -0,0 +1,124 @@ +import socket +import threading +import numpy as np +from typing import Any, Callable +from dataclasses import dataclass + +from ..device_base import DeviceBase, DeviceCfg + + +class HandTrackingDevice(DeviceBase): + """Device for receiving OpenCV handtracking data via UDP and exposing it as 7D hand vectors.""" + + def __init__(self, cfg: 'HandTrackingDeviceCfg', retargeters: list = None): + super().__init__(retargeters) + self._port = cfg.udp_port + self._ip = cfg.udp_ip + self._sock = None + self._socket_available = False + + try: + self._sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + self._sock.bind((self._ip, self._port)) + self._sock.setblocking(False) + self._socket_available = True + except Exception as e: + print(f"[HandTrackingDevice] UDP socket unavailable: {e}") + self._socket_available = False + + zeros = np.zeros(7, dtype=np.float32) + zeros[3] = 1.0 # Set qw=1 for identity quaternion + self._latest_wrist_data = { + self.TrackingTarget.HAND_LEFT: zeros.copy(), + self.TrackingTarget.HAND_RIGHT: zeros.copy(), + } + + ones = np.ones(3, dtype=np.float32) + self._latest_finger_data = { + self.TrackingTarget.HAND_LEFT: ones.copy(), + self.TrackingTarget.HAND_RIGHT: ones.copy(), + } + + self._running = True + if self._socket_available: + self._thread = threading.Thread(target=self._listen, daemon=True) + self._thread.start() + + self._additional_callbacks = dict() + + def __del__(self): + self._running = False + if self._sock: + try: + self._sock.close() + except Exception: + pass + + def add_callback(self, key: str, func: Callable): + self._additional_callbacks[key] = func + + def reset(self): + zeros = np.zeros(7, dtype=np.float32) + zeros[3] = 1.0 # Set qw=1 for identity quaternion + self._latest_wrist_data = { + self.TrackingTarget.HAND_LEFT: zeros.copy(), + self.TrackingTarget.HAND_RIGHT: zeros.copy(), + } + ones = np.ones(3, dtype=np.float32) + self._latest_finger_data = { + self.TrackingTarget.HAND_LEFT: ones.copy(), + self.TrackingTarget.HAND_RIGHT: ones.copy(), + } + + def _listen(self): + while self._running: + try: + data, _ = self._sock.recvfrom(1024) + # Expecting 14 floats (7 for left, 7 for right), e.g. as binary float32 + try: + arr = np.frombuffer(data, dtype=np.float32) + if arr.shape[0] == 21: + self._latest_wrist_data[self.TrackingTarget.HAND_LEFT] = arr[:7] + self._latest_finger_data[self.TrackingTarget.HAND_LEFT] = arr[7:10] + self._latest_wrist_data[self.TrackingTarget.HAND_RIGHT] = arr[10:17] + self._latest_finger_data[self.TrackingTarget.HAND_RIGHT] = arr[17:20] + callback_number = arr[20] + + # print("Left hand:", self._latest_wrist_data[self.TrackingTarget.HAND_LEFT]) + # print("Right hand:", self._latest_wrist_data[self.TrackingTarget.HAND_RIGHT]) + + # Call the corresponding callback if callback_number > 0 + if callback_number > 0: + if callback_number == 1 and "START" in self._additional_callbacks: + print("START callback triggered") + self._additional_callbacks["START"]() + elif callback_number == 2 and "STOP" in self._additional_callbacks: + print("STOP callback triggered") + self._additional_callbacks["STOP"]() + elif callback_number == 3 and "RESET" in self._additional_callbacks: + print("RESET callback triggered") + self._additional_callbacks["RESET"]() + continue + except Exception: + pass + except BlockingIOError: + pass + except Exception: + continue + + def _get_raw_data(self) -> Any: + if not self._socket_available: + print("[HandTrackingDevice] Socket unavailable, returning previous data") + data = { + self.TrackingTarget.HAND_LEFT: {"wrist": self._latest_wrist_data[self.TrackingTarget.HAND_LEFT].copy(), + "finger_values": self._latest_finger_data[self.TrackingTarget.HAND_LEFT].copy()}, + self.TrackingTarget.HAND_RIGHT: {"wrist": self._latest_wrist_data[self.TrackingTarget.HAND_RIGHT].copy(), + "finger_values": self._latest_finger_data[self.TrackingTarget.HAND_RIGHT].copy()}, + } + return data + +@dataclass +class HandTrackingDeviceCfg(DeviceCfg): + udp_ip: str = '0.0.0.0' + udp_port: int = 5005 + class_type: type = HandTrackingDevice diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/__init__.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c6350b885e3c7752c05248e764ae632263a23d83 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Retargeters for mapping input device data to robot commands.""" + +from .humanoid.fourier.gr1t2_retargeter import GR1T2Retargeter, GR1T2RetargeterCfg +from .humanoid.unitree.g1_lower_body_standing import G1LowerBodyStandingRetargeter, G1LowerBodyStandingRetargeterCfg +from .humanoid.unitree.g1_motion_controller_locomotion import ( + G1LowerBodyStandingMotionControllerRetargeter, + G1LowerBodyStandingMotionControllerRetargeterCfg, +) +# from .humanoid.unitree.inspire.g1_upper_body_retargeter import UnitreeG1Retargeter, UnitreeG1RetargeterCfg +# from .humanoid.unitree.trihand.g1_upper_body_motion_ctrl_gripper import ( +# G1TriHandUpperBodyMotionControllerGripperRetargeter, +# G1TriHandUpperBodyMotionControllerGripperRetargeterCfg, +# ) +from .humanoid.unitree.trihand.g1_upper_body_motion_ctrl_retargeter import ( + G1TriHandUpperBodyMotionControllerRetargeter, + G1TriHandUpperBodyMotionControllerRetargeterCfg, +) +from .humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeter, + G1TriHandOpenCVRetargeterCfg, +) +from .manipulator.gripper_retargeter import GripperRetargeter, GripperRetargeterCfg +from .manipulator.se3_abs_retargeter import Se3AbsRetargeter, Se3AbsRetargeterCfg +from .manipulator.se3_rel_retargeter import Se3RelRetargeter, Se3RelRetargeterCfg diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..1e203d11e7e864734f144d22378a164f3ec2349d --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - GR1T2_fourier_hand_6dof_L_thumb_distal_link + - GR1T2_fourier_hand_6dof_L_index_intermediate_link + - GR1T2_fourier_hand_6dof_L_middle_intermediate_link + - GR1T2_fourier_hand_6dof_L_ring_intermediate_link + - GR1T2_fourier_hand_6dof_L_pinky_intermediate_link + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - L_index_proximal_joint + - L_middle_proximal_joint + - L_pinky_proximal_joint + - L_ring_proximal_joint + - L_index_intermediate_joint + - L_middle_intermediate_joint + - L_pinky_intermediate_joint + - L_ring_intermediate_joint + - L_thumb_proximal_yaw_joint + - L_thumb_proximal_pitch_joint + - L_thumb_distal_joint + - L_thumb_distal_joint + type: DexPilot + urdf_path: /tmp/GR1_T2_left_hand.urdf + wrist_link_name: l_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..f67041bd9b60c9571d88d2db21b962cc7aa8d398 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - GR1T2_fourier_hand_6dof_R_thumb_distal_link + - GR1T2_fourier_hand_6dof_R_index_intermediate_link + - GR1T2_fourier_hand_6dof_R_middle_intermediate_link + - GR1T2_fourier_hand_6dof_R_ring_intermediate_link + - GR1T2_fourier_hand_6dof_R_pinky_intermediate_link + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - R_index_proximal_joint + - R_middle_proximal_joint + - R_pinky_proximal_joint + - R_ring_proximal_joint + - R_index_intermediate_joint + - R_middle_intermediate_joint + - R_pinky_intermediate_joint + - R_ring_intermediate_joint + - R_thumb_proximal_yaw_joint + - R_thumb_proximal_pitch_joint + - R_thumb_distal_joint + type: DexPilot + urdf_path: /tmp/GR1_T2_right_hand.urdf + wrist_link_name: r_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aaeb9bda031467bfd49d834a33367a8fd0792de0 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py @@ -0,0 +1,262 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +import os + +import numpy as np +import torch +import yaml +from dex_retargeting.retargeting_config import RetargetingConfig +from scipy.spatial.transform import Rotation as R + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +# import logger +logger = logging.getLogger(__name__) + +# The index to map the OpenXR hand joints to the hand joints used +# in Dex-retargeting. +_HAND_JOINTS_INDEX = [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25] + +# The transformation matrices to convert hand pose to canonical view. +_OPERATOR2MANO_RIGHT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_OPERATOR2MANO_LEFT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_LEFT_HAND_JOINT_NAMES = [ + "L_index_proximal_joint", + "L_index_intermediate_joint", + "L_middle_proximal_joint", + "L_middle_intermediate_joint", + "L_pinky_proximal_joint", + "L_pinky_intermediate_joint", + "L_ring_proximal_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_yaw_joint", + "L_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", +] + + +_RIGHT_HAND_JOINT_NAMES = [ + "R_index_proximal_joint", + "R_index_intermediate_joint", + "R_middle_proximal_joint", + "R_middle_intermediate_joint", + "R_pinky_proximal_joint", + "R_pinky_intermediate_joint", + "R_ring_proximal_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_yaw_joint", + "R_thumb_proximal_pitch_joint", + "R_thumb_distal_joint", +] + + +class GR1TR2DexRetargeting: + """A class for hand retargeting with GR1Fourier. + + Handles retargeting of OpenXRhand tracking data to GR1T2 robot hand joint angles. + """ + + def __init__( + self, + hand_joint_names: list[str], + right_hand_config_filename: str = "fourier_hand_right_dexpilot.yml", + left_hand_config_filename: str = "fourier_hand_left_dexpilot.yml", + left_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/GR1T2_assets/GR1_T2_left_hand.urdf", + right_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/GR1T2_assets/GR1_T2_right_hand.urdf", + ): + """Initialize the hand retargeting. + + Args: + hand_joint_names: Names of hand joints in the robot model + right_hand_config_filename: Config file for right hand retargeting + left_hand_config_filename: Config file for left hand retargeting + """ + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/")) + config_dir = os.path.join(data_dir, "configs/dex-retargeting") + + # Download urdf files from aws + local_left_urdf_path = retrieve_file_path(left_hand_urdf_path, force_download=True) + local_right_urdf_path = retrieve_file_path(right_hand_urdf_path, force_download=True) + + left_config_path = os.path.join(config_dir, left_hand_config_filename) + right_config_path = os.path.join(config_dir, right_hand_config_filename) + + # Update the YAML files with the correct URDF paths + self._update_yaml_with_urdf_path(left_config_path, local_left_urdf_path) + self._update_yaml_with_urdf_path(right_config_path, local_right_urdf_path) + + self._dex_left_hand = RetargetingConfig.load_from_file(left_config_path).build() + self._dex_right_hand = RetargetingConfig.load_from_file(right_config_path).build() + + self.left_dof_names = self._dex_left_hand.optimizer.robot.dof_joint_names + self.right_dof_names = self._dex_right_hand.optimizer.robot.dof_joint_names + self.dof_names = self.left_dof_names + self.right_dof_names + self.isaac_lab_hand_joint_names = hand_joint_names + + logger.info("[GR1T2DexRetargeter] init done.") + + def _update_yaml_with_urdf_path(self, yaml_path: str, urdf_path: str): + """Update YAML file with the correct URDF path. + + Args: + yaml_path: Path to the YAML configuration file + urdf_path: Path to the URDF file to use + """ + try: + # Read the YAML file + with open(yaml_path) as file: + config = yaml.safe_load(file) + + # Update the URDF path in the configuration + if "retargeting" in config: + config["retargeting"]["urdf_path"] = urdf_path + logger.info(f"Updated URDF path in {yaml_path} to {urdf_path}") + else: + logger.warning(f"Unable to find 'retargeting' section in {yaml_path}") + + # Write the updated configuration back to the file + with open(yaml_path, "w") as file: + yaml.dump(config, file) + + except Exception as e: + logger.error(f"Error updating YAML file {yaml_path}: {e}") + + def convert_hand_joints(self, hand_poses: dict[str, np.ndarray], operator2mano: np.ndarray) -> np.ndarray: + """Prepares the hand joints data for retargeting. + + Args: + hand_poses: Dictionary containing hand pose data with joint positions and rotations + operator2mano: Transformation matrix to convert from operator to MANO frame + + Returns: + Joint positions with shape (21, 3) + """ + joint_position = np.zeros((21, 3)) + hand_joints = list(hand_poses.values()) + for i in range(len(_HAND_JOINTS_INDEX)): + joint = hand_joints[_HAND_JOINTS_INDEX[i]] + joint_position[i] = joint[:3] + + # Convert hand pose to the canonical frame. + joint_position = joint_position - joint_position[0:1, :] + xr_wrist_quat = hand_poses.get("wrist")[3:] + # OpenXR hand uses w,x,y,z order for quaternions but scipy uses x,y,z,w order + wrist_rot = R.from_quat([xr_wrist_quat[1], xr_wrist_quat[2], xr_wrist_quat[3], xr_wrist_quat[0]]).as_matrix() + + return joint_position @ wrist_rot @ operator2mano + + def compute_ref_value(self, joint_position: np.ndarray, indices: np.ndarray, retargeting_type: str) -> np.ndarray: + """Computes reference value for retargeting. + + Args: + joint_position: Joint positions array + indices: Target link indices + retargeting_type: Type of retargeting ("POSITION" or other) + + Returns: + Reference value in cartesian space + """ + if retargeting_type == "POSITION": + return joint_position[indices, :] + else: + origin_indices = indices[0, :] + task_indices = indices[1, :] + ref_value = joint_position[task_indices, :] - joint_position[origin_indices, :] + return ref_value + + def compute_one_hand( + self, hand_joints: dict[str, np.ndarray], retargeting: RetargetingConfig, operator2mano: np.ndarray + ) -> np.ndarray: + """Computes retargeted joint angles for one hand. + + Args: + hand_joints: Dictionary containing hand joint data + retargeting: Retargeting configuration object + operator2mano: Transformation matrix from operator to MANO frame + + Returns: + Retargeted joint angles + """ + joint_pos = self.convert_hand_joints(hand_joints, operator2mano) + ref_value = self.compute_ref_value( + joint_pos, + indices=retargeting.optimizer.target_link_human_indices, + retargeting_type=retargeting.optimizer.retargeting_type, + ) + # Enable gradient calculation and inference mode in case some other script has disabled it + # This is necessary for the retargeting to work since it uses gradient features that + # are not available in inference mode + with torch.enable_grad(): + with torch.inference_mode(False): + return retargeting.retarget(ref_value) + + def get_joint_names(self) -> list[str]: + """Returns list of all joint names.""" + return self.dof_names + + def get_left_joint_names(self) -> list[str]: + """Returns list of left hand joint names.""" + return self.left_dof_names + + def get_right_joint_names(self) -> list[str]: + """Returns list of right hand joint names.""" + return self.right_dof_names + + def get_hand_indices(self, robot) -> np.ndarray: + """Gets indices of hand joints in robot's DOF array. + + Args: + robot: Robot object containing DOF information + + Returns: + Array of joint indices + """ + return np.array([robot.dof_names.index(name) for name in self.dof_names], dtype=np.int64) + + def compute_left(self, left_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for left hand. + + Args: + left_hand_poses: Dictionary of left hand joint poses + + Returns: + Retargeted joint angles for left hand + """ + if left_hand_poses is not None: + left_hand_q = self.compute_one_hand(left_hand_poses, self._dex_left_hand, _OPERATOR2MANO_LEFT) + else: + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + return left_hand_q + + def compute_right(self, right_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for right hand. + + Args: + right_hand_poses: Dictionary of right hand joint poses + + Returns: + Retargeted joint angles for right hand + """ + if right_hand_poses is not None: + right_hand_q = self.compute_one_hand(right_hand_poses, self._dex_right_hand, _OPERATOR2MANO_RIGHT) + else: + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + return right_hand_q diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1t2_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1t2_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..0f95d4b9d7585ecd7a1a97177d6676943aa44e18 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/fourier/gr1t2_retargeter.py @@ -0,0 +1,168 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + +# This import exception is suppressed because gr1_t2_dex_retargeting_utils depends +# on pinocchio which is not available on Windows. +with contextlib.suppress(Exception): + from .gr1_t2_dex_retargeting_utils import GR1TR2DexRetargeting + + +class GR1T2Retargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to GR1T2 hand end-effector commands. + + This retargeter maps hand tracking data from OpenXR to joint commands for the GR1T2 robot's hands. + It handles both left and right hands, converting poses of the hands in OpenXR format joint angles + for the GR1T2 robot's hands. + """ + + def __init__( + self, + cfg: GR1T2RetargeterCfg, + ): + """Initialize the GR1T2 hand retargeter. + + Args: + enable_visualization: If True, visualize tracked hand joints + num_open_xr_hand_joints: Number of joints tracked by OpenXR + device: PyTorch device for computations + hand_joint_names: List of robot hand joint names + """ + + super().__init__(cfg) + self._hand_joint_names = cfg.hand_joint_names + self._hands_controller = GR1TR2DexRetargeting(self._hand_joint_names) + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + self._num_open_xr_hand_joints = cfg.num_open_xr_hand_joints + self._sim_device = cfg.sim_device + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.005, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector commands. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + + Returns: + tuple containing: + Left wrist pose + Right wrist pose in USD frame + Retargeted hand joint angles + """ + + # Access the left and right hand data using the enum key + left_hand_poses = data[DeviceBase.TrackingTarget.HAND_LEFT] + right_hand_poses = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + left_wrist = left_hand_poses.get("wrist") + right_wrist = right_hand_poses.get("wrist") + + if self._enable_visualization: + joints_position = np.zeros((self._num_open_xr_hand_joints, 3)) + + joints_position[::2] = np.array([pose[:3] for pose in left_hand_poses.values()]) + joints_position[1::2] = np.array([pose[:3] for pose in right_hand_poses.values()]) + + self._markers.visualize(translations=torch.tensor(joints_position, device=self._sim_device)) + + # Create array of zeros with length matching number of joint names + left_hands_pos = self._hands_controller.compute_left(left_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_left_joint_names()] + left_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + left_retargeted_hand_joints[indexes] = left_hands_pos + left_hand_joints = left_retargeted_hand_joints + + right_hands_pos = self._hands_controller.compute_right(right_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_right_joint_names()] + right_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + right_retargeted_hand_joints[indexes] = right_hands_pos + right_hand_joints = right_retargeted_hand_joints + retargeted_hand_joints = left_hand_joints + right_hand_joints + + # Convert numpy arrays to tensors and concatenate them + left_wrist_tensor = torch.tensor(left_wrist, dtype=torch.float32, device=self._sim_device) + right_wrist_tensor = torch.tensor(self._retarget_abs(right_wrist), dtype=torch.float32, device=self._sim_device) + hand_joints_tensor = torch.tensor(retargeted_hand_joints, dtype=torch.float32, device=self._sim_device) + + # Combine all tensors into a single tensor + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + wrist: Wrist pose data from OpenXR + + Returns: + Retargeted wrist pose in USD control frame + """ + + # Convert wrist data in openxr frame to usd control frame + + # Create pose object for openxr_right_wrist_in_world + # Note: The pose utils require torch tensors + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + openxr_right_wrist_in_world = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + + # The usd control frame is 180 degrees rotated around z axis wrt to the openxr frame + # This was determined through trial and error + zero_pos = torch.zeros(3, dtype=torch.float32) + # 180 degree rotation around z axis + z_axis_rot_quat = torch.tensor([0, 0, 0, 1], dtype=torch.float32) + usd_right_roll_link_in_openxr_right_wrist = PoseUtils.make_pose( + zero_pos, PoseUtils.matrix_from_quat(z_axis_rot_quat) + ) + + # Convert wrist pose in openxr frame to usd control frame + usd_right_roll_link_in_world = PoseUtils.pose_in_A_to_pose_in_B( + usd_right_roll_link_in_openxr_right_wrist, openxr_right_wrist_in_world + ) + + # extract position and rotation + usd_right_roll_link_in_world_pos, usd_right_roll_link_in_world_mat = PoseUtils.unmake_pose( + usd_right_roll_link_in_world + ) + usd_right_roll_link_in_world_quat = PoseUtils.quat_from_matrix(usd_right_roll_link_in_world_mat) + + return np.concatenate([usd_right_roll_link_in_world_pos, usd_right_roll_link_in_world_quat]) + + +@dataclass +class GR1T2RetargeterCfg(RetargeterCfg): + """Configuration for the GR1T2 retargeter.""" + + enable_visualization: bool = False + num_open_xr_hand_joints: int = 100 + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = GR1T2Retargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_lower_body_standing.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_lower_body_standing.py new file mode 100644 index 0000000000000000000000000000000000000000..1692b4a86d9b6ae217b3c89daab5bee9a724b87b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_lower_body_standing.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class G1LowerBodyStandingRetargeter(RetargeterBase): + """Provides lower body standing commands for the G1 robot.""" + + def __init__(self, cfg: G1LowerBodyStandingRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self.cfg = cfg + + def retarget(self, data: dict) -> torch.Tensor: + return torch.tensor([0.0, 0.0, 0.0, self.cfg.hip_height], device=self.cfg.sim_device) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + # This retargeter does not consume any device data + return [] + + +@dataclass +class G1LowerBodyStandingRetargeterCfg(RetargeterCfg): + """Configuration for the G1 lower body standing retargeter.""" + + hip_height: float = 0.72 + """Height of the G1 robot hip in meters. The value is a fixed height suitable for G1 to do tabletop manipulation.""" + retargeter_type: type[RetargeterBase] = G1LowerBodyStandingRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py new file mode 100644 index 0000000000000000000000000000000000000000..8acfe0abc0271ee1745c291df4147768371afcab --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.sim import SimulationContext + + +class G1LowerBodyStandingMotionControllerRetargeter(RetargeterBase): + """Provides lower body standing commands for the G1 robot.""" + + def __init__(self, cfg: G1LowerBodyStandingMotionControllerRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self.cfg = cfg + self._hip_height = cfg.hip_height + + def retarget(self, data: dict) -> torch.Tensor: + left_thumbstick_x = 0.0 + left_thumbstick_y = 0.0 + right_thumbstick_x = 0.0 + right_thumbstick_y = 0.0 + + # Get controller data using enums + if ( + DeviceBase.TrackingTarget.CONTROLLER_LEFT in data + and data[DeviceBase.TrackingTarget.CONTROLLER_LEFT] is not None + ): + left_controller_data = data[DeviceBase.TrackingTarget.CONTROLLER_LEFT] + if len(left_controller_data) > DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + left_inputs = left_controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(left_inputs) > DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value: + left_thumbstick_x = left_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_X.value] + left_thumbstick_y = left_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value] + + if ( + DeviceBase.TrackingTarget.CONTROLLER_RIGHT in data + and data[DeviceBase.TrackingTarget.CONTROLLER_RIGHT] is not None + ): + right_controller_data = data[DeviceBase.TrackingTarget.CONTROLLER_RIGHT] + if len(right_controller_data) > DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + right_inputs = right_controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(right_inputs) > DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value: + right_thumbstick_x = right_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_X.value] + right_thumbstick_y = right_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value] + + # Thumbstick values are in the range of [-1, 1], so we need to scale them to the range of + # [-movement_scale, movement_scale] + left_thumbstick_x = left_thumbstick_x * self.cfg.movement_scale + left_thumbstick_y = left_thumbstick_y * self.cfg.movement_scale + + # Use rendering time step for deterministic hip height adjustment regardless of wall clock time. + dt = SimulationContext.instance().get_rendering_dt() + self._hip_height -= right_thumbstick_y * dt * self.cfg.rotation_scale + self._hip_height = max(0.4, min(1.0, self._hip_height)) + + return torch.tensor( + [-left_thumbstick_y, -left_thumbstick_x, -right_thumbstick_x, self._hip_height], + device=self.cfg.sim_device, + dtype=torch.float32, + ) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + +@dataclass +class G1LowerBodyStandingMotionControllerRetargeterCfg(RetargeterCfg): + """Configuration for the G1 lower body standing retargeter.""" + + hip_height: float = 0.72 + """Height of the G1 robot hip in meters. The value is a fixed height suitable for G1 to do tabletop manipulation.""" + + movement_scale: float = 0.5 + """Scale the movement of the robot to the range of [-movement_scale, movement_scale].""" + + rotation_scale: float = 0.35 + """Scale the rotation of the robot to the range of [-rotation_scale, rotation_scale].""" + retargeter_type: type[RetargeterBase] = G1LowerBodyStandingMotionControllerRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..adb60a61b44ace2e49126f6e016abb9f2ea049e8 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml @@ -0,0 +1,18 @@ +retargeting: + finger_tip_link_names: + - thumb_tip + - index_tip + - middle_tip + low_pass_alpha: 0.2 + scaling_factor: 1.0 + target_joint_names: + - left_hand_thumb_0_joint + - left_hand_thumb_1_joint + - left_hand_thumb_2_joint + - left_hand_middle_0_joint + - left_hand_middle_1_joint + - left_hand_index_0_joint + - left_hand_index_1_joint + type: DexPilot + urdf_path: /tmp/G1_left_hand.urdf + wrist_link_name: base_link diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..bec4782e4c32818e25192b03c4897ba8134c3435 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml @@ -0,0 +1,18 @@ +retargeting: + finger_tip_link_names: + - thumb_tip + - index_tip + - middle_tip + low_pass_alpha: 0.2 + scaling_factor: 1.0 + target_joint_names: + - right_hand_thumb_0_joint + - right_hand_thumb_1_joint + - right_hand_thumb_2_joint + - right_hand_middle_0_joint + - right_hand_middle_1_joint + - right_hand_index_0_joint + - right_hand_index_1_joint + type: DexPilot + urdf_path: /tmp/G1_right_hand.urdf + wrist_link_name: base_link diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2219c04e6e6698d90a79c00d1546adcaa34f272f --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py @@ -0,0 +1,312 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +import os + +import numpy as np +import torch +import yaml +from dex_retargeting.retargeting_config import RetargetingConfig +from scipy.spatial.transform import Rotation as R + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +# import logger +logger = logging.getLogger(__name__) + +# yourdfpy loads visual/collision meshes with the hand URDFs; these aren't needed for +# retargeting and clutter the logs, so we suppress them. +logging.getLogger("dex_retargeting.yourdfpy").setLevel(logging.ERROR) + +# The index to map the OpenXR hand joints to the hand joints used +# in Dex-retargeting. +_HAND_JOINTS_INDEX = [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25] + +# The transformation matrices to convert hand pose to canonical view. +_OPERATOR2MANO_RIGHT = np.array( + [ + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + ] +) + +_OPERATOR2MANO_LEFT = np.array( + [ + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + ] +) + +# G1 robot hand joint names - 2 fingers and 1 thumb configuration +_LEFT_HAND_JOINT_NAMES = [ + "left_hand_index_0_joint", # Index finger proximal + "left_hand_index_1_joint", # Index finger distal + "left_hand_middle_0_joint", # Middle finger proximal + "left_hand_middle_1_joint", # Middle finger distal + "left_hand_thumb_0_joint", # Thumb base (yaw axis) + "left_hand_thumb_1_joint", # Thumb middle (pitch axis) + "left_hand_thumb_2_joint", # Thumb tip +] + +_RIGHT_HAND_JOINT_NAMES = [ + "right_hand_index_0_joint", # Index finger proximal + "right_hand_index_1_joint", # Index finger distal + "right_hand_middle_0_joint", # Middle finger proximal + "right_hand_middle_1_joint", # Middle finger distal + "right_hand_thumb_0_joint", # Thumb base (yaw axis) + "right_hand_thumb_1_joint", # Thumb middle (pitch axis) + "right_hand_thumb_2_joint", # Thumb tip +] + +# Thumb pivot: (-1.04719755, 1.04719755) + +_LEFT_HAND_JOINT_LIMITS = [ + [(-1.57079632, 0.0), (-1.74532925, 0.0)], # Index finger joints + [(-1.57079632, 0.0), (-1.74532925, 0.0)], # Middle finger joints + [(0.0, 0.0), (0.920, -0.72431163), (1.74532925, 0.0)], # Thumb joints +] + +_RIGHT_HAND_JOINT_LIMITS = [ + [(1.57079632, 0.0), (1.74532925, 0.0)], # Index finger joints + [(1.57079632, 0.0), (1.74532925, 0.0)], # Middle finger joints + [(0.0, 0.0), (-0.72431163, 0.920), (-1.74532925, 0.0)], # Thumb joints +] + +class G1TriHandDexRetargeting: + """A class for hand retargeting with G1. + + Handles retargeting of OpenXRhand tracking data to G1 robot hand joint angles. + """ + + def __init__( + self, + hand_joint_names: list[str], + right_hand_config_filename: str = "g1_hand_right_dexpilot.yml", + left_hand_config_filename: str = "g1_hand_left_dexpilot.yml", + left_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_dexpilot_asset/G1_left_hand.urdf", # noqa: E501 + right_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_dexpilot_asset/G1_right_hand.urdf", # noqa: E501 + ): + """Initialize the hand retargeting. + + Args: + hand_joint_names: Names of hand joints in the robot model + right_hand_config_filename: Config file for right hand retargeting + left_hand_config_filename: Config file for left hand retargeting + """ + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/")) + config_dir = os.path.join(data_dir, "configs/dex-retargeting") + + # Download urdf files from aws + local_left_urdf_path = retrieve_file_path(left_hand_urdf_path, force_download=True) + local_right_urdf_path = retrieve_file_path(right_hand_urdf_path, force_download=True) + + left_config_path = os.path.join(config_dir, left_hand_config_filename) + right_config_path = os.path.join(config_dir, right_hand_config_filename) + + # Update the YAML files with the correct URDF paths + self._update_yaml_with_urdf_path(left_config_path, local_left_urdf_path) + self._update_yaml_with_urdf_path(right_config_path, local_right_urdf_path) + + self._dex_left_hand = RetargetingConfig.load_from_file(left_config_path).build() + self._dex_right_hand = RetargetingConfig.load_from_file(right_config_path).build() + + self.left_dof_names = self._dex_left_hand.optimizer.robot.dof_joint_names + self.right_dof_names = self._dex_right_hand.optimizer.robot.dof_joint_names + self.dof_names = self.left_dof_names + self.right_dof_names + self.isaac_lab_hand_joint_names = hand_joint_names + + logger.info("[G1DexRetargeter] init done.") + + def _update_yaml_with_urdf_path(self, yaml_path: str, urdf_path: str): + """Update YAML file with the correct URDF path. + + Args: + yaml_path: Path to the YAML configuration file + urdf_path: Path to the URDF file to use + """ + try: + # Read the YAML file + with open(yaml_path) as file: + config = yaml.safe_load(file) + + # Update the URDF path in the configuration + if "retargeting" in config: + config["retargeting"]["urdf_path"] = urdf_path + logger.info(f"Updated URDF path in {yaml_path} to {urdf_path}") + else: + logger.warning(f"Unable to find 'retargeting' section in {yaml_path}") + + # Write the updated configuration back to the file + with open(yaml_path, "w") as file: + yaml.dump(config, file) + + except Exception as e: + logger.error(f"Error updating YAML file {yaml_path}: {e}") + + def convert_hand_joints(self, hand_poses: dict[str, np.ndarray], operator2mano: np.ndarray) -> np.ndarray: + """Prepares the hand joints data for retargeting. + + Args: + hand_poses: Dictionary containing hand pose data with joint positions and rotations + operator2mano: Transformation matrix to convert from operator to MANO frame + + Returns: + Joint positions with shape (21, 3) + """ + joint_position = np.zeros((21, 3)) + hand_joints = list(hand_poses.values()) + for i, joint_index in enumerate(_HAND_JOINTS_INDEX): + joint = hand_joints[joint_index] + joint_position[i] = joint[:3] + + # # Convert hand pose to the canonical frame. + joint_position = joint_position - joint_position[0:1, :] + xr_wrist_quat = hand_poses.get("wrist")[3:] + # OpenXR hand uses w,x,y,z order for quaternions but scipy uses x,y,z,w order + wrist_rot = R.from_quat([xr_wrist_quat[1], xr_wrist_quat[2], xr_wrist_quat[3], xr_wrist_quat[0]]).as_matrix() + + # return joint_position @ wrist_rot @ operator2mano + return wrist_rot @ operator2mano + + def compute_ref_value(self, joint_position: np.ndarray, indices: np.ndarray, retargeting_type: str) -> np.ndarray: + """Computes reference value for retargeting. + + Args: + joint_position: Joint positions array + indices: Target link indices + retargeting_type: Type of retargeting ("POSITION" or other) + + Returns: + Reference value in cartesian space + """ + if retargeting_type == "POSITION": + return joint_position[indices, :] + else: + origin_indices = indices[0, :] + task_indices = indices[1, :] + ref_value = joint_position[task_indices, :] - joint_position[origin_indices, :] + return ref_value + + def compute_one_hand( + self, hand_joints: dict[str, np.ndarray], retargeting: RetargetingConfig, operator2mano: np.ndarray + ) -> np.ndarray: + """Computes retargeted joint angles for one hand. + + Args: + hand_joints: Dictionary containing hand joint data + retargeting: Retargeting configuration object + operator2mano: Transformation matrix from operator to MANO frame + + Returns: + Retargeted joint angles + """ + joint_pos = self.convert_hand_joints(hand_joints, operator2mano) + ref_value = self.compute_ref_value( + joint_pos, + indices=retargeting.optimizer.target_link_human_indices, + retargeting_type=retargeting.optimizer.retargeting_type, + ) + # Enable gradient calculation and inference mode in case some other script has disabled it + # This is necessary for the retargeting to work since it uses gradient features that + # are not available in inference mode + with torch.enable_grad(): + with torch.inference_mode(False): + return retargeting.retarget(ref_value) + + def get_joint_names(self) -> list[str]: + """Returns list of all joint names.""" + return self.dof_names + + def get_left_joint_names(self) -> list[str]: + """Returns list of left hand joint names.""" + return self.left_dof_names + + def get_right_joint_names(self) -> list[str]: + """Returns list of right hand joint names.""" + return self.right_dof_names + + def get_hand_indices(self, robot) -> np.ndarray: + """Gets indices of hand joints in robot's DOF array. + + Args: + robot: Robot object containing DOF information + + Returns: + Array of joint indices + """ + return np.array([robot.dof_names.index(name) for name in self.dof_names], dtype=np.int64) + + def compute_left(self, left_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for left hand. + + Args: + left_hand_poses: Dictionary of left hand joint poses + + Returns: + Retargeted joint angles for left hand + """ + if left_hand_poses is not None: + left_hand_q = self.compute_one_hand(left_hand_poses, self._dex_left_hand, _OPERATOR2MANO_LEFT) + else: + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + return left_hand_q + + def compute_left_interpolated(self, left_hand_finger_values: np.ndarray) -> np.ndarray: + """Computes retargeted joints for left hand using fingertip positions. + + Args: + left_hand_finger_values: Values from 0 or 1 indicating whether each fingertip is closed or extended + Returns: + Retargeted joint angles for left hand using linear interpolation between open and closed hand poses + """ + # print("Left hand finger values:", left_hand_finger_values) + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + joint_idx = 0 + if left_hand_finger_values is not None: + for finger_idx, finger in enumerate(_LEFT_HAND_JOINT_LIMITS): + for joint in finger: + left_hand_q[joint_idx] = np.interp( + left_hand_finger_values[finger_idx], [0, 1], [joint[0], joint[1]] + ) + joint_idx += 1 + return left_hand_q + + def compute_right(self, right_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for right hand. + + Args: + right_hand_poses: Dictionary of right hand joint poses + + Returns: + Retargeted joint angles for right hand + """ + if right_hand_poses is not None: + right_hand_q = self.compute_one_hand(right_hand_poses, self._dex_right_hand, _OPERATOR2MANO_RIGHT) + else: + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + return right_hand_q + + def compute_right_interpolated(self, right_hand_finger_values: np.ndarray) -> np.ndarray: + """Computes retargeted joints for right hand using fingertip positions. + + Args: + right_hand_finger_values: Values from 0 or 1 indicating whether each fingertip is closed or extended + Returns: + Retargeted joint angles for right hand using linear interpolation between open and closed hand poses + """ + # print("Right hand finger values:", right_hand_finger_values) + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + joint_idx = 0 + if right_hand_finger_values is not None: + for finger_idx, finger in enumerate(_RIGHT_HAND_JOINT_LIMITS): + for joint in finger: + right_hand_q[joint_idx] = np.interp( + right_hand_finger_values[finger_idx], [0, 1], [joint[0], joint[1]] + ) + joint_idx += 1 + return right_hand_q diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py new file mode 100644 index 0000000000000000000000000000000000000000..c22f40a283f3fd9af91936bab4714f5479af46b5 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class G1TriHandUpperBodyMotionControllerGripperRetargeter(RetargeterBase): + """Retargeter for G1 gripper that outputs a boolean state based on controller trigger input, + concatenated with the retargeted wrist pose. + + Gripper: + - Uses hysteresis to prevent flickering when the trigger is near the threshold. + - Output is 0.0 for open, 1.0 for close. + + Wrist: + - Retargets absolute pose from controller to robot frame. + - Applies a fixed offset rotation for comfort/alignment. + """ + + def __init__(self, cfg: G1TriHandUpperBodyMotionControllerGripperRetargeterCfg): + """Initialize the retargeter. + + Args: + cfg: Configuration for the retargeter. + """ + super().__init__(cfg) + self._cfg = cfg + # Track previous state for hysteresis (left, right) + self._prev_left_state: float = 0.0 + self._prev_right_state: float = 0.0 + + def retarget(self, data: dict) -> torch.Tensor: + """Retarget controller inputs to gripper boolean state and wrist pose. + + Args: + data: Dictionary with MotionControllerTrackingTarget.LEFT/RIGHT keys + Each value is a 2D array: [pose(7), inputs(7)] + + Returns: + Tensor: [left_gripper_state(1), right_gripper_state(1), left_wrist(7), right_wrist(7)] + Wrist format: [x, y, z, qw, qx, qy, qz] + """ + # Get controller data + left_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_LEFT, np.array([])) + right_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_RIGHT, np.array([])) + + # --- Gripper Logic --- + # Extract hand state from controller data with hysteresis + left_hand_state: float = self._extract_hand_state(left_controller_data, self._prev_left_state) + right_hand_state: float = self._extract_hand_state(right_controller_data, self._prev_right_state) + + # Update previous states + self._prev_left_state = left_hand_state + self._prev_right_state = right_hand_state + + gripper_tensor = torch.tensor([left_hand_state, right_hand_state], dtype=torch.float32, device=self._sim_device) + + # --- Wrist Logic --- + # Default wrist poses (position + quaternion [w, x, y, z] as per default_wrist init) + # Note: default_wrist is [x, y, z, w, x, y, z] in reference, but seemingly used as [x,y,z, w,x,y,z] + default_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + # Extract poses from controller data + left_wrist = self._extract_wrist_pose(left_controller_data, default_wrist) + right_wrist = self._extract_wrist_pose(right_controller_data, default_wrist) + + # Convert to tensors + left_wrist_tensor = torch.tensor(self._retarget_abs(left_wrist), dtype=torch.float32, device=self._sim_device) + right_wrist_tensor = torch.tensor(self._retarget_abs(right_wrist), dtype=torch.float32, device=self._sim_device) + + # Concatenate: [gripper(2), left_wrist(7), right_wrist(7)] + return torch.cat([gripper_tensor, left_wrist_tensor, right_wrist_tensor]) + + def _extract_hand_state(self, controller_data: np.ndarray, prev_state: float) -> float: + """Extract hand state from controller data with hysteresis. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + prev_state: Previous hand state (0.0 or 1.0) + + Returns: + Hand state as float (0.0 for open, 1.0 for close) + """ + if len(controller_data) <= DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + return 0.0 + + # Extract inputs from second row + inputs = controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(inputs) < len(DeviceBase.MotionControllerInputIndex): + return 0.0 + + # Extract specific inputs using enum + trigger = inputs[DeviceBase.MotionControllerInputIndex.TRIGGER.value] # 0.0 to 1.0 (analog) + + # Apply hysteresis + if prev_state < 0.5: # Currently open + return 1.0 if trigger > self._cfg.threshold_high else 0.0 + else: # Currently closed + return 0.0 if trigger < self._cfg.threshold_low else 1.0 + + def _extract_wrist_pose(self, controller_data: np.ndarray, default_pose: np.ndarray) -> np.ndarray: + """Extract wrist pose from controller data. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + default_pose: Default pose to use if no data + + Returns: + Wrist pose array [x, y, z, w, x, y, z] + """ + if len(controller_data) > DeviceBase.MotionControllerDataRowIndex.POSE.value: + return controller_data[DeviceBase.MotionControllerDataRowIndex.POSE.value] + return default_pose + + def _retarget_abs(self, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting for controller wrists.""" + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + # Combined -75° (rather than -90° for wrist comfort) Y rotation + 90° Z rotation + # This is equivalent to (0, -75, 90) in euler angles + combined_quat = torch.tensor([0.5358, -0.4619, 0.5358, 0.4619], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + +@dataclass +class G1TriHandUpperBodyMotionControllerGripperRetargeterCfg(RetargeterCfg): + """Configuration for the G1 boolean gripper and wrist retargeter.""" + + threshold_high: float = 0.6 # Threshold to close hand + threshold_low: float = 0.4 # Threshold to open hand + retargeter_type: type[RetargeterBase] = G1TriHandUpperBodyMotionControllerGripperRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..0138bdf6d6b94166bce39198a3584e1ea78e2c2b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + + +class G1TriHandUpperBodyMotionControllerRetargeter(RetargeterBase): + """Simple retargeter that maps motion controller inputs to G1 hand joints. + + Mapping: + - A button (digital 0/1) → Thumb joints + - Trigger (analog 0-1) → Index finger joints + - Squeeze (analog 0-1) → Middle finger joints + """ + + def __init__(self, cfg: G1TriHandUpperBodyMotionControllerRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self._sim_device = cfg.sim_device + self._hand_joint_names = cfg.hand_joint_names + self._enable_visualization = cfg.enable_visualization + + if cfg.hand_joint_names is None: + raise ValueError("hand_joint_names must be provided") + + # Initialize visualization if enabled + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/g1_controller_markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert controller inputs to robot commands. + + Args: + data: Dictionary with MotionControllerTrackingTarget.LEFT/RIGHT keys + Each value is a 2D array: [pose(7), inputs(7)] + + Returns: + Tensor: [left_wrist(7), right_wrist(7), hand_joints(14)] + hand_joints order: + [ + left_proximal(3), right_proximal(3), left_distal(2), left_thumb_middle(1), + right_distal(2), right_thumb_middle(1), left_thumb_tip(1), right_thumb_tip(1) + ] + """ + + # Get controller data + left_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_LEFT, np.array([])) + right_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_RIGHT, np.array([])) + + # Default wrist poses (position + quaternion) + default_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + # Extract poses from controller data + left_wrist = self._extract_wrist_pose(left_controller_data, default_wrist) + right_wrist = self._extract_wrist_pose(right_controller_data, default_wrist) + + # Map controller inputs to hand joints + left_hand_joints = self._map_to_hand_joints(left_controller_data, is_left=True) + right_hand_joints = self._map_to_hand_joints(right_controller_data, is_left=False) + + # Negate left hand joints for proper mirroring + left_hand_joints = -left_hand_joints + + # Combine joints in the expected order: + # [left_proximal(3), right_proximal(3), left_distal(2), left_thumb_middle(1), + # right_distal(2), right_thumb_middle(1), left_thumb_tip(1), right_thumb_tip(1)] + all_hand_joints = np.array( + [ + left_hand_joints[3], # left_index_proximal + left_hand_joints[5], # left_middle_proximal + left_hand_joints[0], # left_thumb_base + right_hand_joints[3], # right_index_proximal + right_hand_joints[5], # right_middle_proximal + right_hand_joints[0], # right_thumb_base + left_hand_joints[4], # left_index_distal + left_hand_joints[6], # left_middle_distal + left_hand_joints[1], # left_thumb_middle + right_hand_joints[4], # right_index_distal + right_hand_joints[6], # right_middle_distal + right_hand_joints[1], # right_thumb_middle + left_hand_joints[2], # left_thumb_tip + right_hand_joints[2], # right_thumb_tip + ] + ) + + # Convert to tensors + left_wrist_tensor = torch.tensor( + self._retarget_abs(left_wrist, is_left=True), dtype=torch.float32, device=self._sim_device + ) + right_wrist_tensor = torch.tensor( + self._retarget_abs(right_wrist, is_left=False), dtype=torch.float32, device=self._sim_device + ) + hand_joints_tensor = torch.tensor(all_hand_joints, dtype=torch.float32, device=self._sim_device) + + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + def _extract_wrist_pose(self, controller_data: np.ndarray, default_pose: np.ndarray) -> np.ndarray: + """Extract wrist pose from controller data. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + default_pose: Default pose to use if no data + + Returns: + Wrist pose array [x, y, z, w, x, y, z] + """ + if len(controller_data) > DeviceBase.MotionControllerDataRowIndex.POSE.value: + return controller_data[DeviceBase.MotionControllerDataRowIndex.POSE.value] + return default_pose + + def _map_to_hand_joints(self, controller_data: np.ndarray, is_left: bool) -> np.ndarray: + """Map controller inputs to hand joint angles. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + is_left: True for left hand, False for right hand + + Returns: + Hand joint angles (7 joints per hand) in radians + """ + + # Initialize all joints to zero + hand_joints = np.zeros(7) + + if len(controller_data) <= DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + return hand_joints + + # Extract inputs from second row + inputs = controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + + if len(inputs) < len(DeviceBase.MotionControllerInputIndex): + return hand_joints + + # Extract specific inputs using enum + trigger = inputs[DeviceBase.MotionControllerInputIndex.TRIGGER.value] # 0.0 to 1.0 (analog) + squeeze = inputs[DeviceBase.MotionControllerInputIndex.SQUEEZE.value] # 0.0 to 1.0 (analog) + + # Grasping logic: + # If trigger is pressed, we grasp with index and thumb. + # If squeeze is pressed, we grasp with middle and thumb. + # If both are pressed, we grasp with index, middle, and thumb. + # The thumb rotates towards the direction of the pressing finger. + # If both are pressed, the thumb stays in the middle. + + thumb_button = max(trigger, squeeze) + + # Map to G1 hand joints (in radians) + # Thumb joints (3 joints) - controlled by A button (digital) + thumb_angle = -thumb_button # Max 1 radian ≈ 57° + + # Thumb rotation: + # If trigger is pressed, we rotate the thumb toward the index finger. + # If squeeze is pressed, we rotate the thumb toward the middle finger. + # If both are pressed, the thumb stays between the index and middle fingers. + # Trigger pushes toward +0.5, squeeze pushes toward -0.5 + # trigger=1,squeeze=0 → 0.5; trigger=0,squeeze=1 → -0.5; both=1 → 0 + thumb_rotation = 0.5 * trigger - 0.5 * squeeze + + if not is_left: + thumb_rotation = -thumb_rotation + + # These values were found empirically to get a good gripper pose. + + hand_joints[0] = thumb_rotation # thumb_0_joint (base) + hand_joints[1] = thumb_angle * 0.4 # thumb_1_joint (middle) + hand_joints[2] = thumb_angle * 0.7 # thumb_2_joint (tip) + + # Index finger joints (2 joints) - controlled by trigger (analog) + index_angle = trigger * 1.0 # Max 1.0 radians ≈ 57° + hand_joints[3] = index_angle # index_0_joint (proximal) + hand_joints[4] = index_angle # index_1_joint (distal) + + # Middle finger joints (2 joints) - controlled by squeeze (analog) + middle_angle = squeeze * 1.0 # Max 1.0 radians ≈ 57° + hand_joints[5] = middle_angle # middle_0_joint (proximal) + hand_joints[6] = middle_angle # middle_1_joint (distal) + + return hand_joints + + def _retarget_abs(self, wrist: np.ndarray, is_left: bool) -> np.ndarray: + """Handle absolute pose retargeting for controller wrists.""" + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + # Combined -75° (rather than -90° for wrist comfort) Y rotation + 90° Z rotation + # This is equivalent to (0, -75, 90) in euler angles + combined_quat = torch.tensor([0.5358, -0.4619, 0.5358, 0.4619], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + +@dataclass +class G1TriHandUpperBodyMotionControllerRetargeterCfg(RetargeterCfg): + """Configuration for the G1 Controller Upper Body retargeter.""" + + enable_visualization: bool = False + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = G1TriHandUpperBodyMotionControllerRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..1a5f702d9e45e6fd255e59c5067b174b6136c0d5 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py @@ -0,0 +1,200 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + +# This import exception is suppressed because g1_dex_retargeting_utils depends +# on pinocchio which is not available on Windows. +with contextlib.suppress(Exception): + from .g1_dex_retargeting_utils import G1TriHandDexRetargeting + + +class G1TriHandOpenCVRetargeter(RetargeterBase): + """Retargets OpenXR data to G1 upper body commands. + + This retargeter maps hand tracking data from OpenXR to wrist and hand joint commands for the G1 robot. + It handles both left and right hands, converting poses of the hands in OpenXR format to appropriate wrist poses + and joint angles for the G1 robot's upper body. + """ + + def __init__( + self, + cfg: G1TriHandOpenCVRetargeterCfg, + ): + """Initialize the G1 upper body retargeter. + + Args: + cfg: Configuration for the retargeter. + """ + super().__init__(cfg) + + # Store device name for runtime retrieval + self._sim_device = cfg.sim_device + if cfg.hand_joint_names is None: + raise ValueError("hand_joint_names must be provided in configuration") + self._hand_joint_names = cfg.hand_joint_names + + # Initialize the hands controller (optional). + # For robots without finger joints, allow wrist-only teleop by passing an empty list. + self._hands_controller = None + if len(cfg.hand_joint_names) > 0: + self._hands_controller = G1TriHandDexRetargeting(cfg.hand_joint_names) + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + self._num_open_xr_hand_joints = cfg.num_open_xr_hand_joints + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/g1_hand_markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.005, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + self._disable_hands = cfg.disable_hands + + if self._disable_hands: + print("[G1TriHandOpenCVRetargeter] Hand retargeting is disabled. Only wrist poses will be retargeted.") + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector commands. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + + Returns: + A tensor containing the retargeted commands: + - Left wrist pose (7) + - Right wrist pose (7) + - Hand joint angles (len(hand_joint_names)) + """ + + # Access the left and right hand data using the enum key + left_hand_poses = data[DeviceBase.TrackingTarget.HAND_LEFT] + right_hand_poses = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + left_wrist = left_hand_poses.get("wrist") + right_wrist = right_hand_poses.get("wrist") + + # Handle case where wrist data is not available + if left_wrist is None or right_wrist is None: + # Set to default pose if no data available. + # pos=(0,0,0), quat=(1,0,0,0) (w,x,y,z) + print("[G1TriHandOpenCVRetargeter] Wrist data missing, using default pose.") + default_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + if left_wrist is None: + left_wrist = default_pose + if right_wrist is None: + right_wrist = default_pose + + # Visualization if enabled + # if self._enable_visualization: + # joints_position = np.zeros((self._num_open_xr_hand_joints, 3)) + # joints_position[::2] = np.array([pose[:3] for pose in left_hand_poses.values()]) + # joints_position[1::2] = np.array([pose[:3] for pose in right_hand_poses.values()]) + # self._markers.visualize(translations=torch.tensor(joints_position, device=self._sim_device)) + + # Compute retargeted hand joints (optional). + if self._disable_hands: + retargeted_hand_joints = None + elif self._hands_controller is None: + retargeted_hand_joints = np.zeros(0, dtype=np.float32) + else: + left_hand_finger_values = left_hand_poses.get("finger_values") + left_hand_joint_angles = self._hands_controller.compute_left_interpolated(left_hand_finger_values) + left_hand_joint_angles = np.asarray(left_hand_joint_angles).reshape(-1) + + right_hand_finger_values = right_hand_poses.get("finger_values") + right_hand_joint_angles = self._hands_controller.compute_right_interpolated(right_hand_finger_values) + right_hand_joint_angles = np.asarray(right_hand_joint_angles).reshape(-1) + + retargeted_hand_joints = np.zeros(len(self._hand_joint_names), dtype=np.float32) + left_indices = [ + self._hand_joint_names.index(name) for name in self._hands_controller.get_left_joint_names() + ] + right_indices = [ + self._hand_joint_names.index(name) for name in self._hands_controller.get_right_joint_names() + ] + retargeted_hand_joints[left_indices] = left_hand_joint_angles + retargeted_hand_joints[right_indices] = right_hand_joint_angles + + # Convert numpy arrays to tensors and store in command buffer + left_wrist_tensor = torch.tensor( + self._retarget_abs(left_wrist, is_left=True), dtype=torch.float32, device=self._sim_device + ) + right_wrist_tensor = torch.tensor( + self._retarget_abs(right_wrist, is_left=False), dtype=torch.float32, device=self._sim_device + ) + + # Set fixed position for debugging + # left_wrist_tensor[0:3] = torch.tensor([-0.15, 0.3, 1.0], device=self._sim_device) + # right_wrist_tensor[0:3] = torch.tensor([0.15, 0.3, 1.0], device=self._sim_device) + + # Combine all tensors into a single tensor + if self._disable_hands: + return torch.cat([left_wrist_tensor, right_wrist_tensor]) + else: + hand_joints_tensor = torch.tensor(retargeted_hand_joints, dtype=torch.float32, device=self._sim_device) + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, wrist: np.ndarray, is_left: bool) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + wrist: Wrist pose data from OpenXR. + is_left: True for the left hand, False for the right hand. + + Returns: + Retargeted wrist pose in USD control frame. + """ + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + if is_left: + # Corresponds to a rotation of (0, 90, 90) in euler angles (x,y,z) + combined_quat = torch.tensor([0.7071, 0, 0.7071, 0], dtype=torch.float32) + else: + # Corresponds to a rotation of (0, -90, -90) in euler angles (x,y,z) + combined_quat = torch.tensor([0.7071, 0, 0.7071, 0], dtype=torch.float32) + # combined_quat = torch.tensor([0, -0.7071, 0, 0.7071], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + +@dataclass +class G1TriHandOpenCVRetargeterCfg(RetargeterCfg): + """Configuration for the G1 Controller Upper Body retargeter.""" + + enable_visualization: bool = True + num_open_xr_hand_joints: int = 100 + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = G1TriHandOpenCVRetargeter + disable_hands: bool = False # If True, disables hand retargeting and only retargets wrist poses \ No newline at end of file diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_left_hand.urdf b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_left_hand.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ff0c868c2b7a8081f456bf10226126eb59efa5c6 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_left_hand.urdf @@ -0,0 +1,443 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_right_hand.urdf b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_right_hand.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ba370adf651c671a90e10b0ccd7e14a81321b23f --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/humanoid/unitree/trihand/urdf/G1_right_hand.urdf @@ -0,0 +1,443 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/__init__.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..426b8ac1002c9586eae63696c9023f7aa56ffda3 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Franka manipulator retargeting module. + +This module provides functionality for retargeting motion to Franka robots. +""" + +from .gripper_retargeter import GripperRetargeter, GripperRetargeterCfg +from .se3_abs_retargeter import Se3AbsRetargeter, Se3AbsRetargeterCfg +from .se3_rel_retargeter import Se3RelRetargeter, Se3RelRetargeterCfg diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/gripper_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/gripper_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..9ae2031b4d81146e5b19b7d6de85421faf8126e9 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/gripper_retargeter.py @@ -0,0 +1,98 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass +from typing import Final + +import numpy as np +import torch + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class GripperRetargeter(RetargeterBase): + """Retargeter specifically for gripper control based on hand tracking data. + + This retargeter analyzes the distance between thumb and index finger tips to determine + whether the gripper should be open or closed. It includes hysteresis to prevent rapid + toggling between states when the finger distance is near the threshold. + + Features: + - Tracks thumb and index finger distance + - Implements hysteresis for stable gripper control + - Outputs boolean command (True = close gripper, False = open gripper) + """ + + GRIPPER_CLOSE_METERS: Final[float] = 0.03 + GRIPPER_OPEN_METERS: Final[float] = 0.05 + + def __init__( + self, + cfg: GripperRetargeterCfg, + ): + super().__init__(cfg) + """Initialize the gripper retargeter.""" + # Store the hand to track + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + self.bound_hand = cfg.bound_hand + # Initialize gripper state + self._previous_gripper_command = False + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to gripper command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: Tensor containing a single bool value where True = close gripper, False = open gripper + """ + # Extract key joint poses + hand_data = data[self.bound_hand] + thumb_tip = hand_data["thumb_tip"] + index_tip = hand_data["index_tip"] + + # Calculate gripper command with hysteresis + gripper_command_bool = self._calculate_gripper_command(thumb_tip[:3], index_tip[:3]) + gripper_value = -1.0 if gripper_command_bool else 1.0 + + return torch.tensor([gripper_value], dtype=torch.float32, device=self._sim_device) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _calculate_gripper_command(self, thumb_pos: np.ndarray, index_pos: np.ndarray) -> bool: + """Calculate gripper command from finger positions with hysteresis. + + Args: + thumb_pos: 3D position of thumb tip + index_pos: 3D position of index tip + + Returns: + bool: Gripper command (True = close, False = open) + """ + distance = np.linalg.norm(thumb_pos - index_pos) + + # Apply hysteresis to prevent rapid switching + if distance > self.GRIPPER_OPEN_METERS: + self._previous_gripper_command = False + elif distance < self.GRIPPER_CLOSE_METERS: + self._previous_gripper_command = True + + return self._previous_gripper_command + + +@dataclass +class GripperRetargeterCfg(RetargeterCfg): + """Configuration for gripper retargeter.""" + + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = GripperRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_abs_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_abs_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..d69af88cfccee7f111e0a136fc84025a193b8c31 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_abs_retargeter.py @@ -0,0 +1,172 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation, Slerp + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG + + +class Se3AbsRetargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to end-effector commands using absolute positioning. + + This retargeter maps hand joint poses directly to robot end-effector positions and orientations, + rather than using relative movements. It can either: + - Use the wrist position and orientation + - Use the midpoint between thumb and index finger (pinch position) + + Features: + - Optional constraint to zero out X/Y rotations (keeping only Z-axis rotation) + - Optional visualization of the target end-effector pose + """ + + def __init__( + self, + cfg: Se3AbsRetargeterCfg, + ): + """Initialize the retargeter. + + Args: + bound_hand: The hand to track (DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT) + zero_out_xy_rotation: If True, zero out rotation around x and y axes + use_wrist_rotation: If True, use wrist rotation instead of finger average + use_wrist_position: If True, use wrist position instead of pinch position + enable_visualization: If True, visualize the target pose in the scene + device: The device to place the returned tensor on ('cpu' or 'cuda') + """ + super().__init__(cfg) + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + self.bound_hand = cfg.bound_hand + + self._zero_out_xy_rotation = cfg.zero_out_xy_rotation + self._use_wrist_rotation = cfg.use_wrist_rotation + self._use_wrist_position = cfg.use_wrist_position + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + if cfg.enable_visualization: + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self._goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + self._goal_marker.set_visibility(True) + self._visualization_pos = np.zeros(3) + self._visualization_rot = np.array([1.0, 0.0, 0.0, 0.0]) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: 7D tensor containing position (xyz) and orientation (quaternion) + for the robot end-effector + """ + # Extract key joint poses from the bound hand + hand_data = data[self.bound_hand] + thumb_tip = hand_data.get("thumb_tip") + index_tip = hand_data.get("index_tip") + wrist = hand_data.get("wrist") + + ee_command_np = self._retarget_abs(thumb_tip, index_tip, wrist) + + # Convert to torch tensor + ee_command = torch.tensor(ee_command_np, dtype=torch.float32, device=self._sim_device) + + return ee_command + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, thumb_tip: np.ndarray, index_tip: np.ndarray, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + thumb_tip: 7D array containing position (xyz) and orientation (quaternion) + for the thumb tip + index_tip: 7D array containing position (xyz) and orientation (quaternion) + for the index tip + wrist: 7D array containing position (xyz) and orientation (quaternion) + for the wrist + + Returns: + np.ndarray: 7D array containing position (xyz) and orientation (quaternion) + for the robot end-effector + """ + + # Get position + if self._use_wrist_position: + position = wrist[:3] + else: + position = (thumb_tip[:3] + index_tip[:3]) / 2 + + # Get rotation + if self._use_wrist_rotation: + # wrist is w,x,y,z but scipy expects x,y,z,w + base_rot = Rotation.from_quat([*wrist[4:], wrist[3]]) + else: + # Average the orientations of thumb and index using SLERP + # thumb_tip is w,x,y,z but scipy expects x,y,z,w + r0 = Rotation.from_quat([*thumb_tip[4:], thumb_tip[3]]) + # index_tip is w,x,y,z but scipy expects x,y,z,w + r1 = Rotation.from_quat([*index_tip[4:], index_tip[3]]) + key_times = [0, 1] + slerp = Slerp(key_times, Rotation.concatenate([r0, r1])) + base_rot = slerp([0.5])[0] + + # Apply additional x-axis rotation to align with pinch gesture + final_rot = base_rot * Rotation.from_euler("x", 90, degrees=True) + + if self._zero_out_xy_rotation: + z, y, x = final_rot.as_euler("ZYX") + y = 0.0 # Zero out rotation around y-axis + x = 0.0 # Zero out rotation around x-axis + final_rot = Rotation.from_euler("ZYX", [z, y, x]) * Rotation.from_euler("X", np.pi, degrees=False) + + # Convert back to w,x,y,z format + quat = final_rot.as_quat() + rotation = np.array([quat[3], quat[0], quat[1], quat[2]]) # Output remains w,x,y,z + + # Update visualization if enabled + if self._enable_visualization: + self._visualization_pos = position + self._visualization_rot = rotation + self._update_visualization() + + return np.concatenate([position, rotation]) + + def _update_visualization(self): + """Update visualization markers with current pose. + + If visualization is enabled, the target end-effector pose is visualized in the scene. + """ + if self._enable_visualization: + trans = np.array([self._visualization_pos]) + quat = Rotation.from_matrix(self._visualization_rot).as_quat() + rot = np.array([np.array([quat[3], quat[0], quat[1], quat[2]])]) + self._goal_marker.visualize(translations=trans, orientations=rot) + + +@dataclass +class Se3AbsRetargeterCfg(RetargeterCfg): + """Configuration for absolute position retargeter.""" + + zero_out_xy_rotation: bool = True + use_wrist_rotation: bool = False + use_wrist_position: bool = True + enable_visualization: bool = False + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = Se3AbsRetargeter diff --git a/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_rel_retargeter.py b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_rel_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..360b1c29c347b0fe68b6d4c51fa147eac165af2b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/opencv_handtracking/retargeters/manipulator/se3_rel_retargeter.py @@ -0,0 +1,216 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG + + +class Se3RelRetargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to end-effector commands using relative positioning. + + This retargeter calculates delta poses between consecutive hand joint poses to generate incremental robot movements. + It can either: + - Use the wrist position and orientation + - Use the midpoint between thumb and index finger (pinch position) + + Features: + - Optional constraint to zero out X/Y rotations (keeping only Z-axis rotation) + - Motion smoothing with adjustable parameters + - Optional visualization of the target end-effector pose + """ + + def __init__( + self, + cfg: Se3RelRetargeterCfg, + ): + """Initialize the relative motion retargeter. + + Args: + bound_hand: The hand to track (DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT) + zero_out_xy_rotation: If True, ignore rotations around x and y axes, allowing only z-axis rotation + use_wrist_rotation: If True, use wrist rotation for control instead of averaging finger orientations + use_wrist_position: If True, use wrist position instead of pinch position (midpoint between fingers) + delta_pos_scale_factor: Amplification factor for position changes (higher = larger robot movements) + delta_rot_scale_factor: Amplification factor for rotation changes (higher = larger robot rotations) + alpha_pos: Position smoothing parameter (0-1); higher values track more closely to input, + lower values smooth more + alpha_rot: Rotation smoothing parameter (0-1); higher values track more closely to input, + lower values smooth more + enable_visualization: If True, show a visual marker representing the target end-effector pose + device: The device to place the returned tensor on ('cpu' or 'cuda') + """ + # Store the hand to track + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + super().__init__(cfg) + self.bound_hand = cfg.bound_hand + + self._zero_out_xy_rotation = cfg.zero_out_xy_rotation + self._use_wrist_rotation = cfg.use_wrist_rotation + self._use_wrist_position = cfg.use_wrist_position + self._delta_pos_scale_factor = cfg.delta_pos_scale_factor + self._delta_rot_scale_factor = cfg.delta_rot_scale_factor + self._alpha_pos = cfg.alpha_pos + self._alpha_rot = cfg.alpha_rot + + # Initialize smoothing state + self._smoothed_delta_pos = np.zeros(3) + self._smoothed_delta_rot = np.zeros(3) + + # Define thresholds for small movements + self._position_threshold = 0.001 + self._rotation_threshold = 0.01 + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + if cfg.enable_visualization: + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self._goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + self._goal_marker.set_visibility(True) + self._visualization_pos = np.zeros(3) + self._visualization_rot = np.array([1.0, 0.0, 0.0, 0.0]) + + self._previous_thumb_tip = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + self._previous_index_tip = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + self._previous_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: 6D tensor containing position (xyz) and rotation vector (rx,ry,rz) + for the robot end-effector + """ + # Extract key joint poses from the bound hand + hand_data = data[self.bound_hand] + thumb_tip = hand_data.get("thumb_tip") + index_tip = hand_data.get("index_tip") + wrist = hand_data.get("wrist") + + delta_thumb_tip = self._calculate_delta_pose(thumb_tip, self._previous_thumb_tip) + delta_index_tip = self._calculate_delta_pose(index_tip, self._previous_index_tip) + delta_wrist = self._calculate_delta_pose(wrist, self._previous_wrist) + ee_command_np = self._retarget_rel(delta_thumb_tip, delta_index_tip, delta_wrist) + + self._previous_thumb_tip = thumb_tip.copy() + self._previous_index_tip = index_tip.copy() + self._previous_wrist = wrist.copy() + + # Convert to torch tensor + ee_command = torch.tensor(ee_command_np, dtype=torch.float32, device=self._sim_device) + + return ee_command + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _calculate_delta_pose(self, joint_pose: np.ndarray, previous_joint_pose: np.ndarray) -> np.ndarray: + """Calculate delta pose from previous joint pose. + + Args: + joint_pose: Current joint pose (position and orientation) + previous_joint_pose: Previous joint pose for the same joint + + Returns: + np.ndarray: 6D array with position delta (xyz) and rotation delta as axis-angle (rx,ry,rz) + """ + delta_pos = joint_pose[:3] - previous_joint_pose[:3] + abs_rotation = Rotation.from_quat([*joint_pose[4:7], joint_pose[3]]) + previous_rot = Rotation.from_quat([*previous_joint_pose[4:7], previous_joint_pose[3]]) + relative_rotation = abs_rotation * previous_rot.inv() + return np.concatenate([delta_pos, relative_rotation.as_rotvec()]) + + def _retarget_rel(self, thumb_tip: np.ndarray, index_tip: np.ndarray, wrist: np.ndarray) -> np.ndarray: + """Handle relative (delta) pose retargeting. + + Args: + thumb_tip: Delta pose of thumb tip + index_tip: Delta pose of index tip + wrist: Delta pose of wrist + + Returns: + np.ndarray: 6D array with position delta (xyz) and rotation delta (rx,ry,rz) + """ + # Get position + if self._use_wrist_position: + position = wrist[:3] + else: + position = (thumb_tip[:3] + index_tip[:3]) / 2 + + # Get rotation + if self._use_wrist_rotation: + rotation = wrist[3:6] # rx, ry, rz + else: + rotation = (thumb_tip[3:6] + index_tip[3:6]) / 2 + + # Apply zero_out_xy_rotation regardless of rotation source + if self._zero_out_xy_rotation: + rotation[0] = 0 # x-axis + rotation[1] = 0 # y-axis + + # Smooth and scale position + self._smoothed_delta_pos = self._alpha_pos * position + (1 - self._alpha_pos) * self._smoothed_delta_pos + if np.linalg.norm(self._smoothed_delta_pos) < self._position_threshold: + self._smoothed_delta_pos = np.zeros(3) + position = self._smoothed_delta_pos * self._delta_pos_scale_factor + + # Smooth and scale rotation + self._smoothed_delta_rot = self._alpha_rot * rotation + (1 - self._alpha_rot) * self._smoothed_delta_rot + if np.linalg.norm(self._smoothed_delta_rot) < self._rotation_threshold: + self._smoothed_delta_rot = np.zeros(3) + rotation = self._smoothed_delta_rot * self._delta_rot_scale_factor + + # Update visualization if enabled + if self._enable_visualization: + # Convert rotation vector to quaternion and combine with current rotation + delta_quat = Rotation.from_rotvec(rotation).as_quat() # x, y, z, w format + current_rot = Rotation.from_quat([self._visualization_rot[1:], self._visualization_rot[0]]) + new_rot = Rotation.from_quat(delta_quat) * current_rot + self._visualization_pos = self._visualization_pos + position + # Convert back to w, x, y, z format + self._visualization_rot = np.array([new_rot.as_quat()[3], *new_rot.as_quat()[:3]]) + self._update_visualization() + + return np.concatenate([position, rotation]) + + def _update_visualization(self): + """Update visualization markers with current pose.""" + if self._enable_visualization: + trans = np.array([self._visualization_pos]) + quat = Rotation.from_matrix(self._visualization_rot).as_quat() + rot = np.array([np.array([quat[3], quat[0], quat[1], quat[2]])]) + self._goal_marker.visualize(translations=trans, orientations=rot) + + +@dataclass +class Se3RelRetargeterCfg(RetargeterCfg): + """Configuration for relative position retargeter.""" + + zero_out_xy_rotation: bool = True + use_wrist_rotation: bool = False + use_wrist_position: bool = True + delta_pos_scale_factor: float = 10.0 + delta_rot_scale_factor: float = 10.0 + alpha_pos: float = 0.5 + alpha_rot: float = 0.5 + enable_visualization: bool = False + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = Se3RelRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/__init__.py b/source/isaaclab/isaaclab/devices/openxr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..030fdbdd00b58a3f1cb342dd536bdb2f0fa60183 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Keyboard device for SE(2) and SE(3) control.""" + +from .manus_vive import ManusVive, ManusViveCfg +from .openxr_device import OpenXRDevice, OpenXRDeviceCfg +from .xr_cfg import XrAnchorRotationMode, XrCfg, remove_camera_configs diff --git a/source/isaaclab/isaaclab/devices/openxr/common.py b/source/isaaclab/isaaclab/devices/openxr/common.py new file mode 100644 index 0000000000000000000000000000000000000000..088641c2886ae1772ab14e854431282c715f7911 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/common.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Standard set of hand joint names based on OpenXR specification. +# Input devices for dexterous hands can use this as a reference, +# but may provide any subset or superset of these joints. +HAND_JOINT_NAMES = [ + # Palm + "palm", + # Wrist + "wrist", + # Thumb + "thumb_metacarpal", + "thumb_proximal", + "thumb_distal", + "thumb_tip", + # Index + "index_metacarpal", + "index_proximal", + "index_intermediate", + "index_distal", + "index_tip", + # Middle + "middle_metacarpal", + "middle_proximal", + "middle_intermediate", + "middle_distal", + "middle_tip", + # Ring + "ring_metacarpal", + "ring_proximal", + "ring_intermediate", + "ring_distal", + "ring_tip", + # Little + "little_metacarpal", + "little_proximal", + "little_intermediate", + "little_distal", + "little_tip", +] diff --git a/source/isaaclab/isaaclab/devices/openxr/manus_vive.py b/source/isaaclab/isaaclab/devices/openxr/manus_vive.py new file mode 100644 index 0000000000000000000000000000000000000000..2d5de3528ca7ae036861e4d8266c77a5c9460497 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/manus_vive.py @@ -0,0 +1,245 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Manus and Vive for teleoperation and interaction. +""" + +from __future__ import annotations + +import contextlib +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +from packaging import version + +import carb + +from isaaclab.devices.openxr.common import HAND_JOINT_NAMES +from isaaclab.devices.retargeter_base import RetargeterBase +from isaaclab.utils.version import get_isaac_sim_version + +from ..device_base import DeviceBase, DeviceCfg +from .xr_cfg import XrCfg + +# For testing purposes, we need to mock the XRCore +XRCore = None + +with contextlib.suppress(ModuleNotFoundError, ImportError): + from omni.kit.xr.core import XRCore + +from isaacsim.core.prims import SingleXFormPrim + +from .manus_vive_utils import HAND_JOINT_MAP, ManusViveIntegration + + +class ManusVive(DeviceBase): + """Manus gloves and Vive trackers for teleoperation and interaction. + + This device tracks hand joints using Manus gloves and Vive trackers and makes them available as: + + 1. A dictionary of tracking data (when used without retargeters) + 2. Retargeted commands for robot control (when retargeters are provided) + + The user needs to install the Manus SDK and add `{path_to_manus_sdk}/manus_sdk/lib` to `LD_LIBRARY_PATH`. + Data are acquired by `ManusViveIntegration` from `isaaclab.devices.openxr.manus_vive_utils`, including + + * Vive tracker poses in scene frame, calibrated from AVP wrist poses. + * Hand joints calculated from Vive wrist joints and Manus hand joints (relative to wrist). + * Vive trackers are automatically mapped to the left and right wrist joints. + + Raw data format (_get_raw_data output): consistent with :class:`OpenXRDevice`. + Joint names are defined in `HAND_JOINT_MAP` from `isaaclab.devices.openxr.manus_vive_utils`. + + Teleop commands: consistent with :class:`OpenXRDevice`. + + The device tracks the left hand, right hand, head position, or any combination of these + based on the TrackingTarget enum values. When retargeters are provided, the raw tracking + data is transformed into robot control commands suitable for teleoperation. + """ + + TELEOP_COMMAND_EVENT_TYPE = "teleop_command" + + def __init__(self, cfg: ManusViveCfg, retargeters: list[RetargeterBase] | None = None): + """Initialize the Manus+Vive device. + + Args: + cfg: Configuration object for Manus+Vive settings. + retargeters: List of retargeter instances to use for transforming raw tracking data. + """ + super().__init__(retargeters) + # Enforce minimum Isaac Sim version (>= 5.1) + isaac_sim_version = get_isaac_sim_version() + if isaac_sim_version < version.parse("5.1"): + raise RuntimeError(f"ManusVive requires Isaac Sim >= 5.1. Detected version: '{isaac_sim_version}'.") + self._xr_cfg = cfg.xr_cfg or XrCfg() + self._additional_callbacks = dict() + self._vc_subscription = ( + XRCore.get_singleton() + .get_message_bus() + .create_subscription_to_pop_by_type( + carb.events.type_from_string(self.TELEOP_COMMAND_EVENT_TYPE), self._on_teleop_command + ) + ) + self._manus_vive = ManusViveIntegration() + + # Initialize dictionaries instead of arrays + default_pose = np.array([0, 0, 0, 1, 0, 0, 0], dtype=np.float32) + self._previous_joint_poses_left = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_joint_poses_right = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_headpose = default_pose.copy() + + xr_anchor = SingleXFormPrim("/XRAnchor", position=self._xr_cfg.anchor_pos, orientation=self._xr_cfg.anchor_rot) + carb.settings.get_settings().set_float("/persistent/xr/profile/ar/render/nearPlane", self._xr_cfg.near_plane) + carb.settings.get_settings().set_string("/persistent/xr/profile/ar/anchorMode", "custom anchor") + carb.settings.get_settings().set_string("/xrstage/profile/ar/customAnchor", xr_anchor.prim_path) + + def __del__(self): + """Clean up resources when the object is destroyed. + Properly unsubscribes from the XR message bus to prevent memory leaks + and resource issues when the device is no longer needed. + """ + if hasattr(self, "_vc_subscription") and self._vc_subscription is not None: + self._vc_subscription = None + + # No need to explicitly clean up OpenXR instance as it's managed by NVIDIA Isaac Sim + + def __str__(self) -> str: + """Provide details about the device configuration, tracking settings, + and available gesture commands. + + Returns: + Formatted string with device information. + """ + + msg = f"Manus+Vive Hand Tracking Device: {self.__class__.__name__}\n" + msg += f"\tAnchor Position: {self._xr_cfg.anchor_pos}\n" + msg += f"\tAnchor Rotation: {self._xr_cfg.anchor_rot}\n" + + # Add retargeter information + if self._retargeters: + msg += "\tRetargeters:\n" + for i, retargeter in enumerate(self._retargeters): + msg += f"\t\t{i + 1}. {retargeter.__class__.__name__}\n" + else: + msg += "\tRetargeters: None (raw joint data output)\n" + + # Add available gesture commands + msg += "\t----------------------------------------------\n" + msg += "\tAvailable Gesture Commands:\n" + + # Check which callbacks are registered + start_avail = "START" in self._additional_callbacks + stop_avail = "STOP" in self._additional_callbacks + reset_avail = "RESET" in self._additional_callbacks + + msg += f"\t\tStart Teleoperation: {'✓' if start_avail else '✗'}\n" + msg += f"\t\tStop Teleoperation: {'✓' if stop_avail else '✗'}\n" + msg += f"\t\tReset Environment: {'✓' if reset_avail else '✗'}\n" + + # Add joint tracking information + msg += "\t----------------------------------------------\n" + msg += "\tTracked Joints: 26 XR hand joints including:\n" + msg += "\t\t- Wrist, palm\n" + msg += "\t\t- Thumb (tip, intermediate joints)\n" + msg += "\t\t- Fingers (tip, distal, intermediate, proximal)\n" + + return msg + + def reset(self): + """Reset cached joint and head poses.""" + default_pose = np.array([0, 0, 0, 1, 0, 0, 0], dtype=np.float32) + self._previous_joint_poses_left = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_joint_poses_right = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_headpose = default_pose.copy() + + def add_callback(self, key: str, func: Callable): + """Register a callback for a given key. + + Args: + key: The message key to bind ('START', 'STOP', 'RESET'). + func: The function to invoke when the message key is received. + """ + self._additional_callbacks[key] = func + + def _get_raw_data(self) -> dict: + """Get the latest tracking data from Manus and Vive. + + Returns: + Dictionary with TrackingTarget enum keys (HAND_LEFT, HAND_RIGHT, HEAD) containing: + - Left hand joint poses: Dictionary of 26 joints with position and orientation + - Right hand joint poses: Dictionary of 26 joints with position and orientation + - Head pose: Single 7-element array with position and orientation + + Each pose is represented as a 7-element array: [x, y, z, qw, qx, qy, qz] + where the first 3 elements are position and the last 4 are quaternion orientation. + """ + hand_tracking_data = self._manus_vive.get_all_device_data()["manus_gloves"] + result = {"left": self._previous_joint_poses_left, "right": self._previous_joint_poses_right} + for joint, pose in hand_tracking_data.items(): + hand, index = joint.split("_") + joint_name = HAND_JOINT_MAP[int(index)] + result[hand][joint_name] = np.array(pose["position"] + pose["orientation"], dtype=np.float32) + return { + DeviceBase.TrackingTarget.HAND_LEFT: result["left"], + DeviceBase.TrackingTarget.HAND_RIGHT: result["right"], + DeviceBase.TrackingTarget.HEAD: self._calculate_headpose(), + } + + def _calculate_headpose(self) -> np.ndarray: + """Calculate the head pose from OpenXR. + + Returns: + 7-element numpy.ndarray [x, y, z, qw, qx, qy, qz]. + """ + head_device = XRCore.get_singleton().get_input_device("/user/head") + if head_device: + hmd = head_device.get_virtual_world_pose("") + position = hmd.ExtractTranslation() + quat = hmd.ExtractRotationQuat() + quati = quat.GetImaginary() + quatw = quat.GetReal() + + # Store in w, x, y, z order to match our convention + self._previous_headpose = np.array( + [ + position[0], + position[1], + position[2], + quatw, + quati[0], + quati[1], + quati[2], + ] + ) + + return self._previous_headpose + + def _on_teleop_command(self, event: carb.events.IEvent): + """Handle teleoperation command events. + + Args: + event: The XR message-bus event containing a 'message' payload. + """ + msg = event.payload["message"] + + if "start" in msg: + if "START" in self._additional_callbacks: + self._additional_callbacks["START"]() + elif "stop" in msg: + if "STOP" in self._additional_callbacks: + self._additional_callbacks["STOP"]() + elif "reset" in msg: + if "RESET" in self._additional_callbacks: + self._additional_callbacks["RESET"]() + + +@dataclass +class ManusViveCfg(DeviceCfg): + """Configuration for Manus and Vive.""" + + xr_cfg: XrCfg | None = None + class_type: type[DeviceBase] = ManusVive diff --git a/source/isaaclab/isaaclab/devices/openxr/manus_vive_utils.py b/source/isaaclab/isaaclab/devices/openxr/manus_vive_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a47e32ae49952284fff53731984dab8d7bb7634b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/manus_vive_utils.py @@ -0,0 +1,514 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import contextlib +import logging +from time import time + +import numpy as np + +from isaacsim.core.utils.extensions import enable_extension + +# For testing purposes, we need to mock the XRCore +XRCore, XRPoseValidityFlags = None, None + +with contextlib.suppress(ModuleNotFoundError, ImportError): + from omni.kit.xr.core import XRCore, XRPoseValidityFlags + +from pxr import Gf + +# import logger +logger = logging.getLogger(__name__) + +# Mapping from Manus joint index (0-24) to joint name. Palm (25) is calculated from middle metacarpal and proximal. +HAND_JOINT_MAP = { + # Wrist + 0: "wrist", + # Thumb + 1: "thumb_metacarpal", + 2: "thumb_proximal", + 3: "thumb_distal", + 4: "thumb_tip", + # Index + 5: "index_metacarpal", + 6: "index_proximal", + 7: "index_intermediate", + 8: "index_distal", + 9: "index_tip", + # Middle + 10: "middle_metacarpal", + 11: "middle_proximal", + 12: "middle_intermediate", + 13: "middle_distal", + 14: "middle_tip", + # Ring + 15: "ring_metacarpal", + 16: "ring_proximal", + 17: "ring_intermediate", + 18: "ring_distal", + 19: "ring_tip", + # Little + 20: "little_metacarpal", + 21: "little_proximal", + 22: "little_intermediate", + 23: "little_distal", + 24: "little_tip", + # Palm + 25: "palm", +} + + +class ManusViveIntegration: + def __init__(self): + enable_extension("isaacsim.xr.input_devices") + from isaacsim.xr.input_devices.impl.manus_vive_integration import get_manus_vive_integration + + _manus_vive_integration = get_manus_vive_integration() + self.manus = _manus_vive_integration.manus_tracker + self.vive_tracker = _manus_vive_integration.vive_tracker + self.device_status = _manus_vive_integration.device_status + self.default_pose = {"position": [0, 0, 0], "orientation": [1, 0, 0, 0]} + # 90-degree ccw rotation on Y-axis and 90-degree ccw rotation on Z-axis + self.rot_adjust = Gf.Matrix3d().SetRotate(Gf.Quatd(0.5, Gf.Vec3d(-0.5, 0.5, 0.5))) + self.scene_T_lighthouse_static = None + self._vive_left_id = None + self._vive_right_id = None + self._pairA_candidates = [] # Pair A: WM0->Left, WM1->Right + self._pairB_candidates = [] # Pair B: WM1->Left, WM0->Right + self._pairA_trans_errs = [] + self._pairA_rot_errs = [] + self._pairB_trans_errs = [] + self._pairB_rot_errs = [] + + def get_all_device_data(self) -> dict: + """Get all tracked device data in scene coordinates. + + Returns: + Manus glove joint data and Vive tracker data. + { + 'manus_gloves': { + '{left/right}_{joint_index}': { + 'position': [x, y, z], + 'orientation': [w, x, y, z] + }, + ... + }, + 'vive_trackers': { + '{vive_tracker_id}': { + 'position': [x, y, z], + 'orientation': [w, x, y, z] + }, + ... + } + } + """ + self.update_manus() + self.update_vive() + # Get raw data from trackers + manus_data = self.manus.get_data() + vive_data = self.vive_tracker.get_data() + vive_transformed = self._transform_vive_data(vive_data) + scene_T_wrist = self._get_scene_T_wrist_matrix(vive_transformed) + + return { + "manus_gloves": self._transform_manus_data(manus_data, scene_T_wrist), + "vive_trackers": vive_transformed, + } + + def get_device_status(self) -> dict: + """Get connection and data freshness status for Manus gloves and Vive trackers. + + Returns: + Dictionary containing connection flags and last-data timestamps. + Format: { + 'manus_gloves': {'connected': bool, 'last_data_time': float}, + 'vive_trackers': {'connected': bool, 'last_data_time': float}, + 'left_hand_connected': bool, + 'right_hand_connected': bool + } + """ + return self.device_status + + def update_manus(self): + """Update raw Manus glove data and status flags.""" + self.manus.update() + self.device_status["manus_gloves"]["last_data_time"] = time() + manus_data = self.manus.get_data() + self.device_status["left_hand_connected"] = "left_0" in manus_data + self.device_status["right_hand_connected"] = "right_0" in manus_data + + def update_vive(self): + """Update raw Vive tracker data, and initialize coordinate transformation if it is the first data update.""" + self.vive_tracker.update() + self.device_status["vive_trackers"]["last_data_time"] = time() + try: + # Initialize coordinate transformation from first Vive wrist position + if self.scene_T_lighthouse_static is None: + self._initialize_coordinate_transformation() + except Exception as e: + logger.error(f"Vive tracker update failed: {e}") + + def _initialize_coordinate_transformation(self): + """Initialize the scene to lighthouse coordinate transformation. + + The coordinate transformation is used to transform the wrist pose from lighthouse + coordinate system to isaac sim scene coordinate. It is computed from multiple + frames of AVP/OpenXR wrist pose and Vive wrist pose samples at the beginning of the session. + """ + min_frames = 6 + tolerance = 3.0 + vive_data = self.vive_tracker.get_data() + wm0_id, wm1_id = get_vive_wrist_ids(vive_data) + if wm0_id is None and wm1_id is None: + return + + try: + # Fetch OpenXR wrists + L, R, gloves = None, None, [] + if self.device_status["left_hand_connected"]: + gloves.append("left") + L = get_openxr_wrist_matrix("left") + if self.device_status["right_hand_connected"]: + gloves.append("right") + R = get_openxr_wrist_matrix("right") + + M0, M1, vives = None, None, [] + if wm0_id is not None: + vives.append(wm0_id) + M0 = pose_to_matrix(vive_data[wm0_id]) + if wm1_id is not None: + vives.append(wm1_id) + M1 = pose_to_matrix(vive_data[wm1_id]) + + TL0, TL1, TR0, TR1 = None, None, None, None + # Compute transforms for available pairs + if wm0_id is not None and L is not None: + TL0 = M0.GetInverse() * L + self._pairA_candidates.append(TL0) + if wm1_id is not None and L is not None: + TL1 = M1.GetInverse() * L + self._pairB_candidates.append(TL1) + if wm1_id is not None and R is not None: + TR1 = M1.GetInverse() * R + self._pairA_candidates.append(TR1) + if wm0_id is not None and R is not None: + TR0 = M0.GetInverse() * R + self._pairB_candidates.append(TR0) + + # Per-frame pairing error if both candidates present + if TL0 is not None and TR1 is not None and TL1 is not None and TR0 is not None: + eT, eR = compute_delta_errors(TL0, TR1) + self._pairA_trans_errs.append(eT) + self._pairA_rot_errs.append(eR) + eT, eR = compute_delta_errors(TL1, TR0) + self._pairB_trans_errs.append(eT) + self._pairB_rot_errs.append(eR) + + # Choose a mapping + choose_A = None + if len(self._pairA_candidates) == 0 and len(self._pairB_candidates) >= min_frames: + choose_A = False + elif len(self._pairB_candidates) == 0 and len(self._pairA_candidates) >= min_frames: + choose_A = True + elif len(self._pairA_trans_errs) >= min_frames and len(self._pairB_trans_errs) >= min_frames: + errA = get_pairing_error(self._pairA_trans_errs, self._pairA_rot_errs) + errB = get_pairing_error(self._pairB_trans_errs, self._pairB_rot_errs) + if errA < errB and errA < tolerance: + choose_A = True + elif errB < errA and errB < tolerance: + choose_A = False + elif len(self._pairA_trans_errs) % 10 == 0 or len(self._pairB_trans_errs) % 10 == 0: + print("Computing pairing of Vive trackers with wrists") + logger.info( + f"Pairing Vive trackers with wrists: error of pairing A: {errA}, error of pairing B: {errB}" + ) + if choose_A is None: + return + + if choose_A: + chosen_list = self._pairA_candidates + self._vive_left_id, self._vive_right_id = wm0_id, wm1_id + else: + chosen_list = self._pairB_candidates + self._vive_left_id, self._vive_right_id = wm1_id, wm0_id + + if len(chosen_list) >= min_frames: + cluster = select_mode_cluster(chosen_list) + if len(chosen_list) % 10 == 0: + print( + f"Computing wrist calibration: formed size {len(cluster)} cluster from" + f" {len(chosen_list)} samples" + ) + if len(cluster) >= min_frames // 2: + averaged = average_transforms(cluster) + self.scene_T_lighthouse_static = averaged + print( + f"Wrist calibration computed. Resolved mapping: {self._vive_left_id}->Left," + f" {self._vive_right_id}->Right" + ) + + except Exception as e: + logger.error(f"Failed to initialize coordinate transformation: {e}") + + def _transform_vive_data(self, device_data: dict) -> dict: + """Transform Vive tracker poses to scene coordinates. + + Args: + device_data: raw vive tracker poses, with device id as keys. + + Returns: + Vive tracker poses in scene coordinates, with device id as keys. + """ + transformed_data = {} + for joint_name, joint_data in device_data.items(): + transformed_pose = self.default_pose + if self.scene_T_lighthouse_static is not None: + transformed_matrix = pose_to_matrix(joint_data) * self.scene_T_lighthouse_static + transformed_pose = matrix_to_pose(transformed_matrix) + transformed_data[joint_name] = transformed_pose + return transformed_data + + def _get_scene_T_wrist_matrix(self, vive_data: dict) -> dict: + """Compute scene-frame wrist transforms for left and right hands. + + Args: + vive_data: Vive tracker poses expressed in scene coordinates. + + Returns: + Dictionary with 'left' and 'right' keys mapping to 4x4 transforms. + """ + scene_T_wrist = {"left": Gf.Matrix4d().SetIdentity(), "right": Gf.Matrix4d().SetIdentity()} + # 10 cm offset on Y-axis for change in vive tracker position after flipping the palm + Rcorr = Gf.Matrix4d(self.rot_adjust, Gf.Vec3d(0, -0.1, 0)) + if self._vive_left_id is not None: + scene_T_wrist["left"] = Rcorr * pose_to_matrix(vive_data[self._vive_left_id]) + if self._vive_right_id is not None: + scene_T_wrist["right"] = Rcorr * pose_to_matrix(vive_data[self._vive_right_id]) + return scene_T_wrist + + def _transform_manus_data(self, manus_data: dict, scene_T_wrist: dict) -> dict: + """Transform Manus glove joints from wrist-relative to scene coordinates. + + Args: + manus_data: Raw Manus joint pose dictionary, wrist-relative. + scene_T_wrist: Dictionary of scene transforms for left and right wrists. + + Returns: + Dictionary of Manus joint poses in scene coordinates. + """ + Rcorr = Gf.Matrix4d(self.rot_adjust, Gf.Vec3d(0, 0, 0)).GetInverse() + transformed_data = {} + for joint_name, joint_data in manus_data.items(): + hand, _ = joint_name.split("_") + joint_mat = Rcorr * pose_to_matrix(joint_data) * scene_T_wrist[hand] + transformed_data[joint_name] = matrix_to_pose(joint_mat) + # Calculate palm with middle metacarpal and proximal + transformed_data["left_25"] = self._get_palm(transformed_data, "left") + transformed_data["right_25"] = self._get_palm(transformed_data, "right") + return transformed_data + + def _get_palm(self, transformed_data: dict, hand: str) -> dict: + """Compute palm pose from middle metacarpal and proximal joints. + + Args: + transformed_data: Manus joint poses in scene coordinates. + hand: The hand side, either 'left' or 'right'. + + Returns: + Pose dictionary with 'position' and 'orientation'. + """ + if f"{hand}_6" not in transformed_data or f"{hand}_7" not in transformed_data: + # Joint data not arrived yet + return self.default_pose + metacarpal = transformed_data[f"{hand}_6"] + proximal = transformed_data[f"{hand}_7"] + pos = (np.array(metacarpal["position"]) + np.array(proximal["position"])) / 2.0 + return {"position": [pos[0], pos[1], pos[2]], "orientation": metacarpal["orientation"]} + + +def compute_delta_errors(a: Gf.Matrix4d, b: Gf.Matrix4d) -> tuple[float, float]: + """Compute translation and rotation error between two transforms. + + Args: + a: The first transform. + b: The second transform. + + Returns: + Tuple containing (translation_error_m, rotation_error_deg). + """ + try: + delta = a * b.GetInverse() + t = delta.ExtractTranslation() + trans_err = float(np.linalg.norm([t[0], t[1], t[2]])) + q = delta.ExtractRotation().GetQuat() + w = float(max(min(q.GetReal(), 1.0), -1.0)) + ang = 2.0 * float(np.arccos(w)) + ang_deg = float(np.degrees(ang)) + if ang_deg > 180.0: + ang_deg = 360.0 - ang_deg + return trans_err, ang_deg + except Exception: + return float("inf"), float("inf") + + +def average_transforms(mats: list[Gf.Matrix4d]) -> Gf.Matrix4d: + """Average rigid transforms across translations and quaternions. + + Args: + mats: The list of 4x4 transforms to average. + + Returns: + Averaged 4x4 transform, or None if the list is empty. + """ + if not mats: + return None + ref_quat = mats[0].ExtractRotation().GetQuat() + ref = np.array([ref_quat.GetReal(), *ref_quat.GetImaginary()]) + acc_q = np.zeros(4, dtype=np.float64) + acc_t = np.zeros(3, dtype=np.float64) + for m in mats: + t = m.ExtractTranslation() + acc_t += np.array([t[0], t[1], t[2]], dtype=np.float64) + q = m.ExtractRotation().GetQuat() + qi = np.array([q.GetReal(), *q.GetImaginary()], dtype=np.float64) + if np.dot(qi, ref) < 0.0: + qi = -qi + acc_q += qi + mean_t = acc_t / float(len(mats)) + norm = np.linalg.norm(acc_q) + if norm <= 1e-12: + quat_avg = Gf.Quatd(1.0, Gf.Vec3d(0.0, 0.0, 0.0)) + else: + qn = acc_q / norm + quat_avg = Gf.Quatd(float(qn[0]), Gf.Vec3d(float(qn[1]), float(qn[2]), float(qn[3]))) + rot3 = Gf.Matrix3d().SetRotate(quat_avg) + trans = Gf.Vec3d(float(mean_t[0]), float(mean_t[1]), float(mean_t[2])) + return Gf.Matrix4d(rot3, trans) + + +def select_mode_cluster( + mats: list[Gf.Matrix4d], trans_thresh_m: float = 0.03, rot_thresh_deg: float = 10.0 +) -> list[Gf.Matrix4d]: + """Select the largest cluster of transforms under proximity thresholds. + + Args: + mats: The list of 4x4 transforms to cluster. + trans_thresh_m: The translation threshold in meters. + rot_thresh_deg: The rotation threshold in degrees. + + Returns: + The largest cluster (mode) of transforms. + """ + if not mats: + return [] + best_cluster: list[Gf.Matrix4d] = [] + for center in mats: + cluster: list[Gf.Matrix4d] = [] + for m in mats: + trans_err, rot_err = compute_delta_errors(m, center) + if trans_err <= trans_thresh_m and rot_err <= rot_thresh_deg: + cluster.append(m) + if len(cluster) > len(best_cluster): + best_cluster = cluster + return best_cluster + + +def get_openxr_wrist_matrix(hand: str) -> Gf.Matrix4d: + """Get the OpenXR wrist matrix if valid. + + Args: + hand: The hand side ('left' or 'right'). + + Returns: + 4x4 transform for the wrist if valid, otherwise None. + """ + hand = hand.lower() + try: + hand_device = XRCore.get_singleton().get_input_device(f"/user/hand/{hand}") + if hand_device is None: + return None + joints = hand_device.get_all_virtual_world_poses() + if "wrist" not in joints: + return None + joint = joints["wrist"] + required = XRPoseValidityFlags.POSITION_VALID | XRPoseValidityFlags.ORIENTATION_VALID + if (joint.validity_flags & required) != required: + return None + return joint.pose_matrix + except Exception as e: + logger.warning(f"OpenXR {hand} wrist fetch failed: {e}") + return None + + +def get_vive_wrist_ids(vive_data: dict) -> tuple[str, str]: + """Get the Vive wrist tracker IDs if available. + + Args: + vive_data: The raw Vive data dictionary. + + Returns: + (wm0_id, wm1_id) if available, otherwise None values. + """ + wm_ids = [k for k in vive_data.keys() if len(k) >= 2 and k[:2] == "WM"] + wm_ids.sort() + if len(wm_ids) >= 2: # Assumes the first two vive trackers are the wrist trackers + return wm_ids[0], wm_ids[1] + if len(wm_ids) == 1: + return wm_ids[0], None + return None, None + + +def pose_to_matrix(pose: dict) -> Gf.Matrix4d: + """Convert a pose dictionary to a 4x4 transform matrix. + + Args: + pose: The pose with 'position' and 'orientation' fields. + + Returns: + A 4x4 transform representing the pose. + """ + pos, ori = pose["position"], pose["orientation"] + quat = Gf.Quatd(ori[0], Gf.Vec3d(ori[1], ori[2], ori[3])) + rot = Gf.Matrix3d().SetRotate(quat) + trans = Gf.Vec3d(pos[0], pos[1], pos[2]) + return Gf.Matrix4d(rot, trans) + + +def matrix_to_pose(matrix: Gf.Matrix4d) -> dict: + """Convert a 4x4 transform matrix to a pose dictionary. + + Args: + matrix: The 4x4 transform matrix to convert. + + Returns: + Pose dictionary with 'position' and 'orientation'. + """ + pos = matrix.ExtractTranslation() + rot = matrix.ExtractRotation() + quat = rot.GetQuat() + return { + "position": [pos[0], pos[1], pos[2]], + "orientation": [quat.GetReal(), quat.GetImaginary()[0], quat.GetImaginary()[1], quat.GetImaginary()[2]], + } + + +def get_pairing_error(trans_errs: list, rot_errs: list) -> float: + """Compute a scalar pairing error from translation and rotation errors. + + Args: + trans_errs: The list of translation errors across samples. + rot_errs: The list of rotation errors across samples. + + Returns: + The weighted sum of medians of translation and rotation errors. + """ + + def _median(values: list) -> float: + try: + return float(np.median(np.asarray(values, dtype=np.float64))) + except Exception: + return float("inf") + + return _median(trans_errs) + 0.01 * _median(rot_errs) diff --git a/source/isaaclab/isaaclab/devices/openxr/openxr_device.py b/source/isaaclab/isaaclab/devices/openxr/openxr_device.py new file mode 100644 index 0000000000000000000000000000000000000000..7703c7ce8e6b3b19ef7a025c3823633ad0b01f81 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/openxr_device.py @@ -0,0 +1,511 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""OpenXR-powered device for teleoperation and interaction.""" + +from __future__ import annotations + +import contextlib +import logging +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import numpy as np + +import carb + +# import logger +logger = logging.getLogger(__name__) + +from isaaclab.devices.openxr.common import HAND_JOINT_NAMES +from isaaclab.devices.retargeter_base import RetargeterBase + +from ..device_base import DeviceBase, DeviceCfg +from .xr_anchor_utils import XrAnchorSynchronizer +from .xr_cfg import XrCfg + +# For testing purposes, we need to mock the XRCore, XRPoseValidityFlags classes +XRCore = None +XRPoseValidityFlags = None +XRCoreEventType = None + +with contextlib.suppress(ModuleNotFoundError, ImportError): + from omni.kit.xr.core import XRCore, XRCoreEventType, XRPoseValidityFlags + +from isaacsim.core.prims import SingleXFormPrim + + +class OpenXRDevice(DeviceBase): + """An OpenXR-powered device for teleoperation and interaction. + + This device tracks hand joints using OpenXR and makes them available as: + + 1. A dictionary of tracking data (when used without retargeters) + 2. Retargeted commands for robot control (when retargeters are provided) + + Raw data format (_get_raw_data output): + + * A dictionary with keys matching TrackingTarget enum values (HAND_LEFT, HAND_RIGHT, HEAD) + * Each hand tracking entry contains a dictionary of joint poses + * Each joint pose is a 7D vector (x, y, z, qw, qx, qy, qz) in meters and quaternion units + * Joint names are defined in HAND_JOINT_NAMES from isaaclab.devices.openxr.common + * Supported joints include palm, wrist, and joints for thumb, index, middle, ring and little fingers + + Teleop commands: + The device responds to several teleop commands that can be subscribed to via add_callback(): + + * "START": Resume hand tracking data flow + * "STOP": Pause hand tracking data flow + * "RESET": Reset the tracking and signal simulation reset + + The device tracks the left hand, right hand, head position, or any combination of these + based on the TrackingTarget enum values. When retargeters are provided, the raw tracking + data is transformed into robot control commands suitable for teleoperation. + """ + + TELEOP_COMMAND_EVENT_TYPE = "teleop_command" + + def __init__( + self, + cfg: OpenXRDeviceCfg, + retargeters: list[RetargeterBase] | None = None, + ): + """Initialize the OpenXR device. + + Args: + cfg: Configuration object for OpenXR settings. + retargeters: List of retargeter instances to use for transforming raw tracking data. + """ + super().__init__(retargeters) + self._xr_cfg = cfg.xr_cfg or XrCfg() + self._additional_callbacks = dict() + self._xr_core = XRCore.get_singleton() if XRCore is not None else None + self._vc_subscription = ( + self._xr_core.get_message_bus().create_subscription_to_pop_by_type( + carb.events.type_from_string(self.TELEOP_COMMAND_EVENT_TYPE), self._on_teleop_command + ) + if self._xr_core is not None + else None + ) + + # Initialize dictionaries instead of arrays + default_pose = np.array([0, 0, 0, 1, 0, 0, 0], dtype=np.float32) + self._previous_joint_poses_left = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_joint_poses_right = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_headpose = default_pose.copy() + + if self._xr_cfg.anchor_prim_path is not None: + anchor_path = self._xr_cfg.anchor_prim_path + if anchor_path.endswith("/"): + anchor_path = anchor_path[:-1] + self._xr_anchor_headset_path = f"{anchor_path}/XRAnchor" + else: + self._xr_anchor_headset_path = "/World/XRAnchor" + + _ = SingleXFormPrim( + self._xr_anchor_headset_path, position=self._xr_cfg.anchor_pos, orientation=self._xr_cfg.anchor_rot + ) + + if hasattr(carb, "settings"): + carb.settings.get_settings().set_float( + "/persistent/xr/profile/ar/render/nearPlane", self._xr_cfg.near_plane + ) + carb.settings.get_settings().set_string("/persistent/xr/profile/ar/anchorMode", "custom anchor") + carb.settings.get_settings().set_string("/xrstage/profile/ar/customAnchor", self._xr_anchor_headset_path) + + # Button binding support + self.__button_subscriptions: dict[str, dict] = {} + + # Optional anchor synchronizer + self._anchor_sync: XrAnchorSynchronizer | None = None + if self._xr_core is not None and self._xr_cfg.anchor_prim_path is not None: + try: + self._anchor_sync = XrAnchorSynchronizer( + xr_core=self._xr_core, + xr_cfg=self._xr_cfg, + xr_anchor_headset_path=self._xr_anchor_headset_path, + ) + # Subscribe to pre_sync_update to keep anchor in sync + if XRCoreEventType is not None: + self._xr_pre_sync_update_subscription = ( + self._xr_core.get_message_bus().create_subscription_to_pop_by_type( + XRCoreEventType.pre_sync_update, + lambda _: self._anchor_sync.sync_headset_to_anchor(), + name="isaaclab_xr_pre_sync_update", + ) + ) + except Exception as e: + logger.warning(f"XR: Failed to initialize anchor synchronizer: {e}") + + # Default convenience binding: toggle anchor rotation with right controller 'a' button + with contextlib.suppress(Exception): + self._bind_button_press( + "/user/hand/right", + "a", + "isaaclab_right_a", + lambda ev: self._toggle_anchor_rotation(), + ) + + def __del__(self): + """Clean up resources when the object is destroyed. + + Properly unsubscribes from the XR message bus to prevent memory leaks + and resource issues when the device is no longer needed. + """ + if hasattr(self, "_vc_subscription") and self._vc_subscription is not None: + self._vc_subscription = None + if hasattr(self, "_xr_pre_sync_update_subscription") and self._xr_pre_sync_update_subscription is not None: + self._xr_pre_sync_update_subscription = None + # clear button subscriptions + if hasattr(self, "__button_subscriptions"): + self._unbind_all_buttons() + + # No need to explicitly clean up OpenXR instance as it's managed by NVIDIA Isaac Sim + + def __str__(self) -> str: + """Returns a string containing information about the OpenXR hand tracking device. + + This provides details about the device configuration, tracking settings, + and available gesture commands. + + Returns: + Formatted string with device information + """ + + msg = f"OpenXR Hand Tracking Device: {self.__class__.__name__}\n" + msg += f"\tAnchor Position: {self._xr_cfg.anchor_pos}\n" + msg += f"\tAnchor Rotation: {self._xr_cfg.anchor_rot}\n" + if self._xr_cfg.anchor_prim_path is not None: + msg += f"\tAnchor Prim Path: {self._xr_cfg.anchor_prim_path} (Dynamic Anchoring)\n" + else: + msg += "\tAnchor Mode: Static (Root Level)\n" + + # Add retargeter information + if self._retargeters: + msg += "\tRetargeters:\n" + for i, retargeter in enumerate(self._retargeters): + msg += f"\t\t{i + 1}. {retargeter.__class__.__name__}\n" + else: + msg += "\tRetargeters: None (raw joint data output)\n" + + # Add available gesture commands + msg += "\t----------------------------------------------\n" + msg += "\tAvailable Gesture Commands:\n" + + # Check which callbacks are registered + start_avail = "START" in self._additional_callbacks + stop_avail = "STOP" in self._additional_callbacks + reset_avail = "RESET" in self._additional_callbacks + + msg += f"\t\tStart Teleoperation: {'✓' if start_avail else '✗'}\n" + msg += f"\t\tStop Teleoperation: {'✓' if stop_avail else '✗'}\n" + msg += f"\t\tReset Environment: {'✓' if reset_avail else '✗'}\n" + + # Add joint tracking information + msg += "\t----------------------------------------------\n" + msg += "\tTracked Joints: All 26 XR hand joints including:\n" + msg += "\t\t- Wrist, palm\n" + msg += "\t\t- Thumb (tip, intermediate joints)\n" + msg += "\t\t- Fingers (tip, distal, intermediate, proximal)\n" + + return msg + + """ + Operations + """ + + def reset(self): + default_pose = np.array([0, 0, 0, 1, 0, 0, 0], dtype=np.float32) + self._previous_joint_poses_left = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_joint_poses_right = {name: default_pose.copy() for name in HAND_JOINT_NAMES} + self._previous_headpose = default_pose.copy() + if hasattr(self, "_anchor_sync") and self._anchor_sync is not None: + self._anchor_sync.reset() + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind to client messages. + + Args: + key: The message type to bind to. Valid values are "START", "STOP", and "RESET". + func: The function to call when the message is received. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def _get_raw_data(self) -> Any: + """Get the latest tracking data from the OpenXR runtime. + + Returns: + Dictionary with TrackingTarget enum keys (HAND_LEFT, HAND_RIGHT, HEAD) containing: + - Left hand joint poses: Dictionary of 26 joints with position and orientation + - Right hand joint poses: Dictionary of 26 joints with position and orientation + - Head pose: Single 7-element array with position and orientation + + Each pose is represented as a 7-element array: [x, y, z, qw, qx, qy, qz] + where the first 3 elements are position and the last 4 are quaternion orientation. + """ + data = {} + + if RetargeterBase.Requirement.HAND_TRACKING in self._required_features: + data[DeviceBase.TrackingTarget.HAND_LEFT] = self._calculate_joint_poses( + XRCore.get_singleton().get_input_device("/user/hand/left"), + self._previous_joint_poses_left, + ) + data[DeviceBase.TrackingTarget.HAND_RIGHT] = self._calculate_joint_poses( + XRCore.get_singleton().get_input_device("/user/hand/right"), + self._previous_joint_poses_right, + ) + + if RetargeterBase.Requirement.HEAD_TRACKING in self._required_features: + data[DeviceBase.TrackingTarget.HEAD] = self._calculate_headpose() + + if RetargeterBase.Requirement.MOTION_CONTROLLER in self._required_features: + # Optionally include motion controller pose+inputs if devices are available + try: + left_dev = XRCore.get_singleton().get_input_device("/user/hand/left") + right_dev = XRCore.get_singleton().get_input_device("/user/hand/right") + left_ctrl = self._query_controller(left_dev) if left_dev is not None else np.array([]) + right_ctrl = self._query_controller(right_dev) if right_dev is not None else np.array([]) + if left_ctrl.size: + data[DeviceBase.TrackingTarget.CONTROLLER_LEFT] = left_ctrl + if right_ctrl.size: + data[DeviceBase.TrackingTarget.CONTROLLER_RIGHT] = right_ctrl + except Exception: + # Ignore controller data if XRCore/controller APIs are unavailable + pass + + return data + + """ + Internal helpers. + """ + + def _calculate_joint_poses( + self, hand_device: Any, previous_joint_poses: dict[str, np.ndarray] + ) -> dict[str, np.ndarray]: + """Calculate and update joint poses for a hand device. + + This function retrieves the current joint poses from the OpenXR hand device and updates + the previous joint poses with the new data. If a joint's position or orientation is not + valid, it will use the previous values. + + Args: + hand_device: The OpenXR input device for a hand (/user/hand/left or /user/hand/right). + previous_joint_poses: Dictionary mapping joint names to their previous poses. + Each pose is a 7-element array: [x, y, z, qw, qx, qy, qz]. + + Returns: + Updated dictionary of joint poses with the same structure as previous_joint_poses. + Each pose is represented as a 7-element numpy array: [x, y, z, qw, qx, qy, qz] + where the first 3 elements are position and the last 4 are quaternion orientation. + """ + if hand_device is None: + return previous_joint_poses + + joint_poses = hand_device.get_all_virtual_world_poses() + + # Update each joint that is present in the current data + for joint_name, joint_pose in joint_poses.items(): + if joint_name in HAND_JOINT_NAMES: + # Extract translation and rotation + if joint_pose.validity_flags & XRPoseValidityFlags.POSITION_VALID: + position = joint_pose.pose_matrix.ExtractTranslation() + else: + position = previous_joint_poses[joint_name][:3] + + if joint_pose.validity_flags & XRPoseValidityFlags.ORIENTATION_VALID: + quat = joint_pose.pose_matrix.ExtractRotationQuat() + quati = quat.GetImaginary() + quatw = quat.GetReal() + else: + quatw = previous_joint_poses[joint_name][3] + quati = previous_joint_poses[joint_name][4:] + + # Directly update the dictionary with new data + previous_joint_poses[joint_name] = np.array( + [position[0], position[1], position[2], quatw, quati[0], quati[1], quati[2]], dtype=np.float32 + ) + + # No need for conversion, just return the updated dictionary + return previous_joint_poses + + def _calculate_headpose(self) -> np.ndarray: + """Calculate the head pose from OpenXR. + + Returns: + numpy.ndarray: 7-element array containing head position (xyz) and orientation (wxyz) + """ + head_device = XRCore.get_singleton().get_input_device("/user/head") + if head_device: + hmd = head_device.get_virtual_world_pose("") + position = hmd.ExtractTranslation() + quat = hmd.ExtractRotationQuat() + quati = quat.GetImaginary() + quatw = quat.GetReal() + + # Store in w, x, y, z order to match our convention + self._previous_headpose = np.array( + [ + position[0], + position[1], + position[2], + quatw, + quati[0], + quati[1], + quati[2], + ] + ) + + return self._previous_headpose + + # ----------------------------- + # Controller button binding utilities + # ----------------------------- + def _bind_button_press( + self, + device_path: str, + button_name: str, + event_name: str, + on_button_press: Callable[[carb.events.IEvent], None], + ) -> None: + if self._xr_core is None: + logger.warning("XR core not available; skipping button binding") + return + + sub_key = f"{device_path}/{button_name}" + self.__button_subscriptions[sub_key] = {} + + def try_emit_button_events(): + if self.__button_subscriptions[sub_key].get("generator"): + return + device = self._xr_core.get_input_device(device_path) + if not device: + return + names = {str(n) for n in (device.get_input_names() or ())} + if button_name not in names: + return + gen = device.bind_event_generator(button_name, event_name, ("press",)) + if gen is not None: + logger.info(f"XR: Bound event generator for {sub_key}, {event_name}") + self.__button_subscriptions[sub_key]["generator"] = gen + + def on_inputs_change(_ev: carb.events.IEvent) -> None: + try_emit_button_events() + + def on_disable(_ev: carb.events.IEvent) -> None: + self.__button_subscriptions[sub_key]["generator"] = None + + message_bus = self._xr_core.get_message_bus() + event_suffix = device_path.strip("/").replace("/", "_") + self.__button_subscriptions[sub_key]["press_sub"] = message_bus.create_subscription_to_pop_by_type( + carb.events.type_from_string(f"{event_name}.press"), on_button_press + ) + self.__button_subscriptions[sub_key]["inputs_change_sub"] = message_bus.create_subscription_to_pop_by_type( + carb.events.type_from_string(f"xr_input.{event_suffix}.inputs_change"), on_inputs_change + ) + self.__button_subscriptions[sub_key]["disable_sub"] = message_bus.create_subscription_to_pop_by_type( + carb.events.type_from_string(f"xr_input.{event_suffix}.disable"), on_disable + ) + try_emit_button_events() + + def _unbind_all_buttons(self) -> None: + for sub_key, subs in self.__button_subscriptions.items(): + if "generator" in subs: + subs["generator"] = None + for key in ["press_sub", "inputs_change_sub", "disable_sub"]: + if key in subs: + subs[key] = None + self.__button_subscriptions.clear() + logger.info("XR: Unbound all button event handlers") + + def _toggle_anchor_rotation(self): + if self._anchor_sync is not None: + self._anchor_sync.toggle_anchor_rotation() + + def _query_controller(self, input_device) -> np.ndarray: + """Query motion controller pose and inputs as a 2x7 array. + + Row 0 (POSE): [x, y, z, w, x, y, z] + Row 1 (INPUTS): [thumbstick_x, thumbstick_y, trigger, squeeze, button_0, button_1, padding] + """ + if input_device is None: + return np.array([]) + + pose = input_device.get_virtual_world_pose() + position = pose.ExtractTranslation() + quat = pose.ExtractRotationQuat() + + thumbstick_x = 0.0 + thumbstick_y = 0.0 + trigger = 0.0 + squeeze = 0.0 + button_0 = 0.0 + button_1 = 0.0 + + if input_device.has_input_gesture("thumbstick", "x"): + thumbstick_x = float(input_device.get_input_gesture_value("thumbstick", "x")) + if input_device.has_input_gesture("thumbstick", "y"): + thumbstick_y = float(input_device.get_input_gesture_value("thumbstick", "y")) + if input_device.has_input_gesture("trigger", "value"): + trigger = float(input_device.get_input_gesture_value("trigger", "value")) + if input_device.has_input_gesture("squeeze", "value"): + squeeze = float(input_device.get_input_gesture_value("squeeze", "value")) + + # Determine which button pair exists on this device + if input_device.has_input_gesture("x", "click") or input_device.has_input_gesture("y", "click"): + if input_device.has_input_gesture("x", "click"): + button_0 = float(input_device.get_input_gesture_value("x", "click")) + if input_device.has_input_gesture("y", "click"): + button_1 = float(input_device.get_input_gesture_value("y", "click")) + else: + if input_device.has_input_gesture("a", "click"): + button_0 = float(input_device.get_input_gesture_value("a", "click")) + if input_device.has_input_gesture("b", "click"): + button_1 = float(input_device.get_input_gesture_value("b", "click")) + + pose_row = [ + position[0], + position[1], + position[2], + quat.GetReal(), + quat.GetImaginary()[0], + quat.GetImaginary()[1], + quat.GetImaginary()[2], + ] + + input_row = [ + thumbstick_x, + thumbstick_y, + trigger, + squeeze, + button_0, + button_1, + 0.0, + ] + + return np.array([pose_row, input_row], dtype=np.float32) + + def _on_teleop_command(self, event: carb.events.IEvent): + msg = event.payload["message"] + + if "start" in msg: + if "START" in self._additional_callbacks: + self._additional_callbacks["START"]() + elif "stop" in msg: + if "STOP" in self._additional_callbacks: + self._additional_callbacks["STOP"]() + elif "reset" in msg: + if "RESET" in self._additional_callbacks: + self._additional_callbacks["RESET"]() + self.reset() + + +@dataclass +class OpenXRDeviceCfg(DeviceCfg): + """Configuration for OpenXR devices.""" + + xr_cfg: XrCfg | None = None + class_type: type[DeviceBase] = OpenXRDevice diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/__init__.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94ef9c0e4e5f2e9976ad2f44995bf186c8d2ded4 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Retargeters for mapping input device data to robot commands.""" + +from .humanoid.fourier.gr1t2_retargeter import GR1T2Retargeter, GR1T2RetargeterCfg +from .humanoid.unitree.g1_lower_body_standing import G1LowerBodyStandingRetargeter, G1LowerBodyStandingRetargeterCfg +from .humanoid.unitree.g1_motion_controller_locomotion import ( + G1LowerBodyStandingMotionControllerRetargeter, + G1LowerBodyStandingMotionControllerRetargeterCfg, +) +from .humanoid.unitree.inspire.g1_upper_body_retargeter import UnitreeG1Retargeter, UnitreeG1RetargeterCfg +from .humanoid.unitree.trihand.g1_upper_body_motion_ctrl_gripper import ( + G1TriHandUpperBodyMotionControllerGripperRetargeter, + G1TriHandUpperBodyMotionControllerGripperRetargeterCfg, +) +from .humanoid.unitree.trihand.g1_upper_body_motion_ctrl_retargeter import ( + G1TriHandUpperBodyMotionControllerRetargeter, + G1TriHandUpperBodyMotionControllerRetargeterCfg, +) +from .humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeter, + G1TriHandUpperBodyRetargeterCfg, +) +from .manipulator.gripper_retargeter import GripperRetargeter, GripperRetargeterCfg +from .manipulator.se3_abs_retargeter import Se3AbsRetargeter, Se3AbsRetargeterCfg +from .manipulator.se3_rel_retargeter import Se3RelRetargeter, Se3RelRetargeterCfg diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..1e203d11e7e864734f144d22378a164f3ec2349d --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_left_dexpilot.yml @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - GR1T2_fourier_hand_6dof_L_thumb_distal_link + - GR1T2_fourier_hand_6dof_L_index_intermediate_link + - GR1T2_fourier_hand_6dof_L_middle_intermediate_link + - GR1T2_fourier_hand_6dof_L_ring_intermediate_link + - GR1T2_fourier_hand_6dof_L_pinky_intermediate_link + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - L_index_proximal_joint + - L_middle_proximal_joint + - L_pinky_proximal_joint + - L_ring_proximal_joint + - L_index_intermediate_joint + - L_middle_intermediate_joint + - L_pinky_intermediate_joint + - L_ring_intermediate_joint + - L_thumb_proximal_yaw_joint + - L_thumb_proximal_pitch_joint + - L_thumb_distal_joint + - L_thumb_distal_joint + type: DexPilot + urdf_path: /tmp/GR1_T2_left_hand.urdf + wrist_link_name: l_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..f67041bd9b60c9571d88d2db21b962cc7aa8d398 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/data/configs/dex-retargeting/fourier_hand_right_dexpilot.yml @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - GR1T2_fourier_hand_6dof_R_thumb_distal_link + - GR1T2_fourier_hand_6dof_R_index_intermediate_link + - GR1T2_fourier_hand_6dof_R_middle_intermediate_link + - GR1T2_fourier_hand_6dof_R_ring_intermediate_link + - GR1T2_fourier_hand_6dof_R_pinky_intermediate_link + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - R_index_proximal_joint + - R_middle_proximal_joint + - R_pinky_proximal_joint + - R_ring_proximal_joint + - R_index_intermediate_joint + - R_middle_intermediate_joint + - R_pinky_intermediate_joint + - R_ring_intermediate_joint + - R_thumb_proximal_yaw_joint + - R_thumb_proximal_pitch_joint + - R_thumb_distal_joint + type: DexPilot + urdf_path: /tmp/GR1_T2_right_hand.urdf + wrist_link_name: r_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aaeb9bda031467bfd49d834a33367a8fd0792de0 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1_t2_dex_retargeting_utils.py @@ -0,0 +1,262 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +import os + +import numpy as np +import torch +import yaml +from dex_retargeting.retargeting_config import RetargetingConfig +from scipy.spatial.transform import Rotation as R + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +# import logger +logger = logging.getLogger(__name__) + +# The index to map the OpenXR hand joints to the hand joints used +# in Dex-retargeting. +_HAND_JOINTS_INDEX = [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25] + +# The transformation matrices to convert hand pose to canonical view. +_OPERATOR2MANO_RIGHT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_OPERATOR2MANO_LEFT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_LEFT_HAND_JOINT_NAMES = [ + "L_index_proximal_joint", + "L_index_intermediate_joint", + "L_middle_proximal_joint", + "L_middle_intermediate_joint", + "L_pinky_proximal_joint", + "L_pinky_intermediate_joint", + "L_ring_proximal_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_yaw_joint", + "L_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", +] + + +_RIGHT_HAND_JOINT_NAMES = [ + "R_index_proximal_joint", + "R_index_intermediate_joint", + "R_middle_proximal_joint", + "R_middle_intermediate_joint", + "R_pinky_proximal_joint", + "R_pinky_intermediate_joint", + "R_ring_proximal_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_yaw_joint", + "R_thumb_proximal_pitch_joint", + "R_thumb_distal_joint", +] + + +class GR1TR2DexRetargeting: + """A class for hand retargeting with GR1Fourier. + + Handles retargeting of OpenXRhand tracking data to GR1T2 robot hand joint angles. + """ + + def __init__( + self, + hand_joint_names: list[str], + right_hand_config_filename: str = "fourier_hand_right_dexpilot.yml", + left_hand_config_filename: str = "fourier_hand_left_dexpilot.yml", + left_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/GR1T2_assets/GR1_T2_left_hand.urdf", + right_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/GR1T2_assets/GR1_T2_right_hand.urdf", + ): + """Initialize the hand retargeting. + + Args: + hand_joint_names: Names of hand joints in the robot model + right_hand_config_filename: Config file for right hand retargeting + left_hand_config_filename: Config file for left hand retargeting + """ + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/")) + config_dir = os.path.join(data_dir, "configs/dex-retargeting") + + # Download urdf files from aws + local_left_urdf_path = retrieve_file_path(left_hand_urdf_path, force_download=True) + local_right_urdf_path = retrieve_file_path(right_hand_urdf_path, force_download=True) + + left_config_path = os.path.join(config_dir, left_hand_config_filename) + right_config_path = os.path.join(config_dir, right_hand_config_filename) + + # Update the YAML files with the correct URDF paths + self._update_yaml_with_urdf_path(left_config_path, local_left_urdf_path) + self._update_yaml_with_urdf_path(right_config_path, local_right_urdf_path) + + self._dex_left_hand = RetargetingConfig.load_from_file(left_config_path).build() + self._dex_right_hand = RetargetingConfig.load_from_file(right_config_path).build() + + self.left_dof_names = self._dex_left_hand.optimizer.robot.dof_joint_names + self.right_dof_names = self._dex_right_hand.optimizer.robot.dof_joint_names + self.dof_names = self.left_dof_names + self.right_dof_names + self.isaac_lab_hand_joint_names = hand_joint_names + + logger.info("[GR1T2DexRetargeter] init done.") + + def _update_yaml_with_urdf_path(self, yaml_path: str, urdf_path: str): + """Update YAML file with the correct URDF path. + + Args: + yaml_path: Path to the YAML configuration file + urdf_path: Path to the URDF file to use + """ + try: + # Read the YAML file + with open(yaml_path) as file: + config = yaml.safe_load(file) + + # Update the URDF path in the configuration + if "retargeting" in config: + config["retargeting"]["urdf_path"] = urdf_path + logger.info(f"Updated URDF path in {yaml_path} to {urdf_path}") + else: + logger.warning(f"Unable to find 'retargeting' section in {yaml_path}") + + # Write the updated configuration back to the file + with open(yaml_path, "w") as file: + yaml.dump(config, file) + + except Exception as e: + logger.error(f"Error updating YAML file {yaml_path}: {e}") + + def convert_hand_joints(self, hand_poses: dict[str, np.ndarray], operator2mano: np.ndarray) -> np.ndarray: + """Prepares the hand joints data for retargeting. + + Args: + hand_poses: Dictionary containing hand pose data with joint positions and rotations + operator2mano: Transformation matrix to convert from operator to MANO frame + + Returns: + Joint positions with shape (21, 3) + """ + joint_position = np.zeros((21, 3)) + hand_joints = list(hand_poses.values()) + for i in range(len(_HAND_JOINTS_INDEX)): + joint = hand_joints[_HAND_JOINTS_INDEX[i]] + joint_position[i] = joint[:3] + + # Convert hand pose to the canonical frame. + joint_position = joint_position - joint_position[0:1, :] + xr_wrist_quat = hand_poses.get("wrist")[3:] + # OpenXR hand uses w,x,y,z order for quaternions but scipy uses x,y,z,w order + wrist_rot = R.from_quat([xr_wrist_quat[1], xr_wrist_quat[2], xr_wrist_quat[3], xr_wrist_quat[0]]).as_matrix() + + return joint_position @ wrist_rot @ operator2mano + + def compute_ref_value(self, joint_position: np.ndarray, indices: np.ndarray, retargeting_type: str) -> np.ndarray: + """Computes reference value for retargeting. + + Args: + joint_position: Joint positions array + indices: Target link indices + retargeting_type: Type of retargeting ("POSITION" or other) + + Returns: + Reference value in cartesian space + """ + if retargeting_type == "POSITION": + return joint_position[indices, :] + else: + origin_indices = indices[0, :] + task_indices = indices[1, :] + ref_value = joint_position[task_indices, :] - joint_position[origin_indices, :] + return ref_value + + def compute_one_hand( + self, hand_joints: dict[str, np.ndarray], retargeting: RetargetingConfig, operator2mano: np.ndarray + ) -> np.ndarray: + """Computes retargeted joint angles for one hand. + + Args: + hand_joints: Dictionary containing hand joint data + retargeting: Retargeting configuration object + operator2mano: Transformation matrix from operator to MANO frame + + Returns: + Retargeted joint angles + """ + joint_pos = self.convert_hand_joints(hand_joints, operator2mano) + ref_value = self.compute_ref_value( + joint_pos, + indices=retargeting.optimizer.target_link_human_indices, + retargeting_type=retargeting.optimizer.retargeting_type, + ) + # Enable gradient calculation and inference mode in case some other script has disabled it + # This is necessary for the retargeting to work since it uses gradient features that + # are not available in inference mode + with torch.enable_grad(): + with torch.inference_mode(False): + return retargeting.retarget(ref_value) + + def get_joint_names(self) -> list[str]: + """Returns list of all joint names.""" + return self.dof_names + + def get_left_joint_names(self) -> list[str]: + """Returns list of left hand joint names.""" + return self.left_dof_names + + def get_right_joint_names(self) -> list[str]: + """Returns list of right hand joint names.""" + return self.right_dof_names + + def get_hand_indices(self, robot) -> np.ndarray: + """Gets indices of hand joints in robot's DOF array. + + Args: + robot: Robot object containing DOF information + + Returns: + Array of joint indices + """ + return np.array([robot.dof_names.index(name) for name in self.dof_names], dtype=np.int64) + + def compute_left(self, left_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for left hand. + + Args: + left_hand_poses: Dictionary of left hand joint poses + + Returns: + Retargeted joint angles for left hand + """ + if left_hand_poses is not None: + left_hand_q = self.compute_one_hand(left_hand_poses, self._dex_left_hand, _OPERATOR2MANO_LEFT) + else: + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + return left_hand_q + + def compute_right(self, right_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for right hand. + + Args: + right_hand_poses: Dictionary of right hand joint poses + + Returns: + Retargeted joint angles for right hand + """ + if right_hand_poses is not None: + right_hand_q = self.compute_one_hand(right_hand_poses, self._dex_right_hand, _OPERATOR2MANO_RIGHT) + else: + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + return right_hand_q diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1t2_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1t2_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..0f95d4b9d7585ecd7a1a97177d6676943aa44e18 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/fourier/gr1t2_retargeter.py @@ -0,0 +1,168 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + +# This import exception is suppressed because gr1_t2_dex_retargeting_utils depends +# on pinocchio which is not available on Windows. +with contextlib.suppress(Exception): + from .gr1_t2_dex_retargeting_utils import GR1TR2DexRetargeting + + +class GR1T2Retargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to GR1T2 hand end-effector commands. + + This retargeter maps hand tracking data from OpenXR to joint commands for the GR1T2 robot's hands. + It handles both left and right hands, converting poses of the hands in OpenXR format joint angles + for the GR1T2 robot's hands. + """ + + def __init__( + self, + cfg: GR1T2RetargeterCfg, + ): + """Initialize the GR1T2 hand retargeter. + + Args: + enable_visualization: If True, visualize tracked hand joints + num_open_xr_hand_joints: Number of joints tracked by OpenXR + device: PyTorch device for computations + hand_joint_names: List of robot hand joint names + """ + + super().__init__(cfg) + self._hand_joint_names = cfg.hand_joint_names + self._hands_controller = GR1TR2DexRetargeting(self._hand_joint_names) + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + self._num_open_xr_hand_joints = cfg.num_open_xr_hand_joints + self._sim_device = cfg.sim_device + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.005, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector commands. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + + Returns: + tuple containing: + Left wrist pose + Right wrist pose in USD frame + Retargeted hand joint angles + """ + + # Access the left and right hand data using the enum key + left_hand_poses = data[DeviceBase.TrackingTarget.HAND_LEFT] + right_hand_poses = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + left_wrist = left_hand_poses.get("wrist") + right_wrist = right_hand_poses.get("wrist") + + if self._enable_visualization: + joints_position = np.zeros((self._num_open_xr_hand_joints, 3)) + + joints_position[::2] = np.array([pose[:3] for pose in left_hand_poses.values()]) + joints_position[1::2] = np.array([pose[:3] for pose in right_hand_poses.values()]) + + self._markers.visualize(translations=torch.tensor(joints_position, device=self._sim_device)) + + # Create array of zeros with length matching number of joint names + left_hands_pos = self._hands_controller.compute_left(left_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_left_joint_names()] + left_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + left_retargeted_hand_joints[indexes] = left_hands_pos + left_hand_joints = left_retargeted_hand_joints + + right_hands_pos = self._hands_controller.compute_right(right_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_right_joint_names()] + right_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + right_retargeted_hand_joints[indexes] = right_hands_pos + right_hand_joints = right_retargeted_hand_joints + retargeted_hand_joints = left_hand_joints + right_hand_joints + + # Convert numpy arrays to tensors and concatenate them + left_wrist_tensor = torch.tensor(left_wrist, dtype=torch.float32, device=self._sim_device) + right_wrist_tensor = torch.tensor(self._retarget_abs(right_wrist), dtype=torch.float32, device=self._sim_device) + hand_joints_tensor = torch.tensor(retargeted_hand_joints, dtype=torch.float32, device=self._sim_device) + + # Combine all tensors into a single tensor + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + wrist: Wrist pose data from OpenXR + + Returns: + Retargeted wrist pose in USD control frame + """ + + # Convert wrist data in openxr frame to usd control frame + + # Create pose object for openxr_right_wrist_in_world + # Note: The pose utils require torch tensors + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + openxr_right_wrist_in_world = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + + # The usd control frame is 180 degrees rotated around z axis wrt to the openxr frame + # This was determined through trial and error + zero_pos = torch.zeros(3, dtype=torch.float32) + # 180 degree rotation around z axis + z_axis_rot_quat = torch.tensor([0, 0, 0, 1], dtype=torch.float32) + usd_right_roll_link_in_openxr_right_wrist = PoseUtils.make_pose( + zero_pos, PoseUtils.matrix_from_quat(z_axis_rot_quat) + ) + + # Convert wrist pose in openxr frame to usd control frame + usd_right_roll_link_in_world = PoseUtils.pose_in_A_to_pose_in_B( + usd_right_roll_link_in_openxr_right_wrist, openxr_right_wrist_in_world + ) + + # extract position and rotation + usd_right_roll_link_in_world_pos, usd_right_roll_link_in_world_mat = PoseUtils.unmake_pose( + usd_right_roll_link_in_world + ) + usd_right_roll_link_in_world_quat = PoseUtils.quat_from_matrix(usd_right_roll_link_in_world_mat) + + return np.concatenate([usd_right_roll_link_in_world_pos, usd_right_roll_link_in_world_quat]) + + +@dataclass +class GR1T2RetargeterCfg(RetargeterCfg): + """Configuration for the GR1T2 retargeter.""" + + enable_visualization: bool = False + num_open_xr_hand_joints: int = 100 + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = GR1T2Retargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_lower_body_standing.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_lower_body_standing.py new file mode 100644 index 0000000000000000000000000000000000000000..1692b4a86d9b6ae217b3c89daab5bee9a724b87b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_lower_body_standing.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class G1LowerBodyStandingRetargeter(RetargeterBase): + """Provides lower body standing commands for the G1 robot.""" + + def __init__(self, cfg: G1LowerBodyStandingRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self.cfg = cfg + + def retarget(self, data: dict) -> torch.Tensor: + return torch.tensor([0.0, 0.0, 0.0, self.cfg.hip_height], device=self.cfg.sim_device) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + # This retargeter does not consume any device data + return [] + + +@dataclass +class G1LowerBodyStandingRetargeterCfg(RetargeterCfg): + """Configuration for the G1 lower body standing retargeter.""" + + hip_height: float = 0.72 + """Height of the G1 robot hip in meters. The value is a fixed height suitable for G1 to do tabletop manipulation.""" + retargeter_type: type[RetargeterBase] = G1LowerBodyStandingRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py new file mode 100644 index 0000000000000000000000000000000000000000..943abf0cb6eba69a03fa9093fae487847dd98938 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/g1_motion_controller_locomotion.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.sim import SimulationContext + + +class G1LowerBodyStandingMotionControllerRetargeter(RetargeterBase): + """Provides lower body standing commands for the G1 robot.""" + + def __init__(self, cfg: G1LowerBodyStandingMotionControllerRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self.cfg = cfg + self._hip_height = cfg.hip_height + + def retarget(self, data: dict) -> torch.Tensor: + left_thumbstick_x = 0.0 + left_thumbstick_y = 0.0 + right_thumbstick_x = 0.0 + right_thumbstick_y = 0.0 + + # Get controller data using enums + if ( + DeviceBase.TrackingTarget.CONTROLLER_LEFT in data + and data[DeviceBase.TrackingTarget.CONTROLLER_LEFT] is not None + ): + left_controller_data = data[DeviceBase.TrackingTarget.CONTROLLER_LEFT] + if len(left_controller_data) > DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + left_inputs = left_controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(left_inputs) > DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value: + left_thumbstick_x = left_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_X.value] + left_thumbstick_y = left_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value] + + if ( + DeviceBase.TrackingTarget.CONTROLLER_RIGHT in data + and data[DeviceBase.TrackingTarget.CONTROLLER_RIGHT] is not None + ): + right_controller_data = data[DeviceBase.TrackingTarget.CONTROLLER_RIGHT] + if len(right_controller_data) > DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + right_inputs = right_controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(right_inputs) > DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value: + right_thumbstick_x = right_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_X.value] + right_thumbstick_y = right_inputs[DeviceBase.MotionControllerInputIndex.THUMBSTICK_Y.value] + + # Thumbstick values are in the range of [-1, 1], so we need to scale them to the range of + # [-movement_scale, movement_scale] + left_thumbstick_x = left_thumbstick_x * self.cfg.movement_scale + left_thumbstick_y = left_thumbstick_y * self.cfg.movement_scale + + # Use rendering time step for deterministic hip height adjustment regardless of wall clock time. + dt = SimulationContext.instance().get_rendering_dt() + self._hip_height -= right_thumbstick_y * dt * self.cfg.rotation_scale + self._hip_height = max(0.4, min(1.0, self._hip_height)) + + return torch.tensor( + [-left_thumbstick_y, -left_thumbstick_x, -right_thumbstick_x, self._hip_height], + device=self.cfg.sim_device, + dtype=torch.float32, + ) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + +@dataclass +class G1LowerBodyStandingMotionControllerRetargeterCfg(RetargeterCfg): + """Configuration for the G1 lower body standing retargeter.""" + + hip_height: float = 0.74 + """Height of the G1 robot hip in meters. The value is a fixed height suitable for G1 to do tabletop manipulation.""" + + movement_scale: float = 0.5 + """Scale the movement of the robot to the range of [-movement_scale, movement_scale].""" + + rotation_scale: float = 0.35 + """Scale the rotation of the robot to the range of [-rotation_scale, rotation_scale].""" + retargeter_type: type[RetargeterBase] = G1LowerBodyStandingMotionControllerRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_left_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_left_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..de72352738fd2fb7366bf6f65091b05a8ef1d27b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_left_dexpilot.yml @@ -0,0 +1,24 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - L_thumb_tip + - L_index_tip + - L_middle_tip + - L_ring_tip + - L_pinky_tip + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - L_thumb_proximal_yaw_joint + - L_thumb_proximal_pitch_joint + - L_index_proximal_joint + - L_middle_proximal_joint + - L_ring_proximal_joint + - L_pinky_proximal_joint + type: DexPilot + urdf_path: /tmp/retarget_inspire_white_left_hand.urdf + wrist_link_name: L_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_right_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_right_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..5d0406da4365b6534345c64ba839a326b0a91259 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/data/configs/dex-retargeting/unitree_hand_right_dexpilot.yml @@ -0,0 +1,24 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +retargeting: + finger_tip_link_names: + - R_thumb_tip + - R_index_tip + - R_middle_tip + - R_ring_tip + - R_pinky_tip + low_pass_alpha: 0.2 + scaling_factor: 1.2 + target_joint_names: + - R_thumb_proximal_yaw_joint + - R_thumb_proximal_pitch_joint + - R_index_proximal_joint + - R_middle_proximal_joint + - R_ring_proximal_joint + - R_pinky_proximal_joint + type: DexPilot + urdf_path: /tmp/retarget_inspire_white_right_hand.urdf + wrist_link_name: R_hand_base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_dex_retargeting_utils.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_dex_retargeting_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3d759003f8549f8c18747bf86a2a0e468a0498ca --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_dex_retargeting_utils.py @@ -0,0 +1,266 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +import os + +import numpy as np +import torch +import yaml +from dex_retargeting.retargeting_config import RetargetingConfig +from scipy.spatial.transform import Rotation as R + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +# import logger +logger = logging.getLogger(__name__) + +# The index to map the OpenXR hand joints to the hand joints used +# in Dex-retargeting. +_HAND_JOINTS_INDEX = [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25] + +# The transformation matrices to convert hand pose to canonical view. +_OPERATOR2MANO_RIGHT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_OPERATOR2MANO_LEFT = np.array( + [ + [0, -1, 0], + [-1, 0, 0], + [0, 0, -1], + ] +) + +_LEFT_HAND_JOINT_NAMES = [ + "L_thumb_proximal_yaw_joint", + "L_thumb_proximal_pitch_joint", + "L_thumb_intermediate_joint", + "L_thumb_distal_joint", + "L_index_proximal_joint", + "L_index_intermediate_joint", + "L_middle_proximal_joint", + "L_middle_intermediate_joint", + "L_ring_proximal_joint", + "L_ring_intermediate_joint", + "L_pinky_proximal_joint", + "L_pinky_intermediate_joint", +] + + +_RIGHT_HAND_JOINT_NAMES = [ + "R_thumb_proximal_yaw_joint", + "R_thumb_proximal_pitch_joint", + "R_thumb_intermediate_joint", + "R_thumb_distal_joint", + "R_index_proximal_joint", + "R_index_intermediate_joint", + "R_middle_proximal_joint", + "R_middle_intermediate_joint", + "R_ring_proximal_joint", + "R_ring_intermediate_joint", + "R_pinky_proximal_joint", + "R_pinky_intermediate_joint", +] + + +class UnitreeG1DexRetargeting: + """A class for hand retargeting with GR1Fourier. + + Handles retargeting of OpenXRhand tracking data to GR1T2 robot hand joint angles. + """ + + def __init__( + self, + hand_joint_names: list[str], + right_hand_config_filename: str = "unitree_hand_right_dexpilot.yml", + left_hand_config_filename: str = "unitree_hand_left_dexpilot.yml", + left_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/G1_inspire_assets/retarget_inspire_white_left_hand.urdf", # noqa: E501 + right_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Mimic/G1_inspire_assets/retarget_inspire_white_right_hand.urdf", # noqa: E501 + ): + """Initialize the hand retargeting. + + Args: + hand_joint_names: Names of hand joints in the robot model + right_hand_config_filename: Config file for right hand retargeting + left_hand_config_filename: Config file for left hand retargeting + """ + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/")) + config_dir = os.path.join(data_dir, "configs/dex-retargeting") + + # Download urdf files from aws + local_left_urdf_path = retrieve_file_path(left_hand_urdf_path, force_download=True) + local_right_urdf_path = retrieve_file_path(right_hand_urdf_path, force_download=True) + + left_config_path = os.path.join(config_dir, left_hand_config_filename) + right_config_path = os.path.join(config_dir, right_hand_config_filename) + + # Update the YAML files with the correct URDF paths + self._update_yaml_with_urdf_path(left_config_path, local_left_urdf_path) + self._update_yaml_with_urdf_path(right_config_path, local_right_urdf_path) + + self._dex_left_hand = RetargetingConfig.load_from_file(left_config_path).build() + self._dex_right_hand = RetargetingConfig.load_from_file(right_config_path).build() + + self.left_dof_names = self._dex_left_hand.optimizer.robot.dof_joint_names + self.right_dof_names = self._dex_right_hand.optimizer.robot.dof_joint_names + + self.dof_names = self.left_dof_names + self.right_dof_names + self.isaac_lab_hand_joint_names = hand_joint_names + + logger.info("[UnitreeG1DexRetargeter] init done.") + + def _update_yaml_with_urdf_path(self, yaml_path: str, urdf_path: str): + """Update YAML file with the correct URDF path. + + Args: + yaml_path: Path to the YAML configuration file + urdf_path: Path to the URDF file to use + """ + try: + # Read the YAML file + with open(yaml_path) as file: + config = yaml.safe_load(file) + + # Update the URDF path in the configuration + if "retargeting" in config: + config["retargeting"]["urdf_path"] = urdf_path + logger.info(f"Updated URDF path in {yaml_path} to {urdf_path}") + else: + logger.warning(f"Unable to find 'retargeting' section in {yaml_path}") + + # Write the updated configuration back to the file + with open(yaml_path, "w") as file: + yaml.dump(config, file) + + except Exception as e: + logger.error(f"Error updating YAML file {yaml_path}: {e}") + + def convert_hand_joints(self, hand_poses: dict[str, np.ndarray], operator2mano: np.ndarray) -> np.ndarray: + """Prepares the hand joints data for retargeting. + + Args: + hand_poses: Dictionary containing hand pose data with joint positions and rotations + operator2mano: Transformation matrix to convert from operator to MANO frame + + Returns: + Joint positions with shape (21, 3) + """ + joint_position = np.zeros((21, 3)) + hand_joints = list(hand_poses.values()) + for i in range(len(_HAND_JOINTS_INDEX)): + joint = hand_joints[_HAND_JOINTS_INDEX[i]] + joint_position[i] = joint[:3] + + # Convert hand pose to the canonical frame. + joint_position = joint_position - joint_position[0:1, :] + xr_wrist_quat = hand_poses.get("wrist")[3:] + # OpenXR hand uses w,x,y,z order for quaternions but scipy uses x,y,z,w order + wrist_rot = R.from_quat([xr_wrist_quat[1], xr_wrist_quat[2], xr_wrist_quat[3], xr_wrist_quat[0]]).as_matrix() + + return joint_position @ wrist_rot @ operator2mano + + def compute_ref_value(self, joint_position: np.ndarray, indices: np.ndarray, retargeting_type: str) -> np.ndarray: + """Computes reference value for retargeting. + + Args: + joint_position: Joint positions array + indices: Target link indices + retargeting_type: Type of retargeting ("POSITION" or other) + + Returns: + Reference value in cartesian space + """ + if retargeting_type == "POSITION": + return joint_position[indices, :] + else: + origin_indices = indices[0, :] + task_indices = indices[1, :] + ref_value = joint_position[task_indices, :] - joint_position[origin_indices, :] + return ref_value + + def compute_one_hand( + self, hand_joints: dict[str, np.ndarray], retargeting: RetargetingConfig, operator2mano: np.ndarray + ) -> np.ndarray: + """Computes retargeted joint angles for one hand. + + Args: + hand_joints: Dictionary containing hand joint data + retargeting: Retargeting configuration object + operator2mano: Transformation matrix from operator to MANO frame + + Returns: + Retargeted joint angles + """ + joint_pos = self.convert_hand_joints(hand_joints, operator2mano) + ref_value = self.compute_ref_value( + joint_pos, + indices=retargeting.optimizer.target_link_human_indices, + retargeting_type=retargeting.optimizer.retargeting_type, + ) + + # Enable gradient calculation and inference mode in case some other script has disabled it + # This is necessary for the retargeting to work since it uses gradient features that + # are not available in inference mode + with torch.enable_grad(): + with torch.inference_mode(False): + return retargeting.retarget(ref_value) + + def get_joint_names(self) -> list[str]: + """Returns list of all joint names.""" + return self.dof_names + + def get_left_joint_names(self) -> list[str]: + """Returns list of left hand joint names.""" + return self.left_dof_names + + def get_right_joint_names(self) -> list[str]: + """Returns list of right hand joint names.""" + return self.right_dof_names + + def get_hand_indices(self, robot) -> np.ndarray: + """Gets indices of hand joints in robot's DOF array. + + Args: + robot: Robot object containing DOF information + + Returns: + Array of joint indices + """ + return np.array([robot.dof_names.index(name) for name in self.dof_names], dtype=np.int64) + + def compute_left(self, left_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for left hand. + + Args: + left_hand_poses: Dictionary of left hand joint poses + + Returns: + Retargeted joint angles for left hand + """ + if left_hand_poses is not None: + left_hand_q = self.compute_one_hand(left_hand_poses, self._dex_left_hand, _OPERATOR2MANO_LEFT) + else: + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + return left_hand_q + + def compute_right(self, right_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for right hand. + + Args: + right_hand_poses: Dictionary of right hand joint poses + + Returns: + Retargeted joint angles for right hand + """ + if right_hand_poses is not None: + right_hand_q = self.compute_one_hand(right_hand_poses, self._dex_right_hand, _OPERATOR2MANO_RIGHT) + else: + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + return right_hand_q diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_upper_body_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_upper_body_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..17c73dc7ea40195edcdbddaf6a342dc03da7659e --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/inspire/g1_upper_body_retargeter.py @@ -0,0 +1,164 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + +# This import exception is suppressed because g1_dex_retargeting_utils +# depends on pinocchio which is not available on Windows. +with contextlib.suppress(Exception): + from .g1_dex_retargeting_utils import UnitreeG1DexRetargeting + + +class UnitreeG1Retargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to GR1T2 hand end-effector commands. + + This retargeter maps hand tracking data from OpenXR to joint commands for the GR1T2 robot's hands. + It handles both left and right hands, converting poses of the hands in OpenXR format joint angles + for the GR1T2 robot's hands. + """ + + def __init__( + self, + cfg: UnitreeG1RetargeterCfg, + ): + """Initialize the UnitreeG1 hand retargeter. + + Args: + enable_visualization: If True, visualize tracked hand joints + num_open_xr_hand_joints: Number of joints tracked by OpenXR + device: PyTorch device for computations + hand_joint_names: List of robot hand joint names + """ + + super().__init__(cfg) + self._hand_joint_names = cfg.hand_joint_names + self._hands_controller = UnitreeG1DexRetargeting(self._hand_joint_names) + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + self._num_open_xr_hand_joints = cfg.num_open_xr_hand_joints + self._sim_device = cfg.sim_device + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.005, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector commands. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + + Returns: + tuple containing: + Left wrist pose + Right wrist pose in USD frame + Retargeted hand joint angles + """ + + # Access the left and right hand data using the enum key + left_hand_poses = data[DeviceBase.TrackingTarget.HAND_LEFT] + right_hand_poses = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + left_wrist = left_hand_poses.get("wrist") + right_wrist = right_hand_poses.get("wrist") + + if self._enable_visualization: + joints_position = np.zeros((self._num_open_xr_hand_joints, 3)) + + joints_position[::2] = np.array([pose[:3] for pose in left_hand_poses.values()]) + joints_position[1::2] = np.array([pose[:3] for pose in right_hand_poses.values()]) + + self._markers.visualize(translations=torch.tensor(joints_position, device=self._sim_device)) + + # Create array of zeros with length matching number of joint names + left_hands_pos = self._hands_controller.compute_left(left_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_left_joint_names()] + left_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + left_retargeted_hand_joints[indexes] = left_hands_pos + left_hand_joints = left_retargeted_hand_joints + + right_hands_pos = self._hands_controller.compute_right(right_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_right_joint_names()] + right_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + right_retargeted_hand_joints[indexes] = right_hands_pos + right_hand_joints = right_retargeted_hand_joints + retargeted_hand_joints = left_hand_joints + right_hand_joints + + # Convert numpy arrays to tensors and concatenate them + left_wrist_tensor = torch.tensor( + self._retarget_abs(left_wrist, True), dtype=torch.float32, device=self._sim_device + ) + right_wrist_tensor = torch.tensor( + self._retarget_abs(right_wrist, False), dtype=torch.float32, device=self._sim_device + ) + hand_joints_tensor = torch.tensor(retargeted_hand_joints, dtype=torch.float32, device=self._sim_device) + + # Combine all tensors into a single tensor + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, wrist: np.ndarray, is_left: bool) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + wrist: Wrist pose data from OpenXR. + is_left: True for the left hand, False for the right hand. + + Returns: + Retargeted wrist pose in USD control frame. + """ + # Note: This was determined through trial, use the target quat and cloudXR quat, + # to estimate a most reasonable transformation matrix + + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + if is_left: + # Corresponds to a rotation of (0, 180, 0) in euler angles (x,y,z) + combined_quat = torch.tensor([0.7071, 0, 0.7071, 0], dtype=torch.float32) + else: + # Corresponds to a rotation of (180, 0, 0) in euler angles (x,y,z) + combined_quat = torch.tensor([0, 0.7071, 0, -0.7071], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + +@dataclass +class UnitreeG1RetargeterCfg(RetargeterCfg): + """Configuration for the UnitreeG1 retargeter.""" + + enable_visualization: bool = False + num_open_xr_hand_joints: int = 100 + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = UnitreeG1Retargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..adb60a61b44ace2e49126f6e016abb9f2ea049e8 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_left_dexpilot.yml @@ -0,0 +1,18 @@ +retargeting: + finger_tip_link_names: + - thumb_tip + - index_tip + - middle_tip + low_pass_alpha: 0.2 + scaling_factor: 1.0 + target_joint_names: + - left_hand_thumb_0_joint + - left_hand_thumb_1_joint + - left_hand_thumb_2_joint + - left_hand_middle_0_joint + - left_hand_middle_1_joint + - left_hand_index_0_joint + - left_hand_index_1_joint + type: DexPilot + urdf_path: /tmp/G1_left_hand.urdf + wrist_link_name: base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml new file mode 100644 index 0000000000000000000000000000000000000000..bec4782e4c32818e25192b03c4897ba8134c3435 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/data/configs/dex-retargeting/g1_hand_right_dexpilot.yml @@ -0,0 +1,18 @@ +retargeting: + finger_tip_link_names: + - thumb_tip + - index_tip + - middle_tip + low_pass_alpha: 0.2 + scaling_factor: 1.0 + target_joint_names: + - right_hand_thumb_0_joint + - right_hand_thumb_1_joint + - right_hand_thumb_2_joint + - right_hand_middle_0_joint + - right_hand_middle_1_joint + - right_hand_index_0_joint + - right_hand_index_1_joint + type: DexPilot + urdf_path: /tmp/G1_right_hand.urdf + wrist_link_name: base_link diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6575eaaba41c917ae6bdf985e79c6c815896edfe --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_dex_retargeting_utils.py @@ -0,0 +1,258 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +import os + +import numpy as np +import torch +import yaml +from dex_retargeting.retargeting_config import RetargetingConfig +from scipy.spatial.transform import Rotation as R + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +# import logger +logger = logging.getLogger(__name__) + +# yourdfpy loads visual/collision meshes with the hand URDFs; these aren't needed for +# retargeting and clutter the logs, so we suppress them. +logging.getLogger("dex_retargeting.yourdfpy").setLevel(logging.ERROR) + +# The index to map the OpenXR hand joints to the hand joints used +# in Dex-retargeting. +_HAND_JOINTS_INDEX = [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25] + +# The transformation matrices to convert hand pose to canonical view. +_OPERATOR2MANO_RIGHT = np.array( + [ + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + ] +) + +_OPERATOR2MANO_LEFT = np.array( + [ + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + ] +) + +# G1 robot hand joint names - 2 fingers and 1 thumb configuration +_LEFT_HAND_JOINT_NAMES = [ + "left_hand_thumb_0_joint", # Thumb base (yaw axis) + "left_hand_thumb_1_joint", # Thumb middle (pitch axis) + "left_hand_thumb_2_joint", # Thumb tip + "left_hand_index_0_joint", # Index finger proximal + "left_hand_index_1_joint", # Index finger distal + "left_hand_middle_0_joint", # Middle finger proximal + "left_hand_middle_1_joint", # Middle finger distal +] + +_RIGHT_HAND_JOINT_NAMES = [ + "right_hand_thumb_0_joint", # Thumb base (yaw axis) + "right_hand_thumb_1_joint", # Thumb middle (pitch axis) + "right_hand_thumb_2_joint", # Thumb tip + "right_hand_index_0_joint", # Index finger proximal + "right_hand_index_1_joint", # Index finger distal + "right_hand_middle_0_joint", # Middle finger proximal + "right_hand_middle_1_joint", # Middle finger distal +] + + +class G1TriHandDexRetargeting: + """A class for hand retargeting with G1. + + Handles retargeting of OpenXRhand tracking data to G1 robot hand joint angles. + """ + + def __init__( + self, + hand_joint_names: list[str], + right_hand_config_filename: str = "g1_hand_right_dexpilot.yml", + left_hand_config_filename: str = "g1_hand_left_dexpilot.yml", + left_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_dexpilot_asset/G1_left_hand.urdf", # noqa: E501 + right_hand_urdf_path: str = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_dexpilot_asset/G1_right_hand.urdf", # noqa: E501 + ): + """Initialize the hand retargeting. + + Args: + hand_joint_names: Names of hand joints in the robot model + right_hand_config_filename: Config file for right hand retargeting + left_hand_config_filename: Config file for left hand retargeting + """ + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/")) + config_dir = os.path.join(data_dir, "configs/dex-retargeting") + + # Download urdf files from aws + local_left_urdf_path = retrieve_file_path(left_hand_urdf_path, force_download=True) + local_right_urdf_path = retrieve_file_path(right_hand_urdf_path, force_download=True) + + left_config_path = os.path.join(config_dir, left_hand_config_filename) + right_config_path = os.path.join(config_dir, right_hand_config_filename) + + # Update the YAML files with the correct URDF paths + self._update_yaml_with_urdf_path(left_config_path, local_left_urdf_path) + self._update_yaml_with_urdf_path(right_config_path, local_right_urdf_path) + + self._dex_left_hand = RetargetingConfig.load_from_file(left_config_path).build() + self._dex_right_hand = RetargetingConfig.load_from_file(right_config_path).build() + + self.left_dof_names = self._dex_left_hand.optimizer.robot.dof_joint_names + self.right_dof_names = self._dex_right_hand.optimizer.robot.dof_joint_names + self.dof_names = self.left_dof_names + self.right_dof_names + self.isaac_lab_hand_joint_names = hand_joint_names + + logger.info("[G1DexRetargeter] init done.") + + def _update_yaml_with_urdf_path(self, yaml_path: str, urdf_path: str): + """Update YAML file with the correct URDF path. + + Args: + yaml_path: Path to the YAML configuration file + urdf_path: Path to the URDF file to use + """ + try: + # Read the YAML file + with open(yaml_path) as file: + config = yaml.safe_load(file) + + # Update the URDF path in the configuration + if "retargeting" in config: + config["retargeting"]["urdf_path"] = urdf_path + logger.info(f"Updated URDF path in {yaml_path} to {urdf_path}") + else: + logger.warning(f"Unable to find 'retargeting' section in {yaml_path}") + + # Write the updated configuration back to the file + with open(yaml_path, "w") as file: + yaml.dump(config, file) + + except Exception as e: + logger.error(f"Error updating YAML file {yaml_path}: {e}") + + def convert_hand_joints(self, hand_poses: dict[str, np.ndarray], operator2mano: np.ndarray) -> np.ndarray: + """Prepares the hand joints data for retargeting. + + Args: + hand_poses: Dictionary containing hand pose data with joint positions and rotations + operator2mano: Transformation matrix to convert from operator to MANO frame + + Returns: + Joint positions with shape (21, 3) + """ + joint_position = np.zeros((21, 3)) + hand_joints = list(hand_poses.values()) + for i, joint_index in enumerate(_HAND_JOINTS_INDEX): + joint = hand_joints[joint_index] + joint_position[i] = joint[:3] + + # Convert hand pose to the canonical frame. + joint_position = joint_position - joint_position[0:1, :] + xr_wrist_quat = hand_poses.get("wrist")[3:] + # OpenXR hand uses w,x,y,z order for quaternions but scipy uses x,y,z,w order + wrist_rot = R.from_quat([xr_wrist_quat[1], xr_wrist_quat[2], xr_wrist_quat[3], xr_wrist_quat[0]]).as_matrix() + + return joint_position @ wrist_rot @ operator2mano + + def compute_ref_value(self, joint_position: np.ndarray, indices: np.ndarray, retargeting_type: str) -> np.ndarray: + """Computes reference value for retargeting. + + Args: + joint_position: Joint positions array + indices: Target link indices + retargeting_type: Type of retargeting ("POSITION" or other) + + Returns: + Reference value in cartesian space + """ + if retargeting_type == "POSITION": + return joint_position[indices, :] + else: + origin_indices = indices[0, :] + task_indices = indices[1, :] + ref_value = joint_position[task_indices, :] - joint_position[origin_indices, :] + return ref_value + + def compute_one_hand( + self, hand_joints: dict[str, np.ndarray], retargeting: RetargetingConfig, operator2mano: np.ndarray + ) -> np.ndarray: + """Computes retargeted joint angles for one hand. + + Args: + hand_joints: Dictionary containing hand joint data + retargeting: Retargeting configuration object + operator2mano: Transformation matrix from operator to MANO frame + + Returns: + Retargeted joint angles + """ + joint_pos = self.convert_hand_joints(hand_joints, operator2mano) + ref_value = self.compute_ref_value( + joint_pos, + indices=retargeting.optimizer.target_link_human_indices, + retargeting_type=retargeting.optimizer.retargeting_type, + ) + # Enable gradient calculation and inference mode in case some other script has disabled it + # This is necessary for the retargeting to work since it uses gradient features that + # are not available in inference mode + with torch.enable_grad(): + with torch.inference_mode(False): + return retargeting.retarget(ref_value) + + def get_joint_names(self) -> list[str]: + """Returns list of all joint names.""" + return self.dof_names + + def get_left_joint_names(self) -> list[str]: + """Returns list of left hand joint names.""" + return self.left_dof_names + + def get_right_joint_names(self) -> list[str]: + """Returns list of right hand joint names.""" + return self.right_dof_names + + def get_hand_indices(self, robot) -> np.ndarray: + """Gets indices of hand joints in robot's DOF array. + + Args: + robot: Robot object containing DOF information + + Returns: + Array of joint indices + """ + return np.array([robot.dof_names.index(name) for name in self.dof_names], dtype=np.int64) + + def compute_left(self, left_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for left hand. + + Args: + left_hand_poses: Dictionary of left hand joint poses + + Returns: + Retargeted joint angles for left hand + """ + if left_hand_poses is not None: + left_hand_q = self.compute_one_hand(left_hand_poses, self._dex_left_hand, _OPERATOR2MANO_LEFT) + else: + left_hand_q = np.zeros(len(_LEFT_HAND_JOINT_NAMES)) + return left_hand_q + + def compute_right(self, right_hand_poses: dict[str, np.ndarray]) -> np.ndarray: + """Computes retargeted joints for right hand. + + Args: + right_hand_poses: Dictionary of right hand joint poses + + Returns: + Retargeted joint angles for right hand + """ + if right_hand_poses is not None: + right_hand_q = self.compute_one_hand(right_hand_poses, self._dex_right_hand, _OPERATOR2MANO_RIGHT) + else: + right_hand_q = np.zeros(len(_RIGHT_HAND_JOINT_NAMES)) + return right_hand_q diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py new file mode 100644 index 0000000000000000000000000000000000000000..c22f40a283f3fd9af91936bab4714f5479af46b5 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_gripper.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class G1TriHandUpperBodyMotionControllerGripperRetargeter(RetargeterBase): + """Retargeter for G1 gripper that outputs a boolean state based on controller trigger input, + concatenated with the retargeted wrist pose. + + Gripper: + - Uses hysteresis to prevent flickering when the trigger is near the threshold. + - Output is 0.0 for open, 1.0 for close. + + Wrist: + - Retargets absolute pose from controller to robot frame. + - Applies a fixed offset rotation for comfort/alignment. + """ + + def __init__(self, cfg: G1TriHandUpperBodyMotionControllerGripperRetargeterCfg): + """Initialize the retargeter. + + Args: + cfg: Configuration for the retargeter. + """ + super().__init__(cfg) + self._cfg = cfg + # Track previous state for hysteresis (left, right) + self._prev_left_state: float = 0.0 + self._prev_right_state: float = 0.0 + + def retarget(self, data: dict) -> torch.Tensor: + """Retarget controller inputs to gripper boolean state and wrist pose. + + Args: + data: Dictionary with MotionControllerTrackingTarget.LEFT/RIGHT keys + Each value is a 2D array: [pose(7), inputs(7)] + + Returns: + Tensor: [left_gripper_state(1), right_gripper_state(1), left_wrist(7), right_wrist(7)] + Wrist format: [x, y, z, qw, qx, qy, qz] + """ + # Get controller data + left_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_LEFT, np.array([])) + right_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_RIGHT, np.array([])) + + # --- Gripper Logic --- + # Extract hand state from controller data with hysteresis + left_hand_state: float = self._extract_hand_state(left_controller_data, self._prev_left_state) + right_hand_state: float = self._extract_hand_state(right_controller_data, self._prev_right_state) + + # Update previous states + self._prev_left_state = left_hand_state + self._prev_right_state = right_hand_state + + gripper_tensor = torch.tensor([left_hand_state, right_hand_state], dtype=torch.float32, device=self._sim_device) + + # --- Wrist Logic --- + # Default wrist poses (position + quaternion [w, x, y, z] as per default_wrist init) + # Note: default_wrist is [x, y, z, w, x, y, z] in reference, but seemingly used as [x,y,z, w,x,y,z] + default_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + # Extract poses from controller data + left_wrist = self._extract_wrist_pose(left_controller_data, default_wrist) + right_wrist = self._extract_wrist_pose(right_controller_data, default_wrist) + + # Convert to tensors + left_wrist_tensor = torch.tensor(self._retarget_abs(left_wrist), dtype=torch.float32, device=self._sim_device) + right_wrist_tensor = torch.tensor(self._retarget_abs(right_wrist), dtype=torch.float32, device=self._sim_device) + + # Concatenate: [gripper(2), left_wrist(7), right_wrist(7)] + return torch.cat([gripper_tensor, left_wrist_tensor, right_wrist_tensor]) + + def _extract_hand_state(self, controller_data: np.ndarray, prev_state: float) -> float: + """Extract hand state from controller data with hysteresis. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + prev_state: Previous hand state (0.0 or 1.0) + + Returns: + Hand state as float (0.0 for open, 1.0 for close) + """ + if len(controller_data) <= DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + return 0.0 + + # Extract inputs from second row + inputs = controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + if len(inputs) < len(DeviceBase.MotionControllerInputIndex): + return 0.0 + + # Extract specific inputs using enum + trigger = inputs[DeviceBase.MotionControllerInputIndex.TRIGGER.value] # 0.0 to 1.0 (analog) + + # Apply hysteresis + if prev_state < 0.5: # Currently open + return 1.0 if trigger > self._cfg.threshold_high else 0.0 + else: # Currently closed + return 0.0 if trigger < self._cfg.threshold_low else 1.0 + + def _extract_wrist_pose(self, controller_data: np.ndarray, default_pose: np.ndarray) -> np.ndarray: + """Extract wrist pose from controller data. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + default_pose: Default pose to use if no data + + Returns: + Wrist pose array [x, y, z, w, x, y, z] + """ + if len(controller_data) > DeviceBase.MotionControllerDataRowIndex.POSE.value: + return controller_data[DeviceBase.MotionControllerDataRowIndex.POSE.value] + return default_pose + + def _retarget_abs(self, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting for controller wrists.""" + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + # Combined -75° (rather than -90° for wrist comfort) Y rotation + 90° Z rotation + # This is equivalent to (0, -75, 90) in euler angles + combined_quat = torch.tensor([0.5358, -0.4619, 0.5358, 0.4619], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + +@dataclass +class G1TriHandUpperBodyMotionControllerGripperRetargeterCfg(RetargeterCfg): + """Configuration for the G1 boolean gripper and wrist retargeter.""" + + threshold_high: float = 0.6 # Threshold to close hand + threshold_low: float = 0.4 # Threshold to open hand + retargeter_type: type[RetargeterBase] = G1TriHandUpperBodyMotionControllerGripperRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..0138bdf6d6b94166bce39198a3584e1ea78e2c2b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_motion_ctrl_retargeter.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + + +class G1TriHandUpperBodyMotionControllerRetargeter(RetargeterBase): + """Simple retargeter that maps motion controller inputs to G1 hand joints. + + Mapping: + - A button (digital 0/1) → Thumb joints + - Trigger (analog 0-1) → Index finger joints + - Squeeze (analog 0-1) → Middle finger joints + """ + + def __init__(self, cfg: G1TriHandUpperBodyMotionControllerRetargeterCfg): + """Initialize the retargeter.""" + super().__init__(cfg) + self._sim_device = cfg.sim_device + self._hand_joint_names = cfg.hand_joint_names + self._enable_visualization = cfg.enable_visualization + + if cfg.hand_joint_names is None: + raise ValueError("hand_joint_names must be provided") + + # Initialize visualization if enabled + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/g1_controller_markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert controller inputs to robot commands. + + Args: + data: Dictionary with MotionControllerTrackingTarget.LEFT/RIGHT keys + Each value is a 2D array: [pose(7), inputs(7)] + + Returns: + Tensor: [left_wrist(7), right_wrist(7), hand_joints(14)] + hand_joints order: + [ + left_proximal(3), right_proximal(3), left_distal(2), left_thumb_middle(1), + right_distal(2), right_thumb_middle(1), left_thumb_tip(1), right_thumb_tip(1) + ] + """ + + # Get controller data + left_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_LEFT, np.array([])) + right_controller_data = data.get(DeviceBase.TrackingTarget.CONTROLLER_RIGHT, np.array([])) + + # Default wrist poses (position + quaternion) + default_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + # Extract poses from controller data + left_wrist = self._extract_wrist_pose(left_controller_data, default_wrist) + right_wrist = self._extract_wrist_pose(right_controller_data, default_wrist) + + # Map controller inputs to hand joints + left_hand_joints = self._map_to_hand_joints(left_controller_data, is_left=True) + right_hand_joints = self._map_to_hand_joints(right_controller_data, is_left=False) + + # Negate left hand joints for proper mirroring + left_hand_joints = -left_hand_joints + + # Combine joints in the expected order: + # [left_proximal(3), right_proximal(3), left_distal(2), left_thumb_middle(1), + # right_distal(2), right_thumb_middle(1), left_thumb_tip(1), right_thumb_tip(1)] + all_hand_joints = np.array( + [ + left_hand_joints[3], # left_index_proximal + left_hand_joints[5], # left_middle_proximal + left_hand_joints[0], # left_thumb_base + right_hand_joints[3], # right_index_proximal + right_hand_joints[5], # right_middle_proximal + right_hand_joints[0], # right_thumb_base + left_hand_joints[4], # left_index_distal + left_hand_joints[6], # left_middle_distal + left_hand_joints[1], # left_thumb_middle + right_hand_joints[4], # right_index_distal + right_hand_joints[6], # right_middle_distal + right_hand_joints[1], # right_thumb_middle + left_hand_joints[2], # left_thumb_tip + right_hand_joints[2], # right_thumb_tip + ] + ) + + # Convert to tensors + left_wrist_tensor = torch.tensor( + self._retarget_abs(left_wrist, is_left=True), dtype=torch.float32, device=self._sim_device + ) + right_wrist_tensor = torch.tensor( + self._retarget_abs(right_wrist, is_left=False), dtype=torch.float32, device=self._sim_device + ) + hand_joints_tensor = torch.tensor(all_hand_joints, dtype=torch.float32, device=self._sim_device) + + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.MOTION_CONTROLLER] + + def _extract_wrist_pose(self, controller_data: np.ndarray, default_pose: np.ndarray) -> np.ndarray: + """Extract wrist pose from controller data. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + default_pose: Default pose to use if no data + + Returns: + Wrist pose array [x, y, z, w, x, y, z] + """ + if len(controller_data) > DeviceBase.MotionControllerDataRowIndex.POSE.value: + return controller_data[DeviceBase.MotionControllerDataRowIndex.POSE.value] + return default_pose + + def _map_to_hand_joints(self, controller_data: np.ndarray, is_left: bool) -> np.ndarray: + """Map controller inputs to hand joint angles. + + Args: + controller_data: 2D array [pose(7), inputs(7)] + is_left: True for left hand, False for right hand + + Returns: + Hand joint angles (7 joints per hand) in radians + """ + + # Initialize all joints to zero + hand_joints = np.zeros(7) + + if len(controller_data) <= DeviceBase.MotionControllerDataRowIndex.INPUTS.value: + return hand_joints + + # Extract inputs from second row + inputs = controller_data[DeviceBase.MotionControllerDataRowIndex.INPUTS.value] + + if len(inputs) < len(DeviceBase.MotionControllerInputIndex): + return hand_joints + + # Extract specific inputs using enum + trigger = inputs[DeviceBase.MotionControllerInputIndex.TRIGGER.value] # 0.0 to 1.0 (analog) + squeeze = inputs[DeviceBase.MotionControllerInputIndex.SQUEEZE.value] # 0.0 to 1.0 (analog) + + # Grasping logic: + # If trigger is pressed, we grasp with index and thumb. + # If squeeze is pressed, we grasp with middle and thumb. + # If both are pressed, we grasp with index, middle, and thumb. + # The thumb rotates towards the direction of the pressing finger. + # If both are pressed, the thumb stays in the middle. + + thumb_button = max(trigger, squeeze) + + # Map to G1 hand joints (in radians) + # Thumb joints (3 joints) - controlled by A button (digital) + thumb_angle = -thumb_button # Max 1 radian ≈ 57° + + # Thumb rotation: + # If trigger is pressed, we rotate the thumb toward the index finger. + # If squeeze is pressed, we rotate the thumb toward the middle finger. + # If both are pressed, the thumb stays between the index and middle fingers. + # Trigger pushes toward +0.5, squeeze pushes toward -0.5 + # trigger=1,squeeze=0 → 0.5; trigger=0,squeeze=1 → -0.5; both=1 → 0 + thumb_rotation = 0.5 * trigger - 0.5 * squeeze + + if not is_left: + thumb_rotation = -thumb_rotation + + # These values were found empirically to get a good gripper pose. + + hand_joints[0] = thumb_rotation # thumb_0_joint (base) + hand_joints[1] = thumb_angle * 0.4 # thumb_1_joint (middle) + hand_joints[2] = thumb_angle * 0.7 # thumb_2_joint (tip) + + # Index finger joints (2 joints) - controlled by trigger (analog) + index_angle = trigger * 1.0 # Max 1.0 radians ≈ 57° + hand_joints[3] = index_angle # index_0_joint (proximal) + hand_joints[4] = index_angle # index_1_joint (distal) + + # Middle finger joints (2 joints) - controlled by squeeze (analog) + middle_angle = squeeze * 1.0 # Max 1.0 radians ≈ 57° + hand_joints[5] = middle_angle # middle_0_joint (proximal) + hand_joints[6] = middle_angle # middle_1_joint (distal) + + return hand_joints + + def _retarget_abs(self, wrist: np.ndarray, is_left: bool) -> np.ndarray: + """Handle absolute pose retargeting for controller wrists.""" + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + # Combined -75° (rather than -90° for wrist comfort) Y rotation + 90° Z rotation + # This is equivalent to (0, -75, 90) in euler angles + combined_quat = torch.tensor([0.5358, -0.4619, 0.5358, 0.4619], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + +@dataclass +class G1TriHandUpperBodyMotionControllerRetargeterCfg(RetargeterCfg): + """Configuration for the G1 Controller Upper Body retargeter.""" + + enable_visualization: bool = False + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = G1TriHandUpperBodyMotionControllerRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..9c8651f43de1dc87275eb0a930896fc5afe54f29 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/humanoid/unitree/trihand/g1_upper_body_retargeter.py @@ -0,0 +1,173 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +from dataclasses import dataclass + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as PoseUtils +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg + +# This import exception is suppressed because g1_dex_retargeting_utils depends +# on pinocchio which is not available on Windows. +with contextlib.suppress(Exception): + from .g1_dex_retargeting_utils import G1TriHandDexRetargeting + + +class G1TriHandUpperBodyRetargeter(RetargeterBase): + """Retargets OpenXR data to G1 upper body commands. + + This retargeter maps hand tracking data from OpenXR to wrist and hand joint commands for the G1 robot. + It handles both left and right hands, converting poses of the hands in OpenXR format to appropriate wrist poses + and joint angles for the G1 robot's upper body. + """ + + def __init__( + self, + cfg: G1TriHandUpperBodyRetargeterCfg, + ): + """Initialize the G1 upper body retargeter. + + Args: + cfg: Configuration for the retargeter. + """ + super().__init__(cfg) + + # Store device name for runtime retrieval + self._sim_device = cfg.sim_device + self._hand_joint_names = cfg.hand_joint_names + + # Initialize the hands controller + if cfg.hand_joint_names is not None: + self._hands_controller = G1TriHandDexRetargeting(cfg.hand_joint_names) + else: + raise ValueError("hand_joint_names must be provided in configuration") + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + self._num_open_xr_hand_joints = cfg.num_open_xr_hand_joints + if self._enable_visualization: + marker_cfg = VisualizationMarkersCfg( + prim_path="/Visuals/g1_hand_markers", + markers={ + "joint": sim_utils.SphereCfg( + radius=0.005, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, + ) + self._markers = VisualizationMarkers(marker_cfg) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector commands. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + + Returns: + A tensor containing the retargeted commands: + - Left wrist pose (7) + - Right wrist pose (7) + - Hand joint angles (len(hand_joint_names)) + """ + + # Access the left and right hand data using the enum key + left_hand_poses = data[DeviceBase.TrackingTarget.HAND_LEFT] + right_hand_poses = data[DeviceBase.TrackingTarget.HAND_RIGHT] + + left_wrist = left_hand_poses.get("wrist") + right_wrist = right_hand_poses.get("wrist") + + # Handle case where wrist data is not available + if left_wrist is None or right_wrist is None: + # Set to default pose if no data available. + # pos=(0,0,0), quat=(1,0,0,0) (w,x,y,z) + default_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + if left_wrist is None: + left_wrist = default_pose + if right_wrist is None: + right_wrist = default_pose + + # Visualization if enabled + if self._enable_visualization: + joints_position = np.zeros((self._num_open_xr_hand_joints, 3)) + joints_position[::2] = np.array([pose[:3] for pose in left_hand_poses.values()]) + joints_position[1::2] = np.array([pose[:3] for pose in right_hand_poses.values()]) + self._markers.visualize(translations=torch.tensor(joints_position, device=self._sim_device)) + + # Compute retargeted hand joints + left_hands_pos = self._hands_controller.compute_left(left_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_left_joint_names()] + left_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + left_retargeted_hand_joints[indexes] = left_hands_pos + left_hand_joints = left_retargeted_hand_joints + + right_hands_pos = self._hands_controller.compute_right(right_hand_poses) + indexes = [self._hand_joint_names.index(name) for name in self._hands_controller.get_right_joint_names()] + right_retargeted_hand_joints = np.zeros(len(self._hands_controller.get_joint_names())) + right_retargeted_hand_joints[indexes] = right_hands_pos + right_hand_joints = right_retargeted_hand_joints + retargeted_hand_joints = left_hand_joints + right_hand_joints + + # Convert numpy arrays to tensors and store in command buffer + left_wrist_tensor = torch.tensor( + self._retarget_abs(left_wrist, is_left=True), dtype=torch.float32, device=self._sim_device + ) + right_wrist_tensor = torch.tensor( + self._retarget_abs(right_wrist, is_left=False), dtype=torch.float32, device=self._sim_device + ) + hand_joints_tensor = torch.tensor(retargeted_hand_joints, dtype=torch.float32, device=self._sim_device) + + # Combine all tensors into a single tensor + return torch.cat([left_wrist_tensor, right_wrist_tensor, hand_joints_tensor]) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, wrist: np.ndarray, is_left: bool) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + wrist: Wrist pose data from OpenXR. + is_left: True for the left hand, False for the right hand. + + Returns: + Retargeted wrist pose in USD control frame. + """ + wrist_pos = torch.tensor(wrist[:3], dtype=torch.float32) + wrist_quat = torch.tensor(wrist[3:], dtype=torch.float32) + + if is_left: + # Corresponds to a rotation of (0, 90, 90) in euler angles (x,y,z) + combined_quat = torch.tensor([0.7071, 0, 0.7071, 0], dtype=torch.float32) + else: + # Corresponds to a rotation of (0, -90, -90) in euler angles (x,y,z) + combined_quat = torch.tensor([0, -0.7071, 0, 0.7071], dtype=torch.float32) + + openxr_pose = PoseUtils.make_pose(wrist_pos, PoseUtils.matrix_from_quat(wrist_quat)) + transform_pose = PoseUtils.make_pose(torch.zeros(3), PoseUtils.matrix_from_quat(combined_quat)) + + result_pose = PoseUtils.pose_in_A_to_pose_in_B(transform_pose, openxr_pose) + pos, rot_mat = PoseUtils.unmake_pose(result_pose) + quat = PoseUtils.quat_from_matrix(rot_mat) + + return np.concatenate([pos.numpy(), quat.numpy()]) + + +@dataclass +class G1TriHandUpperBodyRetargeterCfg(RetargeterCfg): + """Configuration for the G1 Controller Upper Body retargeter.""" + + enable_visualization: bool = False + num_open_xr_hand_joints: int = 100 + hand_joint_names: list[str] | None = None # List of robot hand joint names + retargeter_type: type[RetargeterBase] = G1TriHandUpperBodyRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/__init__.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..426b8ac1002c9586eae63696c9023f7aa56ffda3 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Franka manipulator retargeting module. + +This module provides functionality for retargeting motion to Franka robots. +""" + +from .gripper_retargeter import GripperRetargeter, GripperRetargeterCfg +from .se3_abs_retargeter import Se3AbsRetargeter, Se3AbsRetargeterCfg +from .se3_rel_retargeter import Se3RelRetargeter, Se3RelRetargeterCfg diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/gripper_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/gripper_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..9ae2031b4d81146e5b19b7d6de85421faf8126e9 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/gripper_retargeter.py @@ -0,0 +1,98 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass +from typing import Final + +import numpy as np +import torch + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg + + +class GripperRetargeter(RetargeterBase): + """Retargeter specifically for gripper control based on hand tracking data. + + This retargeter analyzes the distance between thumb and index finger tips to determine + whether the gripper should be open or closed. It includes hysteresis to prevent rapid + toggling between states when the finger distance is near the threshold. + + Features: + - Tracks thumb and index finger distance + - Implements hysteresis for stable gripper control + - Outputs boolean command (True = close gripper, False = open gripper) + """ + + GRIPPER_CLOSE_METERS: Final[float] = 0.03 + GRIPPER_OPEN_METERS: Final[float] = 0.05 + + def __init__( + self, + cfg: GripperRetargeterCfg, + ): + super().__init__(cfg) + """Initialize the gripper retargeter.""" + # Store the hand to track + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + self.bound_hand = cfg.bound_hand + # Initialize gripper state + self._previous_gripper_command = False + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to gripper command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: Tensor containing a single bool value where True = close gripper, False = open gripper + """ + # Extract key joint poses + hand_data = data[self.bound_hand] + thumb_tip = hand_data["thumb_tip"] + index_tip = hand_data["index_tip"] + + # Calculate gripper command with hysteresis + gripper_command_bool = self._calculate_gripper_command(thumb_tip[:3], index_tip[:3]) + gripper_value = -1.0 if gripper_command_bool else 1.0 + + return torch.tensor([gripper_value], dtype=torch.float32, device=self._sim_device) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _calculate_gripper_command(self, thumb_pos: np.ndarray, index_pos: np.ndarray) -> bool: + """Calculate gripper command from finger positions with hysteresis. + + Args: + thumb_pos: 3D position of thumb tip + index_pos: 3D position of index tip + + Returns: + bool: Gripper command (True = close, False = open) + """ + distance = np.linalg.norm(thumb_pos - index_pos) + + # Apply hysteresis to prevent rapid switching + if distance > self.GRIPPER_OPEN_METERS: + self._previous_gripper_command = False + elif distance < self.GRIPPER_CLOSE_METERS: + self._previous_gripper_command = True + + return self._previous_gripper_command + + +@dataclass +class GripperRetargeterCfg(RetargeterCfg): + """Configuration for gripper retargeter.""" + + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = GripperRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_abs_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_abs_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..d69af88cfccee7f111e0a136fc84025a193b8c31 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_abs_retargeter.py @@ -0,0 +1,172 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation, Slerp + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG + + +class Se3AbsRetargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to end-effector commands using absolute positioning. + + This retargeter maps hand joint poses directly to robot end-effector positions and orientations, + rather than using relative movements. It can either: + - Use the wrist position and orientation + - Use the midpoint between thumb and index finger (pinch position) + + Features: + - Optional constraint to zero out X/Y rotations (keeping only Z-axis rotation) + - Optional visualization of the target end-effector pose + """ + + def __init__( + self, + cfg: Se3AbsRetargeterCfg, + ): + """Initialize the retargeter. + + Args: + bound_hand: The hand to track (DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT) + zero_out_xy_rotation: If True, zero out rotation around x and y axes + use_wrist_rotation: If True, use wrist rotation instead of finger average + use_wrist_position: If True, use wrist position instead of pinch position + enable_visualization: If True, visualize the target pose in the scene + device: The device to place the returned tensor on ('cpu' or 'cuda') + """ + super().__init__(cfg) + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + self.bound_hand = cfg.bound_hand + + self._zero_out_xy_rotation = cfg.zero_out_xy_rotation + self._use_wrist_rotation = cfg.use_wrist_rotation + self._use_wrist_position = cfg.use_wrist_position + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + if cfg.enable_visualization: + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self._goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + self._goal_marker.set_visibility(True) + self._visualization_pos = np.zeros(3) + self._visualization_rot = np.array([1.0, 0.0, 0.0, 0.0]) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: 7D tensor containing position (xyz) and orientation (quaternion) + for the robot end-effector + """ + # Extract key joint poses from the bound hand + hand_data = data[self.bound_hand] + thumb_tip = hand_data.get("thumb_tip") + index_tip = hand_data.get("index_tip") + wrist = hand_data.get("wrist") + + ee_command_np = self._retarget_abs(thumb_tip, index_tip, wrist) + + # Convert to torch tensor + ee_command = torch.tensor(ee_command_np, dtype=torch.float32, device=self._sim_device) + + return ee_command + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _retarget_abs(self, thumb_tip: np.ndarray, index_tip: np.ndarray, wrist: np.ndarray) -> np.ndarray: + """Handle absolute pose retargeting. + + Args: + thumb_tip: 7D array containing position (xyz) and orientation (quaternion) + for the thumb tip + index_tip: 7D array containing position (xyz) and orientation (quaternion) + for the index tip + wrist: 7D array containing position (xyz) and orientation (quaternion) + for the wrist + + Returns: + np.ndarray: 7D array containing position (xyz) and orientation (quaternion) + for the robot end-effector + """ + + # Get position + if self._use_wrist_position: + position = wrist[:3] + else: + position = (thumb_tip[:3] + index_tip[:3]) / 2 + + # Get rotation + if self._use_wrist_rotation: + # wrist is w,x,y,z but scipy expects x,y,z,w + base_rot = Rotation.from_quat([*wrist[4:], wrist[3]]) + else: + # Average the orientations of thumb and index using SLERP + # thumb_tip is w,x,y,z but scipy expects x,y,z,w + r0 = Rotation.from_quat([*thumb_tip[4:], thumb_tip[3]]) + # index_tip is w,x,y,z but scipy expects x,y,z,w + r1 = Rotation.from_quat([*index_tip[4:], index_tip[3]]) + key_times = [0, 1] + slerp = Slerp(key_times, Rotation.concatenate([r0, r1])) + base_rot = slerp([0.5])[0] + + # Apply additional x-axis rotation to align with pinch gesture + final_rot = base_rot * Rotation.from_euler("x", 90, degrees=True) + + if self._zero_out_xy_rotation: + z, y, x = final_rot.as_euler("ZYX") + y = 0.0 # Zero out rotation around y-axis + x = 0.0 # Zero out rotation around x-axis + final_rot = Rotation.from_euler("ZYX", [z, y, x]) * Rotation.from_euler("X", np.pi, degrees=False) + + # Convert back to w,x,y,z format + quat = final_rot.as_quat() + rotation = np.array([quat[3], quat[0], quat[1], quat[2]]) # Output remains w,x,y,z + + # Update visualization if enabled + if self._enable_visualization: + self._visualization_pos = position + self._visualization_rot = rotation + self._update_visualization() + + return np.concatenate([position, rotation]) + + def _update_visualization(self): + """Update visualization markers with current pose. + + If visualization is enabled, the target end-effector pose is visualized in the scene. + """ + if self._enable_visualization: + trans = np.array([self._visualization_pos]) + quat = Rotation.from_matrix(self._visualization_rot).as_quat() + rot = np.array([np.array([quat[3], quat[0], quat[1], quat[2]])]) + self._goal_marker.visualize(translations=trans, orientations=rot) + + +@dataclass +class Se3AbsRetargeterCfg(RetargeterCfg): + """Configuration for absolute position retargeter.""" + + zero_out_xy_rotation: bool = True + use_wrist_rotation: bool = False + use_wrist_position: bool = True + enable_visualization: bool = False + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = Se3AbsRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_rel_retargeter.py b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_rel_retargeter.py new file mode 100644 index 0000000000000000000000000000000000000000..360b1c29c347b0fe68b6d4c51fa147eac165af2b --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/retargeters/manipulator/se3_rel_retargeter.py @@ -0,0 +1,216 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import torch +from scipy.spatial.transform import Rotation + +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG + + +class Se3RelRetargeter(RetargeterBase): + """Retargets OpenXR hand tracking data to end-effector commands using relative positioning. + + This retargeter calculates delta poses between consecutive hand joint poses to generate incremental robot movements. + It can either: + - Use the wrist position and orientation + - Use the midpoint between thumb and index finger (pinch position) + + Features: + - Optional constraint to zero out X/Y rotations (keeping only Z-axis rotation) + - Motion smoothing with adjustable parameters + - Optional visualization of the target end-effector pose + """ + + def __init__( + self, + cfg: Se3RelRetargeterCfg, + ): + """Initialize the relative motion retargeter. + + Args: + bound_hand: The hand to track (DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT) + zero_out_xy_rotation: If True, ignore rotations around x and y axes, allowing only z-axis rotation + use_wrist_rotation: If True, use wrist rotation for control instead of averaging finger orientations + use_wrist_position: If True, use wrist position instead of pinch position (midpoint between fingers) + delta_pos_scale_factor: Amplification factor for position changes (higher = larger robot movements) + delta_rot_scale_factor: Amplification factor for rotation changes (higher = larger robot rotations) + alpha_pos: Position smoothing parameter (0-1); higher values track more closely to input, + lower values smooth more + alpha_rot: Rotation smoothing parameter (0-1); higher values track more closely to input, + lower values smooth more + enable_visualization: If True, show a visual marker representing the target end-effector pose + device: The device to place the returned tensor on ('cpu' or 'cuda') + """ + # Store the hand to track + if cfg.bound_hand not in [DeviceBase.TrackingTarget.HAND_LEFT, DeviceBase.TrackingTarget.HAND_RIGHT]: + raise ValueError( + "bound_hand must be either DeviceBase.TrackingTarget.HAND_LEFT or DeviceBase.TrackingTarget.HAND_RIGHT" + ) + super().__init__(cfg) + self.bound_hand = cfg.bound_hand + + self._zero_out_xy_rotation = cfg.zero_out_xy_rotation + self._use_wrist_rotation = cfg.use_wrist_rotation + self._use_wrist_position = cfg.use_wrist_position + self._delta_pos_scale_factor = cfg.delta_pos_scale_factor + self._delta_rot_scale_factor = cfg.delta_rot_scale_factor + self._alpha_pos = cfg.alpha_pos + self._alpha_rot = cfg.alpha_rot + + # Initialize smoothing state + self._smoothed_delta_pos = np.zeros(3) + self._smoothed_delta_rot = np.zeros(3) + + # Define thresholds for small movements + self._position_threshold = 0.001 + self._rotation_threshold = 0.01 + + # Initialize visualization if enabled + self._enable_visualization = cfg.enable_visualization + if cfg.enable_visualization: + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self._goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + self._goal_marker.set_visibility(True) + self._visualization_pos = np.zeros(3) + self._visualization_rot = np.array([1.0, 0.0, 0.0, 0.0]) + + self._previous_thumb_tip = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + self._previous_index_tip = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + self._previous_wrist = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], dtype=np.float32) + + def retarget(self, data: dict) -> torch.Tensor: + """Convert hand joint poses to robot end-effector command. + + Args: + data: Dictionary mapping tracking targets to joint data dictionaries. + The joint names are defined in isaaclab.devices.openxr.common.HAND_JOINT_NAMES + + Returns: + torch.Tensor: 6D tensor containing position (xyz) and rotation vector (rx,ry,rz) + for the robot end-effector + """ + # Extract key joint poses from the bound hand + hand_data = data[self.bound_hand] + thumb_tip = hand_data.get("thumb_tip") + index_tip = hand_data.get("index_tip") + wrist = hand_data.get("wrist") + + delta_thumb_tip = self._calculate_delta_pose(thumb_tip, self._previous_thumb_tip) + delta_index_tip = self._calculate_delta_pose(index_tip, self._previous_index_tip) + delta_wrist = self._calculate_delta_pose(wrist, self._previous_wrist) + ee_command_np = self._retarget_rel(delta_thumb_tip, delta_index_tip, delta_wrist) + + self._previous_thumb_tip = thumb_tip.copy() + self._previous_index_tip = index_tip.copy() + self._previous_wrist = wrist.copy() + + # Convert to torch tensor + ee_command = torch.tensor(ee_command_np, dtype=torch.float32, device=self._sim_device) + + return ee_command + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + return [RetargeterBase.Requirement.HAND_TRACKING] + + def _calculate_delta_pose(self, joint_pose: np.ndarray, previous_joint_pose: np.ndarray) -> np.ndarray: + """Calculate delta pose from previous joint pose. + + Args: + joint_pose: Current joint pose (position and orientation) + previous_joint_pose: Previous joint pose for the same joint + + Returns: + np.ndarray: 6D array with position delta (xyz) and rotation delta as axis-angle (rx,ry,rz) + """ + delta_pos = joint_pose[:3] - previous_joint_pose[:3] + abs_rotation = Rotation.from_quat([*joint_pose[4:7], joint_pose[3]]) + previous_rot = Rotation.from_quat([*previous_joint_pose[4:7], previous_joint_pose[3]]) + relative_rotation = abs_rotation * previous_rot.inv() + return np.concatenate([delta_pos, relative_rotation.as_rotvec()]) + + def _retarget_rel(self, thumb_tip: np.ndarray, index_tip: np.ndarray, wrist: np.ndarray) -> np.ndarray: + """Handle relative (delta) pose retargeting. + + Args: + thumb_tip: Delta pose of thumb tip + index_tip: Delta pose of index tip + wrist: Delta pose of wrist + + Returns: + np.ndarray: 6D array with position delta (xyz) and rotation delta (rx,ry,rz) + """ + # Get position + if self._use_wrist_position: + position = wrist[:3] + else: + position = (thumb_tip[:3] + index_tip[:3]) / 2 + + # Get rotation + if self._use_wrist_rotation: + rotation = wrist[3:6] # rx, ry, rz + else: + rotation = (thumb_tip[3:6] + index_tip[3:6]) / 2 + + # Apply zero_out_xy_rotation regardless of rotation source + if self._zero_out_xy_rotation: + rotation[0] = 0 # x-axis + rotation[1] = 0 # y-axis + + # Smooth and scale position + self._smoothed_delta_pos = self._alpha_pos * position + (1 - self._alpha_pos) * self._smoothed_delta_pos + if np.linalg.norm(self._smoothed_delta_pos) < self._position_threshold: + self._smoothed_delta_pos = np.zeros(3) + position = self._smoothed_delta_pos * self._delta_pos_scale_factor + + # Smooth and scale rotation + self._smoothed_delta_rot = self._alpha_rot * rotation + (1 - self._alpha_rot) * self._smoothed_delta_rot + if np.linalg.norm(self._smoothed_delta_rot) < self._rotation_threshold: + self._smoothed_delta_rot = np.zeros(3) + rotation = self._smoothed_delta_rot * self._delta_rot_scale_factor + + # Update visualization if enabled + if self._enable_visualization: + # Convert rotation vector to quaternion and combine with current rotation + delta_quat = Rotation.from_rotvec(rotation).as_quat() # x, y, z, w format + current_rot = Rotation.from_quat([self._visualization_rot[1:], self._visualization_rot[0]]) + new_rot = Rotation.from_quat(delta_quat) * current_rot + self._visualization_pos = self._visualization_pos + position + # Convert back to w, x, y, z format + self._visualization_rot = np.array([new_rot.as_quat()[3], *new_rot.as_quat()[:3]]) + self._update_visualization() + + return np.concatenate([position, rotation]) + + def _update_visualization(self): + """Update visualization markers with current pose.""" + if self._enable_visualization: + trans = np.array([self._visualization_pos]) + quat = Rotation.from_matrix(self._visualization_rot).as_quat() + rot = np.array([np.array([quat[3], quat[0], quat[1], quat[2]])]) + self._goal_marker.visualize(translations=trans, orientations=rot) + + +@dataclass +class Se3RelRetargeterCfg(RetargeterCfg): + """Configuration for relative position retargeter.""" + + zero_out_xy_rotation: bool = True + use_wrist_rotation: bool = False + use_wrist_position: bool = True + delta_pos_scale_factor: float = 10.0 + delta_rot_scale_factor: float = 10.0 + alpha_pos: float = 0.5 + alpha_rot: float = 0.5 + enable_visualization: bool = False + bound_hand: DeviceBase.TrackingTarget = DeviceBase.TrackingTarget.HAND_RIGHT + retargeter_type: type[RetargeterBase] = Se3RelRetargeter diff --git a/source/isaaclab/isaaclab/devices/openxr/xr_anchor_utils.py b/source/isaaclab/isaaclab/devices/openxr/xr_anchor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..43602d1b782d1d324fdac384aabc98da8650d98c --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/xr_anchor_utils.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# SPDX-License-Identifier: BSD-3-Clause +"""Utilities for synchronizing XR anchor pose with a reference prim and XR config.""" + +from __future__ import annotations + +import contextlib +import logging +import math +from typing import Any + +import numpy as np + +# import logger +logger = logging.getLogger(__name__) + +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import get_current_stage_id + +from .xr_cfg import XrAnchorRotationMode + +with contextlib.suppress(ModuleNotFoundError, ImportError): + import usdrt + from pxr import Gf as pxrGf + from usdrt import Rt + + +class XrAnchorSynchronizer: + """Keeps the XR anchor prim aligned with a reference prim according to XR config.""" + + def __init__(self, xr_core: Any, xr_cfg: Any, xr_anchor_headset_path: str): + self._xr_core = xr_core + self._xr_cfg = xr_cfg + self._xr_anchor_headset_path = xr_anchor_headset_path + + self.__anchor_prim_initial_quat = None + self.__anchor_prim_initial_height = None + self.__smoothed_anchor_quat = None + self.__last_anchor_quat = None + self.__anchor_rotation_enabled = True + + # Resolve USD layer identifier of the anchor for updates + try: + from isaacsim.core.utils.stage import get_current_stage + + stage = get_current_stage() + xr_anchor_headset_prim = stage.GetPrimAtPath(self._xr_anchor_headset_path) + prim_stack = xr_anchor_headset_prim.GetPrimStack() if xr_anchor_headset_prim is not None else None + self.__anchor_headset_layer_identifier = prim_stack[0].layer.identifier if prim_stack else None + except Exception: + self.__anchor_headset_layer_identifier = None + + def reset(self): + self.__anchor_prim_initial_quat = None + self.__anchor_prim_initial_height = None + self.__smoothed_anchor_quat = None + self.__last_anchor_quat = None + self.__anchor_rotation_enabled = True + self.sync_headset_to_anchor() + + def toggle_anchor_rotation(self): + self.__anchor_rotation_enabled = not self.__anchor_rotation_enabled + logger.info(f"XR: Toggling anchor rotation: {self.__anchor_rotation_enabled}") + + def sync_headset_to_anchor(self): + """Sync XR anchor pose in USD from reference prim (in Fabric/usdrt).""" + try: + if self._xr_cfg.anchor_prim_path is None: + return + + stage_id = get_current_stage_id() + rt_stage = usdrt.Usd.Stage.Attach(stage_id) + if rt_stage is None: + return + + rt_prim = rt_stage.GetPrimAtPath(self._xr_cfg.anchor_prim_path) + if rt_prim is None: + return + + rt_xformable = Rt.Xformable(rt_prim) + if rt_xformable is None: + return + + world_matrix_attr = rt_xformable.GetFabricHierarchyWorldMatrixAttr() + if world_matrix_attr is None: + return + + rt_matrix = world_matrix_attr.Get() + rt_pos = rt_matrix.ExtractTranslation() + + if self.__anchor_prim_initial_quat is None: + self.__anchor_prim_initial_quat = rt_matrix.ExtractRotationQuat() + + if getattr(self._xr_cfg, "fixed_anchor_height", False): + if self.__anchor_prim_initial_height is None: + self.__anchor_prim_initial_height = rt_pos[2] + rt_pos[2] = self.__anchor_prim_initial_height + + pxr_anchor_pos = pxrGf.Vec3d(*rt_pos) + pxrGf.Vec3d(*self._xr_cfg.anchor_pos) + + w, x, y, z = self._xr_cfg.anchor_rot + pxr_cfg_quat = pxrGf.Quatd(w, pxrGf.Vec3d(x, y, z)) + + pxr_anchor_quat = pxr_cfg_quat + + if self._xr_cfg.anchor_rotation_mode in ( + XrAnchorRotationMode.FOLLOW_PRIM, + XrAnchorRotationMode.FOLLOW_PRIM_SMOOTHED, + ): + rt_prim_quat = rt_matrix.ExtractRotationQuat() + rt_delta_quat = rt_prim_quat * self.__anchor_prim_initial_quat.GetInverse() + pxr_delta_quat = pxrGf.Quatd(rt_delta_quat.GetReal(), pxrGf.Vec3d(*rt_delta_quat.GetImaginary())) + + # yaw-only about Z (right-handed, Z-up) + wq = pxr_delta_quat.GetReal() + ix, iy, iz = pxr_delta_quat.GetImaginary() + yaw = math.atan2(2.0 * (wq * iz + ix * iy), 1.0 - 2.0 * (iy * iy + iz * iz)) + cy = math.cos(yaw * 0.5) + sy = math.sin(yaw * 0.5) + pxr_delta_yaw_only_quat = pxrGf.Quatd(cy, pxrGf.Vec3d(0.0, 0.0, sy)) + pxr_anchor_quat = pxr_delta_yaw_only_quat * pxr_cfg_quat + + if self._xr_cfg.anchor_rotation_mode == XrAnchorRotationMode.FOLLOW_PRIM_SMOOTHED: + if self.__smoothed_anchor_quat is None: + self.__smoothed_anchor_quat = pxr_anchor_quat + else: + dt = SimulationContext.instance().get_rendering_dt() + alpha = 1.0 - math.exp(-dt / max(self._xr_cfg.anchor_rotation_smoothing_time, 1e-6)) + alpha = min(1.0, max(0.05, alpha)) + self.__smoothed_anchor_quat = pxrGf.Slerp(alpha, self.__smoothed_anchor_quat, pxr_anchor_quat) + pxr_anchor_quat = self.__smoothed_anchor_quat + + elif self._xr_cfg.anchor_rotation_mode == XrAnchorRotationMode.CUSTOM: + if self._xr_cfg.anchor_rotation_custom_func is not None: + rt_prim_quat = rt_matrix.ExtractRotationQuat() + anchor_prim_pose = np.array( + [ + rt_pos[0], + rt_pos[1], + rt_pos[2], + rt_prim_quat.GetReal(), + rt_prim_quat.GetImaginary()[0], + rt_prim_quat.GetImaginary()[1], + rt_prim_quat.GetImaginary()[2], + ], + dtype=np.float64, + ) + # Previous headpose must be provided by caller; fall back to zeros. + prev_head = getattr(self, "_previous_headpose", np.zeros(7, dtype=np.float64)) + np_array_quat = self._xr_cfg.anchor_rotation_custom_func(prev_head, anchor_prim_pose) + w, x, y, z = np_array_quat + pxr_anchor_quat = pxrGf.Quatd(w, pxrGf.Vec3d(x, y, z)) + + pxr_mat = pxrGf.Matrix4d() + pxr_mat.SetTranslateOnly(pxr_anchor_pos) + + if self.__anchor_rotation_enabled: + pxr_mat.SetRotateOnly(pxr_anchor_quat) + self.__last_anchor_quat = pxr_anchor_quat + else: + if self.__last_anchor_quat is None: + self.__last_anchor_quat = pxr_anchor_quat + + pxr_mat.SetRotateOnly(self.__last_anchor_quat) + self.__smoothed_anchor_quat = self.__last_anchor_quat + + self._xr_core.set_world_transform_matrix( + self._xr_anchor_headset_path, pxr_mat, self.__anchor_headset_layer_identifier + ) + except Exception as e: + logger.warning(f"XR: Anchor sync failed: {e}") diff --git a/source/isaaclab/isaaclab/devices/openxr/xr_cfg.py b/source/isaaclab/isaaclab/devices/openxr/xr_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..1eaee292eaeed7b4535d0e9395e856df8d3d035d --- /dev/null +++ b/source/isaaclab/isaaclab/devices/openxr/xr_cfg.py @@ -0,0 +1,151 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +import enum +from collections.abc import Callable + +import numpy as np + +from isaaclab.utils import configclass + + +class XrAnchorRotationMode(enum.Enum): + """Enumeration for XR anchor rotation modes.""" + + FIXED = "fixed" + """Fixed rotation mode: sets rotation once and doesn't change it.""" + + FOLLOW_PRIM = "follow_prim" + """Follow prim rotation mode: rotation follows prim's rotation.""" + + FOLLOW_PRIM_SMOOTHED = "follow_prim_smoothed" + """Follow prim rotation mode with smooth interpolation: rotation smoothly follows prim's rotation using slerp.""" + + CUSTOM = "custom_rotation" + """Custom rotation mode: user provided function to calculate the rotation.""" + + +@configclass +class XrCfg: + """Configuration for viewing and interacting with the environment through an XR device.""" + + anchor_pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Specifies the position (in m) of the simulation when viewed in an XR device. + + Specifically: this position will appear at the origin of the XR device's local coordinate frame. + """ + + anchor_rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Specifies the rotation (as a quaternion) of the simulation when viewed in an XR device. + + Specifically: this rotation will determine how the simulation is rotated with respect to the + origin of the XR device's local coordinate frame. + + This quantity is only effective if :attr:`xr_anchor_pos` is set. + """ + + anchor_prim_path: str | None = None + """Specifies the prim path to attach the XR anchor to for dynamic positioning. + + When set, the XR anchor will be attached to the specified prim (e.g., robot root prim), + allowing the XR camera to move with the prim. This is particularly useful for locomotion + robot teleoperation where the robot moves and the XR camera should follow it. + + If None, the anchor will use the static :attr:`anchor_pos` and :attr:`anchor_rot` values. + """ + + anchor_rotation_mode: XrAnchorRotationMode = XrAnchorRotationMode.FIXED + """Specifies how the XR anchor rotation should behave when attached to a prim. + + The available modes are: + - :attr:`XrAnchorRotationMode.FIXED`: Sets rotation once to anchor_rot value + - :attr:`XrAnchorRotationMode.FOLLOW_PRIM`: Rotation follows prim's rotation + - :attr:`XrAnchorRotationMode.FOLLOW_PRIM_SMOOTHED`: Rotation smoothly follows prim's rotation using slerp + - :attr:`XrAnchorRotationMode.CUSTOM`: user provided function to calculate the rotation + """ + + anchor_rotation_smoothing_time: float = 1.0 + """Wall-clock time constant (seconds) for rotation smoothing in FOLLOW_PRIM_SMOOTHED mode. + + This time constant is applied using wall-clock delta time between frames (not physics dt). + Smaller values (e.g., 0.1) result in faster/snappier response but less smoothing. + Larger values (e.g., 0.75–2.0) result in slower/smoother response but more lag. + Typical useful range: 0.3 – 1.5 seconds depending on runtime frame-rate and comfort. + """ + + anchor_rotation_custom_func: Callable[[np.ndarray, np.ndarray], np.ndarray] = lambda headpose, primpose: np.array( + [1, 0, 0, 0], dtype=np.float64 + ) + """Specifies the function to calculate the rotation of the XR anchor when anchor_rotation_mode is CUSTOM. + + Args: + headpose: Previous head pose as numpy array [x, y, z, w, x, y, z] (position + quaternion) + pose: Anchor prim pose as numpy array [x, y, z, w, x, y, z] (position + quaternion) + + Returns: + np.ndarray: Quaternion as numpy array [w, x, y, z] + """ + + near_plane: float = 0.15 + """Specifies the near plane distance for the XR device. + + This value determines the closest distance at which objects will be rendered in the XR device. + """ + + fixed_anchor_height: bool = True + """Specifies if the anchor height should be fixed. + + If True, the anchor height will be fixed to the initial height of the anchor prim. + """ + + +from typing import Any + + +def remove_camera_configs(env_cfg: Any) -> Any: + """Removes cameras from environments when using XR devices. + + XR does not support additional cameras in the environment as they can cause + rendering conflicts and performance issues. This function scans the environment + configuration for camera objects and removes them, along with any associated + observation terms that reference these cameras. + + Args: + env_cfg: The environment configuration to modify. + + Returns: + The modified environment configuration with cameras removed. + """ + + import logging + + # import logger + logger = logging.getLogger(__name__) + + from isaaclab.managers import SceneEntityCfg + from isaaclab.sensors import CameraCfg + + for attr_name in dir(env_cfg.scene): + attr = getattr(env_cfg.scene, attr_name) + if isinstance(attr, CameraCfg): + delattr(env_cfg.scene, attr_name) + logger.info(f"Removed camera config: {attr_name}") + + # Remove any ObsTerms for the camera + if hasattr(env_cfg.observations, "policy"): + for obs_name in dir(env_cfg.observations.policy): + obsterm = getattr(env_cfg.observations.policy, obs_name) + if hasattr(obsterm, "params") and obsterm.params: + for param_value in obsterm.params.values(): + if isinstance(param_value, SceneEntityCfg) and param_value.name == attr_name: + delattr(env_cfg.observations.policy, attr_name) + logger.info(f"Removed camera observation term: {attr_name}") + break + return env_cfg diff --git a/source/isaaclab/isaaclab/devices/retargeter_base.py b/source/isaaclab/isaaclab/devices/retargeter_base.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd443a155b29acef6c23af04adf802a86772d6e --- /dev/null +++ b/source/isaaclab/isaaclab/devices/retargeter_base.py @@ -0,0 +1,69 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from enum import Enum +from typing import Any + + +@dataclass +class RetargeterCfg: + """Base configuration for hand tracking retargeters.""" + + sim_device: str = "cpu" + # Concrete retargeter class to construct for this config. Set by each retargeter module. + retargeter_type: type["RetargeterBase"] | None = None + + +class RetargeterBase(ABC): + """Base interface for input data retargeting. + + This abstract class defines the interface for components that transform + raw device data into robot control commands. Implementations can handle + various types of transformations including: + - Hand joint data to end-effector poses + - Input device commands to robot movements + - Sensor data to control signals + """ + + def __init__(self, cfg: RetargeterCfg): + """Initialize the retargeter. + + Args: + cfg: Configuration for the retargeter + """ + self._sim_device = cfg.sim_device + + class Requirement(Enum): + """Features a retargeter may require from a device's raw data feed.""" + + HAND_TRACKING = "hand_tracking" + HEAD_TRACKING = "head_tracking" + MOTION_CONTROLLER = "motion_controller" + + @abstractmethod + def retarget(self, data: Any) -> Any: + """Retarget input data to desired output format. + + Args: + data: Raw input data to be transformed + + Returns: + Retargeted data in implementation-specific format + """ + pass + + def get_requirements(self) -> list["RetargeterBase.Requirement"]: + """Return the list of required data features for this retargeter. + + Defaults to requesting all available features for backward compatibility. + Implementations should override to narrow to only what they need. + """ + return [ + RetargeterBase.Requirement.HAND_TRACKING, + RetargeterBase.Requirement.HEAD_TRACKING, + RetargeterBase.Requirement.MOTION_CONTROLLER, + ] diff --git a/source/isaaclab/isaaclab/devices/spacemouse/__init__.py b/source/isaaclab/isaaclab/devices/spacemouse/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3cc1c2fd9c4e1c803c5e7d50d932bb801fae2f5 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/spacemouse/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Spacemouse device for SE(2) and SE(3) control.""" + +from .se2_spacemouse import Se2SpaceMouse, Se2SpaceMouseCfg +from .se3_spacemouse import Se3SpaceMouse, Se3SpaceMouseCfg diff --git a/source/isaaclab/isaaclab/devices/spacemouse/se2_spacemouse.py b/source/isaaclab/isaaclab/devices/spacemouse/se2_spacemouse.py new file mode 100644 index 0000000000000000000000000000000000000000..40607ae1de911975cd8ce2027229633f9bd8c3a7 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/spacemouse/se2_spacemouse.py @@ -0,0 +1,173 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Spacemouse controller for SE(2) control.""" + +from __future__ import annotations + +import threading +import time +from collections.abc import Callable +from dataclasses import dataclass + +import hid +import numpy as np +import torch + +from isaaclab.utils.array import convert_to_torch + +from ..device_base import DeviceBase, DeviceCfg +from .utils import convert_buffer + + +class Se2SpaceMouse(DeviceBase): + r"""A space-mouse controller for sending SE(2) commands as delta poses. + + This class implements a space-mouse controller to provide commands to mobile base. + It uses the `HID-API`_ which interfaces with USD and Bluetooth HID-class devices across multiple platforms. + + The command comprises of the base linear and angular velocity: :math:`(v_x, v_y, \omega_z)`. + + Note: + The interface finds and uses the first supported device connected to the computer. + + Currently tested for following devices: + + - SpaceMouse Compact: https://3dconnexion.com/de/product/spacemouse-compact/ + + .. _HID-API: https://github.com/libusb/hidapi + + """ + + def __init__(self, cfg: Se2SpaceMouseCfg): + """Initialize the spacemouse layer. + + Args: + cfg: Configuration for the spacemouse device. + """ + # store inputs + self.v_x_sensitivity = cfg.v_x_sensitivity + self.v_y_sensitivity = cfg.v_y_sensitivity + self.omega_z_sensitivity = cfg.omega_z_sensitivity + self._sim_device = cfg.sim_device + # acquire device interface + self._device = hid.Device(vid=0x256f, pid=0xc62e) + # self._find_device() + # command buffers + self._base_command = np.zeros(3) + # dictionary for additional callbacks + self._additional_callbacks = dict() + # run a thread for listening to device updates + self._thread = threading.Thread(target=self._run_device) + self._thread.daemon = True + self._thread.start() + + def __del__(self): + """Destructor for the class.""" + self._thread.join() + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Spacemouse Controller for SE(2): {self.__class__.__name__}\n" + # msg += f"\tManufacturer: {self._device.get_manufacturer_string()}\n" + # msg += f"\tProduct: {self._device.get_product_string()}\n" + msg += "\t----------------------------------------------\n" + msg += "\tRight button: reset command\n" + msg += "\tMove mouse laterally: move base horizontally in x-y plane\n" + msg += "\tTwist mouse about z-axis: yaw base about a corresponding axis" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._base_command.fill(0.0) + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind spacemouse. + + Args: + key: The keyboard button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from spacemouse event state. + + Returns: + A 3D tensor containing the linear (x,y) and angular velocity (z). + """ + return convert_to_torch(self._base_command, device=self._sim_device) + + """ + Internal helpers. + """ + + def _find_device(self): + """Find the device connected to computer.""" + found = False + # implement a timeout for device search + for _ in range(5): + for device in hid.enumerate(): + if device["product_string"] == "SpaceMouse Compact": + # set found flag + found = True + vendor_id = device["vendor_id"] + product_id = device["product_id"] + # connect to the device + # self._device.open(vendor_id, product_id) + # check if device found + if not found: + time.sleep(1.0) + else: + break + # no device found: return false + if not found: + raise OSError("No device found by SpaceMouse. Is the device connected?") + + def _run_device(self): + """Listener thread that keeps pulling new messages.""" + # keep running + while True: + # read the device data + data = self._device.read(13) + if data is not None: + # readings from 6-DoF sensor + if data[0] == 1: + # along y-axis + self._base_command[1] = self.v_y_sensitivity * convert_buffer(data[1], data[2]) + # along x-axis + self._base_command[0] = self.v_x_sensitivity * convert_buffer(data[3], data[4]) + elif data[0] == 2: + # along z-axis + self._base_command[2] = self.omega_z_sensitivity * convert_buffer(data[3], data[4]) + # readings from the side buttons + elif data[0] == 3: + # press left button + if data[1] == 1: + # additional callbacks + if "L" in self._additional_callbacks: + self._additional_callbacks["L"] + # right button is for reset + if data[1] == 2: + # reset layer + self.reset() + # additional callbacks + if "R" in self._additional_callbacks: + self._additional_callbacks["R"] + + +@dataclass +class Se2SpaceMouseCfg(DeviceCfg): + """Configuration for SE2 space mouse devices.""" + + v_x_sensitivity: float = 0.8 + v_y_sensitivity: float = 0.4 + omega_z_sensitivity: float = 1.0 + class_type: type[DeviceBase] = Se2SpaceMouse diff --git a/source/isaaclab/isaaclab/devices/spacemouse/se3_spacemouse.py b/source/isaaclab/isaaclab/devices/spacemouse/se3_spacemouse.py new file mode 100644 index 0000000000000000000000000000000000000000..569d7c7a74b8e6989075310e920a44d43f3b71d1 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/spacemouse/se3_spacemouse.py @@ -0,0 +1,223 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Spacemouse controller for SE(3) control.""" + +from __future__ import annotations + +import threading +import time +from collections.abc import Callable +from dataclasses import dataclass + +import hid +import numpy as np +import torch +from scipy.spatial.transform import Rotation + +from ..device_base import DeviceBase, DeviceCfg +from .utils import convert_buffer + + +class Se3SpaceMouse(DeviceBase): + """A space-mouse controller for sending SE(3) commands as delta poses. + + This class implements a space-mouse controller to provide commands to a robotic arm with a gripper. + It uses the `HID-API`_ which interfaces with USD and Bluetooth HID-class devices across multiple platforms [1]. + + The command comprises of two parts: + + * delta pose: a 6D vector of (x, y, z, roll, pitch, yaw) in meters and radians. + * gripper: a binary command to open or close the gripper. + + Note: + The interface finds and uses the first supported device connected to the computer. + + Currently tested for following devices: + + - SpaceMouse Compact: https://3dconnexion.com/de/product/spacemouse-compact/ + + .. _HID-API: https://github.com/libusb/hidapi + + """ + + def __init__(self, cfg: Se3SpaceMouseCfg): + """Initialize the space-mouse layer. + + Args: + cfg: Configuration object for space-mouse settings. + """ + # store inputs + self.pos_sensitivity = cfg.pos_sensitivity + self.rot_sensitivity = cfg.rot_sensitivity + self.gripper_term = cfg.gripper_term + self._sim_device = cfg.sim_device + # acquire device interface + self._device = hid.Device(vid=0x256f, pid=0xc62e) + # self._find_device() + # read rotations + self._read_rotation = False + + # command buffers + self._close_gripper = False + self._delta_pos = np.zeros(3) # (x, y, z) + self._delta_rot = np.zeros(3) # (roll, pitch, yaw) + # dictionary for additional callbacks + self._additional_callbacks = dict() + # run a thread for listening to device updates + self._thread = threading.Thread(target=self._run_device) + self._thread.daemon = True + self._thread.start() + + def __del__(self): + """Destructor for the class.""" + self._thread.join() + + def __str__(self) -> str: + """Returns: A string containing the information of joystick.""" + msg = f"Spacemouse Controller for SE(3): {self.__class__.__name__}\n" + # msg += f"\tManufacturer: {self._device.get_manufacturer_string()}\n" + # msg += f"\tProduct: {self._device.get_product_string()}\n" + msg += "\t----------------------------------------------\n" + msg += "\tRight button: reset command\n" + msg += "\tLeft button: toggle gripper command (open/close)\n" + msg += "\tMove mouse laterally: move arm horizontally in x-y plane\n" + msg += "\tMove mouse vertically: move arm vertically\n" + msg += "\tTwist mouse about an axis: rotate arm about a corresponding axis" + return msg + + """ + Operations + """ + + def reset(self): + # default flags + self._close_gripper = False + self._delta_pos = np.zeros(3) # (x, y, z) + self._delta_rot = np.zeros(3) # (roll, pitch, yaw) + + def add_callback(self, key: str, func: Callable): + """Add additional functions to bind spacemouse. + + Args: + key: The keyboard button to check against. + func: The function to call when key is pressed. The callback function should not + take any arguments. + """ + self._additional_callbacks[key] = func + + def advance(self) -> torch.Tensor: + """Provides the result from spacemouse event state. + + Returns: + torch.Tensor: A 7-element tensor containing: + - delta pose: First 6 elements as [x, y, z, rx, ry, rz] in meters and radians. + - gripper command: Last element as a binary value (+1.0 for open, -1.0 for close). + """ + rot_vec = Rotation.from_euler("XYZ", self._delta_rot).as_rotvec() + command = np.concatenate([self._delta_pos, rot_vec]) + if self.gripper_term: + gripper_value = -1.0 if self._close_gripper else 1.0 + command = np.append(command, gripper_value) + + return torch.tensor(command, dtype=torch.float32, device=self._sim_device) + + """ + Internal helpers. + """ + + def _find_device(self): + """Find the device connected to computer.""" + found = False + # implement a timeout for device search + for _ in range(5): + for device in hid.enumerate(): + if ( + device["product_string"] == "SpaceMouse Compact" + or device["product_string"] == "SpaceMouse Wireless" + or device["product_string"] == "3Dconnexion Universal Receiver" + ): + # set found flag + found = True + vendor_id = device["vendor_id"] + product_id = device["product_id"] + # connect to the device + self._device.close() + # self._device.open(vendor_id, product_id) + # self._device_name = device["product_string"] + # check if device found + if not found: + time.sleep(1.0) + else: + break + # no device found: return false + if not found: + raise OSError("No device found by SpaceMouse. Is the device connected?") + + def _run_device(self): + """Listener thread that keeps pulling new messages.""" + # keep running + while True: + # read the device data + # if self._device_name == "3Dconnexion Universal Receiver": + # data = self._device.read(7 + 6) + # else: + data = self._device.read(13) + if data is not None: + # readings from 6-DoF sensor + # if self._device_name == "3Dconnexion Universal Receiver": + # if data[0] == 1: + # self._delta_pos[1] = self.pos_sensitivity * convert_buffer(data[1], data[2]) + # self._delta_pos[0] = self.pos_sensitivity * convert_buffer(data[3], data[4]) + # self._delta_pos[2] = self.pos_sensitivity * convert_buffer(data[5], data[6]) * -1.0 + + # self._delta_rot[1] = self.rot_sensitivity * convert_buffer(data[1 + 6], data[2 + 6]) + # self._delta_rot[0] = self.rot_sensitivity * convert_buffer(data[3 + 6], data[4 + 6]) + # self._delta_rot[2] = self.rot_sensitivity * convert_buffer(data[5 + 6], data[6 + 6]) * -1.0 + # else: + if data[0] == 1: + self._delta_pos[1] = self.pos_sensitivity * convert_buffer(data[1], data[2]) + self._delta_pos[0] = self.pos_sensitivity * convert_buffer(data[3], data[4]) + self._delta_pos[2] = self.pos_sensitivity * convert_buffer(data[5], data[6]) * -1.0 + # print(f"[SpaceMouse] Position:\nraw={data}\npos=({self._delta_pos[0]:.4f}, {self._delta_pos[1]:.4f}, {self._delta_pos[2]:.4f})") + # elif data[0] == 2 and self._read_rotation: + if self._read_rotation: + self._delta_rot[1] = self.rot_sensitivity * convert_buffer(data[7], data[8]) + self._delta_rot[0] = self.rot_sensitivity * convert_buffer(data[9], data[10]) + self._delta_rot[2] = self.rot_sensitivity * convert_buffer(data[11], data[12]) * -1.0 + # print(f"[SpaceMouse] Rotation:\nraw={data}\nrot=({self._delta_rot[0]:.4f}, {self._delta_rot[1]:.4f}, {self._delta_rot[2]:.4f})") + # readings from the side buttons + if data[0] == 3: + # press left button + if data[1] == 1: + # close gripper + self._close_gripper = not self._close_gripper + # additional callbacks + if "L" in self._additional_callbacks: + self._additional_callbacks["L"]() + # right button is for toggling rotation reading + if data[1] == 2: + self._read_rotation = not self._read_rotation + if self._read_rotation: + print(f"Rotation ENABLED") + else: + print(f"Rotation DISABLED") + if data[1] == 3: + self.reset() + # reset layer + # additional callbacks + if "R" in self._additional_callbacks: + self._additional_callbacks["R"]() + + +@dataclass +class Se3SpaceMouseCfg(DeviceCfg): + """Configuration for SE3 space mouse devices.""" + + gripper_term: bool = True + pos_sensitivity: float = 0.4 + rot_sensitivity: float = 0.8 + retargeters: None = None + class_type: type[DeviceBase] = Se3SpaceMouse diff --git a/source/isaaclab/isaaclab/devices/spacemouse/utils.py b/source/isaaclab/isaaclab/devices/spacemouse/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..17510f70bce99ab1967fda9bdbf60577d44604e8 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/spacemouse/utils.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Helper functions for SpaceMouse.""" + +# MIT License +# +# Copyright (c) 2022 Stanford Vision and Learning Lab and UT Robot Perception and Learning Lab +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + +def convert_buffer(b1, b2): + """Converts raw SpaceMouse readings to commands. + + Args: + b1: 8-bit byte + b2: 8-bit byte + + Returns: + Scaled value from Space-mouse message + """ + return _scale_to_control(_to_int16(b1, b2)) + + +""" +Private methods. +""" + + +def _to_int16(y1, y2): + """Convert two 8 bit bytes to a signed 16 bit integer. + + Args: + y1: 8-bit byte + y2: 8-bit byte + + Returns: + 16-bit integer + """ + x = (y1) | (y2 << 8) + if x >= 32768: + x = -(65536 - x) + return x + + +def _scale_to_control(x, axis_scale=350.0, min_v=-1.0, max_v=1.0): + """Normalize raw HID readings to target range. + + Args: + x: Raw reading from HID + axis_scale: (Inverted) scaling factor for mapping raw input value + min_v: Minimum limit after scaling + max_v: Maximum limit after scaling + + Returns: + Clipped, scaled input from HID + """ + x = x / axis_scale + return min(max(x, min_v), max_v) diff --git a/source/isaaclab/isaaclab/devices/teleop_device_factory.py b/source/isaaclab/isaaclab/devices/teleop_device_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..f7265c41c2c6a079d005be40754c2c713f0db346 --- /dev/null +++ b/source/isaaclab/isaaclab/devices/teleop_device_factory.py @@ -0,0 +1,88 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Factory to create teleoperation devices from configuration.""" + +import inspect +import logging +from collections.abc import Callable +from typing import cast + +from isaaclab.devices import DeviceBase, DeviceCfg +from isaaclab.devices.retargeter_base import RetargeterBase + +# import logger +logger = logging.getLogger(__name__) + + +def create_teleop_device( + device_name: str, devices_cfg: dict[str, DeviceCfg], callbacks: dict[str, Callable] | None = None +) -> DeviceBase: + """Create a teleoperation device based on configuration. + + Args: + device_name: The name of the device to create (must exist in devices_cfg) + devices_cfg: Dictionary of device configurations + callbacks: Optional dictionary of callbacks to register with the device + Keys are the button/gesture names, values are callback functions + + Returns: + The configured teleoperation device + + Raises: + ValueError: If the device name is not found in the configuration + ValueError: If the device configuration type is not supported + """ + if device_name not in devices_cfg: + raise ValueError(f"Device '{device_name}' not found in teleop device configurations") + + device_cfg = devices_cfg[device_name] + callbacks = callbacks or {} + + # Determine constructor from the configuration itself + device_constructor = getattr(device_cfg, "class_type", None) + if device_constructor is None: + raise ValueError( + f"Device configuration '{device_name}' does not declare class_type. " + "Set cfg.class_type to the concrete DeviceBase subclass." + ) + if not issubclass(device_constructor, DeviceBase): + raise TypeError(f"class_type for '{device_name}' must be a subclass of DeviceBase; got {device_constructor}") + + # Try to create retargeters if they are configured + retargeters = [] + if hasattr(device_cfg, "retargeters") and device_cfg.retargeters is not None: + try: + # Create retargeters based on configuration using per-config retargeter_type + for retargeter_cfg in device_cfg.retargeters: + retargeter_constructor = getattr(retargeter_cfg, "retargeter_type", None) + if retargeter_constructor is None: + raise ValueError( + f"Retargeter configuration {type(retargeter_cfg).__name__} does not declare retargeter_type. " + "Set cfg.retargeter_type to the concrete RetargeterBase subclass." + ) + if not issubclass(retargeter_constructor, RetargeterBase): + raise TypeError( + f"retargeter_type for {type(retargeter_cfg).__name__} must be a subclass of RetargeterBase; got" + f" {retargeter_constructor}" + ) + retargeters.append(retargeter_constructor(retargeter_cfg)) + + except NameError as e: + raise ValueError(f"Failed to create retargeters: {e}") + + # Build constructor kwargs based on signature + constructor_params = inspect.signature(device_constructor).parameters + params: dict = {"cfg": device_cfg} + if "retargeters" in constructor_params: + params["retargeters"] = retargeters + device = cast(DeviceBase, device_constructor(**params)) + + # Register callbacks + for key, callback in callbacks.items(): + device.add_callback(key, callback) + + logging.info(f"Created teleoperation device: {device_name}") + return device diff --git a/source/isaaclab/isaaclab/envs/__init__.py b/source/isaaclab/isaaclab/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..543ff2ad4bac65aa1dca2b59581e4ed086b7fc4b --- /dev/null +++ b/source/isaaclab/isaaclab/envs/__init__.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package for environment definitions. + +Environments define the interface between the agent and the simulation. +In the simplest case, the environment provides the agent with the current +observations and executes the actions provided by the agent. However, the +environment can also provide additional information such as the current +reward, done flag, and information about the current episode. + +There are two types of environment designing workflows: + +* **Manager-based**: The environment is decomposed into individual components (or managers) + for different aspects (such as computing observations, applying actions, and applying + randomization. The users mainly configure the managers and the environment coordinates the + managers and calls their functions. +* **Direct**: The user implements all the necessary functionality directly into a single class + directly without the need for additional managers. + +Based on these workflows, there are the following environment classes for single and multi-agent RL: + +**Single-Agent RL:** + +* :class:`ManagerBasedEnv`: The manager-based workflow base environment which only provides the + agent with the current observations and executes the actions provided by the agent. +* :class:`ManagerBasedRLEnv`: The manager-based workflow RL task environment which besides the + functionality of the base environment also provides additional Markov Decision Process (MDP) + related information such as the current reward, done flag, and information. +* :class:`DirectRLEnv`: The direct workflow RL task environment which provides implementations for + implementing scene setup, computing dones, performing resets, and computing reward and observation. + +**Multi-Agent RL (MARL):** + +* :class:`DirectMARLEnv`: The direct workflow MARL task environment which provides implementations for + implementing scene setup, computing dones, performing resets, and computing reward and observation. + +For more information about the workflow design patterns, see the `Task Design Workflows`_ section. + +.. _`Task Design Workflows`: https://docs.isaacsim.omniverse.nvidia.com/latest/introduction/workflows.html +""" + +from . import mdp, ui +from .common import VecEnvObs, VecEnvStepReturn, ViewerCfg +from .direct_marl_env import DirectMARLEnv +from .direct_marl_env_cfg import DirectMARLEnvCfg +from .direct_rl_env import DirectRLEnv +from .direct_rl_env_cfg import DirectRLEnvCfg +from .manager_based_env import ManagerBasedEnv +from .manager_based_env_cfg import ManagerBasedEnvCfg +from .manager_based_rl_env import ManagerBasedRLEnv +from .manager_based_rl_env_cfg import ManagerBasedRLEnvCfg +from .manager_based_rl_mimic_env import ManagerBasedRLMimicEnv +from .mimic_env_cfg import * +from .utils.marl import multi_agent_to_single_agent, multi_agent_with_one_agent diff --git a/source/isaaclab/isaaclab/envs/common.py b/source/isaaclab/isaaclab/envs/common.py new file mode 100644 index 0000000000000000000000000000000000000000..f913005d1dbbc206a85db913941742d77d483a22 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/common.py @@ -0,0 +1,148 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import Dict, Literal, TypeVar # noqa: UP035 + +import gymnasium as gym +import torch + +from isaaclab.utils import configclass + +## +# Configuration. +## + + +@configclass +class ViewerCfg: + """Configuration of the scene viewport camera.""" + + eye: tuple[float, float, float] = (7.5, 7.5, 7.5) + """Initial camera position (in m). Default is (7.5, 7.5, 7.5).""" + + lookat: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Initial camera target position (in m). Default is (0.0, 0.0, 0.0).""" + + cam_prim_path: str = "/OmniverseKit_Persp" + """The camera prim path to record images from. Default is "/OmniverseKit_Persp", + which is the default camera in the viewport. + """ + + resolution: tuple[int, int] = (1280, 720) + """The resolution (width, height) of the camera specified using :attr:`cam_prim_path`. + Default is (1280, 720). + """ + + origin_type: Literal["world", "env", "asset_root", "asset_body"] = "world" + """The frame in which the camera position (eye) and target (lookat) are defined in. Default is "world". + + Available options are: + + * ``"world"``: The origin of the world. + * ``"env"``: The origin of the environment defined by :attr:`env_index`. + * ``"asset_root"``: The center of the asset defined by :attr:`asset_name` in environment :attr:`env_index`. + * ``"asset_body"``: The center of the body defined by :attr:`body_name` in asset defined by + :attr:`asset_name` in environment :attr:`env_index`. + """ + + env_index: int = 0 + """The environment index for frame origin. Default is 0. + + This quantity is only effective if :attr:`origin` is set to "env" or "asset_root". + """ + + asset_name: str | None = None + """The asset name in the interactive scene for the frame origin. Default is None. + + This quantity is only effective if :attr:`origin` is set to "asset_root". + """ + + body_name: str | None = None + """The name of the body in :attr:`asset_name` in the interactive scene for the frame origin. Default is None. + + This quantity is only effective if :attr:`origin` is set to "asset_body". + """ + + +## +# Types. +## + +SpaceType = TypeVar("SpaceType", gym.spaces.Space, int, set, tuple, list, dict) +"""A sentinel object to indicate a valid space type to specify states, observations and actions.""" + +VecEnvObs = Dict[str, torch.Tensor | Dict[str, torch.Tensor]] +"""Observation returned by the environment. + +The observations are stored in a dictionary. The keys are the group to which the observations belong. +This is useful for various setups such as reinforcement learning with asymmetric actor-critic or +multi-agent learning. For non-learning paradigms, this may include observations for different components +of a system. + +Within each group, the observations can be stored either as a dictionary with keys as the names of each +observation term in the group, or a single tensor obtained from concatenating all the observation terms. +For example, for asymmetric actor-critic, the observation for the actor and the critic can be accessed +using the keys ``"policy"`` and ``"critic"`` respectively. + +Note: + By default, most learning frameworks deal with default and privileged observations in different ways. + This handling must be taken care of by the wrapper around the :class:`ManagerBasedRLEnv` instance. + + For included frameworks (RSL-RL, RL-Games, skrl), the observations must have the key "policy". In case, + the key "critic" is also present, then the critic observations are taken from the "critic" group. + Otherwise, they are the same as the "policy" group. + +""" + +VecEnvStepReturn = tuple[VecEnvObs, torch.Tensor, torch.Tensor, torch.Tensor, dict] +"""The environment signals processed at the end of each step. + +The tuple contains batched information for each sub-environment. The information is stored in the following order: + +1. **Observations**: The observations from the environment. +2. **Rewards**: The rewards from the environment. +3. **Terminated Dones**: Whether the environment reached a terminal state, such as task success or robot falling etc. +4. **Timeout Dones**: Whether the environment reached a timeout state, such as end of max episode length. +5. **Extras**: A dictionary containing additional information from the environment. +""" + +AgentID = TypeVar("AgentID") +"""Unique identifier for an agent within a multi-agent environment. + +The identifier has to be an immutable object, typically a string (e.g.: ``"agent_0"``). +""" + +ObsType = TypeVar("ObsType", torch.Tensor, Dict[str, torch.Tensor]) +"""A sentinel object to indicate the data type of the observation. +""" + +ActionType = TypeVar("ActionType", torch.Tensor, Dict[str, torch.Tensor]) +"""A sentinel object to indicate the data type of the action. +""" + +StateType = TypeVar("StateType", torch.Tensor, dict) +"""A sentinel object to indicate the data type of the state. +""" + +EnvStepReturn = tuple[ + Dict[AgentID, ObsType], + Dict[AgentID, torch.Tensor], + Dict[AgentID, torch.Tensor], + Dict[AgentID, torch.Tensor], + Dict[AgentID, dict], +] +"""The environment signals processed at the end of each step. + +The tuple contains batched information for each sub-environment (keyed by the agent ID). +The information is stored in the following order: + +1. **Observations**: The observations from the environment. +2. **Rewards**: The rewards from the environment. +3. **Terminated Dones**: Whether the environment reached a terminal state, such as task success or robot falling etc. +4. **Timeout Dones**: Whether the environment reached a timeout state, such as end of max episode length. +5. **Extras**: A dictionary containing additional information from the environment. +""" diff --git a/source/isaaclab/isaaclab/envs/direct_marl_env.py b/source/isaaclab/isaaclab/envs/direct_marl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..206a4e7c01c3e539d13dfbd260ec9fe07a7a79df --- /dev/null +++ b/source/isaaclab/isaaclab/envs/direct_marl_env.py @@ -0,0 +1,759 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import builtins +import inspect +import logging +import math +import weakref +from abc import abstractmethod +from collections.abc import Sequence +from dataclasses import MISSING +from typing import Any, ClassVar + +import gymnasium as gym +import numpy as np +import torch + +import omni.kit.app +import omni.physx + +from isaaclab.managers import EventManager +from isaaclab.scene import InteractiveScene +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import attach_stage_to_usd_context, use_stage +from isaaclab.utils.noise import NoiseModel +from isaaclab.utils.seed import configure_seed +from isaaclab.utils.timer import Timer +from isaaclab.utils.version import get_isaac_sim_version + +from .common import ActionType, AgentID, EnvStepReturn, ObsType, StateType +from .direct_marl_env_cfg import DirectMARLEnvCfg +from .ui import ViewportCameraController +from .utils.spaces import sample_space, spec_to_gym_space + +# import logger +logger = logging.getLogger(__name__) + + +class DirectMARLEnv(gym.Env): + """The superclass for the direct workflow to design multi-agent environments. + + This class implements the core functionality for multi-agent reinforcement learning (MARL) + environments. It is designed to be used with any RL library. The class is designed + to be used with vectorized environments, i.e., the environment is expected to be run + in parallel with multiple sub-environments. + + The design of this class is based on the PettingZoo Parallel API. + While the environment itself is implemented as a vectorized environment, we do not + inherit from :class:`pettingzoo.ParallelEnv` or :class:`gym.vector.VectorEnv`. This is mainly + because the class adds various attributes and methods that are inconsistent with them. + + Note: + For vectorized environments, it is recommended to **only** call the :meth:`reset` + method once before the first call to :meth:`step`, i.e. after the environment is created. + After that, the :meth:`step` function handles the reset of terminated sub-environments. + This is because the simulator does not support resetting individual sub-environments + in a vectorized environment. + + """ + + metadata: ClassVar[dict[str, Any]] = { + "render_modes": [None, "human", "rgb_array"], + } + """Metadata for the environment.""" + + def __init__(self, cfg: DirectMARLEnvCfg, render_mode: str | None = None, **kwargs): + """Initialize the environment. + + Args: + cfg: The configuration object for the environment. + render_mode: The render mode for the environment. Defaults to None, which + is similar to ``"human"``. + + Raises: + RuntimeError: If a simulation context already exists. The environment must always create one + since it configures the simulation context and controls the simulation. + """ + # check that the config is valid + cfg.validate() + # store inputs to class + self.cfg = cfg + # store the render mode + self.render_mode = render_mode + # initialize internal variables + self._is_closed = False + + # set the seed for the environment + if self.cfg.seed is not None: + self.cfg.seed = self.seed(self.cfg.seed) + else: + logger.warning("Seed not set for the environment. The environment creation may not be deterministic.") + + # create a simulation context to control the simulator + if SimulationContext.instance() is None: + self.sim: SimulationContext = SimulationContext(self.cfg.sim) + else: + raise RuntimeError("Simulation context already exists. Cannot create a new one.") + + # make sure torch is running on the correct device + if "cuda" in self.device: + torch.cuda.set_device(self.device) + + # print useful information + print("[INFO]: Base environment:") + print(f"\tEnvironment device : {self.device}") + print(f"\tEnvironment seed : {self.cfg.seed}") + print(f"\tPhysics step-size : {self.physics_dt}") + print(f"\tRendering step-size : {self.physics_dt * self.cfg.sim.render_interval}") + print(f"\tEnvironment step-size : {self.step_dt}") + + if self.cfg.sim.render_interval < self.cfg.decimation: + msg = ( + f"The render interval ({self.cfg.sim.render_interval}) is smaller than the decimation " + f"({self.cfg.decimation}). Multiple render calls will happen for each environment step." + "If this is not intended, set the render interval to be equal to the decimation." + ) + logger.warning(msg) + + # generate scene + with Timer("[INFO]: Time taken for scene creation", "scene_creation"): + # set the stage context for scene creation steps which use the stage + with use_stage(self.sim.get_initial_stage()): + self.scene = InteractiveScene(self.cfg.scene) + self._setup_scene() + attach_stage_to_usd_context() + print("[INFO]: Scene manager: ", self.scene) + + # set up camera viewport controller + # viewport is not available in other rendering modes so the function will throw a warning + # FIXME: This needs to be fixed in the future when we unify the UI functionalities even for + # non-rendering modes. + if self.sim.render_mode >= self.sim.RenderMode.PARTIAL_RENDERING: + self.viewport_camera_controller = ViewportCameraController(self, self.cfg.viewer) + else: + self.viewport_camera_controller = None + + # create event manager + # note: this is needed here (rather than after simulation play) to allow USD-related randomization events + # that must happen before the simulation starts. Example: randomizing mesh scale + if self.cfg.events: + self.event_manager = EventManager(self.cfg.events, self) + + # apply USD-related randomization events + if "prestartup" in self.event_manager.available_modes: + self.event_manager.apply(mode="prestartup") + + # play the simulator to activate physics handles + # note: this activates the physics simulation view that exposes TensorAPIs + # note: when started in extension mode, first call sim.reset_async() and then initialize the managers + if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False: + print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...") + with Timer("[INFO]: Time taken for simulation start", "simulation_start"): + # since the reset can trigger callbacks which use the stage, + # we need to set the stage context here + with use_stage(self.sim.get_initial_stage()): + self.sim.reset() + # update scene to pre populate data buffers for assets and sensors. + # this is needed for the observation manager to get valid tensors for initialization. + # this shouldn't cause an issue since later on, users do a reset over all the environments + # so the lazy buffers would be reset. + self.scene.update(dt=self.physics_dt) + + # check if debug visualization is has been implemented by the environment + source_code = inspect.getsource(self._set_debug_vis_impl) + self.has_debug_vis_implementation = "NotImplementedError" not in source_code + self._debug_vis_handle = None + + # extend UI elements + # we need to do this here after all the managers are initialized + # this is because they dictate the sensors and commands right now + if self.sim.has_gui() and self.cfg.ui_window_class_type is not None: + self._window = self.cfg.ui_window_class_type(self, window_name="IsaacLab") + else: + # if no window, then we don't need to store the window + self._window = None + + # allocate dictionary to store metrics + self.extras = {agent: {} for agent in self.cfg.possible_agents} + + # initialize data and constants + # -- counter for simulation steps + self._sim_step_counter = 0 + # -- counter for curriculum + self.common_step_counter = 0 + # -- init buffers + self.episode_length_buf = torch.zeros(self.num_envs, device=self.device, dtype=torch.long) + self.reset_buf = torch.zeros(self.num_envs, dtype=torch.bool, device=self.sim.device) + + # setup the observation, state and action spaces + self._configure_env_spaces() + + # setup noise cfg for adding action and observation noise + if self.cfg.action_noise_model: + self._action_noise_model: dict[AgentID, NoiseModel] = { + agent: noise_model.class_type(noise_model, num_envs=self.num_envs, device=self.device) + for agent, noise_model in self.cfg.action_noise_model.items() + if noise_model is not None + } + if self.cfg.observation_noise_model: + self._observation_noise_model: dict[AgentID, NoiseModel] = { + agent: noise_model.class_type(noise_model, num_envs=self.num_envs, device=self.device) + for agent, noise_model in self.cfg.observation_noise_model.items() + if noise_model is not None + } + + # perform events at the start of the simulation + if self.cfg.events: + # we print it here to make the logging consistent + print("[INFO] Event Manager: ", self.event_manager) + + if "startup" in self.event_manager.available_modes: + self.event_manager.apply(mode="startup") + + # print the environment information + print("[INFO]: Completed setting up the environment...") + + def __del__(self): + """Cleanup for the environment.""" + self.close() + + """ + Properties. + """ + + @property + def num_envs(self) -> int: + """The number of instances of the environment that are running.""" + return self.scene.num_envs + + @property + def num_agents(self) -> int: + """Number of current agents. + + The number of current agents may change as the environment progresses (e.g.: agents can be added or removed). + """ + return len(self.agents) + + @property + def max_num_agents(self) -> int: + """Number of all possible agents the environment can generate. + + This value remains constant as the environment progresses. + """ + return len(self.possible_agents) + + @property + def unwrapped(self) -> DirectMARLEnv: + """Get the unwrapped environment underneath all the layers of wrappers.""" + return self + + @property + def physics_dt(self) -> float: + """The physics time-step (in s). + + This is the lowest time-decimation at which the simulation is happening. + """ + return self.cfg.sim.dt + + @property + def step_dt(self) -> float: + """The environment stepping time-step (in s). + + This is the time-step at which the environment steps forward. + """ + return self.cfg.sim.dt * self.cfg.decimation + + @property + def device(self): + """The device on which the environment is running.""" + return self.sim.device + + @property + def max_episode_length_s(self) -> float: + """Maximum episode length in seconds.""" + return self.cfg.episode_length_s + + @property + def max_episode_length(self): + """The maximum episode length in steps adjusted from s.""" + return math.ceil(self.max_episode_length_s / (self.cfg.sim.dt * self.cfg.decimation)) + + """ + Space methods + """ + + def observation_space(self, agent: AgentID) -> gym.Space: + """Get the observation space for the specified agent. + + Returns: + The agent's observation space. + """ + return self.observation_spaces[agent] + + def action_space(self, agent: AgentID) -> gym.Space: + """Get the action space for the specified agent. + + Returns: + The agent's action space. + """ + return self.action_spaces[agent] + + """ + Operations. + """ + + def reset( + self, seed: int | None = None, options: dict[str, Any] | None = None + ) -> tuple[dict[AgentID, ObsType], dict[AgentID, dict]]: + """Resets all the environments and returns observations. + + Args: + seed: The seed to use for randomization. Defaults to None, in which case the seed is not set. + options: Additional information to specify how the environment is reset. Defaults to None. + + Note: + This argument is used for compatibility with Gymnasium environment definition. + + Returns: + A tuple containing the observations and extras (keyed by the agent ID). + """ + # set the seed + if seed is not None: + self.seed(seed) + + # reset state of scene + indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device) + self._reset_idx(indices) + + # update observations and the list of current agents (sorted as in possible_agents) + self.obs_dict = self._get_observations() + self.agents = [agent for agent in self.possible_agents if agent in self.obs_dict] + + # return observations + return self.obs_dict, self.extras + + def step(self, actions: dict[AgentID, ActionType]) -> EnvStepReturn: + """Execute one time-step of the environment's dynamics. + + The environment steps forward at a fixed time-step, while the physics simulation is decimated at a + lower time-step. This is to ensure that the simulation is stable. These two time-steps can be configured + independently using the :attr:`DirectMARLEnvCfg.decimation` (number of simulation steps per environment step) + and the :attr:`DirectMARLEnvCfg.sim.physics_dt` (physics time-step). Based on these parameters, the environment + time-step is computed as the product of the two. + + This function performs the following steps: + + 1. Pre-process the actions before stepping through the physics. + 2. Apply the actions to the simulator and step through the physics in a decimated manner. + 3. Compute the reward and done signals. + 4. Reset environments that have terminated or reached the maximum episode length. + 5. Apply interval events if they are enabled. + 6. Compute observations. + + Args: + actions: The actions to apply on the environment (keyed by the agent ID). + Shape of individual tensors is (num_envs, action_dim). + + Returns: + A tuple containing the observations, rewards, resets (terminated and truncated) and + extras (keyed by the agent ID). Shape of individual tensors is (num_envs, ...). + """ + actions = {agent: action.to(self.device) for agent, action in actions.items()} + + # add action noise + if self.cfg.action_noise_model: + for agent, action in actions.items(): + if agent in self._action_noise_model: + actions[agent] = self._action_noise_model[agent](action) + # process actions + self._pre_physics_step(actions) + + # check if we need to do rendering within the physics loop + # note: checked here once to avoid multiple checks within the loop + is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() + + # perform physics stepping + for _ in range(self.cfg.decimation): + self._sim_step_counter += 1 + # set actions into buffers + self._apply_action() + # set actions into simulator + self.scene.write_data_to_sim() + # simulate + self.sim.step(render=False) + # render between steps only if the GUI or an RTX sensor needs it + # note: we assume the render interval to be the shortest accepted rendering interval. + # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. + if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: + self.sim.render() + # update buffers at sim dt + self.scene.update(dt=self.physics_dt) + + # post-step: + # -- update env counters (used for curriculum generation) + self.episode_length_buf += 1 # step in current episode (per env) + self.common_step_counter += 1 # total step (common for all envs) + + self.terminated_dict, self.time_out_dict = self._get_dones() + self.reset_buf[:] = math.prod(self.terminated_dict.values()) | math.prod(self.time_out_dict.values()) + self.reward_dict = self._get_rewards() + + # -- reset envs that terminated/timed-out and log the episode information + reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(reset_env_ids) > 0: + self._reset_idx(reset_env_ids) + + # post-step: step interval event + if self.cfg.events: + if "interval" in self.event_manager.available_modes: + self.event_manager.apply(mode="interval", dt=self.step_dt) + + # update observations and the list of current agents (sorted as in possible_agents) + self.obs_dict = self._get_observations() + self.agents = [agent for agent in self.possible_agents if agent in self.obs_dict] + + # add observation noise + # note: we apply no noise to the state space (since it is used for centralized training or critic networks) + if self.cfg.observation_noise_model: + for agent, obs in self.obs_dict.items(): + if agent in self._observation_noise_model: + self.obs_dict[agent] = self._observation_noise_model[agent](obs) + + # return observations, rewards, resets and extras + return self.obs_dict, self.reward_dict, self.terminated_dict, self.time_out_dict, self.extras + + def state(self) -> StateType | None: + """Returns the state for the environment. + + The state-space is used for centralized training or asymmetric actor-critic architectures. It is configured + using the :attr:`DirectMARLEnvCfg.state_space` parameter. + + Returns: + The states for the environment, or None if :attr:`DirectMARLEnvCfg.state_space` parameter is zero. + """ + if not self.cfg.state_space: + return None + # concatenate and return the observations as state + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + if isinstance(self.cfg.state_space, int) and self.cfg.state_space < 0: + self.state_buf = torch.cat( + [self.obs_dict[agent].reshape(self.num_envs, -1) for agent in self.cfg.possible_agents], dim=-1 + ) + # compute and return custom environment state + else: + self.state_buf = self._get_states() + return self.state_buf + + @staticmethod + def seed(seed: int = -1) -> int: + """Set the seed for the environment. + + Args: + seed: The seed for random generator. Defaults to -1. + + Returns: + The seed used for random generator. + """ + # set seed for replicator + try: + import omni.replicator.core as rep + + rep.set_global_seed(seed) + except ModuleNotFoundError: + pass + # set seed for torch and other libraries + return configure_seed(seed) + + def render(self, recompute: bool = False) -> np.ndarray | None: + """Run rendering without stepping through the physics. + + By convention, if mode is: + + - **human**: Render to the current display and return nothing. Usually for human consumption. + - **rgb_array**: Return a numpy.ndarray with shape (x, y, 3), representing RGB values for an + x-by-y pixel image, suitable for turning into a video. + + Args: + recompute: Whether to force a render even if the simulator has already rendered the scene. + Defaults to False. + + Returns: + The rendered image as a numpy array if mode is "rgb_array". Otherwise, returns None. + + Raises: + RuntimeError: If mode is set to "rgb_data" and simulation render mode does not support it. + In this case, the simulation render mode must be set to ``RenderMode.PARTIAL_RENDERING`` + or ``RenderMode.FULL_RENDERING``. + NotImplementedError: If an unsupported rendering mode is specified. + """ + # run a rendering step of the simulator + # if we have rtx sensors, we do not need to render again sin + if not self.sim.has_rtx_sensors() and not recompute: + self.sim.render() + # decide the rendering mode + if self.render_mode == "human" or self.render_mode is None: + return None + elif self.render_mode == "rgb_array": + # check that if any render could have happened + if self.sim.render_mode.value < self.sim.RenderMode.PARTIAL_RENDERING.value: + raise RuntimeError( + f"Cannot render '{self.render_mode}' when the simulation render mode is" + f" '{self.sim.render_mode.name}'. Please set the simulation render mode to:" + f"'{self.sim.RenderMode.PARTIAL_RENDERING.name}' or '{self.sim.RenderMode.FULL_RENDERING.name}'." + " If running headless, make sure --enable_cameras is set." + ) + # create the annotator if it does not exist + if not hasattr(self, "_rgb_annotator"): + import omni.replicator.core as rep + + # create render product + self._render_product = rep.create.render_product( + self.cfg.viewer.cam_prim_path, self.cfg.viewer.resolution + ) + # create rgb annotator -- used to read data from the render product + self._rgb_annotator = rep.AnnotatorRegistry.get_annotator("rgb", device="cpu") + self._rgb_annotator.attach([self._render_product]) + # obtain the rgb data + rgb_data = self._rgb_annotator.get_data() + # convert to numpy array + rgb_data = np.frombuffer(rgb_data, dtype=np.uint8).reshape(*rgb_data.shape) + # return the rgb data + # note: initially the renderer is warming up and returns empty data + if rgb_data.size == 0: + return np.zeros((self.cfg.viewer.resolution[1], self.cfg.viewer.resolution[0], 3), dtype=np.uint8) + else: + return rgb_data[:, :, :3] + else: + raise NotImplementedError( + f"Render mode '{self.render_mode}' is not supported. Please use: {self.metadata['render_modes']}." + ) + + def close(self): + """Cleanup for the environment.""" + if not self._is_closed: + # close entities related to the environment + # note: this is order-sensitive to avoid any dangling references + if self.cfg.events: + del self.event_manager + del self.scene + if self.viewport_camera_controller is not None: + del self.viewport_camera_controller + + # clear callbacks and instance + if get_isaac_sim_version().major >= 5: + if self.cfg.sim.create_stage_in_memory: + # detach physx stage + omni.physx.get_physx_simulation_interface().detach_stage() + self.sim.stop() + self.sim.clear() + + self.sim.clear_all_callbacks() + self.sim.clear_instance() + + # destroy the window + if self._window is not None: + self._window = None + # update closing status + self._is_closed = True + + """ + Operations - Debug Visualization. + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Toggles the environment debug visualization. + + Args: + debug_vis: Whether to visualize the environment debug visualization. + + Returns: + Whether the debug visualization was successfully set. False if the environment + does not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_debug_vis_implementation: + return False + # toggle debug visualization objects + self._set_debug_vis_impl(debug_vis) + # toggle debug visualization handles + if debug_vis: + # create a subscriber for the post update event if it doesn't exist + if self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + # return success + return True + + """ + Helper functions. + """ + + def _configure_env_spaces(self): + """Configure the spaces for the environment.""" + self.agents = self.cfg.possible_agents + self.possible_agents = self.cfg.possible_agents + + # show deprecation message and overwrite configuration + if self.cfg.num_actions is not None: + logger.warning("DirectMARLEnvCfg.num_actions is deprecated. Use DirectMARLEnvCfg.action_spaces instead.") + if isinstance(self.cfg.action_spaces, type(MISSING)): + self.cfg.action_spaces = self.cfg.num_actions + if self.cfg.num_observations is not None: + logger.warning( + "DirectMARLEnvCfg.num_observations is deprecated. Use DirectMARLEnvCfg.observation_spaces instead." + ) + if isinstance(self.cfg.observation_spaces, type(MISSING)): + self.cfg.observation_spaces = self.cfg.num_observations + if self.cfg.num_states is not None: + logger.warning("DirectMARLEnvCfg.num_states is deprecated. Use DirectMARLEnvCfg.state_space instead.") + if isinstance(self.cfg.state_space, type(MISSING)): + self.cfg.state_space = self.cfg.num_states + + # set up observation and action spaces + self.observation_spaces = { + agent: spec_to_gym_space(self.cfg.observation_spaces[agent]) for agent in self.cfg.possible_agents + } + self.action_spaces = { + agent: spec_to_gym_space(self.cfg.action_spaces[agent]) for agent in self.cfg.possible_agents + } + + # set up state space + if not self.cfg.state_space: + self.state_space = None + if isinstance(self.cfg.state_space, int) and self.cfg.state_space < 0: + self.state_space = gym.spaces.flatten_space( + gym.spaces.Tuple([self.observation_spaces[agent] for agent in self.cfg.possible_agents]) + ) + else: + self.state_space = spec_to_gym_space(self.cfg.state_space) + + # instantiate actions (needed for tasks for which the observations computation is dependent on the actions) + self.actions = { + agent: sample_space(self.action_spaces[agent], self.sim.device, batch_size=self.num_envs, fill_value=0) + for agent in self.cfg.possible_agents + } + + def _reset_idx(self, env_ids: Sequence[int]): + """Reset environments based on specified indices. + + Args: + env_ids: List of environment ids which must be reset + """ + self.scene.reset(env_ids) + + # apply events such as randomization for environments that need a reset + if self.cfg.events: + if "reset" in self.event_manager.available_modes: + env_step_count = self._sim_step_counter // self.cfg.decimation + self.event_manager.apply(mode="reset", env_ids=env_ids, global_env_step_count=env_step_count) + + # reset noise models + if self.cfg.action_noise_model: + for noise_model in self._action_noise_model.values(): + noise_model.reset(env_ids) + if self.cfg.observation_noise_model: + for noise_model in self._observation_noise_model.values(): + noise_model.reset(env_ids) + + # reset the episode length buffer + self.episode_length_buf[env_ids] = 0 + + """ + Implementation-specific functions. + """ + + def _setup_scene(self): + """Setup the scene for the environment. + + This function is responsible for creating the scene objects and setting up the scene for the environment. + The scene creation can happen through :class:`isaaclab.scene.InteractiveSceneCfg` or through + directly creating the scene objects and registering them with the scene manager. + + We leave the implementation of this function to the derived classes. If the environment does not require + any explicit scene setup, the function can be left empty. + """ + pass + + @abstractmethod + def _pre_physics_step(self, actions: dict[AgentID, ActionType]): + """Pre-process actions before stepping through the physics. + + This function is responsible for pre-processing the actions before stepping through the physics. + It is called before the physics stepping (which is decimated). + + Args: + actions: The actions to apply on the environment (keyed by the agent ID). + Shape of individual tensors is (num_envs, action_dim). + """ + raise NotImplementedError(f"Please implement the '_pre_physics_step' method for {self.__class__.__name__}.") + + @abstractmethod + def _apply_action(self): + """Apply actions to the simulator. + + This function is responsible for applying the actions to the simulator. It is called at each + physics time-step. + """ + raise NotImplementedError(f"Please implement the '_apply_action' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_observations(self) -> dict[AgentID, ObsType]: + """Compute and return the observations for the environment. + + Returns: + The observations for the environment (keyed by the agent ID). + """ + raise NotImplementedError(f"Please implement the '_get_observations' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_states(self) -> StateType: + """Compute and return the states for the environment. + + This method is only called (and therefore has to be implemented) when the :attr:`DirectMARLEnvCfg.state_space` + parameter is not a number less than or equal to zero. + + Returns: + The states for the environment. + """ + raise NotImplementedError(f"Please implement the '_get_states' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_rewards(self) -> dict[AgentID, torch.Tensor]: + """Compute and return the rewards for the environment. + + Returns: + The rewards for the environment (keyed by the agent ID). + Shape of individual tensors is (num_envs,). + """ + raise NotImplementedError(f"Please implement the '_get_rewards' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_dones(self) -> tuple[dict[AgentID, torch.Tensor], dict[AgentID, torch.Tensor]]: + """Compute and return the done flags for the environment. + + Returns: + A tuple containing the done flags for termination and time-out (keyed by the agent ID). + Shape of individual tensors is (num_envs,). + """ + raise NotImplementedError(f"Please implement the '_get_dones' method for {self.__class__.__name__}.") + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects. + + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") diff --git a/source/isaaclab/isaaclab/envs/direct_marl_env_cfg.py b/source/isaaclab/isaaclab/envs/direct_marl_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..66b2bcf998d64ce0bbd8088f95f78c7a38e1520c --- /dev/null +++ b/source/isaaclab/isaaclab/envs/direct_marl_env_cfg.py @@ -0,0 +1,232 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.devices.openxr import XrCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass +from isaaclab.utils.noise import NoiseModelCfg + +from .common import AgentID, SpaceType, ViewerCfg +from .ui import BaseEnvWindow + + +@configclass +class DirectMARLEnvCfg: + """Configuration for a MARL environment defined with the direct workflow. + + Please refer to the :class:`isaaclab.envs.direct_marl_env.DirectMARLEnv` class for more details. + """ + + # simulation settings + viewer: ViewerCfg = ViewerCfg() + """Viewer configuration. Default is ViewerCfg().""" + + sim: SimulationCfg = SimulationCfg() + """Physics simulation configuration. Default is SimulationCfg().""" + + # ui settings + ui_window_class_type: type | None = BaseEnvWindow + """The class type of the UI window. Default is None. + + If None, then no UI window is created. + + Note: + If you want to make your own UI window, you can create a class that inherits from + from :class:`isaaclab.envs.ui.base_env_window.BaseEnvWindow`. Then, you can set + this attribute to your class type. + """ + + # general settings + seed: int | None = None + """The seed for the random number generator. Defaults to None, in which case the seed is not set. + + Note: + The seed is set at the beginning of the environment initialization. This ensures that the environment + creation is deterministic and behaves similarly across different runs. + """ + + decimation: int = MISSING + """Number of control action updates @ sim dt per policy dt. + + For instance, if the simulation dt is 0.01s and the policy dt is 0.1s, then the decimation is 10. + This means that the control action is updated every 10 simulation steps. + """ + + is_finite_horizon: bool = False + """Whether the learning task is treated as a finite or infinite horizon problem for the agent. + Defaults to False, which means the task is treated as an infinite horizon problem. + + This flag handles the subtleties of finite and infinite horizon tasks: + + * **Finite horizon**: no penalty or bootstrapping value is required by the the agent for + running out of time. However, the environment still needs to terminate the episode after the + time limit is reached. + * **Infinite horizon**: the agent needs to bootstrap the value of the state at the end of the episode. + This is done by sending a time-limit (or truncated) done signal to the agent, which triggers this + bootstrapping calculation. + + If True, then the environment is treated as a finite horizon problem and no time-out (or truncated) done signal + is sent to the agent. If False, then the environment is treated as an infinite horizon problem and a time-out + (or truncated) done signal is sent to the agent. + + Note: + The base :class:`ManagerBasedRLEnv` class does not use this flag directly. It is used by the environment + wrappers to determine what type of done signal to send to the corresponding learning agent. + """ + + episode_length_s: float = MISSING + """Duration of an episode (in seconds). + + Based on the decimation rate and physics time step, the episode length is calculated as: + + .. code-block:: python + + episode_length_steps = ceil(episode_length_s / (decimation_rate * physics_time_step)) + + For example, if the decimation rate is 10, the physics time step is 0.01, and the episode length is 10 seconds, + then the episode length in steps is 100. + """ + + # environment settings + scene: InteractiveSceneCfg = MISSING + """Scene settings. + + Please refer to the :class:`isaaclab.scene.InteractiveSceneCfg` class for more details. + """ + + events: object = None + """Event settings. Defaults to None, in which case no events are applied through the event manager. + + Please refer to the :class:`isaaclab.managers.EventManager` class for more details. + """ + + observation_spaces: dict[AgentID, SpaceType] = MISSING + """Observation space definition for each agent. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_observations: dict[AgentID, int] | None = None + """The dimension of the observation space for each agent. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectMARLEnvCfg.observation_spaces` instead. + """ + + state_space: SpaceType = MISSING + """State space definition. + + The following values are supported: + + * -1: All the observations from the different agents are automatically concatenated. + * 0: No state-space will be constructed (`state_space` is None). + This is useful to save computational resources when the algorithm to be trained does not need it. + * greater than 0: Custom state-space dimension to be provided by the task implementation. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_states: int | None = None + """The dimension of the state space from each environment instance. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectMARLEnvCfg.state_space` instead. + """ + + observation_noise_model: dict[AgentID, NoiseModelCfg | None] | None = None + """The noise model to apply to the computed observations from the environment. Default is None, + which means no noise is added. + + Please refer to the :class:`isaaclab.utils.noise.NoiseModel` class for more details. + """ + + action_spaces: dict[AgentID, SpaceType] = MISSING + """Action space definition for each agent. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_actions: dict[AgentID, int] | None = None + """The dimension of the action space for each agent. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectMARLEnvCfg.action_spaces` instead. + """ + + action_noise_model: dict[AgentID, NoiseModelCfg | None] | None = None + """The noise model applied to the actions provided to the environment. Default is None, + which means no noise is added. + + Please refer to the :class:`isaaclab.utils.noise.NoiseModel` class for more details. + """ + + possible_agents: list[AgentID] = MISSING + """A list of all possible agents the environment could generate. + + The contents of the list cannot be modified during the entire training process. + """ + + xr: XrCfg | None = None + """Configuration for viewing and interacting with the environment through an XR device.""" + + log_dir: str | None = None + """Directory for logging experiment artifacts. Defaults to None, in which case no specific log directory is set.""" diff --git a/source/isaaclab/isaaclab/envs/direct_rl_env.py b/source/isaaclab/isaaclab/envs/direct_rl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..69be0edb78dd57565e556c60466ba2a88ba417f4 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/direct_rl_env.py @@ -0,0 +1,714 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import builtins +import inspect +import logging +import math +import warnings +import weakref +from abc import abstractmethod +from collections.abc import Sequence +from dataclasses import MISSING +from typing import Any, ClassVar + +import gymnasium as gym +import numpy as np +import torch + +import omni.kit.app +import omni.physx +from isaacsim.core.simulation_manager import SimulationManager + +from isaaclab.managers import EventManager +from isaaclab.scene import InteractiveScene +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import attach_stage_to_usd_context, use_stage +from isaaclab.utils.noise import NoiseModel +from isaaclab.utils.seed import configure_seed +from isaaclab.utils.timer import Timer +from isaaclab.utils.version import get_isaac_sim_version + +from .common import VecEnvObs, VecEnvStepReturn +from .direct_rl_env_cfg import DirectRLEnvCfg +from .ui import ViewportCameraController +from .utils.spaces import sample_space, spec_to_gym_space + +# import logger +logger = logging.getLogger(__name__) + + +class DirectRLEnv(gym.Env): + """The superclass for the direct workflow to design environments. + + This class implements the core functionality for reinforcement learning (RL) + environments. It is designed to be used with any RL library. The class is designed + to be used with vectorized environments, i.e., the environment is expected to be run + in parallel with multiple sub-environments. + + While the environment itself is implemented as a vectorized environment, we do not + inherit from :class:`gym.vector.VectorEnv`. This is mainly because the class adds + various methods (for wait and asynchronous updates) which are not required. + Additionally, each RL library typically has its own definition for a vectorized + environment. Thus, to reduce complexity, we directly use the :class:`gym.Env` over + here and leave it up to library-defined wrappers to take care of wrapping this + environment for their agents. + + Note: + For vectorized environments, it is recommended to **only** call the :meth:`reset` + method once before the first call to :meth:`step`, i.e. after the environment is created. + After that, the :meth:`step` function handles the reset of terminated sub-environments. + This is because the simulator does not support resetting individual sub-environments + in a vectorized environment. + + """ + + is_vector_env: ClassVar[bool] = True + """Whether the environment is a vectorized environment.""" + metadata: ClassVar[dict[str, Any]] = { + "render_modes": [None, "human", "rgb_array"], + } + """Metadata for the environment.""" + + def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs): + """Initialize the environment. + + Args: + cfg: The configuration object for the environment. + render_mode: The render mode for the environment. Defaults to None, which + is similar to ``"human"``. + + Raises: + RuntimeError: If a simulation context already exists. The environment must always create one + since it configures the simulation context and controls the simulation. + """ + # check that the config is valid + cfg.validate() + # store inputs to class + self.cfg = cfg + # store the render mode + self.render_mode = render_mode + # initialize internal variables + self._is_closed = False + + # set the seed for the environment + if self.cfg.seed is not None: + self.cfg.seed = self.seed(self.cfg.seed) + else: + logger.warning("Seed not set for the environment. The environment creation may not be deterministic.") + + # create a simulation context to control the simulator + if SimulationContext.instance() is None: + self.sim: SimulationContext = SimulationContext(self.cfg.sim) + else: + raise RuntimeError("Simulation context already exists. Cannot create a new one.") + + # make sure torch is running on the correct device + if "cuda" in self.device: + torch.cuda.set_device(self.device) + + # print useful information + print("[INFO]: Base environment:") + print(f"\tEnvironment device : {self.device}") + print(f"\tEnvironment seed : {self.cfg.seed}") + print(f"\tPhysics step-size : {self.physics_dt}") + print(f"\tRendering step-size : {self.physics_dt * self.cfg.sim.render_interval}") + print(f"\tEnvironment step-size : {self.step_dt}") + + if self.cfg.sim.render_interval < self.cfg.decimation: + msg = ( + f"The render interval ({self.cfg.sim.render_interval}) is smaller than the decimation " + f"({self.cfg.decimation}). Multiple render calls will happen for each environment step." + "If this is not intended, set the render interval to be equal to the decimation." + ) + logger.warning(msg) + + # generate scene + with Timer("[INFO]: Time taken for scene creation", "scene_creation"): + # set the stage context for scene creation steps which use the stage + with use_stage(self.sim.get_initial_stage()): + self.scene = InteractiveScene(self.cfg.scene) + self._setup_scene() + attach_stage_to_usd_context() + print("[INFO]: Scene manager: ", self.scene) + + # set up camera viewport controller + # viewport is not available in other rendering modes so the function will throw a warning + # FIXME: This needs to be fixed in the future when we unify the UI functionalities even for + # non-rendering modes. + if self.sim.render_mode >= self.sim.RenderMode.PARTIAL_RENDERING: + self.viewport_camera_controller = ViewportCameraController(self, self.cfg.viewer) + else: + self.viewport_camera_controller = None + + # create event manager + # note: this is needed here (rather than after simulation play) to allow USD-related randomization events + # that must happen before the simulation starts. Example: randomizing mesh scale + if self.cfg.events: + self.event_manager = EventManager(self.cfg.events, self) + + # apply USD-related randomization events + if "prestartup" in self.event_manager.available_modes: + self.event_manager.apply(mode="prestartup") + + # play the simulator to activate physics handles + # note: this activates the physics simulation view that exposes TensorAPIs + # note: when started in extension mode, first call sim.reset_async() and then initialize the managers + if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False: + print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...") + with Timer("[INFO]: Time taken for simulation start", "simulation_start"): + # since the reset can trigger callbacks which use the stage, + # we need to set the stage context here + with use_stage(self.sim.get_initial_stage()): + self.sim.reset() + # update scene to pre populate data buffers for assets and sensors. + # this is needed for the observation manager to get valid tensors for initialization. + # this shouldn't cause an issue since later on, users do a reset over all the environments + # so the lazy buffers would be reset. + self.scene.update(dt=self.physics_dt) + + # check if debug visualization is has been implemented by the environment + source_code = inspect.getsource(self._set_debug_vis_impl) + self.has_debug_vis_implementation = "NotImplementedError" not in source_code + self._debug_vis_handle = None + + # extend UI elements + # we need to do this here after all the managers are initialized + # this is because they dictate the sensors and commands right now + if self.sim.has_gui() and self.cfg.ui_window_class_type is not None: + self._window = self.cfg.ui_window_class_type(self, window_name="IsaacLab") + else: + # if no window, then we don't need to store the window + self._window = None + + # allocate dictionary to store metrics + self.extras = {} + + # initialize data and constants + # -- counter for simulation steps + self._sim_step_counter = 0 + # -- counter for curriculum + self.common_step_counter = 0 + # -- init buffers + self.episode_length_buf = torch.zeros(self.num_envs, device=self.device, dtype=torch.long) + self.reset_terminated = torch.zeros(self.num_envs, device=self.device, dtype=torch.bool) + self.reset_time_outs = torch.zeros_like(self.reset_terminated) + self.reset_buf = torch.zeros(self.num_envs, dtype=torch.bool, device=self.sim.device) + + # setup the action and observation spaces for Gym + self._configure_gym_env_spaces() + + # setup noise cfg for adding action and observation noise + if self.cfg.action_noise_model: + self._action_noise_model: NoiseModel = self.cfg.action_noise_model.class_type( + self.cfg.action_noise_model, num_envs=self.num_envs, device=self.device + ) + if self.cfg.observation_noise_model: + self._observation_noise_model: NoiseModel = self.cfg.observation_noise_model.class_type( + self.cfg.observation_noise_model, num_envs=self.num_envs, device=self.device + ) + + # perform events at the start of the simulation + if self.cfg.events: + # we print it here to make the logging consistent + print("[INFO] Event Manager: ", self.event_manager) + + if "startup" in self.event_manager.available_modes: + self.event_manager.apply(mode="startup") + + # set the framerate of the gym video recorder wrapper so that the playback speed of the produced + # video matches the simulation + self.metadata["render_fps"] = 1 / self.step_dt + + # show deprecation message for rerender_on_reset + if self.cfg.rerender_on_reset: + msg = ( + "\033[93m\033[1m[DEPRECATION WARNING] DirectRLEnvCfg.rerender_on_reset is deprecated. Use" + " DirectRLEnvCfg.num_rerenders_on_reset instead.\033[0m" + ) + warnings.warn( + msg, + FutureWarning, + stacklevel=2, + ) + if self.cfg.num_rerenders_on_reset == 0: + self.cfg.num_rerenders_on_reset = 1 + + # print the environment information + print("[INFO]: Completed setting up the environment...") + + def __del__(self): + """Cleanup for the environment.""" + self.close() + + """ + Properties. + """ + + @property + def num_envs(self) -> int: + """The number of instances of the environment that are running.""" + return self.scene.num_envs + + @property + def physics_dt(self) -> float: + """The physics time-step (in s). + + This is the lowest time-decimation at which the simulation is happening. + """ + return self.cfg.sim.dt + + @property + def step_dt(self) -> float: + """The environment stepping time-step (in s). + + This is the time-step at which the environment steps forward. + """ + return self.cfg.sim.dt * self.cfg.decimation + + @property + def device(self): + """The device on which the environment is running.""" + return self.sim.device + + @property + def max_episode_length_s(self) -> float: + """Maximum episode length in seconds.""" + return self.cfg.episode_length_s + + @property + def max_episode_length(self): + """The maximum episode length in steps adjusted from s.""" + return math.ceil(self.max_episode_length_s / (self.cfg.sim.dt * self.cfg.decimation)) + + """ + Operations. + """ + + def reset(self, seed: int | None = None, options: dict[str, Any] | None = None) -> tuple[VecEnvObs, dict]: + """Resets all the environments and returns observations. + + This function calls the :meth:`_reset_idx` function to reset all the environments. + However, certain operations, such as procedural terrain generation, that happened during initialization + are not repeated. + + Args: + seed: The seed to use for randomization. Defaults to None, in which case the seed is not set. + options: Additional information to specify how the environment is reset. Defaults to None. + + Note: + This argument is used for compatibility with Gymnasium environment definition. + + Returns: + A tuple containing the observations and extras. + """ + # set the seed + if seed is not None: + self.seed(seed) + + # reset state of scene + indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device) + self._reset_idx(indices) + + # update articulation kinematics + self.scene.write_data_to_sim() + self.sim.forward() + + # if sensors are added to the scene, make sure we render to reflect changes in reset + if self.sim.has_rtx_sensors() and self.cfg.num_rerenders_on_reset > 0: + for _ in range(self.cfg.num_rerenders_on_reset): + self.sim.render() + + if self.cfg.wait_for_textures and self.sim.has_rtx_sensors(): + while SimulationManager.assets_loading(): + self.sim.render() + + # return observations + return self._get_observations(), self.extras + + def step(self, action: torch.Tensor) -> VecEnvStepReturn: + """Execute one time-step of the environment's dynamics. + + The environment steps forward at a fixed time-step, while the physics simulation is decimated at a + lower time-step. This is to ensure that the simulation is stable. These two time-steps can be configured + independently using the :attr:`DirectRLEnvCfg.decimation` (number of simulation steps per environment step) + and the :attr:`DirectRLEnvCfg.sim.physics_dt` (physics time-step). Based on these parameters, the environment + time-step is computed as the product of the two. + + This function performs the following steps: + + 1. Pre-process the actions before stepping through the physics. + 2. Apply the actions to the simulator and step through the physics in a decimated manner. + 3. Compute the reward and done signals. + 4. Reset environments that have terminated or reached the maximum episode length. + 5. Apply interval events if they are enabled. + 6. Compute observations. + + Args: + action: The actions to apply on the environment. Shape is (num_envs, action_dim). + + Returns: + A tuple containing the observations, rewards, resets (terminated and truncated) and extras. + """ + action = action.to(self.device) + # add action noise + if self.cfg.action_noise_model: + action = self._action_noise_model(action) + + # process actions + self._pre_physics_step(action) + + # check if we need to do rendering within the physics loop + # note: checked here once to avoid multiple checks within the loop + is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() + + # perform physics stepping + for _ in range(self.cfg.decimation): + self._sim_step_counter += 1 + # set actions into buffers + self._apply_action() + # set actions into simulator + self.scene.write_data_to_sim() + # simulate + self.sim.step(render=False) + # render between steps only if the GUI or an RTX sensor needs it + # note: we assume the render interval to be the shortest accepted rendering interval. + # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. + if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: + self.sim.render() + # update buffers at sim dt + self.scene.update(dt=self.physics_dt) + + # post-step: + # -- update env counters (used for curriculum generation) + self.episode_length_buf += 1 # step in current episode (per env) + self.common_step_counter += 1 # total step (common for all envs) + + self.reset_terminated[:], self.reset_time_outs[:] = self._get_dones() + self.reset_buf = self.reset_terminated | self.reset_time_outs + self.reward_buf = self._get_rewards() + + # -- reset envs that terminated/timed-out and log the episode information + reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(reset_env_ids) > 0: + self._reset_idx(reset_env_ids) + # if sensors are added to the scene, make sure we render to reflect changes in reset + if self.sim.has_rtx_sensors() and self.cfg.num_rerenders_on_reset > 0: + for _ in range(self.cfg.num_rerenders_on_reset): + self.sim.render() + + # post-step: step interval event + if self.cfg.events: + if "interval" in self.event_manager.available_modes: + self.event_manager.apply(mode="interval", dt=self.step_dt) + + # update observations + self.obs_buf = self._get_observations() + + # add observation noise + # note: we apply no noise to the state space (since it is used for critic networks) + if self.cfg.observation_noise_model: + self.obs_buf["policy"] = self._observation_noise_model(self.obs_buf["policy"]) + + # return observations, rewards, resets and extras + return self.obs_buf, self.reward_buf, self.reset_terminated, self.reset_time_outs, self.extras + + @staticmethod + def seed(seed: int = -1) -> int: + """Set the seed for the environment. + + Args: + seed: The seed for random generator. Defaults to -1. + + Returns: + The seed used for random generator. + """ + # set seed for replicator + try: + import omni.replicator.core as rep + + rep.set_global_seed(seed) + except ModuleNotFoundError: + pass + # set seed for torch and other libraries + return configure_seed(seed) + + def render(self, recompute: bool = False) -> np.ndarray | None: + """Run rendering without stepping through the physics. + + By convention, if mode is: + + - **human**: Render to the current display and return nothing. Usually for human consumption. + - **rgb_array**: Return a numpy.ndarray with shape (x, y, 3), representing RGB values for an + x-by-y pixel image, suitable for turning into a video. + + Args: + recompute: Whether to force a render even if the simulator has already rendered the scene. + Defaults to False. + + Returns: + The rendered image as a numpy array if mode is "rgb_array". Otherwise, returns None. + + Raises: + RuntimeError: If mode is set to "rgb_data" and simulation render mode does not support it. + In this case, the simulation render mode must be set to ``RenderMode.PARTIAL_RENDERING`` + or ``RenderMode.FULL_RENDERING``. + NotImplementedError: If an unsupported rendering mode is specified. + """ + # run a rendering step of the simulator + # if we have rtx sensors, we do not need to render again sin + if not self.sim.has_rtx_sensors() and not recompute: + self.sim.render() + # decide the rendering mode + if self.render_mode == "human" or self.render_mode is None: + return None + elif self.render_mode == "rgb_array": + # check that if any render could have happened + if self.sim.render_mode.value < self.sim.RenderMode.PARTIAL_RENDERING.value: + raise RuntimeError( + f"Cannot render '{self.render_mode}' when the simulation render mode is" + f" '{self.sim.render_mode.name}'. Please set the simulation render mode to:" + f"'{self.sim.RenderMode.PARTIAL_RENDERING.name}' or '{self.sim.RenderMode.FULL_RENDERING.name}'." + " If running headless, make sure --enable_cameras is set." + ) + # create the annotator if it does not exist + if not hasattr(self, "_rgb_annotator"): + import omni.replicator.core as rep + + # create render product + self._render_product = rep.create.render_product( + self.cfg.viewer.cam_prim_path, self.cfg.viewer.resolution + ) + # create rgb annotator -- used to read data from the render product + self._rgb_annotator = rep.AnnotatorRegistry.get_annotator("rgb", device="cpu") + self._rgb_annotator.attach([self._render_product]) + # obtain the rgb data + rgb_data = self._rgb_annotator.get_data() + # convert to numpy array + rgb_data = np.frombuffer(rgb_data, dtype=np.uint8).reshape(*rgb_data.shape) + # return the rgb data + # note: initially the renerer is warming up and returns empty data + if rgb_data.size == 0: + return np.zeros((self.cfg.viewer.resolution[1], self.cfg.viewer.resolution[0], 3), dtype=np.uint8) + else: + return rgb_data[:, :, :3] + else: + raise NotImplementedError( + f"Render mode '{self.render_mode}' is not supported. Please use: {self.metadata['render_modes']}." + ) + + def close(self): + """Cleanup for the environment.""" + if not self._is_closed: + # close entities related to the environment + # note: this is order-sensitive to avoid any dangling references + if self.cfg.events: + del self.event_manager + del self.scene + if self.viewport_camera_controller is not None: + del self.viewport_camera_controller + + # clear callbacks and instance + if get_isaac_sim_version().major >= 5: + if self.cfg.sim.create_stage_in_memory: + # detach physx stage + omni.physx.get_physx_simulation_interface().detach_stage() + self.sim.stop() + self.sim.clear() + + self.sim.clear_all_callbacks() + self.sim.clear_instance() + + # destroy the window + if self._window is not None: + self._window = None + # update closing status + self._is_closed = True + + """ + Operations - Debug Visualization. + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Toggles the environment debug visualization. + + Args: + debug_vis: Whether to visualize the environment debug visualization. + + Returns: + Whether the debug visualization was successfully set. False if the environment + does not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_debug_vis_implementation: + return False + # toggle debug visualization objects + self._set_debug_vis_impl(debug_vis) + # toggle debug visualization handles + if debug_vis: + # create a subscriber for the post update event if it doesn't exist + if self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + # return success + return True + + """ + Helper functions. + """ + + def _configure_gym_env_spaces(self): + """Configure the action and observation spaces for the Gym environment.""" + # show deprecation message and overwrite configuration + if self.cfg.num_actions is not None: + logger.warning("DirectRLEnvCfg.num_actions is deprecated. Use DirectRLEnvCfg.action_space instead.") + if isinstance(self.cfg.action_space, type(MISSING)): + self.cfg.action_space = self.cfg.num_actions + if self.cfg.num_observations is not None: + logger.warning( + "DirectRLEnvCfg.num_observations is deprecated. Use DirectRLEnvCfg.observation_space instead." + ) + if isinstance(self.cfg.observation_space, type(MISSING)): + self.cfg.observation_space = self.cfg.num_observations + if self.cfg.num_states is not None: + logger.warning("DirectRLEnvCfg.num_states is deprecated. Use DirectRLEnvCfg.state_space instead.") + if isinstance(self.cfg.state_space, type(MISSING)): + self.cfg.state_space = self.cfg.num_states + + # set up spaces + self.single_observation_space = gym.spaces.Dict() + self.single_observation_space["policy"] = spec_to_gym_space(self.cfg.observation_space) + self.single_action_space = spec_to_gym_space(self.cfg.action_space) + + # batch the spaces for vectorized environments + self.observation_space = gym.vector.utils.batch_space(self.single_observation_space["policy"], self.num_envs) + self.action_space = gym.vector.utils.batch_space(self.single_action_space, self.num_envs) + + # optional state space for asymmetric actor-critic architectures + self.state_space = None + if self.cfg.state_space: + self.single_observation_space["critic"] = spec_to_gym_space(self.cfg.state_space) + self.state_space = gym.vector.utils.batch_space(self.single_observation_space["critic"], self.num_envs) + + # instantiate actions (needed for tasks for which the observations computation is dependent on the actions) + self.actions = sample_space(self.single_action_space, self.sim.device, batch_size=self.num_envs, fill_value=0) + + def _reset_idx(self, env_ids: Sequence[int]): + """Reset environments based on specified indices. + + Args: + env_ids: List of environment ids which must be reset + """ + self.scene.reset(env_ids) + + # apply events such as randomization for environments that need a reset + if self.cfg.events: + if "reset" in self.event_manager.available_modes: + env_step_count = self._sim_step_counter // self.cfg.decimation + self.event_manager.apply(mode="reset", env_ids=env_ids, global_env_step_count=env_step_count) + + # reset noise models + if self.cfg.action_noise_model: + self._action_noise_model.reset(env_ids) + if self.cfg.observation_noise_model: + self._observation_noise_model.reset(env_ids) + + # reset the episode length buffer + self.episode_length_buf[env_ids] = 0 + + """ + Implementation-specific functions. + """ + + def _setup_scene(self): + """Setup the scene for the environment. + + This function is responsible for creating the scene objects and setting up the scene for the environment. + The scene creation can happen through :class:`isaaclab.scene.InteractiveSceneCfg` or through + directly creating the scene objects and registering them with the scene manager. + + We leave the implementation of this function to the derived classes. If the environment does not require + any explicit scene setup, the function can be left empty. + """ + pass + + @abstractmethod + def _pre_physics_step(self, actions: torch.Tensor): + """Pre-process actions before stepping through the physics. + + This function is responsible for pre-processing the actions before stepping through the physics. + It is called before the physics stepping (which is decimated). + + Args: + actions: The actions to apply on the environment. Shape is (num_envs, action_dim). + """ + raise NotImplementedError(f"Please implement the '_pre_physics_step' method for {self.__class__.__name__}.") + + @abstractmethod + def _apply_action(self): + """Apply actions to the simulator. + + This function is responsible for applying the actions to the simulator. It is called at each + physics time-step. + """ + raise NotImplementedError(f"Please implement the '_apply_action' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_observations(self) -> VecEnvObs: + """Compute and return the observations for the environment. + + Returns: + The observations for the environment. + """ + raise NotImplementedError(f"Please implement the '_get_observations' method for {self.__class__.__name__}.") + + def _get_states(self) -> VecEnvObs | None: + """Compute and return the states for the environment. + + The state-space is used for asymmetric actor-critic architectures. It is configured + using the :attr:`DirectRLEnvCfg.state_space` parameter. + + Returns: + The states for the environment. If the environment does not have a state-space, the function + returns a None. + """ + return None # noqa: R501 + + @abstractmethod + def _get_rewards(self) -> torch.Tensor: + """Compute and return the rewards for the environment. + + Returns: + The rewards for the environment. Shape is (num_envs,). + """ + raise NotImplementedError(f"Please implement the '_get_rewards' method for {self.__class__.__name__}.") + + @abstractmethod + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + """Compute and return the done flags for the environment. + + Returns: + A tuple containing the done flags for termination and time-out. + Shape of individual tensors is (num_envs,). + """ + raise NotImplementedError(f"Please implement the '_get_dones' method for {self.__class__.__name__}.") + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects. + + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") diff --git a/source/isaaclab/isaaclab/envs/direct_rl_env_cfg.py b/source/isaaclab/isaaclab/envs/direct_rl_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ebb883c65b99b127b2ae70fc02d688a14276ce --- /dev/null +++ b/source/isaaclab/isaaclab/envs/direct_rl_env_cfg.py @@ -0,0 +1,253 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.devices.openxr import XrCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass +from isaaclab.utils.noise import NoiseModelCfg + +from .common import SpaceType, ViewerCfg +from .ui import BaseEnvWindow + + +@configclass +class DirectRLEnvCfg: + """Configuration for an RL environment defined with the direct workflow. + + Please refer to the :class:`isaaclab.envs.direct_rl_env.DirectRLEnv` class for more details. + """ + + # simulation settings + viewer: ViewerCfg = ViewerCfg() + """Viewer configuration. Default is ViewerCfg().""" + + sim: SimulationCfg = SimulationCfg() + """Physics simulation configuration. Default is SimulationCfg().""" + + # ui settings + ui_window_class_type: type | None = BaseEnvWindow + """The class type of the UI window. Default is None. + + If None, then no UI window is created. + + Note: + If you want to make your own UI window, you can create a class that inherits from + from :class:`isaaclab.envs.ui.base_env_window.BaseEnvWindow`. Then, you can set + this attribute to your class type. + """ + + # general settings + seed: int | None = None + """The seed for the random number generator. Defaults to None, in which case the seed is not set. + + Note: + The seed is set at the beginning of the environment initialization. This ensures that the environment + creation is deterministic and behaves similarly across different runs. + """ + + decimation: int = MISSING + """Number of control action updates @ sim dt per policy dt. + + For instance, if the simulation dt is 0.01s and the policy dt is 0.1s, then the decimation is 10. + This means that the control action is updated every 10 simulation steps. + """ + + is_finite_horizon: bool = False + """Whether the learning task is treated as a finite or infinite horizon problem for the agent. + Defaults to False, which means the task is treated as an infinite horizon problem. + + This flag handles the subtleties of finite and infinite horizon tasks: + + * **Finite horizon**: no penalty or bootstrapping value is required by the the agent for + running out of time. However, the environment still needs to terminate the episode after the + time limit is reached. + * **Infinite horizon**: the agent needs to bootstrap the value of the state at the end of the episode. + This is done by sending a time-limit (or truncated) done signal to the agent, which triggers this + bootstrapping calculation. + + If True, then the environment is treated as a finite horizon problem and no time-out (or truncated) done signal + is sent to the agent. If False, then the environment is treated as an infinite horizon problem and a time-out + (or truncated) done signal is sent to the agent. + + Note: + The base :class:`ManagerBasedRLEnv` class does not use this flag directly. It is used by the environment + wrappers to determine what type of done signal to send to the corresponding learning agent. + """ + + episode_length_s: float = MISSING + """Duration of an episode (in seconds). + + Based on the decimation rate and physics time step, the episode length is calculated as: + + .. code-block:: python + + episode_length_steps = ceil(episode_length_s / (decimation_rate * physics_time_step)) + + For example, if the decimation rate is 10, the physics time step is 0.01, and the episode length is 10 seconds, + then the episode length in steps is 100. + """ + + # environment settings + scene: InteractiveSceneCfg = MISSING + """Scene settings. + + Please refer to the :class:`isaaclab.scene.InteractiveSceneCfg` class for more details. + """ + + events: object | None = None + """Event settings. Defaults to None, in which case no events are applied through the event manager. + + Please refer to the :class:`isaaclab.managers.EventManager` class for more details. + """ + + observation_space: SpaceType = MISSING + """Observation space definition. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_observations: int | None = None + """The dimension of the observation space from each environment instance. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectRLEnvCfg.observation_space` instead. + """ + + state_space: SpaceType | None = None + """State space definition. + + This is useful for asymmetric actor-critic and defines the observation space for the critic. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_states: int | None = None + """The dimension of the state-space from each environment instance. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectRLEnvCfg.state_space` instead. + """ + + observation_noise_model: NoiseModelCfg | None = None + """The noise model to apply to the computed observations from the environment. Default is None, + which means no noise is added. + + Please refer to the :class:`isaaclab.utils.noise.NoiseModel` class for more details. + """ + + action_space: SpaceType = MISSING + """Action space definition. + + The space can be defined either using Gymnasium :py:mod:`~gymnasium.spaces` (when a more detailed + specification of the space is desired) or basic Python data types (for simplicity). + + .. list-table:: + :header-rows: 1 + + * - Gymnasium space + - Python data type + * - :class:`~gymnasium.spaces.Box` + - Integer or list of integers (e.g.: ``7``, ``[64, 64, 3]``) + * - :class:`~gymnasium.spaces.Discrete` + - Single-element set (e.g.: ``{2}``) + * - :class:`~gymnasium.spaces.MultiDiscrete` + - List of single-element sets (e.g.: ``[{2}, {5}]``) + * - :class:`~gymnasium.spaces.Dict` + - Dictionary (e.g.: ``{"joints": 7, "rgb": [64, 64, 3], "gripper": {2}}``) + * - :class:`~gymnasium.spaces.Tuple` + - Tuple (e.g.: ``(7, [64, 64, 3], {2})``) + """ + + num_actions: int | None = None + """The dimension of the action space for each environment. + + .. warning:: + + This attribute is deprecated. Use :attr:`~isaaclab.envs.DirectRLEnvCfg.action_space` instead. + """ + + action_noise_model: NoiseModelCfg | None = None + """The noise model applied to the actions provided to the environment. Default is None, + which means no noise is added. + + Please refer to the :class:`isaaclab.utils.noise.NoiseModel` class for more details. + """ + + rerender_on_reset: bool = False + """Whether a render step is performed again after at least one environment has been reset. + Defaults to False, which means no render step will be performed after reset. + + * When this is False, data collected from sensors after performing reset will be stale and will not reflect the + latest states in simulation caused by the reset. + * When this is True, an extra render step will be performed to update the sensor data + to reflect the latest states from the reset. This comes at a cost of performance as an additional render + step will be performed after each time an environment is reset. + + .. deprecated:: 2.3.1 + This attribute is deprecated and will be removed in the future. Please use + :attr:`num_rerenders_on_reset` instead. + + To get the same behaviour as setting this parameter to ``True`` or ``False``, set + :attr:`num_rerenders_on_reset` to 1 or 0, respectively. + """ + + num_rerenders_on_reset: int = 0 + """Number of render steps to perform after reset. Defaults to 0, which means no render step will be performed + after reset. + + * When this is 0, no render step will be performed after reset. Data collected from sensors after performing + reset will be stale and will not reflect the latest states in simulation caused by the reset. + * When this is greater than 0, the specified number of extra render steps will be performed to update the + sensor data to reflect the latest states from the reset. This comes at a cost of performance as additional + render steps will be performed after each time an environment is reset. + """ + + wait_for_textures: bool = True + """True to wait for assets to be loaded completely, False otherwise. Defaults to True.""" + + xr: XrCfg | None = None + """Configuration for viewing and interacting with the environment through an XR device.""" + + log_dir: str | None = None + """Directory for logging experiment artifacts. Defaults to None, in which case no specific log directory is set.""" diff --git a/source/isaaclab/isaaclab/envs/manager_based_env.py b/source/isaaclab/isaaclab/envs/manager_based_env.py new file mode 100644 index 0000000000000000000000000000000000000000..daf7d4225c52b79caa5bfbb7811c23bc0d6b6698 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/manager_based_env.py @@ -0,0 +1,598 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import builtins +import logging +import warnings +from collections.abc import Sequence +from typing import Any + +import torch + +import omni.physx +from isaacsim.core.simulation_manager import SimulationManager + +from isaaclab.managers import ActionManager, EventManager, ObservationManager, RecorderManager +from isaaclab.scene import InteractiveScene +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import attach_stage_to_usd_context, use_stage +from isaaclab.ui.widgets import ManagerLiveVisualizer +from isaaclab.utils.seed import configure_seed +from isaaclab.utils.timer import Timer +from isaaclab.utils.version import get_isaac_sim_version + +from .common import VecEnvObs +from .manager_based_env_cfg import ManagerBasedEnvCfg +from .ui import ViewportCameraController +from .utils.io_descriptors import export_articulations_data, export_scene_data + +# import logger +logger = logging.getLogger(__name__) + + +class ManagerBasedEnv: + """The base environment encapsulates the simulation scene and the environment managers for + the manager-based workflow. + + While a simulation scene or world comprises of different components such as the robots, objects, + and sensors (cameras, lidars, etc.), the environment is a higher level abstraction + that provides an interface for interacting with the simulation. The environment is comprised of + the following components: + + * **Scene**: The scene manager that creates and manages the virtual world in which the robot operates. + This includes defining the robot, static and dynamic objects, sensors, etc. + * **Observation Manager**: The observation manager that generates observations from the current simulation + state and the data gathered from the sensors. These observations may include privileged information + that is not available to the robot in the real world. Additionally, user-defined terms can be added + to process the observations and generate custom observations. For example, using a network to embed + high-dimensional observations into a lower-dimensional space. + * **Action Manager**: The action manager that processes the raw actions sent to the environment and + converts them to low-level commands that are sent to the simulation. It can be configured to accept + raw actions at different levels of abstraction. For example, in case of a robotic arm, the raw actions + can be joint torques, joint positions, or end-effector poses. Similarly for a mobile base, it can be + the joint torques, or the desired velocity of the floating base. + * **Event Manager**: The event manager orchestrates operations triggered based on simulation events. + This includes resetting the scene to a default state, applying random pushes to the robot at different intervals + of time, or randomizing properties such as mass and friction coefficients. This is useful for training + and evaluating the robot in a variety of scenarios. + * **Recorder Manager**: The recorder manager that handles recording data produced during different steps + in the simulation. This includes recording in the beginning and end of a reset and a step. The recorded data + is distinguished per episode, per environment and can be exported through a dataset file handler to a file. + + The environment provides a unified interface for interacting with the simulation. However, it does not + include task-specific quantities such as the reward function, or the termination conditions. These + quantities are often specific to defining Markov Decision Processes (MDPs) while the base environment + is agnostic to the MDP definition. + + The environment steps forward in time at a fixed time-step. The physics simulation is decimated at a + lower time-step. This is to ensure that the simulation is stable. These two time-steps can be configured + independently using the :attr:`ManagerBasedEnvCfg.decimation` (number of simulation steps per environment step) + and the :attr:`ManagerBasedEnvCfg.sim.dt` (physics time-step) parameters. Based on these parameters, the + environment time-step is computed as the product of the two. The two time-steps can be obtained by + querying the :attr:`physics_dt` and the :attr:`step_dt` properties respectively. + """ + + def __init__(self, cfg: ManagerBasedEnvCfg): + """Initialize the environment. + + Args: + cfg: The configuration object for the environment. + + Raises: + RuntimeError: If a simulation context already exists. The environment must always create one + since it configures the simulation context and controls the simulation. + """ + # check that the config is valid + cfg.validate() + # store inputs to class + self.cfg = cfg + # initialize internal variables + self._is_closed = False + + # set the seed for the environment + if self.cfg.seed is not None: + self.cfg.seed = self.seed(self.cfg.seed) + else: + logger.warning("Seed not set for the environment. The environment creation may not be deterministic.") + + # create a simulation context to control the simulator + if SimulationContext.instance() is None: + # the type-annotation is required to avoid a type-checking error + # since it gets confused with Isaac Sim's SimulationContext class + self.sim: SimulationContext = SimulationContext(self.cfg.sim) + else: + # simulation context should only be created before the environment + # when in extension mode + if not builtins.ISAAC_LAUNCHED_FROM_TERMINAL: + raise RuntimeError("Simulation context already exists. Cannot create a new one.") + self.sim: SimulationContext = SimulationContext.instance() + + # make sure torch is running on the correct device + if "cuda" in self.device: + torch.cuda.set_device(self.device) + + # print useful information + print("[INFO]: Base environment:") + print(f"\tEnvironment device : {self.device}") + print(f"\tEnvironment seed : {self.cfg.seed}") + print(f"\tPhysics step-size : {self.physics_dt}") + print(f"\tRendering step-size : {self.physics_dt * self.cfg.sim.render_interval}") + print(f"\tEnvironment step-size : {self.step_dt}") + + if self.cfg.sim.render_interval < self.cfg.decimation: + msg = ( + f"The render interval ({self.cfg.sim.render_interval}) is smaller than the decimation " + f"({self.cfg.decimation}). Multiple render calls will happen for each environment step. " + "If this is not intended, set the render interval to be equal to the decimation." + ) + logger.warning(msg) + + # counter for simulation steps + self._sim_step_counter = 0 + + # allocate dictionary to store metrics + self.extras = {} + + # generate scene + with Timer("[INFO]: Time taken for scene creation", "scene_creation"): + # set the stage context for scene creation steps which use the stage + with use_stage(self.sim.get_initial_stage()): + self.scene = InteractiveScene(self.cfg.scene) + attach_stage_to_usd_context() + # apply any optional post-scene-spawn hooks + # note: these are useful for task-specific USD edits (e.g. material overrides) + post_spawn_fns = getattr(self.cfg, "post_scene_spawn_fns", None) + if post_spawn_fns is None: + post_spawn_fns = getattr(self.cfg, "post_scene_spawn_fn", None) + if post_spawn_fns is not None: + if callable(post_spawn_fns): + post_spawn_fns = [post_spawn_fns] + for fn in post_spawn_fns: + try: + fn(self) + except Exception: + logger.exception("Post scene spawn hook failed: %s", fn) + print("[INFO]: Scene manager: ", self.scene) + + # set up camera viewport controller + # viewport is not available in other rendering modes so the function will throw a warning + # FIXME: This needs to be fixed in the future when we unify the UI functionalities even for + # non-rendering modes. + if self.sim.render_mode >= self.sim.RenderMode.PARTIAL_RENDERING: + self.viewport_camera_controller = ViewportCameraController(self, self.cfg.viewer) + else: + self.viewport_camera_controller = None + + # create event manager + # note: this is needed here (rather than after simulation play) to allow USD-related randomization events + # that must happen before the simulation starts. Example: randomizing mesh scale + self.event_manager = EventManager(self.cfg.events, self) + + # apply USD-related randomization events + if "prestartup" in self.event_manager.available_modes: + self.event_manager.apply(mode="prestartup") + + # play the simulator to activate physics handles + # note: this activates the physics simulation view that exposes TensorAPIs + # note: when started in extension mode, first call sim.reset_async() and then initialize the managers + if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False: + print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...") + with Timer("[INFO]: Time taken for simulation start", "simulation_start"): + # since the reset can trigger callbacks which use the stage, + # we need to set the stage context here + with use_stage(self.sim.get_initial_stage()): + self.sim.reset() + # update scene to pre populate data buffers for assets and sensors. + # this is needed for the observation manager to get valid tensors for initialization. + # this shouldn't cause an issue since later on, users do a reset over all the environments + # so the lazy buffers would be reset. + self.scene.update(dt=self.physics_dt) + # add timeline event to load managers + self.load_managers() + + # extend UI elements + # we need to do this here after all the managers are initialized + # this is because they dictate the sensors and commands right now + if self.sim.has_gui() and self.cfg.ui_window_class_type is not None: + # setup live visualizers + self.setup_manager_visualizers() + self._window = self.cfg.ui_window_class_type(self, window_name="IsaacLab") + else: + # if no window, then we don't need to store the window + self._window = None + + # initialize observation buffers + self.obs_buf = {} + + # export IO descriptors if requested + if self.cfg.export_io_descriptors: + self.export_IO_descriptors() + + # show deprecation message for rerender_on_reset + if self.cfg.rerender_on_reset: + msg = ( + "\033[93m\033[1m[DEPRECATION WARNING] ManagerBasedEnvCfg.rerender_on_reset is deprecated. Use" + " ManagerBasedEnvCfg.num_rerenders_on_reset instead.\033[0m" + ) + warnings.warn( + msg, + FutureWarning, + stacklevel=2, + ) + if self.cfg.num_rerenders_on_reset == 0: + self.cfg.num_rerenders_on_reset = 1 + + def __del__(self): + """Cleanup for the environment.""" + self.close() + + """ + Properties. + """ + + @property + def num_envs(self) -> int: + """The number of instances of the environment that are running.""" + return self.scene.num_envs + + @property + def physics_dt(self) -> float: + """The physics time-step (in s). + + This is the lowest time-decimation at which the simulation is happening. + """ + return self.cfg.sim.dt + + @property + def step_dt(self) -> float: + """The environment stepping time-step (in s). + + This is the time-step at which the environment steps forward. + """ + return self.cfg.sim.dt * self.cfg.decimation + + @property + def device(self): + """The device on which the environment is running.""" + return self.sim.device + + @property + def get_IO_descriptors(self): + """Get the IO descriptors for the environment. + + Returns: + A dictionary with keys as the group names and values as the IO descriptors. + """ + return { + "observations": self.observation_manager.get_IO_descriptors, + "actions": self.action_manager.get_IO_descriptors, + "articulations": export_articulations_data(self), + "scene": export_scene_data(self), + } + + def export_IO_descriptors(self, output_dir: str | None = None): + """Export the IO descriptors for the environment. + + Args: + output_dir: The directory to export the IO descriptors to. + """ + import os + + import yaml + + IO_descriptors = self.get_IO_descriptors + + if output_dir is None: + if self.cfg.log_dir is not None: + output_dir = os.path.join(self.cfg.log_dir, "io_descriptors") + else: + raise ValueError( + "Output directory is not set. Please set the log directory using the `log_dir`" + " configuration or provide an explicit output_dir parameter." + ) + + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + with open(os.path.join(output_dir, "IO_descriptors.yaml"), "w") as f: + print(f"[INFO]: Exporting IO descriptors to {os.path.join(output_dir, 'IO_descriptors.yaml')}") + yaml.safe_dump(IO_descriptors, f) + + """ + Operations - Setup. + """ + + def load_managers(self): + """Load the managers for the environment. + + This function is responsible for creating the various managers (action, observation, + events, etc.) for the environment. Since the managers require access to physics handles, + they can only be created after the simulator is reset (i.e. played for the first time). + + .. note:: + In case of standalone application (when running simulator from Python), the function is called + automatically when the class is initialized. + + However, in case of extension mode, the user must call this function manually after the simulator + is reset. This is because the simulator is only reset when the user calls + :meth:`SimulationContext.reset_async` and it isn't possible to call async functions in the constructor. + + """ + # prepare the managers + # -- event manager (we print it here to make the logging consistent) + print("[INFO] Event Manager: ", self.event_manager) + # -- recorder manager + self.recorder_manager = RecorderManager(self.cfg.recorders, self) + print("[INFO] Recorder Manager: ", self.recorder_manager) + # -- action manager + self.action_manager = ActionManager(self.cfg.actions, self) + print("[INFO] Action Manager: ", self.action_manager) + # -- observation manager + self.observation_manager = ObservationManager(self.cfg.observations, self) + print("[INFO] Observation Manager:", self.observation_manager) + + # perform events at the start of the simulation + # in-case a child implementation creates other managers, the randomization should happen + # when all the other managers are created + if self.__class__ == ManagerBasedEnv and "startup" in self.event_manager.available_modes: + self.event_manager.apply(mode="startup") + + def setup_manager_visualizers(self): + """Creates live visualizers for manager terms.""" + + self.manager_visualizers = { + "action_manager": ManagerLiveVisualizer(manager=self.action_manager), + "observation_manager": ManagerLiveVisualizer(manager=self.observation_manager), + } + + """ + Operations - MDP. + """ + + def reset( + self, seed: int | None = None, env_ids: Sequence[int] | None = None, options: dict[str, Any] | None = None + ) -> tuple[VecEnvObs, dict]: + """Resets the specified environments and returns observations. + + This function calls the :meth:`_reset_idx` function to reset the specified environments. + However, certain operations, such as procedural terrain generation, that happened during initialization + are not repeated. + + Args: + seed: The seed to use for randomization. Defaults to None, in which case the seed is not set. + env_ids: The environment ids to reset. Defaults to None, in which case all environments are reset. + options: Additional information to specify how the environment is reset. Defaults to None. + + Note: + This argument is used for compatibility with Gymnasium environment definition. + + Returns: + A tuple containing the observations and extras. + """ + if env_ids is None: + env_ids = torch.arange(self.num_envs, dtype=torch.int64, device=self.device) + + # trigger recorder terms for pre-reset calls + self.recorder_manager.record_pre_reset(env_ids) + + # set the seed + if seed is not None: + self.seed(seed) + + # reset state of scene + self._reset_idx(env_ids) + + # update articulation kinematics + self.scene.write_data_to_sim() + self.sim.forward() + # if sensors are added to the scene, make sure we render to reflect changes in reset + if self.sim.has_rtx_sensors() and self.cfg.num_rerenders_on_reset > 0: + for _ in range(self.cfg.num_rerenders_on_reset): + self.sim.render() + + # trigger recorder terms for post-reset calls + self.recorder_manager.record_post_reset(env_ids) + + # compute observations + self.obs_buf = self.observation_manager.compute(update_history=True) + + if self.cfg.wait_for_textures and self.sim.has_rtx_sensors(): + while SimulationManager.assets_loading(): + self.sim.render() + + # return observations + return self.obs_buf, self.extras + + def reset_to( + self, + state: dict[str, dict[str, dict[str, torch.Tensor]]], + env_ids: Sequence[int] | None, + seed: int | None = None, + is_relative: bool = False, + ): + """Resets specified environments to provided states. + + This function resets the environments to the provided states. The state is a dictionary + containing the state of the scene entities. Please refer to :meth:`InteractiveScene.get_state` + for the format. + + The function is different from the :meth:`reset` function as it resets the environments to specific states, + instead of using the randomization events for resetting the environments. + + Args: + state: The state to reset the specified environments to. Please refer to + :meth:`InteractiveScene.get_state` for the format. + env_ids: The environment ids to reset. Defaults to None, in which case all environments are reset. + seed: The seed to use for randomization. Defaults to None, in which case the seed is not set. + is_relative: If set to True, the state is considered relative to the environment origins. + Defaults to False. + """ + # reset all envs in the scene if env_ids is None + if env_ids is None: + env_ids = torch.arange(self.num_envs, dtype=torch.int64, device=self.device) + + # trigger recorder terms for pre-reset calls + self.recorder_manager.record_pre_reset(env_ids) + + # set the seed + if seed is not None: + self.seed(seed) + + self._reset_idx(env_ids) + + # set the state + self.scene.reset_to(state, env_ids, is_relative=is_relative) + + # update articulation kinematics + self.sim.forward() + + # if sensors are added to the scene, make sure we render to reflect changes in reset + if self.sim.has_rtx_sensors() and self.cfg.num_rerenders_on_reset > 0: + for _ in range(self.cfg.num_rerenders_on_reset): + self.sim.render() + + # trigger recorder terms for post-reset calls + self.recorder_manager.record_post_reset(env_ids) + + # compute observations + self.obs_buf = self.observation_manager.compute(update_history=True) + + # return observations + return self.obs_buf, self.extras + + def step(self, action: torch.Tensor) -> tuple[VecEnvObs, dict]: + """Execute one time-step of the environment's dynamics. + + The environment steps forward at a fixed time-step, while the physics simulation is + decimated at a lower time-step. This is to ensure that the simulation is stable. These two + time-steps can be configured independently using the :attr:`ManagerBasedEnvCfg.decimation` (number of + simulation steps per environment step) and the :attr:`ManagerBasedEnvCfg.sim.dt` (physics time-step). + Based on these parameters, the environment time-step is computed as the product of the two. + + Args: + action: The actions to apply on the environment. Shape is (num_envs, action_dim). + + Returns: + A tuple containing the observations and extras. + """ + # process actions + self.action_manager.process_action(action.to(self.device)) + + self.recorder_manager.record_pre_step() + + # check if we need to do rendering within the physics loop + # note: checked here once to avoid multiple checks within the loop + is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() + + # perform physics stepping + for _ in range(self.cfg.decimation): + self._sim_step_counter += 1 + # set actions into buffers + self.action_manager.apply_action() + # set actions into simulator + self.scene.write_data_to_sim() + # simulate + self.sim.step(render=False) + # render between steps only if the GUI or an RTX sensor needs it + # note: we assume the render interval to be the shortest accepted rendering interval. + # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. + if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: + self.sim.render() + # update buffers at sim dt + self.scene.update(dt=self.physics_dt) + + # post-step: step interval event + if "interval" in self.event_manager.available_modes: + self.event_manager.apply(mode="interval", dt=self.step_dt) + + # -- compute observations + self.obs_buf = self.observation_manager.compute(update_history=True) + self.recorder_manager.record_post_step() + + # return observations and extras + return self.obs_buf, self.extras + + @staticmethod + def seed(seed: int = -1) -> int: + """Set the seed for the environment. + + Args: + seed: The seed for random generator. Defaults to -1. + + Returns: + The seed used for random generator. + """ + # set seed for replicator + try: + import omni.replicator.core as rep + + rep.set_global_seed(seed) + except ModuleNotFoundError: + pass + # set seed for torch and other libraries + return configure_seed(seed) + + def close(self): + """Cleanup for the environment.""" + if not self._is_closed: + # destructor is order-sensitive + del self.viewport_camera_controller + del self.action_manager + del self.observation_manager + del self.event_manager + del self.recorder_manager + del self.scene + + # clear callbacks and instance + if get_isaac_sim_version().major >= 5: + if self.cfg.sim.create_stage_in_memory: + # detach physx stage + omni.physx.get_physx_simulation_interface().detach_stage() + self.sim.stop() + self.sim.clear() + + self.sim.clear_all_callbacks() + self.sim.clear_instance() + + # destroy the window + if self._window is not None: + self._window = None + # update closing status + self._is_closed = True + + """ + Helper functions. + """ + + def _reset_idx(self, env_ids: Sequence[int]): + """Reset environments based on specified indices. + + Args: + env_ids: List of environment ids which must be reset + """ + # reset the internal buffers of the scene elements + self.scene.reset(env_ids) + + # apply events such as randomization for environments that need a reset + if "reset" in self.event_manager.available_modes: + env_step_count = self._sim_step_counter // self.cfg.decimation + self.event_manager.apply(mode="reset", env_ids=env_ids, global_env_step_count=env_step_count) + + # iterate over all managers and reset them + # this returns a dictionary of information which is stored in the extras + # note: This is order-sensitive! Certain things need be reset before others. + self.extras["log"] = dict() + # -- observation manager + info = self.observation_manager.reset(env_ids) + self.extras["log"].update(info) + # -- action manager + info = self.action_manager.reset(env_ids) + self.extras["log"].update(info) + # -- event manager + info = self.event_manager.reset(env_ids) + self.extras["log"].update(info) + # -- recorder manager + info = self.recorder_manager.reset(env_ids) + self.extras["log"].update(info) diff --git a/source/isaaclab/isaaclab/envs/manager_based_env_cfg.py b/source/isaaclab/isaaclab/envs/manager_based_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e8f583ddfb31daf76f99872c073c100f428f00c5 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/manager_based_env_cfg.py @@ -0,0 +1,150 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Base configuration of the environment. + +This module defines the general configuration of the environment. It includes parameters for +configuring the environment instances, viewer settings, and simulation parameters. +""" + +from dataclasses import MISSING, field + +import isaaclab.envs.mdp as mdp +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import RecorderManagerBaseCfg as DefaultEmptyRecorderManagerCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass + +from .common import ViewerCfg +from .ui import BaseEnvWindow + + +@configclass +class DefaultEventManagerCfg: + """Configuration of the default event manager. + + This manager is used to reset the scene to a default state. The default state is specified + by the scene configuration. + """ + + reset_scene_to_default = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + +@configclass +class ManagerBasedEnvCfg: + """Base configuration of the environment.""" + + # simulation settings + viewer: ViewerCfg = ViewerCfg() + """Viewer configuration. Default is ViewerCfg().""" + + sim: SimulationCfg = SimulationCfg() + """Physics simulation configuration. Default is SimulationCfg().""" + + # ui settings + ui_window_class_type: type | None = BaseEnvWindow + """The class type of the UI window. Default is None. + + If None, then no UI window is created. + + Note: + If you want to make your own UI window, you can create a class that inherits from + from :class:`isaaclab.envs.ui.base_env_window.BaseEnvWindow`. Then, you can set + this attribute to your class type. + """ + + # general settings + seed: int | None = None + """The seed for the random number generator. Defaults to None, in which case the seed is not set. + + Note: + The seed is set at the beginning of the environment initialization. This ensures that the environment + creation is deterministic and behaves similarly across different runs. + """ + + decimation: int = MISSING + """Number of control action updates @ sim dt per policy dt. + + For instance, if the simulation dt is 0.01s and the policy dt is 0.1s, then the decimation is 10. + This means that the control action is updated every 10 simulation steps. + """ + + # environment settings + scene: InteractiveSceneCfg = MISSING + """Scene settings. + + Please refer to the :class:`isaaclab.scene.InteractiveSceneCfg` class for more details. + """ + + recorders: object = DefaultEmptyRecorderManagerCfg() + """Recorder settings. Defaults to recording nothing. + + Please refer to the :class:`isaaclab.managers.RecorderManager` class for more details. + """ + + observations: object = MISSING + """Observation space settings. + + Please refer to the :class:`isaaclab.managers.ObservationManager` class for more details. + """ + + actions: object = MISSING + """Action space settings. + + Please refer to the :class:`isaaclab.managers.ActionManager` class for more details. + """ + + events: object = DefaultEventManagerCfg() + """Event settings. Defaults to the basic configuration that resets the scene to its default state. + + Please refer to the :class:`isaaclab.managers.EventManager` class for more details. + """ + + rerender_on_reset: bool = False + """Whether a render step is performed again after at least one environment has been reset. + Defaults to False, which means no render step will be performed after reset. + + * When this is False, data collected from sensors after performing reset will be stale and will not reflect the + latest states in simulation caused by the reset. + * When this is True, an extra render step will be performed to update the sensor data + to reflect the latest states from the reset. This comes at a cost of performance as an additional render + step will be performed after each time an environment is reset. + + .. deprecated:: 2.3.1 + This attribute is deprecated and will be removed in the future. Please use + :attr:`num_rerenders_on_reset` instead. + + To get the same behaviour as setting this parameter to ``True`` or ``False``, set + :attr:`num_rerenders_on_reset` to 1 or 0, respectively. + """ + + num_rerenders_on_reset: int = 0 + """Number of render steps to perform after reset. Defaults to 0, which means no render step will be + performed after reset. + + * When this is 0, no render step will be performed after reset. Data collected from sensors after performing + reset will be stale and will not reflect the latest states in simulation caused by the reset. + * When this is greater than 0, the specified number of extra render steps will be performed to update the + sensor data to reflect the latest states from the reset. This comes at a cost of performance as additional + render steps will be performed after each time an environment is reset. + """ + + wait_for_textures: bool = True + """True to wait for assets to be loaded completely, False otherwise. Defaults to True.""" + + xr: XrCfg | None = None + """Configuration for viewing and interacting with the environment through an XR device.""" + + teleop_devices: DevicesCfg = field(default_factory=DevicesCfg) + """Configuration for teleoperation devices.""" + + export_io_descriptors: bool = False + """Whether to export the IO descriptors for the environment. Defaults to False.""" + + log_dir: str | None = None + """Directory for logging experiment artifacts. Defaults to None, in which case no specific log directory is set.""" diff --git a/source/isaaclab/isaaclab/envs/manager_based_rl_env.py b/source/isaaclab/isaaclab/envs/manager_based_rl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..ab8c04155d2d8152731a93f87544984635c1c913 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/manager_based_rl_env.py @@ -0,0 +1,394 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed to import for allowing type-hinting: np.ndarray | None +from __future__ import annotations + +import math +from collections.abc import Sequence +from typing import Any, ClassVar + +import gymnasium as gym +import numpy as np +import torch + +from isaaclab.managers import CommandManager, CurriculumManager, RewardManager, TerminationManager +from isaaclab.ui.widgets import ManagerLiveVisualizer + +from .common import VecEnvStepReturn +from .manager_based_env import ManagerBasedEnv +from .manager_based_rl_env_cfg import ManagerBasedRLEnvCfg + + +class ManagerBasedRLEnv(ManagerBasedEnv, gym.Env): + """The superclass for the manager-based workflow reinforcement learning-based environments. + + This class inherits from :class:`ManagerBasedEnv` and implements the core functionality for + reinforcement learning-based environments. It is designed to be used with any RL + library. The class is designed to be used with vectorized environments, i.e., the + environment is expected to be run in parallel with multiple sub-environments. The + number of sub-environments is specified using the ``num_envs``. + + Each observation from the environment is a batch of observations for each sub- + environments. The method :meth:`step` is also expected to receive a batch of actions + for each sub-environment. + + While the environment itself is implemented as a vectorized environment, we do not + inherit from :class:`gym.vector.VectorEnv`. This is mainly because the class adds + various methods (for wait and asynchronous updates) which are not required. + Additionally, each RL library typically has its own definition for a vectorized + environment. Thus, to reduce complexity, we directly use the :class:`gym.Env` over + here and leave it up to library-defined wrappers to take care of wrapping this + environment for their agents. + + Note: + For vectorized environments, it is recommended to **only** call the :meth:`reset` + method once before the first call to :meth:`step`, i.e. after the environment is created. + After that, the :meth:`step` function handles the reset of terminated sub-environments. + This is because the simulator does not support resetting individual sub-environments + in a vectorized environment. + + """ + + is_vector_env: ClassVar[bool] = True + """Whether the environment is a vectorized environment.""" + metadata: ClassVar[dict[str, Any]] = { + "render_modes": [None, "human", "rgb_array"], + } + """Metadata for the environment.""" + + cfg: ManagerBasedRLEnvCfg + """Configuration for the environment.""" + + def __init__(self, cfg: ManagerBasedRLEnvCfg, render_mode: str | None = None, **kwargs): + """Initialize the environment. + + Args: + cfg: The configuration for the environment. + render_mode: The render mode for the environment. Defaults to None, which + is similar to ``"human"``. + """ + # -- counter for curriculum + self.common_step_counter = 0 + + # initialize the episode length buffer BEFORE loading the managers to use it in mdp functions. + self.episode_length_buf = torch.zeros(cfg.scene.num_envs, device=cfg.sim.device, dtype=torch.long) + + # initialize the base class to setup the scene. + super().__init__(cfg=cfg) + # store the render mode + self.render_mode = render_mode + + # initialize data and constants + # -- set the framerate of the gym video recorder wrapper so that the playback speed of the + # produced video matches the simulation + self.metadata["render_fps"] = 1 / self.step_dt + + print("[INFO]: Completed setting up the environment...") + + """ + Properties. + """ + + @property + def max_episode_length_s(self) -> float: + """Maximum episode length in seconds.""" + return self.cfg.episode_length_s + + @property + def max_episode_length(self) -> int: + """Maximum episode length in environment steps.""" + return math.ceil(self.max_episode_length_s / self.step_dt) + + """ + Operations - Setup. + """ + + def load_managers(self): + # note: this order is important since observation manager needs to know the command and action managers + # and the reward manager needs to know the termination manager + # -- command manager + self.command_manager: CommandManager = CommandManager(self.cfg.commands, self) + print("[INFO] Command Manager: ", self.command_manager) + + # call the parent class to load the managers for observations and actions. + super().load_managers() + + # prepare the managers + # -- termination manager + self.termination_manager = TerminationManager(self.cfg.terminations, self) + print("[INFO] Termination Manager: ", self.termination_manager) + # -- reward manager + self.reward_manager = RewardManager(self.cfg.rewards, self) + print("[INFO] Reward Manager: ", self.reward_manager) + # -- curriculum manager + self.curriculum_manager = CurriculumManager(self.cfg.curriculum, self) + print("[INFO] Curriculum Manager: ", self.curriculum_manager) + + # setup the action and observation spaces for Gym + self._configure_gym_env_spaces() + + # perform events at the start of the simulation + if "startup" in self.event_manager.available_modes: + self.event_manager.apply(mode="startup") + + def setup_manager_visualizers(self): + """Creates live visualizers for manager terms.""" + + self.manager_visualizers = { + "action_manager": ManagerLiveVisualizer(manager=self.action_manager), + "observation_manager": ManagerLiveVisualizer(manager=self.observation_manager), + "command_manager": ManagerLiveVisualizer(manager=self.command_manager), + "termination_manager": ManagerLiveVisualizer(manager=self.termination_manager), + "reward_manager": ManagerLiveVisualizer(manager=self.reward_manager), + "curriculum_manager": ManagerLiveVisualizer(manager=self.curriculum_manager), + } + + """ + Operations - MDP + """ + + def step(self, action: torch.Tensor) -> VecEnvStepReturn: + """Execute one time-step of the environment's dynamics and reset terminated environments. + + Unlike the :class:`ManagerBasedEnv.step` class, the function performs the following operations: + + 1. Process the actions. + 2. Perform physics stepping. + 3. Perform rendering if gui is enabled. + 4. Update the environment counters and compute the rewards and terminations. + 5. Reset the environments that terminated. + 6. Compute the observations. + 7. Return the observations, rewards, resets and extras. + + Args: + action: The actions to apply on the environment. Shape is (num_envs, action_dim). + + Returns: + A tuple containing the observations, rewards, resets (terminated and truncated) and extras. + """ + # process actions + self.action_manager.process_action(action.to(self.device)) + + self.recorder_manager.record_pre_step() + + # check if we need to do rendering within the physics loop + # note: checked here once to avoid multiple checks within the loop + is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() + + # perform physics stepping + for _ in range(self.cfg.decimation): + self._sim_step_counter += 1 + # set actions into buffers + self.action_manager.apply_action() + # set actions into simulator + self.scene.write_data_to_sim() + # simulate + self.sim.step(render=False) + self.recorder_manager.record_post_physics_decimation_step() + # render between steps only if the GUI or an RTX sensor needs it + # note: we assume the render interval to be the shortest accepted rendering interval. + # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. + if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: + self.sim.render() + # update buffers at sim dt + self.scene.update(dt=self.physics_dt) + + # post-step: + # -- update env counters (used for curriculum generation) + self.episode_length_buf += 1 # step in current episode (per env) + self.common_step_counter += 1 # total step (common for all envs) + # -- check terminations + self.reset_buf = self.termination_manager.compute() + self.reset_terminated = self.termination_manager.terminated + self.reset_time_outs = self.termination_manager.time_outs + # -- reward computation + self.reward_buf = self.reward_manager.compute(dt=self.step_dt) + + if len(self.recorder_manager.active_terms) > 0: + # update observations for recording if needed + self.obs_buf = self.observation_manager.compute() + self.recorder_manager.record_post_step() + + # -- reset envs that terminated/timed-out and log the episode information + reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(reset_env_ids) > 0: + # trigger recorder terms for pre-reset calls + self.recorder_manager.record_pre_reset(reset_env_ids) + + self._reset_idx(reset_env_ids) + + # if sensors are added to the scene, make sure we render to reflect changes in reset + if self.sim.has_rtx_sensors() and self.cfg.num_rerenders_on_reset > 0: + for _ in range(self.cfg.num_rerenders_on_reset): + self.sim.render() + + # trigger recorder terms for post-reset calls + self.recorder_manager.record_post_reset(reset_env_ids) + + # -- update command + self.command_manager.compute(dt=self.step_dt) + # -- step interval events + if "interval" in self.event_manager.available_modes: + self.event_manager.apply(mode="interval", dt=self.step_dt) + # -- compute observations + # note: done after reset to get the correct observations for reset envs + self.obs_buf = self.observation_manager.compute(update_history=True) + + # return observations, rewards, resets and extras + return self.obs_buf, self.reward_buf, self.reset_terminated, self.reset_time_outs, self.extras + + def render(self, recompute: bool = False) -> np.ndarray | None: + """Run rendering without stepping through the physics. + + By convention, if mode is: + + - **human**: Render to the current display and return nothing. Usually for human consumption. + - **rgb_array**: Return a numpy.ndarray with shape (x, y, 3), representing RGB values for an + x-by-y pixel image, suitable for turning into a video. + + Args: + recompute: Whether to force a render even if the simulator has already rendered the scene. + Defaults to False. + + Returns: + The rendered image as a numpy array if mode is "rgb_array". Otherwise, returns None. + + Raises: + RuntimeError: If mode is set to "rgb_data" and simulation render mode does not support it. + In this case, the simulation render mode must be set to ``RenderMode.PARTIAL_RENDERING`` + or ``RenderMode.FULL_RENDERING``. + NotImplementedError: If an unsupported rendering mode is specified. + """ + # run a rendering step of the simulator + # if we have rtx sensors, we do not need to render again sin + if not self.sim.has_rtx_sensors() and not recompute: + self.sim.render() + # decide the rendering mode + if self.render_mode == "human" or self.render_mode is None: + return None + elif self.render_mode == "rgb_array": + # check that if any render could have happened + if self.sim.render_mode.value < self.sim.RenderMode.PARTIAL_RENDERING.value: + raise RuntimeError( + f"Cannot render '{self.render_mode}' when the simulation render mode is" + f" '{self.sim.render_mode.name}'. Please set the simulation render mode to:" + f"'{self.sim.RenderMode.PARTIAL_RENDERING.name}' or '{self.sim.RenderMode.FULL_RENDERING.name}'." + " If running headless, make sure --enable_cameras is set." + ) + # create the annotator if it does not exist + if not hasattr(self, "_rgb_annotator"): + import omni.replicator.core as rep + + # create render product + self._render_product = rep.create.render_product( + self.cfg.viewer.cam_prim_path, self.cfg.viewer.resolution + ) + # create rgb annotator -- used to read data from the render product + self._rgb_annotator = rep.AnnotatorRegistry.get_annotator("rgb", device="cpu") + self._rgb_annotator.attach([self._render_product]) + # obtain the rgb data + rgb_data = self._rgb_annotator.get_data() + # convert to numpy array + rgb_data = np.frombuffer(rgb_data, dtype=np.uint8).reshape(*rgb_data.shape) + # return the rgb data + # note: initially the renerer is warming up and returns empty data + if rgb_data.size == 0: + return np.zeros((self.cfg.viewer.resolution[1], self.cfg.viewer.resolution[0], 3), dtype=np.uint8) + else: + return rgb_data[:, :, :3] + else: + raise NotImplementedError( + f"Render mode '{self.render_mode}' is not supported. Please use: {self.metadata['render_modes']}." + ) + + def close(self): + if not self._is_closed: + # destructor is order-sensitive + del self.command_manager + del self.reward_manager + del self.termination_manager + del self.curriculum_manager + # call the parent class to close the environment + super().close() + + """ + Helper functions. + """ + + def _configure_gym_env_spaces(self): + """Configure the action and observation spaces for the Gym environment.""" + # observation space (unbounded since we don't impose any limits) + self.single_observation_space = gym.spaces.Dict() + for group_name, group_term_names in self.observation_manager.active_terms.items(): + # extract quantities about the group + has_concatenated_obs = self.observation_manager.group_obs_concatenate[group_name] + group_dim = self.observation_manager.group_obs_dim[group_name] + # check if group is concatenated or not + # if not concatenated, then we need to add each term separately as a dictionary + if has_concatenated_obs: + self.single_observation_space[group_name] = gym.spaces.Box(low=-np.inf, high=np.inf, shape=group_dim) + else: + group_term_cfgs = self.observation_manager._group_obs_term_cfgs[group_name] + term_dict = {} + for term_name, term_dim, term_cfg in zip(group_term_names, group_dim, group_term_cfgs): + low = -np.inf if term_cfg.clip is None else term_cfg.clip[0] + high = np.inf if term_cfg.clip is None else term_cfg.clip[1] + term_dict[term_name] = gym.spaces.Box(low=low, high=high, shape=term_dim) + self.single_observation_space[group_name] = gym.spaces.Dict(term_dict) + # action space (unbounded since we don't impose any limits) + action_dim = sum(self.action_manager.action_term_dim) + self.single_action_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(action_dim,)) + + # batch the spaces for vectorized environments + self.observation_space = gym.vector.utils.batch_space(self.single_observation_space, self.num_envs) + self.action_space = gym.vector.utils.batch_space(self.single_action_space, self.num_envs) + + def _reset_idx(self, env_ids: Sequence[int]): + """Reset environments based on specified indices. + + Args: + env_ids: List of environment ids which must be reset + """ + # update the curriculum for environments that need a reset + self.curriculum_manager.compute(env_ids=env_ids) + # reset the internal buffers of the scene elements + self.scene.reset(env_ids) + # apply events such as randomizations for environments that need a reset + if "reset" in self.event_manager.available_modes: + env_step_count = self._sim_step_counter // self.cfg.decimation + self.event_manager.apply(mode="reset", env_ids=env_ids, global_env_step_count=env_step_count) + + # iterate over all managers and reset them + # this returns a dictionary of information which is stored in the extras + # note: This is order-sensitive! Certain things need be reset before others. + self.extras["log"] = dict() + # -- observation manager + info = self.observation_manager.reset(env_ids) + self.extras["log"].update(info) + # -- action manager + info = self.action_manager.reset(env_ids) + self.extras["log"].update(info) + # -- rewards manager + info = self.reward_manager.reset(env_ids) + self.extras["log"].update(info) + # -- curriculum manager + info = self.curriculum_manager.reset(env_ids) + self.extras["log"].update(info) + # -- command manager + info = self.command_manager.reset(env_ids) + self.extras["log"].update(info) + # -- event manager + info = self.event_manager.reset(env_ids) + self.extras["log"].update(info) + # -- termination manager + info = self.termination_manager.reset(env_ids) + self.extras["log"].update(info) + # -- recorder manager + info = self.recorder_manager.reset(env_ids) + self.extras["log"].update(info) + + # reset the episode length buffer + self.episode_length_buf[env_ids] = 0 diff --git a/source/isaaclab/isaaclab/envs/manager_based_rl_env_cfg.py b/source/isaaclab/isaaclab/envs/manager_based_rl_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..eac633e8b99cb6b5c8ee0f6439e7a8354323a497 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/manager_based_rl_env_cfg.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from .manager_based_env_cfg import ManagerBasedEnvCfg +from .ui import ManagerBasedRLEnvWindow + + +@configclass +class ManagerBasedRLEnvCfg(ManagerBasedEnvCfg): + """Configuration for a reinforcement learning environment with the manager-based workflow.""" + + # ui settings + ui_window_class_type: type | None = ManagerBasedRLEnvWindow + + # general settings + is_finite_horizon: bool = False + """Whether the learning task is treated as a finite or infinite horizon problem for the agent. + Defaults to False, which means the task is treated as an infinite horizon problem. + + This flag handles the subtleties of finite and infinite horizon tasks: + + * **Finite horizon**: no penalty or bootstrapping value is required by the the agent for + running out of time. However, the environment still needs to terminate the episode after the + time limit is reached. + * **Infinite horizon**: the agent needs to bootstrap the value of the state at the end of the episode. + This is done by sending a time-limit (or truncated) done signal to the agent, which triggers this + bootstrapping calculation. + + If True, then the environment is treated as a finite horizon problem and no time-out (or truncated) done signal + is sent to the agent. If False, then the environment is treated as an infinite horizon problem and a time-out + (or truncated) done signal is sent to the agent. + + Note: + The base :class:`ManagerBasedRLEnv` class does not use this flag directly. It is used by the environment + wrappers to determine what type of done signal to send to the corresponding learning agent. + """ + + episode_length_s: float = MISSING + """Duration of an episode (in seconds). + + Based on the decimation rate and physics time step, the episode length is calculated as: + + .. code-block:: python + + episode_length_steps = ceil(episode_length_s / (decimation_rate * physics_time_step)) + + For example, if the decimation rate is 10, the physics time step is 0.01, and the episode length is 10 seconds, + then the episode length in steps is 100. + """ + + # environment settings + rewards: object = MISSING + """Reward settings. + + Please refer to the :class:`isaaclab.managers.RewardManager` class for more details. + """ + + terminations: object = MISSING + """Termination settings. + + Please refer to the :class:`isaaclab.managers.TerminationManager` class for more details. + """ + + curriculum: object | None = None + """Curriculum settings. Defaults to None, in which case no curriculum is applied. + + Please refer to the :class:`isaaclab.managers.CurriculumManager` class for more details. + """ + + commands: object | None = None + """Command settings. Defaults to None, in which case no commands are generated. + + Please refer to the :class:`isaaclab.managers.CommandManager` class for more details. + """ diff --git a/source/isaaclab/isaaclab/envs/manager_based_rl_mimic_env.py b/source/isaaclab/isaaclab/envs/manager_based_rl_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..8518d716332e0abf17aa074ce29341b0375c5d6f --- /dev/null +++ b/source/isaaclab/isaaclab/envs/manager_based_rl_mimic_env.py @@ -0,0 +1,183 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLEnv + + +def optional_method(func): + """Decorator to mark a method as optional.""" + func.__is_optional__ = True + return func + + +class ManagerBasedRLMimicEnv(ManagerBasedRLEnv): + """The superclass for the Isaac Lab Mimic environments. + + This class inherits from :class:`ManagerBasedRLEnv` and provides a template for the functions that + need to be defined to run the Isaac Lab Mimic data generation workflow. The Isaac Lab data generation + pipeline, inspired by the MimicGen system, enables the generation of new datasets based on a few human + collected demonstrations. MimicGen is a novel approach designed to automatically synthesize large-scale, + rich datasets from a sparse set of human demonstrations by adapting them to new contexts. It manages to + replicate the benefits of large datasets while reducing the immense time and effort usually required to + gather extensive human demonstrations. + + The MimicGen system works by parsing demonstrations into object-centric segments. It then adapts + these segments to new scenes by transforming each segment according to the new scene’s context, stitching + them into a coherent trajectory for a robotic end-effector to execute. This approach allows learners to train + proficient agents through imitation learning on diverse configurations of scenes, object instances, etc. + + Key Features: + - Efficient Dataset Generation: Utilizes a small set of human demos to produce large scale demonstrations. + - Broad Applicability: Capable of supporting tasks that require a range of manipulation skills, such as + pick-and-place and interacting with articulated objects. + - Dataset Versatility: The synthetic data retains a quality that compares favorably with additional human demos. + """ + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + raise NotImplementedError + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to compute the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + raise NotImplementedError + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + raise NotImplementedError + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + raise NotImplementedError + + def get_object_poses(self, env_ids: Sequence[int] | None = None): + """ + Gets the pose of each object relevant to Isaac Lab Mimic data generation in the current scene. + + Args: + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A dictionary that maps object names to object pose matrix (4x4 torch.Tensor) + """ + if env_ids is None: + env_ids = slice(None) + + rigid_object_states = self.scene.get_state(is_relative=True)["rigid_object"] + object_pose_matrix = dict() + for obj_name, obj_state in rigid_object_states.items(): + object_pose_matrix[obj_name] = PoseUtils.make_pose( + obj_state["root_pose"][env_ids, :3], PoseUtils.matrix_from_quat(obj_state["root_pose"][env_ids, 3:7]) + ) + return object_pose_matrix + + def get_subtask_start_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of start signal flags for each subtask in a task. The flag is 1 + when the subtask has started and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask start signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask start signal annotation. + + Args: + env_ids: Environment indices to get the start signals for. If None, all envs are considered. + + Returns: + A dictionary start signal flags (False or True) for each subtask. + """ + raise NotImplementedError + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + raise NotImplementedError + + def serialize(self): + """ + Save all information needed to re-instantiate this environment in a dictionary. + This is the same as @env_meta - environment metadata stored in hdf5 datasets, + and used in utils/env_utils.py. + """ + return dict(env_name=self.spec.id, type=2, env_kwargs=dict()) + + @optional_method + def get_navigation_state(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Optional method. Only required when using navigation controller locomanipulation data generation. + + Gets the navigation state of the robot. Required when use of the navigation controller is + enabled. The navigation state includes a boolean flag "is_navigating" to indicate when the + robot is under control by the navigation controller, and a boolean flag "navigation_goal_reached" + to indicate when the navigation goal has been reached. + + Args: + env_ids: The environment index to get the navigation state for. If None, all envs are considered. + + Returns: + A dictionary that of navigation state flags (False or True). + """ + raise NotImplementedError diff --git a/source/isaaclab/isaaclab/envs/mdp/__init__.py b/source/isaaclab/isaaclab/envs/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb507f3eb0ac379a7e62963aa652aca3efa4863 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module with implementation of manager terms. + +The functions can be provided to different managers that are responsible for the +different aspects of the MDP. These include the observation, reward, termination, +actions, events and curriculum managers. + +The terms are defined under the ``envs`` module because they are used to define +the environment. However, they are not part of the environment directly, but +are used to define the environment through their managers. + +""" + +from .actions import * # noqa: F401, F403 +from .commands import * # noqa: F401, F403 +from .curriculums import * # noqa: F401, F403 +from .events import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .recorders import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/__init__.py b/source/isaaclab/isaaclab/envs/mdp/actions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..56b3ae25ff4eaa9e95ab23663e0084e3ebfc826e --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Various action terms that can be used in the environment.""" + +from .actions_cfg import * +from .binary_joint_actions import * +from .joint_actions import * +from .joint_actions_to_limits import * +from .non_holonomic_actions import * +from .surface_gripper_actions import * diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/actions_cfg.py b/source/isaaclab/isaaclab/envs/mdp/actions/actions_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..bf8748bdf3ad26f0cd18cc627a4bac8a6c823eef --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/actions_cfg.py @@ -0,0 +1,379 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.controllers import DifferentialIKControllerCfg, OperationalSpaceControllerCfg +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.utils import configclass + +from . import ( + binary_joint_actions, + joint_actions, + joint_actions_to_limits, + non_holonomic_actions, + surface_gripper_actions, + task_space_actions, +) + +## +# Joint actions. +## + + +@configclass +class JointActionCfg(ActionTermCfg): + """Configuration for the base joint action term. + + See :class:`JointAction` for more details. + """ + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + scale: float | dict[str, float] = 1.0 + """Scale factor for the action (float or dict of regex expressions). Defaults to 1.0.""" + offset: float | dict[str, float] = 0.0 + """Offset factor for the action (float or dict of regex expressions). Defaults to 0.0.""" + preserve_order: bool = False + """Whether to preserve the order of the joint names in the action output. Defaults to False.""" + + +@configclass +class JointPositionActionCfg(JointActionCfg): + """Configuration for the joint position action term. + + See :class:`JointPositionAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions.JointPositionAction + + use_default_offset: bool = True + """Whether to use default joint positions configured in the articulation asset as offset. + Defaults to True. + + If True, this flag results in overwriting the values of :attr:`offset` to the default joint positions + from the articulation asset. + """ + + +@configclass +class RelativeJointPositionActionCfg(JointActionCfg): + """Configuration for the relative joint position action term. + + See :class:`RelativeJointPositionAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions.RelativeJointPositionAction + + use_zero_offset: bool = True + """Whether to ignore the offset defined in articulation asset. Defaults to True. + + If True, this flag results in overwriting the values of :attr:`offset` to zero. + """ + + +@configclass +class JointVelocityActionCfg(JointActionCfg): + """Configuration for the joint velocity action term. + + See :class:`JointVelocityAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions.JointVelocityAction + + use_default_offset: bool = True + """Whether to use default joint velocities configured in the articulation asset as offset. + Defaults to True. + + This overrides the settings from :attr:`offset` if set to True. + """ + + +@configclass +class JointEffortActionCfg(JointActionCfg): + """Configuration for the joint effort action term. + + See :class:`JointEffortAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions.JointEffortAction + + +## +# Joint actions rescaled to limits. +## + + +@configclass +class JointPositionToLimitsActionCfg(ActionTermCfg): + """Configuration for the bounded joint position action term. + + See :class:`JointPositionToLimitsAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions_to_limits.JointPositionToLimitsAction + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + + scale: float | dict[str, float] = 1.0 + """Scale factor for the action (float or dict of regex expressions). Defaults to 1.0.""" + + rescale_to_limits: bool = True + """Whether to rescale the action to the joint limits. Defaults to True. + + If True, the input actions are rescaled to the joint limits, i.e., the action value in + the range [-1, 1] corresponds to the joint lower and upper limits respectively. + + Note: + This operation is performed after applying the scale factor. + """ + + preserve_order: bool = False + """Whether to preserve the order of the joint names in the action output. Defaults to False.""" + + +@configclass +class EMAJointPositionToLimitsActionCfg(JointPositionToLimitsActionCfg): + """Configuration for the exponential moving average (EMA) joint position action term. + + See :class:`EMAJointPositionToLimitsAction` for more details. + """ + + class_type: type[ActionTerm] = joint_actions_to_limits.EMAJointPositionToLimitsAction + + alpha: float | dict[str, float] = 1.0 + """The weight for the moving average (float or dict of regex expressions). Defaults to 1.0. + + If set to 1.0, the processed action is applied directly without any moving average window. + """ + + +## +# Gripper actions. +## + + +@configclass +class BinaryJointActionCfg(ActionTermCfg): + """Configuration for the base binary joint action term. + + See :class:`BinaryJointAction` for more details. + """ + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + open_command_expr: dict[str, float] = MISSING + """The joint command to move to *open* configuration.""" + close_command_expr: dict[str, float] = MISSING + """The joint command to move to *close* configuration.""" + + +@configclass +class BinaryJointPositionActionCfg(BinaryJointActionCfg): + """Configuration for the binary joint position action term. + + See :class:`BinaryJointPositionAction` for more details. + """ + + class_type: type[ActionTerm] = binary_joint_actions.BinaryJointPositionAction + + +@configclass +class BinaryJointVelocityActionCfg(BinaryJointActionCfg): + """Configuration for the binary joint velocity action term. + + See :class:`BinaryJointVelocityAction` for more details. + """ + + class_type: type[ActionTerm] = binary_joint_actions.BinaryJointVelocityAction + + +@configclass +class AbsBinaryJointPositionActionCfg(ActionTermCfg): + """Configuration for the absolute binary joint position action term. + + This action term is used for robust grasping by converting continuous gripper joint position actions + into binary open/close commands. Unlike directly applying continuous gripper joint position actions, this class + applies a threshold-based decision mechanism to determine whether to + open or close the gripper. + + The action works by: + 1. Taking a continuous input action value + 2. Comparing it against a configurable threshold + 3. Mapping the result to either open or close commands based on the threshold comparison + 4. Applying the corresponding gripper open/close commands + + This approach provides more predictable and stable grasping behavior compared to directly applying + continuous gripper joint position actions. + + See :class:`AbsBinaryJointPositionAction` for more details. + """ + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + open_command_expr: dict[str, float] = MISSING + """The joint command to move to *open* configuration.""" + close_command_expr: dict[str, float] = MISSING + """The joint command to move to *close* configuration.""" + threshold: float = 0.5 + """The threshold for the binary action. Defaults to 0.5.""" + positive_threshold: bool = True + """Whether to use positive (Open actions > Close actions) threshold. Defaults to True.""" + + class_type: type[ActionTerm] = binary_joint_actions.AbsBinaryJointPositionAction + + +## +# Non-holonomic actions. +## + + +@configclass +class NonHolonomicActionCfg(ActionTermCfg): + """Configuration for the non-holonomic action term with dummy joints at the base. + + See :class:`NonHolonomicAction` for more details. + """ + + class_type: type[ActionTerm] = non_holonomic_actions.NonHolonomicAction + + body_name: str = MISSING + """Name of the body which has the dummy mechanism connected to.""" + x_joint_name: str = MISSING + """The dummy joint name in the x direction.""" + y_joint_name: str = MISSING + """The dummy joint name in the y direction.""" + yaw_joint_name: str = MISSING + """The dummy joint name in the yaw direction.""" + scale: tuple[float, float] = (1.0, 1.0) + """Scale factor for the action. Defaults to (1.0, 1.0).""" + offset: tuple[float, float] = (0.0, 0.0) + """Offset factor for the action. Defaults to (0.0, 0.0).""" + + +## +# Task-space Actions. +## + + +@configclass +class DifferentialInverseKinematicsActionCfg(ActionTermCfg): + """Configuration for inverse differential kinematics action term. + + See :class:`DifferentialInverseKinematicsAction` for more details. + """ + + @configclass + class OffsetCfg: + """The offset pose from parent frame to child frame. + + On many robots, end-effector frames are fictitious frames that do not have a corresponding + rigid body. In such cases, it is easier to define this transform w.r.t. their parent rigid body. + For instance, for the Franka Emika arm, the end-effector is defined at an offset to the the + "panda_hand" frame. + """ + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation ``(w, x, y, z)`` w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + class_type: type[ActionTerm] = task_space_actions.DifferentialInverseKinematicsAction + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + body_name: str = MISSING + """Name of the body or frame for which IK is performed.""" + body_offset: OffsetCfg | None = None + """Offset of target frame w.r.t. to the body frame. Defaults to None, in which case no offset is applied.""" + scale: float | tuple[float, ...] = 1.0 + """Scale factor for the action. Defaults to 1.0.""" + controller: DifferentialIKControllerCfg = MISSING + """The configuration for the differential IK controller.""" + + +@configclass +class OperationalSpaceControllerActionCfg(ActionTermCfg): + """Configuration for operational space controller action term. + + See :class:`OperationalSpaceControllerAction` for more details. + """ + + @configclass + class OffsetCfg: + """The offset pose from parent frame to child frame. + + On many robots, end-effector frames are fictitious frames that do not have a corresponding + rigid body. In such cases, it is easier to define this transform w.r.t. their parent rigid body. + For instance, for the Franka Emika arm, the end-effector is defined at an offset to the the + "panda_hand" frame. + """ + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation ``(w, x, y, z)`` w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + class_type: type[ActionTerm] = task_space_actions.OperationalSpaceControllerAction + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + + body_name: str = MISSING + """Name of the body or frame for which motion/force control is performed.""" + + body_offset: OffsetCfg | None = None + """Offset of target frame w.r.t. to the body frame. Defaults to None, in which case no offset is applied.""" + + task_frame_rel_path: str = None + """The path of a ``RigidObject``, relative to the sub-environment, representing task frame. Defaults to None.""" + + controller_cfg: OperationalSpaceControllerCfg = MISSING + """The configuration for the operational space controller.""" + + position_scale: float = 1.0 + """Scale factor for the position targets. Defaults to 1.0.""" + + orientation_scale: float = 1.0 + """Scale factor for the orientation (quad for ``pose_abs`` or axis-angle for ``pose_rel``). Defaults to 1.0.""" + + wrench_scale: float = 1.0 + """Scale factor for the wrench targets. Defaults to 1.0.""" + + stiffness_scale: float = 1.0 + """Scale factor for the stiffness commands. Defaults to 1.0.""" + + damping_ratio_scale: float = 1.0 + """Scale factor for the damping ratio commands. Defaults to 1.0.""" + + nullspace_joint_pos_target: str = "none" + """The joint targets for the null-space control: ``"none"``, ``"zero"``, ``"default"``, ``"center"``. + + Note: Functional only when ``nullspace_control`` is set to ``"position"`` within the + ``OperationalSpaceControllerCfg``. + """ + + +## +# Surface Gripper actions. +## + + +@configclass +class SurfaceGripperBinaryActionCfg(ActionTermCfg): + """Configuration for the binary surface gripper action term. + + See :class:`SurfaceGripperBinaryAction` for more details. + """ + + asset_name: str = MISSING + """Name of the surface gripper asset in the scene.""" + open_command: float = -1.0 + """The command value to open the gripper. Defaults to -1.0.""" + close_command: float = 1.0 + """The command value to close the gripper. Defaults to 1.0.""" + + class_type: type[ActionTerm] = surface_gripper_actions.SurfaceGripperBinaryAction diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..289045bd37baa48a173f06f157e5836651b28937 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py @@ -0,0 +1,213 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class BinaryJointAction(ActionTerm): + """Base class for binary joint actions. + + This action term maps a binary action to the *open* or *close* joint configurations. These configurations are + specified through the :class:`BinaryJointActionCfg` object. If the input action is a float vector, the action + is considered binary based on the sign of the action values. + + Based on above, we follow the following convention for the binary action: + + 1. Open action: 1 (bool) or positive values (float). + 2. Close action: 0 (bool) or negative values (float). + + The action term can mostly be used for gripper actions, where the gripper is either open or closed. This + helps in devising a mimicking mechanism for the gripper, since in simulation it is often not possible to + add such constraints to the gripper. + """ + + cfg: actions_cfg.BinaryJointActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv) -> None: + # initialize the action term + super().__init__(cfg, env) + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names) + self._num_joints = len(self._joint_ids) + # log the resolved joint names for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, 1, device=self.device) + self._processed_actions = torch.zeros(self.num_envs, self._num_joints, device=self.device) + + # parse open command + self._open_command = torch.zeros(self._num_joints, device=self.device) + index_list, name_list, value_list = string_utils.resolve_matching_names_values( + self.cfg.open_command_expr, self._joint_names + ) + if len(index_list) != self._num_joints: + raise ValueError( + f"Could not resolve all joints for the action term. Missing: {set(self._joint_names) - set(name_list)}" + ) + self._open_command[index_list] = torch.tensor(value_list, device=self.device) + + # parse close command + self._close_command = torch.zeros_like(self._open_command) + index_list, name_list, value_list = string_utils.resolve_matching_names_values( + self.cfg.close_command_expr, self._joint_names + ) + if len(index_list) != self._num_joints: + raise ValueError( + f"Could not resolve all joints for the action term. Missing: {set(self._joint_names) - set(name_list)}" + ) + self._close_command[index_list] = torch.tensor(value_list, device=self.device) + + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return 1 + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "JointAction" + self._IO_descriptor.joint_names = self._joint_names + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # compute the binary mask + if actions.dtype == torch.bool: + # true: close, false: open + binary_mask = actions == 0 + else: + # true: close, false: open + binary_mask = actions < 0 + # compute the command + self._processed_actions = torch.where(binary_mask, self._close_command, self._open_command) + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 + + +class BinaryJointPositionAction(BinaryJointAction): + """Binary joint action that sets the binary action into joint position targets.""" + + cfg: actions_cfg.BinaryJointPositionActionCfg + """The configuration of the action term.""" + + def apply_actions(self): + self._asset.set_joint_position_target(self._processed_actions, joint_ids=self._joint_ids) + + +class BinaryJointVelocityAction(BinaryJointAction): + """Binary joint action that sets the binary action into joint velocity targets.""" + + cfg: actions_cfg.BinaryJointVelocityActionCfg + """The configuration of the action term.""" + + def apply_actions(self): + self._asset.set_joint_velocity_target(self._processed_actions, joint_ids=self._joint_ids) + + +class AbsBinaryJointPositionAction(BinaryJointAction): + """Absolute Binary joint action that sets the binary action into joint position targets. + + This class extends BinaryJointAction to accept absolute position control + for gripper joints. It converts continuous input actions into binary open/close commands + using a configurable threshold mechanism. + + The key difference from the base BinaryJointAction is that this class: + - Receives absolute joint position actions for gripper control + - Implements a threshold-based decision system to determine open/close state + + The action processing works by: + 1. Taking a continuous input action value + 2. Comparing it against the configured threshold value + 3. Based on the threshold comparison and positive_threshold flag, determining + whether to open or close the gripper + 4. Setting the target joint positions to either the open or close configuration + + """ + + cfg: actions_cfg.AbsBinaryJointPositionActionCfg + """The configuration of the action term.""" + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # compute the binary mask + if self.cfg.positive_threshold: + # true: open 0.785, false: close 0.0 + binary_mask = actions > self.cfg.threshold + else: + # true: close 0.0, false: open 0.785 + binary_mask = actions < self.cfg.threshold + # compute the command + self._processed_actions = torch.where(binary_mask, self._open_command, self._close_command) + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def apply_actions(self): + self._asset.set_joint_position_target(self._processed_actions, joint_ids=self._joint_ids) diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..7ca7fe66c4b4ccc4c9e7e375b87eff4b3f23ac4b --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions.py @@ -0,0 +1,264 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class JointAction(ActionTerm): + r"""Base class for joint actions. + + This action term performs pre-processing of the raw actions using affine transformations (scale and offset). + These transformations can be configured to be applied to a subset of the articulation's joints. + + Mathematically, the action term is defined as: + + .. math:: + + \text{action} = \text{offset} + \text{scaling} \times \text{input action} + + where :math:`\text{action}` is the action that is sent to the articulation's actuated joints, :math:`\text{offset}` + is the offset applied to the input action, :math:`\text{scaling}` is the scaling applied to the input + action, and :math:`\text{input action}` is the input action from the user. + + Based on above, this kind of action transformation ensures that the input and output actions are in the same + units and dimensions. The child classes of this action term can then map the output action to a specific + desired command of the articulation's joints (e.g. position, velocity, etc.). + """ + + cfg: actions_cfg.JointActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor | float + """The scaling factor applied to the input action.""" + _offset: torch.Tensor | float + """The offset applied to the input action.""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> None: + # initialize the action term + super().__init__(cfg, env) + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints( + self.cfg.joint_names, preserve_order=self.cfg.preserve_order + ) + self._num_joints = len(self._joint_ids) + # log the resolved joint names for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + + # Avoid indexing across all joints for efficiency + if self._num_joints == self._asset.num_joints and not self.cfg.preserve_order: + self._joint_ids = slice(None) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # parse scale + if isinstance(cfg.scale, (float, int)): + self._scale = float(cfg.scale) + elif isinstance(cfg.scale, dict): + self._scale = torch.ones(self.num_envs, self.action_dim, device=self.device) + # resolve the dictionary config + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.scale, self._joint_names) + self._scale[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported scale type: {type(cfg.scale)}. Supported types are float and dict.") + # parse offset + if isinstance(cfg.offset, (float, int)): + self._offset = float(cfg.offset) + elif isinstance(cfg.offset, dict): + self._offset = torch.zeros_like(self._raw_actions) + # resolve the dictionary config + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.offset, self._joint_names) + self._offset[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported offset type: {type(cfg.offset)}. Supported types are float and dict.") + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._num_joints + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the joint action. + It adds the following information to the base descriptor: + - joint_names: The names of the joints. + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "JointAction" + self._IO_descriptor.joint_names = self._joint_names + self._IO_descriptor.scale = self._scale + # This seems to be always [4xNum_joints] IDK why. Need to check. + if isinstance(self._offset, torch.Tensor): + self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.offset = self._offset + # FIXME: This is not correct. Add list support. + if self.cfg.clip is not None: + if isinstance(self._clip, torch.Tensor): + self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.clip = self._clip + else: + self._IO_descriptor.clip = None + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # apply the affine transformations + self._processed_actions = self._raw_actions * self._scale + self._offset + # clip actions + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 + + +class JointPositionAction(JointAction): + """Joint action term that applies the processed actions to the articulation's joints as position commands.""" + + cfg: actions_cfg.JointPositionActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: actions_cfg.JointPositionActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + # use default joint positions as offset + if cfg.use_default_offset: + self._offset = self._asset.data.default_joint_pos[:, self._joint_ids].clone() + + def apply_actions(self): + # set position targets + self._asset.set_joint_position_target(self.processed_actions, joint_ids=self._joint_ids) + + +class RelativeJointPositionAction(JointAction): + r"""Joint action term that applies the processed actions to the articulation's joints as relative position commands. + + Unlike :class:`JointPositionAction`, this action term applies the processed actions as relative position commands. + This means that the processed actions are added to the current joint positions of the articulation's joints + before being sent as position commands. + + This means that the action applied at every step is: + + .. math:: + + \text{applied action} = \text{current joint positions} + \text{processed actions} + + where :math:`\text{current joint positions}` are the current joint positions of the articulation's joints. + """ + + cfg: actions_cfg.RelativeJointPositionActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: actions_cfg.RelativeJointPositionActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + # use zero offset for relative position + if cfg.use_zero_offset: + self._offset = 0.0 + + def apply_actions(self): + # add current joint positions to the processed actions + current_actions = self.processed_actions + self._asset.data.joint_pos[:, self._joint_ids] + # set position targets + self._asset.set_joint_position_target(current_actions, joint_ids=self._joint_ids) + + +class JointVelocityAction(JointAction): + """Joint action term that applies the processed actions to the articulation's joints as velocity commands.""" + + cfg: actions_cfg.JointVelocityActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: actions_cfg.JointVelocityActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + # use default joint velocity as offset + if cfg.use_default_offset: + self._offset = self._asset.data.default_joint_vel[:, self._joint_ids].clone() + + def apply_actions(self): + # set joint velocity targets + self._asset.set_joint_velocity_target(self.processed_actions, joint_ids=self._joint_ids) + + +class JointEffortAction(JointAction): + """Joint action term that applies the processed actions to the articulation's joints as effort commands.""" + + cfg: actions_cfg.JointEffortActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: actions_cfg.JointEffortActionCfg, env: ManagerBasedEnv): + super().__init__(cfg, env) + + def apply_actions(self): + # set joint effort targets + self._asset.set_joint_effort_target(self.processed_actions, joint_ids=self._joint_ids) diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions_to_limits.py b/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions_to_limits.py new file mode 100644 index 0000000000000000000000000000000000000000..3fc50ef2d71215117b9e86dc6a0a95bcc21827eb --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/joint_actions_to_limits.py @@ -0,0 +1,292 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class JointPositionToLimitsAction(ActionTerm): + """Joint position action term that scales the input actions to the joint limits and applies them to the + articulation's joints. + + This class is similar to the :class:`JointPositionAction` class. However, it performs additional + re-scaling of input actions to the actuator joint position limits. + + While processing the actions, it performs the following operations: + + 1. Apply scaling to the raw actions based on :attr:`actions_cfg.JointPositionToLimitsActionCfg.scale`. + 2. Clip the scaled actions to the range [-1, 1] and re-scale them to the joint limits if + :attr:`actions_cfg.JointPositionToLimitsActionCfg.rescale_to_limits` is set to True. + + The processed actions are then sent as position commands to the articulation's joints. + """ + + cfg: actions_cfg.JointPositionToLimitsActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor | float + """The scaling factor applied to the input action.""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints( + self.cfg.joint_names, preserve_order=cfg.preserve_order + ) + self._num_joints = len(self._joint_ids) + # log the resolved joint names for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + + # Avoid indexing across all joints for efficiency + if self._num_joints == self._asset.num_joints: + self._joint_ids = slice(None) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # parse scale + if isinstance(cfg.scale, (float, int)): + self._scale = float(cfg.scale) + elif isinstance(cfg.scale, dict): + self._scale = torch.ones(self.num_envs, self.action_dim, device=self.device) + # resolve the dictionary config + index_list, _, value_list = string_utils.resolve_matching_names_values( + self.cfg.scale, self._joint_names, preserve_order=cfg.preserve_order + ) + self._scale[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported scale type: {type(cfg.scale)}. Supported types are float and dict.") + + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values( + self.cfg.clip, self._joint_names, preserve_order=cfg.preserve_order + ) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._num_joints + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the joint position to limits action. + It adds the following information to the base descriptor: + - joint_names: The names of the joints. + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "JointAction" + self._IO_descriptor.joint_names = self._joint_names + self._IO_descriptor.scale = self._scale + # This seems to be always [4xNum_joints] IDK why. Need to check. + if isinstance(self._offset, torch.Tensor): + self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.offset = self._offset + if self.cfg.clip is not None: + self._IO_descriptor.clip = self._clip + else: + self._IO_descriptor.clip = None + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # apply affine transformations + self._processed_actions = self._raw_actions * self._scale + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + # rescale the position targets if configured + # this is useful when the input actions are in the range [-1, 1] + if self.cfg.rescale_to_limits: + # clip to [-1, 1] + actions = self._processed_actions.clamp(-1.0, 1.0) + # rescale within the joint limits + actions = math_utils.unscale_transform( + actions, + self._asset.data.soft_joint_pos_limits[:, self._joint_ids, 0], + self._asset.data.soft_joint_pos_limits[:, self._joint_ids, 1], + ) + self._processed_actions[:] = actions[:] + + def apply_actions(self): + # set position targets + self._asset.set_joint_position_target(self.processed_actions, joint_ids=self._joint_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 + + +class EMAJointPositionToLimitsAction(JointPositionToLimitsAction): + r"""Joint action term that applies exponential moving average (EMA) over the processed actions as the + articulation's joints position commands. + + Exponential moving average (EMA) is a type of moving average that gives more weight to the most recent data points. + This action term applies the processed actions as moving average position action commands. + The moving average is computed as: + + .. math:: + + \text{applied action} = + \alpha \times \text{processed actions} + + (1 - \alpha) \times \text{previous applied action} + + where :math:`\alpha` is the weight for the moving average, :math:`\text{processed actions}` are the + processed actions, and :math:`\text{previous action}` is the previous action that was applied to the articulation's + joints. + + In the trivial case where the weight is 1.0, the action term behaves exactly like + the :class:`JointPositionToLimitsAction` class. + + On reset, the previous action is initialized to the current joint positions of the articulation's joints. + """ + + cfg: actions_cfg.EMAJointPositionToLimitsActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: actions_cfg.EMAJointPositionToLimitsActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + # parse and save the moving average weight + if isinstance(cfg.alpha, float): + # check that the weight is in the valid range + if not 0.0 <= cfg.alpha <= 1.0: + raise ValueError(f"Moving average weight must be in the range [0, 1]. Got {cfg.alpha}.") + self._alpha = cfg.alpha + elif isinstance(cfg.alpha, dict): + self._alpha = torch.ones((env.num_envs, self.action_dim), device=self.device) + # resolve the dictionary config + index_list, names_list, value_list = string_utils.resolve_matching_names_values( + cfg.alpha, self._joint_names + ) + # check that the weights are in the valid range + for name, value in zip(names_list, value_list): + if not 0.0 <= value <= 1.0: + raise ValueError( + f"Moving average weight must be in the range [0, 1]. Got {value} for joint {name}." + ) + self._alpha[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError( + f"Unsupported moving average weight type: {type(cfg.alpha)}. Supported types are float and dict." + ) + + # initialize the previous targets + self._prev_applied_actions = torch.zeros_like(self.processed_actions) + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the EMA joint position to limits action. + It adds the following information to the base descriptor: + - joint_names: The names of the joints. + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + - alpha: The moving average weight. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + if isinstance(self._alpha, float): + self._IO_descriptor.alpha = self._alpha + elif isinstance(self._alpha, torch.Tensor): + self._IO_descriptor.alpha = self._alpha[0].detach().cpu().numpy().tolist() + else: + raise ValueError( + f"Unsupported moving average weight type: {type(self._alpha)}. Supported types are float and" + " torch.Tensor." + ) + return self._IO_descriptor + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + # check if specific environment ids are provided + if env_ids is None: + super().reset(slice(None)) + self._prev_applied_actions[:] = self._asset.data.joint_pos[:, self._joint_ids] + else: + super().reset(env_ids) + curr_applied_actions = self._asset.data.joint_pos[env_ids[:, None], self._joint_ids].view(len(env_ids), -1) + self._prev_applied_actions[env_ids, :] = curr_applied_actions + + def process_actions(self, actions: torch.Tensor): + # apply affine transformations + super().process_actions(actions) + # set position targets as moving average + ema_actions = self._alpha * self._processed_actions + ema_actions += (1.0 - self._alpha) * self._prev_applied_actions + # clamp the targets + self._processed_actions[:] = torch.clamp( + ema_actions, + self._asset.data.soft_joint_pos_limits[:, self._joint_ids, 0], + self._asset.data.soft_joint_pos_limits[:, self._joint_ids, 1], + ) + # update previous targets + self._prev_applied_actions[:] = self._processed_actions[:] diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/non_holonomic_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/non_holonomic_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..0a6c65f910221b63503521612734d3d1e6082da0 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/non_holonomic_actions.py @@ -0,0 +1,197 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.managers.action_manager import ActionTerm +from isaaclab.utils.math import euler_xyz_from_quat + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class NonHolonomicAction(ActionTerm): + r"""Non-holonomic action that maps a two dimensional action to the velocity of the robot in + the x, y and yaw directions. + + This action term helps model a skid-steer robot base. The action is a 2D vector which comprises of the + forward velocity :math:`v_{B,x}` and the turning rate :\omega_{B,z}: in the base frame. Using the current + base orientation, the commands are transformed into dummy joint velocity targets as: + + .. math:: + + \dot{q}_{0, des} &= v_{B,x} \cos(\theta) \\ + \dot{q}_{1, des} &= v_{B,x} \sin(\theta) \\ + \dot{q}_{2, des} &= \omega_{B,z} + + where :math:`\theta` is the yaw of the 2-D base. Since the base is simulated as a dummy joint, the yaw is directly + the value of the revolute joint along z, i.e., :math:`q_2 = \theta`. + + .. note:: + The current implementation assumes that the base is simulated with three dummy joints (prismatic joints along x + and y, and revolute joint along z). This is because it is easier to consider the mobile base as a floating link + controlled by three dummy joints, in comparison to simulating wheels which is at times is tricky because of + friction settings. + + However, the action term can be extended to support other base configurations as well. + + .. tip:: + For velocity control of the base with dummy mechanism, we recommend setting high damping gains to the joints. + This ensures that the base remains unperturbed from external disturbances, such as an arm mounted on the base. + """ + + cfg: actions_cfg.NonHolonomicActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor + """The scaling factor applied to the input action. Shape is (1, 2).""" + _offset: torch.Tensor + """The offset applied to the input action. Shape is (1, 2).""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + # parse the joint information + # -- x joint + x_joint_id, x_joint_name = self._asset.find_joints(self.cfg.x_joint_name) + if len(x_joint_id) != 1: + raise ValueError( + f"Expected a single joint match for the x joint name: {self.cfg.x_joint_name}, got {len(x_joint_id)}" + ) + # -- y joint + y_joint_id, y_joint_name = self._asset.find_joints(self.cfg.y_joint_name) + if len(y_joint_id) != 1: + raise ValueError(f"Found more than one joint match for the y joint name: {self.cfg.y_joint_name}") + # -- yaw joint + yaw_joint_id, yaw_joint_name = self._asset.find_joints(self.cfg.yaw_joint_name) + if len(yaw_joint_id) != 1: + raise ValueError(f"Found more than one joint match for the yaw joint name: {self.cfg.yaw_joint_name}") + # parse the body index + self._body_idx, self._body_name = self._asset.find_bodies(self.cfg.body_name) + if len(self._body_idx) != 1: + raise ValueError(f"Found more than one body match for the body name: {self.cfg.body_name}") + + # process into a list of joint ids + self._joint_ids = [x_joint_id[0], y_joint_id[0], yaw_joint_id[0]] + self._joint_names = [x_joint_name[0], y_joint_name[0], yaw_joint_name[0]] + # log info for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + logger.info( + f"Resolved body name for the action term {self.__class__.__name__}: {self._body_name} [{self._body_idx}]" + ) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + self._joint_vel_command = torch.zeros(self.num_envs, 3, device=self.device) + + # save the scale and offset as tensors + self._scale = torch.tensor(self.cfg.scale, device=self.device).unsqueeze(0) + self._offset = torch.tensor(self.cfg.offset, device=self.device).unsqueeze(0) + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return 2 + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the non-holonomic action. + It adds the following information to the base descriptor: + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + - body_name: The name of the body. + - x_joint_name: The name of the x joint. + - y_joint_name: The name of the y joint. + - yaw_joint_name: The name of the yaw joint. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "non holonomic actions" + self._IO_descriptor.scale = self._scale + self._IO_descriptor.offset = self._offset + self._IO_descriptor.clip = self._clip + self._IO_descriptor.body_name = self._body_name + self._IO_descriptor.x_joint_name = self._joint_names[0] + self._IO_descriptor.y_joint_name = self._joint_names[1] + self._IO_descriptor.yaw_joint_name = self._joint_names[2] + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions): + # store the raw actions + self._raw_actions[:] = actions + self._processed_actions = self.raw_actions * self._scale + self._offset + # clip actions + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def apply_actions(self): + # obtain current heading + quat_w = self._asset.data.body_quat_w[:, self._body_idx].view(self.num_envs, 4) + yaw_w = euler_xyz_from_quat(quat_w)[2] + # compute joint velocities targets + self._joint_vel_command[:, 0] = torch.cos(yaw_w) * self.processed_actions[:, 0] # x + self._joint_vel_command[:, 1] = torch.sin(yaw_w) * self.processed_actions[:, 0] # y + self._joint_vel_command[:, 2] = self.processed_actions[:, 1] # yaw + # set the joint velocity targets + self._asset.set_joint_velocity_target(self._joint_vel_command, joint_ids=self._joint_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/pink_actions_cfg.py b/source/isaaclab/isaaclab/envs/mdp/actions/pink_actions_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a82433be84cc32907487b99b3aafdac1fd205ab0 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/pink_actions_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.controllers.pink_ik import PinkIKControllerCfg +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.utils import configclass + +from . import pink_task_space_actions + + +@configclass +class PinkInverseKinematicsActionCfg(ActionTermCfg): + """Configuration for Pink inverse kinematics action term. + + This configuration is used to define settings for the Pink inverse kinematics action term, + which is a inverse kinematics framework. + """ + + class_type: type[ActionTerm] = pink_task_space_actions.PinkInverseKinematicsAction + """Specifies the action term class type for Pink inverse kinematics action.""" + + pink_controlled_joint_names: list[str] = MISSING + """List of joint names or regular expression patterns that specify the joints controlled by pink IK.""" + + hand_joint_names: list[str] = MISSING + """List of joint names or regular expression patterns that specify the joints controlled by hand retargeting.""" + + controller: PinkIKControllerCfg = MISSING + """Configuration for the Pink IK controller that will be used to solve the inverse kinematics.""" + + enable_gravity_compensation: bool = True + """Whether to compensate for gravity in the Pink IK controller.""" + + target_eef_link_names: dict[str, str] = MISSING + """Dictionary mapping task names to controlled link names for the Pink IK controller. + + This dictionary should map the task names (e.g., 'left_wrist', 'right_wrist') to the + corresponding link names in the URDF that will be controlled by the IK solver. + """ diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/pink_task_space_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/pink_task_space_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..f2284a5c5bb4a256d334f15d3f1ac05046293bf1 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/pink_task_space_actions.py @@ -0,0 +1,371 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from pink.tasks import FrameTask + +import isaaclab.utils.math as math_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.controllers.pink_ik import PinkIKController +from isaaclab.controllers.pink_ik.local_frame_task import LocalFrameTask +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import pink_actions_cfg + + +class PinkInverseKinematicsAction(ActionTerm): + r"""Pink Inverse Kinematics action term. + + This action term processes the action tensor and sets these setpoints in the pink IK framework. + The action tensor is ordered in the order of the tasks defined in PinkIKControllerCfg. + """ + + cfg: pink_actions_cfg.PinkInverseKinematicsActionCfg + """Configuration for the Pink Inverse Kinematics action term.""" + + _asset: Articulation + """The articulation asset to which the action term is applied.""" + + def __init__(self, cfg: pink_actions_cfg.PinkInverseKinematicsActionCfg, env: ManagerBasedEnv): + """Initialize the Pink Inverse Kinematics action term. + + Args: + cfg: The configuration for this action term. + env: The environment in which the action term will be applied. + """ + super().__init__(cfg, env) + + self._env = env + self._sim_dt = env.sim.get_physics_dt() + + # Initialize joint information + self._initialize_joint_info() + + # Initialize IK controllers + self._initialize_ik_controllers() + + # Initialize action tensors + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self._raw_actions) + + # PhysX Articulation Floating joint indices offset from IsaacLab Articulation joint indices + self._physx_floating_joint_indices_offset = 6 + + # Pre-allocate tensors for runtime use + self._initialize_helper_tensors() + + def _initialize_joint_info(self) -> None: + """Initialize joint IDs and names based on configuration.""" + # Resolve pink controlled joints + self._isaaclab_controlled_joint_ids, self._isaaclab_controlled_joint_names = self._asset.find_joints( + self.cfg.pink_controlled_joint_names + ) + self.cfg.controller.joint_names = self._isaaclab_controlled_joint_names + self._isaaclab_all_joint_ids = list(range(len(self._asset.data.joint_names))) + self.cfg.controller.all_joint_names = self._asset.data.joint_names + + # Resolve hand joints + self._hand_joint_ids, self._hand_joint_names = self._asset.find_joints(self.cfg.hand_joint_names) + + # Combine all joint information + self._controlled_joint_ids = self._isaaclab_controlled_joint_ids + self._hand_joint_ids + self._controlled_joint_names = self._isaaclab_controlled_joint_names + self._hand_joint_names + + def _initialize_ik_controllers(self) -> None: + """Initialize Pink IK controllers for all environments.""" + assert self._env.num_envs > 0, "Number of environments specified are less than 1." + + self._ik_controllers = [] + for _ in range(self._env.num_envs): + self._ik_controllers.append( + PinkIKController( + cfg=self.cfg.controller.copy(), + robot_cfg=self._env.scene.cfg.robot, + device=self.device, + controlled_joint_indices=self._isaaclab_controlled_joint_ids, + ) + ) + + def _initialize_helper_tensors(self) -> None: + """Pre-allocate tensors and cache values for performance optimization.""" + # Cache frequently used tensor versions of joint IDs to avoid repeated creation + self._controlled_joint_ids_tensor = torch.tensor(self._controlled_joint_ids, device=self.device) + + # Cache base link index to avoid string lookup every time + articulation_data = self._env.scene[self.cfg.controller.articulation_name].data + self._base_link_idx = articulation_data.body_names.index(self.cfg.controller.base_link_name) + + # Pre-allocate working tensors + # Count only FrameTask instances in variable_input_tasks (not all tasks) + num_frame_tasks = sum( + 1 for task in self._ik_controllers[0].cfg.variable_input_tasks if isinstance(task, FrameTask) + ) + self._num_frame_tasks = num_frame_tasks + self._controlled_frame_poses = torch.zeros(num_frame_tasks, self.num_envs, 4, 4, device=self.device) + + # Pre-allocate tensor for base frame computations + self._base_link_frame_buffer = torch.zeros(self.num_envs, 4, 4, device=self.device) + + # ==================== Properties ==================== + + @property + def hand_joint_dim(self) -> int: + """Dimension for hand joint positions.""" + return self.cfg.controller.num_hand_joints + + @property + def position_dim(self) -> int: + """Dimension for position (x, y, z).""" + return 3 + + @property + def orientation_dim(self) -> int: + """Dimension for orientation (w, x, y, z).""" + return 4 + + @property + def pose_dim(self) -> int: + """Total pose dimension (position + orientation).""" + return self.position_dim + self.orientation_dim + + @property + def action_dim(self) -> int: + """Dimension of the action space (based on number of tasks and pose dimension).""" + # Count only FrameTask instances in variable_input_tasks + frame_tasks_count = sum( + 1 for task in self._ik_controllers[0].cfg.variable_input_tasks if isinstance(task, FrameTask) + ) + return frame_tasks_count * self.pose_dim + self.hand_joint_dim + + @property + def raw_actions(self) -> torch.Tensor: + """Get the raw actions tensor.""" + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + """Get the processed actions tensor.""" + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the pink inverse kinematics action. + It adds the following information to the base descriptor: + - scale: The scale of the action term. + - offset: The offset of the action term. + - clip: The clip of the action term. + - pink_controller_joint_names: The names of the pink controller joints. + - hand_joint_names: The names of the hand joints. + - controller_cfg: The configuration of the pink controller. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "PinkInverseKinematicsAction" + self._IO_descriptor.pink_controller_joint_names = self._isaaclab_controlled_joint_names + self._IO_descriptor.hand_joint_names = self._hand_joint_names + self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller.__dict__ + return self._IO_descriptor + + # """ + # Operations. + # """ + + def process_actions(self, actions: torch.Tensor) -> None: + """Process the input actions and set targets for each task. + + Args: + actions: The input actions tensor. + """ + # Store raw actions + self._raw_actions[:] = actions + + # Extract hand joint positions directly (no cloning needed). + # Note: when hand_joint_dim == 0, slicing with -0 would return the full tensor + # (i.e. actions[:, 0:]), which would incorrectly treat task-space actions as hand joints. + if self.hand_joint_dim > 0: + self._target_hand_joint_positions = actions[:, -self.hand_joint_dim :] + else: + self._target_hand_joint_positions = actions[:, 0:0] + + # Get base link frame transformation + self.base_link_frame_in_world_rf = self._get_base_link_frame_transform() + + # Process controlled frame poses (pass original actions, no clone needed) + controlled_frame_poses = self._extract_controlled_frame_poses(actions) + transformed_poses = self._transform_poses_to_base_link_frame(controlled_frame_poses) + + # Set targets for all tasks + self._set_task_targets(transformed_poses) + + def _get_base_link_frame_transform(self) -> torch.Tensor: + """Get the base link frame transformation matrix. + + Returns: + Base link frame transformation matrix. + """ + # Get base link frame pose in world origin using cached index + articulation_data = self._env.scene[self.cfg.controller.articulation_name].data + base_link_frame_in_world_origin = articulation_data.body_link_state_w[:, self._base_link_idx, :7] + + # Transform to environment origin frame (reuse buffer to avoid allocation) + torch.sub( + base_link_frame_in_world_origin[:, :3], + self._env.scene.env_origins, + out=self._base_link_frame_buffer[:, :3, 3], + ) + + # Copy orientation (avoid clone) + base_link_frame_quat = base_link_frame_in_world_origin[:, 3:7] + + # Create transformation matrix + return math_utils.make_pose( + self._base_link_frame_buffer[:, :3, 3], math_utils.matrix_from_quat(base_link_frame_quat) + ) + + def _extract_controlled_frame_poses(self, actions: torch.Tensor) -> torch.Tensor: + """Extract controlled frame poses from action tensor. + + Args: + actions: The action tensor. + + Returns: + Stacked controlled frame poses tensor. + """ + # Use pre-allocated tensor instead of list operations + for task_index in range(self._num_frame_tasks): + # Extract position and orientation for this task + pos_start = task_index * self.pose_dim + pos_end = pos_start + self.position_dim + quat_start = pos_end + quat_end = (task_index + 1) * self.pose_dim + + position = actions[:, pos_start:pos_end] + quaternion = actions[:, quat_start:quat_end] + + # Create pose matrix directly into pre-allocated tensor + self._controlled_frame_poses[task_index] = math_utils.make_pose( + position, math_utils.matrix_from_quat(quaternion) + ) + + return self._controlled_frame_poses + + def _transform_poses_to_base_link_frame(self, poses: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Transform poses from world frame to base link frame. + + Args: + poses: Poses in world frame. + + Returns: + Tuple of (positions, rotation_matrices) in base link frame. + """ + # Transform poses to base link frame + base_link_inv = math_utils.pose_inv(self.base_link_frame_in_world_rf) + transformed_poses = math_utils.pose_in_A_to_pose_in_B(poses, base_link_inv) + + # Extract position and rotation + positions, rotation_matrices = math_utils.unmake_pose(transformed_poses) + + return positions, rotation_matrices + + def _set_task_targets(self, transformed_poses: tuple[torch.Tensor, torch.Tensor]) -> None: + """Set targets for all tasks across all environments. + + Args: + transformed_poses: Tuple of (positions, rotation_matrices) in base link frame. + """ + positions, rotation_matrices = transformed_poses + + for env_index, ik_controller in enumerate(self._ik_controllers): + for frame_task_index, task in enumerate(ik_controller.cfg.variable_input_tasks): + if isinstance(task, LocalFrameTask): + target = task.transform_target_to_base + elif isinstance(task, FrameTask): + target = task.transform_target_to_world + else: + continue + + # Set position and rotation targets using frame_task_index + target.translation = positions[frame_task_index, env_index, :].cpu().numpy() + target.rotation = rotation_matrices[frame_task_index, env_index, :].cpu().numpy() + + task.set_target(target) + + # ==================== Action Application ==================== + + def apply_actions(self) -> None: + """Apply the computed joint positions based on the inverse kinematics solution.""" + # Compute IK solutions for all environments + ik_joint_positions = self._compute_ik_solutions() + + # Combine IK and hand joint positions + all_joint_positions = torch.cat((ik_joint_positions, self._target_hand_joint_positions), dim=1) + self._processed_actions = all_joint_positions + + # Apply gravity compensation to arm joints + if self.cfg.enable_gravity_compensation: + self._apply_gravity_compensation() + + # Apply joint position targets + self._asset.set_joint_position_target(self._processed_actions, self._controlled_joint_ids) + + def _apply_gravity_compensation(self) -> None: + """Apply gravity compensation to arm joints if not disabled in props.""" + if not self._asset.cfg.spawn.rigid_props.disable_gravity: + # Get gravity compensation forces using cached tensor + if self._asset.is_fixed_base: + gravity = torch.zeros_like( + self._asset.root_physx_view.get_gravity_compensation_forces()[:, self._controlled_joint_ids_tensor] + ) + else: + # If floating base, then need to skip the first 6 joints (base) + gravity = self._asset.root_physx_view.get_gravity_compensation_forces()[ + :, self._controlled_joint_ids_tensor + self._physx_floating_joint_indices_offset + ] + + # Apply gravity compensation to arm joints + self._asset.set_joint_effort_target(gravity, self._controlled_joint_ids) + + def _compute_ik_solutions(self) -> torch.Tensor: + """Compute IK solutions for all environments. + + Returns: + IK joint positions tensor for all environments. + """ + ik_solutions = [] + + for env_index, ik_controller in enumerate(self._ik_controllers): + # Get current joint positions for this environment + current_joint_pos = self._asset.data.joint_pos.cpu().numpy()[env_index] + + # Compute IK solution + joint_pos_des = ik_controller.compute(current_joint_pos, self._sim_dt) + ik_solutions.append(joint_pos_des) + + return torch.stack(ik_solutions) + + # ==================== Reset ==================== + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + """Reset the action term for specified environments. + + Args: + env_ids: A list of environment IDs to reset. If None, all environments are reset. + """ + self._raw_actions[env_ids] = torch.zeros(self.action_dim, device=self.device) diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_actions_cfg.py b/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_actions_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..dfd7a72dcb734c16df538f993b45d2a7618fa225 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_actions_cfg.py @@ -0,0 +1,52 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from dataclasses import MISSING + +from isaaclab.controllers.rmp_flow import RmpFlowControllerCfg +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.utils import configclass + +from . import rmpflow_task_space_actions + + +@configclass +class RMPFlowActionCfg(ActionTermCfg): + @configclass + class OffsetCfg: + """The offset pose from parent frame to child frame. + + On many robots, end-effector frames are fictitious frames that do not have a corresponding + rigid body. In such cases, it is easier to define this transform w.r.t. their parent rigid body. + For instance, for the Franka Emika arm, the end-effector is defined at an offset to the the + "panda_hand" frame. + """ + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + + class_type: type[ActionTerm] = rmpflow_task_space_actions.RMPFlowAction + + joint_names: list[str] = MISSING + """List of joint names or regex expressions that the action will be mapped to.""" + body_name: str = MISSING + """Name of the body or frame for which IK is performed.""" + body_offset: OffsetCfg | None = None + """Offset of target frame w.r.t. to the body frame. Defaults to None, in which case no offset is applied.""" + scale: float | tuple[float, ...] = 1.0 + + controller: RmpFlowControllerCfg = MISSING + + articulation_prim_expr: str = MISSING # The expression to find the articulation prim paths. + """The configuration for the RMPFlow controller.""" + + use_relative_mode: bool = False + """ + Defaults to False. + If True, then the controller treats the input command as a delta change in the position/pose. + Otherwise, the controller treats the input command as the absolute position/pose. + """ diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_task_space_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_task_space_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..83e7bd3e365417c27c56f46c60df4067798c19e6 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/rmpflow_task_space_actions.py @@ -0,0 +1,218 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.controllers.rmp_flow import RmpFlowController +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from . import rmpflow_actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class RMPFlowAction(ActionTerm): + """RMPFlow task space action term.""" + + cfg: rmpflow_actions_cfg.RMPFlowActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor + """The scaling factor applied to the input action. Shape is (1, action_dim).""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: rmpflow_actions_cfg.RMPFlowActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names) + self._num_joints = len(self._joint_ids) + # parse the body index + body_ids, body_names = self._asset.find_bodies(self.cfg.body_name) + if len(body_ids) != 1: + raise ValueError( + f"Expected one match for the body name: {self.cfg.body_name}. Found {len(body_ids)}: {body_names}." + ) + # save only the first body index + self._body_idx = body_ids[0] + self._body_name = body_names[0] + + # check if articulation is fixed-base + # if fixed-base then the jacobian for the base is not computed + # this means that number of bodies is one less than the articulation's number of bodies + if self._asset.is_fixed_base: + self._jacobi_body_idx = self._body_idx - 1 + self._jacobi_joint_ids = self._joint_ids + else: + self._jacobi_body_idx = self._body_idx + self._jacobi_joint_ids = [i + 6 for i in self._joint_ids] + + # log info for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + logger.info( + f"Resolved body name for the action term {self.__class__.__name__}: {self._body_name} [{self._body_idx}]" + ) + # Avoid indexing across all joints for efficiency + if self._num_joints == self._asset.num_joints: + self._joint_ids = slice(None) + + # create the differential IK controller + self._rmpflow_controller = RmpFlowController(cfg=self.cfg.controller, device=self.device) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # save the scale as tensors + self._scale = torch.zeros((self.num_envs, self.action_dim), device=self.device) + self._scale[:] = torch.tensor(self.cfg.scale, device=self.device) + + # convert the fixed offsets to torch tensors of batched shape + if self.cfg.body_offset is not None: + self._offset_pos = torch.tensor(self.cfg.body_offset.pos, device=self.device).repeat(self.num_envs, 1) + self._offset_rot = torch.tensor(self.cfg.body_offset.rot, device=self.device).repeat(self.num_envs, 1) + else: + self._offset_pos, self._offset_rot = None, None + + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + if self.cfg.use_relative_mode: + return 6 # delta_eef_xyz, delta_eef_rpy + else: + return 7 # absolute_eef_xyz, absolute_eef_quat + # self._rmpflow_controller.num_actions = 7 since it use quaternions (w,x,y,z) as command + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def jacobian_w(self) -> torch.Tensor: + return self._asset.root_physx_view.get_jacobians()[:, self._jacobi_body_idx, :, self._jacobi_joint_ids] + + @property + def jacobian_b(self) -> torch.Tensor: + jacobian = self.jacobian_w + base_rot = self._asset.data.root_quat_w + base_rot_matrix = math_utils.matrix_from_quat(math_utils.quat_inv(base_rot)) + jacobian[:, :3, :] = torch.bmm(base_rot_matrix, jacobian[:, :3, :]) + jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :]) + return jacobian + + """ + Operations. + """ + + # This is called each env.step() + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + self._processed_actions[:] = self.raw_actions * self._scale + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + # If use_relative_mode is True, then the controller will apply delta change to the current ee_pose. + if self.cfg.use_relative_mode: + # obtain quantities from simulation + ee_pos_curr, ee_quat_curr = self._compute_frame_pose() + + # compute ee_pose_targets use_relative_actions + if ee_pos_curr is None or ee_quat_curr is None: + raise ValueError( + "Neither end-effector position nor orientation can be None for `pose_rel` command type!" + ) + self.ee_pos_des, self.ee_quat_des = math_utils.apply_delta_pose( + ee_pos_curr, ee_quat_curr, self._processed_actions + ) + else: # If use_relative_mode is False, then the controller will apply absolute ee_pose. + self.ee_pos_des = self._processed_actions[:, 0:3] + self.ee_quat_des = self._processed_actions[:, 3:7] + + self.ee_pose_des = torch.cat([self.ee_pos_des, self.ee_quat_des], dim=1) # shape: [n, 7] + + # set command into controller + self._rmpflow_controller.set_command(self.ee_pose_des) + + # This is called each simulationcontext.step(), step *decimation* times when env.step() update actions + def apply_actions(self): + # obtain quantities from simulation + ee_pos_curr, ee_quat_curr = self._compute_frame_pose() + joint_pos = self._asset.data.joint_pos[:, self._joint_ids] + # compute the delta in joint-space + if ee_quat_curr.norm() != 0: + joint_pos_des, joint_vel_des = self._rmpflow_controller.compute() + else: + joint_pos_des = joint_pos.clone() + # set the joint position command + self._asset.set_joint_position_target(joint_pos_des, self._joint_ids) + self._asset.set_joint_velocity_target(joint_vel_des, self._joint_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 + self._rmpflow_controller.initialize(self.cfg.articulation_prim_expr) + + """ + Helper functions. + """ + + def _compute_frame_pose(self) -> tuple[torch.Tensor, torch.Tensor]: + """Computes the pose of the target frame in the root frame. + + Returns: + A tuple of the body's position and orientation in the root frame. + """ + # obtain quantities from simulation + ee_pos_w = self._asset.data.body_pos_w[:, self._body_idx] + ee_quat_w = self._asset.data.body_quat_w[:, self._body_idx] + root_pos_w = self._asset.data.root_pos_w + root_quat_w = self._asset.data.root_quat_w + # compute the pose of the body in the root frame + ee_pose_b, ee_quat_b = math_utils.subtract_frame_transforms(root_pos_w, root_quat_w, ee_pos_w, ee_quat_w) + # account for the offset + if self.cfg.body_offset is not None: + ee_pose_b, ee_quat_b = math_utils.combine_frame_transforms( + ee_pose_b, ee_quat_b, self._offset_pos, self._offset_rot + ) + + return ee_pose_b, ee_quat_b diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/surface_gripper_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/surface_gripper_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..f16d1403853b17a0825e9253f8bdb4428153a171 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/surface_gripper_actions.py @@ -0,0 +1,109 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets.surface_gripper import SurfaceGripper +from isaaclab.managers.action_manager import ActionTerm + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class SurfaceGripperBinaryAction(ActionTerm): + """Surface gripper binary action. + + This action term maps a binary action to the *open* or *close* surface gripper configurations. + The surface gripper behavior is as follows: + - [-1, -0.3] --> Gripper is Opening + - [-0.3, 0.3] --> Gripper is Idle (do nothing) + - [0.3, 1] --> Gripper is Closing + + Based on above, we follow the following convention for the binary action: + + 1. Open action: 1 (bool) or positive values (float). + 2. Close action: 0 (bool) or negative values (float). + + The action term is specifically designed for surface grippers, which use a different + interface than joint-based grippers. + """ + + cfg: actions_cfg.SurfaceGripperBinaryActionCfg + """The configuration of the action term.""" + _asset: SurfaceGripper + """The surface gripper asset on which the action term is applied.""" + + def __init__(self, cfg: actions_cfg.SurfaceGripperBinaryActionCfg, env: ManagerBasedEnv) -> None: + # initialize the action term + super().__init__(cfg, env) + + # log the resolved asset name for debugging + logger.info( + f"Resolved surface gripper asset for the action term {self.__class__.__name__}: {self.cfg.asset_name}" + ) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, 1, device=self.device) + self._processed_actions = torch.zeros(self.num_envs, 1, device=self.device) + + # parse open command + self._open_command = torch.tensor(self.cfg.open_command, device=self.device) + # parse close command + self._close_command = torch.tensor(self.cfg.close_command, device=self.device) + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return 1 + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # compute the binary mask + if actions.dtype == torch.bool: + # true: close, false: open + binary_mask = actions == 0 + else: + # true: close, false: open + binary_mask = actions < 0 + # compute the command + self._processed_actions = torch.where(binary_mask, self._close_command, self._open_command) + + def apply_actions(self): + """Apply the processed actions to the surface gripper.""" + self._asset.set_grippers_command(self._processed_actions.view(-1)) + self._asset.write_data_to_sim() + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + if env_ids is None: + self._raw_actions[:] = 0.0 + else: + self._raw_actions[env_ids] = 0.0 diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/task_space_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/task_space_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..47f9cec23490d82046abdb5b16a813f140c7f350 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/actions/task_space_actions.py @@ -0,0 +1,781 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from pxr import UsdPhysics + +import isaaclab.utils.math as math_utils +import isaaclab.utils.string as string_utils +from isaaclab.assets.articulation import Articulation +from isaaclab.controllers.differential_ik import DifferentialIKController +from isaaclab.controllers.operational_space import OperationalSpaceController +from isaaclab.managers.action_manager import ActionTerm +from isaaclab.sensors import ContactSensor, ContactSensorCfg, FrameTransformer, FrameTransformerCfg +from isaaclab.sim.utils import find_matching_prims + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class DifferentialInverseKinematicsAction(ActionTerm): + r"""Inverse Kinematics action term. + + This action term performs pre-processing of the raw actions using scaling transformation. + + .. math:: + \text{action} = \text{scaling} \times \text{input action} + \text{joint position} = J^{-} \times \text{action} + + where :math:`\text{scaling}` is the scaling applied to the input action, and :math:`\text{input action}` + is the input action from the user, :math:`J` is the Jacobian over the articulation's actuated joints, + and \text{joint position} is the desired joint position command for the articulation's joints. + """ + + cfg: actions_cfg.DifferentialInverseKinematicsActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor + """The scaling factor applied to the input action. Shape is (1, action_dim).""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: actions_cfg.DifferentialInverseKinematicsActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names) + self._num_joints = len(self._joint_ids) + # parse the body index + body_ids, body_names = self._asset.find_bodies(self.cfg.body_name) + if len(body_ids) != 1: + raise ValueError( + f"Expected one match for the body name: {self.cfg.body_name}. Found {len(body_ids)}: {body_names}." + ) + # save only the first body index + self._body_idx = body_ids[0] + self._body_name = body_names[0] + # check if articulation is fixed-base + # if fixed-base then the jacobian for the base is not computed + # this means that number of bodies is one less than the articulation's number of bodies + if self._asset.is_fixed_base: + self._jacobi_body_idx = self._body_idx - 1 + self._jacobi_joint_ids = self._joint_ids + else: + self._jacobi_body_idx = self._body_idx + self._jacobi_joint_ids = [i + 6 for i in self._joint_ids] + + # log info for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + logger.info( + f"Resolved body name for the action term {self.__class__.__name__}: {self._body_name} [{self._body_idx}]" + ) + # Avoid indexing across all joints for efficiency + if self._num_joints == self._asset.num_joints: + self._joint_ids = slice(None) + + # create the differential IK controller + self._ik_controller = DifferentialIKController( + cfg=self.cfg.controller, num_envs=self.num_envs, device=self.device + ) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # save the scale as tensors + self._scale = torch.zeros((self.num_envs, self.action_dim), device=self.device) + self._scale[:] = torch.tensor(self.cfg.scale, device=self.device) + + # convert the fixed offsets to torch tensors of batched shape + if self.cfg.body_offset is not None: + self._offset_pos = torch.tensor(self.cfg.body_offset.pos, device=self.device).repeat(self.num_envs, 1) + self._offset_rot = torch.tensor(self.cfg.body_offset.rot, device=self.device).repeat(self.num_envs, 1) + else: + self._offset_pos, self._offset_rot = None, None + + # parse clip + if self.cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._ik_controller.action_dim + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def jacobian_w(self) -> torch.Tensor: + return self._asset.root_physx_view.get_jacobians()[:, self._jacobi_body_idx, :, self._jacobi_joint_ids] + + @property + def jacobian_b(self) -> torch.Tensor: + jacobian = self.jacobian_w + base_rot = self._asset.data.root_quat_w + base_rot_matrix = math_utils.matrix_from_quat(math_utils.quat_inv(base_rot)) + jacobian[:, :3, :] = torch.bmm(base_rot_matrix, jacobian[:, :3, :]) + jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :]) + return jacobian + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the pink inverse kinematics action. + It adds the following information to the base descriptor: + - body_name: The name of the body. + - joint_names: The names of the joints. + - scale: The scale of the action term. + - clip: The clip of the action term. + - controller_cfg: The configuration of the controller. + - body_offset: The offset of the body. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "TaskSpaceAction" + self._IO_descriptor.body_name = self._body_name + self._IO_descriptor.joint_names = self._joint_names + self._IO_descriptor.scale = self._scale + if self.cfg.clip is not None: + self._IO_descriptor.clip = self.cfg.clip + else: + self._IO_descriptor.clip = None + self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller.__dict__ + self._IO_descriptor.extras["body_offset"] = self.cfg.body_offset.__dict__ + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + self._processed_actions[:] = self.raw_actions * self._scale + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + # obtain quantities from simulation + ee_pos_curr, ee_quat_curr = self._compute_frame_pose() + # set command into controller + self._ik_controller.set_command(self._processed_actions, ee_pos_curr, ee_quat_curr) + + def apply_actions(self): + # obtain quantities from simulation + ee_pos_curr, ee_quat_curr = self._compute_frame_pose() + joint_pos = self._asset.data.joint_pos[:, self._joint_ids] + # compute the delta in joint-space + if ee_quat_curr.norm() != 0: + jacobian = self._compute_frame_jacobian() + joint_pos_des = self._ik_controller.compute(ee_pos_curr, ee_quat_curr, jacobian, joint_pos) + else: + joint_pos_des = joint_pos.clone() + # set the joint position command + self._asset.set_joint_position_target(joint_pos_des, self._joint_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + self._raw_actions[env_ids] = 0.0 + + """ + Helper functions. + """ + + def _compute_frame_pose(self) -> tuple[torch.Tensor, torch.Tensor]: + """Computes the pose of the target frame in the root frame. + + Returns: + A tuple of the body's position and orientation in the root frame. + """ + # obtain quantities from simulation + ee_pos_w = self._asset.data.body_pos_w[:, self._body_idx] + ee_quat_w = self._asset.data.body_quat_w[:, self._body_idx] + root_pos_w = self._asset.data.root_pos_w + root_quat_w = self._asset.data.root_quat_w + # compute the pose of the body in the root frame + ee_pose_b, ee_quat_b = math_utils.subtract_frame_transforms(root_pos_w, root_quat_w, ee_pos_w, ee_quat_w) + # account for the offset + if self.cfg.body_offset is not None: + ee_pose_b, ee_quat_b = math_utils.combine_frame_transforms( + ee_pose_b, ee_quat_b, self._offset_pos, self._offset_rot + ) + + return ee_pose_b, ee_quat_b + + def _compute_frame_jacobian(self): + """Computes the geometric Jacobian of the target frame in the root frame. + + This function accounts for the target frame offset and applies the necessary transformations to obtain + the right Jacobian from the parent body Jacobian. + """ + # read the parent jacobian + jacobian = self.jacobian_b + # account for the offset + if self.cfg.body_offset is not None: + # Modify the jacobian to account for the offset + # -- translational part + # v_link = v_ee + w_ee x r_link_ee = v_J_ee * q + w_J_ee * q x r_link_ee + # = (v_J_ee + w_J_ee x r_link_ee ) * q + # = (v_J_ee - r_link_ee_[x] @ w_J_ee) * q + jacobian[:, 0:3, :] += torch.bmm(-math_utils.skew_symmetric_matrix(self._offset_pos), jacobian[:, 3:, :]) + # -- rotational part + # w_link = R_link_ee @ w_ee + jacobian[:, 3:, :] = torch.bmm(math_utils.matrix_from_quat(self._offset_rot), jacobian[:, 3:, :]) + + return jacobian + + +class OperationalSpaceControllerAction(ActionTerm): + r"""Operational space controller action term. + + This action term performs pre-processing of the raw actions for operational space control. + + """ + + cfg: actions_cfg.OperationalSpaceControllerActionCfg + """The configuration of the action term.""" + _asset: Articulation + """The articulation asset on which the action term is applied.""" + _contact_sensor: ContactSensor = None + """The contact sensor for the end-effector body.""" + _task_frame_transformer: FrameTransformer = None + """The frame transformer for the task frame.""" + + def __init__(self, cfg: actions_cfg.OperationalSpaceControllerActionCfg, env: ManagerBasedEnv): + # initialize the action term + super().__init__(cfg, env) + + self._sim_dt = env.sim.get_physics_dt() + + # resolve the joints over which the action term is applied + self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names) + self._num_DoF = len(self._joint_ids) + # parse the ee body index + body_ids, body_names = self._asset.find_bodies(self.cfg.body_name) + if len(body_ids) != 1: + raise ValueError( + f"Expected one match for the ee body name: {self.cfg.body_name}. Found {len(body_ids)}: {body_names}." + ) + # save only the first ee body index + self._ee_body_idx = body_ids[0] + self._ee_body_name = body_names[0] + # check if articulation is fixed-base + # if fixed-base then the jacobian for the base is not computed + # this means that number of bodies is one less than the articulation's number of bodies + if self._asset.is_fixed_base: + self._jacobi_ee_body_idx = self._ee_body_idx - 1 + self._jacobi_joint_idx = self._joint_ids + else: + self._jacobi_ee_body_idx = self._ee_body_idx + self._jacobi_joint_idx = [i + 6 for i in self._joint_ids] + + # log info for debugging + logger.info( + f"Resolved joint names for the action term {self.__class__.__name__}:" + f" {self._joint_names} [{self._joint_ids}]" + ) + logger.info( + f"Resolved ee body name for the action term {self.__class__.__name__}:" + f" {self._ee_body_name} [{self._ee_body_idx}]" + ) + # Avoid indexing across all joints for efficiency + if self._num_DoF == self._asset.num_joints: + self._joint_ids = slice(None) + + # convert the fixed offsets to torch tensors of batched shape + if self.cfg.body_offset is not None: + self._offset_pos = torch.tensor(self.cfg.body_offset.pos, device=self.device).repeat(self.num_envs, 1) + self._offset_rot = torch.tensor(self.cfg.body_offset.rot, device=self.device).repeat(self.num_envs, 1) + else: + self._offset_pos, self._offset_rot = None, None + + # create contact sensor if any of the command is wrench_abs, and if stiffness is provided + if ( + "wrench_abs" in self.cfg.controller_cfg.target_types + and self.cfg.controller_cfg.contact_wrench_stiffness_task is not None + ): + self._contact_sensor_cfg = ContactSensorCfg(prim_path=self._asset.cfg.prim_path + "/" + self._ee_body_name) + self._contact_sensor = ContactSensor(self._contact_sensor_cfg) + if not self._contact_sensor.is_initialized: + self._contact_sensor._initialize_impl() + self._contact_sensor._is_initialized = True + + # Initialize the task frame transformer if a relative path for the RigidObject, representing the task frame, + # is provided. + if self.cfg.task_frame_rel_path is not None: + # The source RigidObject can be any child of the articulation asset (we will not use it), + # hence, we will use the first RigidObject child. + root_rigidbody_path = self._first_RigidObject_child_path() + task_frame_transformer_path = "/World/envs/env_.*/" + self.cfg.task_frame_rel_path + task_frame_transformer_cfg = FrameTransformerCfg( + prim_path=root_rigidbody_path, + target_frames=[ + FrameTransformerCfg.FrameCfg( + name="task_frame", + prim_path=task_frame_transformer_path, + ), + ], + ) + self._task_frame_transformer = FrameTransformer(task_frame_transformer_cfg) + if not self._task_frame_transformer.is_initialized: + self._task_frame_transformer._initialize_impl() + self._task_frame_transformer._is_initialized = True + # create tensor for task frame pose in the root frame + self._task_frame_pose_b = torch.zeros(self.num_envs, 7, device=self.device) + else: + # create an empty reference for task frame pose + self._task_frame_pose_b = None + + # create the operational space controller + self._osc = OperationalSpaceController(cfg=self.cfg.controller_cfg, num_envs=self.num_envs, device=self.device) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # create tensors for the dynamic-related quantities + self._jacobian_b = torch.zeros(self.num_envs, 6, self._num_DoF, device=self.device) + self._mass_matrix = torch.zeros(self.num_envs, self._num_DoF, self._num_DoF, device=self.device) + self._gravity = torch.zeros(self.num_envs, self._num_DoF, device=self.device) + + # create tensors for the ee states + self._ee_pose_w = torch.zeros(self.num_envs, 7, device=self.device) + self._ee_pose_b = torch.zeros(self.num_envs, 7, device=self.device) + self._ee_pose_b_no_offset = torch.zeros(self.num_envs, 7, device=self.device) # The original ee without offset + self._ee_vel_w = torch.zeros(self.num_envs, 6, device=self.device) + self._ee_vel_b = torch.zeros(self.num_envs, 6, device=self.device) + self._ee_force_w = torch.zeros(self.num_envs, 3, device=self.device) # Only the forces are used for now + self._ee_force_b = torch.zeros(self.num_envs, 3, device=self.device) # Only the forces are used for now + + # create tensors for the joint states + self._joint_pos = torch.zeros(self.num_envs, self._num_DoF, device=self.device) + self._joint_vel = torch.zeros(self.num_envs, self._num_DoF, device=self.device) + + # create the joint effort tensor + self._joint_efforts = torch.zeros(self.num_envs, self._num_DoF, device=self.device) + + # save the scale as tensors + self._position_scale = torch.tensor(self.cfg.position_scale, device=self.device) + self._orientation_scale = torch.tensor(self.cfg.orientation_scale, device=self.device) + self._wrench_scale = torch.tensor(self.cfg.wrench_scale, device=self.device) + self._stiffness_scale = torch.tensor(self.cfg.stiffness_scale, device=self.device) + self._damping_ratio_scale = torch.tensor(self.cfg.damping_ratio_scale, device=self.device) + + # indexes for the various command elements (e.g., pose_rel, stifness, etc.) within the command tensor + self._pose_abs_idx = None + self._pose_rel_idx = None + self._wrench_abs_idx = None + self._stiffness_idx = None + self._damping_ratio_idx = None + self._resolve_command_indexes() + + # Nullspace position control joint targets + self._nullspace_joint_pos_target = None + self._resolve_nullspace_joint_pos_targets() + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + """Dimension of the action space of operational space control.""" + return self._osc.action_dim + + @property + def raw_actions(self) -> torch.Tensor: + """Raw actions for operational space control.""" + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + """Processed actions for operational space control.""" + return self._processed_actions + + @property + def jacobian_w(self) -> torch.Tensor: + return self._asset.root_physx_view.get_jacobians()[:, self._jacobi_ee_body_idx, :, self._jacobi_joint_idx] + + @property + def jacobian_b(self) -> torch.Tensor: + jacobian = self.jacobian_w + base_rot = self._asset.data.root_quat_w + base_rot_matrix = math_utils.matrix_from_quat(math_utils.quat_inv(base_rot)) + jacobian[:, :3, :] = torch.bmm(base_rot_matrix, jacobian[:, :3, :]) + jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :]) + return jacobian + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term. + + This descriptor is used to describe the action term of the pink inverse kinematics action. + It adds the following information to the base descriptor: + - body_name: The name of the body. + - joint_names: The names of the joints. + - position_scale: The scale of the position. + - orientation_scale: The scale of the orientation. + - wrench_scale: The scale of the wrench. + - stiffness_scale: The scale of the stiffness. + - damping_ratio_scale: The scale of the damping ratio. + - nullspace_joint_pos_target: The nullspace joint pos target. + - clip: The clip of the action term. + - controller_cfg: The configuration of the controller. + - body_offset: The offset of the body. + + Returns: + The IO descriptor of the action term. + """ + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "TaskSpaceAction" + self._IO_descriptor.body_name = self._ee_body_name + self._IO_descriptor.joint_names = self._joint_names + self._IO_descriptor.position_scale = self.cfg.position_scale + self._IO_descriptor.orientation_scale = self.cfg.orientation_scale + self._IO_descriptor.wrench_scale = self.cfg.wrench_scale + self._IO_descriptor.stiffness_scale = self.cfg.stiffness_scale + self._IO_descriptor.damping_ratio_scale = self.cfg.damping_ratio_scale + self._IO_descriptor.nullspace_joint_pos_target = self.cfg.nullspace_joint_pos_target + if self.cfg.clip is not None: + self._IO_descriptor.clip = self.cfg.clip + else: + self._IO_descriptor.clip = None + self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller_cfg.__dict__ + self._IO_descriptor.extras["body_offset"] = self.cfg.body_offset.__dict__ + return self._IO_descriptor + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + """Pre-processes the raw actions and sets them as commands for for operational space control. + + Args: + actions (torch.Tensor): The raw actions for operational space control. It is a tensor of + shape (``num_envs``, ``action_dim``). + """ + + # Update ee pose, which would be used by relative targets (i.e., pose_rel) + self._compute_ee_pose() + + # Update task frame pose w.r.t. the root frame. + self._compute_task_frame_pose() + + # Pre-process the raw actions for operational space control. + self._preprocess_actions(actions) + + # set command into controller + self._osc.set_command( + command=self._processed_actions, + current_ee_pose_b=self._ee_pose_b, + current_task_frame_pose_b=self._task_frame_pose_b, + ) + + def apply_actions(self): + """Computes the joint efforts for operational space control and applies them to the articulation.""" + + # Update the relevant states and dynamical quantities + self._compute_dynamic_quantities() + self._compute_ee_jacobian() + self._compute_ee_pose() + self._compute_ee_velocity() + self._compute_ee_force() + self._compute_joint_states() + # Calculate the joint efforts + self._joint_efforts[:] = self._osc.compute( + jacobian_b=self._jacobian_b, + current_ee_pose_b=self._ee_pose_b, + current_ee_vel_b=self._ee_vel_b, + current_ee_force_b=self._ee_force_b, + mass_matrix=self._mass_matrix, + gravity=self._gravity, + current_joint_pos=self._joint_pos, + current_joint_vel=self._joint_vel, + nullspace_joint_pos_target=self._nullspace_joint_pos_target, + ) + self._asset.set_joint_effort_target(self._joint_efforts, joint_ids=self._joint_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + """Resets the raw actions and the sensors if available. + + Args: + env_ids (Sequence[int] | None): The environment indices to reset. If ``None``, all environments are reset. + """ + self._raw_actions[env_ids] = 0.0 + if self._contact_sensor is not None: + self._contact_sensor.reset(env_ids) + if self._task_frame_transformer is not None: + self._task_frame_transformer.reset(env_ids) + + """ + Helper functions. + + """ + + def _first_RigidObject_child_path(self): + """Finds the first ``RigidObject`` child under the articulation asset. + + Raises: + ValueError: If no child ``RigidObject`` is found under the articulation asset. + + Returns: + str: The path to the first ``RigidObject`` child under the articulation asset. + """ + child_prims = find_matching_prims(self._asset.cfg.prim_path + "/.*") + rigid_child_prim = None + # Loop through the list and stop at the first RigidObject found + for prim in child_prims: + if prim.HasAPI(UsdPhysics.RigidBodyAPI): + rigid_child_prim = prim + break + if rigid_child_prim is None: + raise ValueError("No child rigid body found under the expression: '{self._asset.cfg.prim_path}'/.") + rigid_child_prim_path = rigid_child_prim.GetPath().pathString + # Remove the specific env index from the path string + rigid_child_prim_path = self._asset.cfg.prim_path + "/" + rigid_child_prim_path.split("/")[-1] + return rigid_child_prim_path + + def _resolve_command_indexes(self): + """Resolves the indexes for the various command elements within the command tensor. + + Raises: + ValueError: If any command index is left unresolved. + """ + # First iterate over the target types to find the indexes of the different command elements + cmd_idx = 0 + for target_type in self.cfg.controller_cfg.target_types: + if target_type == "pose_abs": + self._pose_abs_idx = cmd_idx + cmd_idx += 7 + elif target_type == "pose_rel": + self._pose_rel_idx = cmd_idx + cmd_idx += 6 + elif target_type == "wrench_abs": + self._wrench_abs_idx = cmd_idx + cmd_idx += 6 + else: + raise ValueError("Undefined target_type for OSC within OperationalSpaceControllerAction.") + # Then iterate over the impedance parameters depending on the impedance mode + if ( + self.cfg.controller_cfg.impedance_mode == "variable_kp" + or self.cfg.controller_cfg.impedance_mode == "variable" + ): + self._stiffness_idx = cmd_idx + cmd_idx += 6 + if self.cfg.controller_cfg.impedance_mode == "variable": + self._damping_ratio_idx = cmd_idx + cmd_idx += 6 + + # Check if any command is left unresolved + if self.action_dim != cmd_idx: + raise ValueError("Not all command indexes have been resolved.") + + def _resolve_nullspace_joint_pos_targets(self): + """Resolves the nullspace joint pos targets for the operational space controller. + + Raises: + ValueError: If the nullspace joint pos targets are set when null space control is not set to 'position'. + ValueError: If the nullspace joint pos targets are not set when null space control is set to 'position'. + ValueError: If an invalid value is set for nullspace joint pos targets. + """ + + if self.cfg.nullspace_joint_pos_target != "none" and self.cfg.controller_cfg.nullspace_control != "position": + raise ValueError("Nullspace joint targets can only be set when null space control is set to 'position'.") + + if self.cfg.nullspace_joint_pos_target == "none" and self.cfg.controller_cfg.nullspace_control == "position": + raise ValueError("Nullspace joint targets must be set when null space control is set to 'position'.") + + if self.cfg.nullspace_joint_pos_target == "zero" or self.cfg.nullspace_joint_pos_target == "none": + # Keep the nullspace joint targets as None as this is later processed as zero in the controller + self._nullspace_joint_pos_target = None + elif self.cfg.nullspace_joint_pos_target == "center": + # Get the center of the robot soft joint limits + self._nullspace_joint_pos_target = torch.mean( + self._asset.data.soft_joint_pos_limits[:, self._joint_ids, :], dim=-1 + ) + elif self.cfg.nullspace_joint_pos_target == "default": + # Get the default joint positions + self._nullspace_joint_pos_target = self._asset.data.default_joint_pos[:, self._joint_ids] + else: + raise ValueError("Invalid value for nullspace joint pos targets.") + + def _compute_dynamic_quantities(self): + """Computes the dynamic quantities for operational space control.""" + + self._mass_matrix[:] = self._asset.root_physx_view.get_generalized_mass_matrices()[:, self._joint_ids, :][ + :, :, self._joint_ids + ] + self._gravity[:] = self._asset.root_physx_view.get_gravity_compensation_forces()[:, self._joint_ids] + + def _compute_ee_jacobian(self): + """Computes the geometric Jacobian of the ee body frame in root frame. + + This function accounts for the target frame offset and applies the necessary transformations to obtain + the right Jacobian from the parent body Jacobian. + """ + # Get the Jacobian in root frame + self._jacobian_b[:] = self.jacobian_b + + # account for the offset + if self.cfg.body_offset is not None: + # Modify the jacobian to account for the offset + # -- translational part + # v_link = v_ee + w_ee x r_link_ee = v_J_ee * q + w_J_ee * q x r_link_ee + # = (v_J_ee + w_J_ee x r_link_ee ) * q + # = (v_J_ee - r_link_ee_[x] @ w_J_ee) * q + self._jacobian_b[:, 0:3, :] += torch.bmm( + -math_utils.skew_symmetric_matrix(self._offset_pos), self._jacobian_b[:, 3:, :] + ) # type: ignore + # -- rotational part + # w_link = R_link_ee @ w_ee + self._jacobian_b[:, 3:, :] = torch.bmm( + math_utils.matrix_from_quat(self._offset_rot), self._jacobian_b[:, 3:, :] + ) # type: ignore + + def _compute_ee_pose(self): + """Computes the pose of the ee frame in root frame.""" + # Obtain quantities from simulation + self._ee_pose_w[:, 0:3] = self._asset.data.body_pos_w[:, self._ee_body_idx] + self._ee_pose_w[:, 3:7] = self._asset.data.body_quat_w[:, self._ee_body_idx] + # Compute the pose of the ee body in the root frame + self._ee_pose_b_no_offset[:, 0:3], self._ee_pose_b_no_offset[:, 3:7] = math_utils.subtract_frame_transforms( + self._asset.data.root_pos_w, + self._asset.data.root_quat_w, + self._ee_pose_w[:, 0:3], + self._ee_pose_w[:, 3:7], + ) + # Account for the offset + if self.cfg.body_offset is not None: + self._ee_pose_b[:, 0:3], self._ee_pose_b[:, 3:7] = math_utils.combine_frame_transforms( + self._ee_pose_b_no_offset[:, 0:3], self._ee_pose_b_no_offset[:, 3:7], self._offset_pos, self._offset_rot + ) + else: + self._ee_pose_b[:] = self._ee_pose_b_no_offset + + def _compute_ee_velocity(self): + """Computes the velocity of the ee frame in root frame.""" + # Extract end-effector velocity in the world frame + self._ee_vel_w[:] = self._asset.data.body_vel_w[:, self._ee_body_idx, :] + # Compute the relative velocity in the world frame + relative_vel_w = self._ee_vel_w - self._asset.data.root_vel_w + + # Convert ee velocities from world to root frame + self._ee_vel_b[:, 0:3] = math_utils.quat_apply_inverse(self._asset.data.root_quat_w, relative_vel_w[:, 0:3]) + self._ee_vel_b[:, 3:6] = math_utils.quat_apply_inverse(self._asset.data.root_quat_w, relative_vel_w[:, 3:6]) + + # Account for the offset + if self.cfg.body_offset is not None: + # Compute offset vector in root frame + r_offset_b = math_utils.quat_apply(self._ee_pose_b_no_offset[:, 3:7], self._offset_pos) + # Adjust the linear velocity to account for the offset + self._ee_vel_b[:, :3] += torch.cross(self._ee_vel_b[:, 3:], r_offset_b, dim=-1) + # Angular velocity is not affected by the offset + + def _compute_ee_force(self): + """Computes the contact forces on the ee frame in root frame.""" + # Obtain contact forces only if the contact sensor is available + if self._contact_sensor is not None: + self._contact_sensor.update(self._sim_dt) + self._ee_force_w[:] = self._contact_sensor.data.net_forces_w[:, 0, :] # type: ignore + # Rotate forces and torques into root frame + self._ee_force_b[:] = math_utils.quat_apply_inverse(self._asset.data.root_quat_w, self._ee_force_w) + + def _compute_joint_states(self): + """Computes the joint states for operational space control.""" + # Extract joint positions and velocities + self._joint_pos[:] = self._asset.data.joint_pos[:, self._joint_ids] + self._joint_vel[:] = self._asset.data.joint_vel[:, self._joint_ids] + + def _compute_task_frame_pose(self): + """Computes the pose of the task frame in root frame.""" + # Update task frame pose if task frame rigidbody is provided + if self._task_frame_transformer is not None and self._task_frame_pose_b is not None: + self._task_frame_transformer.update(self._sim_dt) + # Calculate the pose of the task frame in the root frame + self._task_frame_pose_b[:, :3], self._task_frame_pose_b[:, 3:] = math_utils.subtract_frame_transforms( + self._asset.data.root_pos_w, + self._asset.data.root_quat_w, + self._task_frame_transformer.data.target_pos_w[:, 0, :], + self._task_frame_transformer.data.target_quat_w[:, 0, :], + ) + + def _preprocess_actions(self, actions: torch.Tensor): + """Pre-processes the raw actions for operational space control. + + Args: + actions (torch.Tensor): The raw actions for operational space control. It is a tensor of + shape (``num_envs``, ``action_dim``). + """ + # Store the raw actions. Please note that the actions contain task space targets + # (in the order of the target_types), and possibly the impedance parameters depending on impedance_mode. + self._raw_actions[:] = actions + # Initialize the processed actions with raw actions. + self._processed_actions[:] = self._raw_actions + # Go through the command types one by one, and apply the pre-processing if needed. + if self._pose_abs_idx is not None: + self._processed_actions[:, self._pose_abs_idx : self._pose_abs_idx + 3] *= self._position_scale + self._processed_actions[:, self._pose_abs_idx + 3 : self._pose_abs_idx + 7] *= self._orientation_scale + if self._pose_rel_idx is not None: + self._processed_actions[:, self._pose_rel_idx : self._pose_rel_idx + 3] *= self._position_scale + self._processed_actions[:, self._pose_rel_idx + 3 : self._pose_rel_idx + 6] *= self._orientation_scale + if self._wrench_abs_idx is not None: + self._processed_actions[:, self._wrench_abs_idx : self._wrench_abs_idx + 6] *= self._wrench_scale + if self._stiffness_idx is not None: + self._processed_actions[:, self._stiffness_idx : self._stiffness_idx + 6] *= self._stiffness_scale + self._processed_actions[:, self._stiffness_idx : self._stiffness_idx + 6] = torch.clamp( + self._processed_actions[:, self._stiffness_idx : self._stiffness_idx + 6], + min=self.cfg.controller_cfg.motion_stiffness_limits_task[0], + max=self.cfg.controller_cfg.motion_stiffness_limits_task[1], + ) + if self._damping_ratio_idx is not None: + self._processed_actions[:, self._damping_ratio_idx : self._damping_ratio_idx + 6] *= ( + self._damping_ratio_scale + ) + self._processed_actions[:, self._damping_ratio_idx : self._damping_ratio_idx + 6] = torch.clamp( + self._processed_actions[:, self._damping_ratio_idx : self._damping_ratio_idx + 6], + min=self.cfg.controller_cfg.motion_damping_ratio_limits_task[0], + max=self.cfg.controller_cfg.motion_damping_ratio_limits_task[1], + ) diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/__init__.py b/source/isaaclab/isaaclab/envs/mdp/commands/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bdfce40473b24f0e3857bf680eae44f0c17b15e0 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Various command terms that can be used in the environment.""" + +from .commands_cfg import ( + NormalVelocityCommandCfg, + NullCommandCfg, + TerrainBasedPose2dCommandCfg, + UniformPose2dCommandCfg, + UniformPoseCommandCfg, + UniformVelocityCommandCfg, +) +from .null_command import NullCommand +from .pose_2d_command import TerrainBasedPose2dCommand, UniformPose2dCommand +from .pose_command import UniformPoseCommand +from .velocity_command import NormalVelocityCommand, UniformVelocityCommand diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/commands_cfg.py b/source/isaaclab/isaaclab/envs/mdp/commands/commands_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..390980f3454d865f49aeca63762b254529702e69 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/commands_cfg.py @@ -0,0 +1,248 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math +from dataclasses import MISSING + +from isaaclab.managers import CommandTermCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.markers.config import BLUE_ARROW_X_MARKER_CFG, FRAME_MARKER_CFG, GREEN_ARROW_X_MARKER_CFG +from isaaclab.utils import configclass + +from .null_command import NullCommand +from .pose_2d_command import TerrainBasedPose2dCommand, UniformPose2dCommand +from .pose_command import UniformPoseCommand +from .velocity_command import NormalVelocityCommand, UniformVelocityCommand + + +@configclass +class NullCommandCfg(CommandTermCfg): + """Configuration for the null command generator.""" + + class_type: type = NullCommand + + def __post_init__(self): + """Post initialization.""" + # set the resampling time range to infinity to avoid resampling + self.resampling_time_range = (math.inf, math.inf) + + +@configclass +class UniformVelocityCommandCfg(CommandTermCfg): + """Configuration for the uniform velocity command generator.""" + + class_type: type = UniformVelocityCommand + + asset_name: str = MISSING + """Name of the asset in the environment for which the commands are generated.""" + + heading_command: bool = False + """Whether to use heading command or angular velocity command. Defaults to False. + + If True, the angular velocity command is computed from the heading error, where the + target heading is sampled uniformly from provided range. Otherwise, the angular velocity + command is sampled uniformly from provided range. + """ + + heading_control_stiffness: float = 1.0 + """Scale factor to convert the heading error to angular velocity command. Defaults to 1.0.""" + + rel_standing_envs: float = 0.0 + """The sampled probability of environments that should be standing still. Defaults to 0.0.""" + + rel_heading_envs: float = 1.0 + """The sampled probability of environments where the robots follow the heading-based angular velocity command + (the others follow the sampled angular velocity command). Defaults to 1.0. + + This parameter is only used if :attr:`heading_command` is True. + """ + + @configclass + class Ranges: + """Uniform distribution ranges for the velocity commands.""" + + lin_vel_x: tuple[float, float] = MISSING + """Range for the linear-x velocity command (in m/s).""" + + lin_vel_y: tuple[float, float] = MISSING + """Range for the linear-y velocity command (in m/s).""" + + ang_vel_z: tuple[float, float] = MISSING + """Range for the angular-z velocity command (in rad/s).""" + + heading: tuple[float, float] | None = None + """Range for the heading command (in rad). Defaults to None. + + This parameter is only used if :attr:`~UniformVelocityCommandCfg.heading_command` is True. + """ + + ranges: Ranges = MISSING + """Distribution ranges for the velocity commands.""" + + goal_vel_visualizer_cfg: VisualizationMarkersCfg = GREEN_ARROW_X_MARKER_CFG.replace( + prim_path="/Visuals/Command/velocity_goal" + ) + """The configuration for the goal velocity visualization marker. Defaults to GREEN_ARROW_X_MARKER_CFG.""" + + current_vel_visualizer_cfg: VisualizationMarkersCfg = BLUE_ARROW_X_MARKER_CFG.replace( + prim_path="/Visuals/Command/velocity_current" + ) + """The configuration for the current velocity visualization marker. Defaults to BLUE_ARROW_X_MARKER_CFG.""" + + # Set the scale of the visualization markers to (0.5, 0.5, 0.5) + goal_vel_visualizer_cfg.markers["arrow"].scale = (0.5, 0.5, 0.5) + current_vel_visualizer_cfg.markers["arrow"].scale = (0.5, 0.5, 0.5) + + +@configclass +class NormalVelocityCommandCfg(UniformVelocityCommandCfg): + """Configuration for the normal velocity command generator.""" + + class_type: type = NormalVelocityCommand + heading_command: bool = False # --> we don't use heading command for normal velocity command. + + @configclass + class Ranges: + """Normal distribution ranges for the velocity commands.""" + + mean_vel: tuple[float, float, float] = MISSING + """Mean velocity for the normal distribution (in m/s). + + The tuple contains the mean linear-x, linear-y, and angular-z velocity. + """ + + std_vel: tuple[float, float, float] = MISSING + """Standard deviation for the normal distribution (in m/s). + + The tuple contains the standard deviation linear-x, linear-y, and angular-z velocity. + """ + + zero_prob: tuple[float, float, float] = MISSING + """Probability of zero velocity for the normal distribution. + + The tuple contains the probability of zero linear-x, linear-y, and angular-z velocity. + """ + + ranges: Ranges = MISSING + """Distribution ranges for the velocity commands.""" + + +@configclass +class UniformPoseCommandCfg(CommandTermCfg): + """Configuration for uniform pose command generator.""" + + class_type: type = UniformPoseCommand + + asset_name: str = MISSING + """Name of the asset in the environment for which the commands are generated.""" + + body_name: str = MISSING + """Name of the body in the asset for which the commands are generated.""" + + make_quat_unique: bool = False + """Whether to make the quaternion unique or not. Defaults to False. + + If True, the quaternion is made unique by ensuring the real part is positive. + """ + + @configclass + class Ranges: + """Uniform distribution ranges for the pose commands.""" + + pos_x: tuple[float, float] = MISSING + """Range for the x position (in m).""" + + pos_y: tuple[float, float] = MISSING + """Range for the y position (in m).""" + + pos_z: tuple[float, float] = MISSING + """Range for the z position (in m).""" + + roll: tuple[float, float] = MISSING + """Range for the roll angle (in rad).""" + + pitch: tuple[float, float] = MISSING + """Range for the pitch angle (in rad).""" + + yaw: tuple[float, float] = MISSING + """Range for the yaw angle (in rad).""" + + ranges: Ranges = MISSING + """Ranges for the commands.""" + + goal_pose_visualizer_cfg: VisualizationMarkersCfg = FRAME_MARKER_CFG.replace(prim_path="/Visuals/Command/goal_pose") + """The configuration for the goal pose visualization marker. Defaults to FRAME_MARKER_CFG.""" + + current_pose_visualizer_cfg: VisualizationMarkersCfg = FRAME_MARKER_CFG.replace( + prim_path="/Visuals/Command/body_pose" + ) + """The configuration for the current pose visualization marker. Defaults to FRAME_MARKER_CFG.""" + + # Set the scale of the visualization markers to (0.1, 0.1, 0.1) + goal_pose_visualizer_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + current_pose_visualizer_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + + +@configclass +class UniformPose2dCommandCfg(CommandTermCfg): + """Configuration for the uniform 2D-pose command generator.""" + + class_type: type = UniformPose2dCommand + + asset_name: str = MISSING + """Name of the asset in the environment for which the commands are generated.""" + + simple_heading: bool = MISSING + """Whether to use simple heading or not. + + If True, the heading is in the direction of the target position. + """ + + @configclass + class Ranges: + """Uniform distribution ranges for the position commands.""" + + pos_x: tuple[float, float] = MISSING + """Range for the x position (in m).""" + + pos_y: tuple[float, float] = MISSING + """Range for the y position (in m).""" + + heading: tuple[float, float] = MISSING + """Heading range for the position commands (in rad). + + Used only if :attr:`simple_heading` is False. + """ + + ranges: Ranges = MISSING + """Distribution ranges for the position commands.""" + + goal_pose_visualizer_cfg: VisualizationMarkersCfg = GREEN_ARROW_X_MARKER_CFG.replace( + prim_path="/Visuals/Command/pose_goal" + ) + """The configuration for the goal pose visualization marker. Defaults to GREEN_ARROW_X_MARKER_CFG.""" + + # Set the scale of the visualization markers to (0.2, 0.2, 0.8) + goal_pose_visualizer_cfg.markers["arrow"].scale = (0.2, 0.2, 0.8) + + +@configclass +class TerrainBasedPose2dCommandCfg(UniformPose2dCommandCfg): + """Configuration for the terrain-based position command generator.""" + + class_type = TerrainBasedPose2dCommand + + @configclass + class Ranges: + """Uniform distribution ranges for the position commands.""" + + heading: tuple[float, float] = MISSING + """Heading range for the position commands (in rad). + + Used only if :attr:`simple_heading` is False. + """ + + ranges: Ranges = MISSING + """Distribution ranges for the sampled commands.""" diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/null_command.py b/source/isaaclab/isaaclab/envs/mdp/commands/null_command.py new file mode 100644 index 0000000000000000000000000000000000000000..0fc2a378810f93f066e87059fc88533bfa1fbcd6 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/null_command.py @@ -0,0 +1,69 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generator that does nothing.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +from isaaclab.managers import CommandTerm + +if TYPE_CHECKING: + from .commands_cfg import NullCommandCfg + + +class NullCommand(CommandTerm): + """Command generator that does nothing. + + This command generator does not generate any commands. It is used for environments that do not + require any commands. + """ + + cfg: NullCommandCfg + """Configuration for the command generator.""" + + def __str__(self) -> str: + msg = "NullCommand:\n" + msg += "\tCommand dimension: N/A\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}" + return msg + + """ + Properties + """ + + @property + def command(self): + """Null command. + + Raises: + RuntimeError: No command is generated. Always raises this error. + """ + raise RuntimeError("NullCommandTerm does not generate any commands.") + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + return {} + + def compute(self, dt: float): + pass + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + pass + + def _resample_command(self, env_ids: Sequence[int]): + pass + + def _update_command(self): + pass diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/pose_2d_command.py b/source/isaaclab/isaaclab/envs/mdp/commands/pose_2d_command.py new file mode 100644 index 0000000000000000000000000000000000000000..a10ee0473e4a310e15cbb9d5cc6aee455ff82542 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/pose_2d_command.py @@ -0,0 +1,204 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generators for the 2D-pose for locomotion tasks.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import CommandTerm +from isaaclab.markers import VisualizationMarkers +from isaaclab.terrains import TerrainImporter +from isaaclab.utils.math import quat_apply_inverse, quat_from_euler_xyz, wrap_to_pi, yaw_quat + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from .commands_cfg import TerrainBasedPose2dCommandCfg, UniformPose2dCommandCfg + + +class UniformPose2dCommand(CommandTerm): + """Command generator that generates pose commands containing a 3-D position and heading. + + The command generator samples uniform 2D positions around the environment origin. It sets + the height of the position command to the default root height of the robot. The heading + command is either set to point towards the target or is sampled uniformly. + This can be configured through the :attr:`Pose2dCommandCfg.simple_heading` parameter in + the configuration. + """ + + cfg: UniformPose2dCommandCfg + """Configuration for the command generator.""" + + def __init__(self, cfg: UniformPose2dCommandCfg, env: ManagerBasedEnv): + """Initialize the command generator class. + + Args: + cfg: The configuration parameters for the command generator. + env: The environment object. + """ + # initialize the base class + super().__init__(cfg, env) + + # obtain the robot and terrain assets + # -- robot + self.robot: Articulation = env.scene[cfg.asset_name] + + # crete buffers to store the command + # -- commands: (x, y, z, heading) + self.pos_command_w = torch.zeros(self.num_envs, 3, device=self.device) + self.heading_command_w = torch.zeros(self.num_envs, device=self.device) + self.pos_command_b = torch.zeros_like(self.pos_command_w) + self.heading_command_b = torch.zeros_like(self.heading_command_w) + # -- metrics + self.metrics["error_pos"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["error_heading"] = torch.zeros(self.num_envs, device=self.device) + + def __str__(self) -> str: + msg = "PositionCommand:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}" + return msg + + """ + Properties + """ + + @property + def command(self) -> torch.Tensor: + """The desired 2D-pose in base frame. Shape is (num_envs, 4).""" + return torch.cat([self.pos_command_b, self.heading_command_b.unsqueeze(1)], dim=1) + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + # logs data + self.metrics["error_pos_2d"] = torch.norm(self.pos_command_w[:, :2] - self.robot.data.root_pos_w[:, :2], dim=1) + self.metrics["error_heading"] = torch.abs(wrap_to_pi(self.heading_command_w - self.robot.data.heading_w)) + + def _resample_command(self, env_ids: Sequence[int]): + # obtain env origins for the environments + self.pos_command_w[env_ids] = self._env.scene.env_origins[env_ids] + # offset the position command by the current root position + r = torch.empty(len(env_ids), device=self.device) + self.pos_command_w[env_ids, 0] += r.uniform_(*self.cfg.ranges.pos_x) + self.pos_command_w[env_ids, 1] += r.uniform_(*self.cfg.ranges.pos_y) + self.pos_command_w[env_ids, 2] += self.robot.data.default_root_state[env_ids, 2] + + if self.cfg.simple_heading: + # set heading command to point towards target + target_vec = self.pos_command_w[env_ids] - self.robot.data.root_pos_w[env_ids] + target_direction = torch.atan2(target_vec[:, 1], target_vec[:, 0]) + flipped_target_direction = wrap_to_pi(target_direction + torch.pi) + + # compute errors to find the closest direction to the current heading + # this is done to avoid the discontinuity at the -pi/pi boundary + curr_to_target = wrap_to_pi(target_direction - self.robot.data.heading_w[env_ids]).abs() + curr_to_flipped_target = wrap_to_pi(flipped_target_direction - self.robot.data.heading_w[env_ids]).abs() + + # set the heading command to the closest direction + self.heading_command_w[env_ids] = torch.where( + curr_to_target < curr_to_flipped_target, + target_direction, + flipped_target_direction, + ) + else: + # random heading command + self.heading_command_w[env_ids] = r.uniform_(*self.cfg.ranges.heading) + + def _update_command(self): + """Re-target the position command to the current root state.""" + target_vec = self.pos_command_w - self.robot.data.root_pos_w[:, :3] + self.pos_command_b[:] = quat_apply_inverse(yaw_quat(self.robot.data.root_quat_w), target_vec) + self.heading_command_b[:] = wrap_to_pi(self.heading_command_w - self.robot.data.heading_w) + + def _set_debug_vis_impl(self, debug_vis: bool): + # create markers if necessary for the first time + if debug_vis: + if not hasattr(self, "goal_pose_visualizer"): + self.goal_pose_visualizer = VisualizationMarkers(self.cfg.goal_pose_visualizer_cfg) + # set their visibility to true + self.goal_pose_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_pose_visualizer"): + self.goal_pose_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # update the box marker + self.goal_pose_visualizer.visualize( + translations=self.pos_command_w, + orientations=quat_from_euler_xyz( + torch.zeros_like(self.heading_command_w), + torch.zeros_like(self.heading_command_w), + self.heading_command_w, + ), + ) + + +class TerrainBasedPose2dCommand(UniformPose2dCommand): + """Command generator that generates pose commands based on the terrain. + + This command generator samples the position commands from the valid patches of the terrain. + The heading commands are either set to point towards the target or are sampled uniformly. + + It expects the terrain to have a valid flat patches under the key 'target'. + """ + + cfg: TerrainBasedPose2dCommandCfg + """Configuration for the command generator.""" + + def __init__(self, cfg: TerrainBasedPose2dCommandCfg, env: ManagerBasedEnv): + # initialize the base class + super().__init__(cfg, env) + + # obtain the terrain asset + self.terrain: TerrainImporter = env.scene["terrain"] + + # obtain the valid targets from the terrain + if "target" not in self.terrain.flat_patches: + raise RuntimeError( + "The terrain-based command generator requires a valid flat patch under 'target' in the terrain." + f" Found: {list(self.terrain.flat_patches.keys())}" + ) + # valid targets: (terrain_level, terrain_type, num_patches, 3) + self.valid_targets: torch.Tensor = self.terrain.flat_patches["target"] + + def _resample_command(self, env_ids: Sequence[int]): + # sample new position targets from the terrain + ids = torch.randint(0, self.valid_targets.shape[2], size=(len(env_ids),), device=self.device) + self.pos_command_w[env_ids] = self.valid_targets[ + self.terrain.terrain_levels[env_ids], self.terrain.terrain_types[env_ids], ids + ] + # offset the position command by the current root height + self.pos_command_w[env_ids, 2] += self.robot.data.default_root_state[env_ids, 2] + + if self.cfg.simple_heading: + # set heading command to point towards target + target_vec = self.pos_command_w[env_ids] - self.robot.data.root_pos_w[env_ids] + target_direction = torch.atan2(target_vec[:, 1], target_vec[:, 0]) + flipped_target_direction = wrap_to_pi(target_direction + torch.pi) + + # compute errors to find the closest direction to the current heading + # this is done to avoid the discontinuity at the -pi/pi boundary + curr_to_target = wrap_to_pi(target_direction - self.robot.data.heading_w[env_ids]).abs() + curr_to_flipped_target = wrap_to_pi(flipped_target_direction - self.robot.data.heading_w[env_ids]).abs() + + # set the heading command to the closest direction + self.heading_command_w[env_ids] = torch.where( + curr_to_target < curr_to_flipped_target, + target_direction, + flipped_target_direction, + ) + else: + # random heading command + r = torch.empty(len(env_ids), device=self.device) + self.heading_command_w[env_ids] = r.uniform_(*self.cfg.ranges.heading) diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/pose_command.py b/source/isaaclab/isaaclab/envs/mdp/commands/pose_command.py new file mode 100644 index 0000000000000000000000000000000000000000..2c62c4baf4b83209f4bb58c27efa8a7d6820afb2 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/pose_command.py @@ -0,0 +1,156 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generators for pose tracking.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import CommandTerm +from isaaclab.markers import VisualizationMarkers +from isaaclab.utils.math import combine_frame_transforms, compute_pose_error, quat_from_euler_xyz, quat_unique + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from .commands_cfg import UniformPoseCommandCfg + + +class UniformPoseCommand(CommandTerm): + """Command generator for generating pose commands uniformly. + + The command generator generates poses by sampling positions uniformly within specified + regions in cartesian space. For orientation, it samples uniformly the euler angles + (roll-pitch-yaw) and converts them into quaternion representation (w, x, y, z). + + The position and orientation commands are generated in the base frame of the robot, and not the + simulation world frame. This means that users need to handle the transformation from the + base frame to the simulation world frame themselves. + + .. caution:: + + Sampling orientations uniformly is not strictly the same as sampling euler angles uniformly. + This is because rotations are defined by 3D non-Euclidean space, and the mapping + from euler angles to rotations is not one-to-one. + + """ + + cfg: UniformPoseCommandCfg + """Configuration for the command generator.""" + + def __init__(self, cfg: UniformPoseCommandCfg, env: ManagerBasedEnv): + """Initialize the command generator class. + + Args: + cfg: The configuration parameters for the command generator. + env: The environment object. + """ + # initialize the base class + super().__init__(cfg, env) + + # extract the robot and body index for which the command is generated + self.robot: Articulation = env.scene[cfg.asset_name] + self.body_idx = self.robot.find_bodies(cfg.body_name)[0][0] + + # create buffers + # -- commands: (x, y, z, qw, qx, qy, qz) in root frame + self.pose_command_b = torch.zeros(self.num_envs, 7, device=self.device) + self.pose_command_b[:, 3] = 1.0 + self.pose_command_w = torch.zeros_like(self.pose_command_b) + # -- metrics + self.metrics["position_error"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["orientation_error"] = torch.zeros(self.num_envs, device=self.device) + + def __str__(self) -> str: + msg = "UniformPoseCommand:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}\n" + return msg + + """ + Properties + """ + + @property + def command(self) -> torch.Tensor: + """The desired pose command. Shape is (num_envs, 7). + + The first three elements correspond to the position, followed by the quaternion orientation in (w, x, y, z). + """ + return self.pose_command_b + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + # transform command from base frame to simulation world frame + self.pose_command_w[:, :3], self.pose_command_w[:, 3:] = combine_frame_transforms( + self.robot.data.root_pos_w, + self.robot.data.root_quat_w, + self.pose_command_b[:, :3], + self.pose_command_b[:, 3:], + ) + # compute the error + pos_error, rot_error = compute_pose_error( + self.pose_command_w[:, :3], + self.pose_command_w[:, 3:], + self.robot.data.body_pos_w[:, self.body_idx], + self.robot.data.body_quat_w[:, self.body_idx], + ) + self.metrics["position_error"] = torch.norm(pos_error, dim=-1) + self.metrics["orientation_error"] = torch.norm(rot_error, dim=-1) + + def _resample_command(self, env_ids: Sequence[int]): + # sample new pose targets + # -- position + r = torch.empty(len(env_ids), device=self.device) + self.pose_command_b[env_ids, 0] = r.uniform_(*self.cfg.ranges.pos_x) + self.pose_command_b[env_ids, 1] = r.uniform_(*self.cfg.ranges.pos_y) + self.pose_command_b[env_ids, 2] = r.uniform_(*self.cfg.ranges.pos_z) + # -- orientation + euler_angles = torch.zeros_like(self.pose_command_b[env_ids, :3]) + euler_angles[:, 0].uniform_(*self.cfg.ranges.roll) + euler_angles[:, 1].uniform_(*self.cfg.ranges.pitch) + euler_angles[:, 2].uniform_(*self.cfg.ranges.yaw) + quat = quat_from_euler_xyz(euler_angles[:, 0], euler_angles[:, 1], euler_angles[:, 2]) + # make sure the quaternion has real part as positive + self.pose_command_b[env_ids, 3:] = quat_unique(quat) if self.cfg.make_quat_unique else quat + + def _update_command(self): + pass + + def _set_debug_vis_impl(self, debug_vis: bool): + # create markers if necessary for the first time + if debug_vis: + if not hasattr(self, "goal_pose_visualizer"): + # -- goal pose + self.goal_pose_visualizer = VisualizationMarkers(self.cfg.goal_pose_visualizer_cfg) + # -- current body pose + self.current_pose_visualizer = VisualizationMarkers(self.cfg.current_pose_visualizer_cfg) + # set their visibility to true + self.goal_pose_visualizer.set_visibility(True) + self.current_pose_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_pose_visualizer"): + self.goal_pose_visualizer.set_visibility(False) + self.current_pose_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # check if robot is initialized + # note: this is needed in-case the robot is de-initialized. we can't access the data + if not self.robot.is_initialized: + return + # update the markers + # -- goal pose + self.goal_pose_visualizer.visualize(self.pose_command_w[:, :3], self.pose_command_w[:, 3:]) + # -- current body pose + body_link_pose_w = self.robot.data.body_link_pose_w[:, self.body_idx] + self.current_pose_visualizer.visualize(body_link_pose_w[:, :3], body_link_pose_w[:, 3:7]) diff --git a/source/isaaclab/isaaclab/envs/mdp/commands/velocity_command.py b/source/isaaclab/isaaclab/envs/mdp/commands/velocity_command.py new file mode 100644 index 0000000000000000000000000000000000000000..38bc076a959182099bfeb2d73ff80b7755bcd99b --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/commands/velocity_command.py @@ -0,0 +1,290 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generators for the velocity-based locomotion task.""" + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.managers import CommandTerm +from isaaclab.markers import VisualizationMarkers + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from .commands_cfg import NormalVelocityCommandCfg, UniformVelocityCommandCfg + +# import logger +logger = logging.getLogger(__name__) + + +class UniformVelocityCommand(CommandTerm): + r"""Command generator that generates a velocity command in SE(2) from uniform distribution. + + The command comprises of a linear velocity in x and y direction and an angular velocity around + the z-axis. It is given in the robot's base frame. + + If the :attr:`cfg.heading_command` flag is set to True, the angular velocity is computed from the heading + error similar to doing a proportional control on the heading error. The target heading is sampled uniformly + from the provided range. Otherwise, the angular velocity is sampled uniformly from the provided range. + + Mathematically, the angular velocity is computed as follows from the heading command: + + .. math:: + + \omega_z = \frac{1}{2} \text{wrap_to_pi}(\theta_{\text{target}} - \theta_{\text{current}}) + + """ + + cfg: UniformVelocityCommandCfg + """The configuration of the command generator.""" + + def __init__(self, cfg: UniformVelocityCommandCfg, env: ManagerBasedEnv): + """Initialize the command generator. + + Args: + cfg: The configuration of the command generator. + env: The environment. + + Raises: + ValueError: If the heading command is active but the heading range is not provided. + """ + # initialize the base class + super().__init__(cfg, env) + + # check configuration + if self.cfg.heading_command and self.cfg.ranges.heading is None: + raise ValueError( + "The velocity command has heading commands active (heading_command=True) but the `ranges.heading`" + " parameter is set to None." + ) + if self.cfg.ranges.heading and not self.cfg.heading_command: + logger.warning( + f"The velocity command has the 'ranges.heading' attribute set to '{self.cfg.ranges.heading}'" + " but the heading command is not active. Consider setting the flag for the heading command to True." + ) + + # obtain the robot asset + # -- robot + self.robot: Articulation = env.scene[cfg.asset_name] + + # crete buffers to store the command + # -- command: x vel, y vel, yaw vel, heading + self.vel_command_b = torch.zeros(self.num_envs, 3, device=self.device) + self.heading_target = torch.zeros(self.num_envs, device=self.device) + self.is_heading_env = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) + self.is_standing_env = torch.zeros_like(self.is_heading_env) + # -- metrics + self.metrics["error_vel_xy"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["error_vel_yaw"] = torch.zeros(self.num_envs, device=self.device) + + def __str__(self) -> str: + """Return a string representation of the command generator.""" + msg = "UniformVelocityCommand:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}\n" + msg += f"\tHeading command: {self.cfg.heading_command}\n" + if self.cfg.heading_command: + msg += f"\tHeading probability: {self.cfg.rel_heading_envs}\n" + msg += f"\tStanding probability: {self.cfg.rel_standing_envs}" + return msg + + """ + Properties + """ + + @property + def command(self) -> torch.Tensor: + """The desired base velocity command in the base frame. Shape is (num_envs, 3).""" + return self.vel_command_b + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + # time for which the command was executed + max_command_time = self.cfg.resampling_time_range[1] + max_command_step = max_command_time / self._env.step_dt + # logs data + self.metrics["error_vel_xy"] += ( + torch.norm(self.vel_command_b[:, :2] - self.robot.data.root_lin_vel_b[:, :2], dim=-1) / max_command_step + ) + self.metrics["error_vel_yaw"] += ( + torch.abs(self.vel_command_b[:, 2] - self.robot.data.root_ang_vel_b[:, 2]) / max_command_step + ) + + def _resample_command(self, env_ids: Sequence[int]): + # sample velocity commands + r = torch.empty(len(env_ids), device=self.device) + # -- linear velocity - x direction + self.vel_command_b[env_ids, 0] = r.uniform_(*self.cfg.ranges.lin_vel_x) + # -- linear velocity - y direction + self.vel_command_b[env_ids, 1] = r.uniform_(*self.cfg.ranges.lin_vel_y) + # -- ang vel yaw - rotation around z + self.vel_command_b[env_ids, 2] = r.uniform_(*self.cfg.ranges.ang_vel_z) + # heading target + if self.cfg.heading_command: + self.heading_target[env_ids] = r.uniform_(*self.cfg.ranges.heading) + # update heading envs + self.is_heading_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.rel_heading_envs + # update standing envs + self.is_standing_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.rel_standing_envs + + def _update_command(self): + """Post-processes the velocity command. + + This function sets velocity command to zero for standing environments and computes angular + velocity from heading direction if the heading_command flag is set. + """ + # Compute angular velocity from heading direction + if self.cfg.heading_command: + # resolve indices of heading envs + env_ids = self.is_heading_env.nonzero(as_tuple=False).flatten() + # compute angular velocity + heading_error = math_utils.wrap_to_pi(self.heading_target[env_ids] - self.robot.data.heading_w[env_ids]) + self.vel_command_b[env_ids, 2] = torch.clip( + self.cfg.heading_control_stiffness * heading_error, + min=self.cfg.ranges.ang_vel_z[0], + max=self.cfg.ranges.ang_vel_z[1], + ) + # Enforce standing (i.e., zero velocity command) for standing envs + # TODO: check if conversion is needed + standing_env_ids = self.is_standing_env.nonzero(as_tuple=False).flatten() + self.vel_command_b[standing_env_ids, :] = 0.0 + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if not hasattr(self, "goal_vel_visualizer"): + # -- goal + self.goal_vel_visualizer = VisualizationMarkers(self.cfg.goal_vel_visualizer_cfg) + # -- current + self.current_vel_visualizer = VisualizationMarkers(self.cfg.current_vel_visualizer_cfg) + # set their visibility to true + self.goal_vel_visualizer.set_visibility(True) + self.current_vel_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_vel_visualizer"): + self.goal_vel_visualizer.set_visibility(False) + self.current_vel_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # check if robot is initialized + # note: this is needed in-case the robot is de-initialized. we can't access the data + if not self.robot.is_initialized: + return + # get marker location + # -- base state + base_pos_w = self.robot.data.root_pos_w.clone() + base_pos_w[:, 2] += 0.5 + # -- resolve the scales and quaternions + vel_des_arrow_scale, vel_des_arrow_quat = self._resolve_xy_velocity_to_arrow(self.command[:, :2]) + vel_arrow_scale, vel_arrow_quat = self._resolve_xy_velocity_to_arrow(self.robot.data.root_lin_vel_b[:, :2]) + # display markers + self.goal_vel_visualizer.visualize(base_pos_w, vel_des_arrow_quat, vel_des_arrow_scale) + self.current_vel_visualizer.visualize(base_pos_w, vel_arrow_quat, vel_arrow_scale) + + """ + Internal helpers. + """ + + def _resolve_xy_velocity_to_arrow(self, xy_velocity: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Converts the XY base velocity command to arrow direction rotation.""" + # obtain default scale of the marker + default_scale = self.goal_vel_visualizer.cfg.markers["arrow"].scale + # arrow-scale + arrow_scale = torch.tensor(default_scale, device=self.device).repeat(xy_velocity.shape[0], 1) + arrow_scale[:, 0] *= torch.linalg.norm(xy_velocity, dim=1) * 3.0 + # arrow-direction + heading_angle = torch.atan2(xy_velocity[:, 1], xy_velocity[:, 0]) + zeros = torch.zeros_like(heading_angle) + arrow_quat = math_utils.quat_from_euler_xyz(zeros, zeros, heading_angle) + # convert everything back from base to world frame + base_quat_w = self.robot.data.root_quat_w + arrow_quat = math_utils.quat_mul(base_quat_w, arrow_quat) + + return arrow_scale, arrow_quat + + +class NormalVelocityCommand(UniformVelocityCommand): + """Command generator that generates a velocity command in SE(2) from a normal distribution. + + The command comprises of a linear velocity in x and y direction and an angular velocity around + the z-axis. It is given in the robot's base frame. + + The command is sampled from a normal distribution with mean and standard deviation specified in + the configuration. With equal probability, the sign of the individual components is flipped. + """ + + cfg: NormalVelocityCommandCfg + """The command generator configuration.""" + + def __init__(self, cfg: NormalVelocityCommandCfg, env: ManagerBasedEnv): + """Initializes the command generator. + + Args: + cfg: The command generator configuration. + env: The environment. + """ + super().__init__(cfg, env) + # create buffers for zero commands envs + self.is_zero_vel_x_env = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) + self.is_zero_vel_y_env = torch.zeros_like(self.is_zero_vel_x_env) + self.is_zero_vel_yaw_env = torch.zeros_like(self.is_zero_vel_x_env) + + def __str__(self) -> str: + """Return a string representation of the command generator.""" + msg = "NormalVelocityCommand:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}\n" + msg += f"\tStanding probability: {self.cfg.rel_standing_envs}" + return msg + + def _resample_command(self, env_ids): + # sample velocity commands + r = torch.empty(len(env_ids), device=self.device) + # -- linear velocity - x direction + self.vel_command_b[env_ids, 0] = r.normal_(mean=self.cfg.ranges.mean_vel[0], std=self.cfg.ranges.std_vel[0]) + self.vel_command_b[env_ids, 0] *= torch.where(r.uniform_(0.0, 1.0) <= 0.5, 1.0, -1.0) + # -- linear velocity - y direction + self.vel_command_b[env_ids, 1] = r.normal_(mean=self.cfg.ranges.mean_vel[1], std=self.cfg.ranges.std_vel[1]) + self.vel_command_b[env_ids, 1] *= torch.where(r.uniform_(0.0, 1.0) <= 0.5, 1.0, -1.0) + # -- angular velocity - yaw direction + self.vel_command_b[env_ids, 2] = r.normal_(mean=self.cfg.ranges.mean_vel[2], std=self.cfg.ranges.std_vel[2]) + self.vel_command_b[env_ids, 2] *= torch.where(r.uniform_(0.0, 1.0) <= 0.5, 1.0, -1.0) + + # update element wise zero velocity command + # TODO what is zero prob ? + self.is_zero_vel_x_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.ranges.zero_prob[0] + self.is_zero_vel_y_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.ranges.zero_prob[1] + self.is_zero_vel_yaw_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.ranges.zero_prob[2] + + # update standing envs + self.is_standing_env[env_ids] = r.uniform_(0.0, 1.0) <= self.cfg.rel_standing_envs + + def _update_command(self): + """Sets velocity command to zero for standing envs.""" + # Enforce standing (i.e., zero velocity command) for standing envs + standing_env_ids = self.is_standing_env.nonzero(as_tuple=False).flatten() # TODO check if conversion is needed + self.vel_command_b[standing_env_ids, :] = 0.0 + + # Enforce zero velocity for individual elements + # TODO: check if conversion is needed + zero_vel_x_env_ids = self.is_zero_vel_x_env.nonzero(as_tuple=False).flatten() + zero_vel_y_env_ids = self.is_zero_vel_y_env.nonzero(as_tuple=False).flatten() + zero_vel_yaw_env_ids = self.is_zero_vel_yaw_env.nonzero(as_tuple=False).flatten() + self.vel_command_b[zero_vel_x_env_ids, 0] = 0.0 + self.vel_command_b[zero_vel_y_env_ids, 1] = 0.0 + self.vel_command_b[zero_vel_yaw_env_ids, 2] = 0.0 diff --git a/source/isaaclab/isaaclab/envs/mdp/curriculums.py b/source/isaaclab/isaaclab/envs/mdp/curriculums.py new file mode 100644 index 0000000000000000000000000000000000000000..8438e5bec9250a9fec7ae09673edd9642e0d8441 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/curriculums.py @@ -0,0 +1,296 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to create curriculum for the learning environment. + +The functions can be passed to the :class:`isaaclab.managers.CurriculumTermCfg` object to enable +the curriculum introduced by the function. +""" + +from __future__ import annotations + +import re +from collections.abc import Sequence +from typing import TYPE_CHECKING, ClassVar + +from isaaclab.managers import CurriculumTermCfg, ManagerTermBase + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class modify_reward_weight(ManagerTermBase): + """Curriculum that modifies the reward weight based on a step-wise schedule.""" + + def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRLEnv): + super().__init__(cfg, env) + + # obtain term configuration + term_name = cfg.params["term_name"] + self._term_cfg = env.reward_manager.get_term_cfg(term_name) + + def __call__( + self, + env: ManagerBasedRLEnv, + env_ids: Sequence[int], + term_name: str, + weight: float, + num_steps: int, + ) -> float: + # update term settings + if env.common_step_counter > num_steps: + self._term_cfg.weight = weight + env.reward_manager.set_term_cfg(term_name, self._term_cfg) + + return self._term_cfg.weight + + +class modify_env_param(ManagerTermBase): + """Curriculum term for modifying an environment parameter at runtime. + + This term helps modify an environment parameter (or attribute) at runtime. + This parameter can be any attribute of the environment, such as the physics material properties, + observation ranges, or any other configurable parameter that can be accessed via a dotted path. + + The term uses the ``address`` parameter to specify the target attribute as a dotted path string. + For instance, "event_manager.cfg.object_physics_material.func.material_buckets" would + refer to the attribute ``material_buckets`` in the event manager's event term "object_physics_material", + which is a tensor of sampled physics material properties. + + The term uses the ``modify_fn`` parameter to specify the function that modifies the value of the target attribute. + The function should have the signature: + + .. code-block:: python + + def modify_fn(env, env_ids, old_value, **modify_params) -> new_value | modify_env_param.NO_CHANGE: + # modify the value based on the old value and the modify parameters + new_value = old_value + modify_params["value"] + return new_value + + where ``env`` is the learning environment, ``env_ids`` are the sub-environment indices, + ``old_value`` is the current value of the target attribute, and ``modify_params`` + are additional parameters that can be passed to the function. The function should return + the new value to be set for the target attribute, or the special token ``modify_env_param.NO_CHANGE`` + to indicate that the value should not be changed. + + At the first call to the term after initialization, it compiles getter and setter functions + for the target attribute specified by the ``address`` parameter. The getter retrieves the + current value, and the setter writes a new value back to the attribute. + + This term processes getter/setter accessors for a target attribute in an(specified by + as an "address" in the term configuration :attr:`cfg.params["address"]`) the first time it is called, + then on each invocation reads the current value, applies a user-provided :attr:`modify_fn`, + and writes back the result. Since :obj:`None` in this case can sometime be desirable value + to write, we use token, :attr:`NO_CHANGE`, as non-modification signal to this class, see usage below. + + Usage: + .. code-block:: python + + def resample_bucket_range( + env, env_id, data, static_friction_range, dynamic_friction_range, restitution_range, num_steps + ): + if env.common_step_counter > num_steps: + range_list = [static_friction_range, dynamic_friction_range, restitution_range] + ranges = torch.tensor(range_list, device="cpu") + new_buckets = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(data), 3), device="cpu") + return new_buckets + + # if the step counter is not reached, return NO_CHANGE to indicate no modification. + # we do this instead of returning None, since None is a valid value to set. + # additionally, returning the input data would not change the value but still lead + # to the setter being called, which may add overhead. + return mdp.modify_env_param.NO_CHANGE + + + object_physics_material_curriculum = CurrTerm( + func=mdp.modify_env_param, + params={ + "address": "event_manager.cfg.object_physics_material.func.material_buckets", + "modify_fn": resample_bucket_range, + "modify_params": { + "static_friction_range": [0.5, 1.0], + "dynamic_friction_range": [0.3, 1.0], + "restitution_range": [0.0, 0.5], + "num_step": 120000, + }, + }, + ) + """ + + NO_CHANGE: ClassVar = object() + """Special token to indicate no change in the value to be set. + + This token is used to signal that the `modify_fn` did not produce a new value. It can + be returned by the `modify_fn` to indicate that the current value should remain unchanged. + """ + + def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRLEnv): + super().__init__(cfg, env) + # resolve term configuration + if "address" not in cfg.params: + raise ValueError("The 'address' parameter must be specified in the curriculum term configuration.") + + # store current address + self._address: str = cfg.params["address"] + # store accessor functions + self._get_fn: callable = None + self._set_fn: callable = None + + def __del__(self): + """Destructor to clean up the compiled functions.""" + # clear the getter and setter functions + self._get_fn = None + self._set_fn = None + self._container = None + self._last_path = None + + """ + Operations. + """ + + def __call__( + self, + env: ManagerBasedRLEnv, + env_ids: Sequence[int], + address: str, + modify_fn: callable, + modify_params: dict | None = None, + ): + # fetch the getter and setter functions if not already compiled + if not self._get_fn: + self._get_fn, self._set_fn = self._process_accessors(self._env, self._address) + + # resolve none type + modify_params = {} if modify_params is None else modify_params + + # get the current value of the target attribute + data = self._get_fn() + # modify the value using the provided function + new_val = modify_fn(self._env, env_ids, data, **modify_params) + # set the modified value back to the target attribute + # note: if the modify_fn return NO_CHANGE signal, we do not invoke self.set_fn + if new_val is not self.NO_CHANGE: + self._set_fn(new_val) + + """ + Helper functions. + """ + + def _process_accessors(self, root: ManagerBasedRLEnv, path: str) -> tuple[callable, callable]: + """Process and return the (getter, setter) functions for a dotted attribute path. + + This function resolves a dotted path string to an attribute in the given root object. + The dotted path can include nested attributes, dictionary keys, and sequence indexing. + + For instance, the path "foo.bar[2].baz" would resolve to `root.foo.bar[2].baz`. This + allows accessing attributes in a nested structure, such as a dictionary or a list. + + Args: + root: The main object from which to resolve the attribute. + path: Dotted path string to the attribute variable. For e.g., "foo.bar[2].baz". + + Returns: + A tuple of two functions (getter, setter), where: + the getter retrieves the current value of the attribute, and + the setter writes a new value back to the attribute. + """ + # Turn "a.b[2].c" into ["a", ("b", 2), "c"] and store in parts + path_parts: list[str | tuple[str, int]] = [] + for part in path.split("."): + m = re.compile(r"^(\w+)\[(\d+)\]$").match(part) + if m: + path_parts.append((m.group(1), int(m.group(2)))) + else: + path_parts.append(part) + + # Traverse the parts to find the container + container = root + for container_path in path_parts[:-1]: + if isinstance(container_path, tuple): + # we are accessing a list element + name, idx = container_path + # find underlying attribute + if isinstance(container_path, dict): + seq = container[name] # type: ignore[assignment] + else: + seq = getattr(container, name) + # save the container for the next iteration + container = seq[idx] + else: + # we are accessing a dictionary key or an attribute + if isinstance(container, dict): + container = container[container_path] + else: + container = getattr(container, container_path) + + # save the container and the last part of the path + self._container = container + self._last_path = path_parts[-1] # for "a.b[2].c", this is "c", while for "a.b[2]" it is 2 + + # build the getter and setter + if isinstance(self._container, tuple): + get_value = lambda: self._container[self._last_path] # noqa: E731 + + def set_value(val): + tuple_list = list(self._container) + tuple_list[self._last_path] = val + self._container = tuple(tuple_list) + + elif isinstance(self._container, (list, dict)): + get_value = lambda: self._container[self._last_path] # noqa: E731 + + def set_value(val): + self._container[self._last_path] = val + + elif isinstance(self._container, object): + get_value = lambda: getattr(self._container, self._last_path) # noqa: E731 + set_value = lambda val: setattr(self._container, self._last_path, val) # noqa: E731 + else: + raise TypeError( + f"Unable to build accessors for address '{path}'. Unknown type found for access variable:" + f" '{type(self._container)}'. Expected a list, dict, or object with attributes." + ) + + return get_value, set_value + + +class modify_term_cfg(modify_env_param): + """Curriculum for modifying a manager term configuration at runtime. + + This class inherits from :class:`modify_env_param` and is specifically designed to modify + the configuration of a manager term in the environment. It mainly adds the convenience of + using a simplified address style that uses "s." as a prefix to refer to the manager's configuration. + + For instance, instead of writing "event_manager.cfg.object_physics_material.func.material_buckets", + you can write "events.object_physics_material.func.material_buckets" to refer to the same term configuration. + The same applies to other managers, such as "observations", "commands", "rewards", and "terminations". + + Internally, it replaces the first occurrence of "s." in the address with "_manager.cfg.", + thus transforming the simplified address into a full manager path. + + Usage: + .. code-block:: python + + def override_value(env, env_ids, data, value, num_steps): + if env.common_step_counter > num_steps: + return value + return mdp.modify_term_cfg.NO_CHANGE + + + command_object_pose_xrange_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "commands.object_pose.ranges.pos_x", # note: `_manager.cfg` is omitted + "modify_fn": override_value, + "modify_params": {"value": (-0.75, -0.25), "num_steps": 12000}, + }, + ) + """ + + def __init__(self, cfg, env): + # initialize the parent + super().__init__(cfg, env) + # overwrite the simplified address with the full manager path + self._address = self._address.replace("s.", "_manager.cfg.", 1) diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py new file mode 100644 index 0000000000000000000000000000000000000000..484121f4e491ae4a854aefd02bddd9af63d5c7bf --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/events.py @@ -0,0 +1,1822 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to enable different events. + +Events include anything related to altering the simulation state. This includes changing the physics +materials, applying external forces, and resetting the state of the asset. + +The functions can be passed to the :class:`isaaclab.managers.EventTermCfg` object to enable +the event introduced by the function. +""" + +from __future__ import annotations + +import logging +import math +import re +from typing import TYPE_CHECKING, Literal + +import torch + +import carb +import omni.physics.tensors.impl.api as physx +from isaacsim.core.utils.extensions import enable_extension +from pxr import Gf, Sdf, UsdGeom, Vt + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.actuators import ImplicitActuator +from isaaclab.assets import Articulation, DeformableObject, RigidObject +from isaaclab.managers import EventTermCfg, ManagerTermBase, SceneEntityCfg +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.terrains import TerrainImporter +from isaaclab.utils.version import compare_versions, get_isaac_sim_version + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + +# import logger +logger = logging.getLogger(__name__) + + +def randomize_rigid_body_scale( + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + scale_range: tuple[float, float] | dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg, + relative_child_path: str | None = None, +): + """Randomize the scale of a rigid body asset in the USD stage. + + This function modifies the "xformOp:scale" property of all the prims corresponding to the asset. + + It takes a tuple or dictionary for the scale ranges. If it is a tuple, then the scaling along + individual axis is performed equally. If it is a dictionary, the scaling is independent across each dimension. + The keys of the dictionary are ``x``, ``y``, and ``z``. The values are tuples of the form ``(min, max)``. + + If the dictionary does not contain a key, the range is set to one for that axis. + + Relative child path can be used to randomize the scale of a specific child prim of the asset. + For example, if the asset at prim path expression ``/World/envs/env_.*/Object`` has a child + with the path ``/World/envs/env_.*/Object/mesh``, then the relative child path should be ``mesh`` or + ``/mesh``. + + .. attention:: + Since this function modifies USD properties that are parsed by the physics engine once the simulation + starts, the term should only be used before the simulation starts playing. This corresponds to the + event mode named "usd". Using it at simulation time, may lead to unpredictable behaviors. + + .. note:: + When randomizing the scale of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + # check if sim is running + if env.sim.is_playing(): + raise RuntimeError( + "Randomizing scale while simulation is running leads to unpredictable behaviors." + " Please ensure that the event term is called before the simulation starts by using the 'usd' mode." + ) + + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + if isinstance(asset, Articulation): + raise ValueError( + "Scaling an articulation randomly is not supported, as it affects joint attributes and can cause" + " unexpected behavior. To achieve different scales, we recommend generating separate USD files for" + " each version of the articulation and using multi-asset spawning. For more details, refer to:" + " https://isaac-sim.github.io/IsaacLab/main/source/how-to/multi_asset_spawning.html" + ) + + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device="cpu") + else: + env_ids = env_ids.cpu() + + # acquire stage + stage = get_current_stage() + # resolve prim paths for spawning and cloning + prim_paths = sim_utils.find_matching_prim_paths(asset.cfg.prim_path) + + # sample scale values + if isinstance(scale_range, dict): + range_list = [scale_range.get(key, (1.0, 1.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device="cpu") + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 3), device="cpu") + else: + rand_samples = math_utils.sample_uniform(*scale_range, (len(env_ids), 1), device="cpu") + rand_samples = rand_samples.repeat(1, 3) + # convert to list for the for loop + rand_samples = rand_samples.tolist() + + # apply the randomization to the parent if no relative child path is provided + # this might be useful if user wants to randomize a particular mesh in the prim hierarchy + if relative_child_path is None: + relative_child_path = "" + elif not relative_child_path.startswith("/"): + relative_child_path = "/" + relative_child_path + + # use sdf changeblock for faster processing of USD properties + with Sdf.ChangeBlock(): + for i, env_id in enumerate(env_ids): + # path to prim to randomize + prim_path = prim_paths[env_id] + relative_child_path + # spawn single instance + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_path) + + # get the attribute to randomize + scale_spec = prim_spec.GetAttributeAtPath(prim_path + ".xformOp:scale") + # if the scale attribute does not exist, create it + has_scale_attr = scale_spec is not None + if not has_scale_attr: + scale_spec = Sdf.AttributeSpec(prim_spec, prim_path + ".xformOp:scale", Sdf.ValueTypeNames.Double3) + + # set the new scale + scale_spec.default = Gf.Vec3f(*rand_samples[i]) + + # ensure the operation is done in the right ordering if we created the scale attribute. + # otherwise, we assume the scale attribute is already in the right order. + # note: by default isaac sim follows this ordering for the transform stack so any asset + # created through it will have the correct ordering + if not has_scale_attr: + op_order_spec = prim_spec.GetAttributeAtPath(prim_path + ".xformOpOrder") + if op_order_spec is None: + op_order_spec = Sdf.AttributeSpec( + prim_spec, UsdGeom.Tokens.xformOpOrder, Sdf.ValueTypeNames.TokenArray + ) + op_order_spec.default = Vt.TokenArray(["xformOp:translate", "xformOp:orient", "xformOp:scale"]) + + +class randomize_rigid_body_material(ManagerTermBase): + """Randomize the physics materials on all geometries of the asset. + + This function creates a set of physics materials with random static friction, dynamic friction, and restitution + values. The number of materials is specified by ``num_buckets``. The materials are generated by sampling + uniform random values from the given ranges. + + The material properties are then assigned to the geometries of the asset. The assignment is done by + creating a random integer tensor of shape (num_instances, max_num_shapes) where ``num_instances`` + is the number of assets spawned and ``max_num_shapes`` is the maximum number of shapes in the asset (over + all bodies). The integer values are used as indices to select the material properties from the + material buckets. + + If the flag ``make_consistent`` is set to ``True``, the dynamic friction is set to be less than or equal to + the static friction. This obeys the physics constraint on friction values. However, it may not always be + essential for the application. Thus, the flag is set to ``False`` by default. + + .. attention:: + This function uses CPU tensors to assign the material properties. It is recommended to use this function + only during the initialization of the environment. Otherwise, it may lead to a significant performance + overhead. + + .. note:: + PhysX only allows 64000 unique physics materials in the scene. If the number of materials exceeds this + limit, the simulation will crash. Due to this reason, we sample the materials only once during initialization. + Afterwards, these materials are randomly assigned to the geometries of the asset. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + + Raises: + ValueError: If the asset is not a RigidObject or an Articulation. + """ + super().__init__(cfg, env) + + # extract the used quantities (to enable type-hinting) + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: RigidObject | Articulation = env.scene[self.asset_cfg.name] + + if not isinstance(self.asset, (RigidObject, Articulation)): + raise ValueError( + f"Randomization term 'randomize_rigid_body_material' not supported for asset: '{self.asset_cfg.name}'" + f" with type: '{type(self.asset)}'." + ) + + # obtain number of shapes per body (needed for indexing the material properties correctly) + # note: this is a workaround since the Articulation does not provide a direct way to obtain the number of shapes + # per body. We use the physics simulation view to obtain the number of shapes per body. + if isinstance(self.asset, Articulation) and self.asset_cfg.body_ids != slice(None): + self.num_shapes_per_body = [] + for link_path in self.asset.root_physx_view.link_paths[0]: + link_physx_view = self.asset._physics_sim_view.create_rigid_body_view(link_path) # type: ignore + self.num_shapes_per_body.append(link_physx_view.max_shapes) + # ensure the parsing is correct + num_shapes = sum(self.num_shapes_per_body) + expected_shapes = self.asset.root_physx_view.max_shapes + if num_shapes != expected_shapes: + raise ValueError( + "Randomization term 'randomize_rigid_body_material' failed to parse the number of shapes per body." + f" Expected total shapes: {expected_shapes}, but got: {num_shapes}." + ) + else: + # in this case, we don't need to do special indexing + self.num_shapes_per_body = None + + # obtain parameters for sampling friction and restitution values + static_friction_range = cfg.params.get("static_friction_range", (1.0, 1.0)) + dynamic_friction_range = cfg.params.get("dynamic_friction_range", (1.0, 1.0)) + restitution_range = cfg.params.get("restitution_range", (0.0, 0.0)) + num_buckets = int(cfg.params.get("num_buckets", 1)) + + # sample material properties from the given ranges + # note: we only sample the materials once during initialization + # afterwards these are randomly assigned to the geometries of the asset + range_list = [static_friction_range, dynamic_friction_range, restitution_range] + ranges = torch.tensor(range_list, device="cpu") + self.material_buckets = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (num_buckets, 3), device="cpu") + + # ensure dynamic friction is always less than static friction + make_consistent = cfg.params.get("make_consistent", False) + if make_consistent: + self.material_buckets[:, 1] = torch.min(self.material_buckets[:, 0], self.material_buckets[:, 1]) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + static_friction_range: tuple[float, float], + dynamic_friction_range: tuple[float, float], + restitution_range: tuple[float, float], + num_buckets: int, + asset_cfg: SceneEntityCfg, + make_consistent: bool = False, + ): + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device="cpu") + else: + env_ids = env_ids.cpu() + + # randomly assign material IDs to the geometries + total_num_shapes = self.asset.root_physx_view.max_shapes + bucket_ids = torch.randint(0, num_buckets, (len(env_ids), total_num_shapes), device="cpu") + material_samples = self.material_buckets[bucket_ids] + + # retrieve material buffer from the physics simulation + materials = self.asset.root_physx_view.get_material_properties() + + # update material buffer with new samples + if self.num_shapes_per_body is not None: + # sample material properties from the given ranges + for body_id in self.asset_cfg.body_ids: + # obtain indices of shapes for the body + start_idx = sum(self.num_shapes_per_body[:body_id]) + end_idx = start_idx + self.num_shapes_per_body[body_id] + # assign the new materials + # material samples are of shape: num_env_ids x total_num_shapes x 3 + materials[env_ids, start_idx:end_idx] = material_samples[:, start_idx:end_idx] + else: + # assign all the materials + materials[env_ids] = material_samples[:] + + # apply to simulation + self.asset.root_physx_view.set_material_properties(materials, env_ids) + + +class randomize_rigid_body_mass(ManagerTermBase): + """Randomize the mass of the bodies by adding, scaling, or setting random values. + + This function allows randomizing the mass of the bodies of the asset. The function samples random + values from the given distribution parameters and adds, scales, or sets the values into the physics + simulation based on the operation. + + If the :attr:`recompute_inertia` flag is set to :obj:`True`, the function recomputes the inertia tensor + of the bodies after setting the mass. This is useful when the mass is changed significantly, as the + inertia tensor depends on the mass. It assumes the body is a uniform density object. If the body is not + a uniform density object, the inertia tensor may not be accurate. + + .. tip:: + This function uses CPU tensors to assign the body masses. It is recommended to use this function + only during the initialization of the environment. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + + Raises: + TypeError: If `params` is not a tuple of two numbers. + ValueError: If the operation is not supported. + ValueError: If the lower bound is negative or zero when not allowed. + ValueError: If the upper bound is less than the lower bound. + """ + super().__init__(cfg, env) + + # extract the used quantities (to enable type-hinting) + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: RigidObject | Articulation = env.scene[self.asset_cfg.name] + # check for valid operation + if cfg.params["operation"] == "scale": + if "mass_distribution_params" in cfg.params: + _validate_scale_range( + cfg.params["mass_distribution_params"], "mass_distribution_params", allow_zero=False + ) + elif cfg.params["operation"] not in ("abs", "add"): + raise ValueError( + "Randomization term 'randomize_rigid_body_mass' does not support operation:" + f" '{cfg.params['operation']}'." + ) + if cfg.params.get("min_mass") is not None: + if cfg.params.get("min_mass") < 1e-6: + raise ValueError( + "Randomization term 'randomize_rigid_body_mass' does not support 'min_mass' less than 1e-6 to avoid" + " physics errors." + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + asset_cfg: SceneEntityCfg, + mass_distribution_params: tuple[float, float], + operation: Literal["add", "scale", "abs"], + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", + recompute_inertia: bool = True, + min_mass: float = 1e-6, + ): + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device="cpu") + else: + env_ids = env_ids.cpu() + + # resolve body indices + if self.asset_cfg.body_ids == slice(None): + body_ids = torch.arange(self.asset.num_bodies, dtype=torch.int, device="cpu") + else: + body_ids = torch.tensor(self.asset_cfg.body_ids, dtype=torch.int, device="cpu") + + # get the current masses of the bodies (num_assets, num_bodies) + masses = self.asset.root_physx_view.get_masses() + + # apply randomization on default values + # this is to make sure when calling the function multiple times, the randomization is applied on the + # default values and not the previously randomized values + masses[env_ids[:, None], body_ids] = self.asset.data.default_mass[env_ids[:, None], body_ids].clone() + + # sample from the given range + # note: we modify the masses in-place for all environments + # however, the setter takes care that only the masses of the specified environments are modified + masses = _randomize_prop_by_op( + masses, mass_distribution_params, env_ids, body_ids, operation=operation, distribution=distribution + ) + masses = torch.clamp(masses, min=min_mass) # ensure masses are positive + + # set the mass into the physics simulation + self.asset.root_physx_view.set_masses(masses, env_ids) + + # recompute inertia tensors if needed + if recompute_inertia: + # compute the ratios of the new masses to the initial masses + ratios = masses[env_ids[:, None], body_ids] / self.asset.data.default_mass[env_ids[:, None], body_ids] + # scale the inertia tensors by the the ratios + # since mass randomization is done on default values, we can use the default inertia tensors + inertias = self.asset.root_physx_view.get_inertias() + if isinstance(self.asset, Articulation): + # inertia has shape: (num_envs, num_bodies, 9) for articulation + inertias[env_ids[:, None], body_ids] = ( + self.asset.data.default_inertia[env_ids[:, None], body_ids] * ratios[..., None] + ) + else: + # inertia has shape: (num_envs, 9) for rigid object + inertias[env_ids] = self.asset.data.default_inertia[env_ids] * ratios + # set the inertia tensors into the physics simulation + self.asset.root_physx_view.set_inertias(inertias, env_ids) + + +def randomize_rigid_body_com( + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + com_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg, +): + """Randomize the center of mass (CoM) of rigid bodies by adding a random value sampled from the given ranges. + + .. note:: + This function uses CPU tensors to assign the CoM. It is recommended to use this function + only during the initialization of the environment. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device="cpu") + else: + env_ids = env_ids.cpu() + + # resolve body indices + if asset_cfg.body_ids == slice(None): + body_ids = torch.arange(asset.num_bodies, dtype=torch.int, device="cpu") + else: + body_ids = torch.tensor(asset_cfg.body_ids, dtype=torch.int, device="cpu") + + # sample random CoM values + range_list = [com_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device="cpu") + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 3), device="cpu").unsqueeze(1) + + # get the current com of the bodies (num_assets, num_bodies) + coms = asset.root_physx_view.get_coms().clone() + + # Randomize the com in range + coms[env_ids[:, None], body_ids, :3] += rand_samples + + # Set the new coms + asset.root_physx_view.set_coms(coms, env_ids) + + +def randomize_rigid_body_collider_offsets( + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + asset_cfg: SceneEntityCfg, + rest_offset_distribution_params: tuple[float, float] | None = None, + contact_offset_distribution_params: tuple[float, float] | None = None, + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", +): + """Randomize the collider parameters of rigid bodies in an asset by adding, scaling, or setting random values. + + This function allows randomizing the collider parameters of the asset, such as rest and contact offsets. + These correspond to the physics engine collider properties that affect the collision checking. + + The function samples random values from the given distribution parameters and applies the operation to + the collider properties. It then sets the values into the physics simulation. If the distribution parameters + are not provided for a particular property, the function does not modify the property. + + Currently, the distribution parameters are applied as absolute values. + + .. tip:: + This function uses CPU tensors to assign the collision properties. It is recommended to use this function + only during the initialization of the environment. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device="cpu") + + # sample collider properties from the given ranges and set into the physics simulation + # -- rest offsets + if rest_offset_distribution_params is not None: + rest_offset = asset.root_physx_view.get_rest_offsets().clone() + rest_offset = _randomize_prop_by_op( + rest_offset, + rest_offset_distribution_params, + None, + slice(None), + operation="abs", + distribution=distribution, + ) + asset.root_physx_view.set_rest_offsets(rest_offset, env_ids.cpu()) + # -- contact offsets + if contact_offset_distribution_params is not None: + contact_offset = asset.root_physx_view.get_contact_offsets().clone() + contact_offset = _randomize_prop_by_op( + contact_offset, + contact_offset_distribution_params, + None, + slice(None), + operation="abs", + distribution=distribution, + ) + asset.root_physx_view.set_contact_offsets(contact_offset, env_ids.cpu()) + + +def randomize_physics_scene_gravity( + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + gravity_distribution_params: tuple[list[float], list[float]], + operation: Literal["add", "scale", "abs"], + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", +): + """Randomize gravity by adding, scaling, or setting random values. + + This function allows randomizing gravity of the physics scene. The function samples random values from the + given distribution parameters and adds, scales, or sets the values into the physics simulation based on the + operation. + + The distribution parameters are lists of two elements each, representing the lower and upper bounds of the + distribution for the x, y, and z components of the gravity vector. The function samples random values for each + component independently. + + .. attention:: + This function applied the same gravity for all the environments. + + .. tip:: + This function uses CPU tensors to assign gravity. + """ + # get the current gravity + gravity = torch.tensor(env.sim.cfg.gravity, device="cpu").unsqueeze(0) + dist_param_0 = torch.tensor(gravity_distribution_params[0], device="cpu") + dist_param_1 = torch.tensor(gravity_distribution_params[1], device="cpu") + gravity = _randomize_prop_by_op( + gravity, + (dist_param_0, dist_param_1), + None, + slice(None), + operation=operation, + distribution=distribution, + ) + # unbatch the gravity tensor into a list + gravity = gravity[0].tolist() + + # set the gravity into the physics simulation + physics_sim_view: physx.SimulationView = sim_utils.SimulationContext.instance().physics_sim_view + physics_sim_view.set_gravity(carb.Float3(*gravity)) + + +class randomize_actuator_gains(ManagerTermBase): + """Randomize the actuator gains in an articulation by adding, scaling, or setting random values. + + This function allows randomizing the actuator stiffness and damping gains. + + The function samples random values from the given distribution parameters and applies the operation to + the joint properties. It then sets the values into the actuator models. If the distribution parameters + are not provided for a particular property, the function does not modify the property. + + .. tip:: + For implicit actuators, this function uses CPU tensors to assign the actuator gains into the simulation. + In such cases, it is recommended to use this function only during the initialization of the environment. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + + Raises: + TypeError: If `params` is not a tuple of two numbers. + ValueError: If the operation is not supported. + ValueError: If the lower bound is negative or zero when not allowed. + ValueError: If the upper bound is less than the lower bound. + """ + super().__init__(cfg, env) + + # extract the used quantities (to enable type-hinting) + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: RigidObject | Articulation = env.scene[self.asset_cfg.name] + # check for valid operation + if cfg.params["operation"] == "scale": + if "stiffness_distribution_params" in cfg.params: + _validate_scale_range( + cfg.params["stiffness_distribution_params"], "stiffness_distribution_params", allow_zero=False + ) + if "damping_distribution_params" in cfg.params: + _validate_scale_range(cfg.params["damping_distribution_params"], "damping_distribution_params") + elif cfg.params["operation"] not in ("abs", "add"): + raise ValueError( + "Randomization term 'randomize_actuator_gains' does not support operation:" + f" '{cfg.params['operation']}'." + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + asset_cfg: SceneEntityCfg, + stiffness_distribution_params: tuple[float, float] | None = None, + damping_distribution_params: tuple[float, float] | None = None, + operation: Literal["add", "scale", "abs"] = "abs", + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", + ): + # Resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device=self.asset.device) + + def randomize(data: torch.Tensor, params: tuple[float, float]) -> torch.Tensor: + return _randomize_prop_by_op( + data, params, dim_0_ids=None, dim_1_ids=actuator_indices, operation=operation, distribution=distribution + ) + + # Loop through actuators and randomize gains + for actuator in self.asset.actuators.values(): + if isinstance(self.asset_cfg.joint_ids, slice): + # we take all the joints of the actuator + actuator_indices = slice(None) + if isinstance(actuator.joint_indices, slice): + global_indices = slice(None) + elif isinstance(actuator.joint_indices, torch.Tensor): + global_indices = actuator.joint_indices.to(self.asset.device) + else: + raise TypeError("Actuator joint indices must be a slice or a torch.Tensor.") + elif isinstance(actuator.joint_indices, slice): + # we take the joints defined in the asset config + global_indices = actuator_indices = torch.tensor(self.asset_cfg.joint_ids, device=self.asset.device) + else: + # we take the intersection of the actuator joints and the asset config joints + actuator_joint_indices = actuator.joint_indices + asset_joint_ids = torch.tensor(self.asset_cfg.joint_ids, device=self.asset.device) + # the indices of the joints in the actuator that have to be randomized + actuator_indices = torch.nonzero(torch.isin(actuator_joint_indices, asset_joint_ids)).view(-1) + if len(actuator_indices) == 0: + continue + # maps actuator indices that have to be randomized to global joint indices + global_indices = actuator_joint_indices[actuator_indices] + # Randomize stiffness + if stiffness_distribution_params is not None: + stiffness = actuator.stiffness[env_ids].clone() + stiffness[:, actuator_indices] = self.asset.data.default_joint_stiffness[env_ids][ + :, global_indices + ].clone() + randomize(stiffness, stiffness_distribution_params) + actuator.stiffness[env_ids] = stiffness + if isinstance(actuator, ImplicitActuator): + self.asset.write_joint_stiffness_to_sim( + stiffness, joint_ids=actuator.joint_indices, env_ids=env_ids + ) + # Randomize damping + if damping_distribution_params is not None: + damping = actuator.damping[env_ids].clone() + damping[:, actuator_indices] = self.asset.data.default_joint_damping[env_ids][:, global_indices].clone() + randomize(damping, damping_distribution_params) + actuator.damping[env_ids] = damping + if isinstance(actuator, ImplicitActuator): + self.asset.write_joint_damping_to_sim(damping, joint_ids=actuator.joint_indices, env_ids=env_ids) + + +class randomize_joint_parameters(ManagerTermBase): + """Randomize the simulated joint parameters of an articulation by adding, scaling, or setting random values. + + This function allows randomizing the joint parameters of the asset. These correspond to the physics engine + joint properties that affect the joint behavior. The properties include the joint friction coefficient, armature, + and joint position limits. + + The function samples random values from the given distribution parameters and applies the operation to the + joint properties. It then sets the values into the physics simulation. If the distribution parameters are + not provided for a particular property, the function does not modify the property. + + .. tip:: + This function uses CPU tensors to assign the joint properties. It is recommended to use this function + only during the initialization of the environment. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + + Raises: + TypeError: If `params` is not a tuple of two numbers. + ValueError: If the operation is not supported. + ValueError: If the lower bound is negative or zero when not allowed. + ValueError: If the upper bound is less than the lower bound. + """ + super().__init__(cfg, env) + + # extract the used quantities (to enable type-hinting) + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: RigidObject | Articulation = env.scene[self.asset_cfg.name] + # check for valid operation + if cfg.params["operation"] == "scale": + if "friction_distribution_params" in cfg.params: + _validate_scale_range(cfg.params["friction_distribution_params"], "friction_distribution_params") + if "armature_distribution_params" in cfg.params: + _validate_scale_range(cfg.params["armature_distribution_params"], "armature_distribution_params") + elif cfg.params["operation"] not in ("abs", "add"): + raise ValueError( + "Randomization term 'randomize_fixed_tendon_parameters' does not support operation:" + f" '{cfg.params['operation']}'." + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + asset_cfg: SceneEntityCfg, + friction_distribution_params: tuple[float, float] | None = None, + armature_distribution_params: tuple[float, float] | None = None, + lower_limit_distribution_params: tuple[float, float] | None = None, + upper_limit_distribution_params: tuple[float, float] | None = None, + operation: Literal["add", "scale", "abs"] = "abs", + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", + ): + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device=self.asset.device) + + # resolve joint indices + if self.asset_cfg.joint_ids == slice(None): + joint_ids = slice(None) # for optimization purposes + else: + joint_ids = torch.tensor(self.asset_cfg.joint_ids, dtype=torch.int, device=self.asset.device) + + if env_ids != slice(None) and joint_ids != slice(None): + env_ids_for_slice = env_ids[:, None] + else: + env_ids_for_slice = env_ids + + # sample joint properties from the given ranges and set into the physics simulation + # joint friction coefficient + if friction_distribution_params is not None: + friction_coeff = _randomize_prop_by_op( + self.asset.data.default_joint_friction_coeff.clone(), + friction_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + + # ensure the friction coefficient is non-negative + friction_coeff = torch.clamp(friction_coeff, min=0.0) + + # Always set static friction (indexed once) + static_friction_coeff = friction_coeff[env_ids_for_slice, joint_ids] + + # if isaacsim version is lower than 5.0.0 we can set only the static friction coefficient + if get_isaac_sim_version().major >= 5: + # Randomize raw tensors + dynamic_friction_coeff = _randomize_prop_by_op( + self.asset.data.default_joint_dynamic_friction_coeff.clone(), + friction_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + viscous_friction_coeff = _randomize_prop_by_op( + self.asset.data.default_joint_viscous_friction_coeff.clone(), + friction_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + + # Clamp to non-negative + dynamic_friction_coeff = torch.clamp(dynamic_friction_coeff, min=0.0) + viscous_friction_coeff = torch.clamp(viscous_friction_coeff, min=0.0) + + # Ensure dynamic ≤ static (same shape before indexing) + dynamic_friction_coeff = torch.minimum(dynamic_friction_coeff, friction_coeff) + + # Index once at the end + dynamic_friction_coeff = dynamic_friction_coeff[env_ids_for_slice, joint_ids] + viscous_friction_coeff = viscous_friction_coeff[env_ids_for_slice, joint_ids] + else: + # For versions < 5.0.0, we do not set these values + dynamic_friction_coeff = None + viscous_friction_coeff = None + + # Single write call for all versions + self.asset.write_joint_friction_coefficient_to_sim( + joint_friction_coeff=static_friction_coeff, + joint_dynamic_friction_coeff=dynamic_friction_coeff, + joint_viscous_friction_coeff=viscous_friction_coeff, + joint_ids=joint_ids, + env_ids=env_ids, + ) + + # joint armature + if armature_distribution_params is not None: + armature = _randomize_prop_by_op( + self.asset.data.default_joint_armature.clone(), + armature_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + self.asset.write_joint_armature_to_sim( + armature[env_ids_for_slice, joint_ids], joint_ids=joint_ids, env_ids=env_ids + ) + + # joint position limits + if lower_limit_distribution_params is not None or upper_limit_distribution_params is not None: + joint_pos_limits = self.asset.data.default_joint_pos_limits.clone() + # -- randomize the lower limits + if lower_limit_distribution_params is not None: + joint_pos_limits[..., 0] = _randomize_prop_by_op( + joint_pos_limits[..., 0], + lower_limit_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + # -- randomize the upper limits + if upper_limit_distribution_params is not None: + joint_pos_limits[..., 1] = _randomize_prop_by_op( + joint_pos_limits[..., 1], + upper_limit_distribution_params, + env_ids, + joint_ids, + operation=operation, + distribution=distribution, + ) + + # extract the position limits for the concerned joints + joint_pos_limits = joint_pos_limits[env_ids_for_slice, joint_ids] + if (joint_pos_limits[..., 0] > joint_pos_limits[..., 1]).any(): + raise ValueError( + "Randomization term 'randomize_joint_parameters' is setting lower joint limits that are greater" + " than upper joint limits. Please check the distribution parameters for the joint position limits." + ) + # set the position limits into the physics simulation + self.asset.write_joint_position_limit_to_sim( + joint_pos_limits, joint_ids=joint_ids, env_ids=env_ids, warn_limit_violation=False + ) + + +class randomize_fixed_tendon_parameters(ManagerTermBase): + """Randomize the simulated fixed tendon parameters of an articulation by adding, scaling, or setting random values. + + This function allows randomizing the fixed tendon parameters of the asset. + These correspond to the physics engine tendon properties that affect the joint behavior. + + The function samples random values from the given distribution parameters and applies the operation to + the tendon properties. It then sets the values into the physics simulation. If the distribution parameters + are not provided for a particular property, the function does not modify the property. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + + Raises: + TypeError: If `params` is not a tuple of two numbers. + ValueError: If the operation is not supported. + ValueError: If the lower bound is negative or zero when not allowed. + ValueError: If the upper bound is less than the lower bound. + """ + super().__init__(cfg, env) + + # extract the used quantities (to enable type-hinting) + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: RigidObject | Articulation = env.scene[self.asset_cfg.name] + # check for valid operation + if cfg.params["operation"] == "scale": + if "stiffness_distribution_params" in cfg.params: + _validate_scale_range( + cfg.params["stiffness_distribution_params"], "stiffness_distribution_params", allow_zero=False + ) + if "damping_distribution_params" in cfg.params: + _validate_scale_range(cfg.params["damping_distribution_params"], "damping_distribution_params") + if "limit_stiffness_distribution_params" in cfg.params: + _validate_scale_range( + cfg.params["limit_stiffness_distribution_params"], "limit_stiffness_distribution_params" + ) + elif cfg.params["operation"] not in ("abs", "add"): + raise ValueError( + "Randomization term 'randomize_fixed_tendon_parameters' does not support operation:" + f" '{cfg.params['operation']}'." + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor | None, + asset_cfg: SceneEntityCfg, + stiffness_distribution_params: tuple[float, float] | None = None, + damping_distribution_params: tuple[float, float] | None = None, + limit_stiffness_distribution_params: tuple[float, float] | None = None, + lower_limit_distribution_params: tuple[float, float] | None = None, + upper_limit_distribution_params: tuple[float, float] | None = None, + rest_length_distribution_params: tuple[float, float] | None = None, + offset_distribution_params: tuple[float, float] | None = None, + operation: Literal["add", "scale", "abs"] = "abs", + distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform", + ): + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device=self.asset.device) + + # resolve joint indices + if self.asset_cfg.fixed_tendon_ids == slice(None): + tendon_ids = slice(None) # for optimization purposes + else: + tendon_ids = torch.tensor(self.asset_cfg.fixed_tendon_ids, dtype=torch.int, device=self.asset.device) + + # sample tendon properties from the given ranges and set into the physics simulation + # stiffness + if stiffness_distribution_params is not None: + stiffness = _randomize_prop_by_op( + self.asset.data.default_fixed_tendon_stiffness.clone(), + stiffness_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + self.asset.set_fixed_tendon_stiffness(stiffness[env_ids[:, None], tendon_ids], tendon_ids, env_ids) + + # damping + if damping_distribution_params is not None: + damping = _randomize_prop_by_op( + self.asset.data.default_fixed_tendon_damping.clone(), + damping_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + self.asset.set_fixed_tendon_damping(damping[env_ids[:, None], tendon_ids], tendon_ids, env_ids) + + # limit stiffness + if limit_stiffness_distribution_params is not None: + limit_stiffness = _randomize_prop_by_op( + self.asset.data.default_fixed_tendon_limit_stiffness.clone(), + limit_stiffness_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + self.asset.set_fixed_tendon_limit_stiffness( + limit_stiffness[env_ids[:, None], tendon_ids], tendon_ids, env_ids + ) + + # position limits + if lower_limit_distribution_params is not None or upper_limit_distribution_params is not None: + limit = self.asset.data.default_fixed_tendon_pos_limits.clone() + # -- lower limit + if lower_limit_distribution_params is not None: + limit[..., 0] = _randomize_prop_by_op( + limit[..., 0], + lower_limit_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + # -- upper limit + if upper_limit_distribution_params is not None: + limit[..., 1] = _randomize_prop_by_op( + limit[..., 1], + upper_limit_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + + # check if the limits are valid + tendon_limits = limit[env_ids[:, None], tendon_ids] + if (tendon_limits[..., 0] > tendon_limits[..., 1]).any(): + raise ValueError( + "Randomization term 'randomize_fixed_tendon_parameters' is setting lower tendon limits that are" + " greater than upper tendon limits." + ) + self.asset.set_fixed_tendon_position_limit(tendon_limits, tendon_ids, env_ids) + + # rest length + if rest_length_distribution_params is not None: + rest_length = _randomize_prop_by_op( + self.asset.data.default_fixed_tendon_rest_length.clone(), + rest_length_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + self.asset.set_fixed_tendon_rest_length(rest_length[env_ids[:, None], tendon_ids], tendon_ids, env_ids) + + # offset + if offset_distribution_params is not None: + offset = _randomize_prop_by_op( + self.asset.data.default_fixed_tendon_offset.clone(), + offset_distribution_params, + env_ids, + tendon_ids, + operation=operation, + distribution=distribution, + ) + self.asset.set_fixed_tendon_offset(offset[env_ids[:, None], tendon_ids], tendon_ids, env_ids) + + # write the fixed tendon properties into the simulation + self.asset.write_fixed_tendon_properties_to_sim(tendon_ids, env_ids) + + +def apply_external_force_torque( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + force_range: tuple[float, float], + torque_range: tuple[float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Randomize the external forces and torques applied to the bodies. + + This function creates a set of random forces and torques sampled from the given ranges. The number of forces + and torques is equal to the number of bodies times the number of environments. The forces and torques are + applied to the bodies by calling ``asset.set_external_force_and_torque``. The forces and torques are only + applied when ``asset.write_data_to_sim()`` is called in the environment. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + # resolve environment ids + if env_ids is None: + env_ids = torch.arange(env.scene.num_envs, device=asset.device) + # resolve number of bodies + num_bodies = len(asset_cfg.body_ids) if isinstance(asset_cfg.body_ids, list) else asset.num_bodies + + # sample random forces and torques + size = (len(env_ids), num_bodies, 3) + forces = math_utils.sample_uniform(*force_range, size, asset.device) + torques = math_utils.sample_uniform(*torque_range, size, asset.device) + # set the forces and torques into the buffers + # note: these are only applied when you call: `asset.write_data_to_sim()` + asset.permanent_wrench_composer.set_forces_and_torques( + forces=forces, + torques=torques, + body_ids=asset_cfg.body_ids, + env_ids=env_ids, + ) + + +def push_by_setting_velocity( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Push the asset by setting the root velocity to a random value within the given ranges. + + This creates an effect similar to pushing the asset with a random impulse that changes the asset's velocity. + It samples the root velocity from the given ranges and sets the velocity into the physics simulation. + + The function takes a dictionary of velocity ranges for each axis and rotation. The keys of the dictionary + are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. The values are tuples of the form ``(min, max)``. + If the dictionary does not contain a key, the velocity is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + + # velocities + vel_w = asset.data.root_vel_w[env_ids] + # sample random velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + vel_w += math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], vel_w.shape, device=asset.device) + # set the velocities into the physics simulation + asset.write_root_velocity_to_sim(vel_w, env_ids=env_ids) + + +def reset_root_state_uniform( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the asset root state to a random position and velocity uniformly within the given ranges. + + This function randomizes the root position and velocity of the asset. + + * It samples the root position from the given ranges and adds them to the default root position, before setting + them into the physics simulation. + * It samples the root orientation from the given ranges and sets them into the physics simulation. + * It samples the root velocity from the given ranges and sets them into the physics simulation. + + The function takes a dictionary of pose and velocity ranges for each axis and rotation. The keys of the + dictionary are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. The values are tuples of the form + ``(min, max)``. If the dictionary does not contain a key, the position or velocity is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + # get default root state + root_states = asset.data.default_root_state[env_ids].clone() + + # poses + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) + # velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + velocities = root_states[:, 7:13] + rand_samples + + # set into the physics simulation + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) + + +def reset_root_state_with_random_orientation( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the asset root position and velocities sampled randomly within the given ranges + and the asset root orientation sampled randomly from the SO(3). + + This function randomizes the root position and velocity of the asset. + + * It samples the root position from the given ranges and adds them to the default root position, before setting + them into the physics simulation. + * It samples the root orientation uniformly from the SO(3) and sets them into the physics simulation. + * It samples the root velocity from the given ranges and sets them into the physics simulation. + + The function takes a dictionary of position and velocity ranges for each axis and rotation: + + * :attr:`pose_range` - a dictionary of position ranges for each axis. The keys of the dictionary are ``x``, + ``y``, and ``z``. The orientation is sampled uniformly from the SO(3). + * :attr:`velocity_range` - a dictionary of velocity ranges for each axis and rotation. The keys of the dictionary + are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. + + The values are tuples of the form ``(min, max)``. If the dictionary does not contain a particular key, + the position is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + # get default root state + root_states = asset.data.default_root_state[env_ids].clone() + + # poses + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 3), device=asset.device) + + positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples + orientations = math_utils.random_orientation(len(env_ids), device=asset.device) + + # velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + velocities = root_states[:, 7:13] + rand_samples + + # set into the physics simulation + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) + + +def reset_root_state_from_terrain( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the asset root state by sampling a random valid pose from the terrain. + + This function samples a random valid pose(based on flat patches) from the terrain and sets the root state + of the asset to this position. The function also samples random velocities from the given ranges and sets them + into the physics simulation. + + The function takes a dictionary of position and velocity ranges for each axis and rotation: + + * :attr:`pose_range` - a dictionary of pose ranges for each axis. The keys of the dictionary are ``roll``, + ``pitch``, and ``yaw``. The position is sampled from the flat patches of the terrain. + * :attr:`velocity_range` - a dictionary of velocity ranges for each axis and rotation. The keys of the dictionary + are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. + + The values are tuples of the form ``(min, max)``. If the dictionary does not contain a particular key, + the position is set to zero for that axis. + + Note: + The function expects the terrain to have valid flat patches under the key "init_pos". The flat patches + are used to sample the random pose for the robot. + + Raises: + ValueError: If the terrain does not have valid flat patches under the key "init_pos". + """ + # access the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + terrain: TerrainImporter = env.scene.terrain + + # obtain all flat patches corresponding to the valid poses + valid_positions: torch.Tensor = terrain.flat_patches.get("init_pos") + if valid_positions is None: + raise ValueError( + "The event term 'reset_root_state_from_terrain' requires valid flat patches under 'init_pos'." + f" Found: {list(terrain.flat_patches.keys())}" + ) + + # sample random valid poses + ids = torch.randint(0, valid_positions.shape[2], size=(len(env_ids),), device=env.device) + positions = valid_positions[terrain.terrain_levels[env_ids], terrain.terrain_types[env_ids], ids] + positions += asset.data.default_root_state[env_ids, :3] + + # sample random orientations + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 3), device=asset.device) + + # convert to quaternions + orientations = math_utils.quat_from_euler_xyz(rand_samples[:, 0], rand_samples[:, 1], rand_samples[:, 2]) + + # sample random velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + velocities = asset.data.default_root_state[env_ids, 7:13] + rand_samples + + # set into the physics simulation + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) + + +def reset_joints_by_scale( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + position_range: tuple[float, float], + velocity_range: tuple[float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the robot joints by scaling the default position and velocity by the given ranges. + + This function samples random values from the given ranges and scales the default joint positions and velocities + by these values. The scaled values are then set into the physics simulation. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + # cast env_ids to allow broadcasting + if asset_cfg.joint_ids != slice(None): + iter_env_ids = env_ids[:, None] + else: + iter_env_ids = env_ids + + # get default joint state + joint_pos = asset.data.default_joint_pos[iter_env_ids, asset_cfg.joint_ids].clone() + joint_vel = asset.data.default_joint_vel[iter_env_ids, asset_cfg.joint_ids].clone() + + # scale these values randomly + joint_pos *= math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device) + joint_vel *= math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device) + + # clamp joint pos to limits + joint_pos_limits = asset.data.soft_joint_pos_limits[iter_env_ids, asset_cfg.joint_ids] + joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) + # clamp joint vel to limits + joint_vel_limits = asset.data.soft_joint_vel_limits[iter_env_ids, asset_cfg.joint_ids] + joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits) + + # set into the physics simulation + asset.write_joint_state_to_sim(joint_pos, joint_vel, joint_ids=asset_cfg.joint_ids, env_ids=env_ids) + + +def reset_joints_by_offset( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + position_range: tuple[float, float], + velocity_range: tuple[float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the robot joints with offsets around the default position and velocity by the given ranges. + + This function samples random values from the given ranges and biases the default joint positions and velocities + by these values. The biased values are then set into the physics simulation. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + # cast env_ids to allow broadcasting + if asset_cfg.joint_ids != slice(None): + iter_env_ids = env_ids[:, None] + else: + iter_env_ids = env_ids + + # get default joint state + joint_pos = asset.data.default_joint_pos[iter_env_ids, asset_cfg.joint_ids].clone() + joint_vel = asset.data.default_joint_vel[iter_env_ids, asset_cfg.joint_ids].clone() + + # bias these values randomly + joint_pos += math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device) + joint_vel += math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device) + + # clamp joint pos to limits + joint_pos_limits = asset.data.soft_joint_pos_limits[iter_env_ids, asset_cfg.joint_ids] + joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) + # clamp joint vel to limits + joint_vel_limits = asset.data.soft_joint_vel_limits[iter_env_ids, asset_cfg.joint_ids] + joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits) + + # set into the physics simulation + asset.write_joint_state_to_sim(joint_pos, joint_vel, joint_ids=asset_cfg.joint_ids, env_ids=env_ids) + + +def reset_nodal_state_uniform( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + position_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the asset nodal state to a random position and velocity uniformly within the given ranges. + + This function randomizes the nodal position and velocity of the asset. + + * It samples the root position from the given ranges and adds them to the default nodal position, before setting + them into the physics simulation. + * It samples the root velocity from the given ranges and sets them into the physics simulation. + + The function takes a dictionary of position and velocity ranges for each axis. The keys of the + dictionary are ``x``, ``y``, ``z``. The values are tuples of the form ``(min, max)``. + If the dictionary does not contain a key, the position or velocity is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: DeformableObject = env.scene[asset_cfg.name] + # get default root state + nodal_state = asset.data.default_nodal_state_w[env_ids].clone() + + # position + range_list = [position_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 1, 3), device=asset.device) + + nodal_state[..., :3] += rand_samples + + # velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 1, 3), device=asset.device) + + nodal_state[..., 3:] += rand_samples + + # set into the physics simulation + asset.write_nodal_state_to_sim(nodal_state, env_ids=env_ids) + + +def reset_scene_to_default(env: ManagerBasedEnv, env_ids: torch.Tensor, reset_joint_targets: bool = False): + """Reset the scene to the default state specified in the scene configuration. + + If :attr:`reset_joint_targets` is True, the joint position and velocity targets of the articulations are + also reset to their default values. This might be useful for some cases to clear out any previously set targets. + However, this is not the default behavior as based on our experience, it is not always desired to reset + targets to default values, especially when the targets should be handled by action terms and not event terms. + """ + # rigid bodies + for rigid_object in env.scene.rigid_objects.values(): + # obtain default and deal with the offset for env origins + default_root_state = rigid_object.data.default_root_state[env_ids].clone() + default_root_state[:, 0:3] += env.scene.env_origins[env_ids] + # set into the physics simulation + rigid_object.write_root_pose_to_sim(default_root_state[:, :7], env_ids=env_ids) + rigid_object.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids=env_ids) + # articulations + for articulation_asset in env.scene.articulations.values(): + # obtain default and deal with the offset for env origins + default_root_state = articulation_asset.data.default_root_state[env_ids].clone() + default_root_state[:, 0:3] += env.scene.env_origins[env_ids] + # set into the physics simulation + articulation_asset.write_root_pose_to_sim(default_root_state[:, :7], env_ids=env_ids) + articulation_asset.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids=env_ids) + # obtain default joint positions + default_joint_pos = articulation_asset.data.default_joint_pos[env_ids].clone() + default_joint_vel = articulation_asset.data.default_joint_vel[env_ids].clone() + # set into the physics simulation + articulation_asset.write_joint_state_to_sim(default_joint_pos, default_joint_vel, env_ids=env_ids) + # reset joint targets if required + if reset_joint_targets: + articulation_asset.set_joint_position_target(default_joint_pos, env_ids=env_ids) + articulation_asset.set_joint_velocity_target(default_joint_vel, env_ids=env_ids) + # deformable objects + for deformable_object in env.scene.deformable_objects.values(): + # obtain default and set into the physics simulation + nodal_state = deformable_object.data.default_nodal_state_w[env_ids].clone() + deformable_object.write_nodal_state_to_sim(nodal_state, env_ids=env_ids) + + +class randomize_visual_texture_material(ManagerTermBase): + """Randomize the visual texture of bodies on an asset using Replicator API. + + This function randomizes the visual texture of the bodies of the asset using the Replicator API. + The function samples random textures from the given texture paths and applies them to the bodies + of the asset. The textures are projected onto the bodies and rotated by the given angles. + + .. note:: + The function assumes that the asset follows the prim naming convention as: + "{asset_prim_path}/{body_name}/visuals" where the body name is the name of the body to + which the texture is applied. This is the default prim ordering when importing assets + from the asset converters in Isaac Lab. + + .. note:: + When randomizing the texture of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + """ + super().__init__(cfg, env) + + # check to make sure replicate_physics is set to False, else raise error + # note: We add an explicit check here since texture randomization can happen outside of 'prestartup' mode + # and the event manager doesn't check in that case. + if env.cfg.scene.replicate_physics: + raise RuntimeError( + "Unable to randomize visual texture material with scene replication enabled." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # enable replicator extension if not already enabled + enable_extension("omni.replicator.core") + + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # read parameters from the configuration + asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg") + + # obtain the asset entity + asset = env.scene[asset_cfg.name] + + # join all bodies in the asset + body_names = asset_cfg.body_names + if isinstance(body_names, str): + body_names_regex = body_names + elif isinstance(body_names, list): + body_names_regex = "|".join(body_names) + else: + body_names_regex = ".*" + + # create the affected prim path + # Check if the pattern with '/visuals' yields results when matching `body_names_regex`. + # If not, fall back to a broader pattern without '/visuals'. + asset_main_prim_path = asset.cfg.prim_path + pattern_with_visuals = f"{asset_main_prim_path}/{body_names_regex}/visuals" + # Use sim_utils to check if any prims currently match this pattern + matching_prims = sim_utils.find_matching_prim_paths(pattern_with_visuals) + if matching_prims: + # If matches are found, use the pattern with /visuals + prim_path = pattern_with_visuals + else: + # If no matches found, fall back to the broader pattern without /visuals + # This pattern (e.g., /World/envs/env_.*/Table/.*) should match visual prims + # whether they end in /visuals or have other structures. + prim_path = f"{asset_main_prim_path}/.*" + logging.info( + f"Pattern '{pattern_with_visuals}' found no prims. Falling back to '{prim_path}' for texture" + " randomization." + ) + + # extract the replicator version + version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) + + # use different path for different version of replicator + if compare_versions(version, "1.12.4") < 0: + texture_paths = cfg.params.get("texture_paths") + event_name = cfg.params.get("event_name") + texture_rotation = cfg.params.get("texture_rotation", (0.0, 0.0)) + + # convert from radians to degrees + texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation) + + # Create the omni-graph node for the randomization term + def rep_texture_randomization(): + prims_group = rep.get.prims(path_pattern=prim_path) + + with prims_group: + rep.randomizer.texture( + textures=texture_paths, + project_uvw=True, + texture_rotate=rep.distribution.uniform(*texture_rotation), + ) + return prims_group.node + + # Register the event to the replicator + with rep.trigger.on_custom_event(event_name=event_name): + rep_texture_randomization() + else: + # acquire stage + stage = get_current_stage() + prims_group = rep.functional.get.prims(path_pattern=prim_path, stage=stage) + + num_prims = len(prims_group) + # rng that randomizes the texture and rotation + self.texture_rng = rep.rng.ReplicatorRNG() + + # Create the material first and bind it to the prims + for i, prim in enumerate(prims_group): + # Disable instancble + if prim.IsInstanceable(): + prim.SetInstanceable(False) + + # TODO: Should we specify the value when creating the material? + self.material_prims = rep.functional.create_batch.material( + mdl="OmniPBR.mdl", bind_prims=prims_group, count=num_prims, project_uvw=True + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + event_name: str, + asset_cfg: SceneEntityCfg, + texture_paths: list[str], + texture_rotation: tuple[float, float] = (0.0, 0.0), + ): + # note: This triggers the nodes for all the environments. + # We need to investigate how to make it happen only for a subset based on env_ids. + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # extract the replicator version + version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) + + # use different path for different version of replicator + if compare_versions(version, "1.12.4") < 0: + rep.utils.send_og_event(event_name) + else: + # read parameters from the configuration + texture_paths = texture_paths if texture_paths else self._cfg.params.get("texture_paths") + texture_rotation = ( + texture_rotation if texture_rotation else self._cfg.params.get("texture_rotation", (0.0, 0.0)) + ) + + # convert from radians to degrees + texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation) + + num_prims = len(self.material_prims) + random_textures = self.texture_rng.generator.choice(texture_paths, size=num_prims) + random_rotations = self.texture_rng.generator.uniform( + texture_rotation[0], texture_rotation[1], size=num_prims + ) + + # modify the material properties + rep.functional.modify.attribute(self.material_prims, "diffuse_texture", random_textures) + rep.functional.modify.attribute(self.material_prims, "texture_rotate", random_rotations) + + +class randomize_visual_color(ManagerTermBase): + """Randomize the visual color of bodies on an asset using Replicator API. + + This function randomizes the visual color of the bodies of the asset using the Replicator API. + The function samples random colors from the given colors and applies them to the bodies + of the asset. + + The function assumes that the asset follows the prim naming convention as: + "{asset_prim_path}/{mesh_name}" where the mesh name is the name of the mesh to + which the color is applied. For instance, if the asset has a prim path "/World/asset" + and a mesh named "body_0/mesh", the prim path for the mesh would be + "/World/asset/body_0/mesh". + + The colors can be specified as a list of tuples of the form ``(r, g, b)`` or as a dictionary + with the keys ``r``, ``g``, ``b`` and values as tuples of the form ``(low, high)``. + If a dictionary is used, the function will sample random colors from the given ranges. + + .. note:: + When randomizing the color of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the randomization term. + + Args: + cfg: The configuration of the event term. + env: The environment instance. + """ + super().__init__(cfg, env) + + # enable replicator extension if not already enabled + enable_extension("omni.replicator.core") + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # read parameters from the configuration + asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg") + mesh_name: str = cfg.params.get("mesh_name", "") # type: ignore + + # check to make sure replicate_physics is set to False, else raise error + # note: We add an explicit check here since texture randomization can happen outside of 'prestartup' mode + # and the event manager doesn't check in that case. + if env.cfg.scene.replicate_physics: + raise RuntimeError( + "Unable to randomize visual color with scene replication enabled." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # obtain the asset entity + asset = env.scene[asset_cfg.name] + + # create the affected prim path + if not mesh_name.startswith("/"): + mesh_name = "/" + mesh_name + mesh_prim_path = f"{asset.cfg.prim_path}{mesh_name}" + # TODO: Need to make it work for multiple meshes. + + # extract the replicator version + version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) + + # use different path for different version of replicator + if compare_versions(version, "1.12.4") < 0: + colors = cfg.params.get("colors") + event_name = cfg.params.get("event_name") + + # parse the colors into replicator format + if isinstance(colors, dict): + # (r, g, b) - low, high --> (low_r, low_g, low_b) and (high_r, high_g, high_b) + color_low = [colors[key][0] for key in ["r", "g", "b"]] + color_high = [colors[key][1] for key in ["r", "g", "b"]] + colors = rep.distribution.uniform(color_low, color_high) + else: + colors = list(colors) + + # Create the omni-graph node for the randomization term + def rep_color_randomization(): + prims_group = rep.get.prims(path_pattern=mesh_prim_path) + with prims_group: + rep.randomizer.color(colors=colors) + + return prims_group.node + + # Register the event to the replicator + with rep.trigger.on_custom_event(event_name=event_name): + rep_color_randomization() + else: + stage = get_current_stage() + prims_group = rep.functional.get.prims(path_pattern=mesh_prim_path, stage=stage) + + num_prims = len(prims_group) + self.color_rng = rep.rng.ReplicatorRNG() + + # Create the material first and bind it to the prims + for i, prim in enumerate(prims_group): + # Disable instancble + if prim.IsInstanceable(): + prim.SetInstanceable(False) + + # TODO: Should we specify the value when creating the material? + self.material_prims = rep.functional.create_batch.material( + mdl="OmniPBR.mdl", bind_prims=prims_group, count=num_prims, project_uvw=True + ) + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + event_name: str, + asset_cfg: SceneEntityCfg, + colors: list[tuple[float, float, float]] | dict[str, tuple[float, float]], + mesh_name: str = "", + ): + # note: This triggers the nodes for all the environments. + # We need to investigate how to make it happen only for a subset based on env_ids. + + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) + + # use different path for different version of replicator + if compare_versions(version, "1.12.4") < 0: + rep.utils.send_og_event(event_name) + else: + colors = colors if colors else self._cfg.params.get("colors") + + # parse the colors into replicator format + if isinstance(colors, dict): + # (r, g, b) - low, high --> (low_r, low_g, low_b) and (high_r, high_g, high_b) + color_low = [colors[key][0] for key in ["r", "g", "b"]] + color_high = [colors[key][1] for key in ["r", "g", "b"]] + colors = [color_low, color_high] + else: + colors = list(colors) + + num_prims = len(self.material_prims) + random_colors = self.color_rng.generator.uniform(colors[0], colors[1], size=(num_prims, 3)) + + rep.functional.modify.attribute(self.material_prims, "diffuse_color_constant", random_colors) + + +""" +Internal helper functions. +""" + + +def _randomize_prop_by_op( + data: torch.Tensor, + distribution_parameters: tuple[float | torch.Tensor, float | torch.Tensor], + dim_0_ids: torch.Tensor | None, + dim_1_ids: torch.Tensor | slice, + operation: Literal["add", "scale", "abs"], + distribution: Literal["uniform", "log_uniform", "gaussian"], +) -> torch.Tensor: + """Perform data randomization based on the given operation and distribution. + + Args: + data: The data tensor to be randomized. Shape is (dim_0, dim_1). + distribution_parameters: The parameters for the distribution to sample values from. + dim_0_ids: The indices of the first dimension to randomize. + dim_1_ids: The indices of the second dimension to randomize. + operation: The operation to perform on the data. Options: 'add', 'scale', 'abs'. + distribution: The distribution to sample the random values from. Options: 'uniform', 'log_uniform'. + + Returns: + The data tensor after randomization. Shape is (dim_0, dim_1). + + Raises: + NotImplementedError: If the operation or distribution is not supported. + """ + # resolve shape + # -- dim 0 + if dim_0_ids is None: + n_dim_0 = data.shape[0] + dim_0_ids = slice(None) + else: + n_dim_0 = len(dim_0_ids) + if not isinstance(dim_1_ids, slice): + dim_0_ids = dim_0_ids[:, None] + # -- dim 1 + if isinstance(dim_1_ids, slice): + n_dim_1 = data.shape[1] + else: + n_dim_1 = len(dim_1_ids) + + # resolve the distribution + if distribution == "uniform": + dist_fn = math_utils.sample_uniform + elif distribution == "log_uniform": + dist_fn = math_utils.sample_log_uniform + elif distribution == "gaussian": + dist_fn = math_utils.sample_gaussian + else: + raise NotImplementedError( + f"Unknown distribution: '{distribution}' for joint properties randomization." + " Please use 'uniform', 'log_uniform', 'gaussian'." + ) + # perform the operation + if operation == "add": + data[dim_0_ids, dim_1_ids] += dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device) + elif operation == "scale": + data[dim_0_ids, dim_1_ids] *= dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device) + elif operation == "abs": + data[dim_0_ids, dim_1_ids] = dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device) + else: + raise NotImplementedError( + f"Unknown operation: '{operation}' for property randomization. Please use 'add', 'scale', or 'abs'." + ) + return data + + +def _validate_scale_range( + params: tuple[float, float] | None, + name: str, + *, + allow_negative: bool = False, + allow_zero: bool = True, +) -> None: + """ + Validates a (low, high) tuple used in scale-based randomization. + + This function ensures the tuple follows expected rules when applying a 'scale' + operation. It performs type and value checks, optionally allowing negative or + zero lower bounds. + + Args: + params (tuple[float, float] | None): The (low, high) range to validate. If None, + validation is skipped. + name (str): The name of the parameter being validated, used for error messages. + allow_negative (bool, optional): If True, allows the lower bound to be negative. + Defaults to False. + allow_zero (bool, optional): If True, allows the lower bound to be zero. + Defaults to True. + + Raises: + TypeError: If `params` is not a tuple of two numbers. + ValueError: If the lower bound is negative or zero when not allowed. + ValueError: If the upper bound is less than the lower bound. + + Example: + _validate_scale_range((0.5, 1.5), "mass_scale") + """ + if params is None: # caller didn’t request randomisation for this field + return + low, high = params + if not isinstance(low, (int, float)) or not isinstance(high, (int, float)): + raise TypeError(f"{name}: expected (low, high) to be a tuple of numbers, got {params}.") + if not allow_negative and not allow_zero and low <= 0: + raise ValueError(f"{name}: lower bound must be > 0 when using the 'scale' operation (got {low}).") + if not allow_negative and allow_zero and low < 0: + raise ValueError(f"{name}: lower bound must be ≥ 0 when using the 'scale' operation (got {low}).") + if high < low: + raise ValueError(f"{name}: upper bound ({high}) must be ≥ lower bound ({low}).") diff --git a/source/isaaclab/isaaclab/envs/mdp/observations.py b/source/isaaclab/isaaclab/envs/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..9839e0d708377ddf12fd2714d93f48a7b6da4267 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/observations.py @@ -0,0 +1,692 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to create observation terms. + +The functions can be passed to the :class:`isaaclab.managers.ObservationTermCfg` object to enable +the observation introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers.manager_base import ManagerTermBase +from isaaclab.managers.manager_term_cfg import ObservationTermCfg +from isaaclab.sensors import Camera, Imu, RayCaster, RayCasterCamera, TiledCamera + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv, ManagerBasedRLEnv + +from isaaclab.envs.utils.io_descriptors import ( + generic_io_descriptor, + record_body_names, + record_dtype, + record_joint_names, + record_joint_pos_offsets, + record_joint_vel_offsets, + record_shape, +) + +""" +Root state. +""" + + +@generic_io_descriptor(units="m", axes=["Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]) +def base_pos_z(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Root height in the simulation world frame.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.root_pos_w[:, 2].unsqueeze(-1) + + +@generic_io_descriptor( + units="m/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def base_lin_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Root linear velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_lin_vel_b + + +@generic_io_descriptor( + units="rad/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def base_ang_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Root angular velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_ang_vel_b + + +@generic_io_descriptor( + units="m/s^2", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def projected_gravity(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Gravity projection on the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.projected_gravity_b + + +@generic_io_descriptor( + units="m", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def root_pos_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Asset root position in the environment frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w - env.scene.env_origins + + +@generic_io_descriptor( + units="unit", axes=["W", "X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def root_quat_w( + env: ManagerBasedEnv, make_quat_unique: bool = False, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Asset root orientation (w, x, y, z) in the environment frame. + + If :attr:`make_quat_unique` is True, then returned quaternion is made unique by ensuring + the quaternion has non-negative real component. This is because both ``q`` and ``-q`` represent + the same orientation. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + quat = asset.data.root_quat_w + # make the quaternion real-part positive if configured + return math_utils.quat_unique(quat) if make_quat_unique else quat + + +@generic_io_descriptor( + units="m/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def root_lin_vel_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Asset root linear velocity in the environment frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_lin_vel_w + + +@generic_io_descriptor( + units="rad/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype] +) +def root_ang_vel_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Asset root angular velocity in the environment frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_ang_vel_w + + +""" +Body state +""" + + +@generic_io_descriptor(observation_type="BodyState", on_inspect=[record_shape, record_dtype, record_body_names]) +def body_pose_w( + env: ManagerBasedEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """The flattened body poses of the asset w.r.t the env.scene.origin. + + Note: Only the bodies configured in :attr:`asset_cfg.body_ids` will have their poses returned. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with this observation. + + Returns: + The poses of bodies in articulation [num_env, 7 * num_bodies]. Pose order is [x,y,z,qw,qx,qy,qz]. + Output is stacked horizontally per body. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + # access the body poses in world frame + pose = asset.data.body_pose_w[:, asset_cfg.body_ids, :7] + if isinstance(asset_cfg.body_ids, (slice, int)): + pose = pose.clone() # if slice or int, make a copy to avoid modifying original data + pose[..., :3] = pose[..., :3] - env.scene.env_origins.unsqueeze(1) + return pose.reshape(env.num_envs, -1) + + +@generic_io_descriptor(observation_type="BodyState", on_inspect=[record_shape, record_dtype, record_body_names]) +def body_projected_gravity_b( + env: ManagerBasedEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """The direction of gravity projected on to bodies of an Articulation. + + Note: Only the bodies configured in :attr:`asset_cfg.body_ids` will have their poses returned. + + Args: + env: The environment. + asset_cfg: The Articulation associated with this observation. + + Returns: + The unit vector direction of gravity projected onto body_name's frame. Gravity projection vector order is + [x,y,z]. Output is stacked horizontally per body. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + body_quat = asset.data.body_quat_w[:, asset_cfg.body_ids] + gravity_dir = asset.data.GRAVITY_VEC_W.unsqueeze(1) + return math_utils.quat_apply_inverse(body_quat, gravity_dir).view(env.num_envs, -1) + + +""" +Joint state. +""" + + +@generic_io_descriptor( + observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="rad" +) +def joint_pos(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint positions of the asset. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.joint_pos[:, asset_cfg.joint_ids] + + +@generic_io_descriptor( + observation_type="JointState", + on_inspect=[record_joint_names, record_dtype, record_shape, record_joint_pos_offsets], + units="rad", +) +def joint_pos_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint positions of the asset w.r.t. the default joint positions. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids] + + +@generic_io_descriptor(observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape]) +def joint_pos_limit_normalized( + env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """The joint positions of the asset normalized with the asset's joint limits. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their normalized positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return math_utils.scale_transform( + asset.data.joint_pos[:, asset_cfg.joint_ids], + asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids, 0], + asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids, 1], + ) + + +@generic_io_descriptor( + observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="rad/s" +) +def joint_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")): + """The joint velocities of the asset. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their velocities returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.joint_vel[:, asset_cfg.joint_ids] + + +@generic_io_descriptor( + observation_type="JointState", + on_inspect=[record_joint_names, record_dtype, record_shape, record_joint_vel_offsets], + units="rad/s", +) +def joint_vel_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")): + """The joint velocities of the asset w.r.t. the default joint velocities. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their velocities returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.joint_vel[:, asset_cfg.joint_ids] - asset.data.default_joint_vel[:, asset_cfg.joint_ids] + + +@generic_io_descriptor( + observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="N.m" +) +def joint_effort(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint applied effort of the robot. + + NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their effort returned. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with this observation. + + Returns: + The joint effort (N or N-m) for joint_names in asset_cfg, shape is [num_env,num_joints]. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return asset.data.applied_torque[:, asset_cfg.joint_ids] + + +""" +Sensors. +""" + + +def height_scan(env: ManagerBasedEnv, sensor_cfg: SceneEntityCfg, offset: float = 0.5) -> torch.Tensor: + """Height scan from the given sensor w.r.t. the sensor's frame. + + The provided offset (Defaults to 0.5) is subtracted from the returned values. + """ + # extract the used quantities (to enable type-hinting) + sensor: RayCaster = env.scene.sensors[sensor_cfg.name] + # height scan: height = sensor_height - hit_point_z - offset + return sensor.data.pos_w[:, 2].unsqueeze(1) - sensor.data.ray_hits_w[..., 2] - offset + + +def body_incoming_wrench(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Incoming spatial wrench on bodies of an articulation in the simulation world frame. + + This is the 6-D wrench (force and torque) applied to the body link by the incoming joint force. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # obtain the link incoming forces in world frame + body_incoming_joint_wrench_b = asset.data.body_incoming_joint_wrench_b[:, asset_cfg.body_ids] + return body_incoming_joint_wrench_b.view(env.num_envs, -1) + + +def imu_orientation(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("imu")) -> torch.Tensor: + """Imu sensor orientation in the simulation world frame. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with an IMU sensor. Defaults to SceneEntityCfg("imu"). + + Returns: + Orientation in the world frame in (w, x, y, z) quaternion form. Shape is (num_envs, 4). + """ + # extract the used quantities (to enable type-hinting) + asset: Imu = env.scene[asset_cfg.name] + # return the orientation quaternion + return asset.data.quat_w + + +def imu_projected_gravity(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("imu")) -> torch.Tensor: + """Imu sensor orientation w.r.t the env.scene.origin. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with an Imu sensor. + + Returns: + Gravity projected on imu_frame, shape of torch.tensor is (num_env,3). + """ + + asset: Imu = env.scene[asset_cfg.name] + return asset.data.projected_gravity_b + + +def imu_ang_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("imu")) -> torch.Tensor: + """Imu sensor angular velocity w.r.t. environment origin expressed in the sensor frame. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with an IMU sensor. Defaults to SceneEntityCfg("imu"). + + Returns: + The angular velocity (rad/s) in the sensor frame. Shape is (num_envs, 3). + """ + # extract the used quantities (to enable type-hinting) + asset: Imu = env.scene[asset_cfg.name] + # return the angular velocity + return asset.data.ang_vel_b + + +def imu_lin_acc(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("imu")) -> torch.Tensor: + """Imu sensor linear acceleration w.r.t. the environment origin expressed in sensor frame. + + Args: + env: The environment. + asset_cfg: The SceneEntity associated with an IMU sensor. Defaults to SceneEntityCfg("imu"). + + Returns: + The linear acceleration (m/s^2) in the sensor frame. Shape is (num_envs, 3). + """ + asset: Imu = env.scene[asset_cfg.name] + return asset.data.lin_acc_b + + +def image( + env: ManagerBasedEnv, + sensor_cfg: SceneEntityCfg = SceneEntityCfg("tiled_camera"), + data_type: str = "rgb", + convert_perspective_to_orthogonal: bool = False, + normalize: bool = True, +) -> torch.Tensor: + """Images of a specific datatype from the camera sensor. + + If the flag :attr:`normalize` is True, post-processing of the images are performed based on their + data-types: + + - "rgb": Scales the image to (0, 1) and subtracts with the mean of the current image batch. + - "depth" or "distance_to_camera" or "distance_to_plane": Replaces infinity values with zero. + + Args: + env: The environment the cameras are placed within. + sensor_cfg: The desired sensor to read from. Defaults to SceneEntityCfg("tiled_camera"). + data_type: The data type to pull from the desired camera. Defaults to "rgb". + convert_perspective_to_orthogonal: Whether to orthogonalize perspective depth images. + This is used only when the data type is "distance_to_camera". Defaults to False. + normalize: Whether to normalize the images. This depends on the selected data type. + Defaults to True. + + Returns: + The images produced at the last time-step + """ + # extract the used quantities (to enable type-hinting) + sensor: TiledCamera | Camera | RayCasterCamera = env.scene.sensors[sensor_cfg.name] + + # obtain the input image + images = sensor.data.output[data_type] + + # depth image conversion + if (data_type == "distance_to_camera") and convert_perspective_to_orthogonal: + images = math_utils.orthogonalize_perspective_depth(images, sensor.data.intrinsic_matrices) + + # rgb/depth/normals image normalization + if normalize: + if data_type == "rgb": + images = images.float() / 255.0 + mean_tensor = torch.mean(images, dim=(1, 2), keepdim=True) + images -= mean_tensor + elif "distance_to" in data_type or "depth" in data_type: + images[images == float("inf")] = 0 + elif "normals" in data_type: + images = (images + 1.0) * 0.5 + + return images.clone() + + +class image_features(ManagerTermBase): + """Extracted image features from a pre-trained frozen encoder. + + This term uses models from the model zoo in PyTorch and extracts features from the images. + + It calls the :func:`image` function to get the images and then processes them using the model zoo. + + A user can provide their own model zoo configuration to use different models for feature extraction. + The model zoo configuration should be a dictionary that maps different model names to a dictionary + that defines the model, preprocess and inference functions. The dictionary should have the following + entries: + + - "model": A callable that returns the model when invoked without arguments. + - "reset": A callable that resets the model. This is useful when the model has a state that needs to be reset. + - "inference": A callable that, when given the model and the images, returns the extracted features. + + If the model zoo configuration is not provided, the default model zoo configurations are used. The default + model zoo configurations include the models from Theia :cite:`shang2024theia` and ResNet :cite:`he2016deep`. + These models are loaded from `Hugging-Face transformers `_ and + `PyTorch torchvision `_ respectively. + + Args: + sensor_cfg: The sensor configuration to poll. Defaults to SceneEntityCfg("tiled_camera"). + data_type: The sensor data type. Defaults to "rgb". + convert_perspective_to_orthogonal: Whether to orthogonalize perspective depth images. + This is used only when the data type is "distance_to_camera". Defaults to False. + model_zoo_cfg: A user-defined dictionary that maps different model names to their respective configurations. + Defaults to None. If None, the default model zoo configurations are used. + model_name: The name of the model to use for inference. Defaults to "resnet18". + model_device: The device to store and infer the model on. This is useful when offloading the computation + from the environment simulation device. Defaults to the environment device. + inference_kwargs: Additional keyword arguments to pass to the inference function. Defaults to None, + which means no additional arguments are passed. + + Returns: + The extracted features tensor. Shape is (num_envs, feature_dim). + + Raises: + ValueError: When the model name is not found in the provided model zoo configuration. + ValueError: When the model name is not found in the default model zoo configuration. + """ + + def __init__(self, cfg: ObservationTermCfg, env: ManagerBasedEnv): + # initialize the base class + super().__init__(cfg, env) + + # extract parameters from the configuration + self.model_zoo_cfg: dict = cfg.params.get("model_zoo_cfg") # type: ignore + self.model_name: str = cfg.params.get("model_name", "resnet18") # type: ignore + self.model_device: str = cfg.params.get("model_device", env.device) # type: ignore + + # List of Theia models - These are configured through `_prepare_theia_transformer_model` function + default_theia_models = [ + "theia-tiny-patch16-224-cddsv", + "theia-tiny-patch16-224-cdiv", + "theia-small-patch16-224-cdiv", + "theia-base-patch16-224-cdiv", + "theia-small-patch16-224-cddsv", + "theia-base-patch16-224-cddsv", + ] + # List of ResNet models - These are configured through `_prepare_resnet_model` function + default_resnet_models = ["resnet18", "resnet34", "resnet50", "resnet101"] + + # Check if model name is specified in the model zoo configuration + if self.model_zoo_cfg is not None and self.model_name not in self.model_zoo_cfg: + raise ValueError( + f"Model name '{self.model_name}' not found in the provided model zoo configuration." + " Please add the model to the model zoo configuration or use a different model name." + f" Available models in the provided list: {list(self.model_zoo_cfg.keys())}." + "\nHint: If you want to use a default model, consider using one of the following models:" + f" {default_theia_models + default_resnet_models}. In this case, you can remove the" + " 'model_zoo_cfg' parameter from the observation term configuration." + ) + if self.model_zoo_cfg is None: + if self.model_name in default_theia_models: + model_config = self._prepare_theia_transformer_model(self.model_name, self.model_device) + elif self.model_name in default_resnet_models: + model_config = self._prepare_resnet_model(self.model_name, self.model_device) + else: + raise ValueError( + f"Model name '{self.model_name}' not found in the default model zoo configuration." + f" Available models: {default_theia_models + default_resnet_models}." + ) + else: + model_config = self.model_zoo_cfg[self.model_name] + + # Retrieve the model, preprocess and inference functions + self._model = model_config["model"]() + self._reset_fn = model_config.get("reset") + self._inference_fn = model_config["inference"] + + def reset(self, env_ids: torch.Tensor | None = None): + # reset the model if a reset function is provided + # this might be useful when the model has a state that needs to be reset + # for example: video transformers + if self._reset_fn is not None: + self._reset_fn(self._model, env_ids) + + def __call__( + self, + env: ManagerBasedEnv, + sensor_cfg: SceneEntityCfg = SceneEntityCfg("tiled_camera"), + data_type: str = "rgb", + convert_perspective_to_orthogonal: bool = False, + model_zoo_cfg: dict | None = None, + model_name: str = "resnet18", + model_device: str | None = None, + inference_kwargs: dict | None = None, + ) -> torch.Tensor: + # obtain the images from the sensor + image_data = image( + env=env, + sensor_cfg=sensor_cfg, + data_type=data_type, + convert_perspective_to_orthogonal=convert_perspective_to_orthogonal, + normalize=False, # we pre-process based on model + ) + # store the device of the image + image_device = image_data.device + # forward the images through the model + features = self._inference_fn(self._model, image_data, **(inference_kwargs or {})) + + # move the features back to the image device + return features.detach().to(image_device) + + """ + Helper functions. + """ + + def _prepare_theia_transformer_model(self, model_name: str, model_device: str) -> dict: + """Prepare the Theia transformer model for inference. + + Args: + model_name: The name of the Theia transformer model to prepare. + model_device: The device to store and infer the model on. + + Returns: + A dictionary containing the model and inference functions. + """ + from transformers import AutoModel + + def _load_model() -> torch.nn.Module: + """Load the Theia transformer model.""" + model = AutoModel.from_pretrained(f"theaiinstitute/{model_name}", trust_remote_code=True).eval() + return model.to(model_device) + + def _inference(model, images: torch.Tensor) -> torch.Tensor: + """Inference the Theia transformer model. + + Args: + model: The Theia transformer model. + images: The preprocessed image tensor. Shape is (num_envs, height, width, channel). + + Returns: + The extracted features tensor. Shape is (num_envs, feature_dim). + """ + # Move the image to the model device + image_proc = images.to(model_device) + # permute the image to (num_envs, channel, height, width) + image_proc = image_proc.permute(0, 3, 1, 2).float() / 255.0 + # Normalize the image + mean = torch.tensor([0.485, 0.456, 0.406], device=model_device).view(1, 3, 1, 1) + std = torch.tensor([0.229, 0.224, 0.225], device=model_device).view(1, 3, 1, 1) + image_proc = (image_proc - mean) / std + + # Taken from Transformers; inference converted to be GPU only + features = model.backbone.model(pixel_values=image_proc, interpolate_pos_encoding=True) + return features.last_hidden_state[:, 1:] + + # return the model, preprocess and inference functions + return {"model": _load_model, "inference": _inference} + + def _prepare_resnet_model(self, model_name: str, model_device: str) -> dict: + """Prepare the ResNet model for inference. + + Args: + model_name: The name of the ResNet model to prepare. + model_device: The device to store and infer the model on. + + Returns: + A dictionary containing the model and inference functions. + """ + from torchvision import models + + def _load_model() -> torch.nn.Module: + """Load the ResNet model.""" + # map the model name to the weights + resnet_weights = { + "resnet18": "ResNet18_Weights.IMAGENET1K_V1", + "resnet34": "ResNet34_Weights.IMAGENET1K_V1", + "resnet50": "ResNet50_Weights.IMAGENET1K_V1", + "resnet101": "ResNet101_Weights.IMAGENET1K_V1", + } + + # load the model + model = getattr(models, model_name)(weights=resnet_weights[model_name]).eval() + return model.to(model_device) + + def _inference(model, images: torch.Tensor) -> torch.Tensor: + """Inference the ResNet model. + + Args: + model: The ResNet model. + images: The preprocessed image tensor. Shape is (num_envs, channel, height, width). + + Returns: + The extracted features tensor. Shape is (num_envs, feature_dim). + """ + # move the image to the model device + image_proc = images.to(model_device) + # permute the image to (num_envs, channel, height, width) + image_proc = image_proc.permute(0, 3, 1, 2).float() / 255.0 + # normalize the image + mean = torch.tensor([0.485, 0.456, 0.406], device=model_device).view(1, 3, 1, 1) + std = torch.tensor([0.229, 0.224, 0.225], device=model_device).view(1, 3, 1, 1) + image_proc = (image_proc - mean) / std + + # forward the image through the model + return model(image_proc) + + # return the model, preprocess and inference functions + return {"model": _load_model, "inference": _inference} + + +""" +Actions. +""" + + +@generic_io_descriptor(dtype=torch.float32, observation_type="Action", on_inspect=[record_shape]) +def last_action(env: ManagerBasedEnv, action_name: str | None = None) -> torch.Tensor: + """The last input action to the environment. + + The name of the action term for which the action is required. If None, the + entire action tensor is returned. + """ + if action_name is None: + return env.action_manager.action + else: + return env.action_manager.get_term(action_name).raw_actions + + +""" +Commands. +""" + + +@generic_io_descriptor(dtype=torch.float32, observation_type="Command", on_inspect=[record_shape]) +def generated_commands(env: ManagerBasedRLEnv, command_name: str | None = None) -> torch.Tensor: + """The generated command from command term in the command manager with the given name.""" + return env.command_manager.get_command(command_name) + + +""" +Time. +""" + + +def current_time_s(env: ManagerBasedRLEnv) -> torch.Tensor: + """The current time in the episode (in seconds).""" + return env.episode_length_buf.unsqueeze(1) * env.step_dt + + +def remaining_time_s(env: ManagerBasedRLEnv) -> torch.Tensor: + """The maximum time remaining in the episode (in seconds).""" + return env.max_episode_length_s - env.episode_length_buf.unsqueeze(1) * env.step_dt diff --git a/source/isaaclab/isaaclab/envs/mdp/recorders/__init__.py b/source/isaaclab/isaaclab/envs/mdp/recorders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cacc5e15c7f570829f4f0eb551c3de2109668223 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/recorders/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Various recorder terms that can be used in the environment.""" + +from .recorders import * +from .recorders_cfg import * diff --git a/source/isaaclab/isaaclab/envs/mdp/recorders/recorders.py b/source/isaaclab/isaaclab/envs/mdp/recorders/recorders.py new file mode 100644 index 0000000000000000000000000000000000000000..a9315bbca63d794b1f9c44e44ae8d4079076da2b --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/recorders/recorders.py @@ -0,0 +1,62 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +from collections.abc import Sequence + +import torch + +from isaaclab.managers.recorder_manager import RecorderTerm + + +class InitialStateRecorder(RecorderTerm): + """Recorder term that records the initial state of the environment after reset.""" + + def record_post_reset(self, env_ids: Sequence[int] | None): + def extract_env_ids_values(value): + nonlocal env_ids + if isinstance(value, dict): + return {k: extract_env_ids_values(v) for k, v in value.items()} + return value[env_ids] + + return "initial_state", extract_env_ids_values(self._env.scene.get_state(is_relative=True)) + + +class PostStepStatesRecorder(RecorderTerm): + """Recorder term that records the state of the environment at the end of each step.""" + + def record_post_step(self): + return "states", self._env.scene.get_state(is_relative=True) + + +class PreStepActionsRecorder(RecorderTerm): + """Recorder term that records the actions in the beginning of each step.""" + + def record_pre_step(self): + return "actions", self._env.action_manager.action + + +class PreStepFlatPolicyObservationsRecorder(RecorderTerm): + """Recorder term that records the policy group observations in each step.""" + + def record_pre_step(self): + return "obs", self._env.obs_buf["policy"] + + +class PostStepProcessedActionsRecorder(RecorderTerm): + """Recorder term that records processed actions at the end of each step.""" + + def record_post_step(self): + processed_actions = None + + # Loop through active terms and concatenate their processed actions + for term_name in self._env.action_manager.active_terms: + term_actions = self._env.action_manager.get_term(term_name).processed_actions.clone() + if processed_actions is None: + processed_actions = term_actions + else: + processed_actions = torch.cat([processed_actions, term_actions], dim=-1) + + return "processed_actions", processed_actions diff --git a/source/isaaclab/isaaclab/envs/mdp/recorders/recorders_cfg.py b/source/isaaclab/isaaclab/envs/mdp/recorders/recorders_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d67350d7f2097494b7efb2d8530476460f7717a8 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/recorders/recorders_cfg.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from isaaclab.managers.recorder_manager import RecorderManagerBaseCfg, RecorderTerm, RecorderTermCfg +from isaaclab.utils import configclass + +from . import recorders + +## +# State recorders. +## + + +@configclass +class InitialStateRecorderCfg(RecorderTermCfg): + """Configuration for the initial state recorder term.""" + + class_type: type[RecorderTerm] = recorders.InitialStateRecorder + + +@configclass +class PostStepStatesRecorderCfg(RecorderTermCfg): + """Configuration for the step state recorder term.""" + + class_type: type[RecorderTerm] = recorders.PostStepStatesRecorder + + +@configclass +class PreStepActionsRecorderCfg(RecorderTermCfg): + """Configuration for the step action recorder term.""" + + class_type: type[RecorderTerm] = recorders.PreStepActionsRecorder + + +@configclass +class PreStepFlatPolicyObservationsRecorderCfg(RecorderTermCfg): + """Configuration for the step policy observation recorder term.""" + + class_type: type[RecorderTerm] = recorders.PreStepFlatPolicyObservationsRecorder + + +@configclass +class PostStepProcessedActionsRecorderCfg(RecorderTermCfg): + """Configuration for the post step processed actions recorder term.""" + + class_type: type[RecorderTerm] = recorders.PostStepProcessedActionsRecorder + + +## +# Recorder manager configurations. +## + + +@configclass +class ActionStateRecorderManagerCfg(RecorderManagerBaseCfg): + """Recorder configurations for recording actions and states.""" + + record_initial_state = InitialStateRecorderCfg() + record_post_step_states = PostStepStatesRecorderCfg() + record_pre_step_actions = PreStepActionsRecorderCfg() + record_pre_step_flat_policy_observations = PreStepFlatPolicyObservationsRecorderCfg() + record_post_step_processed_actions = PostStepProcessedActionsRecorderCfg() diff --git a/source/isaaclab/isaaclab/envs/mdp/rewards.py b/source/isaaclab/isaaclab/envs/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..774c37aefa9155271ebec6f457c1144923e5a9fe --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/rewards.py @@ -0,0 +1,326 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to enable reward functions. + +The functions can be passed to the :class:`isaaclab.managers.RewardTermCfg` object to include +the reward introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers.manager_base import ManagerTermBase +from isaaclab.managers.manager_term_cfg import RewardTermCfg +from isaaclab.sensors import ContactSensor, RayCaster + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + +""" +General. +""" + + +def is_alive(env: ManagerBasedRLEnv) -> torch.Tensor: + """Reward for being alive.""" + return (~env.termination_manager.terminated).float() + + +def is_terminated(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize terminated episodes that don't correspond to episodic timeouts.""" + return env.termination_manager.terminated.float() + + +class is_terminated_term(ManagerTermBase): + """Penalize termination for specific terms that don't correspond to episodic timeouts. + + The parameters are as follows: + + * attr:`term_keys`: The termination terms to penalize. This can be a string, a list of strings + or regular expressions. Default is ".*" which penalizes all terminations. + + The reward is computed as the sum of the termination terms that are not episodic timeouts. + This means that the reward is 0 if the episode is terminated due to an episodic timeout. Otherwise, + if two termination terms are active, the reward is 2. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + # initialize the base class + super().__init__(cfg, env) + # find and store the termination terms + term_keys = cfg.params.get("term_keys", ".*") + self._term_names = env.termination_manager.find_terms(term_keys) + + def __call__(self, env: ManagerBasedRLEnv, term_keys: str | list[str] = ".*") -> torch.Tensor: + # Return the unweighted reward for the termination terms + reset_buf = torch.zeros(env.num_envs, device=env.device) + for term in self._term_names: + # Sums over terminations term values to account for multiple terminations in the same step + reset_buf += env.termination_manager.get_term(term) + + return (reset_buf * (~env.termination_manager.time_outs)).float() + + +""" +Root penalties. +""" + + +def lin_vel_z_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize z-axis base linear velocity using L2 squared kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return torch.square(asset.data.root_lin_vel_b[:, 2]) + + +def ang_vel_xy_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize xy-axis base angular velocity using L2 squared kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return torch.sum(torch.square(asset.data.root_ang_vel_b[:, :2]), dim=1) + + +def flat_orientation_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize non-flat base orientation using L2 squared kernel. + + This is computed by penalizing the xy-components of the projected gravity vector. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return torch.sum(torch.square(asset.data.projected_gravity_b[:, :2]), dim=1) + + +def base_height_l2( + env: ManagerBasedRLEnv, + target_height: float, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + sensor_cfg: SceneEntityCfg | None = None, +) -> torch.Tensor: + """Penalize asset height from its target using L2 squared kernel. + + Note: + For flat terrain, target height is in the world frame. For rough terrain, + sensor readings can adjust the target height to account for the terrain. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + if sensor_cfg is not None: + sensor: RayCaster = env.scene[sensor_cfg.name] + # Adjust the target height using the sensor data + adjusted_target_height = target_height + torch.mean(sensor.data.ray_hits_w[..., 2], dim=1) + else: + # Use the provided target height directly for flat terrain + adjusted_target_height = target_height + # Compute the L2 squared penalty + return torch.square(asset.data.root_pos_w[:, 2] - adjusted_target_height) + + +def body_lin_acc_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize the linear acceleration of bodies using L2-kernel.""" + asset: Articulation = env.scene[asset_cfg.name] + return torch.sum(torch.norm(asset.data.body_lin_acc_w[:, asset_cfg.body_ids, :], dim=-1), dim=1) + + +""" +Joint penalties. +""" + + +def joint_torques_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize joint torques applied on the articulation using L2 squared kernel. + + .. note:: + Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint torques + contribute to the term. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.sum(torch.square(asset.data.applied_torque[:, asset_cfg.joint_ids]), dim=1) + + +def joint_vel_l1(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint velocities on the articulation using an L1-kernel.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.sum(torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids]), dim=1) + + +def joint_vel_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize joint velocities on the articulation using L2 squared kernel. + + .. note:: + Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint velocities + contribute to the term. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.sum(torch.square(asset.data.joint_vel[:, asset_cfg.joint_ids]), dim=1) + + +def joint_acc_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize joint accelerations on the articulation using L2 squared kernel. + + .. note:: + Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint accelerations + contribute to the term. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.sum(torch.square(asset.data.joint_acc[:, asset_cfg.joint_ids]), dim=1) + + +def joint_deviation_l1(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize joint positions that deviate from the default one.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute out of limits constraints + angle = asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids] + return torch.sum(torch.abs(angle), dim=1) + + +def joint_pos_limits(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize joint positions if they cross the soft limits. + + This is computed as a sum of the absolute value of the difference between the joint position and the soft limits. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute out of limits constraints + out_of_limits = -( + asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids, 0] + ).clip(max=0.0) + out_of_limits += ( + asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids, 1] + ).clip(min=0.0) + return torch.sum(out_of_limits, dim=1) + + +def joint_vel_limits( + env: ManagerBasedRLEnv, soft_ratio: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Penalize joint velocities if they cross the soft limits. + + This is computed as a sum of the absolute value of the difference between the joint velocity and the soft limits. + + Args: + soft_ratio: The ratio of the soft limits to be used. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute out of limits constraints + out_of_limits = ( + torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids]) + - asset.data.soft_joint_vel_limits[:, asset_cfg.joint_ids] * soft_ratio + ) + # clip to max error = 1 rad/s per joint to avoid huge penalties + out_of_limits = out_of_limits.clip_(min=0.0, max=1.0) + return torch.sum(out_of_limits, dim=1) + + +""" +Action penalties. +""" + + +def applied_torque_limits(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize applied torques if they cross the limits. + + This is computed as a sum of the absolute value of the difference between the applied torques and the limits. + + .. caution:: + Currently, this only works for explicit actuators since we manually compute the applied torques. + For implicit actuators, we currently cannot retrieve the applied torques from the physics engine. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute out of limits constraints + # TODO: We need to fix this to support implicit joints. + out_of_limits = torch.abs( + asset.data.applied_torque[:, asset_cfg.joint_ids] - asset.data.computed_torque[:, asset_cfg.joint_ids] + ) + return torch.sum(out_of_limits, dim=1) + + +def action_rate_l2(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize the rate of change of the actions using L2 squared kernel.""" + return torch.sum(torch.square(env.action_manager.action - env.action_manager.prev_action), dim=1) + + +def action_l2(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize the actions using L2 squared kernel.""" + return torch.sum(torch.square(env.action_manager.action), dim=1) + + +""" +Contact sensor. +""" + + +def undesired_contacts(env: ManagerBasedRLEnv, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize undesired contacts as the number of violations that are above a threshold.""" + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + # check if contact force is above threshold + net_contact_forces = contact_sensor.data.net_forces_w_history + is_contact = torch.max(torch.norm(net_contact_forces[:, :, sensor_cfg.body_ids], dim=-1), dim=1)[0] > threshold + # sum over contacts for each environment + return torch.sum(is_contact, dim=1) + + +def desired_contacts(env, sensor_cfg: SceneEntityCfg, threshold: float = 1.0) -> torch.Tensor: + """Penalize if none of the desired contacts are present.""" + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + contacts = ( + contact_sensor.data.net_forces_w_history[:, :, sensor_cfg.body_ids, :].norm(dim=-1).max(dim=1)[0] > threshold + ) + zero_contact = (~contacts).all(dim=1) + return 1.0 * zero_contact + + +def contact_forces(env: ManagerBasedRLEnv, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize contact forces as the amount of violations of the net contact force.""" + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + net_contact_forces = contact_sensor.data.net_forces_w_history + # compute the violation + violation = torch.max(torch.norm(net_contact_forces[:, :, sensor_cfg.body_ids], dim=-1), dim=1)[0] - threshold + # compute the penalty + return torch.sum(violation.clip(min=0.0), dim=1) + + +""" +Velocity-tracking rewards. +""" + + +def track_lin_vel_xy_exp( + env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Reward tracking of linear velocity commands (xy axes) using exponential kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + # compute the error + lin_vel_error = torch.sum( + torch.square(env.command_manager.get_command(command_name)[:, :2] - asset.data.root_lin_vel_b[:, :2]), + dim=1, + ) + return torch.exp(-lin_vel_error / std**2) + + +def track_ang_vel_z_exp( + env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Reward tracking of angular velocity commands (yaw) using exponential kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + # compute the error + ang_vel_error = torch.square(env.command_manager.get_command(command_name)[:, 2] - asset.data.root_ang_vel_b[:, 2]) + return torch.exp(-ang_vel_error / std**2) diff --git a/source/isaaclab/isaaclab/envs/mdp/terminations.py b/source/isaaclab/isaaclab/envs/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..5ee3268e48696bb806e6b388d3b29d727b4320d7 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mdp/terminations.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import ContactSensor + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + from isaaclab.managers.command_manager import CommandTerm + +""" +MDP terminations. +""" + + +def time_out(env: ManagerBasedRLEnv) -> torch.Tensor: + """Terminate the episode when the episode length exceeds the maximum episode length.""" + return env.episode_length_buf >= env.max_episode_length + + +def command_resample(env: ManagerBasedRLEnv, command_name: str, num_resamples: int = 1) -> torch.Tensor: + """Terminate the episode based on the total number of times commands have been re-sampled. + + This makes the maximum episode length fluid in nature as it depends on how the commands are + sampled. It is useful in situations where delayed rewards are used :cite:`rudin2022advanced`. + """ + command: CommandTerm = env.command_manager.get_term(command_name) + return torch.logical_and((command.time_left <= env.step_dt), (command.command_counter == num_resamples)) + + +""" +Root terminations. +""" + + +def bad_orientation( + env: ManagerBasedRLEnv, limit_angle: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when the asset's orientation is too far from the desired orientation limits. + + This is computed by checking the angle between the projected gravity vector and the z-axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return torch.acos(-asset.data.projected_gravity_b[:, 2]).abs() > limit_angle + + +def root_height_below_minimum( + env: ManagerBasedRLEnv, minimum_height: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when the asset's root height is below the minimum height. + + Note: + This is currently only supported for flat terrains, i.e. the minimum height is in the world frame. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w[:, 2] < minimum_height + + +def root_pos_behind_robot( + env: ManagerBasedRLEnv, minimum_y: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when the asset's root position is behind the robot. + + Note: + This is currently only supported for flat terrains, i.e. the minimum y position is in the world frame. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w[:, 1] < minimum_y + + + +""" +Joint terminations. +""" + + +def joint_pos_out_of_limit(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Terminate when the asset's joint positions are outside of the soft joint limits.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + if asset_cfg.joint_ids is None: + asset_cfg.joint_ids = slice(None) + + limits = asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids] + out_of_upper_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] > limits[..., 1], dim=1) + out_of_lower_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] < limits[..., 0], dim=1) + return torch.logical_or(out_of_upper_limits, out_of_lower_limits) + + +def joint_pos_out_of_manual_limit( + env: ManagerBasedRLEnv, bounds: tuple[float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when the asset's joint positions are outside of the configured bounds. + + Note: + This function is similar to :func:`joint_pos_out_of_limit` but allows the user to specify the bounds manually. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + if asset_cfg.joint_ids is None: + asset_cfg.joint_ids = slice(None) + # compute any violations + out_of_upper_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] > bounds[1], dim=1) + out_of_lower_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] < bounds[0], dim=1) + return torch.logical_or(out_of_upper_limits, out_of_lower_limits) + + +def joint_vel_out_of_limit(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Terminate when the asset's joint velocities are outside of the soft joint limits.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute any violations + limits = asset.data.soft_joint_vel_limits + return torch.any(torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids]) > limits[:, asset_cfg.joint_ids], dim=1) + + +def joint_vel_out_of_manual_limit( + env: ManagerBasedRLEnv, max_velocity: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when the asset's joint velocities are outside the provided limits.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute any violations + return torch.any(torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids]) > max_velocity, dim=1) + + +def joint_effort_out_of_limit( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Terminate when effort applied on the asset's joints are outside of the soft joint limits. + + In the actuators, the applied torque are the efforts applied on the joints. These are computed by clipping + the computed torques to the joint limits. Hence, we check if the computed torques are equal to the applied + torques. If they are not, it means that clipping has occurred. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # check if any joint effort is out of limit + out_of_limits = ~torch.isclose( + asset.data.computed_torque[:, asset_cfg.joint_ids], asset.data.applied_torque[:, asset_cfg.joint_ids] + ) + return torch.any(out_of_limits, dim=1) + + +""" +Contact sensor. +""" + + +def illegal_contact(env: ManagerBasedRLEnv, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor: + """Terminate when the contact force on the sensor exceeds the force threshold.""" + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + net_contact_forces = contact_sensor.data.net_forces_w_history + # check if any contact force exceeds the threshold + return torch.any( + torch.max(torch.norm(net_contact_forces[:, :, sensor_cfg.body_ids], dim=-1), dim=1)[0] > threshold, dim=1 + ) diff --git a/source/isaaclab/isaaclab/envs/mimic_env_cfg.py b/source/isaaclab/isaaclab/envs/mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c506df7f20bf273dda64fd8e7394b1a3eb6b19a4 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/mimic_env_cfg.py @@ -0,0 +1,320 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the NVIDIA Source Code License [see LICENSE for details]. + +""" +Base MimicEnvCfg object for Isaac Lab Mimic data generation. +""" + +import enum + +from isaaclab.managers.recorder_manager import RecorderManagerBaseCfg +from isaaclab.utils import configclass + + +@configclass +class DataGenConfig: + """Configuration settings for data generation processes within the Isaac Lab Mimic environment.""" + + name: str = "demo" + """The name of the data generation process. Defaults to "demo".""" + + generation_guarantee: bool = True + """Whether to retry generation until generation_num_trials successful demos have been generated. + + If True, generation will be retried until generation_num_trials successful demos are created. + If False, generation will stop after generation_num_trails, regardless of success. + """ + + generation_keep_failed: bool = False + """Whether to keep failed generation trials. + + Keeping failed demonstrations is useful for visualizing and debugging low success rates. + """ + + max_num_failures: int = 50 + """Maximum number of failures allowed before stopping generation.""" + + seed: int = 1 + """Seed for randomization to ensure reproducibility.""" + + """The following configuration values can be changed on the command line, and only serve as defaults.""" + + source_dataset_path: str = None + """Path to the source dataset for mimic generation.""" + + generation_path: str = None + """Path where the generated data will be saved.""" + + generation_num_trials: int = 10 + """Number of trials to be generated.""" + + task_name: str = None + """Name of the task being configured.""" + + """The following configurations are advanced and do not usually need to be changed.""" + + generation_select_src_per_subtask: bool = False + """Whether to select source data per subtask. + + Note: + This requires subtasks to be properly temporally constrained, and may require + additional subtasks to allow for time synchronization. + """ + + generation_select_src_per_arm: bool = False + """Whether to select source data per arm.""" + + generation_transform_first_robot_pose: bool = False + """Whether to transform the first robot pose during generation.""" + + generation_interpolate_from_last_target_pose: bool = True + """Whether to interpolate from last target pose.""" + + use_skillgen: bool = False + """Whether to use skillgen to generate motion trajectories.""" + + use_navigation_controller: bool = False + """Whether to use a navigation controller to generate loco-manipulation trajectories.""" + + +@configclass +class SubTaskConfig: + """ + Configuration settings for specifying subtasks used in Mimic environments. + """ + + """Mandatory options that should be defined for every subtask.""" + + object_ref: str = None + """Reference to the object involved in this subtask. + + Set to None if no object is involved (this is rarely the case). + """ + + subtask_term_signal: str = None + """Subtask termination signal name.""" + + """Advanced options for tuning the generation results.""" + + selection_strategy: str = "random" + """Strategy for selecting a subtask segment. + + Can be one of: + * 'random' + * 'nearest_neighbor_object' + * 'nearest_neighbor_robot_distance' + + Note: + For 'nearest_neighbor_object' and 'nearest_neighbor_robot_distance', the subtask needs + to have 'object_ref' set to a value other than 'None'. These strategies typically yield + higher success rates than the default 'random' strategy when object_ref is set. + """ + + selection_strategy_kwargs: dict = {} + """Additional arguments to the selected strategy. See details on each strategy in + source/isaaclab_mimic/isaaclab_mimic/datagen/selection_strategy.py + Arguments will be passed through to the `select_source_demo` method.""" + + first_subtask_start_offset_range: tuple = (0, 0) + """Range for start offset of the first subtask.""" + + subtask_start_offset_range: tuple = (0, 0) + """Range for start offset of the subtask (only used if use_skillgen is True) + + Note: This value overrides the first_subtask_start_offset_range when skillgen is enabled + """ + + subtask_term_offset_range: tuple = (0, 0) + """Range for offsetting subtask termination.""" + + action_noise: float = 0.03 + """Amplitude of action noise applied.""" + + num_interpolation_steps: int = 5 + """Number of steps for interpolation between waypoints.""" + + num_fixed_steps: int = 0 + """Number of fixed steps for the subtask.""" + + apply_noise_during_interpolation: bool = False + """Whether to apply noise during interpolation.""" + + description: str = "" + """Description of the subtask""" + + next_subtask_description: str = "" + """Instructions for the next subtask""" + + +class SubTaskConstraintType(enum.IntEnum): + """Enum for subtask constraint types.""" + + SEQUENTIAL = 0 + COORDINATION = 1 + + _SEQUENTIAL_FORMER = 2 + _SEQUENTIAL_LATTER = 3 + + +class SubTaskConstraintCoordinationScheme(enum.IntEnum): + """Enum for coordination schemes.""" + + REPLAY = 0 + TRANSFORM = 1 + TRANSLATE = 2 + + +@configclass +class SubTaskConstraintConfig: + """ + Configuration settings for specifying subtask constraints used in multi-eef Mimic environments. + """ + + eef_subtask_constraint_tuple: list[tuple[str, int]] = (("", 0), ("", 0)) + """List of associated subtasks tuples in order. + + The first element of the tuple refers to the eef name. + The second element of the tuple refers to the subtask index of the eef. + """ + + constraint_type: SubTaskConstraintType = None + """Type of constraint to apply between subtasks.""" + + sequential_min_time_diff: int = -1 + """Minimum time difference between two sequential subtasks finishing. + + The second subtask will execute until sequential_min_time_diff steps left in its subtask trajectory + and wait until the first (preconditioned) subtask is finished to continue executing the rest. + If set to -1, the second subtask will start only after the first subtask is finished. + """ + + coordination_scheme: SubTaskConstraintCoordinationScheme = SubTaskConstraintCoordinationScheme.REPLAY + """Scheme to use for coordinating subtasks.""" + + coordination_scheme_pos_noise_scale: float = 0.0 + """Scale of position noise to apply during coordination.""" + + coordination_scheme_rot_noise_scale: float = 0.0 + """Scale of rotation noise to apply during coordination.""" + + coordination_synchronize_start: bool = False + """Whether subtasks should start at the same time.""" + + def generate_runtime_subtask_constraints(self): + """ + Populate expanded task constraints dictionary based on the task constraint config. + The task constraint config contains the configurations set by the user. While the + task_constraints_dict contains flags used to implement the constraint logic in this class. + + The task_constraint_configs may include the following types: + - "sequential" + - "coordination" + + For a "sequential" constraint: + - Data from task_constraint_configs is added to task_constraints_dict as "sequential former" + task constraint. + - The opposite constraint, of type "sequential latter", is also added to task_constraints_dict. + - Additionally, a ("fulfilled", Bool) key-value pair is added to task_constraints_dict. + - This is used to check if the precondition (i.e., the sequential former task) has been met. + - Until the "fulfilled" flag in "sequential latter" is set by "sequential former", + the "sequential latter" subtask will remain paused. + + For a "coordination" constraint: + - Data from task_constraint_configs is added to task_constraints_dict. + - The opposite constraint, of type "coordination", is also added to task_constraints_dict. + - The number of synchronous steps is set to the minimum of subtask_len and concurrent_subtask_len. + - This ensures both concurrent tasks end at the same time step. + - A "selected_src_demo_ind" and "transform" field are used to ensure the transforms used by + both subtasks are the same. + """ + task_constraints_dict = dict() + if self.constraint_type == SubTaskConstraintType.SEQUENTIAL: + constrained_task_spec_key, constrained_subtask_ind = self.eef_subtask_constraint_tuple[1] + assert isinstance(constrained_subtask_ind, int) + pre_condition_task_spec_key, pre_condition_subtask_ind = self.eef_subtask_constraint_tuple[0] + assert isinstance(pre_condition_subtask_ind, int) + assert ( + constrained_task_spec_key, + constrained_subtask_ind, + ) not in task_constraints_dict, "only one constraint per subtask allowed" + task_constraints_dict[(constrained_task_spec_key, constrained_subtask_ind)] = dict( + type=SubTaskConstraintType._SEQUENTIAL_LATTER, + pre_condition_task_spec_key=pre_condition_task_spec_key, + pre_condition_subtask_ind=pre_condition_subtask_ind, + min_time_diff=self.sequential_min_time_diff, + fulfilled=False, + ) + task_constraints_dict[(pre_condition_task_spec_key, pre_condition_subtask_ind)] = dict( + type=SubTaskConstraintType._SEQUENTIAL_FORMER, + constrained_task_spec_key=constrained_task_spec_key, + constrained_subtask_ind=constrained_subtask_ind, + ) + elif self.constraint_type == SubTaskConstraintType.COORDINATION: + constrained_task_spec_key, constrained_subtask_ind = self.eef_subtask_constraint_tuple[0] + assert isinstance(constrained_subtask_ind, int) + concurrent_task_spec_key, concurrent_subtask_ind = self.eef_subtask_constraint_tuple[1] + assert isinstance(concurrent_subtask_ind, int) + if self.coordination_scheme is None: + raise ValueError("Coordination scheme must be specified.") + assert ( + constrained_task_spec_key, + constrained_subtask_ind, + ) not in task_constraints_dict, "only one constraint per subtask allowed" + task_constraints_dict[(constrained_task_spec_key, constrained_subtask_ind)] = dict( + concurrent_task_spec_key=concurrent_task_spec_key, + concurrent_subtask_ind=concurrent_subtask_ind, + type=SubTaskConstraintType.COORDINATION, + fulfilled=False, + finished=False, + selected_src_demo_ind=None, + coordination_scheme=self.coordination_scheme, + coordination_scheme_pos_noise_scale=self.coordination_scheme_pos_noise_scale, + coordination_scheme_rot_noise_scale=self.coordination_scheme_rot_noise_scale, + coordination_synchronize_start=self.coordination_synchronize_start, + synchronous_steps=None, # to be calculated at runtime + ) + task_constraints_dict[(concurrent_task_spec_key, concurrent_subtask_ind)] = dict( + concurrent_task_spec_key=constrained_task_spec_key, + concurrent_subtask_ind=constrained_subtask_ind, + type=SubTaskConstraintType.COORDINATION, + fulfilled=False, + finished=False, + selected_src_demo_ind=None, + coordination_scheme=self.coordination_scheme, + coordination_scheme_pos_noise_scale=self.coordination_scheme_pos_noise_scale, + coordination_scheme_rot_noise_scale=self.coordination_scheme_rot_noise_scale, + coordination_synchronize_start=self.coordination_synchronize_start, + synchronous_steps=None, # to be calculated at runtime + ) + else: + raise ValueError("Constraint type not supported.") + + return task_constraints_dict + + +@configclass +class MimicEnvCfg: + """ + Configuration class for the Mimic environment integration. + + This class consolidates various configuration aspects for the + Isaac Lab Mimic data generation pipeline. + """ + + # Overall configuration for the data generation + datagen_config: DataGenConfig = DataGenConfig() + + # Dictionary of list of subtask configurations for each end-effector. + # Keys are end-effector names. + subtask_configs: dict[str, list[SubTaskConfig]] = {} + + # List of configurations for subtask constraints + task_constraint_configs: list[SubTaskConstraintConfig] = [] + + # Optional recorder configuration + mimic_recorder_config: RecorderManagerBaseCfg | None = None diff --git a/source/isaaclab/isaaclab/envs/ui/__init__.py b/source/isaaclab/isaaclab/envs/ui/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..93db88399e455ff79fc5ef186907646d2e144d96 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/ui/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module providing UI window implementation for environments. + +The UI elements are used to control the environment and visualize the state of the environment. +This includes functionalities such as tracking a robot in the simulation, +toggling different debug visualization tools, and other user-defined functionalities. +""" + +from .base_env_window import BaseEnvWindow +from .empty_window import EmptyWindow +from .manager_based_rl_env_window import ManagerBasedRLEnvWindow +from .viewport_camera_controller import ViewportCameraController diff --git a/source/isaaclab/isaaclab/envs/ui/base_env_window.py b/source/isaaclab/isaaclab/envs/ui/base_env_window.py new file mode 100644 index 0000000000000000000000000000000000000000..2aafe5e6bba2686decd926a38d66bdb523013d15 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/ui/base_env_window.py @@ -0,0 +1,458 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import asyncio +import os +import weakref +from datetime import datetime +from typing import TYPE_CHECKING + +import isaacsim +import omni.kit.app +import omni.kit.commands +import omni.usd +from pxr import PhysxSchema, Sdf, Usd, UsdGeom, UsdPhysics + +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.ui.widgets import ManagerLiveVisualizer + +if TYPE_CHECKING: + import omni.ui + + from ..manager_based_env import ManagerBasedEnv + + +class BaseEnvWindow: + """Window manager for the basic environment. + + This class creates a window that is used to control the environment. The window + contains controls for rendering, debug visualization, and other environment-specific + UI elements. + + Users can add their own UI elements to the window by using the `with` context manager. + This can be done either be inheriting the class or by using the `env.window` object + directly from the standalone execution script. + + Example for adding a UI element from the standalone execution script: + >>> with env.window.ui_window_elements["main_vstack"]: + >>> ui.Label("My UI element") + + """ + + def __init__(self, env: ManagerBasedEnv, window_name: str = "IsaacLab"): + """Initialize the window. + + Args: + env: The environment object. + window_name: The name of the window. Defaults to "IsaacLab". + """ + # store inputs + self.env = env + # prepare the list of assets that can be followed by the viewport camera + # note that the first two options are "World" and "Env" which are special cases + self._viewer_assets_options = [ + "World", + "Env", + *self.env.scene.rigid_objects.keys(), + *self.env.scene.articulations.keys(), + ] + + # get stage handle + self.stage = get_current_stage() + + # Listeners for environment selection changes + self._ui_listeners: list[ManagerLiveVisualizer] = [] + + print("Creating window for environment.") + # create window for UI + self.ui_window = omni.ui.Window( + window_name, width=400, height=500, visible=True, dock_preference=omni.ui.DockPreference.RIGHT_TOP + ) + # dock next to properties window + asyncio.ensure_future(self._dock_window(window_title=self.ui_window.title)) + + # keep a dictionary of stacks so that child environments can add their own UI elements + # this can be done by using the `with` context manager + self.ui_window_elements = dict() + # create main frame + self.ui_window_elements["main_frame"] = self.ui_window.frame + with self.ui_window_elements["main_frame"]: + # create main stack + self.ui_window_elements["main_vstack"] = omni.ui.VStack(spacing=5, height=0) + with self.ui_window_elements["main_vstack"]: + # create collapsable frame for simulation + self._build_sim_frame() + # create collapsable frame for viewer + self._build_viewer_frame() + # create collapsable frame for debug visualization + self._build_debug_vis_frame() + with self.ui_window_elements["debug_frame"]: + with self.ui_window_elements["debug_vstack"]: + self._visualize_manager(title="Actions", class_name="action_manager") + self._visualize_manager(title="Observations", class_name="observation_manager") + + def __del__(self): + """Destructor for the window.""" + # destroy the window + if self.ui_window is not None: + self.ui_window.visible = False + self.ui_window.destroy() + self.ui_window = None + + """ + Build sub-sections of the UI. + """ + + def _build_sim_frame(self): + """Builds the sim-related controls frame for the UI.""" + # create collapsable frame for controls + self.ui_window_elements["sim_frame"] = omni.ui.CollapsableFrame( + title="Simulation Settings", + width=omni.ui.Fraction(1), + height=0, + collapsed=False, + style=isaacsim.gui.components.ui_utils.get_style(), + horizontal_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, + vertical_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, + ) + with self.ui_window_elements["sim_frame"]: + # create stack for controls + self.ui_window_elements["sim_vstack"] = omni.ui.VStack(spacing=5, height=0) + with self.ui_window_elements["sim_vstack"]: + # create rendering mode dropdown + render_mode_cfg = { + "label": "Rendering Mode", + "type": "dropdown", + "default_val": self.env.sim.render_mode.value, + "items": [member.name for member in self.env.sim.RenderMode if member.value >= 0], + "tooltip": "Select a rendering mode\n" + self.env.sim.RenderMode.__doc__, + "on_clicked_fn": lambda value: self.env.sim.set_render_mode(self.env.sim.RenderMode[value]), + } + self.ui_window_elements["render_dropdown"] = isaacsim.gui.components.ui_utils.dropdown_builder( + **render_mode_cfg + ) + + # create animation recording box + record_animate_cfg = { + "label": "Record Animation", + "type": "state_button", + "a_text": "START", + "b_text": "STOP", + "tooltip": "Record the animation of the scene. Only effective if fabric is disabled.", + "on_clicked_fn": lambda value: self._toggle_recording_animation_fn(value), + } + self.ui_window_elements["record_animation"] = isaacsim.gui.components.ui_utils.state_btn_builder( + **record_animate_cfg + ) + # disable the button if fabric is not enabled + self.ui_window_elements["record_animation"].enabled = not self.env.sim.is_fabric_enabled() + + def _build_viewer_frame(self): + """Build the viewer-related control frame for the UI.""" + # create collapsable frame for viewer + self.ui_window_elements["viewer_frame"] = omni.ui.CollapsableFrame( + title="Viewer Settings", + width=omni.ui.Fraction(1), + height=0, + collapsed=False, + style=isaacsim.gui.components.ui_utils.get_style(), + horizontal_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, + vertical_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, + ) + with self.ui_window_elements["viewer_frame"]: + # create stack for controls + self.ui_window_elements["viewer_vstack"] = omni.ui.VStack(spacing=5, height=0) + with self.ui_window_elements["viewer_vstack"]: + # create a number slider to move to environment origin + # NOTE: slider is 1-indexed, whereas the env index is 0-indexed + viewport_origin_cfg = { + "label": "Environment Index", + "type": "button", + "default_val": self.env.cfg.viewer.env_index + 1, + "min": 1, + "max": self.env.num_envs, + "tooltip": "The environment index to follow. Only effective if follow mode is not 'World'.", + } + self.ui_window_elements["viewer_env_index"] = isaacsim.gui.components.ui_utils.int_builder( + **viewport_origin_cfg + ) + # create a number slider to move to environment origin + self.ui_window_elements["viewer_env_index"].add_value_changed_fn(self._set_viewer_env_index_fn) + + # create a tracker for the camera location + viewer_follow_cfg = { + "label": "Follow Mode", + "type": "dropdown", + "default_val": 0, + "items": [name.replace("_", " ").title() for name in self._viewer_assets_options], + "tooltip": "Select the viewport camera following mode.", + "on_clicked_fn": self._set_viewer_origin_type_fn, + } + self.ui_window_elements["viewer_follow"] = isaacsim.gui.components.ui_utils.dropdown_builder( + **viewer_follow_cfg + ) + + # add viewer default eye and lookat locations + self.ui_window_elements["viewer_eye"] = isaacsim.gui.components.ui_utils.xyz_builder( + label="Camera Eye", + tooltip="Modify the XYZ location of the viewer eye.", + default_val=self.env.cfg.viewer.eye, + step=0.1, + on_value_changed_fn=[self._set_viewer_location_fn] * 3, + ) + self.ui_window_elements["viewer_lookat"] = isaacsim.gui.components.ui_utils.xyz_builder( + label="Camera Target", + tooltip="Modify the XYZ location of the viewer target.", + default_val=self.env.cfg.viewer.lookat, + step=0.1, + on_value_changed_fn=[self._set_viewer_location_fn] * 3, + ) + + def _build_debug_vis_frame(self): + """Builds the debug visualization frame for various scene elements. + + This function inquires the scene for all elements that have a debug visualization + implemented and creates a checkbox to toggle the debug visualization for each element + that has it implemented. If the element does not have a debug visualization implemented, + a label is created instead. + """ + # create collapsable frame for debug visualization + self.ui_window_elements["debug_frame"] = omni.ui.CollapsableFrame( + title="Scene Debug Visualization", + width=omni.ui.Fraction(1), + height=0, + collapsed=False, + style=isaacsim.gui.components.ui_utils.get_style(), + horizontal_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, + vertical_scrollbar_policy=omni.ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, + ) + with self.ui_window_elements["debug_frame"]: + # create stack for debug visualization + self.ui_window_elements["debug_vstack"] = omni.ui.VStack(spacing=5, height=0) + with self.ui_window_elements["debug_vstack"]: + elements = [ + self.env.scene.terrain, + *self.env.scene.rigid_objects.values(), + *self.env.scene.articulations.values(), + *self.env.scene.sensors.values(), + ] + names = [ + "terrain", + *self.env.scene.rigid_objects.keys(), + *self.env.scene.articulations.keys(), + *self.env.scene.sensors.keys(), + ] + # create one for the terrain + for elem, name in zip(elements, names): + if elem is not None: + self._create_debug_vis_ui_element(name, elem) + + def _visualize_manager(self, title: str, class_name: str) -> None: + """Checks if the attribute with the name 'class_name' can be visualized. If yes, create vis interface. + + Args: + title: The title of the manager visualization frame. + class_name: The name of the manager to visualize. + """ + + if hasattr(self.env, class_name) and class_name in self.env.manager_visualizers: + manager = self.env.manager_visualizers[class_name] + if hasattr(manager, "has_debug_vis_implementation"): + self._create_debug_vis_ui_element(title, manager) + else: + print( + f"ManagerLiveVisualizer cannot be created for manager: {class_name}, has_debug_vis_implementation" + " does not exist" + ) + else: + print(f"ManagerLiveVisualizer cannot be created for manager: {class_name}, Manager does not exist") + + """ + Custom callbacks for UI elements. + """ + + def _toggle_recording_animation_fn(self, value: bool): + """Toggles the animation recording.""" + if value: + # log directory to save the recording + if not hasattr(self, "animation_log_dir"): + # create a new log directory + log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + self.animation_log_dir = os.path.join(os.getcwd(), "recordings", log_dir) + # start the recording + _ = omni.kit.commands.execute( + "StartRecording", + target_paths=[("/World", True)], + live_mode=True, + use_frame_range=False, + start_frame=0, + end_frame=0, + use_preroll=False, + preroll_frame=0, + record_to="FILE", + fps=0, + apply_root_anim=False, + increment_name=True, + record_folder=self.animation_log_dir, + take_name="TimeSample", + ) + else: + # stop the recording + _ = omni.kit.commands.execute("StopRecording") + # save the current stage + source_layer = self.stage.GetRootLayer() + # output the stage to a file + stage_usd_path = os.path.join(self.animation_log_dir, "Stage.usd") + source_prim_path = "/" + # creates empty anon layer + temp_layer = Sdf.Find(stage_usd_path) + if temp_layer is None: + temp_layer = Sdf.Layer.CreateNew(stage_usd_path) + temp_stage = Usd.Stage.Open(temp_layer) + # update stage data + UsdGeom.SetStageUpAxis(temp_stage, UsdGeom.GetStageUpAxis(self.stage)) + UsdGeom.SetStageMetersPerUnit(temp_stage, UsdGeom.GetStageMetersPerUnit(self.stage)) + # copy the prim + Sdf.CreatePrimInLayer(temp_layer, source_prim_path) + Sdf.CopySpec(source_layer, source_prim_path, temp_layer, source_prim_path) + # set the default prim + temp_layer.defaultPrim = Sdf.Path(source_prim_path).name + # remove all physics from the stage + for prim in temp_stage.TraverseAll(): + # skip if the prim is an instance + if prim.IsInstanceable(): + continue + # if prim has articulation then disable it + if prim.HasAPI(UsdPhysics.ArticulationRootAPI): + prim.RemoveAPI(UsdPhysics.ArticulationRootAPI) + prim.RemoveAPI(PhysxSchema.PhysxArticulationAPI) + # if prim has rigid body then disable it + if prim.HasAPI(UsdPhysics.RigidBodyAPI): + prim.RemoveAPI(UsdPhysics.RigidBodyAPI) + prim.RemoveAPI(PhysxSchema.PhysxRigidBodyAPI) + # if prim is a joint type then disable it + if prim.IsA(UsdPhysics.Joint): + prim.GetAttribute("physics:jointEnabled").Set(False) + # resolve all paths relative to layer path + omni.usd.resolve_paths(source_layer.identifier, temp_layer.identifier) + # save the stage + temp_layer.Save() + # print the path to the saved stage + print("Recording completed.") + print(f"\tSaved recorded stage to : {stage_usd_path}") + print(f"\tSaved recorded animation to: {os.path.join(self.animation_log_dir, 'TimeSample_tk001.usd')}") + print("\nTo play the animation, check the instructions in the following link:") + print( + "\thttps://docs.omniverse.nvidia.com/extensions/latest/ext_animation_stage-recorder.html#using-the-captured-timesamples" + ) + print("\n") + # reset the log directory + self.animation_log_dir = None + + def _set_viewer_origin_type_fn(self, value: str): + """Sets the origin of the viewport's camera. This is based on the drop-down menu in the UI.""" + # Extract the viewport camera controller from environment + vcc = self.env.viewport_camera_controller + if vcc is None: + raise ValueError("Viewport camera controller is not initialized! Please check the rendering mode.") + + # Based on origin type, update the camera view + if value == "World": + vcc.update_view_to_world() + elif value == "Env": + vcc.update_view_to_env() + else: + # find which index the asset is + fancy_names = [name.replace("_", " ").title() for name in self._viewer_assets_options] + # store the desired env index + viewer_asset_name = self._viewer_assets_options[fancy_names.index(value)] + # update the camera view + vcc.update_view_to_asset_root(viewer_asset_name) + + def _set_viewer_location_fn(self, model: omni.ui.SimpleFloatModel): + """Sets the viewport camera location based on the UI.""" + # access the viewport camera controller (for brevity) + vcc = self.env.viewport_camera_controller + if vcc is None: + raise ValueError("Viewport camera controller is not initialized! Please check the rendering mode.") + # obtain the camera locations and set them in the viewpoint camera controller + eye = [self.ui_window_elements["viewer_eye"][i].get_value_as_float() for i in range(3)] + lookat = [self.ui_window_elements["viewer_lookat"][i].get_value_as_float() for i in range(3)] + # update the camera view + vcc.update_view_location(eye, lookat) + + def _set_viewer_env_index_fn(self, model: omni.ui.SimpleIntModel): + """Sets the environment index and updates the camera if in 'env' origin mode.""" + # access the viewport camera controller (for brevity) + vcc = self.env.viewport_camera_controller + if vcc is None: + raise ValueError("Viewport camera controller is not initialized! Please check the rendering mode.") + # store the desired env index, UI is 1-indexed + vcc.set_view_env_index(model.as_int - 1) + # notify additional listeners + for listener in self._ui_listeners: + listener.set_env_selection(model.as_int - 1) + + """ + Helper functions - UI building. + """ + + def _create_debug_vis_ui_element(self, name: str, elem: object): + """Create a checkbox for toggling debug visualization for the given element.""" + from omni.kit.window.extensions import SimpleCheckBox + + with omni.ui.HStack(): + # create the UI element + text = ( + "Toggle debug visualization." + if elem.has_debug_vis_implementation + else "Debug visualization not implemented." + ) + omni.ui.Label( + name.replace("_", " ").title(), + width=isaacsim.gui.components.ui_utils.LABEL_WIDTH - 12, + alignment=omni.ui.Alignment.LEFT_CENTER, + tooltip=text, + ) + has_cfg = hasattr(elem, "cfg") and elem.cfg is not None + is_checked = False + if has_cfg: + is_checked = (hasattr(elem.cfg, "debug_vis") and elem.cfg.debug_vis) or ( + hasattr(elem, "debug_vis") and elem.debug_vis + ) + self.ui_window_elements[f"{name}_cb"] = SimpleCheckBox( + model=omni.ui.SimpleBoolModel(), + enabled=elem.has_debug_vis_implementation, + checked=is_checked, + on_checked_fn=lambda value, e=weakref.proxy(elem): e.set_debug_vis(value), + ) + isaacsim.gui.components.ui_utils.add_line_rect_flourish() + + # Create a panel for the debug visualization + if isinstance(elem, ManagerLiveVisualizer): + self.ui_window_elements[f"{name}_panel"] = omni.ui.Frame(width=omni.ui.Fraction(1)) + if not elem.set_vis_frame(self.ui_window_elements[f"{name}_panel"]): + print(f"Frame failed to set for ManagerLiveVisualizer: {name}") + + # Add listener for environment selection changes + if isinstance(elem, ManagerLiveVisualizer): + self._ui_listeners.append(elem) + + async def _dock_window(self, window_title: str): + """Docks the custom UI window to the property window.""" + # wait for the window to be created + for _ in range(5): + if omni.ui.Workspace.get_window(window_title): + break + await self.env.sim.app.next_update_async() + + # dock next to properties window + custom_window = omni.ui.Workspace.get_window(window_title) + property_window = omni.ui.Workspace.get_window("Property") + if custom_window and property_window: + custom_window.dock_in(property_window, omni.ui.DockPosition.SAME, 1.0) + custom_window.focus() diff --git a/source/isaaclab/isaaclab/envs/ui/empty_window.py b/source/isaaclab/isaaclab/envs/ui/empty_window.py new file mode 100644 index 0000000000000000000000000000000000000000..bc12f862d1b0bc77d191886f2252e4e196269cf9 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/ui/empty_window.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import asyncio +from typing import TYPE_CHECKING + +import omni.kit.app + +if TYPE_CHECKING: + import omni.ui + + from ..manager_based_env import ManagerBasedEnv + + +class EmptyWindow: + """ + Creates an empty UI window that can be docked in the Omniverse Kit environment. + + The class initializes a dockable UI window and provides a main frame with a vertical stack. + You can add custom UI elements to this vertical stack. + + Example for adding a UI element from the standalone execution script: + >>> with env.window.ui_window_elements["main_vstack"]: + >>> ui.Label("My UI element") + + """ + + def __init__(self, env: ManagerBasedEnv, window_name: str): + """Initialize the window. + + Args: + env: The environment object. + window_name: The name of the window. + """ + # store environment + self.env = env + + # create window for UI + self.ui_window = omni.ui.Window( + window_name, width=400, height=500, visible=True, dock_preference=omni.ui.DockPreference.RIGHT_TOP + ) + # dock next to properties window + asyncio.ensure_future(self._dock_window(window_title=self.ui_window.title)) + + # keep a dictionary of stacks so that child environments can add their own UI elements + # this can be done by using the `with` context manager + self.ui_window_elements = dict() + # create main frame + self.ui_window_elements["main_frame"] = self.ui_window.frame + with self.ui_window_elements["main_frame"]: + # create main vstack + self.ui_window_elements["main_vstack"] = omni.ui.VStack(spacing=5, height=0) + + def __del__(self): + """Destructor for the window.""" + # destroy the window + if self.ui_window is not None: + self.ui_window.visible = False + self.ui_window.destroy() + self.ui_window = None + + async def _dock_window(self, window_title: str): + """Docks the custom UI window to the property window.""" + # wait for the window to be created + for _ in range(5): + if omni.ui.Workspace.get_window(window_title): + break + await self.env.sim.app.next_update_async() + + # dock next to properties window + custom_window = omni.ui.Workspace.get_window(window_title) + property_window = omni.ui.Workspace.get_window("Property") + if custom_window and property_window: + custom_window.dock_in(property_window, omni.ui.DockPosition.SAME, 1.0) + custom_window.focus() diff --git a/source/isaaclab/isaaclab/envs/ui/manager_based_rl_env_window.py b/source/isaaclab/isaaclab/envs/ui/manager_based_rl_env_window.py new file mode 100644 index 0000000000000000000000000000000000000000..72f0be06f1998d8e62a04899a72e59d3aa6bb7c3 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/ui/manager_based_rl_env_window.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from .base_env_window import BaseEnvWindow + +if TYPE_CHECKING: + from ..manager_based_rl_env import ManagerBasedRLEnv + + +class ManagerBasedRLEnvWindow(BaseEnvWindow): + """Window manager for the RL environment. + + On top of the basic environment window, this class adds controls for the RL environment. + This includes visualization of the command manager. + """ + + def __init__(self, env: ManagerBasedRLEnv, window_name: str = "IsaacLab"): + """Initialize the window. + + Args: + env: The environment object. + window_name: The name of the window. Defaults to "IsaacLab". + """ + # initialize base window + super().__init__(env, window_name) + + # add custom UI elements + with self.ui_window_elements["main_vstack"]: + with self.ui_window_elements["debug_frame"]: + with self.ui_window_elements["debug_vstack"]: + self._visualize_manager(title="Commands", class_name="command_manager") + self._visualize_manager(title="Rewards", class_name="reward_manager") + self._visualize_manager(title="Curriculum", class_name="curriculum_manager") + self._visualize_manager(title="Termination", class_name="termination_manager") diff --git a/source/isaaclab/isaaclab/envs/ui/viewport_camera_controller.py b/source/isaaclab/isaaclab/envs/ui/viewport_camera_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..0ba5368734b048d875fb72b375ccb590dbf6ec8a --- /dev/null +++ b/source/isaaclab/isaaclab/envs/ui/viewport_camera_controller.py @@ -0,0 +1,233 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import copy +import weakref +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import numpy as np +import torch + +import omni.kit.app +import omni.timeline + +from isaaclab.assets.articulation.articulation import Articulation + +if TYPE_CHECKING: + from isaaclab.envs import DirectRLEnv, ManagerBasedEnv, ViewerCfg + + +class ViewportCameraController: + """This class handles controlling the camera associated with a viewport in the simulator. + + It can be used to set the viewpoint camera to track different origin types: + + - **world**: the center of the world (static) + - **env**: the center of an environment (static) + - **asset_root**: the root of an asset in the scene (e.g. tracking a robot moving in the scene) + + On creation, the camera is set to track the origin type specified in the configuration. + + For the :attr:`asset_root` origin type, the camera is updated at each rendering step to track the asset's + root position. For this, it registers a callback to the post update event stream from the simulation app. + """ + + def __init__(self, env: ManagerBasedEnv | DirectRLEnv, cfg: ViewerCfg): + """Initialize the ViewportCameraController. + + Args: + env: The environment. + cfg: The configuration for the viewport camera controller. + + Raises: + ValueError: If origin type is configured to be "env" but :attr:`cfg.env_index` is out of bounds. + ValueError: If origin type is configured to be "asset_root" but :attr:`cfg.asset_name` is unset. + + """ + # store inputs + self._env = env + self._cfg = copy.deepcopy(cfg) + # cast viewer eye and look-at to numpy arrays + self.default_cam_eye = np.array(self._cfg.eye, dtype=float) + self.default_cam_lookat = np.array(self._cfg.lookat, dtype=float) + + # set the camera origins + if self.cfg.origin_type == "env": + # check that the env_index is within bounds + self.set_view_env_index(self.cfg.env_index) + # set the camera origin to the center of the environment + self.update_view_to_env() + elif self.cfg.origin_type == "asset_root" or self.cfg.origin_type == "asset_body": + # note: we do not yet update camera for tracking an asset origin, as the asset may not yet be + # in the scene when this is called. Instead, we subscribe to the post update event to update the camera + # at each rendering step. + if self.cfg.asset_name is None: + raise ValueError(f"No asset name provided for viewer with origin type: '{self.cfg.origin_type}'.") + if self.cfg.origin_type == "asset_body": + if self.cfg.body_name is None: + raise ValueError(f"No body name provided for viewer with origin type: '{self.cfg.origin_type}'.") + else: + # set the camera origin to the center of the world + self.update_view_to_world() + + # subscribe to post update event so that camera view can be updated at each rendering step + app_interface = omni.kit.app.get_app_interface() + app_event_stream = app_interface.get_post_update_event_stream() + self._viewport_camera_update_handle = app_event_stream.create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._update_tracking_callback(event) + ) + + def __del__(self): + """Unsubscribe from the callback.""" + # use hasattr to handle case where __init__ has not completed before __del__ is called + if hasattr(self, "_viewport_camera_update_handle") and self._viewport_camera_update_handle is not None: + self._viewport_camera_update_handle.unsubscribe() + self._viewport_camera_update_handle = None + + """ + Properties + """ + + @property + def cfg(self) -> ViewerCfg: + """The configuration for the viewer.""" + return self._cfg + + """ + Public Functions + """ + + def set_view_env_index(self, env_index: int): + """Sets the environment index for the camera view. + + Args: + env_index: The index of the environment to set the camera view to. + + Raises: + ValueError: If the environment index is out of bounds. It should be between 0 and num_envs - 1. + """ + # check that the env_index is within bounds + if env_index < 0 or env_index >= self._env.num_envs: + raise ValueError( + f"Out of range value for attribute 'env_index': {env_index}." + f" Expected a value between 0 and {self._env.num_envs - 1} for the current environment." + ) + # update the environment index + self.cfg.env_index = env_index + # update the camera view if the origin is set to env type (since, the camera view is static) + # note: for assets, the camera view is updated at each rendering step + if self.cfg.origin_type == "env": + self.update_view_to_env() + + def update_view_to_world(self): + """Updates the viewer's origin to the origin of the world which is (0, 0, 0).""" + # set origin type to world + self.cfg.origin_type = "world" + # update the camera origins + self.viewer_origin = torch.zeros(3) + # update the camera view + self.update_view_location() + + def update_view_to_env(self): + """Updates the viewer's origin to the origin of the selected environment.""" + # set origin type to world + self.cfg.origin_type = "env" + # update the camera origins + self.viewer_origin = self._env.scene.env_origins[self.cfg.env_index] + # update the camera view + self.update_view_location() + + def update_view_to_asset_root(self, asset_name: str): + """Updates the viewer's origin based upon the root of an asset in the scene. + + Args: + asset_name: The name of the asset in the scene. The name should match the name of the + asset in the scene. + + Raises: + ValueError: If the asset is not in the scene. + """ + # check if the asset is in the scene + if self.cfg.asset_name != asset_name: + asset_entities = [*self._env.scene.rigid_objects.keys(), *self._env.scene.articulations.keys()] + if asset_name not in asset_entities: + raise ValueError(f"Asset '{asset_name}' is not in the scene. Available entities: {asset_entities}.") + # update the asset name + self.cfg.asset_name = asset_name + # set origin type to asset_root + self.cfg.origin_type = "asset_root" + # update the camera origins + self.viewer_origin = self._env.scene[self.cfg.asset_name].data.root_pos_w[self.cfg.env_index] + # update the camera view + self.update_view_location() + + def update_view_to_asset_body(self, asset_name: str, body_name: str): + """Updates the viewer's origin based upon the body of an asset in the scene. + + Args: + asset_name: The name of the asset in the scene. The name should match the name of the + asset in the scene. + body_name: The name of the body in the asset. + + Raises: + ValueError: If the asset is not in the scene or the body is not valid. + """ + # check if the asset is in the scene + if self.cfg.asset_name != asset_name: + asset_entities = [*self._env.scene.rigid_objects.keys(), *self._env.scene.articulations.keys()] + if asset_name not in asset_entities: + raise ValueError(f"Asset '{asset_name}' is not in the scene. Available entities: {asset_entities}.") + # check if the body is in the asset + asset: Articulation = self._env.scene[asset_name] + if body_name not in asset.body_names: + raise ValueError( + f"'{body_name}' is not a body of Asset '{asset_name}'. Available bodies: {asset.body_names}." + ) + # get the body index + body_id, _ = asset.find_bodies(body_name) + # update the asset name + self.cfg.asset_name = asset_name + # set origin type to asset_body + self.cfg.origin_type = "asset_body" + # update the camera origins + self.viewer_origin = self._env.scene[self.cfg.asset_name].data.body_pos_w[self.cfg.env_index, body_id].view(3) + # update the camera view + self.update_view_location() + + def update_view_location(self, eye: Sequence[float] | None = None, lookat: Sequence[float] | None = None): + """Updates the camera view pose based on the current viewer origin and the eye and lookat positions. + + Args: + eye: The eye position of the camera. If None, the current eye position is used. + lookat: The lookat position of the camera. If None, the current lookat position is used. + """ + # store the camera view pose for later use + if eye is not None: + self.default_cam_eye = np.asarray(eye, dtype=float) + if lookat is not None: + self.default_cam_lookat = np.asarray(lookat, dtype=float) + # set the camera locations + viewer_origin = self.viewer_origin.detach().cpu().numpy() + cam_eye = viewer_origin + self.default_cam_eye + cam_target = viewer_origin + self.default_cam_lookat + + # set the camera view + self._env.sim.set_camera_view(eye=cam_eye, target=cam_target) + + """ + Private Functions + """ + + def _update_tracking_callback(self, event): + """Updates the camera view at each rendering step.""" + # update the camera view if the origin is set to asset_root + # in other cases, the camera view is static and does not need to be updated continuously + if self.cfg.origin_type == "asset_root" and self.cfg.asset_name is not None: + self.update_view_to_asset_root(self.cfg.asset_name) + if self.cfg.origin_type == "asset_body" and self.cfg.asset_name is not None and self.cfg.body_name is not None: + self.update_view_to_asset_body(self.cfg.asset_name, self.cfg.body_name) diff --git a/source/isaaclab/isaaclab/envs/utils/__init__.py b/source/isaaclab/isaaclab/envs/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d28381b15b76c01451f1bc3daa10d7c712751cc6 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/utils/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package for environment utils.""" diff --git a/source/isaaclab/isaaclab/envs/utils/io_descriptors.py b/source/isaaclab/isaaclab/envs/utils/io_descriptors.py new file mode 100644 index 0000000000000000000000000000000000000000..84b8a5cf8a0c06281b07a2fab5d55bd639922389 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/utils/io_descriptors.py @@ -0,0 +1,393 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from typing import TYPE_CHECKING, Any, Concatenate, ParamSpec, TypeVar + +from isaaclab.utils import configclass + +if TYPE_CHECKING: + import torch + + from isaaclab.assets.articulation import Articulation + from isaaclab.envs import ManagerBasedEnv + +import dataclasses +import functools +import inspect + + +@configclass +class GenericActionIODescriptor: + """Generic action IO descriptor. + + This descriptor is used to describe the action space of a policy. + It can be extended as needed to add more information about the action term that is being described. + """ + + mdp_type: str = "Action" + """The type of MDP that the action term belongs to.""" + + name: str = None + """The name of the action term. + + By default, the name of the action term class is used. + """ + + full_path: str = None + """The full path of the action term class. + + By default, python's will retrieve the path from the file that the action term class is defined in + and the name of the action term class. + """ + + description: str = None + """The description of the action term. + + By default, the docstring of the action term class is used. + """ + + shape: tuple[int, ...] = None + """The shape of the action term. + + This should be populated by the user.""" + + dtype: str = None + """The dtype of the action term. + + This should be populated by the user.""" + + action_type: str = None + """The type of the action term. + + This attribute is purely informative and should be populated by the user.""" + + extras: dict[str, Any] = {} + """Extra information about the action term. + + This attribute is purely informative and should be populated by the user.""" + + export: bool = True + """Whether to export the action term. + + Should be set to False if the class is not meant to be exported. + """ + + +@configclass +class GenericObservationIODescriptor: + """Generic observation IO descriptor. + + This descriptor is used to describe the observation space of a policy. + It can be extended as needed to add more information about the observation term that is being described. + """ + + mdp_type: str = "Observation" + name: str = None + full_path: str = None + description: str = None + shape: tuple[int, ...] = None + dtype: str = None + observation_type: str = None + extras: dict[str, Any] = {} + + +# These are defined to help with type hinting +P = ParamSpec("P") +R = TypeVar("R") + + +# Automatically builds a descriptor from the kwargs +def _make_descriptor(**kwargs: Any) -> GenericObservationIODescriptor: + """Split *kwargs* into (known dataclass fields) and (extras).""" + field_names = {f.name for f in dataclasses.fields(GenericObservationIODescriptor)} + known = {k: v for k, v in kwargs.items() if k in field_names} + extras = {k: v for k, v in kwargs.items() if k not in field_names} + + desc = GenericObservationIODescriptor(**known) + # User defined extras are stored in the descriptor under the `extras` field + desc.extras = extras + return desc + + +# Decorator factory for generic IO descriptors. +def generic_io_descriptor( + _func: Callable[Concatenate[ManagerBasedEnv, P], R] | None = None, + *, + on_inspect: Callable[..., Any] | list[Callable[..., Any]] | None = None, + **descriptor_kwargs: Any, +) -> Callable[[Callable[Concatenate[ManagerBasedEnv, P], R]], Callable[Concatenate[ManagerBasedEnv, P], R]]: + """Decorator factory for generic IO descriptors. + + This decorator can be used in different ways: + + 1. The default decorator has all the information I need for my use case: + + ..code-block:: python + @generic_io_descriptor(GenericIODescriptor(description="..", dtype="..")) + def my_func(env: ManagerBasedEnv, *args, **kwargs): + ... + + ..note:: If description is not set, the function's docstring is used to populate it. + + 2. I need to add more information to the descriptor: + + ..code-block:: python + @generic_io_descriptor(description="..", new_var_1="a", new_var_2="b") + def my_func(env: ManagerBasedEnv, *args, **kwargs): + ... + + 3. I need to add a hook to the descriptor: + + ..code-block:: python + def record_shape(tensor: torch.Tensor, desc: GenericIODescriptor, **kwargs): + desc.shape = (tensor.shape[-1],) + + @generic_io_descriptor(description="..", new_var_1="a", new_var_2="b", on_inspect=[record_shape, record_dtype]) + def my_func(env: ManagerBasedEnv, *args, **kwargs): + ... + + ..note:: + + The hook is called after the function is called, if and only if the `inspect` flag is set when + calling the function. + + For example: + + ..code-block:: python + my_func(env, inspect=True) + + 4. I need to add a hook to the descriptor and this hook will write to a variable that is not part of + the base descriptor. + + ..code-block:: python + + def record_joint_names(output: torch.Tensor, descriptor: GenericIODescriptor, **kwargs): + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + joint_ids = kwargs["asset_cfg"].joint_ids + if joint_ids == slice(None, None, None): + joint_ids = list(range(len(asset.joint_names))) + descriptor.joint_names = [asset.joint_names[i] for i in joint_ids] + + @generic_io_descriptor( + new_var_1="a", + new_var_2="b", + on_inspect=[record_shape, record_dtype, record_joint_names], + ) + def my_func(env: ManagerBasedEnv, *args, **kwargs): + ... + + ..note:: + + The hook can access all the variables in the wrapped function's signature. While it is useful, + the user should be careful to access only existing variables. + + Args: + _func: The function to decorate. + **descriptor_kwargs: Keyword arguments to pass to the descriptor. + + Returns: + A decorator that can be used to decorate a function. + """ + # If the decorator is used with a descriptor, use it as the descriptor. + if _func is not None and isinstance(_func, GenericObservationIODescriptor): + descriptor = _func + _func = None + else: + descriptor = _make_descriptor(**descriptor_kwargs) + + # Ensures the hook is a list + if callable(on_inspect): + inspect_hooks: list[Callable[..., Any]] = [on_inspect] + else: + inspect_hooks: list[Callable[..., Any]] = list(on_inspect or []) # handles None + + def _apply(func: Callable[Concatenate[ManagerBasedEnv, P], R]) -> Callable[Concatenate[ManagerBasedEnv, P], R]: + # Capture the signature of the function + sig = inspect.signature(func) + + @functools.wraps(func) + def wrapper(env: ManagerBasedEnv, *args: P.args, **kwargs: P.kwargs) -> R: + inspect_flag: bool = kwargs.pop("inspect", False) + out = func(env, *args, **kwargs) + if inspect_flag: + # Injects the function's arguments into the hooks and applies the defaults + bound = sig.bind(env, *args, **kwargs) + bound.apply_defaults() + call_kwargs = { + "output": out, + "descriptor": descriptor, + **bound.arguments, + } + for hook in inspect_hooks: + hook(**call_kwargs) + return out + + # --- Descriptor bookkeeping --- + descriptor.name = func.__name__ + descriptor.full_path = f"{func.__module__}.{func.__name__}" + descriptor.dtype = str(descriptor.dtype) + # Check if description is set in the descriptor + if descriptor.description is None: + descriptor.description = " ".join(func.__doc__.split()) + + # Adds the descriptor to the wrapped function as an attribute + wrapper._descriptor = descriptor + wrapper._has_descriptor = True + # Alters the signature of the wrapped function to make it match the original function. + # This allows the wrapped functions to pass the checks in the managers. + wrapper.__signature__ = sig + return wrapper + + # If the decorator is used without parentheses, _func will be the function itself. + if callable(_func): + return _apply(_func) + return _apply + + +def record_shape(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the shape of the output tensor. + + Args: + output: The output tensor. + descriptor: The descriptor to record the shape to. + **kwargs: Additional keyword arguments. + """ + descriptor.shape = (output.shape[-1],) + + +def record_dtype(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the dtype of the output tensor. + + Args: + output: The output tensor. + descriptor: The descriptor to record the dtype to. + **kwargs: Additional keyword arguments. + """ + descriptor.dtype = str(output.dtype) + + +def record_joint_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the joint names of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the joint names to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + joint_ids = kwargs["asset_cfg"].joint_ids + if joint_ids == slice(None, None, None): + joint_ids = list(range(len(asset.joint_names))) + descriptor.joint_names = [asset.joint_names[i] for i in joint_ids] + + +def record_body_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None: + """Record the body names of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the body names to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + body_ids = kwargs["asset_cfg"].body_ids + if body_ids == slice(None, None, None): + body_ids = list(range(len(asset.body_names))) + descriptor.body_names = [asset.body_names[i] for i in body_ids] + + +def record_joint_pos_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs): + """Record the joint position offsets of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the joint position offsets to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + ids = kwargs["asset_cfg"].joint_ids + # Get the offsets of the joints for the first robot in the scene. + # This assumes that all robots have the same joint offsets. + descriptor.joint_pos_offsets = asset.data.default_joint_pos[:, ids][0] + + +def record_joint_vel_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs): + """Record the joint velocity offsets of the output tensor. + + Expects the `asset_cfg` keyword argument to be set. + + Args: + output: The output tensor. + descriptor: The descriptor to record the joint velocity offsets to. + **kwargs: Additional keyword arguments. + """ + asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name] + ids = kwargs["asset_cfg"].joint_ids + # Get the offsets of the joints for the first robot in the scene. + # This assumes that all robots have the same joint offsets. + descriptor.joint_vel_offsets = asset.data.default_joint_vel[:, ids][0] + + +def export_articulations_data(env: ManagerBasedEnv) -> dict[str, dict[str, list[float]]]: + """Export the articulations data. + + Args: + env: The environment. + + Returns: + A dictionary containing the articulations data. + """ + # Create a dictionary for all the articulations in the scene. + articulation_joint_data = {} + for articulation_name, articulation in env.scene.articulations.items(): + # For each articulation, create a dictionary with the articulation's data. + # Some of the data may be redundant with other information provided by the observation descriptors. + articulation_joint_data[articulation_name] = {} + articulation_joint_data[articulation_name]["joint_names"] = articulation.joint_names + articulation_joint_data[articulation_name]["default_joint_pos"] = ( + articulation.data.default_joint_pos[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_vel"] = ( + articulation.data.default_joint_vel[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_pos_limits"] = ( + articulation.data.default_joint_pos_limits[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_damping"] = ( + articulation.data.default_joint_damping[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_stiffness"] = ( + articulation.data.default_joint_stiffness[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_friction"] = ( + articulation.data.default_joint_friction[0].detach().cpu().numpy().tolist() + ) + articulation_joint_data[articulation_name]["default_joint_armature"] = ( + articulation.data.default_joint_armature[0].detach().cpu().numpy().tolist() + ) + return articulation_joint_data + + +def export_scene_data(env: ManagerBasedEnv) -> dict[str, Any]: + """Export the scene data. + + Args: + env: The environment. + + Returns: + A dictionary containing the scene data. + """ + # Create a dictionary for the scene data. + scene_data = {"physics_dt": env.physics_dt, "dt": env.step_dt, "decimation": env.cfg.decimation} + return scene_data diff --git a/source/isaaclab/isaaclab/envs/utils/marl.py b/source/isaaclab/isaaclab/envs/utils/marl.py new file mode 100644 index 0000000000000000000000000000000000000000..010f6e5e27bd441a927961e02a52d58927446320 --- /dev/null +++ b/source/isaaclab/isaaclab/envs/utils/marl.py @@ -0,0 +1,275 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math +from typing import Any + +import gymnasium as gym +import numpy as np +import torch + +from ..common import ActionType, AgentID, EnvStepReturn, ObsType, StateType, VecEnvObs, VecEnvStepReturn +from ..direct_marl_env import DirectMARLEnv +from ..direct_rl_env import DirectRLEnv + + +def multi_agent_to_single_agent(env: DirectMARLEnv, state_as_observation: bool = False) -> DirectRLEnv: + """Convert the multi-agent environment instance to a single-agent environment instance. + + The converted environment will be an instance of the single-agent environment interface class + (:class:`DirectRLEnv`). As part of the conversion process, the following operations are carried out: + + * The observations of all the agents in the original multi-agent environment are concatenated to compose + the single-agent observation. If the use of the environment state is defined as the observation, + it is returned as is. + * The terminations and time-outs of all the agents in the original multi-agent environment are multiplied + (``AND`` operation) to compose the corresponding single-agent values. + * The rewards of all the agents in the original multi-agent environment are summed to compose the + single-agent reward. + * The action taken by the single-agent is split to compose the actions of each agent in the original + multi-agent environment before stepping it. + + Args: + env: The environment to convert to. + state_as_observation: Weather to use the multi-agent environment state as single-agent observation. + + Returns: + Single-agent environment instance. + + Raises: + AssertionError: If the environment state cannot be used as observation since it was explicitly defined + as unconstructed (:attr:`DirectMARLEnvCfg.state_space`). + """ + + class Env(DirectRLEnv): + def __init__(self, env: DirectMARLEnv) -> None: + self.env: DirectMARLEnv = env.unwrapped + + # check if it is possible to use the multi-agent environment state as single-agent observation + self._state_as_observation = state_as_observation + if self._state_as_observation: + assert self.env.cfg.state_space != 0, ( + "The environment state cannot be used as observation since it was explicitly defined as" + " unconstructed" + ) + + # create single-agent properties to expose in the converted environment + self.cfg = self.env.cfg + self.sim = self.env.sim + self.scene = self.env.scene + self.render_mode = self.env.render_mode + + self.single_observation_space = gym.spaces.Dict() + if self._state_as_observation: + self.single_observation_space["policy"] = self.env.state_space + else: + self.single_observation_space["policy"] = gym.spaces.flatten_space( + gym.spaces.Tuple([self.env.observation_spaces[agent] for agent in self.env.possible_agents]) + ) + self.single_action_space = gym.spaces.flatten_space( + gym.spaces.Tuple([self.env.action_spaces[agent] for agent in self.env.possible_agents]) + ) + + # batch the spaces for vectorized environments + self.observation_space = gym.vector.utils.batch_space( + self.single_observation_space["policy"], self.num_envs + ) + self.action_space = gym.vector.utils.batch_space(self.single_action_space, self.num_envs) + + def reset(self, seed: int | None = None, options: dict[str, Any] | None = None) -> tuple[VecEnvObs, dict]: + obs, extras = self.env.reset(seed, options) + + # use environment state as observation + if self._state_as_observation: + obs = {"policy": self.env.state()} + # concatenate agents' observations + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + else: + obs = { + "policy": torch.cat( + [obs[agent].reshape(self.num_envs, -1) for agent in self.env.possible_agents], dim=-1 + ) + } + + return obs, extras + + def step(self, action: torch.Tensor) -> VecEnvStepReturn: + # split single-agent actions to build the multi-agent ones + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + index = 0 + _actions = {} + for agent in self.env.possible_agents: + delta = gym.spaces.flatdim(self.env.action_spaces[agent]) + _actions[agent] = action[:, index : index + delta] + index += delta + + # step the environment + obs, rewards, terminated, time_outs, extras = self.env.step(_actions) + + # use environment state as observation + if self._state_as_observation: + obs = {"policy": self.env.state()} + # concatenate agents' observations + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + else: + obs = { + "policy": torch.cat( + [obs[agent].reshape(self.num_envs, -1) for agent in self.env.possible_agents], dim=-1 + ) + } + + # process environment outputs to return single-agent data + rewards = sum(rewards.values()) + terminated = math.prod(terminated.values()).to(dtype=torch.bool) + time_outs = math.prod(time_outs.values()).to(dtype=torch.bool) + + return obs, rewards, terminated, time_outs, extras + + def render(self, recompute: bool = False) -> np.ndarray | None: + return self.env.render(recompute) + + def close(self) -> None: + self.env.close() + + return Env(env) + + +def multi_agent_with_one_agent(env: DirectMARLEnv, state_as_observation: bool = False) -> DirectMARLEnv: + """Convert the multi-agent environment instance to a multi-agent environment instance with only one agent. + + The converted environment will be an instance of the multi-agent environment interface class + (:class:`DirectMARLEnv`) but with only one agent available (with ID: ``"single-agent"``). + As part of the conversion process, the following operations are carried out: + + * The observations of all the agents in the original multi-agent environment are concatenated to compose + the agent observation. If the use of the environment state is defined as the observation, it is returned as is. + * The terminations and time-outs of all the agents in the original multi-agent environment are multiplied + (``AND`` operation) to compose the corresponding agent values. + * The rewards of all the agents in the original multi-agent environment are summed to compose the agent reward. + * The action taken by the agent is split to compose the actions of each agent in the original + multi-agent environment before stepping it. + + Args: + env: The environment to convert to. + state_as_observation: Weather to use the multi-agent environment state as agent observation. + + Returns: + Multi-agent environment instance with only one agent. + + Raises: + AssertionError: If the environment state cannot be used as observation since it was explicitly defined + as unconstructed (:attr:`DirectMARLEnvCfg.state_space`). + """ + + class Env(DirectMARLEnv): + def __init__(self, env: DirectMARLEnv) -> None: + self.env: DirectMARLEnv = env.unwrapped + + # check if it is possible to use the multi-agent environment state as agent observation + self._state_as_observation = state_as_observation + if self._state_as_observation: + assert self.env.cfg.state_space != 0, ( + "The environment state cannot be used as observation since it was explicitly defined as" + " unconstructed" + ) + + # create agent properties to expose in the converted environment + self._agent_id = "single-agent" + self._exported_agents = [self._agent_id] + self._exported_possible_agents = [self._agent_id] + if self._state_as_observation: + self._exported_observation_spaces = {self._agent_id: self.env.state_space} + else: + self._exported_observation_spaces = { + self._agent_id: gym.spaces.flatten_space( + gym.spaces.Tuple([self.env.observation_spaces[agent] for agent in self.env.possible_agents]) + ) + } + self._exported_action_spaces = { + self._agent_id: gym.spaces.flatten_space( + gym.spaces.Tuple([self.env.action_spaces[agent] for agent in self.env.possible_agents]) + ) + } + + def __getattr__(self, key: str) -> Any: + return getattr(self.env, key) + + @property + def agents(self) -> list[AgentID]: + return self._exported_agents + + @property + def possible_agents(self) -> list[AgentID]: + return self._exported_possible_agents + + @property + def observation_spaces(self) -> dict[AgentID, gym.Space]: + return self._exported_observation_spaces + + @property + def action_spaces(self) -> dict[AgentID, gym.Space]: + return self._exported_action_spaces + + def reset( + self, seed: int | None = None, options: dict[str, Any] | None = None + ) -> tuple[dict[AgentID, ObsType], dict[AgentID, dict]]: + obs, extras = self.env.reset(seed, options) + + # use environment state as observation + if self._state_as_observation: + obs = {self._agent_id: self.env.state()} + # concatenate agents' observations + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + else: + obs = { + self._agent_id: torch.cat( + [obs[agent].reshape(self.num_envs, -1) for agent in self.env.possible_agents], dim=-1 + ) + } + + return obs, extras + + def step(self, actions: dict[AgentID, ActionType]) -> EnvStepReturn: + # split agent actions to build the multi-agent ones + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + index = 0 + _actions = {} + for agent in self.env.possible_agents: + delta = gym.spaces.flatdim(self.env.action_spaces[agent]) + _actions[agent] = actions[self._agent_id][:, index : index + delta] + index += delta + + # step the environment + obs, rewards, terminated, time_outs, extras = self.env.step(_actions) + + # use environment state as observation + if self._state_as_observation: + obs = {self._agent_id: self.env.state()} + # concatenate agents' observations + # FIXME: This implementation assumes the spaces are fundamental ones. Fix it to support composite spaces + else: + obs = { + self._agent_id: torch.cat( + [obs[agent].reshape(self.num_envs, -1) for agent in self.env.possible_agents], dim=-1 + ) + } + + # process environment outputs to return agent data + rewards = {self._agent_id: sum(rewards.values())} + terminated = {self._agent_id: math.prod(terminated.values()).to(dtype=torch.bool)} + time_outs = {self._agent_id: math.prod(time_outs.values()).to(dtype=torch.bool)} + + return obs, rewards, terminated, time_outs, extras + + def state(self) -> StateType | None: + return self.env.state() + + def render(self, recompute: bool = False) -> np.ndarray | None: + self.env.render(recompute) + + def close(self) -> None: + self.env.close() + + return Env(env) diff --git a/source/isaaclab/isaaclab/envs/utils/spaces.py b/source/isaaclab/isaaclab/envs/utils/spaces.py new file mode 100644 index 0000000000000000000000000000000000000000..a21ecd82667cbe9ebb6459cf3ab2d287a5aec41a --- /dev/null +++ b/source/isaaclab/isaaclab/envs/utils/spaces.py @@ -0,0 +1,229 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import json +from typing import Any + +import gymnasium as gym +import numpy as np +import torch + +from ..common import SpaceType + + +def spec_to_gym_space(spec: SpaceType) -> gym.spaces.Space: + """Generate an appropriate Gymnasium space according to the given space specification. + + Args: + spec: Space specification. + + Returns: + Gymnasium space. + + Raises: + ValueError: If the given space specification is not valid/supported. + """ + if isinstance(spec, gym.spaces.Space): + return spec + # fundamental spaces + # Box + elif isinstance(spec, int): + return gym.spaces.Box(low=-np.inf, high=np.inf, shape=(spec,)) + elif isinstance(spec, list) and all(isinstance(x, int) for x in spec): + return gym.spaces.Box(low=-np.inf, high=np.inf, shape=spec) + # Discrete + elif isinstance(spec, set) and len(spec) == 1: + return gym.spaces.Discrete(n=next(iter(spec))) + # MultiDiscrete + elif isinstance(spec, list) and all(isinstance(x, set) and len(x) == 1 for x in spec): + return gym.spaces.MultiDiscrete(nvec=[next(iter(x)) for x in spec]) + # composite spaces + # Tuple + elif isinstance(spec, tuple): + return gym.spaces.Tuple([spec_to_gym_space(x) for x in spec]) + # Dict + elif isinstance(spec, dict): + return gym.spaces.Dict({k: spec_to_gym_space(v) for k, v in spec.items()}) + raise ValueError(f"Unsupported space specification: {spec}") + + +def sample_space(space: gym.spaces.Space, device: str, batch_size: int = -1, fill_value: float | None = None) -> Any: + """Sample a Gymnasium space where the data container are PyTorch tensors. + + Args: + space: Gymnasium space. + device: The device where the tensor should be created. + batch_size: Batch size. If the specified value is greater than zero, a batched space + will be created and sampled from it. + fill_value: The value to fill the created tensors with. If None (default value), tensors + will keep their random values. + + Returns: + Tensorized sampled space. + """ + + def tensorize(s: gym.spaces.Space, x: Any) -> Any: + if isinstance(s, gym.spaces.Box): + tensor = torch.tensor(x, device=device, dtype=torch.float32).reshape(batch_size, *s.shape) + if fill_value is not None: + tensor.fill_(fill_value) + return tensor + elif isinstance(s, gym.spaces.Discrete): + if isinstance(x, np.ndarray): + tensor = torch.tensor(x, device=device, dtype=torch.int64).reshape(batch_size, 1) + if fill_value is not None: + tensor.fill_(int(fill_value)) + return tensor + elif isinstance(x, np.number) or type(x) in [int, float]: + tensor = torch.tensor([x], device=device, dtype=torch.int64).reshape(batch_size, 1) + if fill_value is not None: + tensor.fill_(int(fill_value)) + return tensor + elif isinstance(s, gym.spaces.MultiDiscrete): + if isinstance(x, np.ndarray): + tensor = torch.tensor(x, device=device, dtype=torch.int64).reshape(batch_size, *s.shape) + if fill_value is not None: + tensor.fill_(int(fill_value)) + return tensor + elif isinstance(s, gym.spaces.Dict): + return {k: tensorize(_s, x[k]) for k, _s in s.items()} + elif isinstance(s, gym.spaces.Tuple): + return tuple([tensorize(_s, v) for _s, v in zip(s, x)]) + + # If the space is not supported, raise an error + raise ValueError(f"Unsupported Gymnasium space for tensorization: {s}") + + sample = (gym.vector.utils.batch_space(space, batch_size) if batch_size > 0 else space).sample() + return tensorize(space, sample) + + +def serialize_space(space: SpaceType) -> str: + """Serialize a space specification as JSON. + + Args: + space: Space specification. + + Returns: + Serialized JSON representation. + """ + # Gymnasium spaces + if isinstance(space, gym.spaces.Discrete): + return json.dumps({"type": "gymnasium", "space": "Discrete", "n": int(space.n)}) + elif isinstance(space, gym.spaces.Box): + return json.dumps( + { + "type": "gymnasium", + "space": "Box", + "low": space.low.tolist(), + "high": space.high.tolist(), + "shape": space.shape, + } + ) + elif isinstance(space, gym.spaces.MultiDiscrete): + return json.dumps({"type": "gymnasium", "space": "MultiDiscrete", "nvec": space.nvec.tolist()}) + elif isinstance(space, gym.spaces.Tuple): + return json.dumps({"type": "gymnasium", "space": "Tuple", "spaces": tuple(map(serialize_space, space.spaces))}) + elif isinstance(space, gym.spaces.Dict): + return json.dumps( + {"type": "gymnasium", "space": "Dict", "spaces": {k: serialize_space(v) for k, v in space.spaces.items()}} + ) + # Python data types + # Box + elif isinstance(space, int) or (isinstance(space, list) and all(isinstance(x, int) for x in space)): + return json.dumps({"type": "python", "space": "Box", "value": space}) + # Discrete + elif isinstance(space, set) and len(space) == 1: + return json.dumps({"type": "python", "space": "Discrete", "value": next(iter(space))}) + # MultiDiscrete + elif isinstance(space, list) and all(isinstance(x, set) and len(x) == 1 for x in space): + return json.dumps({"type": "python", "space": "MultiDiscrete", "value": [next(iter(x)) for x in space]}) + # composite spaces + # Tuple + elif isinstance(space, tuple): + return json.dumps({"type": "python", "space": "Tuple", "value": [serialize_space(x) for x in space]}) + # Dict + elif isinstance(space, dict): + return json.dumps( + {"type": "python", "space": "Dict", "value": {k: serialize_space(v) for k, v in space.items()}} + ) + raise ValueError(f"Unsupported space ({space})") + + +def deserialize_space(string: str) -> gym.spaces.Space: + """Deserialize a space specification encoded as JSON. + + Args: + string: Serialized JSON representation. + + Returns: + Space specification. + """ + obj = json.loads(string) + # Gymnasium spaces + if obj["type"] == "gymnasium": + if obj["space"] == "Discrete": + return gym.spaces.Discrete(n=obj["n"]) + elif obj["space"] == "Box": + return gym.spaces.Box(low=np.array(obj["low"]), high=np.array(obj["high"]), shape=obj["shape"]) + elif obj["space"] == "MultiDiscrete": + return gym.spaces.MultiDiscrete(nvec=np.array(obj["nvec"])) + elif obj["space"] == "Tuple": + return gym.spaces.Tuple(spaces=tuple(map(deserialize_space, obj["spaces"]))) + elif obj["space"] == "Dict": + return gym.spaces.Dict(spaces={k: deserialize_space(v) for k, v in obj["spaces"].items()}) + else: + raise ValueError(f"Unsupported space ({obj['spaces']})") + # Python data types + elif obj["type"] == "python": + if obj["space"] == "Discrete": + return {obj["value"]} + elif obj["space"] == "Box": + return obj["value"] + elif obj["space"] == "MultiDiscrete": + return [{x} for x in obj["value"]] + elif obj["space"] == "Tuple": + return tuple(map(deserialize_space, obj["value"])) + elif obj["space"] == "Dict": + return {k: deserialize_space(v) for k, v in obj["value"].items()} + else: + raise ValueError(f"Unsupported space ({obj['spaces']})") + else: + raise ValueError(f"Unsupported type ({obj['type']})") + + +def replace_env_cfg_spaces_with_strings(env_cfg: object) -> object: + """Replace spaces objects with their serialized JSON representations in an environment config. + + Args: + env_cfg: Environment config instance. + + Returns: + Environment config instance with spaces replaced if any. + """ + for attr in ["observation_space", "action_space", "state_space"]: + if hasattr(env_cfg, attr): + setattr(env_cfg, attr, serialize_space(getattr(env_cfg, attr))) + for attr in ["observation_spaces", "action_spaces"]: + if hasattr(env_cfg, attr): + setattr(env_cfg, attr, {k: serialize_space(v) for k, v in getattr(env_cfg, attr).items()}) + return env_cfg + + +def replace_strings_with_env_cfg_spaces(env_cfg: object) -> object: + """Replace spaces objects with their serialized JSON representations in an environment config. + + Args: + env_cfg: Environment config instance. + + Returns: + Environment config instance with spaces replaced if any. + """ + for attr in ["observation_space", "action_space", "state_space"]: + if hasattr(env_cfg, attr): + setattr(env_cfg, attr, deserialize_space(getattr(env_cfg, attr))) + for attr in ["observation_spaces", "action_spaces"]: + if hasattr(env_cfg, attr): + setattr(env_cfg, attr, {k: deserialize_space(v) for k, v in getattr(env_cfg, attr).items()}) + return env_cfg diff --git a/source/isaaclab/isaaclab/managers/__init__.py b/source/isaaclab/isaaclab/managers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a8266801b477400b7c6051b8aff0a756fa1ca54 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/__init__.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for environment managers. + +The managers are used to handle various aspects of the environment such as randomization events, curriculum, +and observations. Each manager implements a specific functionality for the environment. The managers are +designed to be modular and can be easily extended to support new functionality. +""" + +from .action_manager import ActionManager, ActionTerm +from .command_manager import CommandManager, CommandTerm +from .curriculum_manager import CurriculumManager +from .event_manager import EventManager +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import ( + ActionTermCfg, + CommandTermCfg, + CurriculumTermCfg, + EventTermCfg, + ManagerTermBaseCfg, + ObservationGroupCfg, + ObservationTermCfg, + RecorderTermCfg, + RewardTermCfg, + TerminationTermCfg, +) +from .observation_manager import ObservationManager +from .recorder_manager import DatasetExportMode, RecorderManager, RecorderManagerBaseCfg, RecorderTerm +from .reward_manager import RewardManager +from .scene_entity_cfg import SceneEntityCfg +from .termination_manager import TerminationManager diff --git a/source/isaaclab/isaaclab/managers/action_manager.py b/source/isaaclab/isaaclab/managers/action_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..6d138451f998d6742a11865470ac9ed5e7b98910 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/action_manager.py @@ -0,0 +1,455 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Action manager for processing actions sent to the environment.""" + +from __future__ import annotations + +import inspect +import re +import weakref +from abc import abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +import torch +from prettytable import PrettyTable + +import omni.kit.app + +from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import ActionTermCfg + +if TYPE_CHECKING: + from isaaclab.assets import AssetBase + from isaaclab.envs import ManagerBasedEnv + + +class ActionTerm(ManagerTermBase): + """Base class for action terms. + + The action term is responsible for processing the raw actions sent to the environment + and applying them to the asset managed by the term. The action term is comprised of two + operations: + + * Processing of actions: This operation is performed once per **environment step** and + is responsible for pre-processing the raw actions sent to the environment. + * Applying actions: This operation is performed once per **simulation step** and is + responsible for applying the processed actions to the asset managed by the term. + """ + + def __init__(self, cfg: ActionTermCfg, env: ManagerBasedEnv): + """Initialize the action term. + + Args: + cfg: The configuration object. + env: The environment instance. + """ + # call the base class constructor + super().__init__(cfg, env) + # parse config to obtain asset to which the term is applied + self._asset: AssetBase = self._env.scene[self.cfg.asset_name] + self._IO_descriptor = GenericActionIODescriptor() + self._export_IO_descriptor = True + + # add handle for debug visualization (this is set to a valid handle inside set_debug_vis) + self._debug_vis_handle = None + # set initial state of debug visualization + self.set_debug_vis(self.cfg.debug_vis) + + def __del__(self): + """Unsubscribe from the callbacks.""" + if self._debug_vis_handle: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + + """ + Properties. + """ + + @property + @abstractmethod + def action_dim(self) -> int: + """Dimension of the action term.""" + raise NotImplementedError + + @property + @abstractmethod + def raw_actions(self) -> torch.Tensor: + """The input/raw actions sent to the term.""" + raise NotImplementedError + + @property + @abstractmethod + def processed_actions(self) -> torch.Tensor: + """The actions computed by the term after applying any processing.""" + raise NotImplementedError + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the action term has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_debug_vis_impl) + return "NotImplementedError" not in source_code + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor for the action term.""" + self._IO_descriptor.name = re.sub(r"([a-z])([A-Z])", r"\1_\2", self.__class__.__name__).lower() + self._IO_descriptor.full_path = f"{self.__class__.__module__}.{self.__class__.__name__}" + self._IO_descriptor.description = " ".join(self.__class__.__doc__.split()) + self._IO_descriptor.export = self.export_IO_descriptor + return self._IO_descriptor + + @property + def export_IO_descriptor(self) -> bool: + """Whether to export the IO descriptor for the action term.""" + return self._export_IO_descriptor + + """ + Operations. + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Sets whether to visualize the action term data. + Args: + debug_vis: Whether to visualize the action term data. + Returns: + Whether the debug visualization was successfully set. False if the action term does + not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_debug_vis_implementation: + return False + + # toggle debug visualization objects + self._set_debug_vis_impl(debug_vis) + # toggle debug visualization handles + if debug_vis: + # create a subscriber for the post update event if it doesn't exist + if self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + # return success + return True + + @abstractmethod + def process_actions(self, actions: torch.Tensor): + """Processes the actions sent to the environment. + + Note: + This function is called once per environment step by the manager. + + Args: + actions: The actions to process. + """ + raise NotImplementedError + + @abstractmethod + def apply_actions(self): + """Applies the actions to the asset managed by the term. + + Note: + This is called at every simulation step by the manager. + """ + raise NotImplementedError + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects. + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + def _debug_vis_callback(self, event): + """Callback for debug visualization. + This function calls the visualization objects and sets the data to visualize into them. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + +class ActionManager(ManagerBase): + """Manager for processing and applying actions for a given world. + + The action manager handles the interpretation and application of user-defined + actions on a given world. It is comprised of different action terms that decide + the dimension of the expected actions. + + The action manager performs operations at two stages: + + * processing of actions: It splits the input actions to each term and performs any + pre-processing needed. This should be called once at every environment step. + * apply actions: This operation typically sets the processed actions into the assets in the + scene (such as robots). It should be called before every simulation step. + """ + + def __init__(self, cfg: object, env: ManagerBasedEnv): + """Initialize the action manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, ActionTermCfg]``). + env: The environment instance. + + Raises: + ValueError: If the configuration is None. + """ + # check if config is None + if cfg is None: + raise ValueError("Action manager configuration is None. Please provide a valid configuration.") + + # call the base class constructor (this prepares the terms) + super().__init__(cfg, env) + # create buffers to store actions + self._action = torch.zeros((self.num_envs, self.total_action_dim), device=self.device) + self._prev_action = torch.zeros_like(self._action) + + # check if any term has debug visualization implemented + self.cfg.debug_vis = False + for term in self._terms.values(): + self.cfg.debug_vis |= term.cfg.debug_vis + + def __str__(self) -> str: + """Returns: A string representation for action manager.""" + msg = f" contains {len(self._term_names)} active terms.\n" + + # create table for term information + table = PrettyTable() + table.title = f"Active Action Terms (shape: {self.total_action_dim})" + table.field_names = ["Index", "Name", "Dimension"] + # set alignment of table columns + table.align["Name"] = "l" + table.align["Dimension"] = "r" + # add info on each term + for index, (name, term) in enumerate(self._terms.items()): + table.add_row([index, name, term.action_dim]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def total_action_dim(self) -> int: + """Total dimension of actions.""" + return sum(self.action_term_dim) + + @property + def active_terms(self) -> list[str]: + """Name of active action terms.""" + return self._term_names + + @property + def action_term_dim(self) -> list[int]: + """Shape of each action term.""" + return [term.action_dim for term in self._terms.values()] + + @property + def action(self) -> torch.Tensor: + """The actions sent to the environment. Shape is (num_envs, total_action_dim).""" + return self._action + + @property + def prev_action(self) -> torch.Tensor: + """The previous actions sent to the environment. Shape is (num_envs, total_action_dim).""" + return self._prev_action + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the command terms have debug visualization implemented.""" + # check if function raises NotImplementedError + has_debug_vis = False + for term in self._terms.values(): + has_debug_vis |= term.has_debug_vis_implementation + return has_debug_vis + + @property + def get_IO_descriptors(self) -> list[dict[str, Any]]: + """Get the IO descriptors for the action manager. + + Returns: + A dictionary with keys as the term names and values as the IO descriptors. + """ + + data = [] + + for term_name, term in self._terms.items(): + try: + data.append(term.IO_descriptor.__dict__.copy()) + except Exception as e: + print(f"Error getting IO descriptor for term '{term_name}': {e}") + + formatted_data = [] + for item in data: + name = item.pop("name") + formatted_item = {"name": name, "extras": item.pop("extras")} + print(item["export"]) + if not item.pop("export"): + continue + for k, v in item.items(): + # Check if v is a tuple and convert to list + if isinstance(v, tuple): + v = list(v) + if k in ["description", "units"]: + formatted_item["extras"][k] = v + else: + formatted_item[k] = v + formatted_data.append(formatted_item) + + return formatted_data + + """ + Operations. + """ + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + terms = [] + idx = 0 + for name, term in self._terms.items(): + term_actions = self._action[env_idx, idx : idx + term.action_dim].cpu() + terms.append((name, term_actions.tolist())) + idx += term.action_dim + return terms + + def set_debug_vis(self, debug_vis: bool): + """Sets whether to visualize the action data. + Args: + debug_vis: Whether to visualize the action data. + Returns: + Whether the debug visualization was successfully set. False if the action + does not support debug visualization. + """ + for term in self._terms.values(): + term.set_debug_vis(debug_vis) + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Resets the action history. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + + Returns: + An empty dictionary. + """ + # resolve environment ids + if env_ids is None: + env_ids = slice(None) + # reset the action history + self._prev_action[env_ids] = 0.0 + self._action[env_ids] = 0.0 + # reset all action terms + for term in self._terms.values(): + term.reset(env_ids=env_ids) + # nothing to log here + return {} + + def process_action(self, action: torch.Tensor): + """Processes the actions sent to the environment. + + Note: + This function should be called once per environment step. + + Args: + action: The actions to process. + """ + # check if action dimension is valid + if self.total_action_dim != action.shape[1]: + raise ValueError(f"Invalid action shape, expected: {self.total_action_dim}, received: {action.shape[1]}.") + # store the input actions + self._prev_action[:] = self._action + self._action[:] = action.to(self.device) + + # split the actions and apply to each tensor + idx = 0 + for term in self._terms.values(): + term_actions = action[:, idx : idx + term.action_dim] + term.process_actions(term_actions) + idx += term.action_dim + + def apply_action(self) -> None: + """Applies the actions to the environment/simulation. + + Note: + This should be called at every simulation step. + """ + for term in self._terms.values(): + term.apply_actions() + + def get_term(self, name: str) -> ActionTerm: + """Returns the action term with the specified name. + + Args: + name: The name of the action term. + + Returns: + The action term with the specified name. + """ + return self._terms[name] + + def serialize(self) -> dict: + """Serialize the action manager configuration. + + Returns: + A dictionary of serialized action term configurations. + """ + return {term_name: term.serialize() for term_name, term in self._terms.items()} + + """ + Helper functions. + """ + + def _prepare_terms(self): + # create buffers to parse and store terms + self._term_names: list[str] = list() + self._terms: dict[str, ActionTerm] = dict() + + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # parse action terms from the config + for term_name, term_cfg in cfg_items: + # check if term config is None + if term_cfg is None: + continue + # check valid type + if not isinstance(term_cfg, ActionTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type ActionTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # create the action term + term = term_cfg.class_type(term_cfg, self._env) + # sanity check if term is valid type + if not isinstance(term, ActionTerm): + raise TypeError(f"Returned object for the term '{term_name}' is not of type ActionType.") + # add term name and parameters + self._term_names.append(term_name) + self._terms[term_name] = term diff --git a/source/isaaclab/isaaclab/managers/command_manager.py b/source/isaaclab/isaaclab/managers/command_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..0fe66ff6b963d3db645b6b58776275d5a5735b37 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/command_manager.py @@ -0,0 +1,424 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Command manager for generating and updating commands.""" + +from __future__ import annotations + +import inspect +import weakref +from abc import abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +import omni.kit.app + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import CommandTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class CommandTerm(ManagerTermBase): + """The base class for implementing a command term. + + A command term is used to generate commands for goal-conditioned tasks. For example, + in the case of a goal-conditioned navigation task, the command term can be used to + generate a target position for the robot to navigate to. + + It implements a resampling mechanism that allows the command to be resampled at a fixed + frequency. The resampling frequency can be specified in the configuration object. + Additionally, it is possible to assign a visualization function to the command term + that can be used to visualize the command in the simulator. + """ + + def __init__(self, cfg: CommandTermCfg, env: ManagerBasedRLEnv): + """Initialize the command generator class. + + Args: + cfg: The configuration parameters for the command generator. + env: The environment object. + """ + super().__init__(cfg, env) + + # create buffers to store the command + # -- metrics that can be used for logging + self.metrics = dict() + # -- time left before resampling + self.time_left = torch.zeros(self.num_envs, device=self.device) + # -- counter for the number of times the command has been resampled within the current episode + self.command_counter = torch.zeros(self.num_envs, device=self.device, dtype=torch.long) + + # add handle for debug visualization (this is set to a valid handle inside set_debug_vis) + self._debug_vis_handle = None + # set initial state of debug visualization + self.set_debug_vis(self.cfg.debug_vis) + + def __del__(self): + """Unsubscribe from the callbacks.""" + if self._debug_vis_handle: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + + """ + Properties + """ + + @property + @abstractmethod + def command(self) -> torch.Tensor: + """The command tensor. Shape is (num_envs, command_dim).""" + raise NotImplementedError + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the command generator has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_debug_vis_impl) + return "NotImplementedError" not in source_code + + """ + Operations. + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Sets whether to visualize the command data. + + Args: + debug_vis: Whether to visualize the command data. + + Returns: + Whether the debug visualization was successfully set. False if the command + generator does not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_debug_vis_implementation: + return False + # toggle debug visualization objects + self._set_debug_vis_impl(debug_vis) + # toggle debug visualization handles + if debug_vis: + # create a subscriber for the post update event if it doesn't exist + if self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + # return success + return True + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + """Reset the command generator and log metrics. + + This function resets the command counter and resamples the command. It should be called + at the beginning of each episode. + + Args: + env_ids: The list of environment IDs to reset. Defaults to None. + + Returns: + A dictionary containing the information to log under the "{name}" key. + """ + # resolve the environment IDs + if env_ids is None: + env_ids = slice(None) + + # add logging metrics + extras = {} + for metric_name, metric_value in self.metrics.items(): + # compute the mean metric value + extras[metric_name] = torch.mean(metric_value[env_ids]).item() + # reset the metric value + metric_value[env_ids] = 0.0 + + # set the command counter to zero + self.command_counter[env_ids] = 0 + # resample the command + self._resample(env_ids) + + return extras + + def compute(self, dt: float): + """Compute the command. + + Args: + dt: The time step passed since the last call to compute. + """ + # update the metrics based on current state + self._update_metrics() + # reduce the time left before resampling + self.time_left -= dt + # resample the command if necessary + resample_env_ids = (self.time_left <= 0.0).nonzero().flatten() + if len(resample_env_ids) > 0: + self._resample(resample_env_ids) + # update the command + self._update_command() + + """ + Helper functions. + """ + + def _resample(self, env_ids: Sequence[int]): + """Resample the command. + + This function resamples the command and time for which the command is applied for the + specified environment indices. + + Args: + env_ids: The list of environment IDs to resample. + """ + if len(env_ids) != 0: + # resample the time left before resampling + self.time_left[env_ids] = self.time_left[env_ids].uniform_(*self.cfg.resampling_time_range) + # resample the command + self._resample_command(env_ids) + # increment the command counter + self.command_counter[env_ids] += 1 + + """ + Implementation specific functions. + """ + + @abstractmethod + def _update_metrics(self): + """Update the metrics based on the current state.""" + raise NotImplementedError + + @abstractmethod + def _resample_command(self, env_ids: Sequence[int]): + """Resample the command for the specified environments.""" + raise NotImplementedError + + @abstractmethod + def _update_command(self): + """Update the command based on the current state.""" + raise NotImplementedError + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects. + + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + def _debug_vis_callback(self, event): + """Callback for debug visualization. + + This function calls the visualization objects and sets the data to visualize into them. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + +class CommandManager(ManagerBase): + """Manager for generating commands. + + The command manager is used to generate commands for an agent to execute. It makes it convenient to switch + between different command generation strategies within the same environment. For instance, in an environment + consisting of a quadrupedal robot, the command to it could be a velocity command or position command. + By keeping the command generation logic separate from the environment, it is easy to switch between different + command generation strategies. + + The command terms are implemented as classes that inherit from the :class:`CommandTerm` class. + Each command generator term should also have a corresponding configuration class that inherits from the + :class:`CommandTermCfg` class. + """ + + _env: ManagerBasedRLEnv + """The environment instance.""" + + def __init__(self, cfg: object, env: ManagerBasedRLEnv): + """Initialize the command manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, CommandTermCfg]``). + env: The environment instance. + """ + # create buffers to parse and store terms + self._terms: dict[str, CommandTerm] = dict() + + # call the base class constructor (this prepares the terms) + super().__init__(cfg, env) + # store the commands + self._commands = dict() + if self.cfg: + self.cfg.debug_vis = False + for term in self._terms.values(): + self.cfg.debug_vis |= term.cfg.debug_vis + + def __str__(self) -> str: + """Returns: A string representation for the command manager.""" + msg = f" contains {len(self._terms.values())} active terms.\n" + + # create table for term information + table = PrettyTable() + table.title = "Active Command Terms" + table.field_names = ["Index", "Name", "Type"] + # set alignment of table columns + table.align["Name"] = "l" + # add info on each term + for index, (name, term) in enumerate(self._terms.items()): + table.add_row([index, name, term.__class__.__name__]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def active_terms(self) -> list[str]: + """Name of active command terms.""" + return list(self._terms.keys()) + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the command terms have debug visualization implemented.""" + # check if function raises NotImplementedError + has_debug_vis = False + for term in self._terms.values(): + has_debug_vis |= term.has_debug_vis_implementation + return has_debug_vis + + """ + Operations. + """ + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + + terms = [] + idx = 0 + for name, term in self._terms.items(): + terms.append((name, term.command[env_idx].cpu().tolist())) + idx += term.command.shape[1] + return terms + + def set_debug_vis(self, debug_vis: bool): + """Sets whether to visualize the command data. + + Args: + debug_vis: Whether to visualize the command data. + + Returns: + Whether the debug visualization was successfully set. False if the command + generator does not support debug visualization. + """ + for term in self._terms.values(): + term.set_debug_vis(debug_vis) + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Reset the command terms and log their metrics. + + This function resets the command counter and resamples the command for each term. It should be called + at the beginning of each episode. + + Args: + env_ids: The list of environment IDs to reset. Defaults to None. + + Returns: + A dictionary containing the information to log under the "Metrics/{term_name}/{metric_name}" key. + """ + # resolve environment ids + if env_ids is None: + env_ids = slice(None) + # store information + extras = {} + for name, term in self._terms.items(): + # reset the command term + metrics = term.reset(env_ids=env_ids) + # compute the mean metric value + for metric_name, metric_value in metrics.items(): + extras[f"Metrics/{name}/{metric_name}"] = metric_value + # return logged information + return extras + + def compute(self, dt: float): + """Updates the commands. + + This function calls each command term managed by the class. + + Args: + dt: The time-step interval of the environment. + + """ + # iterate over all the command terms + for term in self._terms.values(): + # compute term's value + term.compute(dt) + + def get_command(self, name: str) -> torch.Tensor: + """Returns the command for the specified command term. + + Args: + name: The name of the command term. + + Returns: + The command tensor of the specified command term. + """ + return self._terms[name].command + + def get_term(self, name: str) -> CommandTerm: + """Returns the command term with the specified name. + + Args: + name: The name of the command term. + + Returns: + The command term with the specified name. + """ + return self._terms[name] + + """ + Helper functions. + """ + + def _prepare_terms(self): + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # check for valid config type + if not isinstance(term_cfg, CommandTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type CommandTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # create the action term + term = term_cfg.class_type(term_cfg, self._env) + # sanity check if term is valid type + if not isinstance(term, CommandTerm): + raise TypeError(f"Returned object for the term '{term_name}' is not of type CommandType.") + # add class to dict + self._terms[term_name] = term diff --git a/source/isaaclab/isaaclab/managers/curriculum_manager.py b/source/isaaclab/isaaclab/managers/curriculum_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..5354641d9e7e8f18b1bf73585d267c042385c152 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/curriculum_manager.py @@ -0,0 +1,204 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Curriculum manager for updating environment quantities subject to a training curriculum.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import CurriculumTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class CurriculumManager(ManagerBase): + """Manager to implement and execute specific curricula. + + The curriculum manager updates various quantities of the environment subject to a training curriculum by + calling a list of terms. These help stabilize learning by progressively making the learning tasks harder + as the agent improves. + + The curriculum terms are parsed from a config class containing the manager's settings and each term's + parameters. Each curriculum term should instantiate the :class:`CurriculumTermCfg` class. + """ + + _env: ManagerBasedRLEnv + """The environment instance.""" + + def __init__(self, cfg: object, env: ManagerBasedRLEnv): + """Initialize the manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, CurriculumTermCfg]``) + env: An environment object. + + Raises: + TypeError: If curriculum term is not of type :class:`CurriculumTermCfg`. + ValueError: If curriculum term configuration does not satisfy its function signature. + """ + # create buffers to parse and store terms + self._term_names: list[str] = list() + self._term_cfgs: list[CurriculumTermCfg] = list() + self._class_term_cfgs: list[CurriculumTermCfg] = list() + + # call the base class constructor (this will parse the terms config) + super().__init__(cfg, env) + + # prepare logging + self._curriculum_state = dict() + for term_name in self._term_names: + self._curriculum_state[term_name] = None + + def __str__(self) -> str: + """Returns: A string representation for curriculum manager.""" + msg = f" contains {len(self._term_names)} active terms.\n" + + # create table for term information + table = PrettyTable() + table.title = "Active Curriculum Terms" + table.field_names = ["Index", "Name"] + # set alignment of table columns + table.align["Name"] = "l" + # add info on each term + for index, name in enumerate(self._term_names): + table.add_row([index, name]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def active_terms(self) -> list[str]: + """Name of active curriculum terms.""" + return self._term_names + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + """Returns the current state of individual curriculum terms. + + Note: + This function does not use the environment indices :attr:`env_ids` + and logs the state of all the terms. The argument is only present + to maintain consistency with other classes. + + Returns: + Dictionary of curriculum terms and their states. + """ + extras = {} + for term_name, term_state in self._curriculum_state.items(): + if term_state is not None: + # deal with dict + if isinstance(term_state, dict): + # each key is a separate state to log + for key, value in term_state.items(): + if isinstance(value, torch.Tensor): + value = value.item() + extras[f"Curriculum/{term_name}/{key}"] = value + else: + # log directly if not a dict + if isinstance(term_state, torch.Tensor): + term_state = term_state.item() + extras[f"Curriculum/{term_name}"] = term_state + # reset all the curriculum terms + for term_cfg in self._class_term_cfgs: + term_cfg.func.reset(env_ids=env_ids) + # return logged information + return extras + + def compute(self, env_ids: Sequence[int] | None = None): + """Update the curriculum terms. + + This function calls each curriculum term managed by the class. + + Args: + env_ids: The list of environment IDs to update. + If None, all the environments are updated. Defaults to None. + """ + # resolve environment indices + if env_ids is None: + env_ids = slice(None) + # iterate over all the curriculum terms + for name, term_cfg in zip(self._term_names, self._term_cfgs): + state = term_cfg.func(self._env, env_ids, **term_cfg.params) + self._curriculum_state[name] = state + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + + terms = [] + + for term_name, term_state in self._curriculum_state.items(): + if term_state is not None: + # deal with dict + data = [] + + if isinstance(term_state, dict): + # each key is a separate state to log + for key, value in term_state.items(): + if isinstance(value, torch.Tensor): + value = value.item() + terms[term_name].append(value) + else: + # log directly if not a dict + if isinstance(term_state, torch.Tensor): + term_state = term_state.item() + data.append(term_state) + terms.append((term_name, data)) + + return terms + + """ + Helper functions. + """ + + def _prepare_terms(self): + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # check if the term is a valid term config + if not isinstance(term_cfg, CurriculumTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type CurriculumTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # resolve common parameters + self._resolve_common_term_cfg(term_name, term_cfg, min_argc=2) + # add name and config to list + self._term_names.append(term_name) + self._term_cfgs.append(term_cfg) + # check if the term is a class + if isinstance(term_cfg.func, ManagerTermBase): + self._class_term_cfgs.append(term_cfg) diff --git a/source/isaaclab/isaaclab/managers/event_manager.py b/source/isaaclab/isaaclab/managers/event_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..8f92d6859c1eb379e66d26877aa4cfd8ba4e90a7 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/event_manager.py @@ -0,0 +1,424 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Event manager for orchestrating operations based on different simulation events.""" + +from __future__ import annotations + +import inspect +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +from .manager_base import ManagerBase +from .manager_term_cfg import EventTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + +# import logger +logger = logging.getLogger(__name__) + + +class EventManager(ManagerBase): + """Manager for orchestrating operations based on different simulation events. + + The event manager applies operations to the environment based on different simulation events. For example, + changing the masses of objects or their friction coefficients during initialization/ reset, or applying random + pushes to the robot at a fixed interval of steps. The user can specify several modes of events to fine-tune the + behavior based on when to apply the event. + + The event terms are parsed from a config class containing the manager's settings and each term's + parameters. Each event term should instantiate the :class:`EventTermCfg` class. + + Event terms can be grouped by their mode. The mode is a user-defined string that specifies when + the event term should be applied. This provides the user complete control over when event + terms should be applied. + + For a typical training process, you may want to apply events in the following modes: + + - "prestartup": Event is applied once at the beginning of the training before the simulation starts. + This is used to randomize USD-level properties of the simulation stage. + - "startup": Event is applied once at the beginning of the training once simulation is started. + - "reset": Event is applied at every reset. + - "interval": Event is applied at pre-specified intervals of time. + + However, you can also define your own modes and use them in the training process as you see fit. + For this you will need to add the triggering of that mode in the environment implementation as well. + + .. note:: + + The triggering of operations corresponding to the mode ``"interval"`` are the only mode that are + directly handled by the manager itself. The other modes are handled by the environment implementation. + + """ + + _env: ManagerBasedEnv + """The environment instance.""" + + def __init__(self, cfg: object, env: ManagerBasedEnv): + """Initialize the event manager. + + Args: + cfg: A configuration object or dictionary (``dict[str, EventTermCfg]``). + env: An environment object. + """ + # create buffers to parse and store terms + self._mode_term_names: dict[str, list[str]] = dict() + self._mode_term_cfgs: dict[str, list[EventTermCfg]] = dict() + self._mode_class_term_cfgs: dict[str, list[EventTermCfg]] = dict() + + # call the base class (this will parse the terms config) + super().__init__(cfg, env) + + def __str__(self) -> str: + """Returns: A string representation for event manager.""" + msg = f" contains {len(self._mode_term_names)} active terms.\n" + + # add info on each mode + for mode in self._mode_term_names: + # create table for term information + table = PrettyTable() + table.title = f"Active Event Terms in Mode: '{mode}'" + # add table headers based on mode + if mode == "interval": + table.field_names = ["Index", "Name", "Interval time range (s)"] + table.align["Name"] = "l" + for index, (name, cfg) in enumerate(zip(self._mode_term_names[mode], self._mode_term_cfgs[mode])): + table.add_row([index, name, cfg.interval_range_s]) + else: + table.field_names = ["Index", "Name"] + table.align["Name"] = "l" + for index, name in enumerate(self._mode_term_names[mode]): + table.add_row([index, name]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def active_terms(self) -> dict[str, list[str]]: + """Name of active event terms. + + The keys are the modes of event and the values are the names of the event terms. + """ + return self._mode_term_names + + @property + def available_modes(self) -> list[str]: + """Modes of events.""" + return list(self._mode_term_names.keys()) + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + # call all terms that are classes + for mode_cfg in self._mode_class_term_cfgs.values(): + for term_cfg in mode_cfg: + term_cfg.func.reset(env_ids=env_ids) + + # resolve number of environments + if env_ids is None: + num_envs = self._env.num_envs + else: + num_envs = len(env_ids) + # if we are doing interval based events then we need to reset the time left + # when the episode starts. otherwise the counter will start from the last time + # for that environment + if "interval" in self._mode_term_cfgs: + for index, term_cfg in enumerate(self._mode_term_cfgs["interval"]): + # sample a new interval and set that as time left + # note: global time events are based on simulation time and not episode time + # so we do not reset them + if not term_cfg.is_global_time: + lower, upper = term_cfg.interval_range_s + sampled_interval = torch.rand(num_envs, device=self.device) * (upper - lower) + lower + self._interval_term_time_left[index][env_ids] = sampled_interval + + # nothing to log here + return {} + + def apply( + self, + mode: str, + env_ids: Sequence[int] | None = None, + dt: float | None = None, + global_env_step_count: int | None = None, + ): + """Calls each event term in the specified mode. + + This function iterates over all the event terms in the specified mode and calls the function + corresponding to the term. The function is called with the environment instance and the environment + indices to apply the event to. + + For the "interval" mode, the function is called when the time interval has passed. This requires + specifying the time step of the environment. + + For the "reset" mode, the function is called when the mode is "reset" and the total number of environment + steps that have happened since the last trigger of the function is equal to its configured parameter for + the number of environment steps between resets. + + Args: + mode: The mode of event. + env_ids: The indices of the environments to apply the event to. + Defaults to None, in which case the event is applied to all environments when applicable. + dt: The time step of the environment. This is only used for the "interval" mode. + Defaults to None to simplify the call for other modes. + global_env_step_count: The total number of environment steps that have happened. This is only used + for the "reset" mode. Defaults to None to simplify the call for other modes. + + Raises: + ValueError: If the mode is ``"interval"`` and the time step is not provided. + ValueError: If the mode is ``"interval"`` and the environment indices are provided. This is an undefined + behavior as the environment indices are computed based on the time left for each environment. + ValueError: If the mode is ``"reset"`` and the total number of environment steps that have happened + is not provided. + """ + # check if mode is valid + if mode not in self._mode_term_names: + logger.warning(f"Event mode '{mode}' is not defined. Skipping event.") + return + + # check if mode is interval and dt is not provided + if mode == "interval" and dt is None: + raise ValueError(f"Event mode '{mode}' requires the time-step of the environment.") + if mode == "interval" and env_ids is not None: + raise ValueError( + f"Event mode '{mode}' does not require environment indices. This is an undefined behavior" + " as the environment indices are computed based on the time left for each environment." + ) + # check if mode is reset and env step count is not provided + if mode == "reset" and global_env_step_count is None: + raise ValueError(f"Event mode '{mode}' requires the total number of environment steps to be provided.") + + # iterate over all the event terms + for index, term_cfg in enumerate(self._mode_term_cfgs[mode]): + if mode == "interval": + # extract time left for this term + time_left = self._interval_term_time_left[index] + # update the time left for each environment + time_left -= dt + + # check if the interval has passed and sample a new interval + # note: we compare with a small value to handle floating point errors + if term_cfg.is_global_time: + if time_left < 1e-6: + lower, upper = term_cfg.interval_range_s + sampled_interval = torch.rand(1) * (upper - lower) + lower + self._interval_term_time_left[index][:] = sampled_interval + + # call the event term (with None for env_ids) + term_cfg.func(self._env, None, **term_cfg.params) + else: + valid_env_ids = (time_left < 1e-6).nonzero().flatten() + if len(valid_env_ids) > 0: + lower, upper = term_cfg.interval_range_s + sampled_time = torch.rand(len(valid_env_ids), device=self.device) * (upper - lower) + lower + self._interval_term_time_left[index][valid_env_ids] = sampled_time + + # call the event term + term_cfg.func(self._env, valid_env_ids, **term_cfg.params) + elif mode == "reset": + # obtain the minimum step count between resets + min_step_count = term_cfg.min_step_count_between_reset + # resolve the environment indices + if env_ids is None: + env_ids = slice(None) + + # We bypass the trigger mechanism if min_step_count is zero, i.e. apply term on every reset call. + # This should avoid the overhead of checking the trigger condition. + if min_step_count == 0: + self._reset_term_last_triggered_step_id[index][env_ids] = global_env_step_count + self._reset_term_last_triggered_once[index][env_ids] = True + + # call the event term with the environment indices + term_cfg.func(self._env, env_ids, **term_cfg.params) + else: + # extract last reset step for this term + last_triggered_step = self._reset_term_last_triggered_step_id[index][env_ids] + triggered_at_least_once = self._reset_term_last_triggered_once[index][env_ids] + # compute the steps since last reset + steps_since_triggered = global_env_step_count - last_triggered_step + + # check if the term can be applied after the minimum step count between triggers has passed + valid_trigger = steps_since_triggered >= min_step_count + # check if the term has not been triggered yet (in that case, we trigger it at least once) + # this is usually only needed at the start of the environment + valid_trigger |= (last_triggered_step == 0) & ~triggered_at_least_once + + # select the valid environment indices based on the trigger + if env_ids == slice(None): + valid_env_ids = valid_trigger.nonzero().flatten() + else: + valid_env_ids = env_ids[valid_trigger] + + # reset the last reset step for each environment to the current env step count + if len(valid_env_ids) > 0: + self._reset_term_last_triggered_once[index][valid_env_ids] = True + self._reset_term_last_triggered_step_id[index][valid_env_ids] = global_env_step_count + + # call the event term + term_cfg.func(self._env, valid_env_ids, **term_cfg.params) + else: + # call the event term + term_cfg.func(self._env, env_ids, **term_cfg.params) + + """ + Operations - Term settings. + """ + + def set_term_cfg(self, term_name: str, cfg: EventTermCfg): + """Sets the configuration of the specified term into the manager. + + The method finds the term by name by searching through all the modes. + It then updates the configuration of the term with the first matching name. + + Args: + term_name: The name of the event term. + cfg: The configuration for the event term. + + Raises: + ValueError: If the term name is not found. + """ + term_found = False + for mode, terms in self._mode_term_names.items(): + if term_name in terms: + self._mode_term_cfgs[mode][terms.index(term_name)] = cfg + term_found = True + break + if not term_found: + raise ValueError(f"Event term '{term_name}' not found.") + + def get_term_cfg(self, term_name: str) -> EventTermCfg: + """Gets the configuration for the specified term. + + The method finds the term by name by searching through all the modes. + It then returns the configuration of the term with the first matching name. + + Args: + term_name: The name of the event term. + + Returns: + The configuration of the event term. + + Raises: + ValueError: If the term name is not found. + """ + for mode, terms in self._mode_term_names.items(): + if term_name in terms: + return self._mode_term_cfgs[mode][terms.index(term_name)] + raise ValueError(f"Event term '{term_name}' not found.") + + """ + Helper functions. + """ + + def _prepare_terms(self): + # buffer to store the time left for "interval" mode + # if interval is global, then it is a single value, otherwise it is per environment + self._interval_term_time_left: list[torch.Tensor] = list() + # buffer to store the step count when the term was last triggered for each environment for "reset" mode + self._reset_term_last_triggered_step_id: list[torch.Tensor] = list() + self._reset_term_last_triggered_once: list[torch.Tensor] = list() + + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # check for valid config type + if not isinstance(term_cfg, EventTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type EventTermCfg." + f" Received: '{type(term_cfg)}'." + ) + + if term_cfg.mode != "reset" and term_cfg.min_step_count_between_reset != 0: + logger.warning( + f"Event term '{term_name}' has 'min_step_count_between_reset' set to a non-zero value" + " but the mode is not 'reset'. Ignoring the 'min_step_count_between_reset' value." + ) + + # resolve common parameters + self._resolve_common_term_cfg(term_name, term_cfg, min_argc=2) + + # check if mode is pre-startup and scene replication is enabled + if term_cfg.mode == "prestartup" and self._env.scene.cfg.replicate_physics: + raise RuntimeError( + "Scene replication is enabled, which may affect USD-level randomization." + " When assets are replicated, their properties are shared across instances," + " potentially leading to unintended behavior." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # for event terms with mode "prestartup", we assume a callable class term + # can be initialized before the simulation starts. + # this is done to ensure that the USD-level randomization is possible before the simulation starts. + if inspect.isclass(term_cfg.func) and term_cfg.mode == "prestartup": + logger.info(f"Initializing term '{term_name}' with class '{term_cfg.func.__name__}'.") + term_cfg.func = term_cfg.func(cfg=term_cfg, env=self._env) + + # check if mode is a new mode + if term_cfg.mode not in self._mode_term_names: + # add new mode + self._mode_term_names[term_cfg.mode] = list() + self._mode_term_cfgs[term_cfg.mode] = list() + self._mode_class_term_cfgs[term_cfg.mode] = list() + # add term name and parameters + self._mode_term_names[term_cfg.mode].append(term_name) + self._mode_term_cfgs[term_cfg.mode].append(term_cfg) + + # check if the term is a class + if inspect.isclass(term_cfg.func): + self._mode_class_term_cfgs[term_cfg.mode].append(term_cfg) + + # resolve the mode of the events + # -- interval mode + if term_cfg.mode == "interval": + if term_cfg.interval_range_s is None: + raise ValueError( + f"Event term '{term_name}' has mode 'interval' but 'interval_range_s' is not specified." + ) + + # sample the time left for global + if term_cfg.is_global_time: + lower, upper = term_cfg.interval_range_s + time_left = torch.rand(1) * (upper - lower) + lower + self._interval_term_time_left.append(time_left) + else: + # sample the time left for each environment + lower, upper = term_cfg.interval_range_s + time_left = torch.rand(self.num_envs, device=self.device) * (upper - lower) + lower + self._interval_term_time_left.append(time_left) + # -- reset mode + elif term_cfg.mode == "reset": + if term_cfg.min_step_count_between_reset < 0: + raise ValueError( + f"Event term '{term_name}' has mode 'reset' but 'min_step_count_between_reset' is" + f" negative: {term_cfg.min_step_count_between_reset}. Please provide a non-negative value." + ) + + # initialize the current step count for each environment to zero + step_count = torch.zeros(self.num_envs, device=self.device, dtype=torch.int32) + self._reset_term_last_triggered_step_id.append(step_count) + # initialize the trigger flag for each environment to zero + no_trigger = torch.zeros(self.num_envs, device=self.device, dtype=torch.bool) + self._reset_term_last_triggered_once.append(no_trigger) diff --git a/source/isaaclab/isaaclab/managers/manager_base.py b/source/isaaclab/isaaclab/managers/manager_base.py new file mode 100644 index 0000000000000000000000000000000000000000..158c713abfa3f68346d4aee1c172f357bfba7f6e --- /dev/null +++ b/source/isaaclab/isaaclab/managers/manager_base.py @@ -0,0 +1,418 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import copy +import inspect +import logging +import weakref +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +import omni.timeline + +import isaaclab.utils.string as string_utils +from isaaclab.utils import class_to_dict, string_to_callable + +from .manager_term_cfg import ManagerTermBaseCfg +from .scene_entity_cfg import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + +# import logger +logger = logging.getLogger(__name__) + + +class ManagerTermBase(ABC): + """Base class for manager terms. + + Manager term implementations can be functions or classes. If the term is a class, it should + inherit from this base class and implement the required methods. + + Each manager is implemented as a class that inherits from the :class:`ManagerBase` class. Each manager + class should also have a corresponding configuration class that defines the configuration terms for the + manager. Each term should the :class:`ManagerTermBaseCfg` class or its subclass. + + Example pseudo-code for creating a manager: + + .. code-block:: python + + from isaaclab.utils import configclass + from isaaclab.utils.mdp import ManagerBase, ManagerTermBaseCfg + + + @configclass + class MyManagerCfg: + my_term_1: ManagerTermBaseCfg = ManagerTermBaseCfg(...) + my_term_2: ManagerTermBaseCfg = ManagerTermBaseCfg(...) + my_term_3: ManagerTermBaseCfg = ManagerTermBaseCfg(...) + + + # define manager instance + my_manager = ManagerBase(cfg=ManagerCfg(), env=env) + + """ + + def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedEnv): + """Initialize the manager term. + + Args: + cfg: The configuration object. + env: The environment instance. + """ + # store the inputs + self.cfg = cfg + self._env = env + + """ + Properties. + """ + + @property + def num_envs(self) -> int: + """Number of environments.""" + return self._env.num_envs + + @property + def device(self) -> str: + """Device on which to perform computations.""" + return self._env.device + + @property + def __name__(self) -> str: + """Return the name of the class or subclass.""" + return self.__class__.__name__ + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + """Resets the manager term. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + """ + pass + + def serialize(self) -> dict: + """General serialization call. Includes the configuration dict.""" + return {"cfg": class_to_dict(self.cfg)} + + def __call__(self, *args) -> Any: + """Returns the value of the term required by the manager. + + In case of a class implementation, this function is called by the manager + to get the value of the term. The arguments passed to this function are + the ones specified in the term configuration (see :attr:`ManagerTermBaseCfg.params`). + + .. attention:: + To be consistent with memory-less implementation of terms with functions, it is + recommended to ensure that the returned mutable quantities are cloned before + returning them. For instance, if the term returns a tensor, it is recommended + to ensure that the returned tensor is a clone of the original tensor. This prevents + the manager from storing references to the tensors and altering the original tensors. + + Args: + *args: Variable length argument list. + + Returns: + The value of the term. + """ + raise NotImplementedError("The method '__call__' should be implemented by the subclass.") + + +class ManagerBase(ABC): + """Base class for all managers.""" + + def __init__(self, cfg: object, env: ManagerBasedEnv): + """Initialize the manager. + + This function is responsible for parsing the configuration object and creating the terms. + + If the simulation is not playing, the scene entities are not resolved immediately. + Instead, the resolution is deferred until the simulation starts. This is done to ensure + that the scene entities are resolved even if the manager is created after the simulation + has already started. + + Args: + cfg: The configuration object. If None, the manager is initialized without any terms. + env: The environment instance. + """ + # store the inputs + self.cfg = copy.deepcopy(cfg) + self._env = env + + # flag for whether the scene entities have been resolved + # if sim is playing, we resolve the scene entities directly while preparing the terms + self._is_scene_entities_resolved = self._env.sim.is_playing() + + # if the simulation is not playing, we use callbacks to trigger the resolution of the scene + # entities configuration. this is needed for cases where the manager is created after the + # simulation, but before the simulation is playing. + # FIXME: Once Isaac Sim supports storing this information as USD schema, we can remove this + # callback and resolve the scene entities directly inside `_prepare_terms`. + if not self._env.sim.is_playing(): + # note: Use weakref on all callbacks to ensure that this object can be deleted when its destructor + # is called + # The order is set to 20 to allow asset/sensor initialization to complete before the scene entities + # are resolved. Those have the order 10. + timeline_event_stream = omni.timeline.get_timeline_interface().get_timeline_event_stream() + self._resolve_terms_handle = timeline_event_stream.create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.PLAY), + lambda event, obj=weakref.proxy(self): obj._resolve_terms_callback(event), + order=20, + ) + else: + self._resolve_terms_handle = None + + # parse config to create terms information + if self.cfg: + self._prepare_terms() + + def __del__(self): + """Delete the manager.""" + if self._resolve_terms_handle: + self._resolve_terms_handle.unsubscribe() + self._resolve_terms_handle = None + + """ + Properties. + """ + + @property + def num_envs(self) -> int: + """Number of environments.""" + return self._env.num_envs + + @property + def device(self) -> str: + """Device on which to perform computations.""" + return self._env.device + + @property + @abstractmethod + def active_terms(self) -> list[str] | dict[str, list[str]]: + """Name of active terms.""" + raise NotImplementedError + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + """Resets the manager and returns logging information for the current time-step. + + Args: + env_ids: The environment ids for which to log data. + Defaults None, which logs data for all environments. + + Returns: + Dictionary containing the logging information. + """ + return {} + + def find_terms(self, name_keys: str | Sequence[str]) -> list[str]: + """Find terms in the manager based on the names. + + This function searches the manager for terms based on the names. The names can be + specified as regular expressions or a list of regular expressions. The search is + performed on the active terms in the manager. + + Please check the :meth:`~isaaclab.utils.string_utils.resolve_matching_names` function for more + information on the name matching. + + Args: + name_keys: A regular expression or a list of regular expressions to match the term names. + + Returns: + A list of term names that match the input keys. + """ + # resolve search keys + if isinstance(self.active_terms, dict): + list_of_strings = [] + for names in self.active_terms.values(): + list_of_strings.extend(names) + else: + list_of_strings = self.active_terms + + # return the matching names + return string_utils.resolve_matching_names(name_keys, list_of_strings)[1] + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Returns: + The active terms. + """ + raise NotImplementedError + + """ + Implementation specific. + """ + + @abstractmethod + def _prepare_terms(self): + """Prepare terms information from the configuration object.""" + raise NotImplementedError + + """ + Internal callbacks. + """ + + def _resolve_terms_callback(self, event): + """Resolve configurations of terms once the simulation starts. + + Please check the :meth:`_process_term_cfg_at_play` method for more information. + """ + # check if scene entities have been resolved + if self._is_scene_entities_resolved: + return + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # process attributes at runtime + # these properties are only resolvable once the simulation starts playing + self._process_term_cfg_at_play(term_name, term_cfg) + + # set the flag + self._is_scene_entities_resolved = True + + """ + Internal functions. + """ + + def _resolve_common_term_cfg(self, term_name: str, term_cfg: ManagerTermBaseCfg, min_argc: int = 1): + """Resolve common attributes of the term configuration. + + Usually, called by the :meth:`_prepare_terms` method to resolve common attributes of the term + configuration. These include: + + * Resolving the term function and checking if it is callable. + * Checking if the term function's arguments are matched by the parameters. + * Resolving special attributes of the term configuration like ``asset_cfg``, ``sensor_cfg``, etc. + * Initializing the term if it is a class. + + The last two steps are only possible once the simulation starts playing. + + By default, all term functions are expected to have at least one argument, which is the + environment object. Some other managers may expect functions to take more arguments, for + instance, the environment indices as the second argument. In such cases, the + ``min_argc`` argument can be used to specify the minimum number of arguments + required by the term function to be called correctly by the manager. + + Args: + term_name: The name of the term. + term_cfg: The term configuration. + min_argc: The minimum number of arguments required by the term function to be called correctly + by the manager. + + Raises: + TypeError: If the term configuration is not of type :class:`ManagerTermBaseCfg`. + ValueError: If the scene entity defined in the term configuration does not exist. + AttributeError: If the term function is not callable. + ValueError: If the term function's arguments are not matched by the parameters. + """ + # check if the term is a valid term config + if not isinstance(term_cfg, ManagerTermBaseCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type ManagerTermBaseCfg." + f" Received: '{type(term_cfg)}'." + ) + + # get the corresponding function or functional class + if isinstance(term_cfg.func, str): + term_cfg.func = string_to_callable(term_cfg.func) + # check if function is callable + if not callable(term_cfg.func): + raise AttributeError(f"The term '{term_name}' is not callable. Received: {term_cfg.func}") + + # check if the term is a class of valid type + if inspect.isclass(term_cfg.func): + if not issubclass(term_cfg.func, ManagerTermBase): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type ManagerTermBase." + f" Received: '{type(term_cfg.func)}'." + ) + func_static = term_cfg.func.__call__ + min_argc += 1 # forward by 1 to account for 'self' argument + else: + func_static = term_cfg.func + # check if function is callable + if not callable(func_static): + raise AttributeError(f"The term '{term_name}' is not callable. Received: {term_cfg.func}") + + # check statically if the term's arguments are matched by params + term_params = list(term_cfg.params.keys()) + args = inspect.signature(func_static).parameters + args_with_defaults = [arg for arg in args if args[arg].default is not inspect.Parameter.empty] + args_without_defaults = [arg for arg in args if args[arg].default is inspect.Parameter.empty] + args = args_without_defaults + args_with_defaults + # ignore first two arguments for env and env_ids + # Think: Check for cases when kwargs are set inside the function? + if len(args) > min_argc: + if set(args[min_argc:]) != set(term_params + args_with_defaults): + raise ValueError( + f"The term '{term_name}' expects mandatory parameters: {args_without_defaults[min_argc:]}" + f" and optional parameters: {args_with_defaults}, but received: {term_params}." + ) + + # process attributes at runtime + # these properties are only resolvable once the simulation starts playing + if self._env.sim.is_playing(): + self._process_term_cfg_at_play(term_name, term_cfg) + + def _process_term_cfg_at_play(self, term_name: str, term_cfg: ManagerTermBaseCfg): + """Process the term configuration at runtime. + + This function is called when the simulation starts playing. It is used to process the term + configuration at runtime. This includes: + + * Resolving the scene entity configuration for the term. + * Initializing the term if it is a class. + + Since the above steps rely on PhysX to parse over the simulation scene, they are deferred + until the simulation starts playing. + + Args: + term_name: The name of the term. + term_cfg: The term configuration. + """ + for key, value in term_cfg.params.items(): + if isinstance(value, SceneEntityCfg): + # load the entity + try: + value.resolve(self._env.scene) + except ValueError as e: + raise ValueError(f"Error while parsing '{term_name}:{key}'. {e}") + # log the entity for checking later + msg = f"[{term_cfg.__class__.__name__}:{term_name}] Found entity '{value.name}'." + if value.joint_ids is not None: + msg += f"\n\tJoint names: {value.joint_names} [{value.joint_ids}]" + if value.body_ids is not None: + msg += f"\n\tBody names: {value.body_names} [{value.body_ids}]" + # print the information + logger.info(msg) + # store the entity + term_cfg.params[key] = value + + # initialize the term if it is a class + if inspect.isclass(term_cfg.func): + logger.info(f"Initializing term '{term_name}' with class '{term_cfg.func.__name__}'.") + term_cfg.func = term_cfg.func(cfg=term_cfg, env=self._env) diff --git a/source/isaaclab/isaaclab/managers/manager_term_cfg.py b/source/isaaclab/isaaclab/managers/manager_term_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..de7c23aa220bb169d5e53b7639940249806a996d --- /dev/null +++ b/source/isaaclab/isaaclab/managers/manager_term_cfg.py @@ -0,0 +1,355 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration terms for different managers.""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING +from typing import TYPE_CHECKING, Any + +import torch + +from isaaclab.utils import configclass +from isaaclab.utils.modifiers import ModifierCfg +from isaaclab.utils.noise import NoiseCfg, NoiseModelCfg + +from .scene_entity_cfg import SceneEntityCfg + +if TYPE_CHECKING: + from .action_manager import ActionTerm + from .command_manager import CommandTerm + from .manager_base import ManagerTermBase + from .recorder_manager import RecorderTerm + + +@configclass +class ManagerTermBaseCfg: + """Configuration for a manager term.""" + + func: Callable | ManagerTermBase = MISSING + """The function or class to be called for the term. + + The function must take the environment object as the first argument. + The remaining arguments are specified in the :attr:`params` attribute. + + It also supports `callable classes`_, i.e. classes that implement the :meth:`__call__` + method. In this case, the class should inherit from the :class:`ManagerTermBase` class + and implement the required methods. + + .. _`callable classes`: https://docs.python.org/3/reference/datamodel.html#object.__call__ + """ + + params: dict[str, Any | SceneEntityCfg] = dict() + """The parameters to be passed to the function as keyword arguments. Defaults to an empty dict. + + .. note:: + If the value is a :class:`SceneEntityCfg` object, the manager will query the scene entity + from the :class:`InteractiveScene` and process the entity's joints and bodies as specified + in the :class:`SceneEntityCfg` object. + """ + + +## +# Recorder manager. +## + + +@configclass +class RecorderTermCfg: + """Configuration for an recorder term.""" + + class_type: type[RecorderTerm] = MISSING + """The associated recorder term class. + + The class should inherit from :class:`isaaclab.managers.recorder_manager.RecorderTerm`. + """ + + +## +# Action manager. +## + + +@configclass +class ActionTermCfg: + """Configuration for an action term.""" + + class_type: type[ActionTerm] = MISSING + """The associated action term class. + + The class should inherit from :class:`isaaclab.managers.action_manager.ActionTerm`. + """ + + asset_name: str = MISSING + """The name of the scene entity. + + This is the name defined in the scene configuration file. See the :class:`InteractiveSceneCfg` + class for more details. + """ + + debug_vis: bool = False + """Whether to visualize debug information. Defaults to False.""" + + clip: dict[str, tuple] | None = None + """Clip range for the action (dict of regex expressions). Defaults to None.""" + + +## +# Command manager. +## + + +@configclass +class CommandTermCfg: + """Configuration for a command generator term.""" + + class_type: type[CommandTerm] = MISSING + """The associated command term class to use. + + The class should inherit from :class:`isaaclab.managers.command_manager.CommandTerm`. + """ + + resampling_time_range: tuple[float, float] = MISSING + """Time before commands are changed [s].""" + debug_vis: bool = False + """Whether to visualize debug information. Defaults to False.""" + + +## +# Curriculum manager. +## + + +@configclass +class CurriculumTermCfg(ManagerTermBaseCfg): + """Configuration for a curriculum term.""" + + func: Callable[..., float | dict[str, float] | None] = MISSING + """The name of the function to be called. + + This function should take the environment object, environment indices + and any other parameters as input and return the curriculum state for + logging purposes. If the function returns None, the curriculum state + is not logged. + """ + + +## +# Observation manager. +## + + +@configclass +class ObservationTermCfg(ManagerTermBaseCfg): + """Configuration for an observation term.""" + + func: Callable[..., torch.Tensor] = MISSING + """The name of the function to be called. + + This function should take the environment object and any other parameters + as input and return the observation signal as torch float tensors of + shape (num_envs, obs_term_dim). + """ + + modifiers: list[ModifierCfg] | None = None + """The list of data modifiers to apply to the observation in order. Defaults to None, + in which case no modifications will be applied. + + Modifiers are applied in the order they are specified in the list. They can be stateless + or stateful, and can be used to apply transformations to the observation data. For example, + a modifier can be used to normalize the observation data or to apply a rolling average. + + For more information on modifiers, see the :class:`~isaaclab.utils.modifiers.ModifierCfg` class. + """ + + noise: NoiseCfg | NoiseModelCfg | None = None + """The noise to add to the observation. Defaults to None, in which case no noise is added.""" + + clip: tuple[float, float] | None = None + """The clipping range for the observation after adding noise. Defaults to None, + in which case no clipping is applied.""" + + scale: tuple[float, ...] | float | None = None + """The scale to apply to the observation after clipping. Defaults to None, + in which case no scaling is applied (same as setting scale to :obj:`1`). + + We leverage PyTorch broadcasting to scale the observation tensor with the provided value. If a tuple is provided, + please make sure the length of the tuple matches the dimensions of the tensor outputted from the term. + """ + + history_length: int = 0 + """Number of past observations to store in the observation buffers. Defaults to 0, meaning no history. + + Observation history initializes to empty, but is filled with the first append after reset or initialization. + Subsequent history only adds a single entry to the history buffer. If flatten_history_dim is set to True, + the source data of shape (N, H, D, ...) where N is the batch dimension and H is the history length will + be reshaped to a 2-D tensor of shape (N, H*D*...). Otherwise, the data will be returned as is. + """ + + flatten_history_dim: bool = True + """Whether or not the observation manager should flatten history-based observation terms to a 2-D (N, D) tensor. + Defaults to True.""" + + +@configclass +class ObservationGroupCfg: + """Configuration for an observation group.""" + + concatenate_terms: bool = True + """Whether to concatenate the observation terms in the group. Defaults to True. + + If true, the observation terms in the group are concatenated along the dimension specified through + :attr:`concatenate_dim`. Otherwise, they are kept separate and returned as a dictionary. + + If the observation group contains terms of different dimensions, it must be set to False. + """ + + concatenate_dim: int = -1 + """Dimension along to concatenate the different observation terms. Defaults to -1, which + means the last dimension of the observation terms. + + If :attr:`concatenate_terms` is True, this parameter specifies the dimension along which the observation + terms are concatenated. The indicated dimension depends on the shape of the observations. For instance, + for a 2-D RGB image of shape (H, W, C), the dimension 0 means concatenating along the height, 1 along the + width, and 2 along the channels. The offset due to the batched environment is handled automatically. + """ + + enable_corruption: bool = False + """Whether to enable corruption for the observation group. Defaults to False. + + If true, the observation terms in the group are corrupted by adding noise (if specified). + Otherwise, no corruption is applied. + """ + + history_length: int | None = None + """Number of past observation to store in the observation buffers for all observation terms in group. + + This parameter will override :attr:`ObservationTermCfg.history_length` if set. Defaults to None. + If None, each terms history will be controlled on a per term basis. See :class:`ObservationTermCfg` + for details on :attr:`ObservationTermCfg.history_length` implementation. + """ + + flatten_history_dim: bool = True + """Flag to flatten history-based observation terms to a 2-D (num_env, D) tensor for all observation terms in group. + Defaults to True. + + This parameter will override all :attr:`ObservationTermCfg.flatten_history_dim` in the group if + ObservationGroupCfg.history_length is set. + """ + + +## +# Event manager +## + + +@configclass +class EventTermCfg(ManagerTermBaseCfg): + """Configuration for a event term.""" + + func: Callable[..., None] = MISSING + """The name of the function to be called. + + This function should take the environment object, environment indices + and any other parameters as input. + """ + + mode: str = MISSING + """The mode in which the event term is applied. + + Note: + The mode name ``"interval"`` is a special mode that is handled by the + manager Hence, its name is reserved and cannot be used for other modes. + """ + + interval_range_s: tuple[float, float] | None = None + """The range of time in seconds at which the term is applied. Defaults to None. + + Based on this, the interval is sampled uniformly between the specified + range for each environment instance. The term is applied on the environment + instances where the current time hits the interval time. + + Note: + This is only used if the mode is ``"interval"``. + """ + + is_global_time: bool = False + """Whether randomization should be tracked on a per-environment basis. Defaults to False. + + If True, the same interval time is used for all the environment instances. + If False, the interval time is sampled independently for each environment instance + and the term is applied when the current time hits the interval time for that instance. + + Note: + This is only used if the mode is ``"interval"``. + """ + + min_step_count_between_reset: int = 0 + """The number of environment steps after which the term is applied since its last application. Defaults to 0. + + When the mode is "reset", the term is only applied if the number of environment steps since + its last application exceeds this quantity. This helps to avoid calling the term too often, + thereby improving performance. + + If the value is zero, the term is applied on every call to the manager with the mode "reset". + + Note: + This is only used if the mode is ``"reset"``. + """ + + +## +# Reward manager. +## + + +@configclass +class RewardTermCfg(ManagerTermBaseCfg): + """Configuration for a reward term.""" + + func: Callable[..., torch.Tensor] = MISSING + """The name of the function to be called. + + This function should take the environment object and any other parameters + as input and return the reward signals as torch float tensors of + shape (num_envs,). + """ + + weight: float = MISSING + """The weight of the reward term. + + This is multiplied with the reward term's value to compute the final + reward. + + Note: + If the weight is zero, the reward term is ignored. + """ + + +## +# Termination manager. +## + + +@configclass +class TerminationTermCfg(ManagerTermBaseCfg): + """Configuration for a termination term.""" + + func: Callable[..., torch.Tensor] = MISSING + """The name of the function to be called. + + This function should take the environment object and any other parameters + as input and return the termination signals as torch boolean tensors of + shape (num_envs,). + """ + + time_out: bool = False + """Whether the termination term contributes towards episodic timeouts. Defaults to False. + + Note: + These usually correspond to tasks that have a fixed time limit. + """ diff --git a/source/isaaclab/isaaclab/managers/observation_manager.py b/source/isaaclab/isaaclab/managers/observation_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..a1bde0266f4b6507f33bf5d4f43142dec75d34f3 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/observation_manager.py @@ -0,0 +1,656 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Observation manager for computing observation signals for a given world.""" + +from __future__ import annotations + +import inspect +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import numpy as np +import torch +from prettytable import PrettyTable + +from isaaclab.utils import class_to_dict, modifiers, noise +from isaaclab.utils.buffers import CircularBuffer + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import ObservationGroupCfg, ObservationTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +class ObservationManager(ManagerBase): + """Manager for computing observation signals for a given world. + + Observations are organized into groups based on their intended usage. This allows having different observation + groups for different types of learning such as asymmetric actor-critic and student-teacher training. Each + group contains observation terms which contain information about the observation function to call, the noise + corruption model to use, and the sensor to retrieve data from. + + Each observation group should inherit from the :class:`ObservationGroupCfg` class. Within each group, each + observation term should instantiate the :class:`ObservationTermCfg` class. Based on the configuration, the + observations in a group can be concatenated into a single tensor or returned as a dictionary with keys + corresponding to the term's name. + + If the observations in a group are concatenated, the shape of the concatenated tensor is computed based on the + shapes of the individual observation terms. This information is stored in the :attr:`group_obs_dim` dictionary + with keys as the group names and values as the shape of the observation tensor. When the terms in a group are not + concatenated, the attribute stores a list of shapes for each term in the group. + + .. note:: + When the observation terms in a group do not have the same shape, the observation terms cannot be + concatenated. In this case, please set the :attr:`ObservationGroupCfg.concatenate_terms` attribute in the + group configuration to False. + + Observations can also have history. This means a running history is updated per sim step. History can be controlled + per :class:`ObservationTermCfg` (See the :attr:`ObservationTermCfg.history_length` and + :attr:`ObservationTermCfg.flatten_history_dim`). History can also be controlled via :class:`ObservationGroupCfg` + where group configuration overwrites per term configuration if set. History follows an oldest to newest ordering. + + The observation manager can be used to compute observations for all the groups or for a specific group. The + observations are computed by calling the registered functions for each term in the group. The functions are + called in the order of the terms in the group. The functions are expected to return a tensor with shape + (num_envs, ...). + + If a noise model or custom modifier is registered for a term, the function is called to corrupt + the observation. The corruption function is expected to return a tensor with the same shape as the observation. + The observations are clipped and scaled as per the configuration settings. + """ + + def __init__(self, cfg: object, env: ManagerBasedEnv): + """Initialize observation manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, ObservationGroupCfg]``). + env: The environment instance. + + Raises: + ValueError: If the configuration is None. + RuntimeError: If the shapes of the observation terms in a group are not compatible for concatenation + and the :attr:`~ObservationGroupCfg.concatenate_terms` attribute is set to True. + """ + # check that cfg is not None + if cfg is None: + raise ValueError("Observation manager configuration is None. Please provide a valid configuration.") + + # call the base class constructor (this will parse the terms config) + super().__init__(cfg, env) + + # compute combined vector for obs group + self._group_obs_dim: dict[str, tuple[int, ...] | list[tuple[int, ...]]] = dict() + for group_name, group_term_dims in self._group_obs_term_dim.items(): + # if terms are concatenated, compute the combined shape into a single tuple + # otherwise, keep the list of shapes as is + if self._group_obs_concatenate[group_name]: + try: + term_dims = torch.stack([torch.tensor(dims, device="cpu") for dims in group_term_dims], dim=0) + if len(term_dims.shape) > 1: + if self._group_obs_concatenate_dim[group_name] >= 0: + dim = self._group_obs_concatenate_dim[group_name] - 1 # account for the batch offset + else: + dim = self._group_obs_concatenate_dim[group_name] + dim_sum = torch.sum(term_dims[:, dim], dim=0) + term_dims[0, dim] = dim_sum + term_dims = term_dims[0] + else: + term_dims = torch.sum(term_dims, dim=0) + self._group_obs_dim[group_name] = tuple(term_dims.tolist()) + except RuntimeError: + raise RuntimeError( + f"Unable to concatenate observation terms in group '{group_name}'." + f" The shapes of the terms are: {group_term_dims}." + " Please ensure that the shapes are compatible for concatenation." + " Otherwise, set 'concatenate_terms' to False in the group configuration." + ) + else: + self._group_obs_dim[group_name] = group_term_dims + + # Stores the latest observations. + self._obs_buffer: dict[str, torch.Tensor | dict[str, torch.Tensor]] | None = None + + def __str__(self) -> str: + """Returns: A string representation for the observation manager.""" + msg = f" contains {len(self._group_obs_term_names)} groups.\n" + + # add info for each group + for group_name, group_dim in self._group_obs_dim.items(): + # create table for term information + table = PrettyTable() + table.title = f"Active Observation Terms in Group: '{group_name}'" + if self._group_obs_concatenate[group_name]: + table.title += f" (shape: {group_dim})" + table.field_names = ["Index", "Name", "Shape"] + # set alignment of table columns + table.align["Name"] = "l" + # add info for each term + obs_terms = zip( + self._group_obs_term_names[group_name], + self._group_obs_term_dim[group_name], + ) + for index, (name, dims) in enumerate(obs_terms): + # resolve inputs to simplify prints + tab_dims = tuple(dims) + # add row + table.add_row([index, name, tab_dims]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + terms = [] + + if self._obs_buffer is None: + self.compute() + obs_buffer: dict[str, torch.Tensor | dict[str, torch.Tensor]] = self._obs_buffer + + for group_name, _ in self._group_obs_dim.items(): + if not self.group_obs_concatenate[group_name]: + for name, term in obs_buffer[group_name].items(): + terms.append((group_name + "-" + name, term[env_idx].cpu().tolist())) + continue + + idx = 0 + concat_dim = self._group_obs_concatenate_dim[group_name] + # handle cases where concat dim is positive, account for the batch dimension + if concat_dim > 0: + concat_dim -= 1 + # add info for each term + data = obs_buffer[group_name] + for name, shape in zip( + self._group_obs_term_names[group_name], + self._group_obs_term_dim[group_name], + ): + # extract the term from the buffer based on the shape + term = data[env_idx].narrow(dim=concat_dim, start=idx, length=shape[concat_dim]) + terms.append((group_name + "-" + name, term.cpu().tolist())) + idx += shape[concat_dim] + + return terms + + """ + Properties. + """ + + @property + def active_terms(self) -> dict[str, list[str]]: + """Name of active observation terms in each group. + + The keys are the group names and the values are the list of observation term names in the group. + """ + return self._group_obs_term_names + + @property + def group_obs_dim(self) -> dict[str, tuple[int, ...] | list[tuple[int, ...]]]: + """Shape of computed observations in each group. + + The key is the group name and the value is the shape of the observation tensor. + If the terms in the group are concatenated, the value is a single tuple representing the + shape of the concatenated observation tensor. Otherwise, the value is a list of tuples, + where each tuple represents the shape of the observation tensor for a term in the group. + """ + return self._group_obs_dim + + @property + def group_obs_term_dim(self) -> dict[str, list[tuple[int, ...]]]: + """Shape of individual observation terms in each group. + + The key is the group name and the value is a list of tuples representing the shape of the observation terms + in the group. The order of the tuples corresponds to the order of the terms in the group. + This matches the order of the terms in the :attr:`active_terms`. + """ + return self._group_obs_term_dim + + @property + def group_obs_concatenate(self) -> dict[str, bool]: + """Whether the observation terms are concatenated in each group or not. + + The key is the group name and the value is a boolean specifying whether the observation terms in the group + are concatenated into a single tensor. If True, the observations are concatenated along the last dimension. + + The values are set based on the :attr:`~ObservationGroupCfg.concatenate_terms` attribute in the group + configuration. + """ + return self._group_obs_concatenate + + @property + def get_IO_descriptors(self, group_names_to_export: list[str] = ["policy"]): + """Get the IO descriptors for the observation manager. + + Returns: + A dictionary with keys as the group names and values as the IO descriptors. + """ + + group_data = {} + + for group_name in self._group_obs_term_names: + group_data[group_name] = [] + # check if group name is valid + if group_name not in self._group_obs_term_names: + raise ValueError( + f"Unable to find the group '{group_name}' in the observation manager." + f" Available groups are: {list(self._group_obs_term_names.keys())}" + ) + # iterate over all the terms in each group + group_term_names = self._group_obs_term_names[group_name] + # read attributes for each term + obs_terms = zip(group_term_names, self._group_obs_term_cfgs[group_name]) + + for term_name, term_cfg in obs_terms: + # Call to the observation function to get the IO descriptor with the inspect flag set to True + try: + term_cfg.func(self._env, **term_cfg.params, inspect=True) + # Copy the descriptor and update with the term's own extra parameters + desc = term_cfg.func._descriptor.__dict__.copy() + # Create a dictionary to store the overloads + overloads = {} + # Iterate over the term's own parameters and add them to the overloads dictionary + for k, v in term_cfg.__dict__.items(): + # For now we do not add the noise modifier + if k in ["modifiers", "clip", "scale", "history_length", "flatten_history_dim"]: + overloads[k] = v + desc.update(overloads) + group_data[group_name].append(desc) + except Exception as e: + print(f"Error getting IO descriptor for term '{term_name}' in group '{group_name}': {e}") + # Format the data for YAML export + formatted_data = {} + for group_name, data in group_data.items(): + formatted_data[group_name] = [] + for item in data: + name = item.pop("name") + formatted_item = {"name": name, "overloads": {}, "extras": item.pop("extras")} + for k, v in item.items(): + # Check if v is a tuple and convert to list + if isinstance(v, tuple): + v = list(v) + # Check if v is a tensor and convert to list + if isinstance(v, torch.Tensor): + v = v.detach().cpu().numpy().tolist() + if k in ["scale", "clip", "history_length", "flatten_history_dim"]: + formatted_item["overloads"][k] = v + elif k in ["modifiers", "description", "units"]: + formatted_item["extras"][k] = v + else: + formatted_item[k] = v + formatted_data[group_name].append(formatted_item) + formatted_data = {k: v for k, v in formatted_data.items() if k in group_names_to_export} + return formatted_data + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]: + # call all terms that are classes + for group_name, group_cfg in self._group_obs_class_term_cfgs.items(): + for term_cfg in group_cfg: + term_cfg.func.reset(env_ids=env_ids) + # reset terms with history + for term_name in self._group_obs_term_names[group_name]: + if term_name in self._group_obs_term_history_buffer[group_name]: + self._group_obs_term_history_buffer[group_name][term_name].reset(batch_ids=env_ids) + # call all modifiers that are classes + for mod in self._group_obs_class_instances: + mod.reset(env_ids=env_ids) + + # nothing to log here + return {} + + def compute(self, update_history: bool = False) -> dict[str, torch.Tensor | dict[str, torch.Tensor]]: + """Compute the observations per group for all groups. + + The method computes the observations for all the groups handled by the observation manager. + Please check the :meth:`compute_group` on the processing of observations per group. + + Args: + update_history: The boolean indicator without return obs should be appended to observation history. + Default to False, in which case calling compute_group does not modify history. This input is no-ops + if the group's history_length == 0. + + Returns: + A dictionary with keys as the group names and values as the computed observations. + The observations are either concatenated into a single tensor or returned as a dictionary + with keys corresponding to the term's name. + """ + # create a buffer for storing obs from all the groups + obs_buffer = dict() + # iterate over all the terms in each group + for group_name in self._group_obs_term_names: + obs_buffer[group_name] = self.compute_group(group_name, update_history=update_history) + # otherwise return a dict with observations of all groups + + # Cache the observations. + self._obs_buffer = obs_buffer + return obs_buffer + + def compute_group(self, group_name: str, update_history: bool = False) -> torch.Tensor | dict[str, torch.Tensor]: + """Computes the observations for a given group. + + The observations for a given group are computed by calling the registered functions for each + term in the group. The functions are called in the order of the terms in the group. The functions + are expected to return a tensor with shape (num_envs, ...). + + The following steps are performed for each observation term: + + 1. Compute observation term by calling the function + 2. Apply custom modifiers in the order specified in :attr:`ObservationTermCfg.modifiers` + 3. Apply corruption/noise model based on :attr:`ObservationTermCfg.noise` + 4. Apply clipping based on :attr:`ObservationTermCfg.clip` + 5. Apply scaling based on :attr:`ObservationTermCfg.scale` + + We apply noise to the computed term first to maintain the integrity of how noise affects the data + as it truly exists in the real world. If the noise is applied after clipping or scaling, the noise + could be artificially constrained or amplified, which might misrepresent how noise naturally occurs + in the data. + + Args: + group_name: The name of the group for which to compute the observations. Defaults to None, + in which case observations for all the groups are computed and returned. + update_history: The boolean indicator without return obs should be appended to observation group's history. + Default to False, in which case calling compute_group does not modify history. This input is no-ops + if the group's history_length == 0. + + Returns: + Depending on the group's configuration, the tensors for individual observation terms are + concatenated along the last dimension into a single tensor. Otherwise, they are returned as + a dictionary with keys corresponding to the term's name. + + Raises: + ValueError: If input ``group_name`` is not a valid group handled by the manager. + """ + # check ig group name is valid + if group_name not in self._group_obs_term_names: + raise ValueError( + f"Unable to find the group '{group_name}' in the observation manager." + f" Available groups are: {list(self._group_obs_term_names.keys())}" + ) + # iterate over all the terms in each group + group_term_names = self._group_obs_term_names[group_name] + # buffer to store obs per group + group_obs = dict.fromkeys(group_term_names, None) + # read attributes for each term + obs_terms = zip(group_term_names, self._group_obs_term_cfgs[group_name]) + + # evaluate terms: compute, add noise, clip, scale, custom modifiers + for term_name, term_cfg in obs_terms: + # compute term's value + obs: torch.Tensor = term_cfg.func(self._env, **term_cfg.params).clone() + # apply post-processing + if term_cfg.modifiers is not None: + for modifier in term_cfg.modifiers: + obs = modifier.func(obs, **modifier.params) + if isinstance(term_cfg.noise, noise.NoiseCfg): + obs = term_cfg.noise.func(obs, term_cfg.noise) + elif isinstance(term_cfg.noise, noise.NoiseModelCfg) and term_cfg.noise.func is not None: + obs = term_cfg.noise.func(obs) + if term_cfg.clip: + obs = obs.clip_(min=term_cfg.clip[0], max=term_cfg.clip[1]) + if term_cfg.scale is not None: + obs = obs.mul_(term_cfg.scale) + # Update the history buffer if observation term has history enabled + if term_cfg.history_length > 0: + circular_buffer = self._group_obs_term_history_buffer[group_name][term_name] + if update_history: + circular_buffer.append(obs) + elif circular_buffer._buffer is None: + # because circular buffer only exits after the simulation steps, + # this guards history buffer from corruption by external calls before simulation start + circular_buffer = CircularBuffer( + max_len=circular_buffer.max_length, + batch_size=circular_buffer.batch_size, + device=circular_buffer.device, + ) + circular_buffer.append(obs) + + if term_cfg.flatten_history_dim: + group_obs[term_name] = circular_buffer.buffer.reshape(self._env.num_envs, -1) + else: + group_obs[term_name] = circular_buffer.buffer + else: + group_obs[term_name] = obs + + # concatenate all observations in the group together + if self._group_obs_concatenate[group_name]: + # set the concatenate dimension, account for the batch dimension if positive dimension is given + return torch.cat(list(group_obs.values()), dim=self._group_obs_concatenate_dim[group_name]) + else: + return group_obs + + def serialize(self) -> dict: + """Serialize the observation term configurations for all active groups. + + Returns: + A dictionary where each group name maps to its serialized observation term configurations. + """ + output = { + group_name: { + term_name: ( + term_cfg.func.serialize() + if isinstance(term_cfg.func, ManagerTermBase) + else {"cfg": class_to_dict(term_cfg)} + ) + for term_name, term_cfg in zip( + self._group_obs_term_names[group_name], + self._group_obs_term_cfgs[group_name], + ) + } + for group_name in self.active_terms.keys() + } + + return output + + """ + Helper functions. + """ + + def _prepare_terms(self): + """Prepares a list of observation terms functions.""" + # create buffers to store information for each observation group + # TODO: Make this more convenient by using data structures. + self._group_obs_term_names: dict[str, list[str]] = dict() + self._group_obs_term_dim: dict[str, list[tuple[int, ...]]] = dict() + self._group_obs_term_cfgs: dict[str, list[ObservationTermCfg]] = dict() + self._group_obs_class_term_cfgs: dict[str, list[ObservationTermCfg]] = dict() + self._group_obs_concatenate: dict[str, bool] = dict() + self._group_obs_concatenate_dim: dict[str, int] = dict() + + self._group_obs_term_history_buffer: dict[str, dict] = dict() + # create a list to store classes instances, e.g., for modifiers and noise models + # we store it as a separate list to only call reset on them and prevent unnecessary calls + self._group_obs_class_instances: list[modifiers.ModifierBase | noise.NoiseModel] = list() + + # make sure the simulation is playing since we compute obs dims which needs asset quantities + if not self._env.sim.is_playing(): + raise RuntimeError( + "Simulation is not playing. Observation manager requires the simulation to be playing" + " to compute observation dimensions. Please start the simulation before using the" + " observation manager." + ) + + # check if config is dict already + if isinstance(self.cfg, dict): + group_cfg_items = self.cfg.items() + else: + group_cfg_items = self.cfg.__dict__.items() + # iterate over all the groups + for group_name, group_cfg in group_cfg_items: + # check for non config + if group_cfg is None: + continue + # check if the term is a curriculum term + if not isinstance(group_cfg, ObservationGroupCfg): + raise TypeError( + f"Observation group '{group_name}' is not of type 'ObservationGroupCfg'." + f" Received: '{type(group_cfg)}'." + ) + # initialize list for the group settings + self._group_obs_term_names[group_name] = list() + self._group_obs_term_dim[group_name] = list() + self._group_obs_term_cfgs[group_name] = list() + self._group_obs_class_term_cfgs[group_name] = list() + + # history buffers + group_entry_history_buffer: dict[str, CircularBuffer] = dict() + + # read common config for the group + self._group_obs_concatenate[group_name] = group_cfg.concatenate_terms + self._group_obs_concatenate_dim[group_name] = ( + group_cfg.concatenate_dim + 1 if group_cfg.concatenate_dim >= 0 else group_cfg.concatenate_dim + ) + + # check if config is dict already + if isinstance(group_cfg, dict): + term_cfg_items = group_cfg.items() + else: + term_cfg_items = group_cfg.__dict__.items() + # iterate over all the terms in each group + for term_name, term_cfg in term_cfg_items: + # skip non-obs settings + if term_name in [ + "enable_corruption", + "concatenate_terms", + "history_length", + "flatten_history_dim", + "concatenate_dim", + ]: + continue + # check for non config + if term_cfg is None: + continue + if not isinstance(term_cfg, ObservationTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type ObservationTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # resolve common terms in the config + self._resolve_common_term_cfg(f"{group_name}/{term_name}", term_cfg, min_argc=1) + + # check noise settings + if not group_cfg.enable_corruption: + term_cfg.noise = None + # check group history params and override terms + if group_cfg.history_length is not None: + term_cfg.history_length = group_cfg.history_length + term_cfg.flatten_history_dim = group_cfg.flatten_history_dim + # add term config to list to list + self._group_obs_term_names[group_name].append(term_name) + self._group_obs_term_cfgs[group_name].append(term_cfg) + + # call function the first time to fill up dimensions + obs_dims = tuple(term_cfg.func(self._env, **term_cfg.params).shape) + + # if scale is set, check if single float or tuple + if term_cfg.scale is not None: + if not isinstance(term_cfg.scale, (float, int, tuple)): + raise TypeError( + f"Scale for observation term '{term_name}' in group '{group_name}'" + f" is not of type float, int or tuple. Received: '{type(term_cfg.scale)}'." + ) + if isinstance(term_cfg.scale, tuple) and len(term_cfg.scale) != obs_dims[1]: + raise ValueError( + f"Scale for observation term '{term_name}' in group '{group_name}'" + f" does not match the dimensions of the observation. Expected: {obs_dims[1]}" + f" but received: {len(term_cfg.scale)}." + ) + + # cast the scale into torch tensor + term_cfg.scale = torch.tensor(term_cfg.scale, dtype=torch.float, device=self._env.device) + + # prepare modifiers for each observation + if term_cfg.modifiers is not None: + # initialize list of modifiers for term + for mod_cfg in term_cfg.modifiers: + # check if class modifier and initialize with observation size when adding + if isinstance(mod_cfg, modifiers.ModifierCfg): + # to list of modifiers + if inspect.isclass(mod_cfg.func): + if not issubclass(mod_cfg.func, modifiers.ModifierBase): + raise TypeError( + f"Modifier function '{mod_cfg.func}' for observation term '{term_name}'" + f" is not a subclass of 'ModifierBase'. Received: '{type(mod_cfg.func)}'." + ) + mod_cfg.func = mod_cfg.func(cfg=mod_cfg, data_dim=obs_dims, device=self._env.device) + + # add to list of class modifiers + self._group_obs_class_instances.append(mod_cfg.func) + else: + raise TypeError( + f"Modifier configuration '{mod_cfg}' of observation term '{term_name}' is not of" + f" required type ModifierCfg, Received: '{type(mod_cfg)}'" + ) + + # check if function is callable + if not callable(mod_cfg.func): + raise AttributeError( + f"Modifier '{mod_cfg}' of observation term '{term_name}' is not callable." + f" Received: {mod_cfg.func}" + ) + + # check if term's arguments are matched by params + term_params = list(mod_cfg.params.keys()) + args = inspect.signature(mod_cfg.func).parameters + args_with_defaults = [arg for arg in args if args[arg].default is not inspect.Parameter.empty] + args_without_defaults = [arg for arg in args if args[arg].default is inspect.Parameter.empty] + args = args_without_defaults + args_with_defaults + # ignore first two arguments for env and env_ids + # Think: Check for cases when kwargs are set inside the function? + if len(args) > 1: + if set(args[1:]) != set(term_params + args_with_defaults): + raise ValueError( + f"Modifier '{mod_cfg}' of observation term '{term_name}' expects" + f" mandatory parameters: {args_without_defaults[1:]}" + f" and optional parameters: {args_with_defaults}, but received: {term_params}." + ) + + # prepare noise model classes + if term_cfg.noise is not None and isinstance(term_cfg.noise, noise.NoiseModelCfg): + noise_model_cls = term_cfg.noise.class_type + if not issubclass(noise_model_cls, noise.NoiseModel): + raise TypeError( + f"Class type for observation term '{term_name}' NoiseModelCfg" + f" is not a subclass of 'NoiseModel'. Received: '{type(noise_model_cls)}'." + ) + # initialize func to be the noise model class instance + term_cfg.noise.func = noise_model_cls( + term_cfg.noise, num_envs=self._env.num_envs, device=self._env.device + ) + self._group_obs_class_instances.append(term_cfg.noise.func) + + # create history buffers and calculate history term dimensions + if term_cfg.history_length > 0: + group_entry_history_buffer[term_name] = CircularBuffer( + max_len=term_cfg.history_length, batch_size=self._env.num_envs, device=self._env.device + ) + old_dims = list(obs_dims) + old_dims.insert(1, term_cfg.history_length) + obs_dims = tuple(old_dims) + if term_cfg.flatten_history_dim: + obs_dims = (obs_dims[0], np.prod(obs_dims[1:])) + + self._group_obs_term_dim[group_name].append(obs_dims[1:]) + + # add term in a separate list if term is a class + if isinstance(term_cfg.func, ManagerTermBase): + self._group_obs_class_term_cfgs[group_name].append(term_cfg) + # call reset (in-case above call to get obs dims changed the state) + term_cfg.func.reset() + # add history buffers for each group + self._group_obs_term_history_buffer[group_name] = group_entry_history_buffer diff --git a/source/isaaclab/isaaclab/managers/recorder_manager.py b/source/isaaclab/isaaclab/managers/recorder_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..51a3f3e8351848c162817e5d6e9a6267557cfac1 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/recorder_manager.py @@ -0,0 +1,583 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Recorder manager for recording data produced from the given world.""" + +from __future__ import annotations + +import enum +import os +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +from isaaclab.utils import configclass +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import RecorderTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +class DatasetExportMode(enum.IntEnum): + """The mode to handle episode exports.""" + + EXPORT_NONE = 0 # Export none of the episodes + EXPORT_ALL = 1 # Export all episodes to a single dataset file + EXPORT_SUCCEEDED_FAILED_IN_SEPARATE_FILES = 2 # Export succeeded and failed episodes in separate files + EXPORT_SUCCEEDED_ONLY = 3 # Export only succeeded episodes to a single dataset file + + +@configclass +class RecorderManagerBaseCfg: + """Base class for configuring recorder manager terms.""" + + dataset_file_handler_class_type: type = HDF5DatasetFileHandler + + dataset_export_dir_path: str = "/tmp/isaaclab/logs" + """The directory path where the recorded datasets are exported.""" + + dataset_filename: str = "dataset" + """Dataset file name without file extension.""" + + dataset_export_mode: DatasetExportMode = DatasetExportMode.EXPORT_ALL + """The mode to handle episode exports.""" + + export_in_record_pre_reset: bool = True + """Whether to export episodes in the record_pre_reset call.""" + + export_in_close: bool = False + """Whether to export episodes in the close call.""" + + +class RecorderTerm(ManagerTermBase): + """Base class for recorder terms. + + The recorder term is responsible for recording data at various stages of the environment's lifecycle. + A recorder term is comprised of four user-defined callbacks to record data in the corresponding stages: + + * Pre-reset recording: This callback is invoked at the beginning of `env.reset()` before the reset is effective. + * Post-reset recording: This callback is invoked at the end of `env.reset()`. + * Pre-step recording: This callback is invoked at the beginning of `env.step()`, after the step action is processed + and before the action is applied by the action manager. + * Post-step recording: This callback is invoked at the end of `env.step()` when all the managers are processed. + """ + + def __init__(self, cfg: RecorderTermCfg, env: ManagerBasedEnv): + """Initialize the recorder term. + + Args: + cfg: The configuration object. + env: The environment instance. + """ + # call the base class constructor + super().__init__(cfg, env) + + """ + User-defined callbacks. + """ + + def record_pre_reset(self, env_ids: Sequence[int] | None) -> tuple[str | None, torch.Tensor | dict | None]: + """Record data at the beginning of env.reset() before reset is effective. + + Args: + env_ids: The environment ids. All environments should be considered when set to None. + + Returns: + A tuple of key and value to be recorded. + The key can contain nested keys separated by '/'. For example, "obs/joint_pos" would add the given + value under ['obs']['policy'] in the underlying dictionary in the recorded episode data. + The value can be a tensor or a nested dictionary of tensors. The shape of a tensor in the value + is (env_ids, ...). + """ + return None, None + + def record_post_reset(self, env_ids: Sequence[int] | None) -> tuple[str | None, torch.Tensor | dict | None]: + """Record data at the end of env.reset(). + + Args: + env_ids: The environment ids. All environments should be considered when set to None. + + Returns: + A tuple of key and value to be recorded. + Please refer to the `record_pre_reset` function for more details. + """ + return None, None + + def record_pre_step(self) -> tuple[str | None, torch.Tensor | dict | None]: + """Record data in the beginning of env.step() after action is cached/processed in the ActionManager. + + Returns: + A tuple of key and value to be recorded. + Please refer to the `record_pre_reset` function for more details. + """ + return None, None + + def record_post_step(self) -> tuple[str | None, torch.Tensor | dict | None]: + """Record data at the end of env.step() when all the managers are processed. + + Returns: + A tuple of key and value to be recorded. + Please refer to the `record_pre_reset` function for more details. + """ + return None, None + + def record_post_physics_decimation_step(self) -> tuple[str | None, torch.Tensor | dict | None]: + """Record data after the physics step is executed in the decimation loop. + + Returns: + A tuple of key and value to be recorded. + Please refer to the `record_pre_reset` function for more details. + """ + return None, None + + def close(self, file_path: str): + """Finalize and "clean up" the recorder term. + + This can include tasks such as appending metadata (e.g. labels) to a file + and properly closing any associated file handles or resources. + + Args: + file_path: the absolute path to the file + """ + pass + + +class RecorderManager(ManagerBase): + """Manager for recording data from recorder terms.""" + + def __init__(self, cfg: object, env: ManagerBasedEnv): + """Initialize the recorder manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, RecorderTermCfg]``). + env: The environment instance. + """ + self._term_names: list[str] = list() + self._terms: dict[str, RecorderTerm] = dict() + + # Do nothing if cfg is None or an empty dict + if not cfg: + return + + super().__init__(cfg, env) + + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + if not isinstance(cfg, RecorderManagerBaseCfg): + raise TypeError("Configuration for the recorder manager is not of type RecorderManagerBaseCfg.") + + # create episode data buffer indexed by environment id + self._episodes: dict[int, EpisodeData] = dict() + for env_id in range(env.num_envs): + self._episodes[env_id] = EpisodeData() + + env_name = getattr(env.cfg, "env_name", None) + + self._dataset_file_handler = None + if cfg.dataset_export_mode != DatasetExportMode.EXPORT_NONE: + self._dataset_file_handler = cfg.dataset_file_handler_class_type() + self._dataset_file_handler.create( + os.path.join(cfg.dataset_export_dir_path, cfg.dataset_filename), env_name=env_name + ) + + self._failed_episode_dataset_file_handler = None + if cfg.dataset_export_mode == DatasetExportMode.EXPORT_SUCCEEDED_FAILED_IN_SEPARATE_FILES: + self._failed_episode_dataset_file_handler = cfg.dataset_file_handler_class_type() + self._failed_episode_dataset_file_handler.create( + os.path.join(cfg.dataset_export_dir_path, f"{cfg.dataset_filename}_failed"), env_name=env_name + ) + + self._exported_successful_episode_count = {} + self._exported_failed_episode_count = {} + + def __str__(self) -> str: + """Returns: A string representation for recorder manager.""" + msg = f" contains {len(self._term_names)} active terms.\n" + # create table for term information + table = PrettyTable() + table.title = "Active Recorder Terms" + table.field_names = ["Index", "Name"] + # set alignment of table columns + table.align["Name"] = "l" + # add info on each term + for index, name in enumerate(self._term_names): + table.add_row([index, name]) + # convert table to string + msg += table.get_string() + msg += "\n" + return msg + + def __del__(self): + """Destructor for recorder.""" + self.close() + + """ + Properties. + """ + + @property + def active_terms(self) -> list[str]: + """Name of active recorder terms.""" + return self._term_names + + @property + def exported_successful_episode_count(self, env_id=None) -> int: + """Number of successful episodes. + + Args: + env_id: The environment id. Defaults to None, in which case all environments are considered. + + Returns: + The number of successful episodes. + """ + if not hasattr(self, "_exported_successful_episode_count"): + return 0 + if env_id is not None: + return self._exported_successful_episode_count.get(env_id, 0) + return sum(self._exported_successful_episode_count.values()) + + @property + def exported_failed_episode_count(self, env_id=None) -> int: + """Number of failed episodes. + + Args: + env_id: The environment id. Defaults to None, in which case all environments are considered. + + Returns: + The number of failed episodes. + """ + if not hasattr(self, "_exported_failed_episode_count"): + return 0 + if env_id is not None: + return self._exported_failed_episode_count.get(env_id, 0) + return sum(self._exported_failed_episode_count.values()) + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Resets the recorder data. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + + Returns: + An empty dictionary. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return {} + + # resolve environment ids + if env_ids is None: + env_ids = list(range(self._env.num_envs)) + if isinstance(env_ids, torch.Tensor): + env_ids = env_ids.tolist() + + for term in self._terms.values(): + term.reset(env_ids=env_ids) + + for env_id in env_ids: + self._episodes[env_id] = EpisodeData() + + # nothing to log here + return {} + + def get_episode(self, env_id: int) -> EpisodeData: + """Returns the episode data for the given environment id. + + Args: + env_id: The environment id. + + Returns: + The episode data for the given environment id. + """ + return self._episodes.get(env_id, EpisodeData()) + + def add_to_episodes(self, key: str, value: torch.Tensor | dict, env_ids: Sequence[int] | None = None): + """Adds the given key-value pair to the episodes for the given environment ids. + + Args: + key: The key of the given value to be added to the episodes. The key can contain nested keys + separated by '/'. For example, "obs/joint_pos" would add the given value under ['obs']['policy'] + in the underlying dictionary in the episode data. + value: The value to be added to the episodes. The value can be a tensor or a nested dictionary of tensors. + The shape of a tensor in the value is (env_ids, ...). + env_ids: The environment ids. Defaults to None, in which case all environments are considered. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + # resolve environment ids + if key is None: + return + if env_ids is None: + env_ids = list(range(self._env.num_envs)) + if isinstance(env_ids, torch.Tensor): + env_ids = env_ids.tolist() + + if isinstance(value, dict): + for sub_key, sub_value in value.items(): + self.add_to_episodes(f"{key}/{sub_key}", sub_value, env_ids) + return + + for value_index, env_id in enumerate(env_ids): + if env_id not in self._episodes: + self._episodes[env_id] = EpisodeData() + self._episodes[env_id].env_id = env_id + self._episodes[env_id].add(key, value[value_index]) + + def set_success_to_episodes(self, env_ids: Sequence[int] | None, success_values: torch.Tensor): + """Sets the task success values to the episodes for the given environment ids. + + Args: + env_ids: The environment ids. Defaults to None, in which case all environments are considered. + success_values: The task success values to be set to the episodes. The shape of the tensor is (env_ids, 1). + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + # resolve environment ids + if env_ids is None: + env_ids = list(range(self._env.num_envs)) + if isinstance(env_ids, torch.Tensor): + env_ids = env_ids.tolist() + + for value_index, env_id in enumerate(env_ids): + self._episodes[env_id].success = success_values[value_index].item() + + def record_pre_step(self) -> None: + """Trigger recorder terms for pre-step functions.""" + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + for term in self._terms.values(): + key, value = term.record_pre_step() + self.add_to_episodes(key, value) + + def record_post_step(self) -> None: + """Trigger recorder terms for post-step functions.""" + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + for term in self._terms.values(): + key, value = term.record_post_step() + self.add_to_episodes(key, value) + + def record_post_physics_decimation_step(self) -> None: + """Trigger recorder terms for post-physics step functions in the decimation loop.""" + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + for term in self._terms.values(): + key, value = term.record_post_physics_decimation_step() + self.add_to_episodes(key, value) + + def record_pre_reset(self, env_ids: Sequence[int] | None, force_export_or_skip=None) -> None: + """Trigger recorder terms for pre-reset functions. + + Args: + env_ids: The environment ids in which a reset is triggered. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + if env_ids is None: + env_ids = list(range(self._env.num_envs)) + if isinstance(env_ids, torch.Tensor): + env_ids = env_ids.tolist() + + for term in self._terms.values(): + key, value = term.record_pre_reset(env_ids) + self.add_to_episodes(key, value, env_ids) + + # Set task success values for the relevant episodes + success_results = torch.zeros(len(env_ids), dtype=bool, device=self._env.device) + # Check success indicator from termination terms + if hasattr(self._env, "termination_manager"): + if "success" in self._env.termination_manager.active_terms: + success_results |= self._env.termination_manager.get_term("success")[env_ids] + self.set_success_to_episodes(env_ids, success_results) + + if force_export_or_skip or (force_export_or_skip is None and self.cfg.export_in_record_pre_reset): + self.export_episodes(env_ids) + + def record_post_reset(self, env_ids: Sequence[int] | None) -> None: + """Trigger recorder terms for post-reset functions. + + Args: + env_ids: The environment ids in which a reset is triggered. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + for term in self._terms.values(): + key, value = term.record_post_reset(env_ids) + self.add_to_episodes(key, value, env_ids) + + def get_ep_meta(self) -> dict: + """Get the episode metadata.""" + if not hasattr(self._env.cfg, "get_ep_meta"): + # Add basic episode metadata + ep_meta = dict() + ep_meta["sim_args"] = { + "dt": self._env.cfg.sim.dt, + "decimation": self._env.cfg.decimation, + "render_interval": self._env.cfg.sim.render_interval, + "num_envs": self._env.cfg.scene.num_envs, + } + return ep_meta + + # Add custom episode metadata if available + ep_meta = self._env.cfg.get_ep_meta() + return ep_meta + + def export_episodes(self, env_ids: Sequence[int] | None = None, demo_ids: Sequence[int] | None = None) -> None: + """Concludes and exports the episodes for the given environment ids. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + demo_ids: Custom identifiers for the exported episodes. + If provided, episodes will be named "demo_{demo_id}" in the dataset. + Should have the same length as env_ids if both are provided. + If None, uses the default sequential naming scheme. Defaults to None. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + + if env_ids is None: + env_ids = list(range(self._env.num_envs)) + if isinstance(env_ids, torch.Tensor): + env_ids = env_ids.tolist() + + # Handle demo_ids processing + if demo_ids is not None: + if isinstance(demo_ids, torch.Tensor): + demo_ids = demo_ids.tolist() + if len(demo_ids) != len(env_ids): + raise ValueError(f"Length of demo_ids ({len(demo_ids)}) must match length of env_ids ({len(env_ids)})") + # Check for duplicate demo_ids + if len(set(demo_ids)) != len(demo_ids): + duplicates = [x for i, x in enumerate(demo_ids) if demo_ids.index(x) != i] + raise ValueError(f"demo_ids must be unique. Found duplicates: {list(set(duplicates))}") + + # Export episode data through dataset exporter + need_to_flush = False + + if any(env_id in self._episodes and not self._episodes[env_id].is_empty() for env_id in env_ids): + ep_meta = self.get_ep_meta() + if self._dataset_file_handler is not None: + self._dataset_file_handler.add_env_args(ep_meta) + if self._failed_episode_dataset_file_handler is not None: + self._failed_episode_dataset_file_handler.add_env_args(ep_meta) + + for i, env_id in enumerate(env_ids): + if env_id in self._episodes and not self._episodes[env_id].is_empty(): + self._episodes[env_id].pre_export() + + episode_succeeded = self._episodes[env_id].success + target_dataset_file_handler = None + if (self.cfg.dataset_export_mode == DatasetExportMode.EXPORT_ALL) or ( + self.cfg.dataset_export_mode == DatasetExportMode.EXPORT_SUCCEEDED_ONLY and episode_succeeded + ): + target_dataset_file_handler = self._dataset_file_handler + elif self.cfg.dataset_export_mode == DatasetExportMode.EXPORT_SUCCEEDED_FAILED_IN_SEPARATE_FILES: + if episode_succeeded: + target_dataset_file_handler = self._dataset_file_handler + else: + target_dataset_file_handler = self._failed_episode_dataset_file_handler + if target_dataset_file_handler is not None: + # Use corresponding demo_id if provided, otherwise None + current_demo_id = demo_ids[i] if demo_ids is not None else None + target_dataset_file_handler.write_episode(self._episodes[env_id], current_demo_id) + need_to_flush = True + # Update episode count + if episode_succeeded: + self._exported_successful_episode_count[env_id] = ( + self._exported_successful_episode_count.get(env_id, 0) + 1 + ) + else: + self._exported_failed_episode_count[env_id] = self._exported_failed_episode_count.get(env_id, 0) + 1 + # Reset the episode buffer for the given environment after export + self._episodes[env_id] = EpisodeData() + + if need_to_flush: + if self._dataset_file_handler is not None: + self._dataset_file_handler.flush() + if self._failed_episode_dataset_file_handler is not None: + self._failed_episode_dataset_file_handler.flush() + + def close(self): + """Closes the recorder manager by exporting any remaining data to file as well as properly + closes the recorder terms. + """ + # Do nothing if no active recorder terms are provided + if len(self.active_terms) == 0: + return + if self._dataset_file_handler is not None: + if self.cfg.export_in_close: + self.export_episodes() + self._dataset_file_handler.close() + if self._failed_episode_dataset_file_handler is not None: + self._failed_episode_dataset_file_handler.close() + for term in self._terms.values(): + term.close(os.path.join(self.cfg.dataset_export_dir_path, self.cfg.dataset_filename)) + + """ + Helper functions. + """ + + def _prepare_terms(self): + """Prepares a list of recorder terms.""" + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + for term_name, term_cfg in cfg_items: + # skip non-term settings + if term_name in [ + "dataset_file_handler_class_type", + "dataset_filename", + "dataset_export_dir_path", + "dataset_export_mode", + "export_in_record_pre_reset", + "export_in_close", + ]: + continue + # check if term config is None + if term_cfg is None: + continue + # check valid type + if not isinstance(term_cfg, RecorderTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type RecorderTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # create the recorder term + term = term_cfg.class_type(term_cfg, self._env) + # sanity check if term is valid type + if not isinstance(term, RecorderTerm): + raise TypeError(f"Returned object for the term '{term_name}' is not of type RecorderTerm.") + # add term name and parameters + self._term_names.append(term_name) + self._terms[term_name] = term diff --git a/source/isaaclab/isaaclab/managers/reward_manager.py b/source/isaaclab/isaaclab/managers/reward_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..d9c66a100a727f07661e4d7aac6c33cfe4365991 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/reward_manager.py @@ -0,0 +1,247 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Reward manager for computing reward signals for a given world.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import RewardTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class RewardManager(ManagerBase): + """Manager for computing reward signals for a given world. + + The reward manager computes the total reward as a sum of the weighted reward terms. The reward + terms are parsed from a nested config class containing the reward manger's settings and reward + terms configuration. + + The reward terms are parsed from a config class containing the manager's settings and each term's + parameters. Each reward term should instantiate the :class:`RewardTermCfg` class. + + .. note:: + + The reward manager multiplies the reward term's ``weight`` with the time-step interval ``dt`` + of the environment. This is done to ensure that the computed reward terms are balanced with + respect to the chosen time-step interval in the environment. + + """ + + _env: ManagerBasedRLEnv + """The environment instance.""" + + def __init__(self, cfg: object, env: ManagerBasedRLEnv): + """Initialize the reward manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, RewardTermCfg]``). + env: The environment instance. + """ + # create buffers to parse and store terms + self._term_names: list[str] = list() + self._term_cfgs: list[RewardTermCfg] = list() + self._class_term_cfgs: list[RewardTermCfg] = list() + + # call the base class constructor (this will parse the terms config) + super().__init__(cfg, env) + # prepare extra info to store individual reward term information + self._episode_sums = dict() + for term_name in self._term_names: + self._episode_sums[term_name] = torch.zeros(self.num_envs, dtype=torch.float, device=self.device) + # create buffer for managing reward per environment + self._reward_buf = torch.zeros(self.num_envs, dtype=torch.float, device=self.device) + + # Buffer which stores the current step reward for each term for each environment + self._step_reward = torch.zeros((self.num_envs, len(self._term_names)), dtype=torch.float, device=self.device) + + def __str__(self) -> str: + """Returns: A string representation for reward manager.""" + msg = f" contains {len(self._term_names)} active terms.\n" + + # create table for term information + table = PrettyTable() + table.title = "Active Reward Terms" + table.field_names = ["Index", "Name", "Weight"] + # set alignment of table columns + table.align["Name"] = "l" + table.align["Weight"] = "r" + # add info on each term + for index, (name, term_cfg) in enumerate(zip(self._term_names, self._term_cfgs)): + table.add_row([index, name, term_cfg.weight]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def active_terms(self) -> list[str]: + """Name of active reward terms.""" + return self._term_names + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Returns the episodic sum of individual reward terms. + + Args: + env_ids: The environment ids for which the episodic sum of + individual reward terms is to be returned. Defaults to all the environment ids. + + Returns: + Dictionary of episodic sum of individual reward terms. + """ + # resolve environment ids + if env_ids is None: + env_ids = slice(None) + # store information + extras = {} + for key in self._episode_sums.keys(): + # store information + # r_1 + r_2 + ... + r_n + episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids]) + extras["Episode_Reward/" + key] = episodic_sum_avg / self._env.max_episode_length_s + # reset episodic sum + self._episode_sums[key][env_ids] = 0.0 + # reset all the reward terms + for term_cfg in self._class_term_cfgs: + term_cfg.func.reset(env_ids=env_ids) + # return logged information + return extras + + def compute(self, dt: float) -> torch.Tensor: + """Computes the reward signal as a weighted sum of individual terms. + + This function calls each reward term managed by the class and adds them to compute the net + reward signal. It also updates the episodic sums corresponding to individual reward terms. + + Args: + dt: The time-step interval of the environment. + + Returns: + The net reward signal of shape (num_envs,). + """ + # reset computation + self._reward_buf[:] = 0.0 + # iterate over all the reward terms + for term_idx, (name, term_cfg) in enumerate(zip(self._term_names, self._term_cfgs)): + # skip if weight is zero (kind of a micro-optimization) + if term_cfg.weight == 0.0: + self._step_reward[:, term_idx] = 0.0 + continue + # compute term's value + value = term_cfg.func(self._env, **term_cfg.params) * term_cfg.weight * dt + # update total reward + self._reward_buf += value + # update episodic sum + self._episode_sums[name] += value + + # Update current reward for this step. + self._step_reward[:, term_idx] = value / dt + + return self._reward_buf + + """ + Operations - Term settings. + """ + + def set_term_cfg(self, term_name: str, cfg: RewardTermCfg): + """Sets the configuration of the specified term into the manager. + + Args: + term_name: The name of the reward term. + cfg: The configuration for the reward term. + + Raises: + ValueError: If the term name is not found. + """ + if term_name not in self._term_names: + raise ValueError(f"Reward term '{term_name}' not found.") + # set the configuration + self._term_cfgs[self._term_names.index(term_name)] = cfg + + def get_term_cfg(self, term_name: str) -> RewardTermCfg: + """Gets the configuration for the specified term. + + Args: + term_name: The name of the reward term. + + Returns: + The configuration of the reward term. + + Raises: + ValueError: If the term name is not found. + """ + if term_name not in self._term_names: + raise ValueError(f"Reward term '{term_name}' not found.") + # return the configuration + return self._term_cfgs[self._term_names.index(term_name)] + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + terms = [] + for idx, name in enumerate(self._term_names): + terms.append((name, [self._step_reward[env_idx, idx].cpu().item()])) + return terms + + """ + Helper functions. + """ + + def _prepare_terms(self): + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # check for valid config type + if not isinstance(term_cfg, RewardTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type RewardTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # check for valid weight type + if not isinstance(term_cfg.weight, (float, int)): + raise TypeError( + f"Weight for the term '{term_name}' is not of type float or int." + f" Received: '{type(term_cfg.weight)}'." + ) + # resolve common parameters + self._resolve_common_term_cfg(term_name, term_cfg, min_argc=1) + # add function to list + self._term_names.append(term_name) + self._term_cfgs.append(term_cfg) + # check if the term is a class + if isinstance(term_cfg.func, ManagerTermBase): + self._class_term_cfgs.append(term_cfg) diff --git a/source/isaaclab/isaaclab/managers/scene_entity_cfg.py b/source/isaaclab/isaaclab/managers/scene_entity_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b01f7eee0d44a8690578b44b844f458ebecc8d --- /dev/null +++ b/source/isaaclab/isaaclab/managers/scene_entity_cfg.py @@ -0,0 +1,292 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration terms for different managers.""" + +from __future__ import annotations + +from dataclasses import MISSING +from typing import TYPE_CHECKING + +from isaaclab.utils import configclass + +if TYPE_CHECKING: + from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection + from isaaclab.scene import InteractiveScene + + +@configclass +class SceneEntityCfg: + """Configuration for a scene entity that is used by the manager's term. + + This class is used to specify the name of the scene entity that is queried from the + :class:`InteractiveScene` and passed to the manager's term function. + """ + + name: str = MISSING + """The name of the scene entity. + + This is the name defined in the scene configuration file. See the :class:`InteractiveSceneCfg` + class for more details. + """ + + joint_names: str | list[str] | None = None + """The names of the joints from the scene entity. Defaults to None. + + The names can be either joint names or a regular expression matching the joint names. + + These are converted to joint indices on initialization of the manager and passed to the term + function as a list of joint indices under :attr:`joint_ids`. + """ + + joint_ids: list[int] | slice = slice(None) + """The indices of the joints from the asset required by the term. Defaults to slice(None), which means + all the joints in the asset (if present). + + If :attr:`joint_names` is specified, this is filled in automatically on initialization of the + manager. + """ + + fixed_tendon_names: str | list[str] | None = None + """The names of the fixed tendons from the scene entity. Defaults to None. + + The names can be either joint names or a regular expression matching the joint names. + + These are converted to fixed tendon indices on initialization of the manager and passed to the term + function as a list of fixed tendon indices under :attr:`fixed_tendon_ids`. + """ + + fixed_tendon_ids: list[int] | slice = slice(None) + """The indices of the fixed tendons from the asset required by the term. Defaults to slice(None), which means + all the fixed tendons in the asset (if present). + + If :attr:`fixed_tendon_names` is specified, this is filled in automatically on initialization of the + manager. + """ + + body_names: str | list[str] | None = None + """The names of the bodies from the asset required by the term. Defaults to None. + + The names can be either body names or a regular expression matching the body names. + + These are converted to body indices on initialization of the manager and passed to the term + function as a list of body indices under :attr:`body_ids`. + """ + + body_ids: list[int] | slice = slice(None) + """The indices of the bodies from the asset required by the term. Defaults to slice(None), which means + all the bodies in the asset. + + If :attr:`body_names` is specified, this is filled in automatically on initialization of the + manager. + """ + + object_collection_names: str | list[str] | None = None + """The names of the objects in the rigid object collection required by the term. Defaults to None. + + The names can be either names or a regular expression matching the object names in the collection. + + These are converted to object indices on initialization of the manager and passed to the term + function as a list of object indices under :attr:`object_collection_ids`. + """ + + object_collection_ids: list[int] | slice = slice(None) + """The indices of the objects from the rigid object collection required by the term. Defaults to slice(None), + which means all the objects in the collection. + + If :attr:`object_collection_names` is specified, this is filled in automatically on initialization of the manager. + """ + + preserve_order: bool = False + """Whether to preserve indices ordering to match with that in the specified joint, body, or object collection names. + Defaults to False. + + If False, the ordering of the indices are sorted in ascending order (i.e. the ordering in the entity's joints, + bodies, or object in the object collection). Otherwise, the indices are preserved in the order of the specified + joint, body, or object collection names. + + For more details, see the :meth:`isaaclab.utils.string.resolve_matching_names` function. + + .. note:: + This attribute is only used when :attr:`joint_names`, :attr:`body_names`, or :attr:`object_collection_names` + are specified. + + """ + + def resolve(self, scene: InteractiveScene): + """Resolves the scene entity and converts the joint and body names to indices. + + This function examines the scene entity from the :class:`InteractiveScene` and resolves the indices + and names of the joints and bodies. It is an expensive operation as it resolves regular expressions + and should be called only once. + + Args: + scene: The interactive scene instance. + + Raises: + ValueError: If the scene entity is not found. + ValueError: If both ``joint_names`` and ``joint_ids`` are specified and are not consistent. + ValueError: If both ``fixed_tendon_names`` and ``fixed_tendon_ids`` are specified and are not consistent. + ValueError: If both ``body_names`` and ``body_ids`` are specified and are not consistent. + ValueError: If both ``object_collection_names`` and ``object_collection_ids`` are specified and + are not consistent. + """ + # check if the entity is valid + if self.name not in scene.keys(): + raise ValueError(f"The scene entity '{self.name}' does not exist. Available entities: {scene.keys()}.") + + # convert joint names to indices based on regex + self._resolve_joint_names(scene) + + # convert fixed tendon names to indices based on regex + self._resolve_fixed_tendon_names(scene) + + # convert body names to indices based on regex + self._resolve_body_names(scene) + + # convert object collection names to indices based on regex + self._resolve_object_collection_names(scene) + + def _resolve_joint_names(self, scene: InteractiveScene): + # convert joint names to indices based on regex + if self.joint_names is not None or self.joint_ids != slice(None): + entity: Articulation = scene[self.name] + # -- if both are not their default values, check if they are valid + if self.joint_names is not None and self.joint_ids != slice(None): + if isinstance(self.joint_names, str): + self.joint_names = [self.joint_names] + if isinstance(self.joint_ids, int): + self.joint_ids = [self.joint_ids] + joint_ids, _ = entity.find_joints(self.joint_names, preserve_order=self.preserve_order) + joint_names = [entity.joint_names[i] for i in self.joint_ids] + if joint_ids != self.joint_ids or joint_names != self.joint_names: + raise ValueError( + "Both 'joint_names' and 'joint_ids' are specified, and are not consistent." + f"\n\tfrom joint names: {self.joint_names} [{joint_ids}]" + f"\n\tfrom joint ids: {joint_names} [{self.joint_ids}]" + "\nHint: Use either 'joint_names' or 'joint_ids' to avoid confusion." + ) + # -- from joint names to joint indices + elif self.joint_names is not None: + if isinstance(self.joint_names, str): + self.joint_names = [self.joint_names] + self.joint_ids, _ = entity.find_joints(self.joint_names, preserve_order=self.preserve_order) + # performance optimization (slice offers faster indexing than list of indices) + # only all joint in the entity order are selected + if len(self.joint_ids) == entity.num_joints and self.joint_names == entity.joint_names: + self.joint_ids = slice(None) + # -- from joint indices to joint names + elif self.joint_ids != slice(None): + if isinstance(self.joint_ids, int): + self.joint_ids = [self.joint_ids] + self.joint_names = [entity.joint_names[i] for i in self.joint_ids] + + def _resolve_fixed_tendon_names(self, scene: InteractiveScene): + # convert tendon names to indices based on regex + if self.fixed_tendon_names is not None or self.fixed_tendon_ids != slice(None): + entity: Articulation = scene[self.name] + # -- if both are not their default values, check if they are valid + if self.fixed_tendon_names is not None and self.fixed_tendon_ids != slice(None): + if isinstance(self.fixed_tendon_names, str): + self.fixed_tendon_names = [self.fixed_tendon_names] + if isinstance(self.fixed_tendon_ids, int): + self.fixed_tendon_ids = [self.fixed_tendon_ids] + fixed_tendon_ids, _ = entity.find_fixed_tendons( + self.fixed_tendon_names, preserve_order=self.preserve_order + ) + fixed_tendon_names = [entity.fixed_tendon_names[i] for i in self.fixed_tendon_ids] + if fixed_tendon_ids != self.fixed_tendon_ids or fixed_tendon_names != self.fixed_tendon_names: + raise ValueError( + "Both 'fixed_tendon_names' and 'fixed_tendon_ids' are specified, and are not consistent." + f"\n\tfrom joint names: {self.fixed_tendon_names} [{fixed_tendon_ids}]" + f"\n\tfrom joint ids: {fixed_tendon_names} [{self.fixed_tendon_ids}]" + "\nHint: Use either 'fixed_tendon_names' or 'fixed_tendon_ids' to avoid confusion." + ) + # -- from fixed tendon names to fixed tendon indices + elif self.fixed_tendon_names is not None: + if isinstance(self.fixed_tendon_names, str): + self.fixed_tendon_names = [self.fixed_tendon_names] + self.fixed_tendon_ids, _ = entity.find_fixed_tendons( + self.fixed_tendon_names, preserve_order=self.preserve_order + ) + # performance optimization (slice offers faster indexing than list of indices) + # only all fixed tendon in the entity order are selected + if ( + len(self.fixed_tendon_ids) == entity.num_fixed_tendons + and self.fixed_tendon_names == entity.fixed_tendon_names + ): + self.fixed_tendon_ids = slice(None) + # -- from fixed tendon indices to fixed tendon names + elif self.fixed_tendon_ids != slice(None): + if isinstance(self.fixed_tendon_ids, int): + self.fixed_tendon_ids = [self.fixed_tendon_ids] + self.fixed_tendon_names = [entity.fixed_tendon_names[i] for i in self.fixed_tendon_ids] + + def _resolve_body_names(self, scene: InteractiveScene): + # convert body names to indices based on regex + if self.body_names is not None or self.body_ids != slice(None): + entity: RigidObject = scene[self.name] + # -- if both are not their default values, check if they are valid + if self.body_names is not None and self.body_ids != slice(None): + if isinstance(self.body_names, str): + self.body_names = [self.body_names] + if isinstance(self.body_ids, int): + self.body_ids = [self.body_ids] + body_ids, _ = entity.find_bodies(self.body_names, preserve_order=self.preserve_order) + body_names = [entity.body_names[i] for i in self.body_ids] + if body_ids != self.body_ids or body_names != self.body_names: + raise ValueError( + "Both 'body_names' and 'body_ids' are specified, and are not consistent." + f"\n\tfrom body names: {self.body_names} [{body_ids}]" + f"\n\tfrom body ids: {body_names} [{self.body_ids}]" + "\nHint: Use either 'body_names' or 'body_ids' to avoid confusion." + ) + # -- from body names to body indices + elif self.body_names is not None: + if isinstance(self.body_names, str): + self.body_names = [self.body_names] + self.body_ids, _ = entity.find_bodies(self.body_names, preserve_order=self.preserve_order) + # performance optimization (slice offers faster indexing than list of indices) + # only all bodies in the entity order are selected + if len(self.body_ids) == entity.num_bodies and self.body_names == entity.body_names: + self.body_ids = slice(None) + # -- from body indices to body names + elif self.body_ids != slice(None): + if isinstance(self.body_ids, int): + self.body_ids = [self.body_ids] + self.body_names = [entity.body_names[i] for i in self.body_ids] + + def _resolve_object_collection_names(self, scene: InteractiveScene): + # convert object names to indices based on regex + if self.object_collection_names is not None or self.object_collection_ids != slice(None): + entity: RigidObjectCollection = scene[self.name] + # -- if both are not their default values, check if they are valid + if self.object_collection_names is not None and self.object_collection_ids != slice(None): + if isinstance(self.object_collection_names, str): + self.object_collection_names = [self.object_collection_names] + if isinstance(self.object_collection_ids, int): + self.object_collection_ids = [self.object_collection_ids] + object_ids, _ = entity.find_objects(self.object_collection_names, preserve_order=self.preserve_order) + object_names = [entity.object_names[i] for i in self.object_collection_ids] + if object_ids != self.object_collection_ids or object_names != self.object_collection_names: + raise ValueError( + "Both 'object_collection_names' and 'object_collection_ids' are specified, and are not" + " consistent.\n\tfrom object collection names:" + f" {self.object_collection_names} [{object_ids}]\n\tfrom object collection ids:" + f" {object_names} [{self.object_collection_ids}]\nHint: Use either 'object_collection_names' or" + " 'object_collection_ids' to avoid confusion." + ) + # -- from object names to object indices + elif self.object_collection_names is not None: + if isinstance(self.object_collection_names, str): + self.object_collection_names = [self.object_collection_names] + self.object_collection_ids, _ = entity.find_objects( + self.object_collection_names, preserve_order=self.preserve_order + ) + # -- from object indices to object names + elif self.object_collection_ids != slice(None): + if isinstance(self.object_collection_ids, int): + self.object_collection_ids = [self.object_collection_ids] + self.object_collection_names = [entity.object_names[i] for i in self.object_collection_ids] diff --git a/source/isaaclab/isaaclab/managers/termination_manager.py b/source/isaaclab/isaaclab/managers/termination_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..0a557df628a9d4afd7d21a7f28ab696da2fb0348 --- /dev/null +++ b/source/isaaclab/isaaclab/managers/termination_manager.py @@ -0,0 +1,276 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Termination manager for computing done signals for a given world.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch +from prettytable import PrettyTable + +from .manager_base import ManagerBase, ManagerTermBase +from .manager_term_cfg import TerminationTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class TerminationManager(ManagerBase): + """Manager for computing done signals for a given world. + + The termination manager computes the termination signal (also called dones) as a combination + of termination terms. Each termination term is a function which takes the environment as an + argument and returns a boolean tensor of shape (num_envs,). The termination manager + computes the termination signal as the union (logical or) of all the termination terms. + + Following the `Gymnasium API `_, + the termination signal is computed as the logical OR of the following signals: + + * **Time-out**: This signal is set to true if the environment has ended after an externally defined condition + (that is outside the scope of a MDP). For example, the environment may be terminated if the episode has + timed out (i.e. reached max episode length). + * **Terminated**: This signal is set to true if the environment has reached a terminal state defined by the + environment. This state may correspond to task success, task failure, robot falling, etc. + + These signals can be individually accessed using the :attr:`time_outs` and :attr:`terminated` properties. + + The termination terms are parsed from a config class containing the manager's settings and each term's + parameters. Each termination term should instantiate the :class:`TerminationTermCfg` class. The term's + configuration :attr:`TerminationTermCfg.time_out` decides whether the term is a timeout or a termination term. + """ + + _env: ManagerBasedRLEnv + """The environment instance.""" + + def __init__(self, cfg: object, env: ManagerBasedRLEnv): + """Initializes the termination manager. + + Args: + cfg: The configuration object or dictionary (``dict[str, TerminationTermCfg]``). + env: An environment object. + """ + # create buffers to parse and store terms + self._term_names: list[str] = list() + self._term_cfgs: list[TerminationTermCfg] = list() + self._class_term_cfgs: list[TerminationTermCfg] = list() + + # call the base class constructor (this will parse the terms config) + super().__init__(cfg, env) + self._term_name_to_term_idx = {name: i for i, name in enumerate(self._term_names)} + # prepare extra info to store individual termination term information + self._term_dones = torch.zeros((self.num_envs, len(self._term_names)), device=self.device, dtype=torch.bool) + # prepare extra info to store last episode done per termination term information + self._last_episode_dones = torch.zeros_like(self._term_dones) + # create buffer for managing termination per environment + self._truncated_buf = torch.zeros(self.num_envs, device=self.device, dtype=torch.bool) + self._terminated_buf = torch.zeros_like(self._truncated_buf) + + def __str__(self) -> str: + """Returns: A string representation for termination manager.""" + msg = f" contains {len(self._term_names)} active terms.\n" + + # create table for term information + table = PrettyTable() + table.title = "Active Termination Terms" + table.field_names = ["Index", "Name", "Time Out"] + # set alignment of table columns + table.align["Name"] = "l" + # add info on each term + for index, (name, term_cfg) in enumerate(zip(self._term_names, self._term_cfgs)): + table.add_row([index, name, term_cfg.time_out]) + # convert table to string + msg += table.get_string() + msg += "\n" + + return msg + + """ + Properties. + """ + + @property + def active_terms(self) -> list[str]: + """Name of active termination terms.""" + return self._term_names + + @property + def dones(self) -> torch.Tensor: + """The net termination signal. Shape is (num_envs,).""" + return self._truncated_buf | self._terminated_buf + + @property + def time_outs(self) -> torch.Tensor: + """The timeout signal (reaching max episode length). Shape is (num_envs,). + + This signal is set to true if the environment has ended after an externally defined condition + (that is outside the scope of a MDP). For example, the environment may be terminated if the episode has + timed out (i.e. reached max episode length). + """ + return self._truncated_buf + + @property + def terminated(self) -> torch.Tensor: + """The terminated signal (reaching a terminal state). Shape is (num_envs,). + + This signal is set to true if the environment has reached a terminal state defined by the environment. + This state may correspond to task success, task failure, robot falling, etc. + """ + return self._terminated_buf + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Returns the episodic counts of individual termination terms. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + + Returns: + Dictionary of episodic sum of individual reward terms. + """ + # resolve environment ids + if env_ids is None: + env_ids = slice(None) + # add to episode dict + extras = {} + last_episode_done_stats = self._last_episode_dones.float().mean(dim=0) + for i, key in enumerate(self._term_names): + # store information + extras["Episode_Termination/" + key] = last_episode_done_stats[i].item() + # reset all the reward terms + for term_cfg in self._class_term_cfgs: + term_cfg.func.reset(env_ids=env_ids) + # return logged information + return extras + + def compute(self) -> torch.Tensor: + """Computes the termination signal as union of individual terms. + + This function calls each termination term managed by the class and performs a logical OR operation + to compute the net termination signal. + + Returns: + The combined termination signal of shape (num_envs,). + """ + # reset computation + self._truncated_buf[:] = False + self._terminated_buf[:] = False + # iterate over all the termination terms + for i, term_cfg in enumerate(self._term_cfgs): + value = term_cfg.func(self._env, **term_cfg.params) + # store timeout signal separately + if term_cfg.time_out: + self._truncated_buf |= value + else: + self._terminated_buf |= value + # add to episode dones + self._term_dones[:, i] = value + # update last-episode dones once per compute: for any env where a term fired, + # reflect exactly which term(s) fired this step and clear others + rows = self._term_dones.any(dim=1).nonzero(as_tuple=True)[0] + if rows.numel() > 0: + self._last_episode_dones[rows] = self._term_dones[rows] + # return combined termination signal + return self._truncated_buf | self._terminated_buf + + def get_term(self, name: str) -> torch.Tensor: + """Returns the termination term value at current step with the specified name. + + Args: + name: The name of the termination term. + + Returns: + The corresponding termination term value. Shape is (num_envs,). + """ + return self._term_dones[:, self._term_name_to_term_idx[name]] + + def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]: + """Returns the active terms as iterable sequence of tuples. + + The first element of the tuple is the name of the term and the second element is the raw value(s) of the term + recorded at current step. + + Args: + env_idx: The specific environment to pull the active terms from. + + Returns: + The active terms. + """ + terms = [] + for i, key in enumerate(self._term_names): + terms.append((key, [self._term_dones[env_idx, i].float().cpu().item()])) + return terms + + """ + Operations - Term settings. + """ + + def set_term_cfg(self, term_name: str, cfg: TerminationTermCfg): + """Sets the configuration of the specified term into the manager. + + Args: + term_name: The name of the termination term. + cfg: The configuration for the termination term. + + Raises: + ValueError: If the term name is not found. + """ + if term_name not in self._term_names: + raise ValueError(f"Termination term '{term_name}' not found.") + # set the configuration + self._term_cfgs[self._term_name_to_term_idx[term_name]] = cfg + + def get_term_cfg(self, term_name: str) -> TerminationTermCfg: + """Gets the configuration for the specified term. + + Args: + term_name: The name of the termination term. + + Returns: + The configuration of the termination term. + + Raises: + ValueError: If the term name is not found. + """ + if term_name not in self._term_names: + raise ValueError(f"Termination term '{term_name}' not found.") + # return the configuration + return self._term_cfgs[self._term_name_to_term_idx[term_name]] + + """ + Helper functions. + """ + + def _prepare_terms(self): + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + # iterate over all the terms + for term_name, term_cfg in cfg_items: + # check for non config + if term_cfg is None: + continue + # check for valid config type + if not isinstance(term_cfg, TerminationTermCfg): + raise TypeError( + f"Configuration for the term '{term_name}' is not of type TerminationTermCfg." + f" Received: '{type(term_cfg)}'." + ) + # resolve common parameters + self._resolve_common_term_cfg(term_name, term_cfg, min_argc=1) + # add function to list + self._term_names.append(term_name) + self._term_cfgs.append(term_cfg) + # check if the term is a class + if isinstance(term_cfg.func, ManagerTermBase): + self._class_term_cfgs.append(term_cfg) diff --git a/source/isaaclab/isaaclab/markers/__init__.py b/source/isaaclab/isaaclab/markers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb7b69761009f55e758c3b92b39f12fb22515557 --- /dev/null +++ b/source/isaaclab/isaaclab/markers/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package for marker utilities to simplify creation of UI elements in the GUI. + +Currently, the sub-package provides the following classes: + +* :class:`VisualizationMarkers` for creating a group of markers using `UsdGeom.PointInstancer + `_. + + +.. note:: + + For some simple use-cases, it may be sufficient to use the debug drawing utilities from Isaac Sim. + The debug drawing API is available in the `isaacsim.util.debug_drawing`_ module. It allows drawing of + points and splines efficiently on the UI. + + .. _isaacsim.util.debug_drawing: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/ext_omni_isaac_debug_drawing.html + +""" + +from .config import * # noqa: F401, F403 +from .visualization_markers import VisualizationMarkers, VisualizationMarkersCfg diff --git a/source/isaaclab/isaaclab/markers/config/__init__.py b/source/isaaclab/isaaclab/markers/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..54d30051259aafacb8e79db3929f74ad57ed4a0f --- /dev/null +++ b/source/isaaclab/isaaclab/markers/config/__init__.py @@ -0,0 +1,156 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.markers.visualization_markers import VisualizationMarkersCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Sensors. +## + +RAY_CASTER_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "hit": sim_utils.SphereCfg( + radius=0.02, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + }, +) +"""Configuration for the ray-caster marker.""" + + +CONTACT_SENSOR_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "contact": sim_utils.SphereCfg( + radius=0.02, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + "no_contact": sim_utils.SphereCfg( + radius=0.02, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + visible=False, + ), + }, +) +"""Configuration for the contact sensor marker.""" + +DEFORMABLE_TARGET_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "target": sim_utils.SphereCfg( + radius=0.02, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.75, 0.8)), + ), + }, +) +"""Configuration for the deformable object's kinematic target marker.""" + +VISUO_TACTILE_SENSOR_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "tacsl_pts": sim_utils.SphereCfg( + radius=0.0002, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + }, +) +"""Configuration for the visuo-tactile sensor marker.""" + +## +# Frames. +## + +FRAME_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "frame": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/frame_prim.usd", + scale=(0.5, 0.5, 0.5), + ), + "connecting_line": sim_utils.CylinderCfg( + radius=0.002, + height=1.0, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 1.0, 0.0), roughness=1.0), + ), + } +) +"""Configuration for the frame marker.""" + + +RED_ARROW_X_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "arrow": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(1.0, 0.1, 0.1), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ) + } +) +"""Configuration for the red arrow marker (along x-direction).""" + + +BLUE_ARROW_X_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "arrow": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(1.0, 0.1, 0.1), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ) + } +) +"""Configuration for the blue arrow marker (along x-direction).""" + +GREEN_ARROW_X_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "arrow": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd", + scale=(1.0, 0.1, 0.1), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ) + } +) +"""Configuration for the green arrow marker (along x-direction).""" + + +## +# Goals. +## + +CUBOID_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "cuboid": sim_utils.CuboidCfg( + size=(0.1, 0.1, 0.1), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + } +) +"""Configuration for the cuboid marker.""" + +SPHERE_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "sphere": sim_utils.SphereCfg( + radius=0.05, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + } +) +"""Configuration for the sphere marker.""" + +POSITION_GOAL_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "target_far": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + "target_near": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + "target_invisible": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + visible=False, + ), + } +) +"""Configuration for the end-effector tracking marker.""" diff --git a/source/isaaclab/isaaclab/markers/visualization_markers.py b/source/isaaclab/isaaclab/markers/visualization_markers.py new file mode 100644 index 0000000000000000000000000000000000000000..bd27009c0ec317e2407e09e730ec892acb68c4dd --- /dev/null +++ b/source/isaaclab/isaaclab/markers/visualization_markers.py @@ -0,0 +1,410 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""A class to coordinate groups of visual markers (such as spheres, frames or arrows) +using `UsdGeom.PointInstancer`_ class. + +The class :class:`VisualizationMarkers` is used to create a group of visual markers and +visualize them in the viewport. The markers are represented as :class:`UsdGeom.PointInstancer` prims +in the USD stage. The markers are created as prototypes in the :class:`UsdGeom.PointInstancer` prim +and are instanced in the :class:`UsdGeom.PointInstancer` prim. The markers can be visualized by +passing the indices of the marker prototypes and their translations, orientations and scales. +The marker prototypes can be configured with the :class:`VisualizationMarkersCfg` class. + +.. _UsdGeom.PointInstancer: https://graphics.pixar.com/usd/dev/api/class_usd_geom_point_instancer.html +""" + +# needed to import for allowing type-hinting: np.ndarray | torch.Tensor | None +from __future__ import annotations + +import logging +from dataclasses import MISSING + +import numpy as np +import torch + +import omni.physx.scripts.utils as physx_utils +from pxr import Gf, PhysxSchema, Sdf, Usd, UsdGeom, UsdPhysics, Vt + +import isaaclab.sim as sim_utils +from isaaclab.sim.spawners import SpawnerCfg +from isaaclab.utils.configclass import configclass +from isaaclab.utils.math import convert_quat + +# import logger +logger = logging.getLogger(__name__) + + +@configclass +class VisualizationMarkersCfg: + """A class to configure a :class:`VisualizationMarkers`.""" + + prim_path: str = MISSING + """The prim path where the :class:`UsdGeom.PointInstancer` will be created.""" + + markers: dict[str, SpawnerCfg] = MISSING + """The dictionary of marker configurations. + + The key is the name of the marker, and the value is the configuration of the marker. + The key is used to identify the marker in the class. + """ + + +class VisualizationMarkers: + """A class to coordinate groups of visual markers (loaded from USD). + + This class allows visualization of different UI markers in the scene, such as points and frames. + The class wraps around the `UsdGeom.PointInstancer`_ for efficient handling of objects + in the stage via instancing the created marker prototype prims. + + A marker prototype prim is a reusable template prim used for defining variations of objects + in the scene. For example, a sphere prim can be used as a marker prototype prim to create + multiple sphere prims in the scene at different locations. Thus, prototype prims are useful + for creating multiple instances of the same prim in the scene. + + The class parses the configuration to create different the marker prototypes into the stage. Each marker + prototype prim is created as a child of the :class:`UsdGeom.PointInstancer` prim. The prim path for the + marker prim is resolved using the key of the marker in the :attr:`VisualizationMarkersCfg.markers` + dictionary. The marker prototypes are created using the :meth:`isaaclab.sim.utils.prims.create_prim` + function, and then instanced using :class:`UsdGeom.PointInstancer` prim to allow creating multiple + instances of the marker prims. + + Switching between different marker prototypes is possible by calling the :meth:`visualize` method with + the prototype indices corresponding to the marker prototype. The prototype indices are based on the order + in the :attr:`VisualizationMarkersCfg.markers` dictionary. For example, if the dictionary has two markers, + "marker1" and "marker2", then their prototype indices are 0 and 1 respectively. The prototype indices + can be passed as a list or array of integers. + + Usage: + The following snippet shows how to create 24 sphere markers with a radius of 1.0 at random translations + within the range [-1.0, 1.0]. The first 12 markers will be colored red and the rest will be colored green. + + .. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.markers import VisualizationMarkersCfg, VisualizationMarkers + + # Create the markers configuration + # This creates two marker prototypes, "marker1" and "marker2" which are spheres with a radius of 1.0. + # The color of "marker1" is red and the color of "marker2" is green. + cfg = VisualizationMarkersCfg( + prim_path="/World/Visuals/testMarkers", + markers={ + "marker1": sim_utils.SphereCfg( + radius=1.0, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + "marker2": VisualizationMarkersCfg.SphereCfg( + radius=1.0, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + }, + ) + # Create the markers instance + # This will create a UsdGeom.PointInstancer prim at the given path along with the marker prototypes. + marker = VisualizationMarkers(cfg) + + # Set position of the marker + # -- randomly sample translations between -1.0 and 1.0 + marker_translations = np.random.uniform(-1.0, 1.0, (24, 3)) + # -- this will create 24 markers at the given translations + # note: the markers will all be `marker1` since the marker indices are not given + marker.visualize(translations=marker_translations) + + # alter the markers based on their prototypes indices + # first 12 markers will be marker1 and the rest will be marker2 + # 0 -> marker1, 1 -> marker2 + marker_indices = [0] * 12 + [1] * 12 + # this will change the marker prototypes at the given indices + # note: the translations of the markers will not be changed from the previous call + # since the translations are not given. + marker.visualize(marker_indices=marker_indices) + + # alter the markers based on their prototypes indices and translations + marker.visualize(marker_indices=marker_indices, translations=marker_translations) + + .. _UsdGeom.PointInstancer: https://graphics.pixar.com/usd/dev/api/class_usd_geom_point_instancer.html + + """ + + def __init__(self, cfg: VisualizationMarkersCfg): + """Initialize the class. + + When the class is initialized, the :class:`UsdGeom.PointInstancer` is created into the stage + and the marker prims are registered into it. + + .. note:: + If a prim already exists at the given path, the function will find the next free path + and create the :class:`UsdGeom.PointInstancer` prim there. + + Args: + cfg: The configuration for the markers. + + Raises: + ValueError: When no markers are provided in the :obj:`cfg`. + """ + # get next free path for the prim + prim_path = sim_utils.get_next_free_prim_path(cfg.prim_path) + # create a new prim + self.stage = sim_utils.get_current_stage() + self._instancer_manager = UsdGeom.PointInstancer.Define(self.stage, prim_path) + # store inputs + self.prim_path = prim_path + self.cfg = cfg + # check if any markers is provided + if len(self.cfg.markers) == 0: + raise ValueError(f"The `cfg.markers` cannot be empty. Received: {self.cfg.markers}") + + # create a child prim for the marker + self._add_markers_prototypes(self.cfg.markers) + # Note: We need to do this the first time to initialize the instancer. + # Otherwise, the instancer will not be "created" and the function `GetInstanceIndices()` will fail. + self._instancer_manager.GetProtoIndicesAttr().Set(list(range(self.num_prototypes))) + self._instancer_manager.GetPositionsAttr().Set([Gf.Vec3f(0.0)] * self.num_prototypes) + self._count = self.num_prototypes + + def __str__(self) -> str: + """Return: A string representation of the class.""" + msg = f"VisualizationMarkers(prim_path={self.prim_path})" + msg += f"\n\tCount: {self.count}" + msg += f"\n\tNumber of prototypes: {self.num_prototypes}" + msg += "\n\tMarkers Prototypes:" + for index, (name, marker) in enumerate(self.cfg.markers.items()): + msg += f"\n\t\t[Index: {index}]: {name}: {marker.to_dict()}" + return msg + + """ + Properties. + """ + + @property + def num_prototypes(self) -> int: + """The number of marker prototypes available.""" + return len(self.cfg.markers) + + @property + def count(self) -> int: + """The total number of marker instances.""" + # TODO: Update this when the USD API is available (Isaac Sim 2023.1) + # return self._instancer_manager.GetInstanceCount() + return self._count + + """ + Operations. + """ + + def set_visibility(self, visible: bool): + """Sets the visibility of the markers. + + The method does this through the USD API. + + Args: + visible: flag to set the visibility. + """ + imageable = UsdGeom.Imageable(self._instancer_manager) + if visible: + imageable.MakeVisible() + else: + imageable.MakeInvisible() + + def is_visible(self) -> bool: + """Checks the visibility of the markers. + + Returns: + True if the markers are visible, False otherwise. + """ + return self._instancer_manager.GetVisibilityAttr().Get() != UsdGeom.Tokens.invisible + + def visualize( + self, + translations: np.ndarray | torch.Tensor | None = None, + orientations: np.ndarray | torch.Tensor | None = None, + scales: np.ndarray | torch.Tensor | None = None, + marker_indices: list[int] | np.ndarray | torch.Tensor | None = None, + ): + """Update markers in the viewport. + + .. note:: + If the prim `PointInstancer` is hidden in the stage, the function will simply return + without updating the markers. This helps in unnecessary computation when the markers + are not visible. + + Whenever updating the markers, the input arrays must have the same number of elements + in the first dimension. If the number of elements is different, the `UsdGeom.PointInstancer` + will raise an error complaining about the mismatch. + + Additionally, the function supports dynamic update of the markers. This means that the + number of markers can change between calls. For example, if you have 24 points that you + want to visualize, you can pass 24 translations, orientations, and scales. If you want to + visualize only 12 points, you can pass 12 translations, orientations, and scales. The + function will automatically update the number of markers in the scene. + + The function will also update the marker prototypes based on their prototype indices. For instance, + if you have two marker prototypes, and you pass the following marker indices: [0, 1, 0, 1], the function + will update the first and third markers with the first prototype, and the second and fourth markers + with the second prototype. This is useful when you want to visualize different markers in the same + scene. The list of marker indices must have the same number of elements as the translations, orientations, + or scales. If the number of elements is different, the function will raise an error. + + .. caution:: + This function will update all the markers instanced from the prototypes. That means + if you have 24 markers, you will need to pass 24 translations, orientations, and scales. + + If you want to update only a subset of the markers, you will need to handle the indices + yourself and pass the complete arrays to this function. + + Args: + translations: Translations w.r.t. parent prim frame. Shape is (M, 3). + Defaults to None, which means left unchanged. + orientations: Quaternion orientations (w, x, y, z) w.r.t. parent prim frame. Shape is (M, 4). + Defaults to None, which means left unchanged. + scales: Scale applied before any rotation is applied. Shape is (M, 3). + Defaults to None, which means left unchanged. + marker_indices: Decides which marker prototype to visualize. Shape is (M). + Defaults to None, which means left unchanged provided that the total number of markers + is the same as the previous call. If the number of markers is different, the function + will update the number of markers in the scene. + + Raises: + ValueError: When input arrays do not follow the expected shapes. + ValueError: When the function is called with all None arguments. + """ + # check if it is visible (if not then let's not waste time) + if not self.is_visible(): + return + # check if we have any markers to visualize + num_markers = 0 + # resolve inputs + # -- position + if translations is not None: + if isinstance(translations, torch.Tensor): + translations = translations.detach().cpu().numpy() + # check that shape is correct + if translations.shape[1] != 3 or len(translations.shape) != 2: + raise ValueError(f"Expected `translations` to have shape (M, 3). Received: {translations.shape}.") + # apply translations + self._instancer_manager.GetPositionsAttr().Set(Vt.Vec3fArray.FromNumpy(translations)) + # update number of markers + num_markers = translations.shape[0] + # -- orientation + if orientations is not None: + if isinstance(orientations, torch.Tensor): + orientations = orientations.detach().cpu().numpy() + # check that shape is correct + if orientations.shape[1] != 4 or len(orientations.shape) != 2: + raise ValueError(f"Expected `orientations` to have shape (M, 4). Received: {orientations.shape}.") + # roll orientations from (w, x, y, z) to (x, y, z, w) + # internally USD expects (x, y, z, w) + orientations = convert_quat(orientations, to="xyzw") + # apply orientations + self._instancer_manager.GetOrientationsAttr().Set(Vt.QuathArray.FromNumpy(orientations)) + # update number of markers + num_markers = orientations.shape[0] + # -- scales + if scales is not None: + if isinstance(scales, torch.Tensor): + scales = scales.detach().cpu().numpy() + # check that shape is correct + if scales.shape[1] != 3 or len(scales.shape) != 2: + raise ValueError(f"Expected `scales` to have shape (M, 3). Received: {scales.shape}.") + # apply scales + self._instancer_manager.GetScalesAttr().Set(Vt.Vec3fArray.FromNumpy(scales)) + # update number of markers + num_markers = scales.shape[0] + # -- status + if marker_indices is not None or num_markers != self._count: + # apply marker indices + if marker_indices is not None: + if isinstance(marker_indices, torch.Tensor): + marker_indices = marker_indices.detach().cpu().numpy() + elif isinstance(marker_indices, list): + marker_indices = np.array(marker_indices) + # check that shape is correct + if len(marker_indices.shape) != 1: + raise ValueError(f"Expected `marker_indices` to have shape (M,). Received: {marker_indices.shape}.") + # apply proto indices + self._instancer_manager.GetProtoIndicesAttr().Set(Vt.IntArray.FromNumpy(marker_indices)) + # update number of markers + num_markers = marker_indices.shape[0] + else: + # check that number of markers is not zero + if num_markers == 0: + raise ValueError("Number of markers cannot be zero! Hint: The function was called with no inputs?") + # set all markers to be the first prototype + self._instancer_manager.GetProtoIndicesAttr().Set([0] * num_markers) + # set number of markers + self._count = num_markers + + """ + Helper functions. + """ + + def _add_markers_prototypes(self, markers_cfg: dict[str, sim_utils.SpawnerCfg]): + """Adds markers prototypes to the scene and sets the markers instancer to use them.""" + # add markers based on config + for name, cfg in markers_cfg.items(): + # resolve prim path + marker_prim_path = f"{self.prim_path}/{name}" + # create a child prim for the marker + marker_prim = cfg.func(prim_path=marker_prim_path, cfg=cfg) + # make the asset uninstanceable (in case it is) + # point instancer defines its own prototypes so if an asset is already instanced, this doesn't work. + self._process_prototype_prim(marker_prim) + # add child reference to point instancer + self._instancer_manager.GetPrototypesRel().AddTarget(marker_prim_path) + # check that we loaded all the prototypes + prototypes = self._instancer_manager.GetPrototypesRel().GetTargets() + if len(prototypes) != len(markers_cfg): + raise RuntimeError( + f"Failed to load all the prototypes. Expected: {len(markers_cfg)}. Received: {len(prototypes)}." + ) + + def _process_prototype_prim(self, prim: Usd.Prim): + """Process a prim and its descendants to make them suitable for defining prototypes. + + Point instancer defines its own prototypes so if an asset is already instanced, this doesn't work. + This function checks if the prim at the specified prim path and its descendants are instanced. + If so, it makes the respective prim uninstanceable by disabling instancing on the prim. + + Additionally, it makes the prim invisible to secondary rays. This is useful when we do not want + to see the marker prims on camera images. + + Args: + prim: The prim to check. + """ + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim.GetPrimAtPath()}' is not valid.") + # iterate over all prims under prim-path + all_prims = [prim] + while len(all_prims) > 0: + # get current prim + child_prim = all_prims.pop(0) + # check if it is physics body -> if so, remove it + if child_prim.HasAPI(UsdPhysics.ArticulationRootAPI): + child_prim.RemoveAPI(UsdPhysics.ArticulationRootAPI) + child_prim.RemoveAPI(PhysxSchema.PhysxArticulationAPI) + if child_prim.HasAPI(UsdPhysics.RigidBodyAPI): + child_prim.RemoveAPI(UsdPhysics.RigidBodyAPI) + child_prim.RemoveAPI(PhysxSchema.PhysxRigidBodyAPI) + if child_prim.IsA(UsdPhysics.Joint): + child_prim.GetAttribute("physics:jointEnabled").Set(False) + # check if prim is instanced -> if so, make it uninstanceable + if child_prim.IsInstance(): + child_prim.SetInstanceable(False) + # check if prim is a mesh -> if so, make it invisible to secondary rays + if child_prim.IsA(UsdGeom.Gprim): + # invisible to secondary rays such as depth images + sim_utils.change_prim_property( + prop_path=f"{child_prim.GetPrimPath().pathString}.primvars:invisibleToSecondaryRays", + value=True, + stage=prim.GetStage(), + type_to_create_if_not_exist=Sdf.ValueTypeNames.Bool, + ) + # add children to list + all_prims += child_prim.GetChildren() + + # remove any physics on the markers because they are only for visualization! + physx_utils.removeRigidBodySubtree(prim) diff --git a/source/isaaclab/isaaclab/scene/__init__.py b/source/isaaclab/isaaclab/scene/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4b614ea4bced0f2c4250bbed1a07f18c13786836 --- /dev/null +++ b/source/isaaclab/isaaclab/scene/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package containing an interactive scene definition. + +A scene is a collection of entities (e.g., terrain, articulations, sensors, lights, etc.) that can be added to the +simulation. However, only a subset of these entities are of direct interest for the user to interact with. +For example, the user may want to interact with a robot in the scene, but not with the terrain or the lights. +For this reason, we integrate the different entities into a single class called :class:`InteractiveScene`. + +The interactive scene performs the following tasks: + +1. It parses the configuration class :class:`InteractiveSceneCfg` to create the scene. This configuration class is + inherited by the user to add entities to the scene. +2. It clones the entities based on the number of environments specified by the user. +3. It clubs the entities into different groups based on their type (e.g., articulations, sensors, etc.). +4. It provides a set of methods to unify the common operations on the entities in the scene (e.g., resetting internal + buffers, writing buffers to simulation and updating buffers from simulation). + +The interactive scene can be passed around to different modules in the framework to perform different tasks. +For instance, computing the observations based on the state of the scene, or randomizing the scene, or applying +actions to the scene. All these are handled by different "managers" in the framework. Please refer to the +:mod:`isaaclab.managers` sub-package for more details. +""" + +from .interactive_scene import InteractiveScene +from .interactive_scene_cfg import InteractiveSceneCfg diff --git a/source/isaaclab/isaaclab/scene/interactive_scene.py b/source/isaaclab/isaaclab/scene/interactive_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..291e371ca39947ca96f96b4b539275f4f6f8f610 --- /dev/null +++ b/source/isaaclab/isaaclab/scene/interactive_scene.py @@ -0,0 +1,800 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging +from collections.abc import Sequence +from typing import Any + +import torch + +import carb +from isaacsim.core.cloner import GridCloner +from pxr import PhysxSchema + +import isaaclab.sim as sim_utils +from isaaclab.assets import ( + Articulation, + ArticulationCfg, + AssetBaseCfg, + DeformableObject, + DeformableObjectCfg, + RigidObject, + RigidObjectCfg, + RigidObjectCollection, + RigidObjectCollectionCfg, + SurfaceGripper, + SurfaceGripperCfg, +) +from isaaclab.sensors import ContactSensorCfg, FrameTransformerCfg, SensorBase, SensorBaseCfg, VisuoTactileSensorCfg +from isaaclab.sim import SimulationContext +from isaaclab.sim.utils.stage import get_current_stage, get_current_stage_id +from isaaclab.sim.views import XformPrimView +from isaaclab.terrains import TerrainImporter, TerrainImporterCfg +from isaaclab.utils.version import get_isaac_sim_version + +from .interactive_scene_cfg import InteractiveSceneCfg + +# import logger +logger = logging.getLogger(__name__) + + +class InteractiveScene: + """A scene that contains entities added to the simulation. + + The interactive scene parses the :class:`InteractiveSceneCfg` class to create the scene. + Based on the specified number of environments, it clones the entities and groups them into different + categories (e.g., articulations, sensors, etc.). + + Cloning can be performed in two ways: + + * For tasks where all environments contain the same assets, a more performant cloning paradigm + can be used to allow for faster environment creation. This is specified by the ``replicate_physics`` flag. + + .. code-block:: python + + scene = InteractiveScene(cfg=InteractiveSceneCfg(replicate_physics=True)) + + * For tasks that require having separate assets in the environments, ``replicate_physics`` would have to + be set to False, which will add some costs to the overall startup time. + + .. code-block:: python + + scene = InteractiveScene(cfg=InteractiveSceneCfg(replicate_physics=False)) + + Each entity is registered to scene based on its name in the configuration class. For example, if the user + specifies a robot in the configuration class as follows: + + .. code-block:: python + + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.utils import configclass + + from isaaclab_assets.robots.anymal import ANYMAL_C_CFG + + + @configclass + class MySceneCfg(InteractiveSceneCfg): + # ANYmal-C robot spawned in each environment + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + Then the robot can be accessed from the scene as follows: + + .. code-block:: python + + from isaaclab.scene import InteractiveScene + + # create 128 environments + scene = InteractiveScene(cfg=MySceneCfg(num_envs=128)) + + # access the robot from the scene + robot = scene["robot"] + # access the robot based on its type + robot = scene.articulations["robot"] + + If the :class:`InteractiveSceneCfg` class does not include asset entities, the cloning process + can still be triggered if assets were added to the stage outside of the :class:`InteractiveScene` class: + + .. code-block:: python + + scene = InteractiveScene(cfg=InteractiveSceneCfg(num_envs=128, replicate_physics=True)) + scene.clone_environments() + + .. note:: + It is important to note that the scene only performs common operations on the entities. For example, + resetting the internal buffers, writing the buffers to the simulation and updating the buffers from the + simulation. The scene does not perform any task specific to the entity. For example, it does not apply + actions to the robot or compute observations from the robot. These tasks are handled by different + modules called "managers" in the framework. Please refer to the :mod:`isaaclab.managers` sub-package + for more details. + """ + + def __init__(self, cfg: InteractiveSceneCfg): + """Initializes the scene. + + Args: + cfg: The configuration class for the scene. + """ + # check that the config is valid + cfg.validate() + # store inputs + self.cfg = cfg + # initialize scene elements + self._terrain = None + self._articulations = dict() + self._deformable_objects = dict() + self._rigid_objects = dict() + self._rigid_object_collections = dict() + self._sensors = dict() + self._surface_grippers = dict() + self._extras = dict() + # get stage handle + self.sim = SimulationContext.instance() + self.stage = get_current_stage() + self.stage_id = get_current_stage_id() + # physics scene path + self._physics_scene_path = None + # prepare cloner for environment replication + self.cloner = GridCloner(spacing=self.cfg.env_spacing, stage=self.stage) + self.cloner.define_base_env(self.env_ns) + self.env_prim_paths = self.cloner.generate_paths(f"{self.env_ns}/env", self.cfg.num_envs) + # create source prim + self.stage.DefinePrim(self.env_prim_paths[0], "Xform") + # allocate env indices + self._ALL_INDICES = torch.arange(self.cfg.num_envs, dtype=torch.long, device=self.device) + # when replicate_physics=False, we assume heterogeneous environments and clone the xforms first. + # this triggers per-object level cloning in the spawner. + if not self.cfg.replicate_physics: + # check version of Isaac Sim to determine whether clone_in_fabric is valid + if get_isaac_sim_version().major < 5: + # clone the env xform + env_origins = self.cloner.clone( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + replicate_physics=False, + copy_from_source=True, + enable_env_ids=( + self.cfg.filter_collisions if self.device != "cpu" else False + ), # this won't do anything because we are not replicating physics + ) + else: + # clone the env xform + env_origins = self.cloner.clone( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + replicate_physics=False, + copy_from_source=True, + enable_env_ids=( + self.cfg.filter_collisions if self.device != "cpu" else False + ), # this won't do anything because we are not replicating physics + clone_in_fabric=self.cfg.clone_in_fabric, + ) + self._default_env_origins = torch.tensor(env_origins, device=self.device, dtype=torch.float32) + else: + # otherwise, environment origins will be initialized during cloning at the end of environment creation + self._default_env_origins = None + + self._global_prim_paths = list() + if self._is_scene_setup_from_cfg(): + # add entities from config + self._add_entities_from_cfg() + # clone environments on a global scope if environment is homogeneous + if self.cfg.replicate_physics: + self.clone_environments(copy_from_source=False) + # replicate physics if we have more than one environment + # this is done to make scene initialization faster at play time + if self.cfg.replicate_physics and self.cfg.num_envs > 1: + # check version of Isaac Sim to determine whether clone_in_fabric is valid + if get_isaac_sim_version().major < 5: + self.cloner.replicate_physics( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + base_env_path=self.env_ns, + root_path=self.env_regex_ns.replace(".*", ""), + enable_env_ids=self.cfg.filter_collisions if self.device != "cpu" else False, + ) + else: + self.cloner.replicate_physics( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + base_env_path=self.env_ns, + root_path=self.env_regex_ns.replace(".*", ""), + enable_env_ids=self.cfg.filter_collisions if self.device != "cpu" else False, + clone_in_fabric=self.cfg.clone_in_fabric, + ) + + # since env_ids is only applicable when replicating physics, we have to fallback to the previous method + # to filter collisions if replicate_physics is not enabled + # additionally, env_ids is only supported in GPU simulation + if (not self.cfg.replicate_physics and self.cfg.filter_collisions) or self.device == "cpu": + self.filter_collisions(self._global_prim_paths) + + def clone_environments(self, copy_from_source: bool = False): + """Creates clones of the environment ``/World/envs/env_0``. + + Args: + copy_from_source: (bool): If set to False, clones inherit from /World/envs/env_0 and mirror its changes. + If True, clones are independent copies of the source prim and won't reflect its changes (start-up time + may increase). Defaults to False. + """ + # check if user spawned different assets in individual environments + # this flag will be None if no multi asset is spawned + carb_settings_iface = carb.settings.get_settings() + has_multi_assets = carb_settings_iface.get("/isaaclab/spawn/multi_assets") + if has_multi_assets and self.cfg.replicate_physics: + logger.warning( + "Varying assets might have been spawned under different environments." + " However, the replicate physics flag is enabled in the 'InteractiveScene' configuration." + " This may adversely affect PhysX parsing. We recommend disabling this property." + ) + + # check version of Isaac Sim to determine whether clone_in_fabric is valid + if get_isaac_sim_version().major < 5: + # clone the environment + env_origins = self.cloner.clone( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + replicate_physics=self.cfg.replicate_physics, + copy_from_source=copy_from_source, + enable_env_ids=( + self.cfg.filter_collisions if self.device != "cpu" else False + ), # this automatically filters collisions between environments + ) + else: + # clone the environment + env_origins = self.cloner.clone( + source_prim_path=self.env_prim_paths[0], + prim_paths=self.env_prim_paths, + replicate_physics=self.cfg.replicate_physics, + copy_from_source=copy_from_source, + enable_env_ids=( + self.cfg.filter_collisions if self.device != "cpu" else False + ), # this automatically filters collisions between environments + clone_in_fabric=self.cfg.clone_in_fabric, + ) + + # since env_ids is only applicable when replicating physics, we have to fallback to the previous method + # to filter collisions if replicate_physics is not enabled + # additionally, env_ids is only supported in GPU simulation + if (not self.cfg.replicate_physics and self.cfg.filter_collisions) or self.device == "cpu": + # if scene is specified through cfg, this is already taken care of + if not self._is_scene_setup_from_cfg(): + logger.warning( + "Collision filtering can only be automatically enabled when replicate_physics=True and using GPU" + " simulation. Please call scene.filter_collisions(global_prim_paths) to filter collisions across" + " environments." + ) + + # in case of heterogeneous cloning, the env origins is specified at init + if self._default_env_origins is None: + self._default_env_origins = torch.tensor(env_origins, device=self.device, dtype=torch.float32) + + def filter_collisions(self, global_prim_paths: list[str] | None = None): + """Filter environments collisions. + + Disables collisions between the environments in ``/World/envs/env_.*`` and enables collisions with the prims + in global prim paths (e.g. ground plane). + + Args: + global_prim_paths: A list of global prim paths to enable collisions with. + Defaults to None, in which case no global prim paths are considered. + """ + # validate paths in global prim paths + if global_prim_paths is None: + global_prim_paths = [] + else: + # remove duplicates in paths + global_prim_paths = list(set(global_prim_paths)) + + # if "/World/collisions" already exists in the stage, we don't filter again + if self.stage.GetPrimAtPath("/World/collisions"): + return + + # set global prim paths list if not previously defined + if len(self._global_prim_paths) < 1: + self._global_prim_paths += global_prim_paths + + # filter collisions within each environment instance + self.cloner.filter_collisions( + self.physics_scene_path, + "/World/collisions", + self.env_prim_paths, + global_paths=self._global_prim_paths, + ) + + def __str__(self) -> str: + """Returns a string representation of the scene.""" + msg = f"\n" + msg += f"\tNumber of environments: {self.cfg.num_envs}\n" + msg += f"\tEnvironment spacing : {self.cfg.env_spacing}\n" + msg += f"\tSource prim name : {self.env_prim_paths[0]}\n" + msg += f"\tGlobal prim paths : {self._global_prim_paths}\n" + msg += f"\tReplicate physics : {self.cfg.replicate_physics}" + return msg + + """ + Properties. + """ + + @property + def physics_scene_path(self) -> str: + """The path to the USD Physics Scene.""" + if self._physics_scene_path is None: + for prim in self.stage.Traverse(): + if prim.HasAPI(PhysxSchema.PhysxSceneAPI): + self._physics_scene_path = prim.GetPrimPath().pathString + logger.info(f"Physics scene prim path: {self._physics_scene_path}") + break + if self._physics_scene_path is None: + raise RuntimeError("No physics scene found! Please make sure one exists.") + return self._physics_scene_path + + @property + def physics_dt(self) -> float: + """The physics timestep of the scene.""" + return sim_utils.SimulationContext.instance().get_physics_dt() # pyright: ignore [reportOptionalMemberAccess] + + @property + def device(self) -> str: + """The device on which the scene is created.""" + return sim_utils.SimulationContext.instance().device # pyright: ignore [reportOptionalMemberAccess] + + @property + def env_ns(self) -> str: + """The namespace ``/World/envs`` in which all environments created. + + The environments are present w.r.t. this namespace under "env_{N}" prim, + where N is a natural number. + """ + return "/World/envs" + + @property + def env_regex_ns(self) -> str: + """The namespace ``/World/envs/env_.*`` in which all environments created.""" + return f"{self.env_ns}/env_.*" + + @property + def num_envs(self) -> int: + """The number of environments handled by the scene.""" + return self.cfg.num_envs + + @property + def env_origins(self) -> torch.Tensor: + """The origins of the environments in the scene. Shape is (num_envs, 3).""" + if self._terrain is not None: + return self._terrain.env_origins + else: + return self._default_env_origins + + @property + def terrain(self) -> TerrainImporter | None: + """The terrain in the scene. If None, then the scene has no terrain. + + Note: + We treat terrain separate from :attr:`extras` since terrains define environment origins and are + handled differently from other miscellaneous entities. + """ + return self._terrain + + @property + def articulations(self) -> dict[str, Articulation]: + """A dictionary of articulations in the scene.""" + return self._articulations + + @property + def deformable_objects(self) -> dict[str, DeformableObject]: + """A dictionary of deformable objects in the scene.""" + return self._deformable_objects + + @property + def rigid_objects(self) -> dict[str, RigidObject]: + """A dictionary of rigid objects in the scene.""" + return self._rigid_objects + + @property + def rigid_object_collections(self) -> dict[str, RigidObjectCollection]: + """A dictionary of rigid object collections in the scene.""" + return self._rigid_object_collections + + @property + def sensors(self) -> dict[str, SensorBase]: + """A dictionary of the sensors in the scene, such as cameras and contact reporters.""" + return self._sensors + + @property + def surface_grippers(self) -> dict[str, SurfaceGripper]: + """A dictionary of the surface grippers in the scene.""" + return self._surface_grippers + + @property + def extras(self) -> dict[str, XformPrimView]: + """A dictionary of miscellaneous simulation objects that neither inherit from assets nor sensors. + + The keys are the names of the miscellaneous objects, and the values are the + :class:`~isaaclab.sim.views.XformPrimView` instances of the corresponding prims. + + As an example, lights or other props in the scene that do not have any attributes or properties that you + want to alter at runtime can be added to this dictionary. + + Note: + These are not reset or updated by the scene. They are mainly other prims that are not necessarily + handled by the interactive scene, but are useful to be accessed by the user. + + """ + return self._extras + + @property + def state(self) -> dict[str, dict[str, dict[str, torch.Tensor]]]: + """A dictionary of the state of the scene entities in the simulation world frame. + + Please refer to :meth:`get_state` for the format. + """ + return self.get_state(is_relative=False) + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None): + """Resets the scene entities. + + Args: + env_ids: The indices of the environments to reset. + Defaults to None (all instances). + """ + # -- assets + for articulation in self._articulations.values(): + articulation.reset(env_ids) + for deformable_object in self._deformable_objects.values(): + deformable_object.reset(env_ids) + for rigid_object in self._rigid_objects.values(): + rigid_object.reset(env_ids) + for surface_gripper in self._surface_grippers.values(): + surface_gripper.reset(env_ids) + for rigid_object_collection in self._rigid_object_collections.values(): + rigid_object_collection.reset(env_ids) + # -- sensors + for sensor in self._sensors.values(): + sensor.reset(env_ids) + + def write_data_to_sim(self): + """Writes the data of the scene entities to the simulation.""" + # -- assets + for articulation in self._articulations.values(): + articulation.write_data_to_sim() + for deformable_object in self._deformable_objects.values(): + deformable_object.write_data_to_sim() + for rigid_object in self._rigid_objects.values(): + rigid_object.write_data_to_sim() + for surface_gripper in self._surface_grippers.values(): + surface_gripper.write_data_to_sim() + for rigid_object_collection in self._rigid_object_collections.values(): + rigid_object_collection.write_data_to_sim() + + def update(self, dt: float) -> None: + """Update the scene entities. + + Args: + dt: The amount of time passed from last :meth:`update` call. + """ + # -- assets + for articulation in self._articulations.values(): + articulation.update(dt) + for deformable_object in self._deformable_objects.values(): + deformable_object.update(dt) + for rigid_object in self._rigid_objects.values(): + rigid_object.update(dt) + for rigid_object_collection in self._rigid_object_collections.values(): + rigid_object_collection.update(dt) + for surface_gripper in self._surface_grippers.values(): + surface_gripper.update(dt) + # -- sensors + for sensor in self._sensors.values(): + sensor.update(dt, force_recompute=not self.cfg.lazy_sensor_update) + + """ + Operations: Scene State. + """ + + def reset_to( + self, + state: dict[str, dict[str, dict[str, torch.Tensor]]], + env_ids: Sequence[int] | None = None, + is_relative: bool = False, + ): + """Resets the entities in the scene to the provided state. + + Args: + state: The state to reset the scene entities to. Please refer to :meth:`get_state` for the format. + env_ids: The indices of the environments to reset. Defaults to None, in which case + all environment instances are reset. + is_relative: If set to True, the state is considered relative to the environment origins. + Defaults to False. + """ + # resolve env_ids + if env_ids is None: + env_ids = self._ALL_INDICES + # articulations + for asset_name, articulation in self._articulations.items(): + asset_state = state["articulation"][asset_name] + # root state + root_pose = asset_state["root_pose"].clone() + if is_relative: + root_pose[:, :3] += self.env_origins[env_ids] + root_velocity = asset_state["root_velocity"].clone() + articulation.write_root_pose_to_sim(root_pose, env_ids=env_ids) + articulation.write_root_velocity_to_sim(root_velocity, env_ids=env_ids) + # joint state + joint_position = asset_state["joint_position"].clone() + joint_velocity = asset_state["joint_velocity"].clone() + articulation.write_joint_state_to_sim(joint_position, joint_velocity, env_ids=env_ids) + # FIXME: This is not generic as it assumes PD control over the joints. + # This assumption does not hold for effort controlled joints. + articulation.set_joint_position_target(joint_position, env_ids=env_ids) + articulation.set_joint_velocity_target(joint_velocity, env_ids=env_ids) + # deformable objects + for asset_name, deformable_object in self._deformable_objects.items(): + asset_state = state["deformable_object"][asset_name] + nodal_position = asset_state["nodal_position"].clone() + if is_relative: + nodal_position[:, :3] += self.env_origins[env_ids] + nodal_velocity = asset_state["nodal_velocity"].clone() + deformable_object.write_nodal_pos_to_sim(nodal_position, env_ids=env_ids) + deformable_object.write_nodal_velocity_to_sim(nodal_velocity, env_ids=env_ids) + # rigid objects + for asset_name, rigid_object in self._rigid_objects.items(): + asset_state = state["rigid_object"][asset_name] + root_pose = asset_state["root_pose"].clone() + if is_relative: + root_pose[:, :3] += self.env_origins[env_ids] + root_velocity = asset_state["root_velocity"].clone() + rigid_object.write_root_pose_to_sim(root_pose, env_ids=env_ids) + rigid_object.write_root_velocity_to_sim(root_velocity, env_ids=env_ids) + # surface grippers + for asset_name, surface_gripper in self._surface_grippers.items(): + asset_state = state["gripper"][asset_name] + surface_gripper.set_grippers_command(asset_state) + + # write data to simulation to make sure initial state is set + # this propagates the joint targets to the simulation + self.write_data_to_sim() + + def get_state(self, is_relative: bool = False) -> dict[str, dict[str, dict[str, torch.Tensor]]]: + """Returns the state of the scene entities. + + Based on the type of the entity, the state comprises of different components. + + * For an articulation, the state comprises of the root pose, root velocity, and joint position and velocity. + * For a deformable object, the state comprises of the nodal position and velocity. + * For a rigid object, the state comprises of the root pose and root velocity. + + The returned state is a dictionary with the following format: + + .. code-block:: python + + { + "articulation": { + "entity_1_name": { + "root_pose": torch.Tensor, + "root_velocity": torch.Tensor, + "joint_position": torch.Tensor, + "joint_velocity": torch.Tensor, + }, + "entity_2_name": { + "root_pose": torch.Tensor, + "root_velocity": torch.Tensor, + "joint_position": torch.Tensor, + "joint_velocity": torch.Tensor, + }, + }, + "deformable_object": { + "entity_3_name": { + "nodal_position": torch.Tensor, + "nodal_velocity": torch.Tensor, + } + }, + "rigid_object": { + "entity_4_name": { + "root_pose": torch.Tensor, + "root_velocity": torch.Tensor, + } + }, + } + + where ``entity_N_name`` is the name of the entity registered in the scene. + + Args: + is_relative: If set to True, the state is considered relative to the environment origins. + Defaults to False. + + Returns: + A dictionary of the state of the scene entities. + """ + state = dict() + # articulations + state["articulation"] = dict() + for asset_name, articulation in self._articulations.items(): + asset_state = dict() + asset_state["root_pose"] = articulation.data.root_pose_w.clone() + if is_relative: + asset_state["root_pose"][:, :3] -= self.env_origins + asset_state["root_velocity"] = articulation.data.root_vel_w.clone() + asset_state["joint_position"] = articulation.data.joint_pos.clone() + asset_state["joint_velocity"] = articulation.data.joint_vel.clone() + state["articulation"][asset_name] = asset_state + # deformable objects + state["deformable_object"] = dict() + for asset_name, deformable_object in self._deformable_objects.items(): + asset_state = dict() + asset_state["nodal_position"] = deformable_object.data.nodal_pos_w.clone() + if is_relative: + asset_state["nodal_position"][:, :3] -= self.env_origins + asset_state["nodal_velocity"] = deformable_object.data.nodal_vel_w.clone() + state["deformable_object"][asset_name] = asset_state + # rigid objects + state["rigid_object"] = dict() + for asset_name, rigid_object in self._rigid_objects.items(): + asset_state = dict() + asset_state["root_pose"] = rigid_object.data.root_pose_w.clone() + if is_relative: + asset_state["root_pose"][:, :3] -= self.env_origins + asset_state["root_velocity"] = rigid_object.data.root_vel_w.clone() + state["rigid_object"][asset_name] = asset_state + # surface grippers + state["gripper"] = dict() + for asset_name, gripper in self._surface_grippers.items(): + state["gripper"][asset_name] = gripper.state.clone() + return state + + """ + Operations: Iteration. + """ + + def keys(self) -> list[str]: + """Returns the keys of the scene entities. + + Returns: + The keys of the scene entities. + """ + all_keys = ["terrain"] + for asset_family in [ + self._articulations, + self._deformable_objects, + self._rigid_objects, + self._rigid_object_collections, + self._sensors, + self._surface_grippers, + self._extras, + ]: + all_keys += list(asset_family.keys()) + return all_keys + + def __getitem__(self, key: str) -> Any: + """Returns the scene entity with the given key. + + Args: + key: The key of the scene entity. + + Returns: + The scene entity. + """ + # check if it is a terrain + if key == "terrain": + return self._terrain + + all_keys = ["terrain"] + # check if it is in other dictionaries + for asset_family in [ + self._articulations, + self._deformable_objects, + self._rigid_objects, + self._rigid_object_collections, + self._sensors, + self._surface_grippers, + self._extras, + ]: + out = asset_family.get(key) + # if found, return + if out is not None: + return out + all_keys += list(asset_family.keys()) + # if not found, raise error + raise KeyError(f"Scene entity with key '{key}' not found. Available Entities: '{all_keys}'") + + """ + Internal methods. + """ + + def _is_scene_setup_from_cfg(self) -> bool: + """Check if scene entities are setup from the config or not. + + Returns: + True if scene entities are setup from the config, False otherwise. + """ + return any( + not (asset_name in InteractiveSceneCfg.__dataclass_fields__ or asset_cfg is None) + for asset_name, asset_cfg in self.cfg.__dict__.items() + ) + + def _add_entities_from_cfg(self): + """Add scene entities from the config.""" + # store paths that are in global collision filter + self._global_prim_paths = list() + # parse the entire scene config and resolve regex + for asset_name, asset_cfg in self.cfg.__dict__.items(): + # skip keywords + # note: easier than writing a list of keywords: [num_envs, env_spacing, lazy_sensor_update] + if asset_name in InteractiveSceneCfg.__dataclass_fields__ or asset_cfg is None: + continue + # resolve regex + if hasattr(asset_cfg, "prim_path"): + asset_cfg.prim_path = asset_cfg.prim_path.format(ENV_REGEX_NS=self.env_regex_ns) + # create asset + if isinstance(asset_cfg, TerrainImporterCfg): + # terrains are special entities since they define environment origins + asset_cfg.num_envs = self.cfg.num_envs + asset_cfg.env_spacing = self.cfg.env_spacing + self._terrain = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, ArticulationCfg): + self._articulations[asset_name] = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, DeformableObjectCfg): + self._deformable_objects[asset_name] = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, RigidObjectCfg): + self._rigid_objects[asset_name] = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, RigidObjectCollectionCfg): + for rigid_object_cfg in asset_cfg.rigid_objects.values(): + rigid_object_cfg.prim_path = rigid_object_cfg.prim_path.format(ENV_REGEX_NS=self.env_regex_ns) + self._rigid_object_collections[asset_name] = asset_cfg.class_type(asset_cfg) + for rigid_object_cfg in asset_cfg.rigid_objects.values(): + if hasattr(rigid_object_cfg, "collision_group") and rigid_object_cfg.collision_group == -1: + asset_paths = sim_utils.find_matching_prim_paths(rigid_object_cfg.prim_path) + self._global_prim_paths += asset_paths + elif isinstance(asset_cfg, SurfaceGripperCfg): + # add surface grippers to scene + self._surface_grippers[asset_name] = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, SensorBaseCfg): + # Update target frame path(s)' regex name space for FrameTransformer + if isinstance(asset_cfg, FrameTransformerCfg): + updated_target_frames = [] + for target_frame in asset_cfg.target_frames: + target_frame.prim_path = target_frame.prim_path.format(ENV_REGEX_NS=self.env_regex_ns) + updated_target_frames.append(target_frame) + asset_cfg.target_frames = updated_target_frames + elif isinstance(asset_cfg, ContactSensorCfg): + updated_filter_prim_paths_expr = [] + for filter_prim_path in asset_cfg.filter_prim_paths_expr: + updated_filter_prim_paths_expr.append(filter_prim_path.format(ENV_REGEX_NS=self.env_regex_ns)) + asset_cfg.filter_prim_paths_expr = updated_filter_prim_paths_expr + elif isinstance(asset_cfg, VisuoTactileSensorCfg): + if hasattr(asset_cfg, "camera_cfg") and asset_cfg.camera_cfg is not None: + asset_cfg.camera_cfg.prim_path = asset_cfg.camera_cfg.prim_path.format( + ENV_REGEX_NS=self.env_regex_ns + ) + if ( + hasattr(asset_cfg, "contact_object_prim_path_expr") + and asset_cfg.contact_object_prim_path_expr is not None + ): + asset_cfg.contact_object_prim_path_expr = asset_cfg.contact_object_prim_path_expr.format( + ENV_REGEX_NS=self.env_regex_ns + ) + + self._sensors[asset_name] = asset_cfg.class_type(asset_cfg) + elif isinstance(asset_cfg, AssetBaseCfg): + # manually spawn asset + if asset_cfg.spawn is not None: + asset_cfg.spawn.func( + asset_cfg.prim_path, + asset_cfg.spawn, + translation=asset_cfg.init_state.pos, + orientation=asset_cfg.init_state.rot, + ) + # store xform prim view corresponding to this asset + # all prims in the scene are Xform prims (i.e. have a transform component) + self._extras[asset_name] = XformPrimView(asset_cfg.prim_path, device=self.device, stage=self.stage) + else: + raise ValueError(f"Unknown asset config type for {asset_name}: {asset_cfg}") + # store global collision paths + if hasattr(asset_cfg, "collision_group") and asset_cfg.collision_group == -1: + asset_paths = sim_utils.find_matching_prim_paths(asset_cfg.prim_path) + self._global_prim_paths += asset_paths diff --git a/source/isaaclab/isaaclab/scene/interactive_scene_cfg.py b/source/isaaclab/isaaclab/scene/interactive_scene_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f4328324152c8399f5640cf01e450b9ec9da8797 --- /dev/null +++ b/source/isaaclab/isaaclab/scene/interactive_scene_cfg.py @@ -0,0 +1,126 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils.configclass import configclass + + +@configclass +class InteractiveSceneCfg: + """Configuration for the interactive scene. + + The users can inherit from this class to add entities to their scene. This is then parsed by the + :class:`InteractiveScene` class to create the scene. + + .. note:: + The adding of entities to the scene is sensitive to the order of the attributes in the configuration. + Please make sure to add the entities in the order you want them to be added to the scene. + The recommended order of specification is terrain, physics-related assets (articulations and rigid bodies), + sensors and non-physics-related assets (lights). + + For example, to add a robot to the scene, the user can create a configuration class as follows: + + .. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.assets import AssetBaseCfg + from isaaclab.scene import InteractiveSceneCfg + from isaaclab.sensors.ray_caster import GridPatternCfg, RayCasterCfg + from isaaclab.utils import configclass + + from isaaclab_assets.robots.anymal import ANYMAL_C_CFG + + + @configclass + class MySceneCfg(InteractiveSceneCfg): + # terrain - flat terrain plane + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + ) + + # articulation - robot 1 + robot_1 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_1") + # articulation - robot 2 + robot_2 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_2") + robot_2.init_state.pos = (0.0, 1.0, 0.6) + + # sensor - ray caster attached to the base of robot 1 that scans the ground + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot_1/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/ground"], + ) + + # extras - light + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, 500.0)), + ) + + """ + + num_envs: int = MISSING + """Number of environment instances handled by the scene.""" + + env_spacing: float = MISSING + """Spacing between environments. + + This is the default distance between environment origins in the scene. Used only when the + number of environments is greater than one. + """ + + lazy_sensor_update: bool = True + """Whether to update sensors only when they are accessed. Default is True. + + If true, the sensor data is only updated when their attribute ``data`` is accessed. Otherwise, the sensor + data is updated every time sensors are updated. + """ + + replicate_physics: bool = True + """Enable/disable replication of physics schemas when using the Cloner APIs. Default is True. + + If True, the simulation will have the same asset instances (USD prims) in all the cloned environments. + Internally, this ensures optimization in setting up the scene and parsing it via the physics stage parser. + + If False, the simulation allows having separate asset instances (USD prims) in each environment. + This flexibility comes at a cost of slowdowns in setting up and parsing the scene. + + .. note:: + Optimized parsing of certain prim types (such as deformable objects) is not currently supported + by the physics engine. In these cases, this flag needs to be set to False. + """ + + filter_collisions: bool = True + """Enable/disable collision filtering between cloned environments. Default is True. + + If True, collisions will not occur between cloned environments. + + If False, the simulation will generate collisions between environments. + + .. note:: + Collisions can only be filtered automatically in direct workflows when physics replication is enabled. + If :attr:`replicated_physics` is ``False`` and collision filtering is desired, make sure to call + ``scene.filter_collisions()``. + """ + + clone_in_fabric: bool = False + """Enable/disable cloning in fabric. Default is False. + + Omniverse Fabric is a more optimized method for performing cloning in scene creation. This reduces the time + taken to create the scene. However, it limits flexibility in accessing the stage through USD APIs and instead, + the stage must be accessed through USDRT. + + .. note:: + Cloning in fabric can only be enabled if :attr:`replicated_physics` is also enabled. + If :attr:`replicated_physics` is ``False``, cloning in Fabric will automatically + default to ``False``. + + """ diff --git a/source/isaaclab/isaaclab/sensors/__init__.py b/source/isaaclab/isaaclab/sensors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1e9273803a07e248a222a1f608b7e2ba83d77d15 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/__init__.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package containing various sensor classes implementations. + +This subpackage contains the sensor classes that are compatible with Isaac Sim. We include both +USD-based and custom sensors: + +* **USD-prim sensors**: Available in Omniverse and require creating a USD prim for them. + For instance, RTX ray tracing camera and lidar sensors. +* **USD-schema sensors**: Available in Omniverse and require creating a USD schema on an existing prim. + For instance, contact sensors and frame transformers. +* **Custom sensors**: Implemented in Python and do not require creating any USD prim or schema. + For instance, warp-based ray-casters. + +Due to the above categorization, the prim paths passed to the sensor's configuration class +are interpreted differently based on the sensor type. The following table summarizes the +interpretation of the prim paths for different sensor types: + ++---------------------+---------------------------+---------------------------------------------------------------+ +| Sensor Type | Example Prim Path | Pre-check | ++=====================+===========================+===============================================================+ +| Camera | /World/robot/base/camera | Leaf is available, and it will spawn a USD camera | ++---------------------+---------------------------+---------------------------------------------------------------+ +| Contact Sensor | /World/robot/feet_* | Leaf is available and checks if the schema exists | ++---------------------+---------------------------+---------------------------------------------------------------+ +| Ray Caster | /World/robot/base | Leaf exists and is a physics body (Articulation / Rigid Body) | ++---------------------+---------------------------+---------------------------------------------------------------+ +| Frame Transformer | /World/robot/base | Leaf exists and is a physics body (Articulation / Rigid Body) | ++---------------------+---------------------------+---------------------------------------------------------------+ +| Imu | /World/robot/base | Leaf exists and is a physics body (Rigid Body) | ++---------------------+---------------------------+---------------------------------------------------------------+ +| Visuo-Tactile Sensor| /World/robot/base | Leaf exists and is a physics body (Rigid Body) | ++---------------------+---------------------------+---------------------------------------------------------------+ + +""" + +from .camera import * # noqa: F401, F403 +from .contact_sensor import * # noqa: F401, F403 +from .frame_transformer import * # noqa: F401 +from .imu import * # noqa: F401, F403 +from .ray_caster import * # noqa: F401, F403 +from .sensor_base import SensorBase # noqa: F401 +from .sensor_base_cfg import SensorBaseCfg # noqa: F401 +from .tacsl_sensor import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/sensors/camera/__init__.py b/source/isaaclab/isaaclab/sensors/camera/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f2318067b586e170fd3bf2218bf223d1e003a187 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for camera wrapper around USD camera prim.""" + +from .camera import Camera +from .camera_cfg import CameraCfg +from .camera_data import CameraData +from .tiled_camera import TiledCamera +from .tiled_camera_cfg import TiledCameraCfg +from .utils import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/sensors/camera/camera.py b/source/isaaclab/isaaclab/sensors/camera/camera.py new file mode 100644 index 0000000000000000000000000000000000000000..ecd9cdac0abd009c6b79797f3bf2486e732ff9a3 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/camera.py @@ -0,0 +1,720 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import json +import logging +import re +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any, Literal + +import numpy as np +import torch +from packaging import version + +import carb +import omni.usd +from pxr import Sdf, UsdGeom + +import isaaclab.sim as sim_utils +import isaaclab.utils.sensors as sensor_utils +from isaaclab.sim.views import XformPrimView +from isaaclab.utils import to_camel_case +from isaaclab.utils.array import convert_to_torch +from isaaclab.utils.math import ( + convert_camera_frame_orientation_convention, + create_rotation_matrix_from_view, + quat_from_matrix, +) +from isaaclab.utils.version import get_isaac_sim_version + +from ..sensor_base import SensorBase +from .camera_data import CameraData + +if TYPE_CHECKING: + from .camera_cfg import CameraCfg + +# import logger +logger = logging.getLogger(__name__) + + +class Camera(SensorBase): + r"""The camera sensor for acquiring visual data. + + This class wraps over the `UsdGeom Camera`_ for providing a consistent API for acquiring visual data. + It ensures that the camera follows the ROS convention for the coordinate system. + + Summarizing from the `replicator extension`_, the following sensor types are supported: + + - ``"rgb"``: A 3-channel rendered color image. + - ``"rgba"``: A 4-channel rendered color image with alpha channel. + - ``"distance_to_camera"``: An image containing the distance to camera optical center. + - ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis. + - ``"depth"``: The same as ``"distance_to_image_plane"``. + - ``"normals"``: An image containing the local surface normal vectors at each pixel. + - ``"motion_vectors"``: An image containing the motion vector data at each pixel. + - ``"semantic_segmentation"``: The semantic segmentation data. + - ``"instance_segmentation_fast"``: The instance segmentation data. + - ``"instance_id_segmentation_fast"``: The instance id segmentation data. + + .. note:: + Currently the following sensor types are not supported in a "view" format: + + - ``"instance_segmentation"``: The instance segmentation data. Please use the fast counterparts instead. + - ``"instance_id_segmentation"``: The instance id segmentation data. Please use the fast counterparts instead. + - ``"bounding_box_2d_tight"``: The tight 2D bounding box data (only contains non-occluded regions). + - ``"bounding_box_2d_tight_fast"``: The tight 2D bounding box data (only contains non-occluded regions). + - ``"bounding_box_2d_loose"``: The loose 2D bounding box data (contains occluded regions). + - ``"bounding_box_2d_loose_fast"``: The loose 2D bounding box data (contains occluded regions). + - ``"bounding_box_3d"``: The 3D view space bounding box data. + - ``"bounding_box_3d_fast"``: The 3D view space bounding box data. + + .. _replicator extension: https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/annotators_details.html#annotator-output + .. _USDGeom Camera: https://graphics.pixar.com/usd/docs/api/class_usd_geom_camera.html + + """ + + cfg: CameraCfg + """The configuration parameters.""" + + UNSUPPORTED_TYPES: set[str] = { + "instance_id_segmentation", + "instance_segmentation", + "bounding_box_2d_tight", + "bounding_box_2d_loose", + "bounding_box_3d", + "bounding_box_2d_tight_fast", + "bounding_box_2d_loose_fast", + "bounding_box_3d_fast", + } + """The set of sensor types that are not supported by the camera class.""" + + def __init__(self, cfg: CameraCfg): + """Initializes the camera sensor. + + Args: + cfg: The configuration parameters. + + Raises: + RuntimeError: If no camera prim is found at the given path. + ValueError: If the provided data types are not supported by the camera. + """ + # check if sensor path is valid + # note: currently we do not handle environment indices if there is a regex pattern in the leaf + # For example, if the prim path is "/World/Sensor_[1,2]". + sensor_path = cfg.prim_path.split("/")[-1] + sensor_path_is_regex = re.match(r"^[a-zA-Z0-9/_]+$", sensor_path) is None + if sensor_path_is_regex: + raise RuntimeError( + f"Invalid prim path for the camera sensor: {self.cfg.prim_path}." + "\n\tHint: Please ensure that the prim path does not contain any regex patterns in the leaf." + ) + # perform check on supported data types + self._check_supported_data_types(cfg) + # initialize base class + super().__init__(cfg) + + # toggle rendering of rtx sensors as True + # this flag is read by SimulationContext to determine if rtx sensors should be rendered + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/isaaclab/render/rtx_sensors", True) + + # spawn the asset + if self.cfg.spawn is not None: + # compute the rotation offset + rot = torch.tensor(self.cfg.offset.rot, dtype=torch.float32, device="cpu").unsqueeze(0) + rot_offset = convert_camera_frame_orientation_convention( + rot, origin=self.cfg.offset.convention, target="opengl" + ) + rot_offset = rot_offset.squeeze(0).cpu().numpy() + # ensure vertical aperture is set, otherwise replace with default for squared pixels + if self.cfg.spawn.vertical_aperture is None: + self.cfg.spawn.vertical_aperture = self.cfg.spawn.horizontal_aperture * self.cfg.height / self.cfg.width + # spawn the asset + self.cfg.spawn.func( + self.cfg.prim_path, self.cfg.spawn, translation=self.cfg.offset.pos, orientation=rot_offset + ) + # check that spawn was successful + matching_prims = sim_utils.find_matching_prims(self.cfg.prim_path) + if len(matching_prims) == 0: + raise RuntimeError(f"Could not find prim with path {self.cfg.prim_path}.") + + # UsdGeom Camera prim for the sensor + self._sensor_prims: list[UsdGeom.Camera] = list() + # Create empty variables for storing output data + self._data = CameraData() + + # HACK: We need to disable instancing for semantic_segmentation and instance_segmentation_fast to work + # checks for Isaac Sim v4.5 as this issue exists there + if get_isaac_sim_version() == version.parse("4.5"): + if "semantic_segmentation" in self.cfg.data_types or "instance_segmentation_fast" in self.cfg.data_types: + logger.warning( + "Isaac Sim 4.5 introduced a bug in Camera and TiledCamera when outputting instance and semantic" + " segmentation outputs for instanceable assets. As a workaround, the instanceable flag on assets" + " will be disabled in the current workflow and may lead to longer load times and increased memory" + " usage." + ) + with Sdf.ChangeBlock(): + for prim in self.stage.Traverse(): + prim.SetInstanceable(False) + + def __del__(self): + """Unsubscribes from callbacks and detach from the replicator registry.""" + # unsubscribe callbacks + super().__del__() + # delete from replicator registry + for _, annotators in self._rep_registry.items(): + for annotator, render_product_path in zip(annotators, self._render_product_paths): + annotator.detach([render_product_path]) + annotator = None + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + # message for class + return ( + f"Camera @ '{self.cfg.prim_path}': \n" + f"\tdata types : {list(self.data.output.keys())} \n" + f"\tsemantic filter : {self.cfg.semantic_filter}\n" + f"\tcolorize semantic segm. : {self.cfg.colorize_semantic_segmentation}\n" + f"\tcolorize instance segm. : {self.cfg.colorize_instance_segmentation}\n" + f"\tcolorize instance id segm.: {self.cfg.colorize_instance_id_segmentation}\n" + f"\tupdate period (s): {self.cfg.update_period}\n" + f"\tshape : {self.image_shape}\n" + f"\tnumber of sensors : {self._view.count}" + ) + + """ + Properties + """ + + @property + def num_instances(self) -> int: + return self._view.count + + @property + def data(self) -> CameraData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + @property + def frame(self) -> torch.tensor: + """Frame number when the measurement took place.""" + return self._frame + + @property + def render_product_paths(self) -> list[str]: + """The path of the render products for the cameras. + + This can be used via replicator interfaces to attach to writes or external annotator registry. + """ + return self._render_product_paths + + @property + def image_shape(self) -> tuple[int, int]: + """A tuple containing (height, width) of the camera sensor.""" + return (self.cfg.height, self.cfg.width) + + """ + Configuration + """ + + def set_intrinsic_matrices( + self, matrices: torch.Tensor, focal_length: float | None = None, env_ids: Sequence[int] | None = None + ): + """Set parameters of the USD camera from its intrinsic matrix. + + The intrinsic matrix is used to set the following parameters to the USD camera: + + - ``focal_length``: The focal length of the camera. + - ``horizontal_aperture``: The horizontal aperture of the camera. + - ``vertical_aperture``: The vertical aperture of the camera. + - ``horizontal_aperture_offset``: The horizontal offset of the camera. + - ``vertical_aperture_offset``: The vertical offset of the camera. + + .. warning:: + + Due to limitations of Omniverse camera, we need to assume that the camera is a spherical lens, + i.e. has square pixels, and the optical center is centered at the camera eye. If this assumption + is not true in the input intrinsic matrix, then the camera will not set up correctly. + + Args: + matrices: The intrinsic matrices for the camera. Shape is (N, 3, 3). + focal_length: Perspective focal length (in cm) used to calculate pixel size. Defaults to None. If None, + focal_length will be calculated 1 / width. + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + """ + # resolve env_ids + if env_ids is None: + env_ids = self._ALL_INDICES + # convert matrices to numpy tensors + if isinstance(matrices, torch.Tensor): + matrices = matrices.cpu().numpy() + else: + matrices = np.asarray(matrices, dtype=float) + # iterate over env_ids + for i, intrinsic_matrix in zip(env_ids, matrices): + height, width = self.image_shape + + params = sensor_utils.convert_camera_intrinsics_to_usd( + intrinsic_matrix=intrinsic_matrix.reshape(-1), height=height, width=width, focal_length=focal_length + ) + + # change data for corresponding camera index + sensor_prim = self._sensor_prims[i] + # set parameters for camera + for param_name, param_value in params.items(): + # convert to camel case (CC) + param_name = to_camel_case(param_name, to="CC") + # get attribute from the class + param_attr = getattr(sensor_prim, f"Get{param_name}Attr") + # set value + # note: We have to do it this way because the camera might be on a different + # layer (default cameras are on session layer), and this is the simplest + # way to set the property on the right layer. + omni.usd.set_prop_val(param_attr(), param_value) + # update the internal buffers + self._update_intrinsic_matrices(env_ids) + + """ + Operations - Set pose. + """ + + def set_world_poses( + self, + positions: torch.Tensor | None = None, + orientations: torch.Tensor | None = None, + env_ids: Sequence[int] | None = None, + convention: Literal["opengl", "ros", "world"] = "ros", + ): + r"""Set the pose of the camera w.r.t. the world frame using specified convention. + + Since different fields use different conventions for camera orientations, the method allows users to + set the camera poses in the specified convention. Possible conventions are: + + - :obj:`"opengl"` - forward axis: -Z - up axis +Y - Offset is applied in the OpenGL (Usd.Camera) convention + - :obj:`"ros"` - forward axis: +Z - up axis -Y - Offset is applied in the ROS convention + - :obj:`"world"` - forward axis: +X - up axis +Z - Offset is applied in the World Frame convention + + See :meth:`isaaclab.sensors.camera.utils.convert_camera_frame_orientation_convention` for more details + on the conventions. + + Args: + positions: The cartesian coordinates (in meters). Shape is (N, 3). + Defaults to None, in which case the camera position in not changed. + orientations: The quaternion orientation in (w, x, y, z). Shape is (N, 4). + Defaults to None, in which case the camera orientation in not changed. + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + convention: The convention in which the poses are fed. Defaults to "ros". + + Raises: + RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first. + """ + # resolve env_ids + if env_ids is None: + env_ids = self._ALL_INDICES + # convert to backend tensor + if positions is not None: + if isinstance(positions, np.ndarray): + positions = torch.from_numpy(positions).to(device=self._device) + elif not isinstance(positions, torch.Tensor): + positions = torch.tensor(positions, device=self._device) + # convert rotation matrix from input convention to OpenGL + if orientations is not None: + if isinstance(orientations, np.ndarray): + orientations = torch.from_numpy(orientations).to(device=self._device) + elif not isinstance(orientations, torch.Tensor): + orientations = torch.tensor(orientations, device=self._device) + orientations = convert_camera_frame_orientation_convention(orientations, origin=convention, target="opengl") + # set the pose + self._view.set_world_poses(positions, orientations, env_ids) + + def set_world_poses_from_view( + self, eyes: torch.Tensor, targets: torch.Tensor, env_ids: Sequence[int] | None = None + ): + """Set the poses of the camera from the eye position and look-at target position. + + Args: + eyes: The positions of the camera's eye. Shape is (N, 3). + targets: The target locations to look at. Shape is (N, 3). + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + + Raises: + RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first. + NotImplementedError: If the stage up-axis is not "Y" or "Z". + """ + # resolve env_ids + if env_ids is None: + env_ids = self._ALL_INDICES + # get up axis of current stage + up_axis = UsdGeom.GetStageUpAxis(self.stage) + # set camera poses using the view + orientations = quat_from_matrix(create_rotation_matrix_from_view(eyes, targets, up_axis, device=self._device)) + self._view.set_world_poses(eyes, orientations, env_ids) + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + if not self._is_initialized: + raise RuntimeError( + "Camera could not be initialized. Please ensure --enable_cameras is used to enable rendering." + ) + # reset the timestamps + super().reset(env_ids) + # resolve None + # note: cannot do smart indexing here since we do a for loop over data. + if env_ids is None: + env_ids = self._ALL_INDICES + # reset the data + # note: this recomputation is useful if one performs events such as randomizations on the camera poses. + self._update_poses(env_ids) + # Reset the frame count + self._frame[env_ids] = 0 + + """ + Implementation. + """ + + def _initialize_impl(self): + """Initializes the sensor handles and internal buffers. + + This function creates handles and registers the provided data types with the replicator registry to + be able to access the data from the sensor. It also initializes the internal buffers to store the data. + + Raises: + RuntimeError: If the number of camera prims in the view does not match the number of environments. + RuntimeError: If replicator was not found. + """ + carb_settings_iface = carb.settings.get_settings() + if not carb_settings_iface.get("/isaaclab/cameras_enabled"): + raise RuntimeError( + "A camera was spawned without the --enable_cameras flag. Please use --enable_cameras to enable" + " rendering." + ) + + import omni.replicator.core as rep + from omni.syntheticdata.scripts.SyntheticData import SyntheticData + + # Initialize parent class + super()._initialize_impl() + # Create a view for the sensor + self._view = XformPrimView(self.cfg.prim_path, device=self._device, stage=self.stage) + # Check that sizes are correct + if self._view.count != self._num_envs: + raise RuntimeError( + f"Number of camera prims in the view ({self._view.count}) does not match" + f" the number of environments ({self._num_envs})." + ) + + # Create all env_ids buffer + self._ALL_INDICES = torch.arange(self._view.count, device=self._device, dtype=torch.long) + # Create frame count buffer + self._frame = torch.zeros(self._view.count, device=self._device, dtype=torch.long) + + # Attach the sensor data types to render node + self._render_product_paths: list[str] = list() + self._rep_registry: dict[str, list[rep.annotators.Annotator]] = {name: list() for name in self.cfg.data_types} + + # Convert all encapsulated prims to Camera + for cam_prim in self._view.prims: + # Obtain the prim path + cam_prim_path = cam_prim.GetPath().pathString + # Check if prim is a camera + if not cam_prim.IsA(UsdGeom.Camera): + raise RuntimeError(f"Prim at path '{cam_prim_path}' is not a Camera.") + # Add to list + sensor_prim = UsdGeom.Camera(cam_prim) + self._sensor_prims.append(sensor_prim) + + # Get render product + # From Isaac Sim 2023.1 onwards, render product is a HydraTexture so we need to extract the path + render_prod_path = rep.create.render_product(cam_prim_path, resolution=(self.cfg.width, self.cfg.height)) + if not isinstance(render_prod_path, str): + render_prod_path = render_prod_path.path + self._render_product_paths.append(render_prod_path) + + # Check if semantic types or semantic filter predicate is provided + if isinstance(self.cfg.semantic_filter, list): + semantic_filter_predicate = ":*; ".join(self.cfg.semantic_filter) + ":*" + elif isinstance(self.cfg.semantic_filter, str): + semantic_filter_predicate = self.cfg.semantic_filter + else: + raise ValueError(f"Semantic types must be a list or a string. Received: {self.cfg.semantic_filter}.") + # set the semantic filter predicate + # copied from rep.scripts.writes_default.basic_writer.py + SyntheticData.Get().set_instance_mapping_semantic_filter(semantic_filter_predicate) + + # Iterate over each data type and create annotator + # TODO: This will move out of the loop once Replicator supports multiple render products within a single + # annotator, i.e.: rep_annotator.attach(self._render_product_paths) + for name in self.cfg.data_types: + # note: we are verbose here to make it easier to understand the code. + # if colorize is true, the data is mapped to colors and a uint8 4 channel image is returned. + # if colorize is false, the data is returned as a uint32 image with ids as values. + if name == "semantic_segmentation": + init_params = { + "colorize": self.cfg.colorize_semantic_segmentation, + "mapping": json.dumps(self.cfg.semantic_segmentation_mapping), + } + elif name == "instance_segmentation_fast": + init_params = {"colorize": self.cfg.colorize_instance_segmentation} + elif name == "instance_id_segmentation_fast": + init_params = {"colorize": self.cfg.colorize_instance_id_segmentation} + else: + init_params = None + + # Resolve device name + if "cuda" in self._device: + device_name = self._device.split(":")[0] + else: + device_name = "cpu" + + # Map special cases to their corresponding annotator names + special_cases = {"rgba": "rgb", "depth": "distance_to_image_plane"} + # Get the annotator name, falling back to the original name if not a special case + annotator_name = special_cases.get(name, name) + # Create the annotator node + rep_annotator = rep.AnnotatorRegistry.get_annotator(annotator_name, init_params, device=device_name) + + # attach annotator to render product + rep_annotator.attach(render_prod_path) + # add to registry + self._rep_registry[name].append(rep_annotator) + + # Create internal buffers + self._create_buffers() + self._update_intrinsic_matrices(self._ALL_INDICES) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + # Increment frame count + self._frame[env_ids] += 1 + # -- pose + if self.cfg.update_latest_camera_pose: + self._update_poses(env_ids) + # -- read the data from annotator registry + # check if buffer is called for the first time. If so then, allocate the memory + if len(self._data.output) == 0: + # this is the first time buffer is called + # it allocates memory for all the sensors + self._create_annotator_data() + else: + # iterate over all the data types + for name, annotators in self._rep_registry.items(): + # iterate over all the annotators + for index in env_ids: + # get the output + output = annotators[index].get_data() + # process the output + data, info = self._process_annotator_output(name, output) + # add data to output + self._data.output[name][index] = data + # add info to output + self._data.info[index][name] = info + # NOTE: The `distance_to_camera` annotator returns the distance to the camera optical center. However, + # the replicator depth clipping is applied w.r.t. to the image plane which may result in values + # larger than the clipping range in the output. We apply an additional clipping to ensure values + # are within the clipping range for all the annotators. + if name == "distance_to_camera": + self._data.output[name][self._data.output[name] > self.cfg.spawn.clipping_range[1]] = torch.inf + # apply defined clipping behavior + if ( + name == "distance_to_camera" or name == "distance_to_image_plane" + ) and self.cfg.depth_clipping_behavior != "none": + self._data.output[name][torch.isinf(self._data.output[name])] = ( + 0.0 if self.cfg.depth_clipping_behavior == "zero" else self.cfg.spawn.clipping_range[1] + ) + + """ + Private Helpers + """ + + def _check_supported_data_types(self, cfg: CameraCfg): + """Checks if the data types are supported by the ray-caster camera.""" + # check if there is any intersection in unsupported types + # reason: these use np structured data types which we can't yet convert to torch tensor + common_elements = set(cfg.data_types) & Camera.UNSUPPORTED_TYPES + if common_elements: + # provide alternative fast counterparts + fast_common_elements = [] + for item in common_elements: + if "instance_segmentation" in item or "instance_id_segmentation" in item: + fast_common_elements.append(item + "_fast") + # raise error + raise ValueError( + f"Camera class does not support the following sensor types: {common_elements}." + "\n\tThis is because these sensor types output numpy structured data types which" + "can't be converted to torch tensors easily." + "\n\tHint: If you need to work with these sensor types, we recommend using their fast counterparts." + f"\n\t\tFast counterparts: {fast_common_elements}" + ) + + def _create_buffers(self): + """Create buffers for storing data.""" + # create the data object + # -- pose of the cameras + self._data.pos_w = torch.zeros((self._view.count, 3), device=self._device) + self._data.quat_w_world = torch.zeros((self._view.count, 4), device=self._device) + # -- intrinsic matrix + self._data.intrinsic_matrices = torch.zeros((self._view.count, 3, 3), device=self._device) + self._data.image_shape = self.image_shape + # -- output data + # lazy allocation of data dictionary + # since the size of the output data is not known in advance, we leave it as None + # the memory will be allocated when the buffer() function is called for the first time. + self._data.output = {} + self._data.info = [{name: None for name in self.cfg.data_types} for _ in range(self._view.count)] + + def _update_intrinsic_matrices(self, env_ids: Sequence[int]): + """Compute camera's matrix of intrinsic parameters. + + Also called calibration matrix. This matrix works for linear depth images. We assume square pixels. + + Note: + The calibration matrix projects points in the 3D scene onto an imaginary screen of the camera. + The coordinates of points on the image plane are in the homogeneous representation. + """ + # iterate over all cameras + for i in env_ids: + # Get corresponding sensor prim + sensor_prim = self._sensor_prims[i] + # get camera parameters + # currently rendering does not use aperture offsets or vertical aperture + focal_length = sensor_prim.GetFocalLengthAttr().Get() + horiz_aperture = sensor_prim.GetHorizontalApertureAttr().Get() + + # get viewport parameters + height, width = self.image_shape + # extract intrinsic parameters + f_x = (width * focal_length) / horiz_aperture + f_y = f_x + c_x = width * 0.5 + c_y = height * 0.5 + # create intrinsic matrix for depth linear + self._data.intrinsic_matrices[i, 0, 0] = f_x + self._data.intrinsic_matrices[i, 0, 2] = c_x + self._data.intrinsic_matrices[i, 1, 1] = f_y + self._data.intrinsic_matrices[i, 1, 2] = c_y + self._data.intrinsic_matrices[i, 2, 2] = 1 + + def _update_poses(self, env_ids: Sequence[int]): + """Computes the pose of the camera in the world frame with ROS convention. + + This methods uses the ROS convention to resolve the input pose. In this convention, + we assume that the camera front-axis is +Z-axis and up-axis is -Y-axis. + + Returns: + A tuple of the position (in meters) and quaternion (w, x, y, z). + """ + # check camera prim exists + if len(self._sensor_prims) == 0: + raise RuntimeError("Camera prim is None. Please call 'sim.play()' first.") + + # get the poses from the view + poses, quat = self._view.get_world_poses(env_ids) + self._data.pos_w[env_ids] = poses + self._data.quat_w_world[env_ids] = convert_camera_frame_orientation_convention( + quat, origin="opengl", target="world" + ) + + def _create_annotator_data(self): + """Create the buffers to store the annotator data. + + We create a buffer for each annotator and store the data in a dictionary. Since the data + shape is not known beforehand, we create a list of buffers and concatenate them later. + + This is an expensive operation and should be called only once. + """ + # add data from the annotators + for name, annotators in self._rep_registry.items(): + # create a list to store the data for each annotator + data_all_cameras = list() + # iterate over all the annotators + for index in self._ALL_INDICES: + # get the output + output = annotators[index].get_data() + # process the output + data, info = self._process_annotator_output(name, output) + # append the data + data_all_cameras.append(data) + # store the info + self._data.info[index][name] = info + # concatenate the data along the batch dimension + self._data.output[name] = torch.stack(data_all_cameras, dim=0) + # NOTE: `distance_to_camera` and `distance_to_image_plane` are not both clipped to the maximum defined + # in the clipping range. The clipping is applied only to `distance_to_image_plane` and then both + # outputs are only clipped where the values in `distance_to_image_plane` exceed the threshold. To + # have a unified behavior between all cameras, we clip both outputs to the maximum value defined. + if name == "distance_to_camera": + self._data.output[name][self._data.output[name] > self.cfg.spawn.clipping_range[1]] = torch.inf + # clip the data if needed + if ( + name == "distance_to_camera" or name == "distance_to_image_plane" + ) and self.cfg.depth_clipping_behavior != "none": + self._data.output[name][torch.isinf(self._data.output[name])] = ( + 0.0 if self.cfg.depth_clipping_behavior == "zero" else self.cfg.spawn.clipping_range[1] + ) + + def _process_annotator_output(self, name: str, output: Any) -> tuple[torch.tensor, dict | None]: + """Process the annotator output. + + This function is called after the data has been collected from all the cameras. + """ + # extract info and data from the output + if isinstance(output, dict): + data = output["data"] + info = output["info"] + else: + data = output + info = None + # convert data into torch tensor + data = convert_to_torch(data, device=self.device) + + # process data for different segmentation types + # Note: Replicator returns raw buffers of dtype int32 for segmentation types + # so we need to convert them to uint8 4 channel images for colorized types + height, width = self.image_shape + if name == "semantic_segmentation": + if self.cfg.colorize_semantic_segmentation: + data = data.view(torch.uint8).reshape(height, width, -1) + else: + data = data.view(height, width, 1) + elif name == "instance_segmentation_fast": + if self.cfg.colorize_instance_segmentation: + data = data.view(torch.uint8).reshape(height, width, -1) + else: + data = data.view(height, width, 1) + elif name == "instance_id_segmentation_fast": + if self.cfg.colorize_instance_id_segmentation: + data = data.view(torch.uint8).reshape(height, width, -1) + else: + data = data.view(height, width, 1) + # make sure buffer dimensions are consistent as (H, W, C) + elif name == "distance_to_camera" or name == "distance_to_image_plane" or name == "depth": + data = data.view(height, width, 1) + # we only return the RGB channels from the RGBA output if rgb is required + # normals return (x, y, z) in first 3 channels, 4th channel is unused + elif name == "rgb" or name == "normals": + data = data[..., :3] + # motion vectors return (x, y) in first 2 channels, 3rd and 4th channels are unused + elif name == "motion_vectors": + data = data[..., :2] + + # return the data and info + return data, info + + """ + Internal simulation callbacks. + """ + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + # call parent + super()._invalidate_initialize_callback(event) + # set all existing views to None to invalidate them + self._view = None diff --git a/source/isaaclab/isaaclab/sensors/camera/camera_cfg.py b/source/isaaclab/isaaclab/sensors/camera/camera_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8fd9f307d180c4baae6dbdd9bb31c3978a0c0045 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/camera_cfg.py @@ -0,0 +1,143 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.sim import FisheyeCameraCfg, PinholeCameraCfg +from isaaclab.utils import configclass + +from ..sensor_base_cfg import SensorBaseCfg +from .camera import Camera + + +@configclass +class CameraCfg(SensorBaseCfg): + """Configuration for a camera sensor.""" + + @configclass + class OffsetCfg: + """The offset pose of the sensor's frame from the sensor's parent frame.""" + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation (w, x, y, z) w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + convention: Literal["opengl", "ros", "world"] = "ros" + """The convention in which the frame offset is applied. Defaults to "ros". + + - ``"opengl"`` - forward axis: ``-Z`` - up axis: ``+Y`` - Offset is applied in the OpenGL (Usd.Camera) + convention. + - ``"ros"`` - forward axis: ``+Z`` - up axis: ``-Y`` - Offset is applied in the ROS convention. + - ``"world"`` - forward axis: ``+X`` - up axis: ``+Z`` - Offset is applied in the World Frame convention. + + """ + + class_type: type = Camera + + offset: OffsetCfg = OffsetCfg() + """The offset pose of the sensor's frame from the sensor's parent frame. Defaults to identity. + + Note: + The parent frame is the frame the sensor attaches to. For example, the parent frame of a + camera at path ``/World/envs/env_0/Robot/Camera`` is ``/World/envs/env_0/Robot``. + """ + + spawn: PinholeCameraCfg | FisheyeCameraCfg | None = MISSING + """Spawn configuration for the asset. + + If None, then the prim is not spawned by the asset. Instead, it is assumed that the + asset is already present in the scene. + """ + + depth_clipping_behavior: Literal["max", "zero", "none"] = "none" + """Clipping behavior for the camera for values exceed the maximum value. Defaults to "none". + + - ``"max"``: Values are clipped to the maximum value. + - ``"zero"``: Values are clipped to zero. + - ``"none``: No clipping is applied. Values will be returned as ``inf``. + """ + + data_types: list[str] = ["rgb"] + """List of sensor names/types to enable for the camera. Defaults to ["rgb"]. + + Please refer to the :class:`Camera` class for a list of available data types. + """ + + width: int = MISSING + """Width of the image in pixels.""" + + height: int = MISSING + """Height of the image in pixels.""" + + update_latest_camera_pose: bool = False + """Whether to update the latest camera pose when fetching the camera's data. Defaults to False. + + If True, the latest camera pose is updated in the camera's data which will slow down performance + due to the use of :class:`XformPrimView`. + If False, the pose of the camera during initialization is returned. + """ + + semantic_filter: str | list[str] = "*:*" + """A string or a list specifying a semantic filter predicate. Defaults to ``"*:*"``. + + If a string, it should be a disjunctive normal form of (semantic type, labels). For examples: + + * ``"typeA : labelA & !labelB | labelC , typeB: labelA ; typeC: labelE"``: + All prims with semantic type "typeA" and label "labelA" but not "labelB" or with label "labelC". + Also, all prims with semantic type "typeB" and label "labelA", or with semantic type "typeC" and label "labelE". + * ``"typeA : * ; * : labelA"``: All prims with semantic type "typeA" or with label "labelA" + + If a list of strings, each string should be a semantic type. The segmentation for prims with + semantics of the specified types will be retrieved. For example, if the list is ["class"], only + the segmentation for prims with semantics of type "class" will be retrieved. + + .. seealso:: + + For more information on the semantics filter, see the documentation on `Replicator Semantics Schema Editor`_. + + .. _Replicator Semantics Schema Editor: https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/semantics_schema_editor.html#semantics-filtering + """ + + colorize_semantic_segmentation: bool = True + """Whether to colorize the semantic segmentation images. Defaults to True. + + If True, semantic segmentation is converted to an image where semantic IDs are mapped to colors + and returned as a ``uint8`` 4-channel array. If False, the output is returned as a ``int32`` array. + """ + + colorize_instance_id_segmentation: bool = True + """Whether to colorize the instance ID segmentation images. Defaults to True. + + If True, instance id segmentation is converted to an image where instance IDs are mapped to colors. + and returned as a ``uint8`` 4-channel array. If False, the output is returned as a ``int32`` array. + """ + + colorize_instance_segmentation: bool = True + """Whether to colorize the instance ID segmentation images. Defaults to True. + + If True, instance segmentation is converted to an image where instance IDs are mapped to colors. + and returned as a ``uint8`` 4-channel array. If False, the output is returned as a ``int32`` array. + """ + + semantic_segmentation_mapping: dict = {} + """Dictionary mapping semantics to specific colours + + Eg. + + .. code-block:: python + + { + "class:cube_1": (255, 36, 66, 255), + "class:cube_2": (255, 184, 48, 255), + "class:cube_3": (55, 255, 139, 255), + "class:table": (255, 237, 218, 255), + "class:ground": (100, 100, 100, 255), + "class:robot": (61, 178, 255, 255), + } + + """ diff --git a/source/isaaclab/isaaclab/sensors/camera/camera_data.py b/source/isaaclab/isaaclab/sensors/camera/camera_data.py new file mode 100644 index 0000000000000000000000000000000000000000..ec3288b04e926ad3bc6807ecf66040febc6941ea --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/camera_data.py @@ -0,0 +1,92 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import dataclass +from typing import Any + +import torch + +from isaaclab.utils.math import convert_camera_frame_orientation_convention + + +@dataclass +class CameraData: + """Data container for the camera sensor.""" + + ## + # Frame state. + ## + + pos_w: torch.Tensor = None + """Position of the sensor origin in world frame, following ROS convention. + + Shape is (N, 3) where N is the number of sensors. + """ + + quat_w_world: torch.Tensor = None + """Quaternion orientation `(w, x, y, z)` of the sensor origin in world frame, following the world coordinate frame + + .. note:: + World frame convention follows the camera aligned with forward axis +X and up axis +Z. + + Shape is (N, 4) where N is the number of sensors. + """ + + ## + # Camera data + ## + + image_shape: tuple[int, int] = None + """A tuple containing (height, width) of the camera sensor.""" + + intrinsic_matrices: torch.Tensor = None + """The intrinsic matrices for the camera. + + Shape is (N, 3, 3) where N is the number of sensors. + """ + + output: dict[str, torch.Tensor] = None + """The retrieved sensor data with sensor types as key. + + The format of the data is available in the `Replicator Documentation`_. For semantic-based data, + this corresponds to the ``"data"`` key in the output of the sensor. + + .. _Replicator Documentation: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/annotators_details.html#annotator-output + """ + + info: list[dict[str, Any]] = None + """The retrieved sensor info with sensor types as key. + + This contains extra information provided by the sensor such as semantic segmentation label mapping, prim paths. + For semantic-based data, this corresponds to the ``"info"`` key in the output of the sensor. For other sensor + types, the info is empty. + """ + + ## + # Additional Frame orientation conventions + ## + + @property + def quat_w_ros(self) -> torch.Tensor: + """Quaternion orientation `(w, x, y, z)` of the sensor origin in the world frame, following ROS convention. + + .. note:: + ROS convention follows the camera aligned with forward axis +Z and up axis -Y. + + Shape is (N, 4) where N is the number of sensors. + """ + return convert_camera_frame_orientation_convention(self.quat_w_world, origin="world", target="ros") + + @property + def quat_w_opengl(self) -> torch.Tensor: + """Quaternion orientation `(w, x, y, z)` of the sensor origin in the world frame, following + Opengl / USD Camera convention. + + .. note:: + OpenGL convention follows the camera aligned with forward axis -Z and up axis +Y. + + Shape is (N, 4) where N is the number of sensors. + """ + return convert_camera_frame_orientation_convention(self.quat_w_world, origin="world", target="opengl") diff --git a/source/isaaclab/isaaclab/sensors/camera/tiled_camera.py b/source/isaaclab/isaaclab/sensors/camera/tiled_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..4b3676158e757634418a8afa06eab6d56d28ab96 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/tiled_camera.py @@ -0,0 +1,425 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import json +import math +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +import numpy as np +import torch +import warp as wp + +import carb +from pxr import UsdGeom + +from isaaclab.sim.views import XformPrimView +from isaaclab.utils.warp.kernels import reshape_tiled_image + +from ..sensor_base import SensorBase +from .camera import Camera + +if TYPE_CHECKING: + from .tiled_camera_cfg import TiledCameraCfg + + +class TiledCamera(Camera): + r"""The tiled rendering based camera sensor for acquiring the same data as the Camera class. + + This class inherits from the :class:`Camera` class but uses the tiled-rendering API to acquire + the visual data. Tiled-rendering concatenates the rendered images from multiple cameras into a single image. + This allows for rendering multiple cameras in parallel and is useful for rendering large scenes with multiple + cameras efficiently. + + The following sensor types are supported: + + - ``"rgb"``: A 3-channel rendered color image. + - ``"rgba"``: A 4-channel rendered color image with alpha channel. + - ``"distance_to_camera"``: An image containing the distance to camera optical center. + - ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis. + - ``"depth"``: Alias for ``"distance_to_image_plane"``. + - ``"normals"``: An image containing the local surface normal vectors at each pixel. + - ``"motion_vectors"``: An image containing the motion vector data at each pixel. + - ``"semantic_segmentation"``: The semantic segmentation data. + - ``"instance_segmentation_fast"``: The instance segmentation data. + - ``"instance_id_segmentation_fast"``: The instance id segmentation data. + + .. note:: + Currently the following sensor types are not supported in a "view" format: + + - ``"instance_segmentation"``: The instance segmentation data. Please use the fast counterparts instead. + - ``"instance_id_segmentation"``: The instance id segmentation data. Please use the fast counterparts instead. + - ``"bounding_box_2d_tight"``: The tight 2D bounding box data (only contains non-occluded regions). + - ``"bounding_box_2d_tight_fast"``: The tight 2D bounding box data (only contains non-occluded regions). + - ``"bounding_box_2d_loose"``: The loose 2D bounding box data (contains occluded regions). + - ``"bounding_box_2d_loose_fast"``: The loose 2D bounding box data (contains occluded regions). + - ``"bounding_box_3d"``: The 3D view space bounding box data. + - ``"bounding_box_3d_fast"``: The 3D view space bounding box data. + + .. _replicator extension: https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/annotators_details.html#annotator-output + .. _USDGeom Camera: https://graphics.pixar.com/usd/docs/api/class_usd_geom_camera.html + + .. versionadded:: v1.0.0 + + This feature is available starting from Isaac Sim 4.2. Before this version, the tiled rendering APIs + were not available. + + """ + + cfg: TiledCameraCfg + """The configuration parameters.""" + + def __init__(self, cfg: TiledCameraCfg): + """Initializes the tiled camera sensor. + + Args: + cfg: The configuration parameters. + + Raises: + RuntimeError: If no camera prim is found at the given path. + ValueError: If the provided data types are not supported by the camera. + """ + super().__init__(cfg) + + def __del__(self): + """Unsubscribes from callbacks and detach from the replicator registry.""" + # unsubscribe from callbacks + SensorBase.__del__(self) + # detach from the replicator registry + for annotator in self._annotators.values(): + annotator.detach(self.render_product_paths) + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + # message for class + return ( + f"Tiled Camera @ '{self.cfg.prim_path}': \n" + f"\tdata types : {list(self.data.output.keys())} \n" + f"\tsemantic filter : {self.cfg.semantic_filter}\n" + f"\tcolorize semantic segm. : {self.cfg.colorize_semantic_segmentation}\n" + f"\tcolorize instance segm. : {self.cfg.colorize_instance_segmentation}\n" + f"\tcolorize instance id segm.: {self.cfg.colorize_instance_id_segmentation}\n" + f"\tupdate period (s): {self.cfg.update_period}\n" + f"\tshape : {self.image_shape}\n" + f"\tnumber of sensors : {self._view.count}" + ) + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + if not self._is_initialized: + raise RuntimeError( + "TiledCamera could not be initialized. Please ensure --enable_cameras is used to enable rendering." + ) + # reset the timestamps + SensorBase.reset(self, env_ids) + # resolve None + if env_ids is None: + env_ids = slice(None) + # reset the frame count + self._frame[env_ids] = 0 + + """ + Implementation. + """ + + def _initialize_impl(self): + """Initializes the sensor handles and internal buffers. + + This function creates handles and registers the provided data types with the replicator registry to + be able to access the data from the sensor. It also initializes the internal buffers to store the data. + + Raises: + RuntimeError: If the number of camera prims in the view does not match the number of environments. + RuntimeError: If replicator was not found. + """ + carb_settings_iface = carb.settings.get_settings() + if not carb_settings_iface.get("/isaaclab/cameras_enabled"): + raise RuntimeError( + "A camera was spawned without the --enable_cameras flag. Please use --enable_cameras to enable" + " rendering." + ) + + import omni.replicator.core as rep + + # Initialize parent class + SensorBase._initialize_impl(self) + # Create a view for the sensor + self._view = XformPrimView(self.cfg.prim_path, device=self._device, stage=self.stage) + # Check that sizes are correct + if self._view.count != self._num_envs: + raise RuntimeError( + f"Number of camera prims in the view ({self._view.count}) does not match" + f" the number of environments ({self._num_envs})." + ) + + # Create all env_ids buffer + self._ALL_INDICES = torch.arange(self._view.count, device=self._device, dtype=torch.long) + # Create frame count buffer + self._frame = torch.zeros(self._view.count, device=self._device, dtype=torch.long) + + # Convert all encapsulated prims to Camera + cam_prim_paths = [] + for cam_prim in self._view.prims: + # Get camera prim + cam_prim_path = cam_prim.GetPath().pathString + # Check if prim is a camera + if not cam_prim.IsA(UsdGeom.Camera): + raise RuntimeError(f"Prim at path '{cam_prim_path}' is not a Camera.") + # Add to list + self._sensor_prims.append(UsdGeom.Camera(cam_prim)) + cam_prim_paths.append(cam_prim_path) + + # Create replicator tiled render product + rp = rep.create.render_product_tiled(cameras=cam_prim_paths, tile_resolution=(self.cfg.width, self.cfg.height)) + self._render_product_paths = [rp.path] + + # Define the annotators based on requested data types + self._annotators = dict() + for annotator_type in self.cfg.data_types: + if annotator_type == "rgba" or annotator_type == "rgb": + annotator = rep.AnnotatorRegistry.get_annotator("rgb", device=self.device, do_array_copy=False) + self._annotators["rgba"] = annotator + elif annotator_type == "depth" or annotator_type == "distance_to_image_plane": + # keep depth for backwards compatibility + annotator = rep.AnnotatorRegistry.get_annotator( + "distance_to_image_plane", device=self.device, do_array_copy=False + ) + self._annotators[annotator_type] = annotator + # note: we are verbose here to make it easier to understand the code. + # if colorize is true, the data is mapped to colors and a uint8 4 channel image is returned. + # if colorize is false, the data is returned as a uint32 image with ids as values. + else: + init_params = None + if annotator_type == "semantic_segmentation": + init_params = { + "colorize": self.cfg.colorize_semantic_segmentation, + "mapping": json.dumps(self.cfg.semantic_segmentation_mapping), + } + elif annotator_type == "instance_segmentation_fast": + init_params = {"colorize": self.cfg.colorize_instance_segmentation} + elif annotator_type == "instance_id_segmentation_fast": + init_params = {"colorize": self.cfg.colorize_instance_id_segmentation} + + annotator = rep.AnnotatorRegistry.get_annotator( + annotator_type, init_params, device=self.device, do_array_copy=False + ) + self._annotators[annotator_type] = annotator + + # Attach the annotator to the render product + for annotator in self._annotators.values(): + annotator.attach(self._render_product_paths) + + # Create internal buffers + self._create_buffers() + + def _update_buffers_impl(self, env_ids: Sequence[int]): + # Increment frame count + self._frame[env_ids] += 1 + + # update latest camera pose + if self.cfg.update_latest_camera_pose: + self._update_poses(env_ids) + + # Extract the flattened image buffer + for data_type, annotator in self._annotators.items(): + # check whether returned data is a dict (used for segmentation) + output = annotator.get_data() + if isinstance(output, dict): + tiled_data_buffer = output["data"] + self._data.info[data_type] = output["info"] + else: + tiled_data_buffer = output + + # convert data buffer to warp array + if isinstance(tiled_data_buffer, np.ndarray): + # Let warp infer the dtype from numpy array instead of hardcoding uint8 + # Different annotators return different dtypes: RGB(uint8), depth(float32), segmentation(uint32) + tiled_data_buffer = wp.array(tiled_data_buffer, device=self.device) + else: + tiled_data_buffer = tiled_data_buffer.to(device=self.device) + + # process data for different segmentation types + # Note: Replicator returns raw buffers of dtype uint32 for segmentation types + # so we need to convert them to uint8 4 channel images for colorized types + if ( + (data_type == "semantic_segmentation" and self.cfg.colorize_semantic_segmentation) + or (data_type == "instance_segmentation_fast" and self.cfg.colorize_instance_segmentation) + or (data_type == "instance_id_segmentation_fast" and self.cfg.colorize_instance_id_segmentation) + ): + tiled_data_buffer = wp.array( + ptr=tiled_data_buffer.ptr, shape=(*tiled_data_buffer.shape, 4), dtype=wp.uint8, device=self.device + ) + + # For motion vectors, we only require the first two channels of the tiled buffer + # Note: Not doing this breaks the alignment of the data (check: https://github.com/isaac-sim/IsaacLab/issues/2003) + if data_type == "motion_vectors": + tiled_data_buffer = tiled_data_buffer[:, :, :2].contiguous() + + # For normals, we only require the first three channels of the tiled buffer + # Note: Not doing this breaks the alignment of the data (check: https://github.com/isaac-sim/IsaacLab/issues/4239) + if data_type == "normals": + tiled_data_buffer = tiled_data_buffer[:, :, :3].contiguous() + + wp.launch( + kernel=reshape_tiled_image, + dim=(self._view.count, self.cfg.height, self.cfg.width), + inputs=[ + tiled_data_buffer.flatten(), + wp.from_torch(self._data.output[data_type]), # zero-copy alias + *list(self._data.output[data_type].shape[1:]), # height, width, num_channels + self._tiling_grid_shape()[0], # num_tiles_x + ], + device=self.device, + ) + + # alias rgb as first 3 channels of rgba + if data_type == "rgba" and "rgb" in self.cfg.data_types: + self._data.output["rgb"] = self._data.output["rgba"][..., :3] + + # NOTE: The `distance_to_camera` annotator returns the distance to the camera optical center. However, + # the replicator depth clipping is applied w.r.t. to the image plane which may result in values + # larger than the clipping range in the output. We apply an additional clipping to ensure values + # are within the clipping range for all the annotators. + if data_type == "distance_to_camera": + self._data.output[data_type][self._data.output[data_type] > self.cfg.spawn.clipping_range[1]] = ( + torch.inf + ) + # apply defined clipping behavior + if ( + data_type == "distance_to_camera" or data_type == "distance_to_image_plane" or data_type == "depth" + ) and self.cfg.depth_clipping_behavior != "none": + self._data.output[data_type][torch.isinf(self._data.output[data_type])] = ( + 0.0 if self.cfg.depth_clipping_behavior == "zero" else self.cfg.spawn.clipping_range[1] + ) + + """ + Private Helpers + """ + + def _check_supported_data_types(self, cfg: TiledCameraCfg): + """Checks if the data types are supported by the ray-caster camera.""" + # check if there is any intersection in unsupported types + # reason: these use np structured data types which we can't yet convert to torch tensor + common_elements = set(cfg.data_types) & Camera.UNSUPPORTED_TYPES + if common_elements: + # provide alternative fast counterparts + fast_common_elements = [] + for item in common_elements: + if "instance_segmentation" in item or "instance_id_segmentation" in item: + fast_common_elements.append(item + "_fast") + # raise error + raise ValueError( + f"TiledCamera class does not support the following sensor types: {common_elements}." + "\n\tThis is because these sensor types output numpy structured data types which" + "can't be converted to torch tensors easily." + "\n\tHint: If you need to work with these sensor types, we recommend using their fast counterparts." + f"\n\t\tFast counterparts: {fast_common_elements}" + ) + + def _create_buffers(self): + """Create buffers for storing data.""" + # create the data object + # -- pose of the cameras + self._data.pos_w = torch.zeros((self._view.count, 3), device=self._device) + self._data.quat_w_world = torch.zeros((self._view.count, 4), device=self._device) + self._update_poses(self._ALL_INDICES) + # -- intrinsic matrix + self._data.intrinsic_matrices = torch.zeros((self._view.count, 3, 3), device=self._device) + self._update_intrinsic_matrices(self._ALL_INDICES) + self._data.image_shape = self.image_shape + # -- output data + data_dict = dict() + if "rgba" in self.cfg.data_types or "rgb" in self.cfg.data_types: + data_dict["rgba"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8 + ).contiguous() + if "rgb" in self.cfg.data_types: + # RGB is the first 3 channels of RGBA + data_dict["rgb"] = data_dict["rgba"][..., :3] + if "distance_to_image_plane" in self.cfg.data_types: + data_dict["distance_to_image_plane"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32 + ).contiguous() + if "depth" in self.cfg.data_types: + data_dict["depth"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32 + ).contiguous() + if "distance_to_camera" in self.cfg.data_types: + data_dict["distance_to_camera"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32 + ).contiguous() + if "normals" in self.cfg.data_types: + data_dict["normals"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 3), device=self.device, dtype=torch.float32 + ).contiguous() + if "motion_vectors" in self.cfg.data_types: + data_dict["motion_vectors"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 2), device=self.device, dtype=torch.float32 + ).contiguous() + if "semantic_segmentation" in self.cfg.data_types: + if self.cfg.colorize_semantic_segmentation: + data_dict["semantic_segmentation"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8 + ).contiguous() + else: + data_dict["semantic_segmentation"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32 + ).contiguous() + if "instance_segmentation_fast" in self.cfg.data_types: + if self.cfg.colorize_instance_segmentation: + data_dict["instance_segmentation_fast"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8 + ).contiguous() + else: + data_dict["instance_segmentation_fast"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32 + ).contiguous() + if "instance_id_segmentation_fast" in self.cfg.data_types: + if self.cfg.colorize_instance_id_segmentation: + data_dict["instance_id_segmentation_fast"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8 + ).contiguous() + else: + data_dict["instance_id_segmentation_fast"] = torch.zeros( + (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32 + ).contiguous() + + self._data.output = data_dict + self._data.info = dict() + + def _tiled_image_shape(self) -> tuple[int, int]: + """Returns a tuple containing the dimension of the tiled image.""" + cols, rows = self._tiling_grid_shape() + return (self.cfg.width * cols, self.cfg.height * rows) + + def _tiling_grid_shape(self) -> tuple[int, int]: + """Returns a tuple containing the tiling grid dimension.""" + cols = math.ceil(math.sqrt(self._view.count)) + rows = math.ceil(self._view.count / cols) + return (cols, rows) + + def _create_annotator_data(self): + # we do not need to create annotator data for the tiled camera sensor + raise RuntimeError("This function should not be called for the tiled camera sensor.") + + def _process_annotator_output(self, name: str, output: Any) -> tuple[torch.tensor, dict | None]: + # we do not need to process annotator output for the tiled camera sensor + raise RuntimeError("This function should not be called for the tiled camera sensor.") + + """ + Internal simulation callbacks. + """ + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + # call parent + super()._invalidate_initialize_callback(event) + # set all existing views to None to invalidate them + self._view = None diff --git a/source/isaaclab/isaaclab/sensors/camera/tiled_camera_cfg.py b/source/isaaclab/isaaclab/sensors/camera/tiled_camera_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2241a0648fd2f7e4619e3563ead8c3559933b584 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/tiled_camera_cfg.py @@ -0,0 +1,16 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .camera_cfg import CameraCfg +from .tiled_camera import TiledCamera + + +@configclass +class TiledCameraCfg(CameraCfg): + """Configuration for a tiled rendering-based camera sensor.""" + + class_type: type = TiledCamera diff --git a/source/isaaclab/isaaclab/sensors/camera/utils.py b/source/isaaclab/isaaclab/sensors/camera/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f9db81551b4fef8be7007da15fdeba1d0270a9dd --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/camera/utils.py @@ -0,0 +1,272 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Helper functions to project between pointcloud and depth images.""" + +# needed to import for allowing type-hinting: torch.device | str | None +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np +import torch +import warp as wp + +import isaaclab.utils.math as math_utils +from isaaclab.utils.array import TensorData, convert_to_torch + +""" +Depth <-> Pointcloud conversions. +""" + + +def transform_points( + points: TensorData, + position: Sequence[float] | None = None, + orientation: Sequence[float] | None = None, + device: torch.device | str | None = None, +) -> np.ndarray | torch.Tensor: + r"""Transform input points in a given frame to a target frame. + + This function transform points from a source frame to a target frame. The transformation is defined by the + position ``t`` and orientation ``R`` of the target frame in the source frame. + + .. math:: + p_{target} = R_{target} \times p_{source} + t_{target} + + If either the inputs `position` and `orientation` are None, the corresponding transformation is not applied. + + Args: + points: a tensor of shape (p, 3) or (n, p, 3) comprising of 3d points in source frame. + position: The position of source frame in target frame. Defaults to None. + orientation: The orientation (w, x, y, z) of source frame in target frame. + Defaults to None. + device: The device for torch where the computation + should be executed. Defaults to None, i.e. takes the device that matches the depth image. + + Returns: + A tensor of shape (N, 3) comprising of 3D points in target frame. + If the input is a numpy array, the output is a numpy array. Otherwise, it is a torch tensor. + """ + # check if numpy + is_numpy = isinstance(points, np.ndarray) + # decide device + if device is None and is_numpy: + device = torch.device("cpu") + # convert to torch + points = convert_to_torch(points, dtype=torch.float32, device=device) + # update the device with the device of the depth image + # note: this is needed since warp does not provide the device directly + device = points.device + # apply rotation + if orientation is not None: + orientation = convert_to_torch(orientation, dtype=torch.float32, device=device) + # apply translation + if position is not None: + position = convert_to_torch(position, dtype=torch.float32, device=device) + # apply transformation + points = math_utils.transform_points(points, position, orientation) + + # return everything according to input type + if is_numpy: + return points.detach().cpu().numpy() + else: + return points + + +def create_pointcloud_from_depth( + intrinsic_matrix: np.ndarray | torch.Tensor | wp.array, + depth: np.ndarray | torch.Tensor | wp.array, + keep_invalid: bool = False, + position: Sequence[float] | None = None, + orientation: Sequence[float] | None = None, + device: torch.device | str | None = None, +) -> np.ndarray | torch.Tensor: + r"""Creates pointcloud from input depth image and camera intrinsic matrix. + + This function creates a pointcloud from a depth image and camera intrinsic matrix. The pointcloud is + computed using the following equation: + + .. math:: + p_{camera} = K^{-1} \times [u, v, 1]^T \times d + + where :math:`K` is the camera intrinsic matrix, :math:`u` and :math:`v` are the pixel coordinates and + :math:`d` is the depth value at the pixel. + + Additionally, the pointcloud can be transformed from the camera frame to a target frame by providing + the position ``t`` and orientation ``R`` of the camera in the target frame: + + .. math:: + p_{target} = R_{target} \times p_{camera} + t_{target} + + Args: + intrinsic_matrix: A (3, 3) array providing camera's calibration matrix. + depth: An array of shape (H, W) with values encoding the depth measurement. + keep_invalid: Whether to keep invalid points in the cloud or not. Invalid points + correspond to pixels with depth values 0.0 or NaN. Defaults to False. + position: The position of the camera in a target frame. Defaults to None. + orientation: The orientation (w, x, y, z) of the camera in a target frame. Defaults to None. + device: The device for torch where the computation should be executed. + Defaults to None, i.e. takes the device that matches the depth image. + + Returns: + An array/tensor of shape (N, 3) comprising of 3D coordinates of points. + The returned datatype is torch if input depth is of type torch.tensor or wp.array. Otherwise, a np.ndarray + is returned. + """ + # We use PyTorch here for matrix multiplication since it is compiled with Intel MKL while numpy + # by default uses OpenBLAS. With PyTorch (CPU), we could process a depth image of size (480, 640) + # in 0.0051 secs, while with numpy it took 0.0292 secs. + + # convert to numpy matrix + is_numpy = isinstance(depth, np.ndarray) + # decide device + if device is None and is_numpy: + device = torch.device("cpu") + # convert depth to torch tensor + depth = convert_to_torch(depth, dtype=torch.float32, device=device) + # update the device with the device of the depth image + # note: this is needed since warp does not provide the device directly + device = depth.device + # convert inputs to torch tensors + intrinsic_matrix = convert_to_torch(intrinsic_matrix, dtype=torch.float32, device=device) + if position is not None: + position = convert_to_torch(position, dtype=torch.float32, device=device) + if orientation is not None: + orientation = convert_to_torch(orientation, dtype=torch.float32, device=device) + # compute pointcloud + depth_cloud = math_utils.unproject_depth(depth, intrinsic_matrix) + # convert 3D points to world frame + depth_cloud = math_utils.transform_points(depth_cloud, position, orientation) + + # keep only valid entries if flag is set + if not keep_invalid: + pts_idx_to_keep = torch.all(torch.logical_and(~torch.isnan(depth_cloud), ~torch.isinf(depth_cloud)), dim=1) + depth_cloud = depth_cloud[pts_idx_to_keep, ...] + + # return everything according to input type + if is_numpy: + return depth_cloud.detach().cpu().numpy() + else: + return depth_cloud + + +def create_pointcloud_from_rgbd( + intrinsic_matrix: torch.Tensor | np.ndarray | wp.array, + depth: torch.Tensor | np.ndarray | wp.array, + rgb: torch.Tensor | wp.array | np.ndarray | tuple[float, float, float] = None, + normalize_rgb: bool = False, + position: Sequence[float] | None = None, + orientation: Sequence[float] | None = None, + device: torch.device | str | None = None, + num_channels: int = 3, +) -> tuple[torch.Tensor, torch.Tensor] | tuple[np.ndarray, np.ndarray]: + """Creates pointcloud from input depth image and camera transformation matrix. + + This function provides the same functionality as :meth:`create_pointcloud_from_depth` but also allows + to provide the RGB values for each point. + + The ``rgb`` attribute is used to resolve the corresponding point's color: + + - If a ``np.array``/``wp.array``/``torch.tensor`` of shape (H, W, 3), then the corresponding channels + encode the RGB values. + - If a tuple, then the point cloud has a single color specified by the values (r, g, b). + - If None, then default color is white, i.e. (0, 0, 0). + + If the input ``normalize_rgb`` is set to :obj:`True`, then the RGB values are normalized to be in the range [0, 1]. + + Args: + intrinsic_matrix: A (3, 3) array/tensor providing camera's calibration matrix. + depth: An array/tensor of shape (H, W) with values encoding the depth measurement. + rgb: Color for generated point cloud. Defaults to None. + normalize_rgb: Whether to normalize input rgb. Defaults to False. + position: The position of the camera in a target frame. Defaults to None. + orientation: The orientation `(w, x, y, z)` of the camera in a target frame. Defaults to None. + device: The device for torch where the computation should be executed. Defaults to None, in which case + it takes the device that matches the depth image. + num_channels: Number of channels in RGB pointcloud. Defaults to 3. + + Returns: + A tuple of (N, 3) arrays or tensors containing the 3D coordinates of points and their RGB color respectively. + The returned datatype is torch if input depth is of type torch.tensor or wp.array. Otherwise, a np.ndarray + is returned. + + Raises: + ValueError: When rgb image is a numpy array but not of shape (H, W, 3) or (H, W, 4). + """ + # check valid inputs + if rgb is not None and not isinstance(rgb, tuple): + if len(rgb.shape) == 3: + if rgb.shape[2] not in [3, 4]: + raise ValueError(f"Input rgb image of invalid shape: {rgb.shape} != (H, W, 3) or (H, W, 4).") + else: + raise ValueError(f"Input rgb image not three-dimensional. Received shape: {rgb.shape}.") + if num_channels not in [3, 4]: + raise ValueError(f"Invalid number of channels: {num_channels} != 3 or 4.") + + # check if input depth is numpy array + is_numpy = isinstance(depth, np.ndarray) + # decide device + if device is None and is_numpy: + device = torch.device("cpu") + # convert depth to torch tensor + if is_numpy: + depth = torch.from_numpy(depth).to(device=device) + # retrieve XYZ pointcloud + points_xyz = create_pointcloud_from_depth(intrinsic_matrix, depth, True, position, orientation, device=device) + + # get image height and width + im_height, im_width = depth.shape[:2] + # total number of points + num_points = im_height * im_width + # extract color value + if rgb is not None: + if isinstance(rgb, (np.ndarray, torch.Tensor, wp.array)): + # copy numpy array to preserve + rgb = convert_to_torch(rgb, device=device, dtype=torch.float32) + rgb = rgb[:, :, :3] + # convert the matrix to (W, H, 3) from (H, W, 3) since depth processing + # is done in the order (u, v) where u: (0, W-1) and v: (0 - H-1) + points_rgb = rgb.permute(1, 0, 2).reshape(-1, 3) + elif isinstance(rgb, (tuple, list)): + # same color for all points + points_rgb = torch.Tensor((rgb,) * num_points, device=device, dtype=torch.uint8) + else: + # default color is white + points_rgb = torch.Tensor(((0, 0, 0),) * num_points, device=device, dtype=torch.uint8) + else: + points_rgb = torch.Tensor(((0, 0, 0),) * num_points, device=device, dtype=torch.uint8) + # normalize color values + if normalize_rgb: + points_rgb = points_rgb.float() / 255 + + # remove invalid points + pts_idx_to_keep = torch.all(torch.logical_and(~torch.isnan(points_xyz), ~torch.isinf(points_xyz)), dim=1) + points_rgb = points_rgb[pts_idx_to_keep, ...] + points_xyz = points_xyz[pts_idx_to_keep, ...] + + # add additional channels if required + if num_channels == 4: + points_rgb = torch.nn.functional.pad(points_rgb, (0, 1), mode="constant", value=1.0) + + # return everything according to input type + if is_numpy: + return points_xyz.cpu().numpy(), points_rgb.cpu().numpy() + else: + return points_xyz, points_rgb + + +def save_images_to_file(images: torch.Tensor, file_path: str): + """Save images to file. + + Args: + images: A tensor of shape (N, H, W, C) containing the images. + file_path: The path to save the images to. + """ + from torchvision.utils import make_grid, save_image + + save_image( + make_grid(torch.swapaxes(images.unsqueeze(1), 1, -1).squeeze(-1), nrow=round(images.shape[0] ** 0.5)), file_path + ) diff --git a/source/isaaclab/isaaclab/sensors/contact_sensor/__init__.py b/source/isaaclab/isaaclab/sensors/contact_sensor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94b402d41a3754bba304c475fe23700d88d18565 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/contact_sensor/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for rigid contact sensor.""" + +from .contact_sensor import ContactSensor +from .contact_sensor_cfg import ContactSensorCfg +from .contact_sensor_data import ContactSensorData diff --git a/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor.py b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..2a85a2661f6acdfd96ba18745a688f714b8c6752 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor.py @@ -0,0 +1,539 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Ignore optional memory usage warning globally +# pyright: reportOptionalSubscript=false + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import carb +import omni.physics.tensors.impl.api as physx +from isaacsim.core.simulation_manager import SimulationManager +from pxr import PhysxSchema + +import isaaclab.sim as sim_utils +import isaaclab.utils.string as string_utils +from isaaclab.markers import VisualizationMarkers +from isaaclab.utils.math import convert_quat + +from ..sensor_base import SensorBase +from .contact_sensor_data import ContactSensorData + +if TYPE_CHECKING: + from .contact_sensor_cfg import ContactSensorCfg + + +class ContactSensor(SensorBase): + """A contact reporting sensor. + + The contact sensor reports the normal contact forces on a rigid body in the world frame. + It relies on the `PhysX ContactReporter`_ API to be activated on the rigid bodies. + + To enable the contact reporter on a rigid body, please make sure to enable the + :attr:`isaaclab.sim.spawner.RigidObjectSpawnerCfg.activate_contact_sensors` on your + asset spawner configuration. This will enable the contact reporter on all the rigid bodies + in the asset. + + The sensor can be configured to report the contact forces on a set of bodies with a given + filter pattern using the :attr:`ContactSensorCfg.filter_prim_paths_expr`. This is useful + when you want to report the contact forces between the sensor bodies and a specific set of + bodies in the scene. The data can be accessed using the :attr:`ContactSensorData.force_matrix_w`. + Please check the documentation on `RigidContact`_ for more details. + + The reporting of the filtered contact forces is only possible as one-to-many. This means that only one + sensor body in an environment can be filtered against multiple bodies in that environment. If you need to + filter multiple sensor bodies against multiple bodies, you need to create separate sensors for each sensor + body. + + As an example, suppose you want to report the contact forces for all the feet of a robot against an object + exclusively. In that case, setting the :attr:`ContactSensorCfg.prim_path` and + :attr:`ContactSensorCfg.filter_prim_paths_expr` with ``{ENV_REGEX_NS}/Robot/.*_FOOT`` and ``{ENV_REGEX_NS}/Object`` + respectively will not work. Instead, you need to create a separate sensor for each foot and filter + it against the object. + + .. _PhysX ContactReporter: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_contact_report_a_p_i.html + .. _RigidContact: https://docs.isaacsim.omniverse.nvidia.com/latest/py/source/extensions/isaacsim.core.api/docs/index.html#isaacsim.core.api.sensors.RigidContactView + """ + + cfg: ContactSensorCfg + """The configuration parameters.""" + + def __init__(self, cfg: ContactSensorCfg): + """Initializes the contact sensor object. + + Args: + cfg: The configuration parameters. + """ + # initialize base class + super().__init__(cfg) + + # Enable contact processing + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/disableContactProcessing", False) + + # Create empty variables for storing output data + self._data: ContactSensorData = ContactSensorData() + # initialize self._body_physx_view for running in extension mode + self._body_physx_view = None + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Contact sensor @ '{self.cfg.prim_path}': \n" + f"\tview type : {self.body_physx_view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of bodies : {self.num_bodies}\n" + f"\tbody names : {self.body_names}\n" + ) + + """ + Properties + """ + + @property + def num_instances(self) -> int: + return self.body_physx_view.count + + @property + def data(self) -> ContactSensorData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + @property + def num_bodies(self) -> int: + """Number of bodies with contact sensors attached.""" + return self._num_bodies + + @property + def body_names(self) -> list[str]: + """Ordered names of bodies with contact sensors attached.""" + prim_paths = self.body_physx_view.prim_paths[: self.num_bodies] + return [path.split("/")[-1] for path in prim_paths] + + @property + def body_physx_view(self) -> physx.RigidBodyView: + """View for the rigid bodies captured (PhysX). + + Note: + Use this view with caution. It requires handling of tensors in a specific way. + """ + return self._body_physx_view + + @property + def contact_physx_view(self) -> physx.RigidContactView: + """Contact reporter view for the bodies (PhysX). + + Note: + Use this view with caution. It requires handling of tensors in a specific way. + """ + return self._contact_physx_view + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + # reset the timers and counters + super().reset(env_ids) + # resolve None + if env_ids is None: + env_ids = slice(None) + # reset accumulative data buffers + self._data.net_forces_w[env_ids] = 0.0 + self._data.net_forces_w_history[env_ids] = 0.0 + # reset force matrix + if len(self.cfg.filter_prim_paths_expr) != 0: + self._data.force_matrix_w[env_ids] = 0.0 + self._data.force_matrix_w_history[env_ids] = 0.0 + # reset the current air time + if self.cfg.track_air_time: + self._data.current_air_time[env_ids] = 0.0 + self._data.last_air_time[env_ids] = 0.0 + self._data.current_contact_time[env_ids] = 0.0 + self._data.last_contact_time[env_ids] = 0.0 + # reset contact positions + if self.cfg.track_contact_points: + self._data.contact_pos_w[env_ids, :] = torch.nan + # reset friction forces + if self.cfg.track_friction_forces: + self._data.friction_forces_w[env_ids, :] = 0.0 + + def find_bodies(self, name_keys: str | Sequence[str], preserve_order: bool = False) -> tuple[list[int], list[str]]: + """Find bodies in the articulation based on the name keys. + + Args: + name_keys: A regular expression or a list of regular expressions to match the body names. + preserve_order: Whether to preserve the order of the name keys in the output. Defaults to False. + + Returns: + A tuple of lists containing the body indices and names. + """ + return string_utils.resolve_matching_names(name_keys, self.body_names, preserve_order) + + def compute_first_contact(self, dt: float, abs_tol: float = 1.0e-8) -> torch.Tensor: + """Checks if bodies that have established contact within the last :attr:`dt` seconds. + + This function checks if the bodies have established contact within the last :attr:`dt` seconds + by comparing the current contact time with the given time period. If the contact time is less + than the given time period, then the bodies are considered to be in contact. + + Note: + The function assumes that :attr:`dt` is a factor of the sensor update time-step. In other + words :math:`dt / dt_sensor = n`, where :math:`n` is a natural number. This is always true + if the sensor is updated by the physics or the environment stepping time-step and the sensor + is read by the environment stepping time-step. + + Args: + dt: The time period since the contact was established. + abs_tol: The absolute tolerance for the comparison. + + Returns: + A boolean tensor indicating the bodies that have established contact within the last + :attr:`dt` seconds. Shape is (N, B), where N is the number of sensors and B is the + number of bodies in each sensor. + + Raises: + RuntimeError: If the sensor is not configured to track contact time. + """ + # check if the sensor is configured to track contact time + if not self.cfg.track_air_time: + raise RuntimeError( + "The contact sensor is not configured to track contact time." + "Please enable the 'track_air_time' in the sensor configuration." + ) + # check if the bodies are in contact + currently_in_contact = self.data.current_contact_time > 0.0 + less_than_dt_in_contact = self.data.current_contact_time < (dt + abs_tol) + return currently_in_contact * less_than_dt_in_contact + + def compute_first_air(self, dt: float, abs_tol: float = 1.0e-8) -> torch.Tensor: + """Checks if bodies that have broken contact within the last :attr:`dt` seconds. + + This function checks if the bodies have broken contact within the last :attr:`dt` seconds + by comparing the current air time with the given time period. If the air time is less + than the given time period, then the bodies are considered to not be in contact. + + Note: + It assumes that :attr:`dt` is a factor of the sensor update time-step. In other words, + :math:`dt / dt_sensor = n`, where :math:`n` is a natural number. This is always true if + the sensor is updated by the physics or the environment stepping time-step and the sensor + is read by the environment stepping time-step. + + Args: + dt: The time period since the contract is broken. + abs_tol: The absolute tolerance for the comparison. + + Returns: + A boolean tensor indicating the bodies that have broken contact within the last :attr:`dt` seconds. + Shape is (N, B), where N is the number of sensors and B is the number of bodies in each sensor. + + Raises: + RuntimeError: If the sensor is not configured to track contact time. + """ + # check if the sensor is configured to track contact time + if not self.cfg.track_air_time: + raise RuntimeError( + "The contact sensor is not configured to track contact time." + "Please enable the 'track_air_time' in the sensor configuration." + ) + # check if the sensor is configured to track contact time + currently_detached = self.data.current_air_time > 0.0 + less_than_dt_detached = self.data.current_air_time < (dt + abs_tol) + return currently_detached * less_than_dt_detached + + """ + Implementation. + """ + + def _initialize_impl(self): + super()._initialize_impl() + # obtain global simulation view + self._physics_sim_view = SimulationManager.get_physics_sim_view() + # check that only rigid bodies are selected + leaf_pattern = self.cfg.prim_path.rsplit("/", 1)[-1] + template_prim_path = self._parent_prims[0].GetPath().pathString + body_names = list() + for prim in sim_utils.find_matching_prims(template_prim_path + "/" + leaf_pattern): + # check if prim has contact reporter API + if prim.HasAPI(PhysxSchema.PhysxContactReportAPI): + prim_path = prim.GetPath().pathString + body_names.append(prim_path.rsplit("/", 1)[-1]) + # check that there is at least one body with contact reporter API + if not body_names: + raise RuntimeError( + f"Sensor at path '{self.cfg.prim_path}' could not find any bodies with contact reporter API." + "\nHINT: Make sure to enable 'activate_contact_sensors' in the corresponding asset spawn configuration." + ) + + # construct regex expression for the body names + body_names_regex = r"(" + "|".join(body_names) + r")" + body_names_regex = f"{self.cfg.prim_path.rsplit('/', 1)[0]}/{body_names_regex}" + # convert regex expressions to glob expressions for PhysX + body_names_glob = body_names_regex.replace(".*", "*") + filter_prim_paths_glob = [expr.replace(".*", "*") for expr in self.cfg.filter_prim_paths_expr] + + # create a rigid prim view for the sensor + self._body_physx_view = self._physics_sim_view.create_rigid_body_view(body_names_glob) + self._contact_physx_view = self._physics_sim_view.create_rigid_contact_view( + body_names_glob, + filter_patterns=filter_prim_paths_glob, + max_contact_data_count=self.cfg.max_contact_data_count_per_prim * len(body_names) * self._num_envs, + ) + # resolve the true count of bodies + self._num_bodies = self.body_physx_view.count // self._num_envs + # check that contact reporter succeeded + if self._num_bodies != len(body_names): + raise RuntimeError( + "Failed to initialize contact reporter for specified bodies." + f"\n\tInput prim path : {self.cfg.prim_path}" + f"\n\tResolved prim paths: {body_names_regex}" + ) + + # prepare data buffers + self._data.net_forces_w = torch.zeros(self._num_envs, self._num_bodies, 3, device=self._device) + # optional buffers + # -- history of net forces + if self.cfg.history_length > 0: + self._data.net_forces_w_history = torch.zeros( + self._num_envs, self.cfg.history_length, self._num_bodies, 3, device=self._device + ) + else: + self._data.net_forces_w_history = self._data.net_forces_w.unsqueeze(1) + # -- pose of sensor origins + if self.cfg.track_pose: + self._data.pos_w = torch.zeros(self._num_envs, self._num_bodies, 3, device=self._device) + self._data.quat_w = torch.zeros(self._num_envs, self._num_bodies, 4, device=self._device) + + # check if filter paths are valid + if self.cfg.track_contact_points or self.cfg.track_friction_forces: + if len(self.cfg.filter_prim_paths_expr) == 0: + raise ValueError( + "The 'filter_prim_paths_expr' is empty. Please specify a valid filter pattern to track" + f" {'contact points' if self.cfg.track_contact_points else 'friction forces'}." + ) + if self.cfg.max_contact_data_count_per_prim < 1: + raise ValueError( + f"The 'max_contact_data_count_per_prim' is {self.cfg.max_contact_data_count_per_prim}. " + "Please set it to a value greater than 0 to track" + f" {'contact points' if self.cfg.track_contact_points else 'friction forces'}." + ) + + # -- position of contact points + if self.cfg.track_contact_points: + self._data.contact_pos_w = torch.full( + (self._num_envs, self._num_bodies, self.contact_physx_view.filter_count, 3), + torch.nan, + device=self._device, + ) + # -- friction forces at contact points + if self.cfg.track_friction_forces: + self._data.friction_forces_w = torch.full( + (self._num_envs, self._num_bodies, self.contact_physx_view.filter_count, 3), + 0.0, + device=self._device, + ) + # -- air/contact time between contacts + if self.cfg.track_air_time: + self._data.last_air_time = torch.zeros(self._num_envs, self._num_bodies, device=self._device) + self._data.current_air_time = torch.zeros(self._num_envs, self._num_bodies, device=self._device) + self._data.last_contact_time = torch.zeros(self._num_envs, self._num_bodies, device=self._device) + self._data.current_contact_time = torch.zeros(self._num_envs, self._num_bodies, device=self._device) + # force matrix: (num_envs, num_bodies, num_filter_shapes, 3) + # force matrix history: (num_envs, history_length, num_bodies, num_filter_shapes, 3) + if len(self.cfg.filter_prim_paths_expr) != 0: + num_filters = self.contact_physx_view.filter_count + self._data.force_matrix_w = torch.zeros( + self._num_envs, self._num_bodies, num_filters, 3, device=self._device + ) + if self.cfg.history_length > 0: + self._data.force_matrix_w_history = torch.zeros( + self._num_envs, self.cfg.history_length, self._num_bodies, num_filters, 3, device=self._device + ) + else: + self._data.force_matrix_w_history = self._data.force_matrix_w.unsqueeze(1) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data.""" + # default to all sensors + if len(env_ids) == self._num_envs: + env_ids = slice(None) + + # obtain the contact forces + # TODO: We are handling the indexing ourself because of the shape; (N, B) vs expected (N * B). + # This isn't the most efficient way to do this, but it's the easiest to implement. + net_forces_w = self.contact_physx_view.get_net_contact_forces(dt=self._sim_physics_dt) + self._data.net_forces_w[env_ids, :, :] = net_forces_w.view(-1, self._num_bodies, 3)[env_ids] + # update contact force history + if self.cfg.history_length > 0: + self._data.net_forces_w_history[env_ids] = self._data.net_forces_w_history[env_ids].roll(1, dims=1) + self._data.net_forces_w_history[env_ids, 0] = self._data.net_forces_w[env_ids] + + # obtain the contact force matrix + if len(self.cfg.filter_prim_paths_expr) != 0: + # shape of the filtering matrix: (num_envs, num_bodies, num_filter_shapes, 3) + num_filters = self.contact_physx_view.filter_count + # acquire and shape the force matrix + force_matrix_w = self.contact_physx_view.get_contact_force_matrix(dt=self._sim_physics_dt) + force_matrix_w = force_matrix_w.view(-1, self._num_bodies, num_filters, 3) + self._data.force_matrix_w[env_ids] = force_matrix_w[env_ids] + if self.cfg.history_length > 0: + self._data.force_matrix_w_history[env_ids] = self._data.force_matrix_w_history[env_ids].roll(1, dims=1) + self._data.force_matrix_w_history[env_ids, 0] = self._data.force_matrix_w[env_ids] + + # obtain the pose of the sensor origin + if self.cfg.track_pose: + pose = self.body_physx_view.get_transforms().view(-1, self._num_bodies, 7)[env_ids] + pose[..., 3:] = convert_quat(pose[..., 3:], to="wxyz") + self._data.pos_w[env_ids], self._data.quat_w[env_ids] = pose.split([3, 4], dim=-1) + + # obtain contact points + if self.cfg.track_contact_points: + _, buffer_contact_points, _, _, buffer_count, buffer_start_indices = ( + self.contact_physx_view.get_contact_data(dt=self._sim_physics_dt) + ) + self._data.contact_pos_w[env_ids] = self._unpack_contact_buffer_data( + buffer_contact_points, buffer_count, buffer_start_indices + )[env_ids] + + # obtain friction forces + if self.cfg.track_friction_forces: + friction_forces, _, buffer_count, buffer_start_indices = self.contact_physx_view.get_friction_data( + dt=self._sim_physics_dt + ) + self._data.friction_forces_w[env_ids] = self._unpack_contact_buffer_data( + friction_forces, buffer_count, buffer_start_indices, avg=False, default=0.0 + )[env_ids] + + # obtain the air time + if self.cfg.track_air_time: + # -- time elapsed since last update + # since this function is called every frame, we can use the difference to get the elapsed time + elapsed_time = self._timestamp[env_ids] - self._timestamp_last_update[env_ids] + # -- check contact state of bodies + is_contact = torch.norm(self._data.net_forces_w[env_ids, :, :], dim=-1) > self.cfg.force_threshold + is_first_contact = (self._data.current_air_time[env_ids] > 0) * is_contact + is_first_detached = (self._data.current_contact_time[env_ids] > 0) * ~is_contact + # -- update the last contact time if body has just become in contact + self._data.last_air_time[env_ids] = torch.where( + is_first_contact, + self._data.current_air_time[env_ids] + elapsed_time.unsqueeze(-1), + self._data.last_air_time[env_ids], + ) + # -- increment time for bodies that are not in contact + self._data.current_air_time[env_ids] = torch.where( + ~is_contact, self._data.current_air_time[env_ids] + elapsed_time.unsqueeze(-1), 0.0 + ) + # -- update the last contact time if body has just detached + self._data.last_contact_time[env_ids] = torch.where( + is_first_detached, + self._data.current_contact_time[env_ids] + elapsed_time.unsqueeze(-1), + self._data.last_contact_time[env_ids], + ) + # -- increment time for bodies that are in contact + self._data.current_contact_time[env_ids] = torch.where( + is_contact, self._data.current_contact_time[env_ids] + elapsed_time.unsqueeze(-1), 0.0 + ) + + def _unpack_contact_buffer_data( + self, + contact_data: torch.Tensor, + buffer_count: torch.Tensor, + buffer_start_indices: torch.Tensor, + avg: bool = True, + default: float = float("nan"), + ) -> torch.Tensor: + """ + Unpacks and aggregates contact data for each (env, body, filter) group. + + This function vectorizes the following nested loop: + + for i in range(self._num_bodies * self._num_envs): + for j in range(self.contact_physx_view.filter_count): + start_index_ij = buffer_start_indices[i, j] + count_ij = buffer_count[i, j] + self._contact_position_aggregate_buffer[i, j, :] = torch.mean( + contact_data[start_index_ij : (start_index_ij + count_ij), :], dim=0 + ) + + For more details, see the `RigidContactView.get_contact_data() documentation `_. + + Args: + contact_data: Flat tensor of contact data, shape (N_envs * N_bodies, 3). + buffer_count: Number of contact points per (env, body, filter), shape (N_envs * N_bodies, N_filters). + buffer_start_indices: Start indices for each (env, body, filter), shape (N_envs * N_bodies, N_filters). + avg: If True, average the contact data for each group; if False, sum the data. Defaults to True. + default: Default value to use for groups with zero contacts. Defaults to NaN. + + Returns: + Aggregated contact data, shape (N_envs, N_bodies, N_filters, 3). + """ + counts, starts = buffer_count.view(-1), buffer_start_indices.view(-1) + n_rows, total = counts.numel(), int(counts.sum()) + agg = torch.full((n_rows, 3), default, device=self._device, dtype=contact_data.dtype) + if total > 0: + row_ids = torch.repeat_interleave(torch.arange(n_rows, device=self._device), counts) + + block_starts = counts.cumsum(0) - counts + deltas = torch.arange(row_ids.numel(), device=counts.device) - block_starts.repeat_interleave(counts) + flat_idx = starts[row_ids] + deltas + + pts = contact_data.index_select(0, flat_idx) + agg = agg.zero_().index_add_(0, row_ids, pts) + agg = agg / counts.clamp_min(1).unsqueeze(-1) if avg else agg + agg[counts == 0] = default + + return agg.view(self._num_envs * self.num_bodies, -1, 3).view( + self._num_envs, self._num_bodies, self.contact_physx_view.filter_count, 3 + ) + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if not hasattr(self, "contact_visualizer"): + self.contact_visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + # set their visibility to true + self.contact_visualizer.set_visibility(True) + else: + if hasattr(self, "contact_visualizer"): + self.contact_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # safely return if view becomes invalid + # note: this invalidity happens because of isaac sim view callbacks + if self.body_physx_view is None: + return + # marker indices + # 0: contact, 1: no contact + net_contact_force_w = torch.norm(self._data.net_forces_w, dim=-1) + marker_indices = torch.where(net_contact_force_w > self.cfg.force_threshold, 0, 1) + # check if prim is visualized + if self.cfg.track_pose: + frame_origins: torch.Tensor = self._data.pos_w + else: + pose = self.body_physx_view.get_transforms() + frame_origins = pose.view(-1, self._num_bodies, 7)[:, :, :3] + # visualize + self.contact_visualizer.visualize(frame_origins.view(-1, 3), marker_indices=marker_indices.view(-1)) + + """ + Internal simulation callbacks. + """ + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + # call parent + super()._invalidate_initialize_callback(event) + # set all existing views to None to invalidate them + self._body_physx_view = None + self._contact_physx_view = None diff --git a/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_cfg.py b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c811a7ca63d127da3ee4004abdc18d68643e2cdb --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_cfg.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.markers.config import CONTACT_SENSOR_MARKER_CFG +from isaaclab.utils import configclass + +from ..sensor_base_cfg import SensorBaseCfg +from .contact_sensor import ContactSensor + + +@configclass +class ContactSensorCfg(SensorBaseCfg): + """Configuration for the contact sensor.""" + + class_type: type = ContactSensor + + track_pose: bool = False + """Whether to track the pose of the sensor's origin. Defaults to False.""" + + track_contact_points: bool = False + """Whether to track the contact point locations. Defaults to False.""" + + track_friction_forces: bool = False + """Whether to track the friction forces at the contact points. Defaults to False.""" + + max_contact_data_count_per_prim: int = 4 + """The maximum number of contacts across all batches of the sensor to keep track of. Default is 4. + + This parameter sets the total maximum counts of the simulation across all bodies and environments. The total number + of contacts allowed is max_contact_data_count_per_prim*num_envs*num_sensor_bodies. + + .. note:: + + If the environment is very contact rich it is suggested to increase this parameter to avoid out of bounds memory + errors and loss of contact data leading to inaccurate measurements. + + """ + + track_air_time: bool = False + """Whether to track the air/contact time of the bodies (time between contacts). Defaults to False.""" + + force_threshold: float = 1.0 + """The threshold on the norm of the contact force that determines whether two bodies are in collision or not. + + This value is only used for tracking the mode duration (the time in contact or in air), + if :attr:`track_air_time` is True. + """ + + filter_prim_paths_expr: list[str] = list() + """The list of primitive paths (or expressions) to filter contacts with. Defaults to an empty list, in which case + no filtering is applied. + + The contact sensor allows reporting contacts between the primitive specified with :attr:`prim_path` and + other primitives in the scene. For instance, in a scene containing a robot, a ground plane and an object, + you can obtain individual contact reports of the base of the robot with the ground plane and the object. + + .. note:: + The expression in the list can contain the environment namespace regex ``{ENV_REGEX_NS}`` which + will be replaced with the environment namespace. + + Example: ``{ENV_REGEX_NS}/Object`` will be replaced with ``/World/envs/env_.*/Object``. + + .. attention:: + The reporting of filtered contacts only works when the sensor primitive :attr:`prim_path` corresponds to a + single primitive in that environment. If the sensor primitive corresponds to multiple primitives, the + filtering will not work as expected. Please check :class:`~isaaclab.sensors.contact_sensor.ContactSensor` + for more details. + If track_contact_points is true, then filter_prim_paths_expr cannot be an empty list! + """ + + visualizer_cfg: VisualizationMarkersCfg = CONTACT_SENSOR_MARKER_CFG.replace(prim_path="/Visuals/ContactSensor") + """The configuration object for the visualization markers. Defaults to CONTACT_SENSOR_MARKER_CFG. + + .. note:: + This attribute is only used when debug visualization is enabled. + """ diff --git a/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_data.py b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_data.py new file mode 100644 index 0000000000000000000000000000000000000000..fd6c15ebe960375d2837ab43a540ae2fd7426f5a --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor_data.py @@ -0,0 +1,153 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed to import for allowing type-hinting: torch.Tensor | None +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass +class ContactSensorData: + """Data container for the contact reporting sensor.""" + + pos_w: torch.Tensor | None = None + """Position of the sensor origin in world frame. + + Shape is (N, 3), where N is the number of sensors. + + Note: + If the :attr:`ContactSensorCfg.track_pose` is False, then this quantity is None. + + """ + + contact_pos_w: torch.Tensor | None = None + """Average of the positions of contact points between sensor body and filter prim in world frame. + + Shape is (N, B, M, 3), where N is the number of sensors, B is number of bodies in each sensor + and M is the number of filtered bodies. + + Collision pairs not in contact will result in NaN. + + Note: + + * If the :attr:`ContactSensorCfg.track_contact_points` is False, then this quantity is None. + * If the :attr:`ContactSensorCfg.track_contact_points` is True, a ValueError will be raised if: + + * If the :attr:`ContactSensorCfg.filter_prim_paths_expr` is empty. + * If the :attr:`ContactSensorCfg.max_contact_data_per_prim` is not specified or less than 1. + will not be calculated. + + """ + + friction_forces_w: torch.Tensor | None = None + """Sum of the friction forces between sensor body and filter prim in world frame. + + Shape is (N, B, M, 3), where N is the number of sensors, B is number of bodies in each sensor + and M is the number of filtered bodies. + + Collision pairs not in contact will result in NaN. + + Note: + + * If the :attr:`ContactSensorCfg.track_friction_forces` is False, then this quantity is None. + * If the :attr:`ContactSensorCfg.track_friction_forces` is True, a ValueError will be raised if: + + * The :attr:`ContactSensorCfg.filter_prim_paths_expr` is empty. + * The :attr:`ContactSensorCfg.max_contact_data_per_prim` is not specified or less than 1. + + """ + + quat_w: torch.Tensor | None = None + """Orientation of the sensor origin in quaternion (w, x, y, z) in world frame. + + Shape is (N, 4), where N is the number of sensors. + + Note: + If the :attr:`ContactSensorCfg.track_pose` is False, then this quantity is None. + """ + + net_forces_w: torch.Tensor | None = None + """The net normal contact forces in world frame. + + Shape is (N, B, 3), where N is the number of sensors and B is the number of bodies in each sensor. + + Note: + This quantity is the sum of the normal contact forces acting on the sensor bodies. It must not be confused + with the total contact forces acting on the sensor bodies (which also includes the tangential forces). + """ + + net_forces_w_history: torch.Tensor | None = None + """The net normal contact forces in world frame. + + Shape is (N, T, B, 3), where N is the number of sensors, T is the configured history length + and B is the number of bodies in each sensor. + + In the history dimension, the first index is the most recent and the last index is the oldest. + + Note: + This quantity is the sum of the normal contact forces acting on the sensor bodies. It must not be confused + with the total contact forces acting on the sensor bodies (which also includes the tangential forces). + """ + + force_matrix_w: torch.Tensor | None = None + """The normal contact forces filtered between the sensor bodies and filtered bodies in world frame. + + Shape is (N, B, M, 3), where N is the number of sensors, B is number of bodies in each sensor + and M is the number of filtered bodies. + + Note: + If the :attr:`ContactSensorCfg.filter_prim_paths_expr` is empty, then this quantity is None. + """ + + force_matrix_w_history: torch.Tensor | None = None + """The normal contact forces filtered between the sensor bodies and filtered bodies in world frame. + + Shape is (N, T, B, M, 3), where N is the number of sensors, T is the configured history length, + B is number of bodies in each sensor and M is the number of filtered bodies. + + In the history dimension, the first index is the most recent and the last index is the oldest. + + Note: + If the :attr:`ContactSensorCfg.filter_prim_paths_expr` is empty, then this quantity is None. + """ + + last_air_time: torch.Tensor | None = None + """Time spent (in s) in the air before the last contact. + + Shape is (N, B), where N is the number of sensors and B is the number of bodies in each sensor. + + Note: + If the :attr:`ContactSensorCfg.track_air_time` is False, then this quantity is None. + """ + + current_air_time: torch.Tensor | None = None + """Time spent (in s) in the air since the last detach. + + Shape is (N, B), where N is the number of sensors and B is the number of bodies in each sensor. + + Note: + If the :attr:`ContactSensorCfg.track_air_time` is False, then this quantity is None. + """ + + last_contact_time: torch.Tensor | None = None + """Time spent (in s) in contact before the last detach. + + Shape is (N, B), where N is the number of sensors and B is the number of bodies in each sensor. + + Note: + If the :attr:`ContactSensorCfg.track_air_time` is False, then this quantity is None. + """ + + current_contact_time: torch.Tensor | None = None + """Time spent (in s) in contact since the last contact. + + Shape is (N, B), where N is the number of sensors and B is the number of bodies in each sensor. + + Note: + If the :attr:`ContactSensorCfg.track_air_time` is False, then this quantity is None. + """ diff --git a/source/isaaclab/isaaclab/sensors/frame_transformer/__init__.py b/source/isaaclab/isaaclab/sensors/frame_transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d5db853e8cc21cdb53cf03a58a525600586b91ee --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/frame_transformer/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for frame transformer sensor.""" + +from .frame_transformer import FrameTransformer +from .frame_transformer_cfg import FrameTransformerCfg, OffsetCfg +from .frame_transformer_data import FrameTransformerData diff --git a/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer.py b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ed83392b3aa7542f9f03c83a7237d30bc2725423 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer.py @@ -0,0 +1,560 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import re +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaacsim.core.simulation_manager import SimulationManager +from pxr import UsdPhysics + +import isaaclab.sim as sim_utils +import isaaclab.utils.string as string_utils +from isaaclab.markers import VisualizationMarkers +from isaaclab.utils.math import ( + combine_frame_transforms, + convert_quat, + is_identity_pose, + normalize, + quat_from_angle_axis, + subtract_frame_transforms, +) + +from ..sensor_base import SensorBase +from .frame_transformer_data import FrameTransformerData + +if TYPE_CHECKING: + from .frame_transformer_cfg import FrameTransformerCfg + +# import logger +logger = logging.getLogger(__name__) + + +class FrameTransformer(SensorBase): + """A sensor for reporting frame transforms. + + This class provides an interface for reporting the transform of one or more frames (target frames) + with respect to another frame (source frame). The source frame is specified by the user as a prim path + (:attr:`FrameTransformerCfg.prim_path`) and the target frames are specified by the user as a list of + prim paths (:attr:`FrameTransformerCfg.target_frames`). + + The source frame and target frames are assumed to be rigid bodies. The transform of the target frames + with respect to the source frame is computed by first extracting the transform of the source frame + and target frames from the physics engine and then computing the relative transform between the two. + + Additionally, the user can specify an offset for the source frame and each target frame. This is useful + for specifying the transform of the desired frame with respect to the body's center of mass, for instance. + + A common example of using this sensor is to track the position and orientation of the end effector of a + robotic manipulator. In this case, the source frame would be the body corresponding to the base frame of the + manipulator, and the target frame would be the body corresponding to the end effector. Since the end-effector is + typically a fictitious body, the user may need to specify an offset from the end-effector to the body of the + manipulator. + + """ + + cfg: FrameTransformerCfg + """The configuration parameters.""" + + def __init__(self, cfg: FrameTransformerCfg): + """Initializes the frame transformer object. + + Args: + cfg: The configuration parameters. + """ + # initialize base class + super().__init__(cfg) + # Create empty variables for storing output data + self._data: FrameTransformerData = FrameTransformerData() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"FrameTransformer @ '{self.cfg.prim_path}': \n" + f"\ttracked body frames: {[self._source_frame_body_name] + self._target_frame_body_names} \n" + f"\tnumber of envs: {self._num_envs}\n" + f"\tsource body frame: {self._source_frame_body_name}\n" + f"\ttarget frames (count: {self._target_frame_names}): {len(self._target_frame_names)}\n" + ) + + """ + Properties + """ + + @property + def data(self) -> FrameTransformerData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + @property + def num_bodies(self) -> int: + """Returns the number of target bodies being tracked. + + Note: + This is an alias used for consistency with other sensors. Otherwise, we recommend using + :attr:`len(data.target_frame_names)` to access the number of target frames. + """ + return len(self._target_frame_body_names) + + @property + def body_names(self) -> list[str]: + """Returns the names of the target bodies being tracked. + + Note: + This is an alias used for consistency with other sensors. Otherwise, we recommend using + :attr:`data.target_frame_names` to access the target frame names. + """ + return self._target_frame_body_names + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + # reset the timers and counters + super().reset(env_ids) + # resolve None + if env_ids is None: + env_ids = ... + + def find_bodies(self, name_keys: str | Sequence[str], preserve_order: bool = False) -> tuple[list[int], list[str]]: + """Find bodies in the articulation based on the name keys. + + Args: + name_keys: A regular expression or a list of regular expressions to match the body names. + preserve_order: Whether to preserve the order of the name keys in the output. Defaults to False. + + Returns: + A tuple of lists containing the body indices and names. + """ + return string_utils.resolve_matching_names(name_keys, self._target_frame_names, preserve_order) + + """ + Implementation. + """ + + def _initialize_impl(self): + super()._initialize_impl() + + # resolve source frame offset + source_frame_offset_pos = torch.tensor(self.cfg.source_frame_offset.pos, device=self.device) + source_frame_offset_quat = torch.tensor(self.cfg.source_frame_offset.rot, device=self.device) + # Only need to perform offsetting of source frame if the position offsets is non-zero and rotation offset is + # not the identity quaternion for efficiency in _update_buffer_impl + self._apply_source_frame_offset = True + # Handle source frame offsets + if is_identity_pose(source_frame_offset_pos, source_frame_offset_quat): + logger.debug(f"No offset application needed for source frame as it is identity: {self.cfg.prim_path}") + self._apply_source_frame_offset = False + else: + logger.debug(f"Applying offset to source frame as it is not identity: {self.cfg.prim_path}") + # Store offsets as tensors (duplicating each env's offsets for ease of multiplication later) + self._source_frame_offset_pos = source_frame_offset_pos.unsqueeze(0).repeat(self._num_envs, 1) + self._source_frame_offset_quat = source_frame_offset_quat.unsqueeze(0).repeat(self._num_envs, 1) + + # Keep track of mapping from the rigid body name to the desired frames and prim path, + # as there may be multiple frames based upon the same body name and we don't want to + # create unnecessary views. + body_names_to_frames: dict[str, dict[str, set[str] | str]] = {} + # The offsets associated with each target frame + target_offsets: dict[str, dict[str, torch.Tensor]] = {} + # The frames whose offsets are not identity + non_identity_offset_frames: list[str] = [] + + # Only need to perform offsetting of target frame if any of the position offsets are non-zero or any of the + # rotation offsets are not the identity quaternion for efficiency in _update_buffer_impl + self._apply_target_frame_offset = False + + # Need to keep track of whether the source frame is also a target frame + self._source_is_also_target_frame = False + + # Collect all target frames, their associated body prim paths and their offsets so that we can extract + # the prim, check that it has the appropriate rigid body API in a single loop. + # First element is None because user can't specify source frame name + frames = [None] + [target_frame.name for target_frame in self.cfg.target_frames] + frame_prim_paths = [self.cfg.prim_path] + [target_frame.prim_path for target_frame in self.cfg.target_frames] + # First element is None because source frame offset is handled separately + frame_offsets = [None] + [target_frame.offset for target_frame in self.cfg.target_frames] + frame_types = ["source"] + ["target"] * len(self.cfg.target_frames) + for frame, prim_path, offset, frame_type in zip(frames, frame_prim_paths, frame_offsets, frame_types): + # Find correct prim + matching_prims = sim_utils.find_matching_prims(prim_path) + if len(matching_prims) == 0: + raise ValueError( + f"Failed to create frame transformer for frame '{frame}' with path '{prim_path}'." + " No matching prims were found." + ) + for prim in matching_prims: + # Get the prim path of the matching prim + matching_prim_path = prim.GetPath().pathString + # Check if it is a rigid prim + if not prim.HasAPI(UsdPhysics.RigidBodyAPI): + raise ValueError( + f"While resolving expression '{prim_path}' found a prim '{matching_prim_path}' which is not a" + " rigid body. The class only supports transformations between rigid bodies." + ) + + # Get the name of the body: use relative prim path for unique identification + body_name = self._get_relative_body_path(matching_prim_path) + # Use leaf name of prim path if frame name isn't specified by user + frame_name = frame if frame is not None else matching_prim_path.rsplit("/", 1)[-1] + + # Keep track of which frames are associated with which bodies + if body_name in body_names_to_frames: + body_names_to_frames[body_name]["frames"].add(frame_name) + + # This is a corner case where the source frame is also a target frame + if body_names_to_frames[body_name]["type"] == "source" and frame_type == "target": + self._source_is_also_target_frame = True + + else: + # Store the first matching prim path and the type of frame + body_names_to_frames[body_name] = { + "frames": {frame_name}, + "prim_path": matching_prim_path, + "type": frame_type, + } + + if offset is not None: + offset_pos = torch.tensor(offset.pos, device=self.device) + offset_quat = torch.tensor(offset.rot, device=self.device) + # Check if we need to apply offsets (optimized code path in _update_buffer_impl) + if not is_identity_pose(offset_pos, offset_quat): + non_identity_offset_frames.append(frame_name) + self._apply_target_frame_offset = True + target_offsets[frame_name] = {"pos": offset_pos, "quat": offset_quat} + + if not self._apply_target_frame_offset: + logger.info( + f"No offsets application needed from '{self.cfg.prim_path}' to target frames as all" + f" are identity: {frames[1:]}" + ) + else: + logger.info( + f"Offsets application needed from '{self.cfg.prim_path}' to the following target frames:" + f" {non_identity_offset_frames}" + ) + + # The names of bodies that RigidPrim will be tracking to later extract transforms from + tracked_prim_paths = [body_names_to_frames[body_name]["prim_path"] for body_name in body_names_to_frames.keys()] + tracked_body_names = [body_name for body_name in body_names_to_frames.keys()] + + body_names_regex = [tracked_prim_path.replace("env_0", "env_*") for tracked_prim_path in tracked_prim_paths] + + # obtain global simulation view + self._physics_sim_view = SimulationManager.get_physics_sim_view() + # Create a prim view for all frames and initialize it + # order of transforms coming out of view will be source frame followed by target frame(s) + self._frame_physx_view = self._physics_sim_view.create_rigid_body_view(body_names_regex) + + # Determine the order in which regex evaluated body names so we can later index into frame transforms + # by frame name correctly + all_prim_paths = self._frame_physx_view.prim_paths + + if "env_" in all_prim_paths[0]: + + def extract_env_num_and_prim_path(item: str) -> tuple[int, str]: + """Separates the environment number and prim_path from the item. + + Args: + item: The item to extract the environment number from. Assumes item is of the form + `/World/envs/env_1/blah` or `/World/envs/env_11/blah`. + Returns: + The environment number and the prim_path. + """ + match = re.search(r"env_(\d+)(.*)", item) + return (int(match.group(1)), match.group(2)) + + # Find the indices that would reorganize output to be per environment. + # We want `env_1/blah` to come before `env_11/blah` and env_1/Robot/base + # to come before env_1/Robot/foot so we need to use custom key function + self._per_env_indices = [ + index + for index, _ in sorted( + list(enumerate(all_prim_paths)), key=lambda x: extract_env_num_and_prim_path(x[1]) + ) + ] + + # Only need 0th env as the names and their ordering are the same across environments + sorted_prim_paths = [ + all_prim_paths[index] for index in self._per_env_indices if "env_0" in all_prim_paths[index] + ] + + else: + # If no environment is present, then the order of the body names is the same as the order of the + # prim paths sorted alphabetically + self._per_env_indices = [index for index, _ in sorted(enumerate(all_prim_paths), key=lambda x: x[1])] + sorted_prim_paths = [all_prim_paths[index] for index in self._per_env_indices] + + # -- target frames: use relative prim path for unique identification + self._target_frame_body_names = [self._get_relative_body_path(prim_path) for prim_path in sorted_prim_paths] + + # -- source frame: use relative prim path for unique identification + self._source_frame_body_name = self._get_relative_body_path(self.cfg.prim_path) + source_frame_index = self._target_frame_body_names.index(self._source_frame_body_name) + + # Only remove source frame from tracked bodies if it is not also a target frame + if not self._source_is_also_target_frame: + self._target_frame_body_names.remove(self._source_frame_body_name) + + # Determine indices into all tracked body frames for both source and target frames + all_ids = torch.arange(self._num_envs * len(tracked_body_names)) + self._source_frame_body_ids = torch.arange(self._num_envs) * len(tracked_body_names) + source_frame_index + + # If source frame is also a target frame, then the target frame body ids are the same as + # the source frame body ids + if self._source_is_also_target_frame: + self._target_frame_body_ids = all_ids + else: + self._target_frame_body_ids = all_ids[~torch.isin(all_ids, self._source_frame_body_ids)] + + # The name of each of the target frame(s) - either user specified or defaulted to the body name + self._target_frame_names: list[str] = [] + # The position and rotation components of target frame offsets + target_frame_offset_pos = [] + target_frame_offset_quat = [] + # Stores the indices of bodies that need to be duplicated. For instance, if body "LF_SHANK" is needed + # for 2 frames, this list enables us to duplicate the body to both frames when doing the calculations + # when updating sensor in _update_buffers_impl + duplicate_frame_indices = [] + + # Go through each body name and determine the number of duplicates we need for that frame + # and extract the offsets. This is all done to handle the case where multiple frames + # reference the same body, but have different names and/or offsets + for i, body_name in enumerate(self._target_frame_body_names): + for frame in body_names_to_frames[body_name]["frames"]: + # Only need to handle target frames here as source frame is handled separately + if frame in target_offsets: + target_frame_offset_pos.append(target_offsets[frame]["pos"]) + target_frame_offset_quat.append(target_offsets[frame]["quat"]) + self._target_frame_names.append(frame) + duplicate_frame_indices.append(i) + + # To handle multiple environments, need to expand so [0, 1, 1, 2] with 2 environments becomes + # [0, 1, 1, 2, 3, 4, 4, 5]. Again, this is a optimization to make _update_buffer_impl more efficient + duplicate_frame_indices = torch.tensor(duplicate_frame_indices, device=self.device) + if self._source_is_also_target_frame: + num_target_body_frames = len(tracked_body_names) + else: + num_target_body_frames = len(tracked_body_names) - 1 + + self._duplicate_frame_indices = torch.cat( + [duplicate_frame_indices + num_target_body_frames * env_num for env_num in range(self._num_envs)] + ) + + # Target frame offsets are only applied if at least one of the offsets are non-identity + if self._apply_target_frame_offset: + # Stack up all the frame offsets for shape (num_envs, num_frames, 3) and (num_envs, num_frames, 4) + self._target_frame_offset_pos = torch.stack(target_frame_offset_pos).repeat(self._num_envs, 1) + self._target_frame_offset_quat = torch.stack(target_frame_offset_quat).repeat(self._num_envs, 1) + + # fill the data buffer + self._data.target_frame_names = self._target_frame_names + self._data.source_pos_w = torch.zeros(self._num_envs, 3, device=self._device) + self._data.source_quat_w = torch.zeros(self._num_envs, 4, device=self._device) + self._data.target_pos_w = torch.zeros(self._num_envs, len(duplicate_frame_indices), 3, device=self._device) + self._data.target_quat_w = torch.zeros(self._num_envs, len(duplicate_frame_indices), 4, device=self._device) + self._data.target_pos_source = torch.zeros_like(self._data.target_pos_w) + self._data.target_quat_source = torch.zeros_like(self._data.target_quat_w) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data.""" + # default to all sensors + if len(env_ids) == self._num_envs: + env_ids = ... + + # Extract transforms from view - shape is: + # (the total number of source and target body frames being tracked * self._num_envs, 7) + transforms = self._frame_physx_view.get_transforms() + + # Reorder the transforms to be per environment as is expected of SensorData + transforms = transforms[self._per_env_indices] + + # Convert quaternions as PhysX uses xyzw form + transforms[:, 3:] = convert_quat(transforms[:, 3:], to="wxyz") + + # Process source frame transform + source_frames = transforms[self._source_frame_body_ids] + # Only apply offset if the offsets will result in a coordinate frame transform + if self._apply_source_frame_offset: + source_pos_w, source_quat_w = combine_frame_transforms( + source_frames[:, :3], + source_frames[:, 3:], + self._source_frame_offset_pos, + self._source_frame_offset_quat, + ) + else: + source_pos_w = source_frames[:, :3] + source_quat_w = source_frames[:, 3:] + + # Process target frame transforms + target_frames = transforms[self._target_frame_body_ids] + duplicated_target_frame_pos_w = target_frames[self._duplicate_frame_indices, :3] + duplicated_target_frame_quat_w = target_frames[self._duplicate_frame_indices, 3:] + + # Only apply offset if the offsets will result in a coordinate frame transform + if self._apply_target_frame_offset: + target_pos_w, target_quat_w = combine_frame_transforms( + duplicated_target_frame_pos_w, + duplicated_target_frame_quat_w, + self._target_frame_offset_pos, + self._target_frame_offset_quat, + ) + else: + target_pos_w = duplicated_target_frame_pos_w + target_quat_w = duplicated_target_frame_quat_w + + # Compute the transform of the target frame with respect to the source frame + total_num_frames = len(self._target_frame_names) + target_pos_source, target_quat_source = subtract_frame_transforms( + source_pos_w.unsqueeze(1).expand(-1, total_num_frames, -1).reshape(-1, 3), + source_quat_w.unsqueeze(1).expand(-1, total_num_frames, -1).reshape(-1, 4), + target_pos_w, + target_quat_w, + ) + + # Update buffers + # note: The frame names / ordering don't change so no need to update them after initialization + self._data.source_pos_w[:] = source_pos_w.view(-1, 3) + self._data.source_quat_w[:] = source_quat_w.view(-1, 4) + self._data.target_pos_w[:] = target_pos_w.view(-1, total_num_frames, 3) + self._data.target_quat_w[:] = target_quat_w.view(-1, total_num_frames, 4) + self._data.target_pos_source[:] = target_pos_source.view(-1, total_num_frames, 3) + self._data.target_quat_source[:] = target_quat_source.view(-1, total_num_frames, 4) + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + if not hasattr(self, "frame_visualizer"): + self.frame_visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + + # set their visibility to true + self.frame_visualizer.set_visibility(True) + else: + if hasattr(self, "frame_visualizer"): + self.frame_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # Get the all frames pose + frames_pos = torch.cat([self._data.source_pos_w, self._data.target_pos_w.view(-1, 3)], dim=0) + frames_quat = torch.cat([self._data.source_quat_w, self._data.target_quat_w.view(-1, 4)], dim=0) + + # Get the all connecting lines between frames pose + lines_pos, lines_quat, lines_length = self._get_connecting_lines( + start_pos=self._data.source_pos_w.repeat_interleave(self._data.target_pos_w.size(1), dim=0), + end_pos=self._data.target_pos_w.view(-1, 3), + ) + + # Initialize default (identity) scales and marker indices for all markers (frames + lines) + marker_scales = torch.ones(frames_pos.size(0) + lines_pos.size(0), 3) + marker_indices = torch.zeros(marker_scales.size(0)) + + # Set the z-scale of line markers to represent their actual length + marker_scales[-lines_length.size(0) :, -1] = lines_length + + # Assign marker config index 1 to line markers + marker_indices[-lines_length.size(0) :] = 1 + + # Update the frame and the connecting line visualizer + self.frame_visualizer.visualize( + translations=torch.cat((frames_pos, lines_pos), dim=0), + orientations=torch.cat((frames_quat, lines_quat), dim=0), + scales=marker_scales, + marker_indices=marker_indices, + ) + + """ + Internal simulation callbacks. + """ + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + # call parent + super()._invalidate_initialize_callback(event) + # set all existing views to None to invalidate them + self._frame_physx_view = None + + """ + Internal helpers. + """ + + def _get_connecting_lines( + self, start_pos: torch.Tensor, end_pos: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Draws connecting lines between frames. + + Given start and end points, this function computes the positions (mid-point), orientations, + and lengths of the connecting lines. + + Args: + start_pos: The start positions of the connecting lines. Shape is (N, 3). + end_pos: The end positions of the connecting lines. Shape is (N, 3). + + Returns: + A tuple containing: + - The positions of each connecting line. Shape is (N, 3). + - The orientations of each connecting line in quaternion. Shape is (N, 4). + - The lengths of each connecting line. Shape is (N,). + """ + direction = end_pos - start_pos + lengths = torch.norm(direction, dim=-1) + positions = (start_pos + end_pos) / 2 + + # Get default direction (along z-axis) + default_direction = torch.tensor([0.0, 0.0, 1.0], device=self.device).expand(start_pos.size(0), -1) + + # Normalize direction vector + direction_norm = normalize(direction) + + # Calculate rotation from default direction to target direction + rotation_axis = torch.linalg.cross(default_direction, direction_norm) + rotation_axis_norm = torch.norm(rotation_axis, dim=-1) + + # Handle case where vectors are parallel + mask = rotation_axis_norm > 1e-6 + rotation_axis = torch.where( + mask.unsqueeze(-1), + normalize(rotation_axis), + torch.tensor([1.0, 0.0, 0.0], device=self.device).expand(start_pos.size(0), -1), + ) + + # Calculate rotation angle + cos_angle = torch.sum(default_direction * direction_norm, dim=-1) + cos_angle = torch.clamp(cos_angle, -1.0, 1.0) + angle = torch.acos(cos_angle) + orientations = quat_from_angle_axis(angle, rotation_axis) + + return positions, orientations, lengths + + @staticmethod + def _get_relative_body_path(prim_path: str) -> str: + """Extract a normalized body path from a prim path. + + Removes the environment instance segment `/envs/env_/` to normalize paths + across multiple environments, while preserving the `/envs/` prefix to + distinguish environment-scoped paths from non-environment paths. + + Examples: + - '/World/envs/env_0/Robot/torso' -> '/World/envs/Robot/torso' + - '/World/envs/env_123/Robot/left_hand' -> '/World/envs/Robot/left_hand' + - '/World/Robot' -> '/World/Robot' + - '/World/Robot_2/left_hand' -> '/World/Robot_2/left_hand' + + Args: + prim_path: The full prim path. + + Returns: + The prim path with `/envs/env_/` removed, preserving `/envs/`. + """ + pattern = re.compile(r"/envs/env_[^/]+/") + return pattern.sub("/envs/", prim_path) diff --git a/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_cfg.py b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ca9b528aa1d9b1f048619b181ae5e420abc5a4c3 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_cfg.py @@ -0,0 +1,76 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.markers.config import FRAME_MARKER_CFG, VisualizationMarkersCfg +from isaaclab.utils import configclass + +from ..sensor_base_cfg import SensorBaseCfg +from .frame_transformer import FrameTransformer + + +@configclass +class OffsetCfg: + """The offset pose of one frame relative to another frame.""" + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation (w, x, y, z) w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + +@configclass +class FrameTransformerCfg(SensorBaseCfg): + """Configuration for the frame transformer sensor.""" + + @configclass + class FrameCfg: + """Information specific to a coordinate frame.""" + + prim_path: str = MISSING + """The prim path corresponding to a rigid body. + + This can be a regex pattern to match multiple prims. For example, "/Robot/.*" + will match all prims under "/Robot". + + This means that if the source :attr:`FrameTransformerCfg.prim_path` is "/Robot/base", + and the target :attr:`FrameTransformerCfg.FrameCfg.prim_path` is "/Robot/.*", then + the frame transformer will track the poses of all the prims under "/Robot", + including "/Robot/base" (even though this will result in an identity pose w.r.t. + the source frame). + """ + + name: str | None = None + """User-defined name for the new coordinate frame. Defaults to None. + + If None, then the name is extracted from the leaf of the prim path. + """ + + offset: OffsetCfg = OffsetCfg() + """The pose offset from the parent prim frame.""" + + class_type: type = FrameTransformer + + prim_path: str = MISSING + """The prim path of the body to transform from (source frame).""" + + source_frame_offset: OffsetCfg = OffsetCfg() + """The pose offset from the source prim frame.""" + + target_frames: list[FrameCfg] = MISSING + """A list of the target frames. + + This allows a single FrameTransformer to handle multiple target prims. For example, in a quadruped, + we can use a single FrameTransformer to track each foot's position and orientation in the body + frame using four frame offsets. + """ + + visualizer_cfg: VisualizationMarkersCfg = FRAME_MARKER_CFG.replace(prim_path="/Visuals/FrameTransformer") + """The configuration object for the visualization markers. Defaults to FRAME_MARKER_CFG. + + Note: + This attribute is only used when debug visualization is enabled. + """ diff --git a/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_data.py b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_data.py new file mode 100644 index 0000000000000000000000000000000000000000..942ddbd5144b74a4b3f9856f840d1ee23ef7ec8a --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/frame_transformer/frame_transformer_data.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import dataclass + +import torch + + +@dataclass +class FrameTransformerData: + """Data container for the frame transformer sensor.""" + + target_frame_names: list[str] = None + """Target frame names (this denotes the order in which that frame data is ordered). + + The frame names are resolved from the :attr:`FrameTransformerCfg.FrameCfg.name` field. + This does not necessarily follow the order in which the frames are defined in the config due to + the regex matching. + """ + + target_pos_source: torch.Tensor = None + """Position of the target frame(s) relative to source frame. + + Shape is (N, M, 3), where N is the number of environments, and M is the number of target frames. + """ + + target_quat_source: torch.Tensor = None + """Orientation of the target frame(s) relative to source frame quaternion (w, x, y, z). + + Shape is (N, M, 4), where N is the number of environments, and M is the number of target frames. + """ + + target_pos_w: torch.Tensor = None + """Position of the target frame(s) after offset (in world frame). + + Shape is (N, M, 3), where N is the number of environments, and M is the number of target frames. + """ + + target_quat_w: torch.Tensor = None + """Orientation of the target frame(s) after offset (in world frame) quaternion (w, x, y, z). + + Shape is (N, M, 4), where N is the number of environments, and M is the number of target frames. + """ + + source_pos_w: torch.Tensor = None + """Position of the source frame after offset (in world frame). + + Shape is (N, 3), where N is the number of environments. + """ + + source_quat_w: torch.Tensor = None + """Orientation of the source frame after offset (in world frame) quaternion (w, x, y, z). + + Shape is (N, 4), where N is the number of environments. + """ diff --git a/source/isaaclab/isaaclab/sensors/imu/__init__.py b/source/isaaclab/isaaclab/sensors/imu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7501e41cf49d3cf3d234009f34d4f203b5ff7bc6 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/imu/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Imu Sensor +""" + +from .imu import Imu +from .imu_cfg import ImuCfg +from .imu_data import ImuData diff --git a/source/isaaclab/isaaclab/sensors/imu/imu.py b/source/isaaclab/isaaclab/sensors/imu/imu.py new file mode 100644 index 0000000000000000000000000000000000000000..e092b39502ee8b6973f31e2e2227310ec5aec8d9 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/imu/imu.py @@ -0,0 +1,299 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaacsim.core.simulation_manager import SimulationManager +from pxr import UsdGeom, UsdPhysics + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.markers import VisualizationMarkers + +from ..sensor_base import SensorBase +from .imu_data import ImuData + +if TYPE_CHECKING: + from .imu_cfg import ImuCfg + + +class Imu(SensorBase): + """The Inertia Measurement Unit (IMU) sensor. + + The sensor can be attached to any prim path with a rigid ancestor in its tree and produces body-frame + linear acceleration and angular velocity, along with world-frame pose and body-frame linear and angular + accelerations/velocities. + + If the provided path is not a rigid body, the closest rigid-body ancestor is used for simulation queries. + The fixed transform from that ancestor to the target prim is computed once during initialization and + composed with the configured sensor offset. + + .. note:: + + We are computing the accelerations using numerical differentiation from the velocities. Consequently, the + IMU sensor accuracy depends on the chosen phsyx timestep. For a sufficient accuracy, we recommend to keep the + timestep at least as 200Hz. + + .. note:: + + The user can configure the sensor offset in the configuration file. The offset is applied relative to the + rigid source prim. If the target prim is not a rigid body, the offset is composed with the fixed transform + from the rigid ancestor to the target prim. The offset is applied in the body frame of the rigid source prim. + The offset is defined as a position vector and a quaternion rotation, which + are applied in the order: position, then rotation. The position is applied as a translation + in the body frame of the rigid source prim, and the rotation is applied as a rotation + in the body frame of the rigid source prim. + + """ + + cfg: ImuCfg + """The configuration parameters.""" + + def __init__(self, cfg: ImuCfg): + """Initializes the Imu sensor. + + Args: + cfg: The configuration parameters. + """ + # initialize base class + super().__init__(cfg) + # Create empty variables for storing output data + self._data = ImuData() + + # Internal: expression used to build the rigid body view (may be different from cfg.prim_path) + self._rigid_parent_expr: str | None = None + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Imu sensor @ '{self.cfg.prim_path}': \n" + f"\tview type : {self._view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of sensors : {self._view.count}\n" + ) + + """ + Properties + """ + + @property + def data(self) -> ImuData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + @property + def num_instances(self) -> int: + return self._view.count + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + # reset the timestamps + super().reset(env_ids) + # resolve None + if env_ids is None: + env_ids = slice(None) + # reset accumulative data buffers + self._data.pos_w[env_ids] = 0.0 + self._data.quat_w[env_ids] = 0.0 + self._data.quat_w[env_ids, 0] = 1.0 + self._data.projected_gravity_b[env_ids] = 0.0 + self._data.projected_gravity_b[env_ids, 2] = -1.0 + self._data.lin_vel_b[env_ids] = 0.0 + self._data.ang_vel_b[env_ids] = 0.0 + self._data.lin_acc_b[env_ids] = 0.0 + self._data.ang_acc_b[env_ids] = 0.0 + self._prev_lin_vel_w[env_ids] = 0.0 + self._prev_ang_vel_w[env_ids] = 0.0 + + def update(self, dt: float, force_recompute: bool = False): + # save timestamp + self._dt = dt + # execute updating + super().update(dt, force_recompute) + + """ + Implementation. + """ + + def _initialize_impl(self): + """Initializes the sensor handles and internal buffers. + + - If the target prim path is a rigid body, build the view directly on it. + - Otherwise find the closest rigid-body ancestor, cache the fixed transform from that ancestor + to the target prim, and build the view on the ancestor expression. + """ + # Initialize parent class + super()._initialize_impl() + # obtain global simulation view + self._physics_sim_view = SimulationManager.get_physics_sim_view() + # check if the prim at path is a rigid prim + prim = sim_utils.find_first_matching_prim(self.cfg.prim_path) + if prim is None: + raise RuntimeError(f"Failed to find a prim at path expression: {self.cfg.prim_path}") + + # Find the first matching ancestor prim that implements rigid body API + ancestor_prim = sim_utils.get_first_matching_ancestor_prim( + prim.GetPath(), predicate=lambda _prim: _prim.HasAPI(UsdPhysics.RigidBodyAPI) + ) + if ancestor_prim is None: + raise RuntimeError(f"Failed to find a rigid body ancestor prim at path expression: {self.cfg.prim_path}") + # Convert ancestor prim path to expression + if ancestor_prim == prim: + self._rigid_parent_expr = self.cfg.prim_path + fixed_pos_b, fixed_quat_b = None, None + else: + # Convert ancestor prim path to expression + relative_path = prim.GetPath().MakeRelativePath(ancestor_prim.GetPath()).pathString + self._rigid_parent_expr = self.cfg.prim_path.replace(relative_path, "") + # Resolve the relative pose between the target prim and the ancestor prim + fixed_pos_b, fixed_quat_b = sim_utils.resolve_prim_pose(prim, ancestor_prim) + + # Create the rigid body view on the ancestor + self._view = self._physics_sim_view.create_rigid_body_view(self._rigid_parent_expr.replace(".*", "*")) + + # Get world gravity + gravity = self._physics_sim_view.get_gravity() + gravity_dir = torch.tensor((gravity[0], gravity[1], gravity[2]), device=self.device) + gravity_dir = math_utils.normalize(gravity_dir.unsqueeze(0)).squeeze(0) + self.GRAVITY_VEC_W = gravity_dir.repeat(self.num_instances, 1) + + # Create internal buffers + self._initialize_buffers_impl() + + # Compose the configured offset with the fixed ancestor->target transform (done once) + # new_offset = fixed * cfg.offset + # where composition is: p = p_fixed + R_fixed * p_cfg, q = q_fixed * q_cfg + if fixed_pos_b is not None and fixed_quat_b is not None: + # Broadcast fixed transform across instances + fixed_p = torch.tensor(fixed_pos_b, device=self._device).repeat(self._view.count, 1) + fixed_q = torch.tensor(fixed_quat_b, device=self._device).repeat(self._view.count, 1) + + cfg_p = self._offset_pos_b.clone() + cfg_q = self._offset_quat_b.clone() + + composed_p = fixed_p + math_utils.quat_apply(fixed_q, cfg_p) + composed_q = math_utils.quat_mul(fixed_q, cfg_q) + + self._offset_pos_b = composed_p + self._offset_quat_b = composed_q + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data.""" + + # default to all sensors + if len(env_ids) == self._num_envs: + env_ids = slice(None) + # world pose of the rigid source (ancestor) from the PhysX view + pos_w, quat_w = self._view.get_transforms()[env_ids].split([3, 4], dim=-1) + quat_w = quat_w.roll(1, dims=-1) + + # sensor pose in world: apply composed offset + self._data.pos_w[env_ids] = pos_w + math_utils.quat_apply(quat_w, self._offset_pos_b[env_ids]) + self._data.quat_w[env_ids] = math_utils.quat_mul(quat_w, self._offset_quat_b[env_ids]) + + # COM of rigid source (body frame) + com_pos_b = self._view.get_coms().to(self.device).split([3, 4], dim=-1)[0] + + # Velocities at rigid source COM + lin_vel_w, ang_vel_w = self._view.get_velocities()[env_ids].split([3, 3], dim=-1) + + # If sensor offset or COM != link origin, account for angular velocity contribution + lin_vel_w += torch.linalg.cross( + ang_vel_w, math_utils.quat_apply(quat_w, self._offset_pos_b[env_ids] - com_pos_b[env_ids]), dim=-1 + ) + + # numerical derivative (world frame) + lin_acc_w = (lin_vel_w - self._prev_lin_vel_w[env_ids]) / self._dt + self._gravity_bias_w[env_ids] + ang_acc_w = (ang_vel_w - self._prev_ang_vel_w[env_ids]) / self._dt + + # batch rotate world->body using current sensor orientation + dynamics_data = torch.stack((lin_vel_w, ang_vel_w, lin_acc_w, ang_acc_w, self.GRAVITY_VEC_W[env_ids]), dim=0) + dynamics_data_rot = math_utils.quat_apply_inverse(self._data.quat_w[env_ids].repeat(5, 1), dynamics_data).chunk( + 5, dim=0 + ) + # store the velocities. + self._data.lin_vel_b[env_ids] = dynamics_data_rot[0] + self._data.ang_vel_b[env_ids] = dynamics_data_rot[1] + # store the accelerations + self._data.lin_acc_b[env_ids] = dynamics_data_rot[2] + self._data.ang_acc_b[env_ids] = dynamics_data_rot[3] + # store projected gravity + self._data.projected_gravity_b[env_ids] = dynamics_data_rot[4] + + self._prev_lin_vel_w[env_ids] = lin_vel_w + self._prev_ang_vel_w[env_ids] = ang_vel_w + + def _initialize_buffers_impl(self): + """Create buffers for storing data.""" + # data buffers + self._data.pos_w = torch.zeros(self._view.count, 3, device=self._device) + self._data.quat_w = torch.zeros(self._view.count, 4, device=self._device) + self._data.quat_w[:, 0] = 1.0 + self._data.projected_gravity_b = torch.zeros(self._view.count, 3, device=self._device) + self._data.lin_vel_b = torch.zeros_like(self._data.pos_w) + self._data.ang_vel_b = torch.zeros_like(self._data.pos_w) + self._data.lin_acc_b = torch.zeros_like(self._data.pos_w) + self._data.ang_acc_b = torch.zeros_like(self._data.pos_w) + self._prev_lin_vel_w = torch.zeros_like(self._data.pos_w) + self._prev_ang_vel_w = torch.zeros_like(self._data.pos_w) + + # store sensor offset (applied relative to rigid source). + # This may be composed later with a fixed ancestor->target transform. + self._offset_pos_b = torch.tensor(list(self.cfg.offset.pos), device=self._device).repeat(self._view.count, 1) + self._offset_quat_b = torch.tensor(list(self.cfg.offset.rot), device=self._device).repeat(self._view.count, 1) + # set gravity bias + self._gravity_bias_w = torch.tensor(list(self.cfg.gravity_bias), device=self._device).repeat( + self._view.count, 1 + ) + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if not hasattr(self, "acceleration_visualizer"): + self.acceleration_visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + # set their visibility to true + self.acceleration_visualizer.set_visibility(True) + else: + if hasattr(self, "acceleration_visualizer"): + self.acceleration_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # safely return if view becomes invalid + # note: this invalidity happens because of isaac sim view callbacks + if self._view is None: + return + # get marker location + # -- base state + base_pos_w = self._data.pos_w.clone() + base_pos_w[:, 2] += 0.5 + # -- resolve the scales + default_scale = self.acceleration_visualizer.cfg.markers["arrow"].scale + arrow_scale = torch.tensor(default_scale, device=self.device).repeat(self._data.lin_acc_b.shape[0], 1) + # get up axis of current stage + up_axis = UsdGeom.GetStageUpAxis(self.stage) + # arrow-direction + quat_opengl = math_utils.quat_from_matrix( + math_utils.create_rotation_matrix_from_view( + self._data.pos_w, + self._data.pos_w + math_utils.quat_apply(self._data.quat_w, self._data.lin_acc_b), + up_axis=up_axis, + device=self._device, + ) + ) + quat_w = math_utils.convert_camera_frame_orientation_convention(quat_opengl, "opengl", "world") + # display markers + self.acceleration_visualizer.visualize(base_pos_w, quat_w, arrow_scale) diff --git a/source/isaaclab/isaaclab/sensors/imu/imu_cfg.py b/source/isaaclab/isaaclab/sensors/imu/imu_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..06aeca5fa95b2226771438f8fb3e7a93ffc3db43 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/imu/imu_cfg.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.markers.config import RED_ARROW_X_MARKER_CFG +from isaaclab.utils import configclass + +from ..sensor_base_cfg import SensorBaseCfg +from .imu import Imu + + +@configclass +class ImuCfg(SensorBaseCfg): + """Configuration for an Inertial Measurement Unit (IMU) sensor.""" + + class_type: type = Imu + + @configclass + class OffsetCfg: + """The offset pose of the sensor's frame from the sensor's parent frame.""" + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation (w, x, y, z) w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + offset: OffsetCfg = OffsetCfg() + """The offset pose of the sensor's frame from the sensor's parent frame. Defaults to identity.""" + + visualizer_cfg: VisualizationMarkersCfg = RED_ARROW_X_MARKER_CFG.replace(prim_path="/Visuals/Command/velocity_goal") + """The configuration object for the visualization markers. Defaults to RED_ARROW_X_MARKER_CFG. + + This attribute is only used when debug visualization is enabled. + """ + gravity_bias: tuple[float, float, float] = (0.0, 0.0, 9.81) + """The linear acceleration bias applied to the linear acceleration in the world frame (x,y,z). + + Imu sensors typically output a positive gravity acceleration in opposition to the direction of gravity. This + config parameter allows users to subtract that bias if set to (0.,0.,0.). By default this is set to (0.0,0.0,9.81) + which results in a positive acceleration reading in the world Z. + """ diff --git a/source/isaaclab/isaaclab/sensors/imu/imu_data.py b/source/isaaclab/isaaclab/sensors/imu/imu_data.py new file mode 100644 index 0000000000000000000000000000000000000000..dd06e09a8b79c3c17082284afa502b697204bc66 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/imu/imu_data.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass +class ImuData: + """Data container for the Imu sensor.""" + + pos_w: torch.Tensor = None + """Position of the sensor origin in world frame. + + Shape is (N, 3), where ``N`` is the number of environments. + """ + + quat_w: torch.Tensor = None + """Orientation of the sensor origin in quaternion ``(w, x, y, z)`` in world frame. + + Shape is (N, 4), where ``N`` is the number of environments. + """ + + projected_gravity_b: torch.Tensor = None + """Gravity direction unit vector projected on the imu frame. + + Shape is (N,3), where ``N`` is the number of environments. + """ + + lin_vel_b: torch.Tensor = None + """IMU frame angular velocity relative to the world expressed in IMU frame. + + Shape is (N, 3), where ``N`` is the number of environments. + """ + + ang_vel_b: torch.Tensor = None + """IMU frame angular velocity relative to the world expressed in IMU frame. + + Shape is (N, 3), where ``N`` is the number of environments. + """ + + lin_acc_b: torch.Tensor = None + """IMU frame linear acceleration relative to the world expressed in IMU frame. + + Shape is (N, 3), where ``N`` is the number of environments. + """ + + ang_acc_b: torch.Tensor = None + """IMU frame angular acceleration relative to the world expressed in IMU frame. + + Shape is (N, 3), where ``N`` is the number of environments. + """ diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/__init__.py b/source/isaaclab/isaaclab/sensors/ray_caster/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..06f479ed2ee8f72cdff32073d7e664e3d0e7cf19 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for Warp-based ray-cast sensor. + +The sub-module contains two implementations of the ray-cast sensor: + +- :class:`isaaclab.sensors.ray_caster.RayCaster`: A basic ray-cast sensor that can be used to ray-cast against a single mesh. +- :class:`isaaclab.sensors.ray_caster.MultiMeshRayCaster`: A multi-mesh ray-cast sensor that can be used to ray-cast against + multiple meshes. For these meshes, it tracks their transformations and updates the warp meshes accordingly. + +Corresponding camera implementations are also provided for each of the sensor implementations. Internally, they perform +the same ray-casting operations as the sensor implementations, but return the results as images. +""" + +from . import patterns +from .multi_mesh_ray_caster import MultiMeshRayCaster +from .multi_mesh_ray_caster_camera import MultiMeshRayCasterCamera +from .multi_mesh_ray_caster_camera_cfg import MultiMeshRayCasterCameraCfg +from .multi_mesh_ray_caster_camera_data import MultiMeshRayCasterCameraData +from .multi_mesh_ray_caster_cfg import MultiMeshRayCasterCfg +from .multi_mesh_ray_caster_data import MultiMeshRayCasterData +from .ray_caster import RayCaster +from .ray_caster_camera import RayCasterCamera +from .ray_caster_camera_cfg import RayCasterCameraCfg +from .ray_caster_cfg import RayCasterCfg +from .ray_caster_data import RayCasterData diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..39be0d7ca0d8586a8a4e625edc41af7534624ff4 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster.py @@ -0,0 +1,427 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import re +from collections.abc import Sequence +from typing import TYPE_CHECKING, ClassVar + +import numpy as np +import torch +import trimesh +import warp as wp + +import omni.physics.tensors.impl.api as physx + +import isaaclab.sim as sim_utils +from isaaclab.sim.views import XformPrimView +from isaaclab.utils.math import matrix_from_quat, quat_mul +from isaaclab.utils.mesh import PRIMITIVE_MESH_TYPES, create_trimesh_from_geom_mesh, create_trimesh_from_geom_shape +from isaaclab.utils.warp import convert_to_warp_mesh, raycast_dynamic_meshes + +from .multi_mesh_ray_caster_data import MultiMeshRayCasterData +from .ray_cast_utils import obtain_world_pose_from_view +from .ray_caster import RayCaster + +if TYPE_CHECKING: + from .multi_mesh_ray_caster_cfg import MultiMeshRayCasterCfg + +# import logger +logger = logging.getLogger(__name__) + + +class MultiMeshRayCaster(RayCaster): + """A multi-mesh ray-casting sensor. + + The ray-caster uses a set of rays to detect collisions with meshes in the scene. The rays are + defined in the sensor's local coordinate frame. The sensor can be configured to ray-cast against + a set of meshes with a given ray pattern. + + The meshes are parsed from the list of primitive paths provided in the configuration. These are then + converted to warp meshes and stored in the :attr:`meshes` list. The ray-caster then ray-casts against + these warp meshes using the ray pattern provided in the configuration. + + Compared to the default RayCaster, the MultiMeshRayCaster provides additional functionality and flexibility as + an extension of the default RayCaster with the following enhancements: + + - Raycasting against multiple target types : Supports primitive shapes (spheres, cubes, etc.) as well as arbitrary + meshes. + - Dynamic mesh tracking : Keeps track of specified meshes, enabling raycasting against moving parts + (e.g., robot links, articulated bodies, or dynamic obstacles). + - Memory-efficient caching : Avoids redundant memory usage by reusing mesh data across environments. + + Example usage to raycast against the visual meshes of a robot (e.g. ANYmal): + + .. code-block:: python + + ray_caster_cfg = MultiMeshRayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot", + mesh_prim_paths=[ + "/World/Ground", + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LF_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RF_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/LH_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/RH_.*/visuals"), + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="{ENV_REGEX_NS}/Robot/base/visuals"), + ], + ray_alignment="world", + pattern_cfg=patterns.GridPatternCfg(resolution=0.02, size=(2.5, 2.5), direction=(0, 0, -1)), + ) + + """ + + cfg: MultiMeshRayCasterCfg + """The configuration parameters.""" + + mesh_offsets: dict[str, tuple[torch.Tensor, torch.Tensor]] = {} + + mesh_views: ClassVar[dict[str, XformPrimView | physx.ArticulationView | physx.RigidBodyView]] = {} + """A dictionary to store mesh views for raycasting, shared across all instances. + + The keys correspond to the prim path for the mesh views, and values are the corresponding view objects. + """ + + def __init__(self, cfg: MultiMeshRayCasterCfg): + """Initializes the ray-caster object. + + Args: + cfg: The configuration parameters. + """ + # Initialize base class + super().__init__(cfg) + + # Create empty variables for storing output data + self._num_meshes_per_env: dict[str, int] = {} + """Keeps track of the number of meshes per env for each ray_cast target. + Since we allow regex indexing (e.g. env_*/object_*) they can differ + """ + + self._raycast_targets_cfg: list[MultiMeshRayCasterCfg.RaycastTargetCfg] = [] + for target in self.cfg.mesh_prim_paths: + # Legacy support for string targets. Treat them as global targets. + if isinstance(target, str): + self._raycast_targets_cfg.append(cfg.RaycastTargetCfg(prim_expr=target, track_mesh_transforms=False)) + else: + self._raycast_targets_cfg.append(target) + + # Resolve regex namespace if set + for cfg in self._raycast_targets_cfg: + cfg.prim_expr = cfg.prim_expr.format(ENV_REGEX_NS="/World/envs/env_.*") + + # overwrite the data class + self._data = MultiMeshRayCasterData() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + + return ( + f"Ray-caster @ '{self.cfg.prim_path}': \n" + f"\tview type : {self._view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of meshes : {self._num_envs} x {sum(self._num_meshes_per_env.values())} \n" + f"\tnumber of sensors : {self._view.count}\n" + f"\tnumber of rays/sensor: {self.num_rays}\n" + f"\ttotal number of rays : {self.num_rays * self._view.count}" + ) + + """ + Properties + """ + + @property + def data(self) -> MultiMeshRayCasterData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + """ + Implementation. + """ + + def _initialize_warp_meshes(self): + """Parse mesh prim expressions, build (or reuse) Warp meshes, and cache per-env mesh IDs. + + High-level steps (per target expression): + + 1. Resolve matching prims by regex/path expression. + 2. Collect supported mesh child prims; merge into a single mesh if configured. + 3. Deduplicate identical vertex buffers (exact match) to avoid uploading duplicates to Warp. + 4. Partition mesh IDs per environment or mark as globally shared. + 5. Optionally create physics views (articulation / rigid body / fallback XForm) and cache local offsets. + + Exceptions: + Raises a RuntimeError if: + + - No prims match the provided expression. + - No supported mesh prims are found under a matched prim. + - Multiple mesh prims are found but merging is disabled. + + """ + multi_mesh_ids: dict[str, list[list[int]]] = {} + for target_cfg in self._raycast_targets_cfg: + # target prim path to ray cast against + target_prim_path = target_cfg.prim_expr + # # check if mesh already casted into warp mesh and skip if so. + if target_prim_path in multi_mesh_ids: + logger.warning( + f"Mesh at target prim path '{target_prim_path}' already exists in the mesh cache. Duplicate entries" + " in `mesh_prim_paths`? This mesh will be skipped." + ) + continue + + # find all matching prim paths to provided expression of the target + target_prims = sim_utils.find_matching_prims(target_prim_path) + if len(target_prims) == 0: + raise RuntimeError(f"Failed to find a prim at path expression: {target_prim_path}") + + # If only one prim is found, treat it as a global prim. + # Either it's a single global object (e.g. ground) or we are only using one env. + is_global_prim = len(target_prims) == 1 + + loaded_vertices: list[np.ndarray | None] = [] + wp_mesh_ids = [] + + for target_prim in target_prims: + # Reuse previously parsed shared mesh instance if possible. + if target_cfg.is_shared and len(wp_mesh_ids) > 0: + # Verify if this mesh has already been registered in an earlier environment. + # Note, this check may fail, if the prim path is not following the env_.* pattern + # Which (worst case) leads to parsing the mesh and skipping registering it at a later stage + curr_prim_base_path = re.sub(r"env_\d+", "env_0", str(target_prim.GetPath())) # + if curr_prim_base_path in MultiMeshRayCaster.meshes: + MultiMeshRayCaster.meshes[str(target_prim.GetPath())] = MultiMeshRayCaster.meshes[ + curr_prim_base_path + ] + # Reuse mesh imported by another ray-cast sensor (global cache). + if str(target_prim.GetPath()) in MultiMeshRayCaster.meshes: + wp_mesh_ids.append(MultiMeshRayCaster.meshes[str(target_prim.GetPath())].id) + loaded_vertices.append(None) + continue + + mesh_prims = sim_utils.get_all_matching_child_prims( + target_prim.GetPath(), lambda prim: prim.GetTypeName() in PRIMITIVE_MESH_TYPES + ["Mesh"] + ) + if len(mesh_prims) == 0: + warn_msg = ( + f"No mesh prims found at path: {target_prim.GetPath()} with supported types:" + f" {PRIMITIVE_MESH_TYPES + ['Mesh']}" + " Skipping this target." + ) + for prim in sim_utils.get_all_matching_child_prims(target_prim.GetPath(), lambda prim: True): + warn_msg += f"\n - Available prim '{prim.GetPath()}' of type '{prim.GetTypeName()}'" + logger.warning(warn_msg) + continue + + trimesh_meshes = [] + + for mesh_prim in mesh_prims: + # check if valid + if mesh_prim is None or not mesh_prim.IsValid(): + raise RuntimeError(f"Invalid mesh prim path: {target_prim}") + + if mesh_prim.GetTypeName() == "Mesh": + mesh = create_trimesh_from_geom_mesh(mesh_prim) + else: + mesh = create_trimesh_from_geom_shape(mesh_prim) + scale = sim_utils.resolve_prim_scale(mesh_prim) + mesh.apply_scale(scale) + + relative_pos, relative_quat = sim_utils.resolve_prim_pose(mesh_prim, target_prim) + relative_pos = torch.tensor(relative_pos, dtype=torch.float32) + relative_quat = torch.tensor(relative_quat, dtype=torch.float32) + + rotation = matrix_from_quat(relative_quat) + transform = np.eye(4) + transform[:3, :3] = rotation.numpy() + transform[:3, 3] = relative_pos.numpy() + mesh.apply_transform(transform) + + # add to list of parsed meshes + trimesh_meshes.append(mesh) + + if len(trimesh_meshes) == 1: + trimesh_mesh = trimesh_meshes[0] + elif target_cfg.merge_prim_meshes: + # combine all trimesh meshes into a single mesh + trimesh_mesh = trimesh.util.concatenate(trimesh_meshes) + else: + raise RuntimeError( + f"Multiple mesh prims found at path: {target_prim.GetPath()} but merging is disabled. Please" + " enable `merge_prim_meshes` in the configuration or specify each mesh separately." + ) + + # check if the mesh is already registered, if so only reference the mesh + registered_idx = _registered_points_idx(trimesh_mesh.vertices, loaded_vertices) + if registered_idx != -1 and self.cfg.reference_meshes: + logger.info("Found a duplicate mesh, only reference the mesh.") + # Found a duplicate mesh, only reference the mesh. + loaded_vertices.append(None) + wp_mesh_ids.append(wp_mesh_ids[registered_idx]) + else: + loaded_vertices.append(trimesh_mesh.vertices) + wp_mesh = convert_to_warp_mesh(trimesh_mesh.vertices, trimesh_mesh.faces, device=self.device) + MultiMeshRayCaster.meshes[str(target_prim.GetPath())] = wp_mesh + wp_mesh_ids.append(wp_mesh.id) + + # print info + if registered_idx != -1: + logger.info(f"Found duplicate mesh for mesh prims under path '{target_prim.GetPath()}'.") + else: + logger.info( + f"Read '{len(mesh_prims)}' mesh prims under path '{target_prim.GetPath()}' with" + f" {len(trimesh_mesh.vertices)} vertices and {len(trimesh_mesh.faces)} faces." + ) + + if is_global_prim: + # reference the mesh for each environment to ray cast against + multi_mesh_ids[target_prim_path] = [wp_mesh_ids] * self._num_envs + self._num_meshes_per_env[target_prim_path] = len(wp_mesh_ids) + else: + # split up the meshes for each environment. Little bit ugly, since + # the current order is interleaved (env1_obj1, env1_obj2, env2_obj1, env2_obj2, ...) + multi_mesh_ids[target_prim_path] = [] + mesh_idx = 0 + n_meshes_per_env = len(wp_mesh_ids) // self._num_envs + self._num_meshes_per_env[target_prim_path] = n_meshes_per_env + for _ in range(self._num_envs): + multi_mesh_ids[target_prim_path].append(wp_mesh_ids[mesh_idx : mesh_idx + n_meshes_per_env]) + mesh_idx += n_meshes_per_env + + if target_cfg.track_mesh_transforms: + MultiMeshRayCaster.mesh_views[target_prim_path], MultiMeshRayCaster.mesh_offsets[target_prim_path] = ( + self._obtain_trackable_prim_view(target_prim_path) + ) + + # throw an error if no meshes are found + if all([target_cfg.prim_expr not in multi_mesh_ids for target_cfg in self._raycast_targets_cfg]): + raise RuntimeError( + f"No meshes found for ray-casting! Please check the mesh prim paths: {self.cfg.mesh_prim_paths}" + ) + + total_n_meshes_per_env = sum(self._num_meshes_per_env.values()) + self._mesh_positions_w = torch.zeros(self._num_envs, total_n_meshes_per_env, 3, device=self.device) + self._mesh_orientations_w = torch.zeros(self._num_envs, total_n_meshes_per_env, 4, device=self.device) + + # Update the mesh positions and rotations + mesh_idx = 0 + for target_cfg in self._raycast_targets_cfg: + n_meshes = self._num_meshes_per_env[target_cfg.prim_expr] + + # update position of the target meshes + pos_w, ori_w = [], [] + for prim in sim_utils.find_matching_prims(target_cfg.prim_expr): + translation, quat = sim_utils.resolve_prim_pose(prim) + pos_w.append(translation) + ori_w.append(quat) + pos_w = torch.tensor(pos_w, device=self.device, dtype=torch.float32).view(-1, n_meshes, 3) + ori_w = torch.tensor(ori_w, device=self.device, dtype=torch.float32).view(-1, n_meshes, 4) + + self._mesh_positions_w[:, mesh_idx : mesh_idx + n_meshes] = pos_w + self._mesh_orientations_w[:, mesh_idx : mesh_idx + n_meshes] = ori_w + mesh_idx += n_meshes + + # flatten the list of meshes that are included in mesh_prim_paths of the specific ray caster + multi_mesh_ids_flattened = [] + for env_idx in range(self._num_envs): + meshes_in_env = [] + for target_cfg in self._raycast_targets_cfg: + meshes_in_env.extend(multi_mesh_ids[target_cfg.prim_expr][env_idx]) + multi_mesh_ids_flattened.append(meshes_in_env) + + self._mesh_views = [ + self.mesh_views[target_cfg.prim_expr] if target_cfg.track_mesh_transforms else None + for target_cfg in self._raycast_targets_cfg + ] + + # save a warp array with mesh ids that is passed to the raycast function + self._mesh_ids_wp = wp.array2d(multi_mesh_ids_flattened, dtype=wp.uint64, device=self.device) + + def _initialize_rays_impl(self): + super()._initialize_rays_impl() + if self.cfg.update_mesh_ids: + self._data.ray_mesh_ids = torch.zeros( + self._num_envs, self.num_rays, 1, device=self.device, dtype=torch.int16 + ) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data. + + Args: + env_ids: The environment ids to update. + """ + + self._update_ray_infos(env_ids) + + # Update the mesh positions and rotations + mesh_idx = 0 + for view, target_cfg in zip(self._mesh_views, self._raycast_targets_cfg): + if not target_cfg.track_mesh_transforms: + mesh_idx += self._num_meshes_per_env[target_cfg.prim_expr] + continue + + # update position of the target meshes + pos_w, ori_w = obtain_world_pose_from_view(view, None) + pos_w = pos_w.squeeze(0) if len(pos_w.shape) == 3 else pos_w + ori_w = ori_w.squeeze(0) if len(ori_w.shape) == 3 else ori_w + + if target_cfg.prim_expr in MultiMeshRayCaster.mesh_offsets: + pos_offset, ori_offset = MultiMeshRayCaster.mesh_offsets[target_cfg.prim_expr] + pos_w -= pos_offset + ori_w = quat_mul(ori_offset.expand(ori_w.shape[0], -1), ori_w) + + count = view.count + if count != 1: # Mesh is not global, i.e. we have different meshes for each env + count = count // self._num_envs + pos_w = pos_w.view(self._num_envs, count, 3) + ori_w = ori_w.view(self._num_envs, count, 4) + + self._mesh_positions_w[:, mesh_idx : mesh_idx + count] = pos_w + self._mesh_orientations_w[:, mesh_idx : mesh_idx + count] = ori_w + mesh_idx += count + + self._data.ray_hits_w[env_ids], _, _, _, mesh_ids = raycast_dynamic_meshes( + self._ray_starts_w[env_ids], + self._ray_directions_w[env_ids], + mesh_ids_wp=self._mesh_ids_wp, # list with shape num_envs x num_meshes_per_env + max_dist=self.cfg.max_distance, + mesh_positions_w=self._mesh_positions_w[env_ids], + mesh_orientations_w=self._mesh_orientations_w[env_ids], + return_mesh_id=self.cfg.update_mesh_ids, + ) + + if self.cfg.update_mesh_ids: + self._data.ray_mesh_ids[env_ids] = mesh_ids + + def __del__(self): + super().__del__() + if RayCaster._instance_count == 0: + MultiMeshRayCaster.mesh_offsets.clear() + MultiMeshRayCaster.mesh_views.clear() + + +""" +Helper functions +""" + + +def _registered_points_idx(points: np.ndarray, registered_points: list[np.ndarray | None]) -> int: + """Check if the points are already registered in the list of registered points. + + Args: + points: The points to check. + registered_points: The list of registered points. + + Returns: + The index of the registered points if found, otherwise -1. + """ + for idx, reg_points in enumerate(registered_points): + if reg_points is None: + continue + if reg_points.shape == points.shape and (reg_points == points).all(): + return idx + return -1 diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..970860fa50ae8df34a1229d5729a22c7ca6836dc --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera.py @@ -0,0 +1,221 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.utils.warp import raycast_dynamic_meshes + +from .multi_mesh_ray_caster import MultiMeshRayCaster +from .multi_mesh_ray_caster_camera_data import MultiMeshRayCasterCameraData +from .ray_cast_utils import obtain_world_pose_from_view +from .ray_caster_camera import RayCasterCamera + +if TYPE_CHECKING: + from .multi_mesh_ray_caster_camera_cfg import MultiMeshRayCasterCameraCfg + + +class MultiMeshRayCasterCamera(RayCasterCamera, MultiMeshRayCaster): + """A multi-mesh ray-casting camera sensor. + + The ray-caster camera uses a set of rays to get the distances to meshes in the scene. The rays are + defined in the sensor's local coordinate frame. The sensor has the same interface as the + :class:`isaaclab.sensors.Camera` that implements the camera class through USD camera prims. + However, this class provides a faster image generation. The sensor converts meshes from the list of + primitive paths provided in the configuration to Warp meshes. The camera then ray-casts against these + Warp meshes only. + + Currently, only the following annotators are supported: + + - ``"distance_to_camera"``: An image containing the distance to camera optical center. + - ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis. + - ``"normals"``: An image containing the local surface normal vectors at each pixel. + """ + + cfg: MultiMeshRayCasterCameraCfg + """The configuration parameters.""" + + def __init__(self, cfg: MultiMeshRayCasterCameraCfg): + """Initializes the camera object. + + Args: + cfg: The configuration parameters. + + Raises: + ValueError: If the provided data types are not supported by the ray-caster camera. + """ + self._check_supported_data_types(cfg) + # initialize base class + MultiMeshRayCaster.__init__(self, cfg) + # create empty variables for storing output data + self._data = MultiMeshRayCasterCameraData() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Multi-Mesh Ray-Caster-Camera @ '{self.cfg.prim_path}': \n" + f"\tview type : {self._view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of meshes : {len(MultiMeshRayCaster.meshes)}\n" + f"\tnumber of sensors : {self._view.count}\n" + f"\tnumber of rays/sensor: {self.num_rays}\n" + f"\ttotal number of rays : {self.num_rays * self._view.count}\n" + f"\timage shape : {self.image_shape}" + ) + + """ + Implementation. + """ + + def _initialize_warp_meshes(self): + MultiMeshRayCaster._initialize_warp_meshes(self) + + def _create_buffers(self): + super()._create_buffers() + self._data.image_mesh_ids = torch.zeros( + self._num_envs, *self.image_shape, 1, device=self.device, dtype=torch.int16 + ) + + def _initialize_rays_impl(self): + # Create all indices buffer + self._ALL_INDICES = torch.arange(self._view.count, device=self._device, dtype=torch.long) + # Create frame count buffer + self._frame = torch.zeros(self._view.count, device=self._device, dtype=torch.long) + # create buffers + self._create_buffers() + # compute intrinsic matrices + self._compute_intrinsic_matrices() + # compute ray stars and directions + self.ray_starts, self.ray_directions = self.cfg.pattern_cfg.func( + self.cfg.pattern_cfg, self._data.intrinsic_matrices, self._device + ) + self.num_rays = self.ray_directions.shape[1] + # create buffer to store ray hits + self.ray_hits_w = torch.zeros(self._view.count, self.num_rays, 3, device=self._device) + # set offsets + quat_w = math_utils.convert_camera_frame_orientation_convention( + torch.tensor([self.cfg.offset.rot], device=self._device), origin=self.cfg.offset.convention, target="world" + ) + self._offset_quat = quat_w.repeat(self._view.count, 1) + self._offset_pos = torch.tensor(list(self.cfg.offset.pos), device=self._device).repeat(self._view.count, 1) + + self._data.quat_w = torch.zeros(self._view.count, 4, device=self.device) + self._data.pos_w = torch.zeros(self._view.count, 3, device=self.device) + + self._ray_starts_w = torch.zeros(self._view.count, self.num_rays, 3, device=self.device) + self._ray_directions_w = torch.zeros(self._view.count, self.num_rays, 3, device=self.device) + + def _update_ray_infos(self, env_ids: Sequence[int]): + """Updates the ray information buffers.""" + + # compute poses from current view + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids] + ) + # update the data + self._data.pos_w[env_ids] = pos_w + self._data.quat_w_world[env_ids] = quat_w + self._data.quat_w_ros[env_ids] = quat_w + + # note: full orientation is considered + ray_starts_w = math_utils.quat_apply(quat_w.repeat(1, self.num_rays), self.ray_starts[env_ids]) + ray_starts_w += pos_w.unsqueeze(1) + ray_directions_w = math_utils.quat_apply(quat_w.repeat(1, self.num_rays), self.ray_directions[env_ids]) + + self._ray_starts_w[env_ids] = ray_starts_w + self._ray_directions_w[env_ids] = ray_directions_w + + def _update_buffers_impl(self, env_ids: Sequence[int] | torch.Tensor | None): + """Fills the buffers of the sensor data.""" + self._update_ray_infos(env_ids) + + # increment frame count + if env_ids is None: + env_ids = torch.arange(self._num_envs, device=self.device) + elif not isinstance(env_ids, torch.Tensor): + env_ids = torch.tensor(env_ids, device=self.device) + + self._frame[env_ids] += 1 + + # Update the mesh positions and rotations + mesh_idx = 0 + for view, target_cfg in zip(self._mesh_views, self._raycast_targets_cfg): + if not target_cfg.track_mesh_transforms: + mesh_idx += self._num_meshes_per_env[target_cfg.prim_expr] + continue + + # update position of the target meshes + pos_w, ori_w = obtain_world_pose_from_view(view, None) + pos_w = pos_w.squeeze(0) if len(pos_w.shape) == 3 else pos_w + ori_w = ori_w.squeeze(0) if len(ori_w.shape) == 3 else ori_w + + if target_cfg.prim_expr in MultiMeshRayCaster.mesh_offsets: + pos_offset, ori_offset = MultiMeshRayCaster.mesh_offsets[target_cfg.prim_expr] + pos_w -= pos_offset + ori_w = math_utils.quat_mul(ori_offset.expand(ori_w.shape[0], -1), ori_w) + + count = view.count + if count != 1: # Mesh is not global, i.e. we have different meshes for each env + count = count // self._num_envs + pos_w = pos_w.view(self._num_envs, count, 3) + ori_w = ori_w.view(self._num_envs, count, 4) + + self._mesh_positions_w[:, mesh_idx : mesh_idx + count] = pos_w + self._mesh_orientations_w[:, mesh_idx : mesh_idx + count] = ori_w + mesh_idx += count + + # ray cast and store the hits + self.ray_hits_w[env_ids], ray_depth, ray_normal, _, ray_mesh_ids = raycast_dynamic_meshes( + self._ray_starts_w[env_ids], + self._ray_directions_w[env_ids], + mesh_ids_wp=self._mesh_ids_wp, # list with shape num_envs x num_meshes_per_env + max_dist=self.cfg.max_distance, + mesh_positions_w=self._mesh_positions_w[env_ids], + mesh_orientations_w=self._mesh_orientations_w[env_ids], + return_distance=any( + [name in self.cfg.data_types for name in ["distance_to_image_plane", "distance_to_camera"]] + ), + return_normal="normals" in self.cfg.data_types, + return_mesh_id=self.cfg.update_mesh_ids, + ) + + # update output buffers + if "distance_to_image_plane" in self.cfg.data_types: + # note: data is in camera frame so we only take the first component (z-axis of camera frame) + distance_to_image_plane = ( + math_utils.quat_apply( + math_utils.quat_inv(self._data.quat_w_world[env_ids]).repeat(1, self.num_rays), + (ray_depth[:, :, None] * self._ray_directions_w[env_ids]), + ) + )[:, :, 0] + # apply the maximum distance after the transformation + if self.cfg.depth_clipping_behavior == "max": + distance_to_image_plane = torch.clip(distance_to_image_plane, max=self.cfg.max_distance) + distance_to_image_plane[torch.isnan(distance_to_image_plane)] = self.cfg.max_distance + elif self.cfg.depth_clipping_behavior == "zero": + distance_to_image_plane[distance_to_image_plane > self.cfg.max_distance] = 0.0 + distance_to_image_plane[torch.isnan(distance_to_image_plane)] = 0.0 + self._data.output["distance_to_image_plane"][env_ids] = distance_to_image_plane.view( + -1, *self.image_shape, 1 + ) + + if "distance_to_camera" in self.cfg.data_types: + if self.cfg.depth_clipping_behavior == "max": + ray_depth = torch.clip(ray_depth, max=self.cfg.max_distance) + elif self.cfg.depth_clipping_behavior == "zero": + ray_depth[ray_depth > self.cfg.max_distance] = 0.0 + self._data.output["distance_to_camera"][env_ids] = ray_depth.view(-1, *self.image_shape, 1) + + if "normals" in self.cfg.data_types: + self._data.output["normals"][env_ids] = ray_normal.view(-1, *self.image_shape, 3) + + if self.cfg.update_mesh_ids: + self._data.image_mesh_ids[env_ids] = ray_mesh_ids.view(-1, *self.image_shape, 1) diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_cfg.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..45df51ce6d80b3e9a123055b096a2f80c4d3511e --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_cfg.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ray-cast camera sensor.""" + +import logging + +from isaaclab.utils import configclass + +from .multi_mesh_ray_caster_camera import MultiMeshRayCasterCamera +from .multi_mesh_ray_caster_cfg import MultiMeshRayCasterCfg +from .ray_caster_camera_cfg import RayCasterCameraCfg + +# import logger +logger = logging.getLogger(__name__) + + +@configclass +class MultiMeshRayCasterCameraCfg(RayCasterCameraCfg, MultiMeshRayCasterCfg): + """Configuration for the multi-mesh ray-cast camera sensor.""" + + class_type: type = MultiMeshRayCasterCamera + + def __post_init__(self): + super().__post_init__() + + # Camera only supports 'base' ray alignment. Ensure this is set correctly. + if self.ray_alignment != "base": + logger.warning( + "Ray alignment for MultiMeshRayCasterCameraCfg only supports 'base' alignment. Overriding from" + f"'{self.ray_alignment}' to 'base'." + ) + self.ray_alignment = "base" diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_data.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_data.py new file mode 100644 index 0000000000000000000000000000000000000000..d2f26abdbf47a3782becf37b689fb53846f1c1aa --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_camera_data.py @@ -0,0 +1,23 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Data container for the multi-mesh ray-cast camera sensor.""" + +import torch + +from isaaclab.sensors.camera import CameraData + +from .ray_caster_data import RayCasterData + + +class MultiMeshRayCasterCameraData(CameraData, RayCasterData): + """Data container for the multi-mesh ray-cast sensor.""" + + image_mesh_ids: torch.Tensor = None + """The mesh ids of the image pixels. + + Shape is (N, H, W, 1), where N is the number of sensors, H and W are the height and width of the image, + and 1 is the number of mesh ids per pixel. + """ diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_cfg.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f5393920162a375677a4099d65f579a05521f0da --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_cfg.py @@ -0,0 +1,71 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Configuration for the ray-cast sensor.""" + +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from .multi_mesh_ray_caster import MultiMeshRayCaster +from .ray_caster_cfg import RayCasterCfg + + +@configclass +class MultiMeshRayCasterCfg(RayCasterCfg): + """Configuration for the multi-mesh ray-cast sensor.""" + + @configclass + class RaycastTargetCfg: + """Configuration for different ray-cast targets.""" + + prim_expr: str = MISSING + """The regex to specify the target prim to ray cast against.""" + + is_shared: bool = False + """Whether the target prim is assumed to be the same mesh across all environments. Defaults to False. + + If True, only the first mesh is read and then reused for all environments, rather than re-parsed. + This provides a startup performance boost when there are many environments that all use the same asset. + + .. note:: + If :attr:`MultiMeshRayCasterCfg.reference_meshes` is False, this flag has no effect. + """ + + merge_prim_meshes: bool = True + """Whether to merge the parsed meshes for a prim that contains multiple meshes. Defaults to True. + + This will create a new mesh that combines all meshes in the parsed prim. The raycast hits mesh IDs + will then refer to the single merged mesh. + """ + + track_mesh_transforms: bool = True + """Whether the mesh transformations should be tracked. Defaults to True. + + .. note:: + Not tracking the mesh transformations is recommended when the meshes are static to increase performance. + """ + + class_type: type = MultiMeshRayCaster + + mesh_prim_paths: list[str | RaycastTargetCfg] = MISSING + """The list of mesh primitive paths to ray cast against. + + If an entry is a string, it is internally converted to :class:`RaycastTargetCfg` with + :attr:`~RaycastTargetCfg.track_mesh_transforms` disabled. These settings ensure backwards compatibility + with the default raycaster. + """ + + update_mesh_ids: bool = False + """Whether to update the mesh ids of the ray hits in the :attr:`data` container.""" + + reference_meshes: bool = True + """Whether to reference duplicated meshes instead of loading each one separately into memory. + Defaults to True. + + When enabled, the raycaster parses all meshes in all environments, but reuses references + for duplicates instead of storing multiple copies. This reduces memory footprint. + """ diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_data.py b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_data.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ae187591bea20b4cd1730ffe3f9b30564485a5 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/multi_mesh_ray_caster_data.py @@ -0,0 +1,22 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Data container for the multi-mesh ray-cast sensor.""" + +import torch + +from .ray_caster_data import RayCasterData + + +class MultiMeshRayCasterData(RayCasterData): + """Data container for the multi-mesh ray-cast sensor.""" + + ray_mesh_ids: torch.Tensor = None + """The mesh ids of the ray hits. + + Shape is (N, B, 1), where N is the number of sensors, B is the number of rays + in the scan pattern per sensor. + """ diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/patterns/__init__.py b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d43f5437ce01606207682700e765a5c7a41cd8af --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for ray-casting patterns used by the ray-caster.""" + +from .patterns import bpearl_pattern, grid_pattern, lidar_pattern, pinhole_camera_pattern +from .patterns_cfg import BpearlPatternCfg, GridPatternCfg, LidarPatternCfg, PatternBaseCfg, PinholeCameraPatternCfg diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns.py b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns.py new file mode 100644 index 0000000000000000000000000000000000000000..d5255f64c75ec5a19009be206ddce43fa4cc2550 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns.py @@ -0,0 +1,180 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from . import patterns_cfg + + +def grid_pattern(cfg: patterns_cfg.GridPatternCfg, device: str) -> tuple[torch.Tensor, torch.Tensor]: + """A regular grid pattern for ray casting. + + The grid pattern is made from rays that are parallel to each other. They span a 2D grid in the sensor's + local coordinates from ``(-length/2, -width/2)`` to ``(length/2, width/2)``, which is defined + by the ``size = (length, width)`` and ``resolution`` parameters in the config. + + Args: + cfg: The configuration instance for the pattern. + device: The device to create the pattern on. + + Returns: + The starting positions and directions of the rays. + + Raises: + ValueError: If the ordering is not "xy" or "yx". + ValueError: If the resolution is less than or equal to 0. + """ + # check valid arguments + if cfg.ordering not in ["xy", "yx"]: + raise ValueError(f"Ordering must be 'xy' or 'yx'. Received: '{cfg.ordering}'.") + if cfg.resolution <= 0: + raise ValueError(f"Resolution must be greater than 0. Received: '{cfg.resolution}'.") + + # resolve mesh grid indexing (note: torch meshgrid is different from numpy meshgrid) + # check: https://github.com/pytorch/pytorch/issues/15301 + indexing = cfg.ordering if cfg.ordering == "xy" else "ij" + # define grid pattern + x = torch.arange(start=-cfg.size[0] / 2, end=cfg.size[0] / 2 + 1.0e-9, step=cfg.resolution, device=device) + y = torch.arange(start=-cfg.size[1] / 2, end=cfg.size[1] / 2 + 1.0e-9, step=cfg.resolution, device=device) + grid_x, grid_y = torch.meshgrid(x, y, indexing=indexing) + + # store into ray starts + num_rays = grid_x.numel() + ray_starts = torch.zeros(num_rays, 3, device=device) + ray_starts[:, 0] = grid_x.flatten() + ray_starts[:, 1] = grid_y.flatten() + + # define ray-cast directions + ray_directions = torch.zeros_like(ray_starts) + ray_directions[..., :] = torch.tensor(list(cfg.direction), device=device) + + return ray_starts, ray_directions + + +def pinhole_camera_pattern( + cfg: patterns_cfg.PinholeCameraPatternCfg, intrinsic_matrices: torch.Tensor, device: str +) -> tuple[torch.Tensor, torch.Tensor]: + """The image pattern for ray casting. + + .. caution:: + This function does not follow the standard pattern interface. It requires the intrinsic matrices + of the cameras to be passed in. This is because we want to be able to randomize the intrinsic + matrices of the cameras, which is not possible with the standard pattern interface. + + Args: + cfg: The configuration instance for the pattern. + intrinsic_matrices: The intrinsic matrices of the cameras. Shape is (N, 3, 3). + device: The device to create the pattern on. + + Returns: + The starting positions and directions of the rays. The shape of the tensors are + (N, H * W, 3) and (N, H * W, 3) respectively. + """ + # get image plane mesh grid + grid = torch.meshgrid( + torch.arange(start=0, end=cfg.width, dtype=torch.int32, device=device), + torch.arange(start=0, end=cfg.height, dtype=torch.int32, device=device), + indexing="xy", + ) + pixels = torch.vstack(list(map(torch.ravel, grid))).T + # convert to homogeneous coordinate system + pixels = torch.hstack([pixels, torch.ones((len(pixels), 1), device=device)]) + # move each pixel coordinate to the center of the pixel + pixels += torch.tensor([[0.5, 0.5, 0]], device=device) + # get pixel coordinates in camera frame + pix_in_cam_frame = torch.matmul(torch.inverse(intrinsic_matrices), pixels.T) + + # robotics camera frame is (x forward, y left, z up) from camera frame with (x right, y down, z forward) + # transform to robotics camera frame + transform_vec = torch.tensor([1, -1, -1], device=device).unsqueeze(0).unsqueeze(2) + pix_in_cam_frame = pix_in_cam_frame[:, [2, 0, 1], :] * transform_vec + # normalize ray directions + ray_directions = (pix_in_cam_frame / torch.norm(pix_in_cam_frame, dim=1, keepdim=True)).permute(0, 2, 1) + # for camera, we always ray-cast from the sensor's origin + ray_starts = torch.zeros_like(ray_directions, device=device) + + return ray_starts, ray_directions + + +def bpearl_pattern(cfg: patterns_cfg.BpearlPatternCfg, device: str) -> tuple[torch.Tensor, torch.Tensor]: + """The RS-Bpearl pattern for ray casting. + + The `Robosense RS-Bpearl`_ is a short-range LiDAR that has a 360 degrees x 90 degrees super wide + field of view. It is designed for near-field blind-spots detection. + + .. _Robosense RS-Bpearl: https://www.roscomponents.com/product/rs-bpearl/ + + Args: + cfg: The configuration instance for the pattern. + device: The device to create the pattern on. + + Returns: + The starting positions and directions of the rays. + """ + h = torch.arange(-cfg.horizontal_fov / 2, cfg.horizontal_fov / 2, cfg.horizontal_res, device=device) + v = torch.tensor(list(cfg.vertical_ray_angles), device=device) + + pitch, yaw = torch.meshgrid(v, h, indexing="xy") + pitch, yaw = torch.deg2rad(pitch.reshape(-1)), torch.deg2rad(yaw.reshape(-1)) + pitch += torch.pi / 2 + x = torch.sin(pitch) * torch.cos(yaw) + y = torch.sin(pitch) * torch.sin(yaw) + z = torch.cos(pitch) + + ray_directions = -torch.stack([x, y, z], dim=1) + ray_starts = torch.zeros_like(ray_directions) + return ray_starts, ray_directions + + +def lidar_pattern(cfg: patterns_cfg.LidarPatternCfg, device: str) -> tuple[torch.Tensor, torch.Tensor]: + """Lidar sensor pattern for ray casting. + + Args: + cfg: The configuration instance for the pattern. + device: The device to create the pattern on. + + Returns: + The starting positions and directions of the rays. + """ + # Vertical angles + vertical_angles = torch.linspace(cfg.vertical_fov_range[0], cfg.vertical_fov_range[1], cfg.channels) + + # If the horizontal field of view is 360 degrees, exclude the last point to avoid overlap + if abs(abs(cfg.horizontal_fov_range[0] - cfg.horizontal_fov_range[1]) - 360.0) < 1e-6: + up_to = -1 + else: + up_to = None + + # Horizontal angles + num_horizontal_angles = math.ceil((cfg.horizontal_fov_range[1] - cfg.horizontal_fov_range[0]) / cfg.horizontal_res) + horizontal_angles = torch.linspace(cfg.horizontal_fov_range[0], cfg.horizontal_fov_range[1], num_horizontal_angles)[ + :up_to + ] + + # Convert degrees to radians + vertical_angles_rad = torch.deg2rad(vertical_angles) + horizontal_angles_rad = torch.deg2rad(horizontal_angles) + + # Meshgrid to create a 2D array of angles + v_angles, h_angles = torch.meshgrid(vertical_angles_rad, horizontal_angles_rad, indexing="ij") + + # Spherical to Cartesian conversion (assuming Z is up) + x = torch.cos(v_angles) * torch.cos(h_angles) + y = torch.cos(v_angles) * torch.sin(h_angles) + z = torch.sin(v_angles) + + # Ray directions + ray_directions = torch.stack([x, y, z], dim=-1).reshape(-1, 3).to(device) + + # Ray starts: Assuming all rays originate from (0,0,0) + ray_starts = torch.zeros_like(ray_directions).to(device) + + return ray_starts, ray_directions diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns_cfg.py b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f50ba272b70864edadf5d8ad7d718fc63a9a607d --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/patterns/patterns_cfg.py @@ -0,0 +1,219 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ray-cast sensor.""" + +from __future__ import annotations + +from collections.abc import Callable, Sequence +from dataclasses import MISSING +from typing import Literal + +import torch + +from isaaclab.utils import configclass + +from . import patterns + + +@configclass +class PatternBaseCfg: + """Base configuration for a pattern.""" + + func: Callable[[PatternBaseCfg, str], tuple[torch.Tensor, torch.Tensor]] = MISSING + """Function to generate the pattern. + + The function should take in the configuration and the device name as arguments. It should return + the pattern's starting positions and directions as a tuple of torch.Tensor. + """ + + +@configclass +class GridPatternCfg(PatternBaseCfg): + """Configuration for the grid pattern for ray-casting. + + Defines a 2D grid of rays in the coordinates of the sensor. + + .. attention:: + The points are ordered based on the :attr:`ordering` attribute. + + """ + + func: Callable = patterns.grid_pattern + + resolution: float = MISSING + """Grid resolution (in meters).""" + + size: tuple[float, float] = MISSING + """Grid size (length, width) (in meters).""" + + direction: tuple[float, float, float] = (0.0, 0.0, -1.0) + """Ray direction. Defaults to (0.0, 0.0, -1.0).""" + + ordering: Literal["xy", "yx"] = "xy" + """Specifies the ordering of points in the generated grid. Defaults to ``"xy"``. + + Consider a grid pattern with points at :math:`(x, y)` where :math:`x` and :math:`y` are the grid indices. + The ordering of the points can be specified as "xy" or "yx". This determines the inner and outer loop order + when iterating over the grid points. + + * If "xy" is selected, the points are ordered with inner loop over "x" and outer loop over "y". + * If "yx" is selected, the points are ordered with inner loop over "y" and outer loop over "x". + + For example, the grid pattern points with :math:`X = (0, 1, 2)` and :math:`Y = (3, 4)`: + + * "xy" ordering: :math:`[(0, 3), (1, 3), (2, 3), (1, 4), (2, 4), (2, 4)]` + * "yx" ordering: :math:`[(0, 3), (0, 4), (1, 3), (1, 4), (2, 3), (2, 4)]` + """ + + +@configclass +class PinholeCameraPatternCfg(PatternBaseCfg): + """Configuration for a pinhole camera depth image pattern for ray-casting. + + .. caution:: + Focal length as well as the aperture sizes and offsets are set as a tenth of the world unit. In our case, the + world unit is meters, so all of these values are in cm. For more information, please check: + https://docs.omniverse.nvidia.com/materials-and-rendering/latest/cameras.html + """ + + func: Callable = patterns.pinhole_camera_pattern + + focal_length: float = 24.0 + """Perspective focal length (in cm). Defaults to 24.0cm. + + Longer lens lengths narrower FOV, shorter lens lengths wider FOV. + """ + + horizontal_aperture: float = 20.955 + """Horizontal aperture (in cm). Defaults to 20.955 cm. + + Emulates sensor/film width on a camera. + + Note: + The default value is the horizontal aperture of a 35 mm spherical projector. + """ + vertical_aperture: float | None = None + r"""Vertical aperture (in cm). Defaults to None. + + Emulates sensor/film height on a camera. If None, then the vertical aperture is calculated based on the + horizontal aperture and the aspect ratio of the image to maintain squared pixels. In this case, the vertical + aperture is calculated as: + + .. math:: + \text{vertical aperture} = \text{horizontal aperture} \times \frac{\text{height}}{\text{width}} + """ + + horizontal_aperture_offset: float = 0.0 + """Offsets Resolution/Film gate horizontally. Defaults to 0.0.""" + + vertical_aperture_offset: float = 0.0 + """Offsets Resolution/Film gate vertically. Defaults to 0.0.""" + + width: int = MISSING + """Width of the image (in pixels).""" + + height: int = MISSING + """Height of the image (in pixels).""" + + @classmethod + def from_intrinsic_matrix( + cls, + intrinsic_matrix: list[float], + width: int, + height: int, + focal_length: float = 24.0, + ) -> PinholeCameraPatternCfg: + r"""Create a :class:`PinholeCameraPatternCfg` class instance from an intrinsic matrix. + + The intrinsic matrix is a 3x3 matrix that defines the mapping between the 3D world coordinates and + the 2D image. The matrix is defined as: + + .. math:: + I_{cam} = \begin{bmatrix} + f_x & 0 & c_x \\ + 0 & f_y & c_y \\ + 0 & 0 & 1 + \end{bmatrix}, + + where :math:`f_x` and :math:`f_y` are the focal length along x and y direction, while + :math:`c_x` and :math:`c_y` are the principle point offsets along x and y direction, respectively. + + Args: + intrinsic_matrix: Intrinsic matrix of the camera in row-major format. + The matrix is defined as [f_x, 0, c_x, 0, f_y, c_y, 0, 0, 1]. Shape is (9,). + width: Width of the image (in pixels). + height: Height of the image (in pixels). + focal_length: Focal length of the camera (in cm). Defaults to 24.0 cm. + + Returns: + An instance of the :class:`PinholeCameraPatternCfg` class. + """ + # extract parameters from matrix + f_x = intrinsic_matrix[0] + c_x = intrinsic_matrix[2] + f_y = intrinsic_matrix[4] + c_y = intrinsic_matrix[5] + # resolve parameters for usd camera + horizontal_aperture = width * focal_length / f_x + vertical_aperture = height * focal_length / f_y + horizontal_aperture_offset = (c_x - width / 2) / f_x + vertical_aperture_offset = (c_y - height / 2) / f_y + + return cls( + focal_length=focal_length, + horizontal_aperture=horizontal_aperture, + vertical_aperture=vertical_aperture, + horizontal_aperture_offset=horizontal_aperture_offset, + vertical_aperture_offset=vertical_aperture_offset, + width=width, + height=height, + ) + + +@configclass +class BpearlPatternCfg(PatternBaseCfg): + """Configuration for the Bpearl pattern for ray-casting.""" + + func: Callable = patterns.bpearl_pattern + + horizontal_fov: float = 360.0 + """Horizontal field of view (in degrees). Defaults to 360.0.""" + + horizontal_res: float = 10.0 + """Horizontal resolution (in degrees). Defaults to 10.0.""" + + # fmt: off + vertical_ray_angles: Sequence[float] = [ + 89.5, 86.6875, 83.875, 81.0625, 78.25, 75.4375, 72.625, 69.8125, 67.0, 64.1875, 61.375, + 58.5625, 55.75, 52.9375, 50.125, 47.3125, 44.5, 41.6875, 38.875, 36.0625, 33.25, 30.4375, + 27.625, 24.8125, 22, 19.1875, 16.375, 13.5625, 10.75, 7.9375, 5.125, 2.3125 + ] + # fmt: on + """Vertical ray angles (in degrees). Defaults to a list of 32 angles. + + Note: + We manually set the vertical ray angles to match the Bpearl sensor. The ray-angles + are not evenly spaced. + """ + + +@configclass +class LidarPatternCfg(PatternBaseCfg): + """Configuration for the LiDAR pattern for ray-casting.""" + + func: Callable = patterns.lidar_pattern + + channels: int = MISSING + """Number of Channels (Beams). Determines the vertical resolution of the LiDAR sensor.""" + + vertical_fov_range: tuple[float, float] = MISSING + """Vertical field of view range in degrees.""" + + horizontal_fov_range: tuple[float, float] = MISSING + """Horizontal field of view range in degrees.""" + + horizontal_res: float = MISSING + """Horizontal resolution (in degrees).""" diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_cast_utils.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_cast_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..543276e8ea2ae12286c091a8fda4d4139b5ce913 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_cast_utils.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utility functions for ray-cast sensors.""" + +from __future__ import annotations + +import torch + +import omni.physics.tensors.impl.api as physx + +from isaaclab.sim.views import XformPrimView +from isaaclab.utils.math import convert_quat + + +def obtain_world_pose_from_view( + physx_view: XformPrimView | physx.ArticulationView | physx.RigidBodyView, + env_ids: torch.Tensor, + clone: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + """Get the world poses of the prim referenced by the prim view. + + Args: + physx_view: The prim view to get the world poses from. + env_ids: The environment ids of the prims to get the world poses for. + clone: Whether to clone the returned tensors (default: False). + + Returns: + A tuple containing the world positions and orientations of the prims. + Orientation is in (w, x, y, z) format. + + Raises: + NotImplementedError: If the prim view is not of the supported type. + """ + if isinstance(physx_view, XformPrimView): + pos_w, quat_w = physx_view.get_world_poses(env_ids) + elif isinstance(physx_view, physx.ArticulationView): + pos_w, quat_w = physx_view.get_root_transforms()[env_ids].split([3, 4], dim=-1) + quat_w = convert_quat(quat_w, to="wxyz") + elif isinstance(physx_view, physx.RigidBodyView): + pos_w, quat_w = physx_view.get_transforms()[env_ids].split([3, 4], dim=-1) + quat_w = convert_quat(quat_w, to="wxyz") + else: + raise NotImplementedError(f"Cannot get world poses for prim view of type '{type(physx_view)}'.") + + if clone: + return pos_w.clone(), quat_w.clone() + else: + return pos_w, quat_w diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..e6735a9f4819b61b0fbdffea8a8ccf61099e6fee --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster.py @@ -0,0 +1,428 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import re +from collections.abc import Sequence +from typing import TYPE_CHECKING, ClassVar + +import numpy as np +import torch +import warp as wp + +import omni +from isaacsim.core.simulation_manager import SimulationManager +from pxr import UsdGeom, UsdPhysics + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.markers import VisualizationMarkers +from isaaclab.sim.views import XformPrimView +from isaaclab.terrains.trimesh.utils import make_plane +from isaaclab.utils.math import quat_apply, quat_apply_yaw +from isaaclab.utils.warp import convert_to_warp_mesh, raycast_mesh + +from ..sensor_base import SensorBase +from .ray_cast_utils import obtain_world_pose_from_view +from .ray_caster_data import RayCasterData + +if TYPE_CHECKING: + from .ray_caster_cfg import RayCasterCfg + +# import logger +logger = logging.getLogger(__name__) + + +class RayCaster(SensorBase): + """A ray-casting sensor. + + The ray-caster uses a set of rays to detect collisions with meshes in the scene. The rays are + defined in the sensor's local coordinate frame. The sensor can be configured to ray-cast against + a set of meshes with a given ray pattern. + + The meshes are parsed from the list of primitive paths provided in the configuration. These are then + converted to warp meshes and stored in the `warp_meshes` list. The ray-caster then ray-casts against + these warp meshes using the ray pattern provided in the configuration. + + .. note:: + Currently, only static meshes are supported. Extending the warp mesh to support dynamic meshes + is a work in progress. + """ + + cfg: RayCasterCfg + """The configuration parameters.""" + + # Class variables to share meshes across instances + meshes: ClassVar[dict[str, wp.Mesh]] = {} + """A dictionary to store warp meshes for raycasting, shared across all instances. + + The keys correspond to the prim path for the meshes, and values are the corresponding warp Mesh objects.""" + _instance_count: ClassVar[int] = 0 + """A counter to track the number of RayCaster instances, used to manage class variable lifecycle.""" + + def __init__(self, cfg: RayCasterCfg): + """Initializes the ray-caster object. + + Args: + cfg: The configuration parameters. + """ + RayCaster._instance_count += 1 + # check if sensor path is valid + # note: currently we do not handle environment indices if there is a regex pattern in the leaf + # For example, if the prim path is "/World/Sensor_[1,2]". + sensor_path = cfg.prim_path.split("/")[-1] + sensor_path_is_regex = re.match(r"^[a-zA-Z0-9/_]+$", sensor_path) is None + if sensor_path_is_regex: + raise RuntimeError( + f"Invalid prim path for the ray-caster sensor: {cfg.prim_path}." + "\n\tHint: Please ensure that the prim path does not contain any regex patterns in the leaf." + ) + # Initialize base class + super().__init__(cfg) + # Create empty variables for storing output data + self._data = RayCasterData() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Ray-caster @ '{self.cfg.prim_path}': \n" + f"\tview type : {self._view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of meshes : {len(RayCaster.meshes)}\n" + f"\tnumber of sensors : {self._view.count}\n" + f"\tnumber of rays/sensor: {self.num_rays}\n" + f"\ttotal number of rays : {self.num_rays * self._view.count}" + ) + + """ + Properties + """ + + @property + def num_instances(self) -> int: + return self._view.count + + @property + def data(self) -> RayCasterData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + """ + Operations. + """ + + def reset(self, env_ids: Sequence[int] | None = None): + # reset the timers and counters + super().reset(env_ids) + # resolve None + if env_ids is None: + env_ids = slice(None) + num_envs_ids = self._view.count + else: + num_envs_ids = len(env_ids) + # resample the drift + r = torch.empty(num_envs_ids, 3, device=self.device) + self.drift[env_ids] = r.uniform_(*self.cfg.drift_range) + # resample the height drift + range_list = [self.cfg.ray_cast_drift_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device=self.device) + self.ray_cast_drift[env_ids] = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (num_envs_ids, 3), device=self.device + ) + + """ + Implementation. + """ + + def _initialize_impl(self): + super()._initialize_impl() + # obtain global simulation view + + self._physics_sim_view = SimulationManager.get_physics_sim_view() + prim = sim_utils.find_first_matching_prim(self.cfg.prim_path) + if prim is None: + available_prims = ",".join([str(p.GetPath()) for p in sim_utils.get_current_stage().Traverse()]) + raise RuntimeError( + f"Failed to find a prim at path expression: {self.cfg.prim_path}. Available prims: {available_prims}" + ) + + self._view, self._offset = self._obtain_trackable_prim_view(self.cfg.prim_path) + + # load the meshes by parsing the stage + self._initialize_warp_meshes() + # initialize the ray start and directions + self._initialize_rays_impl() + + def _initialize_warp_meshes(self): + # check number of mesh prims provided + if len(self.cfg.mesh_prim_paths) != 1: + raise NotImplementedError( + f"RayCaster currently only supports one mesh prim. Received: {len(self.cfg.mesh_prim_paths)}" + ) + + # read prims to ray-cast + for mesh_prim_path in self.cfg.mesh_prim_paths: + # check if mesh already casted into warp mesh + if mesh_prim_path in RayCaster.meshes: + continue + + # check if the prim is a plane - handle PhysX plane as a special case + # if a plane exists then we need to create an infinite mesh that is a plane + mesh_prim = sim_utils.get_first_matching_child_prim( + mesh_prim_path, lambda prim: prim.GetTypeName() == "Plane" + ) + # if we did not find a plane then we need to read the mesh + if mesh_prim is None: + # obtain the mesh prim + mesh_prim = sim_utils.get_first_matching_child_prim( + mesh_prim_path, lambda prim: prim.GetTypeName() == "Mesh" + ) + # check if valid + if mesh_prim is None or not mesh_prim.IsValid(): + raise RuntimeError(f"Invalid mesh prim path: {mesh_prim_path}") + # cast into UsdGeomMesh + mesh_prim = UsdGeom.Mesh(mesh_prim) + # read the vertices and faces + points = np.asarray(mesh_prim.GetPointsAttr().Get()) + transform_matrix = np.array(omni.usd.get_world_transform_matrix(mesh_prim)).T + points = np.matmul(points, transform_matrix[:3, :3].T) + points += transform_matrix[:3, 3] + indices = np.asarray(mesh_prim.GetFaceVertexIndicesAttr().Get()) + wp_mesh = convert_to_warp_mesh(points, indices, device=self.device) + # print info + logger.info( + f"Read mesh prim: {mesh_prim.GetPath()} with {len(points)} vertices and {len(indices)} faces." + ) + else: + mesh = make_plane(size=(2e6, 2e6), height=0.0, center_zero=True) + wp_mesh = convert_to_warp_mesh(mesh.vertices, mesh.faces, device=self.device) + # print info + logger.info(f"Created infinite plane mesh prim: {mesh_prim.GetPath()}.") + # add the warp mesh to the list + RayCaster.meshes[mesh_prim_path] = wp_mesh + + # throw an error if no meshes are found + if all([mesh_prim_path not in RayCaster.meshes for mesh_prim_path in self.cfg.mesh_prim_paths]): + raise RuntimeError( + f"No meshes found for ray-casting! Please check the mesh prim paths: {self.cfg.mesh_prim_paths}" + ) + + def _initialize_rays_impl(self): + # compute ray stars and directions + self.ray_starts, self.ray_directions = self.cfg.pattern_cfg.func(self.cfg.pattern_cfg, self._device) + self.num_rays = len(self.ray_directions) + # apply offset transformation to the rays + offset_pos = torch.tensor(list(self.cfg.offset.pos), device=self._device) + offset_quat = torch.tensor(list(self.cfg.offset.rot), device=self._device) + self.ray_directions = quat_apply(offset_quat.repeat(len(self.ray_directions), 1), self.ray_directions) + self.ray_starts += offset_pos + # repeat the rays for each sensor + self.ray_starts = self.ray_starts.repeat(self._view.count, 1, 1) + self.ray_directions = self.ray_directions.repeat(self._view.count, 1, 1) + # prepare drift + self.drift = torch.zeros(self._view.count, 3, device=self.device) + self.ray_cast_drift = torch.zeros(self._view.count, 3, device=self.device) + # fill the data buffer + self._data.pos_w = torch.zeros(self._view.count, 3, device=self.device) + self._data.quat_w = torch.zeros(self._view.count, 4, device=self.device) + self._data.ray_hits_w = torch.zeros(self._view.count, self.num_rays, 3, device=self.device) + self._ray_starts_w = torch.zeros(self._view.count, self.num_rays, 3, device=self.device) + self._ray_directions_w = torch.zeros(self._view.count, self.num_rays, 3, device=self.device) + + def _update_ray_infos(self, env_ids: Sequence[int]): + """Updates the ray information buffers.""" + + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset[0][env_ids], self._offset[1][env_ids] + ) + # apply drift to ray starting position in world frame + pos_w += self.drift[env_ids] + # store the poses + self._data.pos_w[env_ids] = pos_w + self._data.quat_w[env_ids] = quat_w + + # check if user provided attach_yaw_only flag + if self.cfg.attach_yaw_only is not None: + msg = ( + "Raycaster attribute 'attach_yaw_only' property will be deprecated in a future release." + " Please use the parameter 'ray_alignment' instead." + ) + # set ray alignment to yaw + if self.cfg.attach_yaw_only: + self.cfg.ray_alignment = "yaw" + msg += " Setting ray_alignment to 'yaw'." + else: + self.cfg.ray_alignment = "base" + msg += " Setting ray_alignment to 'base'." + # log the warning + logger.warning(msg) + # ray cast based on the sensor poses + if self.cfg.ray_alignment == "world": + # apply horizontal drift to ray starting position in ray caster frame + pos_w[:, 0:2] += self.ray_cast_drift[env_ids, 0:2] + # no rotation is considered and directions are not rotated + ray_starts_w = self.ray_starts[env_ids] + ray_starts_w += pos_w.unsqueeze(1) + ray_directions_w = self.ray_directions[env_ids] + elif self.cfg.ray_alignment == "yaw": + # apply horizontal drift to ray starting position in ray caster frame + pos_w[:, 0:2] += quat_apply_yaw(quat_w, self.ray_cast_drift[env_ids])[:, 0:2] + # only yaw orientation is considered and directions are not rotated + ray_starts_w = quat_apply_yaw(quat_w.repeat(1, self.num_rays), self.ray_starts[env_ids]) + ray_starts_w += pos_w.unsqueeze(1) + ray_directions_w = self.ray_directions[env_ids] + elif self.cfg.ray_alignment == "base": + # apply horizontal drift to ray starting position in ray caster frame + pos_w[:, 0:2] += quat_apply(quat_w, self.ray_cast_drift[env_ids])[:, 0:2] + # full orientation is considered + ray_starts_w = quat_apply(quat_w.repeat(1, self.num_rays), self.ray_starts[env_ids]) + ray_starts_w += pos_w.unsqueeze(1) + ray_directions_w = quat_apply(quat_w.repeat(1, self.num_rays), self.ray_directions[env_ids]) + else: + raise RuntimeError(f"Unsupported ray_alignment type: {self.cfg.ray_alignment}.") + + self._ray_starts_w[env_ids] = ray_starts_w + self._ray_directions_w[env_ids] = ray_directions_w + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data.""" + self._update_ray_infos(env_ids) + + # ray cast and store the hits + # TODO: Make this work for multiple meshes? + self._data.ray_hits_w[env_ids] = raycast_mesh( + self._ray_starts_w[env_ids], + self._ray_directions_w[env_ids], + max_dist=self.cfg.max_distance, + mesh=RayCaster.meshes[self.cfg.mesh_prim_paths[0]], + )[0] + + # apply vertical drift to ray starting position in ray caster frame + self._data.ray_hits_w[env_ids, :, 2] += self.ray_cast_drift[env_ids, 2].unsqueeze(-1) + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + if not hasattr(self, "ray_visualizer"): + self.ray_visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + # set their visibility to true + self.ray_visualizer.set_visibility(True) + else: + if hasattr(self, "ray_visualizer"): + self.ray_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + if self._data.ray_hits_w is None: + return + # remove possible inf values + viz_points = self._data.ray_hits_w.reshape(-1, 3) + viz_points = viz_points[~torch.any(torch.isinf(viz_points), dim=1)] + + self.ray_visualizer.visualize(viz_points) + + """ + Internal Helpers. + """ + + def _obtain_trackable_prim_view( + self, target_prim_path: str + ) -> tuple[XformPrimView | any, tuple[torch.Tensor, torch.Tensor]]: + """Obtain a prim view that can be used to track the pose of the parget prim. + + The target prim path is a regex expression that matches one or more mesh prims. While we can track its + pose directly using XFormPrim, this is not efficient and can be slow. Instead, we create a prim view + using the physics simulation view, which provides a more efficient way to track the pose of the mesh prims. + + The function additionally resolves the relative pose between the mesh and its corresponding physics prim. + This is especially useful if the mesh is not directly parented to the physics prim. + + Args: + target_prim_path: The target prim path to obtain the prim view for. + + Returns: + A tuple containing: + + - An XFormPrim or a physics prim view (ArticulationView or RigidBodyView). + - A tuple containing the positions and orientations of the mesh prims in the physics prim frame. + + """ + + mesh_prim = sim_utils.find_first_matching_prim(target_prim_path) + current_prim = mesh_prim + current_path_expr = target_prim_path + + prim_view = None + + while prim_view is None: + # TODO: Need to handle the case where API is present but it is disabled + if current_prim.HasAPI(UsdPhysics.ArticulationRootAPI): + prim_view = self._physics_sim_view.create_articulation_view(current_path_expr.replace(".*", "*")) + logger.info(f"Created articulation view for mesh prim at path: {target_prim_path}") + break + + # TODO: Need to handle the case where API is present but it is disabled + if current_prim.HasAPI(UsdPhysics.RigidBodyAPI): + prim_view = self._physics_sim_view.create_rigid_body_view(current_path_expr.replace(".*", "*")) + logger.info(f"Created rigid body view for mesh prim at path: {target_prim_path}") + break + + new_root_prim = current_prim.GetParent() + current_path_expr = current_path_expr.rsplit("/", 1)[0] + if not new_root_prim.IsValid(): + prim_view = XformPrimView(target_prim_path, device=self._device, stage=self.stage) + current_path_expr = target_prim_path + logger.warning( + f"The prim at path {target_prim_path} which is used for raycasting is not a physics prim." + " Defaulting to XFormPrim. \n The pose of the mesh will most likely not" + " be updated correctly when running in headless mode and position lookups will be much slower. \n" + " If possible, ensure that the mesh or its parent is a physics prim (rigid body or articulation)." + ) + break + + # switch the current prim to the parent prim + current_prim = new_root_prim + + # obtain the relative transforms between target prim and the view prims + mesh_prims = sim_utils.find_matching_prims(target_prim_path) + view_prims = sim_utils.find_matching_prims(current_path_expr) + if len(mesh_prims) != len(view_prims): + raise RuntimeError( + f"The number of mesh prims ({len(mesh_prims)}) does not match the number of physics prims" + f" ({len(view_prims)})Please specify the correct mesh and physics prim paths more" + " specifically in your target expressions." + ) + positions = [] + quaternions = [] + for mesh_prim, view_prim in zip(mesh_prims, view_prims): + pos, orientation = sim_utils.resolve_prim_pose(mesh_prim, view_prim) + positions.append(torch.tensor(pos, dtype=torch.float32, device=self.device)) + quaternions.append(torch.tensor(orientation, dtype=torch.float32, device=self.device)) + + positions = torch.stack(positions).to(device=self.device, dtype=torch.float32) + quaternions = torch.stack(quaternions).to(device=self.device, dtype=torch.float32) + + return prim_view, (positions, quaternions) + + """ + Internal simulation callbacks. + """ + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + # call parent + super()._invalidate_initialize_callback(event) + # set all existing views to None to invalidate them + self._view = None + + def __del__(self): + RayCaster._instance_count -= 1 + if RayCaster._instance_count == 0: + RayCaster.meshes.clear() diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..e930d3df183761260245a4495a6a62c5215ea08d --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera.py @@ -0,0 +1,456 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING, ClassVar, Literal + +import torch + +from pxr import UsdGeom + +import isaaclab.utils.math as math_utils +from isaaclab.sensors.camera import CameraData +from isaaclab.utils.warp import raycast_mesh + +from .ray_cast_utils import obtain_world_pose_from_view +from .ray_caster import RayCaster + +if TYPE_CHECKING: + from .ray_caster_camera_cfg import RayCasterCameraCfg + +# import logger +logger = logging.getLogger(__name__) + + +class RayCasterCamera(RayCaster): + """A ray-casting camera sensor. + + The ray-caster camera uses a set of rays to get the distances to meshes in the scene. The rays are + defined in the sensor's local coordinate frame. The sensor has the same interface as the + :class:`isaaclab.sensors.Camera` that implements the camera class through USD camera prims. + However, this class provides a faster image generation. The sensor converts meshes from the list of + primitive paths provided in the configuration to Warp meshes. The camera then ray-casts against these + Warp meshes only. + + Currently, only the following annotators are supported: + + - ``"distance_to_camera"``: An image containing the distance to camera optical center. + - ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis. + - ``"normals"``: An image containing the local surface normal vectors at each pixel. + + .. note:: + Currently, only static meshes are supported. Extending the warp mesh to support dynamic meshes + is a work in progress. + """ + + cfg: RayCasterCameraCfg + """The configuration parameters.""" + UNSUPPORTED_TYPES: ClassVar[set[str]] = { + "rgb", + "instance_id_segmentation", + "instance_id_segmentation_fast", + "instance_segmentation", + "instance_segmentation_fast", + "semantic_segmentation", + "skeleton_data", + "motion_vectors", + "bounding_box_2d_tight", + "bounding_box_2d_tight_fast", + "bounding_box_2d_loose", + "bounding_box_2d_loose_fast", + "bounding_box_3d", + "bounding_box_3d_fast", + } + """A set of sensor types that are not supported by the ray-caster camera.""" + + def __init__(self, cfg: RayCasterCameraCfg): + """Initializes the camera object. + + Args: + cfg: The configuration parameters. + + Raises: + ValueError: If the provided data types are not supported by the ray-caster camera. + """ + # perform check on supported data types + self._check_supported_data_types(cfg) + # initialize base class + super().__init__(cfg) + # create empty variables for storing output data + self._data = CameraData() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Ray-Caster-Camera @ '{self.cfg.prim_path}': \n" + f"\tview type : {self._view.__class__}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tnumber of meshes : {len(RayCaster.meshes)}\n" + f"\tnumber of sensors : {self._view.count}\n" + f"\tnumber of rays/sensor: {self.num_rays}\n" + f"\ttotal number of rays : {self.num_rays * self._view.count}\n" + f"\timage shape : {self.image_shape}" + ) + + """ + Properties + """ + + @property + def data(self) -> CameraData: + # update sensors if needed + self._update_outdated_buffers() + # return the data + return self._data + + @property + def image_shape(self) -> tuple[int, int]: + """A tuple containing (height, width) of the camera sensor.""" + return (self.cfg.pattern_cfg.height, self.cfg.pattern_cfg.width) + + @property + def frame(self) -> torch.tensor: + """Frame number when the measurement took place.""" + return self._frame + + """ + Operations. + """ + + def set_intrinsic_matrices( + self, matrices: torch.Tensor, focal_length: float = 1.0, env_ids: Sequence[int] | None = None + ): + """Set the intrinsic matrix of the camera. + + Args: + matrices: The intrinsic matrices for the camera. Shape is (N, 3, 3). + focal_length: Focal length to use when computing aperture values (in cm). Defaults to 1.0. + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + """ + # resolve env_ids + if env_ids is None: + env_ids = slice(None) + # save new intrinsic matrices and focal length + self._data.intrinsic_matrices[env_ids] = matrices.to(self._device) + self._focal_length = focal_length + # recompute ray directions + self.ray_starts[env_ids], self.ray_directions[env_ids] = self.cfg.pattern_cfg.func( + self.cfg.pattern_cfg, self._data.intrinsic_matrices[env_ids], self._device + ) + + def reset(self, env_ids: Sequence[int] | None = None): + # reset the timestamps + super().reset(env_ids) + # resolve None + if env_ids is None or isinstance(env_ids, slice): + env_ids = self._ALL_INDICES + # reset the data + # note: this recomputation is useful if one performs events such as randomizations on the camera poses. + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids, clone=True) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids] + ) + self._data.pos_w[env_ids] = pos_w + self._data.quat_w_world[env_ids] = quat_w + # Reset the frame count + self._frame[env_ids] = 0 + + def set_world_poses( + self, + positions: torch.Tensor | None = None, + orientations: torch.Tensor | None = None, + env_ids: Sequence[int] | None = None, + convention: Literal["opengl", "ros", "world"] = "ros", + ): + """Set the pose of the camera w.r.t. the world frame using specified convention. + + Since different fields use different conventions for camera orientations, the method allows users to + set the camera poses in the specified convention. Possible conventions are: + + - :obj:`"opengl"` - forward axis: -Z - up axis +Y - Offset is applied in the OpenGL (Usd.Camera) convention + - :obj:`"ros"` - forward axis: +Z - up axis -Y - Offset is applied in the ROS convention + - :obj:`"world"` - forward axis: +X - up axis +Z - Offset is applied in the World Frame convention + + See :meth:`isaaclab.utils.maths.convert_camera_frame_orientation_convention` for more details + on the conventions. + + Args: + positions: The cartesian coordinates (in meters). Shape is (N, 3). + Defaults to None, in which case the camera position in not changed. + orientations: The quaternion orientation in (w, x, y, z). Shape is (N, 4). + Defaults to None, in which case the camera orientation in not changed. + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + convention: The convention in which the poses are fed. Defaults to "ros". + + Raises: + RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first. + """ + # resolve env_ids + if env_ids is None or isinstance(env_ids, slice): + env_ids = self._ALL_INDICES + + # get current positions + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids) + if positions is not None: + # transform to camera frame + pos_offset_world_frame = positions - pos_w + self._offset_pos[env_ids] = math_utils.quat_apply(math_utils.quat_inv(quat_w), pos_offset_world_frame) + if orientations is not None: + # convert rotation matrix from input convention to world + quat_w_set = math_utils.convert_camera_frame_orientation_convention( + orientations, origin=convention, target="world" + ) + self._offset_quat[env_ids] = math_utils.quat_mul(math_utils.quat_inv(quat_w), quat_w_set) + + # update the data + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids, clone=True) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids] + ) + self._data.pos_w[env_ids] = pos_w + self._data.quat_w_world[env_ids] = quat_w + + def set_world_poses_from_view( + self, eyes: torch.Tensor, targets: torch.Tensor, env_ids: Sequence[int] | None = None + ): + """Set the poses of the camera from the eye position and look-at target position. + + Args: + eyes: The positions of the camera's eye. Shape is N, 3). + targets: The target locations to look at. Shape is (N, 3). + env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices. + + Raises: + RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first. + NotImplementedError: If the stage up-axis is not "Y" or "Z". + """ + # get up axis of current stage + up_axis = UsdGeom.GetStageUpAxis(self.stage) + # camera position and rotation in opengl convention + orientations = math_utils.quat_from_matrix( + math_utils.create_rotation_matrix_from_view(eyes, targets, up_axis=up_axis, device=self._device) + ) + self.set_world_poses(eyes, orientations, env_ids, convention="opengl") + + """ + Implementation. + """ + + def _initialize_rays_impl(self): + # Create all indices buffer + self._ALL_INDICES = torch.arange(self._view.count, device=self._device, dtype=torch.long) + # Create frame count buffer + self._frame = torch.zeros(self._view.count, device=self._device, dtype=torch.long) + # create buffers + self._create_buffers() + # compute intrinsic matrices + self._compute_intrinsic_matrices() + # compute ray stars and directions + self.ray_starts, self.ray_directions = self.cfg.pattern_cfg.func( + self.cfg.pattern_cfg, self._data.intrinsic_matrices, self._device + ) + self.num_rays = self.ray_directions.shape[1] + # create buffer to store ray hits + self.ray_hits_w = torch.zeros(self._view.count, self.num_rays, 3, device=self._device) + # set offsets + quat_w = math_utils.convert_camera_frame_orientation_convention( + torch.tensor([self.cfg.offset.rot], device=self._device), origin=self.cfg.offset.convention, target="world" + ) + self._offset_quat = quat_w.repeat(self._view.count, 1) + self._offset_pos = torch.tensor(list(self.cfg.offset.pos), device=self._device).repeat(self._view.count, 1) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data.""" + # increment frame count + self._frame[env_ids] += 1 + + # compute poses from current view + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids, clone=True) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids] + ) + # update the data + self._data.pos_w[env_ids] = pos_w + self._data.quat_w_world[env_ids] = quat_w + + # note: full orientation is considered + ray_starts_w = math_utils.quat_apply(quat_w.repeat(1, self.num_rays), self.ray_starts[env_ids]) + ray_starts_w += pos_w.unsqueeze(1) + ray_directions_w = math_utils.quat_apply(quat_w.repeat(1, self.num_rays), self.ray_directions[env_ids]) + + # ray cast and store the hits + # note: we set max distance to 1e6 during the ray-casting. THis is because we clip the distance + # to the image plane and distance to the camera to the maximum distance afterwards in-order to + # match the USD camera behavior. + + # TODO: Make ray-casting work for multiple meshes? + # necessary for regular dictionaries. + self.ray_hits_w, ray_depth, ray_normal, _ = raycast_mesh( + ray_starts_w, + ray_directions_w, + mesh=RayCaster.meshes[self.cfg.mesh_prim_paths[0]], + max_dist=1e6, + return_distance=any( + [name in self.cfg.data_types for name in ["distance_to_image_plane", "distance_to_camera"]] + ), + return_normal="normals" in self.cfg.data_types, + ) + # update output buffers + if "distance_to_image_plane" in self.cfg.data_types: + # note: data is in camera frame so we only take the first component (z-axis of camera frame) + distance_to_image_plane = ( + math_utils.quat_apply( + math_utils.quat_inv(quat_w).repeat(1, self.num_rays), + (ray_depth[:, :, None] * ray_directions_w), + ) + )[:, :, 0] + # apply the maximum distance after the transformation + if self.cfg.depth_clipping_behavior == "max": + distance_to_image_plane = torch.clip(distance_to_image_plane, max=self.cfg.max_distance) + distance_to_image_plane[torch.isnan(distance_to_image_plane)] = self.cfg.max_distance + elif self.cfg.depth_clipping_behavior == "zero": + distance_to_image_plane[distance_to_image_plane > self.cfg.max_distance] = 0.0 + distance_to_image_plane[torch.isnan(distance_to_image_plane)] = 0.0 + self._data.output["distance_to_image_plane"][env_ids] = distance_to_image_plane.view( + -1, *self.image_shape, 1 + ) + + if "distance_to_camera" in self.cfg.data_types: + if self.cfg.depth_clipping_behavior == "max": + ray_depth = torch.clip(ray_depth, max=self.cfg.max_distance) + elif self.cfg.depth_clipping_behavior == "zero": + ray_depth[ray_depth > self.cfg.max_distance] = 0.0 + self._data.output["distance_to_camera"][env_ids] = ray_depth.view(-1, *self.image_shape, 1) + + if "normals" in self.cfg.data_types: + self._data.output["normals"][env_ids] = ray_normal.view(-1, *self.image_shape, 3) + + def _debug_vis_callback(self, event): + # in case it crashes be safe + if not hasattr(self, "ray_hits_w"): + return + # show ray hit positions + self.ray_visualizer.visualize(self.ray_hits_w.view(-1, 3)) + + """ + Private Helpers + """ + + def _check_supported_data_types(self, cfg: RayCasterCameraCfg): + """Checks if the data types are supported by the ray-caster camera.""" + # check if there is any intersection in unsupported types + # reason: we cannot obtain this data from simplified warp-based ray caster + common_elements = set(cfg.data_types) & RayCasterCamera.UNSUPPORTED_TYPES + if common_elements: + raise ValueError( + f"RayCasterCamera class does not support the following sensor types: {common_elements}." + "\n\tThis is because these sensor types cannot be obtained in a fast way using ''warp''." + "\n\tHint: If you need to work with these sensor types, we recommend using the USD camera" + " interface from the isaaclab.sensors.camera module." + ) + + def _create_buffers(self): + """Create buffers for storing data.""" + # prepare drift + self.drift = torch.zeros(self._view.count, 3, device=self.device) + self.ray_cast_drift = torch.zeros(self._view.count, 3, device=self.device) + # create the data object + # -- pose of the cameras + self._data.pos_w = torch.zeros((self._view.count, 3), device=self._device) + self._data.quat_w_world = torch.zeros((self._view.count, 4), device=self._device) + # -- intrinsic matrix + self._data.intrinsic_matrices = torch.zeros((self._view.count, 3, 3), device=self._device) + self._data.intrinsic_matrices[:, 2, 2] = 1.0 + self._data.image_shape = self.image_shape + # -- output data + # create the buffers to store the annotator data. + self._data.output = {} + self._data.info = [{name: None for name in self.cfg.data_types}] * self._view.count + for name in self.cfg.data_types: + if name in ["distance_to_image_plane", "distance_to_camera"]: + shape = (self.cfg.pattern_cfg.height, self.cfg.pattern_cfg.width, 1) + elif name in ["normals"]: + shape = (self.cfg.pattern_cfg.height, self.cfg.pattern_cfg.width, 3) + else: + raise ValueError(f"Received unknown data type: {name}. Please check the configuration.") + # allocate tensor to store the data + self._data.output[name] = torch.zeros((self._view.count, *shape), device=self._device) + + def _compute_intrinsic_matrices(self): + """Computes the intrinsic matrices for the camera based on the config provided.""" + # get the sensor properties + pattern_cfg = self.cfg.pattern_cfg + + # check if vertical aperture is provided + # if not then it is auto-computed based on the aspect ratio to preserve squared pixels + if pattern_cfg.vertical_aperture is None: + pattern_cfg.vertical_aperture = pattern_cfg.horizontal_aperture * pattern_cfg.height / pattern_cfg.width + + # compute the intrinsic matrix + f_x = pattern_cfg.width * pattern_cfg.focal_length / pattern_cfg.horizontal_aperture + f_y = pattern_cfg.height * pattern_cfg.focal_length / pattern_cfg.vertical_aperture + c_x = pattern_cfg.horizontal_aperture_offset * f_x + pattern_cfg.width / 2 + c_y = pattern_cfg.vertical_aperture_offset * f_y + pattern_cfg.height / 2 + # allocate the intrinsic matrices + self._data.intrinsic_matrices[:, 0, 0] = f_x + self._data.intrinsic_matrices[:, 0, 2] = c_x + self._data.intrinsic_matrices[:, 1, 1] = f_y + self._data.intrinsic_matrices[:, 1, 2] = c_y + + # save focal length + self._focal_length = pattern_cfg.focal_length + + def _compute_view_world_poses(self, env_ids: Sequence[int]) -> tuple[torch.Tensor, torch.Tensor]: + """Obtains the pose of the view the camera is attached to in the world frame. + + .. deprecated v2.3.1: + This function will be removed in a future release in favor of implementation + :meth:`obtain_world_pose_from_view`. + + Returns: + A tuple of the position (in meters) and quaternion (w, x, y, z). + + + """ + # deprecation + logger.warning( + "The function '_compute_view_world_poses' will be deprecated in favor of the util method" + " 'obtain_world_pose_from_view'. Please use 'obtain_world_pose_from_view' instead...." + ) + + return obtain_world_pose_from_view(self._view, env_ids, clone=True) + + def _compute_camera_world_poses(self, env_ids: Sequence[int]) -> tuple[torch.Tensor, torch.Tensor]: + """Computes the pose of the camera in the world frame. + + This function applies the offset pose to the pose of the view the camera is attached to. + + .. deprecated v2.3.1: + This function will be removed in a future release. Instead, use the code block below: + + .. code-block:: python + + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids, clone=True) + pos_w, quat_w = math_utils.combine_frame_transforms( + pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids] + ) + + Returns: + A tuple of the position (in meters) and quaternion (w, x, y, z) in "world" convention. + """ + + # deprecation + logger.warning( + "The function '_compute_camera_world_poses' will be deprecated in favor of the combination of methods" + " 'obtain_world_pose_from_view' and 'math_utils.combine_frame_transforms'. Please use" + " 'obtain_world_pose_from_view' and 'math_utils.combine_frame_transforms' instead...." + ) + + # get the pose of the view the camera is attached to + pos_w, quat_w = obtain_world_pose_from_view(self._view, env_ids, clone=True) + return math_utils.combine_frame_transforms(pos_w, quat_w, self._offset_pos[env_ids], self._offset_quat[env_ids]) diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera_cfg.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..604c586adcc767d72269843ddeb59d7f6e0b386a --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_camera_cfg.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ray-cast camera sensor.""" + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .patterns import PinholeCameraPatternCfg +from .ray_caster_camera import RayCasterCamera +from .ray_caster_cfg import RayCasterCfg + + +@configclass +class RayCasterCameraCfg(RayCasterCfg): + """Configuration for the ray-cast sensor.""" + + @configclass + class OffsetCfg: + """The offset pose of the sensor's frame from the sensor's parent frame.""" + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation (w, x, y, z) w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + convention: Literal["opengl", "ros", "world"] = "ros" + """The convention in which the frame offset is applied. Defaults to "ros". + + - ``"opengl"`` - forward axis: ``-Z`` - up axis: ``+Y`` - Offset is applied in the OpenGL (Usd.Camera) + convention. + - ``"ros"`` - forward axis: ``+Z`` - up axis: ``-Y`` - Offset is applied in the ROS convention. + - ``"world"`` - forward axis: ``+X`` - up axis: ``+Z`` - Offset is applied in the World Frame convention. + + """ + + class_type: type = RayCasterCamera + + offset: OffsetCfg = OffsetCfg() + """The offset pose of the sensor's frame from the sensor's parent frame. Defaults to identity.""" + + data_types: list[str] = ["distance_to_image_plane"] + """List of sensor names/types to enable for the camera. Defaults to ["distance_to_image_plane"].""" + + depth_clipping_behavior: Literal["max", "zero", "none"] = "none" + """Clipping behavior for the camera for values exceed the maximum value. Defaults to "none". + + - ``"max"``: Values are clipped to the maximum value. + - ``"zero"``: Values are clipped to zero. + - ``"none``: No clipping is applied. Values will be returned as ``inf`` for ``distance_to_camera`` and ``nan`` + for ``distance_to_image_plane`` data type. + """ + + pattern_cfg: PinholeCameraPatternCfg = MISSING + """The pattern that defines the local ray starting positions and directions in a pinhole camera pattern.""" + + def __post_init__(self): + # for cameras, this quantity should be False always. + self.ray_alignment = "base" diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_cfg.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..dbdebfad3a5e90e6649012faf7be794a0a220de7 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_cfg.py @@ -0,0 +1,98 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ray-cast sensor.""" + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.markers.config import RAY_CASTER_MARKER_CFG +from isaaclab.utils import configclass + +from ..sensor_base_cfg import SensorBaseCfg +from .patterns.patterns_cfg import PatternBaseCfg +from .ray_caster import RayCaster + + +@configclass +class RayCasterCfg(SensorBaseCfg): + """Configuration for the ray-cast sensor.""" + + @configclass + class OffsetCfg: + """The offset pose of the sensor's frame from the sensor's parent frame.""" + + pos: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Translation w.r.t. the parent frame. Defaults to (0.0, 0.0, 0.0).""" + rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """Quaternion rotation (w, x, y, z) w.r.t. the parent frame. Defaults to (1.0, 0.0, 0.0, 0.0).""" + + class_type: type = RayCaster + + mesh_prim_paths: list[str] = MISSING + """The list of mesh primitive paths to ray cast against. + + Note: + Currently, only a single static mesh is supported. We are working on supporting multiple + static meshes and dynamic meshes. + """ + + offset: OffsetCfg = OffsetCfg() + """The offset pose of the sensor's frame from the sensor's parent frame. Defaults to identity.""" + + attach_yaw_only: bool | None = None + """Whether the rays' starting positions and directions only track the yaw orientation. + Defaults to None, which doesn't raise a warning of deprecated usage. + + This is useful for ray-casting height maps, where only yaw rotation is needed. + + .. deprecated:: 2.1.1 + + This attribute is deprecated and will be removed in the future. Please use + :attr:`ray_alignment` instead. + + To get the same behavior as setting this parameter to ``True`` or ``False``, set + :attr:`ray_alignment` to ``"yaw"`` or "base" respectively. + + """ + + ray_alignment: Literal["base", "yaw", "world"] = "base" + """Specify in what frame the rays are projected onto the ground. Default is "base". + + The options are: + + * ``base`` if the rays' starting positions and directions track the full root position and orientation. + * ``yaw`` if the rays' starting positions and directions track root position and only yaw component of + the orientation. This is useful for ray-casting height maps. + * ``world`` if rays' starting positions and directions are always fixed. This is useful in combination + with a mapping package on the robot and querying ray-casts in a global frame. + """ + + pattern_cfg: PatternBaseCfg = MISSING + """The pattern that defines the local ray starting positions and directions.""" + + max_distance: float = 1e6 + """Maximum distance (in meters) from the sensor to ray cast to. Defaults to 1e6.""" + + drift_range: tuple[float, float] = (0.0, 0.0) + """The range of drift (in meters) to add to the ray starting positions (xyz) in world frame. Defaults to (0.0, 0.0). + + For floating base robots, this is useful for simulating drift in the robot's pose estimation. + """ + + ray_cast_drift_range: dict[str, tuple[float, float]] = {"x": (0.0, 0.0), "y": (0.0, 0.0), "z": (0.0, 0.0)} + """The range of drift (in meters) to add to the projected ray points in local projection frame. Defaults to + a dictionary with zero drift for each x, y and z axis. + + For floating base robots, this is useful for simulating drift in the robot's pose estimation. + """ + + visualizer_cfg: VisualizationMarkersCfg = RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/RayCaster") + """The configuration object for the visualization markers. Defaults to RAY_CASTER_MARKER_CFG. + + Note: + This attribute is only used when debug visualization is enabled. + """ diff --git a/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_data.py b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_data.py new file mode 100644 index 0000000000000000000000000000000000000000..d63e085e752f7e87f41b40098f76b5363a43390f --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/ray_caster/ray_caster_data.py @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import dataclass + +import torch + + +@dataclass +class RayCasterData: + """Data container for the ray-cast sensor.""" + + pos_w: torch.Tensor = None + """Position of the sensor origin in world frame. + + Shape is (N, 3), where N is the number of sensors. + """ + quat_w: torch.Tensor = None + """Orientation of the sensor origin in quaternion (w, x, y, z) in world frame. + + Shape is (N, 4), where N is the number of sensors. + """ + ray_hits_w: torch.Tensor = None + """The ray hit positions in the world frame. + + Shape is (N, B, 3), where N is the number of sensors, B is the number of rays + in the scan pattern per sensor. + """ diff --git a/source/isaaclab/isaaclab/sensors/sensor_base.py b/source/isaaclab/isaaclab/sensors/sensor_base.py new file mode 100644 index 0000000000000000000000000000000000000000..4ece160bbe5a5d6689e0fd6ad4a193d3ff3251cb --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/sensor_base.py @@ -0,0 +1,363 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Base class for sensors. + +This class defines an interface for sensors similar to how the :class:`isaaclab.assets.AssetBase` class works. +Each sensor class should inherit from this class and implement the abstract methods. +""" + +from __future__ import annotations + +import builtins +import inspect +import re +import weakref +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +import torch + +import omni.kit.app +import omni.timeline +from isaacsim.core.simulation_manager import IsaacEvents, SimulationManager + +import isaaclab.sim as sim_utils +from isaaclab.sim.utils.stage import get_current_stage + +if TYPE_CHECKING: + from .sensor_base_cfg import SensorBaseCfg + + +class SensorBase(ABC): + """The base class for implementing a sensor. + + The implementation is based on lazy evaluation. The sensor data is only updated when the user + tries accessing the data through the :attr:`data` property or sets ``force_compute=True`` in + the :meth:`update` method. This is done to avoid unnecessary computation when the sensor data + is not used. + + The sensor is updated at the specified update period. If the update period is zero, then the + sensor is updated at every simulation step. + """ + + def __init__(self, cfg: SensorBaseCfg): + """Initialize the sensor class. + + Args: + cfg: The configuration parameters for the sensor. + """ + # check that config is valid + if cfg.history_length < 0: + raise ValueError(f"History length must be greater than 0! Received: {cfg.history_length}") + # check that the config is valid + cfg.validate() + # store inputs + self.cfg = cfg.copy() + # flag for whether the sensor is initialized + self._is_initialized = False + # flag for whether the sensor is in visualization mode + self._is_visualizing = False + # get stage handle + self.stage = get_current_stage() + + # register various callback functions + self._register_callbacks() + + # add handle for debug visualization (this is set to a valid handle inside set_debug_vis) + self._debug_vis_handle = None + # set initial state of debug visualization + self.set_debug_vis(self.cfg.debug_vis) + + def __del__(self): + """Unsubscribe from the callbacks.""" + # clear physics events handles + self._clear_callbacks() + + """ + Properties + """ + + @property + def is_initialized(self) -> bool: + """Whether the sensor is initialized. + + Returns True if the sensor is initialized, False otherwise. + """ + return self._is_initialized + + @property + def num_instances(self) -> int: + """Number of instances of the sensor. + + This is equal to the number of sensors per environment multiplied by the number of environments. + """ + return self._num_envs + + @property + def device(self) -> str: + """Memory device for computation.""" + return self._device + + @property + @abstractmethod + def data(self) -> Any: + """Data from the sensor. + + This property is only updated when the user tries to access the data. This is done to avoid + unnecessary computation when the sensor data is not used. + + For updating the sensor when this property is accessed, you can use the following + code snippet in your sensor implementation: + + .. code-block:: python + + # update sensors if needed + self._update_outdated_buffers() + # return the data (where `_data` is the data for the sensor) + return self._data + """ + raise NotImplementedError + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the sensor has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_debug_vis_impl) + return "NotImplementedError" not in source_code + + """ + Operations + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Sets whether to visualize the sensor data. + + Args: + debug_vis: Whether to visualize the sensor data. + + Returns: + Whether the debug visualization was successfully set. False if the sensor + does not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_debug_vis_implementation: + return False + # toggle debug visualization objects + self._set_debug_vis_impl(debug_vis) + # toggle debug visualization flag + self._is_visualizing = debug_vis + # toggle debug visualization handles + if debug_vis: + # create a subscriber for the post update event if it doesn't exist + if self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + # return success + return True + + def reset(self, env_ids: Sequence[int] | None = None): + """Resets the sensor internals. + + Args: + env_ids: The sensor ids to reset. Defaults to None. + """ + # Resolve sensor ids + if env_ids is None: + env_ids = slice(None) + # Reset the timestamp for the sensors + self._timestamp[env_ids] = 0.0 + self._timestamp_last_update[env_ids] = 0.0 + # Set all reset sensors to outdated so that they are updated when data is called the next time. + self._is_outdated[env_ids] = True + + def update(self, dt: float, force_recompute: bool = False): + # Update the timestamp for the sensors + self._timestamp += dt + self._is_outdated |= self._timestamp - self._timestamp_last_update + 1e-6 >= self.cfg.update_period + # Update the buffers + # TODO (from @mayank): Why is there a history length here when it doesn't mean anything in the sensor base?!? + # It is only for the contact sensor but there we should redefine the update function IMO. + if force_recompute or self._is_visualizing or (self.cfg.history_length > 0): + self._update_outdated_buffers() + + """ + Implementation specific. + """ + + @abstractmethod + def _initialize_impl(self): + """Initializes the sensor-related handles and internal buffers.""" + # Obtain Simulation Context + sim = sim_utils.SimulationContext.instance() + if sim is None: + raise RuntimeError("Simulation Context is not initialized!") + # Obtain device and backend + self._device = sim.device + self._backend = sim.backend + self._sim_physics_dt = sim.get_physics_dt() + # Count number of environments + env_prim_path_expr = self.cfg.prim_path.rsplit("/", 1)[0] + self._parent_prims = sim_utils.find_matching_prims(env_prim_path_expr) + self._num_envs = len(self._parent_prims) + # Boolean tensor indicating whether the sensor data has to be refreshed + self._is_outdated = torch.ones(self._num_envs, dtype=torch.bool, device=self._device) + # Current timestamp (in seconds) + self._timestamp = torch.zeros(self._num_envs, device=self._device) + # Timestamp from last update + self._timestamp_last_update = torch.zeros_like(self._timestamp) + + # Initialize debug visualization handle + if self._debug_vis_handle is None: + # set initial state of debug visualization + self.set_debug_vis(self.cfg.debug_vis) + + @abstractmethod + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the sensor data for provided environment ids. + + This function does not perform any time-based checks and directly fills the data into the + data container. + + Args: + env_ids: The indices of the sensors that are ready to capture. + """ + raise NotImplementedError + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects. + + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + def _debug_vis_callback(self, event): + """Callback for debug visualization. + + This function calls the visualization objects and sets the data to visualize into them. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + """ + Internal simulation callbacks. + """ + + def _register_callbacks(self): + """Registers the timeline and prim deletion callbacks.""" + + # register simulator callbacks (with weakref safety to avoid crashes on deletion) + def safe_callback(callback_name, event, obj_ref): + """Safely invoke a callback on a weakly-referenced object, ignoring ReferenceError if deleted.""" + try: + obj = obj_ref + getattr(obj, callback_name)(event) + except ReferenceError: + # Object has been deleted; ignore. + pass + + # note: use weakref on callbacks to ensure that this object can be deleted when its destructor is called. + # add callbacks for stage play/stop + obj_ref = weakref.proxy(self) + timeline_event_stream = omni.timeline.get_timeline_interface().get_timeline_event_stream() + + # the order is set to 10 which is arbitrary but should be lower priority than the default order of 0 + # register timeline PLAY event callback (lower priority with order=10) + self._initialize_handle = timeline_event_stream.create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.PLAY), + lambda event, obj_ref=obj_ref: safe_callback("_initialize_callback", event, obj_ref), + order=10, + ) + # register timeline STOP event callback (lower priority with order=10) + self._invalidate_initialize_handle = timeline_event_stream.create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.STOP), + lambda event, obj_ref=obj_ref: safe_callback("_invalidate_initialize_callback", event, obj_ref), + order=10, + ) + # register prim deletion callback + self._prim_deletion_callback_id = SimulationManager.register_callback( + lambda event, obj_ref=obj_ref: safe_callback("_on_prim_deletion", event, obj_ref), + event=IsaacEvents.PRIM_DELETION, + ) + + def _initialize_callback(self, event): + """Initializes the scene elements. + + Note: + PhysX handles are only enabled once the simulator starts playing. Hence, this function needs to be + called whenever the simulator "plays" from a "stop" state. + """ + if not self._is_initialized: + try: + self._initialize_impl() + except Exception as e: + if builtins.ISAACLAB_CALLBACK_EXCEPTION is None: + builtins.ISAACLAB_CALLBACK_EXCEPTION = e + self._is_initialized = True + + def _invalidate_initialize_callback(self, event): + """Invalidates the scene elements.""" + self._is_initialized = False + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + + def _on_prim_deletion(self, prim_path: str) -> None: + """Invalidates and deletes the callbacks when the prim is deleted. + + Args: + prim_path: The path to the prim that is being deleted. + + Note: + This function is called when the prim is deleted. + """ + if prim_path == "/": + self._clear_callbacks() + return + result = re.match( + pattern="^" + "/".join(self.cfg.prim_path.split("/")[: prim_path.count("/") + 1]) + "$", string=prim_path + ) + if result: + self._clear_callbacks() + + def _clear_callbacks(self) -> None: + """Clears the callbacks.""" + if self._prim_deletion_callback_id: + SimulationManager.deregister_callback(self._prim_deletion_callback_id) + self._prim_deletion_callback_id = None + if self._initialize_handle: + self._initialize_handle.unsubscribe() + self._initialize_handle = None + if self._invalidate_initialize_handle: + self._invalidate_initialize_handle.unsubscribe() + self._invalidate_initialize_handle = None + # clear debug visualization + if self._debug_vis_handle: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + + """ + Helper functions. + """ + + def _update_outdated_buffers(self): + """Fills the sensor data for the outdated sensors.""" + outdated_env_ids = self._is_outdated.nonzero().squeeze(-1) + if len(outdated_env_ids) > 0: + # obtain new data + self._update_buffers_impl(outdated_env_ids) + # update the timestamp from last update + self._timestamp_last_update[outdated_env_ids] = self._timestamp[outdated_env_ids] + # set outdated flag to false for the updated sensors + self._is_outdated[outdated_env_ids] = False diff --git a/source/isaaclab/isaaclab/sensors/sensor_base_cfg.py b/source/isaaclab/isaaclab/sensors/sensor_base_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..85875b2e49967a32a3da4fa447297b6df4cbd55d --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/sensor_base_cfg.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from .sensor_base import SensorBase + + +@configclass +class SensorBaseCfg: + """Configuration parameters for a sensor.""" + + class_type: type[SensorBase] = MISSING + """The associated sensor class. + + The class should inherit from :class:`isaaclab.sensors.sensor_base.SensorBase`. + """ + + prim_path: str = MISSING + """Prim path (or expression) to the sensor. + + .. note:: + The expression can contain the environment namespace regex ``{ENV_REGEX_NS}`` which + will be replaced with the environment namespace. + + Example: ``{ENV_REGEX_NS}/Robot/sensor`` will be replaced with ``/World/envs/env_.*/Robot/sensor``. + + """ + + update_period: float = 0.0 + """Update period of the sensor buffers (in seconds). Defaults to 0.0 (update every step).""" + + history_length: int = 0 + """Number of past frames to store in the sensor buffers. Defaults to 0, which means that only + the current data is stored (no history).""" + + debug_vis: bool = False + """Whether to visualize the sensor. Defaults to False.""" diff --git a/source/isaaclab/isaaclab/sensors/tacsl_sensor/__init__.py b/source/isaaclab/isaaclab/sensors/tacsl_sensor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..869b233d166d82f167c0c1c9ce7ed3f46e40cbe4 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/tacsl_sensor/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""TacSL Tactile Sensor implementation for IsaacLab.""" + +from .visuotactile_sensor import VisuoTactileSensor +from .visuotactile_sensor_cfg import GelSightRenderCfg, VisuoTactileSensorCfg +from .visuotactile_sensor_data import VisuoTactileSensorData diff --git a/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_render.py b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_render.py new file mode 100644 index 0000000000000000000000000000000000000000..8992817ec8982bda4240a48528fa866806bd0468 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_render.py @@ -0,0 +1,290 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING + +import cv2 +import numpy as np +import scipy +import torch + +from isaaclab.utils.assets import retrieve_file_path + +logger = logging.getLogger(__name__) + + +if TYPE_CHECKING: + from .visuotactile_sensor_cfg import GelSightRenderCfg + + +def compute_tactile_shear_image( + tactile_normal_force: np.ndarray, + tactile_shear_force: np.ndarray, + normal_force_threshold: float = 0.00008, + shear_force_threshold: float = 0.0005, + resolution: int = 30, +) -> np.ndarray: + """Visualize the tactile shear field. + + This function creates a visualization of tactile forces using arrows to represent shear forces + and color coding to represent normal forces. The thresholds are used to normalize forces for + visualization, chosen empirically to provide clear visual representation. + + Args: + tactile_normal_force: Array of tactile normal forces. Shape: (H, W). + tactile_shear_force: Array of tactile shear forces. Shape: (H, W, 2). + normal_force_threshold: Threshold for normal force visualization. Defaults to 0.00008. + shear_force_threshold: Threshold for shear force visualization. Defaults to 0.0005. + resolution: Resolution for the visualization. Defaults to 30. + + Returns: + Image visualizing the tactile shear forces. Shape: (H * resolution, W * resolution, 3). + """ + nrows = tactile_normal_force.shape[0] + ncols = tactile_normal_force.shape[1] + + imgs_tactile = np.zeros((nrows * resolution, ncols * resolution, 3), dtype=float) + + for row in range(nrows): + for col in range(ncols): + loc0_x = row * resolution + resolution // 2 + loc0_y = col * resolution + resolution // 2 + loc1_x = loc0_x + tactile_shear_force[row, col][0] / shear_force_threshold * resolution + loc1_y = loc0_y + tactile_shear_force[row, col][1] / shear_force_threshold * resolution + color = ( + 0.0, + max(0.0, 1.0 - tactile_normal_force[row][col] / normal_force_threshold), + min(1.0, tactile_normal_force[row][col] / normal_force_threshold), + ) + + cv2.arrowedLine( + imgs_tactile, (int(loc0_y), int(loc0_x)), (int(loc1_y), int(loc1_x)), color, 6, tipLength=0.4 + ) + + return imgs_tactile + + +def compute_penetration_depth( + penetration_depth_img: np.ndarray, resolution: int = 5, depth_multiplier: float = 300.0 +) -> np.ndarray: + """Visualize the penetration depth. + + Args: + penetration_depth_img: Image of penetration depth. Shape: (H, W). + resolution: Resolution for the upsampling; each pixel expands to a (res x res) block. Defaults to 5. + depth_multiplier: Multiplier for the depth values. Defaults to 300.0 (scales ~3.3mm to 1.0). + (e.g. typical Gelsight sensors have maximum penetration depths < 2.5mm, + see https://dspace.mit.edu/handle/1721.1/114627). + + Returns: + Upsampled image visualizing the penetration depth. Shape: (H * resolution, W * resolution). + """ + # penetration_depth_img_upsampled = penetration_depth.repeat(resolution, 0).repeat(resolution, 1) + penetration_depth_img_upsampled = np.kron(penetration_depth_img, np.ones((resolution, resolution))) + penetration_depth_img_upsampled = np.clip(penetration_depth_img_upsampled, 0.0, 1.0) * depth_multiplier + return penetration_depth_img_upsampled + + +class GelsightRender: + """Class to handle GelSight rendering using the Taxim example-based approach. + + Reference: https://arxiv.org/abs/2109.04027 + """ + + def __init__(self, cfg: GelSightRenderCfg, device: str | torch.device): + """Initialize the GelSight renderer. + + Args: + cfg: Configuration object for the GelSight sensor. + device: Device to use ('cpu' or 'cuda'). + + Raises: + ValueError: If :attr:`GelSightRenderCfg.mm_per_pixel` is zero or negative. + FileNotFoundError: If render data files cannot be retrieved. + """ + self.cfg = cfg + self.device = device + + # Validate configuration parameters + eps = 1e-9 + if self.cfg.mm_per_pixel < eps: + raise ValueError(f"mm_per_pixel must be positive (>= {eps}), got {self.cfg.mm_per_pixel}") + + # Retrieve render data files using the configured base path + bg_path = self._get_render_data(self.cfg.sensor_data_dir_name, self.cfg.background_path) + calib_path = self._get_render_data(self.cfg.sensor_data_dir_name, self.cfg.calib_path) + + if bg_path is None or calib_path is None: + raise FileNotFoundError( + "Failed to retrieve GelSight render data files. " + f"Base path: {self.cfg.base_data_path or 'default (Isaac Lab Nucleus)'}, " + f"Data dir: {self.cfg.sensor_data_dir_name}" + ) + + self.background = cv2.cvtColor(cv2.imread(bg_path), cv2.COLOR_BGR2RGB) + + # Load calibration data directly + calib_data = np.load(calib_path) + calib_grad_r = calib_data["grad_r"] + calib_grad_g = calib_data["grad_g"] + calib_grad_b = calib_data["grad_b"] + + image_height = self.cfg.image_height + image_width = self.cfg.image_width + num_bins = self.cfg.num_bins + [xx, yy] = np.meshgrid(range(image_width), range(image_height)) + xf = xx.flatten() + yf = yy.flatten() + self.A = np.array([xf * xf, yf * yf, xf * yf, xf, yf, np.ones(image_height * image_width)]).T + + binm = num_bins - 1 + self.x_binr = 0.5 * np.pi / binm # x [0,pi/2] + self.y_binr = 2 * np.pi / binm # y [-pi, pi] + + kernel = self._get_filtering_kernel(kernel_sz=5) + self.kernel = torch.tensor(kernel, dtype=torch.float, device=self.device) + + self.calib_data_grad_r = torch.tensor(calib_grad_r, device=self.device) + self.calib_data_grad_g = torch.tensor(calib_grad_g, device=self.device) + self.calib_data_grad_b = torch.tensor(calib_grad_b, device=self.device) + + self.A_tensor = torch.tensor(self.A.reshape(image_height, image_width, 6), device=self.device).unsqueeze(0) + self.background_tensor = torch.tensor(self.background, device=self.device) + + # Pre-allocate buffer for RGB output (will be resized if needed) + self._sim_img_rgb_buffer = torch.empty((1, image_height, image_width, 3), device=self.device) + + logger.info("Gelsight initialization done!") + + def render(self, heightMap: torch.Tensor) -> torch.Tensor: + """Render the height map using the GelSight sensor (tensorized version). + + Args: + heightMap: Input height map tensor. Shape: (N, H, W). + + Returns: + Rendered image tensor. Shape: (N, H, W, 3). + """ + height_map = heightMap.clone() + height_map[torch.abs(height_map) < 1e-6] = 0 # remove minor artifact + height_map = height_map * -1000.0 + height_map /= self.cfg.mm_per_pixel + + height_map = self._gaussian_filtering(height_map.unsqueeze(-1), self.kernel).squeeze(-1) + + grad_mag, grad_dir = self._generate_normals(height_map) + + idx_x = torch.floor(grad_mag / self.x_binr).long() + idx_y = torch.floor((grad_dir + np.pi) / self.y_binr).long() + + # Clamp indices to valid range to prevent out-of-bounds errors + max_idx = self.cfg.num_bins - 1 + idx_x = torch.clamp(idx_x, 0, max_idx) + idx_y = torch.clamp(idx_y, 0, max_idx) + + params_r = self.calib_data_grad_r[idx_x, idx_y, :] + params_g = self.calib_data_grad_g[idx_x, idx_y, :] + params_b = self.calib_data_grad_b[idx_x, idx_y, :] + + # Reuse pre-allocated buffer, resize if batch size changed + target_shape = (*idx_x.shape, 3) + if self._sim_img_rgb_buffer.shape != target_shape: + self._sim_img_rgb_buffer = torch.empty(target_shape, device=self.device) + sim_img_rgb = self._sim_img_rgb_buffer + + sim_img_rgb[..., 0] = torch.sum(self.A_tensor * params_r, dim=-1) # R + sim_img_rgb[..., 1] = torch.sum(self.A_tensor * params_g, dim=-1) # G + sim_img_rgb[..., 2] = torch.sum(self.A_tensor * params_b, dim=-1) # B + + # write tactile image + sim_img = sim_img_rgb + self.background_tensor # /255.0 + sim_img = torch.clip(sim_img, 0, 255, out=sim_img).to(torch.uint8) + return sim_img + + """ + Internal Helpers. + """ + + def _get_render_data(self, data_dir: str, file_name: str) -> str: + """Gets the path for the GelSight render data file. + + Args: + data_dir: The data directory name containing the render data. + file_name: The specific file name to retrieve. + + Returns: + The local path to the file. + + Raises: + FileNotFoundError: If the file is not found locally or on Nucleus. + """ + # Construct path using the configured base path + file_path = os.path.join(self.cfg.base_data_path, data_dir, file_name) + + # Cache directory for downloads + cache_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), data_dir) + + # Use retrieve_file_path to handle local/Nucleus paths and caching + return retrieve_file_path(file_path, download_dir=cache_dir, force_download=False) + + def _generate_normals(self, img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Generate the gradient magnitude and direction of the height map. + + Args: + img: Input height map tensor. Shape: (N, H, W). + + Returns: + Tuple containing gradient magnitude tensor and gradient direction tensor. Shape: (N, H, W). + """ + img_grad = torch.gradient(img, dim=(1, 2)) + dzdx, dzdy = img_grad + + grad_mag_orig = torch.sqrt(dzdx**2 + dzdy**2) + grad_mag = torch.arctan(grad_mag_orig) # seems that arctan is used as a squashing function + grad_dir = torch.arctan2(dzdx, dzdy) + grad_dir[grad_mag_orig == 0] = 0 + + # handle edges + grad_mag = torch.nn.functional.pad(grad_mag[:, 1:-1, 1:-1], pad=(1, 1, 1, 1)) + grad_dir = torch.nn.functional.pad(grad_dir[:, 1:-1, 1:-1], pad=(1, 1, 1, 1)) + + return grad_mag, grad_dir + + def _get_filtering_kernel(self, kernel_sz: int = 5) -> np.ndarray: + """Create a Gaussian filtering kernel. + + For kernel derivation, see https://cecas.clemson.edu/~stb/ece847/internal/cvbook/ch03_filtering.pdf + + Args: + kernel_sz: Size of the kernel. Defaults to 5. + + Returns: + Filtering kernel. Shape: (kernel_sz, kernel_sz). + """ + filter_1D = scipy.special.binom(kernel_sz - 1, np.arange(kernel_sz)) + filter_1D /= filter_1D.sum() + filter_1D = filter_1D[..., None] + + kernel = filter_1D @ filter_1D.T + return kernel + + def _gaussian_filtering(self, img: torch.Tensor, kernel: torch.Tensor) -> torch.Tensor: + """Apply Gaussian filtering to the input image tensor. + + Args: + img: Input image tensor. Shape: (N, H, W, 1). + kernel: Filtering kernel tensor. Shape: (K, K). + + Returns: + Filtered image tensor. Shape: (N, H, W, 1). + """ + img_output = torch.nn.functional.conv2d( + img.permute(0, 3, 1, 2), kernel.unsqueeze(0).unsqueeze(0), stride=1, padding="same" + ).permute(0, 2, 3, 1) + return img_output diff --git a/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor.py b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..8f692ca79d92fa40c00367f8985cdf3a14b5f176 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor.py @@ -0,0 +1,912 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +import itertools +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +import numpy as np +import torch + +import isaacsim.core.utils.torch as torch_utils +from isaacsim.core.simulation_manager import SimulationManager +from pxr import Usd, UsdGeom, UsdPhysics + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.markers import VisualizationMarkers +from isaaclab.sensors.camera import Camera, TiledCamera +from isaaclab.sensors.sensor_base import SensorBase +from isaaclab.sensors.tacsl_sensor.visuotactile_render import GelsightRender +from isaaclab.sensors.tacsl_sensor.visuotactile_sensor_data import VisuoTactileSensorData + +if TYPE_CHECKING: + from .visuotactile_sensor_cfg import VisuoTactileSensorCfg + +import trimesh + +logger = logging.getLogger(__name__) + + +class VisuoTactileSensor(SensorBase): + r"""A tactile sensor for both camera-based and force field tactile sensing. + + This sensor provides: + 1. Camera-based tactile sensing: depth images from tactile surface + 2. Force field tactile sensing: Penalty-based normal and shear forces using SDF queries + + The sensor can be configured to use either or both sensing modalities. + + **Computation Pipeline:** + Camera-based sensing computes depth differences from a nominal (no-contact) baseline and + processes them through the tac-sl GelSight renderer to produce realistic tactile images. + + Force field sensing queries Signed Distance Fields (SDF) to compute penetration depths, + then applies penalty-based spring-damper models + (:math:`F_n = k_n \cdot \text{depth}`, :math:`F_t = \min(k_t \cdot \|v_t\|, \mu \cdot F_n)`) + to compute normal and shear forces at discrete tactile points. + + **Example Usage:** + For a complete working example, see: ``scripts/demos/sensors/tacsl/tacsl_example.py`` + + **Current Limitations:** + - SDF collision meshes must be pre-computed and objects specified before simulation starts + - Force field computation requires specific rigid body and mesh configurations + - No support for dynamic addition/removal of interacting objects during runtime + + Configuration Requirements: + The following requirements must be satisfied for proper sensor operation: + + **Camera Tactile Imaging** + If ``enable_camera_tactile=True``, a valid ``camera_cfg`` (TiledCameraCfg) must be + provided with appropriate camera parameters. + + **Force Field Computation** + If ``enable_force_field=True``, the following parameters are required: + + * ``contact_object_prim_path_expr`` - Prim path expression to find the contact object prim + + **SDF Computation** + When force field computation is enabled, penalty-based normal and shear forces are + computed using Signed Distance Field (SDF) queries. To achieve GPU acceleration: + + * Interacting objects should have pre-computed SDF collision meshes + * An SDFView must be defined during initialization, therefore interacting objects + should be specified before simulation. + + """ + + cfg: VisuoTactileSensorCfg + """The configuration parameters.""" + + def __init__(self, cfg: VisuoTactileSensorCfg): + """Initializes the tactile sensor object. + + Args: + cfg: The configuration parameters. + """ + + # Create empty variables for storing output data + self._data: VisuoTactileSensorData = VisuoTactileSensorData() + + # Camera-based tactile sensing + self._camera_sensor: Camera | TiledCamera | None = None + self._nominal_tactile: dict | None = None + + # Force field tactile sensing + self._tactile_pos_local: torch.Tensor | None = None + self._tactile_quat_local: torch.Tensor | None = None + self._sdf_object: Any | None = None + + # COMs for velocity correction + self._elastomer_com_b: torch.Tensor | None = None + self._contact_object_com_b: torch.Tensor | None = None + + # Physics views + self._physics_sim_view = None + self._elastomer_body_view = None + self._elastomer_tip_view = None + self._contact_object_body_view = None + + # Visualization + self._tactile_visualizer: VisualizationMarkers | None = None + + # Tactile points count + self.num_tactile_points: int = 0 + + # Now call parent class constructor + super().__init__(cfg) + + def __del__(self): + """Unsubscribes from callbacks and detach from the replicator registry.""" + if self._camera_sensor is not None: + self._camera_sensor.__del__() + # unsubscribe from callbacks + super().__del__() + + def __str__(self) -> str: + """Returns: A string containing information about the instance.""" + return ( + f"Tactile sensor @ '{self.cfg.prim_path}': \n" + f"\trender config : {self.cfg.render_cfg.base_data_path}/{self.cfg.render_cfg.sensor_data_dir_name}\n" + f"\tupdate period (s) : {self.cfg.update_period}\n" + f"\tcamera enabled : {self.cfg.enable_camera_tactile}\n" + f"\tforce field enabled: {self.cfg.enable_force_field}\n" + f"\tnum instances : {self.num_instances}\n" + ) + + """ + Properties + """ + + @property + def num_instances(self) -> int: + return self._num_envs + + @property + def data(self) -> VisuoTactileSensorData: + # Update sensors if needed + self._update_outdated_buffers() + # Return the data + return self._data + + """ + Operations + """ + + def reset(self, env_ids: Sequence[int] | None = None): + """Resets the sensor internals.""" + # reset the timestamps + super().reset(env_ids) + + # Reset camera sensor if enabled + if self._camera_sensor: + self._camera_sensor.reset(env_ids) + + """ + Implementation + """ + + def _initialize_impl(self): + """Initializes the sensor-related handles and internal buffers.""" + super()._initialize_impl() + + # Obtain global simulation view + self._physics_sim_view = SimulationManager.get_physics_sim_view() + + # Initialize camera-based tactile sensing + if self.cfg.enable_camera_tactile: + self._initialize_camera_tactile() + + # Initialize force field tactile sensing + if self.cfg.enable_force_field: + self._initialize_force_field() + + # Initialize visualization + if self.cfg.debug_vis: + self._initialize_visualization() + + def get_initial_render(self) -> dict | None: + """Get the initial tactile sensor render for baseline comparison. + + This method captures the initial state of the tactile sensor when no contact + is occurring. This baseline is used for computing relative changes during + tactile interactions. + + .. warning:: + It is the user's responsibility to ensure that the sensor is in a "no contact" state + when this method is called. If the sensor is in contact with an object, the baseline + will be incorrect, leading to erroneous tactile readings. + + Returns: + dict | None: Dictionary containing initial render data with sensor output keys + and corresponding tensor values. Returns None if camera tactile + sensing is disabled. + + Raises: + RuntimeError: If camera sensor is not initialized or initial render fails. + """ + if not self.cfg.enable_camera_tactile: + return None + + self._camera_sensor.update(dt=0.0) + + # get the initial render + initial_render = self._camera_sensor.data.output + if initial_render is None: + raise RuntimeError("Initial render is None") + + # Store the initial nominal tactile data + self._nominal_tactile = dict() + for key, value in initial_render.items(): + self._nominal_tactile[key] = value.clone() + + return self._nominal_tactile + + def _initialize_camera_tactile(self): + """Initialize camera-based tactile sensing.""" + if self.cfg.camera_cfg is None: + raise ValueError("Camera configuration is None. Please provide a valid camera configuration.") + # check image size is consistent with the render config + if ( + self.cfg.camera_cfg.height != self.cfg.render_cfg.image_height + or self.cfg.camera_cfg.width != self.cfg.render_cfg.image_width + ): + raise ValueError( + "Camera configuration image size is not consistent with the render config. Camera size:" + f" {self.cfg.camera_cfg.height}x{self.cfg.camera_cfg.width}, Render config:" + f" {self.cfg.render_cfg.image_height}x{self.cfg.render_cfg.image_width}" + ) + # check data types + if not all(data_type in ["distance_to_image_plane", "depth"] for data_type in self.cfg.camera_cfg.data_types): + raise ValueError( + f"Camera configuration data types are not supported. Data types: {self.cfg.camera_cfg.data_types}" + ) + if self.cfg.camera_cfg.update_period != self.cfg.update_period: + logger.warning( + f"Camera configuration update period ({self.cfg.camera_cfg.update_period}) is not equal to sensor" + f" update period ({self.cfg.update_period}), changing camera update period to match sensor update" + " period" + ) + self.cfg.camera_cfg.update_period = self.cfg.update_period + + # gelsightRender + self._tactile_rgb_render = GelsightRender(self.cfg.render_cfg, device=self.device) + + # Create camera sensor + self._camera_sensor = TiledCamera(self.cfg.camera_cfg) + + # Initialize camera + if not self._camera_sensor.is_initialized: + self._camera_sensor._initialize_impl() + self._camera_sensor._is_initialized = True + + # Initialize camera buffers + self._data.tactile_rgb_image = torch.zeros( + (self._num_envs, self.cfg.camera_cfg.height, self.cfg.camera_cfg.width, 3), device=self._device + ) + self._data.tactile_depth_image = torch.zeros( + (self._num_envs, self.cfg.camera_cfg.height, self.cfg.camera_cfg.width, 1), device=self._device + ) + + logger.info("Camera-based tactile sensing initialized.") + + def _initialize_force_field(self): + """Initialize force field tactile sensing components. + + This method sets up all components required for force field based tactile sensing: + + 1. Creates PhysX views for elastomer and contact object rigid bodies + 2. Generates tactile sensing points on the elastomer surface using mesh geometry + 3. Initializes SDF (Signed Distance Field) for collision detection + 4. Creates data buffers for storing force field measurements + + The tactile points are generated by ray-casting onto the elastomer mesh surface + to create a grid of sensing points that will be used for force computation. + + """ + + # Generate tactile points on elastomer surface + self._generate_tactile_points( + num_divs=list(self.cfg.tactile_array_size), + margin=getattr(self.cfg, "tactile_margin", 0.003), + visualize=self.cfg.trimesh_vis_tactile_points, + ) + + self._create_physx_views() + + # Initialize force field data buffers + self._initialize_force_field_buffers() + logger.info("Force field tactile sensing initialized.") + + def _create_physx_views(self) -> None: + """Create PhysX views for contact object and elastomer bodies. + + This method sets up the necessary PhysX views for force field computation: + 1. Creates rigid body view for elastomer + 2. If contact object prim path expression is not None, then: + a. Finds and validates the object prim and its collision mesh + b. Creates SDF view for collision detection + c. Creates rigid body view for object + + """ + elastomer_pattern = self._parent_prims[0].GetPath().pathString.replace("env_0", "env_*") + self._elastomer_body_view = self._physics_sim_view.create_rigid_body_view([elastomer_pattern]) + # Get elastomer COM for velocity correction + self._elastomer_com_b = self._elastomer_body_view.get_coms().to(self._device).split([3, 4], dim=-1)[0] + + if self.cfg.contact_object_prim_path_expr is None: + return + + contact_object_mesh, contact_object_rigid_body = self._find_contact_object_components() + # Create SDF view for collision detection + num_query_points = self.cfg.tactile_array_size[0] * self.cfg.tactile_array_size[1] + mesh_path_pattern = contact_object_mesh.GetPath().pathString.replace("env_0", "env_*") + self._contact_object_sdf_view = self._physics_sim_view.create_sdf_shape_view( + mesh_path_pattern, num_query_points + ) + + # Create rigid body views for contact object and elastomer + body_path_pattern = contact_object_rigid_body.GetPath().pathString.replace("env_0", "env_*") + self._contact_object_body_view = self._physics_sim_view.create_rigid_body_view([body_path_pattern]) + # Get contact object COM for velocity correction + self._contact_object_com_b = self._contact_object_body_view.get_coms().to(self._device).split([3, 4], dim=-1)[0] + + def _find_contact_object_components(self) -> tuple[Any, Any]: + """Find and validate contact object SDF mesh and its parent rigid body. + + This method searches for the contact object prim using the configured filter pattern, + then locates the first SDF collision mesh within that prim hierarchy and + identifies its parent rigid body for physics simulation. + + Returns: + Tuple of (contact_object_mesh, contact_object_rigid_body) + Returns None if contact object components are not found. + + Note: + Only SDF meshes are supported for optimal force field computation performance. + If no SDF mesh is found, the method will log a warning and return None. + """ + # Find the contact object prim using the configured pattern + contact_object_prim = sim_utils.find_first_matching_prim(self.cfg.contact_object_prim_path_expr) + if contact_object_prim is None: + raise RuntimeError( + f"No contact object prim found matching pattern: {self.cfg.contact_object_prim_path_expr}" + ) + + def is_sdf_mesh(prim: Usd.Prim) -> bool: + """Check if a mesh prim is configured for SDF approximation.""" + return ( + prim.HasAPI(UsdPhysics.MeshCollisionAPI) + and UsdPhysics.MeshCollisionAPI(prim).GetApproximationAttr().Get() == "sdf" + ) + + # Find the SDF mesh within the contact object + contact_object_mesh = sim_utils.get_first_matching_child_prim( + contact_object_prim.GetPath(), predicate=is_sdf_mesh + ) + if contact_object_mesh is None: + raise RuntimeError( + f"No SDF mesh found under contact object at path: {contact_object_prim.GetPath().pathString}" + ) + + def find_parent_rigid_body(prim: Usd.Prim) -> Usd.Prim | None: + """Find the first parent prim with RigidBodyAPI.""" + current_prim = prim + while current_prim and current_prim.IsValid(): + if current_prim.HasAPI(UsdPhysics.RigidBodyAPI): + return current_prim + current_prim = current_prim.GetParent() + if current_prim.GetPath() == "/": + break + return None + + # Find the rigid body parent of the SDF mesh + contact_object_rigid_body = find_parent_rigid_body(contact_object_mesh) + if contact_object_rigid_body is None: + raise RuntimeError( + f"No contact object rigid body found for mesh at path: {contact_object_mesh.GetPath().pathString}" + ) + + return contact_object_mesh, contact_object_rigid_body + + def _generate_tactile_points(self, num_divs: list, margin: float, visualize: bool): + """Generate tactile sensing points from elastomer mesh geometry. + + This method creates a grid of tactile sensing points on the elastomer surface + by ray-casting onto the mesh geometry. Visual meshes are used for smoother point sampling. + + Args: + num_divs: Number of divisions [rows, cols] for the tactile grid. + margin: Margin distance from mesh edges in meters. + visualize: Whether to show the generated points in trimesh visualization. + + """ + + # Get the elastomer prim path + elastomer_prim_path = self._parent_prims[0].GetPath().pathString + + def is_visual_mesh(prim) -> bool: + """Check if a mesh prim has visual properties (visual mesh, not collision mesh).""" + return prim.IsA(UsdGeom.Mesh) and not prim.HasAPI(UsdPhysics.CollisionAPI) + + elastomer_mesh_prim = sim_utils.get_first_matching_child_prim(elastomer_prim_path, predicate=is_visual_mesh) + if elastomer_mesh_prim is None: + raise RuntimeError(f"No visual mesh found under elastomer at path: {elastomer_prim_path}") + + logger.info(f"Generating tactile points from USD mesh: {elastomer_mesh_prim.GetPath().pathString}") + + # Extract mesh data + usd_mesh = UsdGeom.Mesh(elastomer_mesh_prim) + points = np.asarray(usd_mesh.GetPointsAttr().Get()) + face_indices = np.asarray(usd_mesh.GetFaceVertexIndicesAttr().Get()) + + # Simple triangulation + faces = face_indices.reshape(-1, 3) + + # Create bounds + mesh_bounds = np.array([points.min(axis=0), points.max(axis=0)]) + + # Create trimesh object + mesh = trimesh.Trimesh(vertices=points, faces=faces) + + # Generate grid on elastomer + elastomer_dims = np.diff(mesh_bounds, axis=0).squeeze() + slim_axis = np.argmin(elastomer_dims) # Determine flat axis of elastomer + + # Determine tip direction using dome geometry + # For dome-shaped elastomers, the center of mass is shifted toward the dome (contact) side + mesh_center_of_mass = mesh.center_mass[slim_axis] + bounding_box_center = (mesh_bounds[0, slim_axis] + mesh_bounds[1, slim_axis]) / 2.0 + + tip_direction_sign = 1.0 if mesh_center_of_mass > bounding_box_center else -1.0 + + # Determine gap between adjacent tactile points + axis_idxs = list(range(3)) + axis_idxs.remove(int(slim_axis)) # Remove slim idx + div_sz = (elastomer_dims[axis_idxs] - margin * 2.0) / (np.array(num_divs) + 1) + tactile_points_dx = min(div_sz) + + # Sample points on the flat plane + planar_grid_points = [] + center = (mesh_bounds[0] + mesh_bounds[1]) / 2.0 + idx = 0 + for axis_i in range(3): + if axis_i == slim_axis: + # On the slim axis, place a point far away so ray is pointing at the elastomer tip + planar_grid_points.append([tip_direction_sign]) + else: + axis_grid_points = np.linspace( + center[axis_i] - tactile_points_dx * (num_divs[idx] + 1.0) / 2.0, + center[axis_i] + tactile_points_dx * (num_divs[idx] + 1.0) / 2.0, + num_divs[idx] + 2, + ) + planar_grid_points.append(axis_grid_points[1:-1]) # Leave out the extreme corners + idx += 1 + + grid_corners = itertools.product(planar_grid_points[0], planar_grid_points[1], planar_grid_points[2]) + grid_corners = np.array(list(grid_corners)) + + # Project ray in positive y direction on the mesh + mesh_data = trimesh.ray.ray_triangle.RayMeshIntersector(mesh) + ray_dir = np.array([0, 0, 0]) + ray_dir[slim_axis] = -tip_direction_sign # Ray points towards elastomer (opposite of tip direction) + + # Handle the ray intersection result + index_tri, index_ray, locations = mesh_data.intersects_id( + grid_corners, np.tile([ray_dir], (grid_corners.shape[0], 1)), return_locations=True, multiple_hits=False + ) + + if visualize: + query_pointcloud = trimesh.PointCloud(locations, colors=(0.0, 0.0, 1.0)) + trimesh.Scene([mesh, query_pointcloud]).show() + + # Sort and store tactile points + tactile_points = locations[index_ray.argsort()] + # in the frame of the elastomer + self._tactile_pos_local = torch.tensor(tactile_points, dtype=torch.float32, device=self._device) + self.num_tactile_points = self._tactile_pos_local.shape[0] + if self.num_tactile_points != self.cfg.tactile_array_size[0] * self.cfg.tactile_array_size[1]: + raise RuntimeError( + f"Number of tactile points does not match expected: {self.num_tactile_points} !=" + f" {self.cfg.tactile_array_size[0] * self.cfg.tactile_array_size[1]}" + ) + + # Assume tactile frame rotation are all the same + rotation = torch.tensor([0, 0, -torch.pi], device=self._device) + self._tactile_quat_local = ( + math_utils.quat_from_euler_xyz(rotation[0], rotation[1], rotation[2]) + .unsqueeze(0) + .repeat(len(tactile_points), 1) + ) + + logger.info(f"Generated {len(tactile_points)} tactile points from USD mesh using ray casting") + + def _initialize_force_field_buffers(self): + """Initialize data buffers for force field sensing.""" + num_pts = self.num_tactile_points + + # Initialize force field data tensors + self._data.tactile_points_pos_w = torch.zeros((self._num_envs, num_pts, 3), device=self._device) + self._data.tactile_points_quat_w = torch.zeros((self._num_envs, num_pts, 4), device=self._device) + self._data.penetration_depth = torch.zeros((self._num_envs, num_pts), device=self._device) + self._data.tactile_normal_force = torch.zeros((self._num_envs, num_pts), device=self._device) + self._data.tactile_shear_force = torch.zeros((self._num_envs, num_pts, 2), device=self._device) + # Pre-compute expanded tactile point tensors to avoid repeated unsqueeze/expand operations + self._tactile_pos_expanded = self._tactile_pos_local.unsqueeze(0).expand(self._num_envs, -1, -1) + self._tactile_quat_expanded = self._tactile_quat_local.unsqueeze(0).expand(self._num_envs, -1, -1) + + def _initialize_visualization(self): + """Initialize visualization markers for tactile points.""" + if self.cfg.visualizer_cfg: + self._visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + + def _update_buffers_impl(self, env_ids: Sequence[int]): + """Fills the buffers of the sensor data. + + This method updates both camera-based and force field tactile sensing data + for the specified environments. + + Args: + env_ids: Sequence of environment indices to update. If length equals + total number of environments, all environments are updated. + """ + # Convert to proper indices for internal methods + if len(env_ids) == self._num_envs: + internal_env_ids = slice(None) + else: + internal_env_ids = env_ids + + # Update camera-based tactile data + if self.cfg.enable_camera_tactile: + self._update_camera_tactile(internal_env_ids) + + # Update force field tactile data + if self.cfg.enable_force_field: + self._update_force_field(internal_env_ids) + + def _update_camera_tactile(self, env_ids: Sequence[int] | slice): + """Update camera-based tactile sensing data. + + This method updates the camera sensor and processes the depth information + to compute tactile measurements. It computes the difference from the nominal + (no-contact) state and renders it using the GelSight tactile renderer. + + Args: + env_ids: Environment indices or slice to update. Can be a sequence of + integers or a slice object for batch processing. + """ + if self._nominal_tactile is None: + raise RuntimeError("Nominal tactile is not set. Please call get_initial_render() first.") + # Update camera sensor + self._camera_sensor.update(self._sim_physics_dt) + + # Get camera data + camera_data = self._camera_sensor.data + + # Check for either distance_to_image_plane or depth (they are equivalent) + depth_key = None + if "distance_to_image_plane" in camera_data.output: + depth_key = "distance_to_image_plane" + elif "depth" in camera_data.output: + depth_key = "depth" + + if depth_key: + self._data.tactile_depth_image[env_ids] = camera_data.output[depth_key][env_ids].clone() + diff = self._nominal_tactile[depth_key][env_ids] - self._data.tactile_depth_image[env_ids] + self._data.tactile_rgb_image[env_ids] = self._tactile_rgb_render.render(diff.squeeze(-1)) + + ######################################################################################### + # Force field tactile sensing + ######################################################################################### + + def _update_force_field(self, env_ids: Sequence[int] | slice): + """Update force field tactile sensing data. + + This method computes penalty-based tactile forces using Signed Distance Field (SDF) + queries. It transforms tactile points to contact object local coordinates, queries the SDF of the + contact object for collision detection, and computes normal and shear forces based on + penetration depth and relative velocities. + + Args: + env_ids: Environment indices or slice to update. Can be a sequence of + integers or a slice object for batch processing. + + Note: + Requires both elastomer and contact object body views to be initialized. Returns + early if tactile points or body views are not available. + """ + # Step 1: Get elastomer pose and precompute pose components + elastomer_pos_w, elastomer_quat_w = self._elastomer_body_view.get_transforms().split([3, 4], dim=-1) + elastomer_quat_w = math_utils.convert_quat(elastomer_quat_w, to="wxyz") + + # Transform tactile points to world coordinates, used for visualization + self._transform_tactile_points_to_world(elastomer_pos_w, elastomer_quat_w) + + # earlly return if contact object body view is not available + # this could happen if the contact object is not specified when tactile_points are required for visualization + if self._contact_object_body_view is None: + return + + # Step 2: Transform tactile points to contact object local frame for SDF queries + contact_object_pos_w, contact_object_quat_w = self._contact_object_body_view.get_transforms().split( + [3, 4], dim=-1 + ) + contact_object_quat_w = math_utils.convert_quat(contact_object_quat_w, to="wxyz") + + world_tactile_points = self._data.tactile_points_pos_w + points_contact_object_local, contact_object_quat_inv = self._transform_points_to_contact_object_local( + world_tactile_points, contact_object_pos_w, contact_object_quat_w + ) + + # Step 3: Query SDF for collision detection + sdf_values_and_gradients = self._contact_object_sdf_view.get_sdf_and_gradients(points_contact_object_local) + sdf_values = sdf_values_and_gradients[..., -1] # Last component is SDF value + sdf_gradients = sdf_values_and_gradients[..., :-1] # First 3 components are gradients + + # Step 4: Compute tactile forces from SDF data + self._compute_tactile_forces_from_sdf( + points_contact_object_local, + sdf_values, + sdf_gradients, + contact_object_pos_w, + contact_object_quat_w, + elastomer_quat_w, + env_ids, + ) + + def _transform_tactile_points_to_world(self, pos_w: torch.Tensor, quat_w: torch.Tensor): + """Transform tactile points from local to world coordinates. + + Args: + pos_w: Elastomer positions in world frame. Shape: (num_envs, 3) + quat_w: Elastomer quaternions in world frame. Shape: (num_envs, 4) + """ + num_pts = self.num_tactile_points + + quat_expanded = quat_w.unsqueeze(1).expand(-1, num_pts, -1) + pos_expanded = pos_w.unsqueeze(1).expand(-1, num_pts, -1) + + # Apply transformation + tactile_pos_w = math_utils.quat_apply(quat_expanded, self._tactile_pos_expanded) + pos_expanded + tactile_quat_w = math_utils.quat_mul(quat_expanded, self._tactile_quat_expanded) + + # Store in data + self._data.tactile_points_pos_w = tactile_pos_w + self._data.tactile_points_quat_w = tactile_quat_w + + def _transform_points_to_contact_object_local( + self, world_points: torch.Tensor, contact_object_pos_w: torch.Tensor, contact_object_quat_w: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + """Optimized version: Transform world coordinates to contact object local frame. + + Args: + world_points: Points in world coordinates. Shape: (num_envs, num_points, 3) + contact_object_pos_w: Contact object positions in world frame. Shape: (num_envs, 3) + contact_object_quat_w: Contact object quaternions in world frame. Shape: (num_envs, 4) + + Returns: + Points in contact object local coordinates and inverse quaternions + """ + # Get inverse transformation (per environment) + # wxyz in torch + contact_object_quat_inv, contact_object_pos_inv = torch_utils.tf_inverse( + contact_object_quat_w, contact_object_pos_w + ) + num_pts = self.num_tactile_points + + contact_object_quat_expanded = contact_object_quat_inv.unsqueeze(1).expand(-1, num_pts, 4) + contact_object_pos_expanded = contact_object_pos_inv.unsqueeze(1).expand(-1, num_pts, 3) + + # Apply transformation + points_sdf = torch_utils.tf_apply(contact_object_quat_expanded, contact_object_pos_expanded, world_points) + + return points_sdf, contact_object_quat_inv + + def _get_tactile_points_velocities( + self, linvel_world: torch.Tensor, angvel_world: torch.Tensor, quat_world: torch.Tensor + ) -> torch.Tensor: + """Optimized version: Compute tactile point velocities from precomputed velocities. + + Args: + linvel_world: Elastomer linear velocities. Shape: (num_envs, 3) + angvel_world: Elastomer angular velocities. Shape: (num_envs, 3) + quat_world: Elastomer quaternions. Shape: (num_envs, 4) + + Returns: + Tactile point velocities in world frame. Shape: (num_envs, num_points, 3) + """ + num_pts = self.num_tactile_points + + # Pre-expand all required tensors once + quat_expanded = quat_world.unsqueeze(1).expand(-1, num_pts, 4) + tactile_pos_expanded = self._tactile_pos_expanded + + # Transform local positions to world frame relative vectors + tactile_pos_world_relative = math_utils.quat_apply(quat_expanded, tactile_pos_expanded) + + # Compute velocity due to angular motion: ω × r + angvel_expanded = angvel_world.unsqueeze(1).expand(-1, num_pts, 3) + angular_velocity_contribution = torch.cross(angvel_expanded, tactile_pos_world_relative, dim=-1) + + # Add linear velocity contribution + linvel_expanded = linvel_world.unsqueeze(1).expand(-1, num_pts, 3) + tactile_velocity_world = angular_velocity_contribution + linvel_expanded + + return tactile_velocity_world + + def _compute_tactile_forces_from_sdf( + self, + points_contact_object_local: torch.Tensor, + sdf_values: torch.Tensor, + sdf_gradients: torch.Tensor, + contact_object_pos_w: torch.Tensor, + contact_object_quat_w: torch.Tensor, + elastomer_quat_w: torch.Tensor, + env_ids: Sequence[int] | slice, + ) -> None: + """Optimized version: Compute tactile forces from SDF values using precomputed parameters. + + This method now operates directly on the pre-allocated data tensors to avoid + unnecessary memory allocation and copying. + + Args: + points_contact_object_local: Points in contact object local frame + sdf_values: SDF values (negative means penetration) + sdf_gradients: SDF gradients (surface normals) + contact_object_pos_w: Contact object positions in world frame + contact_object_quat_w: Contact object quaternions in world frame + elastomer_quat_w: Elastomer quaternions + env_ids: Environment indices being updated + + """ + depth = self._data.penetration_depth[env_ids] + tactile_normal_force = self._data.tactile_normal_force[env_ids] + tactile_shear_force = self._data.tactile_shear_force[env_ids] + + # Clear the output tensors + tactile_normal_force.zero_() + tactile_shear_force.zero_() + depth.zero_() + + # Convert SDF values to penetration depth (positive for penetration) + depth[:] = torch.clamp(-sdf_values[env_ids], min=0.0) # Negative SDF means inside (penetrating) + + # Get collision mask for points that are penetrating + collision_mask = depth > 0.0 + + # Use pre-allocated tensors instead of creating new ones + num_pts = self.num_tactile_points + + if collision_mask.any() or self.cfg.visualize_sdf_closest_pts: + # Get contact object and elastomer velocities (com velocities) + contact_object_velocities = self._contact_object_body_view.get_velocities() + contact_object_linvel_w_com = contact_object_velocities[env_ids, :3] + contact_object_angvel_w = contact_object_velocities[env_ids, 3:] + + elastomer_velocities = self._elastomer_body_view.get_velocities() + elastomer_linvel_w_com = elastomer_velocities[env_ids, :3] + elastomer_angvel_w = elastomer_velocities[env_ids, 3:] + + # Contact object adjustment + contact_object_com_w_offset = math_utils.quat_apply( + contact_object_quat_w[env_ids], self._contact_object_com_b[env_ids] + ) + contact_object_linvel_w = contact_object_linvel_w_com - torch.cross( + contact_object_angvel_w, contact_object_com_w_offset, dim=-1 + ) + # v_origin = v_com - w x (com_world_offset) where com_world_offset = quat_apply(quat, com_b) + elastomer_com_w_offset = math_utils.quat_apply(elastomer_quat_w[env_ids], self._elastomer_com_b[env_ids]) + elastomer_linvel_w = elastomer_linvel_w_com - torch.cross( + elastomer_angvel_w, elastomer_com_w_offset, dim=-1 + ) + + # Normalize gradients to get surface normals in local frame + normals_local = torch.nn.functional.normalize(sdf_gradients[env_ids], dim=-1) + + # Transform normals to world frame (rotate by contact object orientation) - use precomputed quaternions + contact_object_quat_expanded = contact_object_quat_w[env_ids].unsqueeze(1).expand(-1, num_pts, 4) + + # Apply quaternion transformation + normals_world = math_utils.quat_apply(contact_object_quat_expanded, normals_local) + + # Compute normal contact force: F_n = k_n * depth + fc_norm = self.cfg.normal_contact_stiffness * depth + fc_world = fc_norm.unsqueeze(-1) * normals_world + + # Get tactile point velocities using precomputed velocities + tactile_velocity_world = self._get_tactile_points_velocities( + elastomer_linvel_w, elastomer_angvel_w, elastomer_quat_w[env_ids] + ) + + # Use precomputed contact object velocities + closest_points_sdf = points_contact_object_local[env_ids] + depth.unsqueeze(-1) * normals_local + + if self.cfg.visualize_sdf_closest_pts: + debug_closest_points_sdf = ( + points_contact_object_local[env_ids] - sdf_values[env_ids].unsqueeze(-1) * normals_local + ) + self.debug_closest_points_wolrd = math_utils.quat_apply( + contact_object_quat_expanded, debug_closest_points_sdf + ) + contact_object_pos_w[env_ids].unsqueeze(1).expand(-1, num_pts, 3) + + contact_object_linvel_expanded = contact_object_linvel_w.unsqueeze(1).expand(-1, num_pts, 3) + contact_object_angvel_expanded = contact_object_angvel_w.unsqueeze(1).expand(-1, num_pts, 3) + closest_points_vel_world = ( + torch.linalg.cross( + contact_object_angvel_expanded, + math_utils.quat_apply(contact_object_quat_expanded, closest_points_sdf), + ) + + contact_object_linvel_expanded + ) + + # Compute relative velocity at contact points + relative_velocity_world = tactile_velocity_world - closest_points_vel_world + + # Compute tangential velocity (perpendicular to normal) + vt_world = relative_velocity_world - normals_world * torch.sum( + normals_world * relative_velocity_world, dim=-1, keepdim=True + ) + vt_norm = torch.norm(vt_world, dim=-1) + + # Compute friction force: F_t = min(k_t * |v_t|, mu * F_n) + ft_static_norm = self.cfg.tangential_stiffness * vt_norm + ft_dynamic_norm = self.cfg.friction_coefficient * fc_norm + ft_norm = torch.minimum(ft_static_norm, ft_dynamic_norm) + + # Apply friction force opposite to tangential velocity + ft_world = -ft_norm.unsqueeze(-1) * vt_world / (vt_norm.unsqueeze(-1).clamp(min=1e-9)) + + # Total tactile force in world frame + tactile_force_world = fc_world + ft_world + + # Transform forces to tactile frame + tactile_force_tactile = math_utils.quat_apply_inverse( + self._data.tactile_points_quat_w[env_ids], tactile_force_world + ) + + # Extract normal and shear components + # Assume tactile frame has Z as normal direction + tactile_normal_force[:] = tactile_force_tactile[..., 2] # Z component + tactile_shear_force[:] = tactile_force_tactile[..., :2] # X,Y components + + ######################################################################################### + # Debug visualization + ######################################################################################### + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization into visualization objects.""" + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if self._tactile_visualizer is None: + self._tactile_visualizer = VisualizationMarkers(self.cfg.visualizer_cfg) + # set their visibility to true + self._tactile_visualizer.set_visibility(True) + else: + if self._tactile_visualizer: + self._tactile_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + """Callback for debug visualization of tactile sensor data. + + This method is called during each simulation step when debug visualization is enabled. + It visualizes tactile sensing points as 3D markers in the simulation viewport to help + with debugging and understanding sensor behavior. + + The method handles two visualization modes: + + 1. **Standard mode**: Visualizes ``tactile_points_pos_w`` - the world positions of + tactile sensing points on the sensor surface + 2. **SDF debug mode**: When ``cfg.visualize_sdf_closest_pts`` is True, visualizes + ``debug_closest_points_wolrd`` - the closest surface points computed during + SDF-based force calculations + """ + # Safety check - return if not properly initialized + if not hasattr(self, "_tactile_visualizer") or self._tactile_visualizer is None: + return + vis_points = None + + if self.cfg.visualize_sdf_closest_pts and hasattr(self, "debug_closest_points_wolrd"): + vis_points = self.debug_closest_points_wolrd + else: + vis_points = self._data.tactile_points_pos_w + + if vis_points is None or vis_points.numel() == 0: + return + + viz_points = vis_points.view(-1, 3) # Shape: (num_envs * num_points, 3) + + indices = torch.zeros(viz_points.shape[0], dtype=torch.long, device=self._device) + + marker_scales = torch.ones(viz_points.shape[0], 3, device=self._device) + + # Visualize tactile points + self._tactile_visualizer.visualize(translations=viz_points, marker_indices=indices, scales=marker_scales) diff --git a/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_cfg.py b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f7b46bdeaa70bc60861dd39b1d50e5e850b37da1 --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_cfg.py @@ -0,0 +1,190 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +# needed to import for allowing type-hinting: torch.Tensor | None +from __future__ import annotations + +from dataclasses import MISSING + +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.markers.config import VISUO_TACTILE_SENSOR_MARKER_CFG +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from ..camera.tiled_camera_cfg import TiledCameraCfg +from ..sensor_base_cfg import SensorBaseCfg +from .visuotactile_sensor import VisuoTactileSensor + +## +# GelSight Render Configuration +## + + +@configclass +class GelSightRenderCfg: + """Configuration for GelSight sensor rendering parameters. + + This configuration defines the rendering parameters for example-based tactile image synthesis + using the Taxim approach. + + Reference: + Si, Z., & Yuan, W. (2022). Taxim: An example-based simulation model for GelSight + tactile sensors. IEEE Robotics and Automation Letters, 7(2), 2361-2368. + https://arxiv.org/abs/2109.04027 + + Data Directory Structure: + The sensor data should be organized in the following structure:: + + base_data_path/ + └── sensor_data_dir_name/ + ├── bg.jpg # Background image (required) + ├── polycalib.npz # Polynomial calibration data (required) + └── real_bg.npy # Real background data (optional) + + Example: + Using predefined sensor configuration:: + + from isaaclab_assets.sensors import GELSIGHT_R15_CFG + + sensor_cfg = VisuoTactileSensorCfg(render_cfg=GELSIGHT_R15_CFG) + + Using custom sensor data:: + + custom_cfg = GelSightRenderCfg( + base_data_path="/path/to/my/sensors", + sensor_data_dir_name="my_custom_sensor", + image_height=480, + image_width=640, + mm_per_pixel=0.05, + ) + """ + + base_data_path: str | None = f"{ISAACLAB_NUCLEUS_DIR}/TacSL" + """Base path to the directory containing sensor calibration data. + + If ``None``, defaults to Isaac Lab Nucleus directory at + ``{ISAACLAB_NUCLEUS_DIR}/TacSL``. Download the data from Nucleus if not present locally. + If a custom path is provided, uses the data directly from that location without downloading. + """ + + sensor_data_dir_name: str = MISSING + """Directory name containing the sensor calibration and background data. + + This should be a relative path (directory name) inside the :attr:`base_data_path`. + """ + + background_path: str = "bg.jpg" + """Filename of the background image within the data directory.""" + + calib_path: str = "polycalib.npz" + """Filename of the polynomial calibration data within the data directory.""" + + real_background: str = "real_bg.npy" + """Filename of the real background data within the data directory.""" + + image_height: int = MISSING + """Height of the tactile image in pixels.""" + + image_width: int = MISSING + """Width of the tactile image in pixels.""" + + num_bins: int = 120 + """Number of bins for gradient magnitude and direction quantization.""" + + mm_per_pixel: float = MISSING + """Millimeters per pixel conversion factor for reconstructing 2D tactile image from the height map.""" + + +## +# Visuo-Tactile Sensor Configuration +## + + +@configclass +class VisuoTactileSensorCfg(SensorBaseCfg): + """Configuration for the visuo-tactile sensor. + + This sensor provides both camera-based tactile sensing and force field tactile sensing. + It can capture tactile RGB/depth images and compute penalty-based contact forces. + """ + + class_type: type = VisuoTactileSensor + + # Sensor type and capabilities + render_cfg: GelSightRenderCfg = MISSING + """Configuration for GelSight sensor rendering. + + This defines the rendering parameters for converting depth maps to realistic tactile images. + Defaults to GelSight R1.5 parameters. Use predefined configs like GELSIGHT_R15_CFG or + GELSIGHT_MINI_CFG from isaaclab_assets.sensors for standard sensor models. + """ + + enable_camera_tactile: bool = True + """Whether to enable camera-based tactile sensing.""" + + enable_force_field: bool = True + """Whether to enable force field tactile sensing.""" + + # Force field configuration + tactile_array_size: tuple[int, int] = MISSING + """Number of tactile points for force field sensing in (rows, cols) format.""" + + tactile_margin: float = MISSING + """Margin for tactile point generation (in meters). + + This parameter defines the exclusion margin from the edges of the elastomer mesh when generating + the tactile point grid. It ensures that force field points are not generated on the very edges + of the sensor surface where geometry might be unstable or less relevant for contact. + """ + + contact_object_prim_path_expr: str | None = None + """Prim path expression to find the contact object for force field computation. + + This specifies the object that will make contact with the tactile sensor. The sensor will automatically + find the SDF collision mesh within this object for optimal force field computation. + + .. note:: + The expression can contain the environment namespace regex ``{ENV_REGEX_NS}`` which + will be replaced with the environment namespace. + + Example: ``{ENV_REGEX_NS}/ContactObject`` will be replaced with ``/World/envs/env_.*/ContactObject``. + + .. attention:: + For force field computation to work properly, the contact object must have an SDF collision mesh. + The sensor will search for the first SDF mesh within the specified prim hierarchy. + """ + + # Force field physics parameters + normal_contact_stiffness: float = 1.0 + """Normal contact stiffness for penalty-based force computation.""" + + friction_coefficient: float = 2.0 + """Friction coefficient for shear forces.""" + + tangential_stiffness: float = 0.1 + """Tangential stiffness for shear forces.""" + + camera_cfg: TiledCameraCfg | None = None + """Camera configuration for tactile RGB/depth sensing. + + If None, camera-based sensing will be disabled even if :attr:`enable_camera_tactile` is True. + """ + + # Visualization + visualizer_cfg: VisualizationMarkersCfg = VISUO_TACTILE_SENSOR_MARKER_CFG.replace( + prim_path="/Visuals/TactileSensor" + ) + """The configuration object for the visualization markers. + + .. note:: + This attribute is only used when debug visualization is enabled. + """ + + trimesh_vis_tactile_points: bool = False + """Whether to visualize tactile points for debugging using trimesh. Defaults to False.""" + + visualize_sdf_closest_pts: bool = False + """Whether to visualize SDF closest points for debugging. Defaults to False.""" diff --git a/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_data.py b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_data.py new file mode 100644 index 0000000000000000000000000000000000000000..0d2c5b9019233060c03afa97784c4b7de6d994bc --- /dev/null +++ b/source/isaaclab/isaaclab/sensors/tacsl_sensor/visuotactile_sensor_data.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass +class VisuoTactileSensorData: + """Data container for the visuo-tactile sensor. + + This class contains the tactile sensor data that includes: + - Camera-based tactile sensing (RGB and depth images) + - Force field tactile sensing (normal and shear forces) + - Tactile point positions and contact information + """ + + # Camera-based tactile data + tactile_depth_image: torch.Tensor | None = None + """Tactile depth images. Shape is (num_instances, height, width, 1).""" + + tactile_rgb_image: torch.Tensor | None = None + """Tactile RGB images rendered using the Taxim approach (https://arxiv.org/abs/2109.04027). + Shape is (num_instances, height, width, 3). + """ + + # Force field tactile data + tactile_points_pos_w: torch.Tensor | None = None + """Positions of tactile points in world frame. Shape is (num_instances, num_tactile_points, 3).""" + + tactile_points_quat_w: torch.Tensor | None = None + """Orientations of tactile points in world frame. Shape is (num_instances, num_tactile_points, 4).""" + + penetration_depth: torch.Tensor | None = None + """Penetration depth at each tactile point. Shape is (num_instances, num_tactile_points).""" + + tactile_normal_force: torch.Tensor | None = None + """Normal forces at each tactile point in sensor frame. Shape is (num_instances, num_tactile_points).""" + + tactile_shear_force: torch.Tensor | None = None + """Shear forces at each tactile point in sensor frame. Shape is (num_instances, num_tactile_points, 2).""" diff --git a/source/isaaclab/isaaclab/sim/__init__.py b/source/isaaclab/isaaclab/sim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1dc920f4e100b08948eb83a7d1eada2f5cbd1a1d --- /dev/null +++ b/source/isaaclab/isaaclab/sim/__init__.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package containing simulation-specific functionalities. + +These include: + +* Ability to spawn different objects and materials into Omniverse +* Define and modify various schemas on USD prims +* Converters to obtain USD file from other file formats (such as URDF, OBJ, STL, FBX) +* Utility class to control the simulator + +.. note:: + Currently, only a subset of all possible schemas and prims in Omniverse are supported. + We are expanding the these set of functions on a need basis. In case, there are + specific prims or schemas that you would like to include, please open an issue on GitHub + as a feature request elaborating on the required application. + +To make it convenient to use the module, we recommend importing the module as follows: + +.. code-block:: python + + import isaaclab.sim as sim_utils + +""" + +from .converters import * # noqa: F401, F403 +from .schemas import * # noqa: F401, F403 +from .simulation_cfg import PhysxCfg, RenderCfg, SimulationCfg # noqa: F401, F403 +from .simulation_context import SimulationContext, build_simulation_context # noqa: F401, F403 +from .spawners import * # noqa: F401, F403 +from .utils import * # noqa: F401, F403 +from .views import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/sim/converters/__init__.py b/source/isaaclab/isaaclab/sim/converters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7503c53bdd85d6bed3beeac544b0b1ef7e2cb7f1 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing converters for converting various file types to USD. + +In order to support direct loading of various file types into Omniverse, we provide a set of +converters that can convert the file into a USD file. The converters are implemented as +sub-classes of the :class:`AssetConverterBase` class. + +The following converters are currently supported: + +* :class:`UrdfConverter`: Converts a URDF file into a USD file. +* :class:`MeshConverter`: Converts a mesh file into a USD file. This supports OBJ, STL and FBX files. + +""" + +from .asset_converter_base import AssetConverterBase +from .asset_converter_base_cfg import AssetConverterBaseCfg +from .mesh_converter import MeshConverter +from .mesh_converter_cfg import MeshConverterCfg +from .mjcf_converter import MjcfConverter +from .mjcf_converter_cfg import MjcfConverterCfg +from .urdf_converter import UrdfConverter +from .urdf_converter_cfg import UrdfConverterCfg diff --git a/source/isaaclab/isaaclab/sim/converters/asset_converter_base.py b/source/isaaclab/isaaclab/sim/converters/asset_converter_base.py new file mode 100644 index 0000000000000000000000000000000000000000..11c2004223916f84e57d2843441b5035f7840993 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/asset_converter_base.py @@ -0,0 +1,198 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import abc +import hashlib +import json +import os +import pathlib +import random +from datetime import datetime + +from isaaclab.sim.converters.asset_converter_base_cfg import AssetConverterBaseCfg +from isaaclab.utils.assets import check_file_path +from isaaclab.utils.io import dump_yaml + + +class AssetConverterBase(abc.ABC): + """Base class for converting an asset file from different formats into USD format. + + This class provides a common interface for converting an asset file into USD. It does not + provide any implementation for the conversion. The derived classes must implement the + :meth:`_convert_asset` method to provide the actual conversion. + + The file conversion is lazy if the output directory (:obj:`AssetConverterBaseCfg.usd_dir`) is provided. + In the lazy conversion, the USD file is re-generated only if: + + * The asset file is modified. + * The configuration parameters are modified. + * The USD file does not exist. + + To override this behavior to force conversion, the flag :obj:`AssetConverterBaseCfg.force_usd_conversion` + can be set to True. + + When no output directory is defined, lazy conversion is deactivated and the generated USD file is + stored in folder ``/tmp/IsaacLab/usd_{date}_{time}_{random}``, where the parameters in braces are generated + at runtime. The random identifiers help avoid a race condition where two simultaneously triggered conversions + try to use the same directory for reading/writing the generated files. + + .. note:: + Changes to the parameters :obj:`AssetConverterBaseCfg.asset_path`, :obj:`AssetConverterBaseCfg.usd_dir`, and + :obj:`AssetConverterBaseCfg.usd_file_name` are not considered as modifications in the configuration instance + that trigger the USD file re-generation. + + """ + + def __init__(self, cfg: AssetConverterBaseCfg): + """Initializes the class. + + Args: + cfg: The configuration instance for converting an asset file to USD format. + + Raises: + ValueError: When provided asset file does not exist. + """ + # check that the config is valid + cfg.validate() + # check if the asset file exists + if not check_file_path(cfg.asset_path): + raise ValueError(f"The asset path does not exist: {cfg.asset_path}") + # save the inputs + self.cfg = cfg + + # resolve USD directory name + if cfg.usd_dir is None: + # a folder in "/tmp/IsaacLab" by the name: usd_{date}_{time}_{random} + time_tag = datetime.now().strftime("%Y%m%d_%H%M%S") + self._usd_dir = f"/tmp/IsaacLab/usd_{time_tag}_{random.randrange(10000)}" + else: + self._usd_dir = cfg.usd_dir + + # resolve the file name from asset file name if not provided + if cfg.usd_file_name is None: + usd_file_name = pathlib.PurePath(cfg.asset_path).stem + else: + usd_file_name = cfg.usd_file_name + # add USD extension if not provided + if not (usd_file_name.endswith(".usd") or usd_file_name.endswith(".usda")): + self._usd_file_name = usd_file_name + ".usd" + else: + self._usd_file_name = usd_file_name + + # create the USD directory + os.makedirs(self.usd_dir, exist_ok=True) + # check if usd files exist + self._usd_file_exists = os.path.isfile(self.usd_path) + # path to read/write asset hash file + self._dest_hash_path = os.path.join(self.usd_dir, ".asset_hash") + # create asset hash to check if the asset has changed + self._asset_hash = self._config_to_hash(cfg) + # read the saved hash + try: + with open(self._dest_hash_path) as f: + existing_asset_hash = f.readline() + self._is_same_asset = existing_asset_hash == self._asset_hash + except FileNotFoundError: + self._is_same_asset = False + + # convert the asset to USD if the hash is different or USD file does not exist + if cfg.force_usd_conversion or not self._usd_file_exists or not self._is_same_asset: + # write the updated hash + with open(self._dest_hash_path, "w") as f: + f.write(self._asset_hash) + # convert the asset to USD + self._convert_asset(cfg) + # dump the configuration to a file + dump_yaml(os.path.join(self.usd_dir, "config.yaml"), cfg.to_dict()) + # add comment to top of the saved config file with information about the converter + current_date = datetime.now().strftime("%Y-%m-%d") + current_time = datetime.now().strftime("%H:%M:%S") + generation_comment = ( + f"##\n# Generated by {self.__class__.__name__} on {current_date} at {current_time}.\n##\n" + ) + with open(os.path.join(self.usd_dir, "config.yaml"), "a") as f: + f.write(generation_comment) + + """ + Properties. + """ + + @property + def usd_dir(self) -> str: + """The absolute path to the directory where the generated USD files are stored.""" + return self._usd_dir + + @property + def usd_file_name(self) -> str: + """The file name of the generated USD file.""" + return self._usd_file_name + + @property + def usd_path(self) -> str: + """The absolute path to the generated USD file.""" + return os.path.join(self.usd_dir, self.usd_file_name) + + @property + def usd_instanceable_meshes_path(self) -> str: + """The relative path to the USD file with meshes. + + The path is with respect to the USD directory :attr:`usd_dir`. This is to ensure that the + mesh references in the generated USD file are resolved relatively. Otherwise, it becomes + difficult to move the USD asset to a different location. + """ + return os.path.join(".", "Props", "instanceable_meshes.usd") + + """ + Implementation specifics. + """ + + @abc.abstractmethod + def _convert_asset(self, cfg: AssetConverterBaseCfg): + """Converts the asset file to USD. + + Args: + cfg: The configuration instance for the input asset to USD conversion. + """ + raise NotImplementedError() + + """ + Private helpers. + """ + + @staticmethod + def _config_to_hash(cfg: AssetConverterBaseCfg) -> str: + """Converts the configuration object and asset file to an MD5 hash of a string. + + .. warning:: + It only checks the main asset file (:attr:`cfg.asset_path`). + + Args: + config : The asset converter configuration object. + + Returns: + An MD5 hash of a string. + """ + + # convert to dict and remove path related info + config_dic = cfg.to_dict() + _ = config_dic.pop("asset_path") + _ = config_dic.pop("usd_dir") + _ = config_dic.pop("usd_file_name") + # convert config dic to bytes + config_bytes = json.dumps(config_dic).encode() + # hash config + md5 = hashlib.md5() + md5.update(config_bytes) + + # read the asset file to observe changes + with open(cfg.asset_path, "rb") as f: + while True: + # read 64kb chunks to avoid memory issues for the large files! + data = f.read(65536) + if not data: + break + md5.update(data) + # return the hash + return md5.hexdigest() diff --git a/source/isaaclab/isaaclab/sim/converters/asset_converter_base_cfg.py b/source/isaaclab/isaaclab/sim/converters/asset_converter_base_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..79bb8d17d41c9484c149cdbcb4d149691f1bc788 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/asset_converter_base_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + + +@configclass +class AssetConverterBaseCfg: + """The base configuration class for asset converters.""" + + asset_path: str = MISSING + """The absolute path to the asset file to convert into USD.""" + + usd_dir: str | None = None + """The output directory path to store the generated USD file. Defaults to None. + + If None, it is resolved as ``/tmp/IsaacLab/usd_{date}_{time}_{random}``, where + the parameters in braces are runtime generated. + """ + + usd_file_name: str | None = None + """The name of the generated usd file. Defaults to None. + + If None, it is resolved from the asset file name. For example, if the asset file + name is ``"my_asset.urdf"``, then the generated USD file name is ``"my_asset.usd"``. + + If the providing file name does not end with ".usd" or ".usda", then the extension + ".usd" is appended to the file name. + """ + + force_usd_conversion: bool = False + """Force the conversion of the asset file to usd. Defaults to False. + + If True, then the USD file is always generated. It will overwrite the existing USD file if it exists. + """ + + make_instanceable: bool = True + """Make the generated USD file instanceable. Defaults to True. + + Note: + Instancing helps reduce the memory footprint of the asset when multiple copies of the asset are + used in the scene. For more information, please check the USD documentation on + `scene-graph instancing `_. + """ diff --git a/source/isaaclab/isaaclab/sim/converters/mesh_converter.py b/source/isaaclab/isaaclab/sim/converters/mesh_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..4a79a908bab1edda5b55d66ddaa1c05040a758a8 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/mesh_converter.py @@ -0,0 +1,248 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import asyncio +import logging +import os + +import omni +import omni.kit.commands +from isaacsim.core.utils.extensions import enable_extension +from pxr import Gf, Tf, Usd, UsdGeom, UsdPhysics, UsdUtils + +from isaaclab.sim.converters.asset_converter_base import AssetConverterBase +from isaaclab.sim.converters.mesh_converter_cfg import MeshConverterCfg +from isaaclab.sim.schemas import schemas +from isaaclab.sim.utils import delete_prim, export_prim_to_file + +# import logger +logger = logging.getLogger(__name__) + + +class MeshConverter(AssetConverterBase): + """Converter for a mesh file in OBJ / STL / FBX format to a USD file. + + This class wraps around the `omni.kit.asset_converter`_ extension to provide a lazy implementation + for mesh to USD conversion. It stores the output USD file in an instanceable format since that is + what is typically used in all learning related applications. + + To make the asset instanceable, we must follow a certain structure dictated by how USD scene-graph + instancing and physics work. The rigid body component must be added to each instance and not the + referenced asset (i.e. the prototype prim itself). This is because the rigid body component defines + properties that are specific to each instance and cannot be shared under the referenced asset. For + more information, please check the `documentation `_. + + Due to the above, we follow the following structure: + + * ``{prim_path}`` - The root prim that is an Xform with the rigid body and mass APIs if configured. + * ``{prim_path}/geometry`` - The prim that contains the mesh and optionally the materials if configured. + If instancing is enabled, this prim will be an instanceable reference to the prototype prim. + + .. _omni.kit.asset_converter: https://docs.omniverse.nvidia.com/extensions/latest/ext_asset-converter.html + + .. caution:: + When converting STL files, Z-up convention is assumed, even though this is not the default for many CAD + export programs. Asset orientation convention can either be modified directly in the CAD program's export + process or an offset can be added within the config in Isaac Lab. + + """ + + cfg: MeshConverterCfg + """The configuration instance for mesh to USD conversion.""" + + def __init__(self, cfg: MeshConverterCfg): + """Initializes the class. + + Args: + cfg: The configuration instance for mesh to USD conversion. + """ + super().__init__(cfg=cfg) + + """ + Implementation specific methods. + """ + + def _convert_asset(self, cfg: MeshConverterCfg): + """Generate USD from OBJ, STL or FBX. + + The USD file has Y-up axis and is scaled to meters. + The asset hierarchy is arranged as follows: + + .. code-block:: none + mesh_file_basename (default prim) + |- /geometry/Looks + |- /geometry/mesh + + Args: + cfg: The configuration for conversion of mesh to USD. + + Raises: + RuntimeError: If the conversion using the Omniverse asset converter fails. + """ + # resolve mesh name and format + mesh_file_basename, mesh_file_format = os.path.basename(cfg.asset_path).split(".") + mesh_file_format = mesh_file_format.lower() + + # Check if mesh_file_basename is a valid USD identifier + if not Tf.IsValidIdentifier(mesh_file_basename): + # Correct the name to a valid identifier and update the basename + mesh_file_basename_original = mesh_file_basename + mesh_file_basename = Tf.MakeValidIdentifier(mesh_file_basename) + logger.warning( + f"Input file name '{mesh_file_basename_original}' is an invalid identifier for the mesh prim path." + f" Renaming it to '{mesh_file_basename}' for the conversion." + ) + + # Convert USD + asyncio.get_event_loop().run_until_complete( + self._convert_mesh_to_usd(in_file=cfg.asset_path, out_file=self.usd_path) + ) + # Create a new stage, set Z up and meters per unit + temp_stage = Usd.Stage.CreateInMemory() + UsdGeom.SetStageUpAxis(temp_stage, UsdGeom.Tokens.z) + UsdGeom.SetStageMetersPerUnit(temp_stage, 1.0) + UsdPhysics.SetStageKilogramsPerUnit(temp_stage, 1.0) + # Add mesh to stage + base_prim = temp_stage.DefinePrim(f"/{mesh_file_basename}", "Xform") + prim = temp_stage.DefinePrim(f"/{mesh_file_basename}/geometry", "Xform") + prim.GetReferences().AddReference(self.usd_path) + temp_stage.SetDefaultPrim(base_prim) + temp_stage.Export(self.usd_path) + + # Open converted USD stage + stage = Usd.Stage.Open(self.usd_path) + # Need to reload the stage to get the new prim structure, otherwise it can be taken from the cache + stage.Reload() + # Add USD to stage cache + stage_id = UsdUtils.StageCache.Get().Insert(stage) + # Get the default prim (which is the root prim) -- "/{mesh_file_basename}" + xform_prim = stage.GetDefaultPrim() + geom_prim = stage.GetPrimAtPath(f"/{mesh_file_basename}/geometry") + # Move all meshes to underneath new Xform + for child_mesh_prim in geom_prim.GetChildren(): + if child_mesh_prim.GetTypeName() == "Mesh": + # Apply collider properties to mesh + if cfg.collision_props is not None: + # -- Collider properties such as offset, scale, etc. + schemas.define_collision_properties( + prim_path=child_mesh_prim.GetPath(), cfg=cfg.collision_props, stage=stage + ) + # Add collision mesh + if cfg.mesh_collision_props is not None: + schemas.define_mesh_collision_properties( + prim_path=child_mesh_prim.GetPath(), cfg=cfg.mesh_collision_props, stage=stage + ) + # Delete the old Xform and make the new Xform the default prim + stage.SetDefaultPrim(xform_prim) + # Apply default Xform rotation to mesh -> enable to set rotation and scale + omni.kit.commands.execute( + "CreateDefaultXformOnPrimCommand", + prim_path=xform_prim.GetPath(), + **{"stage": stage}, + ) + + # Apply translation, rotation, and scale to the Xform + geom_xform = UsdGeom.Xform(geom_prim) + geom_xform.ClearXformOpOrder() + + # Remove any existing rotation attributes + rotate_attr = geom_prim.GetAttribute("xformOp:rotateXYZ") + if rotate_attr: + geom_prim.RemoveProperty(rotate_attr.GetName()) + + # translation + translate_op = geom_xform.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble) + translate_op.Set(Gf.Vec3d(*cfg.translation)) + # rotation + orient_op = geom_xform.AddOrientOp(UsdGeom.XformOp.PrecisionDouble) + orient_op.Set(Gf.Quatd(*cfg.rotation)) + # scale + scale_op = geom_xform.AddScaleOp(UsdGeom.XformOp.PrecisionDouble) + scale_op.Set(Gf.Vec3d(*cfg.scale)) + + # Handle instanceable + # Create a new Xform prim that will be the prototype prim + if cfg.make_instanceable: + # Export Xform to a file so we can reference it from all instances + export_prim_to_file( + path=os.path.join(self.usd_dir, self.usd_instanceable_meshes_path), + source_prim_path=geom_prim.GetPath(), + stage=stage, + ) + # Delete the original prim that will now be a reference + geom_prim_path = geom_prim.GetPath().pathString + delete_prim(geom_prim_path, stage=stage) + # Update references to exported Xform and make it instanceable + geom_undef_prim = stage.DefinePrim(geom_prim_path) + geom_undef_prim.GetReferences().AddReference(self.usd_instanceable_meshes_path, primPath=geom_prim_path) + geom_undef_prim.SetInstanceable(True) + + # Apply mass and rigid body properties after everything else + # Properties are applied to the top level prim to avoid the case where all instances of this + # asset unintentionally share the same rigid body properties + # apply mass properties + if cfg.mass_props is not None: + schemas.define_mass_properties(prim_path=xform_prim.GetPath(), cfg=cfg.mass_props, stage=stage) + # apply rigid body properties + if cfg.rigid_props is not None: + schemas.define_rigid_body_properties(prim_path=xform_prim.GetPath(), cfg=cfg.rigid_props, stage=stage) + + # Save changes to USD stage + stage.Save() + if stage_id is not None: + UsdUtils.StageCache.Get().Erase(stage_id) + + """ + Helper methods. + """ + + @staticmethod + async def _convert_mesh_to_usd(in_file: str, out_file: str, load_materials: bool = True) -> bool: + """Convert mesh from supported file types to USD. + + This function uses the Omniverse Asset Converter extension to convert a mesh file to USD. + It is an asynchronous function and should be called using `asyncio.get_event_loop().run_until_complete()`. + + The converted asset is stored in the USD format in the specified output file. + The USD file has Y-up axis and is scaled to cm. + + Args: + in_file: The file to convert. + out_file: The path to store the output file. + load_materials: Set to True to enable attaching materials defined in the input file + to the generated USD mesh. Defaults to True. + + Returns: + True if the conversion succeeds. + """ + enable_extension("omni.kit.asset_converter") + + import omni.kit.asset_converter + + # Create converter context + converter_context = omni.kit.asset_converter.AssetConverterContext() + # Set up converter settings + # Don't import/export materials + converter_context.ignore_materials = not load_materials + converter_context.ignore_animations = True + converter_context.ignore_camera = True + converter_context.ignore_light = True + # Merge all meshes into one + converter_context.merge_all_meshes = True + # Sets world units to meters, this will also scale asset if it's centimeters model. + # This does not work right now :(, so we need to scale the mesh manually + converter_context.use_meter_as_world_unit = True + converter_context.baking_scales = True + # Uses double precision for all transform ops. + converter_context.use_double_precision_to_usd_transform_op = True + + # Create converter task + instance = omni.kit.asset_converter.get_instance() + task = instance.create_converter_task(in_file, out_file, None, converter_context) + # Start conversion task and wait for it to finish + success = await task.wait_until_finished() + if not success: + raise RuntimeError(f"Failed to convert {in_file} to USD. Error: {task.get_error_message()}") + return success diff --git a/source/isaaclab/isaaclab/sim/converters/mesh_converter_cfg.py b/source/isaaclab/isaaclab/sim/converters/mesh_converter_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..3d231ac1efeeb9048ecba031af87c8198d11c8a2 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/mesh_converter_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.sim.converters.asset_converter_base_cfg import AssetConverterBaseCfg +from isaaclab.sim.schemas import schemas_cfg +from isaaclab.utils import configclass + + +@configclass +class MeshConverterCfg(AssetConverterBaseCfg): + """The configuration class for MeshConverter.""" + + mass_props: schemas_cfg.MassPropertiesCfg = None + """Mass properties to apply to the USD. Defaults to None. + + Note: + If None, then no mass properties will be added. + """ + + rigid_props: schemas_cfg.RigidBodyPropertiesCfg = None + """Rigid body properties to apply to the USD. Defaults to None. + + Note: + If None, then no rigid body properties will be added. + """ + + collision_props: schemas_cfg.CollisionPropertiesCfg = None + """Collision properties to apply to the USD. Defaults to None. + + Note: + If None, then no collision properties will be added. + """ + mesh_collision_props: schemas_cfg.MeshCollisionPropertiesCfg = None + """Mesh approximation properties to apply to all collision meshes in the USD. + Note: + If None, then no mesh approximation properties will be added. + """ + + translation: tuple[float, float, float] = (0.0, 0.0, 0.0) + """The translation of the mesh to the origin. Defaults to (0.0, 0.0, 0.0).""" + + rotation: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) + """The rotation of the mesh in quaternion format (w, x, y, z). Defaults to (1.0, 0.0, 0.0, 0.0).""" + + scale: tuple[float, float, float] = (1.0, 1.0, 1.0) + """The scale of the mesh. Defaults to (1.0, 1.0, 1.0).""" diff --git a/source/isaaclab/isaaclab/sim/converters/mjcf_converter.py b/source/isaaclab/isaaclab/sim/converters/mjcf_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..17808f59b9484e34883a20da19d3aae3379c4491 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/mjcf_converter.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import os +from typing import TYPE_CHECKING + +import omni.kit.commands + +from .asset_converter_base import AssetConverterBase +from .mjcf_converter_cfg import MjcfConverterCfg + +if TYPE_CHECKING: + import isaacsim.asset.importer.mjcf + + +class MjcfConverter(AssetConverterBase): + """Converter for a MJCF description file to a USD file. + + This class wraps around the `isaacsim.asset.importer.mjcf`_ extension to provide a lazy implementation + for MJCF to USD conversion. It stores the output USD file in an instanceable format since that is + what is typically used in all learning related applications. + + .. caution:: + The current lazy conversion implementation does not automatically trigger USD generation if + only the mesh files used by the MJCF are modified. To force generation, either set + :obj:`AssetConverterBaseCfg.force_usd_conversion` to True or delete the output directory. + + .. note:: + From Isaac Sim 4.5 onwards, the extension name changed from ``omni.importer.mjcf`` to + ``isaacsim.asset.importer.mjcf``. This converter class now uses the latest extension from Isaac Sim. + + .. _isaacsim.asset.importer.mjcf: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_mjcf.html + """ + + cfg: MjcfConverterCfg + """The configuration instance for MJCF to USD conversion.""" + + def __init__(self, cfg: MjcfConverterCfg): + """Initializes the class. + + Args: + cfg: The configuration instance for URDF to USD conversion. + """ + super().__init__(cfg=cfg) + + """ + Implementation specific methods. + """ + + def _convert_asset(self, cfg: MjcfConverterCfg): + """Calls underlying Omniverse command to convert MJCF to USD. + + Args: + cfg: The configuration instance for MJCF to USD conversion. + """ + import_config = self._get_mjcf_import_config() + file_basename, _ = os.path.basename(cfg.asset_path).split(".") + omni.kit.commands.execute( + "MJCFCreateAsset", + mjcf_path=cfg.asset_path, + import_config=import_config, + dest_path=self.usd_path, + prim_path=f"/{file_basename}", + ) + + def _get_mjcf_import_config(self) -> isaacsim.asset.importer.mjcf._mjcf.ImportConfig: + """Returns the import configuration for MJCF to USD conversion. + + Returns: + The constructed ``ImportConfig`` object containing the desired settings. + """ + + _, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") + + # set the unit scaling factor, 1.0 means meters, 100.0 means cm + # import_config.set_distance_scale(1.0) + # set imported robot as default prim + # import_config.set_make_default_prim(True) + # add a physics scene to the stage on import if none exists + # import_config.set_create_physics_scene(False) + # set flag to parse tag + import_config.set_import_sites(True) + + # -- instancing settings + # meshes will be placed in a separate usd file + import_config.set_make_instanceable(self.cfg.make_instanceable) + import_config.set_instanceable_usd_path(self.usd_instanceable_meshes_path) + + # -- asset settings + # default density used for links, use 0 to auto-compute + import_config.set_density(self.cfg.link_density) + # import inertia tensor from urdf, if it is not specified in urdf it will import as identity + import_config.set_import_inertia_tensor(self.cfg.import_inertia_tensor) + + # -- physics settings + # create fix joint for base link + import_config.set_fix_base(self.cfg.fix_base) + # self collisions between links in the articulation + import_config.set_self_collision(self.cfg.self_collision) + + return import_config diff --git a/source/isaaclab/isaaclab/sim/converters/mjcf_converter_cfg.py b/source/isaaclab/isaaclab/sim/converters/mjcf_converter_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7cbd83e3e9f21e2e9ba2e22755f697d9b627ae40 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/mjcf_converter_cfg.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.sim.converters.asset_converter_base_cfg import AssetConverterBaseCfg +from isaaclab.utils import configclass + + +@configclass +class MjcfConverterCfg(AssetConverterBaseCfg): + """The configuration class for MjcfConverter.""" + + link_density = 0.0 + """Default density used for links. Defaults to 0. + + This setting is only effective if ``"inertial"`` properties are missing in the MJCF. + """ + + import_inertia_tensor: bool = True + """Import the inertia tensor from mjcf. Defaults to True. + + If the ``"inertial"`` tag is missing, then it is imported as an identity. + """ + + fix_base: bool = MISSING + """Create a fix joint to the root/base link. Defaults to True.""" + + import_sites: bool = True + """Import the sites from the MJCF. Defaults to True.""" + + self_collision: bool = False + """Activate self-collisions between links of the articulation. Defaults to False.""" diff --git a/source/isaaclab/isaaclab/sim/converters/urdf_converter.py b/source/isaaclab/isaaclab/sim/converters/urdf_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..ba80f520355e177ca0731e90e7b960d8a1671778 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/urdf_converter.py @@ -0,0 +1,342 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +import re +from typing import TYPE_CHECKING + +from packaging.version import Version + +import omni.kit.app +import omni.kit.commands + +from isaaclab.utils.version import get_isaac_sim_version + +from .asset_converter_base import AssetConverterBase +from .urdf_converter_cfg import UrdfConverterCfg + +if TYPE_CHECKING: + import isaacsim.asset.importer.urdf + + +class UrdfConverter(AssetConverterBase): + """Converter for a URDF description file to a USD file. + + This class wraps around the `isaacsim.asset.importer.urdf`_ extension to provide a lazy implementation + for URDF to USD conversion. It stores the output USD file in an instanceable format since that is + what is typically used in all learning related applications. + + .. caution:: + The current lazy conversion implementation does not automatically trigger USD generation if + only the mesh files used by the URDF are modified. To force generation, either set + :obj:`AssetConverterBaseCfg.force_usd_conversion` to True or delete the output directory. + + .. note:: + From Isaac Sim 4.5 onwards, the extension name changed from ``omni.importer.urdf`` to + ``isaacsim.asset.importer.urdf``. + + .. note:: + In Isaac Sim 5.1, the URDF importer changed the default behavior of merging fixed joints. + Links connected through ``fixed_joint`` elements are no longer merged when their URDF link + entries specify mass and inertia, even if ``merge-joint`` is set to True. The new behavior + treats links with mass/inertia as full bodies rather than zero-mass reference frames. + + To maintain backwards compatibility, **this converter pins to an older version of the + URDF importer extension** (version 2.4.31) that still merges fixed joints by default. + This allows existing URDFs to work as expected without modification. + + .. _isaacsim.asset.importer.urdf: https://docs.isaacsim.omniverse.nvidia.com/latest/importer_exporter/ext_isaacsim_asset_importer_urdf.html + """ + + cfg: UrdfConverterCfg + """The configuration instance for URDF to USD conversion.""" + + def __init__(self, cfg: UrdfConverterCfg): + """Initializes the class. + + Args: + cfg: The configuration instance for URDF to USD conversion. + """ + # switch to older version of the URDF importer extension + if get_isaac_sim_version() >= Version("5.1"): + manager = omni.kit.app.get_app().get_extension_manager() + if not manager.is_extension_enabled("isaacsim.asset.importer.urdf-2.4.31"): + manager.set_extension_enabled_immediate("isaacsim.asset.importer.urdf-2.4.31", True) + + # acquire the URDF interface + from isaacsim.asset.importer.urdf._urdf import acquire_urdf_interface + + self._urdf_interface = acquire_urdf_interface() + super().__init__(cfg=cfg) + + """ + Implementation specific methods. + """ + + def _convert_asset(self, cfg: UrdfConverterCfg): + """Calls underlying Omniverse command to convert URDF to USD. + + Args: + cfg: The URDF conversion configuration. + """ + + import_config = self._get_urdf_import_config() + # parse URDF file + result, self._robot_model = omni.kit.commands.execute( + "URDFParseFile", urdf_path=cfg.asset_path, import_config=import_config + ) + + if result: + if cfg.joint_drive: + # modify joint parameters + self._update_joint_parameters() + + # set root link name + if cfg.root_link_name: + self._robot_model.root_link = cfg.root_link_name + + # convert the model to USD + omni.kit.commands.execute( + "URDFImportRobot", + urdf_path=cfg.asset_path, + urdf_robot=self._robot_model, + import_config=import_config, + dest_path=self.usd_path, + ) + else: + raise ValueError(f"Failed to parse URDF file: {cfg.asset_path}") + + """ + Helper methods. + """ + + def _get_urdf_import_config(self) -> isaacsim.asset.importer.urdf._urdf.ImportConfig: + """Create and fill URDF ImportConfig with desired settings + + Returns: + The constructed ``ImportConfig`` object containing the desired settings. + """ + # create a new import config + _, import_config = omni.kit.commands.execute("URDFCreateImportConfig") + + # set the unit scaling factor, 1.0 means meters, 100.0 means cm + import_config.set_distance_scale(1.0) + # set imported robot as default prim + import_config.set_make_default_prim(True) + # add a physics scene to the stage on import if none exists + import_config.set_create_physics_scene(False) + + # -- asset settings + # default density used for links, use 0 to auto-compute + import_config.set_density(self.cfg.link_density) + # mesh simplification settings + convex_decomp = self.cfg.collider_type == "convex_decomposition" + import_config.set_convex_decomp(convex_decomp) + # create collision geometry from visual geometry + import_config.set_collision_from_visuals(self.cfg.collision_from_visuals) + # consolidating links that are connected by fixed joints + import_config.set_merge_fixed_joints(self.cfg.merge_fixed_joints) + import_config.set_merge_fixed_ignore_inertia(self.cfg.merge_fixed_joints) + # -- physics settings + # create fix joint for base link + import_config.set_fix_base(self.cfg.fix_base) + # self collisions between links in the articulation + import_config.set_self_collision(self.cfg.self_collision) + # convert mimic joints to normal joints + import_config.set_parse_mimic(self.cfg.convert_mimic_joints_to_normal_joints) + # replace cylinder shapes with capsule shapes + import_config.set_replace_cylinders_with_capsules(self.cfg.replace_cylinders_with_capsules) + + return import_config + + def _update_joint_parameters(self): + """Update the joint parameters based on the configuration.""" + # set the drive type + self._set_joints_drive_type() + # set the drive target type + self._set_joints_drive_target_type() + # set the drive gains + self._set_joint_drive_gains() + + def _set_joints_drive_type(self): + """Set the joint drive type for all joints in the URDF model.""" + from isaacsim.asset.importer.urdf._urdf import UrdfJointDriveType + + drive_type_mapping = { + "force": UrdfJointDriveType.JOINT_DRIVE_FORCE, + "acceleration": UrdfJointDriveType.JOINT_DRIVE_ACCELERATION, + } + + if isinstance(self.cfg.joint_drive.drive_type, str): + for joint in self._robot_model.joints.values(): + joint.drive.set_drive_type(drive_type_mapping[self.cfg.joint_drive.drive_type]) + elif isinstance(self.cfg.joint_drive.drive_type, dict): + for joint_name, drive_type in self.cfg.joint_drive.drive_type.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the drive type config was not found in the URDF file. The" + f" joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + joint.drive.set_drive_type(drive_type_mapping[drive_type]) + + def _set_joints_drive_target_type(self): + """Set the joint drive target type for all joints in the URDF model.""" + from isaacsim.asset.importer.urdf._urdf import UrdfJointTargetType + + target_type_mapping = { + "none": UrdfJointTargetType.JOINT_DRIVE_NONE, + "position": UrdfJointTargetType.JOINT_DRIVE_POSITION, + "velocity": UrdfJointTargetType.JOINT_DRIVE_VELOCITY, + } + + if isinstance(self.cfg.joint_drive.target_type, str): + for joint in self._robot_model.joints.values(): + joint.drive.set_target_type(target_type_mapping[self.cfg.joint_drive.target_type]) + elif isinstance(self.cfg.joint_drive.target_type, dict): + for joint_name, target_type in self.cfg.joint_drive.target_type.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the target type config was not found in the URDF file. The" + f" joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + joint.drive.set_target_type(target_type_mapping[target_type]) + + def _set_joint_drive_gains(self): + """Set the joint drive gains for all joints in the URDF model.""" + + # set the gains directly from stiffness and damping values + if isinstance(self.cfg.joint_drive.gains, UrdfConverterCfg.JointDriveCfg.PDGainsCfg): + # stiffness + if isinstance(self.cfg.joint_drive.gains.stiffness, (float, int)): + for joint in self._robot_model.joints.values(): + self._set_joint_drive_stiffness(joint, self.cfg.joint_drive.gains.stiffness) + elif isinstance(self.cfg.joint_drive.gains.stiffness, dict): + for joint_name, stiffness in self.cfg.joint_drive.gains.stiffness.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the drive stiffness config was not found in the URDF file." + f" The joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + self._set_joint_drive_stiffness(joint, stiffness) + # damping + if isinstance(self.cfg.joint_drive.gains.damping, (float, int)): + for joint in self._robot_model.joints.values(): + self._set_joint_drive_damping(joint, self.cfg.joint_drive.gains.damping) + elif isinstance(self.cfg.joint_drive.gains.damping, dict): + for joint_name, damping in self.cfg.joint_drive.gains.damping.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the drive damping config was not found in the URDF file." + f" The joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + self._set_joint_drive_damping(joint, damping) + + # set the gains from natural frequency and damping ratio + elif isinstance(self.cfg.joint_drive.gains, UrdfConverterCfg.JointDriveCfg.NaturalFrequencyGainsCfg): + # damping ratio + if isinstance(self.cfg.joint_drive.gains.damping_ratio, (float, int)): + for joint in self._robot_model.joints.values(): + joint.drive.damping_ratio = self.cfg.joint_drive.gains.damping_ratio + elif isinstance(self.cfg.joint_drive.gains.damping_ratio, dict): + for joint_name, damping_ratio in self.cfg.joint_drive.gains.damping_ratio.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the damping ratio config was not found in the URDF file." + f" The joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + joint.drive.damping_ratio = damping_ratio + + # natural frequency (this has to be done after damping ratio is set) + if isinstance(self.cfg.joint_drive.gains.natural_frequency, (float, int)): + for joint in self._robot_model.joints.values(): + joint.drive.natural_frequency = self.cfg.joint_drive.gains.natural_frequency + self._set_joint_drive_gains_from_natural_frequency(joint) + elif isinstance(self.cfg.joint_drive.gains.natural_frequency, dict): + for joint_name, natural_frequency in self.cfg.joint_drive.gains.natural_frequency.items(): + # handle joint name being a regex + matches = [s for s in self._robot_model.joints.keys() if re.search(joint_name, s)] + if not matches: + raise ValueError( + f"The joint name {joint_name} in the natural frequency config was not found in the URDF" + f" file. The joint names in the URDF are {list(self._robot_model.joints.keys())}" + ) + for match in matches: + joint = self._robot_model.joints[match] + joint.drive.natural_frequency = natural_frequency + self._set_joint_drive_gains_from_natural_frequency(joint) + + def _set_joint_drive_stiffness(self, joint, stiffness: float): + """Set the joint drive stiffness. + + Args: + joint: The joint from the URDF robot model. + stiffness: The stiffness value. + """ + from isaacsim.asset.importer.urdf._urdf import UrdfJointType + + if joint.type == UrdfJointType.JOINT_PRISMATIC: + joint.drive.set_strength(stiffness) + else: + # we need to convert the stiffness from radians to degrees + joint.drive.set_strength(math.pi / 180 * stiffness) + + def _set_joint_drive_damping(self, joint, damping: float): + """Set the joint drive damping. + + Args: + joint: The joint from the URDF robot model. + damping: The damping value. + """ + from isaacsim.asset.importer.urdf._urdf import UrdfJointType + + if joint.type == UrdfJointType.JOINT_PRISMATIC: + joint.drive.set_damping(damping) + else: + # we need to convert the damping from radians to degrees + joint.drive.set_damping(math.pi / 180 * damping) + + def _set_joint_drive_gains_from_natural_frequency(self, joint): + """Compute the joint drive gains from the natural frequency and damping ratio. + + Args: + joint: The joint from the URDF robot model. + """ + from isaacsim.asset.importer.urdf._urdf import UrdfJointDriveType, UrdfJointTargetType + + strength = self._urdf_interface.compute_natural_stiffness( + self._robot_model, + joint.name, + joint.drive.natural_frequency, + ) + self._set_joint_drive_stiffness(joint, strength) + + if joint.drive.target_type == UrdfJointTargetType.JOINT_DRIVE_POSITION: + m_eq = 1.0 + if joint.drive.drive_type == UrdfJointDriveType.JOINT_DRIVE_FORCE: + m_eq = joint.inertia + damping = 2 * m_eq * joint.drive.natural_frequency * joint.drive.damping_ratio + self._set_joint_drive_damping(joint, damping) diff --git a/source/isaaclab/isaaclab/sim/converters/urdf_converter_cfg.py b/source/isaaclab/isaaclab/sim/converters/urdf_converter_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c04ede2400aed989b616ddd9aec83d6da4c43f0c --- /dev/null +++ b/source/isaaclab/isaaclab/sim/converters/urdf_converter_cfg.py @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.sim.converters.asset_converter_base_cfg import AssetConverterBaseCfg +from isaaclab.utils import configclass + + +@configclass +class UrdfConverterCfg(AssetConverterBaseCfg): + """The configuration class for UrdfConverter.""" + + @configclass + class JointDriveCfg: + """Configuration for the joint drive.""" + + @configclass + class PDGainsCfg: + """Configuration for the PD gains of the drive.""" + + stiffness: dict[str, float] | float = MISSING + """The stiffness of the joint drive in Nm/rad or N/rad. + + If None, the stiffness is set to the value parsed from the URDF file. + If :attr:`~UrdfConverterCfg.JointDriveCfg.target_type` is set to ``"velocity"``, this value determines + the drive strength in joint velocity space. + """ + + damping: dict[str, float] | float | None = None + """The damping of the joint drive in Nm/(rad/s) or N/(rad/s). Defaults to None. + + If None, the damping is set to the value parsed from the URDF file or 0.0 if no value is found in the URDF. + If :attr:`~UrdfConverterCfg.JointDriveCfg.target_type` is set to ``"velocity"``, this attribute is set to + 0.0 and :attr:`stiffness` serves as the drive's strength in joint velocity space. + """ + + @configclass + class NaturalFrequencyGainsCfg: + r"""Configuration for the natural frequency gains of the drive. + + Computes the joint drive stiffness and damping based on the desired natural frequency using the formula: + + :math:`P = m \cdot f^2`, :math:`D = 2 \cdot r \cdot f \cdot m` + + where :math:`f` is the natural frequency, :math:`r` is the damping ratio, and :math:`m` is the total + equivalent inertia at the joint. The damping ratio is such that: + + * :math:`r = 1.0` is a critically damped system, + * :math:`r < 1.0` is underdamped, + * :math:`r > 1.0` is overdamped. + """ + + natural_frequency: dict[str, float] | float = MISSING + """The natural frequency of the joint drive. + + If :attr:`~UrdfConverterCfg.JointDriveCfg.target_type` is set to ``"velocity"``, this value determines the + drive's natural frequency in joint velocity space. + """ + + damping_ratio: dict[str, float] | float = 0.005 + """The damping ratio of the joint drive. Defaults to 0.005. + + If :attr:`~UrdfConverterCfg.JointDriveCfg.target_type` is set to ``"velocity"``, this value is ignored and + only :attr:`natural_frequency` is used. + """ + + drive_type: dict[str, Literal["acceleration", "force"]] | Literal["acceleration", "force"] = "force" + """The drive type used for the joint. Defaults to ``"force"``. + + * ``"acceleration"``: The joint drive normalizes the inertia before applying the joint effort so it's invariant + to inertia and mass changes (equivalent to ideal damped oscillator). + * ``"force"``: Applies effort through forces, so is subject to variations on the body inertia. + """ + + target_type: dict[str, Literal["none", "position", "velocity"]] | Literal["none", "position", "velocity"] = ( + "position" + ) + """The drive target type used for the joint. Defaults to ``"position"``. + + If the target type is set to ``"none"``, the joint stiffness and damping are set to 0.0. + """ + + gains: PDGainsCfg | NaturalFrequencyGainsCfg = PDGainsCfg() + """The drive gains configuration.""" + + fix_base: bool = MISSING + """Create a fix joint to the root/base link.""" + + root_link_name: str | None = None + """The name of the root link. Defaults to None. + + If None, the root link will be set by PhysX. + """ + + link_density: float = 0.0 + """Default density in ``kg/m^3`` for links whose ``"inertial"`` properties are missing in the URDF. + Defaults to 0.0. + """ + + merge_fixed_joints: bool = True + """Consolidate links that are connected by fixed joints. Defaults to True.""" + + convert_mimic_joints_to_normal_joints: bool = False + """Convert mimic joints to normal joints. Defaults to False.""" + + joint_drive: JointDriveCfg | None = JointDriveCfg() + """The joint drive settings. Defaults to :class:`JointDriveCfg`. + + The parameter can be set to ``None`` for URDFs without joints. + """ + + collision_from_visuals = False + """Whether to create collision geometry from visual geometry. Defaults to False.""" + + collider_type: Literal["convex_hull", "convex_decomposition"] = "convex_hull" + """The collision shape simplification. Defaults to "convex_hull". + + Supported values are: + + * ``"convex_hull"``: The collision shape is simplified to a convex hull. + * ``"convex_decomposition"``: The collision shape is decomposed into smaller convex shapes for a closer fit. + """ + + self_collision: bool = False + """Activate self-collisions between links of the articulation. Defaults to False.""" + + replace_cylinders_with_capsules: bool = False + """Replace cylinder shapes with capsule shapes. Defaults to False.""" diff --git a/source/isaaclab/isaaclab/sim/schemas/__init__.py b/source/isaaclab/isaaclab/sim/schemas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c8402fdb13c8f818d9a7330c1d869fa21b7ae6f2 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/schemas/__init__.py @@ -0,0 +1,127 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing utilities for schemas used in Omniverse. + +We wrap the USD schemas for PhysX and USD Physics in a more convenient API for setting the parameters from +Python. This is done so that configuration objects can define the schema properties to set and make it easier +to tune the physics parameters without requiring to open Omniverse Kit and manually set the parameters into +the respective USD attributes. + +.. caution:: + + Schema properties cannot be applied on prims that are prototypes as they are read-only prims. This + particularly affects instanced assets where some of the prims (usually the visual and collision meshes) + are prototypes so that the instancing can be done efficiently. + + In such cases, it is assumed that the prototypes have sim-ready properties on them that don't need to be modified. + Trying to set properties into prototypes will throw a warning saying that the prim is a prototype and the + properties cannot be set. + +The schemas are defined in the following links: + +* `UsdPhysics schema `_ +* `PhysxSchema schema `_ + +Locally, the schemas are defined in the following files: + +* ``_isaac_sim/extsPhysics/omni.usd.schema.physics/plugins/UsdPhysics/resources/UsdPhysics/schema.usda`` +* ``_isaac_sim/extsPhysics/omni.usd.schema.physx/plugins/PhysxSchema/resources/generatedSchema.usda`` + +""" + +from .schemas import ( + MESH_APPROXIMATION_TOKENS, + PHYSX_MESH_COLLISION_CFGS, + USD_MESH_COLLISION_CFGS, + activate_contact_sensors, + define_articulation_root_properties, + define_collision_properties, + define_deformable_body_properties, + define_mass_properties, + define_mesh_collision_properties, + define_rigid_body_properties, + modify_articulation_root_properties, + modify_collision_properties, + modify_deformable_body_properties, + modify_fixed_tendon_properties, + modify_joint_drive_properties, + modify_mass_properties, + modify_mesh_collision_properties, + modify_rigid_body_properties, + modify_spatial_tendon_properties, +) +from .schemas_cfg import ( + ArticulationRootPropertiesCfg, + BoundingCubePropertiesCfg, + BoundingSpherePropertiesCfg, + CollisionPropertiesCfg, + ConvexDecompositionPropertiesCfg, + ConvexHullPropertiesCfg, + DeformableBodyPropertiesCfg, + FixedTendonPropertiesCfg, + JointDrivePropertiesCfg, + MassPropertiesCfg, + MeshCollisionPropertiesCfg, + RigidBodyPropertiesCfg, + SDFMeshPropertiesCfg, + SpatialTendonPropertiesCfg, + TriangleMeshPropertiesCfg, + TriangleMeshSimplificationPropertiesCfg, +) + +__all__ = [ + # articulation root + "ArticulationRootPropertiesCfg", + "define_articulation_root_properties", + "modify_articulation_root_properties", + # rigid bodies + "RigidBodyPropertiesCfg", + "define_rigid_body_properties", + "modify_rigid_body_properties", + "activate_contact_sensors", + # colliders + "CollisionPropertiesCfg", + "define_collision_properties", + "modify_collision_properties", + # deformables + "DeformableBodyPropertiesCfg", + "define_deformable_body_properties", + "modify_deformable_body_properties", + # joints + "JointDrivePropertiesCfg", + "modify_joint_drive_properties", + # mass + "MassPropertiesCfg", + "define_mass_properties", + "modify_mass_properties", + # mesh colliders + "MeshCollisionPropertiesCfg", + "define_mesh_collision_properties", + "modify_mesh_collision_properties", + # bounding cube + "BoundingCubePropertiesCfg", + # bounding sphere + "BoundingSpherePropertiesCfg", + # convex decomposition + "ConvexDecompositionPropertiesCfg", + # convex hull + "ConvexHullPropertiesCfg", + # sdf mesh + "SDFMeshPropertiesCfg", + # triangle mesh + "TriangleMeshPropertiesCfg", + # triangle mesh simplification + "TriangleMeshSimplificationPropertiesCfg", + # tendons + "FixedTendonPropertiesCfg", + "SpatialTendonPropertiesCfg", + "modify_fixed_tendon_properties", + "modify_spatial_tendon_properties", + # Constants for configs that use PhysX vs USD API + "PHYSX_MESH_COLLISION_CFGS", + "USD_MESH_COLLISION_CFGS", + "MESH_APPROXIMATION_TOKENS", +] diff --git a/source/isaaclab/isaaclab/sim/schemas/schemas.py b/source/isaaclab/isaaclab/sim/schemas/schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..91eb3b2cd6a96a59e0f1b6540fa7383485b445e1 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/schemas/schemas.py @@ -0,0 +1,1126 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed to import for allowing type-hinting: Usd.Stage | None +from __future__ import annotations + +import logging +import math +from collections.abc import Callable +from typing import Any + +import omni.physx.scripts.utils as physx_utils +from omni.physx.scripts import deformableUtils as deformable_utils +from pxr import PhysxSchema, Usd, UsdPhysics + +from isaaclab.sim.utils.stage import get_current_stage + +from ..utils import ( + apply_nested, + find_global_fixed_joint_prim, + get_all_matching_child_prims, + safe_set_attribute_on_usd_schema, +) +from . import schemas_cfg + +# import logger +logger = logging.getLogger(__name__) + + +""" +Constants. +""" + +# Mapping from string names to USD/PhysX tokens for mesh collision approximation +# Refer to omniverse documentation +# https://docs.omniverse.nvidia.com/kit/docs/omni_physics/latest/dev_guide/rigid_bodies_articulations/collision.html#mesh-geometry-colliders +# for available tokens. +MESH_APPROXIMATION_TOKENS = { + "boundingCube": UsdPhysics.Tokens.boundingCube, + "boundingSphere": UsdPhysics.Tokens.boundingSphere, + "convexDecomposition": UsdPhysics.Tokens.convexDecomposition, + "convexHull": UsdPhysics.Tokens.convexHull, + "none": UsdPhysics.Tokens.none, + "meshSimplification": UsdPhysics.Tokens.meshSimplification, + "sdf": PhysxSchema.Tokens.sdf, +} + + +PHYSX_MESH_COLLISION_CFGS = [ + schemas_cfg.ConvexDecompositionPropertiesCfg, + schemas_cfg.ConvexHullPropertiesCfg, + schemas_cfg.TriangleMeshPropertiesCfg, + schemas_cfg.TriangleMeshSimplificationPropertiesCfg, + schemas_cfg.SDFMeshPropertiesCfg, +] + +USD_MESH_COLLISION_CFGS = [ + schemas_cfg.BoundingCubePropertiesCfg, + schemas_cfg.BoundingSpherePropertiesCfg, + schemas_cfg.ConvexDecompositionPropertiesCfg, + schemas_cfg.ConvexHullPropertiesCfg, + schemas_cfg.TriangleMeshSimplificationPropertiesCfg, +] + + +""" +Articulation root properties. +""" + + +def define_articulation_root_properties( + prim_path: str, cfg: schemas_cfg.ArticulationRootPropertiesCfg, stage: Usd.Stage | None = None +): + """Apply the articulation root schema on the input prim and set its properties. + + See :func:`modify_articulation_root_properties` for more details on how the properties are set. + + Args: + prim_path: The prim path where to apply the articulation root schema. + cfg: The configuration for the articulation root. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: When the prim path is not valid. + TypeError: When the prim already has conflicting API schemas. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get articulation USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + # check if prim has articulation applied on it + if not UsdPhysics.ArticulationRootAPI(prim): + UsdPhysics.ArticulationRootAPI.Apply(prim) + # set articulation root properties + modify_articulation_root_properties(prim_path, cfg, stage) + + +@apply_nested +def modify_articulation_root_properties( + prim_path: str, cfg: schemas_cfg.ArticulationRootPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX parameters for an articulation root prim. + + The `articulation root`_ marks the root of an articulation tree. For floating articulations, this should be on + the root body. For fixed articulations, this API can be on a direct or indirect parent of the root joint + which is fixed to the world. + + The schema comprises of attributes that belong to the `ArticulationRootAPI`_ and `PhysxArticulationAPI`_. + schemas. The latter contains the PhysX parameters for the articulation root. + + The properties are applied to the articulation root prim. The common properties (such as solver position + and velocity iteration counts, sleep threshold, stabilization threshold) take precedence over those specified + in the rigid body schemas for all the rigid bodies in the articulation. + + .. caution:: + When the attribute :attr:`schemas_cfg.ArticulationRootPropertiesCfg.fix_root_link` is set to True, + a fixed joint is created between the root link and the world frame (if it does not already exist). However, + to deal with physics parser limitations, the articulation root schema needs to be applied to the parent of + the root link. + + .. note:: + This function is decorated with :func:`apply_nested` that set the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _articulation root: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/Articulations.html + .. _ArticulationRootAPI: https://openusd.org/dev/api/class_usd_physics_articulation_root_a_p_i.html + .. _PhysxArticulationAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_articulation_a_p_i.html + + Args: + prim_path: The prim path to the articulation root. + cfg: The configuration for the articulation root. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + + Raises: + NotImplementedError: When the root prim is not a rigid body and a fixed joint is to be created. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get articulation USD prim + articulation_prim = stage.GetPrimAtPath(prim_path) + # check if prim has articulation applied on it + if not UsdPhysics.ArticulationRootAPI(articulation_prim): + return False + # retrieve the articulation api + physx_articulation_api = PhysxSchema.PhysxArticulationAPI(articulation_prim) + if not physx_articulation_api: + physx_articulation_api = PhysxSchema.PhysxArticulationAPI.Apply(articulation_prim) + + # convert to dict + cfg = cfg.to_dict() + # extract non-USD properties + fix_root_link = cfg.pop("fix_root_link", None) + + # set into physx api + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_articulation_api, attr_name, value, camel_case=True) + + # fix root link based on input + # we do the fixed joint processing later to not interfere with setting other properties + if fix_root_link is not None: + # check if a global fixed joint exists under the root prim + existing_fixed_joint_prim = find_global_fixed_joint_prim(prim_path) + + # if we found a fixed joint, enable/disable it based on the input + # otherwise, create a fixed joint between the world and the root link + if existing_fixed_joint_prim is not None: + logger.info( + f"Found an existing fixed joint for the articulation: '{prim_path}'. Setting it to: {fix_root_link}." + ) + existing_fixed_joint_prim.GetJointEnabledAttr().Set(fix_root_link) + elif fix_root_link: + logger.info(f"Creating a fixed joint for the articulation: '{prim_path}'.") + + # note: we have to assume that the root prim is a rigid body, + # i.e. we don't handle the case where the root prim is not a rigid body but has articulation api on it + # Currently, there is no obvious way to get first rigid body link identified by the PhysX parser + if not articulation_prim.HasAPI(UsdPhysics.RigidBodyAPI): + raise NotImplementedError( + f"The articulation prim '{prim_path}' does not have the RigidBodyAPI applied." + " To create a fixed joint, we need to determine the first rigid body link in" + " the articulation tree. However, this is not implemented yet." + ) + + # create a fixed joint between the root link and the world frame + physx_utils.createJoint(stage=stage, joint_type="Fixed", from_prim=None, to_prim=articulation_prim) + + # Having a fixed joint on a rigid body is not treated as "fixed base articulation". + # instead, it is treated as a part of the maximal coordinate tree. + # Moving the articulation root to the parent solves this issue. This is a limitation of the PhysX parser. + # get parent prim + parent_prim = articulation_prim.GetParent() + # apply api to parent + UsdPhysics.ArticulationRootAPI.Apply(parent_prim) + PhysxSchema.PhysxArticulationAPI.Apply(parent_prim) + + # copy the attributes + # -- usd attributes + usd_articulation_api = UsdPhysics.ArticulationRootAPI(articulation_prim) + for attr_name in usd_articulation_api.GetSchemaAttributeNames(): + attr = articulation_prim.GetAttribute(attr_name) + parent_prim.GetAttribute(attr_name).Set(attr.Get()) + # -- physx attributes + physx_articulation_api = PhysxSchema.PhysxArticulationAPI(articulation_prim) + for attr_name in physx_articulation_api.GetSchemaAttributeNames(): + attr = articulation_prim.GetAttribute(attr_name) + parent_prim.GetAttribute(attr_name).Set(attr.Get()) + + # remove api from root + articulation_prim.RemoveAPI(UsdPhysics.ArticulationRootAPI) + articulation_prim.RemoveAPI(PhysxSchema.PhysxArticulationAPI) + + # success + return True + + +""" +Rigid body properties. +""" + + +def define_rigid_body_properties( + prim_path: str, cfg: schemas_cfg.RigidBodyPropertiesCfg, stage: Usd.Stage | None = None +): + """Apply the rigid body schema on the input prim and set its properties. + + See :func:`modify_rigid_body_properties` for more details on how the properties are set. + + Args: + prim_path: The prim path where to apply the rigid body schema. + cfg: The configuration for the rigid body. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: When the prim path is not valid. + TypeError: When the prim already has conflicting API schemas. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + # check if prim has rigid body applied on it + if not UsdPhysics.RigidBodyAPI(prim): + UsdPhysics.RigidBodyAPI.Apply(prim) + # set rigid body properties + modify_rigid_body_properties(prim_path, cfg, stage) + + +@apply_nested +def modify_rigid_body_properties( + prim_path: str, cfg: schemas_cfg.RigidBodyPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX parameters for a rigid body prim. + + A `rigid body`_ is a single body that can be simulated by PhysX. It can be either dynamic or kinematic. + A dynamic body responds to forces and collisions. A `kinematic body`_ can be moved by the user, but does not + respond to forces. They are similar to having static bodies that can be moved around. + + The schema comprises of attributes that belong to the `RigidBodyAPI`_ and `PhysxRigidBodyAPI`_. + schemas. The latter contains the PhysX parameters for the rigid body. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _rigid body: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/RigidBodyOverview.html + .. _kinematic body: https://openusd.org/release/wp_rigid_body_physics.html#kinematic-bodies + .. _RigidBodyAPI: https://openusd.org/dev/api/class_usd_physics_rigid_body_a_p_i.html + .. _PhysxRigidBodyAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_rigid_body_a_p_i.html + + Args: + prim_path: The prim path to the rigid body. + cfg: The configuration for the rigid body. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get rigid-body USD prim + rigid_body_prim = stage.GetPrimAtPath(prim_path) + # check if prim has rigid-body applied on it + if not UsdPhysics.RigidBodyAPI(rigid_body_prim): + return False + # retrieve the USD rigid-body api + usd_rigid_body_api = UsdPhysics.RigidBodyAPI(rigid_body_prim) + # retrieve the physx rigid-body api + physx_rigid_body_api = PhysxSchema.PhysxRigidBodyAPI(rigid_body_prim) + if not physx_rigid_body_api: + physx_rigid_body_api = PhysxSchema.PhysxRigidBodyAPI.Apply(rigid_body_prim) + + # convert to dict + cfg = cfg.to_dict() + # set into USD API + for attr_name in ["rigid_body_enabled", "kinematic_enabled"]: + value = cfg.pop(attr_name, None) + safe_set_attribute_on_usd_schema(usd_rigid_body_api, attr_name, value, camel_case=True) + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_rigid_body_api, attr_name, value, camel_case=True) + # success + return True + + +""" +Collision properties. +""" + + +def define_collision_properties( + prim_path: str, cfg: schemas_cfg.CollisionPropertiesCfg, stage: Usd.Stage | None = None +): + """Apply the collision schema on the input prim and set its properties. + + See :func:`modify_collision_properties` for more details on how the properties are set. + + Args: + prim_path: The prim path where to apply the rigid body schema. + cfg: The configuration for the collider. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: When the prim path is not valid. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + # check if prim has collision applied on it + if not UsdPhysics.CollisionAPI(prim): + UsdPhysics.CollisionAPI.Apply(prim) + # set collision properties + modify_collision_properties(prim_path, cfg, stage) + + +@apply_nested +def modify_collision_properties( + prim_path: str, cfg: schemas_cfg.CollisionPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX properties of collider prim. + + These properties are based on the `UsdPhysics.CollisionAPI`_ and `PhysxSchema.PhysxCollisionAPI`_ schemas. + For more information on the properties, please refer to the official documentation. + + Tuning these parameters influence the contact behavior of the rigid body. For more information on + tune them and their effect on the simulation, please refer to the + `PhysX documentation `__. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _UsdPhysics.CollisionAPI: https://openusd.org/dev/api/class_usd_physics_collision_a_p_i.html + .. _PhysxSchema.PhysxCollisionAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_collision_a_p_i.html + + Args: + prim_path: The prim path of parent. + cfg: The configuration for the collider. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + collider_prim = stage.GetPrimAtPath(prim_path) + # check if prim has collision applied on it + if not UsdPhysics.CollisionAPI(collider_prim): + return False + # retrieve the USD collision api + usd_collision_api = UsdPhysics.CollisionAPI(collider_prim) + # retrieve the collision api + physx_collision_api = PhysxSchema.PhysxCollisionAPI(collider_prim) + if not physx_collision_api: + physx_collision_api = PhysxSchema.PhysxCollisionAPI.Apply(collider_prim) + + # convert to dict + cfg = cfg.to_dict() + # set into USD API + for attr_name in ["collision_enabled"]: + value = cfg.pop(attr_name, None) + safe_set_attribute_on_usd_schema(usd_collision_api, attr_name, value, camel_case=True) + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_collision_api, attr_name, value, camel_case=True) + # success + return True + + +""" +Mass properties. +""" + + +def define_mass_properties(prim_path: str, cfg: schemas_cfg.MassPropertiesCfg, stage: Usd.Stage | None = None): + """Apply the mass schema on the input prim and set its properties. + + See :func:`modify_mass_properties` for more details on how the properties are set. + + Args: + prim_path: The prim path where to apply the rigid body schema. + cfg: The configuration for the mass properties. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: When the prim path is not valid. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + # check if prim has mass applied on it + if not UsdPhysics.MassAPI(prim): + UsdPhysics.MassAPI.Apply(prim) + # set mass properties + modify_mass_properties(prim_path, cfg, stage) + + +@apply_nested +def modify_mass_properties(prim_path: str, cfg: schemas_cfg.MassPropertiesCfg, stage: Usd.Stage | None = None) -> bool: + """Set properties for the mass of a rigid body prim. + + These properties are based on the `UsdPhysics.MassAPI` schema. If the mass is not defined, the density is used + to compute the mass. However, in that case, a collision approximation of the rigid body is used to + compute the density. For more information on the properties, please refer to the + `documentation `__. + + .. caution:: + + The mass of an object can be specified in multiple ways and have several conflicting settings + that are resolved based on precedence. Please make sure to understand the precedence rules + before using this property. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. UsdPhysics.MassAPI: https://openusd.org/dev/api/class_usd_physics_mass_a_p_i.html + + Args: + prim_path: The prim path of the rigid body. + cfg: The configuration for the mass properties. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + rigid_prim = stage.GetPrimAtPath(prim_path) + # check if prim has mass API applied on it + if not UsdPhysics.MassAPI(rigid_prim): + return False + # retrieve the USD mass api + usd_physics_mass_api = UsdPhysics.MassAPI(rigid_prim) + + # convert to dict + cfg = cfg.to_dict() + # set into USD API + for attr_name in ["mass", "density"]: + value = cfg.pop(attr_name, None) + safe_set_attribute_on_usd_schema(usd_physics_mass_api, attr_name, value, camel_case=True) + # success + return True + + +""" +Contact sensor. +""" + + +def activate_contact_sensors(prim_path: str, threshold: float = 0.0, stage: Usd.Stage = None): + """Activate the contact sensor on all rigid bodies under a specified prim path. + + This function adds the PhysX contact report API to all rigid bodies under the specified prim path. + It also sets the force threshold beyond which the contact sensor reports the contact. The contact + reporting API can only be added to rigid bodies. + + Args: + prim_path: The prim path under which to search and prepare contact sensors. + threshold: The threshold for the contact sensor. Defaults to 0.0. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: If the input prim path is not valid. + ValueError: If there are no rigid bodies under the prim path. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get prim + prim: Usd.Prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + # iterate over all children + num_contact_sensors = 0 + all_prims = [prim] + while len(all_prims) > 0: + # get current prim + child_prim = all_prims.pop(0) + # check if prim is a rigid body + # nested rigid bodies are not allowed by SDK so we can safely assume that + # if a prim has a rigid body API, it is a rigid body and we don't need to + # check its children + if child_prim.HasAPI(UsdPhysics.RigidBodyAPI): + # set sleep threshold to zero + rb = PhysxSchema.PhysxRigidBodyAPI.Get(stage, prim.GetPrimPath()) + rb.CreateSleepThresholdAttr().Set(0.0) + # add contact report API with threshold of zero + if not child_prim.HasAPI(PhysxSchema.PhysxContactReportAPI): + logger.debug(f"Adding contact report API to prim: '{child_prim.GetPrimPath()}'") + cr_api = PhysxSchema.PhysxContactReportAPI.Apply(child_prim) + else: + logger.debug(f"Contact report API already exists on prim: '{child_prim.GetPrimPath()}'") + cr_api = PhysxSchema.PhysxContactReportAPI.Get(stage, child_prim.GetPrimPath()) + # set threshold to zero + cr_api.CreateThresholdAttr().Set(threshold) + # increment number of contact sensors + num_contact_sensors += 1 + else: + # add all children to tree + all_prims += child_prim.GetChildren() + # check if no contact sensors were found + if num_contact_sensors == 0: + raise ValueError( + f"No contact sensors added to the prim: '{prim_path}'. This means that no rigid bodies" + " are present under this prim. Please check the prim path." + ) + # success + return True + + +""" +Joint drive properties. +""" + + +@apply_nested +def modify_joint_drive_properties( + prim_path: str, cfg: schemas_cfg.JointDrivePropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX parameters for a joint prim. + + This function checks if the input prim is a prismatic or revolute joint and applies the joint drive schema + on it. If the joint is a tendon (i.e., it has the `PhysxTendonAxisAPI`_ schema applied on it), then the joint + drive schema is not applied. + + Based on the configuration, this method modifies the properties of the joint drive. These properties are + based on the `UsdPhysics.DriveAPI`_ schema. For more information on the properties, please refer to the + official documentation. + + .. caution:: + + We highly recommend modifying joint properties of articulations through the functionalities in the + :mod:`isaaclab.actuators` module. The methods here are for setting simulation low-level + properties only. + + .. _UsdPhysics.DriveAPI: https://openusd.org/dev/api/class_usd_physics_drive_a_p_i.html + .. _PhysxTendonAxisAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_tendon_axis_a_p_i.html + + Args: + prim_path: The prim path where to apply the joint drive schema. + cfg: The configuration for the joint drive. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + + Raises: + ValueError: If the input prim path is not valid. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + + # check if prim has joint drive applied on it + if prim.IsA(UsdPhysics.RevoluteJoint): + drive_api_name = "angular" + elif prim.IsA(UsdPhysics.PrismaticJoint): + drive_api_name = "linear" + else: + return False + # check that prim is not a tendon child prim + # note: root prim is what "controls" the tendon so we still want to apply the drive to it + if prim.HasAPI(PhysxSchema.PhysxTendonAxisAPI) and not prim.HasAPI(PhysxSchema.PhysxTendonAxisRootAPI): + return False + + # check if prim has joint drive applied on it + usd_drive_api = UsdPhysics.DriveAPI(prim, drive_api_name) + if not usd_drive_api: + usd_drive_api = UsdPhysics.DriveAPI.Apply(prim, drive_api_name) + # check if prim has Physx joint drive applied on it + physx_joint_api = PhysxSchema.PhysxJointAPI(prim) + if not physx_joint_api: + physx_joint_api = PhysxSchema.PhysxJointAPI.Apply(prim) + + # mapping from configuration name to USD attribute name + cfg_to_usd_map = { + "max_velocity": "max_joint_velocity", + "max_effort": "max_force", + "drive_type": "type", + } + # convert to dict + cfg = cfg.to_dict() + + # check if linear drive + is_linear_drive = prim.IsA(UsdPhysics.PrismaticJoint) + # convert values for angular drives from radians to degrees units + if not is_linear_drive: + if cfg["max_velocity"] is not None: + # rad / s --> deg / s + cfg["max_velocity"] = cfg["max_velocity"] * 180.0 / math.pi + if cfg["stiffness"] is not None: + # N-m/rad --> N-m/deg + cfg["stiffness"] = cfg["stiffness"] * math.pi / 180.0 + if cfg["damping"] is not None: + # N-m-s/rad --> N-m-s/deg + cfg["damping"] = cfg["damping"] * math.pi / 180.0 + + # set into PhysX API + for attr_name in ["max_velocity"]: + value = cfg.pop(attr_name, None) + attr_name = cfg_to_usd_map[attr_name] + safe_set_attribute_on_usd_schema(physx_joint_api, attr_name, value, camel_case=True) + # set into USD API + for attr_name, attr_value in cfg.items(): + attr_name = cfg_to_usd_map.get(attr_name, attr_name) + safe_set_attribute_on_usd_schema(usd_drive_api, attr_name, attr_value, camel_case=True) + + return True + + +""" +Fixed tendon properties. +""" + + +@apply_nested +def modify_fixed_tendon_properties( + prim_path: str, cfg: schemas_cfg.FixedTendonPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX parameters for a fixed tendon attachment prim. + + A `fixed tendon`_ can be used to link multiple degrees of freedom of articulation joints + through length and limit constraints. For instance, it can be used to set up an equality constraint + between a driven and passive revolute joints. + + The schema comprises of attributes that belong to the `PhysxTendonAxisRootAPI`_ schema. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _fixed tendon: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/_api_build/classPxArticulationFixedTendon.html + .. _PhysxTendonAxisRootAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_tendon_axis_root_a_p_i.html + + Args: + prim_path: The prim path to the tendon attachment. + cfg: The configuration for the tendon attachment. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + + Raises: + ValueError: If the input prim path is not valid. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + tendon_prim = stage.GetPrimAtPath(prim_path) + # check if prim has fixed tendon applied on it + has_root_fixed_tendon = tendon_prim.HasAPI(PhysxSchema.PhysxTendonAxisRootAPI) + if not has_root_fixed_tendon: + return False + + # resolve all available instances of the schema since it is multi-instance + for schema_name in tendon_prim.GetAppliedSchemas(): + # only consider the fixed tendon schema + if "PhysxTendonAxisRootAPI" not in schema_name: + continue + # retrieve the USD tendon api + instance_name = schema_name.split(":")[-1] + physx_tendon_axis_api = PhysxSchema.PhysxTendonAxisRootAPI(tendon_prim, instance_name) + + # convert to dict + cfg = cfg.to_dict() + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_tendon_axis_api, attr_name, value, camel_case=True) + # success + return True + + +""" +Spatial tendon properties. +""" + + +@apply_nested +def modify_spatial_tendon_properties( + prim_path: str, cfg: schemas_cfg.SpatialTendonPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Modify PhysX parameters for a spatial tendon attachment prim. + + A `spatial tendon`_ can be used to link multiple degrees of freedom of articulation joints + through length and limit constraints. For instance, it can be used to set up an equality constraint + between a driven and passive revolute joints. + + The schema comprises of attributes that belong to the `PhysxTendonAxisRootAPI`_ schema. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _spatial tendon: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/_api_build/classPxArticulationSpatialTendon.html + .. _PhysxTendonAxisRootAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_tendon_axis_root_a_p_i.html + .. _PhysxTendonAttachmentRootAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_tendon_attachment_root_a_p_i.html + .. _PhysxTendonAttachmentLeafAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_tendon_attachment_leaf_a_p_i.html + + Args: + prim_path: The prim path to the tendon attachment. + cfg: The configuration for the tendon attachment. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + + Raises: + ValueError: If the input prim path is not valid. + """ + # obtain stage + if stage is None: + stage = get_current_stage() + # get USD prim + tendon_prim = stage.GetPrimAtPath(prim_path) + # check if prim has spatial tendon applied on it + has_spatial_tendon = tendon_prim.HasAPI(PhysxSchema.PhysxTendonAttachmentRootAPI) or tendon_prim.HasAPI( + PhysxSchema.PhysxTendonAttachmentLeafAPI + ) + if not has_spatial_tendon: + return False + + # resolve all available instances of the schema since it is multi-instance + for schema_name in tendon_prim.GetAppliedSchemas(): + # only consider the spatial tendon schema + # retrieve the USD tendon api + if "PhysxTendonAttachmentRootAPI" in schema_name: + instance_name = schema_name.split(":")[-1] + physx_tendon_spatial_api = PhysxSchema.PhysxTendonAttachmentRootAPI(tendon_prim, instance_name) + elif "PhysxTendonAttachmentLeafAPI" in schema_name: + instance_name = schema_name.split(":")[-1] + physx_tendon_spatial_api = PhysxSchema.PhysxTendonAttachmentLeafAPI(tendon_prim, instance_name) + else: + continue + # convert to dict + cfg = cfg.to_dict() + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_tendon_spatial_api, attr_name, value, camel_case=True) + # success + return True + + +""" +Deformable body properties. +""" + + +def define_deformable_body_properties( + prim_path: str, cfg: schemas_cfg.DeformableBodyPropertiesCfg, stage: Usd.Stage | None = None +): + """Apply the deformable body schema on the input prim and set its properties. + + See :func:`modify_deformable_body_properties` for more details on how the properties are set. + + .. note:: + If the input prim is not a mesh, this function will traverse the prim and find the first mesh + under it. If no mesh or multiple meshes are found, an error is raised. This is because the deformable + body schema can only be applied to a single mesh. + + Args: + prim_path: The prim path where to apply the deformable body schema. + cfg: The configuration for the deformable body. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: When the prim path is not valid. + ValueError: When the prim has no mesh or multiple meshes. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + + # traverse the prim and get the mesh + matching_prims = get_all_matching_child_prims(prim_path, lambda p: p.GetTypeName() == "Mesh") + # check if the mesh is valid + if len(matching_prims) == 0: + raise ValueError(f"Could not find any mesh in '{prim_path}'. Please check asset.") + if len(matching_prims) > 1: + # get list of all meshes found + mesh_paths = [p.GetPrimPath() for p in matching_prims] + raise ValueError( + f"Found multiple meshes in '{prim_path}': {mesh_paths}." + " Deformable body schema can only be applied to one mesh." + ) + + # get deformable-body USD prim + mesh_prim = matching_prims[0] + # check if prim has deformable-body applied on it + if not PhysxSchema.PhysxDeformableBodyAPI(mesh_prim): + PhysxSchema.PhysxDeformableBodyAPI.Apply(mesh_prim) + # set deformable body properties + modify_deformable_body_properties(mesh_prim.GetPrimPath(), cfg, stage) + + +@apply_nested +def modify_deformable_body_properties( + prim_path: str, cfg: schemas_cfg.DeformableBodyPropertiesCfg, stage: Usd.Stage | None = None +): + """Modify PhysX parameters for a deformable body prim. + + A `deformable body`_ is a single body that can be simulated by PhysX. Unlike rigid bodies, deformable bodies + support relative motion of the nodes in the mesh. Consequently, they can be used to simulate deformations + under applied forces. + + PhysX soft body simulation employs Finite Element Analysis (FEA) to simulate the deformations of the mesh. + It uses two tetrahedral meshes to represent the deformable body: + + 1. **Simulation mesh**: This mesh is used for the simulation and is the one that is deformed by the solver. + 2. **Collision mesh**: This mesh only needs to match the surface of the simulation mesh and is used for + collision detection. + + For most applications, we assume that the above two meshes are computed from the "render mesh" of the deformable + body. The render mesh is the mesh that is visible in the scene and is used for rendering purposes. It is composed + of triangles and is the one that is used to compute the above meshes based on PhysX cookings. + + The schema comprises of attributes that belong to the `PhysxDeformableBodyAPI`_. schemas containing the PhysX + parameters for the deformable body. + + .. caution:: + The deformable body schema is still under development by the Omniverse team. The current implementation + works with the PhysX schemas shipped with Isaac Sim 4.0.0 onwards. It may change in future releases. + + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + + .. _deformable body: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/SoftBodies.html + .. _PhysxDeformableBodyAPI: https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/104.2/class_physx_schema_physx_deformable_a_p_i.html + + Args: + prim_path: The prim path to the deformable body. + cfg: The configuration for the deformable body. + stage: The stage where to find the prim. Defaults to None, in which case the + current stage is used. + + Returns: + True if the properties were successfully set, False otherwise. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # get deformable-body USD prim + deformable_body_prim = stage.GetPrimAtPath(prim_path) + + # check if the prim is valid and has the deformable-body API + if not deformable_body_prim.IsValid() or not PhysxSchema.PhysxDeformableBodyAPI(deformable_body_prim): + return False + + # retrieve the physx deformable-body api + physx_deformable_body_api = PhysxSchema.PhysxDeformableBodyAPI(deformable_body_prim) + # retrieve the physx deformable api + physx_deformable_api = PhysxSchema.PhysxDeformableAPI(physx_deformable_body_api) + + # convert to dict + cfg = cfg.to_dict() + # set into deformable body API + attr_kwargs = { + attr_name: cfg.pop(attr_name) + for attr_name in [ + "kinematic_enabled", + "collision_simplification", + "collision_simplification_remeshing", + "collision_simplification_remeshing_resolution", + "collision_simplification_target_triangle_count", + "collision_simplification_force_conforming", + "simulation_hexahedral_resolution", + "solver_position_iteration_count", + "vertex_velocity_damping", + "sleep_damping", + "sleep_threshold", + "settling_threshold", + "self_collision", + "self_collision_filter_distance", + ] + } + status = deformable_utils.add_physx_deformable_body(stage, prim_path=prim_path, **attr_kwargs) + # check if the deformable body was successfully added + if not status: + return False + + # obtain the PhysX collision API (this is set when the deformable body is added) + physx_collision_api = PhysxSchema.PhysxCollisionAPI(deformable_body_prim) + + # set into PhysX API + for attr_name, value in cfg.items(): + if attr_name in ["rest_offset", "contact_offset"]: + safe_set_attribute_on_usd_schema(physx_collision_api, attr_name, value, camel_case=True) + else: + safe_set_attribute_on_usd_schema(physx_deformable_api, attr_name, value, camel_case=True) + + # success + return True + + +""" +Collision mesh properties. +""" + + +def extract_mesh_collision_api_and_attrs( + cfg: schemas_cfg.MeshCollisionPropertiesCfg, +) -> tuple[Callable, dict[str, Any]]: + """Extract the mesh collision API function and custom attributes from the configuration. + + Args: + cfg: The configuration for the mesh collision properties. + + Returns: + A tuple containing the API function to use and a dictionary of custom attributes. + + Raises: + ValueError: When neither USD nor PhysX API can be determined to be used. + """ + # We use the number of user set attributes outside of the API function + # to determine which API to use in ambiguous cases, so collect them here + custom_attrs = { + key: value + for key, value in cfg.to_dict().items() + if value is not None and key not in ["usd_func", "physx_func", "mesh_approximation_name"] + } + + use_usd_api = False + use_phsyx_api = False + + # We have some custom attributes and allow them + if len(custom_attrs) > 0 and type(cfg) in PHYSX_MESH_COLLISION_CFGS: + use_phsyx_api = True + # We have no custom attributes + elif len(custom_attrs) == 0: + if type(cfg) in USD_MESH_COLLISION_CFGS: + # Use the USD API + use_usd_api = True + else: + # Use the PhysX API + use_phsyx_api = True + + elif len(custom_attrs) > 0 and type(cfg) in USD_MESH_COLLISION_CFGS: + raise ValueError("Args are specified but the USD Mesh API doesn't support them!") + + if use_usd_api: + # Use USD API for corresponding attributes + # For mesh collision approximation attribute, we set it explicitly in `modify_mesh_collision_properties`` + api_func = cfg.usd_func + elif use_phsyx_api: + api_func = cfg.physx_func + else: + raise ValueError("Either USD or PhysX API should be used for modifying mesh collision attributes!") + + return api_func, custom_attrs + + +def define_mesh_collision_properties( + prim_path: str, cfg: schemas_cfg.MeshCollisionPropertiesCfg, stage: Usd.Stage | None = None +): + """Apply the mesh collision schema on the input prim and set its properties. + See :func:`modify_collision_mesh_properties` for more details on how the properties are set. + Args: + prim_path : The prim path where to apply the mesh collision schema. + cfg : The configuration for the mesh collision properties. + stage : The stage where to find the prim. Defaults to None, in which case the + current stage is used. + Raises: + ValueError: When the prim path is not valid. + """ + # obtain stage + if stage is None: + stage = get_current_stage() + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim path is valid + if not prim.IsValid(): + raise ValueError(f"Prim path '{prim_path}' is not valid.") + + api_func, _ = extract_mesh_collision_api_and_attrs(cfg=cfg) + + # Only enable if not already enabled + if not api_func(prim): + api_func.Apply(prim) + + modify_mesh_collision_properties(prim_path=prim_path, cfg=cfg, stage=stage) + + +@apply_nested +def modify_mesh_collision_properties( + prim_path: str, cfg: schemas_cfg.MeshCollisionPropertiesCfg, stage: Usd.Stage | None = None +) -> bool: + """Set properties for the mesh collision of a prim. + These properties are based on either the `Phsyx the `UsdPhysics.MeshCollisionAPI` schema. + .. note:: + This function is decorated with :func:`apply_nested` that sets the properties to all the prims + (that have the schema applied on them) under the input prim path. + .. UsdPhysics.MeshCollisionAPI: https://openusd.org/release/api/class_usd_physics_mesh_collision_a_p_i.html + Args: + prim_path : The prim path of the rigid body. This prim should be a Mesh prim. + cfg : The configuration for the mesh collision properties. + stage : The stage where to find the prim. Defaults to None, in which case the + current stage is used. + Returns: + True if the properties were successfully set, False otherwise. + Raises: + ValueError: When the mesh approximation name is invalid. + """ + # obtain stage + if stage is None: + stage = get_current_stage() + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + + # we need MeshCollisionAPI to set mesh collision approximation attribute + if not UsdPhysics.MeshCollisionAPI(prim): + UsdPhysics.MeshCollisionAPI.Apply(prim) + # convert mesh approximation string to token + approximation_name = cfg.mesh_approximation_name + if approximation_name not in MESH_APPROXIMATION_TOKENS: + raise ValueError( + f"Invalid mesh approximation name: '{approximation_name}'. " + f"Valid options are: {list(MESH_APPROXIMATION_TOKENS.keys())}" + ) + approximation_token = MESH_APPROXIMATION_TOKENS[approximation_name] + safe_set_attribute_on_usd_schema( + UsdPhysics.MeshCollisionAPI(prim), "Approximation", approximation_token, camel_case=False + ) + + api_func, custom_attrs = extract_mesh_collision_api_and_attrs(cfg=cfg) + + # retrieve the mesh collision API + mesh_collision_api = api_func(prim) + + # set custom attributes into mesh collision API + for attr_name, value in custom_attrs.items(): + # Only "Attribute" attr should be in format "boundingSphere", so set camel_case to be False + if attr_name == "Attribute": + camel_case = False + else: + camel_case = True + safe_set_attribute_on_usd_schema(mesh_collision_api, attr_name, value, camel_case=camel_case) + + # success + return True diff --git a/source/isaaclab/isaaclab/sim/schemas/schemas_cfg.py b/source/isaaclab/isaaclab/sim/schemas/schemas_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..446d7faa105d1315554625418070a562cb39b76e --- /dev/null +++ b/source/isaaclab/isaaclab/sim/schemas/schemas_cfg.py @@ -0,0 +1,679 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from typing import Literal + +from pxr import PhysxSchema, UsdPhysics + +from isaaclab.utils import configclass + + +@configclass +class ArticulationRootPropertiesCfg: + """Properties to apply to the root of an articulation. + + See :meth:`modify_articulation_root_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + articulation_enabled: bool | None = None + """Whether to enable or disable articulation.""" + + enabled_self_collisions: bool | None = None + """Whether to enable or disable self-collisions.""" + + solver_position_iteration_count: int | None = None + """Solver position iteration counts for the body.""" + + solver_velocity_iteration_count: int | None = None + """Solver velocity iteration counts for the body.""" + + sleep_threshold: float | None = None + """Mass-normalized kinetic energy threshold below which an actor may go to sleep.""" + + stabilization_threshold: float | None = None + """The mass-normalized kinetic energy threshold below which an articulation may participate in stabilization.""" + + fix_root_link: bool | None = None + """Whether to fix the root link of the articulation. + + * If set to None, the root link is not modified. + * If the articulation already has a fixed root link, this flag will enable or disable the fixed joint. + * If the articulation does not have a fixed root link, this flag will create a fixed joint between the world + frame and the root link. The joint is created with the name "FixedJoint" under the articulation prim. + + .. note:: + This is a non-USD schema property. It is handled by the :meth:`modify_articulation_root_properties` function. + + """ + + +@configclass +class RigidBodyPropertiesCfg: + """Properties to apply to a rigid body. + + See :meth:`modify_rigid_body_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + rigid_body_enabled: bool | None = None + """Whether to enable or disable the rigid body.""" + + kinematic_enabled: bool | None = None + """Determines whether the body is kinematic or not. + + A kinematic body is a body that is moved through animated poses or through user defined poses. The simulation + still derives velocities for the kinematic body based on the external motion. + + For more information on kinematic bodies, please refer to the `documentation `_. + """ + + disable_gravity: bool | None = None + """Disable gravity for the actor.""" + + linear_damping: float | None = None + """Linear damping for the body.""" + + angular_damping: float | None = None + """Angular damping for the body.""" + + max_linear_velocity: float | None = None + """Maximum linear velocity for rigid bodies (in m/s).""" + + max_angular_velocity: float | None = None + """Maximum angular velocity for rigid bodies (in deg/s).""" + + max_depenetration_velocity: float | None = None + """Maximum depenetration velocity permitted to be introduced by the solver (in m/s).""" + + max_contact_impulse: float | None = None + """The limit on the impulse that may be applied at a contact.""" + + enable_gyroscopic_forces: bool | None = None + """Enables computation of gyroscopic forces on the rigid body.""" + + retain_accelerations: bool | None = None + """Carries over forces/accelerations over sub-steps.""" + + solver_position_iteration_count: int | None = None + """Solver position iteration counts for the body.""" + + solver_velocity_iteration_count: int | None = None + """Solver position iteration counts for the body.""" + + sleep_threshold: float | None = None + """Mass-normalized kinetic energy threshold below which an actor may go to sleep.""" + + stabilization_threshold: float | None = None + """The mass-normalized kinetic energy threshold below which an actor may participate in stabilization.""" + + +@configclass +class CollisionPropertiesCfg: + """Properties to apply to colliders in a rigid body. + + See :meth:`modify_collision_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + collision_enabled: bool | None = None + """Whether to enable or disable collisions.""" + + contact_offset: float | None = None + """Contact offset for the collision shape (in m). + + The collision detector generates contact points as soon as two shapes get closer than the sum of their + contact offsets. This quantity should be non-negative which means that contact generation can potentially start + before the shapes actually penetrate. + """ + + rest_offset: float | None = None + """Rest offset for the collision shape (in m). + + The rest offset quantifies how close a shape gets to others at rest, At rest, the distance between two + vertically stacked objects is the sum of their rest offsets. If a pair of shapes have a positive rest + offset, the shapes will be separated at rest by an air gap. + """ + + torsional_patch_radius: float | None = None + """Radius of the contact patch for applying torsional friction (in m). + + It is used to approximate rotational friction introduced by the compression of contacting surfaces. + If the radius is zero, no torsional friction is applied. + """ + + min_torsional_patch_radius: float | None = None + """Minimum radius of the contact patch for applying torsional friction (in m).""" + + +@configclass +class MassPropertiesCfg: + """Properties to define explicit mass properties of a rigid body. + + See :meth:`modify_mass_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + mass: float | None = None + """The mass of the rigid body (in kg). + + Note: + If non-zero, the mass is ignored and the density is used to compute the mass. + """ + + density: float | None = None + """The density of the rigid body (in kg/m^3). + + The density indirectly defines the mass of the rigid body. It is generally computed using the collision + approximation of the body. + """ + + +@configclass +class JointDrivePropertiesCfg: + """Properties to define the drive mechanism of a joint. + + See :meth:`modify_joint_drive_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + drive_type: Literal["force", "acceleration"] | None = None + """Joint drive type to apply. + + If the drive type is "force", then the joint is driven by a force. If the drive type is "acceleration", + then the joint is driven by an acceleration (usually used for kinematic joints). + """ + + max_effort: float | None = None + """Maximum effort that can be applied to the joint (in kg-m^2/s^2).""" + + max_velocity: float | None = None + """Maximum velocity of the joint. + + The unit depends on the joint model: + + * For linear joints, the unit is m/s. + * For angular joints, the unit is rad/s. + """ + + stiffness: float | None = None + """Stiffness of the joint drive. + + The unit depends on the joint model: + + * For linear joints, the unit is kg-m/s^2 (N/m). + * For angular joints, the unit is kg-m^2/s^2/rad (N-m/rad). + """ + + damping: float | None = None + """Damping of the joint drive. + + The unit depends on the joint model: + + * For linear joints, the unit is kg-m/s (N-s/m). + * For angular joints, the unit is kg-m^2/s/rad (N-m-s/rad). + """ + + +@configclass +class FixedTendonPropertiesCfg: + """Properties to define fixed tendons of an articulation. + + See :meth:`modify_fixed_tendon_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + tendon_enabled: bool | None = None + """Whether to enable or disable the tendon.""" + + stiffness: float | None = None + """Spring stiffness term acting on the tendon's length.""" + + damping: float | None = None + """The damping term acting on both the tendon length and the tendon-length limits.""" + + limit_stiffness: float | None = None + """Limit stiffness term acting on the tendon's length limits.""" + + offset: float | None = None + """Length offset term for the tendon. + + It defines an amount to be added to the accumulated length computed for the tendon. This allows the application + to actuate the tendon by shortening or lengthening it. + """ + + rest_length: float | None = None + """Spring rest length of the tendon.""" + + +@configclass +class SpatialTendonPropertiesCfg: + """Properties to define spatial tendons of an articulation. + + See :meth:`modify_spatial_tendon_properties` for more information. + + .. note:: + If the values are None, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + tendon_enabled: bool | None = None + """Whether to enable or disable the tendon.""" + + stiffness: float | None = None + """Spring stiffness term acting on the tendon's length.""" + + damping: float | None = None + """The damping term acting on both the tendon length and the tendon-length limits.""" + + limit_stiffness: float | None = None + """Limit stiffness term acting on the tendon's length limits.""" + + offset: float | None = None + """Length offset term for the tendon. + + It defines an amount to be added to the accumulated length computed for the tendon. This allows the application + to actuate the tendon by shortening or lengthening it. + """ + + +@configclass +class DeformableBodyPropertiesCfg: + """Properties to apply to a deformable body. + + A deformable body is a body that can deform under forces. The configuration allows users to specify + the properties of the deformable body, such as the solver iteration counts, damping, and self-collision. + + An FEM-based deformable body is created by providing a collision mesh and simulation mesh. The collision mesh + is used for collision detection and the simulation mesh is used for simulation. The collision mesh is usually + a simplified version of the simulation mesh. + + Based on the above, the PhysX team provides APIs to either set the simulation and collision mesh directly + (by specifying the points) or to simplify the collision mesh based on the simulation mesh. The simplification + process involves remeshing the collision mesh and simplifying it based on the target triangle count. + + Since specifying the collision mesh points directly is not a common use case, we only expose the parameters + to simplify the collision mesh based on the simulation mesh. If you want to provide the collision mesh points, + please open an issue on the repository and we can add support for it. + + See :meth:`modify_deformable_body_properties` for more information. + + .. note:: + If the values are :obj:`None`, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + deformable_enabled: bool | None = None + """Enables deformable body.""" + + kinematic_enabled: bool = False + """Enables kinematic body. Defaults to False, which means that the body is not kinematic. + + Similar to rigid bodies, this allows setting user-driven motion for the deformable body. For more information, + please refer to the `documentation `__. + """ + + self_collision: bool | None = None + """Whether to enable or disable self-collisions for the deformable body based on the rest position distances.""" + + self_collision_filter_distance: float | None = None + """Penetration value that needs to get exceeded before contacts for self collision are generated. + + This parameter must be greater than of equal to twice the :attr:`rest_offset` value. + + This value has an effect only if :attr:`self_collision` is enabled. + """ + + settling_threshold: float | None = None + """Threshold vertex velocity (in m/s) under which sleep damping is applied in addition to velocity damping.""" + + sleep_damping: float | None = None + """Coefficient for the additional damping term if fertex velocity drops below setting threshold.""" + + sleep_threshold: float | None = None + """The velocity threshold (in m/s) under which the vertex becomes a candidate for sleeping in the next step.""" + + solver_position_iteration_count: int | None = None + """Number of the solver positional iterations per step. Range is [1,255]""" + + vertex_velocity_damping: float | None = None + """Coefficient for artificial damping on the vertex velocity. + + This parameter can be used to approximate the effect of air drag on the deformable body. + """ + + simulation_hexahedral_resolution: int = 10 + """The target resolution for the hexahedral mesh used for simulation. Defaults to 10. + + Note: + This value is ignored if the user provides the simulation mesh points directly. However, we assume that + most users will not provide the simulation mesh points directly. If you want to provide the simulation mesh + directly, please set this value to :obj:`None`. + """ + + collision_simplification: bool = True + """Whether or not to simplify the collision mesh before creating a soft body out of it. Defaults to True. + + Note: + This flag is ignored if the user provides the simulation mesh points directly. However, we assume that + most users will not provide the simulation mesh points directly. Hence, this flag is enabled by default. + + If you want to provide the simulation mesh points directly, please set this flag to False. + """ + + collision_simplification_remeshing: bool = True + """Whether or not the collision mesh should be remeshed before simplification. Defaults to True. + + This parameter is ignored if :attr:`collision_simplification` is False. + """ + + collision_simplification_remeshing_resolution: int = 0 + """The resolution used for remeshing. Defaults to 0, which means that a heuristic is used to determine the + resolution. + + This parameter is ignored if :attr:`collision_simplification_remeshing` is False. + """ + + collision_simplification_target_triangle_count: int = 0 + """The target triangle count used for the simplification. Defaults to 0, which means that a heuristic based on + the :attr:`simulation_hexahedral_resolution` is used to determine the target count. + + This parameter is ignored if :attr:`collision_simplification` is False. + """ + + collision_simplification_force_conforming: bool = True + """Whether or not the simplification should force the output mesh to conform to the input mesh. Defaults to True. + + The flag indicates that the tretrahedralizer used to generate the collision mesh should produce tetrahedra + that conform to the triangle mesh. If False, the simplifier uses the output from the tretrahedralizer used. + + This parameter is ignored if :attr:`collision_simplification` is False. + """ + + contact_offset: float | None = None + """Contact offset for the collision shape (in m). + + The collision detector generates contact points as soon as two shapes get closer than the sum of their + contact offsets. This quantity should be non-negative which means that contact generation can potentially start + before the shapes actually penetrate. + """ + + rest_offset: float | None = None + """Rest offset for the collision shape (in m). + + The rest offset quantifies how close a shape gets to others at rest, At rest, the distance between two + vertically stacked objects is the sum of their rest offsets. If a pair of shapes have a positive rest + offset, the shapes will be separated at rest by an air gap. + """ + + max_depenetration_velocity: float | None = None + """Maximum depenetration velocity permitted to be introduced by the solver (in m/s).""" + + +@configclass +class MeshCollisionPropertiesCfg: + """Properties to apply to a mesh in regards to collision. + See :meth:`set_mesh_collision_properties` for more information. + + .. note:: + If the values are MISSING, they are not modified. This is useful when you want to set only a subset of + the properties and leave the rest as-is. + """ + + usd_func: callable = MISSING + """USD API function for modifying mesh collision properties. + Refer to + `original USD Documentation `_ + for more information. + """ + + physx_func: callable = MISSING + """PhysX API function for modifying mesh collision properties. + Refer to + `original PhysX Documentation `_ + for more information. + """ + + mesh_approximation_name: str = "none" + """Name of mesh collision approximation method. Default: "none". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + +@configclass +class BoundingCubePropertiesCfg(MeshCollisionPropertiesCfg): + usd_func: callable = UsdPhysics.MeshCollisionAPI + """Original USD Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_usd_physics_mesh_collision_a_p_i.html + """ + + mesh_approximation_name: str = "boundingCube" + """Name of mesh collision approximation method. Default: "boundingCube". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + +@configclass +class BoundingSpherePropertiesCfg(MeshCollisionPropertiesCfg): + usd_func: callable = UsdPhysics.MeshCollisionAPI + """Original USD Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_usd_physics_mesh_collision_a_p_i.html + """ + + mesh_approximation_name: str = "boundingSphere" + """Name of mesh collision approximation method. Default: "boundingSphere". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + +@configclass +class ConvexDecompositionPropertiesCfg(MeshCollisionPropertiesCfg): + usd_func: callable = UsdPhysics.MeshCollisionAPI + """Original USD Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_usd_physics_mesh_collision_a_p_i.html + """ + + physx_func: callable = PhysxSchema.PhysxConvexDecompositionCollisionAPI + """Original PhysX Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_physx_schema_physx_convex_decomposition_collision_a_p_i.html + """ + + mesh_approximation_name: str = "convexDecomposition" + """Name of mesh collision approximation method. Default: "convexDecomposition". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + hull_vertex_limit: int | None = None + """Convex hull vertex limit used for convex hull cooking. + + Defaults to 64. + """ + max_convex_hulls: int | None = None + """Maximum of convex hulls created during convex decomposition. + Default value is 32. + """ + min_thickness: float | None = None + """Convex hull min thickness. + + Range: [0, inf). Units are distance. Default value is 0.001. + """ + voxel_resolution: int | None = None + """Voxel resolution used for convex decomposition. + + Defaults to 500,000 voxels. + """ + error_percentage: float | None = None + """Convex decomposition error percentage parameter. + + Defaults to 10 percent. Units are percent. + """ + shrink_wrap: bool | None = None + """Attempts to adjust the convex hull points so that they are projected onto the surface of the original graphics + mesh. + + Defaults to False. + """ + + +@configclass +class ConvexHullPropertiesCfg(MeshCollisionPropertiesCfg): + usd_func: callable = UsdPhysics.MeshCollisionAPI + """Original USD Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_usd_physics_mesh_collision_a_p_i.html + """ + + physx_func: callable = PhysxSchema.PhysxConvexHullCollisionAPI + """Original PhysX Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_physx_schema_physx_convex_hull_collision_a_p_i.html + """ + + mesh_approximation_name: str = "convexHull" + """Name of mesh collision approximation method. Default: "convexHull". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + hull_vertex_limit: int | None = None + """Convex hull vertex limit used for convex hull cooking. + + Defaults to 64. + """ + min_thickness: float | None = None + """Convex hull min thickness. + + Range: [0, inf). Units are distance. Default value is 0.001. + """ + + +@configclass +class TriangleMeshPropertiesCfg(MeshCollisionPropertiesCfg): + physx_func: callable = PhysxSchema.PhysxTriangleMeshCollisionAPI + """Triangle mesh is only supported by PhysX API. + + Original PhysX Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_physx_schema_physx_triangle_mesh_collision_a_p_i.html + """ + + mesh_approximation_name: str = "none" + """Name of mesh collision approximation method. Default: "none" (uses triangle mesh). + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + weld_tolerance: float | None = None + """Mesh weld tolerance, controls the distance at which vertices are welded. + + Default -inf will autocompute the welding tolerance based on the mesh size. Zero value will disable welding. + Range: [0, inf) Units: distance + """ + + +@configclass +class TriangleMeshSimplificationPropertiesCfg(MeshCollisionPropertiesCfg): + usd_func: callable = UsdPhysics.MeshCollisionAPI + """Original USD Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_usd_physics_mesh_collision_a_p_i.html + """ + + physx_func: callable = PhysxSchema.PhysxTriangleMeshSimplificationCollisionAPI + """Original PhysX Documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_physx_schema_physx_triangle_mesh_simplification_collision_a_p_i.html + """ + + mesh_approximation_name: str = "meshSimplification" + """Name of mesh collision approximation method. Default: "meshSimplification". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + simplification_metric: float | None = None + """Mesh simplification accuracy. + + Defaults to 0.55. + """ + weld_tolerance: float | None = None + """Mesh weld tolerance, controls the distance at which vertices are welded. + + Default -inf will autocompute the welding tolerance based on the mesh size. Zero value will disable welding. + Range: [0, inf) Units: distance + """ + + +@configclass +class SDFMeshPropertiesCfg(MeshCollisionPropertiesCfg): + physx_func: callable = PhysxSchema.PhysxSDFMeshCollisionAPI + """SDF mesh is only supported by PhysX API. + + Original PhysX documentation: + https://docs.omniverse.nvidia.com/kit/docs/omni_usd_schema_physics/latest/class_physx_schema_physx_s_d_f_mesh_collision_a_p_i.html + + More details and steps for optimizing SDF results can be found here: + https://nvidia-omniverse.github.io/PhysX/physx/5.2.1/docs/RigidBodyCollision.html#dynamic-triangle-meshes-with-sdfs + """ + + mesh_approximation_name: str = "sdf" + """Name of mesh collision approximation method. Default: "sdf". + Refer to :const:`schemas.MESH_APPROXIMATION_TOKENS` for available options. + """ + + sdf_margin: float | None = None + """Margin to increase the size of the SDF relative to the bounding box diagonal length of the mesh. + + + A sdf margin value of 0.01 means the sdf boundary will be enlarged in any direction by 1% of the mesh's bounding + box diagonal length. Representing the margin relative to the bounding box diagonal length ensures that it is scale + independent. Margins allow for precise distance queries in a region slightly outside of the mesh's bounding box. + + Default value is 0.01. + Range: [0, inf) Units: dimensionless + """ + sdf_narrow_band_thickness: float | None = None + """Size of the narrow band around the mesh surface where high resolution SDF samples are available. + + Outside of the narrow band, only low resolution samples are stored. Representing the narrow band thickness as a + fraction of the mesh's bounding box diagonal length ensures that it is scale independent. A value of 0.01 is + usually large enough. The smaller the narrow band thickness, the smaller the memory consumption of the sparse SDF. + + Default value is 0.01. + Range: [0, 1] Units: dimensionless + """ + sdf_resolution: int | None = None + """The spacing of the uniformly sampled SDF is equal to the largest AABB extent of the mesh, + divided by the resolution. + + Choose the lowest possible resolution that provides acceptable performance; very high resolution results in large + memory consumption, and slower cooking and simulation performance. + + Default value is 256. + Range: (1, inf) + """ + sdf_subgrid_resolution: int | None = None + """A positive subgrid resolution enables sparsity on signed-distance-fields (SDF) while a value of 0 leads to the + usage of a dense SDF. + + A value in the range of 4 to 8 is a reasonable compromise between block size and the overhead introduced by block + addressing. The smaller a block, the more memory is spent on the address table. The bigger a block, the less + precisely the sparse SDF can adapt to the mesh's surface. In most cases sparsity reduces the memory consumption of + a SDF significantly. + + Default value is 6. + Range: [0, inf) + """ diff --git a/source/isaaclab/isaaclab/sim/simulation_cfg.py b/source/isaaclab/isaaclab/sim/simulation_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f3bf40b83d0ca1266d3d566a574abbde51f225f5 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/simulation_cfg.py @@ -0,0 +1,444 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Base configuration of the environment. + +This module defines the general configuration of the environment. It includes parameters for +configuring the environment instances, viewer settings, and simulation parameters. +""" + +from typing import Any, Literal + +from isaaclab.utils import configclass + +from .spawners.materials import RigidBodyMaterialCfg + + +@configclass +class PhysxCfg: + """Configuration for PhysX solver-related parameters. + + These parameters are used to configure the PhysX solver. For more information, see the `PhysX 5 SDK + documentation`_. + + PhysX 5 supports GPU-accelerated physics simulation. This is enabled by default, but can be disabled + by setting the :attr:`~SimulationCfg.device` to ``cpu`` in :class:`SimulationCfg`. Unlike CPU PhysX, the GPU + simulation feature is unable to dynamically grow all the buffers. Therefore, it is necessary to provide + a reasonable estimate of the buffer sizes for GPU features. If insufficient buffer sizes are provided, the + simulation will fail with errors and lead to adverse behaviors. The buffer sizes can be adjusted through the + ``gpu_*`` parameters. + + .. _PhysX 5 SDK documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/_api_build/classPxSceneDesc.html + + """ + + solver_type: Literal[0, 1] = 1 + """The type of solver to use.Default is 1 (TGS). + + Available solvers: + + * :obj:`0`: PGS (Projective Gauss-Seidel) + * :obj:`1`: TGS (Temporal Gauss-Seidel) + """ + + min_position_iteration_count: int = 1 + """Minimum number of solver position iterations (rigid bodies, cloth, particles etc.). Default is 1. + + .. note:: + + Each physics actor in Omniverse specifies its own solver iteration count. The solver takes + the number of iterations specified by the actor with the highest iteration and clamps it to + the range ``[min_position_iteration_count, max_position_iteration_count]``. + """ + + max_position_iteration_count: int = 255 + """Maximum number of solver position iterations (rigid bodies, cloth, particles etc.). Default is 255. + + .. note:: + + Each physics actor in Omniverse specifies its own solver iteration count. The solver takes + the number of iterations specified by the actor with the highest iteration and clamps it to + the range ``[min_position_iteration_count, max_position_iteration_count]``. + """ + + min_velocity_iteration_count: int = 0 + """Minimum number of solver velocity iterations (rigid bodies, cloth, particles etc.). Default is 0. + + .. note:: + + Each physics actor in Omniverse specifies its own solver iteration count. The solver takes + the number of iterations specified by the actor with the highest iteration and clamps it to + the range ``[min_velocity_iteration_count, max_velocity_iteration_count]``. + """ + + max_velocity_iteration_count: int = 255 + """Maximum number of solver velocity iterations (rigid bodies, cloth, particles etc.). Default is 255. + + .. note:: + + Each physics actor in Omniverse specifies its own solver iteration count. The solver takes + the number of iterations specified by the actor with the highest iteration and clamps it to + the range ``[min_velocity_iteration_count, max_velocity_iteration_count]``. + """ + + enable_ccd: bool = False + """Enable a second broad-phase pass that makes it possible to prevent objects from tunneling through each other. + Default is False.""" + + enable_stabilization: bool = False + """Enable/disable additional stabilization pass in solver. Default is False. + + .. note:: + + We recommend setting this flag to true only when the simulation step size is large + (i.e., less than 30 Hz or more than 0.0333 seconds). + + .. warning:: + + Enabling this flag may lead to incorrect contact forces report from the contact sensor. + """ + + enable_external_forces_every_iteration: bool = False + """Enable/disable external forces every position iteration in the TGS solver. Default is False. + + When using the TGS solver (:attr:`solver_type` is 1), this flag allows enabling external forces every solver + position iteration. This can help improve the accuracy of velocity updates. Consider enabling this flag if + the velocities generated by the simulation are noisy. Increasing the number of velocity iterations, together + with this flag, can help improve the accuracy of velocity updates. + + .. note:: + + This flag is ignored when using the PGS solver (:attr:`solver_type` is 0). + """ + + enable_enhanced_determinism: bool = False + """Enable/disable improved determinism at the expense of performance. Defaults to False. + + For more information on PhysX determinism, please check `here`_. + + .. _here: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/RigidBodyDynamics.html#enhanced-determinism + """ + + bounce_threshold_velocity: float = 0.5 + """Relative velocity threshold for contacts to bounce (in m/s). Default is 0.5 m/s.""" + + friction_offset_threshold: float = 0.04 + """Threshold for contact point to experience friction force (in m). Default is 0.04 m.""" + + friction_correlation_distance: float = 0.025 + """Distance threshold for merging contacts into a single friction anchor point (in m). Default is 0.025 m.""" + + gpu_max_rigid_contact_count: int = 2**23 + """Size of rigid contact stream buffer allocated in pinned host memory. Default is 2 ** 23.""" + + gpu_max_rigid_patch_count: int = 5 * 2**15 + """Size of the rigid contact patch stream buffer allocated in pinned host memory. Default is 5 * 2 ** 15.""" + + gpu_found_lost_pairs_capacity: int = 2**21 + """Capacity of found and lost buffers allocated in GPU global memory. Default is 2 ** 21. + + This is used for the found/lost pair reports in the BP. + """ + + gpu_found_lost_aggregate_pairs_capacity: int = 2**25 + """Capacity of found and lost buffers in aggregate system allocated in GPU global memory. + Default is 2 ** 25. + + This is used for the found/lost pair reports in AABB manager. + """ + + gpu_total_aggregate_pairs_capacity: int = 2**21 + """Capacity of total number of aggregate pairs allocated in GPU global memory. Default is 2 ** 21.""" + + gpu_collision_stack_size: int = 2**26 + """Size of the collision stack buffer allocated in pinned host memory. Default is 2 ** 26.""" + + gpu_heap_capacity: int = 2**26 + """Initial capacity of the GPU and pinned host memory heaps. Additional memory will be allocated + if more memory is required. Default is 2 ** 26.""" + + gpu_temp_buffer_capacity: int = 2**24 + """Capacity of temp buffer allocated in pinned host memory. Default is 2 ** 24.""" + + gpu_max_num_partitions: int = 8 + """Limitation for the partitions in the GPU dynamics pipeline. Default is 8. + + This variable must be power of 2. A value greater than 32 is currently not supported. Range: (1, 32) + """ + + gpu_max_soft_body_contacts: int = 2**20 + """Size of soft body contacts stream buffer allocated in pinned host memory. Default is 2 ** 20.""" + + gpu_max_particle_contacts: int = 2**20 + """Size of particle contacts stream buffer allocated in pinned host memory. Default is 2 ** 20.""" + + solve_articulation_contact_last: bool = False + """Changes the ordering inside the articulation solver. Default is False. + + PhysX employs a strict ordering for handling constraints in an articulation. The outcome of + each constraint resolution modifies the joint and associated link speeds. However, the default + ordering may not be ideal for gripping scenarios because the solver favours the constraint + types that are resolved last. This is particularly true of stiff constraint systems that are hard + to resolve without resorting to vanishingly small simulation timesteps. + + With dynamic contact resolution being such an important part of gripping, it may make + more sense to solve dynamic contact towards the end of the solver rather than at the + beginning. This parameter modifies the default ordering to enable this change. + + For more information, please check `here `__. + + .. versionadded:: v2.3 + This parameter is only available with Isaac Sim 5.1. + + """ + + +@configclass +class RenderCfg: + """Configuration for Omniverse RTX Renderer. + + These parameters are used to configure the Omniverse RTX Renderer. + + The defaults for IsaacLab are set in the experience files: + + * ``apps/isaaclab.python.rendering.kit``: Setting used when running the simulation with the GUI enabled. + * ``apps/isaaclab.python.headless.rendering.kit``: Setting used when running the simulation in headless mode. + + Setting any value here will override the defaults of the experience files. + + For more information, see the `Omniverse RTX Renderer documentation`_. + + .. _Omniverse RTX Renderer documentation: https://docs.omniverse.nvidia.com/materials-and-rendering/latest/rtx-renderer.html + """ + + enable_translucency: bool | None = None + """Enables translucency for specular transmissive surfaces such as glass at the cost of some performance. + Default is False. + + This is set by the variable: ``/rtx/translucency/enabled``. + """ + + enable_reflections: bool | None = None + """Enables reflections at the cost of some performance. Default is False. + + This is set by the variable: ``/rtx/reflections/enabled``. + """ + + enable_global_illumination: bool | None = None + """Enables Diffused Global Illumination at the cost of some performance. Default is False. + + This is set by the variable: ``/rtx/indirectDiffuse/enabled``. + """ + + antialiasing_mode: Literal["Off", "FXAA", "DLSS", "TAA", "DLAA"] | None = None + """Selects the anti-aliasing mode to use. Defaults to DLSS. + + - **DLSS**: Boosts performance by using AI to output higher resolution frames from a lower resolution input. + DLSS samples multiple lower resolution images and uses motion data and feedback from prior frames to + reconstruct native quality images. + - **DLAA**: Provides higher image quality with an AI-based anti-aliasing technique. DLAA uses the same + Super Resolution technology developed for DLSS, reconstructing a native resolution image to maximize + image quality. + + This is set by the variable: ``/rtx/post/dlss/execMode``. + """ + + enable_dlssg: bool | None = None + """"Enables the use of DLSS-G. Default is False. + + DLSS Frame Generation boosts performance by using AI to generate more frames. DLSS analyzes sequential frames + and motion data to create additional high quality frames. + + .. note:: + + This feature requires an Ada Lovelace architecture GPU. Enabling this feature also enables additional + thread-related activities, which can hurt performance. + + This is set by the variable: ``/rtx-transient/dlssg/enabled``. + """ + + enable_dl_denoiser: bool | None = None + """Enables the use of a DL denoiser. + + The DL denoiser can help improve the quality of renders, but comes at a cost of performance. + + This is set by the variable: ``/rtx-transient/dldenoiser/enabled``. + """ + + dlss_mode: Literal[0, 1, 2, 3] | None = None + """For DLSS anti-aliasing, selects the performance/quality tradeoff mode. Default is 0. + + Valid values are: + + * 0 (Performance) + * 1 (Balanced) + * 2 (Quality) + * 3 (Auto) + + This is set by the variable: ``/rtx/post/dlss/execMode``. + """ + + enable_direct_lighting: bool | None = None + """Enable direct light contributions from lights. Default is False. + + This is set by the variable: ``/rtx/directLighting/enabled``. + """ + + samples_per_pixel: int | None = None + """Defines the Direct Lighting samples per pixel. Default is 1. + + A higher value increases the direct lighting quality at the cost of performance. + + This is set by the variable: ``/rtx/directLighting/sampledLighting/samplesPerPixel``. + """ + + enable_shadows: bool | None = None + """Enables shadows at the cost of performance. Defaults to True. + + When disabled, lights will not cast shadows. + + This is set by the variable: ``/rtx/shadows/enabled``. + """ + + enable_ambient_occlusion: bool | None = None + """Enables ambient occlusion at the cost of some performance. Default is False. + + This is set by the variable: ``/rtx/ambientOcclusion/enabled``. + """ + + dome_light_upper_lower_strategy: Literal[0, 3, 4] | None = None + """Selects how to sample the Dome Light. Default is 0. + For more information, refer to the `documentation`_. + + .. _documentation: https://docs.omniverse.nvidia.com/materials-and-rendering/latest/rtx-renderer_common.html#dome-light + + Valid values are: + + * 0: **Image-Based Lighting (IBL)** - Most accurate even for high-frequency Dome Light textures. + Can introduce sampling artifacts in real-time mode. + * 3: **Limited Image-Based Lighting** - Only sampled for reflection and refraction. Fastest, but least + accurate. Good for cases where the Dome Light contributes less than other light sources. + * 4: **Approximated Image-Based Lighting** - Fast and artifacts-free sampling in real-time mode but only + works well with a low-frequency texture (e.g., a sky with no sun disc where the sun is instead a separate + Distant Light). Requires enabling Direct Lighting denoiser. + + This is set by the variable: ``/rtx/domeLight/upperLowerStrategy``. + """ + + carb_settings: dict[str, Any] | None = None + """A general dictionary for users to supply all carb rendering settings with native names. + + The keys of the dictionary can be formatted like a carb setting, .kit file setting, or python variable. + For instance, a key value pair can be: + + - ``/rtx/translucency/enabled: False`` (carb) + - ``rtx.translucency.enabled: False`` (.kit) + - ``rtx_translucency_enabled: False`` (python) + """ + + rendering_mode: Literal["performance", "balanced", "quality"] | None = None + """The rendering mode. + + This behaves the same as the passing the CLI arg ``--rendering_mode`` to an executable script. + """ + + +@configclass +class SimulationCfg: + """Configuration for simulation physics.""" + + physics_prim_path: str = "/physicsScene" + """The prim path where the USD PhysicsScene is created. Default is "/physicsScene".""" + + device: str = "cuda:0" + """The device to run the simulation on. Default is ``"cuda:0"``. + + Valid options are: + + - ``"cpu"``: Use CPU. + - ``"cuda"``: Use GPU, where the device ID is inferred from :class:`~isaaclab.app.AppLauncher`'s config. + - ``"cuda:N"``: Use GPU, where N is the device ID. For example, "cuda:0". + """ + + dt: float = 1.0 / 60.0 + """The physics simulation time-step (in seconds). Default is 0.0167 seconds.""" + + render_interval: int = 1 + """The number of physics simulation steps per rendering step. Default is 1.""" + + gravity: tuple[float, float, float] = (0.0, 0.0, -9.81) + """The gravity vector (in m/s^2). Default is (0.0, 0.0, -9.81). + + If set to (0.0, 0.0, 0.0), gravity is disabled. + """ + + enable_scene_query_support: bool = False + """Enable/disable scene query support for collision shapes. Default is False. + + This flag allows performing collision queries (raycasts, sweeps, and overlaps) on actors and + attached shapes in the scene. This is useful for implementing custom collision detection logic + outside of the physics engine. + + If set to False, the physics engine does not create the scene query manager and the scene query + functionality will not be available. However, this provides some performance speed-up. + + Note: + This flag is overridden to True inside the :class:`SimulationContext` class when running the simulation + with the GUI enabled. This is to allow certain GUI features to work properly. + """ + + use_fabric: bool = True + """Enable/disable reading of physics buffers directly. Default is True. + + When running the simulation, updates in the states in the scene is normally synchronized with USD. + This leads to an overhead in reading the data and does not scale well with massive parallelization. + This flag allows disabling the synchronization and reading the data directly from the physics buffers. + + It is recommended to set this flag to :obj:`True` when running the simulation with a large number + of primitives in the scene. + + Note: + When enabled, the GUI will not update the physics parameters in real-time. To enable real-time + updates, please set this flag to :obj:`False`. + + When using GPU simulation, it is required to enable Fabric to visualize updates in the renderer. + Transform updates are propagated to the renderer through Fabric. If Fabric is disabled with GPU simulation, + the renderer will not be able to render any updates in the simulation, although simulation will still be + running under the hood. + """ + + physx: PhysxCfg = PhysxCfg() + """PhysX solver settings. Default is PhysxCfg().""" + + physics_material: RigidBodyMaterialCfg = RigidBodyMaterialCfg() + """Default physics material settings for rigid bodies. Default is RigidBodyMaterialCfg(). + + The physics engine defaults to this physics material for all the rigid body prims that do not have any + physics material specified on them. + + The material is created at the path: ``{physics_prim_path}/defaultMaterial``. + """ + + render: RenderCfg = RenderCfg() + """Render settings. Default is RenderCfg().""" + + create_stage_in_memory: bool = False + """If stage is first created in memory. Default is False. + + Creating the stage in memory can reduce start-up time. + """ + + logging_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "WARNING" + """The logging level. Default is "WARNING".""" + + save_logs_to_file: bool = True + """Save logs to a file. Default is True.""" + + log_dir: str | None = None + """The directory to save the logs to. Default is None. + + If :attr:`save_logs_to_file` is True, the logs will be saved to the directory specified by :attr:`log_dir`. + If None, the logs will be saved to the temp directory. + """ diff --git a/source/isaaclab/isaaclab/sim/simulation_context.py b/source/isaaclab/isaaclab/sim/simulation_context.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d3a3d2d685704130da05a2711a5ddbe2425049 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/simulation_context.py @@ -0,0 +1,1130 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import builtins +import enum +import glob +import logging +import os +import re +import time +import traceback +import weakref +from collections.abc import Iterator +from contextlib import contextmanager +from datetime import datetime +from typing import Any + +import flatdict +import numpy as np +import toml +import torch + +import carb +import omni.physx +import omni.usd +from isaacsim.core.api.simulation_context import SimulationContext as _SimulationContext +from isaacsim.core.simulation_manager import SimulationManager +from isaacsim.core.utils.viewports import set_camera_view +from pxr import Gf, PhysxSchema, Sdf, Usd, UsdPhysics, UsdUtils + +import isaaclab.sim as sim_utils +from isaaclab.utils.logger import configure_logging +from isaaclab.utils.version import get_isaac_sim_version + +from .simulation_cfg import SimulationCfg +from .spawners import DomeLightCfg, GroundPlaneCfg +from .utils import bind_physics_material + +# import logger +logger = logging.getLogger(__name__) + + +class SimulationContext(_SimulationContext): + """A class to control simulation-related events such as physics stepping and rendering. + + The simulation context helps control various simulation aspects. This includes: + + * configure the simulator with different settings such as the physics time-step, the number of physics substeps, + and the physics solver parameters (for more information, see :class:`isaaclab.sim.SimulationCfg`) + * playing, pausing, stepping and stopping the simulation + * adding and removing callbacks to different simulation events such as physics stepping, rendering, etc. + + This class inherits from the :class:`isaacsim.core.api.simulation_context.SimulationContext` class and + adds additional functionalities such as setting up the simulation context with a configuration object, + exposing other commonly used simulator-related functions, and performing version checks of Isaac Sim + to ensure compatibility between releases. + + The simulation context is a singleton object. This means that there can only be one instance + of the simulation context at any given time. This is enforced by the parent class. Therefore, it is + not possible to create multiple instances of the simulation context. Instead, the simulation context + can be accessed using the ``instance()`` method. + + .. attention:: + Since we only support the `PyTorch `_ backend for simulation, the + simulation context is configured to use the ``torch`` backend by default. This means that + all the data structures used in the simulation are ``torch.Tensor`` objects. + + The simulation context can be used in two different modes of operations: + + 1. **Standalone python script**: In this mode, the user has full control over the simulation and + can trigger stepping events synchronously (i.e. as a blocking call). In this case the user + has to manually call :meth:`step` step the physics simulation and :meth:`render` to + render the scene. + 2. **Omniverse extension**: In this mode, the user has limited control over the simulation stepping + and all the simulation events are triggered asynchronously (i.e. as a non-blocking call). In this + case, the user can only trigger the simulation to start, pause, and stop. The simulation takes + care of stepping the physics simulation and rendering the scene. + + Based on above, for most functions in this class there is an equivalent function that is suffixed + with ``_async``. The ``_async`` functions are used in the Omniverse extension mode and + the non-``_async`` functions are used in the standalone python script mode. + """ + + class RenderMode(enum.IntEnum): + """Different rendering modes for the simulation. + + Render modes correspond to how the viewport and other UI elements (such as listeners to keyboard or mouse + events) are updated. There are three main components that can be updated when the simulation is rendered: + + 1. **UI elements and other extensions**: These are UI elements (such as buttons, sliders, etc.) and other + extensions that are running in the background that need to be updated when the simulation is running. + 2. **Cameras**: These are typically based on Hydra textures and are used to render the scene from different + viewpoints. They can be attached to a viewport or be used independently to render the scene. + 3. **Viewports**: These are windows where you can see the rendered scene. + + Updating each of the above components has a different overhead. For example, updating the viewports is + computationally expensive compared to updating the UI elements. Therefore, it is useful to be able to + control what is updated when the simulation is rendered. This is where the render mode comes in. There are + four different render modes: + + * :attr:`NO_GUI_OR_RENDERING`: The simulation is running without a GUI and off-screen rendering flag + is disabled, so none of the above are updated. + * :attr:`NO_RENDERING`: No rendering, where only 1 is updated at a lower rate. + * :attr:`PARTIAL_RENDERING`: Partial rendering, where only 1 and 2 are updated. + * :attr:`FULL_RENDERING`: Full rendering, where everything (1, 2, 3) is updated. + + .. _Viewports: https://docs.omniverse.nvidia.com/extensions/latest/ext_viewport.html + """ + + NO_GUI_OR_RENDERING = -1 + """The simulation is running without a GUI and off-screen rendering is disabled.""" + NO_RENDERING = 0 + """No rendering, where only other UI elements are updated at a lower rate.""" + PARTIAL_RENDERING = 1 + """Partial rendering, where the simulation cameras and UI elements are updated.""" + FULL_RENDERING = 2 + """Full rendering, where all the simulation viewports, cameras and UI elements are updated.""" + + def __init__(self, cfg: SimulationCfg | None = None): + """Creates a simulation context to control the simulator. + + Args: + cfg: The configuration of the simulation. Defaults to None, + in which case the default configuration is used. + """ + # store input + if cfg is None: + cfg = SimulationCfg() + # check that the config is valid + cfg.validate() + self.cfg = cfg + # check that simulation is running + if sim_utils.get_current_stage() is None: + raise RuntimeError("The stage has not been created. Did you run the simulator?") + + # setup logger + self.logger = configure_logging( + logging_level=self.cfg.logging_level, + save_logs_to_file=self.cfg.save_logs_to_file, + log_dir=self.cfg.log_dir, + ) + + # create stage in memory if requested + if self.cfg.create_stage_in_memory: + self._initial_stage = sim_utils.create_new_stage_in_memory() + else: + self._initial_stage = omni.usd.get_context().get_stage() + # cache stage if it is not already cached + stage_cache = UsdUtils.StageCache.Get() + stage_id = stage_cache.GetId(self._initial_stage).ToLongInt() + if stage_id < 0: + stage_cache.Insert(self._initial_stage) + + # acquire settings interface + self.carb_settings = carb.settings.get_settings() + + # apply carb physics settings + self._apply_physics_settings() + + # note: we read this once since it is not expected to change during runtime + # read flag for whether a local GUI is enabled + self._local_gui = self.carb_settings.get("/app/window/enabled") + # read flag for whether livestreaming GUI is enabled + self._livestream_gui = self.carb_settings.get("/app/livestream/enabled") + # read flag for whether XR GUI is enabled + self._xr_gui = self.carb_settings.get("/app/xr/enabled") + + # read flags anim recording config and init timestamps + self._setup_anim_recording() + + # read flag for whether the Isaac Lab viewport capture pipeline will be used, + # casting None to False if the flag doesn't exist + # this flag is set from the AppLauncher class + self._offscreen_render = bool(self.carb_settings.get("/isaaclab/render/offscreen")) + # read flag for whether the default viewport should be enabled + self._render_viewport = bool(self.carb_settings.get("/isaaclab/render/active_viewport")) + # flag for whether any GUI will be rendered (local, livestreamed or viewport) + self._has_gui = self._local_gui or self._livestream_gui or self._xr_gui + + # apply render settings from render config + self._apply_render_settings_from_cfg() + + # store the default render mode + if not self._has_gui and not self._offscreen_render: + # set default render mode + # note: this is the terminal state: cannot exit from this render mode + self.render_mode = self.RenderMode.NO_GUI_OR_RENDERING + # set viewport context to None + self._viewport_context = None + self._viewport_window = None + elif not self._has_gui and self._offscreen_render: + # set default render mode + # note: this is the terminal state: cannot exit from this render mode + self.render_mode = self.RenderMode.PARTIAL_RENDERING + # set viewport context to None + self._viewport_context = None + self._viewport_window = None + else: + # note: need to import here in case the UI is not available (ex. headless mode) + import omni.ui as ui + from omni.kit.viewport.utility import get_active_viewport + + # set default render mode + # note: this can be changed by calling the `set_render_mode` function + self.render_mode = self.RenderMode.FULL_RENDERING + # acquire viewport context + self._viewport_context = get_active_viewport() + self._viewport_context.updates_enabled = True # pyright: ignore [reportOptionalMemberAccess] + # acquire viewport window + # TODO @mayank: Why not just use get_active_viewport_and_window() directly? + self._viewport_window = ui.Workspace.get_window("Viewport") + # counter for periodic rendering + self._render_throttle_counter = 0 + # rendering frequency in terms of number of render calls + self._render_throttle_period = 5 + + # check the case where we don't need to render the viewport + # since render_viewport can only be False in headless mode, we only need to check for offscreen_render + if not self._render_viewport and self._offscreen_render: + # disable the viewport if offscreen_render is enabled + from omni.kit.viewport.utility import get_active_viewport + + get_active_viewport().updates_enabled = False + + # override enable scene querying if rendering is enabled + # this is needed for some GUI features + if self._has_gui: + self.cfg.enable_scene_query_support = True + # set up flatcache/fabric interface (default is None) + # this is needed to flush the flatcache data into Hydra manually when calling `render()` + # ref: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_physics.html + # note: need to do this here because super().__init__ calls render and this variable is needed + self._fabric_iface = None + + # create a tensor for gravity + # note: this line is needed to create a "tensor" in the device to avoid issues with torch 2.1 onwards. + # the issue is with some heap memory corruption when torch tensor is created inside the asset class. + # you can reproduce the issue by commenting out this line and running the test `test_articulation.py`. + self._gravity_tensor = torch.tensor(self.cfg.gravity, dtype=torch.float32, device=self.cfg.device) + + # define a global variable to store the exceptions raised in the callback stack + builtins.ISAACLAB_CALLBACK_EXCEPTION = None + + # add callback to deal the simulation app when simulation is stopped. + # this is needed because physics views go invalid once we stop the simulation + if not builtins.ISAAC_LAUNCHED_FROM_TERMINAL: + timeline_event_stream = omni.timeline.get_timeline_interface().get_timeline_event_stream() + self._app_control_on_stop_handle = timeline_event_stream.create_subscription_to_pop_by_type( + int(omni.timeline.TimelineEventType.STOP), + lambda *args, obj=weakref.proxy(self): obj._app_control_on_stop_handle_fn(*args), + order=15, + ) + else: + self._app_control_on_stop_handle = None + self._disable_app_control_on_stop_handle = False + + # flatten out the simulation dictionary + sim_params = self.cfg.to_dict() + if sim_params is not None: + if "physx" in sim_params: + physx_params = sim_params.pop("physx") + sim_params.update(physx_params) + + # add warning about enabling stabilization for large step sizes + if not self.cfg.physx.enable_stabilization and (self.cfg.dt > 0.0333): + self.logger.warning( + "Large simulation step size (> 0.0333 seconds) is not recommended without enabling stabilization." + " Consider setting the `enable_stabilization` flag to True in the PhysxCfg, or reducing the" + " simulation step size if you run into physics issues." + ) + + # set simulation device + # note: Although Isaac Sim sets the physics device in the init function, + # it does a render call which gets the wrong device. + SimulationManager.set_physics_sim_device(self.cfg.device) + + # obtain the parsed device + # This device should be the same as "self.cfg.device". However, for cases, where users specify the device + # as "cuda" and not "cuda:X", then it fetches the current device from SimulationManager. + # Note: Since we fix the device from the configuration and don't expect users to change it at runtime, + # we can obtain the device once from the SimulationManager.get_physics_sim_device() function. + # This reduces the overhead of calling the function. + self._physics_device = SimulationManager.get_physics_sim_device() + + # create a simulation context to control the simulator + if get_isaac_sim_version().major < 5: + # stage arg is not supported before isaac sim 5.0 + super().__init__( + stage_units_in_meters=1.0, + physics_dt=self.cfg.dt, + rendering_dt=self.cfg.dt * self.cfg.render_interval, + backend="torch", + sim_params=sim_params, + physics_prim_path=self.cfg.physics_prim_path, + device=self.cfg.device, + ) + else: + super().__init__( + stage_units_in_meters=1.0, + physics_dt=self.cfg.dt, + rendering_dt=self.cfg.dt * self.cfg.render_interval, + backend="torch", + sim_params=sim_params, + physics_prim_path=self.cfg.physics_prim_path, + device=self.cfg.device, + stage=self._initial_stage, + ) + + """ + Properties - Override. + """ + + @property + def device(self) -> str: + """Device used by the simulation. + + Note: + In Omniverse, it is possible to configure multiple GPUs for rendering, while physics engine + operates on a single GPU. This function returns the device that is used for physics simulation. + """ + return self._physics_device + + """ + Operations - New. + """ + + def has_gui(self) -> bool: + """Returns whether the simulation has a GUI enabled. + + True if the simulation has a GUI enabled either locally or live-streamed. + """ + return self._has_gui + + def has_rtx_sensors(self) -> bool: + """Returns whether the simulation has any RTX-rendering related sensors. + + This function returns the value of the simulation parameter ``"/isaaclab/render/rtx_sensors"``. + The parameter is set to True when instances of RTX-related sensors (cameras or LiDARs) are + created using Isaac Lab's sensor classes. + + True if the simulation has RTX sensors (such as USD Cameras or LiDARs). + + For more information, please check `NVIDIA RTX documentation`_. + + .. _NVIDIA RTX documentation: https://developer.nvidia.com/rendering-technologies + """ + return self._settings.get_as_bool("/isaaclab/render/rtx_sensors") + + def is_fabric_enabled(self) -> bool: + """Returns whether the fabric interface is enabled. + + When fabric interface is enabled, USD read/write operations are disabled. Instead all applications + read and write the simulation state directly from the fabric interface. This reduces a lot of overhead + that occurs during USD read/write operations. + + For more information, please check `Fabric documentation`_. + + .. _Fabric documentation: https://docs.omniverse.nvidia.com/kit/docs/usdrt/latest/docs/usd_fabric_usdrt.html + """ + return self._fabric_iface is not None + + def get_version(self) -> tuple[int, int, int]: + """Returns the version of the simulator. + + The returned tuple contains the following information: + + * Major version: This is the year of the release (e.g. 2022). + * Minor version: This is the half-year of the release (e.g. 1 or 2). + * Patch version: This is the patch number of the release (e.g. 0). + + .. attention:: + This function is deprecated and will be removed in the future. + We recommend using :func:`isaaclab.utils.version.get_isaac_sim_version` + instead of this function. + + Returns: + A tuple containing the major, minor, and patch versions. + + Example: + >>> sim = SimulationContext() + >>> sim.get_version() + (2022, 1, 0) + """ + return get_isaac_sim_version().major, get_isaac_sim_version().minor, get_isaac_sim_version().micro + + """ + Operations - New utilities. + """ + + def set_camera_view( + self, + eye: tuple[float, float, float], + target: tuple[float, float, float], + camera_prim_path: str = "/OmniverseKit_Persp", + ): + """Set the location and target of the viewport camera in the stage. + + Note: + This is a wrapper around the :math:`isaacsim.core.utils.viewports.set_camera_view` function. + It is provided here for convenience to reduce the amount of imports needed. + + Args: + eye: The location of the camera eye. + target: The location of the camera target. + camera_prim_path: The path to the camera primitive in the stage. Defaults to + "/OmniverseKit_Persp". + """ + # safe call only if we have a GUI or viewport rendering enabled + if self._has_gui or self._offscreen_render or self._render_viewport: + set_camera_view(eye, target, camera_prim_path) + + def set_render_mode(self, mode: RenderMode): + """Change the current render mode of the simulation. + + Please see :class:`RenderMode` for more information on the different render modes. + + .. note:: + When no GUI is available (locally or livestreamed), we do not need to choose whether the viewport + needs to render or not (since there is no GUI). Thus, in this case, calling the function will not + change the render mode. + + Args: + mode (RenderMode): The rendering mode. If different than SimulationContext's rendering mode, + SimulationContext's mode is changed to the new mode. + + Raises: + ValueError: If the input mode is not supported. + """ + # check if mode change is possible -- not possible when no GUI is available + if not self._has_gui: + self.logger.warning( + f"Cannot change render mode when GUI is disabled. Using the default render mode: {self.render_mode}." + ) + return + # check if there is a mode change + # note: this is mostly needed for GUI when we want to switch between full rendering and no rendering. + if mode != self.render_mode: + if mode == self.RenderMode.FULL_RENDERING: + # display the viewport and enable updates + self._viewport_context.updates_enabled = True # pyright: ignore [reportOptionalMemberAccess] + self._viewport_window.visible = True # pyright: ignore [reportOptionalMemberAccess] + elif mode == self.RenderMode.PARTIAL_RENDERING: + # hide the viewport and disable updates + self._viewport_context.updates_enabled = False # pyright: ignore [reportOptionalMemberAccess] + self._viewport_window.visible = False # pyright: ignore [reportOptionalMemberAccess] + elif mode == self.RenderMode.NO_RENDERING: + # hide the viewport and disable updates + if self._viewport_context is not None: + self._viewport_context.updates_enabled = False # pyright: ignore [reportOptionalMemberAccess] + self._viewport_window.visible = False # pyright: ignore [reportOptionalMemberAccess] + # reset the throttle counter + self._render_throttle_counter = 0 + else: + raise ValueError(f"Unsupported render mode: {mode}! Please check `RenderMode` for details.") + # update render mode + self.render_mode = mode + + def set_setting(self, name: str, value: Any): + """Set simulation settings using the Carbonite SDK. + + .. note:: + If the input setting name does not exist, it will be created. If it does exist, the value will be + overwritten. Please make sure to use the correct setting name. + + To understand the settings interface, please refer to the + `Carbonite SDK `_ + documentation. + + Args: + name: The name of the setting. + value: The value of the setting. + """ + # Route through typed setters for correctness and consistency for common scalar types. + if isinstance(value, bool): + self.carb_settings.set_bool(name, value) + elif isinstance(value, int): + self.carb_settings.set_int(name, value) + elif isinstance(value, float): + self.carb_settings.set_float(name, value) + elif isinstance(value, str): + self.carb_settings.set_string(name, value) + elif isinstance(value, (list, tuple)): + self.carb_settings.set(name, value) + else: + raise ValueError(f"Unsupported value type for setting '{name}': {type(value)}") + + def get_setting(self, name: str) -> Any: + """Read the simulation setting using the Carbonite SDK. + + Args: + name: The name of the setting. + + Returns: + The value of the setting. + """ + return self.carb_settings.get(name) + + def get_initial_stage(self) -> Usd.Stage: + """Returns stage handle used during scene creation. + + Returns: + The stage used during scene creation. + """ + return self._initial_stage + + """ + Operations - Override (standalone) + """ + + def reset(self, soft: bool = False): + self._disable_app_control_on_stop_handle = True + # check if we need to raise an exception that was raised in a callback + if builtins.ISAACLAB_CALLBACK_EXCEPTION is not None: + exception_to_raise = builtins.ISAACLAB_CALLBACK_EXCEPTION + builtins.ISAACLAB_CALLBACK_EXCEPTION = None + raise exception_to_raise + super().reset(soft=soft) + # app.update() may be changing the cuda device in reset, so we force it back to our desired device here + if "cuda" in self.device: + torch.cuda.set_device(self.device) + # enable kinematic rendering with fabric + if self.physics_sim_view: + self.physics_sim_view._backend.initialize_kinematic_bodies() + # perform additional rendering steps to warm up replicator buffers + # this is only needed for the first time we set the simulation + if not soft: + for _ in range(2): + self.render() + self._disable_app_control_on_stop_handle = False + + def forward(self) -> None: + """Updates articulation kinematics and fabric for rendering.""" + if self._fabric_iface is not None: + if self.physics_sim_view is not None and self.is_playing(): + # Update the articulations' link's poses before rendering + self.physics_sim_view.update_articulations_kinematic() + self._update_fabric(0.0, 0.0) + + def step(self, render: bool = True): + """Steps the simulation. + + .. note:: + This function blocks if the timeline is paused. It only returns when the timeline is playing. + + Args: + render: Whether to render the scene after stepping the physics simulation. + If set to False, the scene is not rendered and only the physics simulation is stepped. + """ + # check if we need to raise an exception that was raised in a callback + if builtins.ISAACLAB_CALLBACK_EXCEPTION is not None: + exception_to_raise = builtins.ISAACLAB_CALLBACK_EXCEPTION + builtins.ISAACLAB_CALLBACK_EXCEPTION = None + raise exception_to_raise + + # update anim recording if needed + if self._anim_recording_enabled: + is_anim_recording_finished = self._update_anim_recording() + if is_anim_recording_finished: + logger.warning("[INFO][SimulationContext]: Animation recording finished. Closing app.") + self._app.shutdown() + + # check if the simulation timeline is paused. in that case keep stepping until it is playing + if not self.is_playing(): + # step the simulator (but not the physics) to have UI still active + while not self.is_playing(): + self.render() + # meantime if someone stops, break out of the loop + if self.is_stopped(): + break + # need to do one step to refresh the app + # reason: physics has to parse the scene again and inform other extensions like hydra-delegate. + # without this the app becomes unresponsive. + # FIXME: This steps physics as well, which we is not good in general. + self.app.update() + + # step the simulation + super().step(render=render) + + # app.update() may be changing the cuda device in step, so we force it back to our desired device here + if "cuda" in self.device: + torch.cuda.set_device(self.device) + + def render(self, mode: RenderMode | None = None): + """Refreshes the rendering components including UI elements and view-ports depending on the render mode. + + This function is used to refresh the rendering components of the simulation. This includes updating the + view-ports, UI elements, and other extensions (besides physics simulation) that are running in the + background. The rendering components are refreshed based on the render mode. + + Please see :class:`RenderMode` for more information on the different render modes. + + Args: + mode: The rendering mode. Defaults to None, in which case the current rendering mode is used. + """ + # check if we need to raise an exception that was raised in a callback + if builtins.ISAACLAB_CALLBACK_EXCEPTION is not None: + exception_to_raise = builtins.ISAACLAB_CALLBACK_EXCEPTION + builtins.ISAACLAB_CALLBACK_EXCEPTION = None + raise exception_to_raise + # check if we need to change the render mode + if mode is not None: + self.set_render_mode(mode) + # render based on the render mode + if self.render_mode == self.RenderMode.NO_GUI_OR_RENDERING: + # we never want to render anything here (this is for complete headless mode) + pass + elif self.render_mode == self.RenderMode.NO_RENDERING: + # throttle the rendering frequency to keep the UI responsive + self._render_throttle_counter += 1 + if self._render_throttle_counter % self._render_throttle_period == 0: + self._render_throttle_counter = 0 + # here we don't render viewport so don't need to flush fabric data + # note: we don't call super().render() anymore because they do flush the fabric data + self.set_setting("/app/player/playSimulations", False) + self._app.update() + self.set_setting("/app/player/playSimulations", True) + else: + # manually flush the fabric data to update Hydra textures + self.forward() + # render the simulation + # note: we don't call super().render() anymore because they do above operation inside + # and we don't want to do it twice. We may remove it once we drop support for Isaac Sim 2022.2. + self.set_setting("/app/player/playSimulations", False) + self._app.update() + self.set_setting("/app/player/playSimulations", True) + + # app.update() may be changing the cuda device, so we force it back to our desired device here + if "cuda" in self.device: + torch.cuda.set_device(self.device) + + """ + Operations - Override (extension) + """ + + async def reset_async(self, soft: bool = False): + # need to load all "physics" information from the USD file + if not soft: + omni.physx.acquire_physx_interface().force_load_physics_from_usd() + # play the simulation + await super().reset_async(soft=soft) + + """ + Initialization/Destruction - Override. + """ + + def _init_stage(self, *args, **kwargs) -> Usd.Stage: + _ = super()._init_stage(*args, **kwargs) + with sim_utils.use_stage(self.get_initial_stage()): + # a stage update here is needed for the case when physics_dt != rendering_dt, otherwise the app crashes + # when in headless mode + self.set_setting("/app/player/playSimulations", False) + self._app.update() + self.set_setting("/app/player/playSimulations", True) + # set additional physx parameters and bind material + self._set_additional_physx_params() + # load flatcache/fabric interface + self._load_fabric_interface() + # return the stage + return self.stage + + async def _initialize_stage_async(self, *args, **kwargs) -> Usd.Stage: + await super()._initialize_stage_async(*args, **kwargs) + # set additional physx parameters and bind material + self._set_additional_physx_params() + # load flatcache/fabric interface + self._load_fabric_interface() + # return the stage + return self.stage + + @classmethod + def clear_instance(cls): + # clear the callback + if cls._instance is not None: + if cls._instance._app_control_on_stop_handle is not None: + cls._instance._app_control_on_stop_handle.unsubscribe() + cls._instance._app_control_on_stop_handle = None + # call parent to clear the instance + super().clear_instance() + + """ + Helper Functions + """ + + def _apply_physics_settings(self): + """Sets various carb physics settings.""" + # enable hydra scene-graph instancing + # note: this allows rendering of instanceable assets on the GUI + self.carb_settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + # change dispatcher to use the default dispatcher in PhysX SDK instead of carb tasking + # note: dispatcher handles how threads are launched for multi-threaded physics + self.carb_settings.set_bool("/physics/physxDispatcher", True) + # disable contact processing in omni.physx + # note: we disable it by default to avoid the overhead of contact processing when it isn't needed. + # The physics flag gets enabled when a contact sensor is created. + if hasattr(self.cfg, "disable_contact_processing"): + self.logger.warning( + "The `disable_contact_processing` attribute is deprecated and always set to True" + " to avoid unnecessary overhead. Contact processing is automatically enabled when" + " a contact sensor is created, so manual configuration is no longer required." + ) + # FIXME: From investigation, it seems this flag only affects CPU physics. For GPU physics, contacts + # are always processed. The issue is reported to the PhysX team by @mmittal. + self.carb_settings.set_bool("/physics/disableContactProcessing", True) + # disable custom geometry for cylinder and cone collision shapes to allow contact reporting for them + # reason: cylinders and cones aren't natively supported by PhysX so we need to use custom geometry flags + # reference: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/Geometry.html?highlight=capsule#geometry + self.carb_settings.set_bool("/physics/collisionConeCustomGeometry", False) + self.carb_settings.set_bool("/physics/collisionCylinderCustomGeometry", False) + # hide the Simulation Settings window + self.carb_settings.set_bool("/physics/autoPopupSimulationOutputWindow", False) + + def _apply_render_settings_from_cfg(self): # noqa: C901 + """Sets rtx settings specified in the RenderCfg.""" + + # define mapping of user-friendly RenderCfg names to native carb names + rendering_setting_name_mapping = { + "enable_translucency": "/rtx/translucency/enabled", + "enable_reflections": "/rtx/reflections/enabled", + "enable_global_illumination": "/rtx/indirectDiffuse/enabled", + "enable_dlssg": "/rtx-transient/dlssg/enabled", + "enable_dl_denoiser": "/rtx-transient/dldenoiser/enabled", + "dlss_mode": "/rtx/post/dlss/execMode", + "enable_direct_lighting": "/rtx/directLighting/enabled", + "samples_per_pixel": "/rtx/directLighting/sampledLighting/samplesPerPixel", + "enable_shadows": "/rtx/shadows/enabled", + "enable_ambient_occlusion": "/rtx/ambientOcclusion/enabled", + "dome_light_upper_lower_strategy": "/rtx/domeLight/upperLowerStrategy", + } + + not_carb_settings = ["rendering_mode", "carb_settings", "antialiasing_mode"] + + # grab the rendering mode using the following priority: + # 1. command line argument --rendering_mode, if provided + # 2. rendering_mode from Render Config, if set + # 3. lastly, default to "balanced" mode, if neither is specified + rendering_mode = self.carb_settings.get("/isaaclab/rendering/rendering_mode") + if not rendering_mode: + rendering_mode = self.cfg.render.rendering_mode + if not rendering_mode: + rendering_mode = "balanced" + + # set preset settings (same behavior as the CLI arg --rendering_mode) + if rendering_mode is not None: + # check if preset is supported + supported_rendering_modes = ["performance", "balanced", "quality"] + if rendering_mode not in supported_rendering_modes: + raise ValueError( + f"RenderCfg rendering mode '{rendering_mode}' not in supported modes {supported_rendering_modes}." + ) + + # grab isaac lab apps path + isaaclab_app_exp_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), *[".."] * 4, "apps") + # for Isaac Sim 4.5 compatibility, we use the 4.5 rendering mode app files in a different folder + if get_isaac_sim_version().major < 5: + isaaclab_app_exp_path = os.path.join(isaaclab_app_exp_path, "isaacsim_4_5") + + # grab preset settings + preset_filename = os.path.join(isaaclab_app_exp_path, f"rendering_modes/{rendering_mode}.kit") + with open(preset_filename) as file: + preset_dict = toml.load(file) + preset_dict = dict(flatdict.FlatDict(preset_dict, delimiter=".")) + + # set presets + for key, value in preset_dict.items(): + key = "/" + key.replace(".", "/") # convert to carb setting format + self.set_setting(key, value) + + # set user-friendly named settings + for key, value in vars(self.cfg.render).items(): + if value is None or key in not_carb_settings: + # skip unset settings and non-carb settings + continue + if key not in rendering_setting_name_mapping: + raise ValueError( + f"'{key}' in RenderCfg not found. Note: internal 'rendering_setting_name_mapping' dictionary might" + " need to be updated." + ) + key = rendering_setting_name_mapping[key] + self.set_setting(key, value) + + # set general carb settings + carb_settings = self.cfg.render.carb_settings + if carb_settings is not None: + for key, value in carb_settings.items(): + if "_" in key: + key = "/" + key.replace("_", "/") # convert from python variable style string + elif "." in key: + key = "/" + key.replace(".", "/") # convert from .kit file style string + if self.get_setting(key) is None: + raise ValueError(f"'{key}' in RenderCfg.general_parameters does not map to a carb setting.") + self.set_setting(key, value) + + # set denoiser mode + if self.cfg.render.antialiasing_mode is not None: + try: + import omni.replicator.core as rep + + rep.settings.set_render_rtx_realtime(antialiasing=self.cfg.render.antialiasing_mode) + except Exception: + pass + + # WAR: Ensure /rtx/renderMode RaytracedLighting is correctly cased. + if self.carb_settings.get("/rtx/rendermode").lower() == "raytracedlighting": + self.carb_settings.set_string("/rtx/rendermode", "RaytracedLighting") + + def _set_additional_physx_params(self): + """Sets additional PhysX parameters that are not directly supported by the parent class.""" + # obtain the physics scene api + physics_scene: UsdPhysics.Scene = self._physics_context._physics_scene + physx_scene_api: PhysxSchema.PhysxSceneAPI = self._physics_context._physx_scene_api + # assert that scene api is not None + if physx_scene_api is None: + raise RuntimeError("Physics scene API is None! Please create the scene first.") + # set parameters not directly supported by the constructor + # -- Continuous Collision Detection (CCD) + # ref: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/AdvancedCollisionDetection.html?highlight=ccd#continuous-collision-detection + self._physics_context.enable_ccd(self.cfg.physx.enable_ccd) + # -- GPU collision stack size + physx_scene_api.CreateGpuCollisionStackSizeAttr(self.cfg.physx.gpu_collision_stack_size) + # -- Improved determinism by PhysX + physx_scene_api.CreateEnableEnhancedDeterminismAttr(self.cfg.physx.enable_enhanced_determinism) + # -- Set solve_articulation_contact_last by add attribute to the PhysxScene prim, and add attribute there. + physx_prim = physx_scene_api.GetPrim() + physx_prim.CreateAttribute("physxScene:solveArticulationContactLast", Sdf.ValueTypeNames.Bool).Set( + self.cfg.physx.solve_articulation_contact_last + ) + # -- Enable external forces every iteration, helps improve the accuracy of velocity updates. + + if self.cfg.physx.solver_type == 1: + if not self.cfg.physx.enable_external_forces_every_iteration: + logger.warning( + "The `enable_external_forces_every_iteration` parameter in the PhysxCfg is set to False. If you are" + " experiencing noisy velocities, consider enabling this flag. You may need to slightly increase the" + " number of velocity iterations (setting it to 1 or 2 rather than 0), together with this flag, to" + " improve the accuracy of velocity updates." + ) + physx_scene_api.CreateEnableExternalForcesEveryIterationAttr( + self.cfg.physx.enable_external_forces_every_iteration + ) + + # -- Gravity + # note: Isaac sim only takes the "up-axis" as the gravity direction. But physics allows any direction so we + # need to convert the gravity vector to a direction and magnitude pair explicitly. + gravity = np.asarray(self.cfg.gravity) + gravity_magnitude = np.linalg.norm(gravity) + + # Avoid division by zero + if gravity_magnitude != 0.0: + gravity_direction = gravity / gravity_magnitude + else: + gravity_direction = gravity + + physics_scene.CreateGravityDirectionAttr(Gf.Vec3f(*gravity_direction)) + physics_scene.CreateGravityMagnitudeAttr(gravity_magnitude) + + # position iteration count + physx_scene_api.CreateMinPositionIterationCountAttr(self.cfg.physx.min_position_iteration_count) + physx_scene_api.CreateMaxPositionIterationCountAttr(self.cfg.physx.max_position_iteration_count) + # velocity iteration count + physx_scene_api.CreateMinVelocityIterationCountAttr(self.cfg.physx.min_velocity_iteration_count) + physx_scene_api.CreateMaxVelocityIterationCountAttr(self.cfg.physx.max_velocity_iteration_count) + + # create the default physics material + # this material is used when no material is specified for a primitive + # check: https://isaac-sim.github.io/IsaacLab/main/source/api/lab/isaaclab.sim.html#isaaclab.sim.SimulationCfg.physics_material + material_path = f"{self.cfg.physics_prim_path}/defaultMaterial" + self.cfg.physics_material.func(material_path, self.cfg.physics_material) + # bind the physics material to the scene + bind_physics_material(self.cfg.physics_prim_path, material_path) + + def _load_fabric_interface(self): + """Loads the fabric interface if enabled.""" + if self.cfg.use_fabric: + from omni.physxfabric import get_physx_fabric_interface + + # acquire fabric interface + self._fabric_iface = get_physx_fabric_interface() + if hasattr(self._fabric_iface, "force_update"): + # The update method in the fabric interface only performs an update if a physics step has occurred. + # However, for rendering, we need to force an update since any element of the scene might have been + # modified in a reset (which occurs after the physics step) and we want the renderer to be aware of + # these changes. + self._update_fabric = self._fabric_iface.force_update + else: + # Needed for backward compatibility with older Isaac Sim versions + self._update_fabric = self._fabric_iface.update + + def _update_anim_recording(self): + """Tracks anim recording timestamps and triggers finish animation recording if the total time has elapsed.""" + if self._anim_recording_started_timestamp is None: + self._anim_recording_started_timestamp = time.time() + + if self._anim_recording_started_timestamp is not None: + anim_recording_total_time = time.time() - self._anim_recording_started_timestamp + if anim_recording_total_time > self._anim_recording_stop_time: + self._finish_anim_recording() + return True + return False + + def _setup_anim_recording(self): + """Sets up anim recording settings and initializes the recording.""" + + self._anim_recording_enabled = bool(self.carb_settings.get("/isaaclab/anim_recording/enabled")) + if not self._anim_recording_enabled: + return + + # Import omni.physx.pvd.bindings here since it is not available by default + from omni.physxpvd.bindings import _physxPvd + + # Init anim recording settings + self._anim_recording_start_time = self.carb_settings.get("/isaaclab/anim_recording/start_time") + self._anim_recording_stop_time = self.carb_settings.get("/isaaclab/anim_recording/stop_time") + self._anim_recording_first_step_timestamp = None + self._anim_recording_started_timestamp = None + + # Make output path relative to repo path + repo_path = os.path.join(carb.tokens.get_tokens_interface().resolve("${app}"), "..") + self._anim_recording_timestamp = datetime.now().strftime("%Y_%m_%d_%H%M%S") + self._anim_recording_output_dir = ( + os.path.join(repo_path, "anim_recordings", self._anim_recording_timestamp).replace("\\", "/").rstrip("/") + + "/" + ) + os.makedirs(self._anim_recording_output_dir, exist_ok=True) + + # Acquire physx pvd interface and set output directory + self._physxPvdInterface = _physxPvd.acquire_physx_pvd_interface() + + # Set carb settings for the output path and enabling pvd recording + self.carb_settings.set_string( + "/persistent/physics/omniPvdOvdRecordingDirectory", self._anim_recording_output_dir + ) + self.carb_settings.set_bool("/physics/omniPvdOutputEnabled", True) + + def _update_usda_start_time(self, file_path, start_time): + """Updates the start time of the USDA baked anim recordingfile.""" + + # Read the USDA file + with open(file_path) as file: + content = file.read() + + # Extract the timeCodesPerSecond value + time_code_match = re.search(r"timeCodesPerSecond\s*=\s*(\d+)", content) + if not time_code_match: + raise ValueError("timeCodesPerSecond not found in the file.") + time_codes_per_second = int(time_code_match.group(1)) + + # Compute the new start time code + new_start_time_code = int(start_time * time_codes_per_second) + + # Replace the startTimeCode in the file + content = re.sub(r"startTimeCode\s*=\s*\d+", f"startTimeCode = {new_start_time_code}", content) + + # Write the updated content back to the file + with open(file_path, "w") as file: + file.write(content) + + def _finish_anim_recording(self): + """Finishes the animation recording and outputs the baked animation recording.""" + + logger.warning( + "[INFO][SimulationContext]: Finishing animation recording. Stage must be saved. Might take a few minutes." + ) + + # Detaching the stage will also close it and force the serialization of the OVD file + physx = omni.physx.get_physx_simulation_interface() + physx.detach_stage() + + # Save stage to disk + stage_path = os.path.join(self._anim_recording_output_dir, "stage_simulation.usdc") + sim_utils.save_stage(stage_path, save_and_reload_in_place=False) + + # Find the latest ovd file not named tmp.ovd + ovd_files = [ + f for f in glob.glob(os.path.join(self._anim_recording_output_dir, "*.ovd")) if not f.endswith("tmp.ovd") + ] + input_ovd_path = max(ovd_files, key=os.path.getctime) + + # Invoke pvd interface to create recording + stage_filename = "baked_animation_recording.usda" + result = self._physxPvdInterface.ovd_to_usd_over_with_layer_creation( + input_ovd_path, + stage_path, + self._anim_recording_output_dir, + stage_filename, + self._anim_recording_start_time, + self._anim_recording_stop_time, + True, # True: ASCII layers / False : USDC layers + False, # True: verify over layer + ) + + # Workaround for manually setting the truncated start time in the baked animation recording + self._update_usda_start_time( + os.path.join(self._anim_recording_output_dir, stage_filename), self._anim_recording_start_time + ) + + # Disable recording + self.carb_settings.set_bool("/physics/omniPvdOutputEnabled", False) + + return result + + """ + Callbacks. + """ + + def _app_control_on_stop_handle_fn(self, event: carb.events.IEvent): + """Callback to deal with the app when the simulation is stopped. + + Once the simulation is stopped, the physics handles go invalid. After that, it is not possible to + resume the simulation from the last state. This leaves the app in an inconsistent state, where + two possible actions can be taken: + + 1. **Keep the app rendering**: In this case, the simulation is kept running and the app is not shutdown. + However, the physics is not updated and the script cannot be resumed from the last state. The + user has to manually close the app to stop the simulation. + 2. **Shutdown the app**: This is the default behavior. In this case, the app is shutdown and + the simulation is stopped. + + Note: + This callback is used only when running the simulation in a standalone python script. In an extension, + it is expected that the user handles the extension shutdown. + """ + if not self._disable_app_control_on_stop_handle: + while not omni.timeline.get_timeline_interface().is_playing(): + self.render() + return + + +@contextmanager +def build_simulation_context( + create_new_stage: bool = True, + gravity_enabled: bool = True, + device: str = "cuda:0", + dt: float = 0.01, + sim_cfg: SimulationCfg | None = None, + add_ground_plane: bool = False, + add_lighting: bool = False, + auto_add_lighting: bool = False, +) -> Iterator[SimulationContext]: + """Context manager to build a simulation context with the provided settings. + + This function facilitates the creation of a simulation context and provides flexibility in configuring various + aspects of the simulation, such as time step, gravity, device, and scene elements like ground plane and + lighting. + + If :attr:`sim_cfg` is None, then an instance of :class:`SimulationCfg` is created with default settings, + with parameters overwritten based on arguments to the function. + + An example usage of the context manager function: + + .. code-block:: python + + with build_simulation_context() as sim: + # Design the scene + + # Play the simulation + sim.reset() + while sim.is_playing(): + sim.step() + + Args: + create_new_stage: Whether to create a new stage. Defaults to True. + gravity_enabled: Whether to enable gravity in the simulation. Defaults to True. + device: Device to run the simulation on. Defaults to "cuda:0". + dt: Time step for the simulation: Defaults to 0.01. + sim_cfg: :class:`isaaclab.sim.SimulationCfg` to use for the simulation. Defaults to None. + add_ground_plane: Whether to add a ground plane to the simulation. Defaults to False. + add_lighting: Whether to add a dome light to the simulation. Defaults to False. + auto_add_lighting: Whether to automatically add a dome light to the simulation if the simulation has a GUI. + Defaults to False. This is useful for debugging tests in the GUI. + + Yields: + The simulation context to use for the simulation. + + """ + try: + if create_new_stage: + sim_utils.create_new_stage() + + if sim_cfg is None: + # Construct one and overwrite the dt, gravity, and device + sim_cfg = SimulationCfg(dt=dt) + + # Set up gravity + if gravity_enabled: + sim_cfg.gravity = (0.0, 0.0, -9.81) + else: + sim_cfg.gravity = (0.0, 0.0, 0.0) + + # Set device + sim_cfg.device = device + + # Construct simulation context + sim = SimulationContext(sim_cfg) + + if add_ground_plane: + # Ground-plane + cfg = GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + + if add_lighting or (auto_add_lighting and sim.has_gui()): + # Lighting + cfg = DomeLightCfg( + color=(0.1, 0.1, 0.1), + enable_color_temperature=True, + color_temperature=5500, + intensity=10000, + ) + # Dome light named specifically to avoid conflicts + cfg.func(prim_path="/World/defaultDomeLight", cfg=cfg, translation=(0.0, 0.0, 10.0)) + + yield sim + + except Exception: + sim.logger.error(traceback.format_exc()) + raise + finally: + if not sim.has_gui(): + # Stop simulation only if we aren't rendering otherwise the app will hang indefinitely + sim.stop() + + # Clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + # check if we need to raise an exception that was raised in a callback + if builtins.ISAACLAB_CALLBACK_EXCEPTION is not None: + exception_to_raise = builtins.ISAACLAB_CALLBACK_EXCEPTION + builtins.ISAACLAB_CALLBACK_EXCEPTION = None + raise exception_to_raise diff --git a/source/isaaclab/isaaclab/sim/spawners/__init__.py b/source/isaaclab/isaaclab/sim/spawners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..916141906e1ebe9d1440ac3cededa6d87e489ba5 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/__init__.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing utilities for creating prims in Omniverse. + +Spawners are used to create prims into Omniverse simulator. At their core, they are calling the +USD Python API or Omniverse Kit Commands to create prims. However, they also provide a convenient +interface for creating prims from their respective config classes. + +There are two main ways of using the spawners: + +1. Using the function from the module + + .. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + + # spawn from USD file + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd") + prim_path = "/World/myAsset" + + # spawn using the function from the module + sim_utils.spawn_from_usd(prim_path, cfg) + +2. Using the `func` reference in the config class + + .. code-block:: python + + import isaaclab.sim as sim_utils + from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + + # spawn from USD file + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd") + prim_path = "/World/myAsset" + + # use the `func` reference in the config class + cfg.func(prim_path, cfg) + +For convenience, we recommend using the second approach, as it allows to easily change the config +class and the function call in a single line of code. + +Depending on the type of prim, the spawning-functions can also deal with the creation of prims +over multiple prim path. These need to be provided as a regex prim path expressions, which are +resolved based on the parent prim paths using the :meth:`isaaclab.sim.utils.clone` function decorator. +For example: + +* ``/World/Table_[1,2]/Robot`` will create the prims ``/World/Table_1/Robot`` and ``/World/Table_2/Robot`` + only if the parent prim ``/World/Table_1`` and ``/World/Table_2`` exist. +* ``/World/Robot_[1,2]`` will **NOT** create the prims ``/World/Robot_1`` and + ``/World/Robot_2`` as the prim path expression can be resolved to multiple prims. + +""" + +from .from_files import * # noqa: F401, F403 +from .lights import * # noqa: F401, F403 +from .materials import * # noqa: F401, F403 +from .meshes import * # noqa: F401, F403 +from .sensors import * # noqa: F401, F403 +from .shapes import * # noqa: F401, F403 +from .spawner_cfg import * # noqa: F401, F403 +from .wrappers import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/sim/spawners/from_files/__init__.py b/source/isaaclab/isaaclab/sim/spawners/from_files/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a95ac491b0a8913d197711c2b1320e9693576beb --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/from_files/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawners that spawn assets from files. + +Currently, the following spawners are supported: + +* :class:`UsdFileCfg`: Spawn an asset from a USD file. +* :class:`UrdfFileCfg`: Spawn an asset from a URDF file. +* :class:`GroundPlaneCfg`: Spawn a ground plane using the grid-world USD file. + +""" + +from .from_files import ( + spawn_from_mjcf, + spawn_from_urdf, + spawn_from_usd, + spawn_from_usd_with_compliant_contact_material, + spawn_ground_plane, +) +from .from_files_cfg import GroundPlaneCfg, MjcfFileCfg, UrdfFileCfg, UsdFileCfg, UsdFileWithCompliantContactCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/from_files/from_files.py b/source/isaaclab/isaaclab/sim/spawners/from_files/from_files.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ad4cb5962fce0a5c5253683d8a6b2f12fdb1be --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/from_files/from_files.py @@ -0,0 +1,465 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import omni.kit.commands +from pxr import Gf, Sdf, Usd + +from isaaclab.sim import converters, schemas +from isaaclab.sim.spawners.materials import RigidBodyMaterialCfg +from isaaclab.sim.utils import ( + add_labels, + bind_physics_material, + bind_visual_material, + change_prim_property, + clone, + create_prim, + get_current_stage, + get_first_matching_child_prim, + select_usd_variants, + set_prim_visibility, +) +from isaaclab.utils.assets import check_usd_path_with_timeout + +if TYPE_CHECKING: + from . import from_files_cfg + +# import logger +logger = logging.getLogger(__name__) + + +@clone +def spawn_from_usd( + prim_path: str, + cfg: from_files_cfg.UsdFileCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Spawn an asset from a USD file and override the settings with the given config. + + In the case of a USD file, the asset is spawned at the default prim specified in the USD file. + If a default prim is not specified, then the asset is spawned at the root prim. + + In case a prim already exists at the given prim path, then the function does not create a new prim + or throw an error that the prim already exists. Instead, it just takes the existing prim and overrides + the settings with the given config. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the USD file is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The prim of the spawned asset. + + Raises: + FileNotFoundError: If the USD file does not exist at the given path. + """ + # spawn asset from the given usd file + return _spawn_from_usd_file(prim_path, cfg.usd_path, cfg, translation, orientation) + + +@clone +def spawn_from_urdf( + prim_path: str, + cfg: from_files_cfg.UrdfFileCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Spawn an asset from a URDF file and override the settings with the given config. + + It uses the :class:`UrdfConverter` class to create a USD file from URDF. This file is then imported + at the specified prim path. + + In case a prim already exists at the given prim path, then the function does not create a new prim + or throw an error that the prim already exists. Instead, it just takes the existing prim and overrides + the settings with the given config. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the generated USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the generated USD file is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The prim of the spawned asset. + + Raises: + FileNotFoundError: If the URDF file does not exist at the given path. + """ + # urdf loader to convert urdf to usd + urdf_loader = converters.UrdfConverter(cfg) + # spawn asset from the generated usd file + return _spawn_from_usd_file(prim_path, urdf_loader.usd_path, cfg, translation, orientation) + + +@clone +def spawn_from_mjcf( + prim_path: str, + cfg: from_files_cfg.MjcfFileCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, +) -> Usd.Prim: + """Spawn an asset from a MJCF file and override the settings with the given config. + + It uses the :class:`MjcfConverter` class to create a USD file from MJCF. This file is then imported + at the specified prim path. + + In case a prim already exists at the given prim path, then the function does not create a new prim + or throw an error that the prim already exists. Instead, it just takes the existing prim and overrides + the settings with the given config. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the generated USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the generated USD file is used. + + Returns: + The prim of the spawned asset. + + Raises: + FileNotFoundError: If the MJCF file does not exist at the given path. + """ + # mjcf loader to convert mjcf to usd + mjcf_loader = converters.MjcfConverter(cfg) + # spawn asset from the generated usd file + return _spawn_from_usd_file(prim_path, mjcf_loader.usd_path, cfg, translation, orientation) + + +def spawn_ground_plane( + prim_path: str, + cfg: from_files_cfg.GroundPlaneCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Spawns a ground plane into the scene. + + This function loads the USD file containing the grid plane asset from Isaac Sim. It may + not work with other assets for ground planes. In those cases, please use the `spawn_from_usd` + function. + + Note: + This function takes keyword arguments to be compatible with other spawners. However, it does not + use any of the kwargs. + + Args: + prim_path: The path to spawn the asset at. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the USD file is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The prim of the spawned asset. + + Raises: + ValueError: If the prim path already exists. + """ + # Obtain current stage + stage = get_current_stage() + + # Spawn Ground-plane + if not stage.GetPrimAtPath(prim_path).IsValid(): + create_prim(prim_path, usd_path=cfg.usd_path, translation=translation, orientation=orientation, stage=stage) + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + # Create physics material + if cfg.physics_material is not None: + cfg.physics_material.func(f"{prim_path}/physicsMaterial", cfg.physics_material) + # Apply physics material to ground plane + collision_prim = get_first_matching_child_prim( + prim_path, + predicate=lambda _prim: _prim.GetTypeName() == "Plane", + stage=stage, + ) + if collision_prim is None: + raise ValueError(f"No collision prim found at path: '{prim_path}'.") + # bind physics material to the collision prim + collision_prim_path = str(collision_prim.GetPath()) + bind_physics_material(collision_prim_path, f"{prim_path}/physicsMaterial", stage=stage) + + # Obtain environment prim + environment_prim = stage.GetPrimAtPath(f"{prim_path}/Environment") + # Scale only the mesh + # Warning: This is specific to the default grid plane asset. + if environment_prim.IsValid(): + # compute scale from size + scale = (cfg.size[0] / 100.0, cfg.size[1] / 100.0, 1.0) + # apply scale to the mesh + environment_prim.GetAttribute("xformOp:scale").Set(scale) + + # Change the color of the plane + # Warning: This is specific to the default grid plane asset. + if cfg.color is not None: + # change the color + change_prim_property( + prop_path=f"{prim_path}/Looks/theGrid/Shader.inputs:diffuse_tint", + value=Gf.Vec3f(*cfg.color), + stage=stage, + type_to_create_if_not_exist=Sdf.ValueTypeNames.Color3f, + ) + # Remove the light from the ground plane + # It isn't bright enough and messes up with the user's lighting settings + omni.kit.commands.execute("ToggleVisibilitySelectedPrims", selected_paths=[f"{prim_path}/SphereLight"], stage=stage) + + # Apply visual material override (if provided) + if getattr(cfg, "visual_material", None) is not None: + visual_material = cfg.visual_material + # resolve material path + if not cfg.visual_material_path.startswith("/"): + material_path = f"{prim_path}/{cfg.visual_material_path}" + else: + material_path = cfg.visual_material_path + + # create material + visual_material.func(material_path, visual_material) + + # bind material to a mesh under the environment prim if possible + bind_root_path = f"{prim_path}/Environment" if environment_prim.IsValid() else prim_path + mesh_prim = get_first_matching_child_prim( + bind_root_path, + predicate=lambda _prim: _prim.GetTypeName() == "Mesh", + stage=stage, + ) + bind_target_path = str(mesh_prim.GetPath()) if mesh_prim is not None else bind_root_path + bind_visual_material(bind_target_path, material_path, stage=stage) + + prim = stage.GetPrimAtPath(prim_path) + # Apply semantic tags + if hasattr(cfg, "semantic_tags") and cfg.semantic_tags is not None: + # note: taken from replicator scripts.utils.utils.py + for semantic_type, semantic_value in cfg.semantic_tags: + # deal with spaces by replacing them with underscores + semantic_type_sanitized = semantic_type.replace(" ", "_") + semantic_value_sanitized = semantic_value.replace(" ", "_") + # add labels to the prim + add_labels(prim, labels=[semantic_value_sanitized], instance_name=semantic_type_sanitized) + + # Apply visibility + set_prim_visibility(prim, cfg.visible) + + # return the prim + return prim + + +""" +Helper functions. +""" + + +def _spawn_from_usd_file( + prim_path: str, + usd_path: str, + cfg: from_files_cfg.FileCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Spawn an asset from a USD file and override the settings with the given config. + + In case a prim already exists at the given prim path, then the function does not create a new prim + or throw an error that the prim already exists. Instead, it just takes the existing prim and overrides + the settings with the given config. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + usd_path: The path to the USD file to spawn the asset from. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the generated USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the generated USD file is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The prim of the spawned asset. + + Raises: + FileNotFoundError: If the USD file does not exist at the given path. + """ + # check if usd path exists with periodic logging until timeout + if not check_usd_path_with_timeout(usd_path): + if "4.5" in usd_path: + usd_5_0_path = usd_path.replace("http", "https").replace("/4.5", "/5.0") + if not check_usd_path_with_timeout(usd_5_0_path): + raise FileNotFoundError(f"USD file not found at path at either: '{usd_path}' or '{usd_5_0_path}'.") + usd_path = usd_5_0_path + else: + raise FileNotFoundError(f"USD file not found at path at: '{usd_path}'.") + + # Obtain current stage + stage = get_current_stage() + # spawn asset if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + # add prim as reference to stage + create_prim( + prim_path, + usd_path=usd_path, + translation=translation, + orientation=orientation, + scale=cfg.scale, + stage=stage, + ) + else: + logger.warning(f"A prim already exists at prim path: '{prim_path}'.") + + # modify variants + if hasattr(cfg, "variants") and cfg.variants is not None: + select_usd_variants(prim_path, cfg.variants) + + # modify rigid body properties + if cfg.rigid_props is not None: + schemas.modify_rigid_body_properties(prim_path, cfg.rigid_props) + # modify collision properties + if cfg.collision_props is not None: + schemas.modify_collision_properties(prim_path, cfg.collision_props) + # modify mass properties + if cfg.mass_props is not None: + schemas.modify_mass_properties(prim_path, cfg.mass_props) + + # modify articulation root properties + if cfg.articulation_props is not None: + schemas.modify_articulation_root_properties(prim_path, cfg.articulation_props) + # modify tendon properties + if cfg.fixed_tendons_props is not None: + schemas.modify_fixed_tendon_properties(prim_path, cfg.fixed_tendons_props) + if cfg.spatial_tendons_props is not None: + schemas.modify_spatial_tendon_properties(prim_path, cfg.spatial_tendons_props) + # define drive API on the joints + # note: these are only for setting low-level simulation properties. all others should be set or are + # and overridden by the articulation/actuator properties. + if cfg.joint_drive_props is not None: + schemas.modify_joint_drive_properties(prim_path, cfg.joint_drive_props) + + # modify deformable body properties + if cfg.deformable_props is not None: + schemas.modify_deformable_body_properties(prim_path, cfg.deformable_props) + + # apply visual material + if cfg.visual_material is not None: + if not cfg.visual_material_path.startswith("/"): + material_path = f"{prim_path}/{cfg.visual_material_path}" + else: + material_path = cfg.visual_material_path + # create material + cfg.visual_material.func(material_path, cfg.visual_material) + # apply material + bind_visual_material(prim_path, material_path, stage=stage) + + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_from_usd_with_compliant_contact_material( + prim_path: str, + cfg: from_files_cfg.UsdFileWithCompliantContactCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Spawn an asset from a USD file and apply physics material to specified prims. + + This function extends the :meth:`spawn_from_usd` function by allowing application of compliant contact + physics materials to specified prims within the spawned asset. This is useful for configuring + contact behavior of specific parts within the asset. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance containing the USD file path and physics material settings. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which + case the translation specified in the USD file is used. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case the orientation specified in the USD file is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The prim of the spawned asset with the physics material applied to the specified prims. + + Raises: + FileNotFoundError: If the USD file does not exist at the given path. + """ + + prim = _spawn_from_usd_file(prim_path, cfg.usd_path, cfg, translation, orientation) + stiff = cfg.compliant_contact_stiffness + damp = cfg.compliant_contact_damping + if cfg.physics_material_prim_path is None: + logger.warning("No physics material prim path specified. Skipping physics material application.") + return prim + + if isinstance(cfg.physics_material_prim_path, str): + prim_paths = [cfg.physics_material_prim_path] + else: + prim_paths = cfg.physics_material_prim_path + + if stiff is not None or damp is not None: + material_kwargs = {} + if stiff is not None: + material_kwargs["compliant_contact_stiffness"] = stiff + if damp is not None: + material_kwargs["compliant_contact_damping"] = damp + material_cfg = RigidBodyMaterialCfg(**material_kwargs) + + for path in prim_paths: + if not path.startswith("/"): + rigid_body_prim_path = f"{prim_path}/{path}" + else: + rigid_body_prim_path = path + + material_path = f"{rigid_body_prim_path}/compliant_material" + + # spawn physics material + material_cfg.func(material_path, material_cfg) + + bind_physics_material( + rigid_body_prim_path, + material_path, + ) + logger.info( + f"Applied physics material to prim: {rigid_body_prim_path} with compliance stiffness: {stiff} and" + f" compliance damping: {damp}." + ) + + return prim diff --git a/source/isaaclab/isaaclab/sim/spawners/from_files/from_files_cfg.py b/source/isaaclab/isaaclab/sim/spawners/from_files/from_files_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b7a5ade5351274e853b24780c5ff98b9f4411fd0 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/from_files/from_files_cfg.py @@ -0,0 +1,233 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING + +from isaaclab.sim import converters, schemas +from isaaclab.sim.spawners import materials +from isaaclab.sim.spawners.spawner_cfg import DeformableObjectSpawnerCfg, RigidObjectSpawnerCfg, SpawnerCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import from_files + + +@configclass +class FileCfg(RigidObjectSpawnerCfg, DeformableObjectSpawnerCfg): + """Configuration parameters for spawning an asset from a file. + + This class is a base class for spawning assets from files. It includes the common parameters + for spawning assets from files, such as the path to the file and the function to use for spawning + the asset. + + Note: + By default, all properties are set to None. This means that no properties will be added or modified + to the prim outside of the properties available by default when spawning the prim. + + If they are set to a value, then the properties are modified on the spawned prim in a nested manner. + This is done by calling the respective function with the specified properties. + """ + + scale: tuple[float, float, float] | None = None + """Scale of the asset. Defaults to None, in which case the scale is not modified.""" + + articulation_props: schemas.ArticulationRootPropertiesCfg | None = None + """Properties to apply to the articulation root.""" + + fixed_tendons_props: schemas.FixedTendonPropertiesCfg | None = None + """Properties to apply to the fixed tendons (if any).""" + + spatial_tendons_props: schemas.SpatialTendonPropertiesCfg | None = None + """Properties to apply to the spatial tendons (if any).""" + + joint_drive_props: schemas.JointDrivePropertiesCfg | None = None + """Properties to apply to a joint. + + .. note:: + The joint drive properties set the USD attributes of all the joint drives in the asset. + We recommend using this attribute sparingly and only when necessary. Instead, please use the + :attr:`~isaaclab.assets.ArticulationCfg.actuators` parameter to set the joint drive properties + for specific joints in an articulation. + """ + + visual_material_path: str = "material" + """Path to the visual material to use for the prim. Defaults to "material". + + If the path is relative, then it will be relative to the prim's path. + This parameter is ignored if `visual_material` is not None. + """ + + visual_material: materials.VisualMaterialCfg | None = None + """Visual material properties to override the visual material properties in the URDF file. + + Note: + If None, then no visual material will be added. + """ + + +@configclass +class UsdFileCfg(FileCfg): + """USD file to spawn asset from. + + USD files are imported directly into the scene. However, given their complexity, there are various different + operations that can be performed on them. For example, selecting variants, applying materials, or modifying + existing properties. + + To prevent the explosion of configuration parameters, the available operations are limited to the most common + ones. These include: + + - **Selecting variants**: This is done by specifying the :attr:`variants` parameter. + - **Creating and applying materials**: This is done by specifying the :attr:`visual_material` parameter. + - **Modifying existing properties**: This is done by specifying the respective properties in the configuration + class. For instance, to modify the scale of the imported prim, set the :attr:`scale` parameter. + + See :meth:`spawn_from_usd` for more information. + + .. note:: + The configuration parameters include various properties. If not `None`, these properties + are modified on the spawned prim in a nested manner. + + If they are set to a value, then the properties are modified on the spawned prim in a nested manner. + This is done by calling the respective function with the specified properties. + """ + + func: Callable = from_files.spawn_from_usd + + usd_path: str = MISSING + """Path to the USD file to spawn asset from.""" + + variants: object | dict[str, str] | None = None + """Variants to select from in the input USD file. Defaults to None, in which case no variants are applied. + + This can either be a configclass object, in which case each attribute is used as a variant set name and + its specified value, or a dictionary mapping between the two. Please check the + :meth:`~isaaclab.sim.utils.select_usd_variants` function for more information. + """ + + +@configclass +class UrdfFileCfg(FileCfg, converters.UrdfConverterCfg): + """URDF file to spawn asset from. + + It uses the :class:`UrdfConverter` class to create a USD file from URDF and spawns the imported + USD file. Similar to the :class:`UsdFileCfg`, the generated USD file can be modified by specifying + the respective properties in the configuration class. + + See :meth:`spawn_from_urdf` for more information. + + .. note:: + The configuration parameters include various properties. If not `None`, these properties + are modified on the spawned prim in a nested manner. + + If they are set to a value, then the properties are modified on the spawned prim in a nested manner. + This is done by calling the respective function with the specified properties. + + """ + + func: Callable = from_files.spawn_from_urdf + + +@configclass +class MjcfFileCfg(FileCfg, converters.MjcfConverterCfg): + """MJCF file to spawn asset from. + + It uses the :class:`MjcfConverter` class to create a USD file from MJCF and spawns the imported + USD file. Similar to the :class:`UsdFileCfg`, the generated USD file can be modified by specifying + the respective properties in the configuration class. + + See :meth:`spawn_from_mjcf` for more information. + + .. note:: + The configuration parameters include various properties. If not `None`, these properties + are modified on the spawned prim in a nested manner. + + If they are set to a value, then the properties are modified on the spawned prim in a nested manner. + This is done by calling the respective function with the specified properties. + + """ + + func: Callable = from_files.spawn_from_mjcf + + +""" +Spawning ground plane. +""" + + +@configclass +class UsdFileWithCompliantContactCfg(UsdFileCfg): + """Configuration for spawning a USD asset with compliant contact physics material. + + This class extends :class:`UsdFileCfg` to support applying compliant contact properties + (stiffness and damping) to specific prims in the spawned asset. It uses the + :meth:`spawn_from_usd_with_compliant_contact_material` function to perform the spawning and + material application. + """ + + func: Callable = from_files.spawn_from_usd_with_compliant_contact_material + + compliant_contact_stiffness: float | None = None + """Stiffness of the compliant contact. Defaults to None. + + This parameter is the same as + :attr:`~isaaclab.sim.spawners.materials.RigidBodyMaterialCfg.compliant_contact_stiffness`. + """ + + compliant_contact_damping: float | None = None + """Damping of the compliant contact. Defaults to None. + + This parameter is the same as + :attr:`isaaclab.sim.spawners.materials.RigidBodyMaterialCfg.compliant_contact_damping`. + """ + + physics_material_prim_path: str | list[str] | None = None + """Path to the prim or prims to apply the physics material to. Defaults to None, in which case the + physics material is not applied. + + If the path is relative, then it will be relative to the prim's path. + If None, then the physics material will not be applied. + """ + + +@configclass +class GroundPlaneCfg(SpawnerCfg): + """Create a ground plane prim. + + This uses the USD for the standard grid-world ground plane from Isaac Sim by default. + """ + + func: Callable = from_files.spawn_ground_plane + + usd_path: str = f"{ISAAC_NUCLEUS_DIR}/Environments/Grid/default_environment.usd" + """Path to the USD file to spawn asset from. Defaults to the grid-world ground plane.""" + + color: tuple[float, float, float] | None = (0.0, 0.0, 0.0) + """The color of the ground plane. Defaults to (0.0, 0.0, 0.0). + + If None, then the color remains unchanged. + """ + + size: tuple[float, float] = (100.0, 100.0) + """The size of the ground plane. Defaults to 100 m x 100 m.""" + + physics_material: materials.RigidBodyMaterialCfg = materials.RigidBodyMaterialCfg() + """Physics material properties. Defaults to the default rigid body material.""" + + visual_material_path: str = "visualMaterial" + """Path to the visual material to use for the ground plane. Defaults to "visualMaterial". + + If the path is relative, then it will be relative to the ground plane prim's path. + This parameter is ignored if `visual_material` is None. + """ + + visual_material: materials.VisualMaterialCfg | None = None + """Optional visual material to bind onto the ground plane visuals. + + Note: + If None, then the default ground plane visual material is kept. + """ diff --git a/source/isaaclab/isaaclab/sim/spawners/lights/__init__.py b/source/isaaclab/isaaclab/sim/spawners/lights/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..df0c638f58f1964ff234148fa1f959cd284d14aa --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/lights/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawners that spawn lights in the simulation. + +There are various different kinds of lights that can be spawned into the USD stage. +Please check the Omniverse documentation for `lighting overview +`_. +""" + +from .lights import spawn_light +from .lights_cfg import CylinderLightCfg, DiskLightCfg, DistantLightCfg, DomeLightCfg, LightCfg, SphereLightCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/lights/lights.py b/source/isaaclab/isaaclab/sim/spawners/lights/lights.py new file mode 100644 index 0000000000000000000000000000000000000000..9b0106c6ecddcbfca3a8b67c5216a796b2b6ee86 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/lights/lights.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pxr import Usd, UsdLux + +from isaaclab.sim.utils import clone, create_prim, get_current_stage, safe_set_attribute_on_usd_prim + +if TYPE_CHECKING: + from . import lights_cfg + + +@clone +def spawn_light( + prim_path: str, + cfg: lights_cfg.LightCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a light prim at the specified prim path with the specified configuration. + + The created prim is based on the `USD.LuxLight `_ API. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration for the light source. + translation: The translation of the prim. Defaults to None, in which case this is set to the origin. + orientation: The orientation of the prim as (w, x, y, z). Defaults to None, in which case this + is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Raises: + ValueError: When a prim already exists at the specified prim path. + """ + # obtain stage handle + stage = get_current_stage() + # check if prim already exists + if stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + # create the prim + prim = create_prim( + prim_path, prim_type=cfg.prim_type, translation=translation, orientation=orientation, stage=stage + ) + + # convert to dict + cfg = cfg.to_dict() + # delete spawner func specific parameters + del cfg["prim_type"] + # delete custom attributes in the config that are not USD parameters + non_usd_cfg_param_names = ["func", "copy_from_source", "visible", "semantic_tags"] + for param_name in non_usd_cfg_param_names: + del cfg[param_name] + # set into USD API + for attr_name, value in cfg.items(): + # special operation for texture properties + # note: this is only used for dome light + if "texture" in attr_name: + light_prim = UsdLux.DomeLight(prim) + if attr_name == "texture_file": + light_prim.CreateTextureFileAttr(value) + elif attr_name == "texture_format": + light_prim.CreateTextureFormatAttr(value) + else: + raise ValueError(f"Unsupported texture attribute: '{attr_name}'.") + else: + if attr_name == "visible_in_primary_ray": + prim_prop_name = attr_name + else: + prim_prop_name = f"inputs:{attr_name}" + # set the attribute + safe_set_attribute_on_usd_prim(prim, prim_prop_name, value, camel_case=True) + # return the prim + return prim diff --git a/source/isaaclab/isaaclab/sim/spawners/lights/lights_cfg.py b/source/isaaclab/isaaclab/sim/spawners/lights/lights_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..60060ea22e52c4ea6b4d2701c4e988937df564fc --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/lights/lights_cfg.py @@ -0,0 +1,190 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Literal + +from isaaclab.sim.spawners.spawner_cfg import SpawnerCfg +from isaaclab.utils import configclass + +from . import lights + + +@configclass +class LightCfg(SpawnerCfg): + """Configuration parameters for creating a light in the scene. + + Please refer to the documentation on `USD LuxLight `_ + for more information. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + func: Callable = lights.spawn_light + + prim_type: str = MISSING + """The prim type name for the light prim.""" + + color: tuple[float, float, float] = (1.0, 1.0, 1.0) + """The color of emitted light, in energy-linear terms. Defaults to white.""" + + enable_color_temperature: bool = False + """Enables color temperature. Defaults to false.""" + + color_temperature: float = 6500.0 + """Color temperature (in Kelvin) representing the white point. The valid range is [1000, 10000]. Defaults to 6500K. + + The `color temperature `_ corresponds to the warmth + or coolness of light. Warmer light has a lower color temperature, while cooler light has a higher + color temperature. + + Note: + It only takes effect when :attr:`enable_color_temperature` is true. + """ + + normalize: bool = False + """Normalizes power by the surface area of the light. Defaults to false. + + This makes it easier to independently adjust the power and shape of the light, by causing the power + to not vary with the area or angular size of the light. + """ + + exposure: float = 0.0 + """Scales the power of the light exponentially as a power of 2. Defaults to 0.0. + + The result is multiplied against the intensity. + """ + + intensity: float = 1.0 + """Scales the power of the light linearly. Defaults to 1.0.""" + + +@configclass +class DiskLightCfg(LightCfg): + """Configuration parameters for creating a disk light in the scene. + + A disk light is a light source that emits light from a disk. It is useful for simulating + fluorescent lights. For more information, please refer to the documentation on + `USDLux DiskLight `_. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + prim_type = "DiskLight" + + radius: float = 0.5 + """Radius of the disk (in m). Defaults to 0.5m.""" + + +@configclass +class DistantLightCfg(LightCfg): + """Configuration parameters for creating a distant light in the scene. + + A distant light is a light source that is infinitely far away, and emits parallel rays of light. + It is useful for simulating sun/moon light. For more information, please refer to the documentation on + `USDLux DistantLight `_. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + prim_type = "DistantLight" + + angle: float = 0.53 + """Angular size of the light (in degrees). Defaults to 0.53 degrees. + + As an example, the Sun is approximately 0.53 degrees as seen from Earth. + Higher values broaden the light and therefore soften shadow edges. + """ + + +@configclass +class DomeLightCfg(LightCfg): + """Configuration parameters for creating a dome light in the scene. + + A dome light is a light source that emits light inwards from all directions. It is also possible to + attach a texture to the dome light, which will be used to emit light. For more information, please refer + to the documentation on `USDLux DomeLight `_. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + prim_type = "DomeLight" + + texture_file: str | None = None + """A color texture to use on the dome, such as an HDR (high dynamic range) texture intended + for IBL (image based lighting). Defaults to None. + + If None, the dome will emit a uniform color. + """ + + texture_format: Literal["automatic", "latlong", "mirroredBall", "angular", "cubeMapVerticalCross"] = "automatic" + """The parametrization format of the color map file. Defaults to "automatic". + + Valid values are: + + * ``"automatic"``: Tries to determine the layout from the file itself. For example, Renderman texture files + embed an explicit parameterization. + * ``"latlong"``: Latitude as X, longitude as Y. + * ``"mirroredBall"``: An image of the environment reflected in a sphere, using an implicitly orthogonal projection. + * ``"angular"``: Similar to mirroredBall but the radial dimension is mapped linearly to the angle, providing better + sampling at the edges. + * ``"cubeMapVerticalCross"``: A cube map with faces laid out as a vertical cross. + """ + + visible_in_primary_ray: bool = True + """Whether the dome light is visible in the primary ray. Defaults to True. + + If true, the texture in the sky is visible, otherwise the sky is black. + """ + + +@configclass +class CylinderLightCfg(LightCfg): + """Configuration parameters for creating a cylinder light in the scene. + + A cylinder light is a light source that emits light from a cylinder. It is useful for simulating + fluorescent lights. For more information, please refer to the documentation on + `USDLux CylinderLight `_. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + prim_type = "CylinderLight" + + length: float = 1.0 + """Length of the cylinder (in m). Defaults to 1.0m.""" + + radius: float = 0.5 + """Radius of the cylinder (in m). Defaults to 0.5m.""" + + treat_as_line: bool = False + """Treats the cylinder as a line source, i.e. a zero-radius cylinder. Defaults to false.""" + + +@configclass +class SphereLightCfg(LightCfg): + """Configuration parameters for creating a sphere light in the scene. + + A sphere light is a light source that emits light outward from a sphere. For more information, + please refer to the documentation on + `USDLux SphereLight `_. + + .. note:: + The default values for the attributes are those specified in the their official documentation. + """ + + prim_type = "SphereLight" + + radius: float = 0.5 + """Radius of the sphere. Defaults to 0.5m.""" + + treat_as_point: bool = False + """Treats the sphere as a point source, i.e. a zero-radius sphere. Defaults to false.""" diff --git a/source/isaaclab/isaaclab/sim/spawners/materials/__init__.py b/source/isaaclab/isaaclab/sim/spawners/materials/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..36bd4ae4628557f899d34a696b2d0cabb8bfd00a --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/materials/__init__.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawners that spawn USD-based and PhysX-based materials. + +`Materials`_ are used to define the appearance and physical properties of objects in the simulation. +In Omniverse, they are defined using NVIDIA's `Material Definition Language (MDL)`_. MDL is based on +the physically-based rendering (PBR) model, which is a set of equations that describe how light +interacts with a surface. The PBR model is used to create realistic-looking materials. + +While MDL is primarily used for defining the appearance of objects, it can be extended to define +the physical properties of objects. For example, the friction and restitution coefficients of a +rubber material. A `physics material`_ can be assigned to a physics object to +define its physical properties. There are different kinds of physics materials, such as rigid body +material, deformable material, and fluid material. + +In order to apply a material to an object, we "bind" the geometry of the object to the material. +For this, we use the `USD Material Binding API`_. The material binding API takes in the path to +the geometry and the path to the material, and binds them together. + +For physics material, the material is bound to the physics object with the 'physics' purpose. +When parsing physics material properties on an object, the following priority is used: + +1. Material binding with a 'physics' purpose (physics material) +2. Material binding with no purpose (visual material) +3. Material binding with a 'physics' purpose on the `Physics Scene`_ prim. +4. Default values of material properties inside PhysX. + +Usage: + .. code-block:: python + + import isaaclab.sim as sim_utils + + # create a visual material + visual_material_cfg = sim_utils.GlassMdlCfg(glass_ior=1.0, thin_walled=True) + visual_material_cfg.func("/World/Looks/glassMaterial", visual_material_cfg) + + # create a mesh prim + cube_cfg = sim_utils.CubeCfg(size=[1.0, 1.0, 1.0]) + cube_cfg.func("/World/Primitives/Cube", cube_cfg) + + # bind the cube to the visual material + sim_utils.bind_visual_material("/World/Primitives/Cube", "/World/Looks/glassMaterial") + + +.. _Material Definition Language (MDL): https://raytracing-docs.nvidia.com/mdl/introduction/index.html#mdl_introduction# +.. _Materials: https://docs.omniverse.nvidia.com/materials-and-rendering/latest/materials.html +.. _physics material: https://isaac-sim.github.io/IsaacLab/main/source/api/lab/isaaclab.sim.html#isaaclab.sim.SimulationCfg.physics_material +.. _USD Material Binding API: https://openusd.org/dev/api/class_usd_shade_material_binding_a_p_i.html +.. _Physics Scene: https://openusd.org/dev/api/usd_physics_page_front.html +""" + +from .physics_materials import spawn_deformable_body_material, spawn_rigid_body_material +from .physics_materials_cfg import DeformableBodyMaterialCfg, PhysicsMaterialCfg, RigidBodyMaterialCfg +from .visual_materials import spawn_from_mdl_file, spawn_preview_surface +from .visual_materials_cfg import GlassMdlCfg, MdlFileCfg, PreviewSurfaceCfg, PreviewSurfaceTextureCfg, VisualMaterialCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials.py b/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials.py new file mode 100644 index 0000000000000000000000000000000000000000..29818d83095140d8866c09e7e82b6200291b0f3b --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pxr import PhysxSchema, Usd, UsdPhysics, UsdShade + +from isaaclab.sim.utils import clone, safe_set_attribute_on_usd_schema +from isaaclab.sim.utils.stage import get_current_stage + +if TYPE_CHECKING: + from . import physics_materials_cfg + + +@clone +def spawn_rigid_body_material(prim_path: str, cfg: physics_materials_cfg.RigidBodyMaterialCfg) -> Usd.Prim: + """Create material with rigid-body physics properties. + + Rigid body materials are used to define the physical properties to meshes of a rigid body. These + include the friction, restitution, and their respective combination modes. For more information on + rigid body material, please refer to the `documentation on PxMaterial `_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration for the physics material. + + Returns: + The spawned rigid body material prim. + + Raises: + ValueError: When a prim already exists at the specified prim path and is not a material. + """ + # get stage handle + stage = get_current_stage() + + # create material prim if no prim exists + if not stage.GetPrimAtPath(prim_path).IsValid(): + _ = UsdShade.Material.Define(stage, prim_path) + + # obtain prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is a material + if not prim.IsA(UsdShade.Material): + raise ValueError(f"A prim already exists at path: '{prim_path}' but is not a material.") + # retrieve the USD rigid-body api + usd_physics_material_api = UsdPhysics.MaterialAPI(prim) + if not usd_physics_material_api: + usd_physics_material_api = UsdPhysics.MaterialAPI.Apply(prim) + # retrieve the collision api + physx_material_api = PhysxSchema.PhysxMaterialAPI(prim) + if not physx_material_api: + physx_material_api = PhysxSchema.PhysxMaterialAPI.Apply(prim) + + # convert to dict + cfg = cfg.to_dict() + del cfg["func"] + # set into USD API + for attr_name in ["static_friction", "dynamic_friction", "restitution"]: + value = cfg.pop(attr_name, None) + safe_set_attribute_on_usd_schema(usd_physics_material_api, attr_name, value, camel_case=True) + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_material_api, attr_name, value, camel_case=True) + # return the prim + return prim + + +@clone +def spawn_deformable_body_material(prim_path: str, cfg: physics_materials_cfg.DeformableBodyMaterialCfg) -> Usd.Prim: + """Create material with deformable-body physics properties. + + Deformable body materials are used to define the physical properties to meshes of a deformable body. These + include the friction and deformable body properties. For more information on deformable body material, + please refer to the documentation on `PxFEMSoftBodyMaterial`_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration for the physics material. + + Returns: + The spawned deformable body material prim. + + Raises: + ValueError: When a prim already exists at the specified prim path and is not a material. + + .. _PxFEMSoftBodyMaterial: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/_api_build/structPxFEMSoftBodyMaterialModel.html + """ + # get stage handle + stage = get_current_stage() + + # create material prim if no prim exists + if not stage.GetPrimAtPath(prim_path).IsValid(): + _ = UsdShade.Material.Define(stage, prim_path) + + # obtain prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is a material + if not prim.IsA(UsdShade.Material): + raise ValueError(f"A prim already exists at path: '{prim_path}' but is not a material.") + # retrieve the deformable-body api + physx_deformable_body_material_api = PhysxSchema.PhysxDeformableBodyMaterialAPI(prim) + if not physx_deformable_body_material_api: + physx_deformable_body_material_api = PhysxSchema.PhysxDeformableBodyMaterialAPI.Apply(prim) + + # convert to dict + cfg = cfg.to_dict() + del cfg["func"] + # set into PhysX API + for attr_name, value in cfg.items(): + safe_set_attribute_on_usd_schema(physx_deformable_body_material_api, attr_name, value, camel_case=True) + # return the prim + return prim diff --git a/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials_cfg.py b/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ce05c2b9ea4631620781376c1b31e267a36c12a8 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/materials/physics_materials_cfg.py @@ -0,0 +1,120 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from . import physics_materials + + +@configclass +class PhysicsMaterialCfg: + """Configuration parameters for creating a physics material. + + Physics material are PhysX schemas that can be applied to a USD material prim to define the + physical properties related to the material. For example, the friction coefficient, restitution + coefficient, etc. For more information on physics material, please refer to the + `PhysX documentation `__. + """ + + func: Callable = MISSING + """Function to use for creating the material.""" + + +@configclass +class RigidBodyMaterialCfg(PhysicsMaterialCfg): + """Physics material parameters for rigid bodies. + + See :meth:`spawn_rigid_body_material` for more information. + """ + + func: Callable = physics_materials.spawn_rigid_body_material + + static_friction: float = 0.5 + """The static friction coefficient. Defaults to 0.5.""" + + dynamic_friction: float = 0.5 + """The dynamic friction coefficient. Defaults to 0.5.""" + + restitution: float = 0.0 + """The restitution coefficient. Defaults to 0.0.""" + + friction_combine_mode: Literal["average", "min", "multiply", "max"] = "average" + """Determines the way friction will be combined during collisions. Defaults to `"average"`. + + .. attention:: + + When two physics materials with different combine modes collide, the combine mode with the higher + priority will be used. The priority order is provided `here + `__. + """ + + restitution_combine_mode: Literal["average", "min", "multiply", "max"] = "average" + """Determines the way restitution coefficient will be combined during collisions. Defaults to `"average"`. + + .. attention:: + + When two physics materials with different combine modes collide, the combine mode with the higher + priority will be used. The priority order is provided `here + `__. + """ + + compliant_contact_stiffness: float = 0.0 + """Spring stiffness for a compliant contact model using implicit springs. Defaults to 0.0. + + A higher stiffness results in behavior closer to a rigid contact. The compliant contact model is only enabled + if the stiffness is larger than 0. + """ + + compliant_contact_damping: float = 0.0 + """Damping coefficient for a compliant contact model using implicit springs. Defaults to 0.0. + + Irrelevant if compliant contacts are disabled when :obj:`compliant_contact_stiffness` is set to zero and + rigid contacts are active. + """ + + +@configclass +class DeformableBodyMaterialCfg(PhysicsMaterialCfg): + """Physics material parameters for deformable bodies. + + See :meth:`spawn_deformable_body_material` for more information. + + """ + + func: Callable = physics_materials.spawn_deformable_body_material + + density: float | None = None + """The material density. Defaults to None, in which case the simulation decides the default density.""" + + dynamic_friction: float = 0.25 + """The dynamic friction. Defaults to 0.25.""" + + youngs_modulus: float = 50000000.0 + """The Young's modulus, which defines the body's stiffness. Defaults to 50000000.0. + + The Young's modulus is a measure of the material's ability to deform under stress. It is measured in Pascals (Pa). + """ + + poissons_ratio: float = 0.45 + """The Poisson's ratio which defines the body's volume preservation. Defaults to 0.45. + + The Poisson's ratio is a measure of the material's ability to expand in the lateral direction when compressed + in the axial direction. It is a dimensionless number between 0 and 0.5. Using a value of 0.5 will make the + material incompressible. + """ + + elasticity_damping: float = 0.005 + """The elasticity damping for the deformable material. Defaults to 0.005.""" + + damping_scale: float = 1.0 + """The damping scale for the deformable material. Defaults to 1.0. + + A scale of 1 corresponds to default damping. A value of 0 will only apply damping to certain motions leading + to special effects that look similar to water filled soft bodies. + """ diff --git a/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials.py b/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials.py new file mode 100644 index 0000000000000000000000000000000000000000..03e9add168604f11aeed2b08c304ada6735cda26 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials.py @@ -0,0 +1,247 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +from omni.usd.commands import CreateMdlMaterialPrimCommand, CreateShaderPrimFromSdrCommand +from pxr import Gf, Sdf, Usd, UsdShade + +from isaaclab.sim.utils import clone, safe_set_attribute_on_usd_prim +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.utils.assets import NVIDIA_NUCLEUS_DIR + +if TYPE_CHECKING: + from . import visual_materials_cfg + +# import logger +logger = logging.getLogger(__name__) + + +@clone +def spawn_preview_surface(prim_path: str, cfg: visual_materials_cfg.PreviewSurfaceCfg) -> Usd.Prim: + """Create a preview surface prim and override the settings with the given config. + + A preview surface is a physically-based surface that handles simple shaders while supporting + both *specular* and *metallic* workflows. All color inputs are in linear color space (RGB). + For more information, see the `documentation `__. + + The function calls the USD command `CreateShaderPrimFromSdrCommand`_ to create the prim. + + .. _CreateShaderPrimFromSdrCommand: https://docs.omniverse.nvidia.com/kit/docs/omni.usd/latest/omni.usd.commands/omni.usd.commands.CreateShaderPrimFromSdrCommand.html + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # get stage handle + stage = get_current_stage() + + # spawn material if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + # note: we don't use Omniverse's CreatePreviewSurfaceMaterialPrimCommand + # since it does not support USD stage as an argument. The created material + # in that case is always the one from USD Context which makes it difficult to + # handle scene creation on a custom stage. + material_prim = UsdShade.Material.Define(stage, prim_path) + if material_prim: + shader_prim = CreateShaderPrimFromSdrCommand( + parent_path=prim_path, + identifier="UsdPreviewSurface", + stage_or_context=stage, + name="Shader", + ).do() + # bind the shader graph to the material + if shader_prim: + surface_out = shader_prim.GetOutput("surface") + if surface_out: + material_prim.CreateSurfaceOutput().ConnectToSource(surface_out) + + displacement_out = shader_prim.GetOutput("displacement") + if displacement_out: + material_prim.CreateDisplacementOutput().ConnectToSource(displacement_out) + else: + raise ValueError(f"Failed to create preview surface shader at path: '{prim_path}'.") + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + # obtain prim + prim = stage.GetPrimAtPath(f"{prim_path}/Shader") + # check prim is valid + if not prim.IsValid(): + raise ValueError(f"Failed to create preview surface material at path: '{prim_path}'.") + # apply properties + cfg = cfg.to_dict() # type: ignore + del cfg["func"] + for attr_name, attr_value in cfg.items(): + safe_set_attribute_on_usd_prim(prim, f"inputs:{attr_name}", attr_value, camel_case=True) + + return prim + + +@clone +def spawn_from_mdl_file( + prim_path: str, cfg: visual_materials_cfg.MdlFileCfg | visual_materials_cfg.GlassMdlCfg +) -> Usd.Prim: + """Load a material from its MDL file and override the settings with the given config. + + NVIDIA's `Material Definition Language (MDL) `__ + is a language for defining physically-based materials. The MDL file format is a binary format + that can be loaded by Omniverse and other applications such as Adobe Substance Designer. + To learn more about MDL, see the `documentation `_. + + The function calls the USD command `CreateMdlMaterialPrim`_ to create the prim. + + .. _CreateMdlMaterialPrim: https://docs.omniverse.nvidia.com/kit/docs/omni.usd/latest/omni.usd.commands/omni.usd.commands.CreateMdlMaterialPrimCommand.html + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # get stage handle + stage = get_current_stage() + + # spawn material if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + # extract material name from path + material_name = cfg.mdl_path.split("/")[-1].split(".")[0] + CreateMdlMaterialPrimCommand( + mtl_url=cfg.mdl_path.format(NVIDIA_NUCLEUS_DIR=NVIDIA_NUCLEUS_DIR), + mtl_name=material_name, + mtl_path=prim_path, + stage=stage, + select_new_prim=False, + ).do() + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + # obtain prim + prim = stage.GetPrimAtPath(f"{prim_path}/Shader") + # check prim is valid + if not prim.IsValid(): + raise ValueError(f"Failed to create MDL material at path: '{prim_path}'.") + # apply properties + cfg = cfg.to_dict() # type: ignore + del cfg["func"] + del cfg["mdl_path"] + for attr_name, attr_value in cfg.items(): + safe_set_attribute_on_usd_prim(prim, f"inputs:{attr_name}", attr_value, camel_case=False) + # return prim + return prim + + +@clone +def spawn_preview_surface_texture(prim_path: str, cfg: visual_materials_cfg.PreviewSurfaceTextureCfg) -> Usd.Prim: + """Create a preview surface material and connect a texture to its diffuse color.""" + stage = get_current_stage() + + # spawn material if it doesn't exist. + if stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + material_prim = UsdShade.Material.Define(stage, prim_path) + if not material_prim: + raise ValueError(f"Failed to create preview surface material at path: '{prim_path}'.") + + # create the shader nodes + preview_shader_prim = CreateShaderPrimFromSdrCommand( + parent_path=prim_path, + identifier="UsdPreviewSurface", + stage_or_context=stage, + name="Shader", + ).do() + texcoord_reader_prim = CreateShaderPrimFromSdrCommand( + parent_path=prim_path, + identifier="UsdPrimvarReader_float2", + stage_or_context=stage, + name="TexCoordReader", + ).do() + uv_transform_prim = CreateShaderPrimFromSdrCommand( + parent_path=prim_path, + identifier="UsdTransform2d", + stage_or_context=stage, + name="UVTransform", + ).do() + uv_texture_prim = CreateShaderPrimFromSdrCommand( + parent_path=prim_path, + identifier="UsdUVTexture", + stage_or_context=stage, + name="UVTexture", + ).do() + + if not (preview_shader_prim and texcoord_reader_prim and uv_transform_prim and uv_texture_prim): + raise ValueError(f"Failed to create shader network under: '{prim_path}'.") + + preview_shader = UsdShade.Shader(preview_shader_prim) + texcoord_reader = UsdShade.Shader(texcoord_reader_prim) + uv_transform = UsdShade.Shader(uv_transform_prim) + uv_texture = UsdShade.Shader(uv_texture_prim) + + # bind the shader graph to the material + surface_out = preview_shader.GetOutput("surface") + if surface_out: + material_prim.CreateSurfaceOutput().ConnectToSource(surface_out) + + displacement_out = preview_shader.GetOutput("displacement") + if displacement_out: + material_prim.CreateDisplacementOutput().ConnectToSource(displacement_out) + + # set preview surface properties + preview_shader.CreateInput("roughness", Sdf.ValueTypeNames.Float).Set(cfg.roughness) + preview_shader.CreateInput("metallic", Sdf.ValueTypeNames.Float).Set(cfg.metallic) + preview_shader.CreateInput("opacity", Sdf.ValueTypeNames.Float).Set(cfg.opacity) + + # configure UV reader (default primvar name is 'st') + texcoord_reader.CreateInput("varname", Sdf.ValueTypeNames.Token).Set("st") + + # configure UV transform (repeat) + uv_transform.CreateInput("scale", Sdf.ValueTypeNames.Float2).Set(Gf.Vec2f(*cfg.texture_repeat)) + + # configure texture + uv_texture.CreateInput("file", Sdf.ValueTypeNames.Asset).Set(Sdf.AssetPath(cfg.texture_file)) + uv_texture.CreateInput("wrapS", Sdf.ValueTypeNames.Token).Set("repeat") + uv_texture.CreateInput("wrapT", Sdf.ValueTypeNames.Token).Set("repeat") + + # connect: st -> transform -> texture + uv_transform_in = uv_transform.CreateInput("in", Sdf.ValueTypeNames.Float2) + uv_transform_in.ConnectToSource(texcoord_reader.ConnectableAPI(), "result") + + uv_texture_st = uv_texture.CreateInput("st", Sdf.ValueTypeNames.Float2) + uv_texture_st.ConnectToSource(uv_transform.ConnectableAPI(), "result") + + # connect texture to preview surface diffuseColor + diffuse_in = preview_shader.CreateInput("diffuseColor", Sdf.ValueTypeNames.Color3f) + diffuse_in.ConnectToSource(uv_texture.ConnectableAPI(), "rgb") + + # return the preview surface shader prim (consistent with spawn_preview_surface) + prim = stage.GetPrimAtPath(f"{prim_path}/Shader") + if not prim.IsValid(): + raise ValueError(f"Failed to create preview surface texture material at path: '{prim_path}'.") + return prim diff --git a/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials_cfg.py b/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6e7b6d8e603cbcbd706ab4adf0b4ae16692321d8 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/materials/visual_materials_cfg.py @@ -0,0 +1,136 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Callable +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from . import visual_materials + + +@configclass +class VisualMaterialCfg: + """Configuration parameters for creating a visual material.""" + + func: Callable = MISSING + """The function to use for creating the material.""" + + +@configclass +class PreviewSurfaceCfg(VisualMaterialCfg): + """Configuration parameters for creating a preview surface. + + See :meth:`spawn_preview_surface` for more information. + """ + + func: Callable = visual_materials.spawn_preview_surface + + diffuse_color: tuple[float, float, float] = (0.18, 0.18, 0.18) + """The RGB diffusion color. This is the base color of the surface. Defaults to a dark gray.""" + emissive_color: tuple[float, float, float] = (0.0, 0.0, 0.0) + """The RGB emission component of the surface. Defaults to black.""" + roughness: float = 0.5 + """The roughness for specular lobe. Ranges from 0 (smooth) to 1 (rough). Defaults to 0.5.""" + metallic: float = 0.0 + """The metallic component. Ranges from 0 (dielectric) to 1 (metal). Defaults to 0.""" + opacity: float = 1.0 + """The opacity of the surface. Ranges from 0 (transparent) to 1 (opaque). Defaults to 1. + + Note: + Opacity only affects the surface's appearance during interactive rendering. + """ + + +@configclass +class MdlFileCfg(VisualMaterialCfg): + """Configuration parameters for loading an MDL material from a file. + + See :meth:`spawn_from_mdl_file` for more information. + """ + + func: Callable = visual_materials.spawn_from_mdl_file + + mdl_path: str = MISSING + """The path to the MDL material. + + NVIDIA Omniverse provides various MDL materials in the NVIDIA Nucleus. + To use these materials, you can set the path of the material in the nucleus directory + using the ``{NVIDIA_NUCLEUS_DIR}`` variable. This is internally resolved to the path of the + NVIDIA Nucleus directory on the host machine through the attribute + :attr:`isaaclab.utils.assets.NVIDIA_NUCLEUS_DIR`. + + For example, to use the "Aluminum_Anodized" material, you can set the path to: + ``{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Anodized.mdl``. + """ + project_uvw: bool | None = None + """Whether to project the UVW coordinates of the material. Defaults to None. + + If None, then the default setting in the MDL material will be used. + """ + albedo_brightness: float | None = None + """Multiplier for the diffuse color of the material. Defaults to None. + + If None, then the default setting in the MDL material will be used. + """ + texture_scale: tuple[float, float] | None = None + """The scale of the texture. Defaults to None. + + If None, then the default setting in the MDL material will be used. + """ + + +@configclass +class PreviewSurfaceTextureCfg(VisualMaterialCfg): + """Configuration parameters for creating a USD preview surface driven by a texture. + + This creates a ``UsdPreviewSurface`` material with a ``UsdUVTexture`` shader connected + to the ``diffuseColor`` input. + """ + + func: Callable = visual_materials.spawn_preview_surface_texture + + texture_file: str = MISSING + """Path to the texture image file (e.g. PNG/JPG).""" + + texture_repeat: tuple[float, float] = (1.0, 1.0) + """UV repeat for the texture (U, V).""" + + roughness: float = 0.0 + """Surface roughness. Ranges from 0 (smooth) to 1 (rough).""" + + metallic: float = 1.0 + """Metallic value. Ranges from 0 (dielectric) to 1 (metal).""" + + opacity: float = 1.0 + """Surface opacity. Ranges from 0 (transparent) to 1 (opaque).""" + + +@configclass +class GlassMdlCfg(VisualMaterialCfg): + """Configuration parameters for loading a glass MDL material. + + This is a convenience class for loading a glass MDL material. For more information on + glass materials, see the `documentation `__. + + .. note:: + The default values are taken from the glass material in the NVIDIA Nucleus. + """ + + func: Callable = visual_materials.spawn_from_mdl_file + + mdl_path: str = "OmniGlass.mdl" + """The path to the MDL material. Defaults to the glass material in the NVIDIA Nucleus.""" + glass_color: tuple[float, float, float] = (1.0, 1.0, 1.0) + """The RGB color or tint of the glass. Defaults to white.""" + frosting_roughness: float = 0.0 + """The amount of reflectivity of the surface. Ranges from 0 (perfectly clear) to 1 (frosted). + Defaults to 0.""" + thin_walled: bool = False + """Whether to perform thin-walled refraction. Defaults to False.""" + glass_ior: float = 1.491 + """The incidence of refraction to control how much light is bent when passing through the glass. + Defaults to 1.491, which is the IOR of glass. + """ diff --git a/source/isaaclab/isaaclab/sim/spawners/meshes/__init__.py b/source/isaaclab/isaaclab/sim/spawners/meshes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..49836dc5cbd4f62ad33714f6d4eff2e3e344514d --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/meshes/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawning meshes in the simulation. + +NVIDIA Omniverse deals with meshes as `USDGeomMesh`_ prims. This sub-module provides various +configurations to spawn different types of meshes. Based on the configuration, the spawned prim can be: + +* a visual mesh (no physics) +* a static collider (no rigid or deformable body) +* a deformable body (with deformable properties) + +.. note:: + While rigid body properties can be set on a mesh, it is recommended to use the + :mod:`isaaclab.sim.spawners.shapes` module to spawn rigid bodies. This is because USD shapes + are more optimized for physics simulations. + +.. _USDGeomMesh: https://openusd.org/release/api/class_usd_geom_mesh.html +""" + +from .meshes import spawn_mesh_capsule, spawn_mesh_cone, spawn_mesh_cuboid, spawn_mesh_cylinder, spawn_mesh_sphere +from .meshes_cfg import MeshCapsuleCfg, MeshCfg, MeshConeCfg, MeshCuboidCfg, MeshCylinderCfg, MeshSphereCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/meshes/meshes.py b/source/isaaclab/isaaclab/sim/spawners/meshes/meshes.py new file mode 100644 index 0000000000000000000000000000000000000000..066ca0bea188a8589294b58398b30299a2a90173 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/meshes/meshes.py @@ -0,0 +1,397 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import trimesh +import trimesh.transformations + +from pxr import Usd, UsdPhysics + +from isaaclab.sim import schemas +from isaaclab.sim.utils import bind_physics_material, bind_visual_material, clone, create_prim, get_current_stage + +from ..materials import DeformableBodyMaterialCfg, RigidBodyMaterialCfg + +if TYPE_CHECKING: + from . import meshes_cfg + + +@clone +def spawn_mesh_sphere( + prim_path: str, + cfg: meshes_cfg.MeshSphereCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD-Mesh sphere prim with the given attributes. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # create a trimesh sphere + sphere = trimesh.creation.uv_sphere(radius=cfg.radius) + + # obtain stage handle + stage = get_current_stage() + # spawn the sphere as a mesh + _spawn_mesh_geom_from_mesh(prim_path, cfg, sphere, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_mesh_cuboid( + prim_path: str, + cfg: meshes_cfg.MeshCuboidCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD-Mesh cuboid prim with the given attributes. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # create a trimesh box + box = trimesh.creation.box(cfg.size) + + # obtain stage handle + stage = get_current_stage() + # spawn the cuboid as a mesh + _spawn_mesh_geom_from_mesh(prim_path, cfg, box, translation, orientation, None, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_mesh_cylinder( + prim_path: str, + cfg: meshes_cfg.MeshCylinderCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD-Mesh cylinder prim with the given attributes. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # align axis from "Z" to input by rotating the cylinder + axis = cfg.axis.upper() + if axis == "X": + transform = trimesh.transformations.rotation_matrix(np.pi / 2, [0, 1, 0]) + elif axis == "Y": + transform = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0]) + else: + transform = None + # create a trimesh cylinder + cylinder = trimesh.creation.cylinder(radius=cfg.radius, height=cfg.height, transform=transform) + + # obtain stage handle + stage = get_current_stage() + # spawn the cylinder as a mesh + _spawn_mesh_geom_from_mesh(prim_path, cfg, cylinder, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_mesh_capsule( + prim_path: str, + cfg: meshes_cfg.MeshCapsuleCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD-Mesh capsule prim with the given attributes. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # align axis from "Z" to input by rotating the cylinder + axis = cfg.axis.upper() + if axis == "X": + transform = trimesh.transformations.rotation_matrix(np.pi / 2, [0, 1, 0]) + elif axis == "Y": + transform = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0]) + else: + transform = None + # create a trimesh capsule + capsule = trimesh.creation.capsule(radius=cfg.radius, height=cfg.height, transform=transform) + + # obtain stage handle + stage = get_current_stage() + # spawn capsule if it doesn't exist. + _spawn_mesh_geom_from_mesh(prim_path, cfg, capsule, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_mesh_cone( + prim_path: str, + cfg: meshes_cfg.MeshConeCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD-Mesh cone prim with the given attributes. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # align axis from "Z" to input by rotating the cylinder + axis = cfg.axis.upper() + if axis == "X": + transform = trimesh.transformations.rotation_matrix(np.pi / 2, [0, 1, 0]) + elif axis == "Y": + transform = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0]) + else: + transform = None + # create a trimesh cone + cone = trimesh.creation.cone(radius=cfg.radius, height=cfg.height, transform=transform) + + # obtain stage handle + stage = get_current_stage() + # spawn cone if it doesn't exist. + _spawn_mesh_geom_from_mesh(prim_path, cfg, cone, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +""" +Helper functions. +""" + + +def _spawn_mesh_geom_from_mesh( + prim_path: str, + cfg: meshes_cfg.MeshCfg, + mesh: trimesh.Trimesh, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + scale: tuple[float, float, float] | None = None, + stage: Usd.Stage | None = None, + **kwargs, +): + """Create a `USDGeomMesh`_ prim from the given mesh. + + This function is similar to :func:`shapes._spawn_geom_from_prim_type` but spawns the prim from a given mesh. + In case of the mesh, it is spawned as a USDGeomMesh prim with the given vertices and faces. + + There is a difference in how the properties are applied to the prim based on the type of object: + + - Deformable body properties: The properties are applied to the mesh prim: ``{prim_path}/geometry/mesh``. + - Collision properties: The properties are applied to the mesh prim: ``{prim_path}/geometry/mesh``. + - Rigid body properties: The properties are applied to the parent prim: ``{prim_path}``. + + Args: + prim_path: The prim path to spawn the asset at. + cfg: The config containing the properties to apply. + mesh: The mesh to spawn the prim from. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + scale: The scale to apply to the prim. Defaults to None, in which case this is set to identity. + stage: The stage to spawn the asset at. Defaults to None, in which case the current stage is used. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Raises: + ValueError: If a prim already exists at the given path. + ValueError: If both deformable and rigid properties are used. + ValueError: If both deformable and collision properties are used. + ValueError: If the physics material is not of the correct type. Deformable properties require a deformable + physics material, and rigid properties require a rigid physics material. + + .. _USDGeomMesh: https://openusd.org/dev/api/class_usd_geom_mesh.html + """ + # obtain stage handle + stage = stage if stage is not None else get_current_stage() + + # spawn geometry if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + create_prim(prim_path, prim_type="Xform", translation=translation, orientation=orientation, stage=stage) + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + # check that invalid schema types are not used + if cfg.deformable_props is not None and cfg.rigid_props is not None: + raise ValueError("Cannot use both deformable and rigid properties at the same time.") + if cfg.deformable_props is not None and cfg.collision_props is not None: + raise ValueError("Cannot use both deformable and collision properties at the same time.") + # check material types are correct + if cfg.deformable_props is not None and cfg.physics_material is not None: + if not isinstance(cfg.physics_material, DeformableBodyMaterialCfg): + raise ValueError("Deformable properties require a deformable physics material.") + if cfg.rigid_props is not None and cfg.physics_material is not None: + if not isinstance(cfg.physics_material, RigidBodyMaterialCfg): + raise ValueError("Rigid properties require a rigid physics material.") + + # create all the paths we need for clarity + geom_prim_path = prim_path + "/geometry" + mesh_prim_path = geom_prim_path + "/mesh" + + # create the mesh prim + mesh_prim = create_prim( + mesh_prim_path, + prim_type="Mesh", + scale=scale, + attributes={ + "points": mesh.vertices, + "faceVertexIndices": mesh.faces.flatten(), + "faceVertexCounts": np.asarray([3] * len(mesh.faces)), + "subdivisionScheme": "bilinear", + }, + stage=stage, + ) + + # note: in case of deformable objects, we need to apply the deformable properties to the mesh prim. + # this is different from rigid objects where we apply the properties to the parent prim. + if cfg.deformable_props is not None: + # apply mass properties + if cfg.mass_props is not None: + schemas.define_mass_properties(mesh_prim_path, cfg.mass_props, stage=stage) + # apply deformable body properties + schemas.define_deformable_body_properties(mesh_prim_path, cfg.deformable_props, stage=stage) + elif cfg.collision_props is not None: + # decide on type of collision approximation based on the mesh + if cfg.__class__.__name__ == "MeshSphereCfg": + collision_approximation = "boundingSphere" + elif cfg.__class__.__name__ == "MeshCuboidCfg": + collision_approximation = "boundingCube" + else: + # for: MeshCylinderCfg, MeshCapsuleCfg, MeshConeCfg + collision_approximation = "convexHull" + # apply collision approximation to mesh + # note: for primitives, we use the convex hull approximation -- this should be sufficient for most cases. + mesh_collision_api = UsdPhysics.MeshCollisionAPI.Apply(mesh_prim) + mesh_collision_api.GetApproximationAttr().Set(collision_approximation) + # apply collision properties + schemas.define_collision_properties(mesh_prim_path, cfg.collision_props, stage=stage) + + # apply visual material + if cfg.visual_material is not None: + if not cfg.visual_material_path.startswith("/"): + material_path = f"{geom_prim_path}/{cfg.visual_material_path}" + else: + material_path = cfg.visual_material_path + # create material + cfg.visual_material.func(material_path, cfg.visual_material) + # apply material + bind_visual_material(mesh_prim_path, material_path, stage=stage) + + # apply physics material + if cfg.physics_material is not None: + if not cfg.physics_material_path.startswith("/"): + material_path = f"{geom_prim_path}/{cfg.physics_material_path}" + else: + material_path = cfg.physics_material_path + # create material + cfg.physics_material.func(material_path, cfg.physics_material) + # apply material + bind_physics_material(mesh_prim_path, material_path, stage=stage) + + # note: we apply the rigid properties to the parent prim in case of rigid objects. + if cfg.rigid_props is not None: + # apply mass properties + if cfg.mass_props is not None: + schemas.define_mass_properties(prim_path, cfg.mass_props, stage=stage) + # apply rigid properties + schemas.define_rigid_body_properties(prim_path, cfg.rigid_props, stage=stage) diff --git a/source/isaaclab/isaaclab/sim/spawners/meshes/meshes_cfg.py b/source/isaaclab/isaaclab/sim/spawners/meshes/meshes_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c39a505b8bc5a5eb4d9ed809673a636cb5caee --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/meshes/meshes_cfg.py @@ -0,0 +1,144 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Literal + +from isaaclab.sim.spawners import materials +from isaaclab.sim.spawners.spawner_cfg import DeformableObjectSpawnerCfg, RigidObjectSpawnerCfg +from isaaclab.utils import configclass + +from . import meshes + + +@configclass +class MeshCfg(RigidObjectSpawnerCfg, DeformableObjectSpawnerCfg): + """Configuration parameters for a USD Geometry or Geom prim. + + This class is similar to :class:`ShapeCfg` but is specifically for meshes. + + Meshes support both rigid and deformable properties. However, their schemas are applied at + different levels in the USD hierarchy based on the type of the object. These are described below: + + - Deformable body properties: Applied to the mesh prim: ``{prim_path}/geometry/mesh``. + - Collision properties: Applied to the mesh prim: ``{prim_path}/geometry/mesh``. + - Rigid body properties: Applied to the parent prim: ``{prim_path}``. + + where ``{prim_path}`` is the path to the prim in the USD stage and ``{prim_path}/geometry/mesh`` + is the path to the mesh prim. + + .. note:: + There are mututally exclusive parameters for rigid and deformable properties. If both are set, + then an error will be raised. This also holds if collision and deformable properties are set together. + + """ + + visual_material_path: str = "material" + """Path to the visual material to use for the prim. Defaults to "material". + + If the path is relative, then it will be relative to the prim's path. + This parameter is ignored if `visual_material` is not None. + """ + + visual_material: materials.VisualMaterialCfg | None = None + """Visual material properties. + + Note: + If None, then no visual material will be added. + """ + + physics_material_path: str = "material" + """Path to the physics material to use for the prim. Defaults to "material". + + If the path is relative, then it will be relative to the prim's path. + This parameter is ignored if `physics_material` is not None. + """ + + physics_material: materials.PhysicsMaterialCfg | None = None + """Physics material properties. + + Note: + If None, then no physics material will be added. + """ + + +@configclass +class MeshSphereCfg(MeshCfg): + """Configuration parameters for a sphere mesh prim with deformable properties. + + See :meth:`spawn_mesh_sphere` for more information. + """ + + func: Callable = meshes.spawn_mesh_sphere + + radius: float = MISSING + """Radius of the sphere (in m).""" + + +@configclass +class MeshCuboidCfg(MeshCfg): + """Configuration parameters for a cuboid mesh prim with deformable properties. + + See :meth:`spawn_mesh_cuboid` for more information. + """ + + func: Callable = meshes.spawn_mesh_cuboid + + size: tuple[float, float, float] = MISSING + """Size of the cuboid (in m).""" + + +@configclass +class MeshCylinderCfg(MeshCfg): + """Configuration parameters for a cylinder mesh prim with deformable properties. + + See :meth:`spawn_cylinder` for more information. + """ + + func: Callable = meshes.spawn_mesh_cylinder + + radius: float = MISSING + """Radius of the cylinder (in m).""" + height: float = MISSING + """Height of the cylinder (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the cylinder. Defaults to "Z".""" + + +@configclass +class MeshCapsuleCfg(MeshCfg): + """Configuration parameters for a capsule mesh prim. + + See :meth:`spawn_capsule` for more information. + """ + + func: Callable = meshes.spawn_mesh_capsule + + radius: float = MISSING + """Radius of the capsule (in m).""" + height: float = MISSING + """Height of the capsule (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the capsule. Defaults to "Z".""" + + +@configclass +class MeshConeCfg(MeshCfg): + """Configuration parameters for a cone mesh prim. + + See :meth:`spawn_cone` for more information. + """ + + func: Callable = meshes.spawn_mesh_cone + + radius: float = MISSING + """Radius of the cone (in m).""" + height: float = MISSING + """Height of the v (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the cone. Defaults to "Z".""" diff --git a/source/isaaclab/isaaclab/sim/spawners/sensors/__init__.py b/source/isaaclab/isaaclab/sim/spawners/sensors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ac61868c02557b9cd9c3cda512ec86ab45a2c262 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/sensors/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawners that spawn sensors in the simulation. + +Currently, the following sensors are supported: + +* Camera: A USD camera prim with settings for pinhole or fisheye projections. + +""" + +from .sensors import spawn_camera +from .sensors_cfg import FisheyeCameraCfg, PinholeCameraCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/sensors/sensors.py b/source/isaaclab/isaaclab/sim/spawners/sensors/sensors.py new file mode 100644 index 0000000000000000000000000000000000000000..6270447169e9164f3b01177ef1a5d943174bb4ed --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/sensors/sensors.py @@ -0,0 +1,146 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +from pxr import Sdf, Usd + +from isaaclab.sim.utils import change_prim_property, clone, create_prim, get_current_stage +from isaaclab.utils import to_camel_case + +if TYPE_CHECKING: + from . import sensors_cfg + +# import logger +logger = logging.getLogger(__name__) + +CUSTOM_PINHOLE_CAMERA_ATTRIBUTES = { + "projection_type": ("cameraProjectionType", Sdf.ValueTypeNames.Token), +} +"""Custom attributes for pinhole camera model. + +The dictionary maps the attribute name in the configuration to the attribute name in the USD prim. +""" + + +CUSTOM_FISHEYE_CAMERA_ATTRIBUTES = { + "projection_type": ("cameraProjectionType", Sdf.ValueTypeNames.Token), + "fisheye_nominal_width": ("fthetaWidth", Sdf.ValueTypeNames.Float), + "fisheye_nominal_height": ("fthetaHeight", Sdf.ValueTypeNames.Float), + "fisheye_optical_centre_x": ("fthetaCx", Sdf.ValueTypeNames.Float), + "fisheye_optical_centre_y": ("fthetaCy", Sdf.ValueTypeNames.Float), + "fisheye_max_fov": ("fthetaMaxFov", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_a": ("fthetaPolyA", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_b": ("fthetaPolyB", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_c": ("fthetaPolyC", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_d": ("fthetaPolyD", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_e": ("fthetaPolyE", Sdf.ValueTypeNames.Float), + "fisheye_polynomial_f": ("fthetaPolyF", Sdf.ValueTypeNames.Float), +} +"""Custom attributes for fisheye camera model. + +The dictionary maps the attribute name in the configuration to the attribute name in the USD prim. +""" + + +@clone +def spawn_camera( + prim_path: str, + cfg: sensors_cfg.PinholeCameraCfg | sensors_cfg.FisheyeCameraCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USD camera prim with given projection type. + + The function creates various attributes on the camera prim that specify the camera's properties. + These are later used by ``omni.replicator.core`` to render the scene with the given camera. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + + # spawn camera if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + create_prim(prim_path, "Camera", translation=translation, orientation=orientation, stage=stage) + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + # lock camera from viewport (this disables viewport movement for camera) + if cfg.lock_camera: + change_prim_property( + prop_path=f"{prim_path}.omni:kit:cameraLock", + value=True, + stage=stage, + type_to_create_if_not_exist=Sdf.ValueTypeNames.Bool, + ) + # decide the custom attributes to add + if cfg.projection_type == "pinhole": + attribute_types = CUSTOM_PINHOLE_CAMERA_ATTRIBUTES + else: + attribute_types = CUSTOM_FISHEYE_CAMERA_ATTRIBUTES + + # TODO: Adjust to handle aperture offsets once supported by omniverse + # Internal ticket from rendering team: OM-42611 + if cfg.horizontal_aperture_offset > 1e-4 or cfg.vertical_aperture_offset > 1e-4: + logger.warning("Camera aperture offsets are not supported by Omniverse. These parameters will be ignored.") + + # custom attributes in the config that are not USD Camera parameters + non_usd_cfg_param_names = [ + "func", + "copy_from_source", + "lock_camera", + "visible", + "semantic_tags", + "from_intrinsic_matrix", + ] + # get camera prim + prim = stage.GetPrimAtPath(prim_path) + # create attributes for the fisheye camera model + # note: for pinhole those are already part of the USD camera prim + for attr_name, attr_type in attribute_types.values(): + # check if attribute does not exist + if prim.GetAttribute(attr_name).Get() is None: + # create attribute based on type + prim.CreateAttribute(attr_name, attr_type) + # set attribute values + for param_name, param_value in cfg.__dict__.items(): + # check if value is valid + if param_value is None or param_name in non_usd_cfg_param_names: + continue + # obtain prim property name + if param_name in attribute_types: + # check custom attributes + prim_prop_name = attribute_types[param_name][0] + else: + # convert attribute name in prim to cfg name + prim_prop_name = to_camel_case(param_name, to="cC") + # get attribute from the class + prim.GetAttribute(prim_prop_name).Set(param_value) + # return the prim + return prim diff --git a/source/isaaclab/isaaclab/sim/spawners/sensors/sensors_cfg.py b/source/isaaclab/isaaclab/sim/spawners/sensors/sensors_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..44e5eb061733873f9c8b4346e8363eb75b282535 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/sensors/sensors_cfg.py @@ -0,0 +1,226 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from typing import Literal + +import isaaclab.utils.sensors as sensor_utils +from isaaclab.sim.spawners.spawner_cfg import SpawnerCfg +from isaaclab.utils import configclass + +from . import sensors + + +@configclass +class PinholeCameraCfg(SpawnerCfg): + """Configuration parameters for a USD camera prim with pinhole camera settings. + + For more information on the parameters, please refer to the `camera documentation `__. + + ..note :: + Focal length as well as the aperture sizes and offsets are set as a tenth of the world unit. In our case, the + world unit is Meter s.t. all of these values are set in cm. + """ + + func: Callable = sensors.spawn_camera + + projection_type: str = "pinhole" + """Type of projection to use for the camera. Defaults to "pinhole". + + Note: + Currently only "pinhole" is supported. + """ + + clipping_range: tuple[float, float] = (0.01, 1e6) + """Near and far clipping distances (in m). Defaults to (0.01, 1e6). + + The minimum clipping range will shift the camera forward by the specified distance. Don't set it too high to + avoid issues for distance related data types (e.g., ``distance_to_image_plane``). + """ + + focal_length: float = 24.0 + """Perspective focal length (in cm). Defaults to 24.0cm. + + Longer lens lengths narrower FOV, shorter lens lengths wider FOV. + """ + + focus_distance: float = 400.0 + """Distance from the camera to the focus plane (in m). Defaults to 400.0. + + The distance at which perfect sharpness is achieved. + """ + + f_stop: float = 0.0 + """Lens aperture. Defaults to 0.0, which turns off focusing. + + Controls Distance Blurring. Lower Numbers decrease focus range, larger numbers increase it. + """ + + horizontal_aperture: float = 20.955 + """Horizontal aperture (in cm). Defaults to 20.955 cm. + + Emulates sensor/film width on a camera. + + Note: + The default value is the horizontal aperture of a 35 mm spherical projector. + """ + + vertical_aperture: float | None = None + r"""Vertical aperture (in mm). Defaults to None. + + Emulates sensor/film height on a camera. If None, then the vertical aperture is calculated based on the + horizontal aperture and the aspect ratio of the image to maintain squared pixels. This is calculated as: + + .. math:: + \text{vertical aperture} = \text{horizontal aperture} \times \frac{\text{height}}{\text{width}} + """ + + horizontal_aperture_offset: float = 0.0 + """Offsets Resolution/Film gate horizontally. Defaults to 0.0.""" + + vertical_aperture_offset: float = 0.0 + """Offsets Resolution/Film gate vertically. Defaults to 0.0.""" + + lock_camera: bool = True + """Locks the camera in the Omniverse viewport. Defaults to True. + + If True, then the camera remains fixed at its configured transform. This is useful when wanting to view + the camera output on the GUI and not accidentally moving the camera through the GUI interactions. + """ + + @classmethod + def from_intrinsic_matrix( + cls, + intrinsic_matrix: list[float], + width: int, + height: int, + clipping_range: tuple[float, float] = (0.01, 1e6), + focal_length: float | None = None, + focus_distance: float = 400.0, + f_stop: float = 0.0, + projection_type: str = "pinhole", + lock_camera: bool = True, + ) -> PinholeCameraCfg: + r"""Create a :class:`PinholeCameraCfg` class instance from an intrinsic matrix. + + The intrinsic matrix is a 3x3 matrix that defines the mapping between the 3D world coordinates and + the 2D image. The matrix is defined as: + + .. math:: + I_{cam} = \begin{bmatrix} + f_x & 0 & c_x \\ + 0 & f_y & c_y \\ + 0 & 0 & 1 + \\end{bmatrix}, + + where :math:`f_x` and :math:`f_y` are the focal length along x and y direction, while :math:`c_x` and + :math:`c_y` are the principle point offsets along x and y direction respectively. + + Args: + intrinsic_matrix: Intrinsic matrix of the camera in row-major format. + The matrix is defined as [f_x, 0, c_x, 0, f_y, c_y, 0, 0, 1]. Shape is (9,). + width: Width of the image (in pixels). + height: Height of the image (in pixels). + clipping_range: Near and far clipping distances (in m). Defaults to (0.01, 1e6). + focal_length: Perspective focal length (in cm) used to calculate pixel size. Defaults to None. If None + focal_length will be calculated 1 / width. + focus_distance: Distance from the camera to the focus plane (in m). Defaults to 400.0 m. + f_stop: Lens aperture. Defaults to 0.0, which turns off focusing. + projection_type: Type of projection to use for the camera. Defaults to "pinhole". + lock_camera: Locks the camera in the Omniverse viewport. Defaults to True. + + Returns: + An instance of the :class:`PinholeCameraCfg` class. + """ + # raise not implemented error is projection type is not pinhole + if projection_type != "pinhole": + raise NotImplementedError("Only pinhole projection type is supported.") + + usd_camera_params = sensor_utils.convert_camera_intrinsics_to_usd( + intrinsic_matrix=intrinsic_matrix, height=height, width=width, focal_length=focal_length + ) + + return cls( + projection_type=projection_type, + clipping_range=clipping_range, + focal_length=usd_camera_params["focal_length"], + focus_distance=focus_distance, + f_stop=f_stop, + horizontal_aperture=usd_camera_params["horizontal_aperture"], + vertical_aperture=usd_camera_params["vertical_aperture"], + horizontal_aperture_offset=usd_camera_params["horizontal_aperture_offset"], + vertical_aperture_offset=usd_camera_params["vertical_aperture_offset"], + lock_camera=lock_camera, + ) + + +@configclass +class FisheyeCameraCfg(PinholeCameraCfg): + """Configuration parameters for a USD camera prim with `fish-eye camera`_ settings. + + For more information on the parameters, please refer to the + `camera documentation `__. + + .. note:: + The default values are taken from the `Replicator camera `__ + function. + + .. _fish-eye camera: https://en.wikipedia.org/wiki/Fisheye_lens + """ + + func: Callable = sensors.spawn_camera + + projection_type: Literal[ + "fisheyePolynomial", + "fisheyeSpherical", + "fisheyeKannalaBrandtK3", + "fisheyeRadTanThinPrism", + "omniDirectionalStereo", + ] = "fisheyePolynomial" + r"""Type of projection to use for the camera. Defaults to "fisheyePolynomial". + + Available options: + + - ``"fisheyePolynomial"``: Fisheye camera model with :math:`360^{\circ}` spherical projection. + - ``"fisheyeSpherical"``: Fisheye camera model with :math:`360^{\circ}` full-frame projection. + - ``"fisheyeKannalaBrandtK3"``: Fisheye camera model using the Kannala-Brandt K3 distortion model. + - ``"fisheyeRadTanThinPrism"``: Fisheye camera model that combines radial and tangential distortions. + - ``"omniDirectionalStereo"``: Fisheye camera model supporting :math:`360^{\circ}` stereoscopic imaging. + """ + + fisheye_nominal_width: float = 1936.0 + """Nominal width of fisheye lens model (in pixels). Defaults to 1936.0.""" + + fisheye_nominal_height: float = 1216.0 + """Nominal height of fisheye lens model (in pixels). Defaults to 1216.0.""" + + fisheye_optical_centre_x: float = 970.94244 + """Horizontal optical centre position of fisheye lens model (in pixels). Defaults to 970.94244.""" + + fisheye_optical_centre_y: float = 600.37482 + """Vertical optical centre position of fisheye lens model (in pixels). Defaults to 600.37482.""" + + fisheye_max_fov: float = 200.0 + """Maximum field of view of fisheye lens model (in degrees). Defaults to 200.0 degrees.""" + + fisheye_polynomial_a: float = 0.0 + """First component of fisheye polynomial. Defaults to 0.0.""" + + fisheye_polynomial_b: float = 0.00245 + """Second component of fisheye polynomial. Defaults to 0.00245.""" + + fisheye_polynomial_c: float = 0.0 + """Third component of fisheye polynomial. Defaults to 0.0.""" + + fisheye_polynomial_d: float = 0.0 + """Fourth component of fisheye polynomial. Defaults to 0.0.""" + + fisheye_polynomial_e: float = 0.0 + """Fifth component of fisheye polynomial. Defaults to 0.0.""" + + fisheye_polynomial_f: float = 0.0 + """Sixth component of fisheye polynomial. Defaults to 0.0.""" diff --git a/source/isaaclab/isaaclab/sim/spawners/shapes/__init__.py b/source/isaaclab/isaaclab/sim/spawners/shapes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8f6cab9439cd1828672e85e43fdf591b5db6c78d --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/shapes/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for spawning primitive shapes in the simulation. + +NVIDIA Omniverse provides various primitive shapes that can be used to create USDGeom prims. Based +on the configuration, the spawned prim can be: + +* a visual mesh (no physics) +* a static collider (no rigid body) +* a rigid body (with collision and rigid body properties). + +""" + +from .shapes import spawn_capsule, spawn_cone, spawn_cuboid, spawn_cylinder, spawn_sphere +from .shapes_cfg import CapsuleCfg, ConeCfg, CuboidCfg, CylinderCfg, ShapeCfg, SphereCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/shapes/shapes.py b/source/isaaclab/isaaclab/sim/spawners/shapes/shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..a7780c25596d9ca04bb07150c04830ba6f4cb662 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/shapes/shapes.py @@ -0,0 +1,325 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pxr import Usd + +from isaaclab.sim import schemas +from isaaclab.sim.utils import bind_physics_material, bind_visual_material, clone, create_prim, get_current_stage + +if TYPE_CHECKING: + from . import shapes_cfg + + +@clone +def spawn_sphere( + prim_path: str, + cfg: shapes_cfg.SphereCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USDGeom-based sphere prim with the given attributes. + + For more information, see `USDGeomSphere `_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + # spawn sphere if it doesn't exist. + attributes = {"radius": cfg.radius} + _spawn_geom_from_prim_type(prim_path, cfg, "Sphere", attributes, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_cuboid( + prim_path: str, + cfg: shapes_cfg.CuboidCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USDGeom-based cuboid prim with the given attributes. + + For more information, see `USDGeomCube `_. + + Note: + Since USD only supports cubes, we set the size of the cube to the minimum of the given size and + scale the cube accordingly. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + # resolve the scale + size = min(cfg.size) + scale = [dim / size for dim in cfg.size] + # spawn cuboid if it doesn't exist. + attributes = {"size": size} + _spawn_geom_from_prim_type(prim_path, cfg, "Cube", attributes, translation, orientation, scale, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_cylinder( + prim_path: str, + cfg: shapes_cfg.CylinderCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USDGeom-based cylinder prim with the given attributes. + + For more information, see `USDGeomCylinder `_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + # spawn cylinder if it doesn't exist. + attributes = {"radius": cfg.radius, "height": cfg.height, "axis": cfg.axis.upper()} + _spawn_geom_from_prim_type(prim_path, cfg, "Cylinder", attributes, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_capsule( + prim_path: str, + cfg: shapes_cfg.CapsuleCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USDGeom-based capsule prim with the given attributes. + + For more information, see `USDGeomCapsule `_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + # spawn capsule if it doesn't exist. + attributes = {"radius": cfg.radius, "height": cfg.height, "axis": cfg.axis.upper()} + _spawn_geom_from_prim_type(prim_path, cfg, "Capsule", attributes, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +@clone +def spawn_cone( + prim_path: str, + cfg: shapes_cfg.ConeCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + **kwargs, +) -> Usd.Prim: + """Create a USDGeom-based cone prim with the given attributes. + + For more information, see `USDGeomCone `_. + + .. note:: + This function is decorated with :func:`clone` that resolves prim path into list of paths + if the input prim path is a regex pattern. This is done to support spawning multiple assets + from a single and cloning the USD prim at the given path expression. + + Args: + prim_path: The prim path or pattern to spawn the asset at. If the prim path is a regex pattern, + then the asset is spawned at all the matching prim paths. + cfg: The configuration instance. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + **kwargs: Additional keyword arguments, like ``clone_in_fabric``. + + Returns: + The created prim. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = get_current_stage() + # spawn cone if it doesn't exist. + attributes = {"radius": cfg.radius, "height": cfg.height, "axis": cfg.axis.upper()} + _spawn_geom_from_prim_type(prim_path, cfg, "Cone", attributes, translation, orientation, stage=stage) + # return the prim + return stage.GetPrimAtPath(prim_path) + + +""" +Helper functions. +""" + + +def _spawn_geom_from_prim_type( + prim_path: str, + cfg: shapes_cfg.ShapeCfg, + prim_type: str, + attributes: dict, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + scale: tuple[float, float, float] | None = None, + stage: Usd.Stage | None = None, +): + """Create a USDGeom-based prim with the given attributes. + + To make the asset instanceable, we must follow a certain structure dictated by how USD scene-graph + instancing and physics work. The rigid body component must be added to each instance and not the + referenced asset (i.e. the prototype prim itself). This is because the rigid body component defines + properties that are specific to each instance and cannot be shared under the referenced asset. For + more information, please check the `documentation `_. + + Due to the above, we follow the following structure: + + * ``{prim_path}`` - The root prim that is an Xform with the rigid body and mass APIs if configured. + * ``{prim_path}/geometry`` - The prim that contains the mesh and optionally the materials if configured. + If instancing is enabled, this prim will be an instanceable reference to the prototype prim. + + Args: + prim_path: The prim path to spawn the asset at. + cfg: The config containing the properties to apply. + prim_type: The type of prim to create. + attributes: The attributes to apply to the prim. + translation: The translation to apply to the prim w.r.t. its parent prim. Defaults to None, in which case + this is set to the origin. + orientation: The orientation in (w, x, y, z) to apply to the prim w.r.t. its parent prim. Defaults to None, + in which case this is set to identity. + scale: The scale to apply to the prim. Defaults to None, in which case this is set to identity. + stage: The stage to spawn the asset at. Defaults to None, in which case the current stage is used. + + Raises: + ValueError: If a prim already exists at the given path. + """ + # obtain stage handle + stage = stage if stage is not None else get_current_stage() + + # spawn geometry if it doesn't exist. + if not stage.GetPrimAtPath(prim_path).IsValid(): + create_prim(prim_path, prim_type="Xform", translation=translation, orientation=orientation, stage=stage) + else: + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + # create all the paths we need for clarity + geom_prim_path = prim_path + "/geometry" + mesh_prim_path = geom_prim_path + "/mesh" + + # create the geometry prim + create_prim(mesh_prim_path, prim_type, scale=scale, attributes=attributes, stage=stage) + # apply collision properties + if cfg.collision_props is not None: + schemas.define_collision_properties(mesh_prim_path, cfg.collision_props, stage=stage) + # apply visual material + if cfg.visual_material is not None: + if not cfg.visual_material_path.startswith("/"): + material_path = f"{geom_prim_path}/{cfg.visual_material_path}" + else: + material_path = cfg.visual_material_path + # create material + cfg.visual_material.func(material_path, cfg.visual_material) + # apply material + bind_visual_material(mesh_prim_path, material_path, stage=stage) + # apply physics material + if cfg.physics_material is not None: + if not cfg.physics_material_path.startswith("/"): + material_path = f"{geom_prim_path}/{cfg.physics_material_path}" + else: + material_path = cfg.physics_material_path + # create material + cfg.physics_material.func(material_path, cfg.physics_material) + # apply material + bind_physics_material(mesh_prim_path, material_path, stage=stage) + + # note: we apply rigid properties in the end to later make the instanceable prim + # apply mass properties + if cfg.mass_props is not None: + schemas.define_mass_properties(prim_path, cfg.mass_props, stage=stage) + # apply rigid body properties + if cfg.rigid_props is not None: + schemas.define_rigid_body_properties(prim_path, cfg.rigid_props, stage=stage) diff --git a/source/isaaclab/isaaclab/sim/spawners/shapes/shapes_cfg.py b/source/isaaclab/isaaclab/sim/spawners/shapes/shapes_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d2de5a7f94165e9c3afc6f217478b4ee15943b4d --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/shapes/shapes_cfg.py @@ -0,0 +1,122 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Literal + +from isaaclab.sim.spawners import materials +from isaaclab.sim.spawners.spawner_cfg import RigidObjectSpawnerCfg +from isaaclab.utils import configclass + +from . import shapes + + +@configclass +class ShapeCfg(RigidObjectSpawnerCfg): + """Configuration parameters for a USD Geometry or Geom prim.""" + + visual_material_path: str = "material" + """Path to the visual material to use for the prim. Defaults to "material". + + If the path is relative, then it will be relative to the prim's path. + This parameter is ignored if `visual_material` is not None. + """ + visual_material: materials.VisualMaterialCfg | None = None + """Visual material properties. + + Note: + If None, then no visual material will be added. + """ + + physics_material_path: str = "material" + """Path to the physics material to use for the prim. Defaults to "material". + + If the path is relative, then it will be relative to the prim's path. + This parameter is ignored if `physics_material` is not None. + """ + physics_material: materials.PhysicsMaterialCfg | None = None + """Physics material properties. + + Note: + If None, then no physics material will be added. + """ + + +@configclass +class SphereCfg(ShapeCfg): + """Configuration parameters for a sphere prim. + + See :meth:`spawn_sphere` for more information. + """ + + func: Callable = shapes.spawn_sphere + + radius: float = MISSING + """Radius of the sphere (in m).""" + + +@configclass +class CuboidCfg(ShapeCfg): + """Configuration parameters for a cuboid prim. + + See :meth:`spawn_cuboid` for more information. + """ + + func: Callable = shapes.spawn_cuboid + + size: tuple[float, float, float] = MISSING + """Size of the cuboid.""" + + +@configclass +class CylinderCfg(ShapeCfg): + """Configuration parameters for a cylinder prim. + + See :meth:`spawn_cylinder` for more information. + """ + + func: Callable = shapes.spawn_cylinder + + radius: float = MISSING + """Radius of the cylinder (in m).""" + height: float = MISSING + """Height of the cylinder (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the cylinder. Defaults to "Z".""" + + +@configclass +class CapsuleCfg(ShapeCfg): + """Configuration parameters for a capsule prim. + + See :meth:`spawn_capsule` for more information. + """ + + func: Callable = shapes.spawn_capsule + + radius: float = MISSING + """Radius of the capsule (in m).""" + height: float = MISSING + """Height of the capsule (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the capsule. Defaults to "Z".""" + + +@configclass +class ConeCfg(ShapeCfg): + """Configuration parameters for a cone prim. + + See :meth:`spawn_cone` for more information. + """ + + func: Callable = shapes.spawn_cone + + radius: float = MISSING + """Radius of the cone (in m).""" + height: float = MISSING + """Height of the v (in m).""" + axis: Literal["X", "Y", "Z"] = "Z" + """Axis of the cone. Defaults to "Z".""" diff --git a/source/isaaclab/isaaclab/sim/spawners/spawner_cfg.py b/source/isaaclab/isaaclab/sim/spawners/spawner_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2dea8db8fcb6c30c457b1520e433da56c4d25a8c --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/spawner_cfg.py @@ -0,0 +1,118 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING + +from pxr import Usd + +from isaaclab.sim import schemas +from isaaclab.utils import configclass + + +@configclass +class SpawnerCfg: + """Configuration parameters for spawning an asset. + + Spawning an asset is done by calling the :attr:`func` function. The function takes in the + prim path to spawn the asset at, the configuration instance and transformation, and returns the + prim path of the spawned asset. + + The function is typically decorated with :func:`isaaclab.sim.spawner.utils.clone` decorator + that checks if input prim path is a regex expression and spawns the asset at all matching prims. + For this, the decorator uses the Cloner API from Isaac Sim and handles the :attr:`copy_from_source` + parameter. + """ + + func: Callable[..., Usd.Prim] = MISSING + """Function to use for spawning the asset. + + The function takes in the prim path (or expression) to spawn the asset at, the configuration instance + and transformation, and returns the source prim spawned. + """ + + visible: bool = True + """Whether the spawned asset should be visible. Defaults to True.""" + + semantic_tags: list[tuple[str, str]] | None = None + """List of semantic tags to add to the spawned asset. Defaults to None, + which means no semantic tags will be added. + + The semantic tags follow the `Replicator Semantic` tagging system. Each tag is a tuple of the + form ``(type, data)``, where ``type`` is the type of the tag and ``data`` is the semantic label + associated with the tag. For example, to annotate a spawned asset in the class avocado, the semantic + tag would be ``[("class", "avocado")]``. + + You can specify multiple semantic tags by passing in a list of tags. For example, to annotate a + spawned asset in the class avocado and the color green, the semantic tags would be + ``[("class", "avocado"), ("color", "green")]``. + + .. seealso:: + + For more information on the semantics filter, see the documentation for the `semantics schema editor`_. + + .. _semantics schema editor: https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/semantics_schema_editor.html#semantics-filtering + + """ + + copy_from_source: bool = True + """Whether to copy the asset from the source prim or inherit it. Defaults to True. + + This parameter is only used when cloning prims. If False, then the asset will be inherited from + the source prim, i.e. all USD changes to the source prim will be reflected in the cloned prims. + """ + + +@configclass +class RigidObjectSpawnerCfg(SpawnerCfg): + """Configuration parameters for spawning a rigid asset. + + Note: + By default, all properties are set to None. This means that no properties will be added or modified + to the prim outside of the properties available by default when spawning the prim. + """ + + mass_props: schemas.MassPropertiesCfg | None = None + """Mass properties.""" + + rigid_props: schemas.RigidBodyPropertiesCfg | None = None + """Rigid body properties. + + For making a rigid object static, set the :attr:`schemas.RigidBodyPropertiesCfg.kinematic_enabled` + as True. This will make the object static and will not be affected by gravity or other forces. + """ + + collision_props: schemas.CollisionPropertiesCfg | None = None + """Properties to apply to all collision meshes.""" + + activate_contact_sensors: bool = False + """Activate contact reporting on all rigid bodies. Defaults to False. + + This adds the PhysxContactReporter API to all the rigid bodies in the given prim path and its children. + """ + + +@configclass +class DeformableObjectSpawnerCfg(SpawnerCfg): + """Configuration parameters for spawning a deformable asset. + + Unlike rigid objects, deformable objects are affected by forces and can deform when subjected to + external forces. This class is used to configure the properties of the deformable object. + + Deformable bodies don't have a separate collision mesh. The collision mesh is the same as the visual mesh. + The collision properties such as rest and collision offsets are specified in the :attr:`deformable_props`. + + Note: + By default, all properties are set to None. This means that no properties will be added or modified + to the prim outside of the properties available by default when spawning the prim. + """ + + mass_props: schemas.MassPropertiesCfg | None = None + """Mass properties.""" + + deformable_props: schemas.DeformableBodyPropertiesCfg | None = None + """Deformable body properties.""" diff --git a/source/isaaclab/isaaclab/sim/spawners/wrappers/__init__.py b/source/isaaclab/isaaclab/sim/spawners/wrappers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4006fa1a6abc4fdda226f1e274eb2d03390c6bd3 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/wrappers/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for wrapping spawner configurations. + +Unlike the other spawner modules, this module provides a way to wrap multiple spawner configurations +into a single configuration. This is useful when the user wants to spawn multiple assets based on +different configurations. +""" + +from .wrappers import spawn_multi_asset, spawn_multi_usd_file +from .wrappers_cfg import MultiAssetSpawnerCfg, MultiUsdFileCfg diff --git a/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers.py b/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..64d0c4f4ab91026514146176ee1e5f61b5297bd3 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers.py @@ -0,0 +1,190 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import random +import re +from typing import TYPE_CHECKING + +import carb +from pxr import Sdf, Usd + +import isaaclab.sim as sim_utils +from isaaclab.sim.spawners.from_files import UsdFileCfg + +if TYPE_CHECKING: + from . import wrappers_cfg + + +def spawn_multi_asset( + prim_path: str, + cfg: wrappers_cfg.MultiAssetSpawnerCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + clone_in_fabric: bool = False, + replicate_physics: bool = False, +) -> Usd.Prim: + """Spawn multiple assets based on the provided configurations. + + This function spawns multiple assets based on the provided configurations. The assets are spawned + in the order they are provided in the list. If the :attr:`~MultiAssetSpawnerCfg.random_choice` parameter is + set to True, a random asset configuration is selected for each spawn. + + Args: + prim_path: The prim path to spawn the assets. + cfg: The configuration for spawning the assets. + translation: The translation of the spawned assets. Default is None. + orientation: The orientation of the spawned assets in (w, x, y, z) order. Default is None. + clone_in_fabric: Whether to clone in fabric. Default is False. + replicate_physics: Whether to replicate physics. Default is False. + + Returns: + The created prim at the first prim path. + """ + # get stage handle + stage = sim_utils.get_current_stage() + + # resolve: {SPAWN_NS}/AssetName + # note: this assumes that the spawn namespace already exists in the stage + root_path, asset_path = prim_path.rsplit("/", 1) + # check if input is a regex expression + # note: a valid prim path can only contain alphanumeric characters, underscores, and forward slashes + is_regex_expression = re.match(r"^[a-zA-Z0-9/_]+$", root_path) is None + + # resolve matching prims for source prim path expression + if is_regex_expression and root_path != "": + source_prim_paths = sim_utils.find_matching_prim_paths(root_path) + # if no matching prims are found, raise an error + if len(source_prim_paths) == 0: + raise RuntimeError( + f"Unable to find source prim path: '{root_path}'. Please create the prim before spawning." + ) + else: + source_prim_paths = [root_path] + + # find a free prim path to hold all the template prims + template_prim_path = sim_utils.get_next_free_prim_path("/World/Template", stage=stage) + sim_utils.create_prim(template_prim_path, "Scope", stage=stage) + + # spawn everything first in a "Dataset" prim + proto_prim_paths = list() + for index, asset_cfg in enumerate(cfg.assets_cfg): + # append semantic tags if specified + if cfg.semantic_tags is not None: + if asset_cfg.semantic_tags is None: + asset_cfg.semantic_tags = cfg.semantic_tags + else: + asset_cfg.semantic_tags += cfg.semantic_tags + # override settings for properties + attr_names = ["mass_props", "rigid_props", "collision_props", "activate_contact_sensors", "deformable_props"] + for attr_name in attr_names: + attr_value = getattr(cfg, attr_name) + if hasattr(asset_cfg, attr_name) and attr_value is not None: + setattr(asset_cfg, attr_name, attr_value) + # spawn single instance + proto_prim_path = f"{template_prim_path}/Asset_{index:04d}" + asset_cfg.func( + proto_prim_path, + asset_cfg, + translation=translation, + orientation=orientation, + clone_in_fabric=clone_in_fabric, + replicate_physics=replicate_physics, + ) + # append to proto prim paths + proto_prim_paths.append(proto_prim_path) + + # resolve prim paths for spawning and cloning + prim_paths = [f"{source_prim_path}/{asset_path}" for source_prim_path in source_prim_paths] + + # manually clone prims if the source prim path is a regex expression + # note: unlike in the cloner API from Isaac Sim, we do not "reset" xforms on the copied prims. + # This is because the "spawn" calls during the creation of the proto prims already handles this operation. + with Sdf.ChangeBlock(): + for index, prim_path in enumerate(prim_paths): + # spawn single instance + env_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_path) + # randomly select an asset configuration + if cfg.random_choice: + proto_path = random.choice(proto_prim_paths) + else: + proto_path = proto_prim_paths[index % len(proto_prim_paths)] + # copy the proto prim + Sdf.CopySpec(env_spec.layer, Sdf.Path(proto_path), env_spec.layer, Sdf.Path(prim_path)) + + # delete the dataset prim after spawning + sim_utils.delete_prim(template_prim_path, stage=stage) + + # set carb setting to indicate Isaac Lab's environments that different prims have been spawned + # at varying prim paths. In this case, PhysX parser shouldn't optimize the stage parsing. + # the flag is mainly used to inform the user that they should disable `InteractiveScene.replicate_physics` + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/isaaclab/spawn/multi_assets", True) + + # return the prim + return stage.GetPrimAtPath(prim_paths[0]) + + +def spawn_multi_usd_file( + prim_path: str, + cfg: wrappers_cfg.MultiUsdFileCfg, + translation: tuple[float, float, float] | None = None, + orientation: tuple[float, float, float, float] | None = None, + clone_in_fabric: bool = False, + replicate_physics: bool = False, +) -> Usd.Prim: + """Spawn multiple USD files based on the provided configurations. + + This function creates configuration instances corresponding the individual USD files and + calls the :meth:`spawn_multi_asset` method to spawn them into the scene. + + Args: + prim_path: The prim path to spawn the assets. + cfg: The configuration for spawning the assets. + translation: The translation of the spawned assets. Default is None. + orientation: The orientation of the spawned assets in (w, x, y, z) order. Default is None. + clone_in_fabric: Whether to clone in fabric. Default is False. + replicate_physics: Whether to replicate physics. Default is False. + + Returns: + The created prim at the first prim path. + """ + # needed here to avoid circular imports + from .wrappers_cfg import MultiAssetSpawnerCfg + + # parse all the usd files + if isinstance(cfg.usd_path, str): + usd_paths = [cfg.usd_path] + else: + usd_paths = cfg.usd_path + + # make a template usd config + usd_template_cfg = UsdFileCfg() + for attr_name, attr_value in cfg.__dict__.items(): + # skip names we know are not present + if attr_name in ["func", "usd_path", "random_choice"]: + continue + # set the attribute into the template + setattr(usd_template_cfg, attr_name, attr_value) + + # create multi asset configuration of USD files + multi_asset_cfg = MultiAssetSpawnerCfg(assets_cfg=[]) + for usd_path in usd_paths: + usd_cfg = usd_template_cfg.replace(usd_path=usd_path) + multi_asset_cfg.assets_cfg.append(usd_cfg) + # set random choice + multi_asset_cfg.random_choice = cfg.random_choice + + # propagate the contact sensor settings + # note: the default value for activate_contact_sensors in MultiAssetSpawnerCfg is False. + # This ends up overwriting the usd-template-cfg's value when the `spawn_multi_asset` + # function is called. We hard-code the value to the usd-template-cfg's value to ensure + # that the contact sensor settings are propagated correctly. + if hasattr(cfg, "activate_contact_sensors"): + multi_asset_cfg.activate_contact_sensors = cfg.activate_contact_sensors + + # call the original function + return spawn_multi_asset(prim_path, multi_asset_cfg, translation, orientation, clone_in_fabric, replicate_physics) diff --git a/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers_cfg.py b/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..07c585f7c4c36ccb93fb61ccdcdc194b85739c61 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/spawners/wrappers/wrappers_cfg.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.sim.spawners.spawner_cfg import DeformableObjectSpawnerCfg, RigidObjectSpawnerCfg, SpawnerCfg +from isaaclab.utils import configclass + +from . import wrappers + + +@configclass +class MultiAssetSpawnerCfg(RigidObjectSpawnerCfg, DeformableObjectSpawnerCfg): + """Configuration parameters for loading multiple assets from their individual configurations. + + Specifying values for any properties at the configuration level will override the settings of + individual assets' configuration. For instance if the attribute + :attr:`MultiAssetSpawnerCfg.mass_props` is specified, its value will overwrite the values of the + mass properties in each configuration inside :attr:`assets_cfg` (wherever applicable). + This is done to simplify configuring similar properties globally. By default, all properties are set to None. + + The following is an exception to the above: + + * :attr:`visible`: This parameter is ignored. Its value for the individual assets is used. + * :attr:`semantic_tags`: If specified, it will be appended to each individual asset's semantic tags. + + """ + + func = wrappers.spawn_multi_asset + + assets_cfg: list[SpawnerCfg] = MISSING + """List of asset configurations to spawn.""" + + random_choice: bool = True + """Whether to randomly select an asset configuration. Default is True. + + If False, the asset configurations are spawned in the order they are provided in the list. + If True, a random asset configuration is selected for each spawn. + """ + + +@configclass +class MultiUsdFileCfg(UsdFileCfg): + """Configuration parameters for loading multiple USD files. + + Specifying values for any properties at the configuration level is applied to all the assets + imported from their USD files. + + .. tip:: + It is recommended that all the USD based assets follow a similar prim-hierarchy. + + """ + + func = wrappers.spawn_multi_usd_file + + usd_path: str | list[str] = MISSING + """Path or a list of paths to the USD files to spawn asset from.""" + + random_choice: bool = True + """Whether to randomly select an asset configuration. Default is True. + + If False, the asset configurations are spawned in the order they are provided in the list. + If True, a random asset configuration is selected for each spawn. + """ diff --git a/source/isaaclab/isaaclab/sim/utils/__init__.py b/source/isaaclab/isaaclab/sim/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3a85ae44c2f1dc9803cff3facad45d1a057ed349 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities built around USD operations.""" + +from .legacy import * # noqa: F401, F403 +from .prims import * # noqa: F401, F403 +from .queries import * # noqa: F401, F403 +from .semantics import * # noqa: F401, F403 +from .stage import * # noqa: F401, F403 +from .transforms import * # noqa: F401, F403 diff --git a/source/isaaclab/isaaclab/sim/utils/legacy.py b/source/isaaclab/isaaclab/sim/utils/legacy.py new file mode 100644 index 0000000000000000000000000000000000000000..0e3aef861733ce87e56a49f79d7fb75111fce5af --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/legacy.py @@ -0,0 +1,342 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for legacy functionality. + +This sub-module contains legacy functions from Isaac Sim that are no longer +required for Isaac Lab. Most functions are simple wrappers around USD APIs +and are provided mainly for convenience. + +It is recommended to use the USD APIs directly whenever possible. +""" + +from __future__ import annotations + +import logging +from collections.abc import Iterable + +from pxr import Usd, UsdGeom + +from .prims import add_usd_reference +from .queries import get_next_free_prim_path +from .stage import get_current_stage + +# import logger +logger = logging.getLogger(__name__) + + +""" +Stage utilities. +""" + + +def add_reference_to_stage(usd_path: str, path: str, prim_type: str = "Xform") -> Usd.Prim: + """Adds a USD reference to the stage at the specified prim path. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the :func:`isaaclab.sim.utils.prims.add_usd_reference` function instead. + + Args: + usd_path: The path to the USD file to reference. + path: The prim path to add the reference to. + prim_type: The type of prim to create if it doesn't exist. Defaults to "Xform". + + Returns: + The USD prim at the specified prim path. + """ + logger.warning("Function 'add_reference_to_stage' is deprecated. Please use 'add_usd_reference' instead.") + return add_usd_reference(prim_path=path, usd_path=usd_path, prim_type=prim_type) + + +def get_stage_up_axis() -> str: + """Gets the up axis of the stage. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> from pxr import UsdGeom + >>> + >>> UsdGeom.GetStageUpAxis(sim_utils.get_current_stage()) + 'Z' + """ + msg = """Function 'get_stage_up_axis' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> from pxr import UsdGeom + >>> + >>> UsdGeom.GetStageUpAxis(sim_utils.get_current_stage()) + 'Z' + """ + logger.warning(msg) + return UsdGeom.GetStageUpAxis(get_current_stage()) + + +def traverse_stage(fabric: bool = False) -> Iterable[Usd.Prim]: + """Traverses the stage and returns all the prims. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> for prim in stage.Traverse(): + >>> print(prim) + Usd.Prim() + Usd.Prim() + Usd.Prim() + Usd.Prim() + + Args: + fabric: True for fabric stage and False for USD stage. Defaults to False. + + Returns: + An iterable of all the prims in the stage. + """ + msg = """Function 'traverse_stage' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> for prim in stage.Traverse(): + >>> print(prim) + """ + logger.warning(msg) + # get current stage + stage = get_current_stage(fabric=fabric) + # traverse stage + return stage.Traverse() + + +""" +Prims utilities. +""" + + +def get_prim_at_path(prim_path: str, fabric: bool = False) -> Usd.Prim | None: + """Gets the USD prim at the specified path. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> stage.GetPrimAtPath("/World/Cube") + Usd.Prim() + + Args: + prim_path: The path of the prim to get. + fabric: Whether to get the prim from the fabric stage. Defaults to False. + + Returns: + The USD prim at the specified path. If stage is not found, returns None. + """ + msg = """Function 'get_prim_at_path' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> stage.GetPrimAtPath("/World/Cube") + Usd.Prim() + """ + logger.warning(msg) + # get current stage + stage = get_current_stage(fabric=fabric) + if stage is not None: + return stage.GetPrimAtPath(prim_path) + return None + + +def get_prim_path(prim: Usd.Prim) -> str: + """Gets the path of the specified USD prim. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.GetPath().pathString + "/World/Cube" + + Args: + prim: The USD prim to get the path of. + + Returns: + The path of the specified USD prim. + """ + msg = """Function 'get_prim_path' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.GetPath().pathString + "/World/Cube" + """ + logger.warning(msg) + return prim.GetPath().pathString if prim.IsValid() else "" + + +def is_prim_path_valid(prim_path: str, fabric: bool = False) -> bool: + """Check if a path has a valid USD Prim on the specified stage. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.IsValid() + True + + Args: + prim_path: path of the prim in the stage + fabric: True for fabric stage and False for USD stage. Defaults to False. + + Returns: + True if the path points to a valid prim. False otherwise. + """ + msg = """Function 'is_prim_path_valid' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.IsValid() + True + """ + logger.warning(msg) + # get prim at path + prim = get_prim_at_path(prim_path, fabric=fabric) + # return validity + return prim.IsValid() if prim else False + + +def define_prim(prim_path: str, prim_type: str = "Xform", fabric: bool = False) -> Usd.Prim: + """Create a USD Prim at the given prim_path of type prim type unless one already exists. + + This function creates a prim of the specified type in the specified path. To apply a + transformation (position, orientation, scale), set attributes or load an USD file while + creating the prim use the :func:`isaaclab.sim.utils.prims.create_prim` function. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + In case, a new prim is needed, use the :func:`isaaclab.sim.utils.prims.create_prim` + function instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> stage.DefinePrim("/World/Shapes", "Xform") + Usd.Prim() + + Args: + prim_path: path of the prim in the stage + prim_type: The type of the prim to create. Defaults to "Xform". + fabric: True for fabric stage and False for USD stage. Defaults to False. + + Returns: + The created USD prim. + + Raises: + ValueError: If there is already a prim at the prim_path + """ + msg = """Function 'define_prim' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> stage.DefinePrim("/World/Shapes", "Xform") + Usd.Prim() + """ + logger.warning(msg) + # get current stage + stage = get_current_stage(fabric=fabric) + # check if prim path is valid + if stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"A prim already exists at prim path: {prim_path}") + # define prim + return stage.DefinePrim(prim_path, prim_type) + + +def get_prim_type_name(prim_path: str | Usd.Prim, fabric: bool = False) -> str: + """Get the type name of the USD Prim at the provided path. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the USD APIs directly instead. + + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.GetTypeName() + "Cube" + + Args: + prim_path: path of the prim in the stage or the prim itself + fabric: True for fabric stage and False for USD stage. Defaults to False. + + Returns: + The type name of the USD Prim at the provided path. + + Raises: + ValueError: If there is not a valid prim at the provided path + """ + msg = """Function 'get_prim_type_name' is deprecated. Please use the USD APIs directly instead. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/Cube") + >>> prim.GetTypeName() + "Cube" + """ + logger.warning(msg) + # check if string + if isinstance(prim_path, str): + stage = get_current_stage(fabric=fabric) + prim = stage.GetPrimAtPath(prim_path) + else: + prim = prim_path + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"A prim does not exist at prim path: {prim_path}") + # return type name + return prim.GetTypeName() + + +""" +Queries utilities. +""" + + +def get_next_free_path(path: str) -> str: + """Gets a new prim path that doesn't exist in the stage given a base path. + + .. deprecated:: 2.3.0 + This function is deprecated. Please use the + :func:`isaaclab.sim.utils.queries.get_next_free_prim_path` function instead. + + Args: + path: The base prim path to check. + stage: The stage to check. Defaults to the current stage. + + Returns: + A new path that is guaranteed to not exist on the current stage + """ + logger.warning("Function 'get_next_free_path' is deprecated. Please use 'get_next_free_prim_path' instead.") + return get_next_free_prim_path(path) diff --git a/source/isaaclab/isaaclab/sim/utils/prims.py b/source/isaaclab/isaaclab/sim/utils/prims.py new file mode 100644 index 0000000000000000000000000000000000000000..6fb7850a5b5d8ae2b161983286854f3924000626 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/prims.py @@ -0,0 +1,1129 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for creating and manipulating USD prims.""" + +from __future__ import annotations + +import functools +import inspect +import logging +import re +from collections.abc import Callable, Sequence +from typing import TYPE_CHECKING, Any + +import torch + +import omni.kit.commands +import omni.usd +from isaacsim.core.cloner import Cloner +from pxr import PhysxSchema, Sdf, Usd, UsdGeom, UsdPhysics, UsdShade, UsdUtils + +from isaaclab.utils.string import to_camel_case +from isaaclab.utils.version import get_isaac_sim_version + +from .queries import find_matching_prim_paths +from .semantics import add_labels +from .stage import get_current_stage, get_current_stage_id +from .transforms import convert_world_pose_to_local, standardize_xform_ops + +if TYPE_CHECKING: + from isaaclab.sim.spawners.spawner_cfg import SpawnerCfg + +# import logger +logger = logging.getLogger(__name__) + + +""" +General Utils +""" + + +def create_prim( + prim_path: str, + prim_type: str = "Xform", + position: Any | None = None, + translation: Any | None = None, + orientation: Any | None = None, + scale: Any | None = None, + usd_path: str | None = None, + semantic_label: str | None = None, + semantic_type: str = "class", + attributes: dict | None = None, + stage: Usd.Stage | None = None, +) -> Usd.Prim: + """Creates a prim in the provided USD stage. + + The method applies the specified transforms, the semantic label and sets the specified attributes. + The transform can be specified either in world space (using ``position``) or local space (using + ``translation``). + + The function determines the coordinate system of the transform based on the provided arguments. + + * If ``position`` is provided, it is assumed the orientation is provided in the world frame as well. + * If ``translation`` is provided, it is assumed the orientation is provided in the local frame as well. + + The scale is always applied in the local frame. + + The function handles various sequence types (list, tuple, numpy array, torch tensor) + and converts them to properly-typed tuples for operations on the prim. + + .. note:: + Transform operations are standardized to the USD convention: translate, orient (quaternion), + and scale, in that order. See :func:`standardize_xform_ops` for more details. + + Args: + prim_path: + The path of the new prim. + prim_type: + Prim type name. Defaults to "Xform", in which case a simple Xform prim is created. + position: + Prim position in world space as (x, y, z). If the prim has a parent, this is + automatically converted to local space relative to the parent. Cannot be used with + ``translation``. Defaults to None, in which case no position is applied. + translation: + Prim translation in local space as (x, y, z). This is applied directly without + any coordinate transformation. Cannot be used with ``position``. Defaults to None, + in which case no translation is applied. + orientation: + Prim rotation as a quaternion (w, x, y, z). When used with ``position``, the + orientation is also converted from world space to local space. When used with ``translation``, + it is applied directly as local orientation. Defaults to None. + scale: + Scaling factor in x, y, z. Applied in local space. Defaults to None, + in which case a uniform scale of 1.0 is applied. + usd_path: + Path to the USD file that this prim will reference. Defaults to None. + semantic_label: + Semantic label to apply to the prim. Defaults to None, in which case no label is added. + semantic_type: + Semantic type for the label. Defaults to "class". + attributes: + Key-value pairs of prim attributes to set. Defaults to None, in which case no attributes are set. + stage: + The stage to create the prim in. Defaults to None, in which case the current stage is used. + + Returns: + The created USD prim. + + Raises: + ValueError: If there is already a prim at the provided prim path. + ValueError: If both position and translation are provided. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # Create a cube at world position (1.0, 0.5, 0.0) + >>> sim_utils.create_prim( + ... prim_path="/World/Parent/Cube", + ... prim_type="Cube", + ... position=(1.0, 0.5, 0.0), + ... attributes={"size": 2.0}, + ... ) + Usd.Prim() + >>> + >>> # Create a sphere with local translation relative to its parent + >>> sim_utils.create_prim( + ... prim_path="/World/Parent/Sphere", + ... prim_type="Sphere", + ... translation=(0.5, 0.0, 0.0), + ... scale=(2.0, 2.0, 2.0), + ... ) + Usd.Prim() + """ + # Ensure that user doesn't provide both position and translation + if position is not None and translation is not None: + raise ValueError("Cannot provide both position and translation. Please provide only one.") + + # obtain stage handle + stage = get_current_stage() if stage is None else stage + + # check if prim already exists + if stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"A prim already exists at path: '{prim_path}'.") + + # create prim in stage + prim = stage.DefinePrim(prim_path, prim_type) + if not prim.IsValid(): + raise ValueError(f"Failed to create prim at path: '{prim_path}' of type: '{prim_type}'.") + # apply attributes into prim + if attributes is not None: + for k, v in attributes.items(): + prim.GetAttribute(k).Set(v) + # add reference to USD file + if usd_path is not None: + add_usd_reference(prim_path=prim_path, usd_path=usd_path, stage=stage) + # add semantic label to prim + if semantic_label is not None: + add_labels(prim, labels=[semantic_label], instance_name=semantic_type) + + # check if prim type is Xformable + if not prim.IsA(UsdGeom.Xformable): + logger.debug( + f"Prim at path '{prim.GetPath().pathString}' is of type '{prim.GetTypeName()}', " + "which is not an Xformable. Transform operations will not be standardized. " + "This is expected for material, shader, and scope prims." + ) + return prim + + # convert input arguments to tuples + position = _to_tuple(position) if position is not None else None + translation = _to_tuple(translation) if translation is not None else None + orientation = _to_tuple(orientation) if orientation is not None else None + scale = _to_tuple(scale) if scale is not None else None + + # convert position and orientation to translation and orientation + # world --> local + if position is not None: + # this means that user provided pose in the world frame + translation, orientation = convert_world_pose_to_local(position, orientation, ref_prim=prim.GetParent()) + + # standardize the xform ops + standardize_xform_ops(prim, translation, orientation, scale) + + return prim + + +def delete_prim(prim_path: str | Sequence[str], stage: Usd.Stage | None = None) -> bool: + """Removes the USD Prim and its descendants from the scene if able. + + Args: + prim_path: The path of the prim to delete. If a list of paths is provided, + the function will delete all the prims in the list. + stage: The stage to delete the prim in. Defaults to None, in which case the current stage is used. + + Returns: + True if the prim or prims were deleted successfully, False otherwise. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.delete_prim("/World/Cube") + """ + # convert prim_path to list if it is a string + if isinstance(prim_path, str): + prim_path = [prim_path] + # get stage handle + stage = get_current_stage() if stage is None else stage + # FIXME: We should not need to cache the stage here. It should + # happen at the creation of the stage. + # the prim command looks for the stage ID in the stage cache + # so we need to ensure the stage is cached + stage_cache = UsdUtils.StageCache.Get() + stage_id = stage_cache.GetId(stage).ToLongInt() + if stage_id < 0: + stage_id = stage_cache.Insert(stage).ToLongInt() + # delete prims + success, _ = omni.kit.commands.execute( + "DeletePrimsCommand", + paths=prim_path, + stage=stage, + ) + return success + + +def move_prim(path_from: str, path_to: str, keep_world_transform: bool = True, stage: Usd.Stage | None = None) -> bool: + """Moves a prim from one path to another within a USD stage. + + This function moves the prim from the source path to the destination path. If the :attr:`keep_world_transform` + is set to True, the world transform of the prim is kept. This implies that the prim's local transform is reset + such that the prim's world transform is the same as the source path's world transform. If it is set to False, + the prim's local transform is preserved. + + .. warning:: + Reparenting or moving prims in USD is an expensive operation that may trigger + significant recomposition costs, especially in large or deeply layered stages. + + Args: + path_from: Path of the USD Prim you wish to move + path_to: Final destination of the prim + keep_world_transform: Whether to keep the world transform of the prim. Defaults to True. + stage: The stage to move the prim in. Defaults to None, in which case the current stage is used. + + Returns: + True if the prim was moved successfully, False otherwise. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # given the stage: /World/Cube. Move the prim Cube outside the prim World + >>> sim_utils.move_prim("/World/Cube", "/Cube") + """ + # get stage handle + stage = get_current_stage() if stage is None else stage + # move prim + success, _ = omni.kit.commands.execute( + "MovePrimCommand", + path_from=path_from, + path_to=path_to, + keep_world_transform=keep_world_transform, + stage_or_context=stage, + ) + return success + + +""" +USD Prim properties and attributes. +""" + + +def make_uninstanceable(prim_path: str | Sdf.Path, stage: Usd.Stage | None = None): + """Check if a prim and its descendants are instanced and make them uninstanceable. + + This function checks if the prim at the specified prim path and its descendants are instanced. + If so, it makes the respective prim uninstanceable by disabling instancing on the prim. + + This is useful when we want to modify the properties of a prim that is instanced. For example, if we + want to apply a different material on an instanced prim, we need to make the prim uninstanceable first. + + Args: + prim_path: The prim path to check. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # make paths str type if they aren't already + prim_path = str(prim_path) + # check if prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + # get prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # iterate over all prims under prim-path + all_prims = [prim] + while len(all_prims) > 0: + # get current prim + child_prim = all_prims.pop(0) + # check if prim is instanced + if child_prim.IsInstance(): + # make the prim uninstanceable + child_prim.SetInstanceable(False) + # add children to list + all_prims += child_prim.GetFilteredChildren(Usd.TraverseInstanceProxies()) + + +def set_prim_visibility(prim: Usd.Prim, visible: bool) -> None: + """Sets the visibility of the prim in the opened stage. + + .. note:: + + The method does this through the USD API. + + Args: + prim: the USD prim + visible: flag to set the visibility of the usd prim in stage. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # given the stage: /World/Cube. Make the Cube not visible + >>> prim = sim_utils.get_prim_at_path("/World/Cube") + >>> sim_utils.set_prim_visibility(prim, False) + """ + imageable = UsdGeom.Imageable(prim) + if visible: + imageable.MakeVisible() + else: + imageable.MakeInvisible() + + +def safe_set_attribute_on_usd_schema(schema_api: Usd.APISchemaBase, name: str, value: Any, camel_case: bool): + """Set the value of an attribute on its USD schema if it exists. + + A USD API schema serves as an interface or API for authoring and extracting a set of attributes. + They typically derive from the :class:`pxr.Usd.SchemaBase` class. This function checks if the + attribute exists on the schema and sets the value of the attribute if it exists. + + Args: + schema_api: The USD schema to set the attribute on. + name: The name of the attribute. + value: The value to set the attribute to. + camel_case: Whether to convert the attribute name to camel case. + + Raises: + TypeError: When the input attribute name does not exist on the provided schema API. + """ + # if value is None, do nothing + if value is None: + return + # convert attribute name to camel case + if camel_case: + attr_name = to_camel_case(name, to="CC") + else: + attr_name = name + # retrieve the attribute + # reference: https://openusd.org/dev/api/_usd__page__common_idioms.html#Usd_Create_Or_Get_Property + attr = getattr(schema_api, f"Create{attr_name}Attr", None) + # check if attribute exists + if attr is not None: + attr().Set(value) + else: + # think: do we ever need to create the attribute if it doesn't exist? + # currently, we are not doing this since the schemas are already created with some defaults. + logger.error(f"Attribute '{attr_name}' does not exist on prim '{schema_api.GetPath()}'.") + raise TypeError(f"Attribute '{attr_name}' does not exist on prim '{schema_api.GetPath()}'.") + + +def safe_set_attribute_on_usd_prim(prim: Usd.Prim, attr_name: str, value: Any, camel_case: bool): + """Set the value of a attribute on its USD prim. + + The function creates a new attribute if it does not exist on the prim. This is because in some cases (such + as with shaders), their attributes are not exposed as USD prim properties that can be altered. This function + allows us to set the value of the attributes in these cases. + + Args: + prim: The USD prim to set the attribute on. + attr_name: The name of the attribute. + value: The value to set the attribute to. + camel_case: Whether to convert the attribute name to camel case. + """ + # if value is None, do nothing + if value is None: + return + # convert attribute name to camel case + if camel_case: + attr_name = to_camel_case(attr_name, to="cC") + # resolve sdf type based on value + if isinstance(value, bool): + sdf_type = Sdf.ValueTypeNames.Bool + elif isinstance(value, int): + sdf_type = Sdf.ValueTypeNames.Int + elif isinstance(value, float): + sdf_type = Sdf.ValueTypeNames.Float + elif isinstance(value, (tuple, list)) and len(value) == 3 and any(isinstance(v, float) for v in value): + sdf_type = Sdf.ValueTypeNames.Float3 + elif isinstance(value, (tuple, list)) and len(value) == 2 and any(isinstance(v, float) for v in value): + sdf_type = Sdf.ValueTypeNames.Float2 + else: + raise NotImplementedError( + f"Cannot set attribute '{attr_name}' with value '{value}'. Please modify the code to support this type." + ) + + # change property using the change_prim_property function + change_prim_property( + prop_path=f"{prim.GetPath()}.{attr_name}", + value=value, + stage=prim.GetStage(), + type_to_create_if_not_exist=sdf_type, + ) + + +def change_prim_property( + prop_path: str | Sdf.Path, + value: Any, + stage: Usd.Stage | None = None, + type_to_create_if_not_exist: Sdf.ValueTypeNames | None = None, + is_custom: bool = False, +) -> bool: + """Change or create a property value on a USD prim. + + This is a simplified property setter that works with the current edit target. If you need + complex layer management, use :class:`omni.kit.commands.ChangePropertyCommand` instead. + + By default, this function changes the value of the property when it exists. If the property + doesn't exist, :attr:`type_to_create_if_not_exist` must be provided to create it. + + Note: + The attribute :attr:`value` must be the correct type for the property. + For example, if the property is a float, the value must be a float. + If it is supposed to be a RGB color, the value must be of type :class:`Gf.Vec3f`. + + Args: + prop_path: Property path in the format ``/World/Prim.propertyName``. + value: Value to set. If None, the attribute value goes to its default value. + If the attribute has no default value, it is a silent no-op. + stage: The USD stage. Defaults to None, in which case the current stage is used. + type_to_create_if_not_exist: If not None and property doesn't exist, a new property will + be created with the given type and value. Defaults to None. + is_custom: If the property is created, specify if it is a custom property (not part of + the schema). Defaults to False. + + Returns: + True if the property was successfully changed, False otherwise. + + Raises: + ValueError: If the prim does not exist at the specified path. + + Example: + >>> import isaaclab.sim as sim_utils + >>> from pxr import Sdf + >>> + >>> # Change an existing property + >>> sim_utils.change_prim_property(prop_path="/World/Cube.size", value=2.0) + True + >>> + >>> # Create a new custom property + >>> sim_utils.change_prim_property( + ... prop_path="/World/Cube.customValue", + ... value=42, + ... type_to_create_if_not_exist=Sdf.ValueTypeNames.Int, + ... is_custom=True, + ... ) + True + """ + # get stage handle + stage = get_current_stage() if stage is None else stage + + # convert to Sdf.Path if needed + prop_path = Sdf.Path(prop_path) if isinstance(prop_path, str) else prop_path + + # get the prim path + prim_path = prop_path.GetAbsoluteRootOrPrimPath() + prim = stage.GetPrimAtPath(prim_path) + if not prim or not prim.IsValid(): + raise ValueError(f"Prim does not exist at path: '{prim_path}'") + + # get or create the property + prop = stage.GetPropertyAtPath(prop_path) + + if not prop: + if type_to_create_if_not_exist is not None: + # create new attribute on the prim + prop = prim.CreateAttribute(prop_path.name, type_to_create_if_not_exist, is_custom) + else: + logger.error(f"Property {prop_path} does not exist and 'type_to_create_if_not_exist' was not provided.") + return False + + if not prop: + logger.error(f"Failed to get or create property at path: '{prop_path}'") + return False + + # set the value + if value is None: + return bool(prop.Clear()) + else: + return bool(prop.Set(value, Usd.TimeCode.Default())) + + +""" +Exporting. +""" + + +def export_prim_to_file( + path: str | Sdf.Path, + source_prim_path: str | Sdf.Path, + target_prim_path: str | Sdf.Path | None = None, + stage: Usd.Stage | None = None, +): + """Exports a prim from a given stage to a USD file. + + The function creates a new layer at the provided path and copies the prim to the layer. + It sets the copied prim as the default prim in the target layer. Additionally, it updates + the stage up-axis and meters-per-unit to match the current stage. + + Args: + path: The filepath path to export the prim to. + source_prim_path: The prim path to export. + target_prim_path: The prim path to set as the default prim in the target layer. + Defaults to None, in which case the source prim path is used. + stage: The stage where the prim exists. Defaults to None, in which case the + current stage is used. + + Raises: + ValueError: If the prim paths are not global (i.e: do not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # automatically casting to str in case args + # are path types + path = str(path) + source_prim_path = str(source_prim_path) + if target_prim_path is not None: + target_prim_path = str(target_prim_path) + + if not source_prim_path.startswith("/"): + raise ValueError(f"Source prim path '{source_prim_path}' is not global. It must start with '/'.") + if target_prim_path is not None and not target_prim_path.startswith("/"): + raise ValueError(f"Target prim path '{target_prim_path}' is not global. It must start with '/'.") + + # get root layer + source_layer = stage.GetRootLayer() + + # only create a new layer if it doesn't exist already + target_layer = Sdf.Find(path) + if target_layer is None: + target_layer = Sdf.Layer.CreateNew(path) + # open the target stage + target_stage = Usd.Stage.Open(target_layer) + + # update stage data + UsdGeom.SetStageUpAxis(target_stage, UsdGeom.GetStageUpAxis(stage)) + UsdGeom.SetStageMetersPerUnit(target_stage, UsdGeom.GetStageMetersPerUnit(stage)) + + # specify the prim to copy + source_prim_path = Sdf.Path(source_prim_path) + if target_prim_path is None: + target_prim_path = source_prim_path + + # copy the prim + Sdf.CreatePrimInLayer(target_layer, target_prim_path) + Sdf.CopySpec(source_layer, source_prim_path, target_layer, target_prim_path) + # set the default prim + target_layer.defaultPrim = Sdf.Path(target_prim_path).name + # resolve all paths relative to layer path + omni.usd.resolve_paths(source_layer.identifier, target_layer.identifier) + # save the stage + target_layer.Save() + + +""" +Decorators +""" + + +def apply_nested(func: Callable) -> Callable: + """Decorator to apply a function to all prims under a specified prim-path. + + The function iterates over the provided prim path and all its children to apply input function + to all prims under the specified prim path. + + If the function succeeds to apply to a prim, it will not look at the children of that prim. + This is based on the physics behavior that nested schemas are not allowed. For example, a parent prim + and its child prim cannot both have a rigid-body schema applied on them, or it is not possible to + have nested articulations. + + While traversing the prims under the specified prim path, the function will throw a warning if it + does not succeed to apply the function to any prim. This is because the user may have intended to + apply the function to a prim that does not have valid attributes, or the prim may be an instanced prim. + + Args: + func: The function to apply to all prims under a specified prim-path. The function + must take the prim-path and other arguments. It should return a boolean indicating whether + the function succeeded or not. + + Returns: + The wrapped function that applies the function to all prims under a specified prim-path. + + Raises: + ValueError: If the prim-path does not exist on the stage. + """ + + @functools.wraps(func) + def wrapper(prim_path: str | Sdf.Path, *args, **kwargs): + # map args and kwargs to function signature so we can get the stage + # note: we do this to check if stage is given in arg or kwarg + sig = inspect.signature(func) + bound_args = sig.bind(prim_path, *args, **kwargs) + # get current stage + stage = bound_args.arguments.get("stage") + if stage is None: + stage = get_current_stage() + + # get USD prim + prim: Usd.Prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # add iterable to check if property was applied on any of the prims + count_success = 0 + instanced_prim_paths = [] + # iterate over all prims under prim-path + all_prims = [prim] + while len(all_prims) > 0: + # get current prim + child_prim = all_prims.pop(0) + child_prim_path = child_prim.GetPath().pathString # type: ignore + # check if prim is a prototype + if child_prim.IsInstance(): + instanced_prim_paths.append(child_prim_path) + continue + # set properties + success = func(child_prim_path, *args, **kwargs) + # if successful, do not look at children + # this is based on the physics behavior that nested schemas are not allowed + if not success: + all_prims += child_prim.GetChildren() + else: + count_success += 1 + # check if we were successful in applying the function to any prim + if count_success == 0: + logger.warning( + f"Could not perform '{func.__name__}' on any prims under: '{prim_path}'." + " This might be because of the following reasons:" + "\n\t(1) The desired attribute does not exist on any of the prims." + "\n\t(2) The desired attribute exists on an instanced prim." + f"\n\t\tDiscovered list of instanced prim paths: {instanced_prim_paths}" + ) + + return wrapper + + +def clone(func: Callable) -> Callable: + """Decorator for cloning a prim based on matching prim paths of the prim's parent. + + The decorator checks if the parent prim path matches any prim paths in the stage. If so, it clones the + spawned prim at each matching prim path. For example, if the input prim path is: ``/World/Table_[0-9]/Bottle``, + the decorator will clone the prim at each matching prim path of the parent prim: ``/World/Table_0/Bottle``, + ``/World/Table_1/Bottle``, etc. + + Note: + For matching prim paths, the decorator assumes that valid prims exist for all matching prim paths. + In case no matching prim paths are found, the decorator raises a ``RuntimeError``. + + Args: + func: The function to decorate. + + Returns: + The decorated function that spawns the prim and clones it at each matching prim path. + It returns the spawned source prim, i.e., the first prim in the list of matching prim paths. + """ + + @functools.wraps(func) + def wrapper(prim_path: str | Sdf.Path, cfg: SpawnerCfg, *args, **kwargs): + # get stage handle + stage = get_current_stage() + + # cast prim_path to str type in case its an Sdf.Path + prim_path = str(prim_path) + # check prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + # resolve: {SPAWN_NS}/AssetName + # note: this assumes that the spawn namespace already exists in the stage + root_path, asset_path = prim_path.rsplit("/", 1) + # check if input is a regex expression + # note: a valid prim path can only contain alphanumeric characters, underscores, and forward slashes + is_regex_expression = re.match(r"^[a-zA-Z0-9/_]+$", root_path) is None + + # resolve matching prims for source prim path expression + if is_regex_expression and root_path != "": + source_prim_paths = find_matching_prim_paths(root_path) + # if no matching prims are found, raise an error + if len(source_prim_paths) == 0: + raise RuntimeError( + f"Unable to find source prim path: '{root_path}'. Please create the prim before spawning." + ) + else: + source_prim_paths = [root_path] + + # resolve prim paths for spawning and cloning + prim_paths = [f"{source_prim_path}/{asset_path}" for source_prim_path in source_prim_paths] + # spawn single instance + prim = func(prim_paths[0], cfg, *args, **kwargs) + # set the prim visibility + if hasattr(cfg, "visible"): + imageable = UsdGeom.Imageable(prim) + if cfg.visible: + imageable.MakeVisible() + else: + imageable.MakeInvisible() + # set the semantic annotations + if hasattr(cfg, "semantic_tags") and cfg.semantic_tags is not None: + # note: taken from replicator scripts.utils.utils.py + for semantic_type, semantic_value in cfg.semantic_tags: + # deal with spaces by replacing them with underscores + semantic_type_sanitized = semantic_type.replace(" ", "_") + semantic_value_sanitized = semantic_value.replace(" ", "_") + # add labels to the prim + add_labels( + prim, labels=[semantic_value_sanitized], instance_name=semantic_type_sanitized, overwrite=False + ) + # activate rigid body contact sensors (lazy import to avoid circular import with schemas) + if hasattr(cfg, "activate_contact_sensors") and cfg.activate_contact_sensors: # type: ignore + from ..schemas import schemas as _schemas + + _schemas.activate_contact_sensors(prim_paths[0]) + # clone asset using cloner API + if len(prim_paths) > 1: + cloner = Cloner(stage=stage) + # check version of Isaac Sim to determine whether clone_in_fabric is valid + if get_isaac_sim_version().major < 5: + # clone the prim + cloner.clone( + prim_paths[0], prim_paths[1:], replicate_physics=False, copy_from_source=cfg.copy_from_source + ) + else: + # clone the prim + clone_in_fabric = kwargs.get("clone_in_fabric", False) + replicate_physics = kwargs.get("replicate_physics", False) + cloner.clone( + prim_paths[0], + prim_paths[1:], + replicate_physics=replicate_physics, + copy_from_source=cfg.copy_from_source, + clone_in_fabric=clone_in_fabric, + ) + # return the source prim + return prim + + return wrapper + + +""" +Material bindings. +""" + + +@apply_nested +def bind_visual_material( + prim_path: str | Sdf.Path, + material_path: str | Sdf.Path, + stage: Usd.Stage | None = None, + stronger_than_descendants: bool = True, +): + """Bind a visual material to a prim. + + This function is a wrapper around the USD command `BindMaterialCommand`_. + + .. note:: + The function is decorated with :meth:`apply_nested` to allow applying the function to a prim path + and all its descendants. + + .. _BindMaterialCommand: https://docs.omniverse.nvidia.com/kit/docs/omni.usd/latest/omni.usd.commands/omni.usd.commands.BindMaterialCommand.html + + Args: + prim_path: The prim path where to apply the material. + material_path: The prim path of the material to apply. + stage: The stage where the prim and material exist. + Defaults to None, in which case the current stage is used. + stronger_than_descendants: Whether the material should override the material of its descendants. + Defaults to True. + + Raises: + ValueError: If the provided prim paths do not exist on stage. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # check if prim and material exists + if not stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"Target prim '{material_path}' does not exist.") + if not stage.GetPrimAtPath(material_path).IsValid(): + raise ValueError(f"Visual material '{material_path}' does not exist.") + + # resolve token for weaker than descendants + # bind material command expects a string token + if stronger_than_descendants: + binding_strength = "strongerThanDescendants" + else: + binding_strength = "weakerThanDescendants" + # obtain material binding API + # note: we prefer using the command here as it is more robust than the USD API + success, _ = omni.kit.commands.execute( + "BindMaterialCommand", + prim_path=prim_path, + material_path=material_path, + strength=binding_strength, + stage=stage, + ) + # return success + return success + + +@apply_nested +def bind_physics_material( + prim_path: str | Sdf.Path, + material_path: str | Sdf.Path, + stage: Usd.Stage | None = None, + stronger_than_descendants: bool = True, +): + """Bind a physics material to a prim. + + `Physics material`_ can be applied only to a prim with physics-enabled on them. This includes having + collision APIs, or deformable body APIs, or being a particle system. In case the prim does not have + any of these APIs, the function will not apply the material and return False. + + .. note:: + The function is decorated with :meth:`apply_nested` to allow applying the function to a prim path + and all its descendants. + + .. _Physics material: https://isaac-sim.github.io/IsaacLab/main/source/api/lab/isaaclab.sim.html#isaaclab.sim.SimulationCfg.physics_material + + Args: + prim_path: The prim path where to apply the material. + material_path: The prim path of the material to apply. + stage: The stage where the prim and material exist. + Defaults to None, in which case the current stage is used. + stronger_than_descendants: Whether the material should override the material of its descendants. + Defaults to True. + + Raises: + ValueError: If the provided prim paths do not exist on stage. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # check if prim and material exists + if not stage.GetPrimAtPath(prim_path).IsValid(): + raise ValueError(f"Target prim '{material_path}' does not exist.") + if not stage.GetPrimAtPath(material_path).IsValid(): + raise ValueError(f"Physics material '{material_path}' does not exist.") + # get USD prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim has collision applied on it + has_physics_scene_api = prim.HasAPI(PhysxSchema.PhysxSceneAPI) + has_collider = prim.HasAPI(UsdPhysics.CollisionAPI) + has_deformable_body = prim.HasAPI(PhysxSchema.PhysxDeformableBodyAPI) + has_particle_system = prim.IsA(PhysxSchema.PhysxParticleSystem) + if not (has_physics_scene_api or has_collider or has_deformable_body or has_particle_system): + logger.debug( + f"Cannot apply physics material '{material_path}' on prim '{prim_path}'. It is neither a" + " PhysX scene, collider, a deformable body, nor a particle system." + ) + return False + + # obtain material binding API + if prim.HasAPI(UsdShade.MaterialBindingAPI): + material_binding_api = UsdShade.MaterialBindingAPI(prim) + else: + material_binding_api = UsdShade.MaterialBindingAPI.Apply(prim) + # obtain the material prim + + material = UsdShade.Material(stage.GetPrimAtPath(material_path)) + # resolve token for weaker than descendants + if stronger_than_descendants: + binding_strength = UsdShade.Tokens.strongerThanDescendants + else: + binding_strength = UsdShade.Tokens.weakerThanDescendants + # apply the material + material_binding_api.Bind(material, bindingStrength=binding_strength, materialPurpose="physics") # type: ignore + # return success + return True + + +""" +USD References and Variants. +""" + + +def add_usd_reference( + prim_path: str, usd_path: str, prim_type: str = "Xform", stage: Usd.Stage | None = None +) -> Usd.Prim: + """Adds a USD reference at the specified prim path on the provided stage. + + This function adds a reference to an external USD file at the specified prim path on the provided stage. + If the prim does not exist, it will be created with the specified type. + + The function also handles stage units verification to ensure compatibility. For instance, + if the current stage is in meters and the referenced USD file is in centimeters, the function will + convert the units to match. This is done using the :mod:`omni.metrics.assembler` functionality. + + Args: + prim_path: The prim path where the reference will be attached. + usd_path: The path to USD file to reference. + prim_type: The type of prim to create if it doesn't exist. Defaults to "Xform". + stage: The stage to add the reference to. Defaults to None, in which case the current stage is used. + + Returns: + The USD prim at the specified prim path. + + Raises: + FileNotFoundError: When the input USD file is not found at the specified path. + """ + # get current stage + stage = get_current_stage() if stage is None else stage + # get prim at path + prim = stage.GetPrimAtPath(prim_path) + if not prim.IsValid(): + prim = stage.DefinePrim(prim_path, prim_type) + + def _add_reference_to_prim(prim: Usd.Prim) -> Usd.Prim: + """Helper function to add a reference to a prim.""" + success_bool = prim.GetReferences().AddReference(usd_path) + if not success_bool: + raise RuntimeError( + f"Unable to add USD reference to the prim at path: {prim_path} from the USD file at path: {usd_path}" + ) + return prim + + # Compatibility with Isaac Sim 4.5 where omni.metrics is not available + if get_isaac_sim_version().major < 5: + return _add_reference_to_prim(prim) + + # check if the USD file is valid and add reference to the prim + sdf_layer = Sdf.Layer.FindOrOpen(usd_path) + if not sdf_layer: + raise FileNotFoundError(f"Unable to open the usd file at path: {usd_path}") + + # import metrics assembler interface + # note: this is only available in Isaac Sim 5.0 and above + from omni.metrics.assembler.core import get_metrics_assembler_interface + + # obtain the stage ID + stage_id = get_current_stage_id() + # check if the layers are compatible (i.e. the same units) + ret_val = get_metrics_assembler_interface().check_layers( + stage.GetRootLayer().identifier, sdf_layer.identifier, stage_id + ) + # log that metric assembler did not detect any issues + if ret_val["ret_val"]: + logger.info( + "Metric assembler detected no issues between the current stage and the referenced USD file at path:" + f" {usd_path}" + ) + # add reference to the prim + return _add_reference_to_prim(prim) + + +def get_usd_references(prim_path: str, stage: Usd.Stage | None = None) -> list[str]: + """Gets the USD references at the specified prim path on the provided stage. + + Args: + prim_path: The prim path to get the USD references from. + stage: The stage to get the USD references from. Defaults to None, in which case the current stage is used. + + Returns: + A list of USD reference paths. + + Raises: + ValueError: If the prim at the specified path is not valid. + """ + # get stage handle + stage = get_current_stage() if stage is None else stage + # get prim at path + prim = stage.GetPrimAtPath(prim_path) + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # get USD references + references = [] + for prim_spec in prim.GetPrimStack(): + for ref in prim_spec.referenceList.prependedItems: + references.append(str(ref.assetPath)) + return references + + +def select_usd_variants(prim_path: str, variants: object | dict[str, str], stage: Usd.Stage | None = None): + """Sets the variant selections from the specified variant sets on a USD prim. + + `USD Variants`_ are a very powerful tool in USD composition that allows prims to have different options on + a single asset. This can be done by modifying variations of the same prim parameters per variant option in a set. + This function acts as a script-based utility to set the variant selections for the specified variant sets on a + USD prim. + + The function takes a dictionary or a config class mapping variant set names to variant selections. For instance, + if we have a prim at ``"/World/Table"`` with two variant sets: "color" and "size", we can set the variant + selections as follows: + + .. code-block:: python + + select_usd_variants( + prim_path="/World/Table", + variants={ + "color": "red", + "size": "large", + }, + ) + + Alternatively, we can use a config class to define the variant selections: + + .. code-block:: python + + @configclass + class TableVariants: + color: Literal["blue", "red"] = "red" + size: Literal["small", "large"] = "large" + + + select_usd_variants( + prim_path="/World/Table", + variants=TableVariants(), + ) + + Args: + prim_path: The path of the USD prim. + variants: A dictionary or config class mapping variant set names to variant selections. + stage: The USD stage. Defaults to None, in which case, the current stage is used. + + Raises: + ValueError: If the prim at the specified path is not valid. + + .. _USD Variants: https://graphics.pixar.com/usd/docs/USD-Glossary.html#USDGlossary-Variant + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # Obtain prim + prim = stage.GetPrimAtPath(prim_path) + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # Convert to dict if we have a configclass object. + if not isinstance(variants, dict): + variants = variants.to_dict() # type: ignore + + existing_variant_sets = prim.GetVariantSets() + for variant_set_name, variant_selection in variants.items(): # type: ignore + # Check if the variant set exists on the prim. + if not existing_variant_sets.HasVariantSet(variant_set_name): + logger.warning(f"Variant set '{variant_set_name}' does not exist on prim '{prim_path}'.") + continue + + variant_set = existing_variant_sets.GetVariantSet(variant_set_name) + # Only set the variant selection if it is different from the current selection. + if variant_set.GetVariantSelection() != variant_selection: + variant_set.SetVariantSelection(variant_selection) + logger.info( + f"Setting variant selection '{variant_selection}' for variant set '{variant_set_name}' on" + f" prim '{prim_path}'." + ) + + +""" +Internal Helpers. +""" + + +def _to_tuple(value: Any) -> tuple[float, ...]: + """Convert various sequence types to a Python tuple of floats. + + This function provides robust conversion from different array-like types (list, tuple, numpy array, + torch tensor) to Python tuples. It handles edge cases like malformed sequences, CUDA tensors, + and arrays with singleton dimensions. + + Args: + value: A sequence-like object containing floats. Supported types include: + - Python list or tuple + - NumPy array (any device) + - PyTorch tensor (CPU or CUDA) + - Mixed sequences with numpy/torch scalar items and float values + + Returns: + A one-dimensional tuple of floats. + + Raises: + ValueError: If the input value is not one-dimensional after squeezing singleton dimensions. + + Example: + >>> import torch + >>> import numpy as np + >>> + >>> _to_tuple([1.0, 2.0, 3.0]) + (1.0, 2.0, 3.0) + >>> _to_tuple(torch.tensor([[1.0, 2.0]])) # Squeezes first dimension + (1.0, 2.0) + >>> _to_tuple(np.array([1.0, 2.0, 3.0])) + (1.0, 2.0, 3.0) + >>> _to_tuple((1.0, 2.0, 3.0)) + (1.0, 2.0, 3.0) + + """ + # Normalize to tensor if value is a plain sequence (list with mixed types, etc.) + # This handles cases like [np.float32(1.0), 2.0, torch.tensor(3.0)] + if not hasattr(value, "tolist"): + value = torch.tensor(value, device="cpu", dtype=torch.float) + + # Remove leading singleton dimension if present (e.g., shape (1, 3) -> (3,)) + # This is common when batched operations produce single-item batches + if value.ndim != 1: + value = value.squeeze() + # Validate that the result is one-dimensional + if value.ndim != 1: + raise ValueError(f"Input value is not one dimensional: {value.shape}") + + # Convert to tuple - works for both numpy arrays and torch tensors + return tuple(value.tolist()) diff --git a/source/isaaclab/isaaclab/sim/utils/queries.py b/source/isaaclab/isaaclab/sim/utils/queries.py new file mode 100644 index 0000000000000000000000000000000000000000..035681a726b4a807fea4f5f3078dad885d0a57d7 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/queries.py @@ -0,0 +1,407 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for querying the USD stage.""" + +from __future__ import annotations + +import logging +import re +from collections.abc import Callable + +import omni +import omni.kit.app +from pxr import Sdf, Usd, UsdPhysics + +from .stage import get_current_stage + +# import logger +logger = logging.getLogger(__name__) + + +def get_next_free_prim_path(path: str, stage: Usd.Stage | None = None) -> str: + """Gets a new prim path that doesn't exist in the stage given a base path. + + If the given path doesn't exist in the stage already, it returns the given path. Otherwise, + it appends a suffix with an incrementing number to the given path. + + Args: + path: The base prim path to check. + stage: The stage to check. Defaults to the current stage. + + Returns: + A new path that is guaranteed to not exist on the current stage + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # given the stage: /World/Cube, /World/Cube_01. + >>> # Get the next available path for /World/Cube + >>> sim_utils.get_next_free_prim_path("/World/Cube") + /World/Cube_02 + """ + # get current stage + stage = get_current_stage() if stage is None else stage + # get next free path + return omni.usd.get_stage_next_free_path(stage, path, True) + + +def get_first_matching_ancestor_prim( + prim_path: str | Sdf.Path, + predicate: Callable[[Usd.Prim], bool], + stage: Usd.Stage | None = None, +) -> Usd.Prim | None: + """Gets the first ancestor prim that passes the predicate function. + + This function walks up the prim hierarchy starting from the target prim and returns the first ancestor prim + that passes the predicate function. This includes the prim itself if it passes the predicate. + + Args: + prim_path: The path of the prim in the stage. + predicate: The function to test the prims against. It takes a prim as input and returns a boolean. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Returns: + The first ancestor prim that passes the predicate. If no ancestor prim passes the predicate, it returns None. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # make paths str type if they aren't already + prim_path = str(prim_path) + # check if prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + # get prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + + # walk up to find the first matching ancestor prim + ancestor_prim = prim + while ancestor_prim and ancestor_prim.IsValid(): + # check if prim passes predicate + if predicate(ancestor_prim): + return ancestor_prim + # get parent prim + ancestor_prim = ancestor_prim.GetParent() + + # If no ancestor prim passes the predicate, return None + return None + + +def get_first_matching_child_prim( + prim_path: str | Sdf.Path, + predicate: Callable[[Usd.Prim], bool], + stage: Usd.Stage | None = None, + traverse_instance_prims: bool = True, +) -> Usd.Prim | None: + """Recursively get the first USD Prim at the path string that passes the predicate function. + + This function performs a depth-first traversal of the prim hierarchy starting from + :attr:`prim_path`, returning the first prim that satisfies the provided :attr:`predicate`. + It optionally supports traversal through instance prims, which are normally skipped in standard USD + traversals. + + USD instance prims are lightweight copies of prototype scene structures and are not included + in default traversals unless explicitly handled. This function allows traversing into instances + when :attr:`traverse_instance_prims` is set to :attr:`True`. + + .. versionchanged:: 2.3.0 + + Added :attr:`traverse_instance_prims` to control whether to traverse instance prims. + By default, instance prims are now traversed. + + Args: + prim_path: The path of the prim in the stage. + predicate: The function to test the prims against. It takes a prim as input and returns a boolean. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + traverse_instance_prims: Whether to traverse instance prims. Defaults to True. + + Returns: + The first prim on the path that passes the predicate. If no prim passes the predicate, it returns None. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # make paths str type if they aren't already + prim_path = str(prim_path) + # check if prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + # get prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # iterate over all prims under prim-path + all_prims = [prim] + while len(all_prims) > 0: + # get current prim + child_prim = all_prims.pop(0) + # check if prim passes predicate + if predicate(child_prim): + return child_prim + # add children to list + if traverse_instance_prims: + all_prims += child_prim.GetFilteredChildren(Usd.TraverseInstanceProxies()) + else: + all_prims += child_prim.GetChildren() + return None + + +def get_all_matching_child_prims( + prim_path: str | Sdf.Path, + predicate: Callable[[Usd.Prim], bool] = lambda _: True, + depth: int | None = None, + stage: Usd.Stage | None = None, + traverse_instance_prims: bool = True, +) -> list[Usd.Prim]: + """Performs a search starting from the root and returns all the prims matching the predicate. + + This function performs a depth-first traversal of the prim hierarchy starting from + :attr:`prim_path`, returning all prims that satisfy the provided :attr:`predicate`. It optionally + supports traversal through instance prims, which are normally skipped in standard USD traversals. + + USD instance prims are lightweight copies of prototype scene structures and are not included + in default traversals unless explicitly handled. This function allows traversing into instances + when :attr:`traverse_instance_prims` is set to :attr:`True`. + + .. versionchanged:: 2.3.0 + + Added :attr:`traverse_instance_prims` to control whether to traverse instance prims. + By default, instance prims are now traversed. + + Args: + prim_path: The root prim path to start the search from. + predicate: The predicate that checks if the prim matches the desired criteria. It takes a prim as input + and returns a boolean. Defaults to a function that always returns True. + depth: The maximum depth for traversal, should be bigger than zero if specified. + Defaults to None (i.e: traversal happens till the end of the tree). + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + traverse_instance_prims: Whether to traverse instance prims. Defaults to True. + + Returns: + A list containing all the prims matching the predicate. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # make paths str type if they aren't already + prim_path = str(prim_path) + # check if prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + # get prim + prim = stage.GetPrimAtPath(prim_path) + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + # check if depth is valid + if depth is not None and depth <= 0: + raise ValueError(f"Depth must be bigger than zero, got {depth}.") + + # iterate over all prims under prim-path + # list of tuples (prim, current_depth) + all_prims_queue = [(prim, 0)] + output_prims = [] + while len(all_prims_queue) > 0: + # get current prim + child_prim, current_depth = all_prims_queue.pop(0) + # check if prim passes predicate + if predicate(child_prim): + output_prims.append(child_prim) + # add children to list + if depth is None or current_depth < depth: + # resolve prims under the current prim + if traverse_instance_prims: + children = child_prim.GetFilteredChildren(Usd.TraverseInstanceProxies()) + else: + children = child_prim.GetChildren() + # add children to list + all_prims_queue += [(child, current_depth + 1) for child in children] + + return output_prims + + +def find_first_matching_prim(prim_path_regex: str, stage: Usd.Stage | None = None) -> Usd.Prim | None: + """Find the first matching prim in the stage based on input regex expression. + + Args: + prim_path_regex: The regex expression for prim path. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Returns: + The first prim that matches input expression. If no prim matches, returns None. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # check prim path is global + if not prim_path_regex.startswith("/"): + raise ValueError(f"Prim path '{prim_path_regex}' is not global. It must start with '/'.") + prim_path_regex = _normalize_legacy_wildcard_pattern(prim_path_regex) + # need to wrap the token patterns in '^' and '$' to prevent matching anywhere in the string + pattern = f"^{prim_path_regex}$" + compiled_pattern = re.compile(pattern) + # obtain matching prim (depth-first search) + for prim in stage.Traverse(): + # check if prim passes predicate + if compiled_pattern.match(prim.GetPath().pathString) is not None: + return prim + return None + + +def _normalize_legacy_wildcard_pattern(prim_path_regex: str) -> str: + """Convert legacy '*' wildcard usage to '.*' and warn users.""" + fixed_regex = re.sub(r"(? list[Usd.Prim]: + """Find all the matching prims in the stage based on input regex expression. + + Args: + prim_path_regex: The regex expression for prim path. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Returns: + A list of prims that match input expression. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # normalize legacy wildcard pattern + prim_path_regex = _normalize_legacy_wildcard_pattern(prim_path_regex) + + # check prim path is global + if not prim_path_regex.startswith("/"): + raise ValueError(f"Prim path '{prim_path_regex}' is not global. It must start with '/'.") + # need to wrap the token patterns in '^' and '$' to prevent matching anywhere in the string + tokens = prim_path_regex.split("/")[1:] + tokens = [f"^{token}$" for token in tokens] + # iterate over all prims in stage (breath-first search) + all_prims = [stage.GetPseudoRoot()] + output_prims = [] + for index, token in enumerate(tokens): + token_compiled = re.compile(token) + for prim in all_prims: + for child in prim.GetAllChildren(): + if token_compiled.match(child.GetName()) is not None: + output_prims.append(child) + if index < len(tokens) - 1: + all_prims = output_prims + output_prims = [] + return output_prims + + +def find_matching_prim_paths(prim_path_regex: str, stage: Usd.Stage | None = None) -> list[str]: + """Find all the matching prim paths in the stage based on input regex expression. + + Args: + prim_path_regex: The regex expression for prim path. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Returns: + A list of prim paths that match input expression. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + """ + # obtain matching prims + output_prims = find_matching_prims(prim_path_regex, stage) + # convert prims to prim paths + output_prim_paths = [] + for prim in output_prims: + output_prim_paths.append(prim.GetPath().pathString) + return output_prim_paths + + +def find_global_fixed_joint_prim( + prim_path: str | Sdf.Path, check_enabled_only: bool = False, stage: Usd.Stage | None = None +) -> UsdPhysics.Joint | None: + """Find the fixed joint prim under the specified prim path that connects the target to the simulation world. + + A joint is a connection between two bodies. A fixed joint is a joint that does not allow relative motion + between the two bodies. When a fixed joint has only one target body, it is considered to attach the body + to the simulation world. + + This function finds the fixed joint prim that has only one target under the specified prim path. If no such + fixed joint prim exists, it returns None. + + Args: + prim_path: The prim path to search for the fixed joint prim. + check_enabled_only: Whether to consider only enabled fixed joints. Defaults to False. + If False, then all joints (enabled or disabled) are considered. + stage: The stage where the prim exists. Defaults to None, in which case the current stage is used. + + Returns: + The fixed joint prim that has only one target. If no such fixed joint prim exists, it returns None. + + Raises: + ValueError: If the prim path is not global (i.e: does not start with '/'). + ValueError: If the prim path does not exist on the stage. + """ + # get stage handle + if stage is None: + stage = get_current_stage() + + # check prim path is global + if not prim_path.startswith("/"): + raise ValueError(f"Prim path '{prim_path}' is not global. It must start with '/'.") + + # check if prim exists + prim = stage.GetPrimAtPath(prim_path) + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim_path}' is not valid.") + + fixed_joint_prim = None + # we check all joints under the root prim and classify the asset as fixed base if there exists + # a fixed joint that has only one target (i.e. the root link). + for prim in Usd.PrimRange(prim): + # note: ideally checking if it is FixedJoint would have been enough, but some assets use "Joint" as the + # schema name which makes it difficult to distinguish between the two. + joint_prim = UsdPhysics.Joint(prim) + if joint_prim: + # if check_enabled_only is True, we only consider enabled joints + if check_enabled_only and not joint_prim.GetJointEnabledAttr().Get(): + continue + # check body 0 and body 1 exist + body_0_exist = joint_prim.GetBody0Rel().GetTargets() != [] + body_1_exist = joint_prim.GetBody1Rel().GetTargets() != [] + # if either body 0 or body 1 does not exist, we have a fixed joint that connects to the world + if not (body_0_exist and body_1_exist): + fixed_joint_prim = joint_prim + break + + return fixed_joint_prim diff --git a/source/isaaclab/isaaclab/sim/utils/semantics.py b/source/isaaclab/isaaclab/sim/utils/semantics.py new file mode 100644 index 0000000000000000000000000000000000000000..c5e95562887e0038f2260930eb17e766fd3c9427 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/semantics.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for applying and removing semantic labels to USD prims.""" + +from __future__ import annotations + +import contextlib +import logging + +from pxr import Usd, UsdGeom + +# USD Semantics is only available in Isaac Sim 5.0 and later. +with contextlib.suppress(ModuleNotFoundError, ImportError, RuntimeError): + from pxr import UsdSemantics + +from isaaclab.utils.version import get_isaac_sim_version + +from .stage import get_current_stage + +# import logger +logger = logging.getLogger(__name__) + + +def add_labels(prim: Usd.Prim, labels: list[str], instance_name: str = "class", overwrite: bool = True) -> None: + """Apply semantic labels to a prim using the :class:`UsdSemantics.LabelsAPI`. + + This function is a wrapper around the :func:`omni.replicator.core.functional.modify.semantics` function. + It applies the labels to the prim using the :class:`UsdSemantics.LabelsAPI`. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later, which introduces the :class:`UsdSemantics.LabelsAPI`. + For previous versions, the function falls back to use the deprecated :class:`UsdSemantics.SemanticsAPI` instead. + + Example: + >>> prim = sim_utils.create_prim("/World/Test/Sphere", "Sphere", stage=stage, attributes={"radius": 10.0}) + >>> sim_utils.add_labels(prim, labels=["sphere"], instance_name="class") + + Args: + prim: The USD prim to add or update labels on. + labels: The list of labels to apply. + instance_name: The name of the semantic instance. Defaults to "class". + overwrite: Whether to overwrite existing labels for this instance. If False, + the new labels are appended to existing ones (if any). Defaults to True. + """ + # Try modern approach (Isaac Sim >= 5.0) + try: + import omni.replicator.core.functional as rep_functional + + mode = "replace" if overwrite else "add" + rep_functional.modify.semantics(prim, {instance_name: labels}, mode=mode) + + return + except (ModuleNotFoundError, ImportError) as e: + # check if we are using isaac sim 5.0 + if get_isaac_sim_version().major >= 5: + logger.warning( + f"Failed to add labels to prim {prim.GetPath()} using Replicator API: {e}. " + "\nPlease ensure Replicator API is enabled by passing '--enable_cameras' to the AppLauncher." + "\nFalling back to legacy approach." + ) + + # Try legacy approach (Isaac Sim < 5.0) + try: + import Semantics + + # check we have only one label + if len(labels) != 1: + raise ValueError(f"Only one label can be applied to a prim. Received: {labels}") + # set the semantic API for the instance + instance_name = f"{instance_name}_{labels[0]}" + sem = Semantics.SemanticsAPI.Apply(prim, instance_name) + # create semantic type and data attributes + sem.CreateSemanticTypeAttr() + sem.CreateSemanticDataAttr() + sem.GetSemanticTypeAttr().Set(instance_name) + sem.GetSemanticDataAttr().Set(labels[0]) + except Exception as e: + logger.warning( + f"Failed to add labels to prim {prim.GetPath()} using legacy API: {e}. " + "\nSemantics functionality may not be available in this Isaac Sim version." + " Please open an issue at https://github.com/isaac-sim/IsaacLab/issues if you believe this is a bug." + ) + + +def get_labels(prim: Usd.Prim) -> dict[str, list[str]]: + """Get all semantic labels (:class:`UsdSemantics.LabelsAPI`) applied to a prim. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For previous versions, + please use :mod:`isaacsim.core.utils.semantics` module instead. + + Args: + prim: The USD prim to return labels for. + + Returns: + A dictionary mapping instance names to a list of labels. + If no labels are found, it returns an empty dictionary. + """ + # Check if UsdSemantics is available + if 'UsdSemantics' not in globals(): + logger.warning("UsdSemantics module is not available. Returning empty dictionary.") + return {} + + result = {} + for schema_name in prim.GetAppliedSchemas(): + if schema_name.startswith("SemanticsLabelsAPI:"): + instance_name = schema_name.split(":", 1)[1] + sem_api = UsdSemantics.LabelsAPI(prim, instance_name) + labels_attr = sem_api.GetLabelsAttr() + if labels_attr: + labels = labels_attr.Get() + result[instance_name] = list(labels) if labels is not None else [] + else: + result[instance_name] = [] + return result + + +def remove_labels(prim: Usd.Prim, instance_name: str | None = None, include_descendants: bool = False): + """Removes semantic labels (:class:`UsdSemantics.LabelsAPI`) from a prim and optionally its descendants. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For previous versions, + please use :mod:`isaacsim.core.utils.semantics` module instead. + + Args: + prim: The USD prim to remove labels from. + instance_name: The specific instance name to remove. Defaults to None, in which case + *all* labels are removed. + include_descendants: Whether to also traverse children and remove labels recursively. + Defaults to False. + """ + # Check if UsdSemantics is available + if 'UsdSemantics' not in globals(): + logger.warning("UsdSemantics module is not available. Cannot remove labels.") + return + + def _remove_single_prim_labels(target_prim: Usd.Prim): + """Helper function to remove labels from a single prim.""" + schemas_to_remove = [] + for schema_name in target_prim.GetAppliedSchemas(): + if schema_name.startswith("SemanticsLabelsAPI:"): + current_instance = schema_name.split(":", 1)[1] + if instance_name is None or current_instance == instance_name: + schemas_to_remove.append(current_instance) + + for inst_to_remove in schemas_to_remove: + target_prim.RemoveAPI(UsdSemantics.LabelsAPI, inst_to_remove) + + if include_descendants: + for p in Usd.PrimRange(prim): + _remove_single_prim_labels(p) + else: + _remove_single_prim_labels(prim) + + +def check_missing_labels(prim_path: str | None = None, stage: Usd.Stage | None = None) -> list[str]: + """Checks whether the prim and its descendants at the provided path have missing + semantic labels (:class:`UsdSemantics.LabelsAPI`). + + .. note:: + The function checks only prims that are :class:`UsdGeom.Gprim` type. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For previous versions, + please use :mod:`isaacsim.core.utils.semantics` module instead. + + Args: + prim_path: The prim path to search from. If None, the entire stage is inspected. + stage: The stage to search from. If None, the current stage is used. + + Returns: + A list containing prim paths to prims with no labels applied. + """ + # check if stage is valid + stage = stage if stage else get_current_stage() + + # check if inspect path is valid + start_prim = stage.GetPrimAtPath(prim_path) if prim_path else stage.GetPseudoRoot() + if not start_prim: + # Allow None prim_path for whole stage check, warn if path specified but not found + if prim_path: + logger.warning(f"No prim found at path '{prim_path}'. Returning from check for semantic labels.") + return [] + + # iterate over prim and its children + prim_paths = [] + for prim in Usd.PrimRange(start_prim): + if prim.IsA(UsdGeom.Gprim): + has_any_label = False + for schema_name in prim.GetAppliedSchemas(): + if schema_name.startswith("SemanticsLabelsAPI:"): + has_any_label = True + break + if not has_any_label: + prim_paths.append(prim.GetPath().pathString) + + return prim_paths + + +def count_total_labels(prim_path: str | None = None, stage: Usd.Stage | None = None) -> dict[str, int]: + """Counts the number of semantic labels (:class:`UsdSemantics.LabelsAPI`) applied to the prims at the provided path. + + This function iterates over all the prims from the provided path and counts the number of times + each label is applied to the prims. It returns a dictionary of labels and their corresponding count. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For previous versions, + please use :mod:`isaacsim.core.utils.semantics` module instead. + + Args: + prim_path: The prim path to search from. If None, the entire stage is inspected. + stage: The stage to search from. If None, the current stage is used. + + Returns: + A dictionary mapping individual labels to their total count across all instances. + The dictionary includes a 'missing_labels' count for prims with no labels. + """ + stage = stage if stage else get_current_stage() + + start_prim = stage.GetPrimAtPath(prim_path) if prim_path else stage.GetPseudoRoot() + if not start_prim: + if prim_path: + logger.warning(f"No prim found at path '{prim_path}'. Returning from count for semantic labels.") + return {"missing_labels": 0} + + labels_counter = {"missing_labels": 0} + for prim in Usd.PrimRange(start_prim): + if prim.IsA(UsdGeom.Gprim): + labels_dict = get_labels(prim) + if not labels_dict: + labels_counter["missing_labels"] += 1 + else: + # Iterate through all labels from all instances on the prim + all_labels = [label for sublist in labels_dict.values() for label in sublist if label] + for label in all_labels: + labels_counter[label] = labels_counter.get(label, 0) + 1 + + return labels_counter diff --git a/source/isaaclab/isaaclab/sim/utils/stage.py b/source/isaaclab/isaaclab/sim/utils/stage.py new file mode 100644 index 0000000000000000000000000000000000000000..3622e3cd607bf942e16ef90ff1fda63d10b78b7b --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/stage.py @@ -0,0 +1,484 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for operating on the USD stage.""" + +import builtins +import contextlib +import logging +import threading +from collections.abc import Callable, Generator + +import omni.kit.app +import omni.usd +from isaacsim.core.utils import stage as sim_stage +from pxr import Sdf, Usd, UsdUtils + +from isaaclab.utils.version import get_isaac_sim_version + +# import logger +logger = logging.getLogger(__name__) +_context = threading.local() # thread-local storage to handle nested contexts and concurrent access + +# _context is a singleton design in isaacsim and for that reason +# until we fully replace all modules that references the singleton(such as XformPrim, Prim ....), we have to point +# that singleton to this _context +sim_stage._context = _context # type: ignore + + +def create_new_stage() -> Usd.Stage: + """Create a new stage attached to the USD context. + + Returns: + Usd.Stage: The created USD stage. + + Raises: + RuntimeError: When failed to create a new stage. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.create_new_stage() + Usd.Stage.Open(rootLayer=Sdf.Find('anon:0x7fba6c04f840:World7.usd'), + sessionLayer=Sdf.Find('anon:0x7fba6c01c5c0:World7-session.usda'), + pathResolverContext=) + """ + result = omni.usd.get_context().new_stage() + if result: + return omni.usd.get_context().get_stage() + else: + raise RuntimeError("Failed to create a new stage. Please check if the USD context is valid.") + + +def create_new_stage_in_memory() -> Usd.Stage: + """Creates a new stage in memory, if supported. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For backwards + compatibility, it falls back to creating a new stage attached to the USD context. + + Returns: + The new stage in memory. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.create_new_stage_in_memory() + Usd.Stage.Open(rootLayer=Sdf.Find('anon:0xf7b00e0:tmp.usda'), + sessionLayer=Sdf.Find('anon:0xf7cd2e0:tmp-session.usda'), + pathResolverContext=) + """ + if get_isaac_sim_version().major < 5: + logger.warning( + "Isaac Sim < 5.0 does not support creating a new stage in memory. Falling back to creating a new" + " stage attached to USD context." + ) + return create_new_stage() + else: + return Usd.Stage.CreateInMemory() + + +def is_current_stage_in_memory() -> bool: + """Checks if the current stage is in memory. + + This function compares the stage id of the current USD stage with the stage id of the USD context stage. + + Returns: + Whether the current stage is in memory. + """ + # grab current stage id + stage_id = get_current_stage_id() + + # grab context stage id + context_stage = omni.usd.get_context().get_stage() + with use_stage(context_stage): + context_stage_id = get_current_stage_id() + + # check if stage ids are the same + return stage_id != context_stage_id + + +def open_stage(usd_path: str) -> bool: + """Open the given usd file and replace currently opened stage. + + Args: + usd_path: The path to the USD file to open. + + Returns: + True if operation is successful, otherwise False. + + Raises: + ValueError: When input path is not a supported file type by USD. + """ + # check if USD file is supported + if not Usd.Stage.IsSupportedFile(usd_path): + raise ValueError(f"The USD file at path '{usd_path}' is not supported.") + + # get USD context + usd_context = omni.usd.get_context() + # disable save to recent files + usd_context.disable_save_to_recent_files() + # open stage + result = usd_context.open_stage(usd_path) + # enable save to recent files + usd_context.enable_save_to_recent_files() + # return result + return result + + +@contextlib.contextmanager +def use_stage(stage: Usd.Stage) -> Generator[None, None, None]: + """Context manager that sets a thread-local stage, if supported. + + This function binds the stage to the thread-local context for the duration of the context manager. + During the context manager, any call to :func:`get_current_stage` will return the stage specified + in the context manager. After the context manager is exited, the stage is restored to the default + stage attached to the USD context. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For backwards + compatibility, it falls back to a no-op context manager in Isaac Sim < 5.0. + + Args: + stage: The stage to set in the context. + + Returns: + A context manager that sets the stage in the context. + + Raises: + AssertionError: If the stage is not a USD stage instance. + + Example: + >>> from pxr import Usd + >>> import isaaclab.sim as sim_utils + >>> + >>> stage_in_memory = Usd.Stage.CreateInMemory() + >>> with sim_utils.use_stage(stage_in_memory): + ... # operate on the specified stage + ... pass + >>> # operate on the default stage attached to the USD context + """ + if get_isaac_sim_version().major < 5: + logger.warning("Isaac Sim < 5.0 does not support thread-local stage contexts. Skipping use_stage().") + yield # no-op + else: + # check stage + if not isinstance(stage, Usd.Stage): + raise TypeError(f"Expected a USD stage instance, got: {type(stage)}") + # store previous context value if it exists + previous_stage = getattr(_context, "stage", None) + # set new context value + try: + _context.stage = stage + yield + # remove context value or restore previous one if it exists + finally: + if previous_stage is None: + delattr(_context, "stage") + else: + _context.stage = previous_stage + + +def update_stage() -> None: + """Updates the current stage by triggering an application update cycle. + + This function triggers a single update cycle of the application interface, which + in turn updates the stage and all associated systems (rendering, physics, etc.). + This is necessary to ensure that changes made to the stage are properly processed + and reflected in the simulation. + + Note: + This function calls the application update interface rather than directly + updating the stage because the stage update is part of the broader + application update cycle that includes rendering, physics, and other systems. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.update_stage() + """ + # TODO: Why is this updating the simulation and not the stage? + omni.kit.app.get_app_interface().update() + + +def save_stage(usd_path: str, save_and_reload_in_place: bool = True) -> bool: + """Saves contents of the root layer of the current stage to the specified USD file. + + If the file already exists, it will be overwritten. + + Args: + usd_path: The file path to save the current stage to + save_and_reload_in_place: Whether to open the saved USD file in place. Defaults to True. + + Returns: + True if operation is successful, otherwise False. + + Raises: + ValueError: When input path is not a supported file type by USD. + RuntimeError: When layer creation or save operation fails. + """ + # check if USD file is supported + if not Usd.Stage.IsSupportedFile(usd_path): + raise ValueError(f"The USD file at path '{usd_path}' is not supported.") + + # create new layer + layer = Sdf.Layer.CreateNew(usd_path) + if layer is None: + raise RuntimeError(f"Failed to create new USD layer at path '{usd_path}'.") + + # get root layer + root_layer = get_current_stage().GetRootLayer() + # transfer content from root layer to new layer + layer.TransferContent(root_layer) + # resolve paths + omni.usd.resolve_paths(root_layer.identifier, layer.identifier) + # save layer + result = layer.Save() + if not result: + logger.error(f"Failed to save USD layer to path '{usd_path}'.") + + # if requested, open the saved USD file in place + if save_and_reload_in_place and result: + open_stage(usd_path) + + return result + + +def close_stage(callback_fn: Callable[[bool, str], None] | None = None) -> bool: + """Closes the current USD stage. + + .. note:: + + Once the stage is closed, it is necessary to open a new stage or create a + new one in order to work on it. + + Args: + callback_fn: A callback function to call while closing the stage. + The function should take two arguments: a boolean indicating whether the stage is closing + and a string indicating the error message if the stage closing fails. Defaults to None, + in which case the stage will be closed without a callback. + + Returns: + True if operation is successful, otherwise False. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.close_stage() + True + >>> + + Example with callback function: + >>> import isaaclab.sim as sim_utils + >>> + >>> def callback(*args, **kwargs): + ... print("callback:", args, kwargs) + >>> sim_utils.close_stage(callback) + True + >>> sim_utils.close_stage(callback) + callback: (False, 'Stage opening or closing already in progress!!') {} + False + """ + if callback_fn is None: + result = omni.usd.get_context().close_stage() + else: + result = omni.usd.get_context().close_stage_with_callback(callback_fn) + return result + + +def clear_stage(predicate: Callable[[Usd.Prim], bool] | None = None) -> None: + """Deletes all prims in the stage without populating the undo command buffer. + + The function will delete all prims in the stage that satisfy the predicate. If the predicate + is None, a default predicate will be used that deletes all prims. The default predicate deletes + all prims that are not the root prim, are not under the /Render namespace, have the ``no_delete`` + metadata, are not ancestral to any other prim, and are not hidden in the stage window. + + Args: + predicate: A user defined function that takes the USD prim as an argument and + returns a boolean indicating if the prim should be deleted. If the predicate is None, + a default predicate will be used that deletes all prims. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # clear the whole stage + >>> sim_utils.clear_stage() + >>> + >>> # given the stage: /World/Cube, /World/Cube_01, /World/Cube_02. + >>> # Delete only the prims of type Cube + >>> predicate = lambda _prim: _prim.GetTypeName() == "Cube" + >>> sim_utils.clear_stage(predicate) # after the execution the stage will be /World + """ + # Note: Need to import this here to prevent circular dependencies. + from .prims import delete_prim + from .queries import get_all_matching_child_prims + + def _default_predicate(prim: Usd.Prim) -> bool: + """Check if the prim should be deleted.""" + prim_path = prim.GetPath().pathString + if prim_path == "/": + return False + if prim_path.startswith("/Render"): + return False + if prim.GetMetadata("no_delete"): + return False + if prim.GetMetadata("hide_in_stage_window"): + return False + if omni.usd.check_ancestral(prim): + return False + return True + + def _predicate_from_path(prim: Usd.Prim) -> bool: + if predicate is None: + return _default_predicate(prim) + return predicate(prim) + + # get all prims to delete + if predicate is None: + prims = get_all_matching_child_prims("/", _default_predicate) + else: + prims = get_all_matching_child_prims("/", _predicate_from_path) + # convert prims to prim paths + prim_paths_to_delete = [prim.GetPath().pathString for prim in prims] + # delete prims + delete_prim(prim_paths_to_delete) + + if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False: # type: ignore + omni.kit.app.get_app_interface().update() + + +def is_stage_loading() -> bool: + """Convenience function to see if any files are being loaded. + + Returns: + bool: True if loading, False otherwise + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.is_stage_loading() + False + """ + context = omni.usd.get_context() + if context is None: + return False + else: + _, _, loading = context.get_stage_loading_status() + return loading > 0 + + +def get_current_stage(fabric: bool = False) -> Usd.Stage: + """Get the current open USD or Fabric stage + + Args: + fabric: True to get the fabric stage. False to get the USD stage. Defaults to False. + + Returns: + The USD or Fabric stage as specified by the input arg fabric. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.get_current_stage() + Usd.Stage.Open(rootLayer=Sdf.Find('anon:0x7fba6c04f840:World7.usd'), + sessionLayer=Sdf.Find('anon:0x7fba6c01c5c0:World7-session.usda'), + pathResolverContext=) + """ + stage = getattr(_context, "stage", omni.usd.get_context().get_stage()) + return stage + + +def get_current_stage_id() -> int: + """Get the current open stage ID. + + Returns: + The current open stage id. + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> sim_utils.get_current_stage_id() + 1234567890 + """ + # get current stage + stage = get_current_stage() + # retrieve stage ID from stage cache + stage_cache = UsdUtils.StageCache.Get() + stage_id = stage_cache.GetId(stage).ToLongInt() + # if stage ID is not found, insert it into the stage cache + if stage_id < 0: + stage_id = stage_cache.Insert(stage).ToLongInt() + # return stage ID + return stage_id + + +def attach_stage_to_usd_context(attaching_early: bool = False): + """Attaches the current USD stage in memory to the USD context. + + This function should be called during or after scene is created and before stage is simulated or rendered. + If the stage is not in memory or rendering is not enabled, this function will return without attaching. + + .. versionadded:: 2.3.0 + This function is available in Isaac Sim 5.0 and later. For backwards + compatibility, it returns without attaching to the USD context. + + Args: + attaching_early: Whether to attach the stage to the usd context before stage is created. Defaults to False. + """ + + import carb + import omni.physx + import omni.usd + from isaacsim.core.simulation_manager import SimulationManager + + from isaaclab.sim.simulation_context import SimulationContext + + # if Isaac Sim version is less than 5.0, stage in memory is not supported + if get_isaac_sim_version().major < 5: + return + + # if stage is not in memory, we can return early + if not is_current_stage_in_memory(): + return + + # attach stage to physx + stage_id = get_current_stage_id() + physx_sim_interface = omni.physx.get_physx_simulation_interface() + physx_sim_interface.attach_stage(stage_id) + + # this carb flag is equivalent to if rendering is enabled + carb_setting = carb.settings.get_settings() # type: ignore + is_rendering_enabled = carb_setting.get("/physics/fabricUpdateTransformations") + + # if rendering is not enabled, we don't need to attach it + if not is_rendering_enabled: + return + + # early attach warning msg + if attaching_early: + logger.warning( + "Attaching stage in memory to USD context early to support an operation which" + " does not support stage in memory." + ) + + # skip this callback to avoid wiping the stage after attachment + SimulationContext.instance().skip_next_stage_open_callback() + + # disable stage open callback to avoid clearing callbacks + SimulationManager.enable_stage_open_callback(False) + + # enable physics fabric + SimulationContext.instance()._physics_context.enable_fabric(True) # type: ignore + + # attach stage to usd context + omni.usd.get_context().attach_stage_with_callback(stage_id) + + # attach stage to physx + physx_sim_interface = omni.physx.get_physx_simulation_interface() + physx_sim_interface.attach_stage(stage_id) + + # re-enable stage open callback + SimulationManager.enable_stage_open_callback(True) diff --git a/source/isaaclab/isaaclab/sim/utils/transforms.py b/source/isaaclab/isaaclab/sim/utils/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ae57a16a6a182969bc0ba14c4c931a1f7b5107 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/utils/transforms.py @@ -0,0 +1,453 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for working with USD transform (xform) operations. + +This module provides utilities for manipulating USD transform operations (xform ops) on prims. +Transform operations in USD define how geometry is positioned, oriented, and scaled in 3D space. + +The utilities in this module help standardize transform stacks, clear operations, and manipulate +transforms in a consistent way across different USD assets. +""" + +from __future__ import annotations + +import logging + +from pxr import Gf, Sdf, Usd, UsdGeom + +# import logger +logger = logging.getLogger(__name__) + +_INVALID_XFORM_OPS = [ + "xformOp:rotateX", + "xformOp:rotateXZY", + "xformOp:rotateY", + "xformOp:rotateYXZ", + "xformOp:rotateYZX", + "xformOp:rotateZ", + "xformOp:rotateZYX", + "xformOp:rotateZXY", + "xformOp:rotateXYZ", + "xformOp:transform", +] +"""List of invalid xform ops that should be removed.""" + + +def standardize_xform_ops( + prim: Usd.Prim, + translation: tuple[float, ...] | None = None, + orientation: tuple[float, ...] | None = None, + scale: tuple[float, ...] | None = None, +) -> bool: + """Standardize the transform operation stack on a USD prim to a canonical form. + + This function converts a prim's transform stack to use the standard USD transform operation + order: [translate, orient, scale]. The function performs the following operations: + + 1. Validates that the prim is Xformable + 2. Captures the current local transform (translation, rotation, scale) + 3. Resolves and bakes unit scale conversions (xformOp:scale:unitsResolve) + 4. Creates or reuses standard transform operations (translate, orient, scale) + 5. Sets the transform operation order to [translate, orient, scale] + 6. Applies the preserved or user-specified transform values + + The entire modification is performed within an ``Sdf.ChangeBlock`` for optimal performance + when processing multiple prims. + + .. note:: + **Standard Transform Order:** The function enforces the USD best practice order: + ``xformOp:translate``, ``xformOp:orient``, ``xformOp:scale``. This order is + compatible with most USD tools and workflows, and uses quaternions for rotation + (avoiding gimbal lock issues). + + .. note:: + **Pose Preservation:** By default, the function preserves the prim's local transform + (relative to its parent). The world-space position of the prim remains unchanged + unless explicit ``translation``, ``orientation``, or ``scale`` values are provided. + + .. warning:: + **Animation Data Loss:** This function only preserves transform values at the default + time code (``Usd.TimeCode.Default()``). Any animation or time-sampled transform data + will be lost. Use this function during asset import or preparation, not on animated prims. + + .. warning:: + **Unit Scale Resolution:** If the prim has a ``xformOp:scale:unitsResolve`` attribute + (common in imported assets with unit mismatches), it will be baked into the scale + and removed. For example, a scale of (1, 1, 1) with unitsResolve of (100, 100, 100) + becomes a final scale of (100, 100, 100). + + Args: + prim: The USD prim to standardize. Must be a valid prim that supports the + UsdGeom.Xformable schema (e.g., Xform, Mesh, Cube, etc.). Material and + Shader prims are not Xformable and will return False. + translation: Optional translation vector (x, y, z) in local space. If provided, + overrides the prim's current translation. If None, preserves the current + local translation. Defaults to None. + orientation: Optional orientation quaternion (w, x, y, z) in local space. If provided, + overrides the prim's current orientation. If None, preserves the current + local orientation. Defaults to None. + scale: Optional scale vector (x, y, z). If provided, overrides the prim's current scale. + If None, preserves the current scale (after unit resolution) or uses (1, 1, 1) + if no scale exists. Defaults to None. + + Returns: + bool: True if the transform operations were successfully standardized. False if the + prim is not Xformable (e.g., Material, Shader prims). The function will log an + error message when returning False. + + Raises: + ValueError: If the prim is not valid (i.e., does not exist or is an invalid prim). + + Example: + >>> import isaaclab.sim as sim_utils + >>> + >>> # Standardize a prim with non-standard transform operations + >>> prim = stage.GetPrimAtPath("/World/ImportedAsset") + >>> result = sim_utils.standardize_xform_ops(prim) + >>> if result: + ... print("Transform stack standardized successfully") + >>> # The prim now uses: [translate, orient, scale] in that order + >>> + >>> # Standardize and set new transform values + >>> sim_utils.standardize_xform_ops( + ... prim, + ... translation=(1.0, 2.0, 3.0), + ... orientation=(1.0, 0.0, 0.0, 0.0), # identity rotation (w, x, y, z) + ... scale=(2.0, 2.0, 2.0), + ... ) + >>> + >>> # Batch processing for performance + >>> prims_to_standardize = [stage.GetPrimAtPath(p) for p in prim_paths] + >>> for prim in prims_to_standardize: + ... sim_utils.standardize_xform_ops(prim) # Each call uses Sdf.ChangeBlock + """ + # Validate prim + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim.GetPath()}' is not valid.") + + # Check if prim is an Xformable + if not prim.IsA(UsdGeom.Xformable): + logger.error( + f"Prim at path '{prim.GetPath().pathString}' is of type '{prim.GetTypeName()}', " + "which is not an Xformable. Transform operations will not be standardized. " + "This is expected for material, shader, and scope prims." + ) + return False + + # Create xformable interface + xformable = UsdGeom.Xformable(prim) + # Get current property names + prop_names = prim.GetPropertyNames() + + # Obtain current local transformations + tf = Gf.Transform(xformable.GetLocalTransformation()) + xform_pos = Gf.Vec3d(tf.GetTranslation()) + xform_quat = Gf.Quatd(tf.GetRotation().GetQuat()) + xform_scale = Gf.Vec3d(tf.GetScale()) + + if translation is not None: + xform_pos = Gf.Vec3d(*translation) + if orientation is not None: + xform_quat = Gf.Quatd(*orientation) + + # Handle scale resolution + if scale is not None: + # User provided scale + xform_scale = Gf.Vec3d(scale) + elif "xformOp:scale" in prop_names: + # Handle unit resolution for scale if present + # This occurs when assets are imported with different unit scales + # Reference: Omniverse Metrics Assembler + if "xformOp:scale:unitsResolve" in prop_names: + units_resolve = prim.GetAttribute("xformOp:scale:unitsResolve").Get() + for i in range(3): + xform_scale[i] = xform_scale[i] * units_resolve[i] + else: + # No scale exists, use default uniform scale + xform_scale = Gf.Vec3d(1.0, 1.0, 1.0) + + # Verify if xform stack is reset + has_reset = xformable.GetResetXformStack() + # Batch the operations + with Sdf.ChangeBlock(): + # Clear the existing transform operation order + for prop_name in prop_names: + if prop_name in _INVALID_XFORM_OPS: + prim.RemoveProperty(prop_name) + + # Remove unitsResolve attribute if present (already handled in scale resolution above) + if "xformOp:scale:unitsResolve" in prop_names: + prim.RemoveProperty("xformOp:scale:unitsResolve") + + # Set up or retrieve scale operation + xform_op_scale = UsdGeom.XformOp(prim.GetAttribute("xformOp:scale")) + if not xform_op_scale: + xform_op_scale = xformable.AddXformOp(UsdGeom.XformOp.TypeScale, UsdGeom.XformOp.PrecisionDouble, "") + + # Set up or retrieve translate operation + xform_op_translate = UsdGeom.XformOp(prim.GetAttribute("xformOp:translate")) + if not xform_op_translate: + xform_op_translate = xformable.AddXformOp( + UsdGeom.XformOp.TypeTranslate, UsdGeom.XformOp.PrecisionDouble, "" + ) + + # Set up or retrieve orient (quaternion rotation) operation + xform_op_orient = UsdGeom.XformOp(prim.GetAttribute("xformOp:orient")) + if not xform_op_orient: + xform_op_orient = xformable.AddXformOp(UsdGeom.XformOp.TypeOrient, UsdGeom.XformOp.PrecisionDouble, "") + + # Handle different floating point precisions + # Existing Xform operations might have floating or double precision. + # We need to cast the data to the correct type to avoid setting the wrong type. + xform_ops = [xform_op_translate, xform_op_orient, xform_op_scale] + xform_values = [xform_pos, xform_quat, xform_scale] + for xform_op, value in zip(xform_ops, xform_values): + # Get current value to determine precision type + current_value = xform_op.Get() + # Cast to existing type to preserve precision (float/double) + xform_op.Set(type(current_value)(value) if current_value is not None else value) + + # Set the transform operation order: translate -> orient -> scale + # This is the standard USD convention and ensures consistent behavior + xformable.SetXformOpOrder([xform_op_translate, xform_op_orient, xform_op_scale], has_reset) + + return True + + +def validate_standard_xform_ops(prim: Usd.Prim) -> bool: + """Validate if the transform operations on a prim are standardized. + + This function checks if the transform operations on a prim are standardized to the canonical form: + [translate, orient, scale]. + + Args: + prim: The USD prim to validate. + """ + # check if prim is valid + if not prim.IsValid(): + logger.error(f"Prim at path '{prim.GetPath().pathString}' is not valid.") + return False + # check if prim is an xformable + if not prim.IsA(UsdGeom.Xformable): + logger.error(f"Prim at path '{prim.GetPath().pathString}' is not an xformable.") + return False + # get the xformable interface + xformable = UsdGeom.Xformable(prim) + # get the xform operation order + xform_op_order = xformable.GetOrderedXformOps() + xform_op_order = [op.GetOpName() for op in xform_op_order] + # check if the xform operation order is the canonical form + if xform_op_order != ["xformOp:translate", "xformOp:orient", "xformOp:scale"]: + msg = f"Xform operation order for prim at path '{prim.GetPath().pathString}' is not the canonical form." + msg += f" Received order: {xform_op_order}" + msg += " Expected order: ['xformOp:translate', 'xformOp:orient', 'xformOp:scale']" + logger.error(msg) + return False + return True + + +def resolve_prim_pose( + prim: Usd.Prim, ref_prim: Usd.Prim | None = None +) -> tuple[tuple[float, float, float], tuple[float, float, float, float]]: + """Resolve the pose of a prim with respect to another prim. + + Note: + This function ignores scale and skew by orthonormalizing the transformation + matrix at the final step. However, if any ancestor prim in the hierarchy + has non-uniform scale, that scale will still affect the resulting position + and orientation of the prim (because it's baked into the transform before + scale removal). + + In other words: scale **is not removed hierarchically**. If you need + completely scale-free poses, you must walk the transform chain and strip + scale at each level. Please open an issue if you need this functionality. + + Args: + prim: The USD prim to resolve the pose for. + ref_prim: The USD prim to compute the pose with respect to. + Defaults to None, in which case the world frame is used. + + Returns: + A tuple containing the position (as a 3D vector) and the quaternion orientation + in the (w, x, y, z) format. + + Raises: + ValueError: If the prim or ref prim is not valid. + + Example: + >>> import isaaclab.sim as sim_utils + >>> from pxr import Usd, UsdGeom + >>> + >>> # Get prim + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/ImportedAsset") + >>> + >>> # Resolve pose + >>> pos, quat = sim_utils.resolve_prim_pose(prim) + >>> print(f"Position: {pos}") + >>> print(f"Orientation: {quat}") + >>> + >>> # Resolve pose with respect to another prim + >>> ref_prim = stage.GetPrimAtPath("/World/Reference") + >>> pos, quat = sim_utils.resolve_prim_pose(prim, ref_prim) + >>> print(f"Position: {pos}") + >>> print(f"Orientation: {quat}") + """ + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim.GetPath().pathString}' is not valid.") + # get prim xform + xform = UsdGeom.Xformable(prim) + prim_tf = xform.ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + # sanitize quaternion + # this is needed, otherwise the quaternion might be non-normalized + prim_tf.Orthonormalize() + + if ref_prim is not None: + # if reference prim is the root, we can skip the computation + if ref_prim.GetPath() != Sdf.Path.absoluteRootPath: + # get ref prim xform + ref_xform = UsdGeom.Xformable(ref_prim) + ref_tf = ref_xform.ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + # make sure ref tf is orthonormal + ref_tf.Orthonormalize() + # compute relative transform to get prim in ref frame + prim_tf = prim_tf * ref_tf.GetInverse() + + # extract position and orientation + prim_pos = [*prim_tf.ExtractTranslation()] + prim_quat = [prim_tf.ExtractRotationQuat().real, *prim_tf.ExtractRotationQuat().imaginary] + return tuple(prim_pos), tuple(prim_quat) + + +def resolve_prim_scale(prim: Usd.Prim) -> tuple[float, float, float]: + """Resolve the scale of a prim in the world frame. + + At an attribute level, a USD prim's scale is a scaling transformation applied to the prim with + respect to its parent prim. This function resolves the scale of the prim in the world frame, + by computing the local to world transform of the prim. This is equivalent to traversing up + the prim hierarchy and accounting for the rotations and scales of the prims. + + For instance, if a prim has a scale of (1, 2, 3) and it is a child of a prim with a scale of (4, 5, 6), + then the scale of the prim in the world frame is (4, 10, 18). + + Args: + prim: The USD prim to resolve the scale for. + + Returns: + The scale of the prim in the x, y, and z directions in the world frame. + + Raises: + ValueError: If the prim is not valid. + + Example: + >>> import isaaclab.sim as sim_utils + >>> from pxr import Usd, UsdGeom + >>> + >>> # Get prim + >>> stage = sim_utils.get_current_stage() + >>> prim = stage.GetPrimAtPath("/World/ImportedAsset") + >>> + >>> # Resolve scale + >>> scale = sim_utils.resolve_prim_scale(prim) + >>> print(f"Scale: {scale}") + """ + # check if prim is valid + if not prim.IsValid(): + raise ValueError(f"Prim at path '{prim.GetPath().pathString}' is not valid.") + # compute local to world transform + xform = UsdGeom.Xformable(prim) + world_transform = xform.ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + # extract scale + return tuple([*(v.GetLength() for v in world_transform.ExtractRotationMatrix())]) + + +def convert_world_pose_to_local( + position: tuple[float, ...], + orientation: tuple[float, ...] | None, + ref_prim: Usd.Prim, +) -> tuple[tuple[float, float, float], tuple[float, float, float, float] | None]: + """Convert a world-space pose to local-space pose relative to a reference prim. + + This function takes a position and orientation in world space and converts them to local space + relative to the given reference prim. This is useful when creating or positioning prims where you + know the desired world position but need to set local transform attributes relative to another prim. + + The conversion uses the standard USD transformation math: + ``local_transform = world_transform * inverse(ref_world_transform)`` + + .. note:: + If the reference prim is the root prim ("/"), the position and orientation are returned + unchanged, as they are already effectively in local/world space. + + Args: + position: The world-space position as (x, y, z). + orientation: The world-space orientation as quaternion (w, x, y, z). If None, only position is converted + and None is returned for orientation. + ref_prim: The reference USD prim to compute the local transform relative to. If this is + the root prim ("/"), the world pose is returned unchanged. + + Returns: + A tuple of (local_translation, local_orientation) where: + + - local_translation is a tuple of (x, y, z) in local space relative to ref_prim + - local_orientation is a tuple of (w, x, y, z) in local space relative to ref_prim, + or None if no orientation was provided + + Raises: + ValueError: If the reference prim is not a valid USD prim. + + Example: + >>> import isaaclab.sim as sim_utils + >>> from pxr import Usd, UsdGeom + >>> + >>> # Get reference prim + >>> stage = sim_utils.get_current_stage() + >>> ref_prim = stage.GetPrimAtPath("/World/Reference") + >>> + >>> # Convert world pose to local (relative to ref_prim) + >>> world_pos = (10.0, 5.0, 0.0) + >>> world_quat = (1.0, 0.0, 0.0, 0.0) # identity rotation + >>> local_pos, local_quat = sim_utils.convert_world_pose_to_local(world_pos, world_quat, ref_prim) + >>> print(f"Local position: {local_pos}") + >>> print(f"Local orientation: {local_quat}") + """ + # Check if prim is valid + if not ref_prim.IsValid(): + raise ValueError(f"Reference prim at path '{ref_prim.GetPath().pathString}' is not valid.") + + # If reference prim is the root, return world pose as-is + if ref_prim.GetPath() == Sdf.Path.absoluteRootPath: + return position, orientation # type: ignore + + # Check if reference prim is a valid xformable + ref_xformable = UsdGeom.Xformable(ref_prim) + # Get reference prim's world transform + ref_world_tf = ref_xformable.ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + + # Create world transform for the desired position and orientation + desired_world_tf = Gf.Matrix4d() + desired_world_tf.SetTranslateOnly(Gf.Vec3d(*position)) + + if orientation is not None: + # Set rotation from quaternion (w, x, y, z) + quat = Gf.Quatd(*orientation) + desired_world_tf.SetRotateOnly(quat) + + # Convert world transform to local: local = world * inv(ref_world) + ref_world_tf_inv = ref_world_tf.GetInverse() + local_tf = desired_world_tf * ref_world_tf_inv + + # Extract local translation and orientation + local_transform = Gf.Transform(local_tf) + local_translation = tuple(local_transform.GetTranslation()) + + local_orientation = None + if orientation is not None: + quat_result = local_transform.GetRotation().GetQuat() + local_orientation = (quat_result.GetReal(), *quat_result.GetImaginary()) + + return local_translation, local_orientation diff --git a/source/isaaclab/isaaclab/sim/views/__init__.py b/source/isaaclab/isaaclab/sim/views/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb5bea7690cb650bf8b66efa4f96642419422ccd --- /dev/null +++ b/source/isaaclab/isaaclab/sim/views/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Views for manipulating USD prims.""" + +from .xform_prim_view import XformPrimView diff --git a/source/isaaclab/isaaclab/sim/views/xform_prim_view.py b/source/isaaclab/isaaclab/sim/views/xform_prim_view.py new file mode 100644 index 0000000000000000000000000000000000000000..f02820272aaa45270f4bb8b1f7a255beda7eb935 --- /dev/null +++ b/source/isaaclab/isaaclab/sim/views/xform_prim_view.py @@ -0,0 +1,601 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np +import torch + +from pxr import Gf, Sdf, Usd, UsdGeom, Vt + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils + + +class XformPrimView: + """Optimized batched interface for reading and writing transforms of multiple USD prims. + + This class provides efficient batch operations for getting and setting poses (position and orientation) + of multiple prims at once using torch tensors. It is designed for scenarios where you need to manipulate + many prims simultaneously, such as in multi-agent simulations or large-scale procedural generation. + + The class supports both world-space and local-space pose operations: + + - **World poses**: Positions and orientations in the global world frame + - **Local poses**: Positions and orientations relative to each prim's parent + + .. warning:: + **Fabric and Physics Simulation:** + + This view operates directly on USD attributes. When **Fabric** (NVIDIA's USD runtime optimization) + is enabled, physics simulation updates are written to Fabric's internal representation and + **not propagated back to USD attributes**. This causes the following issues: + + - Reading poses via :func:`get_world_poses()` or :func:`get_local_poses()` will return + **stale USD data** which does not reflect the actual physics state + - Writing poses via :func:`set_world_poses()` or :func:`set_local_poses()` will update USD, + but **physics simulation will not see these changes**. + + **Solution:** + For prims with physics components (rigid bodies, articulations), use :mod:`isaaclab.assets` + classes (e.g., :class:`~isaaclab.assets.RigidObject`, :class:`~isaaclab.assets.Articulation`) + which use PhysX tensor APIs that work correctly with Fabric. + + **When to use XformPrimView:** + + - Non-physics prims (markers, visual elements, cameras without physics) + - Setting initial poses before simulation starts + - Non-Fabric workflows + + For more information on Fabric, please refer to the `Fabric documentation`_. + + .. _Fabric documentation: https://docs.omniverse.nvidia.com/kit/docs/usdrt/latest/docs/usd_fabric_usdrt.html + + .. note:: + **Performance Considerations:** + + * Tensor operations are performed on the specified device (CPU/CUDA) + * USD write operations use ``Sdf.ChangeBlock`` for batched updates + * Getting poses involves USD API calls and cannot be fully accelerated on GPU + * For maximum performance, minimize get/set operations within tight loops + + .. note:: + **Transform Requirements:** + + All prims in the view must be Xformable and have standardized transform operations: + ``[translate, orient, scale]``. Non-standard prims will raise a ValueError during + initialization if :attr:`validate_xform_ops` is True. Please use the function + :func:`isaaclab.sim.utils.standardize_xform_ops` to prepare prims before using this view. + + .. warning:: + This class operates at the USD default time code. Any animation or time-sampled data + will not be affected by write operations. For animated transforms, you need to handle + time-sampled keyframes separately. + """ + + def __init__( + self, prim_path: str, device: str = "cpu", validate_xform_ops: bool = True, stage: Usd.Stage | None = None + ): + """Initialize the view with matching prims. + + This method searches the USD stage for all prims matching the provided path pattern, + validates that they are Xformable with standard transform operations, and stores + references for efficient batch operations. + + We generally recommend to validate the xform operations, as it ensures that the prims are in a consistent state + and have the standard transform operations (translate, orient, scale in that order). + However, if you are sure that the prims are in a consistent state, you can set this to False to improve + performance. This can save around 45-50% of the time taken to initialize the view. + + Args: + prim_path: USD prim path pattern to match prims. Supports wildcards (``*``) and + regex patterns (e.g., ``"/World/Env_.*/Robot"``). See + :func:`isaaclab.sim.utils.find_matching_prims` for pattern syntax. + device: Device to place the tensors on. Can be ``"cpu"`` or CUDA devices like + ``"cuda:0"``. Defaults to ``"cpu"``. + validate_xform_ops: Whether to validate that the prims have standard xform operations. + Defaults to True. + stage: USD stage to search for prims. Defaults to None, in which case the current active stage + from the simulation context is used. + + Raises: + ValueError: If any matched prim is not Xformable or doesn't have standardized + transform operations (translate, orient, scale in that order). + """ + stage = sim_utils.get_current_stage() if stage is None else stage + + # Store configuration + self._prim_path = prim_path + self._device = device + + # Find and validate matching prims + self._prims: list[Usd.Prim] = sim_utils.find_matching_prims(prim_path, stage=stage) + + # Create indices buffer + # Since we iterate over the indices, we need to use range instead of torch tensor + self._ALL_INDICES = list(range(len(self._prims))) + + # Validate all prims have standard xform operations + if validate_xform_ops: + for prim in self._prims: + if not sim_utils.validate_standard_xform_ops(prim): + raise ValueError( + f"Prim at path '{prim.GetPath().pathString}' is not a xformable prim with standard transform" + f" operations [translate, orient, scale]. Received type: '{prim.GetTypeName()}'." + " Use sim_utils.standardize_xform_ops() to prepare the prim." + ) + + """ + Properties. + """ + + @property + def count(self) -> int: + """Number of prims in this view. + + Returns: + The number of prims being managed by this view. + """ + return len(self._prims) + + @property + def device(self) -> str: + """Device where tensors are allocated (cpu or cuda).""" + return self._device + + @property + def prims(self) -> list[Usd.Prim]: + """List of USD prims being managed by this view.""" + return self._prims + + @property + def prim_paths(self) -> list[str]: + """List of prim paths (as strings) for all prims being managed by this view. + + This property converts each prim to its path string representation. The conversion is + performed lazily on first access and cached for subsequent accesses. + + Note: + For most use cases, prefer using :attr:`prims` directly as it provides direct access + to the USD prim objects without the conversion overhead. This property is mainly useful + for logging, debugging, or when string paths are explicitly required. + + Returns: + List of prim paths (as strings) in the same order as :attr:`prims`. + """ + # we cache it the first time it is accessed. + # we don't compute it in constructor because it is expensive and we don't need it most of the time. + # users should usually deal with prims directly as they typically need to access the prims directly. + if not hasattr(self, "_prim_paths"): + self._prim_paths = [prim.GetPath().pathString for prim in self._prims] + return self._prim_paths + + """ + Operations - Setters. + """ + + def set_world_poses( + self, + positions: torch.Tensor | None = None, + orientations: torch.Tensor | None = None, + indices: Sequence[int] | None = None, + ): + """Set world-space poses for prims in the view. + + This method sets the position and/or orientation of each prim in world space. The world pose + is computed by considering the prim's parent transforms. If a prim has a parent, this method + will convert the world pose to the appropriate local pose before setting it. + + Note: + This operation writes to USD at the default time code. Any animation data will not be affected. + + Args: + positions: World-space positions as a tensor of shape (M, 3) where M is the number of prims + to set (either all prims if indices is None, or the number of indices provided). + Defaults to None, in which case positions are not modified. + orientations: World-space orientations as quaternions (w, x, y, z) with shape (M, 4). + Defaults to None, in which case orientations are not modified. + indices: Indices of prims to set poses for. Defaults to None, in which case poses are set + for all prims in the view. + + Raises: + ValueError: If positions shape is not (M, 3) or orientations shape is not (M, 4). + ValueError: If the number of poses doesn't match the number of indices provided. + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Validate inputs + if positions is not None: + if positions.shape != (len(indices_list), 3): + raise ValueError( + f"Expected positions shape ({len(indices_list)}, 3), got {positions.shape}. " + "Number of positions must match the number of prims in the view." + ) + positions_array = Vt.Vec3dArray.FromNumpy(positions.cpu().numpy()) + else: + positions_array = None + if orientations is not None: + if orientations.shape != (len(indices_list), 4): + raise ValueError( + f"Expected orientations shape ({len(indices_list)}, 4), got {orientations.shape}. " + "Number of orientations must match the number of prims in the view." + ) + # Vt expects quaternions in xyzw order + orientations_array = Vt.QuatdArray.FromNumpy(math_utils.convert_quat(orientations, to="xyzw").cpu().numpy()) + else: + orientations_array = None + + # Create xform cache instance + xform_cache = UsdGeom.XformCache(Usd.TimeCode.Default()) + + # Set poses for each prim + # We use Sdf.ChangeBlock to minimize notification overhead. + with Sdf.ChangeBlock(): + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + # Get parent prim for local space conversion + parent_prim = prim.GetParent() + + # Determine what to set + world_pos = positions_array[idx] if positions_array is not None else None + world_quat = orientations_array[idx] if orientations_array is not None else None + + # Convert world pose to local if we have a valid parent + # Note: We don't use :func:`isaaclab.sim.utils.transforms.convert_world_pose_to_local` + # here since it isn't optimized for batch operations. + if parent_prim.IsValid() and parent_prim.GetPath() != Sdf.Path.absoluteRootPath: + # Get current world pose if we're only setting one component + if positions_array is None or orientations_array is None: + # get prim xform + prim_tf = xform_cache.GetLocalToWorldTransform(prim) + # sanitize quaternion + # this is needed, otherwise the quaternion might be non-normalized + prim_tf.Orthonormalize() + # populate desired world transform + if world_pos is not None: + prim_tf.SetTranslateOnly(world_pos) + if world_quat is not None: + prim_tf.SetRotateOnly(world_quat) + else: + # Both position and orientation are provided, create new transform + prim_tf = Gf.Matrix4d() + prim_tf.SetTranslateOnly(world_pos) + prim_tf.SetRotateOnly(world_quat) + + # Convert to local space + parent_world_tf = xform_cache.GetLocalToWorldTransform(parent_prim) + local_tf = prim_tf * parent_world_tf.GetInverse() + local_pos = local_tf.ExtractTranslation() + local_quat = local_tf.ExtractRotationQuat() + else: + # No parent or parent is root, world == local + local_pos = world_pos + local_quat = world_quat + + # Get or create the standard transform operations + if local_pos is not None: + prim.GetAttribute("xformOp:translate").Set(local_pos) + if local_quat is not None: + prim.GetAttribute("xformOp:orient").Set(local_quat) + + def set_local_poses( + self, + translations: torch.Tensor | None = None, + orientations: torch.Tensor | None = None, + indices: Sequence[int] | None = None, + ): + """Set local-space poses for prims in the view. + + This method sets the position and/or orientation of each prim in local space (relative to + their parent prims). This is useful when you want to directly manipulate the prim's transform + attributes without considering the parent hierarchy. + + Note: + This operation writes to USD at the default time code. Any animation data will not be affected. + + Args: + translations: Local-space translations as a tensor of shape (M, 3) where M is the number of prims + to set (either all prims if indices is None, or the number of indices provided). + Defaults to None, in which case translations are not modified. + orientations: Local-space orientations as quaternions (w, x, y, z) with shape (M, 4). + Defaults to None, in which case orientations are not modified. + indices: Indices of prims to set poses for. Defaults to None, in which case poses are set + for all prims in the view. + + Raises: + ValueError: If translations shape is not (M, 3) or orientations shape is not (M, 4). + ValueError: If the number of poses doesn't match the number of indices provided. + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Validate inputs + if translations is not None: + if translations.shape != (len(indices_list), 3): + raise ValueError( + f"Expected translations shape ({len(indices_list)}, 3), got {translations.shape}. " + "Number of translations must match the number of prims in the view." + ) + translations_array = Vt.Vec3dArray.FromNumpy(translations.cpu().numpy()) + else: + translations_array = None + if orientations is not None: + if orientations.shape != (len(indices_list), 4): + raise ValueError( + f"Expected orientations shape ({len(indices_list)}, 4), got {orientations.shape}. " + "Number of orientations must match the number of prims in the view." + ) + # Vt expects quaternions in xyzw order + orientations_array = Vt.QuatdArray.FromNumpy(math_utils.convert_quat(orientations, to="xyzw").cpu().numpy()) + else: + orientations_array = None + # Set local poses for each prim + # We use Sdf.ChangeBlock to minimize notification overhead. + with Sdf.ChangeBlock(): + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + # Set attributes if provided + if translations_array is not None: + prim.GetAttribute("xformOp:translate").Set(translations_array[idx]) + if orientations_array is not None: + prim.GetAttribute("xformOp:orient").Set(orientations_array[idx]) + + def set_scales(self, scales: torch.Tensor, indices: Sequence[int] | None = None): + """Set scales for prims in the view. + + This method sets the scale of each prim in the view. + + Args: + scales: Scales as a tensor of shape (M, 3) where M is the number of prims + to set (either all prims if indices is None, or the number of indices provided). + indices: Indices of prims to set scales for. Defaults to None, in which case scales are set + for all prims in the view. + + Raises: + ValueError: If scales shape is not (M, 3). + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Validate inputs + if scales.shape != (len(indices_list), 3): + raise ValueError(f"Expected scales shape ({len(indices_list)}, 3), got {scales.shape}.") + + scales_array = Vt.Vec3dArray.FromNumpy(scales.cpu().numpy()) + # Set scales for each prim + # We use Sdf.ChangeBlock to minimize notification overhead. + with Sdf.ChangeBlock(): + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + # Set scale attribute + prim.GetAttribute("xformOp:scale").Set(scales_array[idx]) + + def set_visibility(self, visibility: torch.Tensor, indices: Sequence[int] | None = None): + """Set visibility for prims in the view. + + This method sets the visibility of each prim in the view. + + Args: + visibility: Visibility as a boolean tensor of shape (M,) where M is the + number of prims to set (either all prims if indices is None, or the number of indices provided). + indices: Indices of prims to set visibility for. Defaults to None, in which case visibility is set + for all prims in the view. + + Raises: + ValueError: If visibility shape is not (M,). + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Validate inputs + if visibility.shape != (len(indices_list),): + raise ValueError(f"Expected visibility shape ({len(indices_list)},), got {visibility.shape}.") + + # Set visibility for each prim + with Sdf.ChangeBlock(): + for idx, prim_idx in enumerate(indices_list): + # Convert prim to imageable + imageable = UsdGeom.Imageable(self._prims[prim_idx]) + # Set visibility + if visibility[idx]: + imageable.MakeVisible() + else: + imageable.MakeInvisible() + + """ + Operations - Getters. + """ + + def get_world_poses(self, indices: Sequence[int] | None = None) -> tuple[torch.Tensor, torch.Tensor]: + """Get world-space poses for prims in the view. + + This method retrieves the position and orientation of each prim in world space by computing + the full transform hierarchy from the prim to the world root. + + Note: + Scale and skew are ignored. The returned poses contain only translation and rotation. + + Args: + indices: Indices of prims to get poses for. Defaults to None, in which case poses are retrieved + for all prims in the view. + + Returns: + A tuple of (positions, orientations) where: + + - positions: Torch tensor of shape (M, 3) containing world-space positions (x, y, z), + where M is the number of prims queried. + - orientations: Torch tensor of shape (M, 4) containing world-space quaternions (w, x, y, z) + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Create buffers + positions = Vt.Vec3dArray(len(indices_list)) + orientations = Vt.QuatdArray(len(indices_list)) + # Create xform cache instance + xform_cache = UsdGeom.XformCache(Usd.TimeCode.Default()) + + # Note: We don't use :func:`isaaclab.sim.utils.transforms.resolve_prim_pose` + # here since it isn't optimized for batch operations. + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + # get prim xform + prim_tf = xform_cache.GetLocalToWorldTransform(prim) + # sanitize quaternion + # this is needed, otherwise the quaternion might be non-normalized + prim_tf.Orthonormalize() + # extract position and orientation + positions[idx] = prim_tf.ExtractTranslation() + orientations[idx] = prim_tf.ExtractRotationQuat() + + # move to torch tensors + positions = torch.tensor(np.array(positions), dtype=torch.float32, device=self._device) + orientations = torch.tensor(np.array(orientations), dtype=torch.float32, device=self._device) + # underlying data is in xyzw order, convert to wxyz order + orientations = math_utils.convert_quat(orientations, to="wxyz") + + return positions, orientations # type: ignore + + def get_local_poses(self, indices: Sequence[int] | None = None) -> tuple[torch.Tensor, torch.Tensor]: + """Get local-space poses for prims in the view. + + This method retrieves the position and orientation of each prim in local space (relative to + their parent prims). These are the raw transform values stored on each prim. + + Note: + Scale is ignored. The returned poses contain only translation and rotation. + + Args: + indices: Indices of prims to get poses for. Defaults to None, in which case poses are retrieved + for all prims in the view. + + Returns: + A tuple of (translations, orientations) where: + + - translations: Torch tensor of shape (M, 3) containing local-space translations (x, y, z), + where M is the number of prims queried. + - orientations: Torch tensor of shape (M, 4) containing local-space quaternions (w, x, y, z) + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Create buffers + translations = Vt.Vec3dArray(len(indices_list)) + orientations = Vt.QuatdArray(len(indices_list)) + # Create xform cache instance + xform_cache = UsdGeom.XformCache(Usd.TimeCode.Default()) + + # Note: We don't use :func:`isaaclab.sim.utils.transforms.resolve_prim_pose` + # here since it isn't optimized for batch operations. + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + # get prim xform + prim_tf = xform_cache.GetLocalTransformation(prim)[0] + # sanitize quaternion + # this is needed, otherwise the quaternion might be non-normalized + prim_tf.Orthonormalize() + # extract position and orientation + translations[idx] = prim_tf.ExtractTranslation() + orientations[idx] = prim_tf.ExtractRotationQuat() + + # move to torch tensors + translations = torch.tensor(np.array(translations), dtype=torch.float32, device=self._device) + orientations = torch.tensor(np.array(orientations), dtype=torch.float32, device=self._device) + # underlying data is in xyzw order, convert to wxyz order + orientations = math_utils.convert_quat(orientations, to="wxyz") + + return translations, orientations # type: ignore + + def get_scales(self, indices: Sequence[int] | None = None) -> torch.Tensor: + """Get scales for prims in the view. + + This method retrieves the scale of each prim in the view. + + Args: + indices: Indices of prims to get scales for. Defaults to None, in which case scales are retrieved + for all prims in the view. + + Returns: + A tensor of shape (M, 3) containing the scales of each prim, where M is the number of prims queried. + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Create buffers + scales = Vt.Vec3dArray(len(indices_list)) + + for idx, prim_idx in enumerate(indices_list): + # Get prim + prim = self._prims[prim_idx] + scales[idx] = prim.GetAttribute("xformOp:scale").Get() + + # Convert to tensor + return torch.tensor(np.array(scales), dtype=torch.float32, device=self._device) + + def get_visibility(self, indices: Sequence[int] | None = None) -> torch.Tensor: + """Get visibility for prims in the view. + + This method retrieves the visibility of each prim in the view. + + Args: + indices: Indices of prims to get visibility for. Defaults to None, in which case visibility is retrieved + for all prims in the view. + + Returns: + A tensor of shape (M,) containing the visibility of each prim, where M is the number of prims queried. + The tensor is of type bool. + """ + # Resolve indices + if indices is None or indices == slice(None): + indices_list = self._ALL_INDICES + else: + # Convert to list if it is a tensor array + indices_list = indices.tolist() if isinstance(indices, torch.Tensor) else list(indices) + + # Create buffers + visibility = torch.zeros(len(indices_list), dtype=torch.bool, device=self._device) + + for idx, prim_idx in enumerate(indices_list): + # Get prim + imageable = UsdGeom.Imageable(self._prims[prim_idx]) + # Get visibility + visibility[idx] = imageable.ComputeVisibility() != UsdGeom.Tokens.invisible + + return visibility diff --git a/source/isaaclab/isaaclab/terrains/__init__.py b/source/isaaclab/isaaclab/terrains/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6f0b50185572133058de667aace023022900f87b --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package with utilities for creating terrains procedurally. + +There are two main components in this package: + +* :class:`TerrainGenerator`: This class procedurally generates terrains based on the passed + sub-terrain configuration. It creates a ``trimesh`` mesh object and contains the origins of + each generated sub-terrain. +* :class:`TerrainImporter`: This class mainly deals with importing terrains from different + possible sources and adding them to the simulator as a prim object. + The following functions are available for importing terrains: + + * :meth:`TerrainImporter.import_ground_plane`: spawn a grid plane which is default in Isaac Sim. + * :meth:`TerrainImporter.import_mesh`: spawn a prim from a ``trimesh`` object. + * :meth:`TerrainImporter.import_usd`: spawn a prim as reference to input USD file. + +""" +from .height_field import * # noqa: F401, F403 +from .sub_terrain_cfg import FlatPatchSamplingCfg, SubTerrainBaseCfg +from .terrain_generator import TerrainGenerator +from .terrain_generator_cfg import TerrainGeneratorCfg +from .terrain_importer import TerrainImporter +from .terrain_importer_cfg import TerrainImporterCfg +from .trimesh import * # noqa: F401, F403 +from .utils import color_meshes_by_height, create_prim_from_mesh diff --git a/source/isaaclab/isaaclab/terrains/config/__init__.py b/source/isaaclab/isaaclab/terrains/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..264189e183b45504b1cef27e0730d2d8cf68b1d2 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/config/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Pre-defined terrain configurations for the terrain generator.""" + +from .rough import * # noqa: F401 diff --git a/source/isaaclab/isaaclab/terrains/config/rough.py b/source/isaaclab/isaaclab/terrains/config/rough.py new file mode 100644 index 0000000000000000000000000000000000000000..fde3bf8408b45042e4e6f309bed89f5811d34391 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/config/rough.py @@ -0,0 +1,52 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for custom terrains.""" + +import isaaclab.terrains as terrain_gen + +from ..terrain_generator_cfg import TerrainGeneratorCfg + +ROUGH_TERRAINS_CFG = TerrainGeneratorCfg( + size=(8.0, 8.0), + border_width=20.0, + num_rows=10, + num_cols=20, + horizontal_scale=0.1, + vertical_scale=0.005, + slope_threshold=0.75, + use_cache=False, + sub_terrains={ + "pyramid_stairs": terrain_gen.MeshPyramidStairsTerrainCfg( + proportion=0.2, + step_height_range=(0.05, 0.23), + step_width=0.3, + platform_width=3.0, + border_width=1.0, + holes=False, + ), + "pyramid_stairs_inv": terrain_gen.MeshInvertedPyramidStairsTerrainCfg( + proportion=0.2, + step_height_range=(0.05, 0.23), + step_width=0.3, + platform_width=3.0, + border_width=1.0, + holes=False, + ), + "boxes": terrain_gen.MeshRandomGridTerrainCfg( + proportion=0.2, grid_width=0.45, grid_height_range=(0.05, 0.2), platform_width=2.0 + ), + "random_rough": terrain_gen.HfRandomUniformTerrainCfg( + proportion=0.2, noise_range=(0.02, 0.10), noise_step=0.02, border_width=0.25 + ), + "hf_pyramid_slope": terrain_gen.HfPyramidSlopedTerrainCfg( + proportion=0.1, slope_range=(0.0, 0.4), platform_width=2.0, border_width=0.25 + ), + "hf_pyramid_slope_inv": terrain_gen.HfInvertedPyramidSlopedTerrainCfg( + proportion=0.1, slope_range=(0.0, 0.4), platform_width=2.0, border_width=0.25 + ), + }, +) +"""Rough terrains configuration.""" diff --git a/source/isaaclab/isaaclab/terrains/height_field/__init__.py b/source/isaaclab/isaaclab/terrains/height_field/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3bc28ba3ccfe90c517adb5361b5325b236fd8511 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/height_field/__init__.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This sub-module provides utilities to create different terrains as height fields (HF). + +Height fields are a 2.5D terrain representation that is used in robotics to obtain the +height of the terrain at a given point. This is useful for controls and planning algorithms. + +Each terrain is represented as a 2D numpy array with discretized heights. The shape of the array +is (width, length), where width and length are the number of points along the x and y axis, +respectively. The height of the terrain at a given point is obtained by indexing the array with +the corresponding x and y coordinates. + +.. caution:: + + When working with height field terrains, it is important to remember that the terrain is generated + from a discretized 3D representation. This means that the height of the terrain at a given point + is only an approximation of the real height of the terrain at that point. The discretization + error is proportional to the size of the discretization cells. Therefore, it is important to + choose a discretization size that is small enough for the application. A larger discretization + size will result in a faster simulation, but the terrain will be less accurate. + +""" + +from .hf_terrains_cfg import ( + HfDiscreteObstaclesTerrainCfg, + HfInvertedPyramidSlopedTerrainCfg, + HfInvertedPyramidStairsTerrainCfg, + HfPyramidSlopedTerrainCfg, + HfPyramidStairsTerrainCfg, + HfRandomUniformTerrainCfg, + HfSteppingStonesTerrainCfg, + HfTerrainBaseCfg, + HfWaveTerrainCfg, +) diff --git a/source/isaaclab/isaaclab/terrains/height_field/hf_terrains.py b/source/isaaclab/isaaclab/terrains/height_field/hf_terrains.py new file mode 100644 index 0000000000000000000000000000000000000000..3869eae25c3f7d070e8999ca6b9fb1190fa36dfc --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/height_field/hf_terrains.py @@ -0,0 +1,437 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions to generate height fields for different terrains.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import scipy.interpolate as interpolate + +from .utils import height_field_to_mesh + +if TYPE_CHECKING: + from . import hf_terrains_cfg + + +@height_field_to_mesh +def random_uniform_terrain(difficulty: float, cfg: hf_terrains_cfg.HfRandomUniformTerrainCfg) -> np.ndarray: + """Generate a terrain with height sampled uniformly from a specified range. + + .. image:: ../../_static/terrains/height_field/random_uniform_terrain.jpg + :width: 40% + :align: center + + Note: + The :obj:`difficulty` parameter is ignored for this terrain. + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + + Raises: + ValueError: When the downsampled scale is smaller than the horizontal scale. + """ + # check parameters + # -- horizontal scale + if cfg.downsampled_scale is None: + cfg.downsampled_scale = cfg.horizontal_scale + elif cfg.downsampled_scale < cfg.horizontal_scale: + raise ValueError( + "Downsampled scale must be larger than or equal to the horizontal scale:" + f" {cfg.downsampled_scale} < {cfg.horizontal_scale}." + ) + + # switch parameters to discrete units + # -- horizontal scale + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + # -- downsampled scale + width_downsampled = int(cfg.size[0] / cfg.downsampled_scale) + length_downsampled = int(cfg.size[1] / cfg.downsampled_scale) + # -- height + height_min = int(cfg.noise_range[0] / cfg.vertical_scale) + height_max = int(cfg.noise_range[1] / cfg.vertical_scale) + height_step = int(cfg.noise_step / cfg.vertical_scale) + + # create range of heights possible + height_range = np.arange(height_min, height_max + height_step, height_step) + # sample heights randomly from the range along a grid + height_field_downsampled = np.random.choice(height_range, size=(width_downsampled, length_downsampled)) + # create interpolation function for the sampled heights + x = np.linspace(0, cfg.size[0] * cfg.horizontal_scale, width_downsampled) + y = np.linspace(0, cfg.size[1] * cfg.horizontal_scale, length_downsampled) + func = interpolate.RectBivariateSpline(x, y, height_field_downsampled) + + # interpolate the sampled heights to obtain the height field + x_upsampled = np.linspace(0, cfg.size[0] * cfg.horizontal_scale, width_pixels) + y_upsampled = np.linspace(0, cfg.size[1] * cfg.horizontal_scale, length_pixels) + z_upsampled = func(x_upsampled, y_upsampled) + # round off the interpolated heights to the nearest vertical step + return np.rint(z_upsampled).astype(np.int16) + + +@height_field_to_mesh +def pyramid_sloped_terrain(difficulty: float, cfg: hf_terrains_cfg.HfPyramidSlopedTerrainCfg) -> np.ndarray: + """Generate a terrain with a truncated pyramid structure. + + The terrain is a pyramid-shaped sloped surface with a slope of :obj:`slope` that trims into a flat platform + at the center. The slope is defined as the ratio of the height change along the x axis to the width along the + x axis. For example, a slope of 1.0 means that the height changes by 1 unit for every 1 unit of width. + + If the :obj:`cfg.inverted` flag is set to :obj:`True`, the terrain is inverted such that + the platform is at the bottom. + + .. image:: ../../_static/terrains/height_field/pyramid_sloped_terrain.jpg + :width: 40% + + .. image:: ../../_static/terrains/height_field/inverted_pyramid_sloped_terrain.jpg + :width: 40% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + """ + # resolve terrain configuration + if cfg.inverted: + slope = -cfg.slope_range[0] - difficulty * (cfg.slope_range[1] - cfg.slope_range[0]) + else: + slope = cfg.slope_range[0] + difficulty * (cfg.slope_range[1] - cfg.slope_range[0]) + + # switch parameters to discrete units + # -- horizontal scale + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + # -- height + # we want the height to be 1/2 of the width since the terrain is a pyramid + height_max = int(slope * cfg.size[0] / 2 / cfg.vertical_scale) + # -- center of the terrain + center_x = int(width_pixels / 2) + center_y = int(length_pixels / 2) + + # create a meshgrid of the terrain + x = np.arange(0, width_pixels) + y = np.arange(0, length_pixels) + xx, yy = np.meshgrid(x, y, sparse=True) + # offset the meshgrid to the center of the terrain + xx = (center_x - np.abs(center_x - xx)) / center_x + yy = (center_y - np.abs(center_y - yy)) / center_y + # reshape the meshgrid to be 2D + xx = xx.reshape(width_pixels, 1) + yy = yy.reshape(1, length_pixels) + # create a sloped surface + hf_raw = np.zeros((width_pixels, length_pixels)) + hf_raw = height_max * xx * yy + + # create a flat platform at the center of the terrain + platform_width = int(cfg.platform_width / cfg.horizontal_scale / 2) + # get the height of the platform at the corner of the platform + x_pf = width_pixels // 2 - platform_width + y_pf = length_pixels // 2 - platform_width + z_pf = hf_raw[x_pf, y_pf] + hf_raw = np.clip(hf_raw, min(0, z_pf), max(0, z_pf)) + + # round off the heights to the nearest vertical step + return np.rint(hf_raw).astype(np.int16) + + +@height_field_to_mesh +def pyramid_stairs_terrain(difficulty: float, cfg: hf_terrains_cfg.HfPyramidStairsTerrainCfg) -> np.ndarray: + """Generate a terrain with a pyramid stair pattern. + + The terrain is a pyramid stair pattern which trims to a flat platform at the center of the terrain. + + If the :obj:`cfg.inverted` flag is set to :obj:`True`, the terrain is inverted such that + the platform is at the bottom. + + .. image:: ../../_static/terrains/height_field/pyramid_stairs_terrain.jpg + :width: 40% + + .. image:: ../../_static/terrains/height_field/inverted_pyramid_stairs_terrain.jpg + :width: 40% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + """ + # resolve terrain configuration + step_height = cfg.step_height_range[0] + difficulty * (cfg.step_height_range[1] - cfg.step_height_range[0]) + if cfg.inverted: + step_height *= -1 + # switch parameters to discrete units + # -- terrain + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + # -- stairs + step_width = int(cfg.step_width / cfg.horizontal_scale) + step_height = int(step_height / cfg.vertical_scale) + # -- platform + platform_width = int(cfg.platform_width / cfg.horizontal_scale) + + # create a terrain with a flat platform at the center + hf_raw = np.zeros((width_pixels, length_pixels)) + # add the steps + current_step_height = 0 + start_x, start_y = 0, 0 + stop_x, stop_y = width_pixels, length_pixels + while (stop_x - start_x) > platform_width and (stop_y - start_y) > platform_width: + # increment position + # -- x + start_x += step_width + stop_x -= step_width + # -- y + start_y += step_width + stop_y -= step_width + # increment height + current_step_height += step_height + # add the step + hf_raw[start_x:stop_x, start_y:stop_y] = current_step_height + + # round off the heights to the nearest vertical step + return np.rint(hf_raw).astype(np.int16) + + +@height_field_to_mesh +def discrete_obstacles_terrain(difficulty: float, cfg: hf_terrains_cfg.HfDiscreteObstaclesTerrainCfg) -> np.ndarray: + """Generate a terrain with randomly generated obstacles as pillars with positive and negative heights. + + The terrain is a flat platform at the center of the terrain with randomly generated obstacles as pillars + with positive and negative height. The obstacles are randomly generated cuboids with a random width and + height. They are placed randomly on the terrain with a minimum distance of :obj:`cfg.platform_width` + from the center of the terrain. + + .. image:: ../../_static/terrains/height_field/discrete_obstacles_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + """ + # resolve terrain configuration + obs_height = cfg.obstacle_height_range[0] + difficulty * ( + cfg.obstacle_height_range[1] - cfg.obstacle_height_range[0] + ) + + # switch parameters to discrete units + # -- terrain + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + # -- obstacles + obs_height = int(obs_height / cfg.vertical_scale) + obs_width_min = int(cfg.obstacle_width_range[0] / cfg.horizontal_scale) + obs_width_max = int(cfg.obstacle_width_range[1] / cfg.horizontal_scale) + # -- center of the terrain + platform_width = int(cfg.platform_width / cfg.horizontal_scale) + + # create discrete ranges for the obstacles + # -- shape + obs_width_range = np.arange(obs_width_min, obs_width_max, 4) + obs_length_range = np.arange(obs_width_min, obs_width_max, 4) + # -- position + obs_x_range = np.arange(0, width_pixels, 4) + obs_y_range = np.arange(0, length_pixels, 4) + + # create a terrain with a flat platform at the center + hf_raw = np.zeros((width_pixels, length_pixels)) + # generate the obstacles + for _ in range(cfg.num_obstacles): + # sample size + if cfg.obstacle_height_mode == "choice": + height = np.random.choice([-obs_height, -obs_height // 2, obs_height // 2, obs_height]) + elif cfg.obstacle_height_mode == "fixed": + height = obs_height + else: + raise ValueError(f"Unknown obstacle height mode '{cfg.obstacle_height_mode}'. Must be 'choice' or 'fixed'.") + width = int(np.random.choice(obs_width_range)) + length = int(np.random.choice(obs_length_range)) + # sample position + x_start = int(np.random.choice(obs_x_range)) + y_start = int(np.random.choice(obs_y_range)) + # clip start position to the terrain + if x_start + width > width_pixels: + x_start = width_pixels - width + if y_start + length > length_pixels: + y_start = length_pixels - length + # add to terrain + hf_raw[x_start : x_start + width, y_start : y_start + length] = height + # clip the terrain to the platform + x1 = (width_pixels - platform_width) // 2 + x2 = (width_pixels + platform_width) // 2 + y1 = (length_pixels - platform_width) // 2 + y2 = (length_pixels + platform_width) // 2 + hf_raw[x1:x2, y1:y2] = 0 + # round off the heights to the nearest vertical step + return np.rint(hf_raw).astype(np.int16) + + +@height_field_to_mesh +def wave_terrain(difficulty: float, cfg: hf_terrains_cfg.HfWaveTerrainCfg) -> np.ndarray: + r"""Generate a terrain with a wave pattern. + + The terrain is a flat platform at the center of the terrain with a wave pattern. The wave pattern + is generated by adding sinusoidal waves based on the number of waves and the amplitude of the waves. + + The height of the terrain at a point :math:`(x, y)` is given by: + + .. math:: + + h(x, y) = A \left(\sin\left(\frac{2 \pi x}{\lambda}\right) + \cos\left(\frac{2 \pi y}{\lambda}\right) \right) + + where :math:`A` is the amplitude of the waves, :math:`\lambda` is the wavelength of the waves. + + .. image:: ../../_static/terrains/height_field/wave_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + + Raises: + ValueError: When the number of waves is non-positive. + """ + # check number of waves + if cfg.num_waves < 0: + raise ValueError(f"Number of waves must be a positive integer. Got: {cfg.num_waves}.") + + # resolve terrain configuration + amplitude = cfg.amplitude_range[0] + difficulty * (cfg.amplitude_range[1] - cfg.amplitude_range[0]) + # switch parameters to discrete units + # -- terrain + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + amplitude_pixels = int(0.5 * amplitude / cfg.vertical_scale) + + # compute the wave number: nu = 2 * pi / lambda + wave_length = length_pixels / cfg.num_waves + wave_number = 2 * np.pi / wave_length + # create meshgrid for the terrain + x = np.arange(0, width_pixels) + y = np.arange(0, length_pixels) + xx, yy = np.meshgrid(x, y, sparse=True) + xx = xx.reshape(width_pixels, 1) + yy = yy.reshape(1, length_pixels) + + # create a terrain with a flat platform at the center + hf_raw = np.zeros((width_pixels, length_pixels)) + # add the waves + hf_raw += amplitude_pixels * (np.cos(yy * wave_number) + np.sin(xx * wave_number)) + # round off the heights to the nearest vertical step + return np.rint(hf_raw).astype(np.int16) + + +@height_field_to_mesh +def stepping_stones_terrain(difficulty: float, cfg: hf_terrains_cfg.HfSteppingStonesTerrainCfg) -> np.ndarray: + """Generate a terrain with a stepping stones pattern. + + The terrain is a stepping stones pattern which trims to a flat platform at the center of the terrain. + + .. image:: ../../_static/terrains/height_field/stepping_stones_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + The height field of the terrain as a 2D numpy array with discretized heights. + The shape of the array is (width, length), where width and length are the number of points + along the x and y axis, respectively. + """ + # resolve terrain configuration + stone_width = cfg.stone_width_range[1] - difficulty * (cfg.stone_width_range[1] - cfg.stone_width_range[0]) + stone_distance = cfg.stone_distance_range[0] + difficulty * ( + cfg.stone_distance_range[1] - cfg.stone_distance_range[0] + ) + + # switch parameters to discrete units + # -- terrain + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + # -- stones + stone_distance = int(stone_distance / cfg.horizontal_scale) + stone_width = int(stone_width / cfg.horizontal_scale) + stone_height_max = int(cfg.stone_height_max / cfg.vertical_scale) + # -- holes + holes_depth = int(cfg.holes_depth / cfg.vertical_scale) + # -- platform + platform_width = int(cfg.platform_width / cfg.horizontal_scale) + # create range of heights + stone_height_range = np.arange(-stone_height_max - 1, stone_height_max, step=1) + + # create a terrain with a flat platform at the center + hf_raw = np.full((width_pixels, length_pixels), holes_depth) + # add the stones + start_x, start_y = 0, 0 + # -- if the terrain is longer than it is wide then fill the terrain column by column + if length_pixels >= width_pixels: + while start_y < length_pixels: + # ensure that stone stops along y-axis + stop_y = min(length_pixels, start_y + stone_width) + # randomly sample x-position + start_x = np.random.randint(0, stone_width) + stop_x = max(0, start_x - stone_distance) + # fill first stone + hf_raw[0:stop_x, start_y:stop_y] = np.random.choice(stone_height_range) + # fill row with stones + while start_x < width_pixels: + stop_x = min(width_pixels, start_x + stone_width) + hf_raw[start_x:stop_x, start_y:stop_y] = np.random.choice(stone_height_range) + start_x += stone_width + stone_distance + # update y-position + start_y += stone_width + stone_distance + elif width_pixels > length_pixels: + while start_x < width_pixels: + # ensure that stone stops along x-axis + stop_x = min(width_pixels, start_x + stone_width) + # randomly sample y-position + start_y = np.random.randint(0, stone_width) + stop_y = max(0, start_y - stone_distance) + # fill first stone + hf_raw[start_x:stop_x, 0:stop_y] = np.random.choice(stone_height_range) + # fill column with stones + while start_y < length_pixels: + stop_y = min(length_pixels, start_y + stone_width) + hf_raw[start_x:stop_x, start_y:stop_y] = np.random.choice(stone_height_range) + start_y += stone_width + stone_distance + # update x-position + start_x += stone_width + stone_distance + # add the platform in the center + x1 = (width_pixels - platform_width) // 2 + x2 = (width_pixels + platform_width) // 2 + y1 = (length_pixels - platform_width) // 2 + y2 = (length_pixels + platform_width) // 2 + hf_raw[x1:x2, y1:y2] = 0 + # round off the heights to the nearest vertical step + return np.rint(hf_raw).astype(np.int16) diff --git a/source/isaaclab/isaaclab/terrains/height_field/hf_terrains_cfg.py b/source/isaaclab/isaaclab/terrains/height_field/hf_terrains_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..df1d6dcc21a20f1d768385229b7d5f9cf073547d --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/height_field/hf_terrains_cfg.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + +from ..sub_terrain_cfg import SubTerrainBaseCfg +from . import hf_terrains + + +@configclass +class HfTerrainBaseCfg(SubTerrainBaseCfg): + """The base configuration for height field terrains.""" + + border_width: float = 0.0 + """The width of the border/padding around the terrain (in m). Defaults to 0.0. + + The border width is subtracted from the :obj:`size` of the terrain. If non-zero, it must be + greater than or equal to the :obj:`horizontal scale`. + """ + + horizontal_scale: float = 0.1 + """The discretization of the terrain along the x and y axes (in m). Defaults to 0.1.""" + + vertical_scale: float = 0.005 + """The discretization of the terrain along the z axis (in m). Defaults to 0.005.""" + + slope_threshold: float | None = None + """The slope threshold above which surfaces are made vertical. Defaults to None, + in which case no correction is applied.""" + + +""" +Different height field terrain configurations. +""" + + +@configclass +class HfRandomUniformTerrainCfg(HfTerrainBaseCfg): + """Configuration for a random uniform height field terrain.""" + + function = hf_terrains.random_uniform_terrain + + noise_range: tuple[float, float] = MISSING + """The minimum and maximum height noise (i.e. along z) of the terrain (in m).""" + + noise_step: float = MISSING + """The minimum height (in m) change between two points.""" + + downsampled_scale: float | None = None + """The distance between two randomly sampled points on the terrain. Defaults to None, + in which case the :obj:`horizontal scale` is used. + + The heights are sampled at this resolution and interpolation is performed for intermediate points. + This must be larger than or equal to the :obj:`horizontal scale`. + """ + + +@configclass +class HfPyramidSlopedTerrainCfg(HfTerrainBaseCfg): + """Configuration for a pyramid sloped height field terrain.""" + + function = hf_terrains.pyramid_sloped_terrain + + slope_range: tuple[float, float] = MISSING + """The slope of the terrain (in radians).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + inverted: bool = False + """Whether the pyramid is inverted. Defaults to False. + + If True, the terrain is inverted such that the platform is at the bottom and the slopes are upwards. + """ + + +@configclass +class HfInvertedPyramidSlopedTerrainCfg(HfPyramidSlopedTerrainCfg): + """Configuration for an inverted pyramid sloped height field terrain. + + Note: + This is a subclass of :class:`HfPyramidSlopedTerrainCfg` with :obj:`inverted` set to True. + We make it as a separate class to make it easier to distinguish between the two and match + the naming convention of the other terrains. + """ + + inverted: bool = True + + +@configclass +class HfPyramidStairsTerrainCfg(HfTerrainBaseCfg): + """Configuration for a pyramid stairs height field terrain.""" + + function = hf_terrains.pyramid_stairs_terrain + + step_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the steps (in m).""" + + step_width: float = MISSING + """The width of the steps (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + inverted: bool = False + """Whether the pyramid stairs is inverted. Defaults to False. + + If True, the terrain is inverted such that the platform is at the bottom and the stairs are upwards. + """ + + +@configclass +class HfInvertedPyramidStairsTerrainCfg(HfPyramidStairsTerrainCfg): + """Configuration for an inverted pyramid stairs height field terrain. + + Note: + This is a subclass of :class:`HfPyramidStairsTerrainCfg` with :obj:`inverted` set to True. + We make it as a separate class to make it easier to distinguish between the two and match + the naming convention of the other terrains. + """ + + inverted: bool = True + + +@configclass +class HfDiscreteObstaclesTerrainCfg(HfTerrainBaseCfg): + """Configuration for a discrete obstacles height field terrain.""" + + function = hf_terrains.discrete_obstacles_terrain + + obstacle_height_mode: str = "choice" + """The mode to use for the obstacle height. Defaults to "choice". + + The following modes are supported: "choice", "fixed". + """ + + obstacle_width_range: tuple[float, float] = MISSING + """The minimum and maximum width of the obstacles (in m).""" + + obstacle_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the obstacles (in m).""" + + num_obstacles: int = MISSING + """The number of obstacles to generate.""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + +@configclass +class HfWaveTerrainCfg(HfTerrainBaseCfg): + """Configuration for a wave height field terrain.""" + + function = hf_terrains.wave_terrain + + amplitude_range: tuple[float, float] = MISSING + """The minimum and maximum amplitude of the wave (in m).""" + + num_waves: int = 1 + """The number of waves to generate. Defaults to 1.""" + + +@configclass +class HfSteppingStonesTerrainCfg(HfTerrainBaseCfg): + """Configuration for a stepping stones height field terrain.""" + + function = hf_terrains.stepping_stones_terrain + + stone_height_max: float = MISSING + """The maximum height of the stones (in m).""" + + stone_width_range: tuple[float, float] = MISSING + """The minimum and maximum width of the stones (in m).""" + + stone_distance_range: tuple[float, float] = MISSING + """The minimum and maximum distance between stones (in m).""" + + holes_depth: float = -10.0 + """The depth of the holes (negative obstacles). Defaults to -10.0.""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" diff --git a/source/isaaclab/isaaclab/terrains/height_field/utils.py b/source/isaaclab/isaaclab/terrains/height_field/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..256e8129fe34d5ecd76ec75a9671e05a7bc55ed0 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/height_field/utils.py @@ -0,0 +1,174 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import copy +import functools +from collections.abc import Callable +from typing import TYPE_CHECKING + +import numpy as np +import trimesh + +if TYPE_CHECKING: + from .hf_terrains_cfg import HfTerrainBaseCfg + + +def height_field_to_mesh(func: Callable) -> Callable: + """Decorator to convert a height field function to a mesh function. + + This decorator converts a height field function to a mesh function by sampling the heights + at a specified resolution and performing interpolation to obtain the intermediate heights. + Additionally, it adds a border around the terrain to avoid artifacts at the edges. + + Args: + func: The height field function to convert. The function should return a 2D numpy array + with the heights of the terrain. + + Returns: + The mesh function. The mesh function returns a tuple containing a list of ``trimesh`` + mesh objects and the origin of the terrain. + """ + + @functools.wraps(func) + def wrapper(difficulty: float, cfg: HfTerrainBaseCfg): + # check valid border width + if cfg.border_width > 0 and cfg.border_width < cfg.horizontal_scale: + raise ValueError( + f"The border width ({cfg.border_width}) must be greater than or equal to the" + f" horizontal scale ({cfg.horizontal_scale})." + ) + # allocate buffer for height field (with border) + width_pixels = int(cfg.size[0] / cfg.horizontal_scale) + 1 + length_pixels = int(cfg.size[1] / cfg.horizontal_scale) + 1 + border_pixels = int(cfg.border_width / cfg.horizontal_scale) + 1 + heights = np.zeros((width_pixels, length_pixels), dtype=np.int16) + # override size of the terrain to account for the border + sub_terrain_size = [width_pixels - 2 * border_pixels, length_pixels - 2 * border_pixels] + sub_terrain_size = [dim * cfg.horizontal_scale for dim in sub_terrain_size] + # update the config + terrain_size = copy.deepcopy(cfg.size) + cfg.size = tuple(sub_terrain_size) + # generate the height field + z_gen = func(difficulty, cfg) + # handle the border for the terrain + heights[border_pixels:-border_pixels, border_pixels:-border_pixels] = z_gen + # set terrain size back to config + cfg.size = terrain_size + + # convert to trimesh + vertices, triangles = convert_height_field_to_mesh( + heights, cfg.horizontal_scale, cfg.vertical_scale, cfg.slope_threshold + ) + mesh = trimesh.Trimesh(vertices=vertices, faces=triangles) + # compute origin + x1 = int((cfg.size[0] * 0.5 - 1) / cfg.horizontal_scale) + x2 = int((cfg.size[0] * 0.5 + 1) / cfg.horizontal_scale) + y1 = int((cfg.size[1] * 0.5 - 1) / cfg.horizontal_scale) + y2 = int((cfg.size[1] * 0.5 + 1) / cfg.horizontal_scale) + origin_z = np.max(heights[x1:x2, y1:y2]) * cfg.vertical_scale + origin = np.array([0.5 * cfg.size[0], 0.5 * cfg.size[1], origin_z]) + # return mesh and origin + return [mesh], origin + + return wrapper + + +def convert_height_field_to_mesh( + height_field: np.ndarray, horizontal_scale: float, vertical_scale: float, slope_threshold: float | None = None +) -> tuple[np.ndarray, np.ndarray]: + """Convert a height-field array to a triangle mesh represented by vertices and triangles. + + This function converts a height-field array to a triangle mesh represented by vertices and triangles. + The height-field array is assumed to be a 2D array of floats, where each element represents the height + of the terrain at that location. The height-field array is assumed to be in the form of a matrix, where + the first dimension represents the x-axis and the second dimension represents the y-axis. + + The function can also correct vertical surfaces above the provide slope threshold. This is helpful to + avoid having long vertical surfaces in the mesh. The correction is done by moving the vertices of the + vertical surfaces to minimum of the two neighboring vertices. + + The correction is done in the following way: + If :math:`\\frac{y_2 - y_1}{x_2 - x_1} > threshold`, then move A to A' (i.e., set :math:`x_1' = x_2`). + This is repeated along all directions. + + .. code-block:: none + + B(x_2,y_2) + /| + / | + / | + (x_1,y_1)A---A'(x_1',y_1) + + Args: + height_field: The input height-field array. + horizontal_scale: The discretization of the terrain along the x and y axis. + vertical_scale: The discretization of the terrain along the z axis. + slope_threshold: The slope threshold above which surfaces are made vertical. + Defaults to None, in which case no correction is applied. + + Returns: + The vertices and triangles of the mesh: + - **vertices** (np.ndarray(float)): Array of shape (num_vertices, 3). + Each row represents the location of each vertex (in m). + - **triangles** (np.ndarray(int)): Array of shape (num_triangles, 3). + Each row represents the indices of the 3 vertices connected by this triangle. + """ + # read height field + num_rows, num_cols = height_field.shape + # create a mesh grid of the height field + y = np.linspace(0, (num_cols - 1) * horizontal_scale, num_cols) + x = np.linspace(0, (num_rows - 1) * horizontal_scale, num_rows) + yy, xx = np.meshgrid(y, x) + # copy height field to avoid modifying the original array + hf = height_field.copy() + + # correct vertical surfaces above the slope threshold + if slope_threshold is not None: + # scale slope threshold based on the horizontal and vertical scale + slope_threshold *= horizontal_scale / vertical_scale + # allocate arrays to store the movement of the vertices + move_x = np.zeros((num_rows, num_cols)) + move_y = np.zeros((num_rows, num_cols)) + move_corners = np.zeros((num_rows, num_cols)) + # move vertices along the x-axis + move_x[: num_rows - 1, :] += hf[1:num_rows, :] - hf[: num_rows - 1, :] > slope_threshold + move_x[1:num_rows, :] -= hf[: num_rows - 1, :] - hf[1:num_rows, :] > slope_threshold + # move vertices along the y-axis + move_y[:, : num_cols - 1] += hf[:, 1:num_cols] - hf[:, : num_cols - 1] > slope_threshold + move_y[:, 1:num_cols] -= hf[:, : num_cols - 1] - hf[:, 1:num_cols] > slope_threshold + # move vertices along the corners + move_corners[: num_rows - 1, : num_cols - 1] += ( + hf[1:num_rows, 1:num_cols] - hf[: num_rows - 1, : num_cols - 1] > slope_threshold + ) + move_corners[1:num_rows, 1:num_cols] -= ( + hf[: num_rows - 1, : num_cols - 1] - hf[1:num_rows, 1:num_cols] > slope_threshold + ) + xx += (move_x + move_corners * (move_x == 0)) * horizontal_scale + yy += (move_y + move_corners * (move_y == 0)) * horizontal_scale + + # create vertices for the mesh + vertices = np.zeros((num_rows * num_cols, 3), dtype=np.float32) + vertices[:, 0] = xx.flatten() + vertices[:, 1] = yy.flatten() + vertices[:, 2] = hf.flatten() * vertical_scale + # create triangles for the mesh + triangles = -np.ones((2 * (num_rows - 1) * (num_cols - 1), 3), dtype=np.uint32) + for i in range(num_rows - 1): + ind0 = np.arange(0, num_cols - 1) + i * num_cols + ind1 = ind0 + 1 + ind2 = ind0 + num_cols + ind3 = ind2 + 1 + start = 2 * i * (num_cols - 1) + stop = start + 2 * (num_cols - 1) + triangles[start:stop:2, 0] = ind0 + triangles[start:stop:2, 1] = ind3 + triangles[start:stop:2, 2] = ind1 + triangles[start + 1 : stop : 2, 0] = ind0 + triangles[start + 1 : stop : 2, 1] = ind2 + triangles[start + 1 : stop : 2, 2] = ind3 + + return vertices, triangles diff --git a/source/isaaclab/isaaclab/terrains/sub_terrain_cfg.py b/source/isaaclab/isaaclab/terrains/sub_terrain_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0dab3ea8f3c174d6ee52aafcd70f2bc0a0c289f5 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/sub_terrain_cfg.py @@ -0,0 +1,96 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING + +import numpy as np +import trimesh + +from isaaclab.utils import configclass + + +@configclass +class FlatPatchSamplingCfg: + """Configuration for sampling flat patches on the sub-terrain. + + For a given sub-terrain, this configuration specifies how to sample flat patches on the terrain. + The sampled flat patches can be used for spawning robots, targets, etc. + + Please check the function :meth:`~isaaclab.terrains.utils.find_flat_patches` for more details. + """ + + num_patches: int = MISSING + """Number of patches to sample.""" + + patch_radius: float | list[float] = MISSING + """Radius of the patches. + + A list of radii can be provided to check for patches of different sizes. This is useful to deal with + cases where the terrain may have holes or obstacles in some areas. + """ + + x_range: tuple[float, float] = (-1e6, 1e6) + """The range of x-coordinates to sample from. Defaults to (-1e6, 1e6). + + This range is internally clamped to the size of the terrain mesh. + """ + + y_range: tuple[float, float] = (-1e6, 1e6) + """The range of y-coordinates to sample from. Defaults to (-1e6, 1e6). + + This range is internally clamped to the size of the terrain mesh. + """ + + z_range: tuple[float, float] = (-1e6, 1e6) + """Allowed range of z-coordinates for the sampled patch. Defaults to (-1e6, 1e6).""" + + max_height_diff: float = MISSING + """Maximum allowed height difference between the highest and lowest points on the patch.""" + + +@configclass +class SubTerrainBaseCfg: + """Base class for terrain configurations. + + All the sub-terrain configurations must inherit from this class. + + The :attr:`size` attribute is the size of the generated sub-terrain. Based on this, the terrain must + extend from :math:`(0, 0)` to :math:`(size[0], size[1])`. + """ + + function: Callable[[float, SubTerrainBaseCfg], tuple[list[trimesh.Trimesh], np.ndarray]] = MISSING + """Function to generate the terrain. + + This function must take as input the terrain difficulty and the configuration parameters and + return a tuple with a list of ``trimesh`` mesh objects and the terrain origin. + """ + + proportion: float = 1.0 + """Proportion of the terrain to generate. Defaults to 1.0. + + This is used to generate a mix of terrains. The proportion corresponds to the probability of sampling + the particular terrain. For example, if there are two terrains, A and B, with proportions 0.3 and 0.7, + respectively, then the probability of sampling terrain A is 0.3 and the probability of sampling terrain B + is 0.7. + """ + + size: tuple[float, float] = (10.0, 10.0) + """The width (along x) and length (along y) of the terrain (in m). Defaults to (10.0, 10.0). + + In case the :class:`~isaaclab.terrains.TerrainImporterCfg` is used, this parameter gets overridden by + :attr:`isaaclab.scene.TerrainImporterCfg.size` attribute. + """ + + flat_patch_sampling: dict[str, FlatPatchSamplingCfg] | None = None + """Dictionary of configurations for sampling flat patches on the sub-terrain. Defaults to None, + in which case no flat patch sampling is performed. + + The keys correspond to the name of the flat patch sampling configuration and the values are the + corresponding configurations. + """ diff --git a/source/isaaclab/isaaclab/terrains/terrain_generator.py b/source/isaaclab/isaaclab/terrains/terrain_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..58c0c85be9d1927fc215f6283a7531eea6bc4bae --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/terrain_generator.py @@ -0,0 +1,399 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import os +from typing import TYPE_CHECKING + +import numpy as np +import torch +import trimesh + +from isaaclab.utils.dict import dict_to_md5_hash +from isaaclab.utils.io import dump_yaml +from isaaclab.utils.timer import Timer +from isaaclab.utils.warp import convert_to_warp_mesh + +from .trimesh.utils import make_border +from .utils import color_meshes_by_height, find_flat_patches + +if TYPE_CHECKING: + from .sub_terrain_cfg import FlatPatchSamplingCfg, SubTerrainBaseCfg + from .terrain_generator_cfg import TerrainGeneratorCfg + +# import logger +logger = logging.getLogger(__name__) + + +class TerrainGenerator: + r"""Terrain generator to handle different terrain generation functions. + + The terrains are represented as meshes. These are obtained either from height fields or by using the + `trimesh `__ library. The height field representation is more + flexible, but it is less computationally and memory efficient than the trimesh representation. + + All terrain generation functions take in the argument :obj:`difficulty` which determines the complexity + of the terrain. The difficulty is a number between 0 and 1, where 0 is the easiest and 1 is the hardest. + In most cases, the difficulty is used for linear interpolation between different terrain parameters. + For example, in a pyramid stairs terrain the step height is interpolated between the specified minimum + and maximum step height. + + Each sub-terrain has a corresponding configuration class that can be used to specify the parameters + of the terrain. The configuration classes are inherited from the :class:`SubTerrainBaseCfg` class + which contains the common parameters for all terrains. + + If a curriculum is used, the terrains are generated based on their difficulty parameter. + The difficulty is varied linearly over the number of rows (i.e. along x) with a small random value + added to the difficulty to ensure that the columns with the same sub-terrain type are not exactly + the same. The difficulty parameter for a sub-terrain at a given row is calculated as: + + .. math:: + + \text{difficulty} = + \frac{\text{row_id} + \eta}{\text{num_rows}} \times (\text{upper} - \text{lower}) + \text{lower} + + where :math:`\eta\sim\mathcal{U}(0, 1)` is a random perturbation to the difficulty, and + :math:`(\text{lower}, \text{upper})` is the range of the difficulty parameter, specified using the + :attr:`~TerrainGeneratorCfg.difficulty_range` parameter. + + If a curriculum is not used, the terrains are generated randomly. In this case, the difficulty parameter + is randomly sampled from the specified range, given by the :attr:`~TerrainGeneratorCfg.difficulty_range` + parameter: + + .. math:: + + \text{difficulty} \sim \mathcal{U}(\text{lower}, \text{upper}) + + If the :attr:`~TerrainGeneratorCfg.flat_patch_sampling` is specified for a sub-terrain, flat patches are + sampled on the terrain. These can be used for spawning robots, targets, etc. The sampled patches are stored + in the :obj:`flat_patches` dictionary. The key specifies the intention of the flat patches and the + value is a tensor containing the flat patches for each sub-terrain. + + If the flag :attr:`~TerrainGeneratorCfg.use_cache` is set to True, the terrains are cached based on their + sub-terrain configurations. This means that if the same sub-terrain configuration is used + multiple times, the terrain is only generated once and then reused. This is useful when + generating complex sub-terrains that take a long time to generate. + + .. attention:: + + The terrain generation has its own seed parameter. This is set using the :attr:`TerrainGeneratorCfg.seed` + parameter. If the seed is not set and the caching is disabled, the terrain generation may not be + completely reproducible. + + """ + + terrain_mesh: trimesh.Trimesh + """A single trimesh.Trimesh object for all the generated sub-terrains.""" + terrain_meshes: list[trimesh.Trimesh] + """List of trimesh.Trimesh objects for all the generated sub-terrains.""" + terrain_origins: np.ndarray + """The origin of each sub-terrain. Shape is (num_rows, num_cols, 3).""" + flat_patches: dict[str, torch.Tensor] + """A dictionary of sampled valid (flat) patches for each sub-terrain. + + The dictionary keys are the names of the flat patch sampling configurations. This maps to a + tensor containing the flat patches for each sub-terrain. The shape of the tensor is + (num_rows, num_cols, num_patches, 3). + + For instance, the key "root_spawn" maps to a tensor containing the flat patches for spawning an asset. + Similarly, the key "target_spawn" maps to a tensor containing the flat patches for setting targets. + """ + + def __init__(self, cfg: TerrainGeneratorCfg, device: str = "cpu"): + """Initialize the terrain generator. + + Args: + cfg: Configuration for the terrain generator. + device: The device to use for the flat patches tensor. + """ + # check inputs + if len(cfg.sub_terrains) == 0: + raise ValueError("No sub-terrains specified! Please add at least one sub-terrain.") + # store inputs + self.cfg = cfg + self.device = device + + # set common values to all sub-terrains config + from .height_field import HfTerrainBaseCfg # prevent circular import + + for sub_cfg in self.cfg.sub_terrains.values(): + # size of all terrains + sub_cfg.size = self.cfg.size + # params for height field terrains + if isinstance(sub_cfg, HfTerrainBaseCfg): + sub_cfg.horizontal_scale = self.cfg.horizontal_scale + sub_cfg.vertical_scale = self.cfg.vertical_scale + sub_cfg.slope_threshold = self.cfg.slope_threshold + + # throw a warning if the cache is enabled but the seed is not set + if self.cfg.use_cache and self.cfg.seed is None: + logger.warning( + "Cache is enabled but the seed is not set. The terrain generation will not be reproducible." + " Please set the seed in the terrain generator configuration to make the generation reproducible." + ) + + # if the seed is not set, we assume there is a global seed set and use that. + # this ensures that the terrain is reproducible if the seed is set at the beginning of the program. + if self.cfg.seed is not None: + seed = self.cfg.seed + else: + seed = np.random.get_state()[1][0] + # set the seed for reproducibility + # note: we create a new random number generator to avoid affecting the global state + # in the other places where random numbers are used. + self.np_rng = np.random.default_rng(seed) + + # buffer for storing valid patches + self.flat_patches = {} + # create a list of all sub-terrains + self.terrain_meshes = list() + self.terrain_origins = np.zeros((self.cfg.num_rows, self.cfg.num_cols, 3)) + + # parse configuration and add sub-terrains + # create terrains based on curriculum or randomly + if self.cfg.curriculum: + with Timer("[INFO] Generating terrains based on curriculum took"): + self._generate_curriculum_terrains() + else: + with Timer("[INFO] Generating terrains randomly took"): + self._generate_random_terrains() + # add a border around the terrains + self._add_terrain_border() + # combine all the sub-terrains into a single mesh + self.terrain_mesh = trimesh.util.concatenate(self.terrain_meshes) + + # color the terrain mesh + if self.cfg.color_scheme == "height": + self.terrain_mesh = color_meshes_by_height(self.terrain_mesh) + elif self.cfg.color_scheme == "random": + self.terrain_mesh.visual.vertex_colors = self.np_rng.choice( + range(256), size=(len(self.terrain_mesh.vertices), 4) + ) + elif self.cfg.color_scheme == "none": + pass + else: + raise ValueError(f"Invalid color scheme: {self.cfg.color_scheme}.") + + # offset the entire terrain and origins so that it is centered + # -- terrain mesh + transform = np.eye(4) + transform[:2, -1] = -self.cfg.size[0] * self.cfg.num_rows * 0.5, -self.cfg.size[1] * self.cfg.num_cols * 0.5 + self.terrain_mesh.apply_transform(transform) + # -- terrain origins + self.terrain_origins += transform[:3, -1] + # -- valid patches + terrain_origins_torch = torch.tensor(self.terrain_origins, dtype=torch.float, device=self.device).unsqueeze(2) + for name, value in self.flat_patches.items(): + self.flat_patches[name] = value + terrain_origins_torch + + def __str__(self): + """Return a string representation of the terrain generator.""" + msg = "Terrain Generator:" + msg += f"\n\tSeed: {self.cfg.seed}" + msg += f"\n\tNumber of rows: {self.cfg.num_rows}" + msg += f"\n\tNumber of columns: {self.cfg.num_cols}" + msg += f"\n\tSub-terrain size: {self.cfg.size}" + msg += f"\n\tSub-terrain types: {list(self.cfg.sub_terrains.keys())}" + msg += f"\n\tCurriculum: {self.cfg.curriculum}" + msg += f"\n\tDifficulty range: {self.cfg.difficulty_range}" + msg += f"\n\tColor scheme: {self.cfg.color_scheme}" + msg += f"\n\tUse cache: {self.cfg.use_cache}" + if self.cfg.use_cache: + msg += f"\n\tCache directory: {self.cfg.cache_dir}" + + return msg + + """ + Terrain generator functions. + """ + + def _generate_random_terrains(self): + """Add terrains based on randomly sampled difficulty parameter.""" + # normalize the proportions of the sub-terrains + proportions = np.array([sub_cfg.proportion for sub_cfg in self.cfg.sub_terrains.values()]) + proportions /= np.sum(proportions) + # create a list of all terrain configs + sub_terrains_cfgs = list(self.cfg.sub_terrains.values()) + + # randomly sample sub-terrains + for index in range(self.cfg.num_rows * self.cfg.num_cols): + # coordinate index of the sub-terrain + (sub_row, sub_col) = np.unravel_index(index, (self.cfg.num_rows, self.cfg.num_cols)) + # randomly sample terrain index + sub_index = self.np_rng.choice(len(proportions), p=proportions) + # randomly sample difficulty parameter + difficulty = self.np_rng.uniform(*self.cfg.difficulty_range) + # generate terrain + mesh, origin = self._get_terrain_mesh(difficulty, sub_terrains_cfgs[sub_index]) + # add to sub-terrains + self._add_sub_terrain(mesh, origin, sub_row, sub_col, sub_terrains_cfgs[sub_index]) + + def _generate_curriculum_terrains(self): + """Add terrains based on the difficulty parameter.""" + # normalize the proportions of the sub-terrains + proportions = np.array([sub_cfg.proportion for sub_cfg in self.cfg.sub_terrains.values()]) + proportions /= np.sum(proportions) + + # find the sub-terrain index for each column + # we generate the terrains based on their proportion (not randomly sampled) + sub_indices = [] + for index in range(self.cfg.num_cols): + sub_index = np.min(np.where(index / self.cfg.num_cols + 0.001 < np.cumsum(proportions))[0]) + sub_indices.append(sub_index) + sub_indices = np.array(sub_indices, dtype=np.int32) + # create a list of all terrain configs + sub_terrains_cfgs = list(self.cfg.sub_terrains.values()) + + # curriculum-based sub-terrains + for sub_col in range(self.cfg.num_cols): + for sub_row in range(self.cfg.num_rows): + # vary the difficulty parameter linearly over the number of rows + # note: based on the proportion, multiple columns can have the same sub-terrain type. + # Thus to increase the diversity along the rows, we add a small random value to the difficulty. + # This ensures that the terrains are not exactly the same. For example, if the + # the row index is 2 and the number of rows is 10, the nominal difficulty is 0.2. + # We add a small random value to the difficulty to make it between 0.2 and 0.3. + lower, upper = self.cfg.difficulty_range + difficulty = (sub_row + self.np_rng.uniform()) / self.cfg.num_rows + difficulty = lower + (upper - lower) * difficulty + # generate terrain + mesh, origin = self._get_terrain_mesh(difficulty, sub_terrains_cfgs[sub_indices[sub_col]]) + # add to sub-terrains + self._add_sub_terrain(mesh, origin, sub_row, sub_col, sub_terrains_cfgs[sub_indices[sub_col]]) + + """ + Internal helper functions. + """ + + def _add_terrain_border(self): + """Add a surrounding border over all the sub-terrains into the terrain meshes.""" + # border parameters + border_size = ( + self.cfg.num_rows * self.cfg.size[0] + 2 * self.cfg.border_width, + self.cfg.num_cols * self.cfg.size[1] + 2 * self.cfg.border_width, + ) + inner_size = (self.cfg.num_rows * self.cfg.size[0], self.cfg.num_cols * self.cfg.size[1]) + border_center = ( + self.cfg.num_rows * self.cfg.size[0] / 2, + self.cfg.num_cols * self.cfg.size[1] / 2, + -self.cfg.border_height / 2, + ) + # border mesh + border_meshes = make_border(border_size, inner_size, height=abs(self.cfg.border_height), position=border_center) + border = trimesh.util.concatenate(border_meshes) + # update the faces to have minimal triangles + selector = ~(np.asarray(border.triangles)[:, :, 2] < -0.1).any(1) + border.update_faces(selector) + # add the border to the list of meshes + self.terrain_meshes.append(border) + + def _add_sub_terrain( + self, mesh: trimesh.Trimesh, origin: np.ndarray, row: int, col: int, sub_terrain_cfg: SubTerrainBaseCfg + ): + """Add input sub-terrain to the list of sub-terrains. + + This function adds the input sub-terrain mesh to the list of sub-terrains and updates the origin + of the sub-terrain in the list of origins. It also samples flat patches if specified. + + Args: + mesh: The mesh of the sub-terrain. + origin: The origin of the sub-terrain. + row: The row index of the sub-terrain. + col: The column index of the sub-terrain. + """ + # sample flat patches if specified + if sub_terrain_cfg.flat_patch_sampling is not None: + logger.info(f"Sampling flat patches for sub-terrain at (row, col): ({row}, {col})") + # convert the mesh to warp mesh + wp_mesh = convert_to_warp_mesh(mesh.vertices, mesh.faces, device=self.device) + # sample flat patches based on each patch configuration for that sub-terrain + for name, patch_cfg in sub_terrain_cfg.flat_patch_sampling.items(): + patch_cfg: FlatPatchSamplingCfg + # create the flat patches tensor (if not already created) + if name not in self.flat_patches: + self.flat_patches[name] = torch.zeros( + (self.cfg.num_rows, self.cfg.num_cols, patch_cfg.num_patches, 3), device=self.device + ) + # add the flat patches to the tensor + self.flat_patches[name][row, col] = find_flat_patches( + wp_mesh=wp_mesh, + origin=origin, + num_patches=patch_cfg.num_patches, + patch_radius=patch_cfg.patch_radius, + x_range=patch_cfg.x_range, + y_range=patch_cfg.y_range, + z_range=patch_cfg.z_range, + max_height_diff=patch_cfg.max_height_diff, + ) + + # transform the mesh to the correct position + transform = np.eye(4) + transform[0:2, -1] = (row + 0.5) * self.cfg.size[0], (col + 0.5) * self.cfg.size[1] + mesh.apply_transform(transform) + # add mesh to the list + self.terrain_meshes.append(mesh) + # add origin to the list + self.terrain_origins[row, col] = origin + transform[:3, -1] + + def _get_terrain_mesh(self, difficulty: float, cfg: SubTerrainBaseCfg) -> tuple[trimesh.Trimesh, np.ndarray]: + """Generate a sub-terrain mesh based on the input difficulty parameter. + + If caching is enabled, the sub-terrain is cached and loaded from the cache if it exists. + The cache is stored in the cache directory specified in the configuration. + + .. Note: + This function centers the 2D center of the mesh and its specified origin such that the + 2D center becomes :math:`(0, 0)` instead of :math:`(size[0] / 2, size[1] / 2). + + Args: + difficulty: The difficulty parameter. + cfg: The configuration of the sub-terrain. + + Returns: + The sub-terrain mesh and origin. + """ + # copy the configuration + cfg = cfg.copy() + # add other parameters to the sub-terrain configuration + cfg.difficulty = float(difficulty) + cfg.seed = self.cfg.seed + # generate hash for the sub-terrain + sub_terrain_hash = dict_to_md5_hash(cfg.to_dict()) + # generate the file name + sub_terrain_cache_dir = os.path.join(self.cfg.cache_dir, sub_terrain_hash) + sub_terrain_obj_filename = os.path.join(sub_terrain_cache_dir, "mesh.obj") + sub_terrain_csv_filename = os.path.join(sub_terrain_cache_dir, "origin.csv") + sub_terrain_meta_filename = os.path.join(sub_terrain_cache_dir, "cfg.yaml") + + # check if hash exists - if true, load the mesh and origin and return + if self.cfg.use_cache and os.path.exists(sub_terrain_obj_filename): + # load existing mesh + mesh = trimesh.load_mesh(sub_terrain_obj_filename, process=False) + origin = np.loadtxt(sub_terrain_csv_filename, delimiter=",") + # return the generated mesh + return mesh, origin + + # generate the terrain + meshes, origin = cfg.function(difficulty, cfg) + mesh = trimesh.util.concatenate(meshes) + # offset mesh such that they are in their center + transform = np.eye(4) + transform[0:2, -1] = -cfg.size[0] * 0.5, -cfg.size[1] * 0.5 + mesh.apply_transform(transform) + # change origin to be in the center of the sub-terrain + origin += transform[0:3, -1] + + # if caching is enabled, save the mesh and origin + if self.cfg.use_cache: + # create the cache directory + os.makedirs(sub_terrain_cache_dir, exist_ok=True) + # save the data + mesh.export(sub_terrain_obj_filename) + np.savetxt(sub_terrain_csv_filename, origin, delimiter=",", header="x,y,z") + dump_yaml(sub_terrain_meta_filename, cfg) + # return the generated mesh + return mesh, origin diff --git a/source/isaaclab/isaaclab/terrains/terrain_generator_cfg.py b/source/isaaclab/isaaclab/terrains/terrain_generator_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3238c7cb4a83ad013c51f81a21d1052f5da339 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/terrain_generator_cfg.py @@ -0,0 +1,131 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Configuration classes defining the different terrains available. Each configuration class must +inherit from ``isaaclab.terrains.terrains_cfg.TerrainConfig`` and define the following attributes: + +- ``name``: Name of the terrain. This is used for the prim name in the USD stage. +- ``function``: Function to generate the terrain. This function must take as input the terrain difficulty + and the configuration parameters and return a `tuple with the `trimesh`` mesh object and terrain origin. +""" + +from __future__ import annotations + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .sub_terrain_cfg import SubTerrainBaseCfg +from .terrain_generator import TerrainGenerator + + +@configclass +class TerrainGeneratorCfg: + """Configuration for the terrain generator.""" + + class_type: type = TerrainGenerator + """The class to use for the terrain generator. + + Defaults to :class:`isaaclab.terrains.terrain_generator.TerrainGenerator`. + """ + + seed: int | None = None + """The seed for the random number generator. Defaults to None, in which case the seed from the + current NumPy's random state is used. + + When the seed is set, the random number generator is initialized with the given seed. This ensures + that the generated terrains are deterministic across different runs. If the seed is not set, the + seed from the current NumPy's random state is used. This assumes that the seed is set elsewhere in + the code. + """ + + curriculum: bool = False + """Whether to use the curriculum mode. Defaults to False. + + If True, the terrains are generated based on their difficulty parameter. Otherwise, + they are randomly generated. + """ + + size: tuple[float, float] = MISSING + """The width (along x) and length (along y) of each sub-terrain (in m). + + Note: + This value is passed on to all the sub-terrain configurations. + """ + + border_width: float = 0.0 + """The width of the border around the terrain (in m). Defaults to 0.0.""" + + border_height: float = 1.0 + """The height of the border around the terrain (in m). Defaults to 1.0. + + .. note:: + The default border extends below the ground. If you want to make the border above the ground, + choose a negative value. + + """ + + num_rows: int = 1 + """Number of rows of sub-terrains to generate. Defaults to 1.""" + + num_cols: int = 1 + """Number of columns of sub-terrains to generate. Defaults to 1.""" + + color_scheme: Literal["height", "random", "none"] = "none" + """Color scheme to use for the terrain. Defaults to "none". + + The available color schemes are: + + - "height": Color based on the height of the terrain. + - "random": Random color scheme. + - "none": No color scheme. + """ + + horizontal_scale: float = 0.1 + """The discretization of the terrain along the x and y axes (in m). Defaults to 0.1. + + This value is passed on to all the height field sub-terrain configurations. + """ + + vertical_scale: float = 0.005 + """The discretization of the terrain along the z axis (in m). Defaults to 0.005. + + This value is passed on to all the height field sub-terrain configurations. + """ + + slope_threshold: float | None = 0.75 + """The slope threshold above which surfaces are made vertical. Defaults to 0.75. + + If None no correction is applied. + + This value is passed on to all the height field sub-terrain configurations. + """ + + sub_terrains: dict[str, SubTerrainBaseCfg] = MISSING + """Dictionary of sub-terrain configurations. + + The keys correspond to the name of the sub-terrain configuration and the values are the corresponding + configurations. + """ + + difficulty_range: tuple[float, float] = (0.0, 1.0) + """The range of difficulty values for the sub-terrains. Defaults to (0.0, 1.0). + + If curriculum is enabled, the terrains will be generated based on this range in ascending order + of difficulty. Otherwise, the terrains will be generated based on this range in a random order. + """ + + use_cache: bool = False + """Whether to load the sub-terrain from cache if it exists. Defaults to False. + + If enabled, the generated terrains are stored in the cache directory. When generating terrains, the cache + is checked to see if the terrain already exists. If it does, the terrain is loaded from the cache. Otherwise, + the terrain is generated and stored in the cache. Caching can be used to speed up terrain generation. + """ + + cache_dir: str = "/tmp/isaaclab/terrains" + """The directory where the terrain cache is stored. Defaults to "/tmp/isaaclab/terrains".""" diff --git a/source/isaaclab/isaaclab/terrains/terrain_importer.py b/source/isaaclab/isaaclab/terrains/terrain_importer.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ddc691b35b0779ee4c868571237ca5a13b98a9 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/terrain_importer.py @@ -0,0 +1,399 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import numpy as np +import torch +import trimesh + +import isaaclab.sim as sim_utils +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG + +from .utils import create_prim_from_mesh + +if TYPE_CHECKING: + from .terrain_importer_cfg import TerrainImporterCfg + +# import logger +logger = logging.getLogger(__name__) + + +class TerrainImporter: + r"""A class to handle terrain meshes and import them into the simulator. + + We assume that a terrain mesh comprises of sub-terrains that are arranged in a grid with + rows ``num_rows`` and columns ``num_cols``. The terrain origins are the positions of the sub-terrains + where the robot should be spawned. + + Based on the configuration, the terrain importer handles computing the environment origins from the sub-terrain + origins. In a typical setup, the number of sub-terrains (:math:`num\_rows \times num\_cols`) is smaller than + the number of environments (:math:`num\_envs`). In this case, the environment origins are computed by + sampling the sub-terrain origins. + + If a curriculum is used, it is possible to update the environment origins to terrain origins that correspond + to a harder difficulty. This is done by calling :func:`update_terrain_levels`. The idea comes from game-based + curriculum. For example, in a game, the player starts with easy levels and progresses to harder levels. + """ + + terrain_prim_paths: list[str] + """A list containing the USD prim paths to the imported terrains.""" + + terrain_origins: torch.Tensor | None + """The origins of the sub-terrains in the added terrain mesh. Shape is (num_rows, num_cols, 3). + + If terrain origins is not None, the environment origins are computed based on the terrain origins. + Otherwise, the environment origins are computed based on the grid spacing. + """ + + env_origins: torch.Tensor + """The origins of the environments. Shape is (num_envs, 3).""" + + def __init__(self, cfg: TerrainImporterCfg): + """Initialize the terrain importer. + + Args: + cfg: The configuration for the terrain importer. + + Raises: + ValueError: If input terrain type is not supported. + ValueError: If terrain type is 'generator' and no configuration provided for ``terrain_generator``. + ValueError: If terrain type is 'usd' and no configuration provided for ``usd_path``. + ValueError: If terrain type is 'usd' or 'plane' and no configuration provided for ``env_spacing``. + """ + # check that the config is valid + cfg.validate() + # store inputs + self.cfg = cfg + self.device = sim_utils.SimulationContext.instance().device # type: ignore + + # create buffers for the terrains + self.terrain_prim_paths = list() + self.terrain_origins = None + self.env_origins = None # assigned later when `configure_env_origins` is called + # private variables + self._terrain_flat_patches = dict() + + # auto-import the terrain based on the config + if self.cfg.terrain_type == "generator": + # check config is provided + if self.cfg.terrain_generator is None: + raise ValueError("Input terrain type is 'generator' but no value provided for 'terrain_generator'.") + # generate the terrain + terrain_generator = self.cfg.terrain_generator.class_type( + cfg=self.cfg.terrain_generator, device=self.device + ) + self.import_mesh("terrain", terrain_generator.terrain_mesh) + if self.cfg.use_terrain_origins: + # configure the terrain origins based on the terrain generator + self.configure_env_origins(terrain_generator.terrain_origins) + else: + self.configure_env_origins() + # refer to the flat patches + self._terrain_flat_patches = terrain_generator.flat_patches + elif self.cfg.terrain_type == "usd": + # check if config is provided + if self.cfg.usd_path is None: + raise ValueError("Input terrain type is 'usd' but no value provided for 'usd_path'.") + # import the terrain + self.import_usd("terrain", self.cfg.usd_path) + # configure the origins in a grid + self.configure_env_origins() + elif self.cfg.terrain_type == "plane": + # load the plane + self.import_ground_plane("terrain") + # configure the origins in a grid + self.configure_env_origins() + else: + raise ValueError(f"Terrain type '{self.cfg.terrain_type}' not available.") + + # set initial state of debug visualization + self.set_debug_vis(self.cfg.debug_vis) + + """ + Properties. + """ + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the terrain importer has a debug visualization implemented. + + This always returns True. + """ + return True + + @property + def flat_patches(self) -> dict[str, torch.Tensor]: + """A dictionary containing the sampled valid (flat) patches for the terrain. + + This is only available if the terrain type is 'generator'. For other terrain types, this feature + is not available and the function returns an empty dictionary. + + Please refer to the :attr:`TerrainGenerator.flat_patches` for more information. + """ + return self._terrain_flat_patches + + @property + def terrain_names(self) -> list[str]: + """A list of names of the imported terrains.""" + return [f"'{path.split('/')[-1]}'" for path in self.terrain_prim_paths] + + """ + Operations - Visibility. + """ + + def set_debug_vis(self, debug_vis: bool) -> bool: + """Set the debug visualization of the terrain importer. + + Args: + debug_vis: Whether to visualize the terrain origins. + + Returns: + Whether the debug visualization was successfully set. False if the terrain + importer does not support debug visualization. + + Raises: + RuntimeError: If terrain origins are not configured. + """ + # create a marker if necessary + if debug_vis: + if not hasattr(self, "origin_visualizer"): + self.origin_visualizer = VisualizationMarkers( + cfg=FRAME_MARKER_CFG.replace(prim_path="/Visuals/TerrainOrigin") + ) + if self.terrain_origins is not None: + self.origin_visualizer.visualize(self.terrain_origins.reshape(-1, 3)) + elif self.env_origins is not None: + self.origin_visualizer.visualize(self.env_origins.reshape(-1, 3)) + else: + raise RuntimeError("Terrain origins are not configured.") + # set visibility + self.origin_visualizer.set_visibility(True) + else: + if hasattr(self, "origin_visualizer"): + self.origin_visualizer.set_visibility(False) + # report success + return True + + """ + Operations - Import. + """ + + def import_ground_plane(self, name: str, size: tuple[float, float] = (2.0e6, 2.0e6)): + """Add a plane to the terrain importer. + + Args: + name: The name of the imported terrain. This name is used to create the USD prim + corresponding to the terrain. + size: The size of the plane. Defaults to (2.0e6, 2.0e6). + + Raises: + ValueError: If a terrain with the same name already exists. + """ + # create prim path for the terrain + prim_path = self.cfg.prim_path + f"/{name}" + # check if key exists + if prim_path in self.terrain_prim_paths: + raise ValueError( + f"A terrain with the name '{name}' already exists. Existing terrains: {', '.join(self.terrain_names)}." + ) + # store the mesh name + self.terrain_prim_paths.append(prim_path) + + # obtain ground plane color from the configured visual material + color = (0.0, 0.0, 0.0) + if self.cfg.visual_material is not None: + material = self.cfg.visual_material.to_dict() + # defaults to the `GroundPlaneCfg` color if diffuse color attribute is not found + if "diffuse_color" in material: + color = material["diffuse_color"] + else: + logger.warning( + "Visual material specified for ground plane but no diffuse color found." + " Using default color: (0.0, 0.0, 0.0)" + ) + + # get the mesh + ground_plane_cfg = sim_utils.GroundPlaneCfg(physics_material=self.cfg.physics_material, size=size, color=color) + ground_plane_cfg.func(prim_path, ground_plane_cfg) + + def import_mesh(self, name: str, mesh: trimesh.Trimesh): + """Import a mesh into the simulator. + + The mesh is imported into the simulator under the prim path ``cfg.prim_path/{key}``. The created path + contains the mesh as a :class:`pxr.UsdGeom` instance along with visual or physics material prims. + + Args: + name: The name of the imported terrain. This name is used to create the USD prim + corresponding to the terrain. + mesh: The mesh to import. + + Raises: + ValueError: If a terrain with the same name already exists. + """ + # create prim path for the terrain + prim_path = self.cfg.prim_path + f"/{name}" + # check if key exists + if prim_path in self.terrain_prim_paths: + raise ValueError( + f"A terrain with the name '{name}' already exists. Existing terrains: {', '.join(self.terrain_names)}." + ) + # store the mesh name + self.terrain_prim_paths.append(prim_path) + + # import the mesh + create_prim_from_mesh( + prim_path, mesh, visual_material=self.cfg.visual_material, physics_material=self.cfg.physics_material + ) + + def import_usd(self, name: str, usd_path: str): + """Import a mesh from a USD file. + + This function imports a USD file into the simulator as a terrain. It parses the USD file and + stores the mesh under the prim path ``cfg.prim_path/{key}``. If multiple meshes are present in + the USD file, only the first mesh is imported. + + The function doe not apply any material properties to the mesh. The material properties should + be defined in the USD file. + + Args: + name: The name of the imported terrain. This name is used to create the USD prim + corresponding to the terrain. + usd_path: The path to the USD file. + + Raises: + ValueError: If a terrain with the same name already exists. + """ + # create prim path for the terrain + prim_path = self.cfg.prim_path + f"/{name}" + # check if key exists + if prim_path in self.terrain_prim_paths: + raise ValueError( + f"A terrain with the name '{name}' already exists. Existing terrains: {', '.join(self.terrain_names)}." + ) + # store the mesh name + self.terrain_prim_paths.append(prim_path) + + # add the prim path + cfg = sim_utils.UsdFileCfg(usd_path=usd_path) + cfg.func(prim_path, cfg) + + """ + Operations - Origins. + """ + + def configure_env_origins(self, origins: np.ndarray | torch.Tensor | None = None): + """Configure the origins of the environments based on the added terrain. + + Args: + origins: The origins of the sub-terrains. Shape is (num_rows, num_cols, 3). + """ + # decide whether to compute origins in a grid or based on curriculum + if origins is not None: + # convert to numpy + if isinstance(origins, np.ndarray): + origins = torch.from_numpy(origins) + # store the origins + self.terrain_origins = origins.to(self.device, dtype=torch.float) + # compute environment origins + self.env_origins = self._compute_env_origins_curriculum(self.cfg.num_envs, self.terrain_origins) + else: + self.terrain_origins = None + # check if env spacing is valid + if self.cfg.env_spacing is None: + raise ValueError("Environment spacing must be specified for configuring grid-like origins.") + # compute environment origins + self.env_origins = self._compute_env_origins_grid(self.cfg.num_envs, self.cfg.env_spacing) + + def update_env_origins(self, env_ids: torch.Tensor, move_up: torch.Tensor, move_down: torch.Tensor): + """Update the environment origins based on the terrain levels.""" + # check if grid-like spawning + if self.terrain_origins is None: + return + # update terrain level for the envs + self.terrain_levels[env_ids] += 1 * move_up - 1 * move_down + # robots that solve the last level are sent to a random one + # the minimum level is zero + self.terrain_levels[env_ids] = torch.where( + self.terrain_levels[env_ids] >= self.max_terrain_level, + torch.randint_like(self.terrain_levels[env_ids], self.max_terrain_level), + torch.clip(self.terrain_levels[env_ids], 0), + ) + # update the env origins + self.env_origins[env_ids] = self.terrain_origins[self.terrain_levels[env_ids], self.terrain_types[env_ids]] + + """ + Internal helpers. + """ + + def _compute_env_origins_curriculum(self, num_envs: int, origins: torch.Tensor) -> torch.Tensor: + """Compute the origins of the environments defined by the sub-terrains origins.""" + # extract number of rows and cols + num_rows, num_cols = origins.shape[:2] + # maximum initial level possible for the terrains + if self.cfg.max_init_terrain_level is None: + max_init_level = num_rows - 1 + else: + max_init_level = min(self.cfg.max_init_terrain_level, num_rows - 1) + # store maximum terrain level possible + self.max_terrain_level = num_rows + # define all terrain levels and types available + self.terrain_levels = torch.randint(0, max_init_level + 1, (num_envs,), device=self.device) + self.terrain_types = torch.div( + torch.arange(num_envs, device=self.device), (num_envs / num_cols), rounding_mode="floor" + ).to(torch.long) + # create tensor based on number of environments + env_origins = torch.zeros(num_envs, 3, device=self.device) + env_origins[:] = origins[self.terrain_levels, self.terrain_types] + return env_origins + + def _compute_env_origins_grid(self, num_envs: int, env_spacing: float) -> torch.Tensor: + """Compute the origins of the environments in a grid based on configured spacing.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_envs, 3, device=self.device) + # create a grid of origins + num_rows = np.ceil(num_envs / int(np.sqrt(num_envs))) + num_cols = np.ceil(num_envs / num_rows) + ii, jj = torch.meshgrid( + torch.arange(num_rows, device=self.device), torch.arange(num_cols, device=self.device), indexing="ij" + ) + env_origins[:, 0] = -(ii.flatten()[:num_envs] - (num_rows - 1) / 2) * env_spacing + env_origins[:, 1] = (jj.flatten()[:num_envs] - (num_cols - 1) / 2) * env_spacing + env_origins[:, 2] = 0.0 + return env_origins + + """ + Deprecated. + """ + + @property + def warp_meshes(self): + """A dictionary containing the terrain's names and their warp meshes. + + .. deprecated:: v2.1.0 + The `warp_meshes` attribute is deprecated. It is no longer stored inside the class. + """ + logger.warning( + "The `warp_meshes` attribute is deprecated. It is no longer stored inside the `TerrainImporter` class." + " Returning an empty dictionary." + ) + return {} + + @property + def meshes(self) -> dict[str, trimesh.Trimesh]: + """A dictionary containing the terrain's names and their tri-meshes. + + .. deprecated:: v2.1.0 + The `meshes` attribute is deprecated. It is no longer stored inside the class. + """ + logger.warning( + "The `meshes` attribute is deprecated. It is no longer stored inside the `TerrainImporter` class." + " Returning an empty dictionary." + ) + return {} diff --git a/source/isaaclab/isaaclab/terrains/terrain_importer_cfg.py b/source/isaaclab/isaaclab/terrains/terrain_importer_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7b42e06caaf302bcf257050f7805a8b759a98cd9 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/terrain_importer_cfg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import MISSING +from typing import TYPE_CHECKING, Literal + +import isaaclab.sim as sim_utils +from isaaclab.utils import configclass + +from .terrain_importer import TerrainImporter + +if TYPE_CHECKING: + from .terrain_generator_cfg import TerrainGeneratorCfg + + +@configclass +class TerrainImporterCfg: + """Configuration for the terrain manager.""" + + class_type: type = TerrainImporter + """The class to use for the terrain importer. + + Defaults to :class:`isaaclab.terrains.terrain_importer.TerrainImporter`. + """ + + collision_group: int = -1 + """The collision group of the terrain. Defaults to -1.""" + + prim_path: str = MISSING + """The absolute path of the USD terrain prim. + + All sub-terrains are imported relative to this prim path. + """ + + num_envs: int = 1 + """The number of environment origins to consider. Defaults to 1. + + In case, the :class:`~isaaclab.scene.InteractiveSceneCfg` is used, this parameter gets overridden by + :attr:`isaaclab.scene.InteractiveSceneCfg.num_envs` attribute. + """ + + terrain_type: Literal["generator", "plane", "usd"] = "generator" + """The type of terrain to generate. Defaults to "generator". + + Available options are "plane", "usd", and "generator". + """ + + terrain_generator: TerrainGeneratorCfg | None = None + """The terrain generator configuration. + + Only used if ``terrain_type`` is set to "generator". + """ + + usd_path: str | None = None + """The path to the USD file containing the terrain. + + Only used if ``terrain_type`` is set to "usd". + """ + + env_spacing: float | None = None + """The spacing between environment origins when defined in a grid. Defaults to None. + + Note: + This parameter is used only when the ``terrain_type`` is "plane" or "usd" or if + :attr:`use_terrain_origins` is False. + """ + + use_terrain_origins: bool = True + """Whether to set the environment origins based on the terrain origins or in a grid + according to :attr:`env_spacing`. Defaults to True. + + Note: + This parameter is used only when the :attr:`terrain type` is "generator". + """ + + visual_material: sim_utils.VisualMaterialCfg | None = sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)) + """The visual material of the terrain. Defaults to a dark gray color material. + + This parameter is used for both the "generator" and "plane" terrains. + + - If the ``terrain_type`` is "generator", then the material is created at the path + ``{prim_path}/visualMaterial`` and applied to all the sub-terrains. + - If the ``terrain_type`` is "plane", then the diffuse color of the material is set to + to the grid color of the imported ground plane. + """ + + physics_material: sim_utils.RigidBodyMaterialCfg = sim_utils.RigidBodyMaterialCfg() + """The physics material of the terrain. Defaults to a default physics material. + + The material is created at the path: ``{prim_path}/physicsMaterial``. + + .. note:: + This parameter is used only when the ``terrain_type`` is "generator" or "plane". + """ + + max_init_terrain_level: int | None = None + """The maximum initial terrain level for defining environment origins. Defaults to None. + + The terrain levels are specified by the number of rows in the grid arrangement of + sub-terrains. If None, then the initial terrain level is set to the maximum + terrain level available (``num_rows - 1``). + + Note: + This parameter is used only when sub-terrain origins are defined. + """ + + debug_vis: bool = False + """Whether to enable visualization of terrain origins for the terrain. Defaults to False.""" diff --git a/source/isaaclab/isaaclab/terrains/trimesh/__init__.py b/source/isaaclab/isaaclab/terrains/trimesh/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b27b7a92110914c3dcb2c6372c300b9f3e9dbfcb --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/trimesh/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This sub-module provides methods to create different terrains using the ``trimesh`` library. + +In contrast to the height-field representation, the trimesh representation does not +create arbitrarily small triangles. Instead, the terrain is represented as a single +tri-mesh primitive. Thus, this representation is more computationally and memory +efficient than the height-field representation, but it is not as flexible. +""" + +from .mesh_terrains_cfg import ( + MeshBoxTerrainCfg, + MeshFloatingRingTerrainCfg, + MeshGapTerrainCfg, + MeshInvertedPyramidStairsTerrainCfg, + MeshPitTerrainCfg, + MeshPlaneTerrainCfg, + MeshPyramidStairsTerrainCfg, + MeshRailsTerrainCfg, + MeshRandomGridTerrainCfg, + MeshRepeatedBoxesTerrainCfg, + MeshRepeatedCylindersTerrainCfg, + MeshRepeatedPyramidsTerrainCfg, + MeshStarTerrainCfg, +) diff --git a/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains.py b/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains.py new file mode 100644 index 0000000000000000000000000000000000000000..d5a327aebe5833dc212a29f48684a6c1ac35fa26 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains.py @@ -0,0 +1,863 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions to generate different terrains using the ``trimesh`` library.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import scipy.spatial.transform as tf +import torch +import trimesh + +from .utils import * # noqa: F401, F403 +from .utils import make_border, make_plane + +if TYPE_CHECKING: + from . import mesh_terrains_cfg + + +def flat_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshPlaneTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a flat terrain as a plane. + + .. image:: ../../_static/terrains/trimesh/flat_terrain.jpg + :width: 45% + :align: center + + Note: + The :obj:`difficulty` parameter is ignored for this terrain. + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # compute the position of the terrain + origin = (cfg.size[0] / 2.0, cfg.size[1] / 2.0, 0.0) + # compute the vertices of the terrain + plane_mesh = make_plane(cfg.size, 0.0, center_zero=False) + # return the tri-mesh and the position + return [plane_mesh], np.array(origin) + + +def pyramid_stairs_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshPyramidStairsTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a pyramid stair pattern. + + The terrain is a pyramid stair pattern which trims to a flat platform at the center of the terrain. + + If :obj:`cfg.holes` is True, the terrain will have pyramid stairs of length or width + :obj:`cfg.platform_width` (depending on the direction) with no steps in the remaining area. Additionally, + no border will be added. + + .. image:: ../../_static/terrains/trimesh/pyramid_stairs_terrain.jpg + :width: 45% + + .. image:: ../../_static/terrains/trimesh/pyramid_stairs_terrain_with_holes.jpg + :width: 45% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + step_height = cfg.step_height_range[0] + difficulty * (cfg.step_height_range[1] - cfg.step_height_range[0]) + + # compute number of steps in x and y direction + num_steps_x = (cfg.size[0] - 2 * cfg.border_width - cfg.platform_width) // (2 * cfg.step_width) + 1 + num_steps_y = (cfg.size[1] - 2 * cfg.border_width - cfg.platform_width) // (2 * cfg.step_width) + 1 + # we take the minimum number of steps in x and y direction + num_steps = int(min(num_steps_x, num_steps_y)) + + # initialize list of meshes + meshes_list = list() + + # generate the border if needed + if cfg.border_width > 0.0 and not cfg.holes: + # obtain a list of meshes for the border + border_center = [0.5 * cfg.size[0], 0.5 * cfg.size[1], -step_height / 2] + border_inner_size = (cfg.size[0] - 2 * cfg.border_width, cfg.size[1] - 2 * cfg.border_width) + make_borders = make_border(cfg.size, border_inner_size, step_height, border_center) + # add the border meshes to the list of meshes + meshes_list += make_borders + + # generate the terrain + # -- compute the position of the center of the terrain + terrain_center = [0.5 * cfg.size[0], 0.5 * cfg.size[1], 0.0] + terrain_size = (cfg.size[0] - 2 * cfg.border_width, cfg.size[1] - 2 * cfg.border_width) + # -- generate the stair pattern + for k in range(num_steps): + # check if we need to add holes around the steps + if cfg.holes: + box_size = (cfg.platform_width, cfg.platform_width) + else: + box_size = (terrain_size[0] - 2 * k * cfg.step_width, terrain_size[1] - 2 * k * cfg.step_width) + # compute the quantities of the box + # -- location + box_z = terrain_center[2] + k * step_height / 2.0 + box_offset = (k + 0.5) * cfg.step_width + # -- dimensions + box_height = (k + 2) * step_height + # generate the boxes + # top/bottom + box_dims = (box_size[0], cfg.step_width, box_height) + # -- top + box_pos = (terrain_center[0], terrain_center[1] + terrain_size[1] / 2.0 - box_offset, box_z) + box_top = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- bottom + box_pos = (terrain_center[0], terrain_center[1] - terrain_size[1] / 2.0 + box_offset, box_z) + box_bottom = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # right/left + if cfg.holes: + box_dims = (cfg.step_width, box_size[1], box_height) + else: + box_dims = (cfg.step_width, box_size[1] - 2 * cfg.step_width, box_height) + # -- right + box_pos = (terrain_center[0] + terrain_size[0] / 2.0 - box_offset, terrain_center[1], box_z) + box_right = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- left + box_pos = (terrain_center[0] - terrain_size[0] / 2.0 + box_offset, terrain_center[1], box_z) + box_left = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # add the boxes to the list of meshes + meshes_list += [box_top, box_bottom, box_right, box_left] + + # generate final box for the middle of the terrain + box_dims = ( + terrain_size[0] - 2 * num_steps * cfg.step_width, + terrain_size[1] - 2 * num_steps * cfg.step_width, + (num_steps + 2) * step_height, + ) + box_pos = (terrain_center[0], terrain_center[1], terrain_center[2] + num_steps * step_height / 2) + box_middle = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + meshes_list.append(box_middle) + # origin of the terrain + origin = np.array([terrain_center[0], terrain_center[1], (num_steps + 1) * step_height]) + + return meshes_list, origin + + +def inverted_pyramid_stairs_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshInvertedPyramidStairsTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a inverted pyramid stair pattern. + + The terrain is an inverted pyramid stair pattern which trims to a flat platform at the center of the terrain. + + If :obj:`cfg.holes` is True, the terrain will have pyramid stairs of length or width + :obj:`cfg.platform_width` (depending on the direction) with no steps in the remaining area. Additionally, + no border will be added. + + .. image:: ../../_static/terrains/trimesh/inverted_pyramid_stairs_terrain.jpg + :width: 45% + + .. image:: ../../_static/terrains/trimesh/inverted_pyramid_stairs_terrain_with_holes.jpg + :width: 45% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + step_height = cfg.step_height_range[0] + difficulty * (cfg.step_height_range[1] - cfg.step_height_range[0]) + + # compute number of steps in x and y direction + num_steps_x = (cfg.size[0] - 2 * cfg.border_width - cfg.platform_width) // (2 * cfg.step_width) + 1 + num_steps_y = (cfg.size[1] - 2 * cfg.border_width - cfg.platform_width) // (2 * cfg.step_width) + 1 + # we take the minimum number of steps in x and y direction + num_steps = int(min(num_steps_x, num_steps_y)) + # total height of the terrain + total_height = (num_steps + 1) * step_height + + # initialize list of meshes + meshes_list = list() + + # generate the border if needed + if cfg.border_width > 0.0 and not cfg.holes: + # obtain a list of meshes for the border + border_center = [0.5 * cfg.size[0], 0.5 * cfg.size[1], -0.5 * step_height] + border_inner_size = (cfg.size[0] - 2 * cfg.border_width, cfg.size[1] - 2 * cfg.border_width) + make_borders = make_border(cfg.size, border_inner_size, step_height, border_center) + # add the border meshes to the list of meshes + meshes_list += make_borders + # generate the terrain + # -- compute the position of the center of the terrain + terrain_center = [0.5 * cfg.size[0], 0.5 * cfg.size[1], 0.0] + terrain_size = (cfg.size[0] - 2 * cfg.border_width, cfg.size[1] - 2 * cfg.border_width) + # -- generate the stair pattern + for k in range(num_steps): + # check if we need to add holes around the steps + if cfg.holes: + box_size = (cfg.platform_width, cfg.platform_width) + else: + box_size = (terrain_size[0] - 2 * k * cfg.step_width, terrain_size[1] - 2 * k * cfg.step_width) + # compute the quantities of the box + # -- location + box_z = terrain_center[2] - total_height / 2 - (k + 1) * step_height / 2.0 + box_offset = (k + 0.5) * cfg.step_width + # -- dimensions + box_height = total_height - (k + 1) * step_height + # generate the boxes + # top/bottom + box_dims = (box_size[0], cfg.step_width, box_height) + # -- top + box_pos = (terrain_center[0], terrain_center[1] + terrain_size[1] / 2.0 - box_offset, box_z) + box_top = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- bottom + box_pos = (terrain_center[0], terrain_center[1] - terrain_size[1] / 2.0 + box_offset, box_z) + box_bottom = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # right/left + if cfg.holes: + box_dims = (cfg.step_width, box_size[1], box_height) + else: + box_dims = (cfg.step_width, box_size[1] - 2 * cfg.step_width, box_height) + # -- right + box_pos = (terrain_center[0] + terrain_size[0] / 2.0 - box_offset, terrain_center[1], box_z) + box_right = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- left + box_pos = (terrain_center[0] - terrain_size[0] / 2.0 + box_offset, terrain_center[1], box_z) + box_left = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # add the boxes to the list of meshes + meshes_list += [box_top, box_bottom, box_right, box_left] + # generate final box for the middle of the terrain + box_dims = ( + terrain_size[0] - 2 * num_steps * cfg.step_width, + terrain_size[1] - 2 * num_steps * cfg.step_width, + step_height, + ) + box_pos = (terrain_center[0], terrain_center[1], terrain_center[2] - total_height - step_height / 2) + box_middle = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + meshes_list.append(box_middle) + # origin of the terrain + origin = np.array([terrain_center[0], terrain_center[1], -(num_steps + 1) * step_height]) + + return meshes_list, origin + + +def random_grid_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshRandomGridTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with cells of random heights and fixed width. + + The terrain is generated in the x-y plane and has a height of 1.0. It is then divided into a grid of the + specified size :obj:`cfg.grid_width`. Each grid cell is then randomly shifted in the z-direction by a value + uniformly sampled between :obj:`cfg.grid_height_range`. At the center of the terrain, a platform of the specified + width :obj:`cfg.platform_width` is generated. + + If :obj:`cfg.holes` is True, the terrain will have randomized grid cells only along the plane extending + from the platform (like a plus sign). The remaining area remains empty and no border will be added. + + .. image:: ../../_static/terrains/trimesh/random_grid_terrain.jpg + :width: 45% + + .. image:: ../../_static/terrains/trimesh/random_grid_terrain_with_holes.jpg + :width: 45% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + + Raises: + ValueError: If the terrain is not square. This method only supports square terrains. + RuntimeError: If the grid width is large such that the border width is negative. + """ + # check to ensure square terrain + if cfg.size[0] != cfg.size[1]: + raise ValueError(f"The terrain must be square. Received size: {cfg.size}.") + # resolve the terrain configuration + grid_height = cfg.grid_height_range[0] + difficulty * (cfg.grid_height_range[1] - cfg.grid_height_range[0]) + + # initialize list of meshes + meshes_list = list() + # compute the number of boxes in each direction + num_boxes_x = int(cfg.size[0] / cfg.grid_width) + num_boxes_y = int(cfg.size[1] / cfg.grid_width) + # constant parameters + terrain_height = 1.0 + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + + # generate the border + border_width = cfg.size[0] - min(num_boxes_x, num_boxes_y) * cfg.grid_width + if border_width > 0: + # compute parameters for the border + border_center = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2) + border_inner_size = (cfg.size[0] - border_width, cfg.size[1] - border_width) + # create border meshes + make_borders = make_border(cfg.size, border_inner_size, terrain_height, border_center) + meshes_list += make_borders + else: + raise RuntimeError("Border width must be greater than 0! Adjust the parameter 'cfg.grid_width'.") + + # create a template grid of terrain height + grid_dim = [cfg.grid_width, cfg.grid_width, terrain_height] + grid_position = [0.5 * cfg.grid_width, 0.5 * cfg.grid_width, -terrain_height / 2] + template_box = trimesh.creation.box(grid_dim, trimesh.transformations.translation_matrix(grid_position)) + # extract vertices and faces of the box to create a template + template_vertices = template_box.vertices # (8, 3) + template_faces = template_box.faces + + # repeat the template box vertices to span the terrain (num_boxes_x * num_boxes_y, 8, 3) + vertices = torch.tensor(template_vertices, device=device).repeat(num_boxes_x * num_boxes_y, 1, 1) + # create a meshgrid to offset the vertices + x = torch.arange(0, num_boxes_x, device=device) + y = torch.arange(0, num_boxes_y, device=device) + xx, yy = torch.meshgrid(x, y, indexing="ij") + xx = xx.flatten().view(-1, 1) + yy = yy.flatten().view(-1, 1) + xx_yy = torch.cat((xx, yy), dim=1) + # offset the vertices + offsets = cfg.grid_width * xx_yy + border_width / 2 + vertices[:, :, :2] += offsets.unsqueeze(1) + # mask the vertices to create holes, s.t. only grids along the x and y axis are present + if cfg.holes: + # -- x-axis + mask_x = torch.logical_and( + (vertices[:, :, 0] > (cfg.size[0] - border_width - cfg.platform_width) / 2).all(dim=1), + (vertices[:, :, 0] < (cfg.size[0] + border_width + cfg.platform_width) / 2).all(dim=1), + ) + vertices_x = vertices[mask_x] + # -- y-axis + mask_y = torch.logical_and( + (vertices[:, :, 1] > (cfg.size[1] - border_width - cfg.platform_width) / 2).all(dim=1), + (vertices[:, :, 1] < (cfg.size[1] + border_width + cfg.platform_width) / 2).all(dim=1), + ) + vertices_y = vertices[mask_y] + # -- combine these vertices + vertices = torch.cat((vertices_x, vertices_y)) + # add noise to the vertices to have a random height over each grid cell + num_boxes = len(vertices) + # create noise for the z-axis + h_noise = torch.zeros((num_boxes, 3), device=device) + h_noise[:, 2].uniform_(-grid_height, grid_height) + # reshape noise to match the vertices (num_boxes, 4, 3) + # only the top vertices of the box are affected + vertices_noise = torch.zeros((num_boxes, 4, 3), device=device) + vertices_noise += h_noise.unsqueeze(1) + # add height only to the top vertices of the box + vertices[vertices[:, :, 2] == 0] += vertices_noise.view(-1, 3) + # move to numpy + vertices = vertices.reshape(-1, 3).cpu().numpy() + + # create faces for boxes (num_boxes, 12, 3). Each box has 6 faces, each face has 2 triangles. + faces = torch.tensor(template_faces, device=device).repeat(num_boxes, 1, 1) + face_offsets = torch.arange(0, num_boxes, device=device).unsqueeze(1).repeat(1, 12) * 8 + faces += face_offsets.unsqueeze(2) + # move to numpy + faces = faces.view(-1, 3).cpu().numpy() + # convert to trimesh + grid_mesh = trimesh.Trimesh(vertices=vertices, faces=faces) + meshes_list.append(grid_mesh) + + # add a platform in the center of the terrain that is accessible from all sides + dim = (cfg.platform_width, cfg.platform_width, terrain_height + grid_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2 + grid_height / 2) + box_platform = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(box_platform) + + # specify the origin of the terrain + origin = np.array([0.5 * cfg.size[0], 0.5 * cfg.size[1], grid_height]) + + return meshes_list, origin + + +def rails_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshRailsTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with box rails as extrusions. + + The terrain contains two sets of box rails created as extrusions. The first set (inner rails) is extruded from + the platform at the center of the terrain, and the second set is extruded between the first set of rails + and the terrain border. Each set of rails is extruded to the same height. + + .. image:: ../../_static/terrains/trimesh/rails_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. this is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + rail_height = cfg.rail_height_range[0] + difficulty * (cfg.rail_height_range[1] - cfg.rail_height_range[0]) + + # initialize list of meshes + meshes_list = list() + # extract quantities + rail_1_thickness, rail_2_thickness = cfg.rail_thickness_range + rail_center = (0.5 * cfg.size[0], 0.5 * cfg.size[1], rail_height * 0.5) + # constants for terrain generation + terrain_height = 1.0 + rail_2_ratio = 0.6 + + # generate first set of rails + rail_1_inner_size = (cfg.platform_width, cfg.platform_width) + rail_1_outer_size = (cfg.platform_width + 2.0 * rail_1_thickness, cfg.platform_width + 2.0 * rail_1_thickness) + meshes_list += make_border(rail_1_outer_size, rail_1_inner_size, rail_height, rail_center) + # generate second set of rails + rail_2_inner_x = cfg.platform_width + (cfg.size[0] - cfg.platform_width) * rail_2_ratio + rail_2_inner_y = cfg.platform_width + (cfg.size[1] - cfg.platform_width) * rail_2_ratio + rail_2_inner_size = (rail_2_inner_x, rail_2_inner_y) + rail_2_outer_size = (rail_2_inner_x + 2.0 * rail_2_thickness, rail_2_inner_y + 2.0 * rail_2_thickness) + meshes_list += make_border(rail_2_outer_size, rail_2_inner_size, rail_height, rail_center) + # generate the ground + dim = (cfg.size[0], cfg.size[1], terrain_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2) + ground_meshes = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(ground_meshes) + + # specify the origin of the terrain + origin = np.array([pos[0], pos[1], 0.0]) + + return meshes_list, origin + + +def pit_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshPitTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a pit with levels (stairs) leading out of the pit. + + The terrain contains a platform at the center and a staircase leading out of the pit. + The staircase is a series of steps that are aligned along the x- and y- axis. The steps are + created by extruding a ring along the x- and y- axis. If :obj:`is_double_pit` is True, the pit + contains two levels. + + .. image:: ../../_static/terrains/trimesh/pit_terrain.jpg + :width: 40% + + .. image:: ../../_static/terrains/trimesh/pit_terrain_with_two_levels.jpg + :width: 40% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + pit_depth = cfg.pit_depth_range[0] + difficulty * (cfg.pit_depth_range[1] - cfg.pit_depth_range[0]) + + # initialize list of meshes + meshes_list = list() + # extract quantities + inner_pit_size = (cfg.platform_width, cfg.platform_width) + total_depth = pit_depth + # constants for terrain generation + terrain_height = 1.0 + ring_2_ratio = 0.6 + + # if the pit is double, the inner ring is smaller to fit the second level + if cfg.double_pit: + # increase the total height of the pit + total_depth *= 2.0 + # reduce the size of the inner ring + inner_pit_x = cfg.platform_width + (cfg.size[0] - cfg.platform_width) * ring_2_ratio + inner_pit_y = cfg.platform_width + (cfg.size[1] - cfg.platform_width) * ring_2_ratio + inner_pit_size = (inner_pit_x, inner_pit_y) + + # generate the pit (outer ring) + pit_center = [0.5 * cfg.size[0], 0.5 * cfg.size[1], -total_depth * 0.5] + meshes_list += make_border(cfg.size, inner_pit_size, total_depth, pit_center) + # generate the second level of the pit (inner ring) + if cfg.double_pit: + pit_center[2] = -total_depth + meshes_list += make_border(inner_pit_size, (cfg.platform_width, cfg.platform_width), total_depth, pit_center) + # generate the ground + dim = (cfg.size[0], cfg.size[1], terrain_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -total_depth - terrain_height / 2) + ground_meshes = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(ground_meshes) + + # specify the origin of the terrain + origin = np.array([pos[0], pos[1], -total_depth]) + + return meshes_list, origin + + +def box_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshBoxTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with boxes (similar to a pyramid). + + The terrain has a ground with boxes on top of it that are stacked on top of each other. + The boxes are created by extruding a rectangle along the z-axis. If :obj:`double_box` is True, + then two boxes of height :obj:`box_height` are stacked on top of each other. + + .. image:: ../../_static/terrains/trimesh/box_terrain.jpg + :width: 40% + + .. image:: ../../_static/terrains/trimesh/box_terrain_with_two_boxes.jpg + :width: 40% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + box_height = cfg.box_height_range[0] + difficulty * (cfg.box_height_range[1] - cfg.box_height_range[0]) + + # initialize list of meshes + meshes_list = list() + # extract quantities + total_height = box_height + if cfg.double_box: + total_height *= 2.0 + # constants for terrain generation + terrain_height = 1.0 + box_2_ratio = 0.6 + + # Generate the top box + dim = (cfg.platform_width, cfg.platform_width, terrain_height + total_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], (total_height - terrain_height) / 2) + box_mesh = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(box_mesh) + # Generate the lower box + if cfg.double_box: + # calculate the size of the lower box + outer_box_x = cfg.platform_width + (cfg.size[0] - cfg.platform_width) * box_2_ratio + outer_box_y = cfg.platform_width + (cfg.size[1] - cfg.platform_width) * box_2_ratio + # create the lower box + dim = (outer_box_x, outer_box_y, terrain_height + total_height / 2) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], (total_height - terrain_height) / 2 - total_height / 4) + box_mesh = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(box_mesh) + # Generate the ground + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2) + dim = (cfg.size[0], cfg.size[1], terrain_height) + ground_mesh = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(ground_mesh) + + # specify the origin of the terrain + origin = np.array([pos[0], pos[1], total_height]) + + return meshes_list, origin + + +def gap_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshGapTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a gap around the platform. + + The terrain has a ground with a platform in the middle. The platform is surrounded by a gap + of width :obj:`gap_width` on all sides. + + .. image:: ../../_static/terrains/trimesh/gap_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + gap_width = cfg.gap_width_range[0] + difficulty * (cfg.gap_width_range[1] - cfg.gap_width_range[0]) + + # initialize list of meshes + meshes_list = list() + # constants for terrain generation + terrain_height = 1.0 + terrain_center = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2) + + # Generate the outer ring + inner_size = (cfg.platform_width + 2 * gap_width, cfg.platform_width + 2 * gap_width) + meshes_list += make_border(cfg.size, inner_size, terrain_height, terrain_center) + # Generate the inner box + box_dim = (cfg.platform_width, cfg.platform_width, terrain_height) + box = trimesh.creation.box(box_dim, trimesh.transformations.translation_matrix(terrain_center)) + meshes_list.append(box) + + # specify the origin of the terrain + origin = np.array([terrain_center[0], terrain_center[1], 0.0]) + + return meshes_list, origin + + +def floating_ring_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshFloatingRingTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a floating square ring. + + The terrain has a ground with a floating ring in the middle. The ring extends from the center from + :obj:`platform_width` to :obj:`platform_width` + :obj:`ring_width` in the x and y directions. + The thickness of the ring is :obj:`ring_thickness` and the height of the ring from the terrain + is :obj:`ring_height`. + + .. image:: ../../_static/terrains/trimesh/floating_ring_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + """ + # resolve the terrain configuration + ring_height = cfg.ring_height_range[1] - difficulty * (cfg.ring_height_range[1] - cfg.ring_height_range[0]) + ring_width = cfg.ring_width_range[0] + difficulty * (cfg.ring_width_range[1] - cfg.ring_width_range[0]) + + # initialize list of meshes + meshes_list = list() + # constants for terrain generation + terrain_height = 1.0 + + # Generate the floating ring + ring_center = (0.5 * cfg.size[0], 0.5 * cfg.size[1], ring_height + 0.5 * cfg.ring_thickness) + ring_outer_size = (cfg.platform_width + 2 * ring_width, cfg.platform_width + 2 * ring_width) + ring_inner_size = (cfg.platform_width, cfg.platform_width) + meshes_list += make_border(ring_outer_size, ring_inner_size, cfg.ring_thickness, ring_center) + # Generate the ground + dim = (cfg.size[0], cfg.size[1], terrain_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -terrain_height / 2) + ground = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(ground) + + # specify the origin of the terrain + origin = np.asarray([pos[0], pos[1], 0.0]) + + return meshes_list, origin + + +def star_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshStarTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a star. + + The terrain has a ground with a cylinder in the middle. The star is made of :obj:`num_bars` bars + with a width of :obj:`bar_width` and a height of :obj:`bar_height`. The bars are evenly + spaced around the cylinder and connect to the peripheral of the terrain. + + .. image:: ../../_static/terrains/trimesh/star_terrain.jpg + :width: 40% + :align: center + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + + Raises: + ValueError: If :obj:`num_bars` is less than 2. + """ + # check the number of bars + if cfg.num_bars < 2: + raise ValueError(f"The number of bars in the star must be greater than 2. Received: {cfg.num_bars}") + + # resolve the terrain configuration + bar_height = cfg.bar_height_range[0] + difficulty * (cfg.bar_height_range[1] - cfg.bar_height_range[0]) + bar_width = cfg.bar_width_range[1] - difficulty * (cfg.bar_width_range[1] - cfg.bar_width_range[0]) + + # initialize list of meshes + meshes_list = list() + # Generate a platform in the middle + platform_center = (0.5 * cfg.size[0], 0.5 * cfg.size[1], -bar_height / 2) + platform_transform = trimesh.transformations.translation_matrix(platform_center) + platform = trimesh.creation.cylinder( + cfg.platform_width * 0.5, bar_height, sections=2 * cfg.num_bars, transform=platform_transform + ) + meshes_list.append(platform) + # Generate bars to connect the platform to the terrain + transform = np.eye(4) + transform[:3, -1] = np.asarray(platform_center) + yaw = 0.0 + for _ in range(cfg.num_bars): + # compute the length of the bar based on the yaw + # length changes since the bar is connected to a square border + bar_length = cfg.size[0] + if yaw < 0.25 * np.pi: + bar_length /= np.math.cos(yaw) + elif yaw < 0.75 * np.pi: + bar_length /= np.math.sin(yaw) + else: + bar_length /= np.math.cos(np.pi - yaw) + # compute the transform of the bar + transform[0:3, 0:3] = tf.Rotation.from_euler("z", yaw).as_matrix() + # add the bar to the mesh + dim = [bar_length - bar_width, bar_width, bar_height] + bar = trimesh.creation.box(dim, transform) + meshes_list.append(bar) + # increment the yaw + yaw += np.pi / cfg.num_bars + # Generate the exterior border + inner_size = (cfg.size[0] - 2 * bar_width, cfg.size[1] - 2 * bar_width) + meshes_list += make_border(cfg.size, inner_size, bar_height, platform_center) + # Generate the ground + ground = make_plane(cfg.size, -bar_height, center_zero=False) + meshes_list.append(ground) + # specify the origin of the terrain + origin = np.asarray([0.5 * cfg.size[0], 0.5 * cfg.size[1], 0.0]) + + return meshes_list, origin + + +def repeated_objects_terrain( + difficulty: float, cfg: mesh_terrains_cfg.MeshRepeatedObjectsTerrainCfg +) -> tuple[list[trimesh.Trimesh], np.ndarray]: + """Generate a terrain with a set of repeated objects. + + The terrain has a ground with a platform in the middle. The objects are randomly placed on the + terrain s.t. they do not overlap with the platform. + + Depending on the object type, the objects are generated with different parameters. The objects + The types of objects that can be generated are: ``"cylinder"``, ``"box"``, ``"cone"``. + + The object parameters are specified in the configuration as curriculum parameters. The difficulty + is used to linearly interpolate between the minimum and maximum values of the parameters. + + .. image:: ../../_static/terrains/trimesh/repeated_objects_cylinder_terrain.jpg + :width: 30% + + .. image:: ../../_static/terrains/trimesh/repeated_objects_box_terrain.jpg + :width: 30% + + .. image:: ../../_static/terrains/trimesh/repeated_objects_pyramid_terrain.jpg + :width: 30% + + Args: + difficulty: The difficulty of the terrain. This is a value between 0 and 1. + cfg: The configuration for the terrain. + + Returns: + A tuple containing the tri-mesh of the terrain and the origin of the terrain (in m). + + Raises: + ValueError: If the object type is not supported. It must be either a string or a callable. + """ + # import the object functions -- this is done here to avoid circular imports + from .mesh_terrains_cfg import ( + MeshRepeatedBoxesTerrainCfg, + MeshRepeatedCylindersTerrainCfg, + MeshRepeatedPyramidsTerrainCfg, + ) + + # if object type is a string, get the function: make_{object_type} + if isinstance(cfg.object_type, str): + object_func = globals().get(f"make_{cfg.object_type}") + else: + object_func = cfg.object_type + if not callable(object_func): + raise ValueError(f"The attribute 'object_type' must be a string or a callable. Received: {object_func}") + + # Resolve the terrain configuration + # -- pass parameters to make calling simpler + cp_0 = cfg.object_params_start + cp_1 = cfg.object_params_end + # -- common parameters + num_objects = cp_0.num_objects + int(difficulty * (cp_1.num_objects - cp_0.num_objects)) + height = cp_0.height + difficulty * (cp_1.height - cp_0.height) + platform_height = cfg.platform_height if cfg.platform_height >= 0.0 else height + # -- object specific parameters + # note: SIM114 requires duplicated logical blocks under a single body. + if isinstance(cfg, MeshRepeatedBoxesTerrainCfg): + cp_0: MeshRepeatedBoxesTerrainCfg.ObjectCfg + cp_1: MeshRepeatedBoxesTerrainCfg.ObjectCfg + object_kwargs = { + "length": cp_0.size[0] + difficulty * (cp_1.size[0] - cp_0.size[0]), + "width": cp_0.size[1] + difficulty * (cp_1.size[1] - cp_0.size[1]), + "max_yx_angle": cp_0.max_yx_angle + difficulty * (cp_1.max_yx_angle - cp_0.max_yx_angle), + "degrees": cp_0.degrees, + } + elif isinstance(cfg, MeshRepeatedPyramidsTerrainCfg): # noqa: SIM114 + cp_0: MeshRepeatedPyramidsTerrainCfg.ObjectCfg + cp_1: MeshRepeatedPyramidsTerrainCfg.ObjectCfg + object_kwargs = { + "radius": cp_0.radius + difficulty * (cp_1.radius - cp_0.radius), + "max_yx_angle": cp_0.max_yx_angle + difficulty * (cp_1.max_yx_angle - cp_0.max_yx_angle), + "degrees": cp_0.degrees, + } + elif isinstance(cfg, MeshRepeatedCylindersTerrainCfg): # noqa: SIM114 + cp_0: MeshRepeatedCylindersTerrainCfg.ObjectCfg + cp_1: MeshRepeatedCylindersTerrainCfg.ObjectCfg + object_kwargs = { + "radius": cp_0.radius + difficulty * (cp_1.radius - cp_0.radius), + "max_yx_angle": cp_0.max_yx_angle + difficulty * (cp_1.max_yx_angle - cp_0.max_yx_angle), + "degrees": cp_0.degrees, + } + else: + raise ValueError(f"Unknown terrain configuration: {cfg}") + # constants for the terrain + platform_clearance = 0.1 + + # initialize list of meshes + meshes_list = list() + # compute quantities + origin = np.asarray((0.5 * cfg.size[0], 0.5 * cfg.size[1], 0.5 * platform_height)) + platform_corners = np.asarray( + [ + [origin[0] - cfg.platform_width / 2, origin[1] - cfg.platform_width / 2], + [origin[0] + cfg.platform_width / 2, origin[1] + cfg.platform_width / 2], + ] + ) + platform_corners[0, :] *= 1 - platform_clearance + platform_corners[1, :] *= 1 + platform_clearance + # sample valid center for objects + object_centers = np.zeros((num_objects, 3)) + # use a mask to track invalid objects that still require sampling + mask_objects_left = np.ones((num_objects,), dtype=bool) + # loop until no objects are left to sample + while np.any(mask_objects_left): + # only sample the centers of the remaining invalid objects + num_objects_left = mask_objects_left.sum() + object_centers[mask_objects_left, 0] = np.random.uniform(0, cfg.size[0], num_objects_left) + object_centers[mask_objects_left, 1] = np.random.uniform(0, cfg.size[1], num_objects_left) + # filter out the centers that are on the platform + is_within_platform_x = np.logical_and( + object_centers[mask_objects_left, 0] >= platform_corners[0, 0], + object_centers[mask_objects_left, 0] <= platform_corners[1, 0], + ) + is_within_platform_y = np.logical_and( + object_centers[mask_objects_left, 1] >= platform_corners[0, 1], + object_centers[mask_objects_left, 1] <= platform_corners[1, 1], + ) + # update the mask to track the validity of the objects sampled in this iteration + mask_objects_left[mask_objects_left] = np.logical_and(is_within_platform_x, is_within_platform_y) + + # generate obstacles (but keep platform clean) + for index in range(len(object_centers)): + # randomize the height of the object + abs_height_noise = np.random.uniform(cfg.abs_height_noise[0], cfg.abs_height_noise[1]) + rel_height_noise = np.random.uniform(cfg.rel_height_noise[0], cfg.rel_height_noise[1]) + ob_height = height * rel_height_noise + abs_height_noise + if ob_height > 0.0: + object_mesh = object_func(center=object_centers[index], height=ob_height, **object_kwargs) + meshes_list.append(object_mesh) + + # generate a ground plane for the terrain + ground_plane = make_plane(cfg.size, height=0.0, center_zero=False) + meshes_list.append(ground_plane) + # generate a platform in the middle + dim = (cfg.platform_width, cfg.platform_width, 0.5 * platform_height) + pos = (0.5 * cfg.size[0], 0.5 * cfg.size[1], 0.25 * platform_height) + platform = trimesh.creation.box(dim, trimesh.transformations.translation_matrix(pos)) + meshes_list.append(platform) + + return meshes_list, origin diff --git a/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains_cfg.py b/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4247e21486bea8e6a6ab527eca0cea92501815d3 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/trimesh/mesh_terrains_cfg.py @@ -0,0 +1,320 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import warnings +from dataclasses import MISSING +from typing import Literal + +import isaaclab.terrains.trimesh.mesh_terrains as mesh_terrains +import isaaclab.terrains.trimesh.utils as mesh_utils_terrains +from isaaclab.utils import configclass + +from ..sub_terrain_cfg import SubTerrainBaseCfg + +""" +Different trimesh terrain configurations. +""" + + +@configclass +class MeshPlaneTerrainCfg(SubTerrainBaseCfg): + """Configuration for a plane mesh terrain.""" + + function = mesh_terrains.flat_terrain + + +@configclass +class MeshPyramidStairsTerrainCfg(SubTerrainBaseCfg): + """Configuration for a pyramid stair mesh terrain.""" + + function = mesh_terrains.pyramid_stairs_terrain + + border_width: float = 0.0 + """The width of the border around the terrain (in m). Defaults to 0.0. + + The border is a flat terrain with the same height as the terrain. + """ + + step_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the steps (in m).""" + + step_width: float = MISSING + """The width of the steps (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + holes: bool = False + """If True, the terrain will have holes in the steps. Defaults to False. + + If :obj:`holes` is True, the terrain will have pyramid stairs of length or width + :obj:`platform_width` (depending on the direction) with no steps in the remaining area. Additionally, + no border will be added. + """ + + +@configclass +class MeshInvertedPyramidStairsTerrainCfg(MeshPyramidStairsTerrainCfg): + """Configuration for an inverted pyramid stair mesh terrain. + + Note: + This is the same as :class:`MeshPyramidStairsTerrainCfg` except that the steps are inverted. + """ + + function = mesh_terrains.inverted_pyramid_stairs_terrain + + +@configclass +class MeshRandomGridTerrainCfg(SubTerrainBaseCfg): + """Configuration for a random grid mesh terrain.""" + + function = mesh_terrains.random_grid_terrain + + grid_width: float = MISSING + """The width of the grid cells (in m).""" + + grid_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the grid cells (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + holes: bool = False + """If True, the terrain will have holes in the steps. Defaults to False. + + If :obj:`holes` is True, the terrain will have randomized grid cells only along the plane extending + from the platform (like a plus sign). The remaining area remains empty and no border will be added. + """ + + +@configclass +class MeshRailsTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with box rails as extrusions.""" + + function = mesh_terrains.rails_terrain + + rail_thickness_range: tuple[float, float] = MISSING + """The thickness of the inner and outer rails (in m).""" + + rail_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the rails (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + +@configclass +class MeshPitTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with a pit that leads out of the pit.""" + + function = mesh_terrains.pit_terrain + + pit_depth_range: tuple[float, float] = MISSING + """The minimum and maximum height of the pit (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + double_pit: bool = False + """If True, the pit contains two levels of stairs. Defaults to False.""" + + +@configclass +class MeshBoxTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with boxes (similar to a pyramid).""" + + function = mesh_terrains.box_terrain + + box_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the box (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + double_box: bool = False + """If True, the pit contains two levels of stairs/boxes. Defaults to False.""" + + +@configclass +class MeshGapTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with a gap around the platform.""" + + function = mesh_terrains.gap_terrain + + gap_width_range: tuple[float, float] = MISSING + """The minimum and maximum width of the gap (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + +@configclass +class MeshFloatingRingTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with a floating ring around the center.""" + + function = mesh_terrains.floating_ring_terrain + + ring_width_range: tuple[float, float] = MISSING + """The minimum and maximum width of the ring (in m).""" + + ring_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the ring (in m).""" + + ring_thickness: float = MISSING + """The thickness (along z) of the ring (in m).""" + + platform_width: float = 1.0 + """The width of the square platform at the center of the terrain. Defaults to 1.0.""" + + +@configclass +class MeshStarTerrainCfg(SubTerrainBaseCfg): + """Configuration for a terrain with a star pattern.""" + + function = mesh_terrains.star_terrain + + num_bars: int = MISSING + """The number of bars per-side the star. Must be greater than 2.""" + + bar_width_range: tuple[float, float] = MISSING + """The minimum and maximum width of the bars in the star (in m).""" + + bar_height_range: tuple[float, float] = MISSING + """The minimum and maximum height of the bars in the star (in m).""" + + platform_width: float = 1.0 + """The width of the cylindrical platform at the center of the terrain. Defaults to 1.0.""" + + +@configclass +class MeshRepeatedObjectsTerrainCfg(SubTerrainBaseCfg): + """Base configuration for a terrain with repeated objects.""" + + @configclass + class ObjectCfg: + """Configuration of repeated objects.""" + + num_objects: int = MISSING + """The number of objects to add to the terrain.""" + height: float = MISSING + """The height (along z) of the object (in m).""" + + function = mesh_terrains.repeated_objects_terrain + + object_type: Literal["cylinder", "box", "cone"] | callable = MISSING + """The type of object to generate. + + The type can be a string or a callable. If it is a string, the function will look for a function called + ``make_{object_type}`` in the current module scope. If it is a callable, the function will + use the callable to generate the object. + """ + + object_params_start: ObjectCfg = MISSING + """The object curriculum parameters at the start of the curriculum.""" + + object_params_end: ObjectCfg = MISSING + """The object curriculum parameters at the end of the curriculum.""" + + max_height_noise: float | None = None + """"This parameter is deprecated, but stated here to support backward compatibility""" + + abs_height_noise: tuple[float, float] = (0.0, 0.0) + """The minimum and maximum amount of additive noise for the height of the objects. Default is set to 0.0, + which is no noise. + """ + + rel_height_noise: tuple[float, float] = (1.0, 1.0) + """The minimum and maximum amount of multiplicative noise for the height of the objects. Default is set to 1.0, + which is no noise. + """ + + platform_width: float = 1.0 + """The width of the cylindrical platform at the center of the terrain. Defaults to 1.0.""" + + platform_height: float = -1.0 + """The height of the platform. Defaults to -1.0. + + If the value is negative, the height is the same as the object height. + """ + + def __post_init__(self): + if self.max_height_noise is not None: + warnings.warn( + "MeshRepeatedObjectsTerrainCfg: max_height_noise:float is deprecated and support will be removed in the" + " future. Use abs_height_noise:list[float] instead." + ) + self.abs_height_noise = (-self.max_height_noise, self.max_height_noise) + + +@configclass +class MeshRepeatedPyramidsTerrainCfg(MeshRepeatedObjectsTerrainCfg): + """Configuration for a terrain with repeated pyramids.""" + + @configclass + class ObjectCfg(MeshRepeatedObjectsTerrainCfg.ObjectCfg): + """Configuration for a curriculum of repeated pyramids.""" + + radius: float = MISSING + """The radius of the pyramids (in m).""" + max_yx_angle: float = 0.0 + """The maximum angle along the y and x axis. Defaults to 0.0.""" + degrees: bool = True + """Whether the angle is in degrees. Defaults to True.""" + + object_type = mesh_utils_terrains.make_cone + + object_params_start: ObjectCfg = MISSING + """The object curriculum parameters at the start of the curriculum.""" + + object_params_end: ObjectCfg = MISSING + """The object curriculum parameters at the end of the curriculum.""" + + +@configclass +class MeshRepeatedBoxesTerrainCfg(MeshRepeatedObjectsTerrainCfg): + """Configuration for a terrain with repeated boxes.""" + + @configclass + class ObjectCfg(MeshRepeatedObjectsTerrainCfg.ObjectCfg): + """Configuration for repeated boxes.""" + + size: tuple[float, float] = MISSING + """The width (along x) and length (along y) of the box (in m).""" + max_yx_angle: float = 0.0 + """The maximum angle along the y and x axis. Defaults to 0.0.""" + degrees: bool = True + """Whether the angle is in degrees. Defaults to True.""" + + object_type = mesh_utils_terrains.make_box + + object_params_start: ObjectCfg = MISSING + """The box curriculum parameters at the start of the curriculum.""" + + object_params_end: ObjectCfg = MISSING + """The box curriculum parameters at the end of the curriculum.""" + + +@configclass +class MeshRepeatedCylindersTerrainCfg(MeshRepeatedObjectsTerrainCfg): + """Configuration for a terrain with repeated cylinders.""" + + @configclass + class ObjectCfg(MeshRepeatedObjectsTerrainCfg.ObjectCfg): + """Configuration for repeated cylinder.""" + + radius: float = MISSING + """The radius of the pyramids (in m).""" + max_yx_angle: float = 0.0 + """The maximum angle along the y and x axis. Defaults to 0.0.""" + degrees: bool = True + """Whether the angle is in degrees. Defaults to True.""" + + object_type = mesh_utils_terrains.make_cylinder + + object_params_start: ObjectCfg = MISSING + """The box curriculum parameters at the start of the curriculum.""" + + object_params_end: ObjectCfg = MISSING + """The box curriculum parameters at the end of the curriculum.""" diff --git a/source/isaaclab/isaaclab/terrains/trimesh/utils.py b/source/isaaclab/isaaclab/terrains/trimesh/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aede42f3b7da685b6dfa19e6f2d49e6ec3cd73f0 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/trimesh/utils.py @@ -0,0 +1,194 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import scipy.spatial.transform as tf +import trimesh + +""" +Primitive functions to generate meshes. +""" + + +def make_plane(size: tuple[float, float], height: float, center_zero: bool = True) -> trimesh.Trimesh: + """Generate a plane mesh. + + If :obj:`center_zero` is True, the origin is at center of the plane mesh i.e. the mesh extends from + :math:`(-size[0] / 2, -size[1] / 2, 0)` to :math:`(size[0] / 2, size[1] / 2, height)`. + Otherwise, the origin is :math:`(size[0] / 2, size[1] / 2)` and the mesh extends from + :math:`(0, 0, 0)` to :math:`(size[0], size[1], height)`. + + Args: + size: The length (along x) and width (along y) of the terrain (in m). + height: The height of the plane (in m). + center_zero: Whether the 2D origin of the plane is set to the center of mesh. + Defaults to True. + + Returns: + A trimesh.Trimesh objects for the plane. + """ + # compute the vertices of the terrain + x0 = [size[0], size[1], height] + x1 = [size[0], 0.0, height] + x2 = [0.0, size[1], height] + x3 = [0.0, 0.0, height] + # generate the tri-mesh with two triangles + vertices = np.array([x0, x1, x2, x3]) + faces = np.array([[1, 0, 2], [2, 3, 1]]) + plane_mesh = trimesh.Trimesh(vertices=vertices, faces=faces) + # center the plane at the origin + if center_zero: + plane_mesh.apply_translation(-np.array([size[0] / 2.0, size[1] / 2.0, 0.0])) + # return the tri-mesh and the position + return plane_mesh + + +def make_border( + size: tuple[float, float], inner_size: tuple[float, float], height: float, position: tuple[float, float, float] +) -> list[trimesh.Trimesh]: + """Generate meshes for a rectangular border with a hole in the middle. + + .. code:: text + + +---------------------+ + |#####################| + |##+---------------+##| + |##| |##| + |##| |##| length + |##| |##| (y-axis) + |##| |##| + |##+---------------+##| + |#####################| + +---------------------+ + width (x-axis) + + Args: + size: The length (along x) and width (along y) of the terrain (in m). + inner_size: The inner length (along x) and width (along y) of the hole (in m). + height: The height of the border (in m). + position: The center of the border (in m). + + Returns: + A list of trimesh.Trimesh objects that represent the border. + """ + # compute thickness of the border + thickness_x = (size[0] - inner_size[0]) / 2.0 + thickness_y = (size[1] - inner_size[1]) / 2.0 + # generate tri-meshes for the border + # top/bottom border + box_dims = (size[0], thickness_y, height) + # -- top + box_pos = (position[0], position[1] + inner_size[1] / 2.0 + thickness_y / 2.0, position[2]) + box_mesh_top = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- bottom + box_pos = (position[0], position[1] - inner_size[1] / 2.0 - thickness_y / 2.0, position[2]) + box_mesh_bottom = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # left/right border + box_dims = (thickness_x, inner_size[1], height) + # -- left + box_pos = (position[0] - inner_size[0] / 2.0 - thickness_x / 2.0, position[1], position[2]) + box_mesh_left = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # -- right + box_pos = (position[0] + inner_size[0] / 2.0 + thickness_x / 2.0, position[1], position[2]) + box_mesh_right = trimesh.creation.box(box_dims, trimesh.transformations.translation_matrix(box_pos)) + # return the tri-meshes + return [box_mesh_left, box_mesh_right, box_mesh_top, box_mesh_bottom] + + +def make_box( + length: float, + width: float, + height: float, + center: tuple[float, float, float], + max_yx_angle: float = 0, + degrees: bool = True, +) -> trimesh.Trimesh: + """Generate a box mesh with a random orientation. + + Args: + length: The length (along x) of the box (in m). + width: The width (along y) of the box (in m). + height: The height of the cylinder (in m). + center: The center of the cylinder (in m). + max_yx_angle: The maximum angle along the y and x axis. Defaults to 0. + degrees: Whether the angle is in degrees. Defaults to True. + + Returns: + A trimesh.Trimesh object for the cylinder. + """ + # create a pose for the cylinder + transform = np.eye(4) + transform[0:3, -1] = np.asarray(center) + # -- create a random rotation + euler_zyx = tf.Rotation.random().as_euler("zyx") # returns rotation of shape (3,) + # -- cap the rotation along the y and x axis + if degrees: + max_yx_angle = max_yx_angle / 180.0 + euler_zyx[1:] *= max_yx_angle + # -- apply the rotation + transform[0:3, 0:3] = tf.Rotation.from_euler("zyx", euler_zyx).as_matrix() + # create the box + dims = (length, width, height) + return trimesh.creation.box(dims, transform=transform) + + +def make_cylinder( + radius: float, height: float, center: tuple[float, float, float], max_yx_angle: float = 0, degrees: bool = True +) -> trimesh.Trimesh: + """Generate a cylinder mesh with a random orientation. + + Args: + radius: The radius of the cylinder (in m). + height: The height of the cylinder (in m). + center: The center of the cylinder (in m). + max_yx_angle: The maximum angle along the y and x axis. Defaults to 0. + degrees: Whether the angle is in degrees. Defaults to True. + + Returns: + A trimesh.Trimesh object for the cylinder. + """ + # create a pose for the cylinder + transform = np.eye(4) + transform[0:3, -1] = np.asarray(center) + # -- create a random rotation + euler_zyx = tf.Rotation.random().as_euler("zyx") # returns rotation of shape (3,) + # -- cap the rotation along the y and x axis + if degrees: + max_yx_angle = max_yx_angle / 180.0 + euler_zyx[1:] *= max_yx_angle + # -- apply the rotation + transform[0:3, 0:3] = tf.Rotation.from_euler("zyx", euler_zyx).as_matrix() + # create the cylinder + return trimesh.creation.cylinder(radius, height, sections=np.random.randint(4, 6), transform=transform) + + +def make_cone( + radius: float, height: float, center: tuple[float, float, float], max_yx_angle: float = 0, degrees: bool = True +) -> trimesh.Trimesh: + """Generate a cone mesh with a random orientation. + + Args: + radius: The radius of the cone (in m). + height: The height of the cone (in m). + center: The center of the cone (in m). + max_yx_angle: The maximum angle along the y and x axis. Defaults to 0. + degrees: Whether the angle is in degrees. Defaults to True. + + Returns: + A trimesh.Trimesh object for the cone. + """ + # create a pose for the cylinder + transform = np.eye(4) + transform[0:3, -1] = np.asarray(center) + # -- create a random rotation + euler_zyx = tf.Rotation.random().as_euler("zyx") # returns rotation of shape (3,) + # -- cap the rotation along the y and x axis + if degrees: + max_yx_angle = max_yx_angle / 180.0 + euler_zyx[1:] *= max_yx_angle + # -- apply the rotation + transform[0:3, 0:3] = tf.Rotation.from_euler("zyx", euler_zyx).as_matrix() + # create the cone + return trimesh.creation.cone(radius, height, sections=np.random.randint(4, 6), transform=transform) diff --git a/source/isaaclab/isaaclab/terrains/utils.py b/source/isaaclab/isaaclab/terrains/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0feee6ca51f3342bb105e718bbeddefdbc966ef8 --- /dev/null +++ b/source/isaaclab/isaaclab/terrains/utils.py @@ -0,0 +1,273 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed to import for allowing type-hinting: np.ndarray | torch.Tensor | None +from __future__ import annotations + +import numpy as np +import torch +import trimesh +import warp as wp + +from isaaclab.utils.warp import raycast_mesh + + +def color_meshes_by_height(meshes: list[trimesh.Trimesh], **kwargs) -> trimesh.Trimesh: + """ + Color the vertices of a trimesh object based on the z-coordinate (height) of each vertex, + using the Turbo colormap. If the z-coordinates are all the same, the vertices will be colored + with a single color. + + Args: + meshes: A list of trimesh objects. + + Keyword Args: + color: A list of 3 integers in the range [0,255] representing the RGB + color of the mesh. Used when the z-coordinates of all vertices are the same. + Defaults to [172, 216, 230]. + color_map: The name of the color map to be used. Defaults to "turbo". + + Returns: + A trimesh object with the vertices colored based on the z-coordinate (height) of each vertex. + """ + # Combine all meshes into a single mesh + mesh = trimesh.util.concatenate(meshes) + # Get the z-coordinates of each vertex + heights = mesh.vertices[:, 2] + # Check if the z-coordinates are all the same + if np.max(heights) == np.min(heights): + # Obtain a single color: light blue + color = kwargs.pop("color", (172, 216, 230)) + color = np.asarray(color, dtype=np.uint8) + # Set the color for all vertices + mesh.visual.vertex_colors = color + else: + # Normalize the heights to [0,1] + heights_normalized = (heights - np.min(heights)) / (np.max(heights) - np.min(heights)) + # clip lower and upper bounds to have better color mapping + heights_normalized = np.clip(heights_normalized, 0.1, 0.9) + # Get the color for each vertex based on the height + color_map = kwargs.pop("color_map", "turbo") + colors = trimesh.visual.color.interpolate(heights_normalized, color_map=color_map) + # Set the vertex colors + mesh.visual.vertex_colors = colors + # Return the mesh + return mesh + + +def create_prim_from_mesh(prim_path: str, mesh: trimesh.Trimesh, **kwargs): + """Create a USD prim with mesh defined from vertices and triangles. + + The function creates a USD prim with a mesh defined from vertices and triangles. It performs the + following steps: + + - Create a USD Xform prim at the path :obj:`prim_path`. + - Create a USD prim with a mesh defined from the input vertices and triangles at the path :obj:`{prim_path}/mesh`. + - Assign a physics material to the mesh at the path :obj:`{prim_path}/physicsMaterial`. + - Assign a visual material to the mesh at the path :obj:`{prim_path}/visualMaterial`. + + Args: + prim_path: The path to the primitive to be created. + mesh: The mesh to be used for the primitive. + + Keyword Args: + translation: The translation of the terrain. Defaults to None. + orientation: The orientation of the terrain. Defaults to None. + visual_material: The visual material to apply. Defaults to None. + physics_material: The physics material to apply. Defaults to None. + """ + # need to import these here to prevent isaacsim launching when importing this module + from pxr import UsdGeom + + import isaaclab.sim as sim_utils + + # create parent prim + sim_utils.create_prim(prim_path, "Xform") + # create mesh prim + prim = sim_utils.create_prim( + f"{prim_path}/mesh", + "Mesh", + translation=kwargs.get("translation"), + orientation=kwargs.get("orientation"), + attributes={ + "points": mesh.vertices, + "faceVertexIndices": mesh.faces.flatten(), + "faceVertexCounts": np.asarray([3] * len(mesh.faces)), + "subdivisionScheme": "bilinear", + }, + ) + # apply collider properties + collider_cfg = sim_utils.CollisionPropertiesCfg(collision_enabled=True) + sim_utils.define_collision_properties(prim.GetPrimPath(), collider_cfg) + # add rgba color to the mesh primvars + if mesh.visual.vertex_colors is not None: + # obtain color from the mesh + rgba_colors = np.asarray(mesh.visual.vertex_colors).astype(np.float32) / 255.0 + # displayColor is a primvar attribute that is used to color the mesh + color_prim_attr = prim.GetAttribute("primvars:displayColor") + color_prim_var = UsdGeom.Primvar(color_prim_attr) + color_prim_var.SetInterpolation(UsdGeom.Tokens.vertex) + color_prim_attr.Set(rgba_colors[:, :3]) + # displayOpacity is a primvar attribute that is used to set the opacity of the mesh + display_prim_attr = prim.GetAttribute("primvars:displayOpacity") + display_prim_var = UsdGeom.Primvar(display_prim_attr) + display_prim_var.SetInterpolation(UsdGeom.Tokens.vertex) + display_prim_var.Set(rgba_colors[:, 3]) + + # create visual material + if kwargs.get("visual_material") is not None: + visual_material_cfg: sim_utils.VisualMaterialCfg = kwargs.get("visual_material") + # spawn the material + visual_material_cfg.func(f"{prim_path}/visualMaterial", visual_material_cfg) + sim_utils.bind_visual_material(prim.GetPrimPath(), f"{prim_path}/visualMaterial") + # create physics material + if kwargs.get("physics_material") is not None: + physics_material_cfg: sim_utils.RigidBodyMaterialCfg = kwargs.get("physics_material") + # spawn the material + physics_material_cfg.func(f"{prim_path}/physicsMaterial", physics_material_cfg) + sim_utils.bind_physics_material(prim.GetPrimPath(), f"{prim_path}/physicsMaterial") + + +def find_flat_patches( + wp_mesh: wp.Mesh, + num_patches: int, + patch_radius: float | list[float], + origin: np.ndarray | torch.Tensor | tuple[float, float, float], + x_range: tuple[float, float], + y_range: tuple[float, float], + z_range: tuple[float, float], + max_height_diff: float, +) -> torch.Tensor: + """Finds flat patches of given radius in the input mesh. + + The function finds flat patches of given radius based on the search space defined by the input ranges. + The search space is characterized by origin in the mesh frame, and the x, y, and z ranges. The x and y + ranges are used to sample points in the 2D region around the origin, and the z range is used to filter + patches based on the height of the points. + + The function performs rejection sampling to find the patches based on the following steps: + + 1. Sample patch locations in the 2D region around the origin. + 2. Define a ring of points around each patch location to query the height of the points using ray-casting. + 3. Reject patches that are outside the z range or have a height difference that is too large. + 4. Keep sampling until all patches are valid. + + Args: + wp_mesh: The warp mesh to find patches in. + num_patches: The desired number of patches to find. + patch_radius: The radii used to form patches. If a list is provided, multiple patch sizes are checked. + This is useful to deal with holes or other artifacts in the mesh. + origin: The origin defining the center of the search space. This is specified in the mesh frame. + x_range: The range of X coordinates to sample from. + y_range: The range of Y coordinates to sample from. + z_range: The range of valid Z coordinates used for filtering patches. + max_height_diff: The maximum allowable distance between the lowest and highest points + on a patch to consider it as valid. If the difference is greater than this value, + the patch is rejected. + + Returns: + A tensor of shape (num_patches, 3) containing the flat patches. The patches are defined in the mesh frame. + + Raises: + RuntimeError: If the function fails to find valid patches. This can happen if the input parameters + are not suitable for finding valid patches and maximum number of iterations is reached. + """ + # set device to warp mesh device + device = wp.device_to_torch(wp_mesh.device) + + # resolve inputs to consistent type + # -- patch radii + if isinstance(patch_radius, float): + patch_radius = [patch_radius] + # -- origin + if isinstance(origin, np.ndarray): + origin = torch.from_numpy(origin).to(torch.float).to(device) + elif isinstance(origin, torch.Tensor): + origin = origin.to(device) + else: + origin = torch.tensor(origin, dtype=torch.float, device=device) + + # create ranges for the x and y coordinates around the origin. + # The provided ranges are bounded by the mesh's bounding box. + x_range = ( + max(x_range[0] + origin[0].item(), wp_mesh.points.numpy()[:, 0].min()), + min(x_range[1] + origin[0].item(), wp_mesh.points.numpy()[:, 0].max()), + ) + y_range = ( + max(y_range[0] + origin[1].item(), wp_mesh.points.numpy()[:, 1].min()), + min(y_range[1] + origin[1].item(), wp_mesh.points.numpy()[:, 1].max()), + ) + z_range = ( + z_range[0] + origin[2].item(), + z_range[1] + origin[2].item(), + ) + + # create a circle of points around (0, 0) to query validity of the patches + # the ring of points is uniformly distributed around the circle + angle = torch.linspace(0, 2 * np.pi, 10, device=device) + query_x = [] + query_y = [] + for radius in patch_radius: + query_x.append(radius * torch.cos(angle)) + query_y.append(radius * torch.sin(angle)) + query_x = torch.cat(query_x).unsqueeze(1) # dim: (num_radii * 10, 1) + query_y = torch.cat(query_y).unsqueeze(1) # dim: (num_radii * 10, 1) + # dim: (num_radii * 10, 3) + query_points = torch.cat([query_x, query_y, torch.zeros_like(query_x)], dim=-1) + + # create buffers + # -- a buffer to store indices of points that are not valid + points_ids = torch.arange(num_patches, device=device) + # -- a buffer to store the flat patches locations + flat_patches = torch.zeros(num_patches, 3, device=device) + + # sample points and raycast to find the height. + # 1. Reject points that are outside the z_range or have a height difference that is too large. + # 2. Keep sampling until all points are valid. + iter_count = 0 + while len(points_ids) > 0 and iter_count < 10000: + # sample points in the 2D region around the origin + pos_x = torch.empty(len(points_ids), device=device).uniform_(*x_range) + pos_y = torch.empty(len(points_ids), device=device).uniform_(*y_range) + flat_patches[points_ids, :2] = torch.stack([pos_x, pos_y], dim=-1) + + # define the query points to check validity of the patch + # dim: (num_patches, num_radii * 10, 3) + points = flat_patches[points_ids].unsqueeze(1) + query_points + points[..., 2] = 100.0 + # ray-cast direction is downwards + dirs = torch.zeros_like(points) + dirs[..., 2] = -1.0 + + # ray-cast to find the height of the patches + ray_hits = raycast_mesh(points.view(-1, 3), dirs.view(-1, 3), wp_mesh)[0] + heights = ray_hits.view(points.shape)[..., 2] + # set the height of the patches + # note: for invalid patches, they would be overwritten in the next iteration + # so it's safe to set the height to the last value + flat_patches[points_ids, 2] = heights[..., -1] + + # check validity + # -- height is within the z range + not_valid = torch.any(torch.logical_or(heights < z_range[0], heights > z_range[1]), dim=1) + # -- height difference is within the max height difference + not_valid = torch.logical_or(not_valid, (heights.max(dim=1)[0] - heights.min(dim=1)[0]) > max_height_diff) + + # remove invalid patches indices + points_ids = points_ids[not_valid] + # increment count + iter_count += 1 + + # check all patches are valid + if len(points_ids) > 0: + raise RuntimeError( + "Failed to find valid patches! Please check the input parameters." + f"\n\tMaximum number of iterations reached: {iter_count}" + f"\n\tNumber of invalid patches: {len(points_ids)}" + f"\n\tMaximum height difference: {max_height_diff}" + ) + + # return the flat patches (in the mesh frame) + return flat_patches - origin diff --git a/source/isaaclab/isaaclab/ui/widgets/__init__.py b/source/isaaclab/isaaclab/ui/widgets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd6de31fa2bdf604875a75304f46e07b319eaba --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .image_plot import ImagePlot +from .line_plot import LiveLinePlot +from .manager_live_visualizer import ManagerLiveVisualizer +from .ui_visualizer_base import UiVisualizerBase diff --git a/source/isaaclab/isaaclab/ui/widgets/image_plot.py b/source/isaaclab/isaaclab/ui/widgets/image_plot.py new file mode 100644 index 0000000000000000000000000000000000000000..939ef01dfa9f083da945c195779a2d5161d110a8 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/image_plot.py @@ -0,0 +1,310 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from contextlib import suppress +from typing import TYPE_CHECKING + +import numpy as np +from matplotlib import cm + +import omni + +with suppress(ImportError): + # isaacsim.gui is not available when running in headless mode. + import isaacsim.gui.components.ui_utils + +from .ui_widget_wrapper import UIWidgetWrapper + +if TYPE_CHECKING: + import isaacsim.gui.components + import omni.ui + +# import logger +logger = logging.getLogger(__name__) + + +class ImagePlot(UIWidgetWrapper): + """An image plot widget to display live data. + + It has the following Layout where the mode frame is only useful for depth images: + +-------------------------------------------------------+ + | containing_frame | + |+-----------------------------------------------------+| + | main_plot_frame | + ||+---------------------------------------------------+|| + ||| plot_frames ||| + ||| ||| + ||| ||| + ||| (Image Plot Data) ||| + ||| ||| + ||| ||| + |||+-------------------------------------------------+||| + ||| mode_frame ||| + ||| ||| + ||| [Dropdown: Mode Selection] ||| + ||| [Collapsible: Manual Normalization Options] ||| + ||+---------------------------------------------------+|| + |+-----------------------------------------------------+| + +-------------------------------------------------------+ + + """ + + def __init__( + self, + image: np.ndarray | None = None, + label: str = "", + widget_height: int = 200, + min_value: float = 0.0, + max_value: float = 1.0, + ): + """Create an XY plot UI Widget with axis scaling, legends, and support for multiple plots. + + Overlapping data is most accurately plotted when centered in the frame with reasonable axis scaling. + Pressing down the mouse gives the x and y values of each function at an x coordinate. + + Args: + image: Image to display + label: Short descriptive text to the left of the plot + widget_height: Height of the plot in pixels + min_value: Minimum value for manual normalization/colorization. Defaults to 0.0. + max_value: Maximum value for manual normalization/colorization. Defaults to 1.0. + """ + + self._curr_mode = "None" + + self._has_built = False + + self._enabled = True + + self._byte_provider = omni.ui.ByteImageProvider() + if image is None: + logger.warning("image is NONE") + image = np.ones((480, 640, 3), dtype=np.uint8) * 255 + image[:, :, 0] = 0 + image[:, :240, 1] = 0 + + # if image is channel first, convert to channel last + if image.ndim == 3 and image.shape[0] in [1, 3, 4]: + image = np.moveaxis(image, 0, -1) + + self._aspect_ratio = image.shape[1] / image.shape[0] + self._widget_height = widget_height + self._label = label + self.update_image(image) + + plot_frame = self._create_ui_widget() + + super().__init__(plot_frame) + + def setEnabled(self, enabled: bool): + self._enabled = enabled + + def update_image(self, image: np.ndarray): + if not self._enabled: + return + + # if image is channel first, convert to channel last + if image.ndim == 3 and image.shape[0] in [1, 3, 4]: + image = np.moveaxis(image, 0, -1) + + height, width = image.shape[:2] + + if self._curr_mode == "Normalization": + image = (image - image.min()) / (image.max() - image.min()) + image = (image * 255).astype(np.uint8) + elif self._curr_mode == "Colorization": + if image.ndim == 3 and image.shape[2] == 3: + logger.warning("Colorization mode is only available for single channel images") + else: + image = (image - image.min()) / (image.max() - image.min()) + colormap = cm.get_cmap("jet") + if image.ndim == 3 and image.shape[2] == 1: + image = (colormap(image).squeeze(2) * 255).astype(np.uint8) + else: + image = (colormap(image) * 255).astype(np.uint8) + + # convert image to 4-channel RGBA + if image.ndim == 2 or (image.ndim == 3 and image.shape[2] == 1): + image = np.dstack((image, image, image, np.full((height, width, 1), 255, dtype=np.uint8))) + + elif image.ndim == 3 and image.shape[2] == 3: + image = np.dstack((image, np.full((height, width, 1), 255, dtype=np.uint8))) + + self._byte_provider.set_bytes_data(image.flatten().data, [width, height]) + + def update_min_max(self, image: np.ndarray): + if self._show_min_max and hasattr(self, "_min_max_label"): + non_inf = image[np.isfinite(image)].flatten() + if len(non_inf) > 0: + self._min_max_label.text = self._get_unit_description( + np.min(non_inf), np.max(non_inf), np.median(non_inf) + ) + else: + self._min_max_label.text = self._get_unit_description(0, 0) + + def _create_ui_widget(self): + containing_frame = omni.ui.Frame(build_fn=self._build_widget) + return containing_frame + + def _get_unit_description(self, min_value: float, max_value: float, median_value: float = None): + return ( + f"Min: {min_value * self._unit_scale:.2f} {self._unit_name} Max:" + f" {max_value * self._unit_scale:.2f} {self._unit_name}" + + (f" Median: {median_value * self._unit_scale:.2f} {self._unit_name}" if median_value is not None else "") + ) + + def _build_widget(self): + with omni.ui.VStack(spacing=3): + with omni.ui.HStack(): + # Write the leftmost label for what this plot is + omni.ui.Label( + self._label, + width=isaacsim.gui.components.ui_utils.LABEL_WIDTH, + alignment=omni.ui.Alignment.LEFT_TOP, + ) + with omni.ui.Frame(width=self._aspect_ratio * self._widget_height, height=self._widget_height): + self._base_plot = omni.ui.ImageWithProvider(self._byte_provider) + + if self._show_min_max: + self._min_max_label = omni.ui.Label(self._get_unit_description(0, 0)) + + omni.ui.Spacer(height=8) + self._mode_frame = omni.ui.Frame(build_fn=self._build_mode_frame) + + omni.ui.Spacer(width=5) + self._has_built = True + + def _build_mode_frame(self): + """Build the frame containing the mode selection for the plots. + + This is an internal function to build the frame containing the mode selection for the plots. This function + should only be called from within the build function of a frame. + + The built widget has the following layout: + +-------------------------------------------------------+ + | mode_frame | + ||+---------------------------------------------------+|| + ||| [Dropdown: Mode Selection] ||| + ||| [Collapsible: Manual Normalization Options] ||| + |||+-------------------------------------------------+||| + |+-----------------------------------------------------+| + +-------------------------------------------------------+ + """ + with omni.ui.VStack(spacing=5, style=isaacsim.gui.components.ui_utils.get_style()): + + def _change_mode(value): + self._curr_mode = value + # Update visibility of collapsible frame + show_options = value in ["Normalization", "Colorization"] + if hasattr(self, "_options_collapsable"): + self._options_collapsable.visible = show_options + if show_options: + self._options_collapsable.title = f"{value} Options" + + # Mode dropdown + isaacsim.gui.components.ui_utils.dropdown_builder( + label="Mode", + type="dropdown", + items=["Original", "Normalization", "Colorization"], + tooltip="Select a mode", + on_clicked_fn=_change_mode, + ) + + # Collapsible frame for options (initially hidden) + self._options_collapsable = omni.ui.CollapsableFrame( + "Normalization Options", + height=0, + collapsed=False, + visible=False, + style=isaacsim.gui.components.ui_utils.get_style(), + style_type_name_override="CollapsableFrame", + ) + + with self._options_collapsable: + with omni.ui.VStack(spacing=5, style=isaacsim.gui.components.ui_utils.get_style()): + + def _on_manual_changed(enabled): + self._enabled_min_max = enabled + # Enable/disable the float fields + if hasattr(self, "_min_model"): + self._min_field.enabled = enabled + if hasattr(self, "_max_model"): + self._max_field.enabled = enabled + + def _on_min_changed(model): + self._min_value = model.as_float + + def _on_max_changed(model): + self._max_value = model.as_float + + # Manual values checkbox + isaacsim.gui.components.ui_utils.cb_builder( + label="Use Manual Values", + type="checkbox", + default_val=self._enabled_min_max, + tooltip="Enable manual min/max values", + on_clicked_fn=_on_manual_changed, + ) + + # Min value with reset button + with omni.ui.HStack(): + omni.ui.Label( + "Min", + width=isaacsim.gui.components.ui_utils.LABEL_WIDTH, + alignment=omni.ui.Alignment.LEFT_CENTER, + ) + self._min_field = omni.ui.FloatDrag( + name="FloatField", + width=omni.ui.Fraction(1), + height=0, + alignment=omni.ui.Alignment.LEFT_CENTER, + enabled=self._enabled_min_max, + ) + self._min_model = self._min_field.model + self._min_model.set_value(self._min_value) + self._min_model.add_value_changed_fn(_on_min_changed) + + omni.ui.Spacer(width=5) + omni.ui.Button( + "0", + width=20, + height=20, + clicked_fn=lambda: self._min_model.set_value(0.0), + tooltip="Reset to 0.0", + style=isaacsim.gui.components.ui_utils.get_style(), + ) + isaacsim.gui.components.ui_utils.add_line_rect_flourish(False) + + # Max value with reset button + with omni.ui.HStack(): + omni.ui.Label( + "Max", + width=isaacsim.gui.components.ui_utils.LABEL_WIDTH, + alignment=omni.ui.Alignment.LEFT_CENTER, + ) + self._max_field = omni.ui.FloatDrag( + name="FloatField", + width=omni.ui.Fraction(1), + height=0, + alignment=omni.ui.Alignment.LEFT_CENTER, + enabled=self._enabled_min_max, + ) + self._max_model = self._max_field.model + self._max_model.set_value(self._max_value) + self._max_model.add_value_changed_fn(_on_max_changed) + + omni.ui.Spacer(width=5) + omni.ui.Button( + "1", + width=20, + height=20, + clicked_fn=lambda: self._max_model.set_value(1.0), + tooltip="Reset to 1.0", + style=isaacsim.gui.components.ui_utils.get_style(), + ) + isaacsim.gui.components.ui_utils.add_line_rect_flourish(False) diff --git a/source/isaaclab/isaaclab/ui/widgets/line_plot.py b/source/isaaclab/isaaclab/ui/widgets/line_plot.py new file mode 100644 index 0000000000000000000000000000000000000000..e9914b35503747f86664a8cf4b19556643c024b3 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/line_plot.py @@ -0,0 +1,616 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import colorsys +from contextlib import suppress +from typing import TYPE_CHECKING + +import numpy as np + +import omni +from isaacsim.core.api.simulation_context import SimulationContext + +with suppress(ImportError): + # isaacsim.gui is not available when running in headless mode. + import isaacsim.gui.components.ui_utils + +from .ui_widget_wrapper import UIWidgetWrapper + +if TYPE_CHECKING: + import isaacsim.gui.components + import omni.ui + + +class LiveLinePlot(UIWidgetWrapper): + """A 2D line plot widget to display live data. + + + This widget is used to display live data in a 2D line plot. It can be used to display multiple series + in the same plot. + + It has the following Layout: + +-------------------------------------------------------+ + | containing_frame | + |+-----------------------------------------------------+| + | main_plot_frame | + ||+---------------------------------------------------+|| + ||| plot_frames + grid lines (Z_stacked) ||| + ||| ||| + ||| ||| + ||| (Live Plot Data) ||| + ||| ||| + ||| ||| + |||+-------------------------------------------------+||| + ||| legends_frame ||| + ||| ||| + ||| [x][Series 1] [x][Series 2] [ ][Series 3] ||| + |||+-------------------------------------------------+||| + ||| limits_frame ||| + ||| ||| + ||| [Y-Limits] [min] [max] [Autoscale] ||| + |||+-------------------------------------------------+||| + ||| filter_frame ||| + ||| ||| + ||| ||| + |+-----------------------------------------------------+| + +-------------------------------------------------------+ + + """ + + def __init__( + self, + y_data: list[list[float]], + y_min: float = -10, + y_max: float = 10, + plot_height: int = 150, + show_legend: bool = True, + legends: list[str] | None = None, + max_datapoints: int = 200, + ): + """Create a new LiveLinePlot widget. + + Args: + y_data: A list of lists of floats containing the data to plot. Each list of floats represents a + series in the plot. + y_min: The minimum y value to display. Defaults to -10. + y_max: The maximum y value to display. Defaults to 10. + plot_height: The height of the plot in pixels. Defaults to 150. + show_legend: Whether to display the legend. Defaults to True. + legends: A list of strings containing the legend labels for each series. If None, the default + labels are "Series_0", "Series_1", etc. Defaults to None. + max_datapoints: The maximum number of data points to display. If the number of data points exceeds + this value, the oldest data points are removed. Defaults to 200. + """ + super().__init__(self._create_ui_widget()) + self.plot_height = plot_height + self.show_legend = show_legend + self._legends = legends if legends is not None else ["Series_" + str(i) for i in range(len(y_data))] + self._y_data = y_data + self._colors = self._get_distinct_hex_colors(len(y_data)) + self._y_min = y_min if y_min is not None else -10 + self._y_max = y_max if y_max is not None else 10 + self._max_data_points = max_datapoints + self._show_legend = show_legend + self._series_visible = [True for _ in range(len(y_data))] + self._plot_frames = [] + self._plots = [] + self._plot_selected_values = [] + self._is_built = False + self._filter_frame = None + self._filter_mode = None + self._last_values = None + self._is_paused = False + + # Gets populated when widget is built + self._main_plot_frame = None + + self._autoscale_model = omni.ui.SimpleBoolModel(True) + + """Properties""" + + @property + def autoscale_mode(self) -> bool: + return self._autoscale_model.as_bool + + @property + def y_data(self) -> list[list[float]]: + """The current data in the plot.""" + return self._y_data + + @property + def y_min(self) -> float: + """The current minimum y value.""" + return self._y_min + + @property + def y_max(self) -> float: + """The current maximum y value.""" + return self._y_max + + @property + def legends(self) -> list[str]: + """The current legend labels.""" + return self._legends + + """ General Functions """ + + def clear(self): + """Clears the plot.""" + self._y_data = [[] for _ in range(len(self._y_data))] + self._last_values = None + + for plt in self._plots: + plt.set_data() + + # self._container_frame.rebuild() + + def add_datapoint(self, y_coords: list[float]): + """Add a data point to the plot. + + The data point is added to the end of the plot. If the number of data points exceeds the maximum number + of data points, the oldest data point is removed. + + ``y_coords`` is assumed to be a list of floats with the same length as the number of series in the plot. + + Args: + y_coords: A list of floats containing the y coordinates of the new data points. + """ + + for idx, y_coord in enumerate(y_coords): + if len(self._y_data[idx]) > self._max_data_points: + self._y_data[idx] = self._y_data[idx][1:] + + if self._filter_mode == "Lowpass": + if self._last_values is not None: + alpha = 0.8 + y_coord = self._y_data[idx][-1] * alpha + y_coord * (1 - alpha) + elif self._filter_mode == "Integrate": + if self._last_values is not None: + y_coord = self._y_data[idx][-1] + y_coord + elif self._filter_mode == "Derivative": + if self._last_values is not None: + y_coord = (y_coord - self._last_values[idx]) / SimulationContext.instance().get_rendering_dt() + + self._y_data[idx].append(float(y_coord)) + + if self._main_plot_frame is None: + # Widget not built, not visible + return + + # Check if the widget has been built, i.e. the plot references have been created. + if not self._is_built or self._is_paused: + return + + if len(self._y_data) != len(self._plots): + # Plots gotten out of sync, rebuild the widget + self._main_plot_frame.rebuild() + return + + if self.autoscale_mode: + self._rescale_btn_pressed() + + for idx, plt in enumerate(self._plots): + plt.set_data(*self._y_data[idx]) + + self._last_values = y_coords + # Autoscale the y-axis to the current data + + """ + Internal functions for building the UI. + """ + + def _build_stacked_plots(self, grid: bool = True): + """Builds multiple plots stacked on top of each other to display multiple series. + + This is an internal function to build the plots. It should not be called from outside the class and only + from within the build function of a frame. + + The built widget has the following layout: + +-------------------------------------------------------+ + | main_plot_frame | + ||+---------------------------------------------------+|| + ||| ||| + ||| y_max|*******-------------------*******| ||| + ||| |-------*****-----------**--------| ||| + ||| 0|------------**-----***-----------| ||| + ||| |--------------***----------------| ||| + ||| y_min|---------------------------------| ||| + ||| ||| + |||+-------------------------------------------------+||| + + + Args: + grid: Whether to display grid lines. Defaults to True. + """ + + # Reset lists which are populated in the build function + self._plot_frames = [] + + # Define internal builder function + def _build_single_plot(y_data: list[float], color: int, plot_idx: int): + """Build a single plot. + + This is an internal function to build a single plot with the given data and color. This function + should only be called from within the build function of a frame. + + Args: + y_data: The data to plot. + color: The color of the plot. + """ + plot = omni.ui.Plot( + omni.ui.Type.LINE, + self._y_min, + self._y_max, + *y_data, + height=self.plot_height, + style={"color": color, "background_color": 0x0}, + ) + + if len(self._plots) <= plot_idx: + self._plots.append(plot) + self._plot_selected_values.append(omni.ui.SimpleStringModel("")) + else: + self._plots[plot_idx] = plot + + # Begin building the widget + with omni.ui.HStack(): + # Space to the left to add y-axis labels + omni.ui.Spacer(width=20) + + # Built plots for each time series stacked on top of each other + with omni.ui.ZStack(): + # Background rectangle + omni.ui.Rectangle( + height=self.plot_height, + style={ + "background_color": 0x0, + "border_color": omni.ui.color.white, + "border_width": 0.4, + "margin": 0.0, + }, + ) + + # Draw grid lines and labels + if grid: + # Calculate the number of grid lines to display + # Absolute range of the plot + plot_range = self._y_max - self._y_min + grid_resolution = 10 ** np.floor(np.log10(0.5 * plot_range)) + + plot_range /= grid_resolution + + # Fraction of the plot range occupied by the first and last grid line + first_space = (self._y_max / grid_resolution) - np.floor(self._y_max / grid_resolution) + last_space = np.ceil(self._y_min / grid_resolution) - self._y_min / grid_resolution + + # Number of grid lines to display + n_lines = int(plot_range - first_space - last_space) + + plot_resolution = self.plot_height / plot_range + + with omni.ui.VStack(): + omni.ui.Spacer(height=plot_resolution * first_space) + + # Draw grid lines + with omni.ui.VGrid(row_height=plot_resolution): + for grid_line_idx in range(n_lines): + # Create grid line + with omni.ui.ZStack(): + omni.ui.Line( + style={ + "color": 0xAA8A8777, + "background_color": 0x0, + "border_width": 0.4, + }, + alignment=omni.ui.Alignment.CENTER_TOP, + height=0, + ) + with omni.ui.Placer(offset_x=-20): + label_value = ( + self._y_max + - first_space * grid_resolution + - grid_line_idx * grid_resolution + ) + omni.ui.Label( + f"{label_value:.3f}", + width=8, + height=8, + alignment=omni.ui.Alignment.RIGHT_TOP, + style={ + "color": 0xFFFFFFFF, + "font_size": 8, + }, + ) + + # Create plots for each series + for idx, (data, color) in enumerate(zip(self._y_data, self._colors)): + plot_frame = omni.ui.Frame( + build_fn=lambda y_data=data, plot_idx=idx, color=color: _build_single_plot( + y_data, color, plot_idx + ), + ) + plot_frame.visible = self._series_visible[idx] + self._plot_frames.append(plot_frame) + + # Create an invisible frame on top that will give a helpful tooltip + self._tooltip_frame = omni.ui.Plot( + height=self.plot_height, + style={"color": 0xFFFFFFFF, "background_color": 0x0}, + ) + + self._tooltip_frame.set_mouse_pressed_fn(self._mouse_moved_on_plot) + + # Create top label for the y-axis + with omni.ui.Placer(offset_x=-20, offset_y=-8): + omni.ui.Label( + f"{self._y_max:.3f}", + width=8, + height=2, + alignment=omni.ui.Alignment.LEFT_TOP, + style={"color": 0xFFFFFFFF, "font_size": 8}, + ) + + # Create bottom label for the y-axis + with omni.ui.Placer(offset_x=-20, offset_y=self.plot_height): + omni.ui.Label( + f"{self._y_min:.3f}", + width=8, + height=2, + alignment=omni.ui.Alignment.LEFT_BOTTOM, + style={"color": 0xFFFFFFFF, "font_size": 8}, + ) + + def _mouse_moved_on_plot(self, x, y, *args): + # Show a tooltip with x,y and function values + if len(self._y_data) == 0 or len(self._y_data[0]) == 0: + # There is no data in the plots, so do nothing + return + + for idx, plot in enumerate(self._plots): + x_pos = plot.screen_position_x + width = plot.computed_width + + location_x = (x - x_pos) / width + + data = self._y_data[idx] + n_samples = len(data) + selected_sample = int(location_x * n_samples) + value = data[selected_sample] + # save the value in scientific notation + self._plot_selected_values[idx].set_value(f"{value:.3f}") + + def _build_legends_frame(self): + """Build the frame containing the legend for the plots. + + This is an internal function to build the frame containing the legend for the plots. This function + should only be called from within the build function of a frame. + + The built widget has the following layout: + +-------------------------------------------------------+ + | legends_frame | + ||+---------------------------------------------------+|| + ||| ||| + ||| [x][Series 1] [x][Series 2] [ ][Series 3] ||| + ||| ||| + |||+-------------------------------------------------+||| + |+-----------------------------------------------------+| + +-------------------------------------------------------+ + """ + if not self._show_legend: + return + + with omni.ui.HStack(): + omni.ui.Spacer(width=32) + + # Find the longest legend to determine the width of the frame + max_legend = max([len(legend) for legend in self._legends]) + CHAR_WIDTH = 8 + with omni.ui.VGrid( + row_height=isaacsim.gui.components.ui_utils.LABEL_HEIGHT, + column_width=max_legend * CHAR_WIDTH + 6, + ): + for idx in range(len(self._y_data)): + with omni.ui.HStack(): + model = omni.ui.SimpleBoolModel() + model.set_value(self._series_visible[idx]) + omni.ui.CheckBox(model=model, tooltip="", width=4) + model.add_value_changed_fn(lambda val, idx=idx: self._change_plot_visibility(idx, val.as_bool)) + omni.ui.Spacer(width=2) + with omni.ui.VStack(): + omni.ui.Label( + self._legends[idx], + width=max_legend * CHAR_WIDTH, + alignment=omni.ui.Alignment.LEFT, + style={"color": self._colors[idx], "font_size": 12}, + ) + omni.ui.StringField( + model=self._plot_selected_values[idx], + width=max_legend * CHAR_WIDTH, + alignment=omni.ui.Alignment.LEFT, + style={"color": self._colors[idx], "font_size": 10}, + read_only=True, + ) + + def _build_limits_frame(self): + """Build the frame containing the controls for the y-axis limits. + + This is an internal function to build the frame containing the controls for the y-axis limits. This function + should only be called from within the build function of a frame. + + The built widget has the following layout: + +-------------------------------------------------------+ + | limits_frame | + ||+---------------------------------------------------+|| + ||| ||| + ||| Limits [min] [max] [Re-Sacle] ||| + ||| Autoscale[x] ||| + ||| ------------------------------------------- ||| + |||+-------------------------------------------------+||| + """ + with omni.ui.VStack(): + with omni.ui.HStack(): + omni.ui.Label( + "Limits", + width=isaacsim.gui.components.ui_utils.LABEL_WIDTH, + alignment=omni.ui.Alignment.LEFT_CENTER, + ) + + self.lower_limit_drag = omni.ui.FloatDrag(name="min", enabled=True, alignment=omni.ui.Alignment.CENTER) + y_min_model = self.lower_limit_drag.model + y_min_model.set_value(self._y_min) + y_min_model.add_value_changed_fn(lambda x: self._set_y_min(x.as_float)) + omni.ui.Spacer(width=2) + + self.upper_limit_drag = omni.ui.FloatDrag(name="max", enabled=True, alignment=omni.ui.Alignment.CENTER) + y_max_model = self.upper_limit_drag.model + y_max_model.set_value(self._y_max) + y_max_model.add_value_changed_fn(lambda x: self._set_y_max(x.as_float)) + omni.ui.Spacer(width=2) + + omni.ui.Button( + "Re-Scale", + width=isaacsim.gui.components.ui_utils.BUTTON_WIDTH, + clicked_fn=self._rescale_btn_pressed, + alignment=omni.ui.Alignment.LEFT_CENTER, + style=isaacsim.gui.components.ui_utils.get_style(), + ) + + omni.ui.CheckBox(model=self._autoscale_model, tooltip="", width=4) + + omni.ui.Line( + style={"color": 0x338A8777}, + width=omni.ui.Fraction(1), + alignment=omni.ui.Alignment.CENTER, + ) + + def _build_filter_frame(self): + """Build the frame containing the filter controls. + + This is an internal function to build the frame containing the filter controls. This function + should only be called from within the build function of a frame. + + The built widget has the following layout: + +-------------------------------------------------------+ + | filter_frame | + ||+---------------------------------------------------+|| + ||| ||| + ||| ||| + ||| ||| + |||+-------------------------------------------------+||| + |+-----------------------------------------------------+| + +-------------------------------------------------------+ + """ + with omni.ui.VStack(): + with omni.ui.HStack(): + + def _filter_changed(value): + self.clear() + self._filter_mode = value + + isaacsim.gui.components.ui_utils.dropdown_builder( + label="Filter", + type="dropdown", + items=["None", "Lowpass", "Integrate", "Derivative"], + tooltip="Select a filter", + on_clicked_fn=_filter_changed, + ) + + def _toggle_paused(): + self._is_paused = not self._is_paused + + # Button + omni.ui.Button( + "Play/Pause", + width=isaacsim.gui.components.ui_utils.BUTTON_WIDTH, + clicked_fn=_toggle_paused, + alignment=omni.ui.Alignment.LEFT_CENTER, + style=isaacsim.gui.components.ui_utils.get_style(), + ) + + def _create_ui_widget(self): + """Create the full UI widget.""" + + def _build_widget(): + self._is_built = False + with omni.ui.VStack(): + self._main_plot_frame = omni.ui.Frame(build_fn=self._build_stacked_plots) + omni.ui.Spacer(height=8) + self._legends_frame = omni.ui.Frame(build_fn=self._build_legends_frame) + omni.ui.Spacer(height=8) + self._limits_frame = omni.ui.Frame(build_fn=self._build_limits_frame) + omni.ui.Spacer(height=8) + self._filter_frame = omni.ui.Frame(build_fn=self._build_filter_frame) + self._is_built = True + + containing_frame = omni.ui.Frame(build_fn=_build_widget) + + return containing_frame + + """ UI Actions Listener Functions """ + + def _change_plot_visibility(self, idx: int, visible: bool): + """Change the visibility of a plot at position idx.""" + self._series_visible[idx] = visible + self._plot_frames[idx].visible = visible + # self._main_plot_frame.rebuild() + + def _set_y_min(self, val: float): + """Update the y-axis minimum.""" + self._y_min = val + self.lower_limit_drag.model.set_value(val) + self._main_plot_frame.rebuild() + + def _set_y_max(self, val: float): + """Update the y-axis maximum.""" + self._y_max = val + self.upper_limit_drag.model.set_value(val) + self._main_plot_frame.rebuild() + + def _rescale_btn_pressed(self): + """Autoscale the y-axis to the current data.""" + if any(self._series_visible): + y_min = np.round( + min([min(y) for idx, y in enumerate(self._y_data) if self._series_visible[idx]]), + 4, + ) + y_max = np.round( + max([max(y) for idx, y in enumerate(self._y_data) if self._series_visible[idx]]), + 4, + ) + if y_min == y_max: + y_max += 1e-4 # Make sure axes don't collapse + + self._y_max = y_max + self._y_min = y_min + + if hasattr(self, "lower_limit_drag") and hasattr(self, "upper_limit_drag"): + self.lower_limit_drag.model.set_value(self._y_min) + self.upper_limit_drag.model.set_value(self._y_max) + + self._main_plot_frame.rebuild() + + """ Helper Functions """ + + def _get_distinct_hex_colors(self, num_colors) -> list[int]: + """ + This function returns a list of distinct colors for plotting. + + Args: + num_colors (int): the number of colors to generate + + Returns: + List[int]: a list of distinct colors in hexadecimal format 0xFFBBGGRR + """ + # Generate equally spaced colors in HSV space + rgb_colors = [ + colorsys.hsv_to_rgb(hue / num_colors, 0.75, 1) for hue in np.linspace(0, num_colors - 1, num_colors) + ] + # Convert to 0-255 RGB values + rgb_colors = [[int(c * 255) for c in rgb] for rgb in rgb_colors] + # Convert to 0xFFBBGGRR format + hex_colors = [0xFF * 16**6 + c[2] * 16**4 + c[1] * 16**2 + c[0] for c in rgb_colors] + return hex_colors diff --git a/source/isaaclab/isaaclab/ui/widgets/manager_live_visualizer.py b/source/isaaclab/isaaclab/ui/widgets/manager_live_visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..763443dbbfd1a7b304e0ccc5c3c8c62268afce91 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/manager_live_visualizer.py @@ -0,0 +1,302 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +import weakref +from dataclasses import MISSING +from typing import TYPE_CHECKING + +import numpy + +import omni.kit.app +from isaacsim.core.api.simulation_context import SimulationContext + +from isaaclab.managers import ManagerBase +from isaaclab.utils import configclass + +from .image_plot import ImagePlot +from .line_plot import LiveLinePlot +from .ui_visualizer_base import UiVisualizerBase + +if TYPE_CHECKING: + import omni.ui + +# import logger +logger = logging.getLogger(__name__) + + +@configclass +class ManagerLiveVisualizerCfg: + """Configuration for the :class:`ManagerLiveVisualizer` class.""" + + debug_vis: bool = False + """Flag used to set status of the live visualizers on startup. Defaults to False, which means closed.""" + + manager_name: str = MISSING + """Manager name that corresponds to the manager of interest in the ManagerBasedEnv and ManagerBasedRLEnv""" + + term_names: list[str] | dict[str, list[str]] | None = None + """Specific term names specified in a Manager config that are chosen to be plotted. Defaults to None. + + If None all terms will be plotted. For managers that utilize Groups (i.e. ObservationGroup) use a dictionary of + {group_names: [term_names]}. + """ + + +class ManagerLiveVisualizer(UiVisualizerBase): + """A interface object used to transfer data from a manager to a UI widget. + + This class handles the creation of UI Widgets for selected terms given a :class:`ManagerLiveVisualizerCfg`. + It iterates through the terms of the manager and creates a visualizer for each term. If the term is a single + variable or a multi-variable signal, it creates a :class:`LiveLinePlot`. If the term is an image (2D or RGB), + it creates an :class:`ImagePlot`. The visualizer can be toggled on and off using the + :attr:`ManagerLiveVisualizerCfg.debug_vis` flag in the configuration. + """ + + def __init__(self, manager: ManagerBase, cfg: ManagerLiveVisualizerCfg = ManagerLiveVisualizerCfg()): + """Initialize ManagerLiveVisualizer. + + Args: + manager: The manager with terms to be plotted. The manager must have a + :meth:`~isaaclab.managers.manager_base.ManagerBase.get_active_iterable_terms` method. + cfg: The configuration file used to select desired manager terms to be plotted. + """ + + self._manager = manager + self.debug_vis = cfg.debug_vis + self._env_idx: int = 0 + self.cfg = cfg + self._viewer_env_idx = 0 + self._vis_frame: omni.ui.Frame + self._vis_window: omni.ui.Window + + # evaluate chosen terms if no terms provided use all available. + self.term_names = [] + + if self.cfg.term_names is not None: + # extract chosen terms + if isinstance(self.cfg.term_names, list): + for term_name in self.cfg.term_names: + if term_name in self._manager.active_terms: + self.term_names.append(term_name) + else: + logger.error( + f"ManagerVisualizer Failure: ManagerTerm ({term_name}) does not exist in" + f" Manager({self.cfg.manager_name})" + ) + + # extract chosen group-terms + elif isinstance(self.cfg.term_names, dict): + # if manager is using groups and terms are saved as a dictionary + if isinstance(self._manager.active_terms, dict): + for group, terms in self.cfg.term_names: + if group in self._manager.active_terms.keys(): + for term_name in terms: + if term_name in self._manager.active_terms[group]: + self.term_names.append(f"{group}-{term_name}") + else: + logger.error( + f"ManagerVisualizer Failure: ManagerTerm ({term_name}) does not exist in" + f" Group({group})" + ) + else: + logger.error( + f"ManagerVisualizer Failure: Group ({group}) does not exist in" + f" Manager({self.cfg.manager_name})" + ) + else: + logger.error( + f"ManagerVisualizer Failure: Manager({self.cfg.manager_name}) does not utilize grouping of" + " terms." + ) + + # + # Implementation checks + # + + @property + def get_vis_frame(self) -> omni.ui.Frame: + """Returns the UI Frame object tied to this visualizer.""" + return self._vis_frame + + @property + def get_vis_window(self) -> omni.ui.Window: + """Returns the UI Window object tied to this visualizer.""" + return self._vis_window + + # + # Setters + # + + def set_debug_vis(self, debug_vis: bool): + """Set the debug visualization external facing function. + + Args: + debug_vis: Whether to enable or disable the debug visualization. + """ + self._set_debug_vis_impl(debug_vis) + + # + # Implementations + # + + def _set_env_selection_impl(self, env_idx: int): + """Update the index of the selected environment to display. + + Args: + env_idx: The index of the selected environment. + """ + if env_idx > 0 and env_idx < self._manager.num_envs: + self._env_idx = env_idx + else: + logger.warning(f"Environment index is out of range (0, {self._manager.num_envs - 1})") + + def _set_vis_frame_impl(self, frame: omni.ui.Frame): + """Updates the assigned frame that can be used for visualizations. + + Args: + frame: The debug visualization frame. + """ + self._vis_frame = frame + + def _debug_vis_callback(self, event): + """Callback for the debug visualization event.""" + + if not SimulationContext.instance().is_playing(): + # Visualizers have not been created yet. + return + + # get updated data and update visualization + for (_, term), vis in zip( + self._manager.get_active_iterable_terms(env_idx=self._env_idx), self._term_visualizers + ): + if isinstance(vis, LiveLinePlot): + vis.add_datapoint(term) + elif isinstance(vis, ImagePlot): + vis.update_image(numpy.array(term)) + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set the debug visualization implementation. + + Args: + debug_vis: Whether to enable or disable debug visualization. + """ + + if not hasattr(self, "_vis_frame"): + raise RuntimeError("No frame set for debug visualization.") + + # Clear internal visualizers + self._term_visualizers = [] + self._vis_frame.clear() + + if debug_vis: + # if enabled create a subscriber for the post update event if it doesn't exist + if not hasattr(self, "_debug_vis_handle") or self._debug_vis_handle is None: + app_interface = omni.kit.app.get_app_interface() + self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop( + lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event) + ) + else: + # if disabled remove the subscriber if it exists + if self._debug_vis_handle is not None: + self._debug_vis_handle.unsubscribe() + self._debug_vis_handle = None + + self._vis_frame.visible = False + return + + self._vis_frame.visible = True + + with self._vis_frame: + with omni.ui.VStack(): + # Add a plot in a collapsible frame for each term available + for name, term in self._manager.get_active_iterable_terms(env_idx=self._env_idx): + if name in self.term_names or len(self.term_names) == 0: + frame = omni.ui.CollapsableFrame( + name, + collapsed=False, + style={"border_color": 0xFF8A8777, "padding": 4}, + ) + with frame: + # create line plot for single or multi-variable signals + len_term_shape = len(numpy.array(term).shape) + if len_term_shape <= 2: + plot = LiveLinePlot(y_data=[[elem] for elem in term], plot_height=150, show_legend=True) + self._term_visualizers.append(plot) + # create an image plot for 2d and greater data (i.e. mono and rgb images) + elif len_term_shape == 3: + image = ImagePlot(image=numpy.array(term), label=name) + self._term_visualizers.append(image) + else: + logger.warning( + f"ManagerLiveVisualizer: Term ({name}) is not a supported data type for" + " visualization." + ) + frame.collapsed = True + + self._debug_vis = debug_vis + + +@configclass +class DefaultManagerBasedEnvLiveVisCfg: + """Default configuration to use for the ManagerBasedEnv. Each chosen manager assumes all terms will be plotted.""" + + action_live_vis = ManagerLiveVisualizerCfg(manager_name="action_manager") + observation_live_vis = ManagerLiveVisualizerCfg(manager_name="observation_manager") + + +@configclass +class DefaultManagerBasedRLEnvLiveVisCfg(DefaultManagerBasedEnvLiveVisCfg): + """Default configuration to use for the ManagerBasedRLEnv. Each chosen manager assumes all terms will be plotted.""" + + curriculum_live_vis = ManagerLiveVisualizerCfg(manager_name="curriculum_manager") + command_live_vis = ManagerLiveVisualizerCfg(manager_name="command_manager") + reward_live_vis = ManagerLiveVisualizerCfg(manager_name="reward_manager") + termination_live_vis = ManagerLiveVisualizerCfg(manager_name="termination_manager") + + +class EnvLiveVisualizer: + """A class to handle all ManagerLiveVisualizers used in an Environment.""" + + def __init__(self, cfg: object, managers: dict[str, ManagerBase]): + """Initialize the EnvLiveVisualizer. + + Args: + cfg: The configuration file containing terms of ManagerLiveVisualizers. + managers: A dictionary of labeled managers. i.e. {"manager_name",manager}. + """ + self.cfg = cfg + self.managers = managers + self._prepare_terms() + + def _prepare_terms(self): + self._manager_visualizers: dict[str, ManagerLiveVisualizer] = dict() + + # check if config is dict already + if isinstance(self.cfg, dict): + cfg_items = self.cfg.items() + else: + cfg_items = self.cfg.__dict__.items() + + for term_name, term_cfg in cfg_items: + # check if term config is None + if term_cfg is None: + continue + # check if term config is viable + if isinstance(term_cfg, ManagerLiveVisualizerCfg): + # find appropriate manager name + manager = self.managers[term_cfg.manager_name] + self._manager_visualizers[term_cfg.manager_name] = ManagerLiveVisualizer(manager=manager, cfg=term_cfg) + else: + raise TypeError( + f"Provided EnvLiveVisualizer term: '{term_name}' is not of type ManagerLiveVisualizerCfg" + ) + + @property + def manager_visualizers(self) -> dict[str, ManagerLiveVisualizer]: + """A dictionary of labeled ManagerLiveVisualizers associated manager name as key.""" + return self._manager_visualizers diff --git a/source/isaaclab/isaaclab/ui/widgets/ui_visualizer_base.py b/source/isaaclab/isaaclab/ui/widgets/ui_visualizer_base.py new file mode 100644 index 0000000000000000000000000000000000000000..61a32119f30081a5b3df1d38e70fd74f630a64be --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/ui_visualizer_base.py @@ -0,0 +1,148 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import inspect +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + import omni.ui + + +class UiVisualizerBase: + """Base Class for components that support debug visualizations that requires access to some UI elements. + + This class provides a set of functions that can be used to assign ui interfaces. + + The following functions are provided: + + * :func:`set_debug_vis`: Assigns a debug visualization interface. This function is called by the main UI + when the checkbox for debug visualization is toggled. + * :func:`set_vis_frame`: Assigns a small frame within the isaac lab tab that can be used to visualize debug + information. Such as e.g. plots or images. It is called by the main UI on startup to create the frame. + * :func:`set_window`: Assigngs the main window that is used by the main UI. This allows the user + to have full controller over all UI elements. But be warned, with great power comes great responsibility. + """ + + """ + Exposed Properties + """ + + @property + def has_debug_vis_implementation(self) -> bool: + """Whether the component has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_debug_vis_impl) + return "NotImplementedError" not in source_code + + @property + def has_vis_frame_implementation(self) -> bool: + """Whether the component has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_vis_frame_impl) + return "NotImplementedError" not in source_code + + @property + def has_window_implementation(self) -> bool: + """Whether the component has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_window_impl) + return "NotImplementedError" not in source_code + + @property + def has_env_selection_implementation(self) -> bool: + """Whether the component has a debug visualization implemented.""" + # check if function raises NotImplementedError + source_code = inspect.getsource(self._set_env_selection_impl) + return "NotImplementedError" not in source_code + + """ + Exposed Setters + """ + + def set_env_selection(self, env_selection: int) -> bool: + """Sets the selected environment id. + + This function is called by the main UI when the user selects a different environment. + + Args: + env_selection: The currently selected environment id. + + Returns: + Whether the environment selection was successfully set. False if the component + does not support environment selection. + """ + # check if environment selection is supported + if not self.has_env_selection_implementation: + return False + # set environment selection + self._set_env_selection_impl(env_selection) + return True + + def set_window(self, window: omni.ui.Window) -> bool: + """Sets the current main ui window. + + This function is called by the main UI when the window is created. It allows the component + to add custom UI elements to the window or to control the window and its elements. + + Args: + window: The ui window. + + Returns: + Whether the window was successfully set. False if the component + does not support this functionality. + """ + # check if window is supported + if not self.has_window_implementation: + return False + # set window + self._set_window_impl(window) + return True + + def set_vis_frame(self, vis_frame: omni.ui.Frame) -> bool: + """Sets the debug visualization frame. + + This function is called by the main UI when the window is created. It allows the component + to modify a small frame within the orbit tab that can be used to visualize debug information. + + Args: + vis_frame: The debug visualization frame. + + Returns: + Whether the debug visualization frame was successfully set. False if the component + does not support debug visualization. + """ + # check if debug visualization is supported + if not self.has_vis_frame_implementation: + return False + # set debug visualization frame + self._set_vis_frame_impl(vis_frame) + return True + + """ + Internal Implementation + """ + + def _set_env_selection_impl(self, env_idx: int): + """Set the environment selection.""" + raise NotImplementedError(f"Environment selection is not implemented for {self.__class__.__name__}.") + + def _set_window_impl(self, window: omni.ui.Window): + """Set the window.""" + raise NotImplementedError(f"Window is not implemented for {self.__class__.__name__}.") + + def _set_debug_vis_impl(self, debug_vis: bool): + """Set debug visualization state.""" + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") + + def _set_vis_frame_impl(self, vis_frame: omni.ui.Frame): + """Set debug visualization into visualization objects. + + This function is responsible for creating the visualization objects if they don't exist + and input ``debug_vis`` is True. If the visualization objects exist, the function should + set their visibility into the stage. + """ + raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.") diff --git a/source/isaaclab/isaaclab/ui/widgets/ui_widget_wrapper.py b/source/isaaclab/isaaclab/ui/widgets/ui_widget_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..9025a8c2e93fbefd6d719e458acaec1ea4731b0e --- /dev/null +++ b/source/isaaclab/isaaclab/ui/widgets/ui_widget_wrapper.py @@ -0,0 +1,52 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# This file has been adapted from: +# _isaac_sim/exts/isaacsim.gui.components/isaacsim/gui/components/element_wrappers/base_ui_element_wrappers.py + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import omni + +if TYPE_CHECKING: + import omni.ui + + +class UIWidgetWrapper: + """ + Base class for creating wrappers around any subclass of omni.ui.Widget in order to provide an easy interface + for creating and managing specific types of widgets such as state buttons or file pickers. + """ + + def __init__(self, container_frame: omni.ui.Frame): + self._container_frame = container_frame + + @property + def container_frame(self) -> omni.ui.Frame: + return self._container_frame + + @property + def enabled(self) -> bool: + return self.container_frame.enabled + + @enabled.setter + def enabled(self, value: bool): + self.container_frame.enabled = value + + @property + def visible(self) -> bool: + return self.container_frame.visible + + @visible.setter + def visible(self, value: bool): + self.container_frame.visible = value + + def cleanup(self): + """ + Perform any necessary cleanup + """ + pass diff --git a/source/isaaclab/isaaclab/ui/xr_widgets/__init__.py b/source/isaaclab/isaaclab/ui/xr_widgets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e0b341c8d2d5da23875b49037094e9fc5da9f834 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/xr_widgets/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from .instruction_widget import hide_instruction, show_instruction, update_instruction +from .scene_visualization import DataCollector, TriggerType, VisualizationManager, XRVisualization +from .teleop_visualization_manager import TeleopVisualizationManager diff --git a/source/isaaclab/isaaclab/ui/xr_widgets/instruction_widget.py b/source/isaaclab/isaaclab/ui/xr_widgets/instruction_widget.py new file mode 100644 index 0000000000000000000000000000000000000000..7d6fe00d7f6e35f1375088faa6761f5b54d9f6d5 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/xr_widgets/instruction_widget.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import asyncio +import functools +import textwrap +from typing import Any, TypeAlias + +import omni.kit.commands +import omni.ui as ui +from omni.kit.xr.scene_view.utils import UiContainer, WidgetComponent +from omni.kit.xr.scene_view.utils.spatial_source import SpatialSource +from pxr import Gf + +import isaaclab.sim as sim_utils + +Vec3Type: TypeAlias = Gf.Vec3f | Gf.Vec3d + +camera_facing_widget_container = {} +camera_facing_widget_timers = {} + + +class SimpleTextWidget(ui.Widget): + """A rectangular text label widget for XR overlays. + + The widget renders a centered label over a rectangular background. It keeps + track of the configured style and an original width value used by + higher-level helpers to update the text. + """ + + def __init__( + self, + text: str | None = "Simple Text", + style: dict[str, Any] | None = None, + original_width: float = 0.0, + **kwargs, + ): + """Initialize the text widget. + + Args: + text (str): Initial text to display. + style (dict[str, Any]): Optional style dictionary (for example: ``{"font_size": 1, "color": 0xFFFFFFFF}``). + original_width (float): Width used when updating the text. + **kwargs: Additional keyword arguments forwarded to ``ui.Widget``. + """ + super().__init__(**kwargs) + if style is None: + style = {"font_size": 1, "color": 0xFFFFFFFF} + self._text = text + self._style = style + self._ui_label = None + self._original_width = original_width + self._build_ui() + + def set_label_text(self, text: str): + """Update the text displayed by the label. + + Args: + text (str): New label text to display. + """ + self._text = text + if self._ui_label: + self._ui_label.text = self._text + + def get_font_size(self): + """Return the configured font size. + + Returns: + float: Font size value. + """ + return self._style.get("font_size", 1) + + def get_width(self): + """Return the width used when updating the text. + + Returns: + float: Width used when updating the text. + """ + return self._original_width + + def _build_ui(self): + """Build the UI with a window-like rectangle and centered label.""" + with ui.ZStack(): + ui.Rectangle(style={"Rectangle": {"background_color": 0xFF454545, "border_radius": 0.1}}) + with ui.VStack(alignment=ui.Alignment.CENTER): + self._ui_label = ui.Label(self._text, style=self._style, alignment=ui.Alignment.CENTER) + + +def compute_widget_dimensions( + text: str, font_size: float, max_width: float, min_width: float +) -> tuple[float, float, str]: + """Estimate widget width/height and wrap the text. + + Args: + text (str): Raw text to render. + font_size (float): Font size used for estimating character metrics. + max_width (float): Maximum allowed widget width. + min_width (float): Minimum allowed widget width. + + Returns: + tuple[float, float, str]: A tuple ``(width, height, wrapped_text)`` where + ``width`` and ``height`` are the computed widget dimensions, and + ``wrapped_text`` contains the input text broken into newline-separated + lines to fit within the width constraints. + """ + # Estimate average character width. + char_width = 0.6 * font_size + max_chars_per_line = int(max_width / char_width) + lines = textwrap.wrap(text, width=max_chars_per_line) + if not lines: + lines = [text] + computed_width = max(len(line) for line in lines) * char_width + actual_width = max(min(computed_width, max_width), min_width) + line_height = 1.2 * font_size + actual_height = len(lines) * line_height + wrapped_text = "\n".join(lines) + return actual_width, actual_height, wrapped_text + + +def show_instruction( + text: str, + prim_path_source: str | None = None, + translation: Gf.Vec3d = Gf.Vec3d(0, 0, 0), + display_duration: float | None = 5.0, + max_width: float = 2.5, + min_width: float = 1.0, # Prevent widget from being too narrow. + font_size: float = 0.1, + text_color: int = 0xFFFFFFFF, + target_prim_path: str = "/newPrim", +) -> UiContainer | None: + """Create and display an instruction widget with the given text. + + The widget size is computed from the text and font size, wrapping content + to respect the width limits. If ``display_duration`` is provided and + non-zero, the widget is hidden automatically after the duration elapses. + + Args: + text (str): Instruction text to display. + prim_path_source (str | None): Optional prim path used as a spatial source for the widget. + translation (Gf.Vec3d): World translation to apply to the widget. + display_duration (float | None): Seconds to keep the widget visible. If ``None`` or ``0``, + the widget remains until hidden manually. + max_width (float): Maximum widget width used for wrapping. + min_width (float): Minimum widget width used for wrapping. + font_size (float): Font size of the rendered text. + text_color (int): RGBA color encoded as a 32-bit integer. + target_prim_path (str): Prim path where the widget prim will be created/copied. + + Returns: + UiContainer | None: The container that owns the instruction widget, or ``None`` if creation failed. + """ + global camera_facing_widget_container, camera_facing_widget_timers + + # Check if widget exists and has different text + if target_prim_path in camera_facing_widget_container: + container, current_text = camera_facing_widget_container[target_prim_path] + if current_text == text: + return container + + # Cancel existing timer if there is one + if target_prim_path in camera_facing_widget_timers: + camera_facing_widget_timers[target_prim_path].cancel() + del camera_facing_widget_timers[target_prim_path] + + container.root.clear() + del camera_facing_widget_container[target_prim_path] + + # Obtain stage handle + stage = sim_utils.get_current_stage() + # Clean up existing widget + if stage.GetPrimAtPath(target_prim_path).IsValid(): + sim_utils.delete_prim(target_prim_path) + + width, height, wrapped_text = compute_widget_dimensions(text, font_size, max_width, min_width) + + # Create the widget component. + widget_component = WidgetComponent( + SimpleTextWidget, + width=width, + height=height, + resolution_scale=300, + widget_args=[wrapped_text, {"font_size": font_size, "color": text_color}, width], + ) + + copied_prim = omni.kit.commands.execute( + "CopyPrim", + path_from=prim_path_source, + path_to=target_prim_path, + exclusive_select=False, + copy_to_introducing_layer=False, + ) + + space_stack = [] + if copied_prim is not None: + space_stack.append(SpatialSource.new_prim_path_source(target_prim_path)) + + space_stack.extend( + [ + SpatialSource.new_translation_source(translation), + SpatialSource.new_look_at_camera_source(), + ] + ) + + # Create the UI container with the widget. + container = UiContainer( + widget_component, + space_stack=space_stack, + ) + camera_facing_widget_container[target_prim_path] = (container, text) + + # Schedule auto-hide after the specified display_duration if provided. + if display_duration: + timer = asyncio.get_event_loop().call_later( + display_duration, functools.partial(hide_instruction, target_prim_path) + ) + camera_facing_widget_timers[target_prim_path] = timer + + return container + + +def hide_instruction(target_prim_path: str = "/newPrim") -> None: + """Hide and clean up a specific instruction widget. + + Args: + target_prim_path (str): Prim path of the widget to hide. + + Returns: + None: This function does not return a value. + """ + + global camera_facing_widget_container, camera_facing_widget_timers + + if target_prim_path in camera_facing_widget_container: + container, _ = camera_facing_widget_container[target_prim_path] + container.root.clear() + del camera_facing_widget_container[target_prim_path] + + if target_prim_path in camera_facing_widget_timers: + del camera_facing_widget_timers[target_prim_path] + + +def update_instruction(target_prim_path: str = "/newPrim", text: str = ""): + """Update the text content of an existing instruction widget. + + Args: + target_prim_path (str): Prim path of the widget to update. + text (str): New text content to display. + + Returns: + bool: ``True`` if the widget existed and was updated, otherwise ``False``. + """ + global camera_facing_widget_container + + container_data = camera_facing_widget_container.get(target_prim_path) + if container_data: + container, current_text = container_data + + # Only update if the text has actually changed + if current_text != text: + # Access the widget through the manipulator as shown in ui_container.py + manipulator = container.manipulator + + # The WidgetComponent is stored in the manipulator's components + # Try to access the widget component and then the actual widget + components = getattr(manipulator, "_ComposableManipulator__components") + if len(components) > 0: + simple_text_widget = components[0] + if simple_text_widget and simple_text_widget.component and simple_text_widget.component.widget: + width, height, wrapped_text = compute_widget_dimensions( + text, + simple_text_widget.component.widget.get_font_size(), + simple_text_widget.component.widget.get_width(), + simple_text_widget.component.widget.get_width(), + ) + simple_text_widget.component.widget.set_label_text(wrapped_text) + # Update the stored text in the global dictionary + camera_facing_widget_container[target_prim_path] = (container, text) + return True + + return False diff --git a/source/isaaclab/isaaclab/ui/xr_widgets/scene_visualization.py b/source/isaaclab/isaaclab/ui/xr_widgets/scene_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..0be679a6929b2146760f84340db5115a67288872 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/xr_widgets/scene_visualization.py @@ -0,0 +1,623 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import contextlib +import inspect +import logging +import threading +import time +from collections.abc import Callable +from enum import Enum +from typing import Any, Union + +import numpy as np +import torch + +from pxr import Gf + +from isaaclab.sim import SimulationContext +from isaaclab.ui.xr_widgets import show_instruction + +# import logger +logger = logging.getLogger(__name__) + + +class TriggerType(Enum): + """Enumeration of trigger types for visualization callbacks. + + Defines when callbacks should be executed: + - TRIGGER_ON_EVENT: Execute when a specific event occurs + - TRIGGER_ON_PERIOD: Execute at regular time intervals + - TRIGGER_ON_CHANGE: Execute when a specific data variable changes + - TRIGGER_ON_UPDATE: Execute every frame + """ + + TRIGGER_ON_EVENT = 0 + TRIGGER_ON_PERIOD = 1 + TRIGGER_ON_CHANGE = 2 + TRIGGER_ON_UPDATE = 3 + + +class DataCollector: + """Collects and manages data for visualization purposes. + + This class provides a centralized data store for visualization data, + with change detection and callback mechanisms for real-time updates. + """ + + def __init__(self): + """Initialize the data collector with empty data store and callback system.""" + self._data: dict[str, Any] = {} + self._visualization_callback: Callable | None = None + self._changed_flags: set[str] = set() + + def _values_equal(self, existing_value: Any, new_value: Any) -> bool: + """Compare two values using appropriate method based on their types. + + Handles different data types including None, NumPy arrays, PyTorch tensors, + and standard Python types for accurate change detection. + + Args: + existing_value: The current value stored in the data collector + new_value: The new value to compare against + + Returns: + bool: True if values are equal, False otherwise + """ + # If both are None or one is None + if existing_value is None or new_value is None: + return existing_value is new_value + + # If types are different, they're not equal + if type(existing_value) is not type(new_value): + return False + + # Handle NumPy arrays + if isinstance(existing_value, np.ndarray): + return np.array_equal(existing_value, new_value) + + # Handle torch tensors (if they exist) + if hasattr(existing_value, "equal"): + with contextlib.suppress(Exception): + return torch.equal(existing_value, new_value) + + # For all other types (int, float, string, bool, list, dict, set), use regular equality + with contextlib.suppress(Exception): + return existing_value == new_value + # If comparison fails for any reason, assume they're different + return False + + def update_data(self, name: str, value: Any) -> None: + """Update a data field and trigger change detection. + + This method handles data updates with intelligent change detection. + It also performs pre-processing and post-processing based on the field name. + + Args: + name: The name/key of the data field to update + value: The new value to store (None to remove the field) + """ + existing_value = self.get_data(name) + + if value is None: + self._data.pop(name) + if existing_value is not None: + self._changed_flags.add(name) + return + + # Todo: for list or array, the change won't be detected + # Check if the value has changed using appropriate comparison method + if self._values_equal(existing_value, value): + return + + # Save it + self._data[name] = value + self._changed_flags.add(name) + + def update_loop(self) -> None: + """Process pending changes and trigger visualization callbacks. + + This method should be called regularly to ensure visualization updates + are processed in a timely manner. + """ + if len(self._changed_flags) > 0: + if self._visualization_callback: + self._visualization_callback(self._changed_flags) + self._changed_flags.clear() + + def get_data(self, name: str) -> Any: + """Retrieve data by name. + + Args: + name: The name/key of the data field to retrieve + + Returns: + The stored value, or None if the field doesn't exist + """ + return self._data.get(name) + + def set_visualization_callback(self, callback: Callable) -> None: + """Set the VisualizationManager callback function to be called when data changes. + + Args: + callback: Function to call when data changes, receives set of changed field names + """ + self._visualization_callback = callback + + +class VisualizationManager: + """Base class for managing visualization rules and callbacks. + + Provides a framework for registering and executing callbacks based on + different trigger conditions (events, time periods, data changes). + """ + + # Type aliases for different callback signatures + StandardCallback = Callable[["VisualizationManager", "DataCollector"], None] + EventCallback = Callable[["VisualizationManager", "DataCollector", Any], None] + CallbackType = Union[StandardCallback, EventCallback] # noqa: UP007 + + class TimeCountdown: + """Internal class for managing periodic timer-based callbacks.""" + + period: float + countdown: float + last_time: float + + def __init__(self, period: float, initial_countdown: float = 0.0): + """Initialize a countdown timer. + + Args: + period: Time interval in seconds between callback executions + """ + self.period = period + self.countdown = initial_countdown + self.last_time = time.time() + + def update(self, current_time: float) -> bool: + """Update the countdown timer and check if callback should be triggered. + + Args: + current_time: Current time in seconds + + Returns: + bool: True if callback should be triggered, False otherwise + """ + self.countdown -= current_time - self.last_time + self.last_time = current_time + if self.countdown <= 0.0: + self.countdown = self.period + return True + return False + + # Widget presets for common visualization configurations + @classmethod + def message_widget_preset(cls) -> dict[str, Any]: + """Get the message widget preset configuration. + + Returns: + dict: Configuration dictionary for message widgets + """ + return { + "prim_path_source": "/_xr/stage/xrCamera", + "translation": Gf.Vec3f(0, 0, -2), + "display_duration": 3.0, + "max_width": 2.5, + "min_width": 1.0, + "font_size": 0.1, + "text_color": 0xFF00FFFF, + } + + @classmethod + def panel_widget_preset(cls) -> dict[str, Any]: + """Get the panel widget preset configuration. + + Returns: + dict: Configuration dictionary for panel widgets + """ + return { + "prim_path_source": "/XRAnchor", + "translation": Gf.Vec3f(0, 2, 2), # hard-coded temporarily + "display_duration": 0.0, + "font_size": 0.13, + "max_width": 2, + "min_width": 2, + } + + def display_widget(self, text: str, name: str, args: dict[str, Any]) -> None: + """Display a widget with the given text and configuration. + + Args: + text: Text content to display in the widget + name: Unique identifier for the widget. If duplicated, the old one will be removed from scene. + args: Configuration dictionary for widget appearance and behavior + """ + widget_config = args | {"text": text, "target_prim_path": name} + show_instruction(**widget_config) + + def __init__(self, data_collector: DataCollector): + """Initialize the visualization manager. + + Args: + data_collector: DataCollector instance to access the data for visualization use. + """ + self.data_collector: DataCollector = data_collector + data_collector.set_visualization_callback(self.on_change) + + self._rules_on_period: dict[VisualizationManager.TimeCountdown, VisualizationManager.StandardCallback] = {} + self._rules_on_event: dict[str, list[VisualizationManager.EventCallback]] = {} + self._rules_on_change: dict[str, list[VisualizationManager.StandardCallback]] = {} + self._rules_on_update: list[VisualizationManager.StandardCallback] = [] + + # Todo: add support to registering same callbacks for different names + def on_change(self, names: set[str]) -> None: + """Handle data changes by executing registered callbacks. + + Args: + names: Set of data field names that have changed + """ + for name in names: + callbacks = self._rules_on_change.get(name) + if callbacks: + # Create a copy of the list to avoid modification during iteration + for callback in list(callbacks): + callback(self, self.data_collector) + if len(names) > 0: + self.on_event("default_event_has_change") + + def update_loop(self) -> None: + """Update periodic timers and execute callbacks as needed. + + This method should be called regularly to ensure periodic callbacks + are executed at the correct intervals. + """ + + # Create a copy of the list to avoid modification during iteration + for callback in list(self._rules_on_update): + callback(self, self.data_collector) + + current_time = time.time() + # Create a copy of the items to avoid modification during iteration + for timer, callback in list(self._rules_on_period.items()): + triggered = timer.update(current_time) + if triggered: + callback(self, self.data_collector) + + def on_event(self, event: str, params: Any = None) -> None: + """Handle events by executing registered callbacks. + + Args: + event: Name of the event that occurred + """ + callbacks = self._rules_on_event.get(event) + if callbacks is None: + return + # Create a copy of the list to avoid modification during iteration + for callback in list(callbacks): + callback(self, self.data_collector, params) + + # Todo: better organization of callbacks + def register_callback(self, trigger: TriggerType, arg: dict, callback: CallbackType) -> Any: + """Register a callback function to be executed based on trigger conditions. + + Args: + trigger: Type of trigger that should execute the callback + arg: Dictionary containing trigger-specific parameters: + - For TRIGGER_ON_PERIOD: {"period": float} + - For TRIGGER_ON_EVENT: {"event_name": str} + - For TRIGGER_ON_CHANGE: {"variable_name": str} + - For TRIGGER_ON_UPDATE: {} + callback: Function to execute when trigger condition is met. The callback should have + the following signatures according to the trigger type: + - For TRIGGER_ON_EVENT: + callback( + manager: VisualizationManager, + data_collector: DataCollector, + event_params: Any, + ) + - For others: + callback( + manager: VisualizationManager, + data_collector: DataCollector, + ) + + Raises: + TypeError: If callback signature doesn't match the expected signature for the trigger type + """ + # Validate callback signature based on trigger type + self._validate_callback_signature(trigger, callback) + + match trigger: + case TriggerType.TRIGGER_ON_PERIOD: + period = arg.get("period") + initial_countdown = arg.get("initial_countdown", 0.0) + if isinstance(period, float) and isinstance(initial_countdown, float): + timer = VisualizationManager.TimeCountdown(period=period, initial_countdown=initial_countdown) + # Type cast since we've validated the signature + self._rules_on_period[timer] = callback # type: ignore + return timer + case TriggerType.TRIGGER_ON_EVENT: + event = arg.get("event_name") + if isinstance(event, str): + callbacks = self._rules_on_event.get(event) + if callbacks is None: + # Type cast since we've validated the signature + self._rules_on_event[event] = [callback] # type: ignore + else: + # Type cast since we've validated the signature + self._rules_on_event[event].append(callback) # type: ignore + return event + case TriggerType.TRIGGER_ON_CHANGE: + variable_name = arg.get("variable_name") + if isinstance(variable_name, str): + callbacks = self._rules_on_change.get(variable_name) + if callbacks is None: + # Type cast since we've validated the signature + self._rules_on_change[variable_name] = [callback] # type: ignore + else: + # Type cast since we've validated the signature + self._rules_on_change[variable_name].append(callback) # type: ignore + return variable_name + case TriggerType.TRIGGER_ON_UPDATE: + # Type cast since we've validated the signature + self._rules_on_update.append(callback) # type: ignore + return None + + # Todo: better callback-cancel method + def cancel_rule(self, trigger: TriggerType, arg: str | TimeCountdown, callback: Callable | None = None) -> None: + """Remove a previously registered callback. + + Periodic callbacks are not supported to be cancelled for now. + + Args: + trigger: Type of trigger for the callback to remove + arg: Trigger-specific identifier (event name or variable name) + callback: The callback function to remove + """ + callbacks = None + match trigger: + case TriggerType.TRIGGER_ON_CHANGE: + callbacks = self._rules_on_change.get(arg) + case TriggerType.TRIGGER_ON_EVENT: + callbacks = self._rules_on_event.get(arg) + case TriggerType.TRIGGER_ON_PERIOD: + self._rules_on_period.pop(arg) + case TriggerType.TRIGGER_ON_UPDATE: + callbacks = self._rules_on_update + if callbacks is not None: + if callback is not None: + callbacks.remove(callback) + else: + callbacks.clear() + + def set_attr(self, name: str, value: Any) -> None: + """Set an attribute of the visualization manager. + + Args: + name: Name of the attribute to set + value: Value to set the attribute to + """ + setattr(self, name, value) + + def _validate_callback_signature(self, trigger: TriggerType, callback: Callable) -> None: + """Validate that the callback has the correct signature for the trigger type. + + Args: + trigger: Type of trigger for the callback + callback: The callback function to validate + + Raises: + TypeError: If callback signature doesn't match expected signature + """ + try: + sig = inspect.signature(callback) + params = list(sig.parameters.values()) + + # Remove 'self' parameter if it's a bound method + if params and params[0].name == "self": + params = params[1:] + + param_count = len(params) + + if trigger == TriggerType.TRIGGER_ON_EVENT: + # Event callbacks should have 3 parameters: (manager, data_collector, event_params) + expected_count = 3 + expected_sig = ( + "callback(manager: VisualizationManager, data_collector: DataCollector, event_params: Any)" + ) + else: + # Other callbacks should have 2 parameters: (manager, data_collector) + expected_count = 2 + expected_sig = "callback(manager: VisualizationManager, data_collector: DataCollector)" + + if param_count != expected_count: + raise TypeError( + f"Callback for {trigger.name} must have {expected_count} parameters, " + f"but got {param_count}. Expected signature: {expected_sig}. " + f"Actual signature: {sig}" + ) + + except Exception as e: + if isinstance(e, TypeError): + raise + # If we can't inspect the signature (e.g., built-in functions), + # just log a warning and proceed + logger.warning(f"Could not validate callback signature for {trigger.name}: {e}") + + +class XRVisualization: + """Singleton class providing XR visualization functionality. + + This class implements the singleton pattern to ensure only one instance + of the visualization system exists across the application. It provides + a centralized API for managing XR visualization features. + + When manage a new event ordata field, please add a comment to the following list. + + Event names: + "ik_solver_failed" + + Data fields: + "manipulability_ellipsoid" : list[float] + "device_raw_data" : dict + "joints_distance_percentage_to_limit" : list[float] + "joints_torque" : list[float] + "joints_torque_limit" : list[float] + "joints_name" : list[str] + "wrist_pose" : list[float] + "approximated_working_space" : list[float] + "hand_torque_mapping" : list[str] + """ + + _lock = threading.Lock() + _instance: XRVisualization | None = None + _registered = False + + def __init__(self): + """Prevent direct instantiation.""" + raise RuntimeError("Use VisualizationInterface classmethods instead of direct instantiation") + + @classmethod + def __create_instance(cls, manager: type[VisualizationManager] = VisualizationManager) -> XRVisualization: + """Get the visualization manager instance. + + Returns: + VisualizationManager: The visualization manager instance + """ + with cls._lock: + if cls._instance is None: + # Bypass __init__ by calling __new__ directly + cls._instance = super().__new__(cls) + cls._instance._initialize(manager) + return cls._instance + + @classmethod + def __get_instance(cls) -> XRVisualization: + """Thread-safe singleton access. + + Returns: + XRVisualization: The singleton instance of the visualization system + """ + if cls._instance is None: + return cls.__create_instance() + elif not cls._instance._registered: + cls._instance._register() + return cls._instance + + def _register(self) -> bool: + """Register the visualization system. + + Returns: + bool: True if the visualization system is registered, False otherwise + """ + if self._registered: + return True + + sim = SimulationContext.instance() + if sim is not None: + sim.add_render_callback("visualization_render_callback", self.update_loop) + self._registered = True + return self._registered + + def _initialize(self, manager: type[VisualizationManager]) -> None: + """Initialize the singleton instance with data collector and visualization manager.""" + + self._data_collector = DataCollector() + self._visualization_manager = manager(self._data_collector) + + self._register() + + self._initialized = True + + # APIs + + def update_loop(self, event) -> None: + """Update the visualization system. + + This method should be called regularly (e.g., every frame) to ensure + visualization updates are processed and periodic callbacks are executed. + """ + self._visualization_manager.update_loop() + self._data_collector.update_loop() + + @classmethod + def push_event(cls, name: str, args: Any = None) -> None: + """Push an event to trigger registered callbacks. + + Args: + name: Name of the event to trigger + args: Optional arguments for the event (currently unused) + """ + instance = cls.__get_instance() + instance._visualization_manager.on_event(name, args) + + @classmethod + def push_data(cls, item: dict[str, Any]) -> None: + """Push data to the visualization system. + + Updates multiple data fields at once. Each key-value pair in the + dictionary will be processed by the data collector. + + Args: + item: Dictionary containing data field names and their values + """ + instance = cls.__get_instance() + for name, value in item.items(): + instance._data_collector.update_data(name, value) + + @classmethod + def set_attrs(cls, attributes: dict[str, Any]) -> None: + """Set configuration data for the visualization system. Not currently used. + + Args: + attributes: Dictionary containing configuration keys and values + """ + + instance = cls.__get_instance() + for name, data in attributes.items(): + instance._visualization_manager.set_attr(name, data) + + @classmethod + def get_attr(cls, name: str) -> Any: + """Get configuration data for the visualization system. Not currently used. + + Args: + name: Configuration key + """ + instance = cls.__get_instance() + return getattr(instance._visualization_manager, name) + + @classmethod + def register_callback(cls, trigger: TriggerType, arg: dict, callback: VisualizationManager.CallbackType) -> None: + """Register a callback function for visualization events. + + Args: + trigger: Type of trigger that should execute the callback + arg: Dictionary containing trigger-specific parameters: + - For TRIGGER_ON_PERIOD: {"period": float} + - For TRIGGER_ON_EVENT: {"event_name": str} + - For TRIGGER_ON_CHANGE: {"variable_name": str} + callback: Function to execute when trigger condition is met + """ + instance = cls.__get_instance() + instance._visualization_manager.register_callback(trigger, arg, callback) + + @classmethod + def assign_manager(cls, manager: type[VisualizationManager]) -> None: + """Assign a visualization manager type to the visualization system. + + Args: + manager: Type of the visualization manager to assign + """ + if cls._instance is not None: + logger.error( + f"Visualization system already initialized to {type(cls._instance._visualization_manager).__name__}," + f" cannot assign manager {manager.__name__}" + ) + return + + cls.__create_instance(manager) diff --git a/source/isaaclab/isaaclab/ui/xr_widgets/teleop_visualization_manager.py b/source/isaaclab/isaaclab/ui/xr_widgets/teleop_visualization_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3ba817c71190b3fb2656680b5f1dbd0f76f6ead0 --- /dev/null +++ b/source/isaaclab/isaaclab/ui/xr_widgets/teleop_visualization_manager.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from typing import Any + +from isaaclab.ui.xr_widgets import DataCollector, TriggerType, VisualizationManager +from isaaclab.ui.xr_widgets.instruction_widget import hide_instruction + + +class TeleopVisualizationManager(VisualizationManager): + """Specialized visualization manager for teleoperation scenarios. + For sample and debug use. + + Provides teleoperation-specific visualization features including: + - IK error handling and display + """ + + def __init__(self, data_collector: DataCollector): + """Initialize the teleop visualization manager and register callbacks. + + Args: + data_collector: DataCollector instance to read data for visualization use. + """ + super().__init__(data_collector) + + # Handle error event + self._error_text_color = 0xFF0000FF + self.ik_error_widget_id = "/ik_solver_failed" + + self.register_callback(TriggerType.TRIGGER_ON_EVENT, {"event_name": "ik_error"}, self._handle_ik_error) + + def _handle_ik_error(self, mgr: VisualizationManager, data_collector: DataCollector, params: Any = None) -> None: + """Handle IK error events by displaying an error message widget. + + Args: + data_collector: DataCollector instance (unused in this handler) + """ + # Todo: move display_widget to instruction_widget.py + if not hasattr(mgr, "_ik_error_widget_timer"): + self.display_widget( + "IK Error Detected", + mgr.ik_error_widget_id, + VisualizationManager.message_widget_preset() + | {"text_color": self._error_text_color, "display_duration": None}, + ) + mgr._ik_error_widget_timer = mgr.register_callback( + TriggerType.TRIGGER_ON_PERIOD, {"period": 3.0, "initial_countdown": 3.0}, self._hide_ik_error_widget + ) + if mgr._ik_error_widget_timer is None: + mgr.cancel_rule(TriggerType.TRIGGER_ON_PERIOD, mgr._ik_error_widget_timer) + mgr.cancel_rule(TriggerType.TRIGGER_ON_EVENT, "ik_solver_failed") + raise RuntimeWarning("Failed to register IK error widget timer") + else: + mgr._ik_error_widget_timer.countdown = 3.0 + + def _hide_ik_error_widget(self, mgr: VisualizationManager, data_collector: DataCollector) -> None: + """Hide the IK error widget. + + Args: + data_collector: DataCollector instance (unused in this handler) + """ + + hide_instruction(mgr.ik_error_widget_id) + mgr.cancel_rule(TriggerType.TRIGGER_ON_PERIOD, mgr._ik_error_widget_timer) + delattr(mgr, "_ik_error_widget_timer") diff --git a/source/isaaclab/isaaclab/utils/__init__.py b/source/isaaclab/isaaclab/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1295715857f4a4b39ebf2c4cf0b9d07e60692db1 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package containing utilities for common operations and helper functions.""" + +from .array import * +from .buffers import * +from .configclass import configclass +from .dict import * +from .interpolation import * +from .logger import * +from .mesh import * +from .modifiers import * +from .string import * +from .timer import Timer +from .types import * +from .version import * diff --git a/source/isaaclab/isaaclab/utils/array.py b/source/isaaclab/isaaclab/utils/array.py new file mode 100644 index 0000000000000000000000000000000000000000..d15fbc275dc7eac1d04ae1d47f71973ee31ee0ad --- /dev/null +++ b/source/isaaclab/isaaclab/utils/array.py @@ -0,0 +1,95 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing utilities for working with different array backends.""" + +# needed to import for allowing type-hinting: torch.device | str | None +from __future__ import annotations + +from typing import Union + +import numpy as np +import torch +import warp as wp + +TensorData = Union[np.ndarray, torch.Tensor, wp.array] # noqa: UP007 +"""Type definition for a tensor data. + +Union of numpy, torch, and warp arrays. +""" + +TENSOR_TYPES = { + "numpy": np.ndarray, + "torch": torch.Tensor, + "warp": wp.array, +} +"""A dictionary containing the types for each backend. + +The keys are the name of the backend ("numpy", "torch", "warp") and the values are the corresponding type +(``np.ndarray``, ``torch.Tensor``, ``wp.array``). +""" + +TENSOR_TYPE_CONVERSIONS = { + "numpy": {wp.array: lambda x: x.numpy(), torch.Tensor: lambda x: x.detach().cpu().numpy()}, + "torch": {wp.array: lambda x: wp.torch.to_torch(x), np.ndarray: lambda x: torch.from_numpy(x)}, + "warp": {np.array: lambda x: wp.array(x), torch.Tensor: lambda x: wp.torch.from_torch(x)}, +} +"""A nested dictionary containing the conversion functions for each backend. + +The keys of the outer dictionary are the name of target backend ("numpy", "torch", "warp"). The keys of the +inner dictionary are the source backend (``np.ndarray``, ``torch.Tensor``, ``wp.array``). +""" + + +def convert_to_torch( + array: TensorData, + dtype: torch.dtype = None, + device: torch.device | str | None = None, +) -> torch.Tensor: + """Converts a given array into a torch tensor. + + The function tries to convert the array to a torch tensor. If the array is a numpy/warp arrays, or python + list/tuples, it is converted to a torch tensor. If the array is already a torch tensor, it is returned + directly. + + If ``device`` is None, then the function deduces the current device of the data. For numpy arrays, + this defaults to "cpu", for torch tensors it is "cpu" or "cuda", and for warp arrays it is "cuda". + + Note: + Since PyTorch does not support unsigned integer types, unsigned integer arrays are converted to + signed integer arrays. This is done by casting the array to the corresponding signed integer type. + + Args: + array: The input array. It can be a numpy array, warp array, python list/tuple, or torch tensor. + dtype: Target data-type for the tensor. + device: The target device for the tensor. Defaults to None. + + Returns: + The converted array as torch tensor. + """ + # Convert array to tensor + # if the datatype is not currently supported by torch we need to improvise + # supported types are: https://pytorch.org/docs/stable/tensors.html + if isinstance(array, torch.Tensor): + tensor = array + elif isinstance(array, np.ndarray): + if array.dtype == np.uint32: + array = array.astype(np.int32) + # need to deal with object arrays (np.void) separately + tensor = torch.from_numpy(array) + elif isinstance(array, wp.array): + if array.dtype == wp.uint32: + array = array.view(wp.int32) + tensor = wp.to_torch(array) + else: + tensor = torch.Tensor(array) + # Convert tensor to the right device + if device is not None and str(tensor.device) != str(device): + tensor = tensor.to(device) + # Convert dtype of tensor if requested + if dtype is not None and tensor.dtype != dtype: + tensor = tensor.type(dtype) + + return tensor diff --git a/source/isaaclab/isaaclab/utils/assets.py b/source/isaaclab/isaaclab/utils/assets.py new file mode 100644 index 0000000000000000000000000000000000000000..22c5141587f62183a0e103d28e8089404ff5be8a --- /dev/null +++ b/source/isaaclab/isaaclab/utils/assets.py @@ -0,0 +1,211 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module that defines the host-server where assets and resources are stored. + +By default, we use the Isaac Sim Nucleus Server for hosting assets and resources. This makes +distribution of the assets easier and makes the repository smaller in size code-wise. + +For more information, please check information on `Omniverse Nucleus`_. + +.. _Omniverse Nucleus: https://docs.omniverse.nvidia.com/nucleus/latest/overview/overview.html +""" + +import asyncio +import io +import logging +import os +import tempfile +import time +from typing import Literal + +import carb +import omni.client + +# import logger +logger = logging.getLogger(__name__) + +NUCLEUS_ASSET_ROOT_DIR = carb.settings.get_settings().get("/persistent/isaac/asset_root/cloud") +"""Path to the root directory on the Nucleus Server.""" + +NVIDIA_NUCLEUS_DIR = f"{NUCLEUS_ASSET_ROOT_DIR}/NVIDIA" +"""Path to the root directory on the NVIDIA Nucleus Server.""" + +ISAAC_NUCLEUS_DIR = f"{NUCLEUS_ASSET_ROOT_DIR}/Isaac" +"""Path to the ``Isaac`` directory on the NVIDIA Nucleus Server.""" + +ISAACLAB_NUCLEUS_DIR = f"{ISAAC_NUCLEUS_DIR}/IsaacLab" +"""Path to the ``Isaac/IsaacLab`` directory on the NVIDIA Nucleus Server.""" + + +def check_file_path(path: str) -> Literal[0, 1, 2]: + """Checks if a file exists on the Nucleus Server or locally. + + Args: + path: The path to the file. + + Returns: + The status of the file. Possible values are listed below. + + * :obj:`0` if the file does not exist + * :obj:`1` if the file exists locally + * :obj:`2` if the file exists on the Nucleus Server + """ + if os.path.isfile(path): + return 1 + # we need to convert backslash to forward slash on Windows for omni.client API + elif omni.client.stat(path.replace(os.sep, "/"))[0] == omni.client.Result.OK: + return 2 + else: + return 0 + + +def retrieve_file_path(path: str, download_dir: str | None = None, force_download: bool = True) -> str: + """Retrieves the path to a file on the Nucleus Server or locally. + + If the file exists locally, then the absolute path to the file is returned. + If the file exists on the Nucleus Server, then the file is downloaded to the local machine + and the absolute path to the file is returned. + + Args: + path: The path to the file. + download_dir: The directory where the file should be downloaded. Defaults to None, in which + case the file is downloaded to the system's temporary directory. + force_download: Whether to force download the file from the Nucleus Server. This will overwrite + the local file if it exists. Defaults to True. + + Returns: + The path to the file on the local machine. + + Raises: + FileNotFoundError: When the file not found locally or on Nucleus Server. + RuntimeError: When the file cannot be copied from the Nucleus Server to the local machine. This + can happen when the file already exists locally and :attr:`force_download` is set to False. + """ + # check file status + file_status = check_file_path(path) + if file_status == 1: + return os.path.abspath(path) + elif file_status == 2: + # resolve download directory + if download_dir is None: + download_dir = tempfile.gettempdir() + else: + download_dir = os.path.abspath(download_dir) + # create download directory if it does not exist + if not os.path.exists(download_dir): + os.makedirs(download_dir) + # download file in temp directory using os + file_name = os.path.basename(omni.client.break_url(path.replace(os.sep, "/")).path) + target_path = os.path.join(download_dir, file_name) + # check if file already exists locally + if not os.path.isfile(target_path) or force_download: + # copy file to local machine + result = omni.client.copy(path.replace(os.sep, "/"), target_path, omni.client.CopyBehavior.OVERWRITE) + if result != omni.client.Result.OK and force_download: + raise RuntimeError(f"Unable to copy file: '{path}'. Is the Nucleus Server running?") + return os.path.abspath(target_path) + else: + raise FileNotFoundError(f"Unable to find the file: {path}") + + +def read_file(path: str) -> io.BytesIO: + """Reads a file from the Nucleus Server or locally. + + Args: + path: The path to the file. + + Raises: + FileNotFoundError: When the file not found locally or on Nucleus Server. + + Returns: + The content of the file. + """ + # check file status + file_status = check_file_path(path) + if file_status == 1: + with open(path, "rb") as f: + return io.BytesIO(f.read()) + elif file_status == 2: + file_content = omni.client.read_file(path.replace(os.sep, "/"))[2] + return io.BytesIO(memoryview(file_content).tobytes()) + else: + raise FileNotFoundError(f"Unable to find the file: {path}") + + +""" +Nucleus Connection. +""" + + +def check_usd_path_with_timeout(usd_path: str, timeout: float = 300, log_interval: float = 30) -> bool: + """Checks whether the given USD file path is available on the NVIDIA Nucleus server. + + This function synchronously runs an asynchronous USD path availability check, + logging progress periodically until it completes. The file is available on the server + if the HTTP status code is 200. Otherwise, the file is not available on the server. + + This is useful for checking server responsiveness before attempting to load a remote + asset. It will block execution until the check completes or times out. + + Args: + usd_path: The remote USD file path to check. + timeout: Maximum time (in seconds) to wait for the server check. + log_interval: Interval (in seconds) at which progress is logged. + + Returns: + Whether the given USD path is available on the server. + """ + start_time = time.time() + loop = asyncio.get_event_loop() + + coroutine = _is_usd_path_available(usd_path, timeout) + task = asyncio.ensure_future(coroutine) + + next_log_time = start_time + log_interval + + first_log = True + while not task.done(): + now = time.time() + if now >= next_log_time: + elapsed = int(now - start_time) + if first_log: + logger.warning(f"Checking server availability for USD path: {usd_path} (timeout: {timeout}s)") + first_log = False + logger.warning(f"Waiting for server response... ({elapsed}s elapsed)") + next_log_time += log_interval + loop.run_until_complete(asyncio.sleep(0.1)) # Yield to allow async work + + return task.result() + + +""" +Helper functions. +""" + + +async def _is_usd_path_available(usd_path: str, timeout: float) -> bool: + """Checks whether the given USD path is available on the Omniverse Nucleus server. + + This function is a asynchronous routine to check the availability of the given USD path on + the Omniverse Nucleus server. It will return True if the USD path is available on the server, + False otherwise. + + Args: + usd_path: The remote or local USD file path to check. + timeout: Timeout in seconds for the async stat call. + + Returns: + Whether the given USD path is available on the server. + """ + try: + result, _ = await asyncio.wait_for(omni.client.stat_async(usd_path), timeout=timeout) + return result == omni.client.Result.OK + except asyncio.TimeoutError: + logger.warning(f"Timed out after {timeout}s while checking for USD: {usd_path}") + return False + except Exception as ex: + logger.warning(f"Exception during USD file check: {type(ex).__name__}: {ex}") + return False diff --git a/source/isaaclab/isaaclab/utils/buffers/__init__.py b/source/isaaclab/isaaclab/utils/buffers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..64da4f6e6ae9dd5514d7c269a16e18f7434b38cd --- /dev/null +++ b/source/isaaclab/isaaclab/utils/buffers/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing different buffers.""" + +from .circular_buffer import CircularBuffer +from .delay_buffer import DelayBuffer +from .timestamped_buffer import TimestampedBuffer diff --git a/source/isaaclab/isaaclab/utils/buffers/circular_buffer.py b/source/isaaclab/isaaclab/utils/buffers/circular_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..c5c9fe9ff6ad6b9df88f89a20ff782eab98d320f --- /dev/null +++ b/source/isaaclab/isaaclab/utils/buffers/circular_buffer.py @@ -0,0 +1,172 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Sequence + +import torch + + +class CircularBuffer: + """Circular buffer for storing a history of batched tensor data. + + This class implements a circular buffer for storing a history of batched tensor data. The buffer is + initialized with a maximum length and a batch size. The data is stored in a circular fashion, and the + data can be retrieved in a LIFO (Last-In-First-Out) fashion. The buffer is designed to be used in + multi-environment settings, where each environment has its own data. + + The shape of the appended data is expected to be (batch_size, ...), where the first dimension is the + batch dimension. Correspondingly, the shape of the ring buffer is (max_len, batch_size, ...). + """ + + def __init__(self, max_len: int, batch_size: int, device: str): + """Initialize the circular buffer. + + Args: + max_len: The maximum length of the circular buffer. The minimum allowed value is 1. + batch_size: The batch dimension of the data. + device: The device used for processing. + + Raises: + ValueError: If the buffer size is less than one. + """ + if max_len < 1: + raise ValueError(f"The buffer size should be greater than zero. However, it is set to {max_len}!") + # set the parameters + self._batch_size = batch_size + self._device = device + self._ALL_INDICES = torch.arange(batch_size, device=device) + + # max length tensor for comparisons + self._max_len = torch.full((batch_size,), max_len, dtype=torch.int, device=device) + # number of data pushes passed since the last call to :meth:`reset` + self._num_pushes = torch.zeros(batch_size, dtype=torch.long, device=device) + # the pointer to the current head of the circular buffer (-1 means not initialized) + self._pointer: int = -1 + # the actual buffer for data storage + # note: this is initialized on the first call to :meth:`append` + self._buffer: torch.Tensor = None # type: ignore + + """ + Properties. + """ + + @property + def batch_size(self) -> int: + """The batch size of the ring buffer.""" + return self._batch_size + + @property + def device(self) -> str: + """The device used for processing.""" + return self._device + + @property + def max_length(self) -> int: + """The maximum length of the ring buffer.""" + return int(self._max_len[0].item()) + + @property + def current_length(self) -> torch.Tensor: + """The current length of the buffer. Shape is (batch_size,). + + Since the buffer is circular, the current length is the minimum of the number of pushes + and the maximum length. + """ + return torch.minimum(self._num_pushes, self._max_len) + + @property + def buffer(self) -> torch.Tensor: + """Complete circular buffer with most recent entry at the end and oldest entry at the beginning. + + The shape of the buffer is (batch_size, max_length, ...). + + Note: + The oldest entry is at the beginning of dimension 1. + """ + buf = self._buffer.clone() + buf = torch.roll(buf, shifts=self.max_length - self._pointer - 1, dims=0) + return torch.transpose(buf, dim0=0, dim1=1) + + """ + Operations. + """ + + def reset(self, batch_ids: Sequence[int] | None = None): + """Reset the circular buffer at the specified batch indices. + + Args: + batch_ids: Elements to reset in the batch dimension. Default is None, which resets all the batch indices. + """ + # resolve all indices + if batch_ids is None: + batch_ids = slice(None) + # reset the number of pushes for the specified batch indices + self._num_pushes[batch_ids] = 0 + if self._buffer is not None: + # set buffer at batch_id reset indices to 0.0 so that the buffer() + # getter returns the cleared circular buffer after reset. + self._buffer[:, batch_ids, :] = 0.0 + + def append(self, data: torch.Tensor): + """Append the data to the circular buffer. + + Args: + data: The data to append to the circular buffer. The first dimension should be the batch dimension. + Shape is (batch_size, ...). + + Raises: + ValueError: If the input data has a different batch size than the buffer. + """ + # check the batch size + if data.shape[0] != self.batch_size: + raise ValueError(f"The input data has '{data.shape[0]}' batch size while expecting '{self.batch_size}'") + + # move the data to the device + data = data.to(self._device) + # at the first call, initialize the buffer size + if self._buffer is None: + self._pointer = -1 + self._buffer = torch.empty((self.max_length, *data.shape), dtype=data.dtype, device=self._device) + # move the head to the next slot + self._pointer = (self._pointer + 1) % self.max_length + # add the new data to the last layer + self._buffer[self._pointer] = data + # Check for batches with zero pushes and initialize all values in batch to first append + is_first_push = self._num_pushes == 0 + if torch.any(is_first_push): + self._buffer[:, is_first_push] = data[is_first_push] + # increment number of number of pushes for all batches + self._num_pushes += 1 + + def __getitem__(self, key: torch.Tensor) -> torch.Tensor: + """Retrieve the data from the circular buffer in last-in-first-out (LIFO) fashion. + + If the requested index is larger than the number of pushes since the last call to :meth:`reset`, + the oldest stored data is returned. + + Args: + key: The index to retrieve from the circular buffer. The index should be less than the number of pushes + since the last call to :meth:`reset`. Shape is (batch_size,). + + Returns: + The data from the circular buffer. Shape is (batch_size, ...). + + Raises: + ValueError: If the input key has a different batch size than the buffer. + RuntimeError: If the buffer is empty. + """ + # check the batch size + if len(key) != self.batch_size: + raise ValueError(f"The argument 'key' has length {key.shape[0]}, while expecting {self.batch_size}") + # check if the buffer is empty + if torch.any(self._num_pushes == 0) or self._buffer is None: + raise RuntimeError("Attempting to retrieve data on an empty circular buffer. Please append data first.") + + # admissible lag + valid_keys = torch.minimum(key, self._num_pushes - 1) + # the index in the circular buffer (pointer points to the last+1 index) + index_in_buffer = torch.remainder(self._pointer - valid_keys, self.max_length) + # return output + return self._buffer[index_in_buffer, self._ALL_INDICES] diff --git a/source/isaaclab/isaaclab/utils/buffers/delay_buffer.py b/source/isaaclab/isaaclab/utils/buffers/delay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..dd1a1ef72684002e9142a052f8f429fc3d24d473 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/buffers/delay_buffer.py @@ -0,0 +1,178 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed because we concatenate int and torch.Tensor in the type hints +from __future__ import annotations + +from collections.abc import Sequence + +import torch + +from .circular_buffer import CircularBuffer + + +class DelayBuffer: + """Delay buffer that allows retrieving stored data with delays. + + This class uses a batched circular buffer to store input data. Different to a standard circular buffer, + which uses the LIFO (last-in-first-out) principle to retrieve the data, the delay buffer class allows + retrieving data based on the lag set by the user. For instance, if the delay set inside the buffer + is 1, then the second last entry from the stream is retrieved. If it is 2, then the third last entry + and so on. + + The class supports storing a batched tensor data. This means that the shape of the appended data + is expected to be (batch_size, ...), where the first dimension is the batch dimension. Correspondingly, + the delay can be set separately for each batch index. If the requested delay is larger than the current + length of the underlying buffer, the most recent entry is returned. + + .. note:: + By default, the delay buffer has no delay, meaning that the data is returned as is. + """ + + def __init__(self, history_length: int, batch_size: int, device: str): + """Initialize the delay buffer. + + Args: + history_length: The history of the buffer, i.e., the number of time steps in the past that the data + will be buffered. It is recommended to set this value equal to the maximum time-step lag that + is expected. The minimum acceptable value is zero, which means only the latest data is stored. + batch_size: The batch dimension of the data. + device: The device used for processing. + """ + # set the parameters + self._history_length = max(0, history_length) + + # the buffer size: current data plus the history length + self._circular_buffer = CircularBuffer(self._history_length + 1, batch_size, device) + + # the minimum and maximum lags across all batch indices. + self._min_time_lag = 0 + self._max_time_lag = 0 + # the lags for each batch index. + self._time_lags = torch.zeros(batch_size, dtype=torch.int, device=device) + + """ + Properties. + """ + + @property + def batch_size(self) -> int: + """The batch size of the ring buffer.""" + return self._circular_buffer.batch_size + + @property + def device(self) -> str: + """The device used for processing.""" + return self._circular_buffer.device + + @property + def history_length(self) -> int: + """The history length of the delay buffer. + + If zero, only the latest data is stored. If one, the latest and the previous data are stored, and so on. + """ + return self._history_length + + @property + def min_time_lag(self) -> int: + """Minimum amount of time steps that can be delayed. + + This value cannot be negative or larger than :attr:`max_time_lag`. + """ + return self._min_time_lag + + @property + def max_time_lag(self) -> int: + """Maximum amount of time steps that can be delayed. + + This value cannot be greater than :attr:`history_length`. + """ + return self._max_time_lag + + @property + def time_lags(self) -> torch.Tensor: + """The time lag across each batch index. + + The shape of the tensor is (batch_size, ). The value at each index represents the delay for that index. + This value is used to retrieve the data from the buffer. + """ + return self._time_lags + + """ + Operations. + """ + + def set_time_lag(self, time_lag: int | torch.Tensor, batch_ids: Sequence[int] | None = None): + """Sets the time lag for the delay buffer across the provided batch indices. + + Args: + time_lag: The desired delay for the buffer. + + * If an integer is provided, the same delay is set for the provided batch indices. + * If a tensor is provided, the delay is set for each batch index separately. The shape of the tensor + should be (len(batch_ids),). + + batch_ids: The batch indices for which the time lag is set. Default is None, which sets the time lag + for all batch indices. + + Raises: + TypeError: If the type of the :attr:`time_lag` is not int or integer tensor. + ValueError: If the minimum time lag is negative or the maximum time lag is larger than the history length. + """ + # resolve batch indices + if batch_ids is None: + batch_ids = slice(None) + + # parse requested time_lag + if isinstance(time_lag, int): + # set the time lags across provided batch indices + self._time_lags[batch_ids] = time_lag + elif isinstance(time_lag, torch.Tensor): + # check valid dtype for time_lag: must be int or long + if time_lag.dtype not in [torch.int, torch.long]: + raise TypeError(f"Invalid dtype for time_lag: {time_lag.dtype}. Expected torch.int or torch.long.") + # set the time lags + self._time_lags[batch_ids] = time_lag.to(device=self.device) + else: + raise TypeError(f"Invalid type for time_lag: {type(time_lag)}. Expected int or integer tensor.") + + # compute the min and max time lag + self._min_time_lag = int(torch.min(self._time_lags).item()) + self._max_time_lag = int(torch.max(self._time_lags).item()) + # check that time_lag is feasible + if self._min_time_lag < 0: + raise ValueError(f"The minimum time lag cannot be negative. Received: {self._min_time_lag}") + if self._max_time_lag > self._history_length: + raise ValueError( + f"The maximum time lag cannot be larger than the history length. Received: {self._max_time_lag}" + ) + + def reset(self, batch_ids: Sequence[int] | None = None): + """Reset the data in the delay buffer at the specified batch indices. + + Args: + batch_ids: Elements to reset in the batch dimension. Default is None, which resets all the batch indices. + """ + self._circular_buffer.reset(batch_ids) + + def compute(self, data: torch.Tensor) -> torch.Tensor: + """Append the input data to the buffer and returns a stale version of the data based on time lag delay. + + If the requested delay is larger than the number of buffered data points since the last reset, + the function returns the latest data. For instance, if the delay is set to 2 and only one data point + is stored in the buffer, the function will return the latest data. If the delay is set to 2 and three + data points are stored, the function will return the first data point. + + Args: + data: The input data. Shape is (batch_size, ...). + + Returns: + The delayed version of the data from the stored buffer. Shape is (batch_size, ...). + """ + # add the new data to the last layer + self._circular_buffer.append(data) + # return output + delayed_data = self._circular_buffer[self._time_lags] + return delayed_data.clone() diff --git a/source/isaaclab/isaaclab/utils/buffers/timestamped_buffer.py b/source/isaaclab/isaaclab/utils/buffers/timestamped_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..30b824464ad1d7b78cf1e650f5e5c15582786f1a --- /dev/null +++ b/source/isaaclab/isaaclab/utils/buffers/timestamped_buffer.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import dataclass + +import torch + + +@dataclass +class TimestampedBuffer: + """A buffer class containing data and its timestamp. + + This class is a simple data container that stores a tensor and its timestamp. The timestamp is used to + track the last update of the buffer. The timestamp is set to -1.0 by default, indicating that the buffer + has not been updated yet. The timestamp should be updated whenever the data in the buffer is updated. This + way the buffer can be used to check whether the data is outdated and needs to be refreshed. + + The buffer is useful for creating lazy buffers that only update the data when it is outdated. This can be + useful when the data is expensive to compute or retrieve. For example usage, refer to the data classes in + the :mod:`isaaclab.assets` module. + """ + + data: torch.Tensor = None # type: ignore + """The data stored in the buffer. Default is None, indicating that the buffer is empty.""" + + timestamp: float = -1.0 + """Timestamp at the last update of the buffer. Default is -1.0, indicating that the buffer has not been updated.""" diff --git a/source/isaaclab/isaaclab/utils/configclass.py b/source/isaaclab/isaaclab/utils/configclass.py new file mode 100644 index 0000000000000000000000000000000000000000..e46280a61ccfd1ae58d881dc5a872bc17467e865 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/configclass.py @@ -0,0 +1,502 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module that provides a wrapper around the Python 3.7 onwards ``dataclasses`` module.""" + +import inspect +import types +from collections.abc import Callable +from copy import deepcopy +from dataclasses import MISSING, Field, dataclass, field, replace +from typing import Any, ClassVar + +from .dict import class_to_dict, update_class_from_dict + +_CONFIGCLASS_METHODS = ["to_dict", "from_dict", "replace", "copy", "validate"] +"""List of class methods added at runtime to dataclass.""" + +""" +Wrapper around dataclass. +""" + + +def __dataclass_transform__(): + """Add annotations decorator for PyLance.""" + return lambda a: a + + +@__dataclass_transform__() +def configclass(cls, **kwargs): + """Wrapper around `dataclass` functionality to add extra checks and utilities. + + As of Python 3.7, the standard dataclasses have two main issues which makes them non-generic for + configuration use-cases. These include: + + 1. Requiring a type annotation for all its members. + 2. Requiring explicit usage of :meth:`field(default_factory=...)` to reinitialize mutable variables. + + This function provides a decorator that wraps around Python's `dataclass`_ utility to deal with + the above two issues. It also provides additional helper functions for dictionary <-> class + conversion and easily copying class instances. + + Usage: + + .. code-block:: python + + from dataclasses import MISSING + + from isaaclab.utils.configclass import configclass + + + @configclass + class ViewerCfg: + eye: list = [7.5, 7.5, 7.5] # field missing on purpose + lookat: list = field(default_factory=[0.0, 0.0, 0.0]) + + + @configclass + class EnvCfg: + num_envs: int = MISSING + episode_length: int = 2000 + viewer: ViewerCfg = ViewerCfg() + + + # create configuration instance + env_cfg = EnvCfg(num_envs=24) + + # print information as a dictionary + print(env_cfg.to_dict()) + + # create a copy of the configuration + env_cfg_copy = env_cfg.copy() + + # replace arbitrary fields using keyword arguments + env_cfg_copy = env_cfg_copy.replace(num_envs=32) + + Args: + cls: The class to wrap around. + **kwargs: Additional arguments to pass to :func:`dataclass`. + + Returns: + The wrapped class. + + .. _dataclass: https://docs.python.org/3/library/dataclasses.html + """ + # add type annotations + _add_annotation_types(cls) + # add field factory + _process_mutable_types(cls) + # copy mutable members + # note: we check if user defined __post_init__ function exists and augment it with our own + if hasattr(cls, "__post_init__"): + setattr(cls, "__post_init__", _combined_function(cls.__post_init__, _custom_post_init)) + else: + setattr(cls, "__post_init__", _custom_post_init) + # add helper functions for dictionary conversion + setattr(cls, "to_dict", _class_to_dict) + setattr(cls, "from_dict", _update_class_from_dict) + setattr(cls, "replace", _replace_class_with_kwargs) + setattr(cls, "copy", _copy_class) + setattr(cls, "validate", _validate) + # wrap around dataclass + cls = dataclass(cls, **kwargs) + # return wrapped class + return cls + + +""" +Dictionary <-> Class operations. + +These are redefined here to add new docstrings. +""" + + +def _class_to_dict(obj: object) -> dict[str, Any]: + """Convert an object into dictionary recursively. + + Args: + obj: The object to convert. + + Returns: + Converted dictionary mapping. + """ + return class_to_dict(obj) + + +def _update_class_from_dict(obj, data: dict[str, Any]) -> None: + """Reads a dictionary and sets object variables recursively. + + This function performs in-place update of the class member attributes. + + Args: + obj: The object to update. + data: Input (nested) dictionary to update from. + + Raises: + TypeError: When input is not a dictionary. + ValueError: When dictionary has a value that does not match default config type. + KeyError: When dictionary has a key that does not exist in the default config type. + """ + update_class_from_dict(obj, data, _ns="") + + +def _replace_class_with_kwargs(obj: object, **kwargs) -> object: + """Return a new object replacing specified fields with new values. + + This is especially useful for frozen classes. Example usage: + + .. code-block:: python + + @configclass(frozen=True) + class C: + x: int + y: int + + + c = C(1, 2) + c1 = c.replace(x=3) + assert c1.x == 3 and c1.y == 2 + + Args: + obj: The object to replace. + **kwargs: The fields to replace and their new values. + + Returns: + The new object. + """ + return replace(obj, **kwargs) + + +def _copy_class(obj: object) -> object: + """Return a new object with the same fields as the original.""" + return replace(obj) + + +""" +Private helper functions. +""" + + +def _add_annotation_types(cls): + """Add annotations to all elements in the dataclass. + + By definition in Python, a field is defined as a class variable that has a type annotation. + + In case type annotations are not provided, dataclass ignores those members when :func:`__dict__()` is called. + This function adds these annotations to the class variable to prevent any issues in case the user forgets to + specify the type annotation. + + This makes the following a feasible operation: + + @dataclass + class State: + pos = (0.0, 0.0, 0.0) + ^^ + If the function is NOT used, the following type-error is returned: + TypeError: 'pos' is a field but has no type annotation + """ + # get type hints + hints = {} + # iterate over class inheritance + # we add annotations from base classes first + for base in reversed(cls.__mro__): + # check if base is object + if base is object: + continue + # get base class annotations + ann = base.__dict__.get("__annotations__", {}) + # directly add all annotations from base class + hints.update(ann) + # iterate over base class members + # Note: Do not change this to dir(base) since it orders the members alphabetically. + # This is not desirable since the order of the members is important in some cases. + for key in base.__dict__: + # get class member + value = getattr(base, key) + # skip members + if _skippable_class_member(key, value, hints): + continue + # add type annotations for members that don't have explicit type annotations + # for these, we deduce the type from the default value + if not isinstance(value, type): + if key not in hints: + # check if var type is not MISSING + # we cannot deduce type from MISSING! + if value is MISSING: + raise TypeError( + f"Missing type annotation for '{key}' in class '{cls.__name__}'." + " Please add a type annotation or set a default value." + ) + # add type annotation + hints[key] = type(value) + elif key != value.__name__: + # note: we don't want to add type annotations for nested configclass. Thus, we check if + # the name of the type matches the name of the variable. + # since Python 3.10, type hints are stored as strings + hints[key] = f"type[{value.__name__}]" + + # Note: Do not change this line. `cls.__dict__.get("__annotations__", {})` is different from + # `cls.__annotations__` because of inheritance. + cls.__annotations__ = cls.__dict__.get("__annotations__", {}) + cls.__annotations__ = hints + + +def _validate(obj: object, prefix: str = "") -> list[str]: + """Check the validity of configclass object. + + This function checks if the object is a valid configclass object. A valid configclass object contains no MISSING + entries. + + Args: + obj: The object to check. + prefix: The prefix to add to the missing fields. Defaults to ''. + + Returns: + A list of missing fields. + + Raises: + TypeError: When the object is not a valid configuration object. + """ + missing_fields = [] + + if type(obj).__name__ == "MeshConverterCfg": + return missing_fields + + if type(obj) is type(MISSING): + missing_fields.append(prefix) + return missing_fields + elif isinstance(obj, (list, tuple)): + for index, item in enumerate(obj): + current_path = f"{prefix}[{index}]" + missing_fields.extend(_validate(item, prefix=current_path)) + return missing_fields + elif isinstance(obj, dict): + # Convert any non-string keys to strings to allow validation of dict with non-string keys + if any(not isinstance(key, str) for key in obj.keys()): + obj_dict = {str(key): value for key, value in obj.items()} + else: + obj_dict = obj + elif hasattr(obj, "__dict__"): + obj_dict = obj.__dict__ + else: + return missing_fields + + for key, value in obj_dict.items(): + # disregard builtin attributes + if key.startswith("__"): + continue + current_path = f"{prefix}.{key}" if prefix else key + missing_fields.extend(_validate(value, prefix=current_path)) + + # raise an error only once at the top-level call + if prefix == "" and missing_fields: + formatted_message = "\n".join(f" - {field}" for field in missing_fields) + raise TypeError( + f"Missing values detected in object {obj.__class__.__name__} for the following" + f" fields:\n{formatted_message}\n" + ) + return missing_fields + + +def _process_mutable_types(cls): + """Initialize all mutable elements through :obj:`dataclasses.Field` to avoid unnecessary complaints. + + By default, dataclass requires usage of :obj:`field(default_factory=...)` to reinitialize mutable objects + every time a new class instance is created. If a member has a mutable type and it is created without + specifying the `field(default_factory=...)`, then Python throws an error requiring the usage of `default_factory`. + + Additionally, Python only explicitly checks for field specification when the type is a list, set or dict. + This misses the use-case where the type is class itself. Thus, the code silently carries a bug with it which + can lead to undesirable effects. + + This function deals with this issue + + This makes the following a feasible operation: + + @dataclass + class State: + pos: list = [0.0, 0.0, 0.0] + ^^ + If the function is NOT used, the following value-error is returned: + ValueError: mutable default for field pos is not allowed: use default_factory + """ + # note: Need to set this up in the same order as annotations. Otherwise, it + # complains about missing positional arguments. + ann = cls.__dict__.get("__annotations__", {}) + + # iterate over all class members and store them in a dictionary + class_members = {} + for base in reversed(cls.__mro__): + # check if base is object + if base is object: + continue + # iterate over base class members + for key in base.__dict__: + # get class member + f = getattr(base, key) + # skip members + if _skippable_class_member(key, f): + continue + # store class member if it is not a type or if it is already present in annotations + if not isinstance(f, type) or key in ann: + class_members[key] = f + # iterate over base class data fields + # in previous call, things that became a dataclass field were removed from class members + # so we need to add them back here as a dataclass field directly + for key, f in base.__dict__.get("__dataclass_fields__", {}).items(): + # store class member + if not isinstance(f, type): + class_members[key] = f + + # check that all annotations are present in class members + # note: mainly for debugging purposes + if len(class_members) != len(ann): + raise ValueError( + f"In class '{cls.__name__}', number of annotations ({len(ann)}) does not match number of class members" + f" ({len(class_members)}). Please check that all class members have type annotations and/or a default" + " value. If you don't want to specify a default value, please use the literal `dataclasses.MISSING`." + ) + # iterate over annotations and add field factory for mutable types + for key in ann: + # find matching field in class + value = class_members.get(key, MISSING) + # check if key belongs to ClassVar + # in that case, we cannot use default_factory! + origin = getattr(ann[key], "__origin__", None) + if origin is ClassVar: + continue + # check if f is MISSING + # note: commented out for now since it causes issue with inheritance + # of dataclasses when parent have some positional and some keyword arguments. + # Ref: https://stackoverflow.com/questions/51575931/class-inheritance-in-python-3-7-dataclasses + # TODO: check if this is fixed in Python 3.10 + # if f is MISSING: + # continue + if isinstance(value, Field): + setattr(cls, key, value) + elif not isinstance(value, type): + # create field factory for mutable types + value = field(default_factory=_return_f(value)) + setattr(cls, key, value) + + +def _custom_post_init(obj): + """Deepcopy all elements to avoid shared memory issues for mutable objects in dataclasses initialization. + + This function is called explicitly instead of as a part of :func:`_process_mutable_types()` to prevent mapping + proxy type i.e. a read only proxy for mapping objects. The error is thrown when using hierarchical data-classes + for configuration. + """ + for key in dir(obj): + # skip dunder members + if key.startswith("__"): + continue + # get data member + value = getattr(obj, key) + # check annotation + ann = obj.__class__.__dict__.get(key) + # duplicate data members that are mutable + if not callable(value) and not isinstance(ann, property): + setattr(obj, key, deepcopy(value)) + + +def _combined_function(f1: Callable, f2: Callable) -> Callable: + """Combine two functions into one. + + Args: + f1: The first function. + f2: The second function. + + Returns: + The combined function. + """ + + def _combined(*args, **kwargs): + # call both functions + f1(*args, **kwargs) + f2(*args, **kwargs) + + return _combined + + +""" +Helper functions +""" + + +def _skippable_class_member(key: str, value: Any, hints: dict | None = None) -> bool: + """Check if the class member should be skipped in configclass processing. + + The following members are skipped: + + * Dunder members: ``__name__``, ``__module__``, ``__qualname__``, ``__annotations__``, ``__dict__``. + * Manually-added special class functions: From :obj:`_CONFIGCLASS_METHODS`. + * Members that are already present in the type annotations. + * Functions bounded to class object or class. + * Properties bounded to class object. + + Args: + key: The class member name. + value: The class member value. + hints: The type hints for the class. Defaults to None, in which case, the + members existence in type hints are not checked. + + Returns: + True if the class member should be skipped, False otherwise. + """ + # skip dunder members + if key.startswith("__"): + return True + # skip manually-added special class functions + if key in _CONFIGCLASS_METHODS: + return True + # check if key is already present + if hints is not None and key in hints: + return True + # skip functions bounded to class + if callable(value): + # FIXME: This doesn't yet work for static methods because they are essentially seen as function types. + # check for class methods + if isinstance(value, types.MethodType): + return True + + if "CollisionAPI" in value.__name__: + return False + + # check for instance methods + signature = inspect.signature(value) + if "self" in signature.parameters or "cls" in signature.parameters: + return True + + # skip property methods + if isinstance(value, property): + return True + # Otherwise, don't skip + return False + + +def _return_f(f: Any) -> Callable[[], Any]: + """Returns default factory function for creating mutable/immutable variables. + + This function should be used to create default factory functions for variables. + + Example: + + .. code-block:: python + + value = field(default_factory=_return_f(value)) + setattr(cls, key, value) + """ + + def _wrap(): + if isinstance(f, Field): + if f.default_factory is MISSING: + return deepcopy(f.default) + else: + return f.default_factory + else: + return deepcopy(f) + + return _wrap diff --git a/source/isaaclab/isaaclab/utils/datasets/__init__.py b/source/isaaclab/isaaclab/utils/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fce0fa308fa7d569fdea94f5c3cf3a7f9ad8503f --- /dev/null +++ b/source/isaaclab/isaaclab/utils/datasets/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Submodule for datasets classes and methods. +""" + +from .dataset_file_handler_base import DatasetFileHandlerBase +from .episode_data import EpisodeData +from .hdf5_dataset_file_handler import HDF5DatasetFileHandler diff --git a/source/isaaclab/isaaclab/utils/datasets/dataset_file_handler_base.py b/source/isaaclab/isaaclab/utils/datasets/dataset_file_handler_base.py new file mode 100644 index 0000000000000000000000000000000000000000..201a0be370ec4a9f2f113e2f46be80ccdc058928 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/datasets/dataset_file_handler_base.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from abc import ABC, abstractmethod + +from .episode_data import EpisodeData + + +class DatasetFileHandlerBase(ABC): + """Abstract class for handling dataset files.""" + + def __init__(self): + """Initializes the dataset file handler.""" + pass + + @abstractmethod + def open(self, file_path: str, mode: str = "r"): + """Open a file.""" + return NotImplementedError + + @abstractmethod + def create(self, file_path: str, env_name: str = None): + """Create a new file.""" + return NotImplementedError + + @abstractmethod + def get_env_name(self) -> str | None: + """Get the environment name.""" + return NotImplementedError + + @abstractmethod + def write_episode(self, episode: EpisodeData): + """Write episode data to the file.""" + return NotImplementedError + + @abstractmethod + def flush(self): + """Flush the file.""" + return NotImplementedError + + @abstractmethod + def close(self): + """Close the file.""" + return NotImplementedError + + @abstractmethod + def load_episode(self, episode_name: str) -> EpisodeData | None: + """Load episode data from the file.""" + return NotImplementedError + + @abstractmethod + def get_num_episodes(self) -> int: + """Get number of episodes in the file.""" + return NotImplementedError diff --git a/source/isaaclab/isaaclab/utils/datasets/episode_data.py b/source/isaaclab/isaaclab/utils/datasets/episode_data.py new file mode 100644 index 0000000000000000000000000000000000000000..55df8ebbcbe7cacd5842836452fd40dea36cff14 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/datasets/episode_data.py @@ -0,0 +1,220 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch + + +class EpisodeData: + """Class to store episode data.""" + + def __init__(self) -> None: + """Initializes episode data class.""" + self._data = dict() + self._next_action_index = 0 + self._next_state_index = 0 + self._next_joint_target_index = 0 + self._seed = None + self._env_id = None + self._success = None + + @property + def data(self): + """Returns the episode data.""" + return self._data + + @data.setter + def data(self, data: dict): + """Set the episode data.""" + self._data = data + + @property + def seed(self): + """Returns the random number generator seed.""" + return self._seed + + @seed.setter + def seed(self, seed: int): + """Set the random number generator seed.""" + self._seed = seed + + @property + def env_id(self): + """Returns the environment ID.""" + return self._env_id + + @env_id.setter + def env_id(self, env_id: int): + """Set the environment ID.""" + self._env_id = env_id + + @property + def next_action_index(self): + """Returns the next action index.""" + return self._next_action_index + + @next_action_index.setter + def next_action_index(self, index: int): + """Set the next action index.""" + self._next_action_index = index + + @property + def next_state_index(self): + """Returns the next state index.""" + return self._next_state_index + + @next_state_index.setter + def next_state_index(self, index: int): + """Set the next state index.""" + self._next_state_index = index + + @property + def success(self): + """Returns the success value.""" + return self._success + + @success.setter + def success(self, success: bool): + """Set the success value.""" + self._success = success + + def is_empty(self): + """Check if the episode data is empty.""" + return not bool(self._data) + + def add(self, key: str, value: torch.Tensor | dict): + """Add a key-value pair to the dataset. + + The key can be nested by using the "/" character. + For example: "obs/joint_pos". + + Args: + key: The key name. + value: The corresponding value of tensor type or of dict type. + """ + # check datatype + if isinstance(value, dict): + for sub_key, sub_value in value.items(): + self.add(f"{key}/{sub_key}", sub_value) + return + + sub_keys = key.split("/") + current_dataset_pointer = self._data + for sub_key_index in range(len(sub_keys)): + if sub_key_index == len(sub_keys) - 1: + # Add value to the final dict layer + # Use lists to prevent slow tensor copy during concatenation + if sub_keys[sub_key_index] not in current_dataset_pointer: + current_dataset_pointer[sub_keys[sub_key_index]] = [value.clone()] + else: + current_dataset_pointer[sub_keys[sub_key_index]].append(value.clone()) + break + # key index + if sub_keys[sub_key_index] not in current_dataset_pointer: + current_dataset_pointer[sub_keys[sub_key_index]] = dict() + current_dataset_pointer = current_dataset_pointer[sub_keys[sub_key_index]] + + def get_initial_state(self) -> torch.Tensor | None: + """Get the initial state from the dataset.""" + if "initial_state" not in self._data: + return None + return self._data["initial_state"] + + def get_action(self, action_index) -> torch.Tensor | None: + """Get the action of the specified index from the dataset.""" + if "actions" not in self._data: + return None + if action_index >= len(self._data["actions"]): + return None + return self._data["actions"][action_index] + + def get_next_action(self) -> torch.Tensor | None: + """Get the next action from the dataset.""" + action = self.get_action(self._next_action_index) + if action is not None: + self._next_action_index += 1 + return action + + def get_state(self, state_index) -> dict | None: + """Get the state of the specified index from the dataset.""" + if "states" not in self._data: + return None + + states = self._data["states"] + + def get_state_helper(states, state_index) -> dict | torch.Tensor | None: + if isinstance(states, dict): + output_state = dict() + for key, value in states.items(): + output_state[key] = get_state_helper(value, state_index) + if output_state[key] is None: + return None + elif isinstance(states, torch.Tensor): + if state_index >= len(states): + return None + output_state = states[state_index, None] + else: + raise ValueError(f"Invalid state type: {type(states)}") + return output_state + + output_state = get_state_helper(states, state_index) + return output_state + + def get_next_state(self) -> dict | None: + """Get the next state from the dataset.""" + state = self.get_state(self._next_state_index) + if state is not None: + self._next_state_index += 1 + return state + + def get_joint_target(self, joint_target_index) -> dict | torch.Tensor | None: + """Get the joint target of the specified index from the dataset.""" + if "joint_targets" not in self._data: + return None + + joint_targets = self._data["joint_targets"] + + def get_joint_target_helper(joint_targets, joint_target_index) -> dict | torch.Tensor | None: + if isinstance(joint_targets, dict): + output_joint_targets = dict() + for key, value in joint_targets.items(): + output_joint_targets[key] = get_joint_target_helper(value, joint_target_index) + if output_joint_targets[key] is None: + return None + elif isinstance(joint_targets, torch.Tensor): + if joint_target_index >= len(joint_targets): + return None + output_joint_targets = joint_targets[joint_target_index] + else: + raise ValueError(f"Invalid joint target type: {type(joint_targets)}") + return output_joint_targets + + output_joint_targets = get_joint_target_helper(joint_targets, joint_target_index) + return output_joint_targets + + def get_next_joint_target(self) -> dict | torch.Tensor | None: + """Get the next joint target from the dataset.""" + joint_target = self.get_joint_target(self._next_joint_target_index) + if joint_target is not None: + self._next_joint_target_index += 1 + return joint_target + + def pre_export(self): + """Prepare data for export by converting lists to tensors.""" + + def pre_export_helper(data): + for key, value in data.items(): + if isinstance(value, list): + data[key] = torch.stack(value) + elif isinstance(value, dict): + pre_export_helper(value) + + pre_export_helper(self._data) diff --git a/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..46aeead2fd932e213853e0fad1fdcf261033dd98 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py @@ -0,0 +1,209 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import json +import os +from collections.abc import Iterable + +import h5py +import numpy as np +import torch + +from .dataset_file_handler_base import DatasetFileHandlerBase +from .episode_data import EpisodeData + + +class HDF5DatasetFileHandler(DatasetFileHandlerBase): + """HDF5 dataset file handler for storing and loading episode data.""" + + def __init__(self): + """Initializes the HDF5 dataset file handler.""" + self._hdf5_file_stream = None + self._hdf5_data_group = None + self._demo_count = 0 + self._env_args = {} + + def open(self, file_path: str, mode: str = "r"): + """Open an existing dataset file.""" + if self._hdf5_file_stream is not None: + raise RuntimeError("HDF5 dataset file stream is already in use") + self._hdf5_file_stream = h5py.File(file_path, mode) + self._hdf5_data_group = self._hdf5_file_stream["data"] + self._demo_count = len(self._hdf5_data_group) + + def create(self, file_path: str, env_name: str = None): + """Create a new dataset file.""" + if self._hdf5_file_stream is not None: + raise RuntimeError("HDF5 dataset file stream is already in use") + if not file_path.endswith(".hdf5"): + file_path += ".hdf5" + dir_path = os.path.dirname(file_path) + if not os.path.isdir(dir_path): + os.makedirs(dir_path) + self._hdf5_file_stream = h5py.File(file_path, "w") + + # set up a data group in the file + self._hdf5_data_group = self._hdf5_file_stream.create_group("data") + self._hdf5_data_group.attrs["total"] = 0 + self._demo_count = 0 + + # set environment arguments + # the environment type (we use gym environment type) is set to be compatible with robomimic + # Ref: https://github.com/ARISE-Initiative/robomimic/blob/master/robomimic/envs/env_base.py#L15 + env_name = env_name if env_name is not None else "" + self.add_env_args({"env_name": env_name, "type": 2}) + + def __del__(self): + """Destructor for the file handler.""" + self.close() + + """ + Properties + """ + + def add_env_args(self, env_args: dict): + """Add environment arguments to the dataset.""" + self._raise_if_not_initialized() + self._env_args.update(env_args) + self._hdf5_data_group.attrs["env_args"] = json.dumps(self._env_args) + + def set_env_name(self, env_name: str): + """Set the environment name.""" + self._raise_if_not_initialized() + self.add_env_args({"env_name": env_name}) + + def get_env_name(self) -> str | None: + """Get the environment name.""" + self._raise_if_not_initialized() + env_args = json.loads(self._hdf5_data_group.attrs["env_args"]) + if "env_name" in env_args: + return env_args["env_name"] + return None + + def get_episode_names(self) -> Iterable[str]: + """Get the names of the episodes in the file.""" + self._raise_if_not_initialized() + return self._hdf5_data_group.keys() + + def get_num_episodes(self) -> int: + """Get number of episodes in the file.""" + return self._demo_count + + @property + def demo_count(self) -> int: + """The number of demos collected so far.""" + return self._demo_count + + """ + Operations. + """ + + def load_episode(self, episode_name: str, device: str) -> EpisodeData | None: + """Load episode data from the file.""" + self._raise_if_not_initialized() + if episode_name not in self._hdf5_data_group: + return None + episode = EpisodeData() + h5_episode_group = self._hdf5_data_group[episode_name] + + def load_dataset_helper(group): + """Helper method to load dataset that contains recursive dict objects.""" + data = {} + for key in group: + if isinstance(group[key], h5py.Group): + data[key] = load_dataset_helper(group[key]) + else: + # Converting group[key] to numpy array greatly improves the performance + # when converting to torch tensor + data[key] = torch.tensor(np.array(group[key]), device=device) + return data + + episode.data = load_dataset_helper(h5_episode_group) + + if "seed" in h5_episode_group.attrs: + episode.seed = h5_episode_group.attrs["seed"] + + if "success" in h5_episode_group.attrs: + episode.success = h5_episode_group.attrs["success"] + + episode.env_id = self.get_env_name() + + return episode + + def write_episode(self, episode: EpisodeData, demo_id: int | None = None): + """Add an episode to the dataset. + + Args: + episode: The episode data to add. + demo_id: Custom index for the episode. If None, uses default index. + """ + self._raise_if_not_initialized() + if episode.is_empty(): + return + + # Use custom demo id if provided, otherwise use default naming + if demo_id is not None: + episode_group_name = f"demo_{demo_id}" + else: + episode_group_name = f"demo_{self._demo_count}" + + # create episode group with the specified name + if episode_group_name in self._hdf5_data_group: + raise ValueError(f"Episode group '{episode_group_name}' already exists in the dataset") + h5_episode_group = self._hdf5_data_group.create_group(episode_group_name) + + # store number of steps taken + if "actions" in episode.data: + h5_episode_group.attrs["num_samples"] = len(episode.data["actions"]) + else: + h5_episode_group.attrs["num_samples"] = 0 + + if episode.seed is not None: + h5_episode_group.attrs["seed"] = episode.seed + + if episode.success is not None: + h5_episode_group.attrs["success"] = episode.success + + def create_dataset_helper(group, key, value): + """Helper method to create dataset that contains recursive dict objects.""" + if isinstance(value, dict): + key_group = group.create_group(key) + for sub_key, sub_value in value.items(): + create_dataset_helper(key_group, sub_key, sub_value) + else: + group.create_dataset(key, data=value.cpu().numpy(), compression="gzip") + + for key, value in episode.data.items(): + create_dataset_helper(h5_episode_group, key, value) + + # increment total step counts + self._hdf5_data_group.attrs["total"] += h5_episode_group.attrs["num_samples"] + + # Only increment demo count if using default indexing + if demo_id is None: + # increment total demo counts + self._demo_count += 1 + + def flush(self): + """Flush the episode data to disk.""" + self._raise_if_not_initialized() + + self._hdf5_file_stream.flush() + + def close(self): + """Close the dataset file handler.""" + if self._hdf5_file_stream is not None: + self._hdf5_file_stream.close() + self._hdf5_file_stream = None + + def _raise_if_not_initialized(self): + """Raise an error if the dataset file handler is not initialized.""" + if self._hdf5_file_stream is None: + raise RuntimeError("HDF5 dataset file stream is not initialized") diff --git a/source/isaaclab/isaaclab/utils/dict.py b/source/isaaclab/isaaclab/utils/dict.py new file mode 100644 index 0000000000000000000000000000000000000000..de2062d66979e47af8c1fef116a72a3dfc16321b --- /dev/null +++ b/source/isaaclab/isaaclab/utils/dict.py @@ -0,0 +1,344 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for utilities for working with dictionaries.""" + +import collections.abc +import hashlib +import json +from collections.abc import Iterable, Mapping, Sized +from typing import Any + +import torch + +from .array import TENSOR_TYPE_CONVERSIONS, TENSOR_TYPES +from .string import callable_to_string, string_to_callable, string_to_slice + +""" +Dictionary <-> Class operations. +""" + + +def class_to_dict(obj: object) -> dict[str, Any]: + """Convert an object into dictionary recursively. + + Note: + Ignores all names starting with "__" (i.e. built-in methods). + + Args: + obj: An instance of a class to convert. + + Raises: + ValueError: When input argument is not an object. + + Returns: + Converted dictionary mapping. + """ + # check that input data is class instance + if not hasattr(obj, "__class__"): + raise ValueError(f"Expected a class instance. Received: {type(obj)}.") + # convert object to dictionary + if isinstance(obj, dict): + obj_dict = obj + elif isinstance(obj, torch.Tensor): + # We have to treat torch tensors specially because `torch.tensor.__dict__` returns an empty + # dict, which would mean that a torch.tensor would be stored as an empty dict. Instead we + # want to store it directly as the tensor. + return obj + elif hasattr(obj, "__dict__"): + obj_dict = obj.__dict__ + else: + return obj + + # convert to dictionary + data = dict() + for key, value in obj_dict.items(): + # disregard builtin attributes + if key.startswith("__"): + continue + # check if attribute is callable -- function + if callable(value): + data[key] = callable_to_string(value) + # check if attribute is a dictionary + elif hasattr(value, "__dict__") or isinstance(value, dict): + data[key] = class_to_dict(value) + # check if attribute is a list or tuple + elif isinstance(value, (list, tuple)): + data[key] = type(value)([class_to_dict(v) for v in value]) + else: + data[key] = value + return data + + +def update_class_from_dict(obj, data: dict[str, Any], _ns: str = "") -> None: + """Reads a dictionary and sets object variables recursively. + + This function performs in-place update of the class member attributes. + + Args: + obj: An instance of a class to update. + data: Input dictionary to update from. + _ns: Namespace of the current object. This is useful for nested configuration + classes or dictionaries. Defaults to "". + + Raises: + TypeError: When input is not a dictionary. + ValueError: When dictionary has a value that does not match default config type. + KeyError: When dictionary has a key that does not exist in the default config type. + """ + for key, value in data.items(): + # key_ns is the full namespace of the key + key_ns = _ns + "/" + key + + # -- A) if key is present in the object ------------------------------------ + if hasattr(obj, key) or (isinstance(obj, dict) and key in obj): + obj_mem = obj[key] if isinstance(obj, dict) else getattr(obj, key) + + # -- 1) nested mapping → recurse --------------------------- + if isinstance(value, Mapping): + # recursively call if it is a dictionary + update_class_from_dict(obj_mem, value, _ns=key_ns) + continue + + # -- 2) iterable (list / tuple / etc.) --------------------- + if isinstance(value, Iterable) and not isinstance(value, str): + # ---- 2a) flat iterable → replace wholesale ---------- + if all(not isinstance(el, Mapping) for el in value): + out_val = tuple(value) if isinstance(obj_mem, tuple) else value + if isinstance(obj, dict): + obj[key] = out_val + else: + setattr(obj, key, out_val) + continue + + # ---- 2b) existing value is None → abort ------------- + if obj_mem is None: + raise ValueError( + f"[Config]: Cannot merge list under namespace: {key_ns} because the existing value is None." + ) + + # ---- 2c) length mismatch → abort ------------------- + if isinstance(obj_mem, Sized) and isinstance(value, Sized) and len(obj_mem) != len(value): + raise ValueError( + f"[Config]: Incorrect length under namespace: {key_ns}." + f" Expected: {len(obj_mem)}, Received: {len(value)}." + ) + + # ---- 2d) keep tuple/list parity & recurse ---------- + if isinstance(obj_mem, tuple): + value = tuple(value) + else: + set_obj = True + # recursively call if iterable contains Mappings + for i in range(len(obj_mem)): + if isinstance(value[i], Mapping): + update_class_from_dict(obj_mem[i], value[i], _ns=key_ns) + set_obj = False + # do not set value to obj, otherwise it overwrites the cfg class with the dict + if not set_obj: + continue + + # -- 3) callable attribute → resolve string -------------- + elif callable(obj_mem): + # update function name + value = string_to_callable(value) + + # -- 4) simple scalar / explicit None --------------------- + elif value is None or isinstance(value, type(obj_mem)): + pass + + # -- 5) type mismatch → abort ----------------------------- + else: + raise ValueError( + f"[Config]: Incorrect type under namespace: {key_ns}." + f" Expected: {type(obj_mem)}, Received: {type(value)}." + ) + + # -- 6) final assignment --------------------------------- + if isinstance(obj, dict): + obj[key] = value + else: + setattr(obj, key, value) + + # -- B) if key is not present ------------------------------------ + else: + raise KeyError(f"[Config]: Key not found under namespace: {key_ns}.") + + +""" +Dictionary <-> Hashable operations. +""" + + +def dict_to_md5_hash(data: object) -> str: + """Convert a dictionary into a hashable key using MD5 hash. + + Args: + data: Input dictionary or configuration object to convert. + + Returns: + A string object of double length containing only hexadecimal digits. + """ + # convert to dictionary + if isinstance(data, dict): + encoded_buffer = json.dumps(data, sort_keys=True).encode() + else: + encoded_buffer = json.dumps(class_to_dict(data), sort_keys=True).encode() + # compute hash using MD5 + data_hash = hashlib.md5() + data_hash.update(encoded_buffer) + # return the hash key + return data_hash.hexdigest() + + +""" +Dictionary operations. +""" + + +def convert_dict_to_backend( + data: dict, backend: str = "numpy", array_types: Iterable[str] = ("numpy", "torch", "warp") +) -> dict: + """Convert all arrays or tensors in a dictionary to a given backend. + + This function iterates over the dictionary, converts all arrays or tensors with the given types to + the desired backend, and stores them in a new dictionary. It also works with nested dictionaries. + + Currently supported backends are "numpy", "torch", and "warp". + + Note: + This function only converts arrays or tensors. Other types of data are left unchanged. Mutable types + (e.g. lists) are referenced by the new dictionary, so they are not copied. + + Args: + data: An input dict containing array or tensor data as values. + backend: The backend ("numpy", "torch", "warp") to which arrays in this dict should be converted. + Defaults to "numpy". + array_types: A list containing the types of arrays that should be converted to + the desired backend. Defaults to ("numpy", "torch", "warp"). + + Raises: + ValueError: If the specified ``backend`` or ``array_types`` are unknown, i.e. not in the list of supported + backends ("numpy", "torch", "warp"). + + Returns: + The updated dict with the data converted to the desired backend. + """ + # THINK: Should we also support converting to a specific device, e.g. "cuda:0"? + # Check the backend is valid. + if backend not in TENSOR_TYPE_CONVERSIONS: + raise ValueError(f"Unknown backend '{backend}'. Supported backends are 'numpy', 'torch', and 'warp'.") + # Define the conversion functions for each backend. + tensor_type_conversions = TENSOR_TYPE_CONVERSIONS[backend] + + # Parse the array types and convert them to the corresponding types: "numpy" -> np.ndarray, etc. + parsed_types = list() + for t in array_types: + # Check type is valid. + if t not in TENSOR_TYPES: + raise ValueError(f"Unknown array type: '{t}'. Supported array types are 'numpy', 'torch', and 'warp'.") + # Exclude types that match the backend, since we do not need to convert these. + if t == backend: + continue + # Convert the string types to the corresponding types. + parsed_types.append(TENSOR_TYPES[t]) + + # Convert the data to the desired backend. + output_dict = dict() + for key, value in data.items(): + # Obtain the data type of the current value. + data_type = type(value) + # -- arrays + if data_type in parsed_types: + # check if we have a known conversion. + if data_type not in tensor_type_conversions: + raise ValueError(f"No registered conversion for data type: {data_type} to {backend}!") + # convert the data to the desired backend. + output_dict[key] = tensor_type_conversions[data_type](value) + # -- nested dictionaries + elif isinstance(data[key], dict): + output_dict[key] = convert_dict_to_backend(value) + # -- everything else + else: + output_dict[key] = value + + return output_dict + + +def update_dict(orig_dict: dict, new_dict: collections.abc.Mapping) -> dict: + """Updates existing dictionary with values from a new dictionary. + + This function mimics the dict.update() function. However, it works for + nested dictionaries as well. + + Args: + orig_dict: The original dictionary to insert items to. + new_dict: The new dictionary to insert items from. + + Returns: + The updated dictionary. + """ + for keyname, value in new_dict.items(): + if isinstance(value, collections.abc.Mapping): + orig_dict[keyname] = update_dict(orig_dict.get(keyname, {}), value) + else: + orig_dict[keyname] = value + return orig_dict + + +def replace_slices_with_strings(data: dict) -> dict: + """Replace slice objects with their string representations in a dictionary. + + Args: + data: The dictionary to process. + + Returns: + The dictionary with slice objects replaced by their string representations. + """ + if isinstance(data, dict): + return {k: replace_slices_with_strings(v) for k, v in data.items()} + elif isinstance(data, list): + return [replace_slices_with_strings(v) for v in data] + elif isinstance(data, slice): + return f"slice({data.start},{data.stop},{data.step})" + else: + return data + + +def replace_strings_with_slices(data: dict) -> dict: + """Replace string representations of slices with slice objects in a dictionary. + + Args: + data: The dictionary to process. + + Returns: + The dictionary with string representations of slices replaced by slice objects. + """ + if isinstance(data, dict): + return {k: replace_strings_with_slices(v) for k, v in data.items()} + elif isinstance(data, list): + return [replace_strings_with_slices(v) for v in data] + elif isinstance(data, str) and data.startswith("slice("): + return string_to_slice(data) + else: + return data + + +def print_dict(val, nesting: int = -4, start: bool = True): + """Outputs a nested dictionary.""" + if isinstance(val, dict): + if not start: + print("") + nesting += 4 + for k in val: + print(nesting * " ", end="") + print(k, end=": ") + print_dict(val[k], nesting, start=False) + else: + # deal with functions in print statements + if callable(val): + print(callable_to_string(val)) + else: + print(val) diff --git a/source/isaaclab/isaaclab/utils/interpolation/__init__.py b/source/isaaclab/isaaclab/utils/interpolation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..25f6be5f001457dc61ff5159e6eca1f1509c3393 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/interpolation/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Submodule for different interpolation methods. +""" + +from .linear_interpolation import LinearInterpolation diff --git a/source/isaaclab/isaaclab/utils/interpolation/linear_interpolation.py b/source/isaaclab/isaaclab/utils/interpolation/linear_interpolation.py new file mode 100644 index 0000000000000000000000000000000000000000..84a371280427fd123e1a21d11d3b72f543fb9ab9 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/interpolation/linear_interpolation.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import torch + + +class LinearInterpolation: + """Linearly interpolates a sampled scalar function for arbitrary query points. + + This class implements a linear interpolation for a scalar function. The function maps from real values, x, to + real values, y. It expects a set of samples from the function's domain, x, and the corresponding values, y. + The class allows querying the function's values at any arbitrary point. + + The interpolation is done by finding the two closest points in x to the query point and then linearly + interpolating between the corresponding y values. For the query points that are outside the input points, + the class does a zero-order-hold extrapolation based on the boundary values. This means that the class + returns the value of the closest point in x. + """ + + def __init__(self, x: torch.Tensor, y: torch.Tensor, device: str): + """Initializes the linear interpolation. + + The scalar function maps from real values, x, to real values, y. The input to the class is a set of samples + from the function's domain, x, and the corresponding values, y. + + Note: + The input tensor x should be sorted in ascending order. + + Args: + x: An vector of samples from the function's domain. The values should be sorted in ascending order. + Shape is (num_samples,) + y: The function's values associated to the input x. Shape is (num_samples,) + device: The device used for processing. + + Raises: + ValueError: If the input tensors are empty or have different sizes. + ValueError: If the input tensor x is not sorted in ascending order. + """ + # make sure that input tensors are 1D of size (num_samples,) + self._x = x.view(-1).clone().to(device=device) + self._y = y.view(-1).clone().to(device=device) + + # make sure sizes are correct + if self._x.numel() == 0: + raise ValueError("Input tensor x is empty!") + if self._x.numel() != self._y.numel(): + raise ValueError(f"Input tensors x and y have different sizes: {self._x.numel()} != {self._y.numel()}") + # make sure that x is sorted + if torch.any(self._x[1:] < self._x[:-1]): + raise ValueError("Input tensor x is not sorted in ascending order!") + + def compute(self, q: torch.Tensor) -> torch.Tensor: + """Calculates a linearly interpolated values for the query points. + + Args: + q: The query points. It can have any arbitrary shape. + + Returns: + The interpolated values at query points. It has the same shape as the input tensor. + """ + # serialized q + q_1d = q.view(-1) + # Number of elements in the x that are strictly smaller than query points (use int32 instead of int64) + num_smaller_elements = torch.sum(self._x.unsqueeze(1) < q_1d.unsqueeze(0), dim=0, dtype=torch.int) + + # The index pointing to the first element in x such that x[lower_bound_i] < q_i + # If a point is smaller that all x elements, it will assign 0 + lower_bound = torch.clamp(num_smaller_elements - 1, min=0) + # The index pointing to the first element in x such that x[upper_bound_i] >= q_i + # If a point is greater than all x elements, it will assign the last elements' index + upper_bound = torch.clamp(num_smaller_elements, max=self._x.numel() - 1) + + # compute the weight as: (q_i - x_lb) / (x_ub - x_lb) + weight = (q_1d - self._x[lower_bound]) / (self._x[upper_bound] - self._x[lower_bound]) + # If a point is out of bounds assign weight 0.0 + weight[upper_bound == lower_bound] = 0.0 + + # Perform linear interpolation + fq = self._y[lower_bound] + weight * (self._y[upper_bound] - self._y[lower_bound]) + + # deserialized fq + fq = fq.view(q.shape) + return fq diff --git a/source/isaaclab/isaaclab/utils/io/__init__.py b/source/isaaclab/isaaclab/utils/io/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9a8b16ed157a9047dd0031f13f12689300436d08 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/io/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Submodules for files IO operations. +""" + +from .torchscript import load_torchscript_model +from .yaml import dump_yaml, load_yaml diff --git a/source/isaaclab/isaaclab/utils/io/torchscript.py b/source/isaaclab/isaaclab/utils/io/torchscript.py new file mode 100644 index 0000000000000000000000000000000000000000..df96ebca12337bc0af9e3444d20eb74d093ea147 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/io/torchscript.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""TorchScript I/O utilities.""" + +import os + +import torch + + +def load_torchscript_model(model_path: str, device: str = "cpu") -> torch.nn.Module: + """Load a TorchScript model from the specified path. + + This function only loads TorchScript models (.pt or .pth files created with torch.jit.save). + It will not work with raw PyTorch checkpoints (.pth files created with torch.save). + + Args: + model_path (str): Path to the TorchScript model file (.pt or .pth) + device (str, optional): Device to load the model on. Defaults to 'cpu'. + + Returns: + torch.nn.Module: The loaded TorchScript model in evaluation mode + + Raises: + FileNotFoundError: If the model file does not exist + """ + if not os.path.exists(model_path): + raise FileNotFoundError(f"TorchScript model file not found: {model_path}") + + try: + model = torch.jit.load(model_path, map_location=device) + model.eval() + print(f"Successfully loaded TorchScript model from {model_path}") + return model + except Exception as e: + print(f"Error loading TorchScript model: {e}") + return None diff --git a/source/isaaclab/isaaclab/utils/io/yaml.py b/source/isaaclab/isaaclab/utils/io/yaml.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2dbeeefb9c8c4705a8445ff7f571b97e432969 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/io/yaml.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utilities for file I/O with yaml.""" + +import os + +import yaml + +from isaaclab.utils import class_to_dict + + +def load_yaml(filename: str) -> dict: + """Loads an input PKL file safely. + + Args: + filename: The path to pickled file. + + Raises: + FileNotFoundError: When the specified file does not exist. + + Returns: + The data read from the input file. + """ + if not os.path.exists(filename): + raise FileNotFoundError(f"File not found: {filename}") + with open(filename) as f: + data = yaml.full_load(f) + return data + + +def dump_yaml(filename: str, data: dict | object, sort_keys: bool = False): + """Saves data into a YAML file safely. + + Note: + The function creates any missing directory along the file's path. + + Args: + filename: The path to save the file at. + data: The data to save either a dictionary or class object. + sort_keys: Whether to sort the keys in the output file. Defaults to False. + """ + # check ending + if not filename.endswith("yaml"): + filename += ".yaml" + # create directory + if not os.path.exists(os.path.dirname(filename)): + os.makedirs(os.path.dirname(filename), exist_ok=True) + # convert data into dictionary + if not isinstance(data, dict): + data = class_to_dict(data) + # save data + with open(filename, "w") as f: + yaml.dump(data, f, default_flow_style=False, sort_keys=sort_keys) diff --git a/source/isaaclab/isaaclab/utils/logger.py b/source/isaaclab/isaaclab/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..c9293e931a72be1f54e8432de8489ea327d0e931 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/logger.py @@ -0,0 +1,182 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module with logging utilities. + +To use the logger, you can use the :func:`logging.getLogger` function. + +Example: + >>> import logging + >>> + >>> # define logger for the current module (enables fine-control) + >>> logger = logging.getLogger(__name__) + >>> + >>> # log messages + >>> logger.info("This is an info message") + >>> logger.warning("This is a warning message") + >>> logger.error("This is an error message") + >>> logger.critical("This is a critical message") + >>> logger.debug("This is a debug message") +""" + +from __future__ import annotations + +import logging +import os +import sys +import tempfile +import time +from datetime import datetime +from typing import Literal + + +def configure_logging( + logging_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "WARNING", + save_logs_to_file: bool = True, + log_dir: str | None = None, +) -> logging.Logger: + """Setup the logger with a colored formatter and a rate limit filter. + + This function defines the default logger for IsaacLab. It adds a stream handler with a colored formatter + and a rate limit filter. If :attr:`save_logs_to_file` is True, it also adds a file handler to save the logs + to a file. The log directory can be specified using :attr:`log_dir`. If not provided, the logs will be saved + to the temp directory with the sub-directory "isaaclab/logs". + + The log file name is formatted as "isaaclab_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.log". + The log record format is "%(asctime)s [%(filename)s:%(lineno)d] %(levelname)s: %(message)s". + The date format is "%Y-%m-%d %H:%M:%S". + + Args: + logging_level: The logging level. + save_logs_to_file: Whether to save the logs to a file. + log_dir: The directory to save the logs to. Default is None, in which case the logs + will be saved to the temp directory with the sub-directory "isaaclab/logs". + + Returns: + The root logger. + """ + root_logger = logging.getLogger() + # the root logger must be the lowest level to ensure that all messages are logged + root_logger.setLevel(logging.DEBUG) + + # remove existing handlers + # Note: iterate over a copy [:] to avoid modifying list during iteration + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + # add a stream handler with default level + handler = logging.StreamHandler(sys.stdout) + handler.setLevel(logging_level) + + # add a colored formatter + formatter = ColoredFormatter(fmt="%(asctime)s [%(filename)s] %(levelname)s: %(message)s", datefmt="%H:%M:%S") + handler.setFormatter(formatter) + handler.addFilter(RateLimitFilter(interval_seconds=5)) + root_logger.addHandler(handler) + + # add a file handler + if save_logs_to_file: + # if log_dir is not provided, use the temp directory + if log_dir is None: + log_dir = os.path.join(tempfile.gettempdir(), "isaaclab", "logs") + # create the log directory if it does not exist + os.makedirs(log_dir, exist_ok=True) + # create the log file path + log_file_path = os.path.join(log_dir, f"isaaclab_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.log") + + # create the file handler + file_handler = logging.FileHandler(log_file_path, mode="w", encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + file_formatter = logging.Formatter( + fmt="%(asctime)s [%(filename)s:%(lineno)d] %(levelname)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S" + ) + file_handler.setFormatter(file_formatter) + root_logger.addHandler(file_handler) + + # print the log file path once at startup with nice formatting + cyan = "\033[36m" # cyan color + bold = "\033[1m" # bold text + reset = "\033[0m" # reset formatting + message = f"[INFO][IsaacLab]: Logging to file: {log_file_path}" + border = "=" * len(message) + print(f"\n{cyan}{border}{reset}") + print(f"{cyan}{bold}{message}{reset}") + print(f"{cyan}{border}{reset}\n") + + # return the root logger + return root_logger + + +class ColoredFormatter(logging.Formatter): + """Colored formatter for logging. + + This formatter colors the log messages based on the log level. + """ + + COLORS = { + "WARNING": "\033[33m", # orange/yellow + "ERROR": "\033[31m", # red + "CRITICAL": "\033[1;31m", # bold red + "INFO": "\033[0m", # reset + "DEBUG": "\033[0m", + } + """Colors for different log levels.""" + + RESET = "\033[0m" + """Reset color.""" + + def format(self, record: logging.LogRecord) -> str: + """Format the log record. + + Args: + record: The log record to format. + + Returns: + The formatted log record. + """ + color = self.COLORS.get(record.levelname, self.RESET) + message = super().format(record) + return f"{color}{message}{self.RESET}" + + +class RateLimitFilter(logging.Filter): + """Custom rate-limited warning filter. + + This filter allows warning-level messages only once every few seconds per message. + This is useful to avoid flooding the log with the same message multiple times. + """ + + def __init__(self, interval_seconds: int = 5): + """Initialize the rate limit filter. + + Args: + interval_seconds: The interval in seconds to limit the warnings. + Defaults to 5 seconds. + """ + super().__init__() + self.interval = interval_seconds + self.last_emitted = {} + + def filter(self, record: logging.LogRecord) -> bool: + """Allow warning-level messages only once every few seconds per message. + + Args: + record: The log record to filter. + + Returns: + True if the message should be logged, False otherwise. + """ + # only filter warning-level messages + if record.levelno != logging.WARNING: + return True + # check if the message has been logged in the last interval + now = time.time() + msg_key = record.getMessage() + if msg_key not in self.last_emitted or (now - self.last_emitted[msg_key]) > self.interval: + # if the message has not been logged in the last interval, log it + self.last_emitted[msg_key] = now + return True + # if the message has been logged in the last interval, do not log it + return False diff --git a/source/isaaclab/isaaclab/utils/math.py b/source/isaaclab/isaaclab/utils/math.py new file mode 100644 index 0000000000000000000000000000000000000000..96314abfbd09be50e9cd9e606c4be57f5bcd0ffc --- /dev/null +++ b/source/isaaclab/isaaclab/utils/math.py @@ -0,0 +1,1979 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing utilities for various math operations.""" + +# needed to import for allowing type-hinting: torch.Tensor | np.ndarray +from __future__ import annotations + +import logging +import math +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional + +# import logger +logger = logging.getLogger(__name__) + +""" +General +""" + + +@torch.jit.script +def scale_transform(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor: + """Normalizes a given input tensor to a range of [-1, 1]. + + .. note:: + It uses pytorch broadcasting functionality to deal with batched input. + + Args: + x: Input tensor of shape (N, dims). + lower: The minimum value of the tensor. Shape is (N, dims) or (dims,). + upper: The maximum value of the tensor. Shape is (N, dims) or (dims,). + + Returns: + Normalized transform of the tensor. Shape is (N, dims). + """ + # default value of center + offset = (lower + upper) * 0.5 + # return normalized tensor + return 2 * (x - offset) / (upper - lower) + + +@torch.jit.script +def unscale_transform(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor: + """De-normalizes a given input tensor from range of [-1, 1] to (lower, upper). + + .. note:: + It uses pytorch broadcasting functionality to deal with batched input. + + Args: + x: Input tensor of shape (N, dims). + lower: The minimum value of the tensor. Shape is (N, dims) or (dims,). + upper: The maximum value of the tensor. Shape is (N, dims) or (dims,). + + Returns: + De-normalized transform of the tensor. Shape is (N, dims). + """ + # default value of center + offset = (lower + upper) * 0.5 + # return normalized tensor + return x * (upper - lower) * 0.5 + offset + + +@torch.jit.script +def saturate(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor: + """Clamps a given input tensor to (lower, upper). + + It uses pytorch broadcasting functionality to deal with batched input. + + Args: + x: Input tensor of shape (N, dims). + lower: The minimum value of the tensor. Shape is (N, dims) or (dims,). + upper: The maximum value of the tensor. Shape is (N, dims) or (dims,). + + Returns: + Clamped transform of the tensor. Shape is (N, dims). + """ + return torch.max(torch.min(x, upper), lower) + + +@torch.jit.script +def normalize(x: torch.Tensor, eps: float = 1e-9) -> torch.Tensor: + """Normalizes a given input tensor to unit length. + + Args: + x: Input tensor of shape (N, dims). + eps: A small value to avoid division by zero. Defaults to 1e-9. + + Returns: + Normalized tensor of shape (N, dims). + """ + return x / x.norm(p=2, dim=-1).clamp(min=eps, max=None).unsqueeze(-1) + + +@torch.jit.script +def wrap_to_pi(angles: torch.Tensor) -> torch.Tensor: + r"""Wraps input angles (in radians) to the range :math:`[-\pi, \pi]`. + + This function wraps angles in radians to the range :math:`[-\pi, \pi]`, such that + :math:`\pi` maps to :math:`\pi`, and :math:`-\pi` maps to :math:`-\pi`. In general, + odd positive multiples of :math:`\pi` are mapped to :math:`\pi`, and odd negative + multiples of :math:`\pi` are mapped to :math:`-\pi`. + + The function behaves similar to MATLAB's `wrapToPi `_ + function. + + Args: + angles: Input angles of any shape. + + Returns: + Angles in the range :math:`[-\pi, \pi]`. + """ + # wrap to [0, 2*pi) + wrapped_angle = (angles + torch.pi) % (2 * torch.pi) + # map to [-pi, pi] + # we check for zero in wrapped angle to make it go to pi when input angle is odd multiple of pi + return torch.where((wrapped_angle == 0) & (angles > 0), torch.pi, wrapped_angle - torch.pi) + + +@torch.jit.script +def copysign(mag: float, other: torch.Tensor) -> torch.Tensor: + """Create a new floating-point tensor with the magnitude of input and the sign of other, element-wise. + + Note: + The implementation follows from `torch.copysign`. The function allows a scalar magnitude. + + Args: + mag: The magnitude scalar. + other: The tensor containing values whose signbits are applied to magnitude. + + Returns: + The output tensor. + """ + mag_torch = abs(mag) * torch.ones_like(other) + return torch.copysign(mag_torch, other) + + +""" +Rotation +""" + + +@torch.jit.script +def quat_unique(q: torch.Tensor) -> torch.Tensor: + """Convert a unit quaternion to a standard form where the real part is non-negative. + + Quaternion representations have a singularity since ``q`` and ``-q`` represent the same + rotation. This function ensures the real part of the quaternion is non-negative. + + Args: + q: The quaternion orientation in (w, x, y, z). Shape is (..., 4). + + Returns: + Standardized quaternions. Shape is (..., 4). + """ + return torch.where(q[..., 0:1] < 0, -q, q) + + +@torch.jit.script +def matrix_from_quat(quaternions: torch.Tensor) -> torch.Tensor: + """Convert rotations given as quaternions to rotation matrices. + + Args: + quaternions: The quaternion orientation in (w, x, y, z). Shape is (..., 4). + + Returns: + Rotation matrices. The shape is (..., 3, 3). + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L41-L70 + """ + r, i, j, k = torch.unbind(quaternions, -1) + # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`. + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +def convert_quat(quat: torch.Tensor | np.ndarray, to: Literal["xyzw", "wxyz"] = "xyzw") -> torch.Tensor | np.ndarray: + """Converts quaternion from one convention to another. + + The convention to convert TO is specified as an optional argument. If to == 'xyzw', + then the input is in 'wxyz' format, and vice-versa. + + Args: + quat: The quaternion of shape (..., 4). + to: Convention to convert quaternion to.. Defaults to "xyzw". + + Returns: + The converted quaternion in specified convention. + + Raises: + ValueError: Invalid input argument `to`, i.e. not "xyzw" or "wxyz". + ValueError: Invalid shape of input `quat`, i.e. not (..., 4,). + """ + # check input is correct + if quat.shape[-1] != 4: + msg = f"Expected input quaternion shape mismatch: {quat.shape} != (..., 4)." + raise ValueError(msg) + if to not in ["xyzw", "wxyz"]: + msg = f"Expected input argument `to` to be 'xyzw' or 'wxyz'. Received: {to}." + raise ValueError(msg) + # check if input is numpy array (we support this backend since some classes use numpy) + if isinstance(quat, np.ndarray): + # use numpy functions + if to == "xyzw": + # wxyz -> xyzw + return np.roll(quat, -1, axis=-1) + else: + # xyzw -> wxyz + return np.roll(quat, 1, axis=-1) + else: + # convert to torch (sanity check) + if not isinstance(quat, torch.Tensor): + quat = torch.tensor(quat, dtype=float) + # convert to specified quaternion type + if to == "xyzw": + # wxyz -> xyzw + return quat.roll(-1, dims=-1) + else: + # xyzw -> wxyz + return quat.roll(1, dims=-1) + + +@torch.jit.script +def quat_conjugate(q: torch.Tensor) -> torch.Tensor: + """Computes the conjugate of a quaternion. + + Args: + q: The quaternion orientation in (w, x, y, z). Shape is (..., 4). + + Returns: + The conjugate quaternion in (w, x, y, z). Shape is (..., 4). + """ + shape = q.shape + q = q.reshape(-1, 4) + return torch.cat((q[..., 0:1], -q[..., 1:]), dim=-1).view(shape) + + +@torch.jit.script +def quat_inv(q: torch.Tensor, eps: float = 1e-9) -> torch.Tensor: + """Computes the inverse of a quaternion. + + Args: + q: The quaternion orientation in (w, x, y, z). Shape is (N, 4). + eps: A small value to avoid division by zero. Defaults to 1e-9. + + Returns: + The inverse quaternion in (w, x, y, z). Shape is (N, 4). + """ + return quat_conjugate(q) / q.pow(2).sum(dim=-1, keepdim=True).clamp(min=eps) + + +@torch.jit.script +def quat_from_euler_xyz(roll: torch.Tensor, pitch: torch.Tensor, yaw: torch.Tensor) -> torch.Tensor: + """Convert rotations given as Euler angles in radians to Quaternions. + + Note: + The euler angles are assumed in XYZ convention. + + Args: + roll: Rotation around x-axis (in radians). Shape is (N,). + pitch: Rotation around y-axis (in radians). Shape is (N,). + yaw: Rotation around z-axis (in radians). Shape is (N,). + + Returns: + The quaternion in (w, x, y, z). Shape is (N, 4). + """ + cy = torch.cos(yaw * 0.5) + sy = torch.sin(yaw * 0.5) + cr = torch.cos(roll * 0.5) + sr = torch.sin(roll * 0.5) + cp = torch.cos(pitch * 0.5) + sp = torch.sin(pitch * 0.5) + # compute quaternion + qw = cy * cr * cp + sy * sr * sp + qx = cy * sr * cp - sy * cr * sp + qy = cy * cr * sp + sy * sr * cp + qz = sy * cr * cp - cy * sr * sp + + return torch.stack([qw, qx, qy, qz], dim=-1) + + +@torch.jit.script +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + """Returns torch.sqrt(torch.max(0, x)) but with a zero sub-gradient where x is 0. + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L91-L99 + """ + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + + +@torch.jit.script +def quat_from_matrix(matrix: torch.Tensor) -> torch.Tensor: + """Convert rotations given as rotation matrices to quaternions. + + Args: + matrix: The rotation matrices. Shape is (..., 3, 3). + + Returns: + The quaternion in (w, x, y, z). Shape is (..., 4). + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L102-L161 + """ + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(matrix.reshape(batch_dim + (9,)), dim=-1) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + # we produce the desired quaternion multiplied by each of r, i, j, k + quat_by_rijk = torch.stack( + [ + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`. + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`. + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`. + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`. + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + # We floor here at 0.1 but the exact level is not important; if q_abs is small, + # the candidate won't be picked. + flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device) + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr)) + + # if not for numerical problems, quat_candidates[i] should be same (up to a sign), + # forall i; we pick the best-conditioned one (with the largest denominator) + return quat_candidates[torch.nn.functional.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape( + batch_dim + (4,) + ) + + +def _axis_angle_rotation(axis: Literal["X", "Y", "Z"], angle: torch.Tensor) -> torch.Tensor: + """Return the rotation matrices for one of the rotations about an axis of which Euler angles describe, + for each value of the angle given. + + Args: + axis: Axis label "X" or "Y or "Z". + angle: Euler angles in radians of any shape. + + Returns: + Rotation matrices. Shape is (..., 3, 3). + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L164-L191 + """ + cos = torch.cos(angle) + sin = torch.sin(angle) + one = torch.ones_like(angle) + zero = torch.zeros_like(angle) + + if axis == "X": + R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) + elif axis == "Y": + R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) + elif axis == "Z": + R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) + else: + raise ValueError("letter must be either X, Y or Z.") + + return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) + + +def matrix_from_euler(euler_angles: torch.Tensor, convention: str) -> torch.Tensor: + """ + Convert rotations given as Euler angles (intrinsic) in radians to rotation matrices. + + Args: + euler_angles: Euler angles in radians. Shape is (..., 3). + convention: Convention string of three uppercase letters from {"X", "Y", and "Z"}. + For example, "XYZ" means that the rotations should be applied first about x, + then y, then z. + + Returns: + Rotation matrices. Shape is (..., 3, 3). + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L194-L220 + """ + if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: + raise ValueError("Invalid input euler angles.") + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + matrices = [_axis_angle_rotation(c, e) for c, e in zip(convention, torch.unbind(euler_angles, -1))] + # return functools.reduce(torch.matmul, matrices) + return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2]) + + +@torch.jit.script +def euler_xyz_from_quat( + quat: torch.Tensor, wrap_to_2pi: bool = False +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Convert rotations given as quaternions to Euler angles in radians. + + Note: + The euler angles are assumed in XYZ extrinsic convention. + + Args: + quat: The quaternion orientation in (w, x, y, z). Shape is (N, 4). + wrap_to_2pi (bool): Whether to wrap output Euler angles into [0, 2π). If + False, angles are returned in the default range (−π, π]. Defaults to + False. + + Returns: + A tuple containing roll-pitch-yaw. Each element is a tensor of shape (N,). + + Reference: + https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles + """ + q_w, q_x, q_y, q_z = quat[:, 0], quat[:, 1], quat[:, 2], quat[:, 3] + # roll (x-axis rotation) + sin_roll = 2.0 * (q_w * q_x + q_y * q_z) + cos_roll = 1 - 2 * (q_x * q_x + q_y * q_y) + roll = torch.atan2(sin_roll, cos_roll) + + # pitch (y-axis rotation) + sin_pitch = 2.0 * (q_w * q_y - q_z * q_x) + pitch = torch.where(torch.abs(sin_pitch) >= 1, copysign(torch.pi / 2.0, sin_pitch), torch.asin(sin_pitch)) + + # yaw (z-axis rotation) + sin_yaw = 2.0 * (q_w * q_z + q_x * q_y) + cos_yaw = 1 - 2 * (q_y * q_y + q_z * q_z) + yaw = torch.atan2(sin_yaw, cos_yaw) + + if wrap_to_2pi: + return roll % (2 * torch.pi), pitch % (2 * torch.pi), yaw % (2 * torch.pi) + return roll, pitch, yaw + + +@torch.jit.script +def axis_angle_from_quat(quat: torch.Tensor, eps: float = 1.0e-6) -> torch.Tensor: + """Convert rotations given as quaternions to axis/angle. + + Args: + quat: The quaternion orientation in (w, x, y, z). Shape is (..., 4). + eps: The tolerance for Taylor approximation. Defaults to 1.0e-6. + + Returns: + Rotations given as a vector in axis angle form. Shape is (..., 3). + The vector's magnitude is the angle turned anti-clockwise in radians around the vector's direction. + + Reference: + https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py#L526-L554 + """ + # Modified to take in quat as [q_w, q_x, q_y, q_z] + # Quaternion is [q_w, q_x, q_y, q_z] = [cos(theta/2), n_x * sin(theta/2), n_y * sin(theta/2), n_z * sin(theta/2)] + # Axis-angle is [a_x, a_y, a_z] = [theta * n_x, theta * n_y, theta * n_z] + # Thus, axis-angle is [q_x, q_y, q_z] / (sin(theta/2) / theta) + # When theta = 0, (sin(theta/2) / theta) is undefined + # However, as theta --> 0, we can use the Taylor approximation 1/2 - theta^2 / 48 + quat = quat * (1.0 - 2.0 * (quat[..., 0:1] < 0.0)) + mag = torch.linalg.norm(quat[..., 1:], dim=-1) + half_angle = torch.atan2(mag, quat[..., 0]) + angle = 2.0 * half_angle + # check whether to apply Taylor approximation + sin_half_angles_over_angles = torch.where( + angle.abs() > eps, torch.sin(half_angle) / angle, 0.5 - angle * angle / 48 + ) + return quat[..., 1:4] / sin_half_angles_over_angles.unsqueeze(-1) + + +@torch.jit.script +def quat_from_angle_axis(angle: torch.Tensor, axis: torch.Tensor) -> torch.Tensor: + """Convert rotations given as angle-axis to quaternions. + + Args: + angle: The angle turned anti-clockwise in radians around the vector's direction. Shape is (N,). + axis: The axis of rotation. Shape is (N, 3). + + Returns: + The quaternion in (w, x, y, z). Shape is (N, 4). + """ + theta = (angle / 2).unsqueeze(-1) + xyz = normalize(axis) * theta.sin() + w = theta.cos() + return normalize(torch.cat([w, xyz], dim=-1)) + + +@torch.jit.script +def quat_mul(q1: torch.Tensor, q2: torch.Tensor) -> torch.Tensor: + """Multiply two quaternions together. + + Args: + q1: The first quaternion in (w, x, y, z). Shape is (..., 4). + q2: The second quaternion in (w, x, y, z). Shape is (..., 4). + + Returns: + The product of the two quaternions in (w, x, y, z). Shape is (..., 4). + + Raises: + ValueError: Input shapes of ``q1`` and ``q2`` are not matching. + """ + # check input is correct + if q1.shape != q2.shape: + msg = f"Expected input quaternion shape mismatch: {q1.shape} != {q2.shape}." + raise ValueError(msg) + # reshape to (N, 4) for multiplication + shape = q1.shape + q1 = q1.reshape(-1, 4) + q2 = q2.reshape(-1, 4) + # extract components from quaternions + w1, x1, y1, z1 = q1[:, 0], q1[:, 1], q1[:, 2], q1[:, 3] + w2, x2, y2, z2 = q2[:, 0], q2[:, 1], q2[:, 2], q2[:, 3] + # perform multiplication + ww = (z1 + x1) * (x2 + y2) + yy = (w1 - y1) * (w2 + z2) + zz = (w1 + y1) * (w2 - z2) + xx = ww + yy + zz + qq = 0.5 * (xx + (z1 - x1) * (x2 - y2)) + w = qq - ww + (z1 - y1) * (y2 - z2) + x = qq - xx + (x1 + w1) * (x2 + w2) + y = qq - yy + (w1 - x1) * (y2 + z2) + z = qq - zz + (z1 + y1) * (w2 - x2) + + return torch.stack([w, x, y, z], dim=-1).view(shape) + + +@torch.jit.script +def yaw_quat(quat: torch.Tensor) -> torch.Tensor: + """Extract the yaw component of a quaternion. + + Args: + quat: The orientation in (w, x, y, z). Shape is (..., 4) + + Returns: + A quaternion with only yaw component. + """ + shape = quat.shape + quat_yaw = quat.view(-1, 4) + qw = quat_yaw[:, 0] + qx = quat_yaw[:, 1] + qy = quat_yaw[:, 2] + qz = quat_yaw[:, 3] + yaw = torch.atan2(2 * (qw * qz + qx * qy), 1 - 2 * (qy * qy + qz * qz)) + quat_yaw = torch.zeros_like(quat_yaw) + quat_yaw[:, 3] = torch.sin(yaw / 2) + quat_yaw[:, 0] = torch.cos(yaw / 2) + quat_yaw = normalize(quat_yaw) + return quat_yaw.view(shape) + + +@torch.jit.script +def quat_box_minus(q1: torch.Tensor, q2: torch.Tensor) -> torch.Tensor: + """The box-minus operator (quaternion difference) between two quaternions. + + Args: + q1: The first quaternion in (w, x, y, z). Shape is (N, 4). + q2: The second quaternion in (w, x, y, z). Shape is (N, 4). + + Returns: + The difference between the two quaternions. Shape is (N, 3). + + Reference: + https://github.com/ANYbotics/kindr/blob/master/doc/cheatsheet/cheatsheet_latest.pdf + """ + quat_diff = quat_mul(q1, quat_conjugate(q2)) # q1 * q2^-1 + return axis_angle_from_quat(quat_diff) # log(qd) + + +@torch.jit.script +def quat_box_plus(q: torch.Tensor, delta: torch.Tensor, eps: float = 1.0e-6) -> torch.Tensor: + """The box-plus operator (quaternion update) to apply an increment to a quaternion. + + Args: + q: The initial quaternion in (w, x, y, z). Shape is (N, 4). + delta: The axis-angle perturbation. Shape is (N, 3). + eps: A small value to avoid division by zero. Defaults to 1e-6. + + Returns: + The updated quaternion after applying the perturbation. Shape is (N, 4). + + Reference: + https://github.com/ANYbotics/kindr/blob/master/doc/cheatsheet/cheatsheet_latest.pdf + """ + delta_norm = torch.clamp_min(torch.linalg.norm(delta, dim=-1, keepdim=True), min=eps) + delta_quat = quat_from_angle_axis(delta_norm.squeeze(-1), delta / delta_norm) # exp(dq) + new_quat = quat_mul(delta_quat, q) # Apply perturbation + return quat_unique(new_quat) + + +@torch.jit.script +def quat_apply(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: + """Apply a quaternion rotation to a vector. + + Args: + quat: The quaternion in (w, x, y, z). Shape is (..., 4). + vec: The vector in (x, y, z). Shape is (..., 3). + + Returns: + The rotated vector in (x, y, z). Shape is (..., 3). + """ + # store shape + shape = vec.shape + # reshape to (N, 3) for multiplication + quat = quat.reshape(-1, 4) + vec = vec.reshape(-1, 3) + # extract components from quaternions + xyz = quat[:, 1:] + t = xyz.cross(vec, dim=-1) * 2 + return (vec + quat[:, 0:1] * t + xyz.cross(t, dim=-1)).view(shape) + + +@torch.jit.script +def quat_apply_inverse(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: + """Apply an inverse quaternion rotation to a vector. + + Args: + quat: The quaternion in (w, x, y, z). Shape is (..., 4). + vec: The vector in (x, y, z). Shape is (..., 3). + + Returns: + The rotated vector in (x, y, z). Shape is (..., 3). + """ + # store shape + shape = vec.shape + # reshape to (N, 3) for multiplication + quat = quat.reshape(-1, 4) + vec = vec.reshape(-1, 3) + # extract components from quaternions + xyz = quat[:, 1:] + t = xyz.cross(vec, dim=-1) * 2 + return (vec - quat[:, 0:1] * t + xyz.cross(t, dim=-1)).view(shape) + + +@torch.jit.script +def quat_apply_yaw(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: + """Rotate a vector only around the yaw-direction. + + Args: + quat: The orientation in (w, x, y, z). Shape is (N, 4). + vec: The vector in (x, y, z). Shape is (N, 3). + + Returns: + The rotated vector in (x, y, z). Shape is (N, 3). + """ + quat_yaw = yaw_quat(quat) + return quat_apply(quat_yaw, vec) + + +def quat_rotate(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Rotate a vector by a quaternion along the last dimension of q and v. + + .. deprecated v2.1.0: + This function will be removed in a future release in favor of the faster implementation :meth:`quat_apply`. + + Args: + q: The quaternion in (w, x, y, z). Shape is (..., 4). + v: The vector in (x, y, z). Shape is (..., 3). + + Returns: + The rotated vector in (x, y, z). Shape is (..., 3). + """ + # deprecation + logger.warning( + "The function 'quat_rotate' will be deprecated in favor of the faster method 'quat_apply'." + " Please use 'quat_apply' instead...." + ) + return quat_apply(q, v) + + +def quat_rotate_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Rotate a vector by the inverse of a quaternion along the last dimension of q and v. + + .. deprecated v2.1.0: + This function will be removed in a future release in favor of the faster implementation + :meth:`quat_apply_inverse`. + + Args: + q: The quaternion in (w, x, y, z). Shape is (..., 4). + v: The vector in (x, y, z). Shape is (..., 3). + + Returns: + The rotated vector in (x, y, z). Shape is (..., 3). + """ + logger.warning( + "The function 'quat_rotate_inverse' will be deprecated in favor of the faster method 'quat_apply_inverse'." + " Please use 'quat_apply_inverse' instead...." + ) + return quat_apply_inverse(q, v) + + +@torch.jit.script +def quat_error_magnitude(q1: torch.Tensor, q2: torch.Tensor) -> torch.Tensor: + """Computes the rotation difference between two quaternions. + + Args: + q1: The first quaternion in (w, x, y, z). Shape is (..., 4). + q2: The second quaternion in (w, x, y, z). Shape is (..., 4). + + Returns: + Angular error between input quaternions in radians. + """ + axis_angle_error = quat_box_minus(q1, q2) + return torch.norm(axis_angle_error, dim=-1) + + +@torch.jit.script +def skew_symmetric_matrix(vec: torch.Tensor) -> torch.Tensor: + """Computes the skew-symmetric matrix of a vector. + + Args: + vec: The input vector. Shape is (3,) or (N, 3). + + Returns: + The skew-symmetric matrix. Shape is (1, 3, 3) or (N, 3, 3). + + Raises: + ValueError: If input tensor is not of shape (..., 3). + """ + # check input is correct + if vec.shape[-1] != 3: + raise ValueError(f"Expected input vector shape mismatch: {vec.shape} != (..., 3).") + # unsqueeze the last dimension + if vec.ndim == 1: + vec = vec.unsqueeze(0) + # create a skew-symmetric matrix + skew_sym_mat = torch.zeros(vec.shape[0], 3, 3, device=vec.device, dtype=vec.dtype) + skew_sym_mat[:, 0, 1] = -vec[:, 2] + skew_sym_mat[:, 0, 2] = vec[:, 1] + skew_sym_mat[:, 1, 2] = -vec[:, 0] + skew_sym_mat[:, 1, 0] = vec[:, 2] + skew_sym_mat[:, 2, 0] = -vec[:, 1] + skew_sym_mat[:, 2, 1] = vec[:, 0] + + return skew_sym_mat + + +""" +Transformations +""" + + +def is_identity_pose(pos: torch.tensor, rot: torch.tensor) -> bool: + """Checks if input poses are identity transforms. + + The function checks if the input position and orientation are close to zero and + identity respectively using L2-norm. It does NOT check the error in the orientation. + + Args: + pos: The cartesian position. Shape is (N, 3). + rot: The quaternion in (w, x, y, z). Shape is (N, 4). + + Returns: + True if all the input poses result in identity transform. Otherwise, False. + """ + # create identity transformations + pos_identity = torch.zeros_like(pos) + rot_identity = torch.zeros_like(rot) + rot_identity[..., 0] = 1 + # compare input to identity + return torch.allclose(pos, pos_identity) and torch.allclose(rot, rot_identity) + + +@torch.jit.script +def combine_frame_transforms( + t01: torch.Tensor, q01: torch.Tensor, t12: torch.Tensor | None = None, q12: torch.Tensor | None = None +) -> tuple[torch.Tensor, torch.Tensor]: + r"""Combine transformations between two reference frames into a stationary frame. + + It performs the following transformation operation: :math:`T_{02} = T_{01} \times T_{12}`, + where :math:`T_{AB}` is the homogeneous transformation matrix from frame A to B. + + Args: + t01: Position of frame 1 w.r.t. frame 0. Shape is (N, 3). + q01: Quaternion orientation of frame 1 w.r.t. frame 0 in (w, x, y, z). Shape is (N, 4). + t12: Position of frame 2 w.r.t. frame 1. Shape is (N, 3). + Defaults to None, in which case the position is assumed to be zero. + q12: Quaternion orientation of frame 2 w.r.t. frame 1 in (w, x, y, z). Shape is (N, 4). + Defaults to None, in which case the orientation is assumed to be identity. + + Returns: + A tuple containing the position and orientation of frame 2 w.r.t. frame 0. + Shape of the tensors are (N, 3) and (N, 4) respectively. + """ + # compute orientation + if q12 is not None: + q02 = quat_mul(q01, q12) + else: + q02 = q01 + # compute translation + if t12 is not None: + t02 = t01 + quat_apply(q01, t12) + else: + t02 = t01 + + return t02, q02 + + +def rigid_body_twist_transform( + v0: torch.Tensor, w0: torch.Tensor, t01: torch.Tensor, q01: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + r"""Transform the linear and angular velocity of a rigid body between reference frames. + + Given the twist of 0 relative to frame 0, this function computes the twist of 1 relative to frame 1 + from the position and orientation of frame 1 relative to frame 0. The transformation follows the + equations: + + .. math:: + + w_11 = R_{10} w_00 = R_{01}^{-1} w_00 + v_11 = R_{10} v_00 + R_{10} (w_00 \times t_01) = R_{01}^{-1} (v_00 + (w_00 \times t_01)) + + where + + - :math:`R_{01}` is the rotation matrix from frame 0 to frame 1 derived from quaternion :math:`q_{01}`. + - :math:`t_{01}` is the position of frame 1 relative to frame 0 expressed in frame 0 + - :math:`w_0` is the angular velocity of 0 in frame 0 + - :math:`v_0` is the linear velocity of 0 in frame 0 + + Args: + v0: Linear velocity of 0 in frame 0. Shape is (N, 3). + w0: Angular velocity of 0 in frame 0. Shape is (N, 3). + t01: Position of frame 1 w.r.t. frame 0. Shape is (N, 3). + q01: Quaternion orientation of frame 1 w.r.t. frame 0 in (w, x, y, z). Shape is (N, 4). + + Returns: + A tuple containing: + - The transformed linear velocity in frame 1. Shape is (N, 3). + - The transformed angular velocity in frame 1. Shape is (N, 3). + """ + w1 = quat_rotate_inverse(q01, w0) + v1 = quat_rotate_inverse(q01, v0 + torch.cross(w0, t01, dim=-1)) + return v1, w1 + + +# @torch.jit.script +def subtract_frame_transforms( + t01: torch.Tensor, q01: torch.Tensor, t02: torch.Tensor | None = None, q02: torch.Tensor | None = None +) -> tuple[torch.Tensor, torch.Tensor]: + r"""Subtract transformations between two reference frames into a stationary frame. + + It performs the following transformation operation: :math:`T_{12} = T_{01}^{-1} \times T_{02}`, + where :math:`T_{AB}` is the homogeneous transformation matrix from frame A to B. + + Args: + t01: Position of frame 1 w.r.t. frame 0. Shape is (N, 3). + q01: Quaternion orientation of frame 1 w.r.t. frame 0 in (w, x, y, z). Shape is (N, 4). + t02: Position of frame 2 w.r.t. frame 0. Shape is (N, 3). + Defaults to None, in which case the position is assumed to be zero. + q02: Quaternion orientation of frame 2 w.r.t. frame 0 in (w, x, y, z). Shape is (N, 4). + Defaults to None, in which case the orientation is assumed to be identity. + + Returns: + A tuple containing the position and orientation of frame 2 w.r.t. frame 1. + Shape of the tensors are (N, 3) and (N, 4) respectively. + """ + # compute orientation + q10 = quat_inv(q01) + if q02 is not None: + q12 = quat_mul(q10, q02) + else: + q12 = q10 + # compute translation + if t02 is not None: + t12 = quat_apply(q10, t02 - t01) + else: + t12 = quat_apply(q10, -t01) + return t12, q12 + + +# @torch.jit.script +def compute_pose_error( + t01: torch.Tensor, + q01: torch.Tensor, + t02: torch.Tensor, + q02: torch.Tensor, + rot_error_type: Literal["quat", "axis_angle"] = "axis_angle", +) -> tuple[torch.Tensor, torch.Tensor]: + """Compute the position and orientation error between source and target frames. + + Args: + t01: Position of source frame. Shape is (N, 3). + q01: Quaternion orientation of source frame in (w, x, y, z). Shape is (N, 4). + t02: Position of target frame. Shape is (N, 3). + q02: Quaternion orientation of target frame in (w, x, y, z). Shape is (N, 4). + rot_error_type: The rotation error type to return: "quat", "axis_angle". + Defaults to "axis_angle". + + Returns: + A tuple containing position and orientation error. Shape of position error is (N, 3). + Shape of orientation error depends on the value of :attr:`rot_error_type`: + + - If :attr:`rot_error_type` is "quat", the orientation error is returned + as a quaternion. Shape is (N, 4). + - If :attr:`rot_error_type` is "axis_angle", the orientation error is + returned as an axis-angle vector. Shape is (N, 3). + + Raises: + ValueError: Invalid rotation error type. + """ + # Compute quaternion error (i.e., difference quaternion) + # Reference: https://personal.utdallas.edu/~sxb027100/dock/quaternion.html + # q_current_norm = q_current * q_current_conj + source_quat_norm = quat_mul(q01, quat_conjugate(q01))[:, 0] + # q_current_inv = q_current_conj / q_current_norm + source_quat_inv = quat_conjugate(q01) / source_quat_norm.unsqueeze(-1) + # q_error = q_target * q_current_inv + quat_error = quat_mul(q02, source_quat_inv) + + # Compute position error + pos_error = t02 - t01 + + # return error based on specified type + if rot_error_type == "quat": + return pos_error, quat_error + elif rot_error_type == "axis_angle": + # Convert to axis-angle error + axis_angle_error = axis_angle_from_quat(quat_error) + return pos_error, axis_angle_error + else: + raise ValueError(f"Unsupported orientation error type: {rot_error_type}. Valid: 'quat', 'axis_angle'.") + + +@torch.jit.script +def apply_delta_pose( + source_pos: torch.Tensor, source_rot: torch.Tensor, delta_pose: torch.Tensor, eps: float = 1.0e-6 +) -> tuple[torch.Tensor, torch.Tensor]: + """Applies delta pose transformation on source pose. + + The first three elements of `delta_pose` are interpreted as cartesian position displacement. + The remaining three elements of `delta_pose` are interpreted as orientation displacement + in the angle-axis format. + + Args: + source_pos: Position of source frame. Shape is (N, 3). + source_rot: Quaternion orientation of source frame in (w, x, y, z). Shape is (N, 4).. + delta_pose: Position and orientation displacements. Shape is (N, 6). + eps: The tolerance to consider orientation displacement as zero. Defaults to 1.0e-6. + + Returns: + A tuple containing the displaced position and orientation frames. + Shape of the tensors are (N, 3) and (N, 4) respectively. + """ + # number of poses given + num_poses = source_pos.shape[0] + device = source_pos.device + + # interpret delta_pose[:, 0:3] as target position displacements + target_pos = source_pos + delta_pose[:, 0:3] + # interpret delta_pose[:, 3:6] as target rotation displacements + rot_actions = delta_pose[:, 3:6] + angle = torch.linalg.vector_norm(rot_actions, dim=1) + axis = rot_actions / angle.unsqueeze(-1) + # change from axis-angle to quat convention + identity_quat = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat(num_poses, 1) + rot_delta_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > eps, quat_from_angle_axis(angle, axis), identity_quat + ) + # TODO: Check if this is the correct order for this multiplication. + target_rot = quat_mul(rot_delta_quat, source_rot) + + return target_pos, target_rot + + +# @torch.jit.script +def transform_points( + points: torch.Tensor, pos: torch.Tensor | None = None, quat: torch.Tensor | None = None +) -> torch.Tensor: + r"""Transform input points in a given frame to a target frame. + + This function transform points from a source frame to a target frame. The transformation is defined by the + position :math:`t` and orientation :math:`R` of the target frame in the source frame. + + .. math:: + p_{target} = R_{target} \times p_{source} + t_{target} + + If the input `points` is a batch of points, the inputs `pos` and `quat` must be either a batch of + positions and quaternions or a single position and quaternion. If the inputs `pos` and `quat` are + a single position and quaternion, the same transformation is applied to all points in the batch. + + If either the inputs :attr:`pos` and :attr:`quat` are None, the corresponding transformation is not applied. + + Args: + points: Points to transform. Shape is (N, P, 3) or (P, 3). + pos: Position of the target frame. Shape is (N, 3) or (3,). + Defaults to None, in which case the position is assumed to be zero. + quat: Quaternion orientation of the target frame in (w, x, y, z). Shape is (N, 4) or (4,). + Defaults to None, in which case the orientation is assumed to be identity. + + Returns: + Transformed points in the target frame. Shape is (N, P, 3) or (P, 3). + + Raises: + ValueError: If the inputs `points` is not of shape (N, P, 3) or (P, 3). + ValueError: If the inputs `pos` is not of shape (N, 3) or (3,). + ValueError: If the inputs `quat` is not of shape (N, 4) or (4,). + """ + points_batch = points.clone() + # check if inputs are batched + is_batched = points_batch.dim() == 3 + # -- check inputs + if points_batch.dim() == 2: + points_batch = points_batch[None] # (P, 3) -> (1, P, 3) + if points_batch.dim() != 3: + raise ValueError(f"Expected points to have dim = 2 or dim = 3: got shape {points.shape}") + if not (pos is None or pos.dim() == 1 or pos.dim() == 2): + raise ValueError(f"Expected pos to have dim = 1 or dim = 2: got shape {pos.shape}") + if not (quat is None or quat.dim() == 1 or quat.dim() == 2): + raise ValueError(f"Expected quat to have dim = 1 or dim = 2: got shape {quat.shape}") + # -- rotation + if quat is not None: + # convert to batched rotation matrix + rot_mat = matrix_from_quat(quat) + if rot_mat.dim() == 2: + rot_mat = rot_mat[None] # (3, 3) -> (1, 3, 3) + # convert points to matching batch size (N, P, 3) -> (N, 3, P) + # and apply rotation + points_batch = torch.matmul(rot_mat, points_batch.transpose_(1, 2)) + # (N, 3, P) -> (N, P, 3) + points_batch = points_batch.transpose_(1, 2) + # -- translation + if pos is not None: + # convert to batched translation vector + if pos.dim() == 1: + pos = pos[None, None, :] # (3,) -> (1, 1, 3) + else: + pos = pos[:, None, :] # (N, 3) -> (N, 1, 3) + # apply translation + points_batch += pos + # -- return points in same shape as input + if not is_batched: + points_batch = points_batch.squeeze(0) # (1, P, 3) -> (P, 3) + + return points_batch + + +""" +Projection operations. +""" + + +@torch.jit.script +def orthogonalize_perspective_depth(depth: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor: + """Converts perspective depth image to orthogonal depth image. + + Perspective depth images contain distances measured from the camera's optical center. + Meanwhile, orthogonal depth images provide the distance from the camera's image plane. + This method uses the camera geometry to convert perspective depth to orthogonal depth image. + + The function assumes that the width and height are both greater than 1. + + Args: + depth: The perspective depth images. Shape is (H, W) or or (H, W, 1) or (N, H, W) or (N, H, W, 1). + intrinsics: The camera's calibration matrix. If a single matrix is provided, the same + calibration matrix is used across all the depth images in the batch. + Shape is (3, 3) or (N, 3, 3). + + Returns: + The orthogonal depth images. Shape matches the input shape of depth images. + + Raises: + ValueError: When depth is not of shape (H, W) or (H, W, 1) or (N, H, W) or (N, H, W, 1). + ValueError: When intrinsics is not of shape (3, 3) or (N, 3, 3). + """ + # Clone inputs to avoid in-place modifications + perspective_depth_batch = depth.clone() + intrinsics_batch = intrinsics.clone() + + # Check if inputs are batched + is_batched = perspective_depth_batch.dim() == 4 or ( + perspective_depth_batch.dim() == 3 and perspective_depth_batch.shape[-1] != 1 + ) + + # Track whether the last dimension was singleton + add_last_dim = False + if perspective_depth_batch.dim() == 4 and perspective_depth_batch.shape[-1] == 1: + add_last_dim = True + perspective_depth_batch = perspective_depth_batch.squeeze(dim=3) # (N, H, W, 1) -> (N, H, W) + if perspective_depth_batch.dim() == 3 and perspective_depth_batch.shape[-1] == 1: + add_last_dim = True + perspective_depth_batch = perspective_depth_batch.squeeze(dim=2) # (H, W, 1) -> (H, W) + + if perspective_depth_batch.dim() == 2: + perspective_depth_batch = perspective_depth_batch[None] # (H, W) -> (1, H, W) + + if intrinsics_batch.dim() == 2: + intrinsics_batch = intrinsics_batch[None] # (3, 3) -> (1, 3, 3) + + if is_batched and intrinsics_batch.shape[0] == 1: + intrinsics_batch = intrinsics_batch.expand(perspective_depth_batch.shape[0], -1, -1) # (1, 3, 3) -> (N, 3, 3) + + # Validate input shapes + if perspective_depth_batch.dim() != 3: + raise ValueError(f"Expected depth images to have 2, 3, or 4 dimensions; got {depth.shape}.") + if intrinsics_batch.dim() != 3: + raise ValueError(f"Expected intrinsics to have shape (3, 3) or (N, 3, 3); got {intrinsics.shape}.") + + # Image dimensions + im_height, im_width = perspective_depth_batch.shape[1:] + + # Get the intrinsics parameters + fx = intrinsics_batch[:, 0, 0].view(-1, 1, 1) + fy = intrinsics_batch[:, 1, 1].view(-1, 1, 1) + cx = intrinsics_batch[:, 0, 2].view(-1, 1, 1) + cy = intrinsics_batch[:, 1, 2].view(-1, 1, 1) + + # Create meshgrid of pixel coordinates + u_grid = torch.arange(im_width, device=depth.device, dtype=depth.dtype) + v_grid = torch.arange(im_height, device=depth.device, dtype=depth.dtype) + u_grid, v_grid = torch.meshgrid(u_grid, v_grid, indexing="xy") + + # Expand the grids for batch processing + u_grid = u_grid.unsqueeze(0).expand(perspective_depth_batch.shape[0], -1, -1) + v_grid = v_grid.unsqueeze(0).expand(perspective_depth_batch.shape[0], -1, -1) + + # Compute the squared terms for efficiency + x_term = ((u_grid - cx) / fx) ** 2 + y_term = ((v_grid - cy) / fy) ** 2 + + # Calculate the orthogonal (normal) depth + orthogonal_depth = perspective_depth_batch / torch.sqrt(1 + x_term + y_term) + + # Restore the last dimension if it was present in the input + if add_last_dim: + orthogonal_depth = orthogonal_depth.unsqueeze(-1) + + # Return to original shape if input was not batched + if not is_batched: + orthogonal_depth = orthogonal_depth.squeeze(0) + + return orthogonal_depth + + +@torch.jit.script +def unproject_depth(depth: torch.Tensor, intrinsics: torch.Tensor, is_ortho: bool = True) -> torch.Tensor: + r"""Un-project depth image into a pointcloud. + + This function converts orthogonal or perspective depth images into points given the calibration matrix + of the camera. It uses the following transformation based on camera geometry: + + .. math:: + p_{3D} = K^{-1} \times [u, v, 1]^T \times d + + where :math:`p_{3D}` is the 3D point, :math:`d` is the depth value (measured from the image plane), + :math:`u` and :math:`v` are the pixel coordinates and :math:`K` is the intrinsic matrix. + + The function assumes that the width and height are both greater than 1. This makes the function + deal with many possible shapes of depth images and intrinsics matrices. + + .. note:: + If :attr:`is_ortho` is False, the input depth images are transformed to orthogonal depth images + by using the :meth:`orthogonalize_perspective_depth` method. + + Args: + depth: The depth measurement. Shape is (H, W) or or (H, W, 1) or (N, H, W) or (N, H, W, 1). + intrinsics: The camera's calibration matrix. If a single matrix is provided, the same + calibration matrix is used across all the depth images in the batch. + Shape is (3, 3) or (N, 3, 3). + is_ortho: Whether the input depth image is orthogonal or perspective depth image. If True, the input + depth image is considered as the *orthogonal* type, where the measurements are from the camera's + image plane. If False, the depth image is considered as the *perspective* type, where the + measurements are from the camera's optical center. Defaults to True. + + Returns: + The 3D coordinates of points. Shape is (P, 3) or (N, P, 3). + + Raises: + ValueError: When depth is not of shape (H, W) or (H, W, 1) or (N, H, W) or (N, H, W, 1). + ValueError: When intrinsics is not of shape (3, 3) or (N, 3, 3). + """ + # clone inputs to avoid in-place modifications + intrinsics_batch = intrinsics.clone() + # convert depth image to orthogonal if needed + if not is_ortho: + depth_batch = orthogonalize_perspective_depth(depth, intrinsics) + else: + depth_batch = depth.clone() + + # check if inputs are batched + is_batched = depth_batch.dim() == 4 or (depth_batch.dim() == 3 and depth_batch.shape[-1] != 1) + # make sure inputs are batched + if depth_batch.dim() == 3 and depth_batch.shape[-1] == 1: + depth_batch = depth_batch.squeeze(dim=2) # (H, W, 1) -> (H, W) + if depth_batch.dim() == 2: + depth_batch = depth_batch[None] # (H, W) -> (1, H, W) + if depth_batch.dim() == 4 and depth_batch.shape[-1] == 1: + depth_batch = depth_batch.squeeze(dim=3) # (N, H, W, 1) -> (N, H, W) + if intrinsics_batch.dim() == 2: + intrinsics_batch = intrinsics_batch[None] # (3, 3) -> (1, 3, 3) + # check shape of inputs + if depth_batch.dim() != 3: + raise ValueError(f"Expected depth images to have dim = 2 or 3 or 4: got shape {depth.shape}") + if intrinsics_batch.dim() != 3: + raise ValueError(f"Expected intrinsics to have shape (3, 3) or (N, 3, 3): got shape {intrinsics.shape}") + + # get image height and width + im_height, im_width = depth_batch.shape[1:] + # create image points in homogeneous coordinates (3, H x W) + indices_u = torch.arange(im_width, device=depth.device, dtype=depth.dtype) + indices_v = torch.arange(im_height, device=depth.device, dtype=depth.dtype) + img_indices = torch.stack(torch.meshgrid([indices_u, indices_v], indexing="ij"), dim=0).reshape(2, -1) + pixels = torch.nn.functional.pad(img_indices, (0, 0, 0, 1), mode="constant", value=1.0) + pixels = pixels.unsqueeze(0) # (3, H x W) -> (1, 3, H x W) + + # unproject points into 3D space + points = torch.matmul(torch.inverse(intrinsics_batch), pixels) # (N, 3, H x W) + points = points / points[:, -1, :].unsqueeze(1) # normalize by last coordinate + # flatten depth image (N, H, W) -> (N, H x W) + depth_batch = depth_batch.transpose_(1, 2).reshape(depth_batch.shape[0], -1).unsqueeze(2) + depth_batch = depth_batch.expand(-1, -1, 3) + # scale points by depth + points_xyz = points.transpose_(1, 2) * depth_batch # (N, H x W, 3) + + # return points in same shape as input + if not is_batched: + points_xyz = points_xyz.squeeze(0) + + return points_xyz + + +@torch.jit.script +def project_points(points: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor: + r"""Projects 3D points into 2D image plane. + + This project 3D points into a 2D image plane. The transformation is defined by the intrinsic + matrix of the camera. + + .. math:: + + \begin{align} + p &= K \times p_{3D} = \\ + p_{2D} &= \begin{pmatrix} u \\ v \\ d \end{pmatrix} + = \begin{pmatrix} p[0] / p[2] \\ p[1] / p[2] \\ Z \end{pmatrix} + \end{align} + + where :math:`p_{2D} = (u, v, d)` is the projected 3D point, :math:`p_{3D} = (X, Y, Z)` is the + 3D point and :math:`K \in \mathbb{R}^{3 \times 3}` is the intrinsic matrix. + + If `points` is a batch of 3D points and `intrinsics` is a single intrinsic matrix, the same + calibration matrix is applied to all points in the batch. + + Args: + points: The 3D coordinates of points. Shape is (P, 3) or (N, P, 3). + intrinsics: Camera's calibration matrix. Shape is (3, 3) or (N, 3, 3). + + Returns: + Projected 3D coordinates of points. Shape is (P, 3) or (N, P, 3). + """ + # clone the inputs to avoid in-place operations modifying the original data + points_batch = points.clone() + intrinsics_batch = intrinsics.clone() + + # check if inputs are batched + is_batched = points_batch.dim() == 2 + # make sure inputs are batched + if points_batch.dim() == 2: + points_batch = points_batch[None] # (P, 3) -> (1, P, 3) + if intrinsics_batch.dim() == 2: + intrinsics_batch = intrinsics_batch[None] # (3, 3) -> (1, 3, 3) + # check shape of inputs + if points_batch.dim() != 3: + raise ValueError(f"Expected points to have dim = 3: got shape {points.shape}.") + if intrinsics_batch.dim() != 3: + raise ValueError(f"Expected intrinsics to have shape (3, 3) or (N, 3, 3): got shape {intrinsics.shape}.") + + # project points into 2D image plane + points_2d = torch.matmul(intrinsics_batch, points_batch.transpose(1, 2)) + points_2d = points_2d / points_2d[:, -1, :].unsqueeze(1) # normalize by last coordinate + points_2d = points_2d.transpose_(1, 2) # (N, 3, P) -> (N, P, 3) + # replace last coordinate with depth + points_2d[:, :, -1] = points_batch[:, :, -1] + + # return points in same shape as input + if not is_batched: + points_2d = points_2d.squeeze(0) # (1, 3, P) -> (3, P) + + return points_2d + + +""" +Sampling +""" + + +@torch.jit.script +def default_orientation(num: int, device: str) -> torch.Tensor: + """Returns identity rotation transform. + + Args: + num: The number of rotations to sample. + device: Device to create tensor on. + + Returns: + Identity quaternion in (w, x, y, z). Shape is (num, 4). + """ + quat = torch.zeros((num, 4), dtype=torch.float, device=device) + quat[..., 0] = 1.0 + + return quat + + +@torch.jit.script +def random_orientation(num: int, device: str) -> torch.Tensor: + """Returns sampled rotation in 3D as quaternion. + + Args: + num: The number of rotations to sample. + device: Device to create tensor on. + + Returns: + Sampled quaternion in (w, x, y, z). Shape is (num, 4). + + Reference: + https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.random.html + """ + # sample random orientation from normal distribution + quat = torch.randn((num, 4), dtype=torch.float, device=device) + # normalize the quaternion + return torch.nn.functional.normalize(quat, p=2.0, dim=-1, eps=1e-12) + + +@torch.jit.script +def random_yaw_orientation(num: int, device: str) -> torch.Tensor: + """Returns sampled rotation around z-axis. + + Args: + num: The number of rotations to sample. + device: Device to create tensor on. + + Returns: + Sampled quaternion in (w, x, y, z). Shape is (num, 4). + """ + roll = torch.zeros(num, dtype=torch.float, device=device) + pitch = torch.zeros(num, dtype=torch.float, device=device) + yaw = 2 * torch.pi * torch.rand(num, dtype=torch.float, device=device) + + return quat_from_euler_xyz(roll, pitch, yaw) + + +def sample_triangle(lower: float, upper: float, size: int | tuple[int, ...], device: str) -> torch.Tensor: + """Randomly samples tensor from a triangular distribution. + + Args: + lower: The lower range of the sampled tensor. + upper: The upper range of the sampled tensor. + size: The shape of the tensor. + device: Device to create tensor on. + + Returns: + Sampled tensor. Shape is based on :attr:`size`. + """ + # convert to tuple + if isinstance(size, int): + size = (size,) + # create random tensor in the range [-1, 1] + r = 2 * torch.rand(*size, device=device) - 1 + # convert to triangular distribution + r = torch.where(r < 0.0, -torch.sqrt(-r), torch.sqrt(r)) + # rescale back to [0, 1] + r = (r + 1.0) / 2.0 + # rescale to range [lower, upper] + return (upper - lower) * r + lower + + +def sample_uniform( + lower: torch.Tensor | float, upper: torch.Tensor | float, size: int | tuple[int, ...], device: str +) -> torch.Tensor: + """Sample uniformly within a range. + + Args: + lower: Lower bound of uniform range. + upper: Upper bound of uniform range. + size: The shape of the tensor. + device: Device to create tensor on. + + Returns: + Sampled tensor. Shape is based on :attr:`size`. + """ + # convert to tuple + if isinstance(size, int): + size = (size,) + # return tensor + return torch.rand(*size, device=device) * (upper - lower) + lower + + +def sample_log_uniform( + lower: torch.Tensor | float, upper: torch.Tensor | float, size: int | tuple[int, ...], device: str +) -> torch.Tensor: + r"""Sample using log-uniform distribution within a range. + + The log-uniform distribution is defined as a uniform distribution in the log-space. It + is useful for sampling values that span several orders of magnitude. The sampled values + are uniformly distributed in the log-space and then exponentiated to get the final values. + + .. math:: + + x = \exp(\text{uniform}(\log(\text{lower}), \log(\text{upper}))) + + Args: + lower: Lower bound of uniform range. + upper: Upper bound of uniform range. + size: The shape of the tensor. + device: Device to create tensor on. + + Returns: + Sampled tensor. Shape is based on :attr:`size`. + """ + # cast to tensor if not already + if not isinstance(lower, torch.Tensor): + lower = torch.tensor(lower, dtype=torch.float, device=device) + if not isinstance(upper, torch.Tensor): + upper = torch.tensor(upper, dtype=torch.float, device=device) + # sample in log-space and exponentiate + return torch.exp(sample_uniform(torch.log(lower), torch.log(upper), size, device)) + + +def sample_gaussian( + mean: torch.Tensor | float, std: torch.Tensor | float, size: int | tuple[int, ...], device: str +) -> torch.Tensor: + """Sample using gaussian distribution. + + Args: + mean: Mean of the gaussian. + std: Std of the gaussian. + size: The shape of the tensor. + device: Device to create tensor on. + + Returns: + Sampled tensor. + """ + if isinstance(mean, float): + if isinstance(size, int): + size = (size,) + return torch.normal(mean=mean, std=std, size=size).to(device=device) + else: + return torch.normal(mean=mean, std=std).to(device=device) + + +def sample_cylinder( + radius: float, h_range: tuple[float, float], size: int | tuple[int, ...], device: str +) -> torch.Tensor: + """Sample 3D points uniformly on a cylinder's surface. + + The cylinder is centered at the origin and aligned with the z-axis. The height of the cylinder is + sampled uniformly from the range :obj:`h_range`, while the radius is fixed to :obj:`radius`. + + The sampled points are returned as a tensor of shape :obj:`(*size, 3)`, i.e. the last dimension + contains the x, y, and z coordinates of the sampled points. + + Args: + radius: The radius of the cylinder. + h_range: The minimum and maximum height of the cylinder. + size: The shape of the tensor. + device: Device to create tensor on. + + Returns: + Sampled tensor. Shape is :obj:`(*size, 3)`. + """ + # sample angles + angles = (torch.rand(size, device=device) * 2 - 1) * torch.pi + h_min, h_max = h_range + # add shape + if isinstance(size, int): + size = (size, 3) + else: + size += (3,) + # allocate a tensor + xyz = torch.zeros(size, device=device) + xyz[..., 0] = radius * torch.cos(angles) + xyz[..., 1] = radius * torch.sin(angles) + xyz[..., 2].uniform_(h_min, h_max) + # return positions + return xyz + + +""" +Orientation Conversions +""" + + +def convert_camera_frame_orientation_convention( + orientation: torch.Tensor, + origin: Literal["opengl", "ros", "world"] = "opengl", + target: Literal["opengl", "ros", "world"] = "ros", +) -> torch.Tensor: + r"""Converts a quaternion representing a rotation from one convention to another. + + In USD, the camera follows the ``"opengl"`` convention. Thus, it is always in **Y up** convention. + This means that the camera is looking down the -Z axis with the +Y axis pointing up , and +X axis pointing right. + However, in ROS, the camera is looking down the +Z axis with the +Y axis pointing down, and +X axis pointing right. + Thus, the camera needs to be rotated by :math:`180^{\circ}` around the X axis to follow the ROS convention. + + .. math:: + + T_{ROS} = + \begin{bmatrix} + 1 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 \\ 0 & 0 & -1 & 0 \\ 0 & 0 & 0 & 1 + \end{bmatrix} T_{USD} + + On the other hand, the typical world coordinate system is with +X pointing forward, +Y pointing left, + and +Z pointing up. The camera can also be set in this convention by rotating the camera by :math:`90^{\circ}` + around the X axis and :math:`-90^{\circ}` around the Y axis. + + .. math:: + + T_{WORLD} = + \begin{bmatrix} + 0 & 0 & -1 & 0 \\ -1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 + \end{bmatrix} T_{USD} + + Thus, based on their application, cameras follow different conventions for their orientation. This function + converts a quaternion from one convention to another. + + Possible conventions are: + + - :obj:`"opengl"` - forward axis: -Z - up axis +Y - Offset is applied in the OpenGL (Usd.Camera) convention + - :obj:`"ros"` - forward axis: +Z - up axis -Y - Offset is applied in the ROS convention + - :obj:`"world"` - forward axis: +X - up axis +Z - Offset is applied in the World Frame convention + + Args: + orientation: Quaternion of form `(w, x, y, z)` with shape (..., 4) in source convention. + origin: Convention to convert from. Defaults to "opengl". + target: Convention to convert to. Defaults to "ros". + + Returns: + Quaternion of form `(w, x, y, z)` with shape (..., 4) in target convention + """ + if target == origin: + return orientation.clone() + + # -- unify input type + if origin == "ros": + # convert from ros to opengl convention + rotm = matrix_from_quat(orientation) + rotm[:, :, 2] = -rotm[:, :, 2] + rotm[:, :, 1] = -rotm[:, :, 1] + # convert to opengl convention + quat_gl = quat_from_matrix(rotm) + elif origin == "world": + # convert from world (x forward and z up) to opengl convention + rotm = matrix_from_quat(orientation) + rotm = torch.matmul( + rotm, + matrix_from_euler(torch.tensor([math.pi / 2, -math.pi / 2, 0], device=orientation.device), "XYZ"), + ) + # convert to isaac-sim convention + quat_gl = quat_from_matrix(rotm) + else: + quat_gl = orientation + + # -- convert to target convention + if target == "ros": + # convert from opengl to ros convention + rotm = matrix_from_quat(quat_gl) + rotm[:, :, 2] = -rotm[:, :, 2] + rotm[:, :, 1] = -rotm[:, :, 1] + return quat_from_matrix(rotm) + elif target == "world": + # convert from opengl to world (x forward and z up) convention + rotm = matrix_from_quat(quat_gl) + rotm = torch.matmul( + rotm, + matrix_from_euler(torch.tensor([math.pi / 2, -math.pi / 2, 0], device=orientation.device), "XYZ").T, + ) + return quat_from_matrix(rotm) + else: + return quat_gl.clone() + + +def create_rotation_matrix_from_view( + eyes: torch.Tensor, + targets: torch.Tensor, + up_axis: Literal["Y", "Z"] = "Z", + device: str = "cpu", +) -> torch.Tensor: + """Compute the rotation matrix from world to view coordinates. + + This function takes a vector ''eyes'' which specifies the location + of the camera in world coordinates and the vector ''targets'' which + indicate the position of the object. + The output is a rotation matrix representing the transformation + from world coordinates -> view coordinates. + + The inputs eyes and targets can each be a + - 3 element tuple/list + - torch tensor of shape (1, 3) + - torch tensor of shape (N, 3) + + Args: + eyes: Position of the camera in world coordinates. + targets: Position of the object in world coordinates. + up_axis: The up axis of the camera. Defaults to "Z". + device: The device to create torch tensors on. Defaults to "cpu". + + The vectors are broadcast against each other so they all have shape (N, 3). + + Returns: + R: (N, 3, 3) batched rotation matrices + + Reference: + Based on PyTorch3D (https://github.com/facebookresearch/pytorch3d/blob/eaf0709d6af0025fe94d1ee7cec454bc3054826a/pytorch3d/renderer/cameras.py#L1635-L1685) + """ + if up_axis == "Y": + up_axis_vec = torch.tensor((0, 1, 0), device=device, dtype=torch.float32).repeat(eyes.shape[0], 1) + elif up_axis == "Z": + up_axis_vec = torch.tensor((0, 0, 1), device=device, dtype=torch.float32).repeat(eyes.shape[0], 1) + else: + raise ValueError(f"Invalid up axis: {up_axis}. Valid options are 'Y' and 'Z'.") + + # get rotation matrix in opengl format (-Z forward, +Y up) + z_axis = -torch.nn.functional.normalize(targets - eyes, eps=1e-5) + x_axis = torch.nn.functional.normalize(torch.cross(up_axis_vec, z_axis, dim=1), eps=1e-5) + y_axis = torch.nn.functional.normalize(torch.cross(z_axis, x_axis, dim=1), eps=1e-5) + is_close = torch.isclose(x_axis, torch.tensor(0.0), atol=5e-3).all(dim=1, keepdim=True) + if is_close.any(): + replacement = torch.nn.functional.normalize(torch.cross(y_axis, z_axis, dim=1), eps=1e-5) + x_axis = torch.where(is_close, replacement, x_axis) + R = torch.cat((x_axis[:, None, :], y_axis[:, None, :], z_axis[:, None, :]), dim=1) + return R.transpose(1, 2) + + +def make_pose(pos: torch.Tensor, rot: torch.Tensor) -> torch.Tensor: + """Creates transformation matrices from positions and rotation matrices. + + Args: + pos: Batch of position vectors with last dimension of 3. + rot: Batch of rotation matrices with last 2 dimensions of (3, 3). + + Returns: + Batch of pose matrices with last 2 dimensions of (4, 4). + """ + assert isinstance(pos, torch.Tensor), "Input must be a torch tensor" + assert isinstance(rot, torch.Tensor), "Input must be a torch tensor" + assert pos.shape[:-1] == rot.shape[:-2] + assert pos.shape[-1] == rot.shape[-2] == rot.shape[-1] == 3 + pose = torch.zeros(pos.shape[:-1] + (4, 4), dtype=pos.dtype, device=pos.device) + pose[..., :3, :3] = rot + pose[..., :3, 3] = pos + pose[..., 3, 3] = 1.0 + return pose + + +def unmake_pose(pose: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Splits transformation matrices into positions and rotation matrices. + + Args: + pose: Batch of pose matrices with last 2 dimensions of (4, 4). + + Returns: + Tuple containing: + - Batch of position vectors with last dimension of 3. + - Batch of rotation matrices with last 2 dimensions of (3, 3). + """ + assert isinstance(pose, torch.Tensor), "Input must be a torch tensor" + return pose[..., :3, 3], pose[..., :3, :3] + + +def pose_inv(pose: torch.Tensor) -> torch.Tensor: + """Computes the inverse of transformation matrices. + + The inverse of a pose matrix [R t; 0 1] is [R.T -R.T*t; 0 1]. + + Args: + pose: Batch of pose matrices with last 2 dimensions of (4, 4). + + Returns: + Batch of inverse pose matrices with last 2 dimensions of (4, 4). + """ + assert isinstance(pose, torch.Tensor), "Input must be a torch tensor" + num_axes = len(pose.shape) + assert num_axes >= 2 + + inv_pose = torch.zeros_like(pose) + + # Take transpose of last 2 dimensions + inv_pose[..., :3, :3] = pose[..., :3, :3].transpose(-1, -2) + + # note: PyTorch matmul wants shapes [..., 3, 3] x [..., 3, 1] -> [..., 3, 1] + # so we add a dimension and take it away after + inv_pose[..., :3, 3] = torch.matmul(-inv_pose[..., :3, :3], pose[..., :3, 3:4])[..., 0] + inv_pose[..., 3, 3] = 1.0 + return inv_pose + + +def pose_in_A_to_pose_in_B(pose_in_A: torch.Tensor, pose_A_in_B: torch.Tensor) -> torch.Tensor: + """Converts poses from one coordinate frame to another. + + Transforms matrices representing point C in frame A + to matrices representing the same point C in frame B. + + Example usage: + + frame_C_in_B = pose_in_A_to_pose_in_B(frame_C_in_A, frame_A_in_B) + + Args: + pose_in_A: Batch of transformation matrices of point C in frame A. + pose_A_in_B: Batch of transformation matrices of frame A in frame B. + + Returns: + Batch of transformation matrices of point C in frame B. + """ + assert isinstance(pose_in_A, torch.Tensor), "Input must be a torch tensor" + assert isinstance(pose_A_in_B, torch.Tensor), "Input must be a torch tensor" + return torch.matmul(pose_A_in_B, pose_in_A) + + +def quat_slerp(q1: torch.Tensor, q2: torch.Tensor, tau: float) -> torch.Tensor: + """Performs spherical linear interpolation (SLERP) between two quaternions. + + This function does not support batch processing. + + Args: + q1: First quaternion in (w, x, y, z) format. + q2: Second quaternion in (w, x, y, z) format. + tau: Interpolation coefficient between 0 (q1) and 1 (q2). + + Returns: + Interpolated quaternion in (w, x, y, z) format. + """ + assert isinstance(q1, torch.Tensor), "Input must be a torch tensor" + assert isinstance(q2, torch.Tensor), "Input must be a torch tensor" + if tau == 0.0: + return q1 + elif tau == 1.0: + return q2 + d = torch.dot(q1, q2) + if abs(abs(d) - 1.0) < torch.finfo(q1.dtype).eps * 4.0: + return q1 + if d < 0.0: + # Invert rotation + d = -d + q2 *= -1.0 + angle = torch.acos(torch.clamp(d, -1, 1)) + if abs(angle) < torch.finfo(q1.dtype).eps * 4.0: + return q1 + isin = 1.0 / torch.sin(angle) + q1 = q1 * torch.sin((1.0 - tau) * angle) * isin + q2 = q2 * torch.sin(tau * angle) * isin + q1 = q1 + q2 + return q1 + + +def interpolate_rotations(R1: torch.Tensor, R2: torch.Tensor, num_steps: int, axis_angle: bool = True) -> torch.Tensor: + """Interpolates between two rotation matrices. + + Args: + R1: First rotation matrix. (4x4). + R2: Second rotation matrix. (4x4). + num_steps: Number of desired interpolated rotations (excluding start and end). + axis_angle: If True, interpolate in axis-angle representation; + otherwise use slerp. Defaults to True. + + Returns: + Stack of interpolated rotation matrices of shape (num_steps + 1, 4, 4), + including the start and end rotations. + """ + assert isinstance(R1, torch.Tensor), "Input must be a torch tensor" + assert isinstance(R2, torch.Tensor), "Input must be a torch tensor" + if axis_angle: + # Delta rotation expressed as axis-angle + delta_rot_mat = torch.matmul(R2, R1.transpose(-1, -2)) + delta_quat = quat_from_matrix(delta_rot_mat) + delta_axis_angle = axis_angle_from_quat(delta_quat) + + # Grab angle + delta_angle = torch.linalg.norm(delta_axis_angle) + + # Fix the axis, and chunk the angle up into steps + rot_step_size = delta_angle / num_steps + + # Convert into delta rotation matrices, and then convert to absolute rotations + if delta_angle < 0.05: + # Small angle - don't bother with interpolation + rot_steps = torch.stack([R2 for _ in range(num_steps)]) + else: + # Make sure that axis is a unit vector + delta_axis = delta_axis_angle / delta_angle + delta_rot_steps = [ + matrix_from_quat(quat_from_angle_axis(i * rot_step_size, delta_axis)) for i in range(num_steps) + ] + rot_steps = torch.stack([torch.matmul(delta_rot_steps[i], R1) for i in range(num_steps)]) + else: + q1 = quat_from_matrix(R1) + q2 = quat_from_matrix(R2) + rot_steps = torch.stack( + [matrix_from_quat(quat_slerp(q1, q2, tau=float(i) / num_steps)) for i in range(num_steps)] + ) + + # Add in endpoint + rot_steps = torch.cat([rot_steps, R2[None]], dim=0) + + return rot_steps + + +def interpolate_poses( + pose_1: torch.Tensor, + pose_2: torch.Tensor, + num_steps: int = None, + step_size: float = None, + perturb: bool = False, +) -> tuple[torch.Tensor, int]: + """Performs linear interpolation between two poses. + + Args: + pose_1: 4x4 start pose. + pose_2: 4x4 end pose. + num_steps: If provided, specifies the number of desired interpolated points. + Passing 0 corresponds to no interpolation. If None, step_size must be provided. + step_size: If provided, determines number of steps based on distance between poses. + perturb: If True, randomly perturbs interpolated position points. + + Returns: + Tuple containing: + - Array of shape (N + 2, 4, 4) corresponding to the interpolated pose path. + - Number of interpolated points (N) in the path. + """ + assert isinstance(pose_1, torch.Tensor), "Input must be a torch tensor" + assert isinstance(pose_2, torch.Tensor), "Input must be a torch tensor" + assert step_size is None or num_steps is None + + pos1, rot1 = unmake_pose(pose_1) + pos2, rot2 = unmake_pose(pose_2) + + if num_steps == 0: + # Skip interpolation + return ( + torch.cat([pos1[None], pos2[None]], dim=0), + torch.cat([rot1[None], rot2[None]], dim=0), + num_steps, + ) + + delta_pos = pos2 - pos1 + if num_steps is None: + assert torch.norm(delta_pos) > 0 + num_steps = math.ceil(torch.norm(delta_pos) / step_size) + + num_steps += 1 # Include starting pose + assert num_steps >= 2 + + # Linear interpolation of positions + pos_step_size = delta_pos / num_steps + grid = torch.arange(num_steps, dtype=torch.float32) + if perturb: + # Move interpolation grid points by up to half-size forward or backward + perturbations = torch.rand(num_steps - 2) - 0.5 + grid[1:-1] += perturbations + pos_steps = torch.stack([pos1 + grid[i] * pos_step_size for i in range(num_steps)]) + + # Add in endpoint + pos_steps = torch.cat([pos_steps, pos2[None]], dim=0) + + # Interpolate rotations + rot_steps = interpolate_rotations(R1=rot1, R2=rot2, num_steps=num_steps, axis_angle=True) + + pose_steps = make_pose(pos_steps, rot_steps) + return pose_steps, num_steps - 1 + + +def transform_poses_from_frame_A_to_frame_B( + src_poses: torch.Tensor, frame_A: torch.Tensor, frame_B: torch.Tensor +) -> torch.Tensor: + """Transforms poses from one coordinate frame to another preserving relative poses. + + Args: + src_poses: Input pose sequence (shape [T, 4, 4]) from source demonstration. + frame_A: 4x4 frame A pose. + frame_B: 4x4 frame B pose. + + Returns: + Transformed pose sequence of shape [T, 4, 4]. + """ + # Transform source end effector poses to be relative to source object frame + src_poses_rel_frame_B = pose_in_A_to_pose_in_B( + pose_in_A=src_poses, + pose_A_in_B=pose_inv(frame_B[None]), + ) + + # Apply relative poses to current object frame to obtain new target eef poses + transformed_poses = pose_in_A_to_pose_in_B( + pose_in_A=src_poses_rel_frame_B, + pose_A_in_B=frame_A[None], + ) + return transformed_poses + + +def generate_random_rotation(rot_boundary: float = (2 * math.pi)) -> torch.Tensor: + """Generates a random rotation matrix using Euler angles. + + Args: + rot_boundary: Range for random rotation angles around each axis (x, y, z). + + Returns: + 3x3 rotation matrix. + """ + angles = torch.rand(3) * rot_boundary + Rx = torch.tensor( + [[1, 0, 0], [0, torch.cos(angles[0]), -torch.sin(angles[0])], [0, torch.sin(angles[0]), torch.cos(angles[0])]] + ) + + Ry = torch.tensor( + [[torch.cos(angles[1]), 0, torch.sin(angles[1])], [0, 1, 0], [-torch.sin(angles[1]), 0, torch.cos(angles[1])]] + ) + + Rz = torch.tensor( + [[torch.cos(angles[2]), -torch.sin(angles[2]), 0], [torch.sin(angles[2]), torch.cos(angles[2]), 0], [0, 0, 1]] + ) + + # Combined rotation matrix + R = torch.matmul(torch.matmul(Rz, Ry), Rx) + return R + + +def generate_random_translation(pos_boundary: float = 1) -> torch.Tensor: + """Generates a random translation vector. + + Args: + pos_boundary: Range for random translation values in 3D space. + + Returns: + 3-element translation vector. + """ + return torch.rand(3) * 2 * pos_boundary - pos_boundary # Random translation in 3D space + + +def generate_random_transformation_matrix(pos_boundary: float = 1, rot_boundary: float = (2 * math.pi)) -> torch.Tensor: + """Generates a random transformation matrix combining rotation and translation. + + Args: + pos_boundary: Range for random translation values. + rot_boundary: Range for random rotation angles. + + Returns: + 4x4 transformation matrix. + """ + R = generate_random_rotation(rot_boundary) + translation = generate_random_translation(pos_boundary) + + # Create the transformation matrix + T = torch.eye(4) + T[:3, :3] = R + T[:3, 3] = translation + + return T diff --git a/source/isaaclab/isaaclab/utils/mesh.py b/source/isaaclab/isaaclab/utils/mesh.py new file mode 100644 index 0000000000000000000000000000000000000000..9e6315cc83c7c9b33b7db51b6186c08e0a3b2f3a --- /dev/null +++ b/source/isaaclab/isaaclab/utils/mesh.py @@ -0,0 +1,183 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Utility functions for working with meshes.""" + +from collections.abc import Callable + +import numpy as np +import trimesh + +from pxr import Usd, UsdGeom + +__all__ = [ + "create_trimesh_from_geom_mesh", + "create_trimesh_from_geom_shape", + "convert_faces_to_triangles", + "PRIMITIVE_MESH_TYPES", +] + + +def create_trimesh_from_geom_mesh(mesh_prim: Usd.Prim) -> trimesh.Trimesh: + """Reads the vertices and faces of a mesh prim. + + The function reads the vertices and faces of a mesh prim and returns it. If the underlying mesh is a quad mesh, + it converts it to a triangle mesh. + + Args: + mesh_prim: The mesh prim to read the vertices and faces from. + + Returns: + A trimesh.Trimesh object containing the mesh geometry. + """ + if mesh_prim.GetTypeName() != "Mesh": + raise ValueError(f"Prim at path '{mesh_prim.GetPath()}' is not a mesh.") + # cast into UsdGeomMesh + mesh = UsdGeom.Mesh(mesh_prim) + + # read the vertices and faces + points = np.asarray(mesh.GetPointsAttr().Get()).copy() + + # Load faces and convert to triangle if needed. (Default is quads) + num_vertex_per_face = np.asarray(mesh.GetFaceVertexCountsAttr().Get()) + indices = np.asarray(mesh.GetFaceVertexIndicesAttr().Get()) + return trimesh.Trimesh(points, convert_faces_to_triangles(indices, num_vertex_per_face)) + + +def create_trimesh_from_geom_shape(prim: Usd.Prim) -> trimesh.Trimesh: + """Converts a primitive object to a trimesh. + + Args: + prim: The prim that should be converted to a trimesh. + + Returns: + A trimesh object representing the primitive. + + Raises: + ValueError: If the prim is not a supported primitive. Check PRIMITIVE_MESH_TYPES for supported primitives. + """ + + if prim.GetTypeName() not in PRIMITIVE_MESH_TYPES: + raise ValueError(f"Prim at path '{prim.GetPath()}' is not a primitive mesh. Cannot convert to trimesh.") + + return _MESH_CONVERTERS_CALLBACKS[prim.GetTypeName()](prim) + + +def convert_faces_to_triangles(faces: np.ndarray, point_counts: np.ndarray) -> np.ndarray: + """Converts quad mesh face indices into triangle face indices. + + This function expects an array of faces (indices) and the number of points per face. It then converts potential + quads into triangles and returns the new triangle face indices as a numpy array of shape (n_faces_new, 3). + + Args: + faces: The faces of the quad mesh as a one-dimensional array. Shape is (N,). + point_counts: The number of points per face. Shape is (N,). + + Returns: + The new face ids with triangles. Shape is (n_faces_new, 3). + """ + # check if the mesh is already triangulated + if (point_counts == 3).all(): + return faces.reshape(-1, 3) # already triangulated + all_faces = [] + + vertex_counter = 0 + # Iterates over all faces of the mesh to triangulate them. + # could be very slow for large meshes + for num_points in point_counts: + # Triangulate n-gons (n>4) using fan triangulation + for i in range(num_points - 2): + triangle = np.array([faces[vertex_counter], faces[vertex_counter + 1 + i], faces[vertex_counter + 2 + i]]) + all_faces.append(triangle) + + vertex_counter += num_points + return np.asarray(all_faces) + + +""" +Internal USD Shape Handlers. +""" + + +def _create_plane_trimesh(prim: Usd.Prim) -> trimesh.Trimesh: + """Creates a trimesh for a plane primitive.""" + size = (2e6, 2e6) + vertices = np.array([[size[0], size[1], 0], [size[0], 0.0, 0], [0.0, size[1], 0], [0.0, 0.0, 0]]) - np.array( + [size[0] / 2.0, size[1] / 2.0, 0.0] + ) + faces = np.array([[1, 0, 2], [2, 3, 1]]) + return trimesh.Trimesh(vertices=vertices, faces=faces) + + +def _create_cube_trimesh(prim: Usd.Prim) -> trimesh.Trimesh: + """Creates a trimesh for a cube primitive.""" + size = prim.GetAttribute("size").Get() + extends = [size, size, size] + return trimesh.creation.box(extends) + + +def _create_sphere_trimesh(prim: Usd.Prim, subdivisions: int = 2) -> trimesh.Trimesh: + """Creates a trimesh for a sphere primitive.""" + radius = prim.GetAttribute("radius").Get() + mesh = trimesh.creation.icosphere(radius=radius, subdivisions=subdivisions) + return mesh + + +def _create_cylinder_trimesh(prim: Usd.Prim) -> trimesh.Trimesh: + """Creates a trimesh for a cylinder primitive.""" + radius = prim.GetAttribute("radius").Get() + height = prim.GetAttribute("height").Get() + mesh = trimesh.creation.cylinder(radius=radius, height=height) + axis = prim.GetAttribute("axis").Get() + if axis == "X": + # rotate −90° about Y to point the length along +X + R = trimesh.transformations.rotation_matrix(np.radians(-90), [0, 1, 0]) + mesh.apply_transform(R) + elif axis == "Y": + # rotate +90° about X to point the length along +Y + R = trimesh.transformations.rotation_matrix(np.radians(90), [1, 0, 0]) + mesh.apply_transform(R) + return mesh + + +def _create_capsule_trimesh(prim: Usd.Prim) -> trimesh.Trimesh: + """Creates a trimesh for a capsule primitive.""" + radius = prim.GetAttribute("radius").Get() + height = prim.GetAttribute("height").Get() + mesh = trimesh.creation.capsule(radius=radius, height=height) + axis = prim.GetAttribute("axis").Get() + if axis == "X": + # rotate −90° about Y to point the length along +X + R = trimesh.transformations.rotation_matrix(np.radians(-90), [0, 1, 0]) + mesh.apply_transform(R) + elif axis == "Y": + # rotate +90° about X to point the length along +Y + R = trimesh.transformations.rotation_matrix(np.radians(90), [1, 0, 0]) + mesh.apply_transform(R) + return mesh + + +def _create_cone_trimesh(prim: Usd.Prim) -> trimesh.Trimesh: + """Creates a trimesh for a cone primitive.""" + radius = prim.GetAttribute("radius").Get() + height = prim.GetAttribute("height").Get() + mesh = trimesh.creation.cone(radius=radius, height=height) + # shift all vertices down by height/2 for usd / trimesh cone primitive definition discrepancy + mesh.apply_translation((0.0, 0.0, -height / 2.0)) + return mesh + + +_MESH_CONVERTERS_CALLBACKS: dict[str, Callable[[Usd.Prim], trimesh.Trimesh]] = { + "Plane": _create_plane_trimesh, + "Cube": _create_cube_trimesh, + "Sphere": _create_sphere_trimesh, + "Cylinder": _create_cylinder_trimesh, + "Capsule": _create_capsule_trimesh, + "Cone": _create_cone_trimesh, +} + +PRIMITIVE_MESH_TYPES = list(_MESH_CONVERTERS_CALLBACKS.keys()) +"""List of supported primitive mesh types that can be converted to a trimesh.""" diff --git a/source/isaaclab/isaaclab/utils/modifiers/__init__.py b/source/isaaclab/isaaclab/utils/modifiers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b79a5140a7a829a6b39d2975cdb9de121d4f2a28 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/modifiers/__init__.py @@ -0,0 +1,65 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing different modifiers implementations. + +Modifiers are used to apply stateful or stateless modifications to tensor data. They take +in a tensor and a configuration and return a tensor with the modification applied. This way users +can define custom operations to apply to a tensor. For instance, a modifier can be used to normalize +the input data or to apply a rolling average. + +They are primarily used to apply custom operations in the :class:`~isaaclab.managers.ObservationManager` +as an alternative to the built-in noise, clip and scale post-processing operations. For more details, see +the :class:`~isaaclab.managers.ObservationTermCfg` class. + +Usage with a function modifier: + +.. code-block:: python + + import torch + from isaaclab.utils import modifiers + + # create a random tensor + my_tensor = torch.rand(256, 128, device="cuda") + + # create a modifier configuration + cfg = modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (0.0, torch.inf)}) + + # apply the modifier + my_modified_tensor = cfg.func(my_tensor, cfg) + + +Usage with a class modifier: + +.. code-block:: python + + import torch + from isaaclab.utils import modifiers + + # create a random tensor + my_tensor = torch.rand(256, 128, device="cuda") + + # create a modifier configuration + # a digital filter with a simple delay of 1 timestep + cfg = modifiers.DigitalFilterCfg(A=[0.0], B=[0.0, 1.0]) + + # create the modifier instance + my_modifier = modifiers.DigitalFilter(cfg, my_tensor.shape, "cuda") + + # apply the modifier as a callable object + my_modified_tensor = my_modifier(my_tensor) + +""" + +# isort: off +from .modifier_cfg import ModifierCfg +from .modifier_base import ModifierBase +from .modifier import DigitalFilter +from .modifier_cfg import DigitalFilterCfg +from .modifier import Integrator +from .modifier_cfg import IntegratorCfg + +# isort: on +from .modifier import bias, clip, scale diff --git a/source/isaaclab/isaaclab/utils/modifiers/modifier.py b/source/isaaclab/isaaclab/utils/modifiers/modifier.py new file mode 100644 index 0000000000000000000000000000000000000000..182a606565ad8dfc726d706ff405c4a0d2efa9e1 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/modifiers/modifier.py @@ -0,0 +1,260 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from .modifier_base import ModifierBase + +if TYPE_CHECKING: + from . import modifier_cfg + +## +# Modifiers as functions +## + + +def scale(data: torch.Tensor, multiplier: float) -> torch.Tensor: + """Scales input data by a multiplier. + + Args: + data: The data to apply the scale to. + multiplier: Value to scale input by. + + Returns: + Scaled data. Shape is the same as data. + """ + return data * multiplier + + +def clip(data: torch.Tensor, bounds: tuple[float | None, float | None]) -> torch.Tensor: + """Clips the data to a minimum and maximum value. + + Args: + data: The data to apply the clip to. + bounds: A tuple containing the minimum and maximum values to clip data to. + If the value is None, that bound is not applied. + + Returns: + Clipped data. Shape is the same as data. + """ + return data.clip(min=bounds[0], max=bounds[1]) + + +def bias(data: torch.Tensor, value: float) -> torch.Tensor: + """Adds a uniform bias to the data. + + Args: + data: The data to add bias to. + value: Value of bias to add to data. + + Returns: + Biased data. Shape is the same as data. + """ + return data + value + + +## +# Sample of class based modifiers +## + + +class DigitalFilter(ModifierBase): + r"""Modifier used to apply digital filtering to the input data. + + `Digital filters `_ are used to process discrete-time + signals to extract useful parts of the signal, such as smoothing, noise reduction, or frequency separation. + + The filter can be implemented as a linear difference equation in the time domain. This equation + can be used to calculate the output at each time-step based on the current and previous inputs and outputs. + + .. math:: + y_{i} = X B - Y A = \sum_{j=0}^{N} b_j x_{i-j} - \sum_{j=1}^{M} a_j y_{i-j} + + where :math:`y_{i}` is the current output of the filter. The array :math:`Y` contains previous + outputs from the filter :math:`\{y_{i-j}\}_{j=1}^M` for :math:`M` previous time-steps. The array + :math:`X` contains current :math:`x_{i}` and previous inputs to the filter + :math:`\{x_{i-j}\}_{j=1}^N` for :math:`N` previous time-steps respectively. + The filter coefficients :math:`A` and :math:`B` are used to design the filter. They are column vectors of + length :math:`M` and :math:`N + 1` respectively. + + Different types of filters can be implemented by choosing different values for :math:`A` and :math:`B`. + We provide some examples below. + + Examples + ^^^^^^^^ + + **Unit Delay Filter** + + A filter that delays the input signal by a single time-step simply outputs the previous input value. + + .. math:: y_{i} = x_{i-1} + + This can be implemented as a digital filter with the coefficients :math:`A = [0.0]` and :math:`B = [0.0, 1.0]`. + + **Moving Average Filter** + + A moving average filter is used to smooth out noise in a signal. It is similar to a low-pass filter + but has a finite impulse response (FIR) and is non-recursive. + + The filter calculates the average of the input signal over a window of time-steps. The linear difference + equation for a moving average filter is: + + .. math:: y_{i} = \frac{1}{N} \sum_{j=0}^{N} x_{i-j} + + This can be implemented as a digital filter with the coefficients :math:`A = [0.0]` and + :math:`B = [1/N, 1/N, \cdots, 1/N]`. + + **First-order recursive low-pass filter** + + A recursive low-pass filter is used to smooth out high-frequency noise in a signal. It is a first-order + infinite impulse response (IIR) filter which means it has a recursive component (previous output) in the + linear difference equation. + + A first-order low-pass IIR filter has the difference equation: + + .. math:: y_{i} = \alpha y_{i-1} + (1-\alpha)x_{i} + + where :math:`\alpha` is a smoothing parameter between 0 and 1. Typically, the value of :math:`\alpha` is + chosen based on the desired cut-off frequency of the filter. + + This filter can be implemented as a digital filter with the coefficients :math:`A = [-\alpha]` and + :math:`B = [1 - \alpha]`. + """ + + def __init__(self, cfg: modifier_cfg.DigitalFilterCfg, data_dim: tuple[int, ...], device: str): + """Initializes digital filter. + + Args: + cfg: Configuration parameters. + data_dim: The dimensions of the data to be modified. First element is the batch size + which usually corresponds to number of environments in the simulation. + device: The device to run the modifier on. + + Raises: + ValueError: If filter coefficients are None. + """ + # check that filter coefficients are not None + if cfg.A is None or cfg.B is None: + raise ValueError("Digital filter coefficients A and B must not be None. Please provide valid coefficients.") + + # initialize parent class + super().__init__(cfg, data_dim, device) + + # assign filter coefficients and make sure they are column vectors + self.A = torch.tensor(self._cfg.A, device=self._device).unsqueeze(1) + self.B = torch.tensor(self._cfg.B, device=self._device).unsqueeze(1) + + # create buffer for input and output history + self.x_n = torch.zeros(self._data_dim + (self.B.shape[0],), device=self._device) + self.y_n = torch.zeros(self._data_dim + (self.A.shape[0],), device=self._device) + + def reset(self, env_ids: Sequence[int] | None = None): + """Resets digital filter history. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + """ + if env_ids is None: + env_ids = slice(None) + # reset history buffers + self.x_n[env_ids] = 0.0 + self.y_n[env_ids] = 0.0 + + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Applies digital filter modification with a rolling history window inputs and outputs. + + Args: + data: The data to apply filter to. + + Returns: + Filtered data. Shape is the same as data. + """ + # move history window for input + self.x_n = torch.roll(self.x_n, shifts=1, dims=-1) + self.x_n[..., 0] = data + + # calculate current filter value: y[i] = Y*A - X*B + y_i = torch.matmul(self.x_n, self.B) - torch.matmul(self.y_n, self.A) + y_i.squeeze_(-1) + + # move history window for output and add current filter value to history + self.y_n = torch.roll(self.y_n, shifts=1, dims=-1) + self.y_n[..., 0] = y_i + + return y_i + + +class Integrator(ModifierBase): + r"""Modifier that applies a numerical forward integration based on a middle Reimann sum. + + An integrator is used to calculate the integral of a signal over time. The integral of a signal + is the area under the curve of the signal. The integral can be approximated using numerical methods + such as the `Riemann sum `_. + + The middle Riemann sum is a method to approximate the integral of a function by dividing the area + under the curve into rectangles. The height of each rectangle is the value of the function at the + midpoint of the interval. The area of each rectangle is the width of the interval multiplied by the + height of the rectangle. + + This integral method is useful for signals that are sampled at regular intervals. The integral + can be written as: + + .. math:: + \int_{t_0}^{t_n} f(t) dt & \approx \int_{t_0}^{t_{n-1}} f(t) dt + \frac{f(t_{n-1}) + f(t_n)}{2} \Delta t + + where :math:`f(t)` is the signal to integrate, :math:`t_i` is the time at the i-th sample, and + :math:`\Delta t` is the time step between samples. + """ + + def __init__(self, cfg: modifier_cfg.IntegratorCfg, data_dim: tuple[int, ...], device: str): + """Initializes the integrator configuration and state. + + Args: + cfg: Integral parameters. + data_dim: The dimensions of the data to be modified. First element is the batch size + which usually corresponds to number of environments in the simulation. + device: The device to run the modifier on. + """ + # initialize parent class + super().__init__(cfg, data_dim, device) + + # assign buffer for integral and previous value + self.integral = torch.zeros(self._data_dim, device=self._device) + self.y_prev = torch.zeros(self._data_dim, device=self._device) + + def reset(self, env_ids: Sequence[int] | None = None): + """Resets integrator state to zero. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + """ + if env_ids is None: + env_ids = slice(None) + # reset history buffers + self.integral[env_ids] = 0.0 + self.y_prev[env_ids] = 0.0 + + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Applies integral modification to input data. + + Args: + data: The data to integrate. + + Returns: + Integral of input signal. Shape is the same as data. + """ + # integrate using middle Riemann sum + self.integral += (data + self.y_prev) / 2 * self._cfg.dt + # update previous value + self.y_prev[:] = data + + return self.integral diff --git a/source/isaaclab/isaaclab/utils/modifiers/modifier_base.py b/source/isaaclab/isaaclab/utils/modifiers/modifier_base.py new file mode 100644 index 0000000000000000000000000000000000000000..65a7fe0bb8a29acffd164c295e74eef2424e570c --- /dev/null +++ b/source/isaaclab/isaaclab/utils/modifiers/modifier_base.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from .modifier_cfg import ModifierCfg + + +class ModifierBase(ABC): + """Base class for modifiers implemented as classes. + + Modifiers implementations can be functions or classes. If a modifier is a class, it should + inherit from this class and implement the required methods. + + A class implementation of a modifier can be used to store state information between calls. + This is useful for modifiers that require stateful operations, such as rolling averages + or delays or decaying filters. + + Example pseudo-code to create and use the class: + + .. code-block:: python + + from isaaclab.utils import modifiers + + # define custom keyword arguments to pass to ModifierCfg + kwarg_dict = {"arg_1": VAL_1, "arg_2": VAL_2} + + # create modifier configuration object + # func is the class name of the modifier and params is the dictionary of arguments + modifier_config = modifiers.ModifierCfg(func=modifiers.ModifierBase, params=kwarg_dict) + + # define modifier instance + my_modifier = modifiers.ModifierBase(cfg=modifier_config) + + """ + + def __init__(self, cfg: ModifierCfg, data_dim: tuple[int, ...], device: str) -> None: + """Initializes the modifier class. + + Args: + cfg: Configuration parameters. + data_dim: The dimensions of the data to be modified. First element is the batch size + which usually corresponds to number of environments in the simulation. + device: The device to run the modifier on. + """ + self._cfg = cfg + self._data_dim = data_dim + self._device = device + + @abstractmethod + def reset(self, env_ids: Sequence[int] | None = None): + """Resets the Modifier. + + Args: + env_ids: The environment ids. Defaults to None, in which case + all environments are considered. + """ + raise NotImplementedError + + @abstractmethod + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Abstract method for defining the modification function. + + Args: + data: The data to be modified. Shape should match the data_dim passed during initialization. + + Returns: + Modified data. Shape is the same as the input data. + """ + raise NotImplementedError diff --git a/source/isaaclab/isaaclab/utils/modifiers/modifier_cfg.py b/source/isaaclab/isaaclab/utils/modifiers/modifier_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..cf018fc0716535c44c976767ae2dfa8c0b076720 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/modifiers/modifier_cfg.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Any + +import torch + +from isaaclab.utils import configclass + +from . import modifier + + +@configclass +class ModifierCfg: + """Configuration parameters modifiers""" + + func: Callable[..., torch.Tensor] = MISSING + """Function or callable class used by modifier. + + The function must take a torch tensor as the first argument. The remaining arguments are specified + in the :attr:`params` attribute. + + It also supports `callable classes `_, + i.e. classes that implement the ``__call__()`` method. In this case, the class should inherit from the + :class:`ModifierBase` class and implement the required methods. + """ + + params: dict[str, Any] = dict() + """The parameters to be passed to the function or callable class as keyword arguments. Defaults to + an empty dictionary.""" + + +@configclass +class DigitalFilterCfg(ModifierCfg): + """Configuration parameters for a digital filter modifier. + + For more information, please check the :class:`DigitalFilter` class. + """ + + func: type[modifier.DigitalFilter] = modifier.DigitalFilter + """The digital filter function to be called for applying the filter.""" + + A: list[float] = MISSING + """The coefficients corresponding the the filter's response to past outputs. + + These correspond to the weights of the past outputs of the filter. The first element is the coefficient + for the output at the previous time step, the second element is the coefficient for the output at two + time steps ago, and so on. + + It is the denominator coefficients of the transfer function of the filter. + """ + + B: list[float] = MISSING + """The coefficients corresponding the the filter's response to current and past inputs. + + These correspond to the weights of the current and past inputs of the filter. The first element is the + coefficient for the current input, the second element is the coefficient for the input at the previous + time step, and so on. + + It is the numerator coefficients of the transfer function of the filter. + """ + + +@configclass +class IntegratorCfg(ModifierCfg): + """Configuration parameters for an integrator modifier. + + For more information, please check the :class:`Integrator` class. + """ + + func: type[modifier.Integrator] = modifier.Integrator + """The integrator function to be called for applying the integrator.""" + + dt: float = MISSING + """The time step of the integrator.""" diff --git a/source/isaaclab/isaaclab/utils/noise/__init__.py b/source/isaaclab/isaaclab/utils/noise/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f91067fd0051b7d55ef93c90c82905d3136c259 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/noise/__init__.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing different noise models implementations. + +The noise models are implemented as functions that take in a tensor and a configuration and return a tensor +with the noise applied. These functions are then used in the :class:`NoiseCfg` configuration class. + +Usage: + +.. code-block:: python + + import torch + from isaaclab.utils.noise import AdditiveGaussianNoiseCfg + + # create a random tensor + my_tensor = torch.rand(128, 128, device="cuda") + + # create a noise configuration + cfg = AdditiveGaussianNoiseCfg(mean=0.0, std=1.0) + + # apply the noise + my_noisified_tensor = cfg.func(my_tensor, cfg) + +""" +from .noise_cfg import NoiseCfg # noqa: F401 +from .noise_cfg import ConstantNoiseCfg, GaussianNoiseCfg, NoiseModelCfg, NoiseModelWithAdditiveBiasCfg, UniformNoiseCfg +from .noise_model import NoiseModel, NoiseModelWithAdditiveBias, constant_noise, gaussian_noise, uniform_noise + +# Backward compatibility +ConstantBiasNoiseCfg = ConstantNoiseCfg +AdditiveUniformNoiseCfg = UniformNoiseCfg +AdditiveGaussianNoiseCfg = GaussianNoiseCfg diff --git a/source/isaaclab/isaaclab/utils/noise/noise_cfg.py b/source/isaaclab/isaaclab/utils/noise/noise_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b3275643fd29c7e959dfe3437f4e0f60777c23fd --- /dev/null +++ b/source/isaaclab/isaaclab/utils/noise/noise_cfg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import MISSING +from typing import Literal + +import torch + +from isaaclab.utils import configclass + +from . import noise_model + + +@configclass +class NoiseCfg: + """Base configuration for a noise term.""" + + func: Callable[[torch.Tensor, NoiseCfg], torch.Tensor] = MISSING + """The function to be called for applying the noise. + + Note: + The shape of the input and output tensors must be the same. + """ + operation: Literal["add", "scale", "abs"] = "add" + """The operation to apply the noise on the data. Defaults to "add".""" + + +@configclass +class ConstantNoiseCfg(NoiseCfg): + """Configuration for an additive constant noise term.""" + + func = noise_model.constant_noise + + bias: torch.Tensor | float = 0.0 + """The bias to add. Defaults to 0.0.""" + + +@configclass +class UniformNoiseCfg(NoiseCfg): + """Configuration for a additive uniform noise term.""" + + func = noise_model.uniform_noise + + n_min: torch.Tensor | float = -1.0 + """The minimum value of the noise. Defaults to -1.0.""" + n_max: torch.Tensor | float = 1.0 + """The maximum value of the noise. Defaults to 1.0.""" + + +@configclass +class GaussianNoiseCfg(NoiseCfg): + """Configuration for an additive gaussian noise term.""" + + func = noise_model.gaussian_noise + + mean: torch.Tensor | float = 0.0 + """The mean of the noise. Defaults to 0.0.""" + std: torch.Tensor | float = 1.0 + """The standard deviation of the noise. Defaults to 1.0.""" + + +## +# Noise models +## + + +@configclass +class NoiseModelCfg: + """Configuration for a noise model.""" + + class_type: type = noise_model.NoiseModel + """The class type of the noise model.""" + + noise_cfg: NoiseCfg = MISSING + """The noise configuration to use.""" + + func: Callable[[torch.Tensor], torch.Tensor] | None = None + """Function or callable class used by this noise model. + + The function must take a single `torch.Tensor` (the batch of observations) as input + and return a `torch.Tensor` of the same shape with noise applied. + + It also supports `callable classes `_, + i.e. classes that implement the ``__call__()`` method. In this case, the class should inherit from the + :class:`NoiseModel` class and implement the required methods. + + This field is used internally by :class:ObservationManager and is not meant to be set directly. + """ + + +@configclass +class NoiseModelWithAdditiveBiasCfg(NoiseModelCfg): + """Configuration for an additive gaussian noise with bias model.""" + + class_type: type = noise_model.NoiseModelWithAdditiveBias + + bias_noise_cfg: NoiseCfg = MISSING + """The noise configuration for the bias. + + Based on this configuration, the bias is sampled at every reset of the noise model. + """ + + sample_bias_per_component: bool = True + """Whether to sample a separate bias for each data component. + + Defaults to True. + """ diff --git a/source/isaaclab/isaaclab/utils/noise/noise_model.py b/source/isaaclab/isaaclab/utils/noise/noise_model.py new file mode 100644 index 0000000000000000000000000000000000000000..78b93c9f099bde77413a65a165ad655df25475da --- /dev/null +++ b/source/isaaclab/isaaclab/utils/noise/noise_model.py @@ -0,0 +1,192 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from . import noise_cfg + +## +# Noise as functions. +## + + +def constant_noise(data: torch.Tensor, cfg: noise_cfg.ConstantNoiseCfg) -> torch.Tensor: + """Applies a constant noise bias to a given data set. + + Args: + data: The unmodified data set to apply noise to. + cfg: The configuration parameters for constant noise. + + Returns: + The data modified by the noise parameters provided. + """ + + # fix tensor device for bias on first call and update config parameters + if isinstance(cfg.bias, torch.Tensor): + cfg.bias = cfg.bias.to(device=data.device) + + if cfg.operation == "add": + return data + cfg.bias + elif cfg.operation == "scale": + return data * cfg.bias + elif cfg.operation == "abs": + return torch.zeros_like(data) + cfg.bias + else: + raise ValueError(f"Unknown operation in noise: {cfg.operation}") + + +def uniform_noise(data: torch.Tensor, cfg: noise_cfg.UniformNoiseCfg) -> torch.Tensor: + """Applies a uniform noise to a given data set. + + Args: + data: The unmodified data set to apply noise to. + cfg: The configuration parameters for uniform noise. + + Returns: + The data modified by the noise parameters provided. + """ + + # fix tensor device for n_max on first call and update config parameters + if isinstance(cfg.n_max, torch.Tensor): + cfg.n_max = cfg.n_max.to(data.device) + # fix tensor device for n_min on first call and update config parameters + if isinstance(cfg.n_min, torch.Tensor): + cfg.n_min = cfg.n_min.to(data.device) + + if cfg.operation == "add": + return data + torch.rand_like(data) * (cfg.n_max - cfg.n_min) + cfg.n_min + elif cfg.operation == "scale": + return data * (torch.rand_like(data) * (cfg.n_max - cfg.n_min) + cfg.n_min) + elif cfg.operation == "abs": + return torch.rand_like(data) * (cfg.n_max - cfg.n_min) + cfg.n_min + else: + raise ValueError(f"Unknown operation in noise: {cfg.operation}") + + +def gaussian_noise(data: torch.Tensor, cfg: noise_cfg.GaussianNoiseCfg) -> torch.Tensor: + """Applies a gaussian noise to a given data set. + + Args: + data: The unmodified data set to apply noise to. + cfg: The configuration parameters for gaussian noise. + + Returns: + The data modified by the noise parameters provided. + """ + + # fix tensor device for mean on first call and update config parameters + if isinstance(cfg.mean, torch.Tensor): + cfg.mean = cfg.mean.to(data.device) + # fix tensor device for std on first call and update config parameters + if isinstance(cfg.std, torch.Tensor): + cfg.std = cfg.std.to(data.device) + + if cfg.operation == "add": + return data + cfg.mean + cfg.std * torch.randn_like(data) + elif cfg.operation == "scale": + return data * (cfg.mean + cfg.std * torch.randn_like(data)) + elif cfg.operation == "abs": + return cfg.mean + cfg.std * torch.randn_like(data) + else: + raise ValueError(f"Unknown operation in noise: {cfg.operation}") + + +## +# Noise models as classes +## + + +class NoiseModel: + """Base class for noise models.""" + + def __init__(self, noise_model_cfg: noise_cfg.NoiseModelCfg, num_envs: int, device: str): + """Initialize the noise model. + + Args: + noise_model_cfg: The noise configuration to use. + num_envs: The number of environments. + device: The device to use for the noise model. + """ + self._noise_model_cfg = noise_model_cfg + self._num_envs = num_envs + self._device = device + + def reset(self, env_ids: Sequence[int] | None = None): + """Reset the noise model. + + This method can be implemented by derived classes to reset the noise model. + This is useful when implementing temporal noise models such as random walk. + + Args: + env_ids: The environment ids to reset the noise model for. Defaults to None, + in which case all environments are considered. + """ + pass + + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Apply the noise to the data. + + Args: + data: The data to apply the noise to. Shape is (num_envs, ...). + + Returns: + The data with the noise applied. Shape is the same as the input data. + """ + return self._noise_model_cfg.noise_cfg.func(data, self._noise_model_cfg.noise_cfg) + + +class NoiseModelWithAdditiveBias(NoiseModel): + """Noise model with an additive bias. + + The bias term is sampled from a the specified distribution on reset. + """ + + def __init__(self, noise_model_cfg: noise_cfg.NoiseModelWithAdditiveBiasCfg, num_envs: int, device: str): + # initialize parent class + super().__init__(noise_model_cfg, num_envs, device) + # store the bias noise configuration + self._bias_noise_cfg = noise_model_cfg.bias_noise_cfg + self._bias = torch.zeros((num_envs, 1), device=self._device) + self._num_components: int | None = None + self._sample_bias_per_component = noise_model_cfg.sample_bias_per_component + + def reset(self, env_ids: Sequence[int] | None = None): + """Reset the noise model. + + This method resets the bias term for the specified environments. + + Args: + env_ids: The environment ids to reset the noise model for. Defaults to None, + in which case all environments are considered. + """ + # resolve the environment ids + if env_ids is None: + env_ids = slice(None) + # reset the bias term + self._bias[env_ids] = self._bias_noise_cfg.func(self._bias[env_ids], self._bias_noise_cfg) + + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Apply bias noise to the data. + + Args: + data: The data to apply the noise to. Shape is (num_envs, ...). + + Returns: + The data with the noise applied. Shape is the same as the input data. + """ + # if sample_bias_per_component, on first apply, expand bias to match last dim of data + if self._sample_bias_per_component and self._num_components is None: + *_, self._num_components = data.shape + # expand bias from (num_envs,1) to (num_envs, num_components) + self._bias = self._bias.repeat(1, self._num_components) + # now re-sample that expanded bias in-place + self.reset() + return super().__call__(data) + self._bias diff --git a/source/isaaclab/isaaclab/utils/seed.py b/source/isaaclab/isaaclab/utils/seed.py new file mode 100644 index 0000000000000000000000000000000000000000..6b2a8ff97adfd29ae120a7ccd78f90f8387ac76b --- /dev/null +++ b/source/isaaclab/isaaclab/utils/seed.py @@ -0,0 +1,45 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +import random + +import numpy as np +import torch +import warp as wp + + +def configure_seed(seed: int | None, torch_deterministic: bool = False) -> int: + """Set seed across all random number generators (torch, numpy, random, warp). + + Args: + seed: The random seed value. If None, generates a random seed. + torch_deterministic: If True, enables deterministic mode for torch operations. + + Returns: + The seed value that was set. + """ + if seed is None or seed == -1: + seed = 42 if torch_deterministic else random.randint(0, 10000) + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + wp.rand_init(seed) + + if torch_deterministic: + # refer to https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + torch.use_deterministic_algorithms(True) + else: + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = False + + return seed diff --git a/source/isaaclab/isaaclab/utils/sensors.py b/source/isaaclab/isaaclab/utils/sensors.py new file mode 100644 index 0000000000000000000000000000000000000000..d9016c2f885a5b16e9fa89e28c3b91db9b2af83f --- /dev/null +++ b/source/isaaclab/isaaclab/utils/sensors.py @@ -0,0 +1,61 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import logging + +# import logger +logger = logging.getLogger(__name__) + + +def convert_camera_intrinsics_to_usd( + intrinsic_matrix: list[float], width: int, height: int, focal_length: float | None = None +) -> dict: + """Creates USD camera properties from camera intrinsics and resolution. + + Args: + intrinsic_matrix: Intrinsic matrix of the camera in row-major format. + The matrix is defined as [f_x, 0, c_x, 0, f_y, c_y, 0, 0, 1]. Shape is (9,). + width: Width of the image (in pixels). + height: Height of the image (in pixels). + focal_length: Perspective focal length (in cm) used to calculate pixel size. Defaults to None, + in which case, the focal length will be calculated as 1 / width. + + Returns: + A dictionary of USD camera parameters for focal_length, horizontal_aperture, vertical_aperture, + horizontal_aperture_offset, and vertical_aperture_offset. + """ + # extract parameters from matrix + f_x = intrinsic_matrix[0] + f_y = intrinsic_matrix[4] + c_x = intrinsic_matrix[2] + c_y = intrinsic_matrix[5] + + # warn about non-square pixels + if abs(f_x - f_y) > 1e-4: + logger.warning("Camera non square pixels are not supported by Omniverse. The average of f_x and f_y are used.") + + # warn about aperture offsets + if abs((c_x - float(width) / 2) > 1e-4 or (c_y - float(height) / 2) > 1e-4): + logger.warning( + "Camera aperture offsets are not supported by Omniverse. c_x and c_y will be half of width and height" + ) + + # calculate usd camera parameters + # pixel_size does not need to be exact it is just used for consistent sizing of aperture and focal_length + # https://docs.omniverse.nvidia.com/isaacsim/latest/features/sensors_simulation/isaac_sim_sensors_camera.html#calibrated-camera-sensors + if focal_length is None: + pixel_size = 1 / float(width) + else: + pixel_size = focal_length / ((f_x + f_y) / 2) + + usd_params = { + "horizontal_aperture": pixel_size * float(width), + "vertical_aperture": pixel_size * float(height), + "focal_length": pixel_size * (f_x + f_y) / 2, # The focal length in mm + "horizontal_aperture_offset": 0.0, + "vertical_aperture_offset": 0.0, + } + + return usd_params diff --git a/source/isaaclab/isaaclab/utils/string.py b/source/isaaclab/isaaclab/utils/string.py new file mode 100644 index 0000000000000000000000000000000000000000..dc1cdaf5347792594847a9320c8db93acddb64d8 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/string.py @@ -0,0 +1,416 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing utilities for transforming strings and regular expressions.""" + +import ast +import importlib +import inspect +import re +from collections.abc import Callable, Sequence +from typing import Any + +""" +String formatting. +""" + + +def to_camel_case(snake_str: str, to: str = "cC") -> str: + """Converts a string from snake case to camel case. + + Args: + snake_str: A string in snake case (i.e. with '_') + to: Convention to convert string to. Defaults to "cC". + + Raises: + ValueError: Invalid input argument `to`, i.e. not "cC" or "CC". + + Returns: + A string in camel-case format. + """ + # check input is correct + if to not in ["cC", "CC"]: + msg = "to_camel_case(): Choose a valid `to` argument (CC or cC)" + raise ValueError(msg) + # convert string to lower case and split + components = snake_str.lower().split("_") + if to == "cC": + # We capitalize the first letter of each component except the first one + # with the 'title' method and join them together. + return components[0] + "".join(x.title() for x in components[1:]) + else: + # Capitalize first letter in all the components + return "".join(x.title() for x in components) + + +def to_snake_case(camel_str: str) -> str: + """Converts a string from camel case to snake case. + + Args: + camel_str: A string in camel case. + + Returns: + A string in snake case (i.e. with '_') + """ + camel_str = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", camel_str) + return re.sub("([a-z0-9])([A-Z])", r"\1_\2", camel_str).lower() + + +def string_to_slice(s: str): + """Convert a string representation of a slice to a slice object. + + Args: + s: The string representation of the slice. + + Returns: + The slice object. + """ + # extract the content inside the slice() + match = re.match(r"slice\((.*),(.*),(.*)\)", s) + if not match: + raise ValueError(f"Invalid slice string format: {s}") + + # extract start, stop, and step values + start_str, stop_str, step_str = match.groups() + + # convert 'None' to None and other strings to integers + start = None if start_str == "None" else int(start_str) + stop = None if stop_str == "None" else int(stop_str) + step = None if step_str == "None" else int(step_str) + + # create and return the slice object + return slice(start, stop, step) + + +""" +String <-> Callable operations. +""" + + +def is_lambda_expression(name: str) -> bool: + """Checks if the input string is a lambda expression. + + Args: + name: The input string. + + Returns: + Whether the input string is a lambda expression. + """ + try: + ast.parse(name) + return isinstance(ast.parse(name).body[0], ast.Expr) and isinstance(ast.parse(name).body[0].value, ast.Lambda) + except SyntaxError: + return False + + +def callable_to_string(value: Callable) -> str: + """Converts a callable object to a string. + + Args: + value: A callable object. + + Raises: + ValueError: When the input argument is not a callable object. + + Returns: + A string representation of the callable object. + """ + # check if callable + if not callable(value): + raise ValueError(f"The input argument is not callable: {value}.") + # check if lambda function + if value.__name__ == "": + # we resolve the lambda expression by checking the source code and extracting the line with lambda expression + # we also remove any comments from the line + lambda_line = inspect.getsourcelines(value)[0][0].strip().split("lambda")[1].strip().split(",")[0] + lambda_line = re.sub(r"#.*$", "", lambda_line).rstrip() + return f"lambda {lambda_line}" + else: + # get the module and function name + module_name = value.__module__ + function_name = value.__name__ + # return the string + return f"{module_name}:{function_name}" + + +def string_to_callable(name: str) -> Callable: + """Resolves the module and function names to return the function. + + Args: + name: The function name. The format should be 'module:attribute_name' or a + lambda expression of format: 'lambda x: x'. + + Raises: + ValueError: When the resolved attribute is not a function. + ValueError: When the module cannot be found. + + Returns: + Callable: The function loaded from the module. + """ + try: + if is_lambda_expression(name): + callable_object = eval(name) + else: + mod_name, attr_name = name.split(":") + mod = importlib.import_module(mod_name) + callable_object = getattr(mod, attr_name) + # check if attribute is callable + if callable(callable_object): + return callable_object + else: + raise AttributeError(f"The imported object is not callable: '{name}'") + except (ValueError, ModuleNotFoundError) as e: + msg = ( + f"Could not resolve the input string '{name}' into callable object." + " The format of input should be 'module:attribute_name'.\n" + f"Received the error:\n {e}." + ) + raise ValueError(msg) + + +""" +Regex operations. +""" + + +def resolve_matching_names( + keys: str | Sequence[str], list_of_strings: Sequence[str], preserve_order: bool = False +) -> tuple[list[int], list[str]]: + """Match a list of query regular expressions against a list of strings and return the matched indices and names. + + When a list of query regular expressions is provided, the function checks each target string against each + query regular expression and returns the indices of the matched strings and the matched strings. + + If the :attr:`preserve_order` is True, the ordering of the matched indices and names is the same as the order + of the provided list of strings. This means that the ordering is dictated by the order of the target strings + and not the order of the query regular expressions. + + If the :attr:`preserve_order` is False, the ordering of the matched indices and names is the same as the order + of the provided list of query regular expressions. + + For example, consider the list of strings is ['a', 'b', 'c', 'd', 'e'] and the regular expressions are ['a|c', 'b']. + If :attr:`preserve_order` is False, then the function will return the indices of the matched strings and the + strings as: ([0, 1, 2], ['a', 'b', 'c']). When :attr:`preserve_order` is True, it will return them as: + ([0, 2, 1], ['a', 'c', 'b']). + + Note: + The function does not sort the indices. It returns the indices in the order they are found. + + Args: + keys: A regular expression or a list of regular expressions to match the strings in the list. + list_of_strings: A list of strings to match. + preserve_order: Whether to preserve the order of the query keys in the returned values. Defaults to False. + + Returns: + A tuple of lists containing the matched indices and names. + + Raises: + ValueError: When multiple matches are found for a string in the list. + ValueError: When not all regular expressions are matched. + """ + # resolve name keys + if isinstance(keys, str): + keys = [keys] + # find matching patterns + index_list = [] + names_list = [] + key_idx_list = [] + # book-keeping to check that we always have a one-to-one mapping + # i.e. each target string should match only one regular expression + target_strings_match_found = [None for _ in range(len(list_of_strings))] + keys_match_found = [[] for _ in range(len(keys))] + # loop over all target strings + for target_index, potential_match_string in enumerate(list_of_strings): + for key_index, re_key in enumerate(keys): + if re.fullmatch(re_key, potential_match_string): + # check if match already found + if target_strings_match_found[target_index]: + raise ValueError( + f"Multiple matches for '{potential_match_string}':" + f" '{target_strings_match_found[target_index]}' and '{re_key}'!" + ) + # add to list + target_strings_match_found[target_index] = re_key + index_list.append(target_index) + names_list.append(potential_match_string) + key_idx_list.append(key_index) + # add for regex key + keys_match_found[key_index].append(potential_match_string) + # reorder keys if they should be returned in order of the query keys + if preserve_order: + reordered_index_list = [None] * len(index_list) + global_index = 0 + for key_index in range(len(keys)): + for key_idx_position, key_idx_entry in enumerate(key_idx_list): + if key_idx_entry == key_index: + reordered_index_list[key_idx_position] = global_index + global_index += 1 + # reorder index and names list + index_list_reorder = [None] * len(index_list) + names_list_reorder = [None] * len(index_list) + for idx, reorder_idx in enumerate(reordered_index_list): + index_list_reorder[reorder_idx] = index_list[idx] + names_list_reorder[reorder_idx] = names_list[idx] + # update + index_list = index_list_reorder + names_list = names_list_reorder + # check that all regular expressions are matched + if not all(keys_match_found): + # make this print nicely aligned for debugging + msg = "\n" + for key, value in zip(keys, keys_match_found): + msg += f"\t{key}: {value}\n" + msg += f"Available strings: {list_of_strings}\n" + # raise error + raise ValueError( + f"Not all regular expressions are matched! Please check that the regular expressions are correct: {msg}" + ) + # return + return index_list, names_list + + +def resolve_matching_names_values( + data: dict[str, Any], + list_of_strings: Sequence[str], + preserve_order: bool = False, + strict: bool = True, +) -> tuple[list[int], list[str], list[Any]]: + """Match a list of regular expressions in a dictionary against a list of strings and return + the matched indices, names, and values. + + If the :attr:`preserve_order` is True, the ordering of the matched indices and names is the same as the order + of the provided list of strings. This means that the ordering is dictated by the order of the target strings + and not the order of the query regular expressions. + + If the :attr:`preserve_order` is False, the ordering of the matched indices and names is the same as the order + of the provided list of query regular expressions. + + For example, consider the dictionary is {"a|d|e": 1, "b|c": 2}, the list of strings is ['a', 'b', 'c', 'd', 'e']. + If :attr:`preserve_order` is False, then the function will return the indices of the matched strings, the + matched strings, and the values as: ([0, 1, 2, 3, 4], ['a', 'b', 'c', 'd', 'e'], [1, 2, 2, 1, 1]). When + :attr:`preserve_order` is True, it will return them as: + ([0, 3, 4, 1, 2], ['a', 'd', 'e', 'b', 'c'], [1, 1, 1, 2, 2]). + + Args: + data: A dictionary of regular expressions and values to match the strings in the list. + list_of_strings: A list of strings to match. + preserve_order: Whether to preserve the order of the query keys in the returned values. Defaults to False. + strict: Whether to require that all keys in the dictionary get matched. Defaults to True. + + Returns: + A tuple of lists containing the matched indices, names, and values. + + Raises: + TypeError: When the input argument :attr:`data` is not a dictionary. + ValueError: When multiple matches are found for a string in the dictionary. + ValueError: When not all regular expressions in the data keys are matched (if strict is True). + """ + # check valid input + if not isinstance(data, dict): + raise TypeError(f"Input argument `data` should be a dictionary. Received: {data}") + # find matching patterns + index_list = [] + names_list = [] + values_list = [] + key_idx_list = [] + # book-keeping to check that we always have a one-to-one mapping + # i.e. each target string should match only one regular expression + target_strings_match_found = [None for _ in range(len(list_of_strings))] + keys_match_found = [[] for _ in range(len(data))] + # loop over all target strings + for target_index, potential_match_string in enumerate(list_of_strings): + for key_index, (re_key, value) in enumerate(data.items()): + if re.fullmatch(re_key, potential_match_string): + # check if match already found + if target_strings_match_found[target_index]: + raise ValueError( + f"Multiple matches for '{potential_match_string}':" + f" '{target_strings_match_found[target_index]}' and '{re_key}'!" + ) + # add to list + target_strings_match_found[target_index] = re_key + index_list.append(target_index) + names_list.append(potential_match_string) + values_list.append(value) + key_idx_list.append(key_index) + # add for regex key + keys_match_found[key_index].append(potential_match_string) + # reorder keys if they should be returned in order of the query keys + if preserve_order: + reordered_index_list = [None] * len(index_list) + global_index = 0 + for key_index in range(len(data)): + for key_idx_position, key_idx_entry in enumerate(key_idx_list): + if key_idx_entry == key_index: + reordered_index_list[key_idx_position] = global_index + global_index += 1 + # reorder index and names list + index_list_reorder = [None] * len(index_list) + names_list_reorder = [None] * len(index_list) + values_list_reorder = [None] * len(index_list) + for idx, reorder_idx in enumerate(reordered_index_list): + index_list_reorder[reorder_idx] = index_list[idx] + names_list_reorder[reorder_idx] = names_list[idx] + values_list_reorder[reorder_idx] = values_list[idx] + # update + index_list = index_list_reorder + names_list = names_list_reorder + values_list = values_list_reorder + # check that all regular expressions are matched + if strict and not all(keys_match_found): + # make this print nicely aligned for debugging + msg = "\n" + for key, value in zip(data.keys(), keys_match_found): + msg += f"\t{key}: {value}\n" + msg += f"Available strings: {list_of_strings}\n" + # raise error + raise ValueError( + f"Not all regular expressions are matched! Please check that the regular expressions are correct: {msg}" + ) + # return + return index_list, names_list, values_list + + +def find_unique_string_name(initial_name: str, is_unique_fn: Callable[[str], bool]) -> str: + """Find a unique string name based on the predicate function provided. + The string is appended with "_N", where N is a natural number till the resultant string + is unique. + Args: + initial_name (str): The initial string name. + is_unique_fn (Callable[[str], bool]): The predicate function to validate against. + Returns: + str: A unique string based on input function. + """ + if is_unique_fn(initial_name): + return initial_name + iterator = 1 + result = initial_name + "_" + str(iterator) + while not is_unique_fn(result): + result = initial_name + "_" + str(iterator) + iterator += 1 + return result + + +def find_root_prim_path_from_regex(prim_path_regex: str) -> tuple[str, int]: + """Find the first prim above the regex pattern prim and its position. + Args: + prim_path_regex (str): full prim path including the regex pattern prim. + Returns: + Tuple[str, int]: First position is the prim path to the parent of the regex prim. + Second position represents the level of the regex prim in the USD stage tree representation. + """ + prim_paths_list = str(prim_path_regex).split("/") + root_idx = None + for prim_path_idx in range(len(prim_paths_list)): + chars = set("[]*|^") + if any((c in chars) for c in prim_paths_list[prim_path_idx]): + root_idx = prim_path_idx + break + root_prim_path = None + tree_level = None + if root_idx is not None: + root_prim_path = "/".join(prim_paths_list[:root_idx]) + tree_level = root_idx + return root_prim_path, tree_level diff --git a/source/isaaclab/isaaclab/utils/timer.py b/source/isaaclab/isaaclab/utils/timer.py new file mode 100644 index 0000000000000000000000000000000000000000..4d9951db60c81b193088627f40e9ba6574aa5307 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/timer.py @@ -0,0 +1,171 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for a timer class that can be used for performance measurements.""" + +from __future__ import annotations + +import time +from contextlib import ContextDecorator +from typing import Any, ClassVar + + +class TimerError(Exception): + """A custom exception used to report errors in use of :class:`Timer` class.""" + + pass + + +class Timer(ContextDecorator): + """A timer for performance measurements. + + A class to keep track of time for performance measurement. + It allows timing via context managers and decorators as well. + + It uses the `time.perf_counter` function to measure time. This function + returns the number of seconds since the epoch as a float. It has the + highest resolution available on the system. + + As a regular object: + + .. code-block:: python + + import time + + from isaaclab.utils.timer import Timer + + timer = Timer() + timer.start() + time.sleep(1) + print(1 <= timer.time_elapsed <= 2) # Output: True + + time.sleep(1) + timer.stop() + print(2 <= stopwatch.total_run_time) # Output: True + + As a context manager: + + .. code-block:: python + + import time + + from isaaclab.utils.timer import Timer + + with Timer() as timer: + time.sleep(1) + print(1 <= timer.time_elapsed <= 2) # Output: True + + Reference: https://gist.github.com/sumeet/1123871 + """ + + timing_info: ClassVar[dict[str, float]] = dict() + """Dictionary for storing the elapsed time per timer instances globally. + + This dictionary logs the timer information. The keys are the names given to the timer class + at its initialization. If no :attr:`name` is passed to the constructor, no time + is recorded in the dictionary. + """ + + def __init__(self, msg: str | None = None, name: str | None = None): + """Initializes the timer. + + Args: + msg: The message to display when using the timer + class in a context manager. Defaults to None. + name: The name to use for logging times in a global + dictionary. Defaults to None. + """ + self._msg = msg + self._name = name + self._start_time = None + self._stop_time = None + self._elapsed_time = None + + def __str__(self) -> str: + """A string representation of the class object. + + Returns: + A string containing the elapsed time. + """ + return f"{self.time_elapsed:0.6f} seconds" + + """ + Properties + """ + + @property + def time_elapsed(self) -> float: + """The number of seconds that have elapsed since this timer started timing. + + Note: + This is used for checking how much time has elapsed while the timer is still running. + """ + return time.perf_counter() - self._start_time + + @property + def total_run_time(self) -> float: + """The number of seconds that elapsed from when the timer started to when it ended.""" + return self._elapsed_time + + """ + Operations + """ + + def start(self): + """Start timing.""" + if self._start_time is not None: + raise TimerError("Timer is running. Use .stop() to stop it") + + self._start_time = time.perf_counter() + + def stop(self): + """Stop timing.""" + if self._start_time is None: + raise TimerError("Timer is not running. Use .start() to start it") + + self._stop_time = time.perf_counter() + self._elapsed_time = self._stop_time - self._start_time + self._start_time = None + + if self._name: + Timer.timing_info[self._name] = self._elapsed_time + + """ + Context managers + """ + + def __enter__(self) -> Timer: + """Start timing and return this `Timer` instance.""" + self.start() + return self + + def __exit__(self, *exc_info: Any): + """Stop timing.""" + self.stop() + # print message + if self._msg is not None: + print(self._msg, f": {self._elapsed_time:0.6f} seconds") + + """ + Static Methods + """ + + @staticmethod + def get_timer_info(name: str) -> float: + """Retrieves the time logged in the global dictionary + based on name. + + Args: + name: Name of the the entry to be retrieved. + + Raises: + TimerError: If name doesn't exist in the log. + + Returns: + A float containing the time logged if the name exists. + """ + if name not in Timer.timing_info: + raise TimerError(f"Timer {name} does not exist") + return Timer.timing_info.get(name) diff --git a/source/isaaclab/isaaclab/utils/types.py b/source/isaaclab/isaaclab/utils/types.py new file mode 100644 index 0000000000000000000000000000000000000000..321c361792a4365b1f25ff87b5c995597226b287 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/types.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for different data types.""" + +from __future__ import annotations + +from collections.abc import Sequence +from dataclasses import dataclass + +import torch + + +@dataclass +class ArticulationActions: + """Data container to store articulation's joints actions. + + This class is used to store the actions of the joints of an articulation. + It is used to store the joint positions, velocities, efforts, and indices. + + If the actions are not provided, the values are set to None. + """ + + joint_positions: torch.Tensor | None = None + """The joint positions of the articulation. Defaults to None.""" + + joint_velocities: torch.Tensor | None = None + """The joint velocities of the articulation. Defaults to None.""" + + joint_efforts: torch.Tensor | None = None + """The joint efforts of the articulation. Defaults to None.""" + + joint_indices: torch.Tensor | Sequence[int] | slice | None = None + """The joint indices of the articulation. Defaults to None. + + If the joint indices are a slice, this indicates that the indices are continuous and correspond + to all the joints of the articulation. We use a slice to make the indexing more efficient. + """ diff --git a/source/isaaclab/isaaclab/utils/version.py b/source/isaaclab/isaaclab/utils/version.py new file mode 100644 index 0000000000000000000000000000000000000000..358a5550aa1caa6f9821a0d2974a673ef56b8cc6 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/version.py @@ -0,0 +1,94 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Utility functions for versioning.""" + +from __future__ import annotations + +import functools + +from packaging.version import Version + + +@functools.lru_cache(maxsize=1) +def get_isaac_sim_version() -> Version: + """Get the Isaac Sim version as a Version object, cached for performance. + + This function wraps :func:`isaacsim.core.version.get_version()` and caches the result + to avoid repeated file I/O operations. The underlying Isaac Sim function reads from + a file each time it's called, which can be slow when called frequently. + + Returns: + A :class:`packaging.version.Version` object representing the Isaac Sim version. + This object supports rich comparison operators (<, <=, >, >=, ==, !=). + + Example: + >>> from isaaclab.utils import get_isaac_sim_version + >>> from packaging.version import Version + >>> + >>> isaac_version = get_isaac_sim_version() + >>> print(isaac_version) + 5.0.0 + >>> + >>> # Natural version comparisons + >>> if isaac_version >= Version("5.0.0"): + ... print("Using Isaac Sim 5.0 or later") + >>> + >>> # Access components + >>> print(isaac_version.major, isaac_version.minor, isaac_version.micro) + 5 0 0 + """ + from isaacsim.core.version import get_version + + version_tuple = get_version() + # version_tuple[2] = major (year), [3] = minor (release), [4] = micro (patch) + return Version(f"{version_tuple[2]}.{version_tuple[3]}.{version_tuple[4]}") + + +def compare_versions(v1: str, v2: str) -> int: + """Compare two version strings and return the comparison result. + + The version strings are expected to be in the format "x.y.z" where x, y, + and z are integers. The version strings are compared lexicographically. + + .. note:: + This function is provided for backward compatibility. For new code, + prefer using :class:`packaging.version.Version` objects directly with + comparison operators (``<``, ``<=``, ``>``, ``>=``, ``==``, ``!=``). + + Args: + v1: The first version string. + v2: The second version string. + + Returns: + An integer indicating the comparison result: + + - :attr:`1` if v1 is greater + - :attr:`-1` if v2 is greater + - :attr:`0` if v1 and v2 are equal + + Example: + >>> from isaaclab.utils import compare_versions + >>> compare_versions("5.0.0", "4.5.0") + 1 + >>> compare_versions("4.5.0", "5.0.0") + -1 + >>> compare_versions("5.0.0", "5.0.0") + 0 + >>> + >>> # Better: use Version objects directly + >>> from packaging.version import Version + >>> Version("5.0.0") > Version("4.5.0") + True + """ + ver1 = Version(v1) + ver2 = Version(v2) + + if ver1 > ver2: + return 1 + elif ver1 < ver2: + return -1 + else: + return 0 diff --git a/source/isaaclab/isaaclab/utils/warp/__init__.py b/source/isaaclab/isaaclab/utils/warp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..92a5603fd5dec988cf322036d195f109256b71b2 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/warp/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing operations based on warp.""" + +from .ops import convert_to_warp_mesh, raycast_dynamic_meshes, raycast_mesh, raycast_single_mesh diff --git a/source/isaaclab/isaaclab/utils/warp/kernels.py b/source/isaaclab/isaaclab/utils/warp/kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..cf56e34ed45a030efed497322054f700036441ea --- /dev/null +++ b/source/isaaclab/isaaclab/utils/warp/kernels.py @@ -0,0 +1,469 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Custom kernels for warp.""" + +from typing import Any + +import warp as wp + +## +# Raycasting +## + + +@wp.kernel(enable_backward=False) +def raycast_mesh_kernel( + mesh: wp.uint64, + ray_starts: wp.array(dtype=wp.vec3), + ray_directions: wp.array(dtype=wp.vec3), + ray_hits: wp.array(dtype=wp.vec3), + ray_distance: wp.array(dtype=wp.float32), + ray_normal: wp.array(dtype=wp.vec3), + ray_face_id: wp.array(dtype=wp.int32), + max_dist: float = 1e6, + return_distance: int = False, + return_normal: int = False, + return_face_id: int = False, +): + """Performs ray-casting against a mesh. + + This function performs ray-casting against the given mesh using the provided ray start positions + and directions. The resulting ray hit positions are stored in the :obj:`ray_hits` array. + + Note that the `ray_starts`, `ray_directions`, and `ray_hits` arrays should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + The function utilizes the `mesh_query_ray` method from the `wp` module to perform the actual ray-casting + operation. The maximum ray-cast distance is set to `1e6` units. + + Args: + mesh: The input mesh. The ray-casting is performed against this mesh on the device specified by the + `mesh`'s `device` attribute. + ray_starts: The input ray start positions. Shape is (N, 3). + ray_directions: The input ray directions. Shape is (N, 3). + ray_hits: The output ray hit positions. Shape is (N, 3). + ray_distance: The output ray hit distances. Shape is (N,), if `return_distance` is True. Otherwise, + this array is not used. + ray_normal: The output ray hit normals. Shape is (N, 3), if `return_normal` is True. Otherwise, + this array is not used. + ray_face_id: The output ray hit face ids. Shape is (N,), if `return_face_id` is True. Otherwise, + this array is not used. + max_dist: The maximum ray-cast distance. Defaults to 1e6. + return_distance: Whether to return the ray hit distances. Defaults to False. + return_normal: Whether to return the ray hit normals. Defaults to False`. + return_face_id: Whether to return the ray hit face ids. Defaults to False. + """ + # get the thread id + tid = wp.tid() + + t = float(0.0) # hit distance along ray + u = float(0.0) # hit face barycentric u + v = float(0.0) # hit face barycentric v + sign = float(0.0) # hit face sign + n = wp.vec3() # hit face normal + f = int(0) # hit face index + + # ray cast against the mesh and store the hit position + hit_success = wp.mesh_query_ray(mesh, ray_starts[tid], ray_directions[tid], max_dist, t, u, v, sign, n, f) + # if the ray hit, store the hit data + if hit_success: + ray_hits[tid] = ray_starts[tid] + t * ray_directions[tid] + if return_distance == 1: + ray_distance[tid] = t + if return_normal == 1: + ray_normal[tid] = n + if return_face_id == 1: + ray_face_id[tid] = f + + +@wp.kernel(enable_backward=False) +def raycast_static_meshes_kernel( + mesh: wp.array2d(dtype=wp.uint64), + ray_starts: wp.array2d(dtype=wp.vec3), + ray_directions: wp.array2d(dtype=wp.vec3), + ray_hits: wp.array2d(dtype=wp.vec3), + ray_distance: wp.array2d(dtype=wp.float32), + ray_normal: wp.array2d(dtype=wp.vec3), + ray_face_id: wp.array2d(dtype=wp.int32), + ray_mesh_id: wp.array2d(dtype=wp.int16), + max_dist: float = 1e6, + return_normal: int = False, + return_face_id: int = False, + return_mesh_id: int = False, +): + """Performs ray-casting against multiple static meshes. + + This function performs ray-casting against the given meshes using the provided ray start positions + and directions. The resulting ray hit positions are stored in the :obj:`ray_hits` array. + + The function utilizes the ``mesh_query_ray`` method from the ``wp`` module to perform the actual ray-casting + operation. The maximum ray-cast distance is set to ``1e6`` units. + + .. note:: + That the ``ray_starts``, ``ray_directions``, and ``ray_hits`` arrays should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + This kernel differs from the :meth:`raycast_dynamic_meshes_kernel` in that it does not take into + account the mesh's position and rotation. This kernel is useful for ray-casting against static meshes + that are not expected to move. + + Args: + mesh: The input mesh. The ray-casting is performed against this mesh on the device specified by the + `mesh`'s `device` attribute. + ray_starts: The input ray start positions. Shape is (B, N, 3). + ray_directions: The input ray directions. Shape is (B, N, 3). + ray_hits: The output ray hit positions. Shape is (B, N, 3). + ray_distance: The output ray hit distances. Shape is (B, N,), if ``return_distance`` is True. Otherwise, + this array is not used. + ray_normal: The output ray hit normals. Shape is (B, N, 3), if ``return_normal`` is True. Otherwise, + this array is not used. + ray_face_id: The output ray hit face ids. Shape is (B, N,), if ``return_face_id`` is True. Otherwise, + this array is not used. + ray_mesh_id: The output ray hit mesh ids. Shape is (B, N,), if ``return_mesh_id`` is True. Otherwise, + this array is not used. + max_dist: The maximum ray-cast distance. Defaults to 1e6. + return_normal: Whether to return the ray hit normals. Defaults to False`. + return_face_id: Whether to return the ray hit face ids. Defaults to False. + return_mesh_id: Whether to return the mesh id. Defaults to False. + """ + # get the thread id + tid_mesh_id, tid_env, tid_ray = wp.tid() + + direction = ray_directions[tid_env, tid_ray] + start_pos = ray_starts[tid_env, tid_ray] + + # ray cast against the mesh and store the hit position + mesh_query_ray_t = wp.mesh_query_ray(mesh[tid_env, tid_mesh_id], start_pos, direction, max_dist) + + # if the ray hit, store the hit data + if mesh_query_ray_t.result: + wp.atomic_min(ray_distance, tid_env, tid_ray, mesh_query_ray_t.t) + # check if hit distance is less than the current hit distance, only then update the memory + # TODO, in theory we could use the output of atomic_min to avoid the non-thread safe next comparison + # however, warp atomic_min is returning the wrong values on gpu currently. + # FIXME https://github.com/NVIDIA/warp/issues/1058 + if mesh_query_ray_t.t == ray_distance[tid_env, tid_ray]: + # convert back to world space and update the hit data + ray_hits[tid_env, tid_ray] = start_pos + mesh_query_ray_t.t * direction + + # update the normal and face id if requested + if return_normal == 1: + ray_normal[tid_env, tid_ray] = mesh_query_ray_t.normal + if return_face_id == 1: + ray_face_id[tid_env, tid_ray] = mesh_query_ray_t.face + if return_mesh_id == 1: + ray_mesh_id[tid_env, tid_ray] = wp.int16(tid_mesh_id) + + +@wp.kernel(enable_backward=False) +def raycast_dynamic_meshes_kernel( + mesh: wp.array2d(dtype=wp.uint64), + ray_starts: wp.array2d(dtype=wp.vec3), + ray_directions: wp.array2d(dtype=wp.vec3), + ray_hits: wp.array2d(dtype=wp.vec3), + ray_distance: wp.array2d(dtype=wp.float32), + ray_normal: wp.array2d(dtype=wp.vec3), + ray_face_id: wp.array2d(dtype=wp.int32), + ray_mesh_id: wp.array2d(dtype=wp.int16), + mesh_positions: wp.array2d(dtype=wp.vec3), + mesh_rotations: wp.array2d(dtype=wp.quat), + max_dist: float = 1e6, + return_normal: int = False, + return_face_id: int = False, + return_mesh_id: int = False, +): + """Performs ray-casting against multiple meshes. + + This function performs ray-casting against the given meshes using the provided ray start positions + and directions. The resulting ray hit positions are stored in the :obj:`ray_hits` array. + + The function utilizes the ``mesh_query_ray`` method from the ``wp`` module to perform the actual ray-casting + operation. The maximum ray-cast distance is set to ``1e6`` units. + + + Note: + That the ``ray_starts``, ``ray_directions``, and ``ray_hits`` arrays should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + All arguments are expected to be batched with the first dimension (B, batch) being the number of envs + and the second dimension (N, num_rays) being the number of rays. For Meshes, W is the number of meshes. + + Args: + mesh: The input mesh. The ray-casting is performed against this mesh on the device specified by the + `mesh`'s `device` attribute. + ray_starts: The input ray start positions. Shape is (B, N, 3). + ray_directions: The input ray directions. Shape is (B, N, 3). + ray_hits: The output ray hit positions. Shape is (B, N, 3). + ray_distance: The output ray hit distances. Shape is (B, N,), if ``return_distance`` is True. Otherwise, + this array is not used. + ray_normal: The output ray hit normals. Shape is (B, N, 3), if ``return_normal`` is True. Otherwise, + this array is not used. + ray_face_id: The output ray hit face ids. Shape is (B, N,), if ``return_face_id`` is True. Otherwise, + this array is not used. + ray_mesh_id: The output ray hit mesh ids. Shape is (B, N,), if ``return_mesh_id`` is True. Otherwise, + this array is not used. + mesh_positions: The input mesh positions in world frame. Shape is (W, 3). + mesh_rotations: The input mesh rotations in world frame. Shape is (W, 4). + max_dist: The maximum ray-cast distance. Defaults to 1e6. + return_normal: Whether to return the ray hit normals. Defaults to False`. + return_face_id: Whether to return the ray hit face ids. Defaults to False. + return_mesh_id: Whether to return the mesh id. Defaults to False. + """ + # get the thread id + tid_mesh_id, tid_env, tid_ray = wp.tid() + + mesh_pose = wp.transform(mesh_positions[tid_env, tid_mesh_id], mesh_rotations[tid_env, tid_mesh_id]) + mesh_pose_inv = wp.transform_inverse(mesh_pose) + direction = wp.transform_vector(mesh_pose_inv, ray_directions[tid_env, tid_ray]) + start_pos = wp.transform_point(mesh_pose_inv, ray_starts[tid_env, tid_ray]) + + # ray cast against the mesh and store the hit position + mesh_query_ray_t = wp.mesh_query_ray(mesh[tid_env, tid_mesh_id], start_pos, direction, max_dist) + # if the ray hit, store the hit data + if mesh_query_ray_t.result: + wp.atomic_min(ray_distance, tid_env, tid_ray, mesh_query_ray_t.t) + # check if hit distance is less than the current hit distance, only then update the memory + # TODO, in theory we could use the output of atomic_min to avoid the non-thread safe next comparison + # however, warp atomic_min is returning the wrong values on gpu currently. + # FIXME https://github.com/NVIDIA/warp/issues/1058 + if mesh_query_ray_t.t == ray_distance[tid_env, tid_ray]: + # convert back to world space and update the hit data + hit_pos = start_pos + mesh_query_ray_t.t * direction + ray_hits[tid_env, tid_ray] = wp.transform_point(mesh_pose, hit_pos) + + # update the normal and face id if requested + if return_normal == 1: + n = wp.transform_vector(mesh_pose, mesh_query_ray_t.normal) + ray_normal[tid_env, tid_ray] = n + if return_face_id == 1: + ray_face_id[tid_env, tid_ray] = mesh_query_ray_t.face + if return_mesh_id == 1: + ray_mesh_id[tid_env, tid_ray] = wp.int16(tid_mesh_id) + + +@wp.kernel(enable_backward=False) +def reshape_tiled_image( + tiled_image_buffer: Any, + batched_image: Any, + image_height: int, + image_width: int, + num_channels: int, + num_tiles_x: int, +): + """Reshapes a tiled image into a batch of images. + + This function reshapes the input tiled image buffer into a batch of images. The input image buffer + is assumed to be tiled in the x and y directions. The output image is a batch of images with the + specified height, width, and number of channels. + + Args: + tiled_image_buffer: The input image buffer. Shape is (height * width * num_channels * num_cameras,). + batched_image: The output image. Shape is (num_cameras, height, width, num_channels). + image_width: The width of the image. + image_height: The height of the image. + num_channels: The number of channels in the image. + num_tiles_x: The number of tiles in x-direction. + """ + # get the thread id + camera_id, height_id, width_id = wp.tid() + + # resolve the tile indices + tile_x_id = camera_id % num_tiles_x + tile_y_id = camera_id // num_tiles_x + # compute the start index of the pixel in the tiled image buffer + pixel_start = ( + num_channels * num_tiles_x * image_width * (image_height * tile_y_id + height_id) + + num_channels * tile_x_id * image_width + + num_channels * width_id + ) + + # copy the pixel values into the batched image + for i in range(num_channels): + batched_image[camera_id, height_id, width_id, i] = batched_image.dtype(tiled_image_buffer[pixel_start + i]) + + +# uint32 -> int32 conversion is required for non-colored segmentation annotators +wp.overload( + reshape_tiled_image, + {"tiled_image_buffer": wp.array(dtype=wp.uint32), "batched_image": wp.array(dtype=wp.uint32, ndim=4)}, +) +# uint8 is used for 4 channel annotators +wp.overload( + reshape_tiled_image, + {"tiled_image_buffer": wp.array(dtype=wp.uint8), "batched_image": wp.array(dtype=wp.uint8, ndim=4)}, +) +# float32 is used for single channel annotators +wp.overload( + reshape_tiled_image, + {"tiled_image_buffer": wp.array(dtype=wp.float32), "batched_image": wp.array(dtype=wp.float32, ndim=4)}, +) + +## +# Wrench Composer +## + + +@wp.func +def cast_to_link_frame(position: wp.vec3f, link_position: wp.vec3f, is_global: bool) -> wp.vec3f: + """Casts a position to the link frame of the body. + + Args: + position: The position to cast. + link_position: The link frame position. + is_global: Whether the position is in the global frame. + + Returns: + The position in the link frame of the body. + """ + if is_global: + return position - link_position + else: + return position + + +@wp.func +def cast_force_to_link_frame(force: wp.vec3f, link_quat: wp.quatf, is_global: bool) -> wp.vec3f: + """Casts a force to the link frame of the body. + + Args: + force: The force to cast. + link_quat: The link frame quaternion. + is_global: Whether the force is applied in the global frame. + Returns: + The force in the link frame of the body. + """ + if is_global: + return wp.quat_rotate_inv(link_quat, force) + else: + return force + + +@wp.func +def cast_torque_to_link_frame(torque: wp.vec3f, link_quat: wp.quatf, is_global: bool) -> wp.vec3f: + """Casts a torque to the link frame of the body. + + Args: + torque: The torque to cast. + link_quat: The link frame quaternion. + is_global: Whether the torque is applied in the global frame. + + Returns: + The torque in the link frame of the body. + """ + if is_global: + return wp.quat_rotate_inv(link_quat, torque) + else: + return torque + + +@wp.kernel +def add_forces_and_torques_at_position( + env_ids: wp.array(dtype=wp.int32), + body_ids: wp.array(dtype=wp.int32), + forces: wp.array2d(dtype=wp.vec3f), + torques: wp.array2d(dtype=wp.vec3f), + positions: wp.array2d(dtype=wp.vec3f), + link_positions: wp.array2d(dtype=wp.vec3f), + link_quaternions: wp.array2d(dtype=wp.quatf), + composed_forces_b: wp.array2d(dtype=wp.vec3f), + composed_torques_b: wp.array2d(dtype=wp.vec3f), + is_global: bool, +): + """Adds forces and torques to the composed force and torque at the user-provided positions. + When is_global is False, the user-provided positions are offsetting the application of the force relatively to the + link frame of the body. When is_global is True, the user-provided positions are the global positions of the force + application. + + Args: + env_ids: The environment ids. + body_ids: The body ids. + forces: The forces. + torques: The torques. + positions: The positions. + link_positions: The link frame positions. + link_quaternions: The link frame quaternions. + composed_forces_b: The composed forces. + composed_torques_b: The composed torques. + is_global: Whether the forces and torques are applied in the global frame. + """ + # get the thread id + tid_env, tid_body = wp.tid() + + # add the forces to the composed force, if the positions are provided, also adds a torque to the composed torque. + if forces: + # add the forces to the composed force + composed_forces_b[env_ids[tid_env], body_ids[tid_body]] += cast_force_to_link_frame( + forces[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + # if there is a position offset, add a torque to the composed torque. + if positions: + composed_torques_b[env_ids[tid_env], body_ids[tid_body]] += wp.skew( + cast_to_link_frame( + positions[tid_env, tid_body], link_positions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + ) @ cast_force_to_link_frame( + forces[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + if torques: + composed_torques_b[env_ids[tid_env], body_ids[tid_body]] += cast_torque_to_link_frame( + torques[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + + +@wp.kernel +def set_forces_and_torques_at_position( + env_ids: wp.array(dtype=wp.int32), + body_ids: wp.array(dtype=wp.int32), + forces: wp.array2d(dtype=wp.vec3f), + torques: wp.array2d(dtype=wp.vec3f), + positions: wp.array2d(dtype=wp.vec3f), + link_positions: wp.array2d(dtype=wp.vec3f), + link_quaternions: wp.array2d(dtype=wp.quatf), + composed_forces_b: wp.array2d(dtype=wp.vec3f), + composed_torques_b: wp.array2d(dtype=wp.vec3f), + is_global: bool, +): + """Sets forces and torques to the composed force and torque at the user-provided positions. + When is_global is False, the user-provided positions are offsetting the application of the force relatively + to the link frame of the body. When is_global is True, the user-provided positions are the global positions + of the force application. + + Args: + env_ids: The environment ids. + body_ids: The body ids. + forces: The forces. + torques: The torques. + positions: The positions. + link_positions: The link frame positions. + link_quaternions: The link frame quaternions. + composed_forces_b: The composed forces. + composed_torques_b: The composed torques. + is_global: Whether the forces and torques are applied in the global frame. + """ + # get the thread id + tid_env, tid_body = wp.tid() + + # set the torques to the composed torque + if torques: + composed_torques_b[env_ids[tid_env], body_ids[tid_body]] = cast_torque_to_link_frame( + torques[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + # set the forces to the composed force, if the positions are provided, adds a torque to the composed torque + # from the force at that position. + if forces: + # set the forces to the composed force + composed_forces_b[env_ids[tid_env], body_ids[tid_body]] = cast_force_to_link_frame( + forces[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + # if there is a position offset, set the torque from the force at that position. + if positions: + composed_torques_b[env_ids[tid_env], body_ids[tid_body]] = wp.skew( + cast_to_link_frame( + positions[tid_env, tid_body], link_positions[env_ids[tid_env], body_ids[tid_body]], is_global + ) + ) @ cast_force_to_link_frame( + forces[tid_env, tid_body], link_quaternions[env_ids[tid_env], body_ids[tid_body]], is_global + ) diff --git a/source/isaaclab/isaaclab/utils/warp/ops.py b/source/isaaclab/isaaclab/utils/warp/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f7cc8ac01def5c5069e3cc2d93a854c356882960 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/warp/ops.py @@ -0,0 +1,397 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrapping around warp kernels for compatibility with torch tensors.""" + +# needed to import for allowing type-hinting: torch.Tensor | None +from __future__ import annotations + +import numpy as np +import torch +import warp as wp + +# disable warp module initialization messages +wp.config.quiet = True +# initialize the warp module +wp.init() + +from isaaclab.utils.math import convert_quat + +from . import kernels + + +def raycast_mesh( + ray_starts: torch.Tensor, + ray_directions: torch.Tensor, + mesh: wp.Mesh, + max_dist: float = 1e6, + return_distance: bool = False, + return_normal: bool = False, + return_face_id: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]: + """Performs ray-casting against a mesh. + + Note that the `ray_starts` and `ray_directions`, and `ray_hits` should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + Args: + ray_starts: The starting position of the rays. Shape (N, 3). + ray_directions: The ray directions for each ray. Shape (N, 3). + mesh: The warp mesh to ray-cast against. + max_dist: The maximum distance to ray-cast. Defaults to 1e6. + return_distance: Whether to return the distance of the ray until it hits the mesh. Defaults to False. + return_normal: Whether to return the normal of the mesh face the ray hits. Defaults to False. + return_face_id: Whether to return the face id of the mesh face the ray hits. Defaults to False. + + Returns: + The ray hit position. Shape (N, 3). + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit distance. Shape (N,). + Will only return if :attr:`return_distance` is True, else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit normal. Shape (N, 3). + Will only return if :attr:`return_normal` is True else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit face id. Shape (N,). + Will only return if :attr:`return_face_id` is True else returns None. + The returned tensor contains :obj:`int(-1)` for missed hits. + """ + # extract device and shape information + shape = ray_starts.shape + device = ray_starts.device + # device of the mesh + torch_device = wp.device_to_torch(mesh.device) + # reshape the tensors + ray_starts = ray_starts.to(torch_device).view(-1, 3).contiguous() + ray_directions = ray_directions.to(torch_device).view(-1, 3).contiguous() + num_rays = ray_starts.shape[0] + # create output tensor for the ray hits + ray_hits = torch.full((num_rays, 3), float("inf"), device=torch_device).contiguous() + + # map the memory to warp arrays + ray_starts_wp = wp.from_torch(ray_starts, dtype=wp.vec3) + ray_directions_wp = wp.from_torch(ray_directions, dtype=wp.vec3) + ray_hits_wp = wp.from_torch(ray_hits, dtype=wp.vec3) + + if return_distance: + ray_distance = torch.full((num_rays,), float("inf"), device=torch_device).contiguous() + ray_distance_wp = wp.from_torch(ray_distance, dtype=wp.float32) + else: + ray_distance = None + ray_distance_wp = wp.empty((1,), dtype=wp.float32, device=torch_device) + + if return_normal: + ray_normal = torch.full((num_rays, 3), float("inf"), device=torch_device).contiguous() + ray_normal_wp = wp.from_torch(ray_normal, dtype=wp.vec3) + else: + ray_normal = None + ray_normal_wp = wp.empty((1,), dtype=wp.vec3, device=torch_device) + + if return_face_id: + ray_face_id = torch.ones((num_rays,), dtype=torch.int32, device=torch_device).contiguous() * (-1) + ray_face_id_wp = wp.from_torch(ray_face_id, dtype=wp.int32) + else: + ray_face_id = None + ray_face_id_wp = wp.empty((1,), dtype=wp.int32, device=torch_device) + + # launch the warp kernel + wp.launch( + kernel=kernels.raycast_mesh_kernel, + dim=num_rays, + inputs=[ + mesh.id, + ray_starts_wp, + ray_directions_wp, + ray_hits_wp, + ray_distance_wp, + ray_normal_wp, + ray_face_id_wp, + float(max_dist), + int(return_distance), + int(return_normal), + int(return_face_id), + ], + device=mesh.device, + ) + # NOTE: Synchronize is not needed anymore, but we keep it for now. Check with @dhoeller. + wp.synchronize() + + if return_distance: + ray_distance = ray_distance.to(device).view(shape[0], shape[1]) + if return_normal: + ray_normal = ray_normal.to(device).view(shape) + if return_face_id: + ray_face_id = ray_face_id.to(device).view(shape[0], shape[1]) + + return ray_hits.to(device).view(shape), ray_distance, ray_normal, ray_face_id + + +def raycast_single_mesh( + ray_starts: torch.Tensor, + ray_directions: torch.Tensor, + mesh_id: int, + max_dist: float = 1e6, + return_distance: bool = False, + return_normal: bool = False, + return_face_id: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]: + """Performs ray-casting against a mesh. + + Note that the :attr:`ray_starts` and :attr:`ray_directions`, and :attr:`ray_hits` should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + Args: + ray_starts: The starting position of the rays. Shape (B, N, 3). + ray_directions: The ray directions for each ray. Shape (B, N, 3). + mesh_id: The warp mesh id to ray-cast against. + max_dist: The maximum distance to ray-cast. Defaults to 1e6. + return_distance: Whether to return the distance of the ray until it hits the mesh. Defaults to False. + return_normal: Whether to return the normal of the mesh face the ray hits. Defaults to False. + return_face_id: Whether to return the face id of the mesh face the ray hits. Defaults to False. + + Returns: + The ray hit position. Shape (B, N, 3). + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit distance. Shape (B, N,). + Will only return if :attr:`return_distance` is True, else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit normal. Shape (B, N, 3). + Will only return if :attr:`return_normal` is True else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit face id. Shape (B, N,). + Will only return if :attr:`return_face_id` is True else returns None. + The returned tensor contains :obj:`int(-1)` for missed hits. + """ + # cast mesh id into array + mesh_ids = wp.array2d( + [[mesh_id] for _ in range(ray_starts.shape[0])], dtype=wp.uint64, device=wp.device_from_torch(ray_starts.device) + ) + ray_hits, ray_distance, ray_normal, ray_face_id, ray_mesh_id = raycast_dynamic_meshes( + ray_starts=ray_starts, + ray_directions=ray_directions, + mesh_ids_wp=mesh_ids, + max_dist=max_dist, + return_distance=return_distance, + return_normal=return_normal, + return_face_id=return_face_id, + ) + + return ray_hits, ray_distance, ray_normal, ray_face_id + + +def raycast_dynamic_meshes( + ray_starts: torch.Tensor, + ray_directions: torch.Tensor, + mesh_ids_wp: wp.Array, + mesh_positions_w: torch.Tensor | None = None, + mesh_orientations_w: torch.Tensor | None = None, + max_dist: float = 1e6, + return_distance: bool = False, + return_normal: bool = False, + return_face_id: bool = False, + return_mesh_id: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]: + """Performs ray-casting against multiple, dynamic meshes. + + Note that the :attr:`ray_starts` and :attr:`ray_directions`, and :attr:`ray_hits` should have compatible shapes + and data types to ensure proper execution. Additionally, they all must be in the same frame. + + If mesh positions and rotations are provided, they need to have to have the same shape as the + number of meshes. + + Args: + ray_starts: The starting position of the rays. Shape (B, N, 3). + ray_directions: The ray directions for each ray. Shape (B, N, 3). + mesh_ids_wp: The warp mesh ids to ray-cast against. Length (B, M). + mesh_positions_w: The world positions of the meshes. Shape (B, M, 3). + mesh_orientations_w: The world orientation as quaternion (wxyz) format. Shape (B, M, 4). + max_dist: The maximum distance to ray-cast. Defaults to 1e6. + return_distance: Whether to return the distance of the ray until it hits the mesh. Defaults to False. + return_normal: Whether to return the normal of the mesh face the ray hits. Defaults to False. + return_face_id: Whether to return the face id of the mesh face the ray hits. Defaults to False. + return_mesh_id: Whether to return the mesh id of the mesh face the ray hits. Defaults to False. + NOTE: the type of the returned tensor is torch.int16, so you can't have more than 32767 meshes. + + Returns: + The ray hit position. Shape (B, N, 3). + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit distance. Shape (B, N,). + Will only return if :attr:`return_distance` is True, else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit normal. Shape (B, N, 3). + Will only return if :attr:`return_normal` is True else returns None. + The returned tensor contains :obj:`float('inf')` for missed hits. + The ray hit face id. Shape (B, N,). + Will only return if :attr:`return_face_id` is True else returns None. + The returned tensor contains :obj:`int(-1)` for missed hits. + The ray hit mesh id. Shape (B, N,). + Will only return if :attr:`return_mesh_id` is True else returns None. + The returned tensor contains :obj:`-1` for missed hits. + """ + # extract device and shape information + shape = ray_starts.shape + device = ray_starts.device + + # device of the mesh + torch_device = wp.device_to_torch(mesh_ids_wp.device) + n_meshes = mesh_ids_wp.shape[1] + + n_envs = ray_starts.shape[0] + n_rays_per_env = ray_starts.shape[1] + + # reshape the tensors + ray_starts = ray_starts.to(torch_device).view(n_envs, n_rays_per_env, 3).contiguous() + ray_directions = ray_directions.to(torch_device).view(n_envs, n_rays_per_env, 3).contiguous() + + # create output tensor for the ray hits + ray_hits = torch.full((n_envs, n_rays_per_env, 3), float("inf"), device=torch_device).contiguous() + + # map the memory to warp arrays + ray_starts_wp = wp.from_torch(ray_starts, dtype=wp.vec3) + ray_directions_wp = wp.from_torch(ray_directions, dtype=wp.vec3) + ray_hits_wp = wp.from_torch(ray_hits, dtype=wp.vec3) + # required to check if a closer hit is reported, returned only if return_distance is true + ray_distance = torch.full( + ( + n_envs, + n_rays_per_env, + ), + float("inf"), + device=torch_device, + ).contiguous() + ray_distance_wp = wp.from_torch(ray_distance, dtype=wp.float32) + + if return_normal: + ray_normal = torch.full((n_envs, n_rays_per_env, 3), float("inf"), device=torch_device).contiguous() + ray_normal_wp = wp.from_torch(ray_normal, dtype=wp.vec3) + else: + ray_normal = None + ray_normal_wp = wp.empty((1, 1), dtype=wp.vec3, device=torch_device) + + if return_face_id: + ray_face_id = torch.ones( + ( + n_envs, + n_rays_per_env, + ), + dtype=torch.int32, + device=torch_device, + ).contiguous() * (-1) + ray_face_id_wp = wp.from_torch(ray_face_id, dtype=wp.int32) + else: + ray_face_id = None + ray_face_id_wp = wp.empty((1, 1), dtype=wp.int32, device=torch_device) + + if return_mesh_id: + ray_mesh_id = -torch.ones((n_envs, n_rays_per_env), dtype=torch.int16, device=torch_device).contiguous() + ray_mesh_id_wp = wp.from_torch(ray_mesh_id, dtype=wp.int16) + else: + ray_mesh_id = None + ray_mesh_id_wp = wp.empty((1, 1), dtype=wp.int16, device=torch_device) + + ## + # Call the warp kernels + ### + if mesh_positions_w is None and mesh_orientations_w is None: + # Static mesh case, no need to pass in positions and rotations. + # launch the warp kernel + wp.launch( + kernel=kernels.raycast_static_meshes_kernel, + dim=[n_meshes, n_envs, n_rays_per_env], + inputs=[ + mesh_ids_wp, + ray_starts_wp, + ray_directions_wp, + ray_hits_wp, + ray_distance_wp, + ray_normal_wp, + ray_face_id_wp, + ray_mesh_id_wp, + float(max_dist), + int(return_normal), + int(return_face_id), + int(return_mesh_id), + ], + device=torch_device, + ) + else: + # dynamic mesh case + if mesh_positions_w is None: + mesh_positions_wp_w = wp.zeros((n_envs, n_meshes), dtype=wp.vec3, device=torch_device) + else: + mesh_positions_w = mesh_positions_w.to(torch_device).view(n_envs, n_meshes, 3).contiguous() + mesh_positions_wp_w = wp.from_torch(mesh_positions_w, dtype=wp.vec3) + + if mesh_orientations_w is None: + # Note (zrene): This is a little bit ugly, since it requires to initialize torch memory first + # But I couldn't find a better way to initialize a quaternion identity in warp + # wp.zeros(1, dtype=wp.quat, device=torch_device) gives all zero quaternion + quat_identity = torch.tensor([0, 0, 0, 1], dtype=torch.float32, device=torch_device).repeat( + n_envs, n_meshes, 1 + ) + mesh_quat_wp_w = wp.from_torch(quat_identity, dtype=wp.quat) + else: + mesh_orientations_w = convert_quat( + mesh_orientations_w.to(dtype=torch.float32, device=torch_device), "xyzw" + ).contiguous() + mesh_quat_wp_w = wp.from_torch(mesh_orientations_w, dtype=wp.quat) + + # launch the warp kernel + wp.launch( + kernel=kernels.raycast_dynamic_meshes_kernel, + dim=[n_meshes, n_envs, n_rays_per_env], + inputs=[ + mesh_ids_wp, + ray_starts_wp, + ray_directions_wp, + ray_hits_wp, + ray_distance_wp, + ray_normal_wp, + ray_face_id_wp, + ray_mesh_id_wp, + mesh_positions_wp_w, + mesh_quat_wp_w, + float(max_dist), + int(return_normal), + int(return_face_id), + int(return_mesh_id), + ], + device=torch_device, + ) + ## + # Cleanup and convert back to torch tensors + ## + + # NOTE: Synchronize is not needed anymore, but we keep it for now. Check with @dhoeller. + wp.synchronize() + + if return_distance: + ray_distance = ray_distance.to(device).view(shape[:2]) + if return_normal: + ray_normal = ray_normal.to(device).view(shape) + if return_face_id: + ray_face_id = ray_face_id.to(device).view(shape[:2]) + if return_mesh_id: + ray_mesh_id = ray_mesh_id.to(device).view(shape[:2]) + + return ray_hits.to(device).view(shape), ray_distance, ray_normal, ray_face_id, ray_mesh_id + + +def convert_to_warp_mesh(points: np.ndarray, indices: np.ndarray, device: str) -> wp.Mesh: + """Create a warp mesh object with a mesh defined from vertices and triangles. + + Args: + points: The vertices of the mesh. Shape is (N, 3), where N is the number of vertices. + indices: The triangles of the mesh as references to vertices for each triangle. + Shape is (M, 3), where M is the number of triangles / faces. + device: The device to use for the mesh. + + Returns: + The warp mesh object. + """ + return wp.Mesh( + points=wp.array(points.astype(np.float32), dtype=wp.vec3, device=device), + indices=wp.array(indices.astype(np.int32).flatten(), dtype=wp.int32, device=device), + ) diff --git a/source/isaaclab/isaaclab/utils/wrench_composer.py b/source/isaaclab/isaaclab/utils/wrench_composer.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd42f81e9e6c3734ff69dcc91b9e86eb5a25d86 --- /dev/null +++ b/source/isaaclab/isaaclab/utils/wrench_composer.py @@ -0,0 +1,349 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import warp as wp + +from isaaclab.utils.math import convert_quat +from isaaclab.utils.warp.kernels import add_forces_and_torques_at_position, set_forces_and_torques_at_position + +if TYPE_CHECKING: + from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection + + +class WrenchComposer: + def __init__(self, asset: Articulation | RigidObject | RigidObjectCollection) -> None: + """Wrench composer. + + This class is used to compose forces and torques at the body's link frame. + It can compose global wrenches and local wrenches. The result is always in the link frame of the body. + + Args: + asset: Asset to use. Defaults to None. + """ + self.num_envs = asset.num_instances + # Avoid isinstance to prevent circular import issues, use attribute presence instead. + if hasattr(asset, "num_bodies"): + self.num_bodies = asset.num_bodies + else: + self.num_bodies = asset.num_objects + self.device = asset.device + self._asset = asset + self._active = False + + # Avoid isinstance here due to potential circular import issues; check by attribute presence instead. + if hasattr(self._asset.data, "body_link_pos_w") and hasattr(self._asset.data, "body_link_quat_w"): + self._get_link_position_fn = lambda a=self._asset: a.data.body_link_pos_w[..., :3] + self._get_link_quaternion_fn = lambda a=self._asset: a.data.body_link_quat_w[..., :4] + elif hasattr(self._asset.data, "object_link_pos_w") and hasattr(self._asset.data, "object_link_quat_w"): + self._get_link_position_fn = lambda a=self._asset: a.data.object_link_pos_w[..., :3] + self._get_link_quaternion_fn = lambda a=self._asset: a.data.object_link_quat_w[..., :4] + else: + raise ValueError(f"Unsupported asset type: {self._asset.__class__.__name__}") + + # Create buffers + self._composed_force_b = wp.zeros((self.num_envs, self.num_bodies), dtype=wp.vec3f, device=self.device) + self._composed_torque_b = wp.zeros((self.num_envs, self.num_bodies), dtype=wp.vec3f, device=self.device) + self._ALL_ENV_INDICES_WP = wp.from_torch( + torch.arange(self.num_envs, dtype=torch.int32, device=self.device), dtype=wp.int32 + ) + self._ALL_BODY_INDICES_WP = wp.from_torch( + torch.arange(self.num_bodies, dtype=torch.int32, device=self.device), dtype=wp.int32 + ) + + # Pinning the composed force and torque to the torch tensor to avoid copying the data to the torch tensor + self._composed_force_b_torch = wp.to_torch(self._composed_force_b) + self._composed_torque_b_torch = wp.to_torch(self._composed_torque_b) + # Pinning the environment and body indices to the torch tensor to allow for slicing. + self._ALL_ENV_INDICES_TORCH = wp.to_torch(self._ALL_ENV_INDICES_WP) + self._ALL_BODY_INDICES_TORCH = wp.to_torch(self._ALL_BODY_INDICES_WP) + + # Flag to check if the link poses have been updated. + self._link_poses_updated = False + + @property + def active(self) -> bool: + """Whether the wrench composer is active.""" + return self._active + + @property + def composed_force(self) -> wp.array: + """Composed force at the body's link frame. + + .. note:: If some of the forces are applied in the global frame, the composed force will be in the link frame + of the body. + + Returns: + wp.array: Composed force at the body's link frame. (num_envs, num_bodies, 3) + """ + return self._composed_force_b + + @property + def composed_torque(self) -> wp.array: + """Composed torque at the body's link frame. + + .. note:: If some of the torques are applied in the global frame, the composed torque will be in the link frame + of the body. + + Returns: + wp.array: Composed torque at the body's link frame. (num_envs, num_bodies, 3) + """ + return self._composed_torque_b + + @property + def composed_force_as_torch(self) -> torch.Tensor: + """Composed force at the body's link frame as torch tensor. + + .. note:: If some of the forces are applied in the global frame, the composed force will be in the link frame + of the body. + + Returns: + torch.Tensor: Composed force at the body's link frame. (num_envs, num_bodies, 3) + """ + return self._composed_force_b_torch + + @property + def composed_torque_as_torch(self) -> torch.Tensor: + """Composed torque at the body's link frame as torch tensor. + + .. note:: If some of the torques are applied in the global frame, the composed torque will be in the link frame + of the body. + + Returns: + torch.Tensor: Composed torque at the body's link frame. (num_envs, num_bodies, 3) + """ + return self._composed_torque_b_torch + + def add_forces_and_torques( + self, + forces: wp.array | torch.Tensor | None = None, + torques: wp.array | torch.Tensor | None = None, + positions: wp.array | torch.Tensor | None = None, + body_ids: wp.array | torch.Tensor | None = None, + env_ids: wp.array | torch.Tensor | None = None, + is_global: bool = False, + ): + """Add forces and torques to the composed force and torque. + + Composed force and torque are the sum of all the forces and torques applied to the body. + It can compose global wrenches and local wrenches. The result is always in the link frame of the body. + + The user can provide any combination of forces, torques, and positions. + + .. note:: Users may want to call `reset` function after every simulation step to ensure no force is carried + over to the next step. However, this may not necessary if the user calls `set_forces_and_torques` function + instead of `add_forces_and_torques`. + + Args: + forces: Forces. (num_envs, num_bodies, 3). Defaults to None. + torques: Torques. (num_envs, num_bodies, 3). Defaults to None. + positions: Positions. (num_envs, num_bodies, 3). Defaults to None. + body_ids: Body ids. (num_envs, num_bodies). Defaults to None (all bodies). + env_ids: Environment ids. (num_envs). Defaults to None (all environments). + is_global: Whether the forces and torques are applied in the global frame. Defaults to False. + + Raises: + ValueError: If the type of the input is not supported. + ValueError: If the input is a slice and it is not None. + """ + # Resolve all indices + # -- env_ids + if env_ids is None: + env_ids = self._ALL_ENV_INDICES_WP + elif isinstance(env_ids, torch.Tensor): + env_ids = wp.from_torch(env_ids.to(torch.int32), dtype=wp.int32) + elif isinstance(env_ids, list): + env_ids = wp.array(env_ids, dtype=wp.int32, device=self.device) + elif isinstance(env_ids, slice): + if env_ids == slice(None): + env_ids = self._ALL_ENV_INDICES_WP + else: + raise ValueError(f"Doesn't support slice input for env_ids: {env_ids}") + # -- body_ids + if body_ids is None: + body_ids = self._ALL_BODY_INDICES_WP + elif isinstance(body_ids, torch.Tensor): + body_ids = wp.from_torch(body_ids.to(torch.int32), dtype=wp.int32) + elif isinstance(body_ids, list): + body_ids = wp.array(body_ids, dtype=wp.int32, device=self.device) + elif isinstance(body_ids, slice): + if body_ids == slice(None): + body_ids = self._ALL_BODY_INDICES_WP + else: + raise ValueError(f"Doesn't support slice input for body_ids: {body_ids}") + + # Resolve remaining inputs + # -- don't launch if no forces or torques are provided + if forces is None and torques is None: + return + if isinstance(forces, torch.Tensor): + forces = wp.from_torch(forces, dtype=wp.vec3f) + if isinstance(torques, torch.Tensor): + torques = wp.from_torch(torques, dtype=wp.vec3f) + if isinstance(positions, torch.Tensor): + positions = wp.from_torch(positions, dtype=wp.vec3f) + + # Get the link positions and quaternions + if not self._link_poses_updated: + self._link_positions = wp.from_torch(self._get_link_position_fn().clone(), dtype=wp.vec3f) + self._link_quaternions = wp.from_torch( + convert_quat(self._get_link_quaternion_fn().clone(), to="xyzw"), dtype=wp.quatf + ) + self._link_poses_updated = True + + # Set the active flag to true + self._active = True + + wp.launch( + add_forces_and_torques_at_position, + dim=(env_ids.shape[0], body_ids.shape[0]), + inputs=[ + env_ids, + body_ids, + forces, + torques, + positions, + self._link_positions, + self._link_quaternions, + self._composed_force_b, + self._composed_torque_b, + is_global, + ], + device=self.device, + ) + + def set_forces_and_torques( + self, + forces: wp.array | torch.Tensor | None = None, + torques: wp.array | torch.Tensor | None = None, + positions: wp.array | torch.Tensor | None = None, + body_ids: wp.array | torch.Tensor | None = None, + env_ids: wp.array | torch.Tensor | None = None, + is_global: bool = False, + ): + """Set forces and torques to the composed force and torque. + + Composed force and torque are the sum of all the forces and torques applied to the body. + It can compose global wrenches and local wrenches. The result is always in the link frame of the body. + + The user can provide any combination of forces, torques, and positions. + + Args: + forces: Forces. (num_envs, num_bodies, 3). Defaults to None. + torques: Torques. (num_envs, num_bodies, 3). Defaults to None. + positions: Positions. (num_envs, num_bodies, 3). Defaults to None. + body_ids: Body ids. (num_envs, num_bodies). Defaults to None (all bodies). + env_ids: Environment ids. (num_envs). Defaults to None (all environments). + is_global: Whether the forces and torques are applied in the global frame. Defaults to False. + + Raises: + ValueError: If the type of the input is not supported. + ValueError: If the input is a slice and it is not None. + """ + # Resolve all indices + # -- env_ids + if env_ids is None: + env_ids = self._ALL_ENV_INDICES_WP + elif isinstance(env_ids, torch.Tensor): + env_ids = wp.from_torch(env_ids.to(torch.int32), dtype=wp.int32) + elif isinstance(env_ids, list): + env_ids = wp.array(env_ids, dtype=wp.int32, device=self.device) + elif isinstance(env_ids, slice): + if env_ids == slice(None): + env_ids = self._ALL_ENV_INDICES_WP + else: + raise ValueError(f"Doesn't support slice input for env_ids: {env_ids}") + # -- body_ids + if body_ids is None: + body_ids = self._ALL_BODY_INDICES_WP + elif isinstance(body_ids, torch.Tensor): + body_ids = wp.from_torch(body_ids.to(torch.int32), dtype=wp.int32) + elif isinstance(body_ids, list): + body_ids = wp.array(body_ids, dtype=wp.int32, device=self.device) + elif isinstance(body_ids, slice): + if body_ids == slice(None): + body_ids = self._ALL_BODY_INDICES_WP + else: + raise ValueError(f"Doesn't support slice input for body_ids: {body_ids}") + # Resolve remaining inputs + # -- don't launch if no forces or torques are provided + if forces is None and torques is None: + return + if forces is None: + forces = wp.empty((0, 0), dtype=wp.vec3f, device=self.device) + elif isinstance(forces, torch.Tensor): + forces = wp.from_torch(forces, dtype=wp.vec3f) + if torques is None: + torques = wp.empty((0, 0), dtype=wp.vec3f, device=self.device) + elif isinstance(torques, torch.Tensor): + torques = wp.from_torch(torques, dtype=wp.vec3f) + if positions is None: + positions = wp.empty((0, 0), dtype=wp.vec3f, device=self.device) + elif isinstance(positions, torch.Tensor): + positions = wp.from_torch(positions, dtype=wp.vec3f) + + # Get the link positions and quaternions + if not self._link_poses_updated: + self._link_positions = wp.from_torch(self._get_link_position_fn().clone(), dtype=wp.vec3f) + self._link_quaternions = wp.from_torch( + convert_quat(self._get_link_quaternion_fn().clone(), to="xyzw"), dtype=wp.quatf + ) + self._link_poses_updated = True + + # Set the active flag to true + self._active = True + + wp.launch( + set_forces_and_torques_at_position, + dim=(env_ids.shape[0], body_ids.shape[0]), + inputs=[ + env_ids, + body_ids, + forces, + torques, + positions, + self._link_positions, + self._link_quaternions, + self._composed_force_b, + self._composed_torque_b, + is_global, + ], + device=self.device, + ) + + def reset(self, env_ids: wp.array | torch.Tensor | None = None): + """Reset the composed force and torque. + + This function will reset the composed force and torque to zero. + It will also make sure the link positions and quaternions are updated in the next call of the + `add_forces_and_torques` or `set_forces_and_torques` functions. + + .. note:: This function should be called after every simulation step / reset to ensure no force is carried + over to the next step. + """ + if env_ids is None: + self._composed_force_b.zero_() + self._composed_torque_b.zero_() + self._active = False + else: + indices = env_ids + if isinstance(env_ids, torch.Tensor): + indices = wp.from_torch(env_ids.to(torch.int32), dtype=wp.int32) + elif isinstance(env_ids, list): + indices = wp.array(env_ids, dtype=wp.int32, device=self.device) + elif isinstance(env_ids, slice): + if env_ids == slice(None): + indices = self._ALL_ENV_INDICES_WP + else: + indices = env_ids + + self._composed_force_b[indices].zero_() + self._composed_torque_b[indices].zero_() + + self._link_poses_updated = False diff --git a/source/isaaclab/pyproject.toml b/source/isaaclab/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab/setup.py b/source/isaaclab/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..19c297df7155f46fc91e1124eaba5252d848dfd3 --- /dev/null +++ b/source/isaaclab/setup.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the 'isaaclab' python package.""" + +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Minimum dependencies required prior to installation +INSTALL_REQUIRES = [ + # generic + "numpy<2", + "torch>=2.7", + "onnx>=1.18.0", # 1.16.2 throws access violation on Windows + "prettytable==3.3.0", + "toml", + # devices + "hidapi==0.14.0.post2", + # reinforcement learning + "gymnasium==1.2.1", + # procedural-generation + "trimesh", + "pyglet<2", + # image processing + "transformers", + "einops", # needed for transformers, doesn't always auto-install + "warp-lang", + # make sure this is consistent with isaac sim version + "pillow==11.3.0", + # livestream + "starlette==0.49.1", + # testing + "pytest", + "pytest-mock", + "junitparser", + "flatdict==4.0.1", + "flaky", + "packaging", +] + +# Append Linux x86_64 and ARM64 deps via PEP 508 markers +SUPPORTED_ARCHS_ARM = "platform_machine in 'x86_64,AMD64,aarch64,arm64'" +SUPPORTED_ARCHS = "platform_machine in 'x86_64,AMD64'" +INSTALL_REQUIRES += [ + # required by isaaclab.isaaclab.controllers.pink_ik + f"pin-pink==3.1.0 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS_ARM})", + f"daqp==0.7.2 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS_ARM})", + # required by isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1_t2_dex_retargeting_utils + f"dex-retargeting==0.4.6 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS})", +] + +PYTORCH_INDEX_URL = ["https://download.pytorch.org/whl/cu128"] + +# Installation operation +setup( + name="isaaclab", + author="Isaac Lab Project Developers", + maintainer="Isaac Lab Project Developers", + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + license="BSD-3-Clause", + include_package_data=True, + python_requires=">=3.10", + install_requires=INSTALL_REQUIRES, + dependency_links=PYTORCH_INDEX_URL, + packages=["isaaclab"], + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab/test/actuators/test_dc_motor.py b/source/isaaclab/test/actuators/test_dc_motor.py new file mode 100644 index 0000000000000000000000000000000000000000..26ad2de0526d4b9d94f1db071403f1f4a2a3252c --- /dev/null +++ b/source/isaaclab/test/actuators/test_dc_motor.py @@ -0,0 +1,192 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +HEADLESS = True + +# if not AppLauncher.instance(): +simulation_app = AppLauncher(headless=HEADLESS).app + +"""Rest of imports follows""" + +import pytest +import torch + +from isaaclab.actuators import DCMotorCfg + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_dc_motor_init_minimum(num_envs, num_joints, device): + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + stiffness = 200 + damping = 10 + effort_limit = 60.0 + saturation_effort = 100.0 + velocity_limit = 50 + + actuator_cfg = DCMotorCfg( + joint_names_expr=joint_names, + stiffness=stiffness, + damping=damping, + effort_limit=effort_limit, + saturation_effort=saturation_effort, + velocity_limit=velocity_limit, + ) + # assume Articulation class: + # - finds joints (names and ids) associate with the provided joint_names_expr + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + ) + + # check device and shape + torch.testing.assert_close(actuator.computed_effort, torch.zeros(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.applied_effort, torch.zeros(num_envs, num_joints, device=device)) + torch.testing.assert_close( + actuator.effort_limit, + effort_limit * torch.ones(num_envs, num_joints, device=device), + ) + torch.testing.assert_close( + actuator.velocity_limit, velocity_limit * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda", "cpu"]) +@pytest.mark.parametrize("test_point", range(20)) +def test_dc_motor_clip(num_envs, num_joints, device, test_point): + r"""Test the computation of the dc motor actuator 4 quadrant torque speed curve. + torque_speed_pairs of interest: + + 0 - fully inside torque speed curve and effort limit (quadrant 1) + 1 - greater than effort limit but under torque-speed curve (quadrant 1) + 2 - greater than effort limit and outside torque-speed curve (quadrant 1) + 3 - less than effort limit but outside torque speed curve (quadrant 1) + 4 - less than effort limit but outside torque speed curve and outside corner velocity(quadrant 4) + 5 - fully inside torque speed curve and effort limit (quadrant 4) + 6 - fully outside torque speed curve and -effort limit (quadrant 4) + 7 - fully inside torque speed curve, outside -effort limit, and inside corner velocity (quadrant 4) + 8 - fully inside torque speed curves, outside -effort limit, and outside corner velocity (quadrant 4) + 9 - less than effort limit but outside torque speed curve and inside corner velocity (quadrant 4) + e - effort_limit + s - saturation_effort + v - velocity_limit + c - corner velocity + \ - torque-speed linear boundary between v and s + each torque_speed_point will be tested in quadrant 3 and 4 + =========================================================== + Torque + \ (+) + \ | + Q2 s Q1 + | \ 2 + \ | 1 \ + c ---------------------e-----\ + \ | \ + \ | 0 \ 3 + \ | \ + (-)-----------v -------------o-------------v --------------(+) Speed + \ | \ 9 4 + \ | 5 \ + \ | \ + \ -----e---------------------c + \ | \ 6 + Q3 \ | 7 Q4 \ + \s \ + |\ 8 \ + (-) \ + ============================================================ + """ + effort_lim = 60 + saturation_effort = 100.0 + velocity_limit = 50 + + torque_speed_pairs = [ + (30.0, 10.0), # 0 + (70.0, 10.0), # 1 + (80.0, 40.0), # 2 + (30.0, 40.0), # 3 + (-20.0, 90.0), # 4 + (-30.0, 10.0), # 5 + (-80.0, 110.0), # 6 + (-80.0, 50.0), # 7 + (-120.0, 90.0), # 8 + (-10.0, 70.0), # 9 + (-30.0, -10.0), # -0 + (-70.0, -10.0), # -1 + (-80.0, -40.0), # -2 + (-30.0, -40.0), # -3 + (20.0, -90.0), # -4 + (30.0, -10.0), # -5 + (80.0, -110.0), # -6 + (80.0, -50.0), # -7 + (120.0, -90.0), # -8 + (10.0, -70.0), # -9 + ] + expected_clipped_effort = [ + 30.0, # 0 + 60.0, # 1 + 20.0, # 2 + 20.0, # 3 + -60.0, # 4 + -30.0, # 5 + -60.0, # 6 + -60.0, # 7 + -60.0, # 8 + -40.0, # 9 + -30.0, # -0 + -60.0, # -1 + -20, # -2 + -20, # -3 + 60.0, # -4 + 30.0, # -5 + 60.0, # -6 + 60.0, # -7 + 60.0, # -8 + 40.0, # -9 + ] + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + stiffness = 200 + damping = 10 + actuator_cfg = DCMotorCfg( + joint_names_expr=joint_names, + stiffness=stiffness, + damping=damping, + effort_limit=effort_lim, + velocity_limit=velocity_limit, + saturation_effort=saturation_effort, + ) + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + ) + + ts = torque_speed_pairs[test_point] + torque = ts[0] + speed = ts[1] + actuator._joint_vel[:] = speed * torch.ones(num_envs, num_joints, device=device) + effort = torque * torch.ones(num_envs, num_joints, device=device) + clipped_effort = actuator._clip_effort(effort) + torch.testing.assert_close( + expected_clipped_effort[test_point] * torch.ones(num_envs, num_joints, device=device), + clipped_effort, + ) diff --git a/source/isaaclab/test/actuators/test_ideal_pd_actuator.py b/source/isaaclab/test/actuators/test_ideal_pd_actuator.py new file mode 100644 index 0000000000000000000000000000000000000000..d77e5e12c34ad43748f182a8093a0a6f65c14624 --- /dev/null +++ b/source/isaaclab/test/actuators/test_ideal_pd_actuator.py @@ -0,0 +1,271 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +HEADLESS = True + +# if not AppLauncher.instance(): +simulation_app = AppLauncher(headless=HEADLESS).app + +"""Rest of imports follows""" + +import pytest +import torch + +from isaaclab.actuators import IdealPDActuatorCfg +from isaaclab.utils.types import ArticulationActions + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("usd_default", [False, True]) +def test_ideal_pd_actuator_init_minimum(num_envs, num_joints, device, usd_default): + """Test initialization of ideal pd actuator with minimum configuration.""" + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + stiffness = None if usd_default else 200 + damping = None if usd_default else 10 + friction = None if usd_default else 0.1 + armature = None if usd_default else 0.2 + + actuator_cfg = IdealPDActuatorCfg( + joint_names_expr=joint_names, + stiffness=stiffness, + damping=damping, + armature=armature, + friction=friction, + ) + # assume Articulation class: + # - finds joints (names and ids) associate with the provided joint_names_expr + + # faux usd defaults + stiffness_default = 300 + damping_default = 20 + friction_default = 0.0 + armature_default = 0.0 + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=stiffness_default, + damping=damping_default, + friction=friction_default, + armature=armature_default, + ) + + # check initialized actuator + assert actuator.is_implicit_model is False + # check device and shape + torch.testing.assert_close(actuator.computed_effort, torch.zeros(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.applied_effort, torch.zeros(num_envs, num_joints, device=device)) + + torch.testing.assert_close(actuator.effort_limit, torch.inf * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close( + actuator.effort_limit_sim, actuator._DEFAULT_MAX_EFFORT_SIM * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close(actuator.velocity_limit, torch.inf * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.velocity_limit_sim, torch.inf * torch.ones(num_envs, num_joints, device=device)) + + if not usd_default: + torch.testing.assert_close(actuator.stiffness, stiffness * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.damping, damping * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.armature, armature * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.friction, friction * torch.ones(num_envs, num_joints, device=device)) + else: + torch.testing.assert_close( + actuator.stiffness, stiffness_default * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close(actuator.damping, damping_default * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close( + actuator.armature, armature_default * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.friction, friction_default * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("effort_lim", [None, 300]) +@pytest.mark.parametrize("effort_lim_sim", [None, 400]) +def test_ideal_pd_actuator_init_effort_limits(num_envs, num_joints, device, effort_lim, effort_lim_sim): + """Test initialization of ideal pd actuator with effort limits.""" + # used as a standin for the usd default value read in by articulation. + # This value should not be propagated for ideal pd actuators + effort_lim_default = 5000 + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + + actuator_cfg = IdealPDActuatorCfg( + joint_names_expr=joint_names, + stiffness=200, + damping=10, + effort_limit=effort_lim, + effort_limit_sim=effort_lim_sim, + ) + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + effort_limit=effort_lim_default, + ) + + if effort_lim is not None and effort_lim_sim is None: + effort_lim_expected = effort_lim + effort_lim_sim_expected = actuator._DEFAULT_MAX_EFFORT_SIM + + elif effort_lim is None and effort_lim_sim is not None: + effort_lim_expected = effort_lim_default + effort_lim_sim_expected = effort_lim_sim + + elif effort_lim is None and effort_lim_sim is None: + effort_lim_expected = effort_lim_default + effort_lim_sim_expected = actuator._DEFAULT_MAX_EFFORT_SIM + + elif effort_lim is not None and effort_lim_sim is not None: + effort_lim_expected = effort_lim + effort_lim_sim_expected = effort_lim_sim + + torch.testing.assert_close( + actuator.effort_limit, effort_lim_expected * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.effort_limit_sim, effort_lim_sim_expected * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("velocity_lim", [None, 300]) +@pytest.mark.parametrize("velocity_lim_sim", [None, 400]) +def test_ideal_pd_actuator_init_velocity_limits(num_envs, num_joints, device, velocity_lim, velocity_lim_sim): + """Test initialization of ideal pd actuator with velocity limits. + + Note Ideal PD actuator does not use velocity limits in computation, they are passed to physics via articulations. + """ + velocity_limit_default = 1000 + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + + actuator_cfg = IdealPDActuatorCfg( + joint_names_expr=joint_names, + stiffness=200, + damping=10, + velocity_limit=velocity_lim, + velocity_limit_sim=velocity_lim_sim, + ) + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + velocity_limit=velocity_limit_default, + ) + if velocity_lim is not None and velocity_lim_sim is None: + vel_lim_expected = velocity_lim + vel_lim_sim_expected = velocity_limit_default + elif velocity_lim is None and velocity_lim_sim is not None: + vel_lim_expected = velocity_lim_sim + vel_lim_sim_expected = velocity_lim_sim + elif velocity_lim is None and velocity_lim_sim is None: + vel_lim_expected = velocity_limit_default + vel_lim_sim_expected = velocity_limit_default + elif velocity_lim is not None and velocity_lim_sim is not None: + vel_lim_expected = velocity_lim + vel_lim_sim_expected = velocity_lim_sim + + torch.testing.assert_close( + actuator.velocity_limit, vel_lim_expected * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.velocity_limit_sim, vel_lim_sim_expected * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("effort_lim", [None, 300]) +def test_ideal_pd_compute(num_envs, num_joints, device, effort_lim): + """Test the computation of the ideal pd actuator.""" + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + stiffness = 200 + damping = 10 + actuator_cfg = IdealPDActuatorCfg( + joint_names_expr=joint_names, + stiffness=stiffness, + damping=damping, + effort_limit=effort_lim, + ) + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + ) + desired_pos = 10.0 + desired_vel = 0.1 + measured_joint_pos = 1.0 + measured_joint_vel = -0.1 + + desired_control_action = ArticulationActions() + desired_control_action.joint_positions = desired_pos * torch.ones(num_envs, num_joints, device=device) + desired_control_action.joint_velocities = desired_vel * torch.ones(num_envs, num_joints, device=device) + desired_control_action.joint_efforts = torch.zeros(num_envs, num_joints, device=device) + + expected_comp_joint_effort = stiffness * (desired_pos - measured_joint_pos) + damping * ( + desired_vel - measured_joint_vel + ) + + computed_control_action = actuator.compute( + desired_control_action, + measured_joint_pos * torch.ones(num_envs, num_joints, device=device), + measured_joint_vel * torch.ones(num_envs, num_joints, device=device), + ) + + torch.testing.assert_close( + expected_comp_joint_effort * torch.ones(num_envs, num_joints, device=device), actuator.computed_effort + ) + + if effort_lim is None: + torch.testing.assert_close( + expected_comp_joint_effort * torch.ones(num_envs, num_joints, device=device), actuator.applied_effort + ) + else: + torch.testing.assert_close( + effort_lim * torch.ones(num_envs, num_joints, device=device), actuator.applied_effort + ) + torch.testing.assert_close( + actuator.applied_effort, + computed_control_action.joint_efforts, + ) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "--maxfail=1"]) diff --git a/source/isaaclab/test/actuators/test_implicit_actuator.py b/source/isaaclab/test/actuators/test_implicit_actuator.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a26f2f953382dc620ace12f4ddc076c8c60585 --- /dev/null +++ b/source/isaaclab/test/actuators/test_implicit_actuator.py @@ -0,0 +1,242 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +HEADLESS = True + +# if not AppLauncher.instance(): +simulation_app = AppLauncher(headless=HEADLESS).app + +"""Rest of imports follows""" + +import pytest +import torch + +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.sim import build_simulation_context + + +@pytest.fixture +def sim(request): + """Create simulation context with the specified device.""" + device = request.getfixturevalue("device") + with build_simulation_context(device=device) as sim: + sim._app_control_on_stop_handle = None + yield sim + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("usd_default", [False, True]) +def test_implicit_actuator_init_minimum(sim, num_envs, num_joints, device, usd_default): + """Test initialization of implicit actuator with minimum configuration.""" + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + stiffness = None if usd_default else 200 + damping = None if usd_default else 10 + friction = None if usd_default else 0.1 + armature = None if usd_default else 0.2 + + actuator_cfg = ImplicitActuatorCfg( + joint_names_expr=joint_names, + stiffness=stiffness, + damping=damping, + armature=armature, + friction=friction, + ) + # assume Articulation class: + # - finds joints (names and ids) associate with the provided joint_names_expr + + # faux usd defaults + stiffness_default = 300 + damping_default = 20 + friction_default = 0.0 + armature_default = 0.0 + + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=stiffness_default, + damping=damping_default, + friction=friction_default, + armature=armature_default, + ) + + # check initialized actuator + assert actuator.is_implicit_model is True + # check device and shape + torch.testing.assert_close(actuator.computed_effort, torch.zeros(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.applied_effort, torch.zeros(num_envs, num_joints, device=device)) + + torch.testing.assert_close(actuator.effort_limit, torch.inf * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.effort_limit_sim, torch.inf * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.velocity_limit, torch.inf * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.velocity_limit_sim, torch.inf * torch.ones(num_envs, num_joints, device=device)) + + if not usd_default: + torch.testing.assert_close(actuator.stiffness, stiffness * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.damping, damping * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.armature, armature * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close(actuator.friction, friction * torch.ones(num_envs, num_joints, device=device)) + else: + torch.testing.assert_close( + actuator.stiffness, stiffness_default * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close(actuator.damping, damping_default * torch.ones(num_envs, num_joints, device=device)) + torch.testing.assert_close( + actuator.armature, armature_default * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.friction, friction_default * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("effort_lim", [None, 300, 200]) +@pytest.mark.parametrize("effort_lim_sim", [None, 400, 200]) +def test_implicit_actuator_init_effort_limits(sim, num_envs, num_joints, device, effort_lim, effort_lim_sim): + """Test initialization of implicit actuator with effort limits.""" + effort_limit_default = 5000 + + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + + actuator_cfg = ImplicitActuatorCfg( + joint_names_expr=joint_names, + stiffness=200, + damping=10, + effort_limit=effort_lim, + effort_limit_sim=effort_lim_sim, + ) + + if effort_lim is not None and effort_lim_sim is not None and effort_lim != effort_lim_sim: + with pytest.raises(ValueError): + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + effort_limit=effort_limit_default, + ) + else: + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + effort_limit=effort_limit_default, + ) + if effort_lim is not None and effort_lim_sim is None: + assert actuator.cfg.effort_limit_sim == actuator.cfg.effort_limit + effort_lim_expected = effort_lim + effort_lim_sim_expected = effort_lim + + elif effort_lim is None and effort_lim_sim is not None: + assert actuator.cfg.effort_limit_sim == actuator.cfg.effort_limit + effort_lim_expected = effort_lim_sim + effort_lim_sim_expected = effort_lim_sim + + elif effort_lim is None and effort_lim_sim is None: + assert actuator.cfg.effort_limit_sim is None + assert actuator.cfg.effort_limit is None + effort_lim_expected = effort_limit_default + effort_lim_sim_expected = effort_limit_default + + elif effort_lim is not None and effort_lim_sim is not None: + assert actuator.cfg.effort_limit_sim == actuator.cfg.effort_limit + effort_lim_expected = effort_lim + effort_lim_sim_expected = effort_lim_sim + + torch.testing.assert_close( + actuator.effort_limit, effort_lim_expected * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.effort_limit_sim, effort_lim_sim_expected * torch.ones(num_envs, num_joints, device=device) + ) + + +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.parametrize("num_joints", [1, 2]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("velocity_lim", [None, 300, 200]) +@pytest.mark.parametrize("velocity_lim_sim", [None, 400, 200]) +def test_implicit_actuator_init_velocity_limits(sim, num_envs, num_joints, device, velocity_lim, velocity_lim_sim): + """Test initialization of implicit actuator with velocity limits. + + Note implicit actuators do no use velocity limits in computation, they are passed to physics via articulations. + """ + velocity_limit_default = 1000 + joint_names = [f"joint_{d}" for d in range(num_joints)] + joint_ids = [d for d in range(num_joints)] + + actuator_cfg = ImplicitActuatorCfg( + joint_names_expr=joint_names, + stiffness=200, + damping=10, + velocity_limit=velocity_lim, + velocity_limit_sim=velocity_lim_sim, + ) + + if velocity_lim is not None and velocity_lim_sim is not None and velocity_lim != velocity_lim_sim: + with pytest.raises(ValueError): + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + velocity_limit=velocity_limit_default, + ) + else: + actuator = actuator_cfg.class_type( + actuator_cfg, + joint_names=joint_names, + joint_ids=joint_ids, + num_envs=num_envs, + device=device, + stiffness=actuator_cfg.stiffness, + damping=actuator_cfg.damping, + velocity_limit=velocity_limit_default, + ) + if velocity_lim is not None and velocity_lim_sim is None: + assert actuator.cfg.velocity_limit is None + vel_lim_expected = velocity_limit_default + elif velocity_lim is None and velocity_lim_sim is not None: + assert actuator.cfg.velocity_limit == actuator.cfg.velocity_limit_sim + vel_lim_expected = velocity_lim_sim + elif velocity_lim is None and velocity_lim_sim is None: + assert actuator.cfg.velocity_limit is None + assert actuator.cfg.velocity_limit_sim is None + vel_lim_expected = velocity_limit_default + else: + assert actuator.cfg.velocity_limit == actuator.cfg.velocity_limit_sim + vel_lim_expected = velocity_lim_sim + + torch.testing.assert_close( + actuator.velocity_limit, vel_lim_expected * torch.ones(num_envs, num_joints, device=device) + ) + torch.testing.assert_close( + actuator.velocity_limit_sim, vel_lim_expected * torch.ones(num_envs, num_joints, device=device) + ) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "--maxfail=1"]) diff --git a/source/isaaclab/test/app/test_argparser_launch.py b/source/isaaclab/test/app/test_argparser_launch.py new file mode 100644 index 0000000000000000000000000000000000000000..683409dd190c4f59786e396dbe3f37ebb2f228af --- /dev/null +++ b/source/isaaclab/test/app/test_argparser_launch.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse + +import pytest + +from isaaclab.app import AppLauncher + + +@pytest.mark.usefixtures("mocker") +def test_livestream_launch_with_argparser(mocker): + """Test launching with argparser arguments.""" + # Mock the parse_args method + mocker.patch("argparse.ArgumentParser.parse_args", return_value=argparse.Namespace(livestream=1, headless=True)) + # create argparser + parser = argparse.ArgumentParser() + # add app launcher arguments + AppLauncher.add_app_launcher_args(parser) + # check that argparser has the mandatory arguments + for name in AppLauncher._APPLAUNCHER_CFG_INFO: + assert parser._option_string_actions[f"--{name}"] + # parse args + mock_args = parser.parse_args() + # everything defaults to None + app = AppLauncher(mock_args).app + + # import settings + import carb + + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # check settings + # -- no-gui mode + assert carb_settings_iface.get("/app/window/enabled") is False + # -- livestream + assert carb_settings_iface.get("/app/livestream/enabled") is True + + # close the app on exit + app.close() diff --git a/source/isaaclab/test/app/test_env_var_launch.py b/source/isaaclab/test/app/test_env_var_launch.py new file mode 100644 index 0000000000000000000000000000000000000000..9ec07f93274946137d4f25b7f09c255fa729ee75 --- /dev/null +++ b/source/isaaclab/test/app/test_env_var_launch.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os + +import pytest + +from isaaclab.app import AppLauncher + + +@pytest.mark.usefixtures("mocker") +def test_livestream_launch_with_env_vars(mocker): + """Test launching with environment variables.""" + # Mock the environment variables + mocker.patch.dict(os.environ, {"LIVESTREAM": "1", "HEADLESS": "1"}) + # everything defaults to None + app = AppLauncher().app + + # import settings + import carb + + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # check settings + # -- no-gui mode + assert carb_settings_iface.get("/app/window/enabled") is False + # -- livestream + assert carb_settings_iface.get("/app/livestream/enabled") is True + + # close the app on exit + app.close() diff --git a/source/isaaclab/test/app/test_kwarg_launch.py b/source/isaaclab/test/app/test_kwarg_launch.py new file mode 100644 index 0000000000000000000000000000000000000000..b2781637b722ad955d2598d638f4e69c5a81f9e4 --- /dev/null +++ b/source/isaaclab/test/app/test_kwarg_launch.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import pytest + +from isaaclab.app import AppLauncher + + +@pytest.mark.usefixtures("mocker") +def test_livestream_launch_with_kwargs(mocker): + """Test launching with keyword arguments.""" + # everything defaults to None + app = AppLauncher(headless=True, livestream=1).app + + # import settings + import carb + + # acquire settings interface + carb_settings_iface = carb.settings.get_settings() + # check settings + # -- no-gui mode + assert carb_settings_iface.get("/app/window/enabled") is False + # -- livestream + assert carb_settings_iface.get("/app/livestream/enabled") is True + + # close the app on exit + app.close() diff --git a/source/isaaclab/test/app/test_non_headless_launch.py b/source/isaaclab/test/app/test_non_headless_launch.py new file mode 100644 index 0000000000000000000000000000000000000000..eb8544b995cc1f7a116c4aca82dfb4853a6ee900 --- /dev/null +++ b/source/isaaclab/test/app/test_non_headless_launch.py @@ -0,0 +1,65 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script checks if the app can be launched with non-headless app and start the simulation. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import pytest + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(experience="isaaclab.python.kit", headless=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.utils import configclass + + +@configclass +class SensorsSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + +def run_simulator( + sim: sim_utils.SimulationContext, +): + """Run the simulator.""" + + count = 0 + + # Simulate physics + while simulation_app.is_running() and count < 100: + # perform step + sim.step() + count += 1 + + +@pytest.mark.isaacsim_ci +def test_non_headless_launch(): + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005) + sim = sim_utils.SimulationContext(sim_cfg) + # design scene + scene_cfg = SensorsSceneCfg(num_envs=1, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + print(scene) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim) diff --git a/source/isaaclab/test/controllers/simplified_test_robot.urdf b/source/isaaclab/test/controllers/simplified_test_robot.urdf new file mode 100644 index 0000000000000000000000000000000000000000..b66ce68324bb918cc81fb13ffe82c01db3cf7306 --- /dev/null +++ b/source/isaaclab/test/controllers/simplified_test_robot.urdf @@ -0,0 +1,191 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/isaaclab/test/controllers/test_controller_utils.py b/source/isaaclab/test/controllers/test_controller_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..80a839a847f49c7a99e9c68f2e16307384f3c687 --- /dev/null +++ b/source/isaaclab/test/controllers/test_controller_utils.py @@ -0,0 +1,662 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for Isaac Lab controller utilities.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import os + +# Import the function to test +import tempfile + +import pytest +import torch + +from isaaclab.controllers.utils import change_revolute_to_fixed, change_revolute_to_fixed_regex +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path +from isaaclab.utils.io.torchscript import load_torchscript_model + + +@pytest.fixture +def mock_urdf_content(): + """Create mock URDF content for testing.""" + return """ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +""" + + +@pytest.fixture +def test_urdf_file(mock_urdf_content): + """Create a temporary URDF file for testing.""" + # Create a temporary directory for test files + test_dir = tempfile.mkdtemp() + + # Create the test URDF file + test_urdf_path = os.path.join(test_dir, "test_robot.urdf") + with open(test_urdf_path, "w") as f: + f.write(mock_urdf_content) + + yield test_urdf_path + + # Clean up the temporary directory and all its contents + import shutil + + shutil.rmtree(test_dir) + + +# ============================================================================= +# Test cases for change_revolute_to_fixed function +# ============================================================================= + + +def test_single_joint_conversion(test_urdf_file, mock_urdf_content): + """Test converting a single revolute joint to fixed.""" + # Test converting shoulder_to_elbow joint + fixed_joints = ["shoulder_to_elbow"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the joint was converted + assert '' in modified_content + assert '' not in modified_content + + # Check that other revolute joints remain unchanged + assert '' in modified_content + assert '' in modified_content + + +def test_multiple_joints_conversion(test_urdf_file, mock_urdf_content): + """Test converting multiple revolute joints to fixed.""" + # Test converting multiple joints + fixed_joints = ["base_to_shoulder", "elbow_to_wrist"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that both joints were converted + assert '' in modified_content + assert '' in modified_content + assert '' not in modified_content + assert '' not in modified_content + + # Check that the middle joint remains unchanged + assert '' in modified_content + + +def test_non_existent_joint(test_urdf_file, mock_urdf_content): + """Test behavior when trying to convert a non-existent joint.""" + # Try to convert a joint that doesn't exist + fixed_joints = ["non_existent_joint"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged + assert modified_content == mock_urdf_content + + +def test_mixed_existent_and_non_existent_joints(test_urdf_file, mock_urdf_content): + """Test converting a mix of existent and non-existent joints.""" + # Try to convert both existent and non-existent joints + fixed_joints = ["base_to_shoulder", "non_existent_joint", "elbow_to_wrist"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that existent joints were converted + assert '' in modified_content + assert '' in modified_content + + # Check that non-existent joint didn't cause issues + assert '' not in modified_content + + +def test_already_fixed_joint(test_urdf_file, mock_urdf_content): + """Test behavior when trying to convert an already fixed joint.""" + # Try to convert a joint that is already fixed + fixed_joints = ["wrist_to_gripper"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged (no conversion happened) + assert modified_content == mock_urdf_content + + +def test_empty_joints_list(test_urdf_file, mock_urdf_content): + """Test behavior when passing an empty list of joints.""" + # Try to convert with empty list + fixed_joints = [] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged + assert modified_content == mock_urdf_content + + +def test_file_not_found(test_urdf_file): + """Test behavior when URDF file doesn't exist.""" + non_existent_path = os.path.join(os.path.dirname(test_urdf_file), "non_existent.urdf") + fixed_joints = ["base_to_shoulder"] + + # Should raise FileNotFoundError + with pytest.raises(FileNotFoundError): + change_revolute_to_fixed(non_existent_path, fixed_joints) + + +def test_preserve_other_content(test_urdf_file): + """Test that other content in the URDF file is preserved.""" + fixed_joints = ["shoulder_to_elbow"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that other content is preserved + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + # Check that the fixed joint remains unchanged + assert '' in modified_content + + +def test_joint_attributes_preserved(test_urdf_file): + """Test that joint attributes other than type are preserved.""" + fixed_joints = ["base_to_shoulder"] + change_revolute_to_fixed(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the joint was converted but other attributes preserved + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + +# ============================================================================= +# Test cases for change_revolute_to_fixed_regex function +# ============================================================================= + + +def test_regex_single_joint_conversion(test_urdf_file, mock_urdf_content): + """Test converting a single revolute joint to fixed using regex pattern.""" + # Test converting shoulder_to_elbow joint using exact match + fixed_joints = ["shoulder_to_elbow"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the joint was converted + assert '' in modified_content + assert '' not in modified_content + + # Check that other revolute joints remain unchanged + assert '' in modified_content + assert '' in modified_content + + +def test_regex_pattern_matching(test_urdf_file, mock_urdf_content): + """Test converting joints using regex patterns.""" + # Test converting joints that contain "to" in their name + fixed_joints = [r".*to.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that all joints with "to" in the name were converted + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + # Check that the fixed joint remains unchanged + assert '' in modified_content + + +def test_regex_multiple_patterns(test_urdf_file, mock_urdf_content): + """Test converting joints using multiple regex patterns.""" + # Test converting joints that start with "base" or end with "wrist" + fixed_joints = [r"^base.*", r".*wrist$"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that matching joints were converted + assert '' in modified_content + assert '' in modified_content + + # Check that non-matching joints remain unchanged + assert '' in modified_content + + +def test_regex_case_sensitive_matching(test_urdf_file, mock_urdf_content): + """Test that regex matching is case sensitive.""" + # Test with uppercase pattern that won't match lowercase joint names + fixed_joints = [r".*TO.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that no joints were converted (case sensitive) + assert modified_content == mock_urdf_content + + +def test_regex_partial_word_matching(test_urdf_file, mock_urdf_content): + """Test converting joints using partial word matching.""" + # Test converting joints that contain "shoulder" in their name + fixed_joints = [r".*shoulder.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that shoulder-related joints were converted + assert '' in modified_content + assert '' in modified_content + + # Check that other joints remain unchanged + assert '' in modified_content + + +def test_regex_no_matches(test_urdf_file, mock_urdf_content): + """Test behavior when regex patterns don't match any joints.""" + # Test with pattern that won't match any joint names + fixed_joints = [r"^nonexistent.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged + assert modified_content == mock_urdf_content + + +def test_regex_empty_patterns_list(test_urdf_file, mock_urdf_content): + """Test behavior when passing an empty list of regex patterns.""" + # Try to convert with empty list + fixed_joints = [] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged + assert modified_content == mock_urdf_content + + +def test_regex_file_not_found(test_urdf_file): + """Test behavior when URDF file doesn't exist for regex function.""" + non_existent_path = os.path.join(os.path.dirname(test_urdf_file), "non_existent.urdf") + fixed_joints = [r".*to.*"] + + # Should raise FileNotFoundError + with pytest.raises(FileNotFoundError): + change_revolute_to_fixed_regex(non_existent_path, fixed_joints) + + +def test_regex_preserve_other_content(test_urdf_file): + """Test that other content in the URDF file is preserved with regex function.""" + fixed_joints = [r".*shoulder.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that other content is preserved + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + # Check that the fixed joint remains unchanged + assert '' in modified_content + + +def test_regex_joint_attributes_preserved(test_urdf_file): + """Test that joint attributes other than type are preserved with regex function.""" + fixed_joints = [r"^base.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the joint was converted but other attributes preserved + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + +def test_regex_complex_pattern(test_urdf_file, mock_urdf_content): + """Test converting joints using a complex regex pattern.""" + # Test converting joints that have "to" and end with a word starting with "w" + fixed_joints = [r".*to.*w.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that matching joints were converted + assert '' in modified_content + assert '' in modified_content + + # Check that non-matching joints remain unchanged + assert '' in modified_content + + +def test_regex_already_fixed_joint(test_urdf_file, mock_urdf_content): + """Test behavior when regex pattern matches an already fixed joint.""" + # Try to convert joints that contain "gripper" (which is already fixed) + fixed_joints = [r".*gripper.*"] + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that the file content remains unchanged (no conversion happened) + assert modified_content == mock_urdf_content + + +def test_regex_special_characters(test_urdf_file, mock_urdf_content): + """Test regex patterns with special characters.""" + # Test with pattern that includes special regex characters + fixed_joints = [r".*to.*"] # This should match joints with "to" + change_revolute_to_fixed_regex(test_urdf_file, fixed_joints) + + # Read the modified file + with open(test_urdf_file) as f: + modified_content = f.read() + + # Check that joints with "to" were converted + assert '' in modified_content + assert '' in modified_content + assert '' in modified_content + + # Check that the fixed joint remains unchanged + assert '' in modified_content + + +# ============================================================================= +# Test cases for load_torchscript_model function +# ============================================================================= + + +@pytest.fixture +def policy_model_path(): + """Path to the test TorchScript model.""" + _policy_path = f"{ISAACLAB_NUCLEUS_DIR}/Policies/Agile/agile_locomotion.pt" + return retrieve_file_path(_policy_path) + + +def test_load_torchscript_model_success(policy_model_path): + """Test successful loading of a TorchScript model.""" + model = load_torchscript_model(policy_model_path) + + # Check that model was loaded successfully + assert model is not None + assert isinstance(model, torch.nn.Module) + + # Check that model is in evaluation mode + assert model.training is False + + +def test_load_torchscript_model_cpu_device(policy_model_path): + """Test loading TorchScript model on CPU device.""" + model = load_torchscript_model(policy_model_path, device="cpu") + + # Check that model was loaded successfully + assert model is not None + assert isinstance(model, torch.nn.Module) + + # Check that model is in evaluation mode + assert model.training is False + + +def test_load_torchscript_model_cuda_device(policy_model_path): + """Test loading TorchScript model on CUDA device if available.""" + if torch.cuda.is_available(): + model = load_torchscript_model(policy_model_path, device="cuda") + + # Check that model was loaded successfully + assert model is not None + assert isinstance(model, torch.nn.Module) + + # Check that model is in evaluation mode + assert model.training is False + else: + # Skip test if CUDA is not available + pytest.skip("CUDA not available") + + +def test_load_torchscript_model_file_not_found(): + """Test behavior when TorchScript model file doesn't exist.""" + non_existent_path = "non_existent_model.pt" + + # Should raise FileNotFoundError + with pytest.raises(FileNotFoundError): + load_torchscript_model(non_existent_path) + + +def test_load_torchscript_model_invalid_file(): + """Test behavior when trying to load an invalid TorchScript file.""" + # Create a temporary file with invalid content + import tempfile + + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as temp_file: + temp_file.write(b"invalid torchscript content") + temp_file_path = temp_file.name + + try: + # Should handle the error gracefully and return None + model = load_torchscript_model(temp_file_path) + assert model is None + finally: + # Clean up the temporary file + os.unlink(temp_file_path) + + +def test_load_torchscript_model_empty_file(): + """Test behavior when trying to load an empty TorchScript file.""" + # Create a temporary empty file + import tempfile + + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as temp_file: + temp_file_path = temp_file.name + + try: + # Should handle the error gracefully and return None + model = load_torchscript_model(temp_file_path) + assert model is None + finally: + # Clean up the temporary file + os.unlink(temp_file_path) + + +def test_load_torchscript_model_different_device_mapping(policy_model_path): + """Test loading model with different device mapping.""" + # Test with specific device mapping + model = load_torchscript_model(policy_model_path, device="cpu") + + # Check that model was loaded successfully + assert model is not None + assert isinstance(model, torch.nn.Module) + + +def test_load_torchscript_model_evaluation_mode(policy_model_path): + """Test that loaded model is in evaluation mode.""" + model = load_torchscript_model(policy_model_path) + + # Check that model is in evaluation mode + assert model.training is False + + # Verify we can set it to training mode and back + model.train() + assert model.training is True + model.eval() + assert model.training is False + + +def test_load_torchscript_model_inference_capability(policy_model_path): + """Test that loaded model can perform inference.""" + model = load_torchscript_model(policy_model_path) + + # Check that model was loaded successfully + assert model is not None + + # Try to create a dummy input tensor (actual input shape depends on the model) + # This is a basic test to ensure the model can handle tensor inputs + try: + # Create a dummy input tensor (adjust size based on expected input) + dummy_input = torch.randn(1, 75) # Adjust dimensions as needed + + # Try to run inference (this might fail if input shape is wrong, but shouldn't crash) + with torch.no_grad(): + try: + output = model(dummy_input) + # If successful, check that output is a tensor + assert isinstance(output, torch.Tensor) + except (RuntimeError, ValueError): + # Expected if input shape doesn't match model expectations + # This is acceptable for this test + pass + except Exception: + # If model doesn't accept this input format, that's okay for this test + # The main goal is to ensure the model loads without crashing + pass + + +def test_load_torchscript_model_error_handling(): + """Test error handling when loading fails.""" + # Create a temporary file that will cause a loading error + import tempfile + + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as temp_file: + temp_file.write(b"definitely not a torchscript model") + temp_file_path = temp_file.name + + try: + # Should handle the error gracefully and return None + model = load_torchscript_model(temp_file_path) + assert model is None + finally: + # Clean up the temporary file + os.unlink(temp_file_path) diff --git a/source/isaaclab/test/controllers/test_differential_ik.py b/source/isaaclab/test/controllers/test_differential_ik.py new file mode 100644 index 0000000000000000000000000000000000000000..65ce828129f3ce0359d0bf01626e356e916fb356 --- /dev/null +++ b/source/isaaclab/test/controllers/test_differential_ik.py @@ -0,0 +1,228 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +from isaacsim.core.cloner import GridCloner + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg + +from isaaclab.utils.math import ( # isort:skip + compute_pose_error, + matrix_from_quat, + quat_inv, + random_yaw_orientation, + subtract_frame_transforms, +) + +## +# Pre-defined configs +## +from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG # isort:skip + + +@pytest.fixture +def sim(): + """Create a simulation context for testing.""" + # Wait for spawning + stage = sim_utils.create_new_stage() + # Constants + num_envs = 128 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.01) + sim = sim_utils.SimulationContext(sim_cfg) + # TODO: Remove this once we have a better way to handle this. + sim._app_control_on_stop_handle = None + + # Create a ground plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/GroundPlane", cfg) + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + env_prim_paths = cloner.generate_paths("/World/envs/env", num_envs) + # create source prim + stage.DefinePrim(env_prim_paths[0], "Xform") + # clone the env xform + cloner.clone( + source_prim_path=env_prim_paths[0], + prim_paths=env_prim_paths, + replicate_physics=True, + ) + + # Define goals for the arm + ee_goals_set = [ + [0.5, 0.5, 0.7, 0.707, 0, 0.707, 0], + [0.5, -0.4, 0.6, 0.707, 0.707, 0.0, 0.0], + [0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0], + ] + ee_pose_b_des_set = torch.tensor(ee_goals_set, device=sim.device) + + yield sim, num_envs, ee_pose_b_des_set + + # Cleanup + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +def test_franka_ik_pose_abs(sim): + """Test IK controller for Franka arm with Franka hand.""" + sim_context, num_envs, ee_pose_b_des_set = sim + + # Create robot instance + robot_cfg = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="/World/envs/env_.*/Robot") + robot = Articulation(cfg=robot_cfg) + + # Create IK controller + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=num_envs, device=sim_context.device) + + # Run the controller and check that it converges to the goal + _run_ik_controller( + robot, diff_ik_controller, "panda_hand", ["panda_joint.*"], sim_context, num_envs, ee_pose_b_des_set + ) + + +def test_ur10_ik_pose_abs(sim): + """Test IK controller for UR10 arm.""" + sim_context, num_envs, ee_pose_b_des_set = sim + + # Create robot instance + robot_cfg = UR10_CFG.replace(prim_path="/World/envs/env_.*/Robot") + robot_cfg.spawn.rigid_props.disable_gravity = True + robot = Articulation(cfg=robot_cfg) + + # Create IK controller + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=num_envs, device=sim_context.device) + + # Run the controller and check that it converges to the goal + _run_ik_controller(robot, diff_ik_controller, "ee_link", [".*"], sim_context, num_envs, ee_pose_b_des_set) + + +def _run_ik_controller( + robot: Articulation, + diff_ik_controller: DifferentialIKController, + ee_frame_name: str, + arm_joint_names: list[str], + sim: sim_utils.SimulationContext, + num_envs: int, + ee_pose_b_des_set: torch.Tensor, +): + """Run the IK controller with the given parameters. + + Args: + robot (Articulation): The robot to control. + diff_ik_controller (DifferentialIKController): The differential IK controller. + ee_frame_name (str): The name of the end-effector frame. + arm_joint_names (list[str]): The names of the arm joints. + sim (sim_utils.SimulationContext): The simulation context. + num_envs (int): The number of environments. + ee_pose_b_des_set (torch.Tensor): The set of desired end-effector poses. + """ + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Play the simulator + sim.reset() + + # Obtain the frame index of the end-effector + ee_frame_idx = robot.find_bodies(ee_frame_name)[0][0] + ee_jacobi_idx = ee_frame_idx - 1 + # Obtain joint indices + arm_joint_ids = robot.find_joints(arm_joint_names)[0] + # Update existing buffers + # Note: We need to update buffers before the first step for the controller. + robot.update(dt=sim_dt) + + # Track the given command + current_goal_idx = 0 + # Current goal for the arm + ee_pose_b_des = torch.zeros(num_envs, diff_ik_controller.action_dim, device=sim.device) + ee_pose_b_des[:] = ee_pose_b_des_set[current_goal_idx] + # Compute current pose of the end-effector + ee_pose_w = robot.data.body_pose_w[:, ee_frame_idx] + root_pose_w = robot.data.root_pose_w + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + + # Now we are ready! + for count in range(1500): + # reset every 150 steps + if count % 250 == 0: + # check that we converged to the goal + if count > 0: + pos_error, rot_error = compute_pose_error( + ee_pos_b, ee_quat_b, ee_pose_b_des[:, 0:3], ee_pose_b_des[:, 3:7] + ) + pos_error_norm = torch.norm(pos_error, dim=-1) + rot_error_norm = torch.norm(rot_error, dim=-1) + # desired error (zer) + des_error = torch.zeros_like(pos_error_norm) + # check convergence + torch.testing.assert_close(pos_error_norm, des_error, rtol=0.0, atol=1e-3) + torch.testing.assert_close(rot_error_norm, des_error, rtol=0.0, atol=1e-3) + # reset joint state + joint_pos = robot.data.default_joint_pos.clone() + joint_vel = robot.data.default_joint_vel.clone() + # joint_pos *= sample_uniform(0.9, 1.1, joint_pos.shape, joint_pos.device) + robot.write_joint_state_to_sim(joint_pos, joint_vel) + robot.set_joint_position_target(joint_pos) + robot.write_data_to_sim() + # randomize root state yaw, ik should work regardless base rotation + root_state = robot.data.root_state_w.clone() + root_state[:, 3:7] = random_yaw_orientation(num_envs, sim.device) + robot.write_root_pose_to_sim(root_state[:, :7]) + robot.write_root_velocity_to_sim(root_state[:, 7:]) + robot.reset() + # reset actions + ee_pose_b_des[:] = ee_pose_b_des_set[current_goal_idx] + joint_pos_des = joint_pos[:, arm_joint_ids].clone() + # update goal for next iteration + current_goal_idx = (current_goal_idx + 1) % len(ee_pose_b_des_set) + # set the controller commands + diff_ik_controller.reset() + diff_ik_controller.set_command(ee_pose_b_des) + else: + # at reset, the jacobians are not updated to the latest state + # so we MUST skip the first step + # obtain quantities from simulation + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, arm_joint_ids] + ee_pose_w = robot.data.body_pose_w[:, ee_frame_idx] + root_pose_w = robot.data.root_pose_w + base_rot = root_pose_w[:, 3:7] + base_rot_matrix = matrix_from_quat(quat_inv(base_rot)) + jacobian[:, :3, :] = torch.bmm(base_rot_matrix, jacobian[:, :3, :]) + jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :]) + joint_pos = robot.data.joint_pos[:, arm_joint_ids] + # compute frame in root frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + # compute the joint commands + joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + + # apply actions + robot.set_joint_position_target(joint_pos_des, arm_joint_ids) + robot.write_data_to_sim() + # perform step + sim.step(render=False) + # update buffers + robot.update(sim_dt) diff --git a/source/isaaclab/test/controllers/test_ik_configs/README.md b/source/isaaclab/test/controllers/test_ik_configs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ccbdae06b52e522375801dff2c4a34acf9d251a7 --- /dev/null +++ b/source/isaaclab/test/controllers/test_ik_configs/README.md @@ -0,0 +1,119 @@ +# Test Configuration Generation Guide + +This document explains how to generate test configurations for the Pink IK controller tests used in `test_pink_ik.py`. + +## File Structure + +Test configurations are JSON files with the following structure: + +```json +{ + "tolerances": { + "position": ..., + "pd_position": ..., + "rotation": ..., + "check_errors": true + }, + "allowed_steps_to_settle": ..., + "tests": { + "test_name": { + "left_hand_pose": [...], + "right_hand_pose": [...], + "allowed_steps_per_motion": ..., + "repeat": ... + } + } +} +``` + +## Parameters + +### Tolerances +- **position**: Maximum position error in meters +- **pd_position**: Maximum PD controller error in meters +- **rotation**: Maximum rotation error in radians +- **check_errors**: Whether to verify errors (should be `true`) + +### Test Parameters +- **allowed_steps_to_settle**: Initial settling steps (typically 100) +- **allowed_steps_per_motion**: Steps per motion phase +- **repeat**: Number of test repetitions +- **requires_waist_bending**: Whether the test requires waist bending (boolean) + +## Coordinate System + +### Robot Reset Pose +From `g1_locomanipulation_robot_cfg.py`: +- **Base position**: (0, 0, 0.75) - 75cm above ground +- **Base orientation**: 90° rotation around X-axis (facing forward) +- **Joint positions**: Standing pose with slight knee bend + +### EEF Pose Format +Each pose: `[x, y, z, qw, qx, qy, qz]` +- **Position**: Cartesian coordinates relative to robot base frame +- **Orientation**: Quaternion relative to the world. Typically you want this to start in the same orientation as robot base. (e.g. if robot base is reset to (0.7071, 0.0, 0.0, 0.7071), hand pose should be the same) + +**Note**: The system automatically compensates for hand rotational offsets, so specify orientations relative to the robot's reset orientation. + +## Creating Configurations + +### Step 1: Choose Robot Type +- `pink_ik_g1_test_configs.json` for G1 robot +- `pink_ik_gr1_test_configs.json` for GR1 robot + +### Step 2: Define Tolerances +```json +"tolerances": { + "position": 0.003, + "pd_position": 0.001, + "rotation": 0.017, + "check_errors": true +} +``` + +### Step 3: Create Test Movements +Common test types: +- **stay_still**: Same pose repeated +- **horizontal_movement**: Side-to-side movement +- **vertical_movement**: Up-and-down movement +- **rotation_movements**: Hand orientation changes + +### Step 4: Specify Hand Poses +```json +"horizontal_movement": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.28, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.28, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 100, + "repeat": 2, + "requires_waist_bending": false +} +``` + +## Pose Guidelines + +### Orientation Examples +- **Default**: `[0.7071, 0.0, 0.0, 0.7071]` (90° around X-axis) +- **Z-rotation**: `[0.5, 0.0, 0.0, 0.866]` (60° around Z) +- **Y-rotation**: `[0.866, 0.0, 0.5, 0.0]` (60° around Y) + +## Testing Process + +1. Robot starts in reset pose and settles +2. Moves through each pose in sequence +3. Errors computed and verified against tolerances +4. Sequence repeats specified number of times + +### Waist Bending Logic +Tests marked with `"requires_waist_bending": true` will only run if waist joints are enabled in the environment configuration. The test system automatically detects waist capability by checking if waist joints (`waist_yaw_joint`, `waist_pitch_joint`, `waist_roll_joint`) are included in the `pink_controlled_joint_names` list. + +## Troubleshooting + +- **Can't reach target**: Check if within safe workspace +- **High errors**: Increase tolerances or adjust poses +- **Test failures**: Increase `allowed_steps_per_motion` diff --git a/source/isaaclab/test/controllers/test_ik_configs/pink_ik_g1_test_configs.json b/source/isaaclab/test/controllers/test_ik_configs/pink_ik_g1_test_configs.json new file mode 100644 index 0000000000000000000000000000000000000000..f5d0d60717daf57c828613b926060bae0fa79a86 --- /dev/null +++ b/source/isaaclab/test/controllers/test_ik_configs/pink_ik_g1_test_configs.json @@ -0,0 +1,111 @@ +{ + "tolerances": { + "position": 0.003, + "pd_position": 0.002, + "rotation": 0.017, + "check_errors": true + }, + "allowed_steps_to_settle": 50, + "tests": { + "horizontal_movement": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.28, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.28, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 15, + "repeat": 2, + "requires_waist_bending": false + }, + "horizontal_small_movement": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.19, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.19, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 15, + "repeat": 2, + "requires_waist_bending": false + }, + "stay_still": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 20, + "repeat": 4, + "requires_waist_bending": false + }, + "vertical_movement": { + "left_hand_pose": [ + [-0.18, 0.15, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.15, 0.85, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.15, 0.9, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.15, 0.85, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.15, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.15, 0.85, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.15, 0.9, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.15, 0.85, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 30, + "repeat": 2, + "requires_waist_bending": false + }, + "forward_waist_bending_movement": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.2, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.18, 0.3, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.2, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.18, 0.3, 0.8, 0.7071, 0.0, 0.0, 0.7071] + ], + "allowed_steps_per_motion": 60, + "repeat": 2, + "requires_waist_bending": true + }, + "rotation_movements": { + "left_hand_pose": [ + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.2, 0.11, 0.8, 0.6946, 0.1325, 0.1325, 0.6946], + [-0.2, 0.11, 0.8, 0.6533, 0.2706, 0.2706, 0.6533], + [-0.2, 0.11, 0.8, 0.5848, 0.3975, 0.3975, 0.5848], + [-0.2, 0.11, 0.8, 0.5, 0.5, 0.5, 0.5], + [-0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [-0.2, 0.11, 0.8, 0.6946, -0.1325, -0.1325, 0.6946], + [-0.2, 0.11, 0.8, 0.6533, -0.2706, -0.2706, 0.6533], + [-0.2, 0.11, 0.8, 0.5848, -0.3975, -0.3975, 0.5848], + [-0.2, 0.11, 0.8, 0.5, -0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.2, 0.11, 0.8, 0.6946, -0.1325, -0.1325, 0.6946], + [0.2, 0.11, 0.8, 0.6533, -0.2706, -0.2706, 0.6533], + [0.2, 0.11, 0.8, 0.5848, -0.3975, -0.3975, 0.5848], + [0.2, 0.11, 0.8, 0.5, -0.5, -0.5, 0.5], + [0.18, 0.1, 0.8, 0.7071, 0.0, 0.0, 0.7071], + [0.2, 0.11, 0.8, 0.6946, 0.1325, 0.1325, 0.6946], + [0.2, 0.11, 0.8, 0.6533, 0.2706, 0.2706, 0.6533], + [0.2, 0.11, 0.8, 0.5848, 0.3975, 0.3975, 0.5848], + [0.2, 0.11, 0.8, 0.5, 0.5, 0.5, 0.5] + ], + "allowed_steps_per_motion": 25, + "repeat": 2, + "requires_waist_bending": false + } + } +} diff --git a/source/isaaclab/test/controllers/test_ik_configs/pink_ik_gr1_test_configs.json b/source/isaaclab/test/controllers/test_ik_configs/pink_ik_gr1_test_configs.json new file mode 100644 index 0000000000000000000000000000000000000000..be40d7cf7abc409d1eb6650229b84d16568f51e3 --- /dev/null +++ b/source/isaaclab/test/controllers/test_ik_configs/pink_ik_gr1_test_configs.json @@ -0,0 +1,93 @@ +{ + "tolerances": { + "position": 0.001, + "pd_position": 0.001, + "rotation": 0.02, + "check_errors": true + }, + "allowed_steps_to_settle": 5, + "tests": { + "vertical_movement": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.23, 0.32, 1.2, 0.5, 0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.23, 0.32, 1.2, 0.5, 0.5, -0.5, 0.5] + ], + "allowed_steps_per_motion": 8, + "repeat": 2, + "requires_waist_bending": false + }, + "stay_still": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "allowed_steps_per_motion": 8, + "repeat": 4, + "requires_waist_bending": false + }, + "horizontal_movement": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.13, 0.32, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.13, 0.32, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "allowed_steps_per_motion": 8, + "repeat": 2, + "requires_waist_bending": false + }, + "horizontal_small_movement": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.22, 0.32, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.22, 0.32, 1.1, 0.5, 0.5, -0.5, 0.5] + ], + "allowed_steps_per_motion": 8, + "repeat": 2, + "requires_waist_bending": false + }, + "forward_waist_bending_movement": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.23, 0.5, 1.05, 0.5, 0.5, -0.5, 0.5] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.23, 0.5, 1.05, 0.5, 0.5, -0.5, 0.5] + ], + "allowed_steps_per_motion": 25, + "repeat": 3, + "requires_waist_bending": true + }, + "rotation_movements": { + "left_hand_pose": [ + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.23, 0.32, 1.1, 0.7071, 0.7071, 0.0, 0.0], + [-0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [-0.23, 0.32, 1.1, 0.0, 0.0, -0.7071, 0.7071] + ], + "right_hand_pose": [ + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.23, 0.32, 1.1, 0.0, 0.0, -0.7071, 0.7071], + [0.23, 0.28, 1.1, 0.5, 0.5, -0.5, 0.5], + [0.23, 0.32, 1.1, 0.7071, 0.7071, 0.0, 0.0] + ], + "allowed_steps_per_motion": 10, + "repeat": 2, + "requires_waist_bending": false + } + } +} diff --git a/source/isaaclab/test/controllers/test_local_frame_task.py b/source/isaaclab/test/controllers/test_local_frame_task.py new file mode 100644 index 0000000000000000000000000000000000000000..69790724248ab4906319e2ba76a8943e1f221b0b --- /dev/null +++ b/source/isaaclab/test/controllers/test_local_frame_task.py @@ -0,0 +1,483 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for LocalFrameTask class.""" + +# Import pinocchio in the main script to force the use of the dependencies installed +# by IsaacLab and not the one installed by Isaac Sim +# pinocchio is required by the Pink IK controller +import sys + +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +from pathlib import Path + +import numpy as np +import pinocchio as pin +import pytest + +from isaaclab.controllers.pink_ik.local_frame_task import LocalFrameTask +from isaaclab.controllers.pink_ik.pink_kinematics_configuration import PinkKinematicsConfiguration + +# class TestLocalFrameTask: +# """Test suite for LocalFrameTask class.""" + + +@pytest.fixture +def urdf_path(): + """Path to test URDF file.""" + return Path(__file__).parent / "urdfs" / "test_urdf_two_link_robot.urdf" + + +@pytest.fixture +def controlled_joint_names(): + """List of controlled joint names for testing.""" + return ["joint_1", "joint_2"] + + +@pytest.fixture +def pink_config(urdf_path, controlled_joint_names): + """Create a PinkKinematicsConfiguration instance for testing.""" + return PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + controlled_joint_names=controlled_joint_names, + # copy_data=True, + # forward_kinematics=True, + ) + + +@pytest.fixture +def local_frame_task(): + """Create a LocalFrameTask instance for testing.""" + return LocalFrameTask( + frame="link_2", + base_link_frame_name="base_link", + position_cost=1.0, + orientation_cost=1.0, + lm_damping=0.0, + gain=1.0, + ) + + +def test_initialization(local_frame_task): + """Test proper initialization of LocalFrameTask.""" + # Check that the task is properly initialized + assert local_frame_task.frame == "link_2" + assert local_frame_task.base_link_frame_name == "base_link" + assert np.allclose(local_frame_task.cost[:3], [1.0, 1.0, 1.0]) + assert np.allclose(local_frame_task.cost[3:], [1.0, 1.0, 1.0]) + assert local_frame_task.lm_damping == 0.0 + assert local_frame_task.gain == 1.0 + + # Check that target is initially None + assert local_frame_task.transform_target_to_base is None + + +def test_initialization_with_sequence_costs(): + """Test initialization with sequence costs.""" + task = LocalFrameTask( + frame="link_1", + base_link_frame_name="base_link", + position_cost=[1.0, 1.0, 1.0], + orientation_cost=[1.0, 1.0, 1.0], + lm_damping=0.1, + gain=2.0, + ) + + assert task.frame == "link_1" + assert task.base_link_frame_name == "base_link" + assert np.allclose(task.cost[:3], [1.0, 1.0, 1.0]) + assert np.allclose(task.cost[3:], [1.0, 1.0, 1.0]) + assert task.lm_damping == 0.1 + assert task.gain == 2.0 + + +def test_inheritance_from_frame_task(local_frame_task): + """Test that LocalFrameTask properly inherits from FrameTask.""" + from pink.tasks.frame_task import FrameTask + + # Check inheritance + assert isinstance(local_frame_task, FrameTask) + + # Check that we can call parent class methods + assert hasattr(local_frame_task, "compute_error") + assert hasattr(local_frame_task, "compute_jacobian") + + +def test_set_target(local_frame_task): + """Test setting target with a transform.""" + # Create a test transform + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + target_transform.rotation = pin.exp3(np.array([0.1, 0.0, 0.0])) + + # Set the target + local_frame_task.set_target(target_transform) + + # Check that target was set correctly + assert local_frame_task.transform_target_to_base is not None + assert isinstance(local_frame_task.transform_target_to_base, pin.SE3) + + # Check that it's a copy (not the same object) + assert local_frame_task.transform_target_to_base is not target_transform + + # Check that values match + assert np.allclose(local_frame_task.transform_target_to_base.translation, target_transform.translation) + assert np.allclose(local_frame_task.transform_target_to_base.rotation, target_transform.rotation) + + +def test_set_target_from_configuration(local_frame_task, pink_config): + """Test setting target from a robot configuration.""" + # Set target from configuration + local_frame_task.set_target_from_configuration(pink_config) + + # Check that target was set + assert local_frame_task.transform_target_to_base is not None + assert isinstance(local_frame_task.transform_target_to_base, pin.SE3) + + +def test_set_target_from_configuration_wrong_type(local_frame_task): + """Test that set_target_from_configuration raises error with wrong type.""" + with pytest.raises(ValueError, match="configuration must be a PinkKinematicsConfiguration"): + local_frame_task.set_target_from_configuration("not_a_configuration") + + +def test_compute_error_with_target_set(local_frame_task, pink_config): + """Test computing error when target is set.""" + # Set a target + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + local_frame_task.set_target(target_transform) + + # Compute error + error = local_frame_task.compute_error(pink_config) + + # Check that error is computed correctly + assert isinstance(error, np.ndarray) + assert error.shape == (6,) # 6D error (3 position + 3 orientation) + + # Error should not be all zeros (unless target exactly matches current pose) + # This is a reasonable assumption for a random target + + +def test_compute_error_without_target(local_frame_task, pink_config): + """Test that compute_error raises error when no target is set.""" + with pytest.raises(ValueError, match="no target set for frame 'link_2'"): + local_frame_task.compute_error(pink_config) + + +def test_compute_error_wrong_configuration_type(local_frame_task): + """Test that compute_error raises error with wrong configuration type.""" + # Set a target first + target_transform = pin.SE3.Identity() + local_frame_task.set_target(target_transform) + + with pytest.raises(ValueError, match="configuration must be a PinkKinematicsConfiguration"): + local_frame_task.compute_error("not_a_configuration") + + +def test_compute_jacobian_with_target_set(local_frame_task, pink_config): + """Test computing Jacobian when target is set.""" + # Set a target + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + local_frame_task.set_target(target_transform) + + # Compute Jacobian + jacobian = local_frame_task.compute_jacobian(pink_config) + + # Check that Jacobian is computed correctly + assert isinstance(jacobian, np.ndarray) + assert jacobian.shape == (6, 2) # 6 rows (error), 2 columns (controlled joints) + + # Jacobian should not be all zeros + assert not np.allclose(jacobian, 0.0) + + +def test_compute_jacobian_without_target(local_frame_task, pink_config): + """Test that compute_jacobian raises error when no target is set.""" + with pytest.raises(Exception, match="no target set for frame 'link_2'"): + local_frame_task.compute_jacobian(pink_config) + + +def test_error_consistency_across_configurations(local_frame_task, pink_config): + """Test that error computation is consistent across different configurations.""" + # Set a target + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + local_frame_task.set_target(target_transform) + + # Compute error at initial configuration + error_1 = local_frame_task.compute_error(pink_config) + + # Update configuration + new_q = pink_config.full_q.copy() + new_q[1] = 0.5 # Change first revolute joint + pink_config.update(new_q) + + # Compute error at new configuration + error_2 = local_frame_task.compute_error(pink_config) + + # Errors should be different (not all close) + assert not np.allclose(error_1, error_2) + + +def test_jacobian_consistency_across_configurations(local_frame_task, pink_config): + """Test that Jacobian computation is consistent across different configurations.""" + # Set a target + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + local_frame_task.set_target(target_transform) + + # Compute Jacobian at initial configuration + jacobian_1 = local_frame_task.compute_jacobian(pink_config) + + # Update configuration + new_q = pink_config.full_q.copy() + new_q[1] = 0.3 # Change first revolute joint + pink_config.update(new_q) + + # Compute Jacobian at new configuration + jacobian_2 = local_frame_task.compute_jacobian(pink_config) + + # Jacobians should be different (not all close) + assert not np.allclose(jacobian_1, jacobian_2) + + +def test_error_zero_at_target_pose(local_frame_task, pink_config): + """Test that error is zero when current pose matches target pose.""" + # Get current transform of the frame + current_transform = pink_config.get_transform_frame_to_world("link_2") + + # Set target to current pose + local_frame_task.set_target(current_transform) + + # Compute error + error = local_frame_task.compute_error(pink_config) + + # Error should be very close to zero + assert np.allclose(error, 0.0, atol=1e-10) + + +def test_different_frames(pink_config): + """Test LocalFrameTask with different frame names.""" + # Test with link_1 frame + task_link1 = LocalFrameTask( + frame="link_1", + base_link_frame_name="base_link", + position_cost=1.0, + orientation_cost=1.0, + ) + + # Set target and compute error + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.0, 0.0]) + task_link1.set_target(target_transform) + + error_link1 = task_link1.compute_error(pink_config) + assert error_link1.shape == (6,) + + # Test with base_link frame + task_base = LocalFrameTask( + frame="base_link", + base_link_frame_name="base_link", + position_cost=1.0, + orientation_cost=1.0, + ) + + task_base.set_target(target_transform) + error_base = task_base.compute_error(pink_config) + assert error_base.shape == (6,) + + +def test_different_base_frames(pink_config): + """Test LocalFrameTask with different base frame names.""" + # Test with base_link as base frame + task_base_base = LocalFrameTask( + frame="link_2", + base_link_frame_name="base_link", + position_cost=1.0, + orientation_cost=1.0, + ) + + target_transform = pin.SE3.Identity() + task_base_base.set_target(target_transform) + error_base_base = task_base_base.compute_error(pink_config) + assert error_base_base.shape == (6,) + + # Test with link_1 as base frame + task_link1_base = LocalFrameTask( + frame="link_2", + base_link_frame_name="link_1", + position_cost=1.0, + orientation_cost=1.0, + ) + + task_link1_base.set_target(target_transform) + error_link1_base = task_link1_base.compute_error(pink_config) + assert error_link1_base.shape == (6,) + + +def test_sequence_cost_parameters(): + """Test LocalFrameTask with sequence cost parameters.""" + task = LocalFrameTask( + frame="link_2", + base_link_frame_name="base_link", + position_cost=[1.0, 2.0, 3.0], + orientation_cost=[0.5, 1.0, 1.5], + lm_damping=0.1, + gain=2.0, + ) + + assert np.allclose(task.cost[:3], [1.0, 2.0, 3.0]) # Position costs + assert np.allclose(task.cost[3:], [0.5, 1.0, 1.5]) # Orientation costs + assert task.lm_damping == 0.1 + assert task.gain == 2.0 + + +def test_error_magnitude_consistency(local_frame_task, pink_config): + """Test that error computation produces reasonable results.""" + # Set a small target offset + small_target = pin.SE3.Identity() + small_target.translation = np.array([0.01, 0.01, 0.01]) + local_frame_task.set_target(small_target) + + error_small = local_frame_task.compute_error(pink_config) + + # Set a large target offset + large_target = pin.SE3.Identity() + large_target.translation = np.array([0.5, 0.5, 0.5]) + local_frame_task.set_target(large_target) + + error_large = local_frame_task.compute_error(pink_config) + + # Both errors should be finite and reasonable + assert np.all(np.isfinite(error_small)) + assert np.all(np.isfinite(error_large)) + assert not np.allclose(error_small, error_large) # Different targets should produce different errors + + +def test_jacobian_structure(local_frame_task, pink_config): + """Test that Jacobian has the correct structure.""" + # Set a target + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.1, 0.2, 0.3]) + local_frame_task.set_target(target_transform) + + # Compute Jacobian + jacobian = local_frame_task.compute_jacobian(pink_config) + + # Check structure + assert jacobian.shape == (6, 2) # 6 error dimensions, 2 controlled joints + + # Check that Jacobian is not all zeros (basic functionality check) + assert not np.allclose(jacobian, 0.0) + + +def test_multiple_target_updates(local_frame_task, pink_config): + """Test that multiple target updates work correctly.""" + # Set first target + target1 = pin.SE3.Identity() + target1.translation = np.array([0.1, 0.0, 0.0]) + local_frame_task.set_target(target1) + + error1 = local_frame_task.compute_error(pink_config) + + # Set second target + target2 = pin.SE3.Identity() + target2.translation = np.array([0.0, 0.1, 0.0]) + local_frame_task.set_target(target2) + + error2 = local_frame_task.compute_error(pink_config) + + # Errors should be different + assert not np.allclose(error1, error2) + + +def test_inheritance_behavior(local_frame_task): + """Test that LocalFrameTask properly overrides parent class methods.""" + # Check that the class has the expected methods + assert hasattr(local_frame_task, "set_target") + assert hasattr(local_frame_task, "set_target_from_configuration") + assert hasattr(local_frame_task, "compute_error") + assert hasattr(local_frame_task, "compute_jacobian") + + # Check that these are the overridden methods, not the parent ones + assert local_frame_task.set_target.__qualname__ == "LocalFrameTask.set_target" + assert local_frame_task.compute_error.__qualname__ == "LocalFrameTask.compute_error" + assert local_frame_task.compute_jacobian.__qualname__ == "LocalFrameTask.compute_jacobian" + + +def test_target_copying_behavior(local_frame_task): + """Test that target transforms are properly copied.""" + # Create a target transform + original_target = pin.SE3.Identity() + original_target.translation = np.array([0.1, 0.2, 0.3]) + original_rotation = original_target.rotation.copy() + + # Set the target + local_frame_task.set_target(original_target) + + # Modify the original target + original_target.translation = np.array([0.5, 0.5, 0.5]) + original_target.rotation = pin.exp3(np.array([0.5, 0.0, 0.0])) + + # Check that the stored target is unchanged + assert np.allclose(local_frame_task.transform_target_to_base.translation, np.array([0.1, 0.2, 0.3])) + assert np.allclose(local_frame_task.transform_target_to_base.rotation, original_rotation) + + +def test_error_computation_with_orientation_difference(local_frame_task, pink_config): + """Test error computation when there's an orientation difference.""" + # Set a target with orientation difference + target_transform = pin.SE3.Identity() + target_transform.rotation = pin.exp3(np.array([0.2, 0.0, 0.0])) # Rotation around X-axis + local_frame_task.set_target(target_transform) + + # Compute error + error = local_frame_task.compute_error(pink_config) + + # Check that error is computed correctly + assert isinstance(error, np.ndarray) + assert error.shape == (6,) + + # Error should not be all zeros + assert not np.allclose(error, 0.0) + + +def test_jacobian_rank_consistency(local_frame_task, pink_config): + """Test that Jacobian maintains consistent shape across configurations.""" + # Set a target that we know can be reached by the test robot. + target_transform = pin.SE3.Identity() + target_transform.translation = np.array([0.0, 0.0, 0.45]) + # 90 degrees around x axis = pi/2 radians + target_transform.rotation = pin.exp3(np.array([np.pi / 2, 0.0, 0.0])) + local_frame_task.set_target(target_transform) + + # Compute Jacobian at multiple configurations + jacobians = [] + for i in range(5): + # Update configuration + new_q = pink_config.full_q.copy() + new_q[1] = 0.1 * i # Vary first joint + pink_config.update(new_q) + + # Compute Jacobian + jacobian = local_frame_task.compute_jacobian(pink_config) + jacobians.append(jacobian) + + # All Jacobians should have the same shape + for jacobian in jacobians: + assert jacobian.shape == (6, 2) + + # All Jacobians should have rank 2 (full rank for 2-DOF planar arm) + for jacobian in jacobians: + assert np.linalg.matrix_rank(jacobian) == 2 diff --git a/source/isaaclab/test/controllers/test_null_space_posture_task.py b/source/isaaclab/test/controllers/test_null_space_posture_task.py new file mode 100644 index 0000000000000000000000000000000000000000..a1d0e1ac50d516420a94341b1705a4d43d26f912 --- /dev/null +++ b/source/isaaclab/test/controllers/test_null_space_posture_task.py @@ -0,0 +1,342 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Launch Isaac Sim Simulator first.""" + +# Import pinocchio in the main script to force the use of the dependencies installed +# by IsaacLab and not the one installed by Isaac Sim +# pinocchio is required by the Pink IK controller +import sys + +if sys.platform != "win32": + import pinocchio # noqa: F401 + import pinocchio as pin # noqa: F401 +else: + import pinocchio # noqa: F401 + import pinocchio as pin # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Unit tests for NullSpacePostureTask with simplified robot configuration using Pink library directly.""" + +import numpy as np +import pytest +from pink.configuration import Configuration +from pink.tasks import FrameTask +from pinocchio.robot_wrapper import RobotWrapper + +from isaaclab.controllers.pink_ik.null_space_posture_task import NullSpacePostureTask + + +class TestNullSpacePostureTaskSimplifiedRobot: + """Test cases for NullSpacePostureTask with simplified robot configuration.""" + + @pytest.fixture + def num_joints(self): + """Number of joints in the simplified robot.""" + return 20 + + @pytest.fixture + def joint_configurations(self): + """Pre-generated joint configurations for testing.""" + # Set random seed for reproducible tests + np.random.seed(42) + + return { + "random": np.random.uniform(-0.5, 0.5, 20), + "controlled_only": np.array([0.5] * 5 + [0.0] * 15), # Non-zero for controlled joints only + "sequential": np.linspace(0.1, 2.0, 20), + } + + @pytest.fixture + def robot_urdf(self): + """Load the simplified test robot URDF file.""" + import os + + current_dir = os.path.dirname(os.path.abspath(__file__)) + urdf_path = os.path.join(current_dir, "simplified_test_robot.urdf") + return urdf_path + + @pytest.fixture + def robot_configuration(self, robot_urdf): + """Simplified robot wrapper.""" + wrapper = RobotWrapper.BuildFromURDF(robot_urdf, None, root_joint=None) + return Configuration(wrapper.model, wrapper.data, wrapper.q0) + + @pytest.fixture + def tasks(self): + """pink tasks.""" + return [ + FrameTask("left_hand_pitch_link", position_cost=1.0, orientation_cost=1.0), + NullSpacePostureTask( + cost=1.0, + controlled_frames=["left_hand_pitch_link"], + controlled_joints=[ + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + ], + ), + ] + + def test_null_space_jacobian_zero_end_effector_velocity( + self, robot_configuration, tasks, joint_configurations, num_joints + ): + """Test that velocities projected through null space Jacobian result in zero end-effector velocity.""" + # Set specific joint configuration + robot_configuration.q = joint_configurations["random"] + + # Set frame task target to a specific position in workspace + frame_task = tasks[0] + # Create pin.SE3 from position and quaternion + position = np.array([0.5, 0.3, 0.8]) # x, y, z + quaternion = pin.Quaternion(1.0, 0.0, 0.0, 0.0) # w, x, y, z (identity quaternion) + target_pose = pin.SE3(quaternion, position) + frame_task.set_target(target_pose) + + # Set null space posture task target + null_space_task = tasks[1] + target_posture = np.zeros(num_joints) + null_space_task.set_target(target_posture) + + # Get the null space Jacobian + null_space_jacobian = null_space_task.compute_jacobian(robot_configuration) + + # Get the end-effector Jacobian + frame_task_jacobian = frame_task.compute_jacobian(robot_configuration) + + # Test multiple random velocities in null space + for _ in range(10): + # Generate random joint velocity + random_velocity = np.random.randn(num_joints) * 0.1 + + # Project through null space Jacobian + null_space_velocity = null_space_jacobian @ random_velocity + + # Compute resulting end-effector velocity + ee_velocity = frame_task_jacobian @ null_space_velocity + + # The end-effector velocity should be approximately zero + assert np.allclose(ee_velocity, np.zeros(6), atol=1e-7), f"End-effector velocity not zero: {ee_velocity}" + + def test_null_space_jacobian_properties(self, robot_configuration, tasks, joint_configurations, num_joints): + """Test mathematical properties of the null space Jacobian.""" + # Set specific joint configuration + robot_configuration.q = joint_configurations["random"] + + # Set frame task target + frame_task = tasks[0] + # Create pin.SE3 from position and quaternion + position = np.array([0.3, 0.4, 0.6]) + quaternion = pin.Quaternion(0.707, 0.0, 0.0, 0.707) # w, x, y, z (90-degree rotation around X) + target_pose = pin.SE3(quaternion, position) + frame_task.set_target(target_pose) + + # Set null space posture task target + null_space_task = tasks[1] + target_posture = np.zeros(num_joints) + target_posture[0:5] = [0.1, -0.1, 0.2, -0.2, 0.0] # Set first 5 joints (controlled joints) + null_space_task.set_target(target_posture) + + # Get Jacobians + null_space_jacobian = null_space_task.compute_jacobian(robot_configuration) + ee_jacobian = robot_configuration.get_frame_jacobian("left_hand_pitch_link") + + # Test: N * J^T should be approximately zero (null space property) + # where N is the null space projector and J is the end-effector Jacobian + null_space_projection = null_space_jacobian @ ee_jacobian.T + assert np.allclose(null_space_projection, np.zeros_like(null_space_projection), atol=1e-7), ( + f"Null space projection of end-effector Jacobian not zero: {null_space_projection}" + ) + + def test_null_space_jacobian_identity_when_no_frame_tasks( + self, robot_configuration, joint_configurations, num_joints + ): + """Test that null space Jacobian is identity when no frame tasks are defined.""" + # Create null space task without frame task controlled joints + null_space_task = NullSpacePostureTask(cost=1.0, controlled_frames=[], controlled_joints=[]) + + # Set specific joint configuration + robot_configuration.q = joint_configurations["sequential"] + + # Set target + target_posture = np.zeros(num_joints) + null_space_task.set_target(target_posture) + + # Get the null space Jacobian + null_space_jacobian = null_space_task.compute_jacobian(robot_configuration) + + # Should be identity matrix + expected_identity = np.eye(num_joints) + assert np.allclose(null_space_jacobian, expected_identity), ( + f"Null space Jacobian should be identity when no frame tasks defined: {null_space_jacobian}" + ) + + def test_null_space_jacobian_consistency_across_configurations( + self, robot_configuration, tasks, joint_configurations, num_joints + ): + """Test that null space Jacobian is consistent across different joint configurations.""" + # Test multiple joint configurations + test_configs = [ + np.zeros(num_joints), # Zero configuration + joint_configurations["controlled_only"], # Non-zero for controlled joints + joint_configurations["random"], # Random configuration + ] + + # Set frame task target + frame_task = tasks[0] + # Create pin.SE3 from position and quaternion + position = np.array([0.3, 0.3, 0.5]) + quaternion = pin.Quaternion(1.0, 0.0, 0.0, 0.0) # w, x, y, z (identity quaternion) + target_pose = pin.SE3(quaternion, position) + frame_task.set_target(target_pose) + + # Set null space posture task target + null_space_task = tasks[1] + target_posture = np.zeros(num_joints) + null_space_task.set_target(target_posture) + + jacobians = [] + for config in test_configs: + robot_configuration.q = config + jacobian = null_space_task.compute_jacobian(robot_configuration) + jacobians.append(jacobian) + + # Verify that velocities through this Jacobian result in zero end-effector velocity + ee_jacobian = robot_configuration.get_frame_jacobian("left_hand_pitch_link") + + # Test with random velocity + random_velocity = np.random.randn(num_joints) * 0.1 + null_space_velocity = jacobian @ random_velocity + ee_velocity = ee_jacobian @ null_space_velocity + + assert np.allclose(ee_velocity, np.zeros(6), atol=1e-7), ( + f"End-effector velocity not zero for configuration {config}: {ee_velocity}" + ) + + def test_compute_error_without_target(self, robot_configuration, joint_configurations): + """Test that compute_error raises ValueError when no target is set.""" + null_space_task = NullSpacePostureTask( + cost=1.0, + controlled_frames=["left_hand_pitch_link"], + controlled_joints=["waist_yaw_joint", "waist_pitch_joint"], + ) + + robot_configuration.q = joint_configurations["sequential"] + + # Should raise ValueError when no target is set + with pytest.raises(ValueError, match="No posture target has been set"): + null_space_task.compute_error(robot_configuration) + + def test_joint_masking(self, robot_configuration, joint_configurations, num_joints): + """Test that joint mask correctly filters only controlled joints.""" + + controlled_joint_names = ["waist_pitch_joint", "left_shoulder_pitch_joint", "left_elbow_pitch_joint"] + + # Create task with specific controlled joints + null_space_task = NullSpacePostureTask( + cost=1.0, controlled_frames=["left_hand_pitch_link"], controlled_joints=controlled_joint_names + ) + + # Find the joint indexes in robot_configuration.model.names.tolist()[1:] + joint_names = robot_configuration.model.names.tolist()[1:] + joint_indexes = [joint_names.index(jn) for jn in controlled_joint_names] + + # Set configurations + current_config = joint_configurations["sequential"] + target_config = np.zeros(num_joints) + + robot_configuration.q = current_config + null_space_task.set_target(target_config) + + # Compute error + error = null_space_task.compute_error(robot_configuration) + + # Only controlled joints should have non-zero error + # Joint indices: + # waist_yaw_joint=0, waist_pitch_joint=1, waist_roll_joint=2, left_shoulder_pitch_joint=3, + # left_shoulder_roll_joint=4, etc. + expected_error = np.zeros(num_joints) + for i in joint_indexes: + expected_error[i] = current_config[i] + + assert np.allclose(error, expected_error, atol=1e-7), ( + f"Joint mask not working correctly: expected {expected_error}, got {error}" + ) + + def test_empty_controlled_joints(self, robot_configuration, joint_configurations, num_joints): + """Test behavior when controlled_joints is empty.""" + null_space_task = NullSpacePostureTask( + cost=1.0, controlled_frames=["left_hand_pitch_link"], controlled_joints=[] + ) + + current_config = joint_configurations["sequential"] + target_config = np.zeros(num_joints) + + robot_configuration.q = current_config + null_space_task.set_target(target_config) + + # Error should be all zeros + error = null_space_task.compute_error(robot_configuration) + expected_error = np.zeros(num_joints) + assert np.allclose(error, expected_error), f"Error should be zero when no joints controlled: {error}" + + def test_set_target_from_configuration(self, robot_configuration, joint_configurations): + """Test set_target_from_configuration method.""" + null_space_task = NullSpacePostureTask( + cost=1.0, + controlled_frames=["left_hand_pitch_link"], + controlled_joints=["waist_yaw_joint", "waist_pitch_joint"], + ) + + # Set a specific configuration + test_config = joint_configurations["sequential"] + robot_configuration.q = test_config + + # Set target from configuration + null_space_task.set_target_from_configuration(robot_configuration) + + # Verify target was set correctly + assert null_space_task.target_q is not None + assert np.allclose(null_space_task.target_q, test_config) + + def test_multiple_frame_tasks(self, robot_configuration, joint_configurations, num_joints): + """Test null space projection with multiple frame tasks.""" + # Create task with multiple controlled frames + null_space_task = NullSpacePostureTask( + cost=1.0, + controlled_frames=["left_hand_pitch_link", "right_hand_pitch_link"], + controlled_joints=["waist_yaw_joint", "waist_pitch_joint", "waist_roll_joint"], + ) + + current_config = joint_configurations["sequential"] + robot_configuration.q = current_config + + # Get null space Jacobian + null_space_jacobian = null_space_task.compute_jacobian(robot_configuration) + + # Get Jacobians for both frames + jacobian_left_hand = robot_configuration.get_frame_jacobian("left_hand_pitch_link") + jacobian_right_hand = robot_configuration.get_frame_jacobian("right_hand_pitch_link") + + # Test that null space velocities result in zero velocity for both frames + for _ in range(5): + random_velocity = np.random.randn(num_joints) * 0.1 + null_space_velocity = null_space_jacobian @ random_velocity + + # Check both frames + ee_velocity_left = jacobian_left_hand @ null_space_velocity + ee_velocity_right = jacobian_right_hand @ null_space_velocity + + assert np.allclose(ee_velocity_left, np.zeros(6), atol=1e-7), ( + f"Left hand velocity not zero: {ee_velocity_left}" + ) + assert np.allclose(ee_velocity_right, np.zeros(6), atol=1e-7), ( + f"Right hand velocity not zero: {ee_velocity_right}" + ) diff --git a/source/isaaclab/test/controllers/test_operational_space.py b/source/isaaclab/test/controllers/test_operational_space.py new file mode 100644 index 0000000000000000000000000000000000000000..eeec14d877dc64df2e7f2ddc7c5c0402b301d839 --- /dev/null +++ b/source/isaaclab/test/controllers/test_operational_space.py @@ -0,0 +1,1672 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +from isaacsim.core.cloner import GridCloner + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.controllers import OperationalSpaceController, OperationalSpaceControllerCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.sensors import ContactSensor, ContactSensorCfg +from isaaclab.utils.math import ( + apply_delta_pose, + combine_frame_transforms, + compute_pose_error, + matrix_from_quat, + quat_apply_inverse, + quat_inv, + subtract_frame_transforms, +) + +## +# Pre-defined configs +## +from isaaclab_assets import FRANKA_PANDA_CFG # isort:skip + + +@pytest.fixture +def sim(): + """Create a simulation context for testing.""" + # Wait for spawning + stage = sim_utils.create_new_stage() + # Constants + num_envs = 16 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=0.01) + sim = sim_utils.SimulationContext(sim_cfg) + # TODO: Remove this once we have a better way to handle this. + sim._app_control_on_stop_handle = None + + # Create a ground plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/GroundPlane", cfg) + + # Markers + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current")) + goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + + light_cfg = sim_utils.DistantLightCfg(intensity=5.0, exposure=10.0) + light_cfg.func( + "/Light", + light_cfg, + translation=[0, 0, 1], + ) + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + env_prim_paths = cloner.generate_paths("/World/envs/env", num_envs) + # create source prim + stage.DefinePrim(env_prim_paths[0], "Xform") + # clone the env xform + cloner.clone( + source_prim_path=env_prim_paths[0], + prim_paths=env_prim_paths, + replicate_physics=True, + ) + + robot_cfg = FRANKA_PANDA_CFG.replace(prim_path="/World/envs/env_.*/Robot") + robot_cfg.actuators["panda_shoulder"].stiffness = 0.0 + robot_cfg.actuators["panda_shoulder"].damping = 0.0 + robot_cfg.actuators["panda_forearm"].stiffness = 0.0 + robot_cfg.actuators["panda_forearm"].damping = 0.0 + robot_cfg.spawn.rigid_props.disable_gravity = True + + # Define the ContactSensor + contact_forces = None + + # Define the target sets + ee_goal_abs_pos_set_b = torch.tensor( + [ + [0.5, 0.5, 0.7], + [0.5, -0.4, 0.6], + [0.5, 0, 0.5], + ], + device=sim.device, + ) + ee_goal_abs_quad_set_b = torch.tensor( + [ + [0.707, 0.0, 0.707, 0.0], + [0.707, 0.707, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0], + ], + device=sim.device, + ) + ee_goal_rel_pos_set = torch.tensor( + [ + [0.2, 0.0, 0.0], + [0.2, 0.2, 0.0], + [0.2, 0.2, -0.2], + ], + device=sim.device, + ) + ee_goal_rel_axisangle_set = torch.tensor( + [ + [0.0, torch.pi / 2, 0.0], # for [0.707, 0, 0.707, 0] + [torch.pi / 2, 0.0, 0.0], # for [0.707, 0.707, 0, 0] + [torch.pi / 2, torch.pi / 2, 0.0], # for [0.0, 1.0, 0, 0] + ], + device=sim.device, + ) + ee_goal_abs_wrench_set_b = torch.tensor( + [ + [0.0, 0.0, 10.0, 0.0, -1.0, 0.0], + [0.0, 10.0, 0.0, 0.0, 0.0, 0.0], + [10.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ], + device=sim.device, + ) + kp_set = torch.tensor( + [ + [200.0, 200.0, 200.0, 200.0, 200.0, 200.0], + [240.0, 240.0, 240.0, 240.0, 240.0, 240.0], + [160.0, 160.0, 160.0, 160.0, 160.0, 160.0], + ], + device=sim.device, + ) + d_ratio_set = torch.tensor( + [ + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + [1.1, 1.1, 1.1, 1.1, 1.1, 1.1], + [0.9, 0.9, 0.9, 0.9, 0.9, 0.9], + ], + device=sim.device, + ) + ee_goal_hybrid_set_b = torch.tensor( + [ + [0.6, 0.2, 0.5, 0.0, 0.707, 0.0, 0.707, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.6, -0.29, 0.6, 0.0, 0.707, 0.0, 0.707, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.6, 0.1, 0.8, 0.0, 0.5774, 0.0, 0.8165, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ], + device=sim.device, + ) + ee_goal_pose_set_tilted_b = torch.tensor( + [ + [0.6, 0.15, 0.3, 0.0, 0.92387953, 0.0, 0.38268343], + [0.6, -0.3, 0.3, 0.0, 0.92387953, 0.0, 0.38268343], + [0.8, 0.0, 0.5, 0.0, 0.92387953, 0.0, 0.38268343], + ], + device=sim.device, + ) + ee_goal_wrench_set_tilted_task = torch.tensor( + [ + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 10.0, 0.0, 0.0, 0.0], + ], + device=sim.device, + ) + + # Define goals for the arm [xyz] + target_abs_pos_set_b = ee_goal_abs_pos_set_b.clone() + # Define goals for the arm [xyz + quat_wxyz] + target_abs_pose_set_b = torch.cat([ee_goal_abs_pos_set_b, ee_goal_abs_quad_set_b], dim=-1) + # Define goals for the arm [xyz] + target_rel_pos_set = ee_goal_rel_pos_set.clone() + # Define goals for the arm [xyz + axis-angle] + target_rel_pose_set_b = torch.cat([ee_goal_rel_pos_set, ee_goal_rel_axisangle_set], dim=-1) + # Define goals for the arm [force_xyz + torque_xyz] + target_abs_wrench_set = ee_goal_abs_wrench_set_b.clone() + # Define goals for the arm [xyz + quat_wxyz] and variable kp [kp_xyz + kp_rot_xyz] + target_abs_pose_variable_kp_set = torch.cat([target_abs_pose_set_b, kp_set], dim=-1) + # Define goals for the arm [xyz + quat_wxyz] and the variable imp. [kp_xyz + kp_rot_xyz + d_xyz + d_rot_xyz] + target_abs_pose_variable_set = torch.cat([target_abs_pose_set_b, kp_set, d_ratio_set], dim=-1) + # Define goals for the arm pose [xyz + quat_wxyz] and wrench [force_xyz + torque_xyz] + target_hybrid_set_b = ee_goal_hybrid_set_b.clone() + # Define goals for the arm pose, and wrench, and kp + target_hybrid_variable_kp_set = torch.cat([target_hybrid_set_b, kp_set], dim=-1) + # Define goals for the arm pose [xyz + quat_wxyz] in root and and wrench [force_xyz + torque_xyz] in task frame + target_hybrid_set_tilted = torch.cat([ee_goal_pose_set_tilted_b, ee_goal_wrench_set_tilted_task], dim=-1) + + # Reference frame for targets + frame = "root" + + yield ( + sim, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + target_abs_pos_set_b, + target_abs_pose_set_b, + target_rel_pos_set, + target_rel_pose_set_b, + target_abs_wrench_set, + target_abs_pose_variable_kp_set, + target_abs_pose_variable_set, + target_hybrid_set_b, + target_hybrid_variable_kp_set, + target_hybrid_set_tilted, + frame, + ) + + # Cleanup + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_without_inertial_decoupling(sim): + """Test absolute pose control with fixed impedance and without inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=[400.0, 400.0, 400.0, 100.0, 100.0, 100.0], + motion_damping_ratio_task=[5.0, 5.0, 5.0, 0.001, 0.001, 0.001], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_with_partial_inertial_decoupling(sim): + """Test absolute pose control with fixed impedance and partial inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=True, + gravity_compensation=False, + motion_stiffness_task=1000.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_fixed_impedance_with_gravity_compensation(sim): + """Test absolute pose control with fixed impedance, gravity compensation, and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot_cfg.spawn.rigid_props.disable_gravity = False + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=True, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs(sim): + """Test absolute pose control with fixed impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_rel(sim): + """Test relative pose control with fixed impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + target_rel_pose_set_b, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_rel"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_rel_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_variable_impedance(sim): + """Test absolute pose control with variable impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + _, + _, + target_abs_pose_variable_set, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="variable", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_variable_set, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_wrench_abs_open_loop(sim): + """Test open loop absolute force control.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + target_abs_wrench_set, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(0.7, 0.7, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(0.2, 0.0, 0.93), + orientation=(0.9848, 0.0, -0.1736, 0.0), + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle2", + obstacle_spawn_cfg, + translation=(0.2, 0.35, 0.7), + orientation=(0.707, 0.707, 0.0, 0.0), + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle3", + obstacle_spawn_cfg, + translation=(0.55, 0.0, 0.7), + orientation=(0.707, 0.0, 0.707, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=50, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["wrench_abs"], + motion_control_axes_task=[0, 0, 0, 0, 0, 0], + contact_wrench_control_axes_task=[1, 1, 1, 1, 1, 1], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_wrench_set, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_wrench_abs_closed_loop(sim): + """Test closed loop absolute force control.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + target_abs_wrench_set, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(0.7, 0.7, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(0.2, 0.0, 0.93), + orientation=(0.9848, 0.0, -0.1736, 0.0), + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle2", + obstacle_spawn_cfg, + translation=(0.2, 0.35, 0.7), + orientation=(0.707, 0.707, 0.0, 0.0), + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle3", + obstacle_spawn_cfg, + translation=(0.55, 0.0, 0.7), + orientation=(0.707, 0.0, 0.707, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=2, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["wrench_abs"], + contact_wrench_stiffness_task=[ + 0.2, + 0.2, + 0.2, + 0.0, + 0.0, + 0.0, + ], # Zero torque feedback as we cannot contact torque + motion_control_axes_task=[0, 0, 0, 0, 0, 0], + contact_wrench_control_axes_task=[1, 1, 1, 1, 1, 1], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_wrench_set, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_hybrid_decoupled_motion(sim): + """Test hybrid control with fixed impedance and partial inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + _, + _, + _, + target_hybrid_set_b, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(1.0, 1.0, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(target_hybrid_set_b[0, 0] + 0.05, 0.0, 0.7), + orientation=(0.707, 0.0, 0.707, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=2, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs", "wrench_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=True, + gravity_compensation=False, + motion_stiffness_task=300.0, + motion_damping_ratio_task=1.0, + contact_wrench_stiffness_task=[0.1, 0.0, 0.0, 0.0, 0.0, 0.0], + motion_control_axes_task=[0, 1, 1, 1, 1, 1], + contact_wrench_control_axes_task=[1, 0, 0, 0, 0, 0], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_leftfinger", + ["panda_joint.*"], + target_hybrid_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_hybrid_variable_kp_impedance(sim): + """Test hybrid control with variable kp impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + _, + _, + _, + target_hybrid_set_b, + target_hybrid_variable_kp_set, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(1.0, 1.0, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(target_hybrid_set_b[0, 0] + 0.05, 0.0, 0.7), + orientation=(0.707, 0.0, 0.707, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=2, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs", "wrench_abs"], + impedance_mode="variable_kp", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_damping_ratio_task=0.8, + contact_wrench_stiffness_task=[0.1, 0.0, 0.0, 0.0, 0.0, 0.0], + motion_control_axes_task=[0, 1, 1, 1, 1, 1], + contact_wrench_control_axes_task=[1, 0, 0, 0, 0, 0], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_leftfinger", + ["panda_joint.*"], + target_hybrid_variable_kp_set, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_taskframe_pose_abs(sim): + """Test absolute pose control in task frame with fixed impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + frame = "task" + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_taskframe_pose_rel(sim): + """Test relative pose control in task frame with fixed impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + target_rel_pose_set_b, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + frame = "task" + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_rel"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_rel_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_taskframe_hybrid(sim): + """Test hybrid control in task frame with fixed impedance and inertial dynamics decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + _, + _, + _, + _, + _, + target_hybrid_set_tilted, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + frame = "task" + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(2.0, 1.5, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(target_hybrid_set_tilted[0, 0] + 0.085, 0.0, 0.3), + orientation=(0.9238795325, 0.0, -0.3826834324, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=2, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs", "wrench_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=400.0, + motion_damping_ratio_task=1.0, + contact_wrench_stiffness_task=[0.0, 0.0, 0.1, 0.0, 0.0, 0.0], + motion_control_axes_task=[1, 1, 0, 1, 1, 1], + contact_wrench_control_axes_task=[0, 0, 1, 0, 0, 0], + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_leftfinger", + ["panda_joint.*"], + target_hybrid_set_tilted, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_without_inertial_decoupling_with_nullspace_centering(sim): + """Test absolute pose control with fixed impedance and nullspace centerin but without inertial decoupling.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=[400.0, 400.0, 400.0, 100.0, 100.0, 100.0], + motion_damping_ratio_task=[5.0, 5.0, 5.0, 0.001, 0.001, 0.001], + nullspace_control="position", + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_with_partial_inertial_decoupling_nullspace_centering(sim): + """Test absolute pose control with fixed impedance, partial inertial decoupling and nullspace centering.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=True, + gravity_compensation=False, + motion_stiffness_task=1000.0, + motion_damping_ratio_task=1.0, + nullspace_control="position", + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_pose_abs_with_nullspace_centering(sim): + """Test absolute pose control with fixed impedance, inertial decoupling and nullspace centering.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + target_abs_pose_set_b, + _, + _, + _, + _, + _, + _, + _, + _, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=500.0, + motion_damping_ratio_task=1.0, + nullspace_control="position", + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_hand", + ["panda_joint.*"], + target_abs_pose_set_b, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +@pytest.mark.isaacsim_ci +def test_franka_taskframe_hybrid_with_nullspace_centering(sim): + """Test hybrid control in task frame with fixed impedance, inertial decoupling and nullspace centering.""" + ( + sim_context, + num_envs, + robot_cfg, + ee_marker, + goal_marker, + contact_forces, + _, + _, + _, + _, + _, + _, + _, + _, + _, + target_hybrid_set_tilted, + frame, + ) = sim + + robot = Articulation(cfg=robot_cfg) + frame = "task" + + obstacle_spawn_cfg = sim_utils.CuboidCfg( + size=(2.0, 1.5, 0.01), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), opacity=0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + activate_contact_sensors=True, + ) + obstacle_spawn_cfg.func( + "/World/envs/env_.*/obstacle1", + obstacle_spawn_cfg, + translation=(target_hybrid_set_tilted[0, 0] + 0.085, 0.0, 0.3), + orientation=(0.9238795325, 0.0, -0.3826834324, 0.0), + ) + contact_forces_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/obstacle.*", + update_period=0.0, + history_length=2, + debug_vis=False, + force_threshold=0.1, + ) + contact_forces = ContactSensor(contact_forces_cfg) + + osc_cfg = OperationalSpaceControllerCfg( + target_types=["pose_abs", "wrench_abs"], + impedance_mode="fixed", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=400.0, + motion_damping_ratio_task=1.0, + contact_wrench_stiffness_task=[0.0, 0.0, 0.1, 0.0, 0.0, 0.0], + motion_control_axes_task=[1, 1, 0, 1, 1, 1], + contact_wrench_control_axes_task=[0, 0, 1, 0, 0, 0], + nullspace_control="position", + ) + osc = OperationalSpaceController(osc_cfg, num_envs=num_envs, device=sim_context.device) + + _run_op_space_controller( + robot, + osc, + "panda_leftfinger", + ["panda_joint.*"], + target_hybrid_set_tilted, + sim_context, + num_envs, + ee_marker, + goal_marker, + contact_forces, + frame, + ) + + +def _run_op_space_controller( + robot: Articulation, + osc: OperationalSpaceController, + ee_frame_name: str, + arm_joint_names: list[str], + target_set: torch.tensor, + sim: sim_utils.SimulationContext, + num_envs: int, + ee_marker: VisualizationMarkers, + goal_marker: VisualizationMarkers, + contact_forces: ContactSensor | None, + frame: str, +): + """Run the operational space controller with the given parameters. + + Args: + robot (Articulation): The robot to control. + osc (OperationalSpaceController): The operational space controller. + ee_frame_name (str): The name of the end-effector frame. + arm_joint_names (list[str]): The names of the arm joints. + target_set (torch.tensor): The target set to track. + sim (sim_utils.SimulationContext): The simulation context. + num_envs (int): The number of environments. + ee_marker (VisualizationMarkers): The end-effector marker. + goal_marker (VisualizationMarkers): The goal marker. + contact_forces (ContactSensor | None): The contact forces sensor. + frame (str): The reference frame for targets. + """ + # Initialize the masks for evaluating target convergence according to selection matrices + pos_mask = torch.tensor(osc.cfg.motion_control_axes_task[:3], device=sim.device).view(1, 3) + rot_mask = torch.tensor(osc.cfg.motion_control_axes_task[3:], device=sim.device).view(1, 3) + wrench_mask = torch.tensor(osc.cfg.contact_wrench_control_axes_task, device=sim.device).view(1, 6) + force_mask = wrench_mask[:, 0:3] # Take only the force components as we can measure only these + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Play the simulator + sim.reset() + + # Obtain the frame index of the end-effector + ee_frame_idx = robot.find_bodies(ee_frame_name)[0][0] + # Obtain joint indices + arm_joint_ids = robot.find_joints(arm_joint_names)[0] + + # Update existing buffers + # Note: We need to update buffers before the first step for the controller. + robot.update(dt=sim_dt) + + # Get the center of the robot soft joint limits + joint_centers = torch.mean(robot.data.soft_joint_pos_limits[:, arm_joint_ids, :], dim=-1) + + # get the updated states + ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) = _update_states(robot, ee_frame_idx, arm_joint_ids, sim, contact_forces, num_envs) + + # Track the given target command + current_goal_idx = 0 # Current goal index for the arm + command = torch.zeros( + num_envs, osc.action_dim, device=sim.device + ) # Generic target command, which can be pose, position, force, etc. + ee_target_pose_b = torch.zeros(num_envs, 7, device=sim.device) # Target pose in the body frame + ee_target_pose_w = torch.zeros(num_envs, 7, device=sim.device) # Target pose in the world frame (for marker) + + # Set joint efforts to zero + zero_joint_efforts = torch.zeros(num_envs, robot.num_joints, device=sim.device) + joint_efforts = torch.zeros(num_envs, len(arm_joint_ids), device=sim.device) + + # Now we are ready! + for count in range(1501): + # reset every 500 steps + if count % 500 == 0: + # check that we converged to the goal + if count > 0: + _check_convergence( + osc, ee_pose_b, ee_target_pose_b, ee_force_b, command, pos_mask, rot_mask, force_mask, frame + ) + # reset joint state to default + default_joint_pos = robot.data.default_joint_pos.clone() + default_joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(default_joint_pos, default_joint_vel) + robot.set_joint_effort_target(zero_joint_efforts) # Set zero torques in the initial step + robot.write_data_to_sim() + robot.reset() + # reset contact sensor + if contact_forces is not None: + contact_forces.reset() + # reset target pose + robot.update(sim_dt) + _, _, _, ee_pose_b, _, _, _, _, _, _ = _update_states( + robot, ee_frame_idx, arm_joint_ids, sim, contact_forces, num_envs + ) # at reset, the jacobians are not updated to the latest state + command, ee_target_pose_b, ee_target_pose_w, current_goal_idx = _update_target( + osc, root_pose_w, ee_pose_b, target_set, current_goal_idx + ) + # set the osc command + osc.reset() + command, task_frame_pose_b = _convert_to_task_frame( + osc, command=command, ee_target_pose_b=ee_target_pose_b, frame=frame + ) + osc.set_command(command=command, current_ee_pose_b=ee_pose_b, current_task_frame_pose_b=task_frame_pose_b) + else: + # get the updated states + ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) = _update_states(robot, ee_frame_idx, arm_joint_ids, sim, contact_forces, num_envs) + # compute the joint commands + joint_efforts = osc.compute( + jacobian_b=jacobian_b, + current_ee_pose_b=ee_pose_b, + current_ee_vel_b=ee_vel_b, + current_ee_force_b=ee_force_b, + mass_matrix=mass_matrix, + gravity=gravity, + current_joint_pos=joint_pos, + current_joint_vel=joint_vel, + nullspace_joint_pos_target=joint_centers, + ) + robot.set_joint_effort_target(joint_efforts, joint_ids=arm_joint_ids) + robot.write_data_to_sim() + + # update marker positions + ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]) + goal_marker.visualize(ee_target_pose_w[:, 0:3], ee_target_pose_w[:, 3:7]) + + # perform step + sim.step(render=False) + # update buffers + robot.update(sim_dt) + + +def _update_states( + robot: Articulation, + ee_frame_idx: int, + arm_joint_ids: list[int], + sim: sim_utils.SimulationContext, + contact_forces: ContactSensor | None, + num_envs: int, +): + """Update the states of the robot and obtain the relevant quantities for the operational space controller. + + Args: + robot (Articulation): The robot to control. + ee_frame_idx (int): The index of the end-effector frame. + arm_joint_ids (list[int]): The indices of the arm joints. + sim (sim_utils.SimulationContext): The simulation context. + contact_forces (ContactSensor | None): The contact forces sensor. + num_envs (int): Number of environments. + + Returns: + jacobian_b (torch.tensor): The Jacobian in the root frame. + mass_matrix (torch.tensor): The mass matrix. + gravity (torch.tensor): The gravity vector. + ee_pose_b (torch.tensor): The end-effector pose in the root frame. + ee_vel_b (torch.tensor): The end-effector velocity in the root frame. + root_pose_w (torch.tensor): The root pose in the world frame. + ee_pose_w (torch.tensor): The end-effector pose in the world frame. + ee_force_b (torch.tensor): The end-effector force in the root frame. + joint_pos (torch.tensor): The joint positions. + joint_vel (torch.tensor): The joint velocities. + """ + # obtain dynamics related quantities from simulation + ee_jacobi_idx = ee_frame_idx - 1 + jacobian_w = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, arm_joint_ids] + mass_matrix = robot.root_physx_view.get_generalized_mass_matrices()[:, arm_joint_ids, :][:, :, arm_joint_ids] + gravity = robot.root_physx_view.get_gravity_compensation_forces()[:, arm_joint_ids] + # Convert the Jacobian from world to root frame + jacobian_b = jacobian_w.clone() + root_rot_matrix = matrix_from_quat(quat_inv(robot.data.root_quat_w)) + jacobian_b[:, :3, :] = torch.bmm(root_rot_matrix, jacobian_b[:, :3, :]) + jacobian_b[:, 3:, :] = torch.bmm(root_rot_matrix, jacobian_b[:, 3:, :]) + + # Compute current pose of the end-effector + root_pose_w = robot.data.root_pose_w + ee_pose_w = robot.data.body_pose_w[:, ee_frame_idx] + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + ee_pose_b = torch.cat([ee_pos_b, ee_quat_b], dim=-1) + + # Compute the current velocity of the end-effector + ee_vel_w = robot.data.body_vel_w[:, ee_frame_idx, :] # Extract end-effector velocity in the world frame + root_vel_w = robot.data.root_vel_w # Extract root velocity in the world frame + relative_vel_w = ee_vel_w - root_vel_w # Compute the relative velocity in the world frame + ee_lin_vel_b = quat_apply_inverse(robot.data.root_quat_w, relative_vel_w[:, 0:3]) # From world to root frame + ee_ang_vel_b = quat_apply_inverse(robot.data.root_quat_w, relative_vel_w[:, 3:6]) + ee_vel_b = torch.cat([ee_lin_vel_b, ee_ang_vel_b], dim=-1) + + # Calculate the contact force + ee_force_w = torch.zeros(num_envs, 3, device=sim.device) + if contact_forces is not None: # Only modify if it exist + sim_dt = sim.get_physics_dt() + contact_forces.update(sim_dt) # update contact sensor + # Calculate the contact force by averaging over last four time steps (i.e., to smoothen) and + # taking the max of three surfaces as only one should be the contact of interest + ee_force_w, _ = torch.max(torch.mean(contact_forces.data.net_forces_w_history, dim=1), dim=1) + + # This is a simplification, only for the sake of testing. + ee_force_b = ee_force_w + + # Get joint positions and velocities + joint_pos = robot.data.joint_pos[:, arm_joint_ids] + joint_vel = robot.data.joint_vel[:, arm_joint_ids] + + return ( + jacobian_b, + mass_matrix, + gravity, + ee_pose_b, + ee_vel_b, + root_pose_w, + ee_pose_w, + ee_force_b, + joint_pos, + joint_vel, + ) + + +def _update_target( + osc: OperationalSpaceController, + root_pose_w: torch.tensor, + ee_pose_b: torch.tensor, + target_set: torch.tensor, + current_goal_idx: int, +): + """Update the target for the operational space controller. + + Args: + osc (OperationalSpaceController): The operational space controller. + root_pose_w (torch.tensor): The root pose in the world frame. + ee_pose_b (torch.tensor): The end-effector pose in the body frame. + target_set (torch.tensor): The target set to track. + current_goal_idx (int): The current goal index. + + Returns: + command (torch.tensor): The target command. + ee_target_pose_b (torch.tensor): The end-effector target pose in the body frame. + ee_target_pose_w (torch.tensor): The end-effector target pose in the world frame. + next_goal_idx (int): The next goal index. + + Raises: + ValueError: If the target type is undefined. + """ + # update the ee desired command + command = torch.zeros(osc.num_envs, osc.action_dim, device=osc._device) + command[:] = target_set[current_goal_idx] + + # update the ee desired pose + ee_target_pose_b = torch.zeros(osc.num_envs, 7, device=osc._device) + for target_type in osc.cfg.target_types: + if target_type == "pose_abs": + ee_target_pose_b[:] = command[:, :7] + elif target_type == "pose_rel": + ee_target_pose_b[:, 0:3], ee_target_pose_b[:, 3:7] = apply_delta_pose( + ee_pose_b[:, :3], ee_pose_b[:, 3:], command[:, :7] + ) + elif target_type == "wrench_abs": + pass # ee_target_pose_b could stay at the root frame for force control, what matters is ee_target_b + else: + raise ValueError("Undefined target_type within _update_target().") + + # update the target desired pose in world frame (for marker) + ee_target_pos_w, ee_target_quat_w = combine_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_target_pose_b[:, 0:3], ee_target_pose_b[:, 3:7] + ) + ee_target_pose_w = torch.cat([ee_target_pos_w, ee_target_quat_w], dim=-1) + + next_goal_idx = (current_goal_idx + 1) % len(target_set) + + return command, ee_target_pose_b, ee_target_pose_w, next_goal_idx + + +def _convert_to_task_frame( + osc: OperationalSpaceController, command: torch.tensor, ee_target_pose_b: torch.tensor, frame: str +): + """Convert the target command to the task frame if required. + + Args: + osc (OperationalSpaceController): The operational space controller. + command (torch.tensor): The target command to convert. + ee_target_pose_b (torch.tensor): The end-effector target pose in the body frame. + frame (str): The reference frame for targets. + + Returns: + command (torch.tensor): The converted target command. + task_frame_pose_b (torch.tensor): The task frame pose in the body frame. + + Raises: + ValueError: If the frame is invalid. + """ + command = command.clone() + task_frame_pose_b = None + if frame == "root": + # No need to transform anything if they are already in root frame + pass + elif frame == "task": + # Convert target commands from base to the task frame + command = command.clone() + task_frame_pose_b = ee_target_pose_b.clone() + + cmd_idx = 0 + for target_type in osc.cfg.target_types: + if target_type == "pose_abs": + command[:, :3], command[:, 3:7] = subtract_frame_transforms( + task_frame_pose_b[:, :3], task_frame_pose_b[:, 3:], command[:, :3], command[:, 3:7] + ) + cmd_idx += 7 + elif target_type == "pose_rel": + # Compute rotation matrices + R_task_b = matrix_from_quat(task_frame_pose_b[:, 3:]) # Task frame to base frame + R_b_task = R_task_b.mT # Base frame to task frame + # Transform the delta position and orientation from base to task frame + command[:, :3] = (R_b_task @ command[:, :3].unsqueeze(-1)).squeeze(-1) + command[:, 3:7] = (R_b_task @ command[:, 3:7].unsqueeze(-1)).squeeze(-1) + cmd_idx += 6 + elif target_type == "wrench_abs": + # These are already defined in target frame for ee_goal_wrench_set_tilted_task (since it is + # easier), so not transforming + cmd_idx += 6 + else: + raise ValueError("Undefined target_type within _convert_to_task_frame().") + else: + # Raise error for invalid frame + raise ValueError("Invalid frame selection for target setting inside the test_operational_space.") + + return command, task_frame_pose_b + + +def _check_convergence( + osc: OperationalSpaceController, + ee_pose_b: torch.tensor, + ee_target_pose_b: torch.tensor, + ee_force_b: torch.tensor, + ee_target_b: torch.tensor, + pos_mask: torch.tensor, + rot_mask: torch.tensor, + force_mask: torch.tensor, + frame: str, +): + """Check the convergence to the target. + + Args: + osc (OperationalSpaceController): The operational space controller. + ee_pose_b (torch.tensor): The end-effector pose in the body frame. + ee_target_pose_b (torch.tensor): The end-effector target pose in the body frame. + ee_force_b (torch.tensor): The end-effector force in the body frame. + ee_target_b (torch.tensor): The end-effector target in the body frame. + pos_mask (torch.tensor): The position mask. + rot_mask (torch.tensor): The rotation mask. + force_mask (torch.tensor): The force mask. + frame (str): The reference frame for targets. + + Raises: + AssertionError: If the convergence is not achieved. + ValueError: If the target type is undefined. + """ + cmd_idx = 0 + for target_type in osc.cfg.target_types: + if target_type == "pose_abs": + pos_error, rot_error = compute_pose_error( + ee_pose_b[:, 0:3], ee_pose_b[:, 3:7], ee_target_pose_b[:, 0:3], ee_target_pose_b[:, 3:7] + ) + pos_error_norm = torch.norm(pos_error * pos_mask, dim=-1) + rot_error_norm = torch.norm(rot_error * rot_mask, dim=-1) + # desired error (zer) + des_error = torch.zeros_like(pos_error_norm) + # check convergence + torch.testing.assert_close(pos_error_norm, des_error, rtol=0.0, atol=0.1) + torch.testing.assert_close(rot_error_norm, des_error, rtol=0.0, atol=0.1) + cmd_idx += 7 + elif target_type == "pose_rel": + pos_error, rot_error = compute_pose_error( + ee_pose_b[:, 0:3], ee_pose_b[:, 3:7], ee_target_pose_b[:, 0:3], ee_target_pose_b[:, 3:7] + ) + pos_error_norm = torch.norm(pos_error * pos_mask, dim=-1) + rot_error_norm = torch.norm(rot_error * rot_mask, dim=-1) + # desired error (zer) + des_error = torch.zeros_like(pos_error_norm) + # check convergence + torch.testing.assert_close(pos_error_norm, des_error, rtol=0.0, atol=0.1) + torch.testing.assert_close(rot_error_norm, des_error, rtol=0.0, atol=0.1) + cmd_idx += 6 + elif target_type == "wrench_abs": + force_target_b = ee_target_b[:, cmd_idx : cmd_idx + 3].clone() + # Convert to base frame if the target was defined in task frame + if frame == "task": + task_frame_pose_b = ee_target_pose_b.clone() + R_task_b = matrix_from_quat(task_frame_pose_b[:, 3:]) + force_target_b[:] = (R_task_b @ force_target_b[:].unsqueeze(-1)).squeeze(-1) + force_error = ee_force_b - force_target_b + force_error_norm = torch.norm( + force_error * force_mask, dim=-1 + ) # ignore torque part as we cannot measure it + des_error = torch.zeros_like(force_error_norm) + # check convergence: big threshold here as the force control is not precise when the robot moves + torch.testing.assert_close(force_error_norm, des_error, rtol=0.0, atol=1.0) + cmd_idx += 6 + else: + raise ValueError("Undefined target_type within _check_convergence().") diff --git a/source/isaaclab/test/controllers/test_pink_ik.py b/source/isaaclab/test/controllers/test_pink_ik.py new file mode 100644 index 0000000000000000000000000000000000000000..43ab48ae059004fa7204af4f0ed10686b6eabe48 --- /dev/null +++ b/source/isaaclab/test/controllers/test_pink_ik.py @@ -0,0 +1,445 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +# Import pinocchio in the main script to force the use of the dependencies +# installed by IsaacLab and not the one installed by Isaac Sim +# pinocchio is required by the Pink IK controller +import sys + +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import contextlib +import json +import re +from pathlib import Path + +import gymnasium as gym +import numpy as np +import pytest +import torch +from pink.configuration import Configuration +from pink.tasks import FrameTask + +import omni.usd + +from isaaclab.utils.math import axis_angle_from_quat, matrix_from_quat, quat_from_matrix, quat_inv + +import isaaclab_tasks # noqa: F401 +import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401 +import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +def load_test_config(env_name): + """Load test configuration based on environment type.""" + # Determine which config file to load based on environment name + if "G1" in env_name: + config_file = "pink_ik_g1_test_configs.json" + elif "GR1" in env_name: + config_file = "pink_ik_gr1_test_configs.json" + else: + raise ValueError(f"Unknown environment type in {env_name}. Expected G1 or GR1.") + + config_path = Path(__file__).parent / "test_ik_configs" / config_file + with open(config_path) as f: + return json.load(f) + + +def is_waist_enabled(env_cfg): + """Check if waist joints are enabled in the environment configuration.""" + if not hasattr(env_cfg.actions, "upper_body_ik"): + return False + + pink_controlled_joints = env_cfg.actions.upper_body_ik.pink_controlled_joint_names + + # Also check for pattern-based joint names (e.g., "waist_.*_joint") + return any(re.match("waist", joint) for joint in pink_controlled_joints) + + +def create_test_env(env_name, num_envs): + """Create a test environment with the Pink IK controller.""" + device = "cuda:0" + + omni.usd.get_context().new_stage() + + try: + env_cfg = parse_env_cfg(env_name, device=device, num_envs=num_envs) + # Modify scene config to not spawn the packing table to avoid collision with the robot + del env_cfg.scene.packing_table + del env_cfg.terminations.object_dropping + del env_cfg.terminations.time_out + return gym.make(env_name, cfg=env_cfg).unwrapped, env_cfg + except Exception as e: + print(f"Failed to create environment: {str(e)}") + raise + + +@pytest.fixture( + scope="module", + params=[ + "Isaac-PickPlace-GR1T2-Abs-v0", + "Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0", + "Isaac-PickPlace-FixedBaseUpperBodyIK-G1-Abs-v0", + "Isaac-PickPlace-Locomanipulation-G1-Abs-v0", + ], +) +def env_and_cfg(request): + """Create environment and configuration for tests.""" + env_name = request.param + + # Load the appropriate test configuration based on environment type + test_cfg = load_test_config(env_name) + + env, env_cfg = create_test_env(env_name, num_envs=1) + + # Get only the FrameTasks from variable_input_tasks + variable_input_tasks = [ + task for task in env_cfg.actions.upper_body_ik.controller.variable_input_tasks if isinstance(task, FrameTask) + ] + assert len(variable_input_tasks) == 2, "Expected exactly two FrameTasks (left and right hand)." + frames = [task.frame for task in variable_input_tasks] + # Try to infer which is left and which is right + left_candidates = [f for f in frames if "left" in f.lower()] + right_candidates = [f for f in frames if "right" in f.lower()] + assert len(left_candidates) == 1 and len(right_candidates) == 1, ( + f"Could not uniquely identify left/right frames from: {frames}" + ) + left_eef_urdf_link_name = left_candidates[0] + right_eef_urdf_link_name = right_candidates[0] + + # Set up camera view + env.sim.set_camera_view(eye=[2.5, 2.5, 2.5], target=[0.0, 0.0, 1.0]) + + # Create test parameters from test_cfg + test_params = { + "position": test_cfg["tolerances"]["position"], + "rotation": test_cfg["tolerances"]["rotation"], + "pd_position": test_cfg["tolerances"]["pd_position"], + "check_errors": test_cfg["tolerances"]["check_errors"], + "left_eef_urdf_link_name": left_eef_urdf_link_name, + "right_eef_urdf_link_name": right_eef_urdf_link_name, + } + + try: + yield env, env_cfg, test_cfg, test_params + finally: + env.close() + + +@pytest.fixture +def test_setup(env_and_cfg): + """Set up test case - runs before each test.""" + env, env_cfg, test_cfg, test_params = env_and_cfg + + num_joints_in_robot_hands = env_cfg.actions.upper_body_ik.controller.num_hand_joints + + # Get Action Term and IK controller + action_term = env.action_manager.get_term(name="upper_body_ik") + pink_controllers = action_term._ik_controllers + articulation = action_term._asset + + # Initialize Pink Configuration for forward kinematics + test_kinematics_model = Configuration( + pink_controllers[0].pink_configuration.model, + pink_controllers[0].pink_configuration.data, + pink_controllers[0].pink_configuration.q, + ) + left_target_link_name = env_cfg.actions.upper_body_ik.target_eef_link_names["left_wrist"] + right_target_link_name = env_cfg.actions.upper_body_ik.target_eef_link_names["right_wrist"] + + return { + "env": env, + "env_cfg": env_cfg, + "test_cfg": test_cfg, + "test_params": test_params, + "num_joints_in_robot_hands": num_joints_in_robot_hands, + "action_term": action_term, + "pink_controllers": pink_controllers, + "articulation": articulation, + "test_kinematics_model": test_kinematics_model, + "left_target_link_name": left_target_link_name, + "right_target_link_name": right_target_link_name, + "left_eef_urdf_link_name": test_params["left_eef_urdf_link_name"], + "right_eef_urdf_link_name": test_params["right_eef_urdf_link_name"], + } + + +@pytest.mark.parametrize( + "test_name", + [ + "horizontal_movement", + "horizontal_small_movement", + "stay_still", + "forward_waist_bending_movement", + "vertical_movement", + "rotation_movements", + ], +) +def test_movement_types(test_setup, test_name): + """Test different movement types using parametrization.""" + test_cfg = test_setup["test_cfg"] + env_cfg = test_setup["env_cfg"] + + if test_name not in test_cfg["tests"]: + print(f"Skipping {test_name} test for {env_cfg.__class__.__name__} environment (test not defined)...") + pytest.skip(f"Test {test_name} not defined for {env_cfg.__class__.__name__}") + return + + test_config = test_cfg["tests"][test_name] + + # Check if test requires waist bending and if waist is enabled + requires_waist_bending = test_config.get("requires_waist_bending", False) + waist_enabled = is_waist_enabled(env_cfg) + + if requires_waist_bending and not waist_enabled: + print( + f"Skipping {test_name} test because it requires waist bending but waist is not enabled in" + f" {env_cfg.__class__.__name__}..." + ) + pytest.skip(f"Test {test_name} requires waist bending but waist is not enabled") + return + + print(f"Running {test_name} test...") + run_movement_test(test_setup, test_config, test_cfg) + + +def run_movement_test(test_setup, test_config, test_cfg, aux_function=None): + """Run a movement test with the given configuration.""" + env = test_setup["env"] + num_joints_in_robot_hands = test_setup["num_joints_in_robot_hands"] + + left_hand_poses = np.array(test_config["left_hand_pose"], dtype=np.float32) + right_hand_poses = np.array(test_config["right_hand_pose"], dtype=np.float32) + + curr_pose_idx = 0 + test_counter = 0 + num_runs = 0 + + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + obs, _ = env.reset() + + # Make the first phase longer than subsequent ones + initial_steps = test_cfg["allowed_steps_to_settle"] + phase = "initial" + steps_in_phase = 0 + + while simulation_app.is_running() and not simulation_app.is_exiting(): + num_runs += 1 + steps_in_phase += 1 + + # Call auxiliary function if provided + if aux_function is not None: + aux_function(num_runs) + + # Create actions from hand poses and joint positions + setpoint_poses = np.concatenate([left_hand_poses[curr_pose_idx], right_hand_poses[curr_pose_idx]]) + actions = np.concatenate([setpoint_poses, np.zeros(num_joints_in_robot_hands)]) + actions = torch.tensor(actions, device=env.device, dtype=torch.float32) + # Append base command for Locomanipulation environments with fixed height + if test_setup["env_cfg"].__class__.__name__ == "LocomanipulationG1EnvCfg": + # Use a named variable for base height for clarity and maintainability + BASE_HEIGHT = 0.72 + base_command = torch.zeros(4, device=env.device, dtype=actions.dtype) + base_command[3] = BASE_HEIGHT + actions = torch.cat([actions, base_command]) + actions = actions.repeat(env.num_envs, 1) + + # Step environment + obs, _, _, _, _ = env.step(actions) + + # Determine the step interval for error checking + if phase == "initial": + check_interval = initial_steps + else: + check_interval = test_config["allowed_steps_per_motion"] + + # Check convergence and verify errors + if steps_in_phase % check_interval == 0: + print("Computing errors...") + errors = compute_errors( + test_setup, + env, + left_hand_poses[curr_pose_idx], + right_hand_poses[curr_pose_idx], + test_setup["left_eef_urdf_link_name"], + test_setup["right_eef_urdf_link_name"], + ) + print_debug_info(errors, test_counter) + test_params = test_setup["test_params"] + if test_params["check_errors"]: + verify_errors(errors, test_setup, test_params) + num_runs += 1 + + curr_pose_idx = (curr_pose_idx + 1) % len(left_hand_poses) + if curr_pose_idx == 0: + test_counter += 1 + if test_counter > test_config["repeat"]: + print("Test completed successfully") + break + # After the first phase, switch to normal interval + if phase == "initial": + phase = "normal" + steps_in_phase = 0 + + +def get_link_pose(env, link_name): + """Get the position and orientation of a link.""" + link_index = env.scene["robot"].data.body_names.index(link_name) + link_states = env.scene._articulations["robot"]._data.body_link_state_w + link_pose = link_states[:, link_index, :7] + return link_pose[:, :3], link_pose[:, 3:7] + + +def calculate_rotation_error(current_rot, target_rot): + """Calculate the rotation error between current and target orientations in axis-angle format.""" + if isinstance(target_rot, torch.Tensor): + target_rot_tensor = ( + target_rot.unsqueeze(0).expand(current_rot.shape[0], -1) if target_rot.dim() == 1 else target_rot + ) + else: + target_rot_tensor = torch.tensor(target_rot, device=current_rot.device) + if target_rot_tensor.dim() == 1: + target_rot_tensor = target_rot_tensor.unsqueeze(0).expand(current_rot.shape[0], -1) + + return axis_angle_from_quat( + quat_from_matrix(matrix_from_quat(target_rot_tensor) * matrix_from_quat(quat_inv(current_rot))) + ) + + +def compute_errors( + test_setup, env, left_target_pose, right_target_pose, left_eef_urdf_link_name, right_eef_urdf_link_name +): + """Compute all error metrics for the current state.""" + action_term = test_setup["action_term"] + pink_controllers = test_setup["pink_controllers"] + articulation = test_setup["articulation"] + test_kinematics_model = test_setup["test_kinematics_model"] + left_target_link_name = test_setup["left_target_link_name"] + right_target_link_name = test_setup["right_target_link_name"] + + # Get current hand positions and orientations + left_hand_pos, left_hand_rot = get_link_pose(env, left_target_link_name) + right_hand_pos, right_hand_rot = get_link_pose(env, right_target_link_name) + + # Create setpoint tensors + device = env.device + num_envs = env.num_envs + left_hand_pose_setpoint = torch.tensor(left_target_pose, device=device).unsqueeze(0).repeat(num_envs, 1) + right_hand_pose_setpoint = torch.tensor(right_target_pose, device=device).unsqueeze(0).repeat(num_envs, 1) + + # Calculate position and rotation errors + left_pos_error = left_hand_pose_setpoint[:, :3] - left_hand_pos + right_pos_error = right_hand_pose_setpoint[:, :3] - right_hand_pos + left_rot_error = calculate_rotation_error(left_hand_rot, left_hand_pose_setpoint[:, 3:]) + right_rot_error = calculate_rotation_error(right_hand_rot, right_hand_pose_setpoint[:, 3:]) + + # Calculate PD controller errors + ik_controller = pink_controllers[0] + isaaclab_controlled_joint_ids = action_term._isaaclab_controlled_joint_ids + + # Get current and target positions for controlled joints only + curr_joints = articulation.data.joint_pos[:, isaaclab_controlled_joint_ids].cpu().numpy()[0] + target_joints = action_term.processed_actions[:, : len(isaaclab_controlled_joint_ids)].cpu().numpy()[0] + + # Reorder joints for Pink IK (using controlled joint ordering) + curr_joints = np.array(curr_joints)[ik_controller.isaac_lab_to_pink_controlled_ordering] + target_joints = np.array(target_joints)[ik_controller.isaac_lab_to_pink_controlled_ordering] + + # Run forward kinematics + test_kinematics_model.update(curr_joints) + left_curr_pos = test_kinematics_model.get_transform_frame_to_world(frame=left_eef_urdf_link_name).translation + right_curr_pos = test_kinematics_model.get_transform_frame_to_world(frame=right_eef_urdf_link_name).translation + + test_kinematics_model.update(target_joints) + left_target_pos = test_kinematics_model.get_transform_frame_to_world(frame=left_eef_urdf_link_name).translation + right_target_pos = test_kinematics_model.get_transform_frame_to_world(frame=right_eef_urdf_link_name).translation + + # Calculate PD errors + left_pd_error = ( + torch.tensor(left_target_pos - left_curr_pos, device=device, dtype=torch.float32) + .unsqueeze(0) + .repeat(num_envs, 1) + ) + right_pd_error = ( + torch.tensor(right_target_pos - right_curr_pos, device=device, dtype=torch.float32) + .unsqueeze(0) + .repeat(num_envs, 1) + ) + + return { + "left_pos_error": left_pos_error, + "right_pos_error": right_pos_error, + "left_rot_error": left_rot_error, + "right_rot_error": right_rot_error, + "left_pd_error": left_pd_error, + "right_pd_error": right_pd_error, + } + + +def verify_errors(errors, test_setup, tolerances): + """Verify that all error metrics are within tolerance.""" + env = test_setup["env"] + device = env.device + num_envs = env.num_envs + zero_tensor = torch.zeros(num_envs, device=device) + + for hand in ["left", "right"]: + # Check PD controller errors + pd_error_norm = torch.norm(errors[f"{hand}_pd_error"], dim=1) + torch.testing.assert_close( + pd_error_norm, + zero_tensor, + rtol=0.0, + atol=tolerances["pd_position"], + msg=( + f"{hand.capitalize()} hand PD controller error ({pd_error_norm.item():.6f}) exceeds tolerance" + f" ({tolerances['pd_position']:.6f})" + ), + ) + + # Check IK position errors + pos_error_norm = torch.norm(errors[f"{hand}_pos_error"], dim=1) + torch.testing.assert_close( + pos_error_norm, + zero_tensor, + rtol=0.0, + atol=tolerances["position"], + msg=( + f"{hand.capitalize()} hand IK position error ({pos_error_norm.item():.6f}) exceeds tolerance" + f" ({tolerances['position']:.6f})" + ), + ) + + # Check rotation errors + rot_error_max = torch.max(errors[f"{hand}_rot_error"]) + torch.testing.assert_close( + rot_error_max, + torch.zeros_like(rot_error_max), + rtol=0.0, + atol=tolerances["rotation"], + msg=( + f"{hand.capitalize()} hand IK rotation error ({rot_error_max.item():.6f}) exceeds tolerance" + f" ({tolerances['rotation']:.6f})" + ), + ) + + +def print_debug_info(errors, test_counter): + """Print debug information about the current state.""" + print(f"\nTest iteration {test_counter + 1}:") + for hand in ["left", "right"]: + print(f"Measured {hand} hand position error:", errors[f"{hand}_pos_error"]) + print(f"Measured {hand} hand rotation error:", errors[f"{hand}_rot_error"]) + print(f"Measured {hand} hand PD error:", errors[f"{hand}_pd_error"]) diff --git a/source/isaaclab/test/controllers/test_pink_ik_components.py b/source/isaaclab/test/controllers/test_pink_ik_components.py new file mode 100644 index 0000000000000000000000000000000000000000..6bde4c30a1b6ce0142697241be586b0aaef9221e --- /dev/null +++ b/source/isaaclab/test/controllers/test_pink_ik_components.py @@ -0,0 +1,309 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test cases for PinkKinematicsConfiguration class.""" + +# Import pinocchio in the main script to force the use of the dependencies installed +# by IsaacLab and not the one installed by Isaac Sim +# pinocchio is required by the Pink IK controller +import sys + +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +from pathlib import Path + +import numpy as np +import pinocchio as pin +import pytest +from pink.exceptions import FrameNotFound + +from isaaclab.controllers.pink_ik.pink_kinematics_configuration import PinkKinematicsConfiguration + + +class TestPinkKinematicsConfiguration: + """Test suite for PinkKinematicsConfiguration class.""" + + @pytest.fixture + def urdf_path(self): + """Path to test URDF file.""" + return Path(__file__).parent / "urdfs/test_urdf_two_link_robot.urdf" + + @pytest.fixture + def mesh_path(self): + """Path to mesh directory (empty for simple test).""" + return "" + + @pytest.fixture + def controlled_joint_names(self): + """List of controlled joint names for testing.""" + return ["joint_1", "joint_2"] + + @pytest.fixture + def pink_config(self, urdf_path, mesh_path, controlled_joint_names): + """Create a PinkKinematicsConfiguration instance for testing.""" + return PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + mesh_path=mesh_path, + controlled_joint_names=controlled_joint_names, + copy_data=True, + forward_kinematics=True, + ) + + def test_initialization(self, pink_config, controlled_joint_names): + """Test proper initialization of PinkKinematicsConfiguration.""" + # Check that controlled joint names are stored correctly + assert pink_config._controlled_joint_names == controlled_joint_names + + # Check that both full and controlled models are created + assert pink_config.full_model is not None + assert pink_config.controlled_model is not None + assert pink_config.full_data is not None + assert pink_config.controlled_data is not None + + # Check that configuration vectors are initialized + assert pink_config.full_q is not None + assert pink_config.controlled_q is not None + + # Check that the controlled model has the same number or fewer joints than the full model + assert pink_config.controlled_model.nq == pink_config.full_model.nq + + def test_joint_names_properties(self, pink_config): + """Test joint name properties.""" + # Test controlled joint names in pinocchio order + controlled_names = pink_config.controlled_joint_names_pinocchio_order + assert isinstance(controlled_names, list) + assert len(controlled_names) == len(pink_config._controlled_joint_names) + assert "joint_1" in controlled_names + assert "joint_2" in controlled_names + + # Test all joint names in pinocchio order + all_names = pink_config.all_joint_names_pinocchio_order + assert isinstance(all_names, list) + assert len(all_names) == len(controlled_names) + assert "joint_1" in all_names + assert "joint_2" in all_names + + def test_update_with_valid_configuration(self, pink_config): + """Test updating configuration with valid joint values.""" + # Get initial configuration + initial_q = pink_config.full_q.copy() + + # Create a new configuration with different joint values + new_q = initial_q.copy() + new_q[1] = 0.5 # Change first revolute joint value (index 1, since 0 is fixed joint) + + # Update configuration + pink_config.update(new_q) + + # Check that the configuration was updated + print(pink_config.full_q) + assert not np.allclose(pink_config.full_q, initial_q) + assert np.allclose(pink_config.full_q, new_q) + + def test_update_with_none(self, pink_config): + """Test updating configuration with None (should use current configuration).""" + # Get initial configuration + initial_q = pink_config.full_q.copy() + + # Update with None + pink_config.update(None) + + # Configuration should remain the same + assert np.allclose(pink_config.full_q, initial_q) + + def test_update_with_wrong_dimensions(self, pink_config): + """Test that update raises ValueError with wrong configuration dimensions.""" + # Create configuration with wrong number of joints + wrong_q = np.array([0.1, 0.2, 0.3]) # Wrong number of joints + + with pytest.raises(ValueError, match="q must have the same length as the number of joints"): + pink_config.update(wrong_q) + + def test_get_frame_jacobian_existing_frame(self, pink_config): + """Test getting Jacobian for an existing frame.""" + # Get Jacobian for link_1 frame + jacobian = pink_config.get_frame_jacobian("link_1") + + # Check that Jacobian has correct shape + # Should be 6 rows (linear + angular velocity) and columns equal to controlled joints + expected_rows = 6 + expected_cols = len(pink_config._controlled_joint_names) + assert jacobian.shape == (expected_rows, expected_cols) + + # Check that Jacobian is not all zeros (should have some non-zero values) + assert not np.allclose(jacobian, 0.0) + + def test_get_frame_jacobian_nonexistent_frame(self, pink_config): + """Test that get_frame_jacobian raises FrameNotFound for non-existent frame.""" + with pytest.raises(FrameNotFound): + pink_config.get_frame_jacobian("nonexistent_frame") + + def test_get_transform_frame_to_world_existing_frame(self, pink_config): + """Test getting transform for an existing frame.""" + # Get transform for link_1 frame + transform = pink_config.get_transform_frame_to_world("link_1") + + # Check that transform is a pinocchio SE3 object + assert isinstance(transform, pin.SE3) + + # Check that transform has reasonable values (not identity for non-zero joint angles) + assert not np.allclose(transform.homogeneous, np.eye(4)) + + def test_get_transform_frame_to_world_nonexistent_frame(self, pink_config): + """Test that get_transform_frame_to_world raises FrameNotFound for non-existent frame.""" + with pytest.raises(FrameNotFound): + pink_config.get_transform_frame_to_world("nonexistent_frame") + + def test_multiple_controlled_joints(self, urdf_path, mesh_path): + """Test configuration with multiple controlled joints.""" + # Create configuration with all available joints as controlled + controlled_joint_names = ["joint_1", "joint_2"] # Both revolute joints + + pink_config = PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + mesh_path=mesh_path, + controlled_joint_names=controlled_joint_names, + ) + + # Check that controlled model has correct number of joints + assert pink_config.controlled_model.nq == len(controlled_joint_names) + + def test_no_controlled_joints(self, urdf_path, mesh_path): + """Test configuration with no controlled joints.""" + controlled_joint_names = [] + + pink_config = PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + mesh_path=mesh_path, + controlled_joint_names=controlled_joint_names, + ) + + # Check that controlled model has 0 joints + assert pink_config.controlled_model.nq == 0 + assert len(pink_config.controlled_q) == 0 + + def test_jacobian_consistency(self, pink_config): + """Test that Jacobian computation is consistent across updates.""" + # Get Jacobian at initial configuration + jacobian_1 = pink_config.get_frame_jacobian("link_2") + + # Update configuration + new_q = pink_config.full_q.copy() + new_q[1] = 0.3 # Change first revolute joint (index 1, since 0 is fixed joint) + pink_config.update(new_q) + + # Get Jacobian at new configuration + jacobian_2 = pink_config.get_frame_jacobian("link_2") + + # Jacobians should be different (not all close) + assert not np.allclose(jacobian_1, jacobian_2) + + def test_transform_consistency(self, pink_config): + """Test that transform computation is consistent across updates.""" + # Get transform at initial configuration + transform_1 = pink_config.get_transform_frame_to_world("link_2") + + # Update configuration + new_q = pink_config.full_q.copy() + new_q[1] = 0.5 # Change first revolute joint (index 1, since 0 is fixed joint) + pink_config.update(new_q) + + # Get transform at new configuration + transform_2 = pink_config.get_transform_frame_to_world("link_2") + + # Transforms should be different + assert not np.allclose(transform_1.homogeneous, transform_2.homogeneous) + + def test_inheritance_from_configuration(self, pink_config): + """Test that PinkKinematicsConfiguration properly inherits from Pink Configuration.""" + from pink.configuration import Configuration + + # Check inheritance + assert isinstance(pink_config, Configuration) + + # Check that we can call parent class methods + assert hasattr(pink_config, "update") + assert hasattr(pink_config, "get_transform_frame_to_world") + + def test_controlled_joint_indices_calculation(self, pink_config): + """Test that controlled joint indices are calculated correctly.""" + # Check that controlled joint indices are valid + assert len(pink_config._controlled_joint_indices) == len(pink_config._controlled_joint_names) + + # Check that all indices are within bounds + for idx in pink_config._controlled_joint_indices: + assert 0 <= idx < len(pink_config._all_joint_names) + + # Check that indices correspond to controlled joint names + for i, idx in enumerate(pink_config._controlled_joint_indices): + joint_name = pink_config._all_joint_names[idx] + assert joint_name in pink_config._controlled_joint_names + + def test_full_model_integrity(self, pink_config): + """Test that the full model maintains integrity.""" + # Check that full model has all joints + assert pink_config.full_model.nq > 0 + assert len(pink_config.full_model.names) > 1 # More than just "universe" + + def test_controlled_model_integrity(self, pink_config): + """Test that the controlled model maintains integrity.""" + # Check that controlled model has correct number of joints + assert pink_config.controlled_model.nq == len(pink_config._controlled_joint_names) + + def test_configuration_vector_consistency(self, pink_config): + """Test that configuration vectors are consistent between full and controlled models.""" + # Check that controlled_q is a subset of full_q + controlled_indices = pink_config._controlled_joint_indices + for i, idx in enumerate(controlled_indices): + assert np.isclose(pink_config.controlled_q[i], pink_config.full_q[idx]) + + def test_error_handling_invalid_urdf(self, mesh_path, controlled_joint_names): + """Test error handling with invalid URDF path.""" + with pytest.raises(Exception): # Should raise some exception for invalid URDF + PinkKinematicsConfiguration( + urdf_path="nonexistent.urdf", + mesh_path=mesh_path, + controlled_joint_names=controlled_joint_names, + ) + + def test_error_handling_invalid_joint_names(self, urdf_path, mesh_path): + """Test error handling with invalid joint names.""" + invalid_joint_names = ["nonexistent_joint"] + + # This should not raise an error, but the controlled model should have 0 joints + pink_config = PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + mesh_path=mesh_path, + controlled_joint_names=invalid_joint_names, + ) + + assert pink_config.controlled_model.nq == 0 + assert len(pink_config.controlled_q) == 0 + + def test_undercontrolled_kinematics_model(self, urdf_path, mesh_path): + """Test that the fixed joint to world is properly handled.""" + + test_model = PinkKinematicsConfiguration( + urdf_path=str(urdf_path), + mesh_path=mesh_path, + controlled_joint_names=["joint_1"], + copy_data=True, + forward_kinematics=True, + ) + # Check that the controlled model only includes the revolute joints + assert "joint_1" in test_model.controlled_joint_names_pinocchio_order + assert "joint_2" not in test_model.controlled_joint_names_pinocchio_order + assert len(test_model.controlled_joint_names_pinocchio_order) == 1 # Only the two revolute joints + + # Check that the full configuration has more elements than controlled + assert len(test_model.full_q) > len(test_model.controlled_q) + assert len(test_model.full_q) == len(test_model.all_joint_names_pinocchio_order) + assert len(test_model.controlled_q) == len(test_model.controlled_joint_names_pinocchio_order) diff --git a/source/isaaclab/test/controllers/urdfs/test_urdf_two_link_robot.urdf b/source/isaaclab/test/controllers/urdfs/test_urdf_two_link_robot.urdf new file mode 100644 index 0000000000000000000000000000000000000000..cb1a305c50da337856e1a6e61ec336806c411af2 --- /dev/null +++ b/source/isaaclab/test/controllers/urdfs/test_urdf_two_link_robot.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/isaaclab/test/deps/isaacsim/check_camera.py b/source/isaaclab/test/deps/isaacsim/check_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..81f481f23e3fb61e515ddaa8b355e6f57c826add --- /dev/null +++ b/source/isaaclab/test/deps/isaacsim/check_camera.py @@ -0,0 +1,249 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows the issue with renderer in Isaac Sim that affects episodic resets. + +The first few images of every new episode are not updated. They take multiple steps to update +and have the same image as the previous episode for the first few steps. + +``` +# run with cube +_isaac_sim/python.sh source/isaaclab/test/deps/isaacsim/check_camera.py --scenario cube +# run with anymal +_isaac_sim/python.sh source/isaaclab/test/deps/isaacsim/check_camera.py --scenario anymal +``` +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +# isaaclab +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script shows the issue with renderer in Isaac Sim that affects episodic resets." +) +parser.add_argument("--gpu", action="store_true", default=False, help="Use GPU device for camera rendering output.") +parser.add_argument("--scenario", type=str, default="anymal", help="Scenario to load.", choices=["anymal", "cube"]) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os +import random + +import numpy as np +from PIL import Image, ImageChops + +import isaacsim.core.utils.nucleus as nucleus_utils +import isaacsim.core.utils.prims as prim_utils +import omni.replicator.core as rep +from isaacsim.core.api.world import World +from isaacsim.core.prims import Articulation, RigidPrim, SingleGeometryPrim, SingleRigidPrim +from isaacsim.core.utils.viewports import set_camera_view +from pxr import Gf, UsdGeom + +# check nucleus connection +if nucleus_utils.get_assets_root_path() is None: + msg = ( + "Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n" + "\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus" + ) + raise RuntimeError(msg) + +ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac" +"""Path to the `Isaac` directory on the NVIDIA Nucleus Server.""" + + +def main(): + """Runs a camera sensor from isaaclab.""" + + # Load kit helper + world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cpu") + # Set main camera + set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Enable flatcache which avoids passing data over to USD structure + # this speeds up the read-write operation of GPU buffers + if world.get_physics_context().use_gpu_pipeline: + world.get_physics_context().enable_flatcache(True) + # Enable hydra scene-graph instancing + # this is needed to visualize the scene when flatcache is enabled + world._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Populate scene + # Ground + world.scene.add_default_ground_plane() + # Lights-1 + prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0)) + # Lights-2 + prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0)) + # Xform to hold objects + if args_cli.scenario == "cube": + prim_utils.create_prim("/World/Objects", "Xform") + # Random objects + for i in range(8): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # create prim + prim_type = random.choice(["Cube", "Sphere", "Cylinder"]) + _ = prim_utils.create_prim( + f"/World/Objects/Obj_{i:02d}", + prim_type, + translation=position, + scale=(0.25, 0.25, 0.25), + semantic_label=prim_type, + ) + # add rigid properties + SingleGeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True) + rigid_obj = SingleRigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0) + # cast to geom prim + geom_prim = getattr(UsdGeom, prim_type)(rigid_obj.prim) + # set random color + color = Gf.Vec3f(random.random(), random.random(), random.random()) + geom_prim.CreateDisplayColorAttr() + geom_prim.GetDisplayColorAttr().Set([color]) + # Setup camera sensor on the world + cam_prim_path = "/World/CameraSensor" + else: + # Robot + prim_utils.create_prim( + "/World/Robot", + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_c/anymal_c.usd", + translation=(0.0, 0.0, 0.6), + ) + # Setup camera sensor on the robot + cam_prim_path = "/World/CameraSensor" + + # Create camera + cam_prim = prim_utils.create_prim( + cam_prim_path, + prim_type="Camera", + translation=(5.0, 5.0, 5.0), + orientation=(0.33985113, 0.17591988, 0.42470818, 0.82047324), + ) + _ = UsdGeom.Camera(cam_prim) + # Get render product + render_prod_path = rep.create.render_product(cam_prim_path, resolution=(640, 480)) + # create annotator node + rep_registry = {} + for name in ["rgb", "distance_to_image_plane"]: + # create annotator + rep_annotator = rep.AnnotatorRegistry.get_annotator(name, device="cpu") + rep_annotator.attach(render_prod_path) + # add to registry + rep_registry[name] = rep_annotator + + # Create replicator writer + output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output", "camera", args_cli.scenario) + os.makedirs(output_dir, exist_ok=True) + + # Create a view of the stuff we want to see + if args_cli.scenario == "cube": + view: RigidPrim = world.scene.add(RigidPrim("/World/Objects/.*", name="my_object")) + else: + view: Articulation = world.scene.add(Articulation("/World/Robot", name="my_object")) + # Play simulator + world.reset() + # Get initial state + if args_cli.scenario == "cube": + initial_pos, initial_quat = view.get_world_poses() + initial_joint_pos = None + initial_joint_vel = None + else: + initial_pos, initial_quat = view.get_world_poses() + initial_joint_pos = view.get_joint_positions() + initial_joint_vel = view.get_joint_velocities() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + world.step(render=True) + + # Counter + count = 0 + prev_im = None + # make episode directory + episode_count = 0 + episode_dir = os.path.join(output_dir, f"episode_{episode_count:06d}") + os.makedirs(episode_dir, exist_ok=True) + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if world.is_stopped(): + break + # If simulation is paused, then skip. + if not world.is_playing(): + world.step(render=False) + continue + # Reset on intervals + if count % 25 == 0: + # reset all the state + view.set_world_poses(initial_pos, initial_quat) + if initial_joint_pos is not None: + view.set_joint_positions(initial_joint_pos) + if initial_joint_vel is not None: + view.set_joint_velocities(initial_joint_vel) + # make a new episode directory + episode_dir = os.path.join(output_dir, f"episode_{episode_count:06d}") + os.makedirs(episode_dir, exist_ok=True) + # reset counters + count = 0 + episode_count += 1 + # Step simulation + for _ in range(15): + world.step(render=False) + world.render() + # Update camera data + rgb_data = rep_registry["rgb"].get_data() + depth_data = rep_registry["distance_to_image_plane"].get_data() + + # Show current image number + print(f"[Epi {episode_count:03d}] Current image number: {count:06d}") + # Save data + curr_im = Image.fromarray(rgb_data) + curr_im.save(os.path.join(episode_dir, f"{count:06d}_rgb.png")) + # Save diff + if prev_im is not None: + diff_im = ImageChops.difference(curr_im, prev_im) + # convert to grayscale and threshold + diff_im = diff_im.convert("L") + threshold = 30 + diff_im = diff_im.point(lambda p: p > threshold and 255) + # Save all of them together + dst_im = Image.new("RGB", (curr_im.width + prev_im.width + diff_im.width, diff_im.height)) + dst_im.paste(prev_im, (0, 0)) + dst_im.paste(curr_im, (prev_im.width, 0)) + dst_im.paste(diff_im, (2 * prev_im.width, 0)) + dst_im.save(os.path.join(episode_dir, f"{count:06d}_diff.png")) + + # Save to previous + prev_im = curr_im.copy() + # Update counter + count += 1 + + # Print camera info + print("Received shape of rgb image: ", rgb_data.shape) + print("Received shape of depth image: ", depth_data.shape) + print("-------------------------------") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/deps/isaacsim/check_floating_base_made_fixed.py b/source/isaaclab/test/deps/isaacsim/check_floating_base_made_fixed.py new file mode 100644 index 0000000000000000000000000000000000000000..0be9a55bd4cb832469077339f9813a8d15157e1b --- /dev/null +++ b/source/isaaclab/test/deps/isaacsim/check_floating_base_made_fixed.py @@ -0,0 +1,207 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates how to make a floating robot fixed in Isaac Sim.""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse +import contextlib + +with contextlib.suppress(ModuleNotFoundError): + import isaacsim # noqa: F401 + +from isaacsim import SimulationApp + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script shows the issue in Isaac Sim with making a floating robot fixed." +) +parser.add_argument("--headless", action="store_true", help="Run in headless mode.") +parser.add_argument("--fix-base", action="store_true", help="Whether to fix the base of the robot.") +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +simulation_app = SimulationApp({"headless": args_cli.headless}) + +"""Rest everything follows.""" + +import logging + +import torch + +import isaacsim.core.utils.nucleus as nucleus_utils +import isaacsim.core.utils.prims as prim_utils +import isaacsim.core.utils.stage as stage_utils +import omni.kit.commands +import omni.physx +from isaacsim.core.api.world import World +from isaacsim.core.prims import Articulation +from isaacsim.core.utils.viewports import set_camera_view +from pxr import PhysxSchema, UsdPhysics + +# import logger +logger = logging.getLogger(__name__) + + +# check nucleus connection +if nucleus_utils.get_assets_root_path() is None: + msg = ( + "Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n" + "\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus" + ) + logger.error(msg) + raise RuntimeError(msg) + + +ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac" +"""Path to the `Isaac` directory on the NVIDIA Nucleus Server.""" + +ISAACLAB_NUCLEUS_DIR = f"{ISAAC_NUCLEUS_DIR}/IsaacLab" +"""Path to the `Isaac/IsaacLab` directory on the NVIDIA Nucleus Server.""" + + +""" +Main +""" + + +def main(): + """Spawns the ANYmal robot and makes it fixed.""" + # Load kit helper + world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cpu") + # Set main camera + set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Enable hydra scene-graph instancing + # this is needed to visualize the scene when flatcache is enabled + world._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Spawn things into stage + # Ground-plane + world.scene.add_default_ground_plane(prim_path="/World/defaultGroundPlane", z_position=0.0) + # Lights-1 + prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0)) + # Lights-2 + prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0)) + # -- Robot + # resolve asset + usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd" + root_prim_path = "/World/Robot/base" + # add asset + print("Loading robot from: ", usd_path) + prim_utils.create_prim( + "/World/Robot", + usd_path=usd_path, + translation=(0.0, 0.0, 0.6), + ) + # create fixed joint + if args_cli.fix_base: + # get all necessary information + stage = stage_utils.get_current_stage() + root_prim = stage.GetPrimAtPath(root_prim_path) + parent_prim = root_prim.GetParent() + + # here we assume that the root prim is a rigid body + # there is no clear way to deal with situation where the root prim is not a rigid body but has articulation api + # in that case, it is unclear how to get the link to the first link in the tree + if not root_prim.HasAPI(UsdPhysics.RigidBodyAPI): + raise RuntimeError("The root prim does not have the RigidBodyAPI applied.") + + # create fixed joint + omni.kit.commands.execute( + "CreateJointCommand", + stage=stage, + joint_type="Fixed", + from_prim=None, + to_prim=root_prim, + ) + + # move the root to the parent if this is a rigid body + # having a fixed joint on a rigid body makes physx treat it as a part of the maximal coordinate tree + # if we put to joint on the parent, physx parser treats it as a fixed base articulation + # get parent prim + parent_prim = root_prim.GetParent() + # apply api to parent + UsdPhysics.ArticulationRootAPI.Apply(parent_prim) + PhysxSchema.PhysxArticulationAPI.Apply(parent_prim) + + # copy the attributes + # -- usd attributes + root_usd_articulation_api = UsdPhysics.ArticulationRootAPI(root_prim) + for attr_name in root_usd_articulation_api.GetSchemaAttributeNames(): + attr = root_prim.GetAttribute(attr_name) + parent_prim.GetAttribute(attr_name).Set(attr.Get()) + # -- physx attributes + root_physx_articulation_api = PhysxSchema.PhysxArticulationAPI(root_prim) + for attr_name in root_physx_articulation_api.GetSchemaAttributeNames(): + attr = root_prim.GetAttribute(attr_name) + parent_prim.GetAttribute(attr_name).Set(attr.Get()) + + # remove api from root + root_prim.RemoveAPI(UsdPhysics.ArticulationRootAPI) + root_prim.RemoveAPI(PhysxSchema.PhysxArticulationAPI) + + # rename root path to parent path + root_prim_path = parent_prim.GetPath().pathString + + # Setup robot + robot_view = Articulation(root_prim_path, name="ANYMAL") + world.scene.add(robot_view) + # Play the simulator + world.reset() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # dummy actions + # actions = torch.zeros(robot.count, robot.num_actions, device=robot.device) + + init_root_pos_w, init_root_quat_w = robot_view.get_world_poses() + # Define simulation stepping + sim_dt = world.get_physics_dt() + # episode counter + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if world.is_stopped(): + break + # If simulation is paused, then skip. + if not world.is_playing(): + world.step(render=False) + continue + # do reset + if count % 20 == 0: + # reset + sim_time = 0.0 + count = 0 + # reset root state + root_pos_w = init_root_pos_w.clone() + root_pos_w[:, :2] += torch.rand_like(root_pos_w[:, :2]) * 0.5 + robot_view.set_world_poses(root_pos_w, init_root_quat_w) + # print if it is fixed base + print("Fixed base: ", robot_view._physics_view.shared_metatype.fixed_base) + print("Moving base to: ", root_pos_w[0].cpu().numpy()) + print("-" * 50) + + # apply random joint actions + actions = torch.rand_like(robot_view.get_joint_positions()) * 0.001 + robot_view.set_joint_efforts(actions) + # perform step + world.step() + # update sim-time + sim_time += sim_dt + count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/deps/isaacsim/check_legged_robot_clone.py b/source/isaaclab/test/deps/isaacsim/check_legged_robot_clone.py new file mode 100644 index 0000000000000000000000000000000000000000..57c016d7522d64d31961b265ae33184d36478995 --- /dev/null +++ b/source/isaaclab/test/deps/isaacsim/check_legged_robot_clone.py @@ -0,0 +1,185 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the cloner API from Isaac Sim. + +Reference: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_gym_cloner.html +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse +import contextlib + +with contextlib.suppress(ModuleNotFoundError): + import isaacsim # noqa: F401 + +from isaacsim import SimulationApp + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script shows the issue in Isaac Sim with GPU simulation of floating robots." +) +parser.add_argument("--num_robots", type=int, default=128, help="Number of robots to spawn.") +parser.add_argument( + "--asset", + type=str, + default="isaaclab", + help="The asset source location for the robot. Can be: isaaclab, oige, custom asset path.", +) +parser.add_argument("--headless", action="store_true", help="Run in headless mode.") +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +simulation_app = SimulationApp({"headless": args_cli.headless}) + +"""Rest everything follows.""" + +import logging +import os + +import torch + +import isaacsim.core.utils.nucleus as nucleus_utils +import isaacsim.core.utils.prims as prim_utils +from isaacsim.core.api.world import World +from isaacsim.core.cloner import GridCloner +from isaacsim.core.prims import Articulation +from isaacsim.core.utils.viewports import set_camera_view + +# import logger +logger = logging.getLogger(__name__) + +# check nucleus connection +if nucleus_utils.get_assets_root_path() is None: + msg = ( + "Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n" + "\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus" + ) + logger.error(msg) + raise RuntimeError(msg) + + +ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac" +"""Path to the `Isaac` directory on the NVIDIA Nucleus Server.""" + +ISAACLAB_NUCLEUS_DIR = f"{ISAAC_NUCLEUS_DIR}/IsaacLab" +"""Path to the `Isaac/IsaacLab` directory on the NVIDIA Nucleus Server.""" + + +""" +Main +""" + + +def main(): + """Spawns the ANYmal robot and clones it using Isaac Sim Cloner API.""" + + # Load kit helper + world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cuda:0") + # Set main camera + set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Enable hydra scene-graph instancing + # this is needed to visualize the scene when flatcache is enabled + world._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + prim_utils.define_prim("/World/envs/env_0") + + # Spawn things into stage + # Ground-plane + world.scene.add_default_ground_plane(prim_path="/World/defaultGroundPlane", z_position=0.0) + # Lights-1 + prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0)) + # Lights-2 + prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0)) + # -- Robot + # resolve asset + if args_cli.asset == "isaaclab": + usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd" + root_prim_path = "/World/envs/env_.*/Robot/base" + elif args_cli.asset == "oige": + usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_c/anymal_c.usd" + root_prim_path = "/World/envs/env_.*/Robot" + elif os.path.exists(args_cli.asset): + usd_path = args_cli.asset + else: + raise ValueError(f"Invalid asset: {args_cli.asset}. Must be one of: isaaclab, oige.") + # add asset + print("Loading robot from: ", usd_path) + prim_utils.create_prim( + "/World/envs/env_0/Robot", + usd_path=usd_path, + translation=(0.0, 0.0, 0.6), + ) + + # Clone the scene + num_envs = args_cli.num_robots + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + envs_positions = cloner.clone( + source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True + ) + # convert environment positions to torch tensor + envs_positions = torch.tensor(envs_positions, dtype=torch.float, device=world.device) + # filter collisions within each environment instance + physics_scene_path = world.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", envs_prim_paths, global_paths=["/World/defaultGroundPlane"] + ) + + # Resolve robot prim paths + if args_cli.asset == "isaaclab": + root_prim_path = "/World/envs/env_.*/Robot/base" + elif args_cli.asset == "oige": + root_prim_path = "/World/envs/env_.*/Robot" + elif os.path.exists(args_cli.asset): + usd_path = args_cli.asset + root_prim_path = "/World/envs/env_.*/Robot" + else: + raise ValueError(f"Invalid asset: {args_cli.asset}. Must be one of: isaaclab, oige.") + # Setup robot + robot_view = Articulation(root_prim_path, name="ANYMAL") + world.scene.add(robot_view) + # Play the simulator + world.reset() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # dummy actions + # actions = torch.zeros(robot.count, robot.num_actions, device=robot.device) + + # Define simulation stepping + sim_dt = world.get_physics_dt() + # episode counter + sim_time = 0.0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if world.is_stopped(): + break + # If simulation is paused, then skip. + if not world.is_playing(): + world.step(render=False) + continue + # perform step + world.step() + # update sim-time + sim_time += sim_dt + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/deps/isaacsim/check_ref_count.py b/source/isaaclab/test/deps/isaacsim/check_ref_count.py new file mode 100644 index 0000000000000000000000000000000000000000..7927b8cb01a11c42175c4db82c722c91dc04fa7a --- /dev/null +++ b/source/isaaclab/test/deps/isaacsim/check_ref_count.py @@ -0,0 +1,157 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates reference count of the robot view in Isaac Sim. + +When we make a class instance, the reference count of the class instance should always be 1. +However, in this script, the reference count of the robot view is 2 after the class is created. +This causes a memory leak in the Isaac Sim simulator and the robot view is not garbage collected. + +The issue is observed with torch 2.2 and Isaac Sim 4.0. It works fine with torch 2.0.1 and Isaac Sim 2023.1. +It can be resolved by uncommenting the line that creates a dummy tensor in the main function. + +To reproduce the issue, run this script and check the reference count of the robot view. + +For more details, please check: https://github.com/isaac-sim/IsaacLab/issues/639 +""" + +"""Launch Isaac Sim Simulator first.""" + + +import contextlib + +with contextlib.suppress(ModuleNotFoundError): + import isaacsim # noqa: F401 + +from isaacsim import SimulationApp + +# launch omniverse app +simulation_app = SimulationApp({"headless": True}) + +"""Rest everything follows.""" + +import ctypes +import gc +import logging + +import torch # noqa: F401 + +import isaacsim.core.utils.nucleus as nucleus_utils +import isaacsim.core.utils.prims as prim_utils +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.prims import Articulation + +# import logger +logger = logging.getLogger(__name__) + + +# check nucleus connection +if nucleus_utils.get_assets_root_path() is None: + msg = ( + "Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n" + "\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus" + ) + logger.error(msg) + raise RuntimeError(msg) + + +ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac" +"""Path to the `Isaac` directory on the NVIDIA Nucleus Server.""" + +ISAACLAB_NUCLEUS_DIR = f"{ISAAC_NUCLEUS_DIR}/IsaacLab" +"""Path to the `Isaac/IsaacLab` directory on the NVIDIA Nucleus Server.""" + + +""" +Classes +""" + + +class AnymalArticulation: + """Anymal articulation class.""" + + def __init__(self): + """Initialize the Anymal articulation class.""" + # resolve asset + usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd" + # add asset + print("Loading robot from: ", usd_path) + prim_utils.create_prim("/World/Robot", usd_path=usd_path, translation=(0.0, 0.0, 0.6)) + + # Resolve robot prim paths + root_prim_path = "/World/Robot/base" + # Setup robot + self.view = Articulation(root_prim_path, name="ANYMAL") + + def __del__(self): + """Delete the Anymal articulation class.""" + print("Deleting the Anymal view.") + self.view = None + + def initialize(self): + """Initialize the Anymal view.""" + self.view.initialize() + + +""" +Main +""" + + +def main(): + """Spawns the ANYmal robot and clones it using Isaac Sim Cloner API.""" + + # Load kit helper + sim = SimulationContext(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cuda:0") + + # Enable hydra scene-graph instancing + # this is needed to visualize the scene when flatcache is enabled + sim._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Create a dummy tensor for testing + # Uncommenting the following line will yield a reference count of 1 for the robot (as desired) + # dummy_tensor = torch.zeros(1, device="cuda:0") + + # Robot + robot = AnymalArticulation() + + print("Reference count of the robot view: ", ctypes.c_long.from_address(id(robot)).value) + print("Referrers of the robot view: ", gc.get_referrers(robot)) + print("---" * 10) + + # Play the simulator + sim.reset() + + print("Reference count of the robot view: ", ctypes.c_long.from_address(id(robot)).value) + print("Referrers of the robot view: ", gc.get_referrers(robot)) + print("---" * 10) + + robot.initialize() + + print("Reference count of the robot view: ", ctypes.c_long.from_address(id(robot)).value) + print("Referrers of the robot view: ", gc.get_referrers(robot)) + print("---" * 10) + + # Stop the simulator + sim.stop() + + print("Reference count of the robot view: ", ctypes.c_long.from_address(id(robot)).value) + print("Referrers of the robot view: ", gc.get_referrers(robot)) + print("---" * 10) + + # Clean up + sim.clear() + + print("Reference count of the robot view: ", ctypes.c_long.from_address(id(robot)).value) + print("Referrers of the robot view: ", gc.get_referrers(robot)) + print("---" * 10) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/deps/isaacsim/check_rep_texture_randomizer.py b/source/isaaclab/test/deps/isaacsim/check_rep_texture_randomizer.py new file mode 100644 index 0000000000000000000000000000000000000000..e6d43b00f9f5f16bb9fc093dbbc379384b299fbb --- /dev/null +++ b/source/isaaclab/test/deps/isaacsim/check_rep_texture_randomizer.py @@ -0,0 +1,165 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use replicator to randomly change the textures of a USD scene. + +Note: + Currently this script fails since cloner does not support changing textures of cloned + USD prims. This is because the prims are cloned using `Sdf.ChangeBlock` which does not + allow individual texture changes. + +Usage: + +.. code-block:: bash + + ./isaaclab.sh -p source/isaaclab/test/deps/isaacsim/check_rep_texture_randomizer.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +# isaaclab +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description="This script shows how to use replicator to randomly change the textures of a USD scene." +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import numpy as np +import torch + +import omni.replicator.core as rep +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.objects import DynamicSphere +from isaacsim.core.prims import RigidPrim +from isaacsim.core.utils.viewports import set_camera_view + +import isaaclab.sim.utils.prims as prim_utils + + +def main(): + """Spawn a bunch of balls and randomly change their textures.""" + + # Load kit helper + sim_params = { + "use_gpu": True, + "use_gpu_pipeline": True, + "use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards + "use_fabric": True, # used from Isaac Sim 2023.1 onwards + "enable_scene_query_support": True, + } + sim = SimulationContext( + physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0" + ) + # Set main camera + set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5]) + + # Parameters + num_balls = 128 + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + prim_utils.define_prim("/World/envs/env_0") + + # Define the scene + # -- Ball + DynamicSphere(prim_path="/World/envs/env_0/ball", translation=np.array([0.0, 0.0, 5.0]), mass=0.5, radius=0.25) + + # Clone the scene + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_balls) + env_positions = cloner.clone( + source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True, copy_from_source=True + ) + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"] + ) + + # Use replicator to randomize color on the spheres + with rep.new_layer(): + # Define a function to get all the shapes + def get_shapes(): + shapes = rep.get.prims(path_pattern="/World/envs/env_.*/ball") + with shapes: + rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1))) + return shapes.node + + # Register the function + rep.randomizer.register(get_shapes) + # Specify the frequency of randomization + with rep.trigger.on_frame(): + rep.randomizer.get_shapes() + + # Set ball positions over terrain origins + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + # cache initial state of the balls + ball_initial_positions = torch.tensor(env_positions, dtype=torch.float, device=sim.device) + ball_initial_positions[:, 2] += 5.0 + # set initial poses + # note: setting here writes to USD :) + ball_view.set_world_poses(positions=ball_initial_positions) + + # Play simulator + sim.reset() + # Step replicator to randomize colors + rep.orchestrator.step(pause_timeline=False) + # Stop replicator to prevent further randomization + rep.orchestrator.stop() + # Pause simulator at the beginning for inspection + sim.pause() + + # Initialize the ball views for physics simulation + ball_view.initialize() + ball_initial_velocities = ball_view.get_velocities() + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step() + continue + # Reset the scene + if step_count % 500 == 0: + # reset the balls + ball_view.set_world_poses(positions=ball_initial_positions) + ball_view.set_velocities(ball_initial_velocities) + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update counter + step_count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/deps/test_scipy.py b/source/isaaclab/test/deps/test_scipy.py new file mode 100644 index 0000000000000000000000000000000000000000..d697716aad7a6b7931203344ec21fdd108362dea --- /dev/null +++ b/source/isaaclab/test/deps/test_scipy.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# isort: off +import warnings +import pytest + +warnings.filterwarnings("ignore", category=DeprecationWarning) +# isort: on + +import numpy as np +import scipy.interpolate as interpolate + + +@pytest.mark.isaacsim_ci +def test_interpolation(): + """Test scipy interpolation 2D method.""" + # parameters + size = (10.0, 12.0) + horizontal_scale = 0.1 + vertical_scale = 0.005 + downsampled_scale = 0.2 + noise_range = (-0.02, 0.1) + noise_step = 0.02 + # switch parameters to discrete units + # -- horizontal scale + width_pixels = int(size[0] / horizontal_scale) + length_pixels = int(size[1] / horizontal_scale) + # -- downsampled scale + width_downsampled = int(size[0] / downsampled_scale) + length_downsampled = int(size[1] / downsampled_scale) + # -- height + height_min = int(noise_range[0] / vertical_scale) + height_max = int(noise_range[1] / vertical_scale) + height_step = int(noise_step / vertical_scale) + + # create range of heights possible + height_range = np.arange(height_min, height_max + height_step, height_step) + # sample heights randomly from the range along a grid + height_field_downsampled = np.random.choice(height_range, size=(width_downsampled, length_downsampled)) + # create interpolation function for the sampled heights + x = np.linspace(0, size[0] * horizontal_scale, width_downsampled) + y = np.linspace(0, size[1] * horizontal_scale, length_downsampled) + + # interpolate the sampled heights to obtain the height field + x_upsampled = np.linspace(0, size[0] * horizontal_scale, width_pixels) + y_upsampled = np.linspace(0, size[1] * horizontal_scale, length_pixels) + # -- method 1: RegularGridInterpolator (replacing deprecated interp2d) + func_RegularGridInterpolator = interpolate.RegularGridInterpolator((x, y), height_field_downsampled, method="cubic") + xx_upsampled, yy_upsampled = np.meshgrid(x_upsampled, y_upsampled, indexing="ij", sparse=True) + z_upsampled_RegularGridInterpolator = func_RegularGridInterpolator((xx_upsampled, yy_upsampled)) + # -- method 2: RectBivariateSpline (alternate to interp2d) + func_RectBiVariate = interpolate.RectBivariateSpline(x, y, height_field_downsampled) + z_upsampled_RectBivariant = func_RectBiVariate(x_upsampled, y_upsampled) + + # check if the interpolated height field is the same as the sampled height field + np.testing.assert_allclose(z_upsampled_RegularGridInterpolator, z_upsampled_RectBivariant, atol=1e-2, rtol=1e-2) + np.testing.assert_allclose(z_upsampled_RectBivariant, z_upsampled_RegularGridInterpolator, atol=1e-2, rtol=1e-2) + np.testing.assert_allclose( + z_upsampled_RegularGridInterpolator, z_upsampled_RegularGridInterpolator, atol=1e-2, rtol=1e-2 + ) diff --git a/source/isaaclab/test/deps/test_torch.py b/source/isaaclab/test/deps/test_torch.py new file mode 100644 index 0000000000000000000000000000000000000000..6a50110757deb8d98660942a4a1caf6113fbd417 --- /dev/null +++ b/source/isaaclab/test/deps/test_torch.py @@ -0,0 +1,156 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import pytest +import torch +import torch.utils.benchmark as benchmark + + +@pytest.mark.isaacsim_ci +def test_array_slicing(): + """Check that using ellipsis and slices work for torch tensors.""" + + size = (400, 300, 5) + my_tensor = torch.rand(size, device="cuda:0") + + assert my_tensor[..., 0].shape == (400, 300) + assert my_tensor[:, :, 0].shape == (400, 300) + assert my_tensor[slice(None), slice(None), 0].shape == (400, 300) + with pytest.raises(IndexError): + my_tensor[..., ..., 0] + + assert my_tensor[0, ...].shape == (300, 5) + assert my_tensor[0, :, :].shape == (300, 5) + assert my_tensor[0, slice(None), slice(None)].shape == (300, 5) + assert my_tensor[0, ..., ...].shape == (300, 5) + + assert my_tensor[..., 0, 0].shape == (400,) + assert my_tensor[slice(None), 0, 0].shape == (400,) + assert my_tensor[:, 0, 0].shape == (400,) + + +@pytest.mark.isaacsim_ci +def test_array_circular(): + """Check circular buffer implementation in torch.""" + + size = (10, 30, 5) + my_tensor = torch.rand(size, device="cuda:0") + + # roll up the tensor without cloning + my_tensor_1 = my_tensor.clone() + my_tensor_1[:, 1:, :] = my_tensor_1[:, :-1, :] + my_tensor_1[:, 0, :] = my_tensor[:, -1, :] + # check that circular buffer works as expected + error = torch.max(torch.abs(my_tensor_1 - my_tensor.roll(1, dims=1))) + assert error.item() != 0.0 + assert not torch.allclose(my_tensor_1, my_tensor.roll(1, dims=1)) + + # roll up the tensor with cloning + my_tensor_2 = my_tensor.clone() + my_tensor_2[:, 1:, :] = my_tensor_2[:, :-1, :].clone() + my_tensor_2[:, 0, :] = my_tensor[:, -1, :] + # check that circular buffer works as expected + error = torch.max(torch.abs(my_tensor_2 - my_tensor.roll(1, dims=1))) + assert error.item() == 0.0 + assert torch.allclose(my_tensor_2, my_tensor.roll(1, dims=1)) + + # roll up the tensor with detach operation + my_tensor_3 = my_tensor.clone() + my_tensor_3[:, 1:, :] = my_tensor_3[:, :-1, :].detach() + my_tensor_3[:, 0, :] = my_tensor[:, -1, :] + # check that circular buffer works as expected + error = torch.max(torch.abs(my_tensor_3 - my_tensor.roll(1, dims=1))) + assert error.item() != 0.0 + assert not torch.allclose(my_tensor_3, my_tensor.roll(1, dims=1)) + + # roll up the tensor with roll operation + my_tensor_4 = my_tensor.clone() + my_tensor_4 = my_tensor_4.roll(1, dims=1) + my_tensor_4[:, 0, :] = my_tensor[:, -1, :] + # check that circular buffer works as expected + error = torch.max(torch.abs(my_tensor_4 - my_tensor.roll(1, dims=1))) + assert error.item() == 0.0 + assert torch.allclose(my_tensor_4, my_tensor.roll(1, dims=1)) + + +@pytest.mark.isaacsim_ci +def test_array_circular_copy(): + """Check that circular buffer implementation in torch is copying data.""" + + size = (10, 30, 5) + my_tensor = torch.rand(size, device="cuda:0") + my_tensor_clone = my_tensor.clone() + + # roll up the tensor + my_tensor_1 = my_tensor.clone() + my_tensor_1[:, 1:, :] = my_tensor_1[:, :-1, :].clone() + my_tensor_1[:, 0, :] = my_tensor[:, -1, :] + # change the source tensor + my_tensor[:, 0, :] = 1000 + # check that circular buffer works as expected + assert not torch.allclose(my_tensor_1, my_tensor.roll(1, dims=1)) + assert torch.allclose(my_tensor_1, my_tensor_clone.roll(1, dims=1)) + + +@pytest.mark.isaacsim_ci +def test_array_multi_indexing(): + """Check multi-indexing works for torch tensors.""" + + size = (400, 300, 5) + my_tensor = torch.rand(size, device="cuda:0") + + # this fails since array indexing cannot be broadcasted!! + with pytest.raises(IndexError): + my_tensor[[0, 1, 2, 3], [0, 1, 2, 3, 4]] + + +@pytest.mark.isaacsim_ci +def test_array_single_indexing(): + """Check how indexing effects the returned tensor.""" + + size = (400, 300, 5) + my_tensor = torch.rand(size, device="cuda:0") + + # obtain a slice of the tensor + my_slice = my_tensor[0, ...] + assert my_slice.untyped_storage().data_ptr() == my_tensor.untyped_storage().data_ptr() + + # obtain a slice over ranges + my_slice = my_tensor[0:2, ...] + assert my_slice.untyped_storage().data_ptr() == my_tensor.untyped_storage().data_ptr() + + # obtain a slice over list + my_slice = my_tensor[[0, 1], ...] + assert my_slice.untyped_storage().data_ptr() != my_tensor.untyped_storage().data_ptr() + + # obtain a slice over tensor + my_slice = my_tensor[torch.tensor([0, 1]), ...] + assert my_slice.untyped_storage().data_ptr() != my_tensor.untyped_storage().data_ptr() + + +@pytest.mark.isaacsim_ci +def test_logical_or(): + """Test bitwise or operation.""" + + size = (400, 300, 5) + my_tensor_1 = torch.rand(size, device="cuda:0") > 0.5 + my_tensor_2 = torch.rand(size, device="cuda:0") < 0.5 + + # check the speed of logical or + timer_logical_or = benchmark.Timer( + stmt="torch.logical_or(my_tensor_1, my_tensor_2)", + globals={"my_tensor_1": my_tensor_1, "my_tensor_2": my_tensor_2}, + ) + timer_bitwise_or = benchmark.Timer( + stmt="my_tensor_1 | my_tensor_2", globals={"my_tensor_1": my_tensor_1, "my_tensor_2": my_tensor_2} + ) + + print("Time for logical or:", timer_logical_or.timeit(number=1000)) + print("Time for bitwise or:", timer_bitwise_or.timeit(number=1000)) + # check that logical or works as expected + output_logical_or = torch.logical_or(my_tensor_1, my_tensor_2) + output_bitwise_or = my_tensor_1 | my_tensor_2 + + assert torch.allclose(output_logical_or, output_bitwise_or) diff --git a/source/isaaclab/test/devices/check_keyboard.py b/source/isaaclab/test/devices/check_keyboard.py new file mode 100644 index 0000000000000000000000000000000000000000..4b821bfb11368bb7a05896d6f80e6db7a8772bd1 --- /dev/null +++ b/source/isaaclab/test/devices/check_keyboard.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use a teleoperation device with Isaac Sim. + +The teleoperation device is a keyboard device that allows the user to control the robot. +It is possible to add additional callbacks to it for user-defined operations. +""" + +"""Launch Isaac Sim Simulator first.""" + + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher() +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import ctypes + +from isaacsim.core.api.simulation_context import SimulationContext + +from isaaclab.devices import Se3Keyboard, Se3KeyboardCfg + + +def print_cb(): + """Dummy callback function executed when the key 'L' is pressed.""" + print("Print callback") + + +def quit_cb(): + """Dummy callback function executed when the key 'ESC' is pressed.""" + print("Quit callback") + simulation_app.close() + + +def main(): + # Load kit helper + sim = SimulationContext(physics_dt=0.01, rendering_dt=0.01) + + # Create teleoperation interface + teleop_interface = Se3Keyboard(Se3KeyboardCfg(pos_sensitivity=0.1, rot_sensitivity=0.1)) + # Add teleoperation callbacks + # available key buttons: https://docs.omniverse.nvidia.com/kit/docs/carbonite/latest/docs/python/carb.html?highlight=keyboardeventtype#carb.input.KeyboardInput + teleop_interface.add_callback("L", print_cb) + teleop_interface.add_callback("ESCAPE", quit_cb) + + print("Press 'L' to print a message. Press 'ESC' to quit.") + + # Check that boundedness of articulation is correct + if ctypes.c_long.from_address(id(teleop_interface)).value != 1: + raise RuntimeError("Teleoperation interface is not bounded to a single instance.") + + # Reset interface internals + teleop_interface.reset() + + # Play simulation + sim.reset() + + # Simulate + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step() + continue + # get keyboard command + delta_pose, gripper_command = teleop_interface.advance() + # print command + if gripper_command: + print(f"Gripper command: {gripper_command}") + # step simulation + sim.step() + # check if simulator is stopped + if sim.is_stopped(): + break + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/devices/test_device_constructors.py b/source/isaaclab/test/devices/test_device_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1bded4d61a8751ac71579565aec30d6997a2ff --- /dev/null +++ b/source/isaaclab/test/devices/test_device_constructors.py @@ -0,0 +1,613 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import importlib +import json +from typing import cast + +import pytest +import torch + +# Import device classes to test +from isaaclab.devices import ( + DeviceCfg, + HaplyDevice, + HaplyDeviceCfg, + OpenXRDevice, + OpenXRDeviceCfg, + Se2Gamepad, + Se2GamepadCfg, + Se2Keyboard, + Se2KeyboardCfg, + Se2SpaceMouse, + Se2SpaceMouseCfg, + Se3Gamepad, + Se3GamepadCfg, + Se3Keyboard, + Se3KeyboardCfg, + Se3SpaceMouse, + Se3SpaceMouseCfg, +) +from isaaclab.devices.openxr import XrCfg +from isaaclab.devices.openxr.retargeters import GripperRetargeterCfg, Se3AbsRetargeterCfg + +# Import teleop device factory for testing +from isaaclab.devices.teleop_device_factory import create_teleop_device + + +@pytest.fixture +def mock_environment(mocker): + """Set up common mock objects for tests.""" + # Create mock objects that will be used across tests + carb_mock = mocker.MagicMock() + omni_mock = mocker.MagicMock() + appwindow_mock = mocker.MagicMock() + keyboard_mock = mocker.MagicMock() + gamepad_mock = mocker.MagicMock() + input_mock = mocker.MagicMock() + settings_mock = mocker.MagicMock() + hid_mock = mocker.MagicMock() + device_mock = mocker.MagicMock() + + # Set up the mocks to return appropriate objects + omni_mock.appwindow.get_default_app_window.return_value = appwindow_mock + appwindow_mock.get_keyboard.return_value = keyboard_mock + appwindow_mock.get_gamepad.return_value = gamepad_mock + carb_mock.input.acquire_input_interface.return_value = input_mock + carb_mock.settings.get_settings.return_value = settings_mock + + # Mock keyboard event types + carb_mock.input.KeyboardEventType.KEY_PRESS = 1 + carb_mock.input.KeyboardEventType.KEY_RELEASE = 2 + + # Mock carb events used by OpenXRDevice + events_mock = mocker.MagicMock() + events_mock.type_from_string.return_value = 0 + carb_mock.events = events_mock + + # Mock the SpaceMouse + hid_mock.enumerate.return_value = [{"product_string": "SpaceMouse Compact", "vendor_id": 123, "product_id": 456}] + hid_mock.device.return_value = device_mock + + # Mock OpenXR + # xr_core_mock = mocker.MagicMock() + message_bus_mock = mocker.MagicMock() + singleton_mock = mocker.MagicMock() + omni_mock.kit.xr.core.XRCore.get_singleton.return_value = singleton_mock + singleton_mock.get_message_bus.return_value = message_bus_mock + omni_mock.kit.xr.core.XRPoseValidityFlags.POSITION_VALID = 1 + omni_mock.kit.xr.core.XRPoseValidityFlags.ORIENTATION_VALID = 2 + + # Mock Haply WebSocket + websockets_mock = mocker.MagicMock() + websocket_mock = mocker.MagicMock() + websockets_mock.connect.return_value.__aenter__.return_value = websocket_mock + + return { + "carb": carb_mock, + "omni": omni_mock, + "appwindow": appwindow_mock, + "keyboard": keyboard_mock, + "gamepad": gamepad_mock, + "input": input_mock, + "settings": settings_mock, + "hid": hid_mock, + "device": device_mock, + "websockets": websockets_mock, + "websocket": websocket_mock, + } + + +""" +Test keyboard devices. +""" + + +def test_se2keyboard_constructors(mock_environment, mocker): + """Test constructor for Se2Keyboard.""" + # Test config-based constructor + config = Se2KeyboardCfg( + v_x_sensitivity=0.9, + v_y_sensitivity=0.5, + omega_z_sensitivity=1.2, + ) + device_mod = importlib.import_module("isaaclab.devices.keyboard.se2_keyboard") + mocker.patch.dict("sys.modules", {"carb": mock_environment["carb"], "omni": mock_environment["omni"]}) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "omni", mock_environment["omni"]) + + keyboard = Se2Keyboard(config) + + # Verify configuration was applied correctly + assert keyboard.v_x_sensitivity == 0.9 + assert keyboard.v_y_sensitivity == 0.5 + assert keyboard.omega_z_sensitivity == 1.2 + + # Test advance() returns expected type + result = keyboard.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (3,) # (v_x, v_y, omega_z) + + +def test_se3keyboard_constructors(mock_environment, mocker): + """Test constructor for Se3Keyboard.""" + # Test config-based constructor + config = Se3KeyboardCfg( + pos_sensitivity=0.5, + rot_sensitivity=0.9, + ) + device_mod = importlib.import_module("isaaclab.devices.keyboard.se3_keyboard") + mocker.patch.dict("sys.modules", {"carb": mock_environment["carb"], "omni": mock_environment["omni"]}) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "omni", mock_environment["omni"]) + + keyboard = Se3Keyboard(config) + + # Verify configuration was applied correctly + assert keyboard.pos_sensitivity == 0.5 + assert keyboard.rot_sensitivity == 0.9 + + # Test advance() returns expected type + result = keyboard.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (7,) # (pos_x, pos_y, pos_z, rot_x, rot_y, rot_z, gripper) + + +""" +Test gamepad devices. +""" + + +def test_se2gamepad_constructors(mock_environment, mocker): + """Test constructor for Se2Gamepad.""" + # Test config-based constructor + config = Se2GamepadCfg( + v_x_sensitivity=1.1, + v_y_sensitivity=0.6, + omega_z_sensitivity=1.2, + dead_zone=0.02, + ) + device_mod = importlib.import_module("isaaclab.devices.gamepad.se2_gamepad") + mocker.patch.dict("sys.modules", {"carb": mock_environment["carb"], "omni": mock_environment["omni"]}) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "omni", mock_environment["omni"]) + + gamepad = Se2Gamepad(config) + + # Verify configuration was applied correctly + assert gamepad.v_x_sensitivity == 1.1 + assert gamepad.v_y_sensitivity == 0.6 + assert gamepad.omega_z_sensitivity == 1.2 + assert gamepad.dead_zone == 0.02 + + # Test advance() returns expected type + result = gamepad.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (3,) # (v_x, v_y, omega_z) + + +def test_se3gamepad_constructors(mock_environment, mocker): + """Test constructor for Se3Gamepad.""" + # Test config-based constructor + config = Se3GamepadCfg( + pos_sensitivity=1.1, + rot_sensitivity=1.7, + dead_zone=0.02, + ) + device_mod = importlib.import_module("isaaclab.devices.gamepad.se3_gamepad") + mocker.patch.dict("sys.modules", {"carb": mock_environment["carb"], "omni": mock_environment["omni"]}) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "omni", mock_environment["omni"]) + + gamepad = Se3Gamepad(config) + + # Verify configuration was applied correctly + assert gamepad.pos_sensitivity == 1.1 + assert gamepad.rot_sensitivity == 1.7 + assert gamepad.dead_zone == 0.02 + + # Test advance() returns expected type + result = gamepad.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (7,) # (pos_x, pos_y, pos_z, rot_x, rot_y, rot_z, gripper) + + +""" +Test spacemouse devices. +""" + + +def test_se2spacemouse_constructors(mock_environment, mocker): + """Test constructor for Se2SpaceMouse.""" + # Test config-based constructor + config = Se2SpaceMouseCfg( + v_x_sensitivity=0.9, + v_y_sensitivity=0.5, + omega_z_sensitivity=1.2, + ) + device_mod = importlib.import_module("isaaclab.devices.spacemouse.se2_spacemouse") + mocker.patch.dict("sys.modules", {"hid": mock_environment["hid"]}) + mocker.patch.object(device_mod, "hid", mock_environment["hid"]) + + spacemouse = Se2SpaceMouse(config) + + # Verify configuration was applied correctly + assert spacemouse.v_x_sensitivity == 0.9 + assert spacemouse.v_y_sensitivity == 0.5 + assert spacemouse.omega_z_sensitivity == 1.2 + + # Test advance() returns expected type + mock_environment["device"].read.return_value = [1, 0, 0, 0, 0] + result = spacemouse.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (3,) # (v_x, v_y, omega_z) + + +def test_se3spacemouse_constructors(mock_environment, mocker): + """Test constructor for Se3SpaceMouse.""" + # Test config-based constructor + config = Se3SpaceMouseCfg( + pos_sensitivity=0.5, + rot_sensitivity=0.9, + ) + device_mod = importlib.import_module("isaaclab.devices.spacemouse.se3_spacemouse") + mocker.patch.dict("sys.modules", {"hid": mock_environment["hid"]}) + mocker.patch.object(device_mod, "hid", mock_environment["hid"]) + + spacemouse = Se3SpaceMouse(config) + + # Verify configuration was applied correctly + assert spacemouse.pos_sensitivity == 0.5 + assert spacemouse.rot_sensitivity == 0.9 + + # Test advance() returns expected type + mock_environment["device"].read.return_value = [1, 0, 0, 0, 0, 0, 0] + result = spacemouse.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (7,) # (pos_x, pos_y, pos_z, rot_x, rot_y, rot_z, gripper) + + +""" +Test OpenXR devices. +""" + + +def test_openxr_constructors(mock_environment, mocker): + """Test constructor for OpenXRDevice.""" + # Test config-based constructor with custom XrCfg + xr_cfg = XrCfg( + anchor_pos=(1.0, 2.0, 3.0), + anchor_rot=(0.0, 0.1, 0.2, 0.3), + near_plane=0.2, + ) + config = OpenXRDeviceCfg(xr_cfg=xr_cfg) + + # Create mock retargeters + mock_controller_retargeter = mocker.MagicMock() + mock_head_retargeter = mocker.MagicMock() + retargeters = [mock_controller_retargeter, mock_head_retargeter] + + device_mod = importlib.import_module("isaaclab.devices.openxr.openxr_device") + mocker.patch.dict( + "sys.modules", + { + "carb": mock_environment["carb"], + "omni.kit.xr.core": mock_environment["omni"].kit.xr.core, + "isaacsim.core.prims": mocker.MagicMock(), + }, + ) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "XRCore", mock_environment["omni"].kit.xr.core.XRCore) + mocker.patch.object(device_mod, "XRPoseValidityFlags", mock_environment["omni"].kit.xr.core.XRPoseValidityFlags) + mock_single_xform = mocker.patch.object(device_mod, "SingleXFormPrim") + + # Configure the mock to return a string for prim_path + mock_instance = mock_single_xform.return_value + mock_instance.prim_path = "/XRAnchor" + + # Create the device using the factory + device = OpenXRDevice(config) + + # Verify the device was created successfully + assert device._xr_cfg == xr_cfg + + # Test with retargeters + device = OpenXRDevice(cfg=config, retargeters=retargeters) + + # Verify retargeters were correctly assigned as a list + assert device._retargeters == retargeters + + # Test with config and retargeters + device = OpenXRDevice(cfg=config, retargeters=retargeters) + + # Verify both config and retargeters were correctly assigned + assert device._xr_cfg == xr_cfg + assert device._retargeters == retargeters + + # Test reset functionality + device.reset() + + +""" +Test Haply devices. +""" + + +def test_haply_constructors(mock_environment, mocker): + """Test constructor for HaplyDevice.""" + # Test config-based constructor + config = HaplyDeviceCfg( + websocket_uri="ws://localhost:10001", + pos_sensitivity=1.5, + data_rate=250.0, + ) + + # Mock the websockets module and asyncio + device_mod = importlib.import_module("isaaclab.devices.haply.se3_haply") + mocker.patch.dict("sys.modules", {"websockets": mock_environment["websockets"]}) + mocker.patch.object(device_mod, "websockets", mock_environment["websockets"]) + + # Mock asyncio to prevent actual async operations + asyncio_mock = mocker.MagicMock() + mocker.patch.object(device_mod, "asyncio", asyncio_mock) + + # Mock threading to prevent actual thread creation + threading_mock = mocker.MagicMock() + thread_instance = mocker.MagicMock() + threading_mock.Thread.return_value = thread_instance + thread_instance.is_alive.return_value = False + mocker.patch.object(device_mod, "threading", threading_mock) + + # Mock time.time() for connection timeout simulation + time_mock = mocker.MagicMock() + time_mock.time.side_effect = [0.0, 0.1, 0.2, 0.3, 6.0] # Will timeout + mocker.patch.object(device_mod, "time", time_mock) + + # Create sample WebSocket response data + ws_response = { + "inverse3": [ + { + "device_id": "test_inverse3_123", + "state": {"cursor_position": {"x": 0.1, "y": 0.2, "z": 0.3}}, + } + ], + "wireless_verse_grip": [ + { + "device_id": "test_versegrip_456", + "state": { + "orientation": {"x": 0.0, "y": 0.0, "z": 0.0, "w": 1.0}, + "buttons": {"a": False, "b": False, "c": False}, + }, + } + ], + } + + # Configure websocket mock to return JSON data + mock_environment["websocket"].recv = mocker.AsyncMock(return_value=json.dumps(ws_response)) + mock_environment["websocket"].send = mocker.AsyncMock() + + # The constructor will raise RuntimeError due to timeout, which is expected in test + with pytest.raises(RuntimeError, match="Failed to connect both Inverse3 and VerseGrip devices"): + haply = HaplyDevice(config) + + # Now test successful connection by mocking time to not timeout + time_mock.time.side_effect = [0.0, 0.1, 0.2, 0.3, 0.4] # Won't timeout + + # Mock the connection status + mocker.patch.object(device_mod.HaplyDevice, "_start_websocket_thread") + haply = device_mod.HaplyDevice.__new__(device_mod.HaplyDevice) + haply._sim_device = config.sim_device + haply.websocket_uri = config.websocket_uri + haply.pos_sensitivity = config.pos_sensitivity + haply.data_rate = config.data_rate + haply.limit_force = config.limit_force + haply.connected = True + haply.inverse3_device_id = "test_inverse3_123" + haply.verse_grip_device_id = "test_versegrip_456" + haply.data_lock = threading_mock.Lock() + haply.force_lock = threading_mock.Lock() + haply._connected_lock = threading_mock.Lock() + haply._additional_callbacks = {} + haply._prev_buttons = {"a": False, "b": False, "c": False} + haply._websocket_thread = None # Initialize to prevent AttributeError in __del__ + haply.running = True + haply.cached_data = { + "position": torch.tensor([0.1, 0.2, 0.3], dtype=torch.float32).numpy(), + "quaternion": torch.tensor([0.0, 0.0, 0.0, 1.0], dtype=torch.float32).numpy(), + "buttons": {"a": False, "b": False, "c": False}, + "inverse3_connected": True, + "versegrip_connected": True, + } + haply.feedback_force = {"x": 0.0, "y": 0.0, "z": 0.0} + + # Verify configuration was applied correctly + assert haply.websocket_uri == "ws://localhost:10001" + assert haply.pos_sensitivity == 1.5 + assert haply.data_rate == 250.0 + + # Test advance() returns expected type + result = haply.advance() + assert isinstance(result, torch.Tensor) + assert result.shape == (10,) # (pos_x, pos_y, pos_z, qx, qy, qz, qw, btn_a, btn_b, btn_c) + + # Test push_force with tensor (single force vector) + forces_within = torch.tensor([[1.0, 1.5, -0.5]], dtype=torch.float32) + position_zero = torch.tensor([0], dtype=torch.long) + haply.push_force(forces_within, position_zero) + assert haply.feedback_force["x"] == pytest.approx(1.0) + assert haply.feedback_force["y"] == pytest.approx(1.5) + assert haply.feedback_force["z"] == pytest.approx(-0.5) + + # Test push_force with tensor (force limiting, default limit is 2.0 N) + forces_exceed = torch.tensor([[5.0, -10.0, 1.5]], dtype=torch.float32) + haply.push_force(forces_exceed, position_zero) + assert haply.feedback_force["x"] == pytest.approx(2.0) + assert haply.feedback_force["y"] == pytest.approx(-2.0) + assert haply.feedback_force["z"] == pytest.approx(1.5) + + # Test push_force with position tensor (single index) + forces_multi = torch.tensor([[1.0, 2.0, 3.0], [0.5, 0.8, -0.3], [0.1, 0.2, 0.3]], dtype=torch.float32) + position_single = torch.tensor([1], dtype=torch.long) + haply.push_force(forces_multi, position=position_single) + assert haply.feedback_force["x"] == pytest.approx(0.5) + assert haply.feedback_force["y"] == pytest.approx(0.8) + assert haply.feedback_force["z"] == pytest.approx(-0.3) + + # Test push_force with position tensor (multiple indices) + position_multi = torch.tensor([0, 2], dtype=torch.long) + haply.push_force(forces_multi, position=position_multi) + # Should sum forces[0] and forces[2]: [1.0+0.1, 2.0+0.2, 3.0+0.3] = [1.1, 2.2, 3.3] + # But clipped to [-2.0, 2.0]: [1.1, 2.0, 2.0] + assert haply.feedback_force["x"] == pytest.approx(1.1) + assert haply.feedback_force["y"] == pytest.approx(2.0) + assert haply.feedback_force["z"] == pytest.approx(2.0) + + # Test reset functionality + haply.reset() + assert haply.feedback_force == {"x": 0.0, "y": 0.0, "z": 0.0} + + +""" +Test teleop device factory. +""" + + +def test_create_teleop_device_basic(mock_environment, mocker): + """Test creating devices using the teleop device factory.""" + # Create device configuration + keyboard_cfg = Se3KeyboardCfg(pos_sensitivity=0.8, rot_sensitivity=1.2) + + # Create devices configuration dictionary + devices_cfg: dict[str, DeviceCfg] = {"test_keyboard": keyboard_cfg} + + # Mock Se3Keyboard class + device_mod = importlib.import_module("isaaclab.devices.keyboard.se3_keyboard") + mocker.patch.dict("sys.modules", {"carb": mock_environment["carb"], "omni": mock_environment["omni"]}) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "omni", mock_environment["omni"]) + + # Create the device using the factory + device = create_teleop_device("test_keyboard", devices_cfg) + + # Verify the device was created correctly + assert isinstance(device, Se3Keyboard) + assert device.pos_sensitivity == 0.8 + assert device.rot_sensitivity == 1.2 + + +def test_create_teleop_device_with_callbacks(mock_environment, mocker): + """Test creating device with callbacks.""" + # Create device configuration + xr_cfg = XrCfg(anchor_pos=(0.0, 0.0, 0.0), anchor_rot=(1.0, 0.0, 0.0, 0.0), near_plane=0.15) + openxr_cfg = OpenXRDeviceCfg(xr_cfg=xr_cfg) + + # Create devices configuration dictionary + devices_cfg: dict[str, DeviceCfg] = {"test_xr": openxr_cfg} + + # Create mock callbacks + button_a_callback = mocker.MagicMock() + button_b_callback = mocker.MagicMock() + callbacks = {"button_a": button_a_callback, "button_b": button_b_callback} + + # Mock OpenXRDevice class and dependencies + device_mod = importlib.import_module("isaaclab.devices.openxr.openxr_device") + mocker.patch.dict( + "sys.modules", + { + "carb": mock_environment["carb"], + "omni.kit.xr.core": mock_environment["omni"].kit.xr.core, + "isaacsim.core.prims": mocker.MagicMock(), + }, + ) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "XRCore", mock_environment["omni"].kit.xr.core.XRCore) + mocker.patch.object(device_mod, "XRPoseValidityFlags", mock_environment["omni"].kit.xr.core.XRPoseValidityFlags) + mock_single_xform = mocker.patch.object(device_mod, "SingleXFormPrim") + + # Configure the mock to return a string for prim_path + mock_instance = mock_single_xform.return_value + mock_instance.prim_path = "/XRAnchor" + + # Create the device using the factory + device = create_teleop_device("test_xr", devices_cfg, callbacks) + + # Verify the device was created correctly + assert isinstance(device, OpenXRDevice) + + # Verify callbacks were registered by the factory + assert set(device._additional_callbacks.keys()) == {"button_a", "button_b"} + + +def test_create_teleop_device_with_retargeters(mock_environment, mocker): + """Test creating device with retargeters.""" + # Create retargeter configurations + retargeter_cfg1 = Se3AbsRetargeterCfg() + retargeter_cfg2 = GripperRetargeterCfg() + + # Create device configuration with retargeters + xr_cfg = XrCfg() + device_cfg = OpenXRDeviceCfg(xr_cfg=xr_cfg, retargeters=[retargeter_cfg1, retargeter_cfg2]) + + # Create devices configuration dictionary + devices_cfg: dict[str, DeviceCfg] = {"test_xr": device_cfg} + + # Mock OpenXRDevice class and dependencies + device_mod = importlib.import_module("isaaclab.devices.openxr.openxr_device") + mocker.patch.dict( + "sys.modules", + { + "carb": mock_environment["carb"], + "omni.kit.xr.core": mock_environment["omni"].kit.xr.core, + "isaacsim.core.prims": mocker.MagicMock(), + }, + ) + mocker.patch.object(device_mod, "carb", mock_environment["carb"]) + mocker.patch.object(device_mod, "XRCore", mock_environment["omni"].kit.xr.core.XRCore) + mocker.patch.object(device_mod, "XRPoseValidityFlags", mock_environment["omni"].kit.xr.core.XRPoseValidityFlags) + mock_single_xform = mocker.patch.object(device_mod, "SingleXFormPrim") + + # Configure the mock to return a string for prim_path + mock_instance = mock_single_xform.return_value + mock_instance.prim_path = "/XRAnchor" + + # Create the device using the factory + device = create_teleop_device("test_xr", devices_cfg) + + # Verify retargeters were created + assert len(device._retargeters) == 2 + + +def test_create_teleop_device_device_not_found(): + """Test error when device name is not found in configuration.""" + # Create devices configuration dictionary + devices_cfg: dict[str, DeviceCfg] = {"keyboard": Se3KeyboardCfg()} + + # Try to create a non-existent device + with pytest.raises(ValueError, match="Device 'gamepad' not found"): + create_teleop_device("gamepad", devices_cfg) + + +def test_create_teleop_device_unsupported_config(): + """Test error when device configuration type is not supported.""" + + # Create a custom unsupported configuration class + class UnsupportedCfg: + pass + + # Create devices configuration dictionary with unsupported config + devices_cfg: dict[str, DeviceCfg] = cast(dict[str, DeviceCfg], {"unsupported": UnsupportedCfg()}) + + # Try to create a device with unsupported configuration + with pytest.raises(ValueError, match="does not declare class_type"): + create_teleop_device("unsupported", devices_cfg) diff --git a/source/isaaclab/test/devices/test_oxr_device.py b/source/isaaclab/test/devices/test_oxr_device.py new file mode 100644 index 0000000000000000000000000000000000000000..6402d8e0c187e5cde3a64e051becc4416b1af95b --- /dev/null +++ b/source/isaaclab/test/devices/test_oxr_device.py @@ -0,0 +1,276 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Ignore private usage of variables warning. +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +from isaaclab.app import AppLauncher + +# Can set this to False to see the GUI for debugging. +HEADLESS = True + +# Launch omniverse app. +app_launcher = AppLauncher(headless=HEADLESS) +simulation_app = app_launcher.app + +import importlib + +import numpy as np +import pytest +import torch + +import carb +import omni.usd +from isaacsim.core.prims import XFormPrim + +from isaaclab.devices import OpenXRDevice, OpenXRDeviceCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.devices.retargeter_base import RetargeterBase, RetargeterCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + + +class NoOpRetargeter(RetargeterBase): + """A no-op retargeter that requests hand and head tracking but returns empty tensor.""" + + def __init__(self, cfg: RetargeterCfg): + super().__init__(cfg) + + def get_requirements(self) -> list[RetargeterBase.Requirement]: + """Request hand and head tracking to trigger data collection.""" + return [ + RetargeterBase.Requirement.HAND_TRACKING, + RetargeterBase.Requirement.HEAD_TRACKING, + ] + + def retarget(self, data): + """Return empty tensor.""" + return torch.tensor([], device=self._sim_device) + + +@configclass +class EmptyManagerCfg: + """Empty manager.""" + + pass + + +@configclass +class EmptySceneCfg(InteractiveSceneCfg): + """Configuration for an empty scene.""" + + pass + + +@configclass +class EmptyEnvCfg(ManagerBasedEnvCfg): + """Configuration for the empty test environment.""" + + scene: EmptySceneCfg = EmptySceneCfg(num_envs=1, env_spacing=1.0) + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + + def __post_init__(self): + """Post initialization.""" + self.decimation = 5 + self.episode_length_s = 30.0 + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = 2 + + +@pytest.fixture +def mock_xrcore(mocker): + """Set up a mock for XRCore and related classes.""" + # Create mock for XRCore and XRPoseValidityFlags + xr_core_mock = mocker.MagicMock() + xr_pose_validity_flags_mock = mocker.MagicMock() + + # Set up the validity flags + xr_pose_validity_flags_mock.POSITION_VALID = 1 + xr_pose_validity_flags_mock.ORIENTATION_VALID = 2 + + # Setup the singleton pattern used by XRCore + singleton_mock = mocker.MagicMock() + xr_core_mock.get_singleton.return_value = singleton_mock + + # Setup message bus for teleop commands + message_bus_mock = mocker.MagicMock() + singleton_mock.get_message_bus.return_value = message_bus_mock + message_bus_mock.create_subscription_to_pop_by_type.return_value = mocker.MagicMock() + + # Setup input devices (left hand, right hand, head) + left_hand_mock = mocker.MagicMock() + right_hand_mock = mocker.MagicMock() + head_mock = mocker.MagicMock() + + def get_input_device_mock(device_path): + device_map = { + "/user/hand/left": left_hand_mock, + "/user/hand/right": right_hand_mock, + "/user/head": head_mock, + } + return device_map.get(device_path) + + singleton_mock.get_input_device.side_effect = get_input_device_mock + + # Setup the joint poses for hand tracking + joint_pose_mock = mocker.MagicMock() + joint_pose_mock.validity_flags = ( + xr_pose_validity_flags_mock.POSITION_VALID | xr_pose_validity_flags_mock.ORIENTATION_VALID + ) + + pose_matrix_mock = mocker.MagicMock() + pose_matrix_mock.ExtractTranslation.return_value = [0.1, 0.2, 0.3] + + rotation_quat_mock = mocker.MagicMock() + rotation_quat_mock.GetImaginary.return_value = [0.1, 0.2, 0.3] + rotation_quat_mock.GetReal.return_value = 0.9 + + pose_matrix_mock.ExtractRotationQuat.return_value = rotation_quat_mock + joint_pose_mock.pose_matrix = pose_matrix_mock + + joint_poses = {"palm": joint_pose_mock, "wrist": joint_pose_mock} + left_hand_mock.get_all_virtual_world_poses.return_value = joint_poses + right_hand_mock.get_all_virtual_world_poses.return_value = joint_poses + + head_mock.get_virtual_world_pose.return_value = pose_matrix_mock + + # Patch the modules + device_mod = importlib.import_module("isaaclab.devices.openxr.openxr_device") + mocker.patch.object(device_mod, "XRCore", xr_core_mock) + mocker.patch.object(device_mod, "XRPoseValidityFlags", xr_pose_validity_flags_mock) + + return { + "XRCore": xr_core_mock, + "XRPoseValidityFlags": xr_pose_validity_flags_mock, + "singleton": singleton_mock, + "message_bus": message_bus_mock, + "left_hand": left_hand_mock, + "right_hand": right_hand_mock, + "head": head_mock, + } + + +@pytest.fixture +def empty_env(): + """Fixture to create and cleanup an empty environment.""" + # Create a new stage + omni.usd.get_context().new_stage() + # Create environment with config + env_cfg = EmptyEnvCfg() + env = ManagerBasedEnv(cfg=env_cfg) + + yield env, env_cfg + + # Cleanup + env.close() + + +@pytest.mark.isaacsim_ci +def test_xr_anchor(empty_env, mock_xrcore): + """Test XR anchor creation and configuration.""" + env, env_cfg = empty_env + env_cfg.xr = XrCfg(anchor_pos=(1, 2, 3), anchor_rot=(0, 1, 0, 0)) + + device = OpenXRDevice(OpenXRDeviceCfg(xr_cfg=env_cfg.xr)) + + # Check that the xr anchor prim is created with the correct pose + xr_anchor_prim = XFormPrim("/World/XRAnchor") + assert xr_anchor_prim.is_valid() + + position, orientation = xr_anchor_prim.get_world_poses() + np.testing.assert_almost_equal(position.tolist(), [[1, 2, 3]]) + np.testing.assert_almost_equal(orientation.tolist(), [[0, 1, 0, 0]]) + + # Check that xr anchor mode and custom anchor are set correctly + assert carb.settings.get_settings().get("/persistent/xr/profile/ar/anchorMode") == "custom anchor" + assert carb.settings.get_settings().get("/xrstage/profile/ar/customAnchor") == "/World/XRAnchor" + + device.reset() + + +@pytest.mark.isaacsim_ci +def test_xr_anchor_default(empty_env, mock_xrcore): + """Test XR anchor creation with default configuration.""" + env, _ = empty_env + # Create a proper config object with default values + device = OpenXRDevice(OpenXRDeviceCfg()) + + # Check that the xr anchor prim is created with the correct default pose + xr_anchor_prim = XFormPrim("/World/XRAnchor") + assert xr_anchor_prim.is_valid() + + position, orientation = xr_anchor_prim.get_world_poses() + np.testing.assert_almost_equal(position.tolist(), [[0, 0, 0]]) + np.testing.assert_almost_equal(orientation.tolist(), [[1, 0, 0, 0]]) + + # Check that xr anchor mode and custom anchor are set correctly + assert carb.settings.get_settings().get("/persistent/xr/profile/ar/anchorMode") == "custom anchor" + assert carb.settings.get_settings().get("/xrstage/profile/ar/customAnchor") == "/World/XRAnchor" + + device.reset() + + +@pytest.mark.isaacsim_ci +def test_xr_anchor_multiple_devices(empty_env, mock_xrcore): + """Test XR anchor behavior with multiple devices.""" + env, _ = empty_env + # Create proper config objects with default values + device_1 = OpenXRDevice(OpenXRDeviceCfg()) + device_2 = OpenXRDevice(OpenXRDeviceCfg()) + + # Check that the xr anchor prim is created with the correct default pose + xr_anchor_prim = XFormPrim("/World/XRAnchor") + assert xr_anchor_prim.is_valid() + + position, orientation = xr_anchor_prim.get_world_poses() + np.testing.assert_almost_equal(position.tolist(), [[0, 0, 0]]) + np.testing.assert_almost_equal(orientation.tolist(), [[1, 0, 0, 0]]) + + # Check that xr anchor mode and custom anchor are set correctly + assert carb.settings.get_settings().get("/persistent/xr/profile/ar/anchorMode") == "custom anchor" + assert carb.settings.get_settings().get("/xrstage/profile/ar/customAnchor") == "/World/XRAnchor" + + device_1.reset() + device_2.reset() + + +@pytest.mark.isaacsim_ci +def test_get_raw_data(empty_env, mock_xrcore): + """Test the _get_raw_data method returns correctly formatted tracking data.""" + env, _ = empty_env + # Create a proper config object with default values and a no-op retargeter to trigger data collection + retargeter = NoOpRetargeter(RetargeterCfg()) + device = OpenXRDevice(OpenXRDeviceCfg(), retargeters=[retargeter]) + + # Get raw tracking data + raw_data = device._get_raw_data() + + # Check that the data structure is as expected + from isaaclab.devices.device_base import DeviceBase + + assert DeviceBase.TrackingTarget.HAND_LEFT in raw_data + assert DeviceBase.TrackingTarget.HAND_RIGHT in raw_data + assert DeviceBase.TrackingTarget.HEAD in raw_data + + # Check left hand joints + left_hand = raw_data[DeviceBase.TrackingTarget.HAND_LEFT] + assert "palm" in left_hand + assert "wrist" in left_hand + + # Check that joint pose format is correct + palm_pose = left_hand["palm"] + assert len(palm_pose) == 7 # [x, y, z, qw, qx, qy, qz] + np.testing.assert_almost_equal(palm_pose[:3], [0.1, 0.2, 0.3]) # Position + np.testing.assert_almost_equal(palm_pose[3:], [0.9, 0.1, 0.2, 0.3]) # Orientation + + # Check head pose + head_pose = raw_data[DeviceBase.TrackingTarget.HEAD] + assert len(head_pose) == 7 + np.testing.assert_almost_equal(head_pose[:3], [0.1, 0.2, 0.3]) # Position + np.testing.assert_almost_equal(head_pose[3:], [0.9, 0.1, 0.2, 0.3]) # Orientation diff --git a/source/isaaclab/test/devices/test_retargeters.py b/source/isaaclab/test/devices/test_retargeters.py new file mode 100644 index 0000000000000000000000000000000000000000..c080c4a43d9ca35e424f851e5658eea04182da3a --- /dev/null +++ b/source/isaaclab/test/devices/test_retargeters.py @@ -0,0 +1,370 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Unit tests for retargeters. +""" + +from isaaclab.app import AppLauncher + +# Can set this to False to see the GUI for debugging. +HEADLESS = True + +# Launch omniverse app. +app_launcher = AppLauncher(headless=HEADLESS) +simulation_app = app_launcher.app + +import sys +import unittest +from unittest.mock import MagicMock, patch + +import numpy as np +import torch + +# Mock dependencies that might require a running simulation or specific hardware +sys.modules["isaaclab.markers"] = MagicMock() +sys.modules["isaaclab.markers.config"] = MagicMock() +sys.modules["isaaclab.sim"] = MagicMock() +sys.modules["isaaclab.sim.SimulationContext"] = MagicMock() + +# Mock SimulationContext instance +mock_sim_context = MagicMock() +mock_sim_context.get_rendering_dt.return_value = 0.016 # 60Hz +sys.modules["isaaclab.sim"].SimulationContext.instance.return_value = mock_sim_context + + +# Import after mocking +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.openxr.retargeters.humanoid.unitree.g1_lower_body_standing import ( + G1LowerBodyStandingRetargeter, + G1LowerBodyStandingRetargeterCfg, +) +from isaaclab.devices.openxr.retargeters.humanoid.unitree.g1_motion_controller_locomotion import ( + G1LowerBodyStandingMotionControllerRetargeter, + G1LowerBodyStandingMotionControllerRetargeterCfg, +) +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeter, GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_abs_retargeter import Se3AbsRetargeter, Se3AbsRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_rel_retargeter import Se3RelRetargeter, Se3RelRetargeterCfg + +# Mock dex retargeting utils +with patch.dict( + sys.modules, + { + "isaaclab.devices.openxr.retargeters.humanoid.unitree.inspire.g1_dex_retargeting_utils": MagicMock(), + "isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1_t2_dex_retargeting_utils": MagicMock(), + "isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_dex_retargeting_utils": MagicMock(), + }, +): + from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import ( + GR1T2Retargeter, + GR1T2RetargeterCfg, + ) + from isaaclab.devices.openxr.retargeters.humanoid.unitree.inspire.g1_upper_body_retargeter import ( + UnitreeG1Retargeter, + UnitreeG1RetargeterCfg, + ) + from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_motion_ctrl_gripper import ( + G1TriHandUpperBodyMotionControllerGripperRetargeter, + G1TriHandUpperBodyMotionControllerGripperRetargeterCfg, + ) + from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_motion_ctrl_retargeter import ( + G1TriHandUpperBodyMotionControllerRetargeter, + G1TriHandUpperBodyMotionControllerRetargeterCfg, + ) + from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeter, + G1TriHandUpperBodyRetargeterCfg, + ) + + +class TestSe3AbsRetargeter(unittest.TestCase): + def setUp(self): + self.cfg = Se3AbsRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, enable_visualization=False, sim_device="cpu" + ) + self.retargeter = Se3AbsRetargeter(self.cfg) + + def test_retarget_defaults(self): + # Mock input data + wrist_pose = np.array([0.1, 0.2, 0.3, 1.0, 0.0, 0.0, 0.0]) + thumb_tip_pose = np.array([0.15, 0.25, 0.35, 1.0, 0.0, 0.0, 0.0]) + index_tip_pose = np.array([0.15, 0.20, 0.35, 1.0, 0.0, 0.0, 0.0]) + + data = { + DeviceBase.TrackingTarget.HAND_RIGHT: { + "wrist": wrist_pose, + "thumb_tip": thumb_tip_pose, + "index_tip": index_tip_pose, + } + } + + result = self.retargeter.retarget(data) + + self.assertIsInstance(result, torch.Tensor) + self.assertEqual(result.shape, (7,)) + np.testing.assert_allclose(result[:3].numpy(), wrist_pose[:3], rtol=1e-5) + self.assertAlmostEqual(torch.norm(result[3:]).item(), 1.0, places=4) + + def test_pinch_position(self): + self.cfg.use_wrist_position = False + retargeter = Se3AbsRetargeter(self.cfg) + + wrist_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + thumb_tip_pose = np.array([1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + index_tip_pose = np.array([3.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + data = { + DeviceBase.TrackingTarget.HAND_RIGHT: { + "wrist": wrist_pose, + "thumb_tip": thumb_tip_pose, + "index_tip": index_tip_pose, + } + } + + result = retargeter.retarget(data) + expected_pos = np.array([2.0, 0.0, 0.0]) + np.testing.assert_allclose(result[:3].numpy(), expected_pos, rtol=1e-5) + + +class TestSe3RelRetargeter(unittest.TestCase): + def setUp(self): + self.cfg = Se3RelRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_LEFT, + enable_visualization=False, + sim_device="cpu", + delta_pos_scale_factor=1.0, + delta_rot_scale_factor=1.0, + alpha_pos=1.0, + alpha_rot=1.0, + ) + self.retargeter = Se3RelRetargeter(self.cfg) + + def test_retarget_movement(self): + wrist_pose_1 = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + thumb_tip_pose_1 = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + index_tip_pose_1 = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + data_1 = { + DeviceBase.TrackingTarget.HAND_LEFT: { + "wrist": wrist_pose_1, + "thumb_tip": thumb_tip_pose_1, + "index_tip": index_tip_pose_1, + } + } + + _ = self.retargeter.retarget(data_1) + + wrist_pose_2 = np.array([0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + thumb_tip_pose_2 = np.array([0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + index_tip_pose_2 = np.array([0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + + data_2 = { + DeviceBase.TrackingTarget.HAND_LEFT: { + "wrist": wrist_pose_2, + "thumb_tip": thumb_tip_pose_2, + "index_tip": index_tip_pose_2, + } + } + + result = self.retargeter.retarget(data_2) + self.assertEqual(result.shape, (6,)) + np.testing.assert_allclose(result[:3].numpy(), [0.1, 0.0, 0.0], rtol=1e-4) + + +class TestGripperRetargeter(unittest.TestCase): + def setUp(self): + self.cfg = GripperRetargeterCfg(bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device="cpu") + self.retargeter = GripperRetargeter(self.cfg) + + def test_gripper_logic(self): + data_open = { + DeviceBase.TrackingTarget.HAND_RIGHT: { + "thumb_tip": np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), + "index_tip": np.array([0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), + } + } + result = self.retargeter.retarget(data_open) + self.assertEqual(result.item(), 1.0) + + data_close = { + DeviceBase.TrackingTarget.HAND_RIGHT: { + "thumb_tip": np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), + "index_tip": np.array([0.02, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), + } + } + result = self.retargeter.retarget(data_close) + self.assertEqual(result.item(), -1.0) + + +class TestG1LowerBodyStandingRetargeter(unittest.TestCase): + def test_retarget(self): + cfg = G1LowerBodyStandingRetargeterCfg(hip_height=0.8, sim_device="cpu") + retargeter = G1LowerBodyStandingRetargeter(cfg) + result = retargeter.retarget({}) + self.assertTrue(torch.equal(result, torch.tensor([0.0, 0.0, 0.0, 0.8]))) + + +class TestUnitreeG1Retargeter(unittest.TestCase): + @patch( + "isaaclab.devices.openxr.retargeters.humanoid.unitree.inspire.g1_upper_body_retargeter.UnitreeG1DexRetargeting" + ) + def test_retarget(self, mock_dex_retargeting_cls): + mock_dex_retargeting = mock_dex_retargeting_cls.return_value + mock_dex_retargeting.get_joint_names.return_value = ["joint1", "joint2"] + mock_dex_retargeting.get_left_joint_names.return_value = ["joint1"] + mock_dex_retargeting.get_right_joint_names.return_value = ["joint2"] + mock_dex_retargeting.compute_left.return_value = np.array([0.1]) + mock_dex_retargeting.compute_right.return_value = np.array([0.2]) + + cfg = UnitreeG1RetargeterCfg( + enable_visualization=False, sim_device="cpu", hand_joint_names=["joint1", "joint2"] + ) + retargeter = UnitreeG1Retargeter(cfg) + + wrist_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + data = { + DeviceBase.TrackingTarget.HAND_LEFT: {"wrist": wrist_pose}, + DeviceBase.TrackingTarget.HAND_RIGHT: {"wrist": wrist_pose}, + } + + result = retargeter.retarget(data) + self.assertEqual(result.shape, (16,)) + + +class TestGR1T2Retargeter(unittest.TestCase): + @patch("isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter.GR1TR2DexRetargeting") + def test_retarget(self, mock_dex_retargeting_cls): + mock_dex_retargeting = mock_dex_retargeting_cls.return_value + mock_dex_retargeting.get_joint_names.return_value = ["joint1", "joint2"] + mock_dex_retargeting.get_left_joint_names.return_value = ["joint1"] + mock_dex_retargeting.get_right_joint_names.return_value = ["joint2"] + mock_dex_retargeting.compute_left.return_value = np.array([0.1]) + mock_dex_retargeting.compute_right.return_value = np.array([0.2]) + + cfg = GR1T2RetargeterCfg(enable_visualization=False, sim_device="cpu", hand_joint_names=["joint1", "joint2"]) + retargeter = GR1T2Retargeter(cfg) + + wrist_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + data = { + DeviceBase.TrackingTarget.HAND_LEFT: {"wrist": wrist_pose}, + DeviceBase.TrackingTarget.HAND_RIGHT: {"wrist": wrist_pose}, + } + + result = retargeter.retarget(data) + self.assertEqual(result.shape, (16,)) + + +class TestG1LowerBodyStandingMotionControllerRetargeter(unittest.TestCase): + def test_retarget(self): + cfg = G1LowerBodyStandingMotionControllerRetargeterCfg( + hip_height=0.8, movement_scale=1.0, rotation_scale=1.0, sim_device="cpu" + ) + retargeter = G1LowerBodyStandingMotionControllerRetargeter(cfg) + + # Mock input data + # Inputs array structure: [thumbstick_x, thumbstick_y, trigger, squeeze, button_0, button_1, padding] + left_inputs = np.zeros(7) + left_inputs[0] = 0.5 # thumbstick x + left_inputs[1] = 0.5 # thumbstick y + + right_inputs = np.zeros(7) + right_inputs[0] = -0.5 # thumbstick x + right_inputs[1] = -0.5 # thumbstick y + + data = { + DeviceBase.TrackingTarget.CONTROLLER_LEFT: [np.zeros(7), left_inputs], + DeviceBase.TrackingTarget.CONTROLLER_RIGHT: [np.zeros(7), right_inputs], + } + + result = retargeter.retarget(data) + # Output: [-left_thumbstick_y, -left_thumbstick_x, -right_thumbstick_x, hip_height] + # hip_height modified by right_thumbstick_y + + self.assertEqual(result.shape, (4,)) + self.assertAlmostEqual(result[0].item(), -0.5) # -left y + self.assertAlmostEqual(result[1].item(), -0.5) # -left x + self.assertAlmostEqual(result[2].item(), 0.5) # -right x + # Check hip height modification logic if needed, but basic execution is key here + + +class TestG1TriHandUpperBodyMotionControllerGripperRetargeter(unittest.TestCase): + def test_retarget(self): + cfg = G1TriHandUpperBodyMotionControllerGripperRetargeterCfg( + threshold_high=0.6, threshold_low=0.4, sim_device="cpu" + ) + retargeter = G1TriHandUpperBodyMotionControllerGripperRetargeter(cfg) + + pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + inputs_trigger_high = np.zeros(7) + inputs_trigger_high[2] = 0.8 # Trigger + + inputs_trigger_low = np.zeros(7) + inputs_trigger_low[2] = 0.2 # Trigger + + data = { + DeviceBase.TrackingTarget.CONTROLLER_LEFT: [pose, inputs_trigger_high], + DeviceBase.TrackingTarget.CONTROLLER_RIGHT: [pose, inputs_trigger_low], + } + + result = retargeter.retarget(data) + # Output: [left_state, right_state, left_wrist(7), right_wrist(7)] + self.assertEqual(result.shape, (16,)) + self.assertEqual(result[0].item(), 1.0) # Left closed + self.assertEqual(result[1].item(), 0.0) # Right open + + +class TestG1TriHandUpperBodyMotionControllerRetargeter(unittest.TestCase): + def test_retarget(self): + cfg = G1TriHandUpperBodyMotionControllerRetargeterCfg( + hand_joint_names=["dummy"] * 14, # Not really used in logic, just passed to config + sim_device="cpu", + enable_visualization=False, + ) + retargeter = G1TriHandUpperBodyMotionControllerRetargeter(cfg) + + pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + inputs = np.zeros(7) + + data = { + DeviceBase.TrackingTarget.CONTROLLER_LEFT: [pose, inputs], + DeviceBase.TrackingTarget.CONTROLLER_RIGHT: [pose, inputs], + } + + result = retargeter.retarget(data) + # Output: [left_wrist(7), right_wrist(7), hand_joints(14)] + self.assertEqual(result.shape, (28,)) + + +class TestG1TriHandUpperBodyRetargeter(unittest.TestCase): + @patch( + "isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter.G1TriHandDexRetargeting" + ) + def test_retarget(self, mock_dex_retargeting_cls): + mock_dex_retargeting = mock_dex_retargeting_cls.return_value + mock_dex_retargeting.get_joint_names.return_value = ["joint1", "joint2"] + mock_dex_retargeting.get_left_joint_names.return_value = ["joint1"] + mock_dex_retargeting.get_right_joint_names.return_value = ["joint2"] + mock_dex_retargeting.compute_left.return_value = np.array([0.1]) + mock_dex_retargeting.compute_right.return_value = np.array([0.2]) + + cfg = G1TriHandUpperBodyRetargeterCfg( + enable_visualization=False, sim_device="cpu", hand_joint_names=["joint1", "joint2"] + ) + retargeter = G1TriHandUpperBodyRetargeter(cfg) + + wrist_pose = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) + data = { + DeviceBase.TrackingTarget.HAND_LEFT: {"wrist": wrist_pose}, + DeviceBase.TrackingTarget.HAND_RIGHT: {"wrist": wrist_pose}, + } + + result = retargeter.retarget(data) + # Output: [left_wrist(7), right_wrist(7), joints(2)] + self.assertEqual(result.shape, (16,)) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/isaaclab/test/envs/check_manager_based_env_anymal_locomotion.py b/source/isaaclab/test/envs/check_manager_based_env_anymal_locomotion.py new file mode 100644 index 0000000000000000000000000000000000000000..c6169c94d19752b08245dbb9b9042e9ce4127e0c --- /dev/null +++ b/source/isaaclab/test/envs/check_manager_based_env_anymal_locomotion.py @@ -0,0 +1,254 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates the environment concept that combines a scene with an action, +observation and event manager for a quadruped robot. + +A locomotion policy is loaded and used to control the robot. This shows how to use the +environment with a policy. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to use the concept of an Environment.") +parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import RayCasterCfg, patterns +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR, check_file_path, read_file +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +## +# Pre-defined configs +## +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # add terrain + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + visual_material=sim_utils.MdlFileCfg( + mdl_path=f"{ISAACLAB_NUCLEUS_DIR}/Materials/TilesMarbleSpiderWhiteBrickBondHoned/TilesMarbleSpiderWhiteBrickBondHoned.mdl", + project_uvw=True, + texture_scale=(0.25, 0.25), + ), + debug_vis=False, + ) + + # add robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/ground"], + ) + + # lights + sky_light = AssetBaseCfg( + prim_path="/World/skyLight", + spawn=sim_utils.DomeLightCfg( + intensity=900.0, + texture_file=f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr", + visible_in_primary_ray=False, + ), + ) + + +## +# MDP settings +## + + +def constant_commands(env: ManagerBasedEnv) -> torch.Tensor: + """The generated command from the command generator.""" + return torch.tensor([[1, 0, 0]], device=env.device).repeat(env.num_envs, 1) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2)) + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObsTerm(func=constant_commands) + joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5)) + actions = ObsTerm(func=mdp.last_action) + height_scan = ObsTerm( + func=mdp.height_scan, + params={"sensor_cfg": SceneEntityCfg("height_scanner")}, + noise=Unoise(n_min=-0.1, n_max=0.1), + clip=(-1.0, 1.0), + ) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.5, 0.5), + "y": (-0.5, 0.5), + "z": (-0.5, 0.5), + "roll": (-0.5, 0.5), + "pitch": (-0.5, 0.5), + "yaw": (-0.5, 0.5), + }, + }, + ) + + +## +# Environment configuration +## + + +@configclass +class QuadrupedEnvCfg(ManagerBasedEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 0.005 + # update sensor update periods + # we tick all the sensors based on the smallest update period (physics update period) + if self.scene.height_scanner is not None: + self.scene.height_scanner.update_period = self.decimation * self.sim.dt + + +def main(): + """Main function.""" + + # setup base environment + env = ManagerBasedEnv(cfg=QuadrupedEnvCfg()) + obs, _ = env.reset() + + # load level policy + policy_path = ISAACLAB_NUCLEUS_DIR + "/Policies/ANYmal-C/HeightScan/policy.pt" + + # check if policy file exists + if not check_file_path(policy_path): + raise FileNotFoundError(f"Policy file '{policy_path}' does not exist.") + file_bytes = read_file(policy_path) + # jit load the policy + locomotion_policy = torch.jit.load(file_bytes) + locomotion_policy.to(env.device) + locomotion_policy.eval() + + # simulate physics + count = 0 + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 1000 == 0: + obs, _ = env.reset() + count = 0 + print("[INFO]: Resetting robots state...") + + # infer action + action = locomotion_policy(obs["policy"]) + # step env + obs, _ = env.step(action) + # update counter + count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/envs/check_manager_based_env_floating_cube.py b/source/isaaclab/test/envs/check_manager_based_env_floating_cube.py new file mode 100644 index 0000000000000000000000000000000000000000..fb7622ae67cd7ca4b41c4f4215d61b361ee44cd5 --- /dev/null +++ b/source/isaaclab/test/envs/check_manager_based_env_floating_cube.py @@ -0,0 +1,266 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates the base environment concept that combines a scene with an action, +observation and event manager for a floating cube. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to use the concept of an Environment.") +parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # add terrain + terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False) + + # add cube + cube: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube", + spawn=sim_utils.CuboidCfg( + size=(0.2, 0.2, 0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.0, 0.0)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# Action Term +## + + +class CubeActionTerm(ActionTerm): + """Simple action term that implements a PD controller to track a target position.""" + + _asset: RigidObject + """The articulation asset on which the action term is applied.""" + + def __init__(self, cfg: ActionTermCfg, env: ManagerBasedEnv): + # call super constructor + super().__init__(cfg, env) + # create buffers + self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._vel_command = torch.zeros(self.num_envs, 6, device=self.device) + # gains of controller + self.p_gain = 5.0 + self.d_gain = 0.5 + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._raw_actions.shape[1] + + @property + def raw_actions(self) -> torch.Tensor: + # desired: (x, y, z) + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + """ + Operations + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # no-processing of actions + self._processed_actions[:] = self._raw_actions[:] + + def apply_actions(self): + # implement a PD controller to track the target position + pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins) + vel_error = -self._asset.data.root_lin_vel_w + # set velocity targets + self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error + self._asset.write_root_velocity_to_sim(self._vel_command) + + +@configclass +class CubeActionTermCfg(ActionTermCfg): + """Configuration for the cube action term.""" + + class_type: type = CubeActionTerm + + +## +# Observation Term +## + + +def base_position(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Root linear velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w - env.scene.env_origins + + +## +# Environment settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = CubeActionTermCfg(asset_name="cube") + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # cube velocity + position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube")}) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.5, 0.5), + "y": (-0.5, 0.5), + "z": (-0.5, 0.5), + }, + "asset_cfg": SceneEntityCfg("cube"), + }, + ) + + +## +# Environment configuration +## + + +@configclass +class CubeEnvCfg(ManagerBasedEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + # simulation settings + self.sim.dt = 0.01 + self.sim.physics_material = self.scene.terrain.physics_material + + +def main(): + """Main function.""" + + # setup base environment + env = ManagerBasedEnv(cfg=CubeEnvCfg()) + + # setup target position commands + target_position = torch.rand(env.num_envs, 3, device=env.device) * 2 + target_position[:, 2] += 2.0 + # offset all targets so that they move to the world origin + target_position -= env.scene.env_origins + + # simulate physics + count = 0 + while simulation_app.is_running(): + with torch.inference_mode(): + # reset + if count % 300 == 0: + env.reset() + count = 0 + + # step env + obs, _ = env.step(target_position) + # print mean squared position error between target and current position + error = torch.norm(obs["policy"] - target_position).mean().item() + print(f"[Step: {count:04d}]: Mean position error: {error:.4f}") + # update counter + count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/envs/test_action_state_recorder_term.py b/source/isaaclab/test/envs/test_action_state_recorder_term.py new file mode 100644 index 0000000000000000000000000000000000000000..16ae866dfce2b8dbe43fd57ce75552251555d909 --- /dev/null +++ b/source/isaaclab/test/envs/test_action_state_recorder_term.py @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +simulation_app = AppLauncher(headless=True).app + + +"""Rest everything follows.""" + +import shutil +import tempfile +import uuid + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="session", autouse=True) +def setup_carb_settings(): + """Set up carb settings to prevent simulation getting stuck.""" + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test datasets.""" + temp_dir = tempfile.mkdtemp() + yield temp_dir + shutil.rmtree(temp_dir) + + +def compare_states(compared_state, ground_truth_state, ground_truth_env_id) -> tuple[bool, str]: + """Compare a state with the given ground_truth. + + Args: + compared_state: State to be compared. + ground_truth_state: Ground truth state. + ground_truth_env_id: Index of the environment in the ground_truth states to be compared. + + Returns: + bool: True if states match, False otherwise. + str: Error log if states don't match. + """ + for asset_type in ["articulation", "rigid_object"]: + for asset_name in ground_truth_state[asset_type].keys(): + for state_name in ground_truth_state[asset_type][asset_name].keys(): + runtime_asset_state = ground_truth_state[asset_type][asset_name][state_name][ground_truth_env_id] + dataset_asset_state = compared_state[asset_type][asset_name][state_name][0] + if len(dataset_asset_state) != len(runtime_asset_state): + return False, f"State shape of {state_name} for asset {asset_name} don't match" + for i in range(len(dataset_asset_state)): + if abs(dataset_asset_state[i] - runtime_asset_state[i]) > 0.01: + return ( + False, + f'State ["{asset_type}"]["{asset_name}"]["{state_name}"][{i}] don\'t match\r\n', + ) + return True, "" + + +def check_initial_state_recorder_term(env): + """Check values recorded by the initial state recorder terms. + + Args: + env: Environment instance. + """ + current_state = env.unwrapped.scene.get_state(is_relative=True) + for env_id in range(env.unwrapped.num_envs): + recorded_initial_state = env.unwrapped.recorder_manager.get_episode(env_id).get_initial_state() + are_states_equal, output_log = compare_states(recorded_initial_state, current_state, env_id) + assert are_states_equal, output_log + + +@pytest.mark.parametrize("task_name", ["Isaac-Lift-Cube-Franka-v0"]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 2]) +def test_action_state_recorder_terms(task_name, device, num_envs, temp_dir): + """Check action state recorder terms.""" + omni.usd.get_context().new_stage() + + dummy_dataset_filename = f"{uuid.uuid4()}.hdf5" + + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # set recorder configurations for this test + env_cfg.recorders = ActionStateRecorderManagerCfg() + env_cfg.recorders.dataset_export_dir_path = temp_dir + env_cfg.recorders.dataset_filename = dummy_dataset_filename + + # create environment + env = gym.make(task_name, cfg=env_cfg) + + # reset all environment instances to trigger post-reset recorder callbacks + env.reset() + check_initial_state_recorder_term(env) + + # reset only one environment that is not the first one + env.unwrapped.reset(env_ids=torch.tensor([num_envs - 1], device=env.unwrapped.device)) + check_initial_state_recorder_term(env) + + # close the environment + env.close() diff --git a/source/isaaclab/test/envs/test_color_randomization.py b/source/isaaclab/test/envs/test_color_randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..619c7b3368fcf392fe70af9f7266656b65574e22 --- /dev/null +++ b/source/isaaclab/test/envs/test_color_randomization.py @@ -0,0 +1,173 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script tests the functionality of texture randomization applied to the cartpole scene. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import math + +import pytest +import torch + +import omni.usd + +import isaaclab.envs.mdp as mdp +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass +from isaaclab.utils.version import get_isaac_sim_version + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleSceneCfg + + +@configclass +class ActionsCfg: + """Action specifications for the environment.""" + + joint_efforts = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=5.0) + + +@configclass +class ObservationsCfg: + """Observation specifications for the environment.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) + + def __post_init__(self) -> None: + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # on prestartup apply a new set of textures + # note from @mayank: Changed from 'reset' to 'prestartup' to make test pass. + # The error happens otherwise on Kit thread which is not the main thread. + cart_texture_randomizer = EventTerm( + func=mdp.randomize_visual_color, + mode="prestartup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["cart"]), + "colors": {"r": (0.0, 1.0), "g": (0.0, 1.0), "b": (0.0, 1.0)}, + "event_name": "cart_color_randomizer", + }, + ) + + # on reset apply a new set of textures + pole_texture_randomizer = EventTerm( + func=mdp.randomize_visual_color, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]), + "colors": {"r": (0.0, 1.0), "g": (0.0, 1.0), "b": (0.0, 1.0)}, + "event_name": "pole_color_randomizer", + }, + ) + + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.1, 0.1), + }, + ) + + reset_pole_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), + "position_range": (-0.125 * math.pi, 0.125 * math.pi), + "velocity_range": (-0.01 * math.pi, 0.01 * math.pi), + }, + ) + + +@configclass +class CartpoleEnvCfg(ManagerBasedEnvCfg): + """Configuration for the cartpole environment.""" + + # Scene settings + scene = CartpoleSceneCfg(env_spacing=2.5) + + # Basic settings + actions = ActionsCfg() + observations = ObservationsCfg() + events = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # viewer settings + self.viewer.eye = [4.5, 0.0, 6.0] + self.viewer.lookat = [0.0, 0.0, 2.0] + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_color_randomization(device): + """Test color randomization for cartpole environment.""" + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5: + pytest.skip("Color randomization test hangs in this version of Isaac Sim") + + # Create a new stage + omni.usd.get_context().new_stage() + + try: + # Set the arguments + env_cfg = CartpoleEnvCfg() + env_cfg.scene.num_envs = 16 + env_cfg.scene.replicate_physics = False + env_cfg.sim.device = device + + # Setup base environment + env = ManagerBasedEnv(cfg=env_cfg) + + try: + # Simulate physics + with torch.inference_mode(): + for count in range(50): + # Reset every few steps to check nothing breaks + if count % 10 == 0: + env.reset() + # Sample random actions + joint_efforts = torch.randn_like(env.action_manager.action) + # Step the environment + env.step(joint_efforts) + finally: + env.close() + finally: + # Clean up stage + omni.usd.get_context().close_stage() diff --git a/source/isaaclab/test/envs/test_direct_marl_env.py b/source/isaaclab/test/envs/test_direct_marl_env.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ebd04610b4addc04f38a422fa4d603c2fea6ee --- /dev/null +++ b/source/isaaclab/test/envs/test_direct_marl_env.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest + +import omni.usd + +from isaaclab.envs import DirectMARLEnv, DirectMARLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + + +@configclass +class EmptySceneCfg(InteractiveSceneCfg): + """Configuration for an empty scene.""" + + pass + + +def get_empty_base_env_cfg(device: str = "cuda:0", num_envs: int = 1, env_spacing: float = 1.0): + """Generate base environment config based on device""" + + @configclass + class EmptyEnvCfg(DirectMARLEnvCfg): + """Configuration for the empty test environment.""" + + # Scene settings + scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + # Basic settings + decimation = 1 + possible_agents = ["agent_0", "agent_1"] + action_spaces = {"agent_0": 1, "agent_1": 2} + observation_spaces = {"agent_0": 3, "agent_1": 4} + state_space = -1 + episode_length_s = 100.0 + + return EmptyEnvCfg() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_initialization(device): + """Test initialization of DirectMARLEnv.""" + # create a new stage + omni.usd.get_context().new_stage() + try: + # create environment + env = DirectMARLEnv(cfg=get_empty_base_env_cfg(device=device)) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the DirectMARLEnv environment. Error: {e}") + + # check multi-agent config + assert env.num_agents == 2 + assert env.max_num_agents == 2 + # check spaces + assert env.state_space.shape == (7,) + assert len(env.observation_spaces) == 2 + assert len(env.action_spaces) == 2 + # close the environment + env.close() diff --git a/source/isaaclab/test/envs/test_env_rendering_logic.py b/source/isaaclab/test/envs/test_env_rendering_logic.py new file mode 100644 index 0000000000000000000000000000000000000000..70f0a01f212a26a13d7f3fcaf07d1063a396c098 --- /dev/null +++ b/source/isaaclab/test/envs/test_env_rendering_logic.py @@ -0,0 +1,208 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +# need to set "enable_cameras" true to be able to do rendering tests +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +import omni.usd + +from isaaclab.envs import ( + DirectRLEnv, + DirectRLEnvCfg, + ManagerBasedEnv, + ManagerBasedEnvCfg, + ManagerBasedRLEnv, + ManagerBasedRLEnvCfg, +) +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg, SimulationContext +from isaaclab.utils import configclass + + +@configclass +class EmptyManagerCfg: + """Empty specifications for the environment.""" + + pass + + +def create_manager_based_env(render_interval: int): + """Create a manager based environment.""" + + @configclass + class EnvCfg(ManagerBasedEnvCfg): + """Configuration for the test environment.""" + + decimation: int = 4 + episode_length_s: float = 100.0 + sim: SimulationCfg = SimulationCfg(dt=0.005, render_interval=render_interval) + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=1.0) + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + + return ManagerBasedEnv(cfg=EnvCfg()) + + +def create_manager_based_rl_env(render_interval: int): + """Create a manager based RL environment.""" + + @configclass + class EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the test environment.""" + + decimation: int = 4 + episode_length_s: float = 100.0 + sim: SimulationCfg = SimulationCfg(dt=0.005, render_interval=render_interval) + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=1.0) + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + rewards: EmptyManagerCfg = EmptyManagerCfg() + terminations: EmptyManagerCfg = EmptyManagerCfg() + + return ManagerBasedRLEnv(cfg=EnvCfg()) + + +def create_direct_rl_env(render_interval: int): + """Create a direct RL environment.""" + + @configclass + class EnvCfg(DirectRLEnvCfg): + """Configuration for the test environment.""" + + decimation: int = 4 + action_space: int = 0 + observation_space: int = 0 + episode_length_s: float = 100.0 + sim: SimulationCfg = SimulationCfg(dt=0.005, render_interval=render_interval) + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=1.0) + + class Env(DirectRLEnv): + """Test environment.""" + + def _pre_physics_step(self, actions): + pass + + def _apply_action(self): + pass + + def _get_observations(self): + return {} + + def _get_rewards(self): + return {} + + def _get_dones(self): + return torch.zeros(1, dtype=torch.bool), torch.zeros(1, dtype=torch.bool) + + return Env(cfg=EnvCfg()) + + +@pytest.fixture +def physics_callback(): + """Create a physics callback for tracking physics steps.""" + physics_time = 0.0 + num_physics_steps = 0 + + def callback(dt): + nonlocal physics_time, num_physics_steps + physics_time += dt + num_physics_steps += 1 + + return callback, lambda: (physics_time, num_physics_steps) + + +@pytest.fixture +def render_callback(): + """Create a render callback for tracking render steps.""" + render_time = 0.0 + num_render_steps = 0 + + def callback(event): + nonlocal render_time, num_render_steps + render_time += event.payload["dt"] + num_render_steps += 1 + + return callback, lambda: (render_time, num_render_steps) + + +@pytest.mark.parametrize("env_type", ["manager_based_env", "manager_based_rl_env", "direct_rl_env"]) +@pytest.mark.parametrize("render_interval", [1, 2, 4, 8, 10]) +def test_env_rendering_logic(env_type, render_interval, physics_callback, render_callback): + """Test the rendering logic of the different environment workflows.""" + physics_cb, get_physics_stats = physics_callback + render_cb, get_render_stats = render_callback + + # create a new stage + omni.usd.get_context().new_stage() + try: + # create environment + if env_type == "manager_based_env": + env = create_manager_based_env(render_interval) + elif env_type == "manager_based_rl_env": + env = create_manager_based_rl_env(render_interval) + else: + env = create_direct_rl_env(render_interval) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment {env_type}. Error: {e}") + + # enable the flag to render the environment + # note: this is only done for the unit testing to "fake" camera rendering. + # normally this is set to True when cameras are created. + env.sim.set_setting("/isaaclab/render/rtx_sensors", True) + + # disable the app from shutting down when the environment is closed + # FIXME: Why is this needed in this test but not in the other tests? + # Without it, the test will exit after the environment is closed + env.sim._app_control_on_stop_handle = None # type: ignore + + # check that we are in partial rendering mode for the environment + # this is enabled due to app launcher setting "enable_cameras=True" + assert env.sim.render_mode == SimulationContext.RenderMode.PARTIAL_RENDERING + + # add physics and render callbacks + env.sim.add_physics_callback("physics_step", physics_cb) + env.sim.add_render_callback("render_step", render_cb) + + # create a zero action tensor for stepping the environment + actions = torch.zeros((env.num_envs, 0), device=env.device) + + # run the environment and check the rendering logic + for i in range(50): + # apply zero actions + env.step(action=actions) + + # check that we have completed the correct number of physics steps + _, num_physics_steps = get_physics_stats() + assert num_physics_steps == (i + 1) * env.cfg.decimation, "Physics steps mismatch" + # check that we have simulated physics for the correct amount of time + physics_time, _ = get_physics_stats() + assert abs(physics_time - num_physics_steps * env.cfg.sim.dt) < 1e-6, "Physics time mismatch" + + # check that we have completed the correct number of rendering steps + _, num_render_steps = get_render_stats() + assert num_render_steps == (i + 1) * env.cfg.decimation // env.cfg.sim.render_interval, "Render steps mismatch" + # check that we have rendered for the correct amount of time + render_time, _ = get_render_stats() + assert abs(render_time - num_render_steps * env.cfg.sim.dt * env.cfg.sim.render_interval) < 1e-6, ( + "Render time mismatch" + ) + + # close the environment + env.close() diff --git a/source/isaaclab/test/envs/test_manager_based_env.py b/source/isaaclab/test/envs/test_manager_based_env.py new file mode 100644 index 0000000000000000000000000000000000000000..7ec9ef2d43f87c68a460eaf1dfabe4d5061449c1 --- /dev/null +++ b/source/isaaclab/test/envs/test_manager_based_env.py @@ -0,0 +1,200 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +import omni.usd + +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + + +@configclass +class EmptyManagerCfg: + """Empty manager specifications for the environment.""" + + pass + + +@configclass +class EmptyObservationWithHistoryCfg: + """Empty observation with history specifications for the environment.""" + + @configclass + class EmptyObservationGroupWithHistoryCfg(ObsGroup): + """Empty observation with history specifications for the environment.""" + + dummy_term: ObsTerm = ObsTerm(func=lambda env: torch.randn(env.num_envs, 1, device=env.device)) + + def __post_init__(self): + self.history_length = 5 + + empty_observation: EmptyObservationGroupWithHistoryCfg = EmptyObservationGroupWithHistoryCfg() + + +@configclass +class EmptySceneCfg(InteractiveSceneCfg): + """Configuration for an empty scene.""" + + pass + + +def get_empty_base_env_cfg(device: str = "cuda:0", num_envs: int = 1, env_spacing: float = 1.0): + """Generate base environment config based on device""" + + @configclass + class EmptyEnvCfg(ManagerBasedEnvCfg): + """Configuration for the empty test environment.""" + + # Scene settings + scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + # Basic settings + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + + def __post_init__(self): + """Post initialization.""" + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + self.sim.render_interval = self.decimation # render every 4 sim steps + # pass device down from test + self.sim.device = device + + return EmptyEnvCfg() + + +def get_empty_base_env_cfg_with_history(device: str = "cuda:0", num_envs: int = 1, env_spacing: float = 1.0): + """Generate base environment config based on device""" + + @configclass + class EmptyEnvWithHistoryCfg(ManagerBasedEnvCfg): + """Configuration for the empty test environment.""" + + # Scene settings + scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + # Basic settings + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyObservationWithHistoryCfg = EmptyObservationWithHistoryCfg() + + def __post_init__(self): + """Post initialization.""" + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + self.sim.render_interval = self.decimation # render every 4 sim steps + # pass device down from test + self.sim.device = device + + return EmptyEnvWithHistoryCfg() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_initialization(device): + """Test initialization of ManagerBasedEnv.""" + # create a new stage + omni.usd.get_context().new_stage() + # create environment + env = ManagerBasedEnv(cfg=get_empty_base_env_cfg(device=device)) + # check size of action manager terms + assert env.action_manager.total_action_dim == 0 + assert len(env.action_manager.active_terms) == 0 + assert len(env.action_manager.action_term_dim) == 0 + # check size of observation manager terms + assert len(env.observation_manager.active_terms) == 0 + assert len(env.observation_manager.group_obs_dim) == 0 + assert len(env.observation_manager.group_obs_term_dim) == 0 + assert len(env.observation_manager.group_obs_concatenate) == 0 + # create actions of correct size (1,0) + act = torch.randn_like(env.action_manager.action) + # step environment to verify setup + for _ in range(2): + obs, ext = env.step(action=act) + # close the environment + env.close() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_observation_history_changes_only_after_step(device): + """Test observation history of ManagerBasedEnv. + + The history buffer should only change after a step is taken. + """ + # create a new stage + omni.usd.get_context().new_stage() + # create environment with history length of 5 + env = ManagerBasedEnv(cfg=get_empty_base_env_cfg_with_history(device=device)) + + # check if history buffer is empty + for group_name in env.observation_manager._group_obs_term_names: + group_term_names = env.observation_manager._group_obs_term_names[group_name] + for term_name in group_term_names: + torch.testing.assert_close( + env.observation_manager._group_obs_term_history_buffer[group_name][term_name].current_length, + torch.zeros((env.num_envs,), device=device, dtype=torch.int64), + ) + + # check if history buffer is empty after compute + env.observation_manager.compute() + for group_name in env.observation_manager._group_obs_term_names: + group_term_names = env.observation_manager._group_obs_term_names[group_name] + for term_name in group_term_names: + torch.testing.assert_close( + env.observation_manager._group_obs_term_history_buffer[group_name][term_name].current_length, + torch.zeros((env.num_envs,), device=device, dtype=torch.int64), + ) + + # check if history buffer is not empty after step + act = torch.randn_like(env.action_manager.action) + env.step(act) + group_obs = dict() + for group_name in env.observation_manager._group_obs_term_names: + group_term_names = env.observation_manager._group_obs_term_names[group_name] + group_obs[group_name] = dict() + for term_name in group_term_names: + torch.testing.assert_close( + env.observation_manager._group_obs_term_history_buffer[group_name][term_name].current_length, + torch.ones((env.num_envs,), device=device, dtype=torch.int64), + ) + group_obs[group_name][term_name] = env.observation_manager._group_obs_term_history_buffer[group_name][ + term_name + ].buffer + + # check if history buffer is not empty after compute and is the same as the buffer after step + env.observation_manager.compute() + for group_name in env.observation_manager._group_obs_term_names: + group_term_names = env.observation_manager._group_obs_term_names[group_name] + for term_name in group_term_names: + torch.testing.assert_close( + env.observation_manager._group_obs_term_history_buffer[group_name][term_name].current_length, + torch.ones((env.num_envs,), device=device, dtype=torch.int64), + ) + assert torch.allclose( + group_obs[group_name][term_name], + env.observation_manager._group_obs_term_history_buffer[group_name][term_name].buffer, + ) + + # close the environment + env.close() diff --git a/source/isaaclab/test/envs/test_manager_based_rl_env_obs_spaces.py b/source/isaaclab/test/envs/test_manager_based_rl_env_obs_spaces.py new file mode 100644 index 0000000000000000000000000000000000000000..72525ddb8e0353ad2290cf5f56e0fd3b97dd6468 --- /dev/null +++ b/source/isaaclab/test/envs/test_manager_based_rl_env_obs_spaces.py @@ -0,0 +1,141 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test texture randomization in the cartpole scene using pytest.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +import gymnasium as gym +import numpy as np +import pytest +import torch + +import omni.usd + +from isaaclab.envs import ManagerBasedRLEnv + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_camera_env_cfg import ( + CartpoleDepthCameraEnvCfg, + CartpoleRGBCameraEnvCfg, +) +from isaaclab_tasks.manager_based.locomotion.velocity.config.anymal_c.rough_env_cfg import AnymalCRoughEnvCfg + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_non_concatenated_obs_groups_contain_all_terms(device): + """Test that non-concatenated observation groups contain all defined terms (issue #3133). + + Before the fix, only the last term in each non-concatenated group would be present + in the observation space Dict. This test ensures all terms are correctly included. + """ + from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_joint_pos_env_cfg import ( + FrankaCubeStackEnvCfg, + ) + + # new USD stage + omni.usd.get_context().new_stage() + + # configure the stack env - it has multiple non-concatenated observation groups + env_cfg = FrankaCubeStackEnvCfg() + env_cfg.scene.num_envs = 2 # keep num_envs small for testing + env_cfg.sim.device = device + + env = ManagerBasedRLEnv(cfg=env_cfg) + + # Verify that observation space is properly structured + assert isinstance(env.observation_space, gym.spaces.Dict), "Top-level observation space should be Dict" + + # Test 'policy' group - should have 9 terms (not just the last one due to the bug) + assert "policy" in env.observation_space.spaces, "Policy group missing from observation space" + policy_space = env.observation_space.spaces["policy"] + assert isinstance(policy_space, gym.spaces.Dict), "Policy group should be Dict space" + + expected_policy_terms = [ + "actions", + "joint_pos", + "joint_vel", + "object", + "cube_positions", + "cube_orientations", + "eef_pos", + "eef_quat", + "gripper_pos", + ] + + # This is the key test - before the fix, only "gripper_pos" (last term) would be present + assert len(policy_space.spaces) == len(expected_policy_terms), ( + f"Policy group should have {len(expected_policy_terms)} terms, got {len(policy_space.spaces)}:" + f" {list(policy_space.spaces.keys())}" + ) + + for term_name in expected_policy_terms: + assert term_name in policy_space.spaces, f"Term '{term_name}' missing from policy group" + assert isinstance(policy_space.spaces[term_name], gym.spaces.Box), f"Term '{term_name}' should be Box space" + + # Test 'subtask_terms' group - should have 3 terms (not just the last one) + assert "subtask_terms" in env.observation_space.spaces, "Subtask_terms group missing from observation space" + subtask_space = env.observation_space.spaces["subtask_terms"] + assert isinstance(subtask_space, gym.spaces.Dict), "Subtask_terms group should be Dict space" + + expected_subtask_terms = ["grasp_1", "stack_1", "grasp_2"] + + # Before the fix, only "grasp_2" (last term) would be present + assert len(subtask_space.spaces) == len(expected_subtask_terms), ( + f"Subtask_terms group should have {len(expected_subtask_terms)} terms, got {len(subtask_space.spaces)}:" + f" {list(subtask_space.spaces.keys())}" + ) + + for term_name in expected_subtask_terms: + assert term_name in subtask_space.spaces, f"Term '{term_name}' missing from subtask_terms group" + assert isinstance(subtask_space.spaces[term_name], gym.spaces.Box), f"Term '{term_name}' should be Box space" + + # Test that we can get observations and they match the space structure + env.reset() + action = torch.tensor(env.action_space.sample(), device=env.device) + obs, reward, terminated, truncated, info = env.step(action) + + # Verify all terms are present in actual observations + for term_name in expected_policy_terms: + assert term_name in obs["policy"], f"Term '{term_name}' missing from policy observation" + + for term_name in expected_subtask_terms: + assert term_name in obs["subtask_terms"], f"Term '{term_name}' missing from subtask_terms observation" + + env.close() + + +@pytest.mark.parametrize( + "env_cfg_cls", + [CartpoleRGBCameraEnvCfg, CartpoleDepthCameraEnvCfg, AnymalCRoughEnvCfg], + ids=["RGB", "Depth", "RayCaster"], +) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_obs_space_follows_clip_contraint(env_cfg_cls, device): + """Ensure curriculum terms apply correctly after the fallback and replacement.""" + # new USD stage + omni.usd.get_context().new_stage() + + # configure the cartpole env + env_cfg = env_cfg_cls() + env_cfg.scene.num_envs = 2 # keep num_envs small for testing + env_cfg.observations.policy.concatenate_terms = False + env_cfg.sim.device = device + + env = ManagerBasedRLEnv(cfg=env_cfg) + for group_name, group_space in env.observation_space.spaces.items(): + for term_name, term_space in group_space.spaces.items(): + term_cfg = getattr(getattr(env_cfg.observations, group_name), term_name) + low = -np.inf if term_cfg.clip is None else term_cfg.clip[0] + high = np.inf if term_cfg.clip is None else term_cfg.clip[1] + assert isinstance(term_space, gym.spaces.Box), ( + f"Expected Box space for {term_name} in {group_name}, got {type(term_space)}" + ) + assert np.all(term_space.low == low) + assert np.all(term_space.high == high) + + env.close() diff --git a/source/isaaclab/test/envs/test_manager_based_rl_env_ui.py b/source/isaaclab/test/envs/test_manager_based_rl_env_ui.py new file mode 100644 index 0000000000000000000000000000000000000000..f35c11a1c401ae8fd249e003caad160ca81cffdd --- /dev/null +++ b/source/isaaclab/test/envs/test_manager_based_rl_env_ui.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import carb +import omni.usd +from isaacsim.core.utils.extensions import enable_extension + +from isaaclab.envs import ManagerBasedRLEnv, ManagerBasedRLEnvCfg +from isaaclab.envs.ui import ManagerBasedRLEnvWindow +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + +enable_extension("isaacsim.gui.components") + + +@configclass +class EmptyManagerCfg: + """Empty manager specifications for the environment.""" + + pass + + +@configclass +class EmptySceneCfg(InteractiveSceneCfg): + """Configuration for an empty scene.""" + + pass + + +def get_empty_base_env_cfg(device: str = "cuda:0", num_envs: int = 1, env_spacing: float = 1.0): + """Generate base environment config based on device""" + + @configclass + class EmptyEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the empty test environment.""" + + # Scene settings + scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + # Basic settings + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + rewards: EmptyManagerCfg = EmptyManagerCfg() + terminations: EmptyManagerCfg = EmptyManagerCfg() + # Define window + ui_window_class_type: type[ManagerBasedRLEnvWindow] = ManagerBasedRLEnvWindow + + def __post_init__(self): + """Post initialization.""" + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + self.sim.render_interval = self.decimation # render every 4 sim steps + # pass device down from test + self.sim.device = device + # episode length + self.episode_length_s = 5.0 + + return EmptyEnvCfg() + + +def test_ui_window(): + """Test UI window of ManagerBasedRLEnv.""" + device = "cuda:0" + # override sim setting to enable UI + carb.settings.get_settings().set_bool("/app/window/enabled", True) + # create a new stage + omni.usd.get_context().new_stage() + # create environment + env = ManagerBasedRLEnv(cfg=get_empty_base_env_cfg(device=device)) + # close the environment + env.close() diff --git a/source/isaaclab/test/envs/test_modify_env_param_curr_term.py b/source/isaaclab/test/envs/test_modify_env_param_curr_term.py new file mode 100644 index 0000000000000000000000000000000000000000..a23a29f3860613ff6783507b7c118779f0579811 --- /dev/null +++ b/source/isaaclab/test/envs/test_modify_env_param_curr_term.py @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test texture randomization in the cartpole scene using pytest.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import pytest +import torch + +import omni.usd + +import isaaclab.envs.mdp as mdp +from isaaclab.assets import Articulation +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleEnvCfg + + +def replace_value(env, env_id, data, value, num_steps): + if env.common_step_counter > num_steps and data != value: + return value + # use the sentinel to indicate “no change” + return mdp.modify_env_param.NO_CHANGE + + +@configclass +class CurriculumsCfg: + modify_observation_joint_pos = CurrTerm( + # test writing a term's func. + func=mdp.modify_term_cfg, + params={ + "address": "observations.policy.joint_pos_rel.func", + "modify_fn": replace_value, + "modify_params": {"value": mdp.joint_pos, "num_steps": 1}, + }, + ) + + # test writing a term's param that involves dictionary. + modify_reset_joint_pos = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "events.reset_cart_position.params.position_range", + "modify_fn": replace_value, + "modify_params": {"value": (-0.0, 0.0), "num_steps": 1}, + }, + ) + + # test writing a non_term env parameter using modify_env_param. + modify_episode_max_length = CurrTerm( + func=mdp.modify_env_param, + params={ + "address": "cfg.episode_length_s", + "modify_fn": replace_value, + "modify_params": {"value": 20, "num_steps": 1}, + }, + ) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_curriculum_modify_env_param(device): + """Ensure curriculum terms apply correctly after the fallback and replacement.""" + # new USD stage + omni.usd.get_context().new_stage() + + # configure the cartpole env + env_cfg = CartpoleEnvCfg() + env_cfg.scene.num_envs = 16 + env_cfg.curriculum = CurriculumsCfg() + env_cfg.sim.device = device + + env = ManagerBasedRLEnv(cfg=env_cfg) + robot: Articulation = env.scene["robot"] + + # run a few steps under inference mode + with torch.inference_mode(): + for count in range(3): + env.reset() + actions = torch.randn_like(env.action_manager.action) + + if count == 0: + # test before curriculum kicks in, value agrees with default configuration + joint_ids = env.event_manager.cfg.reset_cart_position.params["asset_cfg"].joint_ids + assert env.observation_manager.cfg.policy.joint_pos_rel.func == mdp.joint_pos_rel + assert torch.any(robot.data.joint_pos[:, joint_ids] != 0.0) + assert env.max_episode_length_s == env_cfg.episode_length_s + + if count == 2: + # test after curriculum makes effect, value agrees with new values + assert env.observation_manager.cfg.policy.joint_pos_rel.func == mdp.joint_pos + joint_ids = env.event_manager.cfg.reset_cart_position.params["asset_cfg"].joint_ids + assert torch.all(robot.data.joint_pos[:, joint_ids] == 0.0) + assert env.max_episode_length_s == 20 + + env.step(actions) + + env.close() diff --git a/source/isaaclab/test/envs/test_null_command_term.py b/source/isaaclab/test/envs/test_null_command_term.py new file mode 100644 index 0000000000000000000000000000000000000000..c394fc94d5ce7131aab58826ea52653ab8558b3f --- /dev/null +++ b/source/isaaclab/test/envs/test_null_command_term.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from collections import namedtuple + +import pytest + +from isaaclab.envs.mdp import NullCommandCfg + + +@pytest.fixture +def env(): + """Create a dummy environment.""" + return namedtuple("ManagerBasedRLEnv", ["num_envs", "dt", "device"])(20, 0.1, "cpu") + + +def test_str(env): + """Test the string representation of the command manager.""" + cfg = NullCommandCfg() + command_term = cfg.class_type(cfg, env) + # print the expected string + print() + print(command_term) + + +def test_compute(env): + """Test the compute function. For null command generator, it does nothing.""" + cfg = NullCommandCfg() + command_term = cfg.class_type(cfg, env) + + # test the reset function + command_term.reset() + # test the compute function + command_term.compute(dt=env.dt) + # expect error + with pytest.raises(RuntimeError): + command_term.command diff --git a/source/isaaclab/test/envs/test_scale_randomization.py b/source/isaaclab/test/envs/test_scale_randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..282c6b2a3d85ca5c732e376a88460c6e0a7fedc0 --- /dev/null +++ b/source/isaaclab/test/envs/test_scale_randomization.py @@ -0,0 +1,350 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +""" +This script checks the functionality of scale randomization. +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import pytest +import torch + +import omni.usd +from pxr import Sdf + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import ActionTerm, ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Custom action term +## + + +class CubeActionTerm(ActionTerm): + """Simple action term that implements a PD controller to track a target position. + + The action term is applied to the cube asset. It involves two steps: + + 1. **Process the raw actions**: Typically, this includes any transformations of the raw actions + that are required to map them to the desired space. This is called once per environment step. + 2. **Apply the processed actions**: This step applies the processed actions to the asset. + It is called once per simulation step. + + In this case, the action term simply applies the raw actions to the cube asset. The raw actions + are the desired target positions of the cube in the environment frame. The pre-processing step + simply copies the raw actions to the processed actions as no additional processing is required. + The processed actions are then applied to the cube asset by implementing a PD controller to + track the target position. + """ + + _asset: RigidObject + """The articulation asset on which the action term is applied.""" + + def __init__(self, cfg: CubeActionTermCfg, env: ManagerBasedEnv): + # call super constructor + super().__init__(cfg, env) + # create buffers + self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device) + self._vel_command = torch.zeros(self.num_envs, 6, device=self.device) + # gains of controller + self.p_gain = cfg.p_gain + self.d_gain = cfg.d_gain + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return self._raw_actions.shape[1] + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + """ + Operations + """ + + def process_actions(self, actions: torch.Tensor): + # store the raw actions + self._raw_actions[:] = actions + # no-processing of actions + self._processed_actions[:] = self._raw_actions[:] + + def apply_actions(self): + # implement a PD controller to track the target position + pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins) + vel_error = -self._asset.data.root_lin_vel_w + # set velocity targets + self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error + self._asset.write_root_velocity_to_sim(self._vel_command) + + +@configclass +class CubeActionTermCfg(ActionTermCfg): + """Configuration for the cube action term.""" + + class_type: type = CubeActionTerm + """The class corresponding to the action term.""" + + p_gain: float = 5.0 + """Proportional gain of the PD controller.""" + d_gain: float = 0.5 + """Derivative gain of the PD controller.""" + + +## +# Custom observation term +## + + +def base_position(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Root linear velocity in the asset's root frame.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return asset.data.root_pos_w - env.scene.env_origins + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration. + + The scene comprises of a ground plane, light source and floating cubes (gravity disabled). + """ + + # add terrain + terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False) + + # add cube for scale randomization + cube1: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube1", + spawn=sim_utils.CuboidCfg( + size=(0.2, 0.2, 0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)), + ) + + # add cube for static scale values + cube2: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube2", + spawn=sim_utils.CuboidCfg( + size=(0.2, 0.2, 0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# Environment settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = CubeActionTermCfg(asset_name="cube1") + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # cube velocity + position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube1")}) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.5, 0.5), + "y": (-0.5, 0.5), + "z": (-0.5, 0.5), + }, + "asset_cfg": SceneEntityCfg("cube1"), + }, + ) + + # Scale randomization as intended + randomize_cube1__scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (0.5, 1.5), "y": (0.5, 1.5), "z": (0.5, 1.5)}, + "asset_cfg": SceneEntityCfg("cube1"), + }, + ) + + # Static scale values + randomize_cube2__scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)}, + "asset_cfg": SceneEntityCfg("cube2"), + }, + ) + + +## +# Environment configuration +## + + +@configclass +class CubeEnvCfg(ManagerBasedEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=10, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + # simulation settings + self.sim.dt = 0.01 + self.sim.physics_material = self.scene.terrain.physics_material + self.sim.render_interval = self.decimation + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_scale_randomization(device): + """Test scale randomization for cube environment.""" + # create a new stage + omni.usd.get_context().new_stage() + + # set the device + env_cfg = CubeEnvCfg() + env_cfg.sim.device = device + + # setup base environment + env = ManagerBasedEnv(cfg=env_cfg) + # setup target position commands + target_position = torch.rand(env.num_envs, 3, device=env.device) * 2 + target_position[:, 2] += 2.0 + # offset all targets so that they move to the world origin + target_position -= env.scene.env_origins + + # test to make sure all assets in the scene are created + all_prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube.*/.*") + assert len(all_prim_paths) == (env.num_envs * 2) + + # test to make sure randomized values are truly random + applied_scaling_randomization = set() + prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube1") + + # get the stage + stage = omni.usd.get_context().get_stage() + + # check if the scale values are truly random + for i in range(3): + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i]) + scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale") + if scale_spec.default in applied_scaling_randomization: + raise ValueError( + "Detected repeat in applied scale values - indication scaling randomization is not working." + ) + applied_scaling_randomization.add(scale_spec.default) + + # test to make sure that fixed values are assigned correctly + prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube2") + for i in range(3): + prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i]) + scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale") + assert tuple(scale_spec.default) == (1.0, 1.0, 1.0) + + # simulate physics + with torch.inference_mode(): + for count in range(200): + # reset every few steps to check nothing breaks + if count % 100 == 0: + env.reset() + # step the environment + env.step(target_position) + + env.close() + + +def test_scale_randomization_failure_replicate_physics(): + """Test scale randomization failure when replicate physics is set to True.""" + # create a new stage + omni.usd.get_context().new_stage() + # set the arguments + cfg_failure = CubeEnvCfg() + cfg_failure.scene.replicate_physics = True + + # run the test + with pytest.raises(RuntimeError): + env = ManagerBasedEnv(cfg_failure) + env.close() diff --git a/source/isaaclab/test/envs/test_spaces_utils.py b/source/isaaclab/test/envs/test_spaces_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f170173ea3844ccb08f08bde92f727d735dbbcb8 --- /dev/null +++ b/source/isaaclab/test/envs/test_spaces_utils.py @@ -0,0 +1,162 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import numpy as np +import torch +from gymnasium.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple + +from isaaclab.envs.utils.spaces import deserialize_space, sample_space, serialize_space, spec_to_gym_space + + +def test_spec_to_gym_space(): + """Test conversion of specs to gym spaces.""" + # fundamental spaces + # Box + space = spec_to_gym_space(1) + assert isinstance(space, Box) + assert space.shape == (1,) + space = spec_to_gym_space([1, 2, 3, 4, 5]) + assert isinstance(space, Box) + assert space.shape == (1, 2, 3, 4, 5) + space = spec_to_gym_space(Box(low=-1.0, high=1.0, shape=(1, 2))) + assert isinstance(space, Box) + # Discrete + space = spec_to_gym_space({2}) + assert isinstance(space, Discrete) + assert space.n == 2 + space = spec_to_gym_space(Discrete(2)) + assert isinstance(space, Discrete) + # MultiDiscrete + space = spec_to_gym_space([{1}, {2}, {3}]) + assert isinstance(space, MultiDiscrete) + assert space.nvec.shape == (3,) + space = spec_to_gym_space(MultiDiscrete(np.array([1, 2, 3]))) + assert isinstance(space, MultiDiscrete) + # composite spaces + # Tuple + space = spec_to_gym_space(([1, 2, 3, 4, 5], {2}, [{1}, {2}, {3}])) + assert isinstance(space, Tuple) + assert len(space) == 3 + assert isinstance(space[0], Box) + assert isinstance(space[1], Discrete) + assert isinstance(space[2], MultiDiscrete) + space = spec_to_gym_space(Tuple((Box(-1, 1, shape=(1,)), Discrete(2)))) + assert isinstance(space, Tuple) + # Dict + space = spec_to_gym_space({"box": [1, 2, 3, 4, 5], "discrete": {2}, "multi_discrete": [{1}, {2}, {3}]}) + assert isinstance(space, Dict) + assert len(space) == 3 + assert isinstance(space["box"], Box) + assert isinstance(space["discrete"], Discrete) + assert isinstance(space["multi_discrete"], MultiDiscrete) + space = spec_to_gym_space(Dict({"box": Box(-1, 1, shape=(1,)), "discrete": Discrete(2)})) + assert isinstance(space, Dict) + + +def test_sample_space(): + """Test sampling from gym spaces.""" + device = "cpu" + # fundamental spaces + # Box + sample = sample_space(Box(low=-1.0, high=1.0, shape=(1, 2)), device, batch_size=1) + assert isinstance(sample, torch.Tensor) + _check_tensorized(sample, batch_size=1) + # Discrete + sample = sample_space(Discrete(2), device, batch_size=2) + assert isinstance(sample, torch.Tensor) + _check_tensorized(sample, batch_size=2) + # MultiDiscrete + sample = sample_space(MultiDiscrete(np.array([1, 2, 3])), device, batch_size=3) + assert isinstance(sample, torch.Tensor) + _check_tensorized(sample, batch_size=3) + # composite spaces + # Tuple + sample = sample_space(Tuple((Box(-1, 1, shape=(1,)), Discrete(2))), device, batch_size=4) + assert isinstance(sample, (tuple, list)) + _check_tensorized(sample, batch_size=4) + # Dict + sample = sample_space(Dict({"box": Box(-1, 1, shape=(1,)), "discrete": Discrete(2)}), device, batch_size=5) + assert isinstance(sample, dict) + _check_tensorized(sample, batch_size=5) + + +def test_space_serialization_deserialization(): + """Test serialization and deserialization of gym spaces.""" + # fundamental spaces + # Box + space = 1 + output = deserialize_space(serialize_space(space)) + assert space == output + space = [1, 2, 3, 4, 5] + output = deserialize_space(serialize_space(space)) + assert space == output + space = Box(low=-1.0, high=1.0, shape=(1, 2)) + output = deserialize_space(serialize_space(space)) + assert isinstance(output, Box) + assert (space.low == output.low).all() + assert (space.high == output.high).all() + assert space.shape == output.shape + # Discrete + space = {2} + output = deserialize_space(serialize_space(space)) + assert space == output + space = Discrete(2) + output = deserialize_space(serialize_space(space)) + assert isinstance(output, Discrete) + assert space.n == output.n + # MultiDiscrete + space = [{1}, {2}, {3}] + output = deserialize_space(serialize_space(space)) + assert space == output + space = MultiDiscrete(np.array([1, 2, 3])) + output = deserialize_space(serialize_space(space)) + assert isinstance(output, MultiDiscrete) + assert (space.nvec == output.nvec).all() + # composite spaces + # Tuple + space = ([1, 2, 3, 4, 5], {2}, [{1}, {2}, {3}]) + output = deserialize_space(serialize_space(space)) + assert space == output + space = Tuple((Box(-1, 1, shape=(1,)), Discrete(2))) + output = deserialize_space(serialize_space(space)) + assert isinstance(output, Tuple) + assert len(output) == 2 + assert isinstance(output[0], Box) + assert isinstance(output[1], Discrete) + # Dict + space = {"box": [1, 2, 3, 4, 5], "discrete": {2}, "multi_discrete": [{1}, {2}, {3}]} + output = deserialize_space(serialize_space(space)) + assert space == output + space = Dict({"box": Box(-1, 1, shape=(1,)), "discrete": Discrete(2)}) + output = deserialize_space(serialize_space(space)) + assert isinstance(output, Dict) + assert len(output) == 2 + assert isinstance(output["box"], Box) + assert isinstance(output["discrete"], Discrete) + + +def _check_tensorized(sample, batch_size): + """Helper function to check if a sample is properly tensorized.""" + if isinstance(sample, (tuple, list)): + list(map(_check_tensorized, sample, [batch_size] * len(sample))) + elif isinstance(sample, dict): + list(map(_check_tensorized, sample.values(), [batch_size] * len(sample))) + else: + assert isinstance(sample, torch.Tensor) + assert sample.shape[0] == batch_size diff --git a/source/isaaclab/test/envs/test_texture_randomization.py b/source/isaaclab/test/envs/test_texture_randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..e2cbe7d54486766baa3d3df530e3662e86114e91 --- /dev/null +++ b/source/isaaclab/test/envs/test_texture_randomization.py @@ -0,0 +1,235 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script tests the functionality of texture randomization applied to the cartpole scene. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import math + +import pytest +import torch + +import omni.usd + +import isaaclab.envs.mdp as mdp +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import NVIDIA_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleSceneCfg + + +@configclass +class ActionsCfg: + """Action specifications for the environment.""" + + joint_efforts = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=5.0) + + +@configclass +class ObservationsCfg: + """Observation specifications for the environment.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) + + def __post_init__(self) -> None: + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # on prestartup apply a new set of textures + # note from @mayank: Changed from 'reset' to 'prestartup' to make test pass. + # The error happens otherwise on Kit thread which is not the main thread. + cart_texture_randomizer = EventTerm( + func=mdp.randomize_visual_texture_material, + mode="prestartup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["cart"]), + "texture_paths": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png", + ], + "event_name": "cart_texture_randomizer", + "texture_rotation": (math.pi / 2, math.pi / 2), + }, + ) + + # on reset apply a new set of textures + pole_texture_randomizer = EventTerm( + func=mdp.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]), + "texture_paths": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png", + ], + "event_name": "pole_texture_randomizer", + "texture_rotation": (math.pi / 2, math.pi / 2), + }, + ) + + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.1, 0.1), + }, + ) + + reset_pole_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), + "position_range": (-0.125 * math.pi, 0.125 * math.pi), + "velocity_range": (-0.01 * math.pi, 0.01 * math.pi), + }, + ) + + +@configclass +class EventCfgFallback: + """Configuration for events that tests the fallback mechanism.""" + + # Test fallback when /visuals pattern doesn't match + test_fallback_texture_randomizer = EventTerm( + func=mdp.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=["slider"]), + "texture_paths": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png", + ], + "event_name": "test_fallback_texture_randomizer", + "texture_rotation": (0.0, 0.0), + }, + ) + + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.1, 0.1), + }, + ) + + +@configclass +class CartpoleEnvCfg(ManagerBasedEnvCfg): + """Configuration for the cartpole environment.""" + + # Scene settings + scene = CartpoleSceneCfg(env_spacing=2.5) + + # Basic settings + actions = ActionsCfg() + observations = ObservationsCfg() + events = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # viewer settings + self.viewer.eye = [4.5, 0.0, 6.0] + self.viewer.lookat = [0.0, 0.0, 2.0] + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_texture_randomization(device): + """Test texture randomization for cartpole environment.""" + # Create a new stage + omni.usd.get_context().new_stage() + + try: + # Set the arguments + env_cfg = CartpoleEnvCfg() + env_cfg.scene.num_envs = 16 + env_cfg.scene.replicate_physics = False + env_cfg.sim.device = device + + # Setup base environment + env = ManagerBasedEnv(cfg=env_cfg) + + try: + # Simulate physics + with torch.inference_mode(): + for count in range(50): + # Reset every few steps to check nothing breaks + if count % 10 == 0: + env.reset() + # Sample random actions + joint_efforts = torch.randn_like(env.action_manager.action) + # Step the environment + env.step(joint_efforts) + finally: + env.close() + finally: + # Clean up stage + omni.usd.get_context().close_stage() + + +def test_texture_randomization_failure_replicate_physics(): + """Test texture randomization failure when replicate physics is set to True.""" + # Create a new stage + omni.usd.get_context().new_stage() + + try: + # Set the arguments + cfg_failure = CartpoleEnvCfg() + cfg_failure.scene.num_envs = 16 + cfg_failure.scene.replicate_physics = True + + # Test that creating the environment raises RuntimeError + with pytest.raises(RuntimeError): + env = ManagerBasedEnv(cfg_failure) + env.close() + finally: + # Clean up stage + omni.usd.get_context().close_stage() diff --git a/source/isaaclab/test/managers/test_event_manager.py b/source/isaaclab/test/managers/test_event_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..171cc8be65e98d996416e23a7ff39047b71ecab8 --- /dev/null +++ b/source/isaaclab/test/managers/test_event_manager.py @@ -0,0 +1,407 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from collections.abc import Sequence + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +from collections import namedtuple + +import pytest +import torch + +from isaaclab.envs import ManagerBasedEnv +from isaaclab.managers import EventManager, EventTermCfg, ManagerTermBase, ManagerTermBaseCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import configclass + +DummyEnv = namedtuple("ManagerBasedRLEnv", ["num_envs", "dt", "device", "sim", "dummy1", "dummy2"]) +"""Dummy environment for testing.""" + + +def reset_dummy1_to_zero(env, env_ids: torch.Tensor): + env.dummy1[env_ids] = 0 + + +def increment_dummy1_by_one(env, env_ids: torch.Tensor): + env.dummy1[env_ids] += 1 + + +def change_dummy1_by_value(env, env_ids: torch.Tensor, value: int): + env.dummy1[env_ids] += value + + +def reset_dummy2_to_zero(env, env_ids: torch.Tensor): + env.dummy2[env_ids] = 0 + + +def increment_dummy2_by_one(env, env_ids: torch.Tensor): + env.dummy2[env_ids] += 1 + + +class reset_dummy2_to_zero_class(ManagerTermBase): + def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedEnv): + super().__init__(cfg, env) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + pass + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + ) -> None: + env.dummy2[env_ids] = 0 + + +class increment_dummy2_by_one_class(ManagerTermBase): + def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedEnv): + super().__init__(cfg, env) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + pass + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + ) -> None: + env.dummy2[env_ids] += 1 + + +@pytest.fixture +def env(): + num_envs = 32 + device = "cpu" + # create dummy tensors + dummy1 = torch.zeros((num_envs, 2), device=device) + dummy2 = torch.zeros((num_envs, 10), device=device) + # create sim + sim = SimulationContext() + # create dummy environment + return DummyEnv(num_envs, 0.01, device, sim, dummy1, dummy2) + + +def test_str(env): + """Test the string representation of the event manager.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset"), + "term_3": EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 10}), + "term_4": EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 2}), + } + event_man = EventManager(cfg, env) + + # print the expected string + print() + print(event_man) + + +def test_config_equivalence(env): + """Test the equivalence of event manager created from different config types.""" + # create from dictionary + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset"), + "term_3": EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 10}), + } + event_man_from_dict = EventManager(cfg, env) + + # create from config class + @configclass + class MyEventManagerCfg: + """Event manager config with no type annotations.""" + + term_1 = EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)) + term_2 = EventTermCfg(func=reset_dummy1_to_zero, mode="reset") + term_3 = EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 10}) + + cfg = MyEventManagerCfg() + event_man_from_cfg = EventManager(cfg, env) + + # create from config class + @configclass + class MyEventManagerAnnotatedCfg: + """Event manager config with type annotations.""" + + term_1: EventTermCfg = EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)) + term_2: EventTermCfg = EventTermCfg(func=reset_dummy1_to_zero, mode="reset") + term_3: EventTermCfg = EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 10}) + + cfg = MyEventManagerAnnotatedCfg() + event_man_from_annotated_cfg = EventManager(cfg, env) + + # check equivalence + # parsed terms + assert event_man_from_dict.active_terms == event_man_from_annotated_cfg.active_terms + assert event_man_from_cfg.active_terms == event_man_from_annotated_cfg.active_terms + assert event_man_from_dict.active_terms == event_man_from_cfg.active_terms + # parsed term configs + assert event_man_from_dict._mode_term_cfgs == event_man_from_annotated_cfg._mode_term_cfgs + assert event_man_from_cfg._mode_term_cfgs == event_man_from_annotated_cfg._mode_term_cfgs + assert event_man_from_dict._mode_term_cfgs == event_man_from_cfg._mode_term_cfgs + + +def test_active_terms(env): + """Test the correct reading of active terms.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset"), + "term_3": EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 10}), + "term_4": EventTermCfg(func=change_dummy1_by_value, mode="custom", params={"value": 2}), + } + event_man = EventManager(cfg, env) + + assert len(event_man.active_terms) == 3 + assert len(event_man.active_terms["interval"]) == 1 + assert len(event_man.active_terms["reset"]) == 1 + assert len(event_man.active_terms["custom"]) == 2 + + +def test_class_terms(env): + """Test the correct preparation of function and class event terms.""" + cfg = { + "term_1": EventTermCfg(func=reset_dummy2_to_zero, mode="reset"), + "term_2": EventTermCfg(func=increment_dummy2_by_one_class, mode="interval", interval_range_s=(0.1, 0.1)), + "term_3": EventTermCfg(func=reset_dummy2_to_zero_class, mode="reset"), + } + + event_man = EventManager(cfg, env) + assert len(event_man.active_terms) == 2 + assert len(event_man.active_terms["interval"]) == 1 + assert len(event_man.active_terms["reset"]) == 2 + assert len(event_man._mode_class_term_cfgs) == 2 + assert len(event_man._mode_class_term_cfgs["reset"]) == 1 + + +def test_config_empty(env): + """Test the creation of reward manager with empty config.""" + event_man = EventManager(None, env) + assert len(event_man.active_terms) == 0 + + # print the expected string + print() + print(event_man) + + +def test_invalid_event_func_module(env): + """Test the handling of invalid event function's module in string representation.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)), + "term_2": EventTermCfg(func="a:reset_dummy1_to_zero", mode="reset"), + } + with pytest.raises(ValueError): + EventManager(cfg, env) + + +def test_invalid_event_config(env): + """Test the handling of invalid event function's config parameters.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="interval", interval_range_s=(0.1, 0.1)), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset"), + "term_3": EventTermCfg(func=change_dummy1_by_value, mode="custom"), + } + with pytest.raises(ValueError): + EventManager(cfg, env) + + +def test_apply_interval_mode_without_global_time(env): + """Test the application of event terms that are in interval mode without global time. + + During local time, each environment instance has its own time for the interval term. + """ + # make two intervals -- one is fixed and the other is random + term_1_interval_range_s = (10 * env.dt, 10 * env.dt) + term_2_interval_range_s = (2 * env.dt, 10 * env.dt) + + cfg = { + "term_1": EventTermCfg( + func=increment_dummy1_by_one, + mode="interval", + interval_range_s=term_1_interval_range_s, + is_global_time=False, + ), + "term_2": EventTermCfg( + func=increment_dummy2_by_one, + mode="interval", + interval_range_s=term_2_interval_range_s, + is_global_time=False, + ), + } + + event_man = EventManager(cfg, env) + + # obtain the initial time left for the interval terms + term_2_interval_time = event_man._interval_term_time_left[1].clone() + expected_dummy2_value = torch.zeros_like(env.dummy2) + + for count in range(50): + # apply the event terms + event_man.apply("interval", dt=env.dt) + # manually decrement the interval time for term2 since it is randomly sampled + term_2_interval_time -= env.dt + + # check the values + # we increment the dummy1 by 1 every 10 steps. at the 9th count (aka 10th apply), the value should be 1 + torch.testing.assert_close(env.dummy1, (count + 1) // 10 * torch.ones_like(env.dummy1)) + + # we increment the dummy2 by 1 every 2 to 10 steps based on the random interval + expected_dummy2_value += term_2_interval_time.unsqueeze(1) < 1e-6 + torch.testing.assert_close(env.dummy2, expected_dummy2_value) + + # check the time sampled at the end of the interval is valid + # -- fixed interval + if (count + 1) % 10 == 0: + term_1_interval_time_init = event_man._interval_term_time_left[0].clone() + expected_time_interval_init = torch.full_like(term_1_interval_time_init, term_1_interval_range_s[1]) + torch.testing.assert_close(term_1_interval_time_init, expected_time_interval_init) + # -- random interval + env_ids = (term_2_interval_time < 1e-6).nonzero(as_tuple=True)[0] + if len(env_ids) > 0: + term_2_interval_time[env_ids] = event_man._interval_term_time_left[1][env_ids] + + +def test_apply_interval_mode_with_global_time(env): + """Test the application of event terms that are in interval mode with global time. + + During global time, all the environment instances share the same time for the interval term. + """ + # make two intervals -- one is fixed and the other is random + term_1_interval_range_s = (10 * env.dt, 10 * env.dt) + term_2_interval_range_s = (2 * env.dt, 10 * env.dt) + + cfg = { + "term_1": EventTermCfg( + func=increment_dummy1_by_one, + mode="interval", + interval_range_s=term_1_interval_range_s, + is_global_time=True, + ), + "term_2": EventTermCfg( + func=increment_dummy2_by_one, + mode="interval", + interval_range_s=term_2_interval_range_s, + is_global_time=True, + ), + } + + event_man = EventManager(cfg, env) + + # obtain the initial time left for the interval terms + term_2_interval_time = event_man._interval_term_time_left[1].clone() + expected_dummy2_value = torch.zeros_like(env.dummy2) + + for count in range(50): + # apply the event terms + event_man.apply("interval", dt=env.dt) + # manually decrement the interval time for term2 since it is randomly sampled + term_2_interval_time -= env.dt + + # check the values + # we increment the dummy1 by 1 every 10 steps. at the 9th count (aka 10th apply), the value should be 1 + torch.testing.assert_close(env.dummy1, (count + 1) // 10 * torch.ones_like(env.dummy1)) + + # we increment the dummy2 by 1 every 2 to 10 steps based on the random interval + expected_dummy2_value += term_2_interval_time < 1e-6 + torch.testing.assert_close(env.dummy2, expected_dummy2_value) + + # check the time sampled at the end of the interval is valid + # -- fixed interval + if (count + 1) % 10 == 0: + term_1_interval_time_init = event_man._interval_term_time_left[0].clone() + expected_time_interval_init = torch.full_like(term_1_interval_time_init, term_1_interval_range_s[1]) + torch.testing.assert_close(term_1_interval_time_init, expected_time_interval_init) + # -- random interval + if term_2_interval_time < 1e-6: + term_2_interval_time = event_man._interval_term_time_left[1].clone() + + +def test_apply_reset_mode(env): + """Test the application of event terms that are in reset mode.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="reset"), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset", min_step_count_between_reset=10), + } + + event_man = EventManager(cfg, env) + + # manually keep track of the expected values for dummy1 and trigger count + expected_dummy1_value = torch.zeros_like(env.dummy1) + term_2_trigger_step_id = torch.zeros((env.num_envs,), dtype=torch.int32, device=env.device) + + for count in range(50): + # apply the event terms for all the env ids + if count % 3 == 0: + event_man.apply("reset", global_env_step_count=count) + + # we increment the dummy1 by 1 every call to reset mode due to term 1 + expected_dummy1_value[:] += 1 + # manually update the expected value for term 2 + if (count - term_2_trigger_step_id[0]) >= 10 or count == 0: + expected_dummy1_value = torch.zeros_like(env.dummy1) + term_2_trigger_step_id[:] = count + + # check the values of trigger count + # -- term 1 + expected_trigger_count = torch.full((env.num_envs,), 3 * (count // 3), dtype=torch.int32, device=env.device) + torch.testing.assert_close(event_man._reset_term_last_triggered_step_id[0], expected_trigger_count) + # -- term 2 + torch.testing.assert_close(event_man._reset_term_last_triggered_step_id[1], term_2_trigger_step_id) + + # check the values of dummy1 + torch.testing.assert_close(env.dummy1, expected_dummy1_value) + + +def test_apply_reset_mode_subset_env_ids(env): + """Test the application of event terms that are in reset mode over a subset of environment ids.""" + cfg = { + "term_1": EventTermCfg(func=increment_dummy1_by_one, mode="reset"), + "term_2": EventTermCfg(func=reset_dummy1_to_zero, mode="reset", min_step_count_between_reset=10), + } + + event_man = EventManager(cfg, env) + + # since we are applying the event terms over a subset of env ids, we need to keep track of the trigger count + # manually for the sake of testing + term_2_trigger_step_id = torch.zeros((env.num_envs,), dtype=torch.int32, device=env.device) + term_2_trigger_once = torch.zeros((env.num_envs,), dtype=torch.bool, device=env.device) + expected_dummy1_value = torch.zeros_like(env.dummy1) + + for count in range(50): + # randomly select a subset of environment ids + env_ids = (torch.rand(env.num_envs, device=env.device) < 0.5).nonzero().flatten() + # apply the event terms for the selected env ids + event_man.apply("reset", env_ids=env_ids, global_env_step_count=count) + + # modify the trigger count for term 2 + trigger_ids = (count - term_2_trigger_step_id[env_ids]) >= 10 + trigger_ids |= (term_2_trigger_step_id[env_ids] == 0) & ~term_2_trigger_once[env_ids] + term_2_trigger_step_id[env_ids[trigger_ids]] = count + term_2_trigger_once[env_ids[trigger_ids]] = True + # we increment the dummy1 by 1 every call to reset mode + # every 10th call, we reset the dummy1 to 0 + expected_dummy1_value[env_ids] += 1 # effect of term 1 + expected_dummy1_value[env_ids[trigger_ids]] = 0 # effect of term 2 + + # check the values of trigger count + # -- term 1 + expected_trigger_count = torch.full((len(env_ids),), count, dtype=torch.int32, device=env.device) + torch.testing.assert_close(event_man._reset_term_last_triggered_step_id[0][env_ids], expected_trigger_count) + # -- term 2 + torch.testing.assert_close(event_man._reset_term_last_triggered_step_id[1], term_2_trigger_step_id) + + # check the values of dummy1 + torch.testing.assert_close(env.dummy1, expected_dummy1_value) diff --git a/source/isaaclab/test/managers/test_observation_manager.py b/source/isaaclab/test/managers/test_observation_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..d738f179da71b3ddc800fa20457c4977684288ab --- /dev/null +++ b/source/isaaclab/test/managers/test_observation_manager.py @@ -0,0 +1,796 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# needed to import for allowing type-hinting: torch.Tensor | None +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from collections import namedtuple +from typing import TYPE_CHECKING + +import pytest +import torch + +import isaaclab.sim as sim_utils +from isaaclab.managers import ( + ManagerTermBase, + ObservationGroupCfg, + ObservationManager, + ObservationTermCfg, + RewardTermCfg, +) +from isaaclab.utils import configclass, modifiers + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def grilled_chicken(env): + return torch.ones(env.num_envs, 4, device=env.device) + + +def grilled_chicken_with_bbq(env, bbq: bool): + return bbq * torch.ones(env.num_envs, 1, device=env.device) + + +def grilled_chicken_with_curry(env, hot: bool): + return hot * 2 * torch.ones(env.num_envs, 1, device=env.device) + + +def grilled_chicken_with_yoghurt(env, hot: bool, bland: float): + return hot * bland * torch.ones(env.num_envs, 5, device=env.device) + + +def grilled_chicken_with_yoghurt_and_bbq(env, hot: bool, bland: float, bbq: bool = False): + return hot * bland * bbq * torch.ones(env.num_envs, 3, device=env.device) + + +def grilled_chicken_image(env, bland: float, channel: int = 1): + return bland * torch.ones(env.num_envs, 128, 256, channel, device=env.device) + + +class complex_function_class(ManagerTermBase): + def __init__(self, cfg: ObservationTermCfg, env: object): + self.cfg = cfg + self.env = env + # define some variables + self._time_passed = torch.zeros(env.num_envs, device=env.device) + + def reset(self, env_ids: torch.Tensor | None = None): + if env_ids is None: + env_ids = slice(None) + self._time_passed[env_ids] = 0.0 + + def __call__(self, env: object, interval: float) -> torch.Tensor: + self._time_passed += interval + return self._time_passed.clone().unsqueeze(-1) + + +class non_callable_complex_function_class(ManagerTermBase): + def __init__(self, cfg: ObservationTermCfg, env: object): + self.cfg = cfg + self.env = env + # define some variables + self._cost = 2 * self.env.num_envs + + def call_me(self, env: object) -> torch.Tensor: + return torch.ones(env.num_envs, 2, device=env.device) * self._cost + + +class MyDataClass: + def __init__(self, num_envs: int, device: str): + self.pos_w = torch.rand((num_envs, 3), device=device) + self.lin_vel_w = torch.rand((num_envs, 3), device=device) + + +def pos_w_data(env) -> torch.Tensor: + return env.data.pos_w + + +def lin_vel_w_data(env) -> torch.Tensor: + return env.data.lin_vel_w + + +@pytest.fixture(autouse=True) +def setup_env(): + dt = 0.01 + num_envs = 20 + device = "cuda:0" + # set up sim + sim_cfg = sim_utils.SimulationCfg(dt=dt, device=device) + sim = sim_utils.SimulationContext(sim_cfg) + # create dummy environment + env = namedtuple("ManagerBasedEnv", ["num_envs", "device", "data", "dt", "sim"])( + num_envs, device, MyDataClass(num_envs, device), dt, sim + ) + # let the simulation play (we need this for observation manager to compute obs dims) + env.sim._app_control_on_stop_handle = None + env.sim.reset() + return env + + +def test_str(setup_env): + env = setup_env + """Test the string representation of the observation manager.""" + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class SampleGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10) + term_2 = ObservationTermCfg(func=grilled_chicken, scale=2) + term_3 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True}) + term_4 = ObservationTermCfg( + func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0} + ) + term_5 = ObservationTermCfg( + func=grilled_chicken_with_yoghurt_and_bbq, scale=1.0, params={"hot": False, "bland": 2.0} + ) + + policy: ObservationGroupCfg = SampleGroupCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + assert len(obs_man.active_terms["policy"]) == 5 + # print the expected string + obs_man_str = str(obs_man) + print() + print(obs_man_str) + obs_man_str_split = obs_man_str.split("|") + term_1_str_index = obs_man_str_split.index(" term_1 ") + term_1_str_shape = obs_man_str_split[term_1_str_index + 1].strip() + assert term_1_str_shape == "(4,)" + + +def test_str_with_history(setup_env): + env = setup_env + """Test the string representation of the observation manager with history terms.""" + + TERM_1_HISTORY = 5 + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class SampleGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10, history_length=TERM_1_HISTORY) + term_2 = ObservationTermCfg(func=grilled_chicken, scale=2) + term_3 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True}) + term_4 = ObservationTermCfg( + func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0} + ) + term_5 = ObservationTermCfg( + func=grilled_chicken_with_yoghurt_and_bbq, scale=1.0, params={"hot": False, "bland": 2.0} + ) + + policy: ObservationGroupCfg = SampleGroupCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + assert len(obs_man.active_terms["policy"]) == 5 + # print the expected string + obs_man_str = str(obs_man) + print() + print(obs_man_str) + obs_man_str_split = obs_man_str.split("|") + term_1_str_index = obs_man_str_split.index(" term_1 ") + term_1_str_shape = obs_man_str_split[term_1_str_index + 1].strip() + assert term_1_str_shape == "(20,)" + + +def test_config_equivalence(setup_env): + env = setup_env + """Test the equivalence of observation manager created from different config types.""" + + # create from config class + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class SampleGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + your_term = ObservationTermCfg(func=grilled_chicken, scale=10) + his_term = ObservationTermCfg(func=grilled_chicken, scale=2) + my_term = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True}) + her_term = ObservationTermCfg( + func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0} + ) + + policy = SampleGroupCfg() + critic = SampleGroupCfg(concatenate_terms=False, her_term=None) + + cfg = MyObservationManagerCfg() + obs_man_from_cfg = ObservationManager(cfg, env) + + # create from config class + @configclass + class MyObservationManagerAnnotatedCfg: + """Test config class for observation manager with annotations on terms.""" + + @configclass + class SampleGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + your_term: ObservationTermCfg = ObservationTermCfg(func=grilled_chicken, scale=10) + his_term: ObservationTermCfg = ObservationTermCfg(func=grilled_chicken, scale=2) + my_term: ObservationTermCfg = ObservationTermCfg( + func=grilled_chicken_with_bbq, scale=5, params={"bbq": True} + ) + her_term: ObservationTermCfg = ObservationTermCfg( + func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0} + ) + + policy: ObservationGroupCfg = SampleGroupCfg() + critic: ObservationGroupCfg = SampleGroupCfg(concatenate_terms=False, her_term=None) + + cfg = MyObservationManagerAnnotatedCfg() + obs_man_from_annotated_cfg = ObservationManager(cfg, env) + + # check equivalence + # parsed terms + assert obs_man_from_cfg.active_terms == obs_man_from_annotated_cfg.active_terms + assert obs_man_from_cfg.group_obs_term_dim == obs_man_from_annotated_cfg.group_obs_term_dim + assert obs_man_from_cfg.group_obs_dim == obs_man_from_annotated_cfg.group_obs_dim + # parsed term configs + assert obs_man_from_cfg._group_obs_term_cfgs == obs_man_from_annotated_cfg._group_obs_term_cfgs + assert obs_man_from_cfg._group_obs_concatenate == obs_man_from_annotated_cfg._group_obs_concatenate + + +def test_config_terms(setup_env): + env = setup_env + """Test the number of terms in the observation manager.""" + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class SampleGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10) + term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False}) + + @configclass + class SampleMixedGroupCfg(ObservationGroupCfg): + """Test config class for policy observation group with a mix of vector and matrix terms.""" + + concatenate_terms = False + term_1 = ObservationTermCfg(func=grilled_chicken, scale=2.0) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5}) + + @configclass + class SampleImageGroupCfg(ObservationGroupCfg): + term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5, "channel": 1}) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=0.5, params={"bland": 0.1, "channel": 3}) + + policy: ObservationGroupCfg = SampleGroupCfg() + critic: ObservationGroupCfg = SampleGroupCfg(term_2=None) + mixed: ObservationGroupCfg = SampleMixedGroupCfg() + image: ObservationGroupCfg = SampleImageGroupCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + + assert len(obs_man.active_terms["policy"]) == 2 + assert len(obs_man.active_terms["critic"]) == 1 + assert len(obs_man.active_terms["mixed"]) == 2 + assert len(obs_man.active_terms["image"]) == 2 + + # create a new obs manager but where mixed group has invalid config + cfg = MyObservationManagerCfg() + cfg.mixed.concatenate_terms = True + + with pytest.raises(RuntimeError): + ObservationManager(cfg, env) + + +def test_compute(setup_env): + env = setup_env + """Test the observation computation.""" + + pos_scale_tuple = (2.0, 3.0, 1.0) + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10) + term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False}) + term_3 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple) + term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5) + + @configclass + class CriticCfg(ObservationGroupCfg): + term_1 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple) + term_2 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5) + term_3 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple) + term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5) + + @configclass + class ImageCfg(ObservationGroupCfg): + term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5, "channel": 1}) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=0.5, params={"bland": 0.1, "channel": 3}) + + policy: ObservationGroupCfg = PolicyCfg() + critic: ObservationGroupCfg = CriticCfg() + image: ObservationGroupCfg = ImageCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + + # obtain the group observations + obs_policy: torch.Tensor = observations["policy"] + obs_critic: torch.Tensor = observations["critic"] + obs_image: torch.Tensor = observations["image"] + + # check the observation shape + assert obs_policy.shape == (env.num_envs, 11) + assert obs_critic.shape == (env.num_envs, 12) + assert obs_image.shape == (env.num_envs, 128, 256, 4) + # check that the scales are applied correctly + assert torch.equal(env.data.pos_w * torch.tensor(pos_scale_tuple, device=env.device), obs_critic[:, :3]) + assert torch.equal(env.data.lin_vel_w * 1.5, obs_critic[:, 3:6]) + # make sure that the data are the same for same terms + # -- within group + assert torch.equal(obs_critic[:, 0:3], obs_critic[:, 6:9]) + assert torch.equal(obs_critic[:, 3:6], obs_critic[:, 9:12]) + # -- between groups + assert torch.equal(obs_policy[:, 5:8], obs_critic[:, 0:3]) + assert torch.equal(obs_policy[:, 8:11], obs_critic[:, 3:6]) + + +def test_compute_with_history(setup_env): + env = setup_env + """Test the observation computation with history buffers.""" + HISTORY_LENGTH = 5 + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, history_length=HISTORY_LENGTH) + # total observation size: term_dim (4) * history_len (5) = 20 + term_2 = ObservationTermCfg(func=lin_vel_w_data) + # total observation size: term_dim (3) = 3 + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + # obtain the group observations + obs_policy: torch.Tensor = observations["policy"] + # check the observation shape + assert obs_policy.shape == (env.num_envs, 23) + # check the observation data + expected_obs_term_1_data = torch.ones(env.num_envs, 4 * HISTORY_LENGTH, device=env.device) + expected_obs_term_2_data = lin_vel_w_data(env) + expected_obs_data_t0 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1) + torch.testing.assert_close(expected_obs_data_t0, obs_policy) + # test that the history buffer holds previous data + for _ in range(HISTORY_LENGTH): + observations = obs_man.compute() + obs_policy = observations["policy"] + expected_obs_term_1_data = torch.ones(env.num_envs, 4 * HISTORY_LENGTH, device=env.device) + expected_obs_data_t5 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1) + assert torch.equal(expected_obs_data_t5, obs_policy) + # test reset + obs_man.reset() + observations = obs_man.compute() + obs_policy = observations["policy"] + torch.testing.assert_close(expected_obs_data_t0, obs_policy) + # test reset of specific env ids + reset_env_ids = [2, 4, 16] + obs_man.reset(reset_env_ids) + torch.testing.assert_close(expected_obs_data_t0[reset_env_ids], obs_policy[reset_env_ids]) + + +def test_compute_with_2d_history(setup_env): + env = setup_env + """Test the observation computation with history buffers for 2D observations.""" + HISTORY_LENGTH = 5 + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class FlattenedPolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg( + func=grilled_chicken_image, params={"bland": 1.0, "channel": 1}, history_length=HISTORY_LENGTH + ) + # total observation size: term_dim (128, 256) * history_len (5) = 163840 + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg( + func=grilled_chicken_image, + params={"bland": 1.0, "channel": 1}, + history_length=HISTORY_LENGTH, + flatten_history_dim=False, + ) + # total observation size: (5, 128, 256, 1) + + flat_obs_policy: ObservationGroupCfg = FlattenedPolicyCfg() + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + # obtain the group observations + obs_policy_flat: torch.Tensor = observations["flat_obs_policy"] + obs_policy: torch.Tensor = observations["policy"] + # check the observation shapes + assert obs_policy_flat.shape == (env.num_envs, 163840) + assert obs_policy.shape == (env.num_envs, HISTORY_LENGTH, 128, 256, 1) + + +def test_compute_with_group_history(setup_env): + env = setup_env + """Test the observation computation with group level history buffer configuration.""" + TERM_HISTORY_LENGTH = 5 + GROUP_HISTORY_LENGTH = 10 + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + history_length = GROUP_HISTORY_LENGTH + # group level history length will override all terms + term_1 = ObservationTermCfg(func=grilled_chicken, history_length=TERM_HISTORY_LENGTH) + # total observation size: term_dim (4) * history_len (5) = 20 + # with override total obs size: term_dim (4) * history_len (10) = 40 + term_2 = ObservationTermCfg(func=lin_vel_w_data) + # total observation size: term_dim (3) = 3 + # with override total obs size: term_dim (3) * history_len (10) = 30 + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + # obtain the group observations + obs_policy: torch.Tensor = observations["policy"] + # check the total observation shape + assert obs_policy.shape == (env.num_envs, 70) + # check the observation data is initialized properly + expected_obs_term_1_data = torch.ones(env.num_envs, 4 * GROUP_HISTORY_LENGTH, device=env.device) + expected_obs_term_2_data = lin_vel_w_data(env).repeat(1, GROUP_HISTORY_LENGTH) + expected_obs_data_t0 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1) + torch.testing.assert_close(expected_obs_data_t0, obs_policy) + # test that the history buffer holds previous data + for _ in range(GROUP_HISTORY_LENGTH): + observations = obs_man.compute() + obs_policy = observations["policy"] + expected_obs_term_1_data = torch.ones(env.num_envs, 4 * GROUP_HISTORY_LENGTH, device=env.device) + expected_obs_term_2_data = lin_vel_w_data(env).repeat(1, GROUP_HISTORY_LENGTH) + expected_obs_data_t10 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1) + torch.testing.assert_close(expected_obs_data_t10, obs_policy) + # test reset + obs_man.reset() + observations = obs_man.compute() + obs_policy = observations["policy"] + torch.testing.assert_close(expected_obs_data_t0, obs_policy) + # test reset of specific env ids + reset_env_ids = [2, 4, 16] + obs_man.reset(reset_env_ids) + torch.testing.assert_close(expected_obs_data_t0[reset_env_ids], obs_policy[reset_env_ids]) + + +def test_invalid_observation_config(setup_env): + env = setup_env + """Test the invalid observation config.""" + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=0.1, params={"hot": False}) + term_2 = ObservationTermCfg(func=grilled_chicken_with_yoghurt, scale=2.0, params={"hot": False}) + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + # check the invalid config + with pytest.raises(ValueError): + ObservationManager(cfg, env) + + +def test_callable_class_term(setup_env): + env = setup_env + """Test the observation computation with callable class term.""" + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10) + term_2 = ObservationTermCfg(func=complex_function_class, scale=0.2, params={"interval": 0.5}) + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + # check the observation + assert observations["policy"].shape == (env.num_envs, 5) + assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5) + + # check memory in term + num_exec_count = 10 + for _ in range(num_exec_count): + observations = obs_man.compute() + assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5 * (num_exec_count + 1)) + + # check reset works + obs_man.reset(env_ids=[0, 4, 9, 14, 19]) + observations = obs_man.compute() + assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5) + assert observations["policy"][1, -1].item() == pytest.approx(0.2 * 0.5 * (num_exec_count + 2)) + + +def test_non_callable_class_term(setup_env): + env = setup_env + """Test the observation computation with non-callable class term.""" + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + term_1 = ObservationTermCfg(func=grilled_chicken, scale=10) + term_2 = ObservationTermCfg(func=non_callable_complex_function_class, scale=0.2) + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager config + cfg = MyObservationManagerCfg() + # create observation manager + with pytest.raises(NotImplementedError): + ObservationManager(cfg, env) + + +def test_modifier_compute(setup_env): + env = setup_env + """Test the observation computation with modifiers.""" + + modifier_1 = modifiers.ModifierCfg(func=modifiers.bias, params={"value": 1.0}) + modifier_2 = modifiers.ModifierCfg(func=modifiers.scale, params={"multiplier": 2.0}) + modifier_3 = modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (-0.5, 0.5)}) + modifier_4 = modifiers.IntegratorCfg(dt=env.dt) + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + concatenate_terms = False + term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[]) + term_2 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1]) + term_3 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_4]) + + @configclass + class CriticCfg(ObservationGroupCfg): + """Test config class for critic observation group""" + + concatenate_terms = False + term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[]) + term_2 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1]) + term_3 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_2]) + term_4 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_2, modifier_3]) + + policy: ObservationGroupCfg = PolicyCfg() + critic: ObservationGroupCfg = CriticCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + + # obtain the group observations + obs_policy: dict[str, torch.Tensor] = observations["policy"] + obs_critic: dict[str, torch.Tensor] = observations["critic"] + + # check correct application of modifications + assert torch.equal(obs_policy["term_1"] + 1.0, obs_policy["term_2"]) + assert torch.equal(obs_critic["term_1"] + 1.0, obs_critic["term_2"]) + assert torch.equal(2.0 * (obs_critic["term_1"] + 1.0), obs_critic["term_3"]) + assert torch.min(obs_critic["term_4"]) >= -0.5 + assert torch.max(obs_critic["term_4"]) <= 0.5 + + +def test_serialize(setup_env): + """Test serialize call for ManagerTermBase terms.""" + env = setup_env + + serialize_data = {"test": 0} + + class test_serialize_term(ManagerTermBase): + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedEnv): + super().__init__(cfg, env) + + def __call__(self, env: ManagerBasedEnv) -> torch.Tensor: + return grilled_chicken(env) + + def serialize(self) -> dict: + return serialize_data + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + concatenate_terms = False + term_1 = ObservationTermCfg(func=test_serialize_term) + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + + # check expected output + assert obs_man.serialize() == {"policy": {"term_1": serialize_data}} + + +def test_modifier_invalid_config(setup_env): + env = setup_env + """Test modifier initialization with invalid config.""" + + modifier = modifiers.ModifierCfg(func=modifiers.clip, params={"min": -0.5, "max": 0.5}) + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + concatenate_terms = False + term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier]) + + policy: ObservationGroupCfg = PolicyCfg() + + # create observation manager + cfg = MyObservationManagerCfg() + + with pytest.raises(ValueError): + ObservationManager(cfg, env) + + +def test_concatenate_dim(setup_env): + """Test concatenation of observations along different dimensions.""" + env = setup_env + + @configclass + class MyObservationManagerCfg: + """Test config class for observation manager.""" + + @configclass + class PolicyCfg(ObservationGroupCfg): + """Test config class for policy observation group.""" + + concatenate_terms = True + concatenate_dim = 1 # Concatenate along dimension 1 + term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + + @configclass + class CriticCfg(ObservationGroupCfg): + """Test config class for critic observation group.""" + + concatenate_terms = True + concatenate_dim = 2 # Concatenate along dimension 2 + term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + + @configclass + class CriticCfg_neg_dim(ObservationGroupCfg): + """Test config class for critic observation group.""" + + concatenate_terms = True + concatenate_dim = -1 # Concatenate along last dimension + term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1}) + + policy: ObservationGroupCfg = PolicyCfg() + critic: ObservationGroupCfg = CriticCfg() + critic_neg_dim: ObservationGroupCfg = CriticCfg_neg_dim() + + # create observation manager + cfg = MyObservationManagerCfg() + obs_man = ObservationManager(cfg, env) + # compute observation using manager + observations = obs_man.compute() + + # obtain the group observations + obs_policy: torch.Tensor = observations["policy"] + obs_critic: torch.Tensor = observations["critic"] + obs_critic_neg_dim: torch.Tensor = observations["critic_neg_dim"] + + # check the observation shapes + # For policy: concatenated along dim 1, so width should be doubled + assert obs_policy.shape == (env.num_envs, 128, 512, 1) + # For critic: concatenated along last dim, so channels should be doubled + assert obs_critic.shape == (env.num_envs, 128, 256, 2) + # For critic_neg_dim: concatenated along last dim, so channels should be doubled + assert obs_critic_neg_dim.shape == (env.num_envs, 128, 256, 2) + + # verify the data is concatenated correctly + # For policy: check that the second half matches the first half + torch.testing.assert_close(obs_policy[:, :, :256, :], obs_policy[:, :, 256:, :]) + # For critic: check that the second channel matches the first channel + torch.testing.assert_close(obs_critic[:, :, :, 0], obs_critic[:, :, :, 1]) + + # For critic_neg_dim: check that it is the same as critic + torch.testing.assert_close(obs_critic_neg_dim, obs_critic) diff --git a/source/isaaclab/test/managers/test_recorder_manager.py b/source/isaaclab/test/managers/test_recorder_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..8a8e8c78a9d235a4c4ee68bf745b6a93f28841e8 --- /dev/null +++ b/source/isaaclab/test/managers/test_recorder_manager.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# needed to import for allowing type-hinting: torch.Tensor | None +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import os +import shutil +import tempfile +import uuid +from collections import namedtuple +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import h5py +import pytest +import torch + +import omni.usd + +from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg +from isaaclab.managers import DatasetExportMode, RecorderManager, RecorderManagerBaseCfg, RecorderTerm, RecorderTermCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import configclass + +if TYPE_CHECKING: + import numpy as np + + +class DummyResetRecorderTerm(RecorderTerm): + """Dummy recorder term that records dummy data.""" + + def __init__(self, cfg: RecorderTermCfg, env: ManagerBasedEnv) -> None: + super().__init__(cfg, env) + + def record_pre_reset(self, env_ids: Sequence[int] | None) -> tuple[str | None, torch.Tensor | None]: + return "record_pre_reset", torch.ones(self._env.num_envs, 2, device=self._env.device) + + def record_post_reset(self, env_ids: Sequence[int] | None) -> tuple[str | None, torch.Tensor | None]: + return "record_post_reset", torch.ones(self._env.num_envs, 3, device=self._env.device) + + +class DummyStepRecorderTerm(RecorderTerm): + """Dummy recorder term that records dummy data.""" + + def __init__(self, cfg: RecorderTermCfg, env: ManagerBasedEnv) -> None: + super().__init__(cfg, env) + + def record_pre_step(self) -> tuple[str | None, torch.Tensor | None]: + return "record_pre_step", torch.ones(self._env.num_envs, 4, device=self._env.device) + + def record_post_step(self) -> tuple[str | None, torch.Tensor | None]: + return "record_post_step", torch.ones(self._env.num_envs, 5, device=self._env.device) + + +@configclass +class DummyRecorderManagerCfg(RecorderManagerBaseCfg): + """Dummy recorder configurations.""" + + @configclass + class DummyResetRecorderTermCfg(RecorderTermCfg): + """Configuration for the dummy reset recorder term.""" + + class_type: type[RecorderTerm] = DummyResetRecorderTerm + + @configclass + class DummyStepRecorderTermCfg(RecorderTermCfg): + """Configuration for the dummy step recorder term.""" + + class_type: type[RecorderTerm] = DummyStepRecorderTerm + + record_reset_term = DummyResetRecorderTermCfg() + record_step_term = DummyStepRecorderTermCfg() + + dataset_export_mode = DatasetExportMode.EXPORT_ALL + + +@configclass +class EmptyManagerCfg: + """Empty manager specifications for the environment.""" + + pass + + +@configclass +class EmptySceneCfg(InteractiveSceneCfg): + """Configuration for an empty scene.""" + + pass + + +def get_empty_base_env_cfg(device: str = "cuda", num_envs: int = 1, env_spacing: float = 1.0): + """Generate base environment config based on device""" + + @configclass + class EmptyEnvCfg(ManagerBasedEnvCfg): + """Configuration for the empty test environment.""" + + # Scene settings + scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + # Basic settings + actions: EmptyManagerCfg = EmptyManagerCfg() + observations: EmptyManagerCfg = EmptyManagerCfg() + recorders: EmptyManagerCfg = EmptyManagerCfg() + + def __post_init__(self): + """Post initialization.""" + # step settings + self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz + # simulation settings + self.sim.dt = 0.005 # sim step every 5ms: 200Hz + self.sim.render_interval = self.decimation # render every 4 sim steps + # pass device down from test + self.sim.device = device + + return EmptyEnvCfg() + + +def get_file_contents(file_name: str, num_steps: int) -> dict[str, np.ndarray]: + """Retrieves the contents of the hdf5 file + Args: + file_name: absolute path to the hdf5 file + num_steps: number of steps taken in the environment + Returns: + dict[str, np.ndarray]: dictionary where keys are HDF5 paths and values are the corresponding data arrays. + """ + data = {} + with h5py.File(file_name, "r") as f: + + def get_data(name, obj): + if isinstance(obj, h5py.Dataset): + if "record_post_step" in name: + assert obj[()].shape == (num_steps, 5) + elif "record_pre_step" in name: + assert obj[()].shape == (num_steps, 4) + else: + raise Exception(f"The hdf5 file contains an unexpected data path, {name}") + data[name] = obj[()] + + f.visititems(get_data) + return data + + +@configclass +class DummyEnvCfg: + """Dummy environment configuration.""" + + @configclass + class DummySimCfg: + """Configuration for the dummy sim.""" + + dt = 0.01 + render_interval = 1 + + @configclass + class DummySceneCfg: + """Configuration for the dummy scene.""" + + num_envs = 1 + + decimation = 1 + sim = DummySimCfg() + scene = DummySceneCfg() + + +def create_dummy_env(device: str = "cpu") -> ManagerBasedEnv: + """Create a dummy environment.""" + + class DummyTerminationManager: + active_terms = [] + + dummy_termination_manager = DummyTerminationManager() + sim = SimulationContext() + dummy_cfg = DummyEnvCfg() + + return namedtuple("ManagerBasedEnv", ["num_envs", "device", "sim", "cfg", "termination_manager"])( + 20, device, sim, dummy_cfg, dummy_termination_manager + ) + + +@pytest.fixture +def dataset_dir(): + """Create directory to dump results.""" + test_dir = tempfile.mkdtemp() + yield test_dir + # Cleanup + shutil.rmtree(test_dir) + + +def test_str(dataset_dir): + """Test the string representation of the recorder manager.""" + # create recorder manager + cfg = DummyRecorderManagerCfg() + recorder_manager = RecorderManager(cfg, create_dummy_env()) + assert len(recorder_manager.active_terms) == 2 + # print the expected string + print(recorder_manager) + + +def test_initialize_dataset_file(dataset_dir): + """Test the initialization of the dataset file.""" + # create recorder manager + cfg = DummyRecorderManagerCfg() + cfg.dataset_export_dir_path = dataset_dir + cfg.dataset_filename = f"{uuid.uuid4()}.hdf5" + _ = RecorderManager(cfg, create_dummy_env()) + + # check if the dataset is created + assert os.path.exists(os.path.join(cfg.dataset_export_dir_path, cfg.dataset_filename)) + + +@pytest.mark.parametrize("device", ("cpu", "cuda")) +def test_record(device, dataset_dir): + """Test the recording of the data.""" + env = create_dummy_env(device) + # create recorder manager + cfg = DummyRecorderManagerCfg() + cfg.dataset_export_dir_path = dataset_dir + cfg.dataset_filename = f"{uuid.uuid4()}.hdf5" + recorder_manager = RecorderManager(cfg, env) + + # record the step data + recorder_manager.record_pre_step() + recorder_manager.record_post_step() + + recorder_manager.record_pre_step() + recorder_manager.record_post_step() + + # check the recorded data + for env_id in range(env.num_envs): + episode = recorder_manager.get_episode(env_id) + assert torch.stack(episode.data["record_pre_step"]).shape == (2, 4) + assert torch.stack(episode.data["record_post_step"]).shape == (2, 5) + + # Trigger pre-reset callbacks which then export and clean the episode data + recorder_manager.record_pre_reset(env_ids=None) + for env_id in range(env.num_envs): + episode = recorder_manager.get_episode(env_id) + assert episode.is_empty() + + recorder_manager.record_post_reset(env_ids=None) + for env_id in range(env.num_envs): + episode = recorder_manager.get_episode(env_id) + assert torch.stack(episode.data["record_post_reset"]).shape == (1, 3) + + +@pytest.mark.parametrize("device", ("cpu", "cuda")) +def test_close(device, dataset_dir): + """Test whether data is correctly exported in the close function when fully integrated with ManagerBasedEnv and + `export_in_close` is True.""" + # create a new stage + omni.usd.get_context().new_stage() + # create environment + env_cfg = get_empty_base_env_cfg(device=device, num_envs=2) + cfg = DummyRecorderManagerCfg() + cfg.export_in_close = True + cfg.dataset_export_dir_path = dataset_dir + cfg.dataset_filename = f"{uuid.uuid4()}.hdf5" + env_cfg.recorders = cfg + env = ManagerBasedEnv(cfg=env_cfg) + num_steps = 3 + for _ in range(num_steps): + act = torch.randn_like(env.action_manager.action) + obs, ext = env.step(act) + # check contents of hdf5 file + file_name = f"{env_cfg.recorders.dataset_export_dir_path}/{env_cfg.recorders.dataset_filename}" + data_pre_close = get_file_contents(file_name, num_steps) + assert len(data_pre_close) == 0 + env.close() + data_post_close = get_file_contents(file_name, num_steps) + assert len(data_post_close.keys()) == 2 * env_cfg.scene.num_envs diff --git a/source/isaaclab/test/managers/test_reward_manager.py b/source/isaaclab/test/managers/test_reward_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..8301fac5b504f9daf5a5d92f06fa34fbcad0a6ff --- /dev/null +++ b/source/isaaclab/test/managers/test_reward_manager.py @@ -0,0 +1,189 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from collections import namedtuple + +import pytest +import torch + +from isaaclab.managers import RewardManager, RewardTermCfg +from isaaclab.sim import SimulationContext +from isaaclab.utils import configclass + + +def grilled_chicken(env): + return 1 + + +def grilled_chicken_with_bbq(env, bbq: bool): + return 0 + + +def grilled_chicken_with_curry(env, hot: bool): + return 0 + + +def grilled_chicken_with_yoghurt(env, hot: bool, bland: float): + return 0 + + +@pytest.fixture +def env(): + sim = SimulationContext() + return namedtuple("ManagerBasedRLEnv", ["num_envs", "dt", "device", "sim"])(20, 0.1, "cpu", sim) + + +def test_str(env): + """Test the string representation of the reward manager.""" + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken, weight=10), + "term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}), + "term_3": RewardTermCfg( + func=grilled_chicken_with_yoghurt, + weight=1.0, + params={"hot": False, "bland": 2.0}, + ), + } + rew_man = RewardManager(cfg, env) + assert len(rew_man.active_terms) == 3 + # print the expected string + print() + print(rew_man) + + +def test_config_equivalence(env): + """Test the equivalence of reward manager created from different config types.""" + # create from dictionary + cfg = { + "my_term": RewardTermCfg(func=grilled_chicken, weight=10), + "your_term": RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True}), + "his_term": RewardTermCfg( + func=grilled_chicken_with_yoghurt, + weight=1.0, + params={"hot": False, "bland": 2.0}, + ), + } + rew_man_from_dict = RewardManager(cfg, env) + + # create from config class + @configclass + class MyRewardManagerCfg: + """Reward manager config with no type annotations.""" + + my_term = RewardTermCfg(func=grilled_chicken, weight=10.0) + your_term = RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True}) + his_term = RewardTermCfg(func=grilled_chicken_with_yoghurt, weight=1.0, params={"hot": False, "bland": 2.0}) + + cfg = MyRewardManagerCfg() + rew_man_from_cfg = RewardManager(cfg, env) + + # create from config class + @configclass + class MyRewardManagerAnnotatedCfg: + """Reward manager config with type annotations.""" + + my_term: RewardTermCfg = RewardTermCfg(func=grilled_chicken, weight=10.0) + your_term: RewardTermCfg = RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True}) + his_term: RewardTermCfg = RewardTermCfg( + func=grilled_chicken_with_yoghurt, weight=1.0, params={"hot": False, "bland": 2.0} + ) + + cfg = MyRewardManagerAnnotatedCfg() + rew_man_from_annotated_cfg = RewardManager(cfg, env) + + # check equivalence + # parsed terms + assert rew_man_from_dict.active_terms == rew_man_from_annotated_cfg.active_terms + assert rew_man_from_cfg.active_terms == rew_man_from_annotated_cfg.active_terms + assert rew_man_from_dict.active_terms == rew_man_from_cfg.active_terms + # parsed term configs + assert rew_man_from_dict._term_cfgs == rew_man_from_annotated_cfg._term_cfgs + assert rew_man_from_cfg._term_cfgs == rew_man_from_annotated_cfg._term_cfgs + assert rew_man_from_dict._term_cfgs == rew_man_from_cfg._term_cfgs + + +def test_compute(env): + """Test the computation of reward.""" + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken, weight=10), + "term_2": RewardTermCfg(func=grilled_chicken_with_curry, weight=0.0, params={"hot": False}), + } + rew_man = RewardManager(cfg, env) + # compute expected reward + expected_reward = cfg["term_1"].weight * env.dt + # compute reward using manager + rewards = rew_man.compute(dt=env.dt) + # check the reward for environment index 0 + assert float(rewards[0]) == expected_reward + assert tuple(rewards.shape) == (env.num_envs,) + + +def test_config_empty(env): + """Test the creation of reward manager with empty config.""" + rew_man = RewardManager(None, env) + assert len(rew_man.active_terms) == 0 + + # print the expected string + print() + print(rew_man) + + # compute reward + rewards = rew_man.compute(dt=env.dt) + + # check all rewards are zero + torch.testing.assert_close(rewards, torch.zeros_like(rewards)) + + +def test_active_terms(env): + """Test the correct reading of active terms.""" + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken, weight=10), + "term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}), + "term_3": RewardTermCfg(func=grilled_chicken_with_curry, weight=0.0, params={"hot": False}), + } + rew_man = RewardManager(cfg, env) + + assert len(rew_man.active_terms) == 3 + + +def test_missing_weight(env): + """Test the missing of weight in the config.""" + # TODO: The error should be raised during the config parsing, not during the reward manager creation. + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken, weight=10), + "term_2": RewardTermCfg(func=grilled_chicken_with_bbq, params={"bbq": True}), + } + with pytest.raises(TypeError): + RewardManager(cfg, env) + + +def test_invalid_reward_func_module(env): + """Test the handling of invalid reward function's module in string representation.""" + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken, weight=10), + "term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}), + "term_3": RewardTermCfg(func="a:grilled_chicken_with_no_bbq", weight=0.1, params={"hot": False}), + } + with pytest.raises(ValueError): + RewardManager(cfg, env) + + +def test_invalid_reward_config(env): + """Test the handling of invalid reward function's config parameters.""" + cfg = { + "term_1": RewardTermCfg(func=grilled_chicken_with_bbq, weight=0.1, params={"hot": False}), + "term_2": RewardTermCfg(func=grilled_chicken_with_yoghurt, weight=2.0, params={"hot": False}), + } + with pytest.raises(ValueError): + RewardManager(cfg, env) diff --git a/source/isaaclab/test/managers/test_termination_manager.py b/source/isaaclab/test/managers/test_termination_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..db96e93675c30db77270ce40583dc2c8d59eed6e --- /dev/null +++ b/source/isaaclab/test/managers/test_termination_manager.py @@ -0,0 +1,139 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +from isaaclab.managers import TerminationManager, TerminationTermCfg +from isaaclab.sim import SimulationContext + + +class DummyEnv: + """Minimal mutable env stub for the termination manager tests.""" + + def __init__(self, num_envs: int, device: str, sim: SimulationContext): + self.num_envs = num_envs + self.device = device + self.sim = sim + self.counter = 0 # mutable step counter used by test terms + + +def fail_every_5_steps(env) -> torch.Tensor: + """Returns True for all envs when counter is a positive multiple of 5.""" + cond = env.counter > 0 and (env.counter % 5 == 0) + return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device) + + +def fail_every_10_steps(env) -> torch.Tensor: + """Returns True for all envs when counter is a positive multiple of 10.""" + cond = env.counter > 0 and (env.counter % 10 == 0) + return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device) + + +def fail_every_3_steps(env) -> torch.Tensor: + """Returns True for all envs when counter is a positive multiple of 3.""" + cond = env.counter > 0 and (env.counter % 3 == 0) + return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device) + + +@pytest.fixture +def env(): + sim = SimulationContext() + return DummyEnv(num_envs=20, device="cpu", sim=sim) + + +def test_initial_state_and_shapes(env): + cfg = { + "term_5": TerminationTermCfg(func=fail_every_5_steps), + "term_10": TerminationTermCfg(func=fail_every_10_steps), + } + tm = TerminationManager(cfg, env) + + # Active term names + assert tm.active_terms == ["term_5", "term_10"] + + # Internal buffers have expected shapes and start as all False + assert tm._term_dones.shape == (env.num_envs, 2) + assert tm._last_episode_dones.shape == (env.num_envs, 2) + assert tm.dones.shape == (env.num_envs,) + assert tm.time_outs.shape == (env.num_envs,) + assert tm.terminated.shape == (env.num_envs,) + assert torch.all(~tm._term_dones) and torch.all(~tm._last_episode_dones) + + +def test_term_transitions_and_persistence(env): + """Concise transitions: single fire, persist, switch, both, persist. + + Uses 3-step and 5-step terms and verifies current-step values and last-episode persistence. + """ + cfg = { + "term_3": TerminationTermCfg(func=fail_every_3_steps, time_out=False), + "term_5": TerminationTermCfg(func=fail_every_5_steps, time_out=False), + } + tm = TerminationManager(cfg, env) + + # step 3: only term_3 -> last_episode [True, False] + env.counter = 3 + out = tm.compute() + assert torch.all(tm.get_term("term_3")) and torch.all(~tm.get_term("term_5")) + assert torch.all(out) + assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(~tm._last_episode_dones[:, 1]) + + # step 4: none -> last_episode persists [True, False] + env.counter = 4 + out = tm.compute() + assert torch.all(~out) + assert torch.all(~tm.get_term("term_3")) and torch.all(~tm.get_term("term_5")) + assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(~tm._last_episode_dones[:, 1]) + + # step 5: only term_5 -> last_episode [False, True] + env.counter = 5 + out = tm.compute() + assert torch.all(~tm.get_term("term_3")) and torch.all(tm.get_term("term_5")) + assert torch.all(out) + assert torch.all(~tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1]) + + # step 15: both -> last_episode [True, True] + env.counter = 15 + out = tm.compute() + assert torch.all(tm.get_term("term_3")) and torch.all(tm.get_term("term_5")) + assert torch.all(out) + assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1]) + + # step 16: none -> persist [True, True] + env.counter = 16 + out = tm.compute() + assert torch.all(~out) + assert torch.all(~tm.get_term("term_3")) and torch.all(~tm.get_term("term_5")) + assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1]) + + +def test_time_out_vs_terminated_split(env): + cfg = { + "term_5": TerminationTermCfg(func=fail_every_5_steps, time_out=False), # terminated + "term_10": TerminationTermCfg(func=fail_every_10_steps, time_out=True), # timeout + } + tm = TerminationManager(cfg, env) + + # Step 5: terminated fires, not timeout + env.counter = 5 + out = tm.compute() + assert torch.all(out) + assert torch.all(tm.terminated) and torch.all(~tm.time_outs) + + # Step 10: both fire; timeout and terminated both True + env.counter = 10 + out = tm.compute() + assert torch.all(out) + assert torch.all(tm.terminated) and torch.all(tm.time_outs) diff --git a/source/isaaclab/test/markers/check_markers_visibility.py b/source/isaaclab/test/markers/check_markers_visibility.py new file mode 100644 index 0000000000000000000000000000000000000000..98dbee8ddcd77077c18dfacba5f4e101937c24de --- /dev/null +++ b/source/isaaclab/test/markers/check_markers_visibility.py @@ -0,0 +1,151 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script checks if the debug markers are visible from the camera. + +To check if the markers are visible on different rendering modalities, you can switch them by going +through the synthetic data generation tool in the Isaac Sim UI. For more information, +please check: https://www.youtube.com/watch?v=vLk-f9LWj48&ab_channel=NVIDIAOmniverse + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p source/isaaclab/test/markers/check_markers_visibility.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Check if the debug markers are visible from the camera.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import RayCasterCfg, patterns +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort:skip + + +@configclass +class SensorsSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + # robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + update_period=0.02, + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/defaultGroundPlane"], + ) + + +def run_simulator( + sim: sim_utils.SimulationContext, + scene: InteractiveScene, +): + """Run the simulator.""" + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + + # Simulate physics + while simulation_app.is_running(): + # Reset + if count % 500 == 0: + # reset counter + count = 0 + # reset the scene entities + # root state + root_state = scene["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + # set joint positions with some noise + joint_pos, joint_vel = ( + scene["robot"].data.default_joint_pos.clone(), + scene["robot"].data.default_joint_vel.clone(), + ) + scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # clear internal buffers + scene.reset() + print("[INFO]: Resetting robot state...") + # Apply default actions to the robot + # -- generate actions/commands + targets = scene["robot"].data.default_joint_pos + # -- apply action to the robot + scene["robot"].set_joint_position_target(targets) + # -- write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + sim_time += sim_dt + count += 1 + # update buffers + scene.update(sim_dt) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0]) + # design scene + scene_cfg = SensorsSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/markers/test_visualization_markers.py b/source/isaaclab/test/markers/test_visualization_markers.py new file mode 100644 index 0000000000000000000000000000000000000000..7183eb15a038e5153e25992c524ba2fedbb73f2f --- /dev/null +++ b/source/isaaclab/test/markers/test_visualization_markers.py @@ -0,0 +1,203 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +from isaacsim.core.api.simulation_context import SimulationContext + +import isaaclab.sim as sim_utils +from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg +from isaaclab.markers.config import FRAME_MARKER_CFG, POSITION_GOAL_MARKER_CFG +from isaaclab.utils.math import random_orientation +from isaaclab.utils.timer import Timer + + +@pytest.fixture +def sim(): + """Create a blank new stage for each test.""" + # Simulation time-step + dt = 0.01 + # Open a new stage + sim_utils.create_new_stage() + # Load kit helper + sim_context = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="torch", device="cuda:0") + yield sim_context + # Cleanup + sim_context.stop() + sim_context.clear_instance() + sim_utils.close_stage() + + +def test_instantiation(sim): + """Test that the class can be initialized properly.""" + config = VisualizationMarkersCfg( + prim_path="/World/Visuals/test", + markers={ + "test": sim_utils.SphereCfg(radius=1.0), + }, + ) + test_marker = VisualizationMarkers(config) + print(test_marker) + # check number of markers + assert test_marker.num_prototypes == 1 + + +def test_usd_marker(sim): + """Test with marker from a USD.""" + # create a marker + config = FRAME_MARKER_CFG.copy() + config.prim_path = "/World/Visuals/test_frames" + test_marker = VisualizationMarkers(config) + + # play the simulation + sim.reset() + # create a buffer + num_frames = 0 + # run with randomization of poses + for count in range(1000): + # sample random poses + if count % 50 == 0: + num_frames = torch.randint(10, 1000, (1,)).item() + frame_translations = torch.randn(num_frames, 3, device=sim.device) + frame_rotations = random_orientation(num_frames, device=sim.device) + # set the marker + test_marker.visualize(translations=frame_translations, orientations=frame_rotations) + # update the kit + sim.step() + # asset that count is correct + assert test_marker.count == num_frames + + +def test_usd_marker_color(sim): + """Test with marker from a USD with its color modified.""" + # create a marker + config = FRAME_MARKER_CFG.copy() + config.prim_path = "/World/Visuals/test_frames" + config.markers["frame"].visual_material = sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)) + test_marker = VisualizationMarkers(config) + + # play the simulation + sim.reset() + # run with randomization of poses + for count in range(1000): + # sample random poses + if count % 50 == 0: + num_frames = torch.randint(10, 1000, (1,)).item() + frame_translations = torch.randn(num_frames, 3, device=sim.device) + frame_rotations = random_orientation(num_frames, device=sim.device) + # set the marker + test_marker.visualize(translations=frame_translations, orientations=frame_rotations) + # update the kit + sim.step() + + +def test_multiple_prototypes_marker(sim): + """Test with multiple prototypes of spheres.""" + # create a marker + config = POSITION_GOAL_MARKER_CFG.copy() + config.prim_path = "/World/Visuals/test_protos" + test_marker = VisualizationMarkers(config) + + # play the simulation + sim.reset() + # run with randomization of poses + for count in range(1000): + # sample random poses + if count % 50 == 0: + num_frames = torch.randint(100, 1000, (1,)).item() + frame_translations = torch.randn(num_frames, 3, device=sim.device) + # randomly choose a prototype + marker_indices = torch.randint(0, test_marker.num_prototypes, (num_frames,), device=sim.device) + # set the marker + test_marker.visualize(translations=frame_translations, marker_indices=marker_indices) + # update the kit + sim.step() + + +def test_visualization_time_based_on_prototypes(sim): + """Test with time taken when number of prototypes is increased.""" + # create a marker + config = POSITION_GOAL_MARKER_CFG.copy() + config.prim_path = "/World/Visuals/test_protos" + test_marker = VisualizationMarkers(config) + + # play the simulation + sim.reset() + # number of frames + num_frames = 4096 + + # check that visibility is true + assert test_marker.is_visible() + # run with randomization of poses and indices + frame_translations = torch.randn(num_frames, 3, device=sim.device) + marker_indices = torch.randint(0, test_marker.num_prototypes, (num_frames,), device=sim.device) + # set the marker + with Timer("Marker visualization with explicit indices") as timer: + test_marker.visualize(translations=frame_translations, marker_indices=marker_indices) + # save the time + time_with_marker_indices = timer.time_elapsed + + with Timer("Marker visualization with no indices") as timer: + test_marker.visualize(translations=frame_translations) + # save the time + time_with_no_marker_indices = timer.time_elapsed + + # update the kit + sim.step() + # check that the time is less + assert time_with_no_marker_indices < time_with_marker_indices + + +def test_visualization_time_based_on_visibility(sim): + """Test with visibility of markers. When invisible, the visualize call should return.""" + # create a marker + config = POSITION_GOAL_MARKER_CFG.copy() + config.prim_path = "/World/Visuals/test_protos" + test_marker = VisualizationMarkers(config) + + # play the simulation + sim.reset() + # number of frames + num_frames = 4096 + + # check that visibility is true + assert test_marker.is_visible() + # run with randomization of poses and indices + frame_translations = torch.randn(num_frames, 3, device=sim.device) + marker_indices = torch.randint(0, test_marker.num_prototypes, (num_frames,), device=sim.device) + # set the marker + with Timer("Marker visualization") as timer: + test_marker.visualize(translations=frame_translations, marker_indices=marker_indices) + # save the time + time_with_visualization = timer.time_elapsed + + # update the kit + sim.step() + # make invisible + test_marker.set_visibility(False) + + # check that visibility is false + assert not test_marker.is_visible() + # run with randomization of poses and indices + frame_translations = torch.randn(num_frames, 3, device=sim.device) + marker_indices = torch.randint(0, test_marker.num_prototypes, (num_frames,), device=sim.device) + # set the marker + with Timer("Marker no visualization") as timer: + test_marker.visualize(translations=frame_translations, marker_indices=marker_indices) + # save the time + time_with_no_visualization = timer.time_elapsed + + # check that the time is less + assert time_with_no_visualization < time_with_visualization diff --git a/source/isaaclab/test/performance/test_kit_startup_performance.py b/source/isaaclab/test/performance/test_kit_startup_performance.py new file mode 100644 index 0000000000000000000000000000000000000000..f4134d04ae14c234c84a6af315137a5024eec9f3 --- /dev/null +++ b/source/isaaclab/test/performance/test_kit_startup_performance.py @@ -0,0 +1,23 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +import time + +from isaaclab.app import AppLauncher + + +def test_kit_start_up_time(): + """Test kit start-up time.""" + start_time = time.time() + app_launcher = AppLauncher(headless=True).app # noqa: F841 + end_time = time.time() + elapsed_time = end_time - start_time + # we are doing some more imports on the automate side - will investigate using warp instead of numba cuda + assert elapsed_time <= 12.0 diff --git a/source/isaaclab/test/performance/test_robot_load_performance.py b/source/isaaclab/test/performance/test_robot_load_performance.py new file mode 100644 index 0000000000000000000000000000000000000000..42d5f1c4fffb0939d098f6ec441f0eaf1eb22a73 --- /dev/null +++ b/source/isaaclab/test/performance/test_robot_load_performance.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +from __future__ import annotations + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import pytest + +import omni +from isaacsim.core.cloner import GridCloner + +from isaaclab.assets import Articulation +from isaaclab.sim import build_simulation_context +from isaaclab.utils.timer import Timer + +from isaaclab_assets import ANYMAL_D_CFG, CARTPOLE_CFG + + +@pytest.mark.parametrize( + "test_config,device", + [ + ({"name": "Cartpole", "robot_cfg": CARTPOLE_CFG, "expected_load_time": 10.0}, "cuda:0"), + ({"name": "Cartpole", "robot_cfg": CARTPOLE_CFG, "expected_load_time": 10.0}, "cpu"), + ({"name": "Anymal_D", "robot_cfg": ANYMAL_D_CFG, "expected_load_time": 40.0}, "cuda:0"), + ({"name": "Anymal_D", "robot_cfg": ANYMAL_D_CFG, "expected_load_time": 40.0}, "cpu"), + ], +) +def test_robot_load_performance(test_config, device): + """Test robot load time.""" + with build_simulation_context(device=device) as sim: + sim._app_control_on_stop_handle = None + cloner = GridCloner(spacing=2) + target_paths = cloner.generate_paths("/World/Robots", 4096) + omni.usd.get_context().get_stage().DefinePrim(target_paths[0], "Xform") + _ = cloner.clone( + source_prim_path=target_paths[0], + prim_paths=target_paths, + replicate_physics=False, + copy_from_source=True, + ) + with Timer(f"{test_config['name']} load time for device {device}") as timer: + robot = Articulation(test_config["robot_cfg"].replace(prim_path="/World/Robots_.*/Robot")) # noqa: F841 + sim.reset() + elapsed_time = timer.time_elapsed + assert elapsed_time <= test_config["expected_load_time"] diff --git a/source/isaaclab/test/scene/check_interactive_scene.py b/source/isaaclab/test/scene/check_interactive_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..5b2463b315a92ac5b4361e5bc85193b14a9b49df --- /dev/null +++ b/source/isaaclab/test/scene/check_interactive_scene.py @@ -0,0 +1,164 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the scene interface to quickly setup a scene with multiple +articulated robots and sensors. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script demonstrates how to use the scene interface.") +parser.add_argument("--headless", action="store_true", default=False, help="Force display off at all times.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(headless=args_cli.headless) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors.ray_caster import RayCasterCfg, patterns +from isaaclab.sim import SimulationContext +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.timer import Timer + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # terrain - flat terrain plane + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + ) + + # articulation - robot 1 + robot_1 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_1") + # articulation - robot 2 + robot_2 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_2") + robot_2.init_state.pos = (0.0, 1.0, 0.6) + + # sensor - ray caster attached to the base of robot 1 that scans the ground + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot_1/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=True, + mesh_prim_paths=["/World/ground"], + ) + + # extras - light + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, 500.0)), + ) + + +def main(): + """Main function.""" + + # Load kit helper + sim = SimulationContext(sim_utils.SimulationCfg(dt=0.005)) + # Set main camera + sim.set_camera_view(eye=[5, 5, 5], target=[0.0, 0.0, 0.0]) + + # Spawn things into stage + with Timer("Setup scene"): + scene = InteractiveScene(MySceneCfg(num_envs=args_cli.num_envs, env_spacing=5.0, lazy_sensor_update=False)) + + # Check that parsing happened as expected + assert len(scene.env_prim_paths) == args_cli.num_envs, "Number of environments does not match." + assert scene.terrain is not None, "Terrain not found." + assert len(scene.articulations) == 2, "Number of robots does not match." + assert len(scene.sensors) == 1, "Number of sensors does not match." + assert len(scene.extras) == 1, "Number of extras does not match." + + # Play the simulator + with Timer("Time taken to play the simulator"): + sim.reset() + + # Now we are ready! + print("[INFO]: Setup complete...") + + # default joint targets + robot_1_actions = scene.articulations["robot_1"].data.default_joint_pos.clone() + robot_2_actions = scene.articulations["robot_2"].data.default_joint_pos.clone() + # Define simulation stepping + sim_dt = sim.get_physics_dt() + sim_time = 0.0 + count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step() + continue + # reset + if count % 50 == 0: + # reset counters + sim_time = 0.0 + count = 0 + # reset root state + root_state = scene.articulations["robot_1"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + joint_pos = scene.articulations["robot_1"].data.default_joint_pos + joint_vel = scene.articulations["robot_1"].data.default_joint_vel + # -- set root state + # -- robot 1 + scene.articulations["robot_1"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot_1"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot_1"].write_joint_state_to_sim(joint_pos, joint_vel) + # -- robot 2 + root_state[:, 1] += 1.0 + scene.articulations["robot_2"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot_2"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot_2"].write_joint_state_to_sim(joint_pos, joint_vel) + # reset buffers + scene.reset() + print(">>>>>>>> Reset!") + # perform this loop at policy control freq (50 Hz) + for _ in range(4): + # set joint targets + scene.articulations["robot_1"].set_joint_position_target(robot_1_actions) + scene.articulations["robot_2"].set_joint_position_target(robot_2_actions) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + # update sim-time + sim_time += sim_dt * 4 + count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/scene/test_interactive_scene.py b/source/isaaclab/test/scene/test_interactive_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..1a42a340baa1470779220d5a63d5b79670fdfa3f --- /dev/null +++ b/source/isaaclab/test/scene/test_interactive_scene.py @@ -0,0 +1,216 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg +from isaaclab.sim import build_simulation_context +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # articulation + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/IsaacSim/SimpleArticulation/revolute_articulation.usd" + ), + actuators={ + "joint": ImplicitActuatorCfg(joint_names_expr=[".*"], stiffness=100.0, damping=1.0), + }, + ) + # rigid object + rigid_obj = RigidObjectCfg( + prim_path="/World/envs/env_.*/RigidObj", + spawn=sim_utils.CuboidCfg( + size=(0.5, 0.5, 0.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + ), + ) + + +@pytest.fixture +def setup_scene(request): + """Create simulation context with the specified device.""" + device = request.getfixturevalue("device") + with build_simulation_context(device=device, auto_add_lighting=True, add_ground_plane=True) as sim: + sim._app_control_on_stop_handle = None + + def make_scene(num_envs: int, env_spacing: float = 1.0): + scene_cfg = MySceneCfg(num_envs=num_envs, env_spacing=env_spacing) + return scene_cfg + + yield make_scene, sim + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_scene_entity_isolation(device, setup_scene): + """Tests that multiple instances of InteractiveScene do not share any data. + + In this test, two InteractiveScene instances are created in a loop and added to a list. + The scene at index 0 of the list will have all of its entities cleared manually, and + the test compares that the data held in the scene at index 1 remained intact. + """ + make_scene, sim = setup_scene + scene_cfg = make_scene(num_envs=1) + # set additional light to test 'extras' attribute of the scene + setattr( + scene_cfg, + "light", + AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(), + ), + ) + # set additional sensor to test 'sensors' attribute of the scene + setattr(scene_cfg, "sensor", ContactSensorCfg(prim_path="/World/envs/env_.*/Robot")) + + scene_list = [] + # create two InteractiveScene instances + for _ in range(2): + with build_simulation_context(device=device, dt=sim.get_physics_dt()) as _: + scene = InteractiveScene(scene_cfg) + scene_list.append(scene) + scene_0 = scene_list[0] + scene_1 = scene_list[1] + # clear entities for scene_0 - this should not affect any data in scene_1 + scene_0.articulations.clear() + scene_0.rigid_objects.clear() + scene_0.sensors.clear() + scene_0.extras.clear() + # check that scene_0 and scene_1 do not share entity data via dictionary comparison + assert scene_0.articulations == dict() + assert scene_0.articulations != scene_1.articulations + assert scene_0.rigid_objects == dict() + assert scene_0.rigid_objects != scene_1.rigid_objects + assert scene_0.sensors == dict() + assert scene_0.sensors != scene_1.sensors + assert scene_0.extras == dict() + assert scene_0.extras != scene_1.extras + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_relative_flag(device, setup_scene): + make_scene, sim = setup_scene + scene_cfg = make_scene(num_envs=4) + scene = InteractiveScene(scene_cfg) + sim.reset() + + # test relative == False produces different result than relative == True + assert_state_different(scene.get_state(is_relative=False), scene.get_state(is_relative=True)) + + # test is relative == False + prev_state = scene.get_state(is_relative=False) + scene["robot"].write_joint_state_to_sim( + position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos) + ) + next_state = scene.get_state(is_relative=False) + assert_state_different(prev_state, next_state) + scene.reset_to(prev_state, is_relative=False) + assert_state_equal(prev_state, scene.get_state(is_relative=False)) + + # test is relative == True + prev_state = scene.get_state(is_relative=True) + scene["robot"].write_joint_state_to_sim( + position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos) + ) + next_state = scene.get_state(is_relative=True) + assert_state_different(prev_state, next_state) + scene.reset_to(prev_state, is_relative=True) + assert_state_equal(prev_state, scene.get_state(is_relative=True)) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_reset_to_env_ids_input_types(device, setup_scene): + make_scene, sim = setup_scene + scene_cfg = make_scene(num_envs=4) + scene = InteractiveScene(scene_cfg) + sim.reset() + + # test env_ids = None + prev_state = scene.get_state() + scene["robot"].write_joint_state_to_sim( + position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos) + ) + scene.reset_to(prev_state, env_ids=None) + assert_state_equal(prev_state, scene.get_state()) + + # test env_ids = torch tensor + scene["robot"].write_joint_state_to_sim( + position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos) + ) + scene.reset_to(prev_state, env_ids=torch.arange(scene.num_envs, device=scene.device)) + assert_state_equal(prev_state, scene.get_state()) + + +def assert_state_equal(s1: dict, s2: dict, path=""): + """ + Recursively assert that s1 and s2 have the same nested keys + and that every tensor leaf is exactly equal. + """ + assert set(s1.keys()) == set(s2.keys()), f"Key mismatch at {path}: {s1.keys()} vs {s2.keys()}" + for k in s1: + v1, v2 = s1[k], s2[k] + subpath = f"{path}.{k}" if path else k + if isinstance(v1, dict): + assert isinstance(v2, dict), f"Type mismatch at {subpath}" + assert_state_equal(v1, v2, path=subpath) + else: + # leaf: should be a torch.Tensor + assert isinstance(v1, torch.Tensor) and isinstance(v2, torch.Tensor), f"Expected tensors at {subpath}" + if not torch.equal(v1, v2): + diff = (v1 - v2).abs().max() + pytest.fail(f"Tensor mismatch at {subpath}, max abs diff = {diff}") + + +def assert_state_different(s1: dict, s2: dict, path=""): + """ + Recursively scan s1 and s2 (which must have identical keys) and + succeed as soon as you find one tensor leaf that differs. + If you reach the end with everything equal, fail the test. + """ + assert set(s1.keys()) == set(s2.keys()), f"Key mismatch at {path}: {s1.keys()} vs {s2.keys()}" + for k in s1: + v1, v2 = s1[k], s2[k] + subpath = f"{path}.{k}" if path else k + if isinstance(v1, dict): + # recurse; if any nested call returns (i.e. finds a diff), we propagate success + try: + assert_state_different(v1, v2, path=subpath) + return + except AssertionError: + continue + else: + assert isinstance(v1, torch.Tensor) and isinstance(v2, torch.Tensor), f"Expected tensors at {subpath}" + if not torch.equal(v1, v2): + return # found a difference → success + pytest.fail(f"No differing tensor found in nested state at {path}") diff --git a/source/isaaclab/test/sensors/check_contact_sensor.py b/source/isaaclab/test/sensors/check_contact_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..c556e7326588c22b5bb71427301ae2e9f71b2261 --- /dev/null +++ b/source/isaaclab/test/sensors/check_contact_sensor.py @@ -0,0 +1,187 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the contact sensor sensor in Isaac Lab. + +.. code-block:: bash + + ./isaaclab.sh -p source/isaaclab/test/sensors/test_contact_sensor.py --num_robots 2 +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Contact Sensor Test Script") +parser.add_argument("--num_robots", type=int, default=128, help="Number of robots to spawn.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import torch + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.utils.viewports import set_camera_view + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.sensors.contact_sensor import ContactSensor, ContactSensorCfg +from isaaclab.utils.timer import Timer + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort:skip + + +""" +Helpers +""" + + +def design_scene(): + """Add prims to the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.DomeLightCfg(intensity=2000) + cfg.func("/World/Light/DomeLight", cfg, translation=(-4.5, 3.5, 10.0)) + + +""" +Main +""" + + +def main(): + """Spawns the ANYmal robot and clones it using Isaac Sim Cloner API.""" + + # Load kit helper + sim = SimulationContext(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cuda:0") + # Set main camera + set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + + # Enable hydra scene-graph instancing + # this is needed to visualize the scene when flatcache is enabled + sim._settings.set_bool("/persistent/omnihydra/useSceneGraphInstancing", True) + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + sim.stage.DefinePrim("/World/envs/env_0", "Xform") + # Clone the scene + num_envs = args_cli.num_robots + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + _ = cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + # Design props + design_scene() + # Spawn things into the scene + robot_cfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot") + robot_cfg.spawn.activate_contact_sensors = True + robot = Articulation(cfg=robot_cfg) + # Contact sensor + contact_sensor_cfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/Robot/.*_FOOT", + track_air_time=True, + track_contact_points=True, + track_friction_forces=True, + debug_vis=False, # not args_cli.headless, + filter_prim_paths_expr=["/World/defaultGroundPlane/GroundPlane/CollisionPlane"], + ) + contact_sensor = ContactSensor(cfg=contact_sensor_cfg) + # filter collisions within each environment instance + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", envs_prim_paths, global_paths=["/World/defaultGroundPlane"] + ) + + # Play the simulator + sim.reset() + # print info + print(contact_sensor) + + # Now we are ready! + print("[INFO]: Setup complete...") + + # Define simulation stepping + decimation = 4 + physics_dt = sim.get_physics_dt() + sim_dt = decimation * physics_dt + sim_time = 0.0 + count = 0 + dt = [] + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step(render=False) + continue + # reset + if count % 1000 == 0 and count != 0: + # reset counters + sim_time = 0.0 + count = 0 + print("=" * 80) + print("avg dt real-time", sum(dt) / len(dt)) + print("=" * 80) + + # reset dof state + joint_pos, joint_vel = robot.data.default_joint_pos, robot.data.default_joint_vel + robot.write_joint_state_to_sim(joint_pos, joint_vel) + robot.reset() + dt = [] + + # perform 4 steps + for _ in range(decimation): + # apply actions + robot.set_joint_position_target(robot.data.default_joint_pos) + # write commands to sim + robot.write_data_to_sim() + # perform step + sim.step() + # fetch data + robot.update(physics_dt) + # update sim-time + sim_time += sim_dt + count += 1 + # update the buffers + if sim.is_playing(): + with Timer() as timer: + contact_sensor.update(sim_dt, force_recompute=True) + dt.append(timer.time_elapsed) + + contact_sensor.update(sim_dt, force_recompute=True) + if count % 100 == 0: + print("Sim-time: ", sim_time) + print("Number of contacts: ", torch.count_nonzero(contact_sensor.data.current_air_time == 0.0).item()) + print("-" * 80) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/sensors/check_imu_sensor.py b/source/isaaclab/test/sensors/check_imu_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..73204f58698aaed43d9b198cc1e47b8a1f0b507a --- /dev/null +++ b/source/isaaclab/test/sensors/check_imu_sensor.py @@ -0,0 +1,200 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Visual test script for the imu sensor from the Orbit framework. +""" + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaacsim import SimulationApp + +# add argparse arguments +parser = argparse.ArgumentParser(description="Imu Test Script") +parser.add_argument("--headless", action="store_true", default=False, help="Force display off at all times.") +parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to clone.") +parser.add_argument( + "--terrain_type", + type=str, + default="generator", + choices=["generator", "usd", "plane"], + help="Type of terrain to import. Can be 'generator' or 'usd' or 'plane'.", +) +args_cli = parser.parse_args() + +# launch omniverse app +config = {"headless": args_cli.headless} +simulation_app = SimulationApp(config) + + +"""Rest everything follows.""" + +import logging +import traceback + +import torch + +import omni +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.utils.viewports import set_camera_view +from pxr import PhysxSchema + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.sensors.imu import Imu, ImuCfg +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.terrains.terrain_importer import TerrainImporter +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.timer import Timer + +# import logger +logger = logging.getLogger(__name__) + + +def design_scene(sim: SimulationContext, num_envs: int = 2048) -> RigidObject: + """Design the scene.""" + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + max_init_terrain_level=None, + num_envs=1, + ) + _ = TerrainImporter(terrain_importer_cfg) + # obtain the current stage + stage = omni.usd.get_context().get_stage() + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + # create source prim + stage.DefinePrim(envs_prim_paths[0], "Xform") + # clone the env xform + cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + # Define the scene + # -- Light + cfg = sim_utils.DistantLightCfg(intensity=2000) + cfg.func("/World/light", cfg) + # -- Balls + cfg = RigidObjectCfg( + spawn=sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + prim_path="/World/envs/env_.*/ball", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5.0)), + ) + balls = RigidObject(cfg) + # Clone the scene + # obtain the current physics scene + physics_scene_prim_path = None + for prim in stage.Traverse(): + if prim.HasAPI(PhysxSchema.PhysxSceneAPI): + physics_scene_prim_path = prim.GetPrimPath() + logging.info(f"Physics scene prim path: {physics_scene_prim_path}") + break + # filter collisions within each environment instance + cloner.filter_collisions( + physics_scene_prim_path, + "/World/collisions", + envs_prim_paths, + ) + return balls + + +def main(): + """Main function.""" + + # Load kit helper + sim_params = { + "use_gpu": True, + "use_gpu_pipeline": True, + "use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards + "use_fabric": True, # used from Isaac Sim 2023.1 onwards + "enable_scene_query_support": True, + } + sim = SimulationContext( + physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0" + ) + # Set main camera + set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5]) + + # Parameters + num_envs = args_cli.num_envs + # Design the scene + balls = design_scene(sim=sim, num_envs=num_envs) + + # Create a ray-caster sensor + imu_cfg = ImuCfg( + prim_path="/World/envs/env_.*/ball", + debug_vis=not args_cli.headless, + ) + # increase scale of the arrows for better visualization + imu_cfg.visualizer_cfg.markers["arrow"].scale = (1.0, 0.2, 0.2) + imu = Imu(cfg=imu_cfg) + + # Play simulator and init the Imu + sim.reset() + + # Print the sensor information + print(imu) + + # Get the ball initial positions + sim.step(render=not args_cli.headless) + balls.update(sim.get_physics_dt()) + ball_initial_positions = balls.data.root_pos_w.clone() + ball_initial_orientations = balls.data.root_quat_w.clone() + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step(render=not args_cli.headless) + continue + # Reset the scene + if step_count % 500 == 0: + # reset ball positions + balls.write_root_pose_to_sim(torch.cat([ball_initial_positions, ball_initial_orientations], dim=-1)) + balls.reset() + # reset the sensor + imu.reset() + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update the imu sensor + with Timer(f"Imu sensor update with {num_envs}"): + imu.update(dt=sim.get_physics_dt(), force_recompute=True) + # Update counter + step_count += 1 + + +if __name__ == "__main__": + try: + # Run the main function + main() + except Exception as err: + logger.error(err) + logger.error(traceback.format_exc()) + raise + finally: + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/sensors/check_multi_mesh_ray_caster.py b/source/isaaclab/test/sensors/check_multi_mesh_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..11e175408dfb8e839d9326dd7e3307177974154a --- /dev/null +++ b/source/isaaclab/test/sensors/check_multi_mesh_ray_caster.py @@ -0,0 +1,213 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +""" +This script shows how to use the multi-mesh ray caster from the Isaac Lab framework. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p source/isaaclab/test/sensors/check_multi_mesh_ray_caster.py --headless + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Ray Caster Test Script") +parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to clone.") +parser.add_argument("--num_objects", type=int, default=0, help="Number of additional objects to clone.") +parser.add_argument( + "--terrain_type", + type=str, + default="generator", + help="Type of terrain to import. Can be 'generator' or 'usd' or 'plane'.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import random + +import torch + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.prims import RigidPrim +from isaacsim.core.utils.viewports import set_camera_view + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.sensors.ray_caster import MultiMeshRayCaster, MultiMeshRayCasterCfg, patterns +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.terrains.terrain_importer import TerrainImporter +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.math import quat_from_euler_xyz +from isaaclab.utils.timer import Timer + + +def design_scene(sim: SimulationContext, num_envs: int = 2048): + """Design the scene.""" + # Create interface to clone the scene + cloner = GridCloner(spacing=10.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + sim.stage.DefinePrim("/World/envs/env_0", "Xform") + # Define the scene + # -- Light + cfg = sim_utils.DistantLightCfg(intensity=2000) + cfg.func("/World/light", cfg) + # -- Balls + cfg = sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ) + cfg.func("/World/envs/env_0/ball", cfg, translation=(0.0, 0.0, 5.0)) + + for i in range(args_cli.num_objects): + object = sim_utils.CuboidCfg( + size=(0.5 + random.random() * 0.5, 0.5 + random.random() * 0.5, 0.1 + random.random() * 0.05), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg( + diffuse_color=(0.0 + i / args_cli.num_objects, 0.0, 1.0 - i / args_cli.num_objects) + ), + ) + object.func( + f"/World/envs/env_0/object_{i}", + object, + translation=(0.0 + random.random(), 0.0 + random.random(), 1.0), + orientation=quat_from_euler_xyz(torch.Tensor(0), torch.Tensor(0), torch.rand(1) * torch.pi).numpy(), + ) + + # Clone the scene + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"] + ) + + +def main(): + """Main function.""" + + # Load kit helper + sim_params = { + "use_gpu": True, + "use_gpu_pipeline": True, + "use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards + "use_fabric": True, # used from Isaac Sim 2023.1 onwards + "enable_scene_query_support": True, + } + sim = SimulationContext( + physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0" + ) + # Set main camera + set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5]) + + # Parameters + num_envs = args_cli.num_envs + # Design the scene + design_scene(sim=sim, num_envs=num_envs) + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + terrain_type=args_cli.terrain_type, + terrain_generator=ROUGH_TERRAINS_CFG, + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + max_init_terrain_level=0, + num_envs=1, + ) + _ = TerrainImporter(terrain_importer_cfg) + + mesh_targets: list[MultiMeshRayCasterCfg.RaycastTargetCfg] = [ + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="/World/ground", track_mesh_transforms=False), + ] + if args_cli.num_objects != 0: + mesh_targets.append( + MultiMeshRayCasterCfg.RaycastTargetCfg(prim_expr="/World/envs/env_.*/object_.*", track_mesh_transforms=True) + ) + # Create a ray-caster sensor + ray_caster_cfg = MultiMeshRayCasterCfg( + prim_path="/World/envs/env_.*/ball", + mesh_prim_paths=mesh_targets, + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=(1.6, 1.0)), + attach_yaw_only=True, + debug_vis=not args_cli.headless, + ) + ray_caster = MultiMeshRayCaster(cfg=ray_caster_cfg) + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + + # Play simulator + sim.reset() + + # Initialize the views + # -- balls + ball_view.initialize() + # Print the sensor information + print(ray_caster) + + # Get the initial positions of the balls + ball_initial_positions, ball_initial_orientations = ball_view.get_world_poses() + ball_initial_velocities = ball_view.get_velocities() + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step(render=False) + continue + # Reset the scene + if step_count % 500 == 0: + # sample random indices to reset + reset_indices = torch.randint(0, num_envs, (num_envs // 2,)) + # reset the balls + ball_view.set_world_poses( + ball_initial_positions[reset_indices], ball_initial_orientations[reset_indices], indices=reset_indices + ) + ball_view.set_velocities(ball_initial_velocities[reset_indices], indices=reset_indices) + # reset the sensor + ray_caster.reset(reset_indices) + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update the ray-caster + with Timer(f"Ray-caster update with {num_envs} x {ray_caster.num_rays} rays"): + ray_caster.update(dt=sim.get_physics_dt(), force_recompute=True) + # Update counter + step_count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/sensors/check_ray_caster.py b/source/isaaclab/test/sensors/check_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..c2e12da4ea62eeea9cc89d7e5d2dcef67bd2d0bc --- /dev/null +++ b/source/isaaclab/test/sensors/check_ray_caster.py @@ -0,0 +1,182 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use the ray caster from the Isaac Lab framework. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p source/isaaclab/test/sensors/test_ray_caster.py --headless +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Ray Caster Test Script") +parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to clone.") +parser.add_argument( + "--terrain_type", + type=str, + default="generator", + help="Type of terrain to import. Can be 'generator' or 'usd' or 'plane'.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import torch + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.prims import RigidPrim +from isaacsim.core.utils.viewports import set_camera_view + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.sensors.ray_caster import RayCaster, RayCasterCfg, patterns +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.terrains.terrain_importer import TerrainImporter +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.timer import Timer + + +def design_scene(sim: SimulationContext, num_envs: int = 2048): + """Design the scene.""" + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + sim.stage.DefinePrim("/World/envs/env_0", "Xform") + # Define the scene + # -- Light + cfg = sim_utils.DistantLightCfg(intensity=2000) + cfg.func("/World/light", cfg) + # -- Balls + cfg = sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ) + cfg.func("/World/envs/env_0/ball", cfg, translation=(0.0, 0.0, 5.0)) + # Clone the scene + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"] + ) + + +def main(): + """Main function.""" + + # Load kit helper + sim_params = { + "use_gpu": True, + "use_gpu_pipeline": True, + "use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards + "use_fabric": True, # used from Isaac Sim 2023.1 onwards + "enable_scene_query_support": True, + } + sim = SimulationContext( + physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0" + ) + # Set main camera + set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5]) + + # Parameters + num_envs = args_cli.num_envs + # Design the scene + design_scene(sim=sim, num_envs=num_envs) + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + terrain_type=args_cli.terrain_type, + terrain_generator=ROUGH_TERRAINS_CFG, + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + max_init_terrain_level=None, + num_envs=1, + ) + _ = TerrainImporter(terrain_importer_cfg) + + # Create a ray-caster sensor + ray_caster_cfg = RayCasterCfg( + prim_path="/World/envs/env_.*/ball", + mesh_prim_paths=["/World/ground"], + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=(1.6, 1.0)), + ray_alignment="yaw", + debug_vis=not args_cli.headless, + ) + ray_caster = RayCaster(cfg=ray_caster_cfg) + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + + # Play simulator + sim.reset() + + # Initialize the views + # -- balls + ball_view.initialize() + # Print the sensor information + print(ray_caster) + + # Get the initial positions of the balls + ball_initial_positions, ball_initial_orientations = ball_view.get_world_poses() + ball_initial_velocities = ball_view.get_velocities() + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step(render=False) + continue + # Reset the scene + if step_count % 500 == 0: + # sample random indices to reset + reset_indices = torch.randint(0, num_envs, (num_envs // 2,)) + # reset the balls + ball_view.set_world_poses( + ball_initial_positions[reset_indices], ball_initial_orientations[reset_indices], indices=reset_indices + ) + ball_view.set_velocities(ball_initial_velocities[reset_indices], indices=reset_indices) + # reset the sensor + ray_caster.reset(reset_indices) + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update the ray-caster + with Timer(f"Ray-caster update with {num_envs} x {ray_caster.num_rays} rays"): + ray_caster.update(dt=sim.get_physics_dt(), force_recompute=True) + # Update counter + step_count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/sensors/test_camera.py b/source/isaaclab/test/sensors/test_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..584394bfd54faae8276c090ecc112ed637c5a011 --- /dev/null +++ b/source/isaaclab/test/sensors/test_camera.py @@ -0,0 +1,908 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import copy +import os +import random + +import numpy as np +import pytest +import scipy.spatial.transform as tf +import torch + +import omni.replicator.core as rep +from isaacsim.core.prims import SingleGeometryPrim, SingleRigidPrim +from pxr import Gf, Usd, UsdGeom + +import isaaclab.sim as sim_utils +from isaaclab.sensors.camera import Camera, CameraCfg +from isaaclab.utils import convert_dict_to_backend +from isaaclab.utils.math import convert_quat +from isaaclab.utils.timer import Timer + +# sample camera poses +POSITION = (2.5, 2.5, 2.5) +QUAT_ROS = (-0.17591989, 0.33985114, 0.82047325, -0.42470819) +QUAT_OPENGL = (0.33985113, 0.17591988, 0.42470818, 0.82047324) +QUAT_WORLD = (-0.3647052, -0.27984815, -0.1159169, 0.88047623) + +# NOTE: setup and teardown are own function to allow calling them in the tests + +# resolutions +HEIGHT = 240 +WIDTH = 320 + + +def setup() -> tuple[sim_utils.SimulationContext, CameraCfg, float]: + camera_cfg = CameraCfg( + height=HEIGHT, + width=WIDTH, + prim_path="/World/Camera", + update_period=0, + data_types=["distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + ) + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt) + sim = sim_utils.SimulationContext(sim_cfg) + # populate scene + _populate_scene() + # load stage + sim_utils.update_stage() + return sim, camera_cfg, dt + + +def teardown(sim: sim_utils.SimulationContext): + # Cleanup + # close all the opened viewport from before. + rep.vp_manager.destroy_hydra_textures("Replicator") + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.fixture +def setup_sim_camera(): + """Create a simulation context.""" + sim, camera_cfg, dt = setup() + yield sim, camera_cfg, dt + teardown(sim) + + +def test_camera_init(setup_sim_camera): + """Test camera initialization.""" + # Create camera configuration + sim, camera_cfg, dt = setup_sim_camera + # Create camera + camera = Camera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[0].GetPath().pathString == camera_cfg.prim_path + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exist and have correct shapes + assert camera.data.pos_w.shape == (1, 3) + assert camera.data.quat_w_ros.shape == (1, 4) + assert camera.data.quat_w_world.shape == (1, 4) + assert camera.data.quat_w_opengl.shape == (1, 4) + assert camera.data.intrinsic_matrices.shape == (1, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + assert camera.data.info == [{camera_cfg.data_types[0]: None}] + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(sim.cfg.dt) + # check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 1) + + +def test_camera_init_offset(setup_sim_camera): + """Test camera initialization with offset using different conventions.""" + sim, camera_cfg, dt = setup_sim_camera + # define the same offset in all conventions + # -- ROS convention + cam_cfg_offset_ros = copy.deepcopy(camera_cfg) + cam_cfg_offset_ros.update_latest_camera_pose = True + cam_cfg_offset_ros.offset = CameraCfg.OffsetCfg( + pos=POSITION, + rot=QUAT_ROS, + convention="ros", + ) + cam_cfg_offset_ros.prim_path = "/World/CameraOffsetRos" + camera_ros = Camera(cam_cfg_offset_ros) + # -- OpenGL convention + cam_cfg_offset_opengl = copy.deepcopy(camera_cfg) + cam_cfg_offset_opengl.update_latest_camera_pose = True + cam_cfg_offset_opengl.offset = CameraCfg.OffsetCfg( + pos=POSITION, + rot=QUAT_OPENGL, + convention="opengl", + ) + cam_cfg_offset_opengl.prim_path = "/World/CameraOffsetOpengl" + camera_opengl = Camera(cam_cfg_offset_opengl) + # -- World convention + cam_cfg_offset_world = copy.deepcopy(camera_cfg) + cam_cfg_offset_world.update_latest_camera_pose = True + cam_cfg_offset_world.offset = CameraCfg.OffsetCfg( + pos=POSITION, + rot=QUAT_WORLD, + convention="world", + ) + cam_cfg_offset_world.prim_path = "/World/CameraOffsetWorld" + camera_world = Camera(cam_cfg_offset_world) + + # play sim + sim.reset() + + # retrieve camera pose using USD API + prim_tf_ros = camera_ros._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + prim_tf_opengl = camera_opengl._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + prim_tf_world = camera_world._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + # convert them from column-major to row-major + prim_tf_ros = np.transpose(prim_tf_ros) + prim_tf_opengl = np.transpose(prim_tf_opengl) + prim_tf_world = np.transpose(prim_tf_world) + + # check that all transforms are set correctly + np.testing.assert_allclose(prim_tf_ros[0:3, 3], cam_cfg_offset_ros.offset.pos) + np.testing.assert_allclose(prim_tf_opengl[0:3, 3], cam_cfg_offset_opengl.offset.pos) + np.testing.assert_allclose(prim_tf_world[0:3, 3], cam_cfg_offset_world.offset.pos) + np.testing.assert_allclose( + convert_quat(tf.Rotation.from_matrix(prim_tf_ros[:3, :3]).as_quat(), "wxyz"), + cam_cfg_offset_opengl.offset.rot, + rtol=1e-5, + ) + np.testing.assert_allclose( + convert_quat(tf.Rotation.from_matrix(prim_tf_opengl[:3, :3]).as_quat(), "wxyz"), + cam_cfg_offset_opengl.offset.rot, + rtol=1e-5, + ) + np.testing.assert_allclose( + convert_quat(tf.Rotation.from_matrix(prim_tf_world[:3, :3]).as_quat(), "wxyz"), + cam_cfg_offset_opengl.offset.rot, + rtol=1e-5, + ) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # check if transform correctly set in output + np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_ros[0].cpu().numpy(), QUAT_ROS, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_opengl[0].cpu().numpy(), QUAT_OPENGL, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_world[0].cpu().numpy(), QUAT_WORLD, rtol=1e-5) + + +def test_multi_camera_init(setup_sim_camera): + """Test multi-camera initialization.""" + sim, camera_cfg, dt = setup_sim_camera + # create two cameras with different prim paths + # -- camera 1 + cam_cfg_1 = copy.deepcopy(camera_cfg) + cam_cfg_1.prim_path = "/World/Camera_1" + cam_1 = Camera(cam_cfg_1) + # -- camera 2 + cam_cfg_2 = copy.deepcopy(camera_cfg) + cam_cfg_2.prim_path = "/World/Camera_2" + cam_2 = Camera(cam_cfg_2) + + # play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + cam_1.update(dt) + cam_2.update(dt) + # check image data + for cam in [cam_1, cam_2]: + for im_data in cam.data.output.values(): + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 1) + + +def test_multi_camera_with_different_resolution(setup_sim_camera): + """Test multi-camera initialization with cameras having different image resolutions.""" + sim, camera_cfg, dt = setup_sim_camera + # create two cameras with different prim paths + # -- camera 1 + cam_cfg_1 = copy.deepcopy(camera_cfg) + cam_cfg_1.prim_path = "/World/Camera_1" + cam_1 = Camera(cam_cfg_1) + # -- camera 2 + cam_cfg_2 = copy.deepcopy(camera_cfg) + cam_cfg_2.prim_path = "/World/Camera_2" + cam_cfg_2.height = 240 + cam_cfg_2.width = 320 + cam_2 = Camera(cam_cfg_2) + + # play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # perform rendering + sim.step() + # update camera + cam_1.update(dt) + cam_2.update(dt) + # check image sizes + assert cam_1.data.output["distance_to_image_plane"].shape == (1, camera_cfg.height, camera_cfg.width, 1) + assert cam_2.data.output["distance_to_image_plane"].shape == (1, cam_cfg_2.height, cam_cfg_2.width, 1) + + +def test_camera_init_intrinsic_matrix(setup_sim_camera): + """Test camera initialization from intrinsic matrix.""" + sim, camera_cfg, dt = setup_sim_camera + # get the first camera + camera_1 = Camera(cfg=camera_cfg) + # get intrinsic matrix + sim.reset() + intrinsic_matrix = camera_1.data.intrinsic_matrices[0].cpu().flatten().tolist() + teardown(sim) + # reinit the first camera + sim, camera_cfg, dt = setup() + camera_1 = Camera(cfg=camera_cfg) + # initialize from intrinsic matrix + intrinsic_camera_cfg = CameraCfg( + height=HEIGHT, + width=WIDTH, + prim_path="/World/Camera_2", + update_period=0, + data_types=["distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsic_matrix, + width=WIDTH, + height=HEIGHT, + focal_length=24.0, + focus_distance=400.0, + clipping_range=(0.1, 1.0e5), + ), + ) + camera_2 = Camera(cfg=intrinsic_camera_cfg) + + # play sim + sim.reset() + + # update cameras + camera_1.update(dt) + camera_2.update(dt) + + # check image data + torch.testing.assert_close( + camera_1.data.output["distance_to_image_plane"], + camera_2.data.output["distance_to_image_plane"], + rtol=5e-3, + atol=1e-4, + ) + # check that both intrinsic matrices are the same + torch.testing.assert_close( + camera_1.data.intrinsic_matrices[0], + camera_2.data.intrinsic_matrices[0], + rtol=5e-3, + atol=1e-4, + ) + + +def test_camera_set_world_poses(setup_sim_camera): + """Test camera function to set specific world pose.""" + sim, camera_cfg, dt = setup_sim_camera + # enable update latest camera pose + camera_cfg.update_latest_camera_pose = True + # init camera + camera = Camera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + position = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + orientation = torch.tensor([QUAT_WORLD], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses(position.clone(), orientation.clone(), convention="world") + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, position) + torch.testing.assert_close(camera.data.quat_w_world, orientation) + + +def test_camera_set_world_poses_from_view(setup_sim_camera): + """Test camera function to set specific world pose from view.""" + sim, camera_cfg, dt = setup_sim_camera + # enable update latest camera pose + camera_cfg.update_latest_camera_pose = True + # init camera + camera = Camera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + eyes = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + quat_ros_gt = torch.tensor([QUAT_ROS], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses_from_view(eyes.clone(), targets.clone()) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, eyes) + torch.testing.assert_close(camera.data.quat_w_ros, quat_ros_gt) + + +def test_intrinsic_matrix(setup_sim_camera): + """Checks that the camera's set and retrieve methods work for intrinsic matrix.""" + sim, camera_cfg, dt = setup_sim_camera + # enable update latest camera pose + camera_cfg.update_latest_camera_pose = True + # init camera + camera = Camera(camera_cfg) + # play sim + sim.reset() + # Desired properties (obtained from realsense camera at 320x240 resolution) + rs_intrinsic_matrix = [229.8, 0.0, 160.0, 0.0, 229.8, 120.0, 0.0, 0.0, 1.0] + rs_intrinsic_matrix = torch.tensor(rs_intrinsic_matrix, device=camera.device).reshape(3, 3).unsqueeze(0) + # Set matrix into simulator + camera.set_intrinsic_matrices(rs_intrinsic_matrix.clone()) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # Check that matrix is correct + torch.testing.assert_close(rs_intrinsic_matrix[0, 0, 0], camera.data.intrinsic_matrices[0, 0, 0]) + torch.testing.assert_close(rs_intrinsic_matrix[0, 1, 1], camera.data.intrinsic_matrices[0, 1, 1]) + torch.testing.assert_close(rs_intrinsic_matrix[0, 0, 2], camera.data.intrinsic_matrices[0, 0, 2]) + torch.testing.assert_close(rs_intrinsic_matrix[0, 1, 2], camera.data.intrinsic_matrices[0, 1, 2]) + + +def test_depth_clipping(setup_sim_camera): + """Test depth clipping. + + .. note:: + + This test is the same for all camera models to enforce the same clipping behavior. + """ + # get camera cfgs + sim, _, dt = setup_sim_camera + camera_cfg_zero = CameraCfg( + prim_path="/World/CameraZero", + offset=CameraCfg.OffsetCfg(pos=(2.5, 2.5, 6.0), rot=(-0.125, 0.362, 0.873, -0.302), convention="ros"), + spawn=sim_utils.PinholeCameraCfg().from_intrinsic_matrix( + focal_length=38.0, + intrinsic_matrix=[380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0], + height=540, + width=960, + clipping_range=(0.1, 10), + ), + height=540, + width=960, + data_types=["distance_to_image_plane", "distance_to_camera"], + depth_clipping_behavior="zero", + ) + camera_zero = Camera(camera_cfg_zero) + + camera_cfg_none = copy.deepcopy(camera_cfg_zero) + camera_cfg_none.prim_path = "/World/CameraNone" + camera_cfg_none.depth_clipping_behavior = "none" + camera_none = Camera(camera_cfg_none) + + camera_cfg_max = copy.deepcopy(camera_cfg_zero) + camera_cfg_max.prim_path = "/World/CameraMax" + camera_cfg_max.depth_clipping_behavior = "max" + camera_max = Camera(camera_cfg_max) + + # Play sim + sim.reset() + + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + camera_zero.update(dt) + camera_none.update(dt) + camera_max.update(dt) + + # none clipping should contain inf values + assert torch.isinf(camera_none.data.output["distance_to_camera"]).any() + assert torch.isinf(camera_none.data.output["distance_to_image_plane"]).any() + assert ( + camera_none.data.output["distance_to_camera"][~torch.isinf(camera_none.data.output["distance_to_camera"])].min() + >= camera_cfg_zero.spawn.clipping_range[0] + ) + assert ( + camera_none.data.output["distance_to_camera"][~torch.isinf(camera_none.data.output["distance_to_camera"])].max() + <= camera_cfg_zero.spawn.clipping_range[1] + ) + assert ( + camera_none.data.output["distance_to_image_plane"][ + ~torch.isinf(camera_none.data.output["distance_to_image_plane"]) + ].min() + >= camera_cfg_zero.spawn.clipping_range[0] + ) + assert ( + camera_none.data.output["distance_to_image_plane"][ + ~torch.isinf(camera_none.data.output["distance_to_camera"]) + ].max() + <= camera_cfg_zero.spawn.clipping_range[1] + ) + + # zero clipping should result in zero values + assert torch.all( + camera_zero.data.output["distance_to_camera"][torch.isinf(camera_none.data.output["distance_to_camera"])] == 0.0 + ) + assert torch.all( + camera_zero.data.output["distance_to_image_plane"][ + torch.isinf(camera_none.data.output["distance_to_image_plane"]) + ] + == 0.0 + ) + assert ( + camera_zero.data.output["distance_to_camera"][camera_zero.data.output["distance_to_camera"] != 0.0].min() + >= camera_cfg_zero.spawn.clipping_range[0] + ) + assert camera_zero.data.output["distance_to_camera"].max() <= camera_cfg_zero.spawn.clipping_range[1] + assert ( + camera_zero.data.output["distance_to_image_plane"][ + camera_zero.data.output["distance_to_image_plane"] != 0.0 + ].min() + >= camera_cfg_zero.spawn.clipping_range[0] + ) + assert camera_zero.data.output["distance_to_image_plane"].max() <= camera_cfg_zero.spawn.clipping_range[1] + + # max clipping should result in max values + assert torch.all( + camera_max.data.output["distance_to_camera"][torch.isinf(camera_none.data.output["distance_to_camera"])] + == camera_cfg_zero.spawn.clipping_range[1] + ) + assert torch.all( + camera_max.data.output["distance_to_image_plane"][ + torch.isinf(camera_none.data.output["distance_to_image_plane"]) + ] + == camera_cfg_zero.spawn.clipping_range[1] + ) + assert camera_max.data.output["distance_to_camera"].min() >= camera_cfg_zero.spawn.clipping_range[0] + assert camera_max.data.output["distance_to_camera"].max() <= camera_cfg_zero.spawn.clipping_range[1] + assert camera_max.data.output["distance_to_image_plane"].min() >= camera_cfg_zero.spawn.clipping_range[0] + assert camera_max.data.output["distance_to_image_plane"].max() <= camera_cfg_zero.spawn.clipping_range[1] + + +def test_camera_resolution_all_colorize(setup_sim_camera): + """Test camera resolution is correctly set for all types with colorization enabled.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + camera_cfg.colorize_instance_id_segmentation = True + camera_cfg.colorize_instance_segmentation = True + camera_cfg.colorize_semantic_segmentation = True + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + + # expected sizes + hw_1c_shape = (1, camera_cfg.height, camera_cfg.width, 1) + hw_2c_shape = (1, camera_cfg.height, camera_cfg.width, 2) + hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 3) + hw_4c_shape = (1, camera_cfg.height, camera_cfg.width, 4) + # access image data and compare shapes + output = camera.data.output + assert output["rgb"].shape == hw_3c_shape + assert output["rgba"].shape == hw_4c_shape + assert output["depth"].shape == hw_1c_shape + assert output["distance_to_camera"].shape == hw_1c_shape + assert output["distance_to_image_plane"].shape == hw_1c_shape + assert output["normals"].shape == hw_3c_shape + assert output["motion_vectors"].shape == hw_2c_shape + assert output["semantic_segmentation"].shape == hw_4c_shape + assert output["instance_segmentation_fast"].shape == hw_4c_shape + assert output["instance_id_segmentation_fast"].shape == hw_4c_shape + + # access image data and compare dtype + output = camera.data.output + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + + +def test_camera_resolution_no_colorize(setup_sim_camera): + """Test camera resolution is correctly set for all types with no colorization enabled.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + camera_cfg.colorize_instance_id_segmentation = False + camera_cfg.colorize_instance_segmentation = False + camera_cfg.colorize_semantic_segmentation = False + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(12): + sim.step() + camera.update(dt) + + # expected sizes + hw_1c_shape = (1, camera_cfg.height, camera_cfg.width, 1) + hw_2c_shape = (1, camera_cfg.height, camera_cfg.width, 2) + hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 3) + hw_4c_shape = (1, camera_cfg.height, camera_cfg.width, 4) + # access image data and compare shapes + output = camera.data.output + assert output["rgb"].shape == hw_3c_shape + assert output["rgba"].shape == hw_4c_shape + assert output["depth"].shape == hw_1c_shape + assert output["distance_to_camera"].shape == hw_1c_shape + assert output["distance_to_image_plane"].shape == hw_1c_shape + assert output["normals"].shape == hw_3c_shape + assert output["motion_vectors"].shape == hw_2c_shape + assert output["semantic_segmentation"].shape == hw_1c_shape + assert output["instance_segmentation_fast"].shape == hw_1c_shape + assert output["instance_id_segmentation_fast"].shape == hw_1c_shape + + # access image data and compare dtype + output = camera.data.output + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.int32 + assert output["instance_segmentation_fast"].dtype == torch.int32 + assert output["instance_id_segmentation_fast"].dtype == torch.int32 + + +def test_camera_large_resolution_all_colorize(setup_sim_camera): + """Test camera resolution is correctly set for all types with colorization enabled.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + camera_cfg.colorize_instance_id_segmentation = True + camera_cfg.colorize_instance_segmentation = True + camera_cfg.colorize_semantic_segmentation = True + camera_cfg.width = 512 + camera_cfg.height = 512 + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + + # expected sizes + hw_1c_shape = (1, camera_cfg.height, camera_cfg.width, 1) + hw_2c_shape = (1, camera_cfg.height, camera_cfg.width, 2) + hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 3) + hw_4c_shape = (1, camera_cfg.height, camera_cfg.width, 4) + # access image data and compare shapes + output = camera.data.output + assert output["rgb"].shape == hw_3c_shape + assert output["rgba"].shape == hw_4c_shape + assert output["depth"].shape == hw_1c_shape + assert output["distance_to_camera"].shape == hw_1c_shape + assert output["distance_to_image_plane"].shape == hw_1c_shape + assert output["normals"].shape == hw_3c_shape + assert output["motion_vectors"].shape == hw_2c_shape + assert output["semantic_segmentation"].shape == hw_4c_shape + assert output["instance_segmentation_fast"].shape == hw_4c_shape + assert output["instance_id_segmentation_fast"].shape == hw_4c_shape + + # access image data and compare dtype + output = camera.data.output + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + + +def test_camera_resolution_rgb_only(setup_sim_camera): + """Test camera resolution is correctly set for RGB only.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = ["rgb"] + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + + # expected sizes + hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 3) + # access image data and compare shapes + output = camera.data.output + assert output["rgb"].shape == hw_3c_shape + # access image data and compare dtype + assert output["rgb"].dtype == torch.uint8 + + +def test_camera_resolution_rgba_only(setup_sim_camera): + """Test camera resolution is correctly set for RGBA only.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = ["rgba"] + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + + # expected sizes + hw_4c_shape = (1, camera_cfg.height, camera_cfg.width, 4) + # access image data and compare shapes + output = camera.data.output + assert output["rgba"].shape == hw_4c_shape + # access image data and compare dtype + assert output["rgba"].dtype == torch.uint8 + + +def test_camera_resolution_depth_only(setup_sim_camera): + """Test camera resolution is correctly set for depth only.""" + # Add all types + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.data_types = ["depth"] + # Create camera + camera = Camera(camera_cfg) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + + # expected sizes + hw_1c_shape = (1, camera_cfg.height, camera_cfg.width, 1) + # access image data and compare shapes + output = camera.data.output + assert output["depth"].shape == hw_1c_shape + # access image data and compare dtype + assert output["depth"].dtype == torch.float + + +def test_throughput(setup_sim_camera): + """Checks that the single camera gets created properly with a rig.""" + # Create directory temp dir to dump the results + file_dir = os.path.dirname(os.path.realpath(__file__)) + temp_dir = os.path.join(file_dir, "output", "camera", "throughput") + os.makedirs(temp_dir, exist_ok=True) + # Create replicator writer + rep_writer = rep.BasicWriter(output_dir=temp_dir, frame_padding=3) + # create camera + sim, camera_cfg, dt = setup_sim_camera + camera_cfg.height = 480 + camera_cfg.width = 640 + camera = Camera(camera_cfg) + + # Play simulator + sim.reset() + + # Set camera pose + eyes = torch.tensor([[2.5, 2.5, 2.5]], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + camera.set_world_poses_from_view(eyes, targets) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(5): + # perform rendering + sim.step() + # update camera + with Timer(f"Time taken for updating camera with shape {camera.image_shape}"): + camera.update(dt) + # Save images + with Timer(f"Time taken for writing data with shape {camera.image_shape} "): + # Pack data back into replicator format to save them using its writer + rep_output = {"annotators": {}} + camera_data = convert_dict_to_backend({k: v[0] for k, v in camera.data.output.items()}, backend="numpy") + for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()): + if info is not None: + rep_output["annotators"][key] = {"render_product": {"data": data, **info}} + else: + rep_output["annotators"][key] = {"render_product": {"data": data}} + # Save images + rep_output["trigger_outputs"] = {"on_time": camera.frame[0]} + rep_writer.write(rep_output) + print("----------------------------------------") + # Check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 1) + + +def test_sensor_print(setup_sim_camera): + """Test sensor print is working correctly.""" + # Create sensor + sim, camera_cfg, dt = setup_sim_camera + sensor = Camera(cfg=camera_cfg) + # Play sim + sim.reset() + # print info + print(sensor) + + +def _populate_scene(): + """Add prims to the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.SphereLightCfg() + cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0)) + cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0)) + # Random objects + random.seed(0) + for i in range(10): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # create prim + prim_type = random.choice(["Cube", "Sphere", "Cylinder"]) + prim = sim_utils.create_prim( + f"/World/Objects/Obj_{i:02d}", + prim_type, + translation=position, + scale=(0.25, 0.25, 0.25), + semantic_label=prim_type, + ) + # cast to geom prim + geom_prim = getattr(UsdGeom, prim_type)(prim) + # set random color + color = Gf.Vec3f(random.random(), random.random(), random.random()) + geom_prim.CreateDisplayColorAttr() + geom_prim.GetDisplayColorAttr().Set([color]) + # add rigid properties + SingleGeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True) + SingleRigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0) diff --git a/source/isaaclab/test/sensors/test_contact_sensor.py b/source/isaaclab/test/sensors/test_contact_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..ed376f97f2d1a7381c3603ef41ffdf137b9c0bbf --- /dev/null +++ b/source/isaaclab/test/sensors/test_contact_sensor.py @@ -0,0 +1,846 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests to verify contact sensor functionality on rigid object prims.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from dataclasses import MISSING +from enum import Enum + +import pytest +import torch +from flaky import flaky + +import carb +from pxr import PhysxSchema + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObject, RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import ContactSensor, ContactSensorCfg +from isaaclab.sim import SimulationCfg, SimulationContext, build_simulation_context +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.terrains import HfRandomUniformTerrainCfg, TerrainGeneratorCfg, TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Custom helper classes. +## + + +class ContactTestMode(Enum): + """Enum to declare the type of contact sensor test to execute.""" + + IN_CONTACT = 0 + """Enum to test the condition where the test object is in contact with the ground plane.""" + NON_CONTACT = 1 + """Enum to test the condition where the test object is not in contact with the ground plane (air time).""" + + +@configclass +class ContactSensorRigidObjectCfg(RigidObjectCfg): + """Configuration for rigid objects used for the contact sensor test. + + This contains the expected values in the configuration to simplify test fixtures. + """ + + contact_pose: torch.Tensor = MISSING + """6D pose of the rigid object under test when it is in contact with the ground surface.""" + non_contact_pose: torch.Tensor = MISSING + """6D pose of the rigid object under test when it is not in contact.""" + + +@configclass +class ContactSensorSceneCfg(InteractiveSceneCfg): + """Configuration of the scene used by the contact sensor test.""" + + terrain: TerrainImporterCfg = MISSING + """Terrain configuration within the scene.""" + + shape: ContactSensorRigidObjectCfg = MISSING + """RigidObject contact prim configuration.""" + + contact_sensor: ContactSensorCfg = MISSING + """Contact sensor configuration.""" + + shape_2: ContactSensorRigidObjectCfg = None + """RigidObject contact prim configuration. Defaults to None, i.e. not included in the scene. + + This is a second prim used for testing contact filtering. + """ + + contact_sensor_2: ContactSensorCfg = None + """Contact sensor configuration. Defaults to None, i.e. not included in the scene. + + This is a second contact sensor used for testing contact filtering. + """ + + +## +# Scene entity configurations. +## + + +CUBE_CFG = ContactSensorRigidObjectCfg( + prim_path="/World/Objects/Cube", + spawn=sim_utils.CuboidCfg( + size=(0.5, 0.5, 0.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + activate_contact_sensors=True, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.6, 0.4)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0, -1.0, 1.0)), + contact_pose=torch.tensor([0, -1.0, 0, 1, 0, 0, 0]), + non_contact_pose=torch.tensor([0, -1.0, 1.0, 1, 0, 0, 0]), +) +"""Configuration of the cube prim.""" + +SPHERE_CFG = ContactSensorRigidObjectCfg( + prim_path="/World/Objects/Sphere", + spawn=sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + activate_contact_sensors=True, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.4, 0.6)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0, 1.0, 1.0)), + contact_pose=torch.tensor([0, 1.0, 0.0, 1, 0, 0, 0]), + non_contact_pose=torch.tensor([0, 1.0, 1.0, 1, 0, 0, 0]), +) +"""Configuration of the sphere prim.""" + +CYLINDER_CFG = ContactSensorRigidObjectCfg( + prim_path="/World/Objects/Cylinder", + spawn=sim_utils.CylinderCfg( + radius=0.5, + height=0.01, + axis="Y", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + activate_contact_sensors=True, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.6, 0.4, 0.4)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0, 0.0, 1.0)), + contact_pose=torch.tensor([0, 0, 0.0, 1, 0, 0, 0]), + non_contact_pose=torch.tensor([0, 0, 1.0, 1, 0, 0, 0]), +) +"""Configuration of the cylinder prim.""" + +CAPSULE_CFG = ContactSensorRigidObjectCfg( + prim_path="/World/Objects/Capsule", + spawn=sim_utils.CapsuleCfg( + radius=0.25, + height=0.5, + axis="Z", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + activate_contact_sensors=True, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.2, 0.4, 0.4)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(1.0, 0.0, 1.5)), + contact_pose=torch.tensor([1.0, 0.0, 0.0, 1, 0, 0, 0]), + non_contact_pose=torch.tensor([1.0, 0.0, 1.5, 1, 0, 0, 0]), +) +"""Configuration of the capsule prim.""" + +CONE_CFG = ContactSensorRigidObjectCfg( + prim_path="/World/Objects/Cone", + spawn=sim_utils.ConeCfg( + radius=0.5, + height=0.5, + axis="Z", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + ), + activate_contact_sensors=True, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.2, 0.4)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0, 0.0, 1.0)), + contact_pose=torch.tensor([-1.0, 0.0, 0.0, 1, 0, 0, 0]), + non_contact_pose=torch.tensor([-1.0, 0.0, 1.0, 1, 0, 0, 0]), +) +"""Configuration of the cone prim.""" + +FLAT_TERRAIN_CFG = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane") +"""Configuration of the flat ground plane.""" + +COBBLESTONE_TERRAIN_CFG = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=TerrainGeneratorCfg( + seed=0, + size=(3.0, 3.0), + border_width=0.0, + num_rows=1, + num_cols=1, + sub_terrains={ + "random_rough": HfRandomUniformTerrainCfg( + proportion=1.0, noise_range=(0.0, 0.05), noise_step=0.01, border_width=0.25 + ), + }, + ), +) +"""Configuration of the generated mesh terrain.""" + + +@pytest.fixture(scope="module") +def setup_simulation(): + """Fixture to set up simulation parameters.""" + sim_dt = 0.0025 + durations = [sim_dt, sim_dt * 2, sim_dt * 32, sim_dt * 128] + terrains = [FLAT_TERRAIN_CFG, COBBLESTONE_TERRAIN_CFG] + devices = ["cuda:0", "cpu"] + carb_settings_iface = carb.settings.get_settings() + return sim_dt, durations, terrains, devices, carb_settings_iface + + +@pytest.mark.parametrize("disable_contact_processing", [True, False]) +@flaky(max_runs=3, min_passes=1) +def test_cube_contact_time(setup_simulation, disable_contact_processing): + """Checks contact sensor values for contact time and air time for a cube collision primitive.""" + # check for both contact processing enabled and disabled + # internally, the contact sensor should enable contact processing so it should always work. + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + carb_settings_iface.set_bool("/physics/disableContactProcessing", disable_contact_processing) + _run_contact_sensor_test(CUBE_CFG, sim_dt, devices, terrains, carb_settings_iface, durations) + + +@pytest.mark.parametrize("disable_contact_processing", [True, False]) +@flaky(max_runs=3, min_passes=1) +def test_sphere_contact_time(setup_simulation, disable_contact_processing): + """Checks contact sensor values for contact time and air time for a sphere collision primitive.""" + # check for both contact processing enabled and disabled + # internally, the contact sensor should enable contact processing so it should always work. + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + carb_settings_iface.set_bool("/physics/disableContactProcessing", disable_contact_processing) + _run_contact_sensor_test(SPHERE_CFG, sim_dt, devices, terrains, carb_settings_iface, durations) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 6, 24]) +def test_cube_stack_contact_filtering(setup_simulation, device, num_envs): + """Checks contact sensor reporting for filtering stacked cube prims.""" + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + with build_simulation_context(device=device, dt=sim_dt, add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Instance new scene for the current terrain and contact prim. + scene_cfg = ContactSensorSceneCfg(num_envs=num_envs, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG.replace(prim_path="/World/ground") + # -- cube 1 + scene_cfg.shape = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_1") + scene_cfg.shape.init_state.pos = (0, -1.0, 1.0) + # -- cube 2 (on top of cube 1) + scene_cfg.shape_2 = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_2") + scene_cfg.shape_2.init_state.pos = (0, -1.0, 1.525) + # -- contact sensor 1 + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + track_pose=True, + debug_vis=False, + update_period=0.0, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_2"], + ) + # -- contact sensor 2 + scene_cfg.contact_sensor_2 = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + track_pose=True, + debug_vis=False, + update_period=0.0, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_1"], + ) + scene = InteractiveScene(scene_cfg) + + # Check that contact processing is enabled + assert not carb_settings_iface.get("/physics/disableContactProcessing") + + # Set variables internally for reference + sim.reset() + + contact_sensor = scene["contact_sensor"] + contact_sensor_2 = scene["contact_sensor_2"] + + # Check that contact processing is enabled + assert contact_sensor.contact_physx_view.filter_count == 1 + assert contact_sensor_2.contact_physx_view.filter_count == 1 + + # Play the simulation + scene.reset() + for _ in range(500): + _perform_sim_step(sim, scene, sim_dt) + + # Check values for cube 2 --> cube 1 is the only collision for cube 2 + torch.testing.assert_close(contact_sensor_2.data.force_matrix_w[:, :, 0], contact_sensor_2.data.net_forces_w) + # Check that forces are opposite and equal + torch.testing.assert_close( + contact_sensor_2.data.force_matrix_w[:, :, 0], -contact_sensor.data.force_matrix_w[:, :, 0] + ) + # Check values are non-zero (contacts are happening and are getting reported) + assert contact_sensor_2.data.net_forces_w.sum().item() > 0.0 + assert contact_sensor.data.net_forces_w.sum().item() > 0.0 + + +def test_no_contact_reporting(setup_simulation): + """Test that forcing the disable of contact processing results in no contact reporting. + + We borrow the test :func:`test_cube_stack_contact_filtering` to test this and force disable contact processing. + """ + # TODO: This test only works on CPU. For GPU, it seems the contact processing is not disabled. + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + with build_simulation_context(device="cpu", dt=sim_dt, add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Instance new scene for the current terrain and contact prim. + scene_cfg = ContactSensorSceneCfg(num_envs=32, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG + # -- cube 1 + scene_cfg.shape = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_1") + scene_cfg.shape.init_state.pos = (0, -1.0, 1.0) + # -- cube 2 (on top of cube 1) + scene_cfg.shape_2 = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_2") + scene_cfg.shape_2.init_state.pos = (0, -1.0, 1.525) + # -- contact sensor 1 + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + track_pose=True, + debug_vis=False, + update_period=0.0, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_2"], + ) + # -- contact sensor 2 + scene_cfg.contact_sensor_2 = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + track_pose=True, + debug_vis=False, + update_period=0.0, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_1"], + ) + scene = InteractiveScene(scene_cfg) + + # Force disable contact processing + carb_settings_iface.set_bool("/physics/disableContactProcessing", True) + + # Set variables internally for reference + sim.reset() + + # Extract from scene for type hinting + contact_sensor: ContactSensor = scene["contact_sensor"] + contact_sensor_2: ContactSensor = scene["contact_sensor_2"] + + # Check buffers have the right size + assert contact_sensor.contact_physx_view.filter_count == 1 + assert contact_sensor_2.contact_physx_view.filter_count == 1 + + # Reset the contact sensors + scene.reset() + # Let the scene come to a rest + for _ in range(500): + _perform_sim_step(sim, scene, sim_dt) + + # check values are zero (contacts are happening but not reported) + assert contact_sensor.data.net_forces_w.sum().item() == 0.0 + assert contact_sensor.data.force_matrix_w.sum().item() == 0.0 + assert contact_sensor_2.data.net_forces_w.sum().item() == 0.0 + assert contact_sensor_2.data.force_matrix_w.sum().item() == 0.0 + + +@pytest.mark.isaacsim_ci +def test_sensor_print(setup_simulation): + """Test sensor print is working correctly.""" + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + with build_simulation_context(device="cuda:0", dt=sim_dt, add_lighting=False) as sim: + sim._app_control_on_stop_handle = None + # Spawn things into stage + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG.replace(prim_path="/World/ground") + scene_cfg.shape = CUBE_CFG + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=scene_cfg.shape.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + ) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + # print info + print(scene.sensors["contact_sensor"]) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_contact_sensor_threshold(setup_simulation, device): + """Test that the contact sensor USD threshold attribute is set to 0.0.""" + sim_dt, durations, terrains, devices, carb_settings_iface = setup_simulation + with build_simulation_context(device=device, dt=sim_dt, add_lighting=False) as sim: + sim._app_control_on_stop_handle = None + # Spawn things into stage + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG.replace(prim_path="/World/ground") + scene_cfg.shape = CUBE_CFG + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=scene_cfg.shape.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + ) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + + stage = get_current_stage() + prim_path = scene_cfg.shape.prim_path + prim = stage.GetPrimAtPath(prim_path) + + # Ensure the contact sensor was created properly + contact_sensor = scene["contact_sensor"] + assert contact_sensor is not None, "Contact sensor was not created" + + # Check if the prim has contact report API and verify threshold is close to 0.0 + if prim.HasAPI(PhysxSchema.PhysxContactReportAPI): + cr_api = PhysxSchema.PhysxContactReportAPI.Get(stage, prim.GetPrimPath()) + threshold_attr = cr_api.GetThresholdAttr() + if threshold_attr.IsValid(): + threshold_value = threshold_attr.Get() + assert pytest.approx(threshold_value, abs=1e-6) == 0.0, ( + f"Expected USD threshold to be close to 0.0, but got {threshold_value}" + ) + + +# minor gravity force in -z to ensure object stays on ground plane +@pytest.mark.parametrize("grav_dir", [(-10.0, 0.0, -0.1), (0.0, -10.0, -0.1)]) +@pytest.mark.isaacsim_ci +def test_friction_reporting(setup_simulation, grav_dir): + """ + Test friction force reporting for contact sensors. + + This test places a contact sensor enabled cube onto a ground plane under different gravity directions. + It then compares the normalized friction force dir with the direction of gravity to ensure they are aligned. + """ + sim_dt, _, _, _, carb_settings_iface = setup_simulation + carb_settings_iface.set_bool("/physics/disableContactProcessing", True) + device = "cuda:0" + sim_cfg = SimulationCfg(dt=sim_dt, device=device, gravity=grav_dir) + with build_simulation_context(sim_cfg=sim_cfg, add_lighting=False) as sim: + sim._app_control_on_stop_handle = None + + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG + scene_cfg.shape = CUBE_CFG + + filter_prim_paths_expr = [scene_cfg.terrain.prim_path + "/terrain/GroundPlane/CollisionPlane"] + + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=scene_cfg.shape.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + track_friction_forces=True, + filter_prim_paths_expr=filter_prim_paths_expr, + ) + + scene = InteractiveScene(scene_cfg) + + sim.reset() + + scene["contact_sensor"].reset() + scene["shape"].write_root_pose_to_sim( + root_pose=torch.tensor([0, 0.0, CUBE_CFG.spawn.size[2] / 2.0, 1, 0, 0, 0]) + ) + + # step sim once to compute friction forces + _perform_sim_step(sim, scene, sim_dt) + + # check that forces are being reported match expected friction forces + expected_friction, _, _, _ = scene["contact_sensor"].contact_physx_view.get_friction_data(dt=sim_dt) + reported_friction = scene["contact_sensor"].data.friction_forces_w[0, 0, :] + + torch.testing.assert_close(expected_friction.sum(dim=0), reported_friction[0], atol=1e-6, rtol=1e-5) + + # check that friction force direction opposes gravity direction + grav = torch.tensor(grav_dir, device=device) + norm_reported_friction = reported_friction / reported_friction.norm() + norm_gravity = grav / grav.norm() + dot = torch.dot(norm_reported_friction[0], norm_gravity) + + torch.testing.assert_close(torch.abs(dot), torch.tensor(1.0, device=device), atol=1e-4, rtol=1e-3) + + +@pytest.mark.isaacsim_ci +def test_invalid_prim_paths_config(setup_simulation): + sim_dt, _, _, _, carb_settings_iface = setup_simulation + carb_settings_iface.set_bool("/physics/disableContactProcessing", True) + device = "cuda:0" + sim_cfg = SimulationCfg(dt=sim_dt, device=device) + with build_simulation_context(sim_cfg=sim_cfg, add_lighting=False) as sim: + sim._app_control_on_stop_handle = None + + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG + scene_cfg.shape = CUBE_CFG + + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=scene_cfg.shape.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + track_friction_forces=True, + filter_prim_paths_expr=[], + ) + + try: + _ = InteractiveScene(scene_cfg) + + sim.reset() + + assert False, "Expected ValueError due to invalid contact sensor configuration." + except ValueError: + pass + + +@pytest.mark.isaacsim_ci +def test_invalid_max_contact_points_config(setup_simulation): + sim_dt, _, _, _, carb_settings_iface = setup_simulation + carb_settings_iface.set_bool("/physics/disableContactProcessing", True) + device = "cuda:0" + sim_cfg = SimulationCfg(dt=sim_dt, device=device) + with build_simulation_context(sim_cfg=sim_cfg, add_lighting=False) as sim: + sim._app_control_on_stop_handle = None + + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = FLAT_TERRAIN_CFG + scene_cfg.shape = CUBE_CFG + filter_prim_paths_expr = [scene_cfg.terrain.prim_path + "/terrain/GroundPlane/CollisionPlane"] + + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=scene_cfg.shape.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + track_friction_forces=True, + filter_prim_paths_expr=filter_prim_paths_expr, + max_contact_data_count_per_prim=0, + ) + + try: + _ = InteractiveScene(scene_cfg) + + sim.reset() + + assert False, "Expected ValueError due to invalid contact sensor configuration." + except ValueError: + pass + + +""" +Internal helpers. +""" + + +def _run_contact_sensor_test( + shape_cfg: ContactSensorRigidObjectCfg, + sim_dt: float, + devices: list[str], + terrains: list[TerrainImporterCfg], + carb_settings_iface, + durations: list[float], +): + """ + Runs a rigid body test for a given contact primitive configuration. + + This method iterates through each device and terrain combination in the simulation environment, + running tests for contact sensors. + """ + for device in devices: + for terrain in terrains: + for track_contact_data in [True, False]: + with build_simulation_context(device=device, dt=sim_dt, add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + + scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False) + scene_cfg.terrain = terrain + scene_cfg.shape = shape_cfg + test_contact_data = False + if (type(shape_cfg.spawn) is sim_utils.SphereCfg) and (terrain.terrain_type == "plane"): + test_contact_data = True + elif track_contact_data: + continue + + if track_contact_data: + if terrain.terrain_type == "plane": + filter_prim_paths_expr = [terrain.prim_path + "/terrain/GroundPlane/CollisionPlane"] + elif terrain.terrain_type == "generator": + filter_prim_paths_expr = [terrain.prim_path + "/terrain/mesh"] + else: + filter_prim_paths_expr = [] + + scene_cfg.contact_sensor = ContactSensorCfg( + prim_path=shape_cfg.prim_path, + track_pose=True, + debug_vis=False, + update_period=0.0, + track_air_time=True, + history_length=3, + track_contact_points=track_contact_data, + track_friction_forces=track_contact_data, + filter_prim_paths_expr=filter_prim_paths_expr, + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulation + sim.reset() + + # Run contact time and air time tests. + _test_sensor_contact( + shape=scene["shape"], + sensor=scene["contact_sensor"], + mode=ContactTestMode.IN_CONTACT, + sim=sim, + scene=scene, + sim_dt=sim_dt, + durations=durations, + test_contact_data=test_contact_data, + ) + _test_sensor_contact( + shape=scene["shape"], + sensor=scene["contact_sensor"], + mode=ContactTestMode.NON_CONTACT, + sim=sim, + scene=scene, + sim_dt=sim_dt, + durations=durations, + test_contact_data=test_contact_data, + ) + + +def _test_sensor_contact( + shape: RigidObject, + sensor: ContactSensor, + mode: ContactTestMode, + sim: SimulationContext, + scene: InteractiveScene, + sim_dt: float, + durations: list[float], + test_contact_data: bool = False, +): + """Test for the contact sensor. + + This test sets the contact prim to a pose either in contact or out of contact with the ground plane for + a known duration. Once the contact duration has elapsed, the data stored inside the contact sensor + associated with the contact prim is checked against the expected values. + + This process is repeated for all elements in :attr:`TestContactSensor.durations`, where each successive + contact timing test is punctuated by setting the contact prim to the complement of the desired contact mode + for 1 sim time-step. + + Args: + shape: The contact prim used for the contact sensor test. + sensor: The sensor reporting data to be verified by the contact sensor test. + mode: The contact test mode: either contact with ground plane or air time. + """ + # reset the test state + sensor.reset() + expected_last_test_contact_time = 0 + expected_last_reset_contact_time = 0 + + # set poses for shape for a given contact sensor test mode. + # desired contact mode to set for a given duration. + test_pose = None + # complement of the desired contact mode used to reset the contact sensor. + reset_pose = None + if mode == ContactTestMode.IN_CONTACT: + test_pose = shape.cfg.contact_pose + reset_pose = shape.cfg.non_contact_pose + elif mode == ContactTestMode.NON_CONTACT: + test_pose = shape.cfg.non_contact_pose + reset_pose = shape.cfg.contact_pose + else: + raise ValueError("Received incompatible contact sensor test mode") + + for idx in range(len(durations)): + current_test_time = 0 + duration = durations[idx] + while current_test_time < duration: + # set object states to contact the ground plane + shape.write_root_pose_to_sim(root_pose=test_pose) + # perform simulation step + _perform_sim_step(sim, scene, sim_dt) + # increment contact time + current_test_time += sim_dt + # set last contact time to the previous desired contact duration plus the extra dt allowance. + expected_last_test_contact_time = durations[idx - 1] + sim_dt if idx > 0 else 0 + # Check the data inside the contact sensor + if mode == ContactTestMode.IN_CONTACT: + _check_prim_contact_state_times( + sensor=sensor, + expected_air_time=0.0, + expected_contact_time=durations[idx], + expected_last_contact_time=expected_last_test_contact_time, + expected_last_air_time=expected_last_reset_contact_time, + dt=duration + sim_dt, + ) + elif mode == ContactTestMode.NON_CONTACT: + _check_prim_contact_state_times( + sensor=sensor, + expected_air_time=durations[idx], + expected_contact_time=0.0, + expected_last_contact_time=expected_last_reset_contact_time, + expected_last_air_time=expected_last_test_contact_time, + dt=duration + sim_dt, + ) + + if test_contact_data: + _test_contact_position(shape, sensor, mode) + _test_friction_forces(shape, sensor, mode) + + # switch the contact mode for 1 dt step before the next contact test begins. + shape.write_root_pose_to_sim(root_pose=reset_pose) + # perform simulation step + _perform_sim_step(sim, scene, sim_dt) + # set the last air time to 2 sim_dt steps, because last_air_time and last_contact_time + # adds an additional sim_dt to the total time spent in the previous contact mode for uncertainty in + # when the contact switch happened in between a dt step. + expected_last_reset_contact_time = 2 * sim_dt + + +def _test_friction_forces(shape: RigidObject, sensor: ContactSensor, mode: ContactTestMode) -> None: + if not sensor.cfg.track_friction_forces: + assert sensor._data.friction_forces_w is None + return + + # check shape of the contact_pos_w tensor + num_bodies = sensor.num_bodies + assert sensor._data.friction_forces_w.shape == (sensor.num_instances // num_bodies, num_bodies, 1, 3) + # compare friction forces + if mode == ContactTestMode.IN_CONTACT: + assert torch.any(torch.abs(sensor._data.friction_forces_w) > 1e-5).item() + friction_forces, _, buffer_count, buffer_start_indices = sensor.contact_physx_view.get_friction_data( + dt=sensor._sim_physics_dt + ) + for i in range(sensor.num_instances * num_bodies): + for j in range(sensor.contact_physx_view.filter_count): + start_index_ij = buffer_start_indices[i, j] + count_ij = buffer_count[i, j] + force = torch.sum(friction_forces[start_index_ij : (start_index_ij + count_ij), :], dim=0) + env_idx = i // num_bodies + body_idx = i % num_bodies + assert torch.allclose(force, sensor._data.friction_forces_w[env_idx, body_idx, j, :], atol=1e-5) + + elif mode == ContactTestMode.NON_CONTACT: + assert torch.all(sensor._data.friction_forces_w == 0.0).item() + + +def _test_contact_position(shape: RigidObject, sensor: ContactSensor, mode: ContactTestMode) -> None: + """Test for the contact positions (only implemented for sphere and flat terrain) + checks that the contact position is radius distance away from the root of the object + Args: + shape: The contact prim used for the contact sensor test. + sensor: The sensor reporting data to be verified by the contact sensor test. + mode: The contact test mode: either contact with ground plane or air time. + """ + if not sensor.cfg.track_contact_points: + assert sensor._data.contact_pos_w is None + return + + # check shape of the contact_pos_w tensor + num_bodies = sensor.num_bodies + assert sensor._data.contact_pos_w.shape == (sensor.num_instances // num_bodies, num_bodies, 1, 3) + # check contact positions + if mode == ContactTestMode.IN_CONTACT: + contact_position = sensor._data.pos_w + torch.tensor( + [[0.0, 0.0, -shape.cfg.spawn.radius]], device=sensor._data.pos_w.device + ) + assert torch.all( + torch.abs(torch.norm(sensor._data.contact_pos_w - contact_position.unsqueeze(1), p=2, dim=-1)) < 1e-2 + ).item() + elif mode == ContactTestMode.NON_CONTACT: + assert torch.all(torch.isnan(sensor._data.contact_pos_w)).item() + + +def _check_prim_contact_state_times( + sensor: ContactSensor, + expected_air_time: float, + expected_contact_time: float, + expected_last_air_time: float, + expected_last_contact_time: float, + dt: float, +): + """Checks contact sensor data matches expected values. + + Args: + sensor: Instance of ContactSensor containing data to be tested. + expected_air_time: Air time ground truth. + expected_contact_time: Contact time ground truth. + expected_last_air_time: Last air time ground truth. + expected_last_contact_time: Last contact time ground truth. + dt: Time since previous contact mode switch. If the contact prim left contact 0.1 seconds ago, + dt should be 0.1 + simulation dt seconds. + """ + # store current state of the contact prim + in_air = False + in_contact = False + if expected_air_time > 0.0: + in_air = True + if expected_contact_time > 0.0: + in_contact = True + measured_contact_time = sensor.data.current_contact_time + measured_air_time = sensor.data.current_air_time + measured_last_contact_time = sensor.data.last_contact_time + measured_last_air_time = sensor.data.last_air_time + # check current contact state + assert pytest.approx(measured_contact_time.item(), 0.01) == expected_contact_time + assert pytest.approx(measured_air_time.item(), 0.01) == expected_air_time + # check last contact state + assert pytest.approx(measured_last_contact_time.item(), 0.01) == expected_last_contact_time + assert pytest.approx(measured_last_air_time.item(), 0.01) == expected_last_air_time + # check current contact mode + assert sensor.compute_first_contact(dt=dt).item() == in_contact + assert sensor.compute_first_air(dt=dt).item() == in_air + + +def _perform_sim_step(sim, scene, sim_dt): + """Updates sensors and steps the contact sensor test scene.""" + # write data to simulation + scene.write_data_to_sim() + # simulate + sim.step(render=False) + # update buffers at sim dt + scene.update(dt=sim_dt) diff --git a/source/isaaclab/test/sensors/test_frame_transformer.py b/source/isaaclab/test/sensors/test_frame_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0ccf8e1f3569a6fc9f28116a001b5da27c2f60 --- /dev/null +++ b/source/isaaclab/test/sensors/test_frame_transformer.py @@ -0,0 +1,796 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import math + +import pytest +import scipy.spatial.transform as tf +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.assets import RigidObjectCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors import FrameTransformerCfg, OffsetCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort:skip + + +def quat_from_euler_rpy(roll, pitch, yaw, degrees=False): + """Converts Euler XYZ to Quaternion (w, x, y, z).""" + quat = tf.Rotation.from_euler("xyz", (roll, pitch, yaw), degrees=degrees).as_quat() + return tuple(quat[[3, 0, 1, 2]].tolist()) + + +def euler_rpy_apply(rpy, xyz, degrees=False): + """Applies rotation from Euler XYZ on position vector.""" + rot = tf.Rotation.from_euler("xyz", rpy, degrees=degrees) + return tuple(rot.apply(xyz).tolist()) + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # terrain - flat terrain plane + terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane") + + # articulation - robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # sensors - frame transformer (filled inside unit test) + frame_transformer: FrameTransformerCfg = None + + # block + cube: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube", + spawn=sim_utils.CuboidCfg( + size=(0.2, 0.2, 0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + physics_material=sim_utils.RigidBodyMaterialCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.0, 0.0)), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(2.0, 0.0, 5)), + ) + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + # Create a new stage + sim_utils.create_new_stage() + # Load kit helper + sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.005, device="cpu")) + # Set main camera + sim.set_camera_view(eye=(5.0, 5.0, 5.0), target=(0.0, 0.0, 0.0)) + yield sim + # Cleanup + sim.clear_all_callbacks() + sim.clear_instance() + + +def test_frame_transformer_feet_wrt_base(sim): + """Test feet transformations w.r.t. base source frame. + + In this test, the source frame is the robot base. + """ + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=32, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg( + name="LF_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/LF_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(0.08795, 0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, -math.pi / 2), + ), + ), + FrameTransformerCfg.FrameCfg( + name="RF_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/RF_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(0.08795, -0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, math.pi / 2), + ), + ), + FrameTransformerCfg.FrameCfg( + name="LH_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/LH_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(-0.08795, 0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, -math.pi / 2), + ), + ), + FrameTransformerCfg.FrameCfg( + name="RH_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/RH_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(-0.08795, -0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, math.pi / 2), + ), + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + # Acquire the index of ground truth bodies + feet_indices, feet_names = scene.articulations["robot"].find_bodies(["LF_FOOT", "RF_FOOT", "LH_FOOT", "RH_FOOT"]) + + target_frame_names = scene.sensors["frame_transformer"].data.target_frame_names + + # Reorder the feet indices to match the order of the target frames with _USER suffix removed + target_frame_names = [name.split("_USER")[0] for name in target_frame_names] + + # Find the indices of the feet in the order of the target frames + reordering_indices = [feet_names.index(name) for name in target_frame_names] + feet_indices = [feet_indices[i] for i in reordering_indices] + + # default joint targets + default_actions = scene.articulations["robot"].data.default_joint_pos.clone() + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Simulate physics + for count in range(100): + # # reset + if count % 25 == 0: + # reset root state + root_state = scene.articulations["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + joint_pos = scene.articulations["robot"].data.default_joint_pos + joint_vel = scene.articulations["robot"].data.default_joint_vel + # -- set root state + # -- robot + scene.articulations["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # reset buffers + scene.reset() + + # set joint targets + robot_actions = default_actions + 0.5 * torch.randn_like(default_actions) + scene.articulations["robot"].set_joint_position_target(robot_actions) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + + # check absolute frame transforms in world frame + # -- ground-truth + root_pose_w = scene.articulations["robot"].data.root_pose_w + feet_pos_w_gt = scene.articulations["robot"].data.body_pos_w[:, feet_indices] + feet_quat_w_gt = scene.articulations["robot"].data.body_quat_w[:, feet_indices] + # -- frame transformer + source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w + source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w + feet_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w + feet_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w + + # check if they are same + torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf) + torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf) + torch.testing.assert_close(feet_pos_w_gt, feet_pos_w_tf) + torch.testing.assert_close(feet_quat_w_gt, feet_quat_w_tf) + + # check if relative transforms are same + feet_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source + feet_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source + for index in range(len(feet_indices)): + # ground-truth + foot_pos_b, foot_quat_b = math_utils.subtract_frame_transforms( + root_pose_w[:, :3], root_pose_w[:, 3:], feet_pos_w_tf[:, index], feet_quat_w_tf[:, index] + ) + # check if they are same + torch.testing.assert_close(feet_pos_source_tf[:, index], foot_pos_b) + torch.testing.assert_close(feet_quat_source_tf[:, index], foot_quat_b) + + +def test_frame_transformer_feet_wrt_thigh(sim): + """Test feet transformation w.r.t. thigh source frame.""" + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=32, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/LF_THIGH", + target_frames=[ + FrameTransformerCfg.FrameCfg( + name="LF_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/LF_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(0.08795, 0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, -math.pi / 2), + ), + ), + FrameTransformerCfg.FrameCfg( + name="RF_FOOT_USER", + prim_path="{ENV_REGEX_NS}/Robot/RF_SHANK", + offset=OffsetCfg( + pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(0.08795, -0.01305, -0.33797)), + rot=quat_from_euler_rpy(0, 0, math.pi / 2), + ), + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + # Acquire the index of ground truth bodies + source_frame_index = scene.articulations["robot"].find_bodies("LF_THIGH")[0][0] + feet_indices, feet_names = scene.articulations["robot"].find_bodies(["LF_FOOT", "RF_FOOT"]) + # Check names are parsed the same order + user_feet_names = [f"{name}_USER" for name in feet_names] + assert scene.sensors["frame_transformer"].data.target_frame_names == user_feet_names + + # default joint targets + default_actions = scene.articulations["robot"].data.default_joint_pos.clone() + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Simulate physics + for count in range(100): + # # reset + if count % 25 == 0: + # reset root state + root_state = scene.articulations["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + joint_pos = scene.articulations["robot"].data.default_joint_pos + joint_vel = scene.articulations["robot"].data.default_joint_vel + # -- set root state + # -- robot + scene.articulations["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # reset buffers + scene.reset() + + # set joint targets + robot_actions = default_actions + 0.5 * torch.randn_like(default_actions) + scene.articulations["robot"].set_joint_position_target(robot_actions) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + + # check absolute frame transforms in world frame + # -- ground-truth + source_pose_w_gt = scene.articulations["robot"].data.body_state_w[:, source_frame_index, :7] + feet_pos_w_gt = scene.articulations["robot"].data.body_pos_w[:, feet_indices] + feet_quat_w_gt = scene.articulations["robot"].data.body_quat_w[:, feet_indices] + # -- frame transformer + source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w + source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w + feet_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w + feet_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w + # check if they are same + torch.testing.assert_close(source_pose_w_gt[:, :3], source_pos_w_tf) + torch.testing.assert_close(source_pose_w_gt[:, 3:], source_quat_w_tf) + torch.testing.assert_close(feet_pos_w_gt, feet_pos_w_tf) + torch.testing.assert_close(feet_quat_w_gt, feet_quat_w_tf) + + # check if relative transforms are same + feet_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source + feet_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source + for index in range(len(feet_indices)): + # ground-truth + foot_pos_b, foot_quat_b = math_utils.subtract_frame_transforms( + source_pose_w_gt[:, :3], source_pose_w_gt[:, 3:], feet_pos_w_tf[:, index], feet_quat_w_tf[:, index] + ) + # check if they are same + torch.testing.assert_close(feet_pos_source_tf[:, index], foot_pos_b) + torch.testing.assert_close(feet_quat_source_tf[:, index], foot_quat_b) + + +def test_frame_transformer_robot_body_to_external_cube(sim): + """Test transformation from robot body to a cube in the scene.""" + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg( + name="CUBE_USER", + prim_path="{ENV_REGEX_NS}/cube", + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + # default joint targets + default_actions = scene.articulations["robot"].data.default_joint_pos.clone() + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Simulate physics + for count in range(100): + # # reset + if count % 25 == 0: + # reset root state + root_state = scene.articulations["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + joint_pos = scene.articulations["robot"].data.default_joint_pos + joint_vel = scene.articulations["robot"].data.default_joint_vel + # -- set root state + # -- robot + scene.articulations["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # reset buffers + scene.reset() + + # set joint targets + robot_actions = default_actions + 0.5 * torch.randn_like(default_actions) + scene.articulations["robot"].set_joint_position_target(robot_actions) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + + # check absolute frame transforms in world frame + # -- ground-truth + root_pose_w = scene.articulations["robot"].data.root_pose_w + cube_pos_w_gt = scene.rigid_objects["cube"].data.root_pos_w + cube_quat_w_gt = scene.rigid_objects["cube"].data.root_quat_w + # -- frame transformer + source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w + source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w + cube_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w.squeeze() + cube_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w.squeeze() + + # check if they are same + torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf) + torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf) + torch.testing.assert_close(cube_pos_w_gt, cube_pos_w_tf) + torch.testing.assert_close(cube_quat_w_gt, cube_quat_w_tf) + + # check if relative transforms are same + cube_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source + cube_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source + # ground-truth + cube_pos_b, cube_quat_b = math_utils.subtract_frame_transforms( + root_pose_w[:, :3], root_pose_w[:, 3:], cube_pos_w_tf, cube_quat_w_tf + ) + # check if they are same + torch.testing.assert_close(cube_pos_source_tf[:, 0], cube_pos_b) + torch.testing.assert_close(cube_quat_source_tf[:, 0], cube_quat_b) + + +@pytest.mark.isaacsim_ci +def test_frame_transformer_offset_frames(sim): + """Test body transformation w.r.t. base source frame. + + In this test, the source frame is the cube frame. + """ + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/cube", + target_frames=[ + FrameTransformerCfg.FrameCfg( + name="CUBE_CENTER", + prim_path="{ENV_REGEX_NS}/cube", + ), + FrameTransformerCfg.FrameCfg( + name="CUBE_TOP", + prim_path="{ENV_REGEX_NS}/cube", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ), + FrameTransformerCfg.FrameCfg( + name="CUBE_BOTTOM", + prim_path="{ENV_REGEX_NS}/cube", + offset=OffsetCfg( + pos=(0.0, 0.0, -0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Simulate physics + for count in range(100): + # # reset + if count % 25 == 0: + # reset root state + root_state = scene["cube"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + # -- set root state + # -- cube + scene["cube"].write_root_pose_to_sim(root_state[:, :7]) + scene["cube"].write_root_velocity_to_sim(root_state[:, 7:]) + # reset buffers + scene.reset() + + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + + # check absolute frame transforms in world frame + # -- ground-truth + cube_pos_w_gt = scene["cube"].data.root_pos_w + cube_quat_w_gt = scene["cube"].data.root_quat_w + # -- frame transformer + source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w + source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w + target_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w.squeeze() + target_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w.squeeze() + target_frame_names = scene.sensors["frame_transformer"].data.target_frame_names + + cube_center_idx = target_frame_names.index("CUBE_CENTER") + cube_bottom_idx = target_frame_names.index("CUBE_BOTTOM") + cube_top_idx = target_frame_names.index("CUBE_TOP") + + # check if they are same + torch.testing.assert_close(cube_pos_w_gt, source_pos_w_tf) + torch.testing.assert_close(cube_quat_w_gt, source_quat_w_tf) + torch.testing.assert_close(cube_pos_w_gt, target_pos_w_tf[:, cube_center_idx]) + torch.testing.assert_close(cube_quat_w_gt, target_quat_w_tf[:, cube_center_idx]) + + # test offsets are applied correctly + # -- cube top + cube_pos_top = target_pos_w_tf[:, cube_top_idx] + cube_quat_top = target_quat_w_tf[:, cube_top_idx] + torch.testing.assert_close(cube_pos_top, cube_pos_w_gt + torch.tensor([0.0, 0.0, 0.1])) + torch.testing.assert_close(cube_quat_top, cube_quat_w_gt) + + # -- cube bottom + cube_pos_bottom = target_pos_w_tf[:, cube_bottom_idx] + cube_quat_bottom = target_quat_w_tf[:, cube_bottom_idx] + torch.testing.assert_close(cube_pos_bottom, cube_pos_w_gt + torch.tensor([0.0, 0.0, -0.1])) + torch.testing.assert_close(cube_quat_bottom, cube_quat_w_gt) + + +@pytest.mark.isaacsim_ci +def test_frame_transformer_all_bodies(sim): + """Test transformation of all bodies w.r.t. base source frame. + + In this test, the source frame is the robot base. + + The target_frames are all bodies in the robot, implemented using .* pattern. + """ + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/.*", + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + target_frame_names = scene.sensors["frame_transformer"].data.target_frame_names + articulation_body_names = scene.articulations["robot"].data.body_names + + reordering_indices = [target_frame_names.index(name) for name in articulation_body_names] + + # default joint targets + default_actions = scene.articulations["robot"].data.default_joint_pos.clone() + # Define simulation stepping + sim_dt = sim.get_physics_dt() + # Simulate physics + for count in range(100): + # # reset + if count % 25 == 0: + # reset root state + root_state = scene.articulations["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + joint_pos = scene.articulations["robot"].data.default_joint_pos + joint_vel = scene.articulations["robot"].data.default_joint_vel + # -- set root state + # -- robot + scene.articulations["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel) + # reset buffers + scene.reset() + + # set joint targets + robot_actions = default_actions + 0.5 * torch.randn_like(default_actions) + scene.articulations["robot"].set_joint_position_target(robot_actions) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim_dt) + + # check absolute frame transforms in world frame + # -- ground-truth + root_pose_w = scene.articulations["robot"].data.root_pose_w + bodies_pos_w_gt = scene.articulations["robot"].data.body_pos_w + bodies_quat_w_gt = scene.articulations["robot"].data.body_quat_w + + # -- frame transformer + source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w + source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w + bodies_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w + bodies_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w + + # check if they are same + torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf) + torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf) + torch.testing.assert_close(bodies_pos_w_gt, bodies_pos_w_tf[:, reordering_indices]) + torch.testing.assert_close(bodies_quat_w_gt, bodies_quat_w_tf[:, reordering_indices]) + + bodies_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source + bodies_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source + + # Go through each body and check if relative transforms are same + for index in range(len(articulation_body_names)): + body_pos_b, body_quat_b = math_utils.subtract_frame_transforms( + root_pose_w[:, :3], root_pose_w[:, 3:], bodies_pos_w_tf[:, index], bodies_quat_w_tf[:, index] + ) + + torch.testing.assert_close(bodies_pos_source_tf[:, index], body_pos_b) + torch.testing.assert_close(bodies_quat_source_tf[:, index], body_quat_b) + + +@pytest.mark.isaacsim_ci +def test_sensor_print(sim): + """Test sensor print is working correctly.""" + # Spawn things into stage + scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False) + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/.*", + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + # print info + print(scene.sensors["frame_transformer"]) + + +@pytest.mark.isaacsim_ci +@pytest.mark.parametrize("source_robot", ["Robot", "Robot_1"]) +@pytest.mark.parametrize("path_prefix", ["{ENV_REGEX_NS}", "/World"]) +def test_frame_transformer_duplicate_body_names(sim, source_robot, path_prefix): + """Test tracking bodies with same leaf name at different hierarchy levels. + + This test verifies that bodies with the same leaf name but different paths + (e.g., Robot/LF_SHANK vs Robot_1/LF_SHANK, or arm/link vs leg/link) are tracked + separately using their full relative paths internally. + + The test uses 4 target frames to cover both scenarios: + + Explicit frame names (recommended when bodies share the same leaf name): + User provides unique names like "Robot_LF_SHANK" and "Robot_1_LF_SHANK" to + distinguish between bodies at different hierarchy levels. This makes it + easy to identify which transform belongs to which body. + + Implicit frame names (backward compatibility): + When no name is provided, it defaults to the leaf body name (e.g., "RF_SHANK"). + This preserves backward compatibility for users who may have existing code like + `idx = target_frame_names.index("RF_SHANK")`. However, when multiple bodies share + the same leaf name, this results in duplicate frame names. The transforms are + still distinct because internal body tracking uses full relative paths. + + Args: + source_robot: The robot to use as the source frame ("Robot" or "Robot_1"). + This tests that both source frames work correctly when there are + duplicate body names. + path_prefix: The path prefix to use ("{ENV_REGEX_NS}" for env patterns or "/World" for direct paths). + """ + + # Create a custom scene config with two robots + @configclass + class MultiRobotSceneCfg(InteractiveSceneCfg): + """Scene with two robots having bodies with same names.""" + + terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane") + + # Frame transformer will be set after config creation (needs source_robot parameter) + frame_transformer: FrameTransformerCfg = None # type: ignore + + # Use multiple envs for env patterns, single env for direct paths + num_envs = 2 if path_prefix == "{ENV_REGEX_NS}" else 1 + env_spacing = 10.0 if path_prefix == "{ENV_REGEX_NS}" else 0.0 + + # Create scene config with appropriate prim paths + scene_cfg = MultiRobotSceneCfg(num_envs=num_envs, env_spacing=env_spacing, lazy_sensor_update=False) + scene_cfg.robot = ANYMAL_C_CFG.replace(prim_path=f"{path_prefix}/Robot") + scene_cfg.robot_1 = ANYMAL_C_CFG.replace( + prim_path=f"{path_prefix}/Robot_1", + init_state=ANYMAL_C_CFG.init_state.replace(pos=(2.0, 0.0, 0.6)), + ) + + # Frame transformer tracking same-named bodies from both robots + # Source frame is parametrized to test both Robot/base and Robot_1/base + scene_cfg.frame_transformer = FrameTransformerCfg( + prim_path=f"{path_prefix}/{source_robot}/base", + target_frames=[ + # Explicit frame names (recommended when bodies share the same leaf name) + FrameTransformerCfg.FrameCfg( + name="Robot_LF_SHANK", + prim_path=f"{path_prefix}/Robot/LF_SHANK", + ), + FrameTransformerCfg.FrameCfg( + name="Robot_1_LF_SHANK", + prim_path=f"{path_prefix}/Robot_1/LF_SHANK", + ), + # Implicit frame names (backward compatibility) + FrameTransformerCfg.FrameCfg( + prim_path=f"{path_prefix}/Robot/RF_SHANK", + ), + FrameTransformerCfg.FrameCfg( + prim_path=f"{path_prefix}/Robot_1/RF_SHANK", + ), + ], + ) + scene = InteractiveScene(scene_cfg) + + # Play the simulator + sim.reset() + + # Get target frame names + target_frame_names = scene.sensors["frame_transformer"].data.target_frame_names + + # Verify explicit frame names are present + assert "Robot_LF_SHANK" in target_frame_names, f"Expected 'Robot_LF_SHANK', got {target_frame_names}" + assert "Robot_1_LF_SHANK" in target_frame_names, f"Expected 'Robot_1_LF_SHANK', got {target_frame_names}" + + # Without explicit names, both RF_SHANK frames default to same name "RF_SHANK" + # This results in duplicate frame names (expected behavior for backwards compatibility) + rf_shank_count = target_frame_names.count("RF_SHANK") + assert rf_shank_count == 2, f"Expected 2 'RF_SHANK' entries (name collision), got {rf_shank_count}" + + # Get indices for explicit named frames + robot_lf_idx = target_frame_names.index("Robot_LF_SHANK") + robot_1_lf_idx = target_frame_names.index("Robot_1_LF_SHANK") + + # Get indices for implicit named frames (both named "RF_SHANK") + rf_shank_indices = [i for i, name in enumerate(target_frame_names) if name == "RF_SHANK"] + assert len(rf_shank_indices) == 2, f"Expected 2 RF_SHANK indices, got {rf_shank_indices}" + + # Acquire ground truth body indices + robot_base_body_idx = scene.articulations["robot"].find_bodies("base")[0][0] + robot_1_base_body_idx = scene.articulations["robot_1"].find_bodies("base")[0][0] + robot_lf_shank_body_idx = scene.articulations["robot"].find_bodies("LF_SHANK")[0][0] + robot_1_lf_shank_body_idx = scene.articulations["robot_1"].find_bodies("LF_SHANK")[0][0] + robot_rf_shank_body_idx = scene.articulations["robot"].find_bodies("RF_SHANK")[0][0] + robot_1_rf_shank_body_idx = scene.articulations["robot_1"].find_bodies("RF_SHANK")[0][0] + + # Determine expected source frame based on parameter + expected_source_robot = "robot" if source_robot == "Robot" else "robot_1" + expected_source_base_body_idx = robot_base_body_idx if source_robot == "Robot" else robot_1_base_body_idx + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + + # Simulate physics + for count in range(20): + # Reset periodically + if count % 10 == 0: + # Reset robot + root_state = scene.articulations["robot"].data.default_root_state.clone() + root_state[:, :3] += scene.env_origins + scene.articulations["robot"].write_root_pose_to_sim(root_state[:, :7]) + scene.articulations["robot"].write_root_velocity_to_sim(root_state[:, 7:]) + scene.articulations["robot"].write_joint_state_to_sim( + scene.articulations["robot"].data.default_joint_pos, + scene.articulations["robot"].data.default_joint_vel, + ) + # Reset robot_1 + root_state_1 = scene.articulations["robot_1"].data.default_root_state.clone() + root_state_1[:, :3] += scene.env_origins + scene.articulations["robot_1"].write_root_pose_to_sim(root_state_1[:, :7]) + scene.articulations["robot_1"].write_root_velocity_to_sim(root_state_1[:, 7:]) + scene.articulations["robot_1"].write_joint_state_to_sim( + scene.articulations["robot_1"].data.default_joint_pos, + scene.articulations["robot_1"].data.default_joint_vel, + ) + scene.reset() + + # Write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + # Read data from sim + scene.update(sim_dt) + + # Get frame transformer data + frame_transformer_data = scene.sensors["frame_transformer"].data + source_pos_w = frame_transformer_data.source_pos_w + source_quat_w = frame_transformer_data.source_quat_w + target_pos_w = frame_transformer_data.target_pos_w + + # Get ground truth positions and orientations (after scene.update() so they're current) + robot_lf_pos_w = scene.articulations["robot"].data.body_pos_w[:, robot_lf_shank_body_idx] + robot_1_lf_pos_w = scene.articulations["robot_1"].data.body_pos_w[:, robot_1_lf_shank_body_idx] + robot_rf_pos_w = scene.articulations["robot"].data.body_pos_w[:, robot_rf_shank_body_idx] + robot_1_rf_pos_w = scene.articulations["robot_1"].data.body_pos_w[:, robot_1_rf_shank_body_idx] + + # Get expected source frame positions and orientations (after scene.update() so they're current) + expected_source_base_pos_w = scene.articulations[expected_source_robot].data.body_pos_w[ + :, expected_source_base_body_idx + ] + expected_source_base_quat_w = scene.articulations[expected_source_robot].data.body_quat_w[ + :, expected_source_base_body_idx + ] + + # TEST 1: Verify source frame is correctly resolved + # The source_pos_w should match the expected source robot's base world position + torch.testing.assert_close(source_pos_w, expected_source_base_pos_w, rtol=1e-5, atol=1e-5) + torch.testing.assert_close(source_quat_w, expected_source_base_quat_w, rtol=1e-5, atol=1e-5) + + # TEST 2: Explicit named frames (LF_SHANK) should have DIFFERENT world positions + lf_pos_difference = torch.norm(target_pos_w[:, robot_lf_idx] - target_pos_w[:, robot_1_lf_idx], dim=-1) + assert torch.all(lf_pos_difference > 1.0), ( + f"Robot_LF_SHANK and Robot_1_LF_SHANK should have different positions (got diff={lf_pos_difference}). " + "This indicates body name collision bug." + ) + + # Verify explicit named frames match correct robot bodies + torch.testing.assert_close(target_pos_w[:, robot_lf_idx], robot_lf_pos_w) + torch.testing.assert_close(target_pos_w[:, robot_1_lf_idx], robot_1_lf_pos_w) + + # TEST 3: Implicit named frames (RF_SHANK) should also have DIFFERENT world positions + # Even though they have the same frame name, internal body tracking uses full paths + rf_pos_difference = torch.norm( + target_pos_w[:, rf_shank_indices[0]] - target_pos_w[:, rf_shank_indices[1]], dim=-1 + ) + assert torch.all(rf_pos_difference > 1.0), ( + f"The two RF_SHANK frames should have different positions (got diff={rf_pos_difference}). " + "This indicates body name collision bug in internal body tracking." + ) + + # Verify implicit named frames match correct robot bodies + # Note: Order depends on internal processing, so we check both match one of the robots + rf_positions = [target_pos_w[:, rf_shank_indices[0]], target_pos_w[:, rf_shank_indices[1]]] + + # Each tracked position should match one of the ground truth positions + for rf_pos in rf_positions: + matches_robot = torch.allclose(rf_pos, robot_rf_pos_w, atol=1e-5) + matches_robot_1 = torch.allclose(rf_pos, robot_1_rf_pos_w, atol=1e-5) + assert matches_robot or matches_robot_1, ( + f"RF_SHANK position {rf_pos} doesn't match either robot's RF_SHANK position" + ) diff --git a/source/isaaclab/test/sensors/test_imu.py b/source/isaaclab/test/sensors/test_imu.py new file mode 100644 index 0000000000000000000000000000000000000000..92c97f0c6d70fac9dadc6cac9fc4b69593b0622c --- /dev/null +++ b/source/isaaclab/test/sensors/test_imu.py @@ -0,0 +1,757 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import pathlib + +import pytest +import torch + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.markers.config import GREEN_ARROW_X_MARKER_CFG, RED_ARROW_X_MARKER_CFG +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.sensors.imu import Imu, ImuCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR # isort: skip + +# offset of imu_link from base_link on anymal_c +POS_OFFSET = (0.2488, 0.00835, 0.04628) +ROT_OFFSET = (0.7071068, 0, 0, 0.7071068) + +# offset of imu_link from link_1 on simple_2_link +PEND_POS_OFFSET = (0.4, 0.0, 0.1) +PEND_ROT_OFFSET = (0.5, 0.5, 0.5, 0.5) + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Example scene configuration.""" + + # terrain - flat terrain plane + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + max_init_terrain_level=None, + ) + + # rigid objects - balls + balls = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/ball", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 0.5)), + spawn=sim_utils.SphereCfg( + radius=0.25, + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + ) + + cube = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/cube", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -2.0, 0.5)), + spawn=sim_utils.CuboidCfg( + size=(0.25, 0.25, 0.25), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.5), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)), + ), + ) + + # articulations - robot + robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/robot") + # pendulum1 + pendulum = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/pendulum", + spawn=sim_utils.UrdfFileCfg( + fix_base=True, + merge_fixed_joints=False, + make_instanceable=False, + asset_path=f"{pathlib.Path(__file__).parent.resolve()}/urdfs/simple_2_link.urdf", + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + joint_drive=sim_utils.UrdfConverterCfg.JointDriveCfg( + gains=sim_utils.UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=None, damping=None) + ), + ), + init_state=ArticulationCfg.InitialStateCfg(), + actuators={ + "joint_1_act": ImplicitActuatorCfg(joint_names_expr=["joint_.*"], stiffness=0.0, damping=0.3), + }, + ) + # pendulum2 + pendulum2 = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/pendulum2", + spawn=sim_utils.UrdfFileCfg( + fix_base=True, + merge_fixed_joints=True, + make_instanceable=False, + asset_path=f"{pathlib.Path(__file__).parent.resolve()}/urdfs/simple_2_link.urdf", + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + joint_drive=sim_utils.UrdfConverterCfg.JointDriveCfg( + gains=sim_utils.UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=None, damping=None) + ), + ), + init_state=ArticulationCfg.InitialStateCfg(), + actuators={ + "joint_1_act": ImplicitActuatorCfg(joint_names_expr=["joint_.*"], stiffness=0.0, damping=0.3), + }, + ) + + # sensors - imu (filled inside unit test) + imu_ball: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/ball", + gravity_bias=(0.0, 0.0, 0.0), + ) + imu_cube: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/cube", + gravity_bias=(0.0, 0.0, 0.0), + ) + imu_robot_imu_link: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/robot/imu_link", + gravity_bias=(0.0, 0.0, 0.0), + ) + imu_robot_base: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/robot/base", + offset=ImuCfg.OffsetCfg( + pos=POS_OFFSET, + rot=ROT_OFFSET, + ), + gravity_bias=(0.0, 0.0, 0.0), + ) + imu_robot_norb: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/robot/LF_HIP/LF_hip_fixed", + offset=ImuCfg.OffsetCfg( + pos=POS_OFFSET, + rot=ROT_OFFSET, + ), + gravity_bias=(0.0, 0.0, 0.0), + ) + imu_indirect_pendulum_link: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/pendulum2/link_1/imu_link", + debug_vis=not app_launcher._headless, + visualizer_cfg=RED_ARROW_X_MARKER_CFG.replace(prim_path="/Visuals/Acceleration/imu_link"), + gravity_bias=(0.0, 0.0, 9.81), + ) + imu_indirect_pendulum_base: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/pendulum2/link_1", + offset=ImuCfg.OffsetCfg( + pos=PEND_POS_OFFSET, + rot=PEND_ROT_OFFSET, + ), + debug_vis=not app_launcher._headless, + visualizer_cfg=GREEN_ARROW_X_MARKER_CFG.replace(prim_path="/Visuals/Acceleration/base"), + gravity_bias=(0.0, 0.0, 9.81), + ) + imu_pendulum_imu_link: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/pendulum/imu_link", + debug_vis=not app_launcher._headless, + visualizer_cfg=RED_ARROW_X_MARKER_CFG.replace(prim_path="/Visuals/Acceleration/imu_link"), + gravity_bias=(0.0, 0.0, 9.81), + ) + imu_pendulum_base: ImuCfg = ImuCfg( + prim_path="{ENV_REGEX_NS}/pendulum/link_1", + offset=ImuCfg.OffsetCfg( + pos=PEND_POS_OFFSET, + rot=PEND_ROT_OFFSET, + ), + debug_vis=not app_launcher._headless, + visualizer_cfg=GREEN_ARROW_X_MARKER_CFG.replace(prim_path="/Visuals/Acceleration/base"), + gravity_bias=(0.0, 0.0, 9.81), + ) + + def __post_init__(self): + """Post initialization.""" + # change position of the robot + self.robot.init_state.pos = (0.0, 2.0, 1.0) + self.pendulum.init_state.pos = (-2.0, 1.0, 0.5) + self.pendulum2.init_state.pos = (2.0, 1.0, 0.5) + + # change asset + self.robot.spawn.usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_c/anymal_c.usd" + # change iterations + self.robot.spawn.articulation_props.solver_position_iteration_count = 32 + self.robot.spawn.articulation_props.solver_velocity_iteration_count = 32 + + +@pytest.fixture +def setup_sim(): + """Create a simulation context and scene.""" + # Create a new stage + sim_utils.create_new_stage() + # Load simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.001) + sim_cfg.physx.solver_type = 0 # 0: PGS, 1: TGS --> use PGS for more accurate results + sim = sim_utils.SimulationContext(sim_cfg) + # construct scene + scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False) + scene = InteractiveScene(scene_cfg) + # Play the simulator + sim.reset() + yield sim, scene + # Cleanup + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_constant_velocity(setup_sim): + """Test the Imu sensor with a constant velocity. + + Expected behavior is that the linear and angular are approx the same at every time step as in each step we set + the same velocity and therefore reset the physx buffers. + """ + sim, scene = setup_sim + prev_lin_acc_ball = torch.zeros((scene.num_envs, 3), dtype=torch.float32, device=scene.device) + prev_ang_acc_ball = torch.zeros((scene.num_envs, 3), dtype=torch.float32, device=scene.device) + prev_lin_acc_cube = torch.zeros((scene.num_envs, 3), dtype=torch.float32, device=scene.device) + prev_ang_acc_cube = torch.zeros((scene.num_envs, 3), dtype=torch.float32, device=scene.device) + + for idx in range(200): + # set velocity + scene.rigid_objects["balls"].write_root_velocity_to_sim( + torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ) + ) + scene.rigid_objects["cube"].write_root_velocity_to_sim( + torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ) + ) + # write data to sim + scene.write_data_to_sim() + + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + if idx > 1: + # check the imu accelerations + torch.testing.assert_close( + scene.sensors["imu_ball"].data.lin_acc_b, + prev_lin_acc_ball, + rtol=1e-3, + atol=1e-3, + ) + torch.testing.assert_close( + scene.sensors["imu_ball"].data.ang_acc_b, + prev_ang_acc_ball, + rtol=1e-3, + atol=1e-3, + ) + + torch.testing.assert_close( + scene.sensors["imu_cube"].data.lin_acc_b, + prev_lin_acc_cube, + rtol=1e-3, + atol=1e-3, + ) + torch.testing.assert_close( + scene.sensors["imu_cube"].data.ang_acc_b, + prev_ang_acc_cube, + rtol=1e-3, + atol=1e-3, + ) + + # check the imu velocities + # NOTE: the expected lin_vel_b is the same as the set velocity, as write_root_velocity_to_sim is + # setting v_0 (initial velocity) and then a calculation step of v_i = v_0 + a*dt. Consequently, + # the data.lin_vel_b is returning approx. v_i. + torch.testing.assert_close( + scene.sensors["imu_ball"].data.lin_vel_b, + torch.tensor([[1.0, 0.0, -scene.physics_dt * 9.81]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ), + rtol=1e-4, + atol=1e-4, + ) + torch.testing.assert_close( + scene.sensors["imu_cube"].data.lin_vel_b, + torch.tensor([[1.0, 0.0, -scene.physics_dt * 9.81]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ), + rtol=1e-4, + atol=1e-4, + ) + + # update previous values + prev_lin_acc_ball = scene.sensors["imu_ball"].data.lin_acc_b.clone() + prev_ang_acc_ball = scene.sensors["imu_ball"].data.ang_acc_b.clone() + prev_lin_acc_cube = scene.sensors["imu_cube"].data.lin_acc_b.clone() + prev_ang_acc_cube = scene.sensors["imu_cube"].data.ang_acc_b.clone() + + +@pytest.mark.isaacsim_ci +def test_constant_acceleration(setup_sim): + """Test the Imu sensor with a constant acceleration.""" + sim, scene = setup_sim + for idx in range(100): + # set acceleration + scene.rigid_objects["balls"].write_root_velocity_to_sim( + torch.tensor([[0.1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ) + * (idx + 1) + ) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + # skip first step where initial velocity is zero + if idx < 1: + continue + + # check the imu data + torch.testing.assert_close( + scene.sensors["imu_ball"].data.lin_acc_b, + math_utils.quat_apply_inverse( + scene.rigid_objects["balls"].data.root_quat_w, + torch.tensor([[0.1, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat(scene.num_envs, 1) + / sim.get_physics_dt(), + ), + rtol=1e-4, + atol=1e-4, + ) + + # check the angular velocity + torch.testing.assert_close( + scene.sensors["imu_ball"].data.ang_vel_b, + scene.rigid_objects["balls"].data.root_ang_vel_b, + rtol=1e-4, + atol=1e-4, + ) + + +@pytest.mark.isaacsim_ci +def test_single_dof_pendulum(setup_sim): + """Test imu against analytical pendulum problem.""" + sim, scene = setup_sim + # pendulum length + pend_length = PEND_POS_OFFSET[0] + + # should achieve same results between the two imu sensors on the robot + for idx in range(500): + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + # get pendulum joint state + joint_pos = scene.articulations["pendulum"].data.joint_pos + joint_vel = scene.articulations["pendulum"].data.joint_vel + joint_acc = scene.articulations["pendulum"].data.joint_acc + + # IMU and base data + imu_data = scene.sensors["imu_pendulum_imu_link"].data + base_data = scene.sensors["imu_pendulum_base"].data + + # extract imu_link imu_sensor dynamics + lin_vel_w_imu_link = math_utils.quat_apply(imu_data.quat_w, imu_data.lin_vel_b) + lin_acc_w_imu_link = math_utils.quat_apply(imu_data.quat_w, imu_data.lin_acc_b) + + # calculate the joint dynamics from the imu_sensor (y axis of imu_link is parallel to joint axis of pendulum) + joint_vel_imu = math_utils.quat_apply(imu_data.quat_w, imu_data.ang_vel_b)[..., 1].unsqueeze(-1) + joint_acc_imu = math_utils.quat_apply(imu_data.quat_w, imu_data.ang_acc_b)[..., 1].unsqueeze(-1) + + # calculate analytical solution + vx = -joint_vel * pend_length * torch.sin(joint_pos) + vy = torch.zeros(2, 1, device=scene.device) + vz = -joint_vel * pend_length * torch.cos(joint_pos) + gt_linear_vel_w = torch.cat([vx, vy, vz], dim=-1) + + ax = -joint_acc * pend_length * torch.sin(joint_pos) - joint_vel**2 * pend_length * torch.cos(joint_pos) + ay = torch.zeros(2, 1, device=scene.device) + az = -joint_acc * pend_length * torch.cos(joint_pos) + joint_vel**2 * pend_length * torch.sin(joint_pos) + 9.81 + gt_linear_acc_w = torch.cat([ax, ay, az], dim=-1) + + # skip first step where initial velocity is zero + if idx < 2: + continue + + # compare imu projected gravity + gravity_dir_w = torch.tensor((0.0, 0.0, -1.0), device=scene.device).repeat(2, 1) + gravity_dir_b = math_utils.quat_apply_inverse(imu_data.quat_w, gravity_dir_w) + torch.testing.assert_close( + imu_data.projected_gravity_b, + gravity_dir_b, + ) + + # compare imu angular velocity with joint velocity + torch.testing.assert_close( + joint_vel, + joint_vel_imu, + rtol=1e-1, + atol=1e-3, + ) + # compare imu angular acceleration with joint acceleration + torch.testing.assert_close( + joint_acc, + joint_acc_imu, + rtol=1e-1, + atol=1e-3, + ) + # compare imu linear velocity with simple pendulum calculation + torch.testing.assert_close( + gt_linear_vel_w, + lin_vel_w_imu_link, + rtol=1e-1, + atol=1e-3, + ) + # compare imu linear acceleration with simple pendulum calculation + torch.testing.assert_close( + gt_linear_acc_w, + lin_acc_w_imu_link, + rtol=1e-1, + atol=1e0, + ) + + # check the position between offset and imu definition + torch.testing.assert_close( + base_data.pos_w, + imu_data.pos_w, + rtol=1e-5, + atol=1e-5, + ) + + # check the orientation between offset and imu definition + torch.testing.assert_close( + base_data.quat_w, + imu_data.quat_w, + rtol=1e-4, + atol=1e-4, + ) + + # check the angular velocities of the imus between offset and imu definition + torch.testing.assert_close( + base_data.ang_vel_b, + imu_data.ang_vel_b, + rtol=1e-4, + atol=1e-4, + ) + # check the angular acceleration of the imus between offset and imu definition + torch.testing.assert_close( + base_data.ang_acc_b, + imu_data.ang_acc_b, + rtol=1e-4, + atol=1e-4, + ) + + # check the linear velocity of the imus between offset and imu definition + torch.testing.assert_close( + base_data.lin_vel_b, + imu_data.lin_vel_b, + rtol=1e-2, + atol=5e-3, + ) + + # check the linear acceleration of the imus between offset and imu definition + torch.testing.assert_close( + base_data.lin_acc_b, + imu_data.lin_acc_b, + rtol=1e-1, + atol=1e-1, + ) + + +@pytest.mark.isaacsim_ci +def test_indirect_attachment(setup_sim): + """Test attaching the imu through an xForm primitive configuration argument.""" + sim, scene = setup_sim + # pendulum length + pend_length = PEND_POS_OFFSET[0] + + # should achieve same results between the two imu sensors on the robot + for idx in range(500): + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + # get pendulum joint state + joint_pos = scene.articulations["pendulum2"].data.joint_pos + joint_vel = scene.articulations["pendulum2"].data.joint_vel + joint_acc = scene.articulations["pendulum2"].data.joint_acc + + imu = scene.sensors["imu_indirect_pendulum_link"] + imu_base = scene.sensors["imu_indirect_pendulum_base"] + + torch.testing.assert_close( + imu._offset_pos_b, + imu_base._offset_pos_b, + ) + torch.testing.assert_close(imu._offset_quat_b, imu_base._offset_quat_b, rtol=1e-4, atol=1e-4) + + # IMU and base data + imu_data = scene.sensors["imu_indirect_pendulum_link"].data + base_data = scene.sensors["imu_indirect_pendulum_base"].data + # extract imu_link imu_sensor dynamics + lin_vel_w_imu_link = math_utils.quat_apply(imu_data.quat_w, imu_data.lin_vel_b) + lin_acc_w_imu_link = math_utils.quat_apply(imu_data.quat_w, imu_data.lin_acc_b) + + # calculate the joint dynamics from the imu_sensor (y axis of imu_link is parallel to joint axis of pendulum) + joint_vel_imu = math_utils.quat_apply(imu_data.quat_w, imu_data.ang_vel_b)[..., 1].unsqueeze(-1) + joint_acc_imu = math_utils.quat_apply(imu_data.quat_w, imu_data.ang_acc_b)[..., 1].unsqueeze(-1) + + # calculate analytical solution + vx = -joint_vel * pend_length * torch.sin(joint_pos) + vy = torch.zeros(2, 1, device=scene.device) + vz = -joint_vel * pend_length * torch.cos(joint_pos) + gt_linear_vel_w = torch.cat([vx, vy, vz], dim=-1) + + ax = -joint_acc * pend_length * torch.sin(joint_pos) - joint_vel**2 * pend_length * torch.cos(joint_pos) + ay = torch.zeros(2, 1, device=scene.device) + az = -joint_acc * pend_length * torch.cos(joint_pos) + joint_vel**2 * pend_length * torch.sin(joint_pos) + 9.81 + gt_linear_acc_w = torch.cat([ax, ay, az], dim=-1) + + # skip first step where initial velocity is zero + if idx < 2: + continue + + # compare imu projected gravity + gravity_dir_w = torch.tensor((0.0, 0.0, -1.0), device=scene.device).repeat(2, 1) + gravity_dir_b = math_utils.quat_apply_inverse(imu_data.quat_w, gravity_dir_w) + torch.testing.assert_close( + imu_data.projected_gravity_b, + gravity_dir_b, + ) + + # compare imu angular velocity with joint velocity + torch.testing.assert_close( + joint_vel, + joint_vel_imu, + rtol=1e-1, + atol=1e-3, + ) + # compare imu angular acceleration with joint acceleration + torch.testing.assert_close( + joint_acc, + joint_acc_imu, + rtol=1e-1, + atol=1e-3, + ) + # compare imu linear velocity with simple pendulum calculation + torch.testing.assert_close( + gt_linear_vel_w, + lin_vel_w_imu_link, + rtol=1e-1, + atol=1e-3, + ) + # compare imu linear acceleration with simple pendulum calculation + torch.testing.assert_close( + gt_linear_acc_w, + lin_acc_w_imu_link, + rtol=1e-1, + atol=1e0, + ) + + # check the position between offset and imu definition + torch.testing.assert_close( + base_data.pos_w, + imu_data.pos_w, + rtol=1e-5, + atol=1e-5, + ) + + # check the orientation between offset and imu definition + torch.testing.assert_close( + base_data.quat_w, + imu_data.quat_w, + rtol=1e-4, + atol=1e-4, + ) + + # check the angular velocities of the imus between offset and imu definition + torch.testing.assert_close( + base_data.ang_vel_b, + imu_data.ang_vel_b, + rtol=1e-4, + atol=1e-4, + ) + # check the angular acceleration of the imus between offset and imu definition + torch.testing.assert_close( + base_data.ang_acc_b, + imu_data.ang_acc_b, + rtol=1e-4, + atol=1e-4, + ) + + # check the linear velocity of the imus between offset and imu definition + torch.testing.assert_close( + base_data.lin_vel_b, + imu_data.lin_vel_b, + rtol=1e-2, + atol=5e-3, + ) + + # check the linear acceleration of the imus between offset and imu definition + torch.testing.assert_close( + base_data.lin_acc_b, + imu_data.lin_acc_b, + rtol=1e-1, + atol=1e-1, + ) + + +@pytest.mark.isaacsim_ci +def test_offset_calculation(setup_sim): + """Test offset configuration argument.""" + sim, scene = setup_sim + + # should achieve same results between the two imu sensors on the robot + for idx in range(500): + # set acceleration + scene.articulations["robot"].write_root_velocity_to_sim( + torch.tensor([[0.05, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ) + * (idx + 1) + ) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + # skip first step where initial velocity is zero + if idx < 1: + continue + + # check the accelerations + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.lin_acc_b, + scene.sensors["imu_robot_imu_link"].data.lin_acc_b, + rtol=1e-4, + atol=1e-4, + ) + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.ang_acc_b, + scene.sensors["imu_robot_imu_link"].data.ang_acc_b, + rtol=1e-4, + atol=1e-4, + ) + + # check the velocities + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.ang_vel_b, + scene.sensors["imu_robot_imu_link"].data.ang_vel_b, + rtol=1e-4, + atol=1e-4, + ) + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.lin_vel_b, + scene.sensors["imu_robot_imu_link"].data.lin_vel_b, + rtol=1e-4, + atol=1e-4, + ) + + # check the orientation + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.quat_w, + scene.sensors["imu_robot_imu_link"].data.quat_w, + rtol=1e-4, + atol=1e-4, + ) + # check the position + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.pos_w, + scene.sensors["imu_robot_imu_link"].data.pos_w, + rtol=1e-4, + atol=1e-4, + ) + # check the projected gravity + torch.testing.assert_close( + scene.sensors["imu_robot_base"].data.projected_gravity_b, + scene.sensors["imu_robot_imu_link"].data.projected_gravity_b, + rtol=1e-4, + atol=1e-4, + ) + + +@pytest.mark.isaacsim_ci +def test_attachment_validity(setup_sim): + """Test invalid imu attachment. An imu cannot be attached directly to the world. It must be somehow attached to + something implementing physics.""" + sim, scene = setup_sim + imu_world_cfg = ImuCfg( + prim_path="/World/envs/env_0", + gravity_bias=(0.0, 0.0, 0.0), + ) + with pytest.raises(RuntimeError) as exc_info: + imu_world = Imu(imu_world_cfg) + imu_world._initialize_impl() + assert exc_info.type is RuntimeError and "find a rigid body ancestor prim" in str(exc_info.value) + + +@pytest.mark.isaacsim_ci +def test_env_ids_propagation(setup_sim): + """Test that env_ids argument propagates through update and reset methods""" + sim, scene = setup_sim + scene.reset() + + for idx in range(10): + # set acceleration + scene.articulations["robot"].write_root_velocity_to_sim( + torch.tensor([[0.5, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype=torch.float32, device=scene.device).repeat( + scene.num_envs, 1 + ) + * (idx + 1) + ) + # write data to sim + scene.write_data_to_sim() + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + # reset scene for env 1 + scene.reset(env_ids=[1]) + # read data from sim + scene.update(sim.get_physics_dt()) + # perform step + sim.step() + # read data from sim + scene.update(sim.get_physics_dt()) + + +@pytest.mark.isaacsim_ci +def test_sensor_print(setup_sim): + """Test sensor print is working correctly.""" + sim, scene = setup_sim + # Create sensor + sensor = scene.sensors["imu_ball"] + # print info + print(sensor) diff --git a/source/isaaclab/test/sensors/test_multi_mesh_ray_caster.py b/source/isaaclab/test/sensors/test_multi_mesh_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..c27b25b53b797dfcc80c36eac0390e49bee935a1 --- /dev/null +++ b/source/isaaclab/test/sensors/test_multi_mesh_ray_caster.py @@ -0,0 +1,250 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +from isaaclab.app import AppLauncher + +# launch omniverse app. Used for warp. +app_launcher = AppLauncher(headless=True) + +import numpy as np +import pytest +import torch +import trimesh +import warp as wp + +from isaaclab.utils.math import matrix_from_quat, quat_from_euler_xyz, random_orientation +from isaaclab.utils.warp.ops import convert_to_warp_mesh, raycast_dynamic_meshes, raycast_single_mesh + + +@pytest.fixture(scope="module") +def device(): + return "cuda" if torch.cuda.is_available() else "cpu" + + +@pytest.fixture +def rays(device): + ray_starts = torch.tensor([[0, -0.35, -5], [0.25, 0.35, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_directions = torch.tensor([[0, 0, 1], [0, 0, 1]], dtype=torch.float32, device=device).unsqueeze(0) + expected_ray_hits = torch.tensor( + [[0, -0.35, -0.5], [0.25, 0.35, -0.5]], dtype=torch.float32, device=device + ).unsqueeze(0) + return ray_starts, ray_directions, expected_ray_hits + + +@pytest.fixture +def trimesh_box(): + return trimesh.creation.box([2, 2, 1]) + + +@pytest.fixture +def single_mesh(trimesh_box, device): + wp_mesh = convert_to_warp_mesh(trimesh_box.vertices, trimesh_box.faces, device) + return wp_mesh, wp_mesh.id + + +def test_raycast_multi_cubes(device, trimesh_box, rays): + """Test raycasting against two cubes.""" + ray_starts, ray_directions, _ = rays + + trimesh_1 = trimesh_box.copy() + wp_mesh_1 = convert_to_warp_mesh(trimesh_1.vertices, trimesh_1.faces, device) + + translation = np.eye(4) + translation[:3, 3] = [0, 2, 0] + trimesh_2 = trimesh_box.copy().apply_transform(translation) + wp_mesh_2 = convert_to_warp_mesh(trimesh_2.vertices, trimesh_2.faces, device) + + # get mesh id array + mesh_ids_wp = wp.array2d([[wp_mesh_1.id, wp_mesh_2.id]], dtype=wp.uint64, device=device) + + # Static positions (no transforms passed) + ray_start = torch.tensor([[0, 0, -5], [0, 2.5, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_ids = raycast_dynamic_meshes( + ray_start, + ray_directions, + mesh_ids_wp, + return_distance=True, + return_normal=True, + return_face_id=True, + return_mesh_id=True, + ) + + torch.testing.assert_close( + ray_hits, torch.tensor([[[0, 0, -0.5], [0, 2.5, -0.5]]], dtype=torch.float32, device=device) + ) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device)) + assert torch.equal(mesh_ids, torch.tensor([[0, 1]], dtype=torch.int32, device=device)) + + # Dynamic positions/orientations + ray_start = torch.tensor([[0, 0, -5], [0, 4.5, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_ids = raycast_dynamic_meshes( + ray_start, + ray_directions, + mesh_ids_wp, + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_positions_w=torch.tensor([[[0, 0, 0], [0, 2, 0]]], dtype=torch.float32, device=device), + mesh_orientations_w=torch.tensor([[[1, 0, 0, 0], [1, 0, 0, 0]]], dtype=torch.float32, device=device), + return_mesh_id=True, + ) + + torch.testing.assert_close( + ray_hits, torch.tensor([[[0, 0, -0.5], [0, 4.5, -0.5]]], dtype=torch.float32, device=device) + ) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device)) + assert torch.equal(mesh_ids, torch.tensor([[0, 1]], dtype=torch.int32, device=device)) + + +def test_raycast_single_cube(device, single_mesh, rays): + """Test raycasting against a single cube.""" + ray_starts, ray_directions, expected_ray_hits = rays + _, single_mesh_id = single_mesh + + ray_hits, ray_distance, ray_normal, ray_face_id = raycast_single_mesh( + ray_starts, + ray_directions, + single_mesh_id, + return_distance=True, + return_normal=True, + return_face_id=True, + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device)) + torch.testing.assert_close( + ray_normal, + torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device), + ) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + # check multiple meshes implementation + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device)) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + +@pytest.mark.parametrize("num_samples", [10]) +def test_raycast_moving_cube(device, single_mesh, rays, num_samples): + r"""Test raycasting against a single cube with different distances. + |-------------| + |\ | + | \ | + | \ 8 | + | \ | + | \ x_1 | + | \ | + | \ | + | \ | + | \ | + | \ | + | 3 x_2 \ | + | \ | + | \| + |-------------| + + """ + ray_starts, ray_directions, expected_ray_hits = rays + _, single_mesh_id = single_mesh + + # move the cube along the z axis + for distance in torch.linspace(0, 1, num_samples, device=device): + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_id = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + return_mesh_id=True, + mesh_positions_w=torch.tensor([[0, 0, distance]], dtype=torch.float32, device=device), + ) + torch.testing.assert_close( + ray_hits, + expected_ray_hits + + torch.tensor([[0, 0, distance], [0, 0, distance]], dtype=torch.float32, device=device).unsqueeze(0), + ) + torch.testing.assert_close( + ray_distance, distance + torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device) + ) + torch.testing.assert_close( + ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device) + ) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + +def test_raycast_rotated_cube(device, single_mesh, rays): + """Test raycasting against a single cube with different 90deg. orientations.""" + ray_starts, ray_directions, expected_ray_hits = rays + _, single_mesh_id = single_mesh + + cube_rotation = quat_from_euler_xyz(torch.tensor([0.0]), torch.tensor([0.0]), torch.tensor([np.pi])).to(device) + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_orientations_w=cube_rotation.unsqueeze(0), + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], dtype=torch.float32, device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], dtype=torch.float32, device=device)) + # Make sure the face ids are correct. The cube is rotated by 90deg. so the face ids are different. + torch.testing.assert_close(ray_face_id, torch.tensor([[8, 3]], dtype=torch.int32, device=device)) + + +@pytest.mark.parametrize("num_random", [10]) +def test_raycast_random_cube(device, trimesh_box, single_mesh, rays, num_random): + """Test raycasting against a single cube with random poses.""" + ray_starts, ray_directions, _ = rays + _, single_mesh_id = single_mesh + + for orientation in random_orientation(num_random, device): + pos = torch.tensor([[0, 0, torch.rand(1)]], dtype=torch.float32, device=device) + tf_hom = np.eye(4) + tf_hom[:3, :3] = matrix_from_quat(orientation).cpu().numpy() + tf_hom[:3, 3] = pos.cpu().numpy() + tf_mesh = trimesh_box.copy().apply_transform(tf_hom) + + # get raycast for transformed, static mesh + wp_mesh = convert_to_warp_mesh(tf_mesh.vertices, tf_mesh.faces, device) + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[wp_mesh.id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + ) + # get raycast for modified mesh + ray_hits_m, ray_distance_m, ray_normal_m, ray_face_id_m, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_positions_w=pos, + mesh_orientations_w=orientation.view(1, 1, -1), + ) + torch.testing.assert_close(ray_hits, ray_hits_m) + torch.testing.assert_close(ray_distance, ray_distance_m) + torch.testing.assert_close(ray_normal, ray_normal_m) + torch.testing.assert_close(ray_face_id, ray_face_id_m) diff --git a/source/isaaclab/test/sensors/test_multi_mesh_ray_caster_camera.py b/source/isaaclab/test/sensors/test_multi_mesh_ray_caster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..6e30a5fcdc981afd2e5e50795d569eb725be68b2 --- /dev/null +++ b/source/isaaclab/test/sensors/test_multi_mesh_ray_caster_camera.py @@ -0,0 +1,862 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import copy +import os + +import numpy as np +import pytest +import torch + +import omni.replicator.core as rep +from pxr import Gf + +import isaaclab.sim as sim_utils +from isaaclab.sensors.camera import Camera, CameraCfg +from isaaclab.sensors.ray_caster import MultiMeshRayCasterCamera, MultiMeshRayCasterCameraCfg, patterns +from isaaclab.sim import PinholeCameraCfg +from isaaclab.terrains.trimesh.utils import make_plane +from isaaclab.terrains.utils import create_prim_from_mesh +from isaaclab.utils import convert_dict_to_backend +from isaaclab.utils.timer import Timer + +# sample camera poses +POSITION = [2.5, 2.5, 2.5] +QUAT_ROS = [-0.17591989, 0.33985114, 0.82047325, -0.42470819] +QUAT_OPENGL = [0.33985113, 0.17591988, 0.42470818, 0.82047324] +QUAT_WORLD = [-0.3647052, -0.27984815, -0.1159169, 0.88047623] + + +@pytest.fixture(scope="function") +def setup_simulation(): + """Fixture to set up and tear down the simulation environment.""" + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt) + sim: sim_utils.SimulationContext = sim_utils.SimulationContext(sim_cfg) + # Ground-plane + mesh = make_plane(size=(100, 100), height=0.0, center_zero=True) + create_prim_from_mesh("/World/defaultGroundPlane", mesh) + # load stage + sim_utils.update_stage() + + camera_cfg = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=480, + width=640, + ), + data_types=["distance_to_image_plane"], + ) + + # create xform because placement of camera directly under world is not supported + sim_utils.create_prim("/World/Camera", "Xform") + + yield sim, dt, camera_cfg + + # Cleanup + # close all the opened viewport from before. + rep.vp_manager.destroy_hydra_textures("Replicator") + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.parametrize( + "convention,quat", + [ + ("ros", QUAT_ROS), + ("opengl", QUAT_OPENGL), + ("world", QUAT_WORLD), + ], +) +@pytest.mark.isaacsim_ci +def test_camera_init_offset(setup_simulation, convention, quat): + """Test camera initialization with offset using different conventions.""" + sim, dt, camera_cfg = setup_simulation + + # Create camera config with specific convention + cam_cfg_offset = copy.deepcopy(camera_cfg) + cam_cfg_offset.offset = MultiMeshRayCasterCameraCfg.OffsetCfg( + pos=POSITION, + rot=quat, + convention=convention, + ) + sim_utils.create_prim(f"/World/CameraOffset{convention.capitalize()}", "Xform") + cam_cfg_offset.prim_path = f"/World/CameraOffset{convention.capitalize()}" + + camera = MultiMeshRayCasterCamera(cam_cfg_offset) + + # play sim + sim.reset() + + # update camera + camera.update(dt) + + # check that transform is set correctly + np.testing.assert_allclose(camera.data.pos_w[0].cpu().numpy(), cam_cfg_offset.offset.pos) + + del camera + + +@pytest.mark.isaacsim_ci +def test_camera_init(setup_simulation): + """Test camera initialization.""" + sim, dt, camera_cfg = setup_simulation + + # Create camera + camera = MultiMeshRayCasterCamera(cfg=camera_cfg) + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (1, 3) + assert camera.data.quat_w_ros.shape == (1, 4) + assert camera.data.quat_w_world.shape == (1, 4) + assert camera.data.quat_w_opengl.shape == (1, 4) + assert camera.data.intrinsic_matrices.shape == (1, 3, 3) + assert camera.data.image_shape == (camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width) + assert camera.data.info == [{camera_cfg.data_types[0]: None}] + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + del camera + + +@pytest.mark.isaacsim_ci +def test_camera_resolution(setup_simulation): + """Test camera resolution is correctly set.""" + sim, dt, camera_cfg = setup_simulation + + # Create camera + camera = MultiMeshRayCasterCamera(cfg=camera_cfg) + # Play sim + sim.reset() + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + camera.update(dt) + # access image data and compare shapes + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + del camera + + +@pytest.mark.isaacsim_ci +def test_camera_init_intrinsic_matrix(setup_simulation): + """Test camera initialization from intrinsic matrix.""" + sim, dt, camera_cfg = setup_simulation + + # get the first camera + camera_1 = MultiMeshRayCasterCamera(cfg=camera_cfg) + # get intrinsic matrix + sim.reset() + intrinsic_matrix = camera_1.data.intrinsic_matrices[0].cpu().flatten().tolist() + + # initialize from intrinsic matrix + intrinsic_camera_cfg = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsic_matrix, + height=camera_cfg.pattern_cfg.height, + width=camera_cfg.pattern_cfg.width, + focal_length=camera_cfg.pattern_cfg.focal_length, + ), + data_types=["distance_to_image_plane"], + ) + camera_2 = MultiMeshRayCasterCamera(cfg=intrinsic_camera_cfg) + + # play sim + sim.reset() + sim.play() + + # update cameras + camera_1.update(dt) + camera_2.update(dt) + + # check image data + torch.testing.assert_close( + camera_1.data.output["distance_to_image_plane"], + camera_2.data.output["distance_to_image_plane"], + ) + # check that both intrinsic matrices are the same + torch.testing.assert_close( + camera_1.data.intrinsic_matrices[0], + camera_2.data.intrinsic_matrices[0], + ) + + del camera_1, camera_2 + + +@pytest.mark.isaacsim_ci +def test_multi_camera_init(setup_simulation): + """Test multi-camera initialization.""" + sim, dt, camera_cfg = setup_simulation + + # -- camera 1 + cam_cfg_1 = copy.deepcopy(camera_cfg) + cam_cfg_1.prim_path = "/World/Camera_0" + sim_utils.create_prim("/World/Camera_0", "Xform") + # Create camera + cam_1 = MultiMeshRayCasterCamera(cam_cfg_1) + + # -- camera 2 + cam_cfg_2 = copy.deepcopy(camera_cfg) + cam_cfg_2.prim_path = "/World/Camera_1" + sim_utils.create_prim("/World/Camera_1", "Xform") + # Create camera + cam_2 = MultiMeshRayCasterCamera(cam_cfg_2) + + # play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + cam_1.update(dt) + cam_2.update(dt) + # check image data + for cam in [cam_1, cam_2]: + for im_data in cam.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + del cam_1, cam_2 + + +@pytest.mark.isaacsim_ci +def test_camera_set_world_poses(setup_simulation): + """Test camera function to set specific world pose.""" + sim, dt, camera_cfg = setup_simulation + + camera = MultiMeshRayCasterCamera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + position = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + orientation = torch.tensor([QUAT_WORLD], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses(position.clone(), orientation.clone(), convention="world") + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, position) + torch.testing.assert_close(camera.data.quat_w_world, orientation) + + del camera + + +@pytest.mark.isaacsim_ci +def test_camera_set_world_poses_from_view(setup_simulation): + """Test camera function to set specific world pose from view.""" + sim, dt, camera_cfg = setup_simulation + + camera = MultiMeshRayCasterCamera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + eyes = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + quat_ros_gt = torch.tensor([QUAT_ROS], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses_from_view(eyes.clone(), targets.clone()) + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, eyes) + torch.testing.assert_close(camera.data.quat_w_ros, quat_ros_gt) + + del camera + + +@pytest.mark.parametrize("height,width", [(240, 320), (480, 640)]) +@pytest.mark.isaacsim_ci +def test_intrinsic_matrix(setup_simulation, height, width): + """Checks that the camera's set and retrieve methods work for intrinsic matrix.""" + sim, dt, camera_cfg = setup_simulation + + camera_cfg_copy = copy.deepcopy(camera_cfg) + camera_cfg_copy.pattern_cfg.height = height + camera_cfg_copy.pattern_cfg.width = width + camera = MultiMeshRayCasterCamera(camera_cfg_copy) + # play sim + sim.reset() + # Desired properties (obtained from realsense camera at 320x240 resolution) + rs_intrinsic_matrix = [229.31640625, 0.0, 164.810546875, 0.0, 229.826171875, 122.1650390625, 0.0, 0.0, 1.0] + rs_intrinsic_matrix = torch.tensor(rs_intrinsic_matrix, device=camera.device).reshape(3, 3).unsqueeze(0) + # Set matrix into simulator + camera.set_intrinsic_matrices(rs_intrinsic_matrix.clone()) + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # Check that matrix is correct + torch.testing.assert_close(rs_intrinsic_matrix, camera.data.intrinsic_matrices) + + del camera + + +@pytest.mark.isaacsim_ci +def test_throughput(setup_simulation): + """Test camera throughput for different image sizes.""" + sim, dt, camera_cfg = setup_simulation + + # Create directory temp dir to dump the results + file_dir = os.path.dirname(os.path.realpath(__file__)) + temp_dir = os.path.join(file_dir, "output", "camera", "throughput") + os.makedirs(temp_dir, exist_ok=True) + # Create replicator writer + rep_writer = rep.BasicWriter(output_dir=temp_dir, frame_padding=3) + # create camera + camera_cfg_copy = copy.deepcopy(camera_cfg) + camera_cfg_copy.pattern_cfg.height = 480 + camera_cfg_copy.pattern_cfg.width = 640 + camera = MultiMeshRayCasterCamera(camera_cfg_copy) + + # Play simulator + sim.reset() + + # Set camera pose + eyes = torch.tensor([[2.5, 2.5, 2.5]], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + camera.set_world_poses_from_view(eyes, targets) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(5): + # perform rendering + sim.step() + # update camera + with Timer(f"Time taken for updating camera with shape {camera.image_shape}"): + camera.update(dt) + # Save images + with Timer(f"Time taken for writing data with shape {camera.image_shape} "): + # Pack data back into replicator format to save them using its writer + rep_output = {"annotators": {}} + camera_data = convert_dict_to_backend(camera.data.output, backend="numpy") + for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()): + if info is not None: + rep_output["annotators"][key] = {"render_product": {"data": data, **info}} + else: + rep_output["annotators"][key] = {"render_product": {"data": data}} + # Save images + rep_output["trigger_outputs"] = {"on_time": camera.frame[0]} + rep_writer.write(rep_output) + print("----------------------------------------") + # Check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg_copy.pattern_cfg.height, camera_cfg_copy.pattern_cfg.width, 1) + + del camera + + +@pytest.mark.parametrize( + "data_types", + [ + ["distance_to_image_plane", "distance_to_camera", "normals"], + ["distance_to_image_plane"], + ["distance_to_camera"], + ], +) +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera(setup_simulation, data_types): + """Test that ray caster camera output equals USD camera output.""" + sim, dt, camera_cfg = setup_simulation + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + sim_utils.create_prim("/World/Camera_warp", "Xform") + camera_cfg_warp = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=data_types, + ) + + camera_warp = MultiMeshRayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd", + update_period=0, + data_types=data_types, + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-4, 1.0e5) + ), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # convert to torch tensors + eyes = torch.tensor([[2.5, 2.5, 4.5]], dtype=torch.float32, device=camera_warp.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera_warp.device) + # set views + camera_warp.set_world_poses_from_view(eyes, targets) + camera_usd.set_world_poses_from_view(eyes, targets) + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check the intrinsic matrices + torch.testing.assert_close( + camera_usd.data.intrinsic_matrices, + camera_warp.data.intrinsic_matrices, + ) + + # check the apertures + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetHorizontalApertureAttr().Get(), + camera_cfg_warp.pattern_cfg.horizontal_aperture, + ) + + # check image data + for data_type in data_types: + if data_type in camera_usd.data.output and data_type in camera_warp.data.output: + if data_type == "distance_to_camera" or data_type == "distance_to_image_plane": + torch.testing.assert_close( + camera_usd.data.output[data_type], + camera_warp.data.output[data_type], + atol=5e-5, + rtol=5e-6, + ) + elif data_type == "normals": + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output[data_type][..., :3], + camera_warp.data.output[data_type], + rtol=1e-5, + atol=1e-4, + ) + else: + torch.testing.assert_close( + camera_usd.data.output[data_type], + camera_warp.data.output[data_type], + ) + + del camera_usd, camera_warp + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera_offset(setup_simulation): + """Test that ray caster camera output equals USD camera output with offset.""" + sim, dt, camera_cfg = setup_simulation + offset_rot = (-0.1251, 0.3617, 0.8731, -0.3020) + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + sim_utils.create_prim("/World/Camera_warp", "Xform") + camera_cfg_warp = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(2.5, 2.5, 4.0), rot=offset_rot, convention="ros"), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + ) + camera_warp = MultiMeshRayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd", + update_period=0, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(2.5, 2.5, 4.0), rot=offset_rot, convention="ros"), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_image_plane"], + camera_warp.data.output["distance_to_image_plane"], + atol=5e-5, + rtol=5e-6, + ) + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + atol=5e-5, + rtol=5e-6, + ) + + # check normals + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output["normals"][..., :3], + camera_warp.data.output["normals"], + rtol=1e-5, + atol=1e-4, + ) + + del camera_usd, camera_warp + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera_prim_offset(setup_simulation): + """Test that the output of the ray caster camera is equal to the output of the usd camera when both are placed + under an XForm prim that is translated and rotated from the world origin.""" + sim, dt, camera_cfg = setup_simulation + + offset_rot = [-0.1251, 0.3617, 0.8731, -0.3020] + + # gf quat + gf_quatf = Gf.Quatd() + gf_quatf.SetReal(QUAT_OPENGL[0]) + gf_quatf.SetImaginary(tuple(QUAT_OPENGL[1:])) + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + prim_raycast_cam = sim_utils.create_prim("/World/Camera_warp", "Xform") + prim_raycast_cam.GetAttribute("xformOp:translate").Set(tuple(POSITION)) + prim_raycast_cam.GetAttribute("xformOp:orient").Set(gf_quatf) + + camera_cfg_warp = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + ) + + camera_warp = MultiMeshRayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd/camera", + update_period=0, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"), + update_latest_camera_pose=True, + ) + prim_usd = sim_utils.create_prim("/World/Camera_usd", "Xform") + prim_usd.GetAttribute("xformOp:translate").Set(tuple(POSITION)) + prim_usd.GetAttribute("xformOp:orient").Set(gf_quatf) + + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check if pos and orientation are correct + torch.testing.assert_close(camera_warp.data.pos_w[0], camera_usd.data.pos_w[0]) + torch.testing.assert_close(camera_warp.data.quat_w_ros[0], camera_usd.data.quat_w_ros[0]) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_image_plane"], + camera_warp.data.output["distance_to_image_plane"], + atol=5e-5, + rtol=5e-6, + ) + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + rtol=4e-6, + atol=2e-5, + ) + + # check normals + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output["normals"][..., :3], + camera_warp.data.output["normals"], + rtol=1e-5, + atol=1e-4, + ) + + del camera_usd, camera_warp + + +@pytest.mark.parametrize("height,width", [(540, 960), (240, 320)]) +@pytest.mark.isaacsim_ci +def test_output_equal_to_usd_camera_intrinsics(setup_simulation, height, width): + """Test that the output of the ray caster camera and usd camera are the same when both are + initialized with the same intrinsic matrix.""" + sim, dt, camera_cfg = setup_simulation + + # create cameras + offset_rot = [-0.1251, 0.3617, 0.8731, -0.3020] + offset_pos = (2.5, 2.5, 4.0) + intrinsics = [380.0831, 0.0, width / 2, 0.0, 380.0831, height / 2, 0.0, 0.0, 1.0] + sim_utils.create_prim("/World/Camera_warp", "Xform") + # get camera cfgs + camera_warp_cfg = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=height, + width=width, + focal_length=38.0, + ), + max_distance=25.0, + data_types=["distance_to_image_plane"], + ) + camera_usd_cfg = CameraCfg( + prim_path="/World/Camera_usd", + offset=CameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + spawn=PinholeCameraCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=height, + width=width, + clipping_range=(0.01, 25), + focal_length=38.0, + ), + height=height, + width=width, + data_types=["distance_to_image_plane"], + ) + + # set aperture offsets to 0, as currently not supported for usd camera + camera_warp_cfg.pattern_cfg.horizontal_aperture_offset = 0 + camera_warp_cfg.pattern_cfg.vertical_aperture_offset = 0 + camera_usd_cfg.spawn.horizontal_aperture_offset = 0 + camera_usd_cfg.spawn.vertical_aperture_offset = 0 + # init cameras + camera_warp = MultiMeshRayCasterCamera(camera_warp_cfg) + camera_usd = Camera(camera_usd_cfg) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # filter nan and inf from output + cam_warp_output = camera_warp.data.output["distance_to_image_plane"].clone() + cam_usd_output = camera_usd.data.output["distance_to_image_plane"].clone() + cam_warp_output[torch.isnan(cam_warp_output)] = 0 + cam_warp_output[torch.isinf(cam_warp_output)] = 0 + cam_usd_output[torch.isnan(cam_usd_output)] = 0 + cam_usd_output[torch.isinf(cam_usd_output)] = 0 + + # check that both have the same intrinsic matrices + torch.testing.assert_close(camera_warp.data.intrinsic_matrices[0], camera_usd.data.intrinsic_matrices[0]) + + # check the apertures + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetHorizontalApertureAttr().Get(), + camera_warp_cfg.pattern_cfg.horizontal_aperture, + ) + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetVerticalApertureAttr().Get(), + camera_warp_cfg.pattern_cfg.vertical_aperture, + ) + + # check image data + torch.testing.assert_close( + cam_warp_output, + cam_usd_output, + atol=5e-5, + rtol=5e-6, + ) + + del camera_usd, camera_warp + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usd_camera_when_intrinsics_set(setup_simulation): + """Test that the output of the ray caster camera is equal to the output of the usd camera when both are placed + under an XForm prim and an intrinsic matrix is set.""" + sim, dt, camera_cfg = setup_simulation + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=540, + width=960, + ) + camera_cfg_warp = MultiMeshRayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=MultiMeshRayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_camera"], + ) + + camera_warp = MultiMeshRayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=540, + width=960, + prim_path="/World/Camera_usd", + update_period=0, + data_types=["distance_to_camera"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-4, 1.0e5) + ), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # set intrinsic matrix + # NOTE: extend the test to cover aperture offsets once supported by the usd camera + intrinsic_matrix = torch.tensor( + [[380.0831, 0.0, camera_cfg_usd.width / 2, 0.0, 380.0831, camera_cfg_usd.height / 2, 0.0, 0.0, 1.0]], + device=camera_warp.device, + ).reshape(1, 3, 3) + camera_warp.set_intrinsic_matrices(intrinsic_matrix, focal_length=10) + camera_usd.set_intrinsic_matrices(intrinsic_matrix, focal_length=10) + + # set camera position + camera_warp.set_world_poses_from_view( + eyes=torch.tensor([[0.0, 0.0, 5.0]], device=camera_warp.device), + targets=torch.tensor([[0.0, 0.0, 0.0]], device=camera_warp.device), + ) + camera_usd.set_world_poses_from_view( + eyes=torch.tensor([[0.0, 0.0, 5.0]], device=camera_usd.device), + targets=torch.tensor([[0.0, 0.0, 0.0]], device=camera_usd.device), + ) + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + rtol=5e-3, + atol=1e-4, + ) + + del camera_usd, camera_warp diff --git a/source/isaaclab/test/sensors/test_multi_tiled_camera.py b/source/isaaclab/test/sensors/test_multi_tiled_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..a1fb9a178351e21d2ee444988b59b3660c58c93e --- /dev/null +++ b/source/isaaclab/test/sensors/test_multi_tiled_camera.py @@ -0,0 +1,508 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import copy +import random + +import numpy as np +import pytest +import torch +from flaky import flaky + +import omni.replicator.core as rep +from isaacsim.core.prims import SingleGeometryPrim, SingleRigidPrim +from pxr import Gf, UsdGeom + +import isaaclab.sim as sim_utils +from isaaclab.sensors.camera import TiledCamera, TiledCameraCfg + + +@pytest.fixture() +def setup_camera(): + """Create a blank new stage for each test.""" + camera_cfg = TiledCameraCfg( + height=128, + width=256, + offset=TiledCameraCfg.OffsetCfg(pos=(0.0, 0.0, 4.0), rot=(0.0, 0.0, 1.0, 0.0), convention="ros"), + prim_path="/World/Camera", + update_period=0, + data_types=["rgb", "distance_to_camera"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + ) + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt) + sim = sim_utils.SimulationContext(sim_cfg) + # populate scene + _populate_scene() + # load stage + sim_utils.update_stage() + yield camera_cfg, sim, dt + # Teardown + rep.vp_manager.destroy_hydra_textures("Replicator") + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_multi_tiled_camera_init(setup_camera): + """Test initialization of multiple tiled cameras.""" + camera_cfg, sim, dt = setup_camera + num_tiled_cameras = 3 + num_cameras_per_tiled_camera = 7 + + tiled_cameras = [] + for i in range(num_tiled_cameras): + for j in range(num_cameras_per_tiled_camera): + sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor" + camera = TiledCamera(camera_cfg) + tiled_cameras.append(camera) + + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + + # Play sim + sim.reset() + + for i, camera in enumerate(tiled_cameras): + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + for camera in tiled_cameras: + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3) + assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3) + assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width) + + # Simulate physics + for _ in range(10): + # Initialize data arrays + rgbs = [] + distances = [] + + # perform rendering + sim.step() + for i, camera in enumerate(tiled_cameras): + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type == "rgb": + im_data = im_data.clone() / 255.0 + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3) + for j in range(num_cameras_per_tiled_camera): + assert (im_data[j]).mean().item() > 0.0 + rgbs.append(im_data) + elif data_type == "distance_to_camera": + im_data = im_data.clone() + im_data[torch.isinf(im_data)] = 0 + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1) + for j in range(num_cameras_per_tiled_camera): + assert im_data[j].mean().item() > 0.0 + distances.append(im_data) + + # Check data from tiled cameras are consistent, assumes >1 tiled cameras + for i in range(1, num_tiled_cameras): + assert torch.abs(rgbs[0] - rgbs[i]).mean() < 0.05 # images of same color should be below 0.001 + assert torch.abs(distances[0] - distances[i]).mean() < 0.01 # distances of same scene should be 0 + + for camera in tiled_cameras: + del camera + + +@pytest.mark.isaacsim_ci +def test_all_annotators_multi_tiled_camera(setup_camera): + """Test initialization of multiple tiled cameras with all supported annotators.""" + camera_cfg, sim, dt = setup_camera + all_annotator_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + + num_tiled_cameras = 2 + num_cameras_per_tiled_camera = 9 + + tiled_cameras = [] + for i in range(num_tiled_cameras): + for j in range(num_cameras_per_tiled_camera): + sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = all_annotator_types + camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor" + camera = TiledCamera(camera_cfg) + tiled_cameras.append(camera) + + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + + # Play sim + sim.reset() + + for i, camera in enumerate(tiled_cameras): + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert sorted(camera.data.output.keys()) == sorted(all_annotator_types) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + for camera in tiled_cameras: + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3) + assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3) + assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + for i, camera in enumerate(tiled_cameras): + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type in ["rgb", "normals"]: + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3) + elif data_type in [ + "rgba", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ]: + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 4) + for i in range(num_cameras_per_tiled_camera): + assert (im_data[i] / 255.0).mean().item() > 0.0 + elif data_type in ["motion_vectors"]: + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 2) + for i in range(num_cameras_per_tiled_camera): + assert im_data[i].mean().item() != 0.0 + elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]: + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1) + for i in range(num_cameras_per_tiled_camera): + assert im_data[i].mean().item() > 0.0 + + for camera in tiled_cameras: + # access image data and compare dtype + output = camera.data.output + info = camera.data.info + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(info["semantic_segmentation"], dict) + assert isinstance(info["instance_segmentation_fast"], dict) + assert isinstance(info["instance_id_segmentation_fast"], dict) + + for camera in tiled_cameras: + del camera + + +@flaky(max_runs=3, min_passes=1) +@pytest.mark.isaacsim_ci +def test_different_resolution_multi_tiled_camera(setup_camera): + """Test multiple tiled cameras with different resolutions.""" + camera_cfg, sim, dt = setup_camera + num_tiled_cameras = 2 + num_cameras_per_tiled_camera = 6 + + tiled_cameras = [] + resolutions = [(16, 16), (23, 765)] + for i in range(num_tiled_cameras): + for j in range(num_cameras_per_tiled_camera): + sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor" + camera_cfg.height, camera_cfg.width = resolutions[i] + camera = TiledCamera(camera_cfg) + tiled_cameras.append(camera) + + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + + # Play sim + sim.reset() + + for i, camera in enumerate(tiled_cameras): + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + for camera in tiled_cameras: + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3) + assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3) + assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + for i, camera in enumerate(tiled_cameras): + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type == "rgb": + im_data = im_data.clone() / 255.0 + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3) + for j in range(num_cameras_per_tiled_camera): + assert (im_data[j]).mean().item() > 0.0 + elif data_type == "distance_to_camera": + im_data = im_data.clone() + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1) + for j in range(num_cameras_per_tiled_camera): + assert im_data[j].mean().item() > 0.0 + + for camera in tiled_cameras: + del camera + + +@pytest.mark.isaacsim_ci +def test_frame_offset_multi_tiled_camera(setup_camera): + """Test frame offset issue with multiple tiled cameras""" + camera_cfg, sim, dt = setup_camera + num_tiled_cameras = 4 + num_cameras_per_tiled_camera = 4 + + tiled_cameras = [] + for i in range(num_tiled_cameras): + for j in range(num_cameras_per_tiled_camera): + sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor" + camera = TiledCamera(camera_cfg) + tiled_cameras.append(camera) + + # modify scene to be less stochastic + stage = sim_utils.get_current_stage() + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + color = Gf.Vec3f(1, 1, 1) + UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color]) + + # play sim + sim.reset() + + # simulate some steps first to make sure objects are settled + for i in range(100): + # step simulation + sim.step() + # update cameras + for camera in tiled_cameras: + camera.update(dt) + + # collect image data + image_befores = [camera.data.output["rgb"].clone() / 255.0 for camera in tiled_cameras] + + # update scene + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + color = Gf.Vec3f(0, 0, 0) + UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color]) + + # update rendering + sim.step() + + # update cameras + for camera in tiled_cameras: + camera.update(dt) + + # make sure the image is different + image_afters = [camera.data.output["rgb"].clone() / 255.0 for camera in tiled_cameras] + + # check difference is above threshold + for i in range(num_tiled_cameras): + image_before = image_befores[i] + image_after = image_afters[i] + assert torch.abs(image_after - image_before).mean() > 0.02 # images of same color should be below 0.001 + + for camera in tiled_cameras: + del camera + + +@flaky(max_runs=3, min_passes=1) +@pytest.mark.isaacsim_ci +def test_frame_different_poses_multi_tiled_camera(setup_camera): + """Test multiple tiled cameras placed at different poses render different images.""" + camera_cfg, sim, dt = setup_camera + num_tiled_cameras = 3 + num_cameras_per_tiled_camera = 4 + positions = [(0.0, 0.0, 4.0), (0.0, 0.0, 2.0), (0.0, 0.0, 3.0)] + rotations = [(0.0, 0.0, 1.0, 0.0), (0.0, 0.0, 1.0, 0.0), (0.0, 0.0, 1.0, 0.0)] + + tiled_cameras = [] + for i in range(num_tiled_cameras): + for j in range(num_cameras_per_tiled_camera): + sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor" + camera_cfg.offset = TiledCameraCfg.OffsetCfg(pos=positions[i], rot=rotations[i], convention="ros") + camera = TiledCamera(camera_cfg) + tiled_cameras.append(camera) + + # Play sim + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Simulate physics + for _ in range(10): + # Initialize data arrays + rgbs = [] + distances = [] + + # perform rendering + sim.step() + for i, camera in enumerate(tiled_cameras): + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type == "rgb": + im_data = im_data.clone() / 255.0 + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3) + for j in range(num_cameras_per_tiled_camera): + assert (im_data[j]).mean().item() > 0.0 + rgbs.append(im_data) + elif data_type == "distance_to_camera": + im_data = im_data.clone() + # replace inf with 0 + im_data[torch.isinf(im_data)] = 0 + assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1) + for j in range(num_cameras_per_tiled_camera): + assert im_data[j].mean().item() > 0.0 + distances.append(im_data) + + # Check data from tiled cameras are different, assumes >1 tiled cameras + for i in range(1, num_tiled_cameras): + assert torch.abs(rgbs[0] - rgbs[i]).mean() > 0.04 # images of same color should be below 0.001 + assert torch.abs(distances[0] - distances[i]).mean() > 0.01 # distances of same scene should be 0 + + for camera in tiled_cameras: + del camera + + +""" +Helper functions. +""" + + +def _populate_scene(): + """Add prims to the scene.""" + # TODO: this causes hang with Kit 107.3??? + # # Ground-plane + # cfg = sim_utils.GroundPlaneCfg() + # cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.SphereLightCfg() + cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0)) + cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0)) + # Random objects + random.seed(0) + for i in range(10): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # create prim + prim_type = random.choice(["Cube", "Sphere", "Cylinder"]) + prim = sim_utils.create_prim( + f"/World/Objects/Obj_{i:02d}", + prim_type, + translation=position, + scale=(0.25, 0.25, 0.25), + semantic_label=prim_type, + ) + # cast to geom prim + geom_prim = getattr(UsdGeom, prim_type)(prim) + # set random color + color = Gf.Vec3f(random.random(), random.random(), random.random()) + geom_prim.CreateDisplayColorAttr() + geom_prim.GetDisplayColorAttr().Set([color]) + # add rigid properties + SingleGeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True) + SingleRigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0) diff --git a/source/isaaclab/test/sensors/test_outdated_sensor.py b/source/isaaclab/test/sensors/test_outdated_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..ac0c989c683930f2f66511b391b147052610e768 --- /dev/null +++ b/source/isaaclab/test/sensors/test_outdated_sensor.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + + +"""Rest everything follows.""" + +import shutil +import tempfile + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture() +def temp_dir(): + """Fixture to create and clean up a temporary directory for test datasets.""" + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + # create a temporary directory to store the test datasets + temp_dir = tempfile.mkdtemp() + yield temp_dir + # delete the temporary directory after the test + shutil.rmtree(temp_dir) + + +@pytest.mark.parametrize("task_name", ["Isaac-Stack-Cube-Franka-IK-Rel-v0"]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 2]) +@pytest.mark.isaacsim_ci +def test_action_state_recorder_terms(temp_dir, task_name, device, num_envs): + """Check FrameTransformer values after reset.""" + omni.usd.get_context().new_stage() + + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + env_cfg.wait_for_textures = False + + # create environment + env = gym.make(task_name, cfg=env_cfg) + + # disable control on stop + env.unwrapped.sim._app_control_on_stop_handle = None # type: ignore + + # reset environment + obs = env.reset()[0] + + # get the end effector position after the reset + pre_reset_eef_pos = obs["policy"]["eef_pos"].clone() + print(pre_reset_eef_pos) + + # step the environment with idle actions + idle_actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device) + obs = env.step(idle_actions)[0] + + # get the end effector position after the first step + post_reset_eef_pos = obs["policy"]["eef_pos"] + print(post_reset_eef_pos) + + # check if the end effector position is the same after the reset and the first step + torch.testing.assert_close(pre_reset_eef_pos, post_reset_eef_pos, atol=1e-5, rtol=1e-3) + + # close the environment + env.close() diff --git a/source/isaaclab/test/sensors/test_ray_caster.py b/source/isaaclab/test/sensors/test_ray_caster.py new file mode 100644 index 0000000000000000000000000000000000000000..01b2dde1ae2aae871384b54972267245763a7c15 --- /dev/null +++ b/source/isaaclab/test/sensors/test_ray_caster.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import numpy as np +import pytest +import torch +import trimesh + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +# Import after app launch +import warp as wp + +from isaaclab.utils.math import matrix_from_quat, quat_from_euler_xyz, random_orientation +from isaaclab.utils.warp.ops import convert_to_warp_mesh, raycast_dynamic_meshes, raycast_mesh + + +@pytest.fixture(scope="module") +def raycast_setup(): + device = "cuda" if torch.cuda.is_available() else "cpu" + # Base trimesh cube and its Warp conversion + trimesh_mesh = trimesh.creation.box([2, 2, 1]) + single_mesh = [ + convert_to_warp_mesh( + trimesh_mesh.vertices, + trimesh_mesh.faces, + device, + ) + ] + single_mesh_id = single_mesh[0].id + + # Rays + ray_starts = torch.tensor([[0, -0.35, -5], [0.25, 0.35, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_directions = torch.tensor([[0, 0, 1], [0, 0, 1]], dtype=torch.float32, device=device).unsqueeze(0) + expected_ray_hits = torch.tensor( + [[0, -0.35, -0.5], [0.25, 0.35, -0.5]], dtype=torch.float32, device=device + ).unsqueeze(0) + + return { + "device": device, + "trimesh_mesh": trimesh_mesh, + "single_mesh_id": single_mesh_id, + "wp_mesh": single_mesh[0], + "ray_starts": ray_starts, + "ray_directions": ray_directions, + "expected_ray_hits": expected_ray_hits, + } + + +def test_raycast_multi_cubes(raycast_setup): + device = raycast_setup["device"] + base_tm = raycast_setup["trimesh_mesh"] + + tm1 = base_tm.copy() + wp_mesh_1 = convert_to_warp_mesh(tm1.vertices, tm1.faces, device) + + translation = np.eye(4) + translation[:3, 3] = [0, 2, 0] + tm2 = base_tm.copy().apply_transform(translation) + wp_mesh_2 = convert_to_warp_mesh(tm2.vertices, tm2.faces, device) + + mesh_ids_wp = wp.array2d([[wp_mesh_1.id, wp_mesh_2.id]], dtype=wp.uint64, device=device) + + ray_directions = raycast_setup["ray_directions"] + + # Case 1 + ray_start = torch.tensor([[0, 0, -5], [0, 2.5, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_ids = raycast_dynamic_meshes( + ray_start, + ray_directions, + mesh_ids_wp, + return_distance=True, + return_normal=True, + return_face_id=True, + return_mesh_id=True, + ) + + torch.testing.assert_close(ray_hits, torch.tensor([[[0, 0, -0.5], [0, 2.5, -0.5]]], device=device)) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32)) + assert torch.equal(mesh_ids, torch.tensor([[0, 1]], dtype=torch.int32, device=device)) + + # Case 2 (explicit poses/orientations) + ray_start = torch.tensor([[0, 0, -5], [0, 4.5, -5]], dtype=torch.float32, device=device).unsqueeze(0) + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_ids = raycast_dynamic_meshes( + ray_start, + ray_directions, + mesh_ids_wp, + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_positions_w=torch.tensor([[[0, 0, 0], [0, 2, 0]]], dtype=torch.float32, device=device), + mesh_orientations_w=torch.tensor([[[1, 0, 0, 0], [1, 0, 0, 0]]], dtype=torch.float32, device=device), + return_mesh_id=True, + ) + + torch.testing.assert_close(ray_hits, torch.tensor([[[0, 0, -0.5], [0, 4.5, -0.5]]], device=device)) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32)) + assert torch.equal(mesh_ids, torch.tensor([[0, 1]], dtype=torch.int32, device=device)) + + +def test_raycast_single_cube(raycast_setup): + device = raycast_setup["device"] + ray_starts = raycast_setup["ray_starts"] + ray_directions = raycast_setup["ray_directions"] + mesh = raycast_setup["wp_mesh"] + expected_ray_hits = raycast_setup["expected_ray_hits"] + single_mesh_id = raycast_setup["single_mesh_id"] + + # Single-mesh helper + ray_hits, ray_distance, ray_normal, ray_face_id = raycast_mesh( + ray_starts, + ray_directions, + mesh, + return_distance=True, + return_normal=True, + return_face_id=True, + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32)) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + # Multi-mesh API with one mesh + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32)) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + +def test_raycast_moving_cube(raycast_setup): + device = raycast_setup["device"] + ray_starts = raycast_setup["ray_starts"] + ray_directions = raycast_setup["ray_directions"] + single_mesh_id = raycast_setup["single_mesh_id"] + expected_ray_hits = raycast_setup["expected_ray_hits"] + + for distance in torch.linspace(0, 1, 10, device=device): + ray_hits, ray_distance, ray_normal, ray_face_id, mesh_id = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + return_mesh_id=True, + mesh_positions_w=torch.tensor([[0, 0, distance.item()]], dtype=torch.float32, device=device), + ) + offset = torch.tensor([[0, 0, distance.item()], [0, 0, distance.item()]], dtype=torch.float32, device=device) + torch.testing.assert_close(ray_hits, expected_ray_hits + offset.unsqueeze(0)) + torch.testing.assert_close(ray_distance, distance + torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close( + ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32) + ) + torch.testing.assert_close(ray_face_id, torch.tensor([[3, 8]], dtype=torch.int32, device=device)) + + +def test_raycast_rotated_cube(raycast_setup): + device = raycast_setup["device"] + ray_starts = raycast_setup["ray_starts"] + ray_directions = raycast_setup["ray_directions"] + single_mesh_id = raycast_setup["single_mesh_id"] + expected_ray_hits = raycast_setup["expected_ray_hits"] + + cube_rotation = quat_from_euler_xyz( + torch.tensor([0.0], device=device), torch.tensor([0.0], device=device), torch.tensor([np.pi], device=device) + ) + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_orientations_w=cube_rotation.unsqueeze(0), + ) + torch.testing.assert_close(ray_hits, expected_ray_hits) + torch.testing.assert_close(ray_distance, torch.tensor([[4.5, 4.5]], device=device)) + torch.testing.assert_close(ray_normal, torch.tensor([[[0, 0, -1], [0, 0, -1]]], device=device, dtype=torch.float32)) + # Rotated cube swaps face IDs + torch.testing.assert_close(ray_face_id, torch.tensor([[8, 3]], dtype=torch.int32, device=device)) + + +def test_raycast_random_cube(raycast_setup): + device = raycast_setup["device"] + base_tm = raycast_setup["trimesh_mesh"] + ray_starts = raycast_setup["ray_starts"] + ray_directions = raycast_setup["ray_directions"] + single_mesh_id = raycast_setup["single_mesh_id"] + + for orientation in random_orientation(10, device): + pos = torch.tensor([[0.0, 0.0, torch.rand(1, device=device).item()]], dtype=torch.float32, device=device) + + tf_hom = np.eye(4) + tf_hom[:3, :3] = matrix_from_quat(orientation).cpu().numpy() + tf_hom[:3, 3] = pos.squeeze(0).cpu().numpy() + + tf_mesh = base_tm.copy().apply_transform(tf_hom) + wp_mesh = convert_to_warp_mesh(tf_mesh.vertices, tf_mesh.faces, device) + + # Raycast transformed, static mesh + ray_hits, ray_distance, ray_normal, ray_face_id, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[wp_mesh.id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + ) + # Raycast original mesh with pose provided + ray_hits_m, ray_distance_m, ray_normal_m, ray_face_id_m, _ = raycast_dynamic_meshes( + ray_starts, + ray_directions, + wp.array2d([[single_mesh_id]], dtype=wp.uint64, device=device), + return_distance=True, + return_normal=True, + return_face_id=True, + mesh_positions_w=pos, + mesh_orientations_w=orientation.view(1, 1, -1), + ) + + torch.testing.assert_close(ray_hits, ray_hits_m) + torch.testing.assert_close(ray_distance, ray_distance_m) + torch.testing.assert_close(ray_normal, ray_normal_m) + torch.testing.assert_close(ray_face_id, ray_face_id_m) diff --git a/source/isaaclab/test/sensors/test_ray_caster_camera.py b/source/isaaclab/test/sensors/test_ray_caster_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4c2f4a973a86fbd30dcee9ded621c26ecf0c31 --- /dev/null +++ b/source/isaaclab/test/sensors/test_ray_caster_camera.py @@ -0,0 +1,1014 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import copy +import os + +import numpy as np +import pytest +import torch + +import omni.replicator.core as rep +from pxr import Gf + +import isaaclab.sim as sim_utils +from isaaclab.sensors.camera import Camera, CameraCfg +from isaaclab.sensors.ray_caster import RayCasterCamera, RayCasterCameraCfg, patterns +from isaaclab.sim import PinholeCameraCfg +from isaaclab.terrains.trimesh.utils import make_plane +from isaaclab.terrains.utils import create_prim_from_mesh +from isaaclab.utils import convert_dict_to_backend +from isaaclab.utils.timer import Timer + +# sample camera poses +POSITION = [2.5, 2.5, 2.5] +QUAT_ROS = [-0.17591989, 0.33985114, 0.82047325, -0.42470819] +QUAT_OPENGL = [0.33985113, 0.17591988, 0.42470818, 0.82047324] +QUAT_WORLD = [-0.3647052, -0.27984815, -0.1159169, 0.88047623] + +DEBUG_PLOTS = False + + +def setup() -> tuple[sim_utils.SimulationContext, RayCasterCameraCfg, float]: + # Create a blank new stage + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=480, + width=640, + ) + camera_cfg = RayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=[ + "distance_to_image_plane", + ], + ) + # Create a new stage + sim_utils.create_new_stage() + # create xform because placement of camera directly under world is not supported + sim_utils.create_prim("/World/Camera", "Xform") + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt) + sim = sim_utils.SimulationContext(sim_cfg) + # Ground-plane + mesh = make_plane(size=(100, 100), height=0.0, center_zero=True) + create_prim_from_mesh("/World/defaultGroundPlane", mesh) + # load stage + sim_utils.update_stage() + return sim, camera_cfg, dt + + +def teardown(sim: sim_utils.SimulationContext): + # Teardown + # close all the opened viewport from before. + rep.vp_manager.destroy_hydra_textures("Replicator") + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.fixture +def setup_sim(): + """Setup and teardown for each test.""" + sim, camera_cfg, dt = setup() + yield sim, camera_cfg, dt + teardown(sim) + + +@pytest.mark.isaacsim_ci +def test_camera_init(setup_sim): + """Test camera initialization.""" + sim, camera_cfg, dt = setup_sim + # Create camera + camera = RayCasterCamera(cfg=camera_cfg) + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check buffers that exist and have correct shapes + assert camera.data.pos_w.shape == (1, 3) + assert camera.data.quat_w_ros.shape == (1, 4) + assert camera.data.quat_w_world.shape == (1, 4) + assert camera.data.quat_w_opengl.shape == (1, 4) + assert camera.data.intrinsic_matrices.shape == (1, 3, 3) + assert camera.data.image_shape == (camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width) + assert camera.data.info == [{camera_cfg.data_types[0]: None}] + # Simulate physics + for _ in range(10): + sim.step() + camera.update(dt) + # check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + # check the camera reset + camera.reset() + assert torch.all(camera.frame == 0) + # Simulate physics + for _ in range(10): + sim.step() + camera.update(dt) + camera.reset(env_ids=[0]) + assert camera.frame[0] == 0 + + +@pytest.mark.isaacsim_ci +def test_camera_resolution(setup_sim): + """Test camera resolution is correctly set.""" + sim, camera_cfg, dt = setup_sim + # Create camera + camera = RayCasterCamera(cfg=camera_cfg) + # Play sim + sim.reset() + camera.update(dt) + # access image data and compare shapes + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + +@pytest.mark.isaacsim_ci +def test_depth_clipping(setup_sim): + """Test depth clipping. + + .. note:: + + This test is the same for all camera models to enforce the same clipping behavior. + """ + sim, camera_cfg, dt = setup_sim + sim_utils.create_prim("/World/CameraZero", "Xform") + sim_utils.create_prim("/World/CameraNone", "Xform") + sim_utils.create_prim("/World/CameraMax", "Xform") + + # get camera cfgs + camera_cfg_zero = RayCasterCameraCfg( + prim_path="/World/CameraZero", + mesh_prim_paths=["/World/defaultGroundPlane"], + offset=RayCasterCameraCfg.OffsetCfg(pos=(2.5, 2.5, 6.0), rot=(0.9914449, 0.0, 0.1305, 0.0), convention="world"), + pattern_cfg=patterns.PinholeCameraPatternCfg().from_intrinsic_matrix( + focal_length=38.0, + intrinsic_matrix=[380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0], + height=540, + width=960, + ), + max_distance=10.0, + data_types=["distance_to_image_plane", "distance_to_camera"], + depth_clipping_behavior="zero", + ) + camera_zero = RayCasterCamera(camera_cfg_zero) + + camera_cfg_none = copy.deepcopy(camera_cfg_zero) + camera_cfg_none.prim_path = "/World/CameraNone" + camera_cfg_none.depth_clipping_behavior = "none" + camera_none = RayCasterCamera(camera_cfg_none) + + camera_cfg_max = copy.deepcopy(camera_cfg_zero) + camera_cfg_max.prim_path = "/World/CameraMax" + camera_cfg_max.depth_clipping_behavior = "max" + camera_max = RayCasterCamera(camera_cfg_max) + + # Play sim + sim.reset() + + camera_zero.update(dt) + camera_none.update(dt) + camera_max.update(dt) + + # none clipping should contain inf values + assert torch.isinf(camera_none.data.output["distance_to_camera"]).any() + assert torch.isnan(camera_none.data.output["distance_to_image_plane"]).any() + assert ( + camera_none.data.output["distance_to_camera"][~torch.isinf(camera_none.data.output["distance_to_camera"])].max() + > camera_cfg_zero.max_distance + ) + assert ( + camera_none.data.output["distance_to_image_plane"][ + ~torch.isnan(camera_none.data.output["distance_to_image_plane"]) + ].max() + > camera_cfg_zero.max_distance + ) + + # zero clipping should result in zero values + assert torch.all( + camera_zero.data.output["distance_to_camera"][torch.isinf(camera_none.data.output["distance_to_camera"])] == 0.0 + ) + assert torch.all( + camera_zero.data.output["distance_to_image_plane"][ + torch.isnan(camera_none.data.output["distance_to_image_plane"]) + ] + == 0.0 + ) + assert camera_zero.data.output["distance_to_camera"].max() <= camera_cfg_zero.max_distance + assert camera_zero.data.output["distance_to_image_plane"].max() <= camera_cfg_zero.max_distance + + # max clipping should result in max values + assert torch.all( + camera_max.data.output["distance_to_camera"][torch.isinf(camera_none.data.output["distance_to_camera"])] + == camera_cfg_zero.max_distance + ) + assert torch.all( + camera_max.data.output["distance_to_image_plane"][ + torch.isnan(camera_none.data.output["distance_to_image_plane"]) + ] + == camera_cfg_zero.max_distance + ) + assert camera_max.data.output["distance_to_camera"].max() <= camera_cfg_zero.max_distance + assert camera_max.data.output["distance_to_image_plane"].max() <= camera_cfg_zero.max_distance + + +@pytest.mark.isaacsim_ci +def test_camera_init_offset(setup_sim): + """Test camera initialization with offset using different conventions.""" + sim, camera_cfg, dt = setup_sim + # define the same offset in all conventions + # -- ROS convention + cam_cfg_offset_ros = copy.deepcopy(camera_cfg) + cam_cfg_offset_ros.offset = RayCasterCameraCfg.OffsetCfg( + pos=(POSITION[0], POSITION[1], POSITION[2]), + rot=(QUAT_ROS[0], QUAT_ROS[1], QUAT_ROS[2], QUAT_ROS[3]), + convention="ros", + ) + sim_utils.create_prim("/World/CameraOffsetRos", "Xform") + cam_cfg_offset_ros.prim_path = "/World/CameraOffsetRos" + camera_ros = RayCasterCamera(cam_cfg_offset_ros) + # -- OpenGL convention + cam_cfg_offset_opengl = copy.deepcopy(camera_cfg) + cam_cfg_offset_opengl.offset = RayCasterCameraCfg.OffsetCfg( + pos=(POSITION[0], POSITION[1], POSITION[2]), + rot=(QUAT_OPENGL[0], QUAT_OPENGL[1], QUAT_OPENGL[2], QUAT_OPENGL[3]), + convention="opengl", + ) + sim_utils.create_prim("/World/CameraOffsetOpengl", "Xform") + cam_cfg_offset_opengl.prim_path = "/World/CameraOffsetOpengl" + camera_opengl = RayCasterCamera(cam_cfg_offset_opengl) + # -- World convention + cam_cfg_offset_world = copy.deepcopy(camera_cfg) + cam_cfg_offset_world.offset = RayCasterCameraCfg.OffsetCfg( + pos=(POSITION[0], POSITION[1], POSITION[2]), + rot=(QUAT_WORLD[0], QUAT_WORLD[1], QUAT_WORLD[2], QUAT_WORLD[3]), + convention="world", + ) + sim_utils.create_prim("/World/CameraOffsetWorld", "Xform") + cam_cfg_offset_world.prim_path = "/World/CameraOffsetWorld" + camera_world = RayCasterCamera(cam_cfg_offset_world) + + # play sim + sim.reset() + + # update cameras + camera_world.update(dt) + camera_opengl.update(dt) + camera_ros.update(dt) + + # check that all transforms are set correctly + np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos) + np.testing.assert_allclose(camera_opengl.data.pos_w[0].cpu().numpy(), cam_cfg_offset_opengl.offset.pos) + np.testing.assert_allclose(camera_world.data.pos_w[0].cpu().numpy(), cam_cfg_offset_world.offset.pos) + + # check if transform correctly set in output + np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_ros[0].cpu().numpy(), QUAT_ROS, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_opengl[0].cpu().numpy(), QUAT_OPENGL, rtol=1e-5) + np.testing.assert_allclose(camera_ros.data.quat_w_world[0].cpu().numpy(), QUAT_WORLD, rtol=1e-5) + + +@pytest.mark.isaacsim_ci +def test_camera_init_intrinsic_matrix(setup_sim): + """Test camera initialization from intrinsic matrix.""" + sim, camera_cfg, dt = setup_sim + # get the first camera + camera_1 = RayCasterCamera(cfg=camera_cfg) + # get intrinsic matrix + sim.reset() + intrinsic_matrix = camera_1.data.intrinsic_matrices[0].cpu().flatten().tolist() + teardown(sim) + # reinit the first camera + sim, camera_cfg, dt = setup() + camera_1 = RayCasterCamera(cfg=camera_cfg) + # initialize from intrinsic matrix + intrinsic_camera_cfg = RayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsic_matrix, + height=camera_cfg.pattern_cfg.height, + width=camera_cfg.pattern_cfg.width, + focal_length=camera_cfg.pattern_cfg.focal_length, + ), + data_types=[ + "distance_to_image_plane", + ], + ) + camera_2 = RayCasterCamera(cfg=intrinsic_camera_cfg) + + # play sim + sim.reset() + sim.play() + + # update cameras + camera_1.update(dt) + camera_2.update(dt) + + # check image data + torch.testing.assert_close( + camera_1.data.output["distance_to_image_plane"], + camera_2.data.output["distance_to_image_plane"], + ) + # check that both intrinsic matrices are the same + torch.testing.assert_close( + camera_1.data.intrinsic_matrices[0], + camera_2.data.intrinsic_matrices[0], + ) + + +@pytest.mark.isaacsim_ci +def test_multi_camera_init(setup_sim): + """Test multi-camera initialization.""" + sim, camera_cfg, dt = setup_sim + # create two cameras with different prim paths + # -- camera 1 + cam_cfg_1 = copy.deepcopy(camera_cfg) + cam_cfg_1.prim_path = "/World/Camera_1" + sim_utils.create_prim("/World/Camera_1", "Xform") + # Create camera + cam_1 = RayCasterCamera(cam_cfg_1) + # -- camera 2 + cam_cfg_2 = copy.deepcopy(camera_cfg) + cam_cfg_2.prim_path = "/World/Camera_2" + sim_utils.create_prim("/World/Camera_2", "Xform") + cam_2 = RayCasterCamera(cam_cfg_2) + + # check that the loaded meshes are equal + assert cam_1.meshes == cam_2.meshes + + # play sim + sim.reset() + + # Simulate for a few steps + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + cam_1.update(dt) + cam_2.update(dt) + # check image data + for cam in [cam_1, cam_2]: + for im_data in cam.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + +@pytest.mark.isaacsim_ci +def test_camera_set_world_poses(setup_sim): + """Test camera function to set specific world pose.""" + sim, camera_cfg, dt = setup_sim + camera = RayCasterCamera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + position = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + orientation = torch.tensor([QUAT_WORLD], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses(position.clone(), orientation.clone(), convention="world") + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, position) + torch.testing.assert_close(camera.data.quat_w_world, orientation) + + +@pytest.mark.isaacsim_ci +def test_camera_set_world_poses_from_view(setup_sim): + """Test camera function to set specific world pose from view.""" + sim, camera_cfg, dt = setup_sim + camera = RayCasterCamera(camera_cfg) + # play sim + sim.reset() + + # convert to torch tensors + eyes = torch.tensor([POSITION], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + quat_ros_gt = torch.tensor([QUAT_ROS], dtype=torch.float32, device=camera.device) + # set new pose + camera.set_world_poses_from_view(eyes.clone(), targets.clone()) + + # check if transform correctly set in output + torch.testing.assert_close(camera.data.pos_w, eyes) + torch.testing.assert_close(camera.data.quat_w_ros, quat_ros_gt) + + +@pytest.mark.isaacsim_ci +def test_intrinsic_matrix(setup_sim): + """Checks that the camera's set and retrieve methods work for intrinsic matrix.""" + sim, camera_cfg, dt = setup_sim + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.pattern_cfg.height = 240 + camera_cfg.pattern_cfg.width = 320 + camera = RayCasterCamera(camera_cfg) + # play sim + sim.reset() + # Desired properties (obtained from realsense camera at 320x240 resolution) + rs_intrinsic_matrix = [229.31640625, 0.0, 164.810546875, 0.0, 229.826171875, 122.1650390625, 0.0, 0.0, 1.0] + rs_intrinsic_matrix = torch.tensor(rs_intrinsic_matrix, device=camera.device).reshape(3, 3).unsqueeze(0) + # Set matrix into simulator + camera.set_intrinsic_matrices(rs_intrinsic_matrix.clone()) + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # Check that matrix is correct + torch.testing.assert_close(rs_intrinsic_matrix, camera.data.intrinsic_matrices) + + +@pytest.mark.isaacsim_ci +def test_throughput(setup_sim): + """Checks that the single camera gets created properly with a rig.""" + sim, camera_cfg, dt = setup_sim + # Create directory temp dir to dump the results + file_dir = os.path.dirname(os.path.realpath(__file__)) + temp_dir = os.path.join(file_dir, "output", "camera", "throughput") + os.makedirs(temp_dir, exist_ok=True) + # Create replicator writer + rep_writer = rep.BasicWriter(output_dir=temp_dir, frame_padding=3) + # create camera + camera_cfg.pattern_cfg.height = 480 + camera_cfg.pattern_cfg.width = 640 + camera = RayCasterCamera(camera_cfg) + + # Play simulator + sim.reset() + + # Set camera pose + eyes = torch.tensor([[2.5, 2.5, 2.5]], dtype=torch.float32, device=camera.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device) + camera.set_world_poses_from_view(eyes, targets) + + # Simulate for a few steps + for _ in range(5): + sim.step() + # Simulate physics + for _ in range(5): + # perform rendering + sim.step() + # update camera + with Timer(f"Time taken for updating camera with shape {camera.image_shape}"): + camera.update(dt) + # Save images + with Timer(f"Time taken for writing data with shape {camera.image_shape} "): + # Pack data back into replicator format to save them using its writer + rep_output = {"annotators": {}} + camera_data = convert_dict_to_backend({k: v[0] for k, v in camera.data.output.items()}, backend="numpy") + for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()): + if info is not None: + rep_output["annotators"][key] = {"render_product": {"data": data, **info}} + else: + rep_output["annotators"][key] = {"render_product": {"data": data}} + # Save images + rep_output["trigger_outputs"] = {"on_time": camera.frame[0]} + rep_writer.write(rep_output) + print("----------------------------------------") + # Check image data + for im_data in camera.data.output.values(): + assert im_data.shape == (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width, 1) + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera(setup_sim): + sim, camera_cfg, dt = setup_sim + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + sim_utils.create_prim("/World/Camera_warp", "Xform") + camera_cfg_warp = RayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + ) + + camera_warp = RayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd", + update_period=0, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-4, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # convert to torch tensors + eyes = torch.tensor([[2.5, 2.5, 4.5]], dtype=torch.float32, device=camera_warp.device) + targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera_warp.device) + # set views + camera_warp.set_world_poses_from_view(eyes, targets) + camera_usd.set_world_poses_from_view(eyes, targets) + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check the intrinsic matrices + torch.testing.assert_close( + camera_usd.data.intrinsic_matrices, + camera_warp.data.intrinsic_matrices, + ) + + # check the apertures + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetHorizontalApertureAttr().Get(), + camera_cfg_warp.pattern_cfg.horizontal_aperture, + ) + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetVerticalApertureAttr().Get(), + ( + camera_cfg_warp.pattern_cfg.horizontal_aperture + * camera_cfg_warp.pattern_cfg.height + / camera_cfg_warp.pattern_cfg.width + ), + ) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_image_plane"], + camera_warp.data.output["distance_to_image_plane"], + ) + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + atol=5e-5, + rtol=5e-6, + ) + + # check normals + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output["normals"][..., :3], + camera_warp.data.output["normals"], + rtol=1e-5, + atol=1e-4, + ) + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera_offset(setup_sim): + sim, camera_cfg, dt = setup_sim + offset_rot = [-0.1251, 0.3617, 0.8731, -0.3020] + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + sim_utils.create_prim("/World/Camera_warp", "Xform") + camera_cfg_warp = RayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(2.5, 2.5, 4.0), rot=tuple(offset_rot), convention="ros"), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + ) + + camera_warp = RayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd", + update_period=0, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5) + ), + offset=CameraCfg.OffsetCfg( + pos=(2.5, 2.5, 4.0), rot=(offset_rot[0], offset_rot[1], offset_rot[2], offset_rot[3]), convention="ros" + ), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_image_plane"], + camera_warp.data.output["distance_to_image_plane"], + rtol=1e-3, + atol=1e-5, + ) + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + rtol=1e-3, + atol=1e-5, + ) + + # check normals + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output["normals"][..., :3], + camera_warp.data.output["normals"], + rtol=1e-5, + atol=1e-4, + ) + + +@pytest.mark.isaacsim_ci +def test_output_equal_to_usdcamera_prim_offset(setup_sim): + """Test that the output of the ray caster camera is equal to the output of the usd camera when both are placed + under an XForm prim that is translated and rotated from the world origin + .""" + sim, camera_cfg, dt = setup_sim + offset_rot = (-0.1251, 0.3617, 0.8731, -0.3020) + + # gf quat + gf_quatf = Gf.Quatd() + gf_quatf.SetReal(QUAT_OPENGL[0]) + gf_quatf.SetImaginary(tuple(QUAT_OPENGL[1:])) + + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=24.0, + horizontal_aperture=20.955, + height=240, + width=320, + ) + prim_raycast_cam = sim_utils.create_prim("/World/Camera_warp", "Xform") + prim_raycast_cam.GetAttribute("xformOp:translate").Set(tuple(POSITION)) + prim_raycast_cam.GetAttribute("xformOp:orient").Set(gf_quatf) + + camera_cfg_warp = RayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + ) + + camera_warp = RayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=240, + width=320, + prim_path="/World/Camera_usd/camera", + update_period=0, + data_types=["distance_to_image_plane", "distance_to_camera", "normals"], + spawn=PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"), + update_latest_camera_pose=True, + ) + prim_usd = sim_utils.create_prim("/World/Camera_usd", "Xform") + prim_usd.GetAttribute("xformOp:translate").Set(tuple(POSITION)) + prim_usd.GetAttribute("xformOp:orient").Set(gf_quatf) + + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # check if pos and orientation are correct + torch.testing.assert_close(camera_warp.data.pos_w[0], camera_usd.data.pos_w[0]) + torch.testing.assert_close(camera_warp.data.quat_w_ros[0], camera_usd.data.quat_w_ros[0]) + + # check image data + torch.testing.assert_close( + camera_usd.data.output["distance_to_image_plane"], + camera_warp.data.output["distance_to_image_plane"], + rtol=1e-3, + atol=1e-5, + ) + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + rtol=4e-6, + atol=2e-5, + ) + + # check normals + # NOTE: floating point issues of ~1e-5, so using atol and rtol in this case + torch.testing.assert_close( + camera_usd.data.output["normals"][..., :3], + camera_warp.data.output["normals"], + rtol=1e-5, + atol=1e-4, + ) + + +@pytest.mark.parametrize("focal_length", [0.193, 1.93, 19.3]) +@pytest.mark.isaacsim_ci +def test_output_equal_to_usd_camera_intrinsics(setup_sim, focal_length): + """ + Test that the output of the ray caster camera and usd camera are the same when both are + initialized with the same intrinsic matrix. + """ + + sim, camera_cfg, dt = setup_sim + # create cameras + offset_rot = (-0.1251, 0.3617, 0.8731, -0.3020) + offset_pos = (2.5, 2.5, 4.0) + intrinsics = [380.0831, 0.0, 480.0, 0.0, 380.0831, 270.0, 0.0, 0.0, 1.0] + sim_utils.create_prim("/World/Camera_warp", "Xform") + # get camera cfgs + camera_warp_cfg = RayCasterCameraCfg( + prim_path="/World/Camera_warp", + mesh_prim_paths=["/World/defaultGroundPlane"], + offset=RayCasterCameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + debug_vis=False, + pattern_cfg=patterns.PinholeCameraPatternCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=540, + width=960, + focal_length=focal_length, + ), + depth_clipping_behavior="max", + max_distance=20.0, + data_types=["distance_to_image_plane"], + ) + camera_usd_cfg = CameraCfg( + prim_path="/World/Camera_usd", + offset=CameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + spawn=PinholeCameraCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=540, + width=960, + clipping_range=(0.01, 20), + focal_length=focal_length, + ), + height=540, + width=960, + depth_clipping_behavior="max", + data_types=["distance_to_image_plane"], + ) + + # set aperture offsets to 0, as currently not supported for usd camera + camera_warp_cfg.pattern_cfg.horizontal_aperture_offset = 0 + camera_warp_cfg.pattern_cfg.vertical_aperture_offset = 0 + camera_usd_cfg.spawn.horizontal_aperture_offset = 0 + camera_usd_cfg.spawn.vertical_aperture_offset = 0 + # init cameras + camera_warp = RayCasterCamera(camera_warp_cfg) + camera_usd = Camera(camera_usd_cfg) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + # filter nan and inf from output + cam_warp_output = camera_warp.data.output["distance_to_image_plane"].clone() + cam_usd_output = camera_usd.data.output["distance_to_image_plane"].clone() + cam_warp_output[torch.isnan(cam_warp_output)] = 0 + cam_warp_output[torch.isinf(cam_warp_output)] = 0 + cam_usd_output[torch.isnan(cam_usd_output)] = 0 + cam_usd_output[torch.isinf(cam_usd_output)] = 0 + + # check that both have the same intrinsic matrices + torch.testing.assert_close(camera_warp.data.intrinsic_matrices[0], camera_usd.data.intrinsic_matrices[0]) + + # check the apertures + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetHorizontalApertureAttr().Get(), + camera_warp_cfg.pattern_cfg.horizontal_aperture, + ) + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetVerticalApertureAttr().Get(), + camera_warp_cfg.pattern_cfg.vertical_aperture, + ) + + if DEBUG_PLOTS: + # plot both images next to each other plus their difference in a 1x3 grid figure + import matplotlib.pyplot as plt + + fig, axs = plt.subplots(1, 3, figsize=(15, 5)) + usd_plt = axs[0].imshow(cam_usd_output[0].cpu().numpy()) + fig.colorbar(usd_plt, ax=axs[0]) + axs[0].set_title("USD") + warp_plt = axs[1].imshow(cam_warp_output[0].cpu().numpy()) + fig.colorbar(warp_plt, ax=axs[1]) + axs[1].set_title("WARP") + diff_plt = axs[2].imshow(torch.abs(cam_usd_output - cam_warp_output)[0].cpu().numpy()) + fig.colorbar(diff_plt, ax=axs[2]) + axs[2].set_title("Difference") + # save figure + plt.tight_layout() + plt.savefig( + f"{os.path.dirname(os.path.abspath(__file__))}/output/test_output_equal_to_usd_camera_intrinsics_{focal_length}.png" + ) + plt.close() + + # check image data + if focal_length != 0.193: + # FIXME: 0.193 is not working on the IsaacSim/ UsdGeom side, add back once fixed + torch.testing.assert_close( + cam_warp_output, + cam_usd_output, + atol=5e-5, + rtol=5e-6, + ) + + del camera_warp, camera_usd + + +@pytest.mark.parametrize("focal_length_aperture", [(0.193, 0.20955), (1.93, 2.0955), (19.3, 20.955), (0.193, 20.955)]) +@pytest.mark.isaacsim_ci +def test_output_equal_to_usd_camera_when_intrinsics_set(setup_sim, focal_length_aperture): + """ + Test that the output of the ray caster camera is equal to the output of the usd camera when both are placed + under an XForm prim and an intrinsic matrix is set. + """ + # unpack focal length and aperture + focal_length, aperture = focal_length_aperture + + sim, camera_cfg, dt = setup_sim + camera_pattern_cfg = patterns.PinholeCameraPatternCfg( + focal_length=focal_length, + horizontal_aperture=aperture, + height=540, + width=960, + ) + camera_cfg_warp = RayCasterCameraCfg( + prim_path="/World/Camera", + mesh_prim_paths=["/World/defaultGroundPlane"], + update_period=0, + offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)), + debug_vis=False, + pattern_cfg=camera_pattern_cfg, + data_types=["distance_to_camera"], + ) + + camera_warp = RayCasterCamera(camera_cfg_warp) + + # create usd camera + camera_cfg_usd = CameraCfg( + height=540, + width=960, + prim_path="/World/Camera_usd", + update_period=0, + data_types=["distance_to_camera"], + spawn=PinholeCameraCfg( + focal_length=focal_length, focus_distance=400.0, horizontal_aperture=aperture, clipping_range=(1e-4, 1.0e5) + ), + ) + camera_usd = Camera(camera_cfg_usd) + + # play sim + sim.reset() + sim.play() + + # set intrinsic matrix + # NOTE: extend the test to cover aperture offsets once supported by the usd camera + # intrinsic_matrix = torch.tensor( + # [[380.0831, 0.0, camera_cfg_usd.width / 2, 0.0, 380.0831, camera_cfg_usd.height / 2, 0.0, 0.0, 1.0]], + # device=camera_warp.device, + # ).reshape(1, 3, 3) + # camera_warp.set_intrinsic_matrices(intrinsic_matrix, focal_length=10) + # camera_usd.set_intrinsic_matrices(intrinsic_matrix, focal_length=10) + + # set camera position + camera_warp.set_world_poses_from_view( + eyes=torch.tensor([[0.0, 0.0, 5.0]], device=camera_warp.device), + targets=torch.tensor([[0.0, 0.0, 0.0]], device=camera_warp.device), + ) + camera_usd.set_world_poses_from_view( + eyes=torch.tensor([[0.0, 0.0, 5.0]], device=camera_usd.device), + targets=torch.tensor([[0.0, 0.0, 0.0]], device=camera_usd.device), + ) + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_warp.update(dt) + + if DEBUG_PLOTS: + # plot both images next to each other plus their difference in a 1x3 grid figure + import matplotlib.pyplot as plt + + fig, axs = plt.subplots(1, 3, figsize=(15, 5)) + usd_plt = axs[0].imshow(camera_usd.data.output["distance_to_camera"][0].cpu().numpy()) + fig.colorbar(usd_plt, ax=axs[0]) + axs[0].set_title("USD") + warp_plt = axs[1].imshow(camera_warp.data.output["distance_to_camera"][0].cpu().numpy()) + fig.colorbar(warp_plt, ax=axs[1]) + axs[1].set_title("WARP") + diff_plt = axs[2].imshow( + torch.abs(camera_usd.data.output["distance_to_camera"] - camera_warp.data.output["distance_to_camera"])[0] + .cpu() + .numpy() + ) + fig.colorbar(diff_plt, ax=axs[2]) + axs[2].set_title("Difference") + # save figure + plt.tight_layout() + plt.savefig( + f"{os.path.dirname(os.path.abspath(__file__))}/output/test_output_equal_to_usd_camera_when_intrinsics_set_{focal_length}_{aperture}.png" + ) + plt.close() + + # check image data + if focal_length != 0.193: + # FIXME: 0.193 is not working on the IsaacSim/ UsdGeom side, add back once fixed + torch.testing.assert_close( + camera_usd.data.output["distance_to_camera"], + camera_warp.data.output["distance_to_camera"], + rtol=5e-3, + atol=1e-4, + ) + + del camera_warp, camera_usd + + +@pytest.mark.isaacsim_ci +def test_sensor_print(setup_sim): + """Test sensor print is working correctly.""" + sim, camera_cfg, dt = setup_sim + # Create sensor + sensor = RayCasterCamera(cfg=camera_cfg) + # Play sim + sim.reset() + # print info + print(sensor) diff --git a/source/isaaclab/test/sensors/test_sensor_base.py b/source/isaaclab/test/sensors/test_sensor_base.py new file mode 100644 index 0000000000000000000000000000000000000000..1f41ba4ab4e5642dedd4efb5b278bee4694533aa --- /dev/null +++ b/source/isaaclab/test/sensors/test_sensor_base.py @@ -0,0 +1,227 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +from collections.abc import Sequence +from dataclasses import dataclass + +import pytest +import torch + +import isaaclab.sim as sim_utils +from isaaclab.sensors import SensorBase, SensorBaseCfg +from isaaclab.utils import configclass + + +@dataclass +class DummyData: + count: torch.Tensor = None + + +class DummySensor(SensorBase): + def __init__(self, cfg): + super().__init__(cfg) + self._data = DummyData() + + def _initialize_impl(self): + super()._initialize_impl() + self._data.count = torch.zeros((self._num_envs), dtype=torch.int, device=self.device) + + @property + def data(self): + # update sensors if needed + self._update_outdated_buffers() + # return the data (where `_data` is the data for the sensor) + return self._data + + def _update_buffers_impl(self, env_ids: Sequence[int]): + self._data.count[env_ids] += 1 + + def reset(self, env_ids: Sequence[int] | None = None): + super().reset(env_ids=env_ids) + # Resolve sensor ids + if env_ids is None: + env_ids = slice(None) + self._data.count[env_ids] = 0 + + +@configclass +class DummySensorCfg(SensorBaseCfg): + class_type = DummySensor + + prim_path = "/World/envs/env_.*/Cube/dummy_sensor" + + +def _populate_scene(): + """""" + + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.SphereLightCfg() + cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0)) + cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0)) + + # create prims + for i in range(5): + _ = sim_utils.create_prim( + f"/World/envs/env_{i:02d}/Cube", + "Cube", + translation=(i * 1.0, 0.0, 0.0), + scale=(0.25, 0.25, 0.25), + ) + + +@pytest.fixture +def create_dummy_sensor(request, device): + # Create a new stage + sim_utils.create_new_stage() + + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt, device=device) + sim = sim_utils.SimulationContext(sim_cfg) + + # create sensor + _populate_scene() + + sensor_cfg = DummySensorCfg() + + sim_utils.update_stage() + + yield sensor_cfg, sim, dt + + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.parametrize("device", ("cpu", "cuda")) +def test_sensor_init(create_dummy_sensor, device): + """Test that the sensor initializes, steps without update, and forces update.""" + + sensor_cfg, sim, dt = create_dummy_sensor + sensor = DummySensor(cfg=sensor_cfg) + + # Play sim + sim.step() + + sim.reset() + + assert sensor.is_initialized + assert int(sensor.num_instances) == 5 + + # test that the data is not updated + for i in range(10): + sim.step() + sensor.update(dt=dt, force_recompute=True) + expected_value = i + 1 + torch.testing.assert_close( + sensor.data.count, + torch.tensor(expected_value, device=device, dtype=torch.int32).repeat(sensor.num_instances), + ) + assert sensor.data.count.shape[0] == 5 + + # test that the data is not updated if sensor.data is not accessed + for _ in range(5): + sim.step() + sensor.update(dt=dt, force_recompute=False) + torch.testing.assert_close( + sensor._data.count, + torch.tensor(expected_value, device=device, dtype=torch.int32).repeat(sensor.num_instances), + ) + + +@pytest.mark.parametrize("device", ("cpu", "cuda")) +def test_sensor_update_rate(create_dummy_sensor, device): + """Test that the update_rate configuration parameter works by checking the value of the data is old for an update + period of 2. + """ + sensor_cfg, sim, dt = create_dummy_sensor + sensor_cfg.update_period = 2 * dt + sensor = DummySensor(cfg=sensor_cfg) + + # Play sim + sim.step() + + sim.reset() + + assert sensor.is_initialized + assert int(sensor.num_instances) == 5 + expected_value = 1 + for i in range(10): + sim.step() + sensor.update(dt=dt, force_recompute=True) + # count should he half of the number of steps + torch.testing.assert_close( + sensor.data.count, + torch.tensor(expected_value, device=device, dtype=torch.int32).repeat(sensor.num_instances), + ) + expected_value += i % 2 + + +@pytest.mark.parametrize("device", ("cpu", "cuda")) +def test_sensor_reset(create_dummy_sensor, device): + """Test that sensor can be reset for all or partial env ids.""" + sensor_cfg, sim, dt = create_dummy_sensor + sensor = DummySensor(cfg=sensor_cfg) + + # Play sim + sim.step() + sim.reset() + + assert sensor.is_initialized + assert int(sensor.num_instances) == 5 + for i in range(5): + sim.step() + sensor.update(dt=dt) + # count should he half of the number of steps + torch.testing.assert_close( + sensor.data.count, + torch.tensor(i + 1, device=device, dtype=torch.int32).repeat(sensor.num_instances), + ) + + sensor.reset() + + for j in range(5): + sim.step() + sensor.update(dt=dt) + # count should he half of the number of steps + torch.testing.assert_close( + sensor.data.count, + torch.tensor(j + 1, device=device, dtype=torch.int32).repeat(sensor.num_instances), + ) + + reset_ids = [2, 4] + cont_ids = [0, 1, 3] + sensor.reset(env_ids=reset_ids) + + for k in range(5): + sim.step() + sensor.update(dt=dt) + # count should he half of the number of steps + torch.testing.assert_close( + sensor.data.count[reset_ids], + torch.tensor(k + 1, device=device, dtype=torch.int32).repeat(len(reset_ids)), + ) + torch.testing.assert_close( + sensor.data.count[cont_ids], + torch.tensor(k + 6, device=device, dtype=torch.int32).repeat(len(cont_ids)), + ) diff --git a/source/isaaclab/test/sensors/test_tiled_camera.py b/source/isaaclab/test/sensors/test_tiled_camera.py new file mode 100644 index 0000000000000000000000000000000000000000..f160ef35df844ba85b215ff9f8ab351af2ae5384 --- /dev/null +++ b/source/isaaclab/test/sensors/test_tiled_camera.py @@ -0,0 +1,1763 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import copy +import random + +import numpy as np +import pytest +import torch + +import omni.replicator.core as rep +from isaacsim.core.prims import SingleGeometryPrim, SingleRigidPrim +from pxr import Gf, UsdGeom + +import isaaclab.sim as sim_utils +from isaaclab.sensors.camera import Camera, CameraCfg, TiledCamera, TiledCameraCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.timer import Timer + + +@pytest.fixture(scope="function") +def setup_camera(device) -> tuple[sim_utils.SimulationContext, TiledCameraCfg, float]: + """Fixture to set up and tear down the camera simulation environment.""" + camera_cfg = TiledCameraCfg( + height=128, + width=256, + offset=TiledCameraCfg.OffsetCfg(pos=(0.0, 0.0, 4.0), rot=(0.0, 0.0, 1.0, 0.0), convention="ros"), + prim_path="/World/Camera", + update_period=0, + data_types=["rgb", "distance_to_camera"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + ) + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.01 + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt, device=device) + sim: sim_utils.SimulationContext = sim_utils.SimulationContext(sim_cfg) + # populate scene + _populate_scene() + # load stage + sim_utils.update_stage() + yield sim, camera_cfg, dt + # Teardown + rep.vp_manager.destroy_hydra_textures("Replicator") + sim._timeline.stop() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_single_camera_init(setup_camera, device): + """Test single camera initialization.""" + sim, camera_cfg, dt = setup_camera + # Create camera + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[0].GetPath().pathString == camera_cfg.prim_path + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (1, 3) + assert camera.data.quat_w_ros.shape == (1, 4) + assert camera.data.quat_w_world.shape == (1, 4) + assert camera.data.quat_w_opengl.shape == (1, 4) + assert camera.data.intrinsic_matrices.shape == (1, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for im_type, im_data in camera.data.output.items(): + if im_type == "rgb": + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 3) + assert (im_data / 255.0).mean() > 0.0 + elif im_type == "distance_to_camera": + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 1) + assert im_data.mean() > 0.0 + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_depth_clipping_max(setup_camera, device): + """Test depth max clipping.""" + sim, _, dt = setup_camera + # get camera cfgs + camera_cfg = TiledCameraCfg( + prim_path="/World/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(2.5, 2.5, 6.0), rot=(-0.125, 0.362, 0.873, -0.302), convention="ros"), + spawn=sim_utils.PinholeCameraCfg().from_intrinsic_matrix( + focal_length=38.0, + intrinsic_matrix=[380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0], + height=540, + width=960, + clipping_range=(4.9, 5.0), + ), + height=540, + width=960, + data_types=["depth"], + depth_clipping_behavior="max", + ) + camera = TiledCamera(camera_cfg) + + # Play sim + sim.reset() + + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + camera.update(dt) + + assert len(camera.data.output["depth"][torch.isinf(camera.data.output["depth"])]) == 0 + assert camera.data.output["depth"].min() >= camera_cfg.spawn.clipping_range[0] + assert camera.data.output["depth"].max() <= camera_cfg.spawn.clipping_range[1] + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_depth_clipping_none(setup_camera, device): + """Test depth none clipping.""" + sim, _, dt = setup_camera + # get camera cfgs + camera_cfg = TiledCameraCfg( + prim_path="/World/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(2.5, 2.5, 6.0), rot=(-0.125, 0.362, 0.873, -0.302), convention="ros"), + spawn=sim_utils.PinholeCameraCfg().from_intrinsic_matrix( + focal_length=38.0, + intrinsic_matrix=[380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0], + height=540, + width=960, + clipping_range=(4.9, 5.0), + ), + height=540, + width=960, + data_types=["depth"], + depth_clipping_behavior="none", + ) + camera = TiledCamera(camera_cfg) + + # Play sim + sim.reset() + + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + camera.update(dt) + + assert len(camera.data.output["depth"][torch.isinf(camera.data.output["depth"])]) > 0 + assert camera.data.output["depth"].min() >= camera_cfg.spawn.clipping_range[0] + if len(camera.data.output["depth"][~torch.isinf(camera.data.output["depth"])]) > 0: + assert ( + camera.data.output["depth"][~torch.isinf(camera.data.output["depth"])].max() + <= camera_cfg.spawn.clipping_range[1] + ) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_depth_clipping_zero(setup_camera, device): + """Test depth zero clipping.""" + sim, _, dt = setup_camera + # get camera cfgs + camera_cfg = TiledCameraCfg( + prim_path="/World/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(2.5, 2.5, 6.0), rot=(-0.125, 0.362, 0.873, -0.302), convention="ros"), + spawn=sim_utils.PinholeCameraCfg().from_intrinsic_matrix( + focal_length=38.0, + intrinsic_matrix=[380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0], + height=540, + width=960, + clipping_range=(4.9, 5.0), + ), + height=540, + width=960, + data_types=["depth"], + depth_clipping_behavior="zero", + ) + camera = TiledCamera(camera_cfg) + + # Play sim + sim.reset() + + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + camera.update(dt) + + assert len(camera.data.output["depth"][torch.isinf(camera.data.output["depth"])]) == 0 + assert camera.data.output["depth"].min() == 0.0 + assert camera.data.output["depth"].max() <= camera_cfg.spawn.clipping_range[1] + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_multi_camera_init(setup_camera, device): + """Test multi-camera initialization.""" + sim, camera_cfg, dt = setup_camera + + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for im_type, im_data in camera.data.output.items(): + if im_type == "rgb": + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + for i in range(4): + assert (im_data[i] / 255.0).mean() > 0.0 + elif im_type == "distance_to_camera": + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(4): + assert im_data[i].mean() > 0.0 + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_rgb_only_camera(setup_camera, device): + """Test initialization with only RGB data type.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["rgb"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["rgba", "rgb"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + im_data = camera.data.output["rgb"] + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + for i in range(4): + assert (im_data[i] / 255.0).mean() > 0.0 + assert camera.data.output["rgb"].dtype == torch.uint8 + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_data_types(setup_camera, device): + """Test different data types for camera initialization.""" + sim, camera_cfg, dt = setup_camera + # Create camera + camera_cfg_distance = copy.deepcopy(camera_cfg) + camera_cfg_distance.data_types = ["distance_to_camera"] + camera_cfg_distance.prim_path = "/World/CameraDistance" + camera_distance = TiledCamera(camera_cfg_distance) + camera_cfg_depth = copy.deepcopy(camera_cfg) + camera_cfg_depth.data_types = ["depth"] + camera_cfg_depth.prim_path = "/World/CameraDepth" + camera_depth = TiledCamera(camera_cfg_depth) + camera_cfg_both = copy.deepcopy(camera_cfg) + camera_cfg_both.data_types = ["distance_to_camera", "depth"] + camera_cfg_both.prim_path = "/World/CameraBoth" + camera_both = TiledCamera(camera_cfg_both) + + # Play sim + sim.reset() + + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check if cameras are initialized + assert camera_distance.is_initialized + assert camera_depth.is_initialized + assert camera_both.is_initialized + + # Check if camera prims are set correctly and that they are camera prims + assert camera_distance._sensor_prims[0].GetPath().pathString == "/World/CameraDistance" + assert isinstance(camera_distance._sensor_prims[0], UsdGeom.Camera) + assert camera_depth._sensor_prims[0].GetPath().pathString == "/World/CameraDepth" + assert isinstance(camera_depth._sensor_prims[0], UsdGeom.Camera) + assert camera_both._sensor_prims[0].GetPath().pathString == "/World/CameraBoth" + assert isinstance(camera_both._sensor_prims[0], UsdGeom.Camera) + assert list(camera_distance.data.output.keys()) == ["distance_to_camera"] + assert list(camera_depth.data.output.keys()) == ["depth"] + assert list(camera_both.data.output.keys()) == ["depth", "distance_to_camera"] + + del camera_distance + del camera_depth + del camera_both + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_depth_only_camera(setup_camera, device): + """Test initialization with only depth.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["distance_to_camera"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["distance_to_camera"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + im_data = camera.data.output["distance_to_camera"] + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(4): + assert im_data[i].mean() > 0.0 + assert camera.data.output["distance_to_camera"].dtype == torch.float + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_rgba_only_camera(setup_camera, device): + """Test initialization with only RGBA.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["rgba"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["rgba"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(4): + assert (im_data[i] / 255.0).mean() > 0.0 + assert camera.data.output["rgba"].dtype == torch.uint8 + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_distance_to_camera_only_camera(setup_camera, device): + """Test initialization with only distance_to_camera.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["distance_to_camera"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["distance_to_camera"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(4): + assert im_data[i].mean() > 0.0 + assert camera.data.output["distance_to_camera"].dtype == torch.float + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_distance_to_image_plane_only_camera(setup_camera, device): + """Test initialization with only distance_to_image_plane.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["distance_to_image_plane"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["distance_to_image_plane"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(4): + assert im_data[i].mean() > 0.0 + assert camera.data.output["distance_to_image_plane"].dtype == torch.float + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_normals_only_camera(setup_camera, device): + """Test initialization with only normals.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["normals"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["normals"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + for i in range(4): + assert im_data[i].mean() > 0.0 + # check normal norm is approximately 1 + norms = torch.norm(im_data, dim=-1) + assert torch.allclose(norms, torch.ones_like(norms), atol=1e-9) + assert camera.data.output["normals"].dtype == torch.float + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_motion_vectors_only_camera(setup_camera, device): + """Test initialization with only motion_vectors.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["motion_vectors"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["motion_vectors"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 2) + for i in range(4): + assert im_data[i].mean() != 0.0 + assert camera.data.output["motion_vectors"].dtype == torch.float + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_semantic_segmentation_colorize_only_camera(setup_camera, device): + """Test initialization with only semantic_segmentation.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["semantic_segmentation"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["semantic_segmentation"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(4): + assert (im_data[i] / 255.0).mean() > 0.0 + assert camera.data.output["semantic_segmentation"].dtype == torch.uint8 + assert isinstance(camera.data.info["semantic_segmentation"], dict) + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_instance_segmentation_fast_colorize_only_camera(setup_camera, device): + """Test initialization with only instance_segmentation_fast.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["instance_segmentation_fast"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["instance_segmentation_fast"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.0 + assert camera.data.output["instance_segmentation_fast"].dtype == torch.uint8 + assert isinstance(camera.data.info["instance_segmentation_fast"], dict) + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_instance_id_segmentation_fast_colorize_only_camera(setup_camera, device): + """Test initialization with only instance_id_segmentation_fast.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["instance_id_segmentation_fast"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["instance_id_segmentation_fast"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.0 + assert camera.data.output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(camera.data.info["instance_id_segmentation_fast"], dict) + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_semantic_segmentation_non_colorize_only_camera(setup_camera, device): + """Test initialization with only semantic_segmentation.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["semantic_segmentation"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera_cfg.colorize_semantic_segmentation = False + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["semantic_segmentation"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].to(dtype=float).mean() > 0.0 + assert camera.data.output["semantic_segmentation"].dtype == torch.int32 + assert isinstance(camera.data.info["semantic_segmentation"], dict) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_instance_segmentation_fast_non_colorize_only_camera(setup_camera, device): + """Test initialization with only instance_segmentation_fast.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["instance_segmentation_fast"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera_cfg.colorize_instance_segmentation = False + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["instance_segmentation_fast"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].to(dtype=float).mean() > 0.0 + assert camera.data.output["instance_segmentation_fast"].dtype == torch.int32 + assert isinstance(camera.data.info["instance_segmentation_fast"], dict) + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_instance_id_segmentation_fast_non_colorize_only_camera(setup_camera, device): + """Test initialization with only instance_id_segmentation_fast.""" + sim, camera_cfg, dt = setup_camera + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = ["instance_id_segmentation_fast"] + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera_cfg.colorize_instance_id_segmentation = False + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert list(camera.data.output.keys()) == ["instance_id_segmentation_fast"] + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for _, im_data in camera.data.output.items(): + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].to(dtype=float).mean() > 0.0 + assert camera.data.output["instance_id_segmentation_fast"].dtype == torch.int32 + assert isinstance(camera.data.info["instance_id_segmentation_fast"], dict) + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_all_annotators_camera(setup_camera, device): + """Test initialization with all supported annotators.""" + sim, camera_cfg, dt = setup_camera + all_annotator_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + + num_cameras = 9 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = all_annotator_types + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert sorted(camera.data.output.keys()) == sorted(all_annotator_types) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type in ["rgb", "normals"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + elif data_type in [ + "rgba", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.0 + elif data_type in ["motion_vectors"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 2) + for i in range(num_cameras): + assert im_data[i].mean() != 0.0 + elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].mean() > 0.0 + + # access image data and compare dtype + output = camera.data.output + info = camera.data.info + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(info["semantic_segmentation"], dict) + assert isinstance(info["instance_segmentation_fast"], dict) + assert isinstance(info["instance_id_segmentation_fast"], dict) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_all_annotators_low_resolution_camera(setup_camera, device): + """Test initialization with all supported annotators.""" + sim, camera_cfg, dt = setup_camera + all_annotator_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + + num_cameras = 2 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.height = 40 + camera_cfg.width = 40 + camera_cfg.data_types = all_annotator_types + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert sorted(camera.data.output.keys()) == sorted(all_annotator_types) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type in ["rgb", "normals"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + elif data_type in [ + "rgba", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.0 + elif data_type in ["motion_vectors"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 2) + for i in range(num_cameras): + assert im_data[i].mean() != 0.0 + elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].mean() > 0.0 + + # access image data and compare dtype + output = camera.data.output + info = camera.data.info + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(info["semantic_segmentation"], dict) + assert isinstance(info["instance_segmentation_fast"], dict) + assert isinstance(info["instance_id_segmentation_fast"], dict) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_all_annotators_non_perfect_square_number_camera(setup_camera, device): + """Test initialization with all supported annotators.""" + sim, camera_cfg, dt = setup_camera + all_annotator_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + + num_cameras = 11 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.data_types = all_annotator_types + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert sorted(camera.data.output.keys()) == sorted(all_annotator_types) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate physics + for _ in range(10): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type in ["rgb", "normals"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + elif data_type in [ + "rgba", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.0 + elif data_type in ["motion_vectors"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 2) + for i in range(num_cameras): + assert im_data[i].mean() != 0.0 + elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].mean() > 0.0 + + # access image data and compare dtype + output = camera.data.output + info = camera.data.info + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(info["semantic_segmentation"], dict) + assert isinstance(info["instance_segmentation_fast"], dict) + assert isinstance(info["instance_id_segmentation_fast"], dict) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_all_annotators_instanceable(setup_camera, device): + """Test initialization with all supported annotators on instanceable assets.""" + sim, camera_cfg, dt = setup_camera + all_annotator_types = [ + "rgb", + "rgba", + "depth", + "distance_to_camera", + "distance_to_image_plane", + "normals", + "motion_vectors", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ] + + num_cameras = 10 + for i in range(num_cameras): + sim_utils.create_prim(f"/World/Origin_{i}", "Xform", translation=(0.0, i, 0.0)) + + # Create a stage with 10 instanceable cubes, where each camera points to one cube + stage = sim_utils.get_current_stage() + for i in range(10): + # Remove objects added to stage by default + stage.RemovePrim(f"/World/Objects/Obj_{i:02d}") + # Add instanceable cubes + sim_utils.create_prim( + f"/World/Cube_{i}", + "Xform", + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + translation=(0.0, i, 5.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(5.0, 5.0, 5.0), + ) + prim = stage.GetPrimAtPath(f"/World/Cube_{i}") + sim_utils.add_labels(prim, labels=["cube"], instance_name="class") + + # Create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.height = 120 + camera_cfg.width = 80 + camera_cfg.data_types = all_annotator_types + camera_cfg.prim_path = "/World/Origin_.*/CameraSensor" + camera_cfg.offset.pos = (0.0, 0.0, 5.5) + camera = TiledCamera(camera_cfg) + # Check simulation parameter is set correctly + assert sim.has_rtx_sensors() + # Play sim + sim.reset() + # Check if camera is initialized + assert camera.is_initialized + # Check if camera prim is set correctly and that it is a camera prim + assert camera._sensor_prims[1].GetPath().pathString == "/World/Origin_1/CameraSensor" + assert isinstance(camera._sensor_prims[0], UsdGeom.Camera) + assert sorted(camera.data.output.keys()) == sorted(all_annotator_types) + + # Check buffers that exists and have correct shapes + assert camera.data.pos_w.shape == (num_cameras, 3) + assert camera.data.quat_w_ros.shape == (num_cameras, 4) + assert camera.data.quat_w_world.shape == (num_cameras, 4) + assert camera.data.quat_w_opengl.shape == (num_cameras, 4) + assert camera.data.intrinsic_matrices.shape == (num_cameras, 3, 3) + assert camera.data.image_shape == (camera_cfg.height, camera_cfg.width) + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Simulate physics + for _ in range(2): + # perform rendering + sim.step() + # update camera + camera.update(dt) + # check image data + for data_type, im_data in camera.data.output.items(): + if data_type in ["rgb", "normals"]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3) + elif data_type in [ + "rgba", + "semantic_segmentation", + "instance_segmentation_fast", + "instance_id_segmentation_fast", + ]: + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 4) + # semantic_segmentation has mean 0.43 + # rgba has mean 0.38 + # instance_segmentation_fast has mean 0.42 + # instance_id_segmentation_fast has mean 0.55-0.62 + for i in range(num_cameras): + assert (im_data[i] / 255.0).mean() > 0.2 + elif data_type in ["motion_vectors"]: + # motion vectors have mean 0.2 + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 2) + for i in range(num_cameras): + assert (im_data[i].abs().mean()) > 0.15 + elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]: + # depth has mean 2.7 + # distance_to_image_plane has mean 3.1 + assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 1) + for i in range(num_cameras): + assert im_data[i].mean() > 2.5 + + # access image data and compare dtype + output = camera.data.output + info = camera.data.info + assert output["rgb"].dtype == torch.uint8 + assert output["rgba"].dtype == torch.uint8 + assert output["depth"].dtype == torch.float + assert output["distance_to_camera"].dtype == torch.float + assert output["distance_to_image_plane"].dtype == torch.float + assert output["normals"].dtype == torch.float + assert output["motion_vectors"].dtype == torch.float + assert output["semantic_segmentation"].dtype == torch.uint8 + assert output["instance_segmentation_fast"].dtype == torch.uint8 + assert output["instance_id_segmentation_fast"].dtype == torch.uint8 + assert isinstance(info["semantic_segmentation"], dict) + assert isinstance(info["instance_segmentation_fast"], dict) + assert isinstance(info["instance_id_segmentation_fast"], dict) + + del camera + + +@pytest.mark.parametrize("device", ["cuda:0"]) +@pytest.mark.isaacsim_ci +def test_throughput(setup_camera, device): + """Test tiled camera throughput.""" + sim, camera_cfg, dt = setup_camera + # create camera + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.height = 480 + camera_cfg.width = 640 + camera = TiledCamera(camera_cfg) + + # Play simulator + sim.reset() + + # Simulate for a few steps + # note: This is a workaround to ensure that the textures are loaded. + # Check "Known Issues" section in the documentation for more details. + for _ in range(5): + sim.step() + + # Simulate physics + for _ in range(5): + # perform rendering + sim.step() + # update camera + with Timer(f"Time taken for updating camera with shape {camera.image_shape}"): + camera.update(dt) + # Check image data + for im_type, im_data in camera.data.output.items(): + if im_type == "rgb": + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 3) + assert (im_data / 255.0).mean() > 0.0 + elif im_type == "distance_to_camera": + assert im_data.shape == (1, camera_cfg.height, camera_cfg.width, 1) + assert im_data.mean() > 0.0 + del camera + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_output_equal_to_usd_camera_intrinsics(setup_camera, device): + """ + Test that the output of the ray caster camera and the usd camera are the same when both are + initialized with the same intrinsic matrix. + """ + sim, _, dt = setup_camera + # create cameras + offset_rot = (-0.1251, 0.3617, 0.8731, -0.3020) + offset_pos = (2.5, 2.5, 4.0) + intrinsics = [380.08, 0.0, 467.79, 0.0, 380.08, 262.05, 0.0, 0.0, 1.0] + # get camera cfgs + # TODO: add clipping range back, once correctly supported by tiled camera + camera_tiled_cfg = TiledCameraCfg( + prim_path="/World/Camera_tiled", + offset=TiledCameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + spawn=sim_utils.PinholeCameraCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=540, + width=960, + ), + height=540, + width=960, + data_types=["depth"], + ) + camera_usd_cfg = CameraCfg( + prim_path="/World/Camera_usd", + offset=CameraCfg.OffsetCfg(pos=offset_pos, rot=offset_rot, convention="ros"), + spawn=sim_utils.PinholeCameraCfg.from_intrinsic_matrix( + intrinsic_matrix=intrinsics, + height=540, + width=960, + ), + height=540, + width=960, + data_types=["distance_to_image_plane"], + ) + + # set aperture offsets to 0, as currently not supported for usd/ tiled camera + camera_tiled_cfg.spawn.horizontal_aperture_offset = 0 + camera_tiled_cfg.spawn.vertical_aperture_offset = 0 + camera_usd_cfg.spawn.horizontal_aperture_offset = 0 + camera_usd_cfg.spawn.vertical_aperture_offset = 0 + # init cameras + camera_tiled = TiledCamera(camera_tiled_cfg) + camera_usd = Camera(camera_usd_cfg) + + # play sim + sim.reset() + sim.play() + + # perform steps + for _ in range(5): + sim.step() + + # update camera + camera_usd.update(dt) + camera_tiled.update(dt) + + # filter nan and inf from output + cam_tiled_output = camera_tiled.data.output["depth"].clone() + cam_usd_output = camera_usd.data.output["distance_to_image_plane"].clone() + cam_tiled_output[torch.isnan(cam_tiled_output)] = 0 + cam_tiled_output[torch.isinf(cam_tiled_output)] = 0 + cam_usd_output[torch.isnan(cam_usd_output)] = 0 + cam_usd_output[torch.isinf(cam_usd_output)] = 0 + + # check that both have the same intrinsic matrices + torch.testing.assert_close(camera_tiled.data.intrinsic_matrices[0], camera_usd.data.intrinsic_matrices[0]) + + # check the apertures + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetHorizontalApertureAttr().Get(), + camera_tiled._sensor_prims[0].GetHorizontalApertureAttr().Get(), + ) + torch.testing.assert_close( + camera_usd._sensor_prims[0].GetVerticalApertureAttr().Get(), + camera_tiled._sensor_prims[0].GetVerticalApertureAttr().Get(), + ) + + # check image data + torch.testing.assert_close( + cam_tiled_output[..., 0], + cam_usd_output[..., 0], + atol=5e-5, + rtol=5e-6, + ) + + del camera_tiled + del camera_usd + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.isaacsim_ci +def test_sensor_print(setup_camera, device): + """Test sensor print is working correctly.""" + sim, camera_cfg, _ = setup_camera + # Create sensor + sensor = TiledCamera(cfg=camera_cfg) + # Play sim + sim.reset() + # print info + print(sensor) + + +@pytest.mark.parametrize("device", ["cuda:0"]) +@pytest.mark.isaacsim_ci +def test_frame_offset_small_resolution(setup_camera, device): + """Test frame offset issue with small resolution camera.""" + sim, camera_cfg, dt = setup_camera + # Create sensor + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.height = 80 + camera_cfg.width = 80 + camera_cfg.offset.pos = (0.0, 0.0, 0.5) + tiled_camera = TiledCamera(camera_cfg) + # play sim + sim.reset() + # simulate some steps first to make sure objects are settled + stage = sim_utils.get_current_stage() + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + UsdGeom.Gprim(prim).GetOrderedXformOps()[2].Set(Gf.Vec3d(1.0, 1.0, 1.0)) + for i in range(100): + # step simulation + sim.step() + # update camera + tiled_camera.update(dt) + # collect image data + image_before = tiled_camera.data.output["rgb"].clone() / 255.0 + + # update scene + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + color = Gf.Vec3f(0, 0, 0) + UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color]) + + # update rendering + sim.step() + # update camera + tiled_camera.update(dt) + + # make sure the image is different + image_after = tiled_camera.data.output["rgb"].clone() / 255.0 + + # check difference is above threshold + assert torch.abs(image_after - image_before).mean() > 0.1 # images of same color should be below 0.01 + + +@pytest.mark.parametrize("device", ["cuda:0"]) +@pytest.mark.isaacsim_ci +def test_frame_offset_large_resolution(setup_camera, device): + """Test frame offset issue with large resolution camera.""" + sim, camera_cfg, dt = setup_camera + # Create sensor + camera_cfg = copy.deepcopy(camera_cfg) + camera_cfg.height = 480 + camera_cfg.width = 480 + tiled_camera = TiledCamera(camera_cfg) + + # modify scene to be less stochastic + stage = sim_utils.get_current_stage() + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + color = Gf.Vec3f(1, 1, 1) + UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color]) + + # play sim + sim.reset() + # simulate some steps first to make sure objects are settled + for i in range(100): + # step simulation + sim.step() + # update camera + tiled_camera.update(dt) + # collect image data + image_before = tiled_camera.data.output["rgb"].clone() / 255.0 + + # update scene + for i in range(10): + prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}") + color = Gf.Vec3f(0, 0, 0) + UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color]) + + # update rendering + sim.step() + # update camera + tiled_camera.update(dt) + + # make sure the image is different + image_after = tiled_camera.data.output["rgb"].clone() / 255.0 + + # check difference is above threshold + assert torch.abs(image_after - image_before).mean() > 0.01 # images of same color should be below 0.001 + + +""" +Helper functions. +""" + + +@staticmethod +def _populate_scene(): + """Add prims to the scene.""" + # Ground-plane + cfg = sim_utils.GroundPlaneCfg() + cfg.func("/World/defaultGroundPlane", cfg) + # Lights + cfg = sim_utils.SphereLightCfg() + cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0)) + cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0)) + # Random objects + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + for i in range(10): + # sample random position + position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0]) + position *= np.asarray([1.5, 1.5, 0.5]) + # create prim + prim_type = random.choice(["Cube", "Sphere", "Cylinder"]) + prim = sim_utils.create_prim( + f"/World/Objects/Obj_{i:02d}", + prim_type, + translation=position, + scale=(0.25, 0.25, 0.25), + semantic_label=prim_type, + ) + # cast to geom prim + geom_prim = getattr(UsdGeom, prim_type)(prim) + # set random color + color = Gf.Vec3f(random.random(), random.random(), random.random()) + geom_prim.CreateDisplayColorAttr() + geom_prim.GetDisplayColorAttr().Set([color]) + # add rigid properties + SingleGeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True) + SingleRigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0) diff --git a/source/isaaclab/test/sensors/test_tiled_camera_env.py b/source/isaaclab/test/sensors/test_tiled_camera_env.py new file mode 100644 index 0000000000000000000000000000000000000000..5c4d33f6a58e322b9db720019740dc6c41579d17 --- /dev/null +++ b/source/isaaclab/test/sensors/test_tiled_camera_env.py @@ -0,0 +1,143 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import argparse +import sys + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser( + description=( + "Test Isaac-Cartpole-RGB-Camera-Direct-v0 environment with different resolutions and number of environments." + ) +) +parser.add_argument("--save_images", action="store_true", default=False, help="Save out renders to file.") +parser.add_argument("unittest_args", nargs="*") + +# parse the arguments +args_cli = parser.parse_args() +# set the sys.argv to the unittest_args +sys.argv[1:] = args_cli.unittest_args + +# launch the simulator +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import sys + +import gymnasium as gym +import pytest + +import omni.usd + +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg, ManagerBasedRLEnv, ManagerBasedRLEnvCfg +from isaaclab.sensors import save_images_to_file + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_resolutions_tiny(): + """Define settings for resolution and number of environments""" + num_envs = 1024 + tile_widths = range(32, 48) + tile_heights = range(32, 48) + _launch_tests(tile_widths, tile_heights, num_envs) + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_resolutions_small(): + """Define settings for resolution and number of environments""" + num_envs = 300 + tile_widths = range(128, 156) + tile_heights = range(128, 156) + _launch_tests(tile_widths, tile_heights, num_envs) + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_resolutions_medium(): + """Define settings for resolution and number of environments""" + num_envs = 64 + tile_widths = range(320, 400, 20) + tile_heights = range(320, 400, 20) + _launch_tests(tile_widths, tile_heights, num_envs) + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_resolutions_large(): + """Define settings for resolution and number of environments""" + num_envs = 4 + tile_widths = range(480, 640, 40) + tile_heights = range(480, 640, 40) + _launch_tests(tile_widths, tile_heights, num_envs) + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_resolutions_edge_cases(): + """Define settings for resolution and number of environments""" + num_envs = 1000 + tile_widths = [12, 67, 93, 147] + tile_heights = [12, 67, 93, 147] + _launch_tests(tile_widths, tile_heights, num_envs) + + +@pytest.mark.skip(reason="Currently takes too long to run") +def test_tiled_num_envs_edge_cases(): + """Define settings for resolution and number of environments""" + num_envs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 53, 359, 733, 927] + tile_widths = [67, 93, 147] + tile_heights = [67, 93, 147] + for n_envs in num_envs: + _launch_tests(tile_widths, tile_heights, n_envs) + + +# Helper functions + + +def _launch_tests(tile_widths: range, tile_heights: range, num_envs: int): + """Run through different resolutions for tiled rendering""" + device = "cuda:0" + task_name = "Isaac-Cartpole-RGB-Camera-Direct-v0" + # iterate over all registered environments + for width in tile_widths: + for height in tile_heights: + # create a new stage + omni.usd.get_context().new_stage() + # parse configuration + env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + env_cfg.tiled_camera.width = width + env_cfg.tiled_camera.height = height + print(f">>> Running test for resolution: {width} x {height}") + # check environment + _run_environment(env_cfg) + # close the environment + print(f">>> Closing environment: {task_name}") + print("-" * 80) + + +def _run_environment(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg): + """Run environment and capture a rendered image.""" + # create environment + env: ManagerBasedRLEnv | DirectRLEnv = gym.make("Isaac-Cartpole-RGB-Camera-Direct-v0", cfg=env_cfg) + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + env.sim.set_setting("/physics/cooking/ujitsoCollisionCooking", False) + + # reset environment + obs, _ = env.reset() + # save image + if args_cli.save_images: + save_images_to_file( + obs["policy"] + 0.93, + f"output_{env.num_envs}_{env_cfg.tiled_camera.width}x{env_cfg.tiled_camera.height}.png", + ) + + # close the environment + env.close() diff --git a/source/isaaclab/test/sensors/test_visuotactile_render.py b/source/isaaclab/test/sensors/test_visuotactile_render.py new file mode 100644 index 0000000000000000000000000000000000000000..8ceafb03eaffddec7d1750a628398dc1e6bd4be6 --- /dev/null +++ b/source/isaaclab/test/sensors/test_visuotactile_render.py @@ -0,0 +1,133 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests for GelSight utility functions - primarily focused on GelsightRender.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +import os +import tempfile + +import cv2 +import numpy as np +import pytest +import torch + +from isaaclab.sensors.tacsl_sensor.visuotactile_render import GelsightRender +from isaaclab.sensors.tacsl_sensor.visuotactile_sensor_cfg import GelSightRenderCfg + + +def test_gelsight_render_custom_path_missing_file(): + """Test initializing GelsightRender with custom path when file doesn't exist.""" + # Assuming 'non_existent_path' is treated as a local path or Nucleus path + # If we pass a path that definitely doesn't exist locally or on Nucleus, it should fail + cfg = GelSightRenderCfg( + base_data_path="non_existent_path", + sensor_data_dir_name="dummy", + image_height=100, + image_width=100, + mm_per_pixel=0.1, + ) + # This should raise FileNotFoundError because the directory/files won't exist + with pytest.raises(FileNotFoundError): + GelsightRender(cfg, device="cpu") + + +def test_gelsight_render_custom_path_success(): + """Test initializing GelsightRender with valid custom path and files.""" + with tempfile.TemporaryDirectory() as tmpdir: + data_dir = "gelsight_r15_data" + full_dir = os.path.join(tmpdir, data_dir) + os.makedirs(full_dir, exist_ok=True) + + # Create dummy configuration + width, height = 10, 10 + cfg = GelSightRenderCfg( + base_data_path=tmpdir, + sensor_data_dir_name=data_dir, + image_width=width, + image_height=height, + num_bins=5, + mm_per_pixel=0.1, + ) + + # 1. Create dummy background image + bg_path = os.path.join(full_dir, cfg.background_path) + dummy_img = np.zeros((height, width, 3), dtype=np.uint8) + cv2.imwrite(bg_path, dummy_img) + + # 2. Create dummy calibration file + calib_path = os.path.join(full_dir, cfg.calib_path) + # Calibration gradients shape: (num_bins, num_bins, 6) + dummy_grad = np.zeros((cfg.num_bins, cfg.num_bins, 6), dtype=np.float32) + np.savez(calib_path, grad_r=dummy_grad, grad_g=dummy_grad, grad_b=dummy_grad) + + # Test initialization + try: + device = torch.device("cpu") + render = GelsightRender(cfg, device=device) + assert render is not None + assert render.device == device + # Verify loaded background dimensions + assert render.background.shape == (height, width, 3) + except Exception as e: + pytest.fail(f"GelsightRender initialization failed with valid custom files: {e}") + + +@pytest.fixture +def gelsight_render_setup(): + """Fixture to set up GelsightRender for testing with default (Nucleus/Cache) files.""" + # Use default GelSight R1.5 configuration + cfg = GelSightRenderCfg( + sensor_data_dir_name="gelsight_r15_data", image_height=320, image_width=240, mm_per_pixel=0.0877 + ) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # Create render instance + try: + render = GelsightRender(cfg, device=device) + yield render, device + except Exception as e: + # If initialization fails (e.g., missing data files), skip tests + pytest.skip(f"GelsightRender initialization failed (likely network/Nucleus issue): {e}") + + +def test_gelsight_render_initialization(gelsight_render_setup): + """Test GelsightRender initialization with default files.""" + render, device = gelsight_render_setup + + # Check that render object was created + assert render is not None + assert render.device == device + + # Check that background was loaded (non-empty) + assert render.background is not None + assert render.background.size > 0 + assert render.background.shape[2] == 3 # RGB + + +def test_gelsight_render_compute(gelsight_render_setup): + """Test the render method of GelsightRender.""" + render, device = gelsight_render_setup + + # Create dummy height map + height, width = render.cfg.image_height, render.cfg.image_width + height_map = torch.zeros((1, height, width), device=device, dtype=torch.float32) + + # Add some features to height map + height_map[0, height // 4 : height // 2, width // 4 : width // 2] = 0.001 # 1mm bump + + # Render + output = render.render(height_map) + + # Check output + assert output is not None + assert output.shape == (1, height, width, 3) + assert output.dtype == torch.uint8 diff --git a/source/isaaclab/test/sensors/test_visuotactile_sensor.py b/source/isaaclab/test/sensors/test_visuotactile_sensor.py new file mode 100644 index 0000000000000000000000000000000000000000..42dd2f3fd85794e9f47ea3586b27ee0cfd921c75 --- /dev/null +++ b/source/isaaclab/test/sensors/test_visuotactile_sensor.py @@ -0,0 +1,451 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import math + +import pytest +import torch + +import isaacsim.core.utils.stage as stage_utils +import omni.replicator.core as rep + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject, RigidObjectCfg +from isaaclab.sensors.camera import TiledCameraCfg +from isaaclab.sensors.tacsl_sensor import VisuoTactileSensor, VisuoTactileSensorCfg +from isaaclab.sensors.tacsl_sensor.visuotactile_sensor_cfg import GelSightRenderCfg +from isaaclab.terrains.trimesh.utils import make_plane +from isaaclab.terrains.utils import create_prim_from_mesh +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +# Sample sensor poses + +TEST_RENDER_CFG = GelSightRenderCfg( + sensor_data_dir_name="gelsight_r15_data", + image_height=320, + image_width=240, + mm_per_pixel=0.0877, +) + + +def get_sensor_cfg_by_type(sensor_type: str) -> VisuoTactileSensorCfg: + """Return a sensor configuration based on the input type. + + Args: + sensor_type: Type of sensor configuration. Options: "minimum_config", "tactile_cam", "nut_rgb_ff". + + Returns: + VisuoTactileSensorCfg: The sensor configuration for the specified type. + + Raises: + ValueError: If the sensor_type is not supported. + """ + + if sensor_type == "minimum_config": + return VisuoTactileSensorCfg( + prim_path="/World/Robot/elastomer/sensor_minimum_config", + enable_camera_tactile=False, + enable_force_field=False, + render_cfg=TEST_RENDER_CFG, + tactile_array_size=(10, 10), + tactile_margin=0.003, + ) + elif sensor_type == "tactile_cam": + return VisuoTactileSensorCfg( + prim_path="/World/Robot/elastomer/tactile_cam", + enable_force_field=False, + camera_cfg=TiledCameraCfg( + height=320, + width=240, + prim_path="/World/Robot/elastomer_tip/cam", + update_period=0, + data_types=["distance_to_image_plane"], + spawn=None, + ), + render_cfg=TEST_RENDER_CFG, + tactile_array_size=(10, 10), + tactile_margin=0.003, + ) + + elif sensor_type == "nut_rgb_ff": + return VisuoTactileSensorCfg( + prim_path="/World/Robot/elastomer/sensor_nut", + update_period=0, + debug_vis=False, + enable_camera_tactile=True, + enable_force_field=True, + camera_cfg=TiledCameraCfg( + height=320, + width=240, + prim_path="/World/Robot/elastomer_tip/cam", + update_period=0, + data_types=["distance_to_image_plane"], + spawn=None, + ), + render_cfg=TEST_RENDER_CFG, + tactile_array_size=(5, 10), + tactile_margin=0.003, + contact_object_prim_path_expr="/World/Nut", + ) + + else: + raise ValueError( + f"Unsupported sensor type: {sensor_type}. Supported types: 'minimum_config', 'tactile_cam', 'nut_rgb_ff'" + ) + + +def setup(sensor_type: str = "cube"): + """Create a new stage and setup simulation environment with robot, objects, and sensor. + + Args: + sensor_type: Type of sensor configuration. Options: "minimum_config", "tactile_cam", "nut_rgb_ff". + + Returns: + Tuple containing simulation context, sensor config, timestep, robot config, cube config, and nut config. + """ + # Create a new stage + stage_utils.create_new_stage() + + # Simulation time-step + dt = 0.01 + + # Load kit helper + sim_cfg = sim_utils.SimulationCfg(dt=dt) + sim = sim_utils.SimulationContext(sim_cfg) + + # Ground-plane + mesh = make_plane(size=(100, 100), height=0.0, center_zero=True) + create_prim_from_mesh("/World/defaultGroundPlane", mesh) + + # gelsightr15 filter + usd_file_path = f"{ISAACLAB_NUCLEUS_DIR}/TacSL/gelsight_r15_finger/gelsight_r15_finger.usd" + # robot + from isaaclab.assets import ArticulationCfg + + robot_cfg = ArticulationCfg( + prim_path="/World/Robot", + spawn=sim_utils.UsdFileWithCompliantContactCfg( + usd_path=usd_file_path, + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=True), + compliant_contact_stiffness=10.0, + compliant_contact_damping=1.0, + physics_material_prim_path="elastomer", + ), + actuators={}, + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(math.sqrt(2) / 2, -math.sqrt(2) / 2, 0.0, 0.0), # 90° rotation + joint_pos={}, + joint_vel={}, + ), + ) + # Cube + cube_cfg = RigidObjectCfg( + prim_path="/World/Cube", + spawn=sim_utils.CuboidCfg( + size=(0.1, 0.1, 0.1), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + ) + # Nut + nut_cfg = RigidObjectCfg( + prim_path="/World/Nut", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Factory/factory_nut_m16.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=False), + articulation_props=sim_utils.ArticulationRootPropertiesCfg(articulation_enabled=False), + mass_props=sim_utils.MassPropertiesCfg(mass=0.1), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.0 + 0.06776, 0.52), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ) + + # Get the requested sensor configuration using the factory function + sensor_cfg = get_sensor_cfg_by_type(sensor_type) + + # load stage + stage_utils.update_stage() + return sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg + + +def teardown(sim): + """Teardown simulation environment.""" + # close all the opened viewport from before. + rep.vp_manager.destroy_hydra_textures("Replicator") + # stop simulation + # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :( + sim._timeline.stop() + # clear the stage + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.fixture +def setup_minimum_config(): + """Create simulation context with minimum config sensor.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup("minimum_config") + yield sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg + teardown(sim) + + +@pytest.fixture +def setup_tactile_cam(): + """Create simulation context with tactile camera sensor.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup("tactile_cam") + yield sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg + teardown(sim) + + +@pytest.fixture +def setup_nut_rgb_ff(): + """Create simulation context with nut RGB force field sensor.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg = setup("nut_rgb_ff") + yield sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg + teardown(sim) + + +@pytest.mark.isaacsim_ci +def test_sensor_minimum_config(setup_minimum_config): + """Test sensor with minimal configuration (no camera, no force field).""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_minimum_config + _ = Articulation(cfg=robot_cfg) + sensor_minimum = VisuoTactileSensor(cfg=sensor_cfg) + sim.reset() + # Simulate physics + for _ in range(10): + sim.step() + sensor_minimum.update(dt) + + # check data should be None, since both camera and force field are disabled + assert sensor_minimum.data.tactile_depth_image is None + assert sensor_minimum.data.tactile_rgb_image is None + assert sensor_minimum.data.tactile_points_pos_w is None + assert sensor_minimum.data.tactile_points_quat_w is None + assert sensor_minimum.data.penetration_depth is None + assert sensor_minimum.data.tactile_normal_force is None + assert sensor_minimum.data.tactile_shear_force is None + + # Check reset functionality + sensor_minimum.reset() + + for i in range(10): + sim.step() + sensor_minimum.update(dt) + sensor_minimum.reset(env_ids=[0]) + + +@pytest.mark.isaacsim_ci +def test_sensor_cam_size_false(setup_tactile_cam): + """Test sensor initialization fails with incorrect camera image size.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_tactile_cam + sensor_cfg.camera_cfg.height = 80 + _ = VisuoTactileSensor(cfg=sensor_cfg) + with pytest.raises(ValueError) as excinfo: + sim.reset() + assert "Camera configuration image size is not consistent with the render config" in str(excinfo.value) + + +@pytest.mark.isaacsim_ci +def test_sensor_cam_type_false(setup_tactile_cam): + """Test sensor initialization fails with unsupported camera data types.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_tactile_cam + sensor_cfg.camera_cfg.data_types = ["rgb"] + _ = VisuoTactileSensor(cfg=sensor_cfg) + with pytest.raises(ValueError) as excinfo: + sim.reset() + assert "Camera configuration data types are not supported" in str(excinfo.value) + + +@pytest.mark.isaacsim_ci +def test_sensor_cam_set(setup_tactile_cam): + """Test sensor with camera configuration using existing camera prim.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_tactile_cam + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + sim.reset() + sensor.get_initial_render() + for _ in range(10): + sim.step() + sensor.update(dt, force_recompute=True) + robot.update(dt) + assert sensor.is_initialized + assert sensor.data.tactile_depth_image.shape == (1, 320, 240, 1) + assert sensor.data.tactile_rgb_image.shape == (1, 320, 240, 3) + assert sensor.data.tactile_points_pos_w is None + + sensor.reset() + for _ in range(10): + sim.step() + sensor.update(dt, force_recompute=True) + robot.update(dt) + sensor.reset(env_ids=[0]) + + +@pytest.mark.isaacsim_ci +def test_sensor_cam_set_wrong_prim(setup_tactile_cam): + """Test sensor initialization fails with invalid camera prim path.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_tactile_cam + sensor_cfg.camera_cfg.prim_path = "/World/Robot/elastomer_tip/cam_wrong" + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + with pytest.raises(RuntimeError) as excinfo: + sim.reset() + robot.update(dt) + sensor.update(dt) + assert "Could not find prim with path" in str(excinfo.value) + + +@pytest.mark.isaacsim_ci +def test_sensor_cam_new_spawn(setup_tactile_cam): + """Test sensor with camera configuration that spawns a new camera.""" + sim, sensor_cfg, dt, robot_cfg, object_cfg, nut_cfg = setup_tactile_cam + sensor_cfg.camera_cfg.prim_path = "/World/Robot/elastomer_tip/cam_new" + sensor_cfg.camera_cfg.spawn = sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.01, 1.0e5) + ) + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + sim.reset() + sensor.get_initial_render() + for _ in range(10): + sim.step() + sensor.update(dt) + robot.update(dt) + # test lazy sensor update + data = sensor.data + assert data is not None + assert data.tactile_depth_image.shape == (1, 320, 240, 1) + assert data.tactile_rgb_image.shape == (1, 320, 240, 3) + assert data.tactile_points_pos_w is None + + assert sensor.is_initialized + + +@pytest.mark.isaacsim_ci +def test_sensor_rgb_forcefield(setup_nut_rgb_ff): + """Test sensor with both camera and force field enabled, detecting contact forces.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg = setup_nut_rgb_ff + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + nut = RigidObject(cfg=nut_cfg) + sim.reset() + sensor.get_initial_render() + for _ in range(10): + sim.step() + sensor.update(dt, force_recompute=True) + robot.update(dt) + nut.update(dt) + # check str + print(sensor) + assert sensor.is_initialized + assert sensor.data.tactile_depth_image.shape == (1, 320, 240, 1) + assert sensor.data.tactile_rgb_image.shape == (1, 320, 240, 3) + assert sensor.data.tactile_points_pos_w.shape == (1, 50, 3) + assert sensor.data.penetration_depth.shape == (1, 50) + assert sensor.data.tactile_normal_force.shape == (1, 50) + assert sensor.data.tactile_shear_force.shape == (1, 50, 2) + sum_depth = torch.sum(sensor.data.penetration_depth) # 0.020887471735477448 + normal_force_sum = torch.sum(sensor.data.tactile_normal_force.abs()) + shear_force_sum = torch.sum(sensor.data.tactile_shear_force.abs()) + assert normal_force_sum > 0.0 + assert sum_depth > 0.0 + assert shear_force_sum > 0.0 + + +@pytest.mark.isaacsim_ci +def test_sensor_no_contact_object(setup_nut_rgb_ff): + """Test sensor with force field but no contact object specified.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg = setup_nut_rgb_ff + sensor_cfg.contact_object_prim_path_expr = None + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + nut = RigidObject(cfg=nut_cfg) + sim.reset() + sensor.get_initial_render() + for _ in range(10): + sim.step() + sensor.update(dt, force_recompute=True) + robot.update(dt) + nut.update(dt) + + assert sensor.is_initialized + assert sensor.data.tactile_depth_image.shape == (1, 320, 240, 1) + assert sensor.data.tactile_rgb_image.shape == (1, 320, 240, 3) + assert sensor.data.tactile_points_pos_w.shape == (1, 50, 3) + # check no forces are detected + assert torch.all(torch.abs(sensor.data.penetration_depth) < 1e-9) + assert torch.all(torch.abs(sensor.data.tactile_normal_force) < 1e-9) + assert torch.all(torch.abs(sensor.data.tactile_shear_force) < 1e-9) + + +@pytest.mark.isaacsim_ci +def test_sensor_force_field_contact_object_not_found(setup_nut_rgb_ff): + """Test sensor initialization fails when contact object prim path is not found.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, NutCfg = setup_nut_rgb_ff + + sensor_cfg.enable_camera_tactile = False + sensor_cfg.contact_object_prim_path_expr = "/World/Nut/wrong_prim" + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + with pytest.raises(RuntimeError) as excinfo: + sim.reset() + robot.update(dt) + sensor.update(dt) + assert "No contact object prim found matching pattern" in str(excinfo.value) + + +@pytest.mark.isaacsim_ci +def test_sensor_force_field_contact_object_no_sdf(setup_nut_rgb_ff): + """Test sensor initialization fails when contact object has no SDF mesh.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, NutCfg = setup_nut_rgb_ff + sensor_cfg.enable_camera_tactile = False + sensor_cfg.contact_object_prim_path_expr = "/World/Cube" + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + cube = RigidObject(cfg=cube_cfg) + with pytest.raises(RuntimeError) as excinfo: + sim.reset() + robot.update(dt) + sensor.update(dt) + cube.update(dt) + assert "No SDF mesh found under contact object at path" in str(excinfo.value) + + +@pytest.mark.isaacsim_ci +def test_sensor_update_period_mismatch(setup_nut_rgb_ff): + """Test sensor with both camera and force field enabled, detecting contact forces.""" + sim, sensor_cfg, dt, robot_cfg, cube_cfg, nut_cfg = setup_nut_rgb_ff + sensor_cfg.update_period = dt + sensor_cfg.camera_cfg.update_period = dt * 2 + robot = Articulation(cfg=robot_cfg) + sensor = VisuoTactileSensor(cfg=sensor_cfg) + nut = RigidObject(cfg=nut_cfg) + sim.reset() + sensor.get_initial_render() + assert sensor.cfg.camera_cfg.update_period == sensor.cfg.update_period + for i in range(10): + sim.step() + sensor.update(dt, force_recompute=True) + robot.update(dt) + nut.update(dt) + assert torch.allclose(sensor._timestamp_last_update, torch.tensor((i + 1) * dt, device=sensor.device)) + assert torch.allclose( + sensor._camera_sensor._timestamp_last_update, torch.tensor((i + 1) * dt, device=sensor.device) + ) diff --git a/source/isaaclab/test/sensors/urdfs/simple_2_link.urdf b/source/isaaclab/test/sensors/urdfs/simple_2_link.urdf new file mode 100644 index 0000000000000000000000000000000000000000..7c09e1b82c048471eb10045c605ff68eeac2994e --- /dev/null +++ b/source/isaaclab/test/sensors/urdfs/simple_2_link.urdf @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/isaaclab/test/sim/check_meshes.py b/source/isaaclab/test/sim/check_meshes.py new file mode 100644 index 0000000000000000000000000000000000000000..705677281d3cb8558106fe0e6d322320b3d1a4fe --- /dev/null +++ b/source/isaaclab/test/sim/check_meshes.py @@ -0,0 +1,167 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script demonstrates different rigid and deformable meshes in the scene. + +It randomly spawns different types of meshes in the scene. The meshes can be rigid or deformable +based on the probability of 0.5. The rigid meshes are spawned with rigid body and collision properties, +while the deformable meshes are spawned with deformable body properties. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p source/isaaclab/test/sim/check_meshes.py + +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# create argparser +parser = argparse.ArgumentParser(description="This script demonstrates different meshes in the scene.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import random + +import numpy as np +import torch +import tqdm + +import isaaclab.sim as sim_utils + + +def define_origins(num_origins: int, spacing: float) -> list[list[float]]: + """Defines the origins of the the scene.""" + # create tensor based on number of environments + env_origins = torch.zeros(num_origins, 3) + # create a grid of origins + num_cols = np.floor(np.sqrt(num_origins)) + num_rows = np.ceil(num_origins / num_cols) + xx, yy = torch.meshgrid(torch.arange(num_rows), torch.arange(num_cols), indexing="xy") + env_origins[:, 0] = spacing * xx.flatten()[:num_origins] - spacing * (num_rows - 1) / 2 + env_origins[:, 1] = spacing * yy.flatten()[:num_origins] - spacing * (num_cols - 1) / 2 + env_origins[:, 2] = torch.rand(num_origins) + 1.0 + # return the origins + return env_origins.tolist() + + +def design_scene(): + """Designs the scene by spawning ground plane, light, and deformable meshes.""" + # Ground-plane + cfg_ground = sim_utils.GroundPlaneCfg() + cfg_ground.func("/World/defaultGroundPlane", cfg_ground) + + # spawn distant light + cfg_light = sim_utils.DomeLightCfg( + intensity=3000.0, + color=(0.75, 0.75, 0.75), + ) + cfg_light.func("/World/light", cfg_light) + + # create new xform prims for all objects to be spawned under + origins = define_origins(num_origins=4, spacing=5.5) + for idx, origin in enumerate(origins): + sim_utils.create_prim(f"/World/Origin{idx:02d}", "Xform", translation=origin) + + # spawn a red cone + cfg_sphere = sim_utils.MeshSphereCfg( + radius=0.25, + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + ) + cfg_cuboid = sim_utils.MeshCuboidCfg( + size=(0.2, 0.2, 0.2), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + ) + cfg_cylinder = sim_utils.MeshCylinderCfg( + radius=0.15, + height=0.5, + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + ) + cfg_capsule = sim_utils.MeshCapsuleCfg( + radius=0.15, + height=0.5, + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + ) + cfg_cone = sim_utils.MeshConeCfg( + radius=0.15, + height=0.5, + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + visual_material=sim_utils.PreviewSurfaceCfg(), + ) + # create a dictionary of all the objects to be spawned + objects_cfg = { + "sphere": cfg_sphere, + "cuboid": cfg_cuboid, + "cylinder": cfg_cylinder, + "capsule": cfg_capsule, + "cone": cfg_cone, + } + + # Create separate groups of deformable objects + origins = define_origins(num_origins=25, spacing=0.5) + print("[INFO]: Spawning objects...") + # Iterate over all the origins and randomly spawn objects + for idx, origin in tqdm.tqdm(enumerate(origins), total=len(origins)): + # randomly select an object to spawn + obj_name = random.choice(list(objects_cfg.keys())) + obj_cfg = objects_cfg[obj_name] + # randomly decide if it is rigid or deformable + if random.random() < 0.5: + obj_cfg.rigid_props = None + obj_cfg.collision_props = None + obj_cfg.deformable_props = sim_utils.DeformableBodyPropertiesCfg(rest_offset=0.0) + else: + obj_cfg.deformable_props = None + obj_cfg.rigid_props = sim_utils.RigidBodyPropertiesCfg() + obj_cfg.collision_props = sim_utils.CollisionPropertiesCfg() + # randomize the color + obj_cfg.visual_material.diffuse_color = (random.random(), random.random(), random.random()) + # spawn the object + obj_cfg.func(f"/World/Origin.*/Object{idx:02d}", obj_cfg, translation=origin) + + +def main(): + """Main function.""" + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.01) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view([8.0, 8.0, 6.0], [0.0, 0.0, 0.0]) + + # Design scene by adding assets to it + design_scene() + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + + # Simulate physics + while simulation_app.is_running(): + # perform step + sim.step() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/sim/test_build_simulation_context_headless.py b/source/isaaclab/test/sim/test_build_simulation_context_headless.py new file mode 100644 index 0000000000000000000000000000000000000000..ebe059bed6667365044ed8bf3bd2bdfd9fe294c2 --- /dev/null +++ b/source/isaaclab/test/sim/test_build_simulation_context_headless.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This test has a lot of duplication with ``test_build_simulation_context_nonheadless.py``. + +This is intentional to ensure that the tests are run in both headless and non-headless modes, +and we currently can't re-build the simulation app in a script. + +If you need to make a change to this test, please make sure to also make the same change to +``test_build_simulation_context_nonheadless.py``. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest + +from isaaclab.sim.simulation_cfg import SimulationCfg +from isaaclab.sim.simulation_context import build_simulation_context + + +@pytest.mark.parametrize("gravity_enabled", [True, False]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("dt", [0.01, 0.1]) +@pytest.mark.isaacsim_ci +def test_build_simulation_context_no_cfg(gravity_enabled, device, dt): + """Test that the simulation context is built when no simulation cfg is passed in.""" + with build_simulation_context(gravity_enabled=gravity_enabled, device=device, dt=dt) as sim: + if gravity_enabled: + assert sim.cfg.gravity == (0.0, 0.0, -9.81) + else: + assert sim.cfg.gravity == (0.0, 0.0, 0.0) + + assert sim.cfg.device == device + assert sim.cfg.dt == dt + + # Ensure that dome light didn't get added automatically as we are headless + assert not sim.stage.GetPrimAtPath("/World/defaultDomeLight").IsValid() + + +@pytest.mark.parametrize("add_ground_plane", [True, False]) +@pytest.mark.isaacsim_ci +def test_build_simulation_context_ground_plane(add_ground_plane): + """Test that the simulation context is built with the correct ground plane.""" + with build_simulation_context(add_ground_plane=add_ground_plane) as sim: + # Ensure that ground plane got added + if add_ground_plane: + assert sim.stage.GetPrimAtPath("/World/defaultGroundPlane").IsValid() + else: + assert not sim.stage.GetPrimAtPath("/World/defaultGroundPlane").IsValid() + + +@pytest.mark.parametrize("add_lighting", [True, False]) +@pytest.mark.parametrize("auto_add_lighting", [True, False]) +@pytest.mark.isaacsim_ci +def test_build_simulation_context_auto_add_lighting(add_lighting, auto_add_lighting): + """Test that the simulation context is built with the correct lighting.""" + with build_simulation_context(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting) as sim: + if add_lighting: + # Ensure that dome light got added + assert sim.stage.GetPrimAtPath("/World/defaultDomeLight").IsValid() + else: + # Ensure that dome light didn't get added as there's no GUI + assert not sim.stage.GetPrimAtPath("/World/defaultDomeLight").IsValid() + + +@pytest.mark.isaacsim_ci +def test_build_simulation_context_cfg(): + """Test that the simulation context is built with the correct cfg and values don't get overridden.""" + dt = 0.001 + # Non-standard gravity + gravity = (0.0, 0.0, -1.81) + device = "cuda:0" + + cfg = SimulationCfg( + gravity=gravity, + device=device, + dt=dt, + ) + + with build_simulation_context(sim_cfg=cfg, gravity_enabled=False, dt=0.01, device="cpu") as sim: + assert sim.cfg.gravity == gravity + assert sim.cfg.device == device + assert sim.cfg.dt == dt diff --git a/source/isaaclab/test/sim/test_build_simulation_context_nonheadless.py b/source/isaaclab/test/sim/test_build_simulation_context_nonheadless.py new file mode 100644 index 0000000000000000000000000000000000000000..ae2203c43b701cd6eb16916bb0cdf90a5973cceb --- /dev/null +++ b/source/isaaclab/test/sim/test_build_simulation_context_nonheadless.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This test has a lot of duplication with ``test_build_simulation_context_headless.py``. + +This is intentional to ensure that the tests are run in both headless and non-headless modes, +and we currently can't re-build the simulation app in a script. + +If you need to make a change to this test, please make sure to also make the same change to +``test_build_simulation_context_headless.py``. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest + +from isaaclab.sim.simulation_cfg import SimulationCfg +from isaaclab.sim.simulation_context import build_simulation_context + + +@pytest.mark.parametrize("gravity_enabled", [True, False]) +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("dt", [0.01, 0.1]) +def test_build_simulation_context_no_cfg(gravity_enabled, device, dt): + """Test that the simulation context is built when no simulation cfg is passed in.""" + with build_simulation_context(gravity_enabled=gravity_enabled, device=device, dt=dt) as sim: + if gravity_enabled: + assert sim.cfg.gravity == (0.0, 0.0, -9.81) + else: + assert sim.cfg.gravity == (0.0, 0.0, 0.0) + + assert sim.cfg.device == device + assert sim.cfg.dt == dt + + +@pytest.mark.parametrize("add_ground_plane", [True, False]) +def test_build_simulation_context_ground_plane(add_ground_plane): + """Test that the simulation context is built with the correct ground plane.""" + with build_simulation_context(add_ground_plane=add_ground_plane) as sim: + # Ensure that ground plane got added + if add_ground_plane: + assert sim.stage.GetPrimAtPath("/World/defaultGroundPlane").IsValid() + else: + assert not sim.stage.GetPrimAtPath("/World/defaultGroundPlane").IsValid() + + +@pytest.mark.parametrize("add_lighting", [True, False]) +@pytest.mark.parametrize("auto_add_lighting", [True, False]) +def test_build_simulation_context_auto_add_lighting(add_lighting, auto_add_lighting): + """Test that the simulation context is built with the correct lighting.""" + with build_simulation_context(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting) as sim: + if auto_add_lighting or add_lighting: + # Ensure that dome light got added + assert sim.stage.GetPrimAtPath("/World/defaultDomeLight").IsValid() + else: + # Ensure that dome light didn't get added + assert not sim.stage.GetPrimAtPath("/World/defaultDomeLight").IsValid() + + +def test_build_simulation_context_cfg(): + """Test that the simulation context is built with the correct cfg and values don't get overridden.""" + dt = 0.001 + # Non-standard gravity + gravity = (0.0, 0.0, -1.81) + device = "cuda:0" + + cfg = SimulationCfg( + gravity=gravity, + device=device, + dt=dt, + ) + + with build_simulation_context(sim_cfg=cfg, gravity_enabled=False, dt=0.01, device="cpu") as sim: + assert sim.cfg.gravity == gravity + assert sim.cfg.device == device + assert sim.cfg.dt == dt diff --git a/source/isaaclab/test/sim/test_mesh_converter.py b/source/isaaclab/test/sim/test_mesh_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..5a986cc0328bd0da1ac7d9b52d5c649b328c3afe --- /dev/null +++ b/source/isaaclab/test/sim/test_mesh_converter.py @@ -0,0 +1,372 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import math +import os +import random +import tempfile + +import pytest + +import omni +from isaacsim.core.api.simulation_context import SimulationContext +from pxr import UsdGeom, UsdPhysics + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import MeshConverter, MeshConverterCfg +from isaaclab.sim.schemas import MESH_APPROXIMATION_TOKENS, schemas_cfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + + +def random_quaternion(): + # Generate four random numbers for the quaternion + u1, u2, u3 = random.random(), random.random(), random.random() + w = math.sqrt(1 - u1) * math.sin(2 * math.pi * u2) + x = math.sqrt(1 - u1) * math.cos(2 * math.pi * u2) + y = math.sqrt(u1) * math.sin(2 * math.pi * u3) + z = math.sqrt(u1) * math.cos(2 * math.pi * u3) + return (w, x, y, z) + + +@pytest.fixture(scope="session") +def assets(): + """Load assets for tests.""" + assets_dir = f"{ISAACLAB_NUCLEUS_DIR}/Tests/MeshConverter/duck" + # Create mapping of file endings to file paths that can be used by tests + assets = { + "obj": f"{assets_dir}/duck.obj", + "stl": f"{assets_dir}/duck.stl", + "fbx": f"{assets_dir}/duck.fbx", + "mtl": f"{assets_dir}/duck.mtl", + "png": f"{assets_dir}/duckCM.png", + } + # Download all these locally + download_dir = tempfile.mkdtemp(suffix="_mesh_converter_test_assets") + for key, value in assets.items(): + assets[key] = retrieve_file_path(value, download_dir=download_dir) + return assets + + +@pytest.fixture(autouse=True) +def sim(): + """Create a blank new stage for each test.""" + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.01 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + yield sim + # stop simulation + sim.stop() + # cleanup stage and context + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +def check_mesh_conversion(mesh_converter: MeshConverter): + """Check that mesh is loadable and stage is valid.""" + # Obtain stage handle + stage = sim_utils.get_current_stage() + + # Load the mesh + prim_path = "/World/Object" + sim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path) + # Check prim can be properly spawned + assert stage.GetPrimAtPath(prim_path).IsValid() + # Load a second time + prim_path = "/World/Object2" + sim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path) + # Check prim can be properly spawned + assert stage.GetPrimAtPath(prim_path).IsValid() + + stage = omni.usd.get_context().get_stage() + # Check axis is z-up + axis = UsdGeom.GetStageUpAxis(stage) + assert axis == "Z" + # Check units is meters + units = UsdGeom.GetStageMetersPerUnit(stage) + assert units == 1.0 + + # Obtain prim handle + prim = stage.GetPrimAtPath("/World/Object/geometry") + # Check mesh settings + pos = tuple(prim.GetAttribute("xformOp:translate").Get()) + assert pos == mesh_converter.cfg.translation + quat = prim.GetAttribute("xformOp:orient").Get() + quat = (quat.GetReal(), quat.GetImaginary()[0], quat.GetImaginary()[1], quat.GetImaginary()[2]) + assert quat == mesh_converter.cfg.rotation + scale = tuple(prim.GetAttribute("xformOp:scale").Get()) + assert scale == mesh_converter.cfg.scale + + +def check_mesh_collider_settings(mesh_converter: MeshConverter): + """Check that mesh collider settings are correct.""" + # Obtain stage handle + stage = sim_utils.get_current_stage() + + # Check prim can be properly spawned + prim_path = "/World/Object" + sim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path) + assert stage.GetPrimAtPath(prim_path).IsValid() + + # Make uninstanceable to check collision settings + geom_prim = stage.GetPrimAtPath(prim_path + "/geometry") + # Check that instancing worked! + assert geom_prim.IsInstanceable() == mesh_converter.cfg.make_instanceable + # Obtain mesh settings + geom_prim.SetInstanceable(False) + mesh_prim = stage.GetPrimAtPath(prim_path + "/geometry/mesh") + + # Check collision settings + # -- if collision is enabled, check that API is present + exp_collision_enabled = ( + mesh_converter.cfg.collision_props is not None and mesh_converter.cfg.collision_props.collision_enabled + ) + collision_api = UsdPhysics.CollisionAPI(mesh_prim) + collision_enabled = collision_api.GetCollisionEnabledAttr().Get() + assert collision_enabled == exp_collision_enabled, "Collision enabled is not the same!" + # -- if collision is enabled, check that collision approximation is correct + if exp_collision_enabled: + if mesh_converter.cfg.mesh_collision_props is not None: + exp_collision_approximation_str = mesh_converter.cfg.mesh_collision_props.mesh_approximation_name + exp_collision_approximation_token = MESH_APPROXIMATION_TOKENS[exp_collision_approximation_str] + mesh_collision_api = UsdPhysics.MeshCollisionAPI(mesh_prim) + collision_approximation = mesh_collision_api.GetApproximationAttr().Get() + # Convert token to string for comparison + assert collision_approximation == exp_collision_approximation_token, ( + "Collision approximation is not the same!" + ) + + +def test_no_change(assets): + """Call conversion twice on the same input asset. + + This should not generate a new USD file if the hash is the same. + """ + # create an initial USD file from asset + mesh_config = MeshConverterCfg(asset_path=assets["obj"]) + mesh_converter = MeshConverter(mesh_config) + time_usd_file_created = os.stat(mesh_converter.usd_path).st_mtime_ns + + # no change to config only define the usd directory + new_config = mesh_config + new_config.usd_dir = mesh_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_mesh_converter = MeshConverter(new_config) + new_time_usd_file_created = os.stat(new_mesh_converter.usd_path).st_mtime_ns + + assert time_usd_file_created == new_time_usd_file_created + + +def test_config_change(assets): + """Call conversion twice but change the config in the second call. This should generate a new USD file.""" + # create an initial USD file from asset + mesh_config = MeshConverterCfg(asset_path=assets["obj"]) + mesh_converter = MeshConverter(mesh_config) + time_usd_file_created = os.stat(mesh_converter.usd_path).st_mtime_ns + + # change the config + new_config = mesh_config + new_config.make_instanceable = not mesh_config.make_instanceable + # define the usd directory + new_config.usd_dir = mesh_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_mesh_converter = MeshConverter(new_config) + new_time_usd_file_created = os.stat(new_mesh_converter.usd_path).st_mtime_ns + + assert time_usd_file_created != new_time_usd_file_created + + +def test_convert_obj(assets): + """Convert an OBJ file""" + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + scale=(random.uniform(0.1, 2.0), random.uniform(0.1, 2.0), random.uniform(0.1, 2.0)), + translation=(random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0)), + rotation=random_quaternion(), + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_conversion(mesh_converter) + + +def test_convert_stl(assets): + """Convert an STL file""" + mesh_config = MeshConverterCfg( + asset_path=assets["stl"], + scale=(random.uniform(0.1, 2.0), random.uniform(0.1, 2.0), random.uniform(0.1, 2.0)), + translation=(random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0)), + rotation=random_quaternion(), + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_conversion(mesh_converter) + + +def test_convert_fbx(assets): + """Convert an FBX file""" + mesh_config = MeshConverterCfg( + asset_path=assets["fbx"], + scale=(random.uniform(0.1, 2.0), random.uniform(0.1, 2.0), random.uniform(0.1, 2.0)), + translation=(random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0)), + rotation=random_quaternion(), + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_conversion(mesh_converter) + + +def test_convert_default_xform_transforms(assets): + """Convert an OBJ file and check that default xform transforms are applied correctly""" + mesh_config = MeshConverterCfg(asset_path=assets["obj"]) + mesh_converter = MeshConverter(mesh_config) + # check that mesh conversion is successful + check_mesh_conversion(mesh_converter) + + +def test_collider_no_approximation(assets): + """Convert an OBJ file using no approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_convex_hull(assets): + """Convert an OBJ file using convex hull approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.ConvexHullPropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_convex_decomposition(assets): + """Convert an OBJ file using convex decomposition approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.ConvexDecompositionPropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_triangle_mesh(assets): + """Convert an OBJ file using triangle mesh approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.TriangleMeshPropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_mesh_simplification(assets): + """Convert an OBJ file using mesh simplification approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.TriangleMeshSimplificationPropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_mesh_bounding_cube(assets): + """Convert an OBJ file using bounding cube approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.BoundingCubePropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_mesh_bounding_sphere(assets): + """Convert an OBJ file using bounding sphere""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.BoundingSpherePropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_mesh_sdf(assets): + """Convert an OBJ file using signed distance field approximation""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True) + mesh_collision_prop = schemas_cfg.SDFMeshPropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) + + +def test_collider_mesh_no_collision(assets): + """Convert an OBJ file using bounding sphere with collision disabled""" + collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=False) + mesh_collision_prop = schemas_cfg.BoundingSpherePropertiesCfg() + mesh_config = MeshConverterCfg( + asset_path=assets["obj"], + mesh_collision_props=mesh_collision_prop, + collision_props=collision_props, + ) + mesh_converter = MeshConverter(mesh_config) + # check that mesh conversion is successful + check_mesh_collider_settings(mesh_converter) diff --git a/source/isaaclab/test/sim/test_mjcf_converter.py b/source/isaaclab/test/sim/test_mjcf_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..9d6499d554f167c07ee5337c3977700c059a502d --- /dev/null +++ b/source/isaaclab/test/sim/test_mjcf_converter.py @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import os + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.utils.extensions import enable_extension, get_extension_path_from_name + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import MjcfConverter, MjcfConverterCfg + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Setup and teardown for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + + # Setup: Create simulation context + dt = 0.01 + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + + # Setup: Create MJCF config + enable_extension("isaacsim.asset.importer.mjcf") + extension_path = get_extension_path_from_name("isaacsim.asset.importer.mjcf") + config = MjcfConverterCfg( + asset_path=f"{extension_path}/data/mjcf/nv_ant.xml", + import_sites=True, + fix_base=False, + make_instanceable=True, + ) + + # Yield the resources for the test + yield sim, config + + # Teardown: Cleanup simulation + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_no_change(test_setup_teardown): + """Call conversion twice. This should not generate a new USD file.""" + sim, mjcf_config = test_setup_teardown + + mjcf_converter = MjcfConverter(mjcf_config) + time_usd_file_created = os.stat(mjcf_converter.usd_path).st_mtime_ns + + # no change to config only define the usd directory + new_config = mjcf_config + new_config.usd_dir = mjcf_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_mjcf_converter = MjcfConverter(new_config) + new_time_usd_file_created = os.stat(new_mjcf_converter.usd_path).st_mtime_ns + + assert time_usd_file_created == new_time_usd_file_created + + +@pytest.mark.isaacsim_ci +def test_config_change(test_setup_teardown): + """Call conversion twice but change the config in the second call. This should generate a new USD file.""" + sim, mjcf_config = test_setup_teardown + + mjcf_converter = MjcfConverter(mjcf_config) + time_usd_file_created = os.stat(mjcf_converter.usd_path).st_mtime_ns + + # change the config + new_config = mjcf_config + new_config.fix_base = not mjcf_config.fix_base + # define the usd directory + new_config.usd_dir = mjcf_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_mjcf_converter = MjcfConverter(new_config) + new_time_usd_file_created = os.stat(new_mjcf_converter.usd_path).st_mtime_ns + + assert time_usd_file_created != new_time_usd_file_created + + +@pytest.mark.isaacsim_ci +def test_create_prim_from_usd(test_setup_teardown): + """Call conversion and create a prim from it.""" + sim, mjcf_config = test_setup_teardown + + urdf_converter = MjcfConverter(mjcf_config) + + prim_path = "/World/Robot" + sim_utils.create_prim(prim_path, usd_path=urdf_converter.usd_path) + + assert sim.stage.GetPrimAtPath(prim_path).IsValid() diff --git a/source/isaaclab/test/sim/test_schemas.py b/source/isaaclab/test/sim/test_schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2b5b61e82e8729bc9c624309968d5dfc76c270 --- /dev/null +++ b/source/isaaclab/test/sim/test_schemas.py @@ -0,0 +1,408 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import math + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext +from pxr import UsdPhysics + +import isaaclab.sim as sim_utils +import isaaclab.sim.schemas as schemas +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.string import to_camel_case + + +@pytest.fixture +def setup_simulation(): + """Fixture to set up and tear down the simulation context.""" + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.1 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + # Set some default values for test + arti_cfg = schemas.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + articulation_enabled=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=1, + sleep_threshold=1.0, + stabilization_threshold=5.0, + fix_root_link=False, + ) + rigid_cfg = schemas.RigidBodyPropertiesCfg( + rigid_body_enabled=True, + kinematic_enabled=False, + disable_gravity=False, + linear_damping=0.1, + angular_damping=0.5, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=10.0, + max_contact_impulse=10.0, + enable_gyroscopic_forces=True, + retain_accelerations=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=1, + sleep_threshold=1.0, + stabilization_threshold=6.0, + ) + collision_cfg = schemas.CollisionPropertiesCfg( + collision_enabled=True, + contact_offset=0.05, + rest_offset=0.001, + min_torsional_patch_radius=0.1, + torsional_patch_radius=1.0, + ) + mass_cfg = schemas.MassPropertiesCfg(mass=1.0, density=100.0) + joint_cfg = schemas.JointDrivePropertiesCfg( + drive_type="acceleration", max_effort=80.0, max_velocity=10.0, stiffness=10.0, damping=0.1 + ) + yield sim, arti_cfg, rigid_cfg, collision_cfg, mass_cfg, joint_cfg + # Teardown + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_valid_properties_cfg(setup_simulation): + """Test that all the config instances have non-None values. + + This is to ensure that we check that all the properties of the schema are set. + """ + sim, arti_cfg, rigid_cfg, collision_cfg, mass_cfg, joint_cfg = setup_simulation + for cfg in [arti_cfg, rigid_cfg, collision_cfg, mass_cfg, joint_cfg]: + # check nothing is none + for k, v in cfg.__dict__.items(): + assert v is not None, f"{cfg.__class__.__name__}:{k} is None. Please make sure schemas are valid." + + +@pytest.mark.isaacsim_ci +def test_modify_properties_on_invalid_prim(setup_simulation): + """Test modifying properties on a prim that does not exist.""" + sim, _, rigid_cfg, _, _, _ = setup_simulation + # set properties + with pytest.raises(ValueError): + schemas.modify_rigid_body_properties("/World/asset_xyz", rigid_cfg) + + +@pytest.mark.isaacsim_ci +def test_modify_properties_on_articulation_instanced_usd(setup_simulation): + """Test modifying properties on articulation instanced usd. + + In this case, modifying collision properties on the articulation instanced usd will fail. + """ + sim, arti_cfg, rigid_cfg, collision_cfg, mass_cfg, joint_cfg = setup_simulation + # spawn asset to the stage + asset_usd_file = f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_c/anymal_c.usd" + if "4.5" in ISAAC_NUCLEUS_DIR: + asset_usd_file = asset_usd_file.replace("http", "https").replace("4.5", "5.0") + sim_utils.create_prim("/World/asset_instanced", usd_path=asset_usd_file, translation=(0.0, 0.0, 0.62)) + + # set properties on the asset and check all properties are set + schemas.modify_articulation_root_properties("/World/asset_instanced", arti_cfg) + schemas.modify_rigid_body_properties("/World/asset_instanced", rigid_cfg) + schemas.modify_mass_properties("/World/asset_instanced", mass_cfg) + schemas.modify_joint_drive_properties("/World/asset_instanced", joint_cfg) + # validate the properties + _validate_articulation_properties_on_prim("/World/asset_instanced/base", arti_cfg, False) + _validate_rigid_body_properties_on_prim("/World/asset_instanced", rigid_cfg) + _validate_mass_properties_on_prim("/World/asset_instanced", mass_cfg) + _validate_joint_drive_properties_on_prim("/World/asset_instanced", joint_cfg) + + # make a fixed joint + arti_cfg.fix_root_link = True + schemas.modify_articulation_root_properties("/World/asset_instanced", arti_cfg) + + +@pytest.mark.isaacsim_ci +def test_modify_properties_on_articulation_usd(setup_simulation): + """Test setting properties on articulation usd.""" + sim, arti_cfg, rigid_cfg, collision_cfg, mass_cfg, joint_cfg = setup_simulation + # spawn asset to the stage + asset_usd_file = f"{ISAAC_NUCLEUS_DIR}/Robots/FrankaRobotics/FrankaPanda/franka.usd" + if "4.5" in ISAAC_NUCLEUS_DIR: + asset_usd_file = asset_usd_file.replace("http", "https").replace("4.5", "5.0") + sim_utils.create_prim("/World/asset", usd_path=asset_usd_file, translation=(0.0, 0.0, 0.62)) + + # set properties on the asset and check all properties are set + schemas.modify_articulation_root_properties("/World/asset", arti_cfg) + schemas.modify_rigid_body_properties("/World/asset", rigid_cfg) + schemas.modify_collision_properties("/World/asset", collision_cfg) + schemas.modify_mass_properties("/World/asset", mass_cfg) + schemas.modify_joint_drive_properties("/World/asset", joint_cfg) + # validate the properties + _validate_articulation_properties_on_prim("/World/asset", arti_cfg, True) + _validate_rigid_body_properties_on_prim("/World/asset", rigid_cfg) + _validate_collision_properties_on_prim("/World/asset", collision_cfg) + _validate_mass_properties_on_prim("/World/asset", mass_cfg) + _validate_joint_drive_properties_on_prim("/World/asset", joint_cfg) + + # make a fixed joint + arti_cfg.fix_root_link = True + schemas.modify_articulation_root_properties("/World/asset", arti_cfg) + # validate the properties + _validate_articulation_properties_on_prim("/World/asset", arti_cfg, True) + + +@pytest.mark.isaacsim_ci +def test_defining_rigid_body_properties_on_prim(setup_simulation): + """Test defining rigid body properties on a prim.""" + sim, _, rigid_cfg, collision_cfg, mass_cfg, _ = setup_simulation + # create a prim + sim_utils.create_prim("/World/parent", prim_type="XForm") + # spawn a prim + sim_utils.create_prim("/World/cube1", prim_type="Cube", translation=(0.0, 0.0, 0.62)) + # set properties on the asset and check all properties are set + schemas.define_rigid_body_properties("/World/cube1", rigid_cfg) + schemas.define_collision_properties("/World/cube1", collision_cfg) + schemas.define_mass_properties("/World/cube1", mass_cfg) + # validate the properties + _validate_rigid_body_properties_on_prim("/World/cube1", rigid_cfg) + _validate_collision_properties_on_prim("/World/cube1", collision_cfg) + _validate_mass_properties_on_prim("/World/cube1", mass_cfg) + + # spawn another prim + sim_utils.create_prim("/World/cube2", prim_type="Cube", translation=(1.0, 1.0, 0.62)) + # set properties on the asset and check all properties are set + schemas.define_rigid_body_properties("/World/cube2", rigid_cfg) + schemas.define_collision_properties("/World/cube2", collision_cfg) + # validate the properties + _validate_rigid_body_properties_on_prim("/World/cube2", rigid_cfg) + _validate_collision_properties_on_prim("/World/cube2", collision_cfg) + + # check if we can play + sim.reset() + for _ in range(100): + sim.step() + + +@pytest.mark.isaacsim_ci +def test_defining_articulation_properties_on_prim(setup_simulation): + """Test defining articulation properties on a prim.""" + sim, arti_cfg, rigid_cfg, collision_cfg, mass_cfg, _ = setup_simulation + # create a parent articulation + sim_utils.create_prim("/World/parent", prim_type="Xform") + schemas.define_articulation_root_properties("/World/parent", arti_cfg) + # validate the properties + _validate_articulation_properties_on_prim("/World/parent", arti_cfg, False) + + # create a child articulation + sim_utils.create_prim("/World/parent/child", prim_type="Cube", translation=(0.0, 0.0, 0.62)) + schemas.define_rigid_body_properties("/World/parent/child", rigid_cfg) + schemas.define_mass_properties("/World/parent/child", mass_cfg) + + # check if we can play + sim.reset() + for _ in range(100): + sim.step() + + +""" +Helper functions. +""" + + +def _validate_articulation_properties_on_prim( + prim_path: str, arti_cfg, has_default_fixed_root: bool, verbose: bool = False +): + """Validate the articulation properties on the prim. + + If :attr:`has_default_fixed_root` is True, then the asset already has a fixed root link. This is used to check the + expected behavior of the fixed root link configuration. + """ + # Obtain stage handle + stage = sim_utils.get_current_stage() + # the root prim + root_prim = stage.GetPrimAtPath(prim_path) + # check articulation properties are set correctly + for attr_name, attr_value in arti_cfg.__dict__.items(): + # skip names we know are not present + if attr_name == "func": + continue + # handle fixed root link + if attr_name == "fix_root_link" and attr_value is not None: + # obtain the fixed joint prim + fixed_joint_prim = sim_utils.find_global_fixed_joint_prim(prim_path) + # if asset does not have a fixed root link then check if the joint is created + if not has_default_fixed_root: + if attr_value: + assert fixed_joint_prim is not None + else: + assert fixed_joint_prim is None + else: + # check a joint exists + assert fixed_joint_prim is not None + # check if the joint is enabled or disabled + is_enabled = fixed_joint_prim.GetJointEnabledAttr().Get() + assert is_enabled == attr_value + # skip the rest of the checks + continue + # convert attribute name in prim to cfg name + prim_prop_name = f"physxArticulation:{to_camel_case(attr_name, to='cC')}" + # validate the values + assert root_prim.GetAttribute(prim_prop_name).Get() == pytest.approx(attr_value, abs=1e-5), ( + f"Failed setting for {prim_prop_name}" + ) + + +def _validate_rigid_body_properties_on_prim(prim_path: str, rigid_cfg, verbose: bool = False): + """Validate the rigid body properties on the prim. + + Note: + Right now this function exploits the hierarchy in the asset to check the properties. This is not a + fool-proof way of checking the properties. + """ + # Obtain stage handle + stage = sim_utils.get_current_stage() + # the root prim + root_prim = stage.GetPrimAtPath(prim_path) + # check rigid body properties are set correctly + for link_prim in root_prim.GetChildren(): + if UsdPhysics.RigidBodyAPI(link_prim): + for attr_name, attr_value in rigid_cfg.__dict__.items(): + # skip names we know are not present + if attr_name in ["func", "rigid_body_enabled", "kinematic_enabled"]: + continue + # convert attribute name in prim to cfg name + prim_prop_name = f"physxRigidBody:{to_camel_case(attr_name, to='cC')}" + # validate the values + assert link_prim.GetAttribute(prim_prop_name).Get() == pytest.approx(attr_value, abs=1e-5), ( + f"Failed setting for {prim_prop_name}" + ) + elif verbose: + print(f"Skipping prim {link_prim.GetPrimPath()} as it is not a rigid body.") + + +def _validate_collision_properties_on_prim(prim_path: str, collision_cfg, verbose: bool = False): + """Validate the collision properties on the prim. + + Note: + Right now this function exploits the hierarchy in the asset to check the properties. This is not a + fool-proof way of checking the properties. + """ + # Obtain stage handle + stage = sim_utils.get_current_stage() + # the root prim + root_prim = stage.GetPrimAtPath(prim_path) + # check collision properties are set correctly + for link_prim in root_prim.GetChildren(): + for mesh_prim in link_prim.GetChildren(): + if UsdPhysics.CollisionAPI(mesh_prim): + for attr_name, attr_value in collision_cfg.__dict__.items(): + # skip names we know are not present + if attr_name in ["func", "collision_enabled"]: + continue + # convert attribute name in prim to cfg name + prim_prop_name = f"physxCollision:{to_camel_case(attr_name, to='cC')}" + # validate the values + assert mesh_prim.GetAttribute(prim_prop_name).Get() == pytest.approx(attr_value, abs=1e-5), ( + f"Failed setting for {prim_prop_name}" + ) + elif verbose: + print(f"Skipping prim {mesh_prim.GetPrimPath()} as it is not a collision mesh.") + + +def _validate_mass_properties_on_prim(prim_path: str, mass_cfg, verbose: bool = False): + """Validate the mass properties on the prim. + + Note: + Right now this function exploits the hierarchy in the asset to check the properties. This is not a + fool-proof way of checking the properties. + """ + # Obtain stage handle + stage = sim_utils.get_current_stage() + # the root prim + root_prim = stage.GetPrimAtPath(prim_path) + # check rigid body mass properties are set correctly + for link_prim in root_prim.GetChildren(): + if UsdPhysics.MassAPI(link_prim): + for attr_name, attr_value in mass_cfg.__dict__.items(): + # skip names we know are not present + if attr_name in ["func"]: + continue + # print(link_prim.GetProperties()) + prim_prop_name = f"physics:{to_camel_case(attr_name, to='cC')}" + # validate the values + assert link_prim.GetAttribute(prim_prop_name).Get() == pytest.approx(attr_value, abs=1e-5), ( + f"Failed setting for {prim_prop_name}" + ) + elif verbose: + print(f"Skipping prim {link_prim.GetPrimPath()} as it is not a mass api.") + + +def _validate_joint_drive_properties_on_prim(prim_path: str, joint_cfg, verbose: bool = False): + """Validate the mass properties on the prim. + + Note: + Right now this function exploits the hierarchy in the asset to check the properties. This is not a + fool-proof way of checking the properties. + """ + # Obtain stage handle + stage = sim_utils.get_current_stage() + # the root prim + root_prim = stage.GetPrimAtPath(prim_path) + # check joint drive properties are set correctly + for link_prim in root_prim.GetAllChildren(): + for joint_prim in link_prim.GetChildren(): + if joint_prim.IsA(UsdPhysics.PrismaticJoint) or joint_prim.IsA(UsdPhysics.RevoluteJoint): + # check it has drive API + assert joint_prim.HasAPI(UsdPhysics.DriveAPI) + # iterate over the joint properties + for attr_name, attr_value in joint_cfg.__dict__.items(): + # skip names we know are not present + if attr_name == "func": + continue + # resolve the drive (linear or angular) + drive_model = "linear" if joint_prim.IsA(UsdPhysics.PrismaticJoint) else "angular" + + # manually check joint type since it is a string type + if attr_name == "drive_type": + prim_attr_name = f"drive:{drive_model}:physics:type" + # check the value + assert attr_value == joint_prim.GetAttribute(prim_attr_name).Get() + continue + + # non-string attributes + if attr_name == "max_velocity": + prim_attr_name = "physxJoint:maxJointVelocity" + elif attr_name == "max_effort": + prim_attr_name = f"drive:{drive_model}:physics:maxForce" + else: + prim_attr_name = f"drive:{drive_model}:physics:{to_camel_case(attr_name, to='cC')}" + + # obtain value from USD API (for angular, these follow degrees unit) + prim_attr_value = joint_prim.GetAttribute(prim_attr_name).Get() + + # for angular drives, we expect user to set in radians + # the values reported by USD are in degrees + if drive_model == "angular": + if attr_name == "max_velocity": + # deg / s --> rad / s + prim_attr_value = prim_attr_value * math.pi / 180.0 + elif attr_name in ["stiffness", "damping"]: + # N-m/deg or N-m-s/deg --> N-m/rad or N-m-s/rad + prim_attr_value = prim_attr_value * 180.0 / math.pi + + # validate the values + assert prim_attr_value == pytest.approx(attr_value, abs=1e-5), ( + f"Failed setting for {prim_attr_name}" + ) + elif verbose: + print(f"Skipping prim {joint_prim.GetPrimPath()} as it is not a joint drive api.") diff --git a/source/isaaclab/test/sim/test_simulation_context.py b/source/isaaclab/test/sim/test_simulation_context.py new file mode 100644 index 0000000000000000000000000000000000000000..88544c2f8423d9b4531c8c73531572dfb21e7019 --- /dev/null +++ b/source/isaaclab/test/sim/test_simulation_context.py @@ -0,0 +1,170 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from collections.abc import Generator + +import numpy as np +import pytest + +import omni.physx +from isaacsim.core.api.simulation_context import SimulationContext as IsaacSimulationContext + +import isaaclab.sim as sim_utils +from isaaclab.sim import SimulationCfg, SimulationContext + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Setup and teardown for each test.""" + # Setup: Clear any existing simulation context + SimulationContext.clear_instance() + + # Yield for the test + yield + + # Teardown: Clear the simulation context after each test + SimulationContext.clear_instance() + + +@pytest.fixture +def sim_with_stage_in_memory() -> Generator[SimulationContext, None, None]: + """Create a simulation context with stage in memory.""" + # create stage in memory + cfg = SimulationCfg(create_stage_in_memory=True) + sim = SimulationContext(cfg=cfg) + # update stage + sim_utils.update_stage() + # yield simulation context + yield sim + # stop simulation + omni.physx.get_physx_simulation_interface().detach_stage() + sim.stop() + # clear simulation context + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_singleton(): + """Tests that the singleton is working.""" + sim1 = SimulationContext() + sim2 = SimulationContext() + sim3 = IsaacSimulationContext() + assert sim1 is sim2 + assert sim1 is sim3 + + # try to delete the singleton + sim2.clear_instance() + assert sim1.instance() is None + # create new instance + sim4 = SimulationContext() + assert sim1 is not sim4 + assert sim3 is not sim4 + assert sim1.instance() is sim4.instance() + assert sim3.instance() is sim4.instance() + # clear instance + sim3.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_initialization(): + """Test the simulation config.""" + cfg = SimulationCfg(physics_prim_path="/Physics/PhysX", render_interval=5, gravity=(0.0, -0.5, -0.5)) + sim = SimulationContext(cfg) + # TODO: Figure out why keyword argument doesn't work. + # note: added a fix in Isaac Sim 2023.1 for this. + # sim = SimulationContext(cfg=cfg) + + # check valid settings + assert sim.get_physics_dt() == cfg.dt + assert sim.get_rendering_dt() == cfg.dt * cfg.render_interval + assert not sim.has_rtx_sensors() + # check valid paths + assert sim.stage.GetPrimAtPath("/Physics/PhysX").IsValid() + assert sim.stage.GetPrimAtPath("/Physics/PhysX/defaultMaterial").IsValid() + # check valid gravity + gravity_dir, gravity_mag = sim.get_physics_context().get_gravity() + gravity = np.array(gravity_dir) * gravity_mag + np.testing.assert_almost_equal(gravity, cfg.gravity) + + +@pytest.mark.isaacsim_ci +def test_sim_version(): + """Test obtaining the version.""" + sim = SimulationContext() + version = sim.get_version() + assert len(version) > 0 + assert version[0] >= 4 + + +@pytest.mark.isaacsim_ci +def test_carb_setting(): + """Test setting carb settings.""" + sim = SimulationContext() + # known carb setting + sim.set_setting("/physics/physxDispatcher", False) + assert sim.get_setting("/physics/physxDispatcher") is False + # unknown carb setting + sim.set_setting("/myExt/using_omniverse_version", sim.get_version()) + assert tuple(sim.get_setting("/myExt/using_omniverse_version")) == tuple(sim.get_version()) + + +@pytest.mark.isaacsim_ci +def test_headless_mode(): + """Test that render mode is headless since we are running in headless mode.""" + sim = SimulationContext() + # check default render mode + assert sim.render_mode == sim.RenderMode.NO_GUI_OR_RENDERING + + +# def test_boundedness(): +# """Test that the boundedness of the simulation context remains constant. +# +# Note: This test fails right now because Isaac Sim does not handle boundedness correctly. On creation, +# it is registering itself to various callbacks and hence the boundedness is more than 1. This may not be +# critical for the simulation context since we usually call various clear functions before deleting the +# simulation context. +# """ +# sim = SimulationContext() +# # manually set the boundedness to 1? -- this is not possible because of Isaac Sim. +# sim.clear_all_callbacks() +# sim._stage_open_callback = None +# sim._physics_timer_callback = None +# sim._event_timer_callback = None +# +# # check that boundedness of simulation context is correct +# sim_ref_count = ctypes.c_long.from_address(id(sim)).value +# # reset the simulation +# sim.reset() +# assert ctypes.c_long.from_address(id(sim)).value == sim_ref_count +# # step the simulation +# for _ in range(10): +# sim.step() +# assert ctypes.c_long.from_address(id(sim)).value == sim_ref_count +# # clear the simulation +# sim.clear_instance() +# assert ctypes.c_long.from_address(id(sim)).value == sim_ref_count - 1 + + +@pytest.mark.isaacsim_ci +def test_zero_gravity(): + """Test that gravity can be properly disabled.""" + cfg = SimulationCfg(gravity=(0.0, 0.0, 0.0)) + + sim = SimulationContext(cfg) + + gravity_dir, gravity_mag = sim.get_physics_context().get_gravity() + gravity = np.array(gravity_dir) * gravity_mag + np.testing.assert_almost_equal(gravity, cfg.gravity) diff --git a/source/isaaclab/test/sim/test_simulation_render_config.py b/source/isaaclab/test/sim/test_simulation_render_config.py new file mode 100644 index 0000000000000000000000000000000000000000..1bab84d11d7ee0511d903dd4d5e88822245a5243 --- /dev/null +++ b/source/isaaclab/test/sim/test_simulation_render_config.py @@ -0,0 +1,209 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + + +"""Rest everything follows.""" + +import os + +import flatdict +import pytest +import toml + +import carb + +from isaaclab.sim.simulation_cfg import RenderCfg, SimulationCfg +from isaaclab.sim.simulation_context import SimulationContext +from isaaclab.utils.version import get_isaac_sim_version + + +@pytest.mark.skip(reason="Timeline not stopped") +@pytest.mark.isaacsim_ci +def test_render_cfg(): + """Test that the simulation context is created with the correct render cfg.""" + enable_translucency = True + enable_reflections = True + enable_global_illumination = True + antialiasing_mode = "DLAA" + enable_dlssg = True + enable_dl_denoiser = True + dlss_mode = 0 + enable_direct_lighting = True + samples_per_pixel = 4 + enable_shadows = True + enable_ambient_occlusion = True + + render_cfg = RenderCfg( + enable_translucency=enable_translucency, + enable_reflections=enable_reflections, + enable_global_illumination=enable_global_illumination, + antialiasing_mode=antialiasing_mode, + enable_dlssg=enable_dlssg, + dlss_mode=dlss_mode, + enable_dl_denoiser=enable_dl_denoiser, + enable_direct_lighting=enable_direct_lighting, + samples_per_pixel=samples_per_pixel, + enable_shadows=enable_shadows, + enable_ambient_occlusion=enable_ambient_occlusion, + ) + + cfg = SimulationCfg(render=render_cfg) + + # FIXME: when running all tests, the timeline is not stopped, force stop it here but also that does not the timeline + # omni.timeline.get_timeline_interface().stop() + + sim = SimulationContext(cfg) + + assert sim.cfg.render.enable_translucency == enable_translucency + assert sim.cfg.render.enable_reflections == enable_reflections + assert sim.cfg.render.enable_global_illumination == enable_global_illumination + assert sim.cfg.render.antialiasing_mode == antialiasing_mode + assert sim.cfg.render.enable_dlssg == enable_dlssg + assert sim.cfg.render.dlss_mode == dlss_mode + assert sim.cfg.render.enable_dl_denoiser == enable_dl_denoiser + assert sim.cfg.render.enable_direct_lighting == enable_direct_lighting + assert sim.cfg.render.samples_per_pixel == samples_per_pixel + assert sim.cfg.render.enable_shadows == enable_shadows + assert sim.cfg.render.enable_ambient_occlusion == enable_ambient_occlusion + + carb_settings_iface = carb.settings.get_settings() + assert carb_settings_iface.get("/rtx/translucency/enabled") == sim.cfg.render.enable_translucency + assert carb_settings_iface.get("/rtx/reflections/enabled") == sim.cfg.render.enable_reflections + assert carb_settings_iface.get("/rtx/indirectDiffuse/enabled") == sim.cfg.render.enable_global_illumination + assert carb_settings_iface.get("/rtx-transient/dlssg/enabled") == sim.cfg.render.enable_dlssg + assert carb_settings_iface.get("/rtx-transient/dldenoiser/enabled") == sim.cfg.render.enable_dl_denoiser + assert carb_settings_iface.get("/rtx/post/dlss/execMode") == sim.cfg.render.dlss_mode + assert carb_settings_iface.get("/rtx/directLighting/enabled") == sim.cfg.render.enable_direct_lighting + assert ( + carb_settings_iface.get("/rtx/directLighting/sampledLighting/samplesPerPixel") + == sim.cfg.render.samples_per_pixel + ) + assert carb_settings_iface.get("/rtx/shadows/enabled") == sim.cfg.render.enable_shadows + assert carb_settings_iface.get("/rtx/ambientOcclusion/enabled") == sim.cfg.render.enable_ambient_occlusion + assert carb_settings_iface.get("/rtx/post/aa/op") == 4 # dlss = 3, dlaa=4 + + +@pytest.mark.isaacsim_ci +def test_render_cfg_presets(): + """Test that the simulation context is created with the correct render cfg preset with overrides.""" + + # carb setting dictionary overrides + carb_settings = {"/rtx/raytracing/subpixel/mode": 3, "/rtx/pathtracing/maxSamplesPerLaunch": 999999} + # user-friendly setting overrides + dlss_mode = ("/rtx/post/dlss/execMode", 5) + + rendering_modes = ["performance", "balanced", "quality"] + + for rendering_mode in rendering_modes: + # grab isaac lab apps path + isaaclab_app_exp_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), *[".."] * 4, "apps") + # for Isaac Sim 4.5 compatibility, we use the 4.5 rendering mode app files in a different folder + if get_isaac_sim_version().major < 5: + isaaclab_app_exp_path = os.path.join(isaaclab_app_exp_path, "isaacsim_4_5") + + # grab preset settings + preset_filename = os.path.join(isaaclab_app_exp_path, f"rendering_modes/{rendering_mode}.kit") + with open(preset_filename) as file: + preset_dict = toml.load(file) + preset_dict = dict(flatdict.FlatDict(preset_dict, delimiter=".")) + + render_cfg = RenderCfg( + rendering_mode=rendering_mode, + dlss_mode=dlss_mode[1], + carb_settings=carb_settings, + ) + + cfg = SimulationCfg(render=render_cfg) + + SimulationContext(cfg) + + carb_settings_iface = carb.settings.get_settings() + for key, val in preset_dict.items(): + setting_name = "/" + key.replace(".", "/") # convert to carb setting format + + if setting_name in carb_settings: + # grab groundtruth from carb setting dictionary overrides + setting_gt = carb_settings[setting_name] + elif setting_name == dlss_mode[0]: + # grab groundtruth from user-friendly setting overrides + setting_gt = dlss_mode[1] + else: + # grab groundtruth from preset + setting_gt = val + + setting_val = carb_settings_iface.get(setting_name) + + assert setting_gt == setting_val + + +@pytest.mark.skip(reason="Timeline not stopped") +@pytest.mark.isaacsim_ci +def test_render_cfg_defaults(): + """Test that the simulation context is created with the correct render cfg.""" + enable_translucency = False + enable_reflections = False + enable_global_illumination = False + antialiasing_mode = "DLSS" + enable_dlssg = False + enable_dl_denoiser = False + dlss_mode = 2 + enable_direct_lighting = False + samples_per_pixel = 1 + enable_shadows = False + enable_ambient_occlusion = False + + render_cfg = RenderCfg( + enable_translucency=enable_translucency, + enable_reflections=enable_reflections, + enable_global_illumination=enable_global_illumination, + antialiasing_mode=antialiasing_mode, + enable_dlssg=enable_dlssg, + enable_dl_denoiser=enable_dl_denoiser, + dlss_mode=dlss_mode, + enable_direct_lighting=enable_direct_lighting, + samples_per_pixel=samples_per_pixel, + enable_shadows=enable_shadows, + enable_ambient_occlusion=enable_ambient_occlusion, + ) + + cfg = SimulationCfg(render=render_cfg) + + sim = SimulationContext(cfg) + + assert sim.cfg.render.enable_translucency == enable_translucency + assert sim.cfg.render.enable_reflections == enable_reflections + assert sim.cfg.render.enable_global_illumination == enable_global_illumination + assert sim.cfg.render.antialiasing_mode == antialiasing_mode + assert sim.cfg.render.enable_dlssg == enable_dlssg + assert sim.cfg.render.enable_dl_denoiser == enable_dl_denoiser + assert sim.cfg.render.dlss_mode == dlss_mode + assert sim.cfg.render.enable_direct_lighting == enable_direct_lighting + assert sim.cfg.render.samples_per_pixel == samples_per_pixel + assert sim.cfg.render.enable_shadows == enable_shadows + assert sim.cfg.render.enable_ambient_occlusion == enable_ambient_occlusion + + carb_settings_iface = carb.settings.get_settings() + assert carb_settings_iface.get("/rtx/translucency/enabled") == sim.cfg.render.enable_translucency + assert carb_settings_iface.get("/rtx/reflections/enabled") == sim.cfg.render.enable_reflections + assert carb_settings_iface.get("/rtx/indirectDiffuse/enabled") == sim.cfg.render.enable_global_illumination + assert carb_settings_iface.get("/rtx-transient/dlssg/enabled") == sim.cfg.render.enable_dlssg + assert carb_settings_iface.get("/rtx-transient/dldenoiser/enabled") == sim.cfg.render.enable_dl_denoiser + assert carb_settings_iface.get("/rtx/post/dlss/execMode") == sim.cfg.render.dlss_mode + assert carb_settings_iface.get("/rtx/directLighting/enabled") == sim.cfg.render.enable_direct_lighting + assert ( + carb_settings_iface.get("/rtx/directLighting/sampledLighting/samplesPerPixel") + == sim.cfg.render.samples_per_pixel + ) + assert carb_settings_iface.get("/rtx/shadows/enabled") == sim.cfg.render.enable_shadows + assert carb_settings_iface.get("/rtx/ambientOcclusion/enabled") == sim.cfg.render.enable_ambient_occlusion + assert carb_settings_iface.get("/rtx/post/aa/op") == 3 # dlss = 3, dlaa=4 diff --git a/source/isaaclab/test/sim/test_simulation_stage_in_memory.py b/source/isaaclab/test/sim/test_simulation_stage_in_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..68d9d86c666e29ef67099d5618d1042b8a03bad5 --- /dev/null +++ b/source/isaaclab/test/sim/test_simulation_stage_in_memory.py @@ -0,0 +1,259 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Integration tests for simulation context with stage in memory.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +# FIXME (mmittal): Stage in memory requires cameras to be enabled. +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + + +import pytest + +import omni +import omni.physx +import omni.usd +import usdrt +from isaacsim.core.cloner import GridCloner + +import isaaclab.sim as sim_utils +from isaaclab.sim.simulation_context import SimulationCfg, SimulationContext +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.version import get_isaac_sim_version + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + cfg = SimulationCfg(create_stage_in_memory=True) + sim = SimulationContext(cfg=cfg) + sim_utils.update_stage() + yield sim + omni.physx.get_physx_simulation_interface().detach_stage() + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +""" +Tests +""" + + +def test_stage_in_memory_with_shapes(sim): + """Test spawning of shapes with stage in memory.""" + + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5: + pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + + # define parameters + num_clones = 10 + + # grab stage in memory and set as current stage via the with statement + stage_in_memory = sim.get_initial_stage() + with sim_utils.use_stage(stage_in_memory): + # create cloned cone stage + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + + cfg = sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.ConeCfg( + radius=0.3, + height=0.6, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + ), + sim_utils.MeshCuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.MdlFileCfg( + mdl_path=f"{ISAACLAB_NUCLEUS_DIR}/Materials/TilesMarbleSpiderWhiteBrickBondHoned/TilesMarbleSpiderWhiteBrickBondHoned.mdl", + project_uvw=True, + texture_scale=(0.25, 0.25), + ), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.MdlFileCfg( + mdl_path=f"{ISAACLAB_NUCLEUS_DIR}/Materials/TilesMarbleSpiderWhiteBrickBondHoned/TilesMarbleSpiderWhiteBrickBondHoned.mdl", + project_uvw=True, + texture_scale=(0.25, 0.25), + ), + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + ), + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ) + prim_path_regex = "/World/env_.*/Cone" + cfg.func(prim_path_regex, cfg) + + # verify stage is in memory + assert sim_utils.is_current_stage_in_memory() + + # verify prims exist in stage in memory + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) == num_clones + + # verify prims do not exist in context stage + context_stage = omni.usd.get_context().get_stage() + with sim_utils.use_stage(context_stage): + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) != num_clones + + # attach stage to context + sim_utils.attach_stage_to_usd_context() + + # verify stage is no longer in memory + assert not sim_utils.is_current_stage_in_memory() + + # verify prims now exist in context stage + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) == num_clones + + +def test_stage_in_memory_with_usds(sim): + """Test spawning of USDs with stage in memory.""" + + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5: + pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + + # define parameters + num_clones = 10 + usd_paths = [ + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d.usd", + ] + + # grab stage in memory and set as current stage via the with statement + stage_in_memory = sim.get_initial_stage() + with sim_utils.use_stage(stage_in_memory): + # create cloned robot stage + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + + cfg = sim_utils.MultiUsdFileCfg( + usd_path=usd_paths, + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=True, + ) + prim_path_regex = "/World/env_.*/Robot" + cfg.func(prim_path_regex, cfg) + + # verify stage is in memory + assert sim_utils.is_current_stage_in_memory() + + # verify prims exist in stage in memory + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) == num_clones + + # verify prims do not exist in context stage + context_stage = omni.usd.get_context().get_stage() + with sim_utils.use_stage(context_stage): + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) != num_clones + + # attach stage to context + sim_utils.attach_stage_to_usd_context() + + # verify stage is no longer in memory + assert not sim_utils.is_current_stage_in_memory() + + # verify prims now exist in context stage + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) == num_clones + + +def test_stage_in_memory_with_clone_in_fabric(sim): + """Test cloning in fabric with stage in memory.""" + + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5: + pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + + # define parameters + usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd" + num_clones = 100 + + # grab stage in memory and set as current stage via the with statement + stage_in_memory = sim.get_initial_stage() + with sim_utils.use_stage(stage_in_memory): + # set up paths + base_env_path = "/World/envs" + source_prim_path = f"{base_env_path}/env_0" + + # create cloner + cloner = GridCloner(spacing=3, stage=stage_in_memory) + cloner.define_base_env(base_env_path) + + # create source prim + sim_utils.create_prim(f"{source_prim_path}/Robot", "Xform", usd_path=usd_path) + + # generate target paths + target_paths = cloner.generate_paths("/World/envs/env", num_clones) + + # clone robots at target paths + cloner.clone( + source_prim_path=source_prim_path, + base_env_path=base_env_path, + prim_paths=target_paths, + replicate_physics=True, + clone_in_fabric=True, + ) + prim_path_regex = "/World/envs/env_.*" + + # verify prims do not exist in context stage + context_stage = omni.usd.get_context().get_stage() + with sim_utils.use_stage(context_stage): + prims = sim_utils.find_matching_prim_paths(prim_path_regex) + assert len(prims) != num_clones + + # attach stage to context + sim_utils.attach_stage_to_usd_context() + + # verify stage is no longer in memory + assert not sim_utils.is_current_stage_in_memory() + + # verify prims now exist in fabric stage using usdrt apis + stage_id = sim_utils.get_current_stage_id() + usdrt_stage = usdrt.Usd.Stage.Attach(stage_id) + for i in range(num_clones): + prim = usdrt_stage.GetPrimAtPath(f"/World/envs/env_{i}/Robot") + assert prim.IsValid() diff --git a/source/isaaclab/test/sim/test_spawn_from_files.py b/source/isaaclab/test/sim/test_spawn_from_files.py new file mode 100644 index 0000000000000000000000000000000000000000..530cc4b99cdbc3b3399bf2b5d5e1bbac58bb7127 --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_from_files.py @@ -0,0 +1,205 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +"""Launch Isaac Sim Simulator first.""" + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +from packaging.version import Version + +import omni.kit.app +from isaacsim.core.api.simulation_context import SimulationContext + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.version import get_isaac_sim_version + + +@pytest.fixture +def sim(): + """Create a blank new stage for each test.""" + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.1 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + # Wait for spawning + sim_utils.update_stage() + + yield sim + + # cleanup after test + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_spawn_usd(sim): + """Test loading prim from Usd file.""" + # Spawn cone + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd") + prim = cfg.func("/World/Franka", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Franka").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + +@pytest.mark.isaacsim_ci +def test_spawn_usd_fails(sim): + """Test loading prim from Usd file fails when asset usd path is invalid.""" + # Spawn cone + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda2_instanceable.usd") + + with pytest.raises(FileNotFoundError): + cfg.func("/World/Franka", cfg) + + +@pytest.mark.isaacsim_ci +def test_spawn_urdf(sim): + """Test loading prim from URDF file.""" + # pin the urdf importer extension to the older version + manager = omni.kit.app.get_app().get_extension_manager() + if get_isaac_sim_version() >= Version("5.1"): + pinned_urdf_extension_name = "isaacsim.asset.importer.urdf-2.4.31" + manager.set_extension_enabled_immediate(pinned_urdf_extension_name, True) + else: + pinned_urdf_extension_name = "isaacsim.asset.importer.urdf" + # retrieve path to urdf importer extension + extension_id = manager.get_enabled_extension_id(pinned_urdf_extension_name) + extension_path = manager.get_extension_path(extension_id) + # Spawn franka from URDF + cfg = sim_utils.UrdfFileCfg( + asset_path=f"{extension_path}/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf", + fix_base=True, + joint_drive=sim_utils.UrdfConverterCfg.JointDriveCfg( + gains=sim_utils.UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=None, damping=None) + ), + ) + prim = cfg.func("/World/Franka", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Franka").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + +@pytest.mark.isaacsim_ci +def test_spawn_ground_plane(sim): + """Test loading prim for the ground plane from grid world USD.""" + # Spawn ground plane + cfg = sim_utils.GroundPlaneCfg(color=(0.1, 0.1, 0.1), size=(10.0, 10.0)) + prim = cfg.func("/World/ground_plane", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/ground_plane").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + +@pytest.mark.isaacsim_ci +def test_spawn_usd_with_compliant_contact_material(sim): + """Test loading prim from USD file with physics material applied to specific prim.""" + # Spawn gelsight finger with physics material on specific prim + usd_file_path = f"{ISAACLAB_NUCLEUS_DIR}/TacSL/gelsight_r15_finger/gelsight_r15_finger.usd" + + # Create spawn configuration + spawn_cfg = sim_utils.UsdFileWithCompliantContactCfg( + usd_path=usd_file_path, + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=True), + compliant_contact_stiffness=1000.0, + compliant_contact_damping=100.0, + physics_material_prim_path="elastomer", + ) + + # Spawn the prim + prim = spawn_cfg.func("/World/Robot", spawn_cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Robot").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + material_prim_path = "/World/Robot/elastomer/compliant_material" + # Check that the physics material was applied to the specified prim + assert sim.stage.GetPrimAtPath(material_prim_path).IsValid() + + # Check properties + material_prim = sim.stage.GetPrimAtPath(material_prim_path) + assert material_prim.IsValid() + assert material_prim.GetAttribute("physxMaterial:compliantContactStiffness").Get() == 1000.0 + assert material_prim.GetAttribute("physxMaterial:compliantContactDamping").Get() == 100.0 + + +@pytest.mark.isaacsim_ci +def test_spawn_usd_with_compliant_contact_material_on_multiple_prims(sim): + """Test loading prim from USD file with physics material applied to multiple prims.""" + # Spawn Panda robot with physics material on specific prims + usd_file_path = f"{ISAACLAB_NUCLEUS_DIR}/TacSL/gelsight_r15_finger/gelsight_r15_finger.usd" + + # Create spawn configuration + spawn_cfg = sim_utils.UsdFileWithCompliantContactCfg( + usd_path=usd_file_path, + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=True), + compliant_contact_stiffness=1000.0, + compliant_contact_damping=100.0, + physics_material_prim_path=["elastomer", "gelsight_finger"], + ) + + # Spawn the prim + prim = spawn_cfg.func("/World/Robot", spawn_cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Robot").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + # Check that the physics material was applied to the specified prims + for link_name in ["elastomer", "gelsight_finger"]: + material_prim_path = f"/World/Robot/{link_name}/compliant_material" + print("checking", material_prim_path) + assert sim.stage.GetPrimAtPath(material_prim_path).IsValid() + + # Check properties + material_prim = sim.stage.GetPrimAtPath(material_prim_path) + assert material_prim.IsValid() + assert material_prim.GetAttribute("physxMaterial:compliantContactStiffness").Get() == 1000.0 + assert material_prim.GetAttribute("physxMaterial:compliantContactDamping").Get() == 100.0 + + +@pytest.mark.isaacsim_ci +def test_spawn_usd_with_compliant_contact_material_no_prim_path(sim): + """Test loading prim from USD file with physics material but no prim path specified.""" + # Spawn gelsight finger without specifying prim path for physics material + usd_file_path = f"{ISAACLAB_NUCLEUS_DIR}/TacSL/gelsight_r15_finger/gelsight_r15_finger.usd" + + # Create spawn configuration without physics material prim path + spawn_cfg = sim_utils.UsdFileWithCompliantContactCfg( + usd_path=usd_file_path, + rigid_props=sim_utils.RigidBodyPropertiesCfg(disable_gravity=True), + compliant_contact_stiffness=1000.0, + compliant_contact_damping=100.0, + physics_material_prim_path=None, + ) + + # Spawn the prim + prim = spawn_cfg.func("/World/Robot", spawn_cfg) + + # Check validity - should still spawn successfully but without physics material + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Robot").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + + material_prim_path = "/World/Robot/elastomer/compliant_material" + material_prim = sim.stage.GetPrimAtPath(material_prim_path) + assert material_prim is not None + assert not material_prim.IsValid() diff --git a/source/isaaclab/test/sim/test_spawn_lights.py b/source/isaaclab/test/sim/test_spawn_lights.py new file mode 100644 index 0000000000000000000000000000000000000000..325cd06866e770ce0769ace30e3806f3fad24006 --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_lights.py @@ -0,0 +1,159 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext +from pxr import Usd, UsdLux + +import isaaclab.sim as sim_utils +from isaaclab.utils.string import to_camel_case + + +@pytest.fixture(autouse=True) +def sim(): + """Setup and teardown for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.1 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + # Wait for spawning + sim_utils.update_stage() + + # Yield the simulation context for the test + yield sim + + # Teardown: Stop simulation + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +def test_spawn_disk_light(sim): + """Test spawning a disk light source.""" + cfg = sim_utils.DiskLightCfg( + color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0 + ) + prim = cfg.func("/World/disk_light", cfg) + + # check if the light is spawned + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/disk_light").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "DiskLight" + # validate properties on the prim + _validate_properties_on_prim(prim, cfg) + + +def test_spawn_distant_light(sim): + """Test spawning a distant light.""" + cfg = sim_utils.DistantLightCfg( + color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, angle=20 + ) + prim = cfg.func("/World/distant_light", cfg) + + # check if the light is spawned + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/distant_light").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "DistantLight" + # validate properties on the prim + _validate_properties_on_prim(prim, cfg) + + +def test_spawn_dome_light(sim): + """Test spawning a dome light source.""" + cfg = sim_utils.DomeLightCfg( + color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100 + ) + prim = cfg.func("/World/dome_light", cfg) + + # check if the light is spawned + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/dome_light").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "DomeLight" + # validate properties on the prim + _validate_properties_on_prim(prim, cfg) + + +def test_spawn_cylinder_light(sim): + """Test spawning a cylinder light source.""" + cfg = sim_utils.CylinderLightCfg( + color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0 + ) + prim = cfg.func("/World/cylinder_light", cfg) + + # check if the light is spawned + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/cylinder_light").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "CylinderLight" + # validate properties on the prim + _validate_properties_on_prim(prim, cfg) + + +def test_spawn_sphere_light(sim): + """Test spawning a sphere light source.""" + cfg = sim_utils.SphereLightCfg( + color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0 + ) + prim = cfg.func("/World/sphere_light", cfg) + + # check if the light is spawned + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/sphere_light").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "SphereLight" + # validate properties on the prim + _validate_properties_on_prim(prim, cfg) + + +""" +Helper functions. +""" + + +def _validate_properties_on_prim(prim: Usd.Prim, cfg: sim_utils.LightCfg): + """Validate the properties on the prim. + + Args: + prim: The prim. + cfg: The configuration for the light source. + """ + # default list of params to skip + non_usd_params = ["func", "prim_type", "visible", "semantic_tags", "copy_from_source"] + # validate the properties + for attr_name, attr_value in cfg.__dict__.items(): + # skip names we know are not present + if attr_name in non_usd_params or attr_value is None: + continue + # deal with texture input names + if "texture" in attr_name: + light_prim = UsdLux.DomeLight(prim) + if attr_name == "texture_file": + configured_value = light_prim.GetTextureFileAttr().Get() + elif attr_name == "texture_format": + configured_value = light_prim.GetTextureFormatAttr().Get() + else: + raise ValueError(f"Unknown texture attribute: '{attr_name}'") + else: + # convert attribute name in prim to cfg name + if attr_name == "visible_in_primary_ray": + prim_prop_name = f"{to_camel_case(attr_name, to='cC')}" + else: + prim_prop_name = f"inputs:{to_camel_case(attr_name, to='cC')}" + # configured value + configured_value = prim.GetAttribute(prim_prop_name).Get() + # validate the values + assert configured_value == attr_value, f"Failed for attribute: '{attr_name}'" diff --git a/source/isaaclab/test/sim/test_spawn_materials.py b/source/isaaclab/test/sim/test_spawn_materials.py new file mode 100644 index 0000000000000000000000000000000000000000..ee8cb38f90a6f24ab7b78dec6398680376c49731 --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_materials.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext +from pxr import UsdPhysics, UsdShade + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import NVIDIA_NUCLEUS_DIR + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + sim_utils.create_new_stage() + dt = 0.1 + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + sim_utils.update_stage() + yield sim + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +def test_spawn_preview_surface(sim): + """Test spawning preview surface.""" + cfg = sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)) + prim = cfg.func("/Looks/PreviewSurface", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/PreviewSurface").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Shader" + # Check properties + assert prim.GetAttribute("inputs:diffuseColor").Get() == cfg.diffuse_color + + +def test_spawn_mdl_material(sim): + """Test spawning mdl material.""" + cfg = sim_utils.materials.MdlFileCfg( + mdl_path=f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Anodized.mdl", + project_uvw=True, + albedo_brightness=0.5, + ) + prim = cfg.func("/Looks/MdlMaterial", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/MdlMaterial").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Shader" + # Check properties + assert prim.GetAttribute("inputs:project_uvw").Get() == cfg.project_uvw + assert prim.GetAttribute("inputs:albedo_brightness").Get() == cfg.albedo_brightness + + +def test_spawn_glass_mdl_material(sim): + """Test spawning a glass mdl material.""" + cfg = sim_utils.materials.GlassMdlCfg(thin_walled=False, glass_ior=1.0, glass_color=(0.0, 1.0, 0.0)) + prim = cfg.func("/Looks/GlassMaterial", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/GlassMaterial").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Shader" + # Check properties + assert prim.GetAttribute("inputs:thin_walled").Get() == cfg.thin_walled + assert prim.GetAttribute("inputs:glass_ior").Get() == cfg.glass_ior + assert prim.GetAttribute("inputs:glass_color").Get() == cfg.glass_color + + +def test_spawn_rigid_body_material(sim): + """Test spawning a rigid body material.""" + cfg = sim_utils.materials.RigidBodyMaterialCfg( + dynamic_friction=1.5, + restitution=1.5, + static_friction=0.5, + restitution_combine_mode="max", + friction_combine_mode="max", + ) + prim = cfg.func("/Looks/RigidBodyMaterial", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/RigidBodyMaterial").IsValid() + # Check properties + assert prim.GetAttribute("physics:staticFriction").Get() == cfg.static_friction + assert prim.GetAttribute("physics:dynamicFriction").Get() == cfg.dynamic_friction + assert prim.GetAttribute("physics:restitution").Get() == cfg.restitution + assert prim.GetAttribute("physxMaterial:restitutionCombineMode").Get() == cfg.restitution_combine_mode + assert prim.GetAttribute("physxMaterial:frictionCombineMode").Get() == cfg.friction_combine_mode + + +def test_spawn_deformable_body_material(sim): + """Test spawning a deformable body material.""" + cfg = sim_utils.materials.DeformableBodyMaterialCfg( + density=1.0, + dynamic_friction=0.25, + youngs_modulus=50000000.0, + poissons_ratio=0.5, + elasticity_damping=0.005, + damping_scale=1.0, + ) + prim = cfg.func("/Looks/DeformableBodyMaterial", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/DeformableBodyMaterial").IsValid() + # Check properties + assert prim.GetAttribute("physxDeformableBodyMaterial:density").Get() == cfg.density + assert prim.GetAttribute("physxDeformableBodyMaterial:dynamicFriction").Get() == cfg.dynamic_friction + assert prim.GetAttribute("physxDeformableBodyMaterial:youngsModulus").Get() == cfg.youngs_modulus + assert prim.GetAttribute("physxDeformableBodyMaterial:poissonsRatio").Get() == cfg.poissons_ratio + assert prim.GetAttribute("physxDeformableBodyMaterial:elasticityDamping").Get() == pytest.approx( + cfg.elasticity_damping + ) + assert prim.GetAttribute("physxDeformableBodyMaterial:dampingScale").Get() == cfg.damping_scale + + +def test_apply_rigid_body_material_on_visual_material(sim): + """Test applying a rigid body material on a visual material.""" + cfg = sim_utils.materials.GlassMdlCfg(thin_walled=False, glass_ior=1.0, glass_color=(0.0, 1.0, 0.0)) + prim = cfg.func("/Looks/Material", cfg) + cfg = sim_utils.materials.RigidBodyMaterialCfg( + dynamic_friction=1.5, + restitution=1.5, + static_friction=0.5, + restitution_combine_mode="max", + friction_combine_mode="max", + ) + prim = cfg.func("/Looks/Material", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/Looks/Material").IsValid() + # Check properties + assert prim.GetAttribute("physics:staticFriction").Get() == cfg.static_friction + assert prim.GetAttribute("physics:dynamicFriction").Get() == cfg.dynamic_friction + assert prim.GetAttribute("physics:restitution").Get() == cfg.restitution + assert prim.GetAttribute("physxMaterial:restitutionCombineMode").Get() == cfg.restitution_combine_mode + assert prim.GetAttribute("physxMaterial:frictionCombineMode").Get() == cfg.friction_combine_mode + + +def test_bind_prim_to_material(sim): + """Test binding a rigid body material on a mesh prim.""" + + # create a mesh prim + object_prim = sim_utils.create_prim("/World/Geometry/box", "Cube") + UsdPhysics.CollisionAPI.Apply(object_prim) + + # create a visual material + visual_material_cfg = sim_utils.GlassMdlCfg(glass_ior=1.0, thin_walled=True) + visual_material_cfg.func("/World/Looks/glassMaterial", visual_material_cfg) + # create a physics material + physics_material_cfg = sim_utils.RigidBodyMaterialCfg(static_friction=0.5, dynamic_friction=1.5, restitution=1.5) + physics_material_cfg.func("/World/Physics/rubberMaterial", physics_material_cfg) + sim_utils.bind_visual_material("/World/Geometry/box", "/World/Looks/glassMaterial") + sim_utils.bind_physics_material("/World/Geometry/box", "/World/Physics/rubberMaterial") + + # check the material binding + material_binding_api = UsdShade.MaterialBindingAPI(object_prim) + # -- visual material + material_direct_binding = material_binding_api.GetDirectBinding() + assert material_direct_binding.GetMaterialPath() == "/World/Looks/glassMaterial" + assert material_direct_binding.GetMaterialPurpose() == "" + # -- physics material + material_direct_binding = material_binding_api.GetDirectBinding("physics") + assert material_direct_binding.GetMaterialPath() == "/World/Physics/rubberMaterial" + assert material_direct_binding.GetMaterialPurpose() == "physics" diff --git a/source/isaaclab/test/sim/test_spawn_meshes.py b/source/isaaclab/test/sim/test_spawn_meshes.py new file mode 100644 index 0000000000000000000000000000000000000000..66a48422c0c43ce86f6df80d1f14b8e085d898af --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_meshes.py @@ -0,0 +1,266 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext + +import isaaclab.sim as sim_utils + + +@pytest.fixture +def sim(): + """Create a simulation context for testing.""" + # Create a new stage + sim_utils.create_new_stage() + # Simulation time-step + dt = 0.1 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, device="cuda:0") + # Wait for spawning + sim_utils.update_stage() + yield sim + # Cleanup + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +""" +Basic spawning. +""" + + +def test_spawn_cone(sim): + """Test spawning of UsdGeomMesh as a cone prim.""" + # Spawn cone + cfg = sim_utils.MeshConeCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Mesh" + + +def test_spawn_capsule(sim): + """Test spawning of UsdGeomMesh as a capsule prim.""" + # Spawn capsule + cfg = sim_utils.MeshCapsuleCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Capsule", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Capsule").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Capsule/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Mesh" + + +def test_spawn_cylinder(sim): + """Test spawning of UsdGeomMesh as a cylinder prim.""" + # Spawn cylinder + cfg = sim_utils.MeshCylinderCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Cylinder", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cylinder").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cylinder/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Mesh" + + +def test_spawn_cuboid(sim): + """Test spawning of UsdGeomMesh as a cuboid prim.""" + # Spawn cuboid + cfg = sim_utils.MeshCuboidCfg(size=(1.0, 2.0, 3.0)) + prim = cfg.func("/World/Cube", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cube").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cube/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Mesh" + + +def test_spawn_sphere(sim): + """Test spawning of UsdGeomMesh as a sphere prim.""" + # Spawn sphere + cfg = sim_utils.MeshSphereCfg(radius=1.0) + prim = cfg.func("/World/Sphere", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Sphere").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Sphere/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Mesh" + + +""" +Physics properties. +""" + + +def test_spawn_cone_with_deformable_props(sim): + """Test spawning of UsdGeomMesh prim for a cone with deformable body API.""" + # Spawn cone + cfg = sim_utils.MeshConeCfg( + radius=1.0, + height=2.0, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(deformable_enabled=True), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + + # Check properties + # Unlike rigid body, deformable body properties are on the mesh prim + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physxDeformable:deformableEnabled").Get() == cfg.deformable_props.deformable_enabled + + +def test_spawn_cone_with_deformable_and_mass_props(sim): + """Test spawning of UsdGeomMesh prim for a cone with deformable body and mass API.""" + # Spawn cone + cfg = sim_utils.MeshConeCfg( + radius=1.0, + height=2.0, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(deformable_enabled=True), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physics:mass").Get() == cfg.mass_props.mass + + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +def test_spawn_cone_with_deformable_and_density_props(sim): + """Test spawning of UsdGeomMesh prim for a cone with deformable body and mass API. + + Note: + In this case, we specify the density instead of the mass. In that case, physics need to know + the collision shape to compute the mass. Thus, we have to set the collider properties. In + order to not have a collision shape, we disable the collision. + """ + # Spawn cone + cfg = sim_utils.MeshConeCfg( + radius=1.0, + height=2.0, + deformable_props=sim_utils.DeformableBodyPropertiesCfg(deformable_enabled=True), + mass_props=sim_utils.MassPropertiesCfg(density=10.0), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physics:density").Get() == cfg.mass_props.density + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +def test_spawn_cone_with_all_deformable_props(sim): + """Test spawning of UsdGeomMesh prim for a cone with all deformable properties.""" + # Spawn cone + cfg = sim_utils.MeshConeCfg( + radius=1.0, + height=2.0, + mass_props=sim_utils.MassPropertiesCfg(mass=5.0), + deformable_props=sim_utils.DeformableBodyPropertiesCfg(), + visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)), + physics_material=sim_utils.materials.DeformableBodyMaterialCfg(), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone/geometry/material").IsValid() + # Check properties + # -- deformable body + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physxDeformable:deformableEnabled").Get() is True + + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +def test_spawn_cone_with_all_rigid_props(sim): + """Test spawning of UsdGeomMesh prim for a cone with all rigid properties.""" + # Spawn cone + cfg = sim_utils.MeshConeCfg( + radius=1.0, + height=2.0, + mass_props=sim_utils.MassPropertiesCfg(mass=5.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1 + ), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)), + physics_material=sim_utils.materials.RigidBodyMaterialCfg(), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone/geometry/material").IsValid() + # Check properties + # -- rigid body + prim = sim.stage.GetPrimAtPath("/World/Cone") + assert prim.GetAttribute("physics:rigidBodyEnabled").Get() == cfg.rigid_props.rigid_body_enabled + assert ( + prim.GetAttribute("physxRigidBody:solverPositionIterationCount").Get() + == cfg.rigid_props.solver_position_iteration_count + ) + assert prim.GetAttribute("physxRigidBody:sleepThreshold").Get() == pytest.approx(cfg.rigid_props.sleep_threshold) + # -- mass + assert prim.GetAttribute("physics:mass").Get() == cfg.mass_props.mass + # -- collision shape + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physics:collisionEnabled").Get() is True + + # check sim playing + sim.play() + for _ in range(10): + sim.step() diff --git a/source/isaaclab/test/sim/test_spawn_sensors.py b/source/isaaclab/test/sim/test_spawn_sensors.py new file mode 100644 index 0000000000000000000000000000000000000000..320a47b3336ee8f45887cc5512c670933d3b05cc --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_sensors.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext +from pxr import Usd + +import isaaclab.sim as sim_utils +from isaaclab.sim.spawners.sensors.sensors import CUSTOM_FISHEYE_CAMERA_ATTRIBUTES, CUSTOM_PINHOLE_CAMERA_ATTRIBUTES +from isaaclab.utils.string import to_camel_case + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + sim_utils.create_new_stage() + dt = 0.1 + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + sim_utils.update_stage() + yield sim + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +""" +Basic spawning. +""" + + +def test_spawn_pinhole_camera(sim): + """Test spawning a pinhole camera.""" + cfg = sim_utils.PinholeCameraCfg( + focal_length=5.0, f_stop=10.0, clipping_range=(0.1, 1000.0), horizontal_aperture=10.0 + ) + prim = cfg.func("/World/pinhole_camera", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/pinhole_camera").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Camera" + # Check properties + _validate_properties_on_prim(prim, cfg, CUSTOM_PINHOLE_CAMERA_ATTRIBUTES) + + +def test_spawn_fisheye_camera(sim): + """Test spawning a fisheye camera.""" + cfg = sim_utils.FisheyeCameraCfg( + projection_type="fisheyePolynomial", + focal_length=5.0, + f_stop=10.0, + clipping_range=(0.1, 1000.0), + horizontal_aperture=10.0, + ) + # FIXME: This throws a warning. Check with Replicator team if this is expected/known. + # [omni.hydra] Camera '/World/fisheye_camera': Unknown projection type, defaulting to pinhole + prim = cfg.func("/World/fisheye_camera", cfg) + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/fisheye_camera").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Camera" + # Check properties + _validate_properties_on_prim(prim, cfg, CUSTOM_FISHEYE_CAMERA_ATTRIBUTES) + + +""" +Helper functions. +""" + + +def _validate_properties_on_prim(prim: Usd.Prim, cfg: object, custom_attr: dict): + """Validate the properties on the prim. + + Args: + prim: The prim. + cfg: The configuration object. + custom_attr: The custom attributes for sensor. + """ + # delete custom attributes in the config that are not USD parameters + non_usd_cfg_param_names = [ + "func", + "copy_from_source", + "lock_camera", + "visible", + "semantic_tags", + "from_intrinsic_matrix", + ] + # validate the properties + for attr_name, attr_value in cfg.__dict__.items(): + # skip names we know are not present + if attr_name in non_usd_cfg_param_names or attr_value is None: + continue + # obtain prim property name + if attr_name in custom_attr: + # check custom attributes + prim_prop_name = custom_attr[attr_name][0] + else: + # convert attribute name in prim to cfg name + prim_prop_name = to_camel_case(attr_name, to="cC") + # validate the values + assert prim.GetAttribute(prim_prop_name).Get() == pytest.approx(attr_value, rel=1e-5) diff --git a/source/isaaclab/test/sim/test_spawn_shapes.py b/source/isaaclab/test/sim/test_spawn_shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..ed7ba68f89b3bacc40d784d9eb4e4a45085fe05e --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_shapes.py @@ -0,0 +1,303 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext + +import isaaclab.sim as sim_utils + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + sim_utils.create_new_stage() + dt = 0.1 + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + sim_utils.update_stage() + yield sim + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +""" +Basic spawning. +""" + + +def test_spawn_cone(sim): + """Test spawning of UsdGeom.Cone prim.""" + cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Cone" + assert prim.GetAttribute("radius").Get() == cfg.radius + assert prim.GetAttribute("height").Get() == cfg.height + assert prim.GetAttribute("axis").Get() == cfg.axis + + +def test_spawn_capsule(sim): + """Test spawning of UsdGeom.Capsule prim.""" + cfg = sim_utils.CapsuleCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Capsule", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Capsule").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Capsule/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Capsule" + assert prim.GetAttribute("radius").Get() == cfg.radius + assert prim.GetAttribute("height").Get() == cfg.height + assert prim.GetAttribute("axis").Get() == cfg.axis + + +def test_spawn_cylinder(sim): + """Test spawning of UsdGeom.Cylinder prim.""" + cfg = sim_utils.CylinderCfg(radius=1.0, height=2.0, axis="Y") + prim = cfg.func("/World/Cylinder", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cylinder").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cylinder/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Cylinder" + assert prim.GetAttribute("radius").Get() == cfg.radius + assert prim.GetAttribute("height").Get() == cfg.height + assert prim.GetAttribute("axis").Get() == cfg.axis + + +def test_spawn_cuboid(sim): + """Test spawning of UsdGeom.Cube prim.""" + cfg = sim_utils.CuboidCfg(size=(1.0, 2.0, 3.0)) + prim = cfg.func("/World/Cube", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cube").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cube/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Cube" + assert prim.GetAttribute("size").Get() == min(cfg.size) + + +def test_spawn_sphere(sim): + """Test spawning of UsdGeom.Sphere prim.""" + cfg = sim_utils.SphereCfg(radius=1.0) + prim = cfg.func("/World/Sphere", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Sphere").IsValid() + assert prim.GetPrimTypeInfo().GetTypeName() == "Xform" + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Sphere/geometry/mesh") + assert prim.GetPrimTypeInfo().GetTypeName() == "Sphere" + assert prim.GetAttribute("radius").Get() == cfg.radius + + +""" +Physics properties. +""" + + +def test_spawn_cone_with_rigid_props(sim): + """Test spawning of UsdGeom.Cone prim with rigid body API. + + Note: + Playing the simulation in this case will give a warning that no mass is specified! + Need to also setup mass and colliders. + """ + cfg = sim_utils.ConeCfg( + radius=1.0, + height=2.0, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1 + ), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone") + assert prim.GetAttribute("physics:rigidBodyEnabled").Get() == cfg.rigid_props.rigid_body_enabled + assert ( + prim.GetAttribute("physxRigidBody:solverPositionIterationCount").Get() + == cfg.rigid_props.solver_position_iteration_count + ) + assert prim.GetAttribute("physxRigidBody:sleepThreshold").Get() == pytest.approx(cfg.rigid_props.sleep_threshold) + + +def test_spawn_cone_with_rigid_and_mass_props(sim): + """Test spawning of UsdGeom.Cone prim with rigid body and mass API.""" + cfg = sim_utils.ConeCfg( + radius=1.0, + height=2.0, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone") + assert prim.GetAttribute("physics:mass").Get() == cfg.mass_props.mass + + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +def test_spawn_cone_with_rigid_and_density_props(sim): + """Test spawning of UsdGeom.Cone prim with rigid body and mass API. + + Note: + In this case, we specify the density instead of the mass. In that case, physics need to know + the collision shape to compute the mass. Thus, we have to set the collider properties. In + order to not have a collision shape, we disable the collision. + """ + cfg = sim_utils.ConeCfg( + radius=1.0, + height=2.0, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1 + ), + mass_props=sim_utils.MassPropertiesCfg(density=10.0), + collision_props=sim_utils.CollisionPropertiesCfg(collision_enabled=False), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + # Check properties + prim = sim.stage.GetPrimAtPath("/World/Cone") + assert prim.GetAttribute("physics:density").Get() == cfg.mass_props.density + + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +def test_spawn_cone_with_all_props(sim): + """Test spawning of UsdGeom.Cone prim with all properties.""" + cfg = sim_utils.ConeCfg( + radius=1.0, + height=2.0, + mass_props=sim_utils.MassPropertiesCfg(mass=5.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)), + physics_material=sim_utils.materials.RigidBodyMaterialCfg(), + ) + prim = cfg.func("/World/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone").IsValid() + assert sim.stage.GetPrimAtPath("/World/Cone/geometry/material").IsValid() + # Check properties + # -- rigid body properties + prim = sim.stage.GetPrimAtPath("/World/Cone") + assert prim.GetAttribute("physics:rigidBodyEnabled").Get() is True + # -- collision properties + prim = sim.stage.GetPrimAtPath("/World/Cone/geometry/mesh") + assert prim.GetAttribute("physics:collisionEnabled").Get() is True + + # check sim playing + sim.play() + for _ in range(10): + sim.step() + + +""" +Cloning. +""" + + +def test_spawn_cone_clones_invalid_paths(sim): + """Test spawning of cone clones on invalid cloning paths.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + # Spawn cone on invalid cloning path -- should raise an error + cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, copy_from_source=True) + with pytest.raises(RuntimeError): + cfg.func("/World/env/env_.*/Cone", cfg) + + +def test_spawn_cone_clones(sim): + """Test spawning of cone clones.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + # Spawn cone on valid cloning path + cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, copy_from_source=True) + prim = cfg.func("/World/env_.*/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert str(prim.GetPath()) == "/World/env_0/Cone" + # find matching prims + prims = sim_utils.find_matching_prim_paths("/World/env_.*/Cone") + assert len(prims) == num_clones + + +def test_spawn_cone_clone_with_all_props_global_material(sim): + """Test spawning of cone clones with global material reference.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + # Spawn cone on valid cloning path + cfg = sim_utils.ConeCfg( + radius=1.0, + height=2.0, + mass_props=sim_utils.MassPropertiesCfg(mass=5.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(), + visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)), + physics_material=sim_utils.materials.RigidBodyMaterialCfg(), + visual_material_path="/Looks/visualMaterial", + physics_material_path="/Looks/physicsMaterial", + ) + prim = cfg.func("/World/env_.*/Cone", cfg) + + # Check validity + assert prim.IsValid() + assert str(prim.GetPath()) == "/World/env_0/Cone" + # find matching prims + prims = sim_utils.find_matching_prim_paths("/World/env_.*/Cone") + assert len(prims) == num_clones + # find matching material prims + prims = sim_utils.find_matching_prim_paths("/Looks/visualMaterial.*") + assert len(prims) == 1 diff --git a/source/isaaclab/test/sim/test_spawn_wrappers.py b/source/isaaclab/test/sim/test_spawn_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..3dd07a54e6f634edd48964d6db55b49ac6bdc813 --- /dev/null +++ b/source/isaaclab/test/sim/test_spawn_wrappers.py @@ -0,0 +1,162 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + + +import pytest + +from isaacsim.core.api.simulation_context import SimulationContext + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + + +@pytest.fixture +def sim(): + """Create a simulation context.""" + sim_utils.create_new_stage() + dt = 0.1 + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, backend="numpy") + sim_utils.update_stage() + yield sim + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +def test_spawn_multiple_shapes_with_global_settings(sim): + """Test spawning of shapes randomly with global rigid body settings.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + + cfg = sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.ConeCfg( + radius=0.3, + height=0.6, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + mass_props=sim_utils.MassPropertiesCfg(mass=100.0), # this one should get overridden + ), + sim_utils.CuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0), metallic=0.2), + ), + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg(), + ) + prim = cfg.func("/World/env_.*/Cone", cfg) + + assert prim.IsValid() + assert str(prim.GetPath()) == "/World/env_0/Cone" + prim_paths = sim_utils.find_matching_prim_paths("/World/env_.*/Cone") + assert len(prim_paths) == num_clones + + for prim_path in prim_paths: + prim = sim.stage.GetPrimAtPath(prim_path) + assert prim.GetAttribute("physics:mass").Get() == cfg.mass_props.mass + + +def test_spawn_multiple_shapes_with_individual_settings(sim): + """Test spawning of shapes randomly with individual rigid object settings.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + + mass_variations = [2.0, 3.0, 4.0] + cfg = sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + sim_utils.ConeCfg( + radius=0.3, + height=0.6, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=mass_variations[0]), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + sim_utils.CuboidCfg( + size=(0.3, 0.3, 0.3), + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=mass_variations[1]), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + sim_utils.SphereCfg( + radius=0.3, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0), metallic=0.2), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=mass_variations[2]), + collision_props=sim_utils.CollisionPropertiesCfg(), + ), + ], + random_choice=True, + ) + prim = cfg.func("/World/env_.*/Cone", cfg) + + assert prim.IsValid() + assert str(prim.GetPath()) == "/World/env_0/Cone" + prim_paths = sim_utils.find_matching_prim_paths("/World/env_.*/Cone") + assert len(prim_paths) == num_clones + + for prim_path in prim_paths: + prim = sim.stage.GetPrimAtPath(prim_path) + assert prim.GetAttribute("physics:mass").Get() in mass_variations + + +""" +Tests - Multiple USDs. +""" + + +def test_spawn_multiple_files_with_global_settings(sim): + """Test spawning of files randomly with global articulation settings.""" + num_clones = 10 + for i in range(num_clones): + sim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0)) + + cfg = sim_utils.MultiUsdFileCfg( + usd_path=[ + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d.usd", + ], + random_choice=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=True, + ) + prim = cfg.func("/World/env_.*/Robot", cfg) + + assert prim.IsValid() + assert str(prim.GetPath()) == "/World/env_0/Robot" + prim_paths = sim_utils.find_matching_prim_paths("/World/env_.*/Robot") + assert len(prim_paths) == num_clones diff --git a/source/isaaclab/test/sim/test_urdf_converter.py b/source/isaaclab/test/sim/test_urdf_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..f350ace9a5b807012d788c0c58125e9d5dcd60c8 --- /dev/null +++ b/source/isaaclab/test/sim/test_urdf_converter.py @@ -0,0 +1,149 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import os + +import numpy as np +import pytest +from packaging.version import Version + +import omni.kit.app +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.prims import Articulation + +import isaaclab.sim as sim_utils +from isaaclab.sim.converters import UrdfConverter, UrdfConverterCfg +from isaaclab.utils.version import get_isaac_sim_version + + +# Create a fixture for setup and teardown +@pytest.fixture +def sim_config(): + # Create a new stage + sim_utils.create_new_stage() + # pin the urdf importer extension to the older version + manager = omni.kit.app.get_app().get_extension_manager() + if get_isaac_sim_version() >= Version("5.1"): + pinned_urdf_extension_name = "isaacsim.asset.importer.urdf-2.4.31" + manager.set_extension_enabled_immediate(pinned_urdf_extension_name, True) + else: + pinned_urdf_extension_name = "isaacsim.asset.importer.urdf" + # obtain the extension path + extension_id = manager.get_enabled_extension_id(pinned_urdf_extension_name) + extension_path = manager.get_extension_path(extension_id) + # default configuration + config = UrdfConverterCfg( + asset_path=f"{extension_path}/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf", + fix_base=True, + joint_drive=UrdfConverterCfg.JointDriveCfg( + gains=UrdfConverterCfg.JointDriveCfg.PDGainsCfg(stiffness=400.0, damping=40.0) + ), + ) + # Simulation time-step + dt = 0.01 + # Load kit helper + sim = SimulationContext(physics_dt=dt, rendering_dt=dt, stage_units_in_meters=1.0, backend="numpy") + yield sim, config + # Teardown + sim.stop() + sim.clear() + sim.clear_all_callbacks() + sim.clear_instance() + + +@pytest.mark.isaacsim_ci +def test_no_change(sim_config): + """Call conversion twice. This should not generate a new USD file.""" + sim, config = sim_config + urdf_converter = UrdfConverter(config) + time_usd_file_created = os.stat(urdf_converter.usd_path).st_mtime_ns + + # no change to config only define the usd directory + new_config = config + new_config.usd_dir = urdf_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_urdf_converter = UrdfConverter(new_config) + new_time_usd_file_created = os.stat(new_urdf_converter.usd_path).st_mtime_ns + + assert time_usd_file_created == new_time_usd_file_created + + +@pytest.mark.isaacsim_ci +def test_config_change(sim_config): + """Call conversion twice but change the config in the second call. This should generate a new USD file.""" + sim, config = sim_config + urdf_converter = UrdfConverter(config) + time_usd_file_created = os.stat(urdf_converter.usd_path).st_mtime_ns + + # change the config + new_config = config + new_config.fix_base = not config.fix_base + # define the usd directory + new_config.usd_dir = urdf_converter.usd_dir + # convert to usd but this time in the same directory as previous step + new_urdf_converter = UrdfConverter(new_config) + new_time_usd_file_created = os.stat(new_urdf_converter.usd_path).st_mtime_ns + + assert time_usd_file_created != new_time_usd_file_created + + +@pytest.mark.isaacsim_ci +def test_create_prim_from_usd(sim_config): + """Call conversion and create a prim from it.""" + sim, config = sim_config + urdf_converter = UrdfConverter(config) + + prim_path = "/World/Robot" + sim_utils.create_prim(prim_path, usd_path=urdf_converter.usd_path) + + assert sim.stage.GetPrimAtPath(prim_path).IsValid() + + +@pytest.mark.isaacsim_ci +def test_config_drive_type(sim_config): + """Change the drive mechanism of the robot to be position.""" + sim, config = sim_config + # Create directory to dump results + test_dir = os.path.dirname(os.path.abspath(__file__)) + output_dir = os.path.join(test_dir, "output", "urdf_converter") + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # change the config + config.force_usd_conversion = True + config.joint_drive.target_type = "position" + config.joint_drive.gains.stiffness = 42.0 + config.joint_drive.gains.damping = 4.2 + config.usd_dir = output_dir + urdf_converter = UrdfConverter(config) + # check the drive type of the robot + prim_path = "/World/Robot" + sim_utils.create_prim(prim_path, usd_path=urdf_converter.usd_path) + + # access the robot + robot = Articulation(prim_path, reset_xform_properties=False) + # play the simulator and initialize the robot + sim.reset() + robot.initialize() + + # check drive values for the robot (read from physx) + drive_stiffness, drive_damping = robot.get_gains() + np.testing.assert_array_equal(drive_stiffness, config.joint_drive.gains.stiffness) + np.testing.assert_array_equal(drive_damping, config.joint_drive.gains.damping) + + # check drive values for the robot (read from usd) + sim.stop() + drive_stiffness, drive_damping = robot.get_gains() + np.testing.assert_array_equal(drive_stiffness, config.joint_drive.gains.stiffness) + np.testing.assert_array_equal(drive_damping, config.joint_drive.gains.damping) diff --git a/source/isaaclab/test/sim/test_utils_prims.py b/source/isaaclab/test/sim/test_utils_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..16584d113ed79c942a35a10fc7deddb19821d5f7 --- /dev/null +++ b/source/isaaclab/test/sim/test_utils_prims.py @@ -0,0 +1,716 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +# note: need to enable cameras to be able to make replicator core available +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import math + +import numpy as np +import pytest +import torch + +from pxr import Gf, Sdf, Usd, UsdGeom + +import isaaclab.sim as sim_utils +from isaaclab.sim.utils.prims import _to_tuple # type: ignore[reportPrivateUsage] +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Create a blank new stage for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + sim_utils.update_stage() + + # Yield for the test + yield + + # Teardown: Clear stage after each test + sim_utils.clear_stage() + + +def assert_quat_close(q1: Gf.Quatf | Gf.Quatd, q2: Gf.Quatf | Gf.Quatd, eps: float = 1e-6): + """Assert two quaternions are close.""" + assert math.isclose(q1.GetReal(), q2.GetReal(), abs_tol=eps) + for i in range(3): + assert math.isclose(q1.GetImaginary()[i], q2.GetImaginary()[i], abs_tol=eps) + + +""" +General Utils +""" + + +def test_create_prim(): + """Test create_prim() function.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create scene + prim = sim_utils.create_prim(prim_path="/World/Test", prim_type="Xform", stage=stage) + # check prim created + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/Test" + assert prim.GetTypeName() == "Xform" + + # check recreation of prim + with pytest.raises(ValueError, match="already exists"): + sim_utils.create_prim(prim_path="/World/Test", prim_type="Xform", stage=stage) + + # check attribute setting + prim = sim_utils.create_prim(prim_path="/World/Test/Cube", prim_type="Cube", stage=stage, attributes={"size": 100}) + # check attribute set + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/Test/Cube" + assert prim.GetTypeName() == "Cube" + assert prim.GetAttribute("size").Get() == 100 + + # check adding USD reference + franka_usd = f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + prim = sim_utils.create_prim("/World/Test/USDReference", usd_path=franka_usd, stage=stage) + # check USD reference set + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/Test/USDReference" + assert prim.GetTypeName() == "Xform" + # get the reference of the prim + references = [] + for prim_spec in prim.GetPrimStack(): + references.extend(prim_spec.referenceList.prependedItems) + assert len(references) == 1 + assert str(references[0].assetPath) == franka_usd + + # check adding semantic label + prim = sim_utils.create_prim( + "/World/Test/Sphere", "Sphere", stage=stage, semantic_label="sphere", attributes={"radius": 10.0} + ) + # check semantic label set + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/Test/Sphere" + assert prim.GetTypeName() == "Sphere" + assert prim.GetAttribute("radius").Get() == 10.0 + assert sim_utils.get_labels(prim)["class"] == ["sphere"] + + # check setting transform + pos = (1.0, 2.0, 3.0) + quat = (0.0, 0.0, 0.0, 1.0) + scale = (1.0, 0.5, 0.5) + prim = sim_utils.create_prim( + "/World/Test/Xform", "Xform", stage=stage, translation=pos, orientation=quat, scale=scale + ) + # check transform set + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/Test/Xform" + assert prim.GetTypeName() == "Xform" + assert prim.GetAttribute("xformOp:translate").Get() == Gf.Vec3d(pos) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), Gf.Quatd(*quat)) + assert prim.GetAttribute("xformOp:scale").Get() == Gf.Vec3d(scale) + # check xform operation order + op_names = [op.GetOpName() for op in UsdGeom.Xformable(prim).GetOrderedXformOps()] + assert op_names == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +@pytest.mark.parametrize( + "input_type", + ["list", "tuple", "numpy", "torch_cpu", "torch_cuda"], + ids=["list", "tuple", "numpy", "torch_cpu", "torch_cuda"], +) +def test_create_prim_with_different_input_types(input_type: str): + """Test create_prim() with different input types (list, tuple, numpy array, torch tensor).""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Define test values + translation_vals = [1.0, 2.0, 3.0] + orientation_vals = [1.0, 0.0, 0.0, 0.0] # w, x, y, z + scale_vals = [2.0, 3.0, 4.0] + + # Convert to the specified input type + if input_type == "list": + translation = translation_vals + orientation = orientation_vals + scale = scale_vals + elif input_type == "tuple": + translation = tuple(translation_vals) + orientation = tuple(orientation_vals) + scale = tuple(scale_vals) + elif input_type == "numpy": + translation = np.array(translation_vals) + orientation = np.array(orientation_vals) + scale = np.array(scale_vals) + elif input_type == "torch_cpu": + translation = torch.tensor(translation_vals) + orientation = torch.tensor(orientation_vals) + scale = torch.tensor(scale_vals) + elif input_type == "torch_cuda": + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + translation = torch.tensor(translation_vals, device="cuda") + orientation = torch.tensor(orientation_vals, device="cuda") + scale = torch.tensor(scale_vals, device="cuda") + + # Create prim with translation (local space) + prim = sim_utils.create_prim( + f"/World/Test/Xform_{input_type}", + "Xform", + stage=stage, + translation=translation, + orientation=orientation, + scale=scale, + ) + + # Verify prim was created correctly + assert prim.IsValid() + assert prim.GetPrimPath() == f"/World/Test/Xform_{input_type}" + + # Verify transform values + assert prim.GetAttribute("xformOp:translate").Get() == Gf.Vec3d(*translation_vals) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), Gf.Quatd(*orientation_vals)) + assert prim.GetAttribute("xformOp:scale").Get() == Gf.Vec3d(*scale_vals) + + # Verify xform operation order + op_names = [op.GetOpName() for op in UsdGeom.Xformable(prim).GetOrderedXformOps()] + assert op_names == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +@pytest.mark.parametrize( + "input_type", + ["list", "tuple", "numpy", "torch_cpu", "torch_cuda"], + ids=["list", "tuple", "numpy", "torch_cpu", "torch_cuda"], +) +def test_create_prim_with_world_position_different_types(input_type: str): + """Test create_prim() with world position using different input types.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a parent prim + _ = sim_utils.create_prim( + "/World/Parent", + "Xform", + stage=stage, + translation=(5.0, 10.0, 15.0), + orientation=(1.0, 0.0, 0.0, 0.0), + ) + + # Define world position and orientation values + world_pos_vals = [10.0, 20.0, 30.0] + world_orient_vals = [0.7071068, 0.0, 0.7071068, 0.0] # 90 deg around Y + + # Convert to the specified input type + if input_type == "list": + world_pos = world_pos_vals + world_orient = world_orient_vals + elif input_type == "tuple": + world_pos = tuple(world_pos_vals) + world_orient = tuple(world_orient_vals) + elif input_type == "numpy": + world_pos = np.array(world_pos_vals) + world_orient = np.array(world_orient_vals) + elif input_type == "torch_cpu": + world_pos = torch.tensor(world_pos_vals) + world_orient = torch.tensor(world_orient_vals) + elif input_type == "torch_cuda": + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + world_pos = torch.tensor(world_pos_vals, device="cuda") + world_orient = torch.tensor(world_orient_vals, device="cuda") + + # Create child prim with world position + child = sim_utils.create_prim( + f"/World/Parent/Child_{input_type}", + "Xform", + stage=stage, + position=world_pos, # Using position (world space) + orientation=world_orient, + ) + + # Verify prim was created + assert child.IsValid() + + # Verify world pose matches what we specified + world_pose = sim_utils.resolve_prim_pose(child) + pos_result, quat_result = world_pose + + # Check position (should be close to world_pos_vals) + for i in range(3): + assert math.isclose(pos_result[i], world_pos_vals[i], abs_tol=1e-4) + + # Check orientation (quaternions may have sign flipped) + quat_match = all(math.isclose(quat_result[i], world_orient_vals[i], abs_tol=1e-4) for i in range(4)) + quat_match_neg = all(math.isclose(quat_result[i], -world_orient_vals[i], abs_tol=1e-4) for i in range(4)) + assert quat_match or quat_match_neg + + +def test_create_prim_non_xformable(): + """Test create_prim() with non-Xformable prim types (Material, Shader, Scope). + + This test verifies that prims which are not Xformable (like Material, Shader, Scope) + are created successfully but transform operations are not applied to them. + This is expected behavior as documented in the create_prim function. + """ + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Test with Material prim (not Xformable) + material_prim = sim_utils.create_prim( + "/World/TestMaterial", + "Material", + stage=stage, + translation=(1.0, 2.0, 3.0), # These should be ignored + orientation=(1.0, 0.0, 0.0, 0.0), # These should be ignored + scale=(2.0, 2.0, 2.0), # These should be ignored + ) + + # Verify prim was created + assert material_prim.IsValid() + assert material_prim.GetPrimPath() == "/World/TestMaterial" + assert material_prim.GetTypeName() == "Material" + + # Verify that it's not Xformable + assert not material_prim.IsA(UsdGeom.Xformable) + + # Verify that no xform operations were applied (Material prims don't support these) + assert not material_prim.HasAttribute("xformOp:translate") + assert not material_prim.HasAttribute("xformOp:orient") + assert not material_prim.HasAttribute("xformOp:scale") + + # Test with Scope prim (not Xformable) + scope_prim = sim_utils.create_prim( + "/World/TestScope", + "Scope", + stage=stage, + translation=(5.0, 6.0, 7.0), # These should be ignored + ) + + # Verify prim was created + assert scope_prim.IsValid() + assert scope_prim.GetPrimPath() == "/World/TestScope" + assert scope_prim.GetTypeName() == "Scope" + + # Verify that it's not Xformable + assert not scope_prim.IsA(UsdGeom.Xformable) + + # Verify that no xform operations were applied (Scope prims don't support these) + assert not scope_prim.HasAttribute("xformOp:translate") + assert not scope_prim.HasAttribute("xformOp:orient") + assert not scope_prim.HasAttribute("xformOp:scale") + + +def test_delete_prim(): + """Test delete_prim() function.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create scene + prim = sim_utils.create_prim("/World/Test/Xform", "Xform", stage=stage) + # delete prim + sim_utils.delete_prim("/World/Test/Xform") + # check prim deleted + assert not prim.IsValid() + + # check for usd reference + prim = sim_utils.create_prim( + "/World/Test/USDReference", + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd", + stage=stage, + ) + # delete prim + sim_utils.delete_prim("/World/Test/USDReference", stage=stage) + # check prim deleted + assert not prim.IsValid() + + # check deleting multiple prims + prim1 = sim_utils.create_prim("/World/Test/Xform1", "Xform", stage=stage) + prim2 = sim_utils.create_prim("/World/Test/Xform2", "Xform", stage=stage) + sim_utils.delete_prim(("/World/Test/Xform1", "/World/Test/Xform2"), stage=stage) + # check prims deleted + assert not prim1.IsValid() + assert not prim2.IsValid() + + +def test_move_prim(): + """Test move_prim() function.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create scene + sim_utils.create_prim("/World/Test", "Xform", stage=stage) + prim = sim_utils.create_prim( + "/World/Test/Xform", + "Xform", + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd", + translation=(1.0, 2.0, 3.0), + orientation=(0.0, 0.0, 0.0, 1.0), + stage=stage, + ) + + # move prim + sim_utils.create_prim("/World/TestMove", "Xform", stage=stage, translation=(1.0, 1.0, 1.0)) + sim_utils.move_prim("/World/Test/Xform", "/World/TestMove/Xform", stage=stage) + # check prim moved + prim = stage.GetPrimAtPath("/World/TestMove/Xform") + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/TestMove/Xform" + assert prim.GetAttribute("xformOp:translate").Get() == Gf.Vec3d((0.0, 1.0, 2.0)) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), Gf.Quatd(0.0, 0.0, 0.0, 1.0)) + + # check moving prim with keep_world_transform=False + # it should preserve the local transform from last move + sim_utils.create_prim( + "/World/TestMove2", "Xform", stage=stage, translation=(2.0, 2.0, 2.0), orientation=(0.0, 0.7071, 0.0, 0.7071) + ) + sim_utils.move_prim("/World/TestMove/Xform", "/World/TestMove2/Xform", keep_world_transform=False, stage=stage) + # check prim moved + prim = stage.GetPrimAtPath("/World/TestMove2/Xform") + assert prim.IsValid() + assert prim.GetPrimPath() == "/World/TestMove2/Xform" + assert prim.GetAttribute("xformOp:translate").Get() == Gf.Vec3d((0.0, 1.0, 2.0)) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), Gf.Quatd(0.0, 0.0, 0.0, 1.0)) + + +""" +USD references and variants. +""" + + +def test_get_usd_references(): + """Test get_usd_references() function.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim without USD reference + sim_utils.create_prim("/World/NoReference", "Xform", stage=stage) + # Check that it has no references + refs = sim_utils.get_usd_references("/World/NoReference", stage=stage) + assert len(refs) == 0 + + # Create a prim with a USD reference + franka_usd = f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + sim_utils.create_prim("/World/WithReference", usd_path=franka_usd, stage=stage) + # Check that it has the expected reference + refs = sim_utils.get_usd_references("/World/WithReference", stage=stage) + assert len(refs) == 1 + assert refs == [franka_usd] + + # Test with invalid prim path + with pytest.raises(ValueError, match="not valid"): + sim_utils.get_usd_references("/World/NonExistent", stage=stage) + + +def test_select_usd_variants(): + """Test select_usd_variants() function.""" + stage = sim_utils.get_current_stage() + + # Create a dummy prim + prim: Usd.Prim = UsdGeom.Xform.Define(stage, Sdf.Path("/World")).GetPrim() + stage.SetDefaultPrim(prim) + + # Create the variant set and add your variants to it. + variants = ["red", "blue", "green"] + variant_set = prim.GetVariantSets().AddVariantSet("colors") + for variant in variants: + variant_set.AddVariant(variant) + + # Set the variant selection + sim_utils.utils.select_usd_variants("/World", {"colors": "red"}, stage) + + # Check if the variant selection is correct + assert variant_set.GetVariantSelection() == "red" + + +def test_select_usd_variants_in_usd_file(): + """Test select_usd_variants() function in USD file.""" + stage = sim_utils.get_current_stage() + + prim = sim_utils.create_prim( + "/World/Test", "Xform", usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/UniversalRobots/ur10e/ur10e.usd", stage=stage + ) + + variant_sets = prim.GetVariantSets() + + # show all variants + for name in variant_sets.GetNames(): + vs = variant_sets.GetVariantSet(name) + options = vs.GetVariantNames() + selected = vs.GetVariantSelection() + + print(f"{name}: {selected} / {options}") + + print("Setting variant 'Gripper' to 'Robotiq_2f_140'.") + # The following performs the operations done internally + # in Isaac Lab. This should be removed in favor of 'select_usd_variants'. + target_vs = variant_sets.GetVariantSet("Gripper") + target_vs.SetVariantSelection("Robotiq_2f_140") + + # show again all variants + variant_sets = prim.GetVariantSets() + + for name in variant_sets.GetNames(): + vs = variant_sets.GetVariantSet(name) + options = vs.GetVariantNames() + selected = vs.GetVariantSelection() + + print(f"{name}: {selected} / {options}") + + # Uncomment the following once resolved + + # Set the variant selection + # sim_utils.select_usd_variants(prim.GetPath(), {"Gripper": "Robotiq_2f_140"}, stage) + + # Obtain variant set + # variant_set = prim.GetVariantSet("Gripper") + # # Check if the variant selection is correct + # assert variant_set.GetVariantSelection() == "Robotiq_2f_140" + + +""" +Property Management. +""" + + +def test_change_prim_property_basic(): + """Test change_prim_property() with existing property.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a cube prim + prim = sim_utils.create_prim("/World/Cube", "Cube", stage=stage, attributes={"size": 1.0}) + + # check initial value + assert prim.GetAttribute("size").Get() == 1.0 + + # change the property + result = sim_utils.change_prim_property( + prop_path="/World/Cube.size", + value=2.0, + stage=stage, + ) + + # check that the change was successful + assert result is True + assert prim.GetAttribute("size").Get() == 2.0 + + +def test_change_prim_property_create_new(): + """Test change_prim_property() creates new property when it doesn't exist.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a prim + prim = sim_utils.create_prim("/World/Test", "Xform", stage=stage) + + # check that the property doesn't exist + assert prim.GetAttribute("customValue").Get() is None + + # create a new property + result = sim_utils.change_prim_property( + prop_path="/World/Test.customValue", + value=42, + stage=stage, + type_to_create_if_not_exist=Sdf.ValueTypeNames.Int, + is_custom=True, + ) + + # check that the property was created successfully + assert result is True + assert prim.GetAttribute("customValue").Get() == 42 + + +def test_change_prim_property_clear_value(): + """Test change_prim_property() clears property value when value is None.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a cube with an attribute + prim = sim_utils.create_prim("/World/Cube", "Cube", stage=stage, attributes={"size": 1.0}) + + # check initial value + assert prim.GetAttribute("size").Get() == 1.0 + + # clear the property value + result = sim_utils.change_prim_property( + prop_path="/World/Cube.size", + value=None, + stage=stage, + ) + + # check that the value was cleared + assert result is True + # Note: After clearing, the attribute should go its default value + assert prim.GetAttribute("size").Get() == 2.0 + + +@pytest.mark.parametrize( + "attr_name,value,value_type,expected", + [ + ("floatValue", 3.14, Sdf.ValueTypeNames.Float, 3.14), + ("boolValue", True, Sdf.ValueTypeNames.Bool, True), + ("intValue", 42, Sdf.ValueTypeNames.Int, 42), + ("stringValue", "test", Sdf.ValueTypeNames.String, "test"), + ("vec3Value", Gf.Vec3f(1.0, 2.0, 3.0), Sdf.ValueTypeNames.Float3, Gf.Vec3f(1.0, 2.0, 3.0)), + ("colorValue", Gf.Vec3f(1.0, 0.0, 0.5), Sdf.ValueTypeNames.Color3f, Gf.Vec3f(1.0, 0.0, 0.5)), + ], + ids=["float", "bool", "int", "string", "vec3", "color"], +) +def test_change_prim_property_different_types(attr_name: str, value, value_type, expected): + """Test change_prim_property() with different value types.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a prim + prim = sim_utils.create_prim("/World/Test", "Xform", stage=stage) + + # change the property + result = sim_utils.change_prim_property( + prop_path=f"/World/Test.{attr_name}", + value=value, + stage=stage, + type_to_create_if_not_exist=value_type, + is_custom=True, + ) + + # check that the change was successful + assert result is True + actual_value = prim.GetAttribute(attr_name).Get() + + # handle float comparison separately for precision + if isinstance(expected, float): + assert math.isclose(actual_value, expected, abs_tol=1e-6) + else: + assert actual_value == expected + + +@pytest.mark.parametrize( + "prop_path_input", + ["/World/Cube.size", Sdf.Path("/World/Cube.size")], + ids=["str_path", "sdf_path"], +) +def test_change_prim_property_path_types(prop_path_input): + """Test change_prim_property() with different path input types.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a cube prim + prim = sim_utils.create_prim("/World/Cube", "Cube", stage=stage, attributes={"size": 1.0}) + + # change property using different path types + result = sim_utils.change_prim_property( + prop_path=prop_path_input, + value=3.0, + stage=stage, + ) + + # check that the change was successful + assert result is True + assert prim.GetAttribute("size").Get() == 3.0 + + +def test_change_prim_property_error_invalid_prim(): + """Test change_prim_property() raises error for invalid prim path.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # try to change property on non-existent prim + with pytest.raises(ValueError, match="Prim does not exist"): + sim_utils.change_prim_property( + prop_path="/World/NonExistent.property", + value=1.0, + stage=stage, + ) + + +def test_change_prim_property_error_missing_type(): + """Test change_prim_property() returns False when property doesn't exist and type not provided.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + # create a prim + prim = sim_utils.create_prim("/World/Test", "Xform", stage=stage) + + # try to create property without providing type + result = sim_utils.change_prim_property( + prop_path="/World/Test.nonExistentProperty", + value=42, + stage=stage, + ) + + # should return False since type was not provided + assert result is False + # property should not have been created + assert prim.GetAttribute("nonExistentProperty").Get() is None + + +""" +Internal Helpers. +""" + + +def test_to_tuple_basic(): + """Test _to_tuple() with basic input types.""" + # Test with list + result = _to_tuple([1.0, 2.0, 3.0]) + assert result == (1.0, 2.0, 3.0) + assert isinstance(result, tuple) + + # Test with tuple + result = _to_tuple((1.0, 2.0, 3.0)) + assert result == (1.0, 2.0, 3.0) + + # Test with numpy array + result = _to_tuple(np.array([1.0, 2.0, 3.0])) + assert result == (1.0, 2.0, 3.0) + + # Test with torch tensor (CPU) + result = _to_tuple(torch.tensor([1.0, 2.0, 3.0])) + assert result == (1.0, 2.0, 3.0) + + # Test squeezing first dimension (batch size 1) + result = _to_tuple(torch.tensor([[1.0, 2.0]])) + assert result == (1.0, 2.0) + + result = _to_tuple(np.array([[1.0, 2.0, 3.0]])) + assert result == (1.0, 2.0, 3.0) + + +def test_to_tuple_raises_error(): + """Test _to_tuple() raises an error for N-dimensional arrays.""" + + with pytest.raises(ValueError, match="not one dimensional"): + _to_tuple(np.array([[1.0, 2.0], [3.0, 4.0]])) + + with pytest.raises(ValueError, match="not one dimensional"): + _to_tuple(torch.tensor([[[1.0, 2.0]], [[3.0, 4.0]]])) + + with pytest.raises(ValueError, match="only one element tensors can be converted"): + _to_tuple((torch.tensor([1.0, 2.0]), 3.0)) + + +def test_to_tuple_mixed_sequences(): + """Test _to_tuple() with mixed type sequences.""" + + # Mixed list with numpy and floats + result = _to_tuple([np.float32(1.0), 2.0, 3.0]) + assert len(result) == 3 + assert all(isinstance(x, float) for x in result) + + # Mixed tuple with torch tensor items and floats + result = _to_tuple([torch.tensor(1.0), 2.0, 3.0]) + assert result == (1.0, 2.0, 3.0) + + # Mixed tuple with numpy array items and torch tensor + result = _to_tuple((np.float32(1.0), 2.0, torch.tensor(3.0))) + assert result == (1.0, 2.0, 3.0) + + +def test_to_tuple_precision(): + """Test _to_tuple() maintains numerical precision.""" + from isaaclab.sim.utils.prims import _to_tuple + + # Test with high precision values + high_precision = [1.123456789, 2.987654321, 3.141592653] + result = _to_tuple(torch.tensor(high_precision, dtype=torch.float64)) + + # Check that precision is maintained reasonably well + for i, val in enumerate(high_precision): + assert math.isclose(result[i], val, abs_tol=1e-6) diff --git a/source/isaaclab/test/sim/test_utils_queries.py b/source/isaaclab/test/sim/test_utils_queries.py new file mode 100644 index 0000000000000000000000000000000000000000..4f5a0758342cdba51adcc5187395c8f5b2d38a0f --- /dev/null +++ b/source/isaaclab/test/sim/test_utils_queries.py @@ -0,0 +1,171 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +# note: need to enable cameras to be able to make replicator core available +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import pytest + +from pxr import UsdPhysics + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Create a blank new stage for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + sim_utils.update_stage() + + # Yield for the test + yield + + # Teardown: Clear stage after each test + sim_utils.clear_stage() + + +""" +USD Stage Querying. +""" + + +def test_get_next_free_prim_path(): + """Test get_next_free_prim_path() function.""" + # create scene + sim_utils.create_prim("/World/Floor") + sim_utils.create_prim("/World/Floor/Box", "Cube", position=[75, 75, -150.1], attributes={"size": 300}) + sim_utils.create_prim("/World/Wall", "Sphere", attributes={"radius": 1e3}) + + # test + isaaclab_result = sim_utils.get_next_free_prim_path("/World/Floor") + assert isaaclab_result == "/World/Floor_01" + + # create another prim + sim_utils.create_prim("/World/Floor/Box_01", "Cube", position=[75, 75, -150.1], attributes={"size": 300}) + + # test again + isaaclab_result = sim_utils.get_next_free_prim_path("/World/Floor/Box") + assert isaaclab_result == "/World/Floor/Box_02" + + +def test_get_first_matching_ancestor_prim(): + """Test get_first_matching_ancestor_prim() function.""" + # create scene + sim_utils.create_prim("/World/Floor") + sim_utils.create_prim("/World/Floor/Box", "Cube", position=[75, 75, -150.1], attributes={"size": 300}) + sim_utils.create_prim("/World/Floor/Box/Sphere", "Sphere", attributes={"radius": 1e3}) + + # test with input prim not having the predicate + isaaclab_result = sim_utils.get_first_matching_ancestor_prim( + "/World/Floor/Box/Sphere", predicate=lambda x: x.GetTypeName() == "Cube" + ) + assert isaaclab_result is not None + assert isaaclab_result.GetPrimPath() == "/World/Floor/Box" + + # test with input prim having the predicate + isaaclab_result = sim_utils.get_first_matching_ancestor_prim( + "/World/Floor/Box", predicate=lambda x: x.GetTypeName() == "Cube" + ) + assert isaaclab_result is not None + assert isaaclab_result.GetPrimPath() == "/World/Floor/Box" + + # test with no predicate match + isaaclab_result = sim_utils.get_first_matching_ancestor_prim( + "/World/Floor/Box/Sphere", predicate=lambda x: x.GetTypeName() == "Cone" + ) + assert isaaclab_result is None + + +def test_get_all_matching_child_prims(): + """Test get_all_matching_child_prims() function.""" + # create scene + sim_utils.create_prim("/World/Floor") + sim_utils.create_prim("/World/Floor/Box", "Cube", position=[75, 75, -150.1], attributes={"size": 300}) + sim_utils.create_prim("/World/Wall", "Sphere", attributes={"radius": 1e3}) + + # add articulation root prim -- this asset has instanced prims + # note: isaac sim function does not support instanced prims so we add it here + # after the above test for the above test to still pass. + sim_utils.create_prim( + "/World/Franka", "Xform", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + ) + + # test with predicate + isaaclab_result = sim_utils.get_all_matching_child_prims("/World", predicate=lambda x: x.GetTypeName() == "Cube") + assert len(isaaclab_result) == 1 + assert isaaclab_result[0].GetPrimPath() == "/World/Floor/Box" + + # test with predicate and instanced prims + isaaclab_result = sim_utils.get_all_matching_child_prims( + "/World/Franka/panda_hand/visuals", predicate=lambda x: x.GetTypeName() == "Mesh" + ) + assert len(isaaclab_result) == 1 + assert isaaclab_result[0].GetPrimPath() == "/World/Franka/panda_hand/visuals/panda_hand" + + # test valid path + with pytest.raises(ValueError): + sim_utils.get_all_matching_child_prims("World/Room") + + +def test_get_first_matching_child_prim(): + """Test get_first_matching_child_prim() function.""" + # create scene + sim_utils.create_prim("/World/Floor") + sim_utils.create_prim( + "/World/env_1/Franka", "Xform", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + ) + sim_utils.create_prim( + "/World/env_2/Franka", "Xform", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + ) + sim_utils.create_prim( + "/World/env_0/Franka", "Xform", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + ) + + # test + isaaclab_result = sim_utils.get_first_matching_child_prim( + "/World", predicate=lambda prim: prim.HasAPI(UsdPhysics.ArticulationRootAPI) + ) + assert isaaclab_result is not None + assert isaaclab_result.GetPrimPath() == "/World/env_1/Franka" + + # test with instanced prims + isaaclab_result = sim_utils.get_first_matching_child_prim( + "/World/env_1/Franka", predicate=lambda prim: prim.GetTypeName() == "Mesh" + ) + assert isaaclab_result is not None + assert isaaclab_result.GetPrimPath() == "/World/env_1/Franka/panda_link0/visuals/panda_link0" + + +def test_find_global_fixed_joint_prim(): + """Test find_global_fixed_joint_prim() function.""" + # create scene + sim_utils.create_prim("/World") + sim_utils.create_prim("/World/ANYmal", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd") + sim_utils.create_prim("/World/Franka", usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd") + if "4.5" in ISAAC_NUCLEUS_DIR: + franka_usd = f"{ISAAC_NUCLEUS_DIR}/Robots/Franka/franka.usd" + else: + franka_usd = f"{ISAAC_NUCLEUS_DIR}/Robots/FrankaRobotics/FrankaPanda/franka.usd" + sim_utils.create_prim("/World/Franka_Isaac", usd_path=franka_usd) + + # test + assert sim_utils.find_global_fixed_joint_prim("/World/ANYmal") is None + assert sim_utils.find_global_fixed_joint_prim("/World/Franka") is not None + assert sim_utils.find_global_fixed_joint_prim("/World/Franka_Isaac") is not None + + # make fixed joint disabled manually + joint_prim = sim_utils.find_global_fixed_joint_prim("/World/Franka") + joint_prim.GetJointEnabledAttr().Set(False) + assert sim_utils.find_global_fixed_joint_prim("/World/Franka") is not None + assert sim_utils.find_global_fixed_joint_prim("/World/Franka", check_enabled_only=True) is None diff --git a/source/isaaclab/test/sim/test_utils_semantics.py b/source/isaaclab/test/sim/test_utils_semantics.py new file mode 100644 index 0000000000000000000000000000000000000000..fe8cbd37187a819c7d4f984900f722cf596282b3 --- /dev/null +++ b/source/isaaclab/test/sim/test_utils_semantics.py @@ -0,0 +1,231 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +# note: need to enable cameras to be able to make replicator core available +simulation_app = AppLauncher(headless=True, enable_cameras=True).app + +"""Rest everything follows.""" + +import pytest + +import isaaclab.sim as sim_utils + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Create a blank new stage for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + sim_utils.update_stage() + + # Yield for the test + yield + + # Teardown: Clear stage after each test + sim_utils.clear_stage() + + +def create_test_environment_with_labels(): + """Creates a test environment with objects with labels.""" + # create 3 cubes with label "cube" + for i in range(3): + sim_utils.create_prim(f"/World/Test/Object{i}", "Cube", semantic_label="cube") + # create a sphere without any labels + sim_utils.create_prim("/World/Test/Object3", "Sphere") + # create a nested prim with label "nested" + nested_prim = sim_utils.create_prim("/World/Test/Object0/Nested", "Cube") + sim_utils.add_labels(nested_prim, ["nested"], instance_name="shape") + + return [f"/World/Test/Object{i}" for i in range(4)] + [str(nested_prim.GetPrimPath())] + + +""" +Tests. +""" + + +def test_add_and_get_labels(): + """Test add_labels() and get_labels() functions.""" + # get stage handle + stage = sim_utils.get_current_stage() + # create a test prim + prim = stage.DefinePrim("/test", "Xform") + nested_prim = stage.DefinePrim("/test/nested", "Xform") + + # Apply semantics + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + sim_utils.add_labels(nested_prim, ["nested_label"], instance_name="class") + + # Get labels + labels_dict = sim_utils.get_labels(prim) + # Check labels are added correctly + assert "class" in labels_dict + assert sorted(labels_dict["class"]) == sorted(["label_a", "label_b"]) + assert "shape" in labels_dict + assert labels_dict["shape"] == ["shape_a"] + nested_labels_dict = sim_utils.get_labels(nested_prim) + assert "class" in nested_labels_dict + assert nested_labels_dict["class"] == ["nested_label"] + + +def test_add_labels_with_overwrite(): + """Test add_labels() function with overwriting existing labels.""" + # get stage handle + stage = sim_utils.get_current_stage() + # create a test prim + prim = stage.DefinePrim("/test", "Xform") + + # Add labels + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + + # Overwrite existing labels for a specific instance + sim_utils.add_labels(prim, ["replaced_label"], instance_name="class", overwrite=True) + labels_dict = sim_utils.get_labels(prim) + assert labels_dict["class"] == ["replaced_label"] + assert "shape" in labels_dict + assert labels_dict["shape"] == ["shape_a"] + + +def test_add_labels_without_overwrite(): + """Test add_labels() function without overwriting existing labels.""" + # get stage handle + stage = sim_utils.get_current_stage() + # create a test prim + prim = stage.DefinePrim("/test", "Xform") + + # Add labels + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + + # Re-add labels with overwrite=False (should append) + sim_utils.add_labels(prim, ["label_c"], instance_name="class", overwrite=False) + labels_dict = sim_utils.get_labels(prim) + assert sorted(labels_dict["class"]) == sorted(["label_a", "label_b", "label_c"]) + + +def test_remove_all_labels(): + """Test removing of all labels from a prim and its descendants.""" + # get stage handle + stage = sim_utils.get_current_stage() + # create a test prim + prim = stage.DefinePrim("/test", "Xform") + nested_prim = stage.DefinePrim("/test/nested", "Xform") + + # Add labels + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + sim_utils.add_labels(nested_prim, ["nested_label"], instance_name="class") + + # Remove all labels + sim_utils.remove_labels(prim) + # Check labels are removed correctly + labels_dict = sim_utils.get_labels(prim) + assert len(labels_dict) == 0 + # Check nested prim labels are not removed + nested_labels_dict = sim_utils.get_labels(nested_prim) + assert "class" in nested_labels_dict + assert nested_labels_dict["class"] == ["nested_label"] + + # Re-add labels + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + sim_utils.add_labels(nested_prim, ["nested_label"], instance_name="class") + # Remove all labels + sim_utils.remove_labels(prim, include_descendants=True) + # Check labels are removed correctly + labels_dict = sim_utils.get_labels(prim) + assert len(labels_dict) == 0 + # Check nested prim labels are removed + nested_labels_dict = sim_utils.get_labels(nested_prim) + assert len(nested_labels_dict) == 0 + + +def test_remove_specific_labels(): + """Test removing of specific labels from a prim and its descendants.""" + # get stage handle + stage = sim_utils.get_current_stage() + # create a test prim + prim = stage.DefinePrim("/test", "Xform") + nested_prim = stage.DefinePrim("/test/nested", "Xform") + + # Add labels + sim_utils.add_labels(prim, ["label_a", "label_b"], instance_name="class") + sim_utils.add_labels(prim, ["shape_a"], instance_name="shape") + sim_utils.add_labels(nested_prim, ["nested_label"], instance_name="class") + sim_utils.add_labels(nested_prim, ["nested_shape"], instance_name="shape") + + # Remove specific labels + sim_utils.remove_labels(prim, instance_name="shape") + # Check labels are removed correctly + labels_dict = sim_utils.get_labels(prim) + assert "shape" not in labels_dict + assert "class" in labels_dict + assert sorted(labels_dict["class"]) == sorted(["label_a", "label_b"]) + # Check nested prim labels are not removed + nested_labels_dict = sim_utils.get_labels(nested_prim) + assert "class" in nested_labels_dict + assert nested_labels_dict["class"] == ["nested_label"] + + # Remove specific labels + sim_utils.remove_labels(prim, instance_name="class", include_descendants=True) + # Check labels are removed correctly + labels_dict = sim_utils.get_labels(prim) + assert len(labels_dict) == 0 + # Check nested prim labels are removed + nested_labels_dict = sim_utils.get_labels(nested_prim) + assert "shape" in nested_labels_dict + assert nested_labels_dict["shape"] == ["nested_shape"] + + +def test_check_missing_labels(): + """Test the check_missing_labels() function.""" + # create a test environment with labels + object_paths = create_test_environment_with_labels() + + # Check from root + missing_paths = sim_utils.check_missing_labels() + + # Only the sphere should be missing + assert len(missing_paths) == 1 + assert object_paths[3] in missing_paths # Object3 should be missing + + # Check from specific subtree + missing_paths_subtree = sim_utils.check_missing_labels(prim_path="/World/Test/Object0") + # Object0 and Nested both have labels + assert len(missing_paths_subtree) == 0 + + # Check from invalid path + missing_paths_invalid = sim_utils.check_missing_labels(prim_path="/World/Test/Invalid") + assert len(missing_paths_invalid) == 0 + + +def test_count_labels_in_scene(): + """Test the count_labels_in_scene() function.""" + # create a test environment with labels + create_test_environment_with_labels() + + # Count from root + labels_dict = sim_utils.count_total_labels() + # Object0 and Nested both have labels + assert labels_dict.get("cube", 0) == 3 + assert labels_dict.get("nested", 0) == 1 + assert labels_dict.get("missing_labels", 0) == 1 + + # Count from specific subtree + labels_dict_subtree = sim_utils.count_total_labels(prim_path="/World/Test/Object0") + assert labels_dict_subtree.get("cube", 0) == 1 + assert labels_dict_subtree.get("nested", 0) == 1 + assert labels_dict_subtree.get("missing_labels", 0) == 0 + + # Count from invalid path + labels_dict_invalid = sim_utils.count_total_labels(prim_path="/World/Test/Invalid") + assert labels_dict_invalid.get("missing_labels", 0) == 0 diff --git a/source/isaaclab/test/sim/test_utils_stage.py b/source/isaaclab/test/sim/test_utils_stage.py new file mode 100644 index 0000000000000000000000000000000000000000..033a461e1a1f3304c06da4ddebae16b081ccc5c6 --- /dev/null +++ b/source/isaaclab/test/sim/test_utils_stage.py @@ -0,0 +1,289 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests for stage utilities.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import tempfile +from pathlib import Path + +import pytest + +from pxr import Usd + +import isaaclab.sim as sim_utils + + +def test_create_new_stage(): + """Test creating a new stage attached to USD context.""" + stage = sim_utils.create_new_stage() + + # Should return a valid stage + assert stage is not None + assert isinstance(stage, Usd.Stage) + + # Stage should be the current stage + current_stage = sim_utils.get_current_stage() + assert stage == current_stage + + # Stage should have a root prim + root_prim = stage.GetPseudoRoot() + assert root_prim.IsValid() + + +def test_create_multiple_stages(): + """Test creating multiple stages.""" + stage1 = sim_utils.create_new_stage() + stage2 = sim_utils.create_new_stage() + stage3 = sim_utils.create_new_stage() + + assert stage1 is not None + assert stage2 is not None + assert stage3 is not None + assert stage1 != stage2 + assert stage1 != stage3 + assert stage2 != stage3 + + +def test_create_new_stage_in_memory(): + """Test creating a new stage in memory (Isaac Sim 5.0+).""" + stage = sim_utils.create_new_stage_in_memory() + + # Should return a valid stage + assert stage is not None + assert isinstance(stage, Usd.Stage) + + # Stage should have a root prim + root_prim = stage.GetPseudoRoot() + assert root_prim.IsValid() + + +def test_is_current_stage_in_memory(): + """Test checking if current stage is in memory.""" + # Create a regular stage (attached to context) + sim_utils.create_new_stage() + is_in_memory = sim_utils.is_current_stage_in_memory() + + # Should return a boolean + assert isinstance(is_in_memory, bool) + assert is_in_memory is False + + # Create a stage in memory + stage = sim_utils.create_new_stage_in_memory() + with sim_utils.use_stage(stage): + is_in_memory = sim_utils.is_current_stage_in_memory() + assert isinstance(is_in_memory, bool) + assert is_in_memory is True + + +def test_save_and_open_stage(): + """Test saving and opening a stage.""" + with tempfile.TemporaryDirectory() as temp_dir: + # Create a stage with some content + stage = sim_utils.create_new_stage() + stage.DefinePrim("/World", "Xform") + stage.DefinePrim("/World/TestCube", "Cube") + + # Save the stage + save_path = Path(temp_dir) / "test_stage.usd" + result = sim_utils.save_stage(str(save_path), save_and_reload_in_place=False) + + # Save should succeed + assert result is True + assert save_path.exists() + + # Open the saved stage + open_result = sim_utils.open_stage(str(save_path)) + assert open_result is True + + # Verify content was preserved + opened_stage = sim_utils.get_current_stage() + test_cube = opened_stage.GetPrimAtPath("/World/TestCube") + assert test_cube.IsValid() + assert test_cube.GetTypeName() == "Cube" + + +def test_open_stage_invalid_path(): + """Test opening a stage with invalid path.""" + with pytest.raises(ValueError, match="not supported"): + sim_utils.open_stage("/invalid/path/to/stage.invalid") + + +def test_use_stage_context_manager(): + """Test use_stage context manager.""" + # Create two stages + stage1 = sim_utils.create_new_stage() + stage1.DefinePrim("/World", "Xform") + stage1.DefinePrim("/World/Stage1Marker", "Xform") + + stage2 = Usd.Stage.CreateInMemory() + stage2.DefinePrim("/World", "Xform") + stage2.DefinePrim("/World/Stage2Marker", "Xform") + + # Initially on stage1 + current = sim_utils.get_current_stage() + marker1 = current.GetPrimAtPath("/World/Stage1Marker") + assert marker1.IsValid() + + # Switch to stage2 temporarily + with sim_utils.use_stage(stage2): + temp_current = sim_utils.get_current_stage() + # Should be on stage2 now + marker2 = temp_current.GetPrimAtPath("/World/Stage2Marker") + assert marker2.IsValid() + + # Should be back on stage1 + final_current = sim_utils.get_current_stage() + marker1_again = final_current.GetPrimAtPath("/World/Stage1Marker") + assert marker1_again.IsValid() + + +def test_use_stage_with_invalid_input(): + """Test use_stage with invalid input.""" + with pytest.raises((TypeError, AssertionError)): + with sim_utils.use_stage("not a stage"): # type: ignore + pass + + +def test_update_stage(): + """Test updating the stage.""" + # Create a new stage + stage = sim_utils.create_new_stage() + + # Add a prim + prim_path = "/World/Test" + stage.DefinePrim(prim_path, "Xform") + + # Update stage should not raise errors + sim_utils.update_stage() + + # Prim should still exist + prim = stage.GetPrimAtPath(prim_path) + assert prim.IsValid() + + +def test_save_stage_with_reload(): + """Test saving stage with reload in place.""" + with tempfile.TemporaryDirectory() as temp_dir: + # Create a stage with content + stage = sim_utils.create_new_stage() + stage.DefinePrim("/World", "Xform") + stage.DefinePrim("/World/TestSphere", "Sphere") + + # Save with reload + save_path = Path(temp_dir) / "test_reload.usd" + result = sim_utils.save_stage(str(save_path), save_and_reload_in_place=True) + + assert result is True + assert save_path.exists() + + # Stage should be reloaded, content should be preserved + current_stage = sim_utils.get_current_stage() + test_sphere = current_stage.GetPrimAtPath("/World/TestSphere") + assert test_sphere.IsValid() + + +def test_save_stage_invalid_path(): + """Test saving stage with invalid path.""" + _ = sim_utils.create_new_stage() + + with pytest.raises(ValueError, match="not supported"): + sim_utils.save_stage("/tmp/test.invalid") + + +def test_close_stage(): + """Test closing the current stage.""" + # Create a stage + stage = sim_utils.create_new_stage() + assert stage is not None + + # Close it + result = sim_utils.close_stage() + + # Should succeed (or return bool) + assert isinstance(result, bool) + + +def test_close_stage_with_callback(): + """Test closing stage with a callback function.""" + # Create a stage + sim_utils.create_new_stage() + + # Track callback invocations + callback_called = [] + + def callback(success: bool, error_msg: str): + callback_called.append((success, error_msg)) + + # Close with callback + result = sim_utils.close_stage(callback_fn=callback) + + # Callback might be called or not depending on implementation + # Just verify no exceptions were raised + assert isinstance(result, bool) + + +def test_clear_stage(): + """Test clearing the stage.""" + # Create a new stage + stage = sim_utils.create_new_stage() + + # Add some prims + stage.DefinePrim("/World", "Xform") + stage.DefinePrim("/World/Cube", "Cube") + stage.DefinePrim("/World/Sphere", "Sphere") + + # Clear the stage + sim_utils.clear_stage() + + # Stage should still exist but prims should be removed + assert stage is not None + + +def test_is_stage_loading(): + """Test checking if stage is loading.""" + # Create a new stage + sim_utils.create_new_stage() + + # Check loading status + is_loading = sim_utils.is_stage_loading() + + # Should return a boolean + assert isinstance(is_loading, bool) + + # After creation, should not be loading + assert is_loading is False + + +def test_get_current_stage(): + """Test getting the current stage.""" + # Create a new stage + created_stage = sim_utils.create_new_stage() + + # Get current stage should return the same stage + current_stage = sim_utils.get_current_stage() + assert current_stage == created_stage + assert isinstance(current_stage, Usd.Stage) + + +def test_get_current_stage_id(): + """Test getting the current stage ID.""" + # Create a new stage + sim_utils.create_new_stage() + + # Get stage ID + stage_id = sim_utils.get_current_stage_id() + + # Should be a valid integer ID + assert isinstance(stage_id, int) + assert stage_id >= 0 diff --git a/source/isaaclab/test/sim/test_utils_transforms.py b/source/isaaclab/test/sim/test_utils_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..040cfe333aa7d48a4f96479c12d549b4b52ea381 --- /dev/null +++ b/source/isaaclab/test/sim/test_utils_transforms.py @@ -0,0 +1,1423 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import math + +import numpy as np +import pytest +import torch + +from pxr import Gf, Sdf, Usd, UsdGeom + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Create a blank new stage for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + sim_utils.update_stage() + + # Yield for the test + yield + + # Teardown: Clear stage after each test + sim_utils.clear_stage() + + +def assert_vec3_close(v1: Gf.Vec3d | Gf.Vec3f, v2: tuple | Gf.Vec3d | Gf.Vec3f, eps: float = 1e-6): + """Assert two 3D vectors are close.""" + if isinstance(v2, tuple): + v2 = Gf.Vec3d(*v2) + for i in range(3): + assert math.isclose(v1[i], v2[i], abs_tol=eps), f"Vector mismatch at index {i}: {v1[i]} != {v2[i]}" + + +def assert_quat_close(q1: Gf.Quatf | Gf.Quatd, q2: Gf.Quatf | Gf.Quatd | tuple, eps: float = 1e-6): + """Assert two quaternions are close, accounting for double-cover (q and -q represent same rotation).""" + if isinstance(q2, tuple): + q2 = Gf.Quatd(*q2) + # Check if quaternions are close (either q1 ≈ q2 or q1 ≈ -q2) + real_match = math.isclose(q1.GetReal(), q2.GetReal(), abs_tol=eps) + imag_match = all(math.isclose(q1.GetImaginary()[i], q2.GetImaginary()[i], abs_tol=eps) for i in range(3)) + + real_match_neg = math.isclose(q1.GetReal(), -q2.GetReal(), abs_tol=eps) + imag_match_neg = all(math.isclose(q1.GetImaginary()[i], -q2.GetImaginary()[i], abs_tol=eps) for i in range(3)) + + assert (real_match and imag_match) or (real_match_neg and imag_match_neg), ( + f"Quaternion mismatch: {q1} != {q2} (and not equal to negative either)" + ) + + +def get_xform_ops(prim: Usd.Prim) -> list[str]: + """Get the ordered list of xform operation names for a prim.""" + xformable = UsdGeom.Xformable(prim) + return [op.GetOpName() for op in xformable.GetOrderedXformOps()] + + +""" +Test standardize_xform_ops() function. +""" + + +def test_standardize_xform_ops_basic(): + """Test basic functionality of standardize_xform_ops on a simple prim.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a simple xform prim with standard operations + prim = sim_utils.create_prim( + "/World/TestXform", + "Xform", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), # w, x, y, z + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + + # Verify the operation succeeded + assert result is True + assert prim.IsValid() + + # Check that the xform operations are in the correct order + xform_ops = get_xform_ops(prim) + assert xform_ops == [ + "xformOp:translate", + "xformOp:orient", + "xformOp:scale", + ], f"Expected standard xform order, got {xform_ops}" + + # Verify the transform values are preserved (approximately) + assert_vec3_close(prim.GetAttribute("xformOp:translate").Get(), (1.0, 2.0, 3.0)) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), (1.0, 0.0, 0.0, 0.0)) + assert_vec3_close(prim.GetAttribute("xformOp:scale").Get(), (1.0, 1.0, 1.0)) + + +def test_standardize_xform_ops_with_rotation_xyz(): + """Test standardize_xform_ops removes deprecated rotateXYZ operations.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim and manually add deprecated rotation operations + prim_path = "/World/TestRotateXYZ" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + # Add deprecated rotateXYZ operation + rotate_xyz_op = xformable.AddRotateXYZOp(UsdGeom.XformOp.PrecisionDouble) + rotate_xyz_op.Set(Gf.Vec3d(45.0, 30.0, 60.0)) + # Add translate operation + translate_op = xformable.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble) + translate_op.Set(Gf.Vec3d(1.0, 2.0, 3.0)) + + # Verify the deprecated operation exists + assert "xformOp:rotateXYZ" in prim.GetPropertyNames() + + # Get pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify world pose is preserved (may have small numeric differences due to rotation conversion) + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-4) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-4) + + # Verify the deprecated operation is removed + assert "xformOp:rotateXYZ" not in prim.GetPropertyNames() + # Verify standard operations exist + assert "xformOp:translate" in prim.GetPropertyNames() + assert "xformOp:orient" in prim.GetPropertyNames() + assert "xformOp:scale" in prim.GetPropertyNames() + # Check the xform operation order + xform_ops = get_xform_ops(prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +def test_standardize_xform_ops_with_transform_matrix(): + """Test standardize_xform_ops removes transform matrix operations.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with a transform matrix + prim_path = "/World/TestTransformMatrix" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add transform matrix operation + transform_op = xformable.AddTransformOp(UsdGeom.XformOp.PrecisionDouble) + # Create a simple translation matrix + matrix = Gf.Matrix4d().SetTranslate(Gf.Vec3d(5.0, 10.0, 15.0)) + transform_op.Set(matrix) + + # Verify the transform operation exists + assert "xformOp:transform" in prim.GetPropertyNames() + + # Get pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + # Verify the transform operation is removed + assert "xformOp:transform" not in prim.GetPropertyNames() + # Verify standard operations exist + assert "xformOp:translate" in prim.GetPropertyNames() + assert "xformOp:orient" in prim.GetPropertyNames() + assert "xformOp:scale" in prim.GetPropertyNames() + + +def test_standardize_xform_ops_preserves_world_pose(): + """Test that standardize_xform_ops preserves the world-space pose of the prim.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with specific world pose + translation = (10.0, 20.0, 30.0) + # Rotation of 90 degrees around Z axis + orientation = (0.7071068, 0.0, 0.0, 0.7071068) # w, x, y, z + scale = (2.0, 3.0, 4.0) + + prim = sim_utils.create_prim( + "/World/TestPreservePose", + "Xform", + translation=translation, + orientation=orientation, + scale=scale, + stage=stage, + ) + + # Get the world pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get the world pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify the world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + +def test_standardize_xform_ops_with_units_resolve(): + """Test standardize_xform_ops handles scale:unitsResolve attribute.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim + prim_path = "/World/TestUnitsResolve" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add scale operation + scale_op = xformable.AddScaleOp(UsdGeom.XformOp.PrecisionDouble) + scale_op.Set(Gf.Vec3d(1.0, 1.0, 1.0)) + + # Manually add a unitsResolve scale attribute (simulating imported asset with different units) + units_resolve_attr = prim.CreateAttribute("xformOp:scale:unitsResolve", Sdf.ValueTypeNames.Double3) + units_resolve_attr.Set(Gf.Vec3d(100.0, 100.0, 100.0)) # e.g., cm to m conversion + + # Verify both attributes exist + assert "xformOp:scale" in prim.GetPropertyNames() + assert "xformOp:scale:unitsResolve" in prim.GetPropertyNames() + + # Get pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + # Verify unitsResolve is removed + assert "xformOp:scale:unitsResolve" not in prim.GetPropertyNames() + + # Verify scale is updated (1.0 * 100.0 = 100.0) + scale = prim.GetAttribute("xformOp:scale").Get() + assert_vec3_close(scale, (100.0, 100.0, 100.0)) + + +def test_standardize_xform_ops_with_hierarchy(): + """Test standardize_xform_ops works correctly with prim hierarchies.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create parent prim + parent_prim = sim_utils.create_prim( + "/World/Parent", + "Xform", + translation=(5.0, 0.0, 0.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + # Create child prim + child_prim = sim_utils.create_prim( + "/World/Parent/Child", + "Xform", + translation=(0.0, 3.0, 0.0), + orientation=(0.7071068, 0.0, 0.7071068, 0.0), # 90 deg around Y + scale=(0.5, 0.5, 0.5), + stage=stage, + ) + + # Get world poses before standardization + parent_pos_before, parent_quat_before = sim_utils.resolve_prim_pose(parent_prim) + child_pos_before, child_quat_before = sim_utils.resolve_prim_pose(child_prim) + + # Apply standardize_xform_ops to both + sim_utils.standardize_xform_ops(parent_prim) + sim_utils.standardize_xform_ops(child_prim) + + # Get world poses after standardization + parent_pos_after, parent_quat_after = sim_utils.resolve_prim_pose(parent_prim) + child_pos_after, child_quat_after = sim_utils.resolve_prim_pose(child_prim) + + # Verify world poses are preserved + assert_vec3_close(Gf.Vec3d(*parent_pos_before), parent_pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*parent_quat_before), parent_quat_after, eps=1e-5) + assert_vec3_close(Gf.Vec3d(*child_pos_before), child_pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*child_quat_before), child_quat_after, eps=1e-5) + + +def test_standardize_xform_ops_multiple_deprecated_ops(): + """Test standardize_xform_ops removes multiple deprecated operations.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with multiple deprecated operations + prim_path = "/World/TestMultipleDeprecated" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add various deprecated rotation operations + rotate_x_op = xformable.AddRotateXOp(UsdGeom.XformOp.PrecisionDouble) + rotate_x_op.Set(45.0) + rotate_y_op = xformable.AddRotateYOp(UsdGeom.XformOp.PrecisionDouble) + rotate_y_op.Set(30.0) + rotate_z_op = xformable.AddRotateZOp(UsdGeom.XformOp.PrecisionDouble) + rotate_z_op.Set(60.0) + + # Verify deprecated operations exist + assert "xformOp:rotateX" in prim.GetPropertyNames() + assert "xformOp:rotateY" in prim.GetPropertyNames() + assert "xformOp:rotateZ" in prim.GetPropertyNames() + + # Obtain current local transformations + pos, quat = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + sim_utils.standardize_xform_ops(prim) + + # Obtain current local transformations + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos), Gf.Vec3d(*pos_after), eps=1e-5) + assert_quat_close(Gf.Quatd(*quat), Gf.Quatd(*quat_after), eps=1e-5) + + # Verify all deprecated operations are removed + assert "xformOp:rotateX" not in prim.GetPropertyNames() + assert "xformOp:rotateY" not in prim.GetPropertyNames() + assert "xformOp:rotateZ" not in prim.GetPropertyNames() + # Verify standard operations exist + xform_ops = get_xform_ops(prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +def test_standardize_xform_ops_with_existing_standard_ops(): + """Test standardize_xform_ops when prim already has standard operations.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with standard operations already in place + prim = sim_utils.create_prim( + "/World/TestExistingStandard", + "Xform", + translation=(7.0, 8.0, 9.0), + orientation=(0.9238795, 0.3826834, 0.0, 0.0), # rotation around X + scale=(1.5, 2.5, 3.5), + stage=stage, + ) + + # Get initial values + initial_translate = prim.GetAttribute("xformOp:translate").Get() + initial_orient = prim.GetAttribute("xformOp:orient").Get() + initial_scale = prim.GetAttribute("xformOp:scale").Get() + + # Get world pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get world pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + # Verify operations still exist and are in correct order + xform_ops = get_xform_ops(prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + # Verify values are approximately preserved + final_translate = prim.GetAttribute("xformOp:translate").Get() + final_orient = prim.GetAttribute("xformOp:orient").Get() + final_scale = prim.GetAttribute("xformOp:scale").Get() + + assert_vec3_close(initial_translate, final_translate, eps=1e-5) + assert_quat_close(initial_orient, final_orient, eps=1e-5) + assert_vec3_close(initial_scale, final_scale, eps=1e-5) + + +def test_standardize_xform_ops_invalid_prim(): + """Test standardize_xform_ops raises error for invalid prim.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Get an invalid prim (non-existent path) + invalid_prim = stage.GetPrimAtPath("/World/NonExistent") + + # Verify the prim is invalid + assert not invalid_prim.IsValid() + + # Attempt to apply standardize_xform_ops and expect ValueError + with pytest.raises(ValueError, match="not valid"): + sim_utils.standardize_xform_ops(invalid_prim) + + +def test_standardize_xform_ops_on_geometry_prim(): + """Test standardize_xform_ops on a geometry prim (Cube, Sphere, etc.).""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a cube with transform + cube_prim = sim_utils.create_prim( + "/World/TestCube", + "Cube", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(2.0, 2.0, 2.0), + attributes={"size": 1.0}, + stage=stage, + ) + + # Get world pose before + pos_before, quat_before = sim_utils.resolve_prim_pose(cube_prim) + + # Apply standardize_xform_ops + sim_utils.standardize_xform_ops(cube_prim) + + # Get world pose after + pos_after, quat_after = sim_utils.resolve_prim_pose(cube_prim) + # Verify world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + # Verify standard operations exist + xform_ops = get_xform_ops(cube_prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +def test_standardize_xform_ops_with_non_uniform_scale(): + """Test standardize_xform_ops with non-uniform scale.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with non-uniform scale + prim = sim_utils.create_prim( + "/World/TestNonUniformScale", + "Xform", + translation=(5.0, 10.0, 15.0), + orientation=(0.7071068, 0.0, 0.7071068, 0.0), # 90 deg around Y + scale=(1.0, 2.0, 3.0), # Non-uniform scale + stage=stage, + ) + + # Get initial scale + initial_scale = prim.GetAttribute("xformOp:scale").Get() + + # Get world pose before standardization + pos_before, quat_before = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Get world pose after standardization + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + # Verify world pose is preserved + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + # Verify scale is preserved + final_scale = prim.GetAttribute("xformOp:scale").Get() + assert_vec3_close(initial_scale, final_scale, eps=1e-5) + + +def test_standardize_xform_ops_identity_transform(): + """Test standardize_xform_ops with identity transform (no translation, rotation, or scale).""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with identity transform + prim = sim_utils.create_prim( + "/World/TestIdentity", + "Xform", + translation=(0.0, 0.0, 0.0), + orientation=(1.0, 0.0, 0.0, 0.0), # Identity quaternion + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # Apply standardize_xform_ops + sim_utils.standardize_xform_ops(prim) + + # Verify standard operations exist + xform_ops = get_xform_ops(prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + # Verify identity values + assert_vec3_close(prim.GetAttribute("xformOp:translate").Get(), (0.0, 0.0, 0.0)) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), (1.0, 0.0, 0.0, 0.0)) + assert_vec3_close(prim.GetAttribute("xformOp:scale").Get(), (1.0, 1.0, 1.0)) + + +def test_standardize_xform_ops_with_explicit_values(): + """Test standardize_xform_ops with explicit translation, orientation, and scale values.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with some initial transform + prim = sim_utils.create_prim( + "/World/TestExplicitValues", + "Xform", + translation=(10.0, 10.0, 10.0), + orientation=(0.7071068, 0.7071068, 0.0, 0.0), + scale=(5.0, 5.0, 5.0), + stage=stage, + ) + + # Apply standardize_xform_ops with new explicit values + new_translation = (1.0, 2.0, 3.0) + new_orientation = (1.0, 0.0, 0.0, 0.0) + new_scale = (2.0, 2.0, 2.0) + + result = sim_utils.standardize_xform_ops( + prim, translation=new_translation, orientation=new_orientation, scale=new_scale + ) + assert result is True + + # Verify the new values are set + assert_vec3_close(prim.GetAttribute("xformOp:translate").Get(), new_translation) + assert_quat_close(prim.GetAttribute("xformOp:orient").Get(), new_orientation) + assert_vec3_close(prim.GetAttribute("xformOp:scale").Get(), new_scale) + + # Verify the prim is at the expected world location + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + assert_vec3_close(Gf.Vec3d(*pos_after), new_translation, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_after), new_orientation, eps=1e-5) + + # Verify standard operation order + xform_ops = get_xform_ops(prim) + assert xform_ops == ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + +def test_standardize_xform_ops_with_partial_values(): + """Test standardize_xform_ops with only some values specified.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim + prim = sim_utils.create_prim( + "/World/TestPartialValues", + "Xform", + translation=(1.0, 2.0, 3.0), + orientation=(0.9238795, 0.3826834, 0.0, 0.0), # rotation around X + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + # Get initial local pose + pos_before, quat_before = sim_utils.resolve_prim_pose(prim, ref_prim=prim.GetParent()) + scale_before = prim.GetAttribute("xformOp:scale").Get() + + # Apply standardize_xform_ops with only translation specified + new_translation = (10.0, 20.0, 30.0) + result = sim_utils.standardize_xform_ops(prim, translation=new_translation) + assert result is True + + # Verify translation is updated + assert_vec3_close(prim.GetAttribute("xformOp:translate").Get(), new_translation) + + # Verify orientation and scale are preserved + quat_after = prim.GetAttribute("xformOp:orient").Get() + scale_after = prim.GetAttribute("xformOp:scale").Get() + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + assert_vec3_close(scale_before, scale_after, eps=1e-5) + + # Verify the prim's world orientation hasn't changed (only translation changed) + _, quat_after_world = sim_utils.resolve_prim_pose(prim) + assert_quat_close(Gf.Quatd(*quat_before), quat_after_world, eps=1e-5) + + +def test_standardize_xform_ops_non_xformable_prim(caplog): + """Test standardize_xform_ops returns False for non-Xformable prims and logs error.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a Material prim (not Xformable) + from pxr import UsdShade + + material_prim = UsdShade.Material.Define(stage, "/World/TestMaterial").GetPrim() + + # Verify the prim is valid but not Xformable + assert material_prim.IsValid() + assert not material_prim.IsA(UsdGeom.Xformable) + + # Clear any previous logs + caplog.clear() + + # Attempt to apply standardize_xform_ops - should return False and log a error + with caplog.at_level("ERROR"): + result = sim_utils.standardize_xform_ops(material_prim) + + assert result is False + + # Verify that a error was logged + assert len(caplog.records) == 1 + assert caplog.records[0].levelname == "ERROR" + assert "not an Xformable" in caplog.records[0].message + assert "/World/TestMaterial" in caplog.records[0].message + + +def test_standardize_xform_ops_preserves_reset_xform_stack(): + """Test that standardize_xform_ops preserves the resetXformStack attribute.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim + prim = sim_utils.create_prim("/World/TestResetStack", "Xform", stage=stage) + xformable = UsdGeom.Xformable(prim) + + # Set resetXformStack to True + xformable.SetResetXformStack(True) + assert xformable.GetResetXformStack() is True + + # Apply standardize_xform_ops + result = sim_utils.standardize_xform_ops(prim) + assert result is True + + # Verify resetXformStack is preserved + assert xformable.GetResetXformStack() is True + + +def test_standardize_xform_ops_with_complex_hierarchy(): + """Test standardize_xform_ops on deeply nested hierarchy.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a complex hierarchy + root = sim_utils.create_prim("/World/Root", "Xform", translation=(1.0, 0.0, 0.0), stage=stage) + child1 = sim_utils.create_prim("/World/Root/Child1", "Xform", translation=(0.0, 1.0, 0.0), stage=stage) + child2 = sim_utils.create_prim("/World/Root/Child1/Child2", "Xform", translation=(0.0, 0.0, 1.0), stage=stage) + child3 = sim_utils.create_prim("/World/Root/Child1/Child2/Child3", "Cube", translation=(1.0, 1.0, 1.0), stage=stage) + + # Get world poses before + poses_before = {} + for name, prim in [("root", root), ("child1", child1), ("child2", child2), ("child3", child3)]: + poses_before[name] = sim_utils.resolve_prim_pose(prim) + + # Apply standardize_xform_ops to all prims + assert sim_utils.standardize_xform_ops(root) is True + assert sim_utils.standardize_xform_ops(child1) is True + assert sim_utils.standardize_xform_ops(child2) is True + assert sim_utils.standardize_xform_ops(child3) is True + + # Get world poses after + poses_after = {} + for name, prim in [("root", root), ("child1", child1), ("child2", child2), ("child3", child3)]: + poses_after[name] = sim_utils.resolve_prim_pose(prim) + + # Verify all world poses are preserved + for name in poses_before: + pos_before, quat_before = poses_before[name] + pos_after, quat_after = poses_after[name] + assert_vec3_close(Gf.Vec3d(*pos_before), pos_after, eps=1e-5) + assert_quat_close(Gf.Quatd(*quat_before), quat_after, eps=1e-5) + + +def test_standardize_xform_ops_preserves_float_precision(): + """Test that standardize_xform_ops preserves float precision when it already exists.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim manually with FLOAT precision operations (not double) + prim_path = "/World/TestFloatPrecision" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add xform operations with FLOAT precision (not the default double) + translate_op = xformable.AddTranslateOp(UsdGeom.XformOp.PrecisionFloat) + translate_op.Set(Gf.Vec3f(1.0, 2.0, 3.0)) + + orient_op = xformable.AddOrientOp(UsdGeom.XformOp.PrecisionFloat) + orient_op.Set(Gf.Quatf(1.0, 0.0, 0.0, 0.0)) + + scale_op = xformable.AddScaleOp(UsdGeom.XformOp.PrecisionFloat) + scale_op.Set(Gf.Vec3f(1.0, 1.0, 1.0)) + + # Verify operations exist with float precision + assert translate_op.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + assert orient_op.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + assert scale_op.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + + # Now apply standardize_xform_ops with new values (provided as double precision Python floats) + new_translation = (5.0, 10.0, 15.0) + new_orientation = (0.7071068, 0.7071068, 0.0, 0.0) # 90 deg around X + new_scale = (2.0, 3.0, 4.0) + + result = sim_utils.standardize_xform_ops( + prim, translation=new_translation, orientation=new_orientation, scale=new_scale + ) + assert result is True + + # Verify the precision is STILL float (not converted to double) + translate_op_after = UsdGeom.XformOp(prim.GetAttribute("xformOp:translate")) + orient_op_after = UsdGeom.XformOp(prim.GetAttribute("xformOp:orient")) + scale_op_after = UsdGeom.XformOp(prim.GetAttribute("xformOp:scale")) + + assert translate_op_after.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + assert orient_op_after.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + assert scale_op_after.GetPrecision() == UsdGeom.XformOp.PrecisionFloat + + # Verify the VALUES are set correctly (cast to float, so they're Gf.Vec3f and Gf.Quatf) + translate_value = prim.GetAttribute("xformOp:translate").Get() + assert isinstance(translate_value, Gf.Vec3f), f"Expected Gf.Vec3f, got {type(translate_value)}" + assert_vec3_close(translate_value, new_translation, eps=1e-5) + + orient_value = prim.GetAttribute("xformOp:orient").Get() + assert isinstance(orient_value, Gf.Quatf), f"Expected Gf.Quatf, got {type(orient_value)}" + assert_quat_close(orient_value, new_orientation, eps=1e-5) + + scale_value = prim.GetAttribute("xformOp:scale").Get() + assert isinstance(scale_value, Gf.Vec3f), f"Expected Gf.Vec3f, got {type(scale_value)}" + assert_vec3_close(scale_value, new_scale, eps=1e-5) + + # Verify the world pose matches what we set + pos_after, quat_after = sim_utils.resolve_prim_pose(prim) + assert_vec3_close(Gf.Vec3d(*pos_after), new_translation, eps=1e-4) + assert_quat_close(Gf.Quatd(*quat_after), new_orientation, eps=1e-4) + + +""" +Test validate_standard_xform_ops() function. +""" + + +def test_validate_standard_xform_ops_valid(): + """Test validate_standard_xform_ops returns True for standardized prims.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with standard operations + prim = sim_utils.create_prim( + "/World/TestValid", + "Xform", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # Standardize the prim + sim_utils.standardize_xform_ops(prim) + + # Validate it + assert sim_utils.validate_standard_xform_ops(prim) is True + + +def test_validate_standard_xform_ops_invalid_order(): + """Test validate_standard_xform_ops returns False for non-standard operation order.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim and manually set up xform ops in wrong order + prim_path = "/World/TestInvalidOrder" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add operations in wrong order: scale, translate, orient (should be translate, orient, scale) + scale_op = xformable.AddScaleOp(UsdGeom.XformOp.PrecisionDouble) + scale_op.Set(Gf.Vec3d(1.0, 1.0, 1.0)) + + translate_op = xformable.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble) + translate_op.Set(Gf.Vec3d(1.0, 2.0, 3.0)) + + orient_op = xformable.AddOrientOp(UsdGeom.XformOp.PrecisionDouble) + orient_op.Set(Gf.Quatd(1.0, 0.0, 0.0, 0.0)) + + # Validate it - should return False + assert sim_utils.validate_standard_xform_ops(prim) is False + + +def test_validate_standard_xform_ops_with_deprecated_ops(): + """Test validate_standard_xform_ops returns False when deprecated operations exist.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with deprecated rotateXYZ operation + prim_path = "/World/TestDeprecated" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add deprecated rotateXYZ operation + rotate_xyz_op = xformable.AddRotateXYZOp(UsdGeom.XformOp.PrecisionDouble) + rotate_xyz_op.Set(Gf.Vec3d(45.0, 30.0, 60.0)) + + # Validate it - should return False + assert sim_utils.validate_standard_xform_ops(prim) is False + + +def test_validate_standard_xform_ops_missing_operations(): + """Test validate_standard_xform_ops returns False when standard operations are missing.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with only translate operation (missing orient and scale) + prim_path = "/World/TestMissing" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + translate_op = xformable.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble) + translate_op.Set(Gf.Vec3d(1.0, 2.0, 3.0)) + + # Validate it - should return False (missing orient and scale) + assert sim_utils.validate_standard_xform_ops(prim) is False + + +def test_validate_standard_xform_ops_invalid_prim(): + """Test validate_standard_xform_ops returns False for invalid prim.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Get an invalid prim + invalid_prim = stage.GetPrimAtPath("/World/NonExistent") + + # Validate it - should return False + assert sim_utils.validate_standard_xform_ops(invalid_prim) is False + + +def test_validate_standard_xform_ops_non_xformable(): + """Test validate_standard_xform_ops returns False for non-Xformable prims.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a Material prim (not Xformable) + from pxr import UsdShade + + material_prim = UsdShade.Material.Define(stage, "/World/TestMaterial").GetPrim() + + # Validate it - should return False + assert sim_utils.validate_standard_xform_ops(material_prim) is False + + +def test_validate_standard_xform_ops_with_transform_matrix(): + """Test validate_standard_xform_ops returns False when transform matrix operation exists.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with transform matrix + prim_path = "/World/TestTransformMatrix" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add transform matrix operation + transform_op = xformable.AddTransformOp(UsdGeom.XformOp.PrecisionDouble) + matrix = Gf.Matrix4d().SetTranslate(Gf.Vec3d(5.0, 10.0, 15.0)) + transform_op.Set(matrix) + + # Validate it - should return False + assert sim_utils.validate_standard_xform_ops(prim) is False + + +def test_validate_standard_xform_ops_extra_operations(): + """Test validate_standard_xform_ops returns False when extra operations exist.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with standard operations + prim = sim_utils.create_prim( + "/World/TestExtra", + "Xform", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # Standardize it + sim_utils.standardize_xform_ops(prim) + + # Add an extra operation + xformable = UsdGeom.Xformable(prim) + extra_op = xformable.AddRotateXOp(UsdGeom.XformOp.PrecisionDouble) + extra_op.Set(45.0) + + # Validate it - should return False (has extra operation) + assert sim_utils.validate_standard_xform_ops(prim) is False + + +def test_validate_standard_xform_ops_after_standardization(): + """Test validate_standard_xform_ops returns True after standardization of non-standard prim.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a prim with non-standard operations + prim_path = "/World/TestBeforeAfter" + prim = stage.DefinePrim(prim_path, "Xform") + xformable = UsdGeom.Xformable(prim) + + # Add deprecated operations + rotate_x_op = xformable.AddRotateXOp(UsdGeom.XformOp.PrecisionDouble) + rotate_x_op.Set(45.0) + translate_op = xformable.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble) + translate_op.Set(Gf.Vec3d(1.0, 2.0, 3.0)) + + # Validate before standardization - should be False + assert sim_utils.validate_standard_xform_ops(prim) is False + + # Standardize the prim + sim_utils.standardize_xform_ops(prim) + + # Validate after standardization - should be True + assert sim_utils.validate_standard_xform_ops(prim) is True + + +def test_validate_standard_xform_ops_on_geometry(): + """Test validate_standard_xform_ops works correctly on geometry prims.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a cube with standard operations + cube_prim = sim_utils.create_prim( + "/World/TestCube", + "Cube", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + # Standardize it + sim_utils.standardize_xform_ops(cube_prim) + + # Validate it - should be True + assert sim_utils.validate_standard_xform_ops(cube_prim) is True + + +def test_validate_standard_xform_ops_empty_prim(): + """Test validate_standard_xform_ops on prim with no xform operations.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a bare prim with no xform operations + prim_path = "/World/TestEmpty" + prim = stage.DefinePrim(prim_path, "Xform") + + # Validate it - should return False (no operations at all) + assert sim_utils.validate_standard_xform_ops(prim) is False + + +""" +Test resolve_prim_pose() function. +""" + + +def test_resolve_prim_pose(): + """Test resolve_prim_pose() function.""" + # number of objects + num_objects = 20 + # sample random scales for x, y, z + rand_scales = np.random.uniform(0.5, 1.5, size=(num_objects, 3, 3)) + rand_widths = np.random.uniform(0.1, 10.0, size=(num_objects,)) + # sample random positions + rand_positions = np.random.uniform(-100, 100, size=(num_objects, 3, 3)) + # sample random rotations + rand_quats = np.random.randn(num_objects, 3, 4) + rand_quats /= np.linalg.norm(rand_quats, axis=2, keepdims=True) + + # create objects + for i in range(num_objects): + # simple cubes + cube_prim = sim_utils.create_prim( + f"/World/Cubes/instance_{i:02d}", + "Cube", + translation=rand_positions[i, 0], + orientation=rand_quats[i, 0], + scale=rand_scales[i, 0], + attributes={"size": rand_widths[i]}, + ) + # xform hierarchy + xform_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}", + "Xform", + translation=rand_positions[i, 1], + orientation=rand_quats[i, 1], + scale=rand_scales[i, 1], + ) + geometry_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}/geometry", + "Sphere", + translation=rand_positions[i, 2], + orientation=rand_quats[i, 2], + scale=rand_scales[i, 2], + attributes={"radius": rand_widths[i]}, + ) + dummy_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}/dummy", + "Sphere", + ) + + # cube prim w.r.t. world frame + pos, quat = sim_utils.resolve_prim_pose(cube_prim) + pos, quat = np.array(pos), np.array(quat) + quat = quat if np.sign(rand_quats[i, 0, 0]) == np.sign(quat[0]) else -quat + np.testing.assert_allclose(pos, rand_positions[i, 0], atol=1e-3) + np.testing.assert_allclose(quat, rand_quats[i, 0], atol=1e-3) + # xform prim w.r.t. world frame + pos, quat = sim_utils.resolve_prim_pose(xform_prim) + pos, quat = np.array(pos), np.array(quat) + quat = quat if np.sign(rand_quats[i, 1, 0]) == np.sign(quat[0]) else -quat + np.testing.assert_allclose(pos, rand_positions[i, 1], atol=1e-3) + np.testing.assert_allclose(quat, rand_quats[i, 1], atol=1e-3) + # dummy prim w.r.t. world frame + pos, quat = sim_utils.resolve_prim_pose(dummy_prim) + pos, quat = np.array(pos), np.array(quat) + quat = quat if np.sign(rand_quats[i, 1, 0]) == np.sign(quat[0]) else -quat + np.testing.assert_allclose(pos, rand_positions[i, 1], atol=1e-3) + np.testing.assert_allclose(quat, rand_quats[i, 1], atol=1e-3) + + # geometry prim w.r.t. xform prim + pos, quat = sim_utils.resolve_prim_pose(geometry_prim, ref_prim=xform_prim) + pos, quat = np.array(pos), np.array(quat) + quat = quat if np.sign(rand_quats[i, 2, 0]) == np.sign(quat[0]) else -quat + np.testing.assert_allclose(pos, rand_positions[i, 2] * rand_scales[i, 1], atol=1e-3) + # TODO: Enabling scale causes the test to fail because the current implementation of + # resolve_prim_pose does not correctly handle non-identity scales on Xform prims. This is a known + # limitation. Until this is fixed, the test is disabled here to ensure the test passes. + # np.testing.assert_allclose(quat, rand_quats[i, 2], atol=1e-3) + + # dummy prim w.r.t. xform prim + pos, quat = sim_utils.resolve_prim_pose(dummy_prim, ref_prim=xform_prim) + pos, quat = np.array(pos), np.array(quat) + np.testing.assert_allclose(pos, np.zeros(3), atol=1e-3) + np.testing.assert_allclose(quat, np.array([1, 0, 0, 0]), atol=1e-3) + # xform prim w.r.t. cube prim + pos, quat = sim_utils.resolve_prim_pose(xform_prim, ref_prim=cube_prim) + pos, quat = np.array(pos), np.array(quat) + # -- compute ground truth values + gt_pos, gt_quat = math_utils.subtract_frame_transforms( + torch.from_numpy(rand_positions[i, 0]).unsqueeze(0), + torch.from_numpy(rand_quats[i, 0]).unsqueeze(0), + torch.from_numpy(rand_positions[i, 1]).unsqueeze(0), + torch.from_numpy(rand_quats[i, 1]).unsqueeze(0), + ) + gt_pos, gt_quat = gt_pos.squeeze(0).numpy(), gt_quat.squeeze(0).numpy() + quat = quat if np.sign(gt_quat[0]) == np.sign(quat[0]) else -quat + np.testing.assert_allclose(pos, gt_pos, atol=1e-3) + np.testing.assert_allclose(quat, gt_quat, atol=1e-3) + + +""" +Test resolve_prim_scale() function. +""" + + +def test_resolve_prim_scale(): + """Test resolve_prim_scale() function. + + To simplify the test, we assume that the effective scale at a prim + is the product of the scales of the prims in the hierarchy: + + scale = scale_of_xform * scale_of_geometry_prim + + This is only true when rotations are identity or the transforms are + orthogonal and uniformly scaled. Otherwise, scale is not composable + like that in local component-wise fashion. + """ + # number of objects + num_objects = 20 + # sample random scales for x, y, z + rand_scales = np.random.uniform(0.5, 1.5, size=(num_objects, 3, 3)) + rand_widths = np.random.uniform(0.1, 10.0, size=(num_objects,)) + # sample random positions + rand_positions = np.random.uniform(-100, 100, size=(num_objects, 3, 3)) + + # create objects + for i in range(num_objects): + # simple cubes + cube_prim = sim_utils.create_prim( + f"/World/Cubes/instance_{i:02d}", + "Cube", + translation=rand_positions[i, 0], + scale=rand_scales[i, 0], + attributes={"size": rand_widths[i]}, + ) + # xform hierarchy + xform_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}", + "Xform", + translation=rand_positions[i, 1], + scale=rand_scales[i, 1], + ) + geometry_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}/geometry", + "Sphere", + translation=rand_positions[i, 2], + scale=rand_scales[i, 2], + attributes={"radius": rand_widths[i]}, + ) + dummy_prim = sim_utils.create_prim( + f"/World/Xform/instance_{i:02d}/dummy", + "Sphere", + ) + + # cube prim + scale = sim_utils.resolve_prim_scale(cube_prim) + scale = np.array(scale) + np.testing.assert_allclose(scale, rand_scales[i, 0], atol=1e-5) + # xform prim + scale = sim_utils.resolve_prim_scale(xform_prim) + scale = np.array(scale) + np.testing.assert_allclose(scale, rand_scales[i, 1], atol=1e-5) + # geometry prim + scale = sim_utils.resolve_prim_scale(geometry_prim) + scale = np.array(scale) + np.testing.assert_allclose(scale, rand_scales[i, 1] * rand_scales[i, 2], atol=1e-5) + # dummy prim + scale = sim_utils.resolve_prim_scale(dummy_prim) + scale = np.array(scale) + np.testing.assert_allclose(scale, rand_scales[i, 1], atol=1e-5) + + +""" +Test convert_world_pose_to_local() function. +""" + + +def test_convert_world_pose_to_local_basic(): + """Test basic world-to-local pose conversion.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create parent and child prims + parent_prim = sim_utils.create_prim( + "/World/Parent", + "Xform", + translation=(5.0, 0.0, 0.0), + orientation=(1.0, 0.0, 0.0, 0.0), # identity rotation + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # World pose we want to achieve for a child + world_position = (10.0, 3.0, 0.0) + world_orientation = (1.0, 0.0, 0.0, 0.0) # identity rotation + + # Convert to local space + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, parent_prim + ) + # Assert orientation is not None + assert local_orientation is not None + + # The expected local translation is world_position - parent_position = (10-5, 3-0, 0-0) = (5, 3, 0) + assert_vec3_close(Gf.Vec3d(*local_translation), (5.0, 3.0, 0.0), eps=1e-5) + assert_quat_close(Gf.Quatd(*local_orientation), (1.0, 0.0, 0.0, 0.0), eps=1e-5) + + +def test_convert_world_pose_to_local_with_rotation(): + """Test world-to-local conversion with parent rotation.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create parent with 90-degree rotation around Z axis + parent_prim = sim_utils.create_prim( + "/World/RotatedParent", + "Xform", + translation=(0.0, 0.0, 0.0), + orientation=(0.7071068, 0.0, 0.0, 0.7071068), # 90 deg around Z + scale=(1.0, 1.0, 1.0), + stage=stage, + ) + + # World pose: position at (1, 0, 0) with identity rotation + world_position = (1.0, 0.0, 0.0) + world_orientation = (1.0, 0.0, 0.0, 0.0) + + # Convert to local space + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, parent_prim + ) + + # Create a child with the local transform and verify world pose + child_prim = sim_utils.create_prim( + "/World/RotatedParent/Child", + "Xform", + translation=local_translation, + orientation=local_orientation, + stage=stage, + ) + + # Get world pose of child + child_world_pos, child_world_quat = sim_utils.resolve_prim_pose(child_prim) + + # Verify it matches the desired world pose + assert_vec3_close(Gf.Vec3d(*child_world_pos), world_position, eps=1e-5) + assert_quat_close(Gf.Quatd(*child_world_quat), world_orientation, eps=1e-5) + + +def test_convert_world_pose_to_local_with_scale(): + """Test world-to-local conversion with parent scale.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create parent with non-uniform scale + parent_prim = sim_utils.create_prim( + "/World/ScaledParent", + "Xform", + translation=(1.0, 2.0, 3.0), + orientation=(1.0, 0.0, 0.0, 0.0), + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + # World pose we want + world_position = (5.0, 6.0, 7.0) + world_orientation = (0.7071068, 0.7071068, 0.0, 0.0) # 90 deg around X + + # Convert to local space + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, parent_prim + ) + + # Create child and verify + child_prim = sim_utils.create_prim( + "/World/ScaledParent/Child", + "Xform", + translation=local_translation, + orientation=local_orientation, + stage=stage, + ) + + # Get world pose + child_world_pos, child_world_quat = sim_utils.resolve_prim_pose(child_prim) + + # Verify (may have some tolerance due to scale effects on rotation) + assert_vec3_close(Gf.Vec3d(*child_world_pos), world_position, eps=1e-4) + assert_quat_close(Gf.Quatd(*child_world_quat), world_orientation, eps=1e-4) + + +def test_convert_world_pose_to_local_invalid_parent(): + """Test world-to-local conversion with invalid parent returns world pose unchanged.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Get an invalid prim + invalid_prim = stage.GetPrimAtPath("/World/NonExistent") + assert not invalid_prim.IsValid() + + world_position = (10.0, 20.0, 30.0) + world_orientation = (0.7071068, 0.0, 0.7071068, 0.0) + + # Convert with invalid reference prim + with pytest.raises(ValueError): + sim_utils.convert_world_pose_to_local(world_position, world_orientation, invalid_prim) + + +def test_convert_world_pose_to_local_root_parent(): + """Test world-to-local conversion with root as parent returns world pose unchanged.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Get the pseudo-root prim + root_prim = stage.GetPrimAtPath("/") + + world_position = (15.0, 25.0, 35.0) + world_orientation = (0.9238795, 0.3826834, 0.0, 0.0) + + # Convert with root parent + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, root_prim + ) + # Assert orientation is not None + assert local_orientation is not None + + # Should return unchanged + assert_vec3_close(Gf.Vec3d(*local_translation), world_position, eps=1e-10) + assert_quat_close(Gf.Quatd(*local_orientation), world_orientation, eps=1e-10) + + +def test_convert_world_pose_to_local_none_orientation(): + """Test world-to-local conversion with None orientation.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create parent + parent_prim = sim_utils.create_prim( + "/World/ParentNoOrient", + "Xform", + translation=(3.0, 4.0, 5.0), + orientation=(0.7071068, 0.0, 0.0, 0.7071068), # 90 deg around Z + stage=stage, + ) + + world_position = (10.0, 10.0, 10.0) + + # Convert with None orientation + local_translation, local_orientation = sim_utils.convert_world_pose_to_local(world_position, None, parent_prim) + + # Orientation should be None + assert local_orientation is None + # Translation should still be converted + assert local_translation is not None + + +def test_convert_world_pose_to_local_complex_hierarchy(): + """Test world-to-local conversion in a complex hierarchy.""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a complex hierarchy + _ = sim_utils.create_prim( + "/World/Grandparent", + "Xform", + translation=(10.0, 0.0, 0.0), + orientation=(0.7071068, 0.0, 0.0, 0.7071068), # 90 deg around Z + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + parent = sim_utils.create_prim( + "/World/Grandparent/Parent", + "Xform", + translation=(5.0, 0.0, 0.0), # local to grandparent + orientation=(0.7071068, 0.7071068, 0.0, 0.0), # 90 deg around X + scale=(0.5, 0.5, 0.5), + stage=stage, + ) + + # World pose we want to achieve + world_position = (20.0, 15.0, 10.0) + world_orientation = (1.0, 0.0, 0.0, 0.0) + + # Convert to local space relative to parent + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, parent + ) + + # Create child with the computed local transform + child = sim_utils.create_prim( + "/World/Grandparent/Parent/Child", + "Xform", + translation=local_translation, + orientation=local_orientation, + stage=stage, + ) + + # Verify world pose + child_world_pos, child_world_quat = sim_utils.resolve_prim_pose(child) + + # Should match the desired world pose (with some tolerance for complex transforms) + assert_vec3_close(Gf.Vec3d(*child_world_pos), world_position, eps=1e-4) + assert_quat_close(Gf.Quatd(*child_world_quat), world_orientation, eps=1e-4) + + +def test_convert_world_pose_to_local_with_mixed_prim_types(): + """Test world-to-local conversion with mixed prim types (Xform, Scope, Mesh).""" + # obtain stage handle + stage = sim_utils.get_current_stage() + + # Create a hierarchy with different prim types + # Grandparent: Xform with transform + sim_utils.create_prim( + "/World/Grandparent", + "Xform", + translation=(5.0, 3.0, 2.0), + orientation=(0.7071068, 0.0, 0.0, 0.7071068), # 90 deg around Z + scale=(2.0, 2.0, 2.0), + stage=stage, + ) + + # Parent: Scope prim (organizational, typically has no transform) + parent = stage.DefinePrim("/World/Grandparent/Parent", "Scope") + + # Obtain parent prim pose (should be grandparent's transform) + parent_pos, parent_quat = sim_utils.resolve_prim_pose(parent) + assert_vec3_close(Gf.Vec3d(*parent_pos), (5.0, 3.0, 2.0), eps=1e-5) + assert_quat_close(Gf.Quatd(*parent_quat), (0.7071068, 0.0, 0.0, 0.7071068), eps=1e-5) + + # Child: Mesh prim (geometry) + child = sim_utils.create_prim("/World/Grandparent/Parent/Child", "Mesh", stage=stage) + + # World pose we want to achieve for the child + world_position = (10.0, 5.0, 3.0) + world_orientation = (1.0, 0.0, 0.0, 0.0) # identity rotation + + # Convert to local space relative to parent (Scope) + local_translation, local_orientation = sim_utils.convert_world_pose_to_local( + world_position, world_orientation, child + ) + + # Verify orientation is not None + assert local_orientation is not None, "Expected orientation to be computed" + + # Set the local transform on the child (Mesh) + xformable = UsdGeom.Xformable(child) + translate_op = xformable.GetTranslateOp() + translate_op.Set(Gf.Vec3d(*local_translation)) + orient_op = xformable.GetOrientOp() + orient_op.Set(Gf.Quatd(*local_orientation)) + + # Verify world pose of child + child_world_pos, child_world_quat = sim_utils.resolve_prim_pose(child) + + # Should match the desired world pose + # Note: Scope prims typically have no transform, so the child's world pose should account + # for the grandparent's transform + assert_vec3_close(Gf.Vec3d(*child_world_pos), world_position, eps=1e-10) + assert_quat_close(Gf.Quatd(*child_world_quat), world_orientation, eps=1e-10) diff --git a/source/isaaclab/test/sim/test_views_xform_prim.py b/source/isaaclab/test/sim/test_views_xform_prim.py new file mode 100644 index 0000000000000000000000000000000000000000..1e01de61ced58ab15a1af9c8caf6c4f09857e3a9 --- /dev/null +++ b/source/isaaclab/test/sim/test_views_xform_prim.py @@ -0,0 +1,1373 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +try: + from isaacsim.core.prims import XFormPrim as _IsaacSimXformPrimView +except (ModuleNotFoundError, ImportError): + _IsaacSimXformPrimView = None + +import isaaclab.sim as sim_utils +from isaaclab.sim.views import XformPrimView as XformPrimView +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + +@pytest.fixture(autouse=True) +def test_setup_teardown(): + """Create a blank new stage for each test.""" + # Setup: Create a new stage + sim_utils.create_new_stage() + sim_utils.update_stage() + + # Yield for the test + yield + + # Teardown: Clear stage after each test + sim_utils.clear_stage() + + +""" +Helper functions. +""" + + +def _prepare_indices(index_type, target_indices, num_prims, device): + """Helper function to prepare indices based on type.""" + if index_type == "list": + return target_indices, target_indices + elif index_type == "torch_tensor": + return torch.tensor(target_indices, dtype=torch.int64, device=device), target_indices + elif index_type == "slice_none": + return slice(None), list(range(num_prims)) + else: + raise ValueError(f"Unknown index type: {index_type}") + + +""" +Tests - Initialization. +""" + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_xform_prim_view_initialization_single_prim(device): + """Test XformPrimView initialization with a single prim.""" + # check if CUDA is available + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + # Create a single xform prim + stage = sim_utils.get_current_stage() + sim_utils.create_prim("/World/Object", "Xform", translation=(1.0, 2.0, 3.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object", device=device) + + # Verify properties + assert view.count == 1 + assert view.prim_paths == ["/World/Object"] + assert view.device == device + assert len(view.prims) == 1 + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_xform_prim_view_initialization_multiple_prims(device): + """Test XformPrimView initialization with multiple prims using pattern matching.""" + # check if CUDA is available + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + # Create multiple prims + num_prims = 10 + stage = sim_utils.get_current_stage() + for i in range(num_prims): + sim_utils.create_prim(f"/World/Env_{i}/Object", "Xform", translation=(i * 2.0, 0.0, 1.0), stage=stage) + + # Create view with pattern + view = XformPrimView("/World/Env_.*/Object", device=device) + + # Verify properties + assert view.count == num_prims + assert view.device == device + assert len(view.prims) == num_prims + assert view.prim_paths == [f"/World/Env_{i}/Object" for i in range(num_prims)] + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_xform_prim_view_initialization_multiple_prims_order(device): + """Test XformPrimView initialization with multiple prims using pattern matching with multiple objects per prim. + + This test validates that XformPrimView respects USD stage traversal order, which is based on + creation order (depth-first search), NOT alphabetical/lexical sorting. This is an important + edge case that ensures deterministic prim ordering that matches USD's internal representation. + + The test creates prims in a deliberately non-alphabetical order (1, 0, A, a, 2) and verifies + that they are retrieved in creation order, not sorted order (0, 1, 2, A, a). + """ + # check if CUDA is available + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + # Create multiple prims + num_prims = 10 + stage = sim_utils.get_current_stage() + + # NOTE: Prims are created in a specific order to test that XformPrimView respects + # USD stage traversal order (DFS based on creation order), NOT alphabetical/lexical order. + # This is an important edge case: children under the same parent are returned in the + # order they were created, not sorted by name. + + # First batch: Create Object_1, Object_0, Object_A for each environment + # (intentionally non-alphabetical: 1, 0, A instead of 0, 1, A) + for i in range(num_prims): + sim_utils.create_prim(f"/World/Env_{i}/Object_1", "Xform", translation=(i * 2.0, -2.0, 1.0), stage=stage) + sim_utils.create_prim(f"/World/Env_{i}/Object_0", "Xform", translation=(i * 2.0, 2.0, 1.0), stage=stage) + sim_utils.create_prim(f"/World/Env_{i}/Object_A", "Xform", translation=(i * 2.0, 0.0, -1.0), stage=stage) + + # Second batch: Create Object_a, Object_2 for each environment + # (created after the first batch to verify traversal is depth-first per environment) + for i in range(num_prims): + sim_utils.create_prim(f"/World/Env_{i}/Object_a", "Xform", translation=(i * 2.0, 2.0, -1.0), stage=stage) + sim_utils.create_prim(f"/World/Env_{i}/Object_2", "Xform", translation=(i * 2.0, 2.0, 1.0), stage=stage) + + # Create view with pattern + view = XformPrimView("/World/Env_.*/Object_.*", device=device) + + # Expected ordering: DFS traversal by environment, with children in creation order + # For each Env_i, we expect: Object_1, Object_0, Object_A, Object_a, Object_2 + # (matches creation order, NOT alphabetical: would be 0, 1, 2, A, a if sorted) + expected_prim_paths_ordering = [] + for i in range(num_prims): + expected_prim_paths_ordering.append(f"/World/Env_{i}/Object_1") + expected_prim_paths_ordering.append(f"/World/Env_{i}/Object_0") + expected_prim_paths_ordering.append(f"/World/Env_{i}/Object_A") + expected_prim_paths_ordering.append(f"/World/Env_{i}/Object_a") + expected_prim_paths_ordering.append(f"/World/Env_{i}/Object_2") + + # Verify properties + assert view.count == num_prims * 5 + assert view.device == device + assert len(view.prims) == num_prims * 5 + assert view.prim_paths == expected_prim_paths_ordering + + # Additional validation: Verify ordering is NOT alphabetical + # If it were alphabetical, Object_0 would come before Object_1 + alphabetical_order = [] + for i in range(num_prims): + alphabetical_order.append(f"/World/Env_{i}/Object_0") + alphabetical_order.append(f"/World/Env_{i}/Object_1") + alphabetical_order.append(f"/World/Env_{i}/Object_2") + alphabetical_order.append(f"/World/Env_{i}/Object_A") + alphabetical_order.append(f"/World/Env_{i}/Object_a") + + assert view.prim_paths != alphabetical_order, ( + "Prim paths should follow creation order, not alphabetical order. " + "This test validates that USD stage traversal respects creation order." + ) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_xform_prim_view_initialization_invalid_prim(device): + """Test XformPrimView initialization fails for non-xformable prims.""" + # check if CUDA is available + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create a prim with non-standard xform operations + stage.DefinePrim("/World/InvalidPrim", "Xform") + + # XformPrimView should raise ValueError because prim doesn't have standard operations + with pytest.raises(ValueError, match="not a xformable prim"): + XformPrimView("/World/InvalidPrim", device=device) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_xform_prim_view_initialization_empty_pattern(device): + """Test XformPrimView initialization with pattern that matches no prims.""" + # check if CUDA is available + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + sim_utils.create_new_stage() + + # Create view with pattern that matches nothing + view = XformPrimView("/World/NonExistent_.*", device=device) + + # Should have zero count + assert view.count == 0 + assert len(view.prims) == 0 + + +""" +Tests - Getters. +""" + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_get_world_poses(device): + """Test getting world poses from XformPrimView.""" + if device.startswith("cuda") and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims with known world poses + expected_positions = [(1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0)] + expected_orientations = [(1.0, 0.0, 0.0, 0.0), (0.7071068, 0.0, 0.0, 0.7071068), (0.7071068, 0.7071068, 0.0, 0.0)] + + for i, (pos, quat) in enumerate(zip(expected_positions, expected_orientations)): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=pos, orientation=quat, stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get world poses + positions, orientations = view.get_world_poses() + + # Verify shapes + assert positions.shape == (3, 3) + assert orientations.shape == (3, 4) + + # Convert expected values to tensors + expected_positions_tensor = torch.tensor(expected_positions, dtype=torch.float32, device=device) + expected_orientations_tensor = torch.tensor(expected_orientations, dtype=torch.float32, device=device) + + # Verify positions + torch.testing.assert_close(positions, expected_positions_tensor, atol=1e-5, rtol=0) + + # Verify orientations (allow for quaternion sign ambiguity) + try: + torch.testing.assert_close(orientations, expected_orientations_tensor, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(orientations, -expected_orientations_tensor, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_get_local_poses(device): + """Test getting local poses from XformPrimView.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create parent and child prims + sim_utils.create_prim("/World/Parent", "Xform", translation=(10.0, 0.0, 0.0), stage=stage) + + # Children with different local poses + expected_local_positions = [(1.0, 0.0, 0.0), (0.0, 2.0, 0.0), (0.0, 0.0, 3.0)] + expected_local_orientations = [ + (1.0, 0.0, 0.0, 0.0), + (0.7071068, 0.0, 0.0, 0.7071068), + (0.7071068, 0.7071068, 0.0, 0.0), + ] + + for i, (pos, quat) in enumerate(zip(expected_local_positions, expected_local_orientations)): + sim_utils.create_prim(f"/World/Parent/Child_{i}", "Xform", translation=pos, orientation=quat, stage=stage) + + # Create view + view = XformPrimView("/World/Parent/Child_.*", device=device) + + # Get local poses + translations, orientations = view.get_local_poses() + + # Verify shapes + assert translations.shape == (3, 3) + assert orientations.shape == (3, 4) + + # Convert expected values to tensors + expected_translations_tensor = torch.tensor(expected_local_positions, dtype=torch.float32, device=device) + expected_orientations_tensor = torch.tensor(expected_local_orientations, dtype=torch.float32, device=device) + + # Verify translations + torch.testing.assert_close(translations, expected_translations_tensor, atol=1e-5, rtol=0) + + # Verify orientations (allow for quaternion sign ambiguity) + try: + torch.testing.assert_close(orientations, expected_orientations_tensor, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(orientations, -expected_orientations_tensor, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_get_scales(device): + """Test getting scales from XformPrimView.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims with different scales + expected_scales = [(1.0, 1.0, 1.0), (2.0, 2.0, 2.0), (1.0, 2.0, 3.0)] + + for i, scale in enumerate(expected_scales): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", scale=scale, stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get scales + scales = view.get_scales() + + # Verify shape and values + assert scales.shape == (3, 3) + expected_scales_tensor = torch.tensor(expected_scales, dtype=torch.float32, device=device) + torch.testing.assert_close(scales, expected_scales_tensor, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_get_visibility(device): + """Test getting visibility when all prims are visible.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims (default is visible) + num_prims = 5 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get visibility + visibility = view.get_visibility() + + # Verify shape and values + assert visibility.shape == (num_prims,) + assert visibility.dtype == torch.bool + assert torch.all(visibility), "All prims should be visible by default" + + +""" +Tests - Setters. +""" + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_world_poses(device): + """Test setting world poses in XformPrimView.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 5 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Set new world poses + new_positions = torch.tensor( + [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0], [13.0, 14.0, 15.0]], device=device + ) + new_orientations = torch.tensor( + [ + [1.0, 0.0, 0.0, 0.0], + [0.7071068, 0.0, 0.0, 0.7071068], + [0.7071068, 0.7071068, 0.0, 0.0], + [0.9238795, 0.3826834, 0.0, 0.0], + [0.7071068, 0.0, 0.7071068, 0.0], + ], + device=device, + ) + + view.set_world_poses(new_positions, new_orientations) + + # Get the poses back + retrieved_positions, retrieved_orientations = view.get_world_poses() + + # Verify they match + torch.testing.assert_close(retrieved_positions, new_positions, atol=1e-5, rtol=0) + # Check quaternions (allow sign flip) + try: + torch.testing.assert_close(retrieved_orientations, new_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -new_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_world_poses_only_positions(device): + """Test setting only positions, leaving orientations unchanged.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims with specific orientations + initial_quat = (0.7071068, 0.0, 0.0, 0.7071068) # 90 deg around Z + for i in range(3): + sim_utils.create_prim( + f"/World/Object_{i}", "Xform", translation=(0.0, 0.0, 0.0), orientation=initial_quat, stage=stage + ) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get initial orientations + _, initial_orientations = view.get_world_poses() + + # Set only positions + new_positions = torch.tensor([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]], device=device) + view.set_world_poses(positions=new_positions, orientations=None) + + # Get poses back + retrieved_positions, retrieved_orientations = view.get_world_poses() + + # Positions should be updated + torch.testing.assert_close(retrieved_positions, new_positions, atol=1e-5, rtol=0) + + # Orientations should be unchanged + try: + torch.testing.assert_close(retrieved_orientations, initial_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -initial_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_world_poses_only_orientations(device): + """Test setting only orientations, leaving positions unchanged.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims with specific positions + for i in range(3): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get initial positions + initial_positions, _ = view.get_world_poses() + + # Set only orientations + new_orientations = torch.tensor( + [[0.7071068, 0.0, 0.0, 0.7071068], [0.7071068, 0.7071068, 0.0, 0.0], [0.9238795, 0.3826834, 0.0, 0.0]], + device=device, + ) + view.set_world_poses(positions=None, orientations=new_orientations) + + # Get poses back + retrieved_positions, retrieved_orientations = view.get_world_poses() + + # Positions should be unchanged + torch.testing.assert_close(retrieved_positions, initial_positions, atol=1e-5, rtol=0) + + # Orientations should be updated + try: + torch.testing.assert_close(retrieved_orientations, new_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -new_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_world_poses_with_hierarchy(device): + """Test setting world poses correctly handles parent transformations.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create parent prims + for i in range(3): + parent_pos = (i * 10.0, 0.0, 0.0) + parent_quat = (0.7071068, 0.0, 0.0, 0.7071068) # 90 deg around Z + sim_utils.create_prim( + f"/World/Parent_{i}", "Xform", translation=parent_pos, orientation=parent_quat, stage=stage + ) + # Create child prims + sim_utils.create_prim(f"/World/Parent_{i}/Child", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + # Create view for children + view = XformPrimView("/World/Parent_.*/Child", device=device) + + # Set world poses for children + desired_world_positions = torch.tensor([[5.0, 5.0, 0.0], [15.0, 5.0, 0.0], [25.0, 5.0, 0.0]], device=device) + desired_world_orientations = torch.tensor( + [[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], device=device + ) + + view.set_world_poses(desired_world_positions, desired_world_orientations) + + # Get world poses back + retrieved_positions, retrieved_orientations = view.get_world_poses() + + # Should match desired world poses + torch.testing.assert_close(retrieved_positions, desired_world_positions, atol=1e-4, rtol=0) + try: + torch.testing.assert_close(retrieved_orientations, desired_world_orientations, atol=1e-4, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -desired_world_orientations, atol=1e-4, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_local_poses(device): + """Test setting local poses in XformPrimView.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create parent + sim_utils.create_prim("/World/Parent", "Xform", translation=(5.0, 5.0, 5.0), stage=stage) + + # Create children + num_prims = 4 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Parent/Child_{i}", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Parent/Child_.*", device=device) + + # Set new local poses + new_translations = torch.tensor([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0], [4.0, 4.0, 4.0]], device=device) + new_orientations = torch.tensor( + [ + [1.0, 0.0, 0.0, 0.0], + [0.7071068, 0.0, 0.0, 0.7071068], + [0.7071068, 0.7071068, 0.0, 0.0], + [0.9238795, 0.3826834, 0.0, 0.0], + ], + device=device, + ) + + view.set_local_poses(new_translations, new_orientations) + + # Get local poses back + retrieved_translations, retrieved_orientations = view.get_local_poses() + + # Verify they match + torch.testing.assert_close(retrieved_translations, new_translations, atol=1e-5, rtol=0) + try: + torch.testing.assert_close(retrieved_orientations, new_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -new_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_local_poses_only_translations(device): + """Test setting only local translations.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create parent and children with specific orientations + sim_utils.create_prim("/World/Parent", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + initial_quat = (0.7071068, 0.0, 0.0, 0.7071068) + + for i in range(3): + sim_utils.create_prim( + f"/World/Parent/Child_{i}", "Xform", translation=(0.0, 0.0, 0.0), orientation=initial_quat, stage=stage + ) + + # Create view + view = XformPrimView("/World/Parent/Child_.*", device=device) + + # Get initial orientations + _, initial_orientations = view.get_local_poses() + + # Set only translations + new_translations = torch.tensor([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]], device=device) + view.set_local_poses(translations=new_translations, orientations=None) + + # Get poses back + retrieved_translations, retrieved_orientations = view.get_local_poses() + + # Translations should be updated + torch.testing.assert_close(retrieved_translations, new_translations, atol=1e-5, rtol=0) + + # Orientations should be unchanged + try: + torch.testing.assert_close(retrieved_orientations, initial_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -initial_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_scales(device): + """Test setting scales in XformPrimView.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 5 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", scale=(1.0, 1.0, 1.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Set new scales + new_scales = torch.tensor( + [[2.0, 2.0, 2.0], [1.0, 2.0, 3.0], [0.5, 0.5, 0.5], [3.0, 1.0, 2.0], [1.5, 1.5, 1.5]], device=device + ) + + view.set_scales(new_scales) + + # Get scales back + retrieved_scales = view.get_scales() + + # Verify they match + torch.testing.assert_close(retrieved_scales, new_scales, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_set_visibility(device): + """Test toggling visibility multiple times.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 3 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Initial state: all visible + visibility = view.get_visibility() + assert torch.all(visibility), "All should be visible initially" + + # Make all invisible + view.set_visibility(torch.zeros(num_prims, dtype=torch.bool, device=device)) + visibility = view.get_visibility() + assert not torch.any(visibility), "All should be invisible" + + # Make all visible again + view.set_visibility(torch.ones(num_prims, dtype=torch.bool, device=device)) + visibility = view.get_visibility() + assert torch.all(visibility), "All should be visible again" + + # Toggle individual prims + view.set_visibility(torch.tensor([False], dtype=torch.bool, device=device), indices=[1]) + visibility = view.get_visibility() + assert visibility[0] and not visibility[1] and visibility[2], "Only middle prim should be invisible" + + +""" +Tests - Index Handling. +""" + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +@pytest.mark.parametrize("index_type", ["list", "torch_tensor", "slice_none"]) +@pytest.mark.parametrize("method", ["world_poses", "local_poses", "scales", "visibility"]) +def test_index_types_get_methods(device, index_type, method): + """Test that getter methods work with different index types.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims based on method type + num_prims = 10 + if method == "local_poses": + # Create parent and children for local poses + sim_utils.create_prim("/World/Parent", "Xform", translation=(10.0, 0.0, 0.0), stage=stage) + for i in range(num_prims): + sim_utils.create_prim( + f"/World/Parent/Child_{i}", "Xform", translation=(float(i), float(i) * 0.5, 0.0), stage=stage + ) + view = XformPrimView("/World/Parent/Child_.*", device=device) + elif method == "scales": + # Create prims with different scales + for i in range(num_prims): + scale = (1.0 + i * 0.5, 1.0 + i * 0.3, 1.0 + i * 0.2) + sim_utils.create_prim(f"/World/Object_{i}", "Xform", scale=scale, stage=stage) + view = XformPrimView("/World/Object_.*", device=device) + else: # world_poses + # Create prims with different positions + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + view = XformPrimView("/World/Object_.*", device=device) + + # Get all data as reference + if method == "world_poses": + all_data1, all_data2 = view.get_world_poses() + elif method == "local_poses": + all_data1, all_data2 = view.get_local_poses() + elif method == "scales": + all_data1 = view.get_scales() + all_data2 = None + else: # visibility + all_data1 = view.get_visibility() + all_data2 = None + + # Prepare indices + target_indices_base = [2, 5, 7] + indices, target_indices = _prepare_indices(index_type, target_indices_base, num_prims, device) + + # Get subset + if method == "world_poses": + subset_data1, subset_data2 = view.get_world_poses(indices=indices) # type: ignore[arg-type] + elif method == "local_poses": + subset_data1, subset_data2 = view.get_local_poses(indices=indices) # type: ignore[arg-type] + elif method == "scales": + subset_data1 = view.get_scales(indices=indices) # type: ignore[arg-type] + subset_data2 = None + else: # visibility + subset_data1 = view.get_visibility(indices=indices) # type: ignore[arg-type] + subset_data2 = None + + # Verify shapes + expected_count = len(target_indices) + if method == "visibility": + assert subset_data1.shape == (expected_count,) + else: + assert subset_data1.shape == (expected_count, 3) + if subset_data2 is not None: + assert subset_data2.shape == (expected_count, 4) + + # Verify values + target_indices_tensor = torch.tensor(target_indices, dtype=torch.int64, device=device) + torch.testing.assert_close(subset_data1, all_data1[target_indices_tensor], atol=1e-5, rtol=0) + if subset_data2 is not None and all_data2 is not None: + torch.testing.assert_close(subset_data2, all_data2[target_indices_tensor], atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +@pytest.mark.parametrize("index_type", ["list", "torch_tensor", "slice_none"]) +@pytest.mark.parametrize("method", ["world_poses", "local_poses", "scales", "visibility"]) +def test_index_types_set_methods(device, index_type, method): + """Test that setter methods work with different index types.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims based on method type + num_prims = 10 + if method == "local_poses": + # Create parent and children for local poses + sim_utils.create_prim("/World/Parent", "Xform", translation=(5.0, 5.0, 0.0), stage=stage) + for i in range(num_prims): + sim_utils.create_prim(f"/World/Parent/Child_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + view = XformPrimView("/World/Parent/Child_.*", device=device) + else: # world_poses or scales + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + view = XformPrimView("/World/Object_.*", device=device) + + # Get initial data + if method == "world_poses": + initial_data1, initial_data2 = view.get_world_poses() + elif method == "local_poses": + initial_data1, initial_data2 = view.get_local_poses() + elif method == "scales": + initial_data1 = view.get_scales() + initial_data2 = None + else: # visibility + initial_data1 = view.get_visibility() + initial_data2 = None + + # Prepare indices + target_indices_base = [2, 5, 7] + indices, target_indices = _prepare_indices(index_type, target_indices_base, num_prims, device) + + # Prepare new data + num_to_set = len(target_indices) + if method in ["world_poses", "local_poses"]: + new_data1 = torch.randn(num_to_set, 3, device=device) * 10.0 + new_data2 = torch.tensor([[1.0, 0.0, 0.0, 0.0]] * num_to_set, dtype=torch.float32, device=device) + elif method == "scales": + new_data1 = torch.rand(num_to_set, 3, device=device) * 2.0 + 0.5 + new_data2 = None + else: # visibility + # Set to False to test change (default is True) + new_data1 = torch.zeros(num_to_set, dtype=torch.bool, device=device) + new_data2 = None + + # Set data + if method == "world_poses": + view.set_world_poses(positions=new_data1, orientations=new_data2, indices=indices) # type: ignore[arg-type] + elif method == "local_poses": + view.set_local_poses(translations=new_data1, orientations=new_data2, indices=indices) # type: ignore[arg-type] + elif method == "scales": + view.set_scales(scales=new_data1, indices=indices) # type: ignore[arg-type] + else: # visibility + view.set_visibility(visibility=new_data1, indices=indices) # type: ignore[arg-type] + + # Get all data after update + if method == "world_poses": + updated_data1, updated_data2 = view.get_world_poses() + elif method == "local_poses": + updated_data1, updated_data2 = view.get_local_poses() + elif method == "scales": + updated_data1 = view.get_scales() + updated_data2 = None + else: # visibility + updated_data1 = view.get_visibility() + updated_data2 = None + + # Verify that specified indices were updated + for i, target_idx in enumerate(target_indices): + torch.testing.assert_close(updated_data1[target_idx], new_data1[i], atol=1e-5, rtol=0) + if new_data2 is not None and updated_data2 is not None: + try: + torch.testing.assert_close(updated_data2[target_idx], new_data2[i], atol=1e-5, rtol=0) + except AssertionError: + # Account for quaternion sign ambiguity + torch.testing.assert_close(updated_data2[target_idx], -new_data2[i], atol=1e-5, rtol=0) + + # Verify that other indices were NOT updated (only for non-slice(None) cases) + if index_type != "slice_none": + for i in range(num_prims): + if i not in target_indices: + torch.testing.assert_close(updated_data1[i], initial_data1[i], atol=1e-5, rtol=0) + if initial_data2 is not None and updated_data2 is not None: + try: + torch.testing.assert_close(updated_data2[i], initial_data2[i], atol=1e-5, rtol=0) + except AssertionError: + # Account for quaternion sign ambiguity + torch.testing.assert_close(updated_data2[i], -initial_data2[i], atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_indices_single_element(device): + """Test with a single index.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 5 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Test with single index + indices = [3] + positions, orientations = view.get_world_poses(indices=indices) + + # Verify shapes + assert positions.shape == (1, 3) + assert orientations.shape == (1, 4) + + # Set pose for single index + new_position = torch.tensor([[100.0, 200.0, 300.0]], device=device) + view.set_world_poses(positions=new_position, indices=indices) + + # Verify it was set + retrieved_positions, _ = view.get_world_poses(indices=indices) + torch.testing.assert_close(retrieved_positions, new_position, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_indices_out_of_order(device): + """Test with indices provided in non-sequential order.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 10 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Use out-of-order indices + indices = [7, 2, 9, 0, 5] + new_positions = torch.tensor( + [[7.0, 0.0, 0.0], [2.0, 0.0, 0.0], [9.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 0.0, 0.0]], device=device + ) + + # Set poses with out-of-order indices + view.set_world_poses(positions=new_positions, indices=indices) + + # Get all poses + all_positions, _ = view.get_world_poses() + + # Verify each index got the correct value + expected_x_values = [0.0, 0.0, 2.0, 0.0, 0.0, 5.0, 0.0, 7.0, 0.0, 9.0] + for i in range(num_prims): + assert abs(all_positions[i, 0].item() - expected_x_values[i]) < 1e-5 + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_indices_with_only_positions_or_orientations(device): + """Test indices work correctly when setting only positions or only orientations.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 5 + for i in range(num_prims): + sim_utils.create_prim( + f"/World/Object_{i}", "Xform", translation=(0.0, 0.0, 0.0), orientation=(1.0, 0.0, 0.0, 0.0), stage=stage + ) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get initial poses + initial_positions, initial_orientations = view.get_world_poses() + + # Set only positions for specific indices + indices = [1, 3] + new_positions = torch.tensor([[10.0, 0.0, 0.0], [30.0, 0.0, 0.0]], device=device) + view.set_world_poses(positions=new_positions, orientations=None, indices=indices) + + # Get updated poses + updated_positions, updated_orientations = view.get_world_poses() + + # Verify positions updated for indices 1 and 3, others unchanged + torch.testing.assert_close(updated_positions[1], new_positions[0], atol=1e-5, rtol=0) + torch.testing.assert_close(updated_positions[3], new_positions[1], atol=1e-5, rtol=0) + torch.testing.assert_close(updated_positions[0], initial_positions[0], atol=1e-5, rtol=0) + + # Verify all orientations unchanged + try: + torch.testing.assert_close(updated_orientations, initial_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(updated_orientations, -initial_orientations, atol=1e-5, rtol=0) + + # Now set only orientations for different indices + indices2 = [0, 4] + new_orientations = torch.tensor([[0.7071068, 0.0, 0.0, 0.7071068], [0.7071068, 0.7071068, 0.0, 0.0]], device=device) + view.set_world_poses(positions=None, orientations=new_orientations, indices=indices2) + + # Get final poses + final_positions, final_orientations = view.get_world_poses() + + # Verify positions unchanged from previous step + torch.testing.assert_close(final_positions, updated_positions, atol=1e-5, rtol=0) + + # Verify orientations updated for indices 0 and 4 + try: + torch.testing.assert_close(final_orientations[0], new_orientations[0], atol=1e-5, rtol=0) + torch.testing.assert_close(final_orientations[4], new_orientations[1], atol=1e-5, rtol=0) + except AssertionError: + # Account for quaternion sign ambiguity + torch.testing.assert_close(final_orientations[0], -new_orientations[0], atol=1e-5, rtol=0) + torch.testing.assert_close(final_orientations[4], -new_orientations[1], atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_index_type_none_equivalent_to_all(device): + """Test that indices=None is equivalent to getting/setting all prims.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create prims + num_prims = 6 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Object_{i}", "Xform", translation=(float(i), 0.0, 0.0), stage=stage) + + # Create view + view = XformPrimView("/World/Object_.*", device=device) + + # Get poses with indices=None + pos_none, quat_none = view.get_world_poses(indices=None) + + # Get poses with no argument (default) + pos_default, quat_default = view.get_world_poses() + + # Get poses with slice(None) + pos_slice, quat_slice = view.get_world_poses(indices=slice(None)) # type: ignore[arg-type] + + # All should be equivalent + torch.testing.assert_close(pos_none, pos_default, atol=1e-10, rtol=0) + torch.testing.assert_close(quat_none, quat_default, atol=1e-10, rtol=0) + torch.testing.assert_close(pos_none, pos_slice, atol=1e-10, rtol=0) + torch.testing.assert_close(quat_none, quat_slice, atol=1e-10, rtol=0) + + # Test the same for set operations + new_positions = torch.randn(num_prims, 3, device=device) * 10.0 + new_orientations = torch.tensor([[1.0, 0.0, 0.0, 0.0]] * num_prims, dtype=torch.float32, device=device) + + # Set with indices=None + view.set_world_poses(positions=new_positions, orientations=new_orientations, indices=None) + pos_after_none, quat_after_none = view.get_world_poses() + + # Reset + view.set_world_poses(positions=torch.zeros(num_prims, 3, device=device), indices=None) + + # Set with slice(None) + view.set_world_poses(positions=new_positions, orientations=new_orientations, indices=slice(None)) # type: ignore[arg-type] + pos_after_slice, quat_after_slice = view.get_world_poses() + + # Should be equivalent + torch.testing.assert_close(pos_after_none, pos_after_slice, atol=1e-5, rtol=0) + torch.testing.assert_close(quat_after_none, quat_after_slice, atol=1e-5, rtol=0) + + +""" +Tests - Integration. +""" + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_with_franka_robots(device): + """Test XformPrimView with real Franka robot USD assets.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Load Franka robot assets + franka_usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/FrankaRobotics/FrankaPanda/franka.usd" + + # Add two Franka robots to the stage + sim_utils.create_prim("/World/Franka_1", "Xform", usd_path=franka_usd_path, stage=stage) + sim_utils.create_prim("/World/Franka_2", "Xform", usd_path=franka_usd_path, stage=stage) + + # Create view for both Frankas + frankas_view = XformPrimView("/World/Franka_.*", device=device) + + # Verify count + assert frankas_view.count == 2 + + # Get initial world poses (should be at origin) + initial_positions, initial_orientations = frankas_view.get_world_poses() + + # Verify initial positions are at origin + expected_initial_positions = torch.zeros(2, 3, device=device) + torch.testing.assert_close(initial_positions, expected_initial_positions, atol=1e-5, rtol=0) + + # Verify initial orientations are identity + expected_initial_orientations = torch.tensor([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], device=device) + try: + torch.testing.assert_close(initial_orientations, expected_initial_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(initial_orientations, -expected_initial_orientations, atol=1e-5, rtol=0) + + # Set new world poses + new_positions = torch.tensor([[10.0, 10.0, 0.0], [-40.0, -40.0, 0.0]], device=device) + # 90° rotation around Z axis for first, -90° for second + new_orientations = torch.tensor( + [[0.7071068, 0.0, 0.0, 0.7071068], [0.7071068, 0.0, 0.0, -0.7071068]], device=device + ) + + frankas_view.set_world_poses(positions=new_positions, orientations=new_orientations) + + # Get poses back and verify + retrieved_positions, retrieved_orientations = frankas_view.get_world_poses() + + torch.testing.assert_close(retrieved_positions, new_positions, atol=1e-5, rtol=0) + try: + torch.testing.assert_close(retrieved_orientations, new_orientations, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(retrieved_orientations, -new_orientations, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_with_nested_targets(device): + """Test with nested frame/target structure similar to Isaac Sim tests.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create frames and targets + for i in range(1, 4): + sim_utils.create_prim(f"/World/Frame_{i}", "Xform", stage=stage) + sim_utils.create_prim(f"/World/Frame_{i}/Target", "Xform", stage=stage) + + # Create views + frames_view = XformPrimView("/World/Frame_.*", device=device) + targets_view = XformPrimView("/World/Frame_.*/Target", device=device) + + assert frames_view.count == 3 + assert targets_view.count == 3 + + # Set local poses for frames + frame_translations = torch.tensor([[0.0, 0.0, 0.0], [0.0, 10.0, 5.0], [0.0, 3.0, 5.0]], device=device) + frames_view.set_local_poses(translations=frame_translations) + + # Set local poses for targets + target_translations = torch.tensor([[0.0, 20.0, 10.0], [0.0, 30.0, 20.0], [0.0, 50.0, 10.0]], device=device) + targets_view.set_local_poses(translations=target_translations) + + # Get world poses of targets + world_positions, _ = targets_view.get_world_poses() + + # Expected world positions are frame_translation + target_translation + expected_positions = torch.tensor([[0.0, 20.0, 10.0], [0.0, 40.0, 25.0], [0.0, 53.0, 15.0]], device=device) + + torch.testing.assert_close(world_positions, expected_positions, atol=1e-5, rtol=0) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_visibility_with_hierarchy(device): + """Test visibility with parent-child hierarchy and inheritance.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + stage = sim_utils.get_current_stage() + + # Create parent and children + sim_utils.create_prim("/World/Parent", "Xform", stage=stage) + + num_children = 4 + for i in range(num_children): + sim_utils.create_prim(f"/World/Parent/Child_{i}", "Xform", stage=stage) + + # Create views for both parent and children + parent_view = XformPrimView("/World/Parent", device=device) + children_view = XformPrimView("/World/Parent/Child_.*", device=device) + + # Verify parent and all children are visible initially + parent_visibility = parent_view.get_visibility() + children_visibility = children_view.get_visibility() + assert parent_visibility[0], "Parent should be visible initially" + assert torch.all(children_visibility), "All children should be visible initially" + + # Make some children invisible directly + new_visibility = torch.tensor([True, False, True, False], dtype=torch.bool, device=device) + children_view.set_visibility(new_visibility) + + # Verify the visibility changes + retrieved_visibility = children_view.get_visibility() + torch.testing.assert_close(retrieved_visibility, new_visibility) + + # Make all children visible again + children_view.set_visibility(torch.ones(num_children, dtype=torch.bool, device=device)) + all_visible = children_view.get_visibility() + assert torch.all(all_visible), "All children should be visible again" + + # Now test parent visibility inheritance: + # Make parent invisible + parent_view.set_visibility(torch.tensor([False], dtype=torch.bool, device=device)) + + # Verify parent is invisible + parent_visibility = parent_view.get_visibility() + assert not parent_visibility[0], "Parent should be invisible" + + # Verify children are also invisible (due to parent being invisible) + children_visibility = children_view.get_visibility() + assert not torch.any(children_visibility), "All children should be invisible when parent is invisible" + + # Make parent visible again + parent_view.set_visibility(torch.tensor([True], dtype=torch.bool, device=device)) + + # Verify parent is visible + parent_visibility = parent_view.get_visibility() + assert parent_visibility[0], "Parent should be visible again" + + # Verify children are also visible again + children_visibility = children_view.get_visibility() + assert torch.all(children_visibility), "All children should be visible again when parent is visible" + + +""" +Tests - Comparison with Isaac Sim Implementation. +""" + + +def test_compare_get_world_poses_with_isaacsim(): + """Compare get_world_poses with Isaac Sim's implementation.""" + stage = sim_utils.get_current_stage() + + # Check if Isaac Sim is available + if _IsaacSimXformPrimView is None: + pytest.skip("Isaac Sim is not available") + + # Create prims with various poses + num_prims = 10 + for i in range(num_prims): + pos = (i * 2.0, i * 0.5, i * 1.5) + # Vary orientations + if i % 3 == 0: + quat = (1.0, 0.0, 0.0, 0.0) # Identity + elif i % 3 == 1: + quat = (0.7071068, 0.0, 0.0, 0.7071068) # 90 deg around Z + else: + quat = (0.7071068, 0.7071068, 0.0, 0.0) # 90 deg around X + sim_utils.create_prim(f"/World/Env_{i}/Object", "Xform", translation=pos, orientation=quat, stage=stage) + + pattern = "/World/Env_.*/Object" + + # Create both views + isaaclab_view = XformPrimView(pattern, device="cpu") + isaacsim_view = _IsaacSimXformPrimView(pattern, reset_xform_properties=False) + + # Get world poses from both + isaaclab_pos, isaaclab_quat = isaaclab_view.get_world_poses() + isaacsim_pos, isaacsim_quat = isaacsim_view.get_world_poses() + + # Convert Isaac Sim results to torch tensors if needed + if not isinstance(isaacsim_pos, torch.Tensor): + isaacsim_pos = torch.tensor(isaacsim_pos, dtype=torch.float32) + if not isinstance(isaacsim_quat, torch.Tensor): + isaacsim_quat = torch.tensor(isaacsim_quat, dtype=torch.float32) + + # Compare results + torch.testing.assert_close(isaaclab_pos, isaacsim_pos, atol=1e-5, rtol=0) + + # Compare quaternions (account for sign ambiguity) + try: + torch.testing.assert_close(isaaclab_quat, isaacsim_quat, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(isaaclab_quat, -isaacsim_quat, atol=1e-5, rtol=0) + + +def test_compare_set_world_poses_with_isaacsim(): + """Compare set_world_poses with Isaac Sim's implementation.""" + stage = sim_utils.get_current_stage() + + # Check if Isaac Sim is available + if _IsaacSimXformPrimView is None: + pytest.skip("Isaac Sim is not available") + + # Create prims + num_prims = 8 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Env_{i}/Object", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + pattern = "/World/Env_.*/Object" + + # Create both views + isaaclab_view = XformPrimView(pattern, device="cpu") + isaacsim_view = _IsaacSimXformPrimView(pattern, reset_xform_properties=False) + + # Generate new poses + new_positions = torch.randn(num_prims, 3) * 10.0 + new_orientations = torch.tensor([[1.0, 0.0, 0.0, 0.0]] * num_prims, dtype=torch.float32) + + # Set poses using both implementations + isaaclab_view.set_world_poses(new_positions.clone(), new_orientations.clone()) + isaacsim_view.set_world_poses(new_positions.clone(), new_orientations.clone()) + + # Get poses back from both + isaaclab_pos, isaaclab_quat = isaaclab_view.get_world_poses() + isaacsim_pos, isaacsim_quat = isaacsim_view.get_world_poses() + + # Convert Isaac Sim results to torch tensors if needed + if not isinstance(isaacsim_pos, torch.Tensor): + isaacsim_pos = torch.tensor(isaacsim_pos, dtype=torch.float32) + if not isinstance(isaacsim_quat, torch.Tensor): + isaacsim_quat = torch.tensor(isaacsim_quat, dtype=torch.float32) + + # Compare results - both implementations should produce the same world poses + torch.testing.assert_close(isaaclab_pos, isaacsim_pos, atol=1e-4, rtol=0) + try: + torch.testing.assert_close(isaaclab_quat, isaacsim_quat, atol=1e-4, rtol=0) + except AssertionError: + torch.testing.assert_close(isaaclab_quat, -isaacsim_quat, atol=1e-4, rtol=0) + + +def test_compare_get_local_poses_with_isaacsim(): + """Compare get_local_poses with Isaac Sim's implementation.""" + stage = sim_utils.get_current_stage() + + # Check if Isaac Sim is available + if _IsaacSimXformPrimView is None: + pytest.skip("Isaac Sim is not available") + + # Create hierarchical prims + num_prims = 5 + for i in range(num_prims): + # Create parent + sim_utils.create_prim(f"/World/Env_{i}", "Xform", translation=(i * 5.0, 0.0, 0.0), stage=stage) + # Create child with local pose + local_pos = (1.0, float(i), 0.0) + local_quat = (1.0, 0.0, 0.0, 0.0) if i % 2 == 0 else (0.7071068, 0.0, 0.0, 0.7071068) + sim_utils.create_prim( + f"/World/Env_{i}/Object", "Xform", translation=local_pos, orientation=local_quat, stage=stage + ) + + pattern = "/World/Env_.*/Object" + + # Create both views + isaaclab_view = XformPrimView(pattern, device="cpu") + isaacsim_view = _IsaacSimXformPrimView(pattern, reset_xform_properties=False) + + # Get local poses from both + isaaclab_trans, isaaclab_quat = isaaclab_view.get_local_poses() + isaacsim_trans, isaacsim_quat = isaacsim_view.get_local_poses() + + # Convert Isaac Sim results to torch tensors if needed + if not isinstance(isaacsim_trans, torch.Tensor): + isaacsim_trans = torch.tensor(isaacsim_trans, dtype=torch.float32) + if not isinstance(isaacsim_quat, torch.Tensor): + isaacsim_quat = torch.tensor(isaacsim_quat, dtype=torch.float32) + + # Compare results + torch.testing.assert_close(isaaclab_trans, isaacsim_trans, atol=1e-5, rtol=0) + try: + torch.testing.assert_close(isaaclab_quat, isaacsim_quat, atol=1e-5, rtol=0) + except AssertionError: + torch.testing.assert_close(isaaclab_quat, -isaacsim_quat, atol=1e-5, rtol=0) + + +def test_compare_set_local_poses_with_isaacsim(): + """Compare set_local_poses with Isaac Sim's implementation.""" + stage = sim_utils.get_current_stage() + + # Check if Isaac Sim is available + if _IsaacSimXformPrimView is None: + pytest.skip("Isaac Sim is not available") + + # Create hierarchical prims + num_prims = 6 + for i in range(num_prims): + sim_utils.create_prim(f"/World/Env_{i}", "Xform", translation=(i * 3.0, 0.0, 0.0), stage=stage) + sim_utils.create_prim(f"/World/Env_{i}/Object", "Xform", translation=(0.0, 0.0, 0.0), stage=stage) + + pattern = "/World/Env_.*/Object" + + # Create both views + isaaclab_view = XformPrimView(pattern, device="cpu") + isaacsim_view = _IsaacSimXformPrimView(pattern, reset_xform_properties=False) + + # Generate new local poses + new_translations = torch.randn(num_prims, 3) * 5.0 + new_orientations = torch.tensor( + [[1.0, 0.0, 0.0, 0.0], [0.7071068, 0.0, 0.0, 0.7071068]] * (num_prims // 2), dtype=torch.float32 + ) + + # Set local poses using both implementations + isaaclab_view.set_local_poses(new_translations.clone(), new_orientations.clone()) + isaacsim_view.set_local_poses(new_translations.clone(), new_orientations.clone()) + + # Get local poses back from both + isaaclab_trans, isaaclab_quat = isaaclab_view.get_local_poses() + isaacsim_trans, isaacsim_quat = isaacsim_view.get_local_poses() + + # Convert Isaac Sim results to torch tensors if needed + if not isinstance(isaacsim_trans, torch.Tensor): + isaacsim_trans = torch.tensor(isaacsim_trans, dtype=torch.float32) + if not isinstance(isaacsim_quat, torch.Tensor): + isaacsim_quat = torch.tensor(isaacsim_quat, dtype=torch.float32) + + # Compare results + torch.testing.assert_close(isaaclab_trans, isaacsim_trans, atol=1e-4, rtol=0) + try: + torch.testing.assert_close(isaaclab_quat, isaacsim_quat, atol=1e-4, rtol=0) + except AssertionError: + torch.testing.assert_close(isaaclab_quat, -isaacsim_quat, atol=1e-4, rtol=0) diff --git a/source/isaaclab/test/terrains/check_height_field_subterrains.py b/source/isaaclab/test/terrains/check_height_field_subterrains.py new file mode 100644 index 0000000000000000000000000000000000000000..972d4dc2288409640e9b65f396cb7f6c36d7d787 --- /dev/null +++ b/source/isaaclab/test/terrains/check_height_field_subterrains.py @@ -0,0 +1,267 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +parser = argparse.ArgumentParser(description="Generate terrains using trimesh") +parser.add_argument( + "--headless", action="store_true", default=False, help="Don't create a window to display each output." +) +args_cli = parser.parse_args() + +from isaaclab.app import AppLauncher + +# launch omniverse app +# note: we only need to do this because of `TerrainImporter` which uses Omniverse functions +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import os + +import trimesh + +import isaaclab.terrains.height_field as hf_gen +from isaaclab.terrains.utils import color_meshes_by_height + + +def test_random_uniform_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfRandomUniformTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + border_width=0.0, + noise_range=(-0.05, 0.05), + noise_step=0.005, + downsampled_scale=0.2, + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "random_uniform_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Random Uniform Terrain") + + +def test_pyramid_sloped_terrain(difficulty: float, inverted: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfPyramidSlopedTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + border_width=0.0, + slope_range=(0.0, 0.4), + platform_width=1.5, + inverted=inverted, + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if inverted: + caption = "Inverted Pyramid Sloped Terrain" + filename = "inverted_pyramid_sloped_terrain.jpg" + else: + caption = "Pyramid Sloped Terrain" + filename = "pyramid_sloped_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_pyramid_stairs_terrain(difficulty: float, inverted: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfPyramidStairsTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + border_width=0.0, + platform_width=1.5, + step_width=0.301, + step_height_range=(0.05, 0.23), + inverted=inverted, + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if inverted: + caption = "Inverted Pyramid Stairs Terrain" + filename = "inverted_pyramid_stairs_terrain.jpg" + else: + caption = "Pyramid Stairs Terrain" + filename = "pyramid_stairs_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_discrete_obstacles_terrain(difficulty: float, obstacle_height_mode: str, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfDiscreteObstaclesTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + border_width=0.0, + num_obstacles=50, + obstacle_height_mode=obstacle_height_mode, + obstacle_width_range=(0.25, 0.75), + obstacle_height_range=(1.0, 2.0), + platform_width=1.5, + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if obstacle_height_mode == "choice": + caption = "Discrete Obstacles Terrain (Sampled Height)" + filename = "discrete_obstacles_terrain_choice.jpg" + elif obstacle_height_mode == "fixed": + caption = "Discrete Obstacles Terrain (Fixed Height)" + filename = "discrete_obstacles_terrain_fixed.jpg" + else: + raise ValueError(f"Unknown obstacle height mode: {obstacle_height_mode}") + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_wave_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfWaveTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + border_width=0.0, + num_waves=5, + amplitude_range=(0.5, 1.0), + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "wave_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Wave Terrain") + + +def test_stepping_stones_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = hf_gen.HfSteppingStonesTerrainCfg( + size=(8.0, 8.0), + horizontal_scale=0.1, + vertical_scale=0.005, + platform_width=1.5, + border_width=0.0, + stone_width_range=(0.25, 1.575), + stone_height_max=0.2, + stone_distance_range=(0.05, 0.1), + holes_depth=-2.0, + ) + # generate terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "stepping_stones_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Stepping Stones Terrain") + + +def main(): + # Create directory to dump results + test_dir = os.path.dirname(os.path.abspath(__file__)) + output_dir = os.path.join(test_dir, "output", "terrains", "height_field") + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + # Read headless mode + headless = args_cli.headless + # generate terrains + test_random_uniform_terrain(difficulty=0.25, output_dir=output_dir, headless=headless) + test_pyramid_sloped_terrain(difficulty=0.25, inverted=False, output_dir=output_dir, headless=headless) + test_pyramid_sloped_terrain(difficulty=0.25, inverted=True, output_dir=output_dir, headless=headless) + test_pyramid_stairs_terrain(difficulty=0.25, inverted=False, output_dir=output_dir, headless=headless) + test_pyramid_stairs_terrain(difficulty=0.25, inverted=True, output_dir=output_dir, headless=headless) + test_discrete_obstacles_terrain( + difficulty=0.25, obstacle_height_mode="choice", output_dir=output_dir, headless=headless + ) + test_discrete_obstacles_terrain( + difficulty=0.25, obstacle_height_mode="fixed", output_dir=output_dir, headless=headless + ) + test_wave_terrain(difficulty=0.25, output_dir=output_dir, headless=headless) + test_stepping_stones_terrain(difficulty=1.0, output_dir=output_dir, headless=headless) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/terrains/check_mesh_subterrains.py b/source/isaaclab/test/terrains/check_mesh_subterrains.py new file mode 100644 index 0000000000000000000000000000000000000000..593b00e8fa233353a13156f0268aa8312236c9ff --- /dev/null +++ b/source/isaaclab/test/terrains/check_mesh_subterrains.py @@ -0,0 +1,431 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +parser = argparse.ArgumentParser(description="Generate terrains using trimesh") +parser.add_argument( + "--headless", action="store_true", default=False, help="Don't create a window to display each output." +) +args_cli = parser.parse_args() + +from isaaclab.app import AppLauncher + +# launch omniverse app +# note: we only need to do this because of `TerrainImporter` which uses Omniverse functions +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import argparse +import os + +import trimesh + +import isaaclab.terrains.trimesh as mesh_gen +from isaaclab.terrains.utils import color_meshes_by_height + + +def test_flat_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshPlaneTerrainCfg(size=(8.0, 8.0)) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "flat_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Flat Terrain") + + +def test_pyramid_stairs_terrain(difficulty: float, holes: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshPyramidStairsTerrainCfg( + size=(8.0, 8.0), + border_width=0.2, + step_width=0.3, + step_height_range=(0.05, 0.23), + platform_width=1.5, + holes=holes, + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if holes: + caption = "Pyramid Stairs Terrain with Holes" + filename = "pyramid_stairs_terrain_with_holes.jpg" + else: + caption = "Pyramid Stairs Terrain" + filename = "pyramid_stairs_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_inverted_pyramid_stairs_terrain(difficulty: float, holes: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshInvertedPyramidStairsTerrainCfg( + size=(8.0, 8.0), + border_width=0.2, + step_width=0.3, + step_height_range=(0.05, 0.23), + platform_width=1.5, + holes=holes, + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if holes: + caption = "Inverted Pyramid Stairs Terrain with Holes" + filename = "inverted_pyramid_stairs_terrain_with_holes.jpg" + else: + caption = "Inverted Pyramid Stairs Terrain" + filename = "inverted_pyramid_stairs_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_random_grid_terrain(difficulty: float, holes: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshRandomGridTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + grid_width=0.75, + grid_height_range=(0.025, 0.2), + holes=holes, + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if holes: + caption = "Random Grid Terrain with Holes" + filename = "random_grid_terrain_with_holes.jpg" + else: + caption = "Random Grid Terrain" + filename = "random_grid_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_rails_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshRailsTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + rail_thickness_range=(0.05, 0.1), + rail_height_range=(0.05, 0.3), + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "rails_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Rail Terrain") + + +def test_pit_terrain(difficulty: float, double_pit: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshPitTerrainCfg( + size=(8.0, 8.0), platform_width=1.5, pit_depth_range=(0.05, 1.1), double_pit=double_pit + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if double_pit: + caption = "Pit Terrain with Two Levels" + filename = "pit_terrain_with_two_levels.jpg" + else: + caption = "Pit Terrain" + filename = "pit_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_box_terrain(difficulty: float, double_box: bool, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshBoxTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + box_height_range=(0.05, 0.2), + double_box=double_box, + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # resolve file name + if double_box: + caption = "Box Terrain with Two Levels" + filename = "box_terrain_with_two_boxes.jpg" + else: + caption = "Box Terrain" + filename = "box_terrain.jpg" + # write the image to a file + with open(os.path.join(output_dir, filename), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=caption) + + +def test_gap_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshGapTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + gap_width_range=(0.05, 1.1), + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "gap_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Gap Terrain") + + +def test_floating_ring_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshFloatingRingTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + ring_height_range=(0.4, 1.0), + ring_width_range=(0.5, 1.0), + ring_thickness=0.05, + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "floating_ring_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Floating Ring Terrain") + + +def test_star_terrain(difficulty: float, output_dir: str, headless: bool): + # parameters for the terrain + cfg = mesh_gen.MeshStarTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + num_bars=5, + bar_width_range=(0.5, 1.0), + bar_height_range=(0.05, 0.2), + ) + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, "star_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption="Star Terrain") + + +def test_repeated_objects_terrain( + difficulty: float, object_type: str, output_dir: str, headless: bool, provide_as_string: bool = False +): + # parameters for the terrain + if object_type == "pyramid": + cfg = mesh_gen.MeshRepeatedPyramidsTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + abs_height_noise=(-0.5, 0.5), + object_params_start=mesh_gen.MeshRepeatedPyramidsTerrainCfg.ObjectCfg( + num_objects=40, height=0.05, radius=0.6, max_yx_angle=0.0, degrees=True + ), + object_params_end=mesh_gen.MeshRepeatedPyramidsTerrainCfg.ObjectCfg( + num_objects=80, height=0.15, radius=0.6, max_yx_angle=60.0, degrees=True + ), + ) + elif object_type == "box": + cfg = mesh_gen.MeshRepeatedBoxesTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + abs_height_noise=(-0.5, 0.5), + object_params_start=mesh_gen.MeshRepeatedBoxesTerrainCfg.ObjectCfg( + num_objects=40, height=0.05, size=(0.6, 0.6), max_yx_angle=0.0, degrees=True + ), + object_params_end=mesh_gen.MeshRepeatedBoxesTerrainCfg.ObjectCfg( + num_objects=80, height=0.15, size=(0.6, 0.6), max_yx_angle=60.0, degrees=True + ), + ) + elif object_type == "cylinder": + cfg = mesh_gen.MeshRepeatedCylindersTerrainCfg( + size=(8.0, 8.0), + platform_width=1.5, + abs_height_noise=(-0.5, 0.5), + object_params_start=mesh_gen.MeshRepeatedCylindersTerrainCfg.ObjectCfg( + num_objects=40, height=0.05, radius=0.6, max_yx_angle=0.0, degrees=True + ), + object_params_end=mesh_gen.MeshRepeatedCylindersTerrainCfg.ObjectCfg( + num_objects=80, height=0.15, radius=0.6, max_yx_angle=60.0, degrees=True + ), + ) + else: + raise ValueError(f"Invalid object type for repeated objects terrain: {object_type}") + + # provide object_type as string (check that the import works) + if provide_as_string: + cfg.object_type = object_type + + # generate the terrain + meshes, origin = cfg.function(difficulty=difficulty, cfg=cfg) + # add colors to the meshes based on the height + colored_mesh = color_meshes_by_height(meshes) + # add a marker for the origin + origin_transform = trimesh.transformations.translation_matrix(origin) + origin_marker = trimesh.creation.axis(origin_size=0.1, transform=origin_transform) + # visualize the meshes + scene = trimesh.Scene([colored_mesh, origin_marker]) + # save the scene to a png file + data = scene.save_image(resolution=(640, 480)) + # write the image to a file + with open(os.path.join(output_dir, f"repeated_objects_{object_type}_terrain.jpg"), "wb") as f: + f.write(data) + # show the scene in a window + if not headless: + trimesh.viewer.SceneViewer(scene=scene, caption=f"Repeated Objects Terrain: {object_type}") + + +def main(): + # Create directory to dump results + test_dir = os.path.dirname(os.path.abspath(__file__)) + output_dir = os.path.join(test_dir, "output", "terrains", "trimesh") + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + # Read headless mode + headless = args_cli.headless + # generate terrains + test_flat_terrain(difficulty=0.0, output_dir=output_dir, headless=headless) + test_pyramid_stairs_terrain(difficulty=0.75, holes=False, output_dir=output_dir, headless=headless) + test_pyramid_stairs_terrain(difficulty=0.75, holes=True, output_dir=output_dir, headless=headless) + test_inverted_pyramid_stairs_terrain(difficulty=0.75, holes=False, output_dir=output_dir, headless=headless) + test_inverted_pyramid_stairs_terrain(difficulty=0.75, holes=True, output_dir=output_dir, headless=headless) + test_random_grid_terrain(difficulty=0.75, holes=False, output_dir=output_dir, headless=headless) + test_random_grid_terrain(difficulty=0.75, holes=True, output_dir=output_dir, headless=headless) + test_star_terrain(difficulty=0.75, output_dir=output_dir, headless=headless) + test_repeated_objects_terrain(difficulty=0.75, object_type="pyramid", output_dir=output_dir, headless=headless) + test_repeated_objects_terrain(difficulty=0.75, object_type="cylinder", output_dir=output_dir, headless=headless) + test_repeated_objects_terrain(difficulty=0.75, object_type="box", output_dir=output_dir, headless=headless) + test_repeated_objects_terrain( + difficulty=0.75, object_type="cylinder", provide_as_string=True, output_dir=output_dir, headless=headless + ) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/terrains/check_terrain_importer.py b/source/isaaclab/test/terrains/check_terrain_importer.py new file mode 100644 index 0000000000000000000000000000000000000000..fdc305a07af807e9465ff4f034d91092d2a80364 --- /dev/null +++ b/source/isaaclab/test/terrains/check_terrain_importer.py @@ -0,0 +1,210 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script shows how to use the terrain generator from the Isaac Lab framework. + +The terrains are generated using the :class:`TerrainGenerator` class and imported using the :class:`TerrainImporter` +class. The terrains can be imported from a file or generated procedurally. + +Example usage: + +.. code-block:: bash + + # generate terrain + # -- use physics sphere mesh + ./isaaclab.sh -p source/isaaclab/test/terrains/check_terrain_importer.py --terrain_type generator + # -- usd usd sphere geom + ./isaaclab.sh -p source/isaaclab/test/terrains/check_terrain_importer.py --terrain_type generator --geom_sphere + + # usd terrain + ./isaaclab.sh -p source/isaaclab/test/terrains/check_terrain_importer.py --terrain_type usd + + # plane terrain + ./isaaclab.sh -p source/isaaclab/test/terrains/check_terrain_importer.py --terrain_type plane +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +# isaaclab +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="This script shows how to use the terrain importer.") +parser.add_argument("--geom_sphere", action="store_true", default=False, help="Whether to use sphere mesh or shape.") +parser.add_argument( + "--terrain_type", + type=str, + choices=["generator", "usd", "plane"], + default="generator", + help="Type of terrain to import. Can be 'generator' or 'usd' or 'plane'.", +) +parser.add_argument( + "--color_scheme", + type=str, + default="height", + choices=["height", "random", "none"], + help="The color scheme to use for the generated terrain.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +import numpy as np + +import omni.kit +import omni.kit.commands +from isaacsim.core.api.materials import PhysicsMaterial +from isaacsim.core.api.materials.preview_surface import PreviewSurface +from isaacsim.core.api.objects import DynamicSphere +from isaacsim.core.api.simulation_context import SimulationContext +from isaacsim.core.cloner import GridCloner +from isaacsim.core.prims import RigidPrim, SingleGeometryPrim, SingleRigidPrim +from isaacsim.core.utils.extensions import enable_extension +from isaacsim.core.utils.viewports import set_camera_view + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.terrains.terrain_importer import TerrainImporter +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +enable_extension("omni.kit.primitive.mesh") + + +def main(): + """Generates a terrain from isaaclab.""" + + # Load kit helper + sim_params = { + "use_gpu": True, + "use_gpu_pipeline": True, + "use_flatcache": True, + "use_fabric": True, + "enable_scene_query_support": True, + } + sim = SimulationContext( + physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0" + ) + # Set main camera + set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5]) + + # Parameters + num_balls = 2048 + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + sim_utils.define_prim("/World/envs/env_0") + + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + num_envs=2048, + env_spacing=3.0, + prim_path="/World/ground", + max_init_terrain_level=None, + terrain_type=args_cli.terrain_type, + terrain_generator=ROUGH_TERRAINS_CFG.replace(curriculum=True, color_scheme=args_cli.color_scheme), + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # Define the scene + # -- Light + cfg = sim_utils.DistantLightCfg(intensity=1000.0) + cfg.func("/World/Light", cfg) + # -- Ball + if args_cli.geom_sphere: + # -- Ball physics + _ = DynamicSphere( + prim_path="/World/envs/env_0/ball", translation=np.array([0.0, 0.0, 5.0]), mass=0.5, radius=0.25 + ) + else: + # -- Ball geometry + cube_prim_path = omni.kit.commands.execute("CreateMeshPrimCommand", prim_type="Sphere")[1] + sim_utils.move_prim(cube_prim_path, "/World/envs/env_0/ball") + # -- Ball physics + SingleRigidPrim( + prim_path="/World/envs/env_0/ball", mass=0.5, scale=(0.5, 0.5, 0.5), translation=(0.0, 0.0, 0.5) + ) + SingleGeometryPrim(prim_path="/World/envs/env_0/ball", collision=True) + # -- Ball material + sphere_geom = SingleGeometryPrim(prim_path="/World/envs/env_0/ball", collision=True) + visual_material = PreviewSurface(prim_path="/World/Looks/ballColorMaterial", color=np.asarray([0.0, 0.0, 1.0])) + physics_material = PhysicsMaterial( + prim_path="/World/Looks/ballPhysicsMaterial", + dynamic_friction=1.0, + static_friction=0.2, + restitution=0.0, + ) + sphere_geom.set_collision_approximation("convexHull") + sphere_geom.apply_visual_material(visual_material) + sphere_geom.apply_physics_material(physics_material) + + # Clone the scene + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_balls) + cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"] + ) + + # Set ball positions over terrain origins + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + # cache initial state of the balls + ball_initial_positions = terrain_importer.env_origins + ball_initial_positions[:, 2] += 5.0 + # set initial poses + # note: setting here writes to USD :) + ball_view.set_world_poses(positions=ball_initial_positions) + + # Play simulator + sim.reset() + # Initialize the ball views for physics simulation + ball_view.initialize() + ball_initial_velocities = ball_view.get_velocities() + + # Create a counter for resetting the scene + step_count = 0 + # Simulate physics + while simulation_app.is_running(): + # If simulation is stopped, then exit. + if sim.is_stopped(): + break + # If simulation is paused, then skip. + if not sim.is_playing(): + sim.step() + continue + # Reset the scene + if step_count % 500 == 0: + # reset the balls + ball_view.set_world_poses(positions=ball_initial_positions) + ball_view.set_velocities(ball_initial_velocities) + # reset the counter + step_count = 0 + # Step simulation + sim.step() + # Update counter + step_count += 1 + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/test/terrains/test_terrain_generator.py b/source/isaaclab/test/terrains/test_terrain_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..804176458cb56db5601992a7af8fccda4387308c --- /dev/null +++ b/source/isaaclab/test/terrains/test_terrain_generator.py @@ -0,0 +1,162 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import os +import shutil + +import numpy as np +import pytest +import torch + +from isaaclab.terrains import FlatPatchSamplingCfg, TerrainGenerator, TerrainGeneratorCfg +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.utils.seed import configure_seed + + +@pytest.fixture +def output_dir(): + """Create directory to dump results.""" + test_dir = os.path.dirname(os.path.abspath(__file__)) + output_dir = os.path.join(test_dir, "output", "generator") + yield output_dir + # Cleanup + if os.path.exists(output_dir): + shutil.rmtree(output_dir) + + +def test_generation(output_dir): + """Generates assorted terrains and tests that the resulting mesh has the expected size.""" + # create terrain generator + cfg = ROUGH_TERRAINS_CFG + terrain_generator = TerrainGenerator(cfg=cfg) + + # print terrain generator info + print(terrain_generator) + + # get size from mesh bounds + bounds = terrain_generator.terrain_mesh.bounds + actualSize = abs(bounds[1] - bounds[0]) + # compute the expected size + expectedSizeX = cfg.size[0] * cfg.num_rows + 2 * cfg.border_width + expectedSizeY = cfg.size[1] * cfg.num_cols + 2 * cfg.border_width + + # check if the size is as expected + assert actualSize[0] == pytest.approx(expectedSizeX) + assert actualSize[1] == pytest.approx(expectedSizeY) + + +@pytest.mark.parametrize("use_global_seed", [True, False]) +@pytest.mark.parametrize("seed", [20, 40, 80]) +def test_generation_reproducibility(use_global_seed, seed): + """Generates assorted terrains and tests that the resulting mesh is reproducible. + + We check both scenarios where the seed is set globally only and when it is set both globally and locally. + Setting only locally is not tested as it is not supported. + """ + # set initial seed + configure_seed(seed) + + # create terrain generator + cfg = ROUGH_TERRAINS_CFG + cfg.use_cache = False + cfg.seed = seed if use_global_seed else None + terrain_generator = TerrainGenerator(cfg=cfg) + + # keep a copy of the generated terrain mesh + terrain_mesh_1 = terrain_generator.terrain_mesh.copy() + + # set seed again + configure_seed(seed) + + # create terrain generator + terrain_generator = TerrainGenerator(cfg=cfg) + + # keep a copy of the generated terrain mesh + terrain_mesh_2 = terrain_generator.terrain_mesh.copy() + + # check if the meshes are equal + np.testing.assert_allclose( + terrain_mesh_1.vertices, terrain_mesh_2.vertices, atol=1e-5, err_msg="Vertices are not equal" + ) + np.testing.assert_allclose(terrain_mesh_1.faces, terrain_mesh_2.faces, atol=1e-5, err_msg="Faces are not equal") + + +@pytest.mark.parametrize("curriculum", [True, False]) +def test_generation_cache(output_dir, curriculum): + """Generate the terrain and check that caching works. + + When caching is enabled, the terrain should be generated only once and the same terrain should be returned + when the terrain generator is created again. + """ + # create terrain generator with cache enabled + cfg: TerrainGeneratorCfg = ROUGH_TERRAINS_CFG + cfg.use_cache = True + cfg.seed = 0 + cfg.cache_dir = output_dir + cfg.curriculum = curriculum + terrain_generator = TerrainGenerator(cfg=cfg) + # keep a copy of the generated terrain mesh + terrain_mesh_1 = terrain_generator.terrain_mesh.copy() + + # check cache exists and is equal to the number of terrains + # with curriculum, all sub-terrains are uniquely generated + hash_ids_1 = set(os.listdir(cfg.cache_dir)) + assert os.listdir(cfg.cache_dir) + + # set a random seed to disturb the process + # this is to ensure that the seed inside the terrain generator makes deterministic results + configure_seed(12456) + + # create terrain generator with cache enabled + terrain_generator = TerrainGenerator(cfg=cfg) + # keep a copy of the generated terrain mesh + terrain_mesh_2 = terrain_generator.terrain_mesh.copy() + + # check no new terrain is generated + hash_ids_2 = set(os.listdir(cfg.cache_dir)) + assert len(hash_ids_1) == len(hash_ids_2) + assert hash_ids_1 == hash_ids_2 + + # check if the mesh is the same + # check they don't point to the same object + assert terrain_mesh_1 is not terrain_mesh_2 + + # check if the meshes are equal + np.testing.assert_allclose( + terrain_mesh_1.vertices, terrain_mesh_2.vertices, atol=1e-5, err_msg="Vertices are not equal" + ) + np.testing.assert_allclose(terrain_mesh_1.faces, terrain_mesh_2.faces, atol=1e-5, err_msg="Faces are not equal") + + +def test_terrain_flat_patches(): + """Test the flat patches generation.""" + # create terrain generator + cfg = ROUGH_TERRAINS_CFG + # add flat patch configuration + for _, sub_terrain_cfg in cfg.sub_terrains.items(): + sub_terrain_cfg.flat_patch_sampling = { + "root_spawn": FlatPatchSamplingCfg(num_patches=8, patch_radius=0.5, max_height_diff=0.05), + "target_spawn": FlatPatchSamplingCfg(num_patches=5, patch_radius=0.35, max_height_diff=0.05), + } + # generate terrain + terrain_generator = TerrainGenerator(cfg=cfg) + + # check if flat patches are generated + assert terrain_generator.flat_patches + # check the size of the flat patches + assert terrain_generator.flat_patches["root_spawn"].shape == (cfg.num_rows, cfg.num_cols, 8, 3) + assert terrain_generator.flat_patches["target_spawn"].shape == (cfg.num_rows, cfg.num_cols, 5, 3) + # check that no flat patches are zero + for _, flat_patches in terrain_generator.flat_patches.items(): + assert not torch.allclose(flat_patches, torch.zeros_like(flat_patches)) diff --git a/source/isaaclab/test/terrains/test_terrain_importer.py b/source/isaaclab/test/terrains/test_terrain_importer.py new file mode 100644 index 0000000000000000000000000000000000000000..05ed76e0811ea5ecfe1de235d899055ca8787b20 --- /dev/null +++ b/source/isaaclab/test/terrains/test_terrain_importer.py @@ -0,0 +1,334 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from typing import Literal + +import numpy as np +import pytest +import torch +import trimesh + +import omni.kit +import omni.kit.commands +from isaacsim.core.api.materials import PhysicsMaterial, PreviewSurface +from isaacsim.core.api.objects import DynamicSphere +from isaacsim.core.cloner import GridCloner +from isaacsim.core.prims import RigidPrim, SingleGeometryPrim, SingleRigidPrim +from isaacsim.core.utils.extensions import enable_extension +from pxr import Usd, UsdGeom + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.sim import PreviewSurfaceCfg, SimulationContext, build_simulation_context, get_first_matching_child_prim +from isaaclab.terrains import TerrainImporter, TerrainImporterCfg +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("env_spacing", [1.0, 4.325, 8.0]) +@pytest.mark.parametrize("num_envs", [1, 4, 125, 379, 1024]) +def test_grid_clone_env_origins(device, env_spacing, num_envs): + """Tests that env origins are consistent when computed using the TerrainImporter and IsaacSim GridCloner.""" + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # create terrain importer + terrain_importer_cfg = TerrainImporterCfg( + num_envs=num_envs, + env_spacing=env_spacing, + prim_path="/World/ground", + terrain_type="plane", # for flat ground, origins are in grid + terrain_generator=None, + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + # obtain env origins using terrain importer + terrain_importer_origins = terrain_importer.env_origins + + # obtain env origins using grid cloner + grid_cloner_origins = _obtain_grid_cloner_env_origins(num_envs, env_spacing, stage=sim.stage, device=sim.device) + + # check if the env origins are the same + torch.testing.assert_close(terrain_importer_origins, grid_cloner_origins, rtol=1e-5, atol=1e-5) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_terrain_generation(device): + """Generates assorted terrains and tests that the resulting mesh has the correct size.""" + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + max_init_terrain_level=None, + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + num_envs=1, + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # check if mesh prim path exists + mesh_prim_path = terrain_importer.cfg.prim_path + "/terrain" + assert mesh_prim_path in terrain_importer.terrain_prim_paths + + # obtain underling mesh + mesh = _obtain_collision_mesh(mesh_prim_path, mesh_type="Mesh") + assert mesh is not None + + # calculate expected size from config + cfg = terrain_importer.cfg.terrain_generator + assert cfg is not None + expectedSizeX = cfg.size[0] * cfg.num_rows + 2 * cfg.border_width + expectedSizeY = cfg.size[1] * cfg.num_cols + 2 * cfg.border_width + + # get size from mesh bounds + bounds = mesh.bounds + actualSize = abs(bounds[1] - bounds[0]) + + assert actualSize[0] == pytest.approx(expectedSizeX) + assert actualSize[1] == pytest.approx(expectedSizeY) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("use_custom_material", [True, False]) +def test_plane(device, use_custom_material): + """Generates a plane and tests that the resulting mesh has the correct size.""" + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + + # create custom material + visual_material = PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)) if use_custom_material else None + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + num_envs=1, + env_spacing=1.0, + visual_material=visual_material, + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # check if mesh prim path exists + mesh_prim_path = terrain_importer.cfg.prim_path + "/terrain" + assert mesh_prim_path in terrain_importer.terrain_prim_paths + + # obtain underling mesh + mesh = _obtain_collision_mesh(mesh_prim_path, mesh_type="Plane") + assert mesh is None + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_usd(device): + """Imports terrain from a usd and tests that the resulting mesh has the correct size.""" + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="usd", + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd", + num_envs=1, + env_spacing=1.0, + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # check if mesh prim path exists + mesh_prim_path = terrain_importer.cfg.prim_path + "/terrain" + assert mesh_prim_path in terrain_importer.terrain_prim_paths + + # obtain underling mesh + mesh = _obtain_collision_mesh(mesh_prim_path, mesh_type="Mesh") + assert mesh is not None + + # expect values from USD file + expectedSizeX = 96 + expectedSizeY = 96 + + # get size from mesh bounds + bounds = mesh.bounds + actualSize = abs(bounds[1] - bounds[0]) + + assert actualSize[0] == pytest.approx(expectedSizeX) + assert actualSize[1] == pytest.approx(expectedSizeY) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_ball_drop(device): + """Generates assorted terrains and spheres created as meshes. + + Tests that spheres fall onto terrain and do not pass through it. This ensures that the triangle mesh + collision works as expected. + """ + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Create a scene with rough terrain and balls + _populate_scene(geom_sphere=False, sim=sim) + + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + + # Play simulator + sim.reset() + # Initialize the ball views for physics simulation + ball_view.initialize() + + # Run simulator + for _ in range(500): + sim.step(render=False) + + # Ball may have some small non-zero velocity if the roll on terrain <~.2 + # If balls fall through terrain velocity is much higher ~82.0 + max_velocity_z = torch.max(torch.abs(ball_view.get_linear_velocities()[:, 2])) + assert max_velocity_z.item() <= 0.5 + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_ball_drop_geom_sphere(device): + """Generates assorted terrains and geom spheres. + + Tests that spheres fall onto terrain and do not pass through it. This ensures that the sphere collision + works as expected. + """ + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # Create a scene with rough terrain and balls + # TODO: Currently the test fails with geom spheres, need to investigate with the PhysX team. + # Setting the geom_sphere as False to pass the test. This test should be enabled once + # the issue is fixed. + _populate_scene(geom_sphere=False, sim=sim) + + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + + # Play simulator + sim.reset() + # Initialize the ball views for physics simulation + ball_view.initialize() + + # Run simulator + for _ in range(500): + sim.step(render=False) + + # Ball may have some small non-zero velocity if the roll on terrain <~.2 + # If balls fall through terrain velocity is much higher ~82.0 + max_velocity_z = torch.max(torch.abs(ball_view.get_linear_velocities()[:, 2])) + assert max_velocity_z.item() <= 0.5 + + +def _obtain_collision_mesh(mesh_prim_path: str, mesh_type: Literal["Mesh", "Plane"]) -> trimesh.Trimesh | None: + """Get the collision mesh from the terrain.""" + # traverse the prim and get the collision mesh + mesh_prim = get_first_matching_child_prim(mesh_prim_path, lambda prim: prim.GetTypeName() == mesh_type) + # check it is valid + assert mesh_prim.IsValid() + + if mesh_prim.GetTypeName() == "Mesh": + # cast into UsdGeomMesh + mesh_prim = UsdGeom.Mesh(mesh_prim) + # store the mesh + vertices = np.asarray(mesh_prim.GetPointsAttr().Get()) + faces = np.asarray(mesh_prim.GetFaceVertexIndicesAttr().Get()).reshape(-1, 3) + return trimesh.Trimesh(vertices=vertices, faces=faces) + else: + return None + + +def _obtain_grid_cloner_env_origins(num_envs: int, env_spacing: float, stage: Usd.Stage, device: str) -> torch.Tensor: + """Obtain the env origins generated by IsaacSim GridCloner (grid_cloner.py).""" + # create grid cloner + cloner = GridCloner(spacing=env_spacing) + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs) + # create source prim + stage.DefinePrim("/World/envs/env_0", "Xform") + # clone envs using grid cloner + env_origins = cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True) + # return as tensor + return torch.tensor(env_origins, dtype=torch.float32, device=device) + + +def _populate_scene(sim: SimulationContext, num_balls: int = 2048, geom_sphere: bool = False): + """Create a scene with terrain and randomly spawned balls. + + The spawned balls are either USD Geom Spheres or are USD Meshes. We check against both these to make sure + both USD-shape and USD-mesh collisions work as expected. + """ + # Handler for terrains importing + terrain_importer_cfg = terrain_gen.TerrainImporterCfg( + prim_path="/World/ground", + max_init_terrain_level=None, + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + num_envs=num_balls, + ) + terrain_importer = TerrainImporter(terrain_importer_cfg) + + # Create interface to clone the scene + cloner = GridCloner(spacing=2.0) + cloner.define_base_env("/World/envs") + # Everything under the namespace "/World/envs/env_0" will be cloned + sim.stage.DefinePrim("/World/envs/env_0", "Xform") + + # Define the scene + # -- Ball + if geom_sphere: + # -- Ball physics + _ = DynamicSphere( + prim_path="/World/envs/env_0/ball", translation=np.array([0.0, 0.0, 5.0]), mass=0.5, radius=0.25 + ) + else: + # -- Ball geometry + enable_extension("omni.kit.primitive.mesh") + cube_prim_path = omni.kit.commands.execute("CreateMeshPrimCommand", prim_type="Sphere")[1] + sim_utils.move_prim(cube_prim_path, "/World/envs/env_0/ball") + # -- Ball physics + SingleRigidPrim( + prim_path="/World/envs/env_0/ball", mass=0.5, scale=(0.5, 0.5, 0.5), translation=(0.0, 0.0, 0.5) + ) + SingleGeometryPrim(prim_path="/World/envs/env_0/ball", collision=True) + + # -- Ball material + sphere_geom = SingleGeometryPrim(prim_path="/World/envs/env_0/ball", collision=True) + visual_material = PreviewSurface(prim_path="/World/Looks/ballColorMaterial", color=np.asarray([0.0, 0.0, 1.0])) + physics_material = PhysicsMaterial( + prim_path="/World/Looks/ballPhysicsMaterial", + dynamic_friction=1.0, + static_friction=0.2, + restitution=0.0, + ) + sphere_geom.set_collision_approximation("convexHull") + sphere_geom.apply_visual_material(visual_material) + sphere_geom.apply_physics_material(physics_material) + + # Clone the scene + cloner.define_base_env("/World/envs") + envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_balls) + cloner.clone( + source_prim_path="/World/envs/env_0", + prim_paths=envs_prim_paths, + replicate_physics=True, + ) + physics_scene_path = sim.get_physics_context().prim_path + cloner.filter_collisions( + physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"] + ) + + # Set ball positions over terrain origins + # Create a view over all the balls + ball_view = RigidPrim("/World/envs/env_.*/ball", reset_xform_properties=False) + # cache initial state of the balls + ball_initial_positions = terrain_importer.env_origins + ball_initial_positions[:, 2] += 5.0 + # set initial poses + # note: setting here writes to USD :) + ball_view.set_world_poses(positions=ball_initial_positions) diff --git a/source/isaaclab/test/utils/test_assets.py b/source/isaaclab/test/utils/test_assets.py new file mode 100644 index 0000000000000000000000000000000000000000..483c7d93d9feed1008a61716a6733a89a5b25b85 --- /dev/null +++ b/source/isaaclab/test/utils/test_assets.py @@ -0,0 +1,52 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import isaaclab.utils.assets as assets_utils + + +def test_nucleus_connection(): + """Test checking the Nucleus connection.""" + # check nucleus connection + assert assets_utils.NUCLEUS_ASSET_ROOT_DIR is not None + + +def test_check_file_path_nucleus(): + """Test checking a file path on the Nucleus server.""" + # robot file path + usd_path = f"{assets_utils.ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + # check file path + assert assets_utils.check_file_path(usd_path) == 2 + + +def test_check_file_path_invalid(): + """Test checking an invalid file path.""" + # robot file path + usd_path = f"{assets_utils.ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_xyz.usd" + # check file path + assert assets_utils.check_file_path(usd_path) == 0 + + +def test_check_usd_path_with_timeout(): + """Test checking a USD path with timeout.""" + # robot file path + usd_path = f"{assets_utils.ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd" + # check file path + assert assets_utils.check_usd_path_with_timeout(usd_path) is True + + # invalid file path + usd_path = f"{assets_utils.ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_xyz.usd" + # check file path + assert assets_utils.check_usd_path_with_timeout(usd_path) is False diff --git a/source/isaaclab/test/utils/test_circular_buffer.py b/source/isaaclab/test/utils/test_circular_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..52a2c16829d86ea28a0d51a67c10118aa22d4f42 --- /dev/null +++ b/source/isaaclab/test/utils/test_circular_buffer.py @@ -0,0 +1,182 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import pytest +import torch + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app in headless mode +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows from here.""" + +from isaaclab.utils import CircularBuffer + + +@pytest.fixture +def circular_buffer(): + """Create a circular buffer for testing.""" + max_len = 5 + batch_size = 3 + device = "cpu" + return CircularBuffer(max_len, batch_size, device) + + +def test_initialization(circular_buffer): + """Test initialization of the circular buffer.""" + assert circular_buffer.max_length == 5 + assert circular_buffer.batch_size == 3 + assert circular_buffer.device == "cpu" + assert circular_buffer.current_length.tolist() == [0, 0, 0] + + +def test_reset(circular_buffer): + """Test resetting the circular buffer.""" + # append some data + data = torch.ones((circular_buffer.batch_size, 2), device=circular_buffer.device) + circular_buffer.append(data) + # reset the buffer + circular_buffer.reset() + + # check if the buffer has zeros entries + assert circular_buffer.current_length.tolist() == [0, 0, 0] + + +def test_reset_subset(circular_buffer): + """Test resetting a subset of batches in the circular buffer.""" + data1 = torch.ones((circular_buffer.batch_size, 2), device=circular_buffer.device) + data2 = 2.0 * data1.clone() + data3 = 3.0 * data1.clone() + circular_buffer.append(data1) + circular_buffer.append(data2) + # reset the buffer + reset_batch_id = 1 + circular_buffer.reset(batch_ids=[reset_batch_id]) + # check that correct batch is reset + assert circular_buffer.current_length.tolist()[reset_batch_id] == 0 + # Append new set of data + circular_buffer.append(data3) + # check if the correct number of entries are in each batch + expected_length = [3, 3, 3] + expected_length[reset_batch_id] = 1 + assert circular_buffer.current_length.tolist() == expected_length + # check that all entries of the recently reset and appended batch are equal + for i in range(circular_buffer.max_length): + torch.testing.assert_close(circular_buffer.buffer[reset_batch_id, 0], circular_buffer.buffer[reset_batch_id, i]) + + +def test_append_and_retrieve(circular_buffer): + """Test appending and retrieving data from the circular buffer.""" + # append some data + data1 = torch.tensor([[1, 1], [1, 1], [1, 1]], device=circular_buffer.device) + data2 = torch.tensor([[2, 2], [2, 2], [2, 2]], device=circular_buffer.device) + + circular_buffer.append(data1) + circular_buffer.append(data2) + + assert circular_buffer.current_length.tolist() == [2, 2, 2] + + retrieved_data = circular_buffer[torch.tensor([0, 0, 0], device=circular_buffer.device)] + assert torch.equal(retrieved_data, data2) + + retrieved_data = circular_buffer[torch.tensor([1, 1, 1], device=circular_buffer.device)] + assert torch.equal(retrieved_data, data1) + + +def test_buffer_overflow(circular_buffer): + """Test buffer overflow. + + If the buffer is full, the oldest data should be overwritten. + """ + # add data in ascending order + for count in range(circular_buffer.max_length + 2): + data = torch.full((circular_buffer.batch_size, 4), count, device=circular_buffer.device) + circular_buffer.append(data) + + # check buffer length is correct + assert circular_buffer.current_length.tolist() == [ + circular_buffer.max_length, + circular_buffer.max_length, + circular_buffer.max_length, + ] + + # retrieve most recent data + key = torch.tensor([0, 0, 0], device=circular_buffer.device) + retrieved_data = circular_buffer[key] + expected_data = torch.full_like(data, circular_buffer.max_length + 1) + + assert torch.equal(retrieved_data, expected_data) + + # retrieve the oldest data + key = torch.tensor( + [circular_buffer.max_length - 1, circular_buffer.max_length - 1, circular_buffer.max_length - 1], + device=circular_buffer.device, + ) + retrieved_data = circular_buffer[key] + expected_data = torch.full_like(data, 2) + + assert torch.equal(retrieved_data, expected_data) + + +def test_empty_buffer_access(circular_buffer): + """Test accessing an empty buffer.""" + with pytest.raises(RuntimeError): + circular_buffer[torch.tensor([0, 0, 0], device=circular_buffer.device)] + + +def test_invalid_batch_size(circular_buffer): + """Test appending data with an invalid batch size.""" + data = torch.ones((circular_buffer.batch_size + 1, 2), device=circular_buffer.device) + with pytest.raises(ValueError): + circular_buffer.append(data) + + with pytest.raises(ValueError): + circular_buffer[torch.tensor([0, 0], device=circular_buffer.device)] + + +def test_key_greater_than_pushes(circular_buffer): + """Test retrieving data with a key greater than the number of pushes. + + In this case, the oldest data should be returned. + """ + data1 = torch.tensor([[1, 1], [1, 1], [1, 1]], device=circular_buffer.device) + data2 = torch.tensor([[2, 2], [2, 2], [2, 2]], device=circular_buffer.device) + + circular_buffer.append(data1) + circular_buffer.append(data2) + + retrieved_data = circular_buffer[torch.tensor([5, 5, 5], device=circular_buffer.device)] + assert torch.equal(retrieved_data, data1) + + +def test_return_buffer_prop(circular_buffer): + """Test retrieving the whole buffer for correct size and contents. + Returning the whole buffer should have the shape [batch_size,max_len,data.shape[1:]] + """ + num_overflow = 2 + for i in range(circular_buffer.max_length + num_overflow): + data = torch.tensor([[i]], device=circular_buffer.device).repeat(3, 2) + circular_buffer.append(data) + + retrieved_buffer = circular_buffer.buffer + # check shape + assert retrieved_buffer.shape == torch.Size([circular_buffer.batch_size, circular_buffer.max_length, 2]) + # check that batch is first dimension + torch.testing.assert_close(retrieved_buffer[0], retrieved_buffer[1]) + # check oldest + torch.testing.assert_close( + retrieved_buffer[:, 0], torch.tensor([[num_overflow]], device=circular_buffer.device).repeat(3, 2) + ) + # check most recent + torch.testing.assert_close( + retrieved_buffer[:, -1], + torch.tensor([[circular_buffer.max_length + num_overflow - 1]], device=circular_buffer.device).repeat(3, 2), + ) + # check that it is returned oldest first + for idx in range(circular_buffer.max_length - 1): + assert torch.all(torch.le(retrieved_buffer[:, idx], retrieved_buffer[:, idx + 1])) diff --git a/source/isaaclab/test/utils/test_configclass.py b/source/isaaclab/test/utils/test_configclass.py new file mode 100644 index 0000000000000000000000000000000000000000..0c024be03f3b891b60bc5412519c93b014e96fc3 --- /dev/null +++ b/source/isaaclab/test/utils/test_configclass.py @@ -0,0 +1,1079 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +# NOTE: While we don't actually use the simulation app in this test, we still need to launch it +# because warp is only available in the context of a running simulation +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import copy +import os +from collections.abc import Callable +from dataclasses import MISSING, asdict, field +from functools import wraps +from typing import Any, ClassVar + +import pytest +import torch + +from isaaclab.utils.configclass import configclass +from isaaclab.utils.dict import class_to_dict, dict_to_md5_hash, update_class_from_dict +from isaaclab.utils.io import dump_yaml, load_yaml + +""" +Mock classes and functions. +""" + + +def dummy_function1() -> int: + """Dummy function 1.""" + return 1 + + +def dummy_function2() -> int: + """Dummy function 2.""" + return 2 + + +def dummy_wrapper(func): + """Decorator for wrapping function.""" + + @wraps(func) + def wrapper(): + return func() + 1 + + return wrapper + + +@dummy_wrapper +def wrapped_dummy_function3(): + """Dummy function 3.""" + return 3 + + +@dummy_wrapper +def wrapped_dummy_function4(): + """Dummy function 4.""" + return 4 + + +class DummyClass: + """Dummy class.""" + + def __init__(self): + """Initialize dummy class.""" + self.a = 1 + self.b = 2 + + +""" +Dummy configuration: Basic +""" + + +def double(x): + """Dummy function.""" + return 2 * x + + +@configclass +class ModifierCfg: + params: dict[str, Any] = {"A": 1, "B": 2} + + +@configclass +class ViewerCfg: + eye: list = [7.5, 7.5, 7.5] # field missing on purpose + lookat: list = field(default_factory=lambda: [0.0, 0.0, 0.0]) + + +@configclass +class EnvCfg: + num_envs: int = double(28) # uses function for assignment + episode_length: int = 2000 + viewer: ViewerCfg = ViewerCfg() + + +@configclass +class RobotDefaultStateCfg: + pos = (0.0, 0.0, 0.0) # type annotation missing on purpose (immutable) + rot: tuple = (1.0, 0.0, 0.0, 0.0) + dof_pos: tuple = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0) + dof_vel = [0.0, 0.0, 0.0, 0.0, 0.0, 1.0] # type annotation missing on purpose (mutable) + + +@configclass +class BasicDemoCfg: + """Dummy configuration class.""" + + device_id: int = 0 + env: EnvCfg = EnvCfg() + robot_default_state: RobotDefaultStateCfg = RobotDefaultStateCfg() + list_config = [ModifierCfg(), ModifierCfg(params={"A": 3, "B": 4})] + + +@configclass +class BasicDemoPostInitCfg: + """Dummy configuration class.""" + + device_id: int = 0 + env: EnvCfg = EnvCfg() + robot_default_state: RobotDefaultStateCfg = RobotDefaultStateCfg() + + def __post_init__(self): + self.device_id = 1 + self.add_variable = 3 + + +@configclass +class BasicDemoTorchCfg: + """Dummy configuration class with a torch tensor .""" + + some_number: int = 0 + some_tensor: torch.Tensor = torch.Tensor([1, 2, 3]) + + +@configclass +class BasicActuatorCfg: + """Dummy configuration class for ActuatorBase config.""" + + joint_names_expr: list[str] = ["some_string"] + joint_parameter_lookup: list[list[float]] = [[1, 2, 3], [4, 5, 6]] + stiffness: float = 1.0 + damping: float = 2.0 + + +""" +Dummy configuration to check type annotations ordering. +""" + + +@configclass +class TypeAnnotationOrderingDemoCfg: + """Config class with type annotations.""" + + anymal: RobotDefaultStateCfg = RobotDefaultStateCfg() + unitree: RobotDefaultStateCfg = RobotDefaultStateCfg() + franka: RobotDefaultStateCfg = RobotDefaultStateCfg() + + +@configclass +class NonTypeAnnotationOrderingDemoCfg: + """Config class without type annotations.""" + + anymal = RobotDefaultStateCfg() + unitree = RobotDefaultStateCfg() + franka = RobotDefaultStateCfg() + + +@configclass +class InheritedNonTypeAnnotationOrderingDemoCfg(NonTypeAnnotationOrderingDemoCfg): + """Inherited config class without type annotations.""" + + pass + + +""" +Dummy configuration: Inheritance +""" + + +@configclass +class ParentDemoCfg: + """Dummy parent configuration with missing fields.""" + + a: int = MISSING # add new missing field + b = 2 # type annotation missing on purpose + c: RobotDefaultStateCfg = MISSING # add new missing field + m: RobotDefaultStateCfg = RobotDefaultStateCfg() # Add class type with defaults + j: list[str] = MISSING # add new missing field + i: list[str] = MISSING # add new missing field + func: Callable = MISSING # add new missing field + + +@configclass +class ChildADemoCfg(ParentDemoCfg): + """Dummy child configuration with missing fields.""" + + func = dummy_function1 # set default value for missing field + c = RobotDefaultStateCfg() # set default value for missing field + + func_2: Callable = MISSING # add new missing field + d: int = MISSING # add new missing field + k: list[str] = ["c", "d"] + e: ViewerCfg = MISSING # add new missing field + + dummy_class = DummyClass + + def __post_init__(self): + self.b = 3 # change value of existing field + self.m.rot = (2.0, 0.0, 0.0, 0.0) # change value of default + self.i = ["a", "b"] # change value of existing field + + +@configclass +class ChildBDemoCfg(ParentDemoCfg): + """Dummy child configuration to test inheritance across instances.""" + + a = 100 # set default value for missing field + j = ["3", "4"] # set default value for missing field + + def __post_init__(self): + self.b = 8 # change value of existing field + self.i = ["1", "2"] # change value of existing field + + +@configclass +class ChildChildDemoCfg(ChildADemoCfg): + """Dummy child configuration with missing fields.""" + + func_2 = dummy_function2 + d = 2 # set default value for missing field + + def __post_init__(self): + """Post initialization function.""" + super().__post_init__() + self.b = 4 # set default value for missing field + self.f = "new" # add new missing field + + +""" +Configuration with class inside. +""" + + +@configclass +class DummyClassCfg: + """Dummy class configuration with class type.""" + + class_name_1: type = DummyClass + class_name_2: type[DummyClass] = DummyClass + class_name_3 = DummyClass + class_name_4: ClassVar[type[DummyClass]] = DummyClass + + b: str = "dummy" + + +""" +Configuration with nested classes. +""" + + +@configclass +class OutsideClassCfg: + """Outermost dummy configuration.""" + + @configclass + class InsideClassCfg: + """Inner dummy configuration.""" + + @configclass + class InsideInsideClassCfg: + """Dummy configuration with class type.""" + + u: list[int] = [1, 2, 3] + + class_type: type = DummyClass + b: str = "dummy" + + inside: InsideClassCfg = InsideClassCfg() + x: int = 20 + + def __post_init__(self): + self.inside.b = "dummy_changed" + + +""" +Dummy configuration: Functions +""" + + +@configclass +class FunctionsDemoCfg: + """Dummy configuration class with functions as attributes.""" + + func = dummy_function1 + wrapped_func = wrapped_dummy_function3 + func_in_dict = {"func": dummy_function1} + + +@configclass +class FunctionImplementedDemoCfg: + """Dummy configuration class with functions as attributes.""" + + func = dummy_function1 + a: int = 5 + k = 100.0 + + def set_a(self, a: int): + self.a = a + + +@configclass +class ClassFunctionImplementedDemoCfg: + """Dummy configuration class with function members defined in the class.""" + + a: int = 5 + + def instance_method(self): + print("Value of a: ", self.a) + + @classmethod + def class_method(cls, value: int) -> ClassFunctionImplementedDemoCfg: + return cls(a=value) + + @property + def a_proxy(self) -> int: + return self.a + + @a_proxy.setter + def a_proxy(self, value: int): + self.a = value + + +""" +Dummy configuration: Nested dictionaries +""" + + +@configclass +class NestedDictAndListCfg: + """Dummy configuration class with nested dictionaries and lists.""" + + dict_1: dict = {"dict_2": {"func": dummy_function1}} + list_1: list[EnvCfg] = [EnvCfg(), EnvCfg()] + + +""" +Dummy configuration: Missing attributes +""" + + +@configclass +class MissingParentDemoCfg: + """Dummy parent configuration with missing fields.""" + + a: int = MISSING + + @configclass + class InsideClassCfg: + """Inner dummy configuration.""" + + @configclass + class InsideInsideClassCfg: + """Inner inner dummy configuration.""" + + a: str = MISSING + + inside: str = MISSING + inside_dict = {"a": MISSING} + inside_nested_dict = {"a": {"b": "hello", "c": MISSING, "d": InsideInsideClassCfg()}} + inside_tuple = (10, MISSING, 20) + inside_list = [MISSING, MISSING, 2] + + b: InsideClassCfg = InsideClassCfg() + + +@configclass +class MissingChildDemoCfg(MissingParentDemoCfg): + """Dummy child configuration with missing fields.""" + + c: Callable = MISSING + d: int | None = None + e: dict = {} + + +""" +Test solutions: Basic +""" + +basic_demo_cfg_correct = { + "env": {"num_envs": 56, "episode_length": 2000, "viewer": {"eye": [7.5, 7.5, 7.5], "lookat": [0.0, 0.0, 0.0]}}, + "robot_default_state": { + "pos": (0.0, 0.0, 0.0), + "rot": (1.0, 0.0, 0.0, 0.0), + "dof_pos": (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + "dof_vel": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + }, + "device_id": 0, + "list_config": [{"params": {"A": 1, "B": 2}}, {"params": {"A": 3, "B": 4}}], +} + +basic_demo_cfg_change_correct = { + "env": {"num_envs": 22, "episode_length": 2000, "viewer": {"eye": (2.0, 2.0, 2.0), "lookat": [0.0, 0.0, 0.0]}}, + "robot_default_state": { + "pos": (0.0, 0.0, 0.0), + "rot": (1.0, 0.0, 0.0, 0.0), + "dof_pos": (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + "dof_vel": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + }, + "device_id": 0, + "list_config": [{"params": {"A": 1, "B": 2}}, {"params": {"A": 3, "B": 4}}], +} + +basic_demo_cfg_change_with_none_correct = { + "env": {"num_envs": 22, "episode_length": 2000, "viewer": None}, + "robot_default_state": { + "pos": (0.0, 0.0, 0.0), + "rot": (1.0, 0.0, 0.0, 0.0), + "dof_pos": (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + "dof_vel": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + }, + "device_id": 0, + "list_config": [{"params": {"A": 1, "B": 2}}, {"params": {"A": 3, "B": 4}}], +} + +basic_demo_cfg_change_with_tuple_correct = { + "env": {"num_envs": 56, "episode_length": 2000, "viewer": {"eye": [7.5, 7.5, 7.5], "lookat": [0.0, 0.0, 0.0]}}, + "robot_default_state": { + "pos": (0.0, 0.0, 0.0), + "rot": (1.0, 0.0, 0.0, 0.0), + "dof_pos": (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + "dof_vel": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + }, + "device_id": 0, + "list_config": [{"params": {"A": -1, "B": -2}}, {"params": {"A": -3, "B": -4}}], +} + +basic_demo_cfg_nested_dict_and_list = { + "dict_1": { + "dict_2": {"func": dummy_function2}, + }, + "list_1": [ + {"num_envs": 23, "episode_length": 3000, "viewer": {"eye": [5.0, 5.0, 5.0], "lookat": [0.0, 0.0, 0.0]}}, + {"num_envs": 24, "episode_length": 2000, "viewer": {"eye": [6.0, 6.0, 6.0], "lookat": [0.0, 0.0, 0.0]}}, + ], +} + +basic_demo_post_init_cfg_correct = { + "env": {"num_envs": 56, "episode_length": 2000, "viewer": {"eye": [7.5, 7.5, 7.5], "lookat": [0.0, 0.0, 0.0]}}, + "robot_default_state": { + "pos": (0.0, 0.0, 0.0), + "rot": (1.0, 0.0, 0.0, 0.0), + "dof_pos": (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + "dof_vel": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + }, + "device_id": 1, + "add_variable": 3, +} + +""" +Test solutions: Functions +""" + +functions_demo_cfg_correct = { + "func": "test_configclass:dummy_function1", + "wrapped_func": "test_configclass:wrapped_dummy_function3", + "func_in_dict": {"func": "test_configclass:dummy_function1"}, +} + +functions_demo_cfg_for_updating = { + "func": "test_configclass:dummy_function2", + "wrapped_func": "test_configclass:wrapped_dummy_function4", + "func_in_dict": {"func": "test_configclass:dummy_function2"}, +} + +""" +Test solutions: Missing attributes +""" + +validity_expected_fields = [ + "a", + "b.inside", + "b.inside_dict.a", + "b.inside_nested_dict.a.c", + "b.inside_nested_dict.a.d.a", + "b.inside_tuple[1]", + "b.inside_list[0]", + "b.inside_list[1]", + "c", +] + +""" +Test fixtures. +""" + + +def test_str(): + """Test printing the configuration.""" + cfg = BasicDemoCfg() + print() + print(cfg) + + +def test_str_dict(): + """Test printing the configuration using dataclass utility.""" + cfg = BasicDemoCfg() + print() + print("Using dataclass function: ", asdict(cfg)) + print("Using internal function: ", cfg.to_dict()) + assert asdict(cfg) == cfg.to_dict() + + +def test_dict_conversion(): + """Test dictionary conversion of configclass instance.""" + cfg = BasicDemoCfg() + # dataclass function + assert asdict(cfg) == basic_demo_cfg_correct + assert asdict(cfg.env) == basic_demo_cfg_correct["env"] + # utility function + assert class_to_dict(cfg) == basic_demo_cfg_correct + assert class_to_dict(cfg.env) == basic_demo_cfg_correct["env"] + # internal function + assert cfg.to_dict() == basic_demo_cfg_correct + assert cfg.env.to_dict() == basic_demo_cfg_correct["env"] + + torch_cfg = BasicDemoTorchCfg() + torch_cfg_dict = torch_cfg.to_dict() + # We have to do a manual check because torch.Tensor does not work with assertDictEqual. + assert torch_cfg_dict["some_number"] == 0 + assert torch.all(torch_cfg_dict["some_tensor"] == torch.tensor([1, 2, 3])) + + +def test_actuator_cfg_dict_conversion(): + """Test dict conversion of ActuatorConfig.""" + # create a basic RemotizedPDActuator config + actuator_cfg = BasicActuatorCfg() + # return writable attributes of config object + actuator_cfg_dict_attr = actuator_cfg.__dict__ + # check if __dict__ attribute of config is not empty + assert len(actuator_cfg_dict_attr) > 0 + # class_to_dict utility function should return a primitive dictionary + actuator_cfg_dict = class_to_dict(actuator_cfg) + assert isinstance(actuator_cfg_dict, dict) + + +def test_dict_conversion_order(): + """Tests that order is conserved when converting to dictionary.""" + true_outer_order = ["device_id", "env", "robot_default_state", "list_config"] + true_env_order = ["num_envs", "episode_length", "viewer"] + # create config + cfg = BasicDemoCfg() + # check ordering + for label, parsed_value in zip(true_outer_order, cfg.__dict__.keys()): + assert label == parsed_value + for label, parsed_value in zip(true_env_order, cfg.env.__dict__.keys()): + assert label == parsed_value + # convert config to dictionary + cfg_dict = class_to_dict(cfg) + # check ordering + for label, parsed_value in zip(true_outer_order, cfg_dict.keys()): + assert label == parsed_value + for label, parsed_value in zip(true_env_order, cfg_dict["env"].keys()): + assert label == parsed_value + # check ordering when copied + cfg_dict_copied = copy.deepcopy(cfg_dict) + cfg_dict_copied.pop("list_config") + # check ordering + for label, parsed_value in zip(true_outer_order, cfg_dict_copied.keys()): + assert label == parsed_value + for label, parsed_value in zip(true_env_order, cfg_dict_copied["env"].keys()): + assert label == parsed_value + + +def test_config_update_via_constructor(): + """Test updating configclass through initialization.""" + cfg = BasicDemoCfg(env=EnvCfg(num_envs=22, viewer=ViewerCfg(eye=(2.0, 2.0, 2.0)))) + assert asdict(cfg) == basic_demo_cfg_change_correct + + +def test_config_update_after_init(): + """Test updating configclass using instance members.""" + cfg = BasicDemoCfg() + cfg.env.num_envs = 22 + cfg.env.viewer.eye = (2.0, 2.0, 2.0) # note: changes from list to tuple + assert asdict(cfg) == basic_demo_cfg_change_correct + + +def test_config_update_dict(): + """Test updating configclass using dictionary.""" + cfg = BasicDemoCfg() + cfg_dict = {"env": {"num_envs": 22, "viewer": {"eye": (2.0, 2.0, 2.0)}}} + update_class_from_dict(cfg, cfg_dict) + assert asdict(cfg) == basic_demo_cfg_change_correct + + # check types are also correct + assert isinstance(cfg.env.viewer, ViewerCfg) + assert isinstance(cfg.env.viewer.eye, tuple) + + +def test_config_update_dict_with_none(): + """Test updating configclass using a dictionary that contains None.""" + cfg = BasicDemoCfg() + cfg_dict = {"env": {"num_envs": 22, "viewer": None}} + update_class_from_dict(cfg, cfg_dict) + assert asdict(cfg) == basic_demo_cfg_change_with_none_correct + + +def test_config_update_dict_tuple(): + """Test updating configclass using a dictionary that modifies a tuple.""" + cfg = BasicDemoCfg() + cfg_dict = {"list_config": [{"params": {"A": -1, "B": -2}}, {"params": {"A": -3, "B": -4}}]} + update_class_from_dict(cfg, cfg_dict) + assert asdict(cfg) == basic_demo_cfg_change_with_tuple_correct + + +def test_config_update_nested_dict(): + """Test updating configclass with sub-dictionaries.""" + cfg = NestedDictAndListCfg() + cfg_dict = { + "dict_1": {"dict_2": {"func": "test_configclass:dummy_function2"}}, + "list_1": [ + {"num_envs": 23, "episode_length": 3000, "viewer": {"eye": [5.0, 5.0, 5.0]}}, + {"num_envs": 24, "viewer": {"eye": [6.0, 6.0, 6.0]}}, + ], + } + update_class_from_dict(cfg, cfg_dict) + assert asdict(cfg) == basic_demo_cfg_nested_dict_and_list + + # check types are also correct + assert isinstance(cfg.list_1[0], EnvCfg) + assert isinstance(cfg.list_1[1], EnvCfg) + assert isinstance(cfg.list_1[0].viewer, ViewerCfg) + assert isinstance(cfg.list_1[1].viewer, ViewerCfg) + + +def test_config_update_different_iterable_lengths(): + """Iterables are whole replaced, even if their lengths are different.""" + + # original cfg has length-6 tuple and list + cfg = RobotDefaultStateCfg() + assert cfg.dof_pos == (0.0, 0.0, 0.0, 0.0, 0.0, 0.0) + assert cfg.dof_vel == [0.0, 0.0, 0.0, 0.0, 0.0, 1.0] + + # patch uses different lengths + patch = { + "dof_pos": (1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0), # longer tuple + "dof_vel": [9.0, 8.0, 7.0], # shorter list + } + + # should not raise + update_class_from_dict(cfg, patch) + + # whole sequences are replaced + assert cfg.dof_pos == (1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0) + assert cfg.dof_vel == [9.0, 8.0, 7.0] + + +def test_config_update_dict_using_internal(): + """Test updating configclass from a dictionary using configclass method.""" + cfg = BasicDemoCfg() + cfg_dict = {"env": {"num_envs": 22, "viewer": {"eye": (2.0, 2.0, 2.0)}}} + cfg.from_dict(cfg_dict) + assert cfg.to_dict() == basic_demo_cfg_change_correct + + +def test_config_update_dict_using_post_init(): + cfg = BasicDemoPostInitCfg() + assert cfg.to_dict() == basic_demo_post_init_cfg_correct + + +def test_invalid_update_key(): + """Test invalid key update.""" + cfg = BasicDemoCfg() + cfg_dict = {"env": {"num_envs": 22, "viewer": {"pos": (2.0, 2.0, 2.0)}}} + with pytest.raises(KeyError): + update_class_from_dict(cfg, cfg_dict) + + +def test_multiple_instances(): + """Test multiple instances with twice instantiation.""" + # create two config instances + cfg1 = BasicDemoCfg() + cfg2 = BasicDemoCfg() + + # check variables + # mutable -- variables should be different + assert id(cfg1.env.viewer.eye) != id(cfg2.env.viewer.eye) + assert id(cfg1.env.viewer.lookat) != id(cfg2.env.viewer.lookat) + assert id(cfg1.robot_default_state) != id(cfg2.robot_default_state) + # immutable -- variables are the same + assert id(cfg1.robot_default_state.dof_pos) == id(cfg2.robot_default_state.dof_pos) + assert id(cfg1.env.num_envs) == id(cfg2.env.num_envs) + assert id(cfg1.device_id) == id(cfg2.device_id) + + # check values + assert cfg1.env.to_dict() == cfg2.env.to_dict() + assert cfg1.robot_default_state.to_dict() == cfg2.robot_default_state.to_dict() + + +def test_alter_values_multiple_instances(): + """Test alterations in multiple instances of the same configclass.""" + # create two config instances + cfg1 = BasicDemoCfg() + cfg2 = BasicDemoCfg() + + # alter configurations + cfg1.env.num_envs = 22 # immutable data: int + cfg1.env.viewer.eye[0] = 1.0 # mutable data: list + cfg1.env.viewer.lookat[2] = 12.0 # mutable data: list + + # check variables + # values should be different + assert cfg1.env.num_envs != cfg2.env.num_envs + assert cfg1.env.viewer.eye != cfg2.env.viewer.eye + assert cfg1.env.viewer.lookat != cfg2.env.viewer.lookat + # mutable -- variables are different ids + assert id(cfg1.env.viewer.eye) != id(cfg2.env.viewer.eye) + assert id(cfg1.env.viewer.lookat) != id(cfg2.env.viewer.lookat) + # immutable -- altered variables are different ids + assert id(cfg1.env.num_envs) != id(cfg2.env.num_envs) + + +def test_multiple_instances_with_replace(): + """Test multiple instances with creation through replace function.""" + # create two config instances + cfg1 = BasicDemoCfg() + cfg2 = cfg1.replace() + + # check variable IDs + # mutable -- variables should be different + assert id(cfg1.env.viewer.eye) != id(cfg2.env.viewer.eye) + assert id(cfg1.env.viewer.lookat) != id(cfg2.env.viewer.lookat) + assert id(cfg1.robot_default_state) != id(cfg2.robot_default_state) + # immutable -- variables are the same + assert id(cfg1.robot_default_state.dof_pos) == id(cfg2.robot_default_state.dof_pos) + assert id(cfg1.env.num_envs) == id(cfg2.env.num_envs) + assert id(cfg1.device_id) == id(cfg2.device_id) + + # check values + assert cfg1.to_dict() == cfg2.to_dict() + + +def test_alter_values_multiple_instances_wth_replace(): + """Test alterations in multiple instances through replace function.""" + # create two config instances + cfg1 = BasicDemoCfg() + cfg2 = cfg1.replace(device_id=1) + + # alter configurations + cfg1.env.num_envs = 22 # immutable data: int + cfg1.env.viewer.eye[0] = 1.0 # mutable data: list + cfg1.env.viewer.lookat[2] = 12.0 # mutable data: list + + # check variables + # values should be different + assert cfg1.env.num_envs != cfg2.env.num_envs + assert cfg1.env.viewer.eye != cfg2.env.viewer.eye + assert cfg1.env.viewer.lookat != cfg2.env.viewer.lookat + # mutable -- variables are different ids + assert id(cfg1.env.viewer.eye) != id(cfg2.env.viewer.eye) + assert id(cfg1.env.viewer.lookat) != id(cfg2.env.viewer.lookat) + # immutable -- altered variables are different ids + assert id(cfg1.env.num_envs) != id(cfg2.env.num_envs) + assert id(cfg1.device_id) != id(cfg2.device_id) + + +def test_configclass_type_ordering(): + """Checks ordering of config objects when no type annotation is provided.""" + + cfg_1 = TypeAnnotationOrderingDemoCfg() + cfg_2 = NonTypeAnnotationOrderingDemoCfg() + cfg_3 = InheritedNonTypeAnnotationOrderingDemoCfg() + + # check ordering + assert list(cfg_1.__dict__.keys()) == list(cfg_2.__dict__.keys()) + assert list(cfg_3.__dict__.keys()) == list(cfg_2.__dict__.keys()) + assert list(cfg_1.__dict__.keys()) == list(cfg_3.__dict__.keys()) + + +def test_functions_config(): + """Tests having functions as values in the configuration instance.""" + cfg = FunctionsDemoCfg() + # check types + assert cfg.__annotations__["func"] is type(dummy_function1) + assert cfg.__annotations__["wrapped_func"] is type(wrapped_dummy_function3) + assert cfg.__annotations__["func_in_dict"] is dict + # check calling + assert cfg.func() == 1 + assert cfg.wrapped_func() == 4 + assert cfg.func_in_dict["func"]() == 1 + + +def test_function_impl_config(): + """Tests having function defined in the class instance.""" + cfg = FunctionImplementedDemoCfg() + # change value + assert cfg.a == 5 + cfg.set_a(10) + assert cfg.a == 10 + + +def test_class_function_impl_config(): + """Tests having class function defined in the class instance.""" + cfg = ClassFunctionImplementedDemoCfg() + + # check that the annotations are correct + assert cfg.__annotations__ == {"a": "int"} + + # check all methods are callable + cfg.instance_method() + new_cfg1 = cfg.class_method(20) + # check value is correct + assert new_cfg1.a == 20 + + # create the same config instance using class method + new_cfg2 = ClassFunctionImplementedDemoCfg.class_method(20) + # check value is correct + assert new_cfg2.a == 20 + + +def test_class_property_impl_config(): + """Tests having class property defined in the class instance.""" + cfg = ClassFunctionImplementedDemoCfg() + + # check that the annotations are correct + assert cfg.__annotations__ == {"a": "int"} + + # check all methods are callable + cfg.instance_method() + + # check value is correct + assert cfg.a == 5 + assert cfg.a_proxy == 5 + + # set through property + cfg.a_proxy = 10 + assert cfg.a == 10 + assert cfg.a_proxy == 10 + + +def test_dict_conversion_functions_config(): + """Tests conversion of config with functions into dictionary.""" + cfg = FunctionsDemoCfg() + cfg_dict = class_to_dict(cfg) + assert cfg_dict["func"] == functions_demo_cfg_correct["func"] + assert cfg_dict["wrapped_func"] == functions_demo_cfg_correct["wrapped_func"] + assert cfg_dict["func_in_dict"]["func"] == functions_demo_cfg_correct["func_in_dict"]["func"] + + +def test_update_functions_config_with_functions(): + """Tests updating config with functions.""" + cfg = FunctionsDemoCfg() + # update config + update_class_from_dict(cfg, functions_demo_cfg_for_updating) + # check calling + assert cfg.func() == 2 + assert cfg.wrapped_func() == 5 + assert cfg.func_in_dict["func"]() == 2 + + +def test_missing_type_in_config(): + """Tests missing type annotation in config. + + Should complain that 'c' is missing type annotation since it cannot be inferred + from 'MISSING' value. + """ + with pytest.raises(TypeError): + + @configclass + class MissingTypeDemoCfg: + a: int = 1 + b = 2 + c = MISSING + + +def test_missing_default_value_in_config(): + """Tests missing default value in config. + + Should complain that 'a' is missing default value since it cannot be inferred + from type annotation. + """ + with pytest.raises(ValueError): + + @configclass + class MissingTypeDemoCfg: + a: int + b = 2 + + +def test_required_argument_for_missing_type_in_config(): + """Tests required positional argument for missing type annotation in config creation.""" + + @configclass + class MissingTypeDemoCfg: + a: int = 1 + b = 2 + c: int = MISSING + + # should complain that 'c' is missed in positional arguments + # TODO: Uncomment this when we move to 3.10. + # with self.assertRaises(TypeError): + # cfg = MissingTypeDemoCfg(a=1) + # should not complain + cfg = MissingTypeDemoCfg(a=1, c=3) + + assert cfg.a == 1 + assert cfg.b == 2 + + +def test_config_inheritance(): + """Tests that inheritance works properly.""" + # check variables + cfg_a = ChildADemoCfg(a=20, d=3, e=ViewerCfg(), j=["c", "d"]) + + assert cfg_a.func == dummy_function1 + assert cfg_a.a == 20 + assert cfg_a.d == 3 + assert cfg_a.j == ["c", "d"] + + # check post init + assert cfg_a.b == 3 + assert cfg_a.i == ["a", "b"] + assert cfg_a.m.rot == (2.0, 0.0, 0.0, 0.0) + + +def test_config_inheritance_independence(): + """Tests that subclass instantions have fully unique members, + rather than references to members of the parent class""" + # instantiate two classes which inherit from a shared parent, + # but which will differently modify their members in their + # __init__ and __post_init__ + cfg_a = ChildADemoCfg() + cfg_b = ChildBDemoCfg() + + # Test various combinations of initialization + # and defaults across inherited members in + # instances to verify independence between the subclasses + assert isinstance(cfg_a.a, type(MISSING)) + assert cfg_b.a == 100 + assert cfg_a.b == 3 + assert cfg_b.b == 8 + assert cfg_a.c == RobotDefaultStateCfg() + assert isinstance(cfg_b.c, type(MISSING)) + assert cfg_a.m.rot == (2.0, 0.0, 0.0, 0.0) + assert cfg_b.m.rot == (1.0, 0.0, 0.0, 0.0) + assert isinstance(cfg_a.j, type(MISSING)) + assert cfg_b.j == ["3", "4"] + assert cfg_a.i == ["a", "b"] + assert cfg_b.i == ["1", "2"] + assert cfg_a.func == dummy_function1 + assert isinstance(cfg_b.func, type(MISSING)) + + # Explicitly assert that members are not the same object + # for different levels and kinds of data types + assert cfg_a.m != cfg_b.m + assert cfg_a.m.rot != cfg_b.m.rot + assert cfg_a.i != cfg_b.i + assert cfg_a.b != cfg_b.b + + +def test_config_double_inheritance(): + """Tests that inheritance works properly when inheriting twice.""" + # check variables + cfg = ChildChildDemoCfg(a=20, d=3, e=ViewerCfg(), j=["c", "d"]) + + assert cfg.func == dummy_function1 + assert cfg.func_2 == dummy_function2 + assert cfg.a == 20 + assert cfg.d == 3 + assert cfg.j == ["c", "d"] + + # check post init + assert cfg.b == 4 + assert cfg.f == "new" + assert cfg.i == ["a", "b"] + + +def test_config_with_class_type(): + """Tests that configclass works properly with class type.""" + + cfg = DummyClassCfg() + + # since python 3.10, annotations are stored as strings + annotations = {k: eval(v) if isinstance(v, str) else v for k, v in cfg.__annotations__.items()} + # check types + assert annotations["class_name_1"] is type + assert annotations["class_name_2"] == type[DummyClass] + assert annotations["class_name_3"] == type[DummyClass] + assert annotations["class_name_4"] is ClassVar[type[DummyClass]] + # check values + assert cfg.class_name_1 == DummyClass + assert cfg.class_name_2 == DummyClass + assert cfg.class_name_3 == DummyClass + assert cfg.class_name_4 == DummyClass + assert cfg.b == "dummy" + + +def test_nested_config_class_declarations(): + """Tests that configclass works properly with nested class class declarations.""" + + cfg = OutsideClassCfg() + + # check types + assert "InsideClassCfg" not in cfg.__annotations__ + assert "InsideClassCfg" not in OutsideClassCfg.__annotations__ + assert "InsideInsideClassCfg" not in OutsideClassCfg.InsideClassCfg.__annotations__ + assert "InsideInsideClassCfg" not in cfg.inside.__annotations__ + # check values + assert cfg.inside.class_type == DummyClass + assert cfg.inside.b == "dummy_changed" + assert cfg.x == 20 + + +def test_config_dumping(): + """Check that config dumping works properly.""" + + # file for dumping + dirname = os.path.dirname(os.path.abspath(__file__)) + filename = os.path.join(dirname, "output", "configclass", "test_config.yaml") + + # create config + cfg = ChildADemoCfg(a=20, d=3, e=ViewerCfg(), j=["c", "d"]) + + # save config + dump_yaml(filename, cfg) + # load config + cfg_loaded = load_yaml(filename) + # check dictionaries are the same + assert list(cfg.to_dict().keys()) == list(cfg_loaded.keys()) + assert cfg.to_dict() == cfg_loaded + + # save config with sorted order won't work! + # save config + dump_yaml(filename, cfg, sort_keys=True) + # load config + cfg_loaded = load_yaml(filename) + # check dictionaries are the same + assert list(cfg.to_dict().keys()) != list(cfg_loaded.keys()) + assert cfg.to_dict() == cfg_loaded + + +def test_config_md5_hash(): + """Check that config md5 hash generation works properly.""" + + # create config + cfg = ChildADemoCfg(a=20, d=3, e=ViewerCfg(), j=["c", "d"]) + + # generate md5 hash + md5_hash_1 = dict_to_md5_hash(cfg.to_dict()) + md5_hash_2 = dict_to_md5_hash(cfg.to_dict()) + + assert md5_hash_1 == md5_hash_2 + + +def test_validity(): + """Check that invalid configurations raise errors.""" + + cfg = MissingChildDemoCfg() + + with pytest.raises(TypeError) as context: + cfg.validate() + + # check that the expected missing fields are in the error message + error_message = str(context.value) + for elem in validity_expected_fields: + assert elem in error_message + + # check that no more than the expected missing fields are in the error message + assert len(error_message.split("\n")) - 2 == len(validity_expected_fields) diff --git a/source/isaaclab/test/utils/test_delay_buffer.py b/source/isaaclab/test/utils/test_delay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..a66802e729786380fb66da2492a362de56448008 --- /dev/null +++ b/source/isaaclab/test/utils/test_delay_buffer.py @@ -0,0 +1,100 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app in headless mode +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows from here.""" + +from collections.abc import Generator + +import pytest +import torch + +from isaaclab.utils import DelayBuffer + + +@pytest.fixture +def delay_buffer(): + """Create a delay buffer for testing.""" + device: str = "cpu" + batch_size: int = 10 + history_length: int = 4 + return DelayBuffer(history_length, batch_size=batch_size, device=device) + + +def _generate_data(batch_size: int, length: int, device: str) -> Generator[torch.Tensor]: + """Data generator for testing the buffer.""" + for step in range(length): + yield torch.full((batch_size, 1), step, dtype=torch.int, device=device) + + +def test_constant_time_lags(delay_buffer): + """Test constant delay.""" + const_lag: int = 3 + batch_size: int = 10 + + delay_buffer.set_time_lag(const_lag) + + all_data = [] + for i, data in enumerate(_generate_data(batch_size, 20, delay_buffer.device)): + all_data.append(data) + # apply delay + delayed_data = delay_buffer.compute(data) + error = delayed_data - all_data[max(0, i - const_lag)] + assert torch.all(error == 0) + + +def test_reset(delay_buffer): + """Test resetting the last two batch indices after iteration `reset_itr`.""" + const_lag: int = 2 + reset_itr = 10 + batch_size: int = 10 + + delay_buffer.set_time_lag(const_lag) + + all_data = [] + for i, data in enumerate(_generate_data(batch_size, 20, delay_buffer.device)): + all_data.append(data) + # from 'reset_itr' iteration reset the last and second-to-last environments + if i == reset_itr: + delay_buffer.reset([-2, -1]) + # apply delay + delayed_data = delay_buffer.compute(data) + # before 'reset_itr' is is similar to test_constant_time_lags + # after that indices [-2, -1] should be treated separately + if i < reset_itr: + error = delayed_data - all_data[max(0, i - const_lag)] + assert torch.all(error == 0) + else: + # error_regular = delayed_data[:-2] - all_data[max(0, i - const_lag)][:-2] + error2_reset = delayed_data[-2, -1] - all_data[max(reset_itr, i - const_lag)][-2, -1] + # assert torch.all(error_regular == 0) + assert torch.all(error2_reset == 0) + + +def test_random_time_lags(delay_buffer): + """Test random delays.""" + max_lag: int = 3 + time_lags = torch.randint( + low=0, high=max_lag + 1, size=(delay_buffer.batch_size,), dtype=torch.int, device=delay_buffer.device + ) + + delay_buffer.set_time_lag(time_lags) + + all_data = [] + for i, data in enumerate(_generate_data(delay_buffer.batch_size, 20, delay_buffer.device)): + all_data.append(data) + # apply delay + delayed_data = delay_buffer.compute(data) + true_delayed_index = torch.maximum(i - delay_buffer.time_lags, torch.zeros_like(delay_buffer.time_lags)) + true_delayed_index = true_delayed_index.tolist() + for i in range(delay_buffer.batch_size): + error = delayed_data[i] - all_data[true_delayed_index[i]][i] + assert torch.all(error == 0) diff --git a/source/isaaclab/test/utils/test_dict.py b/source/isaaclab/test/utils/test_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..35ce35f265778f564c11e34b16917735c2334087 --- /dev/null +++ b/source/isaaclab/test/utils/test_dict.py @@ -0,0 +1,99 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# NOTE: While we don't actually use the simulation app in this test, we still need to launch it +# because warp is only available in the context of a running simulation +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import random + +import isaaclab.utils.dict as dict_utils + + +def _test_function(x): + """Test function for string <-> callable conversion.""" + return x**2 + + +def _test_lambda_function(x): + """Test function for string <-> callable conversion.""" + return x**2 + + +def test_print_dict(): + """Test printing of dictionary.""" + # create a complex nested dictionary + test_dict = { + "a": 1, + "b": 2, + "c": {"d": 3, "e": 4, "f": {"g": 5, "h": 6}}, + "i": 7, + "j": lambda x: x**2, # noqa: E731 + "k": dict_utils.class_to_dict, + } + # print the dictionary + dict_utils.print_dict(test_dict) + + +def test_string_callable_function_conversion(): + """Test string <-> callable conversion for function.""" + + # convert function to string + test_string = dict_utils.callable_to_string(_test_function) + # convert string to function + test_function_2 = dict_utils.string_to_callable(test_string) + # check that functions are the same + assert _test_function(2) == test_function_2(2) + + +def test_string_callable_function_with_lambda_in_name_conversion(): + """Test string <-> callable conversion for function which has lambda in its name.""" + + # convert function to string + test_string = dict_utils.callable_to_string(_test_lambda_function) + # convert string to function + test_function_2 = dict_utils.string_to_callable(test_string) + # check that functions are the same + assert _test_function(2) == test_function_2(2) + + +def test_string_callable_lambda_conversion(): + """Test string <-> callable conversion for lambda expression.""" + + # create lambda function + func = lambda x: x**2 # noqa: E731 + # convert function to string + test_string = dict_utils.callable_to_string(func) + # convert string to function + func_2 = dict_utils.string_to_callable(test_string) + # check that functions are the same + assert test_string == "lambda x: x**2" + assert func(2) == func_2(2) + + +def test_dict_to_md5(): + """Test MD5 hash generation for dictionary.""" + # create a complex nested dictionary + test_dict = { + "a": 1, + "b": 2, + "c": {"d": 3, "e": 4, "f": {"g": 5, "h": 6}}, + "i": random.random(), + "k": dict_utils.callable_to_string(dict_utils.class_to_dict), + } + # generate the MD5 hash + md5_hash_1 = dict_utils.dict_to_md5_hash(test_dict) + + # check that the hash is correct even after multiple calls + for _ in range(200): + md5_hash_2 = dict_utils.dict_to_md5_hash(test_dict) + assert md5_hash_1 == md5_hash_2 diff --git a/source/isaaclab/test/utils/test_episode_data.py b/source/isaaclab/test/utils/test_episode_data.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d14adc8aa6c397329efeb79cb2337fad9ca34d --- /dev/null +++ b/source/isaaclab/test/utils/test_episode_data.py @@ -0,0 +1,153 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app in headless mode +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows from here.""" + +import pytest +import torch + +from isaaclab.utils.datasets import EpisodeData + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_is_empty(device): + """Test checking whether the episode is empty.""" + episode = EpisodeData() + assert episode.is_empty() + + episode.add("key", torch.tensor([1, 2, 3], device=device)) + assert not episode.is_empty() + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_add_tensors(device): + """Test appending tensor data to the episode.""" + dummy_data_0 = torch.tensor([0], device=device) + dummy_data_1 = torch.tensor([1], device=device) + expected_added_data = torch.cat((dummy_data_0.unsqueeze(0), dummy_data_1.unsqueeze(0))) + episode = EpisodeData() + + # test adding data to a key that does not exist + episode.add("key", dummy_data_0) + key_data = torch.stack(episode.data.get("key")) + assert key_data is not None + assert torch.equal(key_data, dummy_data_0.unsqueeze(0)) + + # test adding data to a key that exists + episode.add("key", dummy_data_1) + key_data = torch.stack(episode.data.get("key")) + assert key_data is not None + assert torch.equal(key_data, expected_added_data) + + # test adding data to a key with "/" in the name + episode.add("first/second", dummy_data_0) + first_data = episode.data.get("first") + assert first_data is not None + second_data = torch.stack(first_data.get("second")) + assert second_data is not None + assert torch.equal(second_data, dummy_data_0.unsqueeze(0)) + + # test adding data to a key with "/" in the name that already exists + episode.add("first/second", dummy_data_1) + first_data = episode.data.get("first") + assert first_data is not None + second_data = torch.stack(first_data.get("second")) + assert second_data is not None + assert torch.equal(second_data, expected_added_data) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_add_dict_tensors(device): + """Test appending dict data to the episode.""" + dummy_dict_data_0 = { + "key_0": torch.tensor([0], device=device), + "key_1": {"key_1_0": torch.tensor([1], device=device), "key_1_1": torch.tensor([2], device=device)}, + } + dummy_dict_data_1 = { + "key_0": torch.tensor([3], device=device), + "key_1": {"key_1_0": torch.tensor([4], device=device), "key_1_1": torch.tensor([5], device=device)}, + } + + episode = EpisodeData() + + # test adding dict data to a key that does not exist + episode.add("key", dummy_dict_data_0) + key_data = episode.data.get("key") + assert key_data is not None + key_0_data = torch.stack(key_data.get("key_0")) + assert key_0_data is not None + assert torch.equal(key_0_data, torch.tensor([[0]], device=device)) + key_1_data = key_data.get("key_1") + assert key_1_data is not None + key_1_0_data = torch.stack(key_1_data.get("key_1_0")) + assert key_1_0_data is not None + assert torch.equal(key_1_0_data, torch.tensor([[1]], device=device)) + key_1_1_data = torch.stack(key_1_data.get("key_1_1")) + assert key_1_1_data is not None + assert torch.equal(key_1_1_data, torch.tensor([[2]], device=device)) + + # test adding dict data to a key that exists + episode.add("key", dummy_dict_data_1) + key_data = episode.data.get("key") + assert key_data is not None + key_0_data = torch.stack(key_data.get("key_0")) + assert key_0_data is not None + assert torch.equal(key_0_data, torch.tensor([[0], [3]], device=device)) + key_1_data = key_data.get("key_1") + assert key_1_data is not None + key_1_0_data = torch.stack(key_1_data.get("key_1_0")) + assert key_1_0_data is not None + assert torch.equal(key_1_0_data, torch.tensor([[1], [4]], device=device)) + key_1_1_data = torch.stack(key_1_data.get("key_1_1")) + assert key_1_1_data is not None + assert torch.equal(key_1_1_data, torch.tensor([[2], [5]], device=device)) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_get_initial_state(device): + """Test getting the initial state of the episode.""" + dummy_initial_state = torch.tensor([1, 2, 3], device=device) + episode = EpisodeData() + + episode.add("initial_state", dummy_initial_state) + initial_state = torch.stack(episode.get_initial_state()) + assert initial_state is not None + assert torch.equal(initial_state, dummy_initial_state.unsqueeze(0)) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_get_next_action(device): + """Test getting next actions.""" + # dummy actions + action1 = torch.tensor([1, 2, 3], device=device) + action2 = torch.tensor([4, 5, 6], device=device) + action3 = torch.tensor([7, 8, 9], device=device) + + episode = EpisodeData() + assert episode.get_next_action() is None + + episode.add("actions", action1) + episode.add("actions", action2) + episode.add("actions", action3) + + # check if actions are returned in the correct order + next_action = episode.get_next_action() + assert next_action is not None + assert torch.equal(next_action, action1) + next_action = episode.get_next_action() + assert next_action is not None + assert torch.equal(next_action, action2) + next_action = episode.get_next_action() + assert next_action is not None + assert torch.equal(next_action, action3) + + # check if None is returned when all actions are exhausted + assert episode.get_next_action() is None diff --git a/source/isaaclab/test/utils/test_hdf5_dataset_file_handler.py b/source/isaaclab/test/utils/test_hdf5_dataset_file_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..123ee95a1157f19376646efac5fe977bbc4609b8 --- /dev/null +++ b/source/isaaclab/test/utils/test_hdf5_dataset_file_handler.py @@ -0,0 +1,120 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app in headless mode +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows from here.""" + +import os +import shutil +import tempfile +import uuid + +import pytest +import torch + +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + + +def create_test_episode(device): + """create a test episode with dummy data.""" + test_episode = EpisodeData() + + test_episode.seed = 0 + test_episode.success = True + + test_episode.add("initial_state", torch.tensor([1, 2, 3], device=device)) + + test_episode.add("actions", torch.tensor([1, 2, 3], device=device)) + test_episode.add("actions", torch.tensor([4, 5, 6], device=device)) + test_episode.add("actions", torch.tensor([7, 8, 9], device=device)) + + test_episode.add("obs/policy/term1", torch.tensor([1, 2, 3, 4, 5], device=device)) + test_episode.add("obs/policy/term1", torch.tensor([6, 7, 8, 9, 10], device=device)) + test_episode.add("obs/policy/term1", torch.tensor([11, 12, 13, 14, 15], device=device)) + + return test_episode + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test datasets.""" + temp_dir = tempfile.mkdtemp() + yield temp_dir + # cleanup after tests + shutil.rmtree(temp_dir) + + +def test_create_dataset_file(temp_dir): + """Test creating a new dataset file.""" + # create a dataset file given a file name with extension + dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.hdf5") + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.create(dataset_file_path, "test_env_name") + dataset_file_handler.close() + + # check if the dataset is created + assert os.path.exists(dataset_file_path) + + # create a dataset file given a file name without extension + dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}") + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.create(dataset_file_path, "test_env_name") + dataset_file_handler.close() + + # check if the dataset is created + assert os.path.exists(dataset_file_path + ".hdf5") + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_write_and_load_episode(temp_dir, device): + """Test writing and loading an episode to and from the dataset file.""" + dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.hdf5") + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.create(dataset_file_path, "test_env_name") + + test_episode = create_test_episode(device) + + # write the episode to the dataset + test_episode.pre_export() + dataset_file_handler.write_episode(test_episode) + dataset_file_handler.flush() + + assert dataset_file_handler.get_num_episodes() == 1 + + # write the episode again to test writing 2nd episode + dataset_file_handler.write_episode(test_episode) + dataset_file_handler.flush() + + assert dataset_file_handler.get_num_episodes() == 2 + + # close the dataset file to prepare for testing the load function + dataset_file_handler.close() + + # load the episode from the dataset + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(dataset_file_path) + + assert dataset_file_handler.get_env_name() == "test_env_name" + + loaded_episode_names = dataset_file_handler.get_episode_names() + assert len(list(loaded_episode_names)) == 2 + + for episode_name in loaded_episode_names: + loaded_episode = dataset_file_handler.load_episode(episode_name, device=device) + assert loaded_episode.env_id == "test_env_name" + assert loaded_episode.seed == test_episode.seed + assert loaded_episode.success == test_episode.success + + assert torch.equal(loaded_episode.get_initial_state(), test_episode.get_initial_state()) + + for action in test_episode.data["actions"]: + assert torch.equal(loaded_episode.get_next_action(), action) + + dataset_file_handler.close() diff --git a/source/isaaclab/test/utils/test_logger.py b/source/isaaclab/test/utils/test_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..69df76f4c660c746fda07903c019434465958c9b --- /dev/null +++ b/source/isaaclab/test/utils/test_logger.py @@ -0,0 +1,725 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests for logging utilities.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import logging +import os +import re +import tempfile +import time + +import pytest + +from isaaclab.utils.logger import ColoredFormatter, RateLimitFilter, configure_logging + + +# Fixtures +@pytest.fixture +def formatter(): + """Fixture providing a ColoredFormatter instance.""" + return ColoredFormatter("%(levelname)s: %(message)s") + + +@pytest.fixture +def test_message(): + """Fixture providing a test message string.""" + return "Test message" + + +@pytest.fixture +def rate_limit_filter(): + """Fixture providing a RateLimitFilter instance with 2 second interval.""" + return RateLimitFilter(interval_seconds=2) + + +""" +Tests for the ColoredFormatter class. +""" + + +def test_info_formatting(formatter, test_message): + """Test INFO level message formatting.""" + record = logging.LogRecord( + name="test", + level=logging.INFO, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = formatter.format(record) + + # INFO should use reset color (no color) + assert "\033[0m" in formatted + assert test_message in formatted + assert "INFO" in formatted + + +def test_debug_formatting(formatter, test_message): + """Test DEBUG level message formatting.""" + record = logging.LogRecord( + name="test", + level=logging.DEBUG, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = formatter.format(record) + + # DEBUG should use reset color (no color) + assert "\033[0m" in formatted + assert test_message in formatted + assert "DEBUG" in formatted + + +def test_warning_formatting(formatter, test_message): + """Test WARNING level message formatting.""" + record = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = formatter.format(record) + + # WARNING should use yellow/orange color + assert "\033[33m" in formatted + assert test_message in formatted + assert "WARNING" in formatted + # Should end with reset + assert formatted.endswith("\033[0m") + + +def test_error_formatting(formatter, test_message): + """Test ERROR level message formatting.""" + record = logging.LogRecord( + name="test", + level=logging.ERROR, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = formatter.format(record) + + # ERROR should use red color + assert "\033[31m" in formatted + assert test_message in formatted + assert "ERROR" in formatted + # Should end with reset + assert formatted.endswith("\033[0m") + + +def test_critical_formatting(formatter, test_message): + """Test CRITICAL level message formatting.""" + record = logging.LogRecord( + name="test", + level=logging.CRITICAL, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = formatter.format(record) + + # CRITICAL should use bold red color + assert "\033[1;31m" in formatted + assert test_message in formatted + assert "CRITICAL" in formatted + # Should end with reset + assert formatted.endswith("\033[0m") + + +def test_color_codes_are_ansi(): + """Test that color codes are valid ANSI escape sequences.""" + # Test all defined colors + for level_name, color_code in ColoredFormatter.COLORS.items(): + # ANSI color codes should match pattern \033[m or \033[;m (for bold, etc.) + assert re.match(r"\033\[[\d;]+m", color_code), f"Invalid ANSI color code for {level_name}" + + # Test reset code + assert re.match(r"\033\[[\d;]+m", ColoredFormatter.RESET), "Invalid ANSI reset code" + + +def test_custom_format_string(test_message): + """Test that custom format strings work correctly.""" + custom_formatter = ColoredFormatter("%(name)s - %(levelname)s - %(message)s") + record = logging.LogRecord( + name="custom.logger", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg=test_message, + args=(), + exc_info=None, + ) + formatted = custom_formatter.format(record) + + assert "custom.logger" in formatted + assert "WARNING" in formatted + assert test_message in formatted + assert "\033[33m" in formatted # Warning color + + +""" +Tests for the RateLimitFilter class. +""" + + +def test_non_warning_messages_pass_through(rate_limit_filter): + """Test that non-WARNING messages always pass through the filter.""" + # Test INFO + info_record = logging.LogRecord( + name="test", + level=logging.INFO, + pathname="test.py", + lineno=1, + msg="Info message", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(info_record) is True + + # Test ERROR + error_record = logging.LogRecord( + name="test", + level=logging.ERROR, + pathname="test.py", + lineno=1, + msg="Error message", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(error_record) is True + + # Test DEBUG + debug_record = logging.LogRecord( + name="test", + level=logging.DEBUG, + pathname="test.py", + lineno=1, + msg="Debug message", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(debug_record) is True + + +def test_first_warning_passes(rate_limit_filter): + """Test that the first WARNING message passes through.""" + record = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg="First warning", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(record) is True + + +def test_duplicate_warning_within_interval_blocked(rate_limit_filter): + """Test that duplicate WARNING messages within interval are blocked.""" + message = "Duplicate warning" + + # First warning should pass + record1 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg=message, + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(record1) is True + + # Immediate duplicate should be blocked + record2 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=2, + msg=message, + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(record2) is False + + +def test_warning_after_interval_passes(): + """Test that WARNING messages pass after the rate limit interval.""" + message = "Rate limited warning" + filter_short = RateLimitFilter(interval_seconds=1) + + # First warning should pass + record1 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg=message, + args=(), + exc_info=None, + ) + assert filter_short.filter(record1) is True + + # Immediate duplicate should be blocked + record2 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=2, + msg=message, + args=(), + exc_info=None, + ) + assert filter_short.filter(record2) is False + + # Wait for interval to pass + time.sleep(1.1) + + # After interval, same message should pass again + record3 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=3, + msg=message, + args=(), + exc_info=None, + ) + assert filter_short.filter(record3) is True + + +def test_different_warnings_not_rate_limited(rate_limit_filter): + """Test that different WARNING messages are not rate limited together.""" + # First warning + record1 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg="Warning A", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(record1) is True + + # Different warning should also pass + record2 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=2, + msg="Warning B", + args=(), + exc_info=None, + ) + assert rate_limit_filter.filter(record2) is True + + +def test_custom_interval(): + """Test that custom interval seconds work correctly.""" + custom_filter = RateLimitFilter(interval_seconds=1) + assert custom_filter.interval == 1 + + long_filter = RateLimitFilter(interval_seconds=10) + assert long_filter.interval == 10 + + +def test_last_emitted_tracking(rate_limit_filter): + """Test that the filter correctly tracks last emission times.""" + message1 = "Message 1" + message2 = "Message 2" + + # Emit first message + record1 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg=message1, + args=(), + exc_info=None, + ) + rate_limit_filter.filter(record1) + + # Check that message1 is tracked + assert message1 in rate_limit_filter.last_emitted + + # Emit second message + record2 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=2, + msg=message2, + args=(), + exc_info=None, + ) + rate_limit_filter.filter(record2) + + # Check that both messages are tracked + assert message1 in rate_limit_filter.last_emitted + assert message2 in rate_limit_filter.last_emitted + + # Timestamps should be different (though very close) + assert rate_limit_filter.last_emitted[message1] <= rate_limit_filter.last_emitted[message2] + + +def test_formatted_message_warnings(rate_limit_filter): + """Test rate limiting with formatted WARNING messages.""" + # Test with string formatting + record1 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=1, + msg="Warning: value=%d", + args=(42,), + exc_info=None, + ) + assert rate_limit_filter.filter(record1) is True + + # Same formatted message should be blocked + record2 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=2, + msg="Warning: value=%d", + args=(42,), + exc_info=None, + ) + assert rate_limit_filter.filter(record2) is False + + # Different args create different message, should pass + record3 = logging.LogRecord( + name="test", + level=logging.WARNING, + pathname="test.py", + lineno=3, + msg="Warning: value=%d", + args=(99,), + exc_info=None, + ) + assert rate_limit_filter.filter(record3) is True + + +""" +Integration Tests. + +Tests that the filter and formatter work together in a logger. +""" + + +def test_filter_and_formatter_together(): + """Test that filter and formatter work together in a logger.""" + # Create a logger with both filter and formatter + test_logger = logging.getLogger("test_integration") + test_logger.setLevel(logging.DEBUG) + + # Remove any existing handlers + test_logger.handlers.clear() + + # Create handler with colored formatter + handler = logging.StreamHandler() + handler.setFormatter(ColoredFormatter("%(levelname)s: %(message)s")) + + # Add rate limit filter + rate_filter = RateLimitFilter(interval_seconds=1) + handler.addFilter(rate_filter) + + test_logger.addHandler(handler) + + # Test that logger is set up correctly + assert len(test_logger.handlers) == 1 + assert isinstance(test_logger.handlers[0].formatter, ColoredFormatter) + + # Clean up + test_logger.handlers.clear() + + +def test_default_initialization(): + """Test that classes can be initialized with default parameters.""" + # ColoredFormatter with default format + formatter = ColoredFormatter() + assert formatter is not None + + # RateLimitFilter with default interval + filter_obj = RateLimitFilter() + assert filter_obj.interval == 5 # default is 5 seconds + + +""" +Tests for the configure_logging function. +""" + + +def test_configure_logging_basic(): + """Test basic configure_logging functionality without file logging.""" + # Setup logger without file logging + logger = configure_logging(logging_level="INFO", save_logs_to_file=False) + + # Should return root logger + assert logger is not None + assert logger is logging.getLogger() + # Root logger is always set to DEBUG to ensure all messages are logged + assert logger.level == logging.DEBUG + + # Should have exactly one handler (stream handler) + assert len(logger.handlers) == 1 + + # Stream handler should have ColoredFormatter + stream_handler = logger.handlers[0] + assert isinstance(stream_handler, logging.StreamHandler) + assert isinstance(stream_handler.formatter, ColoredFormatter) + assert stream_handler.level == logging.INFO + + # Should have RateLimitFilter + assert len(stream_handler.filters) > 0 + rate_filter = stream_handler.filters[0] + assert isinstance(rate_filter, RateLimitFilter) + assert rate_filter.interval == 5 + + +def test_configure_logging_with_file(): + """Test configure_logging with file logging enabled.""" + # Setup logger with file logging + with tempfile.TemporaryDirectory() as temp_dir: + logger = configure_logging(logging_level="DEBUG", save_logs_to_file=True, log_dir=temp_dir) + + # Should return root logger + assert logger is not None + # Root logger is always set to DEBUG + assert logger.level == logging.DEBUG + + # Should have two handlers (stream + file) + assert len(logger.handlers) == 2 + + # Check stream handler + stream_handler = logger.handlers[0] + assert isinstance(stream_handler, logging.StreamHandler) + assert isinstance(stream_handler.formatter, ColoredFormatter) + assert stream_handler.level == logging.DEBUG + + # Check file handler + file_handler = logger.handlers[1] + assert isinstance(file_handler, logging.FileHandler) + assert file_handler.level == logging.DEBUG + + # Verify log file was created + log_files = [f for f in os.listdir(temp_dir) if f.startswith("isaaclab_")] + assert len(log_files) == 1 + + +def test_configure_logging_levels(): + """Test configure_logging with different logging levels.""" + from typing import Literal + + levels: list[Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]] = [ + "DEBUG", + "INFO", + "WARNING", + "ERROR", + "CRITICAL", + ] + level_values = { + "DEBUG": logging.DEBUG, + "INFO": logging.INFO, + "WARNING": logging.WARNING, + "ERROR": logging.ERROR, + "CRITICAL": logging.CRITICAL, + } + + for level_str in levels: + logger = configure_logging(logging_level=level_str, save_logs_to_file=False) + # Root logger is always set to DEBUG to ensure all messages are logged + assert logger.level == logging.DEBUG + # Handler level should match the requested level + assert logger.handlers[0].level == level_values[level_str] + + +def test_configure_logging_removes_existing_handlers(): + """Test that configure_logging removes existing handlers.""" + # Get root logger and add a dummy handler + root_logger = logging.getLogger() + dummy_handler = logging.StreamHandler() + root_logger.addHandler(dummy_handler) + + initial_handler_count = len(root_logger.handlers) + assert initial_handler_count > 0 + + # Setup logger should remove existing handlers + logger = configure_logging(logging_level="INFO", save_logs_to_file=False) + + # Should only have the new handler + assert len(logger.handlers) == 1 + assert dummy_handler not in logger.handlers + + +def test_configure_logging_default_log_dir(): + """Test configure_logging uses temp directory when log_dir is None.""" + + logger = configure_logging(logging_level="INFO", save_logs_to_file=True, log_dir=None) + + # Root logger is always set to DEBUG + assert logger.level == logging.DEBUG + + # Should have file handler + assert len(logger.handlers) == 2 + file_handler = logger.handlers[1] + assert isinstance(file_handler, logging.FileHandler) + + # File should be in temp directory + log_file_path = file_handler.baseFilename + assert os.path.dirname(log_file_path) == os.path.join(tempfile.gettempdir(), "isaaclab", "logs") + assert os.path.basename(log_file_path).startswith("isaaclab_") + + # Cleanup + if os.path.exists(log_file_path): + os.remove(log_file_path) + + +def test_configure_logging_custom_log_dir(): + """Test configure_logging with custom log directory.""" + with tempfile.TemporaryDirectory() as temp_dir: + custom_log_dir = os.path.join(temp_dir, "custom_logs") + + logger = configure_logging(logging_level="INFO", save_logs_to_file=True, log_dir=custom_log_dir) + + # Custom directory should be created + assert os.path.exists(custom_log_dir) + assert os.path.isdir(custom_log_dir) + + # Root logger is always set to DEBUG + assert logger.level == logging.DEBUG + + # Log file should be in custom directory + file_handler = logger.handlers[1] + assert isinstance(file_handler, logging.FileHandler) + log_file_path = file_handler.baseFilename + assert os.path.dirname(log_file_path) == custom_log_dir + + +def test_configure_logging_log_file_format(): + """Test that log file has correct timestamp format.""" + with tempfile.TemporaryDirectory() as temp_dir: + logger = configure_logging(logging_level="INFO", save_logs_to_file=True, log_dir=temp_dir) + + # Root logger is always set to DEBUG + assert logger.level == logging.DEBUG + + # Get log file name + file_handler = logger.handlers[1] + assert isinstance(file_handler, logging.FileHandler) + log_file_path = file_handler.baseFilename + log_filename = os.path.basename(log_file_path) + + # Check filename format: isaaclab_YYYY-MM-DD_HH-MM-SS.log + pattern = r"isaaclab_\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}\.log" + assert re.match(pattern, log_filename), f"Log filename {log_filename} doesn't match expected pattern" + + +def test_configure_logging_file_formatter(): + """Test that file handler has more detailed formatter than stream handler.""" + with tempfile.TemporaryDirectory() as temp_dir: + logger = configure_logging(logging_level="INFO", save_logs_to_file=True, log_dir=temp_dir) + + # Root logger is always set to DEBUG + assert logger.level == logging.DEBUG + + stream_handler = logger.handlers[0] + file_handler = logger.handlers[1] + + # Stream formatter should exist and be ColoredFormatter + assert stream_handler.formatter is not None + assert isinstance(stream_handler.formatter, ColoredFormatter) + stream_format = stream_handler.formatter._fmt # type: ignore + assert stream_format is not None + assert "%(asctime)s" in stream_format + assert "%(filename)s" in stream_format + + # File formatter should exist and include line numbers + assert file_handler.formatter is not None + assert isinstance(file_handler.formatter, logging.Formatter) + file_format = file_handler.formatter._fmt # type: ignore + assert file_format is not None + assert "%(asctime)s" in file_format + assert "%(lineno)d" in file_format + + # File handler should always use DEBUG level + assert file_handler.level == logging.DEBUG + + +def test_configure_logging_multiple_calls(): + """Test that multiple configure_logging calls properly cleanup.""" + # First setup + logger1 = configure_logging(logging_level="INFO", save_logs_to_file=False) + handler_count_1 = len(logger1.handlers) + + # Second setup should remove previous handlers + logger2 = configure_logging(logging_level="DEBUG", save_logs_to_file=False) + handler_count_2 = len(logger2.handlers) + + # Should be same logger (root logger) + assert logger1 is logger2 + + # Should have same number of handlers (old ones removed) + assert handler_count_1 == handler_count_2 == 1 + + +def test_configure_logging_actual_logging(): + """Test that logger actually logs messages correctly.""" + import io + + # Capture stdout + captured_output = io.StringIO() + + # Setup logger + logger = configure_logging(logging_level="INFO", save_logs_to_file=False) + + # Temporarily redirect handler to captured output + stream_handler = logger.handlers[0] + assert isinstance(stream_handler, logging.StreamHandler) + original_stream = stream_handler.stream # type: ignore + stream_handler.stream = captured_output # type: ignore + + # Log some messages + test_logger = logging.getLogger("test_module") + test_logger.info("Test info message") + test_logger.warning("Test warning message") + test_logger.debug("Test debug message") # Should not appear (level is INFO) + + # Restore original stream + stream_handler.stream = original_stream # type: ignore + + # Check output + output = captured_output.getvalue() + assert "Test info message" in output + assert "Test warning message" in output + assert "Test debug message" not in output # DEBUG < INFO + assert "INFO" in output + assert "WARNING" in output diff --git a/source/isaaclab/test/utils/test_math.py b/source/isaaclab/test/utils/test_math.py new file mode 100644 index 0000000000000000000000000000000000000000..2f256728e9ee2d95b40a3b5157b33480519db65f --- /dev/null +++ b/source/isaaclab/test/utils/test_math.py @@ -0,0 +1,1320 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first. + +This is only needed because of warp dependency. +""" + +from isaaclab.app import AppLauncher + +# launch omniverse app in headless mode +simulation_app = AppLauncher(headless=True).app + + +"""Rest everything follows.""" + +import math +from math import pi as PI + +import numpy as np +import pytest +import scipy.spatial.transform as scipy_tf +import torch +import torch.utils.benchmark as benchmark + +import isaaclab.utils.math as math_utils + +DECIMAL_PRECISION = 5 +"""Precision of the test. + +This value is used since float operations are inexact. For reference: +https://github.com/pytorch/pytorch/issues/17678 +""" + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("size", ((5, 4, 3), (10, 2))) +def test_scale_unscale_transform(device, size): + """Test scale_transform and unscale_transform.""" + + inputs = torch.tensor(range(math.prod(size)), device=device, dtype=torch.float32).reshape(size) + + # test with same size + scale_same = 2.0 + lower_same = -scale_same * torch.ones(size, device=device) + upper_same = scale_same * torch.ones(size, device=device) + output_same = math_utils.scale_transform(inputs, lower_same, upper_same) + expected_output_same = inputs / scale_same + torch.testing.assert_close(output_same, expected_output_same) + output_unscale_same = math_utils.unscale_transform(output_same, lower_same, upper_same) + torch.testing.assert_close(output_unscale_same, inputs) + + # test with broadcasting + scale_per_batch = 3.0 + lower_per_batch = -scale_per_batch * torch.ones(size[1:], device=device) + upper_per_batch = scale_per_batch * torch.ones(size[1:], device=device) + output_per_batch = math_utils.scale_transform(inputs, lower_per_batch, upper_per_batch) + expected_output_per_batch = inputs / scale_per_batch + torch.testing.assert_close(output_per_batch, expected_output_per_batch) + output_unscale_per_batch = math_utils.unscale_transform(output_per_batch, lower_per_batch, upper_per_batch) + torch.testing.assert_close(output_unscale_per_batch, inputs) + + # test offset between lower and upper + lower_offset = -3.0 * torch.ones(size[1:], device=device) + upper_offset = 2.0 * torch.ones(size[1:], device=device) + output_offset = math_utils.scale_transform(inputs, lower_offset, upper_offset) + expected_output_offset = (inputs + 0.5) / 2.5 + torch.testing.assert_close(output_offset, expected_output_offset) + output_unscale_offset = math_utils.unscale_transform(output_offset, lower_offset, upper_offset) + torch.testing.assert_close(output_unscale_offset, inputs) + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("size", ((5, 4, 3), (10, 2))) +def test_saturate(device, size): + "Test saturate of a tensor of differed shapes and device." + + num_elements = math.prod(size) + input = torch.tensor(range(num_elements), device=device, dtype=torch.float32).reshape(size) + + # testing with same size + lower_same = -2.0 * torch.ones(size, device=device) + upper_same = 2.0 * torch.ones(size, device=device) + output_same = math_utils.saturate(input, lower_same, upper_same) + assert torch.all(torch.greater_equal(output_same, lower_same)).item() + assert torch.all(torch.less_equal(output_same, upper_same)).item() + # testing with broadcasting + lower_per_batch = -2.0 * torch.ones(size[1:], device=device) + upper_per_batch = 3.0 * torch.ones(size[1:], device=device) + output_per_batch = math_utils.saturate(input, lower_per_batch, upper_per_batch) + assert torch.all(torch.greater_equal(output_per_batch, lower_per_batch)).item() + assert torch.all(torch.less_equal(output_per_batch, upper_per_batch)).item() + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("size", ((5, 4, 3), (10, 2))) +def test_normalize(device, size): + """Test normalize of a tensor along its last dimension and check the norm of that dimension is close to 1.0.""" + + num_elements = math.prod(size) + input = torch.tensor(range(num_elements), device=device, dtype=torch.float32).reshape(size) + output = math_utils.normalize(input) + norm = torch.linalg.norm(output, dim=-1) + torch.testing.assert_close(norm, torch.ones(size[0:-1], device=device)) + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +def test_copysign(device): + """Test copysign by copying a sign from both a negative and positive value and + verify that the new sign is the same. + """ + + size = (10, 2) + + input_mag_pos = 2.0 + input_mag_neg = -3.0 + + input = torch.tensor(range(20), device=device, dtype=torch.float32).reshape(size) + value_pos = math_utils.copysign(input_mag_pos, input) + value_neg = math_utils.copysign(input_mag_neg, input) + torch.testing.assert_close(abs(input_mag_pos) * torch.ones_like(input), value_pos) + torch.testing.assert_close(abs(input_mag_neg) * torch.ones_like(input), value_neg) + + input_neg_dim1 = input.clone() + input_neg_dim1[:, 1] = -input_neg_dim1[:, 1] + value_neg_dim1_pos = math_utils.copysign(input_mag_pos, input_neg_dim1) + value_neg_dim1_neg = math_utils.copysign(input_mag_neg, input_neg_dim1) + expected_value_neg_dim1_pos = abs(input_mag_pos) * torch.ones_like(input_neg_dim1) + expected_value_neg_dim1_pos[:, 1] = -expected_value_neg_dim1_pos[:, 1] + expected_value_neg_dim1_neg = abs(input_mag_neg) * torch.ones_like(input_neg_dim1) + expected_value_neg_dim1_neg[:, 1] = -expected_value_neg_dim1_neg[:, 1] + + torch.testing.assert_close(expected_value_neg_dim1_pos, value_neg_dim1_pos) + torch.testing.assert_close(expected_value_neg_dim1_neg, value_neg_dim1_neg) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_is_identity_pose(device): + """Test is_identity_pose method.""" + # Single row identity pose + identity_pos = torch.zeros(3, device=device) + identity_rot = torch.tensor((1.0, 0.0, 0.0, 0.0), device=device) + assert math_utils.is_identity_pose(identity_pos, identity_rot) is True + + # Modified single row pose + identity_pos = torch.tensor([1.0, 0.0, 0.0], device=device) + identity_rot = torch.tensor((1.0, 1.0, 0.0, 0.0), device=device) + assert math_utils.is_identity_pose(identity_pos, identity_rot) is False + + # Multi-row identity pose + identity_pos = torch.zeros(3, 3, device=device) + identity_rot = torch.tensor([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], device=device) + assert math_utils.is_identity_pose(identity_pos, identity_rot) is True + + # Modified multi-row pose + identity_pos = torch.tensor([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=device) + identity_rot = torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], device=device) + assert math_utils.is_identity_pose(identity_pos, identity_rot) is False + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_axis_angle_from_quat(device): + """Test axis_angle_from_quat method.""" + # Quaternions of the form (2,4) and (2,2,4) + quats = [ + torch.Tensor([[1.0, 0.0, 0.0, 0.0], [0.8418536, 0.142006, 0.0, 0.5206887]]).to(device), + torch.Tensor( + [ + [[1.0, 0.0, 0.0, 0.0], [0.8418536, 0.142006, 0.0, 0.5206887]], + [[1.0, 0.0, 0.0, 0.0], [0.9850375, 0.0995007, 0.0995007, 0.0995007]], + ] + ).to(device), + ] + + # Angles of the form (2,3) and (2,2,3) + angles = [ + torch.Tensor([[0.0, 0.0, 0.0], [0.3, 0.0, 1.1]]).to(device), + torch.Tensor([[[0.0, 0.0, 0.0], [0.3, 0.0, 1.1]], [[0.0, 0.0, 0.0], [0.2, 0.2, 0.2]]]).to(device), + ] + + for quat, angle in zip(quats, angles): + torch.testing.assert_close(math_utils.axis_angle_from_quat(quat), angle) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_axis_angle_from_quat_approximation(device): + """Test the Taylor approximation from axis_angle_from_quat method. + + This test checks for unstable conversions where theta is very small. + """ + # Generate a small rotation quaternion + # Small angle + theta = torch.Tensor([0.0000001]).to(device) + # Arbitrary normalized axis of rotation in rads, (x,y,z) + axis = [-0.302286, 0.205494, -0.930803] + # Generate quaternion + qw = torch.cos(theta / 2) + quat_vect = [qw] + [d * torch.sin(theta / 2) for d in axis] + quaternion = torch.tensor(quat_vect, dtype=torch.float32, device=device) + + # Convert quaternion to axis-angle + axis_angle_computed = math_utils.axis_angle_from_quat(quaternion) + + # Expected axis-angle representation + axis_angle_expected = torch.tensor([theta * d for d in axis], dtype=torch.float32, device=device) + + # Assert that the computed values are close to the expected values + torch.testing.assert_close(axis_angle_computed, axis_angle_expected) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_error_magnitude(device): + """Test quat_error_magnitude method.""" + # No rotation + q1 = torch.Tensor([1, 0, 0, 0]).to(device) + q2 = torch.Tensor([1, 0, 0, 0]).to(device) + expected_diff = torch.Tensor([0.0]).to(device) + q12_diff = math_utils.quat_error_magnitude(q1, q2) + assert math.isclose(q12_diff.item(), expected_diff.item(), rel_tol=1e-5) + + # PI/2 rotation + q1 = torch.Tensor([1.0, 0, 0.0, 0]).to(device) + q2 = torch.Tensor([0.7071068, 0.7071068, 0, 0]).to(device) + expected_diff = torch.Tensor([PI / 2]).to(device) + q12_diff = math_utils.quat_error_magnitude(q1, q2) + assert math.isclose(q12_diff.item(), expected_diff.item(), rel_tol=1e-5) + + # PI rotation + q1 = torch.Tensor([1.0, 0, 0.0, 0]).to(device) + q2 = torch.Tensor([0.0, 0.0, 1.0, 0]).to(device) + expected_diff = torch.Tensor([PI]).to(device) + q12_diff = math_utils.quat_error_magnitude(q1, q2) + assert math.isclose(q12_diff.item(), expected_diff.item(), rel_tol=1e-5) + + # Batched inputs + q1 = torch.stack( + [torch.Tensor([1, 0, 0, 0]), torch.Tensor([1.0, 0, 0.0, 0]), torch.Tensor([1.0, 0, 0.0, 0])], dim=0 + ).to(device) + q2 = torch.stack( + [torch.Tensor([1, 0, 0, 0]), torch.Tensor([0.7071068, 0.7071068, 0, 0]), torch.Tensor([0.0, 0.0, 1.0, 0])], + dim=0, + ).to(device) + expected_diff = ( + torch.stack([torch.Tensor([0.0]), torch.Tensor([PI / 2]), torch.Tensor([PI])], dim=0).flatten().to(device) + ) + q12_diff = math_utils.quat_error_magnitude(q1, q2) + torch.testing.assert_close(q12_diff, expected_diff) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_unique(device): + """Test quat_unique method.""" + # Define test cases + quats = math_utils.random_orientation(num=1024, device=device) + + # Test positive real quaternion + pos_real_quats = math_utils.quat_unique(quats) + + # Test that the real part is positive + assert torch.all(pos_real_quats[:, 0] > 0).item() + + non_pos_indices = quats[:, 0] < 0 + # Check imaginary part have sign flipped if real part is negative + torch.testing.assert_close(pos_real_quats[non_pos_indices], -quats[non_pos_indices]) + torch.testing.assert_close(pos_real_quats[~non_pos_indices], quats[~non_pos_indices]) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_mul_with_quat_unique(device): + """Test quat_mul method with different quaternions. + + This test checks that the quaternion multiplication is consistent when using positive real quaternions + and regular quaternions. It makes sure that the result is the same regardless of the input quaternion sign + (i.e. q and -q are same quaternion in the context of rotations). + """ + + quats_1 = math_utils.random_orientation(num=1024, device=device) + quats_2 = math_utils.random_orientation(num=1024, device=device) + # Make quats positive real + quats_1_pos_real = math_utils.quat_unique(quats_1) + quats_2_pos_real = math_utils.quat_unique(quats_2) + + # Option 1: Direct computation on quaternions + quat_result_1 = math_utils.quat_mul(quats_1, math_utils.quat_conjugate(quats_2)) + quat_result_1 = math_utils.quat_unique(quat_result_1) + + # Option 2: Computation on positive real quaternions + quat_result_2 = math_utils.quat_mul(quats_1_pos_real, math_utils.quat_conjugate(quats_2_pos_real)) + quat_result_2 = math_utils.quat_unique(quat_result_2) + + # Option 3: Mixed computation + quat_result_3 = math_utils.quat_mul(quats_1, math_utils.quat_conjugate(quats_2_pos_real)) + quat_result_3 = math_utils.quat_unique(quat_result_3) + + # Check that the result is close to the expected value + torch.testing.assert_close(quat_result_1, quat_result_2) + torch.testing.assert_close(quat_result_2, quat_result_3) + torch.testing.assert_close(quat_result_3, quat_result_1) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_error_mag_with_quat_unique(device): + """Test quat_error_magnitude method with positive real quaternions.""" + + quats_1 = math_utils.random_orientation(num=1024, device=device) + quats_2 = math_utils.random_orientation(num=1024, device=device) + # Make quats positive real + quats_1_pos_real = math_utils.quat_unique(quats_1) + quats_2_pos_real = math_utils.quat_unique(quats_2) + + # Compute the error + error_1 = math_utils.quat_error_magnitude(quats_1, quats_2) + error_2 = math_utils.quat_error_magnitude(quats_1_pos_real, quats_2_pos_real) + error_3 = math_utils.quat_error_magnitude(quats_1, quats_2_pos_real) + error_4 = math_utils.quat_error_magnitude(quats_1_pos_real, quats_2) + + # Check that the error is close to the expected value + torch.testing.assert_close(error_1, error_2) + torch.testing.assert_close(error_2, error_3) + torch.testing.assert_close(error_3, error_4) + torch.testing.assert_close(error_4, error_1) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_convention_converter(device): + """Test convert_camera_frame_orientation_convention to and from ros, opengl, and world conventions.""" + quat_ros = torch.tensor([[-0.17591989, 0.33985114, 0.82047325, -0.42470819]], device=device) + quat_opengl = torch.tensor([[0.33985113, 0.17591988, 0.42470818, 0.82047324]], device=device) + quat_world = torch.tensor([[-0.3647052, -0.27984815, -0.1159169, 0.88047623]], device=device) + + # from ROS + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_ros, "ros", "opengl"), quat_opengl + ) + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_ros, "ros", "world"), quat_world + ) + torch.testing.assert_close(math_utils.convert_camera_frame_orientation_convention(quat_ros, "ros", "ros"), quat_ros) + # from OpenGL + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_opengl, "opengl", "ros"), quat_ros + ) + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_opengl, "opengl", "world"), quat_world + ) + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_opengl, "opengl", "opengl"), quat_opengl + ) + # from World + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_world, "world", "ros"), quat_ros + ) + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_world, "world", "opengl"), quat_opengl + ) + torch.testing.assert_close( + math_utils.convert_camera_frame_orientation_convention(quat_world, "world", "world"), quat_world + ) + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("size", ((10, 4), (5, 3, 4))) +def test_convert_quat(device, size): + """Test convert_quat from "xyzw" to "wxyz" and back to "xyzw" and verify the correct rolling of the tensor. + + Also check the correct exceptions are raised for bad inputs for the quaternion and the 'to'. + """ + + quat = torch.zeros(size, device=device) + quat[..., 0] = 1.0 + + value_default = math_utils.convert_quat(quat) + expected_default = torch.zeros(size, device=device) + expected_default[..., -1] = 1.0 + torch.testing.assert_close(expected_default, value_default) + + value_to_xyzw = math_utils.convert_quat(quat, to="xyzw") + expected_to_xyzw = torch.zeros(size, device=device) + expected_to_xyzw[..., -1] = 1.0 + torch.testing.assert_close(expected_to_xyzw, value_to_xyzw) + + value_to_wxyz = math_utils.convert_quat(quat, to="wxyz") + expected_to_wxyz = torch.zeros(size, device=device) + expected_to_wxyz[..., 1] = 1.0 + torch.testing.assert_close(expected_to_wxyz, value_to_wxyz) + + bad_quat = torch.zeros((10, 5), device=device) + + with pytest.raises(ValueError): + math_utils.convert_quat(bad_quat) + + with pytest.raises(ValueError): + math_utils.convert_quat(quat, to="xwyz") + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +def test_quat_conjugate(device): + """Test quat_conjugate by checking the sign of the imaginary part changes but the magnitudes stay the same.""" + + quat = math_utils.random_orientation(1000, device=device) + + value = math_utils.quat_conjugate(quat) + expected_real = quat[..., 0] + expected_imag = -quat[..., 1:] + torch.testing.assert_close(expected_real, value[..., 0]) + torch.testing.assert_close(expected_imag, value[..., 1:]) + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("num_envs", (1, 10)) +@pytest.mark.parametrize( + "euler_angles", + [ + [0.0, 0.0, 0.0], + [math.pi / 2.0, 0.0, 0.0], + [0.0, math.pi / 2.0, 0.0], + [0.0, 0.0, math.pi / 2.0], + [1.5708, -2.75, 0.1], + [0.1, math.pi, math.pi / 2], + ], +) +def test_quat_from_euler_xyz(device, num_envs, euler_angles): + """Test quat_from_euler_xyz against scipy.""" + + angles = torch.tensor(euler_angles, device=device).unsqueeze(0).repeat((num_envs, 1)) + quat_value = math_utils.quat_unique(math_utils.quat_from_euler_xyz(angles[:, 0], angles[:, 1], angles[:, 2])) + expected_quat = math_utils.convert_quat( + torch.tensor( + scipy_tf.Rotation.from_euler("xyz", euler_angles, degrees=False).as_quat(), + device=device, + dtype=torch.float, + ) + .unsqueeze(0) + .repeat((num_envs, 1)), + to="wxyz", + ) + torch.testing.assert_close(expected_quat, quat_value) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_wrap_to_pi(device): + """Test wrap_to_pi method.""" + # No wrapping needed + angle = torch.Tensor([0.0]).to(device) + expected_angle = torch.Tensor([0.0]).to(device) + wrapped_angle = math_utils.wrap_to_pi(angle) + torch.testing.assert_close(wrapped_angle, expected_angle) + + # Positive angle + angle = torch.Tensor([PI]).to(device) + expected_angle = torch.Tensor([PI]).to(device) + wrapped_angle = math_utils.wrap_to_pi(angle) + torch.testing.assert_close(wrapped_angle, expected_angle) + + # Negative angle + angle = torch.Tensor([-PI]).to(device) + expected_angle = torch.Tensor([-PI]).to(device) + wrapped_angle = math_utils.wrap_to_pi(angle) + torch.testing.assert_close(wrapped_angle, expected_angle) + + # Multiple angles + angle = torch.Tensor([3 * PI, -3 * PI, 4 * PI, -4 * PI]).to(device) + expected_angle = torch.Tensor([PI, -PI, 0.0, 0.0]).to(device) + wrapped_angle = math_utils.wrap_to_pi(angle) + torch.testing.assert_close(wrapped_angle, expected_angle) + + # Multiple angles from MATLAB docs + # fmt: off + angle = torch.Tensor([-2 * PI, - PI - 0.1, -PI, -2.8, 3.1, PI, PI + 0.001, PI + 1, 2 * PI, 2 * PI + 0.1]).to(device) + expected_angle = torch.Tensor([0.0, PI - 0.1, -PI, -2.8, 3.1 , PI, -PI + 0.001, -PI + 1 , 0.0, 0.1]).to(device) + # fmt: on + wrapped_angle = math_utils.wrap_to_pi(angle) + torch.testing.assert_close(wrapped_angle, expected_angle) + + +@pytest.mark.parametrize("device", ("cpu", "cuda:0")) +@pytest.mark.parametrize("shape", ((3,), (1024, 3))) +def test_skew_symmetric_matrix(device, shape): + """Test skew_symmetric_matrix.""" + + vec_rand = torch.zeros(shape, device=device) + vec_rand.uniform_(-1000.0, 1000.0) + + if vec_rand.ndim == 1: + vec_rand_resized = vec_rand.clone().unsqueeze(0) + else: + vec_rand_resized = vec_rand.clone() + + mat_value = math_utils.skew_symmetric_matrix(vec_rand) + if len(shape) == 1: + expected_shape = (1, 3, 3) + else: + expected_shape = (shape[0], 3, 3) + + torch.testing.assert_close( + torch.zeros((expected_shape[0], 3), device=device), torch.diagonal(mat_value, dim1=-2, dim2=-1) + ) + torch.testing.assert_close(-vec_rand_resized[:, 2], mat_value[:, 0, 1]) + torch.testing.assert_close(vec_rand_resized[:, 1], mat_value[:, 0, 2]) + torch.testing.assert_close(-vec_rand_resized[:, 0], mat_value[:, 1, 2]) + torch.testing.assert_close(vec_rand_resized[:, 2], mat_value[:, 1, 0]) + torch.testing.assert_close(-vec_rand_resized[:, 1], mat_value[:, 2, 0]) + torch.testing.assert_close(vec_rand_resized[:, 0], mat_value[:, 2, 1]) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_orthogonalize_perspective_depth(device): + """Test for converting perspective depth to orthogonal depth.""" + # Create a sample perspective depth image (N, H, W) + perspective_depth = torch.tensor([[[10.0, 0.0, 100.0], [0.0, 3000.0, 0.0], [100.0, 0.0, 100.0]]], device=device) + + # Create sample intrinsic matrix (3, 3) + intrinsics = torch.tensor([[500.0, 0.0, 5.0], [0.0, 500.0, 5.0], [0.0, 0.0, 1.0]], device=device) + + # Convert perspective depth to orthogonal depth + orthogonal_depth = math_utils.orthogonalize_perspective_depth(perspective_depth, intrinsics) + + # Manually compute expected orthogonal depth based on the formula for comparison + expected_orthogonal_depth = torch.tensor( + [[[9.9990, 0.0000, 99.9932], [0.0000, 2999.8079, 0.0000], [99.9932, 0.0000, 99.9964]]], device=device + ) + + # Assert that the output is close to the expected result + torch.testing.assert_close(orthogonal_depth, expected_orthogonal_depth) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_combine_frame_transform(device): + """Test combine_frame_transforms function.""" + # create random poses + pose01 = torch.rand(1, 7, device=device) + pose01[:, 3:7] = torch.nn.functional.normalize(pose01[..., 3:7], dim=-1) + + pose12 = torch.rand(1, 7, device=device) + pose12[:, 3:7] = torch.nn.functional.normalize(pose12[..., 3:7], dim=-1) + + # apply combination of poses + pos02, quat02 = math_utils.combine_frame_transforms( + pose01[..., :3], pose01[..., 3:7], pose12[:, :3], pose12[:, 3:7] + ) + # apply combination of poses w.r.t. inverse to get original frame + pos01, quat01 = math_utils.combine_frame_transforms( + pos02, + quat02, + math_utils.quat_rotate(math_utils.quat_inv(pose12[:, 3:7]), -pose12[:, :3]), + math_utils.quat_inv(pose12[:, 3:7]), + ) + + torch.testing.assert_close(pose01, torch.cat((pos01, quat01), dim=-1)) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_interpolate_poses(device): + """Test interpolate_poses function. + + This test checks the output from the :meth:`~isaaclab.utils.math_utils.interpolate_poses` function against + the output from :func:`scipy.spatial.transform.Slerp` and :func:`np.linspace`. + """ + for _ in range(100): + mat1 = math_utils.generate_random_transformation_matrix() + mat2 = math_utils.generate_random_transformation_matrix() + pos_1, rmat1 = math_utils.unmake_pose(mat1) + pos_2, rmat2 = math_utils.unmake_pose(mat2) + + # Compute expected results using scipy's Slerp + key_rots = scipy_tf.Rotation.from_matrix(np.array([rmat1, rmat2])) + + # Create a Slerp object and interpolate create the interpolated rotation matrices + num_steps = np.random.randint(3, 51) + key_times = [0, 1] + slerp = scipy_tf.Slerp(key_times, key_rots) + interp_times = np.linspace(0, 1, num_steps) + expected_quat = slerp(interp_times).as_matrix() + + # Test interpolation against expected result using np.linspace + expected_pos = np.linspace(pos_1, pos_2, num_steps) + + # interpolate_poses using interpolate_poses and quat_slerp + interpolated_poses, _ = math_utils.interpolate_poses( + math_utils.make_pose(pos_1, rmat1), math_utils.make_pose(pos_2, rmat2), num_steps - 2 + ) + result_pos, result_quat = math_utils.unmake_pose(interpolated_poses) + + # Assert that the result is almost equal to the expected quaternion + np.testing.assert_array_almost_equal(result_quat, expected_quat, decimal=DECIMAL_PRECISION) + np.testing.assert_array_almost_equal(result_pos, expected_pos, decimal=DECIMAL_PRECISION) + + +def test_pose_inv(): + """Test pose_inv function. + + This test checks the output from the :meth:`~isaaclab.utils.math_utils.pose_inv` function against + the output from :func:`np.linalg.inv`. Two test cases are performed: + + 1. Checking the inverse of a random transformation matrix matches Numpy's built-in inverse. + 2. Checking the inverse of a batch of random transformation matrices matches Numpy's built-in inverse. + """ + # Check against a single matrix + for _ in range(100): + test_mat = math_utils.generate_random_transformation_matrix(pos_boundary=10, rot_boundary=(2 * np.pi)) + result = np.array(math_utils.pose_inv(test_mat)) + expected = np.linalg.inv(np.array(test_mat)) + np.testing.assert_array_almost_equal(result, expected, decimal=DECIMAL_PRECISION) + + # Check against a batch of matrices + test_mats = torch.stack( + [ + math_utils.generate_random_transformation_matrix(pos_boundary=10, rot_boundary=(2 * math.pi)) + for _ in range(100) + ] + ) + result = np.array(math_utils.pose_inv(test_mats)) + expected = np.linalg.inv(np.array(test_mats)) + np.testing.assert_array_almost_equal(result, expected, decimal=DECIMAL_PRECISION) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_to_and_from_angle_axis(device): + """Test that axis_angle_from_quat against scipy and that quat_from_angle_axis are the inverse of each other.""" + n = 1024 + q_rand = math_utils.quat_unique(math_utils.random_orientation(num=n, device=device)) + rot_vec_value = math_utils.axis_angle_from_quat(q_rand) + rot_vec_scipy = torch.tensor( + scipy_tf.Rotation.from_quat( + math_utils.convert_quat(quat=q_rand.to(device="cpu").numpy(), to="xyzw") + ).as_rotvec(), + device=device, + dtype=torch.float32, + ) + torch.testing.assert_close(rot_vec_scipy, rot_vec_value) + axis = math_utils.normalize(rot_vec_value.clone()) + angle = torch.norm(rot_vec_value.clone(), dim=-1) + q_value = math_utils.quat_unique(math_utils.quat_from_angle_axis(angle, axis)) + torch.testing.assert_close(q_rand, q_value) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_box_minus(device): + """Test quat_box_minus method. + + Ensures that quat_box_minus correctly computes the axis-angle difference + between two quaternions representing rotations around the same axis. + """ + axis_angles = torch.tensor([0.0, 0.0, 1.0], device=device) + angle_a = math.pi - 0.1 + angle_b = -math.pi + 0.1 + quat_a = math_utils.quat_from_angle_axis(torch.tensor([angle_a], device=device), axis_angles) + quat_b = math_utils.quat_from_angle_axis(torch.tensor([angle_b], device=device), axis_angles) + + axis_diff = math_utils.quat_box_minus(quat_a, quat_b).squeeze(0) + expected_diff = axis_angles * math_utils.wrap_to_pi(torch.tensor(angle_a - angle_b, device=device)) + torch.testing.assert_close(expected_diff, axis_diff, atol=1e-06, rtol=1e-06) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_box_minus_and_quat_box_plus(device): + """Test consistency of quat_box_plus and quat_box_minus. + + Checks that applying quat_box_plus to accumulate rotations and then using + quat_box_minus to retrieve differences results in expected values. + """ + + # Perform closed-loop integration using quat_box_plus to accumulate rotations, + # and then use quat_box_minus to compute the incremental differences between quaternions. + # NOTE: Accuracy may decrease for very small angle increments due to numerical precision limits. + for n in (2, 10, 100, 1000): + # Define small incremental rotations around principal axes + delta_angle = torch.tensor( + [ + [0, 0, -math.pi / n], + [0, -math.pi / n, 0], + [-math.pi / n, 0, 0], + [0, 0, math.pi / n], + [0, math.pi / n, 0], + [math.pi / n, 0, 0], + ], + device=device, + ) + + # Initialize quaternion trajectory starting from identity quaternion + quat_trajectory = torch.zeros((len(delta_angle), 2 * n + 1, 4), device=device) + quat_trajectory[:, 0, :] = torch.tensor([[1.0, 0.0, 0.0, 0.0]], device=device).repeat(len(delta_angle), 1) + + # Integrate incremental rotations forward to form a closed loop trajectory + for i in range(1, 2 * n + 1): + quat_trajectory[:, i] = math_utils.quat_box_plus(quat_trajectory[:, i - 1], delta_angle) + + # Validate the loop closure: start and end quaternions should be approximately equal + torch.testing.assert_close(quat_trajectory[:, 0], quat_trajectory[:, -1], atol=1e-04, rtol=1e-04) + + # Validate that the differences between consecutive quaternions match the original increments + for i in range(2 * n): + delta_result = math_utils.quat_box_minus(quat_trajectory[:, i + 1], quat_trajectory[:, i]) + torch.testing.assert_close(delta_result, delta_angle, atol=1e-04, rtol=1e-04) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("t12_inputs", ["True", "False"]) +@pytest.mark.parametrize("q12_inputs", ["True", "False"]) +def test_combine_frame_transforms(device, t12_inputs, q12_inputs): + """Test combine_frame_transforms such that inputs for delta translation and delta rotation + can be :obj:`None` or specified. + """ + n = 1024 + t01 = torch.zeros((n, 3), device=device) + t01.uniform_(-1000.0, 1000.0) + q01 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + + mat_01 = torch.eye(4, 4, device=device).unsqueeze(0).repeat(n, 1, 1) + mat_01[:, 0:3, 3] = t01 + mat_01[:, 0:3, 0:3] = math_utils.matrix_from_quat(q01) + + mat_12 = torch.eye(4, 4, device=device).unsqueeze(0).repeat(n, 1, 1) + if t12_inputs: + t12 = torch.zeros((n, 3), device=device) + t12.uniform_(-1000.0, 1000.0) + mat_12[:, 0:3, 3] = t12 + else: + t12 = None + + if q12_inputs: + q12 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + mat_12[:, 0:3, 0:3] = math_utils.matrix_from_quat(q12) + else: + q12 = None + + mat_expect = torch.einsum("bij,bjk->bik", mat_01, mat_12) + expected_translation = mat_expect[:, 0:3, 3] + expected_quat = math_utils.quat_from_matrix(mat_expect[:, 0:3, 0:3]) + translation_value, quat_value = math_utils.combine_frame_transforms(t01, q01, t12, q12) + + torch.testing.assert_close(expected_translation, translation_value, atol=1e-3, rtol=1e-5) + torch.testing.assert_close(math_utils.quat_unique(expected_quat), math_utils.quat_unique(quat_value)) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("t02_inputs", ["True", "False"]) +@pytest.mark.parametrize("q02_inputs", ["True", "False"]) +def test_subtract_frame_transforms(device, t02_inputs, q02_inputs): + """Test subtract_frame_transforms with specified and unspecified inputs for t02 and q02. + + This test verifies that :meth:`~isaaclab.utils.math_utils.subtract_frame_transforms` is the inverse operation + to :meth:`~isaaclab.utils.math_utils.combine_frame_transforms`. + .""" + n = 1024 + t01 = torch.zeros((n, 3), device=device) + t01.uniform_(-1000.0, 1000.0) + q01 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + + mat_01 = torch.eye(4, 4, device=device).unsqueeze(0).repeat(n, 1, 1) + mat_01[:, 0:3, 3] = t01 + mat_01[:, 0:3, 0:3] = math_utils.matrix_from_quat(q01) + + if t02_inputs: + t02 = torch.zeros((n, 3), device=device) + t02.uniform_(-1000.0, 1000.0) + t02_expected = t02.clone() + else: + t02 = None + t02_expected = torch.zeros((n, 3), device=device) + + if q02_inputs: + q02 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + q02_expected = q02.clone() + else: + q02 = None + q02_expected = math_utils.default_orientation(n, device=device) + + t12_value, q12_value = math_utils.subtract_frame_transforms(t01, q01, t02, q02) + t02_compare, q02_compare = math_utils.combine_frame_transforms(t01, q01, t12_value, q12_value) + + torch.testing.assert_close(t02_expected, t02_compare, atol=1e-3, rtol=1e-4) + torch.testing.assert_close(math_utils.quat_unique(q02_expected), math_utils.quat_unique(q02_compare)) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("rot_error_type", ("quat", "axis_angle")) +def test_compute_pose_error(device, rot_error_type): + """Test compute_pose_error for different rot_error_type.""" + n = 1000 + t01 = torch.zeros((n, 3), device=device) + t01.uniform_(-1000.0, 1000.0) + t02 = torch.zeros((n, 3), device=device) + t02.uniform_(-1000.0, 1000.0) + q01 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + q02 = math_utils.quat_unique(math_utils.random_orientation(n, device=device)) + + diff_pos, diff_rot = math_utils.compute_pose_error(t01, q01, t02, q02, rot_error_type=rot_error_type) + + torch.testing.assert_close(t02 - t01, diff_pos) + if rot_error_type == "axis_angle": + torch.testing.assert_close(math_utils.quat_box_minus(q02, q01), diff_rot) + else: + axis_angle = math_utils.quat_box_minus(q02, q01) + axis = math_utils.normalize(axis_angle) + angle = torch.norm(axis_angle, dim=-1) + + torch.testing.assert_close( + math_utils.quat_unique(math_utils.quat_from_angle_axis(angle, axis)), + math_utils.quat_unique(diff_rot), + ) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_rigid_body_twist_transform(device): + """Test rigid_body_twist_transform method. + + Verifies correct transformation of twists (linear and angular velocity) between coordinate frames. + """ + num_bodies = 100 + # Frame A to B + t_AB = torch.randn((num_bodies, 3), device=device) + q_AB = math_utils.random_orientation(num=num_bodies, device=device) + + # Twists in A in frame A + v_AA = torch.randn((num_bodies, 3), device=device) + w_AA = torch.randn((num_bodies, 3), device=device) + + # Get twists in B in frame B + v_BB, w_BB = math_utils.rigid_body_twist_transform(v_AA, w_AA, t_AB, q_AB) + + # Get back twists in A in frame A + t_BA = -math_utils.quat_rotate_inverse(q_AB, t_AB) + q_BA = math_utils.quat_conjugate(q_AB) + v_AA_, w_AA_ = math_utils.rigid_body_twist_transform(v_BB, w_BB, t_BA, q_BA) + + # Check + torch.testing.assert_close(v_AA_, v_AA) + torch.testing.assert_close(w_AA_, w_AA) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_yaw_quat(device): + """ + Test for yaw_quat methods. + """ + # 90-degree (n/2 radians) rotations about the Y-axis + quat_input = torch.tensor([0.7071, 0, 0.7071, 0], device=device) + cloned_quat_input = quat_input.clone() + + # Calculated output that the function should return + expected_output = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + + # Compute the result using the existing implementation + result = math_utils.yaw_quat(quat_input) + + # Verify original quat is not being modified + torch.testing.assert_close(quat_input, cloned_quat_input) + + # check that the output is equivalent to the expected output + torch.testing.assert_close(result, expected_output) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_slerp(device): + """Test quat_slerp function. + + This test checks the output from the :meth:`~isaaclab.utils.math_utils.quat_slerp` function against + the output from :func:`scipy.spatial.transform.Slerp`. + """ + # Generate 100 random rotation matrices + random_rotation_matrices_1 = [math_utils.generate_random_rotation() for _ in range(100)] + random_rotation_matrices_2 = [math_utils.generate_random_rotation() for _ in range(100)] + + tau_values = np.random.rand(10) # Random values in the range [0, 1] + + for rmat1, rmat2 in zip(random_rotation_matrices_1, random_rotation_matrices_2): + # Convert the rotation matrices to quaternions + q1 = scipy_tf.Rotation.from_matrix(rmat1).as_quat() # (x, y, z, w) + q2 = scipy_tf.Rotation.from_matrix(rmat2).as_quat() # (x, y, z, w) + + # Compute expected results using scipy's Slerp + key_rots = scipy_tf.Rotation.from_quat(np.array([q1, q2])) + key_times = [0, 1] + slerp = scipy_tf.Slerp(key_times, key_rots) + + for tau in tau_values: + expected = slerp(tau).as_quat() # (x, y, z, w) + result = math_utils.quat_slerp(torch.tensor(q1, device=device), torch.tensor(q2, device=device), tau) + # Assert that the result is almost equal to the expected quaternion + np.testing.assert_array_almost_equal(result.cpu(), expected, decimal=DECIMAL_PRECISION) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_matrix_from_quat(device): + """test matrix_from_quat against scipy.""" + # prepare random quaternions and vectors + n = 1024 + # prepare random quaternions and vectors + q_rand = math_utils.quat_unique(math_utils.random_orientation(num=n, device=device)) + rot_mat = math_utils.matrix_from_quat(quaternions=q_rand) + rot_mat_scipy = torch.tensor( + scipy_tf.Rotation.from_quat(math_utils.convert_quat(quat=q_rand.to(device="cpu"), to="xyzw")).as_matrix(), + device=device, + dtype=torch.float32, + ) + torch.testing.assert_close(rot_mat_scipy.to(device=device), rot_mat) + q_value = math_utils.quat_unique(math_utils.quat_from_matrix(rot_mat)) + torch.testing.assert_close(q_rand, q_value) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize( + "euler_angles", + [ + [0.0, 0.0, 0.0], + [math.pi / 2.0, 0.0, 0.0], + [0.0, math.pi / 2.0, 0.0], + [0.0, 0.0, math.pi / 2.0], + [1.5708, -2.75, 0.1], + [0.1, math.pi, math.pi / 2], + ], +) +@pytest.mark.parametrize( + "convention", ("XYZ", "XZY", "YXZ", "YZX", "ZXY", "ZYX", "ZYZ", "YZY", "XYX", "XZX", "ZXZ", "YXY") +) +def test_matrix_from_euler(device, euler_angles, convention): + """Test matrix_from_euler against scipy for different permutations of the X,Y,Z euler angle conventions.""" + + num_envs = 1024 + angles = torch.tensor(euler_angles, device=device).unsqueeze(0).repeat((num_envs, 1)) + mat_value = math_utils.matrix_from_euler(angles, convention=convention) + expected_mag = ( + torch.tensor( + scipy_tf.Rotation.from_euler(convention, euler_angles, degrees=False).as_matrix(), + device=device, + dtype=torch.float, + ) + .unsqueeze(0) + .repeat((num_envs, 1, 1)) + ) + torch.testing.assert_close(expected_mag, mat_value) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_apply(device): + """Test for quat_apply against scipy.""" + # prepare random quaternions and vectors + n = 1024 + q_rand = math_utils.random_orientation(num=n, device=device) + Rotation = scipy_tf.Rotation.from_quat(math_utils.convert_quat(quat=q_rand.to(device="cpu").numpy(), to="xyzw")) + + v_rand = math_utils.sample_uniform(-1000, 1000, (n, 3), device=device) + + # compute the result using the new implementation + scipy_result = torch.tensor(Rotation.apply(v_rand.to(device="cpu").numpy()), device=device, dtype=torch.float) + apply_result = math_utils.quat_apply(q_rand, v_rand) + torch.testing.assert_close(scipy_result.to(device=device), apply_result, atol=2e-4, rtol=2e-4) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_apply_inverse(device): + """Test for quat_apply against scipy.""" + + # prepare random quaternions and vectors + n = 1024 + q_rand = math_utils.random_orientation(num=n, device=device) + Rotation = scipy_tf.Rotation.from_quat(math_utils.convert_quat(quat=q_rand.to(device="cpu").numpy(), to="xyzw")) + + v_rand = math_utils.sample_uniform(-1000, 1000, (n, 3), device=device) + + # compute the result using the new implementation + scipy_result = torch.tensor( + Rotation.apply(v_rand.to(device="cpu").numpy(), inverse=True), device=device, dtype=torch.float + ) + apply_result = math_utils.quat_apply_inverse(q_rand, v_rand) + torch.testing.assert_close(scipy_result.to(device=device), apply_result, atol=2e-4, rtol=2e-4) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_quat_inv(device): + """Test for quat_inv method. + + For random unit and non-unit quaternions q, the Hamilton products + q ⊗ q⁻¹ and q⁻¹ ⊗ q must both equal the identity quaternion (1,0,0,0) + within numerical precision. + """ + num = 2048 + + # -------- non-unit sample (average ‖q‖ ≈ 10) -------- + q_nonunit = torch.randn(num, 4, device=device) * 5.0 + + # -------- unit sample (‖q‖ = 1) -------- + q_unit = torch.randn(num, 4, device=device) + q_unit = q_unit / q_unit.norm(dim=-1, keepdim=True) + + identity = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + + for q in (q_nonunit, q_unit): + q_inv = math_utils.quat_inv(q) + + id_batch = identity.expand_as(q) + + # left and right products must both be identity + torch.testing.assert_close(math_utils.quat_mul(q, q_inv), id_batch, atol=1e-4, rtol=1e-4) + torch.testing.assert_close(math_utils.quat_mul(q_inv, q), id_batch, atol=1e-4, rtol=1e-4) + + +def test_quat_apply_benchmarks(): + """Test for quat_apply and quat_apply_inverse methods compared to old methods using torch.bmm and torch.einsum. + The new implementation uses :meth:`torch.einsum` instead of `torch.bmm` which allows + for more flexibility in the input dimensions and is faster than `torch.bmm`. + """ + + # define old implementation for quat_rotate and quat_rotate_inverse + # Based on commit: cdfa954fcc4394ca8daf432f61994e25a7b8e9e2 + + @torch.jit.script + def bmm_quat_rotate(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + shape = q.shape + q_w = q[:, 0] + q_vec = q[:, 1:] + a = v * (2.0 * q_w**2 - 1.0).unsqueeze(-1) + b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0 + c = q_vec * torch.bmm(q_vec.view(shape[0], 1, 3), v.view(shape[0], 3, 1)).squeeze(-1) * 2.0 + return a + b + c + + @torch.jit.script + def bmm_quat_rotate_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + shape = q.shape + q_w = q[:, 0] + q_vec = q[:, 1:] + a = v * (2.0 * q_w**2 - 1.0).unsqueeze(-1) + b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0 + c = q_vec * torch.bmm(q_vec.view(shape[0], 1, 3), v.view(shape[0], 3, 1)).squeeze(-1) * 2.0 + return a - b + c + + @torch.jit.script + def einsum_quat_rotate(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + q_w = q[..., 0] + q_vec = q[..., 1:] + a = v * (2.0 * q_w**2 - 1.0).unsqueeze(-1) + b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0 + c = q_vec * torch.einsum("...i,...i->...", q_vec, v).unsqueeze(-1) * 2.0 + return a + b + c + + @torch.jit.script + def einsum_quat_rotate_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + q_w = q[..., 0] + q_vec = q[..., 1:] + a = v * (2.0 * q_w**2 - 1.0).unsqueeze(-1) + b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0 + c = q_vec * torch.einsum("...i,...i->...", q_vec, v).unsqueeze(-1) * 2.0 + return a - b + c + + # check that implementation produces the same result as the new implementation + for device in ["cpu", "cuda:0"]: + # prepare random quaternions and vectors + q_rand = math_utils.random_orientation(num=1024, device=device) + v_rand = math_utils.sample_uniform(-1000, 1000, (1024, 3), device=device) + + # compute the result using the old implementation + bmm_result = bmm_quat_rotate(q_rand, v_rand) + bmm_result_inv = bmm_quat_rotate_inverse(q_rand, v_rand) + + # compute the result using the old implementation + einsum_result = einsum_quat_rotate(q_rand, v_rand) + einsum_result_inv = einsum_quat_rotate_inverse(q_rand, v_rand) + + # compute the result using the new implementation + new_result = math_utils.quat_apply(q_rand, v_rand) + new_result_inv = math_utils.quat_apply_inverse(q_rand, v_rand) + + # check that the result is close to the expected value + torch.testing.assert_close(bmm_result, new_result, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(bmm_result_inv, new_result_inv, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(einsum_result, new_result, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(einsum_result_inv, new_result_inv, atol=1e-3, rtol=1e-3) + + # check the performance of the new implementation + for device in ["cpu", "cuda:0"]: + # prepare random quaternions and vectors + # new implementation supports batched inputs + q_shape = (1024, 2, 5, 4) + v_shape = (1024, 2, 5, 3) + # sample random quaternions and vectors + num_quats = math.prod(q_shape[:-1]) + q_rand = math_utils.random_orientation(num=num_quats, device=device).reshape(q_shape) + v_rand = math_utils.sample_uniform(-1000, 1000, v_shape, device=device) + + # create functions to test + def iter_quat_apply(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of new quat_apply.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = math_utils.quat_apply(q_rand[:, i, j], v_rand[:, i, j]) + return out + + def iter_quat_apply_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of new quat_apply_inverse.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = math_utils.quat_apply_inverse(q_rand[:, i, j], v_rand[:, i, j]) + return out + + def iter_bmm_quat_rotate(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of old quat_rotate using torch.bmm.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = bmm_quat_rotate(q_rand[:, i, j], v_rand[:, i, j]) + return out + + def iter_bmm_quat_rotate_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of old quat_rotate_inverse using torch.bmm.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = bmm_quat_rotate_inverse(q_rand[:, i, j], v_rand[:, i, j]) + return out + + def iter_einsum_quat_rotate(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of old quat_rotate using torch.einsum.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = einsum_quat_rotate(q_rand[:, i, j], v_rand[:, i, j]) + return out + + def iter_einsum_quat_rotate_inverse(q: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """Iterative implementation of old quat_rotate_inverse using torch.einsum.""" + out = torch.empty_like(v) + for i in range(q.shape[1]): + for j in range(q.shape[2]): + out[:, i, j] = einsum_quat_rotate_inverse(q_rand[:, i, j], v_rand[:, i, j]) + return out + + # benchmarks for iterative calls + timer_iter_quat_apply = benchmark.Timer( + stmt="iter_quat_apply(q_rand, v_rand)", + globals={"iter_quat_apply": iter_quat_apply, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_iter_quat_apply_inverse = benchmark.Timer( + stmt="iter_quat_apply_inverse(q_rand, v_rand)", + globals={"iter_quat_apply_inverse": iter_quat_apply_inverse, "q_rand": q_rand, "v_rand": v_rand}, + ) + + timer_iter_bmm_quat_rotate = benchmark.Timer( + stmt="iter_bmm_quat_rotate(q_rand, v_rand)", + globals={"iter_bmm_quat_rotate": iter_bmm_quat_rotate, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_iter_bmm_quat_rotate_inverse = benchmark.Timer( + stmt="iter_bmm_quat_rotate_inverse(q_rand, v_rand)", + globals={ + "iter_bmm_quat_rotate_inverse": iter_bmm_quat_rotate_inverse, + "q_rand": q_rand, + "v_rand": v_rand, + }, + ) + + timer_iter_einsum_quat_rotate = benchmark.Timer( + stmt="iter_einsum_quat_rotate(q_rand, v_rand)", + globals={"iter_einsum_quat_rotate": iter_einsum_quat_rotate, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_iter_einsum_quat_rotate_inverse = benchmark.Timer( + stmt="iter_einsum_quat_rotate_inverse(q_rand, v_rand)", + globals={ + "iter_einsum_quat_rotate_inverse": iter_einsum_quat_rotate_inverse, + "q_rand": q_rand, + "v_rand": v_rand, + }, + ) + + # create benchmaks for size independent calls + timer_quat_apply = benchmark.Timer( + stmt="math_utils.quat_apply(q_rand, v_rand)", + globals={"math_utils": math_utils, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_quat_apply_inverse = benchmark.Timer( + stmt="math_utils.quat_apply_inverse(q_rand, v_rand)", + globals={"math_utils": math_utils, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_einsum_quat_rotate = benchmark.Timer( + stmt="einsum_quat_rotate(q_rand, v_rand)", + globals={"einsum_quat_rotate": einsum_quat_rotate, "q_rand": q_rand, "v_rand": v_rand}, + ) + timer_einsum_quat_rotate_inverse = benchmark.Timer( + stmt="einsum_quat_rotate_inverse(q_rand, v_rand)", + globals={"einsum_quat_rotate_inverse": einsum_quat_rotate_inverse, "q_rand": q_rand, "v_rand": v_rand}, + ) + + # run the benchmark + print("--------------------------------") + print(f"Device: {device}") + print("Time for quat_apply:", timer_quat_apply.timeit(number=1000)) + print("Time for einsum_quat_rotate:", timer_einsum_quat_rotate.timeit(number=1000)) + print("Time for iter_quat_apply:", timer_iter_quat_apply.timeit(number=1000)) + print("Time for iter_bmm_quat_rotate:", timer_iter_bmm_quat_rotate.timeit(number=1000)) + print("Time for iter_einsum_quat_rotate:", timer_iter_einsum_quat_rotate.timeit(number=1000)) + print("--------------------------------") + print("Time for quat_apply_inverse:", timer_quat_apply_inverse.timeit(number=1000)) + print("Time for einsum_quat_rotate_inverse:", timer_einsum_quat_rotate_inverse.timeit(number=1000)) + print("Time for iter_quat_apply_inverse:", timer_iter_quat_apply_inverse.timeit(number=1000)) + print("Time for iter_bmm_quat_rotate_inverse:", timer_iter_bmm_quat_rotate_inverse.timeit(number=1000)) + print("Time for iter_einsum_quat_rotate_inverse:", timer_iter_einsum_quat_rotate_inverse.timeit(number=1000)) + print("--------------------------------") + + # check output values are the same + torch.testing.assert_close(math_utils.quat_apply(q_rand, v_rand), iter_quat_apply(q_rand, v_rand)) + torch.testing.assert_close( + math_utils.quat_apply(q_rand, v_rand), iter_bmm_quat_rotate(q_rand, v_rand), atol=1e-3, rtol=1e-3 + ) + torch.testing.assert_close( + math_utils.quat_apply_inverse(q_rand, v_rand), iter_quat_apply_inverse(q_rand, v_rand) + ) + torch.testing.assert_close( + math_utils.quat_apply_inverse(q_rand, v_rand), + iter_bmm_quat_rotate_inverse(q_rand, v_rand), + atol=1e-3, + rtol=1e-3, + ) + + +def test_interpolate_rotations(): + """Test interpolate_rotations function. + + This test checks the output from the :meth:`~isaaclab.utils.math_utils.interpolate_rotations` function against + the output from :func:`scipy.spatial.transform.Slerp`. + """ + # Generate NUM_ITERS random rotation matrices + random_rotation_matrices_1 = [math_utils.generate_random_rotation() for _ in range(100)] + random_rotation_matrices_2 = [math_utils.generate_random_rotation() for _ in range(100)] + + for rmat1, rmat2 in zip(random_rotation_matrices_1, random_rotation_matrices_2): + # Compute expected results using scipy's Slerp + key_rots = scipy_tf.Rotation.from_matrix(np.array([rmat1, rmat2])) + + # Create a Slerp object and interpolate create the interpolated matrices + # Minimum 2 required because Interpolate_rotations returns one extra rotation matrix + num_steps = np.random.randint(2, 51) + key_times = [0, 1] + slerp = scipy_tf.Slerp(key_times, key_rots) + interp_times = np.linspace(0, 1, num_steps) + expected = slerp(interp_times).as_matrix() + + # Test 1: + # Interpolate_rotations using interpolate_rotations and quat_slerp + # interpolate_rotations returns one extra rotation matrix hence num_steps-1 + result_quat = math_utils.interpolate_rotations(rmat1, rmat2, num_steps - 1) + + # Assert that the result is almost equal to the expected quaternion + np.testing.assert_array_almost_equal(result_quat.cpu(), expected, decimal=DECIMAL_PRECISION) + + # Test 2: + # Interpolate_rotations using axis_angle and ensure the result is still the same + # interpolate_rotations returns one extra rotation matrix hence num_steps-1 + result_axis_angle = math_utils.interpolate_rotations(rmat1, rmat2, num_steps - 1, axis_angle=True) + + # Assert that the result is almost equal to the expected quaternion + np.testing.assert_array_almost_equal(result_axis_angle.cpu(), expected, decimal=DECIMAL_PRECISION) + + +def test_euler_xyz_from_quat(): + """Test euler_xyz_from_quat function. + + This test checks the output from the :meth:`~isaaclab.utils.math_utils.euler_xyz_from_quat` function + against the expected output for various quaternions. + The test includes quaternions representing different rotations around the x, y, and z axes. + The test is performed for both the default output range (-π, π] and the wrapped output range [0, 2π). + """ + quats = [ + torch.Tensor([[1.0, 0.0, 0.0, 0.0]]), # 0° around x, y, z + torch.Tensor( + [ + [0.9238795, 0.3826834, 0.0, 0.0], # 45° around x + [0.9238795, 0.0, -0.3826834, 0.0], # -45° around y + [0.9238795, 0.0, 0.0, -0.3826834], # -45° around z + ] + ), + torch.Tensor( + [ + [0.7071068, -0.7071068, 0.0, 0.0], # -90° around x + [0.7071068, 0.0, 0.0, -0.7071068], # -90° around z + ] + ), + torch.Tensor( + [ + [0.3826834, -0.9238795, 0.0, 0.0], # -135° around x + [0.3826834, 0.0, 0.0, -0.9238795], # -135° around y + ] + ), + ] + + expected_euler_angles = [ + torch.Tensor([[0.0, 0.0, 0.0]]), # identity + torch.Tensor( + [ + [torch.pi / 4, 0.0, 0.0], # 45° about x + [0.0, -torch.pi / 4, 0.0], # -45° about y + [0.0, 0.0, -torch.pi / 4], # -45° about z + ] + ), + torch.Tensor( + [ + [-torch.pi / 2, 0.0, 0.0], # -90° about x + [0.0, 0.0, -torch.pi / 2], # -90° about z + ] + ), + torch.Tensor( + [ + [-3 * torch.pi / 4, 0.0, 0.0], # -135° about x + [0.0, 0.0, -3 * torch.pi / 4], # -135° about y + ] + ), + ] + + # Test 1: default no-wrap range from (-π, π] + for quat, expected in zip(quats, expected_euler_angles): + output = torch.stack(math_utils.euler_xyz_from_quat(quat), dim=-1) + torch.testing.assert_close(output, expected) + + # Test 2: wrap to [0, 2π) + for quat, expected in zip(quats, expected_euler_angles): + wrapped = expected % (2 * torch.pi) + output = torch.stack(math_utils.euler_xyz_from_quat(quat, wrap_to_2pi=True), dim=-1) + torch.testing.assert_close(output, wrapped) diff --git a/source/isaaclab/test/utils/test_modifiers.py b/source/isaaclab/test/utils/test_modifiers.py new file mode 100644 index 0000000000000000000000000000000000000000..9cdd9a5d66310fde718fff436b9a099ae7713961 --- /dev/null +++ b/source/isaaclab/test/utils/test_modifiers.py @@ -0,0 +1,226 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +from dataclasses import MISSING + +import pytest +import torch + +import isaaclab.utils.modifiers as modifiers +from isaaclab.utils import configclass + + +@configclass +class ModifierTestCfg: + """Configuration for testing modifiers.""" + + cfg: modifiers.ModifierCfg = MISSING + init_data: torch.Tensor = MISSING + result: torch.Tensor = MISSING + num_iter: int = 10 + + +def test_scale_modifier(): + """Test scale modifier.""" + # create test data + init_data = torch.tensor([1.0, 2.0, 3.0]) + scale = 2.0 + result = torch.tensor([2.0, 4.0, 6.0]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=modifiers.scale, params={"multiplier": scale}), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params) + assert torch.allclose(output, test_cfg.result) + + +def test_bias_modifier(): + """Test bias modifier.""" + # create test data + init_data = torch.tensor([1.0, 2.0, 3.0]) + bias = 1.0 + result = torch.tensor([2.0, 3.0, 4.0]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=modifiers.bias, params={"value": bias}), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params) + assert torch.allclose(output, test_cfg.result) + + +def test_clip_modifier(): + """Test clip modifier.""" + # create test data + init_data = torch.tensor([1.0, 2.0, 3.0]) + min_val = 1.5 + max_val = 2.5 + result = torch.tensor([1.5, 2.0, 2.5]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (min_val, max_val)}), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params) + assert torch.allclose(output, test_cfg.result) + + +def test_clip_no_upper_bound_modifier(): + """Test clip modifier with no upper bound.""" + # create test data + init_data = torch.tensor([1.0, 2.0, 3.0]) + min_val = 1.5 + result = torch.tensor([1.5, 2.0, 3.0]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (min_val, None)}), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params) + assert torch.allclose(output, test_cfg.result) + + +def test_clip_no_lower_bound_modifier(): + """Test clip modifier with no lower bound.""" + # create test data + init_data = torch.tensor([1.0, 2.0, 3.0]) + max_val = 2.5 + result = torch.tensor([1.0, 2.0, 2.5]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (None, max_val)}), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params) + assert torch.allclose(output, test_cfg.result) + + +def test_torch_relu_modifier(): + """Test torch relu modifier.""" + # create test data + init_data = torch.tensor([-1.0, 0.0, 1.0]) + result = torch.tensor([0.0, 0.0, 1.0]) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.ModifierCfg(func=torch.nn.functional.relu), + init_data=init_data, + result=result, + ) + + # test modifier + for _ in range(test_cfg.num_iter): + output = test_cfg.cfg.func(test_cfg.init_data) + assert torch.allclose(output, test_cfg.result) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_digital_filter(device): + """Test digital filter modifier.""" + # create test data + init_data = torch.tensor([0.0, 0.0, 0.0], device=device) + A = [0.0, 0.1] + B = [0.5, 0.5] + result = torch.tensor([-0.45661893, -0.45661893, -0.45661893], device=device) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.DigitalFilterCfg(A=A, B=B), init_data=init_data, result=result, num_iter=16 + ) + + # create a modifier instance + modifier_obj = test_cfg.cfg.func(test_cfg.cfg, test_cfg.init_data.shape, device=device) + + # test the modifier + theta = torch.tensor([0.0], device=device) + delta = torch.pi / torch.tensor([8.0, 8.0, 8.0], device=device) + + for _ in range(5): + # reset the modifier + modifier_obj.reset() + + # apply the modifier multiple times + for i in range(test_cfg.num_iter): + data = torch.sin(theta + i * delta) + processed_data = modifier_obj(data) + + assert data.shape == processed_data.shape, "Modified data shape does not equal original" + + # check if the modified data is close to the expected result + torch.testing.assert_close(processed_data, test_cfg.result) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +def test_integral(device): + """Test integral modifier.""" + # create test data + init_data = torch.tensor([0.0], device=device) + dt = 1.0 + result = torch.tensor([12.5], device=device) + + # create test config + test_cfg = ModifierTestCfg( + cfg=modifiers.IntegratorCfg(dt=dt), + init_data=init_data, + result=result, + num_iter=6, + ) + + # create a modifier instance + modifier_obj = test_cfg.cfg.func(test_cfg.cfg, test_cfg.init_data.shape, device=device) + + # test the modifier + delta = torch.tensor(1.0, device=device) + + for _ in range(5): + # reset the modifier + modifier_obj.reset() + + # clone the data to avoid modifying the original + data = test_cfg.init_data.clone() + # apply the modifier multiple times + for _ in range(test_cfg.num_iter): + processed_data = modifier_obj(data) + data = data + delta + + assert data.shape == processed_data.shape, "Modified data shape does not equal original" + + # check if the modified data is close to the expected result + torch.testing.assert_close(processed_data, test_cfg.result) diff --git a/source/isaaclab/test/utils/test_noise.py b/source/isaaclab/test/utils/test_noise.py new file mode 100644 index 0000000000000000000000000000000000000000..176371d381f66fe2107f4fa1b2126497d924e3d9 --- /dev/null +++ b/source/isaaclab/test/utils/test_noise.py @@ -0,0 +1,110 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +import torch + +import isaaclab.utils.noise as noise + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("op", ["add", "scale", "abs"]) +def test_gaussian_noise(device, noise_device, op): + """Test guassian_noise function.""" + + # create random data set + data = torch.rand(10000, 3, device=device) + # define standard deviation and mean + std = torch.tensor([0.1, 0.2, 0.3], device=noise_device) + mean = torch.tensor([0.4, 0.5, 0.6], device=noise_device) + # create noise config + noise_cfg = noise.GaussianNoiseCfg(std=std, mean=mean, operation=op) + + for i in range(10): + # apply noise + noisy_data = noise_cfg.func(data, cfg=noise_cfg) + # calculate resulting noise compared to original data set + if op == "add": + std_result, mean_result = torch.std_mean(noisy_data - data, dim=0) + elif op == "scale": + std_result, mean_result = torch.std_mean(noisy_data / data, dim=0) + elif op == "abs": + std_result, mean_result = torch.std_mean(noisy_data, dim=0) + + assert str(noise_cfg.mean.device) == device + assert str(noise_cfg.std.device) == device + torch.testing.assert_close(noise_cfg.std, std_result, atol=1e-2, rtol=1e-2) + torch.testing.assert_close(noise_cfg.mean, mean_result, atol=1e-2, rtol=1e-2) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("op", ["add", "scale", "abs"]) +def test_uniform_noise(device, noise_device, op): + """Test uniform_noise function.""" + # create random data set + data = torch.rand(10000, 3, device=device) + # define uniform minimum and maximum + n_min = torch.tensor([0.1, 0.2, 0.3], device=noise_device) + n_max = torch.tensor([0.4, 0.5, 0.6], device=noise_device) + # create noise config + noise_cfg = noise.UniformNoiseCfg(n_max=n_max, n_min=n_min, operation=op) + + for i in range(10): + # apply noise + noisy_data = noise_cfg.func(data, cfg=noise_cfg) + # calculate resulting noise compared to original data set + if op == "add": + min_result, _ = torch.min(noisy_data - data, dim=0) + max_result, _ = torch.max(noisy_data - data, dim=0) + elif op == "scale": + min_result, _ = torch.min(torch.div(noisy_data, data), dim=0) + max_result, _ = torch.max(torch.div(noisy_data, data), dim=0) + elif op == "abs": + min_result, _ = torch.min(noisy_data, dim=0) + max_result, _ = torch.max(noisy_data, dim=0) + + assert str(noise_cfg.n_min.device) == device + assert str(noise_cfg.n_max.device) == device + # add a small epsilon to accommodate for floating point error + assert all(torch.le(noise_cfg.n_min - 1e-5, min_result).tolist()) + assert all(torch.ge(noise_cfg.n_max + 1e-5, max_result).tolist()) + + +@pytest.mark.parametrize("device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"]) +@pytest.mark.parametrize("op", ["add", "scale", "abs"]) +def test_constant_noise(device, noise_device, op): + """Test constant_noise""" + # create random data set + data = torch.rand(10000, 3, device=device) + # define a bias + bias = torch.tensor([0.1, 0.2, 0.3], device=noise_device) + # create noise config + noise_cfg = noise.ConstantNoiseCfg(bias=bias, operation=op) + + for i in range(10): + # apply noise + noisy_data = noise_cfg.func(data, cfg=noise_cfg) + # calculate resulting noise compared to original data set + if op == "add": + bias_result = noisy_data - data + elif op == "scale": + bias_result = noisy_data / data + elif op == "abs": + bias_result = noisy_data + + assert str(noise_cfg.bias.device) == device + torch.testing.assert_close(noise_cfg.bias.repeat(data.shape[0], 1), bias_result) diff --git a/source/isaaclab/test/utils/test_string.py b/source/isaaclab/test/utils/test_string.py new file mode 100644 index 0000000000000000000000000000000000000000..d171a3885e109e322f784973d68133ffab78a989 --- /dev/null +++ b/source/isaaclab/test/utils/test_string.py @@ -0,0 +1,215 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# NOTE: While we don't actually use the simulation app in this test, we still need to launch it +# because warp is only available in the context of a running simulation +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import random + +import pytest + +import isaaclab.utils.string as string_utils + + +def test_case_conversion(): + """Test case conversion between camel case and snake case.""" + # test camel case to snake case + assert string_utils.to_snake_case("CamelCase") == "camel_case" + assert string_utils.to_snake_case("camelCase") == "camel_case" + assert string_utils.to_snake_case("CamelCaseString") == "camel_case_string" + # test snake case to camel case + assert string_utils.to_camel_case("snake_case", to="CC") == "SnakeCase" + assert string_utils.to_camel_case("snake_case_string", to="CC") == "SnakeCaseString" + assert string_utils.to_camel_case("snake_case_string", to="cC") == "snakeCaseString" + + +def test_resolve_matching_names_with_basic_strings(): + """Test resolving matching names with a basic expression.""" + # list of strings + target_names = ["a", "b", "c", "d", "e"] + # test matching names + query_names = ["a|c", "b"] + index_list, names_list = string_utils.resolve_matching_names(query_names, target_names) + assert index_list == [0, 1, 2] + assert names_list == ["a", "b", "c"] + # test matching names with regex + query_names = ["a.*", "b"] + index_list, names_list = string_utils.resolve_matching_names(query_names, target_names) + assert index_list == [0, 1] + assert names_list == ["a", "b"] + # test duplicate names + query_names = ["a|c", "b", "a|c"] + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names(query_names, target_names) + # test no regex match + query_names = ["a|c", "b", "f"] + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names(query_names, target_names) + + +def test_resolve_matching_names_with_joint_name_strings(): + """Test resolving matching names with joint names.""" + # list of strings + robot_joint_names = [] + for i in ["hip", "thigh", "calf"]: + for j in ["FL", "FR", "RL", "RR"]: + robot_joint_names.append(f"{j}_{i}_joint") + # test matching names + index_list, names_list = string_utils.resolve_matching_names(".*", robot_joint_names) + assert index_list == list(range(len(robot_joint_names))) + assert names_list == robot_joint_names + # test matching names with regex + index_list, names_list = string_utils.resolve_matching_names(".*_joint", robot_joint_names) + assert index_list == list(range(len(robot_joint_names))) + assert names_list == robot_joint_names + # test matching names with regex + index_list, names_list = string_utils.resolve_matching_names(["FL.*", "FR.*"], robot_joint_names) + ground_truth_index_list = [0, 1, 4, 5, 8, 9] + assert index_list == ground_truth_index_list + assert names_list == [robot_joint_names[i] for i in ground_truth_index_list] + # test matching names with regex + query_list = [ + "FL_hip_joint", + "FL_thigh_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FL_calf_joint", + "FR_calf_joint", + ] + index_list, names_list = string_utils.resolve_matching_names(query_list, robot_joint_names) + ground_truth_index_list = [0, 1, 4, 5, 8, 9] + assert names_list != query_list + assert index_list == ground_truth_index_list + assert names_list == [robot_joint_names[i] for i in ground_truth_index_list] + # test matching names with regex but shuffled + # randomize order of previous query list + random.shuffle(query_list) + index_list, names_list = string_utils.resolve_matching_names(query_list, robot_joint_names) + ground_truth_index_list = [0, 1, 4, 5, 8, 9] + assert names_list != query_list + assert index_list == ground_truth_index_list + assert names_list == [robot_joint_names[i] for i in ground_truth_index_list] + + +def test_resolve_matching_names_with_preserved_order(): + """Test resolving matching names with preserved order.""" + # list of strings and query list + robot_joint_names = [] + for i in ["hip", "thigh", "calf"]: + for j in ["FL", "FR", "RL", "RR"]: + robot_joint_names.append(f"{j}_{i}_joint") + query_list = [ + "FL_hip_joint", + "FL_thigh_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FL_calf_joint", + "FR_calf_joint", + ] + # test return in target ordering with sublist + query_list.reverse() + index_list, names_list = string_utils.resolve_matching_names(query_list, robot_joint_names, preserve_order=True) + ground_truth_index_list = [9, 8, 5, 1, 4, 0] + assert names_list == query_list + assert index_list == ground_truth_index_list + # test return in target ordering with regex expression + index_list, names_list = string_utils.resolve_matching_names( + ["FR.*", "FL.*"], robot_joint_names, preserve_order=True + ) + ground_truth_index_list = [1, 5, 9, 0, 4, 8] + assert index_list == ground_truth_index_list + assert names_list == [robot_joint_names[i] for i in ground_truth_index_list] + # test return in target ordering with a mix of regex and non-regex expression + index_list, names_list = string_utils.resolve_matching_names( + ["FR.*", "FL_calf_joint", "FL_thigh_joint", "FL_hip_joint"], robot_joint_names, preserve_order=True + ) + ground_truth_index_list = [1, 5, 9, 8, 4, 0] + assert index_list == ground_truth_index_list + assert names_list == [robot_joint_names[i] for i in ground_truth_index_list] + + +def test_resolve_matching_names_values_with_basic_strings(): + """Test resolving matching names with a basic expression.""" + # list of strings + target_names = ["a", "b", "c", "d", "e"] + # test matching names + data = {"a|c": 1, "b": 2} + index_list, names_list, values_list = string_utils.resolve_matching_names_values(data, target_names) + assert index_list == [0, 1, 2] + assert names_list == ["a", "b", "c"] + assert values_list == [1, 2, 1] + # test matching names with regex + data = {"a|d|e": 1, "b|c": 2} + index_list, names_list, values_list = string_utils.resolve_matching_names_values(data, target_names) + assert index_list == [0, 1, 2, 3, 4] + assert names_list == ["a", "b", "c", "d", "e"] + assert values_list == [1, 2, 2, 1, 1] + # test matching names with regex + data = {"a|d|e|b": 1, "b|c": 2} + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names_values(data, target_names) + # test no regex match + query_names = {"a|c": 1, "b": 0, "f": 2} + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names_values(query_names, target_names) + + +def test_resolve_matching_names_values_with_strict_false(): + """Test resolving matching names with strict=False parameter.""" + # list of strings + target_names = ["a", "b", "c", "d", "e"] + # test strict=False + data = {"a|c": 1, "b": 2, "f": 3} + index_list, names_list, values_list = string_utils.resolve_matching_names_values(data, target_names, strict=False) + assert index_list == [0, 1, 2] + assert names_list == ["a", "b", "c"] + assert values_list == [1, 2, 1] + + # test failure case: multiple matches for a string (should still raise ValueError even with strict=False) + data = {"a|c": 1, "a": 2, "b": 3} + with pytest.raises(ValueError, match="Multiple matches for 'a':"): + _ = string_utils.resolve_matching_names_values(data, target_names, strict=False) + + # test failure case: invalid input type (should still raise TypeError even with strict=False) + with pytest.raises(TypeError, match="Input argument `data` should be a dictionary"): + _ = string_utils.resolve_matching_names_values("not_a_dict", target_names, strict=False) + + +def test_resolve_matching_names_values_with_basic_strings_and_preserved_order(): + """Test resolving matching names with a basic expression.""" + # list of strings + target_names = ["a", "b", "c", "d", "e"] + # test matching names + data = {"a|c": 1, "b": 2} + index_list, names_list, values_list = string_utils.resolve_matching_names_values( + data, target_names, preserve_order=True + ) + assert index_list == [0, 2, 1] + assert names_list == ["a", "c", "b"] + assert values_list == [1, 1, 2] + # test matching names with regex + data = {"a|d|e": 1, "b|c": 2} + index_list, names_list, values_list = string_utils.resolve_matching_names_values( + data, target_names, preserve_order=True + ) + assert index_list == [0, 3, 4, 1, 2] + assert names_list == ["a", "d", "e", "b", "c"] + assert values_list == [1, 1, 1, 2, 2] + # test matching names with regex + data = {"a|d|e|b": 1, "b|c": 2} + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names_values(data, target_names, preserve_order=True) + # test no regex match + query_names = {"a|c": 1, "b": 0, "f": 2} + with pytest.raises(ValueError): + _ = string_utils.resolve_matching_names_values(query_names, target_names, preserve_order=True) diff --git a/source/isaaclab/test/utils/test_timer.py b/source/isaaclab/test/utils/test_timer.py new file mode 100644 index 0000000000000000000000000000000000000000..8d99db3b2d801c3d282001533fe12b630e8ef7fe --- /dev/null +++ b/source/isaaclab/test/utils/test_timer.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# NOTE: While we don't actually use the simulation app in this test, we still need to launch it +# because warp is only available in the context of a running simulation +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import time + +from isaaclab.utils.timer import Timer + +# number of decimal places to check +PRECISION_PLACES = 2 + + +def test_timer_as_object(): + """Test using a `Timer` as a regular object.""" + timer = Timer() + timer.start() + assert abs(0 - timer.time_elapsed) < 10 ** (-PRECISION_PLACES) + time.sleep(1) + assert abs(1 - timer.time_elapsed) < 10 ** (-PRECISION_PLACES) + timer.stop() + assert abs(1 - timer.total_run_time) < 10 ** (-PRECISION_PLACES) + + +def test_timer_as_context_manager(): + """Test using a `Timer` as a context manager.""" + with Timer() as timer: + assert abs(0 - timer.time_elapsed) < 10 ** (-PRECISION_PLACES) + time.sleep(1) + assert abs(1 - timer.time_elapsed) < 10 ** (-PRECISION_PLACES) diff --git a/source/isaaclab/test/utils/test_version.py b/source/isaaclab/test/utils/test_version.py new file mode 100644 index 0000000000000000000000000000000000000000..ba737b53643e85c863d42923f5dfe90131c31bdd --- /dev/null +++ b/source/isaaclab/test/utils/test_version.py @@ -0,0 +1,152 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests for version comparison utilities.""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +"""Rest everything follows.""" + +import pytest +from packaging.version import Version + +from isaaclab.utils.version import compare_versions, get_isaac_sim_version + + +def test_get_isaac_sim_version(): + """Test that get_isaac_sim_version returns cached Version object.""" + # Call twice to ensure caching works + version1 = get_isaac_sim_version() + version2 = get_isaac_sim_version() + + # Should return the same object (cached) + assert version1 is version2 + + # Should return a packaging.version.Version object + assert isinstance(version1, Version) + + # Major version should be reasonable + assert version1.major >= 4 + + # Minor and micro should be non-negative + assert version1.minor >= 0 + assert version1.micro >= 0 + + +def test_get_isaac_sim_version_format(): + """Test that get_isaac_sim_version returns correct format.""" + isaac_version = get_isaac_sim_version() + + # Should be able to convert to string + version_str = str(isaac_version) + assert isinstance(version_str, str) + + # Should have proper format (e.g., "5.0.0") + parts = version_str.split(".") + assert len(parts) >= 3 + + # Can access components + assert hasattr(isaac_version, "major") + assert hasattr(isaac_version, "minor") + assert hasattr(isaac_version, "micro") + + +def test_version_caching_performance(): + """Test that caching improves performance for version checks.""" + # First call (will cache) + version1 = get_isaac_sim_version() + + # Subsequent calls should be instant (from cache) + for _ in range(100): + version = get_isaac_sim_version() + assert version == version1 + assert version is version1 # Should be the exact same object + + +def test_version_comparison_operators(): + """Test that Version objects support natural comparisons.""" + isaac_version = get_isaac_sim_version() + + # Should support comparison operators + assert isaac_version >= Version("4.0.0") + assert isaac_version == isaac_version + + # Test less than + if isaac_version.major >= 5: + assert isaac_version > Version("4.5.0") + assert isaac_version >= Version("5.0.0") + + # Test not equal + assert isaac_version != Version("0.0.1") + + +@pytest.mark.parametrize( + "v1,v2,expected", + [ + # Equal versions + ("1.0.0", "1.0.0", 0), + ("2.5.3", "2.5.3", 0), + # Equal with different lengths (implicit zeros) + ("1.0", "1.0.0", 0), + ("1", "1.0.0.0", 0), + ("2.5", "2.5.0.0", 0), + # Major version differences + ("2.0.0", "1.0.0", 1), + ("1.0.0", "2.0.0", -1), + ("2.0.0", "1.99.99", 1), + # Minor version differences + ("1.5.0", "1.4.0", 1), + ("1.4.0", "1.5.0", -1), + ("1.10.0", "1.9.99", 1), + # Patch version differences + ("1.0.5", "1.0.4", 1), + ("1.0.4", "1.0.5", -1), + ("2.5.10", "2.5.9", 1), + # Single/double digit versions + ("2", "1", 1), + ("1", "2", -1), + ("1.5", "1.4", 1), + # Extended versions + ("1.0.0.1", "1.0.0.0", 1), + ("1.2.3.4.5", "1.2.3.4", 1), + # Zero versions + ("0.0.1", "0.0.0", 1), + ("0.1.0", "0.0.9", 1), + ("0", "0.0.0", 0), + # Large numbers + ("100.200.300", "100.200.299", 1), + ("999.999.999", "1000.0.0", -1), + ], +) +def test_version_comparisons(v1, v2, expected): + """Test version comparisons with various scenarios.""" + assert compare_versions(v1, v2) == expected + + +def test_symmetry(): + """Test anti-symmetric property: if v1 < v2, then v2 > v1.""" + test_pairs = [("1.0.0", "2.0.0"), ("1.5.3", "1.4.9"), ("1.0.0", "1.0.0")] + + for v1, v2 in test_pairs: + result1 = compare_versions(v1, v2) + result2 = compare_versions(v2, v1) + + if result1 == 0: + assert result2 == 0 + else: + assert result1 == -result2 + + +def test_transitivity(): + """Test transitive property: if v1 < v2 < v3, then v1 < v3.""" + v1, v2, v3 = "1.0.0", "2.0.0", "3.0.0" + assert compare_versions(v1, v2) == -1 + assert compare_versions(v2, v3) == -1 + assert compare_versions(v1, v3) == -1 diff --git a/source/isaaclab/test/utils/test_wrench_composer.py b/source/isaaclab/test/utils/test_wrench_composer.py new file mode 100644 index 0000000000000000000000000000000000000000..3cc88b3b902835e49622fd7ef0410dfe0bdb6ab2 --- /dev/null +++ b/source/isaaclab/test/utils/test_wrench_composer.py @@ -0,0 +1,712 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import numpy as np +import pytest +import torch +import warp as wp + +from isaaclab.assets import RigidObject +from isaaclab.utils.wrench_composer import WrenchComposer + + +class MockAssetData: + """Mock data class that provides body link positions and quaternions.""" + + def __init__( + self, + num_envs: int, + num_bodies: int, + device: str, + link_pos: torch.Tensor | None = None, + link_quat: torch.Tensor | None = None, + ): + """Initialize mock asset data. + + Args: + num_envs: Number of environments. + num_bodies: Number of bodies. + device: Device to use. + link_pos: Optional link positions (num_envs, num_bodies, 3). Defaults to zeros. + link_quat: Optional link quaternions in (w, x, y, z) format (num_envs, num_bodies, 4). + Defaults to identity quaternion. + """ + if link_pos is not None: + self.body_link_pos_w = link_pos.to(device=device, dtype=torch.float32) + else: + self.body_link_pos_w = torch.zeros((num_envs, num_bodies, 3), dtype=torch.float32, device=device) + + if link_quat is not None: + self.body_link_quat_w = link_quat.to(device=device, dtype=torch.float32) + else: + # Identity quaternion (w, x, y, z) = (1, 0, 0, 0) + self.body_link_quat_w = torch.zeros((num_envs, num_bodies, 4), dtype=torch.float32, device=device) + self.body_link_quat_w[..., 0] = 1.0 + + +class MockRigidObject: + """Mock RigidObject that provides the minimal interface required by WrenchComposer. + + This mock enables testing WrenchComposer in isolation without requiring a full simulation setup. + It passes isinstance checks by registering as a virtual subclass of RigidObject. + """ + + def __init__( + self, + num_envs: int, + num_bodies: int, + device: str, + link_pos: torch.Tensor | None = None, + link_quat: torch.Tensor | None = None, + ): + """Initialize mock rigid object. + + Args: + num_envs: Number of environments. + num_bodies: Number of bodies. + device: Device to use. + link_pos: Optional link positions (num_envs, num_bodies, 3). + link_quat: Optional link quaternions in (w, x, y, z) format (num_envs, num_bodies, 4). + """ + self.num_instances = num_envs + self.num_bodies = num_bodies + self.device = device + self.data = MockAssetData(num_envs, num_bodies, device, link_pos, link_quat) + + +# --- Helper functions for quaternion math --- + + +def quat_rotate_inv_np(quat_wxyz: np.ndarray, vec: np.ndarray) -> np.ndarray: + """Rotate a vector by the inverse of a quaternion (numpy). + + Args: + quat_wxyz: Quaternion in (w, x, y, z) format. Shape: (..., 4) + vec: Vector to rotate. Shape: (..., 3) + + Returns: + Rotated vector. Shape: (..., 3) + """ + # Extract components + w = quat_wxyz[..., 0:1] + xyz = quat_wxyz[..., 1:4] + + # For inverse rotation, we conjugate the quaternion (negate xyz) + # q^-1 * v * q = q_conj * v * q_conj^-1 for unit quaternion + # Using the formula: v' = v + 2*w*(xyz x v) + 2*(xyz x (xyz x v)) + # But for inverse: use -xyz + + # Cross product: xyz x vec + t = 2.0 * np.cross(-xyz, vec, axis=-1) + # Result: vec + w*t + xyz x t + return vec + w * t + np.cross(-xyz, t, axis=-1) + + +def random_unit_quaternion_np(rng: np.random.Generator, shape: tuple) -> np.ndarray: + """Generate random unit quaternions in (w, x, y, z) format. + + Args: + rng: Random number generator. + shape: Output shape, e.g. (num_envs, num_bodies). + + Returns: + Random unit quaternions. Shape: (*shape, 4) + """ + # Generate random quaternion components + q = rng.standard_normal(shape + (4,)).astype(np.float32) + # Normalize to unit quaternion + q = q / np.linalg.norm(q, axis=-1, keepdims=True) + return q + + +# Register MockRigidObject as a virtual subclass of RigidObject +# This allows isinstance(mock, RigidObject) to return True +RigidObject.register(MockRigidObject) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_wrench_composer_add_force(device: str, num_envs: int, num_bodies: int): + # Initialize random number generator + rng = np.random.default_rng(seed=0) + + for _ in range(10): + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Initialize hand-calculated composed force + hand_calculated_composed_force_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for _ in range(10): + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random forces + forces_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + forces = wp.from_numpy(forces_np, dtype=wp.vec3f, device=device) + # Add forces to wrench composer + wrench_composer.add_forces_and_torques(forces=forces, body_ids=body_ids, env_ids=env_ids) + # Add forces to hand-calculated composed force + hand_calculated_composed_force_np[env_ids_np[:, None], body_ids_np[None, :], :] += forces_np + # Get composed force from wrench composer + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, hand_calculated_composed_force_np, atol=1, rtol=1e-7) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_wrench_composer_add_torque(device: str, num_envs: int, num_bodies: int): + # Initialize random number generator + rng = np.random.default_rng(seed=1) + + for _ in range(10): + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Initialize hand-calculated composed torque + hand_calculated_composed_torque_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for _ in range(10): + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random torques + torques_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + torques = wp.from_numpy(torques_np, dtype=wp.vec3f, device=device) + # Add torques to wrench composer + wrench_composer.add_forces_and_torques(torques=torques, body_ids=body_ids, env_ids=env_ids) + # Add torques to hand-calculated composed torque + hand_calculated_composed_torque_np[env_ids_np[:, None], body_ids_np[None, :], :] += torques_np + # Get composed torque from wrench composer + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, hand_calculated_composed_torque_np, atol=1, rtol=1e-7) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_add_forces_at_positons(device: str, num_envs: int, num_bodies: int): + """Test adding forces at local positions (offset from link frame).""" + rng = np.random.default_rng(seed=2) + + for _ in range(10): + # Initialize wrench composer + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Initialize hand-calculated composed force + hand_calculated_composed_force_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + # Initialize hand-calculated composed torque + hand_calculated_composed_torque_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for _ in range(10): + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random forces + forces_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + positions_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + forces = wp.from_numpy(forces_np, dtype=wp.vec3f, device=device) + positions = wp.from_numpy(positions_np, dtype=wp.vec3f, device=device) + # Add forces at positions to wrench composer + wrench_composer.add_forces_and_torques( + forces=forces, positions=positions, body_ids=body_ids, env_ids=env_ids + ) + # Add forces to hand-calculated composed force + hand_calculated_composed_force_np[env_ids_np[:, None], body_ids_np[None, :], :] += forces_np + # Add torques to hand-calculated composed torque: torque = cross(position, force) + torques_from_forces = np.cross(positions_np, forces_np) + for i in range(num_envs_np): + for j in range(num_bodies_np): + hand_calculated_composed_torque_np[env_ids_np[i], body_ids_np[j], :] += torques_from_forces[i, j, :] + + # Get composed force from wrench composer + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, hand_calculated_composed_force_np, atol=1, rtol=1e-7) + # Get composed torque from wrench composer + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, hand_calculated_composed_torque_np, atol=1, rtol=1e-7) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_add_torques_at_position(device: str, num_envs: int, num_bodies: int): + rng = np.random.default_rng(seed=3) + + for _ in range(10): + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Initialize hand-calculated composed torque + hand_calculated_composed_torque_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for _ in range(10): + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random torques + torques_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + positions_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + torques = wp.from_numpy(torques_np, dtype=wp.vec3f, device=device) + positions = wp.from_numpy(positions_np, dtype=wp.vec3f, device=device) + # Add torques at positions to wrench composer + wrench_composer.add_forces_and_torques( + torques=torques, positions=positions, body_ids=body_ids, env_ids=env_ids + ) + # Add torques to hand-calculated composed torque + hand_calculated_composed_torque_np[env_ids_np[:, None], body_ids_np[None, :], :] += torques_np + # Get composed torque from wrench composer + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, hand_calculated_composed_torque_np, atol=1, rtol=1e-7) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_add_forces_and_torques_at_position(device: str, num_envs: int, num_bodies: int): + """Test adding forces and torques at local positions.""" + rng = np.random.default_rng(seed=4) + + for _ in range(10): + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Initialize hand-calculated composed force and torque + hand_calculated_composed_force_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + hand_calculated_composed_torque_np = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for _ in range(10): + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random forces and torques + forces_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + torques_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + positions_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + forces = wp.from_numpy(forces_np, dtype=wp.vec3f, device=device) + torques = wp.from_numpy(torques_np, dtype=wp.vec3f, device=device) + positions = wp.from_numpy(positions_np, dtype=wp.vec3f, device=device) + # Add forces and torques at positions to wrench composer + wrench_composer.add_forces_and_torques( + forces=forces, torques=torques, positions=positions, body_ids=body_ids, env_ids=env_ids + ) + # Add forces to hand-calculated composed force + hand_calculated_composed_force_np[env_ids_np[:, None], body_ids_np[None, :], :] += forces_np + # Add torques to hand-calculated composed torque: torque = cross(position, force) + torque + torques_from_forces = np.cross(positions_np, forces_np) + for i in range(num_envs_np): + for j in range(num_bodies_np): + hand_calculated_composed_torque_np[env_ids_np[i], body_ids_np[j], :] += torques_from_forces[i, j, :] + hand_calculated_composed_torque_np[env_ids_np[:, None], body_ids_np[None, :], :] += torques_np + # Get composed force from wrench composer + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, hand_calculated_composed_force_np, atol=1, rtol=1e-7) + # Get composed torque from wrench composer + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, hand_calculated_composed_torque_np, atol=1, rtol=1e-7) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5, 10]) +def test_wrench_composer_reset(device: str, num_envs: int, num_bodies: int): + rng = np.random.default_rng(seed=5) + for _ in range(10): + mock_asset = MockRigidObject(num_envs, num_bodies, device) + wrench_composer = WrenchComposer(mock_asset) + # Get random number of envs and bodies and their indices + num_envs_np = rng.integers(1, num_envs, endpoint=True) + num_bodies_np = rng.integers(1, num_bodies, endpoint=True) + env_ids_np = rng.choice(num_envs, size=num_envs_np, replace=False) + body_ids_np = rng.choice(num_bodies, size=num_bodies_np, replace=False) + # Convert to warp arrays + env_ids = wp.from_numpy(env_ids_np, dtype=wp.int32, device=device) + body_ids = wp.from_numpy(body_ids_np, dtype=wp.int32, device=device) + # Get random forces and torques + forces_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + torques_np = ( + np.random.uniform(low=-100.0, high=100.0, size=(num_envs_np * num_bodies_np * 3)) + .reshape(num_envs_np, num_bodies_np, 3) + .astype(np.float32) + ) + forces = wp.from_numpy(forces_np, dtype=wp.vec3f, device=device) + torques = wp.from_numpy(torques_np, dtype=wp.vec3f, device=device) + # Add forces and torques to wrench composer + wrench_composer.add_forces_and_torques(forces=forces, torques=torques, body_ids=body_ids, env_ids=env_ids) + # Reset wrench composer + wrench_composer.reset() + # Get composed force and torque from wrench composer + composed_force_np = wrench_composer.composed_force.numpy() + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_force_np, np.zeros((num_envs, num_bodies, 3)), atol=1, rtol=1e-7) + assert np.allclose(composed_torque_np, np.zeros((num_envs, num_bodies, 3)), atol=1, rtol=1e-7) + + +# ============================================================================ +# Global Frame Tests +# ============================================================================ + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5]) +def test_global_forces_with_rotation(device: str, num_envs: int, num_bodies: int): + """Test that global forces are correctly rotated to the local frame.""" + rng = np.random.default_rng(seed=10) + + for _ in range(5): + # Create random link quaternions + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_quat_torch = torch.from_numpy(link_quat_np) + + # Create mock asset with custom quaternions + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random global forces for all envs and bodies + forces_global_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces_global = wp.from_numpy(forces_global_np, dtype=wp.vec3f, device=device) + + # Apply global forces + wrench_composer.add_forces_and_torques(forces=forces_global, is_global=True) + + # Compute expected local forces by rotating global forces by inverse quaternion + expected_forces_local = quat_rotate_inv_np(link_quat_np, forces_global_np) + + # Verify + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, expected_forces_local, atol=1e-4, rtol=1e-5), ( + f"Global force rotation failed.\nExpected:\n{expected_forces_local}\nGot:\n{composed_force_np}" + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 100]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5]) +def test_global_torques_with_rotation(device: str, num_envs: int, num_bodies: int): + """Test that global torques are correctly rotated to the local frame.""" + rng = np.random.default_rng(seed=11) + + for _ in range(5): + # Create random link quaternions + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_quat_torch = torch.from_numpy(link_quat_np) + + # Create mock asset with custom quaternions + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random global torques + torques_global_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + torques_global = wp.from_numpy(torques_global_np, dtype=wp.vec3f, device=device) + + # Apply global torques + wrench_composer.add_forces_and_torques(torques=torques_global, is_global=True) + + # Compute expected local torques + expected_torques_local = quat_rotate_inv_np(link_quat_np, torques_global_np) + + # Verify + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, expected_torques_local, atol=1e-4, rtol=1e-5), ( + f"Global torque rotation failed.\nExpected:\n{expected_torques_local}\nGot:\n{composed_torque_np}" + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 50]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5]) +def test_global_forces_at_global_position(device: str, num_envs: int, num_bodies: int): + """Test global forces at global positions with full coordinate transformation.""" + rng = np.random.default_rng(seed=12) + + for _ in range(5): + # Create random link poses + link_pos_np = rng.uniform(-10.0, 10.0, (num_envs, num_bodies, 3)).astype(np.float32) + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_pos_torch = torch.from_numpy(link_pos_np) + link_quat_torch = torch.from_numpy(link_quat_np) + + # Create mock asset + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_pos=link_pos_torch, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random global forces and positions + forces_global_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + positions_global_np = rng.uniform(-10.0, 10.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces_global = wp.from_numpy(forces_global_np, dtype=wp.vec3f, device=device) + positions_global = wp.from_numpy(positions_global_np, dtype=wp.vec3f, device=device) + + # Apply global forces at global positions + wrench_composer.add_forces_and_torques(forces=forces_global, positions=positions_global, is_global=True) + + # Compute expected results: + # 1. Force in local frame = quat_rotate_inv(link_quat, global_force) + expected_forces_local = quat_rotate_inv_np(link_quat_np, forces_global_np) + + # 2. Position offset in local frame = global_position - link_position (then used for torque) + position_offset_global = positions_global_np - link_pos_np + + # 3. Torque = skew(position_offset_global) @ force_global, then rotate to local + expected_torques_local = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + for i in range(num_envs): + for j in range(num_bodies): + pos_offset = position_offset_global[i, j] # global frame offset + force_local = expected_forces_local[i, j] # local frame force + # skew(pos_offset) @ force_local + expected_torques_local[i, j] = np.cross(pos_offset, force_local) + + # Verify forces + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, expected_forces_local, atol=1e-3, rtol=1e-4), ( + f"Global force at position failed.\nExpected forces:\n{expected_forces_local}\nGot:\n{composed_force_np}" + ) + + # Verify torques + composed_torque_np = wrench_composer.composed_torque.numpy() + assert np.allclose(composed_torque_np, expected_torques_local, atol=1e-3, rtol=1e-4), ( + f"Global force at position failed.\nExpected torques:\n{expected_torques_local}\nGot:\n{composed_torque_np}" + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_local_vs_global_identity_quaternion(device: str): + """Test that local and global give same result with identity quaternion and zero position.""" + rng = np.random.default_rng(seed=13) + num_envs, num_bodies = 10, 5 + + # Create mock with identity pose (default) + mock_asset_local = MockRigidObject(num_envs, num_bodies, device) + mock_asset_global = MockRigidObject(num_envs, num_bodies, device) + + wrench_composer_local = WrenchComposer(mock_asset_local) + wrench_composer_global = WrenchComposer(mock_asset_global) + + # Generate random forces and torques + forces_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + torques_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces = wp.from_numpy(forces_np, dtype=wp.vec3f, device=device) + torques = wp.from_numpy(torques_np, dtype=wp.vec3f, device=device) + + # Apply as local + wrench_composer_local.add_forces_and_torques(forces=forces, torques=torques, is_global=False) + + # Apply as global (should be same with identity quaternion) + wrench_composer_global.add_forces_and_torques(forces=forces, torques=torques, is_global=True) + + # Results should be identical + assert np.allclose( + wrench_composer_local.composed_force.numpy(), + wrench_composer_global.composed_force.numpy(), + atol=1e-6, + ) + assert np.allclose( + wrench_composer_local.composed_torque.numpy(), + wrench_composer_global.composed_torque.numpy(), + atol=1e-6, + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_90_degree_rotation_global_force(device: str): + """Test global force with a known 90-degree rotation for easy verification.""" + num_envs, num_bodies = 1, 1 + + # 90-degree rotation around Z-axis: (w, x, y, z) = (cos(45°), 0, 0, sin(45°)) + # This rotates X -> Y, Y -> -X + angle = np.pi / 2 + link_quat_np = np.array([[[[np.cos(angle / 2), 0, 0, np.sin(angle / 2)]]]], dtype=np.float32).reshape(1, 1, 4) + link_quat_torch = torch.from_numpy(link_quat_np) + + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Apply force in global +X direction + force_global = np.array([[[1.0, 0.0, 0.0]]], dtype=np.float32) + force_wp = wp.from_numpy(force_global, dtype=wp.vec3f, device=device) + + wrench_composer.add_forces_and_torques(forces=force_wp, is_global=True) + + # Expected: After inverse rotation (rotate by -90° around Z), X becomes -Y + # Actually, inverse rotation of +90° around Z applied to (1,0,0) gives (0,-1,0) + expected_force_local = np.array([[[0.0, -1.0, 0.0]]], dtype=np.float32) + + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, expected_force_local, atol=1e-5), ( + f"90-degree rotation test failed.\nExpected:\n{expected_force_local}\nGot:\n{composed_force_np}" + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_composition_mixed_local_and_global(device: str): + """Test that local and global forces can be composed together correctly.""" + rng = np.random.default_rng(seed=14) + num_envs, num_bodies = 5, 3 + + # Create random link quaternions + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_quat_torch = torch.from_numpy(link_quat_np) + + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random local and global forces + forces_local_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces_global_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + + forces_local = wp.from_numpy(forces_local_np, dtype=wp.vec3f, device=device) + forces_global = wp.from_numpy(forces_global_np, dtype=wp.vec3f, device=device) + + # Add local forces first + wrench_composer.add_forces_and_torques(forces=forces_local, is_global=False) + + # Add global forces + wrench_composer.add_forces_and_torques(forces=forces_global, is_global=True) + + # Expected: local forces stay as-is, global forces get rotated, then sum + global_forces_in_local = quat_rotate_inv_np(link_quat_np, forces_global_np) + expected_total = forces_local_np + global_forces_in_local + + composed_force_np = wrench_composer.composed_force.numpy() + assert np.allclose(composed_force_np, expected_total, atol=1e-4, rtol=1e-5), ( + f"Mixed local/global composition failed.\nExpected:\n{expected_total}\nGot:\n{composed_force_np}" + ) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +@pytest.mark.parametrize("num_envs", [1, 10, 50]) +@pytest.mark.parametrize("num_bodies", [1, 3, 5]) +def test_local_forces_at_local_position(device: str, num_envs: int, num_bodies: int): + """Test local forces at local positions (offset from link frame).""" + rng = np.random.default_rng(seed=15) + + for _ in range(5): + # Create random link poses (shouldn't affect local frame calculations) + link_pos_np = rng.uniform(-10.0, 10.0, (num_envs, num_bodies, 3)).astype(np.float32) + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_pos_torch = torch.from_numpy(link_pos_np) + link_quat_torch = torch.from_numpy(link_quat_np) + + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_pos=link_pos_torch, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random local forces and local positions (offsets) + forces_local_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + positions_local_np = rng.uniform(-10.0, 10.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces_local = wp.from_numpy(forces_local_np, dtype=wp.vec3f, device=device) + positions_local = wp.from_numpy(positions_local_np, dtype=wp.vec3f, device=device) + + # Apply local forces at local positions + wrench_composer.add_forces_and_torques(forces=forces_local, positions=positions_local, is_global=False) + + # Expected: forces stay as-is, torque = cross(position, force) + expected_forces = forces_local_np + expected_torques = np.cross(positions_local_np, forces_local_np) + + # Verify + composed_force_np = wrench_composer.composed_force.numpy() + composed_torque_np = wrench_composer.composed_torque.numpy() + + assert np.allclose(composed_force_np, expected_forces, atol=1e-4, rtol=1e-5) + assert np.allclose(composed_torque_np, expected_torques, atol=1e-4, rtol=1e-5) + + +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_global_force_at_link_origin_no_torque(device: str): + """Test that a global force applied at the link origin produces no torque.""" + rng = np.random.default_rng(seed=16) + num_envs, num_bodies = 5, 3 + + # Create random link poses + link_pos_np = rng.uniform(-10.0, 10.0, (num_envs, num_bodies, 3)).astype(np.float32) + link_quat_np = random_unit_quaternion_np(rng, (num_envs, num_bodies)) + link_pos_torch = torch.from_numpy(link_pos_np) + link_quat_torch = torch.from_numpy(link_quat_np) + + mock_asset = MockRigidObject(num_envs, num_bodies, device, link_pos=link_pos_torch, link_quat=link_quat_torch) + wrench_composer = WrenchComposer(mock_asset) + + # Generate random global forces + forces_global_np = rng.uniform(-100.0, 100.0, (num_envs, num_bodies, 3)).astype(np.float32) + forces_global = wp.from_numpy(forces_global_np, dtype=wp.vec3f, device=device) + + # Position = link position (so offset is zero) + positions_at_link = wp.from_numpy(link_pos_np, dtype=wp.vec3f, device=device) + + # Apply global forces at link origin + wrench_composer.add_forces_and_torques(forces=forces_global, positions=positions_at_link, is_global=True) + + # Expected: force rotated to local, torque = 0 (since position offset is zero) + expected_forces = quat_rotate_inv_np(link_quat_np, forces_global_np) + expected_torques = np.zeros((num_envs, num_bodies, 3), dtype=np.float32) + + composed_force_np = wrench_composer.composed_force.numpy() + composed_torque_np = wrench_composer.composed_torque.numpy() + + assert np.allclose(composed_force_np, expected_forces, atol=1e-4, rtol=1e-5) + assert np.allclose(composed_torque_np, expected_torques, atol=1e-4, rtol=1e-5) diff --git a/source/isaaclab/test/visualization/check_scene_xr_visualization.py b/source/isaaclab/test/visualization/check_scene_xr_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..b03fa9e88bd265edad958ea91657f263c4f9af4e --- /dev/null +++ b/source/isaaclab/test/visualization/check_scene_xr_visualization.py @@ -0,0 +1,262 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script checks if the XR visualization widgets are visible from the camera. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p source/isaaclab/test/visualization/check_scene_visualization.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Check XR visualization widgets in Isaac Lab.") +parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.") +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app with XR support +args_cli.xr = True +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +from typing import Any + +from pxr import Gf + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.ui.xr_widgets import DataCollector, TriggerType, VisualizationManager, XRVisualization, update_instruction +from isaaclab.utils import configclass + +## +# Pre-defined configs +## + + +@configclass +class SimpleSceneCfg(InteractiveSceneCfg): + """Design the scene with sensors on the robot.""" + + # ground plane + ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg()) + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) + ) + + +def get_camera_position(): + """Get the current camera position from the USD stage. + + Returns: + tuple: (x, y, z) camera position or None if not available + """ + try: + from pxr import UsdGeom + + stage = sim_utils.get_current_stage() + if stage is not None: + # Get the viewport camera prim + camera_prim_path = "/OmniverseKit_Persp" + camera_prim = stage.GetPrimAtPath(camera_prim_path) + + if camera_prim and camera_prim.IsValid(): + # Get the camera's world transform + camera_xform = UsdGeom.Xformable(camera_prim) + world_transform = camera_xform.ComputeLocalToWorldTransform(0) # 0 = current time + + # Extract position from the transform matrix + camera_pos = world_transform.ExtractTranslation() + return (camera_pos[0], camera_pos[1], camera_pos[2]) + return None + except Exception as e: + print(f"[ERROR]: Failed to get camera position: {e}") + return None + + +def _sample_handle_ik_error(mgr: VisualizationManager, data_collector: DataCollector, params: Any = None) -> None: + error_text_color = getattr(mgr, "_error_text_color", 0xFF0000FF) + mgr.display_widget( + "IK Error Detected", + "/ik_error", + VisualizationManager.message_widget_preset() + | { + "text_color": error_text_color, + "prim_path_source": "/World/defaultGroundPlane/GroundPlane", + "translation": Gf.Vec3f(0, 0, 1), + }, + ) + + +def _sample_update_error_text_color(mgr: VisualizationManager, data_collector: DataCollector) -> None: + current_color = getattr(mgr, "_error_text_color", 0xFF0000FF) + new_color = current_color + 0x100 + if new_color >= 0xFFFFFFFF: + new_color = 0xFF0000FF + mgr.set_attr("_error_text_color", new_color) + + +def _sample_update_left_panel(mgr: VisualizationManager, data_collector: DataCollector) -> None: + left_panel_id = getattr(mgr, "left_panel_id", None) + + if left_panel_id is None: + return + + left_panel_created = getattr(mgr, "_left_panel_created", False) + if left_panel_created is False: + # create a new left panel + mgr.display_widget( + "Left Panel", + left_panel_id, + VisualizationManager.panel_widget_preset() + | { + "text_color": 0xFFFFFFFF, + "prim_path_source": "/World/defaultGroundPlane/GroundPlane", + "translation": Gf.Vec3f(0, -3, 1), + }, + ) + mgr.set_attr("_left_panel_created", True) + + updated_times = getattr(mgr, "_left_panel_updated_times", 0) + # Create a simple panel content since make_panel_content doesn't exist + content = f"Left Panel\nUpdated #{updated_times} times" + update_instruction(left_panel_id, content) + mgr.set_attr("_left_panel_updated_times", updated_times + 1) + + +def _sample_update_right_panel(mgr: VisualizationManager, data_collector: DataCollector) -> None: + right_panel_id = getattr(mgr, "right_panel_id", None) + + if right_panel_id is None: + return + + updated_times = getattr(mgr, "_right_panel_updated_times", 0) + # Create a simple panel content since make_panel_content doesn't exist + right_panel_data = data_collector.get_data("right_panel_data") + if right_panel_data is not None: + assert isinstance(right_panel_data, (tuple, list)), "Right panel data must be a tuple or list" + # Format each element to 3 decimal places + formatted_data = tuple(f"{x:.3f}" for x in right_panel_data) + content = f"Right Panel\nUpdated #{updated_times} times\nData: {formatted_data}" + else: + content = f"Right Panel\nUpdated #{updated_times} times\nData: None" + + right_panel_created = getattr(mgr, "_right_panel_created", False) + if right_panel_created is False: + # create a new left panel + mgr.display_widget( + content, + right_panel_id, + VisualizationManager.panel_widget_preset() + | { + "text_color": 0xFFFFFFFF, + "prim_path_source": "/World/defaultGroundPlane/GroundPlane", + "translation": Gf.Vec3f(0, 3, 1), + }, + ) + mgr.set_attr("_right_panel_created", True) + + update_instruction(right_panel_id, content) + mgr.set_attr("_right_panel_updated_times", updated_times + 1) + + +def apply_sample_visualization(): + # Error Message + XRVisualization.register_callback(TriggerType.TRIGGER_ON_EVENT, {"event_name": "ik_error"}, _sample_handle_ik_error) + + # Display a panel on the left to display DataCollector data + # Refresh periodically + XRVisualization.set_attrs( + { + "left_panel_id": "/left_panel", + "left_panel_translation": Gf.Vec3f(-2, 2.6, 2), + "left_panel_updated_times": 0, + "right_panel_updated_times": 0, + } + ) + XRVisualization.register_callback(TriggerType.TRIGGER_ON_PERIOD, {"period": 1.0}, _sample_update_left_panel) + + # Display a panel on the right to display DataCollector data + # Refresh when camera position changes + XRVisualization.set_attrs( + { + "right_panel_id": "/right_panel", + "right_panel_translation": Gf.Vec3f(1.5, 2, 2), + } + ) + XRVisualization.register_callback( + TriggerType.TRIGGER_ON_CHANGE, {"variable_name": "right_panel_data"}, _sample_update_right_panel + ) + + # Change error text color every second + XRVisualization.set_attrs( + { + "error_text_color": 0xFF0000FF, + } + ) + XRVisualization.register_callback(TriggerType.TRIGGER_ON_UPDATE, {}, _sample_update_error_text_color) + + +def run_simulator( + sim: sim_utils.SimulationContext, + scene: InteractiveScene, +): + """Run the simulator.""" + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + + apply_sample_visualization() + + # Simulate + while simulation_app.is_running(): + if int(time.time()) % 10 < 1: + XRVisualization.push_event("ik_error") + + XRVisualization.push_data({"right_panel_data": get_camera_position()}) + + sim.step() + scene.update(sim_dt) + + +def main(): + """Main function.""" + + # Initialize the simulation context + sim_cfg = sim_utils.SimulationCfg(dt=0.005) + sim = sim_utils.SimulationContext(sim_cfg) + # Set main camera + sim.set_camera_view(eye=(8, 0, 4), target=(0.0, 0.0, 0.0)) + # design scene + scene = InteractiveScene(SimpleSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0)) + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(sim, scene) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab_assets/config/extension.toml b/source/isaaclab_assets/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..3f682d93335843a2a1cf85479eacbc93cd73f05e --- /dev/null +++ b/source/isaaclab_assets/config/extension.toml @@ -0,0 +1,21 @@ +[package] +# Semantic Versioning is used: https://semver.org/ +version = "0.2.4" + +# Description +title = "Isaac Lab Assets" +description="Extension containing configuration instances of different assets and sensors" +readme = "docs/README.md" +repository = "https://github.com/isaac-sim/IsaacLab" +category = "robotics" +keywords = ["kit", "robotics", "assets", "isaaclab"] + +[dependencies] +"isaaclab" = {} + +[core] +reloadable = false + +# Main python module this extension provides. +[[python.module]] +name = "isaaclab_assets" diff --git a/source/isaaclab_assets/data/.gitkeep b/source/isaaclab_assets/data/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..3b41fe31acc6edc88f79e68fca684cfbedfc90ab --- /dev/null +++ b/source/isaaclab_assets/data/.gitkeep @@ -0,0 +1,6 @@ +For Isaac Lab, we primarily store assets on the Omniverse Nucleus server. However, at times, it may be +needed to store the assets locally (for debugging purposes). In such cases, this directory can be +used for temporary hosting of assets. + +Inside the `data` directory, we recommend following the same structure as our Nucleus directory +`Isaac/IsaacLab`. Please check the extension's README for further details. diff --git a/source/isaaclab_assets/docs/CHANGELOG.rst b/source/isaaclab_assets/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..3456213b3e8ea0ab61c7f7b9f1f51ab6e48650e4 --- /dev/null +++ b/source/isaaclab_assets/docs/CHANGELOG.rst @@ -0,0 +1,79 @@ +Changelog +--------- + +0.2.4 (2025-11-26) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Configuration for OpenArm robots used for manipulation tasks. + +0.2.3 (2025-08-11) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Configuration for G1 robot used for locomanipulation tasks. + +0.2.2 (2025-03-10) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configuration for the Fourier GR1T2 robot. + +0.2.1 (2025-01-14) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configuration for the Humanoid-28 robot. + + +0.2.0 (2024-12-27) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Restructured the assets directory into ``robots`` and ``sensors`` subdirectories. + + +0.1.4 (2024-08-21) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configuration for the Inverted Double Pendulum on a Cart robot. + + +0.1.2 (2024-04-03) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configurations for different arms from Kinova Robotics and Rethink Robotics. + + +0.1.1 (2024-03-11) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configurations for allegro and shadow hand assets. + + +0.1.0 (2023-12-20) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Moved all assets' configuration from ``isaaclab`` to ``isaaclab_assets`` extension. diff --git a/source/isaaclab_assets/docs/README.md b/source/isaaclab_assets/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8d37a0dec6d5208d720d49be07839efea6709e59 --- /dev/null +++ b/source/isaaclab_assets/docs/README.md @@ -0,0 +1,41 @@ +# Isaac Lab: Assets for Robots and Objects + +This extension contains configurations for various assets and sensors. The configuration instances are +used to spawn and configure the instances in the simulation. They are passed to their corresponding +classes during construction. + +## Organizing custom assets + +For Isaac Lab, we primarily store assets on the Omniverse Nucleus server. However, at times, it may be +needed to store the assets locally (for debugging purposes). In such cases, the extension's `data` +directory can be used for temporary hosting of assets. + +Inside the `data` directory, we recommend following the same structure as our Nucleus directory +`Isaac/IsaacLab`. This helps us later to move these assets to the Nucleus server seamlessly. + +The recommended directory structure inside `data` is as follows: + +* **`Robots//`**: The USD files should be inside `` directory with + the name of the robot. +* **`Props//`**: The USD files should be inside `` directory with the name + of the prop. This includes mounts, objects and markers. +* **`ActuatorNets/`**: The actuator networks should inside ``**: The policy should be JIT/ONNX compiled with the name `policy.pt`. It should also + contain the parameters used for training the checkpoint. This is to ensure reproducibility. +* **`Test/`**: The asset used for unit testing purposes. + +## Referring to the assets in your code + +You can use the following snippet to refer to the assets: + +```python + +from isaaclab_assets import ISAACLAB_ASSETS_DATA_DIR + + +# ANYmal-C +ANYMAL_C_USD_PATH = f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd" +# ANYmal-D +ANYMAL_D_USD_PATH = f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d.usd" +``` diff --git a/source/isaaclab_assets/isaaclab_assets/__init__.py b/source/isaaclab_assets/isaaclab_assets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d83e15466fc32a1a068b51cc535560e59fdb06b3 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +"""Package containing asset and sensor configurations.""" + +import os +import toml + +# Conveniences to other module directories via relative paths +ISAACLAB_ASSETS_EXT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../")) +"""Path to the extension source directory.""" + +ISAACLAB_ASSETS_DATA_DIR = os.path.join(ISAACLAB_ASSETS_EXT_DIR, "data") +"""Path to the extension data directory.""" + +ISAACLAB_ASSETS_METADATA = toml.load(os.path.join(ISAACLAB_ASSETS_EXT_DIR, "config", "extension.toml")) +"""Extension metadata dictionary parsed from the extension.toml file.""" + +# Configure the module-level variables +__version__ = ISAACLAB_ASSETS_METADATA["package"]["version"] + +from .robots import * +from .sensors import * diff --git a/source/isaaclab_assets/isaaclab_assets/objects/apple/model/configuration/materials/image0.png b/source/isaaclab_assets/isaaclab_assets/objects/apple/model/configuration/materials/image0.png new file mode 100644 index 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--git a/source/isaaclab_assets/isaaclab_assets/objects/plate_pink/model/model.usd b/source/isaaclab_assets/isaaclab_assets/objects/plate_pink/model/model.usd new file mode 100644 index 0000000000000000000000000000000000000000..610ad9f87ff787a54c405709addf49fade80e1f9 Binary files /dev/null and b/source/isaaclab_assets/isaaclab_assets/objects/plate_pink/model/model.usd differ diff --git a/source/isaaclab_assets/isaaclab_assets/robots/__init__.py b/source/isaaclab_assets/isaaclab_assets/robots/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..77bcf04d0a3eb9061a8fd6a56d910108692907c1 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/__init__.py @@ -0,0 +1,32 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +## +# Configuration for different assets. +## + +from .agibot import * +from .agility import * +from .allegro import * +from .ant import * +from .anymal import * +from .cart_double_pendulum import * +from .cartpole import * +from .cassie import * +from .fourier import * +from .franka import * +from .galbot import * +from .humanoid import * +from .humanoid_28 import * +from .kinova import * +from .kuka_allegro import * +from .pick_and_place import * +from .quadcopter import * +from .ridgeback_franka import * +from .sawyer import * +from .shadow_hand import * +from .spot import * +from .unitree import * +from .universal_robots import * diff --git a/source/isaaclab_assets/isaaclab_assets/robots/agibot.py b/source/isaaclab_assets/isaaclab_assets/robots/agibot.py new file mode 100644 index 0000000000000000000000000000000000000000..c5483721d2e06d5fc24f8700a73a423ca55bdeb5 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/agibot.py @@ -0,0 +1,162 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Agibot A2D humanoid robots. + +The following configurations are available: + +* :obj:`AGIBOT_A2D_CFG`: Agibot A2D robot + + +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +AGIBOT_A2D_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Agibot/A2D/A2D_physics.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + # Body joints + "joint_lift_body": 0.1995, + "joint_body_pitch": 0.6025, + # Head joints + "joint_head_yaw": 0.0, + "joint_head_pitch": 0.6708, + # Left arm joints + "left_arm_joint1": -1.0817, + "left_arm_joint2": 0.5907, + "left_arm_joint3": 0.3442, + "left_arm_joint4": -1.2819, + "left_arm_joint5": 0.6928, + "left_arm_joint6": 1.4725, + "left_arm_joint7": -0.1599, + # Right arm joints + "right_arm_joint1": 1.0817, + "right_arm_joint2": -0.5907, + "right_arm_joint3": -0.3442, + "right_arm_joint4": 1.2819, + "right_arm_joint5": -0.6928, + "right_arm_joint6": -0.7, + "right_arm_joint7": 0.0, + # Left gripper joints + "left_Right_1_Joint": 0.0, + "left_hand_joint1": 0.994, + "left_Right_0_Joint": 0.0, + "left_Left_0_Joint": 0.0, + "left_Right_Support_Joint": 0.994, + "left_Left_Support_Joint": 0.994, + "left_Right_RevoluteJoint": 0.0, + "left_Left_RevoluteJoint": 0.0, + # Right gripper joints + "right_Right_1_Joint": 0.0, + "right_hand_joint1": 0.994, + "right_Right_0_Joint": 0.0, + "right_Left_0_Joint": 0.0, + "right_Right_Support_Joint": 0.994, + "right_Left_Support_Joint": 0.994, + "right_Right_RevoluteJoint": 0.0, + "right_Left_RevoluteJoint": 0.0, + }, + pos=(-0.6, 0.0, -1.05), # init pos of the articulation for teleop + ), + actuators={ + # Body lift and torso actuators + "body": ImplicitActuatorCfg( + joint_names_expr=["joint_lift_body", "joint_body_pitch"], + effort_limit_sim=10000.0, + velocity_limit_sim=2.61, + stiffness=10000000.0, + damping=200.0, + ), + # Head actuators + "head": ImplicitActuatorCfg( + joint_names_expr=["joint_head_yaw", "joint_head_pitch"], + effort_limit_sim=50.0, + velocity_limit_sim=1.0, + stiffness=80.0, + damping=4.0, + ), + # Left arm actuator + "left_arm": ImplicitActuatorCfg( + joint_names_expr=["left_arm_joint[1-7]"], + effort_limit_sim={ + "left_arm_joint1": 2000.0, + "left_arm_joint[2-7]": 1000.0, + }, + velocity_limit_sim=1.57, + stiffness={"left_arm_joint1": 10000000.0, "left_arm_joint[2-7]": 20000.0}, + damping={"left_arm_joint1": 0.0, "left_arm_joint[2-7]": 0.0}, + ), + # Right arm actuator + "right_arm": ImplicitActuatorCfg( + joint_names_expr=["right_arm_joint[1-7]"], + effort_limit_sim={ + "right_arm_joint1": 2000.0, + "right_arm_joint[2-7]": 1000.0, + }, + velocity_limit_sim=1.57, + stiffness={"right_arm_joint1": 10000000.0, "right_arm_joint[2-7]": 20000.0}, + damping={"right_arm_joint1": 0.0, "right_arm_joint[2-7]": 0.0}, + ), + # "left_Right_2_Joint" is excluded from Articulation. + # "left_hand_joint1" is the driver joint, and "left_Right_1_Joint" is the mimic joint. + # "left_.*_Support_Joint" driver joint can be set optionally, to disable the driver, + # set stiffness and damping to 0.0 below + "left_gripper": ImplicitActuatorCfg( + joint_names_expr=["left_hand_joint1", "left_.*_Support_Joint"], + effort_limit_sim={"left_hand_joint1": 10.0, "left_.*_Support_Joint": 1.0}, + velocity_limit_sim=2.0, + stiffness={"left_hand_joint1": 20.0, "left_.*_Support_Joint": 2.0}, + damping={"left_hand_joint1": 0.10, "left_.*_Support_Joint": 0.01}, + ), + # set PD to zero for passive joints in close-loop gripper + "left_gripper_passive": ImplicitActuatorCfg( + joint_names_expr=["left_.*_(0|1)_Joint", "left_.*_RevoluteJoint"], + effort_limit_sim=10.0, + velocity_limit_sim=10.0, + stiffness=0.0, + damping=0.0, + ), + # "right_Right_2_Joint" is excluded from Articulation. + # "right_hand_joint1" is the driver joint, and "right_Right_1_Joint" is the mimic joint. + # "right_.*_Support_Joint" driver joint can be set optionally, to disable the driver, + # set stiffness and damping to 0.0 below + "right_gripper": ImplicitActuatorCfg( + joint_names_expr=["right_hand_joint1", "right_.*_Support_Joint"], + effort_limit_sim={"right_hand_joint1": 100.0, "right_.*_Support_Joint": 100.0}, + velocity_limit_sim=10.0, + stiffness={"right_hand_joint1": 20.0, "right_.*_Support_Joint": 2.0}, + damping={"right_hand_joint1": 0.10, "right_.*_Support_Joint": 0.01}, + ), + # set PD to zero for passive joints in close-loop gripper + "right_gripper_passive": ImplicitActuatorCfg( + joint_names_expr=["right_.*_(0|1)_Joint", "right_.*_RevoluteJoint"], + effort_limit_sim=100.0, + velocity_limit_sim=10.0, + stiffness=0.0, + damping=0.0, + ), + }, + soft_joint_pos_limit_factor=1.0, +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/agility.py b/source/isaaclab_assets/isaaclab_assets/robots/agility.py new file mode 100644 index 0000000000000000000000000000000000000000..2c85a42ec6814a04dd849753618d7a9c842b021e --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/agility.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +LEG_JOINT_NAMES = [ + ".*_hip_roll", + ".*_hip_yaw", + ".*_hip_pitch", + ".*_knee", + ".*_toe_a", + ".*_toe_b", +] + +ARM_JOINT_NAMES = [".*_arm_.*"] + + +DIGIT_V4_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Agility/Digit/digit_v4.usd", + activate_contact_sensors=True, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 1.05), + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "all": ImplicitActuatorCfg( + joint_names_expr=".*", + stiffness=None, + damping=None, + ), + }, +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/allegro.py b/source/isaaclab_assets/isaaclab_assets/robots/allegro.py new file mode 100644 index 0000000000000000000000000000000000000000..0e18ef77c13c039e6bcdeb80338ac97f1905f402 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/allegro.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Allegro Hand robots from Wonik Robotics. + +The following configurations are available: + +* :obj:`ALLEGRO_HAND_CFG`: Allegro Hand with implicit actuator model. + +Reference: + +* https://www.wonikrobotics.com/robot-hand + +""" + +import math + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +ALLEGRO_HAND_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/WonikRobotics/AllegroHand/allegro_hand_instanceable.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + retain_accelerations=False, + enable_gyroscopic_forces=False, + angular_damping=0.01, + max_linear_velocity=1000.0, + max_angular_velocity=64 / math.pi * 180.0, + max_depenetration_velocity=1000.0, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0005, + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(0.257551, 0.283045, 0.683330, -0.621782), + joint_pos={"^(?!thumb_joint_0).*": 0.0, "thumb_joint_0": 0.28}, + ), + actuators={ + "fingers": ImplicitActuatorCfg( + joint_names_expr=[".*"], + effort_limit_sim=0.5, + stiffness=3.0, + damping=0.1, + friction=0.01, + ), + }, + soft_joint_pos_limit_factor=1.0, +) +"""Configuration of Allegro Hand robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/ant.py b/source/isaaclab_assets/isaaclab_assets/robots/ant.py new file mode 100644 index 0000000000000000000000000000000000000000..49798ad638dd2b3283964c61ef92ba6e1bc0c3ac --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/ant.py @@ -0,0 +1,55 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Mujoco Ant robot.""" + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +ANT_CFG = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/IsaacSim/Ant/ant_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=10.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + copy_from_source=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + joint_pos={ + ".*_leg": 0.0, + "front_left_foot": 0.785398, # 45 degrees + "front_right_foot": -0.785398, + "left_back_foot": -0.785398, + "right_back_foot": 0.785398, + }, + ), + actuators={ + "body": ImplicitActuatorCfg( + joint_names_expr=[".*"], + stiffness=0.0, + damping=0.0, + ), + }, +) +"""Configuration for the Mujoco Ant robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/anymal.py b/source/isaaclab_assets/isaaclab_assets/robots/anymal.py new file mode 100644 index 0000000000000000000000000000000000000000..ac0e565513f4f4e27a1233263d2083df716710ae --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/anymal.py @@ -0,0 +1,175 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ANYbotics robots. + +The following configuration parameters are available: + +* :obj:`ANYMAL_B_CFG`: The ANYmal-B robot with ANYdrives 3.0 +* :obj:`ANYMAL_C_CFG`: The ANYmal-C robot with ANYdrives 3.0 +* :obj:`ANYMAL_D_CFG`: The ANYmal-D robot with ANYdrives 3.0 + +Reference: + +* https://github.com/ANYbotics/anymal_b_simple_description +* https://github.com/ANYbotics/anymal_c_simple_description +* https://github.com/ANYbotics/anymal_d_simple_description + +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ActuatorNetLSTMCfg, DCMotorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.sensors import RayCasterCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from isaaclab_assets.sensors.velodyne import VELODYNE_VLP_16_RAYCASTER_CFG + +## +# Configuration - Actuators. +## + +ANYDRIVE_3_SIMPLE_ACTUATOR_CFG = DCMotorCfg( + joint_names_expr=[".*HAA", ".*HFE", ".*KFE"], + saturation_effort=120.0, + effort_limit=80.0, + velocity_limit=7.5, + stiffness={".*": 40.0}, + damping={".*": 5.0}, +) +"""Configuration for ANYdrive 3.x with DC actuator model.""" + + +ANYDRIVE_3_LSTM_ACTUATOR_CFG = ActuatorNetLSTMCfg( + joint_names_expr=[".*HAA", ".*HFE", ".*KFE"], + network_file=f"{ISAACLAB_NUCLEUS_DIR}/ActuatorNets/ANYbotics/anydrive_3_lstm_jit.pt", + saturation_effort=120.0, + effort_limit=80.0, + velocity_limit=7.5, +) +"""Configuration for ANYdrive 3.0 (used on ANYmal-C) with LSTM actuator model.""" + + +## +# Configuration - Articulation. +## + +ANYMAL_B_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-B/anymal_b.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.6), + joint_pos={ + ".*HAA": 0.0, # all HAA + ".*F_HFE": 0.4, # both front HFE + ".*H_HFE": -0.4, # both hind HFE + ".*F_KFE": -0.8, # both front KFE + ".*H_KFE": 0.8, # both hind KFE + }, + ), + actuators={"legs": ANYDRIVE_3_LSTM_ACTUATOR_CFG}, + soft_joint_pos_limit_factor=0.95, +) +"""Configuration of ANYmal-B robot using actuator-net.""" + + +ANYMAL_C_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd", + # usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_instanceable.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.6), + joint_pos={ + ".*HAA": 0.0, # all HAA + ".*F_HFE": 0.4, # both front HFE + ".*H_HFE": -0.4, # both hind HFE + ".*F_KFE": -0.8, # both front KFE + ".*H_KFE": 0.8, # both hind KFE + }, + ), + actuators={"legs": ANYDRIVE_3_LSTM_ACTUATOR_CFG}, + soft_joint_pos_limit_factor=0.95, +) +"""Configuration of ANYmal-C robot using actuator-net.""" + + +ANYMAL_D_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d.usd", + # usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-D/anymal_d_minimal.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.6), + joint_pos={ + ".*HAA": 0.0, # all HAA + ".*F_HFE": 0.4, # both front HFE + ".*H_HFE": -0.4, # both hind HFE + ".*F_KFE": -0.8, # both front KFE + ".*H_KFE": 0.8, # both hind KFE + }, + ), + actuators={"legs": ANYDRIVE_3_LSTM_ACTUATOR_CFG}, + soft_joint_pos_limit_factor=0.95, +) +"""Configuration of ANYmal-D robot using actuator-net. + +Note: + Since we don't have a publicly available actuator network for ANYmal-D, we use the same network as ANYmal-C. + This may impact the sim-to-real transfer performance. +""" + + +## +# Configuration - Sensors. +## + +ANYMAL_LIDAR_CFG = VELODYNE_VLP_16_RAYCASTER_CFG.replace( + offset=RayCasterCfg.OffsetCfg(pos=(-0.310, 0.000, 0.159), rot=(0.0, 0.0, 0.0, 1.0)) +) +"""Configuration for the Velodyne VLP-16 sensor mounted on the ANYmal robot's base.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/arl_robot_1.py b/source/isaaclab_assets/isaaclab_assets/robots/arl_robot_1.py new file mode 100644 index 0000000000000000000000000000000000000000..83d974d743e324f698ffa353e1c9c38048f5f40e --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/arl_robot_1.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the ARL robots. + +The following configuration parameters are available: + +* :obj:`ARL_ROBOT_1_CFG`: The ARL_Robot_1 with (TODO add motor propeller combination) +""" + +from isaaclab_contrib.actuators import ThrusterCfg +from isaaclab_contrib.assets import MultirotorCfg + +import isaaclab.sim as sim_utils +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration - Actuators. +## + +ARL_ROBOT_1_THRUSTER = ThrusterCfg( + thrust_range=(0.1, 10.0), + thrust_const_range=(9.26312e-06, 1.826312e-05), + tau_inc_range=(0.05, 0.08), + tau_dec_range=(0.005, 0.005), + torque_to_thrust_ratio=0.07, + thruster_names_expr=["back_left_prop", "back_right_prop", "front_left_prop", "front_right_prop"], +) + +## +# Configuration - Articulation. +## + +ARL_ROBOT_1_CFG = MultirotorCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/NTNU/ARL-Robot-1/arl_robot_1.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=MultirotorCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.0), + lin_vel=(0.0, 0.0, 0.0), + ang_vel=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + rps={ + "back_left_prop": 200.0, + "back_right_prop": 200.0, + "front_left_prop": 200.0, + "front_right_prop": 200.0, + }, + ), + actuators={"thrusters": ARL_ROBOT_1_THRUSTER}, + rotor_directions=[1, -1, 1, -1], + allocation_matrix=[ + [0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0], + [1.0, 1.0, 1.0, 1.0], + [-0.13, -0.13, 0.13, 0.13], + [-0.13, 0.13, 0.13, -0.13], + [-0.07, 0.07, -0.07, 0.07], + ], +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/cart_double_pendulum.py b/source/isaaclab_assets/isaaclab_assets/robots/cart_double_pendulum.py new file mode 100644 index 0000000000000000000000000000000000000000..22028f39baf215c52aa28a8373d0d942607887d3 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/cart_double_pendulum.py @@ -0,0 +1,53 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for a simple inverted Double Pendulum on a Cart robot.""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +CART_DOUBLE_PENDULUM_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Classic/CartDoublePendulum/cart_double_pendulum.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=100.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 2.0), joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0, "pole_to_pendulum": 0.0} + ), + actuators={ + "cart_actuator": ImplicitActuatorCfg( + joint_names_expr=["slider_to_cart"], + effort_limit_sim=400.0, + stiffness=0.0, + damping=10.0, + ), + "pole_actuator": ImplicitActuatorCfg( + joint_names_expr=["cart_to_pole"], effort_limit_sim=400.0, stiffness=0.0, damping=0.0 + ), + "pendulum_actuator": ImplicitActuatorCfg( + joint_names_expr=["pole_to_pendulum"], effort_limit_sim=400.0, stiffness=0.0, damping=0.0 + ), + }, +) +"""Configuration for a simple inverted Double Pendulum on a Cart robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/cartpole.py b/source/isaaclab_assets/isaaclab_assets/robots/cartpole.py new file mode 100644 index 0000000000000000000000000000000000000000..1e236eda6b934d8be6d4abd2b071dd2fc0a07bc7 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/cartpole.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for a simple Cartpole robot.""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +CARTPOLE_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Classic/Cartpole/cartpole.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + rigid_body_enabled=True, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=100.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 2.0), joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0} + ), + actuators={ + "cart_actuator": ImplicitActuatorCfg( + joint_names_expr=["slider_to_cart"], + effort_limit_sim=400.0, + stiffness=0.0, + damping=10.0, + ), + "pole_actuator": ImplicitActuatorCfg( + joint_names_expr=["cart_to_pole"], effort_limit_sim=400.0, stiffness=0.0, damping=0.0 + ), + }, +) +"""Configuration for a simple Cartpole robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/cassie.py b/source/isaaclab_assets/isaaclab_assets/robots/cassie.py new file mode 100644 index 0000000000000000000000000000000000000000..09e75e241fedc64744cfc29526741cb07e5f1b9e --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/cassie.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for Agility robots. + +The following configurations are available: + +* :obj:`CASSIE_CFG`: Agility Cassie robot with simple PD controller for the legs + +Reference: https://github.com/UMich-BipedLab/Cassie_Model/blob/master/urdf/cassie.urdf +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +CASSIE_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Agility/Cassie/cassie.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.9), + joint_pos={ + "hip_abduction_left": 0.1, + "hip_rotation_left": 0.0, + "hip_flexion_left": 1.0, + "thigh_joint_left": -1.8, + "ankle_joint_left": 1.57, + "toe_joint_left": -1.57, + "hip_abduction_right": -0.1, + "hip_rotation_right": 0.0, + "hip_flexion_right": 1.0, + "thigh_joint_right": -1.8, + "ankle_joint_right": 1.57, + "toe_joint_right": -1.57, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "legs": ImplicitActuatorCfg( + joint_names_expr=["hip_.*", "thigh_.*", "ankle_.*"], + effort_limit_sim=200.0, + stiffness={ + "hip_abduction.*": 100.0, + "hip_rotation.*": 100.0, + "hip_flexion.*": 200.0, + "thigh_joint.*": 200.0, + "ankle_joint.*": 200.0, + }, + damping={ + "hip_abduction.*": 3.0, + "hip_rotation.*": 3.0, + "hip_flexion.*": 6.0, + "thigh_joint.*": 6.0, + "ankle_joint.*": 6.0, + }, + ), + "toes": ImplicitActuatorCfg( + joint_names_expr=["toe_.*"], + effort_limit_sim=20.0, + stiffness={ + "toe_joint.*": 20.0, + }, + damping={ + "toe_joint.*": 1.0, + }, + ), + }, +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/fourier.py b/source/isaaclab_assets/isaaclab_assets/robots/fourier.py new file mode 100644 index 0000000000000000000000000000000000000000..58e143d1188575df6ad657c9cf158d9063e950c3 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/fourier.py @@ -0,0 +1,164 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Fourier Robots. + +The following configuration parameters are available: + +* :obj:`GR1T2_CFG`: The GR1T2 humanoid. +* :obj:`GR1T2_HIGH_PD_CFG`: The GR1T2 humanoid configured with high PD gains on upper + body joints for pick-place manipulation tasks. + +Reference: https://www.fftai.com/products-gr1 +""" + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + + +GR1T2_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=( + f"{ISAAC_NUCLEUS_DIR}/Robots/FourierIntelligence/GR-1/GR1T2_fourier_hand_6dof/GR1T2_fourier_hand_6dof.usd" + ), + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=4 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.95), + joint_pos={".*": 0.0}, + joint_vel={".*": 0.0}, + ), + actuators={ + "head": ImplicitActuatorCfg( + joint_names_expr=[ + "head_.*", + ], + effort_limit=None, + velocity_limit=None, + stiffness=None, + damping=None, + ), + "trunk": ImplicitActuatorCfg( + joint_names_expr=[ + "waist_.*", + ], + effort_limit=None, + velocity_limit=None, + stiffness=None, + damping=None, + ), + "legs": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_hip_.*", + ".*_knee_.*", + ".*_ankle_.*", + ], + effort_limit=None, + velocity_limit=None, + stiffness=None, + damping=None, + ), + "right-arm": ImplicitActuatorCfg( + joint_names_expr=[ + "right_shoulder_.*", + "right_elbow_.*", + "right_wrist_.*", + ], + effort_limit=torch.inf, + velocity_limit=torch.inf, + stiffness=None, + damping=None, + armature=0.0, + ), + "left-arm": ImplicitActuatorCfg( + joint_names_expr=[ + "left_shoulder_.*", + "left_elbow_.*", + "left_wrist_.*", + ], + effort_limit=torch.inf, + velocity_limit=torch.inf, + stiffness=None, + damping=None, + armature=0.0, + ), + "right-hand": ImplicitActuatorCfg( + joint_names_expr=[ + "R_.*", + ], + effort_limit=None, + velocity_limit=None, + stiffness=None, + damping=None, + ), + "left-hand": ImplicitActuatorCfg( + joint_names_expr=[ + "L_.*", + ], + effort_limit=None, + velocity_limit=None, + stiffness=None, + damping=None, + ), + }, +) +"""Configuration for the GR1T2 Humanoid robot.""" + + +GR1T2_HIGH_PD_CFG = GR1T2_CFG.replace( + actuators={ + "trunk": ImplicitActuatorCfg( + joint_names_expr=["waist_.*"], + effort_limit=None, + velocity_limit=None, + stiffness=4400, + damping=40.0, + armature=0.01, + ), + "right-arm": ImplicitActuatorCfg( + joint_names_expr=["right_shoulder_.*", "right_elbow_.*", "right_wrist_.*"], + stiffness=4400.0, + damping=40.0, + armature=0.01, + ), + "left-arm": ImplicitActuatorCfg( + joint_names_expr=["left_shoulder_.*", "left_elbow_.*", "left_wrist_.*"], + stiffness=4400.0, + damping=40.0, + armature=0.01, + ), + "right-hand": ImplicitActuatorCfg( + joint_names_expr=["R_.*"], + stiffness=None, + damping=None, + ), + "left-hand": ImplicitActuatorCfg( + joint_names_expr=["L_.*"], + stiffness=None, + damping=None, + ), + }, +) +"""Configuration for the GR1T2 Humanoid robot configured for with high PD gains for pick-place manipulation tasks.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/franka.py b/source/isaaclab_assets/isaaclab_assets/robots/franka.py new file mode 100644 index 0000000000000000000000000000000000000000..caacf214c58f5e2a608649ec84255769f6453d24 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/franka.py @@ -0,0 +1,147 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Franka Emika robots. + +The following configurations are available: + +* :obj:`FRANKA_PANDA_CFG`: Franka Emika Panda robot with Panda hand +* :obj:`FRANKA_PANDA_HIGH_PD_CFG`: Franka Emika Panda robot with Panda hand with stiffer PD control +* :obj:`FRANKA_ROBOTIQ_GRIPPER_CFG`: Franka robot with Robotiq_2f_85 gripper + +Reference: https://github.com/frankaemika/franka_ros +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +FRANKA_PANDA_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 0.0, + "panda_joint2": -0.569, + "panda_joint3": 0.0, + "panda_joint4": -2.810, + "panda_joint5": 0.0, + "panda_joint6": 3.037, + "panda_joint7": 0.741, + "panda_finger_joint.*": 0.04, + }, + ), + actuators={ + "panda_shoulder": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + effort_limit_sim=87.0, + stiffness=80.0, + damping=4.0, + ), + "panda_forearm": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + effort_limit_sim=12.0, + stiffness=80.0, + damping=4.0, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint.*"], + effort_limit_sim=200.0, + stiffness=2e3, + damping=1e2, + ), + }, + soft_joint_pos_limit_factor=1.0, +) +"""Configuration of Franka Emika Panda robot.""" + + +FRANKA_PANDA_HIGH_PD_CFG = FRANKA_PANDA_CFG.copy() +FRANKA_PANDA_HIGH_PD_CFG.spawn.rigid_props.disable_gravity = True +FRANKA_PANDA_HIGH_PD_CFG.actuators["panda_shoulder"].stiffness = 400.0 +FRANKA_PANDA_HIGH_PD_CFG.actuators["panda_shoulder"].damping = 80.0 +FRANKA_PANDA_HIGH_PD_CFG.actuators["panda_forearm"].stiffness = 400.0 +FRANKA_PANDA_HIGH_PD_CFG.actuators["panda_forearm"].damping = 80.0 +"""Configuration of Franka Emika Panda robot with stiffer PD control. + +This configuration is useful for task-space control using differential IK. +""" + + +FRANKA_ROBOTIQ_GRIPPER_CFG = FRANKA_PANDA_CFG.copy() +FRANKA_ROBOTIQ_GRIPPER_CFG.spawn.usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/FrankaRobotics/FrankaPanda/franka.usd" +FRANKA_ROBOTIQ_GRIPPER_CFG.spawn.variants = {"Gripper": "Robotiq_2F_85"} +FRANKA_ROBOTIQ_GRIPPER_CFG.spawn.rigid_props.disable_gravity = True +FRANKA_ROBOTIQ_GRIPPER_CFG.init_state.joint_pos = { + "panda_joint1": 0.0, + "panda_joint2": -0.569, + "panda_joint3": 0.0, + "panda_joint4": -2.810, + "panda_joint5": 0.0, + "panda_joint6": 3.037, + "panda_joint7": 0.741, + "finger_joint": 0.0, + ".*_inner_finger_joint": 0.0, + ".*_inner_finger_knuckle_joint": 0.0, + ".*_outer_.*_joint": 0.0, +} +FRANKA_ROBOTIQ_GRIPPER_CFG.init_state.pos = (-0.85, 0, 0.76) +FRANKA_ROBOTIQ_GRIPPER_CFG.actuators = { + "panda_shoulder": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + effort_limit_sim=5200.0, + velocity_limit_sim=2.175, + stiffness=1100.0, + damping=80.0, + ), + "panda_forearm": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + effort_limit_sim=720.0, + velocity_limit_sim=2.61, + stiffness=1000.0, + damping=80.0, + ), + "gripper_drive": ImplicitActuatorCfg( + joint_names_expr=["finger_joint"], # "right_outer_knuckle_joint" is its mimic joint + effort_limit_sim=1650, + velocity_limit_sim=10.0, + stiffness=17, + damping=0.02, + ), + # enable the gripper to grasp in a parallel manner + "gripper_finger": ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_joint"], + effort_limit_sim=50, + velocity_limit_sim=10.0, + stiffness=0.2, + damping=0.001, + ), + # set PD to zero for passive joints in close-loop gripper + 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@@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a4207216ec90bb76394760583a2056012ff6b9097e10c22937e59f3d51057e5 +size 414984 diff --git a/source/isaaclab_assets/isaaclab_assets/robots/galbot.py b/source/isaaclab_assets/isaaclab_assets/robots/galbot.py new file mode 100644 index 0000000000000000000000000000000000000000..9827c7c8d31e9967ac7df8fc6877258796e9383e --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/galbot.py @@ -0,0 +1,103 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Configuration for the Galbot humanoid robot. + +The following configuration parameters are available: + +* :obj:`GALBOT_ONE_CHARLIE_CFG`: The galbot_one_charlie humanoid robot. + +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + + +GALBOT_ONE_CHARLIE_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Galbot/galbot_one_charlie/galbot_one_charlie.usd", + variants={"Physics": "PhysX"}, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + activate_contact_sensors=True, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "leg_joint1": 0.8, + "leg_joint2": 2.3, + "leg_joint3": 1.55, + "leg_joint4": 0.0, + "head_joint1": 0.0, + "head_joint2": 0.36, + "left_arm_joint1": -0.5480, + "left_arm_joint2": -0.6551, + "left_arm_joint3": 2.407, + "left_arm_joint4": 1.3641, + "left_arm_joint5": -0.4416, + "left_arm_joint6": 0.1168, + "left_arm_joint7": 1.2308, + "left_gripper_left_joint": 0.035, + "left_gripper_right_joint": 0.035, + "right_arm_joint1": 0.1535, + "right_arm_joint2": 1.0087, + "right_arm_joint3": 0.0895, + "right_arm_joint4": 1.5743, + "right_arm_joint5": -0.2422, + "right_arm_joint6": -0.0009, + "right_arm_joint7": -0.9143, + "right_suction_cup_joint1": 0.0, + }, + pos=(-0.6, 0.0, -0.8), + ), + # PD parameters are read from USD file with Gain Tuner + actuators={ + "head": ImplicitActuatorCfg( + joint_names_expr=["head_joint.*"], + velocity_limit_sim=None, + effort_limit_sim=None, + stiffness=None, + damping=None, + ), + "leg": ImplicitActuatorCfg( + joint_names_expr=["leg_joint.*"], + velocity_limit_sim=None, + effort_limit_sim=None, + stiffness=None, + damping=None, + ), + "left_arm": ImplicitActuatorCfg( + joint_names_expr=["left_arm_joint.*"], + velocity_limit_sim=None, + effort_limit_sim=None, + stiffness=None, + damping=None, + ), + "right_arm": ImplicitActuatorCfg( + joint_names_expr=["right_arm_joint.*", "right_suction_cup_joint1"], + velocity_limit_sim=None, + effort_limit_sim=None, + stiffness=None, + damping=None, + ), + "left_gripper": ImplicitActuatorCfg( + joint_names_expr=["left_gripper_.*_joint"], + velocity_limit_sim=1.0, + effort_limit_sim=None, + stiffness=None, + damping=None, + ), + }, +) +"""Configuration of Galbot_one_charlie humanoid using implicit actuator models.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/humanoid.py b/source/isaaclab_assets/isaaclab_assets/robots/humanoid.py new file mode 100644 index 0000000000000000000000000000000000000000..42940b4fa1f4b3e84aefda6e063f095d67ff4a35 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/humanoid.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Mujoco Humanoid robot.""" + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +HUMANOID_CFG = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/IsaacSim/Humanoid/humanoid_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=None, + max_depenetration_velocity=10.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + copy_from_source=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 1.34), + joint_pos={".*": 0.0}, + ), + actuators={ + "body": ImplicitActuatorCfg( + joint_names_expr=[".*"], + stiffness={ + ".*_waist.*": 20.0, + ".*_upper_arm.*": 10.0, + "pelvis": 10.0, + ".*_lower_arm": 2.0, + ".*_thigh:0": 10.0, + ".*_thigh:1": 20.0, + ".*_thigh:2": 10.0, + ".*_shin": 5.0, + ".*_foot.*": 2.0, + }, + damping={ + ".*_waist.*": 5.0, + ".*_upper_arm.*": 5.0, + "pelvis": 5.0, + ".*_lower_arm": 1.0, + ".*_thigh:0": 5.0, + ".*_thigh:1": 5.0, + ".*_thigh:2": 5.0, + ".*_shin": 0.1, + ".*_foot.*": 1.0, + }, + velocity_limit_sim={".*": 100.0}, + ), + }, +) +"""Configuration for the Mujoco Humanoid robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/humanoid_28.py b/source/isaaclab_assets/isaaclab_assets/robots/humanoid_28.py new file mode 100644 index 0000000000000000000000000000000000000000..84f44339a5370ab361998fcd2929cb2dce2ee6ec --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/humanoid_28.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the 28-DOFs Mujoco Humanoid robot.""" + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +HUMANOID_28_CFG = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Classic/Humanoid28/humanoid_28.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=None, + max_depenetration_velocity=10.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + copy_from_source=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.8), + joint_pos={".*": 0.0}, + ), + actuators={ + "body": ImplicitActuatorCfg( + joint_names_expr=[".*"], + stiffness=None, + damping=None, + velocity_limit_sim={".*": 100.0}, + ), + }, +) +"""Configuration for the 28-DOFs Mujoco Humanoid robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/kinova.py b/source/isaaclab_assets/isaaclab_assets/robots/kinova.py new file mode 100644 index 0000000000000000000000000000000000000000..3bef3896232e5b2adce2e90e92e9b9e8b042922e --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/kinova.py @@ -0,0 +1,172 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Kinova Robotics arms. + +The following configuration parameters are available: + +* :obj:`KINOVA_JACO2_N7S300_CFG`: The Kinova JACO2 (7-Dof) arm with a 3-finger gripper. +* :obj:`KINOVA_JACO2_N6S300_CFG`: The Kinova JACO2 (6-Dof) arm with a 3-finger gripper. +* :obj:`KINOVA_GEN3_N7_CFG`: The Kinova Gen3 (7-Dof) arm with no gripper. + +Reference: https://github.com/Kinovarobotics/kinova-ros +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +KINOVA_JACO2_N7S300_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Kinova/Jaco2/J2N7S300/j2n7s300_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "j2n7s300_joint_1": 0.0, + "j2n7s300_joint_2": 2.76, + "j2n7s300_joint_3": 0.0, + "j2n7s300_joint_4": 2.0, + "j2n7s300_joint_5": 2.0, + "j2n7s300_joint_6": 0.0, + "j2n7s300_joint_7": 0.0, + "j2n7s300_joint_finger_[1-3]": 0.2, # close: 1.2, open: 0.2 + "j2n7s300_joint_finger_tip_[1-3]": 0.2, # close: 1.2, open: 0.2 + }, + ), + actuators={ + "arm": ImplicitActuatorCfg( + joint_names_expr=[".*_joint_[1-7]"], + effort_limit_sim={ + ".*_joint_[1-2]": 80.0, + ".*_joint_[3-4]": 40.0, + ".*_joint_[5-7]": 20.0, + }, + stiffness={ + ".*_joint_[1-4]": 40.0, + ".*_joint_[5-7]": 15.0, + }, + damping={ + ".*_joint_[1-4]": 1.0, + ".*_joint_[5-7]": 0.5, + }, + ), + "gripper": ImplicitActuatorCfg( + joint_names_expr=[".*_finger_[1-3]", ".*_finger_tip_[1-3]"], + effort_limit_sim=2.0, + stiffness=1.2, + damping=0.01, + ), + }, +) +"""Configuration of Kinova JACO2 (7-Dof) arm with 3-finger gripper.""" + + +KINOVA_JACO2_N6S300_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Kinova/Jaco2/J2N6S300/j2n6s300_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "j2n6s300_joint_1": 0.0, + "j2n6s300_joint_2": 2.76, + "j2n6s300_joint_3": 2.76, + "j2n6s300_joint_4": 2.5, + "j2n6s300_joint_5": 2.0, + "j2n6s300_joint_6": 0.0, + "j2n6s300_joint_finger_[1-3]": 0.2, # close: 1.2, open: 0.2 + "j2n6s300_joint_finger_tip_[1-3]": 0.2, # close: 1.2, open: 0.2 + }, + ), + actuators={ + "arm": ImplicitActuatorCfg( + joint_names_expr=[".*_joint_[1-6]"], + effort_limit_sim={ + ".*_joint_[1-2]": 80.0, + ".*_joint_3": 40.0, + ".*_joint_[4-6]": 20.0, + }, + stiffness={ + ".*_joint_[1-3]": 40.0, + ".*_joint_[4-6]": 15.0, + }, + damping={ + ".*_joint_[1-3]": 1.0, + ".*_joint_[4-6]": 0.5, + }, + ), + "gripper": ImplicitActuatorCfg( + joint_names_expr=[".*_finger_[1-3]", ".*_finger_tip_[1-3]"], + effort_limit_sim=2.0, + stiffness=1.2, + damping=0.01, + ), + }, +) +"""Configuration of Kinova JACO2 (6-Dof) arm with 3-finger gripper.""" + + +KINOVA_GEN3_N7_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Kinova/Gen3/gen3n7_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "joint_1": 0.0, + "joint_2": 0.65, + "joint_3": 0.0, + "joint_4": 1.89, + "joint_5": 0.0, + "joint_6": 0.6, + "joint_7": -1.57, + }, + ), + actuators={ + "arm": ImplicitActuatorCfg( + joint_names_expr=["joint_[1-7]"], + effort_limit={ + "joint_[1-4]": 39.0, + "joint_[5-7]": 9.0, + }, + stiffness={ + "joint_[1-4]": 40.0, + "joint_[5-7]": 15.0, + }, + damping={ + "joint_[1-4]": 1.0, + "joint_[5-7]": 0.5, + }, + ), + }, +) +"""Configuration of Kinova Gen3 (7-Dof) arm with no gripper.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/kuka_allegro.py b/source/isaaclab_assets/isaaclab_assets/robots/kuka_allegro.py new file mode 100644 index 0000000000000000000000000000000000000000..35b7e0b179a04487c5b670e757b3696b860a26b8 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/kuka_allegro.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Kuka-lbr-iiwa arm robots and Allegro Hand. + +The following configurations are available: + +* :obj:`KUKA_ALLEGRO_CFG`: Kuka Allegro with implicit actuator model. + +Reference: + +* https://www.kuka.com/en-us/products/robotics-systems/industrial-robots/lbr-iiwa +* https://www.wonikrobotics.com/robot-hand + +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +KUKA_ALLEGRO_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/KukaAllegro/kuka.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + retain_accelerations=True, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1000.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=32, + solver_velocity_iteration_count=1, + sleep_threshold=0.005, + stabilization_threshold=0.0005, + ), + joint_drive_props=sim_utils.JointDrivePropertiesCfg(drive_type="force"), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + joint_pos={ + "iiwa7_joint_(1|2|7)": 0.0, + "iiwa7_joint_3": 0.7854, + "iiwa7_joint_4": 1.5708, + "iiwa7_joint_(5|6)": -1.5708, + "(index|middle|ring)_joint_0": 0.0, + "(index|middle|ring)_joint_1": 0.3, + "(index|middle|ring)_joint_2": 0.3, + "(index|middle|ring)_joint_3": 0.3, + "thumb_joint_0": 1.5, + "thumb_joint_1": 0.60147215, + "thumb_joint_2": 0.33795027, + "thumb_joint_3": 0.60845138, + }, + ), + actuators={ + "kuka_allegro_actuators": ImplicitActuatorCfg( + joint_names_expr=[ + "iiwa7_joint_(1|2|3|4|5|6|7)", + "index_joint_(0|1|2|3)", + "middle_joint_(0|1|2|3)", + "ring_joint_(0|1|2|3)", + "thumb_joint_(0|1|2|3)", + ], + effort_limit_sim={ + "iiwa7_joint_(1|2|3|4|5|6|7)": 300.0, + "index_joint_(0|1|2|3)": 0.5, + "middle_joint_(0|1|2|3)": 0.5, + "ring_joint_(0|1|2|3)": 0.5, + "thumb_joint_(0|1|2|3)": 0.5, + }, + stiffness={ + "iiwa7_joint_(1|2|3|4)": 300.0, + "iiwa7_joint_5": 100.0, + "iiwa7_joint_6": 50.0, + "iiwa7_joint_7": 25.0, + "index_joint_(0|1|2|3)": 3.0, + "middle_joint_(0|1|2|3)": 3.0, + "ring_joint_(0|1|2|3)": 3.0, + "thumb_joint_(0|1|2|3)": 3.0, + }, + damping={ + "iiwa7_joint_(1|2|3|4)": 45.0, + "iiwa7_joint_5": 20.0, + "iiwa7_joint_6": 15.0, + "iiwa7_joint_7": 15.0, + "index_joint_(0|1|2|3)": 0.1, + "middle_joint_(0|1|2|3)": 0.1, + "ring_joint_(0|1|2|3)": 0.1, + "thumb_joint_(0|1|2|3)": 0.1, + }, + friction={ + "iiwa7_joint_(1|2|3|4|5|6|7)": 1.0, + "index_joint_(0|1|2|3)": 0.01, + "middle_joint_(0|1|2|3)": 0.01, + "ring_joint_(0|1|2|3)": 0.01, + "thumb_joint_(0|1|2|3)": 0.01, + }, + ), + }, + soft_joint_pos_limit_factor=1.0, +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/openarm.py b/source/isaaclab_assets/isaaclab_assets/robots/openarm.py new file mode 100644 index 0000000000000000000000000000000000000000..02743c5da9158f450ce009fca3c0949f0a9e4277 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/openarm.py @@ -0,0 +1,173 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration of OpenArm robots. + +The following configurations are available: + +* :obj:`OPENARM_BI_CFG`: OpenArm robot with two arms. +* :obj:`OPENARM_BI_HIGH_PD_CFG`: OpenArm robot with two arms and stiffer PD control. +* :obj:`OPENARM_UNI_CFG`: OpenArm robot with one arm. +* :obj:`OPENARM_UNI_HIGH_PD_CFG`: OpenArm robot with one arm and stiffer PD control. + +References: +OpenArm repositories: +* https://github.com/enactic/openarm +* https://github.com/enactic/openarm_isaac_lab + +Motor spec sheets: +* Joint 1–2 (DM-J8009P-2EC): + https://cdn.shopify.com/s/files/1/0673/6848/5000/files/DM-J8009P-2EC_User_Manual.pdf?v=1755481750 +* Joint 3–4 (DM-J4340P-2EC / DM-J4340-2EC): + https://cdn.shopify.com/s/files/1/0673/6848/5000/files/DM-J4340-2EC_User_Manual.pdf?v=1756883905 +* Joint 5–8 (DM-J4310-2EC V1.1): + https://files.seeedstudio.com/products/Damiao/DM-J4310-en.pdf +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +OPENARM_BI_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/OpenArm/openarm_bimanual/openarm_bimanual.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "openarm_left_joint.*": 0.0, + "openarm_right_joint.*": 0.0, + "openarm_left_finger_joint.*": 0.0, + "openarm_right_finger_joint.*": 0.0, + }, + ), + # spec sheet for reference + # DM-J8009P-2EC (Joint 1, 2): + # https://cdn.shopify.com/s/files/1/0673/6848/5000/files/DM-J8009P-2EC_User_Manual.pdf?v=1755481750 + # DM-J4340P-2EC, DM-J4340-2EC (Joint 3, 4): + # https://cdn.shopify.com/s/files/1/0673/6848/5000/files/DM-J4340-2EC_User_Manual.pdf?v=1756883905 + # DM-J4310-2EC V1.1 (Joint 5, 6, 7, 8): + # https://files.seeedstudio.com/products/Damiao/DM-J4310-en.pdf + actuators={ + "openarm_arm": ImplicitActuatorCfg( + joint_names_expr=[ + "openarm_left_joint[1-7]", + "openarm_right_joint[1-7]", + ], + velocity_limit_sim={ + "openarm_left_joint[1-2]": 2.175, + "openarm_right_joint[1-2]": 2.175, + "openarm_left_joint[3-4]": 2.175, + "openarm_right_joint[3-4]": 2.175, + "openarm_left_joint[5-7]": 2.61, + "openarm_right_joint[5-7]": 2.61, + }, + effort_limit_sim={ + "openarm_left_joint[1-2]": 40.0, + "openarm_right_joint[1-2]": 40.0, + "openarm_left_joint[3-4]": 27.0, + "openarm_right_joint[3-4]": 27.0, + "openarm_left_joint[5-7]": 7.0, + "openarm_right_joint[5-7]": 7.0, + }, + stiffness=80.0, + damping=4.0, + ), + "openarm_gripper": ImplicitActuatorCfg( + joint_names_expr=[ + "openarm_left_finger_joint.*", + "openarm_right_finger_joint.*", + ], + velocity_limit_sim=0.2, + effort_limit_sim=333.33, + stiffness=2e3, + damping=1e2, + ), + }, + soft_joint_pos_limit_factor=1.0, +) +"""Configuration of OpenArm Bimanual robot.""" + +OPENARM_UNI_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/OpenArm/openarm_unimanual/openarm_unimanual.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "openarm_joint1": 1.57, + "openarm_joint2": 0.0, + "openarm_joint3": -1.57, + "openarm_joint4": 1.57, + "openarm_joint5": 0.0, + "openarm_joint6": 0.0, + "openarm_joint7": 0.0, + "openarm_finger_joint.*": 0.044, + }, + ), + actuators={ + "openarm_arm": ImplicitActuatorCfg( + joint_names_expr=["openarm_joint[1-7]"], + velocity_limit_sim={ + "openarm_joint[1-2]": 2.175, + "openarm_joint[3-4]": 2.175, + "openarm_joint[5-7]": 2.61, + }, + effort_limit_sim={ + "openarm_joint[1-2]": 40.0, + "openarm_joint[3-4]": 27.0, + "openarm_joint[5-7]": 7.0, + }, + stiffness=80.0, + damping=4.0, + ), + "openarm_gripper": ImplicitActuatorCfg( + joint_names_expr=["openarm_finger_joint.*"], + velocity_limit_sim=0.2, + effort_limit_sim=333.33, + stiffness=2e3, + damping=1e2, + ), + }, + soft_joint_pos_limit_factor=1.0, +) +"""Configuration of OpenArm Unimanual robot.""" + +OPENARM_BI_HIGH_PD_CFG = OPENARM_BI_CFG.copy() +OPENARM_BI_HIGH_PD_CFG.spawn.rigid_props.disable_gravity = True +OPENARM_BI_HIGH_PD_CFG.actuators["openarm_arm"].stiffness = 400.0 +OPENARM_BI_HIGH_PD_CFG.actuators["openarm_arm"].damping = 80.0 +OPENARM_BI_HIGH_PD_CFG.actuators["openarm_gripper"].stiffness = 2e3 +OPENARM_BI_HIGH_PD_CFG.actuators["openarm_gripper"].damping = 1e2 +"""Configuration of OpenArm Bimanual robot with stiffer PD control. + +This configuration is useful for task-space control using differential IK. +""" + +OPENARM_UNI_HIGH_PD_CFG = OPENARM_UNI_CFG.copy() +OPENARM_UNI_HIGH_PD_CFG.spawn.rigid_props.disable_gravity = True +OPENARM_UNI_HIGH_PD_CFG.actuators["openarm_arm"].stiffness = 400.0 +OPENARM_UNI_HIGH_PD_CFG.actuators["openarm_arm"].damping = 80.0 +"""Configuration of OpenArm Unimanual robot with stiffer PD control. + +This configuration is useful for task-space control using differential IK. +""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/pick_and_place.py b/source/isaaclab_assets/isaaclab_assets/robots/pick_and_place.py new file mode 100644 index 0000000000000000000000000000000000000000..988e042fcf655079567a351a0fdf1d235c93278f --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/pick_and_place.py @@ -0,0 +1,69 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for a simple pick and place robot with a suction cup.""" + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +PICK_AND_PLACE_CFG = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Tests/PickAndPlace/pick_and_place_robot.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=10.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + copy_from_source=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.0), + joint_pos={ + "x_axis": 0.0, + "y_axis": 0.0, + "z_axis": 0.0, + }, + ), + actuators={ + "x_gantry": ImplicitActuatorCfg( + joint_names_expr=["x_axis"], + effort_limit=400.0, + velocity_limit=10.0, + stiffness=0.0, + damping=10.0, + ), + "y_gantry": ImplicitActuatorCfg( + joint_names_expr=["y_axis"], + effort_limit=400.0, + velocity_limit=10.0, + stiffness=0.0, + damping=10.0, + ), + "z_gantry": ImplicitActuatorCfg( + joint_names_expr=["z_axis"], + effort_limit=400.0, + velocity_limit=10.0, + stiffness=0.0, + damping=10.0, + ), + }, +) +"""Configuration for a simple pick and place robot with a suction cup.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/quadcopter.py b/source/isaaclab_assets/isaaclab_assets/robots/quadcopter.py new file mode 100644 index 0000000000000000000000000000000000000000..f404a90e3f140f9ffa54696d20e8008dbb3c50a6 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/quadcopter.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the quadcopters""" + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +CRAZYFLIE_CFG = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Bitcraze/Crazyflie/cf2x.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=10.0, + enable_gyroscopic_forces=True, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=4, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.001, + ), + copy_from_source=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + joint_pos={ + ".*": 0.0, + }, + joint_vel={ + "m1_joint": 200.0, + "m2_joint": -200.0, + "m3_joint": 200.0, + "m4_joint": -200.0, + }, + ), + actuators={ + "dummy": ImplicitActuatorCfg( + joint_names_expr=[".*"], + stiffness=0.0, + damping=0.0, + ), + }, +) +"""Configuration for the Crazyflie quadcopter.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/ridgeback_franka.py b/source/isaaclab_assets/isaaclab_assets/robots/ridgeback_franka.py new file mode 100644 index 0000000000000000000000000000000000000000..312236d233739c76beecd825d114a47835a4b44a --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/ridgeback_franka.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Ridgeback-Manipulation robots. + +The following configurations are available: + +* :obj:`RIDGEBACK_FRANKA_PANDA_CFG`: Clearpath Ridgeback base with Franka Emika arm + +Reference: https://github.com/ridgeback/ridgeback_manipulation +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +RIDGEBACK_FRANKA_PANDA_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Clearpath/RidgebackFranka/ridgeback_franka.usd", + articulation_props=sim_utils.ArticulationRootPropertiesCfg(enabled_self_collisions=False), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + # base + "dummy_base_prismatic_y_joint": 0.0, + "dummy_base_prismatic_x_joint": 0.0, + "dummy_base_revolute_z_joint": 0.0, + # franka arm + "panda_joint1": 0.0, + "panda_joint2": -0.569, + "panda_joint3": 0.0, + "panda_joint4": -2.810, + "panda_joint5": 0.0, + "panda_joint6": 2.0, + "panda_joint7": 0.741, + # tool + "panda_finger_joint.*": 0.035, + }, + joint_vel={".*": 0.0}, + ), + actuators={ + "base": ImplicitActuatorCfg( + joint_names_expr=["dummy_base_.*"], + effort_limit_sim=1000.0, + stiffness=0.0, + damping=1e5, + ), + "panda_shoulder": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + effort_limit_sim=87.0, + stiffness=800.0, + damping=40.0, + ), + "panda_forearm": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + effort_limit_sim=12.0, + stiffness=800.0, + damping=40.0, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint.*"], + effort_limit_sim=200.0, + stiffness=1e5, + damping=1e3, + ), + }, +) +"""Configuration of Franka arm with Franka Hand on a Clearpath Ridgeback base using implicit actuator models. + +The following control configuration is used: + +* Base: velocity control +* Arm: position control with damping +* Hand: position control with damping + +""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/sawyer.py b/source/isaaclab_assets/isaaclab_assets/robots/sawyer.py new file mode 100644 index 0000000000000000000000000000000000000000..179df09e7d81ad70bfc027b105ed5325336513b0 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/sawyer.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the Rethink Robotics arms. + +The following configuration parameters are available: + +* :obj:`SAWYER_CFG`: The Sawyer arm without any tool attached. + +Reference: https://github.com/RethinkRobotics/sawyer_robot +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +SAWYER_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/RethinkRobotics/Sawyer/sawyer_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=8, solver_velocity_iteration_count=0 + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "head_pan": 0.0, + "right_j0": 0.0, + "right_j1": -0.785, + "right_j2": 0.0, + "right_j3": 1.05, + "right_j4": 0.0, + "right_j5": 1.3, + "right_j6": 0.0, + }, + ), + actuators={ + "head": ImplicitActuatorCfg( + joint_names_expr=["head_pan"], + effort_limit_sim=8.0, + stiffness=800.0, + damping=40.0, + ), + "arm": ImplicitActuatorCfg( + joint_names_expr=["right_j[0-6]"], + effort_limit_sim={ + "right_j[0-1]": 80.0, + "right_j[2-3]": 40.0, + "right_j[4-6]": 9.0, + }, + stiffness=100.0, + damping=4.0, + ), + }, +) +"""Configuration of Rethink Robotics Sawyer arm.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/shadow_hand.py b/source/isaaclab_assets/isaaclab_assets/robots/shadow_hand.py new file mode 100644 index 0000000000000000000000000000000000000000..d13e90e3b1c0430f853cec76171e4ae43a45d078 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/shadow_hand.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for the dexterous hand from Shadow Robot. + +The following configurations are available: + +* :obj:`SHADOW_HAND_CFG`: Shadow Hand with implicit actuator model. + +Reference: + +* https://www.shadowrobot.com/dexterous-hand-series/ + +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Configuration +## + +SHADOW_HAND_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/ShadowRobot/ShadowHand/shadow_hand_instanceable.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + retain_accelerations=True, + max_depenetration_velocity=1000.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0005, + ), + # collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + joint_drive_props=sim_utils.JointDrivePropertiesCfg(drive_type="force"), + fixed_tendons_props=sim_utils.FixedTendonPropertiesCfg(limit_stiffness=30.0, damping=0.1), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(0.0, 0.0, -0.7071, 0.7071), + joint_pos={".*": 0.0}, + ), + actuators={ + "fingers": ImplicitActuatorCfg( + joint_names_expr=["robot0_WR.*", "robot0_(FF|MF|RF|LF|TH)J(3|2|1)", "robot0_(LF|TH)J4", "robot0_THJ0"], + effort_limit_sim={ + "robot0_WRJ1": 4.785, + "robot0_WRJ0": 2.175, + "robot0_(FF|MF|RF|LF)J1": 0.7245, + "robot0_FFJ(3|2)": 0.9, + "robot0_MFJ(3|2)": 0.9, + "robot0_RFJ(3|2)": 0.9, + "robot0_LFJ(4|3|2)": 0.9, + "robot0_THJ4": 2.3722, + "robot0_THJ3": 1.45, + "robot0_THJ(2|1)": 0.99, + "robot0_THJ0": 0.81, + }, + stiffness={ + "robot0_WRJ.*": 5.0, + "robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 1.0, + "robot0_(LF|TH)J4": 1.0, + "robot0_THJ0": 1.0, + }, + damping={ + "robot0_WRJ.*": 0.5, + "robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 0.1, + "robot0_(LF|TH)J4": 0.1, + "robot0_THJ0": 0.1, + }, + ), + }, + soft_joint_pos_limit_factor=1.0, +) +"""Configuration of Shadow Hand robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/spot.py b/source/isaaclab_assets/isaaclab_assets/robots/spot.py new file mode 100644 index 0000000000000000000000000000000000000000..3bc98b8b2da3d4eb263b0fe489a06f34ded752e7 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/spot.py @@ -0,0 +1,182 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Configuration for the Boston Dynamics robot. + +The following configuration parameters are available: + +* :obj:`SPOT_CFG`: The Spot robot with delay PD and remote PD actuators. +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import DelayedPDActuatorCfg, RemotizedPDActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +# Note: This data was collected by the Boston Dynamics AI Institute. +joint_parameter_lookup = [ + [-2.792900, -24.776718, 37.165077], + [-2.767442, -26.290108, 39.435162], + [-2.741984, -27.793369, 41.690054], + [-2.716526, -29.285997, 43.928996], + [-2.691068, -30.767536, 46.151304], + [-2.665610, -32.237423, 48.356134], + [-2.640152, -33.695168, 50.542751], + [-2.614694, -35.140221, 52.710331], + [-2.589236, -36.572052, 54.858078], + [-2.563778, -37.990086, 56.985128], + [-2.538320, -39.393730, 59.090595], + [-2.512862, -40.782406, 61.173609], + [-2.487404, -42.155487, 63.233231], + [-2.461946, -43.512371, 65.268557], + [-2.436488, -44.852371, 67.278557], + [-2.411030, -46.174873, 69.262310], + [-2.385572, -47.479156, 71.218735], + [-2.360114, -48.764549, 73.146824], + [-2.334656, -50.030334, 75.045502], + [-2.309198, -51.275761, 76.913641], + [-2.283740, -52.500103, 78.750154], + [-2.258282, -53.702587, 80.553881], + [-2.232824, -54.882442, 82.323664], + [-2.207366, -56.038860, 84.058290], + [-2.181908, -57.171028, 85.756542], + [-2.156450, -58.278133, 87.417200], + [-2.130992, -59.359314, 89.038971], + [-2.105534, -60.413738, 90.620607], + [-2.080076, -61.440529, 92.160793], + [-2.054618, -62.438812, 93.658218], + [-2.029160, -63.407692, 95.111538], + [-2.003702, -64.346268, 96.519402], + [-1.978244, -65.253670, 97.880505], + [-1.952786, -66.128944, 99.193417], + [-1.927328, -66.971176, 100.456764], + [-1.901870, -67.779457, 101.669186], + [-1.876412, -68.552864, 102.829296], + [-1.850954, -69.290451, 103.935677], + [-1.825496, -69.991325, 104.986988], + [-1.800038, -70.654541, 105.981812], + [-1.774580, -71.279190, 106.918785], + [-1.749122, -71.864319, 107.796478], + [-1.723664, -72.409088, 108.613632], + [-1.698206, -72.912567, 109.368851], + [-1.672748, -73.373871, 110.060806], + [-1.647290, -73.792130, 110.688194], + [-1.621832, -74.166512, 111.249767], + [-1.596374, -74.496147, 111.744221], + [-1.570916, -74.780251, 112.170376], + [-1.545458, -75.017998, 112.526997], + [-1.520000, -75.208656, 112.812984], + [-1.494542, -75.351448, 113.027172], + [-1.469084, -75.445686, 113.168530], + [-1.443626, -75.490677, 113.236015], + [-1.418168, -75.485771, 113.228657], + [-1.392710, -75.430344, 113.145515], + [-1.367252, -75.323830, 112.985744], + [-1.341794, -75.165688, 112.748531], + [-1.316336, -74.955406, 112.433109], + [-1.290878, -74.692551, 112.038826], + [-1.265420, -74.376694, 111.565041], + [-1.239962, -74.007477, 111.011215], + [-1.214504, -73.584579, 110.376869], + [-1.189046, -73.107742, 109.661613], + [-1.163588, -72.576752, 108.865128], + [-1.138130, -71.991455, 107.987183], + [-1.112672, -71.351707, 107.027561], + [-1.087214, -70.657486, 105.986229], + [-1.061756, -69.908813, 104.863220], + [-1.036298, -69.105721, 103.658581], + [-1.010840, -68.248337, 102.372505], + [-0.985382, -67.336861, 101.005291], + [-0.959924, -66.371513, 99.557270], + [-0.934466, -65.352615, 98.028923], + [-0.909008, -64.280533, 96.420799], + [-0.883550, -63.155693, 94.733540], + [-0.858092, -61.978588, 92.967882], + [-0.832634, -60.749775, 91.124662], + [-0.807176, -59.469845, 89.204767], + [-0.781718, -58.139503, 87.209255], + [-0.756260, -56.759487, 85.139231], + [-0.730802, -55.330616, 82.995924], + [-0.705344, -53.853729, 80.780594], + [-0.679886, -52.329796, 78.494694], + [-0.654428, -50.759762, 76.139643], + [-0.628970, -49.144699, 73.717049], + [-0.603512, -47.485737, 71.228605], + [-0.578054, -45.784004, 68.676006], + [-0.552596, -44.040764, 66.061146], + [-0.527138, -42.257267, 63.385900], + [-0.501680, -40.434883, 60.652325], + [-0.476222, -38.574947, 57.862421], + [-0.450764, -36.678982, 55.018473], + [-0.425306, -34.748432, 52.122648], + [-0.399848, -32.784836, 49.177254], + [-0.374390, -30.789810, 46.184715], + [-0.348932, -28.764952, 43.147428], + [-0.323474, -26.711969, 40.067954], + [-0.298016, -24.632576, 36.948864], + [-0.272558, -22.528547, 33.792821], + [-0.247100, -20.401667, 30.602500], +] +"""The lookup table for the knee joint parameters of the Boston Dynamics Spot robot. + +This table describes the relationship between the joint angle (rad), the transmission ratio (in/out), +and the output torque (N*m). It is used to interpolate the output torque based on the joint angle. +""" + +## +# Configuration +## + + +SPOT_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/BostonDynamics/spot/spot.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + joint_pos={ + "[fh]l_hx": 0.1, # all left hip_x + "[fh]r_hx": -0.1, # all right hip_x + "f[rl]_hy": 0.9, # front hip_y + "h[rl]_hy": 1.1, # hind hip_y + ".*_kn": -1.5, # all knees + }, + joint_vel={".*": 0.0}, + ), + actuators={ + "spot_hip": DelayedPDActuatorCfg( + joint_names_expr=[".*_h[xy]"], + effort_limit=45.0, + stiffness=60.0, + damping=1.5, + min_delay=0, # physics time steps (min: 2.0*0=0.0ms) + max_delay=4, # physics time steps (max: 2.0*4=8.0ms) + ), + "spot_knee": RemotizedPDActuatorCfg( + joint_names_expr=[".*_kn"], + joint_parameter_lookup=joint_parameter_lookup, + effort_limit=None, # torque limits are handled based experimental data (`RemotizedPDActuatorCfg.data`) + stiffness=60.0, + damping=1.5, + min_delay=0, # physics time steps (min: 2.0*0=0.0ms) + max_delay=4, # physics time steps (max: 2.0*4=8.0ms) + ), + }, +) +"""Configuration for the Boston Dynamics Spot robot.""" diff --git a/source/isaaclab_assets/isaaclab_assets/robots/unitree.py b/source/isaaclab_assets/isaaclab_assets/robots/unitree.py new file mode 100644 index 0000000000000000000000000000000000000000..772034b39e3fad1e80fee680fae6ba30c68d7472 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/unitree.py @@ -0,0 +1,773 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for Unitree robots. + +The following configurations are available: + +* :obj:`UNITREE_A1_CFG`: Unitree A1 robot with DC motor model for the legs +* :obj:`UNITREE_GO1_CFG`: Unitree Go1 robot with actuator net model for the legs +* :obj:`UNITREE_GO2_CFG`: Unitree Go2 robot with DC motor model for the legs +* :obj:`H1_CFG`: H1 humanoid robot +* :obj:`H1_MINIMAL_CFG`: H1 humanoid robot with minimal collision bodies +* :obj:`G1_CFG`: G1 humanoid robot +* :obj:`G1_MINIMAL_CFG`: G1 humanoid robot with minimal collision bodies +* :obj:`G1_23DOF_CFG`: G1 humanoid robot with 23 degrees of freedom (no wrists/hands) +* :obj:`G1_29DOF_CFG`: G1 humanoid robot configured for locomanipulation tasks +* :obj:`G1_INSPIRE_FTP_CFG`: G1 29DOF humanoid robot with Inspire 5-finger hand + +Reference: https://github.com/unitreerobotics/unitree_ros +""" + +import os + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ActuatorNetMLPCfg, DCMotorCfg, ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +## +# Configuration - Actuators. +## + +GO1_ACTUATOR_CFG = ActuatorNetMLPCfg( + joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"], + network_file=f"{ISAACLAB_NUCLEUS_DIR}/ActuatorNets/Unitree/unitree_go1.pt", + pos_scale=-1.0, + vel_scale=1.0, + torque_scale=1.0, + input_order="pos_vel", + input_idx=[0, 1, 2], + effort_limit=23.7, # taken from spec sheet + velocity_limit=30.0, # taken from spec sheet + saturation_effort=23.7, # same as effort limit +) +"""Configuration of Go1 actuators using MLP model. + +Actuator specifications: https://shop.unitree.com/products/go1-motor + +This model is taken from: https://github.com/Improbable-AI/walk-these-ways +""" + + +## +# Configuration +## + + +UNITREE_A1_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/A1/a1.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.42), + joint_pos={ + ".*L_hip_joint": 0.1, + ".*R_hip_joint": -0.1, + "F[L,R]_thigh_joint": 0.8, + "R[L,R]_thigh_joint": 1.0, + ".*_calf_joint": -1.5, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "base_legs": DCMotorCfg( + joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"], + effort_limit=33.5, + saturation_effort=33.5, + velocity_limit=21.0, + stiffness=25.0, + damping=0.5, + friction=0.0, + ), + }, +) +"""Configuration of Unitree A1 using DC motor. + +Note: Specifications taken from: https://www.trossenrobotics.com/a1-quadruped#specifications +""" + + +UNITREE_GO1_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/Go1/go1.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.4), + joint_pos={ + ".*L_hip_joint": 0.1, + ".*R_hip_joint": -0.1, + "F[L,R]_thigh_joint": 0.8, + "R[L,R]_thigh_joint": 1.0, + ".*_calf_joint": -1.5, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "base_legs": GO1_ACTUATOR_CFG, + }, +) +"""Configuration of Unitree Go1 using MLP-based actuator model.""" + + +UNITREE_GO2_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/Go2/go2.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.4), + joint_pos={ + ".*L_hip_joint": 0.1, + ".*R_hip_joint": -0.1, + "F[L,R]_thigh_joint": 0.8, + "R[L,R]_thigh_joint": 1.0, + ".*_calf_joint": -1.5, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "base_legs": DCMotorCfg( + joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"], + effort_limit=23.5, + saturation_effort=23.5, + velocity_limit=30.0, + stiffness=25.0, + damping=0.5, + friction=0.0, + ), + }, +) +"""Configuration of Unitree Go2 using DC-Motor actuator model.""" + + +H1_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/H1/h1.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=4 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 1.05), + joint_pos={ + ".*_hip_yaw": 0.0, + ".*_hip_roll": 0.0, + ".*_hip_pitch": -0.28, # -16 degrees + ".*_knee": 0.79, # 45 degrees + ".*_ankle": -0.52, # -30 degrees + "torso": 0.0, + ".*_shoulder_pitch": 0.28, + ".*_shoulder_roll": 0.0, + ".*_shoulder_yaw": 0.0, + ".*_elbow": 0.52, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "legs": ImplicitActuatorCfg( + joint_names_expr=[".*_hip_yaw", ".*_hip_roll", ".*_hip_pitch", ".*_knee", "torso"], + effort_limit_sim=300, + stiffness={ + ".*_hip_yaw": 150.0, + ".*_hip_roll": 150.0, + ".*_hip_pitch": 200.0, + ".*_knee": 200.0, + "torso": 200.0, + }, + damping={ + ".*_hip_yaw": 5.0, + ".*_hip_roll": 5.0, + ".*_hip_pitch": 5.0, + ".*_knee": 5.0, + "torso": 5.0, + }, + ), + "feet": ImplicitActuatorCfg( + joint_names_expr=[".*_ankle"], + effort_limit_sim=100, + stiffness={".*_ankle": 20.0}, + damping={".*_ankle": 4.0}, + ), + "arms": ImplicitActuatorCfg( + joint_names_expr=[".*_shoulder_pitch", ".*_shoulder_roll", ".*_shoulder_yaw", ".*_elbow"], + effort_limit_sim=300, + stiffness={ + ".*_shoulder_pitch": 40.0, + ".*_shoulder_roll": 40.0, + ".*_shoulder_yaw": 40.0, + ".*_elbow": 40.0, + }, + damping={ + ".*_shoulder_pitch": 10.0, + ".*_shoulder_roll": 10.0, + ".*_shoulder_yaw": 10.0, + ".*_elbow": 10.0, + }, + ), + }, +) +"""Configuration for the Unitree H1 Humanoid robot.""" + + +H1_MINIMAL_CFG = H1_CFG.copy() +H1_MINIMAL_CFG.spawn.usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/H1/h1_minimal.usd" +"""Configuration for the Unitree H1 Humanoid robot with fewer collision meshes. + +This configuration removes most collision meshes to speed up simulation. +""" + + +G1_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/G1/g1.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=8, solver_velocity_iteration_count=4 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.74), + joint_pos={ + ".*_hip_pitch_joint": -0.20, + ".*_knee_joint": 0.42, + ".*_ankle_pitch_joint": -0.23, + ".*_elbow_pitch_joint": 0.87, + "left_shoulder_roll_joint": 0.16, + "left_shoulder_pitch_joint": 0.35, + "right_shoulder_roll_joint": -0.16, + "right_shoulder_pitch_joint": 0.35, + "left_one_joint": 1.0, + "right_one_joint": -1.0, + "left_two_joint": 0.52, + "right_two_joint": -0.52, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "legs": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_hip_yaw_joint", + ".*_hip_roll_joint", + ".*_hip_pitch_joint", + ".*_knee_joint", + "torso_joint", + ], + effort_limit_sim=300, + stiffness={ + ".*_hip_yaw_joint": 150.0, + ".*_hip_roll_joint": 150.0, + ".*_hip_pitch_joint": 200.0, + ".*_knee_joint": 200.0, + "torso_joint": 200.0, + }, + damping={ + ".*_hip_yaw_joint": 5.0, + ".*_hip_roll_joint": 5.0, + ".*_hip_pitch_joint": 5.0, + ".*_knee_joint": 5.0, + "torso_joint": 5.0, + }, + armature={ + ".*_hip_.*": 0.01, + ".*_knee_joint": 0.01, + "torso_joint": 0.01, + }, + ), + "feet": ImplicitActuatorCfg( + effort_limit_sim=20, + joint_names_expr=[".*_ankle_pitch_joint", ".*_ankle_roll_joint"], + stiffness=20.0, + damping=2.0, + armature=0.01, + ), + "arms": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_pitch_joint", + ".*_elbow_roll_joint", + ".*_five_joint", + ".*_three_joint", + ".*_six_joint", + ".*_four_joint", + ".*_zero_joint", + ".*_one_joint", + ".*_two_joint", + ], + effort_limit_sim=300, + stiffness=40.0, + damping=10.0, + armature={ + ".*_shoulder_.*": 0.01, + ".*_elbow_.*": 0.01, + ".*_five_joint": 0.001, + ".*_three_joint": 0.001, + ".*_six_joint": 0.001, + ".*_four_joint": 0.001, + ".*_zero_joint": 0.001, + ".*_one_joint": 0.001, + ".*_two_joint": 0.001, + }, + ), + }, +) +"""Configuration for the Unitree G1 Humanoid robot.""" + + +G1_MINIMAL_CFG = G1_CFG.copy() +G1_MINIMAL_CFG.spawn.usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/G1/g1_minimal.usd" +"""Configuration for the Unitree G1 Humanoid robot with fewer collision meshes. + +This configuration removes most collision meshes to speed up simulation. +""" + + +G1_23DOF_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=os.path.join(os.path.dirname(__file__), "g1", "g1_23dof_rev_1_0.usd"), + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + fix_root_link=False, # Configurable - can be set to True for fixed base + solver_position_iteration_count=8, + solver_velocity_iteration_count=4, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.75), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + ".*_hip_pitch_joint": -0.10, + ".*_knee_joint": 0.30, + ".*_ankle_pitch_joint": -0.20, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "legs": DCMotorCfg( + joint_names_expr=[ + ".*_hip_yaw_joint", + ".*_hip_roll_joint", + ".*_hip_pitch_joint", + ".*_knee_joint", + ], + effort_limit={ + ".*_hip_yaw_joint": 88.0, + ".*_hip_roll_joint": 88.0, + ".*_hip_pitch_joint": 88.0, + ".*_knee_joint": 139.0, + }, + velocity_limit={ + ".*_hip_yaw_joint": 32.0, + ".*_hip_roll_joint": 32.0, + ".*_hip_pitch_joint": 32.0, + ".*_knee_joint": 20.0, + }, + stiffness={ + ".*_hip_yaw_joint": 100.0, + ".*_hip_roll_joint": 100.0, + ".*_hip_pitch_joint": 100.0, + ".*_knee_joint": 200.0, + }, + damping={ + ".*_hip_yaw_joint": 2.5, + ".*_hip_roll_joint": 2.5, + ".*_hip_pitch_joint": 2.5, + ".*_knee_joint": 5.0, + }, + armature={ + ".*_hip_.*": 0.03, + ".*_knee_joint": 0.03, + }, + saturation_effort=180.0, + ), + "feet": DCMotorCfg( + joint_names_expr=[".*_ankle_pitch_joint", ".*_ankle_roll_joint"], + stiffness={ + ".*_ankle_pitch_joint": 20.0, + ".*_ankle_roll_joint": 20.0, + }, + damping={ + ".*_ankle_pitch_joint": 0.2, + ".*_ankle_roll_joint": 0.1, + }, + effort_limit={ + ".*_ankle_pitch_joint": 50.0, + ".*_ankle_roll_joint": 50.0, + }, + velocity_limit={ + ".*_ankle_pitch_joint": 37.0, + ".*_ankle_roll_joint": 37.0, + }, + armature=0.03, + saturation_effort=80.0, + ), + "waist": ImplicitActuatorCfg( + joint_names_expr=[ + "waist_.*_joint", + ], + effort_limit={ + "waist_yaw_joint": 88.0, + }, + velocity_limit={ + "waist_yaw_joint": 32.0, + }, + stiffness={ + "waist_yaw_joint": 5000.0, + }, + damping={ + "waist_yaw_joint": 5.0, + }, + armature=0.001, + ), + "arms": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_roll_joint", + ], + effort_limit=300, + velocity_limit=100, + stiffness=3000.0, + damping=10.0, + armature={ + ".*_shoulder_.*": 0.001, + ".*_elbow_.*": 0.001, + ".*_wrist_roll_joint": 0.001, + }, + ), + }, + prim_path="/World/envs/env_.*/Robot", +) + +"""Configuration for the Unitree G1 Humanoid robot with 23 degrees of freedom. + +This configuration is for the G1 23DOF variant which excludes wrist and hand joints, +providing a simpler control interface while maintaining full locomotion and basic +manipulation capabilities. + +DOF breakdown: +- Legs: 12 DOF (6 per leg: hip_yaw, hip_roll, hip_pitch, knee, ankle_pitch, ankle_roll) +- Waist: 3 DOF (yaw, roll, pitch) +- Arms: 8 DOF (4 per arm: shoulder_pitch, shoulder_roll, shoulder_yaw, elbow) +Total: 23 DOF + +Key features: +- No wrist or hand joints compared to 29DOF variant +- Configurable base (fixed or mobile) via fix_root_link parameter +- Optimized for tasks requiring basic manipulation without fine finger control + +Usage examples: + # For fixed base scenarios (upper body manipulation only) + fixed_base_cfg = G1_23DOF_CFG.copy() + fixed_base_cfg.spawn.articulation_props.fix_root_link = True + + # For mobile scenarios (locomotion + manipulation) + mobile_cfg = G1_23DOF_CFG.copy() + mobile_cfg.spawn.articulation_props.fix_root_link = False +""" + + +G1_29DOF_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Unitree/G1/g1.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=1000.0, + max_depenetration_velocity=1.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + fix_root_link=False, # Configurable - can be set to True for fixed base + solver_position_iteration_count=8, + solver_velocity_iteration_count=4, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.75), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + ".*_hip_pitch_joint": -0.10, + ".*_knee_joint": 0.30, + ".*_ankle_pitch_joint": -0.20, + }, + joint_vel={".*": 0.0}, + ), + soft_joint_pos_limit_factor=0.9, + actuators={ + "legs": DCMotorCfg( + joint_names_expr=[ + ".*_hip_yaw_joint", + ".*_hip_roll_joint", + ".*_hip_pitch_joint", + ".*_knee_joint", + ], + effort_limit={ + ".*_hip_yaw_joint": 88.0, + ".*_hip_roll_joint": 88.0, + ".*_hip_pitch_joint": 88.0, + ".*_knee_joint": 139.0, + }, + velocity_limit={ + ".*_hip_yaw_joint": 32.0, + ".*_hip_roll_joint": 32.0, + ".*_hip_pitch_joint": 32.0, + ".*_knee_joint": 20.0, + }, + stiffness={ + ".*_hip_yaw_joint": 100.0, + ".*_hip_roll_joint": 100.0, + ".*_hip_pitch_joint": 100.0, + ".*_knee_joint": 200.0, + }, + damping={ + ".*_hip_yaw_joint": 2.5, + ".*_hip_roll_joint": 2.5, + ".*_hip_pitch_joint": 2.5, + ".*_knee_joint": 5.0, + }, + armature={ + ".*_hip_.*": 0.03, + ".*_knee_joint": 0.03, + }, + saturation_effort=180.0, + ), + "feet": DCMotorCfg( + joint_names_expr=[".*_ankle_pitch_joint", ".*_ankle_roll_joint"], + stiffness={ + ".*_ankle_pitch_joint": 20.0, + ".*_ankle_roll_joint": 20.0, + }, + damping={ + ".*_ankle_pitch_joint": 0.2, + ".*_ankle_roll_joint": 0.1, + }, + effort_limit={ + ".*_ankle_pitch_joint": 50.0, + ".*_ankle_roll_joint": 50.0, + }, + velocity_limit={ + ".*_ankle_pitch_joint": 37.0, + ".*_ankle_roll_joint": 37.0, + }, + armature=0.03, + saturation_effort=80.0, + ), + "waist": ImplicitActuatorCfg( + joint_names_expr=[ + "waist_.*_joint", + ], + effort_limit={ + "waist_yaw_joint": 88.0, + "waist_roll_joint": 50.0, + "waist_pitch_joint": 50.0, + }, + velocity_limit={ + "waist_yaw_joint": 32.0, + "waist_roll_joint": 37.0, + "waist_pitch_joint": 37.0, + }, + stiffness={ + "waist_yaw_joint": 5000.0, + "waist_roll_joint": 5000.0, + "waist_pitch_joint": 5000.0, + }, + damping={ + "waist_yaw_joint": 5.0, + "waist_roll_joint": 5.0, + "waist_pitch_joint": 5.0, + }, + armature=0.001, + ), + "arms": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_.*_joint", + ], + effort_limit=300, + velocity_limit=100, + stiffness=3000.0, + damping=10.0, + armature={ + ".*_shoulder_.*": 0.001, + ".*_elbow_.*": 0.001, + ".*_wrist_.*_joint": 0.001, + }, + ), + "hands": ImplicitActuatorCfg( + joint_names_expr=[ + ".*_index_.*", + ".*_middle_.*", + ".*_thumb_.*", + ], + effort_limit=300, + velocity_limit=100, + stiffness=20, + damping=2, + armature=0.001, + ), + }, + prim_path="/World/envs/env_.*/Robot", +) + +"""Configuration for the Unitree G1 Humanoid robot for locomanipulation tasks. + +This configuration sets up the G1 humanoid robot for locomanipulation tasks, +allowing both locomotion and manipulation capabilities. The robot can be configured +for either fixed base or mobile scenarios by modifying the fix_root_link parameter. + +Key features: +- Configurable base (fixed or mobile) via fix_root_link parameter +- Optimized actuator parameters for locomanipulation tasks +- Enhanced hand and arm configurations for manipulation + +Usage examples: + # For fixed base scenarios (upper body manipulation only) + fixed_base_cfg = G1_29DOF_CFG.copy() + fixed_base_cfg.spawn.articulation_props.fix_root_link = True + + # For mobile scenarios (locomotion + manipulation) + mobile_cfg = G1_29DOF_CFG.copy() + mobile_cfg.spawn.articulation_props.fix_root_link = False +""" + +""" +Configuration for the Unitree G1 Humanoid robot with Inspire 5fingers hand. +The Unitree G1 URDF can be found here: https://github.com/unitreerobotics/unitree_ros/tree/master/robots/g1_description/g1_29dof_with_hand_rev_1_0.urdf +The Inspire hand URDF is available at: https://github.com/unitreerobotics/xr_teleoperate/tree/main/assets/inspire_hand +The merging code for the hand and robot can be found here: https://github.com/unitreerobotics/unitree_ros/blob/master/robots/g1_description/merge_g1_29dof_and_inspire_hand.ipynb, +Necessary modifications should be made to ensure the correct parent–child relationship. +""" +# Inherit PD settings from G1_29DOF_CFG, with minor adjustments for grasping task +G1_INSPIRE_FTP_CFG = G1_29DOF_CFG.copy() +G1_INSPIRE_FTP_CFG.spawn.usd_path = f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/G1/g1_29dof_inspire_hand.usd" +G1_INSPIRE_FTP_CFG.spawn.activate_contact_sensors = True +G1_INSPIRE_FTP_CFG.spawn.rigid_props.disable_gravity = True +G1_INSPIRE_FTP_CFG.spawn.articulation_props.fix_root_link = True +G1_INSPIRE_FTP_CFG.init_state = ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 1.0), + joint_pos={".*": 0.0}, + joint_vel={".*": 0.0}, +) +# Actuator configuration for arms (stability focused for manipulation) +# Increased damping improves stability of arm movements +G1_INSPIRE_FTP_CFG.actuators["arms"] = ImplicitActuatorCfg( + joint_names_expr=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_.*_joint", + ], + effort_limit=300, + velocity_limit=100, + stiffness=3000.0, + damping=100.0, + armature={ + ".*_shoulder_.*": 0.001, + ".*_elbow_.*": 0.001, + ".*_wrist_.*_joint": 0.001, + }, +) +# Actuator configuration for hands (flexibility focused for grasping) +# Lower stiffness and damping to improve finger flexibility when grasping objects +G1_INSPIRE_FTP_CFG.actuators["hands"] = ImplicitActuatorCfg( + joint_names_expr=[ + ".*_index_.*", + ".*_middle_.*", + ".*_thumb_.*", + ".*_ring_.*", + ".*_pinky_.*", + ], + effort_limit_sim=30.0, + velocity_limit_sim=10.0, + stiffness=10.0, + damping=0.2, + armature=0.001, +) diff --git a/source/isaaclab_assets/isaaclab_assets/robots/universal_robots.py b/source/isaaclab_assets/isaaclab_assets/robots/universal_robots.py new file mode 100644 index 0000000000000000000000000000000000000000..1026e00a9713173944d11e7cd9d9aad424fe9456 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/robots/universal_robots.py @@ -0,0 +1,207 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Configuration for the Universal Robots. + +The following configuration parameters are available: + +* :obj:`UR10_CFG`: The UR10 arm without a gripper. +* :obj:`UR10E_ROBOTIQ_GRIPPER_CFG`: The UR10E arm with Robotiq_2f_140 gripper. +* :obj:`UR10e_ROBOTIQ_2F_85_CFG`: The UR10E arm with Robotiq 2F-85 gripper. + +Reference: https://github.com/ros-industrial/universal_robot +""" + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +## +# Configuration +## + +UR10_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/UniversalRobots/UR10/ur10_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "shoulder_pan_joint": 0.0, + "shoulder_lift_joint": -1.712, + "elbow_joint": 1.712, + "wrist_1_joint": 0.0, + "wrist_2_joint": 0.0, + "wrist_3_joint": 0.0, + }, + ), + actuators={ + "arm": ImplicitActuatorCfg( + joint_names_expr=[".*"], + effort_limit_sim=87.0, + stiffness=800.0, + damping=40.0, + ), + }, +) + +UR10e_CFG = ArticulationCfg( + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/UniversalRobots/ur10e/ur10e.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=16, solver_velocity_iteration_count=1 + ), + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "shoulder_pan_joint": 3.141592653589793, + "shoulder_lift_joint": -1.5707963267948966, + "elbow_joint": 1.5707963267948966, + "wrist_1_joint": -1.5707963267948966, + "wrist_2_joint": -1.5707963267948966, + "wrist_3_joint": 0.0, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + actuators={ + # 'shoulder_pan_joint', 'shoulder_lift_joint', 'elbow_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint' + "shoulder": ImplicitActuatorCfg( + joint_names_expr=["shoulder_.*"], + stiffness=1320.0, + damping=72.6636085, + friction=0.0, + armature=0.0, + ), + "elbow": ImplicitActuatorCfg( + joint_names_expr=["elbow_joint"], + stiffness=600.0, + damping=34.64101615, + friction=0.0, + armature=0.0, + ), + "wrist": ImplicitActuatorCfg( + joint_names_expr=["wrist_.*"], + stiffness=216.0, + damping=29.39387691, + friction=0.0, + armature=0.0, + ), + }, +) + +"""Configuration of UR-10 arm using implicit actuator models.""" + +UR10_LONG_SUCTION_CFG = UR10_CFG.copy() +UR10_LONG_SUCTION_CFG.spawn.usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/UniversalRobots/ur10/ur10.usd" +UR10_LONG_SUCTION_CFG.spawn.variants = {"Gripper": "Long_Suction"} +UR10_LONG_SUCTION_CFG.spawn.rigid_props.disable_gravity = True +UR10_LONG_SUCTION_CFG.init_state.joint_pos = { + "shoulder_pan_joint": 0.0, + "shoulder_lift_joint": -1.5707, + "elbow_joint": 1.5707, + "wrist_1_joint": -1.5707, + "wrist_2_joint": 1.5707, + "wrist_3_joint": 0.0, +} + +"""Configuration of UR10 arm with long suction gripper.""" + +UR10_SHORT_SUCTION_CFG = UR10_LONG_SUCTION_CFG.copy() +UR10_SHORT_SUCTION_CFG.spawn.variants = {"Gripper": "Short_Suction"} + +"""Configuration of UR10 arm with short suction gripper.""" + +UR10e_ROBOTIQ_GRIPPER_CFG = UR10e_CFG.copy() +"""Configuration of UR10e arm with Robotiq_2f_140 gripper.""" +UR10e_ROBOTIQ_GRIPPER_CFG.spawn.variants = {"Gripper": "Robotiq_2f_140"} +UR10e_ROBOTIQ_GRIPPER_CFG.spawn.rigid_props.disable_gravity = True +UR10e_ROBOTIQ_GRIPPER_CFG.init_state.joint_pos["finger_joint"] = 0.0 +UR10e_ROBOTIQ_GRIPPER_CFG.init_state.joint_pos[".*_inner_finger_joint"] = 0.0 +UR10e_ROBOTIQ_GRIPPER_CFG.init_state.joint_pos[".*_inner_finger_pad_joint"] = 0.0 +UR10e_ROBOTIQ_GRIPPER_CFG.init_state.joint_pos[".*_outer_.*_joint"] = 0.0 +# the major actuator joint for gripper +UR10e_ROBOTIQ_GRIPPER_CFG.actuators["gripper_drive"] = ImplicitActuatorCfg( + joint_names_expr=["finger_joint"], + effort_limit_sim=10.0, + velocity_limit_sim=1.0, + stiffness=11.25, + damping=0.1, + friction=0.0, + armature=0.0, +) +# the auxiliary actuator joint for gripper +UR10e_ROBOTIQ_GRIPPER_CFG.actuators["gripper_finger"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_joint"], + effort_limit_sim=1.0, + velocity_limit_sim=1.0, + stiffness=0.2, + damping=0.001, + friction=0.0, + armature=0.0, +) +# the passive joints for gripper +UR10e_ROBOTIQ_GRIPPER_CFG.actuators["gripper_passive"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_pad_joint", ".*_outer_finger_joint", "right_outer_knuckle_joint"], + effort_limit_sim=1.0, + velocity_limit_sim=1.0, + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, +) + + +UR10e_ROBOTIQ_2F_85_CFG = UR10e_CFG.copy() +"""Configuration of UR-10E arm with Robotiq_2f_140 gripper.""" +UR10e_ROBOTIQ_2F_85_CFG.spawn.variants = {"Gripper": "Robotiq_2f_85"} +UR10e_ROBOTIQ_2F_85_CFG.spawn.rigid_props.disable_gravity = True +UR10e_ROBOTIQ_2F_85_CFG.init_state.joint_pos["finger_joint"] = 0.0 +UR10e_ROBOTIQ_2F_85_CFG.init_state.joint_pos[".*_inner_finger_joint"] = 0.0 +UR10e_ROBOTIQ_2F_85_CFG.init_state.joint_pos[".*_inner_finger_knuckle_joint"] = 0.0 +UR10e_ROBOTIQ_2F_85_CFG.init_state.joint_pos[".*_outer_.*_joint"] = 0.0 +# the major actuator joint for gripper +UR10e_ROBOTIQ_2F_85_CFG.actuators["gripper_drive"] = ImplicitActuatorCfg( + joint_names_expr=["finger_joint"], # "right_outer_knuckle_joint" is its mimic joint + effort_limit_sim=10.0, + velocity_limit_sim=1.0, + stiffness=11.25, + damping=0.1, + friction=0.0, + armature=0.0, +) +# enable the gripper to grasp in a parallel manner +UR10e_ROBOTIQ_2F_85_CFG.actuators["gripper_finger"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_joint"], + effort_limit_sim=1.0, + velocity_limit_sim=1.0, + stiffness=0.2, + damping=0.001, + friction=0.0, + armature=0.0, +) +# set PD to zero for passive joints in close-loop gripper +UR10e_ROBOTIQ_2F_85_CFG.actuators["gripper_passive"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_knuckle_joint", "right_outer_knuckle_joint"], + effort_limit_sim=1.0, + velocity_limit_sim=1.0, + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, +) + +"""Configuration of UR-10E arm with Robotiq 2F-85 gripper.""" diff --git a/source/isaaclab_assets/isaaclab_assets/sensors/__init__.py b/source/isaaclab_assets/isaaclab_assets/sensors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f5f6c6ac116ea335956cc58cbd2b8c1278781897 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/sensors/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +## +# Configuration for different assets. +## + +from .gelsight import * +from .velodyne import * diff --git a/source/isaaclab_assets/isaaclab_assets/sensors/gelsight.py b/source/isaaclab_assets/isaaclab_assets/sensors/gelsight.py new file mode 100644 index 0000000000000000000000000000000000000000..8010fcef04bb54a1f609eba301a17825e46b1b11 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/sensors/gelsight.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Predefined configurations for GelSight tactile sensors.""" + +from isaaclab.sensors.tacsl_sensor.visuotactile_sensor_cfg import GelSightRenderCfg + +## +# Predefined Configurations +## + +GELSIGHT_R15_CFG = GelSightRenderCfg( + sensor_data_dir_name="gelsight_r15_data", + background_path="bg.jpg", + calib_path="polycalib.npz", + real_background="real_bg.npy", + image_height=320, + image_width=240, + num_bins=120, + mm_per_pixel=0.0877, +) +"""Configuration for GelSight R1.5 sensor rendering parameters. + +The GelSight R1.5 is a high-resolution tactile sensor with a 320x240 pixel tactile image. +It uses a pixel-to-millimeter ratio of 0.0877 mm/pixel. + +Reference: https://www.gelsight.com/gelsightinc-products/ +""" + +GELSIGHT_MINI_CFG = GelSightRenderCfg( + sensor_data_dir_name="gs_mini_data", + background_path="bg.jpg", + calib_path="polycalib.npz", + real_background="real_bg.npy", + image_height=240, + image_width=320, + num_bins=120, + mm_per_pixel=0.065, +) +"""Configuration for GelSight Mini sensor rendering parameters. + +The GelSight Mini is a compact tactile sensor with a 240x320 pixel tactile image. +It uses a pixel-to-millimeter ratio of 0.065 mm/pixel, providing higher spatial resolution +than the R1.5 model. + +Reference: https://www.gelsight.com/gelsightinc-products/ +""" diff --git a/source/isaaclab_assets/isaaclab_assets/sensors/velodyne.py b/source/isaaclab_assets/isaaclab_assets/sensors/velodyne.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd075f4fa25ba444249f6a74f06ac6d09ca2c18 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/sensors/velodyne.py @@ -0,0 +1,25 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for Velodyne LiDAR sensors.""" + +from isaaclab.sensors import RayCasterCfg, patterns + +## +# Configuration +## + +VELODYNE_VLP_16_RAYCASTER_CFG = RayCasterCfg( + ray_alignment="base", + pattern_cfg=patterns.LidarPatternCfg( + channels=16, vertical_fov_range=(-15.0, 15.0), horizontal_fov_range=(-180.0, 180.0), horizontal_res=0.2 + ), + debug_vis=True, + max_distance=100, +) +"""Configuration for Velodyne Puck LiDAR (VLP-16) as a :class:`RayCasterCfg`. + +Reference: https://velodynelidar.com/wp-content/uploads/2019/12/63-9229_Rev-K_Puck-_Datasheet_Web.pdf +""" diff --git a/source/isaaclab_assets/isaaclab_assets/textures/generate_texture.py b/source/isaaclab_assets/isaaclab_assets/textures/generate_texture.py new file mode 100644 index 0000000000000000000000000000000000000000..1a45888bb20ed41d06f74b70b16ffff35421c5ca --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/textures/generate_texture.py @@ -0,0 +1,55 @@ +import os, struct, zlib + +out_path = os.path.join(os.path.dirname(__file__), "mujoco_groundplane_checker.png") +# mujoco colors +rgb1 = (0.2, 0.3, 0.4) +rgb2 = (0.1, 0.2, 0.3) +mark = (0.8, 0.8, 0.8) + +def to_u8(c): + return tuple(max(0, min(255, int(round(v * 255.0)))) for v in c) + +c1 = to_u8(rgb1) +c2 = to_u8(rgb2) +cm = to_u8(mark) + +# texture layout +squares = 102 +square_size_px = 50 +W = H = squares * square_size_px +cell = square_size_px + +# marker lines +marker_stride = cell * 2 +marker_thickness_px = 1 + +# build raw scanlines (filter byte 0 per line) +raw = bytearray() +for y in range(H): + raw.append(0) + for x in range(W): + sx, sy = x // cell, y // cell + base = c1 if (sx + sy) % 2 == 0 else c2 + # marker line every two squares + if (x % marker_stride < marker_thickness_px) or (y % marker_stride < marker_thickness_px): + base = cm + raw.extend(base) + +compressed = zlib.compress(bytes(raw), level=9) + + +def chunk(tag, data): + return struct.pack('>I', len(data)) + tag + data + struct.pack('>I', zlib.crc32(tag + data) & 0xffffffff) + +png = bytearray() +png.extend(b'\x89PNG\r\n\x1a\n') +# IHDR: width, height, bitdepth=8, colortype=2(RGB), compression=0, filter=0, interlace=0 +ihdr = struct.pack('>IIBBBBB', W, H, 8, 2, 0, 0, 0) +png.extend(chunk(b'IHDR', ihdr)) +png.extend(chunk(b'IDAT', compressed)) +png.extend(chunk(b'IEND', b'')) + +with open(out_path, 'wb') as f: + f.write(png) + +print('Wrote', out_path, os.path.getsize(out_path), 'bytes') \ No newline at end of file diff --git a/source/isaaclab_assets/isaaclab_assets/textures/mujoco_groundplane_checker.png b/source/isaaclab_assets/isaaclab_assets/textures/mujoco_groundplane_checker.png new file mode 100644 index 0000000000000000000000000000000000000000..ce731d95d8ea3e063fb4f6cc38f0a10d64493b42 --- /dev/null +++ b/source/isaaclab_assets/isaaclab_assets/textures/mujoco_groundplane_checker.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:419a5e35a379e25cf92cddd2860a1710a8afa906d5b6f195dd1a0e1bf5a16d80 +size 345929 diff --git a/source/isaaclab_assets/pyproject.toml b/source/isaaclab_assets/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab_assets/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab_assets/setup.py b/source/isaaclab_assets/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..ebd9331bd5f890169b9a1a0be419b1c68cd2faac --- /dev/null +++ b/source/isaaclab_assets/setup.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the 'isaaclab_assets' python package.""" + +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Installation operation +setup( + name="isaaclab_assets", + author="Isaac Lab Project Developers", + maintainer="Isaac Lab Project Developers", + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + include_package_data=True, + python_requires=">=3.10", + packages=["isaaclab_assets"], + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab_assets/test/test_valid_configs.py b/source/isaaclab_assets/test/test_valid_configs.py new file mode 100644 index 0000000000000000000000000000000000000000..acd68b260e547979dfe32de1f8f6e3a74b02df24 --- /dev/null +++ b/source/isaaclab_assets/test/test_valid_configs.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ignore private usage of variables warning +# pyright: reportPrivateUsage=none + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +# Define a fixture to replace setUpClass +import pytest + +from isaaclab.assets import AssetBase, AssetBaseCfg +from isaaclab.sim import build_simulation_context + +import isaaclab_assets as lab_assets # noqa: F401 + + +@pytest.fixture(scope="module") +def registered_entities(): + # load all registered entities configurations from the module + registered_entities: dict[str, AssetBaseCfg] = {} + # inspect all classes from the module + for obj_name in dir(lab_assets): + obj = getattr(lab_assets, obj_name) + # store all registered entities configurations + if isinstance(obj, AssetBaseCfg): + registered_entities[obj_name] = obj + # print all existing entities names + print(">>> All registered entities:", list(registered_entities.keys())) + return registered_entities + + +# Add parameterization for the device +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_asset_configs(registered_entities, device): + """Check all registered asset configurations.""" + # iterate over all registered assets + for asset_name, entity_cfg in registered_entities.items(): + # Use pytest's subtests + with build_simulation_context(device=device, auto_add_lighting=True) as sim: + sim._app_control_on_stop_handle = None + # print the asset name + print(f">>> Testing entity {asset_name} on device {device}") + # name the prim path + entity_cfg.prim_path = "/World/asset" + # create the asset / sensors + entity: AssetBase = entity_cfg.class_type(entity_cfg) # type: ignore + + # play the sim + sim.reset() + + # check asset is initialized successfully + assert entity.is_initialized diff --git a/source/isaaclab_contrib/config/extension.toml b/source/isaaclab_contrib/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..9163f552e7978bc9db24dbe34496bb7dd6c95625 --- /dev/null +++ b/source/isaaclab_contrib/config/extension.toml @@ -0,0 +1,21 @@ +[package] +# Semantic Versioning is used: https://semver.org/ +version = "0.0.1" + +# Description +title = "Isaac Lab External Contributions" +description="An extension used to stage and integrate externally contributed features and implementations." +readme = "docs/README.md" +repository = "https://github.com/isaac-sim/IsaacLab" +category = "robotics" +keywords = ["kit", "robotics", "assets", "isaaclab"] + +[dependencies] +"isaaclab" = {} + +[core] +reloadable = false + +# Main python module this extension provides. +[[python.module]] +name = "isaaclab_contrib" diff --git a/source/isaaclab_contrib/docs/CHANGELOG.rst b/source/isaaclab_contrib/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..cd515121e653caa12af7f3df0c3f554b92d466bc --- /dev/null +++ b/source/isaaclab_contrib/docs/CHANGELOG.rst @@ -0,0 +1,10 @@ +Changelog +--------- + +0.0.1 (2025-12-17) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added initial implementation for multi rotor systems. diff --git a/source/isaaclab_contrib/docs/README.md b/source/isaaclab_contrib/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..144ab643b4a1dffd6df047dcd4d6174c64a4ae52 --- /dev/null +++ b/source/isaaclab_contrib/docs/README.md @@ -0,0 +1,137 @@ +# Isaac Lab: MultiRotor Extension + +This extension provides comprehensive support for multirotor systems (drones, quadcopters, hexacopters, etc.) +in Isaac Lab. It includes specialized actuator models, asset classes, and MDP components specifically designed +for multirotor simulation. + +## Features + +The extension provides the following key components: + +### Assets + +* **`Multirotor`**: A specialized articulation class that extends the base `Articulation` class to support + multirotor systems with thruster actuators. This class handles the simulation of multirotor dynamics, + including thrust application at specific body locations and integration with the thruster actuator model. + +### Actuators + +* **`Thruster`**: A low-level motor/thruster dynamics model with separate rise/fall time constants. This + actuator model simulates realistic motor response characteristics including: + - Asymmetric rise and fall time constants + - Thrust limits (minimum and maximum) + - Integration schemes (Euler or RK4) + - Motor spin-up and spin-down dynamics + +### MDP Components + +* **Thrust Actions**: Action terms specifically designed for multirotor control, allowing direct thrust + commands to individual thrusters or groups of thrusters. These integrate seamlessly with Isaac Lab's + MDP framework for reinforcement learning tasks. + +## Using the Extension + +To use this extension in your code, import the required components: + +```python +from isaaclab_contrib.assets import Multirotor, MultirotorCfg +from isaaclab_contrib.actuators import Thruster, ThrusterCfg +from isaaclab_contrib.mdp.actions import ThrustActionCfg +``` + +### Example: Creating a Multirotor Asset + +Here's how to configure and create a multirotor asset: + +```python +import isaaclab.sim as sim_utils +from isaaclab_contrib.assets import MultirotorCfg +from isaaclab_contrib.actuators import ThrusterCfg + +# Define thruster actuator configuration +thruster_cfg = ThrusterCfg( + thrust_limit=(0.0, 10.0), # Min and max thrust in Newtons + rise_time_constant=0.1, # Time constant for thrust increase + fall_time_constant=0.2, # Time constant for thrust decrease +) + +# Create multirotor configuration +multirotor_cfg = MultirotorCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path="path/to/your/multirotor.usd", + ), + actuators={ + "thrusters": thruster_cfg, + }, +) +``` + +### Example: Using Thrust Actions in Environments + +To use thrust actions in your RL environment: + +```python +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab_contrib.mdp.actions import ThrustActionCfg + +@configclass +class MyMultirotorEnvCfg(ManagerBasedRLEnvCfg): + # ... other configuration ... + + # Define actions + actions = ActionsCfg() + + @configclass + class ActionsCfg: + thrust = ThrustActionCfg( + asset_name="robot", + thruster_names=["motor_.*"], # regex pattern for thruster names + ) +``` + +## Extension Structure + +The extension follows Isaac Lab's standard structure: + +```tree +isaaclab_contrib/ +├── actuators/ # Thruster actuator implementations +├── assets/ # Multirotor asset classes +│ └── multirotor/ +├── mdp/ # MDP components for RL +└── utils/ # Utility functions and types +``` + +## Key Concepts + +### Thruster Dynamics + +The `Thruster` actuator model simulates realistic motor response with asymmetric dynamics: + +- **Rise Time**: How quickly thrust increases when commanded +- **Fall Time**: How quickly thrust decreases when commanded +- **Thrust Limits**: Physical constraints on minimum and maximum thrust output + +This asymmetry reflects real-world motor behavior where spinning up often takes longer than spinning down. + +### Multirotor Asset + +The `Multirotor` class extends the standard `Articulation` to provide specialized functionality: + +- Manages multiple thruster actuators as a group +- Applies thrust forces at specific body locations +- Integrates with Isaac Lab's physics simulation +- Provides thruster-specific state information through `MultirotorData` + +## Testing + +The extension includes comprehensive unit tests: + +```bash +# Test thruster actuator +python -m pytest source/isaaclab_contrib/test/actuators/test_thruster.py + +# Test multirotor asset +python -m pytest source/isaaclab_contrib/test/assets/test_multirotor.py +``` diff --git a/source/isaaclab_contrib/isaaclab_contrib/__init__.py b/source/isaaclab_contrib/isaaclab_contrib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cedac62522d8104c7509b8db076215c20ca450dd --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Package containing multirotor (drone) support for Isaac Lab.""" + +import os +import toml + +# Conveniences to other module directories via relative paths +ISAACLAB_CONTRIB_EXT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../")) +"""Path to the extension source directory.""" + +ISAACLAB_CONTRIB_METADATA = toml.load(os.path.join(ISAACLAB_CONTRIB_EXT_DIR, "config", "extension.toml")) +"""Extension metadata dictionary parsed from the extension.toml file.""" + +# Configure the module-level variables +__version__ = ISAACLAB_CONTRIB_METADATA["package"]["version"] diff --git a/source/isaaclab_contrib/isaaclab_contrib/actuators/__init__.py b/source/isaaclab_contrib/isaaclab_contrib/actuators/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f023ae6c7facd997d308a4240e1215faf4933ff6 --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/actuators/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .thruster import Thruster +from .thruster_cfg import ThrusterCfg diff --git a/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster.py b/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster.py new file mode 100644 index 0000000000000000000000000000000000000000..036a817fbfbd1f98ff779d1de7dd88c36eaf816b --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster.py @@ -0,0 +1,229 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils + +from isaaclab_contrib.utils.types import MultiRotorActions + +if TYPE_CHECKING: + from .thruster_cfg import ThrusterCfg + + +class Thruster: + """Low-level motor/thruster dynamics with separate rise/fall time constants. + + Integration scheme is Euler or RK4. All internal buffers are shaped (num_envs, num_motors). + Units: thrust [N], rates [N/s], time [s]. + """ + + computed_thrust: torch.Tensor + """The computed thrust for the actuator group. Shape is (num_envs, num_thrusters).""" + + applied_thrust: torch.Tensor + """The applied thrust for the actuator group. Shape is (num_envs, num_thrusters). + + This is the thrust obtained after clipping the :attr:`computed_thrust` based on the + actuator characteristics. + """ + + cfg: ThrusterCfg + + def __init__( + self, + cfg: ThrusterCfg, + thruster_names: list[str], + thruster_ids: slice | torch.Tensor, + num_envs: int, + device: str, + init_thruster_rps: torch.Tensor, + ): + """Construct buffers and sample per-motor parameters. + + Args: + cfg: Thruster configuration. + thruster_names: List of thruster names belonging to this group. + thruster_ids: Slice or tensor of indices into the articulation thruster array. + num_envs: Number of parallel/vectorized environments. + device: PyTorch device string or device identifier. + init_thruster_rps: Initial per-thruster rotations-per-second tensor used when + the configuration uses RPM-based thrust modelling. + """ + self.cfg = cfg + self._num_envs = num_envs + self._device = device + self._thruster_names = thruster_names + self._thruster_indices = thruster_ids + self._init_thruster_rps = init_thruster_rps + + # Range tensors, shaped (num_envs, 2, num_motors); [:,0,:]=min, [:,1,:]=max + self.num_motors = len(thruster_names) + self.thrust_r = torch.tensor(cfg.thrust_range).to(self._device) + self.tau_inc_r = torch.tensor(cfg.tau_inc_range).to(self._device) + self.tau_dec_r = torch.tensor(cfg.tau_dec_range).to(self._device) + + self.max_rate = torch.tensor(cfg.max_thrust_rate).expand(self._num_envs, self.num_motors).to(self._device) + + self.max_thrust = self.cfg.thrust_range[1] + self.min_thrust = self.cfg.thrust_range[0] + + # State & randomized per-motor parameters + self.tau_inc_s = math_utils.sample_uniform(*self.tau_inc_r, (self._num_envs, self.num_motors), self._device) + self.tau_dec_s = math_utils.sample_uniform(*self.tau_dec_r, (self._num_envs, self.num_motors), self._device) + self.thrust_const_r = torch.tensor(cfg.thrust_const_range, device=self._device, dtype=torch.float32) + self.thrust_const = math_utils.sample_uniform( + *self.thrust_const_r, (self._num_envs, self.num_motors), self._device + ).clamp(min=1e-6) + + self.curr_thrust = self.thrust_const * (self._init_thruster_rps.to(self._device).float() ** 2) + + # Mixing factor (discrete vs continuous form) + if self.cfg.use_discrete_approximation: + self.mixing_factor_function = self.discrete_mixing_factor + else: + self.mixing_factor_function = self.continuous_mixing_factor + + # Choose stepping kernel once (avoids per-step branching) + if self.cfg.integration_scheme == "euler": + self._step_thrust = self.compute_thrust_with_rpm_time_constant + elif self.cfg.integration_scheme == "rk4": + self._step_thrust = self.compute_thrust_with_rpm_time_constant_rk4 + else: + raise ValueError("integration scheme unknown") + + @property + def num_thrusters(self) -> int: + """Number of actuators in the group.""" + return len(self._thruster_names) + + @property + def thruster_names(self) -> list[str]: + """Articulation's thruster names that are part of the group.""" + return self._thruster_names + + @property + def thruster_indices(self) -> slice | torch.Tensor: + """Articulation's thruster indices that are part of the group. + + Note: + If :obj:`slice(None)` is returned, then the group contains all the thrusters in the articulation. + We do this to avoid unnecessary indexing of the thrusters for performance reasons. + """ + return self._thruster_indices + + def compute(self, control_action: MultiRotorActions) -> MultiRotorActions: + """Advance the thruster state one step. + + Applies saturation, chooses rise/fall tau per motor, computes mixing factor, + and integrates with the selected kernel. + + Args: + control_action: (num_envs, num_thrusters) commanded per-thruster thrust [N]. + + Returns: + (num_envs, num_thrusters) updated thrust state [N]. + + """ + des_thrust = control_action.thrusts + des_thrust = torch.clamp(des_thrust, *self.thrust_r) + + thrust_decrease_mask = torch.sign(self.curr_thrust) * torch.sign(des_thrust - self.curr_thrust) + motor_tau = torch.where(thrust_decrease_mask < 0, self.tau_dec_s, self.tau_inc_s) + mixing = self.mixing_factor_function(motor_tau) + + self.curr_thrust[:] = self._step_thrust(des_thrust, self.curr_thrust, mixing) + + self.computed_thrust = self.curr_thrust + self.applied_thrust = torch.clamp(self.computed_thrust, self.min_thrust, self.max_thrust) + + control_action.thrusts = self.applied_thrust + + return control_action + + def reset_idx(self, env_ids=None) -> None: + """Re-sample parameters and reinitialize state. + + Args: + env_ids: Env indices to reset. If ``None``, resets all envs. + """ + if env_ids is None: + env_ids = slice(None) + + if isinstance(env_ids, slice): + num_resets = self._num_envs + else: + num_resets = len(env_ids) + + self.tau_inc_s[env_ids] = math_utils.sample_uniform( + *self.tau_inc_r, + (num_resets, self.num_motors), + self._device, + ) + self.tau_dec_s[env_ids] = math_utils.sample_uniform( + *self.tau_dec_r, + (num_resets, self.num_motors), + self._device, + ) + self.thrust_const[env_ids] = math_utils.sample_uniform( + *self.thrust_const_r, + (num_resets, self.num_motors), + self._device, + ) + self.curr_thrust[env_ids] = self.thrust_const[env_ids] * self._init_thruster_rps[env_ids] ** 2 + + def reset(self, env_ids: Sequence[int]) -> None: + """Reset all envs.""" + self.reset_idx(env_ids) + + def motor_model_rate(self, error: torch.Tensor, mixing_factor: torch.Tensor): + return torch.clamp(mixing_factor * (error), -self.max_rate, self.max_rate) + + def rk4_integration(self, error: torch.Tensor, mixing_factor: torch.Tensor): + k1 = self.motor_model_rate(error, mixing_factor) + k2 = self.motor_model_rate(error + 0.5 * self.cfg.dt * k1, mixing_factor) + k3 = self.motor_model_rate(error + 0.5 * self.cfg.dt * k2, mixing_factor) + k4 = self.motor_model_rate(error + self.cfg.dt * k3, mixing_factor) + return (self.cfg.dt / 6.0) * (k1 + 2.0 * k2 + 2.0 * k3 + k4) + + def discrete_mixing_factor(self, time_constant: torch.Tensor): + return 1.0 / (self.cfg.dt + time_constant) + + def continuous_mixing_factor(self, time_constant: torch.Tensor): + return 1.0 / time_constant + + def compute_thrust_with_rpm_time_constant( + self, + des_thrust: torch.Tensor, + curr_thrust: torch.Tensor, + mixing_factor: torch.Tensor, + ): + # Avoid negative or NaN values inside sqrt by clamping the ratio to >= 0. + current_ratio = torch.clamp(curr_thrust / self.thrust_const, min=0.0) + desired_ratio = torch.clamp(des_thrust / self.thrust_const, min=0.0) + current_rpm = torch.sqrt(current_ratio) + desired_rpm = torch.sqrt(desired_ratio) + rpm_error = desired_rpm - current_rpm + current_rpm += self.motor_model_rate(rpm_error, mixing_factor) * self.cfg.dt + return self.thrust_const * current_rpm**2 + + def compute_thrust_with_rpm_time_constant_rk4( + self, + des_thrust: torch.Tensor, + curr_thrust: torch.Tensor, + mixing_factor: torch.Tensor, + ) -> torch.Tensor: + current_ratio = torch.clamp(curr_thrust / self.thrust_const, min=0.0) + desired_ratio = torch.clamp(des_thrust / self.thrust_const, min=0.0) + current_rpm = torch.sqrt(current_ratio) + desired_rpm = torch.sqrt(desired_ratio) + rpm_error = desired_rpm - current_rpm + current_rpm += self.rk4_integration(rpm_error, mixing_factor) + return self.thrust_const * current_rpm**2 diff --git a/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster_cfg.py b/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..29072f421abbc7fd34ebef192a4b9960a9f104b4 --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/actuators/thruster_cfg.py @@ -0,0 +1,62 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .thruster import Thruster + + +@configclass +class ThrusterCfg: + """Configuration for thruster actuator groups. + + This config defines per-actuator-group parameters used by the low-level + thruster/motor models (time-constants, thrust ranges, integration scheme, + and initial state specifications). Fields left as ``MISSING`` are required + and must be provided by the user configuration. + """ + + class_type: type[Thruster] = Thruster + """Concrete Python class that consumes this config.""" + + dt: float = MISSING + """Simulation/integration timestep used by the thruster update [s].""" + + thrust_range: tuple[float, float] = MISSING + """Per-motor thrust clamp range [N]: values are clipped to this interval.""" + + max_thrust_rate: float = 100000.0 + """Per-motor thrust slew-rate limit applied inside the first-order model [N/s].""" + + thrust_const_range: tuple[float, float] = MISSING + """Range for thrust coefficient :math:`k_f` [N/(rps²)].""" + + tau_inc_range: tuple[float, float] = MISSING + """Range of time constants when commanded output is **increasing** (rise dynamics) [s].""" + + tau_dec_range: tuple[float, float] = MISSING + """Range of time constants when commanded output is **decreasing** (fall dynamics) [s].""" + + torque_to_thrust_ratio: float = MISSING + """Yaw-moment coefficient converting thrust to motor torque about +Z [N·m per N]. + Used as ``tau_z = torque_to_thrust_ratio * thrust_z * direction``. + """ + + use_discrete_approximation: bool = True + """ + Determines how the actuator/motor mixing factor is computed. Defaults to True. + + If True, uses the discrete-time factor ``1 / (dt + tau)``, accounting for the control loop timestep. + If False, uses the continuous-time factor ``1 / tau``. + """ + + integration_scheme: Literal["rk4", "euler"] = "rk4" + """Numerical integrator for the first-order model. Defaults to 'rk4'.""" + + thruster_names_expr: list[str] = MISSING + """Articulation's joint names that are part of the group.""" diff --git a/source/isaaclab_contrib/isaaclab_contrib/mdp/__init__.py b/source/isaaclab_contrib/isaaclab_contrib/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..221164dcf428d87e423d38bfff11e8a22150b72e --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/mdp/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .actions import * # noqa: F401, F403 diff --git a/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/__init__.py b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bd4fa9dc4761253bca114d483ce1d1da0926963f --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .thrust_actions import * # noqa: F401, F403 +from .thrust_actions_cfg import * # noqa: F401, F403 diff --git a/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions.py b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..c6a93a3867579f4d0ff85b6dae2fe4b0db98676a --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions.py @@ -0,0 +1,164 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.string as string_utils +from isaaclab.managers.action_manager import ActionTerm + +from isaaclab_contrib.assets import Multirotor + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor + + from . import thrust_actions_cfg + +# import logger +logger = logging.getLogger(__name__) + + +class ThrustAction(ActionTerm): + """Thrust action term that applies the processed actions as thrust commands. Actions are processed by applying an + affine transformation (scaling and offset) and clipping.""" + + cfg: thrust_actions_cfg.ThrustActionCfg + """The configuration of the action term.""" + _asset: Multirotor + """The articulation asset on which the action term is applied.""" + _scale: torch.Tensor | float + """The scaling factor applied to the input action.""" + _offset: torch.Tensor | float + """The offset applied to the input action.""" + _clip: torch.Tensor + """The clip applied to the input action.""" + + def __init__(self, cfg: thrust_actions_cfg.ThrustActionCfg, env: ManagerBasedEnv) -> None: + # initialize the action term + super().__init__(cfg, env) + + thruster_names_expr = self._asset.actuators["thrusters"].cfg.thruster_names_expr + + # resolve the thrusters over which the action term is applied + self._thruster_ids, self._thruster_names = self._asset.find_bodies( + thruster_names_expr, preserve_order=self.cfg.preserve_order + ) + self._num_thrusters = len(self._thruster_ids) + # log the resolved thruster names for debugging + logger.info( + f"Resolved thruster names for the action term {self.__class__.__name__}:" + f" {self._thruster_names} [{self._thruster_ids}]" + ) + + # Avoid indexing across all thrusters for efficiency + if self._num_thrusters == self._asset.num_thrusters and not self.cfg.preserve_order: + self._thruster_ids = slice(None) + + # create tensors for raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + self._processed_actions = torch.zeros_like(self.raw_actions) + + # parse scale + if isinstance(cfg.scale, (float, int)): + self._scale = float(cfg.scale) + elif isinstance(cfg.scale, dict): + self._scale = torch.ones(self.num_envs, self.action_dim, device=self.device) + # resolve the dictionary config + index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.scale, self._thruster_names) + self._scale[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported scale type: {type(cfg.scale)}. Supported types are float and dict.") + + # parse offset + if isinstance(cfg.offset, (float, int)): + self._offset = float(cfg.offset) + elif isinstance(cfg.offset, dict): + self._offset = torch.zeros_like(self._raw_actions) + # resolve the dictionary config + index_list, _, value_list = string_utils.resolve_matching_names_values( + self.cfg.offset, self._thruster_names + ) + self._offset[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported offset type: {type(cfg.offset)}. Supported types are float and dict.") + + # parse clip + if cfg.clip is not None: + if isinstance(cfg.clip, dict): + self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat( + self.num_envs, self.action_dim, 1 + ) + index_list, _, value_list = string_utils.resolve_matching_names_values( + self.cfg.clip, self._thruster_names + ) + self._clip[:, index_list] = torch.tensor(value_list, device=self.device) + else: + raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.") + + # Handle use_default_offset + if cfg.use_default_offset: + # Use default thruster RPS as offset + self._offset = self._asset.data.default_thruster_rps[:, self._thruster_ids].clone() + + @property + def action_dim(self) -> int: + return self._num_thrusters + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + @property + def IO_descriptor(self) -> GenericActionIODescriptor: + """The IO descriptor of the action term.""" + super().IO_descriptor + self._IO_descriptor.shape = (self.action_dim,) + self._IO_descriptor.dtype = str(self.raw_actions.dtype) + self._IO_descriptor.action_type = "ThrustAction" + self._IO_descriptor.thruster_names = self._thruster_names + self._IO_descriptor.scale = self._scale + if isinstance(self._offset, torch.Tensor): + self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.offset = self._offset + if self.cfg.clip is not None: + if isinstance(self._clip, torch.Tensor): + self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist() + else: + self._IO_descriptor.clip = self._clip + else: + self._IO_descriptor.clip = None + return self._IO_descriptor + + def process_actions(self, actions: torch.Tensor): + """Process actions by applying scaling, offset, and clipping.""" + # store the raw actions + self._raw_actions[:] = actions + # apply the affine transformations + self._processed_actions = self._raw_actions * self._scale + self._offset + # clip actions + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def apply_actions(self): + """Apply the processed actions as thrust commands.""" + # Set thrust targets using thruster IDs + self._asset.set_thrust_target(self.processed_actions, thruster_ids=self._thruster_ids) + + def reset(self, env_ids: Sequence[int] | None = None) -> None: + """Reset the action term.""" + self._raw_actions[env_ids] = 0.0 diff --git a/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions_cfg.py b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a3b7c704fc868bf10c0ca9b305d45361a4ac0b45 --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/mdp/actions/thrust_actions_cfg.py @@ -0,0 +1,45 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.utils import configclass + +from . import thrust_actions + +## +# Drone actions. +## + + +@configclass +class ThrustActionCfg(ActionTermCfg): + """Configuration for the thrust action term. + + See :class:`ThrustAction` for more details. + """ + + class_type: type[ActionTerm] = thrust_actions.ThrustAction + + asset_name: str = MISSING + """Name or regex expression of the asset that the action will be mapped to.""" + + scale: float | dict[str, float] = 1.0 + """Scale factor for the action (float or dict of regex expressions). Defaults to 1.0.""" + + offset: float | dict[str, float] = 0.0 + """Offset factor for the action (float or dict of regex expressions). Defaults to 0.0.""" + + preserve_order: bool = False + """Whether to preserve the order of the asset names in the action output. Defaults to False.""" + + use_default_offset: bool = True + """Whether to use default thrust (e.g. hover thrust) configured in the articulation asset as offset. + Defaults to True. + + If True, this flag results in overwriting the values of :attr:`offset` to the default thrust values + from the articulation asset. + """ diff --git a/source/isaaclab_contrib/isaaclab_contrib/utils/types.py b/source/isaaclab_contrib/isaaclab_contrib/utils/types.py new file mode 100644 index 0000000000000000000000000000000000000000..da01bb79c6854f514d9fd9823ad28ecd09d2dd98 --- /dev/null +++ b/source/isaaclab_contrib/isaaclab_contrib/utils/types.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for different data types.""" + +from __future__ import annotations + +from collections.abc import Sequence +from dataclasses import dataclass + +import torch + + +@dataclass +class MultiRotorActions: + """Data container to store articulation's thruster actions. + + This class is used to store the actions of the thrusters of a multirotor. + It is used to store the thrust values and indices. + + If the actions are not provided, the values are set to None. + """ + + thrusts: torch.Tensor | None = None + """The thrusts of the multirotor. Defaults to None.""" + + thruster_indices: torch.Tensor | Sequence[int] | slice | None = None + """The thruster indices of the multirotor. Defaults to None. + + If the thruster indices are a slice, this indicates that the indices are continuous and correspond + to all the thrusters of the multirotor. We use a slice to make the indexing more efficient. + """ diff --git a/source/isaaclab_contrib/pyproject.toml b/source/isaaclab_contrib/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab_contrib/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab_contrib/setup.py b/source/isaaclab_contrib/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..8de11268f8b92f4c88f150a02b228aefed5c0746 --- /dev/null +++ b/source/isaaclab_contrib/setup.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the 'isaaclab_contrib' python package.""" + +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Installation operation +setup( + name="isaaclab_contrib", + author="Isaac Lab Project Developers", + maintainer="Isaac Lab Project Developers", + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + include_package_data=True, + python_requires=">=3.10", + packages=["isaaclab_contrib"], + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab_contrib/test/actuators/test_thruster.py b/source/isaaclab_contrib/test/actuators/test_thruster.py new file mode 100644 index 0000000000000000000000000000000000000000..322005954737c323db326ad73d2565bacb4fd787 --- /dev/null +++ b/source/isaaclab_contrib/test/actuators/test_thruster.py @@ -0,0 +1,204 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.app import AppLauncher + +HEADLESS = True + +# if not AppLauncher.instance(): +simulation_app = AppLauncher(headless=HEADLESS).app + +"""Rest of imports follows""" + +from types import SimpleNamespace + +import pytest +import torch + + +def make_thruster_cfg(num_motors: int): + """Create a minimal Thruster-like config object for tests.""" + return SimpleNamespace( + dt=0.01, + num_motors=num_motors, + thrust_range=(0.0, 10.0), + max_thrust_rate=100.0, + thrust_const_range=(0.05, 0.1), + tau_inc_range=(0.01, 0.02), + tau_dec_range=(0.01, 0.02), + torque_to_thrust_ratio=0.0, + use_discrete_approximation=True, + use_rps=True, + integration_scheme="euler", + ) + + +@pytest.mark.parametrize("num_envs", [1, 2, 4]) +@pytest.mark.parametrize("num_motors", [1, 2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_zero_thrust_const_is_handled(num_envs, num_motors, device): + """When thrust_const_range contains zeros, Thruster clamps values and compute returns finite outputs.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + cfg.thrust_const_range = (0.0, 0.0) + + thruster_names = [f"t{i}" for i in range(num_motors)] + thruster_ids = slice(None) + init_rps = torch.ones(num_envs, num_motors, device=device) + + thr = Thruster(cfg, thruster_names, thruster_ids, num_envs, device, init_rps) # type: ignore[arg-type] + + command = torch.full((num_envs, num_motors), 1.0, device=device) + action = SimpleNamespace(thrusts=command.clone(), thruster_indices=thruster_ids) + + thr.compute(action) # type: ignore[arg-type] + + assert torch.isfinite(action.thrusts).all() + + +@pytest.mark.parametrize("num_envs", [1, 2, 4]) +@pytest.mark.parametrize("num_motors", [1, 2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_negative_thrust_range_results_finite(num_envs, num_motors, device): + """Negative configured thrust ranges are clamped and yield finite outputs after hardening.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + cfg.thrust_range = (-5.0, -1.0) + cfg.thrust_const_range = (0.05, 0.05) + + thruster_names = [f"t{i}" for i in range(num_motors)] + thruster_ids = slice(None) + init_rps = torch.ones(num_envs, num_motors, device=device) + + thr = Thruster(cfg, thruster_names, thruster_ids, num_envs, device, init_rps) # type: ignore[arg-type] + + command = torch.full((num_envs, num_motors), -2.0, device=device) + action = SimpleNamespace(thrusts=command.clone(), thruster_indices=thruster_ids) + + thr.compute(action) # type: ignore[arg-type] + + assert torch.isfinite(action.thrusts).all() + + +@pytest.mark.parametrize("num_envs", [2, 3, 4]) +@pytest.mark.parametrize("num_motors", [2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_tensor_vs_slice_indices_and_subset_reset(num_envs, num_motors, device): + """Compute should accept tensor or slice thruster indices, and reset_idx should affect only specified envs.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + + thruster_names = [f"t{i}" for i in range(num_motors)] + # Use motor indices that exist for the given num_motors + motor_indices = [0, min(2, num_motors - 1)] + thruster_ids_tensor = torch.tensor(motor_indices, dtype=torch.int64, device=device) + thruster_ids_slice = slice(None) + init_rps = torch.ones(num_envs, num_motors, device=device) + + thr_tensor = Thruster(cfg, thruster_names, thruster_ids_tensor, num_envs, device, init_rps) # type: ignore[arg-type] + thr_slice = Thruster(cfg, thruster_names, thruster_ids_slice, num_envs, device, init_rps) # type: ignore[arg-type] + + command = torch.full((num_envs, num_motors), cfg.thrust_range[1] * 0.5, device=device) + action_tensor = SimpleNamespace(thrusts=command.clone(), thruster_indices=thruster_ids_tensor) + action_slice = SimpleNamespace(thrusts=command.clone(), thruster_indices=thruster_ids_slice) + + thr_tensor.compute(action_tensor) # type: ignore[arg-type] + thr_slice.compute(action_slice) # type: ignore[arg-type] + + assert action_tensor.thrusts.shape == (num_envs, num_motors) + assert action_slice.thrusts.shape == (num_envs, num_motors) + + # Test reset on the last environment + env_to_reset = num_envs - 1 + prev = thr_tensor.curr_thrust.clone() + thr_tensor.reset_idx(torch.tensor([env_to_reset], dtype=torch.int64, device=device)) + assert not torch.allclose(prev[env_to_reset], thr_tensor.curr_thrust[env_to_reset]) + + +@pytest.mark.parametrize("num_envs", [1, 2, 4]) +@pytest.mark.parametrize("num_motors", [1, 2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_mixing_and_integration_modes(num_envs, num_motors, device): + """Verify mixing factor selection and integration kernel choice reflect the config.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + + thruster_names = [f"t{i}" for i in range(num_motors)] + + # discrete mixing + cfg.use_discrete_approximation = True + cfg.integration_scheme = "euler" + thr_d = Thruster( + cfg, thruster_names, slice(None), num_envs, device, torch.ones(num_envs, num_motors, device=device) + ) # type: ignore[arg-type] + # bound method objects are recreated on access; compare underlying functions instead + assert getattr(thr_d.mixing_factor_function, "__func__", None) is Thruster.discrete_mixing_factor + assert getattr(thr_d._step_thrust, "__func__", None) is Thruster.compute_thrust_with_rpm_time_constant + + # continuous mixing and RK4 + cfg.use_discrete_approximation = False + cfg.integration_scheme = "rk4" + thr_c = Thruster( + cfg, thruster_names, slice(None), num_envs, device, torch.ones(num_envs, num_motors, device=device) + ) # type: ignore[arg-type] + assert getattr(thr_c.mixing_factor_function, "__func__", None) is Thruster.continuous_mixing_factor + assert getattr(thr_c._step_thrust, "__func__", None) is Thruster.compute_thrust_with_rpm_time_constant_rk4 + + +@pytest.mark.parametrize("num_envs", [1, 2, 4]) +@pytest.mark.parametrize("num_motors", [1, 2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_thruster_compute_clamps_and_shapes(num_envs, num_motors, device): + """Thruster.compute should return thrusts with correct shape and within clamp bounds.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + + thruster_names = [f"t{i}" for i in range(num_motors)] + thruster_ids = slice(None) + init_rps = torch.ones(num_envs, num_motors, device=device) + + thr = Thruster(cfg, thruster_names, thruster_ids, num_envs, device, init_rps) # type: ignore[arg-type] + + # command above max to check clamping + command = torch.full((num_envs, num_motors), cfg.thrust_range[1] * 2.0, device=device) + action = SimpleNamespace(thrusts=command.clone(), thruster_indices=thruster_ids) + + out = thr.compute(action) # type: ignore[arg-type] + + assert out.thrusts.shape == (num_envs, num_motors) + # values must be clipped to configured range + assert torch.all(out.thrusts <= cfg.thrust_range[1] + 1e-6) + assert torch.all(out.thrusts >= cfg.thrust_range[0] - 1e-6) + + +@pytest.mark.parametrize("num_envs", [1, 2, 4]) +@pytest.mark.parametrize("num_motors", [1, 2, 4]) +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_thruster_reset_idx_changes_state(num_envs, num_motors, device): + """reset_idx should re-sample parameters for specific env indices.""" + from isaaclab_contrib.actuators import Thruster + + cfg = make_thruster_cfg(num_motors) + + thruster_names = [f"t{i}" for i in range(num_motors)] + thruster_ids = slice(None) + init_rps = torch.ones(num_envs, num_motors, device=device) + + thr = Thruster(cfg, thruster_names, thruster_ids, num_envs, device, init_rps) # type: ignore[arg-type] + + # Mutate an internal sampled parameter so reset produces a measurable change. + thr.tau_inc_s[0, 0] = thr.tau_inc_s[0, 0] + 1.0 + prev_val = thr.tau_inc_s[0, 0].item() + + # reset only environment 0 + thr.reset_idx(torch.tensor([0], dtype=torch.int64, device=device)) + + # at least the first tau_inc value for env 0 should differ from the mutated value + assert not torch.isclose(torch.tensor(prev_val, device=device), thr.tau_inc_s[0, 0]) diff --git a/source/isaaclab_mimic/config/extension.toml b/source/isaaclab_mimic/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..5b498ae586526f371b747b3d5c1448459f852d39 --- /dev/null +++ b/source/isaaclab_mimic/config/extension.toml @@ -0,0 +1,29 @@ +[package] + +# Semantic Versioning is used: https://semver.org/ +version = "1.0.16" + +# Description +category = "isaaclab" +readme = "README.md" + +title = "Isaac Lab Mimic" +author = "Isaac Lab Project Developers" +maintainer = "Isaac Lab Project Developers" +description="Mimic for Isaac Lab" +repository = "https://github.com/isaac-sim/IsaacLab.git" +keywords = ["extension", "template", "isaaclab"] + +[dependencies] +"isaaclab" = {} +"isaaclab_assets" = {} +"isaaclab_tasks" = {} +# NOTE: Add additional dependencies here + +[core] +reloadable = false + +[[python.module]] +name = "isaaclab_mimic" + +[isaaclab_settings] diff --git a/source/isaaclab_mimic/docs/CHANGELOG.rst b/source/isaaclab_mimic/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..09b79c8cdd80d884d1c6299d1d31d921fb0b7c3d --- /dev/null +++ b/source/isaaclab_mimic/docs/CHANGELOG.rst @@ -0,0 +1,166 @@ +Changelog +--------- + + +1.0.16 (2025-11-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Add body end effector to Mimic data generation to enable loco-manipulation data generation when a navigation p-controller is provided. + + +1.0.15 (2025-09-25) + +Fixed +^^^^^ + +* Fixed a bug in the instruction UI logic that caused incorrect switching between XR and non-XR display modes. The instruction display now properly detects and updates the UI based on the teleoperation device (e.g., handtracking/XR vs. keyboard). + + +1.0.14 (2025-09-08) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added SkillGen integration for automated demonstration generation using cuRobo; enable via ``--use_skillgen`` in ``scripts/imitation_learning/isaaclab_mimic/generate_dataset.py``. +* Added cuRobo motion planner interface (:class:`CuroboPlanner`, :class:`CuroboPlannerCfg`) +* Added manual subtask start boundary annotation for SkillGen; enable via ``--annotate_subtask_start_signals`` in ``scripts/imitation_learning/isaaclab_mimic/annotate_demos.py``. +* Added Rerun integration for motion plan visualization and debugging; enable via ``visualize_plan = True`` in :class:`CuroboPlannerCfg`. + + +1.0.13 (2025-08-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`PickPlaceGR1T2WaistEnabledEnvCfg` and :class:`PickPlaceGR1T2WaistEnabledMimicEnvCfg` for GR1T2 robot manipulation tasks with waist joint control enabled. + +1.0.12 (2025-07-31) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``from __future__ import annotations`` to utils.py to fix Sphinx + doc warnings for IsaacLab Mimic docs. + + +1.0.11 (2025-07-17) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated test_selection_strategy.py and test_generate_dataset.py test cases to pytest format. +* Updated annotate_demos.py script to return the number of successful task completions as the exit code to support check in test_generate_dataset.py test case. + + +1.0.10 (2025-07-08) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated generate dataset script to cancel remaining async tasks before closing the simulation app. + + +1.0.9 (2025-05-20) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0`` environment for Cosmos vision stacking. + + +1.0.8 (2025-05-01) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`NutPourGR1T2MimicEnv` and :class:`ExhaustPipeGR1T2MimicEnv` for the GR1T2 nut pouring and exhaust pipe tasks. +* Updated instruction display to support all XR handtracking devices. + + +1.0.7 (2025-03-19) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the GR1T2 robot task to a separate directory to prevent import of pinocchio when not needed. This allows use of IsaacLab Mimic in windows. + + +1.0.6 (2025-03-10) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added :class:`FrankaCubeStackIKAbsMimicEnv` and support for the GR1T2 robot task (:class:`PickPlaceGR1T2MimicEnv`). + + +1.0.5 (2025-03-10) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Refactored dataset generation code into leaner modules to prepare for Jupyter notebook. + +Added +^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-Mimic-v0`` environment for blueprint vision stacking. + + +1.0.4 (2025-03-07) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated data generator to support environments with multiple end effectors. +* Updated data generator to support subtask constraints based on DexMimicGen. + + +1.0.3 (2025-03-06) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^ + +* Added absolute pose mimic environment for Franka cube stacking task (:class:`FrankaCubeStackIKAbsMimicEnv`) + + +1.0.2 (2025-01-10) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed test_selection_strategy.py test case by starting omniverse app to import needed dependencies. + + +1.0.1 (2024-12-16) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed the custom :meth:`get_object_poses` function in the:class:`FrankaCubeStackIKRelMimicEnv` + class to use the default implementation from the :class:`ManagerBasedRLMimicEnv` class. + + +1.0.0 (2024-12-06) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Add initial version of Isaac Lab Mimic diff --git a/source/isaaclab_mimic/isaaclab_mimic/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..17f1264a6b59edd0e31cb1c6b732c06ff8e0e759 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Package containing implementation of Isaac Lab Mimic data generation.""" + +__version__ = "1.0.0" diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3ede9a65cb6737ca186c9fb20bc67b28270745fb --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Sub-package with core implementation logic for Isaac Lab Mimic.""" + +from .data_generator import * +from .datagen_info import * +from .datagen_info_pool import * +from .generation import * +from .selection_strategy import * +from .utils import * +from .waypoint import * diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/data_generator.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/data_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..37fabe4a4b472954ae07a227e16cec2628a16015 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/data_generator.py @@ -0,0 +1,1033 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Base class for data generator.""" + +import asyncio +import copy +import logging +from typing import Any + +import numpy as np +import torch + +import isaaclab.utils.math as PoseUtils + +logger = logging.getLogger(__name__) + +from isaaclab.envs import ( + ManagerBasedRLMimicEnv, + MimicEnvCfg, + SubTaskConstraintCoordinationScheme, + SubTaskConstraintType, +) +from isaaclab.managers import TerminationTermCfg + +from isaaclab_mimic.datagen.datagen_info import DatagenInfo +from isaaclab_mimic.datagen.selection_strategy import make_selection_strategy +from isaaclab_mimic.datagen.waypoint import MultiWaypoint, Waypoint, WaypointSequence, WaypointTrajectory + +from .datagen_info_pool import DataGenInfoPool + + +def transform_source_data_segment_using_delta_object_pose( + src_eef_poses: torch.Tensor, + delta_obj_pose: torch.Tensor, +) -> torch.Tensor: + """ + Transform a source data segment (object-centric subtask segment from source demonstration) using + a delta object pose. + + Args: + src_eef_poses: pose sequence (shape [T, 4, 4]) for the sequence of end effector control poses + from the source demonstration + delta_obj_pose: 4x4 delta object pose + + Returns: + transformed_eef_poses: transformed pose sequence (shape [T, 4, 4]) + """ + return PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_poses, + pose_A_in_B=delta_obj_pose[None], + ) + + +def transform_source_data_segment_using_object_pose( + obj_pose: torch.Tensor, + src_eef_poses: torch.Tensor, + src_obj_pose: torch.Tensor, +) -> torch.Tensor: + """ + Transform a source data segment (object-centric subtask segment from source demonstration) such that + the relative poses between the target eef pose frame and the object frame are preserved. Recall that + each object-centric subtask segment corresponds to one object, and consists of a sequence of + target eef poses. + + Args: + obj_pose: 4x4 object pose in current scene + src_eef_poses: pose sequence (shape [T, 4, 4]) for the sequence of end effector control poses + from the source demonstration + src_obj_pose: 4x4 object pose from the source demonstration + + Returns: + transformed_eef_poses: transformed pose sequence (shape [T, 4, 4]) + """ + + # Transform source end effector poses to be relative to source object frame + src_eef_poses_rel_obj = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_poses, + pose_A_in_B=PoseUtils.pose_inv(src_obj_pose[None]), + ) + + # Apply relative poses to current object frame to obtain new target eef poses + transformed_eef_poses = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_poses_rel_obj, + pose_A_in_B=obj_pose[None], + ) + return transformed_eef_poses + + +def get_delta_pose_with_scheme( + src_obj_pose: torch.Tensor, + cur_obj_pose: torch.Tensor, + task_constraint: dict, +) -> torch.Tensor: + """ + Get the delta pose with the given coordination scheme. + + Args: + src_obj_pose: 4x4 object pose in source scene + cur_obj_pose: 4x4 object pose in current scene + task_constraint: task constraint dictionary + + Returns: + delta_pose: 4x4 delta pose + """ + coord_transform_scheme = task_constraint["coordination_scheme"] + device = src_obj_pose.device + if coord_transform_scheme == SubTaskConstraintCoordinationScheme.TRANSFORM: + delta_pose = PoseUtils.get_delta_object_pose(cur_obj_pose, src_obj_pose) + # add noise to delta pose position + elif coord_transform_scheme == SubTaskConstraintCoordinationScheme.TRANSLATE: + delta_pose = torch.eye(4, device=device) + delta_pose[:3, 3] = cur_obj_pose[:3, 3] - src_obj_pose[:3, 3] + elif coord_transform_scheme == SubTaskConstraintCoordinationScheme.REPLAY: + delta_pose = torch.eye(4, device=device) + else: + raise ValueError( + f"coordination coord_transform_scheme {coord_transform_scheme} not supported, only" + f" {[e.value for e in SubTaskConstraintCoordinationScheme]} are supported" + ) + + pos_noise_scale = task_constraint["coordination_scheme_pos_noise_scale"] + rot_noise_scale = task_constraint["coordination_scheme_rot_noise_scale"] + if pos_noise_scale != 0.0 or rot_noise_scale != 0.0: + pos = delta_pose[:3, 3] + rot = delta_pose[:3, :3] + pos_new, rot_new = PoseUtils.add_uniform_noise_to_pose(pos, rot, pos_noise_scale, rot_noise_scale) + delta_pose = torch.eye(4, device=device) + delta_pose[:3, 3] = pos_new + delta_pose[:3, :3] = rot_new + return delta_pose + + +class DataGenerator: + """The main data generator class that generates new trajectories from source datasets. + + The data generator, inspired by the MimicGen, enables the generation of new datasets based on a + few human collected source demonstrations. + + The data generator works by parsing demonstrations into object-centric subtask segments, stored in + :class:`DataGenInfoPool`. It then adapts these subtask segments to new scenes by transforming each + segment according to the new scene's context, stitching them into a coherent trajectory for a robotic + end-effector to execute. + """ + + def __init__( + self, + env: ManagerBasedRLMimicEnv, + src_demo_datagen_info_pool: DataGenInfoPool | None = None, + dataset_path: str | None = None, + demo_keys: list[str] | None = None, + ): + """ + Args: + env: environment to use for data generation + src_demo_datagen_info_pool: source demo datagen info pool + dataset_path: path to hdf5 dataset to use for generation + demo_keys: list of demonstration keys to use in file. If not provided, + all demonstration keys will be used. + """ + self.env = env + self.env_cfg = env.cfg + assert isinstance(self.env_cfg, MimicEnvCfg) + self.dataset_path = dataset_path + + # Sanity check on task spec offset ranges - final subtask should not have any offset randomization + for subtask_configs in self.env_cfg.subtask_configs.values(): + assert subtask_configs[-1].subtask_term_offset_range[0] == 0 + assert subtask_configs[-1].subtask_term_offset_range[1] == 0 + + self.demo_keys = demo_keys + + if src_demo_datagen_info_pool is not None: + self.src_demo_datagen_info_pool = src_demo_datagen_info_pool + elif dataset_path is not None: + self.src_demo_datagen_info_pool = DataGenInfoPool( + env=self.env, env_cfg=self.env_cfg, device=self.env.device + ) + self.src_demo_datagen_info_pool.load_from_dataset_file(dataset_path, select_demo_keys=self.demo_keys) + else: + raise ValueError("Either src_demo_datagen_info_pool or dataset_path must be provided") + + def __repr__(self): + """Pretty print this object.""" + msg = str(self.__class__.__name__) + msg += f" (\n\tdataset_path={self.dataset_path}\n\tdemo_keys={self.demo_keys}\n)" + return msg + + def randomize_subtask_boundaries(self) -> dict[str, np.ndarray]: + """Apply random offsets to sample subtask boundaries according to the task spec. + + Recall that each demonstration is segmented into a set of subtask segments, and the + end index (and start index when skillgen is enabled) of each subtask can have a random offset. + """ + + randomized_subtask_boundaries = {} + + for eef_name, subtask_boundaries in self.src_demo_datagen_info_pool.subtask_boundaries.items(): + # Initial subtask start and end indices - shape (N, S, 2) + subtask_boundaries = np.array(subtask_boundaries) + + # Randomize the start of the first subtask + first_subtask_start_offsets = np.random.randint( + low=self.env_cfg.subtask_configs[eef_name][0].first_subtask_start_offset_range[0], + high=self.env_cfg.subtask_configs[eef_name][0].first_subtask_start_offset_range[0] + 1, + size=subtask_boundaries.shape[0], + ) + subtask_boundaries[:, 0, 0] += first_subtask_start_offsets + + # For each subtask, sample all end offsets at once for each demonstration + # Add them to subtask end indices, and then set them as the start indices of next subtask too + for i in range(subtask_boundaries.shape[1]): + # If skillgen is enabled, sample a random start offset to increase demonstration variety. + if self.env_cfg.datagen_config.use_skillgen: + start_offset = np.random.randint( + low=self.env_cfg.subtask_configs[eef_name][i].subtask_start_offset_range[0], + high=self.env_cfg.subtask_configs[eef_name][i].subtask_start_offset_range[1] + 1, + size=subtask_boundaries.shape[0], + ) + subtask_boundaries[:, i, 0] += start_offset + elif i > 0: + # Without skillgen, the start of a subtask is the end of the previous one. + subtask_boundaries[:, i, 0] = subtask_boundaries[:, i - 1, 1] + + # Sample end offset for each demonstration + end_offsets = np.random.randint( + low=self.env_cfg.subtask_configs[eef_name][i].subtask_term_offset_range[0], + high=self.env_cfg.subtask_configs[eef_name][i].subtask_term_offset_range[1] + 1, + size=subtask_boundaries.shape[0], + ) + subtask_boundaries[:, i, 1] = subtask_boundaries[:, i, 1] + end_offsets + + # Ensure non-empty subtasks + assert np.all((subtask_boundaries[:, :, 1] - subtask_boundaries[:, :, 0]) > 0), "got empty subtasks!" + + # Ensure subtask indices increase (both starts and ends) + assert np.all((subtask_boundaries[:, 1:, :] - subtask_boundaries[:, :-1, :]) > 0), ( + "subtask indices do not strictly increase" + ) + + # Ensure subtasks are in order + subtask_inds_flat = subtask_boundaries.reshape(subtask_boundaries.shape[0], -1) + assert np.all((subtask_inds_flat[:, 1:] - subtask_inds_flat[:, :-1]) >= 0), "subtask indices not in order" + + randomized_subtask_boundaries[eef_name] = subtask_boundaries + + return randomized_subtask_boundaries + + def select_source_demo( + self, + eef_name: str, + eef_pose: np.ndarray, + object_pose: np.ndarray, + src_demo_current_subtask_boundaries: np.ndarray, + subtask_object_name: str, + selection_strategy_name: str, + selection_strategy_kwargs: dict | None = None, + ) -> int: + """Helper method to run source subtask segment selection. + + Args: + eef_name: name of end effector + eef_pose: current end effector pose + object_pose: current object pose for this subtask + src_demo_current_subtask_boundaries: start and end indices for subtask segment + in source demonstrations of shape (N, 2) + subtask_object_name: name of reference object for this subtask + selection_strategy_name: name of selection strategy + selection_strategy_kwargs: extra kwargs for running selection strategy + + Returns: + The selected source demo index + """ + if subtask_object_name is None: + # no reference object - only random selection is supported + assert selection_strategy_name == "random", selection_strategy_name + + # We need to collect the datagen info objects over the timesteps for the subtask segment in each source + # demo, so that it can be used by the selection strategy. + src_subtask_datagen_infos = [] + for i in range(len(self.src_demo_datagen_info_pool.datagen_infos)): + # Datagen info over all timesteps of the src trajectory + src_ep_datagen_info = self.src_demo_datagen_info_pool.datagen_infos[i] + + # Time indices for subtask + subtask_start_ind = src_demo_current_subtask_boundaries[i][0] + subtask_end_ind = src_demo_current_subtask_boundaries[i][1] + + # Get subtask segment using indices + src_subtask_datagen_infos.append( + DatagenInfo( + eef_pose=src_ep_datagen_info.eef_pose[eef_name][subtask_start_ind:subtask_end_ind], + # Only include object pose for relevant object in subtask + object_poses=( + { + subtask_object_name: src_ep_datagen_info.object_poses[subtask_object_name][ + subtask_start_ind:subtask_end_ind + ] + } + if (subtask_object_name is not None) + else None + ), + # Subtask termination signal is unused + subtask_term_signals=None, + target_eef_pose=src_ep_datagen_info.target_eef_pose[eef_name][subtask_start_ind:subtask_end_ind], + gripper_action=src_ep_datagen_info.gripper_action[eef_name][subtask_start_ind:subtask_end_ind], + ) + ) + + # Make selection strategy object + selection_strategy_obj = make_selection_strategy(selection_strategy_name) + + # Run selection + if selection_strategy_kwargs is None: + selection_strategy_kwargs = dict() + selected_src_demo_ind = selection_strategy_obj.select_source_demo( + eef_pose=eef_pose, + object_pose=object_pose, + src_subtask_datagen_infos=src_subtask_datagen_infos, + **selection_strategy_kwargs, + ) + + return selected_src_demo_ind + + def generate_eef_subtask_trajectory( + self, + env_id: int, + eef_name: str, + subtask_ind: int, + all_randomized_subtask_boundaries: dict, + runtime_subtask_constraints_dict: dict, + selected_src_demo_inds: dict, + ) -> WaypointTrajectory: + """Build a transformed waypoint trajectory for a single subtask of an end-effector. + + This method selects a source demonstration segment for the specified subtask, + slices the corresponding EEF poses/targets/gripper actions using the randomized + subtask boundaries, optionally prepends the first robot EEF pose (to interpolate + from the robot pose instead of the first target), applies an object/coordination + based transform to the pose sequence, and returns the result as a `WaypointTrajectory`. + + Selection and transforms: + + - Source demo selection is controlled by `SubTaskConfig.selection_strategy` (and kwargs) and by + `datagen_config.generation_select_src_per_subtask` / `generation_select_src_per_arm`. + - For coordination constraints, the method reuses/sets the selected source demo ID across + concurrent subtasks, computes `synchronous_steps`, and stores the pose `transform` used + to ensure consistent relative motion between tasks. + - Pose transforms are computed either from object poses (`object_ref`) or via a delta pose + provided by a concurrent task/coordination scheme. + + + Args: + env_id: Environment index used to query current robot/object poses. + eef_name: End-effector key whose subtask trajectory is being generated. + subtask_ind: Index of the subtask within `subtask_configs[eef_name]`. + all_randomized_subtask_boundaries: For each EEF, an array of per-demo + randomized (start, end) indices for every subtask. + runtime_subtask_constraints_dict: In/out dictionary carrying runtime fields + for constraints (e.g., selected source ID, delta transform, synchronous steps). + selected_src_demo_inds: Per-EEF mapping for the currently selected source demo index + (may be reused across arms if configured). + + Returns: + WaypointTrajectory: The transformed trajectory for the selected subtask segment. + """ + subtask_configs = self.env_cfg.subtask_configs[eef_name] + # name of object for this subtask + subtask_object_name = self.env_cfg.subtask_configs[eef_name][subtask_ind].object_ref + subtask_object_pose = ( + self.env.get_object_poses(env_ids=[env_id])[subtask_object_name][0] + if (subtask_object_name is not None) + else None + ) + + is_first_subtask = subtask_ind == 0 + + need_source_demo_selection = is_first_subtask or self.env_cfg.datagen_config.generation_select_src_per_subtask + + if not self.env_cfg.datagen_config.generation_select_src_per_arm: + need_source_demo_selection = need_source_demo_selection and selected_src_demo_inds[eef_name] is None + + use_delta_transform = None + coord_transform_scheme = None + if (eef_name, subtask_ind) in runtime_subtask_constraints_dict: + if runtime_subtask_constraints_dict[(eef_name, subtask_ind)]["type"] == SubTaskConstraintType.COORDINATION: + # Avoid selecting source demo if it has already been selected by the concurrent task + concurrent_task_spec_key = runtime_subtask_constraints_dict[(eef_name, subtask_ind)][ + "concurrent_task_spec_key" + ] + concurrent_subtask_ind = runtime_subtask_constraints_dict[(eef_name, subtask_ind)][ + "concurrent_subtask_ind" + ] + concurrent_selected_src_ind = runtime_subtask_constraints_dict[ + (concurrent_task_spec_key, concurrent_subtask_ind) + ]["selected_src_demo_ind"] + if concurrent_selected_src_ind is not None: + # The concurrent task has started, so we should use the same source demo + selected_src_demo_inds[eef_name] = concurrent_selected_src_ind + need_source_demo_selection = False + # This transform is set at after the first data generation iteration/first + # run of the main while loop + use_delta_transform = runtime_subtask_constraints_dict[ + (concurrent_task_spec_key, concurrent_subtask_ind) + ]["transform"] + else: + assert "transform" not in runtime_subtask_constraints_dict[(eef_name, subtask_ind)], ( + "transform should not be set for concurrent task" + ) + # Need to transform demo according to scheme + coord_transform_scheme = runtime_subtask_constraints_dict[(eef_name, subtask_ind)][ + "coordination_scheme" + ] + if coord_transform_scheme != SubTaskConstraintCoordinationScheme.REPLAY: + assert subtask_object_name is not None, ( + f"object reference should not be None for {coord_transform_scheme} coordination scheme" + ) + + if need_source_demo_selection: + selected_src_demo_inds[eef_name] = self.select_source_demo( + eef_name=eef_name, + eef_pose=self.env.get_robot_eef_pose(env_ids=[env_id], eef_name=eef_name)[0], + object_pose=subtask_object_pose, + src_demo_current_subtask_boundaries=all_randomized_subtask_boundaries[eef_name][:, subtask_ind], + subtask_object_name=subtask_object_name, + selection_strategy_name=self.env_cfg.subtask_configs[eef_name][subtask_ind].selection_strategy, + selection_strategy_kwargs=self.env_cfg.subtask_configs[eef_name][subtask_ind].selection_strategy_kwargs, + ) + + assert selected_src_demo_inds[eef_name] is not None + selected_src_demo_ind = selected_src_demo_inds[eef_name] + + if not self.env_cfg.datagen_config.generation_select_src_per_arm and need_source_demo_selection: + for itrated_eef_name in self.env_cfg.subtask_configs.keys(): + selected_src_demo_inds[itrated_eef_name] = selected_src_demo_ind + + # Selected subtask segment time indices + selected_src_subtask_boundary = all_randomized_subtask_boundaries[eef_name][selected_src_demo_ind, subtask_ind] + + if (eef_name, subtask_ind) in runtime_subtask_constraints_dict: + if runtime_subtask_constraints_dict[(eef_name, subtask_ind)]["type"] == SubTaskConstraintType.COORDINATION: + # Store selected source demo ind for concurrent task + runtime_subtask_constraints_dict[(eef_name, subtask_ind)]["selected_src_demo_ind"] = ( + selected_src_demo_ind + ) + concurrent_task_spec_key = runtime_subtask_constraints_dict[(eef_name, subtask_ind)][ + "concurrent_task_spec_key" + ] + concurrent_subtask_ind = runtime_subtask_constraints_dict[(eef_name, subtask_ind)][ + "concurrent_subtask_ind" + ] + concurrent_src_subtask_inds = all_randomized_subtask_boundaries[concurrent_task_spec_key][ + selected_src_demo_ind, concurrent_subtask_ind + ] + subtask_len = selected_src_subtask_boundary[1] - selected_src_subtask_boundary[0] + concurrent_subtask_len = concurrent_src_subtask_inds[1] - concurrent_src_subtask_inds[0] + runtime_subtask_constraints_dict[(eef_name, subtask_ind)]["synchronous_steps"] = min( + subtask_len, concurrent_subtask_len + ) + + # Get subtask segment, consisting of the sequence of robot eef poses, target poses, gripper actions + src_ep_datagen_info = self.src_demo_datagen_info_pool.datagen_infos[selected_src_demo_ind] + src_subtask_eef_poses = src_ep_datagen_info.eef_pose[eef_name][ + selected_src_subtask_boundary[0] : selected_src_subtask_boundary[1] + ] + src_subtask_target_poses = src_ep_datagen_info.target_eef_pose[eef_name][ + selected_src_subtask_boundary[0] : selected_src_subtask_boundary[1] + ] + src_subtask_gripper_actions = src_ep_datagen_info.gripper_action[eef_name][ + selected_src_subtask_boundary[0] : selected_src_subtask_boundary[1] + ] + + # Get reference object pose from source demo + src_subtask_object_pose = ( + src_ep_datagen_info.object_poses[subtask_object_name][selected_src_subtask_boundary[0]] + if (subtask_object_name is not None) + else None + ) + + if is_first_subtask or self.env_cfg.datagen_config.generation_transform_first_robot_pose: + # Source segment consists of first robot eef pose and the target poses. This ensures that + # We will interpolate to the first robot eef pose in this source segment, instead of the + # first robot target pose. + src_eef_poses = torch.cat([src_subtask_eef_poses[0:1], src_subtask_target_poses], dim=0) + # Account for extra timestep added to @src_eef_poses + src_subtask_gripper_actions = torch.cat( + [src_subtask_gripper_actions[0:1], src_subtask_gripper_actions], dim=0 + ) + else: + # Source segment consists of just the target poses. + src_eef_poses = src_subtask_target_poses.clone() + src_subtask_gripper_actions = src_subtask_gripper_actions.clone() + + # Transform source demonstration segment using relevant object pose. + if use_delta_transform is not None: + # Use delta transform from concurrent task + transformed_eef_poses = transform_source_data_segment_using_delta_object_pose( + src_eef_poses, use_delta_transform + ) + + else: + if coord_transform_scheme is not None: + delta_obj_pose = get_delta_pose_with_scheme( + src_subtask_object_pose, + subtask_object_pose, + runtime_subtask_constraints_dict[(eef_name, subtask_ind)], + ) + transformed_eef_poses = transform_source_data_segment_using_delta_object_pose( + src_eef_poses, delta_obj_pose + ) + runtime_subtask_constraints_dict[(eef_name, subtask_ind)]["transform"] = delta_obj_pose + else: + if subtask_object_name is not None: + transformed_eef_poses = transform_source_data_segment_using_object_pose( + subtask_object_pose, + src_eef_poses, + src_subtask_object_pose, + ) + else: + print(f"skipping transformation for {subtask_object_name}") + + # Skip transformation if no reference object is provided + transformed_eef_poses = src_eef_poses + + # Construct trajectory for the transformed segment. + transformed_seq = WaypointSequence.from_poses( + poses=transformed_eef_poses, + gripper_actions=src_subtask_gripper_actions, + action_noise=subtask_configs[subtask_ind].action_noise, + ) + transformed_traj = WaypointTrajectory() + transformed_traj.add_waypoint_sequence(transformed_seq) + + return transformed_traj + + def merge_eef_subtask_trajectory( + self, + env_id: int, + eef_name: str, + subtask_index: int, + prev_executed_traj: list[Waypoint] | None, + subtask_trajectory: WaypointTrajectory, + ) -> list[Waypoint]: + """Merge a subtask trajectory into an executable trajectory for the robot end-effector. + + This constructs a new `WaypointTrajectory` by first creating an initial + interpolation segment, then merging the provided `subtask_trajectory` onto it. + The initial segment begins either from the last executed target waypoint of the + previous subtask (if configured) or from the robot's current end-effector pose. + + Behavior: + + - If `datagen_config.generation_interpolate_from_last_target_pose` is True and + this is not the first subtask, interpolation starts from the last waypoint of + `prev_executed_traj`. + - Otherwise, interpolation starts from the current robot EEF pose (queried from the env) + and uses the first waypoint's gripper action and the subtask's action noise. + - The merge uses `num_interpolation_steps`, `num_fixed_steps`, and optionally + `apply_noise_during_interpolation` from the corresponding `SubTaskConfig`. + - The temporary initial waypoint used to enable interpolation is removed before returning. + + Args: + env_id: Environment index to query the current robot EEF pose when needed. + eef_name: Name/key of the end-effector whose trajectory is being merged. + subtask_index: Index of the subtask within `subtask_configs[eef_name]` driving interpolation parameters. + prev_executed_traj: The previously executed trajectory used to + seed interpolation from its last target waypoint. Required when interpolation-from-last-target + is enabled and this is not the first subtask. + subtask_trajectory: + Trajectory segment for the current subtask that will be merged after the initial interpolation segment. + + Returns: + The full sequence of waypoints to execute (initial interpolation segment followed by the subtask segment), + with the temporary initial waypoint removed. + """ + is_first_subtask = subtask_index == 0 + # We will construct a WaypointTrajectory instance to keep track of robot control targets + # and then execute it once we have the trajectory. + traj_to_execute = WaypointTrajectory() + + if self.env_cfg.datagen_config.generation_interpolate_from_last_target_pose and (not is_first_subtask): + # Interpolation segment will start from last target pose (which may not have been achieved). + assert prev_executed_traj is not None + last_waypoint = prev_executed_traj[-1] + init_sequence = WaypointSequence(sequence=[last_waypoint]) + else: + # Interpolation segment will start from current robot eef pose. + init_sequence = WaypointSequence.from_poses( + poses=self.env.get_robot_eef_pose(env_ids=[env_id], eef_name=eef_name)[0].unsqueeze(0), + gripper_actions=subtask_trajectory[0].gripper_action.unsqueeze(0), + action_noise=self.env_cfg.subtask_configs[eef_name][subtask_index].action_noise, + ) + traj_to_execute.add_waypoint_sequence(init_sequence) + + # Merge this trajectory into our trajectory using linear interpolation. + # Interpolation will happen from the initial pose (@init_sequence) to the first element of @transformed_seq. + traj_to_execute.merge( + subtask_trajectory, + num_steps_interp=self.env_cfg.subtask_configs[eef_name][subtask_index].num_interpolation_steps, + num_steps_fixed=self.env_cfg.subtask_configs[eef_name][subtask_index].num_fixed_steps, + action_noise=( + float(self.env_cfg.subtask_configs[eef_name][subtask_index].apply_noise_during_interpolation) + * self.env_cfg.subtask_configs[eef_name][subtask_index].action_noise + ), + ) + + # We initialized @traj_to_execute with a pose to allow @merge to handle linear interpolation + # for us. However, we can safely discard that first waypoint now, and just start by executing + # the rest of the trajectory (interpolation segment and transformed subtask segment). + traj_to_execute.pop_first() + + # Return the generated trajectory + return traj_to_execute.get_full_sequence().sequence + + async def generate( # noqa: C901 + self, + env_id: int, + success_term: TerminationTermCfg, + env_reset_queue: asyncio.Queue | None = None, + env_action_queue: asyncio.Queue | None = None, + pause_subtask: bool = False, + export_demo: bool = True, + motion_planner: Any | None = None, + ) -> dict: + """Attempt to generate a new demonstration. + + Args: + env_id: environment ID + success_term: success function to check if the task is successful + env_reset_queue: queue to store environment IDs for reset + env_action_queue: queue to store actions for each environment + pause_subtask: whether to pause the subtask generation + export_demo: whether to export the demo + motion_planner: motion planner to use for motion planning + + Returns: + A dictionary containing the following items: + - initial_state (dict): initial simulator state for the executed trajectory + - states (list): simulator state at each timestep + - observations (list): observation dictionary at each timestep + - datagen_infos (list): datagen_info at each timestep + - actions (np.array): action executed at each timestep + - success (bool): whether the trajectory successfully solved the task or not + - src_demo_inds (list): list of selected source demonstration indices for each subtask + - src_demo_labels (np.array): same as @src_demo_inds, but repeated to have a label for + each timestep of the trajectory. + """ + # With skillgen, a motion planner is required to generate collision-free transitions between subtasks. + if self.env_cfg.datagen_config.use_skillgen and motion_planner is None: + raise ValueError("motion_planner must be provided if use_skillgen is True") + + # reset the env to create a new task demo instance + env_id_tensor = torch.tensor([env_id], dtype=torch.int64, device=self.env.device) + self.env.recorder_manager.reset(env_ids=env_id_tensor) + await env_reset_queue.put(env_id) + await env_reset_queue.join() + new_initial_state = self.env.scene.get_state(is_relative=True) + + # create runtime subtask constraint rules from subtask constraint configs + runtime_subtask_constraints_dict = {} + for subtask_constraint in self.env_cfg.task_constraint_configs: + runtime_subtask_constraints_dict.update(subtask_constraint.generate_runtime_subtask_constraints()) + + # save generated data in these variables + generated_states = [] + generated_obs = [] + generated_actions = [] + generated_success = False + + # some eef-specific state variables used during generation + current_eef_selected_src_demo_indices = {} + current_eef_subtask_trajectories: dict[str, list[Waypoint]] = {} + current_eef_subtask_indices = {} + next_eef_subtask_indices_after_motion = {} + next_eef_subtask_trajectories_after_motion = {} + current_eef_subtask_step_indices = {} + eef_subtasks_done = {} + for eef_name in self.env_cfg.subtask_configs.keys(): + current_eef_selected_src_demo_indices[eef_name] = None + current_eef_subtask_trajectories[eef_name] = [] # type of list of Waypoint + current_eef_subtask_indices[eef_name] = 0 + next_eef_subtask_indices_after_motion[eef_name] = None + next_eef_subtask_trajectories_after_motion[eef_name] = None + current_eef_subtask_step_indices[eef_name] = None + eef_subtasks_done[eef_name] = False + + prev_src_demo_datagen_info_pool_size = 0 + + if self.env_cfg.datagen_config.use_navigation_controller: + was_navigating = False + + # While loop that runs per time step + while True: + async with self.src_demo_datagen_info_pool.asyncio_lock: + if len(self.src_demo_datagen_info_pool.datagen_infos) > prev_src_demo_datagen_info_pool_size: + # src_demo_datagen_info_pool at this point may be updated with new demos, + # So we need to update subtask boundaries again + randomized_subtask_boundaries = ( + self.randomize_subtask_boundaries() + ) # shape [N, S, 2], last dim is start and end action lengths + prev_src_demo_datagen_info_pool_size = len(self.src_demo_datagen_info_pool.datagen_infos) + + # Generate trajectory for a subtask for the eef that is currently at the beginning of a subtask + for eef_name, eef_subtask_step_index in current_eef_subtask_step_indices.items(): + if eef_subtask_step_index is None: + # Trajectory stored in current_eef_subtask_trajectories[eef_name] has been executed, + # So we need to determine the next trajectory + # Note: This condition is the "resume-after-motion-plan" gate for skillgen. When + # use_skillgen=False (vanilla Mimic), next_eef_subtask_indices_after_motion[eef_name] + # remains None, so this condition is always True and the else-branch below is never taken. + # The else-branch is only used right after executing a motion-planned transition (skillgen) + # to resume the actual subtask trajectory. + if next_eef_subtask_indices_after_motion[eef_name] is None: + # This is the beginning of a new subtask, so generate a new trajectory accordingly + eef_subtask_trajectory = self.generate_eef_subtask_trajectory( + env_id, + eef_name, + current_eef_subtask_indices[eef_name], + randomized_subtask_boundaries, + runtime_subtask_constraints_dict, + current_eef_selected_src_demo_indices, # updated in the method + ) + # With skillgen, use a motion planner to transition between subtasks. + if self.env_cfg.datagen_config.use_skillgen: + # Define the goal for the motion planner: the start of the next subtask. + target_eef_pose = eef_subtask_trajectory[0].pose + target_gripper_action = eef_subtask_trajectory[0].gripper_action + + # Determine expected object attachment using environment-specific logic (optional) + expected_attached_object = None + if hasattr(self.env, "get_expected_attached_object"): + expected_attached_object = self.env.get_expected_attached_object( + eef_name, current_eef_subtask_indices[eef_name], self.env.cfg + ) + + # Plan motion using motion planner with comprehensive world update + # and attachment handling + if motion_planner: + print(f"\n--- Environment {env_id}: Planning motion to target pose ---") + print(f"Target pose: {target_eef_pose}") + print(f"Expected attached object: {expected_attached_object}") + + # This call updates the planner's world model and computes the trajectory. + planning_success = motion_planner.update_world_and_plan_motion( + target_pose=target_eef_pose, + expected_attached_object=expected_attached_object, + env_id=env_id, + step_size=getattr(motion_planner, "step_size", None), + enable_retiming=hasattr(motion_planner, "step_size") + and motion_planner.step_size is not None, + ) + + # If planning succeeds, execute the planner's trajectory first. + if planning_success: + print(f"Env {env_id}: Motion planning succeeded") + # The original subtask trajectory is stored to be executed after the transition. + next_eef_subtask_trajectories_after_motion[eef_name] = eef_subtask_trajectory + next_eef_subtask_indices_after_motion[eef_name] = current_eef_subtask_indices[ + eef_name + ] + # Mark the current subtask as invalid (-1) until the transition is done. + current_eef_subtask_indices[eef_name] = -1 + + # Convert the planner's output into a sequence of waypoints to be executed. + current_eef_subtask_trajectories[eef_name] = ( + self._convert_planned_trajectory_to_waypoints( + motion_planner, target_gripper_action + ) + ) + current_eef_subtask_step_indices[eef_name] = 0 + print( + f"Generated {len(current_eef_subtask_trajectories[eef_name])} waypoints" + " from motion plan" + ) + + else: + # If planning fails, abort the data generation trial. + print(f"Env {env_id}: Motion planning failed for {eef_name}") + return {"success": False} + else: + # Without skillgen, transition using simple interpolation. + current_eef_subtask_trajectories[eef_name] = self.merge_eef_subtask_trajectory( + env_id, + eef_name, + current_eef_subtask_indices[eef_name], + current_eef_subtask_trajectories[eef_name], + eef_subtask_trajectory, + ) + current_eef_subtask_step_indices[eef_name] = 0 + else: + # Motion-planned trajectory has been executed, so we are ready to move to + # execute the next subtask + print("Finished executing motion-planned trajectory") + # It is important to pass the prev_executed_traj to merge_eef_subtask_trajectory + # so that it can correctly interpolate from the last pose of the motion-planned trajectory + prev_executed_traj = current_eef_subtask_trajectories[eef_name] + current_eef_subtask_indices[eef_name] = next_eef_subtask_indices_after_motion[eef_name] + current_eef_subtask_trajectories[eef_name] = self.merge_eef_subtask_trajectory( + env_id, + eef_name, + current_eef_subtask_indices[eef_name], + prev_executed_traj, + next_eef_subtask_trajectories_after_motion[eef_name], + ) + current_eef_subtask_step_indices[eef_name] = 0 + next_eef_subtask_trajectories_after_motion[eef_name] = None + next_eef_subtask_indices_after_motion[eef_name] = None + + # Determine the next waypoint for each eef based on the current subtask constraints + eef_waypoint_dict = {} + for eef_name in sorted(self.env_cfg.subtask_configs.keys()): + # Handle constraints + step_ind = current_eef_subtask_step_indices[eef_name] + subtask_ind = current_eef_subtask_indices[eef_name] + if (eef_name, subtask_ind) in runtime_subtask_constraints_dict: + task_constraint = runtime_subtask_constraints_dict[(eef_name, subtask_ind)] + if task_constraint["type"] == SubTaskConstraintType._SEQUENTIAL_LATTER: + min_time_diff = task_constraint["min_time_diff"] + if not task_constraint["fulfilled"]: + if ( + min_time_diff == -1 + or step_ind >= len(current_eef_subtask_trajectories[eef_name]) - min_time_diff + ): + if step_ind > 0: + # Wait at the same step + step_ind -= 1 + current_eef_subtask_step_indices[eef_name] = step_ind + + elif task_constraint["type"] == SubTaskConstraintType.COORDINATION: + synchronous_steps = task_constraint["synchronous_steps"] + concurrent_task_spec_key = task_constraint["concurrent_task_spec_key"] + concurrent_subtask_ind = task_constraint["concurrent_subtask_ind"] + concurrent_task_fulfilled = runtime_subtask_constraints_dict[ + (concurrent_task_spec_key, concurrent_subtask_ind) + ]["fulfilled"] + + if ( + task_constraint["coordination_synchronize_start"] + and current_eef_subtask_indices[concurrent_task_spec_key] < concurrent_subtask_ind + ): + # The concurrent eef is not yet at the concurrent subtask, so wait at the first action + # This also makes sure that the concurrent task starts at the same time as this task + step_ind = 0 + current_eef_subtask_step_indices[eef_name] = 0 + else: + if ( + not concurrent_task_fulfilled + and step_ind >= len(current_eef_subtask_trajectories[eef_name]) - synchronous_steps + ): + # Trigger concurrent task + runtime_subtask_constraints_dict[(concurrent_task_spec_key, concurrent_subtask_ind)][ + "fulfilled" + ] = True + + if not task_constraint["fulfilled"]: + if step_ind >= len(current_eef_subtask_trajectories[eef_name]) - synchronous_steps: + if step_ind > 0: + step_ind -= 1 + current_eef_subtask_step_indices[eef_name] = step_ind # wait here + + waypoint = current_eef_subtask_trajectories[eef_name][step_ind] + + # Update visualization if motion planner is available + if motion_planner and motion_planner.visualize_spheres: + current_joints = self.env.scene["robot"].data.joint_pos[env_id] + motion_planner._update_visualization_at_joint_positions(current_joints) + + eef_waypoint_dict[eef_name] = waypoint + multi_waypoint = MultiWaypoint(eef_waypoint_dict) + + # Execute the next waypoints for all eefs + exec_results = await multi_waypoint.execute( + env=self.env, + success_term=success_term, + env_id=env_id, + env_action_queue=env_action_queue, + ) + + # Update execution state buffers + if len(exec_results["states"]) > 0: + generated_states.extend(exec_results["states"]) + generated_obs.extend(exec_results["observations"]) + generated_actions.extend(exec_results["actions"]) + generated_success = generated_success or exec_results["success"] + + # Get the navigation state + if self.env_cfg.datagen_config.use_navigation_controller: + processed_nav_subtask = False + navigation_state = self.env.get_navigation_state(env_id) + assert navigation_state is not None, "Navigation state cannot be None when using navigation controller" + is_navigating = navigation_state["is_navigating"] + navigation_goal_reached = navigation_state["navigation_goal_reached"] + + for eef_name in self.env_cfg.subtask_configs.keys(): + current_eef_subtask_step_indices[eef_name] += 1 + + # Execute locomanip navigation controller if it is enabled via the use_navigation_controller flag + if self.env_cfg.datagen_config.use_navigation_controller: + if "body" not in self.env_cfg.subtask_configs.keys(): + error_msg = ( + 'End effector with name "body" not found in subtask configs. "body" must be a valid end' + " effector to use the navigation controller.\n" + ) + logger.error(error_msg) + raise RuntimeError(error_msg) + + # Repeat the last nav subtask action if the robot is navigating and hasn't reached the waypoint goal + if ( + current_eef_subtask_step_indices["body"] == len(current_eef_subtask_trajectories["body"]) - 1 + and not processed_nav_subtask + ): + if is_navigating and not navigation_goal_reached: + for name in self.env_cfg.subtask_configs.keys(): + current_eef_subtask_step_indices[name] -= 1 + processed_nav_subtask = True + + # Else skip to the end of the nav subtask if the robot has reached the waypoint goal before the end + # of the human recorded trajectory + elif was_navigating and not is_navigating and not processed_nav_subtask: + number_of_steps_to_skip = len(current_eef_subtask_trajectories["body"]) - ( + current_eef_subtask_step_indices["body"] + 1 + ) + for name in self.env_cfg.subtask_configs.keys(): + if current_eef_subtask_step_indices[name] + number_of_steps_to_skip < len( + current_eef_subtask_trajectories[name] + ): + current_eef_subtask_step_indices[name] = ( + current_eef_subtask_step_indices[name] + number_of_steps_to_skip + ) + else: + current_eef_subtask_step_indices[name] = len(current_eef_subtask_trajectories[name]) - 1 + processed_nav_subtask = True + + subtask_ind = current_eef_subtask_indices[eef_name] + if current_eef_subtask_step_indices[eef_name] == len( + current_eef_subtask_trajectories[eef_name] + ): # Subtask done + if (eef_name, subtask_ind) in runtime_subtask_constraints_dict: + task_constraint = runtime_subtask_constraints_dict[(eef_name, subtask_ind)] + if task_constraint["type"] == SubTaskConstraintType._SEQUENTIAL_FORMER: + constrained_task_spec_key = task_constraint["constrained_task_spec_key"] + constrained_subtask_ind = task_constraint["constrained_subtask_ind"] + runtime_subtask_constraints_dict[(constrained_task_spec_key, constrained_subtask_ind)][ + "fulfilled" + ] = True + elif task_constraint["type"] == SubTaskConstraintType.COORDINATION: + concurrent_task_spec_key = task_constraint["concurrent_task_spec_key"] + concurrent_subtask_ind = task_constraint["concurrent_subtask_ind"] + # Concurrent_task_spec_idx = task_spec_keys.index(concurrent_task_spec_key) + task_constraint["finished"] = True + # Check if concurrent task has been finished + assert ( + runtime_subtask_constraints_dict[(concurrent_task_spec_key, concurrent_subtask_ind)][ + "finished" + ] + or current_eef_subtask_step_indices[concurrent_task_spec_key] + >= len(current_eef_subtask_trajectories[concurrent_task_spec_key]) - 1 + ) + + if pause_subtask: + input( + f"Pausing after subtask {current_eef_subtask_indices[eef_name]} of {eef_name} execution." + " Press any key to continue..." + ) + # This is a check to see if this arm has completed all the subtasks + if current_eef_subtask_indices[eef_name] == len(self.env_cfg.subtask_configs[eef_name]) - 1: + eef_subtasks_done[eef_name] = True + # If all subtasks done for this arm, repeat last waypoint to make sure this arm does not move + current_eef_subtask_trajectories[eef_name].append( + current_eef_subtask_trajectories[eef_name][-1] + ) + else: + current_eef_subtask_step_indices[eef_name] = None + current_eef_subtask_indices[eef_name] += 1 + + if self.env_cfg.datagen_config.use_navigation_controller: + was_navigating = copy.deepcopy(is_navigating) + + # Check if all eef_subtasks_done values are True + if all(eef_subtasks_done.values()): + break + + # Merge numpy arrays + if len(generated_actions) > 0: + generated_actions = torch.cat(generated_actions, dim=0) + + # Set success to the recorded episode data and export to file + self.env.recorder_manager.set_success_to_episodes( + env_id_tensor, torch.tensor([[generated_success]], dtype=torch.bool, device=self.env.device) + ) + if export_demo: + self.env.recorder_manager.export_episodes(env_id_tensor) + + results = dict( + initial_state=new_initial_state, + states=generated_states, + observations=generated_obs, + actions=generated_actions, + success=generated_success, + ) + return results + + def _convert_planned_trajectory_to_waypoints( + self, motion_planner: Any, gripper_action: torch.Tensor + ) -> list[Waypoint]: + """ + (skillgen) Convert a motion planner's output trajectory into a list of Waypoint objects. + + The motion planner provides a sequence of planned 4x4 poses. This method wraps each + pose into a `Waypoint`, pairing it with the provided `gripper_action` and an optional + per-timestep noise value sourced from the planner config (`motion_noise_scale`). + + Args: + motion_planner: Planner instance exposing `get_planned_poses()` and an optional + `config.motion_noise_scale` float. + gripper_action: Gripper actuation to associate with each planned pose. + + Returns: + list[Waypoint]: Sequence of waypoints corresponding to the planned trajectory. + """ + # Get motion noise scale from the planner's configuration + motion_noise_scale = getattr(motion_planner.config, "motion_noise_scale", 0.0) + + waypoints = [] + planned_poses = motion_planner.get_planned_poses() + + for planned_pose in planned_poses: + waypoint = Waypoint(pose=planned_pose, gripper_action=gripper_action, noise=motion_noise_scale) + waypoints.append(waypoint) + + return waypoints diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info.py new file mode 100644 index 0000000000000000000000000000000000000000..7e94f3e93838e0f274409cdda610f304e9cd9e02 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info.py @@ -0,0 +1,112 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Defines the structure of information required from an environment for data generation processes.""" + +from copy import deepcopy + + +class DatagenInfo: + """Defines the structure of information required from an environment for data generation processes. + + The :class:`DatagenInfo` class centralizes all essential data elements needed for data generation in one place, + reducing the overhead and complexity of repeatedly querying the environment whenever this information + is needed. + + To allow for flexibility,not all information must be present. + + Core Elements: + + - **eef_pose**: Captures the current 6 dimensional poses of the robot's end-effector. + - **object_poses**: Captures the 6 dimensional poses of relevant objects in the scene. + - **subtask_start_signals**: Captures subtask start signals. Used by skillgen to identify + the precise start of a subtask from a demonstration. + - **subtask_term_signals**: Captures subtask completions signals. + - **target_eef_pose**: Captures the target 6 dimensional poses for robot's end effector at each time step. + - **gripper_action**: Captures the gripper's state. + """ + + def __init__( + self, + eef_pose=None, + object_poses=None, + subtask_term_signals=None, + subtask_start_signals=None, + target_eef_pose=None, + gripper_action=None, + ): + """Initialize the DatagenInfo object. + + Args: + eef_pose (torch.Tensor or None): robot end effector poses of shape [..., 4, 4] + object_poses (dict or None): dictionary mapping object name to object poses + of shape [..., 4, 4] + subtask_start_signals (dict or None): dictionary mapping subtask name to a binary + indicator (0 or 1) on whether subtask has started. This is required when using skillgen. + Each value in the dictionary could be an int, float, or torch.Tensor of shape [..., 1]. + subtask_term_signals (dict or None): dictionary mapping subtask name to a binary + indicator (0 or 1) on whether subtask has been completed. Each value in the + dictionary could be an int, float, or torch.Tensor of shape [..., 1]. + target_eef_pose (torch.Tensor or None): target end effector poses of shape [..., 4, 4] + gripper_action (torch.Tensor or None): gripper actions of shape [..., D] where D + is the dimension of the gripper actuation action for the robot arm + """ + self.eef_pose = None + if eef_pose is not None: + self.eef_pose = eef_pose + + self.object_poses = None + if object_poses is not None: + self.object_poses = {k: object_poses[k] for k in object_poses} + + # When using skillgen, demonstrations must be annotated with subtask start signals. + self.subtask_start_signals = None + if subtask_start_signals is not None: + self.subtask_start_signals = dict() + for k in subtask_start_signals: + if isinstance(subtask_start_signals[k], (float, int)): + self.subtask_start_signals[k] = subtask_start_signals[k] + else: + # Only create torch tensor if value is not a single value + self.subtask_start_signals[k] = subtask_start_signals[k] + + self.subtask_term_signals = None + if subtask_term_signals is not None: + self.subtask_term_signals = dict() + for k in subtask_term_signals: + if isinstance(subtask_term_signals[k], (float, int)): + self.subtask_term_signals[k] = subtask_term_signals[k] + else: + # only create torch tensor if value is not a single value + self.subtask_term_signals[k] = subtask_term_signals[k] + + self.target_eef_pose = None + if target_eef_pose is not None: + self.target_eef_pose = target_eef_pose + + self.gripper_action = None + if gripper_action is not None: + self.gripper_action = gripper_action + + def to_dict(self) -> dict: + """Convert this instance to a dictionary containing the same information. + + Returns: + A dictionary containing the same information as this instance. + """ + ret = dict() + if self.eef_pose is not None: + ret["eef_pose"] = self.eef_pose + if self.object_poses is not None: + ret["object_poses"] = deepcopy(self.object_poses) + if self.subtask_start_signals is not None: + ret["subtask_start_signals"] = deepcopy(self.subtask_start_signals) + if self.subtask_term_signals is not None: + ret["subtask_term_signals"] = deepcopy(self.subtask_term_signals) + if self.target_eef_pose is not None: + ret["target_eef_pose"] = self.target_eef_pose + if self.gripper_action is not None: + ret["gripper_action"] = self.gripper_action + return ret diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info_pool.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..5901944929d6b8c1d7c70a0be5e713cf22f1f5bb --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/datagen_info_pool.py @@ -0,0 +1,225 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import asyncio + +from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler + +from isaaclab_mimic.datagen.datagen_info import DatagenInfo + + +class DataGenInfoPool: + """ + Pool of DatagenInfo for data generation. + + This class is a container for storing `DatagenInfo` objects that are extracted from episodes. + The pool supports the use of an asyncio lock to safely add new episodes to the pool while + consuming the data, so it can be shared across multiple mimic data generators. + """ + + def __init__(self, env, env_cfg, device, asyncio_lock: asyncio.Lock | None = None): + """ + Args: + env_cfg (dict): environment configuration + device (torch.device): device to store the data + asyncio_lock (asyncio.Lock or None): asyncio lock to use for thread safety + """ + self._datagen_infos = [] + + # Start and end step indices of each subtask in each episode for each eef + self._subtask_boundaries: dict[str, list[list[tuple[int, int]]]] = {} + + self.env = env + self.env_cfg = env_cfg + self.device = device + + self._asyncio_lock = asyncio_lock + + # Subtask termination infos for the given environment + self.subtask_term_signal_names: dict[str, list[str]] = {} + self.subtask_term_offset_ranges: dict[str, list[tuple[int, int]]] = {} + self.subtask_start_offset_ranges: dict[str, list[tuple[int, int]]] = {} + + for eef_name, eef_subtask_configs in env_cfg.subtask_configs.items(): + self.subtask_term_signal_names[eef_name] = [ + subtask_config.subtask_term_signal for subtask_config in eef_subtask_configs + ] + self.subtask_start_offset_ranges[eef_name] = [ + subtask_config.subtask_start_offset_range for subtask_config in eef_subtask_configs + ] + self.subtask_term_offset_ranges[eef_name] = [ + subtask_config.subtask_term_offset_range for subtask_config in eef_subtask_configs + ] + + @property + def datagen_infos(self): + """Returns the datagen infos.""" + return self._datagen_infos + + @property + def subtask_boundaries(self) -> dict[str, list[list[tuple[int, int]]]]: + """Returns the subtask boundaries.""" + return self._subtask_boundaries + + @property + def asyncio_lock(self): + """Returns the asyncio lock.""" + return self._asyncio_lock + + @property + def num_datagen_infos(self): + """Returns the number of datagen infos.""" + return len(self._datagen_infos) + + async def add_episode(self, episode: EpisodeData): + """ + Add a datagen info from the given episode. + + Args: + episode (EpisodeData): episode to add + """ + if self._asyncio_lock is not None: + async with self._asyncio_lock: + self._add_episode(episode) + else: + self._add_episode(episode) + + def _add_episode(self, episode: EpisodeData): + """ + Add a datagen info from the given episode. + + Args: + episode: Episode to add. + + Raises: + ValueError: Episode lacks 'datagen_info' annotations in observations. + ValueError: Subtask termination signal is not increasing. + """ + ep_grp = episode.data + + # Extract datagen info + if "datagen_info" in ep_grp["obs"]: + eef_pose = ep_grp["obs"]["datagen_info"]["eef_pose"] + object_poses_dict = ep_grp["obs"]["datagen_info"]["object_pose"] + target_eef_pose = ep_grp["obs"]["datagen_info"]["target_eef_pose"] + subtask_term_signals_dict = ep_grp["obs"]["datagen_info"]["subtask_term_signals"] + # subtask_start_signals is optional + subtask_start_signals_dict = ep_grp["obs"]["datagen_info"].get("subtask_start_signals") + else: + raise ValueError("Episode to be loaded to DatagenInfo pool lacks datagen_info annotations") + + # Extract gripper actions + gripper_actions = self.env.actions_to_gripper_actions(ep_grp["actions"]) + + ep_datagen_info_obj = DatagenInfo( + eef_pose=eef_pose, + object_poses=object_poses_dict, + subtask_start_signals=subtask_start_signals_dict, + subtask_term_signals=subtask_term_signals_dict, + target_eef_pose=target_eef_pose, + gripper_action=gripper_actions, + ) + self._datagen_infos.append(ep_datagen_info_obj) + + # Parse subtask ranges using subtask termination signals and store + # the start and end indices of each subtask for each eef + for eef_name in self.subtask_term_signal_names.keys(): + if eef_name not in self._subtask_boundaries: + self._subtask_boundaries[eef_name] = [] + prev_subtask_term_index = 0 + eef_subtask_boundaries = [] + for eef_subtask_index, eef_subtask_signal_name in enumerate(self.subtask_term_signal_names[eef_name]): + if self.env_cfg.datagen_config.use_skillgen: + # For skillgen, the start of a subtask is explicitly defined in the demonstration data. + if ep_datagen_info_obj.subtask_start_signals is None: + raise ValueError( + "subtask_start_signals field is not present in datagen_info for subtask" + f" {eef_subtask_signal_name} in the loaded episode when use_skillgen is enabled" + ) + # Find the first time step where the start signal transitions from 0 to 1. + subtask_start_indicators = ( + ep_datagen_info_obj.subtask_start_signals[eef_subtask_signal_name].flatten().int() + ) + # Compute the difference between consecutive elements to find the transition point. + diffs = subtask_start_indicators[1:] - subtask_start_indicators[:-1] + # The first non-zero element's index gives the start of the subtask. + start_index = int(diffs.nonzero()[0][0]) + 1 + else: + # Without skillgen, subtasks are assumed to be sequential. + start_index = prev_subtask_term_index + + if eef_subtask_index == len(self.subtask_term_signal_names[eef_name]) - 1: + # Last subtask has no termination signal from the datagen_info + end_index = ep_grp["actions"].shape[0] + else: + # Trick to detect index where first 0 -> 1 transition occurs - this will be the end of the subtask + subtask_term_indicators = ( + ep_datagen_info_obj.subtask_term_signals[eef_subtask_signal_name].flatten().int() + ) + diffs = subtask_term_indicators[1:] - subtask_term_indicators[:-1] + end_index = int(diffs.nonzero()[0][0]) + 1 + end_index = end_index + 1 # increment to support indexing like demo[start:end] + + if end_index <= start_index: + raise ValueError( + f"subtask termination signal is not increasing: {end_index} should be greater than" + f" {start_index}" + ) + eef_subtask_boundaries.append((start_index, end_index)) + prev_subtask_term_index = end_index + + if self.env_cfg.datagen_config.use_skillgen: + # With skillgen, both start and end boundaries can be randomized. + # This checks if the randomized boundaries could result in an invalid (e.g., empty) subtask. + for i in range(len(eef_subtask_boundaries)): + # Ensure that a subtask is not empty in the worst-case randomization scenario. + assert ( + eef_subtask_boundaries[i][0] + self.subtask_start_offset_ranges[eef_name][i][1] + < eef_subtask_boundaries[i][1] + self.subtask_term_offset_ranges[eef_name][i][0] + ), f"subtask {i} is empty in the worst case" + if i == len(eef_subtask_boundaries) - 1: + break + # Make sure that subtasks are not overlapped with the largest offsets + assert ( + eef_subtask_boundaries[i][1] + self.subtask_term_offset_ranges[eef_name][i][1] + < eef_subtask_boundaries[i + 1][0] + self.subtask_start_offset_ranges[eef_name][i + 1][0] + ), f"subtasks {i} and {i + 1} are overlapped with the largest offsets" + else: + # Run sanity check on subtask_term_offset_range in task spec + for i in range(1, len(eef_subtask_boundaries)): + prev_max_offset_range = self.subtask_term_offset_ranges[eef_name][i - 1][1] + # Make sure that subtasks are not overlapped with the largest offsets + assert ( + eef_subtask_boundaries[i - 1][1] + prev_max_offset_range + < eef_subtask_boundaries[i][1] + self.subtask_term_offset_ranges[eef_name][i][0] + ), ( + f"subtask sanity check violation in demo with subtask {i - 1} end ind" + f" {eef_subtask_boundaries[i - 1][1]}, subtask {i - 1} max offset {prev_max_offset_range}," + f" subtask {i} end ind {eef_subtask_boundaries[i][1]}, and subtask {i} min offset" + f" {self.subtask_term_offset_ranges[eef_name][i][0]}" + ) + + self._subtask_boundaries[eef_name].append(eef_subtask_boundaries) + + def load_from_dataset_file(self, file_path, select_demo_keys: str | None = None): + """ + Load from a dataset file. + + Args: + file_path (str): path to the dataset file + select_demo_keys (str or None): keys of the demos to load + """ + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(file_path) + episode_names = dataset_file_handler.get_episode_names() + + if len(episode_names) == 0: + return + + for episode_name in episode_names: + if select_demo_keys is not None and episode_name not in select_demo_keys: + continue + episode = dataset_file_handler.load_episode(episode_name, self.device) + self._add_episode(episode) diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py new file mode 100644 index 0000000000000000000000000000000000000000..18d1f6716d4a5dfe90d61a89fa2c59037d386c94 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py @@ -0,0 +1,262 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import asyncio +import contextlib +import sys +import traceback +from typing import Any + +import torch + +from isaaclab.envs import ManagerBasedRLMimicEnv +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.managers import DatasetExportMode, TerminationTermCfg +from isaaclab.managers.recorder_manager import RecorderManagerBaseCfg + +from isaaclab_mimic.datagen.data_generator import DataGenerator +from isaaclab_mimic.datagen.datagen_info_pool import DataGenInfoPool + +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# global variable to keep track of the data generation statistics +num_success = 0 +num_failures = 0 +num_attempts = 0 + + +async def run_data_generator( + env: ManagerBasedRLMimicEnv, + env_id: int, + env_reset_queue: asyncio.Queue, + env_action_queue: asyncio.Queue, + data_generator: DataGenerator, + success_term: TerminationTermCfg, + pause_subtask: bool = False, + motion_planner: Any = None, +): + """Run mimic data generation from the given data generator in the specified environment index. + + Args: + env: The environment to run the data generator on. + env_id: The environment index to run the data generation on. + env_reset_queue: The asyncio queue to send environment (for this particular env_id) reset requests to. + env_action_queue: The asyncio queue to send actions to for executing actions. + data_generator: The data generator instance to use. + success_term: The success termination term to use. + pause_subtask: Whether to pause the subtask during generation. + motion_planner: The motion planner to use. + """ + global num_success, num_failures, num_attempts + while True: + try: + results = await data_generator.generate( + env_id=env_id, + success_term=success_term, + env_reset_queue=env_reset_queue, + env_action_queue=env_action_queue, + pause_subtask=pause_subtask, + motion_planner=motion_planner, + ) + except Exception as e: + sys.stderr.write(traceback.format_exc()) + sys.stderr.flush() + raise e + + if bool(results["success"]): + num_success += 1 + else: + num_failures += 1 + num_attempts += 1 + + +def env_loop( + env: ManagerBasedRLMimicEnv, + env_reset_queue: asyncio.Queue, + env_action_queue: asyncio.Queue, + shared_datagen_info_pool: DataGenInfoPool, + asyncio_event_loop: asyncio.AbstractEventLoop, +): + """Main asyncio loop for the environment. + + Args: + env: The environment to run the main step loop on. + env_reset_queue: The asyncio queue to handle reset request the environment. + env_action_queue: The asyncio queue to handle actions to for executing actions. + shared_datagen_info_pool: The shared datagen info pool that stores source demo info. + asyncio_event_loop: The main asyncio event loop. + """ + global num_success, num_failures, num_attempts + env_id_tensor = torch.tensor([0], dtype=torch.int64, device=env.device) + prev_num_attempts = 0 + # simulate environment -- run everything in inference mode + with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode(): + while True: + # check if any environment needs to be reset while waiting for actions + while env_action_queue.qsize() != env.num_envs: + asyncio_event_loop.run_until_complete(asyncio.sleep(0)) + while not env_reset_queue.empty(): + env_id_tensor[0] = env_reset_queue.get_nowait() + env.reset(env_ids=env_id_tensor) + env_reset_queue.task_done() + + actions = torch.zeros(env.action_space.shape) + + # get actions from all the data generators + for i in range(env.num_envs): + # an async-blocking call to get an action from a data generator + env_id, action = asyncio_event_loop.run_until_complete(env_action_queue.get()) + actions[env_id] = action + + # perform action on environment + env.step(actions) + + # mark done so the data generators can continue with the step results + for i in range(env.num_envs): + env_action_queue.task_done() + + if prev_num_attempts != num_attempts: + prev_num_attempts = num_attempts + generated_sucess_rate = 100 * num_success / num_attempts if num_attempts > 0 else 0.0 + print("") + print("*" * 50, "\033[K") + print( + f"{num_success}/{num_attempts} ({generated_sucess_rate:.1f}%) successful demos generated by" + " mimic\033[K" + ) + print("*" * 50, "\033[K") + + # termination condition is on enough successes if @guarantee_success or enough attempts otherwise + generation_guarantee = env.cfg.datagen_config.generation_guarantee + generation_num_trials = env.cfg.datagen_config.generation_num_trials + check_val = num_success if generation_guarantee else num_attempts + if check_val >= generation_num_trials: + print(f"Reached {generation_num_trials} successes/attempts. Exiting.") + break + + # check that simulation is stopped or not + if env.sim.is_stopped(): + break + + env.close() + + +def setup_env_config( + env_name: str, + output_dir: str, + output_file_name: str, + num_envs: int, + device: str, + generation_num_trials: int | None = None, + recorder_cfg: RecorderManagerBaseCfg | None = None, +) -> tuple[Any, Any]: + """Configure the environment for data generation. + + Args: + env_name: Name of the environment + output_dir: Directory to save output + output_file_name: Name of output file + num_envs: Number of environments to run + device: Device to run on + generation_num_trials: Optional override for number of trials + + Returns: + tuple containing: + - env_cfg: The environment configuration + - success_term: The success termination condition + + Raises: + NotImplementedError: If no success termination term found + """ + env_cfg = parse_env_cfg(env_name, device=device, num_envs=num_envs) + + if generation_num_trials is not None: + env_cfg.datagen_config.generation_num_trials = generation_num_trials + + env_cfg.env_name = env_name + + # Extract success checking function + success_term = None + if hasattr(env_cfg.terminations, "success"): + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + else: + raise NotImplementedError("No success termination term was found in the environment.") + + # Configure for data generation + env_cfg.terminations = None + env_cfg.observations.policy.concatenate_terms = False + + # Setup recorders + if recorder_cfg is None: + env_cfg.recorders = ActionStateRecorderManagerCfg() + else: + env_cfg.recorders = recorder_cfg + env_cfg.recorders.dataset_export_dir_path = output_dir + env_cfg.recorders.dataset_filename = output_file_name + + if env_cfg.datagen_config.generation_keep_failed: + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_SUCCEEDED_FAILED_IN_SEPARATE_FILES + else: + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_SUCCEEDED_ONLY + + return env_cfg, success_term + + +def setup_async_generation( + env: Any, + num_envs: int, + input_file: str, + success_term: Any, + pause_subtask: bool = False, + motion_planners: Any = None, +) -> dict[str, Any]: + """Setup async data generation tasks. + + Args: + env: The environment instance + num_envs: Number of environments to run + input_file: Path to input dataset file + success_term: Success termination condition + pause_subtask: Whether to pause after subtasks + motion_planners: Motion planner instances for all environments + + Returns: + List of asyncio tasks for data generation + """ + asyncio_event_loop = asyncio.get_event_loop() + env_reset_queue = asyncio.Queue() + env_action_queue = asyncio.Queue() + shared_datagen_info_pool_lock = asyncio.Lock() + shared_datagen_info_pool = DataGenInfoPool(env, env.cfg, env.device, asyncio_lock=shared_datagen_info_pool_lock) + shared_datagen_info_pool.load_from_dataset_file(input_file) + print(f"Loaded {shared_datagen_info_pool.num_datagen_infos} to datagen info pool") + + # Create and schedule data generator tasks + data_generator = DataGenerator(env=env, src_demo_datagen_info_pool=shared_datagen_info_pool) + data_generator_asyncio_tasks = [] + for i in range(num_envs): + env_motion_planner = motion_planners[i] if motion_planners else None + task = asyncio_event_loop.create_task( + run_data_generator( + env, + i, + env_reset_queue, + env_action_queue, + data_generator, + success_term, + pause_subtask=pause_subtask, + motion_planner=env_motion_planner, + ) + ) + data_generator_asyncio_tasks.append(task) + + return { + "tasks": data_generator_asyncio_tasks, + "event_loop": asyncio_event_loop, + "reset_queue": env_reset_queue, + "action_queue": env_action_queue, + "info_pool": shared_datagen_info_pool, + } diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/selection_strategy.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/selection_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..7c8683c64379e6845c3551265ffaac40e9679fc7 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/selection_strategy.py @@ -0,0 +1,310 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +Selection strategies used by Isaac Lab Mimic to select subtask segments from +source human demonstrations. +""" + +import abc # for abstract base class definitions + +import torch + +import isaaclab.utils.math as PoseUtils + +# Global dictionary for remembering name to class mappings. +REGISTERED_SELECTION_STRATEGIES = {} + + +def make_selection_strategy(name, *args, **kwargs): + """ + Creates an instance of a selection strategy class, specified by @name, + which is used to look it up in the registry. + """ + assert_selection_strategy_exists(name) + return REGISTERED_SELECTION_STRATEGIES[name](*args, **kwargs) + + +def register_selection_strategy(cls): + """ + Register selection strategy class into global registry. + """ + ignore_classes = ["SelectionStrategy"] + if cls.__name__ not in ignore_classes: + REGISTERED_SELECTION_STRATEGIES[cls.NAME] = cls + + +def assert_selection_strategy_exists(name): + """ + Allow easy way to check if selection strategy exists. + """ + if name not in REGISTERED_SELECTION_STRATEGIES: + raise Exception( + "assert_selection_strategy_exists: name {} not found. Make sure it is a registered selection strategy" + " among {}".format(name, ", ".join(REGISTERED_SELECTION_STRATEGIES)) + ) + + +class SelectionStrategyMeta(type): + """ + This metaclass adds selection strategy classes into the global registry. + """ + + def __new__(meta, name, bases, class_dict): + cls = super().__new__(meta, name, bases, class_dict) + register_selection_strategy(cls) + return cls + + +class SelectionStrategy(metaclass=SelectionStrategyMeta): + """ + Defines methods and functions for selection strategies to implement. + """ + + def __init__(self): + pass + + @property + @classmethod + def NAME(self): + """ + This name (str) will be used to register the selection strategy class in the global + registry. + """ + raise NotImplementedError + + @abc.abstractmethod + def select_source_demo( + self, + eef_pose, + object_pose, + src_subtask_datagen_infos, + ): + """ + Selects source demonstration index using the current robot pose, relevant object pose + for the current subtask, and relevant information from the source demonstrations for the + current subtask. + + Args: + eef_pose (torch.Tensor): current 4x4 eef pose + object_pose (torch.Tensor): current 4x4 object pose, for the object in this subtask + src_subtask_datagen_infos (list): DatagenInfo instance for the relevant subtask segment + in the source demonstrations + + Returns: + source_demo_ind (int): index of source demonstration - indicates which source subtask segment to use + """ + raise NotImplementedError + + +class RandomStrategy(SelectionStrategy): + """ + Pick source demonstration randomly. + """ + + # name for registering this class into registry + NAME = "random" + + def select_source_demo( + self, + eef_pose, + object_pose, + src_subtask_datagen_infos, + ): + """ + Selects source demonstration index using the current robot pose, relevant object pose + for the current subtask, and relevant information from the source demonstrations for the + current subtask. + + Args: + eef_pose (torch.Tensor): current 4x4 eef pose + object_pose (torch.Tensor): current 4x4 object pose, for the object in this subtask + src_subtask_datagen_infos (list): DatagenInfo instance for the relevant subtask segment + in the source demonstrations + + Returns: + source_demo_ind (int): index of source demonstration - indicates which source subtask segment to use + """ + + # random selection + n_src_demo = len(src_subtask_datagen_infos) + return torch.randint(0, n_src_demo, (1,)).item() + + +class NearestNeighborObjectStrategy(SelectionStrategy): + """ + Pick source demonstration to be the one with the closest object pose to the object + in the current scene. + """ + + # name for registering this class into registry + NAME = "nearest_neighbor_object" + + def select_source_demo( + self, + eef_pose, + object_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=3, + ): + """ + Selects source demonstration index using the current robot pose, relevant object pose + for the current subtask, and relevant information from the source demonstrations for the + current subtask. + + Args: + eef_pose (torch.Tensor): current 4x4 eef pose + object_pose (torch.Tensor): current 4x4 object pose, for the object in this subtask + src_subtask_datagen_infos (list): DatagenInfo instance for the relevant subtask segment + in the source demonstrations + pos_weight (float): weight on position for minimizing pose distance + rot_weight (float): weight on rotation for minimizing pose distance + nn_k (int): pick source demo index uniformly at randomly from the top @nn_k nearest neighbors + + Returns: + source_demo_ind (int): index of source demonstration - indicates which source subtask segment to use + """ + + # collect object poses from start of subtask source segments into tensor of shape [N, 4, 4] + src_object_poses = [] + for di in src_subtask_datagen_infos: + src_obj_pose = list(di.object_poses.values()) + assert len(src_obj_pose) == 1 + # use object pose at start of subtask segment + src_object_poses.append(src_obj_pose[0][0]) + src_object_poses = torch.stack(src_object_poses) + + # split into positions and rotations + all_src_obj_pos, all_src_obj_rot = PoseUtils.unmake_pose(src_object_poses) + obj_pos, obj_rot = PoseUtils.unmake_pose(object_pose) + + # prepare for broadcasting + obj_pos = obj_pos.view(-1, 3) + obj_rot_T = obj_rot.transpose(0, 1).view(-1, 3, 3) + + # pos dist is just L2 between positions + pos_dists = torch.sqrt(((all_src_obj_pos - obj_pos) ** 2).sum(dim=-1)) + + # get angle (in axis-angle representation of delta rotation matrix) using the following formula + # (see http://www.boris-belousov.net/2016/12/01/quat-dist/) + + # batched matrix mult, [N, 3, 3] x [1, 3, 3] -> [N, 3, 3] + delta_R = torch.matmul(all_src_obj_rot, obj_rot_T) + arc_cos_in = (torch.diagonal(delta_R, dim1=-2, dim2=-1).sum(dim=-1) - 1.0) / 2.0 + arc_cos_in = torch.clamp(arc_cos_in, -1.0, 1.0) # clip for numerical stability + rot_dists = torch.acos(arc_cos_in) + + # weight distances with coefficients + dists_to_minimize = pos_weight * pos_dists + rot_weight * rot_dists + + # clip top-k parameter to max possible value + nn_k = min(nn_k, len(dists_to_minimize)) + + # return one of the top-K nearest neighbors uniformly at random + rand_k = torch.randint(0, nn_k, (1,)).item() + top_k_neighbors_in_order = torch.argsort(dists_to_minimize)[:nn_k] + return top_k_neighbors_in_order[rand_k] + + +class NearestNeighborRobotDistanceStrategy(SelectionStrategy): + """ + Pick source demonstration to be the one that minimizes the distance the robot + end effector will need to travel from the current pose to the first pose + in the transformed segment. + """ + + # name for registering this class into registry + NAME = "nearest_neighbor_robot_distance" + + def select_source_demo( + self, + eef_pose, + object_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=3, + ): + """ + Selects source demonstration index using the current robot pose, relevant object pose + for the current subtask, and relevant information from the source demonstrations for the + current subtask. + + Args: + eef_pose (torch.Tensor): current 4x4 eef pose + object_pose (torch.Tensor): current 4x4 object pose, for the object in this subtask + src_subtask_datagen_infos (list): DatagenInfo instance for the relevant subtask segment + in the source demonstrations + pos_weight (float): weight on position for minimizing pose distance + rot_weight (float): weight on rotation for minimizing pose distance + nn_k (int): pick source demo index uniformly at randomly from the top @nn_k nearest neighbors + + Returns: + source_demo_ind (int): index of source demonstration - indicates which source subtask segment to use + """ + + # collect eef and object poses from start of subtask source segments into tensors of shape [N, 4, 4] + src_eef_poses = [] + src_object_poses = [] + for di in src_subtask_datagen_infos: + # use eef pose at start of subtask segment + src_eef_poses.append(di.eef_pose[0]) + # use object pose at start of subtask segment + src_obj_pose = list(di.object_poses.values()) + assert len(src_obj_pose) == 1 + src_object_poses.append(src_obj_pose[0][0]) + src_eef_poses = torch.stack(src_eef_poses) + src_object_poses = torch.stack(src_object_poses) + + # Get source eef poses with respect to object frames. + # note: frame A is world, frame B is object + src_object_poses_inv = PoseUtils.pose_inv(src_object_poses) + src_eef_poses_in_obj = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_poses, + pose_A_in_B=src_object_poses_inv, + ) + + # Use this to find the first pose for the transformed subtask segment for each source demo. + # Note this is the same logic used in PoseUtils.transform_poses_from_frame_A_to_frame_B + transformed_eef_poses = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_poses_in_obj, + pose_A_in_B=object_pose, + ) + + # split into positions and rotations + all_transformed_eef_pos, all_transformed_eef_rot = PoseUtils.unmake_pose(transformed_eef_poses) + eef_pos, eef_rot = PoseUtils.unmake_pose(eef_pose) + + # now measure distance from each of these transformed eef poses to our current eef pose + # and choose the source demo that minimizes this distance + + # prepare for broadcasting + eef_pos = eef_pos.view(-1, 3) + eef_rot_T = eef_rot.transpose(0, 1).view(-1, 3, 3) + + # pos dist is just L2 between positions + pos_dists = torch.sqrt(((all_transformed_eef_pos - eef_pos) ** 2).sum(dim=-1)) + + # get angle (in axis-angle representation of delta rotation matrix) using the following formula + # (see http://www.boris-belousov.net/2016/12/01/quat-dist/) + + # batched matrix mult, [N, 3, 3] x [1, 3, 3] -> [N, 3, 3] + delta_R = torch.matmul(all_transformed_eef_rot, eef_rot_T) + arc_cos_in = (torch.diagonal(delta_R, dim1=-2, dim2=-1).sum(dim=-1) - 1.0) / 2.0 + arc_cos_in = torch.clamp(arc_cos_in, -1.0, 1.0) # clip for numerical stability + rot_dists = torch.acos(arc_cos_in) + + # weight distances with coefficients + dists_to_minimize = pos_weight * pos_dists + rot_weight * rot_dists + + # clip top-k parameter to max possible value + nn_k = min(nn_k, len(dists_to_minimize)) + + # return one of the top-K nearest neighbors uniformly at random + rand_k = torch.randint(0, nn_k, (1,)).item() + top_k_neighbors_in_order = torch.argsort(dists_to_minimize)[:nn_k] + return top_k_neighbors_in_order[rand_k] diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/utils.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5430b75597cb359330b47638d0b21b6cd3603b7d --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/utils.py @@ -0,0 +1,218 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 +from __future__ import annotations + +import os +from collections.abc import Callable +from typing import Any + +from IPython.display import display +from ipywidgets import widgets + +from isaaclab.envs import ManagerBasedEnv +from isaaclab.managers import EventTermCfg +from isaaclab.utils.datasets import HDF5DatasetFileHandler + + +def get_nested_value(d: dict[str, Any], keys: list[str]) -> Any: + """Retrieve a nested value from dictionary d using list of keys.""" + for k in keys: + d = d[k] + return d + + +def update_nested_value(d: dict[str, Any], keys: list[str], value: Any) -> None: + """Update a nested value in dictionary d using list of keys.""" + for k in keys[:-1]: + d = d.setdefault(k, {}) + d[keys[-1]] = value + + +def reset_env(env: ManagerBasedEnv, steps: int = 1) -> None: + """Reset environment and step simulation to stabilize state.""" + # Get sim and scene from unwrapped environment + sim = env.unwrapped.sim + scene = env.unwrapped.scene + + # Reset environment + env.reset() + + # Step simulation multiple times to stabilize + for _ in range(steps): + # Write data to sim + scene.write_data_to_sim() + # Perform step + sim.step() + # Update buffers + scene.update(dt=env.physics_dt) + + +def get_parameter_input( + param_name: str, + current_val: float | tuple[float, float] | list[float], + allowed_range: tuple[float, float, float | None], + update_fn: Callable[[float | tuple[float, float]], None], + env: ManagerBasedEnv | None = None, + event_term_name: str | None = None, +) -> widgets.FloatSlider | widgets.FloatRangeSlider: + """Get parameter input using ipywidgets with immediate value updates.""" + + if isinstance(current_val, (tuple, list)): + step_size = allowed_range[2] if len(allowed_range) > 2 else 0.01 + full_param_name = f"{event_term_name}.{param_name}" if event_term_name else param_name + + # Create container with label and range slider + container = widgets.HBox( + [ + widgets.Label(full_param_name, layout=widgets.Layout(width="auto")), + widgets.FloatRangeSlider( + value=[current_val[0], current_val[1]], + min=allowed_range[0], + max=allowed_range[1], + step=step_size, + layout=widgets.Layout(width="300px"), + readout=True, + readout_format=".3f", + ), + ] + ) + + def on_value_change(change): + new_tuple = (change["new"][0], change["new"][1]) + update_fn(new_tuple) + if env is not None: + reset_env(env, steps=50) + + container.children[1].observe(on_value_change, names="value") + + # Create help text showing the allowed range + help_text = widgets.HTML(value=f'

Allowed range: {allowed_range[:2]}

') + + display(container) + display(help_text) + + return container.children[1] + else: + step_size = allowed_range[2] if len(allowed_range) > 2 else 0.01 + full_param_name = f"{event_term_name}.{param_name}" if event_term_name else param_name + + # Create container with label and slider + container = widgets.HBox( + [ + widgets.Label(full_param_name, layout=widgets.Layout(width="auto")), + widgets.FloatSlider( + value=current_val, + min=allowed_range[0], + max=allowed_range[1], + step=step_size, + layout=widgets.Layout(width="300px"), + readout=True, + readout_format=".3f", + ), + ] + ) + + def on_value_change(change): + update_fn(change["new"]) + if env is not None: + reset_env(env, steps=50) + + container.children[1].observe(on_value_change, names="value") + + # Create help text showing the allowed range + help_text = widgets.HTML(value=f'

Allowed range: {allowed_range[:2]}

') + + display(container) + display(help_text) + + return container.children[1] + + +def interactive_update_randomizable_params( + event_term: EventTermCfg, + event_term_name: str, + param_config: dict[str, dict | tuple[float, float, float | None]], + param_path: str = "", + env: ManagerBasedEnv | None = None, +) -> list[tuple[list[str], widgets.FloatSlider | widgets.FloatRangeSlider]]: + """Interactive parameter updates using ipywidgets.""" + inputs = [] + + for key, allowed_range in param_config.items(): + current_path = f"{param_path}.{key}" if param_path else key + keys = current_path.split(".") + + if isinstance(allowed_range, dict): + interactive_update_randomizable_params(event_term, event_term_name, allowed_range, current_path, env) + else: + try: + current_val = get_nested_value(event_term.params, keys) + + def make_update_fn(k, full_path): + def update_fn(new_val): + update_nested_value(event_term.params, k, new_val) + print(f"Updated '{full_path}' to {new_val}.") + + return update_fn + + input_widget = get_parameter_input( + current_path, + current_val, + allowed_range, + make_update_fn(keys, current_path), + env=env, + event_term_name=event_term_name, + ) + inputs.append((keys, input_widget)) + except KeyError: + print(f"Key '{current_path}' not found in event_term.params; skipping.") + continue + + return inputs + + +def setup_output_paths(output_file_path: str) -> tuple[str, str]: + """Set up output directory and get file name for dataset generation. + + Args: + output_file_path: Full path to the desired output file + + Returns: + tuple containing: + - output_dir: Path to the output directory + - output_file_name: Name of the output file without extension + """ + output_dir = os.path.dirname(output_file_path) + output_file_name = os.path.splitext(os.path.basename(output_file_path))[0] + + # create directory if it does not exist + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + return output_dir, output_file_name + + +def get_env_name_from_dataset(input_file_path: str) -> str: + """Get environment name from an input dataset file. + + Args: + input_file_path: Path to the input dataset file + + Returns: + env_name: Name of the environment from the dataset + + Raises: + FileNotFoundError: If the input file does not exist + """ + if not os.path.exists(input_file_path): + raise FileNotFoundError(f"The dataset file {input_file_path} does not exist.") + + dataset_file_handler = HDF5DatasetFileHandler() + dataset_file_handler.open(input_file_path) + env_name = dataset_file_handler.get_env_name() + if env_name is None: + raise ValueError("Environment name not found in dataset") + + return env_name diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/waypoint.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/waypoint.py new file mode 100644 index 0000000000000000000000000000000000000000..964cc2a49ddaaf623beceb7ba15c4e1b3ae16620 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/waypoint.py @@ -0,0 +1,429 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +""" +A collection of classes used to represent waypoints and trajectories. +""" + +import asyncio +import inspect +from copy import deepcopy + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv +from isaaclab.managers import TerminationTermCfg + + +class Waypoint: + """ + Represents a single desired 6-DoF waypoint, along with corresponding gripper actuation for this point. + """ + + def __init__(self, pose, gripper_action, noise=None): + """ + Args: + pose (torch.Tensor): 4x4 pose target for robot controller + gripper_action (torch.Tensor): gripper action for robot controller + noise (float or None): action noise amplitude to apply during execution at this timestep + (for arm actions, not gripper actions) + """ + self.pose = pose + self.gripper_action = gripper_action + self.noise = noise + + def __str__(self): + """String representation of the waypoint.""" + return f"Waypoint:\n Pose:\n{self.pose}\n" + + +class WaypointSequence: + """ + Represents a sequence of Waypoint objects. + """ + + def __init__(self, sequence=None): + """ + Args: + sequence (list or None): if provided, should be a list of Waypoint objects + """ + if sequence is None: + self.sequence = [] + else: + for waypoint in sequence: + assert isinstance(waypoint, Waypoint) + self.sequence = deepcopy(sequence) + + @classmethod + def from_poses(cls, poses, gripper_actions, action_noise): + """ + Instantiate a WaypointSequence object given a sequence of poses, + gripper actions, and action noise. + + Args: + poses (torch.Tensor): sequence of pose matrices of shape (T, 4, 4) + gripper_actions (torch.Tensor): sequence of gripper actions + that should be applied at each timestep of shape (T, D). + action_noise (float or torch.Tensor): sequence of action noise + magnitudes that should be applied at each timestep. If a + single float is provided, the noise magnitude will be + constant over the trajectory. + """ + assert isinstance(action_noise, (float, torch.Tensor)) + + # handle scalar to tensor conversion + num_timesteps = poses.shape[0] + if isinstance(action_noise, float): + action_noise = action_noise * torch.ones((num_timesteps, 1), dtype=torch.float32) + action_noise = action_noise.reshape(-1, 1) + + # make WaypointSequence instance + sequence = [ + Waypoint( + pose=poses[t], + gripper_action=gripper_actions[t], + noise=action_noise[t, 0], + ) + for t in range(num_timesteps) + ] + return cls(sequence=sequence) + + def get_poses(self): + poses = [] + for waypoint in self.sequence: + poses.append(waypoint.pose[:2, 3]) + return poses + + def __len__(self): + # length of sequence + return len(self.sequence) + + def __getitem__(self, ind): + """ + Returns waypoint at index. + + Returns: + waypoint (Waypoint instance) + """ + return self.sequence[ind] + + def __add__(self, other): + """ + Defines addition (concatenation) of sequences + """ + return WaypointSequence(sequence=(self.sequence + other.sequence)) + + def __str__(self): + """Prints all waypoints in the sequence.""" + output = [] + for idx, waypoint in enumerate(self.sequence): + output.append(f"Waypoint {idx}: {waypoint}") + return "\n".join(output) + + @property + def last_waypoint(self): + """ + Return last waypoint in sequence. + + Returns: + waypoint (Waypoint instance) + """ + return deepcopy(self.sequence[-1]) + + def split(self, ind): + """ + Splits this sequence into 2 pieces, the part up to time index @ind, and the + rest. Returns 2 WaypointSequence objects. + """ + seq_1 = self.sequence[:ind] + seq_2 = self.sequence[ind:] + return WaypointSequence(sequence=seq_1), WaypointSequence(sequence=seq_2) + + +class WaypointTrajectory: + """ + A sequence of WaypointSequence objects that corresponds to a full 6-DoF trajectory. + """ + + def __init__(self): + self.waypoint_sequences = [] + + def __len__(self): + # sum up length of all waypoint sequences + return sum(len(s) for s in self.waypoint_sequences) + + def __getitem__(self, ind): + """ + Returns waypoint at time index. + + Returns: + waypoint (Waypoint instance) + """ + assert len(self.waypoint_sequences) > 0 + assert (ind >= 0) and (ind < len(self)) + + # find correct waypoint sequence we should index + end_ind = 0 + for seq_ind in range(len(self.waypoint_sequences)): + start_ind = end_ind + end_ind += len(self.waypoint_sequences[seq_ind]) + if (ind >= start_ind) and (ind < end_ind): + break + + # index within waypoint sequence + return self.waypoint_sequences[seq_ind][ind - start_ind] + + @property + def last_waypoint(self): + """ + Return last waypoint in sequence. + + Returns: + waypoint (Waypoint instance) + """ + return self.waypoint_sequences[-1].last_waypoint + + def get_poses(self): + poses = [] + for waypoint_sequence in self.waypoint_sequences: + for waypoint in waypoint_sequence: + poses.append(waypoint.pose[:2, 3]) + return poses + + def add_waypoint_sequence(self, sequence): + """ + Directly append sequence to list (no interpolation). + + Args: + sequence (WaypointSequence instance): sequence to add + """ + assert isinstance(sequence, WaypointSequence) + self.waypoint_sequences.append(sequence) + + def add_waypoint_sequence_for_target_pose( + self, + pose, + gripper_action, + num_steps, + skip_interpolation=False, + action_noise=0.0, + ): + """ + Adds a new waypoint sequence corresponding to a desired target pose. A new WaypointSequence + will be constructed consisting of @num_steps intermediate Waypoint objects. These can either + be constructed with linear interpolation from the last waypoint (default) or be a + constant set of target poses (set @skip_interpolation to True). + + Args: + pose (torch.Tensor): 4x4 target pose + + gripper_action (torch.Tensor): value for gripper action + + num_steps (int): number of action steps when trying to reach this waypoint. Will + add intermediate linearly interpolated points between the last pose on this trajectory + and the target pose, so that the total number of steps is @num_steps. + + skip_interpolation (bool): if True, keep the target pose fixed and repeat it @num_steps + times instead of using linearly interpolated targets. + + action_noise (float): scale of random gaussian noise to add during action execution (e.g. + when @execute is called) + """ + if len(self.waypoint_sequences) == 0: + assert skip_interpolation, "cannot interpolate since this is the first waypoint sequence" + + if skip_interpolation: + # repeat the target @num_steps times + assert num_steps is not None + poses = pose.unsqueeze(0).repeat((num_steps, 1, 1)) + gripper_actions = gripper_action.unsqueeze(0).repeat((num_steps, 1)) + else: + # linearly interpolate between the last pose and the new waypoint + last_waypoint = self.last_waypoint + poses, num_steps_2 = PoseUtils.interpolate_poses( + pose_1=last_waypoint.pose, + pose_2=pose, + num_steps=num_steps, + ) + assert num_steps == num_steps_2 + gripper_actions = gripper_action.unsqueeze(0).repeat((num_steps + 2, 1)) + # make sure to skip the first element of the new path, which already exists on the current trajectory path + poses = poses[1:] + gripper_actions = gripper_actions[1:] + + # add waypoint sequence for this set of poses + sequence = WaypointSequence.from_poses( + poses=poses, + gripper_actions=gripper_actions, + action_noise=action_noise, + ) + self.add_waypoint_sequence(sequence) + + def pop_first(self): + """ + Removes first waypoint in first waypoint sequence and returns it. If the first waypoint + sequence is now empty, it is also removed. + + Returns: + waypoint (Waypoint instance) + """ + first, rest = self.waypoint_sequences[0].split(1) + if len(rest) == 0: + # remove empty waypoint sequence + self.waypoint_sequences = self.waypoint_sequences[1:] + else: + # update first waypoint sequence + self.waypoint_sequences[0] = rest + return first + + def merge( + self, + other, + num_steps_interp=None, + num_steps_fixed=None, + action_noise=0.0, + ): + """ + Merge this trajectory with another (@other). + + Args: + other (WaypointTrajectory object): the other trajectory to merge into this one + + num_steps_interp (int or None): if not None, add a waypoint sequence that interpolates + between the end of the current trajectory and the start of @other + + num_steps_fixed (int or None): if not None, add a waypoint sequence that has constant + target poses corresponding to the first target pose in @other + + action_noise (float): noise to use during the interpolation segment + """ + need_interp = (num_steps_interp is not None) and (num_steps_interp > 0) + need_fixed = (num_steps_fixed is not None) and (num_steps_fixed > 0) + use_interpolation_segment = need_interp or need_fixed + + if use_interpolation_segment: + # pop first element of other trajectory + other_first = other.pop_first() + + # Get first target pose of other trajectory. + # The interpolated segment will include this first element as its last point. + target_for_interpolation = other_first[0] + + if need_interp: + # interpolation segment + self.add_waypoint_sequence_for_target_pose( + pose=target_for_interpolation.pose, + gripper_action=target_for_interpolation.gripper_action, + num_steps=num_steps_interp, + action_noise=action_noise, + skip_interpolation=False, + ) + + if need_fixed: + # segment of constant target poses equal to @other's first target pose + + # account for the fact that we pop'd the first element of + # @other in anticipation of an interpolation segment + num_steps_fixed_to_use = num_steps_fixed if need_interp else (num_steps_fixed + 1) + self.add_waypoint_sequence_for_target_pose( + pose=target_for_interpolation.pose, + gripper_action=target_for_interpolation.gripper_action, + num_steps=num_steps_fixed_to_use, + action_noise=action_noise, + skip_interpolation=True, + ) + + # make sure to preserve noise from first element of other trajectory + self.waypoint_sequences[-1][-1].noise = target_for_interpolation.noise + + # concatenate the trajectories + self.waypoint_sequences += other.waypoint_sequences + + def get_full_sequence(self): + """ + Returns the full sequence of waypoints in the trajectory. + + Returns: + sequence (WaypointSequence instance) + """ + return WaypointSequence(sequence=[waypoint for seq in self.waypoint_sequences for waypoint in seq.sequence]) + + +class MultiWaypoint: + """ + A collection of Waypoint objects for multiple end effectors in the environment. + """ + + def __init__(self, waypoints: dict[str, Waypoint]): + """ + Args: + waypoints (dict): a dictionary of waypionts of end effectors + """ + self.waypoints = waypoints + + async def execute( + self, + env: ManagerBasedRLMimicEnv, + success_term: TerminationTermCfg, + env_id: int = 0, + env_action_queue: asyncio.Queue | None = None, + ): + """ + Executes the multi-waypoint eef actions in the environment. + + Args: + env: The environment to execute the multi-waypoint actions in. + success_term: The termination term to check for task success. + env_id: The environment ID to execute the multi-waypoint actions in. + env_action_queue: The asyncio queue to put the action into. + + Returns: + A dictionary containing the state, observation, action, and success of the multi-waypoint actions. + """ + # current state + state = env.scene.get_state(is_relative=True) + + # construct action from target poses and gripper actions + target_eef_pose_dict = {eef_name: waypoint.pose for eef_name, waypoint in self.waypoints.items()} + gripper_action_dict = {eef_name: waypoint.gripper_action for eef_name, waypoint in self.waypoints.items()} + if "action_noise_dict" in inspect.signature(env.target_eef_pose_to_action).parameters: + action_noise_dict = {eef_name: waypoint.noise for eef_name, waypoint in self.waypoints.items()} + play_action = env.target_eef_pose_to_action( + target_eef_pose_dict=target_eef_pose_dict, + gripper_action_dict=gripper_action_dict, + action_noise_dict=action_noise_dict, + env_id=env_id, + ) + else: + # calling user-defined env.target_eef_pose_to_action() with noise parameter is deprecated + # (replaced by action_noise_dict) + play_action = env.target_eef_pose_to_action( + target_eef_pose_dict=target_eef_pose_dict, + gripper_action_dict=gripper_action_dict, + noise=max([waypoint.noise for waypoint in self.waypoints.values()]), + env_id=env_id, + ) + + if play_action.dim() == 1: + play_action = play_action.unsqueeze(0) # Reshape with additional env dimension + + # step environment + if env_action_queue is None: + obs, _, _, _, _ = env.step(play_action) + else: + await env_action_queue.put((env_id, play_action[0])) + await env_action_queue.join() + obs = env.obs_buf + + success = bool(success_term.func(env, **success_term.params)[env_id]) + + result = dict( + states=[state], + observations=[obs], + actions=[play_action], + success=success, + ) + return result diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a2705a68b64baa9fe242a405d73f86ff5d8ea6 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py @@ -0,0 +1,161 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Sub-package with environment wrappers for Isaac Lab Mimic.""" + +import gymnasium as gym + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_stack_ik_rel_mimic_env_cfg:FrankaCubeStackIKRelMimicEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.franka_stack_ik_rel_blueprint_mimic_env_cfg:FrankaCubeStackIKRelBlueprintMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Abs-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_abs_mimic_env:FrankaCubeStackIKAbsMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_stack_ik_abs_mimic_env_cfg:FrankaCubeStackIKAbsMimicEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.franka_stack_ik_rel_visuomotor_mimic_env_cfg:FrankaCubeStackIKRelVisuomotorMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_stack_ik_rel_visuomotor_cosmos_mimic_env_cfg:FrankaCubeStackIKRelVisuomotorCosmosMimicEnvCfg", + }, + disable_env_checker=True, +) + + +## +# SkillGen +## + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_stack_ik_rel_skillgen_env_cfg:FrankaCubeStackIKRelSkillgenEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0", + entry_point=f"{__name__}.franka_stack_ik_rel_mimic_env:FrankaCubeStackIKRelMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_bin_stack_ik_rel_mimic_env_cfg:FrankaBinStackIKRelMimicEnvCfg", + }, + disable_env_checker=True, +) + +## +# Galbot Stack Cube with RmpFlow - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-Rel-Mimic-v0", + entry_point=f"{__name__}.galbot_stack_rmp_rel_mimic_env:RmpFlowGalbotCubeStackRelMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.galbot_stack_rmp_rel_mimic_env_cfg:RmpFlowGalbotLeftArmGripperCubeStackRelMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-Rel-Mimic-v0", + entry_point=f"{__name__}.galbot_stack_rmp_rel_mimic_env:RmpFlowGalbotCubeStackRelMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.galbot_stack_rmp_rel_mimic_env_cfg:RmpFlowGalbotRightArmSuctionCubeStackRelMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +## +# Galbot Stack Cube with RmpFlow - Absolute Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-Abs-Mimic-v0", + entry_point=f"{__name__}.galbot_stack_rmp_abs_mimic_env:RmpFlowGalbotCubeStackAbsMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.galbot_stack_rmp_abs_mimic_env_cfg:RmpFlowGalbotLeftArmGripperCubeStackAbsMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-Abs-Mimic-v0", + entry_point=f"{__name__}.galbot_stack_rmp_abs_mimic_env:RmpFlowGalbotCubeStackAbsMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.galbot_stack_rmp_abs_mimic_env_cfg:RmpFlowGalbotRightArmSuctionCubeStackAbsMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +## +# Agibot Left Arm: Place Upright Mug with RmpFlow - Relative Pose Control +## +gym.register( + id="Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-Rel-Mimic-v0", + entry_point=f"{__name__}.pick_place_mimic_env:PickPlaceRelMimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.agibot_place_upright_mug_mimic_env_cfg:RmpFlowAgibotPlaceUprightMugMimicEnvCfg" + ), + }, + disable_env_checker=True, +) +## +# Agibot Right Arm: Place Toy2Box: RmpFlow - Relative Pose Control +## +gym.register( + id="Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-Rel-Mimic-v0", + entry_point=f"{__name__}.pick_place_mimic_env:PickPlaceRelMimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.agibot_place_toy2box_mimic_env_cfg:RmpFlowAgibotPlaceToy2BoxMimicEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_toy2box_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_toy2box_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..76557802f463e037cc4fb30876b1d17e4ea886b8 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_toy2box_mimic_env_cfg.py @@ -0,0 +1,84 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.place.config.agibot.place_toy2box_rmp_rel_env_cfg import ( + RmpFlowAgibotPlaceToy2BoxEnvCfg, +) + +OBJECT_A_NAME = "toy_truck" +OBJECT_B_NAME = "box" + + +@configclass +class RmpFlowAgibotPlaceToy2BoxMimicEnvCfg(RmpFlowAgibotPlaceToy2BoxEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Agibot Place Toy2Box env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + self.datagen_config.name = "demo_src_place_toy2box_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref=OBJECT_A_NAME, + # End of final subtask does not need to be detected + subtask_term_signal="grasp", + # No time offsets for the final subtask + subtask_term_offset_range=(2, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref=OBJECT_B_NAME, + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["agibot"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_upright_mug_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_upright_mug_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2bfadef874c30518ca71b1aae18edfa8e181048e --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/agibot_place_upright_mug_mimic_env_cfg.py @@ -0,0 +1,81 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.place.config.agibot.place_upright_mug_rmp_rel_env_cfg import ( + RmpFlowAgibotPlaceUprightMugEnvCfg, +) + + +@configclass +class RmpFlowAgibotPlaceUprightMugMimicEnvCfg(RmpFlowAgibotPlaceUprightMugEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Agibot Place Upright Mug env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + self.datagen_config.name = "demo_src_place_upright_mug_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="mug", + # End of final subtask does not need to be detected + subtask_term_signal="grasp", + # No time offsets for the final subtask + subtask_term_offset_range=(15, 30), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="mug", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["agibot"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_bin_stack_ik_rel_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_bin_stack_ik_rel_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ca28719d730628701650482035ecb250450f5faf --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_bin_stack_ik_rel_mimic_env_cfg.py @@ -0,0 +1,92 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.bin_stack_ik_rel_env_cfg import FrankaBinStackEnvCfg + + +@configclass +class FrankaBinStackIKRelMimicEnvCfg(FrankaBinStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + object_ref="cube_2", + subtask_term_signal="grasp_1", + subtask_term_offset_range=(0, 0), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.03, + num_interpolation_steps=0, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + description="Grasp red cube", + next_subtask_description="Stack red cube on top of blue cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_1", + subtask_term_signal="stack_1", + subtask_term_offset_range=(0, 0), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.03, + num_interpolation_steps=0, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + next_subtask_description="Grasp green cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_3", + subtask_term_signal="grasp_2", + subtask_term_offset_range=(0, 0), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.03, + num_interpolation_steps=0, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + next_subtask_description="Stack green cube on top of red cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_2", + subtask_term_signal="stack_2", + subtask_term_offset_range=(0, 0), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.03, + num_interpolation_steps=0, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..45efc58bfc2096076f2ed0795638e37817a627b6 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env.py @@ -0,0 +1,99 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class FrankaCubeStackIKAbsMimicEnv(ManagerBasedRLMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Franka Cube Stack IK Abs env. + """ + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """Get current robot end effector pose.""" + if env_ids is None: + env_ids = slice(None) + + # Retrieve end effector pose from the observation buffer + eef_pos = self.obs_buf["policy"]["eef_pos"][env_ids] + eef_quat = self.obs_buf["policy"]["eef_quat"][env_ids] + # Quaternion format is w,x,y,z + return PoseUtils.make_pose(eef_pos, PoseUtils.matrix_from_quat(eef_quat)) + + def target_eef_pose_to_action( + self, target_eef_pose_dict: dict, gripper_action_dict: dict, noise: float | None = None, env_id: int = 0 + ) -> torch.Tensor: + """Convert target pose to action. + + This method transforms a dictionary of target end-effector poses and gripper actions + into a single action tensor that can be used by the environment. + + The function: + 1. Extracts target position and rotation from the pose dictionary + 2. Extracts gripper action for the end effector + 3. Concatenates position and quaternion rotation into a pose action + 4. Optionally adds noise to the pose action for exploration + 5. Combines pose action with gripper action into a final action tensor + + Args: + target_eef_pose_dict: Dictionary containing target end-effector pose(s), + with keys as eef names and values as pose tensors. + gripper_action_dict: Dictionary containing gripper action(s), + with keys as eef names and values as action tensors. + noise: Optional noise magnitude to apply to the pose action for exploration. + If provided, random noise is generated and added to the pose action. + env_id: Environment ID for multi-environment setups, defaults to 0. + + Returns: + torch.Tensor: A single action tensor combining pose and gripper commands. + """ + # target position and rotation + (target_eef_pose,) = target_eef_pose_dict.values() + target_pos, target_rot = PoseUtils.unmake_pose(target_eef_pose) + + # get gripper action for single eef + (gripper_action,) = gripper_action_dict.values() + + # add noise to action + pose_action = torch.cat([target_pos, PoseUtils.quat_from_matrix(target_rot)], dim=0) + if noise is not None: + noise = noise * torch.randn_like(pose_action) + pose_action += noise + + return torch.cat([pose_action, gripper_action], dim=0).unsqueeze(0) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """Convert action to target pose.""" + eef_name = list(self.cfg.subtask_configs.keys())[0] + + target_pos = action[:, :3] + target_quat = action[:, 3:7] + target_rot = PoseUtils.matrix_from_quat(target_quat) + + target_poses = PoseUtils.make_pose(target_pos, target_rot).clone() + + return {eef_name: target_poses} + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """Extract gripper actions.""" + # last dimension is gripper action + return {list(self.cfg.subtask_configs.keys())[0]: actions[:, -1:]} + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """Get subtask termination signals.""" + if env_ids is None: + env_ids = slice(None) + + signals = dict() + subtask_terms = self.obs_buf["subtask_terms"] + signals["grasp_1"] = subtask_terms["grasp_1"][env_ids] + signals["grasp_2"] = subtask_terms["grasp_2"][env_ids] + signals["stack_1"] = subtask_terms["stack_1"][env_ids] + return signals diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..93e51d8f673e540becfb1300b1c971b4ff352e19 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_abs_mimic_env_cfg.py @@ -0,0 +1,87 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_abs_env_cfg import FrankaCubeStackEnvCfg + + +@configclass +class FrankaCubeStackIKAbsMimicEnvCfg(FrankaCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Abs env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + object_ref="cube_2", + subtask_term_signal="grasp_1", + subtask_term_offset_range=(10, 20), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.01, + num_interpolation_steps=5, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_1", + subtask_term_signal="stack_1", + subtask_term_offset_range=(10, 20), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.01, + num_interpolation_steps=5, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_3", + subtask_term_signal="grasp_2", + subtask_term_offset_range=(10, 20), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.01, + num_interpolation_steps=5, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + object_ref="cube_2", + subtask_term_signal=None, + subtask_term_offset_range=(0, 0), + selection_strategy="nearest_neighbor_object", + selection_strategy_kwargs={"nn_k": 3}, + action_noise=0.01, + num_interpolation_steps=5, + num_fixed_steps=0, + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_blueprint_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_blueprint_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..cd75bea018a128cec53869e31b888b614fd87392 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_blueprint_mimic_env_cfg.py @@ -0,0 +1,127 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_rel_blueprint_env_cfg import ( + FrankaCubeStackBlueprintEnvCfg, +) + + +@configclass +class FrankaCubeStackIKRelBlueprintMimicEnvCfg(FrankaCubeStackBlueprintEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "isaac_lab_franka_stack_ik_rel_blueprint_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(10, 20), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..336db05ca1744327aaec9703341e3de70b6988be --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env.py @@ -0,0 +1,189 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class FrankaCubeStackIKRelMimicEnv(ManagerBasedRLMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Franka Cube Stack IK Rel env. + """ + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + # Retrieve end effector pose from the observation buffer + eef_pos = self.obs_buf["policy"]["eef_pos"][env_ids] + eef_quat = self.obs_buf["policy"]["eef_quat"][env_ids] + # Quaternion format is w,x,y,z + return PoseUtils.make_pose(eef_pos, PoseUtils.matrix_from_quat(eef_quat)) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + noise: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + # target position and rotation + (target_eef_pose,) = target_eef_pose_dict.values() + target_pos, target_rot = PoseUtils.unmake_pose(target_eef_pose) + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=[env_id])[0] + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # normalized delta position action + delta_position = target_pos - curr_pos + + # normalized delta rotation action + delta_rot_mat = target_rot.matmul(curr_rot.transpose(-1, -2)) + delta_quat = PoseUtils.quat_from_matrix(delta_rot_mat) + delta_rotation = PoseUtils.axis_angle_from_quat(delta_quat) + + # get gripper action for single eef + (gripper_action,) = gripper_action_dict.values() + + # add noise to action + pose_action = torch.cat([delta_position, delta_rotation], dim=0) + if action_noise_dict is not None: + noise = action_noise_dict[eef_name] * torch.randn_like(pose_action) + pose_action += noise + pose_action = torch.clamp(pose_action, -1.0, 1.0) + + return torch.cat([pose_action, gripper_action], dim=0) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim) + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + delta_position = action[:, :3] + delta_rotation = action[:, 3:6] + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=None) + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # get pose target + target_pos = curr_pos + delta_position + + # Convert delta_rotation to axis angle form + delta_rotation_angle = torch.linalg.norm(delta_rotation, dim=-1, keepdim=True) + delta_rotation_axis = delta_rotation / delta_rotation_angle + + # Handle invalid division for the case when delta_rotation_angle is close to zero + is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(1) + delta_rotation_axis[is_close_to_zero_angle] = torch.zeros_like(delta_rotation_axis)[is_close_to_zero_angle] + + delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(1), delta_rotation_axis).squeeze(0) + delta_rot_mat = PoseUtils.matrix_from_quat(delta_quat) + target_rot = torch.matmul(delta_rot_mat, curr_rot) + + target_poses = PoseUtils.make_pose(target_pos, target_rot).clone() + + return {eef_name: target_poses} + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + # last dimension is gripper action + return {list(self.cfg.subtask_configs.keys())[0]: actions[:, -1:]} + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + if env_ids is None: + env_ids = slice(None) + + signals = dict() + subtask_terms = self.obs_buf["subtask_terms"] + signals["grasp_1"] = subtask_terms["grasp_1"][env_ids] + signals["grasp_2"] = subtask_terms["grasp_2"][env_ids] + signals["stack_1"] = subtask_terms["stack_1"][env_ids] + # final subtask is placing cubeC on cubeA (motion relative to cubeA) - but final subtask signal is not needed + return signals + + def get_expected_attached_object(self, eef_name: str, subtask_index: int, env_cfg) -> str | None: + """ + (SkillGen) Return the expected attached object for the given EEF/subtask. + + Assumes 'stack' subtasks place the object grasped in the preceding 'grasp' subtask. + Returns None for 'grasp' (or others) at subtask start. + """ + if eef_name not in env_cfg.subtask_configs: + return None + + subtask_configs = env_cfg.subtask_configs[eef_name] + if not (0 <= subtask_index < len(subtask_configs)): + return None + + current_cfg = subtask_configs[subtask_index] + # If stacking, expect we are holding the object grasped in the prior subtask + if "stack" in str(current_cfg.subtask_term_signal).lower(): + if subtask_index > 0: + prev_cfg = subtask_configs[subtask_index - 1] + if "grasp" in str(prev_cfg.subtask_term_signal).lower(): + return prev_cfg.object_ref + return None diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..197852602d39a76ff8bcdf92505a22c629415735 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_mimic_env_cfg.py @@ -0,0 +1,133 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_rel_env_cfg import FrankaCubeStackEnvCfg + + +@configclass +class FrankaCubeStackIKRelMimicEnvCfg(FrankaCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + # # TODO: Figure out how we can move this to the MimicEnvCfg class + # # The __post_init__() above only calls the init for FrankaCubeStackEnvCfg and not MimicEnvCfg + # # https://stackoverflow.com/questions/59986413/achieving-multiple-inheritance-using-python-dataclasses + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(10, 20), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Grasp red cube", + next_subtask_description="Stack red cube on top of blue cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + next_subtask_description="Grasp green cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + next_subtask_description="Stack green cube on top of red cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_skillgen_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_skillgen_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9d26039126be4cd94cee0d1339cf597def4aa2ce --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_skillgen_env_cfg.py @@ -0,0 +1,137 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_rel_env_cfg_skillgen import ( + FrankaCubeStackSkillgenEnvCfg, +) + + +@configclass +class FrankaCubeStackIKRelSkillgenEnvCfg(FrankaCubeStackSkillgenEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + # # TODO: Figure out how we can move this to the MimicEnvCfg class + # # The __post_init__() above only calls the init for FrankaCubeStackEnvCfg and not MimicEnvCfg + # # https://stackoverflow.com/questions/59986413/achieving-multiple-inheritance-using-python-dataclasses + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(0, 0), # (10, 20), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, # 5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Grasp red cube", + next_subtask_description="Stack red cube on top of blue cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), # (10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, # 5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + next_subtask_description="Grasp green cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), # (10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, # 5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + next_subtask_description="Stack green cube on top of red cube", + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected for MimicGen + # Needs to be detected for SkillGen + # Setting this doesn't affect the data generation for MimicGen + subtask_term_signal="stack_2", + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, # 5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_cosmos_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_cosmos_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2f2ebe5ab339f316a8a217eb68fbe16d30b11594 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_cosmos_mimic_env_cfg.py @@ -0,0 +1,128 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_rel_visuomotor_cosmos_env_cfg import ( + FrankaCubeStackVisuomotorCosmosEnvCfg, +) + + +@configclass +class FrankaCubeStackIKRelVisuomotorCosmosMimicEnvCfg(FrankaCubeStackVisuomotorCosmosEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel Visuomotor Cosmos env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "isaac_lab_franka_stack_ik_rel_visuomotor_cosmos_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(10, 20), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ed63e973b2d0b0a03b6924bc271883ab017b07b4 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/franka_stack_ik_rel_visuomotor_mimic_env_cfg.py @@ -0,0 +1,128 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_ik_rel_visuomotor_env_cfg import ( + FrankaCubeStackVisuomotorEnvCfg, +) + + +@configclass +class FrankaCubeStackIKRelVisuomotorMimicEnvCfg(FrankaCubeStackVisuomotorEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel Visuomotor env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "isaac_lab_franka_stack_ik_rel_visuomotor_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(10, 20), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["franka"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..b3d03a149317c21469755eecadc65bf5d576c0de --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env.py @@ -0,0 +1,47 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import isaaclab.utils.math as PoseUtils + +from .franka_stack_ik_abs_mimic_env import FrankaCubeStackIKAbsMimicEnv + + +class RmpFlowGalbotCubeStackAbsMimicEnv(FrankaCubeStackIKAbsMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Galbot Cube Stack RmpFlow Absolute env. + """ + + def get_object_poses(self, env_ids: Sequence[int] | None = None): + """ + Rewrite this function to get the pose of each object in robot base frame, + relevant to Isaac Lab Mimic data generation in the current scene. + + Args: + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A dictionary that maps object names to object pose matrix in base frame of robot (4x4 torch.Tensor) + """ + + if env_ids is None: + env_ids = slice(None) + + rigid_object_states = self.scene.get_state(is_relative=True)["rigid_object"] + robot_states = self.scene.get_state(is_relative=True)["articulation"]["robot"] + root_pose = robot_states["root_pose"] + root_pos = root_pose[env_ids, :3] + root_quat = root_pose[env_ids, 3:7] + + object_pose_matrix = dict() + for obj_name, obj_state in rigid_object_states.items(): + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, obj_state["root_pose"][env_ids, :3], obj_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[obj_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + return object_pose_matrix diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d3de8a9aa3d2cda4820f657bf7e5b4f3b78f022a --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_abs_mimic_env_cfg.py @@ -0,0 +1,255 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.galbot.stack_rmp_rel_env_cfg import ( + RmpFlowGalbotLeftArmCubeStackEnvCfg, + RmpFlowGalbotRightArmCubeStackEnvCfg, +) + + +@configclass +class RmpFlowGalbotLeftArmGripperCubeStackAbsMimicEnvCfg(RmpFlowGalbotLeftArmCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Galbot Gripper Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=( + 18, + 25, + ), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=( + 18, + 25, + ), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=( + 25, + 30, + ), # This should be larger than the other subtasks, because the gripper + # should be lifted higher than two blocks + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["galbot"] = subtask_configs + + +@configclass +class RmpFlowGalbotRightArmSuctionCubeStackAbsMimicEnvCfg(RmpFlowGalbotRightArmCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Galbot Suction Gripper Cube Stack RmpFlow Abs env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(5, 10), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(2, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(5, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["galbot"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..c839cd655b13f822c58a682d1a1b164521284fd5 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +from collections.abc import Sequence + +import isaaclab.utils.math as PoseUtils + +from .franka_stack_ik_rel_mimic_env import FrankaCubeStackIKRelMimicEnv + + +class RmpFlowGalbotCubeStackRelMimicEnv(FrankaCubeStackIKRelMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Galbot Cube Stack RmpFlow Rel env. + """ + + def get_object_poses(self, env_ids: Sequence[int] | None = None): + """ + Rewrite this function to get the pose of each object in robot base frame, + relevant to Isaac Lab Mimic data generation in the current scene. + + Args: + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A dictionary that maps object names to object pose matrix in base frame of robot (4x4 torch.Tensor) + """ + + if env_ids is None: + env_ids = slice(None) + + rigid_object_states = self.scene.get_state(is_relative=True)["rigid_object"] + robot_states = self.scene.get_state(is_relative=True)["articulation"]["robot"] + root_pose = robot_states["root_pose"] + root_pos = root_pose[env_ids, :3] + root_quat = root_pose[env_ids, 3:7] + + object_pose_matrix = dict() + for obj_name, obj_state in rigid_object_states.items(): + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, obj_state["root_pose"][env_ids, :3], obj_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[obj_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + return object_pose_matrix diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ce4d00015a3e94f7e596b02661305628aed9096a --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/galbot_stack_rmp_rel_mimic_env_cfg.py @@ -0,0 +1,253 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack.config.galbot.stack_rmp_rel_env_cfg import ( + RmpFlowGalbotLeftArmCubeStackEnvCfg, + RmpFlowGalbotRightArmCubeStackEnvCfg, +) + + +@configclass +class RmpFlowGalbotLeftArmGripperCubeStackRelMimicEnvCfg(RmpFlowGalbotLeftArmCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Galbot Gripper Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=( + 18, + 25, + ), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=( + 18, + 25, + ), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + # This should be larger than the other subtasks, because the gripper + # should be lifted higher than two blocks + subtask_term_offset_range=(25, 30), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["galbot"] = subtask_configs + + +@configclass +class RmpFlowGalbotRightArmSuctionCubeStackRelMimicEnvCfg(RmpFlowGalbotRightArmCubeStackEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Galbot Suction Gripper Cube Stack RmpFlow Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "demo_src_stack_isaac_lab_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = True + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_1", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(5, 10), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_1", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="stack_1", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(2, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_3", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp_2", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(5, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="cube_2", + # End of final subtask does not need to be detected + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.01, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=15, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["galbot"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pick_place_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pick_place_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..1776528b7970b0331b06a4b78fb6dc1e5a0368fa --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pick_place_mimic_env.py @@ -0,0 +1,179 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils + +from .franka_stack_ik_abs_mimic_env import FrankaCubeStackIKAbsMimicEnv +from .franka_stack_ik_rel_mimic_env import FrankaCubeStackIKRelMimicEnv + + +class PickPlaceRelMimicEnv(FrankaCubeStackIKRelMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for DiffIK / RmpFlow Relative Pose Control env. + + This MimicEnv is used when all observations are in the robot base frame. + """ + + def get_object_poses(self, env_ids: Sequence[int] | None = None): + """ + Gets the pose of each object (including rigid objects and articulated objects) in the robot base frame. + + Args: + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A dictionary that maps object names to object pose matrix in robot base frame (4x4 torch.Tensor) + """ + if env_ids is None: + env_ids = slice(None) + + # Get scene state + scene_state = self.scene.get_state(is_relative=True) + rigid_object_states = scene_state["rigid_object"] + articulation_states = scene_state["articulation"] + + # Get robot root pose + robot_root_pose = articulation_states["robot"]["root_pose"] + root_pos = robot_root_pose[env_ids, :3] + root_quat = robot_root_pose[env_ids, 3:7] + + object_pose_matrix = dict() + + # Process rigid objects + for obj_name, obj_state in rigid_object_states.items(): + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, obj_state["root_pose"][env_ids, :3], obj_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[obj_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + # Process articulated objects (except robot) + for art_name, art_state in articulation_states.items(): + if art_name != "robot": # Skip robot + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, art_state["root_pose"][env_ids, :3], art_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[art_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + return object_pose_matrix + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + if env_ids is None: + env_ids = slice(None) + + signals = dict() + + subtask_terms = self.obs_buf["subtask_terms"] + if "grasp" in subtask_terms: + signals["grasp"] = subtask_terms["grasp"][env_ids] + + # Handle multiple grasp signals + for i in range(0, len(self.cfg.subtask_configs)): + grasp_key = f"grasp_{i + 1}" + if grasp_key in subtask_terms: + signals[grasp_key] = subtask_terms[grasp_key][env_ids] + # final subtask signal is not needed + return signals + + +class PickPlaceAbsMimicEnv(FrankaCubeStackIKAbsMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for DiffIK / RmpFlow Absolute Pose Control env. + + This MimicEnv is used when all observations are in the robot base frame. + """ + + def get_object_poses(self, env_ids: Sequence[int] | None = None): + """ + Gets the pose of each object (including rigid objects and articulated objects) in the robot base frame. + + Args: + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A dictionary that maps object names to object pose matrix in robot base frame (4x4 torch.Tensor) + """ + if env_ids is None: + env_ids = slice(None) + + # Get scene state + scene_state = self.scene.get_state(is_relative=True) + rigid_object_states = scene_state["rigid_object"] + articulation_states = scene_state["articulation"] + + # Get robot root pose + robot_root_pose = articulation_states["robot"]["root_pose"] + root_pos = robot_root_pose[env_ids, :3] + root_quat = robot_root_pose[env_ids, 3:7] + + object_pose_matrix = dict() + + # Process rigid objects + for obj_name, obj_state in rigid_object_states.items(): + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, obj_state["root_pose"][env_ids, :3], obj_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[obj_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + # Process articulated objects (except robot) + for art_name, art_state in articulation_states.items(): + if art_name != "robot": # Skip robot + pos_obj_base, quat_obj_base = PoseUtils.subtract_frame_transforms( + root_pos, root_quat, art_state["root_pose"][env_ids, :3], art_state["root_pose"][env_ids, 3:7] + ) + rot_obj_base = PoseUtils.matrix_from_quat(quat_obj_base) + object_pose_matrix[art_name] = PoseUtils.make_pose(pos_obj_base, rot_obj_base) + + return object_pose_matrix + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + if env_ids is None: + env_ids = slice(None) + + signals = dict() + + subtask_terms = self.obs_buf["subtask_terms"] + if "grasp" in subtask_terms: + signals["grasp"] = subtask_terms["grasp"][env_ids] + + # Handle multiple grasp signals + for i in range(0, len(self.cfg.subtask_configs)): + grasp_key = f"grasp_{i + 1}" + if grasp_key in subtask_terms: + signals[grasp_key] = subtask_terms[grasp_key][env_ids] + # final subtask signal is not needed + return signals diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0a40cc08f715552137a5fe717def4bf9c91dcfb0 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/__init__.py @@ -0,0 +1,77 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Sub-package with environment wrappers for Isaac Lab Mimic.""" + +import gymnasium as gym + +gym.register( + id="Isaac-PickPlace-GR1T2-Abs-Mimic-v0", + entry_point=f"{__name__}.pickplace_gr1t2_mimic_env:PickPlaceGR1T2MimicEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_gr1t2_mimic_env_cfg:PickPlaceGR1T2MimicEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-GR1T2-WaistEnabled-Abs-Mimic-v0", + entry_point=f"{__name__}.pickplace_gr1t2_mimic_env:PickPlaceGR1T2MimicEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.pickplace_gr1t2_waist_enabled_mimic_env_cfg:PickPlaceGR1T2WaistEnabledMimicEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-NutPour-GR1T2-Pink-IK-Abs-Mimic-v0", + entry_point=f"{__name__}.pickplace_gr1t2_mimic_env:PickPlaceGR1T2MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.nutpour_gr1t2_mimic_env_cfg:NutPourGR1T2MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-ExhaustPipe-GR1T2-Pink-IK-Abs-Mimic-v0", + entry_point=f"{__name__}.pickplace_gr1t2_mimic_env:PickPlaceGR1T2MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.exhaustpipe_gr1t2_mimic_env_cfg:ExhaustPipeGR1T2MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Locomanipulation-G1-Abs-Mimic-v0", + entry_point=f"{__name__}.locomanipulation_g1_mimic_env:LocomanipulationG1MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.locomanipulation_g1_mimic_env_cfg:LocomanipulationG1MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-FixedBaseUpperBodyIK-G1-Abs-Mimic-v0", + entry_point=f"{__name__}.fixed_base_upper_body_ik_g1_mimic_env:FixedBaseUpperBodyIKG1MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.fixed_base_upper_body_ik_g1_mimic_env_cfg:FixedBaseUpperBodyIKG1MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-G1-Mimic-v0", + entry_point=f"{__name__}.pick_place_g1_mimic_env:PickPlaceG1MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.pick_place_g1_mimic_env_cfg:PickPlaceG1MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-Camera-G1-Mimic-v0", + entry_point=f"{__name__}.pick_place_camera_g1_mimic_env:PickPlaceCameraG1MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.pick_place_camera_g1_mimic_env_cfg:PickPlaceCameraG1MimicEnvCfg"}, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Apple-PickPlace-G1-Mimic-v0", + entry_point=f"{__name__}.apple_pick_place_g1_mimic_env:ApplePickPlaceG1MimicEnv", + kwargs={"env_cfg_entry_point": f"{__name__}.apple_pick_place_g1_mimic_env_cfg:ApplePickPlaceG1MimicEnvCfg"}, + disable_env_checker=True, +) diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..612bd363276e529b9dc2b74ed36d49500ba1f9a3 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class ApplePickPlaceG1MimicEnv(ManagerBasedRLMimicEnv): + """G1 ApplePickPlace Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:21], "right": actions[:, 21:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..99bddb089e323af6c33575aae0a191e0f4b96ce9 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/apple_pick_place_g1_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.apple_pick_place_g1_env_cfg import ( + ApplePickPlaceG1EnvCfg, +) + + +@configclass +class ApplePickPlaceG1MimicEnvCfg(ApplePickPlaceG1EnvCfg, MimicEnvCfg): + """Configuration for ApplePickPlace G1 Mimic environment.""" + + def __post_init__(self): + # Call parent post-init + super().__post_init__() + + # Override datagen config values for demonstration generation + self.datagen_config.name = "demo_src_apple_pick_place_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # Subtask configs for left arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasping_left", + # Randomization range for starting index of the first subtask + first_subtask_start_offset_range=(0, 0), + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs + + # Subtask configs for right arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/exhaustpipe_gr1t2_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/exhaustpipe_gr1t2_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ed37975c6afe54ba7e8d3e91891cbc065b8343cf --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/exhaustpipe_gr1t2_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.exhaustpipe_gr1t2_pink_ik_env_cfg import ( + ExhaustPipeGR1T2PinkIKEnvCfg, +) + + +@configclass +class ExhaustPipeGR1T2MimicEnvCfg(ExhaustPipeGR1T2PinkIKEnvCfg, MimicEnvCfg): + """Configuration for GR1T2 Exhaust Pipe Mimic environment.""" + + def __post_init__(self): + # Calling post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "gr1t2_exhaust_pipe_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 10 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="blue_exhaust_pipe", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right_1", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="blue_exhaust_pipe", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="blue_exhaust_pipe", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..1370e1a5b646b40a1e6af7c990d7db8976a2066d --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class FixedBaseUpperBodyIKG1MimicEnv(ManagerBasedRLMimicEnv): + """G1 FixedBaseUpperBodyIK Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:21], "right": actions[:, 21:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7c32b1308a0b083340090a36bd8871089859a710 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/fixed_base_upper_body_ik_g1_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.fixed_base_upper_body_ik_g1_env_cfg import ( + FixedBaseUpperBodyIKG1EnvCfg, +) + + +@configclass +class FixedBaseUpperBodyIKG1MimicEnvCfg(FixedBaseUpperBodyIKG1EnvCfg, MimicEnvCfg): + """Configuration for G1 Locomanipulation Mimic environment.""" + + def __post_init__(self): + # Call parent post-init + super().__post_init__() + + # Override datagen config values for demonstration generation + self.datagen_config.name = "demo_src_g1_fixed_base_upper_body_ik_demo_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # Subtask configs for right arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + # Randomization range for starting index of the first subtask + first_subtask_start_offset_range=(0, 0), + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + # Subtask configs for left arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..89b13167315c74f36544248957c2df561da2eb95 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class LocomanipulationG1MimicEnv(ManagerBasedRLMimicEnv): + """G1 Locomanipulation Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:21], "right": actions[:, 21:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2aa766dec33c9b563e366d8e001ab8e41095f753 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/locomanipulation_g1_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.locomanipulation_g1_env_cfg import ( + LocomanipulationG1EnvCfg, +) + + +@configclass +class LocomanipulationG1MimicEnvCfg(LocomanipulationG1EnvCfg, MimicEnvCfg): + """Configuration for G1 Locomanipulation Mimic environment.""" + + def __post_init__(self): + # Call parent post-init + super().__post_init__() + + # Override datagen config values for demonstration generation + self.datagen_config.name = "demo_src_g1_locomanip_demo_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # Subtask configs for right arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + # Randomization range for starting index of the first subtask + first_subtask_start_offset_range=(0, 0), + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + # Subtask configs for left arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/nutpour_gr1t2_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/nutpour_gr1t2_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..683d4be09e44e7d4f6ee6e3e8b00866dcff9873d --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/nutpour_gr1t2_mimic_env_cfg.py @@ -0,0 +1,157 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.nutpour_gr1t2_pink_ik_env_cfg import NutPourGR1T2PinkIKEnvCfg + + +@configclass +class NutPourGR1T2MimicEnvCfg(NutPourGR1T2PinkIKEnvCfg, MimicEnvCfg): + """Configuration for GR1T2 Nut Pouring Mimic environment.""" + + def __post_init__(self): + # Calling post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "gr1t2_nut_pouring_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 10 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="sorting_bowl", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="sorting_bowl", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_right", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="sorting_scale", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="sorting_beaker", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="grasp_left", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generatio + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="sorting_bowl", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..61c48049c3615eccc02e082edc2aff4ef4676d66 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class PickPlaceCameraG1MimicEnv(ManagerBasedRLMimicEnv): + """G1 PickPlaceCamera Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:21], "right": actions[:, 21:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..21397ea5b7c1fdfd5a27b0280c562ec0d334ce70 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_camera_g1_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.pick_place_camera_g1_env_cfg import ( + PickPlaceCameraG1EnvCfg, +) + + +@configclass +class PickPlaceCameraG1MimicEnvCfg(PickPlaceCameraG1EnvCfg, MimicEnvCfg): + """Configuration for G1 PickPlaceCamera Mimic environment.""" + + def __post_init__(self): + # Call parent post-init + super().__post_init__() + + # Override datagen config values for demonstration generation + self.datagen_config.name = "demo_src_g1_pick_place_demo_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # Subtask configs for right arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + # Randomization range for starting index of the first subtask + first_subtask_start_offset_range=(0, 0), + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + # Subtask configs for left arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..f82c8ee5eb8115cd1a2b487b6d14f8ceb1e5bd64 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class PickPlaceG1MimicEnv(ManagerBasedRLMimicEnv): + """G1 PickPlace Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:21], "right": actions[:, 21:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..854303a9b41984c53b12dcc79c580a7eaa8988fe --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pick_place_g1_mimic_env_cfg.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.pick_place_g1_env_cfg import ( + PickPlaceG1EnvCfg, +) + + +@configclass +class PickPlaceG1MimicEnvCfg(PickPlaceG1EnvCfg, MimicEnvCfg): + """Configuration for G1 PickPlace Mimic environment.""" + + def __post_init__(self): + # Call parent post-init + super().__post_init__() + + # Override datagen config values for demonstration generation + self.datagen_config.name = "demo_src_g1_pick_place_demo_task_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # Subtask configs for right arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + # Randomization range for starting index of the first subtask + first_subtask_start_offset_range=(0, 0), + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + # Subtask configs for left arm + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env.py new file mode 100644 index 0000000000000000000000000000000000000000..9ec65040ef9558b8281f81293c2677473ab064a7 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from collections.abc import Sequence + +import torch + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class PickPlaceGR1T2MimicEnv(ManagerBasedRLMimicEnv): + """GR1T2 Pick Place Mimic environment.""" + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + eef_pos_name = f"{eef_name}_eef_pos" + eef_quat_name = f"{eef_name}_eef_quat" + + target_wrist_position = self.obs_buf["policy"][eef_pos_name][env_ids] + target_rot_mat = PoseUtils.matrix_from_quat(self.obs_buf["policy"][eef_quat_name][env_ids]) + + return PoseUtils.make_pose(target_wrist_position, target_rot_mat) + + def target_eef_pose_to_action( + self, + target_eef_pose_dict: dict, + gripper_action_dict: dict, + action_noise_dict: dict | None = None, + env_id: int = 0, # Unused, but required to conform to interface + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + action_noise_dict: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + + # target position and rotation + target_left_eef_pos, left_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["left"]) + target_right_eef_pos, right_target_rot = PoseUtils.unmake_pose(target_eef_pose_dict["right"]) + + target_left_eef_rot_quat = PoseUtils.quat_from_matrix(left_target_rot) + target_right_eef_rot_quat = PoseUtils.quat_from_matrix(right_target_rot) + + # gripper actions + left_gripper_action = gripper_action_dict["left"] + right_gripper_action = gripper_action_dict["right"] + + if action_noise_dict is not None: + pos_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_pos) + pos_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_pos) + quat_noise_left = action_noise_dict["left"] * torch.randn_like(target_left_eef_rot_quat) + quat_noise_right = action_noise_dict["right"] * torch.randn_like(target_right_eef_rot_quat) + + target_left_eef_pos += pos_noise_left + target_right_eef_pos += pos_noise_right + target_left_eef_rot_quat += quat_noise_left + target_right_eef_rot_quat += quat_noise_right + + return torch.cat( + ( + target_left_eef_pos, + target_left_eef_rot_quat, + target_right_eef_pos, + target_right_eef_rot_quat, + left_gripper_action, + right_gripper_action, + ), + dim=0, + ) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim). + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to. + """ + target_poses = {} + + target_left_wrist_position = action[:, 0:3] + target_left_rot_mat = PoseUtils.matrix_from_quat(action[:, 3:7]) + target_pose_left = PoseUtils.make_pose(target_left_wrist_position, target_left_rot_mat) + target_poses["left"] = target_pose_left + + target_right_wrist_position = action[:, 7:10] + target_right_rot_mat = PoseUtils.matrix_from_quat(action[:, 10:14]) + target_pose_right = PoseUtils.make_pose(target_right_wrist_position, target_right_rot_mat) + target_poses["right"] = target_pose_right + + return target_poses + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + return {"left": actions[:, 14:25], "right": actions[:, 25:]} diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0297fb72a1bc182b03c9825fb258418fac654459 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_mimic_env_cfg.py @@ -0,0 +1,110 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.pickplace_gr1t2_env_cfg import PickPlaceGR1T2EnvCfg + + +@configclass +class PickPlaceGR1T2MimicEnvCfg(PickPlaceGR1T2EnvCfg, MimicEnvCfg): + """Configuration for GR1T2 Pick Place Mimic environment.""" + + def __post_init__(self): + # Calling post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "gr1t2_pick_place_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + # selection_strategy="nearest_neighbor_object", + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_waist_enabled_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_waist_enabled_mimic_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f9528b277dba6e00ab19fecc3553c0231bfb80f3 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/pinocchio_envs/pickplace_gr1t2_waist_enabled_mimic_env_cfg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.pickplace_gr1t2_waist_enabled_env_cfg import ( + PickPlaceGR1T2WaistEnabledEnvCfg, +) + + +@configclass +class PickPlaceGR1T2WaistEnabledMimicEnvCfg(PickPlaceGR1T2WaistEnabledEnvCfg, MimicEnvCfg): + """Configuration for GR1T2 Pick Place Waist Enabled Mimic environment.""" + + def __post_init__(self): + # Calling post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "gr1t2_pick_place_waist_enabled_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 1000 + self.datagen_config.generation_select_src_per_subtask = False + self.datagen_config.generation_select_src_per_arm = False + self.datagen_config.generation_relative = False + self.datagen_config.generation_joint_pos = False + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.num_demo_to_render = 10 + self.datagen_config.num_fail_demo_to_render = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="idle_right", + first_subtask_start_offset_range=(0, 0), + # Randomization range for starting index of the first subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + # selection_strategy="nearest_neighbor_object", + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=3, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["right"] = subtask_configs + + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal=None, + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.003, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + self.subtask_configs["left"] = subtask_configs diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3af586c7dfb3707cfb5910810c4e41dccd24813f --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Sub-package with locomanipulation SDG utilities.""" diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/data_classes.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/data_classes.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea2cf68b85e262d7841ec37fa3331b8fc66a813 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/data_classes.py @@ -0,0 +1,86 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from dataclasses import dataclass + +import torch + + +@dataclass +class LocomanipulationSDGInputData: + """Data container for in-place manipulation recording state. Used during locomanipulation replay.""" + + left_hand_pose_target: torch.Tensor + """The pose of the left hand in world coordinates.""" + + right_hand_pose_target: torch.Tensor + """The pose of the right hand in world coordinates.""" + + left_hand_joint_positions_target: torch.Tensor + """The left hand joint positions.""" + + right_hand_joint_positions_target: torch.Tensor + """The right hand joint positions.""" + + base_pose: torch.Tensor + """The robot base pose in world coordinates.""" + + object_pose: torch.Tensor + """The target object pose in world coordinates.""" + + fixture_pose: torch.Tensor + """The fixture (ie: table) pose in world coordinates.""" + + +@dataclass +class LocomanipulationSDGOutputData: + """A container for data that is recorded during locomanipulation replay. + This is the final output of the pipeline. + """ + + left_hand_pose_target: torch.Tensor | None = None + """The left hand's target pose.""" + + right_hand_pose_target: torch.Tensor | None = None + """The right hand's target pose.""" + + left_hand_joint_positions_target: torch.Tensor | None = None + """The left hand's target joint positions""" + + right_hand_joint_positions_target: torch.Tensor | None = None + """The right hand's target joint positions""" + + base_velocity_target: torch.Tensor | None = None + """The target velocity of the robot base. This value is provided to the underlying base controller or policy.""" + + start_fixture_pose: torch.Tensor | None = None + """The pose of the start fixture (ie: pick-up table).""" + + end_fixture_pose: torch.Tensor | None = None + """The pose of the end / destination fixture (ie: drop-off table)""" + + object_pose: torch.Tensor | None = None + """The pose of the target object.""" + + base_pose: torch.Tensor | None = None + """The pose of the robot base.""" + + data_generation_state: int | None = None + """The state of the the locomanipulation SDG replay script's state machine.""" + + base_goal_pose: torch.Tensor | None = None + """The goal pose of the robot base (ie: the final destination before dropping off the object)""" + + base_goal_approach_pose: torch.Tensor | None = None + """The goal pose provided to the path planner (this may be offset from the final destination to enable approach.)""" + + base_path: torch.Tensor | None = None + """The robot base path as determined by the path planner.""" + + recording_step: int | None = None + """The current recording step used for upper body replay.""" + + obstacle_fixture_poses: torch.Tensor | None = None + """The pose of all obstacle fixtures in the scene.""" diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8d16a5ca14b8f357ed7c43fff10c03c9304f3843 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Sub-package with environment wrappers for Locomanipulation SDG.""" + +import gymnasium as gym + +gym.register( + id="Isaac-G1-SteeringWheel-Locomanipulation", + entry_point=f"{__name__}.g1_locomanipulation_sdg_env:G1LocomanipulationSDGEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.g1_locomanipulation_sdg_env:G1LocomanipulationSDGEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/g1_locomanipulation_sdg_env.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/g1_locomanipulation_sdg_env.py new file mode 100644 index 0000000000000000000000000000000000000000..dca2945822a382cf69653670c8525dd77c83552f --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/g1_locomanipulation_sdg_env.py @@ -0,0 +1,279 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.envs.common import ViewerCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import CameraCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path +from isaaclab.utils.datasets import EpisodeData + +from isaaclab_mimic.locomanipulation_sdg.data_classes import LocomanipulationSDGInputData +from isaaclab_mimic.locomanipulation_sdg.scene_utils import HasPose, SceneBody, SceneFixture + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.locomanipulation_g1_env_cfg import ( + LocomanipulationG1EnvCfg, + LocomanipulationG1SceneCfg, + ObservationsCfg, + manip_mdp, +) + +from .locomanipulation_sdg_env import LocomanipulationSDGEnv +from .locomanipulation_sdg_env_cfg import LocomanipulationSDGEnvCfg, LocomanipulationSDGRecorderManagerCfg + +NUM_FORKLIFTS = 6 +NUM_BOXES = 12 + + +@configclass +class G1LocomanipulationSDGSceneCfg(LocomanipulationG1SceneCfg): + packing_table_2 = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable2", + init_state=AssetBaseCfg.InitialStateCfg( + pos=[-2, -3.55, -0.3], + # rot=[0, 0, 0, 1]), + rot=[0.9238795, 0, 0, -0.3826834], + ), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + robot_pov_cam = CameraCfg( + prim_path="/World/envs/env_.*/Robot/torso_link/d435_link/camera", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=8.0, clipping_range=(0.1, 20.0)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.9848078, 0.0, -0.1736482, 0.0), convention="world"), + ) + + +# Add forklifts +for i in range(NUM_FORKLIFTS): + setattr( + G1LocomanipulationSDGSceneCfg, + f"forklift_{i}", + AssetBaseCfg( + prim_path=f"/World/envs/env_.*/Forklift{i}", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.0, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Forklift/forklift.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ), + ) + +# Add boxes +for i in range(NUM_BOXES): + setattr( + G1LocomanipulationSDGSceneCfg, + f"box_{i}", + AssetBaseCfg( + prim_path=f"/World/envs/env_.*/Box{i}", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.0, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_681.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ), + ) + + +@configclass +class G1LocomanipulationSDGObservationsCfg(ObservationsCfg): + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObservationsCfg.PolicyCfg): + robot_pov_cam = ObsTerm( + func=manip_mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + policy: PolicyCfg = PolicyCfg() + + +@configclass +class G1LocomanipulationSDGEnvCfg(LocomanipulationG1EnvCfg, LocomanipulationSDGEnvCfg): + """Configuration for the G1 29DoF environment.""" + + viewer: ViewerCfg = ViewerCfg( + eye=(0.0, 3.0, 1.25), lookat=(0.0, 0.0, 0.5), origin_type="asset_body", asset_name="robot", body_name="pelvis" + ) + + # Scene settings + scene: G1LocomanipulationSDGSceneCfg = G1LocomanipulationSDGSceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + recorders: LocomanipulationSDGRecorderManagerCfg = LocomanipulationSDGRecorderManagerCfg() + observations: G1LocomanipulationSDGObservationsCfg = G1LocomanipulationSDGObservationsCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 100.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 6 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + +class G1LocomanipulationSDGEnv(LocomanipulationSDGEnv): + def __init__(self, cfg: G1LocomanipulationSDGEnvCfg, **kwargs): + super().__init__(cfg) + self.sim.set_camera_view([10.5, 10.5, 10.5], [0.0, 0.0, 0.5]) + self._upper_body_dim = self.action_manager.get_term("upper_body_ik").action_dim + self._waist_dim = 0 # self._env.action_manager.get_term("waist_joint_pos").action_dim + self._lower_body_dim = self.action_manager.get_term("lower_body_joint_pos").action_dim + self._frame_pose_dim = 7 + self._number_of_finger_joints = 7 + + def load_input_data(self, episode_data: EpisodeData, step: int) -> LocomanipulationSDGInputData | None: + dataset_action = episode_data.get_action(step) + dataset_state = episode_data.get_state(step) + + if dataset_action is None: + return None + + if dataset_state is None: + return None + + object_pose = dataset_state["rigid_object"]["object"]["root_pose"] + + data = LocomanipulationSDGInputData( + left_hand_pose_target=dataset_action[0:7], + right_hand_pose_target=dataset_action[7:14], + left_hand_joint_positions_target=dataset_action[14:21], + right_hand_joint_positions_target=dataset_action[21:28], + base_pose=episode_data.get_initial_state()["articulation"]["robot"]["root_pose"], + object_pose=object_pose, + fixture_pose=torch.tensor( + [0.0, 0.55, -0.3, 1.0, 0.0, 0.0, 0.0] + ), # Table pose is not recorded for this env. + ) + + return data + + def build_action_vector( + self, + left_hand_pose_target: torch.Tensor, + right_hand_pose_target: torch.Tensor, + left_hand_joint_positions_target: torch.Tensor, + right_hand_joint_positions_target: torch.Tensor, + base_velocity_target: torch.Tensor, + ): + action = torch.zeros(self.action_space.shape) + + # Set base height + lower_body_index_offset = self._upper_body_dim + self._waist_dim + action[0, lower_body_index_offset + 3 : lower_body_index_offset + 4] = torch.tensor([0.8]) + + # Left hand pose + assert left_hand_pose_target.shape == (self._frame_pose_dim,), ( + f"Expected pose shape ({self._frame_pose_dim},), got {left_hand_pose_target.shape}" + ) + action[0, : self._frame_pose_dim] = left_hand_pose_target + + # Right hand pose + assert right_hand_pose_target.shape == (self._frame_pose_dim,), ( + f"Expected pose shape ({self._frame_pose_dim},), got {right_hand_pose_target.shape}" + ) + action[0, self._frame_pose_dim : 2 * self._frame_pose_dim] = right_hand_pose_target + + # Left hand joint positions + assert left_hand_joint_positions_target.shape == (self._number_of_finger_joints,), ( + f"Expected joint_positions shape ({self._number_of_finger_joints},), got" + f" {left_hand_joint_positions_target.shape}" + ) + action[0, 2 * self._frame_pose_dim : 2 * self._frame_pose_dim + self._number_of_finger_joints] = ( + left_hand_joint_positions_target + ) + + # Right hand joint positions + assert right_hand_joint_positions_target.shape == (self._number_of_finger_joints,), ( + f"Expected joint_positions shape ({self._number_of_finger_joints},), got" + f" {right_hand_joint_positions_target.shape}" + ) + action[ + 0, + 2 * self._frame_pose_dim + self._number_of_finger_joints : 2 * self._frame_pose_dim + + 2 * self._number_of_finger_joints, + ] = right_hand_joint_positions_target + + # Base velocity + assert base_velocity_target.shape == (3,), f"Expected velocity shape (3,), got {base_velocity_target.shape}" + lower_body_index_offset = self._upper_body_dim + self._waist_dim + action[0, lower_body_index_offset : lower_body_index_offset + 3] = base_velocity_target + + return action + + def get_base(self) -> HasPose: + return SceneBody(self.scene, "robot", "pelvis") + + def get_left_hand(self) -> HasPose: + return SceneBody(self.scene, "robot", "left_wrist_yaw_link") + + def get_right_hand(self) -> HasPose: + return SceneBody(self.scene, "robot", "right_wrist_yaw_link") + + def get_object(self) -> HasPose: + return SceneBody(self.scene, "object", "sm_steeringwheel_a01_01") + + def get_start_fixture(self) -> SceneFixture: + return SceneFixture( + self.scene, + "packing_table", + occupancy_map_boundary=np.array([[-1.45, -0.45], [1.45, -0.45], [1.45, 0.45], [-1.45, 0.45]]), + occupancy_map_resolution=0.05, + ) + + def get_end_fixture(self) -> SceneFixture: + return SceneFixture( + self.scene, + "packing_table_2", + occupancy_map_boundary=np.array([[-1.45, -0.45], [1.45, -0.45], [1.45, 0.45], [-1.45, 0.45]]), + occupancy_map_resolution=0.05, + ) + + def get_obstacle_fixtures(self): + obstacles = [ + SceneFixture( + self.scene, + f"forklift_{i}", + occupancy_map_boundary=np.array([[-1.0, -1.9], [1.0, -1.9], [1.0, 2.1], [-1.0, 2.1]]), + occupancy_map_resolution=0.05, + ) + for i in range(NUM_FORKLIFTS) + ] + + obstacles += [ + SceneFixture( + self.scene, + f"box_{i}", + occupancy_map_boundary=np.array([[-0.5, -0.5], [0.5, -0.5], [0.5, 0.5], [-0.5, 0.5]]), + occupancy_map_resolution=0.05, + ) + for i in range(NUM_BOXES) + ] + + return obstacles diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env.py new file mode 100644 index 0000000000000000000000000000000000000000..83ae8e65684619b1e33a8fab47afebbe9a764d54 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env.py @@ -0,0 +1,91 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import torch + +from isaaclab.envs.manager_based_rl_env import ManagerBasedRLEnv +from isaaclab.managers.recorder_manager import RecorderTerm +from isaaclab.utils.datasets import EpisodeData + +from isaaclab_mimic.locomanipulation_sdg.data_classes import LocomanipulationSDGInputData, LocomanipulationSDGOutputData +from isaaclab_mimic.locomanipulation_sdg.scene_utils import HasPose, SceneFixture + + +class LocomanipulationSDGOutputDataRecorder(RecorderTerm): + """Recorder for Locomanipulation SDG output data.""" + + def record_pre_step(self): + output_data: LocomanipulationSDGOutputData = self._env._locomanipulation_sdg_output_data + + output_data_dict = { + "left_hand_pose_target": output_data.left_hand_pose_target[None, :], + "right_hand_pose_target": output_data.right_hand_pose_target[None, :], + "left_hand_joint_positions_target": output_data.left_hand_joint_positions_target[None, :], + "right_hand_joint_positions_target": output_data.right_hand_joint_positions_target[None, :], + "base_velocity_target": output_data.base_velocity_target[None, :], + "start_fixture_pose": output_data.start_fixture_pose, + "end_fixture_pose": output_data.end_fixture_pose, + "object_pose": output_data.object_pose, + "base_pose": output_data.base_pose, + "task": torch.tensor([[output_data.data_generation_state]]), + "base_goal_pose": output_data.base_goal_pose, + "base_goal_approach_pose": output_data.base_goal_approach_pose, + "base_path": output_data.base_path[None, :], + "recording_step": torch.tensor([[output_data.recording_step]]), + "obstacle_fixture_poses": output_data.obstacle_fixture_poses, + } + + return "locomanipulation_sdg_output_data", output_data_dict + + +class LocomanipulationSDGEnv(ManagerBasedRLEnv): + """An abstract base class that wraps the underlying environment, exposing methods needed for integration with + locomanipulation replay. + + This class defines the core methods needed to integrate an environment with the locomanipulation SDG pipeline for + locomanipulation replay. By implementing these methods for a new environment, the environment can be used with + the locomanipulation SDG replay function. + """ + + def load_input_data(self, episode_data: EpisodeData, step: int) -> LocomanipulationSDGInputData: + raise NotImplementedError + + def build_action_vector( + self, + left_hand_pose_target: torch.Tensor, + right_hand_pose_target: torch.Tensor, + left_hand_joint_positions_target: torch.Tensor, + right_hand_joint_positions_target: torch.Tensor, + base_velocity_target: torch.Tensor, + ): + raise NotImplementedError + + def get_base(self) -> HasPose: + """Get the robot base body.""" + raise NotImplementedError + + def get_left_hand(self) -> HasPose: + """Get the robot left hand body.""" + raise NotImplementedError + + def get_right_hand(self) -> HasPose: + """Get the robot right hand body.""" + raise NotImplementedError + + def get_object(self) -> HasPose: + """Get the target object body.""" + raise NotImplementedError + + def get_start_fixture(self) -> SceneFixture: + """Get the start fixture body.""" + raise NotImplementedError + + def get_end_fixture(self) -> SceneFixture: + """Get the end fixture body.""" + raise NotImplementedError + + def get_obstacle_fixtures(self) -> list[SceneFixture]: + """Get the set of obstacle fixtures.""" + raise NotImplementedError diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f3c5dd6c47cb1a3a458a01fd01d56b2e1e753e29 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/envs/locomanipulation_sdg_env_cfg.py @@ -0,0 +1,47 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import isaaclab.envs.mdp as base_mdp +from isaaclab.envs.manager_based_rl_env_cfg import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.managers.recorder_manager import RecorderTerm, RecorderTermCfg +from isaaclab.utils import configclass + +from .locomanipulation_sdg_env import LocomanipulationSDGOutputDataRecorder + + +@configclass +class LocomanipulationSDGOutputDataRecorderCfg(RecorderTermCfg): + """Configuration for the step policy observation recorder term.""" + + class_type: type[RecorderTerm] = LocomanipulationSDGOutputDataRecorder + + +@configclass +class LocomanipulationSDGRecorderManagerCfg(ActionStateRecorderManagerCfg): + record_pre_step_locomanipulation_sdg_output_data = LocomanipulationSDGOutputDataRecorderCfg() + + +@configclass +class LocomanipulationSDGTerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=base_mdp.time_out, time_out=True) + + +@configclass +class LocomanipulationSDGEventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=base_mdp.reset_scene_to_default, mode="reset") + + +@configclass +class LocomanipulationSDGEnvCfg(ManagerBasedRLEnvCfg): + recorders: LocomanipulationSDGRecorderManagerCfg = LocomanipulationSDGRecorderManagerCfg() + terminations: LocomanipulationSDGTerminationsCfg = LocomanipulationSDGTerminationsCfg() + events: LocomanipulationSDGEventCfg = LocomanipulationSDGEventCfg() diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/occupancy_map_utils.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/occupancy_map_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b2fdbdfb8527565bf8d1f4e1e6cb49b10d3944d5 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/occupancy_map_utils.py @@ -0,0 +1,742 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +import enum +import math +import os +import tempfile +from dataclasses import dataclass + +import cv2 +import numpy as np +import PIL.Image +import torch +import yaml +from PIL import ImageDraw + +from pxr import Kind, Sdf, Usd, UsdGeom, UsdShade + + +@dataclass +class Point2d: + x: float + y: float + + +ROS_FREESPACE_THRESH_DEFAULT = 0.196 +ROS_OCCUPIED_THRESH_DEFAULT = 0.65 + +OCCUPANCY_MAP_DEFAULT_Z_MIN = 0.1 +OCCUPANCY_MAP_DEFAULT_Z_MAX = 0.62 +OCCUPANCY_MAP_DEFAULT_CELL_SIZE = 0.05 + + +class OccupancyMapDataValue(enum.IntEnum): + UNKNOWN = 0 + FREESPACE = 1 + OCCUPIED = 2 + + def ros_image_value(self, negate: bool = False) -> int: + values = [0, 127, 255] + + if negate: + values = values[::-1] + + if self == OccupancyMapDataValue.OCCUPIED: + return values[0] + elif self == OccupancyMapDataValue.UNKNOWN: + return values[1] + else: + return values[2] + + +class OccupancyMapMergeMethod(enum.IntEnum): + UNION = 0 + INTERSECTION = 1 + + +class OccupancyMap: + ROS_IMAGE_FILENAME = "map.png" + ROS_YAML_FILENAME = "map.yaml" + ROS_YAML_TEMPLATE = """ +image: {image_filename} +resolution: {resolution} +origin: {origin} +negate: {negate} +occupied_thresh: {occupied_thresh} +free_thresh: {free_thresh} +""" + + def __init__(self, data: np.ndarray, resolution: int, origin: tuple[int, int, int]) -> None: + self.data = data + self.resolution = resolution # meters per pixel + self.origin = origin # x, y, yaw. where (x, y) is the bottom-left of image + self._width_pixels = data.shape[1] + self._height_pixels = data.shape[0] + + def freespace_mask(self) -> np.ndarray: + """Get a binary mask representing the freespace of the occupancy map. + + Returns: + np.ndarray: The binary mask representing freespace of the occupancy map. + """ + return self.data == OccupancyMapDataValue.FREESPACE + + def unknown_mask(self) -> np.ndarray: + """Get a binary mask representing the unknown area of the occupancy map. + + Returns: + np.ndarray: The binary mask representing unknown area of the occupancy map. + """ + return self.data == OccupancyMapDataValue.UNKNOWN + + def occupied_mask(self) -> np.ndarray: + """Get a binary mask representing the occupied area of the occupancy map. + + Returns: + np.ndarray: The binary mask representing occupied area of the occupancy map. + """ + return self.data == OccupancyMapDataValue.OCCUPIED + + def ros_image(self, negate: bool = False) -> PIL.Image.Image: + """Get the ROS image for the occupancy map. + + Args: + negate (bool, optional): See "negate" in ROS occupancy map documentation. Defaults to False. + + Returns: + PIL.Image.Image: The ROS image for the occupancy map as a PIL image. + """ + occupied_mask = self.occupied_mask() + ros_image = np.zeros(self.occupied_mask().shape, dtype=np.uint8) + ros_image[occupied_mask] = OccupancyMapDataValue.OCCUPIED.ros_image_value(negate) + ros_image[self.unknown_mask()] = OccupancyMapDataValue.UNKNOWN.ros_image_value(negate) + ros_image[self.freespace_mask()] = OccupancyMapDataValue.FREESPACE.ros_image_value(negate) + ros_image = PIL.Image.fromarray(ros_image) + return ros_image + + def ros_yaml(self, negate: bool = False) -> str: + """Get the ROS occupancy map YAML file content. + + Args: + negate (bool, optional): See "negate" in ROS occupancy map documentation. Defaults to False. + + Returns: + str: The ROS occupancy map YAML file contents. + """ + return self.ROS_YAML_TEMPLATE.format( + image_filename=self.ROS_IMAGE_FILENAME, + resolution=self.resolution, + origin=list(self.origin), + negate=1 if negate else 0, + occupied_thresh=ROS_OCCUPIED_THRESH_DEFAULT, + free_thresh=ROS_FREESPACE_THRESH_DEFAULT, + ) + + def save_ros(self, path: str): + """Save the occupancy map to a folder in ROS format. + + This method saves both the ROS formatted PNG image, as well + as the corresponding YAML file. + + Args: + path (str): The output path to save the occupancy map. + """ + if not os.path.exists(path): + os.makedirs(path) + assert os.path.isdir(path) # safety check + self.ros_image().save(os.path.join(path, self.ROS_IMAGE_FILENAME)) + with open(os.path.join(path, self.ROS_YAML_FILENAME), "w", encoding="utf-8") as f: + f.write(self.ros_yaml()) + + @staticmethod + def from_ros_yaml(ros_yaml_path: str) -> "OccupancyMap": + """Load an occupancy map from a ROS YAML file. + + This method loads an occupancy map from a ROS yaml file. + This method looks up the occupancy map image from the + value specified in the YAML file, and requires that + the image exists at the specified path. + + Args: + ros_yaml_path (str): The path to the ROS yaml file. + + Returns: + _type_: OccupancyMap + """ + with open(ros_yaml_path, encoding="utf-8") as f: + yaml_data = yaml.safe_load(f) + yaml_dir = os.path.dirname(ros_yaml_path) + image_path = os.path.join(yaml_dir, yaml_data["image"]) + image = PIL.Image.open(image_path).convert("L") + occupancy_map = OccupancyMap.from_ros_image( + ros_image=image, + resolution=yaml_data["resolution"], + origin=yaml_data["origin"], + negate=yaml_data["negate"], + occupied_thresh=yaml_data["occupied_thresh"], + free_thresh=yaml_data["free_thresh"], + ) + return occupancy_map + + @staticmethod + def from_ros_image( + ros_image: PIL.Image.Image, + resolution: float, + origin: tuple[float, float, float], + negate: bool = False, + occupied_thresh: float = ROS_OCCUPIED_THRESH_DEFAULT, + free_thresh: float = ROS_FREESPACE_THRESH_DEFAULT, + ) -> "OccupancyMap": + """Create an occupancy map from a ROS formatted image, and other data. + + This method is intended to be used as a utility by other methods, + but not necessarily useful for end use cases. + + Args: + ros_image (PIL.Image.Image): The ROS formatted PIL image. + resolution (float): The resolution (meter/px) of the occupancy map. + origin (tp.Tuple[float, float, float]): The origin of the occupancy map in world coordinates. + negate (bool, optional): See "negate" in ROS occupancy map documentation. Defaults to False. + occupied_thresh (float, optional): The threshold to consider a value occupied. + Defaults to ROS_OCCUPIED_THRESH_DEFAULT. + free_thresh (float, optional): The threshold to consider a value free. Defaults to + ROS_FREESPACE_THRESH_DEFAULT. + + Returns: + OccupancyMap: The occupancy map. + """ + ros_image = ros_image.convert("L") + + free_thresh = free_thresh * 255 + occupied_thresh = occupied_thresh * 255 + + data = np.asarray(ros_image) + + if not negate: + data = 255 - data + + freespace_mask = data < free_thresh + occupied_mask = data > occupied_thresh + + return OccupancyMap.from_masks( + freespace_mask=freespace_mask, occupied_mask=occupied_mask, resolution=resolution, origin=origin + ) + + @staticmethod + def from_masks( + freespace_mask: np.ndarray, occupied_mask: np.ndarray, resolution: float, origin: tuple[float, float, float] + ) -> "OccupancyMap": + """Creates an occupancy map from binary masks and other data + + This method is intended as a utility by other methods, but not necessarily + useful for end use cases. + + Args: + freespace_mask (np.ndarray): Binary mask for the freespace region. + occupied_mask (np.ndarray): Binary mask for the occupied region. + resolution (float): The resolution of the map (meters/px). + origin (tp.Tuple[float, float, float]): The origin of the map in world coordinates. + + Returns: + OccupancyMap: The occupancy map. + """ + + data = np.zeros(freespace_mask.shape, dtype=np.uint8) + data[...] = OccupancyMapDataValue.UNKNOWN + data[freespace_mask] = OccupancyMapDataValue.FREESPACE + data[occupied_mask] = OccupancyMapDataValue.OCCUPIED + + occupancy_map = OccupancyMap(data=data, resolution=resolution, origin=origin) + + return occupancy_map + + @staticmethod + def from_occupancy_boundary(boundary: np.ndarray, resolution: float) -> "OccupancyMap": + min_xy = boundary.min(axis=0) + max_xy = boundary.max(axis=0) + origin = (float(min_xy[0]), float(min_xy[1]), 0.0) + width_meters = max_xy[0] - min_xy[0] + height_meters = max_xy[1] - min_xy[1] + width_pixels = math.ceil(width_meters / resolution) + height_pixels = math.ceil(height_meters / resolution) + + points = boundary + + bot_left_world = (origin[0], origin[1]) + u = (points[:, 0] - bot_left_world[0]) / width_meters + v = 1.0 - (points[:, 1] - bot_left_world[1]) / height_meters + x_px = u * width_pixels + y_px = v * height_pixels + + xy_px = np.concatenate([x_px[:, None], y_px[:, None]], axis=-1).flatten() + + image = np.zeros((height_pixels, width_pixels, 4), dtype=np.uint8) + image = PIL.Image.fromarray(image) + draw = ImageDraw.Draw(image) + draw.polygon(xy_px.tolist(), fill="white", outline="red") + image = np.asarray(image) + + occupied_mask = image[:, :, 0] > 0 + + freespace_mask = ~occupied_mask + + return OccupancyMap.from_masks(freespace_mask, occupied_mask, resolution, origin) + + @staticmethod + def make_empty(start: tuple[float, float], end: tuple[float, float], resolution: float) -> "OccupancyMap": + origin = (start[0], start[1], 0.0) + width_meters = end[0] - start[0] + height_meters = end[1] - start[1] + width_pixels = math.ceil(width_meters / resolution) + height_pixels = math.ceil(height_meters / resolution) + occupied_mask = np.zeros((height_pixels, width_pixels), dtype=np.uint8) > 0 + freespace_mask = np.ones((height_pixels, width_pixels), dtype=np.uint8) > 0 + return OccupancyMap.from_masks(freespace_mask, occupied_mask, resolution, origin) + + def width_pixels(self) -> int: + """Get the width of the occupancy map in pixels. + + Returns: + int: The width in pixels. + """ + return self._width_pixels + + def height_pixels(self) -> int: + """Get the height of the occupancy map in pixels. + + Returns: + int: The height in pixels. + """ + return self._height_pixels + + def width_meters(self) -> float: + """Get the width of the occupancy map in meters. + + Returns: + float: The width in meters. + """ + return self.resolution * self.width_pixels() + + def height_meters(self) -> float: + """Get the height of the occupancy map in meters. + + Returns: + float: The height in meters. + """ + return self.resolution * self.height_pixels() + + def bottom_left_pixel_world_coords(self) -> tuple[float, float]: + """Get the world coordinates of the bottom left pixel. + + Returns: + tp.Tuple[float, float]: The (x, y) world coordinates of the + bottom left pixel in the occupancy map. + """ + return (self.origin[0], self.origin[1]) + + def top_left_pixel_world_coords(self) -> tuple[float, float]: + """Get the world coordinates of the top left pixel. + + Returns: + tp.Tuple[float, float]: The (x, y) world coordinates of the + top left pixel in the occupancy map. + """ + return (self.origin[0], self.origin[1] + self.height_meters()) + + def bottom_right_pixel_world_coords(self) -> tuple[float, float]: + """Get the world coordinates of the bottom right pixel. + + Returns: + tp.Tuple[float, float]: The (x, y) world coordinates of the + bottom right pixel in the occupancy map. + """ + return (self.origin[0] + self.width_meters(), self.origin[1]) + + def top_right_pixel_world_coords(self) -> tuple[float, float]: + """Get the world coordinates of the top right pixel. + + Returns: + tp.Tuple[float, float]: The (x, y) world coordinates of the + top right pixel in the occupancy map. + """ + return (self.origin[0] + self.width_meters(), self.origin[1] + self.height_meters()) + + def buffered(self, buffer_distance_pixels: int) -> "OccupancyMap": + """Get a buffered occupancy map by dilating the occupied regions. + + This method buffers (aka: pads / dilates) an occupancy map by dilating + the occupied regions using a circular mask with the a radius + specified by "buffer_distance_pixels". + + This is useful for modifying an occupancy map for path planning, + collision checking, or robot spawning with the simple assumption + that the robot has a circular collision profile. + + Args: + buffer_distance_pixels (int): The buffer radius / distance in pixels. + + Returns: + OccupancyMap: The buffered (aka: dilated / padded) occupancy map. + """ + + buffer_distance_pixels = int(buffer_distance_pixels) + + radius = buffer_distance_pixels + diameter = radius * 2 + kernel = np.zeros((diameter, diameter), np.uint8) + cv2.circle(kernel, (radius, radius), radius, 255, -1) + occupied = self.occupied_mask().astype(np.uint8) * 255 + occupied_dilated = cv2.dilate(occupied, kernel, iterations=1) + occupied_mask = occupied_dilated == 255 + free_mask = self.freespace_mask() + free_mask[occupied_mask] = False + + return OccupancyMap.from_masks( + freespace_mask=free_mask, occupied_mask=occupied_mask, resolution=self.resolution, origin=self.origin + ) + + def buffered_meters(self, buffer_distance_meters: float) -> "OccupancyMap": + """Get a buffered occupancy map by dilating the occupied regions. + + See OccupancyMap.buffer() for more details. + + Args: + buffer_distance_meters (int): The buffer radius / distance in pixels. + + Returns: + OccupancyMap: The buffered (aka: dilated / padded) occupancy map. + """ + buffer_distance_pixels = int(buffer_distance_meters / self.resolution) + return self.buffered(buffer_distance_pixels) + + def pixel_to_world(self, point: Point2d) -> Point2d: + """Convert a pixel coordinate to world coordinates. + + Args: + point (Point2d): The pixel coordinate. + + Returns: + Point2d: The world coordinate. + """ + # currently doesn't handle rotations + bot_left = self.bottom_left_pixel_world_coords() + u = point.x / self.width_pixels() + v = 1.0 - point.y / self.height_pixels() + x_world = u * self.width_meters() + bot_left[0] + y_world = v * self.height_meters() + bot_left[1] + return Point2d(x=x_world, y=y_world) + + def pixel_to_world_numpy(self, points: np.ndarray) -> np.ndarray: + """Convert an array of pixel coordinates to world coordinates. + + Args: + points (np.ndarray): The Nx2 numpy array of pixel coordinates. + + Returns: + np.ndarray: The Nx2 numpy array of world coordinates. + """ + bot_left = self.bottom_left_pixel_world_coords() + u = points[:, 0] / self.width_pixels() + v = 1.0 - points[:, 1] / self.height_pixels() + x_world = u * self.width_meters() + bot_left[0] + y_world = v * self.height_meters() + bot_left[1] + return np.concatenate([x_world[:, None], y_world[:, None]], axis=-1) + + def world_to_pixel_numpy(self, points: np.ndarray) -> np.ndarray: + """Convert an array of world coordinates to pixel coordinates. + + Args: + points (np.ndarray): The Nx2 numpy array of world coordinates. + + Returns: + np.ndarray: The Nx2 numpy array of pixel coordinates. + """ + bot_left_world = self.bottom_left_pixel_world_coords() + u = (points[:, 0] - bot_left_world[0]) / self.width_meters() + v = 1.0 - (points[:, 1] - bot_left_world[1]) / self.height_meters() + x_px = u * self.width_pixels() + y_px = v * self.height_pixels() + return np.concatenate([x_px[:, None], y_px[:, None]], axis=-1) + + def check_world_point_in_bounds(self, point: Point2d) -> bool: + """Check if a world coordinate is inside the bounds of the occupancy map. + + Args: + point (Point2d): The world coordinate. + + Returns: + bool: True if the coordinate is inside the bounds of + the occupancy map. False otherwise. + """ + + pixel = self.world_to_pixel_numpy(np.array([[point.x, point.y]])) + x_px = int(pixel[0, 0]) + y_px = int(pixel[0, 1]) + + if (x_px < 0) or (x_px >= self.width_pixels()) or (y_px < 0) or (y_px >= self.height_pixels()): + return False + + return True + + def check_world_point_in_freespace(self, point: Point2d) -> bool: + """Check if a world coordinate is inside the freespace region of the occupancy map + + Args: + point (Point2d): The world coordinate. + + Returns: + bool: True if the world coordinate is inside the freespace region of the occupancy map. + False otherwise. + """ + if not self.check_world_point_in_bounds(point): + return False + pixel = self.world_to_pixel_numpy(np.array([[point.x, point.y]])) + x_px = int(pixel[0, 0]) + y_px = int(pixel[0, 1]) + freespace = self.freespace_mask() + return bool(freespace[y_px, x_px]) + + def transformed(self, transform: np.ndarray) -> "OccupancyMap": + return transform_occupancy_map(self, transform) + + def merged(self, other: "OccupancyMap") -> "OccupancyMap": + return merge_occupancy_maps([self, other]) + + +def _omap_world_to_px( + points: np.ndarray, + origin: tuple[float, float, float], + width_meters: float, + height_meters: float, + width_pixels: int, + height_pixels: int, +) -> np.ndarray: + bot_left_world = (origin[0], origin[1]) + u = (points[:, 0] - bot_left_world[0]) / width_meters + v = 1.0 - (points[:, 1] - bot_left_world[1]) / height_meters + x_px = u * width_pixels + y_px = v * height_pixels + return np.stack([x_px, y_px], axis=-1) + + +def merge_occupancy_maps( + src_omaps: list[OccupancyMap], method: OccupancyMapMergeMethod = OccupancyMapMergeMethod.UNION +) -> OccupancyMap: + """Merge occupancy maps by computing the union or intersection of the occupied regions.""" + dst_resolution = min([o.resolution for o in src_omaps]) + + min_x = min([o.bottom_left_pixel_world_coords()[0] for o in src_omaps]) + min_y = min([o.bottom_left_pixel_world_coords()[1] for o in src_omaps]) + max_x = max([o.top_right_pixel_world_coords()[0] for o in src_omaps]) + max_y = max([o.top_right_pixel_world_coords()[1] for o in src_omaps]) + + dst_origin = (min_x, min_y, 0.0) + + dst_width_meters = max_x - min_x + dst_height_meters = max_y - min_y + dst_width_pixels = math.ceil(dst_width_meters / dst_resolution) + dst_height_pixels = math.ceil(dst_height_meters / dst_resolution) + + dst_occupied_mask: np.ndarray + if method == OccupancyMapMergeMethod.UNION: + dst_occupied_mask = np.zeros((dst_height_pixels, dst_width_pixels), dtype=bool) + elif method == OccupancyMapMergeMethod.INTERSECTION: + dst_occupied_mask = np.ones((dst_height_pixels, dst_width_pixels), dtype=bool) + else: + raise ValueError(f"Unsupported merge method: {method}") + + for src_omap in src_omaps: + omap_corners_in_world_coords = np.array( + [src_omap.top_left_pixel_world_coords(), src_omap.bottom_right_pixel_world_coords()] + ) + + omap_corners_in_dst_image_coords = ( + _omap_world_to_px( + omap_corners_in_world_coords, + dst_origin, + dst_width_meters, + dst_height_meters, + dst_width_pixels, + dst_height_pixels, + ) + .astype(np.int64) + .flatten() + ) + + omap_dst_width = omap_corners_in_dst_image_coords[2] - omap_corners_in_dst_image_coords[0] + omap_dst_height = omap_corners_in_dst_image_coords[3] - omap_corners_in_dst_image_coords[1] + + omap_occupied_image = PIL.Image.fromarray(255 * src_omap.occupied_mask().astype(np.uint8)).resize( + (omap_dst_width, omap_dst_height) + ) + + omap_occupied_image_tmp = omap_occupied_image.copy() + + dst_occupied_image = PIL.Image.fromarray(np.zeros_like(dst_occupied_mask).astype(np.uint8)) + + dst_occupied_image.paste(omap_occupied_image_tmp, box=omap_corners_in_dst_image_coords) + + if method == OccupancyMapMergeMethod.UNION: + dst_occupied_mask = dst_occupied_mask | (np.asarray(dst_occupied_image) > 0) + elif method == OccupancyMapMergeMethod.INTERSECTION: + dst_occupied_mask = dst_occupied_mask & (np.asarray(dst_occupied_image) > 0) + + dst_occupancy_map = OccupancyMap.from_masks( + freespace_mask=~dst_occupied_mask, occupied_mask=dst_occupied_mask, resolution=dst_resolution, origin=dst_origin + ) + + return dst_occupancy_map + + +def intersect_occupancy_maps(src_omaps: list[OccupancyMap]) -> OccupancyMap: + """Compute a new occupancy map by intersecting the occupied regions of a list of occupancy maps.""" + return merge_occupancy_maps(src_omaps=src_omaps, method=OccupancyMapMergeMethod.INTERSECTION) + + +def transform_points(points: np.ndarray, transform: np.ndarray) -> np.ndarray: + """Transform a set of points by a 2D transform.""" + points = np.concatenate([points, np.ones_like(points[:, 0:1])], axis=-1).T + points = transform @ points + points = points.T + points = points[:, :2] + return points + + +def make_rotate_transform(angle: float) -> np.ndarray: + """Create a 2D rotation transform.""" + return np.array([[np.cos(angle), -np.sin(angle), 0.0], [np.sin(angle), np.cos(angle), 0.0], [0.0, 0.0, 1.0]]) + + +def make_translate_transform(dx: float, dy: float) -> np.ndarray: + """Create a 2D translation transform.""" + return np.array([[1.0, 0.0, dx], [0.0, 1.0, dy], [0.0, 0.0, 1.0]]) + + +def transform_occupancy_map(omap: OccupancyMap, transform: np.ndarray) -> OccupancyMap: + """Transform an occupancy map using a 2D transform.""" + + src_box_world_coords = np.array( + [ + [omap.origin[0], omap.origin[1]], + [omap.origin[0] + omap.width_meters(), omap.origin[1]], + [omap.origin[0] + omap.width_meters(), omap.origin[1] + omap.height_meters()], + [omap.origin[0], omap.origin[1] + omap.height_meters()], + ] + ) + + src_box_pixel_coords = omap.world_to_pixel_numpy(src_box_world_coords) + + dst_box_world_coords = transform_points(src_box_world_coords, transform) + + dst_min_xy = np.min(dst_box_world_coords, axis=0) + dst_max_xy = np.max(dst_box_world_coords, axis=0) + + dst_origin = (float(dst_min_xy[0]), float(dst_min_xy[1]), 0) + dst_width_meters = dst_max_xy[0] - dst_min_xy[0] + dst_height_meters = dst_max_xy[1] - dst_min_xy[1] + dst_resolution = omap.resolution + dst_width_pixels = int(dst_width_meters / dst_resolution) + dst_height_pixels = int(dst_height_meters / dst_resolution) + + dst_box_pixel_coords = _omap_world_to_px( + dst_box_world_coords, dst_origin, dst_width_meters, dst_height_meters, dst_width_pixels, dst_height_pixels + ) + + persp_transform = cv2.getPerspectiveTransform( + src_box_pixel_coords.astype(np.float32), dst_box_pixel_coords.astype(np.float32) + ) + + src_occupied_mask = omap.occupied_mask().astype(np.uint8) * 255 + + dst_occupied_mask = cv2.warpPerspective(src_occupied_mask, persp_transform, (dst_width_pixels, dst_height_pixels)) + + dst_occupied_mask = dst_occupied_mask > 0 + dst_freespace_mask = ~dst_occupied_mask + + dst_omap = OccupancyMap.from_masks(dst_freespace_mask, dst_occupied_mask, dst_resolution, dst_origin) + + return dst_omap + + +def occupancy_map_add_to_stage( + occupancy_map: OccupancyMap, + stage: Usd.Stage, + path: str, + z_offset: float = 0.0, + draw_path: np.ndarray | torch.Tensor | None = None, + draw_path_line_width_meter: float = 0.25, +) -> Usd.Prim: + image_path = os.path.join(tempfile.mkdtemp(), "texture.png") + image = occupancy_map.ros_image() + + if draw_path is not None: + if isinstance(draw_path, torch.Tensor): + draw_path = draw_path.detach().cpu().numpy() + image = image.copy().convert("RGBA") + draw = ImageDraw.Draw(image) + line_coordinates = [] + path_pixels = occupancy_map.world_to_pixel_numpy(draw_path) + for i in range(len(path_pixels)): + line_coordinates.append(int(path_pixels[i, 0])) + line_coordinates.append(int(path_pixels[i, 1])) + width_pixels = draw_path_line_width_meter / occupancy_map.resolution + draw.line(line_coordinates, fill="green", width=int(width_pixels / 2), joint="curve") + + # need to flip, ros uses inverted coordinates on y axis + image = image.transpose(PIL.Image.FLIP_TOP_BOTTOM) + image.save(image_path) + + x0, y0 = occupancy_map.top_left_pixel_world_coords() + x1, y1 = occupancy_map.bottom_right_pixel_world_coords() + + # Add model + modelRoot = UsdGeom.Xform.Define(stage, path) + Usd.ModelAPI(modelRoot).SetKind(Kind.Tokens.component) + + # Add mesh + mesh = UsdGeom.Mesh.Define(stage, os.path.join(path, "mesh")) + mesh.CreatePointsAttr([(x0, y0, z_offset), (x1, y0, z_offset), (x1, y1, z_offset), (x0, y1, z_offset)]) + mesh.CreateFaceVertexCountsAttr([4]) + mesh.CreateFaceVertexIndicesAttr([0, 1, 2, 3]) + mesh.CreateExtentAttr([(x0, y0, z_offset), (x1, y1, z_offset)]) + + texCoords = UsdGeom.PrimvarsAPI(mesh).CreatePrimvar( + "st", Sdf.ValueTypeNames.TexCoord2fArray, UsdGeom.Tokens.varying + ) + + texCoords.Set([(0, 0), (1, 0), (1, 1), (0, 1)]) + + # Add material + material_path = os.path.join(path, "material") + material = UsdShade.Material.Define(stage, material_path) + pbrShader = UsdShade.Shader.Define(stage, os.path.join(material_path, "shader")) + pbrShader.CreateIdAttr("UsdPreviewSurface") + pbrShader.CreateInput("roughness", Sdf.ValueTypeNames.Float).Set(1.0) + pbrShader.CreateInput("metallic", Sdf.ValueTypeNames.Float).Set(0.0) + material.CreateSurfaceOutput().ConnectToSource(pbrShader.ConnectableAPI(), "surface") + + # Add texture to material + stReader = UsdShade.Shader.Define(stage, os.path.join(material_path, "st_reader")) + stReader.CreateIdAttr("UsdPrimvarReader_float2") + diffuseTextureSampler = UsdShade.Shader.Define(stage, os.path.join(material_path, "diffuse_texture")) + diffuseTextureSampler.CreateIdAttr("UsdUVTexture") + diffuseTextureSampler.CreateInput("file", Sdf.ValueTypeNames.Asset).Set(image_path) + diffuseTextureSampler.CreateInput("st", Sdf.ValueTypeNames.Float2).ConnectToSource( + stReader.ConnectableAPI(), "result" + ) + diffuseTextureSampler.CreateOutput("rgb", Sdf.ValueTypeNames.Float3) + pbrShader.CreateInput("diffuseColor", Sdf.ValueTypeNames.Color3f).ConnectToSource( + diffuseTextureSampler.ConnectableAPI(), "rgb" + ) + + stInput = material.CreateInput("frame:stPrimvarName", Sdf.ValueTypeNames.Token) + stInput.Set("st") + stReader.CreateInput("varname", Sdf.ValueTypeNames.Token).ConnectToSource(stInput) + mesh.GetPrim().ApplyAPI(UsdShade.MaterialBindingAPI) + UsdShade.MaterialBindingAPI(mesh).Bind(material) + + return modelRoot diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/path_utils.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/path_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3e203f401e85988b1d05c01fe989a381d70c3c0 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/path_utils.py @@ -0,0 +1,215 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + + +import torch + +from isaacsim.replicator.mobility_gen.impl.path_planner import compress_path, generate_paths + +from .occupancy_map_utils import OccupancyMap +from .scene_utils import HasPose2d + + +def nearest_point_on_segment(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Find the nearest point on line segment AB to point C. + + This function computes the closest point on the line segment from A to B + to a given point C, along with the distance from A to that point along the segment. + + Args: + a (torch.Tensor): Start point of the line segment. + b (torch.Tensor): End point of the line segment. + c (torch.Tensor): Query point to find the nearest point to. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: A tuple containing: + - The nearest point on the segment AB to point C + - The distance along the segment from A to the nearest point + """ + a2b = b - a + a2c = c - a + a2b_mag = torch.sqrt(torch.sum(a2b**2)) + a2b_norm = a2b / (a2b_mag + 1e-6) + dist = torch.dot(a2c, a2b_norm) + if dist < 0: + return a, dist + elif dist > a2b_mag: + return b, dist + else: + return a + a2b_norm * dist, dist + + +class ParameterizedPath: + """Path parameterized by arc length for distance-based queries and interpolation.""" + + def __init__(self, points: torch.Tensor) -> None: + """Initialize parameterized path with waypoints. + + Args: + points (torch.Tensor): Sequential waypoints of shape (N, 2). + """ + self.points = points + self._init_point_distances() + + def _init_point_distances(self) -> None: + """Initialize arc length parameterization.""" + self._point_distances = torch.zeros(len(self.points)) + length = 0.0 + for i in range(0, len(self.points) - 1): + self._point_distances[i] = length + a = self.points[i] + b = self.points[i + 1] + dist = torch.sqrt(torch.sum((a - b) ** 2)) + length += dist + self._point_distances[-1] = length + + def point_distances(self) -> torch.Tensor: + """Get arc length parameters for each waypoint. + + Returns: + torch.Tensor: Arc length parameter values. + """ + return self._point_distances + + def get_path_length(self) -> float: + """Calculate total path length. + + Returns: + float: Total euclidean distance from start to end. + """ + length = 0.0 + for i in range(1, len(self.points)): + a = self.points[i - 1] + b = self.points[i] + dist = torch.sqrt(torch.sum((a - b) ** 2)) + length += dist + return length + + def points_x(self) -> torch.Tensor: + """Get x-coordinates of all path points. + + Returns: + torch.Tensor: X-coordinates of all points. + """ + return self.points[:, 0] + + def points_y(self) -> torch.Tensor: + """Get y-coordinates of all path points. + + Returns: + torch.Tensor: Y-coordinates of all points. + """ + return self.points[:, 1] + + def get_segment_by_distance(self, distance: float) -> tuple[int, int]: + """Find path segment containing given distance. + + Args: + distance (float): Distance along path from start. + + Returns: + Tuple[int, int]: Indices of segment endpoints. + """ + for i in range(0, len(self.points) - 1): + d_b = self._point_distances[i + 1] + + if distance < d_b: + return (i, i + 1) + + i = len(self.points) - 2 + return (i, i + 1) + + def get_point_by_distance(self, distance: float) -> torch.Tensor: + """Sample point at specified arc length parameter. + + Args: + distance (float): Arc length parameter from start. + + Returns: + torch.Tensor: Interpolated 2D coordinates. + """ + a_idx, b_idx = self.get_segment_by_distance(distance) + a, b = self.points[a_idx], self.points[b_idx] + a_dist, b_dist = self._point_distances[a_idx], self._point_distances[b_idx] + u = (distance - a_dist) / ((b_dist - a_dist) + 1e-6) + u = torch.clip(u, 0.0, 1.0) + return a + u * (b - a) + + def find_nearest(self, point: torch.Tensor) -> tuple[torch.Tensor, float, tuple[int, int], float]: + """Find nearest point on path to query point. + + Args: + point (torch.Tensor): The query point as a 2D tensor. + + Returns: + Tuple containing: + - torch.Tensor: The nearest point on the path to the query point + - float: Distance along the path from the start to the nearest point + - Tuple[int, int]: Indices of the segment containing the nearest point + - float: Euclidean distance from the query point to the nearest point on path + """ + min_pt_dist_to_seg = 1e9 + min_pt_seg = None + min_pt = None + min_pt_dist_along_path = None + + for a_idx in range(0, len(self.points) - 1): + b_idx = a_idx + 1 + a = self.points[a_idx] + b = self.points[b_idx] + nearest_pt, dist_along_seg = nearest_point_on_segment(a, b, point) + dist_to_seg = torch.sqrt(torch.sum((point - nearest_pt) ** 2)) + + if dist_to_seg < min_pt_dist_to_seg: + min_pt_seg = (a_idx, b_idx) + min_pt_dist_to_seg = dist_to_seg + min_pt = nearest_pt + min_pt_dist_along_path = self._point_distances[a_idx] + dist_along_seg + + return min_pt, min_pt_dist_along_path, min_pt_seg, min_pt_dist_to_seg + + +def plan_path(start: HasPose2d, end: HasPose2d, occupancy_map: OccupancyMap) -> torch.Tensor: + """Plan collision-free path between start and end positions. + + Args: + start (HasPose2d): Start entity with 2D pose. + end (HasPose2d): Target entity with 2D pose. + occupancy_map (OccupancyMap): Occupancy map defining obstacles. + + Returns: + torch.Tensor: A tensor of shape (N, 2) representing the planned path as a + sequence of 2D waypoints from start to end. + """ + + # Extract 2D positions from poses + start_world_pos = start.get_pose_2d()[:, :2].numpy() + end_world_pos = end.get_pose_2d()[:, :2].numpy() + + # Convert world coordinates to pixel coordinates + start_xy_pixels = occupancy_map.world_to_pixel_numpy(start_world_pos) + end_xy_pixels = occupancy_map.world_to_pixel_numpy(end_world_pos) + + # Convert from (x, y) to (y, x) format required by path planner + start_yx_pixels = start_xy_pixels[..., 0, ::-1] + end_yx_pixels = end_xy_pixels[..., 0, ::-1] + + # Generate path using the mobility path planner + path_planner_output = generate_paths(start=start_yx_pixels, freespace=occupancy_map.freespace_mask()) + + # Extract and compress the path + path_yx_pixels = path_planner_output.unroll_path(end_yx_pixels) + path_yx_pixels, _ = compress_path(path_yx_pixels) + + # Convert back from (y, x) to (x, y) format + path_xy_pixels = path_yx_pixels[:, ::-1] + + # Convert pixel coordinates back to world coordinates + path_world = occupancy_map.pixel_to_world_numpy(path_xy_pixels) + + # Convert to torch tensor and return + path_tensor = torch.from_numpy(path_world) + + return path_tensor diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/scene_utils.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/scene_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dc9c969171c05704630abc83ee84a8450060f96d --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/scene_utils.py @@ -0,0 +1,190 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import random + +import numpy as np +import torch + +import isaaclab.utils.math as math_utils + +from .occupancy_map_utils import OccupancyMap, intersect_occupancy_maps +from .transform_utils import transform_mul + + +class HasOccupancyMap: + """An abstract base class for entities that have an associated occupancy map.""" + + def get_occupancy_map(self) -> OccupancyMap: + raise NotImplementedError + + +class HasPose2d: + """An abstract base class for entities that have an associated 2D pose.""" + + def get_pose_2d(self) -> torch.Tensor: + """Get the 2D pose of the entity.""" + raise NotImplementedError + + def get_transform_2d(self): + """Get the 2D transformation matrix of the entity.""" + + pose = self.get_pose_2d() + + x = pose[..., 0] + y = pose[..., 1] + theta = pose[..., 2] + ctheta = torch.cos(theta) + stheta = torch.sin(theta) + + dims = tuple(list(pose.shape)[:-1] + [3, 3]) + transform = torch.zeros(dims) + + transform[..., 0, 0] = ctheta + transform[..., 0, 1] = -stheta + transform[..., 1, 0] = stheta + transform[..., 1, 1] = ctheta + transform[..., 0, 2] = x + transform[..., 1, 2] = y + transform[..., 2, 2] = 1.0 + + return transform + + +class HasPose(HasPose2d): + """An abstract base class for entities that have an associated 3D pose.""" + + def get_pose(self): + """Get the 3D pose of the entity.""" + raise NotImplementedError + + def get_pose_2d(self): + """Get the 2D pose of the entity.""" + pose = self.get_pose() + axis_angle = math_utils.axis_angle_from_quat(pose[..., 3:]) + + yaw = axis_angle[..., 2:3] + xy = pose[..., :2] + + pose_2d = torch.cat([xy, yaw], dim=-1) + + return pose_2d + + +class SceneBody(HasPose): + """A helper class for working with rigid body objects in a scene.""" + + def __init__(self, scene, entity_name: str, body_name: str): + self.scene = scene + self.entity_name = entity_name + self.body_name = body_name + + def get_pose(self): + """Get the 3D pose of the entity.""" + pose = self.scene[self.entity_name].data.body_link_state_w[ + :, + self.scene[self.entity_name].data.body_names.index(self.body_name), + :7, + ] + return pose + + +class SceneAsset(HasPose): + """A helper class for working with assets in a scene.""" + + def __init__(self, scene, entity_name: str): + self.scene = scene + self.entity_name = entity_name + + def get_pose(self): + """Get the 3D pose of the entity.""" + xform_prim = self.scene[self.entity_name] + position, orientation = xform_prim.get_world_poses() + pose = torch.cat([position, orientation], dim=-1) + return pose + + def set_pose(self, pose: torch.Tensor): + """Set the 3D pose of the entity.""" + xform_prim = self.scene[self.entity_name] + position = pose[..., :3] + orientation = pose[..., 3:] + xform_prim.set_world_poses(position, orientation, None) + + +class RelativePose(HasPose): + """A helper class for computing the pose of an entity given it's relative pose to a parent.""" + + def __init__(self, relative_pose: torch.Tensor, parent: HasPose): + self.relative_pose = relative_pose + self.parent = parent + + def get_pose(self): + """Get the 3D pose of the entity.""" + + parent_pose = self.parent.get_pose() + + pose = transform_mul(parent_pose, self.relative_pose) + + return pose + + +class SceneFixture(SceneAsset, HasOccupancyMap): + """A helper class for working with assets in a scene that have an associated occupancy map.""" + + def __init__( + self, scene, entity_name: str, occupancy_map_boundary: np.ndarray, occupancy_map_resolution: float = 0.05 + ): + SceneAsset.__init__(self, scene, entity_name) + self.occupancy_map_boundary = occupancy_map_boundary + self.occupancy_map_resolution = occupancy_map_resolution + + def get_occupancy_map(self): + local_occupancy_map = OccupancyMap.from_occupancy_boundary( + boundary=self.occupancy_map_boundary, resolution=self.occupancy_map_resolution + ) + + transform = self.get_transform_2d().detach().cpu().numpy() + + occupancy_map = local_occupancy_map.transformed(transform) + + return occupancy_map + + +def place_randomly( + fixture: SceneFixture, background_occupancy_map: OccupancyMap, num_iter: int = 100, area_threshold: float = 1e-5 +): + """Place a scene fixture randomly in an unoccupied region of an occupancy.""" + + # sample random xy in bounds + bottom_left = background_occupancy_map.bottom_left_pixel_world_coords() + top_right = background_occupancy_map.top_right_pixel_world_coords() + + initial_pose = fixture.get_pose() + + for i in range(num_iter): + x = random.uniform(bottom_left[0], top_right[0]) + y = random.uniform(bottom_left[1], top_right[1]) + + yaw = torch.tensor([random.uniform(-torch.pi, torch.pi)]) + roll = torch.zeros_like(yaw) + pitch = torch.zeros_like(yaw) + + quat = math_utils.quat_from_euler_xyz(roll, pitch, yaw) + + new_pose = initial_pose.clone() + new_pose[0, 0] = x + new_pose[0, 1] = y + new_pose[0, 3:] = quat + + fixture.set_pose(new_pose) + + intersection_map = intersect_occupancy_maps([fixture.get_occupancy_map(), background_occupancy_map]) + + intersection_area = np.count_nonzero(intersection_map.occupied_mask()) * (intersection_map.resolution**2) + + if intersection_area < area_threshold: + return True + + return False diff --git a/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/transform_utils.py b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/transform_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..73406a187ffd8870672a176b5522964104b16520 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/locomanipulation_sdg/transform_utils.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import torch + +import isaaclab.utils.math as math_utils + + +def transform_mul(transform_a: torch.Tensor, transform_b: torch.Tensor) -> torch.Tensor: + """Multiply two translation, quaternion pose representations by converting to matrices first.""" + # Extract position and quaternion components + pos_a, quat_a = transform_a[..., :3], transform_a[..., 3:] + pos_b, quat_b = transform_b[..., :3], transform_b[..., 3:] + + # Convert quaternions to rotation matrices + rot_a = math_utils.matrix_from_quat(quat_a) + rot_b = math_utils.matrix_from_quat(quat_b) + + # Create pose matrices + pose_a = math_utils.make_pose(pos_a, rot_a) + pose_b = math_utils.make_pose(pos_b, rot_b) + + # Multiply pose matrices + result_pose = torch.matmul(pose_a, pose_b) + + # Extract position and rotation matrix + result_pos, result_rot = math_utils.unmake_pose(result_pose) + + # Convert rotation matrix back to quaternion + result_quat = math_utils.quat_from_matrix(result_rot) + + return torch.cat([result_pos, result_quat], dim=-1) + + +def transform_inv(transform: torch.Tensor) -> torch.Tensor: + """Invert a translation, quaternion format transformation using math_utils.""" + pos, quat = transform[..., :3], transform[..., 3:] + quat_inv = math_utils.quat_inv(quat) + pos_inv = math_utils.quat_apply(quat_inv, -pos) + return torch.cat([pos_inv, quat_inv], dim=-1) + + +def transform_relative_pose(world_pose: torch.Tensor, src_frame_pose: torch.Tensor, dst_frame_pose: torch.Tensor): + """Compute the relative pose with respect to a source frame, and apply this relative pose to a destination frame.""" + pose = transform_mul(dst_frame_pose, transform_mul(transform_inv(src_frame_pose), world_pose)) + return pose diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py new file mode 100644 index 0000000000000000000000000000000000000000..a7e1ebb36252b22f816e0d2e6e32a28c9445c04b --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py @@ -0,0 +1,1951 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import logging +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch + +from curobo.cuda_robot_model.cuda_robot_model import CudaRobotModelState +from curobo.geom.sdf.world import CollisionCheckerType +from curobo.geom.sphere_fit import SphereFitType +from curobo.geom.types import WorldConfig +from curobo.types.base import TensorDeviceType +from curobo.types.math import Pose +from curobo.types.state import JointState +from curobo.util.logger import setup_curobo_logger +from curobo.util.usd_helper import UsdHelper +from curobo.util_file import load_yaml +from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig + +import isaaclab.utils.math as PoseUtils +from isaaclab.assets import Articulation +from isaaclab.envs.manager_based_env import ManagerBasedEnv +from isaaclab.managers import SceneEntityCfg +from isaaclab.sim.spawners.materials import PreviewSurfaceCfg +from isaaclab.sim.spawners.meshes import MeshSphereCfg, spawn_mesh_sphere + +from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg +from isaaclab_mimic.motion_planners.motion_planner_base import MotionPlannerBase + + +class PlannerLogger: + """Logger class for motion planner debugging and monitoring. + + This class provides standard logging functionality while maintaining isolation from + the main application's logging configuration. The logger supports configurable verbosity + levels and formats messages consistently for debugging motion planning operations, + collision checking, and object manipulation. + """ + + def __init__(self, name: str, level: int = logging.INFO): + """Initialize the logger with specified name and level. + + Args: + name: Logger name for identification in log messages + level: Logging level (DEBUG, INFO, WARNING, ERROR) + """ + self._name = name + self._level = level + self._logger = None + + @property + def logger(self): + """Get the underlying logger instance, initializing it if needed. + + Returns: + Configured Python logger instance with stream handler and formatter + """ + if self._logger is None: + self._logger = logging.getLogger(self._name) + if not self._logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s") + handler.setFormatter(formatter) + self._logger.addHandler(handler) + self._logger.setLevel(self._level) + return self._logger + + def debug(self, msg, *args, **kwargs): + """Log debug-level message for detailed internal state information. + + Args: + msg: Message string or format string + *args: Positional arguments for message formatting + **kwargs: Keyword arguments passed to underlying logger + """ + self.logger.debug(msg, *args, **kwargs) + + def info(self, msg, *args, **kwargs): + """Log info-level message for important operational events. + + Args: + msg: Message string or format string + *args: Positional arguments for message formatting + **kwargs: Keyword arguments passed to underlying logger + """ + self.logger.info(msg, *args, **kwargs) + + def warning(self, msg, *args, **kwargs): + """Log warning-level message for potentially problematic conditions. + + Args: + msg: Message string or format string + *args: Positional arguments for message formatting + **kwargs: Keyword arguments passed to underlying logger + """ + self.logger.warning(msg, *args, **kwargs) + + def error(self, msg, *args, **kwargs): + """Log error-level message for serious problems and failures. + + Args: + msg: Message string or format string + *args: Positional arguments for message formatting + **kwargs: Keyword arguments passed to underlying logger + """ + self.logger.error(msg, *args, **kwargs) + + +@dataclass +class Attachment: + """Stores object attachment information for robot manipulation. + + This dataclass tracks the relative pose between an attached object and its parent link, + enabling the robot to maintain consistent object positioning during motion planning. + """ + + pose: Pose # Relative pose from parent link to object + parent: str # Parent link name + + +class CuroboPlanner(MotionPlannerBase): + """Motion planner for robot manipulation using cuRobo. + + This planner provides collision-aware motion planning capabilities for robotic manipulation tasks. + It integrates with Isaac Lab environments to: + + - Update collision world from current stage state + - Plan collision-free paths to target poses + - Handle object attachment and detachment during manipulation + - Execute planned motions with proper collision checking + + The planner uses cuRobo for fast motion generation and supports + multi-phase planning for contact scenarios like grasping and placing objects. + """ + + def __init__( + self, + env: ManagerBasedEnv, + robot: Articulation, + config: CuroboPlannerCfg, + task_name: str | None = None, + env_id: int = 0, + collision_checker: CollisionCheckerType = CollisionCheckerType.MESH, + num_trajopt_seeds: int = 12, + num_graph_seeds: int = 12, + interpolation_dt: float = 0.05, + ) -> None: + """Initialize the motion planner for a specific environment. + + Sets up the cuRobo motion generator with collision checking, configures the robot model, + and prepares visualization components if enabled. The planner is isolated to CUDA device + regardless of Isaac Lab's device configuration. + + Args: + env: The Isaac Lab environment instance containing the robot and scene + robot: Robot articulation to plan motions for + config: Configuration object containing planner parameters and settings + task_name: Task name for auto-configuration + env_id: Environment ID for multi-environment setups (0 to num_envs-1) + collision_checker: Type of collision checker + num_trajopt_seeds: Number of seeds for trajectory optimization + num_graph_seeds: Number of seeds for graph search + interpolation_dt: Time step for interpolating waypoints + + Raises: + ValueError: If ``robot_config_file`` is not provided + """ + # Initialize base class + super().__init__(env=env, robot=robot, env_id=env_id, debug=config.debug_planner) + + # Initialize planner logger with debug level based on config + log_level = logging.DEBUG if config.debug_planner else logging.INFO + self.logger = PlannerLogger(f"CuroboPlanner_{env_id}", log_level) + + # Store instance variables + self.config: CuroboPlannerCfg = config + self.n_repeat: int | None = self.config.n_repeat + self.step_size: float | None = self.config.motion_step_size + self.visualize_plan: bool = self.config.visualize_plan + self.visualize_spheres: bool = self.config.visualize_spheres + + # Log the config parameter values + self.logger.info(f"Config parameter values: {self.config}") + + # Initialize plan visualizer if enabled + if self.visualize_plan: + from isaaclab_mimic.motion_planners.curobo.plan_visualizer import PlanVisualizer + + # Use env-local base translation for multi-env rendering consistency + env_origin = self.env.scene.env_origins[env_id, :3] + base_translation = (self.robot.data.root_pos_w[env_id, :3] - env_origin).detach().cpu().numpy() + self.plan_visualizer = PlanVisualizer( + robot_name=self.config.robot_name, + recording_id=f"curobo_plan_{env_id}", + debug=config.debug_planner, + base_translation=base_translation, + ) + + # Store attached objects as Attachment objects + self.attached_objects: dict[str, Attachment] = {} # object_name -> Attachment + + # Initialize cuRobo components - FORCE CUDA DEVICE FOR ISOLATION + setup_curobo_logger("warn") + + # Force cuRobo to always use CUDA device regardless of Isaac Lab device + # This isolates the motion planner from Isaac Lab's device configuration + self.tensor_args: TensorDeviceType + if torch.cuda.is_available(): + idx = self.config.cuda_device if self.config.cuda_device is not None else torch.cuda.current_device() + self.tensor_args = TensorDeviceType(device=torch.device(f"cuda:{idx}"), dtype=torch.float32) + self.logger.debug(f"cuRobo motion planner initialized on CUDA device {idx}") + else: + # Fallback to CPU if CUDA not available, but this may cause issues + self.tensor_args = TensorDeviceType() + self.logger.warning("CUDA not available, cuRobo using CPU - this may cause device compatibility issues") + + # Load robot configuration + if self.config.robot_config_file is None: + raise ValueError("robot_config_file is required") + robot_cfg_file = self.config.robot_config_file + robot_cfg: dict[str, Any] = load_yaml(robot_cfg_file)["robot_cfg"] + self.logger.info(f"Loaded robot configuration from {robot_cfg_file}") + + # Configure collision spheres + if self.config.collision_spheres_file: + robot_cfg["kinematics"]["collision_spheres"] = self.config.collision_spheres_file + + # Configure extra collision spheres + if self.config.extra_collision_spheres: + robot_cfg["kinematics"]["extra_collision_spheres"] = self.config.extra_collision_spheres + + self.robot_cfg: dict[str, Any] = robot_cfg + + # Load world configuration using the config's method + world_cfg: WorldConfig = self.config.get_world_config() + + # Create motion generator config with parameters from configuration + motion_gen_config: MotionGenConfig = MotionGenConfig.load_from_robot_config( + robot_cfg, + world_cfg, + tensor_args=self.tensor_args, + collision_checker_type=self.config.collision_checker_type, + num_trajopt_seeds=self.config.num_trajopt_seeds, + num_graph_seeds=self.config.num_graph_seeds, + interpolation_dt=self.config.interpolation_dt, + collision_cache=self.config.collision_cache_size, + trajopt_tsteps=self.config.trajopt_tsteps, + collision_activation_distance=self.config.collision_activation_distance, + position_threshold=self.config.position_threshold, + rotation_threshold=self.config.rotation_threshold, + ) + + # Create motion generator + self.motion_gen: MotionGen = MotionGen(motion_gen_config) + + # Set motion generator reference for plan visualizer if enabled + if self.visualize_plan: + self.plan_visualizer.set_motion_generator_reference(self.motion_gen) + + # Create plan config with parameters from configuration + self.plan_config: MotionGenPlanConfig = MotionGenPlanConfig( + enable_graph=self.config.enable_graph, + enable_graph_attempt=self.config.enable_graph_attempt, + max_attempts=self.config.max_planning_attempts, + enable_finetune_trajopt=self.config.enable_finetune_trajopt, + time_dilation_factor=self.config.time_dilation_factor, + ) + + # Create USD helper + self.usd_helper: UsdHelper = UsdHelper() + self.usd_helper.load_stage(env.scene.stage) + + # Initialize planning state + self._current_plan: JointState | None = None + self._plan_index: int = 0 + + # Initialize visualization state + self.frame_counter: int = 0 + self.spheres: list[tuple[str, float]] | None = None + self.sphere_update_freq: int = self.config.sphere_update_freq + + # Warm up planner + self.logger.info("Warming up motion planner...") + self.motion_gen.warmup(enable_graph=True, warmup_js_trajopt=False) + + # Read static world geometry once + self._initialize_static_world() + + # Defer object validation baseline until first update_world() call when scene is fully loaded + self._expected_objects: set[str] | None = None + + # Define supported cuRobo primitive types for object discovery and pose synchronization + self.primitive_types: list[str] = ["mesh", "cuboid", "sphere", "capsule", "cylinder", "voxel", "blox"] + + # Cache object mappings + # Only recompute when objects are added/removed, not when poses change + self._cached_object_mappings: dict[str, str] | None = None + + # ===================================================================================== + # DEVICE CONVERSION UTILITIES + # ===================================================================================== + + def _to_curobo_device(self, tensor: torch.Tensor) -> torch.Tensor: + """Convert tensor to cuRobo device for isolated device management. + + Ensures all tensors used by cuRobo are on CUDA device, providing device isolation + from Isaac Lab's potentially different device configuration. This prevents device + mismatch errors and optimizes cuRobo performance. + + Args: + tensor: Input tensor (may be on any device) + + Returns: + Tensor converted to cuRobo's CUDA device with appropriate dtype + """ + return tensor.to(device=self.tensor_args.device, dtype=self.tensor_args.dtype) + + def _to_env_device(self, tensor: torch.Tensor) -> torch.Tensor: + """Convert tensor back to environment device for Isaac Lab compatibility. + + Converts cuRobo tensors back to the environment's device to ensure compatibility + with Isaac Lab operations that expect tensors on the environment's configured device. + + Args: + tensor: Input tensor from cuRobo operations (typically on CUDA) + + Returns: + Tensor converted to environment's device while preserving dtype + """ + return tensor.to(device=self.env.device, dtype=tensor.dtype) + + # ===================================================================================== + # INITIALIZATION AND CONFIGURATION + # ===================================================================================== + + def _initialize_static_world(self) -> None: + """Initialize static world geometry from USD stage. + + Reads static environment geometry once during planner initialization to establish + the base collision world. This includes walls, tables, bins, and other fixed obstacles + that don't change during the simulation. Dynamic objects are synchronized separately + in update_world() to maintain performance. + """ + env_prim_path = f"/World/envs/env_{self.env_id}" + robot_prim_path = self.config.robot_prim_path or f"{env_prim_path}/Robot" + + ignore_list = self.config.world_ignore_substrings or [ + f"{env_prim_path}/Robot", + f"{env_prim_path}/target", + "/World/defaultGroundPlane", + "/curobo", + ] + + self._static_world_config = self.usd_helper.get_obstacles_from_stage( + only_paths=[env_prim_path], + reference_prim_path=robot_prim_path, + ignore_substring=ignore_list, + ) + self._static_world_config = self._static_world_config.get_collision_check_world() + + # Initialize cuRobo world with static geometry + self.motion_gen.update_world(self._static_world_config) + + # ===================================================================================== + # PROPERTIES AND BASIC GETTERS + # ===================================================================================== + + @property + def attached_link(self) -> str: + """Default link name for object attachment operations.""" + return self.config.attached_object_link_name + + @property + def attachment_links(self) -> set[str]: + """Set of parent link names that currently have attached objects.""" + return {attachment.parent for attachment in self.attached_objects.values()} + + @property + def current_plan(self) -> JointState | None: + """Current plan from cuRobo motion generator.""" + return self._current_plan + + # ===================================================================================== + # WORLD AND OBJECT MANAGEMENT, ATTACHMENT, AND DETACHMENT + # ===================================================================================== + + def get_object_pose(self, object_name: str) -> Pose | None: + """Retrieve object pose from cuRobo's collision world model. + + Searches the collision world model for the specified object and returns its current + pose. This is useful for attachment calculations and debugging collision world state. + The method handles both mesh and cuboid object types automatically. + + Args: + object_name: Short object name used in Isaac Lab scene (e.g., "cube_1") + + Returns: + Object pose in cuRobo coordinate frame, or None if object not found + """ + # Get cached object mappings + object_mappings = self._get_object_mappings() + world_model = self.motion_gen.world_coll_checker.world_model + + object_path = object_mappings.get(object_name) + if not object_path: + self.logger.debug(f"Object {object_name} not found in world model") + return None + + # Search for object in world model + for obj_list, _ in [ + (world_model.mesh, "mesh"), + (world_model.cuboid, "cuboid"), + ]: + if not obj_list: + continue + + for obj in obj_list: + if obj.name and object_path in str(obj.name): + if obj.pose is not None: + return Pose.from_list(obj.pose, tensor_args=self.tensor_args) + + self.logger.debug(f"Object {object_name} found in mappings but pose not available") + return None + + def get_attached_pose(self, link_name: str, joint_state: JointState | None = None) -> Pose: + """Calculate pose of specified link using forward kinematics. + + Computes the world pose of any robot link at the given joint configuration. + This is essential for attachment calculations where we need to know the exact + pose of the parent link to compute relative object positions. + + Args: + link_name: Name of the robot link to get pose for + joint_state: Joint configuration to use for calculation, uses current state if None + + Returns: + World pose of the specified link in cuRobo coordinate frame + + Raises: + KeyError: If link_name is not found in the computed link poses + """ + if joint_state is None: + joint_state = self._get_current_joint_state_for_curobo() + + # Get all link states using the robot model + link_state = self.motion_gen.kinematics.get_state( + q=joint_state.position.detach().clone().to(device=self.tensor_args.device, dtype=self.tensor_args.dtype), + calculate_jacobian=False, + ) + + # Extract all link poses + link_poses = {} + if link_state.links_position is not None and link_state.links_quaternion is not None: + for i, link in enumerate(link_state.link_names): + link_poses[link] = self._make_pose( + position=link_state.links_position[..., i, :], + quaternion=link_state.links_quaternion[..., i, :], + name=link, + ) + + # For attached object link, use ee_link from robot config as parent + if link_name == self.config.attached_object_link_name: + ee_link = self.config.ee_link_name or self.robot_cfg["kinematics"]["ee_link"] + if ee_link in link_poses: + self.logger.debug(f"Using {ee_link} for {link_name}") + return link_poses[ee_link] + + # Return directly for other links + if link_name in link_poses: + return link_poses[link_name] + raise KeyError(f"Link {link_name} not found in computed link poses") + + def create_attachment( + self, object_name: str, link_name: str | None = None, joint_state: JointState | None = None + ) -> Attachment: + """Create attachment relationship between object and robot link. + + Computes the relative pose between an object and a robot link to enable the robot + to carry the object consistently during motion planning. The attachment stores the transform + from the parent link frame to the object frame, which remains constant while grasped. + + Args: + object_name: Name of the object to attach + link_name: Parent link for attachment, uses default attached_object_link if None + joint_state: Robot configuration for calculation, uses current state if None + + Returns: + Attachment object containing relative pose and parent link information + """ + if link_name is None: + link_name = self.attached_link + if joint_state is None: + joint_state = self._get_current_joint_state_for_curobo() + + # Get current link pose + link_pose = self.get_attached_pose(link_name, joint_state) + self.logger.info(f"Getting object pose for {object_name}") + obj_pose = self.get_object_pose(object_name) + + # Compute relative pose + attach_pose = link_pose.inverse().multiply(obj_pose) + + self.logger.debug(f"Creating attachment for {object_name} to {link_name}") + self.logger.debug(f"Link pose: {link_pose.position}") + self.logger.debug(f"Object pose (ACTUAL): {obj_pose.position}") + self.logger.debug(f"Computed relative pose: {attach_pose.position}") + + return Attachment(attach_pose, link_name) + + def update_world(self) -> None: + """Synchronize collision world with current Isaac Lab scene state. + + Updates all dynamic object poses in cuRobo's collision world to match their current + positions in Isaac Lab. This ensures collision checking uses accurate object positions + after simulation steps, resets, or manual object movements. Static world geometry + is loaded once during initialization and not updated here for performance. + + The method validates that the set of objects hasn't changed at runtime, as cuRobo + requires world model reinitialization when objects are added or removed. + + Raises: + RuntimeError: If the set of objects has changed at runtime + """ + + # Establish validation baseline on first call, validate on subsequent calls + if self._expected_objects is None: + self._expected_objects = set(self._get_world_object_names()) + self.logger.debug(f"Established object validation baseline: {len(self._expected_objects)} objects") + else: + # Subsequent calls: validate no changes + current_objects = set(self._get_world_object_names()) + if current_objects != self._expected_objects: + added = current_objects - self._expected_objects + removed = self._expected_objects - current_objects + + error_msg = "World objects changed at runtime!\n" + if added: + error_msg += f"Added: {added}\n" + if removed: + error_msg += f"Removed: {removed}\n" + error_msg += "cuRobo world model must be reinitialized." + + # Invalidate cached mappings since object set changed + self._cached_object_mappings = None + + raise RuntimeError(error_msg) + + # Sync object poses with Isaac Lab + self._sync_object_poses_with_isaaclab() + + if self.visualize_spheres: + self._update_sphere_visualization(force_update=True) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + def _get_world_object_names(self) -> list[str]: + """Extract all object names from cuRobo's collision world model. + + Iterates through all supported primitive types (mesh, cuboid, sphere, etc.) in the + collision world and collects their names. This is used for world validation to detect + when objects are added or removed at runtime. + + Returns: + List of all object names currently in the collision world model + """ + try: + world_model = self.motion_gen.world_coll_checker.world_model + + # Handle case where world_model might be a list + if isinstance(world_model, list): + if len(world_model) <= self.env_id: + return [] + world_model = world_model[self.env_id] + + object_names = [] + + # Get all primitive object names using the defined primitive types + for primitive_type in self.primitive_types: + if hasattr(world_model, primitive_type) and getattr(world_model, primitive_type): + primitive_list = getattr(world_model, primitive_type) + for primitive in primitive_list: + if primitive.name: + object_names.append(str(primitive.name)) + + return object_names + + except Exception as e: + self.logger.debug(f"ERROR getting world object names: {e}") + return [] + + def _sync_object_poses_with_isaaclab(self) -> None: + """Synchronize cuRobo collision world with Isaac Lab object positions. + + Updates all dynamic object poses in cuRobo's world model to match their current + positions in Isaac Lab. This ensures accurate collision checking after simulation + steps or manual object movements. Static objects (bins, tables, walls) are skipped + for performance as they shouldn't move during simulation. + + The method updates both the world model and the collision checker to ensure + consistency across all cuRobo components. + """ + # Get cached object mappings and world model + object_mappings = self._get_object_mappings() + world_model = self.motion_gen.world_coll_checker.world_model + rigid_objects = self.env.scene.rigid_objects + + updated_count = 0 + + for object_name, object_path in object_mappings.items(): + if object_name not in rigid_objects: + continue + + # Skip static mesh objects - they should not be dynamically updated + static_objects = getattr(self.config, "static_objects", []) + if any(static_name in object_name.lower() for static_name in static_objects): + self.logger.debug(f"SYNC: Skipping static object {object_name}") + continue + + # Get current pose from Lab (may be on CPU or CUDA depending on --device flag) + obj = rigid_objects[object_name] + env_origin = self.env.scene.env_origins[self.env_id] + current_pos_raw = obj.data.root_pos_w[self.env_id] - env_origin + current_quat_raw = obj.data.root_quat_w[self.env_id] # (w, x, y, z) + + # Convert to cuRobo device and extract float values for pose list + current_pos = self._to_curobo_device(current_pos_raw) + current_quat = self._to_curobo_device(current_quat_raw) + + # Convert to cuRobo pose format [x, y, z, w, x, y, z] + pose_list = [ + float(current_pos[0].item()), + float(current_pos[1].item()), + float(current_pos[2].item()), + float(current_quat[0].item()), + float(current_quat[1].item()), + float(current_quat[2].item()), + float(current_quat[3].item()), + ] + + # Update object pose in cuRobo's world model + if self._update_object_in_world_model(world_model, object_name, object_path, pose_list): + updated_count += 1 + + self.logger.debug(f"SYNC: Updated {updated_count} object poses in cuRobo world model") + + # Sync object poses with collision checker + if updated_count > 0: + # Update individual obstacle poses in collision checker + # This preserves static mesh objects unlike load_collision_model which rebuilds everything + for object_name, object_path in object_mappings.items(): + if object_name not in rigid_objects: + continue + + # Skip static mesh objects - they should not be dynamically updated + static_objects = getattr(self.config, "static_objects", []) + if any(static_name in object_name.lower() for static_name in static_objects): + continue + + # Get current pose and update in collision checker + obj = rigid_objects[object_name] + env_origin = self.env.scene.env_origins[self.env_id] + current_pos_raw = obj.data.root_pos_w[self.env_id] - env_origin + current_quat_raw = obj.data.root_quat_w[self.env_id] + + current_pos = self._to_curobo_device(current_pos_raw) + current_quat = self._to_curobo_device(current_quat_raw) + + # Create cuRobo pose and update collision checker directly + curobo_pose = self._make_pose(position=current_pos, quaternion=current_quat) + self.motion_gen.world_coll_checker.update_obstacle_pose( # type: ignore + object_path, curobo_pose, update_cpu_reference=True + ) + + self.logger.debug(f"Updated {updated_count} object poses in collision checker") + + def _get_object_mappings(self) -> dict[str, str]: + """Get object mappings with caching for performance optimization. + + Returns cached mappings if available, otherwise computes and caches them. + Cache is invalidated when the object set changes. + + Returns: + Dictionary mapping Isaac Lab object names to their corresponding USD paths + """ + if self._cached_object_mappings is None: + world_model = self.motion_gen.world_coll_checker.world_model + rigid_objects = self.env.scene.rigid_objects + self._cached_object_mappings = self._discover_object_mappings(world_model, rigid_objects) + self.logger.debug(f"Computed and cached object mappings: {len(self._cached_object_mappings)} objects") + + return self._cached_object_mappings + + def _discover_object_mappings(self, world_model, rigid_objects) -> dict[str, str]: + """Build mapping between Isaac Lab object names and cuRobo world paths. + + Automatically discovers the correspondence between Isaac Lab's rigid object names + and their full USD paths in cuRobo's world model. This mapping is essential for + pose synchronization and attachment operations, as cuRobo uses full USD paths + while Isaac Lab uses short object names. + + Args: + world_model: cuRobo's collision world model containing primitive objects + rigid_objects: Isaac Lab's rigid objects dictionary + + Returns: + Dictionary mapping Isaac Lab object names to their corresponding USD paths + """ + mappings = {} + env_prefix = f"/World/envs/env_{self.env_id}/" + world_object_paths = [] + + # Collect all primitive objects from cuRobo world model + for primitive_type in self.primitive_types: + primitive_list = getattr(world_model, primitive_type) + for primitive in primitive_list: + if primitive.name and env_prefix in str(primitive.name): + world_object_paths.append(str(primitive.name)) + + # Match Isaac Lab object names to world paths + for object_name in rigid_objects.keys(): + # Direct name matching + for path in world_object_paths: + if object_name.lower().replace("_", "") in path.lower().replace("_", ""): + mappings[object_name] = path + self.logger.debug(f"MAPPING: {object_name} -> {path}") + break + else: + self.logger.debug(f"WARNING: Could not find world path for {object_name}") + + return mappings + + def _update_object_in_world_model( + self, world_model, object_name: str, object_path: str, pose_list: list[float] + ) -> bool: + """Update a single object's pose in cuRobo's collision world model. + + Searches through all primitive types in the world model to find the specified object + and updates its pose. Uses flexible matching to handle variations in path naming + between Isaac Lab and cuRobo representations. + + Args: + world_model: cuRobo's collision world model + object_name: Short object name from Isaac Lab (e.g., "cube_1") + object_path: Full USD path for the object in cuRobo world + pose_list: New pose as [x, y, z, w, x, y, z] list in cuRobo format + + Returns: + True if object was found and successfully updated, False otherwise + """ + # Handle case where world_model might be a list + if isinstance(world_model, list): + if len(world_model) > self.env_id: + world_model = world_model[self.env_id] + else: + return False + + # Update all primitive types + for primitive_type in self.primitive_types: + primitive_list = getattr(world_model, primitive_type) + for primitive in primitive_list: + if primitive.name: + primitive_name = str(primitive.name) + # Use bidirectional matching for robust path matching + if object_path == primitive_name or object_path in primitive_name or primitive_name in object_path: + primitive.pose = pose_list + self.logger.debug(f"Updated {primitive_type} {object_name} pose") + return True + + self.logger.debug(f"WARNING: Object {object_name} not found in world model") + return False + + def _attach_object(self, object_name: str, object_path: str, env_id: int) -> bool: + """Attach an object to the robot for manipulation planning. + + Establishes an attachment between the specified object and the robot's end-effector + or configured attachment link. This enables the robot to carry the object during + motion planning while maintaining proper collision checking. The object's collision + geometry is disabled in the world model since it's now part of the robot. + + Args: + object_name: Short name of the object to attach (e.g., "cube_2") + object_path: Full USD path for the object in cuRobo world model + env_id: Environment ID for multi-environment support + + Returns: + True if attachment succeeded, False if attachment failed + """ + current_joint_state = self._get_current_joint_state_for_curobo() + + self.logger.debug(f"Attaching {object_name} at path {object_path}") + + # Create attachment record (relative pose object-frame to parent link) + attachment = self.create_attachment( + object_name, + self.config.attached_object_link_name, + current_joint_state, + ) + self.attached_objects[object_name] = attachment + success = self.motion_gen.attach_objects_to_robot( + joint_state=current_joint_state, + object_names=[object_path], + link_name=self.config.attached_object_link_name, + surface_sphere_radius=self.config.surface_sphere_radius, + sphere_fit_type=SphereFitType.SAMPLE_SURFACE, + world_objects_pose_offset=None, + ) + + if success: + self.logger.debug(f"Successfully attached {object_name}") + self.logger.debug(f"Current attached objects: {list(self.attached_objects.keys())}") + + # Force sphere visualization update + if self.visualize_spheres: + self._update_sphere_visualization(force_update=True) + + self.logger.info(f"Sphere count after attach is successful: {self._count_active_spheres()}") + + # Deactivate the original obstacle as it's now carried by the robot + self.motion_gen.world_coll_checker.enable_obstacle(object_path, enable=False) + + return True + else: + self.logger.error(f"cuRobo attach_objects_to_robot failed for {object_name}") + # Clean up on failure + if object_name in self.attached_objects: + del self.attached_objects[object_name] + return False + + def _detach_objects(self, link_names: set[str] | None = None) -> bool: + """Detach objects from robot and restore collision checking. + + Removes object attachments from specified links and re-enables collision checking + for both the objects and the parent links. This is necessary when placing objects + or changing grasps. All attached objects are detached if no specific links are provided. + + Args: + link_names: Set of parent link names to detach objects from, detaches all if None + + Returns: + True if detachment operations completed successfully, False otherwise + """ + if link_names is None: + link_names = self.attachment_links + + self.logger.debug(f"Detaching objects from links: {link_names}") + self.logger.debug(f"Current attached objects: {list(self.attached_objects.keys())}") + + # Get cached object mappings to find the USD path for re-enabling + object_mappings = self._get_object_mappings() + + detached_info = [] + detached_links = set() + for object_name, attachment in list(self.attached_objects.items()): + if attachment.parent not in link_names: + continue + + # Find object path and re-enable it in the world + object_path = object_mappings.get(object_name) + if object_path: + self.motion_gen.world_coll_checker.enable_obstacle(object_path, enable=True) # type: ignore + self.logger.debug(f"Re-enabled obstacle {object_path}") + + # Collect the link that will need re-enabling + detached_links.add(attachment.parent) + + # Remove from attached objects and log info + del self.attached_objects[object_name] + detached_info.append((object_name, attachment.parent)) + + if detached_info: + for obj_name, parent_link in detached_info: + self.logger.debug(f"Detached {obj_name} from {parent_link}") + + # Re-enable collision checking for the attachment links (following the planning pattern) + if detached_links: + self._set_active_links(list(detached_links), active=True) + self.logger.debug(f"Re-enabled collision for attachment links: {detached_links}") + + # Call cuRobo's detach for each link + for link_name in link_names: + self.motion_gen.detach_object_from_robot(link_name=link_name) + self.logger.debug(f"Called cuRobo detach for link {link_name}") + + return True + + def get_attached_objects(self) -> list[str]: + """Get list of currently attached object names. + + Returns the short names of all objects currently attached to the robot. + These names correspond to Isaac Lab scene object names, not full USD paths. + + Returns: + List of attached object names (e.g., ["cube_1", "cube_2"])""" + return list(self.attached_objects.keys()) + + def has_attached_objects(self) -> bool: + """Check if any objects are currently attached to the robot. + + Useful for determining gripper state and collision checking configuration + before planning motions. + + Returns: + True if one or more objects are attached, False if no attachments exist + """ + return len(self.attached_objects) != 0 + + # ===================================================================================== + # JOINT STATE AND KINEMATICS + # ===================================================================================== + + def _get_current_joint_state_for_curobo(self) -> JointState: + """ + Construct the current joint state for cuRobo with zero velocity and acceleration. + + This helper reads the robot's joint positions from Isaac Lab for the current environment + and pairs them with zero velocities and accelerations as required by cuRobo planning. + All tensors are moved to the cuRobo device and reordered to match the kinematic chain + used by the cuRobo motion generator. + + Returns: + JointState on the cuRobo device, ordered according to + `self.motion_gen.kinematics.joint_names`, with position from the robot + and zero velocity/acceleration. + """ + # Fetch joint position (shape: [1, num_joints]) + joint_pos_raw: torch.Tensor = self.robot.data.joint_pos[self.env_id, :].unsqueeze(0) + joint_vel_raw: torch.Tensor = torch.zeros_like(joint_pos_raw) + joint_acc_raw: torch.Tensor = torch.zeros_like(joint_pos_raw) + + # Move to cuRobo device + joint_pos: torch.Tensor = self._to_curobo_device(joint_pos_raw) + joint_vel: torch.Tensor = self._to_curobo_device(joint_vel_raw) + joint_acc: torch.Tensor = self._to_curobo_device(joint_acc_raw) + + cu_js: JointState = JointState( + position=joint_pos, + velocity=joint_vel, + acceleration=joint_acc, + joint_names=self.robot.data.joint_names, + tensor_args=self.tensor_args, + ) + return cu_js.get_ordered_joint_state(self.motion_gen.kinematics.joint_names) + + def get_ee_pose(self, joint_state: JointState) -> Pose: + """Compute end-effector pose from joint configuration. + + Uses cuRobo's forward kinematics to calculate the end-effector pose + at the specified joint configuration. Handles device conversion to ensure + compatibility with cuRobo's CUDA-based computations. + + Args: + joint_state: Robot joint configuration to compute end-effector pose from + + Returns: + End-effector pose in world coordinates + """ + # Ensure joint state is on CUDA device for cuRobo + if isinstance(joint_state.position, torch.Tensor): + cuda_position = self._to_curobo_device(joint_state.position) + else: + cuda_position = self._to_curobo_device(torch.tensor(joint_state.position)) + + # Create new joint state with CUDA tensors + cuda_joint_state = JointState( + position=cuda_position, + velocity=( + self._to_curobo_device(joint_state.velocity.detach().clone()) + if joint_state.velocity is not None + else torch.zeros_like(cuda_position) + ), + acceleration=( + self._to_curobo_device(joint_state.acceleration.detach().clone()) + if joint_state.acceleration is not None + else torch.zeros_like(cuda_position) + ), + joint_names=joint_state.joint_names, + tensor_args=self.tensor_args, + ) + + kin_state: Any = self.motion_gen.rollout_fn.compute_kinematics(cuda_joint_state) + return kin_state.ee_pose + + # ===================================================================================== + # PLANNING CORE METHODS + # ===================================================================================== + + def _make_pose( + self, + position: torch.Tensor | np.ndarray | list[float] | None = None, + quaternion: torch.Tensor | np.ndarray | list[float] | None = None, + *, + name: str | None = None, + normalize_rotation: bool = False, + ) -> Pose: + """Create a cuRobo Pose with sensible defaults and device/dtype alignment. + + Auto-populates missing fields with identity values and ensures tensors are + on the cuRobo device with the correct dtype. + + Args: + position: Optional position as Tensor/ndarray/list. Defaults to [0, 0, 0]. + quaternion: Optional quaternion as Tensor/ndarray/list (w, x, y, z). Defaults to [1, 0, 0, 0]. + name: Optional name of the link that this pose represents. + normalize_rotation: Whether to normalize the quaternion inside Pose. + + Returns: + Pose: A cuRobo Pose on the configured cuRobo device and dtype. + """ + # Defaults + if position is None: + position = torch.tensor([0.0, 0.0, 0.0], dtype=self.tensor_args.dtype, device=self.tensor_args.device) + if quaternion is None: + quaternion = torch.tensor( + [1.0, 0.0, 0.0, 0.0], dtype=self.tensor_args.dtype, device=self.tensor_args.device + ) + + # Convert to tensors if needed + if not isinstance(position, torch.Tensor): + position = torch.tensor(position, dtype=self.tensor_args.dtype, device=self.tensor_args.device) + else: + position = self._to_curobo_device(position) + + if not isinstance(quaternion, torch.Tensor): + quaternion = torch.tensor(quaternion, dtype=self.tensor_args.dtype, device=self.tensor_args.device) + else: + quaternion = self._to_curobo_device(quaternion) + + return Pose(position=position, quaternion=quaternion, name=name, normalize_rotation=normalize_rotation) + + def _set_active_links(self, links: list[str], active: bool) -> None: + """Configure collision checking for specific robot links. + + Enables or disables collision sphere checking for the specified links. + This is essential for contact scenarios where certain links (like fingers + or attachment points) need collision checking disabled to allow contact + with objects being grasped. + + Args: + links: List of link names to enable or disable collision checking for + active: True to enable collision checking, False to disable + """ + for link in links: + if active: + self.motion_gen.kinematics.kinematics_config.enable_link_spheres(link) + else: + self.motion_gen.kinematics.kinematics_config.disable_link_spheres(link) + + def plan_motion( + self, + target_pose: torch.Tensor, + step_size: float | None = None, + enable_retiming: bool | None = None, + ) -> bool: + """Plan collision-free motion to target pose. + + Plans a trajectory from the current robot configuration to the specified target pose. + The method assumes that world updates and locked joint configurations have already + been handled. Supports optional linear retiming for consistent execution speeds. + + Args: + target_pose: Target end-effector pose as 4x4 transformation matrix + step_size: Step size for linear retiming, enables retiming if provided + enable_retiming: Whether to enable linear retiming, auto-detected from step_size if None + + Returns: + True if planning succeeded and a valid trajectory was found, False otherwise + """ + if enable_retiming is None: + enable_retiming = step_size is not None + + # Ensure target pose is on cuRobo device (CUDA) for device isolation + target_pose_cuda = self._to_curobo_device(target_pose) + + target_pos: torch.Tensor + target_rot: torch.Tensor + target_pos, target_rot = PoseUtils.unmake_pose(target_pose_cuda) + target_curobo_pose: Pose = self._make_pose( + position=target_pos, + quaternion=PoseUtils.quat_from_matrix(target_rot), + ) + + start_state: JointState = self._get_current_joint_state_for_curobo() + + self.logger.debug(f"Retiming enabled: {enable_retiming}, Step size: {step_size}") + + success: bool = self._plan_to_contact( + start_state=start_state, + goal_pose=target_curobo_pose, + retreat_distance=self.config.retreat_distance, + approach_distance=self.config.approach_distance, + retime_plan=enable_retiming, + step_size=step_size, + contact=False, + ) + + # Visualize plan if enabled + if success and self.visualize_plan and self._current_plan is not None: + # Get current spheres for visualization + self._sync_object_poses_with_isaaclab() + cu_js = self._get_current_joint_state_for_curobo() + sphere_list = self.motion_gen.kinematics.get_robot_as_spheres(cu_js.position)[0] + + # Split spheres into robot and attached object spheres + robot_spheres = [] + attached_spheres = [] + robot_link_count = 0 + + # Count robot link spheres + robot_links = [ + link + for link in self.robot_cfg["kinematics"]["collision_link_names"] + if link != self.config.attached_object_link_name + ] + for link_name in robot_links: + link_spheres = self.motion_gen.kinematics.kinematics_config.get_link_spheres(link_name) + if link_spheres is not None: + robot_link_count += int(torch.sum(link_spheres[:, 3] > 0).item()) + + # Split spheres + for i, sphere in enumerate(sphere_list): + if i < robot_link_count: + robot_spheres.append(sphere) + else: + attached_spheres.append(sphere) + + # Compute end-effector positions for visualization + ee_positions_list = [] + try: + for i in range(len(self._current_plan.position)): + js: JointState = self._current_plan[i] + kin = self.motion_gen.compute_kinematics(js) + ee_pos = kin.ee_position if hasattr(kin, "ee_position") else kin.ee_pose.position + ee_positions_list.append(ee_pos.cpu().numpy().squeeze()) + + self.logger.debug( + f"Link names from kinematics: {kin.link_names if len(ee_positions_list) > 0 else 'No EE positions'}" + ) + + except Exception as e: + self.logger.debug(f"Failed to compute EE positions for visualization: {e}") + ee_positions_list = None + + try: + world_scene = WorldConfig.get_scene_graph(self.motion_gen.world_coll_checker.world_model) + except Exception: + world_scene = None + + # Visualize plan + self.plan_visualizer.visualize_plan( + plan=self._current_plan, + target_pose=target_pose, + robot_spheres=robot_spheres, + attached_spheres=attached_spheres, + ee_positions=np.array(ee_positions_list) if ee_positions_list else None, + world_scene=world_scene, + ) + + # Animate EE positions over the timeline for playback + if ee_positions_list: + self.plan_visualizer.animate_plan(np.array(ee_positions_list)) + + # Animate spheres along the path for collision visualization + self.plan_visualizer.animate_spheres_along_path( + plan=self._current_plan, + robot_spheres_at_start=robot_spheres, + attached_spheres_at_start=attached_spheres, + timeline="sphere_animation", + interpolation_steps=15, # More steps for smoother animation + ) + + return success + + def _plan_to_contact_pose( + self, + start_state: JointState, + goal_pose: Pose, + contact: bool = True, + ) -> bool: + """Plan motion with configurable collision checking for contact scenarios. + + Plans a trajectory while optionally disabling collision checking for hand links and + attached objects. This is crucial for grasping and placing operations where contact + is expected and collision checking would prevent successful planning. + + Args: + start_state: Starting joint configuration for planning + goal_pose: Target pose to reach in cuRobo coordinate frame + contact: True to disable hand/attached object collisions for contact planning + retime_plan: Whether to apply linear retiming to the resulting trajectory + step_size: Step size for retiming if retime_plan is True + + Returns: + True if planning succeeded, False if no valid trajectory found + """ + # Use configured hand link names instead of hardcoded ones + disable_link_names: list[str] = self.config.hand_link_names.copy() + link_spheres: dict[str, torch.Tensor] = {} + + # Count spheres before planning + sphere_counts_before = self._count_active_spheres() + self.logger.debug( + f"Planning phase contact={contact}: Spheres before - Total: {sphere_counts_before['total']}, Robot:" + f" {sphere_counts_before['robot_links']}, Attached: {sphere_counts_before['attached_objects']}" + ) + + if contact: + # Store current spheres for the attached link so we can restore later + attached_links: list[str] = list(self.attachment_links) + for attached_link in attached_links: + link_spheres[attached_link] = self.motion_gen.kinematics.kinematics_config.get_link_spheres( + attached_link + ).clone() + + self.logger.debug(f"Attached link: {attached_links}") + # Disable all specified links for contact planning + self.logger.debug(f"Disable link names: {disable_link_names}") + self._set_active_links(disable_link_names + attached_links, active=False) + else: + self.logger.debug(f"Disable link names: {disable_link_names}") + + # Count spheres after link disabling + sphere_counts_after_disable = self._count_active_spheres() + self.logger.debug( + f"Planning phase contact={contact}: Spheres after disable - Total:" + f" {sphere_counts_after_disable['total']}, Robot: {sphere_counts_after_disable['robot_links']}," + f" Attached: {sphere_counts_after_disable['attached_objects']}" + ) + + planning_success = False + try: + result: Any = self.motion_gen.plan_single(start_state, goal_pose, self.plan_config) + + if result.success.item(): + if result.optimized_plan is not None and len(result.optimized_plan.position) != 0: + self._current_plan = result.optimized_plan + self.logger.debug(f"Using optimized plan with {len(self._current_plan.position)} waypoints") + else: + self._current_plan = result.get_interpolated_plan() + self.logger.debug(f"Using interpolated plan with {len(self._current_plan.position)} waypoints") + + self._current_plan = self.motion_gen.get_full_js(self._current_plan) + common_js_names: list[str] = [ + x for x in self.robot.data.joint_names if x in self._current_plan.joint_names + ] + self._current_plan = self._current_plan.get_ordered_joint_state(common_js_names) + self._plan_index = 0 + + planning_success = True + self.logger.debug(f"Contact planning succeeded with {len(self._current_plan.position)} waypoints") + else: + self.logger.debug(f"Contact planning failed: {result.status}") + + except Exception as e: + self.logger.debug(f"Error during planning: {e}") + + # Always restore sphere state after planning, regardless of success + if contact: + self._set_active_links(disable_link_names, active=True) + for attached_link, spheres in link_spheres.items(): + self.motion_gen.kinematics.kinematics_config.update_link_spheres(attached_link, spheres) + return planning_success + + def _plan_to_contact( + self, + start_state: JointState, + goal_pose: Pose, + retreat_distance: float, + approach_distance: float, + contact: bool = False, + retime_plan: bool = False, + step_size: float | None = None, + ) -> bool: + """Execute multi-phase contact planning with approach and retreat phases. + + Implements a planning strategy for manipulation tasks that require approach and contact handling. + Plans multiple trajectory segments with different collision checking configurations. + + Args: + start_state: Starting joint state for planning + goal_pose: Target pose to reach + retreat_distance: Distance to retreat before transition to contact + approach_distance: Distance to approach before final pose + contact: Whether to enable contact planning mode + retime_plan: Whether to retime the resulting plan + step_size: Step size for retiming (only used if retime_plan is True) + + Returns: + True if all planning phases succeeded, False if any phase failed + """ + self.logger.debug(f"Multi-phase planning: retreat={retreat_distance}, approach={approach_distance}") + + target_poses: list[Pose] = [] + contacts: list[bool] = [] + + if retreat_distance is not None and retreat_distance > 0: + ee_pose: Pose = self.get_ee_pose(start_state) + retreat_pose: Pose = ee_pose.multiply( + self._make_pose( + position=[0.0, 0.0, -retreat_distance], + ) + ) + target_poses.append(retreat_pose) + contacts.append(True) + contacts.append(contact) + if approach_distance is not None and approach_distance > 0: + approach_pose: Pose = goal_pose.multiply( + self._make_pose( + position=[0.0, 0.0, -approach_distance], + ) + ) + target_poses.append(approach_pose) + contacts.append(True) + + target_poses.append(goal_pose) + + current_state: JointState = start_state + full_plan: JointState | None = None + + for i, (target_pose, contact_flag) in enumerate(zip(target_poses, contacts)): + self.logger.debug( + f"Planning phase {i + 1} of {len(target_poses)}: contact={contact_flag} (collision" + f" {'disabled' if contact_flag else 'enabled'})" + ) + + success: bool = self._plan_to_contact_pose( + start_state=current_state, + goal_pose=target_pose, + contact=contact_flag, + ) + + if not success: + self.logger.debug(f"Phase {i + 1} planning failed") + return False + + if full_plan is None: + full_plan = self._current_plan + else: + full_plan = full_plan.stack(self._current_plan) + + last_waypoint: torch.Tensor = self._current_plan.position[-1] + current_state = JointState( + position=last_waypoint.unsqueeze(0), + velocity=torch.zeros_like(last_waypoint.unsqueeze(0)), + acceleration=torch.zeros_like(last_waypoint.unsqueeze(0)), + joint_names=self._current_plan.joint_names, + ) + current_state = current_state.get_ordered_joint_state(self.motion_gen.kinematics.joint_names) + + self._current_plan = full_plan + self._plan_index = 0 + + if retime_plan and step_size is not None: + original_length: int = len(self._current_plan.position) + self._current_plan = self._linearly_retime_plan(step_size=step_size, plan=self._current_plan) + self.logger.debug( + f"Retimed complete plan from {original_length} to {len(self._current_plan.position)} waypoints" + ) + + self.logger.debug(f"Multi-phase planning succeeded with {len(self._current_plan.position)} total waypoints") + + return True + + def _linearly_retime_plan( + self, + step_size: float = 0.01, + plan: JointState | None = None, + ) -> JointState | None: + """Apply linear retiming to trajectory for consistent execution speed. + + Resamples the trajectory with uniform spacing between waypoints to ensure + consistent motion speed during execution. + + Args: + step_size: Desired spacing between waypoints in joint space + plan: Trajectory to retime, uses current plan if None + + Returns: + Retimed trajectory with uniform waypoint spacing, or None if plan is invalid + """ + if plan is None: + plan = self._current_plan + + if plan is None or len(plan.position) == 0: + return plan + + path = plan.position + + if len(path) <= 1: + return plan + + deltas = path[1:] - path[:-1] + distances = torch.norm(deltas, dim=-1) + + waypoints = [path[0]] + for distance, waypoint in zip(distances, path[1:]): + if distance > 1e-6: + waypoints.append(waypoint) + + if len(waypoints) <= 1: + return plan + + waypoints = torch.stack(waypoints) + + if len(waypoints) > 1: + deltas = waypoints[1:] - waypoints[:-1] + distances = torch.norm(deltas, dim=-1) + cum_distances = torch.cat([torch.zeros(1, device=distances.device), torch.cumsum(distances, dim=0)]) + + if len(waypoints) < 2 or cum_distances[-1] < 1e-6: + return plan + + total_distance = cum_distances[-1] + num_steps = int(torch.ceil(total_distance / step_size).item()) + 1 + + # Create linearly spaced distances + sampled_distances = torch.linspace(cum_distances[0], cum_distances[-1], num_steps, device=cum_distances.device) + + # Linear interpolation + indices = torch.searchsorted(cum_distances, sampled_distances) + indices = torch.clamp(indices, 1, len(cum_distances) - 1) + + # Get interpolation weights + weights = (sampled_distances - cum_distances[indices - 1]) / ( + cum_distances[indices] - cum_distances[indices - 1] + ) + weights = weights.unsqueeze(-1) + + # Interpolate waypoints + sampled_waypoints = (1 - weights) * waypoints[indices - 1] + weights * waypoints[indices] + + self.logger.debug( + f"Retiming: {len(path)} to {len(sampled_waypoints)} waypoints, " + f"Distance: {total_distance:.3f}, Step size: {step_size}" + ) + + retimed_plan = JointState( + position=sampled_waypoints, + velocity=torch.zeros( + (len(sampled_waypoints), plan.velocity.shape[-1]), + device=plan.velocity.device, + dtype=plan.velocity.dtype, + ), + acceleration=torch.zeros( + (len(sampled_waypoints), plan.acceleration.shape[-1]), + device=plan.acceleration.device, + dtype=plan.acceleration.dtype, + ), + joint_names=plan.joint_names, + ) + + return retimed_plan + + def has_next_waypoint(self) -> bool: + """Check if more waypoints remain in the current trajectory. + + Returns: + True if there are unprocessed waypoints, False if trajectory is complete or empty + """ + return self._current_plan is not None and self._plan_index < len(self._current_plan.position) + + def get_next_waypoint_ee_pose(self) -> Pose: + """Get end-effector pose for the next waypoint in the trajectory. + + Advances the trajectory execution index and computes the end-effector pose + for the next waypoint using forward kinematics. + + Returns: + End-effector pose for the next waypoint in world coordinates + + Raises: + IndexError: If no more waypoints remain in the trajectory + """ + if not self.has_next_waypoint(): + raise IndexError("No more waypoints in the plan.") + next_joint_state: JointState = self._current_plan[self._plan_index] + self._plan_index += 1 + eef_state: CudaRobotModelState = self.motion_gen.compute_kinematics(next_joint_state) + return eef_state.ee_pose + + def reset_plan(self) -> None: + """Reset trajectory execution state. + + Clears the current trajectory and resets the execution index to zero. + This prepares the planner for a new planning operation. + """ + self._plan_index = 0 + self._current_plan = None + if self.visualize_plan and hasattr(self, "plan_visualizer"): + self.plan_visualizer.clear_visualization() + self.plan_visualizer.mark_idle() + + def get_planned_poses(self) -> list[torch.Tensor]: + """Extract all end-effector poses from current trajectory. + + Computes end-effector poses for all waypoints in the current trajectory without + affecting the execution state. Optionally repeats the final pose multiple times + if configured for stable goal reaching. + + Returns: + List of end-effector poses as 4x4 transformation matrices, with optional repetition + """ + if self._current_plan is None: + return [] + + # Save current execution state + original_plan_index = self._plan_index + + # Iterate through the plan to get all poses + planned_poses: list[torch.Tensor] = [] + self._plan_index = 0 + while self.has_next_waypoint(): + # Directly use the joint state from the plan to compute pose + # without advancing the main plan index in get_next_waypoint_ee_pose + next_joint_state: JointState = self._current_plan[self._plan_index] + self._plan_index += 1 # Manually advance index for this loop + eef_state: Any = self.motion_gen.compute_kinematics(next_joint_state) + planned_pose: Pose | None = eef_state.ee_pose + + if planned_pose is not None: + # Convert pose to environment device for compatibility + position = ( + self._to_env_device(planned_pose.position) + if isinstance(planned_pose.position, torch.Tensor) + else planned_pose.position + ) + rotation = ( + self._to_env_device(planned_pose.get_rotation()) + if isinstance(planned_pose.get_rotation(), torch.Tensor) + else planned_pose.get_rotation() + ) + planned_poses.append(PoseUtils.make_pose(position, rotation)[0]) + + # Restore the original execution state + self._plan_index = original_plan_index + + if self.n_repeat is not None and self.n_repeat > 0 and len(planned_poses) > 0: + self.logger.info(f"Repeating final pose {self.n_repeat} times") + final_pose: torch.Tensor = planned_poses[-1] + planned_poses.extend([final_pose] * self.n_repeat) + + return planned_poses + + # ===================================================================================== + # VISUALIZATION METHODS + # ===================================================================================== + + def _update_visualization_at_joint_positions(self, joint_positions: torch.Tensor) -> None: + """Update sphere visualization for the robot at specific joint positions. + + Args: + joint_positions: Joint configuration to visualize collision spheres at + """ + if not self.visualize_spheres: + return + + self.frame_counter += 1 + if self.frame_counter % self.sphere_update_freq != 0: + return + + original_joints: torch.Tensor = self.robot.data.joint_pos[self.env_id].clone() + + try: + # Ensure joint positions are on environment device for robot commands + env_joint_positions = ( + self._to_env_device(joint_positions) if joint_positions.device != self.env.device else joint_positions + ) + self.robot.set_joint_position_target(env_joint_positions.view(1, -1), env_ids=[self.env_id]) + self._update_sphere_visualization(force_update=False) + finally: + self.robot.set_joint_position_target(original_joints.unsqueeze(0), env_ids=[self.env_id]) + + def _update_sphere_visualization(self, force_update: bool = True) -> None: + """Update visual representation of robot collision spheres in USD stage. + + Creates or updates sphere primitives in the USD stage to show the robot's + collision model. Different colors are used for robot links (green) and + attached objects (orange) to help distinguish collision boundaries. + + Args: + force_update: True to recreate all spheres, False to update existing positions only + """ + # Get current sphere data + cu_js = self._get_current_joint_state_for_curobo() + sphere_position = self._to_curobo_device( + cu_js.position if isinstance(cu_js.position, torch.Tensor) else torch.tensor(cu_js.position) + ) + sphere_list = self.motion_gen.kinematics.get_robot_as_spheres(sphere_position)[0] + robot_link_count = self._get_robot_link_sphere_count() + + # Remove existing spheres if force update or first time + if (self.spheres is None or force_update) and self.spheres is not None: + self._remove_existing_spheres() + + # Initialize sphere list if needed + if self.spheres is None or force_update: + self.spheres = [] + + # Create or update all spheres + for sphere_idx, sphere in enumerate(sphere_list): + if not self._is_valid_sphere(sphere): + continue + + sphere_config = self._create_sphere_config(sphere_idx, sphere, robot_link_count) + prim_path = f"/curobo/robot_sphere_{sphere_idx}" + + # Remove old sphere if updating + if not (self.spheres is None or force_update): + if sphere_idx < len(self.spheres) and self.usd_helper.stage.GetPrimAtPath(prim_path).IsValid(): + self.usd_helper.stage.RemovePrim(prim_path) + + # Spawn sphere + spawn_mesh_sphere(prim_path=prim_path, translation=sphere_config["position"], cfg=sphere_config["cfg"]) + + # Store reference if creating new + if self.spheres is None or force_update or sphere_idx >= len(self.spheres): + self.spheres.append((prim_path, float(sphere.radius))) + + def _get_robot_link_sphere_count(self) -> int: + """Calculate total number of collision spheres for robot links excluding attached objects. + + Iterates through all robot collision links (excluding the attached object link) and + counts the active collision spheres for each link. This count is used to determine + which spheres in the visualization represent robot links vs attached objects. + + Returns: + Total number of active collision spheres for robot links only + """ + sphere_config = self.motion_gen.kinematics.kinematics_config + robot_links = [ + link + for link in self.robot_cfg["kinematics"]["collision_link_names"] + if link != self.config.attached_object_link_name + ] + return sum( + int(torch.sum(sphere_config.get_link_spheres(link_name)[:, 3] > 0).item()) for link_name in robot_links + ) + + def _remove_existing_spheres(self) -> None: + """Remove all existing sphere visualization primitives from the USD stage. + + Iterates through all stored sphere references and removes their corresponding + USD primitives from the stage. This is used during force updates or when + recreating the sphere visualization from scratch. + """ + stage = self.usd_helper.stage + for prim_path, _ in self.spheres: + if stage.GetPrimAtPath(prim_path).IsValid(): + stage.RemovePrim(prim_path) + + def _is_valid_sphere(self, sphere) -> bool: + """Validate sphere data for visualization rendering. + + Checks if a sphere has valid position coordinates (no NaN values) and a positive + radius. Invalid spheres are skipped during visualization to prevent rendering errors. + + Args: + sphere: Sphere object containing position and radius data + + Returns: + True if sphere has valid position and positive radius, False otherwise + """ + pos_tensor = torch.tensor(sphere.position, dtype=torch.float32) + return not torch.isnan(pos_tensor).any() and sphere.radius > 0 + + def _create_sphere_config(self, sphere_idx: int, sphere, robot_link_count: int) -> dict: + """Create sphere configuration with position and visual properties for USD rendering. + + Determines sphere type (robot link vs attached object), calculates world position, + and creates the appropriate visual configuration including colors and materials. + Robot link spheres are green with lower opacity, while attached object spheres + are orange with higher opacity for better distinction. + + Args: + sphere_idx: Index of the sphere in the sphere list + sphere: Sphere object containing position and radius data + robot_link_count: Total number of robot link spheres (for type determination) + + Returns: + Dictionary containing 'position' (world coordinates) and 'cfg' (MeshSphereCfg) + """ + + is_attached = sphere_idx >= robot_link_count + color = (1.0, 0.5, 0.0) if is_attached else (0.0, 1.0, 0.0) + opacity = 0.9 if is_attached else 0.5 + + # Calculate position in world frame (do not use env_origin) + root_translation = (self.robot.data.root_pos_w[self.env_id, :3]).detach().cpu().numpy() + position = sphere.position.cpu().numpy() if hasattr(sphere.position, "cpu") else sphere.position + if not is_attached: + position = position + root_translation + + return { + "position": position, + "cfg": MeshSphereCfg( + radius=float(sphere.radius), + visual_material=PreviewSurfaceCfg(diffuse_color=color, opacity=opacity, emissive_color=color), + ), + } + + def _is_sphere_attached_object(self, sphere_index: int, sphere_config: Any) -> bool: + """Check if a sphere belongs to attached_object link. + + Args: + sphere_index: Index of the sphere to check + sphere_config: Sphere configuration object + + Returns: + True if sphere belongs to an attached object, False if it's a robot link sphere + """ + # Get total number of robot link spheres (excluding attached_object) + robot_links = [ + link + for link in self.robot_cfg["kinematics"]["collision_link_names"] + if link != self.config.attached_object_link_name + ] + + total_robot_spheres = 0 + for link_name in robot_links: + try: + link_spheres = sphere_config.get_link_spheres(link_name) + active_spheres = torch.sum(link_spheres[:, 3] > 0).item() + total_robot_spheres += int(active_spheres) + except Exception: + continue + + # If sphere_index >= total_robot_spheres, it's an attached object sphere + is_attached = sphere_index >= total_robot_spheres + + if sphere_index < 5: # Debug first few spheres + self.logger.debug( + f"SPHERE {sphere_index}: total_robot_spheres={total_robot_spheres}, is_attached={is_attached}" + ) + + return is_attached + + # ===================================================================================== + # HIGH-LEVEL PLANNING INTERFACE + # ===================================================================================== + + def update_world_and_plan_motion( + self, + target_pose: torch.Tensor, + expected_attached_object: str | None = None, + env_id: int = 0, + step_size: float | None = None, + enable_retiming: bool | None = None, + ) -> bool: + """Complete planning pipeline with world updates and object attachment handling. + + Provides a high-level interface that handles the complete planning workflow: + world synchronization, object attachment/detachment, gripper configuration, + and motion planning. + + Args: + target_pose: Target end-effector pose as 4x4 transformation matrix + expected_attached_object: Name of object that should be attached, None for no attachment + env_id: Environment ID for multi-environment setups + step_size: Step size for linear retiming if retiming is enabled + enable_retiming: Whether to enable linear retiming of trajectory + + Returns: + True if complete planning pipeline succeeded, False if any step failed + """ + # Always reset the plan before starting a new one to ensure a clean state + self.reset_plan() + + self.logger.debug("=== MOTION PLANNING DEBUG ===") + self.logger.debug(f"Expected attached object: {expected_attached_object}") + + self.update_world() + gripper_closed = expected_attached_object is not None + self._set_gripper_state(gripper_closed) + current_attached = self.get_attached_objects() + gripper_pos = self.robot.data.joint_pos[env_id, -2:] + + self.logger.debug(f"Current attached objects: {current_attached}") + + # Attach object if expected but not currently attached + if expected_attached_object and expected_attached_object not in current_attached: + self.logger.debug(f"Need to attach {expected_attached_object}") + + object_mappings = self._get_object_mappings() + + self.logger.debug(f"Object mappings found: {list(object_mappings.keys())}") + + if expected_attached_object in object_mappings: + expected_path = object_mappings[expected_attached_object] + + self.logger.debug(f"Object path: {expected_path}") + + # Debug object poses + rigid_objects = self.env.scene.rigid_objects + if expected_attached_object in rigid_objects: + obj = rigid_objects[expected_attached_object] + origin = self.env.scene.env_origins[env_id] + obj_pos = obj.data.root_pos_w[env_id] - origin + self.logger.debug(f"Isaac Lab object position: {obj_pos}") + + # Debug end-effector position + ee_frame_cfg = SceneEntityCfg("ee_frame") + ee_frame = self.env.scene[ee_frame_cfg.name] + ee_pos = ee_frame.data.target_pos_w[env_id, 0, :] - origin + self.logger.debug(f"End-effector position: {ee_pos}") + + # Debug distance + distance = torch.linalg.vector_norm(obj_pos - ee_pos).item() + self.logger.debug(f"Distance EE to object: {distance:.4f}") + + # Debug gripper state + gripper_open_val = self.config.grasp_gripper_open_val + self.logger.debug(f"Gripper positions: {gripper_pos}") + self.logger.debug(f"Gripper open val: {gripper_open_val}") + + is_grasped = self._check_object_grasped(gripper_pos, expected_attached_object) + + self.logger.debug(f"Is grasped check result: {is_grasped}") + + if is_grasped: + self._attach_object(expected_attached_object, expected_path, env_id) + self.logger.debug(f"Attached {expected_attached_object}") + else: + self.logger.debug( + "Object not detected as grasped - attachment skipped" + ) # This will cause collision with ghost object! + else: + self.logger.debug(f"Object {expected_attached_object} not found in world mappings") + + # Detach objects if no object should be attached (i.e., placing/releasing) + if expected_attached_object is None and current_attached: + self.logger.debug("Detaching all objects as no object expected to be attached") + self._detach_objects() + + self.logger.debug(f"Planning motion with attached objects: {self.get_attached_objects()}") + + plan_success = self.plan_motion(target_pose, step_size, enable_retiming) + + self.logger.debug(f"Planning result: {plan_success}") + self.logger.debug("=== END POST-GRASP DEBUG ===") + + self._detach_objects() + + return plan_success + + # ===================================================================================== + # UTILITY METHODS + # ===================================================================================== + + def _check_object_grasped(self, gripper_pos: torch.Tensor, object_name: str) -> bool: + """Check if a specific object is currently grasped by the robot. + + Uses gripper position to determine if an object is grasped. + + Args: + gripper_pos: Gripper position tensor + object_name: Name of object to check (e.g., "cube_1") + + Returns: + True if object is detected as grasped + """ + gripper_open_val = self.config.grasp_gripper_open_val + object_grasped = gripper_pos[0].item() < gripper_open_val + + self.logger.info( + f"Object {object_name} is grasped: {object_grasped}" + if object_grasped + else f"Object {object_name} is not grasped" + ) + + return object_grasped + + def _set_gripper_state(self, has_attached_objects: bool) -> None: + """Configure gripper joint positions based on object attachment status. + + Sets the gripper to closed position when objects are attached and open position + when no objects are attached. This ensures proper collision checking and planning + with the correct gripper configuration. + + Args: + has_attached_objects: True if robot currently has attached objects requiring closed gripper + """ + if has_attached_objects: + # Closed gripper for grasping + locked_joints = self.config.gripper_closed_positions + else: + # Open gripper for manipulation + locked_joints = self.config.gripper_open_positions + + self.motion_gen.update_locked_joints(locked_joints, self.robot_cfg) + + def _count_active_spheres(self) -> dict[str, int]: + """Count active collision spheres by category for debugging. + + Analyzes the current collision sphere configuration to provide detailed + statistics about robot links vs attached object spheres. This is helpful + for debugging collision checking issues and attachment problems. + + Returns: + Dictionary containing sphere counts by category (total, robot_links, attached_objects) + """ + cu_js = self._get_current_joint_state_for_curobo() + + # Ensure position tensor is on CUDA for cuRobo + if isinstance(cu_js.position, torch.Tensor): + sphere_position = self._to_curobo_device(cu_js.position) + else: + # Convert list to tensor and move to CUDA + sphere_position = self._to_curobo_device(torch.tensor(cu_js.position)) + + sphere_list = self.motion_gen.kinematics.get_robot_as_spheres(sphere_position)[0] + + # Get sphere configuration + sphere_config = self.motion_gen.kinematics.kinematics_config + + # Count robot link spheres (excluding attached_object) + robot_links = [ + link + for link in self.robot_cfg["kinematics"]["collision_link_names"] + if link != self.config.attached_object_link_name + ] + robot_sphere_count = 0 + for link_name in robot_links: + if hasattr(sphere_config, "get_link_spheres"): + link_spheres = sphere_config.get_link_spheres(link_name) + if link_spheres is not None: + active_spheres = torch.sum(link_spheres[:, 3] > 0).item() + robot_sphere_count += int(active_spheres) + + # Count attached object spheres by checking actual sphere list + attached_sphere_count = 0 + + # Handle sphere_list as either a list or single Sphere object + total_spheres = len(list(sphere_list)) + + # Any spheres beyond robot_sphere_count are attached object spheres + attached_sphere_count = max(0, total_spheres - robot_sphere_count) + + self.logger.debug( + f"SPHERE COUNT: Total={total_spheres}, Robot={robot_sphere_count},Attached={attached_sphere_count}" + ) + + return { + "total": total_spheres, + "robot_links": robot_sphere_count, + "attached_objects": attached_sphere_count, + } diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b3b3f7cce827be9fa8bddca6e628564dd9b46f02 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py @@ -0,0 +1,466 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import os +import tempfile + +import yaml + +from curobo.geom.sdf.world import CollisionCheckerType +from curobo.geom.types import WorldConfig +from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path +from isaaclab.utils.configclass import configclass + + +@configclass +class CuroboPlannerCfg: + """Configuration for CuRobo motion planner. + + This dataclass provides a flexible configuration system for the CuRobo motion planner. + The base configuration is robot-agnostic, with factory methods providing pre-configured + settings for specific robots and tasks. + + Example Usage: + >>> # Use a pre-configured robot + >>> config = CuroboPlannerCfg.franka_config() + >>> + >>> # Or create from task name + >>> config = CuroboPlannerCfg.from_task_name("Isaac-Stack-Cube-Franka-v0") + >>> + >>> # Initialize planner with config + >>> planner = CuroboPlanner(env, robot, config) + + To add support for a new robot, see the factory methods section below for detailed instructions. + """ + + # Robot configuration + robot_config_file: str | None = None + """cuRobo robot configuration file (path defined by curobo api).""" + + robot_name: str = "" + """Robot name for visualization and identification.""" + + ee_link_name: str | None = None + """End-effector link name (auto-detected from robot config if None).""" + + # Gripper configuration + gripper_joint_names: list[str] = [] + """Names of gripper joints.""" + + gripper_open_positions: dict[str, float] = {} + """Open gripper positions for cuRobo to update spheres""" + + gripper_closed_positions: dict[str, float] = {} + """Closed gripper positions for cuRobo to update spheres""" + + # Hand link configuration (for contact planning) + hand_link_names: list[str] = [] + """Names of hand/finger links to disable during contact planning.""" + + # Attachment configuration + attached_object_link_name: str = "attached_object" + """Name of the link used for attaching objects.""" + + # World configuration + world_config_file: str = "collision_table.yml" + """CuRobo world configuration file (without path).""" + + # Static objects to not update in the world model + static_objects: list[str] = [] + """Names of static objects to not update in the world model.""" + + # Optional prim path configuration + robot_prim_path: str | None = None + """Absolute USD prim path to the robot root for world extraction; None derives it from environment root.""" + + world_ignore_substrings: list[str] | None = None + """List of substring patterns to ignore when extracting world obstacles + (e.g., default ground plane, debug prims). + """ + + # Motion planning parameters + collision_checker_type: CollisionCheckerType = CollisionCheckerType.MESH + """Type of collision checker to use.""" + + num_trajopt_seeds: int = 12 + """Number of seeds for trajectory optimization.""" + + num_graph_seeds: int = 12 + """Number of seeds for graph search.""" + + interpolation_dt: float = 0.05 + """Time step for interpolating waypoints.""" + + collision_cache_size: dict[str, int] = {"obb": 150, "mesh": 150} + """Cache sizes for different collision types.""" + + trajopt_tsteps: int = 32 + """Number of trajectory optimization time steps.""" + + collision_activation_distance: float = 0.0 + """Distance at which collision constraints are activated.""" + + approach_distance: float = 0.05 + """Distance to approach at the end of the plan.""" + + retreat_distance: float = 0.05 + """Distance to retreat at the start of the plan.""" + + grasp_gripper_open_val: float = 0.04 + """Gripper joint value when considered open for grasp detection.""" + + # Planning configuration + enable_graph: bool = True + """Whether to enable graph-based planning.""" + + enable_graph_attempt: int = 5 + """Number of graph planning attempts.""" + + max_planning_attempts: int = 15 + """Maximum number of planning attempts.""" + + enable_finetune_trajopt: bool = True + """Whether to enable trajectory optimization fine-tuning.""" + + time_dilation_factor: float = 1.0 + """Time dilation factor for planning.""" + + surface_sphere_radius: float = 0.005 + """Radius of surface spheres for collision checking.""" + + # Debug and visualization + n_repeat: int | None = None + """Number of times to repeat final waypoint for stabilization. If None, no repetition.""" + + motion_step_size: float | None = None + """Step size (in radians) for retiming motion plans. If None, no retiming.""" + + visualize_spheres: bool = False + """Visualize robot collision spheres. Note: only works for env 0.""" + + visualize_plan: bool = False + """Visualize motion plan in Rerun. Note: only works for env 0.""" + + debug_planner: bool = False + """Enable detailed motion planning debug information.""" + + sphere_update_freq: int = 5 + """Frequency to update sphere visualization, specified in number of frames.""" + + motion_noise_scale: float = 0.0 + """Scale of Gaussian noise to add to the planned waypoints. Defaults to 0.0 (no noise).""" + + # Collision sphere configuration + collision_spheres_file: str | None = None + """Collision spheres configuration file (auto-detected if None).""" + + extra_collision_spheres: dict[str, int] = {"attached_object": 100} + """Extra collision spheres for attached objects.""" + + position_threshold: float = 0.005 + """Position threshold for motion planning.""" + + rotation_threshold: float = 0.05 + """Rotation threshold for motion planning.""" + + cuda_device: int | None = 0 + """Preferred CUDA device index; None uses torch.cuda.current_device() (respects CUDA_VISIBLE_DEVICES).""" + + def get_world_config(self) -> WorldConfig: + """Load and prepare the world configuration. + + This method can be overridden in subclasses or customized per task + to provide different world configuration setups. + + Returns: + WorldConfig: The configured world for collision checking + """ + # Default implementation: just load the world config file + world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), self.world_config_file))) + return world_cfg + + def _get_world_config_with_table_adjustment(self) -> WorldConfig: + """Load world config with standard table adjustments. + + This is a helper method that implements the common pattern of adjusting + table height and combining mesh/cuboid worlds. Used by specific task configs. + + Returns: + WorldConfig: World configuration with adjusted table + """ + # Load the base world config + world_cfg_table = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), self.world_config_file))) + + # Adjust table height if cuboid exists and has a pose + if world_cfg_table.cuboid and len(world_cfg_table.cuboid) > 0 and world_cfg_table.cuboid[0].pose: + world_cfg_table.cuboid[0].pose[2] -= 0.02 + + # Get mesh world for additional collision objects + world_cfg_mesh = WorldConfig.from_dict( + load_yaml(join_path(get_world_configs_path(), self.world_config_file)) + ).get_mesh_world() + + # Adjust mesh configuration if it exists + if world_cfg_mesh.mesh and len(world_cfg_mesh.mesh) > 0: + mesh_obj = world_cfg_mesh.mesh[0] + if mesh_obj.name: + mesh_obj.name += "_mesh" + if mesh_obj.pose: + mesh_obj.pose[2] = -10.5 # Move mesh below scene + + # Combine cuboid and mesh worlds + world_cfg = WorldConfig(cuboid=world_cfg_table.cuboid, mesh=world_cfg_mesh.mesh) + return world_cfg + + @classmethod + def _create_temp_robot_yaml(cls, base_yaml: str, urdf_path: str) -> str: + """Create a temporary robot configuration YAML with custom URDF path. + + Args: + base_yaml: Base robot configuration file name + urdf_path: Absolute path to the URDF file + + Returns: + Path to the temporary YAML file + + Raises: + FileNotFoundError: If the URDF file doesn't exist + """ + # Validate URDF path + if not os.path.isabs(urdf_path) or not os.path.isfile(urdf_path): + raise FileNotFoundError(f"URDF must be a local file: {urdf_path}") + + # Load base configuration + robot_cfg_path = get_robot_configs_path() + base_path = join_path(robot_cfg_path, base_yaml) + data = load_yaml(base_path) + print(f"urdf_path: {urdf_path}") + # Update URDF path + data["robot_cfg"]["kinematics"]["urdf_path"] = urdf_path + + # Write to temporary file + tmp_dir = tempfile.mkdtemp(prefix="curobo_robot_cfg_") + out_path = os.path.join(tmp_dir, base_yaml) + with open(out_path, "w") as f: + yaml.safe_dump(data, f, sort_keys=False) + + return out_path + + # ===================================================================================== + # FACTORY METHODS FOR ROBOT CONFIGURATIONS + # ===================================================================================== + """ + Creating Custom Robot Configurations + ===================================== + + To create a configuration for your own robot, follow these steps: + + 1. Create a Factory Method + --------------------------- + Define a classmethod that returns a configured instance: + + .. code-block:: python + + @classmethod + def my_robot_config(cls) -> "CuroboPlannerCfg": + # Option 1: Download from Nucleus (like Franka example) + urdf_path = f"{ISAACLAB_NUCLEUS_DIR}/path/to/my_robot.urdf" + local_urdf = retrieve_file_path(urdf_path, force_download=True) + + # Option 2: Use local file directly + # local_urdf = "/absolute/path/to/my_robot.urdf" + + # Create temporary YAML with custom URDF path + robot_cfg_file = cls._create_temp_robot_yaml("my_robot.yml", local_urdf) + + return cls( + # Required: Specify robot configuration file + robot_config_file=robot_cfg_file, # Use the generated YAML with custom URDF + robot_name="my_robot", + + # Gripper configuration (if robot has grippers) + gripper_joint_names=["gripper_left", "gripper_right"], + gripper_open_positions={"gripper_left": 0.05, "gripper_right": 0.05}, + gripper_closed_positions={"gripper_left": 0.01, "gripper_right": 0.01}, + + # Hand/finger links to disable during contact planning + hand_link_names=["finger_link_1", "finger_link_2", "palm_link"], + + # Optional: Absolute USD prim path to the robot root for world extraction; + # None derives it from environment root. + robot_prim_path=None, + + # Optional: List of substring patterns to ignore when extracting world obstacles + # (e.g., default ground plane, debug prims). + # None derives it from the environment root and adds some default patterns. + # This is useful for environments with a lot of prims. + world_ignore_substrings=None, + + # Optional: Custom collision spheres configuration + # Path relative to curobo (can override with custom spheres file) + collision_spheres_file="spheres/my_robot_spheres.yml", + + # Grasp detection threshold + grasp_gripper_open_val=0.05, + + # Motion planning parameters (tune for your robot) + approach_distance=0.05, # Distance to approach before grasping + retreat_distance=0.05, # Distance to retreat after grasping + time_dilation_factor=0.5, # Speed factor (0.5 = half speed) + + # Visualization options + visualize_spheres=False, + visualize_plan=False, + debug_planner=False, + ) + + 2. Task-Specific Configurations + -------------------------------- + For task-specific variants, create methods that modify the base config: + + .. code-block:: python + + @classmethod + def my_robot_pick_place_config(cls) -> "CuroboPlannerCfg": + config = cls.my_robot_config() # Start from base config + + # Override for pick-and-place tasks + config.approach_distance = 0.08 + config.retreat_distance = 0.10 + config.enable_finetune_trajopt = True + config.collision_activation_distance = 0.02 + + # Custom world configuration if needed + config.get_world_config = lambda: config._get_world_config_with_table_adjustment() + + return config + + 3. Register in from_task_name() + -------------------------------- + Add your robot detection logic to the from_task_name method: + + .. code-block:: python + + @classmethod + def from_task_name(cls, task_name: str) -> "CuroboPlannerCfg": + task_lower = task_name.lower() + + # Add your robot detection + if "my-robot" in task_lower: + if "pick-place" in task_lower: + return cls.my_robot_pick_place_config() + else: + return cls.my_robot_config() + + # ... existing robot checks ... + + Important Notes + --------------- + - The _create_temp_robot_yaml() helper creates a temporary YAML with your custom URDF + - If using Nucleus assets, retrieve_file_path() downloads them to a local temp directory + - The base robot YAML (e.g., "my_robot.yml") should exist in cuRobo's robot configs + + Best Practices + -------------- + 1. Start with conservative parameters (slow speed, large distances) + 2. Test with visualization enabled (visualize_plan=True) for debugging + 3. Tune collision_activation_distance based on controller precision to follow collision-free motion + 4. Adjust sphere counts in extra_collision_spheres for attached objects + 5. Use debug_planner=True when developing new configurations + """ + + @classmethod + def franka_config(cls) -> "CuroboPlannerCfg": + """Create configuration for Franka Panda robot. + + This method uses a custom URDF from Nucleus for the Franka robot. + + Returns: + CuroboPlannerCfg: Configuration for Franka robot + """ + urdf_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/SkillGenAssets/FrankaPanda/franka_panda.urdf" + local_urdf = retrieve_file_path(urdf_path, force_download=True) + + robot_cfg_file = cls._create_temp_robot_yaml("franka.yml", local_urdf) + + return cls( + robot_config_file=robot_cfg_file, + robot_name="franka", + gripper_joint_names=["panda_finger_joint1", "panda_finger_joint2"], + gripper_open_positions={"panda_finger_joint1": 0.04, "panda_finger_joint2": 0.04}, + gripper_closed_positions={"panda_finger_joint1": 0.023, "panda_finger_joint2": 0.023}, + hand_link_names=["panda_leftfinger", "panda_rightfinger", "panda_hand"], + collision_spheres_file="spheres/franka_mesh.yml", + grasp_gripper_open_val=0.04, + approach_distance=0.0, + retreat_distance=0.0, + max_planning_attempts=1, + time_dilation_factor=0.6, + enable_finetune_trajopt=True, + n_repeat=None, + motion_step_size=None, + visualize_spheres=False, + visualize_plan=False, + debug_planner=False, + sphere_update_freq=5, + motion_noise_scale=0.02, + # World extraction tuning for Franka envs + world_ignore_substrings=["/World/defaultGroundPlane", "/curobo"], + ) + + @classmethod + def franka_stack_cube_bin_config(cls) -> "CuroboPlannerCfg": + """Create configuration for Franka stacking cube in a bin.""" + config = cls.franka_config() + config.static_objects = ["bin", "table"] + config.gripper_closed_positions = {"panda_finger_joint1": 0.024, "panda_finger_joint2": 0.024} + config.approach_distance = 0.05 + config.retreat_distance = 0.07 + config.surface_sphere_radius = 0.01 + config.debug_planner = False + config.collision_activation_distance = 0.02 + config.visualize_plan = False + config.enable_finetune_trajopt = True + config.motion_noise_scale = 0.02 + config.get_world_config = lambda: config._get_world_config_with_table_adjustment() + return config + + @classmethod + def franka_stack_cube_config(cls) -> "CuroboPlannerCfg": + """Create configuration for Franka stacking a normal cube.""" + config = cls.franka_config() + config.static_objects = ["table"] + config.visualize_plan = False + config.debug_planner = False + config.motion_noise_scale = 0.02 + config.collision_activation_distance = 0.01 + config.approach_distance = 0.05 + config.retreat_distance = 0.05 + config.surface_sphere_radius = 0.01 + config.get_world_config = lambda: config._get_world_config_with_table_adjustment() + return config + + @classmethod + def from_task_name(cls, task_name: str) -> "CuroboPlannerCfg": + """Create configuration from task name. + + Args: + task_name: Task name (e.g., "Isaac-Stack-Cube-Bin-Franka-v0") + + Returns: + CuroboPlannerCfg: Configuration for the specified task + """ + task_lower = task_name.lower() + + if "stack-cube-bin" in task_lower: + return cls.franka_stack_cube_bin_config() + elif "stack-cube" in task_lower: + return cls.franka_stack_cube_config() + else: + # Default to Franka configuration + print(f"Warning: Unknown robot in task '{task_name}', using Franka configuration") + return cls.franka_config() diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/plan_visualizer.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/plan_visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..0f5252e208c94ffced7dbf2386fc9fb04a30da9e --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/plan_visualizer.py @@ -0,0 +1,937 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Utility for visualizing motion plans using Rerun. + +This module provides tools to visualize motion plans, robot poses, and collision spheres +using Rerun's visualization capabilities. It helps in debugging and validating collision-free paths. +""" + +import atexit +import os +import signal +import subprocess +import threading +import time +import weakref +from typing import TYPE_CHECKING, Any, Optional + +import numpy as np +import torch + +# Check if rerun is installed +try: + import rerun as rr +except ImportError: + raise ImportError("Rerun is not installed!") + +from curobo.types.state import JointState + +import isaaclab.utils.math as PoseUtils + +# Import psutil for process management +try: + import psutil + + PSUTIL_AVAILABLE = True +except ImportError: + PSUTIL_AVAILABLE = False + print("Warning: psutil not available. Process monitoring will be limited.") + +if TYPE_CHECKING: # For type hints only + import trimesh + + +# Global registry to track all PlanVisualizer instances for cleanup +_GLOBAL_PLAN_VISUALIZERS: list["PlanVisualizer"] = [] + + +def _cleanup_all_plan_visualizers(): + """Enhanced global cleanup function with better process killing.""" + global _GLOBAL_PLAN_VISUALIZERS + + if PSUTIL_AVAILABLE: + killed_count = 0 + for proc in psutil.process_iter(["pid", "name", "cmdline"]): + # Check if it's a rerun process + if (proc.info["name"] and "rerun" in proc.info["name"].lower()) or ( + proc.info["cmdline"] and any("rerun" in str(arg).lower() for arg in proc.info["cmdline"]) + ): + proc.kill() + killed_count += 1 + + print(f"Killed {killed_count} Rerun viewer processes on script exit") + else: + # Fallback to pkill + subprocess.run(["pkill", "-f", "rerun"], stderr=subprocess.DEVNULL, check=False) + print("Used pkill fallback to close Rerun processes") + + # Also clean up individual instances + for visualizer in _GLOBAL_PLAN_VISUALIZERS[:]: + if not visualizer._closed: + visualizer.close() + + _GLOBAL_PLAN_VISUALIZERS.clear() + + +# Register global cleanup on module import +atexit.register(_cleanup_all_plan_visualizers) + + +class PlanVisualizer: + """Visualizes motion plans using Rerun. + + This class provides methods to visualize: + 1. Robot poses along a planned trajectory + 2. Attached objects and their collision spheres + 3. Robot collision spheres + 4. Target poses and waypoints + """ + + def __init__( + self, + robot_name: str = "panda", + recording_id: str | None = None, + debug: bool = False, + save_path: str | None = None, + base_translation: np.ndarray | None = None, + ): + """Initialize the plan visualizer. + + Args: + robot_name: Name of the robot for visualization + recording_id: Optional ID for the Rerun recording + debug: Whether to print debug information + save_path: Optional path to save the recording + base_translation: Optional base translation to apply to all visualized entities + """ + self.robot_name = robot_name + self.debug = debug + self.recording_id = recording_id or f"motion_plan_{robot_name}" + self.save_path = save_path + self._closed = False + # Translation offset applied to all visualized entities (for multi-env setups) + self._base_translation = ( + np.array(base_translation, dtype=float) if base_translation is not None else np.zeros(3) + ) + + # Enhanced process management + self._parent_pid = os.getpid() + self._monitor_thread = None + self._monitor_active = False + + # Motion generator reference for sphere animation (set by CuroboPlanner) + self._motion_gen_ref = None + + # Register this instance globally for cleanup + global _GLOBAL_PLAN_VISUALIZERS + _GLOBAL_PLAN_VISUALIZERS.append(self) + + # Initialize Rerun + rr.init(self.recording_id, spawn=False) + + # Spawn viewer and keep handle if provided so we can terminate it later + try: + self._rerun_process = rr.spawn() + except Exception: + # Older versions of Rerun may not return a process handle + self._rerun_process = None + + # Set up coordinate system + rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Y_UP) + + # Store visualization state + self._current_frame = 0 + self._sphere_entities: dict[str, list[str]] = {"robot": [], "attached": [], "target": []} + + # Start enhanced parent process monitoring + self._start_parent_process_monitoring() + + # Use weakref.finalize for cleanup when object is garbage collected + self._finalizer = weakref.finalize( + self, self._cleanup_class_resources, self.recording_id, self.save_path, debug + ) + + # Also register atexit handler as backup for normal script termination + # Store values locally to avoid capturing self in the closure + recording_id = self.recording_id + save_path = self.save_path + debug_flag = debug + atexit.register(self._cleanup_class_resources, recording_id, save_path, debug_flag) + + # Store original signal handlers so we can restore them after cleanup + self._original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_DFL) + self._original_sigterm_handler = signal.signal(signal.SIGTERM, signal.SIG_DFL) + + # Handle Ctrl+C (SIGINT) and termination (SIGTERM) signals + def signal_handler(signum, frame): + if self.debug: + print(f"Received signal {signum}, closing Rerun viewer...") + self._cleanup_on_exit() + + # Restore original signal handler and re-raise the signal + if signum == signal.SIGINT: + signal.signal(signal.SIGINT, self._original_sigint_handler) + elif signum == signal.SIGTERM: + signal.signal(signal.SIGTERM, self._original_sigterm_handler) + os.kill(os.getpid(), signum) + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + if self.debug: + print(f"Initialized Rerun visualization with recording ID: {self.recording_id}") + if np.linalg.norm(self._base_translation) > 0: + print(f"Applying translation offset: {self._base_translation}") + if PSUTIL_AVAILABLE: + print("Enhanced process monitoring enabled") + + def _start_parent_process_monitoring(self) -> None: + """Start monitoring the parent process and cleanup when it dies.""" + if not PSUTIL_AVAILABLE: + if self.debug: + print("psutil not available, skipping parent process monitoring") + return + + self._monitor_active = True + + def monitor_parent_process() -> None: + """Monitor thread function that watches the parent process.""" + if self.debug: + print(f"Starting parent process monitor for PID {self._parent_pid}") + + # Get parent process handle + parent_process = psutil.Process(self._parent_pid) + + # Monitor parent process + while self._monitor_active: + try: + if not parent_process.is_running(): + if self.debug: + print(f"Parent process {self._parent_pid} died, cleaning up Rerun...") + self._kill_rerun_processes() + break + + # Check every 2 seconds + time.sleep(2) + + except (psutil.NoSuchProcess, psutil.AccessDenied): + if self.debug: + print(f"Parent process {self._parent_pid} no longer accessible, cleaning up...") + self._kill_rerun_processes() + break + except Exception as e: + if self.debug: + print(f"Error in parent process monitor: {e}") + break + + # Start monitor thread + self._monitor_thread = threading.Thread(target=monitor_parent_process, daemon=True) + self._monitor_thread.start() + + def _kill_rerun_processes(self) -> None: + """Enhanced method to kill Rerun viewer processes using psutil.""" + try: + if PSUTIL_AVAILABLE: + killed_count = 0 + for proc in psutil.process_iter(["pid", "name", "cmdline"]): + try: + # Check if it's a rerun process + is_rerun = False + + # Check process name + if proc.info["name"] and "rerun" in proc.info["name"].lower(): + is_rerun = True + + # Check command line arguments + if proc.info["cmdline"] and any("rerun" in str(arg).lower() for arg in proc.info["cmdline"]): + is_rerun = True + + if is_rerun: + proc.kill() + killed_count += 1 + if self.debug: + print(f"Killed Rerun process {proc.info['pid']} ({proc.info['name']})") + + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + # Process already dead or inaccessible + pass + except Exception as e: + if self.debug: + print(f"Error killing process: {e}") + + if self.debug: + print(f"Killed {killed_count} Rerun processes using psutil") + + else: + # Fallback to pkill if psutil not available + result = subprocess.run(["pkill", "-f", "rerun"], stderr=subprocess.DEVNULL, check=False) + if self.debug: + print(f"Used pkill fallback (return code: {result.returncode})") + + except Exception as e: + if self.debug: + print(f"Error killing rerun processes: {e}") + + @staticmethod + def _cleanup_class_resources(recording_id: str, save_path: str | None, debug: bool) -> None: + """Static method for cleanup that doesn't hold references to the instance. + + This is called by weakref.finalize when the object is garbage collected. + """ + if debug: + print(f"Cleaning up Rerun visualization for {recording_id}") + + # Disconnect from Rerun + rr.disconnect() + + # Save to file if requested + if save_path is not None: + rr.save(save_path) + if debug: + print(f"Saved Rerun recording to {save_path}") + + # Enhanced process killing + if PSUTIL_AVAILABLE: + killed_count = 0 + for proc in psutil.process_iter(["pid", "name", "cmdline"]): + if (proc.info["name"] and "rerun" in proc.info["name"].lower()) or ( + proc.info["cmdline"] and any("rerun" in str(arg).lower() for arg in proc.info["cmdline"]) + ): + proc.kill() + killed_count += 1 + + if debug: + print(f"Killed {killed_count} Rerun processes during cleanup") + else: + subprocess.run(["pkill", "-f", "rerun"], stderr=subprocess.DEVNULL, check=False) + + if debug: + print("Cleanup completed") + + def _cleanup_on_exit(self) -> None: + """Manual cleanup method for signal handlers.""" + if not self._closed: + # Stop monitoring thread + self._monitor_active = False + + self.close() + self._kill_rerun_processes() + + def close(self) -> None: + """Close the Rerun visualization with enhanced cleanup.""" + if self._closed: + return + + # Stop parent process monitoring + self._monitor_active = False + if self._monitor_thread and self._monitor_thread.is_alive(): + # Give the thread a moment to stop gracefully + time.sleep(0.1) + + # Disconnect from Rerun (closes connections, servers, and files) + rr.disconnect() + + # Save to file if requested + if self.save_path is not None: + rr.save(self.save_path) + if self.debug: + print(f"Saved Rerun recording to {self.save_path}") + + self._closed = True + + # Terminate viewer process if we have a handle + try: + process = getattr(self, "_rerun_process", None) + if process is not None and process.poll() is None: + process.terminate() + try: + process.wait(timeout=5) + except Exception: + process.kill() + except Exception: + pass + + # Enhanced process killing + self._kill_rerun_processes() + + # Remove from global registry + global _GLOBAL_PLAN_VISUALIZERS + if self in _GLOBAL_PLAN_VISUALIZERS: + _GLOBAL_PLAN_VISUALIZERS.remove(self) + + if self.debug: + print("Closed Rerun visualization with enhanced cleanup") + + def visualize_plan( + self, + plan: JointState, + target_pose: torch.Tensor, + robot_spheres: list[Any] | None = None, + attached_spheres: list[Any] | None = None, + ee_positions: np.ndarray | None = None, + world_scene: Optional["trimesh.Scene"] = None, + ) -> None: + """Visualize a complete motion plan with all components. + + Args: + plan: Joint state trajectory to visualize + target_pose: Target end-effector pose + robot_spheres: Optional list of robot collision spheres + attached_spheres: Optional list of attached object spheres + ee_positions: Optional end-effector positions + world_scene: Optional world scene to visualize + """ + if self.debug: + robot_count = len(robot_spheres) if robot_spheres else 0 + attached_count = len(attached_spheres) if attached_spheres else 0 + offset_info = ( + f"offset={self._base_translation}" if np.linalg.norm(self._base_translation) > 0 else "no offset" + ) + print( + f"Visualizing plan: {len(plan.position)} waypoints, {robot_count} robot spheres (with offset)," + f" {attached_count} attached spheres (no offset), {offset_info}" + ) + + # Set timeline for static visualization (separate from animation) + rr.set_time("static_plan", sequence=self._current_frame) + self._current_frame += 1 + + # Clear previous visualization of dynamic entities (keep static meshes) + self._clear_visualization() + + # If a scene is supplied and not yet logged, draw it once + if world_scene is not None: + self._visualize_world_scene(world_scene) + + # Visualize target pose + self._visualize_target_pose(target_pose) + + # Visualize trajectory (end-effector positions if provided) + self._visualize_trajectory(plan, ee_positions) + + # Visualize spheres if provided + if robot_spheres: + self._visualize_robot_spheres(robot_spheres) + if attached_spheres: + self._visualize_attached_spheres(attached_spheres) + else: + # Clear any existing attached sphere visualization when no objects are attached + self._clear_attached_spheres() + + def _clear_visualization(self) -> None: + """Clear all visualization entities.""" + # Clear dynamic trajectory, target, and finger logs to avoid artifacts between visualizations + dynamic_paths = [ + "trajectory", + "target", + "anim", + ] + + for path in dynamic_paths: + rr.log(f"world/{path}", rr.Clear(recursive=True)) + + for entity_type, entities in self._sphere_entities.items(): + for entity in entities: + rr.log(f"world/{entity_type}/{entity}", rr.Clear(recursive=True)) + self._sphere_entities[entity_type] = [] + self._current_frame = 0 + + def clear_visualization(self) -> None: + """Public method to clear the visualization.""" + self._clear_visualization() + + def _visualize_target_pose(self, target_pose: torch.Tensor) -> None: + """Visualize the target end-effector pose. + + Args: + target_pose: Target pose as 4x4 transformation matrix + """ + pos, rot = PoseUtils.unmake_pose(target_pose) + + # Convert to numpy arrays + pos_np = pos.detach().cpu().numpy() if torch.is_tensor(pos) else np.array(pos) + rot_np = rot.detach().cpu().numpy() if torch.is_tensor(rot) else np.array(rot) + + # Ensure arrays are the right shape + pos_np = pos_np.reshape(-1) + rot_np = rot_np.reshape(3, 3) + + # Apply translation offset + pos_np += self._base_translation + + # Log target position + rr.log( + "world/target/position", + rr.Points3D( + positions=np.array([pos_np]), + colors=[[255, 0, 0]], # Red + radii=[0.02], + ), + ) + + # Log target orientation as coordinate frame + rr.log( + "world/target/frame", + rr.Transform3D( + translation=pos_np, + mat3x3=rot_np, + ), + ) + + def _visualize_trajectory( + self, + plan: JointState, + ee_positions: np.ndarray | None = None, + ) -> None: + """Visualize the robot trajectory. + + Args: + plan: Joint state trajectory + ee_positions: Optional end-effector positions + """ + if ee_positions is None: + raw = plan.position.detach().cpu().numpy() if torch.is_tensor(plan.position) else np.array(plan.position) + if raw.shape[1] >= 3: + positions = raw[:, :3] + else: + raise ValueError("ee_positions not provided and joint positions are not 3-D") + else: + positions = ee_positions + + # Apply translation offset + positions = positions + self._base_translation + + # Log trajectory points + rr.log( + "world/trajectory", + rr.LineStrips3D( + [positions], # single strip consisting of all waypoints + colors=[[0, 100, 255]], # Blue + radii=[0.005], + ), + static=True, + ) + + # Log keyframes + for i, pos in enumerate(positions): + rr.log( + f"world/trajectory/keyframe_{i}", + rr.Points3D( + positions=np.array([pos]), + colors=[[0, 100, 255]], # Blue + radii=[0.01], + ), + static=True, + ) + + def _visualize_robot_spheres(self, spheres: list[Any]) -> None: + """Visualize robot collision spheres. + + Args: + spheres: List of robot collision spheres + """ + self._log_spheres( + spheres=spheres, + entity_type="robot", + color=[0, 255, 100, 128], # Semi-transparent green + apply_offset=True, + ) + + def _visualize_attached_spheres(self, spheres: list[Any]) -> None: + """Visualize attached object collision spheres. + + Args: + spheres: List of attached object spheres + """ + self._log_spheres( + spheres=spheres, + entity_type="attached", + color=[255, 0, 0, 128], # Semi-transparent red + apply_offset=False, + ) + + def _clear_attached_spheres(self) -> None: + """Clear all attached object spheres.""" + for entity_id in self._sphere_entities.get("attached", []): + rr.log(f"world/attached/{entity_id}", rr.Clear(recursive=True)) + self._sphere_entities["attached"] = [] + + # --------------------------------------------------------------------- + # PRIVATE UTILITIES + # --------------------------------------------------------------------- + + def _log_spheres( + self, + spheres: list[Any], + entity_type: str, + color: list[int], + apply_offset: bool = False, + ) -> None: + """Generic helper for sphere visualization. + + Args: + spheres: List of CuRobo ``Sphere`` objects. + entity_type: Log path prefix (``robot`` or ``attached``). + color: RGBA color for the spheres. + apply_offset: Whether to add ``self._base_translation`` to sphere positions. + """ + for i, sphere in enumerate(spheres): + entity_id = f"sphere_{i}" + # Track entities so we can clear them later + self._sphere_entities.setdefault(entity_type, []).append(entity_id) + + # Convert position to numpy and optionally apply offset + pos = ( + sphere.position.detach().cpu().numpy() + if torch.is_tensor(sphere.position) + else np.array(sphere.position) + ) + if apply_offset: + pos = pos + self._base_translation + pos = pos.reshape(-1) # Ensure 1-D + + # Log sphere via Rerun + rr.log( + f"world/{entity_type}/{entity_id}", + rr.Points3D(positions=np.array([pos]), colors=[color], radii=[float(sphere.radius)]), + ) + + def _visualize_world_scene(self, scene: "trimesh.Scene") -> None: + """Log world geometry and dynamic transforms each call. + + Geometry is sent once (cached), but transforms are updated every invocation + so objects that move (cubes after randomization) appear at the correct + pose per episode/frame. + """ + import trimesh # local import to avoid hard dependency at top + + def _to_rerun_mesh(mesh: "trimesh.Trimesh") -> "rr.Mesh3D": + # Basic conversion without materials + return rr.Mesh3D( + vertex_positions=mesh.vertices, + triangle_indices=mesh.faces, + vertex_normals=mesh.vertex_normals if mesh.vertex_normals is not None else None, + ) + + if not hasattr(self, "_logged_geometry"): + self._logged_geometry = set() + + for node in scene.graph.nodes_geometry: + tform, geom_key = scene.graph.get(node) + mesh = scene.geometry.get(geom_key) + if mesh is None: + continue + rr_path = f"world/scene/{node.replace('/', '_')}" + + # Always update transform (objects may move between calls) + # NOTE: World scene objects are already in correct world coordinates, no offset needed + rr.log( + rr_path, + rr.Transform3D( + translation=tform[:3, 3], + mat3x3=tform[:3, :3], + axis_length=0.25, + ), + static=False, + ) + + # Geometry: send only once per node to avoid duplicates + if rr_path not in self._logged_geometry: + if isinstance(mesh, trimesh.Trimesh): + rr_mesh = _to_rerun_mesh(mesh) + elif isinstance(mesh, trimesh.PointCloud): + rr_mesh = rr.Points3D(positions=mesh.vertices, colors=mesh.colors) + else: + continue + + rr.log(rr_path, rr_mesh, static=True) + self._logged_geometry.add(rr_path) + + if self.debug: + print(f"Logged/updated {len(scene.graph.nodes_geometry)} world nodes in Rerun") + + def animate_plan( + self, + ee_positions: np.ndarray, + object_positions: dict[str, np.ndarray] | None = None, + timeline: str = "plan", + point_radius: float = 0.01, + ) -> None: + """Animate robot end-effector and (optionally) attached object positions over time using Rerun. + + This helper logs a single 3-D point per timestep so that Rerun can play back the + trajectory on the provided ``timeline``. It is intentionally lightweight and does + not attempt to render the full robot geometry—only key points—keeping the data + transfer to the viewer minimal. + + Args: + ee_positions: Array of shape (T, 3) with end-effector world positions. + object_positions: Mapping from object name to an array (T, 3) with that + object's world positions. Each trajectory must be at least as long as + ``ee_positions``; extra entries are ignored. + timeline: Name of the Rerun timeline used for the animation frames. + point_radius: Visual radius (in metres) of the rendered points. + """ + if ee_positions is None or len(ee_positions) == 0: + return + + # Iterate over timesteps and log a frame on the chosen timeline + for idx, pos in enumerate(ee_positions): + # Set time on the chosen timeline (creates it if needed) + rr.set_time(timeline, sequence=idx) + + # Log end-effector marker (needs offset for multi-env) + rr.log( + "world/anim/ee", + rr.Points3D( + positions=np.array([pos + self._base_translation]), + colors=[[0, 100, 255]], # Blue + radii=[point_radius], + ), + ) + + # Optionally log attached object markers + # NOTE: Object positions are already in world coordinates, no offset needed + if object_positions is not None: + for name, traj in object_positions.items(): + if idx >= len(traj): + continue + rr.log( + f"world/anim/{name}", + rr.Points3D( + positions=np.array([traj[idx]]), + colors=[[255, 128, 0]], # Orange + radii=[point_radius], + ), + ) + + def animate_spheres_along_path( + self, + plan: JointState, + robot_spheres_at_start: list[Any] | None = None, + attached_spheres_at_start: list[Any] | None = None, + timeline: str = "sphere_animation", + interpolation_steps: int = 10, + ) -> None: + """Animate robot and attached object spheres along the planned trajectory with smooth interpolation. + + This method creates a dense, interpolated trajectory and computes sphere positions + at many intermediate points to create smooth animation of the robot moving along the path. + + Args: + plan: Joint state trajectory to animate spheres along + robot_spheres_at_start: Initial robot collision spheres (for reference) + attached_spheres_at_start: Initial attached object spheres (for reference) + timeline: Name of the Rerun timeline for the animation + interpolation_steps: Number of interpolated steps between each waypoint pair + """ + if plan is None or len(plan.position) == 0: + if self.debug: + print("No plan available for sphere animation") + return + + if self.debug: + robot_count = len(robot_spheres_at_start) if robot_spheres_at_start else 0 + attached_count = len(attached_spheres_at_start) if attached_spheres_at_start else 0 + print(f"Creating smooth animation for {robot_count} robot spheres and {attached_count} attached spheres") + print( + f"Original plan: {len(plan.position)} waypoints, interpolating with {interpolation_steps} steps between" + " waypoints" + ) + + # We need access to the motion generator to compute spheres at each waypoint + if not hasattr(self, "_motion_gen_ref") or self._motion_gen_ref is None: + if self.debug: + print("Motion generator reference not available for sphere animation") + return + + motion_gen = self._motion_gen_ref + + # Validate motion generator has required attributes + if not hasattr(motion_gen, "kinematics") or motion_gen.kinematics is None: + if self.debug: + print("Motion generator kinematics not available for sphere animation") + return + + # Clear static spheres to avoid visual clutter during animation + self._hide_static_spheres_for_animation() + + # Count robot link spheres (excluding attached objects) for consistent splitting + robot_link_count = 0 + if robot_spheres_at_start: + robot_link_count = len(robot_spheres_at_start) + + # Create interpolated trajectory for smooth animation + interpolated_positions = self._create_interpolated_trajectory(plan, interpolation_steps) + + if self.debug: + print(f"Created interpolated trajectory with {len(interpolated_positions)} total frames") + + # Animate spheres along the interpolated trajectory + for frame_idx, joint_positions in enumerate(interpolated_positions): + # Set time on the animation timeline with consistent timing + rr.set_time(timeline, sequence=frame_idx) + + # Create joint state for this interpolated position + if isinstance(joint_positions, torch.Tensor): + sphere_position = joint_positions + else: + sphere_position = torch.tensor(joint_positions) + + # Ensure tensor is on the right device for CuRobo + if hasattr(motion_gen, "tensor_args") and motion_gen.tensor_args is not None: + sphere_position = motion_gen.tensor_args.to_device(sphere_position) + + # Get spheres at this configuration + try: + sphere_list = motion_gen.kinematics.get_robot_as_spheres(sphere_position)[0] + except Exception as e: + if self.debug: + print(f"Failed to compute spheres for frame {frame_idx}: {e}") + continue + + # Handle sphere_list as either a list or single Sphere object + if hasattr(sphere_list, "__iter__") and not hasattr(sphere_list, "position"): + sphere_items = list(sphere_list) + else: + sphere_items = [sphere_list] + + # Separate robot and attached object spheres with different colors + robot_sphere_positions = [] + robot_sphere_radii = [] + attached_sphere_positions = [] + attached_sphere_radii = [] + + for i, sphere in enumerate(sphere_items): + # Convert position to numpy + pos = ( + sphere.position.detach().cpu().numpy() + if torch.is_tensor(sphere.position) + else np.array(sphere.position) + ) + pos = pos.reshape(-1) + radius = float(sphere.radius) + + if i < robot_link_count: + # Robot sphere - needs base translation offset + robot_sphere_positions.append(pos + self._base_translation) + robot_sphere_radii.append(radius) + else: + # Attached object sphere - already in world coordinates + attached_sphere_positions.append(pos) + attached_sphere_radii.append(radius) + + # Log robot spheres with green color + if robot_sphere_positions: + rr.log( + "world/robot_animation", + rr.Points3D( + positions=np.array(robot_sphere_positions), + colors=[[0, 255, 100, 220]] * len(robot_sphere_positions), # Bright green + radii=robot_sphere_radii, + ), + ) + + # Log attached object spheres with orange color (or clear if no attached objects) + if attached_sphere_positions: + rr.log( + "world/attached_animation", + rr.Points3D( + positions=np.array(attached_sphere_positions), + colors=[[255, 150, 0, 220]] * len(attached_sphere_positions), # Bright orange + radii=attached_sphere_radii, + ), + ) + else: + # Clear attached object spheres when no objects are attached + rr.log("world/attached_animation", rr.Clear(recursive=True)) + + if self.debug: + print( + f"Completed smooth sphere animation with {len(interpolated_positions)} frames on timeline '{timeline}'" + ) + + def _hide_static_spheres_for_animation(self) -> None: + """Hide static sphere visualization during animation to reduce visual clutter.""" + # Clear static robot spheres + for entity_id in self._sphere_entities.get("robot", []): + rr.log(f"world/robot/{entity_id}", rr.Clear(recursive=True)) + + # Clear static attached spheres + for entity_id in self._sphere_entities.get("attached", []): + rr.log(f"world/attached/{entity_id}", rr.Clear(recursive=True)) + + if self.debug: + print("Hidden static spheres for cleaner animation view") + + def _create_interpolated_trajectory(self, plan: JointState, interpolation_steps: int) -> list[torch.Tensor]: + """Create a smooth interpolated trajectory between waypoints. + + Args: + plan: Original joint state trajectory + interpolation_steps: Number of interpolation steps between each waypoint pair + + Returns: + List of interpolated joint positions + """ + if len(plan.position) < 2: + # If only one waypoint, just return it + return [plan.position[0] if isinstance(plan.position[0], torch.Tensor) else torch.tensor(plan.position[0])] # type: ignore + + interpolated_positions = [] + + # Convert plan positions to tensors if needed + waypoints = [] + for i in range(len(plan.position)): + pos = plan.position[i] + if isinstance(pos, torch.Tensor): + waypoints.append(pos) + else: + waypoints.append(torch.tensor(pos)) + + # Interpolate between each pair of consecutive waypoints + for i in range(len(waypoints) - 1): + start_pos = waypoints[i] + end_pos = waypoints[i + 1] + + # Create interpolation steps between start and end + for step in range(interpolation_steps): + alpha = step / interpolation_steps + interpolated_pos = start_pos * (1 - alpha) + end_pos * alpha + interpolated_positions.append(interpolated_pos) + + # Add the final waypoint + interpolated_positions.append(waypoints[-1]) + + return interpolated_positions + + def set_motion_generator_reference(self, motion_gen: Any) -> None: + """Set the motion generator reference for sphere animation. + + Args: + motion_gen: CuRobo motion generator instance + """ + self._motion_gen_ref = motion_gen + + def mark_idle(self) -> None: + """Signal that the planner is idle, clearing animations. + + This method advances the animation timelines and logs empty data to ensure that + no leftover visualizations from the previous plan are shown. It's useful for + creating a clean state between planning episodes. + """ + # Advance plan timeline and emit empty anim so latest frame is blank + rr.set_time("plan", sequence=self._current_frame) + self._current_frame += 1 + empty = np.empty((0, 3), dtype=float) + rr.log("world/anim/ee", rr.Points3D(positions=empty)) + rr.log("world/robot_animation", rr.Points3D(positions=empty)) + rr.log("world/attached_animation", rr.Points3D(positions=empty)) + + # Also advance sphere animation timeline + rr.set_time("sphere_animation", sequence=self._current_frame) + rr.log("world/robot_animation", rr.Points3D(positions=empty)) + rr.log("world/attached_animation", rr.Points3D(positions=empty)) diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py new file mode 100644 index 0000000000000000000000000000000000000000..43bf6b1405155f2dc54d41d4519b243f5c9aff81 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py @@ -0,0 +1,134 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from abc import ABC, abstractmethod +from typing import Any + +import torch + +from isaaclab.assets import Articulation +from isaaclab.envs.manager_based_env import ManagerBasedEnv + + +class MotionPlannerBase(ABC): + """Abstract base class for motion planners. + + This class defines the public interface that all motion planners must implement. + It focuses on the essential functionality that users interact with, while leaving + implementation details to specific planner backends. + + The core workflow is: + 1. Initialize planner with environment and robot + 2. Call update_world_and_plan_motion() to plan to a target + 3. Execute plan using has_next_waypoint() and get_next_waypoint_ee_pose() + + Example: + >>> from isaaclab_mimic.motion_planners.curobo.curobo_planner import CuroboPlanner + >>> from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg + >>> config = CuroboPlannerCfg.franka_config() + >>> planner = CuroboPlanner(env, robot, config) + >>> success = planner.update_world_and_plan_motion(target_pose) + >>> if success: + >>> while planner.has_next_waypoint(): + >>> action = planner.get_next_waypoint_ee_pose() + >>> obs, info = env.step(action) + """ + + def __init__( + self, env: ManagerBasedEnv, robot: Articulation, env_id: int = 0, debug: bool = False, **kwargs + ) -> None: + """Initialize the motion planner. + + Args: + env: The environment instance + robot: Robot articulation to plan motions for + env_id: Environment ID (0 to num_envs-1) + debug: Whether to print detailed debugging information + **kwargs: Additional planner-specific arguments + """ + self.env = env + self.robot = robot + self.env_id = env_id + self.debug = debug + + @abstractmethod + def update_world_and_plan_motion(self, target_pose: torch.Tensor, **kwargs: Any) -> bool: + """Update collision world and plan motion to target pose. + + This is the main entry point for motion planning. It should: + 1. Update the planner's internal world representation + 2. Plan a collision-free path to the target pose + 3. Store the plan internally for execution + + Args: + target_pose: Target pose to plan motion to (4x4 transformation matrix) + **kwargs: Planner-specific arguments (e.g., retiming, contact planning) + + Returns: + bool: True if planning succeeded, False otherwise + """ + raise NotImplementedError + + @abstractmethod + def has_next_waypoint(self) -> bool: + """Check if there are more waypoints in current plan. + + Returns: + bool: True if there are more waypoints, False otherwise + """ + raise NotImplementedError + + @abstractmethod + def get_next_waypoint_ee_pose(self) -> Any: + """Get next waypoint's end-effector pose from current plan. + + This method should only be called after checking has_next_waypoint(). + + Returns: + Any: End-effector pose for the next waypoint in the plan. + """ + raise NotImplementedError + + def get_planned_poses(self) -> list[Any]: + """Get all planned poses from current plan. + + Returns: + list[Any]: List of planned poses. + + Note: + Default implementation iterates through waypoints. + Child classes can override for a more efficient implementation. + """ + planned_poses = [] + # Create a copy of the planner state to not affect the original plan execution + # This is a placeholder and may need to be implemented by child classes + # if they manage complex internal state. + # For now, we assume the planner can be reset and we can iterate through the plan. + # A more robust solution might involve a dedicated method to get the full plan. + self.reset_plan() + while self.has_next_waypoint(): + pose = self.get_next_waypoint_ee_pose() + planned_poses.append(pose) + return planned_poses + + @abstractmethod + def reset_plan(self) -> None: + """Reset the current plan and execution state. + + This should clear any stored plan and reset the execution index or iterator. + """ + raise NotImplementedError + + def get_planner_info(self) -> dict[str, Any]: + """Get information about the planner. + + Returns: + dict: Information about the planner (name, version, capabilities, etc.) + """ + return { + "name": self.__class__.__name__, + "env_id": self.env_id, + "debug": self.debug, + } diff --git a/source/isaaclab_mimic/isaaclab_mimic/ui/instruction_display.py b/source/isaaclab_mimic/isaaclab_mimic/ui/instruction_display.py new file mode 100644 index 0000000000000000000000000000000000000000..274038d3d9ebec2fcfcef6a1b14133381066b9dd --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/ui/instruction_display.py @@ -0,0 +1,99 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Copyright (c) 2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Module for handling instruction displays in Isaac Lab environments.""" + +from typing import Any + +from pxr import Gf + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg + + +class InstructionDisplay: + """Handles instruction display for different teleop devices.""" + + def __init__(self, xr: bool): + self.xr = xr + + if self.xr: + from isaaclab.ui.xr_widgets import show_instruction + + self._display_subtask = lambda text: show_instruction( + text, "/_xr/stage/xrCamera", Gf.Vec3f(1.25, 0.3, -2), target_prim_path="/subtask_instruction" + ) + self._display_demo = lambda text: show_instruction( + text, "/_xr/stage/xrCamera", Gf.Vec3f(-1.25, 0.3, -2), target_prim_path="/demo_complete" + ) + else: + self.subtask_label = None + self.demo_label = None + self._display_subtask = lambda text: setattr(self.subtask_label, "text", text) + self._display_demo = lambda text: setattr(self.demo_label, "text", text) + + def set_labels(self, subtask_label, demo_label): + """Set the instruction labels for non-handtracking displays.""" + self.subtask_label = subtask_label + self.demo_label = demo_label + + def show_subtask(self, text): + """Display subtask instruction.""" + self._display_subtask(text) + + def show_demo(self, text): + """Display demo completion message.""" + self._display_demo(text) + + +def show_subtask_instructions( + instruction_display: InstructionDisplay, prev_subtasks: dict, obv: dict, env_cfg: Any +) -> None: + """ + Detect changes in subtasks and display the changes. + + Args: + instruction_display: Display handler for showing instructions + prev_subtasks: Previous subtask terms + obv: Current observation with subtask terms + env_cfg: Environment configuration containing subtask descriptions + """ + if not isinstance(env_cfg, MimicEnvCfg): + return + subtasks = obv[0].get("subtask_terms") + if subtasks is None: + return + + # Currently only supports one end effector + eef_name = list(env_cfg.subtask_configs.keys())[0] + subtask_configs = env_cfg.subtask_configs[eef_name] + + all_false = True + for subtask_config in subtask_configs: + term_signal = subtask_config.subtask_term_signal + if term_signal is None: + continue + + current_state = subtasks[term_signal].item() + prev_state = prev_subtasks.get(term_signal, False) + + if current_state: + all_false = False + + # Show message when state changes from False to True + if current_state and not prev_state: + instruction_display.show_subtask(f"Next objective: {subtask_config.next_subtask_description}") + + # Update the previous state + prev_subtasks[term_signal] = current_state + + # If all tasks are false, show the first task's description + if all_false and subtask_configs: + first_task = subtask_configs[0] + instruction_display.show_subtask(f"Current objective: {first_task.description}") diff --git a/source/isaaclab_mimic/pyproject.toml b/source/isaaclab_mimic/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab_mimic/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab_mimic/setup.py b/source/isaaclab_mimic/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..c43d268fe2606ca0cd0dba1d0b6c93c64c62c056 --- /dev/null +++ b/source/isaaclab_mimic/setup.py @@ -0,0 +1,63 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Installation script for the 'isaaclab_mimic' python package.""" + +import itertools +import os +import platform + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Minimum dependencies required prior to installation +INSTALL_REQUIRES = [ + "tomli", + # jupyter notebook + "ipywidgets==8.1.5", +] + +# Extra dependencies for IL agents +EXTRAS_REQUIRE = {"robomimic": []} + +# Check if the platform is Linux and add the dependency +if platform.system() == "Linux": + EXTRAS_REQUIRE["robomimic"].append("robomimic@git+https://github.com/ARISE-Initiative/robomimic.git@v0.4.0") + +# Cumulation of all extra-requires +EXTRAS_REQUIRE["all"] = list(itertools.chain.from_iterable(EXTRAS_REQUIRE.values())) +# Remove duplicates in the all list to avoid double installations +EXTRAS_REQUIRE["all"] = list(set(EXTRAS_REQUIRE["all"])) + +# Installation operation +setup( + name="isaaclab_mimic", + packages=["isaaclab_mimic"], + author=EXTENSION_TOML_DATA["package"]["author"], + maintainer=EXTENSION_TOML_DATA["package"]["maintainer"], + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + install_requires=INSTALL_REQUIRES, + extras_require=EXTRAS_REQUIRE, + license="Apache-2.0", + include_package_data=True, + python_requires=">=3.10", + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab_mimic/test/test_curobo_planner_cube_stack.py b/source/isaaclab_mimic/test/test_curobo_planner_cube_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..4c532f62ef621bc2b91e1782b39550e272d92fde --- /dev/null +++ b/source/isaaclab_mimic/test/test_curobo_planner_cube_stack.py @@ -0,0 +1,249 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 +from __future__ import annotations + +import random +from typing import Any + +import pytest + +SEED: int = 42 +random.seed(SEED) + +from isaaclab.app import AppLauncher + +headless = True +app_launcher = AppLauncher(headless=headless) +simulation_app: Any = app_launcher.app + +from collections.abc import Generator + +import gymnasium as gym +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs.manager_based_env import ManagerBasedEnv +from isaaclab.markers import FRAME_MARKER_CFG, VisualizationMarkers + +from isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env_cfg import FrankaCubeStackIKRelMimicEnvCfg +from isaaclab_mimic.motion_planners.curobo.curobo_planner import CuroboPlanner +from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg + +GRIPPER_OPEN_CMD: float = 1.0 +GRIPPER_CLOSE_CMD: float = -1.0 + + +def _eef_name(env: ManagerBasedEnv) -> str: + return list(env.cfg.subtask_configs.keys())[0] + + +def _action_from_pose( + env: ManagerBasedEnv, target_pose: torch.Tensor, gripper_binary_action: float, env_id: int = 0 +) -> torch.Tensor: + eef = _eef_name(env) + play_action = env.target_eef_pose_to_action( + target_eef_pose_dict={eef: target_pose}, + gripper_action_dict={eef: torch.tensor([gripper_binary_action], device=env.device, dtype=torch.float32)}, + env_id=env_id, + ) + if play_action.dim() == 1: + play_action = play_action.unsqueeze(0) + return play_action + + +def _env_step_with_action(env: ManagerBasedEnv, action: torch.Tensor) -> None: + env.step(action) + + +def _execute_plan(env: ManagerBasedEnv, planner: CuroboPlanner, gripper_binary_action: float, env_id: int = 0) -> None: + """Execute planner's EEF planned poses using env.step with IK-relative controller actions.""" + planned_poses = planner.get_planned_poses() + if not planned_poses: + return + for pose in planned_poses: + action = _action_from_pose(env, pose, gripper_binary_action, env_id=env_id) + _env_step_with_action(env, action) + + +def _execute_gripper_action( + env: ManagerBasedEnv, robot: Articulation, gripper_binary_action: float, steps: int = 12, env_id: int = 0 +) -> None: + """Hold current EEF pose and toggle gripper for a few steps.""" + eef = _eef_name(env) + curr_pose = env.get_robot_eef_pose(eef_name=eef, env_ids=[env_id])[0] + for _ in range(steps): + action = _action_from_pose(env, curr_pose, gripper_binary_action, env_id=env_id) + _env_step_with_action(env, action) + + +DOWN_FACING_QUAT = torch.tensor([0.0, 1.0, 0.0, 0.0], dtype=torch.float32) + + +@pytest.fixture(scope="class") +def cube_stack_test_env() -> Generator[dict[str, Any], None, None]: + """Create the environment and motion planner once for the test suite and yield them.""" + random.seed(SEED) + torch.manual_seed(SEED) + + env_cfg = FrankaCubeStackIKRelMimicEnvCfg() + env_cfg.scene.num_envs = 1 + for frame in env_cfg.scene.ee_frame.target_frames: + if frame.name == "end_effector": + print(f"Setting end effector offset from {frame.offset.pos} to (0.0, 0.0, 0.0) for SkillGen parity") + frame.offset.pos = (0.0, 0.0, 0.0) + + env: ManagerBasedEnv = gym.make( + "Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0", + cfg=env_cfg, + headless=headless, + ).unwrapped + env.reset() + + robot: Articulation = env.scene["robot"] + planner_cfg = CuroboPlannerCfg.franka_stack_cube_config() + planner_cfg.visualize_plan = False + planner_cfg.visualize_spheres = False + planner_cfg.debug_planner = True + planner_cfg.retreat_distance = 0.05 + planner_cfg.approach_distance = 0.05 + planner_cfg.time_dilation_factor = 1.0 + + planner = CuroboPlanner( + env=env, + robot=robot, + config=planner_cfg, + env_id=0, + ) + + goal_pose_visualizer = None + if not headless: + marker_cfg = FRAME_MARKER_CFG.replace(prim_path="/World/Visuals/goal_pose") + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + goal_pose_visualizer = VisualizationMarkers(marker_cfg) + + yield { + "env": env, + "robot": robot, + "planner": planner, + "goal_pose_visualizer": goal_pose_visualizer, + } + + env.close() + + +class TestCubeStackPlanner: + @pytest.fixture(autouse=True) + def setup(self, cube_stack_test_env) -> None: + self.env: ManagerBasedEnv = cube_stack_test_env["env"] + self.robot: Articulation = cube_stack_test_env["robot"] + self.planner: CuroboPlanner = cube_stack_test_env["planner"] + self.goal_pose_visualizer: VisualizationMarkers | None = cube_stack_test_env["goal_pose_visualizer"] + + def _visualize_goal_pose(self, pos: torch.Tensor, quat: torch.Tensor) -> None: + """Visualize the goal frame markers at pos, quat (xyzw).""" + if headless or self.goal_pose_visualizer is None: + return + self.goal_pose_visualizer.visualize(translations=pos.unsqueeze(0), orientations=quat.unsqueeze(0)) + + def _pose_from_xy_quat(self, xy: torch.Tensor, z: float, quat: torch.Tensor) -> torch.Tensor: + """Build a 4×4 pose given xy (Tensor[2]), z, and quaternion.""" + device = xy.device + dtype = xy.dtype + pos = torch.cat([xy, torch.tensor([z], dtype=dtype, device=device)]) + rot = math_utils.matrix_from_quat(quat.to(device).unsqueeze(0))[0] + return math_utils.make_pose(pos, rot) + + def _get_cube_pos(self, cube_name: str) -> torch.Tensor: + """Return the current world position of a cube's root (x, y, z).""" + obj: RigidObject = self.env.scene[cube_name] + return obj.data.root_pos_w[0, :3].clone().detach() + + def _place_pose_over_cube(self, cube_name: str, height_offset: float) -> torch.Tensor: + """Compute a goal pose directly above the named cube using the latest pose.""" + base_pos = self._get_cube_pos(cube_name) + return self._pose_from_xy_quat(base_pos[:2], base_pos[2].item() + height_offset, DOWN_FACING_QUAT) + + def test_pick_and_stack(self) -> None: + """Plan and execute pick-and-place to stack cube_1 on cube_2, then cube_3 on the stack.""" + cube_1_pos = self._get_cube_pos("cube_1") + cube_2_pos = self._get_cube_pos("cube_2") + cube_3_pos = self._get_cube_pos("cube_3") + print(f"Cube 1 position: {cube_1_pos}") + print(f"Cube 2 position: {cube_2_pos}") + print(f"Cube 3 position: {cube_3_pos}") + + # Approach above cube_1 + pre_grasp_height = 0.1 + pre_grasp_pose = self._pose_from_xy_quat(cube_1_pos[:2], pre_grasp_height, DOWN_FACING_QUAT) + print(f"Pre-grasp pose: {pre_grasp_pose}") + if not headless: + pos_pg = pre_grasp_pose[:3, 3].detach().cpu() + quat_pg = math_utils.quat_from_matrix(pre_grasp_pose[:3, :3].unsqueeze(0))[0].detach().cpu() + self._visualize_goal_pose(pos_pg, quat_pg) + + # Plan to pre-grasp + assert self.planner.update_world_and_plan_motion(pre_grasp_pose), "Failed to plan to pre-grasp pose" + _execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_OPEN_CMD) + + # Close gripper to grasp cube_1 (hold pose while closing) + _execute_gripper_action(self.env, self.robot, GRIPPER_CLOSE_CMD, steps=16) + + # Plan placement with cube_1 attached (above latest cube_2) + place_pose = self._place_pose_over_cube("cube_2", 0.15) + + if not headless: + pos_place = place_pose[:3, 3].detach().cpu() + quat_place = math_utils.quat_from_matrix(place_pose[:3, :3].unsqueeze(0))[0].detach().cpu() + self._visualize_goal_pose(pos_place, quat_place) + + # Plan with attached object + assert self.planner.update_world_and_plan_motion(place_pose, expected_attached_object="cube_1"), ( + "Failed to plan placement trajectory with attached cube" + ) + _execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_CLOSE_CMD) + + # Release cube 1 + _execute_gripper_action(self.env, self.robot, GRIPPER_OPEN_CMD, steps=16) + + # Go to cube 3 + cube_3_pos_now = self._get_cube_pos("cube_3") + pre_grasp_pose = self._pose_from_xy_quat(cube_3_pos_now[:2], pre_grasp_height, DOWN_FACING_QUAT) + print(f"Pre-grasp pose: {pre_grasp_pose}") + if not headless: + pos_pg = pre_grasp_pose[:3, 3].detach().cpu() + quat_pg = math_utils.quat_from_matrix(pre_grasp_pose[:3, :3].unsqueeze(0))[0].detach().cpu() + self._visualize_goal_pose(pos_pg, quat_pg) + + assert self.planner.update_world_and_plan_motion(pre_grasp_pose, expected_attached_object=None), ( + "Failed to plan retract motion" + ) + _execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_OPEN_CMD) + + # Grasp cube 3 + _execute_gripper_action(self.env, self.robot, GRIPPER_CLOSE_CMD) + + # Plan placement with cube_3 attached, to cube 2 (use latest cube_2 pose) + place_pose = self._place_pose_over_cube("cube_2", 0.18) + + if not headless: + pos_place = place_pose[:3, 3].detach().cpu() + quat_place = math_utils.quat_from_matrix(place_pose[:3, :3].unsqueeze(0))[0].detach().cpu() + self._visualize_goal_pose(pos_place, quat_place) + + assert self.planner.update_world_and_plan_motion(place_pose, expected_attached_object="cube_3"), ( + "Failed to plan placement trajectory with attached cube" + ) + _execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_CLOSE_CMD) + + # Release cube 3 + _execute_gripper_action(self.env, self.robot, GRIPPER_OPEN_CMD) + + print("Pick-and-place stacking test completed successfully!") diff --git a/source/isaaclab_mimic/test/test_curobo_planner_franka.py b/source/isaaclab_mimic/test/test_curobo_planner_franka.py new file mode 100644 index 0000000000000000000000000000000000000000..9e5adf724c2f9776563af13a1d50e786c5038b90 --- /dev/null +++ b/source/isaaclab_mimic/test/test_curobo_planner_franka.py @@ -0,0 +1,173 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import random +from collections.abc import Generator +from typing import Any + +import pytest + +SEED: int = 42 +random.seed(SEED) + +from isaaclab.app import AppLauncher + +headless = True +app_launcher = AppLauncher(headless=headless) +simulation_app: Any = app_launcher.app + +import gymnasium as gym +import torch + +import isaaclab.utils.assets as _al_assets +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObjectCfg +from isaaclab.envs.manager_based_env import ManagerBasedEnv +from isaaclab.markers import FRAME_MARKER_CFG, VisualizationMarkers +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg + +ISAAC_NUCLEUS_DIR: str = getattr(_al_assets, "ISAAC_NUCLEUS_DIR", "/Isaac") + +from isaaclab_mimic.motion_planners.curobo.curobo_planner import CuroboPlanner +from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg + +from isaaclab_tasks.manager_based.manipulation.stack.config.franka.stack_joint_pos_env_cfg import FrankaCubeStackEnvCfg + +# Predefined EE goals for the test +# Each entry is a tuple of: (goal specification, goal ID) +predefined_ee_goals_and_ids = [ + ({"pos": [0.70, -0.25, 0.25], "quat": [0.0, 0.707, 0.0, 0.707]}, "Behind wall, left"), + ({"pos": [0.70, 0.25, 0.25], "quat": [0.0, 0.707, 0.0, 0.707]}, "Behind wall, right"), + ({"pos": [0.65, 0.0, 0.45], "quat": [0.0, 1.0, 0.0, 0.0]}, "Behind wall, center, high"), + ({"pos": [0.80, -0.15, 0.35], "quat": [0.0, 0.5, 0.0, 0.866]}, "Behind wall, far left"), + ({"pos": [0.80, 0.15, 0.35], "quat": [0.0, 0.5, 0.0, 0.866]}, "Behind wall, far right"), +] + + +@pytest.fixture(scope="class") +def curobo_test_env() -> Generator[dict[str, Any], None, None]: + """Set up the environment for the Curobo test and yield test-critical data.""" + random.seed(SEED) + torch.manual_seed(SEED) + + env_cfg = FrankaCubeStackEnvCfg() + env_cfg.scene.num_envs = 1 + + # Add a static wall for the robot to avoid + wall_props = RigidBodyPropertiesCfg(kinematic_enabled=True, disable_gravity=True) + wall_cfg = RigidObjectCfg( + prim_path="/World/envs/env_0/moving_wall", + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(0.5, 4.5, 7.0), + rigid_props=wall_props, + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.55, 0.0, 0.80)), + ) + setattr(env_cfg.scene, "moving_wall", wall_cfg) + + env: ManagerBasedEnv = gym.make("Isaac-Stack-Cube-Franka-v0", cfg=env_cfg, headless=headless).unwrapped + env.reset() + + robot = env.scene["robot"] + planner = CuroboPlanner(env=env, robot=robot, config=CuroboPlannerCfg.franka_config()) + + goal_pose_visualizer = None + if not headless: + goal_marker_cfg = FRAME_MARKER_CFG.replace(prim_path="/World/Visuals/goal_poses") + goal_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + goal_pose_visualizer = VisualizationMarkers(goal_marker_cfg) + + # Allow the simulation to settle + for _ in range(3): + env.sim.step(render=False) + + planner.update_world() + + # Default joint positions for the Franka arm (7-DOF) + home_j = torch.tensor([0.0, -0.4, 0.0, -2.1, 0.0, 2.1, 0.7]) + + # Yield the necessary objects for the test + yield { + "env": env, + "robot": robot, + "planner": planner, + "goal_pose_visualizer": goal_pose_visualizer, + "home_j": home_j, + } + + # Teardown: close the environment and simulation app + env.close() + + +class TestCuroboPlanner: + """Test suite for the Curobo motion planner, focusing on obstacle avoidance.""" + + @pytest.fixture(autouse=True) + def setup(self, curobo_test_env) -> None: + """Inject the test environment into the test class instance.""" + self.env: ManagerBasedEnv = curobo_test_env["env"] + self.robot: Articulation = curobo_test_env["robot"] + self.planner: CuroboPlanner = curobo_test_env["planner"] + self.goal_pose_visualizer: VisualizationMarkers | None = curobo_test_env["goal_pose_visualizer"] + self.home_j: torch.Tensor = curobo_test_env["home_j"] + + def _visualize_goal_pose(self, pos: torch.Tensor, quat: torch.Tensor) -> None: + """Visualize the goal pose using frame markers if not in headless mode.""" + if headless or self.goal_pose_visualizer is None: + return + pos_vis = pos.unsqueeze(0) + quat_vis = quat.unsqueeze(0) + self.goal_pose_visualizer.visualize(translations=pos_vis, orientations=quat_vis) + + def _execute_current_plan(self) -> None: + """Replay the waypoints of the current plan in the simulator for visualization.""" + if headless or self.planner.current_plan is None: + return + for q in self.planner.current_plan.position: + q_tensor = q if isinstance(q, torch.Tensor) else torch.as_tensor(q, dtype=torch.float32) + self._set_arm_positions(q_tensor) + self.env.sim.step(render=True) + + def _set_arm_positions(self, q: torch.Tensor) -> None: + """Set the joint positions of the robot's arm, appending default gripper values if necessary.""" + if q.dim() == 1: + q = q.unsqueeze(0) + if q.shape[-1] == 7: # Arm only + fingers = torch.tensor([0.04, 0.04], device=q.device, dtype=q.dtype).repeat(q.shape[0], 1) + q_full = torch.cat([q, fingers], dim=-1) + else: + q_full = q + self.robot.write_joint_position_to_sim(q_full) + + @pytest.mark.parametrize("goal_spec, goal_id", predefined_ee_goals_and_ids) + def test_plan_to_predefined_goal(self, goal_spec, goal_id) -> None: + """Test planning to a predefined goal, ensuring the planner can find a path around an obstacle.""" + print(f"Planning for goal: {goal_id}") + + # Reset robot to a known home position before each test + self._set_arm_positions(self.home_j) + self.env.sim.step() + + pos = torch.tensor(goal_spec["pos"], dtype=torch.float32) + quat = torch.tensor(goal_spec["quat"], dtype=torch.float32) + + if not headless: + self._visualize_goal_pose(pos, quat) + + # Ensure the goal is actually behind the wall + assert pos[0] > 0.55, f"Goal '{goal_id}' is not behind the wall (x={pos[0]:.3f})" + + rot_matrix = math_utils.matrix_from_quat(quat.unsqueeze(0))[0] + ee_goal = math_utils.make_pose(pos, rot_matrix) + + result = self.planner.plan_motion(ee_goal) + print(f"Planning result for '{goal_id}': {'Success' if result else 'Failure'}") + + assert result, f"Failed to find a motion plan for the goal: '{goal_id}'" + + if result and not headless: + self._execute_current_plan() diff --git a/source/isaaclab_mimic/test/test_generate_dataset.py b/source/isaaclab_mimic/test/test_generate_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8568ab4ec01d92d1937b9ee977e467b9e19aa56d --- /dev/null +++ b/source/isaaclab_mimic/test/test_generate_dataset.py @@ -0,0 +1,142 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Test dataset generation for Isaac Lab Mimic workflow.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import os +import subprocess +import tempfile + +import pytest + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +DATASETS_DOWNLOAD_DIR = tempfile.mkdtemp(suffix="_Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0") +NUCLEUS_DATASET_PATH = os.path.join(ISAACLAB_NUCLEUS_DIR, "Tests", "Mimic", "dataset.hdf5") +EXPECTED_SUCCESSFUL_ANNOTATIONS = 10 + + +@pytest.fixture +def setup_test_environment(): + """Set up the environment for testing.""" + # Create the datasets directory if it does not exist + if not os.path.exists(DATASETS_DOWNLOAD_DIR): + print("Creating directory : ", DATASETS_DOWNLOAD_DIR) + os.makedirs(DATASETS_DOWNLOAD_DIR) + + # Try to download the dataset from Nucleus + try: + retrieve_file_path(NUCLEUS_DATASET_PATH, DATASETS_DOWNLOAD_DIR) + except Exception as e: + print(e) + print("Could not download dataset from Nucleus") + pytest.fail( + "The dataset required for this test is currently unavailable. Dataset path: " + NUCLEUS_DATASET_PATH + ) + + # Set the environment variable PYTHONUNBUFFERED to 1 to get all text outputs in result.stdout + pythonunbuffered_env_var_ = os.environ.get("PYTHONUNBUFFERED") + os.environ["PYTHONUNBUFFERED"] = "1" + + # Automatically detect the workflow root (backtrack from current file location) + current_dir = os.path.dirname(os.path.abspath(__file__)) + workflow_root = os.path.abspath(os.path.join(current_dir, "../../..")) + + # Run the command to generate core configs + config_command = [ + workflow_root + "/isaaclab.sh", + "-p", + os.path.join(workflow_root, "scripts/imitation_learning/isaaclab_mimic/annotate_demos.py"), + "--task", + "Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0", + "--input_file", + DATASETS_DOWNLOAD_DIR + "/dataset.hdf5", + "--output_file", + DATASETS_DOWNLOAD_DIR + "/annotated_dataset.hdf5", + "--auto", + "--headless", + ] + print(config_command) + + # Execute the command and capture the result + result = subprocess.run(config_command, capture_output=True, text=True) + + print(f"Annotate demos result: {result.returncode}\n\n\n\n\n\n\n\n\n\n\n\n") + + # Print the result for debugging purposes + print("Config generation result:") + print(result.stdout) # Print standard output from the command + print(result.stderr) # Print standard error from the command + + # Check if the config generation was successful + assert result.returncode == 0, result.stderr + + # Check that at least one task was completed successfully by parsing stdout + # Look for the line that reports successful task completions + success_line = None + for line in result.stdout.split("\n"): + if "Successful task completions:" in line: + success_line = line + break + + assert success_line is not None, "Could not find 'Successful task completions:' in output" + + # Extract the number from the line + try: + successful_count = int(success_line.split(":")[-1].strip()) + assert successful_count == EXPECTED_SUCCESSFUL_ANNOTATIONS, ( + f"Expected 10 successful annotations but got {successful_count}" + ) + except (ValueError, IndexError) as e: + pytest.fail(f"Could not parse successful task count from line: '{success_line}'. Error: {e}") + + # Yield the workflow root for use in tests + yield workflow_root + + # Cleanup: restore the original environment variable + if pythonunbuffered_env_var_: + os.environ["PYTHONUNBUFFERED"] = pythonunbuffered_env_var_ + else: + del os.environ["PYTHONUNBUFFERED"] + + +@pytest.mark.isaacsim_ci +def test_generate_dataset(setup_test_environment): + """Test the dataset generation script.""" + workflow_root = setup_test_environment + + # Define the command to run the dataset generation script + command = [ + workflow_root + "/isaaclab.sh", + "-p", + os.path.join(workflow_root, "scripts/imitation_learning/isaaclab_mimic/generate_dataset.py"), + "--input_file", + DATASETS_DOWNLOAD_DIR + "/annotated_dataset.hdf5", + "--output_file", + DATASETS_DOWNLOAD_DIR + "/generated_dataset.hdf5", + "--generation_num_trials", + "1", + "--headless", + ] + + # Call the script and capture output + result = subprocess.run(command, capture_output=True, text=True) + + # Print the result for debugging purposes + print("Dataset generation result:") + print(result.stdout) # Print standard output from the command + print(result.stderr) # Print standard error from the command + + # Check if the script executed successfully + assert result.returncode == 0, result.stderr + + # Check for specific output + expected_output = "successes/attempts. Exiting" + assert expected_output in result.stdout diff --git a/source/isaaclab_mimic/test/test_generate_dataset_skillgen.py b/source/isaaclab_mimic/test/test_generate_dataset_skillgen.py new file mode 100644 index 0000000000000000000000000000000000000000..7f5afc7d664c5ac800739fd050ac9d290a522c06 --- /dev/null +++ b/source/isaaclab_mimic/test/test_generate_dataset_skillgen.py @@ -0,0 +1,91 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Test dataset generation with SkillGen for Isaac Lab Mimic workflow.""" + +from isaaclab.app import AppLauncher + +# Launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import os +import subprocess +import tempfile + +import pytest + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +DATASETS_DOWNLOAD_DIR = tempfile.mkdtemp(suffix="_Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0") +NUCLEUS_SKILLGEN_ANNOTATED_DATASET_PATH = os.path.join( + ISAACLAB_NUCLEUS_DIR, "Mimic", "franka_stack_datasets", "annotated_dataset_skillgen.hdf5" +) + + +@pytest.fixture +def setup_skillgen_test_environment(): + """Prepare environment for SkillGen dataset generation test.""" + # Create the datasets directory if it does not exist + if not os.path.exists(DATASETS_DOWNLOAD_DIR): + print("Creating directory : ", DATASETS_DOWNLOAD_DIR) + os.makedirs(DATASETS_DOWNLOAD_DIR) + + # Download the SkillGen annotated dataset from Nucleus into DATASETS_DOWNLOAD_DIR + retrieve_file_path(NUCLEUS_SKILLGEN_ANNOTATED_DATASET_PATH, DATASETS_DOWNLOAD_DIR) + + # Set the environment variable PYTHONUNBUFFERED to 1 to get all text outputs in result.stdout + pythonunbuffered_env_var_ = os.environ.get("PYTHONUNBUFFERED") + os.environ["PYTHONUNBUFFERED"] = "1" + + # Automatically detect the workflow root (backtrack from current file location) + current_dir = os.path.dirname(os.path.abspath(__file__)) + workflow_root = os.path.abspath(os.path.join(current_dir, "../../..")) + + # Yield the workflow root for use in tests + yield workflow_root + + # Cleanup: restore the original environment variable + if pythonunbuffered_env_var_: + os.environ["PYTHONUNBUFFERED"] = pythonunbuffered_env_var_ + else: + del os.environ["PYTHONUNBUFFERED"] + + +def test_generate_dataset_skillgen(setup_skillgen_test_environment): + """Test dataset generation with SkillGen enabled.""" + workflow_root = setup_skillgen_test_environment + + input_file = os.path.join(DATASETS_DOWNLOAD_DIR, "annotated_dataset_skillgen.hdf5") + output_file = os.path.join(DATASETS_DOWNLOAD_DIR, "generated_dataset_skillgen.hdf5") + + command = [ + workflow_root + "/isaaclab.sh", + "-p", + os.path.join(workflow_root, "scripts/imitation_learning/isaaclab_mimic/generate_dataset.py"), + "--device", + "cpu", + "--input_file", + input_file, + "--output_file", + output_file, + "--num_envs", + "1", + "--generation_num_trials", + "1", + "--use_skillgen", + "--headless", + "--task", + "Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0", + ] + + result = subprocess.run(command, capture_output=True, text=True) + + print("SkillGen dataset generation result:") + print(result.stdout) + print(result.stderr) + + assert result.returncode == 0, result.stderr + expected_output = "successes/attempts. Exiting" + assert expected_output in result.stdout diff --git a/source/isaaclab_mimic/test/test_selection_strategy.py b/source/isaaclab_mimic/test/test_selection_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..ac58be34db0feacf7d9396299862b36e68434786 --- /dev/null +++ b/source/isaaclab_mimic/test/test_selection_strategy.py @@ -0,0 +1,278 @@ +# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.app import AppLauncher + +# launch omniverse app +simulation_app = AppLauncher(headless=True).app + +import numpy as np +import pytest +import torch + +import isaaclab.utils.math as PoseUtils + +from isaaclab_mimic.datagen.datagen_info import DatagenInfo + +# Importing the necessary classes for the testing +from isaaclab_mimic.datagen.selection_strategy import ( + NearestNeighborObjectStrategy, + NearestNeighborRobotDistanceStrategy, +) + +# Number of iterations to run the batched tests +NUM_ITERS = 1000 + + +@pytest.fixture +def nearest_neighbor_object_strategy(): + """Fixture for NearestNeighborObjectStrategy.""" + return NearestNeighborObjectStrategy() + + +@pytest.fixture +def nearest_neighbor_robot_distance_strategy(): + """Fixture for NearestNeighborRobotDistanceStrategy.""" + return NearestNeighborRobotDistanceStrategy() + + +def test_select_source_demo_identity_orientations_object_strategy(nearest_neighbor_object_strategy): + """Test the selection of source demonstrations using two distinct object_pose clusters. + + This method generates two clusters of object poses and randomly adjusts the current object pose within + specified deviations. It then simulates multiple selections to verify that when the current pose is close + to cluster 1, all selected indices correspond to that cluster, and that the same holds true for cluster 2. + """ + + # Define ranges for two clusters of object poses + cluster_1_range_min = 0 + cluster_1_range_max = 4 + cluster_2_range_min = 25 + cluster_2_range_max = 35 + + # Generate object poses for cluster 1 with varying translations + src_object_poses_in_world_cluster_1 = [ + torch.eye(4) + torch.tensor([[0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, -1.0]]) + for i in range(cluster_1_range_min, cluster_1_range_max) + ] + + # Generate object poses for cluster 2 similarly + src_object_poses_in_world_cluster_2 = [ + torch.eye(4) + torch.tensor([[0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, i], [0.0, 0.0, 0.0, -1.0]]) + for i in range(cluster_2_range_min, cluster_2_range_max) + ] + + # Combine the poses from both clusters into a single list + src_object_poses_in_world = src_object_poses_in_world_cluster_1 + src_object_poses_in_world_cluster_2 + + # Create DatagenInfo instances for these positions + src_subtask_datagen_infos = [ + DatagenInfo(object_poses={0: object_pose.unsqueeze(0)}) for object_pose in src_object_poses_in_world + ] + + # Define the end-effector pose (not used in the nearest neighbor selection) + eef_pose = torch.eye(4) + + # Test 1: + # Set the current object pose to the first value of cluster 1 and add some noise + # Check that the nearest neighbor is always part of cluster 1 + max_deviation = 3 # Define a maximum deviation for the current pose + # Randomly select an index from cluster 1 + random_index_cluster_1 = np.random.randint(0, len(src_object_poses_in_world_cluster_1)) + cluster_1_curr_object_pose = src_object_poses_in_world_cluster_1[ + random_index_cluster_1 + ].clone() # Use clone to avoid reference issues + # Randomly adjust the current pose within the maximum deviation + cluster_1_curr_object_pose[0, 3] += torch.rand(1).item() * max_deviation + cluster_1_curr_object_pose[1, 3] += torch.rand(1).item() * max_deviation + cluster_1_curr_object_pose[2, 3] += torch.rand(1).item() * max_deviation + + # Select source demonstrations multiple times to check randomness + selected_indices = [ + nearest_neighbor_object_strategy.select_source_demo( + eef_pose, + cluster_1_curr_object_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=3, # Check among the top 3 nearest neighbors + ) + for _ in range(NUM_ITERS) + ] + + # Assert that all selected indices are valid indices within cluster 1 + assert np.all(np.array(selected_indices) < len(src_object_poses_in_world_cluster_1)), ( + "Some selected indices are not part of cluster 1." + ) + + # Test 2: + # Set the current object pose to the first value of cluster 2 and add some noise + # Check that the nearest neighbor is always part of cluster 2 + max_deviation = 5 # Define a maximum deviation for the current pose in cluster 2 + # Randomly select an index from cluster 2 + random_index_cluster_2 = np.random.randint(0, len(src_object_poses_in_world_cluster_2)) + cluster_2_curr_object_pose = src_object_poses_in_world_cluster_2[ + random_index_cluster_2 + ].clone() # Use clone to avoid reference issues + # Randomly adjust the current pose within the maximum deviation + cluster_2_curr_object_pose[0, 3] += torch.rand(1).item() * max_deviation + cluster_2_curr_object_pose[1, 3] += torch.rand(1).item() * max_deviation + cluster_2_curr_object_pose[2, 3] += torch.rand(1).item() * max_deviation + + # Select source demonstrations multiple times to check randomness + selected_indices = [ + nearest_neighbor_object_strategy.select_source_demo( + eef_pose, + cluster_2_curr_object_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=6, # Check among the top 6 nearest neighbors + ) + for _ in range(20) + ] + + # Assert that all selected indices are valid indices within cluster 2 + assert np.all(np.array(selected_indices) < len(src_object_poses_in_world)), ( + "Some selected indices are not part of cluster 2." + ) + assert np.all(np.array(selected_indices) > (len(src_object_poses_in_world_cluster_1) - 1)), ( + "Some selected indices are not part of cluster 2." + ) + + +def test_select_source_demo_identity_orientations_robot_distance_strategy(nearest_neighbor_robot_distance_strategy): + """Test the selection of source demonstrations based on identity-oriented poses with varying positions. + + This method generates two clusters of object poses and randomly adjusts the current object pose within + specified deviations. It then simulates multiple selections to verify that when the current pose is close + to cluster 1, all selected indices correspond to that cluster, and that the same holds true for cluster 2. + """ + + # Define ranges for two clusters of object poses + cluster_1_range_min = 0 + cluster_1_range_max = 4 + cluster_2_range_min = 25 + cluster_2_range_max = 35 + + # Generate random transformed object poses for cluster 1 with varying translations + # This represents the first object pose for the transformed subtask segment for each source demo + transformed_eef_pose_cluster_1 = [ + torch.eye(4) + torch.tensor([[0, 0, 0, i], [0, 0, 0, i], [0, 0, 0, i], [0, 0, 0, -1]]) + for i in range(cluster_1_range_min, cluster_1_range_max) + ] + + # Generate object poses for cluster 2 similarly + transformed_eef_pose_cluster_2 = [ + torch.eye(4) + torch.tensor([[0, 0, 0, i], [0, 0, 0, i], [0, 0, 0, i], [0, 0, 0, -1]]) + for i in range(cluster_2_range_min, cluster_2_range_max) + ] + + # Combine the poses from both clusters into a single list + # This represents the first end effector pose for the transformed subtask segment for each source demo + transformed_eef_in_world_poses_tensor = torch.stack(transformed_eef_pose_cluster_1 + transformed_eef_pose_cluster_2) + + # Create transformation matrices corresponding to each source object pose + src_obj_in_world_poses = torch.stack( + [ + PoseUtils.generate_random_transformation_matrix(pos_boundary=10, rot_boundary=(2 * np.pi)) + for _ in range(transformed_eef_in_world_poses_tensor.shape[0]) + ] + ) + + # Calculate the src_eef poses from the transformed eef poses, src_obj_in_world and curr_obj_pose_in_world + # This is the inverse of the transformation of the eef pose done in NearestNeighborRobotDistanceStrategy + # Refer to NearestNeighborRobotDistanceStrategy.select_source_demo for more details + curr_object_in_world_pose = PoseUtils.generate_random_transformation_matrix( + pos_boundary=10, rot_boundary=(2 * np.pi) + ) + world_in_curr_obj_pose = PoseUtils.pose_inv(curr_object_in_world_pose) + + src_eef_in_src_obj_poses = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=transformed_eef_in_world_poses_tensor, + pose_A_in_B=world_in_curr_obj_pose, + ) + + src_eef_in_world_poses = PoseUtils.pose_in_A_to_pose_in_B( + pose_in_A=src_eef_in_src_obj_poses, + pose_A_in_B=src_obj_in_world_poses, + ) + + # Check that both lists have the same length + assert src_obj_in_world_poses.shape[0] == src_eef_in_world_poses.shape[0], ( + "Source object poses and end effector poses does not have the same length. " + "This is a bug in the test code and not the source code." + ) + + # Create DatagenInfo instances for these positions + src_subtask_datagen_infos = [ + DatagenInfo(eef_pose=src_eef_in_world_pose.unsqueeze(0), object_poses={0: src_obj_in_world_pose.unsqueeze(0)}) + for src_obj_in_world_pose, src_eef_in_world_pose in zip(src_obj_in_world_poses, src_eef_in_world_poses) + ] + + # Test 1: Ensure the nearest neighbor is always part of cluster 1 + max_deviation = 3 # Define a maximum deviation for the current pose + # Define the end-effector pose + # Set the current object pose to the first value of cluster 1 and add some noise + random_index_cluster_1 = np.random.randint(0, len(transformed_eef_pose_cluster_1)) + curr_eef_in_world_pose = transformed_eef_pose_cluster_1[ + random_index_cluster_1 + ].clone() # Use clone to avoid reference issues + # Randomly adjust the current pose within the maximum deviation + curr_eef_in_world_pose[0, 3] += torch.rand(1).item() * max_deviation + curr_eef_in_world_pose[1, 3] += torch.rand(1).item() * max_deviation + curr_eef_in_world_pose[2, 3] += torch.rand(1).item() * max_deviation + + # Select source demonstrations multiple times to check randomness + selected_indices = [ + nearest_neighbor_robot_distance_strategy.select_source_demo( + curr_eef_in_world_pose, + curr_object_in_world_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=3, # Check among the top 3 nearest neighbors + ) + for _ in range(20) + ] + + # Assert that all selected indices are valid indices within cluster 1 + assert np.all(np.array(selected_indices) < len(transformed_eef_pose_cluster_1)), ( + "Some selected indices are not part of cluster 1." + ) + + # Test 2: Ensure the nearest neighbor is always part of cluster 2 + max_deviation = 3 # Define a maximum deviation for the current pose + # Define the end-effector pose + # Set the current object pose to the first value of cluster 2 and add some noise + random_index_cluster_2 = np.random.randint(0, len(transformed_eef_pose_cluster_2)) + curr_eef_in_world_pose = transformed_eef_pose_cluster_2[ + random_index_cluster_2 + ].clone() # Use clone to avoid reference issues + # Randomly adjust the current pose within the maximum deviation + curr_eef_in_world_pose[0, 3] += torch.rand(1).item() * max_deviation + curr_eef_in_world_pose[1, 3] += torch.rand(1).item() * max_deviation + curr_eef_in_world_pose[2, 3] += torch.rand(1).item() * max_deviation + + # Select source demonstrations multiple times to check randomness + selected_indices = [ + nearest_neighbor_robot_distance_strategy.select_source_demo( + curr_eef_in_world_pose, + curr_object_in_world_pose, + src_subtask_datagen_infos, + pos_weight=1.0, + rot_weight=1.0, + nn_k=3, # Check among the top 3 nearest neighbors + ) + for _ in range(20) + ] + + # Assert that all selected indices are valid indices within cluster 2 + assert np.all(np.array(selected_indices) < transformed_eef_in_world_poses_tensor.shape[0]), ( + "Some selected indices are not part of cluster 2." + ) + assert np.all(np.array(selected_indices) > (len(transformed_eef_pose_cluster_1) - 1)), ( + "Some selected indices are not part of cluster 2." + ) diff --git a/source/isaaclab_rl/config/extension.toml b/source/isaaclab_rl/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..79f171d3398c80afb87f8349c0ef708deb4d5239 --- /dev/null +++ b/source/isaaclab_rl/config/extension.toml @@ -0,0 +1,23 @@ +[package] + +# Note: Semantic Versioning is used: https://semver.org/ +version = "0.4.7" + +# Description +title = "Isaac Lab RL" +description="Extension containing reinforcement learning related utilities." +readme = "docs/README.md" +repository = "https://github.com/isaac-sim/IsaacLab" +category = "robotics" +keywords = ["robotics", "rl", "wrappers", "learning"] + +[dependencies] +"isaaclab" = {} +"isaaclab_assets" = {} +"isaaclab_tasks" = {} + +[core] +reloadable = false + +[[python.module]] +name = "isaaclab_rl" diff --git a/source/isaaclab_rl/docs/CHANGELOG.rst b/source/isaaclab_rl/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..6fe0be78d0a59d1a7d4f680b9c195cde14a8d799 --- /dev/null +++ b/source/isaaclab_rl/docs/CHANGELOG.rst @@ -0,0 +1,204 @@ +Changelog +--------- + +0.4.7 (2025-12-29) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added :mod:`isaaclab_rl.utils.pretrained_checkpoint` sub-module to handle various pre-trained checkpoint tasks. + This module was previously located in the :mod:`isaaclab.utils` module. + + +0.4.6 (2025-11-10) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added support for decoupling RL device from simulation device in for RL games wrapper. + This allows users to run simulation on one device (e.g., CPU) while running RL training/inference on another device. + + +0.4.5 (2025-12-01) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added state_dependent_std rsl_rl param to RSL-RL wrapper. + + +0.4.4 (2025-10-15) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added onnxscript package to isaaclab_rl setup.py to fix onnxscript package missing issue in aarch64 platform. + + +0.4.3 (2025-10-15) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Isaac-Ant-v0's sb3_ppo_cfg default value, so it trains under reasonable amount of time. + + +0.4.2 (2025-10-14) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Updated opset version from 11 to 18 in RSL-RL OnnxPolicyExporter to avoid onnex downcast issue seen in aarch64. + + +0.4.1 (2025-09-09) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Made PBT a bit nicer by +* 1. added resume logic to allow wandb to continue on the same run_id +* 2. corrected broadcasting order in distributed setup +* 3. made score query general by using dotted keys to access dictionary of arbitrary depth + + +0.4.0 (2025-09-09) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Introduced PBT to rl-games. + + +0.3.0 (2025-09-03) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Enhanced rl-games wrapper to allow dict observation. + + +0.2.4 (2025-08-07) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Disallowed string values in ``sb3_ppo_cfg.yaml`` from being passed to ``eval()`` in + :meth:`~isaaclab_rl.sb3.process_sb3_cfg`. This change prevents accidental or malicious + code execution when loading configuration files, improving overall security and reliability. + + +0.2.3 (2025-06-29) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Support SB3 VecEnv wrapper to configure with composite observation spaces properly so that the cnn creation pipelines + natively supported by sb3 can be automatically triggered + + +0.2.2 (2025-06-30) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Call :meth:`eval` during :meth:`forward`` RSL-RL OnnxPolicyExporter + + +0.2.1 (2025-06-26) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Relaxed upper range pin for protobuf python dependency for more permissive installation. + + +0.2.0 (2025-04-24) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Switched to a 3.11 compatible branch for rl-games as Isaac Sim 5.0 is now using Python 3.11. + + +0.1.5 (2025-04-11) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Optimized Stable-Baselines3 wrapper ``Sb3VecEnvWrapper`` (now 4x faster) by using Numpy buffers and only logging episode and truncation information by default. +* Upgraded minimum SB3 version to 2.6.0 and added optional dependencies for progress bar + + +0.1.4 (2025-04-10) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added configurations for distillation implementation in RSL-RL. +* Added configuration for recurrent actor-critic in RSL-RL. + + +0.1.3 (2025-03-31) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the location of :meth:`isaaclab_rl.rsl_rl.RslRlOnPolicyRunnerCfg._modify_action_space` + to be called only after retrieving the dimensions of the environment, preventing errors + related to accessing uninitialized attributes. + + +0.1.2 (2025-03-28) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added symmetry and curiosity-based exploration configurations for RSL-RL wrapper. + + +0.1.1 (2025-03-10) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a parameter to clip the actions in the action space inside the RSL-RL wrapper. + This parameter is set to None by default, which is the same as not clipping the actions. +* Added attribute :attr:`isaaclab_rl.rsl_rl.RslRlOnPolicyRunnerCfg.clip_actions` to set + the clipping range for the actions in the RSL-RL on-policy runner. + + +0.1.0 (2024-12-27) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +Initial version of the extension. +This extension is split off from ``isaaclab_tasks`` to include the wrapper scripts for the supported RL libraries. + +Supported RL libraries are: + +* RL Games +* RSL RL +* SKRL +* Stable Baselines3 diff --git a/source/isaaclab_rl/isaaclab_rl/__init__.py b/source/isaaclab_rl/isaaclab_rl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f9e67f543a74d0d26b775b2ada796821eedf3350 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Package for environment wrappers to different learning frameworks. + +Wrappers allow you to modify the behavior of an environment without modifying the environment itself. +This is useful for modifying the observation space, action space, or reward function. Additionally, +they can be used to cast a given environment into the respective environment class definition used by +different learning frameworks. This operation may include handling of asymmetric actor-critic observations, +casting the data between different backends such `numpy` and `pytorch`, or organizing the returned data +into the expected data structure by the learning framework. + +All wrappers work similar to the :class:`gymnasium.Wrapper` class. Using a wrapper is as simple as passing +the initialized environment instance to the wrapper constructor. However, since learning frameworks +expect different input and output data structures, their wrapper classes are not compatible with each other. +Thus, they should always be used in conjunction with the respective learning framework. +""" diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/__init__.py b/source/isaaclab_rl/isaaclab_rl/rl_games/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f1d006ae6cc99f015c70e25a7772a42fe13c5412 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrappers and utilities to configure an environment for rl-games library.""" + +from .pbt import * +from .rl_games import * diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/__init__.py b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c56bf4f40e514b833cd515d5da328666f267e449 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .pbt import MultiObserver, PbtAlgoObserver +from .pbt_cfg import PbtCfg diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/mutation.py b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/mutation.py new file mode 100644 index 0000000000000000000000000000000000000000..ad942de8eec93582cebb786a8ef251cbcf39183b --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/mutation.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import random +from collections.abc import Callable +from typing import Any + + +def mutate_float(x: float, change_min: float = 1.1, change_max: float = 1.5) -> float: + """Multiply or divide by a random factor in [change_min, change_max].""" + k = random.uniform(change_min, change_max) + return x / k if random.random() < 0.5 else x * k + + +def mutate_discount(x: float, **kwargs) -> float: + """Conservative change near 1.0 by mutating (1 - x) in [1.1, 1.2].""" + inv = 1.0 - x + new_inv = mutate_float(inv, change_min=1.1, change_max=1.2) + return 1.0 - new_inv + + +MUTATION_FUNCS: dict[str, Callable[..., Any]] = { + "mutate_float": mutate_float, + "mutate_discount": mutate_discount, +} + + +def mutate( + params: dict[str, Any], + mutations: dict[str, str], + mutation_rate: float, + change_range: tuple[float, float], +) -> dict[str, Any]: + cmin, cmax = change_range + out: dict[str, Any] = {} + for name, val in params.items(): + fn_name = mutations.get(name) + # skip if no rule or coin flip says "no" + if fn_name is None or random.random() > mutation_rate: + out[name] = val + continue + fn = MUTATION_FUNCS.get(fn_name) + if fn is None: + raise KeyError(f"Unknown mutation function: {fn_name!r}") + out[name] = fn(val, change_min=cmin, change_max=cmax) + return out diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt.py b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt.py new file mode 100644 index 0000000000000000000000000000000000000000..aeec36055eb188d9de9ce23d45c84897ea3c5972 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt.py @@ -0,0 +1,268 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +import random +import sys + +import numpy as np +import torch +import torch.distributed as dist +from rl_games.common.algo_observer import AlgoObserver + +from . import pbt_utils +from .mutation import mutate +from .pbt_cfg import PbtCfg + +# i.e. value for target objective when it is not known +_UNINITIALIZED_VALUE = float(-1e9) + + +class PbtAlgoObserver(AlgoObserver): + """rl_games observer that implements Population-Based Training for a single policy process.""" + + def __init__(self, params, args_cli): + """Initialize observer, print the mutation table, and allocate the restart flag. + + Args: + params (dict): Full agent/task params (Hydra style). + args_cli: Parsed CLI args used to reconstruct a restart command. + """ + super().__init__() + self.printer = pbt_utils.PbtTablePrinter() + self.dir = params["pbt"]["directory"] + + self.rendering_args = pbt_utils.RenderingArgs(args_cli) + self.wandb_args = pbt_utils.WandbArgs(args_cli) + self.env_args = pbt_utils.EnvArgs(args_cli) + self.distributed_args = pbt_utils.DistributedArgs(args_cli) + self.cfg = PbtCfg(**params["pbt"]) + self.pbt_it = -1 # dummy value, stands for "not initialized" + self.score = _UNINITIALIZED_VALUE + self.pbt_params = pbt_utils.filter_params(pbt_utils.flatten_dict({"agent": params}), self.cfg.mutation) + + assert len(self.pbt_params) > 0, "[DANGER]: Dictionary that contains params to mutate is empty" + self.printer.print_params_table(self.pbt_params, header="List of params to mutate") + + self.device = params["params"]["config"]["device"] + self.restart_flag = torch.tensor([0], device=self.device) + + def after_init(self, algo): + """Capture training directories on rank 0 and create this policy's workspace folder. + + Args: + algo: rl_games algorithm object (provides writer, train_dir, frame counter, etc.). + """ + if self.distributed_args.rank != 0: + return + + self.algo = algo + self.root_dir = algo.train_dir + self.ws_dir = os.path.join(self.root_dir, self.cfg.workspace) + self.curr_policy_dir = os.path.join(self.ws_dir, f"{self.cfg.policy_idx:03d}") + os.makedirs(self.curr_policy_dir, exist_ok=True) + + def process_infos(self, infos, done_indices): + """Extract the scalar objective from environment infos and store in `self.score`. + + Notes: + Expects the objective to be at `infos[self.cfg.objective]` where self.cfg.objective is dotted address. + """ + score = infos + for part in self.cfg.objective.split("."): + score = score[part] + self.score = score + + def after_steps(self): + """Main PBT tick executed every train step. + + Flow: + 1) Non-zero ranks: exit immediately if `restart_flag == 1`, else return. + 2) Rank 0: if `restart_flag == 1`, restart this process with new params. + 3) Rank 0: on PBT cadence boundary (`interval_steps`), save checkpoint, + load population checkpoints, compute bands, and if this policy is an + underperformer, select a replacement (random leader or self), mutate + whitelisted params, set `restart_flag`, broadcast (if distributed), + and print a mutation diff table. + """ + if self.distributed_args.distributed: + dist.broadcast(self.restart_flag, src=0) + + if self.distributed_args.rank != 0: + if self.restart_flag.cpu().item() == 1: + os._exit(0) + return + + elif self.restart_flag.cpu().item() == 1: + self._restart_with_new_params(self.new_params, self.restart_from_checkpoint) + return + + # Non-zero can continue + if self.distributed_args.rank != 0: + return + + if self.pbt_it == -1: + self.pbt_it = self.algo.frame // self.cfg.interval_steps + return + + if self.algo.frame // self.cfg.interval_steps <= self.pbt_it: + return + + self.pbt_it = self.algo.frame // self.cfg.interval_steps + frame_left = (self.pbt_it + 1) * self.cfg.interval_steps - self.algo.frame + print(f"Policy {self.cfg.policy_idx}, frames_left {frame_left}, PBT it {self.pbt_it}") + try: + pbt_utils.save_pbt_checkpoint(self.curr_policy_dir, self.score, self.pbt_it, self.algo, self.pbt_params) + ckpts = pbt_utils.load_pbt_ckpts(self.ws_dir, self.cfg.policy_idx, self.cfg.num_policies, self.pbt_it) + pbt_utils.cleanup(ckpts, self.curr_policy_dir) + except Exception as exc: + print(f"Policy {self.cfg.policy_idx}: Exception {exc} during sanity log!") + return + + sumry = {i: None if c is None else {k: v for k, v in c.items() if k != "params"} for i, c in ckpts.items()} + self.printer.print_ckpt_summary(sumry) + + policies = list(range(self.cfg.num_policies)) + target_objectives = [ckpts[p]["true_objective"] if ckpts[p] else _UNINITIALIZED_VALUE for p in policies] + initialized = [(obj, p) for obj, p in zip(target_objectives, policies) if obj > _UNINITIALIZED_VALUE] + if not initialized: + print("No policies initialized; skipping PBT iteration.") + return + initialized_objectives, initialized_policies = zip(*initialized) + + # 1) Stats + mean_obj = float(np.mean(initialized_objectives)) + std_obj = float(np.std(initialized_objectives)) + upper_cut = max(mean_obj + self.cfg.threshold_std * std_obj, mean_obj + self.cfg.threshold_abs) + lower_cut = min(mean_obj - self.cfg.threshold_std * std_obj, mean_obj - self.cfg.threshold_abs) + leaders = [p for obj, p in zip(initialized_objectives, initialized_policies) if obj > upper_cut] + underperformers = [p for obj, p in zip(initialized_objectives, initialized_policies) if obj < lower_cut] + + print(f"mean={mean_obj:.4f}, std={std_obj:.4f}, upper={upper_cut:.4f}, lower={lower_cut:.4f}") + print(f"Leaders: {leaders} Underperformers: {underperformers}") + + # 3) Only replace if *this* policy is an underperformer + if self.cfg.policy_idx in underperformers: + # 4) If there are any leaders, pick one at random; else simply mutate with no replacement + replacement_policy_candidate = random.choice(leaders) if leaders else self.cfg.policy_idx + print(f"Replacing policy {self.cfg.policy_idx} with {replacement_policy_candidate}.") + + if self.distributed_args.rank == 0: + for param, value in self.pbt_params.items(): + self.algo.writer.add_scalar(f"pbt/{param}", value, self.algo.frame) + self.algo.writer.add_scalar("pbt/00_best_objective", max(initialized_objectives), self.algo.frame) + self.algo.writer.flush() + + # Decided to replace the policy weights! + cur_params = ckpts[replacement_policy_candidate]["params"] + self.new_params = mutate(cur_params, self.cfg.mutation, self.cfg.mutation_rate, self.cfg.change_range) + self.restart_from_checkpoint = os.path.abspath(ckpts[replacement_policy_candidate]["checkpoint"]) + self.restart_flag[0] = 1 + self.printer.print_mutation_diff(cur_params, self.new_params) + + def _restart_with_new_params(self, new_params, restart_from_checkpoint): + """Re-exec the current process with a filtered/augmented CLI to apply new params. + + Notes: + - Filters out existing Hydra-style overrides that will be replaced, + and appends `--checkpoint=` and new param overrides. + - On distributed runs, assigns a fresh master port and forwards + distributed args to the python.sh launcher. + """ + cli_args = sys.argv + print(f"previous command line args: {cli_args}") + + SKIP = ["checkpoint"] + is_hydra = lambda arg: ( # noqa: E731 + (name := arg.split("=", 1)[0]) not in new_params and not any(k in name for k in SKIP) + ) + modified_args = [cli_args[0]] + [arg for arg in cli_args[1:] if "=" not in arg or is_hydra(arg)] + + modified_args.append(f"--checkpoint={restart_from_checkpoint}") + modified_args.extend(self.wandb_args.get_args_list()) + modified_args.extend(self.rendering_args.get_args_list()) + + # add all of the new (possibly mutated) parameters + for param, value in new_params.items(): + modified_args.append(f"{param}={value}") + + self.algo.writer.flush() + self.algo.writer.close() + + if self.wandb_args.enabled: + import wandb + + # note setdefault will only affect child process, that mean don't have to worry it env variable + # propagate beyond restarted child process + os.environ.setdefault("WANDB_RUN_ID", wandb.run.id) # continue with the same run id + os.environ.setdefault("WANDB_RESUME", "allow") # allow wandb to resume + os.environ.setdefault("WANDB_INIT_TIMEOUT", "300") # give wandb init more time to be fault tolerant + wandb.run.finish() + + # Get the directory of the current file + thisfile_dir = os.path.dirname(os.path.abspath(__file__)) + isaac_sim_path = os.path.abspath(os.path.join(thisfile_dir, "../../../../../_isaac_sim")) + command = [f"{isaac_sim_path}/python.sh"] + + if self.distributed_args.distributed: + self.distributed_args.master_port = str(pbt_utils.find_free_port()) + command.extend(self.distributed_args.get_args_list()) + command += [modified_args[0]] + command.extend(self.env_args.get_args_list()) + command += modified_args[1:] + if self.distributed_args.distributed: + command += ["--distributed"] + + print("Running command:", command, flush=True) + print("sys.executable = ", sys.executable) + print(f"Policy {self.cfg.policy_idx}: Restarting self with args {modified_args}", flush=True) + + if self.distributed_args.rank == 0: + pbt_utils.dump_env_sizes() + + # after any sourcing (or before exec’ing python.sh) prevent kept increasing arg_length: + for var in ("PATH", "PYTHONPATH", "LD_LIBRARY_PATH", "OMNI_USD_RESOLVER_MDL_BUILTIN_PATHS"): + val = os.environ.get(var) + if not val or os.pathsep not in val: + continue + seen = set() + new_parts = [] + for p in val.split(os.pathsep): + if p and p not in seen: + seen.add(p) + new_parts.append(p) + os.environ[var] = os.pathsep.join(new_parts) + + os.execv(f"{isaac_sim_path}/python.sh", command) + + +class MultiObserver(AlgoObserver): + """Meta-observer that allows the user to add several observers.""" + + def __init__(self, observers_): + super().__init__() + self.observers = observers_ + + def _call_multi(self, method, *args_, **kwargs_): + for o in self.observers: + getattr(o, method)(*args_, **kwargs_) + + def before_init(self, base_name, config, experiment_name): + self._call_multi("before_init", base_name, config, experiment_name) + + def after_init(self, algo): + self._call_multi("after_init", algo) + + def process_infos(self, infos, done_indices): + self._call_multi("process_infos", infos, done_indices) + + def after_steps(self): + self._call_multi("after_steps") + + def after_clear_stats(self): + self._call_multi("after_clear_stats") + + def after_print_stats(self, frame, epoch_num, total_time): + self._call_multi("after_print_stats", frame, epoch_num, total_time) diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_cfg.py b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b494dd1fdefedebe1a40371e5df2fef926f252b6 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_cfg.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + + +@configclass +class PbtCfg: + """ + Population-Based Training (PBT) configuration. + + leaders are policies with score > max(mean + threshold_std*std, mean + threshold_abs). + underperformers are policies with score < min(mean - threshold_std*std, mean - threshold_abs). + On replacement, selected hyperparameters are mutated multiplicatively in [change_min, change_max]. + """ + + enabled: bool = False + """Enable/disable PBT logic.""" + + policy_idx: int = 0 + """Index of this learner in the population (unique in [0, num_policies-1]).""" + + num_policies: int = 8 + """Total number of learners participating in PBT.""" + + directory: str = "" + """Root directory for PBT artifacts (checkpoints, metadata).""" + + workspace: str = "pbt_workspace" + """Subfolder under the training dir to isolate this PBT run.""" + + objective: str = "Episode_Reward/success" + """The key in info returned by env.step that pbt measures to determine leaders and underperformers, + If reward is stationary, using the term that corresponds to task success is usually enough, when reward + are non-stationary, consider uses better objectives. + """ + + interval_steps: int = 100_000 + """Environment steps between PBT iterations (save, compare, replace/mutate).""" + + threshold_std: float = 0.10 + """Std-based margin k in max(mean ± k·std, mean ± threshold_abs) for leader/underperformer cuts.""" + + threshold_abs: float = 0.05 + """Absolute margin A in max(mean ± threshold_std·std, mean ± A) for leader/underperformer cuts.""" + + mutation_rate: float = 0.25 + """Per-parameter probability of mutation when a policy is replaced.""" + + change_range: tuple[float, float] = (1.1, 2.0) + """Lower and upper bound of multiplicative change factor (sampled in [change_min, change_max]).""" + + mutation: dict[str, str] = {} + """Mutation strings indicating which parameter will be mutated when pbt restart + example: + { + "agent.params.config.learning_rate": "mutate_float" + "agent.params.config.grad_norm": "mutate_float" + "agent.params.config.entropy_coef": "mutate_float" + } + """ diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_utils.py b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..959c24e410313998834991f492c6a5e1a5f74903 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/pbt/pbt_utils.py @@ -0,0 +1,297 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import datetime +import os +import random +import socket +from collections import OrderedDict +from pathlib import Path + +import yaml +from prettytable import PrettyTable +from rl_games.algos_torch.torch_ext import safe_filesystem_op, safe_save + + +class DistributedArgs: + def __init__(self, args_cli): + self.distributed = args_cli.distributed + self.nproc_per_node = int(os.environ.get("WORLD_SIZE", 1)) + self.rank = int(os.environ.get("RANK", 0)) + self.nnodes = 1 + self.master_port = getattr(args_cli, "master_port", None) + + def get_args_list(self) -> list[str]: + args = ["-m", "torch.distributed.run", f"--nnodes={self.nnodes}", f"--nproc_per_node={self.nproc_per_node}"] + if self.master_port: + args.append(f"--master_port={self.master_port}") + return args + + +class EnvArgs: + def __init__(self, args_cli): + self.task = args_cli.task + self.seed = args_cli.seed if args_cli.seed is not None else -1 + self.headless = args_cli.headless + self.num_envs = args_cli.num_envs + + def get_args_list(self) -> list[str]: + list = [] + list.append(f"--task={self.task}") + list.append(f"--seed={self.seed}") + list.append(f"--num_envs={self.num_envs}") + if self.headless: + list.append("--headless") + return list + + +class RenderingArgs: + def __init__(self, args_cli): + self.camera_enabled = args_cli.enable_cameras + self.video = args_cli.video + self.video_length = args_cli.video_length + self.video_interval = args_cli.video_interval + + def get_args_list(self) -> list[str]: + args = [] + if self.camera_enabled: + args.append("--enable_cameras") + if self.video: + args.extend(["--video", f"--video_length={self.video_length}", f"--video_interval={self.video_interval}"]) + return args + + +class WandbArgs: + def __init__(self, args_cli): + self.enabled = args_cli.track + self.project_name = args_cli.wandb_project_name + self.name = args_cli.wandb_name + self.entity = args_cli.wandb_entity + + def get_args_list(self) -> list[str]: + args = [] + if self.enabled: + args.append("--track") + if self.entity: + args.append(f"--wandb-entity={self.entity}") + else: + raise ValueError("entity must be specified if wandb is enabled") + if self.project_name: + args.append(f"--wandb-project-name={self.project_name}") + if self.name: + args.append(f"--wandb-name={self.name}") + return args + + +def dump_env_sizes(): + """Print summary of environment variable usage (count, bytes, top-5 largest, SC_ARG_MAX).""" + + n = len(os.environ) + # total bytes in "KEY=VAL\0" for all envp entries + total = sum(len(k) + 1 + len(v) + 1 for k, v in os.environ.items()) + # find the 5 largest values + biggest = sorted(os.environ.items(), key=lambda kv: len(kv[1]), reverse=True)[:5] + + print(f"[ENV MONITOR] vars={n}, total_bytes={total}") + for k, v in biggest: + print(f" {k!r} length={len(v)} → {v[:60]}{'…' if len(v) > 60 else ''}") + + try: + argmax = os.sysconf("SC_ARG_MAX") + print(f"[ENV MONITOR] SC_ARG_MAX = {argmax}") + except (ValueError, AttributeError): + pass + + +def flatten_dict(d, prefix="", separator="."): + """Flatten nested dictionaries into a flat dict with keys joined by `separator`.""" + + res = dict() + for key, value in d.items(): + if isinstance(value, (dict, OrderedDict)): + res.update(flatten_dict(value, prefix + key + separator, separator)) + else: + res[prefix + key] = value + + return res + + +def find_free_port(max_tries: int = 20) -> int: + """Return an OS-assigned free TCP port, with a few retries; fall back to a random high port.""" + for _ in range(max_tries): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + try: + s.bind(("", 0)) + return s.getsockname()[1] + except OSError: + continue + return random.randint(20000, 65000) + + +def filter_params(params, params_to_mutate): + """Filter `params` to only those in `params_to_mutate`, converting str floats (e.g. '1e-4') to float.""" + + def try_float(v): + if isinstance(v, str): + try: + return float(v) + except ValueError: + return v + return v + + return {k: try_float(v) for k, v in params.items() if k in params_to_mutate} + + +def save_pbt_checkpoint(workspace_dir, curr_policy_score, curr_iter, algo, params): + """Save a PBT checkpoint (.pth and .yaml) with policy state, score, and metadata (rank 0 only).""" + if int(os.environ.get("RANK", "0")) == 0: + checkpoint_file = os.path.join(workspace_dir, f"{curr_iter:06d}.pth") + safe_save(algo.get_full_state_weights(), checkpoint_file) + pbt_checkpoint_file = os.path.join(workspace_dir, f"{curr_iter:06d}.yaml") + + pbt_checkpoint = { + "iteration": curr_iter, + "true_objective": curr_policy_score, + "frame": algo.frame, + "params": params, + "checkpoint": os.path.abspath(checkpoint_file), + "pbt_checkpoint": os.path.abspath(pbt_checkpoint_file), + "experiment_name": algo.experiment_name, + } + + with open(pbt_checkpoint_file, "w") as fobj: + yaml.dump(pbt_checkpoint, fobj) + + +def load_pbt_ckpts(workspace_dir, cur_policy_id, num_policies, pbt_iteration) -> dict | None: + """ + Load the latest available PBT checkpoint for each policy (≤ current iteration). + Returns a dict mapping policy_idx → checkpoint dict or None. (rank 0 only) + """ + if int(os.environ.get("RANK", "0")) != 0: + return None + checkpoints = dict() + for policy_idx in range(num_policies): + checkpoints[policy_idx] = None + policy_dir = os.path.join(workspace_dir, f"{policy_idx:03d}") + + if not os.path.isdir(policy_dir): + continue + + pbt_checkpoint_files = sorted([f for f in os.listdir(policy_dir) if f.endswith(".yaml")], reverse=True) + for pbt_checkpoint_file in pbt_checkpoint_files: + iteration = int(pbt_checkpoint_file.split(".")[0]) + + # current local time + now_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ctime_ts = os.path.getctime(os.path.join(policy_dir, pbt_checkpoint_file)) + created_str = datetime.datetime.fromtimestamp(ctime_ts).strftime("%Y-%m-%d %H:%M:%S") + + if iteration <= pbt_iteration: + with open(os.path.join(policy_dir, pbt_checkpoint_file)) as fobj: + now_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + print( + f"Policy {cur_policy_id} [{now_str}]: Loading" + f" policy-{policy_idx} {pbt_checkpoint_file} (created at {created_str})" + ) + checkpoints[policy_idx] = safe_filesystem_op(yaml.load, fobj, Loader=yaml.FullLoader) + break + + return checkpoints + + +def cleanup(checkpoints: dict[int, dict], policy_dir, keep_back: int = 20, max_yaml: int = 50) -> None: + """ + Cleanup old checkpoints for the current policy directory (rank 0 only). + - Delete files older than (oldest iteration - keep_back). + - Keep at most `max_yaml` latest YAML iterations. + """ + if int(os.environ.get("RANK", "0")) == 0: + oldest = min((ckpt["iteration"] if ckpt else 0) for ckpt in checkpoints.values()) + threshold = max(0, oldest - keep_back) + root = Path(policy_dir) + + # group files by numeric iteration (only *.yaml / *.pth) + groups: dict[int, list[Path]] = {} + for p in root.iterdir(): + if p.suffix in (".yaml", ".pth") and p.stem.isdigit(): + groups.setdefault(int(p.stem), []).append(p) + + # 1) drop anything older than threshold + for it in [i for i in groups if i <= threshold]: + for p in groups[it]: + p.unlink(missing_ok=True) + groups.pop(it, None) + + # 2) cap total YAML checkpoints: keep newest `max_yaml` iters + yaml_iters = sorted((i for i, ps in groups.items() if any(p.suffix == ".yaml" for p in ps)), reverse=True) + for it in yaml_iters[max_yaml:]: + for p in groups.get(it, []): + p.unlink(missing_ok=True) + groups.pop(it, None) + + +class PbtTablePrinter: + """All PrettyTable-related rendering lives here.""" + + def __init__(self, *, float_digits: int = 6, path_maxlen: int = 52): + self.float_digits = float_digits + self.path_maxlen = path_maxlen + + # format helpers + def fmt(self, v): + return f"{v:.{self.float_digits}g}" if isinstance(v, float) else v + + def short(self, s: str) -> str: + s = str(s) + L = self.path_maxlen + return s if len(s) <= L else s[: L // 2 - 1] + "…" + s[-L // 2 :] + + # tables + def print_params_table(self, params: dict, header: str = "Parameters"): + table = PrettyTable(field_names=["Parameter", "Value"]) + table.align["Parameter"] = "l" + table.align["Value"] = "r" + for k in sorted(params): + table.add_row([k, self.fmt(params[k])]) + print(header + ":") + print(table.get_string()) + + def print_ckpt_summary(self, sumry: dict[int, dict | None]): + t = PrettyTable(["Policy", "Status", "Objective", "Iter", "Frame", "Experiment", "Checkpoint", "YAML"]) + t.align["Policy"] = "r" + t.align["Status"] = "l" + t.align["Objective"] = "r" + t.align["Iter"] = "r" + t.align["Frame"] = "r" + t.align["Experiment"] = "l" + t.align["Checkpoint"] = "l" + t.align["YAML"] = "l" + for p in sorted(sumry.keys()): + c = sumry[p] + if c is None: + t.add_row([p, "—", "", "", "", "", "", ""]) + else: + t.add_row( + [ + p, + "OK", + self.fmt(c.get("true_objective", "")), + c.get("iteration", ""), + c.get("frame", ""), + c.get("experiment_name", ""), + self.short(c.get("checkpoint", "")), + self.short(c.get("pbt_checkpoint", "")), + ] + ) + print(t) + + def print_mutation_diff(self, before: dict, after: dict, *, header: str = "Mutated params (changed only)"): + t = PrettyTable(["Parameter", "Old", "New"]) + for k in sorted(before): + if before[k] != after[k]: + t.add_row([k, self.fmt(before[k]), self.fmt(after[k])]) + print(header + ":") + print(t if t._rows else "(no changes)") diff --git a/source/isaaclab_rl/isaaclab_rl/rl_games/rl_games.py b/source/isaaclab_rl/isaaclab_rl/rl_games/rl_games.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c786c7c9e7018239de13eafd72866750242e2c --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rl_games/rl_games.py @@ -0,0 +1,417 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrapper to configure an environment instance to RL-Games vectorized environment. + +The following example shows how to wrap an environment for RL-Games and register the environment construction +for RL-Games :class:`Runner` class: + +.. code-block:: python + + from rl_games.common import env_configurations, vecenv + + from isaaclab_rl.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper + + # configuration parameters + rl_device = "cuda:0" + clip_obs = 10.0 + clip_actions = 1.0 + + # wrap around environment for rl-games + env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions) + + # register the environment to rl-games registry + # note: in agents configuration: environment name must be "rlgpu" + vecenv.register( + "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs) + ) + env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env}) + +""" + +# needed to import for allowing type-hinting:gym.spaces.Box | None +from __future__ import annotations + +from collections.abc import Callable + +import gym.spaces # needed for rl-games incompatibility: https://github.com/Denys88/rl_games/issues/261 +import gymnasium +import torch +from rl_games.common import env_configurations +from rl_games.common.vecenv import IVecEnv + +from isaaclab.envs import DirectRLEnv, ManagerBasedRLEnv, VecEnvObs + +""" +Vectorized environment wrapper. +""" + + +class RlGamesVecEnvWrapper(IVecEnv): + """Wraps around Isaac Lab environment for RL-Games. + + This class wraps around the Isaac Lab environment. Since RL-Games works directly on + GPU buffers, the wrapper handles moving of buffers from the simulation environment + to the same device as the learning agent. Additionally, it performs clipping of + observations and actions. + + For algorithms like asymmetric actor-critic, RL-Games expects a dictionary for + observations. This dictionary contains "obs" and "states" which typically correspond + to the actor and critic observations respectively. + + To use asymmetric actor-critic, map privileged observation groups under ``"states"`` (e.g. ``["critic"]``). + + The wrapper supports **either** concatenated tensors (default) **or** Dict inputs: + when wrapper is concate mode, rl-games sees {"obs": Tensor, (optional)"states": Tensor} + when wrapper is not concate mode, rl-games sees {"obs": dict[str, Tensor], (optional)"states": dict[str, Tensor]} + + - Concatenated mode (``concate_obs_group=True``): ``observation_space``/``state_space`` are ``gym.spaces.Box``. + - Dict mode (``concate_obs_group=False``): ``observation_space``/``state_space`` are ``gym.spaces.Dict`` keyed by + the requested groups. When no ``"states"`` groups are provided, the states Dict is omitted at runtime. + + .. caution:: + + This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow + the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this + wrapper. + + + Reference: + https://github.com/Denys88/rl_games/blob/master/rl_games/common/ivecenv.py + https://github.com/NVIDIA-Omniverse/IsaacGymEnvs + """ + + def __init__( + self, + env: ManagerBasedRLEnv | DirectRLEnv, + rl_device: str, + clip_obs: float, + clip_actions: float, + obs_groups: dict[str, list[str]] | None = None, + concate_obs_group: bool = True, + ): + """Initializes the wrapper instance. + + Args: + env: The environment to wrap around. + rl_device: The device on which agent computations are performed. + clip_obs: The clipping value for observations. + clip_actions: The clipping value for actions. + obs_groups: The remapping from isaaclab observation to rl-games, default to None for backward compatible. + concate_obs_group: The boolean value indicates if input to rl-games network is dict or tensor. Default to + True for backward compatible. + + Raises: + ValueError: The environment is not inherited from :class:`ManagerBasedRLEnv` or :class:`DirectRLEnv`. + ValueError: If specified, the privileged observations (critic) are not of type :obj:`gym.spaces.Box`. + """ + # check that input is valid + if not isinstance(env.unwrapped, ManagerBasedRLEnv) and not isinstance(env.unwrapped, DirectRLEnv): + raise ValueError( + "The environment must be inherited from ManagerBasedRLEnv or DirectRLEnv. Environment type:" + f" {type(env)}" + ) + # initialize the wrapper + self.env = env + # store provided arguments + self._rl_device = rl_device + self._clip_obs = clip_obs + self._clip_actions = clip_actions + self._sim_device = env.unwrapped.device + + # resolve the observation group + self._concate_obs_groups = concate_obs_group + self._obs_groups = obs_groups + if obs_groups is None: + self._obs_groups = {"obs": ["policy"], "states": []} + if not self.unwrapped.single_observation_space.get("policy"): + raise KeyError("Policy observation group is expected if no explicit groups is defined") + if self.unwrapped.single_observation_space.get("critic"): + self._obs_groups["states"] = ["critic"] + + if ( + self._concate_obs_groups + and isinstance(self.state_space, gym.spaces.Box) + and isinstance(self.observation_space, gym.spaces.Box) + ): + self.rlg_num_states = self.state_space.shape[0] + elif ( + not self._concate_obs_groups + and isinstance(self.state_space, gym.spaces.Dict) + and isinstance(self.observation_space, gym.spaces.Dict) + ): + space = [space.shape[0] for space in self.state_space.values()] + self.rlg_num_states = sum(space) + else: + raise TypeError( + "only valid combination for state space is gym.space.Box when concate_obs_groups is True, " + " and gym.space.Dict when concate_obs_groups is False. You have concate_obs_groups: " + f" {self._concate_obs_groups}, and state_space: {self.state_space.__class__}" + ) + + def __str__(self): + """Returns the wrapper name and the :attr:`env` representation string.""" + return ( + f"<{type(self).__name__}{self.env}>" + f"\n\tObservations clipping: {self._clip_obs}" + f"\n\tActions clipping : {self._clip_actions}" + f"\n\tAgent device : {self._rl_device}" + f"\n\tAsymmetric-learning : {self.rlg_num_states != 0}" + ) + + def __repr__(self): + """Returns the string representation of the wrapper.""" + return str(self) + + """ + Properties -- Gym.Wrapper + """ + + @property + def render_mode(self) -> str | None: + """Returns the :attr:`Env` :attr:`render_mode`.""" + return self.env.render_mode + + @property + def observation_space(self) -> gym.spaces.Box | gym.spaces.Dict: + """Returns the :attr:`Env` :attr:`observation_space` (``Box`` if concatenated, otherwise ``Dict``).""" + # note: rl-games only wants single observation space + space = self.unwrapped.single_observation_space + clip = self._clip_obs + if not self._concate_obs_groups: + policy_space = {grp: gym.spaces.Box(-clip, clip, space.get(grp).shape) for grp in self._obs_groups["obs"]} + return gym.spaces.Dict(policy_space) + else: + shapes = [space.get(group).shape for group in self._obs_groups["obs"]] + cat_shape, self._obs_concat_fn = make_concat_plan(shapes) + return gym.spaces.Box(-clip, clip, cat_shape) + + @property + def action_space(self) -> gym.Space: + """Returns the :attr:`Env` :attr:`action_space`.""" + # note: rl-games only wants single action space + action_space = self.unwrapped.single_action_space + if not isinstance(action_space, gymnasium.spaces.Box): + raise NotImplementedError( + f"The RL-Games wrapper does not currently support action space: '{type(action_space)}'." + f" If you need to support this, please modify the wrapper: {self.__class__.__name__}," + " and if you are nice, please send a merge-request." + ) + # return casted space in gym.spaces.Box (OpenAI Gym) + # note: maybe should check if we are a sub-set of the actual space. don't do it right now since + # in ManagerBasedRLEnv we are setting action space as (-inf, inf). + return gym.spaces.Box(-self._clip_actions, self._clip_actions, action_space.shape) + + @classmethod + def class_name(cls) -> str: + """Returns the class name of the wrapper.""" + return cls.__name__ + + @property + def unwrapped(self) -> ManagerBasedRLEnv | DirectRLEnv: + """Returns the base environment of the wrapper. + + This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers. + """ + return self.env.unwrapped + + """ + Properties + """ + + @property + def num_envs(self) -> int: + """Returns the number of sub-environment instances.""" + return self.unwrapped.num_envs + + @property + def device(self) -> str: + """Returns the base environment simulation device.""" + return self.unwrapped.device + + @property + def state_space(self) -> gym.spaces.Box | gym.spaces.Dict | None: + """Returns the privileged observation space for the critic (``Box`` if concatenated, otherwise ``Dict``).""" + # # note: rl-games only wants single observation space + space = self.unwrapped.single_observation_space + clip = self._clip_obs + if not self._concate_obs_groups: + state_space = {grp: gym.spaces.Box(-clip, clip, space.get(grp).shape) for grp in self._obs_groups["states"]} + return gym.spaces.Dict(state_space) + else: + shapes = [space.get(group).shape for group in self._obs_groups["states"]] + cat_shape, self._states_concat_fn = make_concat_plan(shapes) + return gym.spaces.Box(-self._clip_obs, self._clip_obs, cat_shape) + + def get_number_of_agents(self) -> int: + """Returns number of actors in the environment.""" + return getattr(self, "num_agents", 1) + + def get_env_info(self) -> dict: + """Returns the Gym spaces for the environment.""" + return { + "observation_space": self.observation_space, + "action_space": self.action_space, + "state_space": self.state_space, + } + + """ + Operations - MDP + """ + + def seed(self, seed: int = -1) -> int: # noqa: D102 + return self.unwrapped.seed(seed) + + def reset(self): # noqa: D102 + obs_dict, _ = self.env.reset() + # process observations and states + return self._process_obs(obs_dict) + + def step(self, actions): # noqa: D102 + # move actions to sim-device + actions = actions.detach().clone().to(device=self._sim_device) + # clip the actions + actions = torch.clamp(actions, -self._clip_actions, self._clip_actions) + # perform environment step + obs_dict, rew, terminated, truncated, extras = self.env.step(actions) + + # move time out information to the extras dict + # this is only needed for infinite horizon tasks + # note: only useful when `value_bootstrap` is True in the agent configuration + if not self.unwrapped.cfg.is_finite_horizon: + extras["time_outs"] = truncated.to(device=self._rl_device) + # process observations and states + obs_and_states = self._process_obs(obs_dict) + # move buffers to rl-device + # note: we perform clone to prevent issues when rl-device and sim-device are the same. + rew = rew.to(device=self._rl_device) + dones = (terminated | truncated).to(device=self._rl_device) + extras = { + k: v.to(device=self._rl_device, non_blocking=True) if hasattr(v, "to") else v for k, v in extras.items() + } + # remap extras from "log" to "episode" + if "log" in extras: + extras["episode"] = extras.pop("log") + + return obs_and_states, rew, dones, extras + + def close(self): # noqa: D102 + return self.env.close() + + """ + Helper functions + """ + + def _process_obs(self, obs_dict: VecEnvObs) -> dict[str, torch.Tensor] | dict[str, dict[str, torch.Tensor]]: + """Processing of the observations and states from the environment. + + Note: + States typically refers to privileged observations for the critic function. It is typically used in + asymmetric actor-critic algorithms. + + Args: + obs_dict: The current observations from environment. + + Returns: + A dictionary for RL-Games with keys: + - ``"obs"``: either a concatenated tensor (``concate_obs_group=True``) or a Dict of group tensors. + - ``"states"`` (optional): same structure as above when state groups are configured; omitted otherwise. + """ + # move observations to RL device if different from sim device + if self._rl_device != self._sim_device: + obs_dict = {key: obs.to(device=self._rl_device) for key, obs in obs_dict.items()} + + # clip the observations + for key, obs in obs_dict.items(): + obs_dict[key] = torch.clamp(obs, -self._clip_obs, self._clip_obs) + + # process input obs dict + rl_games_obs = {"obs": {group: obs_dict[group] for group in self._obs_groups["obs"]}} + if len(self._obs_groups["states"]) > 0: + rl_games_obs["states"] = {group: obs_dict[group] for group in self._obs_groups["states"]} + + if self._concate_obs_groups: + rl_games_obs["obs"] = self._obs_concat_fn(list(rl_games_obs["obs"].values())) + if "states" in rl_games_obs: + rl_games_obs["states"] = self._states_concat_fn(list(rl_games_obs["states"].values())) + + return rl_games_obs + + +def make_concat_plan(shapes: list[tuple[int, ...]]) -> tuple[tuple[int, ...], Callable]: + """ + Given per-sample shapes (no batch dim), return: + - the concatenated per-sample shape + - a function that concatenates a list of batch tensors accordingly. + + Rules: + 0) Empty -> (0,), No-op + 1) All 1D -> concat features (dim=1). + 2) Same rank > 1: + 2a) If all s[:-1] equal -> concat along last dim (channels-last, dim=-1). + 2b) If all s[1:] equal -> concat along first dim (channels-first, dim=1). + """ + if len(shapes) == 0: + return (0,), lambda x: x + # case 1: all vectors + if all(len(s) == 1 for s in shapes): + return (sum(s[0] for s in shapes),), lambda x: torch.cat(x, dim=1) + # case 2: same rank > 1 + rank = len(shapes[0]) + if all(len(s) == rank for s in shapes) and rank > 1: + # 2a: concat along last axis (…C) + if all(s[:-1] == shapes[0][:-1] for s in shapes): + out_shape = shapes[0][:-1] + (sum(s[-1] for s in shapes),) + return out_shape, lambda x: torch.cat(x, dim=-1) + # 2b: concat along first axis (C…) + if all(s[1:] == shapes[0][1:] for s in shapes): + out_shape = (sum(s[0] for s in shapes),) + shapes[0][1:] + return out_shape, lambda x: torch.cat(x, dim=1) + else: + raise ValueError(f"Could not find a valid concatenation plan for rank {[(len(s),) for s in shapes]}") + else: + raise ValueError("Could not find a valid concatenation plan, please make sure all value share the same size") + + +""" +Environment Handler. +""" + + +class RlGamesGpuEnv(IVecEnv): + """Thin wrapper to create instance of the environment to fit RL-Games runner.""" + + # TODO: Adding this for now but do we really need this? + + def __init__(self, config_name: str, num_actors: int, **kwargs): + """Initialize the environment. + + Args: + config_name: The name of the environment configuration. + num_actors: The number of actors in the environment. This is not used in this wrapper. + """ + self.env: RlGamesVecEnvWrapper = env_configurations.configurations[config_name]["env_creator"](**kwargs) + + def step(self, action): # noqa: D102 + return self.env.step(action) + + def reset(self): # noqa: D102 + return self.env.reset() + + def get_number_of_agents(self) -> int: + """Get number of agents in the environment. + + Returns: + The number of agents in the environment. + """ + return self.env.get_number_of_agents() + + def get_env_info(self) -> dict: + """Get the Gym spaces for the environment. + + Returns: + The Gym spaces for the environment. + """ + return self.env.get_env_info() diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/__init__.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d8c629689949dde2078738b156eacc4f7b37ca7 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrappers and utilities to configure an environment for RSL-RL library. + +The following example shows how to wrap an environment for RSL-RL: + +.. code-block:: python + + from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper + + env = RslRlVecEnvWrapper(env) + +""" + +from .distillation_cfg import * +from .exporter import export_policy_as_jit, export_policy_as_onnx +from .rl_cfg import * +from .rnd_cfg import RslRlRndCfg +from .symmetry_cfg import RslRlSymmetryCfg +from .vecenv_wrapper import RslRlVecEnvWrapper diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/distillation_cfg.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/distillation_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..1a631eeffdaae6a07110403a06b9f7e782bad8c1 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/distillation_cfg.py @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .rl_cfg import RslRlBaseRunnerCfg + +######################### +# Policy configurations # +######################### + + +@configclass +class RslRlDistillationStudentTeacherCfg: + """Configuration for the distillation student-teacher networks.""" + + class_name: str = "StudentTeacher" + """The policy class name. Default is StudentTeacher.""" + + init_noise_std: float = MISSING + """The initial noise standard deviation for the student policy.""" + + noise_std_type: Literal["scalar", "log"] = "scalar" + """The type of noise standard deviation for the policy. Default is scalar.""" + + student_obs_normalization: bool = MISSING + """Whether to normalize the observation for the student network.""" + + teacher_obs_normalization: bool = MISSING + """Whether to normalize the observation for the teacher network.""" + + student_hidden_dims: list[int] = MISSING + """The hidden dimensions of the student network.""" + + teacher_hidden_dims: list[int] = MISSING + """The hidden dimensions of the teacher network.""" + + activation: str = MISSING + """The activation function for the student and teacher networks.""" + + +@configclass +class RslRlDistillationStudentTeacherRecurrentCfg(RslRlDistillationStudentTeacherCfg): + """Configuration for the distillation student-teacher recurrent networks.""" + + class_name: str = "StudentTeacherRecurrent" + """The policy class name. Default is StudentTeacherRecurrent.""" + + rnn_type: str = MISSING + """The type of the RNN network. Either "lstm" or "gru".""" + + rnn_hidden_dim: int = MISSING + """The hidden dimension of the RNN network.""" + + rnn_num_layers: int = MISSING + """The number of layers of the RNN network.""" + + teacher_recurrent: bool = MISSING + """Whether the teacher network is recurrent too.""" + + +############################ +# Algorithm configurations # +############################ + + +@configclass +class RslRlDistillationAlgorithmCfg: + """Configuration for the distillation algorithm.""" + + class_name: str = "Distillation" + """The algorithm class name. Default is Distillation.""" + + num_learning_epochs: int = MISSING + """The number of updates performed with each sample.""" + + learning_rate: float = MISSING + """The learning rate for the student policy.""" + + gradient_length: int = MISSING + """The number of environment steps the gradient flows back.""" + + max_grad_norm: None | float = None + """The maximum norm the gradient is clipped to.""" + + optimizer: Literal["adam", "adamw", "sgd", "rmsprop"] = "adam" + """The optimizer to use for the student policy.""" + + loss_type: Literal["mse", "huber"] = "mse" + """The loss type to use for the student policy.""" + + +######################### +# Runner configurations # +######################### + + +@configclass +class RslRlDistillationRunnerCfg(RslRlBaseRunnerCfg): + """Configuration of the runner for distillation algorithms.""" + + class_name: str = "DistillationRunner" + """The runner class name. Default is DistillationRunner.""" + + policy: RslRlDistillationStudentTeacherCfg = MISSING + """The policy configuration.""" + + algorithm: RslRlDistillationAlgorithmCfg = MISSING + """The algorithm configuration.""" diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/exporter.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/exporter.py new file mode 100644 index 0000000000000000000000000000000000000000..d5b8a248adb805685146df090e5d9c2dd9d5674d --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/exporter.py @@ -0,0 +1,216 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import copy +import os + +import torch + + +def export_policy_as_jit(policy: object, normalizer: object | None, path: str, filename="policy.pt"): + """Export policy into a Torch JIT file. + + Args: + policy: The policy torch module. + normalizer: The empirical normalizer module. If None, Identity is used. + path: The path to the saving directory. + filename: The name of exported JIT file. Defaults to "policy.pt". + """ + policy_exporter = _TorchPolicyExporter(policy, normalizer) + policy_exporter.export(path, filename) + + +def export_policy_as_onnx( + policy: object, path: str, normalizer: object | None = None, filename="policy.onnx", verbose=False +): + """Export policy into a Torch ONNX file. + + Args: + policy: The policy torch module. + normalizer: The empirical normalizer module. If None, Identity is used. + path: The path to the saving directory. + filename: The name of exported ONNX file. Defaults to "policy.onnx". + verbose: Whether to print the model summary. Defaults to False. + """ + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) + policy_exporter = _OnnxPolicyExporter(policy, normalizer, verbose) + policy_exporter.export(path, filename) + + +""" +Helper Classes - Private. +""" + + +class _TorchPolicyExporter(torch.nn.Module): + """Exporter of actor-critic into JIT file.""" + + def __init__(self, policy, normalizer=None): + super().__init__() + self.is_recurrent = policy.is_recurrent + # copy policy parameters + if hasattr(policy, "actor"): + self.actor = copy.deepcopy(policy.actor) + if self.is_recurrent: + self.rnn = copy.deepcopy(policy.memory_a.rnn) + elif hasattr(policy, "student"): + self.actor = copy.deepcopy(policy.student) + if self.is_recurrent: + self.rnn = copy.deepcopy(policy.memory_s.rnn) + else: + raise ValueError("Policy does not have an actor/student module.") + # set up recurrent network + if self.is_recurrent: + self.rnn.cpu() + self.rnn_type = type(self.rnn).__name__.lower() # 'lstm' or 'gru' + self.register_buffer("hidden_state", torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size)) + if self.rnn_type == "lstm": + self.register_buffer("cell_state", torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size)) + self.forward = self.forward_lstm + self.reset = self.reset_memory + elif self.rnn_type == "gru": + self.forward = self.forward_gru + self.reset = self.reset_memory + else: + raise NotImplementedError(f"Unsupported RNN type: {self.rnn_type}") + # copy normalizer if exists + if normalizer: + self.normalizer = copy.deepcopy(normalizer) + else: + self.normalizer = torch.nn.Identity() + + def forward_lstm(self, x): + x = self.normalizer(x) + x, (h, c) = self.rnn(x.unsqueeze(0), (self.hidden_state, self.cell_state)) + self.hidden_state[:] = h + self.cell_state[:] = c + x = x.squeeze(0) + return self.actor(x) + + def forward_gru(self, x): + x = self.normalizer(x) + x, h = self.rnn(x.unsqueeze(0), self.hidden_state) + self.hidden_state[:] = h + x = x.squeeze(0) + return self.actor(x) + + def forward(self, x): + return self.actor(self.normalizer(x)) + + @torch.jit.export + def reset(self): + pass + + def reset_memory(self): + self.hidden_state[:] = 0.0 + if hasattr(self, "cell_state"): + self.cell_state[:] = 0.0 + + def export(self, path, filename): + os.makedirs(path, exist_ok=True) + path = os.path.join(path, filename) + self.to("cpu") + traced_script_module = torch.jit.script(self) + traced_script_module.save(path) + + +class _OnnxPolicyExporter(torch.nn.Module): + """Exporter of actor-critic into ONNX file.""" + + def __init__(self, policy, normalizer=None, verbose=False): + super().__init__() + self.verbose = verbose + self.is_recurrent = policy.is_recurrent + # copy policy parameters + if hasattr(policy, "actor"): + self.actor = copy.deepcopy(policy.actor) + if self.is_recurrent: + self.rnn = copy.deepcopy(policy.memory_a.rnn) + elif hasattr(policy, "student"): + self.actor = copy.deepcopy(policy.student) + if self.is_recurrent: + self.rnn = copy.deepcopy(policy.memory_s.rnn) + else: + raise ValueError("Policy does not have an actor/student module.") + # set up recurrent network + if self.is_recurrent: + self.rnn.cpu() + self.rnn_type = type(self.rnn).__name__.lower() # 'lstm' or 'gru' + if self.rnn_type == "lstm": + self.forward = self.forward_lstm + elif self.rnn_type == "gru": + self.forward = self.forward_gru + else: + raise NotImplementedError(f"Unsupported RNN type: {self.rnn_type}") + # copy normalizer if exists + if normalizer: + self.normalizer = copy.deepcopy(normalizer) + else: + self.normalizer = torch.nn.Identity() + + def forward_lstm(self, x_in, h_in, c_in): + x_in = self.normalizer(x_in) + x, (h, c) = self.rnn(x_in.unsqueeze(0), (h_in, c_in)) + x = x.squeeze(0) + return self.actor(x), h, c + + def forward_gru(self, x_in, h_in): + x_in = self.normalizer(x_in) + x, h = self.rnn(x_in.unsqueeze(0), h_in) + x = x.squeeze(0) + return self.actor(x), h + + def forward(self, x): + return self.actor(self.normalizer(x)) + + def export(self, path, filename): + self.to("cpu") + self.eval() + opset_version = 18 # was 11, but it caused problems with linux-aarch, and 18 worked well across all systems. + if self.is_recurrent: + obs = torch.zeros(1, self.rnn.input_size) + h_in = torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size) + + if self.rnn_type == "lstm": + c_in = torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size) + torch.onnx.export( + self, + (obs, h_in, c_in), + os.path.join(path, filename), + export_params=True, + opset_version=opset_version, + verbose=self.verbose, + input_names=["obs", "h_in", "c_in"], + output_names=["actions", "h_out", "c_out"], + dynamic_axes={}, + ) + elif self.rnn_type == "gru": + torch.onnx.export( + self, + (obs, h_in), + os.path.join(path, filename), + export_params=True, + opset_version=opset_version, + verbose=self.verbose, + input_names=["obs", "h_in"], + output_names=["actions", "h_out"], + dynamic_axes={}, + ) + else: + raise NotImplementedError(f"Unsupported RNN type: {self.rnn_type}") + else: + obs = torch.zeros(1, self.actor[0].in_features) + torch.onnx.export( + self, + obs, + os.path.join(path, filename), + export_params=True, + opset_version=opset_version, + verbose=self.verbose, + input_names=["obs"], + output_names=["actions"], + dynamic_axes={}, + ) diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/rl_cfg.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/rl_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7be991174dec49eec7a2258502ddb1ffaa5c0fec --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/rl_cfg.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import MISSING +from typing import Literal + +from isaaclab.utils import configclass + +from .rnd_cfg import RslRlRndCfg +from .symmetry_cfg import RslRlSymmetryCfg + +######################### +# Policy configurations # +######################### + + +@configclass +class RslRlPpoActorCriticCfg: + """Configuration for the PPO actor-critic networks.""" + + class_name: str = "ActorCritic" + """The policy class name. Default is ActorCritic.""" + + init_noise_std: float = MISSING + """The initial noise standard deviation for the policy.""" + + noise_std_type: Literal["scalar", "log"] = "scalar" + """The type of noise standard deviation for the policy. Default is scalar.""" + + state_dependent_std: bool = False + """Whether to use state-dependent standard deviation for the policy. Default is False.""" + + actor_obs_normalization: bool = MISSING + """Whether to normalize the observation for the actor network.""" + + critic_obs_normalization: bool = MISSING + """Whether to normalize the observation for the critic network.""" + + actor_hidden_dims: list[int] = MISSING + """The hidden dimensions of the actor network.""" + + critic_hidden_dims: list[int] = MISSING + """The hidden dimensions of the critic network.""" + + activation: str = MISSING + """The activation function for the actor and critic networks.""" + + +@configclass +class RslRlPpoActorCriticRecurrentCfg(RslRlPpoActorCriticCfg): + """Configuration for the PPO actor-critic networks with recurrent layers.""" + + class_name: str = "ActorCriticRecurrent" + """The policy class name. Default is ActorCriticRecurrent.""" + + rnn_type: str = MISSING + """The type of RNN to use. Either "lstm" or "gru".""" + + rnn_hidden_dim: int = MISSING + """The dimension of the RNN layers.""" + + rnn_num_layers: int = MISSING + """The number of RNN layers.""" + + +############################ +# Algorithm configurations # +############################ + + +@configclass +class RslRlPpoAlgorithmCfg: + """Configuration for the PPO algorithm.""" + + class_name: str = "PPO" + """The algorithm class name. Default is PPO.""" + + num_learning_epochs: int = MISSING + """The number of learning epochs per update.""" + + num_mini_batches: int = MISSING + """The number of mini-batches per update.""" + + learning_rate: float = MISSING + """The learning rate for the policy.""" + + schedule: str = MISSING + """The learning rate schedule.""" + + gamma: float = MISSING + """The discount factor.""" + + lam: float = MISSING + """The lambda parameter for Generalized Advantage Estimation (GAE).""" + + entropy_coef: float = MISSING + """The coefficient for the entropy loss.""" + + desired_kl: float = MISSING + """The desired KL divergence.""" + + max_grad_norm: float = MISSING + """The maximum gradient norm.""" + + value_loss_coef: float = MISSING + """The coefficient for the value loss.""" + + use_clipped_value_loss: bool = MISSING + """Whether to use clipped value loss.""" + + clip_param: float = MISSING + """The clipping parameter for the policy.""" + + normalize_advantage_per_mini_batch: bool = False + """Whether to normalize the advantage per mini-batch. Default is False. + + If True, the advantage is normalized over the mini-batches only. + Otherwise, the advantage is normalized over the entire collected trajectories. + """ + + rnd_cfg: RslRlRndCfg | None = None + """The RND configuration. Default is None, in which case RND is not used.""" + + symmetry_cfg: RslRlSymmetryCfg | None = None + """The symmetry configuration. Default is None, in which case symmetry is not used.""" + + +######################### +# Runner configurations # +######################### + + +@configclass +class RslRlBaseRunnerCfg: + """Base configuration of the runner.""" + + seed: int = 42 + """The seed for the experiment. Default is 42.""" + + device: str = "cuda:0" + """The device for the rl-agent. Default is cuda:0.""" + + num_steps_per_env: int = MISSING + """The number of steps per environment per update.""" + + max_iterations: int = MISSING + """The maximum number of iterations.""" + + empirical_normalization: bool | None = None + """This parameter is deprecated and will be removed in the future. + + Use `actor_obs_normalization` and `critic_obs_normalization` instead. + """ + + obs_groups: dict[str, list[str]] = MISSING + """A mapping from observation groups to observation sets. + + The keys of the dictionary are predefined observation sets used by the underlying algorithm + and values are lists of observation groups provided by the environment. + + For instance, if the environment provides a dictionary of observations with groups "policy", "images", + and "privileged", these can be mapped to algorithmic observation sets as follows: + + .. code-block:: python + + obs_groups = { + "policy": ["policy", "images"], + "critic": ["policy", "privileged"], + } + + This way, the policy will receive the "policy" and "images" observations, and the critic will + receive the "policy" and "privileged" observations. + + For more details, please check ``vec_env.py`` in the rsl_rl library. + """ + + clip_actions: float | None = None + """The clipping value for actions. If None, then no clipping is done. Defaults to None. + + .. note:: + This clipping is performed inside the :class:`RslRlVecEnvWrapper` wrapper. + """ + + save_interval: int = MISSING + """The number of iterations between saves.""" + + experiment_name: str = MISSING + """The experiment name.""" + + run_name: str = "" + """The run name. Default is empty string. + + The name of the run directory is typically the time-stamp at execution. If the run name is not empty, + then it is appended to the run directory's name, i.e. the logging directory's name will become + ``{time-stamp}_{run_name}``. + """ + + logger: Literal["tensorboard", "neptune", "wandb"] = "tensorboard" + """The logger to use. Default is tensorboard.""" + + neptune_project: str = "isaaclab" + """The neptune project name. Default is "isaaclab".""" + + wandb_project: str = "isaaclab" + """The wandb project name. Default is "isaaclab".""" + + resume: bool = False + """Whether to resume a previous training. Default is False. + + This flag will be ignored for distillation. + """ + + load_run: str = ".*" + """The run directory to load. Default is ".*" (all). + + If regex expression, the latest (alphabetical order) matching run will be loaded. + """ + + load_checkpoint: str = "model_.*.pt" + """The checkpoint file to load. Default is ``"model_.*.pt"`` (all). + + If regex expression, the latest (alphabetical order) matching file will be loaded. + """ + + +@configclass +class RslRlOnPolicyRunnerCfg(RslRlBaseRunnerCfg): + """Configuration of the runner for on-policy algorithms.""" + + class_name: str = "OnPolicyRunner" + """The runner class name. Default is OnPolicyRunner.""" + + policy: RslRlPpoActorCriticCfg = MISSING + """The policy configuration.""" + + algorithm: RslRlPpoAlgorithmCfg = MISSING + """The algorithm configuration.""" diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/rnd_cfg.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/rnd_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0cc698dc881303c12f02bb63a8e10a9906b0c8f1 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/rnd_cfg.py @@ -0,0 +1,99 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + + +@configclass +class RslRlRndCfg: + """Configuration for the Random Network Distillation (RND) module. + + For more information, please check the work from :cite:`schwarke2023curiosity`. + """ + + @configclass + class WeightScheduleCfg: + """Configuration for the weight schedule.""" + + mode: str = "constant" + """The type of weight schedule. Default is "constant".""" + + @configclass + class LinearWeightScheduleCfg(WeightScheduleCfg): + """Configuration for the linear weight schedule. + + This schedule decays the weight linearly from the initial value to the final value + between :attr:`initial_step` and before :attr:`final_step`. + """ + + mode: str = "linear" + + final_value: float = MISSING + """The final value of the weight parameter.""" + + initial_step: int = MISSING + """The initial step of the weight schedule. + + For steps before this step, the weight is the initial value specified in :attr:`RslRlRndCfg.weight`. + """ + + final_step: int = MISSING + """The final step of the weight schedule. + + For steps after this step, the weight is the final value specified in :attr:`final_value`. + """ + + @configclass + class StepWeightScheduleCfg(WeightScheduleCfg): + """Configuration for the step weight schedule. + + This schedule sets the weight to the value specified in :attr:`final_value` at step :attr:`final_step`. + """ + + mode: str = "step" + + final_step: int = MISSING + """The final step of the weight schedule. + + For steps after this step, the weight is the value specified in :attr:`final_value`. + """ + + final_value: float = MISSING + """The final value of the weight parameter.""" + + weight: float = 0.0 + """The weight for the RND reward (also known as intrinsic reward). Default is 0.0. + + Similar to other reward terms, the RND reward is scaled by this weight. + """ + + weight_schedule: WeightScheduleCfg | None = None + """The weight schedule for the RND reward. Default is None, which means the weight is constant.""" + + reward_normalization: bool = False + """Whether to normalize the RND reward. Default is False.""" + + state_normalization: bool = False + """Whether to normalize the RND state. Default is False.""" + + learning_rate: float = 1e-3 + """The learning rate for the RND module. Default is 1e-3.""" + + num_outputs: int = 1 + """The number of outputs for the RND module. Default is 1.""" + + predictor_hidden_dims: list[int] = [-1] + """The hidden dimensions for the RND predictor network. Default is [-1]. + + If the list contains -1, then the hidden dimensions are the same as the input dimensions. + """ + + target_hidden_dims: list[int] = [-1] + """The hidden dimensions for the RND target network. Default is [-1]. + + If the list contains -1, then the hidden dimensions are the same as the input dimensions. + """ diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/symmetry_cfg.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/symmetry_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8f60c7430686ae5611073e54d9f98a5c38eb9354 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/symmetry_cfg.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.utils import configclass + + +@configclass +class RslRlSymmetryCfg: + """Configuration for the symmetry-augmentation in the training. + + When :meth:`use_data_augmentation` is True, the :meth:`data_augmentation_func` is used to generate + augmented observations and actions. These are then used to train the model. + + When :meth:`use_mirror_loss` is True, the :meth:`mirror_loss_coeff` is used to weight the + symmetry-mirror loss. This loss is directly added to the agent's loss function. + + If both :meth:`use_data_augmentation` and :meth:`use_mirror_loss` are False, then no symmetry-based + training is enabled. However, the :meth:`data_augmentation_func` is called to compute and log + symmetry metrics. This is useful for performing ablations. + + For more information, please check the work from :cite:`mittal2024symmetry`. + """ + + use_data_augmentation: bool = False + """Whether to use symmetry-based data augmentation. Default is False.""" + + use_mirror_loss: bool = False + """Whether to use the symmetry-augmentation loss. Default is False.""" + + data_augmentation_func: callable = MISSING + """The symmetry data augmentation function. + + The function signature should be as follows: + + Args: + + env (VecEnv): The environment object. This is used to access the environment's properties. + obs (tensordict.TensorDict | None): The observation tensor dictionary. If None, the observation is not used. + action (torch.Tensor | None): The action tensor. If None, the action is not used. + + Returns: + A tuple containing the augmented observation dictionary and action tensors. The tensors can be None, + if their respective inputs are None. + """ + + mirror_loss_coeff: float = 0.0 + """The weight for the symmetry-mirror loss. Default is 0.0.""" diff --git a/source/isaaclab_rl/isaaclab_rl/rsl_rl/vecenv_wrapper.py b/source/isaaclab_rl/isaaclab_rl/rsl_rl/vecenv_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..dde20f2bb165fdc7d647ad0100f68cbebee07398 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/rsl_rl/vecenv_wrapper.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym +import torch +from rsl_rl.env import VecEnv +from tensordict import TensorDict + +from isaaclab.envs import DirectRLEnv, ManagerBasedRLEnv + + +class RslRlVecEnvWrapper(VecEnv): + """Wraps around Isaac Lab environment for the RSL-RL library + + .. caution:: + This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow + the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this + wrapper. + + Reference: + https://github.com/leggedrobotics/rsl_rl/blob/master/rsl_rl/env/vec_env.py + """ + + def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv, clip_actions: float | None = None): + """Initializes the wrapper. + + Note: + The wrapper calls :meth:`reset` at the start since the RSL-RL runner does not call reset. + + Args: + env: The environment to wrap around. + clip_actions: The clipping value for actions. If ``None``, then no clipping is done. + + Raises: + ValueError: When the environment is not an instance of :class:`ManagerBasedRLEnv` or :class:`DirectRLEnv`. + """ + + # check that input is valid + if not isinstance(env.unwrapped, ManagerBasedRLEnv) and not isinstance(env.unwrapped, DirectRLEnv): + raise ValueError( + "The environment must be inherited from ManagerBasedRLEnv or DirectRLEnv. Environment type:" + f" {type(env)}" + ) + + # initialize the wrapper + self.env = env + self.clip_actions = clip_actions + + # store information required by wrapper + self.num_envs = self.unwrapped.num_envs + self.device = self.unwrapped.device + self.max_episode_length = self.unwrapped.max_episode_length + + # obtain dimensions of the environment + if hasattr(self.unwrapped, "action_manager"): + self.num_actions = self.unwrapped.action_manager.total_action_dim + else: + self.num_actions = gym.spaces.flatdim(self.unwrapped.single_action_space) + + # modify the action space to the clip range + self._modify_action_space() + + # reset at the start since the RSL-RL runner does not call reset + self.env.reset() + + def __str__(self): + """Returns the wrapper name and the :attr:`env` representation string.""" + return f"<{type(self).__name__}{self.env}>" + + def __repr__(self): + """Returns the string representation of the wrapper.""" + return str(self) + + """ + Properties -- Gym.Wrapper + """ + + @property + def cfg(self) -> object: + """Returns the configuration class instance of the environment.""" + return self.unwrapped.cfg + + @property + def render_mode(self) -> str | None: + """Returns the :attr:`Env` :attr:`render_mode`.""" + return self.env.render_mode + + @property + def observation_space(self) -> gym.Space: + """Returns the :attr:`Env` :attr:`observation_space`.""" + return self.env.observation_space + + @property + def action_space(self) -> gym.Space: + """Returns the :attr:`Env` :attr:`action_space`.""" + return self.env.action_space + + @classmethod + def class_name(cls) -> str: + """Returns the class name of the wrapper.""" + return cls.__name__ + + @property + def unwrapped(self) -> ManagerBasedRLEnv | DirectRLEnv: + """Returns the base environment of the wrapper. + + This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers. + """ + return self.env.unwrapped + + """ + Properties + """ + + @property + def episode_length_buf(self) -> torch.Tensor: + """The episode length buffer.""" + return self.unwrapped.episode_length_buf + + @episode_length_buf.setter + def episode_length_buf(self, value: torch.Tensor): + """Set the episode length buffer. + + Note: + This is needed to perform random initialization of episode lengths in RSL-RL. + """ + self.unwrapped.episode_length_buf = value + + """ + Operations - MDP + """ + + def seed(self, seed: int = -1) -> int: # noqa: D102 + return self.unwrapped.seed(seed) + + def reset(self) -> tuple[TensorDict, dict]: # noqa: D102 + # reset the environment + obs_dict, extras = self.env.reset() + return TensorDict(obs_dict, batch_size=[self.num_envs]), extras + + def get_observations(self) -> TensorDict: + """Returns the current observations of the environment.""" + if hasattr(self.unwrapped, "observation_manager"): + obs_dict = self.unwrapped.observation_manager.compute() + else: + obs_dict = self.unwrapped._get_observations() + return TensorDict(obs_dict, batch_size=[self.num_envs]) + + def step(self, actions: torch.Tensor) -> tuple[TensorDict, torch.Tensor, torch.Tensor, dict]: + # clip actions + if self.clip_actions is not None: + actions = torch.clamp(actions, -self.clip_actions, self.clip_actions) + # record step information + obs_dict, rew, terminated, truncated, extras = self.env.step(actions) + # compute dones for compatibility with RSL-RL + dones = (terminated | truncated).to(dtype=torch.long) + # move time out information to the extras dict + # this is only needed for infinite horizon tasks + if not self.unwrapped.cfg.is_finite_horizon: + extras["time_outs"] = truncated + # return the step information + return TensorDict(obs_dict, batch_size=[self.num_envs]), rew, dones, extras + + def close(self): # noqa: D102 + return self.env.close() + + """ + Helper functions + """ + + def _modify_action_space(self): + """Modifies the action space to the clip range.""" + if self.clip_actions is None: + return + + # modify the action space to the clip range + # note: this is only possible for the box action space. we need to change it in the future for other + # action spaces. + self.env.unwrapped.single_action_space = gym.spaces.Box( + low=-self.clip_actions, high=self.clip_actions, shape=(self.num_actions,) + ) + self.env.unwrapped.action_space = gym.vector.utils.batch_space( + self.env.unwrapped.single_action_space, self.num_envs + ) diff --git a/source/isaaclab_rl/isaaclab_rl/sb3.py b/source/isaaclab_rl/isaaclab_rl/sb3.py new file mode 100644 index 0000000000000000000000000000000000000000..2177bc6252c4bcde34788c4f3f2eb2fe1ec8560b --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/sb3.py @@ -0,0 +1,433 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrapper to configure an environment instance to Stable-Baselines3 vectorized environment. + +The following example shows how to wrap an environment for Stable-Baselines3: + +.. code-block:: python + + from isaaclab_rl.sb3 import Sb3VecEnvWrapper + + env = Sb3VecEnvWrapper(env) + +""" + +# needed to import for allowing type-hinting: torch.Tensor | dict[str, torch.Tensor] +from __future__ import annotations + +import warnings +from typing import Any + +import gymnasium as gym +import numpy as np +import torch +import torch.nn as nn # noqa: F401 +from stable_baselines3.common.preprocessing import is_image_space, is_image_space_channels_first +from stable_baselines3.common.utils import constant_fn +from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn + +from isaaclab.envs import DirectRLEnv, ManagerBasedRLEnv + +# remove SB3 warnings because PPO with bigger net actually benefits from GPU +warnings.filterwarnings("ignore", message="You are trying to run PPO on the GPU") + +""" +Configuration Parser. +""" + + +def process_sb3_cfg(cfg: dict, num_envs: int) -> dict: + """Convert simple YAML types to Stable-Baselines classes/components. + + Args: + cfg: A configuration dictionary. + num_envs: the number of parallel environments (used to compute `batch_size` for a desired number of minibatches) + + Returns: + A dictionary containing the converted configuration. + + Reference: + https://github.com/DLR-RM/rl-baselines3-zoo/blob/0e5eb145faefa33e7d79c7f8c179788574b20da5/utils/exp_manager.py#L358 + """ + + def update_dict(hyperparams: dict[str, Any], depth: int) -> dict[str, Any]: + for key, value in hyperparams.items(): + if isinstance(value, dict): + update_dict(value, depth + 1) + if isinstance(value, str): + if value.startswith("nn."): + hyperparams[key] = getattr(nn, value[3:]) + if depth == 0: + if key in ["learning_rate", "clip_range", "clip_range_vf"]: + if isinstance(value, str): + _, initial_value = value.split("_") + initial_value = float(initial_value) + hyperparams[key] = lambda progress_remaining: progress_remaining * initial_value + elif isinstance(value, (float, int)): + # negative value: ignore (ex: for clipping) + if value < 0: + continue + hyperparams[key] = constant_fn(float(value)) + else: + raise ValueError(f"Invalid value for {key}: {hyperparams[key]}") + + # Convert to a desired batch_size (n_steps=2048 by default for SB3 PPO) + if "n_minibatches" in hyperparams: + hyperparams["batch_size"] = (hyperparams.get("n_steps", 2048) * num_envs) // hyperparams["n_minibatches"] + del hyperparams["n_minibatches"] + + return hyperparams + + # parse agent configuration and convert to classes + return update_dict(cfg, depth=0) + + +""" +Vectorized environment wrapper. +""" + + +class Sb3VecEnvWrapper(VecEnv): + """Wraps around Isaac Lab environment for Stable Baselines3. + + Isaac Sim internally implements a vectorized environment. However, since it is + still considered a single environment instance, Stable Baselines tries to wrap + around it using the :class:`DummyVecEnv`. This is only done if the environment + is not inheriting from their :class:`VecEnv`. Thus, this class thinly wraps + over the environment from :class:`ManagerBasedRLEnv` or :class:`DirectRLEnv`. + + Note: + While Stable-Baselines3 supports Gym 0.26+ API, their vectorized environment + uses their own API (i.e. it is closer to Gym 0.21). Thus, we implement + the API for the vectorized environment. + + We also add monitoring functionality that computes the un-discounted episode + return and length. This information is added to the info dicts under key `episode`. + + In contrast to the Isaac Lab environment, stable-baselines expect the following: + + 1. numpy datatype for MDP signals + 2. a list of info dicts for each sub-environment (instead of a dict) + 3. when environment has terminated, the observations from the environment should correspond + to the one after reset. The "real" final observation is passed using the info dicts + under the key ``terminal_observation``. + + .. warning:: + + By the nature of physics stepping in Isaac Sim, it is not possible to forward the + simulation buffers without performing a physics step. Thus, reset is performed + inside the :meth:`step()` function after the actual physics step is taken. + Thus, the returned observations for terminated environments is the one after the reset. + + .. caution:: + + This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow + the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this + wrapper. + + Reference: + + 1. https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html + 2. https://stable-baselines3.readthedocs.io/en/master/common/monitor.html + + """ + + def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv, fast_variant: bool = True): + """Initialize the wrapper. + + Args: + env: The environment to wrap around. + fast_variant: Use fast variant for processing info + (Only episodic reward, lengths and truncation info are included) + Raises: + ValueError: When the environment is not an instance of :class:`ManagerBasedRLEnv` or :class:`DirectRLEnv`. + """ + # check that input is valid + if not isinstance(env.unwrapped, ManagerBasedRLEnv) and not isinstance(env.unwrapped, DirectRLEnv): + raise ValueError( + "The environment must be inherited from ManagerBasedRLEnv or DirectRLEnv. Environment type:" + f" {type(env)}" + ) + # initialize the wrapper + self.env = env + self.fast_variant = fast_variant + # collect common information + self.num_envs = self.unwrapped.num_envs + self.sim_device = self.unwrapped.device + self.render_mode = self.unwrapped.render_mode + self.observation_processors = {} + self._process_spaces() + # add buffer for logging episodic information + self._ep_rew_buf = np.zeros(self.num_envs) + self._ep_len_buf = np.zeros(self.num_envs) + + def __str__(self): + """Returns the wrapper name and the :attr:`env` representation string.""" + return f"<{type(self).__name__}{self.env}>" + + def __repr__(self): + """Returns the string representation of the wrapper.""" + return str(self) + + """ + Properties -- Gym.Wrapper + """ + + @classmethod + def class_name(cls) -> str: + """Returns the class name of the wrapper.""" + return cls.__name__ + + @property + def unwrapped(self) -> ManagerBasedRLEnv | DirectRLEnv: + """Returns the base environment of the wrapper. + + This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers. + """ + return self.env.unwrapped + + """ + Properties + """ + + def get_episode_rewards(self) -> list[float]: + """Returns the rewards of all the episodes.""" + return self._ep_rew_buf.tolist() + + def get_episode_lengths(self) -> list[int]: + """Returns the number of time-steps of all the episodes.""" + return self._ep_len_buf.tolist() + + """ + Operations - MDP + """ + + def seed(self, seed: int | None = None) -> list[int | None]: # noqa: D102 + return [self.unwrapped.seed(seed)] * self.unwrapped.num_envs + + def reset(self) -> VecEnvObs: # noqa: D102 + obs_dict, _ = self.env.reset() + # reset episodic information buffers + self._ep_rew_buf = np.zeros(self.num_envs) + self._ep_len_buf = np.zeros(self.num_envs) + # convert data types to numpy depending on backend + return self._process_obs(obs_dict) + + def step_async(self, actions): # noqa: D102 + # convert input to numpy array + if not isinstance(actions, torch.Tensor): + actions = np.asarray(actions) + actions = torch.from_numpy(actions).to(device=self.sim_device, dtype=torch.float32) + else: + actions = actions.to(device=self.sim_device, dtype=torch.float32) + # convert to tensor + self._async_actions = actions + + def step_wait(self) -> VecEnvStepReturn: # noqa: D102 + # record step information + obs_dict, rew, terminated, truncated, extras = self.env.step(self._async_actions) + # compute reset ids + dones = terminated | truncated + + # convert data types to numpy depending on backend + # note: ManagerBasedRLEnv uses torch backend (by default). + obs = self._process_obs(obs_dict) + rewards = rew.detach().cpu().numpy() + terminated = terminated.detach().cpu().numpy() + truncated = truncated.detach().cpu().numpy() + dones = dones.detach().cpu().numpy() + + reset_ids = dones.nonzero()[0] + + # update episode un-discounted return and length + self._ep_rew_buf += rewards + self._ep_len_buf += 1 + # convert extra information to list of dicts + infos = self._process_extras(obs, terminated, truncated, extras, reset_ids) + + # reset info for terminated environments + self._ep_rew_buf[reset_ids] = 0.0 + self._ep_len_buf[reset_ids] = 0 + + return obs, rewards, dones, infos + + def close(self): # noqa: D102 + self.env.close() + + def get_attr(self, attr_name, indices=None): # noqa: D102 + # resolve indices + if indices is None: + indices = slice(None) + num_indices = self.num_envs + else: + num_indices = len(indices) + # obtain attribute value + attr_val = getattr(self.env, attr_name) + # return the value + if not isinstance(attr_val, torch.Tensor): + return [attr_val] * num_indices + else: + return attr_val[indices].detach().cpu().numpy() + + def set_attr(self, attr_name, value, indices=None): # noqa: D102 + raise NotImplementedError("Setting attributes is not supported.") + + def env_method(self, method_name: str, *method_args, indices=None, **method_kwargs): # noqa: D102 + if method_name == "render": + # gymnasium does not support changing render mode at runtime + return self.env.render() + else: + # this isn't properly implemented but it is not necessary. + # mostly done for completeness. + env_method = getattr(self.env, method_name) + return env_method(*method_args, indices=indices, **method_kwargs) + + def env_is_wrapped(self, wrapper_class, indices=None): # noqa: D102 + # fake implementation to be able to use `evaluate_policy()` helper + return [False] + + def get_images(self): # noqa: D102 + raise NotImplementedError("Getting images is not supported.") + + """ + Helper functions. + """ + + def _process_spaces(self): + # process observation space + observation_space = self.unwrapped.single_observation_space["policy"] + if isinstance(observation_space, gym.spaces.Dict): + for obs_key, obs_space in observation_space.spaces.items(): + processors: list[callable[[torch.Tensor], Any]] = [] + # assume normalized, if not, it won't pass is_image_space, which check [0-255]. + # for scale like image space that has right shape but not scaled, we will scale it later + if is_image_space(obs_space, check_channels=True, normalized_image=True): + actually_normalized = np.all(obs_space.low == -1.0) and np.all(obs_space.high == 1.0) + if not actually_normalized: + if np.any(obs_space.low != 0) or np.any(obs_space.high != 255): + raise ValueError( + "Your image observation is not normalized in environment, and will not be" + "normalized by sb3 if its min is not 0 and max is not 255." + ) + # sb3 will handle normalization and transpose, but sb3 expects uint8 images + if obs_space.dtype != np.uint8: + processors.append(lambda obs: obs.to(torch.uint8)) + observation_space.spaces[obs_key] = gym.spaces.Box(0, 255, obs_space.shape, np.uint8) + else: + # sb3 will NOT handle the normalization, while sb3 will transpose, its transpose applies to all + # image terms and maybe non-ideal, more, if we can do it in torch on gpu, it will be faster then + # sb3 transpose it in numpy with cpu. + if not is_image_space_channels_first(obs_space): + + def tranp(img: torch.Tensor) -> torch.Tensor: + return img.permute(2, 0, 1) if len(img.shape) == 3 else img.permute(0, 3, 1, 2) + + processors.append(tranp) + h, w, c = obs_space.shape + observation_space.spaces[obs_key] = gym.spaces.Box(-1.0, 1.0, (c, h, w), obs_space.dtype) + + def chained_processor(obs: torch.Tensor, procs=processors) -> Any: + for proc in procs: + obs = proc(obs) + return obs + + # add processor to the dictionary + if len(processors) > 0: + self.observation_processors[obs_key] = chained_processor + + # obtain gym spaces + # note: stable-baselines3 does not like when we have unbounded action space so + # we set it to some high value here. Maybe this is not general but something to think about. + action_space = self.unwrapped.single_action_space + if isinstance(action_space, gym.spaces.Box) and not action_space.is_bounded("both"): + action_space = gym.spaces.Box(low=-100, high=100, shape=action_space.shape) + + # initialize vec-env + VecEnv.__init__(self, self.num_envs, observation_space, action_space) + + def _process_obs(self, obs_dict: torch.Tensor | dict[str, torch.Tensor]) -> np.ndarray | dict[str, np.ndarray]: + """Convert observations into NumPy data type.""" + # Sb3 doesn't support asymmetric observation spaces, so we only use "policy" + obs = obs_dict["policy"] + # note: ManagerBasedRLEnv uses torch backend (by default). + if isinstance(obs, dict): + for key, value in obs.items(): + if key in self.observation_processors: + obs[key] = self.observation_processors[key](value) + obs[key] = obs[key].detach().cpu().numpy() + elif isinstance(obs, torch.Tensor): + obs = obs.detach().cpu().numpy() + else: + raise NotImplementedError(f"Unsupported data type: {type(obs)}") + return obs + + def _process_extras( + self, obs: np.ndarray, terminated: np.ndarray, truncated: np.ndarray, extras: dict, reset_ids: np.ndarray + ) -> list[dict[str, Any]]: + """Convert miscellaneous information into dictionary for each sub-environment.""" + # faster version: only process env that terminated and add bootstrapping info + if self.fast_variant: + infos = [{} for _ in range(self.num_envs)] + + for idx in reset_ids: + # fill-in episode monitoring info + infos[idx]["episode"] = { + "r": self._ep_rew_buf[idx], + "l": self._ep_len_buf[idx], + } + + # fill-in bootstrap information + infos[idx]["TimeLimit.truncated"] = truncated[idx] and not terminated[idx] + + # add information about terminal observation separately + if isinstance(obs, dict): + terminal_obs = {key: value[idx] for key, value in obs.items()} + else: + terminal_obs = obs[idx] + infos[idx]["terminal_observation"] = terminal_obs + + return infos + + # create empty list of dictionaries to fill + infos: list[dict[str, Any]] = [dict.fromkeys(extras.keys()) for _ in range(self.num_envs)] + # fill-in information for each sub-environment + # note: This loop becomes slow when number of environments is large. + for idx in range(self.num_envs): + # fill-in episode monitoring info + if idx in reset_ids: + infos[idx]["episode"] = dict() + infos[idx]["episode"]["r"] = float(self._ep_rew_buf[idx]) + infos[idx]["episode"]["l"] = float(self._ep_len_buf[idx]) + else: + infos[idx]["episode"] = None + # fill-in bootstrap information + infos[idx]["TimeLimit.truncated"] = truncated[idx] and not terminated[idx] + # fill-in information from extras + for key, value in extras.items(): + # 1. remap extra episodes information safely + # 2. for others just store their values + if key == "log": + # only log this data for episodes that are terminated + if infos[idx]["episode"] is not None: + for sub_key, sub_value in value.items(): + infos[idx]["episode"][sub_key] = sub_value + else: + infos[idx][key] = value[idx] + # add information about terminal observation separately + if idx in reset_ids: + # extract terminal observations + if isinstance(obs, dict): + terminal_obs = dict.fromkeys(obs.keys()) + for key, value in obs.items(): + terminal_obs[key] = value[idx] + else: + terminal_obs = obs[idx] + # add info to dict + infos[idx]["terminal_observation"] = terminal_obs + else: + infos[idx]["terminal_observation"] = None + # return list of dictionaries + return infos diff --git a/source/isaaclab_rl/isaaclab_rl/skrl.py b/source/isaaclab_rl/isaaclab_rl/skrl.py new file mode 100644 index 0000000000000000000000000000000000000000..5aba121523f279754d87b25030bbea61aa0fec8e --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/skrl.py @@ -0,0 +1,86 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Wrapper to configure an environment instance to skrl environment. + +The following example shows how to wrap an environment for skrl: + +.. code-block:: python + + from isaaclab_rl.skrl import SkrlVecEnvWrapper + + env = SkrlVecEnvWrapper(env, ml_framework="torch") # or ml_framework="jax" + +Or, equivalently, by directly calling the skrl library API as follows: + +.. code-block:: python + + from skrl.envs.torch.wrappers import wrap_env # for PyTorch, or... + from skrl.envs.jax.wrappers import wrap_env # for JAX + + env = wrap_env(env, wrapper="isaaclab") + +""" + +# needed to import for type hinting: Agent | list[Agent] +from __future__ import annotations + +from typing import Literal + +from isaaclab.envs import DirectMARLEnv, DirectRLEnv, ManagerBasedRLEnv + +""" +Vectorized environment wrapper. +""" + + +def SkrlVecEnvWrapper( + env: ManagerBasedRLEnv | DirectRLEnv | DirectMARLEnv, + ml_framework: Literal["torch", "jax", "jax-numpy"] = "torch", + wrapper: Literal["auto", "isaaclab", "isaaclab-single-agent", "isaaclab-multi-agent"] = "isaaclab", +): + """Wraps around Isaac Lab environment for skrl. + + This function wraps around the Isaac Lab environment. Since the wrapping + functionality is defined within the skrl library itself, this implementation + is maintained for compatibility with the structure of the extension that contains it. + Internally it calls the :func:`wrap_env` from the skrl library API. + + Args: + env: The environment to wrap around. + ml_framework: The ML framework to use for the wrapper. Defaults to "torch". + wrapper: The wrapper to use. Defaults to "isaaclab": leave it to skrl to determine if the environment + will be wrapped as single-agent or multi-agent. + + Raises: + ValueError: When the environment is not an instance of any Isaac Lab environment interface. + ValueError: If the specified ML framework is not valid. + + Reference: + https://skrl.readthedocs.io/en/latest/api/envs/wrapping.html + """ + # check that input is valid + if ( + not isinstance(env.unwrapped, ManagerBasedRLEnv) + and not isinstance(env.unwrapped, DirectRLEnv) + and not isinstance(env.unwrapped, DirectMARLEnv) + ): + raise ValueError( + "The environment must be inherited from ManagerBasedRLEnv, DirectRLEnv or DirectMARLEnv. Environment type:" + f" {type(env)}" + ) + + # import statements according to the ML framework + if ml_framework.startswith("torch"): + from skrl.envs.wrappers.torch import wrap_env + elif ml_framework.startswith("jax"): + from skrl.envs.wrappers.jax import wrap_env + else: + ValueError( + f"Invalid ML framework for skrl: {ml_framework}. Available options are: 'torch', 'jax' or 'jax-numpy'" + ) + + # wrap and return the environment + return wrap_env(env, wrapper) diff --git a/source/isaaclab_rl/isaaclab_rl/utils/__init__.py b/source/isaaclab_rl/isaaclab_rl/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_rl/isaaclab_rl/utils/pretrained_checkpoint.py b/source/isaaclab_rl/isaaclab_rl/utils/pretrained_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..c2ada0e9b5e8aac00c3e99cd7cd098c5f5c1ce2f --- /dev/null +++ b/source/isaaclab_rl/isaaclab_rl/utils/pretrained_checkpoint.py @@ -0,0 +1,171 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module for handling various pre-trained checkpoint tasks""" + +import glob +import json +import os + +import carb.settings + +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry # noqa: F401 + +PRETRAINED_CHECKPOINTS_ASSET_ROOT_DIR = carb.settings.get_settings().get( + "/persistent/isaaclab/asset_root/pretrained_checkpoints" +) +"""Path to the root directory on the Nucleus Server.""" + +PRETRAINED_CHECKPOINT_PATH = str(PRETRAINED_CHECKPOINTS_ASSET_ROOT_DIR) + "/Isaac/IsaacLab/PretrainedCheckpoints" +"""URL for where we store all the pre-trained checkpoints""" + +WORKFLOWS = ["rl_games", "rsl_rl", "sb3", "skrl"] +"""The supported workflows for pre-trained checkpoints""" + +WORKFLOW_TRAINER = {w: f"scripts/reinforcement_learning/{w}/train.py" for w in WORKFLOWS} +"""A dict mapping workflow to their training program path""" + +WORKFLOW_PLAYER = {w: f"scripts/reinforcement_learning/{w}/play.py" for w in WORKFLOWS} +"""A dict mapping workflow to their play program path""" + +WORKFLOW_PRETRAINED_CHECKPOINT_FILENAMES = { + "rl_games": "checkpoint.pth", + "rsl_rl": "checkpoint.pt", + "sb3": "checkpoint.zip", + "skrl": "checkpoint.pt", +} +"""The filename for checkpoints used by the different workflows""" + +WORKFLOW_EXPERIMENT_NAME_VARIABLE = { + "rl_games": "agent.params.config.name", + "rsl_rl": "agent.experiment_name", + "sb3": None, + "skrl": "agent.agent.experiment.directory", +} +"""Maps workflow to the agent variable name that determines the logging directory logs/{workflow}/{variable}""" + + +def has_pretrained_checkpoints_asset_root_dir() -> bool: + """Returns True if and only if /persistent/isaaclab/asset_root/pretrained_checkpoints exists""" + return PRETRAINED_CHECKPOINTS_ASSET_ROOT_DIR is not None + + +def get_log_root_path(workflow: str, task_name: str) -> str: + """Returns the absolute path where the logs are written for a specific workflow and task_name""" + return os.path.abspath(os.path.join("logs", workflow, task_name)) + + +def get_latest_job_run_path(workflow: str, task_name: str) -> str | None: + """The local logs path of the most recent run of this workflow and task name""" + log_root_path = get_log_root_path(workflow, task_name) + return _get_latest_file_or_directory(log_root_path) + + +def get_pretrained_checkpoint_path(workflow: str, task_name: str) -> str: + """The local logs path where we get the pre-trained checkpoints from""" + + path = get_latest_job_run_path(workflow, task_name) + if not path: + return None + + if workflow == "rl_games": + return os.path.join(path, "nn", f"{task_name}.pth") + elif workflow == "rsl_rl": + return _get_latest_file_or_directory(path, "*.pt") # type: ignore + elif workflow == "sb3": + return os.path.join(path, "model.zip") + elif workflow == "skrl": + return os.path.join(path, "checkpoints", "best_agent.pt") + else: + raise Exception(f"Unsupported workflow ({workflow})") + + +def get_pretrained_checkpoint_publish_path(workflow: str, task_name: str) -> str: + """The path where pre-trained checkpoints are published to""" + return os.path.join( + PRETRAINED_CHECKPOINT_PATH, workflow, task_name, WORKFLOW_PRETRAINED_CHECKPOINT_FILENAMES[workflow] + ) + + +def get_published_pretrained_checkpoint_path(workflow: str, task_name: str) -> str: + """The path where pre-trained checkpoints are fetched from""" + return os.path.join( + ISAACLAB_NUCLEUS_DIR, + "PretrainedCheckpoints", + workflow, + task_name, + WORKFLOW_PRETRAINED_CHECKPOINT_FILENAMES[workflow], + ) + + +def get_published_pretrained_checkpoint(workflow: str, task_name: str) -> str | None: + """Gets the path for the pre-trained checkpoint. + + If the checkpoint is not cached locally then the file is downloaded. + The cached path is then returned. + + Args: + workflow: The workflow. + task_name: The task name. + + Returns: + The path. + """ + ov_path = get_published_pretrained_checkpoint_path(workflow, task_name) + download_dir = os.path.join(".pretrained_checkpoints", workflow, task_name) + resume_path = os.path.join(download_dir, WORKFLOW_PRETRAINED_CHECKPOINT_FILENAMES[workflow]) + + if not os.path.exists(resume_path): + print(f"Fetching pre-trained checkpoint : {ov_path}") + try: + resume_path = retrieve_file_path(ov_path, download_dir) + except Exception: + print("A pre-trained checkpoint is currently unavailable for this task.") + return None + else: + print("Using pre-fetched pre-trained checkpoint") + return resume_path + + +def has_pretrained_checkpoint_job_run(workflow: str, task_name: str) -> bool: + """Returns true if an experiment exists in the logs for the workflow and task""" + return os.path.exists(get_log_root_path(workflow, task_name)) + + +def has_pretrained_checkpoint_job_finished(workflow: str, task_name: str) -> bool: + """Returns true if an experiment has results which may or may not be final depending on workflow""" + local_path = get_pretrained_checkpoint_path(workflow, task_name) + return local_path is not None and os.path.exists(local_path) + + +def get_pretrained_checkpoint_review_path(workflow: str, task_name: str) -> str | None: + """The path of the review JSON file for a workflow and task""" + run_path = get_latest_job_run_path(workflow, task_name) + if not run_path: + return None + return os.path.join(run_path, "pretrained_checkpoint_review.json") + + +def get_pretrained_checkpoint_review(workflow: str, task_name: str) -> dict | None: + """Returns the review JSON file as a dict if it exists""" + review_path = get_pretrained_checkpoint_review_path(workflow, task_name) + if not review_path: + return None + + if os.path.exists(review_path): + with open(review_path) as f: + return json.load(f) + + return None + + +def _get_latest_file_or_directory(path: str, pattern: str = "*"): + """Returns the path to the most recently modified file or directory at a path matching an optional pattern""" + g = glob.glob(f"{path}/{pattern}") + if len(g): + return max(g, key=os.path.getmtime) + return None diff --git a/source/isaaclab_rl/pyproject.toml b/source/isaaclab_rl/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab_rl/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab_rl/setup.py b/source/isaaclab_rl/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..998fba147a2a002bf21475fc43d9e46ad6ad73aa --- /dev/null +++ b/source/isaaclab_rl/setup.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the 'isaaclab_rl' python package.""" + +import itertools +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Minimum dependencies required prior to installation +INSTALL_REQUIRES = [ + # generic + "numpy<2", + "torch>=2.7", + "torchvision>=0.14.1", # ensure compatibility with torch 1.13.1 + "protobuf>=4.25.8,!=5.26.0", + # configuration management + "hydra-core", + # data collection + "h5py", + # basic logger + "tensorboard", + # video recording + "moviepy", + # make sure this is consistent with isaac sim version + "pillow==11.3.0", + "packaging<24", +] + +PYTORCH_INDEX_URL = ["https://download.pytorch.org/whl/cu128"] + +# Extra dependencies for RL agents +EXTRAS_REQUIRE = { + "sb3": ["stable-baselines3>=2.6", "tqdm", "rich"], # tqdm/rich for progress bar + "skrl": ["skrl>=1.4.3"], + "rl-games": [ + "rl-games @ git+https://github.com/isaac-sim/rl_games.git@python3.11", + "gym", + ], # rl-games still needs gym :( + "rsl-rl": ["rsl-rl-lib==3.1.2", "onnxscript>=0.5"], # linux aarch 64 requires manual onnxscript installation +} +# Add the names with hyphens as aliases for convenience +EXTRAS_REQUIRE["rl_games"] = EXTRAS_REQUIRE["rl-games"] +EXTRAS_REQUIRE["rsl_rl"] = EXTRAS_REQUIRE["rsl-rl"] + +# Cumulation of all extra-requires +EXTRAS_REQUIRE["all"] = list(itertools.chain.from_iterable(EXTRAS_REQUIRE.values())) +# Remove duplicates in the all list to avoid double installations +EXTRAS_REQUIRE["all"] = list(set(EXTRAS_REQUIRE["all"])) + +# Installation operation +setup( + name="isaaclab_rl", + author="Isaac Lab Project Developers", + maintainer="Isaac Lab Project Developers", + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + include_package_data=True, + python_requires=">=3.10", + install_requires=INSTALL_REQUIRES, + dependency_links=PYTORCH_INDEX_URL, + extras_require=EXTRAS_REQUIRE, + packages=["isaaclab_rl"], + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab_rl/test/test_rl_games_wrapper.py b/source/isaaclab_rl/test/test_rl_games_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f01a561b99291618fbf05dbefbe50b3f4b1705 --- /dev/null +++ b/source/isaaclab_rl/test/test_rl_games_wrapper.py @@ -0,0 +1,142 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import os + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent + +from isaaclab_rl.rl_games import RlGamesVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="module") +def registered_tasks(): + # disable interactive mode for wandb for automate environments + os.environ["WANDB_DISABLED"] = "true" + # acquire all Isaac environments names + registered_tasks = list() + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id: + cfg_entry_point = gym.spec(task_spec.id).kwargs.get("rl_games_cfg_entry_point") + if cfg_entry_point is not None: + # skip automate environments as they require cuda installation + if "assembly" in task_spec.id.lower(): + continue + registered_tasks.append(task_spec.id) + # sort environments by name + registered_tasks.sort() + registered_tasks = registered_tasks[:5] + + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + # print all existing task names + print(">>> All registered environments:", registered_tasks) + return registered_tasks + + +def test_random_actions(registered_tasks): + """Run random actions and check environments return valid signals.""" + # common parameters + num_envs = 64 + device = "cuda" + for task_name in registered_tasks: + # Use pytest's subtests + print(f">>> Running test for environment: {task_name}") + # create a new stage + omni.usd.get_context().new_stage() + # reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + try: + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # create environment + env = gym.make(task_name, cfg=env_cfg) + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + # wrap environment + env = RlGamesVecEnvWrapper(env, "cuda:0", 100, 100) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # avoid shutdown of process on simulation stop + env.unwrapped.sim._app_control_on_stop_handle = None + + # reset environment + obs = env.reset() + # check signal + assert _check_valid_tensor(obs) + + # simulate environment for 100 steps + with torch.inference_mode(): + for _ in range(100): + # sample actions from -1 to 1 + actions = 2 * torch.rand(env.num_envs, *env.action_space.shape, device=env.device) - 1 + # apply actions + transition = env.step(actions) + # check signals + for data in transition: + assert _check_valid_tensor(data), f"Invalid data: {data}" + + # close the environment + print(f">>> Closing environment: {task_name}") + env.close() + + +""" +Helper functions. +""" + + +@staticmethod +def _check_valid_tensor(data: torch.Tensor | dict) -> bool: + """Checks if given data does not have corrupted values. + + Args: + data: Data buffer. + + Returns: + True if the data is valid. + """ + if isinstance(data, torch.Tensor): + return not torch.any(torch.isnan(data)) + elif isinstance(data, dict): + valid_tensor = True + for value in data.values(): + if isinstance(value, dict): + valid_tensor &= _check_valid_tensor(value) + elif isinstance(value, torch.Tensor): + valid_tensor &= not torch.any(torch.isnan(value)) + return valid_tensor + else: + raise ValueError(f"Input data of invalid type: {type(data)}.") diff --git a/source/isaaclab_rl/test/test_rsl_rl_wrapper.py b/source/isaaclab_rl/test/test_rsl_rl_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..2ae427df2797a970ff7a9306020d15c5192541a0 --- /dev/null +++ b/source/isaaclab_rl/test/test_rsl_rl_wrapper.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import gymnasium as gym +import pytest +import torch +from tensordict import TensorDict + +import carb +import omni.usd + +from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent + +from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="module") +def registered_tasks(): + # acquire all Isaac environments names + registered_tasks = list() + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id: + cfg_entry_point = gym.spec(task_spec.id).kwargs.get("rsl_rl_cfg_entry_point") + if cfg_entry_point is not None: + registered_tasks.append(task_spec.id) + # sort environments by name + registered_tasks.sort() + registered_tasks = registered_tasks[:5] + + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + # print all existing task names + print(">>> All registered environments:", registered_tasks) + return registered_tasks + + +def test_random_actions(registered_tasks): + """Run random actions and check environments return valid signals.""" + # common parameters + num_envs = 64 + device = "cuda" + for task_name in registered_tasks: + # Use pytest's subtests + print(f">>> Running test for environment: {task_name}") + # create a new stage + omni.usd.get_context().new_stage() + # reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + try: + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # create environment + env = gym.make(task_name, cfg=env_cfg) + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + # wrap environment + env = RslRlVecEnvWrapper(env) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # reset environment + obs, extras = env.reset() + # check signal + assert _check_valid_tensor(obs) + assert _check_valid_tensor(extras) + + # simulate environment for 100 steps + with torch.inference_mode(): + for _ in range(100): + # sample actions from -1 to 1 + actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions + transition = env.step(actions) + # check signals + for data in transition: + assert _check_valid_tensor(data), f"Invalid data: {data}" + + # close the environment + print(f">>> Closing environment: {task_name}") + env.close() + + +def test_no_time_outs(registered_tasks): + """Check that environments with finite horizon do not send time-out signals.""" + # common parameters + num_envs = 64 + device = "cuda" + for task_name in registered_tasks: + # Use pytest's subtests + print(f">>> Running test for environment: {task_name}") + # create a new stage + omni.usd.get_context().new_stage() + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # change to finite horizon + env_cfg.is_finite_horizon = True + + # create environment + env = gym.make(task_name, cfg=env_cfg) + # wrap environment + env = RslRlVecEnvWrapper(env) + + # reset environment + _, extras = env.reset() + # check signal + assert "time_outs" not in extras, "Time-out signal found in finite horizon environment." + + # simulate environment for 10 steps + with torch.inference_mode(): + for _ in range(10): + # sample actions from -1 to 1 + actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions + extras = env.step(actions)[-1] + # check signals + assert "time_outs" not in extras, "Time-out signal found in finite horizon environment." + + # close the environment + print(f">>> Closing environment: {task_name}") + env.close() + + +""" +Helper functions. +""" + + +@staticmethod +def _check_valid_tensor(data: torch.Tensor | dict) -> bool: + """Checks if given data does not have corrupted values. + + Args: + data: Data buffer. + + Returns: + True if the data is valid. + """ + if isinstance(data, torch.Tensor): + return not torch.any(torch.isnan(data)) + elif isinstance(data, TensorDict): + return not data.isnan().any() + elif isinstance(data, dict): + valid_tensor = True + for value in data.values(): + if isinstance(value, dict): + valid_tensor &= _check_valid_tensor(value) + elif isinstance(value, torch.Tensor): + valid_tensor &= not torch.any(torch.isnan(value)) + return valid_tensor + else: + raise ValueError(f"Input data of invalid type: {type(data)}.") diff --git a/source/isaaclab_rl/test/test_sb3_wrapper.py b/source/isaaclab_rl/test/test_sb3_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..be5dc46741e756d2fbb288500293c3ed14520493 --- /dev/null +++ b/source/isaaclab_rl/test/test_sb3_wrapper.py @@ -0,0 +1,138 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import gymnasium as gym +import numpy as np +import pytest +import torch + +import carb +import omni.usd + +from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent + +from isaaclab_rl.sb3 import Sb3VecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="module") +def registered_tasks(): + # acquire all Isaac environments names + registered_tasks = list() + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id: + cfg_entry_point = gym.spec(task_spec.id).kwargs.get("sb3_cfg_entry_point") + if cfg_entry_point is not None: + registered_tasks.append(task_spec.id) + # sort environments by name + registered_tasks.sort() + registered_tasks = registered_tasks[:5] + + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + # print all existing task names + print(">>> All registered environments:", registered_tasks) + return registered_tasks + + +def test_random_actions(registered_tasks): + """Run random actions and check environments return valid signals.""" + # common parameters + num_envs = 64 + device = "cuda" + for task_name in registered_tasks: + # Use pytest's subtests + print(f">>> Running test for environment: {task_name}") + # create a new stage + omni.usd.get_context().new_stage() + # reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + try: + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # create environment + env = gym.make(task_name, cfg=env_cfg) + # convert to single-agent instance if required by the RL algorithm + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + # wrap environment + env = Sb3VecEnvWrapper(env) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # reset environment + obs = env.reset() + # check signal + assert _check_valid_array(obs) + + # simulate environment for 100 steps + with torch.inference_mode(): + for _ in range(100): + # sample actions from -1 to 1 + actions = 2 * np.random.rand(env.num_envs, *env.action_space.shape) - 1 + # apply actions + transition = env.step(actions) + # check signals + for data in transition: + assert _check_valid_array(data), f"Invalid data: {data}" + + # close the environment + print(f">>> Closing environment: {task_name}") + env.close() + + +""" +Helper functions. +""" + + +@staticmethod +def _check_valid_array(data: np.ndarray | dict | list) -> bool: + """Checks if given data does not have corrupted values. + + Args: + data: Data buffer. + + Returns: + True if the data is valid. + """ + if isinstance(data, np.ndarray): + return not np.any(np.isnan(data)) + elif isinstance(data, dict): + valid_array = True + for value in data.values(): + if isinstance(value, dict): + valid_array &= _check_valid_array(value) + elif isinstance(value, np.ndarray): + valid_array &= not np.any(np.isnan(value)) + return valid_array + elif isinstance(data, list): + valid_array = True + for value in data: + valid_array &= _check_valid_array(value) + return valid_array + else: + raise ValueError(f"Input data of invalid type: {type(data)}.") diff --git a/source/isaaclab_rl/test/test_skrl_wrapper.py b/source/isaaclab_rl/test/test_skrl_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..25ce35a1c476e4e6223236a8c9d4a70a4bebb75e --- /dev/null +++ b/source/isaaclab_rl/test/test_skrl_wrapper.py @@ -0,0 +1,135 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent + +from isaaclab_rl.skrl import SkrlVecEnvWrapper + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="module") +def registered_tasks(): + # acquire all Isaac environments names + registered_tasks = list() + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id: + cfg_entry_point = gym.spec(task_spec.id).kwargs.get("skrl_cfg_entry_point") + if cfg_entry_point is not None: + registered_tasks.append(task_spec.id) + # sort environments by name + registered_tasks.sort() + registered_tasks = registered_tasks[:3] + + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + # print all existing task names + print(">>> All registered environments:", registered_tasks) + return registered_tasks + + +def test_random_actions(registered_tasks): + """Run random actions and check environments return valid signals.""" + # common parameters + num_envs = 64 + device = "cuda" + for task_name in registered_tasks: + # Use pytest's subtests + print(f">>> Running test for environment: {task_name}") + # create a new stage + omni.usd.get_context().new_stage() + # reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + try: + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # create environment + env = gym.make(task_name, cfg=env_cfg) + if isinstance(env.unwrapped, DirectMARLEnv): + env = multi_agent_to_single_agent(env) + # wrap environment + env = SkrlVecEnvWrapper(env) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # avoid shutdown of process on simulation stop + env.unwrapped.sim._app_control_on_stop_handle = None + + # reset environment + obs, extras = env.reset() + # check signal + assert _check_valid_tensor(obs) + assert _check_valid_tensor(extras) + + # simulate environment for 100 steps + with torch.inference_mode(): + for _ in range(100): + # sample actions from -1 to 1 + actions = 2 * torch.rand(num_envs, *env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions + transition = env.step(actions) + # check signals + for data in transition: + assert _check_valid_tensor(data), f"Invalid data: {data}" + + # close the environment + print(f">>> Closing environment: {task_name}") + env.close() + + +""" +Helper functions. +""" + + +@staticmethod +def _check_valid_tensor(data: torch.Tensor | dict) -> bool: + """Checks if given data does not have corrupted values. + + Args: + data: Data buffer. + + Returns: + True if the data is valid. + """ + if isinstance(data, torch.Tensor): + return not torch.any(torch.isnan(data)) + elif isinstance(data, dict): + valid_tensor = True + for value in data.values(): + if isinstance(value, dict): + valid_tensor &= _check_valid_tensor(value) + elif isinstance(value, torch.Tensor): + valid_tensor &= not torch.any(torch.isnan(value)) + return valid_tensor + else: + raise ValueError(f"Input data of invalid type: {type(data)}.") diff --git a/source/isaaclab_tasks/config/extension.toml b/source/isaaclab_tasks/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..ea211a93e2a3eac9b0a16b2e21db777be2397a00 --- /dev/null +++ b/source/isaaclab_tasks/config/extension.toml @@ -0,0 +1,22 @@ +[package] + +# Note: Semantic Versioning is used: https://semver.org/ +version = "0.11.12" + +# Description +title = "Isaac Lab Environments" +description="Extension containing suite of environments for robot learning." +readme = "docs/README.md" +repository = "https://github.com/isaac-sim/IsaacLab" +category = "robotics" +keywords = ["robotics", "rl", "il", "learning"] + +[dependencies] +"isaaclab" = {} +"isaaclab_assets" = {} + +[core] +reloadable = false + +[[python.module]] +name = "isaaclab_tasks" diff --git a/source/isaaclab_tasks/docs/CHANGELOG.rst b/source/isaaclab_tasks/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..f9100882c5d56f95f0c1298e10602d2276be32c9 --- /dev/null +++ b/source/isaaclab_tasks/docs/CHANGELOG.rst @@ -0,0 +1,1144 @@ +Changelog +--------- + +0.11.12 (2025-12-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Deploy-GearAssembly`` environments. + + +0.11.11 (2025-12-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added reaching task environments for OpenArm unimanual robot: + * :class:`OpenArmReachEnvCfg`; Gym ID ``Isaac-Reach-OpenArm-v0``. + * :class:`OpenArmReachEnvCfg_PLAY`; Gym ID ``Isaac-Reach-OpenArm-Play-v0``. +* Added lifting a cube task environments for OpenArm unimanual robot: + * :class:`OpenArmCubeLiftEnvCfg`; Gym ID ``Isaac-Lift-Cube-OpenArm-v0``. + * :class:`OpenArmCubeLiftEnvCfg_PLAY`; Gym ID ``Isaac-Lift-Cube-OpenArm-Play-v0``. +* Added opening a drawer task environments for OpenArm unimanual robot: + * :class:`OpenArmCabinetEnvCfg`; Gym ID ``Isaac-Open-Drawer-OpenArm-v0``. + * :class:`OpenArmCabinetEnvCfg_PLAY`; Gym ID ``Isaac-Open-Drawer-OpenArm-Play-v0``. +* Added reaching task environments for OpenArm bimanual robot: + * :class:`OpenArmReachEnvCfg`; Gym ID ``Isaac-Reach-OpenArm-Bi-v0``. + * :class:`OpenArmReachEnvCfg_PLAY`; Gym ID ``Isaac-Reach-OpenArm-Bi-Play-v0``. + + +0.11.10 (2025-12-13) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added obs_groups to the RSL-RL PPO agent configuration for the ``Isaac-Reach-UR10e-v0`` environment. +* Changed self.state_space to 19 in the ``Isaac-Reach-UR10e-ROS-Inference-v0`` environment. + + +0.11.9 (2025-11-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added OpenXR motion controller support for the G1 robot locomanipulation environment + ``Isaac-PickPlace-Locomanipulation-G1-Abs-v0``. This enables teleoperation using XR motion controllers + in addition to hand tracking. +* Added :class:`OpenXRDeviceMotionController` for motion controller-based teleoperation with headset anchoring control. +* Added motion controller-specific retargeters: + * :class:`G1TriHandControllerUpperBodyRetargeterCfg` for upper body and hand control using motion controllers. + * :class:`G1LowerBodyStandingControllerRetargeterCfg` for lower body control using motion controllers. + + +0.11.8 (2025-11-06) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed to use of ``num_rerenders_on_reset`` and ``DLAA`` in visuomotor imitation learning environments. + + +0.11.7 (2025-10-22) +~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Ensured all imports follows the string import style instead of direct import of environment. + + +0.11.6 (2025-10-23) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Refined further the anchor position for the XR anchor in the world frame for the G1 robot tasks. + + +0.11.5 (2025-10-22) +~~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Removed scikit-learn dependency because we are no longer using this package. + + +0.11.4 (2025-10-20) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Fixed the anchor position for the XR anchor in the world frame for the G1 robot tasks. + + +0.11.3 (2025-10-15) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed how the Sim rendering settings are modified by the Cosmos-Mimic env cfg. + + +0.11.2 (2025-10-10) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added OpenXRteleoperation devices to the Galbot stack environments. + + +0.11.1 (2025-09-24) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added dextrous lifting pbt configuration example cfg for rl_games. + + +0.11.0 (2025-09-07) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added dextrous lifting and dextrous reorientation manipulation rl environments. + + +0.10.51 (2025-09-08) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added SkillGen-specific cube stacking environments: + * :class:`FrankaCubeStackSkillgenEnvCfg`; Gym ID ``Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0``. +* Added bin cube stacking environment for SkillGen/Mimic: + * :class:`FrankaBinStackEnvCfg`; Gym ID ``Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0``. + + +0.10.50 (2025-09-05) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added stacking environments for Galbot with suction grippers. + + +0.10.49 (2025-09-05) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added suction gripper stacking environments with UR10 that can be used with teleoperation. + + +0.10.48 (2025-09-03) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Deploy-Reach-UR10e-v0`` environment. + + +0.10.47 (2025-07-25) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* New ``Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0`` environment that enables the waist degrees-of-freedom for the GR1T2 robot. + + +Changed +^^^^^^^ + +* Updated pink inverse kinematics controller configuration for the following tasks (``Isaac-PickPlace-GR1T2``, ``Isaac-NutPour-GR1T2``, ``Isaac-ExhaustPipe-GR1T2``) + to increase end-effector tracking accuracy and speed. Also added a null-space regularizer that enables turning on of waist degrees-of-freedom without + the robot control drifting to a bending posture. +* Tuned the pink inverse kinematics controller and joint PD controllers for the following tasks (``Isaac-PickPlace-GR1T2``, ``Isaac-NutPour-GR1T2``, ``Isaac-ExhaustPipe-GR1T2``) + to improve the end-effector tracking accuracy and speed. Achieving position and orientation accuracy test within **(2 mm, 1 degree)**. + + +0.10.46 (2025-08-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added symmetry data augmentation example with RSL-RL for cartpole and anymal locomotion environments. +* Added :attr:`--agent` to RL workflow scripts to allow switching between different configurations. + + +0.10.45 (2025-07-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``from __future__ import annotations`` to isaaclab_tasks files to fix Sphinx + doc warnings for IsaacLab Mimic docs. + + +0.10.44 (2025-07-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Forge-PegInsert-Direct-v0``, ``Isaac-Forge-GearMesh-Direct-v0``, + and ``Isaac-Forge-NutThread-Direct-v0`` environments as direct RL envs. These + environments extend ``Isaac-Factory-*-v0`` with force sensing, an excessive force + penalty, dynamics randomization, and success prediction. + + +0.10.43 (2025-07-24) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed un-set camera observations in the ``Isaac-Stack-Cube-Instance-Randomize-Franka-v0`` environment. + + +0.10.42 (2025-07-11) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Organized environment unit tests + + +0.10.41 (2025-07-01) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the rendering settings used for the Mimic-Cosmos pipeline. + + +0.10.40 (2025-06-26) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Relaxed upper range pin for protobuf python dependency for more permissive installation. + + +0.10.39 (2025-05-22) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed redundant body_names assignment in rough_env_cfg.py for H1 robot. + + +0.10.38 (2025-06-16) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Show available RL library configs on error message when an entry point key is not available for a given task. + + +0.10.37 (2025-05-15) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Assembly-Direct-v0`` environment as a direct RL env that + implements assembly tasks to insert pegs into their corresponding sockets. + + +0.10.36 (2025-05-21) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added unit tests for benchmarking environments with configurable settings. Output KPI payloads + can be pushed to a visualization dashboard to track improvements or regressions. + + +0.10.35 (2025-05-21) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0`` stacking environment with multi-modality camera inputs at higher resolution. + +Changed +^^^^^^^ + +* Updated the ``Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0`` stacking environment to support visual domain randomization events during model evaluation. +* Made the task termination condition for the stacking task more strict. + + +0.10.34 (2025-05-22) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed ``Isaac-PickPlace-GR1T2-Abs-v0`` object asset to a steering wheel. + + +0.10.33 (2025-05-12) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Increase ``Isaac-PickPlace-GR1T2-Abs-v0`` sim dt to 120Hz for improved stability. +* Fix object initial state in ``Isaac-PickPlace-GR1T2-Abs-v0`` to be above the table. + + +0.10.32 (2025-05-01) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added new GR1 tasks (``Isaac-NutPour-GR1T2-Pink-IK-Abs-v0``, and ``Isaac-ExhaustPipe-GR1T2-Pink-IK-Abs-v0``). + + +0.10.31 (2025-04-02) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Adds an idle action parameter to the ``Isaac-PickPlace-GR1T2-Abs-v0`` environment configuration. + + +0.10.30 (2025-03-25) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed environment test failure for ``Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0``. + + +0.10.29 (2025-03-18) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added Gymnasium spaces showcase tasks (``Isaac-Cartpole-Showcase-*-Direct-v0``, and ``Isaac-Cartpole-Camera-Showcase-*-Direct-v0``). + + +0.10.28 (2025-03-19) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated the ``Isaac-PickPlace-GR1T2-Abs-v0`` environment with auto termination when the object falls off the table + and refined the success criteria to be more accurate. + + +0.10.27 (2025-03-13) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Blacklisted pick_place task from being imported automatically by isaaclab_tasks. It now has to be imported + manually by the script due to dependencies on the pinocchio import. + + +0.10.26 (2025-03-10) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the ``Isaac-PickPlace-GR1T2-Abs-v0`` environment that implements a humanoid arm picking and placing a steering wheel task using the PinkIKController. + + +0.10.25 (2025-03-06) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0`` stacking environment with camera inputs. + + +0.10.24 (2025-02-13) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Set ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` to use sim parameters from base ``StackEnvCfg``, improving simulation stability. + + +0.10.23 (2025-02-11) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the inconsistent object pos observations in the ``Isaac-Stack-Cube-Franka`` environment when using parallel envs by + subtracting out the env origin from each object pos observation. + + +0.10.22 (2025-01-14) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Humanoid-AMP-Dance-Direct-v0``, ``Isaac-Humanoid-AMP-Run-Direct-v0`` and ``Isaac-Humanoid-AMP-Walk-Direct-v0`` + environments as a direct RL env that implements the Humanoid AMP task. + + +0.10.21 (2025-01-03) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the reset of the actions in the function overriding of the low level observations of :class:`isaaclab_tasks.manager_based.navigation.mdp.PreTrainedPolicyAction`. + + +0.10.20 (2024-12-17) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the configuration of + :class:`isaaclab.envs.mdp.actions.OperationalSpaceControllerAction` + inside the ``Isaac-Reach-Franka-OSC-v0`` environment to enable nullspace control. + + +0.10.19 (2024-12-17) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed :meth:`isaaclab_tasks.manager_based.manipulation.stack.mdp.ee_frame_pos` to output + ``ee_frame_pos`` with respect to the environment's origin. + + +0.10.18 (2024-12-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Factory-Direct-v0`` environment as a direct RL env that + implements contact-rich manipulation tasks including peg insertion, + gear meshing, and nut threading. + + +0.10.17 (2024-12-16) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Reach-Franka-OSC-v0`` and ``Isaac-Reach-Franka-OSC-Play-v0`` + variations of the manager based reach environment that uses + :class:`isaaclab.envs.mdp.actions.OperationalSpaceControllerAction`. + + +0.10.16 (2024-12-03) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` and ``Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0`` environments + as manager-based RL envs that implement a three cube stacking task. + + +0.10.15 (2024-10-30) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Defined the Gymnasium task entry points with configuration strings instead of class types. + This avoids unnecessary imports and improves the load types. +* Blacklisted ``mdp`` directories during the recursive module search. + + +0.10.14 (2024-10-28) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed manager-based vision cartpole environment names from Isaac-Cartpole-RGB-Camera-v0 + and Isaac-Cartpole-Depth-Camera-v0 to Isaac-Cartpole-RGB-v0 and Isaac-Cartpole-Depth-v0 + +0.10.13 (2024-10-28) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added feature extracted observation cartpole examples. + + +0.10.12 (2024-10-25) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed issues with defining Gymnasium spaces in Direct workflows due to Hydra/OmegaConf limitations with non-primitive types. + + +0.10.11 (2024-10-22) +~~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Sets curriculum and commands to None in manager-based environment configurations when not needed. + Earlier, this was done by making an empty configuration object, which is now unnecessary. + + +0.10.10 (2024-10-22) +~~~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the wrong selection of body id's in the :meth:`isaaclab_tasks.manager_based.locomotion.velocity.mdp.rewards.feet_slide` + reward function. This makes sure the right IDs are selected for the bodies. + + +0.10.9 (2024-10-01) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` to align with Robosuite stacking env. + + +0.10.8 (2024-09-25) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` environment as a manager-based RL env that implements a three cube stacking task. + + +0.10.7 (2024-10-02) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Replace deprecated :attr:`num_observations`, :attr:`num_actions` and :attr:`num_states` in single-agent direct tasks + by :attr:`observation_space`, :attr:`action_space` and :attr:`state_space` respectively. +* Replace deprecated :attr:`num_observations`, :attr:`num_actions` and :attr:`num_states` in multi-agent direct tasks + by :attr:`observation_spaces`, :attr:`action_spaces` and :attr:`state_space` respectively. + + +0.10.6 (2024-09-25) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Cartpole-RGB-Camera-v0`` and ``Isaac-Cartpole-Depth-Camera-v0`` + manager based camera cartpole environments. + + +0.10.5 (2024-09-11) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated the skrl RL library integration to the latest release (skrl-v1.3.0) + + +0.10.4 (2024-09-10) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Repose-Cube-Shadow-Vision-Direct-v0`` environment with heterogeneous proprioception and vision observations. + + +0.10.3 (2024-09-05) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added environment config flag ``rerender_on_reset`` to allow updating sensor data after a reset. + + +0.10.2 (2024-08-23) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Shadow-Hand-Over-Direct-v0`` multi-agent environment + + +0.10.1 (2024-08-21) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Cart-Double-Pendulum-Direct-v0`` multi-agent environment + +Changed +^^^^^^^ + +* Update skrl wrapper to support multi-agent environments. + + +0.10.0 (2024-08-14) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added support for the Hydra configuration system to all the train scripts. As a result, parameters of the environment + and the agent can be modified using command line arguments, for example ``env.actions.joint_effort.scale=10``. + + +0.9.0 (2024-08-05) +~~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Replaced the command line input ``--cpu`` with ``--device`` in the train and play scripts. Running on cpu is + supported by passing ``--device cpu``. Running on a specific gpu is now supported by passing ``--device cuda:``, + where ```` is the id of the GPU to use, for example ``--device cuda:0``. + + +0.8.2 (2024-08-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Isaac-Repose-Cube-Allegro-Direct-v0`` environment + +Changed +^^^^^^^ + +* Renamed ``Isaac-Shadow-Hand-Direct-v0`` environments to ``Isaac-Repose-Cube-Shadow-Direct-v0``. +* Renamed ``Isaac-Shadow-Hand-OpenAI-FF-Direct-v0`` environments to ``Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0``. +* Renamed ``Isaac-Shadow-Hand-OpenAI-LSTM-Direct-v0`` environments to ``Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0``. + + +0.8.1 (2024-08-02) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed the folder names for Unitree robots in the manager-based locomotion tasks. Earlier, there was an inconsistency + in the folder names as some had ``unitree_`` prefix and some didn't. Now, none of the folders have the prefix. + + +0.8.0 (2024-07-26) +~~~~~~~~~~~~~~~~~~ + +Removed +^^^^^^^ + +* Renamed the action term names inside the manager-based lift-manipulation task. Earlier, they were called + ``body_joint_pos`` and ``gripper_joint_pos``. Now, they are called ``arm_action`` and ``gripper_action``. + + +0.7.10 (2024-07-02) +~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Extended skrl wrapper to support training/evaluation using JAX. + + +0.7.9 (2024-07-01) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the action space check in the Stable-Baselines3 wrapper. Earlier, the wrapper checked + the action space via :meth:`gymnasium.spaces.Box.is_bounded` method, which returned a bool + value instead of a string. + + +0.7.8 (2024-06-26) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated the skrl RL library integration to the latest release (>= 1.2.0) + + +0.7.7 (2024-06-14) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Updated the tasks to use the renamed attribute :attr:`isaaclab.sim.SimulationCfg.render_interval`. + + +0.7.6 (2024-06-13) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added option to save images for Cartpole Camera environment. + + +0.7.5 (2024-05-31) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added exporting of empirical normalization layer to ONNX and JIT when exporting the model using + :meth:`isaaclab.actuators.ActuatorNetMLP.export` method. Previously, the normalization layer + was not exported to the ONNX and JIT models. This caused the exported model to not work properly + when used for inference. + + +0.7.5 (2024-05-28) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new environment ``Isaac-Navigation-Flat-Anymal-C-v0`` to navigate towards a target position on flat terrain. + + +0.7.4 (2024-05-21) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Made default device for RSL RL and SB3 configs to "cuda:0". + +0.7.3 (2024-05-21) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Introduced ``--max_iterations`` argument to training scripts for specifying number of training iterations. + +0.7.2 (2024-05-13) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added Shadow Hand environments: ``Isaac-Shadow-Hand-Direct-v0``, ``Isaac-Shadow-Hand-OpenAI-FF-Direct-v0``, + and ``Isaac-Shadow-Hand-OpenAI-LSTM-Direct-v0``. + + +0.7.1 (2024-05-09) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added the skrl agent configurations for the config and direct workflow tasks + + +0.7.0 (2024-05-07) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Renamed all references of ``BaseEnv``, ``RLTaskEnv``, and ``OIGEEnv`` to + :class:`isaaclab.envs.ManagerBasedEnv`, :class:`isaaclab.envs.ManagerBasedRLEnv`, + and :class:`isaaclab.envs.DirectRLEnv` respectively. +* Split environments into ``manager_based`` and ``direct`` folders. + +Added +^^^^^ + +* Added direct workflow environments: + * ``Isaac-Cartpole-Direct-v0``, ``Isaac-Cartpole-Camera-Direct-v0``, ``Isaac-Ant-Direct-v0``, ``Isaac-Humanoid-Direct-v0``. + * ``Isaac-Velocity-Flat-Anymal-C-Direct-v0``, ``Isaac-Velocity-Rough-Anymal-C-Direct-v0``, ``Isaac-Quadcopter-Direct-v0``. + + +0.6.1 (2024-04-16) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new environment ``Isaac-Repose-Cube-Allegro-v0`` and ``Isaac-Repose-Allegro-Cube-NoVelObs-v0`` + for the Allegro hand to reorient a cube. It is based on the IsaacGymEnvs Allegro hand environment. + + +0.6.0 (2024-03-10) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new environment ``Isaac-Open-Drawer-Franka-v0`` for the Franka arm to open a drawer. It is + based on the IsaacGymEnvs cabinet environment. + +Fixed +^^^^^ + +* Fixed logging of extra information for RL-Games wrapper. It expected the extra information to be under the + key ``"episode"``, but Isaac Lab used the key ``"log"``. The wrapper now remaps the key to ``"episode"``. + + +0.5.7 (2024-02-28) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Updated the RL wrapper for the skrl library to the latest release (>= 1.1.0) + + +0.5.6 (2024-02-21) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the configuration parsing to support a pre-initialized configuration object. + + +0.5.5 (2024-02-05) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Pinned :mod:`torch` version to 2.0.1 in the setup.py to keep parity version of :mod:`torch` supplied by + Isaac 2023.1.1, and prevent version incompatibility between :mod:`torch` ==2.2 and + :mod:`typing-extensions` ==3.7.4.3 + + +0.5.4 (2024-02-06) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a check for the flag :attr:`isaaclab.envs.ManagerBasedRLEnvCfg.is_finite_horizon` + in the RSL-RL and RL-Games wrappers to handle the finite horizon tasks properly. Earlier, + the wrappers were always assuming the tasks to be infinite horizon tasks and returning a + time-out signals when the episode length was reached. + + +0.5.3 (2023-11-16) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added raising of error in the :meth:`isaaclab_tasks.utils.importer.import_all` method to make sure + all the packages are imported properly. Previously, error was being caught and ignored. + + +0.5.2 (2023-11-08) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the RL wrappers for Stable-Baselines3 and RL-Games. It now works with their most recent versions. +* Fixed the :meth:`get_checkpoint_path` to allow any in-between sub-folders between the run directory and the + checkpoint directory. + + +0.5.1 (2023-11-04) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the wrappers to different learning frameworks to use the new :class:`isaaclab_tasks.ManagerBasedRLEnv` class. + The :class:`ManagerBasedRLEnv` class inherits from the :class:`gymnasium.Env` class (Gym 0.29.0). +* Fixed the registration of tasks in the Gym registry based on Gym 0.29.0 API. + +Changed +^^^^^^^ + +* Removed the inheritance of all the RL-framework specific wrappers from the :class:`gymnasium.Wrapper` class. + This is because the wrappers don't comply with the new Gym 0.29.0 API. The wrappers are now only inherit + from their respective RL-framework specific base classes. + + +0.5.0 (2023-10-30) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the way agent configs are handled for environments and learning agents. Switched from yaml to configclasses. + +Fixed +^^^^^ + +* Fixed the way package import automation is handled in the :mod:`isaaclab_tasks` module. Earlier it was + not skipping the blacklisted packages properly. + + +0.4.3 (2023-09-25) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Added future import of ``annotations`` to have a consistent behavior across Python versions. +* Removed the type-hinting from docstrings to simplify maintenance of the documentation. All type-hints are + now in the code itself. + + +0.4.2 (2023-08-29) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Moved the base environment definition to the :class:`isaaclab.envs.RLEnv` class. The :class:`RLEnv` + contains RL-specific managers such as the reward, termination, randomization and curriculum managers. These + are all configured using the :class:`isaaclab.envs.RLEnvConfig` class. The :class:`RLEnv` class + inherits from the :class:`isaaclab.envs.ManagerBasedEnv` and ``gym.Env`` classes. + +Fixed +^^^^^ + +* Adapted the wrappers to use the new :class:`isaaclab.envs.RLEnv` class. + + +0.4.1 (2023-08-02) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Adapted the base :class:`IsaacEnv` class to use the :class:`SimulationContext` class from the + :mod:`isaaclab.sim` module. This simplifies setting of simulation parameters. + + +0.4.0 (2023-07-26) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Removed the resetting of environment indices in the step call of the :class:`IsaacEnv` class. + This must be handled in the :math:`_step_impl`` function by the inherited classes. +* Adapted the wrapper for RSL-RL library its new API. + +Fixed +^^^^^ + +* Added handling of no checkpoint available error in the :meth:`get_checkpoint_path`. +* Fixed the locomotion environment for rough terrain locomotion training. + + +0.3.2 (2023-07-22) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^^^ + +* Added a UI to the :class:`IsaacEnv` class to enable/disable rendering of the viewport when not running in + headless mode. + +Fixed +^^^^^ + +* Fixed the the issue with environment returning transition tuples even when the simulation is paused. +* Fixed the shutdown of the simulation when the environment is closed. + + +0.3.1 (2023-06-23) +~~~~~~~~~~~~~~~~~~ + +Changed +^^^^^^^ + +* Changed the argument ``headless`` in :class:`IsaacEnv` class to ``render``, in order to cause less confusion + about rendering and headless-ness, i.e. that you can render while headless. + + +0.3.0 (2023-04-14) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a new flag ``viewport`` to the :class:`IsaacEnv` class to enable/disable rendering of the viewport. + If the flag is set to ``True``, the viewport is enabled and the environment is rendered in the background. +* Updated the training scripts in the ``scripts/reinforcement_learning`` directory to use the new flag ``viewport``. + If the CLI argument ``--video`` is passed, videos are recorded in the ``videos/train`` directory using the + :class:`gym.wrappers.RecordVideo` wrapper. + +Changed +^^^^^^^ + +* The :class:`IsaacEnv` class supports different rendering mode as referenced in OpenAI Gym's ``render`` method. + These modes are: + + * ``rgb_array``: Renders the environment in the background and returns the rendered image as a numpy array. + * ``human``: Renders the environment in the background and displays the rendered image in a window. + +* Changed the constructor in the classes inheriting from :class:`IsaacEnv` to pass all the keyword arguments to the + constructor of :class:`IsaacEnv` class. + +Fixed +^^^^^ + +* Clarified the documentation of ``headless`` flag in the :class:`IsaacEnv` class. It refers to whether or not + to render at every sim step, not whether to render the viewport or not. +* Fixed the unit tests for running random agent on included environments. + +0.2.3 (2023-03-06) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Tuned the observations and rewards for ``Isaac-Lift-Franka-v0`` environment. + +0.2.2 (2023-03-04) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed the issue with rigid object not working in the ``Isaac-Lift-Franka-v0`` environment. + +0.2.1 (2023-03-01) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added a flag ``disable_contact_processing`` to the :class:`SimCfg` class to handle + contact processing effectively when using TensorAPIs for contact reporting. +* Added verbosity flag to :meth:`export_policy_as_onnx` to print model summary. + +Fixed +^^^^^ + +* Clarified the documentation of flags in the :class:`SimCfg` class. +* Added enabling of ``omni.kit.viewport`` and ``isaacsim.replicator`` extensions + dynamically to maintain order in the startup of extensions. +* Corrected the experiment names in the configuration files for training environments with ``rsl_rl``. + +Changed +^^^^^^^ + +* Changed the default value of ``enable_scene_query_support`` in :class:`SimCfg` class to False. + The flag is overridden to True inside :class:`IsaacEnv` class when running the simulation in + non-headless mode. + +0.2.0 (2023-01-25) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added environment wrapper and sequential trainer for the skrl RL library +* Added training/evaluation configuration files for the skrl RL library + +0.1.2 (2023-01-19) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Added the flag ``replicate_physics`` to the :class:`SimCfg` class. +* Increased the default value of ``gpu_found_lost_pairs_capacity`` in :class:`PhysxCfg` class + +0.1.1 (2023-01-18) +~~~~~~~~~~~~~~~~~~ + +Fixed +^^^^^ + +* Fixed a bug in ``Isaac-Velocity-Anymal-C-v0`` where the domain randomization is + not applicable if cloning the environments with ``replicate_physics=True``. + +0.1.0 (2023-01-17) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Initial release of the extension. +* Includes the following environments: + + * ``Isaac-Cartpole-v0``: A cartpole environment with a continuous action space. + * ``Isaac-Ant-v0``: A 3D ant environment with a continuous action space. + * ``Isaac-Humanoid-v0``: A 3D humanoid environment with a continuous action space. + * ``Isaac-Reach-Franka-v0``: A end-effector pose tracking task for the Franka arm. + * ``Isaac-Lift-Franka-v0``: A 3D object lift and reposing task for the Franka arm. + * ``Isaac-Velocity-Anymal-C-v0``: An SE(2) velocity tracking task for legged robot on flat terrain. diff --git a/source/isaaclab_tasks/docs/README.md b/source/isaaclab_tasks/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..155ea05e9a572708a3a3b4654c6ca0d80f4f6f32 --- /dev/null +++ b/source/isaaclab_tasks/docs/README.md @@ -0,0 +1,60 @@ +# Isaac Lab: Environment Suite + +Using the core framework developed as part of Isaac Lab, we provide various learning environments for robotics research. +These environments follow the `gym.Env` API from OpenAI Gym version `0.21.0`. The environments are registered using +the Gym registry. + +Each environment's name is composed of `Isaac---v`, where `` indicates the skill to learn +in the environment, `` indicates the embodiment of the acting agent, and `` represents the version of +the environment (which can be used to suggest different observation or action spaces). + +The environments are configured using either Python classes (wrapped using `configclass` decorator) or through +YAML files. The template structure of the environment is always put at the same level as the environment file +itself. However, its various instances are included in directories within the environment directory itself. +This looks like as follows: + +```tree +isaaclab_tasks/locomotion/ +├── __init__.py +└── velocity + ├── config + │ └── anymal_c + │ ├── agent # <- this is where we store the learning agent configurations + │ ├── __init__.py # <- this is where we register the environment and configurations to gym registry + │ ├── flat_env_cfg.py + │ └── rough_env_cfg.py + ├── __init__.py + └── velocity_env_cfg.py # <- this is the base task configuration +``` + +The environments are then registered in the `isaaclab_tasks/locomotion/velocity/config/anymal_c/__init__.py`: + +```python +gym.register( + id="Isaac-Velocity-Rough-Anymal-C-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalCRoughEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_rough_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Anymal-C-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalCFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_flat_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) +``` + +> **Note:** As a practice, we specify all the environments in a single file to avoid name conflicts between different +> tasks or environments. However, this practice is debatable and we are open to suggestions to deal with a large +> scaling in the number of tasks or environments. diff --git a/source/isaaclab_tasks/isaaclab_tasks/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1907733fa75b2025ef334ee1d962d5cb14484621 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/__init__.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Package containing task implementations for various robotic environments. + +The package is structured as follows: + +- ``direct``: These include single-file implementations of tasks. +- ``manager_based``: These include task implementations that use the manager-based API. +- ``utils``: These include utility functions for the tasks. + +""" + +import os +import toml + +# Conveniences to other module directories via relative paths +ISAACLAB_TASKS_EXT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../")) +"""Path to the extension source directory.""" + +ISAACLAB_TASKS_METADATA = toml.load(os.path.join(ISAACLAB_TASKS_EXT_DIR, "config", "extension.toml")) +"""Extension metadata dictionary parsed from the extension.toml file.""" + +# Configure the module-level variables +__version__ = ISAACLAB_TASKS_METADATA["package"]["version"] + +## +# Register Gym environments. +## + +from .utils import import_packages + +# The blacklist is used to prevent importing configs from sub-packages +# TODO(@ashwinvk): Remove pick_place from the blacklist once pinocchio from Isaac Sim is compatibility +_BLACKLIST_PKGS = ["utils", ".mdp", "direct.humanoid_amp.motions"] +# Import all configs in this package +import_packages(__name__, _BLACKLIST_PKGS) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3e2b7945ebde3707389bc6779f35c5cb63eadc7e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Direct workflow environments. +""" + +import gymnasium as gym diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..636ad36616594898b6681dc9f0343f6d2b91ad43 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/__init__.py @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Allegro Inhand Manipulation environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +inhand_task_entry = "isaaclab_tasks.direct.inhand_manipulation" + +gym.register( + id="Isaac-Repose-Cube-Allegro-Direct-v0", + entry_point=f"{inhand_task_entry}.inhand_manipulation_env:InHandManipulationEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.allegro_hand_env_cfg:AllegroHandEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AllegroHandPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..36d441d26dd81eb349d89cebc1259de3561f012b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [1024, 512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: allegro_hand + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 5000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 32768 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..871250fd0b17f78d48e0ee654ddd1f7aa73be828 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class AllegroHandPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 10000 + save_interval = 250 + experiment_name = "allegro_hand" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[1024, 512, 256, 128], + critic_hidden_dims=[1024, 512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.016, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..de44a576eb98991660d35b3e2de52a9f89b8857f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "allegro_hand" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/allegro_hand_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/allegro_hand_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..75087bbe019964229c681deb8aa09ba95a389d32 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/allegro_hand/allegro_hand_env_cfg.py @@ -0,0 +1,121 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_assets.robots.allegro import ALLEGRO_HAND_CFG + + +@configclass +class AllegroHandEnvCfg(DirectRLEnvCfg): + # env + decimation = 4 + episode_length_s = 10.0 + action_space = 16 + observation_space = 124 # (full) + state_space = 0 + asymmetric_obs = False + obs_type = "full" + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 120, + render_interval=decimation, + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + physx=PhysxCfg( + bounce_threshold_velocity=0.2, + ), + ) + # robot + robot_cfg: ArticulationCfg = ALLEGRO_HAND_CFG.replace(prim_path="/World/envs/env_.*/Robot") + + actuated_joint_names = [ + "index_joint_0", + "middle_joint_0", + "ring_joint_0", + "thumb_joint_0", + "index_joint_1", + "index_joint_2", + "index_joint_3", + "middle_joint_1", + "middle_joint_2", + "middle_joint_3", + "ring_joint_1", + "ring_joint_2", + "ring_joint_3", + "thumb_joint_1", + "thumb_joint_2", + "thumb_joint_3", + ] + fingertip_body_names = [ + "index_link_3", + "middle_link_3", + "ring_link_3", + "thumb_link_3", + ] + + # in-hand object + object_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/object", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + kinematic_enabled=False, + disable_gravity=False, + enable_gyroscopic_forces=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0025, + max_depenetration_velocity=1000.0, + ), + mass_props=sim_utils.MassPropertiesCfg(density=400.0), + scale=(1.2, 1.2, 1.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -0.17, 0.56), rot=(1.0, 0.0, 0.0, 0.0)), + ) + # goal object + goal_object_cfg: VisualizationMarkersCfg = VisualizationMarkersCfg( + prim_path="/Visuals/goal_marker", + markers={ + "goal": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(1.2, 1.2, 1.2), + ) + }, + ) + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=8192, env_spacing=0.75, replicate_physics=True, clone_in_fabric=True + ) + # reset + reset_position_noise = 0.01 # range of position at reset + reset_dof_pos_noise = 0.2 # range of dof pos at reset + reset_dof_vel_noise = 0.0 # range of dof vel at reset + # reward scales + dist_reward_scale = -10.0 + rot_reward_scale = 1.0 + rot_eps = 0.1 + action_penalty_scale = -0.0002 + reach_goal_bonus = 250 + fall_penalty = 0 + fall_dist = 0.24 + vel_obs_scale = 0.2 + success_tolerance = 0.2 + max_consecutive_success = 0 + av_factor = 0.1 + act_moving_average = 1.0 + force_torque_obs_scale = 10.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9881cd66ca74e3fae7dc865be0396ac95bd729aa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Ant locomotion environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Ant-Direct-v0", + entry_point=f"{__name__}.ant_env:AntEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.ant_env:AntEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AntPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a8c24a530962dddc052b1687ceb8749641844152 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: ant_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 3e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 500 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 32768 + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..00eefc843e202252687fafd25c3c8703739dca21 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class AntPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 32 + max_iterations = 1000 + save_interval = 50 + experiment_name = "ant_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[400, 200, 100], + critic_hidden_dims=[400, 200, 100], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb7382d4a2b835845b218d754308ea0b7548492f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 4 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "ant_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 8000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/ant/ant_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/ant_env.py new file mode 100644 index 0000000000000000000000000000000000000000..39ae57b2967754981f03dbce7523b9f17a6fd554 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/ant/ant_env.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.locomotion.locomotion_env import LocomotionEnv + +from isaaclab_assets.robots.ant import ANT_CFG + + +@configclass +class AntEnvCfg(DirectRLEnvCfg): + # env + episode_length_s = 15.0 + decimation = 2 + action_scale = 0.5 + action_space = 8 + observation_space = 36 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="average", + restitution_combine_mode="average", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + debug_vis=False, + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=4096, env_spacing=4.0, replicate_physics=True, clone_in_fabric=True + ) + + # robot + robot: ArticulationCfg = ANT_CFG.replace(prim_path="/World/envs/env_.*/Robot") + joint_gears: list = [15, 15, 15, 15, 15, 15, 15, 15] + + heading_weight: float = 0.5 + up_weight: float = 0.1 + + energy_cost_scale: float = 0.05 + actions_cost_scale: float = 0.005 + alive_reward_scale: float = 0.5 + dof_vel_scale: float = 0.2 + + death_cost: float = -2.0 + termination_height: float = 0.31 + + angular_velocity_scale: float = 1.0 + contact_force_scale: float = 0.1 + + +class AntEnv(LocomotionEnv): + cfg: AntEnvCfg + + def __init__(self, cfg: AntEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..26275c97fff95c8806a9b836c71e3e46f28c50b4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Ant locomotion environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Anymal-C-Direct-v0", + entry_point=f"{__name__}.anymal_c_env:AnymalCEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.anymal_c_env_cfg:AnymalCFlatEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_flat_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-C-Direct-v0", + entry_point=f"{__name__}.anymal_c_env:AnymalCEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.anymal_c_env_cfg:AnymalCRoughEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_rough_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e8fc0d4ca91dba8e577fdfeb0a5671786d7c52a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_flat_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [128, 128, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: anymal_c_flat_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: False + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.005 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2.0 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ef2670326e208bca8c5694d3bd6b6bbbb31d6d0f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rl_games_rough_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: anymal_c_rough_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: False + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.005 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2.0 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..117ad6e75bed9db2c3b387f76ee0d30d1e6b7619 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class AnymalCFlatPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 500 + save_interval = 50 + experiment_name = "anymal_c_flat_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[128, 128, 128], + critic_hidden_dims=[128, 128, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class AnymalCRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "anymal_c_rough_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33bc471a477824ce760a16145c7e3feb53a82fe1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_c_flat_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c31294b7fa865f447f7a0423675327154786b9d5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_c_rough_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env.py new file mode 100644 index 0000000000000000000000000000000000000000..2f7e792f93ad42f5212e68bf4b0d1fa1d58c8ce0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env.py @@ -0,0 +1,197 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import gymnasium as gym +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectRLEnv +from isaaclab.sensors import ContactSensor, RayCaster + +from .anymal_c_env_cfg import AnymalCFlatEnvCfg, AnymalCRoughEnvCfg + + +class AnymalCEnv(DirectRLEnv): + cfg: AnymalCFlatEnvCfg | AnymalCRoughEnvCfg + + def __init__(self, cfg: AnymalCFlatEnvCfg | AnymalCRoughEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + # Joint position command (deviation from default joint positions) + self._actions = torch.zeros(self.num_envs, gym.spaces.flatdim(self.single_action_space), device=self.device) + self._previous_actions = torch.zeros( + self.num_envs, gym.spaces.flatdim(self.single_action_space), device=self.device + ) + + # X/Y linear velocity and yaw angular velocity commands + self._commands = torch.zeros(self.num_envs, 3, device=self.device) + + # Logging + self._episode_sums = { + key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device) + for key in [ + "track_lin_vel_xy_exp", + "track_ang_vel_z_exp", + "lin_vel_z_l2", + "ang_vel_xy_l2", + "dof_torques_l2", + "dof_acc_l2", + "action_rate_l2", + "feet_air_time", + "undesired_contacts", + "flat_orientation_l2", + ] + } + # Get specific body indices + self._base_id, _ = self._contact_sensor.find_bodies("base") + self._feet_ids, _ = self._contact_sensor.find_bodies(".*FOOT") + self._undesired_contact_body_ids, _ = self._contact_sensor.find_bodies(".*THIGH") + + def _setup_scene(self): + self._robot = Articulation(self.cfg.robot) + self.scene.articulations["robot"] = self._robot + self._contact_sensor = ContactSensor(self.cfg.contact_sensor) + self.scene.sensors["contact_sensor"] = self._contact_sensor + if isinstance(self.cfg, AnymalCRoughEnvCfg): + # we add a height scanner for perceptive locomotion + self._height_scanner = RayCaster(self.cfg.height_scanner) + self.scene.sensors["height_scanner"] = self._height_scanner + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self._terrain = self.cfg.terrain.class_type(self.cfg.terrain) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path]) + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor): + self._actions = actions.clone() + self._processed_actions = self.cfg.action_scale * self._actions + self._robot.data.default_joint_pos + + def _apply_action(self): + self._robot.set_joint_position_target(self._processed_actions) + + def _get_observations(self) -> dict: + self._previous_actions = self._actions.clone() + height_data = None + if isinstance(self.cfg, AnymalCRoughEnvCfg): + height_data = ( + self._height_scanner.data.pos_w[:, 2].unsqueeze(1) - self._height_scanner.data.ray_hits_w[..., 2] - 0.5 + ).clip(-1.0, 1.0) + obs = torch.cat( + [ + tensor + for tensor in ( + self._robot.data.root_lin_vel_b, + self._robot.data.root_ang_vel_b, + self._robot.data.projected_gravity_b, + self._commands, + self._robot.data.joint_pos - self._robot.data.default_joint_pos, + self._robot.data.joint_vel, + height_data, + self._actions, + ) + if tensor is not None + ], + dim=-1, + ) + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + # linear velocity tracking + lin_vel_error = torch.sum(torch.square(self._commands[:, :2] - self._robot.data.root_lin_vel_b[:, :2]), dim=1) + lin_vel_error_mapped = torch.exp(-lin_vel_error / 0.25) + # yaw rate tracking + yaw_rate_error = torch.square(self._commands[:, 2] - self._robot.data.root_ang_vel_b[:, 2]) + yaw_rate_error_mapped = torch.exp(-yaw_rate_error / 0.25) + # z velocity tracking + z_vel_error = torch.square(self._robot.data.root_lin_vel_b[:, 2]) + # angular velocity x/y + ang_vel_error = torch.sum(torch.square(self._robot.data.root_ang_vel_b[:, :2]), dim=1) + # joint torques + joint_torques = torch.sum(torch.square(self._robot.data.applied_torque), dim=1) + # joint acceleration + joint_accel = torch.sum(torch.square(self._robot.data.joint_acc), dim=1) + # action rate + action_rate = torch.sum(torch.square(self._actions - self._previous_actions), dim=1) + # feet air time + first_contact = self._contact_sensor.compute_first_contact(self.step_dt)[:, self._feet_ids] + last_air_time = self._contact_sensor.data.last_air_time[:, self._feet_ids] + air_time = torch.sum((last_air_time - 0.5) * first_contact, dim=1) * ( + torch.norm(self._commands[:, :2], dim=1) > 0.1 + ) + # undesired contacts + net_contact_forces = self._contact_sensor.data.net_forces_w_history + is_contact = ( + torch.max(torch.norm(net_contact_forces[:, :, self._undesired_contact_body_ids], dim=-1), dim=1)[0] > 1.0 + ) + contacts = torch.sum(is_contact, dim=1) + # flat orientation + flat_orientation = torch.sum(torch.square(self._robot.data.projected_gravity_b[:, :2]), dim=1) + + rewards = { + "track_lin_vel_xy_exp": lin_vel_error_mapped * self.cfg.lin_vel_reward_scale * self.step_dt, + "track_ang_vel_z_exp": yaw_rate_error_mapped * self.cfg.yaw_rate_reward_scale * self.step_dt, + "lin_vel_z_l2": z_vel_error * self.cfg.z_vel_reward_scale * self.step_dt, + "ang_vel_xy_l2": ang_vel_error * self.cfg.ang_vel_reward_scale * self.step_dt, + "dof_torques_l2": joint_torques * self.cfg.joint_torque_reward_scale * self.step_dt, + "dof_acc_l2": joint_accel * self.cfg.joint_accel_reward_scale * self.step_dt, + "action_rate_l2": action_rate * self.cfg.action_rate_reward_scale * self.step_dt, + "feet_air_time": air_time * self.cfg.feet_air_time_reward_scale * self.step_dt, + "undesired_contacts": contacts * self.cfg.undesired_contact_reward_scale * self.step_dt, + "flat_orientation_l2": flat_orientation * self.cfg.flat_orientation_reward_scale * self.step_dt, + } + reward = torch.sum(torch.stack(list(rewards.values())), dim=0) + # Logging + for key, value in rewards.items(): + self._episode_sums[key] += value + return reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + time_out = self.episode_length_buf >= self.max_episode_length - 1 + net_contact_forces = self._contact_sensor.data.net_forces_w_history + died = torch.any(torch.max(torch.norm(net_contact_forces[:, :, self._base_id], dim=-1), dim=1)[0] > 1.0, dim=1) + return died, time_out + + def _reset_idx(self, env_ids: torch.Tensor | None): + if env_ids is None or len(env_ids) == self.num_envs: + env_ids = self._robot._ALL_INDICES + self._robot.reset(env_ids) + super()._reset_idx(env_ids) + if len(env_ids) == self.num_envs: + # Spread out the resets to avoid spikes in training when many environments reset at a similar time + self.episode_length_buf[:] = torch.randint_like(self.episode_length_buf, high=int(self.max_episode_length)) + self._actions[env_ids] = 0.0 + self._previous_actions[env_ids] = 0.0 + # Sample new commands + self._commands[env_ids] = torch.zeros_like(self._commands[env_ids]).uniform_(-1.0, 1.0) + # Reset robot state + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_vel = self._robot.data.default_joint_vel[env_ids] + default_root_state = self._robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self._terrain.env_origins[env_ids] + self._robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self._robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + # Logging + extras = dict() + for key in self._episode_sums.keys(): + episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids]) + extras["Episode_Reward/" + key] = episodic_sum_avg / self.max_episode_length_s + self._episode_sums[key][env_ids] = 0.0 + self.extras["log"] = dict() + self.extras["log"].update(extras) + extras = dict() + extras["Episode_Termination/base_contact"] = torch.count_nonzero(self.reset_terminated[env_ids]).item() + extras["Episode_Termination/time_out"] = torch.count_nonzero(self.reset_time_outs[env_ids]).item() + self.extras["log"].update(extras) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f5e12b599122f962baa96a3fec8bde692f999fed --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_env_cfg.py @@ -0,0 +1,148 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg, RayCasterCfg, patterns +from isaaclab.sim import SimulationCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for randomization.""" + + physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.8, 0.8), + "dynamic_friction_range": (0.6, 0.6), + "restitution_range": (0.0, 0.0), + "num_buckets": 64, + }, + ) + + add_base_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "mass_distribution_params": (-5.0, 5.0), + "operation": "add", + }, + ) + + +@configclass +class AnymalCFlatEnvCfg(DirectRLEnvCfg): + # env + episode_length_s = 20.0 + decimation = 4 + action_scale = 0.5 + action_space = 12 + observation_space = 48 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 200, + render_interval=decimation, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + debug_vis=False, + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # events + events: EventCfg = EventCfg() + + # robot + robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot") + contact_sensor: ContactSensorCfg = ContactSensorCfg( + prim_path="/World/envs/env_.*/Robot/.*", history_length=3, update_period=0.005, track_air_time=True + ) + + # reward scales + lin_vel_reward_scale = 1.0 + yaw_rate_reward_scale = 0.5 + z_vel_reward_scale = -2.0 + ang_vel_reward_scale = -0.05 + joint_torque_reward_scale = -2.5e-5 + joint_accel_reward_scale = -2.5e-7 + action_rate_reward_scale = -0.01 + feet_air_time_reward_scale = 0.5 + undesired_contact_reward_scale = -1.0 + flat_orientation_reward_scale = -5.0 + + +@configclass +class AnymalCRoughEnvCfg(AnymalCFlatEnvCfg): + # env + observation_space = 235 + + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + max_init_terrain_level=9, + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + visual_material=sim_utils.MdlFileCfg( + mdl_path="{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl", + project_uvw=True, + ), + debug_vis=False, + ) + + # we add a height scanner for perceptive locomotion + height_scanner = RayCasterCfg( + prim_path="/World/envs/env_.*/Robot/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=False, + mesh_prim_paths=["/World/ground"], + ) + + # reward scales (override from flat config) + flat_orientation_reward_scale = 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9da64598c0c4eb2c8d3f04956828bcb6d8be5363 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/__init__.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-AutoMate-Assembly-Direct-v0", + entry_point=f"{__name__}.assembly_env:AssemblyEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.assembly_env:AssemblyEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-AutoMate-Disassembly-Direct-v0", + entry_point=f"{__name__}.disassembly_env:DisassemblyEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.disassembly_env:DisassemblyEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3d8d070248cf29f83f9b159c864eae05192d8a17 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,105 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 0 + algo: + name: a2c_continuous + env: + clip_actions: 1.0 + model: + name: continuous_a2c_logstd + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 256 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: False + load_checkpoint: False + load_path: "" + config: + name: Assembly + device: cuda:0 + full_experiment_name: test + env_name: rlgpu + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 128 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: fixed + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 20000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 300 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: False + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 4096 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches + mini_epochs: 8 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + central_value_config: + minibatch_size: 256 + mini_epochs: 4 + learning_rate: 1e-3 + lr_schedule: linear + kl_threshold: 0.016 + clip_value: True + normalize_input: True + truncate_grads: True + network: + name: actor_critic + central_value: True + + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + initializer: + name: default + regularizer: + name: None + + player: + deterministic: False diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env.py new file mode 100644 index 0000000000000000000000000000000000000000..35e999120380dcc76889f5a4b8b3443248a09595 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env.py @@ -0,0 +1,877 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import json +import os + +import numpy as np +import torch +import warp as wp + +import carb +import isaacsim.core.utils.torch as torch_utils + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs import DirectRLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, retrieve_file_path +from isaaclab.utils.math import axis_angle_from_quat + +from . import automate_algo_utils as automate_algo +from . import automate_log_utils as automate_log +from . import factory_control as fc +from . import industreal_algo_utils as industreal_algo +from .assembly_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, AssemblyEnvCfg +from .soft_dtw_cuda import SoftDTW + + +class AssemblyEnv(DirectRLEnv): + cfg: AssemblyEnvCfg + + def __init__(self, cfg: AssemblyEnvCfg, render_mode: str | None = None, **kwargs): + # Update number of obs/states + cfg.observation_space = sum([OBS_DIM_CFG[obs] for obs in cfg.obs_order]) + cfg.state_space = sum([STATE_DIM_CFG[state] for state in cfg.state_order]) + self.cfg_task = cfg.tasks[cfg.task_name] + + super().__init__(cfg, render_mode, **kwargs) + + self._set_body_inertias() + self._init_tensors() + self._set_default_dynamics_parameters() + self._compute_intermediate_values(dt=self.physics_dt) + + # Load asset meshes in warp for SDF-based dense reward + wp.init() + self.wp_device = wp.get_preferred_device() + self.plug_mesh, self.plug_sample_points, self.socket_mesh = industreal_algo.load_asset_mesh_in_warp( + self.cfg_task.assembly_dir + self.cfg_task.held_asset_cfg.obj_path, + self.cfg_task.assembly_dir + self.cfg_task.fixed_asset_cfg.obj_path, + self.cfg_task.num_mesh_sample_points, + self.wp_device, + ) + + # Get the gripper open width based on plug object bounding box + self.gripper_open_width = automate_algo.get_gripper_open_width( + self.cfg_task.assembly_dir + self.cfg_task.held_asset_cfg.obj_path + ) + + # Create criterion for dynamic time warping (later used for imitation reward) + cuda_version = automate_algo.get_cuda_version() + if (cuda_version is not None) and (cuda_version < (13, 0, 0)): + self.soft_dtw_criterion = SoftDTW(use_cuda=True, device=self.device, gamma=self.cfg_task.soft_dtw_gamma) + else: + self.soft_dtw_criterion = SoftDTW(use_cuda=False, device=self.device, gamma=self.cfg_task.soft_dtw_gamma) + + # Evaluate + if self.cfg_task.if_logging_eval: + self._init_eval_logging() + + def _init_eval_logging(self): + self.held_asset_pose_log = torch.empty( + (0, 7), dtype=torch.float32, device=self.device + ) # (position, quaternion) + self.fixed_asset_pose_log = torch.empty((0, 7), dtype=torch.float32, device=self.device) + self.success_log = torch.empty((0, 1), dtype=torch.float32, device=self.device) + + # Turn off SBC during evaluation so all plugs are initialized outside of the socket + self.cfg_task.if_sbc = False + + def _set_body_inertias(self): + """Note: this is to account for the asset_options.armature parameter in IGE.""" + inertias = self._robot.root_physx_view.get_inertias() + offset = torch.zeros_like(inertias) + offset[:, :, [0, 4, 8]] += 0.01 + new_inertias = inertias + offset + self._robot.root_physx_view.set_inertias(new_inertias, torch.arange(self.num_envs)) + + def _set_default_dynamics_parameters(self): + """Set parameters defining dynamic interactions.""" + self.default_gains = torch.tensor(self.cfg.ctrl.default_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + + self.pos_threshold = torch.tensor(self.cfg.ctrl.pos_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.rot_threshold = torch.tensor(self.cfg.ctrl.rot_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + + # Set masses and frictions. + self._set_friction(self._held_asset, self.cfg_task.held_asset_cfg.friction) + self._set_friction(self._fixed_asset, self.cfg_task.fixed_asset_cfg.friction) + self._set_friction(self._robot, self.cfg_task.robot_cfg.friction) + + def _set_friction(self, asset, value): + """Update material properties for a given asset.""" + materials = asset.root_physx_view.get_material_properties() + materials[..., 0] = value # Static friction. + materials[..., 1] = value # Dynamic friction. + env_ids = torch.arange(self.scene.num_envs, device="cpu") + asset.root_physx_view.set_material_properties(materials, env_ids) + + def _init_tensors(self): + """Initialize tensors once.""" + self.identity_quat = ( + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + + # Control targets. + self.ctrl_target_joint_pos = torch.zeros((self.num_envs, self._robot.num_joints), device=self.device) + self.ctrl_target_fingertip_midpoint_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.ctrl_target_fingertip_midpoint_quat = torch.zeros((self.num_envs, 4), device=self.device) + + # Fixed asset. + self.fixed_pos_action_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.fixed_pos_obs_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.init_fixed_pos_obs_noise = torch.zeros((self.num_envs, 3), device=self.device) + + # Held asset + held_base_x_offset = 0.0 + held_base_z_offset = 0.0 + + self.held_base_pos_local = torch.tensor([0.0, 0.0, 0.0], device=self.device).repeat((self.num_envs, 1)) + self.held_base_pos_local[:, 0] = held_base_x_offset + self.held_base_pos_local[:, 2] = held_base_z_offset + self.held_base_quat_local = self.identity_quat.clone().detach() + + self.held_base_pos = torch.zeros_like(self.held_base_pos_local) + self.held_base_quat = self.identity_quat.clone().detach() + + self.plug_grasps, self.disassembly_dists = self._load_assembly_info() + self.curriculum_height_bound, self.curriculum_height_step = self._get_curriculum_info(self.disassembly_dists) + self._load_disassembly_data() + + # Load grasp pose from json files given assembly ID + # Grasp pose tensors + self.palm_to_finger_center = ( + torch.tensor([0.0, 0.0, -self.cfg_task.palm_to_finger_dist], device=self.device) + .unsqueeze(0) + .repeat(self.num_envs, 1) + ) + self.robot_to_gripper_quat = ( + torch.tensor([0.0, 1.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + self.plug_grasp_pos_local = self.plug_grasps[: self.num_envs, :3] + self.plug_grasp_quat_local = torch.roll(self.plug_grasps[: self.num_envs, 3:], -1, 1) + + # Computer body indices. + self.left_finger_body_idx = self._robot.body_names.index("panda_leftfinger") + self.right_finger_body_idx = self._robot.body_names.index("panda_rightfinger") + self.fingertip_body_idx = self._robot.body_names.index("panda_fingertip_centered") + + # Tensors for finite-differencing. + self.last_update_timestamp = 0.0 # Note: This is for finite differencing body velocities. + self.prev_fingertip_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.prev_fingertip_quat = self.identity_quat.clone() + self.prev_joint_pos = torch.zeros((self.num_envs, 7), device=self.device) + + # Keypoint tensors. + self.target_held_base_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.target_held_base_quat = self.identity_quat.clone().detach() + + offsets = self._get_keypoint_offsets(self.cfg_task.num_keypoints) + self.keypoint_offsets = offsets * self.cfg_task.keypoint_scale + self.keypoints_held = torch.zeros((self.num_envs, self.cfg_task.num_keypoints, 3), device=self.device) + self.keypoints_fixed = torch.zeros_like(self.keypoints_held, device=self.device) + + # Used to compute target poses. + self.fixed_success_pos_local = torch.zeros((self.num_envs, 3), device=self.device) + self.fixed_success_pos_local[:, 2] = 0.0 + + self.ep_succeeded = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + self.ep_success_times = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + + # SBC + if self.cfg_task.if_sbc: + self.curr_max_disp = self.curriculum_height_bound[:, 0] + else: + self.curr_max_disp = self.curriculum_height_bound[:, 1] + + def _load_assembly_info(self): + """Load grasp pose and disassembly distance for plugs in each environment.""" + + retrieve_file_path(self.cfg_task.plug_grasp_json, download_dir="./") + with open(os.path.basename(self.cfg_task.plug_grasp_json)) as f: + plug_grasp_dict = json.load(f) + plug_grasps = [plug_grasp_dict[f"asset_{self.cfg_task.assembly_id}"] for i in range(self.num_envs)] + + retrieve_file_path(self.cfg_task.disassembly_dist_json, download_dir="./") + with open(os.path.basename(self.cfg_task.disassembly_dist_json)) as f: + disassembly_dist_dict = json.load(f) + disassembly_dists = [disassembly_dist_dict[f"asset_{self.cfg_task.assembly_id}"] for i in range(self.num_envs)] + + return torch.as_tensor(plug_grasps).to(self.device), torch.as_tensor(disassembly_dists).to(self.device) + + def _get_curriculum_info(self, disassembly_dists): + """Calculate the ranges and step sizes for Sampling-based Curriculum (SBC) in each environment.""" + + curriculum_height_bound = torch.zeros((self.num_envs, 2), dtype=torch.float32, device=self.device) + curriculum_height_step = torch.zeros((self.num_envs, 2), dtype=torch.float32, device=self.device) + + curriculum_height_bound[:, 1] = disassembly_dists + self.cfg_task.curriculum_freespace_range + + curriculum_height_step[:, 0] = curriculum_height_bound[:, 1] / self.cfg_task.num_curriculum_step + curriculum_height_step[:, 1] = -curriculum_height_step[:, 0] / 2.0 + + return curriculum_height_bound, curriculum_height_step + + def _load_disassembly_data(self): + """Load pre-collected disassembly trajectories (end-effector position only).""" + + retrieve_file_path(self.cfg_task.disassembly_path_json, download_dir="./") + with open(os.path.basename(self.cfg_task.disassembly_path_json)) as f: + disassembly_traj = json.load(f) + + eef_pos_traj = [] + + for i in range(len(disassembly_traj)): + curr_ee_traj = np.asarray(disassembly_traj[i]["fingertip_centered_pos"]).reshape((-1, 3)) + curr_ee_goal = np.asarray(disassembly_traj[i]["fingertip_centered_pos"]).reshape((-1, 3))[0, :] + + # offset each trajectory to be relative to the goal + eef_pos_traj.append(curr_ee_traj - curr_ee_goal) + + self.eef_pos_traj = torch.tensor(np.array(eef_pos_traj), dtype=torch.float32, device=self.device).squeeze() + + def _get_keypoint_offsets(self, num_keypoints): + """Get uniformly-spaced keypoints along a line of unit length, centered at 0.""" + keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device) + keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5 + + return keypoint_offsets + + def _setup_scene(self): + """Initialize simulation scene.""" + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg(), translation=(0.0, 0.0, -0.4)) + + # spawn a usd file of a table into the scene + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func( + "/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711) + ) + + self._robot = Articulation(self.cfg.robot) + self._fixed_asset = Articulation(self.cfg_task.fixed_asset) + self._held_asset = RigidObject(self.cfg_task.held_asset) + + self.scene.clone_environments(copy_from_source=False) + self.scene.filter_collisions() + + self.scene.articulations["robot"] = self._robot + self.scene.articulations["fixed_asset"] = self._fixed_asset + self.scene.rigid_objects["held_asset"] = self._held_asset + + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _compute_intermediate_values(self, dt): + """Get values computed from raw tensors. This includes adding noise.""" + # TODO: A lot of these can probably only be set once? + self.fixed_pos = self._fixed_asset.data.root_pos_w - self.scene.env_origins + self.fixed_quat = self._fixed_asset.data.root_quat_w + + self.held_pos = self._held_asset.data.root_pos_w - self.scene.env_origins + self.held_quat = self._held_asset.data.root_quat_w + + self.fingertip_midpoint_pos = self._robot.data.body_pos_w[:, self.fingertip_body_idx] - self.scene.env_origins + self.fingertip_midpoint_quat = self._robot.data.body_quat_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_linvel = self._robot.data.body_lin_vel_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_angvel = self._robot.data.body_ang_vel_w[:, self.fingertip_body_idx] + + jacobians = self._robot.root_physx_view.get_jacobians() + + self.left_finger_jacobian = jacobians[:, self.left_finger_body_idx - 1, 0:6, 0:7] + self.right_finger_jacobian = jacobians[:, self.right_finger_body_idx - 1, 0:6, 0:7] + self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 + self.arm_mass_matrix = self._robot.root_physx_view.get_generalized_mass_matrices()[:, 0:7, 0:7] + self.joint_pos = self._robot.data.joint_pos.clone() + self.joint_vel = self._robot.data.joint_vel.clone() + + # Compute pose of gripper goal and top of socket in socket frame + self.gripper_goal_quat, self.gripper_goal_pos = torch_utils.tf_combine( + self.fixed_quat, + self.fixed_pos, + self.plug_grasp_quat_local, + self.plug_grasp_pos_local, + ) + + self.gripper_goal_quat, self.gripper_goal_pos = torch_utils.tf_combine( + self.gripper_goal_quat, + self.gripper_goal_pos, + self.robot_to_gripper_quat, + self.palm_to_finger_center, + ) + + # Finite-differencing results in more reliable velocity estimates. + self.ee_linvel_fd = (self.fingertip_midpoint_pos - self.prev_fingertip_pos) / dt + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + + # Add state differences if velocity isn't being added. + rot_diff_quat = torch_utils.quat_mul( + self.fingertip_midpoint_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) + ) + rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) + rot_diff_aa = axis_angle_from_quat(rot_diff_quat) + self.ee_angvel_fd = rot_diff_aa / dt + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + joint_diff = self.joint_pos[:, 0:7] - self.prev_joint_pos + self.joint_vel_fd = joint_diff / dt + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + + # Keypoint tensors. + self.held_base_quat[:], self.held_base_pos[:] = torch_utils.tf_combine( + self.held_quat, self.held_pos, self.held_base_quat_local, self.held_base_pos_local + ) + self.target_held_base_quat[:], self.target_held_base_pos[:] = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, self.identity_quat, self.fixed_success_pos_local + ) + + # Compute pos of keypoints on held asset, and fixed asset in world frame + for idx, keypoint_offset in enumerate(self.keypoint_offsets): + self.keypoints_held[:, idx] = torch_utils.tf_combine( + self.held_base_quat, self.held_base_pos, self.identity_quat, keypoint_offset.repeat(self.num_envs, 1) + )[1] + self.keypoints_fixed[:, idx] = torch_utils.tf_combine( + self.target_held_base_quat, + self.target_held_base_pos, + self.identity_quat, + keypoint_offset.repeat(self.num_envs, 1), + )[1] + + self.keypoint_dist = torch.norm(self.keypoints_held - self.keypoints_fixed, p=2, dim=-1).mean(-1) + self.last_update_timestamp = self._robot._data._sim_timestamp + + def _get_observations(self): + """Get actor/critic inputs using asymmetric critic.""" + + obs_dict = { + "joint_pos": self.joint_pos[:, 0:7], + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "fingertip_goal_pos": self.gripper_goal_pos, + "fingertip_goal_quat": self.gripper_goal_quat, + "delta_pos": self.gripper_goal_pos - self.fingertip_midpoint_pos, + } + + state_dict = { + "joint_pos": self.joint_pos[:, 0:7], + "joint_vel": self.joint_vel[:, 0:7], + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.fingertip_midpoint_linvel, + "ee_angvel": self.fingertip_midpoint_angvel, + "fingertip_goal_pos": self.gripper_goal_pos, + "fingertip_goal_quat": self.gripper_goal_quat, + "held_pos": self.held_pos, + "held_quat": self.held_quat, + "delta_pos": self.gripper_goal_pos - self.fingertip_midpoint_pos, + } + # obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order + ['prev_actions']] + obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order] + obs_tensors = torch.cat(obs_tensors, dim=-1) + + # state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order + ['prev_actions']] + state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order] + state_tensors = torch.cat(state_tensors, dim=-1) + + return {"policy": obs_tensors, "critic": state_tensors} + + def _reset_buffers(self, env_ids): + """Reset buffers.""" + self.ep_succeeded[env_ids] = 0 + + def _pre_physics_step(self, action): + """Apply policy actions with smoothing.""" + env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(env_ids) > 0: + self._reset_buffers(env_ids) + + self.actions = ( + self.cfg.ctrl.ema_factor * action.clone().to(self.device) + (1 - self.cfg.ctrl.ema_factor) * self.actions + ) + + def move_gripper_in_place(self, ctrl_target_gripper_dof_pos): + """Keep gripper in current position as gripper closes.""" + actions = torch.zeros((self.num_envs, 6), device=self.device) + ctrl_target_gripper_dof_pos = 0.0 + + # Interpret actions as target pos displacements and set pos target + pos_actions = actions[:, 0:3] * self.pos_threshold + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = actions[:, 3:6] + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1.0e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos + self.generate_ctrl_signals() + + def _apply_action(self): + """Apply actions for policy as delta targets from current position.""" + # Get current yaw for success checking. + _, _, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) + self.curr_yaw = torch.where(curr_yaw > np.deg2rad(235), curr_yaw - 2 * np.pi, curr_yaw) + + # Note: We use finite-differenced velocities for control and observations. + # Check if we need to re-compute velocities within the decimation loop. + if self.last_update_timestamp < self._robot._data._sim_timestamp: + self._compute_intermediate_values(dt=self.physics_dt) + + # Interpret actions as target pos displacements and set pos target + pos_actions = self.actions[:, 0:3] * self.pos_threshold + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = self.actions[:, 3:6] + if self.cfg_task.unidirectional_rot: + rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0] + rot_actions = rot_actions * self.rot_threshold + + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + # To speed up learning, never allow the policy to move more than 5cm away from the base. + delta_pos = self.ctrl_target_fingertip_midpoint_pos - self.fixed_pos_action_frame + pos_error_clipped = torch.clip( + delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1] + ) + self.ctrl_target_fingertip_midpoint_pos = self.fixed_pos_action_frame + pos_error_clipped + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 # Restrict actions to be upright. + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = 0.0 + self.generate_ctrl_signals() + + def _set_gains(self, prop_gains, rot_deriv_scale=1.0): + """Set robot gains using critical damping.""" + self.task_prop_gains = prop_gains + self.task_deriv_gains = 2 * torch.sqrt(prop_gains) + self.task_deriv_gains[:, 3:6] /= rot_deriv_scale + + def generate_ctrl_signals(self): + """Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm).""" + self.joint_torque, self.applied_wrench = fc.compute_dof_torque( + cfg=self.cfg, + dof_pos=self.joint_pos, + dof_vel=self.joint_vel, # _fd, + fingertip_midpoint_pos=self.fingertip_midpoint_pos, + fingertip_midpoint_quat=self.fingertip_midpoint_quat, + fingertip_midpoint_linvel=self.ee_linvel_fd, + fingertip_midpoint_angvel=self.ee_angvel_fd, + jacobian=self.fingertip_midpoint_jacobian, + arm_mass_matrix=self.arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat, + task_prop_gains=self.task_prop_gains, + task_deriv_gains=self.task_deriv_gains, + device=self.device, + ) + + # set target for gripper joints to use GYM's PD controller + self.ctrl_target_joint_pos[:, 7:9] = self.ctrl_target_gripper_dof_pos + self.joint_torque[:, 7:9] = 0.0 + + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + self._robot.set_joint_effort_target(self.joint_torque) + + def _get_dones(self): + """Update intermediate values used for rewards and observations.""" + self._compute_intermediate_values(dt=self.physics_dt) + time_out = self.episode_length_buf >= self.max_episode_length - 1 + return time_out, time_out + + def _get_rewards(self): + """Update rewards and compute success statistics.""" + # Get successful and failed envs at current timestep + + curr_successes = automate_algo.check_plug_inserted_in_socket( + self.held_pos, + self.fixed_pos, + self.disassembly_dists, + self.keypoints_held, + self.keypoints_fixed, + self.cfg_task.close_error_thresh, + self.episode_length_buf, + ) + + rew_buf = self._update_rew_buf(curr_successes) + self.ep_succeeded = torch.logical_or(self.ep_succeeded, curr_successes) + + # Only log episode success rates at the end of an episode. + if torch.any(self.reset_buf): + self.extras["successes"] = torch.count_nonzero(self.ep_succeeded) / self.num_envs + + sbc_rwd_scale = automate_algo.get_curriculum_reward_scale( + curr_max_disp=self.curr_max_disp, + curriculum_height_bound=self.curriculum_height_bound, + ) + + rew_buf *= sbc_rwd_scale + + if self.cfg_task.if_sbc: + self.curr_max_disp = automate_algo.get_new_max_disp( + curr_success=torch.count_nonzero(self.ep_succeeded) / self.num_envs, + cfg_task=self.cfg_task, + curriculum_height_bound=self.curriculum_height_bound, + curriculum_height_step=self.curriculum_height_step, + curr_max_disp=self.curr_max_disp, + ) + + self.extras["curr_max_disp"] = self.curr_max_disp + + if self.cfg_task.if_logging_eval: + self.success_log = torch.cat([self.success_log, self.ep_succeeded.reshape((self.num_envs, 1))], dim=0) + + if self.success_log.shape[0] >= self.cfg_task.num_eval_trials: + automate_log.write_log_to_hdf5( + self.held_asset_pose_log, + self.fixed_asset_pose_log, + self.success_log, + self.cfg_task.eval_filename, + ) + exit(0) + + self.prev_actions = self.actions.clone() + return rew_buf + + def _update_rew_buf(self, curr_successes): + """Compute reward at current timestep.""" + rew_dict = dict({}) + + # SDF-based reward. + rew_dict["sdf"] = industreal_algo.get_sdf_reward( + self.plug_mesh, + self.plug_sample_points, + self.held_pos, + self.held_quat, + self.fixed_pos, + self.fixed_quat, + self.wp_device, + self.device, + ) + + rew_dict["curr_successes"] = curr_successes.clone().float() + + # Imitation Reward: Calculate reward + curr_eef_pos = (self.fingertip_midpoint_pos - self.gripper_goal_pos).reshape( + -1, 3 + ) # relative position instead of absolute position + rew_dict["imitation"] = automate_algo.get_imitation_reward_from_dtw( + self.eef_pos_traj, curr_eef_pos, self.prev_fingertip_midpoint_pos, self.soft_dtw_criterion, self.device + ) + + self.prev_fingertip_midpoint_pos = torch.cat( + (self.prev_fingertip_midpoint_pos[:, 1:, :], curr_eef_pos.unsqueeze(1).clone().detach()), dim=1 + ) + + rew_buf = ( + self.cfg_task.sdf_rwd_scale * rew_dict["sdf"] + + self.cfg_task.imitation_rwd_scale * rew_dict["imitation"] + + rew_dict["curr_successes"] + ) + + for rew_name, rew in rew_dict.items(): + self.extras[f"logs_rew_{rew_name}"] = rew.mean() + + return rew_buf + + def _reset_idx(self, env_ids): + """ + We assume all envs will always be reset at the same time. + """ + super()._reset_idx(env_ids) + + self._set_assets_to_default_pose(env_ids) + self._set_franka_to_default_pose(joints=self.cfg.ctrl.reset_joints, env_ids=env_ids) + self.step_sim_no_action() + + self.randomize_initial_state(env_ids) + + if self.cfg_task.if_logging_eval: + self.held_asset_pose_log = torch.cat( + [self.held_asset_pose_log, torch.cat([self.held_pos, self.held_quat], dim=1)], dim=0 + ) + self.fixed_asset_pose_log = torch.cat( + [self.fixed_asset_pose_log, torch.cat([self.fixed_pos, self.fixed_quat], dim=1)], dim=0 + ) + + prev_fingertip_midpoint_pos = (self.fingertip_midpoint_pos - self.gripper_goal_pos).unsqueeze( + 1 + ) # (num_envs, 1, 3) + self.prev_fingertip_midpoint_pos = torch.repeat_interleave( + prev_fingertip_midpoint_pos, self.cfg_task.num_point_robot_traj, dim=1 + ) # (num_envs, num_point_robot_traj, 3) + + def _set_assets_to_default_pose(self, env_ids): + """Move assets to default pose before randomization.""" + held_state = self._held_asset.data.default_root_state.clone()[env_ids] + held_state[:, 0:3] += self.scene.env_origins[env_ids] + held_state[:, 7:] = 0.0 + self._held_asset.write_root_pose_to_sim(held_state[:, 0:7], env_ids=env_ids) + self._held_asset.write_root_velocity_to_sim(held_state[:, 7:], env_ids=env_ids) + self._held_asset.reset() + + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + fixed_state[:, 0:3] += self.scene.env_origins[env_ids] + fixed_state[:, 7:] = 0.0 + self._fixed_asset.write_root_pose_to_sim(fixed_state[:, 0:7], env_ids=env_ids) + self._fixed_asset.write_root_velocity_to_sim(fixed_state[:, 7:], env_ids=env_ids) + self._fixed_asset.reset() + + def _move_gripper_to_grasp_pose(self, env_ids): + """Define grasp pose for plug and move gripper to pose.""" + + gripper_goal_quat, gripper_goal_pos = torch_utils.tf_combine( + self.held_quat, + self.held_pos, + self.plug_grasp_quat_local, + self.plug_grasp_pos_local, + ) + + gripper_goal_quat, gripper_goal_pos = torch_utils.tf_combine( + gripper_goal_quat, + gripper_goal_pos, + self.robot_to_gripper_quat, + self.palm_to_finger_center, + ) + + # Set target_pos + self.ctrl_target_fingertip_midpoint_pos = gripper_goal_pos.clone() + + # Set target rot + self.ctrl_target_fingertip_midpoint_quat = gripper_goal_quat.clone() + + self.set_pos_inverse_kinematics(env_ids) + self.step_sim_no_action() + + def set_pos_inverse_kinematics(self, env_ids): + """Set robot joint position using DLS IK.""" + ik_time = 0.0 + while ik_time < 0.50: + # Compute error to target. + pos_error, axis_angle_error = fc.get_pose_error( + fingertip_midpoint_pos=self.fingertip_midpoint_pos[env_ids], + fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos[env_ids], + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat[env_ids], + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + + # Solve DLS problem. + delta_dof_pos = fc._get_delta_dof_pos( + delta_pose=delta_hand_pose, + ik_method="dls", + jacobian=self.fingertip_midpoint_jacobian[env_ids], + device=self.device, + ) + self.joint_pos[env_ids, 0:7] += delta_dof_pos[:, 0:7] + self.joint_vel[env_ids, :] = torch.zeros_like(self.joint_pos[env_ids,]) + + self.ctrl_target_joint_pos[env_ids, 0:7] = self.joint_pos[env_ids, 0:7] + # Update dof state. + self._robot.write_joint_state_to_sim(self.joint_pos, self.joint_vel) + self._robot.reset() + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + + # Simulate and update tensors. + self.step_sim_no_action() + ik_time += self.physics_dt + + return pos_error, axis_angle_error + + def _set_franka_to_default_pose(self, joints, env_ids): + """Return Franka to its default joint position.""" + gripper_width = self.gripper_open_width + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_pos[:, 7:] = gripper_width # MIMIC + joint_pos[:, :7] = torch.tensor(joints, device=self.device)[None, :] + joint_vel = torch.zeros_like(joint_pos) + joint_effort = torch.zeros_like(joint_pos) + self.ctrl_target_joint_pos[env_ids, :] = joint_pos + self._robot.set_joint_position_target(self.ctrl_target_joint_pos[env_ids], env_ids=env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + self._robot.reset() + self._robot.set_joint_effort_target(joint_effort, env_ids=env_ids) + + self.step_sim_no_action() + + def step_sim_no_action(self): + """Step the simulation without an action. Used for resets.""" + self.scene.write_data_to_sim() + self.sim.step(render=True) + self.scene.update(dt=self.physics_dt) + self._compute_intermediate_values(dt=self.physics_dt) + + def randomize_fixed_initial_state(self, env_ids): + # (1.) Randomize fixed asset pose. + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + # (1.a.) Position + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] + fixed_asset_init_pos_rand = torch.tensor( + self.cfg_task.fixed_asset_init_pos_noise, dtype=torch.float32, device=self.device + ) + fixed_pos_init_rand = fixed_pos_init_rand @ torch.diag(fixed_asset_init_pos_rand) + fixed_state[:, 0:3] += fixed_pos_init_rand + self.scene.env_origins[env_ids] + fixed_state[:, 2] += self.cfg_task.fixed_asset_z_offset + + # (1.b.) Orientation + fixed_orn_init_yaw = np.deg2rad(self.cfg_task.fixed_asset_init_orn_deg) + fixed_orn_yaw_range = np.deg2rad(self.cfg_task.fixed_asset_init_orn_range_deg) + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_orn_euler = fixed_orn_init_yaw + fixed_orn_yaw_range * rand_sample + fixed_orn_euler[:, 0:2] = 0.0 # Only change yaw. + fixed_orn_quat = torch_utils.quat_from_euler_xyz( + fixed_orn_euler[:, 0], fixed_orn_euler[:, 1], fixed_orn_euler[:, 2] + ) + fixed_state[:, 3:7] = fixed_orn_quat + # (1.c.) Velocity + fixed_state[:, 7:] = 0.0 # vel + # (1.d.) Update values. + self._fixed_asset.write_root_state_to_sim(fixed_state, env_ids=env_ids) + self._fixed_asset.reset() + + # (1.e.) Noisy position observation. + fixed_asset_pos_noise = torch.randn((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_asset_pos_rand = torch.tensor(self.cfg.obs_rand.fixed_asset_pos, dtype=torch.float32, device=self.device) + fixed_asset_pos_noise = fixed_asset_pos_noise @ torch.diag(fixed_asset_pos_rand) + self.init_fixed_pos_obs_noise[:] = fixed_asset_pos_noise + + self.step_sim_no_action() + + def randomize_held_initial_state(self, env_ids, pre_grasp): + curr_curriculum_disp_range = self.curriculum_height_bound[:, 1] - self.curr_max_disp + if pre_grasp: + self.curriculum_disp = self.curr_max_disp + curr_curriculum_disp_range * ( + torch.rand((self.num_envs,), dtype=torch.float32, device=self.device) + ) + + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + held_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] + held_asset_init_pos_rand = torch.tensor( + self.cfg_task.held_asset_init_pos_noise, dtype=torch.float32, device=self.device + ) + self.held_pos_init_rand = held_pos_init_rand @ torch.diag(held_asset_init_pos_rand) + + # Set plug pos to assembled state, but offset plug Z-coordinate by height of socket, + # minus curriculum displacement + held_state = self._held_asset.data.default_root_state.clone() + held_state[env_ids, 0:3] = self.fixed_pos[env_ids].clone() + self.scene.env_origins[env_ids] + held_state[env_ids, 3:7] = self.fixed_quat[env_ids].clone() + held_state[env_ids, 7:] = 0.0 + + held_state[env_ids, 2] += self.curriculum_disp + + plug_in_freespace_idx = torch.argwhere(self.curriculum_disp > self.disassembly_dists) + held_state[plug_in_freespace_idx, :2] += self.held_pos_init_rand[plug_in_freespace_idx, :2] + + self._held_asset.write_root_state_to_sim(held_state) + self._held_asset.reset() + + self.step_sim_no_action() + + def randomize_initial_state(self, env_ids): + """Randomize initial state and perform any episode-level randomization.""" + # Disable gravity. + physics_sim_view = sim_utils.SimulationContext.instance().physics_sim_view + physics_sim_view.set_gravity(carb.Float3(0.0, 0.0, 0.0)) + + self.randomize_fixed_initial_state(env_ids) + + # Compute the frame on the bolt that would be used as observation: fixed_pos_obs_frame + # For example, the tip of the bolt can be used as the observation frame + fixed_tip_pos_local = torch.zeros_like(self.fixed_pos) + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height + + _, fixed_tip_pos = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, self.identity_quat, fixed_tip_pos_local + ) + self.fixed_pos_obs_frame[:] = fixed_tip_pos + + self.randomize_held_initial_state(env_ids, pre_grasp=True) + + self._move_gripper_to_grasp_pose(env_ids) + + self.randomize_held_initial_state(env_ids, pre_grasp=False) + + # Close hand + # Set gains to use for quick resets. + reset_task_prop_gains = torch.tensor(self.cfg.ctrl.reset_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + reset_rot_deriv_scale = self.cfg.ctrl.reset_rot_deriv_scale + self._set_gains(reset_task_prop_gains, reset_rot_deriv_scale) + + self.step_sim_no_action() + + grasp_time = 0.0 + while grasp_time < 0.25: + self.ctrl_target_joint_pos[env_ids, 7:] = 0.0 # Close gripper. + self.ctrl_target_gripper_dof_pos = 0.0 + self.move_gripper_in_place(ctrl_target_gripper_dof_pos=0.0) + self.step_sim_no_action() + grasp_time += self.sim.get_physics_dt() + + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + # Set initial actions to involve no-movement. Needed for EMA/correct penalties. + self.actions = torch.zeros_like(self.actions) + self.prev_actions = torch.zeros_like(self.actions) + self.fixed_pos_action_frame[:] = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + + # Zero initial velocity. + self.ee_angvel_fd[:, :] = 0.0 + self.ee_linvel_fd[:, :] = 0.0 + + # Set initial gains for the episode. + self._set_gains(self.default_gains) + + physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5d4f66ec8969758df9fcb36b51656712c466a92a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env_cfg.py @@ -0,0 +1,200 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass + +from .assembly_tasks_cfg import ASSET_DIR, Insertion + +OBS_DIM_CFG = { + "joint_pos": 7, + "fingertip_pos": 3, + "fingertip_quat": 4, + "fingertip_goal_pos": 3, + "fingertip_goal_quat": 4, + "delta_pos": 3, +} + +STATE_DIM_CFG = { + "joint_pos": 7, + "joint_vel": 7, + "fingertip_pos": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "fingertip_goal_pos": 3, + "fingertip_goal_quat": 4, + "held_pos": 3, + "held_quat": 4, + "delta_pos": 3, +} + + +@configclass +class ObsRandCfg: + fixed_asset_pos = [0.001, 0.001, 0.001] + + +@configclass +class CtrlCfg: + ema_factor = 0.2 + + pos_action_bounds = [0.1, 0.1, 0.1] + rot_action_bounds = [0.01, 0.01, 0.01] + + pos_action_threshold = [0.1, 0.1, 0.1] + rot_action_threshold = [0.01, 0.01, 0.01] + + reset_joints = [0.0, 0.0, 0.0, -1.870, 0.0, 1.8675, 0.785398] + reset_task_prop_gains = [1000, 1000, 1000, 50, 50, 50] + # reset_rot_deriv_scale = 1.0 + # default_task_prop_gains = [1000, 1000, 1000, 50, 50, 50] + # reset_task_prop_gains = [300, 300, 300, 20, 20, 20] + reset_rot_deriv_scale = 10.0 + default_task_prop_gains = [100, 100, 100, 30, 30, 30] + + # Null space parameters. + default_dof_pos_tensor = [0.0, 0.0, 0.0, -1.870, 0.0, 1.8675, 0.785398] + kp_null = 10.0 + kd_null = 6.3246 + + +@configclass +class AssemblyEnvCfg(DirectRLEnvCfg): + decimation = 8 + action_space = 6 + # num_*: will be overwritten to correspond to obs_order, state_order. + observation_space = 24 + state_space = 44 + obs_order: list = [ + "joint_pos", + "fingertip_pos", + "fingertip_quat", + "fingertip_goal_pos", + "fingertip_goal_quat", + "delta_pos", + ] + state_order: list = [ + "joint_pos", + "joint_vel", + "fingertip_pos", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "fingertip_goal_pos", + "fingertip_goal_quat", + "held_pos", + "held_quat", + "delta_pos", + ] + + task_name: str = "insertion" # peg_insertion, gear_meshing, nut_threading + tasks: dict = {"insertion": Insertion()} + obs_rand: ObsRandCfg = ObsRandCfg() + ctrl: CtrlCfg = CtrlCfg() + + # episode_length_s = 10.0 # Probably need to override. + episode_length_s = 5.0 + sim: SimulationCfg = SimulationCfg( + device="cuda:0", + dt=1 / 120, + gravity=(0.0, 0.0, -9.81), + physx=PhysxCfg( + solver_type=1, + max_position_iteration_count=192, # Important to avoid interpenetration. + max_velocity_iteration_count=1, + bounce_threshold_velocity=0.2, + friction_offset_threshold=0.01, + friction_correlation_distance=0.00625, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + gpu_max_num_partitions=1, # Important for stable simulation. + ), + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + ) + + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=128, env_spacing=2.0) + + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ASSET_DIR}/franka_mimic.usd", + # usd_path=f'{ASSET_DIR}/automate_franka.usd', + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 0.00871, + "panda_joint2": -0.10368, + "panda_joint3": -0.00794, + "panda_joint4": -1.49139, + "panda_joint5": -0.00083, + "panda_joint6": 1.38774, + "panda_joint7": 0.0, + "panda_finger_joint2": 0.04, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + actuators={ + "panda_arm1": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=87, + velocity_limit=124.6, + ), + "panda_arm2": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=12, + velocity_limit=149.5, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint[1-2]"], + effort_limit=40.0, + velocity_limit=0.04, + stiffness=7500.0, + damping=173.0, + friction=0.1, + armature=0.0, + ), + }, + ) + # contact_sensor: ContactSensorCfg = ContactSensorCfg( + # prim_path="/World/envs/env_.*/Robot/.*", update_period=0.0, history_length=1, debug_vis=True + # ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_tasks_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_tasks_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b651767f7f3574d838a2a8425a22d1280c02518d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_tasks_cfg.py @@ -0,0 +1,270 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +ASSET_DIR = f"{ISAACLAB_NUCLEUS_DIR}/AutoMate" + +OBS_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, +} + +STATE_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "joint_pos": 7, + "held_pos": 3, + "held_pos_rel_fixed": 3, + "held_quat": 4, + "fixed_pos": 3, + "fixed_quat": 4, + "task_prop_gains": 6, + "ema_factor": 1, + "pos_threshold": 3, + "rot_threshold": 3, +} + + +@configclass +class FixedAssetCfg: + usd_path: str = "" + diameter: float = 0.0 + height: float = 0.0 + base_height: float = 0.0 # Used to compute held asset CoM. + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class HeldAssetCfg: + usd_path: str = "" + diameter: float = 0.0 # Used for gripper width. + height: float = 0.0 + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class RobotCfg: + robot_usd: str = "" + franka_fingerpad_length: float = 0.017608 + friction: float = 0.75 + + +@configclass +class AssemblyTask: + robot_cfg: RobotCfg = RobotCfg() + name: str = "" + duration_s = 5.0 + + fixed_asset_cfg: FixedAssetCfg = FixedAssetCfg() + held_asset_cfg: HeldAssetCfg = HeldAssetCfg() + asset_size: float = 0.0 + + # palm_to_finger_dist: float = 0.1034 + palm_to_finger_dist: float = 0.1134 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 2.356] + hand_init_orn_noise: list = [0.0, 0.0, 1.57] + + # Action + unidirectional_rot: bool = False + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 10.0 + + # Held Asset (applies to all tasks) + # held_asset_pos_noise: list = [0.0, 0.006, 0.003] # noise level of the held asset in gripper + held_asset_init_pos_noise: list = [0.01, 0.01, 0.01] + held_asset_pos_noise: list = [0.0, 0.0, 0.0] + held_asset_rot_init: float = 0.0 + + # Reward + ee_success_yaw: float = 0.0 # nut_threading task only. + action_penalty_scale: float = 0.0 + action_grad_penalty_scale: float = 0.0 + # Reward function details can be found in Appendix B of https://arxiv.org/pdf/2408.04587. + # Multi-scale keypoints are used to capture different phases of the task. + # Each reward passes the keypoint distance, x, through a squashing function: + # r(x) = 1/(exp(-ax) + b + exp(ax)). + # Each list defines [a, b] which control the slope and maximum of the squashing function. + num_keypoints: int = 4 + keypoint_scale: float = 0.15 + + # Fixed-asset height fraction for which different bonuses are rewarded (see individual tasks). + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + + # SDF reward + sdf_rwd_scale: float = 1.0 + num_mesh_sample_points: int = 1000 + + # Imitation reward + imitation_rwd_scale: float = 1.0 + soft_dtw_gamma: float = 0.01 # set to 0 if want to use the original DTW without any smoothing + num_point_robot_traj: int = 10 # number of waypoints included in the end-effector trajectory + + # SBC + initial_max_disp: float = 0.01 # max initial downward displacement of plug at beginning of curriculum + curriculum_success_thresh: float = 0.8 # success rate threshold for increasing curriculum difficulty + curriculum_failure_thresh: float = 0.5 # success rate threshold for decreasing curriculum difficulty + curriculum_freespace_range: float = 0.01 + num_curriculum_step: int = 10 + curriculum_height_step: list = [ + -0.005, + 0.003, + ] # how much to increase max initial downward displacement after hitting success or failure thresh + + if_sbc: bool = True + + # Logging evaluation results + if_logging_eval: bool = False + num_eval_trials: int = 100 + eval_filename: str = "evaluation_00015.h5" + + +@configclass +class Peg8mm(HeldAssetCfg): + usd_path = "plug.usd" + obj_path = "plug.obj" + diameter = 0.007986 + height = 0.050 + mass = 0.019 + + +@configclass +class Hole8mm(FixedAssetCfg): + usd_path = "socket.usd" + obj_path = "socket.obj" + diameter = 0.0081 + height = 0.050896 + base_height = 0.0 + + +@configclass +class Insertion(AssemblyTask): + name = "insertion" + + assembly_id = "00015" + assembly_dir = f"{ASSET_DIR}/{assembly_id}/" + + fixed_asset_cfg = Hole8mm() + held_asset_cfg = Peg8mm() + asset_size = 8.0 + duration_s = 10.0 + + plug_grasp_json = f"{ASSET_DIR}/plug_grasps.json" + disassembly_dist_json = f"{ASSET_DIR}/disassembly_dist.json" + disassembly_path_json = f"{assembly_dir}/disassemble_traj.json" + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.047] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 0.0] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] + hand_width_max: float = 0.080 # maximum opening width of gripper + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 10.0 + fixed_asset_z_offset: float = 0.1435 + + # Held Asset (applies to all tasks) + # held_asset_pos_noise: list = [0.003, 0.0, 0.003] # noise level of the held asset in gripper + held_asset_init_pos_noise: list = [0.01, 0.01, 0.01] + held_asset_pos_noise: list = [0.0, 0.0, 0.0] + held_asset_rot_init: float = 0.0 + + # Rewards + keypoint_coef_baseline: list = [5, 4] + keypoint_coef_coarse: list = [50, 2] + keypoint_coef_fine: list = [100, 0] + # Fraction of socket height. + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + engage_height_thresh: float = 0.01 + success_height_thresh: float = 0.003 + close_error_thresh: float = 0.015 + + fixed_asset: ArticulationCfg = ArticulationCfg( + # fixed_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{assembly_dir}{fixed_asset_cfg.usd_path}", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + fix_root_link=True, # add this so the fixed asset is set to have a fixed base + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + # init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), + rot=(1.0, 0.0, 0.0, 0.0), + joint_pos={}, + joint_vel={}, + ), + actuators={}, + ) + # held_asset: ArticulationCfg = ArticulationCfg( + held_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{assembly_dir}{held_asset_cfg.usd_path}", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + # init_state=ArticulationCfg.InitialStateCfg( + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + # joint_pos={}, + # joint_vel={} + ), + # actuators={} + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_algo_utils.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_algo_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4588d4e227fd80592b3326b1cb442525d7c27250 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_algo_utils.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +import re +import subprocess +import sys + +import torch +import trimesh +import warp as wp + +print("Python Executable:", sys.executable) +print("Python Path:", sys.path) + +base_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".")) +sys.path.append(base_dir) + +from isaaclab.utils.assets import retrieve_file_path + +""" +Util Functions +""" + + +def parse_cuda_version(version_string): + """ + Parse CUDA version string into comparable tuple of (major, minor, patch). + + Args: + version_string: Version string like "12.8.9" or "11.2" + + Returns: + Tuple of (major, minor, patch) as integers, where patch defaults to 0 iff + not present. + + Example: + "12.8.9" -> (12, 8, 9) + "11.2" -> (11, 2, 0) + """ + parts = version_string.split(".") + major = int(parts[0]) + minor = int(parts[1]) if len(parts) > 1 else 0 + patch = int(parts[2]) if len(parts) > 2 else 0 + return (major, minor, patch) + + +def get_cuda_version(): + try: + # Execute nvcc --version command + result = subprocess.run(["nvcc", "--version"], capture_output=True, text=True, check=True) + output = result.stdout + + # Use regex to find the CUDA version (e.g., V11.2.67) + match = re.search(r"V(\d+\.\d+(\.\d+)?)", output) + if match: + return parse_cuda_version(match.group(1)) + else: + print("CUDA version not found in output.") + return None + except FileNotFoundError: + print("nvcc command not found. Is CUDA installed and in your PATH?") + return None + except subprocess.CalledProcessError as e: + print(f"Error executing nvcc: {e.stderr}") + return None + except Exception as e: + print(f"An unexpected error occurred: {e}") + return None + + +def get_gripper_open_width(obj_filepath): + retrieve_file_path(obj_filepath, download_dir="./") + obj_mesh = trimesh.load_mesh(os.path.basename(obj_filepath)) + # obj_mesh = trimesh.load_mesh(obj_filepath) + aabb = obj_mesh.bounds + + return min(0.04, (aabb[1][1] - aabb[0][1]) / 1.25) + + +""" +Imitation Reward +""" + + +def get_closest_state_idx(ref_traj, curr_ee_pos): + """Find the index of the closest state in reference trajectory.""" + + # ref_traj.shape = (num_trajs, traj_len, 3) + traj_len = ref_traj.shape[1] + num_envs = curr_ee_pos.shape[0] + + # dist_from_all_state.shape = (num_envs, num_trajs, traj_len, 1) + dist_from_all_state = torch.cdist(ref_traj.unsqueeze(0), curr_ee_pos.reshape(-1, 1, 1, 3), p=2) + + # dist_from_all_state_flatten.shape = (num_envs, num_trajs * traj_len) + dist_from_all_state_flatten = dist_from_all_state.reshape(num_envs, -1) + + # min_dist_per_env.shape = (num_envs) + min_dist_per_env = torch.amin(dist_from_all_state_flatten, dim=-1) + + # min_dist_idx.shape = (num_envs) + min_dist_idx = torch.argmin(dist_from_all_state_flatten, dim=-1) + + # min_dist_traj_idx.shape = (num_envs) + # min_dist_step_idx.shape = (num_envs) + min_dist_traj_idx = min_dist_idx // traj_len + min_dist_step_idx = min_dist_idx % traj_len + + return min_dist_traj_idx, min_dist_step_idx, min_dist_per_env + + +def get_reward_mask(ref_traj, curr_ee_pos, tolerance): + _, min_dist_step_idx, _ = get_closest_state_idx(ref_traj, curr_ee_pos) + selected_steps = torch.index_select( + ref_traj, dim=1, index=min_dist_step_idx + ) # selected_steps.shape = (num_trajs, num_envs, 3) + + x_min = torch.amin(selected_steps[:, :, 0], dim=0) - tolerance + x_max = torch.amax(selected_steps[:, :, 0], dim=0) + tolerance + y_min = torch.amin(selected_steps[:, :, 1], dim=0) - tolerance + y_max = torch.amax(selected_steps[:, :, 1], dim=0) + tolerance + + x_in_range = torch.logical_and(torch.lt(curr_ee_pos[:, 0], x_max), torch.gt(curr_ee_pos[:, 0], x_min)) + y_in_range = torch.logical_and(torch.lt(curr_ee_pos[:, 1], y_max), torch.gt(curr_ee_pos[:, 1], y_min)) + pos_in_range = torch.logical_and(x_in_range, y_in_range).int() + + return pos_in_range + + +def get_imitation_reward_from_dtw(ref_traj, curr_ee_pos, prev_ee_traj, criterion, device): + """Get imitation reward based on dynamic time warping.""" + + soft_dtw = torch.zeros((curr_ee_pos.shape[0]), device=device) + prev_ee_pos = prev_ee_traj[:, 0, :] # select the first ee pos in robot traj + min_dist_traj_idx, min_dist_step_idx, min_dist_per_env = get_closest_state_idx(ref_traj, prev_ee_pos) + + for i in range(curr_ee_pos.shape[0]): + traj_idx = min_dist_traj_idx[i] + step_idx = min_dist_step_idx[i] + curr_ee_pos_i = curr_ee_pos[i].reshape(1, 3) + + # NOTE: in reference trajectories, larger index -> closer to goal + traj = ref_traj[traj_idx, step_idx:, :].reshape((1, -1, 3)) + + _, curr_step_idx, _ = get_closest_state_idx(traj, curr_ee_pos_i) + + if curr_step_idx == 0: + selected_pos = ref_traj[traj_idx, step_idx, :].reshape((1, 1, 3)) + selected_traj = torch.cat([selected_pos, selected_pos], dim=1) + else: + selected_traj = ref_traj[traj_idx, step_idx : (curr_step_idx + step_idx), :].reshape((1, -1, 3)) + eef_traj = torch.cat((prev_ee_traj[i, 1:, :], curr_ee_pos_i)).reshape((1, -1, 3)) + soft_dtw[i] = criterion(eef_traj, selected_traj) + + w_task_progress = 1 - (min_dist_step_idx / ref_traj.shape[1]) + + # imitation_rwd = torch.exp(-soft_dtw) + imitation_rwd = 1 - torch.tanh(soft_dtw) + + return imitation_rwd * w_task_progress + + +""" +Sampling-Based Curriculum (SBC) +""" + + +def get_new_max_disp(curr_success, cfg_task, curriculum_height_bound, curriculum_height_step, curr_max_disp): + """Update max downward displacement of plug at beginning of episode, based on success rate.""" + + if curr_success > cfg_task.curriculum_success_thresh: + # If success rate is above threshold, increase min downward displacement until max value + new_max_disp = torch.where( + curr_max_disp + curriculum_height_step[:, 0] < curriculum_height_bound[:, 1], + curr_max_disp + curriculum_height_step[:, 0], + curriculum_height_bound[:, 1], + ) + elif curr_success < cfg_task.curriculum_failure_thresh: + # If success rate is below threshold, decrease min downward displacement until min value + new_max_disp = torch.where( + curr_max_disp + curriculum_height_step[:, 1] > curriculum_height_bound[:, 0], + curr_max_disp + curriculum_height_step[:, 1], + curriculum_height_bound[:, 0], + ) + else: + # Maintain current max downward displacement + new_max_disp = curr_max_disp + + return new_max_disp + + +""" +Bonus and Success Checking +""" + + +def check_plug_close_to_socket(keypoints_plug, keypoints_socket, dist_threshold, progress_buf): + """Check if plug is close to socket.""" + + # Compute keypoint distance between plug and socket + keypoint_dist = torch.norm(keypoints_socket - keypoints_plug, p=2, dim=-1) + + # Check if keypoint distance is below threshold + is_plug_close_to_socket = torch.where( + torch.mean(keypoint_dist, dim=-1) < dist_threshold, + torch.ones_like(progress_buf), + torch.zeros_like(progress_buf), + ) + + return is_plug_close_to_socket + + +def check_plug_inserted_in_socket( + plug_pos, socket_pos, disassembly_dist, keypoints_plug, keypoints_socket, close_error_thresh, progress_buf +): + """Check if plug is inserted in socket.""" + + # Check if plug is within threshold distance of assembled state + is_plug_below_insertion_height = plug_pos[:, 2] < socket_pos[:, 2] + disassembly_dist + is_plug_above_table_height = plug_pos[:, 2] > socket_pos[:, 2] + + is_plug_height_success = torch.logical_and(is_plug_below_insertion_height, is_plug_above_table_height) + + # Check if plug is close to socket + # NOTE: This check addresses edge case where plug is within threshold distance of + # assembled state, but plug is outside socket + is_plug_close_to_socket = check_plug_close_to_socket( + keypoints_plug=keypoints_plug, + keypoints_socket=keypoints_socket, + dist_threshold=close_error_thresh, + progress_buf=progress_buf, + ) + + # Combine both checks + is_plug_inserted_in_socket = torch.logical_and(is_plug_height_success, is_plug_close_to_socket) + + return is_plug_inserted_in_socket + + +def get_curriculum_reward_scale(curr_max_disp, curriculum_height_bound): + """Compute reward scale for SBC.""" + + # Compute difference between max downward displacement at beginning of training (easiest condition) + # and current max downward displacement (based on current curriculum stage) + # NOTE: This number increases as curriculum gets harder + curr_stage_diff = curr_max_disp - curriculum_height_bound[:, 0] + + # Compute difference between max downward displacement at beginning of training (easiest condition) + # and min downward displacement (hardest condition) + final_stage_diff = curriculum_height_bound[:, 1] - curriculum_height_bound[:, 0] + + # Compute reward scale + reward_scale = curr_stage_diff / final_stage_diff + 1.0 + + return reward_scale.mean() + + +""" +Warp Kernels +""" + + +# Transform points from source coordinate frame to destination coordinate frame +@wp.kernel +def transform_points(src: wp.array(dtype=wp.vec3), dest: wp.array(dtype=wp.vec3), xform: wp.transform): + tid = wp.tid() + + p = src[tid] + m = wp.transform_point(xform, p) + + dest[tid] = m + + +# Return interpenetration distances between query points (e.g., plug vertices in current pose) +# and mesh surfaces (e.g., of socket mesh in current pose) +@wp.kernel +def get_interpen_dist( + queries: wp.array(dtype=wp.vec3), + mesh: wp.uint64, + interpen_dists: wp.array(dtype=wp.float32), +): + tid = wp.tid() + + # Declare arguments to wp.mesh_query_point() that will not be modified + q = queries[tid] # query point + max_dist = 1.5 # max distance on mesh from query point + + # Declare arguments to wp.mesh_query_point() that will be modified + sign = float( + 0.0 + ) # -1 if query point inside mesh; 0 if on mesh; +1 if outside mesh (NOTE: Mesh must be watertight!) + face_idx = int(0) # index of closest face + face_u = float(0.0) # barycentric u-coordinate of closest point + face_v = float(0.0) # barycentric v-coordinate of closest point + + # Get closest point on mesh to query point + closest_mesh_point_exists = wp.mesh_query_point(mesh, q, max_dist, sign, face_idx, face_u, face_v) + + # If point exists within max_dist + if closest_mesh_point_exists: + # Get 3D position of point on mesh given face index and barycentric coordinates + p = wp.mesh_eval_position(mesh, face_idx, face_u, face_v) + + # Get signed distance between query point and mesh point + delta = q - p + signed_dist = sign * wp.length(delta) + + # If signed distance is negative + if signed_dist < 0.0: + # Store interpenetration distance + interpen_dists[tid] = signed_dist diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_log_utils.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_log_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f46fcb3479bc606dd352b8fc27271c61c6677e9a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/automate_log_utils.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import h5py + + +def write_log_to_hdf5(held_asset_pose_log, fixed_asset_pose_log, success_log, eval_logging_filename): + with h5py.File(eval_logging_filename, "w") as hf: + hf.create_dataset("held_asset_pose", data=held_asset_pose_log.cpu().numpy()) + hf.create_dataset("fixed_asset_pose", data=fixed_asset_pose_log.cpu().numpy()) + hf.create_dataset("success", data=success_log.cpu().numpy()) + + +def load_log_from_hdf5(eval_logging_filename): + with h5py.File(eval_logging_filename, "r") as hf: + held_asset_pose = hf["held_asset_pose"][:] + fixed_asset_pose = hf["fixed_asset_pose"][:] + success = hf["success"][:] + + # held_asset_pose = torch.from_numpy(held_asset_pose).to(device) + # fixed_asset_pose = torch.from_numpy(fixed_asset_pose).to(device) + # success = torch.from_numpy(success).to(device) + + return held_asset_pose, fixed_asset_pose, success diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b454829eacac00c86232fb8c95155e5068fed8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env.py @@ -0,0 +1,872 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import json +import os + +import numpy as np +import torch + +import carb +import isaacsim.core.utils.torch as torch_utils + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs import DirectRLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, retrieve_file_path +from isaaclab.utils.math import axis_angle_from_quat + +from . import automate_algo_utils as automate_algo +from . import factory_control as fc +from .disassembly_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, DisassemblyEnvCfg + + +class DisassemblyEnv(DirectRLEnv): + cfg: DisassemblyEnvCfg + + def __init__(self, cfg: DisassemblyEnvCfg, render_mode: str | None = None, **kwargs): + # Update number of obs/states + cfg.observation_space = sum([OBS_DIM_CFG[obs] for obs in cfg.obs_order]) + cfg.state_space = sum([STATE_DIM_CFG[state] for state in cfg.state_order]) + self.cfg_task = cfg.tasks[cfg.task_name] + + super().__init__(cfg, render_mode, **kwargs) + + self._set_body_inertias() + self._init_tensors() + self._set_default_dynamics_parameters() + self._compute_intermediate_values(dt=self.physics_dt) + + # Get the gripper open width based on plug object bounding box + self.gripper_open_width = automate_algo.get_gripper_open_width( + self.cfg_task.assembly_dir + self.cfg_task.held_asset_cfg.obj_path + ) + + # initialized logging variables for disassembly paths + self._init_log_data_per_assembly() + + def _set_body_inertias(self): + """Note: this is to account for the asset_options.armature parameter in IGE.""" + inertias = self._robot.root_physx_view.get_inertias() + offset = torch.zeros_like(inertias) + offset[:, :, [0, 4, 8]] += 0.01 + new_inertias = inertias + offset + self._robot.root_physx_view.set_inertias(new_inertias, torch.arange(self.num_envs)) + + def _set_default_dynamics_parameters(self): + """Set parameters defining dynamic interactions.""" + self.default_gains = torch.tensor(self.cfg.ctrl.default_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + + self.pos_threshold = torch.tensor(self.cfg.ctrl.pos_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.rot_threshold = torch.tensor(self.cfg.ctrl.rot_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + + # Set masses and frictions. + self._set_friction(self._held_asset, self.cfg_task.held_asset_cfg.friction) + self._set_friction(self._fixed_asset, self.cfg_task.fixed_asset_cfg.friction) + self._set_friction(self._robot, self.cfg_task.robot_cfg.friction) + + def _set_friction(self, asset, value): + """Update material properties for a given asset.""" + materials = asset.root_physx_view.get_material_properties() + materials[..., 0] = value # Static friction. + materials[..., 1] = value # Dynamic friction. + env_ids = torch.arange(self.scene.num_envs, device="cpu") + asset.root_physx_view.set_material_properties(materials, env_ids) + + def _init_tensors(self): + """Initialize tensors once.""" + self.identity_quat = ( + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + + # Control targets. + self.ctrl_target_joint_pos = torch.zeros((self.num_envs, self._robot.num_joints), device=self.device) + self.ctrl_target_fingertip_midpoint_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.ctrl_target_fingertip_midpoint_quat = torch.zeros((self.num_envs, 4), device=self.device) + + # Fixed asset. + self.fixed_pos_action_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.fixed_pos_obs_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.init_fixed_pos_obs_noise = torch.zeros((self.num_envs, 3), device=self.device) + + # Held asset + held_base_x_offset = 0.0 + held_base_z_offset = 0.0 + + self.held_base_pos_local = torch.tensor([0.0, 0.0, 0.0], device=self.device).repeat((self.num_envs, 1)) + self.held_base_pos_local[:, 0] = held_base_x_offset + self.held_base_pos_local[:, 2] = held_base_z_offset + self.held_base_quat_local = self.identity_quat.clone().detach() + + self.held_base_pos = torch.zeros_like(self.held_base_pos_local) + self.held_base_quat = self.identity_quat.clone().detach() + + self.plug_grasps, self.disassembly_dists = self._load_assembly_info() + + # Load grasp pose from json files given assembly ID + # Grasp pose tensors + self.palm_to_finger_center = ( + torch.tensor([0.0, 0.0, -self.cfg_task.palm_to_finger_dist], device=self.device) + .unsqueeze(0) + .repeat(self.num_envs, 1) + ) + self.robot_to_gripper_quat = ( + torch.tensor([0.0, 1.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + self.plug_grasp_pos_local = self.plug_grasps[: self.num_envs, :3] + self.plug_grasp_quat_local = torch.roll(self.plug_grasps[: self.num_envs, 3:], -1, 1) + + # Computer body indices. + self.left_finger_body_idx = self._robot.body_names.index("panda_leftfinger") + self.right_finger_body_idx = self._robot.body_names.index("panda_rightfinger") + self.fingertip_body_idx = self._robot.body_names.index("panda_fingertip_centered") + + # Tensors for finite-differencing. + self.last_update_timestamp = 0.0 # Note: This is for finite differencing body velocities. + self.prev_fingertip_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.prev_fingertip_quat = self.identity_quat.clone() + self.prev_joint_pos = torch.zeros((self.num_envs, 7), device=self.device) + + # Keypoint tensors. + self.target_held_base_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.target_held_base_quat = self.identity_quat.clone().detach() + + # Used to compute target poses. + self.fixed_success_pos_local = torch.zeros((self.num_envs, 3), device=self.device) + self.fixed_success_pos_local[:, 2] = 0.0 + + self.ep_succeeded = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + self.ep_success_times = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + + def _load_assembly_info(self): + """Load grasp pose and disassembly distance for plugs in each environment.""" + + retrieve_file_path(self.cfg_task.plug_grasp_json, download_dir="./") + with open(os.path.basename(self.cfg_task.plug_grasp_json)) as f: + plug_grasp_dict = json.load(f) + plug_grasps = [plug_grasp_dict[f"asset_{self.cfg_task.assembly_id}"] for i in range(self.num_envs)] + + retrieve_file_path(self.cfg_task.disassembly_dist_json, download_dir="./") + with open(os.path.basename(self.cfg_task.disassembly_dist_json)) as f: + disassembly_dist_dict = json.load(f) + disassembly_dists = [disassembly_dist_dict[f"asset_{self.cfg_task.assembly_id}"] for i in range(self.num_envs)] + + return torch.as_tensor(plug_grasps).to(self.device), torch.as_tensor(disassembly_dists).to(self.device) + + def _setup_scene(self): + """Initialize simulation scene.""" + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg(), translation=(0.0, 0.0, -0.4)) + + # spawn a usd file of a table into the scene + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func( + "/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711) + ) + + self._robot = Articulation(self.cfg.robot) + self._fixed_asset = Articulation(self.cfg_task.fixed_asset) + # self._held_asset = Articulation(self.cfg_task.held_asset) + # self._fixed_asset = RigidObject(self.cfg_task.fixed_asset) + self._held_asset = RigidObject(self.cfg_task.held_asset) + + self.scene.clone_environments(copy_from_source=False) + self.scene.filter_collisions() + + self.scene.articulations["robot"] = self._robot + self.scene.articulations["fixed_asset"] = self._fixed_asset + # self.scene.articulations["held_asset"] = self._held_asset + # self.scene.rigid_objects["fixed_asset"] = self._fixed_asset + self.scene.rigid_objects["held_asset"] = self._held_asset + + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _compute_intermediate_values(self, dt): + """Get values computed from raw tensors. This includes adding noise.""" + # TODO: A lot of these can probably only be set once? + self.fixed_pos = self._fixed_asset.data.root_pos_w - self.scene.env_origins + self.fixed_quat = self._fixed_asset.data.root_quat_w + + self.held_pos = self._held_asset.data.root_pos_w - self.scene.env_origins + self.held_quat = self._held_asset.data.root_quat_w + + self.fingertip_midpoint_pos = self._robot.data.body_pos_w[:, self.fingertip_body_idx] - self.scene.env_origins + self.fingertip_midpoint_quat = self._robot.data.body_quat_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_linvel = self._robot.data.body_lin_vel_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_angvel = self._robot.data.body_ang_vel_w[:, self.fingertip_body_idx] + + jacobians = self._robot.root_physx_view.get_jacobians() + + self.left_finger_jacobian = jacobians[:, self.left_finger_body_idx - 1, 0:6, 0:7] + self.right_finger_jacobian = jacobians[:, self.right_finger_body_idx - 1, 0:6, 0:7] + self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 + self.arm_mass_matrix = self._robot.root_physx_view.get_generalized_mass_matrices()[:, 0:7, 0:7] + self.joint_pos = self._robot.data.joint_pos.clone() + self.joint_vel = self._robot.data.joint_vel.clone() + + # Compute pose of gripper goal and top of socket in socket frame + self.gripper_goal_quat, self.gripper_goal_pos = torch_utils.tf_combine( + self.fixed_quat, + self.fixed_pos, + self.plug_grasp_quat_local, + self.plug_grasp_pos_local, + ) + + self.gripper_goal_quat, self.gripper_goal_pos = torch_utils.tf_combine( + self.gripper_goal_quat, + self.gripper_goal_pos, + self.robot_to_gripper_quat, + self.palm_to_finger_center, + ) + + # Finite-differencing results in more reliable velocity estimates. + self.ee_linvel_fd = (self.fingertip_midpoint_pos - self.prev_fingertip_pos) / dt + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + + # Add state differences if velocity isn't being added. + rot_diff_quat = torch_utils.quat_mul( + self.fingertip_midpoint_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) + ) + rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) + rot_diff_aa = axis_angle_from_quat(rot_diff_quat) + self.ee_angvel_fd = rot_diff_aa / dt + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + joint_diff = self.joint_pos[:, 0:7] - self.prev_joint_pos + self.joint_vel_fd = joint_diff / dt + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + + # Keypoint tensors. + self.held_base_quat[:], self.held_base_pos[:] = torch_utils.tf_combine( + self.held_quat, self.held_pos, self.held_base_quat_local, self.held_base_pos_local + ) + self.target_held_base_quat[:], self.target_held_base_pos[:] = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, self.identity_quat, self.fixed_success_pos_local + ) + + self.last_update_timestamp = self._robot._data._sim_timestamp + + def _get_observations(self): + """Get actor/critic inputs using asymmetric critic.""" + + obs_dict = { + "joint_pos": self.joint_pos[:, 0:7], + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "fingertip_goal_pos": self.gripper_goal_pos, + "fingertip_goal_quat": self.gripper_goal_quat, + "delta_pos": self.gripper_goal_pos - self.fingertip_midpoint_pos, + } + + state_dict = { + "joint_pos": self.joint_pos[:, 0:7], + "joint_vel": self.joint_vel[:, 0:7], + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.fingertip_midpoint_linvel, + "ee_angvel": self.fingertip_midpoint_angvel, + "fingertip_goal_pos": self.gripper_goal_pos, + "fingertip_goal_quat": self.gripper_goal_quat, + "held_pos": self.held_pos, + "held_quat": self.held_quat, + "delta_pos": self.gripper_goal_pos - self.fingertip_midpoint_pos, + } + # obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order + ['prev_actions']] + obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order] + obs_tensors = torch.cat(obs_tensors, dim=-1) + + # state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order + ['prev_actions']] + state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order] + state_tensors = torch.cat(state_tensors, dim=-1) + + return {"policy": obs_tensors, "critic": state_tensors} + + def _reset_buffers(self, env_ids): + """Reset buffers.""" + self.ep_succeeded[env_ids] = 0 + + def _pre_physics_step(self, action): + """Apply policy actions with smoothing.""" + env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(env_ids) > 0: + self._reset_buffers(env_ids) + + def move_gripper_in_place(self, ctrl_target_gripper_dof_pos): + """Keep gripper in current position as gripper closes.""" + actions = torch.zeros((self.num_envs, 6), device=self.device) + ctrl_target_gripper_dof_pos = 0.0 + + # Interpret actions as target pos displacements and set pos target + pos_actions = actions[:, 0:3] * self.pos_threshold + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = actions[:, 3:6] + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1.0e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos + self.generate_ctrl_signals() + + def _apply_action(self): + """Apply actions for policy as delta targets from current position.""" + # Get current yaw for success checking. + _, _, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) + self.curr_yaw = torch.where(curr_yaw > np.deg2rad(235), curr_yaw - 2 * np.pi, curr_yaw) + + # Note: We use finite-differenced velocities for control and observations. + # Check if we need to re-compute velocities within the decimation loop. + if self.last_update_timestamp < self._robot._data._sim_timestamp: + self._compute_intermediate_values(dt=self.physics_dt) + + # Interpret actions as target pos displacements and set pos target + pos_actions = self.actions[:, 0:3] * self.pos_threshold + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = self.actions[:, 3:6] + if self.cfg_task.unidirectional_rot: + rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0] + rot_actions = rot_actions * self.rot_threshold + + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + # To speed up learning, never allow the policy to move more than 5cm away from the base. + delta_pos = self.ctrl_target_fingertip_midpoint_pos - self.fixed_pos_action_frame + pos_error_clipped = torch.clip( + delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1] + ) + self.ctrl_target_fingertip_midpoint_pos = self.fixed_pos_action_frame + pos_error_clipped + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 # Restrict actions to be upright. + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = 0.0 + self.generate_ctrl_signals() + + def _set_gains(self, prop_gains, rot_deriv_scale=1.0): + """Set robot gains using critical damping.""" + self.task_prop_gains = prop_gains + self.task_deriv_gains = 2 * torch.sqrt(prop_gains) + self.task_deriv_gains[:, 3:6] /= rot_deriv_scale + + def generate_ctrl_signals(self): + """Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm).""" + self.joint_torque, self.applied_wrench = fc.compute_dof_torque( + cfg=self.cfg, + dof_pos=self.joint_pos, + dof_vel=self.joint_vel, # _fd, + fingertip_midpoint_pos=self.fingertip_midpoint_pos, + fingertip_midpoint_quat=self.fingertip_midpoint_quat, + fingertip_midpoint_linvel=self.ee_linvel_fd, + fingertip_midpoint_angvel=self.ee_angvel_fd, + jacobian=self.fingertip_midpoint_jacobian, + arm_mass_matrix=self.arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat, + task_prop_gains=self.task_prop_gains, + task_deriv_gains=self.task_deriv_gains, + device=self.device, + ) + + # set target for gripper joints to use GYM's PD controller + self.ctrl_target_joint_pos[:, 7:9] = self.ctrl_target_gripper_dof_pos + self.joint_torque[:, 7:9] = 0.0 + + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + self._robot.set_joint_effort_target(self.joint_torque) + + def _get_dones(self): + """Update intermediate values used for rewards and observations.""" + self._compute_intermediate_values(dt=self.physics_dt) + time_out = self.episode_length_buf >= self.max_episode_length - 1 + + if time_out[0]: + self.close_gripper(env_ids=np.array(range(self.num_envs)).reshape(-1)) + self._disassemble_plug_from_socket() + + if_intersect = (self.held_pos[:, 2] < self.fixed_pos[:, 2] + self.disassembly_dists).cpu().numpy() + success_env_ids = np.argwhere(if_intersect == 0).reshape(-1) + + self._log_robot_state(success_env_ids) + self._log_object_state(success_env_ids) + self._save_log_traj() + + return time_out, time_out + + def _get_rewards(self): + """Update rewards and compute success statistics.""" + # Get successful and failed envs at current timestep + + rew_buf = self._update_rew_buf() + return rew_buf + + def _update_rew_buf(self): + """Compute reward at current timestep.""" + return torch.zeros((self.num_envs,), device=self.device) + + def _reset_idx(self, env_ids): + """ + We assume all envs will always be reset at the same time. + """ + super()._reset_idx(env_ids) + + self._set_assets_to_default_pose(env_ids) + self._set_franka_to_default_pose(joints=self.cfg.ctrl.reset_joints, env_ids=env_ids) + self.step_sim_no_action() + + self.randomize_initial_state(env_ids) + + prev_fingertip_midpoint_pos = (self.fingertip_midpoint_pos - self.gripper_goal_pos).unsqueeze( + 1 + ) # (num_envs, 1, 3) + self.prev_fingertip_midpoint_pos = torch.repeat_interleave( + prev_fingertip_midpoint_pos, self.cfg_task.num_point_robot_traj, dim=1 + ) # (num_envs, num_point_robot_traj, 3) + self._init_log_data_per_episode() + + def _set_assets_to_default_pose(self, env_ids): + """Move assets to default pose before randomization.""" + held_state = self._held_asset.data.default_root_state.clone()[env_ids] + held_state[:, 0:3] += self.scene.env_origins[env_ids] + held_state[:, 7:] = 0.0 + self._held_asset.write_root_pose_to_sim(held_state[:, 0:7], env_ids=env_ids) + self._held_asset.write_root_velocity_to_sim(held_state[:, 7:], env_ids=env_ids) + self._held_asset.reset() + + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + fixed_state[:, 0:3] += self.scene.env_origins[env_ids] + fixed_state[:, 7:] = 0.0 + self._fixed_asset.write_root_pose_to_sim(fixed_state[:, 0:7], env_ids=env_ids) + self._fixed_asset.write_root_velocity_to_sim(fixed_state[:, 7:], env_ids=env_ids) + self._fixed_asset.reset() + + def _move_gripper_to_grasp_pose(self, env_ids): + """Define grasp pose for plug and move gripper to pose.""" + + gripper_goal_quat, gripper_goal_pos = torch_utils.tf_combine( + self.held_quat, + self.held_pos, + self.plug_grasp_quat_local, + self.plug_grasp_pos_local, + ) + + gripper_goal_quat, gripper_goal_pos = torch_utils.tf_combine( + gripper_goal_quat, + gripper_goal_pos, + self.robot_to_gripper_quat, + self.palm_to_finger_center, + ) + + # Set target_pos + self.ctrl_target_fingertip_midpoint_pos = gripper_goal_pos.clone() + + # Set target rot + self.ctrl_target_fingertip_midpoint_quat = gripper_goal_quat.clone() + + self.set_pos_inverse_kinematics(env_ids) + self.step_sim_no_action() + + def set_pos_inverse_kinematics(self, env_ids): + """Set robot joint position using DLS IK.""" + ik_time = 0.0 + while ik_time < 0.50: + # Compute error to target. + pos_error, axis_angle_error = fc.get_pose_error( + fingertip_midpoint_pos=self.fingertip_midpoint_pos[env_ids], + fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos[env_ids], + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat[env_ids], + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + + # Solve DLS problem. + delta_dof_pos = fc._get_delta_dof_pos( + delta_pose=delta_hand_pose, + ik_method="dls", + jacobian=self.fingertip_midpoint_jacobian[env_ids], + device=self.device, + ) + self.joint_pos[env_ids, 0:7] += delta_dof_pos[:, 0:7] + self.joint_vel[env_ids, :] = torch.zeros_like(self.joint_pos[env_ids,]) + + self.ctrl_target_joint_pos[env_ids, 0:7] = self.joint_pos[env_ids, 0:7] + # Update dof state. + self._robot.write_joint_state_to_sim(self.joint_pos, self.joint_vel) + self._robot.reset() + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + + # Simulate and update tensors. + self.step_sim_no_action() + ik_time += self.physics_dt + + return pos_error, axis_angle_error + + def _move_gripper_to_eef_pose(self, env_ids, goal_pos, goal_quat, sim_steps, if_log=False): + for _ in range(sim_steps): + if if_log: + self._log_robot_state_per_timestep() + + # Compute error to target. + pos_error, axis_angle_error = fc.get_pose_error( + fingertip_midpoint_pos=self.fingertip_midpoint_pos[env_ids], + fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], + ctrl_target_fingertip_midpoint_pos=goal_pos[env_ids], + ctrl_target_fingertip_midpoint_quat=goal_quat[env_ids], + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + self.actions *= 0.0 + self.actions[env_ids, :6] = delta_hand_pose + + is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() + # perform physics stepping + for _ in range(self.cfg.decimation): + self._sim_step_counter += 1 + # set actions into buffers + self._apply_action() + # set actions into simulator + self.scene.write_data_to_sim() + # simulate + self.sim.step(render=False) + # render between steps only if the GUI or an RTX sensor needs it + # note: we assume the render interval to be the shortest accepted rendering interval. + # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. + if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: + self.sim.render() + # update buffers at sim dt + self.scene.update(dt=self.physics_dt) + + # Simulate and update tensors. + self.step_sim_no_action() + + def _set_franka_to_default_pose(self, joints, env_ids): + """Return Franka to its default joint position.""" + # gripper_width = self.cfg_task.held_asset_cfg.diameter / 2 * 1.25 + # gripper_width = self.cfg_task.hand_width_max / 3.0 + gripper_width = self.gripper_open_width + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_pos[:, 7:] = gripper_width # MIMIC + joint_pos[:, :7] = torch.tensor(joints, device=self.device)[None, :] + joint_vel = torch.zeros_like(joint_pos) + joint_effort = torch.zeros_like(joint_pos) + self.ctrl_target_joint_pos[env_ids, :] = joint_pos + self._robot.set_joint_position_target(self.ctrl_target_joint_pos[env_ids], env_ids=env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + self._robot.reset() + self._robot.set_joint_effort_target(joint_effort, env_ids=env_ids) + + self.step_sim_no_action() + + def step_sim_no_action(self): + """Step the simulation without an action. Used for resets.""" + self.scene.write_data_to_sim() + self.sim.step(render=True) + self.scene.update(dt=self.physics_dt) + self._compute_intermediate_values(dt=self.physics_dt) + + def randomize_fixed_initial_state(self, env_ids): + # (1.) Randomize fixed asset pose. + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + # (1.a.) Position + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] + fixed_asset_init_pos_rand = torch.tensor( + self.cfg_task.fixed_asset_init_pos_noise, dtype=torch.float32, device=self.device + ) + fixed_pos_init_rand = fixed_pos_init_rand @ torch.diag(fixed_asset_init_pos_rand) + fixed_state[:, 0:3] += fixed_pos_init_rand + self.scene.env_origins[env_ids] + fixed_state[:, 2] += self.cfg_task.fixed_asset_z_offset + + # (1.b.) Orientation + fixed_orn_init_yaw = np.deg2rad(self.cfg_task.fixed_asset_init_orn_deg) + fixed_orn_yaw_range = np.deg2rad(self.cfg_task.fixed_asset_init_orn_range_deg) + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_orn_euler = fixed_orn_init_yaw + fixed_orn_yaw_range * rand_sample + fixed_orn_euler[:, 0:2] = 0.0 # Only change yaw. + fixed_orn_quat = torch_utils.quat_from_euler_xyz( + fixed_orn_euler[:, 0], fixed_orn_euler[:, 1], fixed_orn_euler[:, 2] + ) + fixed_state[:, 3:7] = fixed_orn_quat + # (1.c.) Velocity + fixed_state[:, 7:] = 0.0 # vel + # (1.d.) Update values. + self._fixed_asset.write_root_state_to_sim(fixed_state, env_ids=env_ids) + self._fixed_asset.reset() + + # (1.e.) Noisy position observation. + fixed_asset_pos_noise = torch.randn((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_asset_pos_rand = torch.tensor(self.cfg.obs_rand.fixed_asset_pos, dtype=torch.float32, device=self.device) + fixed_asset_pos_noise = fixed_asset_pos_noise @ torch.diag(fixed_asset_pos_rand) + self.init_fixed_pos_obs_noise[:] = fixed_asset_pos_noise + + self.step_sim_no_action() + + def randomize_held_initial_state(self, env_ids, pre_grasp): + # Set plug pos to assembled state + held_state = self._held_asset.data.default_root_state.clone() + held_state[env_ids, 0:3] = self.fixed_pos[env_ids].clone() + self.scene.env_origins[env_ids] + held_state[env_ids, 3:7] = self.fixed_quat[env_ids].clone() + held_state[env_ids, 7:] = 0.0 + + self._held_asset.write_root_state_to_sim(held_state) + self._held_asset.reset() + + self.step_sim_no_action() + + def close_gripper(self, env_ids): + # Close hand + # Set gains to use for quick resets. + reset_task_prop_gains = torch.tensor(self.cfg.ctrl.reset_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + reset_rot_deriv_scale = self.cfg.ctrl.reset_rot_deriv_scale + self._set_gains(reset_task_prop_gains, reset_rot_deriv_scale) + + self.step_sim_no_action() + + grasp_time = 0.0 + while grasp_time < 0.25: + self.ctrl_target_joint_pos[env_ids, 7:] = 0.0 # Close gripper. + self.ctrl_target_gripper_dof_pos = 0.0 + self.move_gripper_in_place(ctrl_target_gripper_dof_pos=0.0) + self.step_sim_no_action() + grasp_time += self.sim.get_physics_dt() + + def randomize_initial_state(self, env_ids): + """Randomize initial state and perform any episode-level randomization.""" + # Disable gravity. + physics_sim_view = sim_utils.SimulationContext.instance().physics_sim_view + physics_sim_view.set_gravity(carb.Float3(0.0, 0.0, 0.0)) + + self.randomize_fixed_initial_state(env_ids) + + # Compute the frame on the bolt that would be used as observation: fixed_pos_obs_frame + # For example, the tip of the bolt can be used as the observation frame + fixed_tip_pos_local = torch.zeros_like(self.fixed_pos) + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height + + _, fixed_tip_pos = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, self.identity_quat, fixed_tip_pos_local + ) + self.fixed_pos_obs_frame[:] = fixed_tip_pos + + self.randomize_held_initial_state(env_ids, pre_grasp=True) + + self._move_gripper_to_grasp_pose(env_ids) + + self.randomize_held_initial_state(env_ids, pre_grasp=False) + + self.close_gripper(env_ids) + + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + # Set initial actions to involve no-movement. Needed for EMA/correct penalties. + self.actions = torch.zeros_like(self.actions) + self.prev_actions = torch.zeros_like(self.actions) + self.fixed_pos_action_frame[:] = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + + # Zero initial velocity. + self.ee_angvel_fd[:, :] = 0.0 + self.ee_linvel_fd[:, :] = 0.0 + + # Set initial gains for the episode. + self._set_gains(self.default_gains) + + physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) + + def _disassemble_plug_from_socket(self): + """Lift plug from socket till disassembly and then randomize end-effector pose.""" + + if_intersect = np.ones(self.num_envs, dtype=np.float32) + + env_ids = np.argwhere(if_intersect == 1).reshape(-1) + self._lift_gripper(self.disassembly_dists * 3.0, self.cfg_task.disassemble_sim_steps, env_ids) + + self.step_sim_no_action() + + if_intersect = (self.held_pos[:, 2] < self.fixed_pos[:, 2] + self.disassembly_dists).cpu().numpy() + env_ids = np.argwhere(if_intersect == 0).reshape(-1) + self._randomize_gripper_pose(self.cfg_task.move_gripper_sim_steps, env_ids) + + def _lift_gripper(self, lift_distance, sim_steps, env_ids=None): + """Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode).""" + + ctrl_tgt_pos = torch.empty_like(self.fingertip_midpoint_pos).copy_(self.fingertip_midpoint_pos) + ctrl_tgt_quat = torch.empty_like(self.fingertip_midpoint_quat).copy_(self.fingertip_midpoint_quat) + ctrl_tgt_pos[:, 2] += lift_distance + if len(env_ids) == 0: + env_ids = np.array(range(self.num_envs)).reshape(-1) + + self._move_gripper_to_eef_pose(env_ids, ctrl_tgt_pos, ctrl_tgt_quat, sim_steps, if_log=True) + + def _randomize_gripper_pose(self, sim_steps, env_ids): + """Move gripper to random pose.""" + + ctrl_tgt_pos = torch.empty_like(self.gripper_goal_pos).copy_(self.gripper_goal_pos) + ctrl_tgt_pos[:, 2] += self.cfg_task.gripper_rand_z_offset + + # ctrl_tgt_pos = torch.empty_like(self.fingertip_midpoint_pos).copy_(self.fingertip_midpoint_pos) + + fingertip_centered_pos_noise = 2 * ( + torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5 + ) # [-1, 1] + fingertip_centered_pos_noise = fingertip_centered_pos_noise @ torch.diag( + torch.tensor(self.cfg_task.gripper_rand_pos_noise, device=self.device) + ) + ctrl_tgt_pos += fingertip_centered_pos_noise + + # Set target rot + ctrl_target_fingertip_centered_euler = ( + torch.tensor(self.cfg_task.fingertip_centered_rot_initial, device=self.device) + .unsqueeze(0) + .repeat(self.num_envs, 1) + ) + + fingertip_centered_rot_noise = 2 * ( + torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5 + ) # [-1, 1] + fingertip_centered_rot_noise = fingertip_centered_rot_noise @ torch.diag( + torch.tensor(self.cfg_task.gripper_rand_rot_noise, device=self.device) + ) + ctrl_target_fingertip_centered_euler += fingertip_centered_rot_noise + ctrl_tgt_quat = torch_utils.quat_from_euler_xyz( + ctrl_target_fingertip_centered_euler[:, 0], + ctrl_target_fingertip_centered_euler[:, 1], + ctrl_target_fingertip_centered_euler[:, 2], + ) + + # ctrl_tgt_quat = torch.empty_like(self.fingertip_midpoint_quat).copy_(self.fingertip_midpoint_quat) + + self._move_gripper_to_eef_pose(env_ids, ctrl_tgt_pos, ctrl_tgt_quat, sim_steps, if_log=True) + + def _init_log_data_per_assembly(self): + self.log_assembly_id = [] + self.log_plug_pos = [] + self.log_plug_quat = [] + self.log_init_plug_pos = [] + self.log_init_plug_quat = [] + self.log_plug_grasp_pos = [] + self.log_plug_grasp_quat = [] + self.log_fingertip_centered_pos = [] + self.log_fingertip_centered_quat = [] + self.log_arm_dof_pos = [] + + def _init_log_data_per_episode(self): + self.log_fingertip_centered_pos_traj = [] + self.log_fingertip_centered_quat_traj = [] + self.log_arm_dof_pos_traj = [] + self.log_plug_pos_traj = [] + self.log_plug_quat_traj = [] + + self.init_plug_grasp_pos = self.gripper_goal_pos.clone().detach() + self.init_plug_grasp_quat = self.gripper_goal_quat.clone().detach() + self.init_plug_pos = self.held_pos.clone().detach() + self.init_plug_quat = self.held_quat.clone().detach() + + def _log_robot_state(self, env_ids): + self.log_plug_pos += torch.stack(self.log_plug_pos_traj, dim=1)[env_ids].cpu().tolist() + self.log_plug_quat += torch.stack(self.log_plug_quat_traj, dim=1)[env_ids].cpu().tolist() + self.log_arm_dof_pos += torch.stack(self.log_arm_dof_pos_traj, dim=1)[env_ids].cpu().tolist() + self.log_fingertip_centered_pos += ( + torch.stack(self.log_fingertip_centered_pos_traj, dim=1)[env_ids].cpu().tolist() + ) + self.log_fingertip_centered_quat += ( + torch.stack(self.log_fingertip_centered_quat_traj, dim=1)[env_ids].cpu().tolist() + ) + + def _log_robot_state_per_timestep(self): + self.log_plug_pos_traj.append(self.held_pos.clone().detach()) + self.log_plug_quat_traj.append(self.held_quat.clone().detach()) + self.log_arm_dof_pos_traj.append(self.joint_pos[:, 0:7].clone().detach()) + self.log_fingertip_centered_pos_traj.append(self.fingertip_midpoint_pos.clone().detach()) + self.log_fingertip_centered_quat_traj.append(self.fingertip_midpoint_quat.clone().detach()) + + def _log_object_state(self, env_ids): + self.log_plug_grasp_pos += self.init_plug_grasp_pos[env_ids].cpu().tolist() + self.log_plug_grasp_quat += self.init_plug_grasp_quat[env_ids].cpu().tolist() + self.log_init_plug_pos += self.init_plug_pos[env_ids].cpu().tolist() + self.log_init_plug_quat += self.init_plug_quat[env_ids].cpu().tolist() + + def _save_log_traj(self): + if len(self.log_arm_dof_pos) > self.cfg_task.num_log_traj: + log_item = [] + for i in range(self.cfg_task.num_log_traj): + curr_dict = dict({}) + curr_dict["fingertip_centered_pos"] = self.log_fingertip_centered_pos[i] + curr_dict["fingertip_centered_quat"] = self.log_fingertip_centered_quat[i] + curr_dict["arm_dof_pos"] = self.log_arm_dof_pos[i] + curr_dict["plug_grasp_pos"] = self.log_plug_grasp_pos[i] + curr_dict["plug_grasp_quat"] = self.log_plug_grasp_quat[i] + curr_dict["init_plug_pos"] = self.log_init_plug_pos[i] + curr_dict["init_plug_quat"] = self.log_init_plug_quat[i] + curr_dict["plug_pos"] = self.log_plug_pos[i] + curr_dict["plug_quat"] = self.log_plug_quat[i] + + log_item.append(curr_dict) + + log_filename = os.path.join( + os.getcwd(), self.cfg_task.disassembly_dir, self.cfg_task.assembly_id + "_disassemble_traj.json" + ) + + with open(log_filename, "w+") as out_file: + json.dump(log_item, out_file, indent=6) + + print(f"Trajectory collection complete! Collected {len(self.log_arm_dof_pos)} trajectories!") + exit(0) + else: + print( + f"Collected {len(self.log_arm_dof_pos)} trajectories so far (target: > {self.cfg_task.num_log_traj})." + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9d17f35975876c8e0cfbc834e4c60ea261e837d7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_env_cfg.py @@ -0,0 +1,196 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass + +from .disassembly_tasks_cfg import ASSET_DIR, Extraction + +OBS_DIM_CFG = { + "joint_pos": 7, + "fingertip_pos": 3, + "fingertip_quat": 4, + "fingertip_goal_pos": 3, + "fingertip_goal_quat": 4, + "delta_pos": 3, +} + +STATE_DIM_CFG = { + "joint_pos": 7, + "joint_vel": 7, + "fingertip_pos": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "fingertip_goal_pos": 3, + "fingertip_goal_quat": 4, + "held_pos": 3, + "held_quat": 4, + "delta_pos": 3, +} + + +@configclass +class ObsRandCfg: + fixed_asset_pos = [0.001, 0.001, 0.001] + + +@configclass +class CtrlCfg: + ema_factor = 0.2 + + pos_action_bounds = [0.1, 0.1, 0.1] + rot_action_bounds = [0.01, 0.01, 0.01] + + pos_action_threshold = [0.01, 0.01, 0.01] + rot_action_threshold = [0.01, 0.01, 0.01] + + reset_joints = [0.0, 0.0, 0.0, -1.870, 0.0, 1.8675, 0.785398] + reset_task_prop_gains = [1000, 1000, 1000, 50, 50, 50] + # reset_rot_deriv_scale = 1.0 + # default_task_prop_gains = [1000, 1000, 1000, 50, 50, 50] + # reset_task_prop_gains = [300, 300, 300, 20, 20, 20] + reset_rot_deriv_scale = 10.0 + default_task_prop_gains = [100, 100, 100, 30, 30, 30] + + # Null space parameters. + default_dof_pos_tensor = [0.0, 0.0, 0.0, -1.870, 0.0, 1.8675, 0.785398] + kp_null = 10.0 + kd_null = 6.3246 + + +@configclass +class DisassemblyEnvCfg(DirectRLEnvCfg): + decimation = 8 + action_space = 6 + # num_*: will be overwritten to correspond to obs_order, state_order. + observation_space = 24 + state_space = 44 + obs_order: list = [ + "joint_pos", + "fingertip_pos", + "fingertip_quat", + "fingertip_goal_pos", + "fingertip_goal_quat", + "delta_pos", + ] + state_order: list = [ + "joint_pos", + "joint_vel", + "fingertip_pos", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "fingertip_goal_pos", + "fingertip_goal_quat", + "held_pos", + "held_quat", + "delta_pos", + ] + + task_name: str = "extraction" # peg_insertion, gear_meshing, nut_threading + tasks: dict = {"extraction": Extraction()} + obs_rand: ObsRandCfg = ObsRandCfg() + ctrl: CtrlCfg = CtrlCfg() + + # episode_length_s = 10.0 # Probably need to override. + episode_length_s = 5.0 + sim: SimulationCfg = SimulationCfg( + device="cuda:0", + dt=1 / 120, + gravity=(0.0, 0.0, -9.81), + physx=PhysxCfg( + solver_type=1, + max_position_iteration_count=192, # Important to avoid interpenetration. + max_velocity_iteration_count=1, + bounce_threshold_velocity=0.2, + friction_offset_threshold=0.01, + friction_correlation_distance=0.00625, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + gpu_max_num_partitions=1, # Important for stable simulation. + ), + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + ) + + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=128, env_spacing=2.0) + + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ASSET_DIR}/franka_mimic.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 0.00871, + "panda_joint2": -0.10368, + "panda_joint3": -0.00794, + "panda_joint4": -1.49139, + "panda_joint5": -0.00083, + "panda_joint6": 1.38774, + "panda_joint7": 0.0, + "panda_finger_joint2": 0.04, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + actuators={ + "panda_arm1": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=87, + velocity_limit=124.6, + ), + "panda_arm2": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=12, + velocity_limit=149.5, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint[1-2]"], + effort_limit=40.0, + velocity_limit=0.04, + stiffness=7500.0, + damping=173.0, + friction=0.1, + armature=0.0, + ), + }, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_tasks_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_tasks_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d21bd0166e5cf93161f25da16170f3b67042a050 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_tasks_cfg.py @@ -0,0 +1,220 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +ASSET_DIR = f"{ISAACLAB_NUCLEUS_DIR}/AutoMate" + +OBS_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, +} + +STATE_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "joint_pos": 7, + "held_pos": 3, + "held_pos_rel_fixed": 3, + "held_quat": 4, + "fixed_pos": 3, + "fixed_quat": 4, + "task_prop_gains": 6, + "ema_factor": 1, + "pos_threshold": 3, + "rot_threshold": 3, +} + + +@configclass +class FixedAssetCfg: + usd_path: str = "" + diameter: float = 0.0 + height: float = 0.0 + base_height: float = 0.0 # Used to compute held asset CoM. + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class HeldAssetCfg: + usd_path: str = "" + diameter: float = 0.0 # Used for gripper width. + height: float = 0.0 + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class RobotCfg: + robot_usd: str = "" + franka_fingerpad_length: float = 0.017608 + friction: float = 0.75 + + +@configclass +class DisassemblyTask: + robot_cfg: RobotCfg = RobotCfg() + name: str = "" + duration_s = 5.0 + + fixed_asset_cfg: FixedAssetCfg = FixedAssetCfg() + held_asset_cfg: HeldAssetCfg = HeldAssetCfg() + asset_size: float = 0.0 + + # palm_to_finger_dist: float = 0.1034 + palm_to_finger_dist: float = 0.1134 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 2.356] + hand_init_orn_noise: list = [0.0, 0.0, 1.57] + + # Action + unidirectional_rot: bool = False + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 10.0 + + num_point_robot_traj: int = 10 # number of waypoints included in the end-effector trajectory + + +@configclass +class Peg8mm(HeldAssetCfg): + usd_path = "plug.usd" + obj_path = "plug.obj" + diameter = 0.007986 + height = 0.050 + mass = 0.019 + + +@configclass +class Hole8mm(FixedAssetCfg): + usd_path = "socket.usd" + obj_path = "socket.obj" + diameter = 0.0081 + height = 0.050896 + base_height = 0.0 + + +@configclass +class Extraction(DisassemblyTask): + name = "extraction" + + assembly_id = "00015" + assembly_dir = f"{ASSET_DIR}/{assembly_id}/" + disassembly_dir = "disassembly_dir" + num_log_traj = 100 + + fixed_asset_cfg = Hole8mm() + held_asset_cfg = Peg8mm() + asset_size = 8.0 + duration_s = 10.0 + + plug_grasp_json = f"{ASSET_DIR}/plug_grasps.json" + disassembly_dist_json = f"{ASSET_DIR}/disassembly_dist.json" + + move_gripper_sim_steps = 64 + disassemble_sim_steps = 64 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.047] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 0.0] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] + hand_width_max: float = 0.080 # maximum opening width of gripper + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 10.0 + fixed_asset_z_offset: float = 0.1435 + + fingertip_centered_pos_initial: list = [ + 0.0, + 0.0, + 0.2, + ] # initial position of midpoint between fingertips above table + fingertip_centered_rot_initial: list = [3.141593, 0.0, 0.0] # initial rotation of fingertips (Euler) + gripper_rand_pos_noise: list = [0.05, 0.05, 0.05] + gripper_rand_rot_noise: list = [0.174533, 0.174533, 0.174533] # +-10 deg for roll/pitch/yaw + gripper_rand_z_offset: float = 0.05 + + fixed_asset: ArticulationCfg = ArticulationCfg( + # fixed_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{assembly_dir}{fixed_asset_cfg.usd_path}", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + fix_root_link=True, # add this so the fixed asset is set to have a fixed base + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + # init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), + rot=(1.0, 0.0, 0.0, 0.0), + joint_pos={}, + joint_vel={}, + ), + actuators={}, + ) + # held_asset: ArticulationCfg = ArticulationCfg( + held_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{assembly_dir}{held_asset_cfg.usd_path}", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + # init_state=ArticulationCfg.InitialStateCfg( + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + # joint_pos={}, + # joint_vel={} + ), + # actuators={} + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/factory_control.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/factory_control.py new file mode 100644 index 0000000000000000000000000000000000000000..0e51b6e41f6cdd24ec04b42a8a4baf813f607b95 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/factory_control.py @@ -0,0 +1,196 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Factory: control module. + +Imported by base, environment, and task classes. Not directly executed. +""" + +import math + +import torch + +import isaacsim.core.utils.torch as torch_utils + +from isaaclab.utils.math import axis_angle_from_quat + + +def compute_dof_torque( + cfg, + dof_pos, + dof_vel, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + fingertip_midpoint_linvel, + fingertip_midpoint_angvel, + jacobian, + arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + task_prop_gains, + task_deriv_gains, + device, +): + """Compute Franka DOF torque to move fingertips towards target pose.""" + # References: + # 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + # 2) Modern Robotics + + num_envs = cfg.scene.num_envs + dof_torque = torch.zeros((num_envs, dof_pos.shape[1]), device=device) + task_wrench = torch.zeros((num_envs, 6), device=device) + + pos_error, axis_angle_error = get_pose_error( + fingertip_midpoint_pos=fingertip_midpoint_pos, + fingertip_midpoint_quat=fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) + + # Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98) + task_wrench_motion = _apply_task_space_gains( + delta_fingertip_pose=delta_fingertip_pose, + fingertip_midpoint_linvel=fingertip_midpoint_linvel, + fingertip_midpoint_angvel=fingertip_midpoint_angvel, + task_prop_gains=task_prop_gains, + task_deriv_gains=task_deriv_gains, + ) + task_wrench += task_wrench_motion + + # Set tau = J^T * tau, i.e., map tau into joint space as desired + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + dof_torque[:, 0:7] = (jacobian_T @ task_wrench.unsqueeze(-1)).squeeze(-1) + + # adapted from roboticsproceedings.org/rss07/p31.pdf + + # useful tensors + arm_mass_matrix_inv = torch.inverse(arm_mass_matrix) + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + arm_mass_matrix_task = torch.inverse( + jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T + ) # ETH eq. 3.86; geometric Jacobian is assumed + j_eef_inv = arm_mass_matrix_task @ jacobian @ arm_mass_matrix_inv + default_dof_pos_tensor = torch.tensor(cfg.ctrl.default_dof_pos_tensor, device=device).repeat((num_envs, 1)) + # nullspace computation + distance_to_default_dof_pos = default_dof_pos_tensor - dof_pos[:, :7] + distance_to_default_dof_pos = (distance_to_default_dof_pos + math.pi) % ( + 2 * math.pi + ) - math.pi # normalize to [-pi, pi] + u_null = cfg.ctrl.kd_null * -dof_vel[:, :7] + cfg.ctrl.kp_null * distance_to_default_dof_pos + u_null = arm_mass_matrix @ u_null.unsqueeze(-1) + torque_null = (torch.eye(7, device=device).unsqueeze(0) - torch.transpose(jacobian, 1, 2) @ j_eef_inv) @ u_null + dof_torque[:, 0:7] += torque_null.squeeze(-1) + + # TODO: Verify it's okay to no longer do gripper control here. + dof_torque = torch.clamp(dof_torque, min=-100.0, max=100.0) + return dof_torque, task_wrench + + +def get_pose_error( + fingertip_midpoint_pos, + fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + jacobian_type, + rot_error_type, +): + """Compute task-space error between target Franka fingertip pose and current pose.""" + # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + + # Compute pos error + pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos + + # Compute rot error + if jacobian_type == "geometric": # See example 2.9.8; note use of J_g and transformation between rotation vectors + # Compute quat error (i.e., difference quat) + # Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html + + # Check for shortest path using quaternion dot product. + quat_dot = (ctrl_target_fingertip_midpoint_quat * fingertip_midpoint_quat).sum(dim=1, keepdim=True) + ctrl_target_fingertip_midpoint_quat = torch.where( + quat_dot.expand(-1, 4) >= 0, ctrl_target_fingertip_midpoint_quat, -ctrl_target_fingertip_midpoint_quat + ) + + fingertip_midpoint_quat_norm = torch_utils.quat_mul( + fingertip_midpoint_quat, torch_utils.quat_conjugate(fingertip_midpoint_quat) + )[:, 0] # scalar component + fingertip_midpoint_quat_inv = torch_utils.quat_conjugate( + fingertip_midpoint_quat + ) / fingertip_midpoint_quat_norm.unsqueeze(-1) + quat_error = torch_utils.quat_mul(ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv) + + # Convert to axis-angle error + axis_angle_error = axis_angle_from_quat(quat_error) + + if rot_error_type == "quat": + return pos_error, quat_error + elif rot_error_type == "axis_angle": + return pos_error, axis_angle_error + else: + raise ValueError(f"Unsupported rotation error type: {rot_error_type}. Valid: 'quat', 'axis_angle'.") + + +def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device): + """Get delta Franka DOF position from delta pose using specified IK method.""" + # References: + # 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf + # 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47) # noqa: E501 + + if ik_method == "pinv": # Jacobian pseudoinverse + k_val = 1.0 + jacobian_pinv = torch.linalg.pinv(jacobian) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "trans": # Jacobian transpose + k_val = 1.0 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "dls": # damped least squares (Levenberg-Marquardt) + lambda_val = 0.1 # 0.1 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + lambda_matrix = (lambda_val**2) * torch.eye(n=jacobian.shape[1], device=device) + delta_dof_pos = jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "svd": # adaptive SVD + k_val = 1.0 + U, S, Vh = torch.linalg.svd(jacobian) + S_inv = 1.0 / S + min_singular_value = 1.0e-5 + S_inv = torch.where(min_singular_value < S, S_inv, torch.zeros_like(S_inv)) + jacobian_pinv = ( + torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2) + ) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + return delta_dof_pos + + +def _apply_task_space_gains( + delta_fingertip_pose, fingertip_midpoint_linvel, fingertip_midpoint_angvel, task_prop_gains, task_deriv_gains +): + """Interpret PD gains as task-space gains. Apply to task-space error.""" + + task_wrench = torch.zeros_like(delta_fingertip_pose) + + # Apply gains to lin error components + lin_error = delta_fingertip_pose[:, 0:3] + task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + task_deriv_gains[:, 0:3] * ( + 0.0 - fingertip_midpoint_linvel + ) + + # Apply gains to rot error components + rot_error = delta_fingertip_pose[:, 3:6] + task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + task_deriv_gains[:, 3:6] * ( + 0.0 - fingertip_midpoint_angvel + ) + return task_wrench diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/industreal_algo_utils.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/industreal_algo_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c324eb46f46f0f6a93532219f21a039002fd0d77 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/industreal_algo_utils.py @@ -0,0 +1,378 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Copyright (c) 2023, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +"""IndustReal: algorithms module. + +Contains functions that implement: + +- Simulation-Aware Policy Update (SAPU) +- SDF-Based Reward +- Sampling-Based Curriculum (SBC) + +Not intended to be executed as a standalone script. +""" + +# Force garbage collection for large arrays +import gc +import os + +import numpy as np + +# from pysdf import SDF +import torch +import trimesh + +# from urdfpy import URDF +import warp as wp +from trimesh.exchange.load import load + +from isaaclab.utils.assets import retrieve_file_path + +""" +Simulation-Aware Policy Update (SAPU) +""" + + +def load_asset_mesh_in_warp(held_asset_obj, fixed_asset_obj, num_samples, device): + """Create mesh objects in Warp for all environments.""" + retrieve_file_path(held_asset_obj, download_dir="./") + plug_trimesh = load(os.path.basename(held_asset_obj)) + # plug_trimesh = load(held_asset_obj) + retrieve_file_path(fixed_asset_obj, download_dir="./") + socket_trimesh = load(os.path.basename(fixed_asset_obj)) + # socket_trimesh = load(fixed_asset_obj) + + plug_wp_mesh = wp.Mesh( + points=wp.array(plug_trimesh.vertices, dtype=wp.vec3, device=device), + indices=wp.array(plug_trimesh.faces.flatten(), dtype=wp.int32, device=device), + ) + + # Sample points on surface of mesh + sampled_points, _ = trimesh.sample.sample_surface_even(plug_trimesh, num_samples) + wp_mesh_sampled_points = wp.array(sampled_points, dtype=wp.vec3, device=device) + + socket_wp_mesh = wp.Mesh( + points=wp.array(socket_trimesh.vertices, dtype=wp.vec3, device=device), + indices=wp.array(socket_trimesh.faces.flatten(), dtype=wp.int32, device=device), + ) + + return plug_wp_mesh, wp_mesh_sampled_points, socket_wp_mesh + + +""" +SDF-Based Reward +""" + + +def get_sdf_reward( + wp_plug_mesh, + wp_plug_mesh_sampled_points, + plug_pos, + plug_quat, + socket_pos, + socket_quat, + wp_device, + device, +): + """Calculate SDF-based reward.""" + + num_envs = len(plug_pos) + sdf_reward = torch.zeros((num_envs,), dtype=torch.float32, device=device) + + for i in range(num_envs): + # Create copy of plug mesh + mesh_points = wp.clone(wp_plug_mesh.points) + mesh_indices = wp.clone(wp_plug_mesh.indices) + mesh_copy = wp.Mesh(points=mesh_points, indices=mesh_indices) + + # Transform plug mesh from current pose to goal pose + # NOTE: In source OBJ files, when plug and socket are assembled, + # their poses are identical + goal_transform = wp.transform(socket_pos[i], socket_quat[i]) + wp.launch( + kernel=transform_points, + dim=len(mesh_copy.points), + inputs=[mesh_copy.points, mesh_copy.points, goal_transform], + device=wp_device, + ) + + # Rebuild BVH (see https://nvidia.github.io/warp/_build/html/modules/runtime.html#meshes) + mesh_copy.refit() + + # Create copy of sampled points + sampled_points = wp.clone(wp_plug_mesh_sampled_points) + + # Transform sampled points from original plug pose to current plug pose + curr_transform = wp.transform(plug_pos[i], plug_quat[i]) + wp.launch( + kernel=transform_points, + dim=len(sampled_points), + inputs=[sampled_points, sampled_points, curr_transform], + device=wp_device, + ) + + # Get SDF values at transformed points + sdf_dist = wp.zeros((len(sampled_points),), dtype=wp.float32, device=wp_device) + wp.launch( + kernel=get_batch_sdf, + dim=len(sampled_points), + inputs=[mesh_copy.id, sampled_points, sdf_dist], + device=wp_device, + ) + sdf_dist = wp.to_torch(sdf_dist) + + # Clamp values outside isosurface and take absolute value + sdf_dist = torch.where(sdf_dist < 0.0, 0.0, sdf_dist) + + sdf_reward[i] = torch.mean(sdf_dist) + + del mesh_copy + del mesh_points + del mesh_indices + del sampled_points + + sdf_reward = -torch.log(sdf_reward) + + gc.collect() # Force garbage collection to free memory + return sdf_reward + + +""" +Sampling-Based Curriculum (SBC) +""" + + +def get_curriculum_reward_scale(cfg_task, curr_max_disp): + """Compute reward scale for SBC.""" + + # Compute difference between max downward displacement at beginning of training (easiest condition) + # and current max downward displacement (based on current curriculum stage) + # NOTE: This number increases as curriculum gets harder + curr_stage_diff = cfg_task.curriculum_height_bound[1] - curr_max_disp + + # Compute difference between max downward displacement at beginning of training (easiest condition) + # and min downward displacement (hardest condition) + final_stage_diff = cfg_task.curriculum_height_bound[1] - cfg_task.curriculum_height_bound[0] + + # Compute reward scale + reward_scale = curr_stage_diff / final_stage_diff + 1.0 + + return reward_scale + + +def get_new_max_disp(curr_success, cfg_task, curr_max_disp): + """Update max downward displacement of plug at beginning of episode, based on success rate.""" + + if curr_success > cfg_task.curriculum_success_thresh: + # If success rate is above threshold, reduce max downward displacement until min value + # NOTE: height_step[0] is negative + new_max_disp = max( + curr_max_disp + cfg_task.curriculum_height_step[0], + cfg_task.curriculum_height_bound[0], + ) + + elif curr_success < cfg_task.curriculum_failure_thresh: + # If success rate is below threshold, increase max downward displacement until max value + # NOTE: height_step[1] is positive + new_max_disp = min( + curr_max_disp + cfg_task.curriculum_height_step[1], + cfg_task.curriculum_height_bound[1], + ) + + else: + # Maintain current max downward displacement + new_max_disp = curr_max_disp + + return new_max_disp + + +""" +Bonus and Success Checking +""" + + +def get_keypoint_offsets(num_keypoints, device): + """Get uniformly-spaced keypoints along a line of unit length, centered at 0.""" + + keypoint_offsets = torch.zeros((num_keypoints, 3), device=device) + keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=device) - 0.5 + + return keypoint_offsets + + +def check_plug_close_to_socket(keypoints_plug, keypoints_socket, dist_threshold, progress_buf): + """Check if plug is close to socket.""" + + # Compute keypoint distance between plug and socket + keypoint_dist = torch.norm(keypoints_socket - keypoints_plug, p=2, dim=-1) + + # Check if keypoint distance is below threshold + is_plug_close_to_socket = torch.where( + torch.sum(keypoint_dist, dim=-1) < dist_threshold, + torch.ones_like(progress_buf), + torch.zeros_like(progress_buf), + ) + + return is_plug_close_to_socket + + +def check_plug_inserted_in_socket( + plug_pos, socket_pos, keypoints_plug, keypoints_socket, success_height_thresh, close_error_thresh, progress_buf +): + """Check if plug is inserted in socket.""" + + # Check if plug is within threshold distance of assembled state + is_plug_below_insertion_height = plug_pos[:, 2] < socket_pos[:, 2] + success_height_thresh + + # Check if plug is close to socket + # NOTE: This check addresses edge case where plug is within threshold distance of + # assembled state, but plug is outside socket + is_plug_close_to_socket = check_plug_close_to_socket( + keypoints_plug=keypoints_plug, + keypoints_socket=keypoints_socket, + dist_threshold=close_error_thresh, + progress_buf=progress_buf, + ) + + # Combine both checks + is_plug_inserted_in_socket = torch.logical_and(is_plug_below_insertion_height, is_plug_close_to_socket) + + return is_plug_inserted_in_socket + + +def get_engagement_reward_scale(plug_pos, socket_pos, is_plug_engaged_w_socket, success_height_thresh, device): + """Compute scale on reward. If plug is not engaged with socket, scale is zero. + If plug is engaged, scale is proportional to distance between plug and bottom of socket.""" + + # Set default value of scale to zero + num_envs = len(plug_pos) + reward_scale = torch.zeros((num_envs,), dtype=torch.float32, device=device) + + # For envs in which plug and socket are engaged, compute positive scale + engaged_idx = np.argwhere(is_plug_engaged_w_socket.cpu().numpy().copy()).squeeze() + height_dist = plug_pos[engaged_idx, 2] - socket_pos[engaged_idx, 2] + # NOTE: Edge case: if success_height_thresh is greater than 0.1, + # denominator could be negative + reward_scale[engaged_idx] = 1.0 / ((height_dist - success_height_thresh) + 0.1) + + return reward_scale + + +""" +Warp Functions +""" + + +@wp.func +def mesh_sdf(mesh: wp.uint64, point: wp.vec3, max_dist: float): + face_index = int(0) + face_u = float(0.0) + face_v = float(0.0) + sign = float(0.0) + res = wp.mesh_query_point(mesh, point, max_dist, sign, face_index, face_u, face_v) + if res: + closest = wp.mesh_eval_position(mesh, face_index, face_u, face_v) + return wp.length(point - closest) * sign + return max_dist + + +""" +Warp Kernels +""" + + +@wp.kernel +def get_batch_sdf( + mesh: wp.uint64, + queries: wp.array(dtype=wp.vec3), + sdf_dist: wp.array(dtype=wp.float32), +): + tid = wp.tid() + + q = queries[tid] # query point + max_dist = 1.5 # max distance on mesh from query point + # max_dist = 0.0 + + # sdf_dist[tid] = wp.mesh_query_point_sign_normal(mesh, q, max_dist) + sdf_dist[tid] = mesh_sdf(mesh, q, max_dist) + + +# Transform points from source coordinate frame to destination coordinate frame +@wp.kernel +def transform_points(src: wp.array(dtype=wp.vec3), dest: wp.array(dtype=wp.vec3), xform: wp.transform): + tid = wp.tid() + + p = src[tid] + m = wp.transform_point(xform, p) + + dest[tid] = m + + +# Return interpenetration distances between query points (e.g., plug vertices in current pose) +# and mesh surfaces (e.g., of socket mesh in current pose) +@wp.kernel +def get_interpen_dist( + queries: wp.array(dtype=wp.vec3), + mesh: wp.uint64, + interpen_dists: wp.array(dtype=wp.float32), +): + tid = wp.tid() + + # Declare arguments to wp.mesh_query_point() that will not be modified + q = queries[tid] # query point + max_dist = 1.5 # max distance on mesh from query point + + # Declare arguments to wp.mesh_query_point() that will be modified + sign = float( + 0.0 + ) # -1 if query point inside mesh; 0 if on mesh; +1 if outside mesh (NOTE: Mesh must be watertight!) + face_idx = int(0) # index of closest face + face_u = float(0.0) # barycentric u-coordinate of closest point + face_v = float(0.0) # barycentric v-coordinate of closest point + + # Get closest point on mesh to query point + closest_mesh_point_exists = wp.mesh_query_point(mesh, q, max_dist, sign, face_idx, face_u, face_v) + + # If point exists within max_dist + if closest_mesh_point_exists: + # Get 3D position of point on mesh given face index and barycentric coordinates + p = wp.mesh_eval_position(mesh, face_idx, face_u, face_v) + + # Get signed distance between query point and mesh point + delta = q - p + signed_dist = sign * wp.length(delta) + + # If signed distance is negative + if signed_dist < 0.0: + # Store interpenetration distance + interpen_dists[tid] = signed_dist diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py new file mode 100644 index 0000000000000000000000000000000000000000..89c8a39650bdcdd82a1911e81726b0f068a3f61d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import os +import re +import subprocess +import sys + + +def update_task_param(task_cfg, assembly_id, disassembly_dir): + # Read the file lines. + with open(task_cfg) as f: + lines = f.readlines() + + updated_lines = [] + + # Regex patterns to capture the assignment lines + assembly_pattern = re.compile(r"^(.*assembly_id\s*=\s*).*$") + disassembly_dir_pattern = re.compile(r"^(.*disassembly_dir\s*=\s*).*$") + + for line in lines: + if "assembly_id =" in line: + line = assembly_pattern.sub(rf"\1'{assembly_id}'", line) + elif "disassembly_dir = " in line: + line = disassembly_dir_pattern.sub(rf"\1'{disassembly_dir}'", line) + + updated_lines.append(line) + + # Write the modified lines back to the file. + with open(task_cfg, "w") as f: + f.writelines(updated_lines) + + +def main(): + parser = argparse.ArgumentParser(description="Update assembly_id and run training script.") + parser.add_argument( + "--disassembly_dir", + type=str, + help="Path to the directory containing output disassembly trajectories.", + default="disassembly_dir", + ) + parser.add_argument( + "--cfg_path", + type=str, + help="Path to the file containing assembly_id.", + default="source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_tasks_cfg.py", + ) + parser.add_argument("--assembly_id", type=str, default="00731", help="New assembly ID to set.") + parser.add_argument("--num_envs", type=int, default=128, help="Number of parallel environment.") + parser.add_argument("--seed", type=int, default=-1, help="Random seed.") + parser.add_argument("--headless", action="store_true", help="Run in headless mode.") + args = parser.parse_args() + + os.makedirs(args.disassembly_dir, exist_ok=True) + + update_task_param( + args.cfg_path, + args.assembly_id, + args.disassembly_dir, + ) + + if sys.platform.startswith("win"): + bash_command = "isaaclab.bat -p" + elif sys.platform.startswith("linux"): + bash_command = "./isaaclab.sh -p" + + bash_command += " scripts/reinforcement_learning/rl_games/train.py --task=Isaac-AutoMate-Disassembly-Direct-v0" + + bash_command += f" --num_envs={str(args.num_envs)}" + bash_command += f" --seed={str(args.seed)}" + + if args.headless: + bash_command += " --headless" + + # Run the bash command + subprocess.run(bash_command, shell=True, check=True) + + +if __name__ == "__main__": + main() diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py new file mode 100644 index 0000000000000000000000000000000000000000..18e8914e67047c5922d63ff753d0cd3f5f170285 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py @@ -0,0 +1,89 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import argparse +import re +import subprocess +import sys + + +def update_task_param(task_cfg, assembly_id, if_sbc, if_log_eval): + # Read the file lines. + with open(task_cfg) as f: + lines = f.readlines() + + updated_lines = [] + + # Regex patterns to capture the assignment lines + assembly_pattern = re.compile(r"^(.*assembly_id\s*=\s*).*$") + if_sbc_pattern = re.compile(r"^(.*if_sbc\s*:\s*bool\s*=\s*).*$") + if_log_eval_pattern = re.compile(r"^(.*if_logging_eval\s*:\s*bool\s*=\s*).*$") + eval_file_pattern = re.compile(r"^(.*eval_filename\s*:\s*str\s*=\s*).*$") + + for line in lines: + if "assembly_id =" in line: + line = assembly_pattern.sub(rf"\1'{assembly_id}'", line) + elif "if_sbc: bool =" in line: + line = if_sbc_pattern.sub(rf"\1{str(if_sbc)}", line) + elif "if_logging_eval: bool =" in line: + line = if_log_eval_pattern.sub(rf"\1{str(if_log_eval)}", line) + elif "eval_filename: str = " in line: + line = eval_file_pattern.sub(r"\1'{}'".format(f"evaluation_{assembly_id}.h5"), line) + + updated_lines.append(line) + + # Write the modified lines back to the file. + with open(task_cfg, "w") as f: + f.writelines(updated_lines) + + +def main(): + parser = argparse.ArgumentParser(description="Update assembly_id and run training script.") + parser.add_argument( + "--cfg_path", + type=str, + help="Path to the file containing assembly_id.", + default="source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_tasks_cfg.py", + ) + parser.add_argument("--assembly_id", type=str, help="New assembly ID to set.") + parser.add_argument("--checkpoint", type=str, help="Checkpoint path.") + parser.add_argument("--num_envs", type=int, default=128, help="Number of parallel environment.") + parser.add_argument("--seed", type=int, default=-1, help="Random seed.") + parser.add_argument("--train", action="store_true", help="Run training mode.") + parser.add_argument("--log_eval", action="store_true", help="Log evaluation results.") + parser.add_argument("--headless", action="store_true", help="Run in headless mode.") + parser.add_argument("--max_iterations", type=int, default=1500, help="Number of iteration for policy learning.") + args = parser.parse_args() + + update_task_param(args.cfg_path, args.assembly_id, args.train, args.log_eval) + + # avoid the warning of low GPU occupancy for SoftDTWCUDA function + bash_command = "NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS=0" + if sys.platform.startswith("win"): + bash_command += " isaaclab.bat -p" + elif sys.platform.startswith("linux"): + bash_command += " ./isaaclab.sh -p" + if args.train: + bash_command += " scripts/reinforcement_learning/rl_games/train.py --task=Isaac-AutoMate-Assembly-Direct-v0" + bash_command += f" --seed={str(args.seed)} --max_iterations={str(args.max_iterations)}" + else: + if not args.checkpoint: + raise ValueError("No checkpoint provided for evaluation.") + bash_command += " scripts/reinforcement_learning/rl_games/play.py --task=Isaac-AutoMate-Assembly-Direct-v0" + + bash_command += f" --num_envs={str(args.num_envs)}" + + if args.checkpoint: + bash_command += f" --checkpoint={args.checkpoint}" + + if args.headless: + bash_command += " --headless" + + # Run the bash command + subprocess.run(bash_command, shell=True, check=True) + + +if __name__ == "__main__": + main() diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/automate/soft_dtw_cuda.py b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/soft_dtw_cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..a979ec449381fcd8fd5db27e24c1ddd84d0f1385 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/automate/soft_dtw_cuda.py @@ -0,0 +1,451 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# MIT License +# +# Copyright (c) 2020 Mehran Maghoumi +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# ---------------------------------------------------------------------------------------------------------------------- + +import math + +import numpy as np +import torch +import torch.cuda +from numba import cuda, jit, prange +from torch.autograd import Function + + +# ---------------------------------------------------------------------------------------------------------------------- +@cuda.jit +def compute_softdtw_cuda(D, gamma, bandwidth, max_i, max_j, n_passes, R): + """ + :param seq_len: The length of the sequence (both inputs are assumed to be of the same size) + :param n_passes: 2 * seq_len - 1 (The number of anti-diagonals) + """ + # Each block processes one pair of examples + b = cuda.blockIdx.x + # We have as many threads as seq_len, because the most number of threads we need + # is equal to the number of elements on the largest anti-diagonal + tid = cuda.threadIdx.x + + inv_gamma = 1.0 / gamma + + # Go over each anti-diagonal. Only process threads that fall on the current on the anti-diagonal + for p in range(n_passes): + # The index is actually 'p - tid' but need to force it in-bounds + J = max(0, min(p - tid, max_j - 1)) + + # For simplicity, we define i, j which start from 1 (offset from I, J) + i = tid + 1 + j = J + 1 + + # Only compute if element[i, j] is on the current anti-diagonal, and also is within bounds + if tid + J == p and (tid < max_i and max_j > J): + # Don't compute if outside bandwidth + if not (abs(i - j) > bandwidth > 0): + r0 = -R[b, i - 1, j - 1] * inv_gamma + r1 = -R[b, i - 1, j] * inv_gamma + r2 = -R[b, i, j - 1] * inv_gamma + rmax = max(max(r0, r1), r2) + rsum = math.exp(r0 - rmax) + math.exp(r1 - rmax) + math.exp(r2 - rmax) + softmin = -gamma * (math.log(rsum) + rmax) + R[b, i, j] = D[b, i - 1, j - 1] + softmin + + # Wait for other threads in this block + cuda.syncthreads() + + +# ---------------------------------------------------------------------------------------------------------------------- +@cuda.jit +def compute_softdtw_backward_cuda(D, R, inv_gamma, bandwidth, max_i, max_j, n_passes, E): + k = cuda.blockIdx.x + tid = cuda.threadIdx.x + + # Indexing logic is the same as above, however, the anti-diagonal needs to + # progress backwards + + for p in range(n_passes): + # Reverse the order to make the loop go backward + rev_p = n_passes - p - 1 + + # convert tid to I, J, then i, j + J = max(0, min(rev_p - tid, max_j - 1)) + + i = tid + 1 + j = J + 1 + + # Only compute if element[i, j] is on the current anti-diagonal, and also is within bounds + if tid + J == rev_p and (tid < max_i and max_j > J): + if math.isinf(R[k, i, j]): + R[k, i, j] = -math.inf + + # Don't compute if outside bandwidth + if not (abs(i - j) > bandwidth > 0): + a = math.exp((R[k, i + 1, j] - R[k, i, j] - D[k, i + 1, j]) * inv_gamma) + b = math.exp((R[k, i, j + 1] - R[k, i, j] - D[k, i, j + 1]) * inv_gamma) + c = math.exp((R[k, i + 1, j + 1] - R[k, i, j] - D[k, i + 1, j + 1]) * inv_gamma) + E[k, i, j] = E[k, i + 1, j] * a + E[k, i, j + 1] * b + E[k, i + 1, j + 1] * c + + # Wait for other threads in this block + cuda.syncthreads() + + +# ---------------------------------------------------------------------------------------------------------------------- +class _SoftDTWCUDA(Function): + """ + CUDA implementation is inspired by the diagonal one proposed in https://ieeexplore.ieee.org/document/8400444: + "Developing a pattern discovery method in time series data and its GPU acceleration" + """ + + @staticmethod + def forward(ctx, D, device, gamma, bandwidth): + dev = D.device + dtype = D.dtype + gamma = torch.tensor([gamma], dtype=torch.float, device=device) + bandwidth = torch.tensor([bandwidth], dtype=torch.float, device=device) + + B = D.shape[0] + N = D.shape[1] + M = D.shape[2] + threads_per_block = max(N, M) + n_passes = 2 * threads_per_block - 1 + + # Prepare the output array + R = torch.ones((B, N + 2, M + 2), device=dev, dtype=dtype) * math.inf + R[:, 0, 0] = 0 + + # Run the CUDA kernel. + # Set CUDA's grid size to be equal to the batch size (every CUDA block processes one sample pair) + # Set the CUDA block size to be equal to the length of the longer sequence + # (equal to the size of the largest diagonal) + compute_softdtw_cuda[B, threads_per_block]( + cuda.as_cuda_array(D.detach()), gamma.item(), bandwidth.item(), N, M, n_passes, cuda.as_cuda_array(R) + ) + ctx.save_for_backward(D, R.clone(), gamma, bandwidth) + return R[:, -2, -2] + + @staticmethod + def backward(ctx, grad_output): + dev = grad_output.device + dtype = grad_output.dtype + D, R, gamma, bandwidth = ctx.saved_tensors + + B = D.shape[0] + N = D.shape[1] + M = D.shape[2] + threads_per_block = max(N, M) + n_passes = 2 * threads_per_block - 1 + + D_ = torch.zeros((B, N + 2, M + 2), dtype=dtype, device=dev) + D_[:, 1 : N + 1, 1 : M + 1] = D + + R[:, :, -1] = -math.inf + R[:, -1, :] = -math.inf + R[:, -1, -1] = R[:, -2, -2] + + E = torch.zeros((B, N + 2, M + 2), dtype=dtype, device=dev) + E[:, -1, -1] = 1 + + # Grid and block sizes are set same as done above for the forward() call + compute_softdtw_backward_cuda[B, threads_per_block]( + cuda.as_cuda_array(D_), + cuda.as_cuda_array(R), + 1.0 / gamma.item(), + bandwidth.item(), + N, + M, + n_passes, + cuda.as_cuda_array(E), + ) + E = E[:, 1 : N + 1, 1 : M + 1] + return grad_output.view(-1, 1, 1).expand_as(E) * E, None, None + + +# ---------------------------------------------------------------------------------------------------------------------- +# +# The following is the CPU implementation based on https://github.com/Sleepwalking/pytorch-softdtw +# Credit goes to Kanru Hua. +# I've added support for batching and pruning. +# +# ---------------------------------------------------------------------------------------------------------------------- +@jit(nopython=True, parallel=True) +def compute_softdtw(D, gamma, bandwidth): + B = D.shape[0] + N = D.shape[1] + M = D.shape[2] + R = np.ones((B, N + 2, M + 2)) * np.inf + R[:, 0, 0] = 0 + for b in prange(B): + for j in range(1, M + 1): + for i in range(1, N + 1): + # Check the pruning condition + if 0 < bandwidth < np.abs(i - j): + continue + + r0 = -R[b, i - 1, j - 1] / gamma + r1 = -R[b, i - 1, j] / gamma + r2 = -R[b, i, j - 1] / gamma + rmax = max(max(r0, r1), r2) + rsum = np.exp(r0 - rmax) + np.exp(r1 - rmax) + np.exp(r2 - rmax) + softmin = -gamma * (np.log(rsum) + rmax) + R[b, i, j] = D[b, i - 1, j - 1] + softmin + return R + + +# ---------------------------------------------------------------------------------------------------------------------- +@jit(nopython=True, parallel=True) +def compute_softdtw_backward(D_, R, gamma, bandwidth): + B = D_.shape[0] + N = D_.shape[1] + M = D_.shape[2] + D = np.zeros((B, N + 2, M + 2)) + E = np.zeros((B, N + 2, M + 2)) + D[:, 1 : N + 1, 1 : M + 1] = D_ + E[:, -1, -1] = 1 + R[:, :, -1] = -np.inf + R[:, -1, :] = -np.inf + R[:, -1, -1] = R[:, -2, -2] + for k in prange(B): + for j in range(M, 0, -1): + for i in range(N, 0, -1): + if np.isinf(R[k, i, j]): + R[k, i, j] = -np.inf + + # Check the pruning condition + if 0 < bandwidth < np.abs(i - j): + continue + + a0 = (R[k, i + 1, j] - R[k, i, j] - D[k, i + 1, j]) / gamma + b0 = (R[k, i, j + 1] - R[k, i, j] - D[k, i, j + 1]) / gamma + c0 = (R[k, i + 1, j + 1] - R[k, i, j] - D[k, i + 1, j + 1]) / gamma + a = np.exp(a0) + b = np.exp(b0) + c = np.exp(c0) + E[k, i, j] = E[k, i + 1, j] * a + E[k, i, j + 1] * b + E[k, i + 1, j + 1] * c + return E[:, 1 : N + 1, 1 : M + 1] + + +# ---------------------------------------------------------------------------------------------------------------------- +class _SoftDTW(Function): + """ + CPU implementation based on https://github.com/Sleepwalking/pytorch-softdtw + """ + + @staticmethod + def forward(ctx, D, device, gamma, bandwidth): + dev = D.device + dtype = D.dtype + gamma = torch.Tensor([gamma]).to(dev).type(dtype) # dtype fixed + bandwidth = torch.Tensor([bandwidth]).to(dev).type(dtype) + D_ = D.detach().cpu().numpy() + g_ = gamma.item() + b_ = bandwidth.item() + R = torch.Tensor(compute_softdtw(D_, g_, b_)).to(dev).type(dtype) + ctx.save_for_backward(D, R, gamma, bandwidth) + return R[:, -2, -2] + + @staticmethod + def backward(ctx, grad_output): + dev = grad_output.device + dtype = grad_output.dtype + D, R, gamma, bandwidth = ctx.saved_tensors + D_ = D.detach().cpu().numpy() + R_ = R.detach().cpu().numpy() + g_ = gamma.item() + b_ = bandwidth.item() + E = torch.Tensor(compute_softdtw_backward(D_, R_, g_, b_)).to(dev).type(dtype) + return grad_output.view(-1, 1, 1).expand_as(E) * E, None, None + + +# ---------------------------------------------------------------------------------------------------------------------- +class SoftDTW(torch.nn.Module): + """ + The soft DTW implementation that optionally supports CUDA + """ + + def __init__(self, use_cuda, device, gamma=1.0, normalize=False, bandwidth=None, dist_func=None): + """Initializes a new instance using the supplied parameters + + Args: + + use_cuda: Whether to use the CUDA implementation. + device: The device to run the SoftDTW computation. + gamma: The SoftDTW's gamma parameter. Default is 1.0. + normalize: Whether to perform normalization. Default is False. + (as discussed in https://github.com/mblondel/soft-dtw/issues/10#issuecomment-383564790) + bandwidth: Sakoe-Chiba bandwidth for pruning. Default is None, which disables pruning. + If provided, must be a float. + dist_func: The point-wise distance function to use. Default is None, which + uses a default Euclidean distance function. + """ + super().__init__() + self.normalize = normalize + self.gamma = gamma + self.bandwidth = 0 if bandwidth is None else float(bandwidth) + self.use_cuda = use_cuda + self.device = device + + # Set the distance function + if dist_func is not None: + self.dist_func = dist_func + else: + self.dist_func = SoftDTW._euclidean_dist_func + + def _get_func_dtw(self, x, y): + """ + Checks the inputs and selects the proper implementation to use. + """ + bx, lx, dx = x.shape + by, ly, dy = y.shape + # Make sure the dimensions match + assert bx == by # Equal batch sizes + assert dx == dy # Equal feature dimensions + + use_cuda = self.use_cuda + + if use_cuda and (lx > 1024 or ly > 1024): # We should be able to spawn enough threads in CUDA + print( + "SoftDTW: Cannot use CUDA because the sequence length > 1024 (the maximum block size supported by CUDA)" + ) + use_cuda = False + + # Finally, return the correct function + return _SoftDTWCUDA.apply if use_cuda else _SoftDTW.apply + + @staticmethod + def _euclidean_dist_func(x, y): + """ + Calculates the Euclidean distance between each element in x and y per timestep + """ + n = x.size(1) + m = y.size(1) + d = x.size(2) + x = x.unsqueeze(2).expand(-1, n, m, d) + y = y.unsqueeze(1).expand(-1, n, m, d) + return torch.pow(x - y, 2).sum(3) + + def forward(self, X, Y): + """ + Compute the soft-DTW value between X and Y + :param X: One batch of examples, batch_size x seq_len x dims + :param Y: The other batch of examples, batch_size x seq_len x dims + :return: The computed results + """ + + # Check the inputs and get the correct implementation + func_dtw = self._get_func_dtw(X, Y) + + if self.normalize: + # Stack everything up and run + x = torch.cat([X, X, Y]) + y = torch.cat([Y, X, Y]) + D = self.dist_func(x, y) + out = func_dtw(D, self.device, self.gamma, self.bandwidth) + out_xy, out_xx, out_yy = torch.split(out, X.shape[0]) + return out_xy - 1 / 2 * (out_xx + out_yy) + else: + D_xy = self.dist_func(X, Y) + return func_dtw(D_xy, self.device, self.gamma, self.bandwidth) + + +# ---------------------------------------------------------------------------------------------------------------------- +def timed_run(a, b, sdtw): + """ + Runs a and b through sdtw, and times the forward and backward passes. + Assumes that a requires gradients. + :return: timing, forward result, backward result + """ + + from timeit import default_timer as timer + + # Forward pass + start = timer() + forward = sdtw(a, b) + end = timer() + t = end - start + + grad_outputs = torch.ones_like(forward) + + # Backward + start = timer() + grads = torch.autograd.grad(forward, a, grad_outputs=grad_outputs)[0] + end = timer() + + # Total time + t += end - start + + return t, forward, grads + + +# ---------------------------------------------------------------------------------------------------------------------- +def profile(batch_size, seq_len_a, seq_len_b, dims, tol_backward): + sdtw = SoftDTW(False, gamma=1.0, normalize=False) + sdtw_cuda = SoftDTW(True, gamma=1.0, normalize=False) + n_iters = 6 + + print( + f"Profiling forward() + backward() times for batch_size={batch_size}, seq_len_a={seq_len_a}," + f" seq_len_b={seq_len_b}, dims={dims}..." + ) + + times_cpu = [] + times_gpu = [] + + for i in range(n_iters): + a_cpu = torch.rand((batch_size, seq_len_a, dims), requires_grad=True) + b_cpu = torch.rand((batch_size, seq_len_b, dims)) + a_gpu = a_cpu.cuda() + b_gpu = b_cpu.cuda() + + # GPU + t_gpu, forward_gpu, backward_gpu = timed_run(a_gpu, b_gpu, sdtw_cuda) + + # CPU + t_cpu, forward_cpu, backward_cpu = timed_run(a_cpu, b_cpu, sdtw) + + # Verify the results + assert torch.allclose(forward_cpu, forward_gpu.cpu()) + assert torch.allclose(backward_cpu, backward_gpu.cpu(), atol=tol_backward) + + # Ignore the first time we run, in case this is a cold start + # (because timings are off at a cold start of the script) + if i > 0: + times_cpu += [t_cpu] + times_gpu += [t_gpu] + + # Average and log + avg_cpu = np.mean(times_cpu) + avg_gpu = np.mean(times_gpu) + print(" CPU: ", avg_cpu) + print(" GPU: ", avg_gpu) + print(" Speedup: ", avg_cpu / avg_gpu) + print() + + +# ---------------------------------------------------------------------------------------------------------------------- +if __name__ == "__main__": + torch.manual_seed(1234) + + profile(128, 17, 15, 2, tol_backward=1e-6) + profile(512, 64, 64, 2, tol_backward=1e-4) + profile(512, 256, 256, 2, tol_backward=1e-3) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b4913fb2c3a98812d69594f5571e76177f020f85 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Inverted Double Pendulum on a Cart balancing environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Cart-Double-Pendulum-Direct-v0", + entry_point=f"{__name__}.cart_double_pendulum_env:CartDoublePendulumEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cart_double_pendulum_env:CartDoublePendulumEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "skrl_ippo_cfg_entry_point": f"{agents.__name__}:skrl_ippo_cfg.yaml", + "skrl_mappo_cfg_entry_point": f"{agents.__name__}:skrl_mappo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e97a86a62ff415e56030dd1b9e1156cac20d2b5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [32, 32] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cart_double_pendulum_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.1 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 150 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 16384 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ippo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ippo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f9298c9252ac81f07bff4aab44d26bbefeb52252 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ippo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# IPPO agent configuration (field names are from IPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/ippo.html +agent: + class: IPPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 1 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cart_double_pendulum_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_mappo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_mappo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8f192cf2988d0e0c98d8d1f27b946bd0e63884c7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_mappo_cfg.yaml @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# MAPPO agent configuration (field names are from MAPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/mappo.html +agent: + class: MAPPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 1 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + shared_state_preprocessor: RunningStandardScaler + shared_state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cart_double_pendulum_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c5f7943b99d93d995b04490a1edb413920c80ea7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 1 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cart_double_pendulum_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/cart_double_pendulum_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/cart_double_pendulum_env.py new file mode 100644 index 0000000000000000000000000000000000000000..e0464a7201c8df661a02f1459a3a977baac2f451 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cart_double_pendulum/cart_double_pendulum_env.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +from collections.abc import Sequence + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, ArticulationCfg +from isaaclab.envs import DirectMARLEnv, DirectMARLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils import configclass +from isaaclab.utils.math import sample_uniform + +from isaaclab_assets.robots.cart_double_pendulum import CART_DOUBLE_PENDULUM_CFG + + +@configclass +class CartDoublePendulumEnvCfg(DirectMARLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + possible_agents = ["cart", "pendulum"] + action_spaces = {"cart": 1, "pendulum": 1} + observation_spaces = {"cart": 4, "pendulum": 3} + state_space = -1 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot + robot_cfg: ArticulationCfg = CART_DOUBLE_PENDULUM_CFG.replace(prim_path="/World/envs/env_.*/Robot") + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + pendulum_dof_name = "pole_to_pendulum" + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # reset + max_cart_pos = 3.0 # the cart is reset if it exceeds that position [m] + initial_pole_angle_range = [-0.25, 0.25] # the range in which the pole angle is sampled from on reset [rad] + initial_pendulum_angle_range = [-0.25, 0.25] # the range in which the pendulum angle is sampled from on reset [rad] + + # action scales + cart_action_scale = 100.0 # [N] + pendulum_action_scale = 50.0 # [Nm] + + # reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_cart_pos = 0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_pos = -1.0 + rew_scale_pole_vel = -0.01 + rew_scale_pendulum_pos = -1.0 + rew_scale_pendulum_vel = -0.01 + + +class CartDoublePendulumEnv(DirectMARLEnv): + cfg: CartDoublePendulumEnvCfg + + def __init__(self, cfg: CartDoublePendulumEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self._cart_dof_idx, _ = self.robot.find_joints(self.cfg.cart_dof_name) + self._pole_dof_idx, _ = self.robot.find_joints(self.cfg.pole_dof_name) + self._pendulum_dof_idx, _ = self.robot.find_joints(self.cfg.pendulum_dof_name) + + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[]) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: dict[str, torch.Tensor]) -> None: + self.actions = actions + + def _apply_action(self) -> None: + self.robot.set_joint_effort_target( + self.actions["cart"] * self.cfg.cart_action_scale, joint_ids=self._cart_dof_idx + ) + self.robot.set_joint_effort_target( + self.actions["pendulum"] * self.cfg.pendulum_action_scale, joint_ids=self._pendulum_dof_idx + ) + + def _get_observations(self) -> dict[str, torch.Tensor]: + pole_joint_pos = normalize_angle(self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1)) + pendulum_joint_pos = normalize_angle(self.joint_pos[:, self._pendulum_dof_idx[0]].unsqueeze(dim=1)) + observations = { + "cart": torch.cat( + ( + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + pole_joint_pos, + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ), + "pendulum": torch.cat( + ( + pole_joint_pos + pendulum_joint_pos, + pendulum_joint_pos, + self.joint_vel[:, self._pendulum_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ), + } + return observations + + def _get_rewards(self) -> dict[str, torch.Tensor]: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_cart_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_pole_vel, + self.cfg.rew_scale_pendulum_pos, + self.cfg.rew_scale_pendulum_vel, + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + normalize_angle(self.joint_pos[:, self._pole_dof_idx[0]]), + self.joint_vel[:, self._pole_dof_idx[0]], + normalize_angle(self.joint_pos[:, self._pendulum_dof_idx[0]]), + self.joint_vel[:, self._pendulum_dof_idx[0]], + math.prod(self.terminated_dict.values()), + ) + return total_reward + + def _get_dones(self) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + out_of_bounds = torch.any(torch.abs(self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, dim=1) + out_of_bounds = out_of_bounds | torch.any(torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, dim=1) + + terminated = {agent: out_of_bounds for agent in self.cfg.possible_agents} + time_outs = {agent: time_out for agent in self.cfg.possible_agents} + return terminated, time_outs + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.robot._ALL_INDICES + super()._reset_idx(env_ids) + + joint_pos = self.robot.data.default_joint_pos[env_ids] + joint_pos[:, self._pole_dof_idx] += sample_uniform( + self.cfg.initial_pole_angle_range[0] * math.pi, + self.cfg.initial_pole_angle_range[1] * math.pi, + joint_pos[:, self._pole_dof_idx].shape, + joint_pos.device, + ) + joint_pos[:, self._pendulum_dof_idx] += sample_uniform( + self.cfg.initial_pendulum_angle_range[0] * math.pi, + self.cfg.initial_pendulum_angle_range[1] * math.pi, + joint_pos[:, self._pendulum_dof_idx].shape, + joint_pos.device, + ) + joint_vel = self.robot.data.default_joint_vel[env_ids] + + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self.robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self.robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + +@torch.jit.script +def normalize_angle(angle): + return (angle + math.pi) % (2 * math.pi) - math.pi + + +@torch.jit.script +def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_cart_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_pos: float, + rew_scale_pole_vel: float, + rew_scale_pendulum_pos: float, + rew_scale_pendulum_vel: float, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + pendulum_pos: torch.Tensor, + pendulum_vel: torch.Tensor, + reset_terminated: torch.Tensor, +): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos).unsqueeze(dim=1), dim=-1) + rew_pendulum_pos = rew_scale_pendulum_pos * torch.sum( + torch.square(pole_pos + pendulum_pos).unsqueeze(dim=1), dim=-1 + ) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel).unsqueeze(dim=1), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel).unsqueeze(dim=1), dim=-1) + rew_pendulum_vel = rew_scale_pendulum_vel * torch.sum(torch.abs(pendulum_vel).unsqueeze(dim=1), dim=-1) + + total_reward = { + "cart": rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel, + "pendulum": rew_alive + rew_termination + rew_pendulum_pos + rew_pendulum_vel, + } + return total_reward diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f72ee0a6f8dc944a1c55057b21c495e685b05fe2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/__init__.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Cartpole balancing environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Cartpole-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env:CartpoleEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-RGB-Camera-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env:CartpoleRGBCameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_camera_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Depth-Camera-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env:CartpoleDepthCameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_camera_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_camera_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_camera_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60c37b40476d755cf43154b4f5fa03fb14423f1f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_camera_ppo_cfg.yaml @@ -0,0 +1,100 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + cnn: + type: conv2d + activation: relu + initializer: + name: default + regularizer: + name: None + convs: + - filters: 32 + kernel_size: 8 + strides: 4 + padding: 0 + - filters: 64 + kernel_size: 4 + strides: 2 + padding: 0 + - filters: 64 + kernel_size: 3 + strides: 1 + padding: 0 + + mlp: + units: [512] + activation: elu + initializer: + name: default + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole_camera_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: False + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 500 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 64 + minibatch_size: 2048 + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a673a29257ea89fce161c569449b50c22f2abf39 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [32, 32] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.1 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 150 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 16384 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..097b7b43a672a18b5d2a7c94477a4e6ed4099c33 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class CartpolePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 150 + save_interval = 50 + experiment_name = "cartpole_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[32, 32], + critic_hidden_dims=[32, 32], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fcb32cd51dd95679da4b32f938f5bc1583899e66 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,25 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +n_timesteps: !!float 1e6 +policy: 'MlpPolicy' +n_steps: 16 +batch_size: 4096 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 20 +ent_coef: 0.01 +learning_rate: !!float 3e-4 +clip_range: !!float 0.2 +policy_kwargs: + activation_fn: 'nn.ELU' + net_arch: [32, 32] + squash_output: False +vf_coef: 1.0 +max_grad_norm: 1.0 +device: "cuda:0" diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_camera_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_camera_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..282ebb0020c5cfaed904a323b765db534aa23939 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_camera_ppo_cfg.yaml @@ -0,0 +1,101 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b50b4bea69bbfbe9f2d1528ef75ff7287bdfb2dd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cartpole_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_camera_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_camera_env.py new file mode 100644 index 0000000000000000000000000000000000000000..9606008ccf19a0e6b2bcd166c4c3afd13f165800 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_camera_env.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +from collections.abc import Sequence + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, ArticulationCfg +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg, ViewerCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import TiledCamera, TiledCameraCfg, save_images_to_file +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass +from isaaclab.utils.math import sample_uniform + +from isaaclab_assets.robots.cartpole import CARTPOLE_CFG + + +@configclass +class CartpoleRGBCameraEnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + action_scale = 100.0 # [N] + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot + robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + + # camera + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="/World/envs/env_.*/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-5.0, 0.0, 2.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=100, + height=100, + ) + write_image_to_file = False + + # spaces + action_space = 1 + state_space = 0 + observation_space = [tiled_camera.height, tiled_camera.width, 3] + + # change viewer settings + viewer = ViewerCfg(eye=(20.0, 20.0, 20.0)) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=512, env_spacing=20.0, replicate_physics=True) + + # reset + max_cart_pos = 3.0 # the cart is reset if it exceeds that position [m] + initial_pole_angle_range = [-0.125, 0.125] # the range in which the pole angle is sampled from on reset [rad] + + # reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_pole_pos = -1.0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_vel = -0.005 + + +@configclass +class CartpoleDepthCameraEnvCfg(CartpoleRGBCameraEnvCfg): + # camera + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="/World/envs/env_.*/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-5.0, 0.0, 2.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + data_types=["depth"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=100, + height=100, + ) + + # spaces + observation_space = [tiled_camera.height, tiled_camera.width, 1] + + +class CartpoleCameraEnv(DirectRLEnv): + """Cartpole Camera Environment.""" + + cfg: CartpoleRGBCameraEnvCfg | CartpoleDepthCameraEnvCfg + + def __init__( + self, cfg: CartpoleRGBCameraEnvCfg | CartpoleDepthCameraEnvCfg, render_mode: str | None = None, **kwargs + ): + super().__init__(cfg, render_mode, **kwargs) + + self._cart_dof_idx, _ = self._cartpole.find_joints(self.cfg.cart_dof_name) + self._pole_dof_idx, _ = self._cartpole.find_joints(self.cfg.pole_dof_name) + self.action_scale = self.cfg.action_scale + + self.joint_pos = self._cartpole.data.joint_pos + self.joint_vel = self._cartpole.data.joint_vel + + if len(self.cfg.tiled_camera.data_types) != 1: + raise ValueError( + "The Cartpole camera environment only supports one image type at a time but the following were" + f" provided: {self.cfg.tiled_camera.data_types}" + ) + + def close(self): + """Cleanup for the environment.""" + super().close() + + def _setup_scene(self): + """Setup the scene with the cartpole and camera.""" + self._cartpole = Articulation(self.cfg.robot_cfg) + self._tiled_camera = TiledCamera(self.cfg.tiled_camera) + + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + if self.device == "cpu": + # we need to explicitly filter collisions for CPU simulation + self.scene.filter_collisions(global_prim_paths=[]) + + # add articulation and sensors to scene + self.scene.articulations["cartpole"] = self._cartpole + self.scene.sensors["tiled_camera"] = self._tiled_camera + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = self.action_scale * actions.clone() + + def _apply_action(self) -> None: + self._cartpole.set_joint_effort_target(self.actions, joint_ids=self._cart_dof_idx) + + def _get_observations(self) -> dict: + data_type = "rgb" if "rgb" in self.cfg.tiled_camera.data_types else "depth" + if "rgb" in self.cfg.tiled_camera.data_types: + camera_data = self._tiled_camera.data.output[data_type] / 255.0 + # normalize the camera data for better training results + mean_tensor = torch.mean(camera_data, dim=(1, 2), keepdim=True) + camera_data -= mean_tensor + elif "depth" in self.cfg.tiled_camera.data_types: + camera_data = self._tiled_camera.data.output[data_type] + camera_data[camera_data == float("inf")] = 0 + observations = {"policy": camera_data.clone()} + + if self.cfg.write_image_to_file: + save_images_to_file(observations["policy"], f"cartpole_{data_type}.png") + + return observations + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_vel, + self.joint_pos[:, self._pole_dof_idx[0]], + self.joint_vel[:, self._pole_dof_idx[0]], + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + self.reset_terminated, + ) + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + self.joint_pos = self._cartpole.data.joint_pos + self.joint_vel = self._cartpole.data.joint_vel + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + out_of_bounds = torch.any(torch.abs(self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, dim=1) + out_of_bounds = out_of_bounds | torch.any(torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, dim=1) + return out_of_bounds, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self._cartpole._ALL_INDICES + super()._reset_idx(env_ids) + + joint_pos = self._cartpole.data.default_joint_pos[env_ids] + joint_pos[:, self._pole_dof_idx] += sample_uniform( + self.cfg.initial_pole_angle_range[0] * math.pi, + self.cfg.initial_pole_angle_range[1] * math.pi, + joint_pos[:, self._pole_dof_idx].shape, + joint_pos.device, + ) + joint_vel = self._cartpole.data.default_joint_vel[env_ids] + + default_root_state = self._cartpole.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self._cartpole.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self._cartpole.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self._cartpole.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + +@torch.jit.script +def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_pole_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_vel: float, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + reset_terminated: torch.Tensor, +): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos).unsqueeze(dim=1), dim=-1) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel).unsqueeze(dim=1), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel).unsqueeze(dim=1), dim=-1) + total_reward = rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel + return total_reward diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py new file mode 100644 index 0000000000000000000000000000000000000000..f897b64f3ec903c319f84617ff83da5954fa30ee --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole/cartpole_env.py @@ -0,0 +1,175 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +from collections.abc import Sequence + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, ArticulationCfg +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils import configclass +from isaaclab.utils.math import sample_uniform + +from isaaclab_assets.robots.cartpole import CARTPOLE_CFG + + +@configclass +class CartpoleEnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + action_scale = 100.0 # [N] + action_space = 1 + observation_space = 4 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot + robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=4096, env_spacing=4.0, replicate_physics=True, clone_in_fabric=True + ) + + # reset + max_cart_pos = 3.0 # the cart is reset if it exceeds that position [m] + initial_pole_angle_range = [-0.25, 0.25] # the range in which the pole angle is sampled from on reset [rad] + + # reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_pole_pos = -1.0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_vel = -0.005 + + +class CartpoleEnv(DirectRLEnv): + cfg: CartpoleEnvCfg + + def __init__(self, cfg: CartpoleEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self._cart_dof_idx, _ = self.cartpole.find_joints(self.cfg.cart_dof_name) + self._pole_dof_idx, _ = self.cartpole.find_joints(self.cfg.pole_dof_name) + self.action_scale = self.cfg.action_scale + + self.joint_pos = self.cartpole.data.joint_pos + self.joint_vel = self.cartpole.data.joint_vel + + def _setup_scene(self): + self.cartpole = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[]) + # add articulation to scene + self.scene.articulations["cartpole"] = self.cartpole + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = self.action_scale * actions.clone() + + def _apply_action(self) -> None: + self.cartpole.set_joint_effort_target(self.actions, joint_ids=self._cart_dof_idx) + + def _get_observations(self) -> dict: + obs = torch.cat( + ( + self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ) + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_vel, + self.joint_pos[:, self._pole_dof_idx[0]], + self.joint_vel[:, self._pole_dof_idx[0]], + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + self.reset_terminated, + ) + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + self.joint_pos = self.cartpole.data.joint_pos + self.joint_vel = self.cartpole.data.joint_vel + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + out_of_bounds = torch.any(torch.abs(self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, dim=1) + out_of_bounds = out_of_bounds | torch.any(torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, dim=1) + return out_of_bounds, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.cartpole._ALL_INDICES + super()._reset_idx(env_ids) + + joint_pos = self.cartpole.data.default_joint_pos[env_ids] + joint_pos[:, self._pole_dof_idx] += sample_uniform( + self.cfg.initial_pole_angle_range[0] * math.pi, + self.cfg.initial_pole_angle_range[1] * math.pi, + joint_pos[:, self._pole_dof_idx].shape, + joint_pos.device, + ) + joint_vel = self.cartpole.data.default_joint_vel[env_ids] + + default_root_state = self.cartpole.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self.cartpole.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.cartpole.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self.cartpole.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + +@torch.jit.script +def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_pole_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_vel: float, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + reset_terminated: torch.Tensor, +): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos).unsqueeze(dim=1), dim=-1) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel).unsqueeze(dim=1), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel).unsqueeze(dim=1), dim=-1) + total_reward = rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel + return total_reward diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d401c413967d58773c733d474e6c9959859cad9b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Cartpole environment showcase for the supported Gymnasium spaces.""" + +from .cartpole import * # noqa +from .cartpole_camera import * # noqa diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..576ccc822edf14776b9dc16a9f01edce8e66ab2b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/__init__.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Cartpole balancing environment. +""" + +import gymnasium as gym + +from . import agents + +########################### +# Register Gym environments +########################### + +### +# Observation space as Box +### + +gym.register( + id="Isaac-Cartpole-Showcase-Box-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:BoxBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Box-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:BoxDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Box-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:BoxMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as Discrete +### + +gym.register( + id="Isaac-Cartpole-Showcase-Discrete-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DiscreteBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_discrete_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Discrete-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DiscreteDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_discrete_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Discrete-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DiscreteMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_discrete_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as MultiDiscrete +### + +gym.register( + id="Isaac-Cartpole-Showcase-MultiDiscrete-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:MultiDiscreteBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_multidiscrete_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-MultiDiscrete-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:MultiDiscreteDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_multidiscrete_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-MultiDiscrete-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:MultiDiscreteMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_multidiscrete_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as Dict +### + +gym.register( + id="Isaac-Cartpole-Showcase-Dict-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DictBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Dict-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DictDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Dict-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:DictMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as Tuple +### + +gym.register( + id="Isaac-Cartpole-Showcase-Tuple-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:TupleBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Tuple-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:TupleDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Showcase-Tuple-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_env:CartpoleShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:TupleMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_multidiscrete_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..08d0e729708a9a27142bb3fd716f95abd334fd45 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_box_ppo_cfg.yaml @@ -0,0 +1,106 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_box_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4d7852d6665c08edd02e240266a7ba395df5fc38 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_discrete_ppo_cfg.yaml @@ -0,0 +1,102 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_box_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..47a764f6117d8b20af7621d350e2e62fd0f458c9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_box_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,102 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_box_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7153e46bcf133e13e545eb7002cc2f831fca7475 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_box_ppo_cfg.yaml @@ -0,0 +1,129 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["joint-positions"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(+)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_dict_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..67bb6b932dcf006a3927a9f001ca52edc4a44038 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_discrete_ppo_cfg.yaml @@ -0,0 +1,125 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["joint-positions"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(+)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_dict_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d51d71764315ba3a7d976ff17d62c324b4c432c8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_dict_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,125 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["joint-positions"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(+)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS["joint-positions"] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS["joint-velocities"] + layers: [16, 16] + activations: elu + - name: net + input: net_pos + net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_dict_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f55aa1f21c6744ff6e8385b23d3799c9b94deaa8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_box_ppo_cfg.yaml @@ -0,0 +1,108 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_discrete_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae513a50183503b2baf581a82dbdc6aeb9497c85 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_discrete_ppo_cfg.yaml @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_discrete_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7310ed646ca4a52c61ea0f1e67ebb757774741f5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_discrete_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_discrete_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ed10841aa026d79952cb49e204ada3773742ec1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_box_ppo_cfg.yaml @@ -0,0 +1,108 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_multidiscrete_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ed49c9f6692a08bebdfa699b1dab6b3f9140b51 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_discrete_ppo_cfg.yaml @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_multidiscrete_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad95dab1ba63bf3ff0c7c67273090c4f83b9ee68 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_multidiscrete_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,104 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# one_hot(obs) +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: one_hot_encoding(OBSERVATION_SPACE, OBSERVATIONS) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null # pre-processor should not be used with Discrete/MultiDiscrete observations + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_multidiscrete_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..291610cc73d33af5d0a6bfd96c9ff382b8749cf9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_box_ppo_cfg.yaml @@ -0,0 +1,129 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(*)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_tuple_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cd48d89491ed027f734db3b6cdae842c33947d63 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_discrete_ppo_cfg.yaml @@ -0,0 +1,125 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(*)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_tuple_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..84ba7d6506be8aeabe9a01dc53ff357f89ec7648 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,125 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━▼━━━━━┓ ┏━━━━━━▼━━━━━┓ +# ┃ net_pos ┃ ┃ net_vel ┃ +# ┡━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━┩ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# │ linear(16) │ │ linear(16) │ +# │ elu │ │ elu │ +# └──────┬─────┘ └─────┬──────┘ +# │ │ +# └─────────▶(*)◀─────────┘ +# │ +# ┏━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━┩ +# │ identity │ +# shared └─────┬─────┘ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net_pos + input: OBSERVATIONS[0] + layers: [16, 16] + activations: elu + - name: net_vel + input: OBSERVATIONS[1] + layers: [16, 16] + activations: elu + - name: net + input: net_pos * net_vel + layers: [] + activations: [] + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_direct_tuple_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env.py new file mode 100644 index 0000000000000000000000000000000000000000..dc03eb299d0d4f1cfc6b2edf59709415e0feaa27 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env.py @@ -0,0 +1,133 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import gymnasium as gym +import torch + +from isaaclab_tasks.direct.cartpole.cartpole_env import CartpoleEnv, CartpoleEnvCfg + + +class CartpoleShowcaseEnv(CartpoleEnv): + cfg: CartpoleEnvCfg + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + + def _apply_action(self) -> None: + # fundamental spaces + # - Box + if isinstance(self.single_action_space, gym.spaces.Box): + target = self.cfg.action_scale * self.actions + # - Discrete + elif isinstance(self.single_action_space, gym.spaces.Discrete): + target = torch.zeros((self.num_envs, 1), dtype=torch.float32, device=self.device) + target = torch.where(self.actions == 1, -self.cfg.action_scale, target) + target = torch.where(self.actions == 2, self.cfg.action_scale, target) + # - MultiDiscrete + elif isinstance(self.single_action_space, gym.spaces.MultiDiscrete): + # value + target = torch.zeros((self.num_envs, 1), dtype=torch.float32, device=self.device) + target = torch.where(self.actions[:, [0]] == 1, self.cfg.action_scale / 2.0, target) + target = torch.where(self.actions[:, [0]] == 2, self.cfg.action_scale, target) + # direction + target = torch.where(self.actions[:, [1]] == 0, -target, target) + else: + raise NotImplementedError(f"Action space {type(self.single_action_space)} not implemented") + + # set target + self.cartpole.set_joint_effort_target(target, joint_ids=self._cart_dof_idx) + + def _get_observations(self) -> dict: + # fundamental spaces + # - Box + if isinstance(self.single_observation_space["policy"], gym.spaces.Box): + obs = torch.cat( + ( + self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ) + # - Discrete + elif isinstance(self.single_observation_space["policy"], gym.spaces.Discrete): + data = ( + torch.cat( + ( + self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ) + >= 0 + ) + obs = torch.zeros((self.num_envs,), dtype=torch.int32, device=self.device) + obs = torch.where(discretization_indices(data, [False, False, False, True]), 1, obs) + obs = torch.where(discretization_indices(data, [False, False, True, False]), 2, obs) + obs = torch.where(discretization_indices(data, [False, False, True, True]), 3, obs) + obs = torch.where(discretization_indices(data, [False, True, False, False]), 4, obs) + obs = torch.where(discretization_indices(data, [False, True, False, True]), 5, obs) + obs = torch.where(discretization_indices(data, [False, True, True, False]), 6, obs) + obs = torch.where(discretization_indices(data, [False, True, True, True]), 7, obs) + obs = torch.where(discretization_indices(data, [True, False, False, False]), 8, obs) + obs = torch.where(discretization_indices(data, [True, False, False, True]), 9, obs) + obs = torch.where(discretization_indices(data, [True, False, True, False]), 10, obs) + obs = torch.where(discretization_indices(data, [True, False, True, True]), 11, obs) + obs = torch.where(discretization_indices(data, [True, True, False, False]), 12, obs) + obs = torch.where(discretization_indices(data, [True, True, False, True]), 13, obs) + obs = torch.where(discretization_indices(data, [True, True, True, False]), 14, obs) + obs = torch.where(discretization_indices(data, [True, True, True, True]), 15, obs) + # - MultiDiscrete + elif isinstance(self.single_observation_space["policy"], gym.spaces.MultiDiscrete): + zeros = torch.zeros((self.num_envs,), dtype=torch.int32, device=self.device) + ones = torch.ones_like(zeros) + obs = torch.cat( + ( + torch.where( + discretization_indices(self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1) >= 0, [True]), + ones, + zeros, + ).unsqueeze(dim=1), + torch.where( + discretization_indices(self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1) >= 0, [True]), + ones, + zeros, + ).unsqueeze(dim=1), + torch.where( + discretization_indices(self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1) >= 0, [True]), + ones, + zeros, + ).unsqueeze(dim=1), + torch.where( + discretization_indices(self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1) >= 0, [True]), + ones, + zeros, + ).unsqueeze(dim=1), + ), + dim=-1, + ) + # composite spaces + # - Tuple + elif isinstance(self.single_observation_space["policy"], gym.spaces.Tuple): + obs = (self.joint_pos, self.joint_vel) + # - Dict + elif isinstance(self.single_observation_space["policy"], gym.spaces.Dict): + obs = {"joint-positions": self.joint_pos, "joint-velocities": self.joint_vel} + else: + raise NotImplementedError( + f"Observation space {type(self.single_observation_space['policy'])} not implemented" + ) + + observations = {"policy": obs} + return observations + + +def discretization_indices(x: torch.Tensor, condition: list[bool]) -> torch.Tensor: + return torch.prod(x == torch.tensor(condition, device=x.device), axis=-1).to(torch.bool) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e8169b1ba0dc69da81d93cfb74898d10a095a0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole/cartpole_env_cfg.py @@ -0,0 +1,607 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from gymnasium import spaces + +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.cartpole.cartpole_env import CartpoleEnvCfg + +### +# Observation space as Box +### + + +@configclass +class BoxBoxEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (4,)) + + === === + Idx Observation + === === + 0 Pole DOF position + 1 Pole DOF velocity + 2 Cart DOF position + 3 Cart DOF velocity + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + observation_space = spaces.Box(low=float("-inf"), high=float("inf"), shape=(4,)) # or for simplicity: 4 or [4] + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class BoxDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (4,)) + + === === + Idx Observation + === === + 0 Pole DOF position + 1 Pole DOF velocity + 2 Cart DOF position + 3 Cart DOF velocity + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + observation_space = spaces.Box(low=float("-inf"), high=float("inf"), shape=(4,)) # or for simplicity: 4 or [4] + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class BoxMultiDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (4,)) + + === === + Idx Observation + === === + 0 Pole DOF position + 1 Pole DOF velocity + 2 Cart DOF position + 3 Cart DOF velocity + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + observation_space = spaces.Box(low=float("-inf"), high=float("inf"), shape=(4,)) # or for simplicity: 4 or [4] + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as Discrete +### + + +@configclass +class DiscreteBoxEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Discrete`` with 16 elements) + + === === + N Observation (Value signs: pole position, cart position, pole velocity, cart velocity) + === === + 0 - - - - + 1 - - - + + 2 - - + - + 3 - - + + + 4 - + - - + 5 - + - + + 6 - + + - + 7 - + + + + 8 + - - - + 9 + - - + + 10 + - + - + 11 + - + + + 12 + + - - + 13 + + - + + 14 + + + - + 15 + + + + + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + observation_space = spaces.Discrete(16) # or for simplicity: {16} + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class DiscreteDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Discrete`` with 16 elements) + + === === + N Observation (Value signs: pole position, cart position, pole velocity, cart velocity) + === === + 0 - - - - + 1 - - - + + 2 - - + - + 3 - - + + + 4 - + - - + 5 - + - + + 6 - + + - + 7 - + + + + 8 + - - - + 9 + - - + + 10 + - + - + 11 + - + + + 12 + + - - + 13 + + - + + 14 + + + - + 15 + + + + + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + observation_space = spaces.Discrete(16) # or for simplicity: {16} + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class DiscreteMultiDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Discrete`` with 16 elements) + + === === + N Observation (Value signs: pole position, cart position, pole velocity, cart velocity) + === === + 0 - - - - + 1 - - - + + 2 - - + - + 3 - - + + + 4 - + - - + 5 - + - + + 6 - + + - + 7 - + + + + 8 + - - - + 9 + - - + + 10 + - + - + 11 + - + + + 12 + + - - + 13 + + - + + 14 + + + - + 15 + + + + + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + observation_space = spaces.Discrete(16) # or for simplicity: {16} + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as MultiDiscrete +### + + +@configclass +class MultiDiscreteBoxEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.MultiDiscrete`` with 4 discrete spaces) + + === === + N Observation (Discrete 0) + === === + 0 Negative pole position (-) + 1 Zero or positive pole position (+) + === === + + === === + N Observation (Discrete 1) + === === + 0 Negative cart position (-) + 1 Zero or positive cart position (+) + === === + + === === + N Observation (Discrete 2) + === === + 0 Negative pole velocity (-) + 1 Zero or positive pole velocity (+) + === === + + === === + N Observation (Discrete 3) + === === + 0 Negative cart velocity (-) + 1 Zero or positive cart velocity (+) + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + observation_space = spaces.MultiDiscrete([2, 2, 2, 2]) # or for simplicity: [{2}, {2}, {2}, {2}] + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class MultiDiscreteDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.MultiDiscrete`` with 4 discrete spaces) + + === === + N Observation (Discrete 0) + === === + 0 Negative pole position (-) + 1 Zero or positive pole position (+) + === === + + === === + N Observation (Discrete 1) + === === + 0 Negative cart position (-) + 1 Zero or positive cart position (+) + === === + + === === + N Observation (Discrete 2) + === === + 0 Negative pole velocity (-) + 1 Zero or positive pole velocity (+) + === === + + === === + N Observation (Discrete 3) + === === + 0 Negative cart velocity (-) + 1 Zero or positive cart velocity (+) + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + observation_space = spaces.MultiDiscrete([2, 2, 2, 2]) # or for simplicity: [{2}, {2}, {2}, {2}] + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class MultiDiscreteMultiDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.MultiDiscrete`` with 4 discrete spaces) + + === === + N Observation (Discrete 0) + === === + 0 Negative pole position (-) + 1 Zero or positive pole position (+) + === === + + === === + N Observation (Discrete 1) + === === + 0 Negative cart position (-) + 1 Zero or positive cart position (+) + === === + + === === + N Observation (Discrete 2) + === === + 0 Negative pole velocity (-) + 1 Zero or positive pole velocity (+) + === === + + === === + N Observation (Discrete 3) + === === + 0 Negative cart velocity (-) + 1 Zero or positive cart velocity (+) + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + observation_space = spaces.MultiDiscrete([2, 2, 2, 2]) # or for simplicity: [{2}, {2}, {2}, {2}] + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as Dict +### + + +@configclass +class DictBoxEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-positions DOF positions + joint-velocities DOF velocities + ================ === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + observation_space = spaces.Dict( + { + "joint-positions": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + } + ) # or for simplicity: {"joint-positions": 2, "joint-velocities": 2} + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class DictDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-positions DOF positions + joint-velocities DOF velocities + ================ === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + observation_space = spaces.Dict( + { + "joint-positions": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + } + ) # or for simplicity: {"joint-positions": 2, "joint-velocities": 2} + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class DictMultiDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-positions DOF positions + joint-velocities DOF velocities + ================ === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + observation_space = spaces.Dict( + { + "joint-positions": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + } + ) # or for simplicity: {"joint-positions": 2, "joint-velocities": 2} + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as Tuple +### + + +@configclass +class TupleBoxEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 DOF positions + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: (2, 2) + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class TupleDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 DOF positions + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: (2, 2) + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class TupleMultiDiscreteEnvCfg(CartpoleEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 DOF positions + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: (2, 2) + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2953ce1a29c01f5411c65ed70bc74e44bf579a24 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/__init__.py @@ -0,0 +1,118 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Cartpole balancing environment with camera. +""" + +import gymnasium as gym + +from . import agents + +########################### +# Register Gym environments +########################### + +### +# Observation space as Box +### + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Box-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:BoxBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Box-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:BoxDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Box-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:BoxMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_box_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as Dict +### + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Dict-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:DictBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Dict-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:DictDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Dict-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:DictMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_dict_multidiscrete_ppo_cfg.yaml", + }, +) + +### +# Observation space as Tuple +### + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Tuple-Box-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:TupleBoxEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_box_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Tuple-Discrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:TupleDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_discrete_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Camera-Showcase-Tuple-MultiDiscrete-Direct-v0", + entry_point=f"{__name__}.cartpole_camera_env:CartpoleCameraShowcaseEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:TupleMultiDiscreteEnvCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_tuple_multidiscrete_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77bb2c8eab503d7c29ec27c9dd59ac1739cef527 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_box_ppo_cfg.yaml @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ +# ┃ features_extractor ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ +# │ relu │ +# │ conv2d(64, 4, 2) │ +# │ relu │ +# │ conv2d(64, 3, 1) │ +# │ relu │ +# │ flatten │ +# └──────────┬──────────┘ +# │ +# ┏━━━━━━▼━━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━━┩ +# │ linear(512) │ +# │ elu │ +# └──────┬──────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_box_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac047e6b9361a519f8097181ea8d6366fdb7d94f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_discrete_ppo_cfg.yaml @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ +# ┃ features_extractor ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ +# │ relu │ +# │ conv2d(64, 4, 2) │ +# │ relu │ +# │ conv2d(64, 3, 1) │ +# │ relu │ +# │ flatten │ +# └──────────┬──────────┘ +# │ +# ┏━━━━━━▼━━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━━┩ +# │ linear(512) │ +# │ elu │ +# └──────┬──────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_box_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8710230931d0563ac5d13abb9df7d56072ab6a7a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_box_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs +# │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ +# ┃ features_extractor ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ +# │ relu │ +# │ conv2d(64, 4, 2) │ +# │ relu │ +# │ conv2d(64, 3, 1) │ +# │ relu │ +# │ flatten │ +# └──────────┬──────────┘ +# │ +# ┏━━━━━━▼━━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━━┩ +# │ linear(512) │ +# │ elu │ +# └──────┬──────┘ +# shared │ +# ......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + activations: relu + - name: net + input: features_extractor + layers: [512] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_box_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..09f598574135e1367d729ecd52d26c7cb1b83303 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_box_ppo_cfg.yaml @@ -0,0 +1,144 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["camera"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ │ +# ┃ features_extractor ┃ │ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ │ +# │ conv2d(32, 8, 4) │ │ +# │ relu │ │ +# │ conv2d(64, 4, 2) │ │ +# │ relu │ │ +# │ conv2d(64, 3, 1) │ │ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀────────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: features_extractor + input: permute(OBSERVATIONS["camera"], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_dict_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92a5c1f52b4ebab43a0be9468862c639e3599346 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_discrete_ppo_cfg.yaml @@ -0,0 +1,140 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["camera"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ │ +# ┃ features_extractor ┃ │ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ │ +# │ conv2d(32, 8, 4) │ │ +# │ relu │ │ +# │ conv2d(64, 4, 2) │ │ +# │ relu │ │ +# │ conv2d(64, 3, 1) │ │ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀────────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS["camera"], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_dict_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2dfd9f889eafc479a9d385d2adaa8386ccec4b7c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_dict_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,140 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs["camera"] obs["joint-velocities"] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ │ +# ┃ features_extractor ┃ │ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ │ +# │ conv2d(32, 8, 4) │ │ +# │ relu │ │ +# │ conv2d(64, 4, 2) │ │ +# │ relu │ │ +# │ conv2d(64, 3, 1) │ │ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀────────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS["camera"], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS, (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: net + input: concatenate([features_extractor, OBSERVATIONS["joint-velocities"]]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_dict_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_box_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_box_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..423f17203ccce1d5ad5a1f5d4ed88e66ab5dfaa3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_box_ppo_cfg.yaml @@ -0,0 +1,152 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ ┏━━━━━━━▼━━━━━━━━┓ +# ┃ features_extractor ┃ ┃ proprioception ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ │ linear(16) │ +# │ relu │ │ elu │ +# │ conv2d(64, 4, 2) │ │ linear(8) │ +# │ relu │ │ elu │ +# │ conv2d(64, 3, 1) │ └───────┬────────┘ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀───────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_tuple_box" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_discrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_discrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f5aafefa90605143be5deca80d123f8e2e01e89d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_discrete_ppo_cfg.yaml @@ -0,0 +1,148 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ ┏━━━━━━━▼━━━━━━━━┓ +# ┃ features_extractor ┃ ┃ proprioception ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ │ linear(16) │ +# │ relu │ │ elu │ +# │ conv2d(64, 4, 2) │ │ linear(8) │ +# │ relu │ │ elu │ +# │ conv2d(64, 3, 1) │ └───────┬────────┘ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀───────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see categorical_model parameters + class: CategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_tuple_discrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5e3637aeb7b2b6ea843c17a11b198c70d1cf600d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/agents/skrl_tuple_multidiscrete_ppo_cfg.yaml @@ -0,0 +1,148 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +# +# obs[0] obs[1] +# │ │ +# ┏━━━━━━━━━━▼━━━━━━━━━━┓ ┏━━━━━━━▼━━━━━━━━┓ +# ┃ features_extractor ┃ ┃ proprioception ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━┩ ┡━━━━━━━━━━━━━━━━┩ +# │ conv2d(32, 8, 4) │ │ linear(16) │ +# │ relu │ │ elu │ +# │ conv2d(64, 4, 2) │ │ linear(8) │ +# │ relu │ │ elu │ +# │ conv2d(64, 3, 1) │ └───────┬────────┘ +# │ relu │ │ +# │ flatten │ │ +# │ linear(512) │ │ +# │ tanh │ │ +# │ linear(16) │ │ +# │ tanh │ │ +# └──────────┬──────────┘ | +# │ │ +# └─▶(concatenate)◀───────┘ +# │ +# ┏━━━━━━▼━━━━━┓ +# ┃ net ┃ +# ┡━━━━━━━━━━━━┩ +# │ linear(32) │ +# │ elu │ +# │ linear(32) │ +# │ elu │ +# └──────┬─────┘ +# shared │ +# .......................│....................... +# non-shared │ +# ┏━━━━━━━━━━━▼━━━━━━━━━━━┓ +# ┃ policy|value output ┃ +# ┡━━━━━━━━━━━━━━━━━━━━━━━┩ +# │ linear(num_actions|1) │ +# └───────────┬───────────┘ +# ▼ +models: + separate: False + policy: # see multicategorical_model parameters + class: MultiCategoricalMixin + unnormalized_log_prob: True + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: features_extractor + input: permute(OBSERVATIONS[0], (0, 3, 1, 2)) # PyTorch NHWC -> NCHW. Warning: don't permute for JAX since it expects NHWC + layers: + - conv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0} + - conv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0} + - flatten + - linear: 512 + - linear: 16 + activations: [relu, relu, relu, null, tanh, tanh] + - name: proprioception + input: OBSERVATIONS[1] + layers: [16, 8] + activations: elu + - name: net + input: concatenate([features_extractor, proprioception]) + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 64 + learning_epochs: 4 + mini_batches: 32 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + mixed_precision: False + # logging and checkpoint + experiment: + directory: "cartpole_camera_direct_tuple_multidiscrete" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8dc6b4a078c453892b3372ec40887a7f1b54b7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import gymnasium as gym +import torch + +from isaaclab_tasks.direct.cartpole.cartpole_camera_env import CartpoleCameraEnv, CartpoleRGBCameraEnvCfg + + +class CartpoleCameraShowcaseEnv(CartpoleCameraEnv): + cfg: CartpoleRGBCameraEnvCfg + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + + def _apply_action(self) -> None: + # fundamental spaces + # - Box + if isinstance(self.single_action_space, gym.spaces.Box): + target = self.cfg.action_scale * self.actions + # - Discrete + elif isinstance(self.single_action_space, gym.spaces.Discrete): + target = torch.zeros((self.num_envs, 1), dtype=torch.float32, device=self.device) + target = torch.where(self.actions == 1, -self.cfg.action_scale, target) + target = torch.where(self.actions == 2, self.cfg.action_scale, target) + # - MultiDiscrete + elif isinstance(self.single_action_space, gym.spaces.MultiDiscrete): + # value + target = torch.zeros((self.num_envs, 1), dtype=torch.float32, device=self.device) + target = torch.where(self.actions[:, [0]] == 1, self.cfg.action_scale / 2.0, target) + target = torch.where(self.actions[:, [0]] == 2, self.cfg.action_scale, target) + # direction + target = torch.where(self.actions[:, [1]] == 0, -target, target) + else: + raise NotImplementedError(f"Action space {type(self.single_action_space)} not implemented") + + # set target + self._cartpole.set_joint_effort_target(target, joint_ids=self._cart_dof_idx) + + def _get_observations(self) -> dict: + # get camera data + data_type = "rgb" if "rgb" in self.cfg.tiled_camera.data_types else "depth" + if "rgb" in self.cfg.tiled_camera.data_types: + camera_data = self._tiled_camera.data.output[data_type] / 255.0 + # normalize the camera data for better training results + mean_tensor = torch.mean(camera_data, dim=(1, 2), keepdim=True) + camera_data -= mean_tensor + elif "depth" in self.cfg.tiled_camera.data_types: + camera_data = self._tiled_camera.data.output[data_type] + camera_data[camera_data == float("inf")] = 0 + + # fundamental spaces + # - Box + if isinstance(self.single_observation_space["policy"], gym.spaces.Box): + obs = camera_data + # composite spaces + # - Tuple + elif isinstance(self.single_observation_space["policy"], gym.spaces.Tuple): + obs = (camera_data, self.joint_vel) + # - Dict + elif isinstance(self.single_observation_space["policy"], gym.spaces.Dict): + obs = {"joint-velocities": self.joint_vel, "camera": camera_data} + else: + raise NotImplementedError( + f"Observation space {type(self.single_observation_space['policy'])} not implemented" + ) + + observations = {"policy": obs} + return observations diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5e146041b79b97b0bbb0f5c386a8d3d43396eb67 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/cartpole_showcase/cartpole_camera/cartpole_camera_env_cfg.py @@ -0,0 +1,375 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from gymnasium import spaces + +import isaaclab.sim as sim_utils +from isaaclab.sensors import TiledCameraCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.cartpole.cartpole_camera_env import CartpoleRGBCameraEnvCfg as CartpoleCameraEnvCfg + + +def get_tiled_camera_cfg(data_type: str, width: int = 100, height: int = 100) -> TiledCameraCfg: + return TiledCameraCfg( + prim_path="/World/envs/env_.*/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-5.0, 0.0, 2.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + data_types=[data_type], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=width, + height=height, + ) + + +### +# Observation space as Box +### + + +@configclass +class BoxBoxEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (height, width, 3)) + + === === + Idx Observation + === === + - RGB image + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ) # or for simplicity: [height, width, 3] + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class BoxDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (height, width, 3)) + + === === + Idx Observation + === === + - RGB image + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ) # or for simplicity: [height, width, 3] + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class BoxMultiDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Box`` with shape (height, width, 3)) + + === === + Idx Observation + === === + - RGB image + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ) # or for simplicity: [height, width, 3] + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as Dict +### + + +@configclass +class DictBoxEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-velocities DOF velocities + camera RGB image + ================ === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Dict( + { + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "camera": spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ), + } + ) # or for simplicity: {"joint-velocities": 2, "camera": [height, width, 3]} + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class DictDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-velocities DOF velocities + camera RGB image + ================ === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Dict( + { + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "camera": spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ), + } + ) # or for simplicity: {"joint-velocities": 2, "camera": [height, width, 3]} + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class DictMultiDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Dict`` with 2 constituent spaces) + + ================ === + Key Observation + ================ === + joint-velocities DOF velocities + camera RGB image + ================ === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Dict( + { + "joint-velocities": spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + "camera": spaces.Box( + low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3) + ), + } + ) # or for simplicity: {"joint-velocities": 2, "camera": [height, width, 3]} + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] + + +### +# Observation space as Tuple +### + + +@configclass +class TupleBoxEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 RGB image + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.Box`` with shape (1,)) + + === === + Idx Action + === === + 0 Cart DOF effort scale: [-1, 1] + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: ([height, width, 3], 2) + action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,)) # or for simplicity: 1 or [1] + + +@configclass +class TupleDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 RGB image + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.Discrete`` with 3 elements) + + === === + N Action + === === + 0 Zero cart DOF effort + 1 Negative maximum cart DOF effort + 2 Positive maximum cart DOF effort + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: ([height, width, 3], 2) + action_space = spaces.Discrete(3) # or for simplicity: {3} + + +@configclass +class TupleMultiDiscreteEnvCfg(CartpoleCameraEnvCfg): + """ + * Observation space (``~gymnasium.spaces.Tuple`` with 2 constituent spaces) + + === === + Idx Observation + === === + 0 RGB image + 1 DOF velocities + === === + + * Action space (``~gymnasium.spaces.MultiDiscrete`` with 2 discrete spaces) + + === === + N Action (Discrete 0) + === === + 0 Zero cart DOF effort + 1 Half of maximum cart DOF effort + 2 Maximum cart DOF effort + === === + + === === + N Action (Discrete 1) + === === + 0 Negative effort (one side) + 1 Positive effort (other side) + === === + """ + + # camera + tiled_camera: TiledCameraCfg = get_tiled_camera_cfg("rgb") + + # spaces + observation_space = spaces.Tuple( + ( + spaces.Box(low=float("-inf"), high=float("inf"), shape=(tiled_camera.height, tiled_camera.width, 3)), + spaces.Box(low=float("-inf"), high=float("inf"), shape=(2,)), + ) + ) # or for simplicity: ([height, width, 3], 2) + action_space = spaces.MultiDiscrete([3, 2]) # or for simplicity: [{3}, {2}] diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..54d69f31f679af346176aa11d5af8d952f95556d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/__init__.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Factory-PegInsert-Direct-v0", + entry_point=f"{__name__}.factory_env:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.factory_env_cfg:FactoryTaskPegInsertCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Factory-GearMesh-Direct-v0", + entry_point=f"{__name__}.factory_env:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.factory_env_cfg:FactoryTaskGearMeshCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Factory-NutThread-Direct-v0", + entry_point=f"{__name__}.factory_env:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.factory_env_cfg:FactoryTaskNutThreadCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f11f6d996745df22be262a95cd7bcf0ff1eb2256 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 0 + algo: + name: a2c_continuous + + env: + clip_actions: 1.0 + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: False + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + load_checkpoint: False + load_path: "" + + config: + name: Factory + device: cuda:0 + full_experiment_name: test + env_name: rlgpu + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 128 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.995 + tau: 0.95 + learning_rate: 1.0e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 10 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 # 0.0001 # 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 128 + minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 128 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 512 + mini_epochs: 4 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + player: + deterministic: False diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_control.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_control.py new file mode 100644 index 0000000000000000000000000000000000000000..f8ccb0e134512b6a43c08ea2229113ea1565ecaa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_control.py @@ -0,0 +1,206 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Factory: control module. + +Imported by base, environment, and task classes. Not directly executed. +""" + +import math + +import torch + +import isaacsim.core.utils.torch as torch_utils + +from isaaclab.utils.math import axis_angle_from_quat + + +def compute_dof_torque( + cfg, + dof_pos, + dof_vel, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + fingertip_midpoint_linvel, + fingertip_midpoint_angvel, + jacobian, + arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + task_prop_gains, + task_deriv_gains, + device, + dead_zone_thresholds=None, +): + """Compute Franka DOF torque to move fingertips towards target pose.""" + # References: + # 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + # 2) Modern Robotics + + num_envs = cfg.scene.num_envs + dof_torque = torch.zeros((num_envs, dof_pos.shape[1]), device=device) + task_wrench = torch.zeros((num_envs, 6), device=device) + + pos_error, axis_angle_error = get_pose_error( + fingertip_midpoint_pos=fingertip_midpoint_pos, + fingertip_midpoint_quat=fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) + + # Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98) + task_wrench_motion = _apply_task_space_gains( + delta_fingertip_pose=delta_fingertip_pose, + fingertip_midpoint_linvel=fingertip_midpoint_linvel, + fingertip_midpoint_angvel=fingertip_midpoint_angvel, + task_prop_gains=task_prop_gains, + task_deriv_gains=task_deriv_gains, + ) + task_wrench += task_wrench_motion + + # Offset task_wrench motion by random amount to simulate unreliability at low forces. + # Check if absolute value is less than specified amount. If so, 0 out, otherwise, subtract. + if dead_zone_thresholds is not None: + task_wrench = torch.where( + task_wrench.abs() < dead_zone_thresholds, + torch.zeros_like(task_wrench), + task_wrench.sign() * (task_wrench.abs() - dead_zone_thresholds), + ) + + # Set tau = J^T * tau, i.e., map tau into joint space as desired + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + dof_torque[:, 0:7] = (jacobian_T @ task_wrench.unsqueeze(-1)).squeeze(-1) + + # adapted from roboticsproceedings.org/rss07/p31.pdf + + # useful tensors + arm_mass_matrix_inv = torch.inverse(arm_mass_matrix) + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + arm_mass_matrix_task = torch.inverse( + jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T + ) # ETH eq. 3.86; geometric Jacobian is assumed + j_eef_inv = arm_mass_matrix_task @ jacobian @ arm_mass_matrix_inv + default_dof_pos_tensor = torch.tensor(cfg.ctrl.default_dof_pos_tensor, device=device).repeat((num_envs, 1)) + # nullspace computation + distance_to_default_dof_pos = default_dof_pos_tensor - dof_pos[:, :7] + distance_to_default_dof_pos = (distance_to_default_dof_pos + math.pi) % ( + 2 * math.pi + ) - math.pi # normalize to [-pi, pi] + u_null = cfg.ctrl.kd_null * -dof_vel[:, :7] + cfg.ctrl.kp_null * distance_to_default_dof_pos + u_null = arm_mass_matrix @ u_null.unsqueeze(-1) + torque_null = (torch.eye(7, device=device).unsqueeze(0) - torch.transpose(jacobian, 1, 2) @ j_eef_inv) @ u_null + dof_torque[:, 0:7] += torque_null.squeeze(-1) + + # TODO: Verify it's okay to no longer do gripper control here. + dof_torque = torch.clamp(dof_torque, min=-100.0, max=100.0) + return dof_torque, task_wrench + + +def get_pose_error( + fingertip_midpoint_pos, + fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + jacobian_type, + rot_error_type, +): + """Compute task-space error between target Franka fingertip pose and current pose.""" + # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + + # Compute pos error + pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos + + # Compute rot error + if jacobian_type == "geometric": # See example 2.9.8; note use of J_g and transformation between rotation vectors + # Compute quat error (i.e., difference quat) + # Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html + + # Check for shortest path using quaternion dot product. + quat_dot = (ctrl_target_fingertip_midpoint_quat * fingertip_midpoint_quat).sum(dim=1, keepdim=True) + ctrl_target_fingertip_midpoint_quat = torch.where( + quat_dot.expand(-1, 4) >= 0, ctrl_target_fingertip_midpoint_quat, -ctrl_target_fingertip_midpoint_quat + ) + + fingertip_midpoint_quat_norm = torch_utils.quat_mul( + fingertip_midpoint_quat, torch_utils.quat_conjugate(fingertip_midpoint_quat) + )[:, 0] # scalar component + fingertip_midpoint_quat_inv = torch_utils.quat_conjugate( + fingertip_midpoint_quat + ) / fingertip_midpoint_quat_norm.unsqueeze(-1) + quat_error = torch_utils.quat_mul(ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv) + + # Convert to axis-angle error + axis_angle_error = axis_angle_from_quat(quat_error) + + if rot_error_type == "quat": + return pos_error, quat_error + elif rot_error_type == "axis_angle": + return pos_error, axis_angle_error + else: + raise ValueError(f"Unsupported rotation error type: {rot_error_type}. Valid: 'quat', 'axis_angle'.") + + +def get_delta_dof_pos(delta_pose, ik_method, jacobian, device): + """Get delta Franka DOF position from delta pose using specified IK method.""" + # References: + # 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf + # 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47) # noqa: E501 + + if ik_method == "pinv": # Jacobian pseudoinverse + k_val = 1.0 + jacobian_pinv = torch.linalg.pinv(jacobian) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "trans": # Jacobian transpose + k_val = 1.0 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "dls": # damped least squares (Levenberg-Marquardt) + lambda_val = 0.1 # 0.1 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + lambda_matrix = (lambda_val**2) * torch.eye(n=jacobian.shape[1], device=device) + delta_dof_pos = jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == "svd": # adaptive SVD + k_val = 1.0 + U, S, Vh = torch.linalg.svd(jacobian) + S_inv = 1.0 / S + min_singular_value = 1.0e-5 + S_inv = torch.where(min_singular_value < S, S_inv, torch.zeros_like(S_inv)) + jacobian_pinv = ( + torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2) + ) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + return delta_dof_pos + + +def _apply_task_space_gains( + delta_fingertip_pose, fingertip_midpoint_linvel, fingertip_midpoint_angvel, task_prop_gains, task_deriv_gains +): + """Interpret PD gains as task-space gains. Apply to task-space error.""" + + task_wrench = torch.zeros_like(delta_fingertip_pose) + + # Apply gains to lin error components + lin_error = delta_fingertip_pose[:, 0:3] + task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + task_deriv_gains[:, 0:3] * ( + 0.0 - fingertip_midpoint_linvel + ) + + # Apply gains to rot error components + rot_error = delta_fingertip_pose[:, 3:6] + task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + task_deriv_gains[:, 3:6] * ( + 0.0 - fingertip_midpoint_angvel + ) + return task_wrench diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env.py new file mode 100644 index 0000000000000000000000000000000000000000..a4e9c6d9ece9005ddd35a464d676f2c60efeb7f7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env.py @@ -0,0 +1,820 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import torch + +import carb +import isaacsim.core.utils.torch as torch_utils + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectRLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.math import axis_angle_from_quat + +from . import factory_control, factory_utils +from .factory_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, FactoryEnvCfg + + +class FactoryEnv(DirectRLEnv): + cfg: FactoryEnvCfg + + def __init__(self, cfg: FactoryEnvCfg, render_mode: str | None = None, **kwargs): + # Update number of obs/states + cfg.observation_space = sum([OBS_DIM_CFG[obs] for obs in cfg.obs_order]) + cfg.state_space = sum([STATE_DIM_CFG[state] for state in cfg.state_order]) + cfg.observation_space += cfg.action_space + cfg.state_space += cfg.action_space + self.cfg_task = cfg.task + + super().__init__(cfg, render_mode, **kwargs) + + factory_utils.set_body_inertias(self._robot, self.scene.num_envs) + self._init_tensors() + self._set_default_dynamics_parameters() + + def _set_default_dynamics_parameters(self): + """Set parameters defining dynamic interactions.""" + self.default_gains = torch.tensor(self.cfg.ctrl.default_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + + self.pos_threshold = torch.tensor(self.cfg.ctrl.pos_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.rot_threshold = torch.tensor(self.cfg.ctrl.rot_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + + # Set masses and frictions. + factory_utils.set_friction(self._held_asset, self.cfg_task.held_asset_cfg.friction, self.scene.num_envs) + factory_utils.set_friction(self._fixed_asset, self.cfg_task.fixed_asset_cfg.friction, self.scene.num_envs) + factory_utils.set_friction(self._robot, self.cfg_task.robot_cfg.friction, self.scene.num_envs) + + def _init_tensors(self): + """Initialize tensors once.""" + # Control targets. + self.ctrl_target_joint_pos = torch.zeros((self.num_envs, self._robot.num_joints), device=self.device) + self.ema_factor = self.cfg.ctrl.ema_factor + self.dead_zone_thresholds = None + + # Fixed asset. + self.fixed_pos_obs_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.init_fixed_pos_obs_noise = torch.zeros((self.num_envs, 3), device=self.device) + + # Computer body indices. + self.left_finger_body_idx = self._robot.body_names.index("panda_leftfinger") + self.right_finger_body_idx = self._robot.body_names.index("panda_rightfinger") + self.fingertip_body_idx = self._robot.body_names.index("panda_fingertip_centered") + + # Tensors for finite-differencing. + self.last_update_timestamp = 0.0 # Note: This is for finite differencing body velocities. + self.prev_fingertip_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.prev_fingertip_quat = ( + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + self.prev_joint_pos = torch.zeros((self.num_envs, 7), device=self.device) + + self.ep_succeeded = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + self.ep_success_times = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + + def _setup_scene(self): + """Initialize simulation scene.""" + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg(), translation=(0.0, 0.0, -1.05)) + + # spawn a usd file of a table into the scene + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func( + "/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711) + ) + + self._robot = Articulation(self.cfg.robot) + self._fixed_asset = Articulation(self.cfg_task.fixed_asset) + self._held_asset = Articulation(self.cfg_task.held_asset) + if self.cfg_task.name == "gear_mesh": + self._small_gear_asset = Articulation(self.cfg_task.small_gear_cfg) + self._large_gear_asset = Articulation(self.cfg_task.large_gear_cfg) + + self.scene.clone_environments(copy_from_source=False) + if self.device == "cpu": + # we need to explicitly filter collisions for CPU simulation + self.scene.filter_collisions() + + self.scene.articulations["robot"] = self._robot + self.scene.articulations["fixed_asset"] = self._fixed_asset + self.scene.articulations["held_asset"] = self._held_asset + if self.cfg_task.name == "gear_mesh": + self.scene.articulations["small_gear"] = self._small_gear_asset + self.scene.articulations["large_gear"] = self._large_gear_asset + + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _compute_intermediate_values(self, dt): + """Get values computed from raw tensors. This includes adding noise.""" + # TODO: A lot of these can probably only be set once? + self.fixed_pos = self._fixed_asset.data.root_pos_w - self.scene.env_origins + self.fixed_quat = self._fixed_asset.data.root_quat_w + + self.held_pos = self._held_asset.data.root_pos_w - self.scene.env_origins + self.held_quat = self._held_asset.data.root_quat_w + + self.fingertip_midpoint_pos = self._robot.data.body_pos_w[:, self.fingertip_body_idx] - self.scene.env_origins + self.fingertip_midpoint_quat = self._robot.data.body_quat_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_linvel = self._robot.data.body_lin_vel_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_angvel = self._robot.data.body_ang_vel_w[:, self.fingertip_body_idx] + + jacobians = self._robot.root_physx_view.get_jacobians() + + self.left_finger_jacobian = jacobians[:, self.left_finger_body_idx - 1, 0:6, 0:7] + self.right_finger_jacobian = jacobians[:, self.right_finger_body_idx - 1, 0:6, 0:7] + self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 + self.arm_mass_matrix = self._robot.root_physx_view.get_generalized_mass_matrices()[:, 0:7, 0:7] + self.joint_pos = self._robot.data.joint_pos.clone() + self.joint_vel = self._robot.data.joint_vel.clone() + + # Finite-differencing results in more reliable velocity estimates. + self.ee_linvel_fd = (self.fingertip_midpoint_pos - self.prev_fingertip_pos) / dt + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + + # Add state differences if velocity isn't being added. + rot_diff_quat = torch_utils.quat_mul( + self.fingertip_midpoint_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) + ) + rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) + rot_diff_aa = axis_angle_from_quat(rot_diff_quat) + self.ee_angvel_fd = rot_diff_aa / dt + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + joint_diff = self.joint_pos[:, 0:7] - self.prev_joint_pos + self.joint_vel_fd = joint_diff / dt + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + + self.last_update_timestamp = self._robot._data._sim_timestamp + + def _get_factory_obs_state_dict(self): + """Populate dictionaries for the policy and critic.""" + noisy_fixed_pos = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + + prev_actions = self.actions.clone() + + obs_dict = { + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_pos_rel_fixed": self.fingertip_midpoint_pos - noisy_fixed_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.ee_linvel_fd, + "ee_angvel": self.ee_angvel_fd, + "prev_actions": prev_actions, + } + + state_dict = { + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_pos_rel_fixed": self.fingertip_midpoint_pos - self.fixed_pos_obs_frame, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.fingertip_midpoint_linvel, + "ee_angvel": self.fingertip_midpoint_angvel, + "joint_pos": self.joint_pos[:, 0:7], + "held_pos": self.held_pos, + "held_pos_rel_fixed": self.held_pos - self.fixed_pos_obs_frame, + "held_quat": self.held_quat, + "fixed_pos": self.fixed_pos, + "fixed_quat": self.fixed_quat, + "task_prop_gains": self.task_prop_gains, + "pos_threshold": self.pos_threshold, + "rot_threshold": self.rot_threshold, + "prev_actions": prev_actions, + } + return obs_dict, state_dict + + def _get_observations(self): + """Get actor/critic inputs using asymmetric critic.""" + obs_dict, state_dict = self._get_factory_obs_state_dict() + + obs_tensors = factory_utils.collapse_obs_dict(obs_dict, self.cfg.obs_order + ["prev_actions"]) + state_tensors = factory_utils.collapse_obs_dict(state_dict, self.cfg.state_order + ["prev_actions"]) + return {"policy": obs_tensors, "critic": state_tensors} + + def _reset_buffers(self, env_ids): + """Reset buffers.""" + self.ep_succeeded[env_ids] = 0 + self.ep_success_times[env_ids] = 0 + + def _pre_physics_step(self, action): + """Apply policy actions with smoothing.""" + env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(env_ids) > 0: + self._reset_buffers(env_ids) + + self.actions = self.ema_factor * action.clone().to(self.device) + (1 - self.ema_factor) * self.actions + + def close_gripper_in_place(self): + """Keep gripper in current position as gripper closes.""" + actions = torch.zeros((self.num_envs, 6), device=self.device) + + # Interpret actions as target pos displacements and set pos target + pos_actions = actions[:, 0:3] * self.pos_threshold + ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = actions[:, 3:6] + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1.0e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 + target_euler_xyz[:, 1] = 0.0 + + ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.generate_ctrl_signals( + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + ctrl_target_gripper_dof_pos=0.0, + ) + + def _apply_action(self): + """Apply actions for policy as delta targets from current position.""" + # Note: We use finite-differenced velocities for control and observations. + # Check if we need to re-compute velocities within the decimation loop. + if self.last_update_timestamp < self._robot._data._sim_timestamp: + self._compute_intermediate_values(dt=self.physics_dt) + + # Interpret actions as target pos displacements and set pos target + pos_actions = self.actions[:, 0:3] * self.pos_threshold + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = self.actions[:, 3:6] + if self.cfg_task.unidirectional_rot: + rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0] + rot_actions = rot_actions * self.rot_threshold + + ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + # To speed up learning, never allow the policy to move more than 5cm away from the base. + fixed_pos_action_frame = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + delta_pos = ctrl_target_fingertip_midpoint_pos - fixed_pos_action_frame + pos_error_clipped = torch.clip( + delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1] + ) + ctrl_target_fingertip_midpoint_pos = fixed_pos_action_frame + pos_error_clipped + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), + ) + ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 # Restrict actions to be upright. + target_euler_xyz[:, 1] = 0.0 + + ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] + ) + + self.generate_ctrl_signals( + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + ctrl_target_gripper_dof_pos=0.0, + ) + + def generate_ctrl_signals( + self, ctrl_target_fingertip_midpoint_pos, ctrl_target_fingertip_midpoint_quat, ctrl_target_gripper_dof_pos + ): + """Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm).""" + self.joint_torque, self.applied_wrench = factory_control.compute_dof_torque( + cfg=self.cfg, + dof_pos=self.joint_pos, + dof_vel=self.joint_vel, + fingertip_midpoint_pos=self.fingertip_midpoint_pos, + fingertip_midpoint_quat=self.fingertip_midpoint_quat, + fingertip_midpoint_linvel=self.fingertip_midpoint_linvel, + fingertip_midpoint_angvel=self.fingertip_midpoint_angvel, + jacobian=self.fingertip_midpoint_jacobian, + arm_mass_matrix=self.arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + task_prop_gains=self.task_prop_gains, + task_deriv_gains=self.task_deriv_gains, + device=self.device, + dead_zone_thresholds=self.dead_zone_thresholds, + ) + + # set target for gripper joints to use physx's PD controller + self.ctrl_target_joint_pos[:, 7:9] = ctrl_target_gripper_dof_pos + self.joint_torque[:, 7:9] = 0.0 + + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + self._robot.set_joint_effort_target(self.joint_torque) + + def _get_dones(self): + """Check which environments are terminated. + + For Factory reset logic, it is important that all environments + stay in sync (i.e., _get_dones should return all true or all false). + """ + self._compute_intermediate_values(dt=self.physics_dt) + time_out = self.episode_length_buf >= self.max_episode_length - 1 + return time_out, time_out + + def _get_curr_successes(self, success_threshold, check_rot=False): + """Get success mask at current timestep.""" + curr_successes = torch.zeros((self.num_envs,), dtype=torch.bool, device=self.device) + + held_base_pos, held_base_quat = factory_utils.get_held_base_pose( + self.held_pos, self.held_quat, self.cfg_task.name, self.cfg_task.fixed_asset_cfg, self.num_envs, self.device + ) + target_held_base_pos, target_held_base_quat = factory_utils.get_target_held_base_pose( + self.fixed_pos, + self.fixed_quat, + self.cfg_task.name, + self.cfg_task.fixed_asset_cfg, + self.num_envs, + self.device, + ) + + xy_dist = torch.linalg.vector_norm(target_held_base_pos[:, 0:2] - held_base_pos[:, 0:2], dim=1) + z_disp = held_base_pos[:, 2] - target_held_base_pos[:, 2] + + is_centered = torch.where(xy_dist < 0.0025, torch.ones_like(curr_successes), torch.zeros_like(curr_successes)) + # Height threshold to target + fixed_cfg = self.cfg_task.fixed_asset_cfg + if self.cfg_task.name == "peg_insert" or self.cfg_task.name == "gear_mesh": + height_threshold = fixed_cfg.height * success_threshold + elif self.cfg_task.name == "nut_thread": + height_threshold = fixed_cfg.thread_pitch * success_threshold + else: + raise NotImplementedError("Task not implemented") + is_close_or_below = torch.where( + z_disp < height_threshold, torch.ones_like(curr_successes), torch.zeros_like(curr_successes) + ) + curr_successes = torch.logical_and(is_centered, is_close_or_below) + + if check_rot: + _, _, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) + curr_yaw = factory_utils.wrap_yaw(curr_yaw) + is_rotated = curr_yaw < self.cfg_task.ee_success_yaw + curr_successes = torch.logical_and(curr_successes, is_rotated) + + return curr_successes + + def _log_factory_metrics(self, rew_dict, curr_successes): + """Keep track of episode statistics and log rewards.""" + # Only log episode success rates at the end of an episode. + if torch.any(self.reset_buf): + self.extras["successes"] = torch.count_nonzero(curr_successes) / self.num_envs + + # Get the time at which an episode first succeeds. + first_success = torch.logical_and(curr_successes, torch.logical_not(self.ep_succeeded)) + self.ep_succeeded[curr_successes] = 1 + + first_success_ids = first_success.nonzero(as_tuple=False).squeeze(-1) + self.ep_success_times[first_success_ids] = self.episode_length_buf[first_success_ids] + nonzero_success_ids = self.ep_success_times.nonzero(as_tuple=False).squeeze(-1) + + if len(nonzero_success_ids) > 0: # Only log for successful episodes. + success_times = self.ep_success_times[nonzero_success_ids].sum() / len(nonzero_success_ids) + self.extras["success_times"] = success_times + + for rew_name, rew in rew_dict.items(): + self.extras[f"logs_rew_{rew_name}"] = rew.mean() + + def _get_rewards(self): + """Update rewards and compute success statistics.""" + # Get successful and failed envs at current timestep + check_rot = self.cfg_task.name == "nut_thread" + curr_successes = self._get_curr_successes( + success_threshold=self.cfg_task.success_threshold, check_rot=check_rot + ) + + rew_dict, rew_scales = self._get_factory_rew_dict(curr_successes) + + rew_buf = torch.zeros_like(rew_dict["kp_coarse"]) + for rew_name, rew in rew_dict.items(): + rew_buf += rew_dict[rew_name] * rew_scales[rew_name] + + self.prev_actions = self.actions.clone() + + self._log_factory_metrics(rew_dict, curr_successes) + return rew_buf + + def _get_factory_rew_dict(self, curr_successes): + """Compute reward terms at current timestep.""" + rew_dict, rew_scales = {}, {} + + # Compute pos of keypoints on held asset, and fixed asset in world frame + held_base_pos, held_base_quat = factory_utils.get_held_base_pose( + self.held_pos, self.held_quat, self.cfg_task.name, self.cfg_task.fixed_asset_cfg, self.num_envs, self.device + ) + target_held_base_pos, target_held_base_quat = factory_utils.get_target_held_base_pose( + self.fixed_pos, + self.fixed_quat, + self.cfg_task.name, + self.cfg_task.fixed_asset_cfg, + self.num_envs, + self.device, + ) + + keypoints_held = torch.zeros((self.num_envs, self.cfg_task.num_keypoints, 3), device=self.device) + keypoints_fixed = torch.zeros((self.num_envs, self.cfg_task.num_keypoints, 3), device=self.device) + offsets = factory_utils.get_keypoint_offsets(self.cfg_task.num_keypoints, self.device) + keypoint_offsets = offsets * self.cfg_task.keypoint_scale + for idx, keypoint_offset in enumerate(keypoint_offsets): + keypoints_held[:, idx] = torch_utils.tf_combine( + held_base_quat, + held_base_pos, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1), + keypoint_offset.repeat(self.num_envs, 1), + )[1] + keypoints_fixed[:, idx] = torch_utils.tf_combine( + target_held_base_quat, + target_held_base_pos, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1), + keypoint_offset.repeat(self.num_envs, 1), + )[1] + keypoint_dist = torch.norm(keypoints_held - keypoints_fixed, p=2, dim=-1).mean(-1) + + a0, b0 = self.cfg_task.keypoint_coef_baseline + a1, b1 = self.cfg_task.keypoint_coef_coarse + a2, b2 = self.cfg_task.keypoint_coef_fine + # Action penalties. + action_penalty_ee = torch.norm(self.actions, p=2) + action_grad_penalty = torch.norm(self.actions - self.prev_actions, p=2, dim=-1) + curr_engaged = self._get_curr_successes(success_threshold=self.cfg_task.engage_threshold, check_rot=False) + + rew_dict = { + "kp_baseline": factory_utils.squashing_fn(keypoint_dist, a0, b0), + "kp_coarse": factory_utils.squashing_fn(keypoint_dist, a1, b1), + "kp_fine": factory_utils.squashing_fn(keypoint_dist, a2, b2), + "action_penalty_ee": action_penalty_ee, + "action_grad_penalty": action_grad_penalty, + "curr_engaged": curr_engaged.float(), + "curr_success": curr_successes.float(), + } + rew_scales = { + "kp_baseline": 1.0, + "kp_coarse": 1.0, + "kp_fine": 1.0, + "action_penalty_ee": -self.cfg_task.action_penalty_ee_scale, + "action_grad_penalty": -self.cfg_task.action_grad_penalty_scale, + "curr_engaged": 1.0, + "curr_success": 1.0, + } + return rew_dict, rew_scales + + def _reset_idx(self, env_ids): + """We assume all envs will always be reset at the same time.""" + super()._reset_idx(env_ids) + + self._set_assets_to_default_pose(env_ids) + self._set_franka_to_default_pose(joints=self.cfg.ctrl.reset_joints, env_ids=env_ids) + self.step_sim_no_action() + + self.randomize_initial_state(env_ids) + + def _set_assets_to_default_pose(self, env_ids): + """Move assets to default pose before randomization.""" + held_state = self._held_asset.data.default_root_state.clone()[env_ids] + held_state[:, 0:3] += self.scene.env_origins[env_ids] + held_state[:, 7:] = 0.0 + self._held_asset.write_root_pose_to_sim(held_state[:, 0:7], env_ids=env_ids) + self._held_asset.write_root_velocity_to_sim(held_state[:, 7:], env_ids=env_ids) + self._held_asset.reset() + + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + fixed_state[:, 0:3] += self.scene.env_origins[env_ids] + fixed_state[:, 7:] = 0.0 + self._fixed_asset.write_root_pose_to_sim(fixed_state[:, 0:7], env_ids=env_ids) + self._fixed_asset.write_root_velocity_to_sim(fixed_state[:, 7:], env_ids=env_ids) + self._fixed_asset.reset() + + def set_pos_inverse_kinematics( + self, ctrl_target_fingertip_midpoint_pos, ctrl_target_fingertip_midpoint_quat, env_ids + ): + """Set robot joint position using DLS IK.""" + ik_time = 0.0 + while ik_time < 0.25: + # Compute error to target. + pos_error, axis_angle_error = factory_control.get_pose_error( + fingertip_midpoint_pos=self.fingertip_midpoint_pos[env_ids], + fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos[env_ids], + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat[env_ids], + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + + # Solve DLS problem. + delta_dof_pos = factory_control.get_delta_dof_pos( + delta_pose=delta_hand_pose, + ik_method="dls", + jacobian=self.fingertip_midpoint_jacobian[env_ids], + device=self.device, + ) + self.joint_pos[env_ids, 0:7] += delta_dof_pos[:, 0:7] + self.joint_vel[env_ids, :] = torch.zeros_like(self.joint_pos[env_ids,]) + + self.ctrl_target_joint_pos[env_ids, 0:7] = self.joint_pos[env_ids, 0:7] + # Update dof state. + self._robot.write_joint_state_to_sim(self.joint_pos, self.joint_vel) + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + + # Simulate and update tensors. + self.step_sim_no_action() + ik_time += self.physics_dt + + return pos_error, axis_angle_error + + def get_handheld_asset_relative_pose(self): + """Get default relative pose between help asset and fingertip.""" + if self.cfg_task.name == "peg_insert": + held_asset_relative_pos = torch.zeros((self.num_envs, 3), device=self.device) + held_asset_relative_pos[:, 2] = self.cfg_task.held_asset_cfg.height + held_asset_relative_pos[:, 2] -= self.cfg_task.robot_cfg.franka_fingerpad_length + elif self.cfg_task.name == "gear_mesh": + held_asset_relative_pos = torch.zeros((self.num_envs, 3), device=self.device) + gear_base_offset = self.cfg_task.fixed_asset_cfg.medium_gear_base_offset + held_asset_relative_pos[:, 0] += gear_base_offset[0] + held_asset_relative_pos[:, 2] += gear_base_offset[2] + held_asset_relative_pos[:, 2] += self.cfg_task.held_asset_cfg.height / 2.0 * 1.1 + elif self.cfg_task.name == "nut_thread": + held_asset_relative_pos = factory_utils.get_held_base_pos_local( + self.cfg_task.name, self.cfg_task.fixed_asset_cfg, self.num_envs, self.device + ) + else: + raise NotImplementedError("Task not implemented") + + held_asset_relative_quat = ( + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) + if self.cfg_task.name == "nut_thread": + # Rotate along z-axis of frame for default position. + initial_rot_deg = self.cfg_task.held_asset_rot_init + rot_yaw_euler = torch.tensor([0.0, 0.0, initial_rot_deg * np.pi / 180.0], device=self.device).repeat( + self.num_envs, 1 + ) + held_asset_relative_quat = torch_utils.quat_from_euler_xyz( + roll=rot_yaw_euler[:, 0], pitch=rot_yaw_euler[:, 1], yaw=rot_yaw_euler[:, 2] + ) + + return held_asset_relative_pos, held_asset_relative_quat + + def _set_franka_to_default_pose(self, joints, env_ids): + """Return Franka to its default joint position.""" + gripper_width = self.cfg_task.held_asset_cfg.diameter / 2 * 1.25 + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_pos[:, 7:] = gripper_width # MIMIC + joint_pos[:, :7] = torch.tensor(joints, device=self.device)[None, :] + joint_vel = torch.zeros_like(joint_pos) + joint_effort = torch.zeros_like(joint_pos) + self.ctrl_target_joint_pos[env_ids, :] = joint_pos + self._robot.set_joint_position_target(self.ctrl_target_joint_pos[env_ids], env_ids=env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + self._robot.reset() + self._robot.set_joint_effort_target(joint_effort, env_ids=env_ids) + + self.step_sim_no_action() + + def step_sim_no_action(self): + """Step the simulation without an action. Used for resets only. + + This method should only be called during resets when all environments + reset at the same time. + """ + self.scene.write_data_to_sim() + self.sim.step(render=False) + self.scene.update(dt=self.physics_dt) + self._compute_intermediate_values(dt=self.physics_dt) + + def randomize_initial_state(self, env_ids): + """Randomize initial state and perform any episode-level randomization.""" + # Disable gravity. + physics_sim_view = sim_utils.SimulationContext.instance().physics_sim_view + physics_sim_view.set_gravity(carb.Float3(0.0, 0.0, 0.0)) + + # (1.) Randomize fixed asset pose. + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + # (1.a.) Position + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] + fixed_asset_init_pos_rand = torch.tensor( + self.cfg_task.fixed_asset_init_pos_noise, dtype=torch.float32, device=self.device + ) + fixed_pos_init_rand = fixed_pos_init_rand @ torch.diag(fixed_asset_init_pos_rand) + fixed_state[:, 0:3] += fixed_pos_init_rand + self.scene.env_origins[env_ids] + # (1.b.) Orientation + fixed_orn_init_yaw = np.deg2rad(self.cfg_task.fixed_asset_init_orn_deg) + fixed_orn_yaw_range = np.deg2rad(self.cfg_task.fixed_asset_init_orn_range_deg) + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_orn_euler = fixed_orn_init_yaw + fixed_orn_yaw_range * rand_sample + fixed_orn_euler[:, 0:2] = 0.0 # Only change yaw. + fixed_orn_quat = torch_utils.quat_from_euler_xyz( + fixed_orn_euler[:, 0], fixed_orn_euler[:, 1], fixed_orn_euler[:, 2] + ) + fixed_state[:, 3:7] = fixed_orn_quat + # (1.c.) Velocity + fixed_state[:, 7:] = 0.0 # vel + # (1.d.) Update values. + self._fixed_asset.write_root_pose_to_sim(fixed_state[:, 0:7], env_ids=env_ids) + self._fixed_asset.write_root_velocity_to_sim(fixed_state[:, 7:], env_ids=env_ids) + self._fixed_asset.reset() + + # (1.e.) Noisy position observation. + fixed_asset_pos_noise = torch.randn((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_asset_pos_rand = torch.tensor(self.cfg.obs_rand.fixed_asset_pos, dtype=torch.float32, device=self.device) + fixed_asset_pos_noise = fixed_asset_pos_noise @ torch.diag(fixed_asset_pos_rand) + self.init_fixed_pos_obs_noise[:] = fixed_asset_pos_noise + + self.step_sim_no_action() + + # Compute the frame on the bolt that would be used as observation: fixed_pos_obs_frame + # For example, the tip of the bolt can be used as the observation frame + fixed_tip_pos_local = torch.zeros((self.num_envs, 3), device=self.device) + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height + if self.cfg_task.name == "gear_mesh": + fixed_tip_pos_local[:, 0] = self.cfg_task.fixed_asset_cfg.medium_gear_base_offset[0] + + _, fixed_tip_pos = torch_utils.tf_combine( + self.fixed_quat, + self.fixed_pos, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1), + fixed_tip_pos_local, + ) + self.fixed_pos_obs_frame[:] = fixed_tip_pos + + # (2) Move gripper to randomizes location above fixed asset. Keep trying until IK succeeds. + # (a) get position vector to target + bad_envs = env_ids.clone() + ik_attempt = 0 + + hand_down_quat = torch.zeros((self.num_envs, 4), dtype=torch.float32, device=self.device) + while True: + n_bad = bad_envs.shape[0] + + above_fixed_pos = fixed_tip_pos.clone() + above_fixed_pos[:, 2] += self.cfg_task.hand_init_pos[2] + + rand_sample = torch.rand((n_bad, 3), dtype=torch.float32, device=self.device) + above_fixed_pos_rand = 2 * (rand_sample - 0.5) # [-1, 1] + hand_init_pos_rand = torch.tensor(self.cfg_task.hand_init_pos_noise, device=self.device) + above_fixed_pos_rand = above_fixed_pos_rand @ torch.diag(hand_init_pos_rand) + above_fixed_pos[bad_envs] += above_fixed_pos_rand + + # (b) get random orientation facing down + hand_down_euler = ( + torch.tensor(self.cfg_task.hand_init_orn, device=self.device).unsqueeze(0).repeat(n_bad, 1) + ) + + rand_sample = torch.rand((n_bad, 3), dtype=torch.float32, device=self.device) + above_fixed_orn_noise = 2 * (rand_sample - 0.5) # [-1, 1] + hand_init_orn_rand = torch.tensor(self.cfg_task.hand_init_orn_noise, device=self.device) + above_fixed_orn_noise = above_fixed_orn_noise @ torch.diag(hand_init_orn_rand) + hand_down_euler += above_fixed_orn_noise + hand_down_quat[bad_envs, :] = torch_utils.quat_from_euler_xyz( + roll=hand_down_euler[:, 0], pitch=hand_down_euler[:, 1], yaw=hand_down_euler[:, 2] + ) + + # (c) iterative IK Method + pos_error, aa_error = self.set_pos_inverse_kinematics( + ctrl_target_fingertip_midpoint_pos=above_fixed_pos, + ctrl_target_fingertip_midpoint_quat=hand_down_quat, + env_ids=bad_envs, + ) + pos_error = torch.linalg.norm(pos_error, dim=1) > 1e-3 + angle_error = torch.norm(aa_error, dim=1) > 1e-3 + any_error = torch.logical_or(pos_error, angle_error) + bad_envs = bad_envs[any_error.nonzero(as_tuple=False).squeeze(-1)] + + # Check IK succeeded for all envs, otherwise try again for those envs + if bad_envs.shape[0] == 0: + break + + self._set_franka_to_default_pose( + joints=[0.00871, -0.10368, -0.00794, -1.49139, -0.00083, 1.38774, 0.0], env_ids=bad_envs + ) + + ik_attempt += 1 + + self.step_sim_no_action() + + # Add flanking gears after servo (so arm doesn't move them). + if self.cfg_task.name == "gear_mesh" and self.cfg_task.add_flanking_gears: + small_gear_state = self._small_gear_asset.data.default_root_state.clone()[env_ids] + small_gear_state[:, 0:7] = fixed_state[:, 0:7] + small_gear_state[:, 7:] = 0.0 # vel + self._small_gear_asset.write_root_pose_to_sim(small_gear_state[:, 0:7], env_ids=env_ids) + self._small_gear_asset.write_root_velocity_to_sim(small_gear_state[:, 7:], env_ids=env_ids) + self._small_gear_asset.reset() + + large_gear_state = self._large_gear_asset.data.default_root_state.clone()[env_ids] + large_gear_state[:, 0:7] = fixed_state[:, 0:7] + large_gear_state[:, 7:] = 0.0 # vel + self._large_gear_asset.write_root_pose_to_sim(large_gear_state[:, 0:7], env_ids=env_ids) + self._large_gear_asset.write_root_velocity_to_sim(large_gear_state[:, 7:], env_ids=env_ids) + self._large_gear_asset.reset() + + # (3) Randomize asset-in-gripper location. + # flip gripper z orientation + flip_z_quat = torch.tensor([0.0, 0.0, 1.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + fingertip_flipped_quat, fingertip_flipped_pos = torch_utils.tf_combine( + q1=self.fingertip_midpoint_quat, + t1=self.fingertip_midpoint_pos, + q2=flip_z_quat, + t2=torch.zeros((self.num_envs, 3), device=self.device), + ) + + # get default gripper in asset transform + held_asset_relative_pos, held_asset_relative_quat = self.get_handheld_asset_relative_pose() + asset_in_hand_quat, asset_in_hand_pos = torch_utils.tf_inverse( + held_asset_relative_quat, held_asset_relative_pos + ) + + translated_held_asset_quat, translated_held_asset_pos = torch_utils.tf_combine( + q1=fingertip_flipped_quat, t1=fingertip_flipped_pos, q2=asset_in_hand_quat, t2=asset_in_hand_pos + ) + + # Add asset in hand randomization + rand_sample = torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) + held_asset_pos_noise = 2 * (rand_sample - 0.5) # [-1, 1] + if self.cfg_task.name == "gear_mesh": + held_asset_pos_noise[:, 2] = -rand_sample[:, 2] # [-1, 0] + + held_asset_pos_noise_level = torch.tensor(self.cfg_task.held_asset_pos_noise, device=self.device) + held_asset_pos_noise = held_asset_pos_noise @ torch.diag(held_asset_pos_noise_level) + translated_held_asset_quat, translated_held_asset_pos = torch_utils.tf_combine( + q1=translated_held_asset_quat, + t1=translated_held_asset_pos, + q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1), + t2=held_asset_pos_noise, + ) + + held_state = self._held_asset.data.default_root_state.clone() + held_state[:, 0:3] = translated_held_asset_pos + self.scene.env_origins + held_state[:, 3:7] = translated_held_asset_quat + held_state[:, 7:] = 0.0 + self._held_asset.write_root_pose_to_sim(held_state[:, 0:7]) + self._held_asset.write_root_velocity_to_sim(held_state[:, 7:]) + self._held_asset.reset() + + # Close hand + # Set gains to use for quick resets. + reset_task_prop_gains = torch.tensor(self.cfg.ctrl.reset_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + self.task_prop_gains = reset_task_prop_gains + self.task_deriv_gains = factory_utils.get_deriv_gains( + reset_task_prop_gains, self.cfg.ctrl.reset_rot_deriv_scale + ) + + self.step_sim_no_action() + + grasp_time = 0.0 + while grasp_time < 0.25: + self.ctrl_target_joint_pos[env_ids, 7:] = 0.0 # Close gripper. + self.close_gripper_in_place() + self.step_sim_no_action() + grasp_time += self.sim.get_physics_dt() + + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + # Set initial actions to involve no-movement. Needed for EMA/correct penalties. + self.actions = torch.zeros_like(self.actions) + self.prev_actions = torch.zeros_like(self.actions) + + # Zero initial velocity. + self.ee_angvel_fd[:, :] = 0.0 + self.ee_linvel_fd[:, :] = 0.0 + + # Set initial gains for the episode. + self.task_prop_gains = self.default_gains + self.task_deriv_gains = factory_utils.get_deriv_gains(self.default_gains) + + physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0e6b5db0a73c9a081cc7a44179a2607e412a7eb7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_env_cfg.py @@ -0,0 +1,209 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass + +from .factory_tasks_cfg import ASSET_DIR, FactoryTask, GearMesh, NutThread, PegInsert + +OBS_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, +} + +STATE_DIM_CFG = { + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "joint_pos": 7, + "held_pos": 3, + "held_pos_rel_fixed": 3, + "held_quat": 4, + "fixed_pos": 3, + "fixed_quat": 4, + "task_prop_gains": 6, + "ema_factor": 1, + "pos_threshold": 3, + "rot_threshold": 3, +} + + +@configclass +class ObsRandCfg: + fixed_asset_pos = [0.001, 0.001, 0.001] + + +@configclass +class CtrlCfg: + ema_factor = 0.2 + + pos_action_bounds = [0.05, 0.05, 0.05] + rot_action_bounds = [1.0, 1.0, 1.0] + + pos_action_threshold = [0.02, 0.02, 0.02] + rot_action_threshold = [0.097, 0.097, 0.097] + + reset_joints = [1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01] + reset_task_prop_gains = [300, 300, 300, 20, 20, 20] + reset_rot_deriv_scale = 10.0 + default_task_prop_gains = [100, 100, 100, 30, 30, 30] + + # Null space parameters. + default_dof_pos_tensor = [-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754] + kp_null = 10.0 + kd_null = 6.3246 + + +@configclass +class FactoryEnvCfg(DirectRLEnvCfg): + decimation = 8 + action_space = 6 + # num_*: will be overwritten to correspond to obs_order, state_order. + observation_space = 21 + state_space = 72 + obs_order: list = ["fingertip_pos_rel_fixed", "fingertip_quat", "ee_linvel", "ee_angvel"] + state_order: list = [ + "fingertip_pos", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "joint_pos", + "held_pos", + "held_pos_rel_fixed", + "held_quat", + "fixed_pos", + "fixed_quat", + ] + + task_name: str = "peg_insert" # peg_insert, gear_mesh, nut_thread + task: FactoryTask = FactoryTask() + obs_rand: ObsRandCfg = ObsRandCfg() + ctrl: CtrlCfg = CtrlCfg() + + episode_length_s = 10.0 # Probably need to override. + sim: SimulationCfg = SimulationCfg( + device="cuda:0", + dt=1 / 120, + gravity=(0.0, 0.0, -9.81), + physx=PhysxCfg( + solver_type=1, + max_position_iteration_count=192, # Important to avoid interpenetration. + max_velocity_iteration_count=1, + bounce_threshold_velocity=0.2, + friction_offset_threshold=0.01, + friction_correlation_distance=0.00625, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + gpu_collision_stack_size=2**28, + gpu_max_num_partitions=1, # Important for stable simulation. + ), + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + ) + + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=128, env_spacing=2.0, clone_in_fabric=True) + + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ASSET_DIR}/franka_mimic.usd", + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 0.00871, + "panda_joint2": -0.10368, + "panda_joint3": -0.00794, + "panda_joint4": -1.49139, + "panda_joint5": -0.00083, + "panda_joint6": 1.38774, + "panda_joint7": 0.0, + "panda_finger_joint2": 0.04, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + actuators={ + "panda_arm1": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit_sim=87, + velocity_limit_sim=124.6, + ), + "panda_arm2": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit_sim=12, + velocity_limit_sim=149.5, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint[1-2]"], + effort_limit_sim=40.0, + velocity_limit_sim=0.04, + stiffness=7500.0, + damping=173.0, + friction=0.1, + armature=0.0, + ), + }, + ) + + +@configclass +class FactoryTaskPegInsertCfg(FactoryEnvCfg): + task_name = "peg_insert" + task = PegInsert() + episode_length_s = 10.0 + + +@configclass +class FactoryTaskGearMeshCfg(FactoryEnvCfg): + task_name = "gear_mesh" + task = GearMesh() + episode_length_s = 20.0 + + +@configclass +class FactoryTaskNutThreadCfg(FactoryEnvCfg): + task_name = "nut_thread" + task = NutThread() + episode_length_s = 30.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_tasks_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_tasks_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c631856816cb1cf5390fd0b86c61f5f475ab3173 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_tasks_cfg.py @@ -0,0 +1,447 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +ASSET_DIR = f"{ISAACLAB_NUCLEUS_DIR}/Factory" + + +@configclass +class FixedAssetCfg: + usd_path: str = "" + diameter: float = 0.0 + height: float = 0.0 + base_height: float = 0.0 # Used to compute held asset CoM. + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class HeldAssetCfg: + usd_path: str = "" + diameter: float = 0.0 # Used for gripper width. + height: float = 0.0 + friction: float = 0.75 + mass: float = 0.05 + + +@configclass +class RobotCfg: + robot_usd: str = "" + franka_fingerpad_length: float = 0.017608 + friction: float = 0.75 + + +@configclass +class FactoryTask: + robot_cfg: RobotCfg = RobotCfg() + name: str = "" + duration_s = 5.0 + + fixed_asset_cfg: FixedAssetCfg = FixedAssetCfg() + held_asset_cfg: HeldAssetCfg = HeldAssetCfg() + asset_size: float = 0.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 2.356] + hand_init_orn_noise: list = [0.0, 0.0, 1.57] + + # Action + unidirectional_rot: bool = False + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 360.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.0, 0.006, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + # Reward + ee_success_yaw: float = 0.0 # nut_thread task only. + action_penalty_ee_scale: float = 0.0 + action_grad_penalty_scale: float = 0.0 + # Reward function details can be found in Appendix B of https://arxiv.org/pdf/2408.04587. + # Multi-scale keypoints are used to capture different phases of the task. + # Each reward passes the keypoint distance, x, through a squashing function: + # r(x) = 1/(exp(-ax) + b + exp(ax)). + # Each list defines [a, b] which control the slope and maximum of the squashing function. + num_keypoints: int = 4 + keypoint_scale: float = 0.15 + keypoint_coef_baseline: list = [5, 4] # General movement towards fixed object. + keypoint_coef_coarse: list = [50, 2] # Movement to align the assets. + keypoint_coef_fine: list = [100, 0] # Smaller distances for threading or last-inch insertion. + # Fixed-asset height fraction for which different bonuses are rewarded (see individual tasks). + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + + +@configclass +class Peg8mm(HeldAssetCfg): + usd_path = f"{ASSET_DIR}/factory_peg_8mm.usd" + diameter = 0.007986 + height = 0.050 + mass = 0.019 + + +@configclass +class Hole8mm(FixedAssetCfg): + usd_path = f"{ASSET_DIR}/factory_hole_8mm.usd" + diameter = 0.0081 + height = 0.025 + base_height = 0.0 + + +@configclass +class PegInsert(FactoryTask): + name = "peg_insert" + fixed_asset_cfg = Hole8mm() + held_asset_cfg = Peg8mm() + asset_size = 8.0 + duration_s = 10.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.047] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 0.0] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 360.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.003, 0.0, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = 0.0 + + # Rewards + keypoint_coef_baseline: list = [5, 4] + keypoint_coef_coarse: list = [50, 2] + keypoint_coef_fine: list = [100, 0] + # Fraction of socket height. + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + + fixed_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + held_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + + +@configclass +class GearBase(FixedAssetCfg): + usd_path = f"{ASSET_DIR}/factory_gear_base.usd" + height = 0.02 + base_height = 0.005 + small_gear_base_offset = [5.075e-2, 0.0, 0.0] + medium_gear_base_offset = [2.025e-2, 0.0, 0.0] + large_gear_base_offset = [-3.025e-2, 0.0, 0.0] + + +@configclass +class MediumGear(HeldAssetCfg): + usd_path = f"{ASSET_DIR}/factory_gear_medium.usd" + diameter = 0.03 # Used for gripper width. + height: float = 0.03 + mass = 0.012 + + +@configclass +class GearMesh(FactoryTask): + name = "gear_mesh" + fixed_asset_cfg = GearBase() + held_asset_cfg = MediumGear() + duration_s = 20.0 + + small_gear_usd = f"{ASSET_DIR}/factory_gear_small.usd" + large_gear_usd = f"{ASSET_DIR}/factory_gear_large.usd" + + small_gear_cfg: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/SmallGearAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=small_gear_usd, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=0.019), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + + large_gear_cfg: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/LargeGearAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=large_gear_usd, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=0.019), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + + # Gears Asset + add_flanking_gears = True + add_flanking_gears_prob = 1.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.035] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 0.0] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 15.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.003, 0.0, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + keypoint_coef_baseline: list = [5, 4] + keypoint_coef_coarse: list = [50, 2] + keypoint_coef_fine: list = [100, 0] + # Fraction of gear peg height. + success_threshold: float = 0.05 + engage_threshold: float = 0.9 + + fixed_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + held_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + + +@configclass +class NutM16(HeldAssetCfg): + usd_path = f"{ASSET_DIR}/factory_nut_m16.usd" + diameter = 0.024 + height = 0.01 + mass = 0.03 + friction = 0.01 # Additive with the nut means friction is (-0.25 + 0.75)/2 = 0.25 + + +@configclass +class BoltM16(FixedAssetCfg): + usd_path = f"{ASSET_DIR}/factory_bolt_m16.usd" + diameter = 0.024 + height = 0.025 + base_height = 0.01 + thread_pitch = 0.002 + + +@configclass +class NutThread(FactoryTask): + name = "nut_thread" + fixed_asset_cfg = BoltM16() + held_asset_cfg = NutM16() + asset_size = 16.0 + duration_s = 30.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 1.83] + hand_init_orn_noise: list = [0.0, 0.0, 0.26] + + # Action + unidirectional_rot: bool = True + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 120.0 + fixed_asset_init_orn_range_deg: float = 30.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.0, 0.003, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + # Reward. + ee_success_yaw = 0.0 + keypoint_coef_baseline: list = [100, 2] + keypoint_coef_coarse: list = [500, 2] # 100, 2 + keypoint_coef_fine: list = [1500, 0] # 500, 0 + # Fraction of thread-height. + success_threshold: float = 0.375 + engage_threshold: float = 0.5 + keypoint_scale: float = 0.05 + + fixed_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) + held_asset: ArticulationCfg = ArticulationCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} + ), + actuators={}, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_utils.py b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..962b3872bf09841263bc216b5f4d7862f8b2264f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/factory/factory_utils.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import torch + +import isaacsim.core.utils.torch as torch_utils + + +def get_keypoint_offsets(num_keypoints, device): + """Get uniformly-spaced keypoints along a line of unit length, centered at 0.""" + keypoint_offsets = torch.zeros((num_keypoints, 3), device=device) + keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=device) - 0.5 + return keypoint_offsets + + +def get_deriv_gains(prop_gains, rot_deriv_scale=1.0): + """Set robot gains using critical damping.""" + deriv_gains = 2 * torch.sqrt(prop_gains) + deriv_gains[:, 3:6] /= rot_deriv_scale + return deriv_gains + + +def wrap_yaw(angle): + """Ensure yaw stays within range.""" + return torch.where(angle > np.deg2rad(235), angle - 2 * np.pi, angle) + + +def set_friction(asset, value, num_envs): + """Update material properties for a given asset.""" + materials = asset.root_physx_view.get_material_properties() + materials[..., 0] = value # Static friction. + materials[..., 1] = value # Dynamic friction. + env_ids = torch.arange(num_envs, device="cpu") + asset.root_physx_view.set_material_properties(materials, env_ids) + + +def set_body_inertias(robot, num_envs): + """Note: this is to account for the asset_options.armature parameter in IGE.""" + inertias = robot.root_physx_view.get_inertias() + offset = torch.zeros_like(inertias) + offset[:, :, [0, 4, 8]] += 0.01 + new_inertias = inertias + offset + robot.root_physx_view.set_inertias(new_inertias, torch.arange(num_envs)) + + +def get_held_base_pos_local(task_name, fixed_asset_cfg, num_envs, device): + """Get transform between asset default frame and geometric base frame.""" + held_base_x_offset = 0.0 + if task_name == "peg_insert": + held_base_z_offset = 0.0 + elif task_name == "gear_mesh": + gear_base_offset = fixed_asset_cfg.medium_gear_base_offset + held_base_x_offset = gear_base_offset[0] + held_base_z_offset = gear_base_offset[2] + elif task_name == "nut_thread": + held_base_z_offset = fixed_asset_cfg.base_height + else: + raise NotImplementedError("Task not implemented") + + held_base_pos_local = torch.tensor([0.0, 0.0, 0.0], device=device).repeat((num_envs, 1)) + held_base_pos_local[:, 0] = held_base_x_offset + held_base_pos_local[:, 2] = held_base_z_offset + + return held_base_pos_local + + +def get_held_base_pose(held_pos, held_quat, task_name, fixed_asset_cfg, num_envs, device): + """Get current poses for keypoint and success computation.""" + held_base_pos_local = get_held_base_pos_local(task_name, fixed_asset_cfg, num_envs, device) + held_base_quat_local = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).unsqueeze(0).repeat(num_envs, 1) + + held_base_quat, held_base_pos = torch_utils.tf_combine( + held_quat, held_pos, held_base_quat_local, held_base_pos_local + ) + return held_base_pos, held_base_quat + + +def get_target_held_base_pose(fixed_pos, fixed_quat, task_name, fixed_asset_cfg, num_envs, device): + """Get target poses for keypoint and success computation.""" + fixed_success_pos_local = torch.zeros((num_envs, 3), device=device) + if task_name == "peg_insert": + fixed_success_pos_local[:, 2] = 0.0 + elif task_name == "gear_mesh": + gear_base_offset = fixed_asset_cfg.medium_gear_base_offset + fixed_success_pos_local[:, 0] = gear_base_offset[0] + fixed_success_pos_local[:, 2] = gear_base_offset[2] + elif task_name == "nut_thread": + head_height = fixed_asset_cfg.base_height + shank_length = fixed_asset_cfg.height + thread_pitch = fixed_asset_cfg.thread_pitch + fixed_success_pos_local[:, 2] = head_height + shank_length - thread_pitch * 1.5 + else: + raise NotImplementedError("Task not implemented") + fixed_success_quat_local = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).unsqueeze(0).repeat(num_envs, 1) + + target_held_base_quat, target_held_base_pos = torch_utils.tf_combine( + fixed_quat, fixed_pos, fixed_success_quat_local, fixed_success_pos_local + ) + return target_held_base_pos, target_held_base_quat + + +def squashing_fn(x, a, b): + """Compute bounded reward function.""" + return 1 / (torch.exp(a * x) + b + torch.exp(-a * x)) + + +def collapse_obs_dict(obs_dict, obs_order): + """Stack observations in given order.""" + obs_tensors = [obs_dict[obs_name] for obs_name in obs_order] + obs_tensors = torch.cat(obs_tensors, dim=-1) + return obs_tensors diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6532e3c3b6b54d91e3a444f00375ba9dc0e81cd8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/__init__.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Forge-PegInsert-Direct-v0", + entry_point=f"{__name__}.forge_env:ForgeEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.forge_env_cfg:ForgeTaskPegInsertCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Forge-GearMesh-Direct-v0", + entry_point=f"{__name__}.forge_env:ForgeEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.forge_env_cfg:ForgeTaskGearMeshCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Forge-NutThread-Direct-v0", + entry_point=f"{__name__}.forge_env:ForgeEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.forge_env_cfg:ForgeTaskNutThreadCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg_nut_thread.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..08081f97e03567dcbdc3725b888871876b0f95c0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 0 + algo: + name: a2c_continuous + + env: + clip_actions: 1.0 + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: False + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + load_checkpoint: False + load_path: "" + + config: + name: Forge + device: cuda:0 + full_experiment_name: test + env_name: rlgpu + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 128 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.995 + tau: 0.95 + learning_rate: 1.0e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 10 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 128 + minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 128 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 512 + mini_epochs: 4 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + player: + deterministic: False diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg_nut_thread.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg_nut_thread.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a73dd178f6e0bf338017611b4827b112cb72d7d0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/agents/rl_games_ppo_cfg_nut_thread.yaml @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 0 + algo: + name: a2c_continuous + + env: + clip_actions: 1.0 + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: False + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + load_checkpoint: False + load_path: "" + + config: + name: Forge + device: cuda:0 + full_experiment_name: test + env_name: rlgpu + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 128 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.995 + tau: 0.95 + learning_rate: 1.0e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 10 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 256 + minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 128 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 512 + mini_epochs: 4 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + player: + deterministic: False diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env.py new file mode 100644 index 0000000000000000000000000000000000000000..75484cbd8f17968dafcbd02030ca19fcb6c0680e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env.py @@ -0,0 +1,388 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import numpy as np +import torch + +import isaacsim.core.utils.torch as torch_utils + +from isaaclab.utils.math import axis_angle_from_quat + +from isaaclab_tasks.direct.factory import factory_utils +from isaaclab_tasks.direct.factory.factory_env import FactoryEnv + +from . import forge_utils +from .forge_env_cfg import ForgeEnvCfg + + +class ForgeEnv(FactoryEnv): + cfg: ForgeEnvCfg + + def __init__(self, cfg: ForgeEnvCfg, render_mode: str | None = None, **kwargs): + """Initialize additional randomization and logging tensors.""" + super().__init__(cfg, render_mode, **kwargs) + + # Success prediction. + self.success_pred_scale = 0.0 + self.first_pred_success_tx = {} + for thresh in [0.5, 0.6, 0.7, 0.8, 0.9]: + self.first_pred_success_tx[thresh] = torch.zeros(self.num_envs, device=self.device, dtype=torch.long) + + # Flip quaternions. + self.flip_quats = torch.ones((self.num_envs,), dtype=torch.float32, device=self.device) + + # Force sensor information. + self.force_sensor_body_idx = self._robot.body_names.index("force_sensor") + self.force_sensor_smooth = torch.zeros((self.num_envs, 6), device=self.device) + self.force_sensor_world_smooth = torch.zeros((self.num_envs, 6), device=self.device) + + # Set nominal dynamics parameters for randomization. + self.default_gains = torch.tensor(self.cfg.ctrl.default_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + self.default_pos_threshold = torch.tensor(self.cfg.ctrl.pos_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.default_rot_threshold = torch.tensor(self.cfg.ctrl.rot_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.default_dead_zone = torch.tensor(self.cfg.ctrl.default_dead_zone, device=self.device).repeat( + (self.num_envs, 1) + ) + + self.pos_threshold = self.default_pos_threshold.clone() + self.rot_threshold = self.default_rot_threshold.clone() + + def _compute_intermediate_values(self, dt): + """Add noise to observations for force sensing.""" + super()._compute_intermediate_values(dt) + + # Add noise to fingertip position. + pos_noise_level, rot_noise_level_deg = self.cfg.obs_rand.fingertip_pos, self.cfg.obs_rand.fingertip_rot_deg + fingertip_pos_noise = torch.randn((self.num_envs, 3), dtype=torch.float32, device=self.device) + fingertip_pos_noise = fingertip_pos_noise @ torch.diag( + torch.tensor([pos_noise_level, pos_noise_level, pos_noise_level], dtype=torch.float32, device=self.device) + ) + self.noisy_fingertip_pos = self.fingertip_midpoint_pos + fingertip_pos_noise + + rot_noise_axis = torch.randn((self.num_envs, 3), dtype=torch.float32, device=self.device) + rot_noise_axis /= torch.linalg.norm(rot_noise_axis, dim=1, keepdim=True) + rot_noise_angle = torch.randn((self.num_envs,), dtype=torch.float32, device=self.device) * np.deg2rad( + rot_noise_level_deg + ) + self.noisy_fingertip_quat = torch_utils.quat_mul( + self.fingertip_midpoint_quat, torch_utils.quat_from_angle_axis(rot_noise_angle, rot_noise_axis) + ) + self.noisy_fingertip_quat[:, [0, 3]] = 0.0 + self.noisy_fingertip_quat = self.noisy_fingertip_quat * self.flip_quats.unsqueeze(-1) + + # Repeat finite differencing with noisy fingertip positions. + self.ee_linvel_fd = (self.noisy_fingertip_pos - self.prev_fingertip_pos) / dt + self.prev_fingertip_pos = self.noisy_fingertip_pos.clone() + + # Add state differences if velocity isn't being added. + rot_diff_quat = torch_utils.quat_mul( + self.noisy_fingertip_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) + ) + rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) + rot_diff_aa = axis_angle_from_quat(rot_diff_quat) + self.ee_angvel_fd = rot_diff_aa / dt + self.ee_angvel_fd[:, 0:2] = 0.0 + self.prev_fingertip_quat = self.noisy_fingertip_quat.clone() + + # Update and smooth force values. + self.force_sensor_world = self._robot.root_physx_view.get_link_incoming_joint_force()[ + :, self.force_sensor_body_idx + ] + + alpha = self.cfg.ft_smoothing_factor + self.force_sensor_world_smooth = alpha * self.force_sensor_world + (1 - alpha) * self.force_sensor_world_smooth + + self.force_sensor_smooth = torch.zeros_like(self.force_sensor_world) + identity_quat = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + self.force_sensor_smooth[:, :3], self.force_sensor_smooth[:, 3:6] = forge_utils.change_FT_frame( + self.force_sensor_world_smooth[:, 0:3], + self.force_sensor_world_smooth[:, 3:6], + (identity_quat, torch.zeros((self.num_envs, 3), device=self.device)), + (identity_quat, self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise), + ) + + # Compute noisy force values. + force_noise = torch.randn((self.num_envs, 3), dtype=torch.float32, device=self.device) + force_noise *= self.cfg.obs_rand.ft_force + self.noisy_force = self.force_sensor_smooth[:, 0:3] + force_noise + + def _get_observations(self): + """Add additional FORGE observations.""" + obs_dict, state_dict = self._get_factory_obs_state_dict() + + noisy_fixed_pos = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + prev_actions = self.actions.clone() + prev_actions[:, 3:5] = 0.0 + + obs_dict.update( + { + "fingertip_pos": self.noisy_fingertip_pos, + "fingertip_pos_rel_fixed": self.noisy_fingertip_pos - noisy_fixed_pos, + "fingertip_quat": self.noisy_fingertip_quat, + "force_threshold": self.contact_penalty_thresholds[:, None], + "ft_force": self.noisy_force, + "prev_actions": prev_actions, + } + ) + + state_dict.update( + { + "ema_factor": self.ema_factor, + "ft_force": self.force_sensor_smooth[:, 0:3], + "force_threshold": self.contact_penalty_thresholds[:, None], + "prev_actions": prev_actions, + } + ) + + obs_tensors = factory_utils.collapse_obs_dict(obs_dict, self.cfg.obs_order + ["prev_actions"]) + state_tensors = factory_utils.collapse_obs_dict(state_dict, self.cfg.state_order + ["prev_actions"]) + return {"policy": obs_tensors, "critic": state_tensors} + + def _apply_action(self): + """FORGE actions are defined as targets relative to the fixed asset.""" + if self.last_update_timestamp < self._robot._data._sim_timestamp: + self._compute_intermediate_values(dt=self.physics_dt) + + # Step (0): Scale actions to allowed range. + pos_actions = self.actions[:, 0:3] + pos_actions = pos_actions @ torch.diag(torch.tensor(self.cfg.ctrl.pos_action_bounds, device=self.device)) + + rot_actions = self.actions[:, 3:6] + rot_actions = rot_actions @ torch.diag(torch.tensor(self.cfg.ctrl.rot_action_bounds, device=self.device)) + + # Step (1): Compute desired pose targets in EE frame. + # (1.a) Position. Action frame is assumed to be the top of the bolt (noisy estimate). + fixed_pos_action_frame = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + ctrl_target_fingertip_preclipped_pos = fixed_pos_action_frame + pos_actions + # (1.b) Enforce rotation action constraints. + rot_actions[:, 0:2] = 0.0 + + # Assumes joint limit is in (+x, -y)-quadrant of world frame. + rot_actions[:, 2] = np.deg2rad(-180.0) + np.deg2rad(270.0) * (rot_actions[:, 2] + 1.0) / 2.0 # Joint limit. + # (1.c) Get desired orientation target. + bolt_frame_quat = torch_utils.quat_from_euler_xyz( + roll=rot_actions[:, 0], pitch=rot_actions[:, 1], yaw=rot_actions[:, 2] + ) + + rot_180_euler = torch.tensor([np.pi, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) + quat_bolt_to_ee = torch_utils.quat_from_euler_xyz( + roll=rot_180_euler[:, 0], pitch=rot_180_euler[:, 1], yaw=rot_180_euler[:, 2] + ) + + ctrl_target_fingertip_preclipped_quat = torch_utils.quat_mul(quat_bolt_to_ee, bolt_frame_quat) + + # Step (2): Clip targets if they are too far from current EE pose. + # (2.a): Clip position targets. + self.delta_pos = ctrl_target_fingertip_preclipped_pos - self.fingertip_midpoint_pos # Used for action_penalty. + pos_error_clipped = torch.clip(self.delta_pos, -self.pos_threshold, self.pos_threshold) + ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_error_clipped + + # (2.b) Clip orientation targets. Use Euler angles. We assume we are near upright, so + # clipping yaw will effectively cause slow motions. When we clip, we also need to make + # sure we avoid the joint limit. + + # (2.b.i) Get current and desired Euler angles. + curr_roll, curr_pitch, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) + desired_roll, desired_pitch, desired_yaw = torch_utils.get_euler_xyz(ctrl_target_fingertip_preclipped_quat) + desired_xyz = torch.stack([desired_roll, desired_pitch, desired_yaw], dim=1) + + # (2.b.ii) Correct the direction of motion to avoid joint limit. + # Map yaws between [-125, 235] degrees + # (so that angles appear on a continuous span uninterrupted by the joint limit) + curr_yaw = factory_utils.wrap_yaw(curr_yaw) + desired_yaw = factory_utils.wrap_yaw(desired_yaw) + + # (2.b.iii) Clip motion in the correct direction. + self.delta_yaw = desired_yaw - curr_yaw # Used later for action_penalty. + clipped_yaw = torch.clip(self.delta_yaw, -self.rot_threshold[:, 2], self.rot_threshold[:, 2]) + desired_xyz[:, 2] = curr_yaw + clipped_yaw + + # (2.b.iv) Clip roll and pitch. + desired_roll = torch.where(desired_roll < 0.0, desired_roll + 2 * torch.pi, desired_roll) + desired_pitch = torch.where(desired_pitch < 0.0, desired_pitch + 2 * torch.pi, desired_pitch) + + delta_roll = desired_roll - curr_roll + clipped_roll = torch.clip(delta_roll, -self.rot_threshold[:, 0], self.rot_threshold[:, 0]) + desired_xyz[:, 0] = curr_roll + clipped_roll + + curr_pitch = torch.where(curr_pitch > torch.pi, curr_pitch - 2 * torch.pi, curr_pitch) + desired_pitch = torch.where(desired_pitch > torch.pi, desired_pitch - 2 * torch.pi, desired_pitch) + + delta_pitch = desired_pitch - curr_pitch + clipped_pitch = torch.clip(delta_pitch, -self.rot_threshold[:, 1], self.rot_threshold[:, 1]) + desired_xyz[:, 1] = curr_pitch + clipped_pitch + + ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=desired_xyz[:, 0], pitch=desired_xyz[:, 1], yaw=desired_xyz[:, 2] + ) + + self.generate_ctrl_signals( + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + ctrl_target_gripper_dof_pos=0.0, + ) + + def _get_rewards(self): + """FORGE reward includes a contact penalty and success prediction error.""" + # Use same base rewards as Factory. + rew_buf = super()._get_rewards() + + rew_dict, rew_scales = {}, {} + # Calculate action penalty for the asset-relative action space. + pos_error = torch.norm(self.delta_pos, p=2, dim=-1) / self.cfg.ctrl.pos_action_threshold[0] + rot_error = torch.abs(self.delta_yaw) / self.cfg.ctrl.rot_action_threshold[0] + # Contact penalty. + contact_force = torch.norm(self.force_sensor_smooth[:, 0:3], p=2, dim=-1, keepdim=False) + contact_penalty = torch.nn.functional.relu(contact_force - self.contact_penalty_thresholds) + # Add success prediction rewards. + check_rot = self.cfg_task.name == "nut_thread" + true_successes = self._get_curr_successes( + success_threshold=self.cfg_task.success_threshold, check_rot=check_rot + ) + policy_success_pred = (self.actions[:, 6] + 1) / 2 # rescale from [-1, 1] to [0, 1] + success_pred_error = (true_successes.float() - policy_success_pred).abs() + # Delay success prediction penalty until some successes have occurred. + if true_successes.float().mean() >= self.cfg_task.delay_until_ratio: + self.success_pred_scale = 1.0 + + # Add new FORGE reward terms. + rew_dict = { + "action_penalty_asset": pos_error + rot_error, + "contact_penalty": contact_penalty, + "success_pred_error": success_pred_error, + } + rew_scales = { + "action_penalty_asset": -self.cfg_task.action_penalty_asset_scale, + "contact_penalty": -self.cfg_task.contact_penalty_scale, + "success_pred_error": -self.success_pred_scale, + } + for rew_name, rew in rew_dict.items(): + rew_buf += rew_dict[rew_name] * rew_scales[rew_name] + + self._log_forge_metrics(rew_dict, policy_success_pred) + return rew_buf + + def _reset_idx(self, env_ids): + """Perform additional randomizations.""" + super()._reset_idx(env_ids) + + # Compute initial action for correct EMA computation. + fixed_pos_action_frame = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + pos_actions = self.fingertip_midpoint_pos - fixed_pos_action_frame + pos_action_bounds = torch.tensor(self.cfg.ctrl.pos_action_bounds, device=self.device) + pos_actions = pos_actions @ torch.diag(1.0 / pos_action_bounds) + self.actions[:, 0:3] = self.prev_actions[:, 0:3] = pos_actions + + # Relative yaw to bolt. + unrot_180_euler = torch.tensor([-np.pi, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) + unrot_quat = torch_utils.quat_from_euler_xyz( + roll=unrot_180_euler[:, 0], pitch=unrot_180_euler[:, 1], yaw=unrot_180_euler[:, 2] + ) + + fingertip_quat_rel_bolt = torch_utils.quat_mul(unrot_quat, self.fingertip_midpoint_quat) + fingertip_yaw_bolt = torch_utils.get_euler_xyz(fingertip_quat_rel_bolt)[-1] + fingertip_yaw_bolt = torch.where( + fingertip_yaw_bolt > torch.pi / 2, fingertip_yaw_bolt - 2 * torch.pi, fingertip_yaw_bolt + ) + fingertip_yaw_bolt = torch.where( + fingertip_yaw_bolt < -torch.pi, fingertip_yaw_bolt + 2 * torch.pi, fingertip_yaw_bolt + ) + + yaw_action = (fingertip_yaw_bolt + np.deg2rad(180.0)) / np.deg2rad(270.0) * 2.0 - 1.0 + self.actions[:, 5] = self.prev_actions[:, 5] = yaw_action + self.actions[:, 6] = self.prev_actions[:, 6] = -1.0 + + # EMA randomization. + ema_rand = torch.rand((self.num_envs, 1), dtype=torch.float32, device=self.device) + ema_lower, ema_upper = self.cfg.ctrl.ema_factor_range + self.ema_factor = ema_lower + ema_rand * (ema_upper - ema_lower) + + # Set initial gains for the episode. + prop_gains = self.default_gains.clone() + self.pos_threshold = self.default_pos_threshold.clone() + self.rot_threshold = self.default_rot_threshold.clone() + prop_gains = forge_utils.get_random_prop_gains( + prop_gains, self.cfg.ctrl.task_prop_gains_noise_level, self.num_envs, self.device + ) + self.pos_threshold = forge_utils.get_random_prop_gains( + self.pos_threshold, self.cfg.ctrl.pos_threshold_noise_level, self.num_envs, self.device + ) + self.rot_threshold = forge_utils.get_random_prop_gains( + self.rot_threshold, self.cfg.ctrl.rot_threshold_noise_level, self.num_envs, self.device + ) + self.task_prop_gains = prop_gains + self.task_deriv_gains = factory_utils.get_deriv_gains(prop_gains) + + contact_rand = torch.rand((self.num_envs,), dtype=torch.float32, device=self.device) + contact_lower, contact_upper = self.cfg.task.contact_penalty_threshold_range + self.contact_penalty_thresholds = contact_lower + contact_rand * (contact_upper - contact_lower) + + self.dead_zone_thresholds = ( + torch.rand((self.num_envs, 6), dtype=torch.float32, device=self.device) * self.default_dead_zone + ) + + self.force_sensor_world_smooth[:, :] = 0.0 + + self.flip_quats = torch.ones((self.num_envs,), dtype=torch.float32, device=self.device) + rand_flips = torch.rand(self.num_envs) > 0.5 + self.flip_quats[rand_flips] = -1.0 + + def _reset_buffers(self, env_ids): + """Reset additional logging metrics.""" + super()._reset_buffers(env_ids) + # Reset success pred metrics. + for thresh in [0.5, 0.6, 0.7, 0.8, 0.9]: + self.first_pred_success_tx[thresh][env_ids] = 0 + + def _log_forge_metrics(self, rew_dict, policy_success_pred): + """Log metrics to evaluate success prediction performance.""" + for rew_name, rew in rew_dict.items(): + self.extras[f"logs_rew_{rew_name}"] = rew.mean() + + for thresh, first_success_tx in self.first_pred_success_tx.items(): + curr_predicted_success = policy_success_pred > thresh + first_success_idxs = torch.logical_and(curr_predicted_success, first_success_tx == 0) + + first_success_tx[:] = torch.where(first_success_idxs, self.episode_length_buf, first_success_tx) + + # Only log at the end. + if torch.any(self.reset_buf): + # Log prediction delay. + delay_ids = torch.logical_and(self.ep_success_times != 0, first_success_tx != 0) + delay_times = (first_success_tx[delay_ids] - self.ep_success_times[delay_ids]).sum() / delay_ids.sum() + if delay_ids.sum().item() > 0: + self.extras[f"early_term_delay_all/{thresh}"] = delay_times + + correct_delay_ids = torch.logical_and(delay_ids, first_success_tx > self.ep_success_times) + correct_delay_times = ( + first_success_tx[correct_delay_ids] - self.ep_success_times[correct_delay_ids] + ).sum() / correct_delay_ids.sum() + if correct_delay_ids.sum().item() > 0: + self.extras[f"early_term_delay_correct/{thresh}"] = correct_delay_times.item() + + # Log early-term success rate (for all episodes we have "stopped", did we succeed?). + pred_success_idxs = first_success_tx != 0 # Episodes which we have predicted success. + + true_success_preds = torch.logical_and( + self.ep_success_times[pred_success_idxs] > 0, # Success has actually occurred. + self.ep_success_times[pred_success_idxs] + < first_success_tx[pred_success_idxs], # Success occurred before we predicted it. + ) + + num_pred_success = pred_success_idxs.sum().item() + et_prec = true_success_preds.sum() / num_pred_success + if num_pred_success > 0: + self.extras[f"early_term_precision/{thresh}"] = et_prec + + true_success_idxs = self.ep_success_times > 0 + num_true_success = true_success_idxs.sum().item() + et_recall = true_success_preds.sum() / num_true_success + if num_true_success > 0: + self.extras[f"early_term_recall/{thresh}"] = et_recall diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5da73aa2ae352926692d6aa40174241b7a13a3c8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_env_cfg.py @@ -0,0 +1,150 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.envs.mdp as mdp +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.factory.factory_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, CtrlCfg, FactoryEnvCfg, ObsRandCfg + +from .forge_events import randomize_dead_zone +from .forge_tasks_cfg import ForgeGearMesh, ForgeNutThread, ForgePegInsert, ForgeTask + +OBS_DIM_CFG.update({"force_threshold": 1, "ft_force": 3}) + +STATE_DIM_CFG.update({"force_threshold": 1, "ft_force": 3}) + + +@configclass +class ForgeCtrlCfg(CtrlCfg): + ema_factor_range = [0.025, 0.1] + default_task_prop_gains = [565.0, 565.0, 565.0, 28.0, 28.0, 28.0] + task_prop_gains_noise_level = [0.41, 0.41, 0.41, 0.41, 0.41, 0.41] + pos_threshold_noise_level = [0.25, 0.25, 0.25] + rot_threshold_noise_level = [0.29, 0.29, 0.29] + default_dead_zone = [5.0, 5.0, 5.0, 1.0, 1.0, 1.0] + + +@configclass +class ForgeObsRandCfg(ObsRandCfg): + fingertip_pos = 0.00025 + fingertip_rot_deg = 0.1 + ft_force = 1.0 + + +@configclass +class EventCfg: + object_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("held_asset"), + "mass_distribution_params": (-0.005, 0.005), + "operation": "add", + "distribution": "uniform", + }, + ) + + held_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("held_asset"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 1, + }, + ) + + fixed_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("fixed_asset"), + "static_friction_range": (0.25, 1.25), # TODO: Set these values based on asset type. + "dynamic_friction_range": (0.25, 0.25), + "restitution_range": (0.0, 0.0), + "num_buckets": 128, + }, + ) + + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 1, + }, + ) + + dead_zone_thresholds = EventTerm( + func=randomize_dead_zone, + mode="interval", + interval_range_s=(2.0, 2.0), # (0.25, 0.25) + ) + + +@configclass +class ForgeEnvCfg(FactoryEnvCfg): + action_space: int = 7 + obs_rand: ForgeObsRandCfg = ForgeObsRandCfg() + ctrl: ForgeCtrlCfg = ForgeCtrlCfg() + task: ForgeTask = ForgeTask() + events: EventCfg = EventCfg() + + ft_smoothing_factor: float = 0.25 + + obs_order: list = [ + "fingertip_pos_rel_fixed", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "ft_force", + "force_threshold", + ] + state_order: list = [ + "fingertip_pos", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "joint_pos", + "held_pos", + "held_pos_rel_fixed", + "held_quat", + "fixed_pos", + "fixed_quat", + "task_prop_gains", + "ema_factor", + "ft_force", + "pos_threshold", + "rot_threshold", + "force_threshold", + ] + + +@configclass +class ForgeTaskPegInsertCfg(ForgeEnvCfg): + task_name = "peg_insert" + task = ForgePegInsert() + episode_length_s = 10.0 + + +@configclass +class ForgeTaskGearMeshCfg(ForgeEnvCfg): + task_name = "gear_mesh" + task = ForgeGearMesh() + episode_length_s = 20.0 + + +@configclass +class ForgeTaskNutThreadCfg(ForgeEnvCfg): + task_name = "nut_thread" + task = ForgeNutThread() + episode_length_s = 30.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_events.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_events.py new file mode 100644 index 0000000000000000000000000000000000000000..15ced1c2b1a9e0a0ffc906fd93bfd9a420f1897b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_events.py @@ -0,0 +1,15 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import annotations + +import torch + +from isaaclab.envs import DirectRLEnv + + +def randomize_dead_zone(env: DirectRLEnv, env_ids: torch.Tensor | None): + env.dead_zone_thresholds = ( + torch.rand((env.num_envs, 6), dtype=torch.float32, device=env.device) * env.default_dead_zone + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_tasks_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_tasks_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..1529543e18895dd1ca31c6d1a95f18049ee2ca30 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_tasks_cfg.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.factory.factory_tasks_cfg import FactoryTask, GearMesh, NutThread, PegInsert + + +@configclass +class ForgeTask(FactoryTask): + action_penalty_ee_scale: float = 0.0 + action_penalty_asset_scale: float = 0.001 + action_grad_penalty_scale: float = 0.1 + contact_penalty_scale: float = 0.05 + delay_until_ratio: float = 0.25 + contact_penalty_threshold_range = [5.0, 10.0] + + +@configclass +class ForgePegInsert(PegInsert, ForgeTask): + contact_penalty_scale: float = 0.2 + + +@configclass +class ForgeGearMesh(GearMesh, ForgeTask): + contact_penalty_scale: float = 0.05 + + +@configclass +class ForgeNutThread(NutThread, ForgeTask): + contact_penalty_scale: float = 0.05 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_utils.py b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e966cf93f218fa317126209d2ef7bfb7b0d118e8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/forge/forge_utils.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import torch + +import isaacsim.core.utils.torch as torch_utils + + +def get_random_prop_gains(default_values, noise_levels, num_envs, device): + """Helper function to randomize controller gains.""" + c_param_noise = torch.rand((num_envs, default_values.shape[1]), dtype=torch.float32, device=device) + c_param_noise = c_param_noise @ torch.diag(torch.tensor(noise_levels, dtype=torch.float32, device=device)) + c_param_multiplier = 1.0 + c_param_noise + decrease_param_flag = torch.rand((num_envs, default_values.shape[1]), dtype=torch.float32, device=device) > 0.5 + c_param_multiplier = torch.where(decrease_param_flag, 1.0 / c_param_multiplier, c_param_multiplier) + + prop_gains = default_values * c_param_multiplier + + return prop_gains + + +def change_FT_frame(source_F, source_T, source_frame, target_frame): + """Convert force/torque reading from source to target frame.""" + # Modern Robotics eq. 3.95 + source_frame_inv = torch_utils.tf_inverse(source_frame[0], source_frame[1]) + target_T_source_quat, target_T_source_pos = torch_utils.tf_combine( + source_frame_inv[0], source_frame_inv[1], target_frame[0], target_frame[1] + ) + target_F = torch_utils.quat_apply(target_T_source_quat, source_F) + target_T = torch_utils.quat_apply( + target_T_source_quat, (source_T + torch.cross(target_T_source_pos, source_F, dim=-1)) + ) + return target_F, target_T diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c282be85730fc01d8344021a284a81f74f2ee1fd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/__init__.py @@ -0,0 +1,27 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +""" +Franka-Cabinet environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Franka-Cabinet-Direct-v0", + entry_point=f"{__name__}.franka_cabinet_env:FrankaCabinetEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.franka_cabinet_env:FrankaCabinetEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaCabinetPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4d882905a6c5ede6fc7d27a949e45fc6c87b691 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: franka_cabinet_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + # value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 100000000 + max_epochs: 1500 + save_best_after: 200 + save_frequency: 100 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 8192 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a2304fb2c4b7299fffb6c71384656fefe77b04b8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class FrankaCabinetPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 1500 + save_interval = 50 + experiment_name = "franka_cabinet_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=8, + num_mini_batches=8, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.008, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e1c7fe9676e2bd9ce3c74456f9849685bf75d70c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "franka_cabinet_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/franka_cabinet_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/franka_cabinet_env.py new file mode 100644 index 0000000000000000000000000000000000000000..8b87e1bdb258238e5270a1bed0fca833fbb3111d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/franka_cabinet/franka_cabinet_env.py @@ -0,0 +1,491 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch + +from isaacsim.core.utils.torch.transformations import tf_combine, tf_inverse, tf_vector +from pxr import UsdGeom + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import Articulation, ArticulationCfg +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.sim.utils.stage import get_current_stage +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.math import sample_uniform + + +@configclass +class FrankaCabinetEnvCfg(DirectRLEnvCfg): + # env + episode_length_s = 8.3333 # 500 timesteps + decimation = 2 + action_space = 9 + observation_space = 23 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 120, + render_interval=decimation, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=4096, env_spacing=3.0, replicate_physics=True, clone_in_fabric=True + ) + + # robot + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=12, solver_velocity_iteration_count=1 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 1.157, + "panda_joint2": -1.066, + "panda_joint3": -0.155, + "panda_joint4": -2.239, + "panda_joint5": -1.841, + "panda_joint6": 1.003, + "panda_joint7": 0.469, + "panda_finger_joint.*": 0.035, + }, + pos=(1.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), + ), + actuators={ + "panda_shoulder": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + effort_limit_sim=87.0, + stiffness=80.0, + damping=4.0, + ), + "panda_forearm": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + effort_limit_sim=12.0, + stiffness=80.0, + damping=4.0, + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint.*"], + effort_limit_sim=200.0, + stiffness=2e3, + damping=1e2, + ), + }, + ) + + # cabinet + cabinet = ArticulationCfg( + prim_path="/World/envs/env_.*/Cabinet", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Sektion_Cabinet/sektion_cabinet_instanceable.usd", + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0, 0.4), + rot=(0.1, 0.0, 0.0, 0.0), + joint_pos={ + "door_left_joint": 0.0, + "door_right_joint": 0.0, + "drawer_bottom_joint": 0.0, + "drawer_top_joint": 0.0, + }, + ), + actuators={ + "drawers": ImplicitActuatorCfg( + joint_names_expr=["drawer_top_joint", "drawer_bottom_joint"], + effort_limit_sim=87.0, + stiffness=10.0, + damping=1.0, + ), + "doors": ImplicitActuatorCfg( + joint_names_expr=["door_left_joint", "door_right_joint"], + effort_limit_sim=87.0, + stiffness=10.0, + damping=2.5, + ), + }, + ) + + # ground plane + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + + action_scale = 7.5 + dof_velocity_scale = 0.1 + + # reward scales + dist_reward_scale = 1.5 + rot_reward_scale = 1.5 + open_reward_scale = 10.0 + action_penalty_scale = 0.05 + finger_reward_scale = 2.0 + + +class FrankaCabinetEnv(DirectRLEnv): + # pre-physics step calls + # |-- _pre_physics_step(action) + # |-- _apply_action() + # post-physics step calls + # |-- _get_dones() + # |-- _get_rewards() + # |-- _reset_idx(env_ids) + # |-- _get_observations() + + cfg: FrankaCabinetEnvCfg + + def __init__(self, cfg: FrankaCabinetEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + def get_env_local_pose(env_pos: torch.Tensor, xformable: UsdGeom.Xformable, device: torch.device): + """Compute pose in env-local coordinates""" + world_transform = xformable.ComputeLocalToWorldTransform(0) + world_pos = world_transform.ExtractTranslation() + world_quat = world_transform.ExtractRotationQuat() + + px = world_pos[0] - env_pos[0] + py = world_pos[1] - env_pos[1] + pz = world_pos[2] - env_pos[2] + qx = world_quat.imaginary[0] + qy = world_quat.imaginary[1] + qz = world_quat.imaginary[2] + qw = world_quat.real + + return torch.tensor([px, py, pz, qw, qx, qy, qz], device=device) + + self.dt = self.cfg.sim.dt * self.cfg.decimation + + # create auxiliary variables for computing applied action, observations and rewards + self.robot_dof_lower_limits = self._robot.data.soft_joint_pos_limits[0, :, 0].to(device=self.device) + self.robot_dof_upper_limits = self._robot.data.soft_joint_pos_limits[0, :, 1].to(device=self.device) + + self.robot_dof_speed_scales = torch.ones_like(self.robot_dof_lower_limits) + self.robot_dof_speed_scales[self._robot.find_joints("panda_finger_joint1")[0]] = 0.1 + self.robot_dof_speed_scales[self._robot.find_joints("panda_finger_joint2")[0]] = 0.1 + + self.robot_dof_targets = torch.zeros((self.num_envs, self._robot.num_joints), device=self.device) + + stage = get_current_stage() + hand_pose = get_env_local_pose( + self.scene.env_origins[0], + UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/Robot/panda_link7")), + self.device, + ) + lfinger_pose = get_env_local_pose( + self.scene.env_origins[0], + UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/Robot/panda_leftfinger")), + self.device, + ) + rfinger_pose = get_env_local_pose( + self.scene.env_origins[0], + UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/Robot/panda_rightfinger")), + self.device, + ) + + finger_pose = torch.zeros(7, device=self.device) + finger_pose[0:3] = (lfinger_pose[0:3] + rfinger_pose[0:3]) / 2.0 + finger_pose[3:7] = lfinger_pose[3:7] + hand_pose_inv_rot, hand_pose_inv_pos = tf_inverse(hand_pose[3:7], hand_pose[0:3]) + + robot_local_grasp_pose_rot, robot_local_pose_pos = tf_combine( + hand_pose_inv_rot, hand_pose_inv_pos, finger_pose[3:7], finger_pose[0:3] + ) + robot_local_pose_pos += torch.tensor([0, 0.04, 0], device=self.device) + self.robot_local_grasp_pos = robot_local_pose_pos.repeat((self.num_envs, 1)) + self.robot_local_grasp_rot = robot_local_grasp_pose_rot.repeat((self.num_envs, 1)) + + drawer_local_grasp_pose = torch.tensor([0.3, 0.01, 0.0, 1.0, 0.0, 0.0, 0.0], device=self.device) + self.drawer_local_grasp_pos = drawer_local_grasp_pose[0:3].repeat((self.num_envs, 1)) + self.drawer_local_grasp_rot = drawer_local_grasp_pose[3:7].repeat((self.num_envs, 1)) + + self.gripper_forward_axis = torch.tensor([0, 0, 1], device=self.device, dtype=torch.float32).repeat( + (self.num_envs, 1) + ) + self.drawer_inward_axis = torch.tensor([-1, 0, 0], device=self.device, dtype=torch.float32).repeat( + (self.num_envs, 1) + ) + self.gripper_up_axis = torch.tensor([0, 1, 0], device=self.device, dtype=torch.float32).repeat( + (self.num_envs, 1) + ) + self.drawer_up_axis = torch.tensor([0, 0, 1], device=self.device, dtype=torch.float32).repeat( + (self.num_envs, 1) + ) + + self.hand_link_idx = self._robot.find_bodies("panda_link7")[0][0] + self.left_finger_link_idx = self._robot.find_bodies("panda_leftfinger")[0][0] + self.right_finger_link_idx = self._robot.find_bodies("panda_rightfinger")[0][0] + self.drawer_link_idx = self._cabinet.find_bodies("drawer_top")[0][0] + + self.robot_grasp_rot = torch.zeros((self.num_envs, 4), device=self.device) + self.robot_grasp_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.drawer_grasp_rot = torch.zeros((self.num_envs, 4), device=self.device) + self.drawer_grasp_pos = torch.zeros((self.num_envs, 3), device=self.device) + + def _setup_scene(self): + self._robot = Articulation(self.cfg.robot) + self._cabinet = Articulation(self.cfg.cabinet) + self.scene.articulations["robot"] = self._robot + self.scene.articulations["cabinet"] = self._cabinet + + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self._terrain = self.cfg.terrain.class_type(self.cfg.terrain) + + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path]) + + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + # pre-physics step calls + + def _pre_physics_step(self, actions: torch.Tensor): + self.actions = actions.clone().clamp(-1.0, 1.0) + targets = self.robot_dof_targets + self.robot_dof_speed_scales * self.dt * self.actions * self.cfg.action_scale + self.robot_dof_targets[:] = torch.clamp(targets, self.robot_dof_lower_limits, self.robot_dof_upper_limits) + + def _apply_action(self): + self._robot.set_joint_position_target(self.robot_dof_targets) + + # post-physics step calls + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + terminated = self._cabinet.data.joint_pos[:, 3] > 0.39 + truncated = self.episode_length_buf >= self.max_episode_length - 1 + return terminated, truncated + + def _get_rewards(self) -> torch.Tensor: + # Refresh the intermediate values after the physics steps + self._compute_intermediate_values() + robot_left_finger_pos = self._robot.data.body_pos_w[:, self.left_finger_link_idx] + robot_right_finger_pos = self._robot.data.body_pos_w[:, self.right_finger_link_idx] + + return self._compute_rewards( + self.actions, + self._cabinet.data.joint_pos, + self.robot_grasp_pos, + self.drawer_grasp_pos, + self.robot_grasp_rot, + self.drawer_grasp_rot, + robot_left_finger_pos, + robot_right_finger_pos, + self.gripper_forward_axis, + self.drawer_inward_axis, + self.gripper_up_axis, + self.drawer_up_axis, + self.num_envs, + self.cfg.dist_reward_scale, + self.cfg.rot_reward_scale, + self.cfg.open_reward_scale, + self.cfg.action_penalty_scale, + self.cfg.finger_reward_scale, + self._robot.data.joint_pos, + ) + + def _reset_idx(self, env_ids: torch.Tensor | None): + super()._reset_idx(env_ids) + # robot state + joint_pos = self._robot.data.default_joint_pos[env_ids] + sample_uniform( + -0.125, + 0.125, + (len(env_ids), self._robot.num_joints), + self.device, + ) + joint_pos = torch.clamp(joint_pos, self.robot_dof_lower_limits, self.robot_dof_upper_limits) + joint_vel = torch.zeros_like(joint_pos) + self._robot.set_joint_position_target(joint_pos, env_ids=env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + # cabinet state + zeros = torch.zeros((len(env_ids), self._cabinet.num_joints), device=self.device) + self._cabinet.write_joint_state_to_sim(zeros, zeros, env_ids=env_ids) + + # Need to refresh the intermediate values so that _get_observations() can use the latest values + self._compute_intermediate_values(env_ids) + + def _get_observations(self) -> dict: + dof_pos_scaled = ( + 2.0 + * (self._robot.data.joint_pos - self.robot_dof_lower_limits) + / (self.robot_dof_upper_limits - self.robot_dof_lower_limits) + - 1.0 + ) + to_target = self.drawer_grasp_pos - self.robot_grasp_pos + + obs = torch.cat( + ( + dof_pos_scaled, + self._robot.data.joint_vel * self.cfg.dof_velocity_scale, + to_target, + self._cabinet.data.joint_pos[:, 3].unsqueeze(-1), + self._cabinet.data.joint_vel[:, 3].unsqueeze(-1), + ), + dim=-1, + ) + return {"policy": torch.clamp(obs, -5.0, 5.0)} + + # auxiliary methods + + def _compute_intermediate_values(self, env_ids: torch.Tensor | None = None): + if env_ids is None: + env_ids = self._robot._ALL_INDICES + + hand_pos = self._robot.data.body_pos_w[env_ids, self.hand_link_idx] + hand_rot = self._robot.data.body_quat_w[env_ids, self.hand_link_idx] + drawer_pos = self._cabinet.data.body_pos_w[env_ids, self.drawer_link_idx] + drawer_rot = self._cabinet.data.body_quat_w[env_ids, self.drawer_link_idx] + ( + self.robot_grasp_rot[env_ids], + self.robot_grasp_pos[env_ids], + self.drawer_grasp_rot[env_ids], + self.drawer_grasp_pos[env_ids], + ) = self._compute_grasp_transforms( + hand_rot, + hand_pos, + self.robot_local_grasp_rot[env_ids], + self.robot_local_grasp_pos[env_ids], + drawer_rot, + drawer_pos, + self.drawer_local_grasp_rot[env_ids], + self.drawer_local_grasp_pos[env_ids], + ) + + def _compute_rewards( + self, + actions, + cabinet_dof_pos, + franka_grasp_pos, + drawer_grasp_pos, + franka_grasp_rot, + drawer_grasp_rot, + franka_lfinger_pos, + franka_rfinger_pos, + gripper_forward_axis, + drawer_inward_axis, + gripper_up_axis, + drawer_up_axis, + num_envs, + dist_reward_scale, + rot_reward_scale, + open_reward_scale, + action_penalty_scale, + finger_reward_scale, + joint_positions, + ): + # distance from hand to the drawer + d = torch.norm(franka_grasp_pos - drawer_grasp_pos, p=2, dim=-1) + dist_reward = 1.0 / (1.0 + d**2) + dist_reward *= dist_reward + dist_reward = torch.where(d <= 0.02, dist_reward * 2, dist_reward) + + axis1 = tf_vector(franka_grasp_rot, gripper_forward_axis) + axis2 = tf_vector(drawer_grasp_rot, drawer_inward_axis) + axis3 = tf_vector(franka_grasp_rot, gripper_up_axis) + axis4 = tf_vector(drawer_grasp_rot, drawer_up_axis) + + dot1 = ( + torch.bmm(axis1.view(num_envs, 1, 3), axis2.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1) + ) # alignment of forward axis for gripper + dot2 = ( + torch.bmm(axis3.view(num_envs, 1, 3), axis4.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1) + ) # alignment of up axis for gripper + # reward for matching the orientation of the hand to the drawer (fingers wrapped) + rot_reward = 0.5 * (torch.sign(dot1) * dot1**2 + torch.sign(dot2) * dot2**2) + + # regularization on the actions (summed for each environment) + action_penalty = torch.sum(actions**2, dim=-1) + + # how far the cabinet has been opened out + open_reward = cabinet_dof_pos[:, 3] # drawer_top_joint + + # penalty for distance of each finger from the drawer handle + lfinger_dist = franka_lfinger_pos[:, 2] - drawer_grasp_pos[:, 2] + rfinger_dist = drawer_grasp_pos[:, 2] - franka_rfinger_pos[:, 2] + finger_dist_penalty = torch.zeros_like(lfinger_dist) + finger_dist_penalty += torch.where(lfinger_dist < 0, lfinger_dist, torch.zeros_like(lfinger_dist)) + finger_dist_penalty += torch.where(rfinger_dist < 0, rfinger_dist, torch.zeros_like(rfinger_dist)) + + rewards = ( + dist_reward_scale * dist_reward + + rot_reward_scale * rot_reward + + open_reward_scale * open_reward + + finger_reward_scale * finger_dist_penalty + - action_penalty_scale * action_penalty + ) + + self.extras["log"] = { + "dist_reward": (dist_reward_scale * dist_reward).mean(), + "rot_reward": (rot_reward_scale * rot_reward).mean(), + "open_reward": (open_reward_scale * open_reward).mean(), + "action_penalty": (-action_penalty_scale * action_penalty).mean(), + "left_finger_distance_reward": (finger_reward_scale * lfinger_dist).mean(), + "right_finger_distance_reward": (finger_reward_scale * rfinger_dist).mean(), + "finger_dist_penalty": (finger_reward_scale * finger_dist_penalty).mean(), + } + + # bonus for opening drawer properly + rewards = torch.where(cabinet_dof_pos[:, 3] > 0.01, rewards + 0.25, rewards) + rewards = torch.where(cabinet_dof_pos[:, 3] > 0.2, rewards + 0.25, rewards) + rewards = torch.where(cabinet_dof_pos[:, 3] > 0.35, rewards + 0.25, rewards) + + return rewards + + def _compute_grasp_transforms( + self, + hand_rot, + hand_pos, + franka_local_grasp_rot, + franka_local_grasp_pos, + drawer_rot, + drawer_pos, + drawer_local_grasp_rot, + drawer_local_grasp_pos, + ): + global_franka_rot, global_franka_pos = tf_combine( + hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos + ) + global_drawer_rot, global_drawer_pos = tf_combine( + drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos + ) + + return global_franka_rot, global_franka_pos, global_drawer_rot, global_drawer_pos diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f7e0f518b666893a295584b7c31d0d7cd171b006 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Humanoid locomotion environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Humanoid-Direct-v0", + entry_point=f"{__name__}.humanoid_env:HumanoidEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.humanoid_env:HumanoidEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:HumanoidPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4ff1aced918c4866f0b26c54ac5ca83c91782b1d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [400, 200, 100] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: humanoid_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 1000 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 32768 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..778d73f0911933cd8807e3bd06deed28c9ef55db --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class HumanoidPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 32 + max_iterations = 1000 + save_interval = 50 + experiment_name = "humanoid_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[400, 200, 100], + critic_hidden_dims=[400, 200, 100], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.008, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f56d1fc45336aebf49ea4bb8f46e89461a784fa3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [400, 200, 100] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [400, 200, 100] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/humanoid_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/humanoid_env.py new file mode 100644 index 0000000000000000000000000000000000000000..402409e9d35ba1b881d1c1cecd187a7dbb21311e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid/humanoid_env.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.direct.locomotion.locomotion_env import LocomotionEnv + +from isaaclab_assets import HUMANOID_CFG + + +@configclass +class HumanoidEnvCfg(DirectRLEnvCfg): + # env + episode_length_s = 15.0 + decimation = 2 + action_scale = 1.0 + action_space = 21 + observation_space = 75 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="average", + restitution_combine_mode="average", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + debug_vis=False, + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=4096, env_spacing=4.0, replicate_physics=True, clone_in_fabric=True + ) + + # robot + robot: ArticulationCfg = HUMANOID_CFG.replace(prim_path="/World/envs/env_.*/Robot") + joint_gears: list = [ + 67.5000, # lower_waist + 67.5000, # lower_waist + 67.5000, # right_upper_arm + 67.5000, # right_upper_arm + 67.5000, # left_upper_arm + 67.5000, # left_upper_arm + 67.5000, # pelvis + 45.0000, # right_lower_arm + 45.0000, # left_lower_arm + 45.0000, # right_thigh: x + 135.0000, # right_thigh: y + 45.0000, # right_thigh: z + 45.0000, # left_thigh: x + 135.0000, # left_thigh: y + 45.0000, # left_thigh: z + 90.0000, # right_knee + 90.0000, # left_knee + 22.5, # right_foot + 22.5, # right_foot + 22.5, # left_foot + 22.5, # left_foot + ] + + heading_weight: float = 0.5 + up_weight: float = 0.1 + + energy_cost_scale: float = 0.05 + actions_cost_scale: float = 0.01 + alive_reward_scale: float = 2.0 + dof_vel_scale: float = 0.1 + + death_cost: float = -1.0 + termination_height: float = 0.8 + + angular_velocity_scale: float = 0.25 + contact_force_scale: float = 0.01 + + +class HumanoidEnv(LocomotionEnv): + cfg: HumanoidEnvCfg + + def __init__(self, cfg: HumanoidEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..36c93d5a1c53f822b05fae9f33d70a95e74520e7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/__init__.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +AMP Humanoid locomotion environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Humanoid-AMP-Dance-Direct-v0", + entry_point=f"{__name__}.humanoid_amp_env:HumanoidAmpEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.humanoid_amp_env_cfg:HumanoidAmpDanceEnvCfg", + "skrl_amp_cfg_entry_point": f"{agents.__name__}:skrl_dance_amp_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Humanoid-AMP-Run-Direct-v0", + entry_point=f"{__name__}.humanoid_amp_env:HumanoidAmpEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.humanoid_amp_env_cfg:HumanoidAmpRunEnvCfg", + "skrl_amp_cfg_entry_point": f"{agents.__name__}:skrl_run_amp_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Humanoid-AMP-Walk-Direct-v0", + entry_point=f"{__name__}.humanoid_amp_env:HumanoidAmpEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.humanoid_amp_env_cfg:HumanoidAmpWalkEnvCfg", + "skrl_amp_cfg_entry_point": f"{agents.__name__}:skrl_walk_amp_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_dance_amp_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_dance_amp_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3071d039b88ca88602c32cd95e4c4580cda27185 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_dance_amp_cfg.yaml @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: -2.9 + fixed_log_std: True + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + discriminator: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + +# AMP memory (reference motion dataset) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +motion_dataset: + class: RandomMemory + memory_size: 200000 + +# AMP memory (preventing discriminator overfitting) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +reply_buffer: + class: RandomMemory + memory_size: 1000000 + + +# AMP agent configuration (field names are from AMP_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/amp.html +agent: + class: AMP + rollouts: 16 + learning_epochs: 6 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-05 + learning_rate_scheduler: null + learning_rate_scheduler_kwargs: null + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + amp_state_preprocessor: RunningStandardScaler + amp_state_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 0.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.5 + discriminator_loss_scale: 5.0 + amp_batch_size: 512 + task_reward_weight: 0.0 + style_reward_weight: 1.0 + discriminator_batch_size: 4096 + discriminator_reward_scale: 2.0 + discriminator_logit_regularization_scale: 0.05 + discriminator_gradient_penalty_scale: 5.0 + discriminator_weight_decay_scale: 1.0e-04 + # rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid_amp_dance" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_run_amp_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_run_amp_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f6fcdc1a03fc36395ab1155e12ed57e6dbf6787 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_run_amp_cfg.yaml @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: -2.9 + fixed_log_std: True + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + discriminator: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + +# AMP memory (reference motion dataset) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +motion_dataset: + class: RandomMemory + memory_size: 200000 + +# AMP memory (preventing discriminator overfitting) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +reply_buffer: + class: RandomMemory + memory_size: 1000000 + + +# AMP agent configuration (field names are from AMP_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/amp.html +agent: + class: AMP + rollouts: 16 + learning_epochs: 6 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-05 + learning_rate_scheduler: null + learning_rate_scheduler_kwargs: null + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + amp_state_preprocessor: RunningStandardScaler + amp_state_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 0.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.5 + discriminator_loss_scale: 5.0 + amp_batch_size: 512 + task_reward_weight: 0.0 + style_reward_weight: 1.0 + discriminator_batch_size: 4096 + discriminator_reward_scale: 2.0 + discriminator_logit_regularization_scale: 0.05 + discriminator_gradient_penalty_scale: 5.0 + discriminator_weight_decay_scale: 1.0e-04 + # rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid_amp_run" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_walk_amp_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_walk_amp_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..efb34f0d2f5bddb34460f8a6bfc7dab633aaf41b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/agents/skrl_walk_amp_cfg.yaml @@ -0,0 +1,116 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: -2.9 + fixed_log_std: True + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + discriminator: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + +# AMP memory (reference motion dataset) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +motion_dataset: + class: RandomMemory + memory_size: 200000 + +# AMP memory (preventing discriminator overfitting) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +reply_buffer: + class: RandomMemory + memory_size: 1000000 + + +# AMP agent configuration (field names are from AMP_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/amp.html +agent: + class: AMP + rollouts: 16 + learning_epochs: 6 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-05 + learning_rate_scheduler: null + learning_rate_scheduler_kwargs: null + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + amp_state_preprocessor: RunningStandardScaler + amp_state_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 0.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.5 + discriminator_loss_scale: 5.0 + amp_batch_size: 512 + task_reward_weight: 0.0 + style_reward_weight: 1.0 + discriminator_batch_size: 4096 + discriminator_reward_scale: 2.0 + discriminator_logit_regularization_scale: 0.05 + discriminator_gradient_penalty_scale: 5.0 + discriminator_weight_decay_scale: 1.0e-04 + # rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid_amp_walk" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env.py new file mode 100644 index 0000000000000000000000000000000000000000..5d6a01e9d4e456cdb5963c1e9181b2e4852eee0e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env.py @@ -0,0 +1,242 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import gymnasium as gym +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectRLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.math import quat_apply + +from .humanoid_amp_env_cfg import HumanoidAmpEnvCfg +from .motions import MotionLoader + + +class HumanoidAmpEnv(DirectRLEnv): + cfg: HumanoidAmpEnvCfg + + def __init__(self, cfg: HumanoidAmpEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + # action offset and scale + dof_lower_limits = self.robot.data.soft_joint_pos_limits[0, :, 0] + dof_upper_limits = self.robot.data.soft_joint_pos_limits[0, :, 1] + self.action_offset = 0.5 * (dof_upper_limits + dof_lower_limits) + self.action_scale = dof_upper_limits - dof_lower_limits + + # load motion + self._motion_loader = MotionLoader(motion_file=self.cfg.motion_file, device=self.device) + + # DOF and key body indexes + key_body_names = ["right_hand", "left_hand", "right_foot", "left_foot"] + self.ref_body_index = self.robot.data.body_names.index(self.cfg.reference_body) + self.key_body_indexes = [self.robot.data.body_names.index(name) for name in key_body_names] + self.motion_dof_indexes = self._motion_loader.get_dof_index(self.robot.data.joint_names) + self.motion_ref_body_index = self._motion_loader.get_body_index([self.cfg.reference_body])[0] + self.motion_key_body_indexes = self._motion_loader.get_body_index(key_body_names) + + # reconfigure AMP observation space according to the number of observations and create the buffer + self.amp_observation_size = self.cfg.num_amp_observations * self.cfg.amp_observation_space + self.amp_observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.amp_observation_size,)) + self.amp_observation_buffer = torch.zeros( + (self.num_envs, self.cfg.num_amp_observations, self.cfg.amp_observation_space), device=self.device + ) + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot) + # add ground plane + spawn_ground_plane( + prim_path="/World/ground", + cfg=GroundPlaneCfg( + physics_material=sim_utils.RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ), + ) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=["/World/ground"]) + + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor): + self.actions = actions.clone() + + def _apply_action(self): + target = self.action_offset + self.action_scale * self.actions + self.robot.set_joint_position_target(target) + + def _get_observations(self) -> dict: + # build task observation + obs = compute_obs( + self.robot.data.joint_pos, + self.robot.data.joint_vel, + self.robot.data.body_pos_w[:, self.ref_body_index], + self.robot.data.body_quat_w[:, self.ref_body_index], + self.robot.data.body_lin_vel_w[:, self.ref_body_index], + self.robot.data.body_ang_vel_w[:, self.ref_body_index], + self.robot.data.body_pos_w[:, self.key_body_indexes], + ) + + # update AMP observation history + for i in reversed(range(self.cfg.num_amp_observations - 1)): + self.amp_observation_buffer[:, i + 1] = self.amp_observation_buffer[:, i] + # build AMP observation + self.amp_observation_buffer[:, 0] = obs.clone() + self.extras = {"amp_obs": self.amp_observation_buffer.view(-1, self.amp_observation_size)} + + return {"policy": obs} + + def _get_rewards(self) -> torch.Tensor: + return torch.ones((self.num_envs,), dtype=torch.float32, device=self.sim.device) + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + time_out = self.episode_length_buf >= self.max_episode_length - 1 + if self.cfg.early_termination: + died = self.robot.data.body_pos_w[:, self.ref_body_index, 2] < self.cfg.termination_height + else: + died = torch.zeros_like(time_out) + return died, time_out + + def _reset_idx(self, env_ids: torch.Tensor | None): + if env_ids is None or len(env_ids) == self.num_envs: + env_ids = self.robot._ALL_INDICES + self.robot.reset(env_ids) + super()._reset_idx(env_ids) + + if self.cfg.reset_strategy == "default": + root_state, joint_pos, joint_vel = self._reset_strategy_default(env_ids) + elif self.cfg.reset_strategy.startswith("random"): + start = "start" in self.cfg.reset_strategy + root_state, joint_pos, joint_vel = self._reset_strategy_random(env_ids, start) + else: + raise ValueError(f"Unknown reset strategy: {self.cfg.reset_strategy}") + + self.robot.write_root_link_pose_to_sim(root_state[:, :7], env_ids) + self.robot.write_root_com_velocity_to_sim(root_state[:, 7:], env_ids) + self.robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + # reset strategies + + def _reset_strategy_default(self, env_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + root_state = self.robot.data.default_root_state[env_ids].clone() + root_state[:, :3] += self.scene.env_origins[env_ids] + joint_pos = self.robot.data.default_joint_pos[env_ids].clone() + joint_vel = self.robot.data.default_joint_vel[env_ids].clone() + return root_state, joint_pos, joint_vel + + def _reset_strategy_random( + self, env_ids: torch.Tensor, start: bool = False + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + # sample random motion times (or zeros if start is True) + num_samples = env_ids.shape[0] + times = np.zeros(num_samples) if start else self._motion_loader.sample_times(num_samples) + # sample random motions + ( + dof_positions, + dof_velocities, + body_positions, + body_rotations, + body_linear_velocities, + body_angular_velocities, + ) = self._motion_loader.sample(num_samples=num_samples, times=times) + + # get root transforms (the humanoid torso) + motion_torso_index = self._motion_loader.get_body_index(["torso"])[0] + root_state = self.robot.data.default_root_state[env_ids].clone() + root_state[:, 0:3] = body_positions[:, motion_torso_index] + self.scene.env_origins[env_ids] + root_state[:, 2] += 0.15 # lift the humanoid slightly to avoid collisions with the ground + root_state[:, 3:7] = body_rotations[:, motion_torso_index] + root_state[:, 7:10] = body_linear_velocities[:, motion_torso_index] + root_state[:, 10:13] = body_angular_velocities[:, motion_torso_index] + # get DOFs state + dof_pos = dof_positions[:, self.motion_dof_indexes] + dof_vel = dof_velocities[:, self.motion_dof_indexes] + + # update AMP observation + amp_observations = self.collect_reference_motions(num_samples, times) + self.amp_observation_buffer[env_ids] = amp_observations.view(num_samples, self.cfg.num_amp_observations, -1) + + return root_state, dof_pos, dof_vel + + # env methods + + def collect_reference_motions(self, num_samples: int, current_times: np.ndarray | None = None) -> torch.Tensor: + # sample random motion times (or use the one specified) + if current_times is None: + current_times = self._motion_loader.sample_times(num_samples) + times = ( + np.expand_dims(current_times, axis=-1) + - self._motion_loader.dt * np.arange(0, self.cfg.num_amp_observations) + ).flatten() + # get motions + ( + dof_positions, + dof_velocities, + body_positions, + body_rotations, + body_linear_velocities, + body_angular_velocities, + ) = self._motion_loader.sample(num_samples=num_samples, times=times) + # compute AMP observation + amp_observation = compute_obs( + dof_positions[:, self.motion_dof_indexes], + dof_velocities[:, self.motion_dof_indexes], + body_positions[:, self.motion_ref_body_index], + body_rotations[:, self.motion_ref_body_index], + body_linear_velocities[:, self.motion_ref_body_index], + body_angular_velocities[:, self.motion_ref_body_index], + body_positions[:, self.motion_key_body_indexes], + ) + return amp_observation.view(-1, self.amp_observation_size) + + +@torch.jit.script +def quaternion_to_tangent_and_normal(q: torch.Tensor) -> torch.Tensor: + ref_tangent = torch.zeros_like(q[..., :3]) + ref_normal = torch.zeros_like(q[..., :3]) + ref_tangent[..., 0] = 1 + ref_normal[..., -1] = 1 + tangent = quat_apply(q, ref_tangent) + normal = quat_apply(q, ref_normal) + return torch.cat([tangent, normal], dim=len(tangent.shape) - 1) + + +@torch.jit.script +def compute_obs( + dof_positions: torch.Tensor, + dof_velocities: torch.Tensor, + root_positions: torch.Tensor, + root_rotations: torch.Tensor, + root_linear_velocities: torch.Tensor, + root_angular_velocities: torch.Tensor, + key_body_positions: torch.Tensor, +) -> torch.Tensor: + obs = torch.cat( + ( + dof_positions, + dof_velocities, + root_positions[:, 2:3], # root body height + quaternion_to_tangent_and_normal(root_rotations), + root_linear_velocities, + root_angular_velocities, + (key_body_positions - root_positions.unsqueeze(-2)).view(key_body_positions.shape[0], -1), + ), + dim=-1, + ) + return obs diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c7178f746c3f4fb497b492bd8d92aa062f36c60d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/humanoid_amp_env_cfg.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import os +from dataclasses import MISSING + +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.utils import configclass + +from isaaclab_assets import HUMANOID_28_CFG + +MOTIONS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "motions") + + +@configclass +class HumanoidAmpEnvCfg(DirectRLEnvCfg): + """Humanoid AMP environment config (base class).""" + + # env + episode_length_s = 10.0 + decimation = 2 + + # spaces + observation_space = 81 + action_space = 28 + state_space = 0 + num_amp_observations = 2 + amp_observation_space = 81 + + early_termination = True + termination_height = 0.5 + + motion_file: str = MISSING + reference_body = "torso" + reset_strategy = "random" # default, random, random-start + """Strategy to be followed when resetting each environment (humanoid's pose and joint states). + + * default: pose and joint states are set to the initial state of the asset. + * random: pose and joint states are set by sampling motions at random, uniform times. + * random-start: pose and joint states are set by sampling motion at the start (time zero). + """ + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 60, + render_interval=decimation, + physx=PhysxCfg( + gpu_found_lost_pairs_capacity=2**23, + gpu_total_aggregate_pairs_capacity=2**23, + ), + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=10.0, replicate_physics=True) + + # robot + robot: ArticulationCfg = HUMANOID_28_CFG.replace(prim_path="/World/envs/env_.*/Robot").replace( + actuators={ + "body": ImplicitActuatorCfg( + joint_names_expr=[".*"], + stiffness=None, + damping=None, + velocity_limit_sim={ + ".*": 100.0, + }, + ), + }, + ) + + +@configclass +class HumanoidAmpDanceEnvCfg(HumanoidAmpEnvCfg): + motion_file = os.path.join(MOTIONS_DIR, "humanoid_dance.npz") + + +@configclass +class HumanoidAmpRunEnvCfg(HumanoidAmpEnvCfg): + motion_file = os.path.join(MOTIONS_DIR, "humanoid_run.npz") + + +@configclass +class HumanoidAmpWalkEnvCfg(HumanoidAmpEnvCfg): + motion_file = os.path.join(MOTIONS_DIR, "humanoid_walk.npz") diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/README.md b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/README.md new file mode 100644 index 0000000000000000000000000000000000000000..32b98bcaa61607c206e254ba362cf2b4aacf665b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/README.md @@ -0,0 +1,33 @@ +# Motion files + +The motion files are in NumPy-file format that contains data from the skeleton DOFs and bodies that perform the motion. + +The data (accessed by key) is described in the following table, where: + +* `N` is the number of motion frames recorded +* `D` is the number of skeleton DOFs +* `B` is the number of skeleton bodies + +| Key | Dtype | Shape | Description | +| --- | ---- | ----- | ----------- | +| `fps` | int64 | () | FPS at which motion was sampled | +| `dof_names` | unicode string | (D,) | Skeleton DOF names | +| `body_names` | unicode string | (B,) | Skeleton body names | +| `dof_positions` | float32 | (N, D) | Skeleton DOF positions | +| `dof_velocities` | float32 | (N, D) | Skeleton DOF velocities | +| `body_positions` | float32 | (N, B, 3) | Skeleton body positions | +| `body_rotations` | float32 | (N, B, 4) | Skeleton body rotations (as `wxyz` quaternion) | +| `body_linear_velocities` | float32 | (N, B, 3) | Skeleton body linear velocities | +| `body_angular_velocities` | float32 | (N, B, 3) | Skeleton body angular velocities | + +## Motion visualization + +The `motion_viewer.py` file allows to visualize the skeleton motion recorded in a motion file. + +Open an terminal in the `motions` folder and run the following command. + +```bash +python motion_viewer.py --file MOTION_FILE_NAME.npz +``` + +See `python motion_viewer.py --help` for available arguments. diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..06a047fc65ec46e37a20116ac32216e769bf43fb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +AMP Motion Loader and motion files. +""" + +from .motion_loader import MotionLoader +from .motion_viewer import MotionViewer diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_dance.npz b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_dance.npz new file mode 100644 index 0000000000000000000000000000000000000000..2ac9ef8cc0d55632447fc0bf5ce97dec921377bb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_dance.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f2b07fd6f2c12f89b60690f9937cf82a9d613e91eaec7e6ca29e126925f9d12 +size 910662 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_run.npz b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_run.npz new file mode 100644 index 0000000000000000000000000000000000000000..38ca22598d508cdc3778dc610b88b65f3943b51e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_run.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98dd7cf75b130a593d5f0254d5ece6a2123c2fefad0ee02dcb13ec6d57db99c1 +size 87382 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_walk.npz b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_walk.npz new file mode 100644 index 0000000000000000000000000000000000000000..c61bbc64188fad367d28282d0b7c2ea2995c3e09 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/humanoid_walk.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91a7af4e7fa59d8a3a2bb2c6e0dad45d73f6e5b5ee1a284d733435e32c418542 +size 159670 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_loader.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..354332de1b2408dd4b59f70eda1a04175bc50b53 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_loader.py @@ -0,0 +1,283 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import os + +import numpy as np +import torch + + +class MotionLoader: + """ + Helper class to load and sample motion data from NumPy-file format. + """ + + def __init__(self, motion_file: str, device: torch.device) -> None: + """Load a motion file and initialize the internal variables. + + Args: + motion_file: Motion file path to load. + device: The device to which to load the data. + + Raises: + AssertionError: If the specified motion file doesn't exist. + """ + assert os.path.isfile(motion_file), f"Invalid file path: {motion_file}" + data = np.load(motion_file) + + self.device = device + self._dof_names = data["dof_names"].tolist() + self._body_names = data["body_names"].tolist() + + self.dof_positions = torch.tensor(data["dof_positions"], dtype=torch.float32, device=self.device) + self.dof_velocities = torch.tensor(data["dof_velocities"], dtype=torch.float32, device=self.device) + self.body_positions = torch.tensor(data["body_positions"], dtype=torch.float32, device=self.device) + self.body_rotations = torch.tensor(data["body_rotations"], dtype=torch.float32, device=self.device) + self.body_linear_velocities = torch.tensor( + data["body_linear_velocities"], dtype=torch.float32, device=self.device + ) + self.body_angular_velocities = torch.tensor( + data["body_angular_velocities"], dtype=torch.float32, device=self.device + ) + + self.dt = 1.0 / data["fps"] + self.num_frames = self.dof_positions.shape[0] + self.duration = self.dt * (self.num_frames - 1) + print(f"Motion loaded ({motion_file}): duration: {self.duration} sec, frames: {self.num_frames}") + + @property + def dof_names(self) -> list[str]: + """Skeleton DOF names.""" + return self._dof_names + + @property + def body_names(self) -> list[str]: + """Skeleton rigid body names.""" + return self._body_names + + @property + def num_dofs(self) -> int: + """Number of skeleton's DOFs.""" + return len(self._dof_names) + + @property + def num_bodies(self) -> int: + """Number of skeleton's rigid bodies.""" + return len(self._body_names) + + def _interpolate( + self, + a: torch.Tensor, + *, + b: torch.Tensor | None = None, + blend: torch.Tensor | None = None, + start: np.ndarray | None = None, + end: np.ndarray | None = None, + ) -> torch.Tensor: + """Linear interpolation between consecutive values. + + Args: + a: The first value. Shape is (N, X) or (N, M, X). + b: The second value. Shape is (N, X) or (N, M, X). + blend: Interpolation coefficient between 0 (a) and 1 (b). + start: Indexes to fetch the first value. If both, ``start`` and ``end` are specified, + the first and second values will be fetches from the argument ``a`` (dimension 0). + end: Indexes to fetch the second value. If both, ``start`` and ``end` are specified, + the first and second values will be fetches from the argument ``a`` (dimension 0). + + Returns: + Interpolated values. Shape is (N, X) or (N, M, X). + """ + if start is not None and end is not None: + return self._interpolate(a=a[start], b=a[end], blend=blend) + if a.ndim >= 2: + blend = blend.unsqueeze(-1) + if a.ndim >= 3: + blend = blend.unsqueeze(-1) + return (1.0 - blend) * a + blend * b + + def _slerp( + self, + q0: torch.Tensor, + *, + q1: torch.Tensor | None = None, + blend: torch.Tensor | None = None, + start: np.ndarray | None = None, + end: np.ndarray | None = None, + ) -> torch.Tensor: + """Interpolation between consecutive rotations (Spherical Linear Interpolation). + + Args: + q0: The first quaternion (wxyz). Shape is (N, 4) or (N, M, 4). + q1: The second quaternion (wxyz). Shape is (N, 4) or (N, M, 4). + blend: Interpolation coefficient between 0 (q0) and 1 (q1). + start: Indexes to fetch the first quaternion. If both, ``start`` and ``end` are specified, + the first and second quaternions will be fetches from the argument ``q0`` (dimension 0). + end: Indexes to fetch the second quaternion. If both, ``start`` and ``end` are specified, + the first and second quaternions will be fetches from the argument ``q0`` (dimension 0). + + Returns: + Interpolated quaternions. Shape is (N, 4) or (N, M, 4). + """ + if start is not None and end is not None: + return self._slerp(q0=q0[start], q1=q0[end], blend=blend) + if q0.ndim >= 2: + blend = blend.unsqueeze(-1) + if q0.ndim >= 3: + blend = blend.unsqueeze(-1) + + qw, qx, qy, qz = 0, 1, 2, 3 # wxyz + cos_half_theta = ( + q0[..., qw] * q1[..., qw] + + q0[..., qx] * q1[..., qx] + + q0[..., qy] * q1[..., qy] + + q0[..., qz] * q1[..., qz] + ) + + neg_mask = cos_half_theta < 0 + q1 = q1.clone() + q1[neg_mask] = -q1[neg_mask] + cos_half_theta = torch.abs(cos_half_theta) + cos_half_theta = torch.unsqueeze(cos_half_theta, dim=-1) + + half_theta = torch.acos(cos_half_theta) + sin_half_theta = torch.sqrt(1.0 - cos_half_theta * cos_half_theta) + + ratio_a = torch.sin((1 - blend) * half_theta) / sin_half_theta + ratio_b = torch.sin(blend * half_theta) / sin_half_theta + + new_q_x = ratio_a * q0[..., qx : qx + 1] + ratio_b * q1[..., qx : qx + 1] + new_q_y = ratio_a * q0[..., qy : qy + 1] + ratio_b * q1[..., qy : qy + 1] + new_q_z = ratio_a * q0[..., qz : qz + 1] + ratio_b * q1[..., qz : qz + 1] + new_q_w = ratio_a * q0[..., qw : qw + 1] + ratio_b * q1[..., qw : qw + 1] + + new_q = torch.cat([new_q_w, new_q_x, new_q_y, new_q_z], dim=len(new_q_w.shape) - 1) + new_q = torch.where(torch.abs(sin_half_theta) < 0.001, 0.5 * q0 + 0.5 * q1, new_q) + new_q = torch.where(torch.abs(cos_half_theta) >= 1, q0, new_q) + return new_q + + def _compute_frame_blend(self, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Compute the indexes of the first and second values, as well as the blending time + to interpolate between them and the given times. + + Args: + times: Times, between 0 and motion duration, to sample motion values. + Specified times will be clipped to fall within the range of the motion duration. + + Returns: + First value indexes, Second value indexes, and blending time between 0 (first value) and 1 (second value). + """ + phase = np.clip(times / self.duration, 0.0, 1.0) + index_0 = (phase * (self.num_frames - 1)).round(decimals=0).astype(int) + index_1 = np.minimum(index_0 + 1, self.num_frames - 1) + blend = ((times - index_0 * self.dt) / self.dt).round(decimals=5) + return index_0, index_1, blend + + def sample_times(self, num_samples: int, duration: float | None = None) -> np.ndarray: + """Sample random motion times uniformly. + + Args: + num_samples: Number of time samples to generate. + duration: Maximum motion duration to sample. + If not defined samples will be within the range of the motion duration. + + Raises: + AssertionError: If the specified duration is longer than the motion duration. + + Returns: + Time samples, between 0 and the specified/motion duration. + """ + duration = self.duration if duration is None else duration + assert duration <= self.duration, ( + f"The specified duration ({duration}) is longer than the motion duration ({self.duration})" + ) + return duration * np.random.uniform(low=0.0, high=1.0, size=num_samples) + + def sample( + self, num_samples: int, times: np.ndarray | None = None, duration: float | None = None + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Sample motion data. + + Args: + num_samples: Number of time samples to generate. If ``times`` is defined, this parameter is ignored. + times: Motion time used for sampling. + If not defined, motion data will be random sampled uniformly in time. + duration: Maximum motion duration to sample. + If not defined, samples will be within the range of the motion duration. + If ``times`` is defined, this parameter is ignored. + + Returns: + A tuple containing sampled motion data: + - DOF positions (with shape (N, num_dofs)) + - DOF velocities (with shape (N, num_dofs)) + - Body positions (with shape (N, num_bodies, 3)) + - Body rotations (with shape (N, num_bodies, 4), as wxyz quaternion) + - Body linear velocities (with shape (N, num_bodies, 3)) + - Body angular velocities (with shape (N, num_bodies, 3)) + """ + times = self.sample_times(num_samples, duration) if times is None else times + index_0, index_1, blend = self._compute_frame_blend(times) + blend = torch.tensor(blend, dtype=torch.float32, device=self.device) + + return ( + self._interpolate(self.dof_positions, blend=blend, start=index_0, end=index_1), + self._interpolate(self.dof_velocities, blend=blend, start=index_0, end=index_1), + self._interpolate(self.body_positions, blend=blend, start=index_0, end=index_1), + self._slerp(self.body_rotations, blend=blend, start=index_0, end=index_1), + self._interpolate(self.body_linear_velocities, blend=blend, start=index_0, end=index_1), + self._interpolate(self.body_angular_velocities, blend=blend, start=index_0, end=index_1), + ) + + def get_dof_index(self, dof_names: list[str]) -> list[int]: + """Get skeleton DOFs indexes by DOFs names. + + Args: + dof_names: List of DOFs names. + + Raises: + AssertionError: If the specified DOFs name doesn't exist. + + Returns: + List of DOFs indexes. + """ + indexes = [] + for name in dof_names: + assert name in self._dof_names, f"The specified DOF name ({name}) doesn't exist: {self._dof_names}" + indexes.append(self._dof_names.index(name)) + return indexes + + def get_body_index(self, body_names: list[str]) -> list[int]: + """Get skeleton body indexes by body names. + + Args: + dof_names: List of body names. + + Raises: + AssertionError: If the specified body name doesn't exist. + + Returns: + List of body indexes. + """ + indexes = [] + for name in body_names: + assert name in self._body_names, f"The specified body name ({name}) doesn't exist: {self._body_names}" + indexes.append(self._body_names.index(name)) + return indexes + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--file", type=str, required=True, help="Motion file") + args, _ = parser.parse_known_args() + + motion = MotionLoader(args.file, "cpu") + + print("- number of frames:", motion.num_frames) + print("- number of DOFs:", motion.num_dofs) + print("- number of bodies:", motion.num_bodies) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_viewer.py b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..62438f5e3c68e63cdaaa27572670de66a74fd6bd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/humanoid_amp/motions/motion_viewer.py @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import matplotlib +import matplotlib.animation +import matplotlib.pyplot as plt +import mpl_toolkits.mplot3d # noqa: F401 +import numpy as np +import torch + +try: + from .motion_loader import MotionLoader +except ImportError: + from motion_loader import MotionLoader + + +class MotionViewer: + """ + Helper class to visualize motion data from NumPy-file format. + """ + + def __init__(self, motion_file: str, device: torch.device | str = "cpu", render_scene: bool = False) -> None: + """Load a motion file and initialize the internal variables. + + Args: + motion_file: Motion file path to load. + device: The device to which to load the data. + render_scene: Whether the scene (space occupied by the skeleton during movement) + is rendered instead of a reduced view of the skeleton. + + Raises: + AssertionError: If the specified motion file doesn't exist. + """ + self._figure = None + self._figure_axes = None + self._render_scene = render_scene + + # load motions + self._motion_loader = MotionLoader(motion_file=motion_file, device=device) + + self._num_frames = self._motion_loader.num_frames + self._current_frame = 0 + self._body_positions = self._motion_loader.body_positions.cpu().numpy() + + print("\nBody") + for i, name in enumerate(self._motion_loader.body_names): + minimum = np.min(self._body_positions[:, i], axis=0).round(decimals=2) + maximum = np.max(self._body_positions[:, i], axis=0).round(decimals=2) + print(f" |-- [{name}] minimum position: {minimum}, maximum position: {maximum}") + + def _drawing_callback(self, frame: int) -> None: + """Drawing callback called each frame""" + # get current motion frame + # get data + vertices = self._body_positions[self._current_frame] + # draw skeleton state + self._figure_axes.clear() + self._figure_axes.scatter(*vertices.T, color="black", depthshade=False) + # adjust exes according to motion view + # - scene + if self._render_scene: + # compute axes limits + minimum = np.min(self._body_positions.reshape(-1, 3), axis=0) + maximum = np.max(self._body_positions.reshape(-1, 3), axis=0) + center = 0.5 * (maximum + minimum) + diff = 0.75 * (maximum - minimum) + # - skeleton + else: + # compute axes limits + minimum = np.min(vertices, axis=0) + maximum = np.max(vertices, axis=0) + center = 0.5 * (maximum + minimum) + diff = np.array([0.75 * np.max(maximum - minimum).item()] * 3) + # scale view + self._figure_axes.set_xlim((center[0] - diff[0], center[0] + diff[0])) + self._figure_axes.set_ylim((center[1] - diff[1], center[1] + diff[1])) + self._figure_axes.set_zlim((center[2] - diff[2], center[2] + diff[2])) + self._figure_axes.set_box_aspect(aspect=diff / diff[0]) + # plot ground plane + x, y = np.meshgrid([center[0] - diff[0], center[0] + diff[0]], [center[1] - diff[1], center[1] + diff[1]]) + self._figure_axes.plot_surface(x, y, np.zeros_like(x), color="green", alpha=0.2) + # print metadata + self._figure_axes.set_xlabel("X") + self._figure_axes.set_ylabel("Y") + self._figure_axes.set_zlabel("Z") + self._figure_axes.set_title(f"frame: {self._current_frame}/{self._num_frames}") + # increase frame counter + self._current_frame += 1 + if self._current_frame >= self._num_frames: + self._current_frame = 0 + + def show(self) -> None: + """Show motion""" + # create a 3D figure + self._figure = plt.figure() + self._figure_axes = self._figure.add_subplot(projection="3d") + # matplotlib animation (the instance must live as long as the animation will run) + self._animation = matplotlib.animation.FuncAnimation( + fig=self._figure, + func=self._drawing_callback, + frames=self._num_frames, + interval=1000 * self._motion_loader.dt, + ) + plt.show() + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--file", type=str, required=True, help="Motion file") + parser.add_argument( + "--render-scene", + action="store_true", + default=False, + help=( + "Whether the scene (space occupied by the skeleton during movement) is rendered instead of a reduced view" + " of the skeleton." + ), + ) + parser.add_argument("--matplotlib-backend", type=str, default="TkAgg", help="Matplotlib interactive backend") + args, _ = parser.parse_known_args() + + # https://matplotlib.org/stable/users/explain/figure/backends.html#interactive-backends + matplotlib.use(args.matplotlib_backend) + + viewer = MotionViewer(args.file, render_scene=args.render_scene) + viewer.show() diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/inhand_manipulation_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/inhand_manipulation_env.py new file mode 100644 index 0000000000000000000000000000000000000000..c8d4fbf9e2d01efd909011d634b8f7a1638afb1c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/inhand_manipulation/inhand_manipulation_env.py @@ -0,0 +1,433 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs import DirectRLEnv +from isaaclab.markers import VisualizationMarkers +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.math import quat_conjugate, quat_from_angle_axis, quat_mul, sample_uniform, saturate + +if TYPE_CHECKING: + from isaaclab_tasks.direct.allegro_hand.allegro_hand_env_cfg import AllegroHandEnvCfg + from isaaclab_tasks.direct.shadow_hand.shadow_hand_env_cfg import ShadowHandEnvCfg + + +class InHandManipulationEnv(DirectRLEnv): + cfg: AllegroHandEnvCfg | ShadowHandEnvCfg + + def __init__(self, cfg: AllegroHandEnvCfg | ShadowHandEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self.num_hand_dofs = self.hand.num_joints + + # buffers for position targets + self.hand_dof_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device) + self.prev_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device) + self.cur_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device) + + # list of actuated joints + self.actuated_dof_indices = list() + for joint_name in cfg.actuated_joint_names: + self.actuated_dof_indices.append(self.hand.joint_names.index(joint_name)) + self.actuated_dof_indices.sort() + + # finger bodies + self.finger_bodies = list() + for body_name in self.cfg.fingertip_body_names: + self.finger_bodies.append(self.hand.body_names.index(body_name)) + self.finger_bodies.sort() + self.num_fingertips = len(self.finger_bodies) + + # joint limits + joint_pos_limits = self.hand.root_physx_view.get_dof_limits().to(self.device) + self.hand_dof_lower_limits = joint_pos_limits[..., 0] + self.hand_dof_upper_limits = joint_pos_limits[..., 1] + + # track goal resets + self.reset_goal_buf = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) + # used to compare object position + self.in_hand_pos = self.object.data.default_root_state[:, 0:3].clone() + self.in_hand_pos[:, 2] -= 0.04 + # default goal positions + self.goal_rot = torch.zeros((self.num_envs, 4), dtype=torch.float, device=self.device) + self.goal_rot[:, 0] = 1.0 + self.goal_pos = torch.zeros((self.num_envs, 3), dtype=torch.float, device=self.device) + self.goal_pos[:, :] = torch.tensor([-0.2, -0.45, 0.68], device=self.device) + # initialize goal marker + self.goal_markers = VisualizationMarkers(self.cfg.goal_object_cfg) + + # track successes + self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device) + self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device) + + # unit tensors + self.x_unit_tensor = torch.tensor([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + self.y_unit_tensor = torch.tensor([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + self.z_unit_tensor = torch.tensor([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + + def _setup_scene(self): + # add hand, in-hand object, and goal object + self.hand = Articulation(self.cfg.robot_cfg) + self.object = RigidObject(self.cfg.object_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate (no need to filter for this environment) + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene - we must register to scene to randomize with EventManager + self.scene.articulations["robot"] = self.hand + self.scene.rigid_objects["object"] = self.object + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + + def _apply_action(self) -> None: + self.cur_targets[:, self.actuated_dof_indices] = scale( + self.actions, + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + self.cur_targets[:, self.actuated_dof_indices] = ( + self.cfg.act_moving_average * self.cur_targets[:, self.actuated_dof_indices] + + (1.0 - self.cfg.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices] + ) + self.cur_targets[:, self.actuated_dof_indices] = saturate( + self.cur_targets[:, self.actuated_dof_indices], + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + + self.prev_targets[:, self.actuated_dof_indices] = self.cur_targets[:, self.actuated_dof_indices] + + self.hand.set_joint_position_target( + self.cur_targets[:, self.actuated_dof_indices], joint_ids=self.actuated_dof_indices + ) + + def _get_observations(self) -> dict: + if self.cfg.asymmetric_obs: + self.fingertip_force_sensors = self.hand.root_physx_view.get_link_incoming_joint_force()[ + :, self.finger_bodies + ] + + if self.cfg.obs_type == "openai": + obs = self.compute_reduced_observations() + elif self.cfg.obs_type == "full": + obs = self.compute_full_observations() + else: + print("Unknown observations type!") + + if self.cfg.asymmetric_obs: + states = self.compute_full_state() + + observations = {"policy": obs} + if self.cfg.asymmetric_obs: + observations = {"policy": obs, "critic": states} + return observations + + def _get_rewards(self) -> torch.Tensor: + ( + total_reward, + self.reset_goal_buf, + self.successes[:], + self.consecutive_successes[:], + ) = compute_rewards( + self.reset_buf, + self.reset_goal_buf, + self.successes, + self.consecutive_successes, + self.max_episode_length, + self.object_pos, + self.object_rot, + self.in_hand_pos, + self.goal_rot, + self.cfg.dist_reward_scale, + self.cfg.rot_reward_scale, + self.cfg.rot_eps, + self.actions, + self.cfg.action_penalty_scale, + self.cfg.success_tolerance, + self.cfg.reach_goal_bonus, + self.cfg.fall_dist, + self.cfg.fall_penalty, + self.cfg.av_factor, + ) + + if "log" not in self.extras: + self.extras["log"] = dict() + self.extras["log"]["consecutive_successes"] = self.consecutive_successes.mean() + + # reset goals if the goal has been reached + goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1) + if len(goal_env_ids) > 0: + self._reset_target_pose(goal_env_ids) + + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + self._compute_intermediate_values() + + # reset when cube has fallen + goal_dist = torch.norm(self.object_pos - self.in_hand_pos, p=2, dim=-1) + out_of_reach = goal_dist >= self.cfg.fall_dist + + if self.cfg.max_consecutive_success > 0: + # Reset progress (episode length buf) on goal envs if max_consecutive_success > 0 + rot_dist = rotation_distance(self.object_rot, self.goal_rot) + self.episode_length_buf = torch.where( + torch.abs(rot_dist) <= self.cfg.success_tolerance, + torch.zeros_like(self.episode_length_buf), + self.episode_length_buf, + ) + max_success_reached = self.successes >= self.cfg.max_consecutive_success + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + if self.cfg.max_consecutive_success > 0: + time_out = time_out | max_success_reached + return out_of_reach, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.hand._ALL_INDICES + # resets articulation and rigid body attributes + super()._reset_idx(env_ids) + + # reset goals + self._reset_target_pose(env_ids) + + # reset object + object_default_state = self.object.data.default_root_state.clone()[env_ids] + pos_noise = sample_uniform(-1.0, 1.0, (len(env_ids), 3), device=self.device) + # global object positions + object_default_state[:, 0:3] = ( + object_default_state[:, 0:3] + self.cfg.reset_position_noise * pos_noise + self.scene.env_origins[env_ids] + ) + + rot_noise = sample_uniform(-1.0, 1.0, (len(env_ids), 2), device=self.device) # noise for X and Y rotation + object_default_state[:, 3:7] = randomize_rotation( + rot_noise[:, 0], rot_noise[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids] + ) + + object_default_state[:, 7:] = torch.zeros_like(self.object.data.default_root_state[env_ids, 7:]) + self.object.write_root_pose_to_sim(object_default_state[:, :7], env_ids) + self.object.write_root_velocity_to_sim(object_default_state[:, 7:], env_ids) + + # reset hand + delta_max = self.hand_dof_upper_limits[env_ids] - self.hand.data.default_joint_pos[env_ids] + delta_min = self.hand_dof_lower_limits[env_ids] - self.hand.data.default_joint_pos[env_ids] + + dof_pos_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + rand_delta = delta_min + (delta_max - delta_min) * 0.5 * dof_pos_noise + dof_pos = self.hand.data.default_joint_pos[env_ids] + self.cfg.reset_dof_pos_noise * rand_delta + + dof_vel_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + dof_vel = self.hand.data.default_joint_vel[env_ids] + self.cfg.reset_dof_vel_noise * dof_vel_noise + + self.prev_targets[env_ids] = dof_pos + self.cur_targets[env_ids] = dof_pos + self.hand_dof_targets[env_ids] = dof_pos + + self.hand.set_joint_position_target(dof_pos, env_ids=env_ids) + self.hand.write_joint_state_to_sim(dof_pos, dof_vel, env_ids=env_ids) + + self.successes[env_ids] = 0 + self._compute_intermediate_values() + + def _reset_target_pose(self, env_ids): + # reset goal rotation + rand_floats = sample_uniform(-1.0, 1.0, (len(env_ids), 2), device=self.device) + new_rot = randomize_rotation( + rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids] + ) + + # update goal pose and markers + self.goal_rot[env_ids] = new_rot + goal_pos = self.goal_pos + self.scene.env_origins + self.goal_markers.visualize(goal_pos, self.goal_rot) + + self.reset_goal_buf[env_ids] = 0 + + def _compute_intermediate_values(self): + # data for hand + self.fingertip_pos = self.hand.data.body_pos_w[:, self.finger_bodies] + self.fingertip_rot = self.hand.data.body_quat_w[:, self.finger_bodies] + self.fingertip_pos -= self.scene.env_origins.repeat((1, self.num_fingertips)).reshape( + self.num_envs, self.num_fingertips, 3 + ) + self.fingertip_velocities = self.hand.data.body_vel_w[:, self.finger_bodies] + + self.hand_dof_pos = self.hand.data.joint_pos + self.hand_dof_vel = self.hand.data.joint_vel + + # data for object + self.object_pos = self.object.data.root_pos_w - self.scene.env_origins + self.object_rot = self.object.data.root_quat_w + self.object_velocities = self.object.data.root_vel_w + self.object_linvel = self.object.data.root_lin_vel_w + self.object_angvel = self.object.data.root_ang_vel_w + + def compute_reduced_observations(self): + # Per https://arxiv.org/pdf/1808.00177.pdf Table 2 + # Fingertip positions + # Object Position, but not orientation + # Relative target orientation + obs = torch.cat( + ( + self.fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + self.object_pos, + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + self.actions, + ), + dim=-1, + ) + + return obs + + def compute_full_observations(self): + obs = torch.cat( + ( + # hand + unscale(self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + self.cfg.vel_obs_scale * self.hand_dof_vel, + # object + self.object_pos, + self.object_rot, + self.object_linvel, + self.cfg.vel_obs_scale * self.object_angvel, + # goal + self.in_hand_pos, + self.goal_rot, + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + # fingertips + self.fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + self.fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + self.fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # actions + self.actions, + ), + dim=-1, + ) + return obs + + def compute_full_state(self): + states = torch.cat( + ( + # hand + unscale(self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + self.cfg.vel_obs_scale * self.hand_dof_vel, + # object + self.object_pos, + self.object_rot, + self.object_linvel, + self.cfg.vel_obs_scale * self.object_angvel, + # goal + self.in_hand_pos, + self.goal_rot, + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + # fingertips + self.fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + self.fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + self.fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + self.cfg.force_torque_obs_scale + * self.fingertip_force_sensors.view(self.num_envs, self.num_fingertips * 6), + # actions + self.actions, + ), + dim=-1, + ) + return states + + +@torch.jit.script +def scale(x, lower, upper): + return 0.5 * (x + 1.0) * (upper - lower) + lower + + +@torch.jit.script +def unscale(x, lower, upper): + return (2.0 * x - upper - lower) / (upper - lower) + + +@torch.jit.script +def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor): + return quat_mul( + quat_from_angle_axis(rand0 * np.pi, x_unit_tensor), quat_from_angle_axis(rand1 * np.pi, y_unit_tensor) + ) + + +@torch.jit.script +def rotation_distance(object_rot, target_rot): + # Orientation alignment for the cube in hand and goal cube + quat_diff = quat_mul(object_rot, quat_conjugate(target_rot)) + return 2.0 * torch.asin(torch.clamp(torch.norm(quat_diff[:, 1:4], p=2, dim=-1), max=1.0)) # changed quat convention + + +@torch.jit.script +def compute_rewards( + reset_buf: torch.Tensor, + reset_goal_buf: torch.Tensor, + successes: torch.Tensor, + consecutive_successes: torch.Tensor, + max_episode_length: float, + object_pos: torch.Tensor, + object_rot: torch.Tensor, + target_pos: torch.Tensor, + target_rot: torch.Tensor, + dist_reward_scale: float, + rot_reward_scale: float, + rot_eps: float, + actions: torch.Tensor, + action_penalty_scale: float, + success_tolerance: float, + reach_goal_bonus: float, + fall_dist: float, + fall_penalty: float, + av_factor: float, +): + goal_dist = torch.norm(object_pos - target_pos, p=2, dim=-1) + rot_dist = rotation_distance(object_rot, target_rot) + + dist_rew = goal_dist * dist_reward_scale + rot_rew = 1.0 / (torch.abs(rot_dist) + rot_eps) * rot_reward_scale + + action_penalty = torch.sum(actions**2, dim=-1) + + # Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty + reward = dist_rew + rot_rew + action_penalty * action_penalty_scale + + # Find out which envs hit the goal and update successes count + goal_resets = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.ones_like(reset_goal_buf), reset_goal_buf) + successes = successes + goal_resets + + # Success bonus: orientation is within `success_tolerance` of goal orientation + reward = torch.where(goal_resets == 1, reward + reach_goal_bonus, reward) + + # Fall penalty: distance to the goal is larger than a threshold + reward = torch.where(goal_dist >= fall_dist, reward + fall_penalty, reward) + + # Check env termination conditions, including maximum success number + resets = torch.where(goal_dist >= fall_dist, torch.ones_like(reset_buf), reset_buf) + + num_resets = torch.sum(resets) + finished_cons_successes = torch.sum(successes * resets.float()) + + cons_successes = torch.where( + num_resets > 0, + av_factor * finished_cons_successes / num_resets + (1.0 - av_factor) * consecutive_successes, + consecutive_successes, + ) + + return reward, goal_resets, successes, cons_successes diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/locomotion_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/locomotion_env.py new file mode 100644 index 0000000000000000000000000000000000000000..faac10e1a7182d9bbd404b1bcc32fbc5068adf0c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/locomotion/locomotion_env.py @@ -0,0 +1,280 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch + +import isaacsim.core.utils.torch as torch_utils +from isaacsim.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg + + +def normalize_angle(x): + return torch.atan2(torch.sin(x), torch.cos(x)) + + +class LocomotionEnv(DirectRLEnv): + cfg: DirectRLEnvCfg + + def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self.action_scale = self.cfg.action_scale + self.joint_gears = torch.tensor(self.cfg.joint_gears, dtype=torch.float32, device=self.sim.device) + self.motor_effort_ratio = torch.ones_like(self.joint_gears, device=self.sim.device) + self._joint_dof_idx, _ = self.robot.find_joints(".*") + + self.potentials = torch.zeros(self.num_envs, dtype=torch.float32, device=self.sim.device) + self.prev_potentials = torch.zeros_like(self.potentials) + self.targets = torch.tensor([1000, 0, 0], dtype=torch.float32, device=self.sim.device).repeat( + (self.num_envs, 1) + ) + self.targets += self.scene.env_origins + self.start_rotation = torch.tensor([1, 0, 0, 0], device=self.sim.device, dtype=torch.float32) + self.up_vec = torch.tensor([0, 0, 1], dtype=torch.float32, device=self.sim.device).repeat((self.num_envs, 1)) + self.heading_vec = torch.tensor([1, 0, 0], dtype=torch.float32, device=self.sim.device).repeat( + (self.num_envs, 1) + ) + self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1)) + self.basis_vec0 = self.heading_vec.clone() + self.basis_vec1 = self.up_vec.clone() + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot) + # add ground plane + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self.terrain = self.cfg.terrain.class_type(self.cfg.terrain) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path]) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor): + self.actions = actions.clone() + + def _apply_action(self): + forces = self.action_scale * self.joint_gears * self.actions + self.robot.set_joint_effort_target(forces, joint_ids=self._joint_dof_idx) + + def _compute_intermediate_values(self): + self.torso_position, self.torso_rotation = self.robot.data.root_pos_w, self.robot.data.root_quat_w + self.velocity, self.ang_velocity = self.robot.data.root_lin_vel_w, self.robot.data.root_ang_vel_w + self.dof_pos, self.dof_vel = self.robot.data.joint_pos, self.robot.data.joint_vel + + ( + self.up_proj, + self.heading_proj, + self.up_vec, + self.heading_vec, + self.vel_loc, + self.angvel_loc, + self.roll, + self.pitch, + self.yaw, + self.angle_to_target, + self.dof_pos_scaled, + self.prev_potentials, + self.potentials, + ) = compute_intermediate_values( + self.targets, + self.torso_position, + self.torso_rotation, + self.velocity, + self.ang_velocity, + self.dof_pos, + self.robot.data.soft_joint_pos_limits[0, :, 0], + self.robot.data.soft_joint_pos_limits[0, :, 1], + self.inv_start_rot, + self.basis_vec0, + self.basis_vec1, + self.potentials, + self.prev_potentials, + self.cfg.sim.dt, + ) + + def _get_observations(self) -> dict: + obs = torch.cat( + ( + self.torso_position[:, 2].view(-1, 1), + self.vel_loc, + self.angvel_loc * self.cfg.angular_velocity_scale, + normalize_angle(self.yaw).unsqueeze(-1), + normalize_angle(self.roll).unsqueeze(-1), + normalize_angle(self.angle_to_target).unsqueeze(-1), + self.up_proj.unsqueeze(-1), + self.heading_proj.unsqueeze(-1), + self.dof_pos_scaled, + self.dof_vel * self.cfg.dof_vel_scale, + self.actions, + ), + dim=-1, + ) + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards( + self.actions, + self.reset_terminated, + self.cfg.up_weight, + self.cfg.heading_weight, + self.heading_proj, + self.up_proj, + self.dof_vel, + self.dof_pos_scaled, + self.potentials, + self.prev_potentials, + self.cfg.actions_cost_scale, + self.cfg.energy_cost_scale, + self.cfg.dof_vel_scale, + self.cfg.death_cost, + self.cfg.alive_reward_scale, + self.motor_effort_ratio, + ) + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + self._compute_intermediate_values() + time_out = self.episode_length_buf >= self.max_episode_length - 1 + died = self.torso_position[:, 2] < self.cfg.termination_height + return died, time_out + + def _reset_idx(self, env_ids: torch.Tensor | None): + if env_ids is None or len(env_ids) == self.num_envs: + env_ids = self.robot._ALL_INDICES + self.robot.reset(env_ids) + super()._reset_idx(env_ids) + + joint_pos = self.robot.data.default_joint_pos[env_ids] + joint_vel = self.robot.data.default_joint_vel[env_ids] + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self.robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + to_target = self.targets[env_ids] - default_root_state[:, :3] + to_target[:, 2] = 0.0 + self.potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.cfg.sim.dt + + self._compute_intermediate_values() + + +@torch.jit.script +def compute_rewards( + actions: torch.Tensor, + reset_terminated: torch.Tensor, + up_weight: float, + heading_weight: float, + heading_proj: torch.Tensor, + up_proj: torch.Tensor, + dof_vel: torch.Tensor, + dof_pos_scaled: torch.Tensor, + potentials: torch.Tensor, + prev_potentials: torch.Tensor, + actions_cost_scale: float, + energy_cost_scale: float, + dof_vel_scale: float, + death_cost: float, + alive_reward_scale: float, + motor_effort_ratio: torch.Tensor, +): + heading_weight_tensor = torch.ones_like(heading_proj) * heading_weight + heading_reward = torch.where(heading_proj > 0.8, heading_weight_tensor, heading_weight * heading_proj / 0.8) + + # aligning up axis of robot and environment + up_reward = torch.zeros_like(heading_reward) + up_reward = torch.where(up_proj > 0.93, up_reward + up_weight, up_reward) + + # energy penalty for movement + actions_cost = torch.sum(actions**2, dim=-1) + electricity_cost = torch.sum( + torch.abs(actions * dof_vel * dof_vel_scale) * motor_effort_ratio.unsqueeze(0), + dim=-1, + ) + + # dof at limit cost + dof_at_limit_cost = torch.sum(dof_pos_scaled > 0.98, dim=-1) + + # reward for duration of staying alive + alive_reward = torch.ones_like(potentials) * alive_reward_scale + progress_reward = potentials - prev_potentials + + total_reward = ( + progress_reward + + alive_reward + + up_reward + + heading_reward + - actions_cost_scale * actions_cost + - energy_cost_scale * electricity_cost + - dof_at_limit_cost + ) + # adjust reward for fallen agents + total_reward = torch.where(reset_terminated, torch.ones_like(total_reward) * death_cost, total_reward) + return total_reward + + +@torch.jit.script +def compute_intermediate_values( + targets: torch.Tensor, + torso_position: torch.Tensor, + torso_rotation: torch.Tensor, + velocity: torch.Tensor, + ang_velocity: torch.Tensor, + dof_pos: torch.Tensor, + dof_lower_limits: torch.Tensor, + dof_upper_limits: torch.Tensor, + inv_start_rot: torch.Tensor, + basis_vec0: torch.Tensor, + basis_vec1: torch.Tensor, + potentials: torch.Tensor, + prev_potentials: torch.Tensor, + dt: float, +): + to_target = targets - torso_position + to_target[:, 2] = 0.0 + + torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up( + torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2 + ) + + vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot( + torso_quat, velocity, ang_velocity, targets, torso_position + ) + + dof_pos_scaled = torch_utils.maths.unscale(dof_pos, dof_lower_limits, dof_upper_limits) + + to_target = targets - torso_position + to_target[:, 2] = 0.0 + prev_potentials[:] = potentials + potentials = -torch.norm(to_target, p=2, dim=-1) / dt + + return ( + up_proj, + heading_proj, + up_vec, + heading_vec, + vel_loc, + angvel_loc, + roll, + pitch, + yaw, + angle_to_target, + dof_pos_scaled, + prev_potentials, + potentials, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a1a5c9ef913a5ff2745e8b42052a93647a0c9c98 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Quacopter environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Quadcopter-Direct-v0", + entry_point=f"{__name__}.quadcopter_env:QuadcopterEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.quadcopter_env:QuadcopterEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:QuadcopterPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..36e2d8f61fbf2a1f934f6f116805e822e19e55c9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [64, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: quadcopter_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.016 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 100 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..607d9f0fb0ea6dc0e52351e9c3a56c0055f4005d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class QuadcopterPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 200 + save_interval = 50 + experiment_name = "quadcopter_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[64, 64], + critic_hidden_dims=[64, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c84e3f5674bda78331de2e51dde26b4e73c042d6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "quadcopter_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/quadcopter_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/quadcopter_env.py new file mode 100644 index 0000000000000000000000000000000000000000..02857c63d3485e7b26e5e37e58736e918e37d797 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/quadcopter/quadcopter_env.py @@ -0,0 +1,255 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import gymnasium as gym +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, ArticulationCfg +from isaaclab.envs import DirectRLEnv, DirectRLEnvCfg +from isaaclab.envs.ui import BaseEnvWindow +from isaaclab.markers import VisualizationMarkers +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.math import subtract_frame_transforms + +## +# Pre-defined configs +## +from isaaclab_assets import CRAZYFLIE_CFG # isort: skip +from isaaclab.markers import CUBOID_MARKER_CFG # isort: skip + + +class QuadcopterEnvWindow(BaseEnvWindow): + """Window manager for the Quadcopter environment.""" + + def __init__(self, env: QuadcopterEnv, window_name: str = "IsaacLab"): + """Initialize the window. + + Args: + env: The environment object. + window_name: The name of the window. Defaults to "IsaacLab". + """ + # initialize base window + super().__init__(env, window_name) + # add custom UI elements + with self.ui_window_elements["main_vstack"]: + with self.ui_window_elements["debug_frame"]: + with self.ui_window_elements["debug_vstack"]: + # add command manager visualization + self._create_debug_vis_ui_element("targets", self.env) + + +@configclass +class QuadcopterEnvCfg(DirectRLEnvCfg): + # env + episode_length_s = 10.0 + decimation = 2 + action_space = 4 + observation_space = 12 + state_space = 0 + debug_vis = True + + ui_window_class_type = QuadcopterEnvWindow + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 100, + render_interval=decimation, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + ) + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + debug_vis=False, + ) + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=4096, env_spacing=2.5, replicate_physics=True, clone_in_fabric=True + ) + + # robot + robot: ArticulationCfg = CRAZYFLIE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + thrust_to_weight = 1.9 + moment_scale = 0.01 + + # reward scales + lin_vel_reward_scale = -0.05 + ang_vel_reward_scale = -0.01 + distance_to_goal_reward_scale = 15.0 + + +class QuadcopterEnv(DirectRLEnv): + cfg: QuadcopterEnvCfg + + def __init__(self, cfg: QuadcopterEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + # Total thrust and moment applied to the base of the quadcopter + self._actions = torch.zeros(self.num_envs, gym.spaces.flatdim(self.single_action_space), device=self.device) + self._thrust = torch.zeros(self.num_envs, 1, 3, device=self.device) + self._moment = torch.zeros(self.num_envs, 1, 3, device=self.device) + # Goal position + self._desired_pos_w = torch.zeros(self.num_envs, 3, device=self.device) + + # Logging + self._episode_sums = { + key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device) + for key in [ + "lin_vel", + "ang_vel", + "distance_to_goal", + ] + } + # Get specific body indices + self._body_id = self._robot.find_bodies("body")[0] + self._robot_mass = self._robot.root_physx_view.get_masses()[0].sum() + self._gravity_magnitude = torch.tensor(self.sim.cfg.gravity, device=self.device).norm() + self._robot_weight = (self._robot_mass * self._gravity_magnitude).item() + + # add handle for debug visualization (this is set to a valid handle inside set_debug_vis) + self.set_debug_vis(self.cfg.debug_vis) + + def _setup_scene(self): + self._robot = Articulation(self.cfg.robot) + self.scene.articulations["robot"] = self._robot + + self.cfg.terrain.num_envs = self.scene.cfg.num_envs + self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing + self._terrain = self.cfg.terrain.class_type(self.cfg.terrain) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path]) + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor): + self._actions = actions.clone().clamp(-1.0, 1.0) + self._thrust[:, 0, 2] = self.cfg.thrust_to_weight * self._robot_weight * (self._actions[:, 0] + 1.0) / 2.0 + self._moment[:, 0, :] = self.cfg.moment_scale * self._actions[:, 1:] + + def _apply_action(self): + self._robot.permanent_wrench_composer.set_forces_and_torques( + body_ids=self._body_id, forces=self._thrust, torques=self._moment + ) + + def _get_observations(self) -> dict: + desired_pos_b, _ = subtract_frame_transforms( + self._robot.data.root_pos_w, self._robot.data.root_quat_w, self._desired_pos_w + ) + obs = torch.cat( + [ + self._robot.data.root_lin_vel_b, + self._robot.data.root_ang_vel_b, + self._robot.data.projected_gravity_b, + desired_pos_b, + ], + dim=-1, + ) + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + lin_vel = torch.sum(torch.square(self._robot.data.root_lin_vel_b), dim=1) + ang_vel = torch.sum(torch.square(self._robot.data.root_ang_vel_b), dim=1) + distance_to_goal = torch.linalg.norm(self._desired_pos_w - self._robot.data.root_pos_w, dim=1) + distance_to_goal_mapped = 1 - torch.tanh(distance_to_goal / 0.8) + rewards = { + "lin_vel": lin_vel * self.cfg.lin_vel_reward_scale * self.step_dt, + "ang_vel": ang_vel * self.cfg.ang_vel_reward_scale * self.step_dt, + "distance_to_goal": distance_to_goal_mapped * self.cfg.distance_to_goal_reward_scale * self.step_dt, + } + reward = torch.sum(torch.stack(list(rewards.values())), dim=0) + # Logging + for key, value in rewards.items(): + self._episode_sums[key] += value + return reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + time_out = self.episode_length_buf >= self.max_episode_length - 1 + died = torch.logical_or(self._robot.data.root_pos_w[:, 2] < 0.1, self._robot.data.root_pos_w[:, 2] > 2.0) + return died, time_out + + def _reset_idx(self, env_ids: torch.Tensor | None): + if env_ids is None or len(env_ids) == self.num_envs: + env_ids = self._robot._ALL_INDICES + + # Logging + final_distance_to_goal = torch.linalg.norm( + self._desired_pos_w[env_ids] - self._robot.data.root_pos_w[env_ids], dim=1 + ).mean() + extras = dict() + for key in self._episode_sums.keys(): + episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids]) + extras["Episode_Reward/" + key] = episodic_sum_avg / self.max_episode_length_s + self._episode_sums[key][env_ids] = 0.0 + self.extras["log"] = dict() + self.extras["log"].update(extras) + extras = dict() + extras["Episode_Termination/died"] = torch.count_nonzero(self.reset_terminated[env_ids]).item() + extras["Episode_Termination/time_out"] = torch.count_nonzero(self.reset_time_outs[env_ids]).item() + extras["Metrics/final_distance_to_goal"] = final_distance_to_goal.item() + self.extras["log"].update(extras) + + self._robot.reset(env_ids) + super()._reset_idx(env_ids) + if len(env_ids) == self.num_envs: + # Spread out the resets to avoid spikes in training when many environments reset at a similar time + self.episode_length_buf = torch.randint_like(self.episode_length_buf, high=int(self.max_episode_length)) + + self._actions[env_ids] = 0.0 + # Sample new commands + self._desired_pos_w[env_ids, :2] = torch.zeros_like(self._desired_pos_w[env_ids, :2]).uniform_(-2.0, 2.0) + self._desired_pos_w[env_ids, :2] += self._terrain.env_origins[env_ids, :2] + self._desired_pos_w[env_ids, 2] = torch.zeros_like(self._desired_pos_w[env_ids, 2]).uniform_(0.5, 1.5) + # Reset robot state + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_vel = self._robot.data.default_joint_vel[env_ids] + default_root_state = self._robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self._terrain.env_origins[env_ids] + self._robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self._robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + def _set_debug_vis_impl(self, debug_vis: bool): + # create markers if necessary for the first time + if debug_vis: + if not hasattr(self, "goal_pos_visualizer"): + marker_cfg = CUBOID_MARKER_CFG.copy() + marker_cfg.markers["cuboid"].size = (0.05, 0.05, 0.05) + # -- goal pose + marker_cfg.prim_path = "/Visuals/Command/goal_position" + self.goal_pos_visualizer = VisualizationMarkers(marker_cfg) + # set their visibility to true + self.goal_pos_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_pos_visualizer"): + self.goal_pos_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # update the markers + self.goal_pos_visualizer.visualize(self._desired_pos_w) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4d2f0f3eee50ff53f361782426879811a9a47fcb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/__init__.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Shadow Hand environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +inhand_task_entry = "isaaclab_tasks.direct.inhand_manipulation" + +gym.register( + id="Isaac-Repose-Cube-Shadow-Direct-v0", + entry_point=f"{inhand_task_entry}.inhand_manipulation_env:InHandManipulationEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_env_cfg:ShadowHandEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ShadowHandPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0", + entry_point=f"{inhand_task_entry}.inhand_manipulation_env:InHandManipulationEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_env_cfg:ShadowHandOpenAIEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_ff_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ShadowHandAsymFFPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ff_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0", + entry_point=f"{inhand_task_entry}.inhand_manipulation_env:InHandManipulationEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_env_cfg:ShadowHandOpenAIEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_lstm_cfg.yaml", + }, +) + +# ------- +# Vision +# ------- + +gym.register( + id="Isaac-Repose-Cube-Shadow-Vision-Direct-v0", + entry_point=f"{__name__}.shadow_hand_vision_env:ShadowHandVisionEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_vision_env:ShadowHandVisionEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ShadowHandVisionFFPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_vision_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Repose-Cube-Shadow-Vision-Direct-Play-v0", + entry_point=f"{__name__}.shadow_hand_vision_env:ShadowHandVisionEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_vision_env:ShadowHandVisionEnvPlayCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ShadowHandVisionFFPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_vision_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..30b3b0b012f4a9186180bd051fd0843f75bbf96d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512, 512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: shadow_hand + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 5000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 32768 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_ff_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_ff_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6724046a9a5933b420cba760037f17018b3e0a58 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_ff_cfg.yaml @@ -0,0 +1,112 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [400, 400, 200, 100] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: shadow_hand_openai_ff + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.998 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 10000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 16384 + mini_epochs: 4 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 32768 + mini_epochs: 4 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + mlp: + units: [512, 512, 256, 128] + activation: elu + d2rl: False + initializer: + name: default + regularizer: + name: None + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_lstm_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_lstm_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0aea26e9cde63bc657dc5ff4a6021b5400a30429 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_lstm_cfg.yaml @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512] + activation: relu + d2rl: False + + initializer: + name: default + regularizer: + name: None + rnn: + name: lstm + units: 1024 + layers: 1 + before_mlp: True + layer_norm: True + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: shadow_hand_openai_lstm + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.998 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 10000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 16384 + mini_epochs: 4 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 32768 + mini_epochs: 4 + learning_rate: 1e-4 + kl_threshold: 0.016 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + mlp: + units: [512] + activation: relu + d2rl: False + initializer: + name: default + regularizer: + name: None + rnn: + name: lstm + units: 1024 + layers: 1 + before_mlp: True + layer_norm: True + zero_rnn_on_done: False + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_vision_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_vision_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..43fa1d20fb8871d4ceff1d54345036b04f9f99a7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rl_games_ppo_vision_cfg.yaml @@ -0,0 +1,112 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [1024, 512, 512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: shadow_hand_vision + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.01 + score_to_win: 100000 + max_epochs: 50000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 64 + minibatch_size: 19600 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 19600 + mini_epochs: 5 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.01 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + mlp: + units: [1024, 512, 512, 256, 128] + activation: elu + d2rl: False + initializer: + name: default + regularizer: + name: None + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6ab4c9e56f5aa0f56404f49fbdca66559345edae --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,98 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class ShadowHandPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 10000 + save_interval = 250 + experiment_name = "shadow_hand" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[512, 512, 256, 128], + critic_hidden_dims=[512, 512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.016, + max_grad_norm=1.0, + ) + + +@configclass +class ShadowHandAsymFFPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 10000 + save_interval = 250 + experiment_name = "shadow_hand_openai_ff" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[400, 400, 200, 100], + critic_hidden_dims=[512, 512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=4, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class ShadowHandVisionFFPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 64 + max_iterations = 50000 + save_interval = 250 + experiment_name = "shadow_hand_vision" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[1024, 512, 512, 256, 128], + critic_hidden_dims=[1024, 512, 512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ff_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ff_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0831dd7b4125fee56964283b97c01269b586c776 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ff_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [400, 400, 200, 100] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 4 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "shadow_hand_openai_ff" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 160000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9bba87a54552460539e1633b4283d4e1558a0b38 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "shadow_hand" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/feature_extractor.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/feature_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..75a7b6d04a2013309cbcbece2edb7d830e0bbfd4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/feature_extractor.py @@ -0,0 +1,198 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import glob +import os + +import torch +import torch.nn as nn +import torchvision + +from isaaclab.sensors import save_images_to_file +from isaaclab.utils import configclass + + +class FeatureExtractorNetwork(nn.Module): + """CNN architecture used to regress keypoint positions of the in-hand cube from image data.""" + + def __init__(self): + super().__init__() + num_channel = 7 + self.cnn = nn.Sequential( + nn.Conv2d(num_channel, 16, kernel_size=6, stride=2, padding=0), + nn.ReLU(), + nn.LayerNorm([16, 58, 58]), + nn.Conv2d(16, 32, kernel_size=4, stride=2, padding=0), + nn.ReLU(), + nn.LayerNorm([32, 28, 28]), + nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=0), + nn.ReLU(), + nn.LayerNorm([64, 13, 13]), + nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=0), + nn.ReLU(), + nn.LayerNorm([128, 6, 6]), + nn.AvgPool2d(6), + ) + + self.linear = nn.Sequential( + nn.Linear(128, 27), + ) + + self.data_transforms = torchvision.transforms.Compose( + [ + torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ] + ) + + def forward(self, x): + x = x.permute(0, 3, 1, 2) + x[:, 0:3, :, :] = self.data_transforms(x[:, 0:3, :, :]) + x[:, 4:7, :, :] = self.data_transforms(x[:, 4:7, :, :]) + cnn_x = self.cnn(x) + out = self.linear(cnn_x.view(-1, 128)) + return out + + +@configclass +class FeatureExtractorCfg: + """Configuration for the feature extractor model.""" + + train: bool = True + """If True, the feature extractor model is trained during the rollout process. Default is False.""" + + load_checkpoint: bool = False + """If True, the feature extractor model is loaded from a checkpoint. Default is False.""" + + write_image_to_file: bool = False + """If True, the images from the camera sensor are written to file. Default is False.""" + + +class FeatureExtractor: + """Class for extracting features from image data. + + It uses a CNN to regress keypoint positions from normalized RGB, depth, and segmentation images. + If the train flag is set to True, the CNN is trained during the rollout process. + """ + + def __init__(self, cfg: FeatureExtractorCfg, device: str, log_dir: str | None = None): + """Initialize the feature extractor model. + + Args: + cfg: Configuration for the feature extractor model. + device: Device to run the model on. + log_dir: Directory to save checkpoints. Default is None, which uses the local + "logs" folder resolved relative to this file. + """ + + self.cfg = cfg + self.device = device + + # Feature extractor model + self.feature_extractor = FeatureExtractorNetwork() + self.feature_extractor.to(self.device) + + self.step_count = 0 + if log_dir is not None: + self.log_dir = log_dir + else: + self.log_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "logs") + if not os.path.exists(self.log_dir): + os.makedirs(self.log_dir) + + if self.cfg.load_checkpoint: + list_of_files = glob.glob(self.log_dir + "/*.pth") + latest_file = max(list_of_files, key=os.path.getctime) + checkpoint = os.path.join(self.log_dir, latest_file) + print(f"[INFO]: Loading feature extractor checkpoint from {checkpoint}") + self.feature_extractor.load_state_dict(torch.load(checkpoint, weights_only=True)) + + if self.cfg.train: + self.optimizer = torch.optim.Adam(self.feature_extractor.parameters(), lr=1e-4) + self.l2_loss = nn.MSELoss() + self.feature_extractor.train() + else: + self.feature_extractor.eval() + + def _preprocess_images( + self, rgb_img: torch.Tensor, depth_img: torch.Tensor, segmentation_img: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Preprocesses the input images. + + Args: + rgb_img (torch.Tensor): RGB image tensor. Shape: (N, H, W, 3). + depth_img (torch.Tensor): Depth image tensor. Shape: (N, H, W, 1). + segmentation_img (torch.Tensor): Segmentation image tensor. Shape: (N, H, W, 3) + + Returns: + tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Preprocessed RGB, depth, and segmentation + """ + rgb_img = rgb_img / 255.0 + # process depth image + depth_img[depth_img == float("inf")] = 0 + depth_img /= 5.0 + depth_img /= torch.max(depth_img) + # process segmentation image + segmentation_img = segmentation_img / 255.0 + mean_tensor = torch.mean(segmentation_img, dim=(1, 2), keepdim=True) + segmentation_img -= mean_tensor + return rgb_img, depth_img, segmentation_img + + def _save_images(self, rgb_img: torch.Tensor, depth_img: torch.Tensor, segmentation_img: torch.Tensor): + """Writes image buffers to file. + + Args: + rgb_img (torch.Tensor): RGB image tensor. Shape: (N, H, W, 3). + depth_img (torch.Tensor): Depth image tensor. Shape: (N, H, W, 1). + segmentation_img (torch.Tensor): Segmentation image tensor. Shape: (N, H, W, 3). + """ + save_images_to_file(rgb_img, "shadow_hand_rgb.png") + save_images_to_file(depth_img, "shadow_hand_depth.png") + save_images_to_file(segmentation_img, "shadow_hand_segmentation.png") + + def step( + self, rgb_img: torch.Tensor, depth_img: torch.Tensor, segmentation_img: torch.Tensor, gt_pose: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + """Extracts the features using the images and trains the model if the train flag is set to True. + + Args: + rgb_img (torch.Tensor): RGB image tensor. Shape: (N, H, W, 3). + depth_img (torch.Tensor): Depth image tensor. Shape: (N, H, W, 1). + segmentation_img (torch.Tensor): Segmentation image tensor. Shape: (N, H, W, 3). + gt_pose (torch.Tensor): Ground truth pose tensor (position and corners). Shape: (N, 27). + + Returns: + tuple[torch.Tensor, torch.Tensor]: Pose loss and predicted pose. + """ + + rgb_img, depth_img, segmentation_img = self._preprocess_images(rgb_img, depth_img, segmentation_img) + + if self.cfg.write_image_to_file: + self._save_images(rgb_img, depth_img, segmentation_img) + + if self.cfg.train: + with torch.enable_grad(): + with torch.inference_mode(False): + img_input = torch.cat((rgb_img, depth_img, segmentation_img), dim=-1) + self.optimizer.zero_grad() + + predicted_pose = self.feature_extractor(img_input) + pose_loss = self.l2_loss(predicted_pose, gt_pose.clone()) * 100 + + pose_loss.backward() + self.optimizer.step() + + if self.step_count % 50000 == 0: + torch.save( + self.feature_extractor.state_dict(), + os.path.join(self.log_dir, f"cnn_{self.step_count}_{pose_loss.detach().cpu().numpy()}.pth"), + ) + + self.step_count += 1 + + return pose_loss, predicted_pose + else: + img_input = torch.cat((rgb_img, depth_img, segmentation_img), dim=-1) + predicted_pose = self.feature_extractor(img_input) + return None, predicted_pose diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f9c92f18fbe1482298353e1ecde7d20d52a8fe0a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_env_cfg.py @@ -0,0 +1,286 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import GaussianNoiseCfg, NoiseModelWithAdditiveBiasCfg + +from isaaclab_assets.robots.shadow_hand import SHADOW_HAND_CFG + + +@configclass +class EventCfg: + """Configuration for randomization.""" + + # -- robot + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="reset", + min_step_count_between_reset=720, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + robot_joint_pos_limits = EventTerm( + func=mdp.randomize_joint_parameters, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "lower_limit_distribution_params": (0.00, 0.01), + "upper_limit_distribution_params": (0.00, 0.01), + "operation": "add", + "distribution": "gaussian", + }, + ) + robot_tendon_properties = EventTerm( + func=mdp.randomize_fixed_tendon_parameters, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", fixed_tendon_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + + # -- object + object_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("object"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + object_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("object"), + "mass_distribution_params": (0.5, 1.5), + "operation": "scale", + "distribution": "uniform", + }, + ) + + # -- scene + reset_gravity = EventTerm( + func=mdp.randomize_physics_scene_gravity, + mode="interval", + is_global_time=True, + interval_range_s=(36.0, 36.0), # time_s = num_steps * (decimation * dt) + params={ + "gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.4]), + "operation": "add", + "distribution": "gaussian", + }, + ) + + +@configclass +class ShadowHandEnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 10.0 + action_space = 20 + observation_space = 157 # (full) + state_space = 0 + asymmetric_obs = False + obs_type = "full" + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 120, + render_interval=decimation, + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + physx=PhysxCfg( + bounce_threshold_velocity=0.2, + ), + ) + # robot + robot_cfg: ArticulationCfg = SHADOW_HAND_CFG.replace(prim_path="/World/envs/env_.*/Robot").replace( + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(1.0, 0.0, 0.0, 0.0), + joint_pos={".*": 0.0}, + ) + ) + actuated_joint_names = [ + "robot0_WRJ1", + "robot0_WRJ0", + "robot0_FFJ3", + "robot0_FFJ2", + "robot0_FFJ1", + "robot0_MFJ3", + "robot0_MFJ2", + "robot0_MFJ1", + "robot0_RFJ3", + "robot0_RFJ2", + "robot0_RFJ1", + "robot0_LFJ4", + "robot0_LFJ3", + "robot0_LFJ2", + "robot0_LFJ1", + "robot0_THJ4", + "robot0_THJ3", + "robot0_THJ2", + "robot0_THJ1", + "robot0_THJ0", + ] + fingertip_body_names = [ + "robot0_ffdistal", + "robot0_mfdistal", + "robot0_rfdistal", + "robot0_lfdistal", + "robot0_thdistal", + ] + + # in-hand object + object_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/object", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + kinematic_enabled=False, + disable_gravity=False, + enable_gyroscopic_forces=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0025, + max_depenetration_velocity=1000.0, + ), + mass_props=sim_utils.MassPropertiesCfg(density=567.0), + semantic_tags=[("class", "cube")], + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -0.39, 0.6), rot=(1.0, 0.0, 0.0, 0.0)), + ) + # goal object + goal_object_cfg: VisualizationMarkersCfg = VisualizationMarkersCfg( + prim_path="/Visuals/goal_marker", + markers={ + "goal": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(1.0, 1.0, 1.0), + ) + }, + ) + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=8192, env_spacing=0.75, replicate_physics=True, clone_in_fabric=True + ) + + # reset + reset_position_noise = 0.01 # range of position at reset + reset_dof_pos_noise = 0.2 # range of dof pos at reset + reset_dof_vel_noise = 0.0 # range of dof vel at reset + # reward scales + dist_reward_scale = -10.0 + rot_reward_scale = 1.0 + rot_eps = 0.1 + action_penalty_scale = -0.0002 + reach_goal_bonus = 250 + fall_penalty = 0 + fall_dist = 0.24 + vel_obs_scale = 0.2 + success_tolerance = 0.1 + max_consecutive_success = 0 + av_factor = 0.1 + act_moving_average = 1.0 + force_torque_obs_scale = 10.0 + + +@configclass +class ShadowHandOpenAIEnvCfg(ShadowHandEnvCfg): + # env + decimation = 3 + episode_length_s = 8.0 + action_space = 20 + observation_space = 42 + state_space = 187 + asymmetric_obs = True + obs_type = "openai" + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 60, + render_interval=decimation, + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + physx=PhysxCfg( + bounce_threshold_velocity=0.2, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + ), + ) + # reset + reset_position_noise = 0.01 # range of position at reset + reset_dof_pos_noise = 0.2 # range of dof pos at reset + reset_dof_vel_noise = 0.0 # range of dof vel at reset + # reward scales + dist_reward_scale = -10.0 + rot_reward_scale = 1.0 + rot_eps = 0.1 + action_penalty_scale = -0.0002 + reach_goal_bonus = 250 + fall_penalty = -50 + fall_dist = 0.24 + vel_obs_scale = 0.2 + success_tolerance = 0.4 + max_consecutive_success = 50 + av_factor = 0.1 + act_moving_average = 0.3 + force_torque_obs_scale = 10.0 + # domain randomization config + events: EventCfg = EventCfg() + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + action_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.05, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.015, operation="abs"), + ) + # at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset + observation_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg( + noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.002, operation="add"), + bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.0001, operation="abs"), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_vision_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_vision_env.py new file mode 100644 index 0000000000000000000000000000000000000000..b5c781c1a9f27c9e457d447329cb61b65d9922df --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand/shadow_hand_vision_env.py @@ -0,0 +1,187 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import TiledCamera, TiledCameraCfg +from isaaclab.utils import configclass +from isaaclab.utils.math import quat_apply + +from isaaclab_tasks.direct.inhand_manipulation.inhand_manipulation_env import InHandManipulationEnv, unscale + +from .feature_extractor import FeatureExtractor, FeatureExtractorCfg +from .shadow_hand_env_cfg import ShadowHandEnvCfg + + +@configclass +class ShadowHandVisionEnvCfg(ShadowHandEnvCfg): + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1225, env_spacing=2.0, replicate_physics=True) + + # camera + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="/World/envs/env_.*/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(0, -0.35, 1.0), rot=(0.7071, 0.0, 0.7071, 0.0), convention="world"), + data_types=["rgb", "depth", "semantic_segmentation"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=120, + height=120, + ) + feature_extractor = FeatureExtractorCfg() + + # env + observation_space = 164 + 27 # state observation + vision CNN embedding + state_space = 187 + 27 # asymettric states + vision CNN embedding + + +@configclass +class ShadowHandVisionEnvPlayCfg(ShadowHandVisionEnvCfg): + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=64, env_spacing=2.0, replicate_physics=True) + # inference for CNN + feature_extractor = FeatureExtractorCfg(train=False, load_checkpoint=True) + + +class ShadowHandVisionEnv(InHandManipulationEnv): + cfg: ShadowHandVisionEnvCfg + + def __init__(self, cfg: ShadowHandVisionEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + # Use the log directory from the configuration + self.feature_extractor = FeatureExtractor(self.cfg.feature_extractor, self.device, self.cfg.log_dir) + # hide goal cubes + self.goal_pos[:, :] = torch.tensor([-0.2, 0.1, 0.6], device=self.device) + # keypoints buffer + self.gt_keypoints = torch.ones(self.num_envs, 8, 3, dtype=torch.float32, device=self.device) + self.goal_keypoints = torch.ones(self.num_envs, 8, 3, dtype=torch.float32, device=self.device) + + def _setup_scene(self): + # add hand, in-hand object, and goal object + self.hand = Articulation(self.cfg.robot_cfg) + self.object = RigidObject(self.cfg.object_cfg) + self._tiled_camera = TiledCamera(self.cfg.tiled_camera) + # clone and replicate (no need to filter for this environment) + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene - we must register to scene to randomize with EventManager + self.scene.articulations["robot"] = self.hand + self.scene.rigid_objects["object"] = self.object + self.scene.sensors["tiled_camera"] = self._tiled_camera + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _compute_image_observations(self): + # generate ground truth keypoints for in-hand cube + compute_keypoints(pose=torch.cat((self.object_pos, self.object_rot), dim=1), out=self.gt_keypoints) + + object_pose = torch.cat([self.object_pos, self.gt_keypoints.view(-1, 24)], dim=-1) + + # train CNN to regress on keypoint positions + pose_loss, embeddings = self.feature_extractor.step( + self._tiled_camera.data.output["rgb"], + self._tiled_camera.data.output["depth"], + self._tiled_camera.data.output["semantic_segmentation"][..., :3], + object_pose, + ) + + self.embeddings = embeddings.clone().detach() + # compute keypoints for goal cube + compute_keypoints( + pose=torch.cat((torch.zeros_like(self.goal_pos), self.goal_rot), dim=-1), out=self.goal_keypoints + ) + + obs = torch.cat( + ( + self.embeddings, + self.goal_keypoints.view(-1, 24), + ), + dim=-1, + ) + + # log pose loss from CNN training + if "log" not in self.extras: + self.extras["log"] = dict() + self.extras["log"]["pose_loss"] = pose_loss + + return obs + + def _compute_proprio_observations(self): + """Proprioception observations from physics.""" + obs = torch.cat( + ( + # hand + unscale(self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + self.cfg.vel_obs_scale * self.hand_dof_vel, + # goal + self.in_hand_pos, + self.goal_rot, + # fingertips + self.fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + self.fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + self.fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # actions + self.actions, + ), + dim=-1, + ) + return obs + + def _compute_states(self): + """Asymmetric states for the critic.""" + sim_states = self.compute_full_state() + state = torch.cat((sim_states, self.embeddings), dim=-1) + return state + + def _get_observations(self) -> dict: + # proprioception observations + state_obs = self._compute_proprio_observations() + # vision observations from CMM + image_obs = self._compute_image_observations() + obs = torch.cat((state_obs, image_obs), dim=-1) + # asymmetric critic states + self.fingertip_force_sensors = self.hand.root_physx_view.get_link_incoming_joint_force()[:, self.finger_bodies] + state = self._compute_states() + + observations = {"policy": obs, "critic": state} + return observations + + +@torch.jit.script +def compute_keypoints( + pose: torch.Tensor, + num_keypoints: int = 8, + size: tuple[float, float, float] = (2 * 0.03, 2 * 0.03, 2 * 0.03), + out: torch.Tensor | None = None, +): + """Computes positions of 8 corner keypoints of a cube. + + Args: + pose: Position and orientation of the center of the cube. Shape is (N, 7) + num_keypoints: Number of keypoints to compute. Default = 8 + size: Length of X, Y, Z dimensions of cube. Default = [0.06, 0.06, 0.06] + out: Buffer to store keypoints. If None, a new buffer will be created. + """ + num_envs = pose.shape[0] + if out is None: + out = torch.ones(num_envs, num_keypoints, 3, dtype=torch.float32, device=pose.device) + else: + out[:] = 1.0 + for i in range(num_keypoints): + # which dimensions to negate + n = [((i >> k) & 1) == 0 for k in range(3)] + corner_loc = ([(1 if n[k] else -1) * s / 2 for k, s in enumerate(size)],) + corner = torch.tensor(corner_loc, dtype=torch.float32, device=pose.device) * out[:, i, :] + # express corner position in the world frame + out[:, i, :] = pose[:, :3] + quat_apply(pose[:, 3:7], corner) + + return out diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0fbb815b98f2d48de7df9ee9ff4264975c61a46b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +ShadowHand Over environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Shadow-Hand-Over-Direct-v0", + entry_point=f"{__name__}.shadow_hand_over_env:ShadowHandOverEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.shadow_hand_over_env_cfg:ShadowHandOverEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "skrl_ippo_cfg_entry_point": f"{agents.__name__}:skrl_ippo_cfg.yaml", + "skrl_mappo_cfg_entry_point": f"{agents.__name__}:skrl_mappo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3849c1dd8dab4be7ec569d04b43cc8cd1dd9b016 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512, 512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: shadow_hand_over + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 5000 + save_best_after: 100 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 32768 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 + + player: + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ippo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ippo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60be7d18110e3505bab09842b2733268dc515c7b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ippo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# IPPO agent configuration (field names are from IPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/ippo.html +agent: + class: IPPO + rollouts: 16 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "shadow_hand_over" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_mappo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_mappo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..57c1c455185d320ea25a902341596df2a1f5c840 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_mappo_cfg.yaml @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# MAPPO agent configuration (field names are from MAPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/mappo.html +agent: + class: MAPPO + rollouts: 16 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + shared_state_preprocessor: RunningStandardScaler + shared_state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "shadow_hand_over" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ab45d2683375b6ad78444fcfb1a45d74713f40f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "shadow_hand_over" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env.py new file mode 100644 index 0000000000000000000000000000000000000000..09bbff6e97c0b6d43fc76d8bc2163ce67769fe7d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env.py @@ -0,0 +1,424 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np +import torch + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs import DirectMARLEnv +from isaaclab.markers import VisualizationMarkers +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.math import quat_conjugate, quat_from_angle_axis, quat_mul, sample_uniform, saturate + +from .shadow_hand_over_env_cfg import ShadowHandOverEnvCfg + + +class ShadowHandOverEnv(DirectMARLEnv): + cfg: ShadowHandOverEnvCfg + + def __init__(self, cfg: ShadowHandOverEnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self.num_hand_dofs = self.right_hand.num_joints + + # buffers for position targets + self.right_hand_dof_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + self.right_hand_prev_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + self.right_hand_curr_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + self.left_hand_dof_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + self.left_hand_prev_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + self.left_hand_curr_targets = torch.zeros( + (self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device + ) + + # list of actuated joints + self.actuated_dof_indices = list() + for joint_name in cfg.actuated_joint_names: + self.actuated_dof_indices.append(self.right_hand.joint_names.index(joint_name)) + self.actuated_dof_indices.sort() + + # finger bodies + self.finger_bodies = list() + for body_name in self.cfg.fingertip_body_names: + self.finger_bodies.append(self.right_hand.body_names.index(body_name)) + self.finger_bodies.sort() + self.num_fingertips = len(self.finger_bodies) + + # joint limits + joint_pos_limits = self.right_hand.root_physx_view.get_dof_limits().to(self.device) + self.hand_dof_lower_limits = joint_pos_limits[..., 0] + self.hand_dof_upper_limits = joint_pos_limits[..., 1] + + # used to compare object position + self.in_hand_pos = self.object.data.default_root_state[:, 0:3].clone() + self.in_hand_pos[:, 2] -= 0.04 + # default goal positions + self.goal_rot = torch.zeros((self.num_envs, 4), dtype=torch.float, device=self.device) + self.goal_rot[:, 0] = 1.0 + self.goal_pos = torch.zeros((self.num_envs, 3), dtype=torch.float, device=self.device) + self.goal_pos[:, :] = torch.tensor([0.0, -0.64, 0.54], device=self.device) + # initialize goal marker + self.goal_markers = VisualizationMarkers(self.cfg.goal_object_cfg) + + # unit tensors + self.x_unit_tensor = torch.tensor([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + self.y_unit_tensor = torch.tensor([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + self.z_unit_tensor = torch.tensor([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1)) + + def _setup_scene(self): + # add hand, in-hand object, and goal object + self.right_hand = Articulation(self.cfg.right_robot_cfg) + self.left_hand = Articulation(self.cfg.left_robot_cfg) + self.object = RigidObject(self.cfg.object_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate (no need to filter for this environment) + self.scene.clone_environments(copy_from_source=False) + # add articulation to scene - we must register to scene to randomize with EventManager + self.scene.articulations["right_robot"] = self.right_hand + self.scene.articulations["left_robot"] = self.left_hand + self.scene.rigid_objects["object"] = self.object + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: dict[str, torch.Tensor]) -> None: + self.actions = actions + + def _apply_action(self) -> None: + # right hand target + self.right_hand_curr_targets[:, self.actuated_dof_indices] = scale( + self.actions["right_hand"], + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + self.right_hand_curr_targets[:, self.actuated_dof_indices] = ( + self.cfg.act_moving_average * self.right_hand_curr_targets[:, self.actuated_dof_indices] + + (1.0 - self.cfg.act_moving_average) * self.right_hand_prev_targets[:, self.actuated_dof_indices] + ) + self.right_hand_curr_targets[:, self.actuated_dof_indices] = saturate( + self.right_hand_curr_targets[:, self.actuated_dof_indices], + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + + # left hand target + self.left_hand_curr_targets[:, self.actuated_dof_indices] = scale( + self.actions["left_hand"], + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + self.left_hand_curr_targets[:, self.actuated_dof_indices] = ( + self.cfg.act_moving_average * self.left_hand_curr_targets[:, self.actuated_dof_indices] + + (1.0 - self.cfg.act_moving_average) * self.left_hand_prev_targets[:, self.actuated_dof_indices] + ) + self.left_hand_curr_targets[:, self.actuated_dof_indices] = saturate( + self.left_hand_curr_targets[:, self.actuated_dof_indices], + self.hand_dof_lower_limits[:, self.actuated_dof_indices], + self.hand_dof_upper_limits[:, self.actuated_dof_indices], + ) + + # save current targets + self.right_hand_prev_targets[:, self.actuated_dof_indices] = self.right_hand_curr_targets[ + :, self.actuated_dof_indices + ] + self.left_hand_prev_targets[:, self.actuated_dof_indices] = self.left_hand_curr_targets[ + :, self.actuated_dof_indices + ] + + # set targets + self.right_hand.set_joint_position_target( + self.right_hand_curr_targets[:, self.actuated_dof_indices], joint_ids=self.actuated_dof_indices + ) + self.left_hand.set_joint_position_target( + self.left_hand_curr_targets[:, self.actuated_dof_indices], joint_ids=self.actuated_dof_indices + ) + + def _get_observations(self) -> dict[str, torch.Tensor]: + observations = { + "right_hand": torch.cat( + ( + # ---- right hand ---- + # DOF positions (24) + unscale(self.right_hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + # DOF velocities (24) + self.cfg.vel_obs_scale * self.right_hand_dof_vel, + # fingertip positions (5 * 3) + self.right_fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + # fingertip rotations (5 * 4) + self.right_fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + # fingertip linear and angular velocities (5 * 6) + self.right_fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # applied actions (20) + self.actions["right_hand"], + # ---- object ---- + # positions (3) + self.object_pos, + # rotations (4) + self.object_rot, + # linear velocities (3) + self.object_linvel, + # angular velocities (3) + self.cfg.vel_obs_scale * self.object_angvel, + # ---- goal ---- + # positions (3) + self.goal_pos, + # rotations (4) + self.goal_rot, + # goal-object rotation diff (4) + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + ), + dim=-1, + ), + "left_hand": torch.cat( + ( + # ---- left hand ---- + # DOF positions (24) + unscale(self.left_hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + # DOF velocities (24) + self.cfg.vel_obs_scale * self.left_hand_dof_vel, + # fingertip positions (5 * 3) + self.left_fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + # fingertip rotations (5 * 4) + self.left_fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + # fingertip linear and angular velocities (5 * 6) + self.left_fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # applied actions (20) + self.actions["left_hand"], + # ---- object ---- + # positions (3) + self.object_pos, + # rotations (4) + self.object_rot, + # linear velocities (3) + self.object_linvel, + # angular velocities (3) + self.cfg.vel_obs_scale * self.object_angvel, + # ---- goal ---- + # positions (3) + self.goal_pos, + # rotations (4) + self.goal_rot, + # goal-object rotation diff (4) + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + ), + dim=-1, + ), + } + return observations + + def _get_states(self) -> torch.Tensor: + states = torch.cat( + ( + # ---- right hand ---- + # DOF positions (24) + unscale(self.right_hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + # DOF velocities (24) + self.cfg.vel_obs_scale * self.right_hand_dof_vel, + # fingertip positions (5 * 3) + self.right_fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + # fingertip rotations (5 * 4) + self.right_fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + # fingertip linear and angular velocities (5 * 6) + self.right_fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # applied actions (20) + self.actions["right_hand"], + # ---- left hand ---- + # DOF positions (24) + unscale(self.left_hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits), + # DOF velocities (24) + self.cfg.vel_obs_scale * self.left_hand_dof_vel, + # fingertip positions (5 * 3) + self.left_fingertip_pos.view(self.num_envs, self.num_fingertips * 3), + # fingertip rotations (5 * 4) + self.left_fingertip_rot.view(self.num_envs, self.num_fingertips * 4), + # fingertip linear and angular velocities (5 * 6) + self.left_fingertip_velocities.view(self.num_envs, self.num_fingertips * 6), + # applied actions (20) + self.actions["left_hand"], + # ---- object ---- + # positions (3) + self.object_pos, + # rotations (4) + self.object_rot, + # linear velocities (3) + self.object_linvel, + # angular velocities (3) + self.cfg.vel_obs_scale * self.object_angvel, + # ---- goal ---- + # positions (3) + self.goal_pos, + # rotations (4) + self.goal_rot, + # goal-object rotation diff (4) + quat_mul(self.object_rot, quat_conjugate(self.goal_rot)), + ), + dim=-1, + ) + return states + + def _get_rewards(self) -> dict[str, torch.Tensor]: + # compute reward + goal_dist = torch.norm(self.object_pos - self.goal_pos, p=2, dim=-1) + rew_dist = 2 * torch.exp(-self.cfg.dist_reward_scale * goal_dist) + + # log reward components + if "log" not in self.extras: + self.extras["log"] = dict() + self.extras["log"]["dist_reward"] = rew_dist.mean() + self.extras["log"]["dist_goal"] = goal_dist.mean() + + return {"right_hand": rew_dist, "left_hand": rew_dist} + + def _get_dones(self) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + self._compute_intermediate_values() + + # reset when object has fallen + out_of_reach = self.object_pos[:, 2] <= self.cfg.fall_dist + # reset when episode ends + time_out = self.episode_length_buf >= self.max_episode_length - 1 + + terminated = {agent: out_of_reach for agent in self.cfg.possible_agents} + time_outs = {agent: time_out for agent in self.cfg.possible_agents} + return terminated, time_outs + + def _reset_idx(self, env_ids: Sequence[int] | torch.Tensor | None): + if env_ids is None: + env_ids = self.right_hand._ALL_INDICES + # reset articulation and rigid body attributes + super()._reset_idx(env_ids) + + # reset goals + self._reset_target_pose(env_ids) + + # reset object + object_default_state = self.object.data.default_root_state.clone()[env_ids] + pos_noise = sample_uniform(-1.0, 1.0, (len(env_ids), 3), device=self.device) + + object_default_state[:, 0:3] = ( + object_default_state[:, 0:3] + self.cfg.reset_position_noise * pos_noise + self.scene.env_origins[env_ids] + ) + + rot_noise = sample_uniform(-1.0, 1.0, (len(env_ids), 2), device=self.device) # noise for X and Y rotation + object_default_state[:, 3:7] = randomize_rotation( + rot_noise[:, 0], rot_noise[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids] + ) + + object_default_state[:, 7:] = torch.zeros_like(self.object.data.default_root_state[env_ids, 7:]) + self.object.write_root_pose_to_sim(object_default_state[:, :7], env_ids) + self.object.write_root_velocity_to_sim(object_default_state[:, 7:], env_ids) + + # reset right hand + delta_max = self.hand_dof_upper_limits[env_ids] - self.right_hand.data.default_joint_pos[env_ids] + delta_min = self.hand_dof_lower_limits[env_ids] - self.right_hand.data.default_joint_pos[env_ids] + + dof_pos_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + rand_delta = delta_min + (delta_max - delta_min) * 0.5 * dof_pos_noise + dof_pos = self.right_hand.data.default_joint_pos[env_ids] + self.cfg.reset_dof_pos_noise * rand_delta + + dof_vel_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + dof_vel = self.right_hand.data.default_joint_vel[env_ids] + self.cfg.reset_dof_vel_noise * dof_vel_noise + + self.right_hand_prev_targets[env_ids] = dof_pos + self.right_hand_curr_targets[env_ids] = dof_pos + self.right_hand_dof_targets[env_ids] = dof_pos + + self.right_hand.set_joint_position_target(dof_pos, env_ids=env_ids) + self.right_hand.write_joint_state_to_sim(dof_pos, dof_vel, env_ids=env_ids) + + # reset left hand + delta_max = self.hand_dof_upper_limits[env_ids] - self.left_hand.data.default_joint_pos[env_ids] + delta_min = self.hand_dof_lower_limits[env_ids] - self.left_hand.data.default_joint_pos[env_ids] + + dof_pos_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + rand_delta = delta_min + (delta_max - delta_min) * 0.5 * dof_pos_noise + dof_pos = self.left_hand.data.default_joint_pos[env_ids] + self.cfg.reset_dof_pos_noise * rand_delta + + dof_vel_noise = sample_uniform(-1.0, 1.0, (len(env_ids), self.num_hand_dofs), device=self.device) + dof_vel = self.left_hand.data.default_joint_vel[env_ids] + self.cfg.reset_dof_vel_noise * dof_vel_noise + + self.left_hand_prev_targets[env_ids] = dof_pos + self.left_hand_curr_targets[env_ids] = dof_pos + self.left_hand_dof_targets[env_ids] = dof_pos + + self.left_hand.set_joint_position_target(dof_pos, env_ids=env_ids) + self.left_hand.write_joint_state_to_sim(dof_pos, dof_vel, env_ids=env_ids) + + self._compute_intermediate_values() + + def _reset_target_pose(self, env_ids): + # reset goal rotation + rand_floats = sample_uniform(-1.0, 1.0, (len(env_ids), 2), device=self.device) + new_rot = randomize_rotation( + rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids] + ) + + # update goal pose and markers + self.goal_rot[env_ids] = new_rot + goal_pos = self.goal_pos + self.scene.env_origins + self.goal_markers.visualize(goal_pos, self.goal_rot) + + def _compute_intermediate_values(self): + # data for right hand + self.right_fingertip_pos = self.right_hand.data.body_pos_w[:, self.finger_bodies] + self.right_fingertip_rot = self.right_hand.data.body_quat_w[:, self.finger_bodies] + self.right_fingertip_pos -= self.scene.env_origins.repeat((1, self.num_fingertips)).reshape( + self.num_envs, self.num_fingertips, 3 + ) + self.right_fingertip_velocities = self.right_hand.data.body_vel_w[:, self.finger_bodies] + + self.right_hand_dof_pos = self.right_hand.data.joint_pos + self.right_hand_dof_vel = self.right_hand.data.joint_vel + + # data for left hand + self.left_fingertip_pos = self.left_hand.data.body_pos_w[:, self.finger_bodies] + self.left_fingertip_rot = self.left_hand.data.body_quat_w[:, self.finger_bodies] + self.left_fingertip_pos -= self.scene.env_origins.repeat((1, self.num_fingertips)).reshape( + self.num_envs, self.num_fingertips, 3 + ) + self.left_fingertip_velocities = self.left_hand.data.body_vel_w[:, self.finger_bodies] + + self.left_hand_dof_pos = self.left_hand.data.joint_pos + self.left_hand_dof_vel = self.left_hand.data.joint_vel + + # data for object + self.object_pos = self.object.data.root_pos_w - self.scene.env_origins + self.object_rot = self.object.data.root_quat_w + self.object_velocities = self.object.data.root_vel_w + self.object_linvel = self.object.data.root_lin_vel_w + self.object_angvel = self.object.data.root_ang_vel_w + + +@torch.jit.script +def scale(x, lower, upper): + return 0.5 * (x + 1.0) * (upper - lower) + lower + + +@torch.jit.script +def unscale(x, lower, upper): + return (2.0 * x - upper - lower) / (upper - lower) + + +@torch.jit.script +def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor): + return quat_mul( + quat_from_angle_axis(rand0 * np.pi, x_unit_tensor), quat_from_angle_axis(rand1 * np.pi, y_unit_tensor) + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..855939392a22347ebff2db6b54e3725ed0fb4f47 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env_cfg.py @@ -0,0 +1,226 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.envs.mdp as mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, RigidObjectCfg +from isaaclab.envs import DirectMARLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass + +from isaaclab_assets.robots.shadow_hand import SHADOW_HAND_CFG + + +@configclass +class EventCfg: + """Configuration for randomization.""" + + # -- robot + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="reset", + min_step_count_between_reset=720, + params={ + "asset_cfg": SceneEntityCfg("right_hand"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("right_hand", joint_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + robot_joint_pos_limits = EventTerm( + func=mdp.randomize_joint_parameters, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("right_hand", joint_names=".*"), + "lower_limit_distribution_params": (0.00, 0.01), + "upper_limit_distribution_params": (0.00, 0.01), + "operation": "add", + "distribution": "gaussian", + }, + ) + robot_tendon_properties = EventTerm( + func=mdp.randomize_fixed_tendon_parameters, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("right_hand", fixed_tendon_names=".*"), + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + + # -- object + object_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("object"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (1.0, 1.0), + "restitution_range": (1.0, 1.0), + "num_buckets": 250, + }, + ) + object_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + min_step_count_between_reset=720, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("object"), + "mass_distribution_params": (0.5, 1.5), + "operation": "scale", + "distribution": "uniform", + }, + ) + + # -- scene + reset_gravity = EventTerm( + func=mdp.randomize_physics_scene_gravity, + mode="interval", + is_global_time=True, + interval_range_s=(36.0, 36.0), # time_s = num_steps * (decimation * dt) + params={ + "gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.4]), + "operation": "add", + "distribution": "gaussian", + }, + ) + + +@configclass +class ShadowHandOverEnvCfg(DirectMARLEnvCfg): + # env + decimation = 2 + episode_length_s = 7.5 + possible_agents = ["right_hand", "left_hand"] + action_spaces = {"right_hand": 20, "left_hand": 20} + observation_spaces = {"right_hand": 157, "left_hand": 157} + state_space = 290 + + # simulation + sim: SimulationCfg = SimulationCfg( + dt=1 / 120, + render_interval=decimation, + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + physx=PhysxCfg( + bounce_threshold_velocity=0.2, + ), + ) + # robot + right_robot_cfg: ArticulationCfg = SHADOW_HAND_CFG.replace(prim_path="/World/envs/env_.*/RightRobot").replace( + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.5), + rot=(1.0, 0.0, 0.0, 0.0), + joint_pos={".*": 0.0}, + ) + ) + left_robot_cfg: ArticulationCfg = SHADOW_HAND_CFG.replace(prim_path="/World/envs/env_.*/LeftRobot").replace( + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, -1.0, 0.5), + rot=(0.0, 0.0, 0.0, 1.0), + joint_pos={".*": 0.0}, + ) + ) + actuated_joint_names = [ + "robot0_WRJ1", + "robot0_WRJ0", + "robot0_FFJ3", + "robot0_FFJ2", + "robot0_FFJ1", + "robot0_MFJ3", + "robot0_MFJ2", + "robot0_MFJ1", + "robot0_RFJ3", + "robot0_RFJ2", + "robot0_RFJ1", + "robot0_LFJ4", + "robot0_LFJ3", + "robot0_LFJ2", + "robot0_LFJ1", + "robot0_THJ4", + "robot0_THJ3", + "robot0_THJ2", + "robot0_THJ1", + "robot0_THJ0", + ] + fingertip_body_names = [ + "robot0_ffdistal", + "robot0_mfdistal", + "robot0_rfdistal", + "robot0_lfdistal", + "robot0_thdistal", + ] + + # in-hand object + object_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/object", + spawn=sim_utils.SphereCfg( + radius=0.0335, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 1.0, 0.0)), + physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=0.7), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + kinematic_enabled=False, + disable_gravity=False, + enable_gyroscopic_forces=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0025, + max_depenetration_velocity=1000.0, + ), + collision_props=sim_utils.CollisionPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(density=500.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -0.39, 0.54), rot=(1.0, 0.0, 0.0, 0.0)), + ) + # goal object + goal_object_cfg: VisualizationMarkersCfg = VisualizationMarkersCfg( + prim_path="/Visuals/goal_marker", + markers={ + "goal": sim_utils.SphereCfg( + radius=0.0335, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.3, 1.0)), + ), + }, + ) + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=2048, env_spacing=1.5, replicate_physics=True) + + # reset + reset_position_noise = 0.01 # range of position at reset + reset_dof_pos_noise = 0.2 # range of dof pos at reset + reset_dof_vel_noise = 0.0 # range of dof vel at reset + # scales and constants + fall_dist = 0.24 + vel_obs_scale = 0.2 + act_moving_average = 1.0 + # reward-related scales + dist_reward_scale = 20.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..47e4a4aac39273614ee4acc4ec3a1dc4c1e1facc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Config-based workflow environments. +""" + +import gymnasium as gym diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79c13e2aa8f41106a7da840c8639b2cbca269a28 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Classic environments for control. + +These environments are based on the MuJoCo environments provided by OpenAI. + +Reference: + https://github.com/openai/gym/tree/master/gym/envs/mujoco +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..df1a4db3a042b8d3ae662265911e49af20e2588c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Ant locomotion environment (similar to OpenAI Gym Ant-v2). +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Ant-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.ant_env_cfg:AntEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AntPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aad56e76525cebff9b3ddcd7ac97b195afcb91cc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: ant + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 3e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 500 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 32768 + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..986461733663c4db6f553bdf8bb95ed42531dd87 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class AntPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 32 + max_iterations = 1000 + save_interval = 50 + experiment_name = "ant" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[400, 200, 100], + critic_hidden_dims=[400, 200, 100], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..126885cbcd441ac71c14cbbeca0fe55e5be2f0d5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L161 +seed: 42 + +n_timesteps: !!float 1e8 +policy: 'MlpPolicy' +batch_size: 32768 +n_steps: 16 +gamma: 0.99 +gae_lambda: 0.9 +n_epochs: 4 +ent_coef: 0.0 +sde_sample_freq: 4 +max_grad_norm: 0.5 +vf_coef: 0.5 +learning_rate: !!float 3e-5 +use_sde: False +clip_range: 0.4 +device: "cuda:0" +policy_kwargs: + log_std_init: -1 + ortho_init: False + activation_fn: 'nn.ReLU' + net_arch: + pi: [256, 256] + vf: [256, 256] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3ff54f7e76e85f22fd590c2bfbe6cd3b4a1970d7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 4 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "ant" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 8000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/ant_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/ant_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..289d4f75f8c431eb97048b2850ab83c16090e1a6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/ant_env_cfg.py @@ -0,0 +1,184 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.classic.humanoid.mdp as mdp + +## +# Pre-defined configs +## +from isaaclab_assets.robots.ant import ANT_CFG # isort: skip + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Configuration for the terrain scene with an ant robot.""" + + # terrain + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="average", + restitution_combine_mode="average", + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.0, + ), + debug_vis=False, + ) + + # robot + robot = ANT_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=[".*"], scale=7.5) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for the policy.""" + + base_height = ObsTerm(func=mdp.base_pos_z) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel) + base_yaw_roll = ObsTerm(func=mdp.base_yaw_roll) + base_angle_to_target = ObsTerm(func=mdp.base_angle_to_target, params={"target_pos": (1000.0, 0.0, 0.0)}) + base_up_proj = ObsTerm(func=mdp.base_up_proj) + base_heading_proj = ObsTerm(func=mdp.base_heading_proj, params={"target_pos": (1000.0, 0.0, 0.0)}) + joint_pos_norm = ObsTerm(func=mdp.joint_pos_limit_normalized) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel, scale=0.2) + feet_body_forces = ObsTerm( + func=mdp.body_incoming_wrench, + scale=0.1, + params={ + "asset_cfg": SceneEntityCfg( + "robot", body_names=["front_left_foot", "front_right_foot", "left_back_foot", "right_back_foot"] + ) + }, + ) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={"pose_range": {}, "velocity_range": {}}, + ) + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.2, 0.2), + "velocity_range": (-0.1, 0.1), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # (1) Reward for moving forward + progress = RewTerm(func=mdp.progress_reward, weight=1.0, params={"target_pos": (1000.0, 0.0, 0.0)}) + # (2) Stay alive bonus + alive = RewTerm(func=mdp.is_alive, weight=0.5) + # (3) Reward for non-upright posture + upright = RewTerm(func=mdp.upright_posture_bonus, weight=0.1, params={"threshold": 0.93}) + # (4) Reward for moving in the right direction + move_to_target = RewTerm( + func=mdp.move_to_target_bonus, weight=0.5, params={"threshold": 0.8, "target_pos": (1000.0, 0.0, 0.0)} + ) + # (5) Penalty for large action commands + action_l2 = RewTerm(func=mdp.action_l2, weight=-0.005) + # (6) Penalty for energy consumption + energy = RewTerm(func=mdp.power_consumption, weight=-0.05, params={"gear_ratio": {".*": 15.0}}) + # (7) Penalty for reaching close to joint limits + joint_pos_limits = RewTerm( + func=mdp.joint_pos_limits_penalty_ratio, weight=-0.1, params={"threshold": 0.99, "gear_ratio": {".*": 15.0}} + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + # (1) Terminate if the episode length is exceeded + time_out = DoneTerm(func=mdp.time_out, time_out=True) + # (2) Terminate if the robot falls + torso_height = DoneTerm(func=mdp.root_height_below_minimum, params={"minimum_height": 0.31}) + + +@configclass +class AntEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the MuJoCo-style Ant walking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=5.0, clone_in_fabric=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 16.0 + # simulation settings + self.sim.dt = 1 / 120.0 + self.sim.render_interval = self.decimation + self.sim.physx.bounce_threshold_velocity = 0.2 + # default friction material + self.sim.physics_material.static_friction = 1.0 + self.sim.physics_material.dynamic_friction = 1.0 + self.sim.physics_material.restitution = 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..68eef31a0cc8fa060fffd073eb725c8bfe206f4c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/__init__.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Cartpole balancing environment. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Cartpole-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerWithSymmetryCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-RGB-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleRGBCameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-Depth-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleDepthCameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-RGB-ResNet18-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleResNet18CameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_feature_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Cartpole-RGB-TheiaTiny-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleTheiaTinyCameraEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_feature_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_camera_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_camera_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..abdccfc4b4aca96abaa9dc021a8c9b8cbf75c745 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_camera_ppo_cfg.yaml @@ -0,0 +1,100 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + cnn: + type: conv2d + activation: relu + initializer: + name: default + regularizer: + name: None + convs: + - filters: 32 + kernel_size: 8 + strides: 4 + padding: 0 + - filters: 64 + kernel_size: 4 + strides: 2 + padding: 0 + - filters: 64 + kernel_size: 3 + strides: 1 + padding: 0 + + mlp: + units: [512] + activation: elu + initializer: + name: default + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole_camera + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: False + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 500 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 64 + minibatch_size: 2048 + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_feature_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_feature_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f28e2c85d54a60750d079e78c9cef2af0e49a241 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_feature_ppo_cfg.yaml @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole_features + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 3e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 2048 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eae63873c89d602d978674e4b93012ad6b6e0119 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [32, 32] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: False + normalize_value: False + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1.0 + normalize_advantage: False + gamma: 0.99 + tau : 0.95 + learning_rate: 3e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 150 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 16 + minibatch_size: 8192 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2a266a098df2b2b9669409687a434a00d82a8ff7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg, RslRlSymmetryCfg + +import isaaclab_tasks.manager_based.classic.cartpole.mdp.symmetry as symmetry + + +@configclass +class CartpolePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 150 + save_interval = 50 + experiment_name = "cartpole" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[32, 32], + critic_hidden_dims=[32, 32], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class CartpolePPORunnerWithSymmetryCfg(CartpolePPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=symmetry.compute_symmetric_states + ), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fcb32cd51dd95679da4b32f938f5bc1583899e66 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,25 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +n_timesteps: !!float 1e6 +policy: 'MlpPolicy' +n_steps: 16 +batch_size: 4096 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 20 +ent_coef: 0.01 +learning_rate: !!float 3e-4 +clip_range: !!float 0.2 +policy_kwargs: + activation_fn: 'nn.ELU' + net_arch: [32, 32] + squash_output: False +vf_coef: 1.0 +max_grad_norm: 1.0 +device: "cuda:0" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6485d4ed57fbed616085cce3cf415f21bfc6e6ec --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cartpole" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 2400 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_camera_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_camera_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d0840c4c1ed65d52fb029aa44b9048ca38e93324 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_camera_env_cfg.py @@ -0,0 +1,176 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import TiledCameraCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.classic.cartpole.mdp as mdp + +from .cartpole_env_cfg import CartpoleEnvCfg, CartpoleSceneCfg + +## +# Scene definition +## + + +@configclass +class CartpoleRGBCameraSceneCfg(CartpoleSceneCfg): + """Configuration for the cartpole environment with RGB camera.""" + + # add camera to the scene + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="{ENV_REGEX_NS}/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-7.0, 0.0, 3.0), rot=(0.9945, 0.0, 0.1045, 0.0), convention="world"), + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=100, + height=100, + ) + + +@configclass +class CartpoleDepthCameraSceneCfg(CartpoleSceneCfg): + # add camera to the scene + tiled_camera: TiledCameraCfg = TiledCameraCfg( + prim_path="{ENV_REGEX_NS}/Camera", + offset=TiledCameraCfg.OffsetCfg(pos=(-7.0, 0.0, 3.0), rot=(0.9945, 0.0, 0.1045, 0.0), convention="world"), + data_types=["distance_to_camera"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 20.0) + ), + width=100, + height=100, + ) + + +## +# MDP settings +## + + +@configclass +class RGBObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + image = ObsTerm(func=mdp.image, params={"sensor_cfg": SceneEntityCfg("tiled_camera"), "data_type": "rgb"}) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True + + policy: ObsGroup = RGBCameraPolicyCfg() + + +@configclass +class DepthObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class DepthCameraPolicyCfg(ObsGroup): + """Observations for policy group with depth images.""" + + image = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("tiled_camera"), "data_type": "distance_to_camera"} + ) + + policy: ObsGroup = DepthCameraPolicyCfg() + + +@configclass +class ResNet18ObservationCfg: + """Observation specifications for the MDP.""" + + @configclass + class ResNet18FeaturesCameraPolicyCfg(ObsGroup): + """Observations for policy group with features extracted from RGB images with a frozen ResNet18.""" + + image = ObsTerm( + func=mdp.image_features, + params={"sensor_cfg": SceneEntityCfg("tiled_camera"), "data_type": "rgb", "model_name": "resnet18"}, + ) + + policy: ObsGroup = ResNet18FeaturesCameraPolicyCfg() + + +@configclass +class TheiaTinyObservationCfg: + """Observation specifications for the MDP.""" + + @configclass + class TheiaTinyFeaturesCameraPolicyCfg(ObsGroup): + """Observations for policy group with features extracted from RGB images with a frozen Theia-Tiny Transformer""" + + image = ObsTerm( + func=mdp.image_features, + params={ + "sensor_cfg": SceneEntityCfg("tiled_camera"), + "data_type": "rgb", + "model_name": "theia-tiny-patch16-224-cddsv", + "model_device": "cuda:0", + }, + ) + + policy: ObsGroup = TheiaTinyFeaturesCameraPolicyCfg() + + +## +# Environment configuration +## + + +@configclass +class CartpoleRGBCameraEnvCfg(CartpoleEnvCfg): + """Configuration for the cartpole environment with RGB camera.""" + + scene: CartpoleRGBCameraSceneCfg = CartpoleRGBCameraSceneCfg(num_envs=512, env_spacing=20) + observations: RGBObservationsCfg = RGBObservationsCfg() + + def __post_init__(self): + super().__post_init__() + # remove ground as it obstructs the camera + self.scene.ground = None + # viewer settings + self.viewer.eye = (7.0, 0.0, 2.5) + self.viewer.lookat = (0.0, 0.0, 2.5) + + +@configclass +class CartpoleDepthCameraEnvCfg(CartpoleEnvCfg): + """Configuration for the cartpole environment with depth camera.""" + + scene: CartpoleDepthCameraSceneCfg = CartpoleDepthCameraSceneCfg(num_envs=512, env_spacing=20) + observations: DepthObservationsCfg = DepthObservationsCfg() + + def __post_init__(self): + super().__post_init__() + # remove ground as it obstructs the camera + self.scene.ground = None + # viewer settings + self.viewer.eye = (7.0, 0.0, 2.5) + self.viewer.lookat = (0.0, 0.0, 2.5) + + +@configclass +class CartpoleResNet18CameraEnvCfg(CartpoleRGBCameraEnvCfg): + """Configuration for the cartpole environment with ResNet18 features as observations.""" + + observations: ResNet18ObservationCfg = ResNet18ObservationCfg() + + +@configclass +class CartpoleTheiaTinyCameraEnvCfg(CartpoleRGBCameraEnvCfg): + """Configuration for the cartpole environment with Theia-Tiny features as observations.""" + + observations: TheiaTinyObservationCfg = TheiaTinyObservationCfg() diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..788920af058cff47f55a2042d608f325f7b285ba --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/cartpole_env_cfg.py @@ -0,0 +1,181 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.classic.cartpole.mdp as mdp + +## +# Pre-defined configs +## +from isaaclab_assets.robots.cartpole import CARTPOLE_CFG # isort:skip + + +## +# Scene definition +## + + +@configclass +class CartpoleSceneCfg(InteractiveSceneCfg): + """Configuration for a cart-pole scene.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(size=(100.0, 100.0)), + ) + + # cartpole + robot: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/DomeLight", + spawn=sim_utils.DomeLightCfg(color=(0.9, 0.9, 0.9), intensity=500.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=100.0) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) + + def __post_init__(self) -> None: + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # reset + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.5, 0.5), + }, + ) + + reset_pole_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), + "position_range": (-0.25 * math.pi, 0.25 * math.pi), + "velocity_range": (-0.25 * math.pi, 0.25 * math.pi), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # (1) Constant running reward + alive = RewTerm(func=mdp.is_alive, weight=1.0) + # (2) Failure penalty + terminating = RewTerm(func=mdp.is_terminated, weight=-2.0) + # (3) Primary task: keep pole upright + pole_pos = RewTerm( + func=mdp.joint_pos_target_l2, + weight=-1.0, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "target": 0.0}, + ) + # (4) Shaping tasks: lower cart velocity + cart_vel = RewTerm( + func=mdp.joint_vel_l1, + weight=-0.01, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"])}, + ) + # (5) Shaping tasks: lower pole angular velocity + pole_vel = RewTerm( + func=mdp.joint_vel_l1, + weight=-0.005, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"])}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + # (1) Time out + time_out = DoneTerm(func=mdp.time_out, time_out=True) + # (2) Cart out of bounds + cart_out_of_bounds = DoneTerm( + func=mdp.joint_pos_out_of_manual_limit, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "bounds": (-3.0, 3.0)}, + ) + + +## +# Environment configuration +## + + +@configclass +class CartpoleEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the cartpole environment.""" + + # Scene settings + scene: CartpoleSceneCfg = CartpoleSceneCfg(num_envs=4096, env_spacing=4.0, clone_in_fabric=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + + # Post initialization + def __post_init__(self) -> None: + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 5 + # viewer settings + self.viewer.eye = (8.0, 0.0, 5.0) + # simulation settings + self.sim.dt = 1 / 120 + self.sim.render_interval = self.decimation diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..155079c558f610d1c09011137bee2e9627415036 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the cartpole environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..5500089d7f94b1b9ffa6a3805603f8325d0b8862 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/rewards.py @@ -0,0 +1,27 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import wrap_to_pi + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def joint_pos_target_l2(env: ManagerBasedRLEnv, target: float, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint position deviation from a target value.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # wrap the joint positions to (-pi, pi) + joint_pos = wrap_to_pi(asset.data.joint_pos[:, asset_cfg.joint_ids]) + # compute the reward + return torch.sum(torch.square(joint_pos - target), dim=1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/symmetry.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/symmetry.py new file mode 100644 index 0000000000000000000000000000000000000000..3997d2ae1395f3b74e2df91ca01e4d3170baf09f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole/mdp/symmetry.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions to specify the symmetry in the observation and action space for cartpole.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +from tensordict import TensorDict + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + +# specify the functions that are available for import +__all__ = ["compute_symmetric_states"] + + +@torch.no_grad() +def compute_symmetric_states( + env: ManagerBasedRLEnv, + obs: TensorDict | None = None, + actions: torch.Tensor | None = None, +): + """Augments the given observations and actions by applying symmetry transformations. + + This function creates augmented versions of the provided observations and actions by applying + two symmetrical transformations: original, left-right. The symmetry + transformations are beneficial for reinforcement learning tasks by providing additional + diverse data without requiring additional data collection. + + Args: + env: The environment instance. + obs: The original observation tensor dictionary. Defaults to None. + actions: The original actions tensor. Defaults to None. + + Returns: + Augmented observations and actions tensors, or None if the respective input was None. + """ + + # observations + if obs is not None: + batch_size = obs.batch_size[0] + # since we have 2 different symmetries, we need to augment the batch size by 2 + obs_aug = obs.repeat(2) + # -- original + obs_aug["policy"][:batch_size] = obs["policy"][:] + # -- left-right + obs_aug["policy"][batch_size : 2 * batch_size] = -obs["policy"] + else: + obs_aug = None + + # actions + if actions is not None: + batch_size = actions.shape[0] + # since we have 4 different symmetries, we need to augment the batch size by 4 + actions_aug = torch.zeros(batch_size * 2, actions.shape[1], device=actions.device) + # -- original + actions_aug[:batch_size] = actions[:] + # -- left-right + actions_aug[batch_size : 2 * batch_size] = -actions + else: + actions_aug = None + + return obs_aug, actions_aug diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..67c17ab3bf34867e5bb1d80bf51e5462b7cb0dbc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Humanoid locomotion environment (similar to OpenAI Gym Humanoid-v2). +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Humanoid-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.humanoid_env_cfg:HumanoidEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:HumanoidPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..efb7a8afef899d4776629158c91969c059473da9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,88 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ========================================= IMPORTANT NOTICE ========================================= +# +# This file defines the agent configuration used to generate the "Training Performance" table in +# https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_frameworks.html. +# Ensure that the configurations for the other RL libraries are updated if this one is modified. +# +# ==================================================================================================== + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [400, 200, 100] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: humanoid + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 1000 + save_best_after: 100 + save_frequency: 100 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 32768 # num_envs * horizon_length / num_mini_batches + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f2c7f48e4558c80b43a4165e6fa7c7c8c04e7ab0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +========================================= IMPORTANT NOTICE ========================================= + +This file defines the agent configuration used to generate the "Training Performance" table in +https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_frameworks.html. +Ensure that the configurations for the other RL libraries are updated if this one is modified. + +==================================================================================================== +""" + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class HumanoidPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 32 + max_iterations = 1000 + save_interval = 100 + experiment_name = "humanoid" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[400, 200, 100], + critic_hidden_dims=[400, 200, 100], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=2.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cd8fd98874167502c498e4bdafbfe8c0005a69ff --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ========================================= IMPORTANT NOTICE ========================================= +# +# This file defines the agent configuration used to generate the "Training Performance" table in +# https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_frameworks.html. +# Ensure that the configurations for the other RL libraries are updated if this one is modified. +# +# ==================================================================================================== + +seed: 42 +policy: "MlpPolicy" +n_timesteps: !!float 5e7 +# For 4 minibatches with 4096 envs +# batch_size = (n_envs * n_steps) / n_minibatches = 32768 +n_minibatches: 4 +n_steps: 32 +gamma: 0.99 +learning_rate: !!float 5e-4 +ent_coef: 0.0 +clip_range: 0.2 +n_epochs: 5 +gae_lambda: 0.95 +max_grad_norm: 1.0 +vf_coef: 2.0 +policy_kwargs: + activation_fn: 'nn.ELU' + net_arch: [400, 200, 100] + optimizer_kwargs: + eps: !!float 1e-8 + ortho_init: False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..468421e4253f8229e9a71363d557dc27f1e2cb6f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,92 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# ========================================= IMPORTANT NOTICE ========================================= +# +# This file defines the agent configuration used to generate the "Training Performance" table in +# https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_frameworks.html. +# Ensure that the configurations for the other RL libraries are updated if this one is modified. +# +# ==================================================================================================== + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [400, 200, 100] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [400, 200, 100] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid" + experiment_name: "" + write_interval: 32 + checkpoint_interval: 3200 + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 32000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/humanoid_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/humanoid_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..37b9426df9b6616a4bd63c06f9db7139acd96ee4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/humanoid_env_cfg.py @@ -0,0 +1,221 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.classic.humanoid.mdp as mdp + +from isaaclab_assets.robots.humanoid import HUMANOID_CFG # isort:skip + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Configuration for the terrain scene with a humanoid robot.""" + + # terrain + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="plane", + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0, dynamic_friction=1.0, restitution=0.0), + debug_vis=False, + ) + + # robot + robot = HUMANOID_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_effort = mdp.JointEffortActionCfg( + asset_name="robot", + joint_names=[".*"], + scale={ + ".*_waist.*": 67.5, + ".*_upper_arm.*": 67.5, + "pelvis": 67.5, + ".*_lower_arm": 45.0, + ".*_thigh:0": 45.0, + ".*_thigh:1": 135.0, + ".*_thigh:2": 45.0, + ".*_shin": 90.0, + ".*_foot.*": 22.5, + }, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for the policy.""" + + base_height = ObsTerm(func=mdp.base_pos_z) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel, scale=0.25) + base_yaw_roll = ObsTerm(func=mdp.base_yaw_roll) + base_angle_to_target = ObsTerm(func=mdp.base_angle_to_target, params={"target_pos": (1000.0, 0.0, 0.0)}) + base_up_proj = ObsTerm(func=mdp.base_up_proj) + base_heading_proj = ObsTerm(func=mdp.base_heading_proj, params={"target_pos": (1000.0, 0.0, 0.0)}) + joint_pos_norm = ObsTerm(func=mdp.joint_pos_limit_normalized) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel, scale=0.1) + feet_body_forces = ObsTerm( + func=mdp.body_incoming_wrench, + scale=0.01, + params={"asset_cfg": SceneEntityCfg("robot", body_names=["left_foot", "right_foot"])}, + ) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={"pose_range": {}, "velocity_range": {}}, + ) + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.2, 0.2), + "velocity_range": (-0.1, 0.1), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # (1) Reward for moving forward + progress = RewTerm(func=mdp.progress_reward, weight=1.0, params={"target_pos": (1000.0, 0.0, 0.0)}) + # (2) Stay alive bonus + alive = RewTerm(func=mdp.is_alive, weight=2.0) + # (3) Reward for non-upright posture + upright = RewTerm(func=mdp.upright_posture_bonus, weight=0.1, params={"threshold": 0.93}) + # (4) Reward for moving in the right direction + move_to_target = RewTerm( + func=mdp.move_to_target_bonus, weight=0.5, params={"threshold": 0.8, "target_pos": (1000.0, 0.0, 0.0)} + ) + # (5) Penalty for large action commands + action_l2 = RewTerm(func=mdp.action_l2, weight=-0.01) + # (6) Penalty for energy consumption + energy = RewTerm( + func=mdp.power_consumption, + weight=-0.005, + params={ + "gear_ratio": { + ".*_waist.*": 67.5, + ".*_upper_arm.*": 67.5, + "pelvis": 67.5, + ".*_lower_arm": 45.0, + ".*_thigh:0": 45.0, + ".*_thigh:1": 135.0, + ".*_thigh:2": 45.0, + ".*_shin": 90.0, + ".*_foot.*": 22.5, + } + }, + ) + # (7) Penalty for reaching close to joint limits + joint_pos_limits = RewTerm( + func=mdp.joint_pos_limits_penalty_ratio, + weight=-0.25, + params={ + "threshold": 0.98, + "gear_ratio": { + ".*_waist.*": 67.5, + ".*_upper_arm.*": 67.5, + "pelvis": 67.5, + ".*_lower_arm": 45.0, + ".*_thigh:0": 45.0, + ".*_thigh:1": 135.0, + ".*_thigh:2": 45.0, + ".*_shin": 90.0, + ".*_foot.*": 22.5, + }, + }, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + # (1) Terminate if the episode length is exceeded + time_out = DoneTerm(func=mdp.time_out, time_out=True) + # (2) Terminate if the robot falls + torso_height = DoneTerm(func=mdp.root_height_below_minimum, params={"minimum_height": 0.8}) + + +@configclass +class HumanoidEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the MuJoCo-style Humanoid walking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=5.0, clone_in_fabric=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 16.0 + # simulation settings + self.sim.dt = 1 / 120.0 + self.sim.render_interval = self.decimation + self.sim.physx.bounce_threshold_velocity = 0.2 + # default friction material + self.sim.physics_material.static_friction = 1.0 + self.sim.physics_material.dynamic_friction = 1.0 + self.sim.physics_material.restitution = 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd05f5563503de06ca90f5e0d8022ceef0c3662 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the humanoid environment.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * +from .rewards import * diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..123c4eb7de34b6139db14e8cc4a3af144934831d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/observations.py @@ -0,0 +1,76 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def base_yaw_roll(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Yaw and roll of the base in the simulation world frame.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # extract euler angles (in world frame) + roll, _, yaw = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) + # normalize angle to [-pi, pi] + roll = torch.atan2(torch.sin(roll), torch.cos(roll)) + yaw = torch.atan2(torch.sin(yaw), torch.cos(yaw)) + + return torch.cat((yaw.unsqueeze(-1), roll.unsqueeze(-1)), dim=-1) + + +def base_up_proj(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Projection of the base up vector onto the world up vector.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute base up vector + base_up_vec = -asset.data.projected_gravity_b + + return base_up_vec[:, 2].unsqueeze(-1) + + +def base_heading_proj( + env: ManagerBasedEnv, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Projection of the base forward vector onto the world forward vector.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute desired heading direction + to_target_pos = torch.tensor(target_pos, device=env.device) - asset.data.root_pos_w[:, :3] + to_target_pos[:, 2] = 0.0 + to_target_dir = math_utils.normalize(to_target_pos) + # compute base forward vector + heading_vec = math_utils.quat_apply(asset.data.root_quat_w, asset.data.FORWARD_VEC_B) + # compute dot product between heading and target direction + heading_proj = torch.bmm(heading_vec.view(env.num_envs, 1, 3), to_target_dir.view(env.num_envs, 3, 1)) + + return heading_proj.view(env.num_envs, 1) + + +def base_angle_to_target( + env: ManagerBasedEnv, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Angle between the base forward vector and the vector to the target.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute desired heading direction + to_target_pos = torch.tensor(target_pos, device=env.device) - asset.data.root_pos_w[:, :3] + walk_target_angle = torch.atan2(to_target_pos[:, 1], to_target_pos[:, 0]) + # compute base forward vector + _, _, yaw = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) + # normalize angle to target to [-pi, pi] + angle_to_target = walk_target_angle - yaw + angle_to_target = torch.atan2(torch.sin(angle_to_target), torch.cos(angle_to_target)) + + return angle_to_target.unsqueeze(-1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..51b47d11449ece212ebbf465064710c82ba88c4e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/humanoid/mdp/rewards.py @@ -0,0 +1,144 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +import isaaclab.utils.string as string_utils +from isaaclab.assets import Articulation +from isaaclab.managers import ManagerTermBase, RewardTermCfg, SceneEntityCfg + +from . import observations as obs + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def upright_posture_bonus( + env: ManagerBasedRLEnv, threshold: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Reward for maintaining an upright posture.""" + up_proj = obs.base_up_proj(env, asset_cfg).squeeze(-1) + return (up_proj > threshold).float() + + +def move_to_target_bonus( + env: ManagerBasedRLEnv, + threshold: float, + target_pos: tuple[float, float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """Reward for moving to the target heading.""" + heading_proj = obs.base_heading_proj(env, target_pos, asset_cfg).squeeze(-1) + return torch.where(heading_proj > threshold, 1.0, heading_proj / threshold) + + +class progress_reward(ManagerTermBase): + """Reward for making progress towards the target.""" + + def __init__(self, env: ManagerBasedRLEnv, cfg: RewardTermCfg): + # initialize the base class + super().__init__(cfg, env) + # create history buffer + self.potentials = torch.zeros(env.num_envs, device=env.device) + self.prev_potentials = torch.zeros_like(self.potentials) + + def reset(self, env_ids: torch.Tensor): + # extract the used quantities (to enable type-hinting) + asset: Articulation = self._env.scene["robot"] + # compute projection of current heading to desired heading vector + target_pos = torch.tensor(self.cfg.params["target_pos"], device=self.device) + to_target_pos = target_pos - asset.data.root_pos_w[env_ids, :3] + # reward terms + self.potentials[env_ids] = -torch.norm(to_target_pos, p=2, dim=-1) / self._env.step_dt + self.prev_potentials[env_ids] = self.potentials[env_ids] + + def __call__( + self, + env: ManagerBasedRLEnv, + target_pos: tuple[float, float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + ) -> torch.Tensor: + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute vector to target + target_pos = torch.tensor(target_pos, device=env.device) + to_target_pos = target_pos - asset.data.root_pos_w[:, :3] + to_target_pos[:, 2] = 0.0 + # update history buffer and compute new potential + self.prev_potentials[:] = self.potentials[:] + self.potentials[:] = -torch.norm(to_target_pos, p=2, dim=-1) / env.step_dt + + return self.potentials - self.prev_potentials + + +class joint_pos_limits_penalty_ratio(ManagerTermBase): + """Penalty for violating joint position limits weighted by the gear ratio.""" + + def __init__(self, env: ManagerBasedRLEnv, cfg: RewardTermCfg): + # add default argument + asset_cfg = cfg.params.get("asset_cfg", SceneEntityCfg("robot")) + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + # resolve the gear ratio for each joint + self.gear_ratio = torch.ones(env.num_envs, asset.num_joints, device=env.device) + index_list, _, value_list = string_utils.resolve_matching_names_values( + cfg.params["gear_ratio"], asset.joint_names + ) + self.gear_ratio[:, index_list] = torch.tensor(value_list, device=env.device) + self.gear_ratio_scaled = self.gear_ratio / torch.max(self.gear_ratio) + + def __call__( + self, + env: ManagerBasedRLEnv, + threshold: float, + gear_ratio: dict[str, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + ) -> torch.Tensor: + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # compute the penalty over normalized joints + joint_pos_scaled = math_utils.scale_transform( + asset.data.joint_pos, asset.data.soft_joint_pos_limits[..., 0], asset.data.soft_joint_pos_limits[..., 1] + ) + # scale the violation amount by the gear ratio + violation_amount = (torch.abs(joint_pos_scaled) - threshold) / (1 - threshold) + violation_amount = violation_amount * self.gear_ratio_scaled + + return torch.sum((torch.abs(joint_pos_scaled) > threshold) * violation_amount, dim=-1) + + +class power_consumption(ManagerTermBase): + """Penalty for the power consumed by the actions to the environment. + + This is computed as commanded torque times the joint velocity. + """ + + def __init__(self, env: ManagerBasedRLEnv, cfg: RewardTermCfg): + # add default argument + asset_cfg = cfg.params.get("asset_cfg", SceneEntityCfg("robot")) + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + + # resolve the gear ratio for each joint + self.gear_ratio = torch.ones(env.num_envs, asset.num_joints, device=env.device) + index_list, _, value_list = string_utils.resolve_matching_names_values( + cfg.params["gear_ratio"], asset.joint_names + ) + self.gear_ratio[:, index_list] = torch.tensor(value_list, device=env.device) + self.gear_ratio_scaled = self.gear_ratio / torch.max(self.gear_ratio) + + def __call__( + self, env: ManagerBasedRLEnv, gear_ratio: dict[str, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") + ) -> torch.Tensor: + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # return power = torque * velocity (here actions: joint torques) + return torch.sum(torch.abs(env.action_manager.action * asset.data.joint_vel * self.gear_ratio_scaled), dim=-1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0fbb155619048bf09db1f8bb1591bda5c64a8afb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Drone ARL environments.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc4d65f5b1f22e4ec6be944abc8f3209dc7606fb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the drone ARL environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from isaaclab_contrib.mdp import * # noqa: F401, F403 + +from .commands import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7386d3ce53993311a9373ae8b53fd9afccbf323 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Various command terms that can be used in the environment.""" + +from .commands_cfg import DroneUniformPoseCommandCfg +from .drone_pose_command import DroneUniformPoseCommand diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/commands_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/commands_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f12cf1be082f722c9b0a57069a34eca366fc0ce9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/commands_cfg.py @@ -0,0 +1,16 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.envs.mdp.commands.commands_cfg import UniformPoseCommandCfg +from isaaclab.utils import configclass + +from .drone_pose_command import DroneUniformPoseCommand + + +@configclass +class DroneUniformPoseCommandCfg(UniformPoseCommandCfg): + """Configuration for uniform drone pose command generator.""" + + class_type: type = DroneUniformPoseCommand diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/drone_pose_command.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/drone_pose_command.py new file mode 100644 index 0000000000000000000000000000000000000000..f33aa41be4c95e0a1ec5ff0e32f9e96f561b2fb4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/commands/drone_pose_command.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generators for pose tracking.""" + +from __future__ import annotations + +import torch + +from isaaclab.envs.mdp.commands.pose_command import UniformPoseCommand +from isaaclab.utils.math import combine_frame_transforms, compute_pose_error + + +class DroneUniformPoseCommand(UniformPoseCommand): + """Drone-specific UniformPoseCommand extensions. + + This class customizes the generic :class:`UniformPoseCommand` for drone (multirotor) + use-cases. Main differences and additions: + + - Transforms pose commands from the drone's base frame to the world frame before use. + - Accounts for per-environment origin offsets (``scene.env_origins``) when computing + position errors so tasks running on shifted/sub-terrain environments compute + meaningful errors. + - Computes and exposes simple metrics used by higher-level code: ``position_error`` + and ``orientation_error`` (stored in ``self.metrics``). + - Provides a debug visualization callback that renders the goal pose (with + sub-terrain shift) and current body pose using the existing visualizers. + + The implementation overrides :meth:`_update_metrics` and :meth:`_debug_vis_callback` + from the base class to implement these drone-specific behaviors. + """ + + def _update_metrics(self): + # transform command from base frame to simulation world frame + self.pose_command_w[:, :3], self.pose_command_w[:, 3:] = combine_frame_transforms( + self.robot.data.root_pos_w, + self.robot.data.root_quat_w, + self.pose_command_b[:, :3], + self.pose_command_b[:, 3:], + ) + # compute the error + pos_error, rot_error = compute_pose_error( + # Sub-terrain shift for correct position error calculation @grzemal + self.pose_command_b[:, :3] + self._env.scene.env_origins, + self.pose_command_w[:, 3:], + self.robot.data.body_pos_w[:, self.body_idx], + self.robot.data.body_quat_w[:, self.body_idx], + ) + self.metrics["position_error"] = torch.norm(pos_error, dim=-1) + self.metrics["orientation_error"] = torch.norm(rot_error, dim=-1) + + def _debug_vis_callback(self, event): + # check if robot is initialized + # note: this is needed in-case the robot is de-initialized. we can't access the data + if not self.robot.is_initialized: + return + # update the markers + # -- goal pose + # Sub-terrain shift for visualization purposes @grzemal + self.goal_pose_visualizer.visualize( + self.pose_command_b[:, :3] + self._env.scene.env_origins, self.pose_command_b[:, 3:] + ) + # -- current body pose + body_link_pose_w = self.robot.data.body_link_pose_w[:, self.body_idx] + self.current_pose_visualizer.visualize(body_link_pose_w[:, :3], body_link_pose_w[:, 3:7]) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..475e59d38a46c928def9216b37f28d021fd90e5a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/observations.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to create drone observation terms. + +The functions can be passed to the :class:`isaaclab.managers.ObservationTermCfg` object to enable +the observation introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +import torch.jit +from isaaclab_contrib.assets import Multirotor + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv, ManagerBasedRLEnv + +from isaaclab.envs.utils.io_descriptors import generic_io_descriptor, record_shape + +""" +State. +""" + + +def base_roll_pitch(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Return the base roll and pitch in the simulation world frame. + + Parameters: + env: Manager-based environment providing the scene and tensors. + asset_cfg: Scene entity config pointing to the target robot (default: "robot"). + + Returns: + torch.Tensor: Shape (num_envs, 2). Column 0 is roll, column 1 is pitch. + Values are radians normalized to [-pi, pi], expressed in the world frame. + + Notes: + - Euler angles are computed from asset.data.root_quat_w using XYZ convention. + - Only roll and pitch are returned; yaw is omitted. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # extract euler angles (in world frame) + roll, pitch, _ = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) + # normalize angle to [-pi, pi] + roll = math_utils.wrap_to_pi(roll) + pitch = math_utils.wrap_to_pi(pitch) + + return torch.cat((roll.unsqueeze(-1), pitch.unsqueeze(-1)), dim=-1) + + +""" +Commands. +""" + + +@generic_io_descriptor(dtype=torch.float32, observation_type="Command", on_inspect=[record_shape]) +def generated_drone_commands( + env: ManagerBasedRLEnv, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Generate a body-frame direction and distance to the commanded position. + + This observation reads a command from env.command_manager identified by command_name, + interprets its first three components as a target position in the world frame, and + returns: + [dir_x, dir_y, dir_z, distance] + where dir_* is the unit vector from the current body origin to the target, expressed + in the multirotor body (root link) frame, and distance is the Euclidean separation. + + Parameters: + env: Manager-based RL environment providing scene and command manager. + command_name: Name of the command term to query from the command manager. + asset_cfg: Scene entity config for the multirotor asset (default: "robot"). + + Returns: + torch.Tensor: Shape (num_envs, 4) with body-frame unit direction (3) and distance (1). + + Frame conventions: + - Current position is asset.data.root_pos_w relative to env.scene.env_origins (world frame). + - Body orientation uses asset.data.root_link_quat_w to rotate world vectors into the body frame. + + Assumptions: + - env.command_manager.get_command(command_name) returns at least three values + representing a world-frame target position per environment. + - A small epsilon (1e-8) is used to guard against zero-length direction vectors. + """ + asset: Multirotor = env.scene[asset_cfg.name] + current_position_w = asset.data.root_pos_w - env.scene.env_origins + command = env.command_manager.get_command(command_name) + current_position_b = math_utils.quat_apply_inverse(asset.data.root_link_quat_w, command[:, :3] - current_position_w) + current_position_b_dir = current_position_b / (torch.norm(current_position_b, dim=-1, keepdim=True) + 1e-8) + current_position_b_mag = torch.norm(current_position_b, dim=-1, keepdim=True) + return torch.cat((current_position_b_dir, current_position_b_mag), dim=-1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..4ad040563a42546ebf8e3b17c524e4b9da47774f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/mdp/rewards.py @@ -0,0 +1,147 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg + +""" +Drone control rewards. +""" + + +def distance_to_goal_exp( + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + std: float = 1.0, + command_name: str = "target_pose", +) -> torch.Tensor: + """Reward the distance to a goal position using an exponential kernel. + + This reward computes an exponential falloff of the squared Euclidean distance + between the commanded target position and the asset (robot) root position. + + Args: + env: The manager-based RL environment instance. + asset_cfg: SceneEntityCfg identifying the asset (defaults to "robot"). + std: Standard deviation used in the exponential kernel; larger values + produce a gentler falloff. + command_name: Name of the command to read the target pose from the + environment's command manager. The function expects the command + tensor to contain positions in its first three columns. + + Returns: + A 1-D tensor of shape (num_envs,) containing the per-environment reward + values in [0, 1], with 1.0 when the position error is zero. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + + target_position_w = command[:, :3].clone() + current_position = asset.data.root_pos_w - env.scene.env_origins + + # compute the error + position_error_square = torch.sum(torch.square(target_position_w - current_position), dim=1) + return torch.exp(-position_error_square / std**2) + + +def ang_vel_xyz_exp( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), std: float = 1.0 +) -> torch.Tensor: + """Penalize angular velocity magnitude with an exponential kernel. + + This reward computes exp(-||omega||^2 / std^2) where omega is the body-frame + angular velocity of the asset. It is useful for encouraging low rotational + rates. + + Args: + env: The manager-based RL environment instance. + asset_cfg: SceneEntityCfg identifying the asset (defaults to "robot"). + std: Standard deviation used in the exponential kernel; controls + sensitivity to angular velocity magnitude. + + Returns: + A 1-D tensor of shape (num_envs,) with values in (0, 1], where 1 indicates + zero angular velocity. + """ + + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + # compute squared magnitude of angular velocity (all axes) + ang_vel_squared = torch.sum(torch.square(asset.data.root_ang_vel_b), dim=1) + + return torch.exp(-ang_vel_squared / std**2) + + +def lin_vel_xyz_exp( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), std: float = 1.0 +) -> torch.Tensor: + """Penalize linear velocity magnitude with an exponential kernel. + + Computes exp(-||v||^2 / std^2) where v is the asset's linear velocity in + world frame. Useful for encouraging the agent to reduce translational speed. + + Args: + env: The manager-based RL environment instance. + asset_cfg: SceneEntityCfg identifying the asset (defaults to "robot"). + std: Standard deviation used in the exponential kernel. + + Returns: + A 1-D tensor of shape (num_envs,) with values in (0, 1], where 1 indicates + zero linear velocity. + """ + + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + # compute squared magnitude of linear velocity (all axes) + lin_vel_squared = torch.sum(torch.square(asset.data.root_lin_vel_w), dim=1) + + return torch.exp(-lin_vel_squared / std**2) + + +def yaw_aligned( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), std: float = 0.5 +) -> torch.Tensor: + """Reward alignment of the vehicle's yaw to zero using an exponential kernel. + + The function extracts the yaw (rotation about Z) from the world-frame root + quaternion and computes exp(-yaw^2 / std^2). This encourages heading + alignment to a zero-yaw reference. + + Args: + env: The manager-based RL environment instance. + asset_cfg: SceneEntityCfg identifying the asset (defaults to "robot"). + std: Standard deviation used in the exponential kernel; smaller values + make the reward more sensitive to yaw deviations. + + Returns: + A 1-D tensor of shape (num_envs,) with values in (0, 1], where 1 indicates + perfect yaw alignment (yaw == 0). + """ + + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + # extract yaw from current orientation + _, _, yaw = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) + + # normalize yaw to [-pi, pi] (target is 0) + yaw = math_utils.wrap_to_pi(yaw) + + # return exponential reward (1 when yaw=0, approaching 0 when rotated) + return torch.exp(-(yaw**2) / std**2) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..36cc6105ac85ca6fed2692c49dee2492ed7a8fc9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Drone ARL state-based control environments.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d69799b587f6152000858b855683082dbb29002 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for state-based control environments.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0dddd02bc027d5f3c90f7465a871a60b417208b1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-TrackPositionNoObstacles-ARL-Robot-1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.no_obstacle_env_cfg:NoObstacleEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:TrackPositionNoObstaclesEnvPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-TrackPositionNoObstacles-ARL-Robot-1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.no_obstacle_env_cfg:NoObstacleEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:TrackPositionNoObstaclesEnvPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d454c4caee229d0661cf57e1bcc477890cceb4fc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256,128,64] + d2rl: False + activation: elu + initializer: + name: default + scale: 2 + rnn: + name: gru + units: 64 + layers: 1 + # before_mlp: False + # layer_norm: True + config: + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + env_config: + num_envs: 8192 + + name: arl_robot_1_track_position_state_based + reward_shaper: + # min_val: -1 + scale_value: 0.1 + + normalize_advantage: True + gamma: 0.98 + tau: 0.95 + ppo: True + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.016 + save_best_after: 10 + score_to_win: 100000 + grad_norm: 1.0 + entropy_coef: 0 + truncate_grads: True + e_clip: 0.2 + clip_value: False + num_actors: 1024 + horizon_length: 32 + minibatch_size: 2048 + mini_epochs: 4 + critic_coef: 2 + normalize_input: True + bounds_loss_coef: 0.0001 + max_epochs: 1500 + normalize_value: True + use_diagnostics: True + value_bootstrap: True + #weight_decay: 0.0001 + use_smooth_clamp: False + + player: + render: True + deterministic: True + games_num: 100000 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b53c53dbdd0c1f24db30f70ccb00d20fc85d5135 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class TrackPositionNoObstaclesEnvPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "arl_robot_1_track_position_state_based" + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=0.5, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.001, + num_learning_epochs=4, + num_mini_batches=4, + learning_rate=4.0e-4, + schedule="adaptive", + gamma=0.98, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3a5779e0106a2e3d3db993f6ab69a84b3c6603b4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,95 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: mlp + input: STATES + layers: [256, 128, 64] + activations: elu + - name: gru + input: mlp + type: GRU + layers: [64] + num_layers: 1 + output: ACTIONS + value: + class: DeterministicMixin + clip_actions: False + network: + - name: mlp + input: STATES + layers: [256, 128, 64] + activations: elu + - name: gru + input: mlp + type: GRU + layers: [64] + num_layers: 1 + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "arl_robot_1_track_position_state_based" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/no_obstacle_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/no_obstacle_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..92a11d82442069e56370302dc696d5b571d6956c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/no_obstacle_env_cfg.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_assets.robots.arl_robot_1 import ARL_ROBOT_1_CFG + +from .track_position_state_based_env_cfg import TrackPositionNoObstaclesEnvCfg + +## +# Pre-defined configs +## + + +@configclass +class NoObstacleEnvCfg(TrackPositionNoObstaclesEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # switch robot to arl_robot_1 + self.scene.robot = ARL_ROBOT_1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.actuators["thrusters"].dt = self.sim.dt + + +@configclass +class NoObstacleEnvCfg_PLAY(NoObstacleEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/track_position_state_based_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/track_position_state_based_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..aea404326e3a65d5e93708b642f0ee8865cb4678 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/drone_arl/track_position_state_based/config/arl_robot_1/track_position_state_based_env_cfg.py @@ -0,0 +1,229 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math +from dataclasses import MISSING + +from isaaclab_contrib.assets import MultirotorCfg + +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.drone_arl.mdp as mdp + + +## +# Scene definition +## +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Configuration for the terrain scene with a flying robot.""" + + # robots + robot: MultirotorCfg = MISSING + + # lights + sky_light = AssetBaseCfg( + prim_path="/World/skyLight", + spawn=sim_utils.DomeLightCfg( + intensity=750.0, + texture_file=f"{ISAAC_NUCLEUS_DIR}/Materials/Textures/Skies/PolyHaven/kloofendal_43d_clear_puresky_4k.hdr", + ), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command specifications for the MDP.""" + + target_pose = mdp.DroneUniformPoseCommandCfg( + asset_name="robot", + body_name="base_link", + resampling_time_range=(10.0, 10.0), + debug_vis=True, + ranges=mdp.DroneUniformPoseCommandCfg.Ranges( + pos_x=(-0.0, 0.0), + pos_y=(-0.0, 0.0), + pos_z=(-0.0, 0.0), + roll=(-0.0, 0.0), + pitch=(-0.0, 0.0), + yaw=(-0.0, 0.0), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + thrust_command = mdp.ThrustActionCfg( + asset_name="robot", + scale=3.0, + offset=3.0, + preserve_order=False, + use_default_offset=False, + clip={ + "back_left_prop": (0.0, 6.0), + "back_right_prop": (0.0, 6.0), + "front_left_prop": (0.0, 6.0), + "front_right_prop": (0.0, 6.0), + }, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + base_link_position = ObsTerm(func=mdp.root_pos_w, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_orientation = ObsTerm(func=mdp.root_quat_w, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) + last_action = ObsTerm(func=mdp.last_action, noise=Unoise(n_min=-0.0, n_max=0.0)) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # reset + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": (-1.0, 1.0), + "y": (-1.0, 1.0), + "z": (-1.0, 1.0), + "yaw": (-math.pi / 6.0, math.pi / 6.0), + "roll": (-math.pi / 6.0, math.pi / 6.0), + "pitch": (-math.pi / 6.0, math.pi / 6.0), + }, + "velocity_range": { + "x": (-0.2, 0.2), + "y": (-0.2, 0.2), + "z": (-0.2, 0.2), + "roll": (-0.2, 0.2), + "pitch": (-0.2, 0.2), + "yaw": (-0.2, 0.2), + }, + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + distance_to_goal_exp = RewTerm( + func=mdp.distance_to_goal_exp, + weight=25.0, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "std": 1.5, + "command_name": "target_pose", + }, + ) + flat_orientation_l2 = RewTerm( + func=mdp.flat_orientation_l2, + weight=1.0, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + yaw_aligned = RewTerm( + func=mdp.yaw_aligned, + weight=2.0, + params={"asset_cfg": SceneEntityCfg("robot"), "std": 1.0}, + ) + lin_vel_xyz_exp = RewTerm( + func=mdp.lin_vel_xyz_exp, + weight=2.5, + params={"asset_cfg": SceneEntityCfg("robot"), "std": 2.0}, + ) + ang_vel_xyz_exp = RewTerm( + func=mdp.ang_vel_xyz_exp, + weight=10.0, + params={"asset_cfg": SceneEntityCfg("robot"), "std": 10.0}, + ) + action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.05) + action_magnitude_l2 = RewTerm(func=mdp.action_l2, weight=-0.05) + + termination_penalty = RewTerm( + func=mdp.is_terminated, + weight=-5.0, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + crash = DoneTerm(func=mdp.root_height_below_minimum, params={"minimum_height": -3.0}) + + +## +# Environment configuration +## + + +@configclass +class TrackPositionNoObstaclesEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the state-based drone pose-control environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 10 + self.episode_length_s = 5.0 + # simulation settings + self.sim.dt = 0.01 + self.sim.render_interval = self.decimation + self.sim.physics_material = sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ) + self.sim.physx.gpu_max_rigid_patch_count = 10 * 2**15 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..72b01368b4990408f11ab69ff79fb0328836fd7b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""This sub-module contains the functions that are specific to the locomanipulation environments.""" + +from .tracking import * # noqa diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f093231f95f021116a5eec4124c177efb59a986e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/__init__.py @@ -0,0 +1,72 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""This sub-module contains the functions that are specific to the locomanipulation environments.""" + +import gymnasium as gym +import os + +from . import agents, apple_pick_place_g1_env_cfg, fixed_base_upper_body_ik_g1_env_cfg, locomanipulation_g1_env_cfg, pick_place_g1_env_cfg, pick_place_camera_g1_env_cfg, pick_place_g1_23_env_cfg + +gym.register( + id="Isaac-PickPlace-Locomanipulation-G1-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": locomanipulation_g1_env_cfg.LocomanipulationG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-FixedBaseUpperBodyIK-G1-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": fixed_base_upper_body_ik_g1_env_cfg.FixedBaseUpperBodyIKG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-G1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": pick_place_g1_env_cfg.PickPlaceG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-Camera-G1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": pick_place_camera_g1_env_cfg.PickPlaceCameraG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_image_pick_place.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-G1-23-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": pick_place_g1_23_env_cfg.PickPlaceG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Apple-PickPlace-G1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": apple_pick_place_g1_env_cfg.ApplePickPlaceG1EnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_image_pick_place.json"), + }, + disable_env_checker=True, +) \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_image_pick_place.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_image_pick_place.json new file mode 100644 index 0000000000000000000000000000000000000000..461e48d94d71e08f5696a3af59bbcb41928234ee --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_image_pick_place.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_pick_place", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state" + ], + "rgb": [ + "robot_pov_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 144, + "crop_width": 236, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json new file mode 100644 index 0000000000000000000000000000000000000000..c1dce5f832c8cda3677e6da5554518aa24410ce2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,117 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_g1", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": false, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/apple_pick_place_g1_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/apple_pick_place_g1_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e6b7189dc19b03398744bc1849647d90908bef27 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/apple_pick_place_g1_env_cfg.py @@ -0,0 +1,334 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +import isaaclab_assets +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_29DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + +from isaaclab.sensors import CameraCfg + +from pathlib import Path +from pxr import Gf, Sdf + + +_OBJECTS_ROOT_DIR = Path(isaaclab_assets.__file__).resolve().parent / "objects" + + +def _set_material_albedo_rgb(stage, material_prim_path: str, rgb: tuple[float, float, float]) -> bool: + """Best-effort set of an Omniverse material's albedo/base color. + + This targets the common shader input names used by OmniPBR and UsdPreviewSurface. + """ + material_prim = stage.GetPrimAtPath(material_prim_path) + if not material_prim.IsValid(): + return False + + shader_prim = sim_utils.get_first_matching_child_prim( + material_prim_path, + predicate=lambda p: p.GetTypeName() == "Shader", + stage=stage, + ) + if shader_prim is None or not shader_prim.IsValid(): + return False + + shader_path = str(shader_prim.GetPath()) + shader_id_attr = shader_prim.GetAttribute("info:id") + shader_id = shader_id_attr.Get() if shader_id_attr.IsValid() else None + + if shader_id == "UsdPreviewSurface": + input_names = ["inputs:diffuseColor"] + elif isinstance(shader_id, str) and "OmniPBR" in shader_id: + input_names = ["inputs:diffuse_color_constant"] + else: + input_names = ["inputs:diffuse_color_constant", "inputs:diffuseColor"] + + for input_name in input_names: + sim_utils.change_prim_property( + prop_path=f"{shader_path}.{input_name}", + value=Gf.Vec3f(*rgb), + stage=stage, + type_to_create_if_not_exist=Sdf.ValueTypeNames.Color3f, + ) + return True + + +def _apply_g1_hand_color_overrides(env) -> None: + """Post-scene-spawn hook to recolor the G1 hands.""" + stage = env.scene.stage + rgb = (0.1, 0.1, 0.1) + for env_prim_path in env.scene.env_prim_paths: + _set_material_albedo_rgb(stage, f"{env_prim_path}/Robot/right_hand/Looks/DefaultMaterial", rgb) + _set_material_albedo_rgb(stage, f"{env_prim_path}/Robot/left_hand/Looks/DefaultMaterial", rgb) + +## +# Scene definition +## +@configclass +class ApplePickPlaceG1SceneCfg(InteractiveSceneCfg): + # Table - spawn from USD + packing_table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/LabTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.5, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{_OBJECTS_ROOT_DIR}/lab_table/model/model.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + blue_plate = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/BluePlate", + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.4, 0.69), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{_OBJECTS_ROOT_DIR}/plate_blue/model/model.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + # pink_plate = AssetBaseCfg( + # prim_path="{ENV_REGEX_NS}/PinkPlate", + # init_state=AssetBaseCfg.InitialStateCfg(pos=(0.15, 0.40, 0.69), rot=(1.0, 0.0, 0.0, 0.0)), + # spawn=UsdFileCfg( + # usd_path=f"{_OBJECTS_ROOT_DIR}/objects/plate_pink/model/model.usd", + # scale=(0.75, 0.75, 0.75), + # rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + # ), + # ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Apple", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.15, 0.4, 0.7125], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{_OBJECTS_ROOT_DIR}/apple/model/model.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + ), + ) + + # Unitree G1 Humanoid robot - fixed base configuration + robot: ArticulationCfg = G1_29DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg( + # keep Isaac Sim's physics/collision setup, but override visuals to match Mujoco + color=None, + visual_material=sim_utils.PreviewSurfaceTextureCfg( + texture_file=f"{_OBJECTS_ROOT_DIR}/textures/mujoco_groundplane_checker.png", + texture_repeat=(5.0, 5.0), + roughness=1.0, + metallic=0.0, + ), + ), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=1500.0), + ) + + # Set table view camera + robot_pov_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/torso_link/d435_link/camera", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + # Match a known-good G1 D435 camera setup. + spawn=sim_utils.PinholeCameraCfg(focal_length=8.0, clipping_range=(0.1, 20.0)), + # Offset is relative to the D435 link frame. + # The rotation applies a small downward pitch to look at the table. + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + ) + + + def __post_init__(self): + """Post initialization.""" + # Set the robot to fixed base + self.robot.spawn.articulation_props.fix_root_link = True + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + hand_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": [".*_hand.*"]}) + + robot_pov_cam = ObsTerm( + func=base_mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + object_behind_robot = DoneTerm( + func=base_mdp.root_pos_behind_robot, params={"minimum_y": 0.0, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={ + "task_link_name": "left_wrist_yaw_link", + "min_x": -0.05, + "max_x": 0.05, + "min_y": 0.35, + "max_y": 0.45, + "max_height": 0.725, + "min_vel": 0.1, + "wrist_max_x": -0.15, + }) + + +## +# MDP settings +## + + +@configclass +class ApplePickPlaceG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 pick and place environment. + + This environment is designed for manipulation tasks where the G1 humanoid robot + has a fixed pelvis and legs, allowing only arm and hand movements for manipulation. The robot is + controlled using upper body IK. + """ + + # Scene settings + scene: ApplePickPlaceG1SceneCfg = ApplePickPlaceG1SceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + self.teleop_devices = DevicesCfg( + devices={ + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + ), + } + ) + + # Post scene spawn: override hand material albedo. + # This runs after the scene (including the robot) is spawned. + self.post_scene_spawn_fn = _apply_g1_hand_color_overrides + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/action_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/action_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b195334ba684a5fdd3f7700fc5f0c811b9f064ac --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/action_cfg.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +from isaaclab.managers.action_manager import ActionTerm, ActionTermCfg +from isaaclab.utils import configclass + +from ..mdp.actions import AgileBasedLowerBodyAction + + +@configclass +class AgileBasedLowerBodyActionCfg(ActionTermCfg): + """Configuration for the lower body action term that is based on Agile lower body RL policy.""" + + class_type: type[ActionTerm] = AgileBasedLowerBodyAction + """The class type for the lower body action term.""" + + joint_names: list[str] = MISSING + """The names of the joints to control.""" + + obs_group_name: str = MISSING + """The name of the observation group to use.""" + + policy_path: str = MISSING + """The path to the policy model.""" + + policy_output_offset: float = 0.0 + """Offsets the output of the policy.""" + + policy_output_scale: float = 1.0 + """Scales the output of the policy.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/agile_locomotion_observation_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/agile_locomotion_observation_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6fd0b6dbdf9d983191f9086155d0d54a608cdd5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/agile_locomotion_observation_cfg.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.envs import mdp +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + + +@configclass +class AgileTeacherPolicyObservationsCfg(ObsGroup): + """Observation specifications for the Agile lower body policy. + + Note: This configuration defines only part of the observation input to the Agile lower body policy. + The lower body command portion is appended to the observation tensor in the action term, as that + is where the environment has access to those commands. + """ + + base_lin_vel = ObsTerm( + func=mdp.base_lin_vel, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + base_ang_vel = ObsTerm( + func=mdp.base_ang_vel, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + scale=1.0, + ) + + joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + ".*_shoulder_.*_joint", + ".*_elbow_joint", + ".*_wrist_.*_joint", + ".*_hip_.*_joint", + ".*_knee_joint", + ".*_ankle_.*_joint", + "waist_.*_joint", + ], + ), + }, + ) + + joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + scale=0.1, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + ".*_shoulder_.*_joint", + ".*_elbow_joint", + ".*_wrist_.*_joint", + ".*_hip_.*_joint", + ".*_knee_joint", + ".*_ankle_.*_joint", + "waist_.*_joint", + ], + ), + }, + ) + + actions = ObsTerm( + func=mdp.last_action, + scale=1.0, + params={ + "action_name": "lower_body_joint_pos", + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/pink_controller_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/pink_controller_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..488d22c137b818d9373ca967c259f923eac2ba4a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/configs/pink_controller_cfg.py @@ -0,0 +1,126 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration for pink controller. + +This module provides configurations for humanoid robot pink IK controllers, +including both fixed base and mobile configurations for upper body manipulation. +""" + +from isaaclab.controllers.pink_ik.local_frame_task import LocalFrameTask +from isaaclab.controllers.pink_ik.null_space_posture_task import NullSpacePostureTask +from isaaclab.controllers.pink_ik.pink_ik_cfg import PinkIKControllerCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg + +## +# Pink IK Controller Configuration for G1 +## + +G1_UPPER_BODY_IK_CONTROLLER_CFG = PinkIKControllerCfg( + articulation_name="robot", + base_link_name="pelvis", + num_hand_joints=14, + show_ik_warnings=True, + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + LocalFrameTask( + "g1_29dof_with_hand_rev_1_0_left_wrist_yaw_link", + base_link_frame_name="g1_29dof_with_hand_rev_1_0_pelvis", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + LocalFrameTask( + "g1_29dof_with_hand_rev_1_0_right_wrist_yaw_link", + base_link_frame_name="g1_29dof_with_hand_rev_1_0_pelvis", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "g1_29dof_with_hand_rev_1_0_left_wrist_yaw_link", + "g1_29dof_with_hand_rev_1_0_right_wrist_yaw_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + gain=0.3, + ), + ], + fixed_input_tasks=[], +) +"""Base configuration for the G1 pink IK controller. + +This configuration sets up the pink IK controller for the G1 humanoid robot with +left and right wrist control tasks. The controller is designed for upper body +manipulation tasks. +""" + + +## +# Pink IK Action Configuration for G1 +## + +G1_UPPER_BODY_IK_ACTION_CFG = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_pitch_joint", + ".*_wrist_roll_joint", + ".*_wrist_yaw_joint", + "waist_.*_joint", + ], + hand_joint_names=[ + "left_hand_index_0_joint", # Index finger proximal + "left_hand_middle_0_joint", # Middle finger proximal + "left_hand_thumb_0_joint", # Thumb base (yaw axis) + "right_hand_index_0_joint", # Index finger proximal + "right_hand_middle_0_joint", # Middle finger proximal + "right_hand_thumb_0_joint", # Thumb base (yaw axis) + "left_hand_index_1_joint", # Index finger distal + "left_hand_middle_1_joint", # Middle finger distal + "left_hand_thumb_1_joint", # Thumb middle (pitch axis) + "right_hand_index_1_joint", # Index finger distal + "right_hand_middle_1_joint", # Middle finger distal + "right_hand_thumb_1_joint", # Thumb middle (pitch axis) + "left_hand_thumb_2_joint", # Thumb tip + "right_hand_thumb_2_joint", # Thumb tip + ], + target_eef_link_names={ + "left_wrist": "left_wrist_yaw_link", + "right_wrist": "right_wrist_yaw_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=G1_UPPER_BODY_IK_CONTROLLER_CFG, +) +"""Base configuration for the G1 pink IK action. + +This configuration sets up the pink IK action for the G1 humanoid robot, +defining which joints are controlled by the IK solver and which are fixed. +The configuration includes: +- Upper body joints controlled by IK (shoulders, elbows, wrists) +- Fixed joints (pelvis, legs, hands) +- Hand joint names for additional control +- Reference to the pink IK controller configuration +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/fixed_base_upper_body_ik_g1_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/fixed_base_upper_body_ik_g1_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..409802b228b9de966ba8093b023b66e7b33e6559 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/fixed_base_upper_body_ik_g1_env_cfg.py @@ -0,0 +1,233 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_29DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + + +## +# Scene definition +## +@configclass +class FixedBaseUpperBodyIKG1SceneCfg(InteractiveSceneCfg): + """Scene configuration for fixed base upper body IK environment with G1 robot. + + This configuration sets up the G1 humanoid robot with fixed pelvis and legs, + allowing only arm manipulation while the base remains stationary. The robot is + controlled using upper body IK. + """ + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, -0.3], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.0, 0.45, 0.6996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Unitree G1 Humanoid robot - fixed base configuration + robot: ArticulationCfg = G1_29DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + def __post_init__(self): + """Post initialization.""" + # Set the robot to fixed base + self.robot.spawn.articulation_props.fix_root_link = True + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + + hand_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": [".*_hand.*"]}) + # head_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": []}) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +## +# MDP settings +## + + +@configclass +class FixedBaseUpperBodyIKG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 fixed base upper body IK environment. + + This environment is designed for manipulation tasks where the G1 humanoid robot + has a fixed pelvis and legs, allowing only arm and hand movements for manipulation. The robot is + controlled using upper body IK. + """ + + # Scene settings + scene: FixedBaseUpperBodyIKG1SceneCfg = FixedBaseUpperBodyIKG1SceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + # xr: XrCfg = XrCfg( + # anchor_pos=(0.0, 0.0, -0.45), + # anchor_rot=(1.0, 0.0, 0.0, 0.0), + # ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + G1TriHandUpperBodyRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/locomanipulation_g1_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/locomanipulation_g1_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0c86d502bbd37d242eca60fcbc5af8b666cbdbc0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/locomanipulation_g1_env_cfg.py @@ -0,0 +1,274 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.g1_lower_body_standing import G1LowerBodyStandingRetargeterCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.g1_motion_controller_locomotion import ( + G1LowerBodyStandingMotionControllerRetargeterCfg, +) +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_motion_ctrl_retargeter import ( + G1TriHandUpperBodyMotionControllerRetargeterCfg, +) +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) +from isaaclab.devices.openxr.xr_cfg import XrAnchorRotationMode + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.action_cfg import AgileBasedLowerBodyActionCfg +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.agile_locomotion_observation_cfg import ( + AgileTeacherPolicyObservationsCfg, +) +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_29DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + + +## +# Scene definition +## +@configclass +class LocomanipulationG1SceneCfg(InteractiveSceneCfg): + """Scene configuration for locomanipulation environment with G1 robot. + + This configuration sets up the G1 humanoid robot for locomanipulation tasks, + allowing both locomotion and manipulation capabilities. The robot can move its + base and use its arms for manipulation tasks. + """ + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, -0.3], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.35, 0.45, 0.6996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Humanoid robot w/ arms higher + robot: ArticulationCfg = G1_29DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + lower_body_joint_pos = AgileBasedLowerBodyActionCfg( + asset_name="robot", + joint_names=[ + ".*_hip_.*_joint", + ".*_knee_joint", + ".*_ankle_.*_joint", + ], + policy_output_scale=0.25, + obs_group_name="lower_body_policy", # need to be the same name as the on in ObservationCfg + policy_path=f"{ISAACLAB_NUCLEUS_DIR}/Policies/Agile/agile_locomotion.pt", + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + + hand_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": [".*_hand.*"]}) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + lower_body_policy: AgileTeacherPolicyObservationsCfg = AgileTeacherPolicyObservationsCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +## +# MDP settings +## + + +@configclass +class LocomanipulationG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 locomanipulation environment. + + This environment is designed for locomanipulation tasks where the G1 humanoid robot + can perform both locomotion and manipulation simultaneously. The robot can move its + base and use its arms for manipulation tasks, enabling complex mobile manipulation + behaviors. + """ + + # Scene settings + scene: LocomanipulationG1SceneCfg = LocomanipulationG1SceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # MDP settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands = None + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, -0.35), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + self.xr.anchor_prim_path = "/World/envs/env_0/Robot/pelvis" + self.xr.fixed_anchor_height = True + # Ensure XR anchor rotation follows the robot pelvis (yaw only), with smoothing for comfort + self.xr.anchor_rotation_mode = XrAnchorRotationMode.FOLLOW_PRIM_SMOOTHED + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + G1TriHandUpperBodyRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + G1LowerBodyStandingRetargeterCfg( + sim_device=self.sim.device, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "motion_controllers": OpenXRDeviceCfg( + retargeters=[ + G1TriHandUpperBodyMotionControllerRetargeterCfg( + enable_visualization=True, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + G1LowerBodyStandingMotionControllerRetargeterCfg( + sim_device=self.sim.device, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + G1LowerBodyStandingMotionControllerRetargeterCfg( + sim_device=self.sim.device, + ), + ], + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7e559309b5cf74cb2db1db2fdb07ff7e255d9bd4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""This sub-module contains the functions that are specific to the locomanipulation environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .actions import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/actions.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/actions.py new file mode 100644 index 0000000000000000000000000000000000000000..64d27dbc2f2a5909bc04b32cec61275f0b41f1ba --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/actions.py @@ -0,0 +1,126 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets.articulation import Articulation +from isaaclab.managers.action_manager import ActionTerm +from isaaclab.utils.assets import retrieve_file_path +from isaaclab.utils.io.torchscript import load_torchscript_model + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from .configs.action_cfg import AgileBasedLowerBodyActionCfg + + +class AgileBasedLowerBodyAction(ActionTerm): + """Action term that is based on Agile lower body RL policy.""" + + cfg: AgileBasedLowerBodyActionCfg + """The configuration of the action term.""" + + _asset: Articulation + """The articulation asset to which the action term is applied.""" + + def __init__(self, cfg: AgileBasedLowerBodyActionCfg, env: ManagerBasedEnv): + super().__init__(cfg, env) + + # Save the observation config from cfg + self._observation_cfg = env.cfg.observations + self._obs_group_name = cfg.obs_group_name + + # Load policy here if needed + _temp_policy_path = retrieve_file_path(cfg.policy_path) + self._policy = load_torchscript_model(_temp_policy_path, device=env.device) + self._env = env + + # Find joint ids for the lower body joints + self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names) + + # Get the scale and offset from the configuration + self._policy_output_scale = torch.tensor(cfg.policy_output_scale, device=env.device) + self._policy_output_offset = self._asset.data.default_joint_pos[:, self._joint_ids].clone() + + # Create tensors to store raw and processed actions + self._raw_actions = torch.zeros(self.num_envs, len(self._joint_ids), device=self.device) + self._processed_actions = torch.zeros(self.num_envs, len(self._joint_ids), device=self.device) + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + """Lower Body Action: [vx, vy, wz, hip_height]""" + return 4 + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self._processed_actions + + def _compose_policy_input(self, base_command: torch.Tensor, obs_tensor: torch.Tensor) -> torch.Tensor: + """Compose the policy input by concatenating repeated commands with observations. + + Args: + base_command: The base command tensor [vx, vy, wz, hip_height]. + obs_tensor: The observation tensor from the environment. + + Returns: + The composed policy input tensor with repeated commands concatenated to observations. + """ + # Get history length from observation configuration + history_length = getattr(self._observation_cfg, self._obs_group_name).history_length + # Default to 1 if history_length is None (no history, just current observation) + if history_length is None: + history_length = 1 + + # Repeat commands based on history length and concatenate with observations + repeated_commands = base_command.unsqueeze(1).repeat(1, history_length, 1).reshape(base_command.shape[0], -1) + policy_input = torch.cat([repeated_commands, obs_tensor], dim=-1) + + return policy_input + + def process_actions(self, actions: torch.Tensor): + """Process the input actions using the locomotion policy. + + Args: + actions: The lower body commands. + """ + + # Extract base command from the action tensor + # Assuming the base command [vx, vy, wz, hip_height] + base_command = actions + + obs_tensor = self._env.obs_buf["lower_body_policy"] + + # Compose policy input using helper function + policy_input = self._compose_policy_input(base_command, obs_tensor) + + joint_actions = self._policy.forward(policy_input) + + self._raw_actions[:] = joint_actions + + # Apply scaling and offset to the raw actions from the policy + self._processed_actions = joint_actions * self._policy_output_scale + self._policy_output_offset + + # Clip actions if configured + if self.cfg.clip is not None: + self._processed_actions = torch.clamp( + self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1] + ) + + def apply_actions(self): + """Apply the actions to the environment.""" + # Store the raw actions + self._asset.set_joint_position_target(self._processed_actions, joint_ids=self._joint_ids) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..d4b3f2b4bdf4b686d6967d079979accd024f957c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/mdp/observations.py @@ -0,0 +1,32 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import torch + +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import SceneEntityCfg + + +def upper_body_last_action( + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """Extract the last action of the upper body.""" + asset = env.scene[asset_cfg.name] + joint_pos_target = asset.data.joint_pos_target + + # Use joint_names from asset_cfg to find indices + joint_names = asset_cfg.joint_names if hasattr(asset_cfg, "joint_names") else None + if joint_names is None: + raise ValueError("asset_cfg must have 'joint_names' attribute for upper_body_last_action.") + + # Find joint indices matching the provided joint_names (supports regex) + joint_indices, _ = asset.find_joints(joint_names) + joint_indices = torch.tensor(joint_indices, dtype=torch.long) + + # Get upper body joint positions for all environments + upper_body_joint_pos_target = joint_pos_target[:, joint_indices] + + return upper_body_joint_pos_target diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_camera_g1_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_camera_g1_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..387c2f8795d3e0cc88b843de0e8aef2f65dddcd9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_camera_g1_env_cfg.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import os +import sys +from copy import deepcopy + +import torch + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_29DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + +from isaaclab.sensors import CameraCfg +from isaaclab.utils.math import convert_camera_frame_orientation_convention + + +def _are_cameras_enabled() -> bool: + """Best-effort check mirroring AppLauncher camera enable logic. + + We key off `--enable_cameras` (CLI) and `ENABLE_CAMERAS` (env var). This keeps task configs + usable in scripts like `record_demos.py` without forcing the rendering experience. + """ + + if "--enable_cameras" in sys.argv: + return True + try: + return bool(int(os.environ.get("ENABLE_CAMERAS", "0"))) + except ValueError: + return False + + +## +# Scene definition +## +@configclass +class PickPlaceCameraG1SceneCfg(InteractiveSceneCfg): + """Scene configuration for pick and place environment with G1 robot. + + This configuration sets up the G1 humanoid robot with fixed pelvis and legs, + allowing only arm manipulation while the base remains stationary. The robot is + controlled using upper body IK. + """ + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.55, -0.3), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.45, 0.6996), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Unitree G1 Humanoid robot - fixed base configuration + robot: ArticulationCfg = G1_29DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + # Set table view camera + robot_pov_cam: CameraCfg | AssetBaseCfg = CameraCfg( + prim_path="{ENV_REGEX_NS}/RobotPOVCam", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=18.15, clipping_range=(0.1, 2)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.12, 1.67675), rot=(-0.19848, 0.9801, 0.0, 0.0), convention="ros"), + ) + + # Set camera on robot D435 link + # robot_pov_cam = CameraCfg( + # prim_path="{ENV_REGEX_NS}/Robot/torso_link/d435_link/camera", + # update_period=0.0, + # height=160, + # width=256, + # data_types=["rgb"], + # # Match a known-good G1 D435 camera setup. + # spawn=sim_utils.PinholeCameraCfg(focal_length=8.0, clipping_range=(0.1, 20.0)), + # # Offset is relative to the D435 link frame. + # # The rotation applies a small downward pitch to look at the table. + # offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"), + # ) + + def __post_init__(self): + """Post initialization.""" + # Set the robot to fixed base + self.robot.spawn.articulation_props.fix_root_link = True + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + hand_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": [".*_hand.*"]}) + + robot_pov_cam: ObsTerm | None = ObsTerm( + func=base_mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + object_behind_robot = DoneTerm( + func=base_mdp.root_pos_behind_robot, params={"minimum_y": 0.0, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +## +# MDP settings +## + + +@configclass +class PickPlaceCameraG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 pick and place environment. + + This environment is designed for manipulation tasks where the G1 humanoid robot + has a fixed pelvis and legs, allowing only arm and hand movements for manipulation. The robot is + controlled using upper body IK. + """ + + # Scene settings + scene: PickPlaceCameraG1SceneCfg = PickPlaceCameraG1SceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + self.teleop_devices = DevicesCfg( + devices={ + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + ), + } + ) + + cameras_enabled = _are_cameras_enabled() + if cameras_enabled: + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] + else: + # Without `--enable_cameras`, Isaac Lab typically launches a non-rendering experience. + # RTX/Replicator-based sensors (Camera) won't function, but we still spawn a USD Camera prim + # so users can select it in the stage/viewport. + cam_cfg = self.scene.robot_pov_cam + if isinstance(cam_cfg, AssetBaseCfg): + self.observations.policy.robot_pov_cam = None + self.image_obs_list = [] + return + + spawn_cfg = deepcopy(cam_cfg.spawn) + if spawn_cfg is not None and getattr(spawn_cfg, "vertical_aperture", None) is None: + spawn_cfg.vertical_aperture = spawn_cfg.horizontal_aperture * cam_cfg.height / cam_cfg.width + + rot = torch.tensor(cam_cfg.offset.rot, dtype=torch.float32, device="cpu").unsqueeze(0) + rot_offset = convert_camera_frame_orientation_convention( + rot, origin=cam_cfg.offset.convention, target="opengl" + ).squeeze(0) + rot_offset_list = rot_offset.cpu().numpy().tolist() + rot_offset_tuple = ( + float(rot_offset_list[0]), + float(rot_offset_list[1]), + float(rot_offset_list[2]), + float(rot_offset_list[3]), + ) + + self.scene.robot_pov_cam = AssetBaseCfg( + prim_path=cam_cfg.prim_path, + init_state=AssetBaseCfg.InitialStateCfg( + pos=cam_cfg.offset.pos, + rot=rot_offset_tuple, + ), + spawn=spawn_cfg, + ) + + # Skip image observations (they would error / be stale without rendering). + self.observations.policy.robot_pov_cam = None + self.image_obs_list = [] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_23_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_23_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..29faeb3e92010c2395eb9d6211ffffddf120f3c5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_23_env_cfg.py @@ -0,0 +1,352 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +from copy import deepcopy + +import isaaclab_assets +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.controllers.pink_ik.local_frame_task import LocalFrameTask +from isaaclab.controllers.pink_ik.null_space_posture_task import NullSpacePostureTask +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_23DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + + +## +# Scene definition +## +@configclass +class PickPlaceG1SceneCfg(InteractiveSceneCfg): + """Scene configuration for pick and place environment with G1 robot. + + This configuration sets up the G1 humanoid robot with fixed pelvis and legs, + allowing only arm manipulation while the base remains stationary. The robot is + controlled using upper body IK. + """ + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, -0.3], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + # Red block + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Block", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0., 0.3, 0.745], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=sim_utils.CuboidCfg( + size=(0.06, 0.06, 0.06), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + retain_accelerations=False + ), + mass_props=sim_utils.MassPropertiesCfg(mass=1.0), + collision_props=sim_utils.CollisionPropertiesCfg( + collision_enabled=True, + contact_offset=0.01, + rest_offset=0.0 + ), + visual_material=sim_utils.PreviewSurfaceCfg( + diffuse_color=(1.0, 0.0, 0.0), metallic=0.0 + ), + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="max", + restitution_combine_mode="min", + static_friction=1, + dynamic_friction=0.15, + restitution=0.01, + ), + ), + ) + + # object = RigidObjectCfg( + # prim_path="{ENV_REGEX_NS}/Object", + # init_state=RigidObjectCfg.InitialStateCfg(pos=[0.0, 0.45, 0.6996], rot=[1, 0, 0, 0]), + # spawn=UsdFileCfg( + # usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + # scale=(0.75, 0.75, 0.75), + # rigid_props=sim_utils.RigidBodyPropertiesCfg(), + # ), + # ) + + # object = RigidObjectCfg( + # prim_path="{ENV_REGEX_NS}/Cylinder", + # init_state=RigidObjectCfg.InitialStateCfg(pos=[0.0, 0.40, 0.87], rot=[1.0, 0.0, 0.0, 0.0]), + # spawn=sim_utils.CylinderCfg( + # radius=0.018, # cylinder radius (radius) + # height=0.35, # cylinder height (height) + + # rigid_props=sim_utils.RigidBodyPropertiesCfg( + # ), # rigid body properties configuration (rigid_props) + # mass_props=sim_utils.MassPropertiesCfg(mass=0.4), # mass properties configuration (mass) + # collision_props=sim_utils.CollisionPropertiesCfg(), # collision properties configuration (collision_props) + # visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.15, 0.15, 0.15), metallic=1.0), # visual material configuration (visual_material) + # physics_material=sim_utils.RigidBodyMaterialCfg( + # friction_combine_mode="max", # friction combine mode + # restitution_combine_mode="min", # restitution combine mode + # static_friction=1.5, # static friction coefficient + # dynamic_friction=1.5, # dynamic friction coefficient + # restitution=0.0, # restitution coefficient (no restitution) + # ), + # ), + # ) + + # Unitree G1 Humanoid robot - fixed base configuration + robot: ArticulationCfg = G1_23DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + def __post_init__(self): + """Post initialization.""" + # Set the robot to fixed base + self.robot.spawn.articulation_props.fix_root_link = True + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_roll_rubber_hand"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_roll_rubber_hand"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_roll_rubber_hand"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_roll_rubber_hand"}) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={ + "left_eef_link_name": "left_wrist_roll_rubber_hand", + "right_eef_link_name": "right_wrist_roll_rubber_hand", + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + object_behind_robot = DoneTerm( + func=base_mdp.root_pos_behind_robot, params={"minimum_y": 0.0, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={"task_link_name": "right_wrist_roll_rubber_hand"}) + + +## +# MDP settings +## + + +@configclass +class PickPlaceG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 pick and place environment. + + This environment is designed for manipulation tasks where the G1 humanoid robot + has a fixed pelvis and legs, allowing only arm and hand movements for manipulation. The robot is + controlled using upper body IK. + """ + + # Scene settings + scene: PickPlaceG1SceneCfg = PickPlaceG1SceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = os.path.join( + os.path.dirname(isaaclab_assets.__file__), + "robots", + "g1", + "g1_23dof_rev_1_0.urdf", + ) + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + urdf_path = retrieve_file_path(urdf_omniverse_path) + self.actions.upper_body_ik.controller.urdf_path = urdf_path + # Pinocchio resolves relative mesh filenames (e.g. "meshes/foo.STL") using `mesh_path`. + # Here we point it at the URDF directory which contains the `meshes/` folder. + self.actions.upper_body_ik.controller.mesh_path = os.path.dirname(urdf_path) + + # --------------------------------------------------------------------- + # Pink IK configuration for the 23-DoF G1 + # + # The shared `G1_UPPER_BODY_IK_ACTION_CFG` is authored for the 29-DoF+hands kinematics URDF + # (includes wrist pitch/yaw + finger joints). The 23-DoF model only has wrist roll joints and + # no finger joints; therefore we must override the controlled-joint patterns and task frames. + # --------------------------------------------------------------------- + self.actions.upper_body_ik.pink_controlled_joint_names = [ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_roll_joint", + "waist_.*_joint", + ] + # No finger joints in the 23-DoF model. + self.actions.upper_body_ik.hand_joint_names = [] + + # End-effector link names must match the URDF/scene for Pink IK. + self.actions.upper_body_ik.target_eef_link_names = { + "left_wrist": "left_wrist_roll_rubber_hand", + "right_wrist": "right_wrist_roll_rubber_hand", + } + + controller_cfg = deepcopy(self.actions.upper_body_ik.controller) + controller_cfg.num_hand_joints = 0 + controller_cfg.variable_input_tasks = [ + LocalFrameTask( + "left_wrist_roll_rubber_hand", + base_link_frame_name="pelvis", + position_cost=8.0, + orientation_cost=2.0, + lm_damping=10, + gain=0.5, + ), + LocalFrameTask( + "right_wrist_roll_rubber_hand", + base_link_frame_name="pelvis", + position_cost=8.0, + orientation_cost=2.0, + lm_damping=10, + gain=0.5, + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "left_wrist_roll_rubber_hand", + "right_wrist_roll_rubber_hand", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_joint", + "left_wrist_roll_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_joint", + "right_wrist_roll_joint", + "waist_yaw_joint", + ], + gain=0.3, + ), + ] + self.actions.upper_body_ik.controller = controller_cfg + + self.teleop_devices = DevicesCfg( + devices={ + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + disable_hands=True, # disable hand retargeting since the 23-DoF model has no finger joints + ), + ], + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa16c1e4ffb5aec6fe0df443483dafd679d5d51 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/pick_place/pick_place_g1_env_cfg.py @@ -0,0 +1,238 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandUpperBodyRetargeterCfg, +) + +from isaaclab.devices.opencv_handtracking.opencv_handtracking_device import HandTrackingDeviceCfg +from isaaclab.devices.opencv_handtracking.retargeters.humanoid.unitree.trihand.g1_upper_body_retargeter import ( + G1TriHandOpenCVRetargeterCfg, +) + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR, retrieve_file_path + +from isaaclab_tasks.manager_based.locomanipulation.pick_place import mdp as locomanip_mdp +from isaaclab_tasks.manager_based.manipulation.pick_place import mdp as manip_mdp + +from isaaclab_assets.robots.unitree import G1_29DOF_CFG + +from isaaclab_tasks.manager_based.locomanipulation.pick_place.configs.pink_controller_cfg import ( # isort: skip + G1_UPPER_BODY_IK_ACTION_CFG, +) + + +## +# Scene definition +## +@configclass +class PickPlaceG1SceneCfg(InteractiveSceneCfg): + """Scene configuration for pick and place environment with G1 robot. + + This configuration sets up the G1 humanoid robot with fixed pelvis and legs, + allowing only arm manipulation while the base remains stationary. The robot is + controlled using upper body IK. + """ + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, -0.3], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.0, 0.45, 0.6996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # object = RigidObjectCfg( + # prim_path="{ENV_REGEX_NS}/Cylinder", + # init_state=RigidObjectCfg.InitialStateCfg(pos=[0.0, 0.40, 0.87], rot=[1.0, 0.0, 0.0, 0.0]), + # spawn=sim_utils.CylinderCfg( + # radius=0.018, # cylinder radius (radius) + # height=0.35, # cylinder height (height) + + # rigid_props=sim_utils.RigidBodyPropertiesCfg( + # ), # rigid body properties configuration (rigid_props) + # mass_props=sim_utils.MassPropertiesCfg(mass=0.4), # mass properties configuration (mass) + # collision_props=sim_utils.CollisionPropertiesCfg(), # collision properties configuration (collision_props) + # visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.15, 0.15, 0.15), metallic=1.0), # visual material configuration (visual_material) + # physics_material=sim_utils.RigidBodyMaterialCfg( + # friction_combine_mode="max", # friction combine mode + # restitution_combine_mode="min", # restitution combine mode + # static_friction=1.5, # static friction coefficient + # dynamic_friction=1.5, # dynamic friction coefficient + # restitution=0.0, # restitution coefficient (no restitution) + # ), + # ), + # ) + + # Unitree G1 Humanoid robot - fixed base configuration + robot: ArticulationCfg = G1_29DOF_CFG + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + def __post_init__(self): + """Post initialization.""" + # Set the robot to fixed base + self.robot.spawn.articulation_props.fix_root_link = True + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = G1_UPPER_BODY_IK_ACTION_CFG + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP. + This class is required by the environment configuration but not used in this implementation + """ + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=manip_mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=manip_mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=manip_mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=manip_mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + hand_joint_state = ObsTerm(func=manip_mdp.get_robot_joint_state, params={"joint_names": [".*_hand.*"]}) + + object = ObsTerm( + func=manip_mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=locomanip_mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=base_mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + object_behind_robot = DoneTerm( + func=base_mdp.root_pos_behind_robot, params={"minimum_y": 0.0, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=manip_mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +## +# MDP settings +## + + +@configclass +class PickPlaceG1EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the G1 pick and place environment. + + This environment is designed for manipulation tasks where the G1 humanoid robot + has a fixed pelvis and legs, allowing only arm and hand movements for manipulation. The robot is + controlled using upper body IK. + """ + + # Scene settings + scene: PickPlaceG1SceneCfg = PickPlaceG1SceneCfg( + num_envs=1, env_spacing=2.5, replicate_physics=True + ) + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 200 # 200Hz + self.sim.render_interval = 2 + + # Set the URDF and mesh paths for the IK controller + urdf_omniverse_path = f"{ISAACLAB_NUCLEUS_DIR}/Controllers/LocomanipulationAssets/unitree_g1_kinematics_asset/g1_29dof_with_hand_only_kinematics.urdf" # noqa: E501 + + # Retrieve local paths for the URDF and mesh files. Will be cached for call after the first time. + self.actions.upper_body_ik.controller.urdf_path = retrieve_file_path(urdf_omniverse_path) + + self.teleop_devices = DevicesCfg( + devices={ + "opencv_handtracking": HandTrackingDeviceCfg( + retargeters=[ + G1TriHandOpenCVRetargeterCfg( + enable_visualization=True, + # OpenXR hand tracking has 26 joints per hand + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e952f370f8237742f668dc0963f2e1a174e5987e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/__init__.py @@ -0,0 +1,32 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## +gym.register( + id="Isaac-Tracking-LocoManip-Digit-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.loco_manip_env_cfg:DigitLocoManipEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitLocoManipPPORunnerCfg", + }, +) + + +gym.register( + id="Isaac-Tracking-LocoManip-Digit-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.loco_manip_env_cfg:DigitLocoManipEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitLocoManipPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c98c2030a2ca578d72b603a6dfe50f87a5cddb43 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class DigitLocoManipPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 2000 + save_interval = 50 + experiment_name = "digit_loco_manip" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[256, 128, 128], + critic_hidden_dims=[256, 128, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/loco_manip_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/loco_manip_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a91ff0907dc7721df4485e356ab5a147afab1ddb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomanipulation/tracking/config/digit/loco_manip_env_cfg.py @@ -0,0 +1,249 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.managers import EventTermCfg, SceneEntityCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.utils import configclass +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +import isaaclab_tasks.manager_based.manipulation.reach.mdp as manipulation_mdp +from isaaclab_tasks.manager_based.locomotion.velocity.config.digit.rough_env_cfg import DigitRewards, DigitRoughEnvCfg +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import EventCfg + +from isaaclab_assets.robots.agility import ARM_JOINT_NAMES, LEG_JOINT_NAMES + + +@configclass +class DigitLocoManipRewards(DigitRewards): + joint_deviation_arms = None + + joint_vel_hip_yaw = RewTerm( + func=mdp.joint_vel_l2, + weight=-0.001, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_leg_hip_yaw"])}, + ) + + left_ee_pos_tracking = RewTerm( + func=manipulation_mdp.position_command_error, + weight=-2.0, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="left_arm_wrist_yaw"), + "command_name": "left_ee_pose", + }, + ) + + left_ee_pos_tracking_fine_grained = RewTerm( + func=manipulation_mdp.position_command_error_tanh, + weight=2.0, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="left_arm_wrist_yaw"), + "std": 0.05, + "command_name": "left_ee_pose", + }, + ) + + left_end_effector_orientation_tracking = RewTerm( + func=manipulation_mdp.orientation_command_error, + weight=-0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="left_arm_wrist_yaw"), + "command_name": "left_ee_pose", + }, + ) + + right_ee_pos_tracking = RewTerm( + func=manipulation_mdp.position_command_error, + weight=-2.0, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="right_arm_wrist_yaw"), + "command_name": "right_ee_pose", + }, + ) + + right_ee_pos_tracking_fine_grained = RewTerm( + func=manipulation_mdp.position_command_error_tanh, + weight=2.0, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="right_arm_wrist_yaw"), + "std": 0.05, + "command_name": "right_ee_pose", + }, + ) + + right_end_effector_orientation_tracking = RewTerm( + func=manipulation_mdp.orientation_command_error, + weight=-0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="right_arm_wrist_yaw"), + "command_name": "right_ee_pose", + }, + ) + + +@configclass +class DigitLocoManipObservations: + """Configuration for the Digit Locomanipulation environment.""" + + @configclass + class PolicyCfg(ObsGroup): + base_lin_vel = ObsTerm( + func=mdp.base_lin_vel, + noise=Unoise(n_min=-0.1, n_max=0.1), + ) + base_ang_vel = ObsTerm( + func=mdp.base_ang_vel, + noise=Unoise(n_min=-0.2, n_max=0.2), + ) + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObsTerm( + func=mdp.generated_commands, + params={"command_name": "base_velocity"}, + ) + left_ee_pose_command = ObsTerm( + func=mdp.generated_commands, + params={"command_name": "left_ee_pose"}, + ) + right_ee_pose_command = ObsTerm( + func=mdp.generated_commands, + params={"command_name": "right_ee_pose"}, + ) + joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES)}, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES)}, + noise=Unoise(n_min=-1.5, n_max=1.5), + ) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + policy = PolicyCfg() + + +@configclass +class DigitLocoManipCommands: + base_velocity = mdp.UniformVelocityCommandCfg( + asset_name="robot", + resampling_time_range=(10.0, 10.0), + rel_standing_envs=0.25, + rel_heading_envs=1.0, + heading_command=True, + debug_vis=True, + ranges=mdp.UniformVelocityCommandCfg.Ranges( + lin_vel_x=(-1.0, 1.0), + lin_vel_y=(-1.0, 1.0), + ang_vel_z=(-1.0, 1.0), + heading=(-math.pi, math.pi), + ), + ) + + left_ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name="left_arm_wrist_yaw", + resampling_time_range=(1.0, 3.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.10, 0.50), + pos_y=(0.05, 0.50), + pos_z=(-0.20, 0.20), + roll=(-0.1, 0.1), + pitch=(-0.1, 0.1), + yaw=(math.pi / 2.0 - 0.1, math.pi / 2.0 + 0.1), + ), + ) + + right_ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name="right_arm_wrist_yaw", + resampling_time_range=(1.0, 3.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.10, 0.50), + pos_y=(-0.50, -0.05), + pos_z=(-0.20, 0.20), + roll=(-0.1, 0.1), + pitch=(-0.1, 0.1), + yaw=(-math.pi / 2.0 - 0.1, -math.pi / 2.0 + 0.1), + ), + ) + + +@configclass +class DigitEvents(EventCfg): + # Add an external force to simulate a payload being carried. + left_hand_force = EventTermCfg( + func=mdp.apply_external_force_torque, + mode="interval", + interval_range_s=(10.0, 15.0), + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="left_arm_wrist_yaw"), + "force_range": (-10.0, 10.0), + "torque_range": (-1.0, 1.0), + }, + ) + + right_hand_force = EventTermCfg( + func=mdp.apply_external_force_torque, + mode="interval", + interval_range_s=(10.0, 15.0), + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="right_arm_wrist_yaw"), + "force_range": (-10.0, 10.0), + "torque_range": (-1.0, 1.0), + }, + ) + + +@configclass +class DigitLocoManipEnvCfg(DigitRoughEnvCfg): + rewards: DigitLocoManipRewards = DigitLocoManipRewards() + observations: DigitLocoManipObservations = DigitLocoManipObservations() + commands: DigitLocoManipCommands = DigitLocoManipCommands() + + def __post_init__(self): + super().__post_init__() + + self.episode_length_s = 14.0 + + # Rewards: + self.rewards.flat_orientation_l2.weight = -10.5 + self.rewards.termination_penalty.weight = -100.0 + + # Change terrain to flat. + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # Remove height scanner. + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # Remove terrain curriculum. + self.curriculum.terrain_levels = None + + +class DigitLocoManipEnvCfg_PLAY(DigitLocoManipEnvCfg): + def __post_init__(self) -> None: + super().__post_init__() + + # Make a smaller scene for play. + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # Disable randomization for play. + self.observations.policy.enable_corruption = False + # Remove random pushing. + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5ce57dc1bd563da9a0fdaec6c30099f88cee023f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Locomotion environments for legged robots.""" + +from .velocity import * # noqa diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7971b7cd36400187c323e12189c4d8b6035e99d7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Locomotion environments with velocity-tracking commands. + +These environments are based on the `legged_gym` environments provided by Rudin et al. + +Reference: + https://github.com/leggedrobotics/legged_gym +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..26f3257daef40572e05fdb4a4282b6c7a1a05262 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for velocity-based locomotion environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..99ead146751dbe34d7bf8491a17d672563a44710 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/__init__.py @@ -0,0 +1,60 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Unitree-A1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeA1FlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Unitree-A1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeA1FlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-A1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeA1RoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-A1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeA1RoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..972ebf937367bb80c6287205eddeed7f43d3f0ca --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class UnitreeA1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "unitree_a1_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class UnitreeA1FlatPPORunnerCfg(UnitreeA1RoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "unitree_a1_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc16b3ce79bdf650c00fe64333ae033b10203fe3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Adapted from rsl_rl config +seed: 42 +n_timesteps: !!float 5e7 +policy: 'MlpPolicy' +n_steps: 24 +n_minibatches: 4 # batch_size=24576 for n_envs=4096 and n_steps=24 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 5 +ent_coef: 0.005 +learning_rate: !!float 1e-3 +clip_range: !!float 0.2 +policy_kwargs: + activation_fn: 'nn.ELU' + net_arch: [512, 256, 128] + optimizer_kwargs: + eps: !!float 1e-8 + ortho_init: False +vf_coef: 1.0 +max_grad_norm: 1.0 +normalize_input: True +normalize_value: False +clip_obs: 10.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eeb09d2b8a13ed79c902a54e0e2a18a8fad5d44d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_a1_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e7eff6aa9a83410c4a6ea99fa3549a8c7c612b88 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_a1_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..eb239d3637754e563daa0b013a85f3dc67533ae6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import UnitreeA1RoughEnvCfg + + +@configclass +class UnitreeA1FlatEnvCfg(UnitreeA1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -2.5 + self.rewards.feet_air_time.weight = 0.25 + + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class UnitreeA1FlatEnvCfg_PLAY(UnitreeA1FlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..371ccb5b0cd47f215eefc90f8d9bf2d5efce5228 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/a1/rough_env_cfg.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.unitree import UNITREE_A1_CFG # isort: skip + + +@configclass +class UnitreeA1RoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.robot = UNITREE_A1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/trunk" + # scale down the terrains because the robot is small + self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01 + + # reduce action scale + self.actions.joint_pos.scale = 0.25 + + # event + self.events.push_robot = None + self.events.add_base_mass.params["mass_distribution_params"] = (-1.0, 3.0) + self.events.add_base_mass.params["asset_cfg"].body_names = "trunk" + self.events.base_external_force_torque.params["asset_cfg"].body_names = "trunk" + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # rewards + self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot" + self.rewards.feet_air_time.weight = 0.01 + self.rewards.undesired_contacts = None + self.rewards.dof_torques_l2.weight = -0.0002 + self.rewards.track_lin_vel_xy_exp.weight = 1.5 + self.rewards.track_ang_vel_z_exp.weight = 0.75 + self.rewards.dof_acc_l2.weight = -2.5e-7 + + # terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = "trunk" + + +@configclass +class UnitreeA1RoughEnvCfg_PLAY(UnitreeA1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e0f3eafd39e57bb999c9150e823e914f901108a6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/__init__.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Anymal-B-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalBFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBFlatPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBFlatPPORunnerWithSymmetryCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Anymal-B-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalBFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBFlatPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBFlatPPORunnerWithSymmetryCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-B-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalBRoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBRoughPPORunnerWithSymmetryCfg" + ), + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-B-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalBRoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalBRoughPPORunnerWithSymmetryCfg" + ), + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f6d0c585dd15fa46c9f673a925e4d2e0c7fe84cb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,99 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg, RslRlSymmetryCfg + +from isaaclab_tasks.manager_based.locomotion.velocity.mdp.symmetry import anymal + + +@configclass +class AnymalBRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "anymal_b_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class AnymalBFlatPPORunnerCfg(AnymalBRoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "anymal_b_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] + + +@configclass +class AnymalBFlatPPORunnerWithSymmetryCfg(AnymalBFlatPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) + + +@configclass +class AnymalBRoughPPORunnerWithSymmetryCfg(AnymalBRoughPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4daadf17383319cc09c1f85c73ff3229e2773fb8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_b_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..51dd9c72723655c2a01645c595a5502976a61bb1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_b_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..134fd0154bf85d780dad2535e2ab4df197ebc6fb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import AnymalBRoughEnvCfg + + +@configclass +class AnymalBFlatEnvCfg(AnymalBRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -5.0 + self.rewards.dof_torques_l2.weight = -2.5e-5 + self.rewards.feet_air_time.weight = 0.5 + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class AnymalBFlatEnvCfg_PLAY(AnymalBFlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..dd11ad8584799cdb8ca27d35f6e0df6c8f960731 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/rough_env_cfg.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets import ANYMAL_B_CFG # isort: skip + + +@configclass +class AnymalBRoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # switch robot to anymal-d + self.scene.robot = ANYMAL_B_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + +@configclass +class AnymalBRoughEnvCfg_PLAY(AnymalBRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..39b8e5caeaa8a06a0dcf5fe7367e59c3fd3ce793 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/__init__.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Anymal-C-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalCFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerWithSymmetryCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_flat_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Anymal-C-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalCFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCFlatPPORunnerWithSymmetryCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_flat_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-C-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalCRoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerWithSymmetryCfg" + ), + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_rough_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-C-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalCRoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalCRoughPPORunnerWithSymmetryCfg" + ), + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_rough_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..95916252ac262a042b6a8e1154e2c2680d1685de --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_flat_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [128, 128, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: anymal_c_flat + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: False + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 300 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.005 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2.0 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..54a9847bbef2b26f64123e4d433386f01ba3b950 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rl_games_rough_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: anymal_c_rough + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: False + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.6 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + grad_norm: 1.0 + entropy_coef: 0.005 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2.0 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..45f434fe7f0dc2105975957c426b13fba7b7073a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,99 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg, RslRlSymmetryCfg + +from isaaclab_tasks.manager_based.locomotion.velocity.mdp.symmetry import anymal + + +@configclass +class AnymalCRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "anymal_c_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class AnymalCFlatPPORunnerCfg(AnymalCRoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "anymal_c_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] + + +@configclass +class AnymalCFlatPPORunnerWithSymmetryCfg(AnymalCFlatPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) + + +@configclass +class AnymalCRoughPPORunnerWithSymmetryCfg(AnymalCRoughPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..087eed1e266c6bc2bb3bac3be9498c9100bf64ed --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_c_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1baab1c851bcb1d64ba4b4598366f3087bb0935f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.6 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_c_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..76ccb79b48abe8851c7b391cc02165552211dc82 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import AnymalCRoughEnvCfg + + +@configclass +class AnymalCFlatEnvCfg(AnymalCRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -5.0 + self.rewards.dof_torques_l2.weight = -2.5e-5 + self.rewards.feet_air_time.weight = 0.5 + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class AnymalCFlatEnvCfg_PLAY(AnymalCFlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ed62e06fc9448034c2dfbe32187a72cc61867d15 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/rough_env_cfg.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip + + +@configclass +class AnymalCRoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # switch robot to anymal-c + self.scene.robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + +@configclass +class AnymalCRoughEnvCfg_PLAY(AnymalCRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..41b0398e5206ab4fe99ee83b1992d1bb561098fa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/__init__.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Anymal-D-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalDFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDFlatPPORunnerCfg", + "rsl_rl_distillation_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_distillation_cfg:AnymalDFlatDistillationRunnerCfg" + ), + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDFlatPPORunnerWithSymmetryCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Anymal-D-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalDFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDFlatPPORunnerCfg", + "rsl_rl_distillation_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_distillation_cfg:AnymalDFlatDistillationRunnerCfg" + ), + "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDFlatPPORunnerWithSymmetryCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-D-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalDRoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDRoughPPORunnerWithSymmetryCfg" + ), + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Anymal-D-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalDRoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDRoughPPORunnerCfg", + "rsl_rl_with_symmetry_cfg_entry_point": ( + f"{agents.__name__}.rsl_rl_ppo_cfg:AnymalDRoughPPORunnerWithSymmetryCfg" + ), + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_distillation_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_distillation_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ea3d5f521ac3dd81589dd2d786f9472c238fb432 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_distillation_cfg.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import ( + RslRlDistillationAlgorithmCfg, + RslRlDistillationRunnerCfg, + RslRlDistillationStudentTeacherCfg, +) + + +@configclass +class AnymalDFlatDistillationRunnerCfg(RslRlDistillationRunnerCfg): + num_steps_per_env = 120 + max_iterations = 300 + save_interval = 50 + experiment_name = "anymal_d_flat" + obs_groups = {"policy": ["policy"], "teacher": ["policy"]} + policy = RslRlDistillationStudentTeacherCfg( + init_noise_std=0.1, + noise_std_type="scalar", + student_obs_normalization=False, + teacher_obs_normalization=False, + student_hidden_dims=[128, 128, 128], + teacher_hidden_dims=[128, 128, 128], + activation="elu", + ) + algorithm = RslRlDistillationAlgorithmCfg( + num_learning_epochs=2, + learning_rate=1.0e-3, + gradient_length=15, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..220efdd6e8c4c938a90eb48312c6e839c6b16f90 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,98 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg, RslRlSymmetryCfg + +from isaaclab_tasks.manager_based.locomotion.velocity.mdp.symmetry import anymal + + +@configclass +class AnymalDRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "anymal_d_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class AnymalDFlatPPORunnerCfg(AnymalDRoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "anymal_d_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] + + +@configclass +class AnymalDFlatPPORunnerWithSymmetryCfg(AnymalDFlatPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) + + +@configclass +class AnymalDRoughPPORunnerWithSymmetryCfg(AnymalDRoughPPORunnerCfg): + """Configuration for the PPO agent with symmetry augmentation.""" + + # all the other settings are inherited from the parent class + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + symmetry_cfg=RslRlSymmetryCfg( + use_data_augmentation=True, data_augmentation_func=anymal.compute_symmetric_states + ), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f5510e337706ce576394b5c78e7daf99907e85d6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_d_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a612f624db17131b5f2595f665673a7949152760 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_d_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7abab44fdb9b7062e33a2882608a937562122e55 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import AnymalDRoughEnvCfg + + +@configclass +class AnymalDFlatEnvCfg(AnymalDRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -5.0 + self.rewards.dof_torques_l2.weight = -2.5e-5 + self.rewards.feet_air_time.weight = 0.5 + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class AnymalDFlatEnvCfg_PLAY(AnymalDFlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c672dcacc0ce5589613d5564d3f76924ec64436f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_d/rough_env_cfg.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.anymal import ANYMAL_D_CFG # isort: skip + + +@configclass +class AnymalDRoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # switch robot to anymal-d + self.scene.robot = ANYMAL_D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + +@configclass +class AnymalDRoughEnvCfg_PLAY(AnymalDRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7cb2be15696166b9654d7dec33f5bba59b75d598 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/__init__.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Cassie-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:CassieFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CassieFlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Cassie-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:CassieFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CassieFlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Cassie-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:CassieRoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CassieRoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Cassie-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:CassieRoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CassieRoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..93cce1bb929452697c04dab1c5cec9073b22731a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class CassieRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "cassie_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class CassieFlatPPORunnerCfg(CassieRoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 1000 + self.experiment_name = "cassie_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f55bd81a18cea7d8c3343bcb9b2152b9ad31951 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cassie_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ddd65baaa3ab449915a147b3ca8ff9c52d8cacdb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cassie_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5ca23455cd0e189d34390aadb5574720a71c2d0e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/flat_env_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import CassieRoughEnvCfg + + +@configclass +class CassieFlatEnvCfg(CassieRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # rewards + self.rewards.flat_orientation_l2.weight = -2.5 + self.rewards.feet_air_time.weight = 5.0 + self.rewards.joint_deviation_hip.params["asset_cfg"].joint_names = ["hip_rotation_.*"] + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class CassieFlatEnvCfg_PLAY(CassieFlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2a13f35213caf1383901074c3bd64be8617953cc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/cassie/rough_env_cfg.py @@ -0,0 +1,115 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.cassie import CASSIE_CFG # isort: skip + + +@configclass +class CassieRewardsCfg(RewardsCfg): + termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0) + feet_air_time = RewTerm( + func=mdp.feet_air_time_positive_biped, + weight=2.5, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*toe"), + "command_name": "base_velocity", + "threshold": 0.3, + }, + ) + joint_deviation_hip = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["hip_abduction_.*", "hip_rotation_.*"])}, + ) + joint_deviation_toes = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["toe_joint_.*"])}, + ) + # penalize toe joint limits + dof_pos_limits = RewTerm( + func=mdp.joint_pos_limits, + weight=-1.0, + params={"asset_cfg": SceneEntityCfg("robot", joint_names="toe_joint_.*")}, + ) + + +@configclass +class CassieRoughEnvCfg(LocomotionVelocityRoughEnvCfg): + """Cassie rough environment configuration.""" + + rewards: CassieRewardsCfg = CassieRewardsCfg() + + def __post_init__(self): + super().__post_init__() + # scene + self.scene.robot = CASSIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/pelvis" + + # actions + self.actions.joint_pos.scale = 0.5 + + # events + self.events.push_robot = None + self.events.add_base_mass = None + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.base_external_force_torque.params["asset_cfg"].body_names = [".*pelvis"] + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = [".*pelvis"] + + # rewards + self.rewards.undesired_contacts = None + self.rewards.dof_torques_l2.weight = -5.0e-6 + self.rewards.track_lin_vel_xy_exp.weight = 2.0 + self.rewards.track_ang_vel_z_exp.weight = 1.0 + self.rewards.action_rate_l2.weight *= 1.5 + self.rewards.dof_acc_l2.weight *= 1.5 + + +@configclass +class CassieRoughEnvCfg_PLAY(CassieRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + self.commands.base_velocity.ranges.lin_vel_x = (0.7, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) + self.commands.base_velocity.ranges.heading = (0.0, 0.0) + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8311b225698df548a5c9e9f8f0b7600059b68821 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/__init__.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## +gym.register( + id="Isaac-Velocity-Flat-Digit-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:DigitFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitFlatPPORunnerCfg", + }, +) + + +gym.register( + id="Isaac-Velocity-Flat-Digit-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:DigitFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitFlatPPORunnerCfg", + }, +) + + +gym.register( + id="Isaac-Velocity-Rough-Digit-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:DigitRoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitRoughPPORunnerCfg", + }, +) + + +gym.register( + id="Isaac-Velocity-Rough-Digit-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:DigitRoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DigitRoughPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..72eb4a2aa3ff013612ad5869d07a4d1849fa9b6b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class DigitRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 3000 + save_interval = 50 + experiment_name = "digit_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class DigitFlatPPORunnerCfg(DigitRoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 2000 + self.experiment_name = "digit_flat" + + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..48a647e17a64a07c56098cbeada6e164a0501802 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/flat_env_cfg.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import DigitRoughEnvCfg + + +@configclass +class DigitFlatEnvCfg(DigitRoughEnvCfg): + def __post_init__(self): + super().__post_init__() + + # Change terrain to flat. + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # Remove height scanner. + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # Remove terrain curriculum. + self.curriculum.terrain_levels = None + + +class DigitFlatEnvCfg_PLAY(DigitFlatEnvCfg): + def __post_init__(self) -> None: + super().__post_init__() + + # Make a smaller scene for play. + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # Disable randomization for play. + self.observations.policy.enable_corruption = False + # Remove random pushing. + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..792e6f6394791137204b8a31fc2311e15e6b6b1f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/rough_env_cfg.py @@ -0,0 +1,266 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.managers import ObservationGroupCfg, ObservationTermCfg, RewardTermCfg, SceneEntityCfg, TerminationTermCfg +from isaaclab.utils import configclass +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +from isaaclab_assets.robots.agility import ARM_JOINT_NAMES, DIGIT_V4_CFG, LEG_JOINT_NAMES + + +@configclass +class DigitRewards: + termination_penalty = RewardTermCfg( + func=mdp.is_terminated, + weight=-100.0, + ) + track_lin_vel_xy_exp = RewardTermCfg( + func=mdp.track_lin_vel_xy_yaw_frame_exp, + weight=1.0, + params={"command_name": "base_velocity", "std": math.sqrt(0.25)}, + ) + track_ang_vel_z_exp = RewardTermCfg( + func=mdp.track_ang_vel_z_world_exp, + weight=1.0, + params={ + "command_name": "base_velocity", + "std": math.sqrt(0.25), + }, + ) + feet_air_time = RewardTermCfg( + func=mdp.feet_air_time_positive_biped, + weight=0.25, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_leg_toe_roll"), + "threshold": 0.8, + "command_name": "base_velocity", + }, + ) + feet_slide = RewardTermCfg( + func=mdp.feet_slide, + weight=-0.25, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_leg_toe_roll"), + "asset_cfg": SceneEntityCfg("robot", body_names=".*_leg_toe_roll"), + }, + ) + dof_torques_l2 = RewardTermCfg( + func=mdp.joint_torques_l2, + weight=-1.0e-6, + ) + dof_acc_l2 = RewardTermCfg( + func=mdp.joint_acc_l2, + weight=-2.0e-7, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES)}, + ) + action_rate_l2 = RewardTermCfg( + func=mdp.action_rate_l2, + weight=-0.008, + ) + flat_orientation_l2 = RewardTermCfg( + func=mdp.flat_orientation_l2, + weight=-2.5, + ) + stand_still = RewardTermCfg( + func=mdp.stand_still_joint_deviation_l1, + weight=-0.4, + params={ + "command_name": "base_velocity", + "asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES), + }, + ) + lin_vel_z_l2 = RewardTermCfg( + func=mdp.lin_vel_z_l2, + weight=-2.0, + ) + ang_vel_xy_l2 = RewardTermCfg( + func=mdp.ang_vel_xy_l2, + weight=-0.1, + ) + no_jumps = RewardTermCfg( + func=mdp.desired_contacts, + weight=-0.5, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=[".*_leg_toe_roll"])}, + ) + dof_pos_limits = RewardTermCfg( + func=mdp.joint_pos_limits, + weight=-1.0, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_leg_toe_roll", ".*_leg_toe_pitch", ".*_tarsus"])}, + ) + joint_deviation_hip_roll = RewardTermCfg( + func=mdp.joint_deviation_l1, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_leg_hip_roll")}, + ) + joint_deviation_hip_yaw = RewardTermCfg( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_leg_hip_yaw")}, + ) + joint_deviation_knee = RewardTermCfg( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_tarsus")}, + ) + joint_deviation_feet = RewardTermCfg( + func=mdp.joint_deviation_l1, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_toe_a", ".*_toe_b"])}, + ) + joint_deviation_arms = RewardTermCfg( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*_arm_.*"), + }, + ) + + undesired_contacts = RewardTermCfg( + func=mdp.undesired_contacts, + weight=-0.1, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=[".*_rod", ".*_tarsus"]), + "threshold": 1.0, + }, + ) + + +@configclass +class DigitObservations: + @configclass + class PolicyCfg(ObservationGroupCfg): + base_lin_vel = ObservationTermCfg( + func=mdp.base_lin_vel, + noise=Unoise(n_min=-0.1, n_max=0.1), + ) + base_ang_vel = ObservationTermCfg( + func=mdp.base_ang_vel, + noise=Unoise(n_min=-0.2, n_max=0.2), + ) + projected_gravity = ObservationTermCfg( + func=mdp.projected_gravity, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObservationTermCfg( + func=mdp.generated_commands, + params={"command_name": "base_velocity"}, + ) + joint_pos = ObservationTermCfg( + func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES)}, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + joint_vel = ObservationTermCfg( + func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES)}, + noise=Unoise(n_min=-1.5, n_max=1.5), + ) + actions = ObservationTermCfg(func=mdp.last_action) + height_scan = ObservationTermCfg( + func=mdp.height_scan, + params={"sensor_cfg": SceneEntityCfg("height_scanner")}, + noise=Unoise(n_min=-0.1, n_max=0.1), + clip=(-1.0, 1.0), + ) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # Observation groups: + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = TerminationTermCfg(func=mdp.time_out, time_out=True) + base_contact = TerminationTermCfg( + func=mdp.illegal_contact, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=["torso_base"]), + "threshold": 1.0, + }, + ) + base_orientation = TerminationTermCfg( + func=mdp.bad_orientation, + params={"limit_angle": 0.7}, + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=LEG_JOINT_NAMES + ARM_JOINT_NAMES, + scale=0.5, + use_default_offset=True, + ) + + +@configclass +class DigitRoughEnvCfg(LocomotionVelocityRoughEnvCfg): + rewards: DigitRewards = DigitRewards() + observations: DigitObservations = DigitObservations() + terminations: TerminationsCfg = TerminationsCfg() + actions: ActionsCfg = ActionsCfg() + + def __post_init__(self): + super().__post_init__() + self.decimation = 4 + self.sim.dt = 0.005 + + # Scene + self.scene.robot = DIGIT_V4_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/torso_base" + self.scene.contact_forces.history_length = self.decimation + self.scene.contact_forces.update_period = self.sim.dt + self.scene.height_scanner.update_period = self.decimation * self.sim.dt + + # Events: + self.events.add_base_mass.params["asset_cfg"] = SceneEntityCfg("robot", body_names="torso_base") + self.events.base_external_force_torque.params["asset_cfg"] = SceneEntityCfg("robot", body_names="torso_base") + # Don't randomize the initial joint positions because we have closed loops. + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + # remove COM randomization + self.events.base_com = None + + # Commands + self.commands.base_velocity.ranges.lin_vel_x = (-0.8, 0.8) + self.commands.base_velocity.ranges.lin_vel_y = (-0.5, 0.5) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + self.commands.base_velocity.rel_standing_envs = 0.1 + self.commands.base_velocity.resampling_time_range = (3.0, 8.0) + + +@configclass +class DigitRoughEnvCfg_PLAY(DigitRoughEnvCfg): + def __post_init__(self): + super().__post_init__() + + # Make a smaller scene for play. + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # Spawn the robot randomly in the grid (instead of their terrain levels). + self.scene.terrain.max_init_terrain_level = None + # Reduce the number of terrains to save memory. + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # Disable randomization for play. + self.observations.policy.enable_corruption = False + # Remove random pushing. + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..30861ec5a3a5f9da34f534c3c1847343eea40863 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/__init__.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Rough-G1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:G1RoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:G1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Rough-G1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:G1RoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:G1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Flat-G1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:G1FlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:G1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Flat-G1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:G1FlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:G1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..61a6d0261b9fc209f2dcb039c6df49206efd1b3f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class G1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 3000 + save_interval = 50 + experiment_name = "g1_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.008, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class G1FlatPPORunnerCfg(G1RoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 1500 + self.experiment_name = "g1_flat" + self.policy.actor_hidden_dims = [256, 128, 128] + self.policy.critic_hidden_dims = [256, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..711b7190245e86cacec8067fce9664db7929b861 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.008 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "g1_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b54682b45cd12c8c616f6b0658e1128fd1db0b0e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.008 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "g1_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 72000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e8d3b5edc451a4211def4013f9332f23834f2836 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/flat_env_cfg.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +from .rough_env_cfg import G1RoughEnvCfg + + +@configclass +class G1FlatEnvCfg(G1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + # Rewards + self.rewards.track_ang_vel_z_exp.weight = 1.0 + self.rewards.lin_vel_z_l2.weight = -0.2 + self.rewards.action_rate_l2.weight = -0.005 + self.rewards.dof_acc_l2.weight = -1.0e-7 + self.rewards.feet_air_time.weight = 0.75 + self.rewards.feet_air_time.params["threshold"] = 0.4 + self.rewards.dof_torques_l2.weight = -2.0e-6 + self.rewards.dof_torques_l2.params["asset_cfg"] = SceneEntityCfg( + "robot", joint_names=[".*_hip_.*", ".*_knee_joint"] + ) + # Commands + self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (-0.5, 0.5) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + + +class G1FlatEnvCfg_PLAY(G1FlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..04971c3d9f2066205cc99ddd6e9850f3cfc476fe --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py @@ -0,0 +1,181 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg + +## +# Pre-defined configs +## +from isaaclab_assets import G1_MINIMAL_CFG # isort: skip + + +@configclass +class G1Rewards(RewardsCfg): + """Reward terms for the MDP.""" + + termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0) + track_lin_vel_xy_exp = RewTerm( + func=mdp.track_lin_vel_xy_yaw_frame_exp, + weight=1.0, + params={"command_name": "base_velocity", "std": 0.5}, + ) + track_ang_vel_z_exp = RewTerm( + func=mdp.track_ang_vel_z_world_exp, weight=2.0, params={"command_name": "base_velocity", "std": 0.5} + ) + feet_air_time = RewTerm( + func=mdp.feet_air_time_positive_biped, + weight=0.25, + params={ + "command_name": "base_velocity", + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_ankle_roll_link"), + "threshold": 0.4, + }, + ) + feet_slide = RewTerm( + func=mdp.feet_slide, + weight=-0.1, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_ankle_roll_link"), + "asset_cfg": SceneEntityCfg("robot", body_names=".*_ankle_roll_link"), + }, + ) + + # Penalize ankle joint limits + dof_pos_limits = RewTerm( + func=mdp.joint_pos_limits, + weight=-1.0, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_ankle_pitch_joint", ".*_ankle_roll_joint"])}, + ) + # Penalize deviation from default of the joints that are not essential for locomotion + joint_deviation_hip = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_hip_yaw_joint", ".*_hip_roll_joint"])}, + ) + joint_deviation_arms = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.1, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_pitch_joint", + ".*_elbow_roll_joint", + ], + ) + }, + ) + joint_deviation_fingers = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.05, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + ".*_five_joint", + ".*_three_joint", + ".*_six_joint", + ".*_four_joint", + ".*_zero_joint", + ".*_one_joint", + ".*_two_joint", + ], + ) + }, + ) + joint_deviation_torso = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", joint_names="torso_joint")}, + ) + + +@configclass +class G1RoughEnvCfg(LocomotionVelocityRoughEnvCfg): + rewards: G1Rewards = G1Rewards() + + def __post_init__(self): + # post init of parent + super().__post_init__() + # Scene + self.scene.robot = G1_MINIMAL_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/torso_link" + + # Randomization + self.events.push_robot = None + self.events.add_base_mass = None + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.base_external_force_torque.params["asset_cfg"].body_names = ["torso_link"] + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # Rewards + self.rewards.lin_vel_z_l2.weight = 0.0 + self.rewards.undesired_contacts = None + self.rewards.flat_orientation_l2.weight = -1.0 + self.rewards.action_rate_l2.weight = -0.005 + self.rewards.dof_acc_l2.weight = -1.25e-7 + self.rewards.dof_acc_l2.params["asset_cfg"] = SceneEntityCfg( + "robot", joint_names=[".*_hip_.*", ".*_knee_joint"] + ) + self.rewards.dof_torques_l2.weight = -1.5e-7 + self.rewards.dof_torques_l2.params["asset_cfg"] = SceneEntityCfg( + "robot", joint_names=[".*_hip_.*", ".*_knee_joint", ".*_ankle_.*"] + ) + + # Commands + self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (-0.0, 0.0) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + + # terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = "torso_link" + + +@configclass +class G1RoughEnvCfg_PLAY(G1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + self.episode_length_s = 40.0 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + self.commands.base_velocity.ranges.lin_vel_x = (1.0, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + self.commands.base_velocity.ranges.heading = (0.0, 0.0) + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..def24b8e14421781108a4e42ba977c7cb04b2ee4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/__init__.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Unitree-Go1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeGo1FlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Unitree-Go1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeGo1FlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-Go1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeGo1RoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-Go1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeGo1RoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9baa2b371ea39e9c23fb65137f60b80f512f7e80 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class UnitreeGo1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "unitree_go1_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class UnitreeGo1FlatPPORunnerCfg(UnitreeGo1RoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "unitree_go1_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d125c913446f53b294a77a41886ba5179c6a2b91 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_go1_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..47888d623e910f705a31dea61ce890a333a89012 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_go1_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..760c1f5f5d073d07aa4c7e2cef47638268fda6c7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import UnitreeGo1RoughEnvCfg + + +@configclass +class UnitreeGo1FlatEnvCfg(UnitreeGo1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -2.5 + self.rewards.feet_air_time.weight = 0.25 + + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class UnitreeGo1FlatEnvCfg_PLAY(UnitreeGo1FlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..91efcc17024511b57b447a05e4458f42edb09fe2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go1/rough_env_cfg.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.unitree import UNITREE_GO1_CFG # isort: skip + + +@configclass +class UnitreeGo1RoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.robot = UNITREE_GO1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/trunk" + # scale down the terrains because the robot is small + self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01 + + # reduce action scale + self.actions.joint_pos.scale = 0.25 + + # event + self.events.push_robot = None + self.events.add_base_mass.params["mass_distribution_params"] = (-1.0, 3.0) + self.events.add_base_mass.params["asset_cfg"].body_names = "trunk" + self.events.base_external_force_torque.params["asset_cfg"].body_names = "trunk" + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # rewards + self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot" + self.rewards.feet_air_time.weight = 0.01 + self.rewards.undesired_contacts = None + self.rewards.dof_torques_l2.weight = -0.0002 + self.rewards.track_lin_vel_xy_exp.weight = 1.5 + self.rewards.track_ang_vel_z_exp.weight = 0.75 + self.rewards.dof_acc_l2.weight = -2.5e-7 + + # terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = "trunk" + + +@configclass +class UnitreeGo1RoughEnvCfg_PLAY(UnitreeGo1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4ea7d3fce71864c71abf0b557aa5c4c72b649942 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/__init__.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Unitree-Go2-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeGo2FlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo2FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Unitree-Go2-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeGo2FlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo2FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-Go2-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeGo2RoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo2RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Rough-Unitree-Go2-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeGo2RoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeGo2RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9777785f7e300aaaba4b13d959706544aa5bb651 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class UnitreeGo2RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "unitree_go2_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class UnitreeGo2FlatPPORunnerCfg(UnitreeGo2RoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 300 + self.experiment_name = "unitree_go2_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e36d3a5748675ec49a953926a4591e301bce7d67 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_go2_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 7200 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c89ca249f01ab42dfbaac9faf7684df2f0b1275 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "unitree_go2_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8bf8bb1373f90facb178be4e929af0fbf2ee2e59 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/flat_env_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import UnitreeGo2RoughEnvCfg + + +@configclass +class UnitreeGo2FlatEnvCfg(UnitreeGo2RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # override rewards + self.rewards.flat_orientation_l2.weight = -2.5 + self.rewards.feet_air_time.weight = 0.25 + + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + + +class UnitreeGo2FlatEnvCfg_PLAY(UnitreeGo2FlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..69e6adddd04248baf50d267dba40c3c07d97f4fc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/go2/rough_env_cfg.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.unitree import UNITREE_GO2_CFG # isort: skip + + +@configclass +class UnitreeGo2RoughEnvCfg(LocomotionVelocityRoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.robot = UNITREE_GO2_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/base" + # scale down the terrains because the robot is small + self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06) + self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01 + + # reduce action scale + self.actions.joint_pos.scale = 0.25 + + # event + self.events.push_robot = None + self.events.add_base_mass.params["mass_distribution_params"] = (-1.0, 3.0) + self.events.add_base_mass.params["asset_cfg"].body_names = "base" + self.events.base_external_force_torque.params["asset_cfg"].body_names = "base" + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # rewards + self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot" + self.rewards.feet_air_time.weight = 0.01 + self.rewards.undesired_contacts = None + self.rewards.dof_torques_l2.weight = -0.0002 + self.rewards.track_lin_vel_xy_exp.weight = 1.5 + self.rewards.track_ang_vel_z_exp.weight = 0.75 + self.rewards.dof_acc_l2.weight = -2.5e-7 + + # terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = "base" + + +@configclass +class UnitreeGo2RoughEnvCfg_PLAY(UnitreeGo2RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6a218243e3716bcd00ae825b54a90cbc3be2a96f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/__init__.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Rough-H1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:H1RoughEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:H1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Rough-H1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.rough_env_cfg:H1RoughEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:H1RoughPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_rough_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Flat-H1-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:H1FlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:H1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + + +gym.register( + id="Isaac-Velocity-Flat-H1-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:H1FlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:H1FlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..102359770864f121e87242a4731f513303d7ca1d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class H1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 3000 + save_interval = 50 + experiment_name = "h1_rough" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class H1FlatPPORunnerCfg(H1RoughPPORunnerCfg): + def __post_init__(self): + super().__post_init__() + + self.max_iterations = 1000 + self.experiment_name = "h1_flat" + self.policy.actor_hidden_dims = [128, 128, 128] + self.policy.critic_hidden_dims = [128, 128, 128] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed1dbeb89d1165b5914e24434de363b8c0e98072 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "h1_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_rough_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_rough_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c5f49d24efdb6dceeff56561ea67510683674d5d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/agents/skrl_rough_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.995 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "h1_rough" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 72000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e9b9e2a1fa2708e61f90d63e00e657481f5de815 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/flat_env_cfg.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .rough_env_cfg import H1RoughEnvCfg + + +@configclass +class H1FlatEnvCfg(H1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # change terrain to flat + self.scene.terrain.terrain_type = "plane" + self.scene.terrain.terrain_generator = None + # no height scan + self.scene.height_scanner = None + self.observations.policy.height_scan = None + # no terrain curriculum + self.curriculum.terrain_levels = None + self.rewards.feet_air_time.weight = 1.0 + self.rewards.feet_air_time.params["threshold"] = 0.6 + + +class H1FlatEnvCfg_PLAY(H1FlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/rough_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/rough_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..799a7b95cc402c5bd427924451b20b17d68cdf17 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/h1/rough_env_cfg.py @@ -0,0 +1,142 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg + +## +# Pre-defined configs +## +from isaaclab_assets import H1_MINIMAL_CFG # isort: skip + + +@configclass +class H1Rewards(RewardsCfg): + """Reward terms for the MDP.""" + + termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0) + lin_vel_z_l2 = None + track_lin_vel_xy_exp = RewTerm( + func=mdp.track_lin_vel_xy_yaw_frame_exp, + weight=1.0, + params={"command_name": "base_velocity", "std": 0.5}, + ) + track_ang_vel_z_exp = RewTerm( + func=mdp.track_ang_vel_z_world_exp, weight=1.0, params={"command_name": "base_velocity", "std": 0.5} + ) + feet_air_time = RewTerm( + func=mdp.feet_air_time_positive_biped, + weight=0.25, + params={ + "command_name": "base_velocity", + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*ankle_link"), + "threshold": 0.4, + }, + ) + feet_slide = RewTerm( + func=mdp.feet_slide, + weight=-0.25, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*ankle_link"), + "asset_cfg": SceneEntityCfg("robot", body_names=".*ankle_link"), + }, + ) + # Penalize ankle joint limits + dof_pos_limits = RewTerm( + func=mdp.joint_pos_limits, weight=-1.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_ankle")} + ) + # Penalize deviation from default of the joints that are not essential for locomotion + joint_deviation_hip = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_hip_yaw", ".*_hip_roll"])}, + ) + joint_deviation_arms = RewTerm( + func=mdp.joint_deviation_l1, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*_shoulder_.*", ".*_elbow"])}, + ) + joint_deviation_torso = RewTerm( + func=mdp.joint_deviation_l1, weight=-0.1, params={"asset_cfg": SceneEntityCfg("robot", joint_names="torso")} + ) + + +@configclass +class H1RoughEnvCfg(LocomotionVelocityRoughEnvCfg): + rewards: H1Rewards = H1Rewards() + + def __post_init__(self): + # post init of parent + super().__post_init__() + # Scene + self.scene.robot = H1_MINIMAL_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + if self.scene.height_scanner: + self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/torso_link" + + # Randomization + self.events.push_robot = None + self.events.add_base_mass = None + self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) + self.events.base_external_force_torque.params["asset_cfg"].body_names = [".*torso_link"] + self.events.reset_base.params = { + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.0, 0.0), + "roll": (0.0, 0.0), + "pitch": (0.0, 0.0), + "yaw": (0.0, 0.0), + }, + } + self.events.base_com = None + + # Rewards + self.rewards.undesired_contacts = None + self.rewards.flat_orientation_l2.weight = -1.0 + self.rewards.dof_torques_l2.weight = 0.0 + self.rewards.action_rate_l2.weight = -0.005 + self.rewards.dof_acc_l2.weight = -1.25e-7 + + # Commands + self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + + # Terminations + self.terminations.base_contact.params["sensor_cfg"].body_names = ".*torso_link" + + +@configclass +class H1RoughEnvCfg_PLAY(H1RoughEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + self.episode_length_s = 40.0 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + self.commands.base_velocity.ranges.lin_vel_x = (1.0, 1.0) + self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) + self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) + self.commands.base_velocity.ranges.heading = (0.0, 0.0) + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing + self.events.base_external_force_torque = None + self.events.push_robot = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/README.md b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/README.md new file mode 100644 index 0000000000000000000000000000000000000000..eec0d4431094d5dc819733fe464b68c21db913f0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/README.md @@ -0,0 +1,9 @@ +# Acknowledgment + +We would like to acknowledge [The AI Institute](https://theaiinstitute.com/)'s efforts in developing +the Spot RL environment from the specifications provided by Boston Dynamics. +The team at The AI Institute trained, verified, and deployed the resulting policy on the Spot hardware. +They demonstrated its capability and reliability out in the real world. + +The accompanying deployment code and access to Spot's low-level API is available with the [Spot RL +Researcher Kit](https://bostondynamics.com/reinforcement-learning-researcher-kit/). diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..28572a7dfa5d74005825c61213ca404a7018aefb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/__init__.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Velocity-Flat-Spot-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:SpotFlatEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:SpotFlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Velocity-Flat-Spot-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.flat_env_cfg:SpotFlatEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:SpotFlatPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..3985f6b3b4911bce0a01a971fbe0eaecadfcfc10 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class SpotFlatPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 20000 + save_interval = 50 + experiment_name = "spot_flat" + store_code_state = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=0.5, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0025, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dcbf8926268b25d3b73b87166a205b9161a14044 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0025 + value_loss_scale: 0.5 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "spot_flat" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 480000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/flat_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/flat_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6bf334e24536e15ba44b47232718cc53c6e0fd01 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/flat_env_cfg.py @@ -0,0 +1,378 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +import isaaclab.terrains as terrain_gen +from isaaclab.envs import ViewerCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg, SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.locomotion.velocity.config.spot.mdp as spot_mdp +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.spot import SPOT_CFG # isort: skip + + +COBBLESTONE_ROAD_CFG = terrain_gen.TerrainGeneratorCfg( + size=(8.0, 8.0), + border_width=20.0, + num_rows=9, + num_cols=21, + horizontal_scale=0.1, + vertical_scale=0.005, + slope_threshold=0.75, + difficulty_range=(0.0, 1.0), + use_cache=False, + sub_terrains={ + "flat": terrain_gen.MeshPlaneTerrainCfg(proportion=0.2), + "random_rough": terrain_gen.HfRandomUniformTerrainCfg( + proportion=0.2, noise_range=(0.02, 0.05), noise_step=0.02, border_width=0.25 + ), + }, +) + + +@configclass +class SpotActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.2, use_default_offset=True) + + +@configclass +class SpotCommandsCfg: + """Command specifications for the MDP.""" + + base_velocity = mdp.UniformVelocityCommandCfg( + asset_name="robot", + resampling_time_range=(10.0, 10.0), + rel_standing_envs=0.1, + rel_heading_envs=0.0, + heading_command=False, + debug_vis=True, + ranges=mdp.UniformVelocityCommandCfg.Ranges( + lin_vel_x=(-2.0, 3.0), lin_vel_y=(-1.5, 1.5), ang_vel_z=(-2.0, 2.0) + ), + ) + + +@configclass +class SpotObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # `` observation terms (order preserved) + base_lin_vel = ObsTerm( + func=mdp.base_lin_vel, params={"asset_cfg": SceneEntityCfg("robot")}, noise=Unoise(n_min=-0.1, n_max=0.1) + ) + base_ang_vel = ObsTerm( + func=mdp.base_ang_vel, params={"asset_cfg": SceneEntityCfg("robot")}, noise=Unoise(n_min=-0.1, n_max=0.1) + ) + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + params={"asset_cfg": SceneEntityCfg("robot")}, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObsTerm(func=mdp.generated_commands, params={"command_name": "base_velocity"}) + joint_pos = ObsTerm( + func=mdp.joint_pos_rel, params={"asset_cfg": SceneEntityCfg("robot")}, noise=Unoise(n_min=-0.05, n_max=0.05) + ) + joint_vel = ObsTerm( + func=mdp.joint_vel_rel, params={"asset_cfg": SceneEntityCfg("robot")}, noise=Unoise(n_min=-0.5, n_max=0.5) + ) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class SpotEventCfg: + """Configuration for randomization.""" + + # startup + physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.3, 1.0), + "dynamic_friction_range": (0.3, 0.8), + "restitution_range": (0.0, 0.0), + "num_buckets": 64, + }, + ) + + add_base_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="body"), + "mass_distribution_params": (-2.5, 2.5), + "operation": "add", + }, + ) + + # reset + base_external_force_torque = EventTerm( + func=mdp.apply_external_force_torque, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="body"), + "force_range": (0.0, 0.0), + "torque_range": (-0.0, 0.0), + }, + ) + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot"), + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-1.5, 1.5), + "y": (-1.0, 1.0), + "z": (-0.5, 0.5), + "roll": (-0.7, 0.7), + "pitch": (-0.7, 0.7), + "yaw": (-1.0, 1.0), + }, + }, + ) + + reset_robot_joints = EventTerm( + func=spot_mdp.reset_joints_around_default, + mode="reset", + params={ + "position_range": (-0.2, 0.2), + "velocity_range": (-2.5, 2.5), + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + # interval + push_robot = EventTerm( + func=mdp.push_by_setting_velocity, + mode="interval", + interval_range_s=(10.0, 15.0), + params={ + "asset_cfg": SceneEntityCfg("robot"), + "velocity_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5)}, + }, + ) + + +@configclass +class SpotRewardsCfg: + # -- task + air_time = RewardTermCfg( + func=spot_mdp.air_time_reward, + weight=5.0, + params={ + "mode_time": 0.3, + "velocity_threshold": 0.5, + "asset_cfg": SceneEntityCfg("robot"), + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_foot"), + }, + ) + base_angular_velocity = RewardTermCfg( + func=spot_mdp.base_angular_velocity_reward, + weight=5.0, + params={"std": 2.0, "asset_cfg": SceneEntityCfg("robot")}, + ) + base_linear_velocity = RewardTermCfg( + func=spot_mdp.base_linear_velocity_reward, + weight=5.0, + params={"std": 1.0, "ramp_rate": 0.5, "ramp_at_vel": 1.0, "asset_cfg": SceneEntityCfg("robot")}, + ) + foot_clearance = RewardTermCfg( + func=spot_mdp.foot_clearance_reward, + weight=0.5, + params={ + "std": 0.05, + "tanh_mult": 2.0, + "target_height": 0.1, + "asset_cfg": SceneEntityCfg("robot", body_names=".*_foot"), + }, + ) + gait = RewardTermCfg( + func=spot_mdp.GaitReward, + weight=10.0, + params={ + "std": 0.1, + "max_err": 0.2, + "velocity_threshold": 0.5, + "synced_feet_pair_names": (("fl_foot", "hr_foot"), ("fr_foot", "hl_foot")), + "asset_cfg": SceneEntityCfg("robot"), + "sensor_cfg": SceneEntityCfg("contact_forces"), + }, + ) + + # -- penalties + action_smoothness = RewardTermCfg(func=spot_mdp.action_smoothness_penalty, weight=-1.0) + air_time_variance = RewardTermCfg( + func=spot_mdp.air_time_variance_penalty, + weight=-1.0, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_foot")}, + ) + base_motion = RewardTermCfg( + func=spot_mdp.base_motion_penalty, weight=-2.0, params={"asset_cfg": SceneEntityCfg("robot")} + ) + base_orientation = RewardTermCfg( + func=spot_mdp.base_orientation_penalty, weight=-3.0, params={"asset_cfg": SceneEntityCfg("robot")} + ) + foot_slip = RewardTermCfg( + func=spot_mdp.foot_slip_penalty, + weight=-0.5, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*_foot"), + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_foot"), + "threshold": 1.0, + }, + ) + joint_acc = RewardTermCfg( + func=spot_mdp.joint_acceleration_penalty, + weight=-1.0e-4, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_h[xy]")}, + ) + joint_pos = RewardTermCfg( + func=spot_mdp.joint_position_penalty, + weight=-0.7, + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stand_still_scale": 5.0, + "velocity_threshold": 0.5, + }, + ) + joint_torques = RewardTermCfg( + func=spot_mdp.joint_torques_penalty, + weight=-5.0e-4, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")}, + ) + joint_vel = RewardTermCfg( + func=spot_mdp.joint_velocity_penalty, + weight=-1.0e-2, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*_h[xy]")}, + ) + + +@configclass +class SpotTerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + body_contact = DoneTerm( + func=mdp.illegal_contact, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=["body", ".*leg"]), "threshold": 1.0}, + ) + terrain_out_of_bounds = DoneTerm( + func=mdp.terrain_out_of_bounds, + params={"asset_cfg": SceneEntityCfg("robot"), "distance_buffer": 3.0}, + time_out=True, + ) + + +@configclass +class SpotFlatEnvCfg(LocomotionVelocityRoughEnvCfg): + """Configuration for the Spot robot in a flat environment.""" + + # Basic settings + observations: SpotObservationsCfg = SpotObservationsCfg() + actions: SpotActionsCfg = SpotActionsCfg() + commands: SpotCommandsCfg = SpotCommandsCfg() + + # MDP setting + rewards: SpotRewardsCfg = SpotRewardsCfg() + terminations: SpotTerminationsCfg = SpotTerminationsCfg() + events: SpotEventCfg = SpotEventCfg() + + # Viewer + viewer = ViewerCfg(eye=(10.5, 10.5, 0.3), origin_type="world", env_index=0, asset_name="robot") + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # general settings + self.decimation = 10 # 50 Hz + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 0.002 # 500 Hz + self.sim.render_interval = self.decimation + self.sim.physics_material.static_friction = 1.0 + self.sim.physics_material.dynamic_friction = 1.0 + self.sim.physics_material.friction_combine_mode = "multiply" + self.sim.physics_material.restitution_combine_mode = "multiply" + # update sensor update periods + # we tick all the sensors based on the smallest update period (physics update period) + self.scene.contact_forces.update_period = self.sim.dt + + # switch robot to Spot-d + self.scene.robot = SPOT_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # terrain + self.scene.terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=COBBLESTONE_ROAD_CFG, + max_init_terrain_level=COBBLESTONE_ROAD_CFG.num_rows - 1, + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + visual_material=sim_utils.MdlFileCfg( + mdl_path=f"{ISAACLAB_NUCLEUS_DIR}/Materials/TilesMarbleSpiderWhiteBrickBondHoned/TilesMarbleSpiderWhiteBrickBondHoned.mdl", + project_uvw=True, + texture_scale=(0.25, 0.25), + ), + debug_vis=True, + ) + + # no height scan + self.scene.height_scanner = None + + +class SpotFlatEnvCfg_PLAY(SpotFlatEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # spawn the robot randomly in the grid (instead of their terrain levels) + self.scene.terrain.max_init_terrain_level = None + + # reduce the number of terrains to save memory + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.num_rows = 5 + self.scene.terrain.terrain_generator.num_cols = 5 + self.scene.terrain.terrain_generator.curriculum = False + + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove random pushing event diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf460b5f33fe444c046ba90ab05ffed6db02fcbb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""This sub-module contains the functions that are specific to the Spot locomotion task.""" + +from .events import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/events.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a47934d95e9f8696987a5daeafcf1570f453b3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/events.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""This sub-module contains the functions that can be used to enable Spot randomizations. + +The functions can be passed to the :class:`isaaclab.managers.EventTermCfg` object to enable +the randomization introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import sample_uniform + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def reset_joints_around_default( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + position_range: tuple[float, float], + velocity_range: tuple[float, float], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """Reset the robot joints in the interval around the default position and velocity by the given ranges. + + This function samples random values from the given ranges around the default joint positions and velocities. + The ranges are clipped to fit inside the soft joint limits. The sampled values are then set into the physics + simulation. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # get default joint state + joint_min_pos = asset.data.default_joint_pos[env_ids] + position_range[0] + joint_max_pos = asset.data.default_joint_pos[env_ids] + position_range[1] + joint_min_vel = asset.data.default_joint_vel[env_ids] + velocity_range[0] + joint_max_vel = asset.data.default_joint_vel[env_ids] + velocity_range[1] + # clip pos to range + joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids, ...] + joint_min_pos = torch.clamp(joint_min_pos, min=joint_pos_limits[..., 0], max=joint_pos_limits[..., 1]) + joint_max_pos = torch.clamp(joint_max_pos, min=joint_pos_limits[..., 0], max=joint_pos_limits[..., 1]) + # clip vel to range + joint_vel_abs_limits = asset.data.soft_joint_vel_limits[env_ids] + joint_min_vel = torch.clamp(joint_min_vel, min=-joint_vel_abs_limits, max=joint_vel_abs_limits) + joint_max_vel = torch.clamp(joint_max_vel, min=-joint_vel_abs_limits, max=joint_vel_abs_limits) + # sample these values randomly + joint_pos = sample_uniform(joint_min_pos, joint_max_pos, joint_min_pos.shape, joint_min_pos.device) + joint_vel = sample_uniform(joint_min_vel, joint_max_vel, joint_min_vel.shape, joint_min_vel.device) + # set into the physics simulation + asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..05680e43735543e4b1ba5a25c0db1c1d4dc1ff05 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/spot/mdp/rewards.py @@ -0,0 +1,284 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the reward functions that can be used for Spot's locomotion task. + +The functions can be passed to the :class:`isaaclab.managers.RewardTermCfg` object to +specify the reward function and its parameters. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import ManagerTermBase, SceneEntityCfg +from isaaclab.sensors import ContactSensor + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + from isaaclab.managers import RewardTermCfg + + +## +# Task Rewards +## + + +def air_time_reward( + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg, + sensor_cfg: SceneEntityCfg, + mode_time: float, + velocity_threshold: float, +) -> torch.Tensor: + """Reward longer feet air and contact time.""" + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + asset: Articulation = env.scene[asset_cfg.name] + if contact_sensor.cfg.track_air_time is False: + raise RuntimeError("Activate ContactSensor's track_air_time!") + # compute the reward + current_air_time = contact_sensor.data.current_air_time[:, sensor_cfg.body_ids] + current_contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids] + + t_max = torch.max(current_air_time, current_contact_time) + t_min = torch.clip(t_max, max=mode_time) + stance_cmd_reward = torch.clip(current_contact_time - current_air_time, -mode_time, mode_time) + cmd = torch.norm(env.command_manager.get_command("base_velocity"), dim=1).unsqueeze(dim=1).expand(-1, 4) + body_vel = torch.linalg.norm(asset.data.root_lin_vel_b[:, :2], dim=1).unsqueeze(dim=1).expand(-1, 4) + reward = torch.where( + torch.logical_or(cmd > 0.0, body_vel > velocity_threshold), + torch.where(t_max < mode_time, t_min, 0), + stance_cmd_reward, + ) + return torch.sum(reward, dim=1) + + +def base_angular_velocity_reward(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, std: float) -> torch.Tensor: + """Reward tracking of angular velocity commands (yaw) using abs exponential kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + # compute the error + target = env.command_manager.get_command("base_velocity")[:, 2] + ang_vel_error = torch.linalg.norm((target - asset.data.root_ang_vel_b[:, 2]).unsqueeze(1), dim=1) + return torch.exp(-ang_vel_error / std) + + +def base_linear_velocity_reward( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, std: float, ramp_at_vel: float = 1.0, ramp_rate: float = 0.5 +) -> torch.Tensor: + """Reward tracking of linear velocity commands (xy axes) using abs exponential kernel.""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + # compute the error + target = env.command_manager.get_command("base_velocity")[:, :2] + lin_vel_error = torch.linalg.norm((target - asset.data.root_lin_vel_b[:, :2]), dim=1) + # fixed 1.0 multiple for tracking below the ramp_at_vel value, then scale by the rate above + vel_cmd_magnitude = torch.linalg.norm(target, dim=1) + velocity_scaling_multiple = torch.clamp(1.0 + ramp_rate * (vel_cmd_magnitude - ramp_at_vel), min=1.0) + return torch.exp(-lin_vel_error / std) * velocity_scaling_multiple + + +class GaitReward(ManagerTermBase): + """Gait enforcing reward term for quadrupeds. + + This reward penalizes contact timing differences between selected foot pairs defined in + :attr:`synced_feet_pair_names` to bias the policy towards a desired gait, i.e trotting, + bounding, or pacing. Note that this reward is only for quadrupedal gaits with two pairs + of synchronized feet. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the term. + + Args: + cfg: The configuration of the reward. + env: The RL environment instance. + """ + super().__init__(cfg, env) + self.std: float = cfg.params["std"] + self.max_err: float = cfg.params["max_err"] + self.velocity_threshold: float = cfg.params["velocity_threshold"] + self.contact_sensor: ContactSensor = env.scene.sensors[cfg.params["sensor_cfg"].name] + self.asset: Articulation = env.scene[cfg.params["asset_cfg"].name] + # match foot body names with corresponding foot body ids + synced_feet_pair_names = cfg.params["synced_feet_pair_names"] + if ( + len(synced_feet_pair_names) != 2 + or len(synced_feet_pair_names[0]) != 2 + or len(synced_feet_pair_names[1]) != 2 + ): + raise ValueError("This reward only supports gaits with two pairs of synchronized feet, like trotting.") + synced_feet_pair_0 = self.contact_sensor.find_bodies(synced_feet_pair_names[0])[0] + synced_feet_pair_1 = self.contact_sensor.find_bodies(synced_feet_pair_names[1])[0] + self.synced_feet_pairs = [synced_feet_pair_0, synced_feet_pair_1] + + def __call__( + self, + env: ManagerBasedRLEnv, + std: float, + max_err: float, + velocity_threshold: float, + synced_feet_pair_names, + asset_cfg: SceneEntityCfg, + sensor_cfg: SceneEntityCfg, + ) -> torch.Tensor: + """Compute the reward. + + This reward is defined as a multiplication between six terms where two of them enforce pair feet + being in sync and the other four rewards if all the other remaining pairs are out of sync + + Args: + env: The RL environment instance. + Returns: + The reward value. + """ + # for synchronous feet, the contact (air) times of two feet should match + sync_reward_0 = self._sync_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[0][1]) + sync_reward_1 = self._sync_reward_func(self.synced_feet_pairs[1][0], self.synced_feet_pairs[1][1]) + sync_reward = sync_reward_0 * sync_reward_1 + # for asynchronous feet, the contact time of one foot should match the air time of the other one + async_reward_0 = self._async_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[1][0]) + async_reward_1 = self._async_reward_func(self.synced_feet_pairs[0][1], self.synced_feet_pairs[1][1]) + async_reward_2 = self._async_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[1][1]) + async_reward_3 = self._async_reward_func(self.synced_feet_pairs[1][0], self.synced_feet_pairs[0][1]) + async_reward = async_reward_0 * async_reward_1 * async_reward_2 * async_reward_3 + # only enforce gait if cmd > 0 + cmd = torch.norm(env.command_manager.get_command("base_velocity"), dim=1) + body_vel = torch.linalg.norm(self.asset.data.root_lin_vel_b[:, :2], dim=1) + return torch.where( + torch.logical_or(cmd > 0.0, body_vel > self.velocity_threshold), sync_reward * async_reward, 0.0 + ) + + """ + Helper functions. + """ + + def _sync_reward_func(self, foot_0: int, foot_1: int) -> torch.Tensor: + """Reward synchronization of two feet.""" + air_time = self.contact_sensor.data.current_air_time + contact_time = self.contact_sensor.data.current_contact_time + # penalize the difference between the most recent air time and contact time of synced feet pairs. + se_air = torch.clip(torch.square(air_time[:, foot_0] - air_time[:, foot_1]), max=self.max_err**2) + se_contact = torch.clip(torch.square(contact_time[:, foot_0] - contact_time[:, foot_1]), max=self.max_err**2) + return torch.exp(-(se_air + se_contact) / self.std) + + def _async_reward_func(self, foot_0: int, foot_1: int) -> torch.Tensor: + """Reward anti-synchronization of two feet.""" + air_time = self.contact_sensor.data.current_air_time + contact_time = self.contact_sensor.data.current_contact_time + # penalize the difference between opposing contact modes air time of feet 1 to contact time of feet 2 + # and contact time of feet 1 to air time of feet 2) of feet pairs that are not in sync with each other. + se_act_0 = torch.clip(torch.square(air_time[:, foot_0] - contact_time[:, foot_1]), max=self.max_err**2) + se_act_1 = torch.clip(torch.square(contact_time[:, foot_0] - air_time[:, foot_1]), max=self.max_err**2) + return torch.exp(-(se_act_0 + se_act_1) / self.std) + + +def foot_clearance_reward( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, target_height: float, std: float, tanh_mult: float +) -> torch.Tensor: + """Reward the swinging feet for clearing a specified height off the ground""" + asset: RigidObject = env.scene[asset_cfg.name] + foot_z_target_error = torch.square(asset.data.body_pos_w[:, asset_cfg.body_ids, 2] - target_height) + foot_velocity_tanh = torch.tanh(tanh_mult * torch.norm(asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2], dim=2)) + reward = foot_z_target_error * foot_velocity_tanh + return torch.exp(-torch.sum(reward, dim=1) / std) + + +## +# Regularization Penalties +## + + +def action_smoothness_penalty(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize large instantaneous changes in the network action output""" + return torch.linalg.norm((env.action_manager.action - env.action_manager.prev_action), dim=1) + + +def air_time_variance_penalty(env: ManagerBasedRLEnv, sensor_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize variance in the amount of time each foot spends in the air/on the ground relative to each other""" + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + if contact_sensor.cfg.track_air_time is False: + raise RuntimeError("Activate ContactSensor's track_air_time!") + # compute the reward + last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids] + last_contact_time = contact_sensor.data.last_contact_time[:, sensor_cfg.body_ids] + return torch.var(torch.clip(last_air_time, max=0.5), dim=1) + torch.var( + torch.clip(last_contact_time, max=0.5), dim=1 + ) + + +# ! look into simplifying the kernel here; it's a little oddly complex +def base_motion_penalty(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize base vertical and roll/pitch velocity""" + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return 0.8 * torch.square(asset.data.root_lin_vel_b[:, 2]) + 0.2 * torch.sum( + torch.abs(asset.data.root_ang_vel_b[:, :2]), dim=1 + ) + + +def base_orientation_penalty(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize non-flat base orientation + + This is computed by penalizing the xy-components of the projected gravity vector. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + return torch.linalg.norm((asset.data.projected_gravity_b[:, :2]), dim=1) + + +def foot_slip_penalty( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, sensor_cfg: SceneEntityCfg, threshold: float +) -> torch.Tensor: + """Penalize foot planar (xy) slip when in contact with the ground""" + asset: RigidObject = env.scene[asset_cfg.name] + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + + # check if contact force is above threshold + net_contact_forces = contact_sensor.data.net_forces_w_history + is_contact = torch.max(torch.norm(net_contact_forces[:, :, sensor_cfg.body_ids], dim=-1), dim=1)[0] > threshold + foot_planar_velocity = torch.linalg.norm(asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2], dim=2) + + reward = is_contact * foot_planar_velocity + return torch.sum(reward, dim=1) + + +def joint_acceleration_penalty(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint accelerations on the articulation.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.linalg.norm((asset.data.joint_acc), dim=1) + + +def joint_position_penalty( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, stand_still_scale: float, velocity_threshold: float +) -> torch.Tensor: + """Penalize joint position error from default on the articulation.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + cmd = torch.linalg.norm(env.command_manager.get_command("base_velocity"), dim=1) + body_vel = torch.linalg.norm(asset.data.root_lin_vel_b[:, :2], dim=1) + reward = torch.linalg.norm((asset.data.joint_pos - asset.data.default_joint_pos), dim=1) + return torch.where(torch.logical_or(cmd > 0.0, body_vel > velocity_threshold), reward, stand_still_scale * reward) + + +def joint_torques_penalty(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint torques on the articulation.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.linalg.norm((asset.data.applied_torque), dim=1) + + +def joint_velocity_penalty(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint velocities on the articulation.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + return torch.linalg.norm((asset.data.joint_vel), dim=1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6cad007128c1e94e83bd5b04902e7d3d3de77b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the locomotion environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .curriculums import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/curriculums.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/curriculums.py new file mode 100644 index 0000000000000000000000000000000000000000..88187a6b816b3155f3c54890a5cf6e8d97c42e91 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/curriculums.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to create curriculum for the learning environment. + +The functions can be passed to the :class:`isaaclab.managers.CurriculumTermCfg` object to enable +the curriculum introduced by the function. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg +from isaaclab.terrains import TerrainImporter + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def terrain_levels_vel( + env: ManagerBasedRLEnv, env_ids: Sequence[int], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Curriculum based on the distance the robot walked when commanded to move at a desired velocity. + + This term is used to increase the difficulty of the terrain when the robot walks far enough and decrease the + difficulty when the robot walks less than half of the distance required by the commanded velocity. + + .. note:: + It is only possible to use this term with the terrain type ``generator``. For further information + on different terrain types, check the :class:`isaaclab.terrains.TerrainImporter` class. + + Returns: + The mean terrain level for the given environment ids. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + terrain: TerrainImporter = env.scene.terrain + command = env.command_manager.get_command("base_velocity") + # compute the distance the robot walked + distance = torch.norm(asset.data.root_pos_w[env_ids, :2] - env.scene.env_origins[env_ids, :2], dim=1) + # robots that walked far enough progress to harder terrains + move_up = distance > terrain.cfg.terrain_generator.size[0] / 2 + # robots that walked less than half of their required distance go to simpler terrains + move_down = distance < torch.norm(command[env_ids, :2], dim=1) * env.max_episode_length_s * 0.5 + move_down *= ~move_up + # update terrain levels + terrain.update_env_origins(env_ids, move_up, move_down) + # return the mean terrain level + return torch.mean(terrain.terrain_levels.float()) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..f804aa6884c50c31975d612822a596b06805e0fd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/rewards.py @@ -0,0 +1,119 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to define rewards for the learning environment. + +The functions can be passed to the :class:`isaaclab.managers.RewardTermCfg` object to +specify the reward function and its parameters. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.envs import mdp +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import ContactSensor +from isaaclab.utils.math import quat_apply_inverse, yaw_quat + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def feet_air_time( + env: ManagerBasedRLEnv, command_name: str, sensor_cfg: SceneEntityCfg, threshold: float +) -> torch.Tensor: + """Reward long steps taken by the feet using L2-kernel. + + This function rewards the agent for taking steps that are longer than a threshold. This helps ensure + that the robot lifts its feet off the ground and takes steps. The reward is computed as the sum of + the time for which the feet are in the air. + + If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero. + """ + # extract the used quantities (to enable type-hinting) + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + # compute the reward + first_contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids] + last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids] + reward = torch.sum((last_air_time - threshold) * first_contact, dim=1) + # no reward for zero command + reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1 + return reward + + +def feet_air_time_positive_biped(env, command_name: str, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor: + """Reward long steps taken by the feet for bipeds. + + This function rewards the agent for taking steps up to a specified threshold and also keep one foot at + a time in the air. + + If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero. + """ + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + # compute the reward + air_time = contact_sensor.data.current_air_time[:, sensor_cfg.body_ids] + contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids] + in_contact = contact_time > 0.0 + in_mode_time = torch.where(in_contact, contact_time, air_time) + single_stance = torch.sum(in_contact.int(), dim=1) == 1 + reward = torch.min(torch.where(single_stance.unsqueeze(-1), in_mode_time, 0.0), dim=1)[0] + reward = torch.clamp(reward, max=threshold) + # no reward for zero command + reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1 + return reward + + +def feet_slide(env, sensor_cfg: SceneEntityCfg, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Penalize feet sliding. + + This function penalizes the agent for sliding its feet on the ground. The reward is computed as the + norm of the linear velocity of the feet multiplied by a binary contact sensor. This ensures that the + agent is penalized only when the feet are in contact with the ground. + """ + # Penalize feet sliding + contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name] + contacts = contact_sensor.data.net_forces_w_history[:, :, sensor_cfg.body_ids, :].norm(dim=-1).max(dim=1)[0] > 1.0 + asset = env.scene[asset_cfg.name] + + body_vel = asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2] + reward = torch.sum(body_vel.norm(dim=-1) * contacts, dim=1) + return reward + + +def track_lin_vel_xy_yaw_frame_exp( + env, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Reward tracking of linear velocity commands (xy axes) in the gravity aligned + robot frame using an exponential kernel. + """ + # extract the used quantities (to enable type-hinting) + asset = env.scene[asset_cfg.name] + vel_yaw = quat_apply_inverse(yaw_quat(asset.data.root_quat_w), asset.data.root_lin_vel_w[:, :3]) + lin_vel_error = torch.sum( + torch.square(env.command_manager.get_command(command_name)[:, :2] - vel_yaw[:, :2]), dim=1 + ) + return torch.exp(-lin_vel_error / std**2) + + +def track_ang_vel_z_world_exp( + env, command_name: str, std: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Reward tracking of angular velocity commands (yaw) in world frame using exponential kernel.""" + # extract the used quantities (to enable type-hinting) + asset = env.scene[asset_cfg.name] + ang_vel_error = torch.square(env.command_manager.get_command(command_name)[:, 2] - asset.data.root_ang_vel_w[:, 2]) + return torch.exp(-ang_vel_error / std**2) + + +def stand_still_joint_deviation_l1( + env, command_name: str, command_threshold: float = 0.06, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") +) -> torch.Tensor: + """Penalize offsets from the default joint positions when the command is very small.""" + command = env.command_manager.get_command(command_name) + # Penalize motion when command is nearly zero. + return mdp.joint_deviation_l1(env, asset_cfg) * (torch.norm(command[:, :2], dim=1) < command_threshold) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abbd6c26ca5147e0456185193510cd72ff6118fe --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Symmetry functions for the velocity tasks. + +These functions are used to augment the observations and actions of the environment. +They are specific to the velocity task and the choice of the robot. +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/anymal.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/anymal.py new file mode 100644 index 0000000000000000000000000000000000000000..f4197ccbe76ee73f2594e208899dcd4966a61488 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/symmetry/anymal.py @@ -0,0 +1,261 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Functions to specify the symmetry in the observation and action space for ANYmal.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch +from tensordict import TensorDict + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + +# specify the functions that are available for import +__all__ = ["compute_symmetric_states"] + + +@torch.no_grad() +def compute_symmetric_states( + env: ManagerBasedRLEnv, + obs: TensorDict | None = None, + actions: torch.Tensor | None = None, +): + """Augments the given observations and actions by applying symmetry transformations. + + This function creates augmented versions of the provided observations and actions by applying + four symmetrical transformations: original, left-right, front-back, and diagonal. The symmetry + transformations are beneficial for reinforcement learning tasks by providing additional + diverse data without requiring additional data collection. + + Args: + env: The environment instance. + obs: The original observation tensor dictionary. Defaults to None. + actions: The original actions tensor. Defaults to None. + + Returns: + Augmented observations and actions tensors, or None if the respective input was None. + """ + + # observations + if obs is not None: + batch_size = obs.batch_size[0] + # since we have 4 different symmetries, we need to augment the batch size by 4 + obs_aug = obs.repeat(4) + + # policy observation group + # -- original + obs_aug["policy"][:batch_size] = obs["policy"][:] + # -- left-right + obs_aug["policy"][batch_size : 2 * batch_size] = _transform_policy_obs_left_right(env.unwrapped, obs["policy"]) + # -- front-back + obs_aug["policy"][2 * batch_size : 3 * batch_size] = _transform_policy_obs_front_back( + env.unwrapped, obs["policy"] + ) + # -- diagonal + obs_aug["policy"][3 * batch_size :] = _transform_policy_obs_front_back( + env.unwrapped, obs_aug["policy"][batch_size : 2 * batch_size] + ) + else: + obs_aug = None + + # actions + if actions is not None: + batch_size = actions.shape[0] + # since we have 4 different symmetries, we need to augment the batch size by 4 + actions_aug = torch.zeros(batch_size * 4, actions.shape[1], device=actions.device) + # -- original + actions_aug[:batch_size] = actions[:] + # -- left-right + actions_aug[batch_size : 2 * batch_size] = _transform_actions_left_right(actions) + # -- front-back + actions_aug[2 * batch_size : 3 * batch_size] = _transform_actions_front_back(actions) + # -- diagonal + actions_aug[3 * batch_size :] = _transform_actions_front_back(actions_aug[batch_size : 2 * batch_size]) + else: + actions_aug = None + + return obs_aug, actions_aug + + +""" +Symmetry functions for observations. +""" + + +def _transform_policy_obs_left_right(env: ManagerBasedRLEnv, obs: torch.Tensor) -> torch.Tensor: + """Apply a left-right symmetry transformation to the observation tensor. + + This function modifies the given observation tensor by applying transformations + that represent a symmetry with respect to the left-right axis. This includes + negating certain components of the linear and angular velocities, projected gravity, + velocity commands, and flipping the joint positions, joint velocities, and last actions + for the ANYmal robot. Additionally, if height-scan data is present, it is flipped + along the relevant dimension. + + Args: + env: The environment instance from which the observation is obtained. + obs: The observation tensor to be transformed. + + Returns: + The transformed observation tensor with left-right symmetry applied. + """ + # copy observation tensor + obs = obs.clone() + device = obs.device + # lin vel + obs[:, :3] = obs[:, :3] * torch.tensor([1, -1, 1], device=device) + # ang vel + obs[:, 3:6] = obs[:, 3:6] * torch.tensor([-1, 1, -1], device=device) + # projected gravity + obs[:, 6:9] = obs[:, 6:9] * torch.tensor([1, -1, 1], device=device) + # velocity command + obs[:, 9:12] = obs[:, 9:12] * torch.tensor([1, -1, -1], device=device) + # joint pos + obs[:, 12:24] = _switch_anymal_joints_left_right(obs[:, 12:24]) + # joint vel + obs[:, 24:36] = _switch_anymal_joints_left_right(obs[:, 24:36]) + # last actions + obs[:, 36:48] = _switch_anymal_joints_left_right(obs[:, 36:48]) + + # note: this is hard-coded for grid-pattern of ordering "xy" and size (1.6, 1.0) + if "height_scan" in env.observation_manager.active_terms["policy"]: + obs[:, 48:235] = obs[:, 48:235].view(-1, 11, 17).flip(dims=[1]).view(-1, 11 * 17) + + return obs + + +def _transform_policy_obs_front_back(env: ManagerBasedRLEnv, obs: torch.Tensor) -> torch.Tensor: + """Applies a front-back symmetry transformation to the observation tensor. + + This function modifies the given observation tensor by applying transformations + that represent a symmetry with respect to the front-back axis. This includes negating + certain components of the linear and angular velocities, projected gravity, velocity commands, + and flipping the joint positions, joint velocities, and last actions for the ANYmal robot. + Additionally, if height-scan data is present, it is flipped along the relevant dimension. + + Args: + env: The environment instance from which the observation is obtained. + obs: The observation tensor to be transformed. + + Returns: + The transformed observation tensor with front-back symmetry applied. + """ + # copy observation tensor + obs = obs.clone() + device = obs.device + # lin vel + obs[:, :3] = obs[:, :3] * torch.tensor([-1, 1, 1], device=device) + # ang vel + obs[:, 3:6] = obs[:, 3:6] * torch.tensor([1, -1, -1], device=device) + # projected gravity + obs[:, 6:9] = obs[:, 6:9] * torch.tensor([-1, 1, 1], device=device) + # velocity command + obs[:, 9:12] = obs[:, 9:12] * torch.tensor([-1, 1, -1], device=device) + # joint pos + obs[:, 12:24] = _switch_anymal_joints_front_back(obs[:, 12:24]) + # joint vel + obs[:, 24:36] = _switch_anymal_joints_front_back(obs[:, 24:36]) + # last actions + obs[:, 36:48] = _switch_anymal_joints_front_back(obs[:, 36:48]) + + # note: this is hard-coded for grid-pattern of ordering "xy" and size (1.6, 1.0) + if "height_scan" in env.observation_manager.active_terms["policy"]: + obs[:, 48:235] = obs[:, 48:235].view(-1, 11, 17).flip(dims=[2]).view(-1, 11 * 17) + + return obs + + +""" +Symmetry functions for actions. +""" + + +def _transform_actions_left_right(actions: torch.Tensor) -> torch.Tensor: + """Applies a left-right symmetry transformation to the actions tensor. + + This function modifies the given actions tensor by applying transformations + that represent a symmetry with respect to the left-right axis. This includes + flipping the joint positions, joint velocities, and last actions for the + ANYmal robot. + + Args: + actions: The actions tensor to be transformed. + + Returns: + The transformed actions tensor with left-right symmetry applied. + """ + actions = actions.clone() + actions[:] = _switch_anymal_joints_left_right(actions[:]) + return actions + + +def _transform_actions_front_back(actions: torch.Tensor) -> torch.Tensor: + """Applies a front-back symmetry transformation to the actions tensor. + + This function modifies the given actions tensor by applying transformations + that represent a symmetry with respect to the front-back axis. This includes + flipping the joint positions, joint velocities, and last actions for the + ANYmal robot. + + Args: + actions: The actions tensor to be transformed. + + Returns: + The transformed actions tensor with front-back symmetry applied. + """ + actions = actions.clone() + actions[:] = _switch_anymal_joints_front_back(actions[:]) + return actions + + +""" +Helper functions for symmetry. + +In Isaac Sim, the joint ordering is as follows: +[ + 'LF_HAA', 'LH_HAA', 'RF_HAA', 'RH_HAA', + 'LF_HFE', 'LH_HFE', 'RF_HFE', 'RH_HFE', + 'LF_KFE', 'LH_KFE', 'RF_KFE', 'RH_KFE' +] + +Correspondingly, the joint ordering for the ANYmal robot is: + +* LF = left front --> [0, 4, 8] +* LH = left hind --> [1, 5, 9] +* RF = right front --> [2, 6, 10] +* RH = right hind --> [3, 7, 11] +""" + + +def _switch_anymal_joints_left_right(joint_data: torch.Tensor) -> torch.Tensor: + """Applies a left-right symmetry transformation to the joint data tensor.""" + joint_data_switched = torch.zeros_like(joint_data) + # left <-- right + joint_data_switched[..., [0, 4, 8, 1, 5, 9]] = joint_data[..., [2, 6, 10, 3, 7, 11]] + # right <-- left + joint_data_switched[..., [2, 6, 10, 3, 7, 11]] = joint_data[..., [0, 4, 8, 1, 5, 9]] + + # Flip the sign of the HAA joints + joint_data_switched[..., [0, 1, 2, 3]] *= -1.0 + + return joint_data_switched + + +def _switch_anymal_joints_front_back(joint_data: torch.Tensor) -> torch.Tensor: + """Applies a front-back symmetry transformation to the joint data tensor.""" + joint_data_switched = torch.zeros_like(joint_data) + # front <-- hind + joint_data_switched[..., [0, 4, 8, 2, 6, 10]] = joint_data[..., [1, 5, 9, 3, 7, 11]] + # hind <-- front + joint_data_switched[..., [1, 5, 9, 3, 7, 11]] = joint_data[..., [0, 4, 8, 2, 6, 10]] + + # Flip the sign of the HFE and KFE joints + joint_data_switched[..., 4:] *= -1 + + return joint_data_switched diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..6c037d01ea51b06aec0c77ee7cb2b9b37a3674be --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/mdp/terminations.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def terrain_out_of_bounds( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), distance_buffer: float = 3.0 +) -> torch.Tensor: + """Terminate when the actor move too close to the edge of the terrain. + + If the actor moves too close to the edge of the terrain, the termination is activated. The distance + to the edge of the terrain is calculated based on the size of the terrain and the distance buffer. + """ + if env.scene.cfg.terrain.terrain_type == "plane": + # we have infinite terrain because it is a plane + return torch.zeros(env.num_envs, dtype=torch.bool, device=env.device) + elif env.scene.cfg.terrain.terrain_type == "generator": + # obtain the size of the sub-terrains + terrain_gen_cfg = env.scene.terrain.cfg.terrain_generator + grid_width, grid_length = terrain_gen_cfg.size + n_rows, n_cols = terrain_gen_cfg.num_rows, terrain_gen_cfg.num_cols + border_width = terrain_gen_cfg.border_width + # compute the size of the map + map_width = n_rows * grid_width + 2 * border_width + map_height = n_cols * grid_length + 2 * border_width + + # extract the used quantities (to enable type-hinting) + asset: RigidObject = env.scene[asset_cfg.name] + + # check if the agent is out of bounds + x_out_of_bounds = torch.abs(asset.data.root_pos_w[:, 0]) > 0.5 * map_width - distance_buffer + y_out_of_bounds = torch.abs(asset.data.root_pos_w[:, 1]) > 0.5 * map_height - distance_buffer + return torch.logical_or(x_out_of_bounds, y_out_of_bounds) + else: + raise ValueError("Received unsupported terrain type, must be either 'plane' or 'generator'.") diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/velocity_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/velocity_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d7094e777014134c3d79d644309a2d08ffd9f23a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/velocity_env_cfg.py @@ -0,0 +1,329 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg, RayCasterCfg, patterns +from isaaclab.terrains import TerrainImporterCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdp + +## +# Pre-defined configs +## +from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip + + +## +# Scene definition +## + + +@configclass +class MySceneCfg(InteractiveSceneCfg): + """Configuration for the terrain scene with a legged robot.""" + + # ground terrain + terrain = TerrainImporterCfg( + prim_path="/World/ground", + terrain_type="generator", + terrain_generator=ROUGH_TERRAINS_CFG, + max_init_terrain_level=5, + collision_group=-1, + physics_material=sim_utils.RigidBodyMaterialCfg( + friction_combine_mode="multiply", + restitution_combine_mode="multiply", + static_friction=1.0, + dynamic_friction=1.0, + ), + visual_material=sim_utils.MdlFileCfg( + mdl_path=f"{ISAACLAB_NUCLEUS_DIR}/Materials/TilesMarbleSpiderWhiteBrickBondHoned/TilesMarbleSpiderWhiteBrickBondHoned.mdl", + project_uvw=True, + texture_scale=(0.25, 0.25), + ), + debug_vis=False, + ) + # robots + robot: ArticulationCfg = MISSING + # sensors + height_scanner = RayCasterCfg( + prim_path="{ENV_REGEX_NS}/Robot/base", + offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), + ray_alignment="yaw", + pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), + debug_vis=False, + mesh_prim_paths=["/World/ground"], + ) + contact_forces = ContactSensorCfg(prim_path="{ENV_REGEX_NS}/Robot/.*", history_length=3, track_air_time=True) + # lights + sky_light = AssetBaseCfg( + prim_path="/World/skyLight", + spawn=sim_utils.DomeLightCfg( + intensity=750.0, + texture_file=f"{ISAAC_NUCLEUS_DIR}/Materials/Textures/Skies/PolyHaven/kloofendal_43d_clear_puresky_4k.hdr", + ), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command specifications for the MDP.""" + + base_velocity = mdp.UniformVelocityCommandCfg( + asset_name="robot", + resampling_time_range=(10.0, 10.0), + rel_standing_envs=0.02, + rel_heading_envs=1.0, + heading_command=True, + heading_control_stiffness=0.5, + debug_vis=True, + ranges=mdp.UniformVelocityCommandCfg.Ranges( + lin_vel_x=(-1.0, 1.0), lin_vel_y=(-1.0, 1.0), ang_vel_z=(-1.0, 1.0), heading=(-math.pi, math.pi) + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2)) + projected_gravity = ObsTerm( + func=mdp.projected_gravity, + noise=Unoise(n_min=-0.05, n_max=0.05), + ) + velocity_commands = ObsTerm(func=mdp.generated_commands, params={"command_name": "base_velocity"}) + joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5)) + actions = ObsTerm(func=mdp.last_action) + height_scan = ObsTerm( + func=mdp.height_scan, + params={"sensor_cfg": SceneEntityCfg("height_scanner")}, + noise=Unoise(n_min=-0.1, n_max=0.1), + clip=(-1.0, 1.0), + ) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # startup + physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.8, 0.8), + "dynamic_friction_range": (0.6, 0.6), + "restitution_range": (0.0, 0.0), + "num_buckets": 64, + }, + ) + + add_base_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "mass_distribution_params": (-5.0, 5.0), + "operation": "add", + }, + ) + + base_com = EventTerm( + func=mdp.randomize_rigid_body_com, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "com_range": {"x": (-0.05, 0.05), "y": (-0.05, 0.05), "z": (-0.01, 0.01)}, + }, + ) + + # reset + base_external_force_torque = EventTerm( + func=mdp.apply_external_force_torque, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names="base"), + "force_range": (0.0, 0.0), + "torque_range": (-0.0, 0.0), + }, + ) + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.5, 0.5), + "y": (-0.5, 0.5), + "z": (-0.5, 0.5), + "roll": (-0.5, 0.5), + "pitch": (-0.5, 0.5), + "yaw": (-0.5, 0.5), + }, + }, + ) + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_scale, + mode="reset", + params={ + "position_range": (0.5, 1.5), + "velocity_range": (0.0, 0.0), + }, + ) + + # interval + push_robot = EventTerm( + func=mdp.push_by_setting_velocity, + mode="interval", + interval_range_s=(10.0, 15.0), + params={"velocity_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5)}}, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # -- task + track_lin_vel_xy_exp = RewTerm( + func=mdp.track_lin_vel_xy_exp, weight=1.0, params={"command_name": "base_velocity", "std": math.sqrt(0.25)} + ) + track_ang_vel_z_exp = RewTerm( + func=mdp.track_ang_vel_z_exp, weight=0.5, params={"command_name": "base_velocity", "std": math.sqrt(0.25)} + ) + # -- penalties + lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0) + ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=-0.05) + dof_torques_l2 = RewTerm(func=mdp.joint_torques_l2, weight=-1.0e-5) + dof_acc_l2 = RewTerm(func=mdp.joint_acc_l2, weight=-2.5e-7) + action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01) + feet_air_time = RewTerm( + func=mdp.feet_air_time, + weight=0.125, + params={ + "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*FOOT"), + "command_name": "base_velocity", + "threshold": 0.5, + }, + ) + undesired_contacts = RewTerm( + func=mdp.undesired_contacts, + weight=-1.0, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*THIGH"), "threshold": 1.0}, + ) + # -- optional penalties + flat_orientation_l2 = RewTerm(func=mdp.flat_orientation_l2, weight=0.0) + dof_pos_limits = RewTerm(func=mdp.joint_pos_limits, weight=0.0) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + base_contact = DoneTerm( + func=mdp.illegal_contact, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="base"), "threshold": 1.0}, + ) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + terrain_levels = CurrTerm(func=mdp.terrain_levels_vel) + + +## +# Environment configuration +## + + +@configclass +class LocomotionVelocityRoughEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the locomotion velocity-tracking environment.""" + + # Scene settings + scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 0.005 + self.sim.render_interval = self.decimation + self.sim.physics_material = self.scene.terrain.physics_material + self.sim.physx.gpu_max_rigid_patch_count = 10 * 2**15 + # update sensor update periods + # we tick all the sensors based on the smallest update period (physics update period) + if self.scene.height_scanner is not None: + self.scene.height_scanner.update_period = self.decimation * self.sim.dt + if self.scene.contact_forces is not None: + self.scene.contact_forces.update_period = self.sim.dt + + # check if terrain levels curriculum is enabled - if so, enable curriculum for terrain generator + # this generates terrains with increasing difficulty and is useful for training + if getattr(self.curriculum, "terrain_levels", None) is not None: + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.curriculum = True + else: + if self.scene.terrain.terrain_generator is not None: + self.scene.terrain.terrain_generator.curriculum = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eaf0b09fbb6619dab14e346a21dc7868eb0d8d3c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Manipulation environments for fixed-arm robots.""" + +from .reach import * # noqa diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7ea0d7159142fd3d3d488bee61a324b88e451246 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Manipulation environments to open drawers in a cabinet.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/cabinet_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/cabinet_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..85b7e5ae9ba8b2d32724eefa906a8c4a7e2cde8c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/cabinet_env_cfg.py @@ -0,0 +1,278 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer import OffsetCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +FRAME_MARKER_SMALL_CFG = FRAME_MARKER_CFG.copy() +FRAME_MARKER_SMALL_CFG.markers["frame"].scale = (0.10, 0.10, 0.10) + + +## +# Scene definition +## + + +@configclass +class CabinetSceneCfg(InteractiveSceneCfg): + """Configuration for the cabinet scene with a robot and a cabinet. + + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the robot and end-effector frames + """ + + # robots, Will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # End-effector, Will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + cabinet = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Cabinet", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Sektion_Cabinet/sektion_cabinet_instanceable.usd", + activate_contact_sensors=False, + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.8, 0, 0.4), + rot=(0.0, 0.0, 0.0, 1.0), + joint_pos={ + "door_left_joint": 0.0, + "door_right_joint": 0.0, + "drawer_bottom_joint": 0.0, + "drawer_top_joint": 0.0, + }, + ), + actuators={ + "drawers": ImplicitActuatorCfg( + joint_names_expr=["drawer_top_joint", "drawer_bottom_joint"], + effort_limit_sim=87.0, + stiffness=10.0, + damping=1.0, + ), + "doors": ImplicitActuatorCfg( + joint_names_expr=["door_left_joint", "door_right_joint"], + effort_limit_sim=87.0, + stiffness=10.0, + damping=2.5, + ), + }, + ) + + # Frame definitions for the cabinet. + cabinet_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Cabinet/sektion", + debug_vis=True, + visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/CabinetFrameTransformer"), + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Cabinet/drawer_handle_top", + name="drawer_handle_top", + offset=OffsetCfg( + pos=(0.305, 0.0, 0.01), + rot=(0.5, 0.5, -0.5, -0.5), # align with end-effector frame + ), + ), + ], + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(), + spawn=sim_utils.GroundPlaneCfg(), + collision_group=-1, + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + cabinet_joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, + ) + cabinet_joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, + ) + rel_ee_drawer_distance = ObsTerm(func=mdp.rel_ee_drawer_distance) + + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.8, 1.25), + "dynamic_friction_range": (0.8, 1.25), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + cabinet_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("cabinet", body_names="drawer_handle_top"), + "static_friction_range": (1.0, 1.25), + "dynamic_friction_range": (1.25, 1.5), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.1, 0.1), + "velocity_range": (0.0, 0.0), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # 1. Approach the handle + approach_ee_handle = RewTerm(func=mdp.approach_ee_handle, weight=2.0, params={"threshold": 0.2}) + align_ee_handle = RewTerm(func=mdp.align_ee_handle, weight=0.5) + + # 2. Grasp the handle + approach_gripper_handle = RewTerm(func=mdp.approach_gripper_handle, weight=5.0, params={"offset": MISSING}) + align_grasp_around_handle = RewTerm(func=mdp.align_grasp_around_handle, weight=0.125) + grasp_handle = RewTerm( + func=mdp.grasp_handle, + weight=0.5, + params={ + "threshold": 0.03, + "open_joint_pos": MISSING, + "asset_cfg": SceneEntityCfg("robot", joint_names=MISSING), + }, + ) + + # 3. Open the drawer + open_drawer_bonus = RewTerm( + func=mdp.open_drawer_bonus, + weight=7.5, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, + ) + multi_stage_open_drawer = RewTerm( + func=mdp.multi_stage_open_drawer, + weight=1.0, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, + ) + + # 4. Penalize actions for cosmetic reasons + action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-1e-2) + joint_vel = RewTerm(func=mdp.joint_vel_l2, weight=-0.0001) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +## +# Environment configuration +## + + +@configclass +class CabinetEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the cabinet environment.""" + + # Scene settings + scene: CabinetSceneCfg = CabinetSceneCfg(num_envs=4096, env_spacing=2.0) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 1 + self.episode_length_s = 8.0 + self.viewer.eye = (-2.0, 2.0, 2.0) + self.viewer.lookat = (0.8, 0.0, 0.5) + # simulation settings + self.sim.dt = 1 / 60 # 60Hz + self.sim.render_interval = self.decimation + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c38f5c0b04e678d2cd1dc8253d031cb6db4a67 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the cabinet environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1f8b763b4228c5f040483bf36f1e3d46cbf859ca --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/__init__.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Open-Drawer-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCabinetEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CabinetPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Open-Drawer-Franka-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCabinetEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CabinetPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + + +## +# Inverse Kinematics - Absolute Pose Control +## + +gym.register( + id="Isaac-Open-Drawer-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaCabinetEnvCfg", + }, + disable_env_checker=True, +) + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Open-Drawer-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_rel_env_cfg:FrankaCabinetEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8fc9b9773d5d93e5330c88037b6f31caf1a5996e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 5.0 + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False + load_path: '' + + config: + name: franka_open_drawer + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: False + normalize_value: False + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1 + normalize_advantage: False + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 200 + max_epochs: 400 + save_best_after: 50 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 96 + minibatch_size: 4096 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccb4787cdd6d7075d87b272b5b06acdef986a4e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class CabinetPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 96 + max_iterations = 400 + save_interval = 50 + experiment_name = "franka_open_drawer" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=1e-3, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.02, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ca95cb45ececf0c4e62854236a5297095f5e6235 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 96 + learning_epochs: 5 + mini_batches: 96 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.001 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "franka_open_drawer" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 38400 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_abs_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2f47f3239580e9f8db7c4de854fe6a47a64f3a17 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_abs_env_cfg.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCabinetEnvCfg(joint_pos_env_cfg.FrankaCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCabinetEnvCfg_PLAY(FrankaCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..aaaa644ce1c2b6a6d1640ada7a50889b44a3d00a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/ik_rel_env_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCabinetEnvCfg(joint_pos_env_cfg.FrankaCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCabinetEnvCfg_PLAY(FrankaCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..04624c0e6389d9a00cdce799294e0717bbe99c66 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/franka/joint_pos_env_cfg.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cabinet import mdp + +from isaaclab_tasks.manager_based.manipulation.cabinet.cabinet_env_cfg import ( # isort: skip + FRAME_MARKER_SMALL_CFG, + CabinetEnvCfg, +) + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class FrankaCabinetEnvCfg(CabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set Actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + scale=1.0, + use_default_offset=True, + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + + # Listens to the required transforms + # IMPORTANT: The order of the frames in the list is important. The first frame is the tool center point (TCP) + # the other frames are the fingers + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/EndEffectorFrameTransformer"), + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="ee_tcp", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.1034), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) + + # override rewards + self.rewards.approach_gripper_handle.params["offset"] = 0.04 + self.rewards.grasp_handle.params["open_joint_pos"] = 0.04 + self.rewards.grasp_handle.params["asset_cfg"].joint_names = ["panda_finger_.*"] + + +@configclass +class FrankaCabinetEnvCfg_PLAY(FrankaCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a4630e8b3bfd0d6086a8e31b4abf37d622da9802 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/__init__.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Open-Drawer-OpenArm-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmCabinetEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmCabinetPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Open-Drawer-OpenArm-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmCabinetEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmCabinetPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52d5a7dfae87b17b0b5c88bc231f6ec4fbf9df6a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,81 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 5.0 + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False + load_path: '' + + config: + name: openarm_open_drawer + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: False + normalize_value: False + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 1 + normalize_advantage: False + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 200 + max_epochs: 400 + save_best_after: 50 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 96 + minibatch_size: 4096 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..67f3498c361d6d9a11485c1de722c929392930fa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class OpenArmCabinetPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 96 + max_iterations = 600 + save_interval = 50 + experiment_name = "openarm_open_drawer" + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=1e-3, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.02, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/cabinet_openarm_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/cabinet_openarm_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6e3eecb59382d65722a09008c89ae51bb4cfe3e4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/cabinet_openarm_env_cfg.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +We modified parts of the environment, such as the target's position and orientation, +as well as certain object properties, to better suit the smaller robot. +""" + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.actuators.actuator_cfg import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer import OffsetCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + +FRAME_MARKER_SMALL_CFG = FRAME_MARKER_CFG.copy() +FRAME_MARKER_SMALL_CFG.markers["frame"].scale = (0.10, 0.10, 0.10) + +from ... import mdp + +## +# Scene definition +## + + +@configclass +class CabinetSceneCfg(InteractiveSceneCfg): + """Configuration for the cabinet scene with a robot and a cabinet. + + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the robot and end-effector frames + """ + + # robots, Will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # End-effector, Will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + cabinet = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Cabinet", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Sektion_Cabinet/sektion_cabinet_instanceable.usd", + activate_contact_sensors=False, + scale=(0.75, 0.75, 0.75), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.7, 0, 0.3), + rot=(0.0, 0.0, 0.0, 1.0), + joint_pos={ + "door_left_joint": 0.0, + "door_right_joint": 0.0, + "drawer_bottom_joint": 0.0, + "drawer_top_joint": 0.0, + }, + ), + actuators={ + "drawers": ImplicitActuatorCfg( + joint_names_expr=["drawer_top_joint", "drawer_bottom_joint"], + effort_limit=87.0, + velocity_limit=100.0, + stiffness=10.0, + damping=1.0, + ), + "doors": ImplicitActuatorCfg( + joint_names_expr=["door_left_joint", "door_right_joint"], + effort_limit=87.0, + velocity_limit=100.0, + stiffness=10.0, + damping=2.5, + ), + }, + ) + + # Frame definitions for the cabinet. + cabinet_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Cabinet/sektion", + debug_vis=True, + visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/CabinetFrameTransformer"), + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Cabinet/drawer_handle_bottom", + name="drawer_handle_bottom", + offset=OffsetCfg( + pos=(0.222, 0.0, 0.005), + rot=(0.5, 0.5, -0.5, -0.5), # align with end-effector frame + ), + ), + ], + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(), + spawn=sim_utils.GroundPlaneCfg(), + collision_group=-1, + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + cabinet_joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_bottom_joint"])}, + ) + cabinet_joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_bottom_joint"])}, + ) + rel_ee_drawer_distance = ObsTerm(func=mdp.rel_ee_drawer_distance) + + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.8, 1.25), + "dynamic_friction_range": (0.8, 1.25), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + cabinet_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("cabinet", body_names="drawer_handle_bottom"), + "static_friction_range": (2.25, 2.5), + "dynamic_friction_range": (2.0, 2.25), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.1, 0.1), + "velocity_range": (0.0, 0.0), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # 1. Approach the handle + approach_ee_handle = RewTerm(func=mdp.approach_ee_handle, weight=2.0, params={"threshold": 0.2}) + align_ee_handle = RewTerm(func=mdp.align_ee_handle, weight=0.5) + + # 2. Grasp the handle + approach_gripper_handle = RewTerm(func=mdp.approach_gripper_handle, weight=5.0, params={"offset": MISSING}) + align_grasp_around_handle = RewTerm(func=mdp.align_grasp_around_handle, weight=0.125) + grasp_handle = RewTerm( + func=mdp.grasp_handle, + weight=0.5, + params={ + "threshold": 0.03, + "open_joint_pos": MISSING, + "asset_cfg": SceneEntityCfg("robot", joint_names=MISSING), + }, + ) + + # 3. Open the drawer + open_drawer_bonus = RewTerm( + func=mdp.open_drawer_bonus, + weight=7.5, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_bottom_joint"])}, + ) + multi_stage_open_drawer = RewTerm( + func=mdp.multi_stage_open_drawer, + weight=1.0, + params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_bottom_joint"])}, + ) + + # 4. Penalize actions for cosmetic reasons + action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-1e-2) + joint_vel = RewTerm(func=mdp.joint_vel_l2, weight=-0.0001) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +## +# Environment configuration +## + + +@configclass +class CabinetEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the cabinet environment.""" + + # Scene settings + scene: CabinetSceneCfg = CabinetSceneCfg(num_envs=4096, env_spacing=2.0) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 1 + self.episode_length_s = 8.0 + self.viewer.eye = (-2.0, 2.0, 2.0) + self.viewer.lookat = (0.8, 0.0, 0.5) + # simulation settings + self.sim.dt = 1 / 60 # 60Hz + self.sim.render_interval = self.decimation + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..05d03942700e35281b9b08bf1da8fd17139ebb66 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/config/openarm/joint_pos_env_cfg.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +## +# Pre-defined configs +## +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cabinet import mdp + +from isaaclab_assets.robots.openarm import OPENARM_UNI_CFG + +from isaaclab_tasks.manager_based.manipulation.cabinet.config.openarm.cabinet_openarm_env_cfg import ( # isort: skip + FRAME_MARKER_SMALL_CFG, + CabinetEnvCfg, +) + + +@configclass +class OpenArmCabinetEnvCfg(CabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set OpenArm as robot + self.scene.robot = OPENARM_UNI_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set Actions for the specific robot type (OpenArm) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=["openarm_joint.*"], + scale=1.0, + use_default_offset=True, + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["openarm_finger_joint.*"], + open_command_expr={"openarm_finger_joint.*": 0.044}, + close_command_expr={"openarm_finger_joint.*": 0.0}, + ) + + # Listens to the required transforms + # IMPORTANT: The order of the frames in the list is important. The first frame is the tool center point (TCP) + # the other frames are the fingers + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_link0", + visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/EndEffectorFrameTransformer"), + debug_vis=False, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_ee_tcp", + name="ee_tcp", + offset=OffsetCfg( + pos=(0.0, 0.0, -0.003), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_left_finger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, -0.005, 0.075), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_right_finger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.005, 0.075), + ), + ), + ], + ) + + # override rewards + self.rewards.approach_gripper_handle.params["offset"] = 0.04 + self.rewards.grasp_handle.params["open_joint_pos"] = 0.044 + self.rewards.grasp_handle.params["asset_cfg"].joint_names = ["openarm_finger_joint.*"] + + +@configclass +class OpenArmCabinetEnvCfg_PLAY(OpenArmCabinetEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79a9af2f736b5abd160620b54503c3c51c82290f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the cabinet environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..66fb8bb38e9701268262f813f627ff8c39168946 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/observations.py @@ -0,0 +1,60 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import ArticulationData +from isaaclab.sensors import FrameTransformerData + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def rel_ee_object_distance(env: ManagerBasedRLEnv) -> torch.Tensor: + """The distance between the end-effector and the object.""" + ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data + object_data: ArticulationData = env.scene["object"].data + + return object_data.root_pos_w - ee_tf_data.target_pos_w[..., 0, :] + + +def rel_ee_drawer_distance(env: ManagerBasedRLEnv) -> torch.Tensor: + """The distance between the end-effector and the object.""" + ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data + cabinet_tf_data: FrameTransformerData = env.scene["cabinet_frame"].data + + return cabinet_tf_data.target_pos_w[..., 0, :] - ee_tf_data.target_pos_w[..., 0, :] + + +def fingertips_pos(env: ManagerBasedRLEnv) -> torch.Tensor: + """The position of the fingertips relative to the environment origins.""" + ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data + fingertips_pos = ee_tf_data.target_pos_w[..., 1:, :] - env.scene.env_origins.unsqueeze(1) + + return fingertips_pos.view(env.num_envs, -1) + + +def ee_pos(env: ManagerBasedRLEnv) -> torch.Tensor: + """The position of the end-effector relative to the environment origins.""" + ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data + ee_pos = ee_tf_data.target_pos_w[..., 0, :] - env.scene.env_origins + + return ee_pos + + +def ee_quat(env: ManagerBasedRLEnv, make_quat_unique: bool = True) -> torch.Tensor: + """The orientation of the end-effector in the environment frame. + + If :attr:`make_quat_unique` is True, the quaternion is made unique by ensuring the real part is positive. + """ + ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data + ee_quat = ee_tf_data.target_quat_w[..., 0, :] + # make first element of quaternion positive + return math_utils.quat_unique(ee_quat) if make_quat_unique else ee_quat diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..433a2a87732abc437d5c112ff95b190e39bdccb8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cabinet/mdp/rewards.py @@ -0,0 +1,163 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import matrix_from_quat + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def approach_ee_handle(env: ManagerBasedRLEnv, threshold: float) -> torch.Tensor: + r"""Reward the robot for reaching the drawer handle using inverse-square law. + + It uses a piecewise function to reward the robot for reaching the handle. + + .. math:: + + reward = \begin{cases} + 2 * (1 / (1 + distance^2))^2 & \text{if } distance \leq threshold \\ + (1 / (1 + distance^2))^2 & \text{otherwise} + \end{cases} + + """ + ee_tcp_pos = env.scene["ee_frame"].data.target_pos_w[..., 0, :] + handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] + + # Compute the distance of the end-effector to the handle + distance = torch.norm(handle_pos - ee_tcp_pos, dim=-1, p=2) + + # Reward the robot for reaching the handle + reward = 1.0 / (1.0 + distance**2) + reward = torch.pow(reward, 2) + return torch.where(distance <= threshold, 2 * reward, reward) + + +def align_ee_handle(env: ManagerBasedRLEnv) -> torch.Tensor: + """Reward for aligning the end-effector with the handle. + + The reward is based on the alignment of the gripper with the handle. It is computed as follows: + + .. math:: + + reward = 0.5 * (align_z^2 + align_x^2) + + where :math:`align_z` is the dot product of the z direction of the gripper and the -x direction of the handle + and :math:`align_x` is the dot product of the x direction of the gripper and the -y direction of the handle. + """ + ee_tcp_quat = env.scene["ee_frame"].data.target_quat_w[..., 0, :] + handle_quat = env.scene["cabinet_frame"].data.target_quat_w[..., 0, :] + + ee_tcp_rot_mat = matrix_from_quat(ee_tcp_quat) + handle_mat = matrix_from_quat(handle_quat) + + # get current x and y direction of the handle + handle_x, handle_y = handle_mat[..., 0], handle_mat[..., 1] + # get current x and z direction of the gripper + ee_tcp_x, ee_tcp_z = ee_tcp_rot_mat[..., 0], ee_tcp_rot_mat[..., 2] + + # make sure gripper aligns with the handle + # in this case, the z direction of the gripper should be close to the -x direction of the handle + # and the x direction of the gripper should be close to the -y direction of the handle + # dot product of z and x should be large + align_z = torch.bmm(ee_tcp_z.unsqueeze(1), -handle_x.unsqueeze(-1)).squeeze(-1).squeeze(-1) + align_x = torch.bmm(ee_tcp_x.unsqueeze(1), -handle_y.unsqueeze(-1)).squeeze(-1).squeeze(-1) + return 0.5 * (torch.sign(align_z) * align_z**2 + torch.sign(align_x) * align_x**2) + + +def align_grasp_around_handle(env: ManagerBasedRLEnv) -> torch.Tensor: + """Bonus for correct hand orientation around the handle. + + The correct hand orientation is when the left finger is above the handle and the right finger is below the handle. + """ + # Target object position: (num_envs, 3) + handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] + # Fingertips position: (num_envs, n_fingertips, 3) + ee_fingertips_w = env.scene["ee_frame"].data.target_pos_w[..., 1:, :] + lfinger_pos = ee_fingertips_w[..., 0, :] + rfinger_pos = ee_fingertips_w[..., 1, :] + + # Check if hand is in a graspable pose + is_graspable = (rfinger_pos[:, 2] < handle_pos[:, 2]) & (lfinger_pos[:, 2] > handle_pos[:, 2]) + + # bonus if left finger is above the drawer handle and right below + return is_graspable + + +def approach_gripper_handle(env: ManagerBasedRLEnv, offset: float = 0.04) -> torch.Tensor: + """Reward the robot's gripper reaching the drawer handle with the right pose. + + This function returns the distance of fingertips to the handle when the fingers are in a grasping orientation + (i.e., the left finger is above the handle and the right finger is below the handle). Otherwise, it returns zero. + """ + # Target object position: (num_envs, 3) + handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] + # Fingertips position: (num_envs, n_fingertips, 3) + ee_fingertips_w = env.scene["ee_frame"].data.target_pos_w[..., 1:, :] + lfinger_pos = ee_fingertips_w[..., 0, :] + rfinger_pos = ee_fingertips_w[..., 1, :] + + # Compute the distance of each finger from the handle + lfinger_dist = torch.abs(lfinger_pos[:, 2] - handle_pos[:, 2]) + rfinger_dist = torch.abs(rfinger_pos[:, 2] - handle_pos[:, 2]) + + # Check if hand is in a graspable pose + is_graspable = (rfinger_pos[:, 2] < handle_pos[:, 2]) & (lfinger_pos[:, 2] > handle_pos[:, 2]) + + return is_graspable * ((offset - lfinger_dist) + (offset - rfinger_dist)) + + +def grasp_handle( + env: ManagerBasedRLEnv, threshold: float, open_joint_pos: float, asset_cfg: SceneEntityCfg +) -> torch.Tensor: + """Reward for closing the fingers when being close to the handle. + + The :attr:`threshold` is the distance from the handle at which the fingers should be closed. + The :attr:`open_joint_pos` is the joint position when the fingers are open. + + Note: + It is assumed that zero joint position corresponds to the fingers being closed. + """ + ee_tcp_pos = env.scene["ee_frame"].data.target_pos_w[..., 0, :] + handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] + gripper_joint_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids] + + distance = torch.norm(handle_pos - ee_tcp_pos, dim=-1, p=2) + is_close = distance <= threshold + + return is_close * torch.sum(open_joint_pos - gripper_joint_pos, dim=-1) + + +def open_drawer_bonus(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Bonus for opening the drawer given by the joint position of the drawer. + + The bonus is given when the drawer is open. If the grasp is around the handle, the bonus is doubled. + """ + drawer_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids[0]] + is_graspable = align_grasp_around_handle(env).float() + + return (is_graspable + 1.0) * drawer_pos + + +def multi_stage_open_drawer(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Multi-stage bonus for opening the drawer. + + Depending on the drawer's position, the reward is given in three stages: easy, medium, and hard. + This helps the agent to learn to open the drawer in a controlled manner. + """ + drawer_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids[0]] + is_graspable = align_grasp_around_handle(env).float() + + open_easy = (drawer_pos > 0.01) * 0.5 + open_medium = (drawer_pos > 0.2) * is_graspable + open_hard = (drawer_pos > 0.3) * is_graspable + + return open_easy + open_medium + open_hard diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..61fccf6b2c1402307217a8f9a29bb51c843ebe07 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Deployment environments for manipulation tasks. + +These environments are designed for real-world deployment of manipulation tasks. +They containconfigurations and implementations that have been tested +and deployed on physical robots. + +The deploy module includes: +- Reach environments for end-effector pose tracking + +""" + +from .reach import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7b2bf171a20a670f0f9eef29daaa0eabe36a66d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Assemble 3 gears into a base.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..177e08ed7349116172b0b0a47e9d92319cb1dbc2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for arm-based gear assembly environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ad5dff78db64373c8eba0534c78e1c20469b174b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/__init__.py @@ -0,0 +1,75 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + + +# UR10e with 2F-140 gripper +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F140-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10e2F140GearAssemblyEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10GearAssemblyRNNPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F140-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10e2F140GearAssemblyEnvCfg_PLAY", + }, +) + +# UR10e with 2F-85 gripper +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F85-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10e2F85GearAssemblyEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10GearAssemblyRNNPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F85-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10e2F85GearAssemblyEnvCfg_PLAY", + }, +) + +# UR10e with 2F-140 gripper - ROS Inference +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F140-ROS-Inference-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.ros_inference_env_cfg:UR10e2F140GearAssemblyROSInferenceEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10GearAssemblyRNNPPORunnerCfg", + }, +) + +# UR10e with 2F-85 gripper - ROS Inference +gym.register( + id="Isaac-Deploy-GearAssembly-UR10e-2F85-ROS-Inference-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.ros_inference_env_cfg:UR10e2F85GearAssemblyROSInferenceEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10GearAssemblyRNNPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf59b16a1e2e10613c813c3d808e783886f400c7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ac1ecba8463d83b2e3459c5f4b3a2755b832d253 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticRecurrentCfg, RslRlPpoAlgorithmCfg + + +@configclass +class UR10GearAssemblyRNNPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 512 + max_iterations = 1500 + save_interval = 50 + experiment_name = "gear_assembly_ur10e" + clip_actions = 1.0 + resume = False + obs_groups = { + "policy": ["policy"], + "critic": ["critic"], + } + policy = RslRlPpoActorCriticRecurrentCfg( + state_dependent_std=True, + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + noise_std_type="log", + activation="elu", + rnn_type="lstm", + rnn_hidden_dim=256, + rnn_num_layers=2, + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=8, + num_mini_batches=16, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.008, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..22921e717895c9e6738dc34f4c570f70d689475f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/joint_pos_env_cfg.py @@ -0,0 +1,520 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +import torch + +import isaaclab.sim as sim_utils +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab.assets import ArticulationCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.deploy.mdp as mdp +import isaaclab_tasks.manager_based.manipulation.deploy.mdp.events as gear_assembly_events +from isaaclab_tasks.manager_based.manipulation.deploy.gear_assembly.gear_assembly_env_cfg import GearAssemblyEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG, UR10e_ROBOTIQ_2F_85_CFG # isort: skip + + +## +# Gripper-specific helper functions +## + + +def set_finger_joint_pos_robotiq_2f140( + joint_pos: torch.Tensor, + reset_ind_joint_pos: list[int], + finger_joints: list[int], + finger_joint_position: float, +): + """Set finger joint positions for Robotiq 2F-140 gripper. + + Args: + joint_pos: Joint positions tensor + reset_ind_joint_pos: Row indices into the sliced joint_pos tensor + finger_joints: List of finger joint indices + finger_joint_position: Target position for finger joints + """ + for idx in reset_ind_joint_pos: + # For 2F-140 gripper (8 joints expected) + # Joint structure: [finger_joint, finger_joint, outer_joints x2, inner_finger_joints x2, pad_joints x2] + if len(finger_joints) < 8: + raise ValueError(f"2F-140 gripper requires at least 8 finger joints, got {len(finger_joints)}") + + joint_pos[idx, finger_joints[0]] = finger_joint_position + joint_pos[idx, finger_joints[1]] = finger_joint_position + + # outer finger joints set to 0 + joint_pos[idx, finger_joints[2]] = 0 + joint_pos[idx, finger_joints[3]] = 0 + + # inner finger joints: multiply by -1 + joint_pos[idx, finger_joints[4]] = -finger_joint_position + joint_pos[idx, finger_joints[5]] = -finger_joint_position + + joint_pos[idx, finger_joints[6]] = finger_joint_position + joint_pos[idx, finger_joints[7]] = finger_joint_position + + +def set_finger_joint_pos_robotiq_2f85( + joint_pos: torch.Tensor, + reset_ind_joint_pos: list[int], + finger_joints: list[int], + finger_joint_position: float, +): + """Set finger joint positions for Robotiq 2F-85 gripper. + + Args: + joint_pos: Joint positions tensor + reset_ind_joint_pos: Row indices into the sliced joint_pos tensor + finger_joints: List of finger joint indices + finger_joint_position: Target position for finger joints + """ + for idx in reset_ind_joint_pos: + # For 2F-85 gripper (6 joints expected) + # Joint structure: [finger_joint, finger_joint, inner_finger_joints x2, inner_finger_knuckle_joints x2] + if len(finger_joints) < 6: + raise ValueError(f"2F-85 gripper requires at least 6 finger joints, got {len(finger_joints)}") + + # Multiply specific indices by -1: [2, 4, 5] + # These correspond to: + # ['left_inner_finger_joint', 'right_inner_finger_knuckle_joint', 'left_inner_finger_knuckle_joint'] + joint_pos[idx, finger_joints[0]] = finger_joint_position + joint_pos[idx, finger_joints[1]] = finger_joint_position + joint_pos[idx, finger_joints[2]] = -finger_joint_position + joint_pos[idx, finger_joints[3]] = finger_joint_position + joint_pos[idx, finger_joints[4]] = -finger_joint_position + joint_pos[idx, finger_joints[5]] = -finger_joint_position + + +## +# Environment configuration +## + + +@configclass +class EventCfg: + """Configuration for events.""" + + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg( + "robot", joint_names=["shoulder_.*", "elbow_.*", "wrist_.*"] + ), # only the arm joints are randomized + "stiffness_distribution_params": (0.75, 1.5), + "damping_distribution_params": (0.3, 3.0), + "operation": "scale", + "distribution": "log_uniform", + }, + ) + + joint_friction = EventTerm( + func=mdp.randomize_joint_parameters, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["shoulder_.*", "elbow_.*", "wrist_.*"]), + "friction_distribution_params": (0.3, 0.7), + "operation": "add", + "distribution": "uniform", + }, + ) + + small_gear_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_gear_small", body_names=".*"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + medium_gear_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_gear_medium", body_names=".*"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + large_gear_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_gear_large", body_names=".*"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + gear_base_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_gear_base", body_names=".*"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*finger"), + "static_friction_range": (0.75, 0.75), + "dynamic_friction_range": (0.75, 0.75), + "restitution_range": (0.0, 0.0), + "num_buckets": 16, + }, + ) + + randomize_gear_type = EventTerm( + func=gear_assembly_events.randomize_gear_type, + mode="reset", + params={"gear_types": ["gear_small", "gear_medium", "gear_large"]}, + ) + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + randomize_gears_and_base_pose = EventTerm( + func=gear_assembly_events.randomize_gears_and_base_pose, + mode="reset", + params={ + "pose_range": { + "x": [-0.1, 0.1], + "y": [-0.25, 0.25], + "z": [-0.1, 0.1], + "roll": [-math.pi / 90, math.pi / 90], # 2 degree + "pitch": [-math.pi / 90, math.pi / 90], # 2 degree + "yaw": [-math.pi / 6, math.pi / 6], # 2 degree + }, + "gear_pos_range": { + "x": [-0.02, 0.02], + "y": [-0.02, 0.02], + "z": [0.0575, 0.0775], # 0.045 + 0.0225 + }, + "velocity_range": {}, + }, + ) + + set_robot_to_grasp_pose = EventTerm( + func=gear_assembly_events.set_robot_to_grasp_pose, + mode="reset", + params={ + "robot_asset_cfg": SceneEntityCfg("robot"), + "pos_randomization_range": {"x": [-0.0, 0.0], "y": [-0.005, 0.005], "z": [-0.003, 0.003]}, + }, + ) + + +@configclass +class UR10eGearAssemblyEnvCfg(GearAssemblyEnvCfg): + """Base configuration for UR10e Gear Assembly Environment. + + This class contains common setup shared across different gripper configurations. + Subclasses should configure gripper-specific parameters. + """ + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Robot-specific parameters (can be overridden for other robots) + self.end_effector_body_name = "wrist_3_link" # End effector body name for IK and termination checks + self.num_arm_joints = 6 # Number of arm joints (excluding gripper) + self.grasp_rot_offset = [ + 0.0, + math.sqrt(2) / 2, + math.sqrt(2) / 2, + 0.0, + ] # Rotation offset for grasp pose (quaternion [w, x, y, z]) + self.gripper_joint_setter_func = None # Gripper-specific joint setter function (set in subclass) + + # Gear orientation termination thresholds (in degrees) + self.gear_orientation_roll_threshold_deg = 15.0 # Maximum allowed roll deviation + self.gear_orientation_pitch_threshold_deg = 15.0 # Maximum allowed pitch deviation + self.gear_orientation_yaw_threshold_deg = 180.0 # Maximum allowed yaw deviation + + # Common observation configuration + self.observations.policy.joint_pos.params["asset_cfg"].joint_names = [ + "shoulder_pan_joint", + "shoulder_lift_joint", + "elbow_joint", + "wrist_1_joint", + "wrist_2_joint", + "wrist_3_joint", + ] + self.observations.policy.joint_vel.params["asset_cfg"].joint_names = [ + "shoulder_pan_joint", + "shoulder_lift_joint", + "elbow_joint", + "wrist_1_joint", + "wrist_2_joint", + "wrist_3_joint", + ] + + # override events + self.events = EventCfg() + + # Update termination thresholds from config + self.terminations.gear_orientation_exceeded.params["roll_threshold_deg"] = ( + self.gear_orientation_roll_threshold_deg + ) + self.terminations.gear_orientation_exceeded.params["pitch_threshold_deg"] = ( + self.gear_orientation_pitch_threshold_deg + ) + self.terminations.gear_orientation_exceeded.params["yaw_threshold_deg"] = ( + self.gear_orientation_yaw_threshold_deg + ) + + # override command generator body + self.joint_action_scale = 0.025 + self.actions.arm_action = mdp.RelativeJointPositionActionCfg( + asset_name="robot", + joint_names=[ + "shoulder_pan_joint", + "shoulder_lift_joint", + "elbow_joint", + "wrist_1_joint", + "wrist_2_joint", + "wrist_3_joint", + ], + scale=self.joint_action_scale, + use_zero_offset=True, + ) + + +@configclass +class UR10e2F140GearAssemblyEnvCfg(UR10eGearAssemblyEnvCfg): + """Configuration for UR10e with Robotiq 2F-140 gripper.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to ur10e with 2F-140 gripper + self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=UR10e_ROBOTIQ_GRIPPER_CFG.spawn.replace( + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=1 + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + # Joint positions based on IK from center of distribution for randomized gear positions + # This is done so that the start for the differential IK search after randomizing + # is close to the optimal grasp pose + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "shoulder_pan_joint": 2.7228, + "shoulder_lift_joint": -8.3962e-01, + "elbow_joint": 1.3684, + "wrist_1_joint": -2.1048, + "wrist_2_joint": -1.5691, + "wrist_3_joint": -1.9896, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ) + + # 2F-140 gripper actuator configuration + self.scene.robot.actuators["gripper_finger"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_joint"], + effort_limit_sim=10.0, + velocity_limit_sim=10.0, + stiffness=10.0, + damping=0.05, + friction=0.0, + armature=0.0, + ) + + # Set gripper-specific joint setter function + self.gripper_joint_setter_func = set_finger_joint_pos_robotiq_2f140 + + # gear offsets and grasp positions for the 2F-140 gripper + self.gear_offsets_grasp = { + "gear_small": [0.0, self.gear_offsets["gear_small"][0], -0.26], + "gear_medium": [0.0, self.gear_offsets["gear_medium"][0], -0.26], + "gear_large": [0.0, self.gear_offsets["gear_large"][0], -0.26], + } + + # Grasp widths for 2F-140 gripper + self.hand_grasp_width = {"gear_small": 0.64, "gear_medium": 0.54, "gear_large": 0.51} + + # Close widths for 2F-140 gripper + self.hand_close_width = {"gear_small": 0.69, "gear_medium": 0.59, "gear_large": 0.56} + + # Populate event term parameters + self.events.set_robot_to_grasp_pose.params["gear_offsets_grasp"] = self.gear_offsets_grasp + self.events.set_robot_to_grasp_pose.params["end_effector_body_name"] = self.end_effector_body_name + self.events.set_robot_to_grasp_pose.params["num_arm_joints"] = self.num_arm_joints + self.events.set_robot_to_grasp_pose.params["grasp_rot_offset"] = self.grasp_rot_offset + self.events.set_robot_to_grasp_pose.params["gripper_joint_setter_func"] = self.gripper_joint_setter_func + + # Populate termination term parameters + self.terminations.gear_dropped.params["gear_offsets_grasp"] = self.gear_offsets_grasp + self.terminations.gear_dropped.params["end_effector_body_name"] = self.end_effector_body_name + self.terminations.gear_dropped.params["grasp_rot_offset"] = self.grasp_rot_offset + + self.terminations.gear_orientation_exceeded.params["end_effector_body_name"] = self.end_effector_body_name + self.terminations.gear_orientation_exceeded.params["grasp_rot_offset"] = self.grasp_rot_offset + + +@configclass +class UR10e2F85GearAssemblyEnvCfg(UR10eGearAssemblyEnvCfg): + """Configuration for UR10e with Robotiq 2F-85 gripper.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to ur10e with 2F-85 gripper + self.scene.robot = UR10e_ROBOTIQ_2F_85_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=UR10e_ROBOTIQ_2F_85_CFG.spawn.replace( + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=4, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=1 + ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), + ), + # Joint positions based on IK from center of distribution for randomized gear positions + # This is done so that the start for the differential IK search after randomizing + # is close to the optimal grasp pose + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "shoulder_pan_joint": 2.7228, + "shoulder_lift_joint": -8.3962e-01, + "elbow_joint": 1.3684, + "wrist_1_joint": -2.1048, + "wrist_2_joint": -1.5691, + "wrist_3_joint": -1.9896, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + ) + + # 2F-85 gripper actuator configuration (higher effort limits than 2F-140) + self.scene.robot.actuators["gripper_finger"] = ImplicitActuatorCfg( + joint_names_expr=[".*_inner_finger_joint"], + effort_limit_sim=10.0, + velocity_limit_sim=10.0, + stiffness=10.0, + damping=0.05, + friction=0.0, + armature=0.0, + ) + self.scene.robot.actuators["gripper_drive"] = ImplicitActuatorCfg( + joint_names_expr=["finger_joint"], + effort_limit_sim=10.0, + velocity_limit_sim=1.0, + stiffness=40.0, + damping=1.0, + friction=0.0, + armature=0.0, + ) + + # Set gripper-specific joint setter function + self.gripper_joint_setter_func = set_finger_joint_pos_robotiq_2f85 + + # gear offsets and grasp positions for the 2F-85 gripper + self.gear_offsets_grasp = { + "gear_small": [0.0, self.gear_offsets["gear_small"][0], -0.19], + "gear_medium": [0.0, self.gear_offsets["gear_medium"][0], -0.19], + "gear_large": [0.0, self.gear_offsets["gear_large"][0], -0.19], + } + + # Grasp widths for 2F-85 gripper + self.hand_grasp_width = {"gear_small": 0.64, "gear_medium": 0.46, "gear_large": 0.4} + + # Close widths for 2F-85 gripper + self.hand_close_width = {"gear_small": 0.69, "gear_medium": 0.51, "gear_large": 0.45} + + # Populate event term parameters + self.events.set_robot_to_grasp_pose.params["gear_offsets_grasp"] = self.gear_offsets_grasp + self.events.set_robot_to_grasp_pose.params["end_effector_body_name"] = self.end_effector_body_name + self.events.set_robot_to_grasp_pose.params["num_arm_joints"] = self.num_arm_joints + self.events.set_robot_to_grasp_pose.params["grasp_rot_offset"] = self.grasp_rot_offset + self.events.set_robot_to_grasp_pose.params["gripper_joint_setter_func"] = self.gripper_joint_setter_func + + # Populate termination term parameters + self.terminations.gear_dropped.params["gear_offsets_grasp"] = self.gear_offsets_grasp + self.terminations.gear_dropped.params["end_effector_body_name"] = self.end_effector_body_name + self.terminations.gear_dropped.params["grasp_rot_offset"] = self.grasp_rot_offset + + self.terminations.gear_orientation_exceeded.params["end_effector_body_name"] = self.end_effector_body_name + self.terminations.gear_orientation_exceeded.params["grasp_rot_offset"] = self.grasp_rot_offset + + +@configclass +class UR10e2F140GearAssemblyEnvCfg_PLAY(UR10e2F140GearAssemblyEnvCfg): + """Play configuration for UR10e with Robotiq 2F-140 gripper.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + + +@configclass +class UR10e2F85GearAssemblyEnvCfg_PLAY(UR10e2F85GearAssemblyEnvCfg): + """Play configuration for UR10e with Robotiq 2F-85 gripper.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/ros_inference_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/ros_inference_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..450a454f78f06922fdf13ee2208c79b61253505f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/config/ur_10e/ros_inference_env_cfg.py @@ -0,0 +1,197 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.assets import RigidObjectCfg +from isaaclab.utils import configclass + +from .joint_pos_env_cfg import UR10e2F85GearAssemblyEnvCfg, UR10e2F140GearAssemblyEnvCfg + + +@configclass +class UR10e2F140GearAssemblyROSInferenceEnvCfg(UR10e2F140GearAssemblyEnvCfg): + """Configuration for ROS inference with UR10e and Robotiq 2F-140 gripper. + + This configuration: + - Exposes variables needed for ROS inference + - Overrides robot and gear initial poses for fixed/deterministic setup + """ + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Variables used by Isaac Manipulator for on robot inference + # These parameters allow the ROS inference node to validate environment configuration, + # perform checks during inference, and correctly interpret observations and actions. + self.obs_order = ["arm_dof_pos", "arm_dof_vel", "shaft_pos", "shaft_quat"] + self.policy_action_space = "joint" + # Use inherited joint names from parent's observation configuration + self.arm_joint_names = self.observations.policy.joint_pos.params["asset_cfg"].joint_names + # Use inherited num_arm_joints from parent + self.action_space = self.num_arm_joints + # State space and observation space are set as constants for now + self.state_space = 42 + self.observation_space = 19 + + # Set joint_action_scale from the existing arm_action.scale + self.joint_action_scale = self.actions.arm_action.scale + + # Dynamically generate action_scale_joint_space based on action_space + self.action_scale_joint_space = [self.joint_action_scale] * self.action_space + + # Override robot initial pose for ROS inference (fixed pose, no randomization) + # Note: The policy is trained to work with respect to the UR robot's 'base' frame + # (rotated 180° around Z from base_link), not the base_link frame (USD origin). + # See: https://docs.universal-robots.com/Universal_Robots_ROS2_Documentation/doc/ur_description/doc/robot_frames.html + # Joint positions and pos are inherited from parent, only override rotation to be deterministic + self.scene.robot.init_state.rot = (0.0, 0.0, 0.0, 1.0) + + # Override gear base initial pose (fixed pose for ROS inference) + self.scene.factory_gear_base.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Override gear initial poses (fixed poses for ROS inference) + # Small gear + self.scene.factory_gear_small.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), # z = base_z + 0.1675 (above base) + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Medium gear + self.scene.factory_gear_medium.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Large gear + self.scene.factory_gear_large.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Fixed asset parameters for ROS inference - derived from configuration + # These parameters are used by the ROS inference node to validate the environment setup + # and apply appropriate noise models for robust real-world deployment. + # Derive position center from gear base init state + self.fixed_asset_init_pos_center = list(self.scene.factory_gear_base.init_state.pos) + # Derive position range from parent's randomize_gears_and_base_pose event pose_range + pose_range = self.events.randomize_gears_and_base_pose.params["pose_range"] + self.fixed_asset_init_pos_range = [ + pose_range["x"][1], # max value + pose_range["y"][1], # max value + pose_range["z"][1], # max value + ] + # Orientation in degrees (quaternion (-0.70711, 0.0, 0.0, 0.70711) = -90° around Z) + self.fixed_asset_init_orn_deg = [0.0, 0.0, -90.0] + # Derive orientation range from parent's pose_range (radians to degrees) + self.fixed_asset_init_orn_deg_range = [ + math.degrees(pose_range["roll"][1]), # convert radians to degrees + math.degrees(pose_range["pitch"][1]), + math.degrees(pose_range["yaw"][1]), + ] + # Derive observation noise level from parent's gear_shaft_pos noise configuration + gear_shaft_pos_noise = self.observations.policy.gear_shaft_pos.noise.noise_cfg.n_max + self.fixed_asset_pos_obs_noise_level = [ + gear_shaft_pos_noise, + gear_shaft_pos_noise, + gear_shaft_pos_noise, + ] + + +@configclass +class UR10e2F85GearAssemblyROSInferenceEnvCfg(UR10e2F85GearAssemblyEnvCfg): + """Configuration for ROS inference with UR10e and Robotiq 2F-85 gripper. + + This configuration: + - Exposes variables needed for ROS inference + - Overrides robot and gear initial poses for fixed/deterministic setup + """ + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Variables used by Isaac Manipulator for on robot inference + # These parameters allow the ROS inference node to validate environment configuration, + # perform checks during inference, and correctly interpret observations and actions. + self.obs_order = ["arm_dof_pos", "arm_dof_vel", "shaft_pos", "shaft_quat"] + self.policy_action_space = "joint" + # Use inherited joint names from parent's observation configuration + self.arm_joint_names = self.observations.policy.joint_pos.params["asset_cfg"].joint_names + # Use inherited num_arm_joints from parent + self.action_space = self.num_arm_joints + # State space and observation space are set as constants for now + self.state_space = 38 + self.observation_space = 19 + + # Set joint_action_scale from the existing arm_action.scale + self.joint_action_scale = self.actions.arm_action.scale + + # Dynamically generate action_scale_joint_space based on action_space + self.action_scale_joint_space = [self.joint_action_scale] * self.action_space + + # Override robot initial pose for ROS inference (fixed pose, no randomization) + # Note: The policy is trained to work with respect to the UR robot's 'base' frame + # (rotated 180° around Z from base_link), not the base_link frame (USD origin). + # See: https://docs.universal-robots.com/Universal_Robots_ROS2_Documentation/doc/ur_description/doc/robot_frames.html + # Joint positions and pos are inherited from parent, only override rotation to be deterministic + self.scene.robot.init_state.rot = (0.0, 0.0, 0.0, 1.0) + + # Override gear base initial pose (fixed pose for ROS inference) + self.scene.factory_gear_base.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Override gear initial poses (fixed poses for ROS inference) + # Small gear + self.scene.factory_gear_small.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), # z = base_z + 0.1675 (above base) + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Medium gear + self.scene.factory_gear_medium.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Large gear + self.scene.factory_gear_large.init_state = RigidObjectCfg.InitialStateCfg( + pos=(1.0200, -0.2100, -0.1), + rot=(-0.70711, 0.0, 0.0, 0.70711), + ) + + # Fixed asset parameters for ROS inference - derived from configuration + # These parameters are used by the ROS inference node to validate the environment setup + # and apply appropriate noise models for robust real-world deployment. + # Derive position center from gear base init state + self.fixed_asset_init_pos_center = list(self.scene.factory_gear_base.init_state.pos) + # Derive position range from parent's randomize_gears_and_base_pose event pose_range + pose_range = self.events.randomize_gears_and_base_pose.params["pose_range"] + self.fixed_asset_init_pos_range = [ + pose_range["x"][1], # max value + pose_range["y"][1], # max value + pose_range["z"][1], # max value + ] + # Orientation in degrees (quaternion (-0.70711, 0.0, 0.0, 0.70711) = -90° around Z) + self.fixed_asset_init_orn_deg = [0.0, 0.0, -90.0] + # Derive orientation range from parent's pose_range (radians to degrees) + self.fixed_asset_init_orn_deg_range = [ + math.degrees(pose_range["roll"][1]), # convert radians to degrees + math.degrees(pose_range["pitch"][1]), + math.degrees(pose_range["yaw"][1]), + ] + # Derive observation noise level from parent's gear_shaft_pos noise configuration + gear_shaft_pos_noise = self.observations.policy.gear_shaft_pos.noise.noise_cfg.n_max + self.fixed_asset_pos_obs_noise_level = [ + gear_shaft_pos_noise, + gear_shaft_pos_noise, + gear_shaft_pos_noise, + ] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/gear_assembly_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/gear_assembly_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8a15d7b3a52d137887916081f83056676953b6eb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/gear_assembly/gear_assembly_env_cfg.py @@ -0,0 +1,330 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg as ActionTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.simulation_cfg import PhysxCfg, SimulationCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import UniformNoiseCfg + +import isaaclab_tasks.manager_based.manipulation.deploy.mdp as mdp +import isaaclab_tasks.manager_based.manipulation.deploy.mdp.terminations as gear_assembly_terminations +from isaaclab_tasks.manager_based.manipulation.deploy.mdp.noise_models import ResetSampledConstantNoiseModelCfg + +# Get the directory where this configuration file is located +CONFIG_DIR = os.path.dirname(os.path.abspath(__file__)) +ASSETS_DIR = os.path.join(CONFIG_DIR, "assets") + +## +# Environment configuration +## + + +@configclass +class GearAssemblySceneCfg(InteractiveSceneCfg): + """Configuration for the scene with a robotic arm.""" + + # Disable scene replication to allow USD-level randomization + replicate_physics = False + + # world + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), + ) + + factory_gear_base = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryGearBase", + # TODO: change to common isaac sim directory + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Factory/gear_assets/factory_gear_base/factory_gear_base.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + kinematic_enabled=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=32, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=None), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0200, 0.2100, -0.1), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + factory_gear_small = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryGearSmall", + # TODO: change to common isaac sim directory + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Factory/gear_assets/factory_gear_small/factory_gear_small.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + kinematic_enabled=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=32, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=None), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0200, 0.2100, -0.1), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + factory_gear_medium = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryGearMedium", + # TODO: change to common isaac sim directory + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Factory/gear_assets/factory_gear_medium/factory_gear_medium.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + kinematic_enabled=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=32, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=None), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0200, 0.2100, -0.1), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + factory_gear_large = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryGearLarge", + # TODO: change to common isaac sim directory + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Factory/gear_assets/factory_gear_large/factory_gear_large.usd", + activate_contact_sensors=False, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + kinematic_enabled=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=32, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=None), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.02, rest_offset=0.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0200, 0.2100, -0.1), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + # robots + robot: ArticulationCfg = MISSING + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), + ) + + stand = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Stand", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: ActionTerm = MISSING + gripper_action: ActionTerm | None = None + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos = ObsTerm(func=mdp.joint_pos, params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*"])}) + joint_vel = ObsTerm(func=mdp.joint_vel, params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*"])}) + gear_shaft_pos = ObsTerm( + func=mdp.gear_shaft_pos_w, + params={}, # Will be populated in __post_init__ + noise=ResetSampledConstantNoiseModelCfg( + noise_cfg=UniformNoiseCfg(n_min=-0.005, n_max=0.005, operation="add") + ), + ) + gear_shaft_quat = ObsTerm(func=mdp.gear_shaft_quat_w) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + @configclass + class CriticCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos = ObsTerm(func=mdp.joint_pos, params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*"])}) + joint_vel = ObsTerm(func=mdp.joint_vel, params={"asset_cfg": SceneEntityCfg("robot", joint_names=[".*"])}) + gear_shaft_pos = ObsTerm(func=mdp.gear_shaft_pos_w, params={}) # Will be populated in __post_init__ + gear_shaft_quat = ObsTerm(func=mdp.gear_shaft_quat_w) + + gear_pos = ObsTerm(func=mdp.gear_pos_w) + gear_quat = ObsTerm(func=mdp.gear_quat_w) + + # observation groups + policy: PolicyCfg = PolicyCfg() + critic: CriticCfg = CriticCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_gear = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.05, 0.05], + "y": [-0.05, 0.05], + "z": [0.1, 0.15], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("factory_gear_small"), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + end_effector_gear_keypoint_tracking = RewTerm( + func=mdp.keypoint_entity_error, + weight=-1.5, + params={ + "asset_cfg_1": SceneEntityCfg("factory_gear_base"), + "keypoint_scale": 0.15, + }, + ) + + end_effector_gear_keypoint_tracking_exp = RewTerm( + func=mdp.keypoint_entity_error_exp, + weight=1.5, + params={ + "asset_cfg_1": SceneEntityCfg("factory_gear_base"), + "kp_exp_coeffs": [(50, 0.0001), (300, 0.0001)], + "kp_use_sum_of_exps": False, + "keypoint_scale": 0.15, + }, + ) + + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-5.0e-06) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + gear_dropped = DoneTerm( + func=gear_assembly_terminations.reset_when_gear_dropped, + params={ + "distance_threshold": 0.15, # 15cm from gripper + "robot_asset_cfg": SceneEntityCfg("robot"), + }, + ) + + gear_orientation_exceeded = DoneTerm( + func=gear_assembly_terminations.reset_when_gear_orientation_exceeds_threshold, + params={ + "roll_threshold_deg": 7.0, # Maximum roll deviation in degrees + "pitch_threshold_deg": 7.0, # Maximum pitch deviation in degrees + "yaw_threshold_deg": 180.0, # Maximum yaw deviation in degrees + "robot_asset_cfg": SceneEntityCfg("robot"), + }, + ) + + +@configclass +class GearAssemblyEnvCfg(ManagerBasedRLEnvCfg): + # Scene settings + scene: GearAssemblySceneCfg = GearAssemblySceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + sim: SimulationCfg = SimulationCfg( + physx=PhysxCfg( + # Important to prevent collisionStackSize buffer overflow in contact-rich environments. + gpu_collision_stack_size=2**28, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + ), + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.episode_length_s = 6.66 + self.viewer.eye = (3.5, 3.5, 3.5) + # simulation settings + self.decimation = 4 + self.sim.render_interval = self.decimation + self.sim.dt = 1.0 / 120.0 + + self.gear_offsets = { + "gear_small": [0.076125, 0.0, 0.0], + "gear_medium": [0.030375, 0.0, 0.0], + "gear_large": [-0.045375, 0.0, 0.0], + } + + # Populate observation term parameters with gear offsets + self.observations.policy.gear_shaft_pos.params["gear_offsets"] = self.gear_offsets + self.observations.critic.gear_shaft_pos.params["gear_offsets"] = self.gear_offsets diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..10ab3ea7e7fd926d24ac778e16408dd621acb817 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the locomotion environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .events import * # noqa: F401, F403 +from .noise_models import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/events.py new file mode 100644 index 0000000000000000000000000000000000000000..7666875435fb58f53ddfae1145e0ad8833c5255c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/events.py @@ -0,0 +1,481 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Class-based event terms specific to the gear assembly manipulation environments.""" + +from __future__ import annotations + +import random +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import EventTermCfg, ManagerTermBase, SceneEntityCfg + +from isaaclab_tasks.direct.automate import factory_control as fc + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +class randomize_gear_type(ManagerTermBase): + """Randomize and manage the gear type being used for each environment. + + This class stores the current gear type for each environment and provides a mapping + from gear type names to indices. It serves as the central manager for gear type state + that other MDP terms depend on. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the gear type randomization term. + + Args: + cfg: Event term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Extract gear types from config (required parameter) + if "gear_types" not in cfg.params: + raise ValueError("'gear_types' parameter is required in randomize_gear_type configuration") + self.gear_types: list[str] = cfg.params["gear_types"] + + # Create gear type mapping (shared across all terms) + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + + # Store current gear type for each environment (as list for easy access) + # Initialize all to first gear type in the list + self._current_gear_type = [self.gear_types[0]] * env.num_envs + + # Store current gear type indices as tensor for efficient vectorized access + # Initialize all to first gear type index + first_gear_idx = self.gear_type_map[self.gear_types[0]] + self._current_gear_type_indices = torch.full( + (env.num_envs,), first_gear_idx, device=env.device, dtype=torch.long + ) + + # Store reference on environment for other terms to access + env._gear_type_manager = self + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + gear_types: list[str] = ["gear_small", "gear_medium", "gear_large"], + ): + """Randomize the gear type for specified environments. + + Args: + env: The environment containing the assets + env_ids: Environment IDs to randomize + gear_types: List of available gear types to choose from + """ + # Randomly select gear type for each environment + # Use the parameter passed to __call__ (not self.gear_types) to allow runtime overrides + for env_id in env_ids.tolist(): + chosen_gear = random.choice(gear_types) + self._current_gear_type[env_id] = chosen_gear + self._current_gear_type_indices[env_id] = self.gear_type_map[chosen_gear] + + def get_gear_type(self, env_id: int) -> str: + """Get the current gear type for a specific environment.""" + return self._current_gear_type[env_id] + + def get_all_gear_types(self) -> list[str]: + """Get current gear types for all environments.""" + return self._current_gear_type + + def get_all_gear_type_indices(self) -> torch.Tensor: + """Get current gear type indices for all environments as a tensor. + + Returns: + Tensor of shape (num_envs,) with gear type indices (0=small, 1=medium, 2=large) + """ + return self._current_gear_type_indices + + +class set_robot_to_grasp_pose(ManagerTermBase): + """Set robot to grasp pose using IK with pre-cached tensors. + + This class-based term caches all required tensors and gear offsets during initialization, + avoiding repeated allocations and lookups during execution. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the set robot to grasp pose term. + + Args: + cfg: Event term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Get robot asset configuration + self.robot_asset_cfg: SceneEntityCfg = cfg.params.get("robot_asset_cfg", SceneEntityCfg("robot")) + self.robot_asset: Articulation = env.scene[self.robot_asset_cfg.name] + + # Get robot-specific parameters from environment config (all required) + # Validate required parameters + if "end_effector_body_name" not in cfg.params: + raise ValueError( + "'end_effector_body_name' parameter is required in set_robot_to_grasp_pose configuration. " + "Example: 'wrist_3_link'" + ) + if "num_arm_joints" not in cfg.params: + raise ValueError( + "'num_arm_joints' parameter is required in set_robot_to_grasp_pose configuration. Example: 6 for UR10e" + ) + if "grasp_rot_offset" not in cfg.params: + raise ValueError( + "'grasp_rot_offset' parameter is required in set_robot_to_grasp_pose configuration. " + "It should be a quaternion [w, x, y, z]. Example: [0.0, 0.707, 0.707, 0.0]" + ) + if "gripper_joint_setter_func" not in cfg.params: + raise ValueError( + "'gripper_joint_setter_func' parameter is required in set_robot_to_grasp_pose configuration. " + "It should be a function to set gripper joint positions." + ) + + self.end_effector_body_name = cfg.params["end_effector_body_name"] + self.num_arm_joints = cfg.params["num_arm_joints"] + self.gripper_joint_setter_func = cfg.params["gripper_joint_setter_func"] + + # Pre-cache gear grasp offsets as tensors (required parameter) + if "gear_offsets_grasp" not in cfg.params: + raise ValueError( + "'gear_offsets_grasp' parameter is required in set_robot_to_grasp_pose configuration. " + "It should be a dict with keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + gear_offsets_grasp = cfg.params["gear_offsets_grasp"] + if not isinstance(gear_offsets_grasp, dict): + raise TypeError( + f"'gear_offsets_grasp' parameter must be a dict, got {type(gear_offsets_grasp).__name__}. " + "It should have keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + + self.gear_grasp_offset_tensors = {} + for gear_type in ["gear_small", "gear_medium", "gear_large"]: + if gear_type not in gear_offsets_grasp: + raise ValueError( + f"'{gear_type}' offset is required in 'gear_offsets_grasp' parameter. " + f"Found keys: {list(gear_offsets_grasp.keys())}" + ) + self.gear_grasp_offset_tensors[gear_type] = torch.tensor( + gear_offsets_grasp[gear_type], device=env.device, dtype=torch.float32 + ) + + # Stack grasp offset tensors for vectorized indexing (shape: 3, 3) + # Index 0=small, 1=medium, 2=large + self.gear_grasp_offsets_stacked = torch.stack( + [ + self.gear_grasp_offset_tensors["gear_small"], + self.gear_grasp_offset_tensors["gear_medium"], + self.gear_grasp_offset_tensors["gear_large"], + ], + dim=0, + ) + + # Pre-cache grasp rotation offset tensor + grasp_rot_offset = cfg.params["grasp_rot_offset"] + self.grasp_rot_offset_tensor = ( + torch.tensor(grasp_rot_offset, device=env.device, dtype=torch.float32).unsqueeze(0).repeat(env.num_envs, 1) + ) + + # Pre-allocate buffers for batch operations + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.local_env_indices = torch.arange(env.num_envs, device=env.device) + self.gear_grasp_offsets_buffer = torch.zeros(env.num_envs, 3, device=env.device, dtype=torch.float32) + + # Cache hand grasp/close widths + self.hand_grasp_width = env.cfg.hand_grasp_width + self.hand_close_width = env.cfg.hand_close_width + + # Find end effector index once + eef_indices, _ = self.robot_asset.find_bodies([self.end_effector_body_name]) + if len(eef_indices) == 0: + raise ValueError(f"End effector body '{self.end_effector_body_name}' not found in robot") + self.eef_idx = eef_indices[0] + + # Find jacobian body index (for fixed-base robots, subtract 1) + self.jacobi_body_idx = self.eef_idx - 1 + + # Find all joints once + all_joints, all_joints_names = self.robot_asset.find_joints([".*"]) + self.all_joints = all_joints + self.finger_joints = all_joints[self.num_arm_joints :] + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + robot_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + pos_threshold: float = 1e-6, + rot_threshold: float = 1e-6, + max_iterations: int = 10, + pos_randomization_range: dict | None = None, + gear_offsets_grasp: dict | None = None, + end_effector_body_name: str | None = None, + num_arm_joints: int | None = None, + grasp_rot_offset: list | None = None, + gripper_joint_setter_func: callable | None = None, + ): + """Set robot to grasp pose using IK. + + Args: + env: Environment instance + env_ids: Environment IDs to reset + robot_asset_cfg: Robot asset configuration (unused, kept for compatibility) + pos_threshold: Position convergence threshold + rot_threshold: Rotation convergence threshold + max_iterations: Maximum IK iterations + pos_randomization_range: Optional position randomization range + """ + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this event term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + + # Slice buffers for current batch size + num_reset_envs = len(env_ids) + gear_type_indices = self.gear_type_indices[:num_reset_envs] + local_env_indices = self.local_env_indices[:num_reset_envs] + gear_grasp_offsets = self.gear_grasp_offsets_buffer[:num_reset_envs] + grasp_rot_offset_tensor = self.grasp_rot_offset_tensor[env_ids] + + # IK loop + for i in range(max_iterations): + # Get current joint state + joint_pos = self.robot_asset.data.joint_pos[env_ids].clone() + joint_vel = self.robot_asset.data.joint_vel[env_ids].clone() + + # Stack all gear positions and quaternions + all_gear_pos = torch.stack( + [ + env.scene["factory_gear_small"].data.root_link_pos_w, + env.scene["factory_gear_medium"].data.root_link_pos_w, + env.scene["factory_gear_large"].data.root_link_pos_w, + ], + dim=1, + )[env_ids] + + all_gear_quat = torch.stack( + [ + env.scene["factory_gear_small"].data.root_link_quat_w, + env.scene["factory_gear_medium"].data.root_link_quat_w, + env.scene["factory_gear_large"].data.root_link_quat_w, + ], + dim=1, + )[env_ids] + + # Get gear type indices directly as tensor + all_gear_type_indices = gear_type_manager.get_all_gear_type_indices() + gear_type_indices[:] = all_gear_type_indices[env_ids] + + # Select gear data using advanced indexing + grasp_object_pos_world = all_gear_pos[local_env_indices, gear_type_indices] + grasp_object_quat = all_gear_quat[local_env_indices, gear_type_indices] + + # Apply rotation offset + grasp_object_quat = math_utils.quat_mul(grasp_object_quat, grasp_rot_offset_tensor) + + # Get grasp offsets (vectorized) + gear_grasp_offsets[:] = self.gear_grasp_offsets_stacked[gear_type_indices] + + # Add position randomization if specified + if pos_randomization_range is not None: + pos_keys = ["x", "y", "z"] + range_list_pos = [pos_randomization_range.get(key, (0.0, 0.0)) for key in pos_keys] + ranges_pos = torch.tensor(range_list_pos, device=env.device) + rand_pos_offsets = math_utils.sample_uniform( + ranges_pos[:, 0], ranges_pos[:, 1], (len(env_ids), 3), device=env.device + ) + gear_grasp_offsets = gear_grasp_offsets + rand_pos_offsets + + # Transform offsets from gear frame to world frame + grasp_object_pos_world = grasp_object_pos_world + math_utils.quat_apply( + grasp_object_quat, gear_grasp_offsets + ) + + # Get end effector pose + eef_pos = self.robot_asset.data.body_pos_w[env_ids, self.eef_idx] + eef_quat = self.robot_asset.data.body_quat_w[env_ids, self.eef_idx] + + # Compute pose error + pos_error, axis_angle_error = fc.get_pose_error( + fingertip_midpoint_pos=eef_pos, + fingertip_midpoint_quat=eef_quat, + ctrl_target_fingertip_midpoint_pos=grasp_object_pos_world, + ctrl_target_fingertip_midpoint_quat=grasp_object_quat, + jacobian_type="geometric", + rot_error_type="axis_angle", + ) + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + + # Check convergence + pos_error_norm = torch.norm(pos_error, dim=-1) + rot_error_norm = torch.norm(axis_angle_error, dim=-1) + + if torch.all(pos_error_norm < pos_threshold) and torch.all(rot_error_norm < rot_threshold): + break + + # Solve IK using jacobian + jacobians = self.robot_asset.root_physx_view.get_jacobians().clone() + jacobian = jacobians[env_ids, self.jacobi_body_idx, :, :] + + delta_dof_pos = fc._get_delta_dof_pos( + delta_pose=delta_hand_pose, + ik_method="dls", + jacobian=jacobian, + device=env.device, + ) + + # Update joint positions + joint_pos = joint_pos + delta_dof_pos + joint_vel = torch.zeros_like(joint_pos) + + # Write to sim + self.robot_asset.set_joint_position_target(joint_pos, env_ids=env_ids) + self.robot_asset.set_joint_velocity_target(joint_vel, env_ids=env_ids) + self.robot_asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + # Set gripper to grasp position + joint_pos = self.robot_asset.data.joint_pos[env_ids].clone() + + # Get gear types for all environments + all_gear_types = gear_type_manager.get_all_gear_types() + for row_idx, env_id in enumerate(env_ids.tolist()): + gear_key = all_gear_types[env_id] + hand_grasp_width = self.hand_grasp_width[gear_key] + self.gripper_joint_setter_func(joint_pos, [row_idx], self.finger_joints, hand_grasp_width) + + self.robot_asset.set_joint_position_target(joint_pos, joint_ids=self.all_joints, env_ids=env_ids) + self.robot_asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + # Set gripper to closed position + for row_idx, env_id in enumerate(env_ids.tolist()): + gear_key = all_gear_types[env_id] + hand_close_width = self.hand_close_width[gear_key] + self.gripper_joint_setter_func(joint_pos, [row_idx], self.finger_joints, hand_close_width) + + self.robot_asset.set_joint_position_target(joint_pos, joint_ids=self.all_joints, env_ids=env_ids) + + +class randomize_gears_and_base_pose(ManagerTermBase): + """Randomize both the gear base pose and individual gear poses. + + This class-based term pre-caches all tensors needed for randomization. + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + """Initialize the randomize gears and base pose term. + + Args: + cfg: Event term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Pre-allocate gear type mapping and indices + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + + # Cache asset names + self.gear_asset_names = ["factory_gear_small", "factory_gear_medium", "factory_gear_large"] + self.base_asset_name = "factory_gear_base" + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict = {}, + velocity_range: dict = {}, + gear_pos_range: dict = {}, + ): + """Randomize gear base and gear poses. + + Args: + env: Environment instance + env_ids: Environment IDs to randomize + pose_range: Pose randomization range for base and all gears + velocity_range: Velocity randomization range + gear_pos_range: Additional position randomization for selected gear only + """ + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this event term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + device = env.device + + # Shared pose samples for all assets + pose_keys = ["x", "y", "z", "roll", "pitch", "yaw"] + range_list_pose = [pose_range.get(key, (0.0, 0.0)) for key in pose_keys] + ranges_pose = torch.tensor(range_list_pose, device=device) + rand_pose_samples = math_utils.sample_uniform( + ranges_pose[:, 0], ranges_pose[:, 1], (len(env_ids), 6), device=device + ) + + orientations_delta = math_utils.quat_from_euler_xyz( + rand_pose_samples[:, 3], rand_pose_samples[:, 4], rand_pose_samples[:, 5] + ) + + # Shared velocity samples + range_list_vel = [velocity_range.get(key, (0.0, 0.0)) for key in pose_keys] + ranges_vel = torch.tensor(range_list_vel, device=device) + rand_vel_samples = math_utils.sample_uniform( + ranges_vel[:, 0], ranges_vel[:, 1], (len(env_ids), 6), device=device + ) + + # Prepare poses for all assets + positions_by_asset = {} + orientations_by_asset = {} + velocities_by_asset = {} + + asset_names_to_process = [self.base_asset_name] + self.gear_asset_names + for asset_name in asset_names_to_process: + asset: RigidObject | Articulation = env.scene[asset_name] + root_states = asset.data.default_root_state[env_ids].clone() + positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_pose_samples[:, 0:3] + orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) + velocities = root_states[:, 7:13] + rand_vel_samples + positions_by_asset[asset_name] = positions + orientations_by_asset[asset_name] = orientations + velocities_by_asset[asset_name] = velocities + + # Per-env gear offset (gear_pos_range) applied only to selected gear + range_list_gear = [gear_pos_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges_gear = torch.tensor(range_list_gear, device=device) + rand_gear_offsets = math_utils.sample_uniform( + ranges_gear[:, 0], ranges_gear[:, 1], (len(env_ids), 3), device=device + ) + + # Get gear type indices directly as tensor + num_reset_envs = len(env_ids) + gear_type_indices = self.gear_type_indices[:num_reset_envs] + all_gear_type_indices = gear_type_manager.get_all_gear_type_indices() + gear_type_indices[:] = all_gear_type_indices[env_ids] + + # Apply offsets using vectorized operations with masks + for gear_idx, asset_name in enumerate(self.gear_asset_names): + if asset_name in positions_by_asset: + mask = gear_type_indices == gear_idx + positions_by_asset[asset_name][mask] = positions_by_asset[asset_name][mask] + rand_gear_offsets[mask] + + # Write to sim + for asset_name in positions_by_asset.keys(): + asset = env.scene[asset_name] + positions = positions_by_asset[asset_name] + orientations = orientations_by_asset[asset_name] + velocities = velocities_by_asset[asset_name] + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/noise_models.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/noise_models.py new file mode 100644 index 0000000000000000000000000000000000000000..2d5411e9697715bd24f779aa0175a2e1279a0908 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/noise_models.py @@ -0,0 +1,109 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Noise models specific to deployment tasks.""" + +from __future__ import annotations + +__all__ = ["ResetSampledConstantNoiseModel", "ResetSampledConstantNoiseModelCfg"] + +from collections.abc import Sequence +from dataclasses import MISSING +from typing import TYPE_CHECKING + +import torch + +from isaaclab.utils import configclass +from isaaclab.utils.noise import NoiseModel, NoiseModelCfg + +if TYPE_CHECKING: + from isaaclab.utils.noise import NoiseCfg + + +class ResetSampledConstantNoiseModel(NoiseModel): + """Noise model that samples noise ONLY during reset and applies it consistently. + + The noise is sampled from the configured distribution ONLY during reset and applied consistently + until the next reset. Unlike regular noise that generates new random values every step, + this model maintains the same noise values throughout an episode. + + Note: + This noise model was used since the noise randimization should only be done at reset time. + Other noise models(Eg: GaussianNoise) were not used since this randomizes the noise at every time-step. + """ + + def __init__(self, noise_model_cfg: NoiseModelCfg, num_envs: int, device: str): + # initialize parent class + super().__init__(noise_model_cfg, num_envs, device) + # store the noise configuration + self._noise_cfg = noise_model_cfg.noise_cfg + self._sampled_noise = torch.zeros((num_envs, 1), device=self._device) + self._num_components: int | None = None + + def reset(self, env_ids: Sequence[int] | None = None): + """Reset the noise model by sampling NEW noise values. + + This method samples new noise for the specified environments using the configured noise function. + The sampled noise will remain constant until the next reset. + + Args: + env_ids: The environment ids to reset the noise model for. Defaults to None, + in which case all environments are considered. + """ + # resolve the environment ids + if env_ids is None: + env_ids = slice(None) + + # Use the existing noise function to sample new noise + # Create dummy data to sample from the noise function + dummy_data = torch.zeros( + (env_ids.stop - env_ids.start if isinstance(env_ids, slice) else len(env_ids), 1), device=self._device + ) + + # Sample noise using the configured noise function + sampled_noise = self._noise_model_cfg.noise_cfg.func(dummy_data, self._noise_model_cfg.noise_cfg) + + self._sampled_noise[env_ids] = sampled_noise + + def __call__(self, data: torch.Tensor) -> torch.Tensor: + """Apply the pre-sampled noise to the data. + + This method applies the noise that was sampled during the last reset. + No new noise is generated - the same values are used consistently. + + Args: + data: The data to apply the noise to. Shape is (num_envs, ...). + + Returns: + The data with the noise applied. Shape is the same as the input data. + """ + # on first apply, expand noise to match last dim of data + if self._num_components is None: + *_, self._num_components = data.shape + # expand noise from (num_envs,1) to (num_envs, num_components) + self._sampled_noise = self._sampled_noise.repeat(1, self._num_components) + + # apply the noise based on operation + if self._noise_cfg.operation == "add": + return data + self._sampled_noise + elif self._noise_cfg.operation == "scale": + return data * self._sampled_noise + elif self._noise_cfg.operation == "abs": + return self._sampled_noise + else: + raise ValueError(f"Unknown operation in noise: {self._noise_cfg.operation}") + + +@configclass +class ResetSampledConstantNoiseModelCfg(NoiseModelCfg): + """Configuration for a noise model that samples noise ONLY during reset.""" + + class_type: type = ResetSampledConstantNoiseModel + + noise_cfg: NoiseCfg = MISSING + """The noise configuration for the noise. + + Based on this configuration, the noise is sampled at every reset of the noise model. + """ diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..cf9ae56ee2da5bf0637b86fe46edbe113fce1fbd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/observations.py @@ -0,0 +1,342 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Class-based observation terms for the gear assembly manipulation environment.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import ManagerTermBase, ObservationTermCfg, SceneEntityCfg +from isaaclab.utils.math import combine_frame_transforms + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + from .events import randomize_gear_type + + +class gear_shaft_pos_w(ManagerTermBase): + """Gear shaft position in world frame with offset applied. + + This class-based term caches gear offset tensors and identity quaternions for efficient computation + across all environments. It transforms the gear base position by the appropriate offset based on the + active gear type in each environment. + + Args: + asset_cfg: The asset configuration for the gear base. Defaults to SceneEntityCfg("factory_gear_base"). + gear_offsets: A dictionary mapping gear type names to their shaft offsets in the gear base frame. + Required keys are "gear_small", "gear_medium", and "gear_large", each mapping to a 3D offset + list [x, y, z]. This parameter is required and must be provided in the configuration. + + Returns: + Gear shaft position tensor in the environment frame with shape (num_envs, 3). + + Raises: + ValueError: If the 'gear_offsets' parameter is not provided in the configuration. + TypeError: If the 'gear_offsets' parameter is not a dictionary. + ValueError: If any of the required gear type keys are missing from 'gear_offsets'. + RuntimeError: If the gear type manager is not initialized in the environment. + """ + + def __init__(self, cfg: ObservationTermCfg, env: ManagerBasedRLEnv): + """Initialize the gear shaft position observation term. + + Args: + cfg: Observation term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset + self.asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("factory_gear_base")) + self.asset: RigidObject = env.scene[self.asset_cfg.name] + + # Pre-cache gear offset tensors (required parameter) + if "gear_offsets" not in cfg.params: + raise ValueError( + "'gear_offsets' parameter is required in gear_shaft_pos_w configuration. " + "It should be a dict with keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + gear_offsets = cfg.params["gear_offsets"] + if not isinstance(gear_offsets, dict): + raise TypeError( + f"'gear_offsets' parameter must be a dict, got {type(gear_offsets).__name__}. " + "It should have keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + + self.gear_offset_tensors = {} + for gear_type in ["gear_small", "gear_medium", "gear_large"]: + if gear_type not in gear_offsets: + raise ValueError( + f"'{gear_type}' offset is required in 'gear_offsets' parameter. " + f"Found keys: {list(gear_offsets.keys())}" + ) + self.gear_offset_tensors[gear_type] = torch.tensor( + gear_offsets[gear_type], device=env.device, dtype=torch.float32 + ) + + # Stack offset tensors for vectorized indexing (shape: 3, 3) + # Index 0=small, 1=medium, 2=large + self.gear_offsets_stacked = torch.stack( + [ + self.gear_offset_tensors["gear_small"], + self.gear_offset_tensors["gear_medium"], + self.gear_offset_tensors["gear_large"], + ], + dim=0, + ) + + # Pre-allocate buffers + self.offsets_buffer = torch.zeros(env.num_envs, 3, device=env.device, dtype=torch.float32) + self.env_indices = torch.arange(env.num_envs, device=env.device) + self.identity_quat = ( + torch.tensor([[1.0, 0.0, 0.0, 0.0]], device=env.device, dtype=torch.float32) + .repeat(env.num_envs, 1) + .contiguous() + ) + + def __call__( + self, + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("factory_gear_base"), + gear_offsets: dict | None = None, + ) -> torch.Tensor: + """Compute gear shaft position in world frame. + + Args: + env: Environment instance + asset_cfg: Configuration of the gear base asset (unused, kept for compatibility) + + Returns: + Gear shaft position tensor of shape (num_envs, 3) + """ + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this observation term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor (no Python loops!) + gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Get base gear position and orientation + base_pos = self.asset.data.root_pos_w + base_quat = self.asset.data.root_quat_w + + # Update offsets using vectorized indexing + self.offsets_buffer = self.gear_offsets_stacked[gear_type_indices] + + # Transform offsets + shaft_pos, _ = combine_frame_transforms(base_pos, base_quat, self.offsets_buffer, self.identity_quat) + + return shaft_pos - env.scene.env_origins + + +class gear_shaft_quat_w(ManagerTermBase): + """Gear shaft orientation in world frame. + + This class-based term returns the orientation of the gear base (which is the same as the gear shaft + orientation). The quaternion is canonicalized to ensure the w component is positive, reducing + observation variation for the policy. + + Args: + asset_cfg: The asset configuration for the gear base. Defaults to SceneEntityCfg("factory_gear_base"). + + Returns: + Gear shaft orientation tensor as a quaternion (w, x, y, z) with shape (num_envs, 4). + """ + + def __init__(self, cfg: ObservationTermCfg, env: ManagerBasedRLEnv): + """Initialize the gear shaft orientation observation term. + + Args: + cfg: Observation term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset + self.asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("factory_gear_base")) + self.asset: RigidObject = env.scene[self.asset_cfg.name] + + def __call__( + self, + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("factory_gear_base"), + ) -> torch.Tensor: + """Compute gear shaft orientation in world frame. + + Args: + env: Environment instance + asset_cfg: Configuration of the gear base asset (unused, kept for compatibility) + + Returns: + Gear shaft orientation tensor of shape (num_envs, 4) + """ + # Get base quaternion + base_quat = self.asset.data.root_quat_w + + # Ensure w component is positive (q and -q represent the same rotation) + # Pick one canonical form to reduce observation variation seen by the policy + w_negative = base_quat[:, 0] < 0 + positive_quat = base_quat.clone() + positive_quat[w_negative] = -base_quat[w_negative] + + return positive_quat + + +class gear_pos_w(ManagerTermBase): + """Gear position in world frame. + + This class-based term returns the position of the active gear in each environment. It uses + vectorized indexing to efficiently select the correct gear position based on the gear type + (small, medium, or large) active in each environment. + + Returns: + Gear position tensor in the environment frame with shape (num_envs, 3). + + Raises: + RuntimeError: If the gear type manager is not initialized in the environment. + """ + + def __init__(self, cfg: ObservationTermCfg, env: ManagerBasedRLEnv): + """Initialize the gear position observation term. + + Args: + cfg: Observation term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Pre-allocate gear type mapping and indices + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + def __call__(self, env: ManagerBasedRLEnv) -> torch.Tensor: + """Compute gear position in world frame. + + Args: + env: Environment instance + + Returns: + Gear position tensor of shape (num_envs, 3) + """ + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this observation term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor (no Python loops!) + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Stack all gear positions + all_gear_positions = torch.stack( + [ + self.gear_assets["gear_small"].data.root_pos_w, + self.gear_assets["gear_medium"].data.root_pos_w, + self.gear_assets["gear_large"].data.root_pos_w, + ], + dim=1, + ) + + # Select gear positions using advanced indexing + gear_positions = all_gear_positions[self.env_indices, self.gear_type_indices] + + return gear_positions - env.scene.env_origins + + +class gear_quat_w(ManagerTermBase): + """Gear orientation in world frame. + + This class-based term returns the orientation of the active gear in each environment. It uses + vectorized indexing to efficiently select the correct gear orientation based on the gear type + (small, medium, or large) active in each environment. The quaternion is canonicalized to ensure + the w component is positive, reducing observation variation for the policy. + + Returns: + Gear orientation tensor as a quaternion (w, x, y, z) with shape (num_envs, 4). + + Raises: + RuntimeError: If the gear type manager is not initialized in the environment. + """ + + def __init__(self, cfg: ObservationTermCfg, env: ManagerBasedRLEnv): + """Initialize the gear orientation observation term. + + Args: + cfg: Observation term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Pre-allocate gear type mapping and indices + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + def __call__(self, env: ManagerBasedRLEnv) -> torch.Tensor: + """Compute gear orientation in world frame. + + Args: + env: Environment instance + + Returns: + Gear orientation tensor of shape (num_envs, 4) + """ + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this observation term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor (no Python loops!) + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Stack all gear quaternions + all_gear_quat = torch.stack( + [ + self.gear_assets["gear_small"].data.root_quat_w, + self.gear_assets["gear_medium"].data.root_quat_w, + self.gear_assets["gear_large"].data.root_quat_w, + ], + dim=1, + ) + + # Select gear quaternions using advanced indexing + gear_quat = all_gear_quat[self.env_indices, self.gear_type_indices] + + # Ensure w component is positive (q and -q represent the same rotation) + # Pick one canonical form to reduce observation variation seen by the policy + w_negative = gear_quat[:, 0] < 0 + gear_positive_quat = gear_quat.clone() + gear_positive_quat[w_negative] = -gear_quat[w_negative] + + return gear_positive_quat diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..482cab8f69b87ea5b36b793775e7729f0d31afe3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/rewards.py @@ -0,0 +1,513 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Class-based reward terms for the gear assembly manipulation environment.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.managers import ManagerTermBase, RewardTermCfg, SceneEntityCfg +from isaaclab.sensors.frame_transformer.frame_transformer import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + from .events import randomize_gear_type + + +class keypoint_command_error(ManagerTermBase): + """Compute keypoint distance between current and desired poses from command. + + This class-based term uses _compute_keypoint_distance internally. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the keypoint command error term. + + Args: + cfg: Reward term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset configuration + self.asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("ee_frame")) + self.command_name: str = cfg.params.get("command_name", "ee_pose") + + # Create keypoint distance computer + self.keypoint_computer = _compute_keypoint_distance(cfg, env) + + def __call__( + self, + env: ManagerBasedRLEnv, + command_name: str, + asset_cfg: SceneEntityCfg, + keypoint_scale: float = 1.0, + add_cube_center_kp: bool = True, + ) -> torch.Tensor: + """Compute keypoint distance error. + + Args: + env: Environment instance + command_name: Name of the command containing desired pose + asset_cfg: Configuration of the asset to track + keypoint_scale: Scale factor for keypoint offsets + add_cube_center_kp: Whether to include center keypoint + + Returns: + Mean keypoint distance tensor of shape (num_envs,) + """ + # Extract frame transformer sensor + asset: FrameTransformer = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + + # Get desired pose from command + des_pos_w = command[:, :3] + des_quat_w = command[:, 3:7] + + # Get current pose from frame transformer + curr_pos_w = asset.data.target_pos_source[:, 0] + curr_quat_w = asset.data.target_quat_source[:, 0] + + # Compute keypoint distance + keypoint_dist_sep = self.keypoint_computer.compute( + current_pos=curr_pos_w, + current_quat=curr_quat_w, + target_pos=des_pos_w, + target_quat=des_quat_w, + keypoint_scale=keypoint_scale, + ) + + return keypoint_dist_sep.mean(-1) + + +class keypoint_command_error_exp(ManagerTermBase): + """Compute exponential keypoint reward between current and desired poses from command. + + This class-based term uses _compute_keypoint_distance internally and applies + exponential reward transformation. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the keypoint command error exponential term. + + Args: + cfg: Reward term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset configuration + self.asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("ee_frame")) + self.command_name: str = cfg.params.get("command_name", "ee_pose") + + # Create keypoint distance computer + self.keypoint_computer = _compute_keypoint_distance(cfg, env) + + def __call__( + self, + env: ManagerBasedRLEnv, + command_name: str, + asset_cfg: SceneEntityCfg, + kp_exp_coeffs: list[tuple[float, float]] = [(1.0, 0.1)], + kp_use_sum_of_exps: bool = True, + keypoint_scale: float = 1.0, + add_cube_center_kp: bool = True, + ) -> torch.Tensor: + """Compute exponential keypoint reward. + + Args: + env: Environment instance + command_name: Name of the command containing desired pose + asset_cfg: Configuration of the asset to track + kp_exp_coeffs: List of (a, b) coefficient pairs for exponential reward + kp_use_sum_of_exps: Whether to use sum of exponentials + keypoint_scale: Scale factor for keypoint offsets + add_cube_center_kp: Whether to include center keypoint + + Returns: + Exponential keypoint reward tensor of shape (num_envs,) + """ + # Extract frame transformer sensor + asset: FrameTransformer = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + + # Get desired pose from command + des_pos_w = command[:, :3] + des_quat_w = command[:, 3:7] + + # Get current pose from frame transformer + curr_pos_w = asset.data.target_pos_source[:, 0] + curr_quat_w = asset.data.target_quat_source[:, 0] + + # Compute keypoint distance + keypoint_dist_sep = self.keypoint_computer.compute( + current_pos=curr_pos_w, + current_quat=curr_quat_w, + target_pos=des_pos_w, + target_quat=des_quat_w, + keypoint_scale=keypoint_scale, + ) + + # Compute exponential reward + keypoint_reward_exp = torch.zeros_like(keypoint_dist_sep[:, 0]) + + if kp_use_sum_of_exps: + for coeff in kp_exp_coeffs: + a, b = coeff + keypoint_reward_exp += ( + 1.0 / (torch.exp(a * keypoint_dist_sep) + b + torch.exp(-a * keypoint_dist_sep)) + ).mean(-1) + else: + keypoint_dist = keypoint_dist_sep.mean(-1) + for coeff in kp_exp_coeffs: + a, b = coeff + keypoint_reward_exp += 1.0 / (torch.exp(a * keypoint_dist) + b + torch.exp(-a * keypoint_dist)) + + return keypoint_reward_exp + + +class keypoint_entity_error(ManagerTermBase): + """Compute keypoint distance between a RigidObject and the dynamically selected gear. + + This class-based term pre-caches gear type mapping and asset references. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the keypoint entity error term. + + Args: + cfg: Reward term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset configuration + self.asset_cfg_1: SceneEntityCfg = cfg.params.get("asset_cfg_1", SceneEntityCfg("factory_gear_base")) + self.asset_1 = env.scene[self.asset_cfg_1.name] + + # Pre-allocate gear type mapping and indices + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + # Create keypoint distance computer + self.keypoint_computer = _compute_keypoint_distance(cfg, env) + + def __call__( + self, + env: ManagerBasedRLEnv, + asset_cfg_1: SceneEntityCfg, + keypoint_scale: float = 1.0, + add_cube_center_kp: bool = True, + ) -> torch.Tensor: + """Compute keypoint distance error. + + Args: + env: Environment instance + asset_cfg_1: Configuration of the first asset (RigidObject) + keypoint_scale: Scale factor for keypoint offsets + add_cube_center_kp: Whether to include center keypoint + + Returns: + Mean keypoint distance tensor of shape (num_envs,) + """ + # Get current pose of asset_1 (RigidObject) + curr_pos_1 = self.asset_1.data.body_pos_w[:, 0] + curr_quat_1 = self.asset_1.data.body_quat_w[:, 0] + + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this reward term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Stack all gear positions and quaternions + all_gear_pos = torch.stack( + [ + self.gear_assets["gear_small"].data.body_pos_w[:, 0], + self.gear_assets["gear_medium"].data.body_pos_w[:, 0], + self.gear_assets["gear_large"].data.body_pos_w[:, 0], + ], + dim=1, + ) + + all_gear_quat = torch.stack( + [ + self.gear_assets["gear_small"].data.body_quat_w[:, 0], + self.gear_assets["gear_medium"].data.body_quat_w[:, 0], + self.gear_assets["gear_large"].data.body_quat_w[:, 0], + ], + dim=1, + ) + + # Select positions and quaternions using advanced indexing + curr_pos_2 = all_gear_pos[self.env_indices, self.gear_type_indices] + curr_quat_2 = all_gear_quat[self.env_indices, self.gear_type_indices] + + # Compute keypoint distance + keypoint_dist_sep = self.keypoint_computer.compute( + current_pos=curr_pos_1, + current_quat=curr_quat_1, + target_pos=curr_pos_2, + target_quat=curr_quat_2, + keypoint_scale=keypoint_scale, + ) + + return keypoint_dist_sep.mean(-1) + + +class keypoint_entity_error_exp(ManagerTermBase): + """Compute exponential keypoint reward between a RigidObject and the dynamically selected gear. + + This class-based term pre-caches gear type mapping and asset references. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the keypoint entity error exponential term. + + Args: + cfg: Reward term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Cache asset configuration + self.asset_cfg_1: SceneEntityCfg = cfg.params.get("asset_cfg_1", SceneEntityCfg("factory_gear_base")) + self.asset_1 = env.scene[self.asset_cfg_1.name] + + # Pre-allocate gear type mapping and indices + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + # Create keypoint distance computer + self.keypoint_computer = _compute_keypoint_distance(cfg, env) + + def __call__( + self, + env: ManagerBasedRLEnv, + asset_cfg_1: SceneEntityCfg, + kp_exp_coeffs: list[tuple[float, float]] = [(1.0, 0.1)], + kp_use_sum_of_exps: bool = True, + keypoint_scale: float = 1.0, + add_cube_center_kp: bool = True, + ) -> torch.Tensor: + """Compute exponential keypoint reward. + + Args: + env: Environment instance + asset_cfg_1: Configuration of the first asset (RigidObject) + kp_exp_coeffs: List of (a, b) coefficient pairs for exponential reward + kp_use_sum_of_exps: Whether to use sum of exponentials + keypoint_scale: Scale factor for keypoint offsets + add_cube_center_kp: Whether to include center keypoint + + Returns: + Exponential keypoint reward tensor of shape (num_envs,) + """ + # Get current pose of asset_1 (RigidObject) + curr_pos_1 = self.asset_1.data.body_pos_w[:, 0] + curr_quat_1 = self.asset_1.data.body_quat_w[:, 0] + + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this reward term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Stack all gear positions and quaternions + all_gear_pos = torch.stack( + [ + self.gear_assets["gear_small"].data.body_pos_w[:, 0], + self.gear_assets["gear_medium"].data.body_pos_w[:, 0], + self.gear_assets["gear_large"].data.body_pos_w[:, 0], + ], + dim=1, + ) + + all_gear_quat = torch.stack( + [ + self.gear_assets["gear_small"].data.body_quat_w[:, 0], + self.gear_assets["gear_medium"].data.body_quat_w[:, 0], + self.gear_assets["gear_large"].data.body_quat_w[:, 0], + ], + dim=1, + ) + + # Select positions and quaternions using advanced indexing + curr_pos_2 = all_gear_pos[self.env_indices, self.gear_type_indices] + curr_quat_2 = all_gear_quat[self.env_indices, self.gear_type_indices] + + # Compute keypoint distance + keypoint_dist_sep = self.keypoint_computer.compute( + current_pos=curr_pos_1, + current_quat=curr_quat_1, + target_pos=curr_pos_2, + target_quat=curr_quat_2, + keypoint_scale=keypoint_scale, + ) + + # Compute exponential reward + keypoint_reward_exp = torch.zeros_like(keypoint_dist_sep[:, 0]) + + if kp_use_sum_of_exps: + for coeff in kp_exp_coeffs: + a, b = coeff + keypoint_reward_exp += ( + 1.0 / (torch.exp(a * keypoint_dist_sep) + b + torch.exp(-a * keypoint_dist_sep)) + ).mean(-1) + else: + keypoint_dist = keypoint_dist_sep.mean(-1) + for coeff in kp_exp_coeffs: + a, b = coeff + keypoint_reward_exp += 1.0 / (torch.exp(a * keypoint_dist) + b + torch.exp(-a * keypoint_dist)) + + return keypoint_reward_exp + + +## +# Helper functions and classes +## + + +def _get_keypoint_offsets_full_6d(add_cube_center_kp: bool = False, device: torch.device | None = None) -> torch.Tensor: + """Get keypoints for pose alignment comparison. Pose is aligned if all axis are aligned. + + Args: + add_cube_center_kp: Whether to include the center keypoint (0, 0, 0) + device: Device to create the tensor on + + Returns: + Keypoint offsets tensor of shape (num_keypoints, 3) + """ + if add_cube_center_kp: + keypoint_corners = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]] + else: + keypoint_corners = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] + + keypoint_corners = torch.tensor(keypoint_corners, device=device, dtype=torch.float32) + keypoint_corners = torch.cat((keypoint_corners, -keypoint_corners[-3:]), dim=0) + + return keypoint_corners + + +class _compute_keypoint_distance: + """Compute keypoint distance between current and target poses. + + This helper class pre-caches keypoint offsets and identity quaternions + to avoid repeated allocations during reward computation. + """ + + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRLEnv): + """Initialize the compute keypoint distance helper. + + Args: + cfg: Reward term configuration + env: Environment instance + """ + # Get keypoint configuration + add_cube_center_kp = cfg.params.get("add_cube_center_kp", True) + + # Pre-compute base keypoint offsets (unscaled) + self.keypoint_offsets_base = _get_keypoint_offsets_full_6d( + add_cube_center_kp=add_cube_center_kp, device=env.device + ) + self.num_keypoints = self.keypoint_offsets_base.shape[0] + + # Pre-allocate identity quaternion for keypoint transforms + self.identity_quat_keypoints = ( + torch.tensor([[1.0, 0.0, 0.0, 0.0]], device=env.device, dtype=torch.float32) + .repeat(env.num_envs * self.num_keypoints, 1) + .contiguous() + ) + + # Pre-allocate buffer for batched keypoint offsets + self.keypoint_offsets_buffer = torch.zeros( + env.num_envs, self.num_keypoints, 3, device=env.device, dtype=torch.float32 + ) + + def compute( + self, + current_pos: torch.Tensor, + current_quat: torch.Tensor, + target_pos: torch.Tensor, + target_quat: torch.Tensor, + keypoint_scale: float = 1.0, + ) -> torch.Tensor: + """Compute keypoint distance between current and target poses. + + Args: + current_pos: Current position tensor of shape (num_envs, 3) + current_quat: Current quaternion tensor of shape (num_envs, 4) + target_pos: Target position tensor of shape (num_envs, 3) + target_quat: Target quaternion tensor of shape (num_envs, 4) + keypoint_scale: Scale factor for keypoint offsets + + Returns: + Keypoint distance tensor of shape (num_envs, num_keypoints) + """ + num_envs = current_pos.shape[0] + + # Scale keypoint offsets + keypoint_offsets = self.keypoint_offsets_base * keypoint_scale + + # Create batched keypoints (in-place operation) + self.keypoint_offsets_buffer[:num_envs] = keypoint_offsets.unsqueeze(0) + + # Flatten for batch processing + keypoint_offsets_flat = self.keypoint_offsets_buffer[:num_envs].reshape(-1, 3) + identity_quat = self.identity_quat_keypoints[: num_envs * self.num_keypoints] + + # Expand quaternions and positions for all keypoints + current_quat_expanded = current_quat.unsqueeze(1).expand(-1, self.num_keypoints, -1).reshape(-1, 4) + current_pos_expanded = current_pos.unsqueeze(1).expand(-1, self.num_keypoints, -1).reshape(-1, 3) + target_quat_expanded = target_quat.unsqueeze(1).expand(-1, self.num_keypoints, -1).reshape(-1, 4) + target_pos_expanded = target_pos.unsqueeze(1).expand(-1, self.num_keypoints, -1).reshape(-1, 3) + + # Transform all keypoints at once + keypoints_current_flat, _ = combine_frame_transforms( + current_pos_expanded, current_quat_expanded, keypoint_offsets_flat, identity_quat + ) + keypoints_target_flat, _ = combine_frame_transforms( + target_pos_expanded, target_quat_expanded, keypoint_offsets_flat, identity_quat + ) + + # Reshape back + keypoints_current = keypoints_current_flat.reshape(num_envs, self.num_keypoints, 3) + keypoints_target = keypoints_target_flat.reshape(num_envs, self.num_keypoints, 3) + + # Calculate L2 norm distance + keypoint_dist_sep = torch.norm(keypoints_target - keypoints_current, p=2, dim=-1) + + return keypoint_dist_sep diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..b623148c5b3bad45972c4b2be224b0ceff14a9ec --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/mdp/terminations.py @@ -0,0 +1,331 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Class-based termination terms specific to the gear assembly manipulation environments.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import carb + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.managers import ManagerTermBase, SceneEntityCfg, TerminationTermCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from .events import randomize_gear_type + + +class reset_when_gear_dropped(ManagerTermBase): + """Check if the gear has fallen out of the gripper and return reset flags. + + This class-based term pre-caches all required tensors and gear offsets. + """ + + def __init__(self, cfg: TerminationTermCfg, env: ManagerBasedEnv): + """Initialize the reset when gear dropped term. + + Args: + cfg: Termination term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Get robot asset configuration + self.robot_asset_cfg: SceneEntityCfg = cfg.params.get("robot_asset_cfg", SceneEntityCfg("robot")) + self.robot_asset: Articulation = env.scene[self.robot_asset_cfg.name] + + # Validate required parameters + if "end_effector_body_name" not in cfg.params: + raise ValueError( + "'end_effector_body_name' parameter is required in reset_when_gear_dropped configuration. " + "Example: 'wrist_3_link'" + ) + if "grasp_rot_offset" not in cfg.params: + raise ValueError( + "'grasp_rot_offset' parameter is required in reset_when_gear_dropped configuration. " + "It should be a quaternion [w, x, y, z]. Example: [0.0, 0.707, 0.707, 0.0]" + ) + + self.end_effector_body_name = cfg.params["end_effector_body_name"] + + # Pre-cache gear grasp offsets as tensors (required parameter) + if "gear_offsets_grasp" not in cfg.params: + raise ValueError( + "'gear_offsets_grasp' parameter is required in reset_when_gear_dropped configuration. " + "It should be a dict with keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + gear_offsets_grasp = cfg.params["gear_offsets_grasp"] + if not isinstance(gear_offsets_grasp, dict): + raise TypeError( + f"'gear_offsets_grasp' parameter must be a dict, got {type(gear_offsets_grasp).__name__}. " + "It should have keys 'gear_small', 'gear_medium', 'gear_large' mapping to [x, y, z] offsets." + ) + + self.gear_grasp_offset_tensors = {} + for gear_type in ["gear_small", "gear_medium", "gear_large"]: + if gear_type not in gear_offsets_grasp: + raise ValueError( + f"'{gear_type}' offset is required in 'gear_offsets_grasp' parameter. " + f"Found keys: {list(gear_offsets_grasp.keys())}" + ) + self.gear_grasp_offset_tensors[gear_type] = torch.tensor( + gear_offsets_grasp[gear_type], device=env.device, dtype=torch.float32 + ) + + # Stack grasp offset tensors for vectorized indexing (shape: 3, 3) + # Index 0=small, 1=medium, 2=large + self.gear_grasp_offsets_stacked = torch.stack( + [ + self.gear_grasp_offset_tensors["gear_small"], + self.gear_grasp_offset_tensors["gear_medium"], + self.gear_grasp_offset_tensors["gear_large"], + ], + dim=0, + ) + + # Pre-cache grasp rotation offset tensor + grasp_rot_offset = cfg.params["grasp_rot_offset"] + self.grasp_rot_offset_tensor = ( + torch.tensor(grasp_rot_offset, device=env.device, dtype=torch.float32).unsqueeze(0).repeat(env.num_envs, 1) + ) + + # Pre-allocate buffers + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + self.gear_grasp_offsets_buffer = torch.zeros(env.num_envs, 3, device=env.device, dtype=torch.float32) + self.all_gear_pos_buffer = torch.zeros(env.num_envs, 3, 3, device=env.device, dtype=torch.float32) + self.all_gear_quat_buffer = torch.zeros(env.num_envs, 3, 4, device=env.device, dtype=torch.float32) + self.reset_flags = torch.zeros(env.num_envs, dtype=torch.bool, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + # Find end effector index once + eef_indices, _ = self.robot_asset.find_bodies([self.end_effector_body_name]) + if len(eef_indices) == 0: + carb.log_warn( + f"{self.end_effector_body_name} not found in robot body names. Cannot check gear drop condition." + ) + self.eef_idx = None + else: + self.eef_idx = eef_indices[0] + + def __call__( + self, + env: ManagerBasedEnv, + distance_threshold: float = 0.1, + robot_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + gear_offsets_grasp: dict | None = None, + end_effector_body_name: str | None = None, + grasp_rot_offset: list | None = None, + ) -> torch.Tensor: + """Check if gear has dropped and return reset flags. + + Args: + env: Environment instance + distance_threshold: Maximum allowed distance between gear grasp point and gripper + robot_asset_cfg: Configuration for the robot asset (unused, kept for compatibility) + + Returns: + Boolean tensor indicating which environments should be reset + """ + # Reset flags + self.reset_flags.fill_(False) + + if self.eef_idx is None: + return self.reset_flags + + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this termination term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor (no Python loops!) + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Get end effector position + eef_pos_world = self.robot_asset.data.body_link_pos_w[:, self.eef_idx] + + # Update gear positions and quaternions in buffers + self.all_gear_pos_buffer[:, 0, :] = self.gear_assets["gear_small"].data.root_link_pos_w + self.all_gear_pos_buffer[:, 1, :] = self.gear_assets["gear_medium"].data.root_link_pos_w + self.all_gear_pos_buffer[:, 2, :] = self.gear_assets["gear_large"].data.root_link_pos_w + + self.all_gear_quat_buffer[:, 0, :] = self.gear_assets["gear_small"].data.root_link_quat_w + self.all_gear_quat_buffer[:, 1, :] = self.gear_assets["gear_medium"].data.root_link_quat_w + self.all_gear_quat_buffer[:, 2, :] = self.gear_assets["gear_large"].data.root_link_quat_w + + # Select gear data using advanced indexing + gear_pos_world = self.all_gear_pos_buffer[self.env_indices, self.gear_type_indices] + gear_quat_world = self.all_gear_quat_buffer[self.env_indices, self.gear_type_indices] + + # Apply rotation offset + gear_quat_world = math_utils.quat_mul(gear_quat_world, self.grasp_rot_offset_tensor) + + # Get grasp offsets (vectorized) + self.gear_grasp_offsets_buffer = self.gear_grasp_offsets_stacked[self.gear_type_indices] + + # Transform grasp offsets to world frame + gear_grasp_pos_world = gear_pos_world + math_utils.quat_apply(gear_quat_world, self.gear_grasp_offsets_buffer) + + # Compute distances + distances = torch.norm(gear_grasp_pos_world - eef_pos_world, dim=-1) + + # Check distance threshold + self.reset_flags[:] = distances > distance_threshold + + return self.reset_flags + + +class reset_when_gear_orientation_exceeds_threshold(ManagerTermBase): + """Check if the gear's orientation relative to the gripper exceeds thresholds. + + This class-based term pre-caches all required tensors and thresholds. + """ + + def __init__(self, cfg: TerminationTermCfg, env: ManagerBasedEnv): + """Initialize the reset when gear orientation exceeds threshold term. + + Args: + cfg: Termination term configuration + env: Environment instance + """ + super().__init__(cfg, env) + + # Get robot asset configuration + self.robot_asset_cfg: SceneEntityCfg = cfg.params.get("robot_asset_cfg", SceneEntityCfg("robot")) + self.robot_asset: Articulation = env.scene[self.robot_asset_cfg.name] + + # Validate required parameters + if "end_effector_body_name" not in cfg.params: + raise ValueError( + "'end_effector_body_name' parameter is required in reset_when_gear_orientation_exceeds_threshold" + " configuration. Example: 'wrist_3_link'" + ) + if "grasp_rot_offset" not in cfg.params: + raise ValueError( + "'grasp_rot_offset' parameter is required in reset_when_gear_orientation_exceeds_threshold" + " configuration. It should be a quaternion [w, x, y, z]. Example: [0.0, 0.707, 0.707, 0.0]" + ) + + self.end_effector_body_name = cfg.params["end_effector_body_name"] + + # Pre-cache grasp rotation offset tensor + grasp_rot_offset = cfg.params["grasp_rot_offset"] + self.grasp_rot_offset_tensor = ( + torch.tensor(grasp_rot_offset, device=env.device, dtype=torch.float32).unsqueeze(0).repeat(env.num_envs, 1) + ) + + # Pre-allocate buffers + self.gear_type_map = {"gear_small": 0, "gear_medium": 1, "gear_large": 2} + self.gear_type_indices = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self.env_indices = torch.arange(env.num_envs, device=env.device) + self.all_gear_quat_buffer = torch.zeros(env.num_envs, 3, 4, device=env.device, dtype=torch.float32) + self.reset_flags = torch.zeros(env.num_envs, dtype=torch.bool, device=env.device) + + # Cache gear assets + self.gear_assets = { + "gear_small": env.scene["factory_gear_small"], + "gear_medium": env.scene["factory_gear_medium"], + "gear_large": env.scene["factory_gear_large"], + } + + # Find end effector index once + eef_indices, _ = self.robot_asset.find_bodies([self.end_effector_body_name]) + if len(eef_indices) == 0: + carb.log_warn( + f"{self.end_effector_body_name} not found in robot body names. Cannot check gear orientation condition." + ) + self.eef_idx = None + else: + self.eef_idx = eef_indices[0] + + def __call__( + self, + env: ManagerBasedEnv, + roll_threshold_deg: float = 30.0, + pitch_threshold_deg: float = 30.0, + yaw_threshold_deg: float = 180.0, + robot_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + end_effector_body_name: str | None = None, + grasp_rot_offset: list | None = None, + ) -> torch.Tensor: + """Check if gear orientation exceeds thresholds and return reset flags. + + Args: + env: Environment instance + roll_threshold_deg: Maximum allowed roll angle deviation in degrees + pitch_threshold_deg: Maximum allowed pitch angle deviation in degrees + yaw_threshold_deg: Maximum allowed yaw angle deviation in degrees + robot_asset_cfg: Configuration for the robot asset (unused, kept for compatibility) + + Returns: + Boolean tensor indicating which environments should be reset + """ + # Reset flags + self.reset_flags.fill_(False) + + if self.eef_idx is None: + return self.reset_flags + + # Check if gear type manager exists + if not hasattr(env, "_gear_type_manager"): + raise RuntimeError( + "Gear type manager not initialized. Ensure randomize_gear_type event is configured " + "in your environment's event configuration before this termination term is used." + ) + + gear_type_manager: randomize_gear_type = env._gear_type_manager + # Get gear type indices directly as tensor (no Python loops!) + self.gear_type_indices = gear_type_manager.get_all_gear_type_indices() + + # Convert thresholds to radians + roll_threshold_rad = torch.deg2rad(torch.tensor(roll_threshold_deg, device=env.device)) + pitch_threshold_rad = torch.deg2rad(torch.tensor(pitch_threshold_deg, device=env.device)) + yaw_threshold_rad = torch.deg2rad(torch.tensor(yaw_threshold_deg, device=env.device)) + + # Get end effector orientation + eef_quat_world = self.robot_asset.data.body_link_quat_w[:, self.eef_idx] + + # Update gear quaternions in buffer + self.all_gear_quat_buffer[:, 0, :] = self.gear_assets["gear_small"].data.root_link_quat_w + self.all_gear_quat_buffer[:, 1, :] = self.gear_assets["gear_medium"].data.root_link_quat_w + self.all_gear_quat_buffer[:, 2, :] = self.gear_assets["gear_large"].data.root_link_quat_w + + # Select gear data using advanced indexing + gear_quat_world = self.all_gear_quat_buffer[self.env_indices, self.gear_type_indices] + + # Apply rotation offset + gear_quat_world = math_utils.quat_mul(gear_quat_world, self.grasp_rot_offset_tensor) + + # Compute relative orientation: q_rel = q_gear * q_eef^-1 + eef_quat_inv = math_utils.quat_conjugate(eef_quat_world) + relative_quat = math_utils.quat_mul(gear_quat_world, eef_quat_inv) + + # Convert relative quaternion to Euler angles + roll, pitch, yaw = math_utils.euler_xyz_from_quat(relative_quat) + + # Check if any angle exceeds its threshold + self.reset_flags[:] = ( + (torch.abs(roll) > roll_threshold_rad) + | (torch.abs(pitch) > pitch_threshold_rad) + | (torch.abs(yaw) > yaw_threshold_rad) + ) + + return self.reset_flags diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..69fa0010cd0016baaa9a5ada0e0fe1cacc9227d8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""end-effector pose tracking tasks that have been deployed on a real robot.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..05f9849b508ab0fb493086bb5ba70686961db843 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configuration package for manipulation tasks that have been deployed on a real robot.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b1626c78218646ebb4fe3049ce64a88722d99d73 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/__init__.py @@ -0,0 +1,42 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Deploy-Reach-UR10e-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10eReachEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:URReachPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Deploy-Reach-UR10e-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10eReachEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:URReachPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Deploy-Reach-UR10e-ROS-Inference-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.ros_inference_env_cfg:UR10eReachROSInferenceEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:URReachPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf59b16a1e2e10613c813c3d808e783886f400c7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..02af65eec9a0aebce76e5a755d75d4c3d296b2d9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class URReachPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 512 + max_iterations = 1500 + save_interval = 50 + experiment_name = "reach_ur10e" + empirical_normalization = True + obs_groups = {"policy": ["policy"], "critic": ["policy"]} + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.0, + num_learning_epochs=8, + num_mini_batches=8, + learning_rate=5.0e-4, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.008, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4abcf4369764740c5ddba7a6c2d5e6568d079179 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/joint_pos_env_cfg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.managers import SceneEntityCfg +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg, OffsetCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.deploy.mdp as mdp +from isaaclab_tasks.manager_based.manipulation.deploy.reach.reach_env_cfg import ReachEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets import UR10e_CFG # isort: skip + + +## +# Environment configuration +## + + +@configclass +class UR10eReachEnvCfg(ReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.events.robot_joint_stiffness_and_damping.params["asset_cfg"].joint_names = [ + "shoulder_.*", + "elbow_.*", + "wrist_.*", + ] + self.events.joint_friction.params["asset_cfg"].joint_names = ["shoulder_.*", "elbow_.*", "wrist_.*"] + + # switch robot to ur10e + self.scene.robot = UR10e_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # The real UR10e robots polyscore software uses the "base" frame for reference + # But the USD model and UR10e ROS interface uses the "base_link" frame + # We are training this policy to track the end-effector pose in the "base" frame + # The base frame is 180 offset from the base_link frame + # And hence the source_frame_offset is set to 180 degrees around the z-axis + self.rewards.end_effector_keypoint_tracking.params["asset_cfg"] = SceneEntityCfg("ee_frame_wrt_base_frame") + self.rewards.end_effector_keypoint_tracking_exp.params["asset_cfg"] = SceneEntityCfg("ee_frame_wrt_base_frame") + self.scene.ee_frame_wrt_base_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + visualizer_cfg=FRAME_MARKER_CFG.replace(prim_path="/Visuals/FrameTransformer"), + source_frame_offset=OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.0, 0.0, 0.0, 1.0)), + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/wrist_3_link", + name="end_effector", + ), + ], + ) + # Disable visualization for the goal pose because the commands are generated wrt to the base frame + # But the visualization will visualizing it wrt to the base_link frame + self.commands.ee_pose.debug_vis = False + + # Incremental joint position action configuration + self.actions.arm_action = mdp.RelativeJointPositionActionCfg( + asset_name="robot", joint_names=[".*"], scale=0.0625, use_zero_offset=True + ) + # override command generator body + # end-effector is along x-direction + self.target_pos_centre = (0.8875, -0.225, 0.2) + self.target_pos_range = (0.25, 0.125, 0.1) + self.commands.ee_pose.body_name = "wrist_3_link" + self.commands.ee_pose.ranges.pos_x = ( + self.target_pos_centre[0] - self.target_pos_range[0], + self.target_pos_centre[0] + self.target_pos_range[0], + ) + self.commands.ee_pose.ranges.pos_y = ( + self.target_pos_centre[1] - self.target_pos_range[1], + self.target_pos_centre[1] + self.target_pos_range[1], + ) + self.commands.ee_pose.ranges.pos_z = ( + self.target_pos_centre[2] - self.target_pos_range[2], + self.target_pos_centre[2] + self.target_pos_range[2], + ) + + self.target_rot_centre = (math.pi, 0.0, -math.pi / 2) # end-effector facing down + self.target_rot_range = (math.pi / 6, math.pi / 6, math.pi * 2 / 3) + self.commands.ee_pose.ranges.roll = ( + self.target_rot_centre[0] - self.target_rot_range[0], + self.target_rot_centre[0] + self.target_rot_range[0], + ) + self.commands.ee_pose.ranges.pitch = ( + self.target_rot_centre[1] - self.target_rot_range[1], + self.target_rot_centre[1] + self.target_rot_range[1], + ) + self.commands.ee_pose.ranges.yaw = ( + self.target_rot_centre[2] - self.target_rot_range[2], + self.target_rot_centre[2] + self.target_rot_range[2], + ) + + +@configclass +class UR10eReachEnvCfg_PLAY(UR10eReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/ros_inference_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/ros_inference_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2324b32b001c4480c10d2cd72075aa91f34bf1a7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/config/ur_10e/ros_inference_env_cfg.py @@ -0,0 +1,46 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from .joint_pos_env_cfg import UR10eReachEnvCfg + + +@configclass +class UR10eReachROSInferenceEnvCfg(UR10eReachEnvCfg): + """Exposing variables for ROS inferences""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Variables used by Isaac Manipuulator for on robot inference + # TODO: @ashwinvk: Remove these from env cfg once the generic inference node has been implemented + self.obs_order = ["arm_dof_pos", "arm_dof_vel", "target_pos", "target_quat"] + self.policy_action_space = "joint" + self.arm_joint_names = [ + "shoulder_pan_joint", + "shoulder_lift_joint", + "elbow_joint", + "wrist_1_joint", + "wrist_2_joint", + "wrist_3_joint", + ] + self.policy_action_space = "joint" + self.action_space = 6 + self.state_space = 19 + self.observation_space = 19 + + # Set joint_action_scale from the existing arm_action.scale + self.joint_action_scale = self.actions.arm_action.scale + + self.action_scale_joint_space = [ + self.joint_action_scale, + self.joint_action_scale, + self.joint_action_scale, + self.joint_action_scale, + self.joint_action_scale, + self.joint_action_scale, + ] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/reach_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/reach_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..90b65a0f96c2586608a179c06a6e4088e598f03b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/deploy/reach/reach_env_cfg.py @@ -0,0 +1,215 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg as ActionTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.manipulation.deploy.mdp as mdp + +## +# Scene definition +## + + +@configclass +class SceneCfg(InteractiveSceneCfg): + """Configuration for the scene with a robotic arm.""" + + # world + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), + ) + + # robots + robot: ArticulationCfg = MISSING + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), + ) + + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) + ), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, + resampling_time_range=(4.0, 4.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.35, 0.65), + pos_y=(-0.2, 0.2), + pos_z=(0.15, 0.5), + roll=(0.0, 0.0), + pitch=MISSING, # depends on end-effector axis + yaw=(-3.14, 3.14), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: ActionTerm = MISSING + gripper_action: ActionTerm | None = None + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos = ObsTerm(func=mdp.joint_pos, noise=Unoise(n_min=-0.0, n_max=0.0)) + joint_vel = ObsTerm(func=mdp.joint_vel, noise=Unoise(n_min=-0.0, n_max=0.0)) + pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "ee_pose"}) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.125, 0.125), + "velocity_range": (0.0, 0.0), + }, + ) + + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + min_step_count_between_reset=200, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot"), + "stiffness_distribution_params": (0.9, 1.1), + "damping_distribution_params": (0.75, 1.5), + "operation": "scale", + "distribution": "uniform", + }, + ) + + joint_friction = EventTerm( + func=mdp.randomize_joint_parameters, + min_step_count_between_reset=200, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot"), + "friction_distribution_params": (0.0, 0.1), + "operation": "add", + "distribution": "uniform", + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + end_effector_keypoint_tracking = RewTerm( + func=mdp.keypoint_command_error, + weight=-1.5, + params={ + "asset_cfg": SceneEntityCfg("ee_frame"), + "command_name": "ee_pose", + "keypoint_scale": 0.45, + }, + ) + end_effector_keypoint_tracking_exp = RewTerm( + func=mdp.keypoint_command_error_exp, + weight=1.5, + params={ + "asset_cfg": SceneEntityCfg("ee_frame"), + "command_name": "ee_pose", + "kp_exp_coeffs": [(50, 0.0001), (300, 0.0001), (5000, 0.0001)], + "kp_use_sum_of_exps": False, + "keypoint_scale": 0.45, + }, + ) + + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.005) + action = RewTerm(func=mdp.action_l2, weight=-0.005) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +## +# Environment configuration +## + + +@configclass +class ReachEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the end-effector pose tracking environment that has been deployed on a real robot.""" + + # Scene settings + scene: SceneCfg = SceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.sim.render_interval = self.decimation + self.episode_length_s = 12.0 + self.viewer.eye = (3.5, 3.5, 3.5) + # simulation settings + self.sim.dt = 1.0 / 120.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7aaceec9c2de056eddeb33c025c75627c757b5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Dexsuite environments. + +Implementation Reference: + +Reorient: +@article{petrenko2023dexpbt, + title={Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training}, + author={Petrenko, Aleksei and Allshire, Arthur and State, Gavriel and Handa, Ankur and Makoviychuk, Viktor}, + journal={arXiv preprint arXiv:2305.12127}, + year={2023} +} + +Lift: +@article{singh2024dextrah, + title={Dextrah-rgb: Visuomotor policies to grasp anything with dexterous hands}, + author={Singh, Ritvik and Allshire, Arthur and Handa, Ankur and Ratliff, Nathan and Van Wyk, Karl}, + journal={arXiv preprint arXiv:2412.01791}, + year={2024} +} + +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/adr_curriculum.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/adr_curriculum.py new file mode 100644 index 0000000000000000000000000000000000000000..de3aca917f751b43209089525a7358f077aea268 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/adr_curriculum.py @@ -0,0 +1,122 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.utils import configclass + +from . import mdp + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + # adr stands for automatic/adaptive domain randomization + adr = CurrTerm( + func=mdp.DifficultyScheduler, params={"init_difficulty": 0, "min_difficulty": 0, "max_difficulty": 10} + ) + + joint_pos_unoise_min_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.joint_pos.noise.n_min", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.1, "difficulty_term_str": "adr"}, + }, + ) + + joint_pos_unoise_max_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.joint_pos.noise.n_max", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": 0.1, "difficulty_term_str": "adr"}, + }, + ) + + joint_vel_unoise_min_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.joint_vel.noise.n_min", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.2, "difficulty_term_str": "adr"}, + }, + ) + + joint_vel_unoise_max_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.joint_vel.noise.n_max", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": 0.2, "difficulty_term_str": "adr"}, + }, + ) + + hand_tips_pos_unoise_min_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.hand_tips_state_b.noise.n_min", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.01, "difficulty_term_str": "adr"}, + }, + ) + + hand_tips_pos_unoise_max_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.proprio.hand_tips_state_b.noise.n_max", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": 0.01, "difficulty_term_str": "adr"}, + }, + ) + + object_quat_unoise_min_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.policy.object_quat_b.noise.n_min", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.03, "difficulty_term_str": "adr"}, + }, + ) + + object_quat_unoise_max_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.policy.object_quat_b.noise.n_max", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": 0.03, "difficulty_term_str": "adr"}, + }, + ) + + object_obs_unoise_min_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.perception.object_point_cloud.noise.n_min", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.01, "difficulty_term_str": "adr"}, + }, + ) + + object_obs_unoise_max_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "observations.perception.object_point_cloud.noise.n_max", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": {"initial_value": 0.0, "final_value": -0.01, "difficulty_term_str": "adr"}, + }, + ) + + gravity_adr = CurrTerm( + func=mdp.modify_term_cfg, + params={ + "address": "events.variable_gravity.params.gravity_distribution_params", + "modify_fn": mdp.initial_final_interpolate_fn, + "modify_params": { + "initial_value": ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0)), + "final_value": ((0.0, 0.0, -9.81), (0.0, 0.0, -9.81)), + "difficulty_term_str": "adr", + }, + }, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d6250c0195732065adba6257748cc665dea2b501 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the dexsuite environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8c9e9617fce3920d98b45e21e17eb9c21fdb1fce --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/__init__.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Dextra Kuka Allegro environments. +""" + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +# State Observation +gym.register( + id="Isaac-Dexsuite-Kuka-Allegro-Reorient-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.dexsuite_kuka_allegro_env_cfg:DexsuiteKukaAllegroReorientEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DexsuiteKukaAllegroPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Dexsuite-Kuka-Allegro-Reorient-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.dexsuite_kuka_allegro_env_cfg:DexsuiteKukaAllegroReorientEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DexsuiteKukaAllegroPPORunnerCfg", + }, +) + +# Dexsuite Lift Environments +gym.register( + id="Isaac-Dexsuite-Kuka-Allegro-Lift-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.dexsuite_kuka_allegro_env_cfg:DexsuiteKukaAllegroLiftEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DexsuiteKukaAllegroPPORunnerCfg", + }, +) + + +gym.register( + id="Isaac-Dexsuite-Kuka-Allegro-Lift-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.dexsuite_kuka_allegro_env_cfg:DexsuiteKukaAllegroLiftEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DexsuiteKukaAllegroPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3a9e96eaeb052df21e45a58c3a6e61aa3ceb830c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,111 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + obs_groups: + obs: ["policy", "proprio", "perception"] + states: ["policy", "proprio", "perception"] + concate_obs_groups: True + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: True + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [512, 256, 128] + activation: elu + d2rl: False + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: reorient + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: False + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 100000000 + max_epochs: 750000 + save_best_after: 100 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 36 + minibatch_size: 36864 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + clip_actions: False + seq_len: 4 + bounds_loss_coef: 0.0001 + +pbt: + enabled: False + policy_idx: 0 # policy index in a population + num_policies: 8 # total number of policies in the population + directory: . + workspace: "pbt_workspace" # suffix of the workspace dir name inside train_dir + objective: episode.Curriculum/adr + + # PBT hyperparams + interval_steps: 50000000 + threshold_std: 0.1 + threshold_abs: 0.025 + mutation_rate: 0.25 + change_range: [1.1, 2.0] + mutation: + + agent.params.config.learning_rate: "mutate_float" + agent.params.config.grad_norm: "mutate_float" + agent.params.config.entropy_coef: "mutate_float" + agent.params.config.critic_coef: "mutate_float" + agent.params.config.bounds_loss_coef: "mutate_float" + agent.params.config.kl_threshold: "mutate_float" + agent.params.config.gamma: "mutate_discount" + agent.params.config.tau: "mutate_discount" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9bc92bd8f69bbc1abeb236141b71796eecea99c7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class DexsuiteKukaAllegroPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 32 + obs_groups = {"policy": ["policy", "proprio", "perception"], "critic": ["policy", "proprio", "perception"]} + max_iterations = 15000 + save_interval = 250 + experiment_name = "dexsuite_kuka_allegro" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/dexsuite_kuka_allegro_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/dexsuite_kuka_allegro_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6b7f82fde06e453652942cafe1338697e1c63299 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/config/kuka_allegro/dexsuite_kuka_allegro_env_cfg.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import ContactSensorCfg +from isaaclab.utils import configclass + +from isaaclab_assets.robots import KUKA_ALLEGRO_CFG + +from ... import dexsuite_env_cfg as dexsuite +from ... import mdp + + +@configclass +class KukaAllegroRelJointPosActionCfg: + action = mdp.RelativeJointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.1) + + +@configclass +class KukaAllegroReorientRewardCfg(dexsuite.RewardsCfg): + # bool awarding term if 2 finger tips are in contact with object, one of the contacting fingers has to be thumb. + good_finger_contact = RewTerm( + func=mdp.contacts, + weight=0.5, + params={"threshold": 1.0}, + ) + + +@configclass +class KukaAllegroMixinCfg: + rewards: KukaAllegroReorientRewardCfg = KukaAllegroReorientRewardCfg() + actions: KukaAllegroRelJointPosActionCfg = KukaAllegroRelJointPosActionCfg() + + def __post_init__(self: dexsuite.DexsuiteReorientEnvCfg): + super().__post_init__() + self.commands.object_pose.body_name = "palm_link" + self.scene.robot = KUKA_ALLEGRO_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + finger_tip_body_list = ["index_link_3", "middle_link_3", "ring_link_3", "thumb_link_3"] + for link_name in finger_tip_body_list: + setattr( + self.scene, + f"{link_name}_object_s", + ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link/" + link_name, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Object"], + ), + ) + self.observations.proprio.contact = ObsTerm( + func=mdp.fingers_contact_force_b, + params={"contact_sensor_names": [f"{link}_object_s" for link in finger_tip_body_list]}, + clip=(-20.0, 20.0), # contact force in finger tips is under 20N normally + ) + self.observations.proprio.hand_tips_state_b.params["body_asset_cfg"].body_names = ["palm_link", ".*_tip"] + self.rewards.fingers_to_object.params["asset_cfg"] = SceneEntityCfg("robot", body_names=["palm_link", ".*_tip"]) + + +@configclass +class DexsuiteKukaAllegroReorientEnvCfg(KukaAllegroMixinCfg, dexsuite.DexsuiteReorientEnvCfg): + pass + + +@configclass +class DexsuiteKukaAllegroReorientEnvCfg_PLAY(KukaAllegroMixinCfg, dexsuite.DexsuiteReorientEnvCfg_PLAY): + pass + + +@configclass +class DexsuiteKukaAllegroLiftEnvCfg(KukaAllegroMixinCfg, dexsuite.DexsuiteLiftEnvCfg): + pass + + +@configclass +class DexsuiteKukaAllegroLiftEnvCfg_PLAY(KukaAllegroMixinCfg, dexsuite.DexsuiteLiftEnvCfg_PLAY): + pass diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/dexsuite_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/dexsuite_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee00105e57a1635943172b1ce9193fb27bdd37a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/dexsuite_env_cfg.py @@ -0,0 +1,467 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.envs import ManagerBasedEnvCfg, ViewerCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import CapsuleCfg, ConeCfg, CuboidCfg, RigidBodyMaterialCfg, SphereCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +from . import mdp +from .adr_curriculum import CurriculumCfg + + +@configclass +class SceneCfg(InteractiveSceneCfg): + """Dexsuite Scene for multi-objects Lifting""" + + # robot + robot: ArticulationCfg = MISSING + + # object + object: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + spawn=sim_utils.MultiAssetSpawnerCfg( + assets_cfg=[ + CuboidCfg(size=(0.05, 0.1, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CuboidCfg(size=(0.05, 0.05, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CuboidCfg(size=(0.025, 0.1, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CuboidCfg(size=(0.025, 0.05, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CuboidCfg(size=(0.025, 0.025, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CuboidCfg(size=(0.01, 0.1, 0.1), physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + SphereCfg(radius=0.05, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + SphereCfg(radius=0.025, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.04, height=0.025, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.04, height=0.01, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.04, height=0.1, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.025, height=0.1, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.025, height=0.2, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + CapsuleCfg(radius=0.01, height=0.2, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + ConeCfg(radius=0.05, height=0.1, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + ConeCfg(radius=0.025, height=0.1, physics_material=RigidBodyMaterialCfg(static_friction=0.5)), + ], + rigid_props=sim_utils.RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=0, + disable_gravity=False, + ), + collision_props=sim_utils.CollisionPropertiesCfg(), + mass_props=sim_utils.MassPropertiesCfg(mass=0.2), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-0.55, 0.1, 0.35)), + ) + + # table + table: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/table", + spawn=sim_utils.CuboidCfg( + size=(0.8, 1.5, 0.04), + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + collision_props=sim_utils.CollisionPropertiesCfg(), + # trick: we let visualizer's color to show the table with success coloring + visible=False, + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(-0.55, 0.0, 0.235), rot=(1.0, 0.0, 0.0, 0.0)), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(), + spawn=sim_utils.GroundPlaneCfg(), + collision_group=-1, + ) + + # lights + sky_light = AssetBaseCfg( + prim_path="/World/skyLight", + spawn=sim_utils.DomeLightCfg( + intensity=750.0, + texture_file=f"{ISAAC_NUCLEUS_DIR}/Materials/Textures/Skies/PolyHaven/kloofendal_43d_clear_puresky_4k.hdr", + ), + ) + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + object_pose = mdp.ObjectUniformPoseCommandCfg( + asset_name="robot", + object_name="object", + resampling_time_range=(3.0, 5.0), + debug_vis=False, + ranges=mdp.ObjectUniformPoseCommandCfg.Ranges( + pos_x=(-0.7, -0.3), + pos_y=(-0.25, 0.25), + pos_z=(0.55, 0.95), + roll=(-3.14, 3.14), + pitch=(-3.14, 3.14), + yaw=(0.0, 0.0), + ), + success_vis_asset_name="table", + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + object_quat_b = ObsTerm(func=mdp.object_quat_b, noise=Unoise(n_min=-0.0, n_max=0.0)) + target_object_pose_b = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + self.history_length = 5 + + @configclass + class ProprioObsCfg(ObsGroup): + """Observations for proprioception group.""" + + joint_pos = ObsTerm(func=mdp.joint_pos, noise=Unoise(n_min=-0.0, n_max=0.0)) + joint_vel = ObsTerm(func=mdp.joint_vel, noise=Unoise(n_min=-0.0, n_max=0.0)) + hand_tips_state_b = ObsTerm( + func=mdp.body_state_b, + noise=Unoise(n_min=-0.0, n_max=0.0), + # good behaving number for position in m, velocity in m/s, rad/s, + # and quaternion are unlikely to exceed -2 to 2 range + clip=(-2.0, 2.0), + params={ + "body_asset_cfg": SceneEntityCfg("robot"), + "base_asset_cfg": SceneEntityCfg("robot"), + }, + ) + contact: ObsTerm = MISSING + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + self.history_length = 5 + + @configclass + class PerceptionObsCfg(ObsGroup): + """Observations for perception group.""" + + object_point_cloud = ObsTerm( + func=mdp.object_point_cloud_b, + noise=Unoise(n_min=-0.0, n_max=0.0), + clip=(-2.0, 2.0), # clamp between -2 m to 2 m + params={"num_points": 64, "flatten": True}, + ) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_dim = 0 + self.concatenate_terms = True + self.flatten_history_dim = True + self.history_length = 5 + + # observation groups + policy: PolicyCfg = PolicyCfg() + proprio: ProprioObsCfg = ProprioObsCfg() + perception: PerceptionObsCfg = PerceptionObsCfg() + + +@configclass +class EventCfg: + """Configuration for randomization.""" + + # -- pre-startup + randomize_object_scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={"scale_range": (0.75, 1.5), "asset_cfg": SceneEntityCfg("object")}, + ) + + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": [0.5, 1.0], + "dynamic_friction_range": [0.5, 1.0], + "restitution_range": [0.0, 0.0], + "num_buckets": 250, + }, + ) + + object_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("object", body_names=".*"), + "static_friction_range": [0.5, 1.0], + "dynamic_friction_range": [0.5, 1.0], + "restitution_range": [0.0, 0.0], + "num_buckets": 250, + }, + ) + + joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stiffness_distribution_params": [0.5, 2.0], + "damping_distribution_params": [0.5, 2.0], + "operation": "scale", + }, + ) + + joint_friction = EventTerm( + func=mdp.randomize_joint_parameters, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "friction_distribution_params": [0.0, 5.0], + "operation": "scale", + }, + ) + + object_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("object"), + "mass_distribution_params": [0.2, 2.0], + "operation": "scale", + }, + ) + + reset_table = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": [-0.05, 0.05], "y": [-0.05, 0.05], "z": [0.0, 0.0]}, + "velocity_range": {"x": [-0.0, 0.0], "y": [-0.0, 0.0], "z": [-0.0, 0.0]}, + "asset_cfg": SceneEntityCfg("table"), + }, + ) + + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.2, 0.2], + "y": [-0.2, 0.2], + "z": [0.0, 0.4], + "roll": [-3.14, 3.14], + "pitch": [-3.14, 3.14], + "yaw": [-3.14, 3.14], + }, + "velocity_range": {"x": [-0.0, 0.0], "y": [-0.0, 0.0], "z": [-0.0, 0.0]}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + reset_root = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": [-0.0, 0.0], "y": [-0.0, 0.0], "yaw": [-0.0, 0.0]}, + "velocity_range": {"x": [-0.0, 0.0], "y": [-0.0, 0.0], "z": [-0.0, 0.0]}, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": [-0.50, 0.50], + "velocity_range": [0.0, 0.0], + }, + ) + + reset_robot_wrist_joint = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names="iiwa7_joint_7"), + "position_range": [-3, 3], + "velocity_range": [0.0, 0.0], + }, + ) + + # Note (Octi): This is a deliberate trick in Remake to accelerate learning. + # By scheduling gravity as a curriculum — starting with no gravity (easy) + # and gradually introducing full gravity (hard) — the agent learns more smoothly. + # This removes the need for a special "Lift" reward (often required to push the + # agent to counter gravity), which has bonus effect of simplifying reward composition overall. + variable_gravity = EventTerm( + func=mdp.randomize_physics_scene_gravity, + mode="reset", + params={ + "gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.0]), + "operation": "abs", + }, + ) + + +@configclass +class ActionsCfg: + pass + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + action_l2 = RewTerm(func=mdp.action_l2_clamped, weight=-0.005) + + action_rate_l2 = RewTerm(func=mdp.action_rate_l2_clamped, weight=-0.005) + + fingers_to_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.4}, weight=1.0) + + position_tracking = RewTerm( + func=mdp.position_command_error_tanh, + weight=2.0, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "std": 0.2, + "command_name": "object_pose", + "align_asset_cfg": SceneEntityCfg("object"), + }, + ) + + orientation_tracking = RewTerm( + func=mdp.orientation_command_error_tanh, + weight=4.0, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "std": 1.5, + "command_name": "object_pose", + "align_asset_cfg": SceneEntityCfg("object"), + }, + ) + + success = RewTerm( + func=mdp.success_reward, + weight=10, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "pos_std": 0.1, + "rot_std": 0.5, + "command_name": "object_pose", + "align_asset_cfg": SceneEntityCfg("object"), + }, + ) + + early_termination = RewTerm(func=mdp.is_terminated_term, weight=-1, params={"term_keys": "abnormal_robot"}) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_out_of_bound = DoneTerm( + func=mdp.out_of_bound, + params={ + "in_bound_range": {"x": (-1.5, 0.5), "y": (-2.0, 2.0), "z": (0.0, 2.0)}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + abnormal_robot = DoneTerm(func=mdp.abnormal_robot_state) + + +@configclass +class DexsuiteReorientEnvCfg(ManagerBasedEnvCfg): + """Dexsuite reorientation task definition, also the base definition for derivative Lift task and evaluation task""" + + # Scene settings + viewer: ViewerCfg = ViewerCfg(eye=(-2.25, 0.0, 0.75), lookat=(0.0, 0.0, 0.45), origin_type="env") + scene: SceneCfg = SceneCfg(num_envs=4096, env_spacing=3, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg | None = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 # 50 Hz + + # *single-goal setup + self.commands.object_pose.resampling_time_range = (10.0, 10.0) + self.commands.object_pose.position_only = False + self.commands.object_pose.success_visualizer_cfg.markers["failure"] = self.scene.table.spawn.replace( + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.25, 0.15, 0.15), roughness=0.25), visible=True + ) + self.commands.object_pose.success_visualizer_cfg.markers["success"] = self.scene.table.spawn.replace( + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.15, 0.25, 0.15), roughness=0.25), visible=True + ) + + self.episode_length_s = 4.0 + self.is_finite_horizon = True + + # simulation settings + self.sim.dt = 1 / 120 + self.sim.render_interval = self.decimation + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_max_rigid_patch_count = 4 * 5 * 2**15 + + if self.curriculum is not None: + self.curriculum.adr.params["pos_tol"] = self.rewards.success.params["pos_std"] / 2 + self.curriculum.adr.params["rot_tol"] = self.rewards.success.params["rot_std"] / 2 + + +class DexsuiteLiftEnvCfg(DexsuiteReorientEnvCfg): + """Dexsuite lift task definition""" + + def __post_init__(self): + super().__post_init__() + self.rewards.orientation_tracking = None # no orientation reward + self.commands.object_pose.position_only = True + if self.curriculum is not None: + self.rewards.success.params["rot_std"] = None # make success reward not consider orientation + self.curriculum.adr.params["rot_tol"] = None # make adr not tracking orientation + + +class DexsuiteReorientEnvCfg_PLAY(DexsuiteReorientEnvCfg): + """Dexsuite reorientation task evaluation environment definition""" + + def __post_init__(self): + super().__post_init__() + self.commands.object_pose.resampling_time_range = (2.0, 3.0) + self.commands.object_pose.debug_vis = True + self.curriculum.adr.params["init_difficulty"] = self.curriculum.adr.params["max_difficulty"] + + +class DexsuiteLiftEnvCfg_PLAY(DexsuiteLiftEnvCfg): + """Dexsuite lift task evaluation environment definition""" + + def __post_init__(self): + super().__post_init__() + self.commands.object_pose.resampling_time_range = (2.0, 3.0) + self.commands.object_pose.debug_vis = True + self.commands.object_pose.position_only = True + self.curriculum.adr.params["init_difficulty"] = self.curriculum.adr.params["max_difficulty"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a6537b1a5e198b06182af08d48b4d64de70383b2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .commands import * # noqa: F401, F403 +from .curriculums import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..83f55101029b62d179a9df21570b42a535293da1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from .pose_commands_cfg import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands.py new file mode 100644 index 0000000000000000000000000000000000000000..ade464360a07e6f76b5c13412ab4bac8897f1694 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands.py @@ -0,0 +1,180 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +"""Sub-module containing command generators for pose tracking.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import CommandTerm +from isaaclab.markers import VisualizationMarkers +from isaaclab.utils.math import combine_frame_transforms, compute_pose_error, quat_from_euler_xyz, quat_unique + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + from . import pose_commands_cfg as dex_cmd_cfgs + + +class ObjectUniformPoseCommand(CommandTerm): + """Uniform pose command generator for an object (in the robot base frame). + + This command term samples target object poses by: + • Drawing (x, y, z) uniformly within configured Cartesian bounds, and + • Drawing roll-pitch-yaw uniformly within configured ranges, then converting + to a quaternion (w, x, y, z). Optionally makes quaternions unique by enforcing + a positive real part. + + Frames: + Targets are defined in the robot's *base frame*. For metrics/visualization, + targets are transformed into the *world frame* using the robot root pose. + + Outputs: + The command buffer has shape (num_envs, 7): `(x, y, z, qw, qx, qy, qz)`. + + Metrics: + `position_error` and `orientation_error` are computed between the commanded + world-frame pose and the object's current world-frame pose. + + Config: + `cfg` must provide the sampling ranges, whether to enforce quaternion uniqueness, + and optional visualization settings. + """ + + cfg: dex_cmd_cfgs.ObjectUniformPoseCommandCfg + """Configuration for the command generator.""" + + def __init__(self, cfg: dex_cmd_cfgs.ObjectUniformPoseCommandCfg, env: ManagerBasedEnv): + """Initialize the command generator class. + + Args: + cfg: The configuration parameters for the command generator. + env: The environment object. + """ + # initialize the base class + super().__init__(cfg, env) + + # extract the robot and body index for which the command is generated + self.robot: Articulation = env.scene[cfg.asset_name] + self.object: RigidObject = env.scene[cfg.object_name] + self.success_vis_asset: RigidObject = env.scene[cfg.success_vis_asset_name] + + # create buffers + # -- commands: (x, y, z, qw, qx, qy, qz) in root frame + self.pose_command_b = torch.zeros(self.num_envs, 7, device=self.device) + self.pose_command_b[:, 3] = 1.0 + self.pose_command_w = torch.zeros_like(self.pose_command_b) + # -- metrics + self.metrics["position_error"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["orientation_error"] = torch.zeros(self.num_envs, device=self.device) + + self.success_visualizer = VisualizationMarkers(self.cfg.success_visualizer_cfg) + self.success_visualizer.set_visibility(True) + + def __str__(self) -> str: + msg = "UniformPoseCommand:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + msg += f"\tResampling time range: {self.cfg.resampling_time_range}\n" + return msg + + """ + Properties + """ + + @property + def command(self) -> torch.Tensor: + """The desired pose command. Shape is (num_envs, 7). + + The first three elements correspond to the position, followed by the quaternion orientation in (w, x, y, z). + """ + return self.pose_command_b + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + # transform command from base frame to simulation world frame + self.pose_command_w[:, :3], self.pose_command_w[:, 3:] = combine_frame_transforms( + self.robot.data.root_pos_w, + self.robot.data.root_quat_w, + self.pose_command_b[:, :3], + self.pose_command_b[:, 3:], + ) + # compute the error + pos_error, rot_error = compute_pose_error( + self.pose_command_w[:, :3], + self.pose_command_w[:, 3:], + self.object.data.root_state_w[:, :3], + self.object.data.root_state_w[:, 3:7], + ) + self.metrics["position_error"] = torch.norm(pos_error, dim=-1) + self.metrics["orientation_error"] = torch.norm(rot_error, dim=-1) + + success_id = self.metrics["position_error"] < 0.05 + if not self.cfg.position_only: + success_id &= self.metrics["orientation_error"] < 0.5 + self.success_visualizer.visualize(self.success_vis_asset.data.root_pos_w, marker_indices=success_id.int()) + + def _resample_command(self, env_ids: Sequence[int]): + # sample new pose targets + # -- position + r = torch.empty(len(env_ids), device=self.device) + self.pose_command_b[env_ids, 0] = r.uniform_(*self.cfg.ranges.pos_x) + self.pose_command_b[env_ids, 1] = r.uniform_(*self.cfg.ranges.pos_y) + self.pose_command_b[env_ids, 2] = r.uniform_(*self.cfg.ranges.pos_z) + # -- orientation + euler_angles = torch.zeros_like(self.pose_command_b[env_ids, :3]) + euler_angles[:, 0].uniform_(*self.cfg.ranges.roll) + euler_angles[:, 1].uniform_(*self.cfg.ranges.pitch) + euler_angles[:, 2].uniform_(*self.cfg.ranges.yaw) + quat = quat_from_euler_xyz(euler_angles[:, 0], euler_angles[:, 1], euler_angles[:, 2]) + # make sure the quaternion has real part as positive + self.pose_command_b[env_ids, 3:] = quat_unique(quat) if self.cfg.make_quat_unique else quat + + def _update_command(self): + pass + + def _set_debug_vis_impl(self, debug_vis: bool): + # create markers if necessary for the first tome + if debug_vis: + if not hasattr(self, "goal_visualizer"): + # -- goal pose + self.goal_visualizer = VisualizationMarkers(self.cfg.goal_pose_visualizer_cfg) + # -- current body pose + self.curr_visualizer = VisualizationMarkers(self.cfg.curr_pose_visualizer_cfg) + # set their visibility to true + self.goal_visualizer.set_visibility(True) + self.curr_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_visualizer"): + self.goal_visualizer.set_visibility(False) + self.curr_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # check if robot is initialized + # note: this is needed in-case the robot is de-initialized. we can't access the data + if not self.robot.is_initialized: + return + # update the markers + if not self.cfg.position_only: + # -- goal pose + self.goal_visualizer.visualize(self.pose_command_w[:, :3], self.pose_command_w[:, 3:]) + # -- current object pose + self.curr_visualizer.visualize(self.object.data.root_pos_w, self.object.data.root_quat_w) + else: + distance = torch.norm(self.pose_command_w[:, :3] - self.object.data.root_pos_w[:, :3], dim=1) + success_id = (distance < 0.05).int() + # note: since marker indices for position is 1(far) and 2(near), we can simply shift the success_id by 1. + # -- goal position + self.goal_visualizer.visualize(self.pose_command_w[:, :3], marker_indices=success_id + 1) + # -- current object position + self.curr_visualizer.visualize(self.object.data.root_pos_w, marker_indices=success_id + 1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e3c83882a3f5691b96b6e68a0faec10c701cdde9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/commands/pose_commands_cfg.py @@ -0,0 +1,92 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.managers import CommandTermCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import pose_commands as dex_cmd + +ALIGN_MARKER_CFG = VisualizationMarkersCfg( + markers={ + "frame": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/frame_prim.usd", + scale=(0.1, 0.1, 0.1), + ), + "position_far": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)), + ), + "position_near": sim_utils.SphereCfg( + radius=0.01, + visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)), + ), + } +) + + +@configclass +class ObjectUniformPoseCommandCfg(CommandTermCfg): + """Configuration for uniform pose command generator.""" + + class_type: type = dex_cmd.ObjectUniformPoseCommand + + asset_name: str = MISSING + """Name of the coordinate referencing asset in the environment for which the commands are generated respect to.""" + + object_name: str = MISSING + """Name of the object in the environment for which the commands are generated.""" + + make_quat_unique: bool = False + """Whether to make the quaternion unique or not. Defaults to False. + + If True, the quaternion is made unique by ensuring the real part is positive. + """ + + @configclass + class Ranges: + """Uniform distribution ranges for the pose commands.""" + + pos_x: tuple[float, float] = MISSING + """Range for the x position (in m).""" + + pos_y: tuple[float, float] = MISSING + """Range for the y position (in m).""" + + pos_z: tuple[float, float] = MISSING + """Range for the z position (in m).""" + + roll: tuple[float, float] = MISSING + """Range for the roll angle (in rad).""" + + pitch: tuple[float, float] = MISSING + """Range for the pitch angle (in rad).""" + + yaw: tuple[float, float] = MISSING + """Range for the yaw angle (in rad).""" + + ranges: Ranges = MISSING + """Ranges for the commands.""" + + position_only: bool = True + """Command goal position only. Command includes goal quat if False""" + + # Pose Markers + goal_pose_visualizer_cfg: VisualizationMarkersCfg = ALIGN_MARKER_CFG.replace(prim_path="/Visuals/Command/goal_pose") + """The configuration for the goal pose visualization marker. Defaults to FRAME_MARKER_CFG.""" + + curr_pose_visualizer_cfg: VisualizationMarkersCfg = ALIGN_MARKER_CFG.replace(prim_path="/Visuals/Command/body_pose") + """The configuration for the current pose visualization marker. Defaults to FRAME_MARKER_CFG.""" + + success_vis_asset_name: str = MISSING + """Name of the asset in the environment for which the success color are indicated.""" + + # success markers + success_visualizer_cfg = VisualizationMarkersCfg(prim_path="/Visuals/SuccessMarkers", markers={}) + """The configuration for the success visualization marker. User needs to add the markers""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/curriculums.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/curriculums.py new file mode 100644 index 0000000000000000000000000000000000000000..148046f012c7c8463ba562cf89cf758482999692 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/curriculums.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.envs import mdp +from isaaclab.managers import ManagerTermBase, SceneEntityCfg +from isaaclab.utils.math import combine_frame_transforms, compute_pose_error + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def initial_final_interpolate_fn(env: ManagerBasedRLEnv, env_id, data, initial_value, final_value, difficulty_term_str): + """ + Interpolate between initial value iv and final value fv, for any arbitrarily + nested structure of lists/tuples in 'data'. Scalars (int/float) are handled + at the leaves. + """ + # get the fraction scalar on the device + difficulty_term: DifficultyScheduler = getattr(env.curriculum_manager.cfg, difficulty_term_str).func + frac = difficulty_term.difficulty_frac + if frac < 0.1: + # no-op during start, since the difficulty fraction near 0 is wasting of resource. + return mdp.modify_env_param.NO_CHANGE + + # convert iv/fv to tensors, but we'll peel them apart in recursion + initial_value_tensor = torch.tensor(initial_value, device=env.device) + final_value_tensor = torch.tensor(final_value, device=env.device) + + return _recurse(initial_value_tensor.tolist(), final_value_tensor.tolist(), data, frac) + + +def _recurse(iv_elem, fv_elem, data_elem, frac): + # If it's a sequence, rebuild the same type with each element recursed + if isinstance(data_elem, Sequence) and not isinstance(data_elem, (str, bytes)): + # Note: we assume initial value element and final value element have the same structure as data + return type(data_elem)(_recurse(iv_e, fv_e, d_e, frac) for iv_e, fv_e, d_e in zip(iv_elem, fv_elem, data_elem)) + # Otherwise it's a leaf scalar: do the interpolation + new_val = frac * (fv_elem - iv_elem) + iv_elem + if isinstance(data_elem, int): + return int(new_val.item()) + else: + # cast floats or any numeric + return new_val.item() + + +class DifficultyScheduler(ManagerTermBase): + """Adaptive difficulty scheduler for curriculum learning. + + Tracks per-environment difficulty levels and adjusts them based on task performance. Difficulty increases when + position/orientation errors fall below given tolerances, and decreases otherwise (unless `promotion_only` is set). + The normalized average difficulty across environments is exposed as `difficulty_frac` for use in curriculum + interpolation. + + Args: + cfg: Configuration object specifying scheduler parameters. + env: The manager-based RL environment. + + """ + + def __init__(self, cfg, env): + super().__init__(cfg, env) + init_difficulty = self.cfg.params.get("init_difficulty", 0) + self.current_adr_difficulties = torch.ones(env.num_envs, device=env.device) * init_difficulty + self.difficulty_frac = 0 + + def get_state(self): + return self.current_adr_difficulties + + def set_state(self, state: torch.Tensor): + self.current_adr_difficulties = state.clone().to(self._env.device) + + def __call__( + self, + env: ManagerBasedRLEnv, + env_ids: Sequence[int], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + pos_tol: float = 0.1, + rot_tol: float | None = None, + init_difficulty: int = 0, + min_difficulty: int = 0, + max_difficulty: int = 50, + promotion_only: bool = False, + ): + asset: Articulation = env.scene[asset_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command("object_pose") + des_pos_w, des_quat_w = combine_frame_transforms( + asset.data.root_pos_w[env_ids], asset.data.root_quat_w[env_ids], command[env_ids, :3], command[env_ids, 3:7] + ) + pos_err, rot_err = compute_pose_error( + des_pos_w, des_quat_w, object.data.root_pos_w[env_ids], object.data.root_quat_w[env_ids] + ) + pos_dist = torch.norm(pos_err, dim=1) + rot_dist = torch.norm(rot_err, dim=1) + move_up = (pos_dist < pos_tol) & (rot_dist < rot_tol) if rot_tol else pos_dist < pos_tol + demot = self.current_adr_difficulties[env_ids] if promotion_only else self.current_adr_difficulties[env_ids] - 1 + self.current_adr_difficulties[env_ids] = torch.where( + move_up, + self.current_adr_difficulties[env_ids] + 1, + demot, + ).clamp(min=min_difficulty, max=max_difficulty) + self.difficulty_frac = torch.mean(self.current_adr_difficulties) / max(max_difficulty, 1) + return self.difficulty_frac diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..604c74320b0153fe51d4b6053c30cd3630413de6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/observations.py @@ -0,0 +1,198 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import ManagerTermBase, SceneEntityCfg +from isaaclab.utils.math import quat_apply, quat_apply_inverse, quat_inv, quat_mul, subtract_frame_transforms + +from .utils import sample_object_point_cloud + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_pos_b( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +): + """Object position in the robot's root frame. + + Args: + env: The environment. + robot_cfg: Scene entity for the robot (reference frame). Defaults to ``SceneEntityCfg("robot")``. + object_cfg: Scene entity for the object. Defaults to ``SceneEntityCfg("object")``. + + Returns: + Tensor of shape ``(num_envs, 3)``: object position [x, y, z] expressed in the robot root frame. + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + return quat_apply_inverse(robot.data.root_quat_w, object.data.root_pos_w - robot.data.root_pos_w) + + +def object_quat_b( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Object orientation in the robot's root frame. + + Args: + env: The environment. + robot_cfg: Scene entity for the robot (reference frame). Defaults to ``SceneEntityCfg("robot")``. + object_cfg: Scene entity for the object. Defaults to ``SceneEntityCfg("object")``. + + Returns: + Tensor of shape ``(num_envs, 4)``: object quaternion ``(w, x, y, z)`` in the robot root frame. + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + return quat_mul(quat_inv(robot.data.root_quat_w), object.data.root_quat_w) + + +def body_state_b( + env: ManagerBasedRLEnv, + body_asset_cfg: SceneEntityCfg, + base_asset_cfg: SceneEntityCfg, +) -> torch.Tensor: + """Body state (pos, quat, lin vel, ang vel) in the base asset's root frame. + + The state for each body is stacked horizontally as + ``[position(3), quaternion(4)(wxyz), linvel(3), angvel(3)]`` and then concatenated over bodies. + + Args: + env: The environment. + body_asset_cfg: Scene entity for the articulated body whose links are observed. + base_asset_cfg: Scene entity providing the reference (root) frame. + + Returns: + Tensor of shape ``(num_envs, num_bodies * 13)`` with per-body states expressed in the base root frame. + """ + body_asset: Articulation = env.scene[body_asset_cfg.name] + base_asset: Articulation = env.scene[base_asset_cfg.name] + # get world pose of bodies + body_pos_w = body_asset.data.body_pos_w[:, body_asset_cfg.body_ids].view(-1, 3) + body_quat_w = body_asset.data.body_quat_w[:, body_asset_cfg.body_ids].view(-1, 4) + body_lin_vel_w = body_asset.data.body_lin_vel_w[:, body_asset_cfg.body_ids].view(-1, 3) + body_ang_vel_w = body_asset.data.body_ang_vel_w[:, body_asset_cfg.body_ids].view(-1, 3) + num_bodies = int(body_pos_w.shape[0] / env.num_envs) + # get world pose of base frame + root_pos_w = base_asset.data.root_link_pos_w.unsqueeze(1).repeat_interleave(num_bodies, dim=1).view(-1, 3) + root_quat_w = base_asset.data.root_link_quat_w.unsqueeze(1).repeat_interleave(num_bodies, dim=1).view(-1, 4) + # transform from world body pose to local body pose + body_pos_b, body_quat_b = subtract_frame_transforms(root_pos_w, root_quat_w, body_pos_w, body_quat_w) + body_lin_vel_b = quat_apply_inverse(root_quat_w, body_lin_vel_w) + body_ang_vel_b = quat_apply_inverse(root_quat_w, body_ang_vel_w) + # concate and return + out = torch.cat((body_pos_b, body_quat_b, body_lin_vel_b, body_ang_vel_b), dim=1) + return out.view(env.num_envs, -1) + + +class object_point_cloud_b(ManagerTermBase): + """Object surface point cloud expressed in a reference asset's root frame. + + Points are pre-sampled on the object's surface in its local frame and transformed to world, + then into the reference (e.g., robot) root frame. Optionally visualizes the points. + + Args (from ``cfg.params``): + object_cfg: Scene entity for the object to sample. Defaults to ``SceneEntityCfg("object")``. + ref_asset_cfg: Scene entity providing the reference frame. Defaults to ``SceneEntityCfg("robot")``. + num_points: Number of points to sample on the object surface. Defaults to ``10``. + visualize: Whether to draw markers for the points. Defaults to ``True``. + static: If ``True``, cache world-space points on reset and reuse them (no per-step resampling). + + Returns (from ``__call__``): + If ``flatten=False``: tensor of shape ``(num_envs, num_points, 3)``. + If ``flatten=True``: tensor of shape ``(num_envs, 3 * num_points)``. + """ + + def __init__(self, cfg, env: ManagerBasedRLEnv): + super().__init__(cfg, env) + + self.object_cfg: SceneEntityCfg = cfg.params.get("object_cfg", SceneEntityCfg("object")) + self.ref_asset_cfg: SceneEntityCfg = cfg.params.get("ref_asset_cfg", SceneEntityCfg("robot")) + num_points: int = cfg.params.get("num_points", 10) + self.object: RigidObject = env.scene[self.object_cfg.name] + self.ref_asset: Articulation = env.scene[self.ref_asset_cfg.name] + # lazy initialize visualizer and point cloud + if cfg.params.get("visualize", True): + from isaaclab.markers import VisualizationMarkers + from isaaclab.markers.config import RAY_CASTER_MARKER_CFG + + ray_cfg = RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/ObservationPointCloud") + ray_cfg.markers["hit"].radius = 0.0025 + self.visualizer = VisualizationMarkers(ray_cfg) + self.points_local = sample_object_point_cloud( + env.num_envs, num_points, self.object.cfg.prim_path, device=env.device + ) + self.points_w = torch.zeros_like(self.points_local) + + def __call__( + self, + env: ManagerBasedRLEnv, + ref_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + num_points: int = 10, + flatten: bool = False, + visualize: bool = True, + ): + """Compute the object point cloud in the reference asset's root frame. + + Note: + Points are pre-sampled at initialization using ``self.num_points``; the ``num_points`` argument is + kept for API symmetry and does not change the sampled set at runtime. + + Args: + env: The environment. + ref_asset_cfg: Reference frame provider (root). Defaults to ``SceneEntityCfg("robot")``. + object_cfg: Object to sample. Defaults to ``SceneEntityCfg("object")``. + num_points: Unused at runtime; see note above. + flatten: If ``True``, return a flattened tensor ``(num_envs, 3 * num_points)``. + visualize: If ``True``, draw markers for the points. + + Returns: + Tensor of shape ``(num_envs, num_points, 3)`` or flattened if requested. + """ + ref_pos_w = self.ref_asset.data.root_pos_w.unsqueeze(1).repeat(1, num_points, 1) + ref_quat_w = self.ref_asset.data.root_quat_w.unsqueeze(1).repeat(1, num_points, 1) + + object_pos_w = self.object.data.root_pos_w.unsqueeze(1).repeat(1, num_points, 1) + object_quat_w = self.object.data.root_quat_w.unsqueeze(1).repeat(1, num_points, 1) + # apply rotation + translation + self.points_w = quat_apply(object_quat_w, self.points_local) + object_pos_w + if visualize: + self.visualizer.visualize(translations=self.points_w.view(-1, 3)) + object_point_cloud_pos_b, _ = subtract_frame_transforms(ref_pos_w, ref_quat_w, self.points_w, None) + + return object_point_cloud_pos_b.view(env.num_envs, -1) if flatten else object_point_cloud_pos_b + + +def fingers_contact_force_b( + env: ManagerBasedRLEnv, + contact_sensor_names: list[str], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """base-frame contact forces from listed sensors, concatenated per env. + + Args: + env: The environment. + contact_sensor_names: Names of contact sensors in ``env.scene.sensors`` to read. + + Returns: + Tensor of shape ``(num_envs, 3 * num_sensors)`` with forces stacked horizontally as + ``[fx, fy, fz]`` per sensor. + """ + force_w = [env.scene.sensors[name].data.force_matrix_w.view(env.num_envs, 3) for name in contact_sensor_names] + force_w = torch.stack(force_w, dim=1) + robot: Articulation = env.scene[asset_cfg.name] + forces_b = quat_apply_inverse(robot.data.root_link_quat_w.unsqueeze(1).repeat(1, force_w.shape[1], 1), force_w) + return forces_b diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..a6ddab0f908125af4be09a312d54825fb2873f32 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/rewards.py @@ -0,0 +1,127 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import ContactSensor +from isaaclab.utils import math as math_utils +from isaaclab.utils.math import combine_frame_transforms, compute_pose_error + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def action_rate_l2_clamped(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize the rate of change of the actions using L2 squared kernel.""" + return torch.sum(torch.square(env.action_manager.action - env.action_manager.prev_action), dim=1).clamp(-1000, 1000) + + +def action_l2_clamped(env: ManagerBasedRLEnv) -> torch.Tensor: + """Penalize the actions using L2 squared kernel.""" + return torch.sum(torch.square(env.action_manager.action), dim=1).clamp(-1000, 1000) + + +def object_ee_distance( + env: ManagerBasedRLEnv, + std: float, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """Reward reaching the object using a tanh-kernel on end-effector distance. + + The reward is close to 1 when the maximum distance between the object and any end-effector body is small. + """ + asset: RigidObject = env.scene[asset_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + asset_pos = asset.data.body_pos_w[:, asset_cfg.body_ids] + object_pos = object.data.root_pos_w + object_ee_distance = torch.norm(asset_pos - object_pos[:, None, :], dim=-1).max(dim=-1).values + return 1 - torch.tanh(object_ee_distance / std) + + +def contacts(env: ManagerBasedRLEnv, threshold: float) -> torch.Tensor: + """Penalize undesired contacts as the number of violations that are above a threshold.""" + + thumb_contact_sensor: ContactSensor = env.scene.sensors["thumb_link_3_object_s"] + index_contact_sensor: ContactSensor = env.scene.sensors["index_link_3_object_s"] + middle_contact_sensor: ContactSensor = env.scene.sensors["middle_link_3_object_s"] + ring_contact_sensor: ContactSensor = env.scene.sensors["ring_link_3_object_s"] + # check if contact force is above threshold + thumb_contact = thumb_contact_sensor.data.force_matrix_w.view(env.num_envs, 3) + index_contact = index_contact_sensor.data.force_matrix_w.view(env.num_envs, 3) + middle_contact = middle_contact_sensor.data.force_matrix_w.view(env.num_envs, 3) + ring_contact = ring_contact_sensor.data.force_matrix_w.view(env.num_envs, 3) + + thumb_contact_mag = torch.norm(thumb_contact, dim=-1) + index_contact_mag = torch.norm(index_contact, dim=-1) + middle_contact_mag = torch.norm(middle_contact, dim=-1) + ring_contact_mag = torch.norm(ring_contact, dim=-1) + good_contact_cond1 = (thumb_contact_mag > threshold) & ( + (index_contact_mag > threshold) | (middle_contact_mag > threshold) | (ring_contact_mag > threshold) + ) + + return good_contact_cond1 + + +def success_reward( + env: ManagerBasedRLEnv, + command_name: str, + asset_cfg: SceneEntityCfg, + align_asset_cfg: SceneEntityCfg, + pos_std: float, + rot_std: float | None = None, +) -> torch.Tensor: + """Reward success by comparing commanded pose to the object pose using tanh kernels on error.""" + + asset: RigidObject = env.scene[asset_cfg.name] + object: RigidObject = env.scene[align_asset_cfg.name] + command = env.command_manager.get_command(command_name) + des_pos_w, des_quat_w = combine_frame_transforms( + asset.data.root_pos_w, asset.data.root_quat_w, command[:, :3], command[:, 3:7] + ) + pos_err, rot_err = compute_pose_error(des_pos_w, des_quat_w, object.data.root_pos_w, object.data.root_quat_w) + pos_dist = torch.norm(pos_err, dim=1) + if not rot_std: + # square is not necessary but this help to keep the final value between having rot_std or not roughly the same + return (1 - torch.tanh(pos_dist / pos_std)) ** 2 + rot_dist = torch.norm(rot_err, dim=1) + return (1 - torch.tanh(pos_dist / pos_std)) * (1 - torch.tanh(rot_dist / rot_std)) + + +def position_command_error_tanh( + env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg, align_asset_cfg: SceneEntityCfg +) -> torch.Tensor: + """Reward tracking of commanded position using tanh kernel, gated by contact presence.""" + + asset: RigidObject = env.scene[asset_cfg.name] + object: RigidObject = env.scene[align_asset_cfg.name] + command = env.command_manager.get_command(command_name) + # obtain the desired and current positions + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(asset.data.root_pos_w, asset.data.root_quat_w, des_pos_b) + distance = torch.norm(object.data.root_pos_w - des_pos_w, dim=1) + return (1 - torch.tanh(distance / std)) * contacts(env, 1.0).float() + + +def orientation_command_error_tanh( + env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg, align_asset_cfg: SceneEntityCfg +) -> torch.Tensor: + """Reward tracking of commanded orientation using tanh kernel, gated by contact presence.""" + + asset: RigidObject = env.scene[asset_cfg.name] + object: RigidObject = env.scene[align_asset_cfg.name] + command = env.command_manager.get_command(command_name) + # obtain the desired and current orientations + des_quat_b = command[:, 3:7] + des_quat_w = math_utils.quat_mul(asset.data.root_state_w[:, 3:7], des_quat_b) + quat_distance = math_utils.quat_error_magnitude(object.data.root_quat_w, des_quat_w) + + return (1 - torch.tanh(quat_distance / std)) * contacts(env, 1.0).float() diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..91bf2d0e3aaff1c3ab4ccfd07c154497b2f6e257 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/terminations.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the dexsuite task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def out_of_bound( + env: ManagerBasedRLEnv, + asset_cfg: SceneEntityCfg = SceneEntityCfg("object"), + in_bound_range: dict[str, tuple[float, float]] = {}, +) -> torch.Tensor: + """Termination condition for the object falls out of bound. + + Args: + env: The environment. + asset_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + in_bound_range: The range in x, y, z such that the object is considered in range + """ + object: RigidObject = env.scene[asset_cfg.name] + range_list = [in_bound_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z"]] + ranges = torch.tensor(range_list, device=env.device) + + object_pos_local = object.data.root_pos_w - env.scene.env_origins + outside_bounds = ((object_pos_local < ranges[:, 0]) | (object_pos_local > ranges[:, 1])).any(dim=1) + return outside_bounds + + +def abnormal_robot_state(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """Terminating environment when violation of velocity limits detects, this usually indicates unstable physics caused + by very bad, or aggressive action""" + robot: Articulation = env.scene[asset_cfg.name] + return (robot.data.joint_vel.abs() > (robot.data.joint_vel_limits * 2)).any(dim=1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/utils.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8cae308d3843d35cd29082c4fe361a8c7a41de5a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/dexsuite/mdp/utils.py @@ -0,0 +1,249 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import hashlib +import logging + +import numpy as np +import torch +import trimesh +from trimesh.sample import sample_surface + +from pxr import UsdGeom + +import isaaclab.sim.utils as sim_utils + +# ---- module-scope caches ---- +_PRIM_SAMPLE_CACHE: dict[tuple[str, int], np.ndarray] = {} # (prim_hash, num_points) -> (N,3) in root frame +_FINAL_SAMPLE_CACHE: dict[str, np.ndarray] = {} # env_hash -> (num_points,3) in root frame + + +def clear_pointcloud_caches(): + _PRIM_SAMPLE_CACHE.clear() + _FINAL_SAMPLE_CACHE.clear() + + +def sample_object_point_cloud(num_envs: int, num_points: int, prim_path: str, device: str = "cpu") -> torch.Tensor: + """ + Samples point clouds for each environment instance by collecting points + from all matching USD prims under `prim_path`, then downsamples to + exactly `num_points` per env using farthest-point sampling. + + Caching is in-memory within this module: + - per-prim raw samples: _PRIM_SAMPLE_CACHE[(prim_hash, num_points)] + - final downsampled env: _FINAL_SAMPLE_CACHE[env_hash] + + Returns: + torch.Tensor: Shape (num_envs, num_points, 3) on `device`. + """ + points = torch.zeros((num_envs, num_points, 3), dtype=torch.float32, device=device) + xform_cache = UsdGeom.XformCache() + # Obtain stage handle + stage = sim_utils.get_current_stage() + + for i in range(num_envs): + # Resolve prim path + obj_path = prim_path.replace(".*", str(i)) + + # Gather prims + prims = sim_utils.get_all_matching_child_prims( + obj_path, predicate=lambda p: p.GetTypeName() in ("Mesh", "Cube", "Sphere", "Cylinder", "Capsule", "Cone") + ) + if not prims: + raise KeyError(f"No valid prims under {obj_path}") + + object_prim = stage.GetPrimAtPath(obj_path) + world_root = xform_cache.GetLocalToWorldTransform(object_prim) + + # hash each child prim by its rel transform + geometry + prim_hashes = [] + for prim in prims: + prim_type = prim.GetTypeName() + hasher = hashlib.sha256() + + rel = world_root.GetInverse() * xform_cache.GetLocalToWorldTransform(prim) # prim -> root + mat_np = np.array([[rel[r][c] for c in range(4)] for r in range(4)], dtype=np.float32) + hasher.update(mat_np.tobytes()) + + if prim_type == "Mesh": + mesh = UsdGeom.Mesh(prim) + verts = np.asarray(mesh.GetPointsAttr().Get(), dtype=np.float32) + hasher.update(verts.tobytes()) + else: + if prim_type == "Cube": + size = UsdGeom.Cube(prim).GetSizeAttr().Get() + hasher.update(np.float32(size).tobytes()) + elif prim_type == "Sphere": + r = UsdGeom.Sphere(prim).GetRadiusAttr().Get() + hasher.update(np.float32(r).tobytes()) + elif prim_type == "Cylinder": + c = UsdGeom.Cylinder(prim) + hasher.update(np.float32(c.GetRadiusAttr().Get()).tobytes()) + hasher.update(np.float32(c.GetHeightAttr().Get()).tobytes()) + elif prim_type == "Capsule": + c = UsdGeom.Capsule(prim) + hasher.update(np.float32(c.GetRadiusAttr().Get()).tobytes()) + hasher.update(np.float32(c.GetHeightAttr().Get()).tobytes()) + elif prim_type == "Cone": + c = UsdGeom.Cone(prim) + hasher.update(np.float32(c.GetRadiusAttr().Get()).tobytes()) + hasher.update(np.float32(c.GetHeightAttr().Get()).tobytes()) + + prim_hashes.append(hasher.hexdigest()) + + # scale on root (default to 1 if missing) + attr = object_prim.GetAttribute("xformOp:scale") + scale_val = attr.Get() if attr else None + if scale_val is None: + base_scale = torch.ones(3, dtype=torch.float32, device=device) + else: + base_scale = torch.tensor(scale_val, dtype=torch.float32, device=device) + + # env-level cache key (includes num_points) + env_key = "_".join(sorted(prim_hashes)) + f"_{num_points}" + env_hash = hashlib.sha256(env_key.encode()).hexdigest() + + # load from env-level in-memory cache + if env_hash in _FINAL_SAMPLE_CACHE: + arr = _FINAL_SAMPLE_CACHE[env_hash] # (num_points,3) in root frame + points[i] = torch.from_numpy(arr).to(device) * base_scale.unsqueeze(0) + continue + + # otherwise build per-prim samples (with per-prim cache) + all_samples_np: list[np.ndarray] = [] + for prim, ph in zip(prims, prim_hashes): + key = (ph, num_points) + if key in _PRIM_SAMPLE_CACHE: + samples = _PRIM_SAMPLE_CACHE[key] + else: + prim_type = prim.GetTypeName() + if prim_type == "Mesh": + mesh = UsdGeom.Mesh(prim) + verts = np.asarray(mesh.GetPointsAttr().Get(), dtype=np.float32) + faces = _triangulate_faces(prim) + mesh_tm = trimesh.Trimesh(vertices=verts, faces=faces, process=False) + else: + mesh_tm = create_primitive_mesh(prim) + + face_weights = mesh_tm.area_faces + samples_np, _ = sample_surface(mesh_tm, num_points * 2, face_weight=face_weights) + + # FPS to num_points on chosen device + tensor_pts = torch.from_numpy(samples_np.astype(np.float32)).to(device) + prim_idxs = farthest_point_sampling(tensor_pts, num_points) + local_pts = tensor_pts[prim_idxs] + + # prim -> root transform + rel = xform_cache.GetLocalToWorldTransform(prim) * world_root.GetInverse() + mat_np = np.array([[rel[r][c] for c in range(4)] for r in range(4)], dtype=np.float32) + mat_t = torch.from_numpy(mat_np).to(device) + + ones = torch.ones((num_points, 1), device=device) + pts_h = torch.cat([local_pts, ones], dim=1) + root_h = pts_h @ mat_t + samples = root_h[:, :3].detach().cpu().numpy() + + if prim_type == "Cone": + samples[:, 2] -= UsdGeom.Cone(prim).GetHeightAttr().Get() / 2 + + _PRIM_SAMPLE_CACHE[key] = samples # cache in root frame @ num_points + + all_samples_np.append(samples) + + # combine & env-level FPS (if needed) + if len(all_samples_np) == 1: + samples_final = torch.from_numpy(all_samples_np[0]).to(device) + else: + combined = torch.from_numpy(np.concatenate(all_samples_np, axis=0)).to(device) + idxs = farthest_point_sampling(combined, num_points) + samples_final = combined[idxs] + + # store env-level cache in root frame (CPU) + _FINAL_SAMPLE_CACHE[env_hash] = samples_final.detach().cpu().numpy() + + # apply root scale and write out + points[i] = samples_final * base_scale.unsqueeze(0) + + return points + + +def _triangulate_faces(prim) -> np.ndarray: + """Convert a USD Mesh prim into triangulated face indices (N, 3).""" + mesh = UsdGeom.Mesh(prim) + counts = mesh.GetFaceVertexCountsAttr().Get() + indices = mesh.GetFaceVertexIndicesAttr().Get() + faces = [] + it = iter(indices) + for cnt in counts: + poly = [next(it) for _ in range(cnt)] + for k in range(1, cnt - 1): + faces.append([poly[0], poly[k], poly[k + 1]]) + return np.asarray(faces, dtype=np.int64) + + +def create_primitive_mesh(prim) -> trimesh.Trimesh: + """Create a trimesh mesh from a USD primitive (Cube, Sphere, Cylinder, etc.).""" + prim_type = prim.GetTypeName() + if prim_type == "Cube": + size = UsdGeom.Cube(prim).GetSizeAttr().Get() + return trimesh.creation.box(extents=(size, size, size)) + elif prim_type == "Sphere": + r = UsdGeom.Sphere(prim).GetRadiusAttr().Get() + return trimesh.creation.icosphere(subdivisions=3, radius=r) + elif prim_type == "Cylinder": + c = UsdGeom.Cylinder(prim) + return trimesh.creation.cylinder(radius=c.GetRadiusAttr().Get(), height=c.GetHeightAttr().Get()) + elif prim_type == "Capsule": + c = UsdGeom.Capsule(prim) + return trimesh.creation.capsule(radius=c.GetRadiusAttr().Get(), height=c.GetHeightAttr().Get()) + elif prim_type == "Cone": # Cone + c = UsdGeom.Cone(prim) + return trimesh.creation.cone(radius=c.GetRadiusAttr().Get(), height=c.GetHeightAttr().Get()) + else: + raise KeyError(f"{prim_type} is not a valid primitive mesh type") + + +def farthest_point_sampling( + points: torch.Tensor, n_samples: int, memory_threashold=2 * 1024**3 +) -> torch.Tensor: # 2 GiB + """ + Farthest Point Sampling (FPS) for point sets. + + Selects `n_samples` points such that each new point is farthest from the + already chosen ones. Uses a full pairwise distance matrix if memory allows, + otherwise falls back to an iterative version. + + Args: + points (torch.Tensor): Input points of shape (N, D). + n_samples (int): Number of samples to select. + memory_threashold (int): Max allowed bytes for distance matrix. Default 2 GiB. + + Returns: + torch.Tensor: Indices of sampled points (n_samples,). + """ + device = points.device + N = points.shape[0] + elem_size = points.element_size() + bytes_needed = N * N * elem_size + if bytes_needed <= memory_threashold: + dist_mat = torch.cdist(points, points) + sampled_idx = torch.zeros(n_samples, dtype=torch.long, device=device) + min_dists = torch.full((N,), float("inf"), device=device) + farthest = torch.randint(0, N, (1,), device=device) + for j in range(n_samples): + sampled_idx[j] = farthest + min_dists = torch.minimum(min_dists, dist_mat[farthest].view(-1)) + farthest = torch.argmax(min_dists) + return sampled_idx + logging.warning(f"FPS fallback to iterative (needed {bytes_needed} > {memory_threashold})") + sampled_idx = torch.zeros(n_samples, dtype=torch.long, device=device) + distances = torch.full((N,), float("inf"), device=device) + farthest = torch.randint(0, N, (1,), device=device) + for j in range(n_samples): + sampled_idx[j] = farthest + dist = torch.norm(points - points[farthest], dim=1) + distances = torch.minimum(distances, dist) + farthest = torch.argmax(distances) + return sampled_idx diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5dfbb3b40111afd7066250609b668c0f1ecbb4f2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""In-hand object reorientation environment. + +These environments are based on the `dexterous cube manipulation`_ environments +provided in IsaacGymEnvs repository from NVIDIA. However, they contain certain +modifications and additional features. + +.. _dexterous cube manipulation: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs/blob/main/isaacgymenvs/tasks/allegro_hand.py + +""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c586540a04d268d8bcdc275829b1eeed155ded6a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for in-hand manipulation environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9f53828ec4b665f09670ffd917b0afd593606af1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/__init__.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Full kinematic state observations. +## + +gym.register( + id="Isaac-Repose-Cube-Allegro-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.allegro_env_cfg:AllegroCubeEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AllegroCubePPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Repose-Cube-Allegro-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.allegro_env_cfg:AllegroCubeEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AllegroCubePPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +## +# Kinematic state observations without velocity information. +## + +gym.register( + id="Isaac-Repose-Cube-Allegro-NoVelObs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.allegro_env_cfg:AllegroCubeNoVelObsEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AllegroCubeNoVelObsPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Repose-Cube-Allegro-NoVelObs-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.allegro_env_cfg:AllegroCubeNoVelObsEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:AllegroCubeNoVelObsPPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2fa70902ab611577e1d8b6ff7f7e8dd35eb9c93b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,90 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 5.0 + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + + mlp: + units: [512, 256, 128] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False + load_path: '' + + config: + name: allegro_cube + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.1 + normalize_advantage: True + gamma: 0.998 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.016 + score_to_win: 100000 + max_epochs: 5000 + save_best_after: 500 + save_frequency: 200 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.002 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 16384 # 32768 + mini_epochs: 5 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0005 + + player: + #render: True + deterministic: True + games_num: 100000 + print_stats: True diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b1d3d4be17562b74510387dd5547f0bc7ff02003 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class AllegroCubePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 5000 + save_interval = 50 + experiment_name = "allegro_cube" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=True, + critic_obs_normalization=True, + actor_hidden_dims=[512, 256, 128], + critic_hidden_dims=[512, 256, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.002, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=0.001, + schedule="adaptive", + gamma=0.998, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) + + +@configclass +class AllegroCubeNoVelObsPPORunnerCfg(AllegroCubePPORunnerCfg): + experiment_name = "allegro_cube_no_vel_obs" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f61e7f50132daa752dd44857906d68bee4fb5fa1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [512, 256, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 12 + discount_factor: 0.998 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.016 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.002 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "allegro_cube" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 120000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/allegro_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/allegro_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7223ce0234fee56a370a60de3c796319cb29298e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/config/allegro_hand/allegro_env_cfg.py @@ -0,0 +1,66 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.inhand.inhand_env_cfg as inhand_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets import ALLEGRO_HAND_CFG # isort: skip + + +@configclass +class AllegroCubeEnvCfg(inhand_env_cfg.InHandObjectEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to allegro hand + self.scene.robot = ALLEGRO_HAND_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + # enable clone in fabric + self.scene.clone_in_fabric = True + + +@configclass +class AllegroCubeEnvCfg_PLAY(AllegroCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove termination due to timeouts + self.terminations.time_out = None + + +## +# Environment configuration with no velocity observations. +## + + +@configclass +class AllegroCubeNoVelObsEnvCfg(AllegroCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch observation group to no velocity group + self.observations.policy = inhand_env_cfg.ObservationsCfg.NoVelocityKinematicObsGroupCfg() + + +@configclass +class AllegroCubeNoVelObsEnvCfg_PLAY(AllegroCubeNoVelObsEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + # disable randomization for play + self.observations.policy.enable_corruption = False + # remove termination due to timeouts + self.terminations.time_out = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/inhand_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/inhand_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..71594ae210d8e35eaecdb04af92b2547dd030a63 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/inhand_env_cfg.py @@ -0,0 +1,346 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.simulation_cfg import PhysxCfg, SimulationCfg +from isaaclab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveGaussianNoiseCfg as Gnoise + +import isaaclab_tasks.manager_based.manipulation.inhand.mdp as mdp + +## +# Scene definition +## + + +@configclass +class InHandObjectSceneCfg(InteractiveSceneCfg): + """Configuration for a scene with an object and a dexterous hand.""" + + # robots + robot: ArticulationCfg = MISSING + + # objects + object: RigidObjectCfg = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/object", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg( + kinematic_enabled=False, + disable_gravity=False, + enable_gyroscopic_forces=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=0, + sleep_threshold=0.005, + stabilization_threshold=0.0025, + max_depenetration_velocity=1000.0, + ), + mass_props=sim_utils.MassPropertiesCfg(density=400.0), + ), + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, -0.19, 0.56), rot=(1.0, 0.0, 0.0, 0.0)), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.95, 0.95, 0.95), intensity=1000.0), + ) + + dome_light = AssetBaseCfg( + prim_path="/World/domeLight", + spawn=sim_utils.DomeLightCfg(color=(0.02, 0.02, 0.02), intensity=1000.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command specifications for the MDP.""" + + object_pose = mdp.InHandReOrientationCommandCfg( + asset_name="object", + init_pos_offset=(0.0, 0.0, -0.04), + update_goal_on_success=True, + orientation_success_threshold=0.1, + make_quat_unique=False, + marker_pos_offset=(-0.2, -0.06, 0.08), + debug_vis=True, + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_pos = mdp.EMAJointPositionToLimitsActionCfg( + asset_name="robot", + joint_names=[".*"], + alpha=0.95, + rescale_to_limits=True, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class KinematicObsGroupCfg(ObsGroup): + """Observations with full-kinematic state information. + + This does not include acceleration or force information. + """ + + # observation terms (order preserved) + # -- robot terms + joint_pos = ObsTerm(func=mdp.joint_pos_limit_normalized, noise=Gnoise(std=0.005)) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, scale=0.2, noise=Gnoise(std=0.01)) + + # -- object terms + object_pos = ObsTerm( + func=mdp.root_pos_w, noise=Gnoise(std=0.002), params={"asset_cfg": SceneEntityCfg("object")} + ) + object_quat = ObsTerm( + func=mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object"), "make_quat_unique": False} + ) + object_lin_vel = ObsTerm( + func=mdp.root_lin_vel_w, noise=Gnoise(std=0.002), params={"asset_cfg": SceneEntityCfg("object")} + ) + object_ang_vel = ObsTerm( + func=mdp.root_ang_vel_w, + scale=0.2, + noise=Gnoise(std=0.002), + params={"asset_cfg": SceneEntityCfg("object")}, + ) + + # -- command terms + goal_pose = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + goal_quat_diff = ObsTerm( + func=mdp.goal_quat_diff, + params={"asset_cfg": SceneEntityCfg("object"), "command_name": "object_pose", "make_quat_unique": False}, + ) + + # -- action terms + last_action = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + @configclass + class NoVelocityKinematicObsGroupCfg(KinematicObsGroupCfg): + """Observations with partial kinematic state information. + + In contrast to the full-kinematic state group, this group does not include velocity information + about the robot joints and the object root frame. This is useful for tasks where velocity information + is not available or has a lot of noise. + """ + + def __post_init__(self): + # call parent post init + super().__post_init__() + # set unused terms to None + self.joint_vel = None + self.object_lin_vel = None + self.object_ang_vel = None + + # observation groups + policy: KinematicObsGroupCfg = KinematicObsGroupCfg() + + +@configclass +class EventCfg: + """Configuration for randomization.""" + + # startup + # -- robot + robot_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (0.7, 1.3), + "restitution_range": (0.0, 0.0), + "num_buckets": 250, + }, + ) + robot_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=".*"), + "mass_distribution_params": (0.95, 1.05), + "operation": "scale", + }, + ) + robot_joint_stiffness_and_damping = EventTerm( + func=mdp.randomize_actuator_gains, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=".*"), + "stiffness_distribution_params": (0.3, 3.0), # default: 3.0 + "damping_distribution_params": (0.75, 1.5), # default: 0.1 + "operation": "scale", + "distribution": "log_uniform", + }, + ) + + # -- object + object_physics_material = EventTerm( + func=mdp.randomize_rigid_body_material, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("object", body_names=".*"), + "static_friction_range": (0.7, 1.3), + "dynamic_friction_range": (0.7, 1.3), + "restitution_range": (0.0, 0.0), + "num_buckets": 250, + }, + ) + object_scale_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("object"), + "mass_distribution_params": (0.4, 1.6), + "operation": "scale", + }, + ) + + # reset + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": [-0.01, 0.01], "y": [-0.01, 0.01], "z": [-0.01, 0.01]}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names=".*"), + }, + ) + reset_robot_joints = EventTerm( + func=mdp.reset_joints_within_limits_range, + mode="reset", + params={ + "position_range": {".*": [0.2, 0.2]}, + "velocity_range": {".*": [0.0, 0.0]}, + "use_default_offset": True, + "operation": "scale", + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # -- task + # track_pos_l2 = RewTerm( + # func=mdp.track_pos_l2, + # weight=-10.0, + # params={"object_cfg": SceneEntityCfg("object"), "command_name": "object_pose"}, + # ) + track_orientation_inv_l2 = RewTerm( + func=mdp.track_orientation_inv_l2, + weight=1.0, + params={"object_cfg": SceneEntityCfg("object"), "rot_eps": 0.1, "command_name": "object_pose"}, + ) + success_bonus = RewTerm( + func=mdp.success_bonus, + weight=250.0, + params={"object_cfg": SceneEntityCfg("object"), "command_name": "object_pose"}, + ) + + # -- penalties + joint_vel_l2 = RewTerm(func=mdp.joint_vel_l2, weight=-2.5e-5) + action_l2 = RewTerm(func=mdp.action_l2, weight=-0.0001) + action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01) + + # -- optional penalties (these are disabled by default) + # object_away_penalty = RewTerm( + # func=mdp.is_terminated_term, + # weight=-0.0, + # params={"term_keys": "object_out_of_reach"}, + # ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + max_consecutive_success = DoneTerm( + func=mdp.max_consecutive_success, params={"num_success": 50, "command_name": "object_pose"} + ) + + object_out_of_reach = DoneTerm(func=mdp.object_away_from_robot, params={"threshold": 0.3}) + + # object_out_of_reach = DoneTerm( + # func=mdp.object_away_from_goal, params={"threshold": 0.24, "command_name": "object_pose"} + # ) + + +## +# Environment configuration +## + + +@configclass +class InHandObjectEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the in hand reorientation environment.""" + + # Scene settings + scene: InHandObjectSceneCfg = InHandObjectSceneCfg(num_envs=8192, env_spacing=0.6) + # Simulation settings + sim: SimulationCfg = SimulationCfg( + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + physx=PhysxCfg( + bounce_threshold_velocity=0.2, + gpu_max_rigid_contact_count=2**20, + gpu_max_rigid_patch_count=2**23, + ), + ) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 4 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1.0 / 120.0 + self.sim.render_interval = self.decimation + # change viewer settings + self.viewer.eye = (2.0, 2.0, 2.0) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0fafe45003672f76c378fe0e9683c5a660657368 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the in-hand manipulation environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .commands import * # noqa: F401, F403 +from .events import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ab3a9cf3e11c7b25af78b917dc264f477e26ca7e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command terms for 3D orientation goals.""" + +from .commands_cfg import InHandReOrientationCommandCfg # noqa: F401 +from .orientation_command import InHandReOrientationCommand # noqa: F401 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/commands_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/commands_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8c020137c0c9c201dc48a864f904a83d92f5302b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/commands_cfg.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.managers import CommandTermCfg +from isaaclab.markers import VisualizationMarkersCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from .orientation_command import InHandReOrientationCommand + + +@configclass +class InHandReOrientationCommandCfg(CommandTermCfg): + """Configuration for the uniform 3D orientation command term. + + Please refer to the :class:`InHandReOrientationCommand` class for more details. + """ + + class_type: type = InHandReOrientationCommand + resampling_time_range: tuple[float, float] = (1e6, 1e6) # no resampling based on time + + asset_name: str = MISSING + """Name of the asset in the environment for which the commands are generated.""" + + init_pos_offset: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Position offset of the asset from its default position. + + This is used to account for the offset typically present in the object's default position + so that the object is spawned at a height above the robot's palm. When the position command + is generated, the object's default position is used as the reference and the offset specified + is added to it to get the desired position of the object. + """ + + make_quat_unique: bool = MISSING + """Whether to make the quaternion unique or not. + + If True, the quaternion is made unique by ensuring the real part is positive. + """ + + orientation_success_threshold: float = MISSING + """Threshold for the orientation error to consider the goal orientation to be reached.""" + + update_goal_on_success: bool = MISSING + """Whether to update the goal orientation when the goal orientation is reached.""" + + marker_pos_offset: tuple[float, float, float] = (0.0, 0.0, 0.0) + """Position offset of the marker from the object's desired position. + + This is useful to position the marker at a height above the object's desired position. + Otherwise, the marker may occlude the object in the visualization. + """ + + goal_pose_visualizer_cfg: VisualizationMarkersCfg = VisualizationMarkersCfg( + prim_path="/Visuals/Command/goal_marker", + markers={ + "goal": sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(1.0, 1.0, 1.0), + ), + }, + ) + """The configuration for the goal pose visualization marker. Defaults to a DexCube marker.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/orientation_command.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/orientation_command.py new file mode 100644 index 0000000000000000000000000000000000000000..3f116a48c497fbd712707bc2f3332498ffffc29d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/commands/orientation_command.py @@ -0,0 +1,145 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module containing command generators for 3D orientation goals for objects.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import RigidObject +from isaaclab.managers import CommandTerm +from isaaclab.markers.visualization_markers import VisualizationMarkers + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + from .commands_cfg import InHandReOrientationCommandCfg + + +class InHandReOrientationCommand(CommandTerm): + """Command term that generates 3D pose commands for in-hand manipulation task. + + This command term generates 3D orientation commands for the object. The orientation commands + are sampled uniformly from the 3D orientation space. The position commands are the default + root state of the object. + + The constant position commands is to encourage that the object does not move during the task. + For instance, the object should not fall off the robot's palm. + + Unlike typical command terms, where the goals are resampled based on time, this command term + does not resample the goals based on time. Instead, the goals are resampled when the object + reaches the goal orientation. The goal orientation is considered to be reached when the + orientation error is below a certain threshold. + """ + + cfg: InHandReOrientationCommandCfg + """Configuration for the command term.""" + + def __init__(self, cfg: InHandReOrientationCommandCfg, env: ManagerBasedRLEnv): + """Initialize the command term class. + + Args: + cfg: The configuration parameters for the command term. + env: The environment object. + """ + # initialize the base class + super().__init__(cfg, env) + + # object + self.object: RigidObject = env.scene[cfg.asset_name] + + # create buffers to store the command + # -- command: (x, y, z) + init_pos_offset = torch.tensor(cfg.init_pos_offset, dtype=torch.float, device=self.device) + self.pos_command_e = self.object.data.default_root_state[:, :3] + init_pos_offset + self.pos_command_w = self.pos_command_e + self._env.scene.env_origins + # -- orientation: (w, x, y, z) + self.quat_command_w = torch.zeros(self.num_envs, 4, device=self.device) + self.quat_command_w[:, 0] = 1.0 # set the scalar component to 1.0 + + # -- unit vectors + self._X_UNIT_VEC = torch.tensor([1.0, 0, 0], device=self.device).repeat((self.num_envs, 1)) + self._Y_UNIT_VEC = torch.tensor([0, 1.0, 0], device=self.device).repeat((self.num_envs, 1)) + self._Z_UNIT_VEC = torch.tensor([0, 0, 1.0], device=self.device).repeat((self.num_envs, 1)) + + # -- metrics + self.metrics["orientation_error"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["position_error"] = torch.zeros(self.num_envs, device=self.device) + self.metrics["consecutive_success"] = torch.zeros(self.num_envs, device=self.device) + + def __str__(self) -> str: + msg = "InHandManipulationCommandGenerator:\n" + msg += f"\tCommand dimension: {tuple(self.command.shape[1:])}\n" + return msg + + """ + Properties + """ + + @property + def command(self) -> torch.Tensor: + """The desired goal pose in the environment frame. Shape is (num_envs, 7).""" + return torch.cat((self.pos_command_e, self.quat_command_w), dim=-1) + + """ + Implementation specific functions. + """ + + def _update_metrics(self): + # logs data + # -- compute the orientation error + self.metrics["orientation_error"] = math_utils.quat_error_magnitude( + self.object.data.root_quat_w, self.quat_command_w + ) + # -- compute the position error + self.metrics["position_error"] = torch.norm(self.object.data.root_pos_w - self.pos_command_w, dim=1) + # -- compute the number of consecutive successes + successes = self.metrics["orientation_error"] < self.cfg.orientation_success_threshold + self.metrics["consecutive_success"] += successes.float() + + def _resample_command(self, env_ids: Sequence[int]): + # sample new orientation targets + rand_floats = 2.0 * torch.rand((len(env_ids), 2), device=self.device) - 1.0 + # rotate randomly about x-axis and then y-axis + quat = math_utils.quat_mul( + math_utils.quat_from_angle_axis(rand_floats[:, 0] * torch.pi, self._X_UNIT_VEC[env_ids]), + math_utils.quat_from_angle_axis(rand_floats[:, 1] * torch.pi, self._Y_UNIT_VEC[env_ids]), + ) + # make sure the quaternion real-part is always positive + self.quat_command_w[env_ids] = math_utils.quat_unique(quat) if self.cfg.make_quat_unique else quat + + def _update_command(self): + # update the command if goal is reached + if self.cfg.update_goal_on_success: + # compute the goal resets + goal_resets = self.metrics["orientation_error"] < self.cfg.orientation_success_threshold + goal_reset_ids = goal_resets.nonzero(as_tuple=False).squeeze(-1) + # resample the goals + self._resample(goal_reset_ids) + + def _set_debug_vis_impl(self, debug_vis: TYPE_CHECKING): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if not hasattr(self, "goal_pose_visualizer"): + self.goal_pose_visualizer = VisualizationMarkers(self.cfg.goal_pose_visualizer_cfg) + # set visibility + self.goal_pose_visualizer.set_visibility(True) + else: + if hasattr(self, "goal_pose_visualizer"): + self.goal_pose_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # add an offset to the marker position to visualize the goal + marker_pos = self.pos_command_w + torch.tensor(self.cfg.marker_pos_offset, device=self.device) + marker_quat = self.quat_command_w + # visualize the goal marker + self.goal_pose_visualizer.visualize(translations=marker_pos, orientations=marker_quat) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/events.py new file mode 100644 index 0000000000000000000000000000000000000000..dad2e88107e8b62142c0c16d288891b9bcd31da8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/events.py @@ -0,0 +1,184 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions specific to the in-hand dexterous manipulation environments.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import EventTermCfg, ManagerTermBase, SceneEntityCfg +from isaaclab.utils.math import sample_uniform + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +class reset_joints_within_limits_range(ManagerTermBase): + """Reset an articulation's joints to a random position in the given limit ranges. + + This function samples random values for the joint position and velocities from the given limit ranges. + The values are then set into the physics simulation. + + The parameters to the function are: + + * :attr:`position_range` - a dictionary of position ranges for each joint. The keys of the dictionary are the + joint names (or regular expressions) of the asset. + * :attr:`velocity_range` - a dictionary of velocity ranges for each joint. The keys of the dictionary are the + joint names (or regular expressions) of the asset. + * :attr:`use_default_offset` - a boolean flag to indicate if the ranges are offset by the default joint state. + Defaults to False. + * :attr:`asset_cfg` - the configuration of the asset to reset. Defaults to the entity named "robot" in the scene. + * :attr:`operation` - whether the ranges are scaled values of the joint limits, or absolute limits. + Defaults to "abs". + + The dictionary values are a tuple of the form ``(a, b)``. Based on the operation, these values are + interpreted differently: + + * If the operation is "abs", the values are the absolute minimum and maximum values for the joint, i.e. + the joint range becomes ``[a, b]``. + * If the operation is "scale", the values are the scaling factors for the joint limits, i.e. the joint range + becomes ``[a * min_joint_limit, b * max_joint_limit]``. + + If the ``a`` or the ``b`` value is ``None``, the joint limits are used instead. + + Note: + If the dictionary does not contain a key, the joint position or joint velocity is set to the default value for + that joint. + + """ + + def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): + # initialize the base class + super().__init__(cfg, env) + + # check if the cfg has the required parameters + if "position_range" not in cfg.params or "velocity_range" not in cfg.params: + raise ValueError( + "The term 'reset_joints_within_range' requires parameters: 'position_range' and 'velocity_range'." + f" Received: {list(cfg.params.keys())}." + ) + + # parse the parameters + asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("robot")) + use_default_offset = cfg.params.get("use_default_offset", False) + operation = cfg.params.get("operation", "abs") + # check if the operation is valid + if operation not in ["abs", "scale"]: + raise ValueError( + f"For event 'reset_joints_within_limits_range', unknown operation: '{operation}'." + " Please use 'abs' or 'scale'." + ) + + # extract the used quantities (to enable type-hinting) + self._asset: Articulation = env.scene[asset_cfg.name] + default_joint_pos = self._asset.data.default_joint_pos[0] + default_joint_vel = self._asset.data.default_joint_vel[0] + + # create buffers to store the joint position range + self._pos_ranges = self._asset.data.soft_joint_pos_limits[0].clone() + # parse joint position ranges + pos_joint_ids = [] + for joint_name, joint_range in cfg.params["position_range"].items(): + # find the joint ids + joint_ids = self._asset.find_joints(joint_name)[0] + pos_joint_ids.extend(joint_ids) + + # set the joint position ranges based on the given values + if operation == "abs": + if joint_range[0] is not None: + self._pos_ranges[joint_ids, 0] = joint_range[0] + if joint_range[1] is not None: + self._pos_ranges[joint_ids, 1] = joint_range[1] + elif operation == "scale": + if joint_range[0] is not None: + self._pos_ranges[joint_ids, 0] *= joint_range[0] + if joint_range[1] is not None: + self._pos_ranges[joint_ids, 1] *= joint_range[1] + else: + raise ValueError( + f"Unknown operation: '{operation}' for joint position ranges. Please use 'abs' or 'scale'." + ) + # add the default offset + if use_default_offset: + self._pos_ranges[joint_ids] += default_joint_pos[joint_ids].unsqueeze(1) + + # store the joint pos ids (used later to sample the joint positions) + self._pos_joint_ids = torch.tensor(pos_joint_ids, device=self._pos_ranges.device) + self._pos_ranges = self._pos_ranges[self._pos_joint_ids] + + # create buffers to store the joint velocity range + self._vel_ranges = torch.stack( + [-self._asset.data.soft_joint_vel_limits[0], self._asset.data.soft_joint_vel_limits[0]], dim=1 + ) + # parse joint velocity ranges + vel_joint_ids = [] + for joint_name, joint_range in cfg.params["velocity_range"].items(): + # find the joint ids + joint_ids = self._asset.find_joints(joint_name)[0] + vel_joint_ids.extend(joint_ids) + + # set the joint position ranges based on the given values + if operation == "abs": + if joint_range[0] is not None: + self._vel_ranges[joint_ids, 0] = joint_range[0] + if joint_range[1] is not None: + self._vel_ranges[joint_ids, 1] = joint_range[1] + elif operation == "scale": + if joint_range[0] is not None: + self._vel_ranges[joint_ids, 0] = joint_range[0] * self._vel_ranges[joint_ids, 0] + if joint_range[1] is not None: + self._vel_ranges[joint_ids, 1] = joint_range[1] * self._vel_ranges[joint_ids, 1] + else: + raise ValueError( + f"Unknown operation: '{operation}' for joint velocity ranges. Please use 'abs' or 'scale'." + ) + # add the default offset + if use_default_offset: + self._vel_ranges[joint_ids] += default_joint_vel[joint_ids].unsqueeze(1) + + # store the joint vel ids (used later to sample the joint positions) + self._vel_joint_ids = torch.tensor(vel_joint_ids, device=self._vel_ranges.device) + self._vel_ranges = self._vel_ranges[self._vel_joint_ids] + + def __call__( + self, + env: ManagerBasedEnv, + env_ids: torch.Tensor, + position_range: dict[str, tuple[float | None, float | None]], + velocity_range: dict[str, tuple[float | None, float | None]], + use_default_offset: bool = False, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + operation: Literal["abs", "scale"] = "abs", + ): + # get default joint state + joint_pos = self._asset.data.default_joint_pos[env_ids].clone() + joint_vel = self._asset.data.default_joint_vel[env_ids].clone() + + # sample random joint positions for each joint + if len(self._pos_joint_ids) > 0: + joint_pos_shape = (len(env_ids), len(self._pos_joint_ids)) + joint_pos[:, self._pos_joint_ids] = sample_uniform( + self._pos_ranges[:, 0], self._pos_ranges[:, 1], joint_pos_shape, device=joint_pos.device + ) + # clip the joint positions to the joint limits + joint_pos_limits = self._asset.data.soft_joint_pos_limits[0, self._pos_joint_ids] + joint_pos = joint_pos.clamp(joint_pos_limits[:, 0], joint_pos_limits[:, 1]) + + # sample random joint velocities for each joint + if len(self._vel_joint_ids) > 0: + joint_vel_shape = (len(env_ids), len(self._vel_joint_ids)) + joint_vel[:, self._vel_joint_ids] = sample_uniform( + self._vel_ranges[:, 0], self._vel_ranges[:, 1], joint_vel_shape, device=joint_vel.device + ) + # clip the joint velocities to the joint limits + joint_vel_limits = self._asset.data.soft_joint_vel_limits[0, self._vel_joint_ids] + joint_vel = joint_vel.clamp(-joint_vel_limits, joint_vel_limits) + + # set into the physics simulation + self._asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..e9059792563421122d20cd80f2f6228edb280445 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/observations.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions specific to the in-hand dexterous manipulation environments.""" + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import RigidObject +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from .commands import InHandReOrientationCommand + + +def goal_quat_diff( + env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, command_name: str, make_quat_unique: bool +) -> torch.Tensor: + """Goal orientation relative to the asset's root frame. + + The quaternion is represented as (w, x, y, z). The real part is always positive. + """ + # extract useful elements + asset: RigidObject = env.scene[asset_cfg.name] + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + + # obtain the orientations + goal_quat_w = command_term.command[:, 3:7] + asset_quat_w = asset.data.root_quat_w + + # compute quaternion difference + quat = math_utils.quat_mul(asset_quat_w, math_utils.quat_conjugate(goal_quat_w)) + # make sure the quaternion real-part is always positive + return math_utils.quat_unique(quat) if make_quat_unique else quat diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..f928b92fb3678e322891806de1cfa50386d9984a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/rewards.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions specific to the in-hand dexterous manipulation environments.""" + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import RigidObject +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from .commands import InHandReOrientationCommand + + +def success_bonus( + env: ManagerBasedRLEnv, command_name: str, object_cfg: SceneEntityCfg = SceneEntityCfg("object") +) -> torch.Tensor: + """Bonus reward for successfully reaching the goal. + + The object is considered to have reached the goal when the object orientation is within the threshold. + The reward is 1.0 if the object has reached the goal, otherwise 0.0. + + Args: + env: The environment object. + command_name: The command term to be used for extracting the goal. + object_cfg: The configuration for the scene entity. Default is "object". + """ + # extract useful elements + asset: RigidObject = env.scene[object_cfg.name] + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + + # obtain the goal orientation + goal_quat_w = command_term.command[:, 3:7] + # obtain the threshold for the orientation error + threshold = command_term.cfg.orientation_success_threshold + # calculate the orientation error + dtheta = math_utils.quat_error_magnitude(asset.data.root_quat_w, goal_quat_w) + + return dtheta <= threshold + + +def track_pos_l2( + env: ManagerBasedRLEnv, command_name: str, object_cfg: SceneEntityCfg = SceneEntityCfg("object") +) -> torch.Tensor: + """Reward for tracking the object position using the L2 norm. + + The reward is the distance between the object position and the goal position. + + Args: + env: The environment object. + command_term: The command term to be used for extracting the goal. + object_cfg: The configuration for the scene entity. Default is "object". + """ + # extract useful elements + asset: RigidObject = env.scene[object_cfg.name] + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + + # obtain the goal position + goal_pos_e = command_term.command[:, 0:3] + # obtain the object position in the environment frame + object_pos_e = asset.data.root_pos_w - env.scene.env_origins + + return torch.norm(goal_pos_e - object_pos_e, p=2, dim=-1) + + +def track_orientation_inv_l2( + env: ManagerBasedRLEnv, + command_name: str, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + rot_eps: float = 1e-3, +) -> torch.Tensor: + """Reward for tracking the object orientation using the inverse of the orientation error. + + The reward is the inverse of the orientation error between the object orientation and the goal orientation. + + Args: + env: The environment object. + command_name: The command term to be used for extracting the goal. + object_cfg: The configuration for the scene entity. Default is "object". + rot_eps: The threshold for the orientation error. Default is 1e-3. + """ + # extract useful elements + asset: RigidObject = env.scene[object_cfg.name] + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + + # obtain the goal orientation + goal_quat_w = command_term.command[:, 3:7] + # calculate the orientation error + dtheta = math_utils.quat_error_magnitude(asset.data.root_quat_w, goal_quat_w) + + return 1.0 / (dtheta + rot_eps) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..1d4f36f1e62b7c0eaded9143e44a7dea62cc72e4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/inhand/mdp/terminations.py @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Functions specific to the in-hand dexterous manipulation environments.""" + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from .commands import InHandReOrientationCommand + + +def max_consecutive_success(env: ManagerBasedRLEnv, num_success: int, command_name: str) -> torch.Tensor: + """Check if the task has been completed consecutively for a certain number of times. + + Args: + env: The environment object. + num_success: Threshold for the number of consecutive successes required. + command_name: The command term to be used for extracting the goal. + """ + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + + return command_term.metrics["consecutive_success"] >= num_success + + +def object_away_from_goal( + env: ManagerBasedRLEnv, + threshold: float, + command_name: str, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Check if object has gone far from the goal. + + The object is considered to be out-of-reach if the distance between the goal and the object is greater + than the threshold. + + Args: + env: The environment object. + threshold: The threshold for the distance between the robot and the object. + command_name: The command term to be used for extracting the goal. + object_cfg: The configuration for the scene entity. Default is "object". + """ + # extract useful elements + command_term: InHandReOrientationCommand = env.command_manager.get_term(command_name) + asset = env.scene[object_cfg.name] + + # object pos + asset_pos_e = asset.data.root_pos_w - env.scene.env_origins + goal_pos_e = command_term.command[:, :3] + + return torch.norm(asset_pos_e - goal_pos_e, p=2, dim=1) > threshold + + +def object_away_from_robot( + env: ManagerBasedRLEnv, + threshold: float, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Check if object has gone far from the robot. + + The object is considered to be out-of-reach if the distance between the robot and the object is greater + than the threshold. + + Args: + env: The environment object. + threshold: The threshold for the distance between the robot and the object. + asset_cfg: The configuration for the robot entity. Default is "robot". + object_cfg: The configuration for the object entity. Default is "object". + """ + # extract useful elements + robot = env.scene[asset_cfg.name] + object = env.scene[object_cfg.name] + + # compute distance + dist = torch.norm(robot.data.root_pos_w - object.data.root_pos_w, dim=1) + + return dist > threshold diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1211ee056649353b42f5ea54f7b431bf39e4dc61 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1211ee056649353b42f5ea54f7b431bf39e4dc61 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d72fd6ebb5963098d3e0daabd9447e0c9dd2ff68 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/__init__.py @@ -0,0 +1,77 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Lift-Cube-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCubeLiftEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Lift-Cube-Franka-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCubeLiftEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +## +# Inverse Kinematics - Absolute Pose Control +## + +gym.register( + id="Isaac-Lift-Cube-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaCubeLiftEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaTeddyBearLiftEnvCfg", + }, + disable_env_checker=True, +) + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Lift-Cube-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_rel_env_cfg:FrankaCubeLiftEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc.json", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77360c1b91bbf5a47cf2785fdfc0debcce3e57c8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: franka_lift + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: False + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 100000000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + clip_actions: False + seq_len: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bc.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bc.json new file mode 100644 index 0000000000000000000000000000000000000000..e96f7f7e194ab6a4b6766f6341bb7e4a0205d16f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bc.json @@ -0,0 +1,264 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../bc_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 1, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 200, + "seed": 1 + }, + "algo": { + "optim_params": { + "policy": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": false, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": false, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "joint_pos", + "joint_vel", + "object_position", + "target_object_position" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + }, + "conv_activation": "relu", + "conv_kwargs": { + "out_channels": [ + 32, + 64, + 64 + ], + "kernel_size": [ + 8, + 4, + 2 + ], + "stride": [ + 4, + 2, + 1 + ] + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bcq.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bcq.json new file mode 100644 index 0000000000000000000000000000000000000000..1d80b50d2873ac5465d5fda3b2ad05cf16fa0bf7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/robomimic/bcq.json @@ -0,0 +1,299 @@ +{ + "algo_name": "bcq", + "experiment": { + "name": "bcq", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../bcq_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "seq_length": 1, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 200, + "seed": 1 + }, + "algo": { + "optim_params": { + "critic": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "action_sampler": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "actor": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + } + }, + "discount": 0.99, + "n_step": 1, + "target_tau": 0.005, + "infinite_horizon": false, + "critic": { + "use_huber": false, + "max_gradient_norm": null, + "value_bounds": null, + "num_action_samples": 10, + "num_action_samples_rollout": 100, + "ensemble": { + "n": 2, + "weight": 0.75 + }, + "distributional": { + "enabled": false, + "num_atoms": 51 + }, + "layer_dims": [ + 300, + 400 + ] + }, + "action_sampler": { + "actor_layer_dims": [ + 1024, + 1024 + ], + "gmm": { + "enabled": false, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": true, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "freeze_encoder_epoch": -1 + }, + "actor": { + "enabled": false, + "perturbation_scale": 0.05, + "layer_dims": [ + 300, + 400 + ] + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "tool_dof_pos_scaled", + "tool_positions", + "object_relative_tool_positions", + "object_desired_positions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + }, + "conv_activation": "relu", + "conv_kwargs": { + "out_channels": [ + 32, + 64, + 64 + ], + "kernel_size": [ + 8, + 4, + 2 + ], + "stride": [ + 4, + 2, + 1 + ] + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7a94614d8c976f566cc522340d92475b5136e1d7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class LiftCubePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "franka_lift" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.006, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-4, + schedule="adaptive", + gamma=0.98, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/sb3_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..593de544a83a609afb8cd06164cefb320fb6e6e7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +# epoch * n_steps * nenvs: 500×512*8*8 +n_timesteps: 16384000 +policy: 'MlpPolicy' +n_steps: 64 +# mini batch size: num_envs * nsteps / nminibatches 2048×512÷2048 +batch_size: 192 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 8 +ent_coef: 0.00 +vf_coef: 0.0001 +learning_rate: !!float 3e-4 +clip_range: 0.2 +policy_kwargs: + activation_fn: 'nn.ELU' + net_arch: + pi: [256, 128, 64] + vf: [256, 128, 64] +target_kl: 0.01 +max_grad_norm: 1.0 + +# # Uses VecNormalize class to normalize obs +# normalize_input: True +# # Uses VecNormalize class to normalize rew +# normalize_value: True +# clip_obs: 5 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3f39d0c4afc4877638df9f921d9d8105eca5233c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 8 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.001 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "franka_lift" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_abs_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9441a22341934f387d5e8c5773471b69bc851c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_abs_env_cfg.py @@ -0,0 +1,110 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import DeformableObjectCfg +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sim.spawners import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +import isaaclab_tasks.manager_based.manipulation.lift.mdp as mdp + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +## +# Rigid object lift environment. +## + + +@configclass +class FrankaCubeLiftEnvCfg(joint_pos_env_cfg.FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCubeLiftEnvCfg_PLAY(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + + +## +# Deformable object lift environment. +## + + +@configclass +class FrankaTeddyBearLiftEnvCfg(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.object = DeformableObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=DeformableObjectCfg.InitialStateCfg(pos=(0.5, 0, 0.05), rot=(0.707, 0, 0, 0.707)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/Teddy_Bear/teddy_bear.usd", + scale=(0.01, 0.01, 0.01), + ), + ) + + # Make the end effector less stiff to not hurt the poor teddy bear + self.scene.robot.actuators["panda_hand"].effort_limit_sim = 50.0 + self.scene.robot.actuators["panda_hand"].stiffness = 40.0 + self.scene.robot.actuators["panda_hand"].damping = 10.0 + + # Disable replicate physics as it doesn't work for deformable objects + # FIXME: This should be fixed by the PhysX replication system. + self.scene.replicate_physics = False + + # Set events for the specific object type (deformable cube) + self.events.reset_object_position = EventTerm( + func=mdp.reset_nodal_state_uniform, + mode="reset", + params={ + "position_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + # Remove all the terms for the state machine demo + # TODO: Computing the root pose of deformable object from nodal positions is expensive. + # We need to fix that part before enabling these terms for the training. + self.terminations.object_dropping = None + self.rewards.reaching_object = None + self.rewards.lifting_object = None + self.rewards.object_goal_tracking = None + self.rewards.object_goal_tracking_fine_grained = None + self.observations.policy.object_position = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5e5c95e7d472e514e17a59cc641e3efef5e41331 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/ik_rel_env_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeLiftEnvCfg(joint_pos_env_cfg.FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCubeLiftEnvCfg_PLAY(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5c5754c53e43086916c2cc18f42f9219bac3ae02 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/franka/joint_pos_env_cfg.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.lift import mdp +from isaaclab_tasks.manager_based.manipulation.lift.lift_env_cfg import LiftEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class FrankaCubeLiftEnvCfg(LiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + + # Set Cube as object + self.scene.object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.5, 0, 0.055], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(0.8, 0.8, 0.8), + rigid_props=RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ), + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + ], + ) + + +@configclass +class FrankaCubeLiftEnvCfg_PLAY(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..40ce61a45f83c94e50ea10fec3d7abbfa7aa0d31 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/__init__.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Lift-Cube-OpenArm-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmCubeLiftEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmLiftCubePPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Lift-Cube-OpenArm-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmCubeLiftEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmLiftCubePPORunnerCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3363b1ea73489988d817de3af7734f056c0b0659 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: openarm_lift + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: False + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 100000000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + clip_actions: False + seq_len: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f079552f1f4a34d8db2ca626990c82daeb6dc6ec --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class OpenArmLiftCubePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 2000 + save_interval = 50 + experiment_name = "openarm_lift" + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.006, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-4, + schedule="adaptive", + gamma=0.98, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f29f1fc32fc39d69e532ace3945a16412749bd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/joint_pos_env_cfg.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import math + +from isaaclab.assets import RigidObjectCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.lift import mdp +from isaaclab_tasks.manager_based.manipulation.lift.config.openarm.lift_openarm_env_cfg import LiftEnvCfg + +from isaaclab_assets.robots.openarm import OPENARM_UNI_CFG + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +@configclass +class OpenArmCubeLiftEnvCfg(LiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set OpenArm as robot + self.scene.robot = OPENARM_UNI_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (OpenArm) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=[ + "openarm_joint.*", + ], + scale=0.5, + use_default_offset=True, + ) + + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["openarm_finger_joint.*"], + open_command_expr={"openarm_finger_joint.*": 0.044}, + close_command_expr={"openarm_finger_joint.*": 0.0}, + ) + + # Set the body name for the end effector + self.commands.object_pose.body_name = "openarm_hand" + self.commands.object_pose.ranges.pitch = (math.pi / 2, math.pi / 2) + + # Set Cube as object + self.scene.object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0, 0.055], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", + scale=(0.8, 0.8, 0.8), + rigid_props=RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ), + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/openarm_ee_tcp", + name="end_effector", + ), + ], + ) + + +@configclass +class OpenArmCubeLiftEnvCfg_PLAY(OpenArmCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/lift_openarm_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/lift_openarm_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..491b713c14f901d0ed112df124067604124cf9c4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/config/openarm/lift_openarm_env_cfg.py @@ -0,0 +1,240 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +We modified parts of the environment, such as the target's position and orientation, +as well as certain object properties, to better suit the smaller robot. +""" + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, DeformableObjectCfg, RigidObjectCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from ... import mdp + +## +# Scene definition +## + + +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + # target object: will be populated by agent env cfg + object: RigidObjectCfg | DeformableObjectCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + object_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, # will be set by agent env cfg + resampling_time_range=(5.0, 5.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.2, 0.4), + pos_y=(-0.2, 0.2), + pos_z=(0.15, 0.4), + roll=(0.0, 0.0), + pitch=(0.0, 0.0), + yaw=(0.0, 0.0), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg | mdp.DifferentialInverseKinematicsActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["openarm_joint.*", "openarm_finger_joint.*"])}, + ) + joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["openarm_joint.*", "openarm_finger_joint.*"])}, + ) + object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) + target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object_position = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + reaching_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.1}, weight=1.1) + + lifting_object = RewTerm(func=mdp.object_is_lifted, params={"minimal_height": 0.04}, weight=15.0) + + object_goal_tracking = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.3, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=16.0, + ) + + object_goal_tracking_fine_grained = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.05, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=5.0, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-1e-4) + + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-1e-4, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["openarm_joint.*", "openarm_finger_joint.*"])}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, + params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")}, + ) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "action_rate", "weight": -1e-1, "num_steps": 10000}, + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "joint_vel", "weight": -1e-1, "num_steps": 10000}, + ) + + +## +# Environment configuration +## + + +@configclass +class LiftEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the lifting environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 5.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/lift_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..272661bda61dfd593fadcbd4bf7d61181c1f34de --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/lift_env_cfg.py @@ -0,0 +1,222 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, DeformableObjectCfg, RigidObjectCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + +## +# Scene definition +## + + +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + # target object: will be populated by agent env cfg + object: RigidObjectCfg | DeformableObjectCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + object_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, # will be set by agent env cfg + resampling_time_range=(5.0, 5.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.4, 0.6), pos_y=(-0.25, 0.25), pos_z=(0.25, 0.5), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg | mdp.DifferentialInverseKinematicsActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) + target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object_position = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + reaching_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.1}, weight=1.0) + + lifting_object = RewTerm(func=mdp.object_is_lifted, params={"minimal_height": 0.04}, weight=15.0) + + object_goal_tracking = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.3, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=16.0, + ) + + object_goal_tracking_fine_grained = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.05, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=5.0, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-1e-4) + + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-1e-4, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} + ) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -1e-1, "num_steps": 10000} + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "joint_vel", "weight": -1e-1, "num_steps": 10000} + ) + + +## +# Environment configuration +## + + +@configclass +class LiftEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the lifting environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 5.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3dd0fecdf8e53ca937c0f6ed596e323175eb100 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..8654933a9aae726491c43ae8e7d61ad021014f54 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/observations.py @@ -0,0 +1,30 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import subtract_frame_transforms + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_position_in_robot_root_frame( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms(robot.data.root_pos_w, robot.data.root_quat_w, object_pos_w) + return object_pos_b diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..34e60773a068a8c7acb8d31c2165dcd3312665d9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/rewards.py @@ -0,0 +1,68 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_is_lifted( + env: ManagerBasedRLEnv, minimal_height: float, object_cfg: SceneEntityCfg = SceneEntityCfg("object") +) -> torch.Tensor: + """Reward the agent for lifting the object above the minimal height.""" + object: RigidObject = env.scene[object_cfg.name] + return torch.where(object.data.root_pos_w[:, 2] > minimal_height, 1.0, 0.0) + + +def object_ee_distance( + env: ManagerBasedRLEnv, + std: float, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +) -> torch.Tensor: + """Reward the agent for reaching the object using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + object: RigidObject = env.scene[object_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + # Target object position: (num_envs, 3) + cube_pos_w = object.data.root_pos_w + # End-effector position: (num_envs, 3) + ee_w = ee_frame.data.target_pos_w[..., 0, :] + # Distance of the end-effector to the object: (num_envs,) + object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) + + return 1 - torch.tanh(object_ee_distance / std) + + +def object_goal_distance( + env: ManagerBasedRLEnv, + std: float, + minimal_height: float, + command_name: str, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Reward the agent for tracking the goal pose using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_pos_w, robot.data.root_quat_w, des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w, dim=1) + # rewarded if the object is lifted above the threshold + return (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std)) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..68fe0e011b854564155984aae35e241a8c6f267d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/lift/mdp/terminations.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import combine_frame_transforms + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_reached_goal( + env: ManagerBasedRLEnv, + command_name: str = "object_pose", + threshold: float = 0.02, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_pos_w, robot.data.root_quat_w, des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) + + # rewarded if the object is lifted above the threshold + return distance < threshold diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f2bd7d0f707533e31c8cfc327bca615cd533fe1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/__init__.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +gym.register( + id="Isaac-PickPlace-GR1T2-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_gr1t2_env_cfg:PickPlaceGR1T2EnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-NutPour-GR1T2-Pink-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.nutpour_gr1t2_pink_ik_env_cfg:NutPourGR1T2PinkIKEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_nut_pouring.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-ExhaustPipe-GR1T2-Pink-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.exhaustpipe_gr1t2_pink_ik_env_cfg:ExhaustPipeGR1T2PinkIKEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_exhaust_pipe.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_gr1t2_waist_enabled_env_cfg:PickPlaceGR1T2WaistEnabledEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-G1-InspireFTP-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_unitree_g1_inspire_hand_env_cfg:PickPlaceG1InspireFTPEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_exhaust_pipe.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_exhaust_pipe.json new file mode 100644 index 0000000000000000000000000000000000000000..5af2a9f4a4f8379eadb1ea908e08a4ca2c3d8ca8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_exhaust_pipe.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_gr1_exhaust_pipe", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + 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b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_nut_pouring.json new file mode 100644 index 0000000000000000000000000000000000000000..dbe527d72dde835737ea35591572a7cf6e5b44ca --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_nut_pouring.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_gr1_nut_pouring", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + 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b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_image_pick_place.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_pick_place", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + 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false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state" + ], + "rgb": [ + "robot_pov_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 144, + "crop_width": 236, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json new file mode 100644 index 0000000000000000000000000000000000000000..d2e0f8fc6d940a9abbf1506502cd40f8845ef540 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,117 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_gr1t2", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": false, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_base_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_base_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6e14d2e1fdd235d5a8fe7505306f05b3928cccf1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_base_env_cfg.py @@ -0,0 +1,336 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile +from dataclasses import MISSING + +import torch + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import CameraCfg + +# from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Exhaust Pipe Base Scene.""" + + # Table + table = AssetBaseCfg( + prim_path="/World/envs/env_.*/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/table.usd", + scale=(1.0, 1.0, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + blue_exhaust_pipe = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueExhaustPipe", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.04904, 0.31, 1.2590], rot=[0, 0, 1.0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/blue_exhaust_pipe.usd", + scale=(0.5, 0.5, 1.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + blue_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.16605, 0.39, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/blue_sorting_bin.usd", + scale=(1.0, 1.7, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + black_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlackSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.40132, 0.39, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/black_sorting_bin.usd", + scale=(1.0, 1.7, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Humanoid robot w/ arms higher + robot: ArticulationCfg = GR1T2_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": -0.10933163, + "left_shoulder_roll_joint": 0.43292055, + "left_shoulder_yaw_joint": -0.15983289, + "left_elbow_pitch_joint": -1.48233023, + "left_wrist_yaw_joint": 0.2359135, + "left_wrist_roll_joint": 0.26184522, + "left_wrist_pitch_joint": 0.00830735, + # right hand + "R_index_intermediate_joint": 0.0, + "R_index_proximal_joint": 0.0, + "R_middle_intermediate_joint": 0.0, + "R_middle_proximal_joint": 0.0, + "R_pinky_intermediate_joint": 0.0, + "R_pinky_proximal_joint": 0.0, + "R_ring_intermediate_joint": 0.0, + "R_ring_proximal_joint": 0.0, + "R_thumb_distal_joint": 0.0, + "R_thumb_proximal_pitch_joint": 0.0, + "R_thumb_proximal_yaw_joint": -1.57, + # left hand + "L_index_intermediate_joint": 0.0, + "L_index_proximal_joint": 0.0, + "L_middle_intermediate_joint": 0.0, + "L_middle_proximal_joint": 0.0, + "L_pinky_intermediate_joint": 0.0, + "L_pinky_proximal_joint": 0.0, + "L_ring_intermediate_joint": 0.0, + "L_ring_proximal_joint": 0.0, + "L_thumb_distal_joint": 0.0, + "L_thumb_proximal_pitch_joint": 0.0, + "L_thumb_proximal_yaw_joint": -1.57, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Set table view camera + robot_pov_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/RobotPOVCam", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=18.15, clipping_range=(0.1, 2)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.12, 1.85418), rot=(-0.17246, 0.98502, 0.0, 0.0), convention="ros"), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + gr1_action: ActionTermCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + robot_pov_cam = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + blue_exhaust_pipe_dropped = DoneTerm( + func=mdp.root_height_below_minimum, + params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("blue_exhaust_pipe")}, + ) + + success = DoneTerm(func=mdp.task_done_exhaust_pipe) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_blue_exhaust_pipe = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("blue_exhaust_pipe"), + }, + ) + + +@configclass +class ExhaustPipeGR1T2BaseEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), + # right arm quat (4), left/right hand joint pos (22)] + idle_action = torch.tensor( + [ + [ + -0.2909, + 0.2778, + 1.1247, + 0.5253, + 0.5747, + -0.4160, + 0.4699, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 100 + self.sim.render_interval = 2 + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_pink_ik_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_pink_ik_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..66ebfcad8a1869da65fdd04c0bcd47e49b94f2e9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/exhaustpipe_gr1t2_pink_ik_env_cfg.py @@ -0,0 +1,155 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GR1T2RetargeterCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.exhaustpipe_gr1t2_base_env_cfg import ( + ExhaustPipeGR1T2BaseEnvCfg, +) + + +@configclass +class ExhaustPipeGR1T2PinkIKEnvCfg(ExhaustPipeGR1T2BaseEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.gr1_action = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.2, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.gr1_action.controller.urdf_path = temp_urdf_output_path + self.actions.gr1_action.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.gr1_action.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..555bfb7cbe8f3e73aa5bc809545619f211db9cbc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .pick_place_events import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..01e52e73f242b86298370fa7573fb9ac3ee925d2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/observations.py @@ -0,0 +1,86 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_obs( + env: ManagerBasedRLEnv, + left_eef_link_name: str, + right_eef_link_name: str, +) -> torch.Tensor: + """ + Object observations (in world frame): + object pos, + object quat, + left_eef to object, + right_eef_to object, + """ + + body_pos_w = env.scene["robot"].data.body_pos_w + left_eef_idx = env.scene["robot"].data.body_names.index(left_eef_link_name) + right_eef_idx = env.scene["robot"].data.body_names.index(right_eef_link_name) + left_eef_pos = body_pos_w[:, left_eef_idx] - env.scene.env_origins + right_eef_pos = body_pos_w[:, right_eef_idx] - env.scene.env_origins + + object_pos = env.scene["object"].data.root_pos_w - env.scene.env_origins + object_quat = env.scene["object"].data.root_quat_w + + left_eef_to_object = object_pos - left_eef_pos + right_eef_to_object = object_pos - right_eef_pos + + return torch.cat( + ( + object_pos, + object_quat, + left_eef_to_object, + right_eef_to_object, + ), + dim=1, + ) + + +def get_eef_pos(env: ManagerBasedRLEnv, link_name: str) -> torch.Tensor: + body_pos_w = env.scene["robot"].data.body_pos_w + left_eef_idx = env.scene["robot"].data.body_names.index(link_name) + left_eef_pos = body_pos_w[:, left_eef_idx] - env.scene.env_origins + + return left_eef_pos + + +def get_eef_quat(env: ManagerBasedRLEnv, link_name: str) -> torch.Tensor: + body_quat_w = env.scene["robot"].data.body_quat_w + left_eef_idx = env.scene["robot"].data.body_names.index(link_name) + left_eef_quat = body_quat_w[:, left_eef_idx] + + return left_eef_quat + + +def get_robot_joint_state( + env: ManagerBasedRLEnv, + joint_names: list[str], +) -> torch.Tensor: + # hand_joint_names is a list of regex, use find_joints + indexes, _ = env.scene["robot"].find_joints(joint_names) + indexes = torch.tensor(indexes, dtype=torch.long) + robot_joint_states = env.scene["robot"].data.joint_pos[:, indexes] + + return robot_joint_states + + +def get_all_robot_link_state( + env: ManagerBasedRLEnv, +) -> torch.Tensor: + body_pos_w = env.scene["robot"].data.body_link_state_w[:, :, :] + all_robot_link_pos = body_pos_w + + return all_robot_link_pos diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/pick_place_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/pick_place_events.py new file mode 100644 index 0000000000000000000000000000000000000000..ca1fd940fea84e7d9ba09548143a5055ae770e3a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/pick_place_events.py @@ -0,0 +1,96 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def reset_object_poses_nut_pour( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + sorting_beaker_cfg: SceneEntityCfg = SceneEntityCfg("sorting_beaker"), + factory_nut_cfg: SceneEntityCfg = SceneEntityCfg("factory_nut"), + sorting_bowl_cfg: SceneEntityCfg = SceneEntityCfg("sorting_bowl"), + sorting_scale_cfg: SceneEntityCfg = SceneEntityCfg("sorting_scale"), +): + """Reset the asset root states to a random position and orientation uniformly within the given ranges. + + Args: + env: The RL environment instance. + env_ids: The environment IDs to reset the object poses for. + sorting_beaker_cfg: The configuration for the sorting beaker asset. + factory_nut_cfg: The configuration for the factory nut asset. + sorting_bowl_cfg: The configuration for the sorting bowl asset. + sorting_scale_cfg: The configuration for the sorting scale asset. + pose_range: The dictionary of pose ranges for the objects. Keys are + ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. + """ + # extract the used quantities (to enable type-hinting) + sorting_beaker = env.scene[sorting_beaker_cfg.name] + factory_nut = env.scene[factory_nut_cfg.name] + sorting_bowl = env.scene[sorting_bowl_cfg.name] + sorting_scale = env.scene[sorting_scale_cfg.name] + + # get default root state + sorting_beaker_root_states = sorting_beaker.data.default_root_state[env_ids].clone() + factory_nut_root_states = factory_nut.data.default_root_state[env_ids].clone() + sorting_bowl_root_states = sorting_bowl.data.default_root_state[env_ids].clone() + sorting_scale_root_states = sorting_scale.data.default_root_state[env_ids].clone() + + # get pose ranges + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=sorting_beaker.device) + + # randomize sorting beaker and factory nut together + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_beaker = ( + sorting_beaker_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + ) + positions_factory_nut = factory_nut_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_beaker = math_utils.quat_mul(sorting_beaker_root_states[:, 3:7], orientations_delta) + orientations_factory_nut = math_utils.quat_mul(factory_nut_root_states[:, 3:7], orientations_delta) + + # randomize sorting bowl + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_bowl = sorting_bowl_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_bowl = math_utils.quat_mul(sorting_bowl_root_states[:, 3:7], orientations_delta) + + # randomize scorting scale + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_scale = sorting_scale_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_scale = math_utils.quat_mul(sorting_scale_root_states[:, 3:7], orientations_delta) + + # set into the physics simulation + sorting_beaker.write_root_pose_to_sim( + torch.cat([positions_sorting_beaker, orientations_sorting_beaker], dim=-1), env_ids=env_ids + ) + factory_nut.write_root_pose_to_sim( + torch.cat([positions_factory_nut, orientations_factory_nut], dim=-1), env_ids=env_ids + ) + sorting_bowl.write_root_pose_to_sim( + torch.cat([positions_sorting_bowl, orientations_sorting_bowl], dim=-1), env_ids=env_ids + ) + sorting_scale.write_root_pose_to_sim( + torch.cat([positions_sorting_scale, orientations_sorting_scale], dim=-1), env_ids=env_ids + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..1122e06d16c3327093dabd3e3621a294104d64cf --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/mdp/terminations.py @@ -0,0 +1,225 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def task_done_pick_place( + env: ManagerBasedRLEnv, + task_link_name: str = "", + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + wrist_min_x: float = -1.0, + wrist_max_x: float = 1.0, + min_x: float = 0.20, + max_x: float = 1.05, + min_y: float = 0.15, + max_y: float = 0.80, + max_height: float = 1.10, + min_vel: float = 0.20, +) -> torch.Tensor: + """Determine if the object placement task is complete. + + This function checks whether all success conditions for the task have been met: + 1. object is within the target x/y range + 2. object is below a minimum height + 3. object velocity is below threshold + 4. Robot wrist is within the specified x/y position range + + Args: + env: The RL environment instance. + object_cfg: Configuration for the object entity. + wrist_max_x: Maximum x position of the wrist for task completion. + wrist_min_y: Minimum y position of the wrist for task completion. + min_x: Minimum x position of the object for task completion. + max_x: Maximum x position of the object for task completion. + min_y: Minimum y position of the object for task completion. + max_y: Maximum y position of the object for task completion. + max_height: Maximum height (z position) of the object for task completion. + min_vel: Minimum velocity magnitude of the object for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + if task_link_name == "": + raise ValueError("task_link_name must be provided to task_done_pick_place") + + # Get object entity from the scene + object: RigidObject = env.scene[object_cfg.name] + + # Extract wheel position relative to environment origin + object_x = object.data.root_pos_w[:, 0] - env.scene.env_origins[:, 0] + object_y = object.data.root_pos_w[:, 1] - env.scene.env_origins[:, 1] + object_height = object.data.root_pos_w[:, 2] - env.scene.env_origins[:, 2] + object_vel = torch.abs(object.data.root_vel_w) + + # Get wrist position relative to environment origin + robot_body_pos_w = env.scene["robot"].data.body_pos_w + eef_idx = env.scene["robot"].data.body_names.index(task_link_name) + wrist_x = robot_body_pos_w[:, eef_idx, 0] - env.scene.env_origins[:, 0] + + # Check all success conditions and combine with logical AND + done = object_x < max_x + done = torch.logical_and(done, object_x > min_x) + done = torch.logical_and(done, object_y < max_y) + done = torch.logical_and(done, object_y > min_y) + done = torch.logical_and(done, object_height < max_height) + done = torch.logical_and(done, wrist_x > wrist_min_x) + done = torch.logical_and(done, wrist_x < wrist_max_x) + done = torch.logical_and(done, object_vel[:, 0] < min_vel) + done = torch.logical_and(done, object_vel[:, 1] < min_vel) + done = torch.logical_and(done, object_vel[:, 2] < min_vel) + + return done + + +def task_done_nut_pour( + env: ManagerBasedRLEnv, + sorting_scale_cfg: SceneEntityCfg = SceneEntityCfg("sorting_scale"), + sorting_bowl_cfg: SceneEntityCfg = SceneEntityCfg("sorting_bowl"), + sorting_beaker_cfg: SceneEntityCfg = SceneEntityCfg("sorting_beaker"), + factory_nut_cfg: SceneEntityCfg = SceneEntityCfg("factory_nut"), + sorting_bin_cfg: SceneEntityCfg = SceneEntityCfg("black_sorting_bin"), + max_bowl_to_scale_x: float = 0.055, + max_bowl_to_scale_y: float = 0.055, + max_bowl_to_scale_z: float = 0.025, + max_nut_to_bowl_x: float = 0.050, + max_nut_to_bowl_y: float = 0.050, + max_nut_to_bowl_z: float = 0.019, + max_beaker_to_bin_x: float = 0.08, + max_beaker_to_bin_y: float = 0.12, + max_beaker_to_bin_z: float = 0.07, +) -> torch.Tensor: + """Determine if the nut pouring task is complete. + + This function checks whether all success conditions for the task have been met: + 1. The factory nut is in the sorting bowl + 2. The sorting beaker is in the sorting bin + 3. The sorting bowl is placed on the sorting scale + + Args: + env: The RL environment instance. + sorting_scale_cfg: Configuration for the sorting scale entity. + sorting_bowl_cfg: Configuration for the sorting bowl entity. + sorting_beaker_cfg: Configuration for the sorting beaker entity. + factory_nut_cfg: Configuration for the factory nut entity. + sorting_bin_cfg: Configuration for the sorting bin entity. + max_bowl_to_scale_x: Maximum x position of the sorting bowl relative to the sorting scale for task completion. + max_bowl_to_scale_y: Maximum y position of the sorting bowl relative to the sorting scale for task completion. + max_bowl_to_scale_z: Maximum z position of the sorting bowl relative to the sorting scale for task completion. + max_nut_to_bowl_x: Maximum x position of the factory nut relative to the sorting bowl for task completion. + max_nut_to_bowl_y: Maximum y position of the factory nut relative to the sorting bowl for task completion. + max_nut_to_bowl_z: Maximum z position of the factory nut relative to the sorting bowl for task completion. + max_beaker_to_bin_x: Maximum x position of the sorting beaker relative to the sorting bin for task completion. + max_beaker_to_bin_y: Maximum y position of the sorting beaker relative to the sorting bin for task completion. + max_beaker_to_bin_z: Maximum z position of the sorting beaker relative to the sorting bin for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + # Get object entities from the scene + sorting_scale: RigidObject = env.scene[sorting_scale_cfg.name] + sorting_bowl: RigidObject = env.scene[sorting_bowl_cfg.name] + factory_nut: RigidObject = env.scene[factory_nut_cfg.name] + sorting_beaker: RigidObject = env.scene[sorting_beaker_cfg.name] + sorting_bin: RigidObject = env.scene[sorting_bin_cfg.name] + + # Get positions relative to environment origin + scale_pos = sorting_scale.data.root_pos_w - env.scene.env_origins + bowl_pos = sorting_bowl.data.root_pos_w - env.scene.env_origins + sorting_beaker_pos = sorting_beaker.data.root_pos_w - env.scene.env_origins + nut_pos = factory_nut.data.root_pos_w - env.scene.env_origins + bin_pos = sorting_bin.data.root_pos_w - env.scene.env_origins + + # nut to bowl + nut_to_bowl_x = torch.abs(nut_pos[:, 0] - bowl_pos[:, 0]) + nut_to_bowl_y = torch.abs(nut_pos[:, 1] - bowl_pos[:, 1]) + nut_to_bowl_z = nut_pos[:, 2] - bowl_pos[:, 2] + + # bowl to scale + bowl_to_scale_x = torch.abs(bowl_pos[:, 0] - scale_pos[:, 0]) + bowl_to_scale_y = torch.abs(bowl_pos[:, 1] - scale_pos[:, 1]) + bowl_to_scale_z = bowl_pos[:, 2] - scale_pos[:, 2] + + # beaker to bin + beaker_to_bin_x = torch.abs(sorting_beaker_pos[:, 0] - bin_pos[:, 0]) + beaker_to_bin_y = torch.abs(sorting_beaker_pos[:, 1] - bin_pos[:, 1]) + beaker_to_bin_z = sorting_beaker_pos[:, 2] - bin_pos[:, 2] + + done = nut_to_bowl_x < max_nut_to_bowl_x + done = torch.logical_and(done, nut_to_bowl_y < max_nut_to_bowl_y) + done = torch.logical_and(done, nut_to_bowl_z < max_nut_to_bowl_z) + done = torch.logical_and(done, bowl_to_scale_x < max_bowl_to_scale_x) + done = torch.logical_and(done, bowl_to_scale_y < max_bowl_to_scale_y) + done = torch.logical_and(done, bowl_to_scale_z < max_bowl_to_scale_z) + done = torch.logical_and(done, beaker_to_bin_x < max_beaker_to_bin_x) + done = torch.logical_and(done, beaker_to_bin_y < max_beaker_to_bin_y) + done = torch.logical_and(done, beaker_to_bin_z < max_beaker_to_bin_z) + + return done + + +def task_done_exhaust_pipe( + env: ManagerBasedRLEnv, + blue_exhaust_pipe_cfg: SceneEntityCfg = SceneEntityCfg("blue_exhaust_pipe"), + blue_sorting_bin_cfg: SceneEntityCfg = SceneEntityCfg("blue_sorting_bin"), + max_blue_exhaust_to_bin_x: float = 0.085, + max_blue_exhaust_to_bin_y: float = 0.200, + min_blue_exhaust_to_bin_y: float = -0.090, + max_blue_exhaust_to_bin_z: float = 0.070, +) -> torch.Tensor: + """Determine if the exhaust pipe task is complete. + + This function checks whether all success conditions for the task have been met: + 1. The blue exhaust pipe is placed in the correct position + + Args: + env: The RL environment instance. + blue_exhaust_pipe_cfg: Configuration for the blue exhaust pipe entity. + blue_sorting_bin_cfg: Configuration for the blue sorting bin entity. + max_blue_exhaust_to_bin_x: Maximum x position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + max_blue_exhaust_to_bin_y: Maximum y position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + max_blue_exhaust_to_bin_z: Maximum z position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + # Get object entities from the scene + blue_exhaust_pipe: RigidObject = env.scene[blue_exhaust_pipe_cfg.name] + blue_sorting_bin: RigidObject = env.scene[blue_sorting_bin_cfg.name] + + # Get positions relative to environment origin + blue_exhaust_pipe_pos = blue_exhaust_pipe.data.root_pos_w - env.scene.env_origins + blue_sorting_bin_pos = blue_sorting_bin.data.root_pos_w - env.scene.env_origins + + # blue exhaust to bin + blue_exhaust_to_bin_x = torch.abs(blue_exhaust_pipe_pos[:, 0] - blue_sorting_bin_pos[:, 0]) + blue_exhaust_to_bin_y = blue_exhaust_pipe_pos[:, 1] - blue_sorting_bin_pos[:, 1] + blue_exhaust_to_bin_z = blue_exhaust_pipe_pos[:, 2] - blue_sorting_bin_pos[:, 2] + + done = blue_exhaust_to_bin_x < max_blue_exhaust_to_bin_x + done = torch.logical_and(done, blue_exhaust_to_bin_y < max_blue_exhaust_to_bin_y) + done = torch.logical_and(done, blue_exhaust_to_bin_y > min_blue_exhaust_to_bin_y) + done = torch.logical_and(done, blue_exhaust_to_bin_z < max_blue_exhaust_to_bin_z) + + return done diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_base_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_base_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..01caf58a8af2a461166e092196ffe88d47783f5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_base_env_cfg.py @@ -0,0 +1,371 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile +from dataclasses import MISSING + +import torch + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import CameraCfg + +# from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Nut Pour Base Scene.""" + + # Table + table = AssetBaseCfg( + prim_path="/World/envs/env_.*/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/table.usd", + scale=(1.0, 1.0, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + sorting_scale = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingScale", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.22236, 0.56, 0.9859], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_scale.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + sorting_bowl = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingBowl", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.02779, 0.43007, 0.9860], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bowl_yellow.usd", + scale=(1.0, 1.0, 1.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005), + ), + ) + + sorting_beaker = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingBeaker", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.13739, 0.45793, 0.9861], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_beaker_red.usd", + scale=(0.45, 0.45, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + factory_nut = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryNut", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.13739, 0.45793, 0.9995], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/factory_m16_nut_green.usd", + scale=(0.5, 0.5, 0.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005), + ), + ) + + black_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlackSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.32688, 0.46793, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bin_blue.usd", + scale=(0.75, 1.0, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + robot: ArticulationCfg = GR1T2_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_pitch_joint": -1.5708, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # right hand + "R_index_intermediate_joint": 0.0, + "R_index_proximal_joint": 0.0, + "R_middle_intermediate_joint": 0.0, + "R_middle_proximal_joint": 0.0, + "R_pinky_intermediate_joint": 0.0, + "R_pinky_proximal_joint": 0.0, + "R_ring_intermediate_joint": 0.0, + "R_ring_proximal_joint": 0.0, + "R_thumb_distal_joint": 0.0, + "R_thumb_proximal_pitch_joint": 0.0, + "R_thumb_proximal_yaw_joint": -1.57, + # left hand + "L_index_intermediate_joint": 0.0, + "L_index_proximal_joint": 0.0, + "L_middle_intermediate_joint": 0.0, + "L_middle_proximal_joint": 0.0, + "L_pinky_intermediate_joint": 0.0, + "L_pinky_proximal_joint": 0.0, + "L_ring_intermediate_joint": 0.0, + "L_ring_proximal_joint": 0.0, + "L_thumb_distal_joint": 0.0, + "L_thumb_proximal_pitch_joint": 0.0, + "L_thumb_proximal_yaw_joint": -1.57, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Set table view camera + robot_pov_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/RobotPOVCam", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=18.15, clipping_range=(0.1, 2)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.12, 1.67675), rot=(-0.19848, 0.9801, 0.0, 0.0), convention="ros"), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + gr1_action: ActionTermCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + robot_pov_cam = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + sorting_bowl_dropped = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("sorting_bowl")} + ) + sorting_beaker_dropped = DoneTerm( + func=mdp.root_height_below_minimum, + params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("sorting_beaker")}, + ) + factory_nut_dropped = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("factory_nut")} + ) + + success = DoneTerm(func=mdp.task_done_nut_pour) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + set_factory_nut_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_nut"), + "mass_distribution_params": (0.2, 0.2), + "operation": "abs", + }, + ) + + reset_object = EventTerm( + func=mdp.reset_object_poses_nut_pour, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + }, + ) + + +@configclass +class NutPourGR1T2BaseEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), + # right arm quat (4), left/right hand joint pos (22)] + idle_action = torch.tensor( + [ + [ + -0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 100 + self.sim.render_interval = 2 + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_pink_ik_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_pink_ik_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..818fba1fc80512a21ebb70caa6b531e58e77ede2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/nutpour_gr1t2_pink_ik_env_cfg.py @@ -0,0 +1,153 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GR1T2RetargeterCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.nutpour_gr1t2_base_env_cfg import NutPourGR1T2BaseEnvCfg + + +@configclass +class NutPourGR1T2PinkIKEnvCfg(NutPourGR1T2BaseEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.gr1_action = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.2, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.gr1_action.controller.urdf_path = temp_urdf_output_path + self.actions.gr1_action.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.gr1_action.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ba6c5d385135219301b737927b3998babe2898e5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_env_cfg.py @@ -0,0 +1,420 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile + +import torch +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import ManusViveCfg, OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import GR1T2RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_HIGH_PD_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Pick Place Base Scene.""" + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.45, 0.45, 0.9996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Humanoid robot configured for pick-place manipulation tasks + robot: ArticulationCfg = GR1T2_HIGH_PD_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_pitch_joint": -1.5708, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + "R_.*": 0.0, + "L_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=12, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=12, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + object = ObsTerm( + func=mdp.object_obs, + params={"left_eef_link_name": "left_hand_roll_link", "right_eef_link_name": "right_hand_roll_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=mdp.task_done_pick_place, params={"task_link_name": "right_hand_roll_link"}) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + +@configclass +class PickPlaceGR1T2EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), right arm quat (4), + # left hand joint pos (11), right hand joint pos (11)] + idle_action = torch.tensor( + [ + -0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.upper_body_ik.controller.urdf_path = temp_urdf_output_path + self.actions.upper_body_ik.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "manusvive": ManusViveCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_waist_enabled_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_waist_enabled_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..23ed8d984bcd8c641571e355d66edbef7cc9d88b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_gr1t2_waist_enabled_env_cfg.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import GR1T2RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.utils import configclass + +from .pickplace_gr1t2_env_cfg import ActionsCfg, EventCfg, ObjectTableSceneCfg, ObservationsCfg, TerminationsCfg + + +@configclass +class PickPlaceGR1T2WaistEnabledEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Add waist joint to pink_ik_cfg + waist_joint_names = ["waist_yaw_joint", "waist_pitch_joint", "waist_roll_joint"] + for joint_name in waist_joint_names: + self.actions.upper_body_ik.pink_controlled_joint_names.append(joint_name) + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.upper_body_ik.controller.urdf_path = temp_urdf_output_path + self.actions.upper_body_ik.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_unitree_g1_inspire_hand_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_unitree_g1_inspire_hand_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..85af79e7fb19cddc5488bd94f5b65f35300d7d29 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place/pickplace_unitree_g1_inspire_hand_env_cfg.py @@ -0,0 +1,415 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import tempfile + +import torch +from pink.tasks import FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import ManusViveCfg, OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.inspire.g1_upper_body_retargeter import UnitreeG1RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.schemas.schemas_cfg import MassPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.unitree import G1_INSPIRE_FTP_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the Unitree G1 Inspire Hand Pick Place Base Scene.""" + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.35, 0.45, 0.9996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=MassPropertiesCfg( + mass=0.05, + ), + ), + ) + + # Humanoid robot w/ arms higher + robot: ArticulationCfg = G1_INSPIRE_FTP_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 1.0), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_joint": 0.0, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_joint": 0.0, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # -- + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + # -- left/right hand + ".*_thumb_.*": 0.0, + ".*_index_.*": 0.0, + ".*_middle_.*": 0.0, + ".*_ring_.*": 0.0, + ".*_pinky_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + pink_ik_cfg = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_yaw_joint", + ".*_wrist_roll_joint", + ".*_wrist_pitch_joint", + ], + hand_joint_names=[ + # All the drive and mimic joints, total 24 joints + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_intermediate_joint", + "R_thumb_intermediate_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_wrist_yaw_link", + "right_wrist": "right_wrist_yaw_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="pelvis", + num_hand_joints=24, + show_ik_warnings=False, + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "g1_29dof_rev_1_0_left_wrist_yaw_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "g1_29dof_rev_1_0_right_wrist_yaw_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "g1_29dof_rev_1_0_left_wrist_yaw_link", + "g1_29dof_rev_1_0_right_wrist_yaw_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + gain=0.3, + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + enable_gravity_compensation=False, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + + object = ObsTerm( + func=mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + +@configclass +class PickPlaceG1InspireFTPEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), right arm quat (4), + # left hand joint pos (12), right hand joint pos (12)] + idle_action = torch.tensor( + [ + # 14 hand joints for EEF control + -0.1487, + 0.2038, + 1.0952, + 0.707, + 0.0, + 0.0, + 0.707, + 0.1487, + 0.2038, + 1.0952, + 0.707, + 0.0, + 0.0, + 0.707, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.pink_ik_cfg.controller.urdf_path = temp_urdf_output_path + self.actions.pink_ik_cfg.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + UnitreeG1RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + # Please confirm that self.actions.pink_ik_cfg.hand_joint_names is + # consistent with robot.joint_names[-24:] + # The order of the joints does matter as it will be used for + # converting pink_ik actions to final control actions in IsaacLab. + hand_joint_names=self.actions.pink_ik_cfg.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "manusvive": ManusViveCfg( + retargeters=[ + UnitreeG1RetargeterCfg( + enable_visualization=True, + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.pink_ik_cfg.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + }, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f2bd7d0f707533e31c8cfc327bca615cd533fe1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/__init__.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +gym.register( + id="Isaac-PickPlace-GR1T2-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_gr1t2_env_cfg:PickPlaceGR1T2EnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-NutPour-GR1T2-Pink-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.nutpour_gr1t2_pink_ik_env_cfg:NutPourGR1T2PinkIKEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_nut_pouring.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-ExhaustPipe-GR1T2-Pink-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.exhaustpipe_gr1t2_pink_ik_env_cfg:ExhaustPipeGR1T2PinkIKEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_exhaust_pipe.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-GR1T2-WaistEnabled-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_gr1t2_waist_enabled_env_cfg:PickPlaceGR1T2WaistEnabledEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-PickPlace-G1-InspireFTP-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pickplace_unitree_g1_inspire_hand_env_cfg:PickPlaceG1InspireFTPEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_exhaust_pipe.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_exhaust_pipe.json new file mode 100644 index 0000000000000000000000000000000000000000..5af2a9f4a4f8379eadb1ea908e08a4ca2c3d8ca8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_exhaust_pipe.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_gr1_exhaust_pipe", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state" + ], + "rgb": [ + "robot_pov_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 144, + "crop_width": 236, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_nut_pouring.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_nut_pouring.json new file mode 100644 index 0000000000000000000000000000000000000000..dbe527d72dde835737ea35591572a7cf6e5b44ca --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_image_nut_pouring.json @@ -0,0 +1,220 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_gr1_nut_pouring", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state" + ], + "rgb": [ + "robot_pov_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 144, + "crop_width": 236, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_low_dim.json new file mode 100644 index 0000000000000000000000000000000000000000..d2e0f8fc6d940a9abbf1506502cd40f8845ef540 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,117 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_gr1t2", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": false, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "left_eef_pos", + "left_eef_quat", + "right_eef_pos", + "right_eef_quat", + "hand_joint_state", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_base_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_base_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6e14d2e1fdd235d5a8fe7505306f05b3928cccf1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_base_env_cfg.py @@ -0,0 +1,336 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile +from dataclasses import MISSING + +import torch + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import CameraCfg + +# from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Exhaust Pipe Base Scene.""" + + # Table + table = AssetBaseCfg( + prim_path="/World/envs/env_.*/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/table.usd", + scale=(1.0, 1.0, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + blue_exhaust_pipe = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueExhaustPipe", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.04904, 0.31, 1.2590], rot=[0, 0, 1.0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/blue_exhaust_pipe.usd", + scale=(0.5, 0.5, 1.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + blue_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.16605, 0.39, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/blue_sorting_bin.usd", + scale=(1.0, 1.7, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + black_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlackSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.40132, 0.39, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/exhaust_pipe_task/exhaust_pipe_assets/black_sorting_bin.usd", + scale=(1.0, 1.7, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Humanoid robot w/ arms higher + robot: ArticulationCfg = GR1T2_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": -0.10933163, + "left_shoulder_roll_joint": 0.43292055, + "left_shoulder_yaw_joint": -0.15983289, + "left_elbow_pitch_joint": -1.48233023, + "left_wrist_yaw_joint": 0.2359135, + "left_wrist_roll_joint": 0.26184522, + "left_wrist_pitch_joint": 0.00830735, + # right hand + "R_index_intermediate_joint": 0.0, + "R_index_proximal_joint": 0.0, + "R_middle_intermediate_joint": 0.0, + "R_middle_proximal_joint": 0.0, + "R_pinky_intermediate_joint": 0.0, + "R_pinky_proximal_joint": 0.0, + "R_ring_intermediate_joint": 0.0, + "R_ring_proximal_joint": 0.0, + "R_thumb_distal_joint": 0.0, + "R_thumb_proximal_pitch_joint": 0.0, + "R_thumb_proximal_yaw_joint": -1.57, + # left hand + "L_index_intermediate_joint": 0.0, + "L_index_proximal_joint": 0.0, + "L_middle_intermediate_joint": 0.0, + "L_middle_proximal_joint": 0.0, + "L_pinky_intermediate_joint": 0.0, + "L_pinky_proximal_joint": 0.0, + "L_ring_intermediate_joint": 0.0, + "L_ring_proximal_joint": 0.0, + "L_thumb_distal_joint": 0.0, + "L_thumb_proximal_pitch_joint": 0.0, + "L_thumb_proximal_yaw_joint": -1.57, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Set table view camera + robot_pov_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/RobotPOVCam", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=18.15, clipping_range=(0.1, 2)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.12, 1.85418), rot=(-0.17246, 0.98502, 0.0, 0.0), convention="ros"), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + gr1_action: ActionTermCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + robot_pov_cam = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + blue_exhaust_pipe_dropped = DoneTerm( + func=mdp.root_height_below_minimum, + params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("blue_exhaust_pipe")}, + ) + + success = DoneTerm(func=mdp.task_done_exhaust_pipe) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_blue_exhaust_pipe = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("blue_exhaust_pipe"), + }, + ) + + +@configclass +class ExhaustPipeGR1T2BaseEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), + # right arm quat (4), left/right hand joint pos (22)] + idle_action = torch.tensor( + [ + [ + -0.2909, + 0.2778, + 1.1247, + 0.5253, + 0.5747, + -0.4160, + 0.4699, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 100 + self.sim.render_interval = 2 + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_pink_ik_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_pink_ik_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..66ebfcad8a1869da65fdd04c0bcd47e49b94f2e9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/exhaustpipe_gr1t2_pink_ik_env_cfg.py @@ -0,0 +1,155 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GR1T2RetargeterCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.exhaustpipe_gr1t2_base_env_cfg import ( + ExhaustPipeGR1T2BaseEnvCfg, +) + + +@configclass +class ExhaustPipeGR1T2PinkIKEnvCfg(ExhaustPipeGR1T2BaseEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.gr1_action = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.2, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.gr1_action.controller.urdf_path = temp_urdf_output_path + self.actions.gr1_action.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.gr1_action.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..555bfb7cbe8f3e73aa5bc809545619f211db9cbc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .pick_place_events import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..3cf1d57d260039fe915e343b01f65cb4e52bf02d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/observations.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_obs( + env: ManagerBasedRLEnv, + left_eef_link_name: str, + right_eef_link_name: str, + object_name: str, +) -> torch.Tensor: + """ + Object observations (in world frame): + object pos, + object quat, + left_eef to object, + right_eef_to object, + """ + + body_pos_w = env.scene["robot"].data.body_pos_w + left_eef_idx = env.scene["robot"].data.body_names.index(left_eef_link_name) + right_eef_idx = env.scene["robot"].data.body_names.index(right_eef_link_name) + left_eef_pos = body_pos_w[:, left_eef_idx] - env.scene.env_origins + right_eef_pos = body_pos_w[:, right_eef_idx] - env.scene.env_origins + + object_pos = env.scene[object_name].data.root_pos_w - env.scene.env_origins + object_quat = env.scene[object_name].data.root_quat_w + + left_eef_to_object = object_pos - left_eef_pos + right_eef_to_object = object_pos - right_eef_pos + + return torch.cat( + ( + object_pos, + object_quat, + left_eef_to_object, + right_eef_to_object, + ), + dim=1, + ) + + +def get_eef_pos(env: ManagerBasedRLEnv, link_name: str) -> torch.Tensor: + body_pos_w = env.scene["robot"].data.body_pos_w + left_eef_idx = env.scene["robot"].data.body_names.index(link_name) + left_eef_pos = body_pos_w[:, left_eef_idx] - env.scene.env_origins + + return left_eef_pos + + +def get_eef_quat(env: ManagerBasedRLEnv, link_name: str) -> torch.Tensor: + body_quat_w = env.scene["robot"].data.body_quat_w + left_eef_idx = env.scene["robot"].data.body_names.index(link_name) + left_eef_quat = body_quat_w[:, left_eef_idx] + + return left_eef_quat + + +def get_robot_joint_state( + env: ManagerBasedRLEnv, + joint_names: list[str], +) -> torch.Tensor: + # hand_joint_names is a list of regex, use find_joints + indexes, _ = env.scene["robot"].find_joints(joint_names) + indexes = torch.tensor(indexes, dtype=torch.long) + robot_joint_states = env.scene["robot"].data.joint_pos[:, indexes] + + return robot_joint_states + + +def get_all_robot_link_state( + env: ManagerBasedRLEnv, +) -> torch.Tensor: + body_pos_w = env.scene["robot"].data.body_link_state_w[:, :, :] + all_robot_link_pos = body_pos_w + + return all_robot_link_pos diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/pick_place_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/pick_place_events.py new file mode 100644 index 0000000000000000000000000000000000000000..ca1fd940fea84e7d9ba09548143a5055ae770e3a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/pick_place_events.py @@ -0,0 +1,96 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def reset_object_poses_nut_pour( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + sorting_beaker_cfg: SceneEntityCfg = SceneEntityCfg("sorting_beaker"), + factory_nut_cfg: SceneEntityCfg = SceneEntityCfg("factory_nut"), + sorting_bowl_cfg: SceneEntityCfg = SceneEntityCfg("sorting_bowl"), + sorting_scale_cfg: SceneEntityCfg = SceneEntityCfg("sorting_scale"), +): + """Reset the asset root states to a random position and orientation uniformly within the given ranges. + + Args: + env: The RL environment instance. + env_ids: The environment IDs to reset the object poses for. + sorting_beaker_cfg: The configuration for the sorting beaker asset. + factory_nut_cfg: The configuration for the factory nut asset. + sorting_bowl_cfg: The configuration for the sorting bowl asset. + sorting_scale_cfg: The configuration for the sorting scale asset. + pose_range: The dictionary of pose ranges for the objects. Keys are + ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. + """ + # extract the used quantities (to enable type-hinting) + sorting_beaker = env.scene[sorting_beaker_cfg.name] + factory_nut = env.scene[factory_nut_cfg.name] + sorting_bowl = env.scene[sorting_bowl_cfg.name] + sorting_scale = env.scene[sorting_scale_cfg.name] + + # get default root state + sorting_beaker_root_states = sorting_beaker.data.default_root_state[env_ids].clone() + factory_nut_root_states = factory_nut.data.default_root_state[env_ids].clone() + sorting_bowl_root_states = sorting_bowl.data.default_root_state[env_ids].clone() + sorting_scale_root_states = sorting_scale.data.default_root_state[env_ids].clone() + + # get pose ranges + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=sorting_beaker.device) + + # randomize sorting beaker and factory nut together + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_beaker = ( + sorting_beaker_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + ) + positions_factory_nut = factory_nut_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_beaker = math_utils.quat_mul(sorting_beaker_root_states[:, 3:7], orientations_delta) + orientations_factory_nut = math_utils.quat_mul(factory_nut_root_states[:, 3:7], orientations_delta) + + # randomize sorting bowl + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_bowl = sorting_bowl_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_bowl = math_utils.quat_mul(sorting_bowl_root_states[:, 3:7], orientations_delta) + + # randomize scorting scale + rand_samples = math_utils.sample_uniform( + ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=sorting_beaker.device + ) + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + positions_sorting_scale = sorting_scale_root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_sorting_scale = math_utils.quat_mul(sorting_scale_root_states[:, 3:7], orientations_delta) + + # set into the physics simulation + sorting_beaker.write_root_pose_to_sim( + torch.cat([positions_sorting_beaker, orientations_sorting_beaker], dim=-1), env_ids=env_ids + ) + factory_nut.write_root_pose_to_sim( + torch.cat([positions_factory_nut, orientations_factory_nut], dim=-1), env_ids=env_ids + ) + sorting_bowl.write_root_pose_to_sim( + torch.cat([positions_sorting_bowl, orientations_sorting_bowl], dim=-1), env_ids=env_ids + ) + sorting_scale.write_root_pose_to_sim( + torch.cat([positions_sorting_scale, orientations_sorting_scale], dim=-1), env_ids=env_ids + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..6252b9c67a218c0e7ec347775e8e2e24a0667add --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/mdp/terminations.py @@ -0,0 +1,222 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def task_done_pick_place( + env: ManagerBasedRLEnv, + task_link_name: str = "", + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + right_wrist_max_x: float = 0.26, + min_x: float = 0.40, + max_x: float = 0.85, + min_y: float = 0.35, + max_y: float = 0.60, + max_height: float = 1.10, + min_vel: float = 0.20, +) -> torch.Tensor: + """Determine if the object placement task is complete. + + This function checks whether all success conditions for the task have been met: + 1. object is within the target x/y range + 2. object is below a minimum height + 3. object velocity is below threshold + 4. Right robot wrist is retracted back towards body (past a given x pos threshold) + + Args: + env: The RL environment instance. + object_cfg: Configuration for the object entity. + right_wrist_max_x: Maximum x position of the right wrist for task completion. + min_x: Minimum x position of the object for task completion. + max_x: Maximum x position of the object for task completion. + min_y: Minimum y position of the object for task completion. + max_y: Maximum y position of the object for task completion. + max_height: Maximum height (z position) of the object for task completion. + min_vel: Minimum velocity magnitude of the object for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + if task_link_name == "": + raise ValueError("task_link_name must be provided to task_done_pick_place") + + # Get object entity from the scene + object: RigidObject = env.scene[object_cfg.name] + + # Extract wheel position relative to environment origin + object_x = object.data.root_pos_w[:, 0] - env.scene.env_origins[:, 0] + object_y = object.data.root_pos_w[:, 1] - env.scene.env_origins[:, 1] + object_height = object.data.root_pos_w[:, 2] - env.scene.env_origins[:, 2] + object_vel = torch.abs(object.data.root_vel_w) + + # Get right wrist position relative to environment origin + robot_body_pos_w = env.scene["robot"].data.body_pos_w + right_eef_idx = env.scene["robot"].data.body_names.index(task_link_name) + right_wrist_x = robot_body_pos_w[:, right_eef_idx, 0] - env.scene.env_origins[:, 0] + + # Check all success conditions and combine with logical AND + done = object_x < max_x + done = torch.logical_and(done, object_x > min_x) + done = torch.logical_and(done, object_y < max_y) + done = torch.logical_and(done, object_y > min_y) + done = torch.logical_and(done, object_height < max_height) + done = torch.logical_and(done, right_wrist_x < right_wrist_max_x) + done = torch.logical_and(done, object_vel[:, 0] < min_vel) + done = torch.logical_and(done, object_vel[:, 1] < min_vel) + done = torch.logical_and(done, object_vel[:, 2] < min_vel) + + return done + + +def task_done_nut_pour( + env: ManagerBasedRLEnv, + sorting_scale_cfg: SceneEntityCfg = SceneEntityCfg("sorting_scale"), + sorting_bowl_cfg: SceneEntityCfg = SceneEntityCfg("sorting_bowl"), + sorting_beaker_cfg: SceneEntityCfg = SceneEntityCfg("sorting_beaker"), + factory_nut_cfg: SceneEntityCfg = SceneEntityCfg("factory_nut"), + sorting_bin_cfg: SceneEntityCfg = SceneEntityCfg("black_sorting_bin"), + max_bowl_to_scale_x: float = 0.055, + max_bowl_to_scale_y: float = 0.055, + max_bowl_to_scale_z: float = 0.025, + max_nut_to_bowl_x: float = 0.050, + max_nut_to_bowl_y: float = 0.050, + max_nut_to_bowl_z: float = 0.019, + max_beaker_to_bin_x: float = 0.08, + max_beaker_to_bin_y: float = 0.12, + max_beaker_to_bin_z: float = 0.07, +) -> torch.Tensor: + """Determine if the nut pouring task is complete. + + This function checks whether all success conditions for the task have been met: + 1. The factory nut is in the sorting bowl + 2. The sorting beaker is in the sorting bin + 3. The sorting bowl is placed on the sorting scale + + Args: + env: The RL environment instance. + sorting_scale_cfg: Configuration for the sorting scale entity. + sorting_bowl_cfg: Configuration for the sorting bowl entity. + sorting_beaker_cfg: Configuration for the sorting beaker entity. + factory_nut_cfg: Configuration for the factory nut entity. + sorting_bin_cfg: Configuration for the sorting bin entity. + max_bowl_to_scale_x: Maximum x position of the sorting bowl relative to the sorting scale for task completion. + max_bowl_to_scale_y: Maximum y position of the sorting bowl relative to the sorting scale for task completion. + max_bowl_to_scale_z: Maximum z position of the sorting bowl relative to the sorting scale for task completion. + max_nut_to_bowl_x: Maximum x position of the factory nut relative to the sorting bowl for task completion. + max_nut_to_bowl_y: Maximum y position of the factory nut relative to the sorting bowl for task completion. + max_nut_to_bowl_z: Maximum z position of the factory nut relative to the sorting bowl for task completion. + max_beaker_to_bin_x: Maximum x position of the sorting beaker relative to the sorting bin for task completion. + max_beaker_to_bin_y: Maximum y position of the sorting beaker relative to the sorting bin for task completion. + max_beaker_to_bin_z: Maximum z position of the sorting beaker relative to the sorting bin for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + # Get object entities from the scene + sorting_scale: RigidObject = env.scene[sorting_scale_cfg.name] + sorting_bowl: RigidObject = env.scene[sorting_bowl_cfg.name] + factory_nut: RigidObject = env.scene[factory_nut_cfg.name] + sorting_beaker: RigidObject = env.scene[sorting_beaker_cfg.name] + sorting_bin: RigidObject = env.scene[sorting_bin_cfg.name] + + # Get positions relative to environment origin + scale_pos = sorting_scale.data.root_pos_w - env.scene.env_origins + bowl_pos = sorting_bowl.data.root_pos_w - env.scene.env_origins + sorting_beaker_pos = sorting_beaker.data.root_pos_w - env.scene.env_origins + nut_pos = factory_nut.data.root_pos_w - env.scene.env_origins + bin_pos = sorting_bin.data.root_pos_w - env.scene.env_origins + + # nut to bowl + nut_to_bowl_x = torch.abs(nut_pos[:, 0] - bowl_pos[:, 0]) + nut_to_bowl_y = torch.abs(nut_pos[:, 1] - bowl_pos[:, 1]) + nut_to_bowl_z = nut_pos[:, 2] - bowl_pos[:, 2] + + # bowl to scale + bowl_to_scale_x = torch.abs(bowl_pos[:, 0] - scale_pos[:, 0]) + bowl_to_scale_y = torch.abs(bowl_pos[:, 1] - scale_pos[:, 1]) + bowl_to_scale_z = bowl_pos[:, 2] - scale_pos[:, 2] + + # beaker to bin + beaker_to_bin_x = torch.abs(sorting_beaker_pos[:, 0] - bin_pos[:, 0]) + beaker_to_bin_y = torch.abs(sorting_beaker_pos[:, 1] - bin_pos[:, 1]) + beaker_to_bin_z = sorting_beaker_pos[:, 2] - bin_pos[:, 2] + + done = nut_to_bowl_x < max_nut_to_bowl_x + done = torch.logical_and(done, nut_to_bowl_y < max_nut_to_bowl_y) + done = torch.logical_and(done, nut_to_bowl_z < max_nut_to_bowl_z) + done = torch.logical_and(done, bowl_to_scale_x < max_bowl_to_scale_x) + done = torch.logical_and(done, bowl_to_scale_y < max_bowl_to_scale_y) + done = torch.logical_and(done, bowl_to_scale_z < max_bowl_to_scale_z) + done = torch.logical_and(done, beaker_to_bin_x < max_beaker_to_bin_x) + done = torch.logical_and(done, beaker_to_bin_y < max_beaker_to_bin_y) + done = torch.logical_and(done, beaker_to_bin_z < max_beaker_to_bin_z) + + return done + + +def task_done_exhaust_pipe( + env: ManagerBasedRLEnv, + blue_exhaust_pipe_cfg: SceneEntityCfg = SceneEntityCfg("blue_exhaust_pipe"), + blue_sorting_bin_cfg: SceneEntityCfg = SceneEntityCfg("blue_sorting_bin"), + max_blue_exhaust_to_bin_x: float = 0.085, + max_blue_exhaust_to_bin_y: float = 0.200, + min_blue_exhaust_to_bin_y: float = -0.090, + max_blue_exhaust_to_bin_z: float = 0.070, +) -> torch.Tensor: + """Determine if the exhaust pipe task is complete. + + This function checks whether all success conditions for the task have been met: + 1. The blue exhaust pipe is placed in the correct position + + Args: + env: The RL environment instance. + blue_exhaust_pipe_cfg: Configuration for the blue exhaust pipe entity. + blue_sorting_bin_cfg: Configuration for the blue sorting bin entity. + max_blue_exhaust_to_bin_x: Maximum x position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + max_blue_exhaust_to_bin_y: Maximum y position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + max_blue_exhaust_to_bin_z: Maximum z position of the blue exhaust pipe + relative to the blue sorting bin for task completion. + + Returns: + Boolean tensor indicating which environments have completed the task. + """ + # Get object entities from the scene + blue_exhaust_pipe: RigidObject = env.scene[blue_exhaust_pipe_cfg.name] + blue_sorting_bin: RigidObject = env.scene[blue_sorting_bin_cfg.name] + + # Get positions relative to environment origin + blue_exhaust_pipe_pos = blue_exhaust_pipe.data.root_pos_w - env.scene.env_origins + blue_sorting_bin_pos = blue_sorting_bin.data.root_pos_w - env.scene.env_origins + + # blue exhaust to bin + blue_exhaust_to_bin_x = torch.abs(blue_exhaust_pipe_pos[:, 0] - blue_sorting_bin_pos[:, 0]) + blue_exhaust_to_bin_y = blue_exhaust_pipe_pos[:, 1] - blue_sorting_bin_pos[:, 1] + blue_exhaust_to_bin_z = blue_exhaust_pipe_pos[:, 2] - blue_sorting_bin_pos[:, 2] + + done = blue_exhaust_to_bin_x < max_blue_exhaust_to_bin_x + done = torch.logical_and(done, blue_exhaust_to_bin_y < max_blue_exhaust_to_bin_y) + done = torch.logical_and(done, blue_exhaust_to_bin_y > min_blue_exhaust_to_bin_y) + done = torch.logical_and(done, blue_exhaust_to_bin_z < max_blue_exhaust_to_bin_z) + + return done diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_base_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_base_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..01caf58a8af2a461166e092196ffe88d47783f5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_base_env_cfg.py @@ -0,0 +1,371 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile +from dataclasses import MISSING + +import torch + +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg, SceneEntityCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import CameraCfg + +# from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Nut Pour Base Scene.""" + + # Table + table = AssetBaseCfg( + prim_path="/World/envs/env_.*/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/table.usd", + scale=(1.0, 1.0, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + sorting_scale = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingScale", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.22236, 0.56, 0.9859], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_scale.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + sorting_bowl = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingBowl", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.02779, 0.43007, 0.9860], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bowl_yellow.usd", + scale=(1.0, 1.0, 1.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005), + ), + ) + + sorting_beaker = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/SortingBeaker", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.13739, 0.45793, 0.9861], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_beaker_red.usd", + scale=(0.45, 0.45, 1.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + factory_nut = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/FactoryNut", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.13739, 0.45793, 0.9995], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/factory_m16_nut_green.usd", + scale=(0.5, 0.5, 0.5), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005), + ), + ) + + black_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlackSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.32688, 0.46793, 0.98634], rot=[1.0, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bin_blue.usd", + scale=(0.75, 1.0, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + robot: ArticulationCfg = GR1T2_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_pitch_joint": -1.5708, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # right hand + "R_index_intermediate_joint": 0.0, + "R_index_proximal_joint": 0.0, + "R_middle_intermediate_joint": 0.0, + "R_middle_proximal_joint": 0.0, + "R_pinky_intermediate_joint": 0.0, + "R_pinky_proximal_joint": 0.0, + "R_ring_intermediate_joint": 0.0, + "R_ring_proximal_joint": 0.0, + "R_thumb_distal_joint": 0.0, + "R_thumb_proximal_pitch_joint": 0.0, + "R_thumb_proximal_yaw_joint": -1.57, + # left hand + "L_index_intermediate_joint": 0.0, + "L_index_proximal_joint": 0.0, + "L_middle_intermediate_joint": 0.0, + "L_middle_proximal_joint": 0.0, + "L_pinky_intermediate_joint": 0.0, + "L_pinky_proximal_joint": 0.0, + "L_ring_intermediate_joint": 0.0, + "L_ring_proximal_joint": 0.0, + "L_thumb_distal_joint": 0.0, + "L_thumb_proximal_pitch_joint": 0.0, + "L_thumb_proximal_yaw_joint": -1.57, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Set table view camera + robot_pov_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/RobotPOVCam", + update_period=0.0, + height=160, + width=256, + data_types=["rgb"], + spawn=sim_utils.PinholeCameraCfg(focal_length=18.15, clipping_range=(0.1, 2)), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.12, 1.67675), rot=(-0.19848, 0.9801, 0.0, 0.0), convention="ros"), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + gr1_action: ActionTermCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + robot_pov_cam = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("robot_pov_cam"), "data_type": "rgb", "normalize": False}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + sorting_bowl_dropped = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("sorting_bowl")} + ) + sorting_beaker_dropped = DoneTerm( + func=mdp.root_height_below_minimum, + params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("sorting_beaker")}, + ) + factory_nut_dropped = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("factory_nut")} + ) + + success = DoneTerm(func=mdp.task_done_nut_pour) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + set_factory_nut_mass = EventTerm( + func=mdp.randomize_rigid_body_mass, + mode="startup", + params={ + "asset_cfg": SceneEntityCfg("factory_nut"), + "mass_distribution_params": (0.2, 0.2), + "operation": "abs", + }, + ) + + reset_object = EventTerm( + func=mdp.reset_object_poses_nut_pour, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + }, + ) + + +@configclass +class NutPourGR1T2BaseEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), + # right arm quat (4), left/right hand joint pos (22)] + idle_action = torch.tensor( + [ + [ + -0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 100 + self.sim.render_interval = 2 + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["robot_pov_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_pink_ik_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_pink_ik_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..818fba1fc80512a21ebb70caa6b531e58e77ede2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/nutpour_gr1t2_pink_ik_env_cfg.py @@ -0,0 +1,153 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GR1T2RetargeterCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.pick_place.nutpour_gr1t2_base_env_cfg import NutPourGR1T2BaseEnvCfg + + +@configclass +class NutPourGR1T2PinkIKEnvCfg(NutPourGR1T2BaseEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.gr1_action = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.2, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.gr1_action.controller.urdf_path = temp_urdf_output_path + self.actions.gr1_action.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.gr1_action.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ba6c5d385135219301b737927b3998babe2898e5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_env_cfg.py @@ -0,0 +1,420 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile + +import torch +from pink.tasks import DampingTask, FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import ManusViveCfg, OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import GR1T2RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.fourier import GR1T2_HIGH_PD_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the GR1T2 Pick Place Base Scene.""" + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.45, 0.45, 0.9996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Humanoid robot configured for pick-place manipulation tasks + robot: ArticulationCfg = GR1T2_HIGH_PD_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 0.93), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_pitch_joint": -1.5708, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_pitch_joint": -1.5708, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # -- + "head_.*": 0.0, + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + "R_.*": 0.0, + "L_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + upper_body_ik = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "left_wrist_yaw_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "right_wrist_yaw_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + ], + hand_joint_names=[ + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_hand_pitch_link", + "right_wrist": "right_hand_pitch_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + # Configuration for the IK controller + # The frames names are the ones present in the URDF file + # The urdf has to be generated from the USD that is being used in the scene + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="base_link", + num_hand_joints=22, + show_ik_warnings=False, + # Determines whether Pink IK solver will fail due to a joint limit violation + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=12, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=1.0, # [cost] / [rad] + lm_damping=12, # dampening for solver for step jumps + gain=0.5, + ), + DampingTask( + cost=0.5, # [cost] * [s] / [rad] + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "GR1T2_fourier_hand_6dof_left_hand_pitch_link", + "GR1T2_fourier_hand_6dof_right_hand_pitch_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_pitch_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_pitch_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_hand_roll_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_hand_roll_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_hand_roll_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_hand_roll_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + head_joint_state = ObsTerm( + func=mdp.get_robot_joint_state, + params={"joint_names": ["head_pitch_joint", "head_roll_joint", "head_yaw_joint"]}, + ) + + object = ObsTerm( + func=mdp.object_obs, + params={"left_eef_link_name": "left_hand_roll_link", "right_eef_link_name": "right_hand_roll_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=mdp.task_done_pick_place, params={"task_link_name": "right_hand_roll_link"}) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + +@configclass +class PickPlaceGR1T2EnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), right arm quat (4), + # left hand joint pos (11), right hand joint pos (11)] + idle_action = torch.tensor( + [ + -0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.22878, + 0.2536, + 1.0953, + 0.5, + 0.5, + -0.5, + 0.5, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.upper_body_ik.controller.urdf_path = temp_urdf_output_path + self.actions.upper_body_ik.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "manusvive": ManusViveCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_waist_enabled_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_waist_enabled_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..23ed8d984bcd8c641571e355d66edbef7cc9d88b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_gr1t2_waist_enabled_env_cfg.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import tempfile + +import isaaclab.controllers.utils as ControllerUtils +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import GR1T2RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.utils import configclass + +from .pickplace_gr1t2_env_cfg import ActionsCfg, EventCfg, ObjectTableSceneCfg, ObservationsCfg, TerminationsCfg + + +@configclass +class PickPlaceGR1T2WaistEnabledEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # OpenXR hand tracking has 26 joints per hand + NUM_OPENXR_HAND_JOINTS = 26 + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Add waist joint to pink_ik_cfg + waist_joint_names = ["waist_yaw_joint", "waist_pitch_joint", "waist_roll_joint"] + for joint_name in waist_joint_names: + self.actions.upper_body_ik.pink_controlled_joint_names.append(joint_name) + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.upper_body_ik.controller.urdf_path = temp_urdf_output_path + self.actions.upper_body_ik.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + GR1T2RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * self.NUM_OPENXR_HAND_JOINTS, + sim_device=self.sim.device, + hand_joint_names=self.actions.upper_body_ik.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_unitree_g1_inspire_hand_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_unitree_g1_inspire_hand_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..85af79e7fb19cddc5488bd94f5b65f35300d7d29 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/pick_place_multi/pickplace_unitree_g1_inspire_hand_env_cfg.py @@ -0,0 +1,415 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import tempfile + +import torch +from pink.tasks import FrameTask + +import carb + +import isaaclab.controllers.utils as ControllerUtils +import isaaclab.envs.mdp as base_mdp +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg +from isaaclab.controllers.pink_ik import NullSpacePostureTask, PinkIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr import ManusViveCfg, OpenXRDeviceCfg, XrCfg +from isaaclab.devices.openxr.retargeters.humanoid.unitree.inspire.g1_upper_body_retargeter import UnitreeG1RetargeterCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.actions.pink_actions_cfg import PinkInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim.schemas.schemas_cfg import MassPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from . import mdp + +from isaaclab_assets.robots.unitree import G1_INSPIRE_FTP_CFG # isort: skip + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the Unitree G1 Inspire Hand Pick Place Base Scene.""" + + # Table + packing_table = AssetBaseCfg( + prim_path="/World/envs/env_.*/PackingTable", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.0, 0.55, 0.0], rot=[1.0, 0.0, 0.0, 0.0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/PackingTable/packing_table.usd", + rigid_props=sim_utils.RigidBodyPropertiesCfg(kinematic_enabled=True), + ), + ) + + object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg(pos=[-0.35, 0.45, 0.9996], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/pick_place_task/pick_place_assets/steering_wheel.usd", + scale=(0.75, 0.75, 0.75), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + mass_props=MassPropertiesCfg( + mass=0.05, + ), + ), + ) + + # Humanoid robot w/ arms higher + robot: ArticulationCfg = G1_INSPIRE_FTP_CFG.replace( + prim_path="/World/envs/env_.*/Robot", + init_state=ArticulationCfg.InitialStateCfg( + pos=(0, 0, 1.0), + rot=(0.7071, 0, 0, 0.7071), + joint_pos={ + # right-arm + "right_shoulder_pitch_joint": 0.0, + "right_shoulder_roll_joint": 0.0, + "right_shoulder_yaw_joint": 0.0, + "right_elbow_joint": 0.0, + "right_wrist_yaw_joint": 0.0, + "right_wrist_roll_joint": 0.0, + "right_wrist_pitch_joint": 0.0, + # left-arm + "left_shoulder_pitch_joint": 0.0, + "left_shoulder_roll_joint": 0.0, + "left_shoulder_yaw_joint": 0.0, + "left_elbow_joint": 0.0, + "left_wrist_yaw_joint": 0.0, + "left_wrist_roll_joint": 0.0, + "left_wrist_pitch_joint": 0.0, + # -- + "waist_.*": 0.0, + ".*_hip_.*": 0.0, + ".*_knee_.*": 0.0, + ".*_ankle_.*": 0.0, + # -- left/right hand + ".*_thumb_.*": 0.0, + ".*_index_.*": 0.0, + ".*_middle_.*": 0.0, + ".*_ring_.*": 0.0, + ".*_pinky_.*": 0.0, + }, + joint_vel={".*": 0.0}, + ), + ) + + # Ground plane + ground = AssetBaseCfg( + prim_path="/World/GroundPlane", + spawn=GroundPlaneCfg(), + ) + + # Lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + pink_ik_cfg = PinkInverseKinematicsActionCfg( + pink_controlled_joint_names=[ + ".*_shoulder_pitch_joint", + ".*_shoulder_roll_joint", + ".*_shoulder_yaw_joint", + ".*_elbow_joint", + ".*_wrist_yaw_joint", + ".*_wrist_roll_joint", + ".*_wrist_pitch_joint", + ], + hand_joint_names=[ + # All the drive and mimic joints, total 24 joints + "L_index_proximal_joint", + "L_middle_proximal_joint", + "L_pinky_proximal_joint", + "L_ring_proximal_joint", + "L_thumb_proximal_yaw_joint", + "R_index_proximal_joint", + "R_middle_proximal_joint", + "R_pinky_proximal_joint", + "R_ring_proximal_joint", + "R_thumb_proximal_yaw_joint", + "L_index_intermediate_joint", + "L_middle_intermediate_joint", + "L_pinky_intermediate_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_pitch_joint", + "R_index_intermediate_joint", + "R_middle_intermediate_joint", + "R_pinky_intermediate_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_pitch_joint", + "L_thumb_intermediate_joint", + "R_thumb_intermediate_joint", + "L_thumb_distal_joint", + "R_thumb_distal_joint", + ], + target_eef_link_names={ + "left_wrist": "left_wrist_yaw_link", + "right_wrist": "right_wrist_yaw_link", + }, + # the robot in the sim scene we are controlling + asset_name="robot", + controller=PinkIKControllerCfg( + articulation_name="robot", + base_link_name="pelvis", + num_hand_joints=24, + show_ik_warnings=False, + fail_on_joint_limit_violation=False, + variable_input_tasks=[ + FrameTask( + "g1_29dof_rev_1_0_left_wrist_yaw_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + FrameTask( + "g1_29dof_rev_1_0_right_wrist_yaw_link", + position_cost=8.0, # [cost] / [m] + orientation_cost=2.0, # [cost] / [rad] + lm_damping=10, # dampening for solver for step jumps + gain=0.5, + ), + NullSpacePostureTask( + cost=0.5, + lm_damping=1, + controlled_frames=[ + "g1_29dof_rev_1_0_left_wrist_yaw_link", + "g1_29dof_rev_1_0_right_wrist_yaw_link", + ], + controlled_joints=[ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "waist_yaw_joint", + "waist_pitch_joint", + "waist_roll_joint", + ], + gain=0.3, + ), + ], + fixed_input_tasks=[], + xr_enabled=bool(carb.settings.get_settings().get("/app/xr/enabled")), + ), + enable_gravity_compensation=False, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + robot_joint_pos = ObsTerm( + func=base_mdp.joint_pos, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + robot_root_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("robot")}) + robot_root_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("robot")}) + object_pos = ObsTerm(func=base_mdp.root_pos_w, params={"asset_cfg": SceneEntityCfg("object")}) + object_rot = ObsTerm(func=base_mdp.root_quat_w, params={"asset_cfg": SceneEntityCfg("object")}) + robot_links_state = ObsTerm(func=mdp.get_all_robot_link_state) + + left_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "left_wrist_yaw_link"}) + left_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "left_wrist_yaw_link"}) + right_eef_pos = ObsTerm(func=mdp.get_eef_pos, params={"link_name": "right_wrist_yaw_link"}) + right_eef_quat = ObsTerm(func=mdp.get_eef_quat, params={"link_name": "right_wrist_yaw_link"}) + + hand_joint_state = ObsTerm(func=mdp.get_robot_joint_state, params={"joint_names": ["R_.*", "L_.*"]}) + + object = ObsTerm( + func=mdp.object_obs, + params={"left_eef_link_name": "left_wrist_yaw_link", "right_eef_link_name": "right_wrist_yaw_link"}, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": 0.5, "asset_cfg": SceneEntityCfg("object")} + ) + + success = DoneTerm(func=mdp.task_done_pick_place, params={"task_link_name": "right_wrist_yaw_link"}) + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": { + "x": [-0.01, 0.01], + "y": [-0.01, 0.01], + }, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + +@configclass +class PickPlaceG1InspireFTPEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the GR1T2 environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=1, env_spacing=2.5, replicate_physics=True) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + events = EventCfg() + + # Unused managers + commands = None + rewards = None + curriculum = None + + # Position of the XR anchor in the world frame + xr: XrCfg = XrCfg( + anchor_pos=(0.0, 0.0, 0.0), + anchor_rot=(1.0, 0.0, 0.0, 0.0), + ) + + # Temporary directory for URDF files + temp_urdf_dir = tempfile.gettempdir() + + # Idle action to hold robot in default pose + # Action format: [left arm pos (3), left arm quat (4), right arm pos (3), right arm quat (4), + # left hand joint pos (12), right hand joint pos (12)] + idle_action = torch.tensor( + [ + # 14 hand joints for EEF control + -0.1487, + 0.2038, + 1.0952, + 0.707, + 0.0, + 0.0, + 0.707, + 0.1487, + 0.2038, + 1.0952, + 0.707, + 0.0, + 0.0, + 0.707, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 6 + self.episode_length_s = 20.0 + # simulation settings + self.sim.dt = 1 / 120 # 120Hz + self.sim.render_interval = 2 + + # Convert USD to URDF and change revolute joints to fixed + temp_urdf_output_path, temp_urdf_meshes_output_path = ControllerUtils.convert_usd_to_urdf( + self.scene.robot.spawn.usd_path, self.temp_urdf_dir, force_conversion=True + ) + + # Set the URDF and mesh paths for the IK controller + self.actions.pink_ik_cfg.controller.urdf_path = temp_urdf_output_path + self.actions.pink_ik_cfg.controller.mesh_path = temp_urdf_meshes_output_path + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + UnitreeG1RetargeterCfg( + enable_visualization=True, + # number of joints in both hands + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + # Please confirm that self.actions.pink_ik_cfg.hand_joint_names is + # consistent with robot.joint_names[-24:] + # The order of the joints does matter as it will be used for + # converting pink_ik actions to final control actions in IsaacLab. + hand_joint_names=self.actions.pink_ik_cfg.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "manusvive": ManusViveCfg( + retargeters=[ + UnitreeG1RetargeterCfg( + enable_visualization=True, + num_open_xr_hand_joints=2 * 26, + sim_device=self.sim.device, + hand_joint_names=self.actions.pink_ik_cfg.hand_joint_names, + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + }, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..696e138c3e427993b33a926a9262921ade560e06 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the place environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..696e138c3e427993b33a926a9262921ade560e06 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the place environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7b99bd4a5d3439577ed64c2a5f03a398bc35b27b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/__init__.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +## +# Register Gym environments. +## + +## +# Agibot Right Arm: place toy2box task, with RmpFlow +## +gym.register( + id="Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.place_toy2box_rmp_rel_env_cfg:RmpFlowAgibotPlaceToy2BoxEnvCfg", + }, + disable_env_checker=True, +) + +## +# Agibot Left Arm: place upright mug task, with RmpFlow +## +gym.register( + id="Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.place_upright_mug_rmp_rel_env_cfg:RmpFlowAgibotPlaceUprightMugEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_toy2box_rmp_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_toy2box_rmp_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ffe842b0202a74c8afb4328c793abf0d9023a366 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_toy2box_rmp_rel_env_cfg.py @@ -0,0 +1,347 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +from dataclasses import MISSING + +from isaaclab.assets import AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.envs.mdp.actions.rmpflow_actions_cfg import RMPFlowActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.sensors import ContactSensorCfg, FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import MassPropertiesCfg, RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.place import mdp as place_mdp +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_env_cfg import ObjectTableSceneCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.agibot import AGIBOT_A2D_CFG # isort: skip +from isaaclab.controllers.config.rmp_flow import AGIBOT_RIGHT_ARM_RMPFLOW_CFG # isort: skip + +## +# Event settings +## + + +@configclass +class EventCfgPlaceToy2Box: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset", params={"reset_joint_targets": True}) + + init_toy_position = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": { + "x": (-0.15, 0.20), + "y": (-0.3, -0.15), + "z": (-0.65, -0.65), + "yaw": (-3.14, 3.14), + }, + "asset_cfgs": [SceneEntityCfg("toy_truck")], + }, + ) + init_box_position = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": { + "x": (0.25, 0.35), + "y": (0.0, 0.10), + "z": (-0.55, -0.55), + "yaw": (-3.14, 3.14), + }, + "asset_cfgs": [SceneEntityCfg("box")], + }, + ) + + +# +# MDP settings +## + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + toy_truck_positions = ObsTerm( + func=place_mdp.object_poses_in_base_frame, + params={"object_cfg": SceneEntityCfg("toy_truck"), "return_key": "pos"}, + ) + toy_truck_orientations = ObsTerm( + func=place_mdp.object_poses_in_base_frame, + params={"object_cfg": SceneEntityCfg("toy_truck"), "return_key": "quat"}, + ) + box_positions = ObsTerm( + func=place_mdp.object_poses_in_base_frame, params={"object_cfg": SceneEntityCfg("box"), "return_key": "pos"} + ) + box_orientations = ObsTerm( + func=place_mdp.object_poses_in_base_frame, + params={"object_cfg": SceneEntityCfg("box"), "return_key": "quat"}, + ) + eef_pos = ObsTerm(func=mdp.ee_frame_pose_in_base_frame, params={"return_key": "pos"}) + eef_quat = ObsTerm(func=mdp.ee_frame_pose_in_base_frame, params={"return_key": "quat"}) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp = ObsTerm( + func=place_mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("toy_truck"), + "diff_threshold": 0.05, + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + toy_truck_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.85, "asset_cfg": SceneEntityCfg("toy_truck")} + ) + + success = DoneTerm( + func=place_mdp.object_a_is_into_b, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "object_a_cfg": SceneEntityCfg("toy_truck"), + "object_b_cfg": SceneEntityCfg("box"), + "xy_threshold": 0.10, + "height_diff": 0.06, + "height_threshold": 0.04, + }, + ) + + +@configclass +class PlaceToy2BoxEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the stacking environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=3.0, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + commands = None + rewards = None + events = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + + self.sim.render_interval = self.decimation + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 + + # set viewer to see the whole scene + self.viewer.eye = [1.5, -1.0, 1.5] + self.viewer.lookat = [0.5, 0.0, 0.0] + + +""" +Env to Replay Sim2Lab Demonstrations with JointSpaceAction +""" + + +class RmpFlowAgibotPlaceToy2BoxEnvCfg(PlaceToy2BoxEnvCfg): + """Configuration for the Agibot Place Toy2Box RMP Rel Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.events = EventCfgPlaceToy2Box() + + # Set Agibot as robot + self.scene.robot = AGIBOT_A2D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # add table + self.scene.table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0.0, -0.7], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", + scale=(1.8, 1.0, 0.30), + ), + ) + + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Agibot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["right_arm_joint.*"], + body_name="right_gripper_center", + controller=AGIBOT_RIGHT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + + # Enable Parallel Gripper: + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["right_hand_joint1", "right_.*_Support_Joint"], + open_command_expr={"right_hand_joint1": 0.994, "right_.*_Support_Joint": 0.994}, + close_command_expr={"right_hand_joint1": 0.20, "right_.*_Support_Joint": 0.20}, + ) + + # find joint ids for grippers + self.gripper_joint_names = ["right_hand_joint1", "right_Right_1_Joint"] + self.gripper_open_val = 0.994 + self.gripper_threshold = 0.2 + + # Rigid body properties of toy_truck and box + toy_truck_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + box_properties = toy_truck_properties.copy() + + # Notes: remember to add Physics/Mass properties to the toy_truck mesh to make grasping successful, + # then you can use below MassPropertiesCfg to set the mass of the toy_truck + toy_mass_properties = MassPropertiesCfg( + mass=0.05, + ) + + self.scene.toy_truck = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/ToyTruck", + init_state=RigidObjectCfg.InitialStateCfg(), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/ToyTruck/toy_truck.usd", + rigid_props=toy_truck_properties, + mass_props=toy_mass_properties, + ), + ) + + self.scene.box = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Box", + init_state=RigidObjectCfg.InitialStateCfg(), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/Box/box.usd", + rigid_props=box_properties, + ), + ) + + # Listens to the required transforms + self.marker_cfg = FRAME_MARKER_CFG.copy() + self.marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self.marker_cfg.prim_path = "/Visuals/FrameTransformer" + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_gripper_center", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + ), + ), + ], + ) + + # add contact force sensor for grasped checking + self.scene.contact_grasp = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_.*_Pad_Link", + update_period=0.05, + history_length=6, + debug_vis=True, + filter_prim_paths_expr=["{ENV_REGEX_NS}/ToyTruck"], + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + # Set the simulation parameters + self.sim.dt = 1 / 60 + self.sim.render_interval = 6 + + self.decimation = 3 + self.episode_length_s = 30.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_upright_mug_rmp_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_upright_mug_rmp_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..14841036d66eb3a4fd06e28e1557ef45bc808d44 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/config/agibot/place_upright_mug_rmp_rel_env_cfg.py @@ -0,0 +1,283 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +from dataclasses import MISSING + +from isaaclab.assets import AssetBaseCfg, RigidObjectCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs.mdp.actions.rmpflow_actions_cfg import RMPFlowActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.sensors import ContactSensorCfg, FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.place import mdp as place_mdp +from isaaclab_tasks.manager_based.manipulation.place.config.agibot import place_toy2box_rmp_rel_env_cfg +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.agibot import AGIBOT_A2D_CFG # isort: skip +from isaaclab.controllers.config.rmp_flow import AGIBOT_LEFT_ARM_RMPFLOW_CFG # isort: skip + +## +# Event settings +## + + +@configclass +class EventCfgPlaceUprightMug: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset", params={"reset_joint_targets": True}) + + randomize_mug_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": { + "x": (-0.05, 0.2), + "y": (-0.10, 0.10), + "z": (0.75, 0.75), + "roll": (-1.57, -1.57), + "yaw": (-0.57, 0.57), + }, + "asset_cfgs": [SceneEntityCfg("mug")], + }, + ) + + +# +# MDP settings +## + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + mug_positions = ObsTerm( + func=place_mdp.object_poses_in_base_frame, params={"object_cfg": SceneEntityCfg("mug"), "return_key": "pos"} + ) + mug_orientations = ObsTerm( + func=place_mdp.object_poses_in_base_frame, + params={"object_cfg": SceneEntityCfg("mug"), "return_key": "quat"}, + ) + eef_pos = ObsTerm(func=mdp.ee_frame_pose_in_base_frame, params={"return_key": "pos"}) + eef_quat = ObsTerm(func=mdp.ee_frame_pose_in_base_frame, params={"return_key": "quat"}) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp = ObsTerm( + func=place_mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("mug"), + "diff_threshold": 0.05, + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + mug_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.85, "asset_cfg": SceneEntityCfg("mug")} + ) + + success = DoneTerm( + func=place_mdp.object_placed_upright, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "object_cfg": SceneEntityCfg("mug"), + "target_height": 0.6, + }, + ) + + +""" +Env to Place Upright Mug with AgiBot Left Arm using RMPFlow +""" + + +class RmpFlowAgibotPlaceUprightMugEnvCfg(place_toy2box_rmp_rel_env_cfg.PlaceToy2BoxEnvCfg): + """Configuration for the Agibot Place Upright Mug RMP Rel Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.events = EventCfgPlaceUprightMug() + + # Set Agibot as robot + self.scene.robot = AGIBOT_A2D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.init_state.pos = (-0.60, 0.0, 0.0) + + # reset obs and termination terms + self.observations = ObservationsCfg() + self.terminations = TerminationsCfg() + + # Table + self.scene.table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.50, 0.0, 0.60], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", + scale=(1.0, 1.0, 0.60), + ), + ) + + # add contact force sensor for grasped checking + self.scene.contact_grasp = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_.*_Pad_Link", + update_period=0.0, + history_length=6, + debug_vis=True, + filter_prim_paths_expr=["{ENV_REGEX_NS}/Mug"], + ) + + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Agibot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["left_arm_joint.*"], + body_name="gripper_center", + controller=AGIBOT_LEFT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0], rot=[0.7071, 0.0, -0.7071, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + + # Enable Parallel Gripper: + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["left_hand_joint1", "left_.*_Support_Joint"], + open_command_expr={"left_hand_joint1": 0.994, "left_.*_Support_Joint": 0.994}, + close_command_expr={"left_hand_joint1": 0.0, "left_.*_Support_Joint": 0.0}, + ) + + # find joint ids for grippers + self.gripper_joint_names = ["left_hand_joint1", "left_Right_1_Joint"] + self.gripper_open_val = 0.994 + self.gripper_threshold = 0.2 + + # Rigid body properties of mug + mug_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + self.scene.mug = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Mug", + init_state=RigidObjectCfg.InitialStateCfg(), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/Mug/mug.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=mug_properties, + ), + ) + + # Listens to the required transforms + self.marker_cfg = FRAME_MARKER_CFG.copy() + self.marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self.marker_cfg.prim_path = "/Visuals/FrameTransformer" + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/gripper_center", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + rot=[ + 0.7071, + 0.0, + -0.7071, + 0.0, + ], + ), + ), + ], + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + # Set the simulation parameters + self.sim.dt = 1 / 60 + self.sim.render_interval = 6 + + self.decimation = 3 + self.episode_length_s = 10.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..41f76fcdb1bb7353bf142a9ad0b112aeaf4f740f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the pick and place environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..b0a9107beca14ffe93c5264cbb54ea3a525dc628 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/observations.py @@ -0,0 +1,119 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_poses_in_base_frame( + env: ManagerBasedRLEnv, + object_cfg: SceneEntityCfg = SceneEntityCfg("mug"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + return_key: Literal["pos", "quat", None] = None, +) -> torch.Tensor: + """The pose of the object in the robot base frame.""" + object: RigidObject = env.scene[object_cfg.name] + + pos_object_world = object.data.root_pos_w + quat_object_world = object.data.root_quat_w + + """The position of the robot in the world frame.""" + robot: Articulation = env.scene[robot_cfg.name] + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + + pos_object_base, quat_object_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_object_world, quat_object_world + ) + if return_key == "pos": + return pos_object_base + elif return_key == "quat": + return quat_object_base + else: + return torch.cat((pos_object_base, quat_object_base), dim=1) + + +def object_grasped( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + ee_frame_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + diff_threshold: float = 0.06, + force_threshold: float = 1.0, +) -> torch.Tensor: + """ + Check if an object is grasped by the specified robot. + Support both surface gripper and parallel gripper. + If contact_grasp sensor is found, check if the contact force is greater than force_threshold. + """ + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + if "contact_grasp" in env.scene.keys() and env.scene["contact_grasp"] is not None: + contact_force_grasp = env.scene["contact_grasp"].data.net_forces_w # shape:(N, 2, 3) for two fingers + contact_force_norm = torch.linalg.vector_norm( + contact_force_grasp, dim=2 + ) # shape:(N, 2) - force magnitude per finger + both_fingers_force_ok = torch.all( + contact_force_norm > force_threshold, dim=1 + ) # both fingers must exceed threshold + grasped = torch.logical_and(pose_diff < diff_threshold, both_fingers_force_ok) + elif ( + f"contact_grasp_{object_cfg.name}" in env.scene.keys() + and env.scene[f"contact_grasp_{object_cfg.name}"] is not None + ): + contact_force_object = env.scene[ + f"contact_grasp_{object_cfg.name}" + ].data.net_forces_w # shape:(N, 2, 3) for two fingers + contact_force_norm = torch.linalg.vector_norm( + contact_force_object, dim=2 + ) # shape:(N, 2) - force magnitude per finger + both_fingers_force_ok = torch.all( + contact_force_norm > force_threshold, dim=1 + ) # both fingers must exceed threshold + grasped = torch.logical_and(pose_diff < diff_threshold, both_fingers_force_ok) + else: + grasped = (pose_diff < diff_threshold).clone().detach() + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_closed = (suction_cup_status == 1).to(torch.float32) + grasped = torch.logical_and(suction_cup_is_closed, pose_diff < diff_threshold) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + grasped = torch.logical_and( + grasped, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[0]]) - env.cfg.gripper_open_val) + > env.cfg.gripper_threshold, + ) + grasped = torch.logical_and( + grasped, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[1]]) - env.cfg.gripper_open_val) + > env.cfg.gripper_threshold, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return grasped diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..cf7248d2934866e2e46f16f37338b777a6e61a5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/place/mdp/terminations.py @@ -0,0 +1,123 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the place task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_placed_upright( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + target_height: float = 0.927, + euler_xy_threshold: float = 0.10, +): + """Check if an object placed upright by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + # Compute mug euler angles of X, Y axis, to check if it is placed upright + object_euler_x, object_euler_y, _ = math_utils.euler_xyz_from_quat(object.data.root_quat_w) # (N,4) [0, 2*pi] + + object_euler_x_err = torch.abs(math_utils.wrap_to_pi(object_euler_x)) # (N,) + object_euler_y_err = torch.abs(math_utils.wrap_to_pi(object_euler_y)) # (N,) + + success = torch.logical_and(object_euler_x_err < euler_xy_threshold, object_euler_y_err < euler_xy_threshold) + + # Check if current mug height is greater than target height + height_success = object.data.root_pos_w[:, 2] > target_height + + success = torch.logical_and(height_success, success) + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + success = torch.logical_and(suction_cup_is_open, success) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + success = torch.logical_and( + success, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[0]]) - env.cfg.gripper_open_val) + < env.cfg.gripper_threshold, + ) + success = torch.logical_and( + success, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[1]]) - env.cfg.gripper_open_val) + < env.cfg.gripper_threshold, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return success + + +def object_a_is_into_b( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_a_cfg: SceneEntityCfg = SceneEntityCfg("object_a"), + object_b_cfg: SceneEntityCfg = SceneEntityCfg("object_b"), + xy_threshold: float = 0.03, # xy_distance_threshold + height_threshold: float = 0.04, # height_distance_threshold + height_diff: float = 0.0, # expected height_diff +) -> torch.Tensor: + """Check if an object a is put into another object b by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + object_a: RigidObject = env.scene[object_a_cfg.name] + object_b: RigidObject = env.scene[object_b_cfg.name] + + # check object a is into object b + pos_diff = object_a.data.root_pos_w - object_b.data.root_pos_w + height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) + xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + + success = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) + + # Check gripper positions + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + success = torch.logical_and(suction_cup_is_open, success) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Terminations only support parallel gripper for now" + + success = torch.logical_and( + success, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[0]]) - env.cfg.gripper_open_val) + < env.cfg.gripper_threshold, + ) + success = torch.logical_and( + success, + torch.abs(torch.abs(robot.data.joint_pos[:, gripper_joint_ids[1]]) - env.cfg.gripper_open_val) + < env.cfg.gripper_threshold, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return success diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..be11b529e2c2faf3d769f156af87a1fbb8484670 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Fixed-arm environments with end-effector pose tracking commands.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..acf853fcf647259a5addc376102671e03fbd75c6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for arm-based reach-tracking environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..47158f64a650532ecda3dc5668eff1c388bee492 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/__init__.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Reach-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaReachEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Reach-Franka-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaReachEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + + +## +# Inverse Kinematics - Absolute Pose Control +## + +gym.register( + id="Isaac-Reach-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaReachEnvCfg", + }, + disable_env_checker=True, +) + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Reach-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_rel_env_cfg:FrankaReachEnvCfg", + }, + disable_env_checker=True, +) + +## +# Operational Space Control +## + +gym.register( + id="Isaac-Reach-Franka-OSC-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.osc_env_cfg:FrankaReachEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaReachPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Reach-Franka-OSC-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.osc_env_cfg:FrankaReachEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaReachPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..09e4f9d48b5985475120cc7af59c4a6fead6115a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [64, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: reach_franka + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 10000 + max_epochs: 1000 + save_best_after: 200 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.01 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2 + clip_value: True + clip_actions: False + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ede70559fd567cd7a24d6dc71926573a73d6d2b6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class FrankaReachPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1000 + save_interval = 50 + experiment_name = "franka_reach" + run_name = "" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[64, 64], + critic_hidden_dims=[64, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.001, + num_learning_epochs=8, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..986b35fff6b39175320970052ee068ea16c4077c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "reach_franka" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_abs_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..b090e568965e19da1e55326102bea03fac5c5911 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_abs_env_cfg.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaReachEnvCfg(joint_pos_env_cfg.FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..024a42270d8512d64128e6bf105fa349f42c919f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/ik_rel_env_cfg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaReachEnvCfg(joint_pos_env_cfg.FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..a848ddb87667d5a390e3400d2b65ae759f70fa4d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/joint_pos_env_cfg.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp +from isaaclab_tasks.manager_based.manipulation.reach.reach_env_cfg import ReachEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets import FRANKA_PANDA_CFG # isort: skip + + +## +# Environment configuration +## + + +@configclass +class FrankaReachEnvCfg(ReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to franka + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + # override rewards + self.rewards.end_effector_position_tracking.params["asset_cfg"].body_names = ["panda_hand"] + self.rewards.end_effector_position_tracking_fine_grained.params["asset_cfg"].body_names = ["panda_hand"] + self.rewards.end_effector_orientation_tracking.params["asset_cfg"].body_names = ["panda_hand"] + + # override actions + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + # override command generator body + # end-effector is along z-direction + self.commands.ee_pose.body_name = "panda_hand" + self.commands.ee_pose.ranges.pitch = (math.pi, math.pi) + + +@configclass +class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/osc_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/osc_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e612439fda70f666084a5cdacb8d2fe917405cfa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/osc_env_cfg.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.operational_space_cfg import OperationalSpaceControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import OperationalSpaceControllerActionCfg +from isaaclab.utils import configclass + +from . import joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class FrankaReachEnvCfg(joint_pos_env_cfg.FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We remove stiffness and damping for the shoulder and forearm joints for effort control + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.actuators["panda_shoulder"].stiffness = 0.0 + self.scene.robot.actuators["panda_shoulder"].damping = 0.0 + self.scene.robot.actuators["panda_forearm"].stiffness = 0.0 + self.scene.robot.actuators["panda_forearm"].damping = 0.0 + self.scene.robot.spawn.rigid_props.disable_gravity = True + + # If closed-loop contact force control is desired, contact sensors should be enabled for the robot + # self.scene.robot.spawn.activate_contact_sensors = True + + self.actions.arm_action = OperationalSpaceControllerActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + # If a task frame different from articulation root/base is desired, a RigidObject, e.g., "task_frame", + # can be added to the scene and its relative path could provided as task_frame_rel_path + # task_frame_rel_path="task_frame", + controller_cfg=OperationalSpaceControllerCfg( + target_types=["pose_abs"], + impedance_mode="variable_kp", + inertial_dynamics_decoupling=True, + partial_inertial_dynamics_decoupling=False, + gravity_compensation=False, + motion_stiffness_task=100.0, + motion_damping_ratio_task=1.0, + motion_stiffness_limits_task=(50.0, 200.0), + nullspace_control="position", + ), + nullspace_joint_pos_target="center", + position_scale=1.0, + orientation_scale=1.0, + stiffness_scale=100.0, + ) + # Removing these observations as they are not needed for OSC and we want keep the observation space small + self.observations.policy.joint_pos = None + self.observations.policy.joint_vel = None + + +@configclass +class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 16 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd284b1838e652fa2090697a2c3a1409ace61c1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/__init__.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Reach-OpenArm-Bi-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmReachEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmReachPPORunnerCfg", + }, +) + +gym.register( + id="Isaac-Reach-OpenArm-Bi-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmReachEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmReachPPORunnerCfg", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..71526744500eff5712d919f5ffef67947775c1fb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [64, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: openarm_bi_reach + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 10000 + max_epochs: 1000 + save_best_after: 200 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.01 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2 + clip_value: True + clip_actions: False + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..d1dd736a2ed74e8e66359887c185e655e929d3d6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class OpenArmReachPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 550 + save_interval = 50 + experiment_name = "openarm_bi_reach" + run_name = "" + resume = False + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[64, 64], + critic_hidden_dims=[64, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.001, + num_learning_epochs=8, + num_mini_batches=4, + learning_rate=1.0e-2, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6b17b4174cb009f988a0d16f9f00823b1a1e5477 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/joint_pos_env_cfg.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +## +# Pre-defined configs +## + +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp +from isaaclab_tasks.manager_based.manipulation.reach.config.openarm.bimanual.reach_openarm_bi_env_cfg import ReachEnvCfg + +from isaaclab_assets.robots.openarm import OPENARM_BI_HIGH_PD_CFG + +## +# Environment configuration +## + + +@configclass +class OpenArmReachEnvCfg(ReachEnvCfg): + """Configuration for the Bimanual OpenArm Reach Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to OpenArm + self.scene.robot = OPENARM_BI_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # override rewards + self.rewards.left_end_effector_position_tracking.params["asset_cfg"].body_names = ["openarm_left_hand"] + self.rewards.left_end_effector_position_tracking_fine_grained.params["asset_cfg"].body_names = [ + "openarm_left_hand" + ] + self.rewards.left_end_effector_orientation_tracking.params["asset_cfg"].body_names = ["openarm_left_hand"] + + self.rewards.right_end_effector_position_tracking.params["asset_cfg"].body_names = ["openarm_right_hand"] + self.rewards.right_end_effector_position_tracking_fine_grained.params["asset_cfg"].body_names = [ + "openarm_right_hand" + ] + self.rewards.right_end_effector_orientation_tracking.params["asset_cfg"].body_names = ["openarm_right_hand"] + + # override actions + self.actions.left_arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=[ + "openarm_left_joint.*", + ], + scale=0.5, + use_default_offset=True, + ) + + self.actions.right_arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=[ + "openarm_right_joint.*", + ], + scale=0.5, + use_default_offset=True, + ) + + # override command generator body + # end-effector is along z-direction + self.commands.left_ee_pose.body_name = "openarm_left_hand" + self.commands.right_ee_pose.body_name = "openarm_right_hand" + + +@configclass +class OpenArmReachEnvCfg_PLAY(OpenArmReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/reach_openarm_bi_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/reach_openarm_bi_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..7ccdfa0f851ba2b584a1994279dd18ce4096ea15 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/bimanual/reach_openarm_bi_env_cfg.py @@ -0,0 +1,335 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +We modified parts of the environment—such as the target’s position and orientation—to better suit the smaller robot. +""" + +import math +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg as ActionTerm +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp + +## +# Scene definition +## + + +@configclass +class ReachSceneCfg(InteractiveSceneCfg): + """Configuration for the scene with a robotic arm.""" + + # world + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, 0)), + ) + + # robots + robot: ArticulationCfg = MISSING + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + left_ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, + resampling_time_range=(4.0, 4.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.15, 0.3), + pos_y=(0.15, 0.25), + pos_z=(0.3, 0.5), + roll=(-math.pi / 6, math.pi / 6), + pitch=(3 * math.pi / 2, 3 * math.pi / 2), + yaw=(8 * math.pi / 9, 10 * math.pi / 9), + ), + ) + + right_ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, + resampling_time_range=(4.0, 4.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.15, 0.3), + pos_y=(-0.25, -0.15), + pos_z=(0.3, 0.5), + roll=(-math.pi / 6, math.pi / 6), + pitch=(3 * math.pi / 2, 3 * math.pi / 2), + yaw=(8 * math.pi / 9, 10 * math.pi / 9), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + left_arm_action: ActionTerm = MISSING + right_arm_action: ActionTerm = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + left_joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_left_joint.*", + ], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + + right_joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_right_joint.*", + ], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + + left_joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_left_joint.*", + ], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + right_joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_right_joint.*", + ], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + left_pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "left_ee_pose"}) + right_pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "right_ee_pose"}) + left_actions = ObsTerm(func=mdp.last_action, params={"action_name": "left_arm_action"}) + right_actions = ObsTerm(func=mdp.last_action, params={"action_name": "right_arm_action"}) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_scale, + mode="reset", + params={ + "position_range": (0.5, 1.5), + "velocity_range": (0.0, 0.0), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # task terms + left_end_effector_position_tracking = RewTerm( + func=mdp.position_command_error, + weight=-0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "left_ee_pose", + }, + ) + + right_end_effector_position_tracking = RewTerm( + func=mdp.position_command_error, + weight=-0.25, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "right_ee_pose", + }, + ) + + left_end_effector_position_tracking_fine_grained = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "std": 0.1, + "command_name": "left_ee_pose", + }, + ) + + right_end_effector_position_tracking_fine_grained = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "std": 0.1, + "command_name": "right_ee_pose", + }, + ) + + left_end_effector_orientation_tracking = RewTerm( + func=mdp.orientation_command_error, + weight=-0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "left_ee_pose", + }, + ) + + right_end_effector_orientation_tracking = RewTerm( + func=mdp.orientation_command_error, + weight=-0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "right_ee_pose", + }, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.0001) + left_joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-0.0001, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_left_joint.*", + ], + ) + }, + ) + right_joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-0.0001, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=[ + "openarm_right_joint.*", + ], + ) + }, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "action_rate", "weight": -0.005, "num_steps": 4500}, + ) + + left_joint_vel = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "left_joint_vel", "weight": -0.001, "num_steps": 4500}, + ) + + right_joint_vel = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "right_joint_vel", "weight": -0.001, "num_steps": 4500}, + ) + + +## +# Environment configuration +## + + +@configclass +class ReachEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the reach end-effector pose tracking environment.""" + + # Scene settings + scene: ReachSceneCfg = ReachSceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.sim.render_interval = self.decimation + self.episode_length_s = 24.0 + self.viewer.eye = (3.5, 3.5, 3.5) + # simulation settings + self.sim.dt = 1.0 / 60.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d0003b6080944404b0018e2956e27d4670653ca7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/__init__.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Reach-OpenArm-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmReachEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Reach-OpenArm-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:OpenArmReachEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:OpenArmReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..29349e1e389f3e3811c9ac4b8e55ed8a82988d41 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,84 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [64, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: openarm_reach + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 10000 + max_epochs: 1000 + save_best_after: 200 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.01 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2 + clip_value: True + clip_actions: False + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4d43c35741954352fadc035a5ea047b84d921208 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class OpenArmReachPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1000 + save_interval = 50 + experiment_name = "openarm_reach" + run_name = "" + resume = False + empirical_normalization = True + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[64, 64], + critic_hidden_dims=[64, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.001, + num_learning_epochs=8, + num_mini_batches=4, + learning_rate=1.0e-2, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93bd0e506bb452adf0960c3bc2ef6036235cc9cd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: STATES + layers: [64, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: STATES + layers: [64, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "openarm_reach" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2bfd6e326a5aa05828136c92cfd14df2a548b687 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/joint_pos_env_cfg.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets.articulation import ArticulationCfg +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp +from isaaclab_tasks.manager_based.manipulation.reach.config.openarm.unimanual.reach_openarm_uni_env_cfg import ( + ReachEnvCfg, +) + +## +# Pre-defined configs +## +from isaaclab_assets.robots.openarm import OPENARM_UNI_CFG + +## +# Environment configuration +## + + +@configclass +class OpenArmReachEnvCfg(ReachEnvCfg): + """Configuration for the single-arm OpenArm Reach Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to OpenArm + self.scene.robot = OPENARM_UNI_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "openarm_joint1": 1.57, + "openarm_joint2": 0.0, + "openarm_joint3": -1.57, + "openarm_joint4": 1.57, + "openarm_joint5": 0.0, + "openarm_joint6": 0.0, + "openarm_joint7": 0.0, + "openarm_finger_joint.*": 0.0, + }, # Close the gripper + ), + ) + + # override rewards + self.rewards.end_effector_position_tracking.params["asset_cfg"].body_names = ["openarm_hand"] + self.rewards.end_effector_position_tracking_fine_grained.params["asset_cfg"].body_names = ["openarm_hand"] + self.rewards.end_effector_orientation_tracking.params["asset_cfg"].body_names = ["openarm_hand"] + + # override actions + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=[ + "openarm_joint.*", + ], + scale=0.5, + use_default_offset=True, + ) + + # override command generator body + # end-effector is along z-direction + self.commands.ee_pose.body_name = "openarm_hand" + + +@configclass +class OpenArmReachEnvCfg_PLAY(OpenArmReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/reach_openarm_uni_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/reach_openarm_uni_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..ed9bcbfc08be7d314b23219a9daa6f383606bde3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/openarm/unimanual/reach_openarm_uni_env_cfg.py @@ -0,0 +1,248 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +We modified parts of the environment—such as the target’s position and orientation—to better suit the smaller robot. +""" + +import math +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg as ActionTerm +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp + +## +# Scene definition +## + + +@configclass +class ReachSceneCfg(InteractiveSceneCfg): + """Configuration for the scene with a robotic arm.""" + + # world + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), + ) + + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", + ), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.55, 0.0, 0.0), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + # robots + robot: ArticulationCfg = MISSING + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, + resampling_time_range=(4.0, 4.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.25, 0.35), + pos_y=(-0.2, 0.2), + pos_z=(0.3, 0.4), + roll=(-math.pi / 6, math.pi / 6), + pitch=(math.pi / 2, math.pi / 2), + yaw=(-math.pi / 9, math.pi / 9), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: ActionTerm = MISSING + gripper_action: ActionTerm | None = None + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos = ObsTerm( + func=mdp.joint_pos_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=["openarm_joint.*"], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + joint_vel = ObsTerm( + func=mdp.joint_vel_rel, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=["openarm_joint.*"], + ) + }, + noise=Unoise(n_min=-0.01, n_max=0.01), + ) + pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "ee_pose"}) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_scale, + mode="reset", + params={ + "position_range": (0.5, 1.5), + "velocity_range": (0.0, 0.0), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # task terms + end_effector_position_tracking = RewTerm( + func=mdp.position_command_error, + weight=-0.2, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "ee_pose", + }, + ) + end_effector_position_tracking_fine_grained = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "std": 0.1, + "command_name": "ee_pose", + }, + ) + end_effector_orientation_tracking = RewTerm( + func=mdp.orientation_command_error, + weight=-0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", body_names=MISSING), + "command_name": "ee_pose", + }, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.0001) + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-0.0001, + params={ + "asset_cfg": SceneEntityCfg( + "robot", + joint_names=["openarm_joint.*"], + ) + }, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "action_rate", "weight": -0.005, "num_steps": 4500}, + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, + params={"term_name": "joint_vel", "weight": -0.001, "num_steps": 4500}, + ) + + +## +# Environment configuration +## + + +@configclass +class ReachEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the reach end-effector pose tracking environment.""" + + # Scene settings + scene: ReachSceneCfg = ReachSceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.sim.render_interval = self.decimation + self.episode_length_s = 12.0 + self.viewer.eye = (3.5, 3.5, 3.5) + # simulation settings + self.sim.dt = 1.0 / 60.0 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fafe5b75820ceb7d1502392bbab6284cf72c4c7e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Reach-UR10-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10ReachEnvCfg", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10ReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Reach-UR10-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:UR10ReachEnvCfg_PLAY", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10ReachPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..06a3c70554e38650dcfda47cb2b67c8b93ef5133 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,83 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [64, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: reach_ur10 + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: -1 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-3 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 10000 + max_epochs: 1000 + save_best_after: 200 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.01 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 5 + critic_coef: 2 + clip_value: True + clip_actions: False + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c445786c44c788876cc27ea94e19407e0e27b9fa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class UR10ReachPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1000 + save_interval = 50 + experiment_name = "reach_ur10" + run_name = "" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[64, 64], + critic_hidden_dims=[64, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.01, + num_learning_epochs=8, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/skrl_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..327a9b9ca80effe098d1ae90225cb0f6763e7046 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [64, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.01 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "reach_ur10" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 24000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..6ddf935768b83fd0ef716fcb17eb7c7ab249b0a8 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/ur_10/joint_pos_env_cfg.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.utils import configclass + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp +from isaaclab_tasks.manager_based.manipulation.reach.reach_env_cfg import ReachEnvCfg + +## +# Pre-defined configs +## +from isaaclab_assets import UR10_CFG # isort: skip + + +## +# Environment configuration +## + + +@configclass +class UR10ReachEnvCfg(ReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # switch robot to ur10 + self.scene.robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + # override events + self.events.reset_robot_joints.params["position_range"] = (0.75, 1.25) + # override rewards + self.rewards.end_effector_position_tracking.params["asset_cfg"].body_names = ["ee_link"] + self.rewards.end_effector_position_tracking_fine_grained.params["asset_cfg"].body_names = ["ee_link"] + self.rewards.end_effector_orientation_tracking.params["asset_cfg"].body_names = ["ee_link"] + # override actions + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True + ) + # override command generator body + # end-effector is along x-direction + self.commands.ee_pose.body_name = "ee_link" + self.commands.ee_pose.ranges.pitch = (math.pi / 2, math.pi / 2) + + +@configclass +class UR10ReachEnvCfg_PLAY(UR10ReachEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3fec83fe70afbdb1d7a0838eca175803996d7b63 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the locomotion environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..76f2fe36db41dd570bb41831e9134bed768e17ec --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/mdp/rewards.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import combine_frame_transforms, quat_error_magnitude, quat_mul + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def position_command_error(env: ManagerBasedRLEnv, command_name: str, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize tracking of the position error using L2-norm. + + The function computes the position error between the desired position (from the command) and the + current position of the asset's body (in world frame). The position error is computed as the L2-norm + of the difference between the desired and current positions. + """ + # extract the asset (to enable type hinting) + asset: RigidObject = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + # obtain the desired and current positions + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(asset.data.root_pos_w, asset.data.root_quat_w, des_pos_b) + curr_pos_w = asset.data.body_pos_w[:, asset_cfg.body_ids[0]] # type: ignore + return torch.norm(curr_pos_w - des_pos_w, dim=1) + + +def position_command_error_tanh( + env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg +) -> torch.Tensor: + """Reward tracking of the position using the tanh kernel. + + The function computes the position error between the desired position (from the command) and the + current position of the asset's body (in world frame) and maps it with a tanh kernel. + """ + # extract the asset (to enable type hinting) + asset: RigidObject = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + # obtain the desired and current positions + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(asset.data.root_pos_w, asset.data.root_quat_w, des_pos_b) + curr_pos_w = asset.data.body_pos_w[:, asset_cfg.body_ids[0]] # type: ignore + distance = torch.norm(curr_pos_w - des_pos_w, dim=1) + return 1 - torch.tanh(distance / std) + + +def orientation_command_error(env: ManagerBasedRLEnv, command_name: str, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize tracking orientation error using shortest path. + + The function computes the orientation error between the desired orientation (from the command) and the + current orientation of the asset's body (in world frame). The orientation error is computed as the shortest + path between the desired and current orientations. + """ + # extract the asset (to enable type hinting) + asset: RigidObject = env.scene[asset_cfg.name] + command = env.command_manager.get_command(command_name) + # obtain the desired and current orientations + des_quat_b = command[:, 3:7] + des_quat_w = quat_mul(asset.data.root_quat_w, des_quat_b) + curr_quat_w = asset.data.body_quat_w[:, asset_cfg.body_ids[0]] # type: ignore + return quat_error_magnitude(curr_quat_w, des_quat_w) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/reach_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/reach_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..bad88b401c7cd04d6f6b15b7129b56e84609ef1f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/reach_env_cfg.py @@ -0,0 +1,229 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.gamepad import Se3GamepadCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ActionTermCfg as ActionTerm +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise + +import isaaclab_tasks.manager_based.manipulation.reach.mdp as mdp + +## +# Scene definition +## + + +@configclass +class ReachSceneCfg(InteractiveSceneCfg): + """Configuration for the scene with a robotic arm.""" + + # world + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), + ) + + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", + ), + init_state=AssetBaseCfg.InitialStateCfg(pos=(0.55, 0.0, 0.0), rot=(0.70711, 0.0, 0.0, 0.70711)), + ) + + # robots + robot: ArticulationCfg = MISSING + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), + ) + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + ee_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, + resampling_time_range=(4.0, 4.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + pos_x=(0.35, 0.65), + pos_y=(-0.2, 0.2), + pos_z=(0.15, 0.5), + roll=(0.0, 0.0), + pitch=MISSING, # depends on end-effector axis + yaw=(-3.14, 3.14), + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + arm_action: ActionTerm = MISSING + gripper_action: ActionTerm | None = None + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) + pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "ee_pose"}) + actions = ObsTerm(func=mdp.last_action) + + def __post_init__(self): + self.enable_corruption = True + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_robot_joints = EventTerm( + func=mdp.reset_joints_by_scale, + mode="reset", + params={ + "position_range": (0.5, 1.5), + "velocity_range": (0.0, 0.0), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # task terms + end_effector_position_tracking = RewTerm( + func=mdp.position_command_error, + weight=-0.2, + params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"}, + ) + end_effector_position_tracking_fine_grained = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.1, + params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "std": 0.1, "command_name": "ee_pose"}, + ) + end_effector_orientation_tracking = RewTerm( + func=mdp.orientation_command_error, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"}, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.0001) + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-0.0001, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -0.005, "num_steps": 4500} + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "joint_vel", "weight": -0.001, "num_steps": 4500} + ) + + +## +# Environment configuration +## + + +@configclass +class ReachEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the reach end-effector pose tracking environment.""" + + # Scene settings + scene: ReachSceneCfg = ReachSceneCfg(num_envs=4096, env_spacing=2.5) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 2 + self.sim.render_interval = self.decimation + self.episode_length_s = 12.0 + self.viewer.eye = (3.5, 3.5, 3.5) + # simulation settings + self.sim.dt = 1.0 / 60.0 + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + gripper_term=False, + sim_device=self.sim.device, + ), + "gamepad": Se3GamepadCfg( + gripper_term=False, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + gripper_term=False, + sim_device=self.sim.device, + ), + }, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9e0ebd8436530ac9fd0cb2dfc4e9dff629636593 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object stack environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9e0ebd8436530ac9fd0cb2dfc4e9dff629636593 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object stack environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0c93d83ff1f1bf4dfc5850d861af2eb77f8f78e3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/__init__.py @@ -0,0 +1,122 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Stack-Cube-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_joint_pos_env_cfg:FrankaCubeStackEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Instance-Randomize-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.stack_joint_pos_instance_randomize_env_cfg:FrankaCubeStackInstanceRandomizeEnvCfg" + ), + }, + disable_env_checker=True, +) + + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg:FrankaCubeStackEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_visuomotor_env_cfg:FrankaCubeStackVisuomotorEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_200.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.stack_ik_rel_visuomotor_cosmos_env_cfg:FrankaCubeStackVisuomotorCosmosEnvCfg" + ), + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_image_cosmos.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_abs_env_cfg:FrankaCubeStackEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.stack_ik_rel_instance_randomize_env_cfg:FrankaCubeStackInstanceRandomizeEnvCfg" + ), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_blueprint_env_cfg:FrankaCubeStackBlueprintEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg_skillgen:FrankaCubeStackSkillgenEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.bin_stack_ik_rel_env_cfg:FrankaBinStackEnvCfg", + "robomimic_bc_cfg_entry_point": f"{agents.__name__}:robomimic/bc_rnn_low_dim.json", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_200.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_200.json new file mode 100644 index 0000000000000000000000000000000000000000..33117b90e3f30a502f29eea388057149b3bcd1a9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_200.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_franka_stack", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos" + ], + "rgb": [ + "table_cam", + "wrist_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 181, + "crop_width": 181, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_cosmos.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_cosmos.json new file mode 100644 index 0000000000000000000000000000000000000000..5f68551765b9f4f01387b3a3d29f8e5f124f275b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_image_cosmos.json @@ -0,0 +1,218 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_franka_stack_cosmos", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos" + ], + "rgb": [ + "table_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 180, + "crop_width": 180, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_low_dim.json new file mode 100644 index 0000000000000000000000000000000000000000..23e490971f39cdd324db2c5fd5b89b8b966a0784 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,101 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_franka_stack", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..91ddcbb851cc20e389f923b1a840bf754675f908 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_ik_rel_env_cfg.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import bin_stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaBinStackEnvCfg(bin_stack_joint_pos_env_cfg.FrankaBinStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4121c1bb2e20a6b2dea3a7b9090dcde6763bfb72 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/bin_stack_joint_pos_env_cfg.py @@ -0,0 +1,207 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObjectCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_env_cfg import StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + # mode="startup", + mode="reset", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + # Reset blue bin position + reset_blue_bin_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + # Keep bin at fixed position - no randomization + "pose_range": {"x": (0.4, 0.4), "y": (0.0, 0.0), "z": (0.0203, 0.0203), "yaw": (0.0, 0.0)}, + "min_separation": 0.0, + "asset_cfgs": [SceneEntityCfg("blue_sorting_bin")], + }, + ) + + # Reset cube 1 to initial position (inside the bin) + reset_cube_1_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.4), "y": (0.0, 0.0), "z": (0.0203, 0.0203), "yaw": (0.0, 0.0)}, + "min_separation": 0.0, + "asset_cfgs": [SceneEntityCfg("cube_1")], + }, + ) + + # Reset cube 2 and 3 to initial position (outside the bin, to the left and right) + reset_cube_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.65, 0.70), "y": (-0.18, 0.18), "z": (0.0203, 0.0203), "yaw": (-1.0, 1.0, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class FrankaBinStackEnvCfg(StackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Add semantics to table + self.scene.table.spawn.semantic_tags = [("class", "table")] + + # Add semantics to ground + self.scene.plane.semantic_tags = [("class", "ground")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=40, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Blue sorting bin positioned at table center + self.scene.blue_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.4, 0.0, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bin_blue.usd", + scale=(1.1, 1.6, 3.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Cube 1 positioned at the bottom center of the blue bin + # The bin is at (0.4, 0.0, 0.0203), so cube_1 should be slightly above it + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.4, 0.0, 0.025), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Cube 2 positioned outside the bin (to the right) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.85, 0.25, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Cube 3 positioned outside the bin (to the left) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.85, -0.25, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.0), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_abs_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..8d9b18bcd95f81ebba54e4289b3137cfc1e67b5e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_abs_env_cfg.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DeviceBase, DevicesCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_abs_retargeter import Se3AbsRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3AbsRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_blueprint_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_blueprint_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..3586508df2dcb2215ee216c6eed7132dd8d3d7a4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_blueprint_env_cfg.py @@ -0,0 +1,274 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os + +import torch +from torchvision.utils import save_image + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs import ManagerBasedEnv +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import Camera, CameraCfg, RayCasterCamera, TiledCamera +from isaaclab.utils import configclass + +from ... import mdp +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +def image( + env: ManagerBasedEnv, + sensor_cfg: SceneEntityCfg = SceneEntityCfg("tiled_camera"), + data_type: str = "rgb", + convert_perspective_to_orthogonal: bool = False, + normalize: bool = True, + save_image_to_file: bool = False, + image_path: str = "image", +) -> torch.Tensor: + """Images of a specific datatype from the camera sensor. + + If the flag :attr:`normalize` is True, post-processing of the images are performed based on their + data-types: + + - "rgb": Scales the image to (0, 1) and subtracts with the mean of the current image batch. + - "depth" or "distance_to_camera" or "distance_to_plane": Replaces infinity values with zero. + + Args: + env: The environment the cameras are placed within. + sensor_cfg: The desired sensor to read from. Defaults to SceneEntityCfg("tiled_camera"). + data_type: The data type to pull from the desired camera. Defaults to "rgb". + convert_perspective_to_orthogonal: Whether to orthogonalize perspective depth images. + This is used only when the data type is "distance_to_camera". Defaults to False. + normalize: Whether to normalize the images. This depends on the selected data type. + Defaults to True. + + Returns: + The images produced at the last time-step + """ + # extract the used quantities (to enable type-hinting) + sensor: TiledCamera | Camera | RayCasterCamera = env.scene.sensors[sensor_cfg.name] + + # obtain the input image + images = sensor.data.output[data_type] + + # depth image conversion + if (data_type == "distance_to_camera") and convert_perspective_to_orthogonal: + images = math_utils.orthogonalize_perspective_depth(images, sensor.data.intrinsic_matrices) + + # rgb/depth image normalization + if normalize: + if data_type == "rgb": + images = images.float() / 255.0 + mean_tensor = torch.mean(images, dim=(1, 2), keepdim=True) + images -= mean_tensor + elif "distance_to" in data_type or "depth" in data_type: + images[images == float("inf")] = 0 + elif data_type == "normals": + images = (images + 1.0) * 0.5 + + if save_image_to_file: + dir_path, _ = os.path.split(image_path) + if dir_path: + os.makedirs(dir_path, exist_ok=True) + if images.dtype == torch.uint8: + images = images.float() / 255.0 + # Get total successful episodes + total_successes = 0 + if hasattr(env, "recorder_manager") and env.recorder_manager is not None: + total_successes = env.recorder_manager.exported_successful_episode_count + + for tile in range(images.shape[0]): + tile_chw = torch.swapaxes(images[tile : tile + 1].unsqueeze(1), 1, -1).squeeze(-1) + filename = ( + f"{image_path}_{data_type}_trial_{total_successes}_tile_{tile}_step_{env.common_step_counter}.png" + ) + save_image(tile_chw, filename) + + return images.clone() + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + table_cam_normals = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "normals", + "normalize": True, + "save_image_to_file": True, + "image_path": "table_cam", + }, + ) + table_cam_segmentation = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "semantic_segmentation", + "normalize": False, + "save_image_to_file": True, + "image_path": "table_cam", + }, + ) + table_high_cam_normals = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_high_cam"), + "data_type": "normals", + "normalize": True, + "save_image_to_file": True, + "image_path": "table_high_cam", + }, + ) + table_high_cam_segmentation = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_high_cam"), + "data_type": "semantic_segmentation", + "normalize": False, + "save_image_to_file": True, + "image_path": "table_high_cam", + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackBlueprintEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + MAPPING = { + "class:cube_1": (255, 36, 66, 255), + "class:cube_2": (255, 184, 48, 255), + "class:cube_3": (55, 255, 139, 255), + "class:table": (255, 237, 218, 255), + "class:ground": (100, 100, 100, 255), + "class:robot": (125, 125, 125, 255), + "class:UNLABELLED": (125, 125, 125, 255), + "class:BACKGROUND": (10, 10, 10, 255), + } + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0333, + height=704, + width=1280, + data_types=["rgb", "semantic_segmentation", "normals"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.0, 0.0, 0.33), rot=(-0.3799, 0.5963, 0.5963, -0.3799), convention="ros"), + ) + + # Set table view camera + self.scene.table_high_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_high_cam", + update_period=0.0333, + height=704, + width=1280, + data_types=["rgb", "semantic_segmentation", "normals"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1.5, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.4, 1.8, 1.2), rot=(-0.1393, 0.2025, 0.8185, -0.5192), convention="ros"), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..16eb9b9f087ecdc3de7928913be784fcdb4e4cce --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg.py @@ -0,0 +1,69 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DeviceBase, DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_rel_retargeter import Se3RelRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg_skillgen.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg_skillgen.py new file mode 100644 index 0000000000000000000000000000000000000000..d2a2bd62100ad2354dcab3a83efd257c97e00e16 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg_skillgen.py @@ -0,0 +1,167 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DeviceBase, DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_rel_retargeter import Se3RelRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +from ... import mdp +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + stack_2 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_3"), + "lower_object_cfg": SceneEntityCfg("cube_2"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackSkillgenEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Override observations with SkillGen-specific config + self.observations = ObservationsCfg() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + # Apply skillgen-specific cube position randomization + self.events.randomize_cube_positions.params["pose_range"] = { + "x": (0.45, 0.6), + "y": (-0.23, 0.23), + "z": (0.0203, 0.0203), + "yaw": (-1.0, 1, 0), + } + + # Set the offset for the end effector to be 0.0 + for f in self.scene.ee_frame.target_frames: + if f.name == "end_effector": + f.offset.pos = [0.0, 0.0, 0.0] + break diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_instance_randomize_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4f31184585ddb8fe7a17a2e61bfe762230bf3c82 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_instance_randomize_env_cfg.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_instance_randomize_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackInstanceRandomizeEnvCfg( + stack_joint_pos_instance_randomize_env_cfg.FrankaCubeStackInstanceRandomizeEnvCfg +): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Reduce the number of environments due to camera resources + self.scene.num_envs = 2 + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..2cdd8ef39ee42b746f92f2f57faac56c90026e3b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py @@ -0,0 +1,160 @@ +# Copyright (c) 2025-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import CameraCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack import mdp + +from . import stack_ik_rel_visuomotor_env_cfg + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + table_cam_segmentation = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "semantic_segmentation", "normalize": True}, + ) + table_cam_normals = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "normals", "normalize": True}, + ) + table_cam_depth = ObsTerm( + func=mdp.image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "distance_to_image_plane", + "normalize": True, + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackVisuomotorCosmosEnvCfg(stack_ik_rel_visuomotor_env_cfg.FrankaCubeStackVisuomotorEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # set domeLight.upperLowerStrategy to 4 to remove rendering noise + self.sim.render.dome_light_upper_lower_strategy = 4 + + SEMANTIC_MAPPING = { + "class:cube_1": (120, 230, 255, 255), + "class:cube_2": (255, 36, 66, 255), + "class:cube_3": (55, 255, 139, 255), + "class:table": (255, 237, 218, 255), + "class:ground": (100, 100, 100, 255), + "class:robot": (204, 110, 248, 255), + "class:UNLABELLED": (150, 150, 150, 255), + "class:BACKGROUND": (200, 200, 200, 255), + } + + # Set cameras + # Set wrist camera + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "semantic_segmentation", "normals", "distance_to_image_plane"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=SEMANTIC_MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + # Set settings for camera rendering + self.num_rerenders_on_reset = 1 + self.sim.render.antialiasing_mode = "OFF" + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..13040620b788914b08eece1a3830ab1b6af9c7c9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_visuomotor_env_cfg.py @@ -0,0 +1,238 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import CameraCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class EventCfg(stack_joint_pos_env_cfg.EventCfg): + """Configuration for events.""" + + randomize_light = EventTerm( + func=franka_stack_events.randomize_scene_lighting_domelight, + mode="reset", + params={ + "intensity_range": (1500.0, 10000.0), + "color_variation": 0.4, + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/abandoned_parking_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/evening_road_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/lakeside_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/autoshop_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/carpentry_shop_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hospital_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hotel_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/old_bus_depot_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/small_empty_house_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/surgery_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Studio/photo_studio_01_4k.hdr", + ], + "default_intensity": 3000.0, + "default_color": (0.75, 0.75, 0.75), + "default_texture": "", + }, + ) + + randomize_table_visual_material = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("table"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Ash/Ash_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Birch/Birch_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Mahogany_Planks/Mahogany_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Plywood/Plywood_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Stone/Marble/Marble_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + "default_texture": ( + f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/Materials/Textures/DemoTable_TableBase_BaseColor.png" + ), + }, + ) + + randomize_robot_arm_visual_texture = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Cast/Aluminum_Cast_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Polished/Aluminum_Polished_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brass/Brass_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Bronze/Bronze_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brushed_Antique_Copper/Brushed_Antique_Copper_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Cast_Metal_Silver_Vein/Cast_Metal_Silver_Vein_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Copper/Copper_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Gold/Gold_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Iron/Iron_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/RustedMetal/RustedMetal_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Silver/Silver_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Carbon/Steel_Carbon_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + }, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackVisuomotorEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + # Evaluation settings + eval_mode = False + eval_type = None + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + # Set cameras + # Set wrist camera + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e344f0021db2cb035e02d78e93312d61eabf054d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_env_cfg.py @@ -0,0 +1,169 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_env_cfg import StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="reset", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class FrankaCubeStackEnvCfg(StackEnvCfg): + """Configuration for the Franka Cube Stack Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Add semantics to table + self.scene.table.spawn.semantic_tags = [("class", "table")] + + # Add semantics to ground + self.scene.plane.semantic_tags = [("class", "ground")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_1")], + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_2")], + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_3")], + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_instance_randomize_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..3e14199a263026a15b10ac8efe78c887ff9550d6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/franka/stack_joint_pos_instance_randomize_env_cfg.py @@ -0,0 +1,204 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import torch + +from isaaclab.assets import RigidObjectCfg, RigidObjectCollectionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_instance_randomize_env_cfg import ( + StackInstanceRandomizeEnvCfg, +) + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="startup", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cubes_in_focus = EventTerm( + func=franka_stack_events.randomize_rigid_objects_in_focus, + mode="reset", + params={ + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + "out_focus_state": torch.tensor([10.0, 10.0, 10.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, + "min_separation": 0.1, + }, + ) + + +@configclass +class FrankaCubeStackInstanceRandomizeEnvCfg(StackInstanceRandomizeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Reduce the number of environments due to camera resources + self.scene.num_envs = 2 + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Set each stacking cube to be a collection of rigid objects + cube_1_config_dict = { + "blue_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1_Blue", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "red_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1_Red", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + cube_2_config_dict = { + "red_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Red", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "yellow_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Yellow", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + cube_3_config_dict = { + "yellow_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3_Yellow", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "green_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Green", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + self.scene.cube_1 = RigidObjectCollectionCfg(rigid_objects=cube_1_config_dict) + self.scene.cube_2 = RigidObjectCollectionCfg(rigid_objects=cube_2_config_dict) + self.scene.cube_3 = RigidObjectCollectionCfg(rigid_objects=cube_3_config_dict) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..760669cca6298500bec4b3fa8b2dfa83b15b116c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/__init__.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import gymnasium as gym + +## +# Register Gym environments. +## + +## +# RMPFlow (with Joint Limit Constraint and Obstacle Avoidance) for Galbot Single Arm Cube Stack Task +# you can use for both absolute and relative mode, by given the USE_RELATIVE_MODE environment variable +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotLeftArmCubeStackEnvCfg", + }, + disable_env_checker=True, +) + + +gym.register( + id="Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotRightArmCubeStackEnvCfg", + }, + disable_env_checker=True, +) + + +## +# Visuomotor Task for Galbot Left ArmCube Stack Task +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg", + }, + disable_env_checker=True, +) + +## +# Policy Close-loop Evaluation Task for Galbot Left Arm Cube Stack Task (in Joint Space) +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Joint-Position-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.stack_rmp_rel_env_cfg:GalbotLeftArmJointPositionCubeStackVisuomotorEnvCfg_PLAY" + ), + }, + disable_env_checker=True, +) + +## +# Policy Close-loop Evaluation Task for Galbot Left Arm Cube Stack Task (in Task Space) +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-RmpFlow-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:GalbotLeftArmRmpFlowCubeStackVisuomotorEnvCfg_PLAY", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..4506a95eaba9b206df95240ab0c2da16629c0c2c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_joint_pos_env_cfg.py @@ -0,0 +1,325 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.assets import RigidObjectCfg, SurfaceGripperCfg +from isaaclab.devices import DevicesCfg +from isaaclab.devices.device_base import DeviceBase +from isaaclab.devices.openxr.openxr_device import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GripperRetargeterCfg, Se3AbsRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import SurfaceGripperBinaryActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import CollisionPropertiesCfg, RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_env_cfg import ObservationsCfg, StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.galbot import GALBOT_ONE_CHARLIE_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset", params={"reset_joint_targets": True}) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": { + "x": (-0.2, 0.0), + "y": (0.20, 0.40), + "z": (0.0203, 0.0203), + "yaw": (-1.0, 1.0, 0.0), + }, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class ObservationGalbotLeftArmGripperCfg: + """Observations for the Galbot Left Arm Gripper.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + + object = ObsTerm( + func=mdp.object_abs_obs_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + }, + ) + cube_positions = ObsTerm( + func=mdp.cube_poses_in_base_frame, params={"robot_cfg": SceneEntityCfg("robot"), "return_key": "pos"} + ) + cube_orientations = ObsTerm( + func=mdp.cube_poses_in_base_frame, params={"robot_cfg": SceneEntityCfg("robot"), "return_key": "quat"} + ) + + eef_pos = ObsTerm( + func=mdp.ee_frame_pose_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "return_key": "pos", + }, + ) + eef_quat = ObsTerm( + func=mdp.ee_frame_pose_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "return_key": "quat", + }, + ) + gripper_pos = ObsTerm( + func=mdp.gripper_pos, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObservationsCfg.SubtaskCfg): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + super().__post_init__() + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + subtask_terms: SubtaskCfg = SubtaskCfg() + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + + +@configclass +class GalbotLeftArmCubeStackEnvCfg(StackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # MDP settings + + # Set events + self.events = EventCfg() + self.observations.policy = ObservationGalbotLeftArmGripperCfg().PolicyCfg() + self.observations.subtask_terms = ObservationGalbotLeftArmGripperCfg().SubtaskCfg() + + # Set galbot as robot + self.scene.robot = GALBOT_ONE_CHARLIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (galbot) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["left_arm_joint.*"], scale=0.5, use_default_offset=True + ) + # Enable Parallel Gripper + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.0}, + ) + self.gripper_joint_names = ["left_gripper_.*_joint"] + self.gripper_open_val = 0.035 + self.gripper_threshold = 0.010 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + cube_collision_properties = CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + collision_props=cube_collision_properties, + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + collision_props=cube_collision_properties, + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + collision_props=cube_collision_properties, + ), + ) + + # Listens to the required transforms + self.marker_cfg = FRAME_MARKER_CFG.copy() + self.marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self.marker_cfg.prim_path = "/Visuals/FrameTransformer" + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_gripper_tcp_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + ), + ), + ], + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3AbsRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_LEFT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_LEFT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) + + +@configclass +class GalbotRightArmCubeStackEnvCfg(GalbotLeftArmCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Move to area below right hand (invert y-axis) + left, right = self.events.randomize_cube_positions.params["pose_range"]["y"] + self.events.randomize_cube_positions.params["pose_range"]["y"] = (-right, -left) + + # Set actions for the specific robot type (galbot) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["right_arm_joint.*"], scale=0.5, use_default_offset=True + ) + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_suction_cup_tcp_link/SurfaceGripper", + max_grip_distance=0.0075, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + # Set surface gripper action + self.actions.gripper_action = SurfaceGripperBinaryActionCfg( + asset_name="surface_gripper", + open_command=-1.0, + close_command=1.0, + ) + + self.scene.ee_frame.target_frames[0].prim_path = "{ENV_REGEX_NS}/Robot/right_suction_cup_tcp_link" + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3AbsRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_rmp_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_rmp_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..eebb79e1d315568f2291245e7aabd2bc96d77369 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/galbot/stack_rmp_rel_env_cfg.py @@ -0,0 +1,324 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import os + +import isaaclab.sim as sim_utils +from isaaclab.devices.device_base import DeviceBase, DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters import GripperRetargeterCfg, Se3RelRetargeterCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs.mdp.actions.rmpflow_actions_cfg import RMPFlowActionCfg +from isaaclab.sensors import CameraCfg, FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack import mdp + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab.controllers.config.rmp_flow import ( # isort: skip + GALBOT_LEFT_ARM_RMPFLOW_CFG, + GALBOT_RIGHT_ARM_RMPFLOW_CFG, +) +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +## +# RmpFlow Controller for Galbot Left Arm Cube Stack Task (with Parallel Gripper) +## +@configclass +class RmpFlowGalbotLeftArmCubeStackEnvCfg(stack_joint_pos_env_cfg.GalbotLeftArmCubeStackEnvCfg): + """Configuration for the Galbot Left Arm Cube Stack Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # read use_relative_mode from environment variable + # True for record_demos, and False for replay_demos, annotate_demos and generate_demos + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Galbot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["left_arm_joint.*"], + body_name="left_gripper_tcp_link", + controller=GALBOT_LEFT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + + # Set the simulation parameters + self.sim.dt = 1 / 60 + self.sim.render_interval = 6 + + self.decimation = 3 + self.episode_length_s = 30.0 + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_LEFT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_LEFT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) + + +## +# RmpFlow Controller for Galbot Right Arm Cube Stack Task (with Surface Gripper) +## +@configclass +class RmpFlowGalbotRightArmCubeStackEnvCfg(stack_joint_pos_env_cfg.GalbotRightArmCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # read use_relative_mode from environment variable + # True for record_demos, and False for replay_demos, annotate_demos and generate_demos + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Galbot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["right_arm_joint.*"], + body_name="right_suction_cup_tcp_link", + controller=GALBOT_RIGHT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + # Set the simulation parameters + self.sim.dt = 1 / 120 + self.sim.render_interval = 6 + + self.decimation = 6 + self.episode_length_s = 30.0 + + # Enable CCD to avoid tunneling + self.sim.physx.enable_ccd = True + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=DeviceBase.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) + + +## +# Visuomotor Env for Record, Generate and Replay (in Task Space) +## +@configclass +class RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg(RmpFlowGalbotLeftArmCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set left and right wrist cameras for VLA policy training + self.scene.right_wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_arm_camera_sim_view_frame/right_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + self.scene.left_wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_arm_camera_sim_view_frame/left_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + # Set ego view camera + self.scene.ego_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/head_camera_sim_view_frame/head_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + # Set front view camera + self.scene.front_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/front_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.0, 0.0, 0.6), rot=(-0.3799, 0.5963, 0.5963, -0.3799), convention="ros"), + ) + + marker_right_camera_cfg = FRAME_MARKER_CFG.copy() + marker_right_camera_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_right_camera_cfg.prim_path = "/Visuals/FrameTransformerRightCamera" + + self.scene.right_arm_camera_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=marker_right_camera_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_arm_camera_sim_view_frame", + name="right_camera", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + rot=(0.5, -0.5, 0.5, -0.5), + ), + ), + ], + ) + + marker_left_camera_cfg = FRAME_MARKER_CFG.copy() + marker_left_camera_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_left_camera_cfg.prim_path = "/Visuals/FrameTransformerLeftCamera" + + self.scene.left_arm_camera_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=marker_left_camera_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_arm_camera_sim_view_frame", + name="left_camera", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + rot=(0.5, -0.5, 0.5, -0.5), + ), + ), + ], + ) + + # Set settings for camera rendering + self.num_rerenders_on_reset = 3 + self.sim.render.antialiasing_mode = "DLAA" # Use DLAA for higher quality rendering + + # List of image observations in policy observations + self.image_obs_list = ["ego_cam", "left_wrist_cam", "right_wrist_cam"] + + +## +# Task Env for VLA Policy Close-loop Evaluation (in Joint Space) +## + + +@configclass +class GalbotLeftArmJointPositionCubeStackVisuomotorEnvCfg_PLAY(RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["left_arm_joint.*"], scale=1.0, use_default_offset=False + ) + # Enable Parallel Gripper with AbsBinaryJointPosition Control + self.actions.gripper_action = mdp.AbsBinaryJointPositionActionCfg( + asset_name="robot", + threshold=0.030, + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.023}, + # real gripper close data is 0.0235, close to it to meet data distribution, + # but smaller to ensure robust grasping. + # during VLA inference, we set the close command to '0.023' since the VLA + # has never seen the gripper fully closed. + ) + + +## +# Task Envs for VLA Policy Close-loop Evaluation (in Task Space) +## +@configclass +class GalbotLeftArmRmpFlowCubeStackVisuomotorEnvCfg_PLAY(RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Enable Parallel Gripper with AbsBinaryJointPosition Control + self.actions.gripper_action = mdp.AbsBinaryJointPositionActionCfg( + asset_name="robot", + threshold=0.030, + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.023}, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81165e4a28a4a97a4454e83a728c4c5fddd2a954 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/__init__.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +## +# Register Gym environments. +## + + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg:UR10LongSuctionCubeStackEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg:UR10ShortSuctionCubeStackEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_ik_rel_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0e6b2df08b29d49a312cee4204c45a8440670fbb --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_ik_rel_env_cfg.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + + +@configclass +class UR10LongSuctionCubeStackEnvCfg(stack_joint_pos_env_cfg.UR10LongSuctionCubeStackEnvCfg): + """Configuration for the UR10 Long Suction Cube Stack Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set actions for the specific robot type (UR10 LONG SUCTION) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=[".*_joint"], + body_name="ee_link", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=1.0, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, -0.22]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.02, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + +@configclass +class UR10ShortSuctionCubeStackEnvCfg(stack_joint_pos_env_cfg.UR10ShortSuctionCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set actions for the specific robot type (UR10 SHORT SUCTION) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=[".*_joint"], + body_name="ee_link", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=1.0, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, -0.159]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.02, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_joint_pos_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d73a3fb3c9c8e9201a5803e696a8a7985c4240 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/config/ur10_gripper/stack_joint_pos_env_cfg.py @@ -0,0 +1,212 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, SurfaceGripperCfg +from isaaclab.envs.mdp.actions.actions_cfg import SurfaceGripperBinaryActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack import mdp +from isaaclab_tasks.manager_based.manipulation.stack.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack.stack_env_cfg import StackEnvCfg + +from isaaclab_assets.robots.universal_robots import ( # isort: skip + UR10_LONG_SUCTION_CFG, + UR10_SHORT_SUCTION_CFG, +) + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +@configclass +class EventCfgLongSuction: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="reset", + params={ + "default_pose": [0.0, -1.5707, 1.5707, -1.5707, -1.5707, 0.0], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1.0, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class UR10CubeStackEnvCfg(StackEnvCfg): + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + cube_scale = (1.0, 1.0, 1.0) + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfgLongSuction() + + # Set actions for the specific robot type (ur10) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=[".*_joint"], scale=0.5, use_default_offset=True + ) + # Set surface gripper action + self.actions.gripper_action = SurfaceGripperBinaryActionCfg( + asset_name="surface_gripper", + open_command=-1.0, + close_command=1.0, + ) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + + self.decimation = 5 + self.episode_length_s = 30.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = 5 + + +@configclass +class UR10LongSuctionCubeStackEnvCfg(UR10CubeStackEnvCfg): + """Configuration for the UR10 Long Suction Cube Stack Environment.""" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Suction grippers currently require CPU simulation + self.device = "cpu" + + # Set events + self.events = EventCfgLongSuction() + + # Set UR10 as robot + self.scene.robot = UR10_LONG_SUCTION_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link/SurfaceGripper", + max_grip_distance=0.0075, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=True, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.22, 0.0, 0.0], + ), + ), + ], + ) + + +@configclass +class UR10ShortSuctionCubeStackEnvCfg(UR10CubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Suction grippers currently require CPU simulation + self.device = "cpu" + + # Set UR10 as robot + self.scene.robot = UR10_SHORT_SUCTION_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link/SurfaceGripper", + max_grip_distance=0.0075, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=True, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.1585, 0.0, 0.0], + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea04fcc468e9ce046eca400e34137aae6adfdb3b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/franka_stack_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/franka_stack_events.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9e1db4862966ba6e01420bbb111c0f8575c22a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/franka_stack_events.py @@ -0,0 +1,315 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +import math +import random +from typing import TYPE_CHECKING + +import torch + +from isaacsim.core.utils.extensions import enable_extension + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, AssetBase +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def set_default_joint_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + default_pose: torch.Tensor, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + # Set the default pose for robots in all envs + asset = env.scene[asset_cfg.name] + asset.data.default_joint_pos = torch.tensor(default_pose, device=env.device).repeat(env.num_envs, 1) + + +def randomize_joint_by_gaussian_offset( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + mean: float, + std: float, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + asset: Articulation = env.scene[asset_cfg.name] + + # Add gaussian noise to joint states + joint_pos = asset.data.default_joint_pos[env_ids].clone() + joint_vel = asset.data.default_joint_vel[env_ids].clone() + joint_pos += math_utils.sample_gaussian(mean, std, joint_pos.shape, joint_pos.device) + + # Clamp joint pos to limits + joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids] + joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) + + # Don't noise the gripper poses + joint_pos[:, -2:] = asset.data.default_joint_pos[env_ids, -2:] + + # Set into the physics simulation + asset.set_joint_position_target(joint_pos, env_ids=env_ids) + asset.set_joint_velocity_target(joint_vel, env_ids=env_ids) + asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + +def sample_random_color(base=(0.75, 0.75, 0.75), variation=0.1): + """ + Generates a randomized color that stays close to the base color while preserving overall brightness. + The relative balance between the R, G, and B components is maintained by ensuring that + the sum of random offsets is zero. + + Parameters: + base (tuple): The base RGB color with each component between 0 and 1. + variation (float): Maximum deviation to sample for each channel before balancing. + + Returns: + tuple: A new RGB color with balanced random variation. + """ + # Generate random offsets for each channel in the range [-variation, variation] + offsets = [random.uniform(-variation, variation) for _ in range(3)] + # Compute the average offset + avg_offset = sum(offsets) / 3 + # Adjust offsets so their sum is zero (maintaining brightness) + balanced_offsets = [offset - avg_offset for offset in offsets] + + # Apply the balanced offsets to the base color and clamp each channel between 0 and 1 + new_color = tuple(max(0, min(1, base_component + offset)) for base_component, offset in zip(base, balanced_offsets)) + + return new_color + + +def randomize_scene_lighting_domelight( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + intensity_range: tuple[float, float], + color_variation: float, + textures: list[str], + default_intensity: float = 3000.0, + default_color: tuple[float, float, float] = (0.75, 0.75, 0.75), + default_texture: str = "", + asset_cfg: SceneEntityCfg = SceneEntityCfg("light"), +): + asset: AssetBase = env.scene[asset_cfg.name] + light_prim = asset.prims[0] + + intensity_attr = light_prim.GetAttribute("inputs:intensity") + intensity_attr.Set(default_intensity) + + color_attr = light_prim.GetAttribute("inputs:color") + color_attr.Set(default_color) + + texture_file_attr = light_prim.GetAttribute("inputs:texture:file") + texture_file_attr.Set(default_texture) + + if not hasattr(env.cfg, "eval_mode") or not env.cfg.eval_mode: + return + + if env.cfg.eval_type in ["light_intensity", "all"]: + # Sample new light intensity + new_intensity = random.uniform(intensity_range[0], intensity_range[1]) + # Set light intensity to light prim + intensity_attr.Set(new_intensity) + + if env.cfg.eval_type in ["light_color", "all"]: + # Sample new light color + new_color = sample_random_color(base=default_color, variation=color_variation) + # Set light color to light prim + color_attr.Set(new_color) + + if env.cfg.eval_type in ["light_texture", "all"]: + # Sample new light texture (background) + new_texture = random.sample(textures, 1)[0] + # Set light texture to light prim + texture_file_attr.Set(new_texture) + + +def sample_object_poses( + num_objects: int, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + pose_list = [] + + for i in range(num_objects): + for j in range(max_sample_tries): + sample = [random.uniform(range[0], range[1]) for range in range_list] + + # Accept pose if it is the first one, or if reached max num tries + if len(pose_list) == 0 or j == max_sample_tries - 1: + pose_list.append(sample) + break + + # Check if pose of object is sufficiently far away from all other objects + separation_check = [math.dist(sample[:3], pose[:3]) > min_separation for pose in pose_list] + if False not in separation_check: + pose_list.append(sample) + break + + return pose_list + + +def randomize_object_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # Randomize poses in each environment independently + for cur_env in env_ids.tolist(): + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + # Randomize pose for each object + for i in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[i] + asset = env.scene[asset_cfg.name] + + # Write pose to simulation + pose_tensor = torch.tensor([pose_list[i]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + asset.write_root_pose_to_sim( + torch.cat([positions, orientations], dim=-1), env_ids=torch.tensor([cur_env], device=env.device) + ) + asset.write_root_velocity_to_sim( + torch.zeros(1, 6, device=env.device), env_ids=torch.tensor([cur_env], device=env.device) + ) + + +def randomize_rigid_objects_in_focus( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + out_focus_state: torch.Tensor, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # List of rigid objects in focus for each env (dim = [num_envs, num_rigid_objects]) + env.rigid_objects_in_focus = [] + + for cur_env in env_ids.tolist(): + # Sample in focus object poses + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + selected_ids = [] + for asset_idx in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[asset_idx] + asset = env.scene[asset_cfg.name] + + # Randomly select an object to bring into focus + object_id = random.randint(0, asset.num_objects - 1) + selected_ids.append(object_id) + + # Create object state tensor + object_states = torch.stack([out_focus_state] * asset.num_objects).to(device=env.device) + pose_tensor = torch.tensor([pose_list[asset_idx]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + object_states[object_id, 0:3] = positions + object_states[object_id, 3:7] = orientations + + asset.write_object_state_to_sim( + object_state=object_states, env_ids=torch.tensor([cur_env], device=env.device) + ) + + env.rigid_objects_in_focus.append(selected_ids) + + +def randomize_visual_texture_material( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfg: SceneEntityCfg, + textures: list[str], + default_texture: str = "", + texture_rotation: tuple[float, float] = (0.0, 0.0), +): + """Randomize the visual texture of bodies on an asset using Replicator API. + + This function randomizes the visual texture of the bodies of the asset using the Replicator API. + The function samples random textures from the given texture paths and applies them to the bodies + of the asset. The textures are projected onto the bodies and rotated by the given angles. + + .. note:: + The function assumes that the asset follows the prim naming convention as: + "{asset_prim_path}/{body_name}/visuals" where the body name is the name of the body to + which the texture is applied. This is the default prim ordering when importing assets + from the asset converters in Isaac Lab. + + .. note:: + When randomizing the texture of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + if hasattr(env.cfg, "eval_mode") and ( + not env.cfg.eval_mode or env.cfg.eval_type not in [f"{asset_cfg.name}_texture", "all"] + ): + return + # textures = [default_texture] + + # enable replicator extension if not already enabled + enable_extension("omni.replicator.core") + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # check to make sure replicate_physics is set to False, else raise error + # note: We add an explicit check here since texture randomization can happen outside of 'prestartup' mode + # and the event manager doesn't check in that case. + if env.cfg.scene.replicate_physics: + raise RuntimeError( + "Unable to randomize visual texture material with scene replication enabled." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # convert from radians to degrees + texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation) + + # obtain the asset entity + asset = env.scene[asset_cfg.name] + + # join all bodies in the asset + body_names = asset_cfg.body_names + if isinstance(body_names, str): + body_names_regex = body_names + elif isinstance(body_names, list): + body_names_regex = "|".join(body_names) + else: + body_names_regex = ".*" + + if not hasattr(asset, "cfg"): + prims_group = rep.get.prims(path_pattern=f"{asset.prim_paths[0]}/visuals") + else: + prims_group = rep.get.prims(path_pattern=f"{asset.cfg.prim_path}/{body_names_regex}/visuals") + + with prims_group: + rep.randomizer.texture( + textures=textures, project_uvw=True, texture_rotate=rep.distribution.uniform(*texture_rotation) + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/observations.py new file mode 100644 index 0000000000000000000000000000000000000000..31123e71a30849a59f1b2c3cbcbaf80e7833240a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/observations.py @@ -0,0 +1,534 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def cube_positions_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The position of the cubes in the world frame.""" + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + return torch.cat((cube_1.data.root_pos_w, cube_2.data.root_pos_w, cube_3.data.root_pos_w), dim=1) + + +def instance_randomize_cube_positions_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The position of the cubes in the world frame.""" + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + + cube_1_pos_w = [] + cube_2_pos_w = [] + cube_3_pos_w = [] + for env_id in range(env.num_envs): + cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) + cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) + cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) + cube_1_pos_w = torch.stack(cube_1_pos_w) + cube_2_pos_w = torch.stack(cube_2_pos_w) + cube_3_pos_w = torch.stack(cube_3_pos_w) + + return torch.cat((cube_1_pos_w, cube_2_pos_w, cube_3_pos_w), dim=1) + + +def cube_orientations_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +): + """The orientation of the cubes in the world frame.""" + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + return torch.cat((cube_1.data.root_quat_w, cube_2.data.root_quat_w, cube_3.data.root_quat_w), dim=1) + + +def instance_randomize_cube_orientations_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The orientation of the cubes in the world frame.""" + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + + cube_1_quat_w = [] + cube_2_quat_w = [] + cube_3_quat_w = [] + for env_id in range(env.num_envs): + cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) + cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) + cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) + cube_1_quat_w = torch.stack(cube_1_quat_w) + cube_2_quat_w = torch.stack(cube_2_quat_w) + cube_3_quat_w = torch.stack(cube_3_quat_w) + + return torch.cat((cube_1_quat_w, cube_2_quat_w, cube_3_quat_w), dim=1) + + +def object_obs( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +): + """ + Object observations (in world frame): + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper to cube_1, + gripper to cube_2, + gripper to cube_3, + cube_1 to cube_2, + cube_2 to cube_3, + cube_1 to cube_3, + """ + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + + cube_1_pos_w = cube_1.data.root_pos_w + cube_1_quat_w = cube_1.data.root_quat_w + + cube_2_pos_w = cube_2.data.root_pos_w + cube_2_quat_w = cube_2.data.root_quat_w + + cube_3_pos_w = cube_3.data.root_pos_w + cube_3_quat_w = cube_3.data.root_quat_w + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + gripper_to_cube_1 = cube_1_pos_w - ee_pos_w + gripper_to_cube_2 = cube_2_pos_w - ee_pos_w + gripper_to_cube_3 = cube_3_pos_w - ee_pos_w + + cube_1_to_2 = cube_1_pos_w - cube_2_pos_w + cube_2_to_3 = cube_2_pos_w - cube_3_pos_w + cube_1_to_3 = cube_1_pos_w - cube_3_pos_w + + return torch.cat( + ( + cube_1_pos_w - env.scene.env_origins, + cube_1_quat_w, + cube_2_pos_w - env.scene.env_origins, + cube_2_quat_w, + cube_3_pos_w - env.scene.env_origins, + cube_3_quat_w, + gripper_to_cube_1, + gripper_to_cube_2, + gripper_to_cube_3, + cube_1_to_2, + cube_2_to_3, + cube_1_to_3, + ), + dim=1, + ) + + +def instance_randomize_object_obs( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +): + """ + Object observations (in world frame): + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper to cube_1, + gripper to cube_2, + gripper to cube_3, + cube_1 to cube_2, + cube_2 to cube_3, + cube_1 to cube_3, + """ + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + + cube_1_pos_w = [] + cube_2_pos_w = [] + cube_3_pos_w = [] + cube_1_quat_w = [] + cube_2_quat_w = [] + cube_3_quat_w = [] + for env_id in range(env.num_envs): + cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) + cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) + cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) + cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) + cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) + cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) + cube_1_pos_w = torch.stack(cube_1_pos_w) + cube_2_pos_w = torch.stack(cube_2_pos_w) + cube_3_pos_w = torch.stack(cube_3_pos_w) + cube_1_quat_w = torch.stack(cube_1_quat_w) + cube_2_quat_w = torch.stack(cube_2_quat_w) + cube_3_quat_w = torch.stack(cube_3_quat_w) + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + gripper_to_cube_1 = cube_1_pos_w - ee_pos_w + gripper_to_cube_2 = cube_2_pos_w - ee_pos_w + gripper_to_cube_3 = cube_3_pos_w - ee_pos_w + + cube_1_to_2 = cube_1_pos_w - cube_2_pos_w + cube_2_to_3 = cube_2_pos_w - cube_3_pos_w + cube_1_to_3 = cube_1_pos_w - cube_3_pos_w + + return torch.cat( + ( + cube_1_pos_w - env.scene.env_origins, + cube_1_quat_w, + cube_2_pos_w - env.scene.env_origins, + cube_2_quat_w, + cube_3_pos_w - env.scene.env_origins, + cube_3_quat_w, + gripper_to_cube_1, + gripper_to_cube_2, + gripper_to_cube_3, + cube_1_to_2, + cube_2_to_3, + cube_1_to_3, + ), + dim=1, + ) + + +def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] + + return ee_frame_pos + + +def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] + + return ee_frame_quat + + +def gripper_pos( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """ + Obtain the versatile gripper position of both Gripper and Suction Cup. + """ + robot: Articulation = env.scene[robot_cfg.name] + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + # Handle multiple surface grippers by concatenating their states + gripper_states = [] + for gripper_name, surface_gripper in env.scene.surface_grippers.items(): + gripper_states.append(surface_gripper.state.view(-1, 1)) + + if len(gripper_states) == 1: + return gripper_states[0] + else: + return torch.cat(gripper_states, dim=1) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observation gripper_pos only support parallel gripper for now" + finger_joint_1 = robot.data.joint_pos[:, gripper_joint_ids[0]].clone().unsqueeze(1) + finger_joint_2 = -1 * robot.data.joint_pos[:, gripper_joint_ids[1]].clone().unsqueeze(1) + return torch.cat((finger_joint_1, finger_joint_2), dim=1) + else: + raise NotImplementedError("[Error] Cannot find gripper_joint_names in the environment config") + + +def object_grasped( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + ee_frame_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + diff_threshold: float = 0.06, +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_closed = (suction_cup_status == 1).to(torch.float32) + grasped = torch.logical_and(suction_cup_is_closed, pose_diff < diff_threshold) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observations only support parallel gripper for now" + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs( + robot.data.joint_pos[:, gripper_joint_ids[0]] + - torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device) + ) + > env.cfg.gripper_threshold, + ) + grasped = torch.logical_and( + grasped, + torch.abs( + robot.data.joint_pos[:, gripper_joint_ids[1]] + - torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device) + ) + > env.cfg.gripper_threshold, + ) + + return grasped + + +def object_stacked( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + upper_object_cfg: SceneEntityCfg, + lower_object_cfg: SceneEntityCfg, + xy_threshold: float = 0.05, + height_threshold: float = 0.005, + height_diff: float = 0.0468, +) -> torch.Tensor: + """Check if an object is stacked by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + upper_object: RigidObject = env.scene[upper_object_cfg.name] + lower_object: RigidObject = env.scene[lower_object_cfg.name] + + pos_diff = upper_object.data.root_pos_w - lower_object.data.root_pos_w + height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) + xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + + stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + stacked = torch.logical_and(suction_cup_is_open, stacked) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observations only support parallel gripper for now" + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=1e-4, + rtol=1e-4, + ), + stacked, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=1e-4, + rtol=1e-4, + ), + stacked, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return stacked + + +def cube_poses_in_base_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + return_key: Literal["pos", "quat", None] = None, +) -> torch.Tensor: + """The position and orientation of the cubes in the robot base frame.""" + + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + pos_cube_1_world = cube_1.data.root_pos_w + pos_cube_2_world = cube_2.data.root_pos_w + pos_cube_3_world = cube_3.data.root_pos_w + + quat_cube_1_world = cube_1.data.root_quat_w + quat_cube_2_world = cube_2.data.root_quat_w + quat_cube_3_world = cube_3.data.root_quat_w + + robot: Articulation = env.scene[robot_cfg.name] + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + + pos_cube_1_base, quat_cube_1_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_1_world, quat_cube_1_world + ) + pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_2_world, quat_cube_2_world + ) + pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_3_world, quat_cube_3_world + ) + + pos_cubes_base = torch.cat((pos_cube_1_base, pos_cube_2_base, pos_cube_3_base), dim=1) + quat_cubes_base = torch.cat((quat_cube_1_base, quat_cube_2_base, quat_cube_3_base), dim=1) + + if return_key == "pos": + return pos_cubes_base + elif return_key == "quat": + return quat_cubes_base + else: + return torch.cat((pos_cubes_base, quat_cubes_base), dim=1) + + +def object_abs_obs_in_base_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """ + Object Abs observations (in base frame): remove the relative observations, + and add abs gripper pos and quat in robot base frame + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper pos, + gripper quat, + """ + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + robot: Articulation = env.scene[robot_cfg.name] + + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + + cube_1_pos_w = cube_1.data.root_pos_w + cube_1_quat_w = cube_1.data.root_quat_w + + cube_2_pos_w = cube_2.data.root_pos_w + cube_2_quat_w = cube_2.data.root_quat_w + + cube_3_pos_w = cube_3.data.root_pos_w + cube_3_quat_w = cube_3.data.root_quat_w + + pos_cube_1_base, quat_cube_1_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_1_pos_w, cube_1_quat_w + ) + pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_2_pos_w, cube_2_quat_w + ) + pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_3_pos_w, cube_3_quat_w + ) + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + ee_quat_w = ee_frame.data.target_quat_w[:, 0, :] + ee_pos_base, ee_quat_base = math_utils.subtract_frame_transforms(root_pos_w, root_quat_w, ee_pos_w, ee_quat_w) + + return torch.cat( + ( + pos_cube_1_base, + quat_cube_1_base, + pos_cube_2_base, + quat_cube_2_base, + pos_cube_3_base, + quat_cube_3_base, + ee_pos_base, + ee_quat_base, + ), + dim=1, + ) + + +def ee_frame_pose_in_base_frame( + env: ManagerBasedRLEnv, + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + return_key: Literal["pos", "quat", None] = None, +) -> torch.Tensor: + """ + The end effector pose in the robot base frame. + """ + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos_w = ee_frame.data.target_pos_w[:, 0, :] + ee_frame_quat_w = ee_frame.data.target_quat_w[:, 0, :] + + robot: Articulation = env.scene[robot_cfg.name] + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + ee_pos_in_base, ee_quat_in_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, ee_frame_pos_w, ee_frame_quat_w + ) + + if return_key == "pos": + return ee_pos_in_base + elif return_key == "quat": + return ee_quat_in_base + else: + return torch.cat((ee_pos_in_base, ee_quat_in_base), dim=1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/terminations.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4c14afcea1eb85a95677db10adf1769ba7a0af --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/mdp/terminations.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def cubes_stacked( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + xy_threshold: float = 0.04, + height_threshold: float = 0.005, + height_diff: float = 0.0468, + atol=0.0001, + rtol=0.0001, +): + robot: Articulation = env.scene[robot_cfg.name] + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + pos_diff_c12 = cube_1.data.root_pos_w - cube_2.data.root_pos_w + pos_diff_c23 = cube_2.data.root_pos_w - cube_3.data.root_pos_w + + # Compute cube position difference in x-y plane + xy_dist_c12 = torch.norm(pos_diff_c12[:, :2], dim=1) + xy_dist_c23 = torch.norm(pos_diff_c23[:, :2], dim=1) + + # Compute cube height difference + h_dist_c12 = torch.norm(pos_diff_c12[:, 2:], dim=1) + h_dist_c23 = torch.norm(pos_diff_c23[:, 2:], dim=1) + + # Check cube positions + stacked = torch.logical_and(xy_dist_c12 < xy_threshold, xy_dist_c23 < xy_threshold) + stacked = torch.logical_and(h_dist_c12 - height_diff < height_threshold, stacked) + stacked = torch.logical_and(pos_diff_c12[:, 2] < 0.0, stacked) + stacked = torch.logical_and(h_dist_c23 - height_diff < height_threshold, stacked) + stacked = torch.logical_and(pos_diff_c23[:, 2] < 0.0, stacked) + + # Check gripper positions + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + stacked = torch.logical_and(suction_cup_is_open, stacked) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Terminations only support parallel gripper for now" + + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return stacked diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..5c772a11760e5d40d20f78f588c74d2af0d41e9c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_env_cfg.py @@ -0,0 +1,199 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + cube_1_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_1")} + ) + + cube_2_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_2")} + ) + + cube_3_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_3")} + ) + + success = DoneTerm(func=mdp.cubes_stacked) + + +@configclass +class StackEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the stacking environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + commands = None + rewards = None + events = None + curriculum = None + + xr: XrCfg = XrCfg( + anchor_pos=(-0.1, -0.5, -1.05), + anchor_rot=(0.866, 0, 0, -0.5), + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 30.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = 2 + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_instance_randomize_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..526297b956172c5d3930818a79074d5e886e60fc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack/stack_instance_randomize_env_cfg.py @@ -0,0 +1,135 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.instance_randomize_object_obs) + cube_positions = ObsTerm(func=mdp.instance_randomize_cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.instance_randomize_cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +@configclass +class StackInstanceRandomizeEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the stacking environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + commands = None + rewards = None + events = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 30.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f611f9e8eb4d4c1a782630477924f949a537f61d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Navigation environments.""" + +from .config import anymal_c diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..99d0035ef26444bba22ba9db8a9359b981e5d873 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for navigation environments.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bcedc58f3f0ad00283b4e1201d4fc6462bb29acf --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/__init__.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + +gym.register( + id="Isaac-Navigation-Flat-Anymal-C-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.navigation_env_cfg:NavigationEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:NavigationEnvPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) + +gym.register( + id="Isaac-Navigation-Flat-Anymal-C-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.navigation_env_cfg:NavigationEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:NavigationEnvPPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml", + }, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/rsl_rl_ppo_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..93ec98732f8cccb1232c2a0df70dec48c2fc7b06 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class NavigationEnvPPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 8 + max_iterations = 1500 + save_interval = 50 + experiment_name = "anymal_c_navigation" + policy = RslRlPpoActorCriticCfg( + init_noise_std=0.5, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[128, 128], + critic_hidden_dims=[128, 128], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3eba30e7fc18356dae4e2999cb915a4330812b09 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/agents/skrl_flat_ppo_cfg.yaml @@ -0,0 +1,85 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: -0.6931471805599453 + network: + - name: net + input: OBSERVATIONS + layers: [128, 128] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [128, 128] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 8 + learning_epochs: 5 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-03 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: null + state_preprocessor_kwargs: null + value_preprocessor: null + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.005 + value_loss_scale: 1.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "anymal_c_navigation" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 12000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/navigation_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/navigation_env_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..96b60705bb50218c68fc9d5cf461404709bb32aa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/config/anymal_c/navigation_env_cfg.py @@ -0,0 +1,160 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +import isaaclab_tasks.manager_based.navigation.mdp as mdp +from isaaclab_tasks.manager_based.locomotion.velocity.config.anymal_c.flat_env_cfg import AnymalCFlatEnvCfg + +LOW_LEVEL_ENV_CFG = AnymalCFlatEnvCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_base = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, + "velocity_range": { + "x": (-0.0, 0.0), + "y": (-0.0, 0.0), + "z": (-0.0, 0.0), + "roll": (-0.0, 0.0), + "pitch": (-0.0, 0.0), + "yaw": (-0.0, 0.0), + }, + }, + ) + + +@configclass +class ActionsCfg: + """Action terms for the MDP.""" + + pre_trained_policy_action: mdp.PreTrainedPolicyActionCfg = mdp.PreTrainedPolicyActionCfg( + asset_name="robot", + policy_path=f"{ISAACLAB_NUCLEUS_DIR}/Policies/ANYmal-C/Blind/policy.pt", + low_level_decimation=4, + low_level_actions=LOW_LEVEL_ENV_CFG.actions.joint_pos, + low_level_observations=LOW_LEVEL_ENV_CFG.observations.policy, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + base_lin_vel = ObsTerm(func=mdp.base_lin_vel) + projected_gravity = ObsTerm(func=mdp.projected_gravity) + pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "pose_command"}) + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + termination_penalty = RewTerm(func=mdp.is_terminated, weight=-400.0) + position_tracking = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.5, + params={"std": 2.0, "command_name": "pose_command"}, + ) + position_tracking_fine_grained = RewTerm( + func=mdp.position_command_error_tanh, + weight=0.5, + params={"std": 0.2, "command_name": "pose_command"}, + ) + orientation_tracking = RewTerm( + func=mdp.heading_command_error_abs, + weight=-0.2, + params={"command_name": "pose_command"}, + ) + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" + + pose_command = mdp.UniformPose2dCommandCfg( + asset_name="robot", + simple_heading=False, + resampling_time_range=(8.0, 8.0), + debug_vis=True, + ranges=mdp.UniformPose2dCommandCfg.Ranges(pos_x=(-3.0, 3.0), pos_y=(-3.0, 3.0), heading=(-math.pi, math.pi)), + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + base_contact = DoneTerm( + func=mdp.illegal_contact, + params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="base"), "threshold": 1.0}, + ) + + +@configclass +class NavigationEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the navigation environment.""" + + # environment settings + scene: SceneEntityCfg = LOW_LEVEL_ENV_CFG.scene + actions: ActionsCfg = ActionsCfg() + observations: ObservationsCfg = ObservationsCfg() + events: EventCfg = EventCfg() + # mdp settings + commands: CommandsCfg = CommandsCfg() + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + + def __post_init__(self): + """Post initialization.""" + + self.sim.dt = LOW_LEVEL_ENV_CFG.sim.dt + self.sim.render_interval = LOW_LEVEL_ENV_CFG.decimation + self.decimation = LOW_LEVEL_ENV_CFG.decimation * 10 + self.episode_length_s = self.commands.pose_command.resampling_time_range[1] + + if self.scene.height_scanner is not None: + self.scene.height_scanner.update_period = ( + self.actions.pre_trained_policy_action.low_level_decimation * self.sim.dt + ) + if self.scene.contact_forces is not None: + self.scene.contact_forces.update_period = self.sim.dt + + +class NavigationEnvCfg_PLAY(NavigationEnvCfg): + def __post_init__(self) -> None: + # post init of parent + super().__post_init__() + + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..213391d362b13c3591bb89dd7647b1c7edccc4d6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the locomotion environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .pre_trained_policy_action import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/pre_trained_policy_action.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/pre_trained_policy_action.py new file mode 100644 index 0000000000000000000000000000000000000000..c25558c788469b5820dd6fb590fe3ec0e7a1165d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/pre_trained_policy_action.py @@ -0,0 +1,189 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from dataclasses import MISSING +from typing import TYPE_CHECKING + +import torch + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation +from isaaclab.managers import ActionTerm, ActionTermCfg, ObservationGroupCfg, ObservationManager +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import BLUE_ARROW_X_MARKER_CFG, GREEN_ARROW_X_MARKER_CFG +from isaaclab.utils import configclass +from isaaclab.utils.assets import check_file_path, read_file + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +class PreTrainedPolicyAction(ActionTerm): + r"""Pre-trained policy action term. + + This action term infers a pre-trained policy and applies the corresponding low-level actions to the robot. + The raw actions correspond to the commands for the pre-trained policy. + + """ + + cfg: PreTrainedPolicyActionCfg + """The configuration of the action term.""" + + def __init__(self, cfg: PreTrainedPolicyActionCfg, env: ManagerBasedRLEnv) -> None: + # initialize the action term + super().__init__(cfg, env) + + self.robot: Articulation = env.scene[cfg.asset_name] + + # load policy + if not check_file_path(cfg.policy_path): + raise FileNotFoundError(f"Policy file '{cfg.policy_path}' does not exist.") + file_bytes = read_file(cfg.policy_path) + self.policy = torch.jit.load(file_bytes).to(env.device).eval() + + self._raw_actions = torch.zeros(self.num_envs, self.action_dim, device=self.device) + + # prepare low level actions + self._low_level_action_term: ActionTerm = cfg.low_level_actions.class_type(cfg.low_level_actions, env) + self.low_level_actions = torch.zeros(self.num_envs, self._low_level_action_term.action_dim, device=self.device) + + def last_action(): + # reset the low level actions if the episode was reset + if hasattr(env, "episode_length_buf"): + self.low_level_actions[env.episode_length_buf == 0, :] = 0 + return self.low_level_actions + + # remap some of the low level observations to internal observations + cfg.low_level_observations.actions.func = lambda dummy_env: last_action() + cfg.low_level_observations.actions.params = dict() + cfg.low_level_observations.velocity_commands.func = lambda dummy_env: self._raw_actions + cfg.low_level_observations.velocity_commands.params = dict() + + # add the low level observations to the observation manager + self._low_level_obs_manager = ObservationManager({"ll_policy": cfg.low_level_observations}, env) + + self._counter = 0 + + """ + Properties. + """ + + @property + def action_dim(self) -> int: + return 3 + + @property + def raw_actions(self) -> torch.Tensor: + return self._raw_actions + + @property + def processed_actions(self) -> torch.Tensor: + return self.raw_actions + + """ + Operations. + """ + + def process_actions(self, actions: torch.Tensor): + self._raw_actions[:] = actions + + def apply_actions(self): + if self._counter % self.cfg.low_level_decimation == 0: + low_level_obs = self._low_level_obs_manager.compute_group("ll_policy") + self.low_level_actions[:] = self.policy(low_level_obs) + self._low_level_action_term.process_actions(self.low_level_actions) + self._counter = 0 + self._low_level_action_term.apply_actions() + self._counter += 1 + + """ + Debug visualization. + """ + + def _set_debug_vis_impl(self, debug_vis: bool): + # set visibility of markers + # note: parent only deals with callbacks. not their visibility + if debug_vis: + # create markers if necessary for the first time + if not hasattr(self, "base_vel_goal_visualizer"): + # -- goal + marker_cfg = GREEN_ARROW_X_MARKER_CFG.copy() + marker_cfg.prim_path = "/Visuals/Actions/velocity_goal" + marker_cfg.markers["arrow"].scale = (0.5, 0.5, 0.5) + self.base_vel_goal_visualizer = VisualizationMarkers(marker_cfg) + # -- current + marker_cfg = BLUE_ARROW_X_MARKER_CFG.copy() + marker_cfg.prim_path = "/Visuals/Actions/velocity_current" + marker_cfg.markers["arrow"].scale = (0.5, 0.5, 0.5) + self.base_vel_visualizer = VisualizationMarkers(marker_cfg) + # set their visibility to true + self.base_vel_goal_visualizer.set_visibility(True) + self.base_vel_visualizer.set_visibility(True) + else: + if hasattr(self, "base_vel_goal_visualizer"): + self.base_vel_goal_visualizer.set_visibility(False) + self.base_vel_visualizer.set_visibility(False) + + def _debug_vis_callback(self, event): + # check if robot is initialized + # note: this is needed in-case the robot is de-initialized. we can't access the data + if not self.robot.is_initialized: + return + # get marker location + # -- base state + base_pos_w = self.robot.data.root_pos_w.clone() + base_pos_w[:, 2] += 0.5 + # -- resolve the scales and quaternions + vel_des_arrow_scale, vel_des_arrow_quat = self._resolve_xy_velocity_to_arrow(self.raw_actions[:, :2]) + vel_arrow_scale, vel_arrow_quat = self._resolve_xy_velocity_to_arrow(self.robot.data.root_lin_vel_b[:, :2]) + # display markers + self.base_vel_goal_visualizer.visualize(base_pos_w, vel_des_arrow_quat, vel_des_arrow_scale) + self.base_vel_visualizer.visualize(base_pos_w, vel_arrow_quat, vel_arrow_scale) + + """ + Internal helpers. + """ + + def _resolve_xy_velocity_to_arrow(self, xy_velocity: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Converts the XY base velocity command to arrow direction rotation.""" + # obtain default scale of the marker + default_scale = self.base_vel_goal_visualizer.cfg.markers["arrow"].scale + # arrow-scale + arrow_scale = torch.tensor(default_scale, device=self.device).repeat(xy_velocity.shape[0], 1) + arrow_scale[:, 0] *= torch.linalg.norm(xy_velocity, dim=1) * 3.0 + # arrow-direction + heading_angle = torch.atan2(xy_velocity[:, 1], xy_velocity[:, 0]) + zeros = torch.zeros_like(heading_angle) + arrow_quat = math_utils.quat_from_euler_xyz(zeros, zeros, heading_angle) + # convert everything back from base to world frame + base_quat_w = self.robot.data.root_quat_w + arrow_quat = math_utils.quat_mul(base_quat_w, arrow_quat) + + return arrow_scale, arrow_quat + + +@configclass +class PreTrainedPolicyActionCfg(ActionTermCfg): + """Configuration for pre-trained policy action term. + + See :class:`PreTrainedPolicyAction` for more details. + """ + + class_type: type[ActionTerm] = PreTrainedPolicyAction + """ Class of the action term.""" + asset_name: str = MISSING + """Name of the asset in the environment for which the commands are generated.""" + policy_path: str = MISSING + """Path to the low level policy (.pt files).""" + low_level_decimation: int = 4 + """Decimation factor for the low level action term.""" + low_level_actions: ActionTermCfg = MISSING + """Low level action configuration.""" + low_level_observations: ObservationGroupCfg = MISSING + """Low level observation configuration.""" + debug_vis: bool = True + """Whether to visualize debug information. Defaults to False.""" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..ccaad755d087b79ad665ece7755f08d13555096d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/navigation/mdp/rewards.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def position_command_error_tanh(env: ManagerBasedRLEnv, std: float, command_name: str) -> torch.Tensor: + """Reward position tracking with tanh kernel.""" + command = env.command_manager.get_command(command_name) + des_pos_b = command[:, :3] + distance = torch.norm(des_pos_b, dim=1) + return 1 - torch.tanh(distance / std) + + +def heading_command_error_abs(env: ManagerBasedRLEnv, command_name: str) -> torch.Tensor: + """Penalize tracking orientation error.""" + command = env.command_manager.get_command(command_name) + heading_b = command[:, 3] + return heading_b.abs() diff --git a/source/isaaclab_tasks/isaaclab_tasks/utils/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..495b207c319472d05eba1bc21afcaff37938f460 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-package with utilities, data collectors and environment wrappers.""" + +from .importer import import_packages +from .parse_cfg import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg diff --git a/source/isaaclab_tasks/isaaclab_tasks/utils/hydra.py b/source/isaaclab_tasks/isaaclab_tasks/utils/hydra.py new file mode 100644 index 0000000000000000000000000000000000000000..525b425917fa2817bd8272706df8cf7d77f89d75 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/utils/hydra.py @@ -0,0 +1,107 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module with utilities for the hydra configuration system.""" + +import functools +from collections.abc import Callable + +try: + import hydra + from hydra.core.config_store import ConfigStore + from omegaconf import DictConfig, OmegaConf +except ImportError: + raise ImportError("Hydra is not installed. Please install it by running 'pip install hydra-core'.") + +from isaaclab.envs import DirectRLEnvCfg, ManagerBasedRLEnvCfg +from isaaclab.envs.utils.spaces import replace_env_cfg_spaces_with_strings, replace_strings_with_env_cfg_spaces +from isaaclab.utils import replace_slices_with_strings, replace_strings_with_slices + +from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry + + +def register_task_to_hydra( + task_name: str, agent_cfg_entry_point: str +) -> tuple[ManagerBasedRLEnvCfg | DirectRLEnvCfg, dict]: + """Register the task configuration to the Hydra configuration store. + + This function resolves the configuration file for the environment and agent based on the task's name. + It then registers the configurations to the Hydra configuration store. + + Args: + task_name: The name of the task. + agent_cfg_entry_point: The entry point key to resolve the agent's configuration file. + + Returns: + A tuple containing the parsed environment and agent configuration objects. + """ + # load the configurations + env_cfg = load_cfg_from_registry(task_name, "env_cfg_entry_point") + agent_cfg = None + if agent_cfg_entry_point: + agent_cfg = load_cfg_from_registry(task_name, agent_cfg_entry_point) + # replace gymnasium spaces with strings because OmegaConf does not support them. + # this must be done before converting the env configs to dictionary to avoid internal reinterpretations + env_cfg = replace_env_cfg_spaces_with_strings(env_cfg) + # convert the configs to dictionary + env_cfg_dict = env_cfg.to_dict() + if isinstance(agent_cfg, dict) or agent_cfg is None: + agent_cfg_dict = agent_cfg + else: + agent_cfg_dict = agent_cfg.to_dict() + cfg_dict = {"env": env_cfg_dict, "agent": agent_cfg_dict} + # replace slices with strings because OmegaConf does not support slices + cfg_dict = replace_slices_with_strings(cfg_dict) + # store the configuration to Hydra + ConfigStore.instance().store(name=task_name, node=cfg_dict) + return env_cfg, agent_cfg + + +def hydra_task_config(task_name: str, agent_cfg_entry_point: str) -> Callable: + """Decorator to handle the Hydra configuration for a task. + + This decorator registers the task to Hydra and updates the environment and agent configurations from Hydra parsed + command line arguments. + + Args: + task_name: The name of the task. + agent_cfg_entry_point: The entry point key to resolve the agent's configuration file. + + Returns: + The decorated function with the envrionment's and agent's configurations updated from command line arguments. + """ + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + # register the task to Hydra + env_cfg, agent_cfg = register_task_to_hydra(task_name.split(":")[-1], agent_cfg_entry_point) + + # define the new Hydra main function + @hydra.main(config_path=None, config_name=task_name.split(":")[-1], version_base="1.3") + def hydra_main(hydra_env_cfg: DictConfig, env_cfg=env_cfg, agent_cfg=agent_cfg): + # convert to a native dictionary + hydra_env_cfg = OmegaConf.to_container(hydra_env_cfg, resolve=True) + # replace string with slices because OmegaConf does not support slices + hydra_env_cfg = replace_strings_with_slices(hydra_env_cfg) + # update the configs with the Hydra command line arguments + env_cfg.from_dict(hydra_env_cfg["env"]) + # replace strings that represent gymnasium spaces because OmegaConf does not support them. + # this must be done after converting the env configs from dictionary to avoid internal reinterpretations + env_cfg = replace_strings_with_env_cfg_spaces(env_cfg) + # get agent configs + if isinstance(agent_cfg, dict) or agent_cfg is None: + agent_cfg = hydra_env_cfg["agent"] + else: + agent_cfg.from_dict(hydra_env_cfg["agent"]) + # call the original function + func(env_cfg, agent_cfg, *args, **kwargs) + + # call the new Hydra main function + hydra_main() + + return wrapper + + return decorator diff --git a/source/isaaclab_tasks/isaaclab_tasks/utils/importer.py b/source/isaaclab_tasks/isaaclab_tasks/utils/importer.py new file mode 100644 index 0000000000000000000000000000000000000000..ddbab7ede4122483cf5a5793e12ae349e3d51c46 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/utils/importer.py @@ -0,0 +1,96 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module with utility for importing all modules in a package recursively.""" + +from __future__ import annotations + +import importlib +import pkgutil +import sys + + +def import_packages(package_name: str, blacklist_pkgs: list[str] | None = None): + """Import all sub-packages in a package recursively. + + It is easier to use this function to import all sub-packages in a package recursively + than to manually import each sub-package. + + It replaces the need of the following code snippet on the top of each package's ``__init__.py`` file: + + .. code-block:: python + + import .locomotion.velocity + import .manipulation.reach + import .manipulation.lift + + Args: + package_name: The package name. + blacklist_pkgs: The list of blacklisted packages to skip. Defaults to None, + which means no packages are blacklisted. + """ + # Default blacklist + if blacklist_pkgs is None: + blacklist_pkgs = [] + # Import the package itself + package = importlib.import_module(package_name) + # Import all Python files + for _ in _walk_packages(package.__path__, package.__name__ + ".", blacklist_pkgs=blacklist_pkgs): + pass + + +""" +Internal helpers. +""" + + +def _walk_packages( + path: str | None = None, + prefix: str = "", + onerror: callable | None = None, + blacklist_pkgs: list[str] | None = None, +): + """Yields ModuleInfo for all modules recursively on path, or, if path is None, all accessible modules. + + Note: + This function is a modified version of the original ``pkgutil.walk_packages`` function. It adds + the ``blacklist_pkgs`` argument to skip blacklisted packages. Please refer to the original + ``pkgutil.walk_packages`` function for more details. + + """ + # Default blacklist + if blacklist_pkgs is None: + blacklist_pkgs = [] + + def seen(p: str, m: dict[str, bool] = {}) -> bool: + """Check if a package has been seen before.""" + if p in m: + return True + m[p] = True + return False + + for info in pkgutil.iter_modules(path, prefix): + # check blacklisted + if any([black_pkg_name in info.name for black_pkg_name in blacklist_pkgs]): + continue + + # yield the module info + yield info + + if info.ispkg: + try: + __import__(info.name) + except Exception: + if onerror is not None: + onerror(info.name) + else: + raise + else: + path: list = getattr(sys.modules[info.name], "__path__", []) + + # don't traverse path items we've seen before + path = [p for p in path if not seen(p)] + + yield from _walk_packages(path, info.name + ".", onerror, blacklist_pkgs) diff --git a/source/isaaclab_tasks/isaaclab_tasks/utils/parse_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/utils/parse_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..0002c5d58d9ac8918856d32c726c6217d6c60171 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/utils/parse_cfg.py @@ -0,0 +1,219 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Sub-module with utilities for parsing and loading configurations.""" + +import collections +import importlib +import inspect +import os +import re + +import gymnasium as gym +import yaml + +from isaaclab.envs import DirectRLEnvCfg, ManagerBasedRLEnvCfg + + +def load_cfg_from_registry(task_name: str, entry_point_key: str) -> dict | object: + """Load default configuration given its entry point from the gym registry. + + This function loads the configuration object from the gym registry for the given task name. + It supports both YAML and Python configuration files. + + It expects the configuration to be registered in the gym registry as: + + .. code-block:: python + + gym.register( + id="My-Awesome-Task-v0", + ... + kwargs={"env_entry_point_cfg": "path.to.config:ConfigClass"}, + ) + + + The parsed configuration object for above example can be obtained as: + + .. code-block:: python + + from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry + + cfg = load_cfg_from_registry("My-Awesome-Task-v0", "env_entry_point_cfg") + + + Args: + task_name: The name of the environment. + entry_point_key: The entry point key to resolve the configuration file. + + Returns: + The parsed configuration object. If the entry point is a YAML file, it is parsed into a dictionary. + If the entry point is a Python class, it is instantiated and returned. + + Raises: + ValueError: If the entry point key is not available in the gym registry for the task. + """ + # obtain the configuration entry point + cfg_entry_point = gym.spec(task_name.split(":")[-1]).kwargs.get(entry_point_key) + # check if entry point exists + if cfg_entry_point is None: + # get existing agents and algorithms + agents = collections.defaultdict(list) + for k in gym.spec(task_name.split(":")[-1]).kwargs: + if k.endswith("_cfg_entry_point") and k != "env_cfg_entry_point": + spec = ( + k.replace("_cfg_entry_point", "") + .replace("rl_games", "rl-games") + .replace("rsl_rl", "rsl-rl") + .split("_") + ) + agent = spec[0].replace("-", "_") + algorithms = [item.upper() for item in (spec[1:] if len(spec) > 1 else ["PPO"])] + agents[agent].extend(algorithms) + msg = "\nExisting RL library (and algorithms) config entry points: " + for agent, algorithms in agents.items(): + msg += f"\n |-- {agent}: {', '.join(algorithms)}" + # raise error + raise ValueError( + f"Could not find configuration for the environment: '{task_name}'." + f"\nPlease check that the gym registry has the entry point: '{entry_point_key}'." + f"{msg if agents else ''}" + ) + # parse the default config file + if isinstance(cfg_entry_point, str) and cfg_entry_point.endswith(".yaml"): + if os.path.exists(cfg_entry_point): + # absolute path for the config file + config_file = cfg_entry_point + else: + # resolve path to the module location + mod_name, file_name = cfg_entry_point.split(":") + mod_path = os.path.dirname(importlib.import_module(mod_name).__file__) + # obtain the configuration file path + config_file = os.path.join(mod_path, file_name) + # load the configuration + print(f"[INFO]: Parsing configuration from: {config_file}") + with open(config_file, encoding="utf-8") as f: + cfg = yaml.full_load(f) + else: + if callable(cfg_entry_point): + # resolve path to the module location + mod_path = inspect.getfile(cfg_entry_point) + # load the configuration + cfg_cls = cfg_entry_point() + elif isinstance(cfg_entry_point, str): + # resolve path to the module location + mod_name, attr_name = cfg_entry_point.split(":") + mod = importlib.import_module(mod_name) + cfg_cls = getattr(mod, attr_name) + else: + cfg_cls = cfg_entry_point + # load the configuration + print(f"[INFO]: Parsing configuration from: {cfg_entry_point}") + if callable(cfg_cls): + cfg = cfg_cls() + else: + cfg = cfg_cls + return cfg + + +def parse_env_cfg( + task_name: str, device: str = "cuda:0", num_envs: int | None = None, use_fabric: bool | None = None +) -> ManagerBasedRLEnvCfg | DirectRLEnvCfg: + """Parse configuration for an environment and override based on inputs. + + Args: + task_name: The name of the environment. + device: The device to run the simulation on. Defaults to "cuda:0". + num_envs: Number of environments to create. Defaults to None, in which case it is left unchanged. + use_fabric: Whether to enable/disable fabric interface. If false, all read/write operations go through USD. + This slows down the simulation but allows seeing the changes in the USD through the USD stage. + Defaults to None, in which case it is left unchanged. + + Returns: + The parsed configuration object. + + Raises: + RuntimeError: If the configuration for the task is not a class. We assume users always use a class for the + environment configuration. + """ + # load the default configuration + cfg = load_cfg_from_registry(task_name.split(":")[-1], "env_cfg_entry_point") + + # check that it is not a dict + # we assume users always use a class for the configuration + if isinstance(cfg, dict): + raise RuntimeError(f"Configuration for the task: '{task_name}' is not a class. Please provide a class.") + + # simulation device + cfg.sim.device = device + # disable fabric to read/write through USD + if use_fabric is not None: + cfg.sim.use_fabric = use_fabric + # number of environments + if num_envs is not None: + cfg.scene.num_envs = num_envs + + return cfg + + +def get_checkpoint_path( + log_path: str, run_dir: str = ".*", checkpoint: str = ".*", other_dirs: list[str] = None, sort_alpha: bool = True +) -> str: + """Get path to the model checkpoint in input directory. + + The checkpoint file is resolved as: ``//<*other_dirs>/``, where the + :attr:`other_dirs` are intermediate folder names to concatenate. These cannot be regex expressions. + + If :attr:`run_dir` and :attr:`checkpoint` are regex expressions then the most recent (highest alphabetical order) + run and checkpoint are selected. To disable this behavior, set the flag :attr:`sort_alpha` to False. + + Args: + log_path: The log directory path to find models in. + run_dir: The regex expression for the name of the directory containing the run. Defaults to the most + recent directory created inside :attr:`log_path`. + other_dirs: The intermediate directories between the run directory and the checkpoint file. Defaults to + None, which implies that checkpoint file is directly under the run directory. + checkpoint: The regex expression for the model checkpoint file. Defaults to the most recent + torch-model saved in the :attr:`run_dir` directory. + sort_alpha: Whether to sort the runs by alphabetical order. Defaults to True. + If False, the folders in :attr:`run_dir` are sorted by the last modified time. + + Returns: + The path to the model checkpoint. + + Raises: + ValueError: When no runs are found in the input directory. + ValueError: When no checkpoints are found in the input directory. + + """ + # check if runs present in directory + try: + # find all runs in the directory that math the regex expression + runs = [ + os.path.join(log_path, run) for run in os.scandir(log_path) if run.is_dir() and re.match(run_dir, run.name) + ] + # sort matched runs by alphabetical order (latest run should be last) + if sort_alpha: + runs.sort() + else: + runs = sorted(runs, key=os.path.getmtime) + # create last run file path + if other_dirs is not None: + run_path = os.path.join(runs[-1], *other_dirs) + else: + run_path = runs[-1] + except IndexError: + raise ValueError(f"No runs present in the directory: '{log_path}' match: '{run_dir}'.") + + # list all model checkpoints in the directory + model_checkpoints = [f for f in os.listdir(run_path) if re.match(checkpoint, f)] + # check if any checkpoints are present + if len(model_checkpoints) == 0: + raise ValueError(f"No checkpoints in the directory: '{run_path}' match '{checkpoint}'.") + # sort alphabetically while ensuring that *_10 comes after *_9 + model_checkpoints.sort(key=lambda m: f"{m:0>15}") + # get latest matched checkpoint file + checkpoint_file = model_checkpoints[-1] + + return os.path.join(run_path, checkpoint_file) diff --git a/source/isaaclab_tasks/pyproject.toml b/source/isaaclab_tasks/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/source/isaaclab_tasks/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/source/isaaclab_tasks/setup.py b/source/isaaclab_tasks/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..455c56689ce0162b4b8fc8bdee05709046c03447 --- /dev/null +++ b/source/isaaclab_tasks/setup.py @@ -0,0 +1,55 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the 'isaaclab_tasks' python package.""" + +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Minimum dependencies required prior to installation +INSTALL_REQUIRES = [ + # generic + "numpy<2", + "torch>=2.7", + "torchvision>=0.14.1", # ensure compatibility with torch 1.13.1 + "protobuf>=4.25.8,!=5.26.0", + # basic logger + "tensorboard", + "numba", +] + +PYTORCH_INDEX_URL = ["https://download.pytorch.org/whl/cu128"] + +# Installation operation +setup( + name="isaaclab_tasks", + author="Isaac Lab Project Developers", + maintainer="Isaac Lab Project Developers", + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + include_package_data=True, + python_requires=">=3.10", + install_requires=INSTALL_REQUIRES, + dependency_links=PYTORCH_INDEX_URL, + packages=["isaaclab_tasks"], + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/source/isaaclab_tasks/test/benchmarking/configs.yaml b/source/isaaclab_tasks/test/benchmarking/configs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5df21551b73263a0b78565b212e3be38edbc969 --- /dev/null +++ b/source/isaaclab_tasks/test/benchmarking/configs.yaml @@ -0,0 +1,303 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# mode for very simple functional testing without checking thresholds +fast_test: + rl_games:Isaac-Ant-v0: + max_iterations: 10 + lower_thresholds: + reward: -99999 + episode_length: -99999 + upper_thresholds: + duration: 99999 + +# mode for capturing KPIs across all environments without checking thresholds +full_test: + Isaac-*: + max_iterations: 500 + lower_thresholds: + reward: -99999 + episode_length: -99999 + upper_thresholds: + duration: 99999 + +# mode for PR tests (default mode) +fast: + rl_games:Isaac-Ant-v0: + max_iterations: 200 + lower_thresholds: + reward: 45 + episode_length: 900 + upper_thresholds: + duration: 750 + skrl:Isaac-Cartpole-RGB-Camera-Direct-v0: + max_iterations: 50 + lower_thresholds: + reward: 190 + episode_length: 230 + upper_thresholds: + duration: 450 + rsl_rl:Isaac-Humanoid-v0: + max_iterations: 200 + lower_thresholds: + reward: 50 + episode_length: 750 + upper_thresholds: + duration: 500 + rl_games:Isaac-Quadcopter-Direct-v0: + max_iterations: 200 + lower_thresholds: + reward: 100 + episode_length: 400 + upper_thresholds: + duration: 250 + skrl:Isaac-Shadow-Hand-Over-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 30 + episode_length: 250 + upper_thresholds: + duration: 600 + rsl_rl:Isaac-Velocity-Rough-Anymal-C-v0: + max_iterations: 300 + lower_thresholds: + reward: 7 + episode_length: 900 + upper_thresholds: + duration: 1800 + +# mode for weekly CI +full: + Isaac-Ant-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 7000 + episode_length: 700 + upper_thresholds: + duration: 500 + Isaac-Ant-v0: + max_iterations: 1000 + lower_thresholds: + reward: 100 + episode_length: 700 + upper_thresholds: + duration: 800 + Isaac-Cart-Double-Pendulum-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 400 + episode_length: 150 + upper_thresholds: + duration: 500 + Isaac-Cartpole-Depth-Camera-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 200 + episode_length: 150 + upper_thresholds: + duration: 3000 + Isaac-Cartpole-Depth-v0: + max_iterations: 300 + lower_thresholds: + reward: 1 + episode_length: 150 + upper_thresholds: + duration: 3000 + Isaac-Cartpole-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 200 + episode_length: 150 + upper_thresholds: + duration: 500 + Isaac-Cartpole-RGB-Camera-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 200 + episode_length: 150 + upper_thresholds: + duration: 3000 + Isaac-Cartpole-RGB-ResNet18-v0: + max_iterations: 300 + lower_thresholds: + reward: 1 + episode_length: 100 + upper_thresholds: + duration: 4000 + Isaac-Cartpole-RGB-TheiaTiny-v0: + max_iterations: 300 + lower_thresholds: + reward: 1 + episode_length: 150 + upper_thresholds: + duration: 4000 + Isaac-Cartpole-RGB-v0: + max_iterations: 300 + lower_thresholds: + reward: -2 + episode_length: 150 + upper_thresholds: + duration: 4000 + Isaac-Cartpole-v0: + max_iterations: 1000 + lower_thresholds: + reward: 3 + episode_length: 150 + upper_thresholds: + duration: 1500 + Isaac-Factory-GearMesh-Direct-v0: + max_iterations: 100 + lower_thresholds: + reward: 200 + episode_length: 250 + upper_thresholds: + duration: 6000 + Isaac-Factory-NutThread-Direct-v0: + max_iterations: 100 + lower_thresholds: + reward: 400 + episode_length: 400 + upper_thresholds: + duration: 5000 + Isaac-Factory-PegInsert-Direct-v0: + max_iterations: 100 + lower_thresholds: + reward: 125 + episode_length: 130 + upper_thresholds: + duration: 4000 + Isaac-Franka-Cabinet-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 2000 + episode_length: 400 + upper_thresholds: + duration: 1000 + Isaac-Humanoid-Direct-v0: + max_iterations: 300 + lower_thresholds: + reward: 2000 + episode_length: 600 + upper_thresholds: + duration: 1000 + Isaac-Humanoid-v0: + max_iterations: 1000 + lower_thresholds: + reward: 100 + episode_length: 600 + upper_thresholds: + duration: 2500 + Isaac-Lift-Cube-Franka-v0: + max_iterations: 300 + lower_thresholds: + reward: 90 + episode_length: 100 + upper_thresholds: + duration: 1000 + Isaac-Navigation-Flat-Anymal-C-v0: + max_iterations: 300 + lower_thresholds: + reward: 0.5 + episode_length: 20 + upper_thresholds: + duration: 2000 + Isaac-Open-Drawer-Franka-v0: + max_iterations: 200 + lower_thresholds: + reward: 60 + episode_length: 150 + upper_thresholds: + duration: 3000 + Isaac-Quadcopter-Direct-v0: + max_iterations: 500 + lower_thresholds: + reward: 90 + episode_length: 300 + upper_thresholds: + duration: 500 + Isaac-Reach-Franka-*: + max_iterations: 1000 + lower_thresholds: + reward: 0.25 + episode_length: 150 + upper_thresholds: + duration: 1500 + Isaac-Reach-UR10-v0: + max_iterations: 1000 + lower_thresholds: + reward: 0.25 + episode_length: 150 + upper_thresholds: + duration: 1500 + Isaac-Repose-Cube-Allegro-Direct-v0: + max_iterations: 500 + lower_thresholds: + reward: 200 + episode_length: 150 + upper_thresholds: + duration: 1500 + Isaac-Repose-Cube-Allegro-*: + max_iterations: 500 + lower_thresholds: + reward: 15 + episode_length: 300 + upper_thresholds: + duration: 1500 + Isaac-Repose-Cube-Shadow-Direct-v0: + max_iterations: 3000 + lower_thresholds: + reward: 1000 + episode_length: 300 + upper_thresholds: + duration: 10000 + Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0: + max_iterations: 3000 + lower_thresholds: + reward: 1000 + episode_length: 50 + upper_thresholds: + duration: 15000 + Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0: + max_iterations: 3000 + lower_thresholds: + reward: 1000 + episode_length: 100 + upper_thresholds: + duration: 30000 + Isaac-Repose-Cube-Shadow-Vision-Direct-v0: + max_iterations: 3000 + lower_thresholds: + reward: 1000 + episode_length: 400 + upper_thresholds: + duration: 40000 + Isaac-Shadow-Hand-Over-Direct-v0: + max_iterations: 3000 + lower_thresholds: + reward: 1000 + episode_length: 150 + upper_thresholds: + duration: 10000 + Isaac-Velocity-Flat-*: + max_iterations: 1000 + lower_thresholds: + reward: 15 + episode_length: 700 + upper_thresholds: + duration: 3000 + Isaac-Velocity-Flat-Spot-v0: + max_iterations: 1000 + lower_thresholds: + reward: 150 + episode_length: 700 + upper_thresholds: + duration: 6000 + Isaac-Velocity-Rough-*: + max_iterations: 1000 + lower_thresholds: + reward: 7 + episode_length: 700 + upper_thresholds: + duration: 6000 diff --git a/source/isaaclab_tasks/test/benchmarking/conftest.py b/source/isaaclab_tasks/test/benchmarking/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..6a13b1898a5283e6f66c47d478dfacf71f0a23f4 --- /dev/null +++ b/source/isaaclab_tasks/test/benchmarking/conftest.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import json + +import pytest + +# Local imports should be imported last +import env_benchmark_test_utils as utils # isort: skip + +# Global variable for storing KPI data +GLOBAL_KPI_STORE = {} + + +def pytest_addoption(parser): + parser.addoption( + "--workflows", + action="store", + nargs="+", + default=["rl_games", "rsl_rl", "sb3", "skrl"], + help="List of workflows. Must be equal to or a subset of the default list.", + ) + parser.addoption( + "--config_path", + action="store", + default="configs.yaml", + help="Path to config file for environment training and evaluation.", + ) + parser.addoption( + "--mode", + action="store", + default="fast", + help="Coverage mode defined in the config file.", + ) + parser.addoption("--num_gpus", action="store", type=int, default=1, help="Number of GPUs for distributed training.") + parser.addoption( + "--save_kpi_payload", + action="store_true", + help="To collect output metrics into a KPI payload that can be uploaded to a dashboard.", + ) + parser.addoption( + "--tag", + action="store", + default="", + help="Optional tag to add to the KPI payload for filtering on the Grafana dashboard.", + ) + + +@pytest.fixture +def workflows(request): + return request.config.getoption("--workflows") + + +@pytest.fixture +def config_path(request): + return request.config.getoption("--config_path") + + +@pytest.fixture +def mode(request): + return request.config.getoption("--mode") + + +@pytest.fixture +def num_gpus(request): + return request.config.getoption("--num_gpus") + + +@pytest.fixture +def save_kpi_payload(request): + return request.config.getoption("--save_kpi_payload") + + +@pytest.fixture +def tag(request): + return request.config.getoption("--tag") + + +# Fixture for storing KPI data in a global variable +@pytest.fixture(scope="session") +def kpi_store(): + return GLOBAL_KPI_STORE # Using global variable for storing KPI data + + +# This hook dynamically generates test cases based on the --workflows option. +# For any test that includes a 'workflow' fixture, this will parametrize it +# with all values passed via the command line option --workflows. +def pytest_generate_tests(metafunc): + if "workflow" in metafunc.fixturenames: + workflows = metafunc.config.getoption("workflows") + metafunc.parametrize("workflow", workflows) + + +# The pytest session finish hook +def pytest_sessionfinish(session, exitstatus): + # Access global variable instead of fixture + tag = session.config.getoption("--tag") + utils.process_kpi_data(GLOBAL_KPI_STORE, tag=tag) + print(json.dumps(GLOBAL_KPI_STORE, indent=2)) + save_kpi_payload = session.config.getoption("--save_kpi_payload") + if save_kpi_payload: + print("Saving KPI data...") + utils.output_payloads(GLOBAL_KPI_STORE) diff --git a/source/isaaclab_tasks/test/benchmarking/env_benchmark_test_utils.py b/source/isaaclab_tasks/test/benchmarking/env_benchmark_test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..52dbeadda3a02340e90a4ecd505f57e53acc45b7 --- /dev/null +++ b/source/isaaclab_tasks/test/benchmarking/env_benchmark_test_utils.py @@ -0,0 +1,239 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import glob +import json +import math +import os +import re +from datetime import datetime + +import numpy as np +import yaml +from tensorboard.backend.event_processing import event_accumulator + +import carb + + +def get_env_configs(configs_path): + """Get environment configurations from yaml filepath.""" + with open(configs_path) as env_configs_file: + env_configs = yaml.safe_load(env_configs_file) + return env_configs + + +def get_env_config(env_configs, mode, workflow, task): + """Get the environment configuration.""" + if mode not in env_configs: + raise ValueError(f"Mode {mode} is not supported in the config file.") + + extended_task = f"{workflow}:{task}" + # return a direct match with extended task name + if extended_task in env_configs[mode]: + return env_configs[mode][extended_task] + + # else, return a direct match with task name + if task in env_configs[mode]: + return env_configs[mode][task] + + # else, return a regex match with extended task name + for env_config_key in env_configs[mode].keys(): + if re.match(env_config_key, extended_task): + return env_configs[mode][env_config_key] + + # else, return a regex match with task name + for env_config_key in env_configs[mode].keys(): + if re.match(env_config_key, task): + return env_configs[mode][env_config_key] + + # if no match is found, return None + return None + + +def evaluate_job(workflow, task, env_config, duration): + """Evaluate the job.""" + log_data = _retrieve_logs(workflow, task) + + kpi_payload = {"success": True, "msg": ""} + + # handle case where no log files are found + if not log_data: + kpi_payload["success"] = False + kpi_payload["msg"] = "error: training did not finish!" + return kpi_payload + + thresholds = {**env_config.get("lower_thresholds", {}), **env_config.get("upper_thresholds", {})} + + # evaluate all thresholds from the config + for threshold_name, threshold_val in thresholds.items(): + uses_lower_threshold = threshold_name in env_config.get("lower_thresholds", {}) + if threshold_name == "duration": + val = duration + else: + val = _extract_log_val(threshold_name, log_data, uses_lower_threshold, workflow) + # skip non-numeric values + if val is None or not isinstance(val, (int, float)) or (isinstance(val, float) and math.isnan(val)): + continue + val = round(val, 4) + if uses_lower_threshold: + # print(f"{threshold_name}: {val} > {round(threshold_val, 4)}") + if val < threshold_val: + kpi_payload["success"] = False + else: + # print(f"{threshold_name}: {val} < {round(threshold_val, 4)}") + if val > threshold_val: + kpi_payload["success"] = False + kpi_payload[threshold_name] = val + if threshold_name == "reward": + normalized_reward = val / threshold_val + kpi_payload[f"{threshold_name}_normalized"] = normalized_reward + kpi_payload[f"{threshold_name}_threshold"] = threshold_val + + # add max iterations to the payload + max_iterations = env_config.get("max_iterations") + if max_iterations is not None: + kpi_payload["max_iterations"] = max_iterations + + return kpi_payload + + +def process_kpi_data(kpi_payloads, tag=""): + """Combine and augment the KPI payloads.""" + # accumulate workflow outcomes + totals = {} + successes = {} + failures_did_not_finish = {} + failures_did_not_pass_thresholds = {} + for job_id, kpi_payload in kpi_payloads.items(): + workflow = job_id.split(":")[0] + if workflow not in totals: + totals[workflow] = 0 + successes[workflow] = 0 + failures_did_not_finish[workflow] = 0 + failures_did_not_pass_thresholds[workflow] = 0 + totals[workflow] += 1 + if kpi_payload["success"]: + successes[workflow] += 1 + else: + if kpi_payload["msg"] == "error: training did not finish!": + failures_did_not_finish[workflow] += 1 + else: + failures_did_not_pass_thresholds[workflow] += 1 + + kpi_payloads["overall"] = { + "totals": totals, + "successes": successes, + "failures_did_not_finish": failures_did_not_finish, + "failures_did_not_pass_thresholds": failures_did_not_pass_thresholds, + "timestamp": datetime.now().isoformat(), + "tag": tag, + } + + return kpi_payloads + + +def output_payloads(payloads): + """Output the KPI payloads to a json file.""" + # first grab all log files + repo_path = os.path.join(carb.tokens.get_tokens_interface().resolve("${app}"), "..") + output_path = os.path.join(repo_path, "logs/kpi.json") + # create directory if it doesn't exist + if not os.path.exists(os.path.dirname(output_path)): + os.makedirs(os.path.dirname(output_path)) + # save file + with open(output_path, "w") as payload_file: + json.dump(payloads, payload_file, indent=4) + + +def _retrieve_logs(workflow, task): + """Retrieve training logs.""" + # first grab all log files + repo_path = os.path.join(carb.tokens.get_tokens_interface().resolve("${app}"), "..") + from isaaclab.utils.version import get_isaac_sim_version + + if get_isaac_sim_version().major < 5: + repo_path = os.path.join(repo_path, "..") + if workflow == "rl_games": + log_files_path = os.path.join(repo_path, f"logs/{workflow}/{task}/*/summaries/*") + else: + log_files_path = os.path.join(repo_path, f"logs/{workflow}/{task}/*/*.tfevents.*") + log_files = glob.glob(log_files_path) + # handle case where no log files are found + if not log_files: + return None + # find most recent + latest_log_file = max(log_files, key=os.path.getctime) + # parse tf file into a dictionary + log_data = _parse_tf_logs(latest_log_file) + return log_data + + +def _parse_tf_logs(log): + """Parse the tensorflow filepath into a dictionary.""" + log_data = {} + ea = event_accumulator.EventAccumulator(log) + ea.Reload() + tags = ea.Tags()["scalars"] + for tag in tags: + log_data[tag] = [] + for event in ea.Scalars(tag): + log_data[tag].append((event.step, event.value)) + return log_data + + +def _extract_log_val(name, log_data, uses_lower_threshold, workflow): + """Extract the value from the log data.""" + try: + if name == "reward": + reward_tags = { + "rl_games": "rewards/iter", + "rsl_rl": "Train/mean_reward", + "sb3": None, # TODO: complete when sb3 is fixed + "skrl": "Reward / Total reward (mean)", + } + tag = reward_tags.get(workflow) + if tag: + return _extract_reward(log_data, tag) + + elif name == "episode_length": + episode_tags = { + "rl_games": "episode_lengths/iter", + "rsl_rl": "Train/mean_episode_length", + "sb3": None, # TODO: complete when sb3 is fixed + "skrl": "Episode / Total timesteps (mean)", + } + tag = episode_tags.get(workflow) + if tag: + return _extract_feature(log_data, tag, uses_lower_threshold) + + elif name == "training_time": + return {"rl_games": log_data["rewards/time"][-1][0], "rsl_rl": None, "sb3": None, "skrl": None}.get( + workflow + ) + except Exception: + return None + + raise ValueError(f"Env Config name {name} is not supported.") + + +def _extract_feature(log_data, feature, uses_lower_threshold): + """Extract the feature from the log data.""" + log_data = np.array(log_data[feature])[:, 1] + + if uses_lower_threshold: + return max(log_data) + else: + return min(log_data) + + +def _extract_reward(log_data, feature, k=8): + """Extract the averaged max reward from the log data.""" + log_data = np.array(log_data[feature])[:, 1] + + # find avg of k max values + k = min(len(log_data), k) + averaged_reward = np.mean(np.partition(log_data, -k)[-k:]) + + return averaged_reward diff --git a/source/isaaclab_tasks/test/benchmarking/test_environments_training.py b/source/isaaclab_tasks/test/benchmarking/test_environments_training.py new file mode 100644 index 0000000000000000000000000000000000000000..70fd562089a170eea914fd29d0a0d5461c7923f9 --- /dev/null +++ b/source/isaaclab_tasks/test/benchmarking/test_environments_training.py @@ -0,0 +1,118 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# Launch omniverse app +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +import os +import subprocess +import sys +import time + +import env_benchmark_test_utils as utils +import gymnasium as gym +import pytest + +import carb + +from isaaclab_rl.utils.pretrained_checkpoint import WORKFLOW_EXPERIMENT_NAME_VARIABLE, WORKFLOW_TRAINER + + +def setup_environment(): + """Setup environment for testing.""" + # Acquire all Isaac environments names + registered_task_specs = [] + for task_spec in gym.registry.values(): + if "Isaac" in task_spec.id and not task_spec.id.endswith("Play-v0"): + registered_task_specs.append(task_spec) + + # Sort environments by name + registered_task_specs.sort(key=lambda x: x.id) + + # This flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + return registered_task_specs + + +def train_job(workflow, task, env_config, num_gpus): + """Train a single job for a given workflow, task, and configuration, and return the duration.""" + cmd = [ + sys.executable, + WORKFLOW_TRAINER[workflow], + "--task", + task, + "--enable_cameras", + "--headless", + ] + + # Add max iterations if specified + max_iterations = env_config.get("max_iterations") + if max_iterations is not None: + cmd.extend(["--max_iterations", str(max_iterations)]) + + if num_gpus > 1: + cmd.append(f"--nnprod_per_node={num_gpus}") + cmd.append("--distributed") + + # Add experiment name variable + cmd.append(f"{WORKFLOW_EXPERIMENT_NAME_VARIABLE[workflow]}={task}") + + print("Running : " + " ".join(cmd)) + + start_time = time.time() + subprocess.run(cmd) + duration = time.time() - start_time + + return duration + + +@pytest.mark.parametrize("task_spec", setup_environment()) +def test_train_environments(workflow, task_spec, config_path, mode, num_gpus, kpi_store): + """Train environments provided in the config file, save KPIs, and evaluate against thresholds""" + # Skip if workflow not supported for this task + if workflow + "_cfg_entry_point" not in task_spec.kwargs: + pytest.skip(f"Workflow {workflow} not supported for task {task_spec.id}") + + # Load environment config + task = task_spec.id + if config_path.startswith("/"): + full_config_path = config_path + else: + full_config_path = os.path.join(os.path.dirname(__file__), config_path) + env_configs = utils.get_env_configs(full_config_path) + env_config = utils.get_env_config(env_configs, mode, workflow, task) + + # Skip if config not found + if env_config is None: + pytest.skip(f"No config found for task {task} in {mode} mode") + + job_name = f"{workflow}:{task}" + print(f">>> Training: {job_name}") + + # Train and capture duration + duration = train_job(workflow, task, env_config, num_gpus) + + print(f">>> Evaluating trained: {job_name}") + # Check if training logs were output and all thresholds passed + kpi_payload = utils.evaluate_job(workflow, task, env_config, duration) + + success_flag = kpi_payload["success"] + print(f">>> Trained {job_name} success flag: {success_flag}.") + print("-" * 80) + + # Save KPI + kpi_store[job_name] = kpi_payload + + # Verify job was successful + if not kpi_payload["success"]: + pytest.fail(f"Job {job_name} failed to meet success criteria") diff --git a/source/isaaclab_tasks/test/env_test_utils.py b/source/isaaclab_tasks/test/env_test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b6f0383abee1749dfd88b6c68f1105762022a05d --- /dev/null +++ b/source/isaaclab_tasks/test/env_test_utils.py @@ -0,0 +1,283 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Shared test utilities for Isaac Lab environments.""" + +import inspect +import os + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +from isaaclab.envs.utils.spaces import sample_space +from isaaclab.utils.version import get_isaac_sim_version + +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +def setup_environment( + include_play: bool = False, + factory_envs: bool | None = None, + multi_agent: bool | None = None, +) -> list[str]: + """ + Acquire all registered Isaac environment task IDs with optional filters. + + Args: + include_play: If True, include environments ending in 'Play-v0'. + factory_envs: + - True: include only Factory environments + - False: exclude Factory environments + - None: include both Factory and non-Factory environments + multi_agent: + - True: include only multi-agent environments + - False: include only single-agent environments + - None: include all environments regardless of agent type + + Returns: + A sorted list of task IDs matching the selected filters. + """ + # disable interactive mode for wandb for automate environments + os.environ["WANDB_DISABLED"] = "true" + + # acquire all Isaac environment names + registered_tasks = [] + for task_spec in gym.registry.values(): + # only consider Isaac environments + if "Isaac" not in task_spec.id: + continue + + # filter Play environments, if needed + if not include_play and task_spec.id.endswith("Play-v0"): + continue + + # TODO: factory environments cause tests to fail if run together with other envs, + # so we collect these environments separately to run in a separate unit test. + # apply factory filter + if (factory_envs is True and ("Factory" not in task_spec.id and "Forge" not in task_spec.id)) or ( + factory_envs is False and ("Factory" in task_spec.id or "Forge" in task_spec.id) + ): + continue + # if None: no filter + + # apply multi agent filter + if multi_agent is not None: + # parse config + env_cfg = parse_env_cfg(task_spec.id) + if (multi_agent is True and not hasattr(env_cfg, "possible_agents")) or ( + multi_agent is False and hasattr(env_cfg, "possible_agents") + ): + continue + # if None: no filter + + registered_tasks.append(task_spec.id) + + # sort environments alphabetically + registered_tasks.sort() + + # this flag is necessary to prevent a bug where the simulation gets stuck randomy when running many environments + carb.settings.get_settings().set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + print(">>> All registered environments:", registered_tasks) + + return registered_tasks + + +def _run_environments( + task_name, + device, + num_envs, + num_steps=100, + multi_agent=False, + create_stage_in_memory=False, + disable_clone_in_fabric=False, +): + """Run all environments and check environments return valid signals. + + Args: + task_name: Name of the environment. + device: Device to use (e.g., 'cuda'). + num_envs: Number of environments. + num_steps: Number of simulation steps. + multi_agent: Whether the environment is multi-agent. + create_stage_in_memory: Whether to create stage in memory. + disable_clone_in_fabric: Whether to disable fabric cloning. + """ + + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5 and create_stage_in_memory: + pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + + # skip suction gripper environments as they require CPU simulation and cannot be run with GPU simulation + if "Suction" in task_name and device != "cpu": + return + + # skip these environments as they cannot be run with 32 environments within reasonable VRAM + if num_envs == 32 and task_name in [ + "Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0", + "Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0", + "Isaac-Stack-Cube-Instance-Randomize-Franka-v0", + ]: + return + + # skip these environments as they cannot be run with 32 environments within reasonable VRAM + if "Visuomotor" in task_name and num_envs == 32: + return + + # skip automate environments as they require cuda installation + if task_name in ["Isaac-AutoMate-Assembly-Direct-v0", "Isaac-AutoMate-Disassembly-Direct-v0"]: + return + + # Check if this is the teddy bear environment and if it's being called from the right test file + if task_name == "Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0": + # Get the calling frame to check which test file is calling this function + frame = inspect.currentframe() + while frame: + filename = frame.f_code.co_filename + if "test_lift_teddy_bear.py" in filename: + # Called from the dedicated test file, allow it to run + break + frame = frame.f_back + + # If not called from the dedicated test file, skip it + if not frame: + return + + print(f""">>> Running test for environment: {task_name}""") + _check_random_actions( + task_name, + device, + num_envs, + num_steps=num_steps, + multi_agent=multi_agent, + create_stage_in_memory=create_stage_in_memory, + disable_clone_in_fabric=disable_clone_in_fabric, + ) + print(f""">>> Closing environment: {task_name}""") + print("-" * 80) + + +def _check_random_actions( + task_name: str, + device: str, + num_envs: int, + num_steps: int = 100, + multi_agent: bool = False, + create_stage_in_memory: bool = False, + disable_clone_in_fabric: bool = False, +): + """Run random actions and check environments return valid signals. + + Args: + task_name: Name of the environment. + device: Device to use (e.g., 'cuda'). + num_envs: Number of environments. + num_steps: Number of simulation steps. + multi_agent: Whether the environment is multi-agent. + create_stage_in_memory: Whether to create stage in memory. + disable_clone_in_fabric: Whether to disable fabric cloning. + """ + # create a new context stage, if stage in memory is not enabled + if not create_stage_in_memory: + omni.usd.get_context().new_stage() + + # reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + try: + # parse config + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # set config args + env_cfg.sim.create_stage_in_memory = create_stage_in_memory + if disable_clone_in_fabric: + env_cfg.scene.clone_in_fabric = False + + # filter based off multi agents mode and create env + if multi_agent: + if not hasattr(env_cfg, "possible_agents"): + print(f"[INFO]: Skipping {task_name} as it is not a multi-agent task") + return + env = gym.make(task_name, cfg=env_cfg) + else: + if hasattr(env_cfg, "possible_agents"): + print(f"[INFO]: Skipping {task_name} as it is a multi-agent task") + return + env = gym.make(task_name, cfg=env_cfg) + + except Exception as e: + # try to close environment on exception + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # disable control on stop + env.unwrapped.sim._app_control_on_stop_handle = None # type: ignore + + # override action space if set to inf for `Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0` + if task_name == "Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0": + for i in range(env.unwrapped.single_action_space.shape[0]): + if env.unwrapped.single_action_space.low[i] == float("-inf"): + env.unwrapped.single_action_space.low[i] = -1.0 + if env.unwrapped.single_action_space.high[i] == float("inf"): + env.unwrapped.single_action_space.low[i] = 1.0 + + # reset environment + obs, _ = env.reset() + + # check signal + assert _check_valid_tensor(obs) + + # simulate environment for num_steps + with torch.inference_mode(): + for _ in range(num_steps): + # sample actions according to the defined space + if multi_agent: + actions = { + agent: sample_space( + env.unwrapped.action_spaces[agent], device=env.unwrapped.device, batch_size=num_envs + ) + for agent in env.unwrapped.possible_agents + } + else: + actions = sample_space( + env.unwrapped.single_action_space, device=env.unwrapped.device, batch_size=num_envs + ) + # apply actions + transition = env.step(actions) + # check signals + for data in transition[:-1]: # exclude info + if multi_agent: + for agent, agent_data in data.items(): + assert _check_valid_tensor(agent_data), f"Invalid data ('{agent}'): {agent_data}" + else: + assert _check_valid_tensor(data), f"Invalid data: {data}" + + # close environment + env.close() + + +def _check_valid_tensor(data: torch.Tensor | dict) -> bool: + """Checks if given data does not have corrupted values. + + Args: + data: Data buffer. + + Returns: + True if the data is valid. + """ + if isinstance(data, torch.Tensor): + return not torch.any(torch.isnan(data)) + elif isinstance(data, (tuple, list)): + return all(_check_valid_tensor(value) for value in data) + elif isinstance(data, dict): + return all(_check_valid_tensor(value) for value in data.values()) + else: + raise ValueError(f"Input data of invalid type: {type(data)}.") diff --git a/source/isaaclab_tasks/test/test_environment_determinism.py b/source/isaaclab_tasks/test/test_environment_determinism.py new file mode 100644 index 0000000000000000000000000000000000000000..52ac1f34980ed081d16069cc5f4f6a25ae52f816 --- /dev/null +++ b/source/isaaclab_tasks/test/test_environment_determinism.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + + +@pytest.fixture(scope="module", autouse=True) +def setup_environment(): + # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the + # test on many environments. + carb_settings_iface = carb.settings.get_settings() + carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False) + + +@pytest.mark.parametrize( + "task_name", + [ + "Isaac-Open-Drawer-Franka-v0", + "Isaac-Lift-Cube-Franka-v0", + ], +) +@pytest.mark.parametrize("device", ["cuda", "cpu"]) +def test_manipulation_env_determinism(task_name, device): + """Check deterministic environment creation for manipulation.""" + _test_environment_determinism(task_name, device) + + +@pytest.mark.parametrize( + "task_name", + [ + "Isaac-Velocity-Flat-Anymal-C-v0", + "Isaac-Velocity-Rough-Anymal-C-v0", + "Isaac-Velocity-Rough-Anymal-C-Direct-v0", + ], +) +@pytest.mark.parametrize("device", ["cuda", "cpu"]) +def test_locomotion_env_determinism(task_name, device): + """Check deterministic environment creation for locomotion.""" + _test_environment_determinism(task_name, device) + + +@pytest.mark.parametrize( + "task_name", + [ + "Isaac-Repose-Cube-Allegro-v0", + # "Isaac-Repose-Cube-Allegro-Direct-v0", # FIXME: @kellyg, any idea why it is not deterministic? + ], +) +@pytest.mark.parametrize("device", ["cuda", "cpu"]) +def test_dextrous_env_determinism(task_name, device): + """Check deterministic environment creation for dextrous manipulation.""" + _test_environment_determinism(task_name, device) + + +def _test_environment_determinism(task_name: str, device: str): + """Check deterministic environment creation.""" + # fix number of steps + num_envs = 32 + num_steps = 100 + # call function to create and step the environment + obs_1, rew_1 = _obtain_transition_tuples(task_name, num_envs, device, num_steps) + obs_2, rew_2 = _obtain_transition_tuples(task_name, num_envs, device, num_steps) + + # check everything is as expected + # -- rewards should be the same + torch.testing.assert_close(rew_1, rew_2) + # -- observations should be the same + for key in obs_1.keys(): + torch.testing.assert_close(obs_1[key], obs_2[key]) + + +def _obtain_transition_tuples(task_name: str, num_envs: int, device: str, num_steps: int) -> tuple[dict, torch.Tensor]: + """Run random actions and obtain transition tuples after fixed number of steps.""" + # create a new stage + omni.usd.get_context().new_stage() + try: + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + # set seed + env_cfg.seed = 42 + # create environment + env = gym.make(task_name, cfg=env_cfg) + except Exception as e: + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}") + + # disable control on stop + env.unwrapped.sim._app_control_on_stop_handle = None # type: ignore + + # reset environment + obs, _ = env.reset() + # simulate environment for fixed steps + with torch.inference_mode(): + for _ in range(num_steps): + # sample actions from -1 to 1 + actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions and get initial observation + obs, rewards = env.step(actions)[:2] + + # close the environment + env.close() + + return obs, rewards diff --git a/source/isaaclab_tasks/test/test_environments.py b/source/isaaclab_tasks/test/test_environments.py new file mode 100644 index 0000000000000000000000000000000000000000..879948f9d9a84e4e48a1e8c3a457d9073fa1527c --- /dev/null +++ b/source/isaaclab_tasks/test/test_environments.py @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import sys + +# Import pinocchio in the main script to force the use of the dependencies +# installed by IsaacLab and not the one installed by Isaac Sim. +# pinocchio is required by the Pink IK controller +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import pytest + +import isaaclab_tasks # noqa: F401 + +# Local imports should be imported last +from env_test_utils import _run_environments, setup_environment # isort: skip + + +@pytest.mark.parametrize("num_envs, device", [(32, "cuda"), (1, "cuda")]) +@pytest.mark.parametrize("task_name", setup_environment(include_play=False, factory_envs=False, multi_agent=False)) +@pytest.mark.isaacsim_ci +def test_environments(task_name, num_envs, device): + # run environments without stage in memory + _run_environments(task_name, device, num_envs, create_stage_in_memory=False) diff --git a/source/isaaclab_tasks/test/test_environments_with_stage_in_memory.py b/source/isaaclab_tasks/test/test_environments_with_stage_in_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..8a99436b91c70fad431cabf118a0bf2ebebed146 --- /dev/null +++ b/source/isaaclab_tasks/test/test_environments_with_stage_in_memory.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import sys + +# Import pinocchio in the main script to force the use of the dependencies +# installed by IsaacLab and not the one installed by Isaac Sim. +# pinocchio is required by the Pink IK controller +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +from isaaclab.utils.version import get_isaac_sim_version + +"""Rest everything follows.""" + +import pytest + +import isaaclab_tasks # noqa: F401 + +# Local imports should be imported last +from env_test_utils import _run_environments, setup_environment # isort: skip + + +# note, running an env test without stage in memory then +# running an env test with stage in memory causes IsaacLab to hang. +# so, here we run all envs with stage in memory separately + +# TODO(mtrepte): re-enable with fabric cloning fix +# @pytest.mark.parametrize("num_envs, device", [(2, "cuda")]) +# @pytest.mark.parametrize("task_name", setup_environment(include_play=False,factory_envs=False, multi_agent=False)) +# def test_environments_with_stage_in_memory_and_clone_in_fabric_disabled(task_name, num_envs, device): +# # skip test if stage in memory is not supported +# if get_isaac_sim_version().major < 5: +# pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + +# # run environments with stage in memory +# _run_environments(task_name, device, num_envs, create_stage_in_memory=True) + + +@pytest.mark.parametrize("num_envs, device", [(2, "cuda")]) +@pytest.mark.parametrize("task_name", setup_environment(include_play=False, factory_envs=False, multi_agent=False)) +def test_environments_with_stage_in_memory_and_clone_in_fabric_disabled(task_name, num_envs, device): + # skip test if stage in memory is not supported + if get_isaac_sim_version().major < 5: + pytest.skip("Stage in memory is not supported in this version of Isaac Sim") + + # run environments with stage in memory + _run_environments(task_name, device, num_envs, create_stage_in_memory=True, disable_clone_in_fabric=True) diff --git a/source/isaaclab_tasks/test/test_factory_environments.py b/source/isaaclab_tasks/test/test_factory_environments.py new file mode 100644 index 0000000000000000000000000000000000000000..059080006557f9e8f16e8cda14d50438f6c41e9c --- /dev/null +++ b/source/isaaclab_tasks/test/test_factory_environments.py @@ -0,0 +1,32 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import pytest + +import isaaclab_tasks # noqa: F401 + +# Local imports should be imported last +from env_test_utils import _check_random_actions, setup_environment # isort: skip + + +@pytest.mark.parametrize("num_envs, device", [(32, "cuda"), (1, "cuda")]) +@pytest.mark.parametrize("task_name", setup_environment(factory_envs=True, multi_agent=False)) +@pytest.mark.isaacsim_ci +def test_factory_environments(task_name, num_envs, device): + """Run all factory environments and check environments return valid signals.""" + print(f">>> Running test for environment: {task_name}") + _check_random_actions(task_name, device, num_envs) + print(f">>> Closing environment: {task_name}") + print("-" * 80) diff --git a/source/isaaclab_tasks/test/test_hydra.py b/source/isaaclab_tasks/test/test_hydra.py new file mode 100644 index 0000000000000000000000000000000000000000..5c81cb3e650fe364327fed3e62db6dd9fa36568b --- /dev/null +++ b/source/isaaclab_tasks/test/test_hydra.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import sys + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import functools +from collections.abc import Callable + +import hydra +from hydra import compose, initialize +from omegaconf import OmegaConf + +from isaaclab.utils import replace_strings_with_slices + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.hydra import register_task_to_hydra + + +def hydra_task_config_test(task_name: str, agent_cfg_entry_point: str) -> Callable: + """Copied from hydra.py hydra_task_config, since hydra.main requires a single point of entry, + which will not work with multiple tests. Here, we replace hydra.main with hydra initialize + and compose.""" + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + # register the task to Hydra + env_cfg, agent_cfg = register_task_to_hydra(task_name, agent_cfg_entry_point) + + # replace hydra.main with initialize and compose + with initialize(config_path=None, version_base="1.3"): + hydra_env_cfg = compose(config_name=task_name, overrides=sys.argv[1:]) + # convert to a native dictionary + hydra_env_cfg = OmegaConf.to_container(hydra_env_cfg, resolve=True) + # replace string with slices because OmegaConf does not support slices + hydra_env_cfg = replace_strings_with_slices(hydra_env_cfg) + # update the configs with the Hydra command line arguments + env_cfg.from_dict(hydra_env_cfg["env"]) + if isinstance(agent_cfg, dict): + agent_cfg = hydra_env_cfg["agent"] + else: + agent_cfg.from_dict(hydra_env_cfg["agent"]) + # call the original function + func(env_cfg, agent_cfg, *args, **kwargs) + + return wrapper + + return decorator + + +def test_hydra(): + """Test the hydra configuration system.""" + + # set hardcoded command line arguments + sys.argv = [ + sys.argv[0], + "env.decimation=42", # test simple env modification + "env.events.physics_material.params.asset_cfg.joint_ids='slice(0 ,1, 2)'", # test slice setting + "env.scene.robot.init_state.joint_vel={.*: 4.0}", # test regex setting + "env.rewards.feet_air_time=null", # test setting to none + "agent.max_iterations=3", # test simple agent modification + ] + + @hydra_task_config_test("Isaac-Velocity-Flat-H1-v0", "rsl_rl_cfg_entry_point") + def main(env_cfg, agent_cfg): + # env + assert env_cfg.decimation == 42 + assert env_cfg.events.physics_material.params["asset_cfg"].joint_ids == slice(0, 1, 2) + assert env_cfg.scene.robot.init_state.joint_vel == {".*": 4.0} + assert env_cfg.rewards.feet_air_time is None + # agent + assert agent_cfg.max_iterations == 3 + + main() + # clean up + sys.argv = [sys.argv[0]] + hydra.core.global_hydra.GlobalHydra.instance().clear() + + +def test_nested_iterable_dict(): + """Test the hydra configuration system when dict is nested in an Iterable.""" + + @hydra_task_config_test("Isaac-Lift-Cube-Franka-v0", "rsl_rl_cfg_entry_point") + def main(env_cfg, agent_cfg): + # env + assert env_cfg.scene.ee_frame.target_frames[0].name == "end_effector" + assert env_cfg.scene.ee_frame.target_frames[0].offset.pos[2] == 0.1034 + + main() + # clean up + sys.argv = [sys.argv[0]] + hydra.core.global_hydra.GlobalHydra.instance().clear() diff --git a/source/isaaclab_tasks/test/test_lift_teddy_bear.py b/source/isaaclab_tasks/test/test_lift_teddy_bear.py new file mode 100644 index 0000000000000000000000000000000000000000..e131e0357498af9a9d3a50c7345545e3388aaefd --- /dev/null +++ b/source/isaaclab_tasks/test/test_lift_teddy_bear.py @@ -0,0 +1,44 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +import sys + +# Import pinocchio in the main script to force the use of the dependencies +# installed by IsaacLab and not the one installed by Isaac Sim. +# pinocchio is required by the Pink IK controller +if sys.platform != "win32": + import pinocchio # noqa: F401 + +from isaaclab.app import AppLauncher + +# launch the simulator with specific settings for teddy bear environment +app_launcher = AppLauncher( + headless=True, enable_cameras=False, kit_args='--/app/extensions/excluded=["omni.usd.metrics.assembler.ui"]' +) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import pytest + +import isaaclab_tasks # noqa: F401 + +# Local imports should be imported last +from env_test_utils import _run_environments # isort: skip + + +@pytest.mark.parametrize("num_envs, device", [(32, "cuda"), (1, "cuda")]) +def test_lift_teddy_bear_environment(num_envs, device): + """Test the Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0 environment in isolation.""" + task_name = "Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0" + + # Try to run the environment with specific settings for this problematic case + try: + _run_environments(task_name, device, num_envs, create_stage_in_memory=False) + except Exception as e: + # If it still fails, skip the test with a descriptive message + pytest.skip(f"Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0 environment failed to load: {e}") diff --git a/source/isaaclab_tasks/test/test_multi_agent_environments.py b/source/isaaclab_tasks/test/test_multi_agent_environments.py new file mode 100644 index 0000000000000000000000000000000000000000..478cb3942e105fc27f0536ac68527bca2a2f194d --- /dev/null +++ b/source/isaaclab_tasks/test/test_multi_agent_environments.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + + +"""Rest everything follows.""" + +import pytest + +import isaaclab_tasks # noqa: F401 + +# Local imports should be imported last +from env_test_utils import _check_random_actions, setup_environment # isort: skip + + +@pytest.mark.parametrize("num_envs, device", [(32, "cuda"), (1, "cuda")]) +@pytest.mark.parametrize("task_name", setup_environment(multi_agent=True)) +def test_environments(task_name, num_envs, device): + """Run all environments with given parameters and check environments return valid signals.""" + print(f">>> Running test for environment: {task_name} with num_envs={num_envs} and device={device}") + # check environment + _check_random_actions(task_name, device, num_envs, multi_agent=True) + # close the environment + print(f">>> Closing environment: {task_name}") + print("-" * 80) diff --git a/source/isaaclab_tasks/test/test_record_video.py b/source/isaaclab_tasks/test/test_record_video.py new file mode 100644 index 0000000000000000000000000000000000000000..a84eb846e887dc6fe7d2961c346d37be0dc50db8 --- /dev/null +++ b/source/isaaclab_tasks/test/test_record_video.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True, enable_cameras=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import os + +import gymnasium as gym +import pytest +import torch + +import omni.usd + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils import parse_env_cfg + +# Local imports should be imported last +from env_test_utils import setup_environment # isort: skip + + +@pytest.fixture(scope="function") +def setup_video_params(): + # common parameters + num_envs = 16 + device = "cuda" + # video parameters + step_trigger = lambda step: step % 225 == 0 # noqa: E731 + video_length = 200 + return num_envs, device, step_trigger, video_length + + +@pytest.mark.parametrize("task_name", setup_environment(include_play=True)) +def test_record_video(task_name, setup_video_params): + """Run random actions agent with recording of videos.""" + num_envs, device, step_trigger, video_length = setup_video_params + videos_dir = os.path.join(os.path.dirname(__file__), "output", "videos", "train") + # create a new stage + omni.usd.get_context().new_stage() + + # parse configuration + env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs) + + # create environment + env = gym.make(task_name, cfg=env_cfg, render_mode="rgb_array") + + # directory to save videos + task_videos_dir = os.path.join(videos_dir, task_name) + # wrap environment to record videos + env = gym.wrappers.RecordVideo( + env, + task_videos_dir, + step_trigger=step_trigger, + video_length=video_length, + disable_logger=True, + ) + + # reset environment + env.reset() + # simulate environment + with torch.inference_mode(): + for _ in range(500): + # compute zero actions + actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 + # apply actions + _ = env.step(actions) + + # close the simulator + env.close() diff --git a/source/isaaclab_tasks/test/test_rl_device_separation.py b/source/isaaclab_tasks/test/test_rl_device_separation.py new file mode 100644 index 0000000000000000000000000000000000000000..ef6bd1e093f122c73fadd666f1b326d06f137139 --- /dev/null +++ b/source/isaaclab_tasks/test/test_rl_device_separation.py @@ -0,0 +1,379 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Test RL device separation across all supported RL libraries. + +This test verifies that RL library wrappers correctly handle device transfers when the +simulation device differs from the RL training device. + +Device Architecture: + 1. sim_device: Where physics simulation runs and environment buffers live + 2. rl_device: Where policy networks and training computations occur + +Test Scenarios: + - GPU simulation + GPU RL: Same device (no transfers needed, optimal performance) + - GPU simulation + CPU RL: Cross-device transfers (wrapper handles transfers) + - CPU simulation + CPU RL: CPU-only operation + +Each test verifies the wrapper correctly: + 1. Unwrapped env: operates entirely on sim_device + 2. Wrapper: accepts actions on rl_device (where policy generates them) + 3. Wrapper: internally transfers actions from rl_device → sim_device for env.step() + 4. Wrapper: transfers outputs from sim_device → rl_device (for policy to use) + +Tested Libraries: + - RSL-RL: TensorDict observations, device separation via OnPolicyRunner (agent_cfg.device) + * Wrapper returns data on sim_device, Runner handles transfers to rl_device + - RL Games: Dict observations, explicit rl_device parameter in wrapper + * Wrapper transfers data from sim_device to rl_device + - Stable-Baselines3: Numpy arrays (CPU-only by design) + * Wrapper converts tensors to/from numpy on CPU + - skrl: Dict observations, uses skrl.config.torch.device for RL device + * Wrapper keeps observations on sim_device, only transfers actions + +""" + +from isaaclab.app import AppLauncher + +# launch the simulator +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import gymnasium as gym +import pytest +import torch + +import carb +import omni.usd + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg + +# Test environment - use Cartpole as it's simple and fast +TEST_ENV = "Isaac-Cartpole-v0" +NUM_ENVS = 4 + + +def _create_env(sim_device: str): + """Create and initialize a test environment. + + Args: + sim_device: Device for simulation (e.g., "cuda:0", "cpu") + + Returns: + Initialized gym environment + """ + # Create a new stage + omni.usd.get_context().new_stage() + # Reset the rtx sensors carb setting to False + carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False) + + try: + env_cfg = parse_env_cfg(TEST_ENV, device=sim_device, num_envs=NUM_ENVS) + env = gym.make(TEST_ENV, cfg=env_cfg) + except Exception as e: + # Try to close environment on exception + if "env" in locals() and hasattr(env, "_is_closed"): + env.close() + else: + if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"): + e.obj.close() + pytest.fail(f"Failed to set-up the environment for task {TEST_ENV}. Error: {e}") + + # Disable control on stop + env.unwrapped.sim._app_control_on_stop_handle = None + return env + + +def _verify_unwrapped_env(env, sim_device: str): + """Verify unwrapped environment operates entirely on sim_device. + + Args: + env: Unwrapped gym environment + sim_device: Expected simulation device + """ + assert env.unwrapped.device == sim_device, ( + f"Environment device mismatch: expected {sim_device}, got {env.unwrapped.device}" + ) + + # Verify reset returns data on sim device + obs_dict, _ = env.reset() + for key, value in obs_dict.items(): + if isinstance(value, torch.Tensor): + assert value.device.type == torch.device(sim_device).type, ( + f"Unwrapped env obs '{key}' should be on {sim_device}, got {value.device}" + ) + + # Verify step returns data on sim device + action_space = env.unwrapped.single_action_space + test_action = torch.zeros(NUM_ENVS, action_space.shape[0], device=sim_device) + obs_dict, rew, term, trunc, extras = env.step(test_action) + assert rew.device.type == torch.device(sim_device).type, ( + f"Unwrapped env rewards should be on {sim_device}, got {rew.device}" + ) + assert term.device.type == torch.device(sim_device).type, ( + f"Unwrapped env terminated should be on {sim_device}, got {term.device}" + ) + + +def _verify_tensor_device(data, expected_device: str, name: str): + """Verify tensor or dict of tensors is on expected device. + + Args: + data: Tensor, dict of tensors, or numpy array + expected_device: Expected device string + name: Name for error messages + """ + if isinstance(data, torch.Tensor): + assert data.device.type == torch.device(expected_device).type, ( + f"{name} should be on {expected_device}, got {data.device}" + ) + elif isinstance(data, dict): + for key, value in data.items(): + if isinstance(value, torch.Tensor): + assert value.device.type == torch.device(expected_device).type, ( + f"{name}['{key}'] should be on {expected_device}, got {value.device}" + ) + + +def _test_rsl_rl_device_separation(sim_device: str, rl_device: str): + """Helper function to test RSL-RL with specified device configuration. + + Note: RSL-RL device separation is handled by the OnPolicyRunner, not the wrapper. + The wrapper returns observations on sim_device, and the runner handles device transfers. + This test verifies the wrapper works correctly when actions come from a different device. + + Args: + sim_device: Device for simulation (e.g., "cuda:0", "cpu") + rl_device: Device for RL agent (e.g., "cuda:0", "cpu") - where policy generates actions + """ + from tensordict import TensorDict + + from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper + + env = _create_env(sim_device) + _verify_unwrapped_env(env, sim_device) + + # Create wrapper - it uses sim_device, runner handles rl_device + env = RslRlVecEnvWrapper(env) + assert env.device == sim_device, f"Wrapper device should be {sim_device}" + + # Test reset - wrapper returns observations on sim_device + obs, extras = env.reset() + assert isinstance(obs, TensorDict), f"Expected TensorDict, got {type(obs)}" + _verify_tensor_device(obs, sim_device, "Observation") + + # Test step with action from RL device (simulating policy output) + # The wrapper should handle transferring action to sim_device internally + action = 2 * torch.rand(env.action_space.shape, device=rl_device) - 1 + obs, reward, dones, extras = env.step(action) + + # Verify outputs are on sim_device (runner would transfer to rl_device) + assert isinstance(obs, TensorDict), f"Expected TensorDict, got {type(obs)}" + _verify_tensor_device(obs, sim_device, "Step observation") + _verify_tensor_device(reward, sim_device, "Reward") + _verify_tensor_device(dones, sim_device, "Dones") + + env.close() + + +def _test_rl_games_device_separation(sim_device: str, rl_device: str): + """Helper function to test RL Games with specified device configuration. + + Args: + sim_device: Device for simulation (e.g., "cuda:0", "cpu") + rl_device: Device for RL agent (e.g., "cuda:0", "cpu") + """ + from isaaclab_rl.rl_games import RlGamesVecEnvWrapper + + env = _create_env(sim_device) + _verify_unwrapped_env(env, sim_device) + + # Create wrapper + env = RlGamesVecEnvWrapper(env, rl_device=rl_device, clip_obs=10.0, clip_actions=1.0) + + # Test reset + obs = env.reset() + _verify_tensor_device(obs, rl_device, "Observation") + + # Test step with action on RL device + action = 2 * torch.rand(NUM_ENVS, *env.action_space.shape, device=rl_device) - 1 + obs, reward, dones, info = env.step(action) + + # Verify outputs are on RL device + _verify_tensor_device(obs, rl_device, "Observation") + _verify_tensor_device(reward, rl_device, "Reward") + _verify_tensor_device(dones, rl_device, "Dones") + + env.close() + + +def _test_sb3_device_separation(sim_device: str): + """Helper function to test Stable-Baselines3 with specified device configuration. + + Note: SB3 always converts to CPU/numpy, so we don't test rl_device parameter. + + Args: + sim_device: Device for simulation (e.g., "cuda:0", "cpu") + """ + import numpy as np + + from isaaclab_rl.sb3 import Sb3VecEnvWrapper + + env = _create_env(sim_device) + _verify_unwrapped_env(env, sim_device) + + # Create wrapper + env = Sb3VecEnvWrapper(env) + + # Test reset - SB3 should return numpy arrays + obs = env.reset() + assert isinstance(obs, np.ndarray), f"SB3 observations should be numpy arrays, got {type(obs)}" + + # Test step with numpy action + action = 2 * np.random.rand(env.num_envs, *env.action_space.shape) - 1 + obs, reward, done, info = env.step(action) + + # Verify outputs are numpy arrays + assert isinstance(obs, np.ndarray), f"Observations should be numpy arrays, got {type(obs)}" + assert isinstance(reward, np.ndarray), f"Rewards should be numpy arrays, got {type(reward)}" + assert isinstance(done, np.ndarray), f"Dones should be numpy arrays, got {type(done)}" + + env.close() + + +def _test_skrl_device_separation(sim_device: str, rl_device: str): + """Helper function to test skrl with specified device configuration. + + Note: skrl uses skrl.config.torch.device for device configuration. + Observations remain on sim_device; only actions are transferred from rl_device. + + Args: + sim_device: Device for simulation (e.g., "cuda:0", "cpu") + rl_device: Device for RL agent (e.g., "cuda:0", "cpu") + """ + try: + import skrl + from skrl.envs.wrappers.torch import wrap_env + except ImportError: + pytest.skip("skrl not installed") + + # Configure skrl device + skrl.config.torch.device = torch.device(rl_device) + + env = _create_env(sim_device) + _verify_unwrapped_env(env, sim_device) + + # Wrap with skrl + env = wrap_env(env, wrapper="isaaclab") + + # Test reset + obs, info = env.reset() + assert isinstance(obs, (dict, torch.Tensor)), f"Observations should be dict or tensor, got {type(obs)}" + + # Test step with action on RL device + action = 2 * torch.rand(NUM_ENVS, *env.action_space.shape, device=skrl.config.torch.device) - 1 + transition = env.step(action) + + # Verify outputs - skrl keeps them on sim_device + if len(transition) == 5: + obs, reward, terminated, truncated, info = transition + _verify_tensor_device(obs, sim_device, "Observation") + _verify_tensor_device(reward, sim_device, "Reward") + _verify_tensor_device(terminated, sim_device, "Terminated") + _verify_tensor_device(truncated, sim_device, "Truncated") + elif len(transition) == 4: + obs, reward, done, info = transition + _verify_tensor_device(obs, sim_device, "Observation") + _verify_tensor_device(reward, sim_device, "Reward") + _verify_tensor_device(done, sim_device, "Done") + else: + pytest.fail(f"Unexpected number of return values from step: {len(transition)}") + + env.close() + + +# ============================================================================ +# Test Functions +# ============================================================================ + + +def test_rsl_rl_device_separation_gpu_to_gpu(): + """Test RSL-RL with GPU simulation and GPU RL (default configuration).""" + try: + import isaaclab_rl.rsl_rl # noqa: F401 + except ImportError: + pytest.skip("RSL-RL not installed") + + _test_rsl_rl_device_separation(sim_device="cuda:0", rl_device="cuda:0") + + +def test_rsl_rl_device_separation_gpu_to_cpu(): + """Test RSL-RL with GPU simulation and CPU RL (cross-device transfer).""" + try: + import isaaclab_rl.rsl_rl # noqa: F401 + except ImportError: + pytest.skip("RSL-RL not installed") + + _test_rsl_rl_device_separation(sim_device="cuda:0", rl_device="cpu") + + +def test_rl_games_device_separation_gpu_to_gpu(): + """Test RL Games with GPU simulation and GPU RL (default configuration).""" + try: + import isaaclab_rl.rl_games # noqa: F401 + except ImportError: + pytest.skip("RL Games not installed") + + _test_rl_games_device_separation(sim_device="cuda:0", rl_device="cuda:0") + + +def test_rl_games_device_separation_gpu_to_cpu(): + """Test RL Games with GPU simulation and CPU RL (cross-device transfer).""" + try: + import isaaclab_rl.rl_games # noqa: F401 + except ImportError: + pytest.skip("RL Games not installed") + + _test_rl_games_device_separation(sim_device="cuda:0", rl_device="cpu") + + +def test_sb3_device_separation_gpu(): + """Test Stable-Baselines3 with GPU simulation. + + Note: SB3 always converts to CPU/numpy, so only GPU simulation is tested. + """ + try: + import isaaclab_rl.sb3 # noqa: F401 + except ImportError: + pytest.skip("Stable-Baselines3 not installed") + + _test_sb3_device_separation(sim_device="cuda:0") + + +def test_skrl_device_separation_gpu(): + """Test skrl with GPU simulation and GPU policy (matching devices).""" + try: + import skrl # noqa: F401 + except ImportError: + pytest.skip("skrl not installed") + + _test_skrl_device_separation(sim_device="cuda:0", rl_device="cuda:0") + + +def test_skrl_device_separation_cpu_to_gpu(): + """Test skrl with CPU simulation and GPU policy. + + Note: Uses skrl.config.torch.device to set the policy device to GPU + while the environment runs on CPU. + """ + try: + import skrl # noqa: F401 + except ImportError: + pytest.skip("skrl not installed") + + _test_skrl_device_separation(sim_device="cpu", rl_device="cuda:0") diff --git a/tools/conftest.py b/tools/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..a61c94f2c474e7b89798b26d92897a37841c7b6d --- /dev/null +++ b/tools/conftest.py @@ -0,0 +1,420 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import contextlib +import os +import select +import subprocess +import sys +import time + +import pytest +from junitparser import Error, JUnitXml, TestCase, TestSuite +from prettytable import PrettyTable + +# Local imports +import test_settings as test_settings # isort: skip + + +def pytest_ignore_collect(collection_path, config): + # Skip collection and run each test script individually + return True + + +def capture_test_output_with_timeout(cmd, timeout, env): + """Run a command with timeout and capture all output while streaming in real-time.""" + stdout_data = b"" + stderr_data = b"" + + try: + # Use Popen to capture output in real-time + process = subprocess.Popen( + cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=0, universal_newlines=False + ) + + # Set up file descriptors for non-blocking reads + stdout_fd = process.stdout.fileno() + stderr_fd = process.stderr.fileno() + + # Set non-blocking mode (Unix systems only) + try: + import fcntl + + for fd in [stdout_fd, stderr_fd]: + flags = fcntl.fcntl(fd, fcntl.F_GETFL) + fcntl.fcntl(fd, fcntl.F_SETFL, flags | os.O_NONBLOCK) + except ImportError: + # fcntl not available on Windows, use a simpler approach + pass + + start_time = time.time() + + while process.poll() is None: + # Check for timeout + if time.time() - start_time > timeout: + process.kill() + try: + remaining_stdout, remaining_stderr = process.communicate(timeout=5) + stdout_data += remaining_stdout + stderr_data += remaining_stderr + except subprocess.TimeoutExpired: + process.terminate() + remaining_stdout, remaining_stderr = process.communicate(timeout=1) + stdout_data += remaining_stdout + stderr_data += remaining_stderr + return -1, stdout_data, stderr_data, True # -1 indicates timeout + + # Check for available output + try: + ready_fds, _, _ = select.select([stdout_fd, stderr_fd], [], [], 0.1) + + for fd in ready_fds: + with contextlib.suppress(OSError): + if fd == stdout_fd: + chunk = process.stdout.read(1024) + if chunk: + stdout_data += chunk + # Print to stdout in real-time + sys.stdout.buffer.write(chunk) + sys.stdout.buffer.flush() + elif fd == stderr_fd: + chunk = process.stderr.read(1024) + if chunk: + stderr_data += chunk + # Print to stderr in real-time + sys.stderr.buffer.write(chunk) + sys.stderr.buffer.flush() + except OSError: + # select failed, fall back to simple polling + time.sleep(0.1) + continue + + # Get any remaining output + remaining_stdout, remaining_stderr = process.communicate() + stdout_data += remaining_stdout + stderr_data += remaining_stderr + + return process.returncode, stdout_data, stderr_data, False + + except Exception as e: + return -1, str(e).encode(), b"", False + + +def create_timeout_test_case(test_file, timeout, stdout_data, stderr_data): + """Create a test case entry for a timeout test with captured logs.""" + test_suite = TestSuite(name=f"timeout_{os.path.splitext(os.path.basename(test_file))[0]}") + test_case = TestCase(name="test_execution", classname=os.path.splitext(os.path.basename(test_file))[0]) + + # Create error message with timeout info and captured logs + error_msg = f"Test timed out after {timeout} seconds" + + # Add captured output to error details + details = f"Timeout after {timeout} seconds\n\n" + + if stdout_data: + details += "=== STDOUT ===\n" + details += stdout_data.decode("utf-8", errors="replace") + "\n" + + if stderr_data: + details += "=== STDERR ===\n" + details += stderr_data.decode("utf-8", errors="replace") + "\n" + + error = Error(message=error_msg) + error.text = details + test_case.result = error + + test_suite.add_testcase(test_case) + return test_suite + + +def run_individual_tests(test_files, workspace_root, isaacsim_ci): + """Run each test file separately, ensuring one finishes before starting the next.""" + failed_tests = [] + test_status = {} + + for test_file in test_files: + print(f"\n\n🚀 Running {test_file} independently...\n") + # get file name from path + file_name = os.path.basename(test_file) + env = os.environ.copy() + + # Determine timeout for this test + timeout = test_settings.PER_TEST_TIMEOUTS.get(file_name, test_settings.DEFAULT_TIMEOUT) + + # Prepare command + # Note: Command options matter as they are used for cleanups inside AppLauncher + cmd = [ + sys.executable, + "-m", + "pytest", + "--no-header", + f"--config-file={workspace_root}/pyproject.toml", + f"--junitxml=tests/test-reports-{str(file_name)}.xml", + "--tb=short", + ] + + if isaacsim_ci: + cmd.append("-m") + cmd.append("isaacsim_ci") + + # Add the test file path last + cmd.append(str(test_file)) + + # Run test with timeout and capture output + returncode, stdout_data, stderr_data, timed_out = capture_test_output_with_timeout(cmd, timeout, env) + + if timed_out: + print(f"Test {test_file} timed out after {timeout} seconds...") + failed_tests.append(test_file) + + # Create a special XML report for timeout tests with captured logs + timeout_suite = create_timeout_test_case(test_file, timeout, stdout_data, stderr_data) + timeout_report = JUnitXml() + timeout_report.add_testsuite(timeout_suite) + + # Write timeout report + report_file = f"tests/test-reports-{str(file_name)}.xml" + timeout_report.write(report_file) + + test_status[test_file] = { + "errors": 1, + "failures": 0, + "skipped": 0, + "tests": 1, + "result": "TIMEOUT", + "time_elapsed": timeout, + } + continue + + if returncode != 0: + failed_tests.append(test_file) + + # check report for any failures + report_file = f"tests/test-reports-{str(file_name)}.xml" + if not os.path.exists(report_file): + print(f"Warning: Test report not found at {report_file}") + failed_tests.append(test_file) + test_status[test_file] = { + "errors": 1, # Assume error since we can't read the report + "failures": 0, + "skipped": 0, + "tests": 0, + "result": "FAILED", + "time_elapsed": 0.0, + } + continue + + try: + report = JUnitXml.fromfile(report_file) + + # Rename test suites to be more descriptive + for suite in report: + if suite.name == "pytest": + # Remove .py extension and use the filename as the test suite name + suite_name = os.path.splitext(file_name)[0] + suite.name = suite_name + + # Write the updated report back + report.write(report_file) + + # Parse the integer values with None handling + errors = int(report.errors) if report.errors is not None else 0 + failures = int(report.failures) if report.failures is not None else 0 + skipped = int(report.skipped) if report.skipped is not None else 0 + tests = int(report.tests) if report.tests is not None else 0 + time_elapsed = float(report.time) if report.time is not None else 0.0 + except Exception as e: + print(f"Error reading test report {report_file}: {e}") + failed_tests.append(test_file) + test_status[test_file] = { + "errors": 1, + "failures": 0, + "skipped": 0, + "tests": 0, + "result": "FAILED", + "time_elapsed": 0.0, + } + continue + + # Check if there were any failures + if errors > 0 or failures > 0: + failed_tests.append(test_file) + + test_status[test_file] = { + "errors": errors, + "failures": failures, + "skipped": skipped, + "tests": tests, + "result": "FAILED" if errors > 0 or failures > 0 else "passed", + "time_elapsed": time_elapsed, + } + + print("~~~~~~~~~~~~ Finished running all tests") + + return failed_tests, test_status + + +def pytest_sessionstart(session): + """Intercept pytest startup to execute tests in the correct order.""" + # Get the workspace root directory (one level up from tools) + workspace_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + source_dirs = [ + os.path.join(workspace_root, "scripts"), + os.path.join(workspace_root, "source"), + ] + + # Get filter pattern from environment variable or command line + filter_pattern = os.environ.get("TEST_FILTER_PATTERN", "") + exclude_pattern = os.environ.get("TEST_EXCLUDE_PATTERN", "") + + isaacsim_ci = os.environ.get("ISAACSIM_CI_SHORT", "false") == "true" + + # Also try to get from pytest config + if hasattr(session.config, "option") and hasattr(session.config.option, "filter_pattern"): + filter_pattern = filter_pattern or getattr(session.config.option, "filter_pattern", "") + if hasattr(session.config, "option") and hasattr(session.config.option, "exclude_pattern"): + exclude_pattern = exclude_pattern or getattr(session.config.option, "exclude_pattern", "") + + print("=" * 50) + print("CONFTEST.PY DEBUG INFO") + print("=" * 50) + print(f"Filter pattern: '{filter_pattern}'") + print(f"Exclude pattern: '{exclude_pattern}'") + print(f"TEST_FILTER_PATTERN env var: '{os.environ.get('TEST_FILTER_PATTERN', 'NOT_SET')}'") + print(f"TEST_EXCLUDE_PATTERN env var: '{os.environ.get('TEST_EXCLUDE_PATTERN', 'NOT_SET')}'") + print("=" * 50) + + # Get all test files in the source directories + test_files = [] + + for source_dir in source_dirs: + if not os.path.exists(source_dir): + print(f"Error: source directory not found at {source_dir}") + pytest.exit("Source directory not found", returncode=1) + + for root, _, files in os.walk(source_dir): + for file in files: + if file.startswith("test_") and file.endswith(".py"): + # Skip if the file is in TESTS_TO_SKIP + if file in test_settings.TESTS_TO_SKIP: + print(f"Skipping {file} as it's in the skip list") + continue + + full_path = os.path.join(root, file) + + # Apply include filter + if filter_pattern and filter_pattern not in full_path: + print(f"Skipping {full_path} (does not match include pattern: {filter_pattern})") + continue + + # Apply exclude filter + if exclude_pattern and exclude_pattern in full_path: + print(f"Skipping {full_path} (matches exclude pattern: {exclude_pattern})") + continue + + test_files.append(full_path) + + if isaacsim_ci: + new_test_files = [] + for test_file in test_files: + with open(test_file) as f: + if "@pytest.mark.isaacsim_ci" in f.read(): + new_test_files.append(test_file) + test_files = new_test_files + + if not test_files: + print("No test files found in source directory") + pytest.exit("No test files found", returncode=1) + + print(f"Found {len(test_files)} test files after filtering:") + for test_file in test_files: + print(f" - {test_file}") + + # Run all tests individually + failed_tests, test_status = run_individual_tests(test_files, workspace_root, isaacsim_ci) + + print("failed tests:", failed_tests) + + # Collect reports + print("~~~~~~~~~ Collecting final report...") + + # create new full report + full_report = JUnitXml() + # read all reports and merge them + for report in os.listdir("tests"): + if report.endswith(".xml"): + print(report) + report_file = JUnitXml.fromfile(f"tests/{report}") + full_report += report_file + print("~~~~~~~~~~~~ Writing final report...") + # write content to full report + result_file = os.environ.get("TEST_RESULT_FILE", "full_report.xml") + full_report_path = f"tests/{result_file}" + print(f"Using result file: {result_file}") + full_report.write(full_report_path) + print("~~~~~~~~~~~~ Report written to", full_report_path) + + # print test status in a nice table + # Calculate the number and percentage of passing tests + num_tests = len(test_status) + num_passing = len([test_path for test_path in test_files if test_status[test_path]["result"] == "passed"]) + num_failing = len([test_path for test_path in test_files if test_status[test_path]["result"] == "FAILED"]) + num_timeout = len([test_path for test_path in test_files if test_status[test_path]["result"] == "TIMEOUT"]) + + if num_tests == 0: + passing_percentage = 100 + else: + passing_percentage = num_passing / num_tests * 100 + + # Print summaries of test results + summary_str = "\n\n" + summary_str += "===================\n" + summary_str += "Test Result Summary\n" + summary_str += "===================\n" + + summary_str += f"Total: {num_tests}\n" + summary_str += f"Passing: {num_passing}\n" + summary_str += f"Failing: {num_failing}\n" + summary_str += f"Timeout: {num_timeout}\n" + summary_str += f"Passing Percentage: {passing_percentage:.2f}%\n" + + # Print time elapsed in hours, minutes, seconds + total_time = sum([test_status[test_path]["time_elapsed"] for test_path in test_files]) + + summary_str += f"Total Time Elapsed: {total_time // 3600}h" + summary_str += f"{total_time // 60 % 60}m" + summary_str += f"{total_time % 60:.2f}s" + + summary_str += "\n\n=======================\n" + summary_str += "Per Test Result Summary\n" + summary_str += "=======================\n" + + # Construct table of results per test + per_test_result_table = PrettyTable(field_names=["Test Path", "Result", "Time (s)", "# Tests"]) + per_test_result_table.align["Test Path"] = "l" + per_test_result_table.align["Time (s)"] = "r" + for test_path in test_files: + num_tests_passed = ( + test_status[test_path]["tests"] + - test_status[test_path]["failures"] + - test_status[test_path]["errors"] + - test_status[test_path]["skipped"] + ) + per_test_result_table.add_row( + [ + test_path, + test_status[test_path]["result"], + f"{test_status[test_path]['time_elapsed']:0.2f}", + f"{num_tests_passed}/{test_status[test_path]['tests']}", + ] + ) + + summary_str += per_test_result_table.get_string() + + # Print summary to console and log file + print(summary_str) + + # Exit pytest after custom execution to prevent normal pytest from overwriting our report + pytest.exit("Custom test execution completed", returncode=0 if num_failing == 0 else 1) diff --git a/tools/install_deps.py b/tools/install_deps.py new file mode 100644 index 0000000000000000000000000000000000000000..d2f08bb52e7cc1d878de2878a5b4f4b4fab34de8 --- /dev/null +++ b/tools/install_deps.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script is a utility to install dependencies mentioned in an extension.toml file of an extension. + +The script takes in two arguments: + +1. type: The type of dependencies to install. It can be one of the following: ['all', 'apt', 'rosdep']. +2. extensions_dir: The path to the directory beneath which we search for extensions. + +The script will search for all extensions in the extensions_dir and then look for an extension.toml file in each +extension's config directory. If the extension.toml file exists, the script will look for the following keys in the +[isaac_lab_settings] section: + +* **apt_deps**: A list of apt packages to install. +* **ros_ws**: The path to the ROS workspace in the extension. If the path is not absolute, the script assumes that + the path is relative to the extension root and resolves it accordingly. + +If the type is 'all', the script will install both apt and rosdep packages. If the type is 'apt', the script will only +install apt packages. If the type is 'rosdep', the script will only install rosdep packages. + +For more information, please check the `documentation`_. + +.. _documentation: https://isaac-sim.github.io/IsaacLab/source/setup/developer.html#extension-dependency-management +""" + +import argparse +import os +import shutil +from subprocess import PIPE, STDOUT, Popen + +import toml + +# add argparse arguments +parser = argparse.ArgumentParser(description="A utility to install dependencies based on extension.toml files.") +parser.add_argument("type", type=str, choices=["all", "apt", "rosdep"], help="The type of packages to install.") +parser.add_argument("extensions_dir", type=str, help="The path to the directory containing extensions.") +parser.add_argument("--ros_distro", type=str, default="humble", help="The ROS distribution to use for rosdep.") + + +def install_apt_packages(paths: list[str]): + """Installs apt packages listed in the extension.toml file for Isaac Lab extensions. + + For each path in the input list of paths, the function looks in ``{path}/config/extension.toml`` for + the ``[isaac_lab_settings][apt_deps]`` key. It then attempts to install the packages listed in the + value of the key. The function exits on failure to stop the build process from continuing despite missing + dependencies. + + Args: + paths: A list of paths to the extension's root. + + Raises: + SystemError: If 'apt' is not a known command. This is a system error. + """ + for path in paths: + if shutil.which("apt"): + # Check if the extension.toml file exists + if not os.path.exists(f"{path}/config/extension.toml"): + print( + "[WARN] During the installation of 'apt' dependencies, unable to find a" + f" valid file at: {path}/config/extension.toml." + ) + continue + # Load the extension.toml file and check for apt_deps + with open(f"{path}/config/extension.toml") as fd: + ext_toml = toml.load(fd) + if "isaac_lab_settings" in ext_toml and "apt_deps" in ext_toml["isaac_lab_settings"]: + deps = ext_toml["isaac_lab_settings"]["apt_deps"] + print(f"[INFO] Installing the following apt packages: {deps}") + run_and_print(["apt-get", "update"]) + run_and_print(["apt-get", "install", "-y"] + deps) + else: + print(f"[INFO] No apt packages specified for the extension at: {path}") + else: + raise SystemError("Unable to find 'apt' command. Please ensure that 'apt' is installed on your system.") + + +def install_rosdep_packages(paths: list[str], ros_distro: str = "humble"): + """Installs ROS dependencies listed in the extension.toml file for Isaac Lab extensions. + + For each path in the input list of paths, the function looks in ``{path}/config/extension.toml`` for + the ``[isaac_lab_settings][ros_ws]`` key. It then attempts to install the ROS dependencies under the workspace + listed in the value of the key. The function exits on failure to stop the build process from continuing despite + missing dependencies. + + If the path to the ROS workspace is not absolute, the function assumes that the path is relative to the extension + root and resolves it accordingly. The function also checks if the ROS workspace exists before proceeding with + the installation of ROS dependencies. If the ROS workspace does not exist, the function raises an error. + + Args: + path: A list of paths to the extension roots. + ros_distro: The ROS distribution to use for rosdep. Default is 'humble'. + + Raises: + FileNotFoundError: If a valid ROS workspace is not found while installing ROS dependencies. + SystemError: If 'rosdep' is not a known command. This is raised if 'rosdep' is not installed on the system. + """ + for path in paths: + if shutil.which("rosdep"): + # Check if the extension.toml file exists + if not os.path.exists(f"{path}/config/extension.toml"): + print( + "[WARN] During the installation of 'rosdep' dependencies, unable to find a" + f" valid file at: {path}/config/extension.toml." + ) + continue + # Load the extension.toml file and check for ros_ws + with open(f"{path}/config/extension.toml") as fd: + ext_toml = toml.load(fd) + if "isaac_lab_settings" in ext_toml and "ros_ws" in ext_toml["isaac_lab_settings"]: + # resolve the path to the ROS workspace + ws_path = ext_toml["isaac_lab_settings"]["ros_ws"] + if not os.path.isabs(ws_path): + ws_path = os.path.join(path, ws_path) + # check if the workspace exists + if not os.path.exists(f"{ws_path}/src"): + raise FileNotFoundError( + "During the installation of 'rosdep' dependencies, unable to find a" + f" valid ROS workspace at: {ws_path}." + ) + # install rosdep if not already installed + if not os.path.exists("/etc/ros/rosdep/sources.list.d/20-default.list"): + run_and_print(["rosdep", "init"]) + run_and_print(["rosdep", "update", f"--rosdistro={ros_distro}"]) + # install rosdep packages + run_and_print( + [ + "rosdep", + "install", + "--from-paths", + f"{ws_path}/src", + "--ignore-src", + "-y", + f"--rosdistro={ros_distro}", + ] + ) + else: + print(f"[INFO] No rosdep packages specified for the extension at: {path}") + else: + raise SystemError( + "Unable to find 'rosdep' command. Please ensure that 'rosdep' is installed on your system." + "You can install it by running:\n\t sudo apt-get install python3-rosdep" + ) + + +def run_and_print(args: list[str]): + """Runs a subprocess and prints the output to stdout. + + This function wraps Popen and prints the output to stdout in real-time. + + Args: + args: A list of arguments to pass to Popen. + """ + print(f'Running "{args}"') + with Popen(args, stdout=PIPE, stderr=STDOUT, env=os.environ) as p: + while p.poll() is None: + text = p.stdout.read1().decode("utf-8") + print(text, end="", flush=True) + return_code = p.poll() + if return_code != 0: + raise RuntimeError(f'Subprocess with args: "{args}" failed. The returned error code was: {return_code}') + + +def main(): + # Parse the command line arguments + args = parser.parse_args() + # Get immediate children of args.extensions_dir + extension_paths = [os.path.join(args.extensions_dir, x) for x in next(os.walk(args.extensions_dir))[1]] + + # Install dependencies based on the type + if args.type == "all": + install_apt_packages(extension_paths) + install_rosdep_packages(extension_paths, args.ros_distro) + elif args.type == "apt": + install_apt_packages(extension_paths) + elif args.type == "rosdep": + install_rosdep_packages(extension_paths, args.ros_distro) + else: + raise ValueError(f"'Invalid dependency type: '{args.type}'. Available options: ['all', 'apt', 'rosdep'].") + + +if __name__ == "__main__": + main() diff --git a/tools/run_all_tests.py b/tools/run_all_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..bbec83183584d350db00008f34df922e400d922c --- /dev/null +++ b/tools/run_all_tests.py @@ -0,0 +1,401 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""A runner script for all the tests within source directory. + +.. code-block:: bash + + ./isaaclab.sh -p tools/run_all_tests.py + + # for dry run + ./isaaclab.sh -p tools/run_all_tests.py --discover_only + + # for quiet run + ./isaaclab.sh -p tools/run_all_tests.py --quiet + + # for increasing timeout (default is 600 seconds) + ./isaaclab.sh -p tools/run_all_tests.py --timeout 1000 + +""" + +import argparse +import logging +import os +import re +import subprocess +import sys +import time +from datetime import datetime +from pathlib import Path + +from prettytable import PrettyTable + +# Local imports +from test_settings import DEFAULT_TIMEOUT, ISAACLAB_PATH, PER_TEST_TIMEOUTS, TESTS_TO_SKIP + + +def parse_args() -> argparse.Namespace: + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description="Run all tests under current directory.") + # add arguments + parser.add_argument( + "--skip_tests", + default="", + help="Space separated list of tests to skip in addition to those in tests_to_skip.py.", + type=str, + nargs="*", + ) + + # configure default test directory (source directory) + default_test_dir = os.path.join(ISAACLAB_PATH, "source") + + parser.add_argument( + "--test_dir", type=str, default=default_test_dir, help="Path to the directory containing the tests." + ) + + # configure default logging path based on time stamp + log_file_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".log" + default_log_path = os.path.join(ISAACLAB_PATH, "logs", "test_results", log_file_name) + + parser.add_argument( + "--log_path", type=str, default=default_log_path, help="Path to the log file to store the results in." + ) + parser.add_argument("--discover_only", action="store_true", help="Only discover and print tests, don't run them.") + parser.add_argument("--quiet", action="store_true", help="Don't print to console, only log to file.") + parser.add_argument("--timeout", type=int, default=DEFAULT_TIMEOUT, help="Timeout for each test in seconds.") + parser.add_argument("--extension", type=str, default=None, help="Run tests only for the given extension.") + # parse arguments + args = parser.parse_args() + return args + + +def test_all( + test_dir: str, + tests_to_skip: list[str], + log_path: str, + timeout: float = DEFAULT_TIMEOUT, + per_test_timeouts: dict[str, float] = {}, + discover_only: bool = False, + quiet: bool = False, + extension: str | None = None, +) -> bool: + """Run all tests under the given directory. + + Args: + test_dir: Path to the directory containing the tests. + tests_to_skip: List of tests to skip. + log_path: Path to the log file to store the results in. + timeout: Timeout for each test in seconds. Defaults to DEFAULT_TIMEOUT. + per_test_timeouts: A dictionary of tests and their timeouts in seconds. Any tests not listed here will use the + timeout specified by `timeout`. Defaults to an empty dictionary. + discover_only: If True, only discover and print the tests without running them. Defaults to False. + quiet: If False, print the output of the tests to the terminal console (in addition to the log file). + Defaults to False. + extension: Run tests only for the given extension. Defaults to None, which means all extensions' + tests will be run. + Returns: + True if all un-skipped tests pass or `discover_only` is True. Otherwise, False. + + Raises: + ValueError: If any test to skip is not found under the given `test_dir`. + + """ + # Create the log directory if it doesn't exist + os.makedirs(os.path.dirname(log_path), exist_ok=True) + + # Add file handler to log to file + logging_handlers = [logging.FileHandler(log_path)] + # We also want to print to console + if not quiet: + logging_handlers.append(logging.StreamHandler()) + # Set up logger + logging.basicConfig(level=logging.INFO, format="%(message)s", handlers=logging_handlers) + + all_test_paths, test_paths, skipped_test_paths, test_timeouts = extract_tests_and_timeouts( + test_dir, extension, tests_to_skip, timeout, per_test_timeouts + ) + + # Print tests to be run + logging.info("\n" + "=" * 60 + "\n") + logging.info(f"The following {len(all_test_paths)} tests were found:") + for i, test_path in enumerate(all_test_paths): + logging.info(f"{i + 1:02d}: {test_path}, timeout: {test_timeouts[test_path]}") + logging.info("\n" + "=" * 60 + "\n") + + logging.info(f"The following {len(skipped_test_paths)} tests are marked to be skipped:") + for i, test_path in enumerate(skipped_test_paths): + logging.info(f"{i + 1:02d}: {test_path}") + logging.info("\n" + "=" * 60 + "\n") + + # Exit if only discovering tests + if discover_only: + return True + + results = {} + + # Run each script and store results + for test_path in test_paths: + results[test_path] = {} + before = time.time() + logging.info("\n" + "-" * 60 + "\n") + logging.info(f"[INFO] Running '{test_path}'\n") + try: + completed_process = subprocess.run( + [sys.executable, test_path], check=True, capture_output=True, timeout=test_timeouts[test_path] + ) + except subprocess.TimeoutExpired as e: + logging.error(f"Timeout occurred: {e}") + result = "TIMEDOUT" + stdout = e.stdout + stderr = e.stderr + except subprocess.CalledProcessError as e: + # When check=True is passed to subprocess.run() above, CalledProcessError is raised if the process returns a + # non-zero exit code. The caveat is returncode is not correctly updated in this case, so we simply + # catch the exception and set this test as FAILED + result = "FAILED" + stdout = e.stdout + stderr = e.stderr + except Exception as e: + logging.error(f"Unexpected exception {e}. Please report this issue on the repository.") + result = "FAILED" + stdout = None + stderr = None + else: + result = "COMPLETED" + stdout = completed_process.stdout + stderr = completed_process.stderr + + after = time.time() + time_elapsed = after - before + + # Decode stdout and stderr + stdout = stdout.decode("utf-8") if stdout is not None else "" + stderr = stderr.decode("utf-8") if stderr is not None else "" + + if result == "COMPLETED": + # Check for success message in the output + success_pattern = r"Ran \d+ tests? in [\d.]+s\s+OK" + if re.search(success_pattern, stdout) or re.search(success_pattern, stderr): + result = "PASSED" + else: + result = "FAILED" + + # Write to log file + logging.info(stdout) + logging.info(stderr) + logging.info(f"[INFO] Time elapsed: {time_elapsed:.2f} s") + logging.info(f"[INFO] Result '{test_path}': {result}") + # Collect results + results[test_path]["time_elapsed"] = time_elapsed + results[test_path]["result"] = result + + # Calculate the number and percentage of passing tests + num_tests = len(all_test_paths) + num_passing = len([test_path for test_path in test_paths if results[test_path]["result"] == "PASSED"]) + num_failing = len([test_path for test_path in test_paths if results[test_path]["result"] == "FAILED"]) + num_timing_out = len([test_path for test_path in test_paths if results[test_path]["result"] == "TIMEDOUT"]) + num_skipped = len(skipped_test_paths) + + if num_tests == 0: + passing_percentage = 100 + else: + passing_percentage = (num_passing + num_skipped) / num_tests * 100 + + # Print summaries of test results + summary_str = "\n\n" + summary_str += "===================\n" + summary_str += "Test Result Summary\n" + summary_str += "===================\n" + + summary_str += f"Total: {num_tests}\n" + summary_str += f"Passing: {num_passing}\n" + summary_str += f"Failing: {num_failing}\n" + summary_str += f"Skipped: {num_skipped}\n" + summary_str += f"Timing Out: {num_timing_out}\n" + + summary_str += f"Passing Percentage: {passing_percentage:.2f}%\n" + + # Print time elapsed in hours, minutes, seconds + total_time = sum([results[test_path]["time_elapsed"] for test_path in test_paths]) + + summary_str += f"Total Time Elapsed: {total_time // 3600}h" + summary_str += f"{total_time // 60 % 60}m" + summary_str += f"{total_time % 60:.2f}s" + + summary_str += "\n\n=======================\n" + summary_str += "Per Test Result Summary\n" + summary_str += "=======================\n" + + # Construct table of results per test + per_test_result_table = PrettyTable(field_names=["Test Path", "Result", "Time (s)"]) + per_test_result_table.align["Test Path"] = "l" + per_test_result_table.align["Time (s)"] = "r" + for test_path in test_paths: + per_test_result_table.add_row( + [test_path, results[test_path]["result"], f"{results[test_path]['time_elapsed']:0.2f}"] + ) + + for test_path in skipped_test_paths: + per_test_result_table.add_row([test_path, "SKIPPED", "N/A"]) + + summary_str += per_test_result_table.get_string() + + # Print summary to console and log file + logging.info(summary_str) + + # Only count failing and timing out tests towards failure + return num_failing + num_timing_out == 0 + + +def extract_tests_and_timeouts( + test_dir: str, + extension: str | None = None, + tests_to_skip: list[str] = [], + timeout: float = DEFAULT_TIMEOUT, + per_test_timeouts: dict[str, float] = {}, +) -> tuple[list[str], list[str], list[str], dict[str, float]]: + """Extract all tests under the given directory or extension and their respective timeouts. + + Args: + test_dir: Path to the directory containing the tests. + extension: Run tests only for the given extension. Defaults to None, which means all extensions' + tests will be run. + tests_to_skip: List of tests to skip. + timeout: Timeout for each test in seconds. Defaults to DEFAULT_TIMEOUT. + per_test_timeouts: A dictionary of tests and their timeouts in seconds. Any tests not listed here will use the + timeout specified by `timeout`. Defaults to an empty dictionary. + + Returns: + A tuple containing the paths of all tests, tests to run, tests to skip, and their respective timeouts. + + Raises: + ValueError: If any test to skip is not found under the given `test_dir`. + """ + + # Discover all tests under current directory + all_test_paths = [str(path) for path in Path(test_dir).resolve().rglob("*test_*.py")] + skipped_test_paths = [] + test_paths = [] + # Check that all tests to skip are actually in the tests + for test_to_skip in tests_to_skip: + for test_path in all_test_paths: + if test_to_skip in test_path: + break + else: + raise ValueError(f"Test to skip '{test_to_skip}' not found in tests.") + + # Filter tests by extension + if extension is not None: + all_tests_in_selected_extension = [] + + for test_path in all_test_paths: + # Extract extension name from test path + extension_name = test_path[test_path.find("extensions") :].split("/")[1] + + # Skip tests that are not in the selected extension + if extension_name != extension: + continue + + all_tests_in_selected_extension.append(test_path) + + all_test_paths = all_tests_in_selected_extension + + # Remove tests to skip from the list of tests to run + if len(tests_to_skip) != 0: + for test_path in all_test_paths: + if any([test_to_skip in test_path for test_to_skip in tests_to_skip]): + skipped_test_paths.append(test_path) + else: + test_paths.append(test_path) + else: + test_paths = all_test_paths + + # Sort test paths so they're always in the same order + all_test_paths.sort() + test_paths.sort() + skipped_test_paths.sort() + + # Initialize all tests to have the same timeout + test_timeouts = {test_path: timeout for test_path in all_test_paths} + + # Overwrite timeouts for specific tests + for test_path_with_timeout, test_timeout in per_test_timeouts.items(): + for test_path in all_test_paths: + if test_path_with_timeout in test_path: + test_timeouts[test_path] = test_timeout + + return all_test_paths, test_paths, skipped_test_paths, test_timeouts + + +def warm_start_app(): + """Warm start the app to compile shaders before running the tests.""" + + print("[INFO] Warm starting the simulation app before running tests.") + before = time.time() + # headless experience + warm_start_output = subprocess.run( + [ + sys.executable, + "-c", + "from isaaclab.app import AppLauncher; app_launcher = AppLauncher(headless=True); app_launcher.app.close()", + ], + capture_output=True, + ) + if len(warm_start_output.stderr) > 0: + if "omni::fabric::IStageReaderWriter" not in str(warm_start_output.stderr) and "scaling_governor" not in str( + warm_start_output.stderr + ): + logging.error(f"Error warm starting the app: {str(warm_start_output.stderr)}") + exit(1) + + # headless experience with rendering + warm_start_rendering_output = subprocess.run( + [ + sys.executable, + "-c", + ( + "from isaaclab.app import AppLauncher; app_launcher = AppLauncher(headless=True," + " enable_cameras=True); app_launcher.app.close()" + ), + ], + capture_output=True, + ) + if len(warm_start_rendering_output.stderr) > 0: + if "omni::fabric::IStageReaderWriter" not in str( + warm_start_rendering_output.stderr + ) and "scaling_governor" not in str(warm_start_output.stderr): + logging.error(f"Error warm starting the app with rendering: {str(warm_start_rendering_output.stderr)}") + exit(1) + + after = time.time() + time_elapsed = after - before + print(f"[INFO] Warm start completed successfully in {time_elapsed:.2f} s") + + +if __name__ == "__main__": + # parse command line arguments + args = parse_args() + + # warm start the app + warm_start_app() + + # add tests to skip to the list of tests to skip + tests_to_skip = TESTS_TO_SKIP + tests_to_skip += args.skip_tests + + # run all tests + test_success = test_all( + test_dir=args.test_dir, + tests_to_skip=tests_to_skip, + log_path=args.log_path, + timeout=args.timeout, + per_test_timeouts=PER_TEST_TIMEOUTS, + discover_only=args.discover_only, + quiet=args.quiet, + extension=args.extension, + ) + # update exit status based on all tests passing or not + if not test_success: + exit(1) diff --git a/tools/run_train_envs.py b/tools/run_train_envs.py new file mode 100644 index 0000000000000000000000000000000000000000..efc85c0265bac59a79b1d33e31eae42cd954a862 --- /dev/null +++ b/tools/run_train_envs.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This scripts run training with different RL libraries over a subset of the environments. + +It calls the script ``scripts/reinforcement_learning/${args.lib_name}/train.py`` with the appropriate arguments. +Each training run has the corresponding "commit tag" appended to the run name, which allows comparing different +training logs of the same environments. + +Example usage: + +.. code-block:: bash + # for rsl-rl + python run_train_envs.py --lib-name rsl_rl + +""" + +import argparse +import subprocess + +from test_settings import ISAACLAB_PATH, TEST_RL_ENVS + + +def parse_args() -> argparse.Namespace: + """Parse the command line arguments.""" + parser = argparse.ArgumentParser() + parser.add_argument( + "--lib-name", + type=str, + default="rsl_rl", + choices=["rsl_rl", "skrl", "rl_games", "sb3"], + help="The name of the library to use for training.", + ) + return parser.parse_args() + + +def main(args: argparse.Namespace): + """The main function.""" + # get the git commit hash + git_commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip() + + # add run name based on library + if args.lib_name == "rsl_rl": + extra_args = ["--run_name", git_commit_hash] + else: + # TODO: Modify this for other libraries as well to have commit tag in their saved run logs + extra_args = [] + + # train on each environment + for env_name in TEST_RL_ENVS: + # print a colored output to catch the attention of the user + # this should be a multi-line print statement + print("\033[91m==============================================\033[0m") + print("\033[91m==============================================\033[0m") + print(f"\033[91mTraining on {env_name} with {args.lib_name}...\033[0m") + print("\033[91m==============================================\033[0m") + print("\033[91m==============================================\033[0m") + + # run the training script + subprocess.run( + [ + f"{ISAACLAB_PATH}/isaaclab.sh", + "-p", + f"{ISAACLAB_PATH}/scripts/reinforcement_learning/{args.lib_name}/train.py", + "--task", + env_name, + "--headless", + ] + + extra_args, + check=False, # do not raise an error if the script fails + ) + + +if __name__ == "__main__": + args_cli = parse_args() + main(args_cli) diff --git a/tools/template/__init__.py b/tools/template/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..460a30569089052537f30b25ddab421f8aba8568 --- /dev/null +++ b/tools/template/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/tools/template/cli.py b/tools/template/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..d922025e070ff90cbe13e415aa528666455a85b6 --- /dev/null +++ b/tools/template/cli.py @@ -0,0 +1,262 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import enum +import importlib +import os +from collections.abc import Callable + +import rich.console +import rich.table +from common import ROOT_DIR +from generator import generate, get_algorithms_per_rl_library +from InquirerPy import inquirer, separator + + +class CLIHandler: + """CLI handler for the Isaac Lab template.""" + + def __init__(self): + self.console = rich.console.Console() + + @staticmethod + def get_choices(choices: list[str], default: list[str]) -> list[str]: + return default if "all" in choices or "both" in choices else choices + + def output_table(self, table: rich.table.Table, new_line_start: bool = True) -> None: + """Print a rich table to the console. + + Args: + table: The table to print. + new_line_start: Whether to print a new line before the table. + """ + self.console.print(table, new_line_start=new_line_start) + + def input_select( + self, message: str, choices: list[str], default: str | None = None, long_instruction: str = "" + ) -> str: + """Prompt the user to select an option from a list of choices. + + Args: + message: The message to display to the user. + choices: The list of choices to display to the user. + default: The default choice. + long_instruction: The long instruction to display to the user. + + Returns: + str: The selected choice. + """ + return inquirer.select( + message=message, + choices=choices, + cycle=True, + default=default, + style=None, + wrap_lines=True, + long_instruction=long_instruction, + ).execute() + + def input_checkbox(self, message: str, choices: list[str], default: str | None = None) -> list[str]: + """Prompt the user to select one or more options from a list of choices. + + Args: + message: The message to display to the user. + choices: The list of choices to display to the user. + default: The default choice. + + Returns: + The selected choices. + """ + + def transformer(result: list[str]) -> str: + if "all" in result or "both" in result: + token = "all" if "all" in result else "both" + return f"{token} ({', '.join(choices[: choices.index('---')])})" + return ", ".join(result) + + return inquirer.checkbox( + message=message, + choices=[separator.Separator() if "---" in item else item for item in choices], + cycle=True, + default=default, + style=None, + wrap_lines=True, + validate=lambda result: len(result) >= 1, + invalid_message="No option selected (SPACE: select/deselect an option, ENTER: confirm selection)", + transformer=transformer, + ).execute() + + def input_path( + self, + message: str, + default: str | None = None, + validate: Callable[[str], bool] | None = None, + invalid_message: str = "", + ) -> str: + """Prompt the user to input a path. + + Args: + message: The message to display to the user. + default: The default path. + validate: A callable to validate the path. + invalid_message: The message to display to the user if the path is invalid. + + Returns: + The input path. + """ + return inquirer.filepath( + message=message, + default=default if default is not None else "", + validate=validate, + invalid_message=invalid_message, + ).execute() + + def input_text( + self, + message: str, + default: str | None = None, + validate: Callable[[str], bool] | None = None, + invalid_message: str = "", + ) -> str: + """Prompt the user to input a text. + + Args: + message: The message to display to the user. + default: The default text. + validate: A callable to validate the text. + invalid_message: The message to display to the user if the text is invalid. + + Returns: + The input text. + """ + return inquirer.text( + message=message, + default=default if default is not None else "", + validate=validate, + invalid_message=invalid_message, + ).execute() + + +class State(str, enum.Enum): + Yes = "[green]yes[/green]" + No = "[red]no[/red]" + + +def main() -> None: + """Main function to run template generation from CLI.""" + cli_handler = CLIHandler() + + lab_module = importlib.import_module("isaaclab") + lab_path = os.path.realpath(getattr(lab_module, "__file__", "") or (getattr(lab_module, "__path__", [""])[0])) + is_lab_pip_installed = ("site-packages" in lab_path) or ("dist-packages" in lab_path) + + if not is_lab_pip_installed: + # project type + is_external_project = ( + cli_handler.input_select( + "Task type:", + choices=["External", "Internal"], + long_instruction=( + "External (recommended): task/project is in its own folder/repo outside the Isaac Lab project.\n" + "Internal: the task is implemented within the Isaac Lab project (in source/isaaclab_tasks)." + ), + ).lower() + == "external" + ) + else: + is_external_project = True + + # project path (if 'external') + project_path = None + if is_external_project: + project_path = cli_handler.input_path( + "Project path:", + default=os.path.dirname(ROOT_DIR) + os.sep, + validate=lambda path: not os.path.abspath(path).startswith(os.path.abspath(ROOT_DIR)), + invalid_message="External project path cannot be within the Isaac Lab project", + ) + + # project/task name + project_name = cli_handler.input_text( + "Project name:" if is_external_project else "Task's folder name:", + validate=lambda name: name.isidentifier(), + invalid_message=( + "Project/task name must be a valid identifier (Letters, numbers and underscores only. No spaces, etc.)" + ), + ) + + # Isaac Lab workflow + # - show supported workflows and features + workflow_table = rich.table.Table(title="RL environment features support according to Isaac Lab workflows") + workflow_table.add_column("Environment feature", no_wrap=True) + workflow_table.add_column("Direct", justify="center") + workflow_table.add_column("Manager-based", justify="center") + workflow_table.add_row("Single-agent", State.Yes, State.Yes) + workflow_table.add_row("Multi-agent", State.Yes, State.No) + workflow_table.add_row("Fundamental/composite spaces (apart from 'Box')", State.Yes, State.No) + cli_handler.output_table(workflow_table) + # - prompt for workflows + supported_workflows = ["Direct | single-agent", "Direct | multi-agent", "Manager-based | single-agent"] + workflow = cli_handler.get_choices( + cli_handler.input_checkbox("Isaac Lab workflow:", choices=[*supported_workflows, "---", "all"]), + default=supported_workflows, + ) + workflow = [{"name": item.split(" | ")[0].lower(), "type": item.split(" | ")[1].lower()} for item in workflow] + single_agent_workflow = [item for item in workflow if item["type"] == "single-agent"] + multi_agent_workflow = [item for item in workflow if item["type"] == "multi-agent"] + + # RL library + rl_library_algorithms = [] + algorithms_per_rl_library = get_algorithms_per_rl_library() + # - show supported RL libraries and features + rl_library_table = rich.table.Table(title="Supported RL libraries") + rl_library_table.add_column("RL/training feature", no_wrap=True) + rl_library_table.add_column("rl_games") + rl_library_table.add_column("rsl_rl") + rl_library_table.add_column("skrl") + rl_library_table.add_column("sb3") + rl_library_table.add_row("ML frameworks", "PyTorch", "PyTorch", "PyTorch, JAX", "PyTorch") + rl_library_table.add_row("Relative performance", "~1X", "~1X", "~1X", "~0.03X") + rl_library_table.add_row( + "Algorithms", + ", ".join(algorithms_per_rl_library.get("rl_games", [])), + ", ".join(algorithms_per_rl_library.get("rsl_rl", [])), + ", ".join(algorithms_per_rl_library.get("skrl", [])), + ", ".join(algorithms_per_rl_library.get("sb3", [])), + ) + rl_library_table.add_row("Multi-agent support", State.No, State.No, State.Yes, State.No) + rl_library_table.add_row("Distributed training", State.Yes, State.No, State.Yes, State.No) + rl_library_table.add_row("Vectorized training", State.Yes, State.Yes, State.Yes, State.No) + rl_library_table.add_row("Fundamental/composite spaces", State.No, State.No, State.Yes, State.No) + cli_handler.output_table(rl_library_table) + # - prompt for RL libraries + supported_rl_libraries = ["rl_games", "rsl_rl", "skrl", "sb3"] if len(single_agent_workflow) else ["skrl"] + selected_rl_libraries = cli_handler.get_choices( + cli_handler.input_checkbox("RL library:", choices=[*supported_rl_libraries, "---", "all"]), + default=supported_rl_libraries, + ) + # - prompt for algorithms per RL library + algorithms_per_rl_library = get_algorithms_per_rl_library(len(single_agent_workflow), len(multi_agent_workflow)) + for rl_library in selected_rl_libraries: + algorithms = algorithms_per_rl_library.get(rl_library, []) + if len(algorithms) > 1: + algorithms = cli_handler.get_choices( + cli_handler.input_checkbox(f"RL algorithms for {rl_library}:", choices=[*algorithms, "---", "all"]), + default=algorithms, + ) + rl_library_algorithms.append({"name": rl_library, "algorithms": [item.lower() for item in algorithms]}) + + specification = { + "external": is_external_project, + "path": project_path, + "name": project_name, + "workflows": workflow, + "rl_libraries": rl_library_algorithms, + } + generate(specification) + + +if __name__ == "__main__": + main() diff --git a/tools/template/common.py b/tools/template/common.py new file mode 100644 index 0000000000000000000000000000000000000000..08d2732a19114fdca14a0cd4aa5e9bbf2a8f0199 --- /dev/null +++ b/tools/template/common.py @@ -0,0 +1,15 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os + +# paths +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +TASKS_DIR = os.path.join(ROOT_DIR, "source", "isaaclab_tasks", "isaaclab_tasks") +TEMPLATE_DIR = os.path.join(ROOT_DIR, "tools", "template", "templates") + +# RL algorithms +SINGLE_AGENT_ALGORITHMS = ["AMP", "PPO"] +MULTI_AGENT_ALGORITHMS = ["IPPO", "MAPPO"] diff --git a/tools/template/generator.py b/tools/template/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..04f4bae6f63da07662cd2997284a72e783ee99b6 --- /dev/null +++ b/tools/template/generator.py @@ -0,0 +1,323 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import glob +import os +import shutil +import subprocess +import sys +from datetime import datetime + +import jinja2 +from common import MULTI_AGENT_ALGORITHMS, ROOT_DIR, SINGLE_AGENT_ALGORITHMS, TASKS_DIR, TEMPLATE_DIR + +jinja_env = jinja2.Environment( + loader=jinja2.FileSystemLoader(TEMPLATE_DIR), + trim_blocks=True, + lstrip_blocks=True, +) + + +def _setup_git_repo(project_dir: str) -> None: + """Setup the git repository. + + Args: + project_dir: The directory of the project. + """ + commands = [ + ["git", "init"], + ["git", "add", "-f", "."], + ["git", "commit", "-q", "-m", "Initial commit"], + ] + for command in commands: + result = subprocess.run(command, capture_output=True, text=True, cwd=project_dir) + for line in result.stdout.splitlines(): + print(f" | {line}") + + +def _replace_in_file(replacements: list[tuple[str, str]], src: str, dst: str | None = None) -> None: + """Replace the placeholders in the file. + + Args: + replacements: The replacements to make. + src: The source file. + dst: The destination file. If not provided, the source file will be overwritten. + """ + with open(src) as file: + content = file.read() + for old, new in replacements: + content = content.replace(old, new) + with open(src if dst is None else dst, "w") as file: + file.write(content) + + +def _write_file(dst: str, content: str) -> None: + """Write the content to a file. + + Args: + dst: The path to the file. + content: The content to write to the file. + """ + with open(dst, "w") as file: + file.write(content) + + +def _generate_task_per_workflow(task_dir: str, specification: dict) -> None: + """Generate the task files for a single workflow. + + Args: + task_dir: The directory where the task files will be generated. + specification: The specification of the project/task. + """ + task_spec = specification["task"] + agents_dir = os.path.join(task_dir, "agents") + os.makedirs(agents_dir, exist_ok=True) + # common content + # - task/__init__.py + template = jinja_env.get_template("tasks/__init__task") + _write_file(os.path.join(task_dir, "__init__.py"), content=template.render(**specification)) + # - task/agents/__init__.py + template = jinja_env.get_template("tasks/__init__agents") + _write_file(os.path.join(agents_dir, "__init__.py"), content=template.render(**specification)) + # - task/agents/*cfg* + for rl_library in specification["rl_libraries"]: + rl_library_name = rl_library["name"] + for algorithm in rl_library.get("algorithms", []): + file_name = f"{rl_library_name}_{algorithm.lower()}_cfg" + file_ext = ".py" if rl_library_name == "rsl_rl" else ".yaml" + try: + template = jinja_env.get_template(f"agents/{file_name}") + except jinja2.exceptions.TemplateNotFound: + print(f"Template not found: agents/{file_name}") + continue + _write_file(os.path.join(agents_dir, file_name + file_ext), content=template.render(**specification)) + # workflow-specific content + if task_spec["workflow"]["name"] == "direct": + # - task/*env_cfg.py + template = jinja_env.get_template(f"tasks/direct_{task_spec['workflow']['type']}/env_cfg") + _write_file( + os.path.join(task_dir, f"{task_spec['filename']}_env_cfg.py"), content=template.render(**specification) + ) + # - task/*env.py + template = jinja_env.get_template(f"tasks/direct_{task_spec['workflow']['type']}/env") + _write_file(os.path.join(task_dir, f"{task_spec['filename']}_env.py"), content=template.render(**specification)) + elif task_spec["workflow"]["name"] == "manager-based": + # - task/*env_cfg.py + template = jinja_env.get_template(f"tasks/manager-based_{task_spec['workflow']['type']}/env_cfg") + _write_file( + os.path.join(task_dir, f"{task_spec['filename']}_env_cfg.py"), content=template.render(**specification) + ) + # - task/mdp folder + shutil.copytree( + os.path.join(TEMPLATE_DIR, "tasks", f"manager-based_{task_spec['workflow']['type']}", "mdp"), + os.path.join(task_dir, "mdp"), + dirs_exist_ok=True, + ) + + +def _generate_tasks(specification: dict, task_dir: str) -> list[dict]: + """Generate the task files for an external project or an internal task. + + Args: + specification: The specification of the project/task. + task_dir: The directory where the tasks will be generated. + + Returns: + A list of specifications for the tasks. + """ + specifications = [] + task_name_prefix = "Template" if specification["external"] else "Isaac" + general_task_name = "-".join([item.capitalize() for item in specification["name"].split("_")]) + for workflow in specification["workflows"]: + task_name = general_task_name + ("-Marl" if workflow["type"] == "multi-agent" else "") + filename = task_name.replace("-", "_").lower() + task = { + "workflow": workflow, + "filename": filename, + "classname": task_name.replace("-", ""), + "dir": os.path.join(task_dir, workflow["name"].replace("-", "_"), filename), + } + if task["workflow"]["name"] == "direct": + task["id"] = f"{task_name_prefix}-{task_name}-Direct-v0" + elif task["workflow"]["name"] == "manager-based": + task["id"] = f"{task_name_prefix}-{task_name}-v0" + print(f" | |-- Generating '{task['id']}' task...") + _generate_task_per_workflow(task["dir"], {**specification, "task": task}) + specifications.append({**specification, "task": task}) + return specifications + + +def _external(specification: dict) -> None: + """Generate an external project. + + Args: + specification: The specification of the project/task. + """ + name = specification["name"] + project_dir = os.path.join(specification["path"], name) + os.makedirs(project_dir, exist_ok=True) + # repo files + print(" |-- Copying repo files...") + shutil.copyfile(os.path.join(ROOT_DIR, ".dockerignore"), os.path.join(project_dir, ".dockerignore")) + shutil.copyfile(os.path.join(ROOT_DIR, "pyproject.toml"), os.path.join(project_dir, "pyproject.toml")) + shutil.copyfile(os.path.join(ROOT_DIR, ".gitattributes"), os.path.join(project_dir, ".gitattributes")) + if os.path.exists(os.path.join(ROOT_DIR, ".gitignore")): + shutil.copyfile(os.path.join(ROOT_DIR, ".gitignore"), os.path.join(project_dir, ".gitignore")) + shutil.copyfile( + os.path.join(ROOT_DIR, ".pre-commit-config.yaml"), os.path.join(project_dir, ".pre-commit-config.yaml") + ) + template = jinja_env.get_template("external/README.md") + _write_file(os.path.join(project_dir, "README.md"), content=template.render(**specification)) + # scripts + print(" |-- Copying scripts...") + # reinforcement learning libraries + dir = os.path.join(project_dir, "scripts") + os.makedirs(dir, exist_ok=True) + for rl_library in specification["rl_libraries"]: + shutil.copytree( + os.path.join(ROOT_DIR, "scripts", "reinforcement_learning", rl_library["name"]), + os.path.join(dir, rl_library["name"]), + dirs_exist_ok=True, + ) + # replace placeholder in scripts + for file in glob.glob(os.path.join(dir, rl_library["name"], "*.py")): + _replace_in_file( + [ + ( + "# PLACEHOLDER: Extension template (do not remove this comment)", + f"import {name}.tasks # noqa: F401", + ) + ], + src=file, + ) + # - other scripts + _replace_in_file( + [("import isaaclab_tasks", f"import {name}.tasks"), ("isaaclab_tasks", name), ('"Isaac"', '"Template-"')], + src=os.path.join(ROOT_DIR, "scripts", "environments", "list_envs.py"), + dst=os.path.join(dir, "list_envs.py"), + ) + for script in ["zero_agent.py", "random_agent.py"]: + _replace_in_file( + [ + ( + "# PLACEHOLDER: Extension template (do not remove this comment)", + f"import {name}.tasks # noqa: F401", + ) + ], + src=os.path.join(ROOT_DIR, "scripts", "environments", script), + dst=os.path.join(dir, script), + ) + # # docker files + # print(" |-- Copying docker files...") + # dir = os.path.join(project_dir, "docker") + # os.makedirs(dir, exist_ok=True) + # template = jinja_env.get_template("external/docker/.env.base") + # _write_file(os.path.join(dir, ".env.base"), content=template.render(**specification)) + # template = jinja_env.get_template("external/docker/docker-compose.yaml") + # _write_file(os.path.join(dir, "docker-compose.yaml"), content=template.render(**specification)) + # template = jinja_env.get_template("external/docker/Dockerfile") + # _write_file(os.path.join(dir, "Dockerfile"), content=template.render(**specification)) + # extension files + print(" |-- Copying extension files...") + # - config/extension.toml + dir = os.path.join(project_dir, "source", name, "config") + os.makedirs(dir, exist_ok=True) + template = jinja_env.get_template("extension/config/extension.toml") + _write_file(os.path.join(dir, "extension.toml"), content=template.render(**specification)) + # - docs/CHANGELOG.rst + dir = os.path.join(project_dir, "source", name, "docs") + os.makedirs(dir, exist_ok=True) + template = jinja_env.get_template("extension/docs/CHANGELOG.rst") + _write_file( + os.path.join(dir, "CHANGELOG.rst"), content=template.render({"date": datetime.now().strftime("%Y-%m-%d")}) + ) + # - setup.py and pyproject.toml + dir = os.path.join(project_dir, "source", name) + template = jinja_env.get_template("extension/setup.py") + _write_file(os.path.join(dir, "setup.py"), content=template.render(**specification)) + shutil.copyfile(os.path.join(TEMPLATE_DIR, "extension", "pyproject.toml"), os.path.join(dir, "pyproject.toml")) + # - tasks + print(" |-- Generating tasks...") + dir = os.path.join(project_dir, "source", name, name, "tasks") + os.makedirs(dir, exist_ok=True) + specifications = _generate_tasks(specification, dir) + shutil.copyfile(os.path.join(TEMPLATE_DIR, "extension", "__init__tasks"), os.path.join(dir, "__init__.py")) + for workflow in specification["workflows"]: + shutil.copyfile( + os.path.join(TEMPLATE_DIR, "extension", "__init__workflow"), + os.path.join(dir, workflow["name"].replace("-", "_"), "__init__.py"), + ) + # - other files + dir = os.path.join(project_dir, "source", name, name) + template = jinja_env.get_template("extension/ui_extension_example.py") + _write_file(os.path.join(dir, "ui_extension_example.py"), content=template.render(**specification)) + shutil.copyfile(os.path.join(TEMPLATE_DIR, "extension", "__init__ext"), os.path.join(dir, "__init__.py")) + # .vscode files + print(" |-- Copying vscode files...") + dir = os.path.join(project_dir, ".vscode") + shutil.copytree(os.path.join(TEMPLATE_DIR, "external", ".vscode"), dir, dirs_exist_ok=True) + template = jinja_env.get_template("external/.vscode/tasks.json") + _write_file(os.path.join(dir, "tasks.json"), content=template.render(**specification)) + template = jinja_env.get_template("external/.vscode/tools/launch.template.json") + _write_file( + os.path.join(dir, "tools", "launch.template.json"), content=template.render(specifications=specifications) + ) + # setup git repo + print(f"Setting up git repo in {project_dir} path...") + _setup_git_repo(project_dir) + # show end message + print("\n" + "-" * 80) + print(f"Project '{name}' generated successfully in {project_dir} path.") + print(f"See {project_dir}/README.md to get started!") + print("-" * 80) + + +def get_algorithms_per_rl_library(single_agent: bool = True, multi_agent: bool = True): + assert single_agent or multi_agent, "At least one of 'single_agent' or 'multi_agent' must be True" + data = {"rl_games": [], "rsl_rl": [], "skrl": [], "sb3": []} + # get algorithms + for file in glob.glob(os.path.join(TEMPLATE_DIR, "agents", "*_cfg")): + for rl_library in data.keys(): + basename = os.path.basename(file).replace("_cfg", "") + if basename.startswith(f"{rl_library}_"): + algorithm = basename.replace(f"{rl_library}_", "").upper() + assert algorithm in SINGLE_AGENT_ALGORITHMS or algorithm in MULTI_AGENT_ALGORITHMS, ( + f"{algorithm} algorithm is not listed in the supported algorithms" + ) + if single_agent and algorithm in SINGLE_AGENT_ALGORITHMS: + data[rl_library].append(algorithm) + if multi_agent and algorithm in MULTI_AGENT_ALGORITHMS: + data[rl_library].append(algorithm) + # remove duplicates and sort + for rl_library in data.keys(): + data[rl_library] = sorted(list(set(data[rl_library]))) + return data + + +def generate(specification: dict) -> None: + """Generate the project/task. + + Args: + specification: The specification of the project/task. + """ + # validate specification + print("\nValidating specification...") + assert "external" in specification, "External flag is required" + assert specification.get("name", "").isidentifier(), "Name must be a valid identifier" + for workflow in specification["workflows"]: + assert workflow["name"] in ["direct", "manager-based"], f"Invalid workflow: {workflow}" + assert workflow["type"] in ["single-agent", "multi-agent"], f"Invalid workflow type: {workflow}" + if specification["external"]: + assert "path" in specification, "Path is required for external projects" + # add other information to specification + specification["platform"] = sys.platform + # generate project/task + if specification["external"]: + print("Generating external project...") + _external(specification) + else: + print("Generating internal task...") + print(" |-- Generating tasks...") + _generate_tasks(specification, TASKS_DIR) diff --git a/tools/template/requirements.txt b/tools/template/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a1a0c0f691ae75f7fc59ab5f9ff0a60e22562dc7 --- /dev/null +++ b/tools/template/requirements.txt @@ -0,0 +1,5 @@ +# CLI management +InquirerPy +rich +# templating +Jinja2 diff --git a/tools/template/templates/agents/rl_games_ppo_cfg b/tools/template/templates/agents/rl_games_ppo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..e78116e70cef1edcb6f58b63cb5f1768e3a8e995 --- /dev/null +++ b/tools/template/templates/agents/rl_games_ppo_cfg @@ -0,0 +1,78 @@ +params: + seed: 42 + + # environment wrapper clipping + env: + # added to the wrapper + clip_observations: 5.0 + # can make custom wrapper? + clip_actions: 1.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + # doesn't have this fine grained control but made it close + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [32, 32] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: cartpole_direct + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + num_actors: -1 # configured from the script (based on num_envs) + reward_shaper: + scale_value: 0.1 + normalize_advantage: True + gamma: 0.99 + tau : 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 150 + save_best_after: 50 + save_frequency: 25 + grad_norm: 1.0 + entropy_coef: 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 32 + minibatch_size: 16384 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + seq_length: 4 + bounds_loss_coef: 0.0001 diff --git a/tools/template/templates/agents/rsl_rl_ppo_cfg b/tools/template/templates/agents/rsl_rl_ppo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..85970dfc2ce494aad14bab2636ccf05e5a2ef1d1 --- /dev/null +++ b/tools/template/templates/agents/rsl_rl_ppo_cfg @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class PPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 16 + max_iterations = 150 + save_interval = 50 + experiment_name = "cartpole_direct" + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_obs_normalization=False, + critic_obs_normalization=False, + actor_hidden_dims=[32, 32], + critic_hidden_dims=[32, 32], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.005, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/tools/template/templates/agents/sb3_ppo_cfg b/tools/template/templates/agents/sb3_ppo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..4ac83212e440efb99b63ceaa70616073ee33a0a0 --- /dev/null +++ b/tools/template/templates/agents/sb3_ppo_cfg @@ -0,0 +1,20 @@ +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +n_timesteps: !!float 1e6 +policy: 'MlpPolicy' +n_steps: 16 +batch_size: 4096 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 20 +ent_coef: 0.01 +learning_rate: !!float 3e-4 +clip_range: !!float 0.2 +policy_kwargs: + activation_fn: nn.ELU + net_arch: [32, 32] + squash_output: False +vf_coef: 1.0 +max_grad_norm: 1.0 +device: "cuda:0" diff --git a/tools/template/templates/agents/skrl_amp_cfg b/tools/template/templates/agents/skrl_amp_cfg new file mode 100644 index 0000000000000000000000000000000000000000..0946e4c6e6fab07bd310553f2335187033cf76a8 --- /dev/null +++ b/tools/template/templates/agents/skrl_amp_cfg @@ -0,0 +1,111 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: -2.9 + fixed_log_std: True + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + discriminator: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [1024, 512] + activations: relu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + +# AMP memory (reference motion dataset) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +motion_dataset: + class: RandomMemory + memory_size: 200000 + +# AMP memory (preventing discriminator overfitting) +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +reply_buffer: + class: RandomMemory + memory_size: 1000000 + + +# AMP agent configuration (field names are from AMP_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/amp.html +agent: + class: AMP + rollouts: 16 + learning_epochs: 6 + mini_batches: 2 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-05 + learning_rate_scheduler: null + learning_rate_scheduler_kwargs: null + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + amp_state_preprocessor: RunningStandardScaler + amp_state_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 0.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.5 + discriminator_loss_scale: 5.0 + amp_batch_size: 512 + task_reward_weight: 0.0 + style_reward_weight: 1.0 + discriminator_batch_size: 4096 + discriminator_reward_scale: 2.0 + discriminator_logit_regularization_scale: 0.05 + discriminator_gradient_penalty_scale: 5.0 + discriminator_weight_decay_scale: 1.0e-04 + # rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "humanoid_amp_run" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 80000 + environment_info: log diff --git a/tools/template/templates/agents/skrl_ippo_cfg b/tools/template/templates/agents/skrl_ippo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..a89939f9554311af51fcf7f324ad757895e857f7 --- /dev/null +++ b/tools/template/templates/agents/skrl_ippo_cfg @@ -0,0 +1,80 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# IPPO agent configuration (field names are from IPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/ippo.html +agent: + class: IPPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 1 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cart_double_pendulum_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/tools/template/templates/agents/skrl_mappo_cfg b/tools/template/templates/agents/skrl_mappo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..255b30eac810b6e03bb7c2836b31f52aab07c87a --- /dev/null +++ b/tools/template/templates/agents/skrl_mappo_cfg @@ -0,0 +1,82 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: True + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# MAPPO agent configuration (field names are from MAPPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/multi_agents/mappo.html +agent: + class: MAPPO + rollouts: 16 + learning_epochs: 8 + mini_batches: 1 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 3.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + shared_state_preprocessor: RunningStandardScaler + shared_state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 1.0 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cart_double_pendulum_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/tools/template/templates/agents/skrl_ppo_cfg b/tools/template/templates/agents/skrl_ppo_cfg new file mode 100644 index 0000000000000000000000000000000000000000..96515145faba20dbdba1728a4b2179599446042e --- /dev/null +++ b/tools/template/templates/agents/skrl_ppo_cfg @@ -0,0 +1,80 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: OBSERVATIONS + layers: [32, 32] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 32 + learning_epochs: 8 + mini_batches: 8 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 5.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.008 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.0 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.1 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "cartpole_direct" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 4800 + environment_info: log diff --git a/tools/template/templates/extension/__init__ext b/tools/template/templates/extension/__init__ext new file mode 100644 index 0000000000000000000000000000000000000000..6705ede8b0a493668fd41c9bbb4dddc1e5e2be6e --- /dev/null +++ b/tools/template/templates/extension/__init__ext @@ -0,0 +1,14 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Python module serving as a project/extension template. +""" + +# Register Gym environments. +from .tasks import * + +# Register UI extensions. +from .ui_extension_example import * diff --git a/tools/template/templates/extension/__init__tasks b/tools/template/templates/extension/__init__tasks new file mode 100644 index 0000000000000000000000000000000000000000..13df3c3210fa12afbd1b6dbdbaee492b80915175 --- /dev/null +++ b/tools/template/templates/extension/__init__tasks @@ -0,0 +1,17 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Package containing task implementations for the extension.""" + +## +# Register Gym environments. +## + +from isaaclab_tasks.utils import import_packages + +# The blacklist is used to prevent importing configs from sub-packages +_BLACKLIST_PKGS = ["utils", ".mdp"] +# Import all configs in this package +import_packages(__name__, _BLACKLIST_PKGS) diff --git a/tools/template/templates/extension/__init__workflow b/tools/template/templates/extension/__init__workflow new file mode 100644 index 0000000000000000000000000000000000000000..65d6e5a24441a1dd26e3826dca71107d0a559813 --- /dev/null +++ b/tools/template/templates/extension/__init__workflow @@ -0,0 +1,6 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym # noqa: F401 diff --git a/tools/template/templates/extension/config/extension.toml b/tools/template/templates/extension/config/extension.toml new file mode 100644 index 0000000000000000000000000000000000000000..dbe4b064fbc4b0fbe8eb694a7557e07dbf95d27f --- /dev/null +++ b/tools/template/templates/extension/config/extension.toml @@ -0,0 +1,35 @@ +[package] + +# Semantic Versioning is used: https://semver.org/ +version = "0.1.0" + +# Description +category = "isaaclab" +readme = "README.md" + +title = "Extension Template" +author = "Isaac Lab Project Developers" +maintainer = "Isaac Lab Project Developers" +description="Extension Template for Isaac Lab" +repository = "https://github.com/isaac-sim/IsaacLab.git" +keywords = ["extension", "template", "isaaclab"] + +[dependencies] +"isaaclab" = {} +"isaaclab_assets" = {} +"isaaclab_mimic" = {} +"isaaclab_rl" = {} +"isaaclab_tasks" = {} +# NOTE: Add additional dependencies here + +[[python.module]] +name = "{{ name }}" + +[isaac_lab_settings] +# TODO: Uncomment and list any apt dependencies here. +# If none, leave it commented out. +# apt_deps = ["example_package"] +# TODO: Uncomment and provide path to a ros_ws +# with rosdeps to be installed. If none, +# leave it commented out. +# ros_ws = "path/from/extension_root/to/ros_ws" diff --git a/tools/template/templates/extension/docs/CHANGELOG.rst b/tools/template/templates/extension/docs/CHANGELOG.rst new file mode 100644 index 0000000000000000000000000000000000000000..9b888993542df590e3fb873c663ece2c606f23a3 --- /dev/null +++ b/tools/template/templates/extension/docs/CHANGELOG.rst @@ -0,0 +1,10 @@ +Changelog +--------- + +0.1.0 ({{ date }}) +~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Created an initial template for building an extension or project based on Isaac Lab diff --git a/tools/template/templates/extension/pyproject.toml b/tools/template/templates/extension/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d90ac3536f168228bdb8bb40c178ffa22f08bed2 --- /dev/null +++ b/tools/template/templates/extension/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/tools/template/templates/extension/setup.py b/tools/template/templates/extension/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..e991708e34d867cdd95de4dac0eb2560c4d32273 --- /dev/null +++ b/tools/template/templates/extension/setup.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Installation script for the '{{ name }}' python package.""" + +import os + +import toml +from setuptools import setup + +# Obtain the extension data from the extension.toml file +EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) +# Read the extension.toml file +EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) + +# Minimum dependencies required prior to installation +INSTALL_REQUIRES = [ + # NOTE: Add dependencies + "psutil", +] + +# Installation operation +setup( + name="{{ name }}", + packages=["{{ name }}"], + author=EXTENSION_TOML_DATA["package"]["author"], + maintainer=EXTENSION_TOML_DATA["package"]["maintainer"], + url=EXTENSION_TOML_DATA["package"]["repository"], + version=EXTENSION_TOML_DATA["package"]["version"], + description=EXTENSION_TOML_DATA["package"]["description"], + keywords=EXTENSION_TOML_DATA["package"]["keywords"], + install_requires=INSTALL_REQUIRES, + license="Apache-2.0", + include_package_data=True, + python_requires=">=3.10", + classifiers=[ + "Natural Language :: English", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Isaac Sim :: 4.5.0", + "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", + ], + zip_safe=False, +) diff --git a/tools/template/templates/extension/ui_extension_example.py b/tools/template/templates/extension/ui_extension_example.py new file mode 100644 index 0000000000000000000000000000000000000000..483f323954a7cc56fe32579f4167b587520c1f34 --- /dev/null +++ b/tools/template/templates/extension/ui_extension_example.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import omni.ext + + +# Functions and vars are available to other extension as usual in python: `example.python_ext.some_public_function(x)` +def some_public_function(x: int): + print("[{{ name }}] some_public_function was called with x: ", x) + return x**x + + +# Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be +# instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled +# on_shutdown() is called. +class ExampleExtension(omni.ext.IExt): + # ext_id is current extension id. It can be used with extension manager to query additional information, like where + # this extension is located on filesystem. + def on_startup(self, ext_id): + print("[{{ name }}] startup") + + self._count = 0 + + self._window = omni.ui.Window("My Window", width=300, height=300) + with self._window.frame: + with omni.ui.VStack(): + label = omni.ui.Label("") + + def on_click(): + self._count += 1 + label.text = f"count: {self._count}" + + def on_reset(): + self._count = 0 + label.text = "empty" + + on_reset() + + with omni.ui.HStack(): + omni.ui.Button("Add", clicked_fn=on_click) + omni.ui.Button("Reset", clicked_fn=on_reset) + + def on_shutdown(self): + print("[{{ name }}] shutdown") diff --git a/tools/template/templates/external/.vscode/.gitignore b/tools/template/templates/external/.vscode/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..10b0af342ce38ac614e378cac00d334c5106edd0 --- /dev/null +++ b/tools/template/templates/external/.vscode/.gitignore @@ -0,0 +1,10 @@ +# Note: These files are kept for development purposes only. +!tools/launch.template.json +!tools/settings.template.json +!tools/setup_vscode.py +!extensions.json +!tasks.json + +# Ignore all other files +.python.env +*.json diff --git a/tools/template/templates/external/.vscode/extensions.json b/tools/template/templates/external/.vscode/extensions.json new file mode 100644 index 0000000000000000000000000000000000000000..6306e43497082f89fa3eac81377aed8f0ac6c30e --- /dev/null +++ b/tools/template/templates/external/.vscode/extensions.json @@ -0,0 +1,12 @@ +{ + // See http://go.microsoft.com/fwlink/?LinkId=827846 + // for the documentation about the extensions.json format + "recommendations": [ + "ms-python.python", + "ms-python.vscode-pylance", + "ban.spellright", + "ms-iot.vscode-ros", + "ms-python.black-formatter", + "ms-python.flake8", + ] +} diff --git a/tools/template/templates/external/.vscode/tasks.json b/tools/template/templates/external/.vscode/tasks.json new file mode 100644 index 0000000000000000000000000000000000000000..0ebe2101cff6cbb82bb2b6667bb3050dd2b70ba9 --- /dev/null +++ b/tools/template/templates/external/.vscode/tasks.json @@ -0,0 +1,27 @@ +{ + "version": "2.0.0", + "tasks": [ + { + "label": "setup_python_env", + "type": "shell", + "linux": { + "command": "${input:isaac_path}/python.sh ${workspaceFolder}/.vscode/tools/setup_vscode.py --isaac_path ${input:isaac_path}" + }, + "windows": { + "command": "${input:isaac_path}/python.bat ${workspaceFolder}/.vscode/tools/setup_vscode.py --isaac_path ${input:isaac_path}" + } + } + ], + "inputs": [ + { + "id": "isaac_path", + "description": "Absolute path to the current Isaac Sim installation. If you installed IsaacSim from pip, the import of it failed. Please make sure you run the task with the correct python environment. As fallback, you can directly execute the python script by running: ``python.sh /.vscode/tools/setup_vscode.py``", +{% if platform == "win32" %} + "default": "C:/isaacsim", +{% else %} + "default": "${HOME}/isaacsim", +{% endif %} + "type": "promptString" + }, + ] +} diff --git a/tools/template/templates/external/.vscode/tools/launch.template.json b/tools/template/templates/external/.vscode/tools/launch.template.json new file mode 100644 index 0000000000000000000000000000000000000000..e660203981c12f44164202a71605a20636c60be3 --- /dev/null +++ b/tools/template/templates/external/.vscode/tools/launch.template.json @@ -0,0 +1,93 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + // For standalone script execution + { + "name": "Python: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + }, +{% for specification in specifications %} + {% for rl_library in specification.rl_libraries %} + {% for rl_algorithm in rl_library.algorithms %} + { + "name": "Python: Train {{ specification.task.id }} with {{ rl_library.name }} ({{ rl_algorithm|upper }})", + "type": "debugpy", + "request": "launch", + {% if rl_library.name == "skrl" %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "4096", "--headless", "--algorithm", "{{ rl_algorithm|upper }}"], + {% else %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "4096", "--headless"], + {% endif %} + "program": "${workspaceFolder}/scripts/{{ rl_library.name }}/train.py", + "console": "integratedTerminal", + }, + { + "name": "Python: Play {{ specification.task.id }} with {{ rl_library.name }} ({{ rl_algorithm|upper }})", + "type": "debugpy", + "request": "launch", + {% if rl_library.name == "skrl" %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "32", "--algorithm", "{{ rl_algorithm|upper }}"], + {% else %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "32"], + {% endif %} + "program": "${workspaceFolder}/scripts/{{ rl_library.name }}/play.py", + "console": "integratedTerminal", + }, + {% endfor %} + {% endfor %} +{% endfor %} + // For script execution inside a Docker + { + "name": "Docker: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "env": { + "PYTHONPATH": "${env:PYTHONPATH}:${workspaceFolder}" + } + }, +{% for specification in specifications %} + {% for rl_library in specification.rl_libraries %} + {% for rl_algorithm in rl_library.algorithms %} + { + "name": "Docker: Train {{ specification.task.id }} with {{ rl_library.name }} ({{ rl_algorithm|upper }})", + "type": "debugpy", + "request": "launch", + {% if rl_library.name == "skrl" %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "4096", "--headless", "--algorithm", "{{ rl_algorithm|upper }}"], + {% else %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "4096", "--headless"], + {% endif %} + "program": "${workspaceFolder}/scripts/{{ rl_library.name }}/train.py", + "console": "integratedTerminal", + "env": { + "PYTHONPATH": "${env:PYTHONPATH}:${workspaceFolder}" + }, + }, + { + "name": "Docker: Play {{ specification.task.id }} with {{ rl_library.name }} ({{ rl_algorithm|upper }})", + "type": "debugpy", + "request": "launch", + {% if rl_library.name == "skrl" %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "32", "--algorithm", "{{ rl_algorithm|upper }}"], + {% else %} + "args" : ["--task", "{{ specification.task.id }}", "--num_envs", "32"], + {% endif %} + "program": "${workspaceFolder}/scripts/{{ rl_library.name }}/play.py", + "console": "integratedTerminal", + "env": { + "PYTHONPATH": "${env:PYTHONPATH}:${workspaceFolder}" + }, + }, + {% endfor %} + {% endfor %} +{% endfor %} + ] +} diff --git a/tools/template/templates/external/.vscode/tools/settings.template.json b/tools/template/templates/external/.vscode/tools/settings.template.json new file mode 100644 index 0000000000000000000000000000000000000000..c1528d65dd73aa45958e638e580c64ce7f41cea4 --- /dev/null +++ b/tools/template/templates/external/.vscode/tools/settings.template.json @@ -0,0 +1,79 @@ +{ + "files.associations": { + "*.tpp": "cpp", + "*.kit": "toml", + "*.rst": "restructuredtext" + }, + "editor.rulers": [120], + + // files to be ignored by the linter + "files.watcherExclude": { + "**/.git/objects/**": true, + "**/.git/subtree-cache/**": true, + "**/node_modules/**": true, + "**/_isaac_sim/**": true, + "**/_compiler/**": true + }, + // Configuration for spelling checker + "spellright.language": [ + "en-US-10-1." + ], + "spellright.documentTypes": [ + "markdown", + "latex", + "plaintext", + "cpp", + "asciidoc", + "python", + "restructuredtext" + ], + "cSpell.words": [ + "literalinclude", + "linenos", + "instanceable", + "isaacSim", + "jacobians", + "pointcloud", + "ridgeback", + "rllib", + "robomimic", + "teleoperation", + "xform", + "numpy", + "tensordict", + "flatcache", + "physx", + "dpad", + "gamepad", + "linspace", + "upsampled", + "downsampled", + "arange", + "discretization", + "trimesh", + "uninstanceable" + ], + // This enables python language server. Seems to work slightly better than jedi: + "python.languageServer": "Pylance", + // Use ruff as a formatter and linter + "ruff.configuration": "${workspaceFolder}/pyproject.toml", + // Use docstring generator + "autoDocstring.docstringFormat": "google", + "autoDocstring.guessTypes": true, + // Python environment path + // note: the default interpreter is overridden when user selects a workspace interpreter + // in the status bar. For example, the virtual environment python interpreter + "python.defaultInterpreterPath": "", + // ROS distribution + "ros.distro": "noetic", + // Language specific settings + "[python]": { + "editor.tabSize": 4 + }, + "[restructuredtext]": { + "editor.tabSize": 2 + }, + // Python extra paths + // Note: this is filled up when vscode is set up for the first time + "python.analysis.extraPaths": [] +} diff --git a/tools/template/templates/external/.vscode/tools/setup_vscode.py b/tools/template/templates/external/.vscode/tools/setup_vscode.py new file mode 100644 index 0000000000000000000000000000000000000000..f8c61b76c5b730e409e22d4517649caf83bfe040 --- /dev/null +++ b/tools/template/templates/external/.vscode/tools/setup_vscode.py @@ -0,0 +1,220 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This script sets up the vs-code settings for the Isaac Lab project. + +This script merges the python.analysis.extraPaths from the "{ISAACSIM_DIR}/.vscode/settings.json" file into +the ".vscode/settings.json" file. + +This is necessary because Isaac Sim 2022.2.1 onwards does not add the necessary python packages to the python path +when the "setup_python_env.sh" is run as part of the vs-code launch configuration. +""" + +import argparse +import os +import pathlib +import platform +import re +import sys + +PROJECT_DIR = pathlib.Path(__file__).parents[2] +"""Path to the the project directory.""" + +try: + import isaacsim # noqa: F401 + + isaacsim_dir = os.environ.get("ISAAC_PATH", "") +except ModuleNotFoundError or ImportError: + # Create a parser to get the isaac-sim path + parser = argparse.ArgumentParser(description="Setup the VSCode settings for the project.") + parser.add_argument("--isaac_path", type=str, help="The absolute path to the Isaac Sim installation.") + args = parser.parse_args() + + # parse the isaac-sim directory + isaacsim_dir = args.isaac_path + # check if the isaac-sim directory is provided + if not os.path.exists(isaacsim_dir): + raise FileNotFoundError( + f"Could not find the isaac-sim directory: {isaacsim_dir}. Please provide the correct path to the Isaac Sim" + " installation." + ) +except EOFError: + print("Unable to trigger EULA acceptance. This is likely due to the script being run in a non-interactive shell.") + print("Please run the script in an interactive shell to accept the EULA.") + print("Skipping the setup of the VSCode settings...") + sys.exit(0) + +# check if the isaac-sim directory exists +if not os.path.exists(isaacsim_dir): + raise FileNotFoundError( + f"Could not find the isaac-sim directory: {isaacsim_dir}. There are two possible reasons for this:\n\t1. The" + " Isaac Sim directory does not exist as provided CLI path.\n\t2. The script couldn't import the 'isaacsim'" + " package. This could be due to the 'isaacsim' package not being installed in the Python" + " environment.\n\nPlease make sure that the Isaac Sim directory exists or that the 'isaacsim' package is" + " installed." + ) + +ISAACSIM_DIR = isaacsim_dir +"""Path to the isaac-sim directory.""" + + +def overwrite_python_analysis_extra_paths(isaaclab_settings: str) -> str: + """Overwrite the python.analysis.extraPaths in the Isaac Lab settings file. + + The extraPaths are replaced with the path names from the isaac-sim settings file that exists in the + "{ISAACSIM_DIR}/.vscode/settings.json" file. + + If the isaac-sim settings file does not exist, the extraPaths are not overwritten. + + Args: + isaaclab_settings: The settings string to use as template. + + Returns: + The settings string with overwritten python analysis extra paths. + """ + # isaac-sim settings + isaacsim_vscode_filename = os.path.join(ISAACSIM_DIR, ".vscode", "settings.json") + + # we use the isaac-sim settings file to get the python.analysis.extraPaths for kit extensions + # if this file does not exist, we will not add any extra paths + if os.path.exists(isaacsim_vscode_filename): + # read the path names from the isaac-sim settings file + with open(isaacsim_vscode_filename) as f: + vscode_settings = f.read() + # extract the path names + # search for the python.analysis.extraPaths section and extract the contents + settings = re.search( + r"\"python.analysis.extraPaths\": \[.*?\]", vscode_settings, flags=re.MULTILINE | re.DOTALL + ) + settings = settings.group(0) + settings = settings.split('"python.analysis.extraPaths": [')[-1] + settings = settings.split("]")[0] + + # read the path names from the isaac-sim settings file + path_names = settings.split(",") + path_names = [path_name.strip().strip('"') for path_name in path_names] + path_names = [path_name for path_name in path_names if len(path_name) > 0] + + # change the path names to be relative to the Isaac Lab directory + rel_path = os.path.relpath(ISAACSIM_DIR, PROJECT_DIR) + path_names = ['"${workspaceFolder}/' + rel_path + "/" + path_name + '"' for path_name in path_names] + else: + path_names = [] + print( + f"[WARN] Could not find Isaac Sim VSCode settings: {isaacsim_vscode_filename}." + "\n\tThis will result in missing 'python.analysis.extraPaths' in the VSCode" + "\n\tsettings, which limits the functionality of the Python language server." + "\n\tHowever, it does not affect the functionality of the Isaac Lab project." + "\n\tWe are working on a fix for this issue with the Isaac Sim team." + ) + + # add the path names that are in the Isaac Lab extensions directory + isaaclab_extensions = os.listdir(os.path.join(PROJECT_DIR, "source")) + path_names.extend(['"${workspaceFolder}/source/' + ext + '"' for ext in isaaclab_extensions]) + + # combine them into a single string + path_names = ",\n\t\t".expandtabs(4).join(path_names) + # deal with the path separator being different on Windows and Unix + path_names = path_names.replace("\\", "/") + + # replace the path names in the Isaac Lab settings file with the path names parsed + isaaclab_settings = re.sub( + r"\"python.analysis.extraPaths\": \[.*?\]", + '"python.analysis.extraPaths": [\n\t\t'.expandtabs(4) + path_names + "\n\t]".expandtabs(4), + isaaclab_settings, + flags=re.DOTALL, + ) + # return the Isaac Lab settings string + return isaaclab_settings + + +def overwrite_default_python_interpreter(isaaclab_settings: str) -> str: + """Overwrite the default python interpreter in the Isaac Lab settings file. + + The default python interpreter is replaced with the path to the python interpreter used by the + isaac-sim project. This is necessary because the default python interpreter is the one shipped with + isaac-sim. + + Args: + isaaclab_settings: The settings string to use as template. + + Returns: + The settings string with overwritten default python interpreter. + """ + # read executable name + python_exe = os.path.normpath(sys.executable) + + # replace with Isaac Sim's python.sh or python.bat scripts to make sure python with correct + # source paths is set as default + if f"kit{os.sep}python{os.sep}bin{os.sep}python" in python_exe: + # Check if the OS is Windows or Linux to use appropriate shell file + if platform.system() == "Windows": + python_exe = python_exe.replace(f"kit{os.sep}python{os.sep}bin{os.sep}python3", "python.bat") + else: + python_exe = python_exe.replace(f"kit{os.sep}python{os.sep}bin{os.sep}python3", "python.sh") + + # replace the default python interpreter in the Isaac Lab settings file with the path to the + # python interpreter in the Isaac Lab directory + isaaclab_settings = re.sub( + r"\"python.defaultInterpreterPath\": \".*?\"", + f'"python.defaultInterpreterPath": "{python_exe}"', + isaaclab_settings, + flags=re.DOTALL, + ) + # return the Isaac Lab settings file + return isaaclab_settings + + +def main(): + # Isaac Lab template settings + isaaclab_vscode_template_filename = os.path.join(PROJECT_DIR, ".vscode", "tools", "settings.template.json") + # make sure the Isaac Lab template settings file exists + if not os.path.exists(isaaclab_vscode_template_filename): + raise FileNotFoundError( + f"Could not find the Isaac Lab template settings file: {isaaclab_vscode_template_filename}" + ) + # read the Isaac Lab template settings file + with open(isaaclab_vscode_template_filename) as f: + isaaclab_template_settings = f.read() + + # overwrite the python.analysis.extraPaths in the Isaac Lab settings file with the path names + isaaclab_settings = overwrite_python_analysis_extra_paths(isaaclab_template_settings) + # overwrite the default python interpreter in the Isaac Lab settings file with the path to the + # python interpreter used to call this script + isaaclab_settings = overwrite_default_python_interpreter(isaaclab_settings) + + # add template notice to the top of the file + header_message = ( + "// This file is a template and is automatically generated by the setup_vscode.py script.\n" + "// Do not edit this file directly.\n" + "// \n" + f"// Generated from: {isaaclab_vscode_template_filename}\n" + ) + isaaclab_settings = header_message + isaaclab_settings + + # write the Isaac Lab settings file + isaaclab_vscode_filename = os.path.join(PROJECT_DIR, ".vscode", "settings.json") + with open(isaaclab_vscode_filename, "w") as f: + f.write(isaaclab_settings) + + # copy the launch.json file if it doesn't exist + isaaclab_vscode_launch_filename = os.path.join(PROJECT_DIR, ".vscode", "launch.json") + isaaclab_vscode_template_launch_filename = os.path.join(PROJECT_DIR, ".vscode", "tools", "launch.template.json") + if not os.path.exists(isaaclab_vscode_launch_filename): + # read template launch settings + with open(isaaclab_vscode_template_launch_filename) as f: + isaaclab_template_launch_settings = f.read() + # add header + header_message = header_message.replace( + isaaclab_vscode_template_filename, isaaclab_vscode_template_launch_filename + ) + isaaclab_launch_settings = header_message + isaaclab_template_launch_settings + # write the Isaac Lab launch settings file + with open(isaaclab_vscode_launch_filename, "w") as f: + f.write(isaaclab_launch_settings) + + +if __name__ == "__main__": + main() diff --git a/tools/template/templates/external/README.md b/tools/template/templates/external/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3b11b5407a85ae97ac5139b2959d24c66cd95c81 --- /dev/null +++ b/tools/template/templates/external/README.md @@ -0,0 +1,135 @@ +# Template for Isaac Lab Projects + +## Overview + +This project/repository serves as a template for building projects or extensions based on Isaac Lab. +It allows you to develop in an isolated environment, outside of the core Isaac Lab repository. + +**Key Features:** + +- `Isolation` Work outside the core Isaac Lab repository, ensuring that your development efforts remain self-contained. +- `Flexibility` This template is set up to allow your code to be run as an extension in Omniverse. + +**Keywords:** extension, template, isaaclab + +## Installation + +- Install Isaac Lab by following the [installation guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html). + We recommend using the conda or uv installation as it simplifies calling Python scripts from the terminal. + +- Clone or copy this project/repository separately from the Isaac Lab installation (i.e. outside the `IsaacLab` directory): + +- Using a python interpreter that has Isaac Lab installed, install the library in editable mode using: + + ```bash + # use 'PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda + python -m pip install -e source/{{ name }} + +- Verify that the extension is correctly installed by: + + - Listing the available tasks: + + Note: It the task name changes, it may be necessary to update the search pattern `"Template-"` + (in the `scripts/list_envs.py` file) so that it can be listed. + + ```bash + # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda + python scripts/list_envs.py + ``` + + - Running a task: + + ```bash + # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda + python scripts//train.py --task= + ``` + + - Running a task with dummy agents: + + These include dummy agents that output zero or random agents. They are useful to ensure that the environments are configured correctly. + + - Zero-action agent + + ```bash + # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda + python scripts/zero_agent.py --task= + ``` + - Random-action agent + + ```bash + # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda + python scripts/random_agent.py --task= + ``` + +### Set up IDE (Optional) + +To setup the IDE, please follow these instructions: + +- Run VSCode Tasks, by pressing `Ctrl+Shift+P`, selecting `Tasks: Run Task` and running the `setup_python_env` in the drop down menu. + When running this task, you will be prompted to add the absolute path to your Isaac Sim installation. + +If everything executes correctly, it should create a file .python.env in the `.vscode` directory. +The file contains the python paths to all the extensions provided by Isaac Sim and Omniverse. +This helps in indexing all the python modules for intelligent suggestions while writing code. + +### Setup as Omniverse Extension (Optional) + +We provide an example UI extension that will load upon enabling your extension defined in `source/{{ name }}/{{ name }}/ui_extension_example.py`. + +To enable your extension, follow these steps: + +1. **Add the search path of this project/repository** to the extension manager: + - Navigate to the extension manager using `Window` -> `Extensions`. + - Click on the **Hamburger Icon**, then go to `Settings`. + - In the `Extension Search Paths`, enter the absolute path to the `source` directory of this project/repository. + - If not already present, in the `Extension Search Paths`, enter the path that leads to Isaac Lab's extension directory directory (`IsaacLab/source`) + - Click on the **Hamburger Icon**, then click `Refresh`. + +2. **Search and enable your extension**: + - Find your extension under the `Third Party` category. + - Toggle it to enable your extension. + +## Code formatting + +We have a pre-commit template to automatically format your code. +To install pre-commit: + +```bash +pip install pre-commit +``` + +Then you can run pre-commit with: + +```bash +pre-commit run --all-files +``` + +## Troubleshooting + +### Pylance Missing Indexing of Extensions + +In some VsCode versions, the indexing of part of the extensions is missing. +In this case, add the path to your extension in `.vscode/settings.json` under the key `"python.analysis.extraPaths"`. + +```json +{ + "python.analysis.extraPaths": [ + "/source/{{ name }}" + ] +} +``` + +### Pylance Crash + +If you encounter a crash in `pylance`, it is probable that too many files are indexed and you run out of memory. +A possible solution is to exclude some of omniverse packages that are not used in your project. +To do so, modify `.vscode/settings.json` and comment out packages under the key `"python.analysis.extraPaths"` +Some examples of packages that can likely be excluded are: + +```json +"/extscache/omni.anim.*" // Animation packages +"/extscache/omni.kit.*" // Kit UI tools +"/extscache/omni.graph.*" // Graph UI tools +"/extscache/omni.services.*" // Services tools +... +``` diff --git a/tools/template/templates/external/docker/.env.base b/tools/template/templates/external/docker/.env.base new file mode 100644 index 0000000000000000000000000000000000000000..a837c47640feb84db9e86a7bf89faf47a956a505 --- /dev/null +++ b/tools/template/templates/external/docker/.env.base @@ -0,0 +1,8 @@ +### +# General settings +### + +# Isaac Lab base image +ISAACLAB_BASE_IMAGE=isaac-lab-base +# The Isaac Lab Extension Template path in the container +DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH=/workspace/isaaclab_extension_template diff --git a/tools/template/templates/external/docker/Dockerfile b/tools/template/templates/external/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..895968d2451c72061107961014b9170d42952585 --- /dev/null +++ b/tools/template/templates/external/docker/Dockerfile @@ -0,0 +1,21 @@ +ARG ISAACLAB_BASE_IMAGE_ARG + +# we use the basic isaaclab image as the base +FROM ${ISAACLAB_BASE_IMAGE_ARG} AS base + +ARG DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH_ARG +ENV DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH=${DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH_ARG} + +USER root + +# Copy the Isaac Lab Extension Template directory (files to exclude are defined in .dockerignore) +COPY ../ ${DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH} + +# # Install whatever you need as additional dependencies. +RUN bash -i -c "source ${HOME}/.bashrc && \ + cd ${DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH}/source/{{ name }} && \ + pip install -e ." + +# make working directory as the Isaac Lab directory +# this is the default directory when the container is run +WORKDIR /workspace diff --git a/tools/template/templates/external/docker/docker-compose.yaml b/tools/template/templates/external/docker/docker-compose.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f1505b90d22503a96c670c6ace9e4da76de28ece --- /dev/null +++ b/tools/template/templates/external/docker/docker-compose.yaml @@ -0,0 +1,38 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +x-default-isaac-lab-template-environment: &default-isaac-lab-template-environment + - OMNI_KIT_ALLOW_ROOT=1 + +x-default-isaac-lab-template-deploy: &default-isaac-lab-template-deploy + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + +services: + isaac-lab-template: + env_file: .env.base + build: + context: ../ + dockerfile: docker/Dockerfile + args: + - ISAACLAB_BASE_IMAGE_ARG=${ISAACLAB_BASE_IMAGE} + - DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH_ARG=${DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH} + image: isaac-lab-template + container_name: isaac-lab-template + volumes: + - type: bind + source: ../ + target: ${DOCKER_ISAACLAB_EXTENSION_TEMPLATE_PATH} + network_mode: host + environment: *default-isaac-lab-template-environment + deploy: *default-isaac-lab-template-deploy + # This is the entrypoint for the container + entrypoint: bash + stdin_open: true + tty: true diff --git a/tools/template/templates/tasks/__init__agents b/tools/template/templates/tasks/__init__agents new file mode 100644 index 0000000000000000000000000000000000000000..2e924fbf1b135c29b4cb5d7d8f86bec2b1085b4a --- /dev/null +++ b/tools/template/templates/tasks/__init__agents @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/tools/template/templates/tasks/__init__task b/tools/template/templates/tasks/__init__task new file mode 100644 index 0000000000000000000000000000000000000000..e8890743df1da79b69aac3f39edbe1e19488e60f --- /dev/null +++ b/tools/template/templates/tasks/__init__task @@ -0,0 +1,43 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +from . import agents + +## +# Register Gym environments. +## + + +gym.register( + id="{{ task.id }}", +{% if task.workflow.name == "direct" %} + entry_point=f"{__name__}.{{ task.filename }}_env:{{ task.classname }}Env", +{% else %} + entry_point="isaaclab.envs:ManagerBasedRLEnv", +{% endif %} + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": f"{__name__}.{{ task.filename }}_env_cfg:{{ task.classname }}EnvCfg", +{# RL libraries configurations #} +{% for rl_library in rl_libraries %} + {% for algorithm in rl_library.algorithms %} + {# configuration file #} + {% if rl_library.name == "rsl_rl" %} + {% set agent_config = "." ~ rl_library.name ~ "_" ~ algorithm ~ "_cfg:" ~ algorithm|upper ~ "RunnerCfg" %} + {% else %} + {% set agent_config = ":" ~ rl_library.name ~ "_" ~ algorithm ~ "_cfg.yaml" %} + {% endif %} + {# library configuration #} + {% if algorithm == "ppo" %} + "{{ rl_library.name }}_cfg_entry_point": f"{agents.__name__}{{ agent_config }}", + {% else %} + "{{ rl_library.name }}_{{ algorithm }}_cfg_entry_point": f"{agents.__name__}{{ agent_config }}", + {% endif %} + {% endfor %} +{% endfor %} + }, +) diff --git a/tools/template/templates/tasks/direct_multi-agent/env b/tools/template/templates/tasks/direct_multi-agent/env new file mode 100644 index 0000000000000000000000000000000000000000..eec2331722e83671fbe7319630186b791520b102 --- /dev/null +++ b/tools/template/templates/tasks/direct_multi-agent/env @@ -0,0 +1,184 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +import torch +from collections.abc import Sequence + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectMARLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.math import sample_uniform + +from .{{ task.filename }}_env_cfg import {{ task.classname }}EnvCfg + + +class {{ task.classname }}Env(DirectMARLEnv): + cfg: {{ task.classname }}EnvCfg + + def __init__(self, cfg: {{ task.classname }}EnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self._cart_dof_idx, _ = self.robot.find_joints(self.cfg.cart_dof_name) + self._pole_dof_idx, _ = self.robot.find_joints(self.cfg.pole_dof_name) + self._pendulum_dof_idx, _ = self.robot.find_joints(self.cfg.pendulum_dof_name) + + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[]) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: dict[str, torch.Tensor]) -> None: + self.actions = actions + + def _apply_action(self) -> None: + self.robot.set_joint_effort_target( + self.actions["cart"] * self.cfg.cart_action_scale, joint_ids=self._cart_dof_idx + ) + self.robot.set_joint_effort_target( + self.actions["pendulum"] * self.cfg.pendulum_action_scale, joint_ids=self._pendulum_dof_idx + ) + + def _get_observations(self) -> dict[str, torch.Tensor]: + pole_joint_pos = normalize_angle(self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1)) + pendulum_joint_pos = normalize_angle(self.joint_pos[:, self._pendulum_dof_idx[0]].unsqueeze(dim=1)) + observations = { + "cart": torch.cat( + ( + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + pole_joint_pos, + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ), + "pendulum": torch.cat( + ( + pole_joint_pos + pendulum_joint_pos, + pendulum_joint_pos, + self.joint_vel[:, self._pendulum_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ), + } + return observations + + def _get_rewards(self) -> dict[str, torch.Tensor]: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_cart_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_pole_vel, + self.cfg.rew_scale_pendulum_pos, + self.cfg.rew_scale_pendulum_vel, + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + normalize_angle(self.joint_pos[:, self._pole_dof_idx[0]]), + self.joint_vel[:, self._pole_dof_idx[0]], + normalize_angle(self.joint_pos[:, self._pendulum_dof_idx[0]]), + self.joint_vel[:, self._pendulum_dof_idx[0]], + math.prod(self.terminated_dict.values()), + ) + return total_reward + + def _get_dones(self) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + out_of_bounds = torch.any(torch.abs(self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, dim=1) + out_of_bounds = out_of_bounds | torch.any(torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, dim=1) + + terminated = {agent: out_of_bounds for agent in self.cfg.possible_agents} + time_outs = {agent: time_out for agent in self.cfg.possible_agents} + return terminated, time_outs + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.robot._ALL_INDICES + super()._reset_idx(env_ids) + + joint_pos = self.robot.data.default_joint_pos[env_ids] + joint_pos[:, self._pole_dof_idx] += sample_uniform( + self.cfg.initial_pole_angle_range[0] * math.pi, + self.cfg.initial_pole_angle_range[1] * math.pi, + joint_pos[:, self._pole_dof_idx].shape, + joint_pos.device, + ) + joint_pos[:, self._pendulum_dof_idx] += sample_uniform( + self.cfg.initial_pendulum_angle_range[0] * math.pi, + self.cfg.initial_pendulum_angle_range[1] * math.pi, + joint_pos[:, self._pendulum_dof_idx].shape, + joint_pos.device, + ) + joint_vel = self.robot.data.default_joint_vel[env_ids] + + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self.robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self.robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + +@torch.jit.script +def normalize_angle(angle): + return (angle + math.pi) % (2 * math.pi) - math.pi + + +@torch.jit.script +def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_cart_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_pos: float, + rew_scale_pole_vel: float, + rew_scale_pendulum_pos: float, + rew_scale_pendulum_vel: float, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + pendulum_pos: torch.Tensor, + pendulum_vel: torch.Tensor, + reset_terminated: torch.Tensor, +): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos).unsqueeze(dim=1), dim=-1) + rew_pendulum_pos = rew_scale_pendulum_pos * torch.sum( + torch.square(pole_pos + pendulum_pos).unsqueeze(dim=1), dim=-1 + ) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel).unsqueeze(dim=1), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel).unsqueeze(dim=1), dim=-1) + rew_pendulum_vel = rew_scale_pendulum_vel * torch.sum(torch.abs(pendulum_vel).unsqueeze(dim=1), dim=-1) + + total_reward = { + "cart": rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel, + "pendulum": rew_alive + rew_termination + rew_pendulum_pos + rew_pendulum_vel, + } + return total_reward diff --git a/tools/template/templates/tasks/direct_multi-agent/env_cfg b/tools/template/templates/tasks/direct_multi-agent/env_cfg new file mode 100644 index 0000000000000000000000000000000000000000..3b207209b736b489132561165e0222f47e4111b6 --- /dev/null +++ b/tools/template/templates/tasks/direct_multi-agent/env_cfg @@ -0,0 +1,55 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab_assets.robots.cart_double_pendulum import CART_DOUBLE_PENDULUM_CFG + +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectMARLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass + + +@configclass +class {{ task.classname }}EnvCfg(DirectMARLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + # multi-agent specification and spaces definition + possible_agents = ["cart", "pendulum"] + action_spaces = {"cart": 1, "pendulum": 1} + observation_spaces = {"cart": 4, "pendulum": 3} + state_space = -1 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot(s) + robot_cfg: ArticulationCfg = CART_DOUBLE_PENDULUM_CFG.replace(prim_path="/World/envs/env_.*/Robot") + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # custom parameters/scales + # - controllable joint + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + pendulum_dof_name = "pole_to_pendulum" + # - action scale + cart_action_scale = 100.0 # [N] + pendulum_action_scale = 50.0 # [Nm] + # - reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_cart_pos = 0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_pos = -1.0 + rew_scale_pole_vel = -0.01 + rew_scale_pendulum_pos = -1.0 + rew_scale_pendulum_vel = -0.01 + # - reset states/conditions + initial_pendulum_angle_range = [-0.25, 0.25] # pendulum angle sample range on reset [rad] + initial_pole_angle_range = [-0.25, 0.25] # pole angle sample range on reset [rad] + max_cart_pos = 3.0 # reset if cart exceeds this position [m] diff --git a/tools/template/templates/tasks/direct_single-agent/env b/tools/template/templates/tasks/direct_single-agent/env new file mode 100644 index 0000000000000000000000000000000000000000..e6f47fd3366b571a862185f6cd83eebfd695116f --- /dev/null +++ b/tools/template/templates/tasks/direct_single-agent/env @@ -0,0 +1,135 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import math +import torch +from collections.abc import Sequence + +import isaaclab.sim as sim_utils +from isaaclab.assets import Articulation +from isaaclab.envs import DirectRLEnv +from isaaclab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from isaaclab.utils.math import sample_uniform + +from .{{ task.filename }}_env_cfg import {{ task.classname }}EnvCfg + + +class {{ task.classname }}Env(DirectRLEnv): + cfg: {{ task.classname }}EnvCfg + + def __init__(self, cfg: {{ task.classname }}EnvCfg, render_mode: str | None = None, **kwargs): + super().__init__(cfg, render_mode, **kwargs) + + self._cart_dof_idx, _ = self.robot.find_joints(self.cfg.cart_dof_name) + self._pole_dof_idx, _ = self.robot.find_joints(self.cfg.pole_dof_name) + + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + def _setup_scene(self): + self.robot = Articulation(self.cfg.robot_cfg) + # add ground plane + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()) + # clone and replicate + self.scene.clone_environments(copy_from_source=False) + # we need to explicitly filter collisions for CPU simulation + if self.device == "cpu": + self.scene.filter_collisions(global_prim_paths=[]) + # add articulation to scene + self.scene.articulations["robot"] = self.robot + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _pre_physics_step(self, actions: torch.Tensor) -> None: + self.actions = actions.clone() + + def _apply_action(self) -> None: + self.robot.set_joint_effort_target(self.actions * self.cfg.action_scale, joint_ids=self._cart_dof_idx) + + def _get_observations(self) -> dict: + obs = torch.cat( + ( + self.joint_pos[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._pole_dof_idx[0]].unsqueeze(dim=1), + self.joint_pos[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + self.joint_vel[:, self._cart_dof_idx[0]].unsqueeze(dim=1), + ), + dim=-1, + ) + observations = {"policy": obs} + return observations + + def _get_rewards(self) -> torch.Tensor: + total_reward = compute_rewards( + self.cfg.rew_scale_alive, + self.cfg.rew_scale_terminated, + self.cfg.rew_scale_pole_pos, + self.cfg.rew_scale_cart_vel, + self.cfg.rew_scale_pole_vel, + self.joint_pos[:, self._pole_dof_idx[0]], + self.joint_vel[:, self._pole_dof_idx[0]], + self.joint_pos[:, self._cart_dof_idx[0]], + self.joint_vel[:, self._cart_dof_idx[0]], + self.reset_terminated, + ) + return total_reward + + def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]: + self.joint_pos = self.robot.data.joint_pos + self.joint_vel = self.robot.data.joint_vel + + time_out = self.episode_length_buf >= self.max_episode_length - 1 + out_of_bounds = torch.any(torch.abs(self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos, dim=1) + out_of_bounds = out_of_bounds | torch.any(torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2, dim=1) + return out_of_bounds, time_out + + def _reset_idx(self, env_ids: Sequence[int] | None): + if env_ids is None: + env_ids = self.robot._ALL_INDICES + super()._reset_idx(env_ids) + + joint_pos = self.robot.data.default_joint_pos[env_ids] + joint_pos[:, self._pole_dof_idx] += sample_uniform( + self.cfg.initial_pole_angle_range[0] * math.pi, + self.cfg.initial_pole_angle_range[1] * math.pi, + joint_pos[:, self._pole_dof_idx].shape, + joint_pos.device, + ) + joint_vel = self.robot.data.default_joint_vel[env_ids] + + default_root_state = self.robot.data.default_root_state[env_ids] + default_root_state[:, :3] += self.scene.env_origins[env_ids] + + self.joint_pos[env_ids] = joint_pos + self.joint_vel[env_ids] = joint_vel + + self.robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids) + self.robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids) + self.robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids) + + +@torch.jit.script +def compute_rewards( + rew_scale_alive: float, + rew_scale_terminated: float, + rew_scale_pole_pos: float, + rew_scale_cart_vel: float, + rew_scale_pole_vel: float, + pole_pos: torch.Tensor, + pole_vel: torch.Tensor, + cart_pos: torch.Tensor, + cart_vel: torch.Tensor, + reset_terminated: torch.Tensor, +): + rew_alive = rew_scale_alive * (1.0 - reset_terminated.float()) + rew_termination = rew_scale_terminated * reset_terminated.float() + rew_pole_pos = rew_scale_pole_pos * torch.sum(torch.square(pole_pos).unsqueeze(dim=1), dim=-1) + rew_cart_vel = rew_scale_cart_vel * torch.sum(torch.abs(cart_vel).unsqueeze(dim=1), dim=-1) + rew_pole_vel = rew_scale_pole_vel * torch.sum(torch.abs(pole_vel).unsqueeze(dim=1), dim=-1) + total_reward = rew_alive + rew_termination + rew_pole_pos + rew_cart_vel + rew_pole_vel + return total_reward diff --git a/tools/template/templates/tasks/direct_single-agent/env_cfg b/tools/template/templates/tasks/direct_single-agent/env_cfg new file mode 100644 index 0000000000000000000000000000000000000000..10588cd3e845cd64a21c43e8196e8a4f97e04046 --- /dev/null +++ b/tools/template/templates/tasks/direct_single-agent/env_cfg @@ -0,0 +1,48 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab_assets.robots.cartpole import CARTPOLE_CFG + +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import DirectRLEnvCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sim import SimulationCfg +from isaaclab.utils import configclass + + +@configclass +class {{ task.classname }}EnvCfg(DirectRLEnvCfg): + # env + decimation = 2 + episode_length_s = 5.0 + # - spaces definition + action_space = 1 + observation_space = 4 + state_space = 0 + + # simulation + sim: SimulationCfg = SimulationCfg(dt=1 / 120, render_interval=decimation) + + # robot(s) + robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="/World/envs/env_.*/Robot") + + # scene + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=4.0, replicate_physics=True) + + # custom parameters/scales + # - controllable joint + cart_dof_name = "slider_to_cart" + pole_dof_name = "cart_to_pole" + # - action scale + action_scale = 100.0 # [N] + # - reward scales + rew_scale_alive = 1.0 + rew_scale_terminated = -2.0 + rew_scale_pole_pos = -1.0 + rew_scale_cart_vel = -0.01 + rew_scale_pole_vel = -0.005 + # - reset states/conditions + initial_pole_angle_range = [-0.25, 0.25] # pole angle sample range on reset [rad] + max_cart_pos = 3.0 # reset if cart exceeds this position [m] diff --git a/tools/template/templates/tasks/manager-based_single-agent/env_cfg b/tools/template/templates/tasks/manager-based_single-agent/env_cfg new file mode 100644 index 0000000000000000000000000000000000000000..3ab42ecf166b4c185a7b9d31d7cb2b00f6792bc3 --- /dev/null +++ b/tools/template/templates/tasks/manager-based_single-agent/env_cfg @@ -0,0 +1,180 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import math + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.utils import configclass + +from . import mdp + +## +# Pre-defined configs +## + +from isaaclab_assets.robots.cartpole import CARTPOLE_CFG # isort:skip + + +## +# Scene definition +## + + +@configclass +class {{ task.classname }}SceneCfg(InteractiveSceneCfg): + """Configuration for a cart-pole scene.""" + + # ground plane + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg(size=(100.0, 100.0)), + ) + + # robot + robot: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # lights + dome_light = AssetBaseCfg( + prim_path="/World/DomeLight", + spawn=sim_utils.DomeLightCfg(color=(0.9, 0.9, 0.9), intensity=500.0), + ) + + +## +# MDP settings +## + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=100.0) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + # observation terms (order preserved) + joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) + joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) + + def __post_init__(self) -> None: + self.enable_corruption = False + self.concatenate_terms = True + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class EventCfg: + """Configuration for events.""" + + # reset + reset_cart_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), + "position_range": (-1.0, 1.0), + "velocity_range": (-0.5, 0.5), + }, + ) + + reset_pole_position = EventTerm( + func=mdp.reset_joints_by_offset, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), + "position_range": (-0.25 * math.pi, 0.25 * math.pi), + "velocity_range": (-0.25 * math.pi, 0.25 * math.pi), + }, + ) + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + # (1) Constant running reward + alive = RewTerm(func=mdp.is_alive, weight=1.0) + # (2) Failure penalty + terminating = RewTerm(func=mdp.is_terminated, weight=-2.0) + # (3) Primary task: keep pole upright + pole_pos = RewTerm( + func=mdp.joint_pos_target_l2, + weight=-1.0, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "target": 0.0}, + ) + # (4) Shaping tasks: lower cart velocity + cart_vel = RewTerm( + func=mdp.joint_vel_l1, + weight=-0.01, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"])}, + ) + # (5) Shaping tasks: lower pole angular velocity + pole_vel = RewTerm( + func=mdp.joint_vel_l1, + weight=-0.005, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"])}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + # (1) Time out + time_out = DoneTerm(func=mdp.time_out, time_out=True) + # (2) Cart out of bounds + cart_out_of_bounds = DoneTerm( + func=mdp.joint_pos_out_of_manual_limit, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "bounds": (-3.0, 3.0)}, + ) + + +## +# Environment configuration +## + + +@configclass +class {{ task.classname }}EnvCfg(ManagerBasedRLEnvCfg): + # Scene settings + scene: {{ task.classname }}SceneCfg = {{ task.classname }}SceneCfg(num_envs=4096, env_spacing=4.0) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + events: EventCfg = EventCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + + # Post initialization + def __post_init__(self) -> None: + """Post initialization.""" + # general settings + self.decimation = 2 + self.episode_length_s = 5 + # viewer settings + self.viewer.eye = (8.0, 0.0, 5.0) + # simulation settings + self.sim.dt = 1 / 120 + self.sim.render_interval = self.decimation diff --git a/tools/template/templates/tasks/manager-based_single-agent/mdp/__init__.py b/tools/template/templates/tasks/manager-based_single-agent/mdp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..966d4a3f4b7c30726bcfdc7263f1d8f505d6b523 --- /dev/null +++ b/tools/template/templates/tasks/manager-based_single-agent/mdp/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the environment.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .rewards import * # noqa: F401, F403 diff --git a/tools/template/templates/tasks/manager-based_single-agent/mdp/rewards.py b/tools/template/templates/tasks/manager-based_single-agent/mdp/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..5500089d7f94b1b9ffa6a3805603f8325d0b8862 --- /dev/null +++ b/tools/template/templates/tasks/manager-based_single-agent/mdp/rewards.py @@ -0,0 +1,27 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from isaaclab.assets import Articulation +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import wrap_to_pi + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def joint_pos_target_l2(env: ManagerBasedRLEnv, target: float, asset_cfg: SceneEntityCfg) -> torch.Tensor: + """Penalize joint position deviation from a target value.""" + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + # wrap the joint positions to (-pi, pi) + joint_pos = wrap_to_pi(asset.data.joint_pos[:, asset_cfg.joint_ids]) + # compute the reward + return torch.sum(torch.square(joint_pos - target), dim=1) diff --git a/tools/test_settings.py b/tools/test_settings.py new file mode 100644 index 0000000000000000000000000000000000000000..61ed16992994d45a57550e30896630b4bb000b17 --- /dev/null +++ b/tools/test_settings.py @@ -0,0 +1,71 @@ +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This file contains the settings for the tests. +""" + +import os + +ISAACLAB_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +"""Path to the root directory of the Isaac Lab repository.""" + +DEFAULT_TIMEOUT = 300 +"""The default timeout for each test in seconds.""" + +PER_TEST_TIMEOUTS = { + "test_articulation.py": 500, + "test_stage_in_memory.py": 500, + "test_environments.py": 2500, # This test runs through all the environments for 100 steps each + "test_environments_with_stage_in_memory.py": ( + 2500 + ), # Like the above, with stage in memory and with and without fabric cloning + "test_environment_determinism.py": 1000, # This test runs through many the environments for 100 steps each + "test_factory_environments.py": 1000, # This test runs through Factory environments for 100 steps each + "test_multi_agent_environments.py": 800, # This test runs through multi-agent environments for 100 steps each + "test_generate_dataset.py": 500, # This test runs annotation for 10 demos and generation until one succeeds + "test_pink_ik.py": 1000, # This test runs through all the pink IK environments through various motions + "test_environments_training.py": ( + 6000 + ), # This test runs through training for several environments and compares thresholds + "test_simulation_render_config.py": 500, + "test_operational_space.py": 500, + "test_non_headless_launch.py": 1000, # This test launches the app in non-headless mode and starts simulation + "test_rl_games_wrapper.py": 500, + "test_skrl_wrapper.py": 500, +} +"""A dictionary of tests and their timeouts in seconds. + +Note: Any tests not listed here will use the default timeout. +""" + +TESTS_TO_SKIP = [ + # lab + "test_argparser_launch.py", # app.close issue + "test_build_simulation_context_nonheadless.py", # headless + "test_env_var_launch.py", # app.close issue + "test_kwarg_launch.py", # app.close issue + "test_differential_ik.py", # Failing + # lab_tasks + "test_record_video.py", # Failing + "test_tiled_camera_env.py", # Need to improve the logic +] +"""A list of tests to skip by run_tests.py""" + +TEST_RL_ENVS = [ + # classic control + "Isaac-Ant-v0", + "Isaac-Cartpole-v0", + # manipulation + "Isaac-Lift-Cube-Franka-v0", + "Isaac-Open-Drawer-Franka-v0", + # dexterous manipulation + "Isaac-Repose-Cube-Allegro-v0", + # locomotion + "Isaac-Velocity-Flat-Unitree-Go2-v0", + "Isaac-Velocity-Rough-Anymal-D-v0", + "Isaac-Velocity-Rough-G1-v0", +] +"""A list of RL environments to test training on by run_train_envs.py"""